We describe a prototype ontology-driven information system (ODIS) that exploits what we call Portion of Reality (POR) representations. The system takes both sensor data and natural language text as inputs and composes on this basis logically structured POR assertions. The goal of our prototype is to represent both natural language and sensor data within a single framework that is able to support both axiomatic reasoning and computation. In addition, the framework should be capable of discovering and (...) representing new kinds of situations and thematic roles, (e.g., roles such as agent, patient and instrument), based on new compositions of existing representations. We applied our prototype in an intelligence analysis use case to test the hypothesis that a framework of this sort can produce usefully structured information from combined natural language and sensor data inputs. We further tested our hypothesis by adding an enhanced US Air Force ontology framework to our ODIS in order to (1) process a collection of sensor data, intel reports, and mission plans; (2) build composite POR representations from these data; and (3) machine analyze the fused results to infer mission threats. (shrink)

This paper explores the development of a sensor-based system for detecting water impurities and designing an elevator-equipped robotic boat for autonomous waste collection. Leveraging RaspberryPi for real- time data processing and motor drivers for precise navigation, this project aims to address the pressing issue of water pollution. The proposed system integrates advanced sensors, robotics, and IoTtechnologies to enhance water quality management. Water pollution poses a significant threat to environmental and public health, driven by the accumulation of chemical impurities (...) and solid waste in natural water bodies. This project proposes a smart, autonomous system that combines water quality monitoring and floating waste collection into a single mobile platform. Utilizing a Raspberry Pi as the core controller, the system integrates various water quality sensors— including pH, turbidity, and Total Dissolved Solids (TDS) sensors—to detect pollutants in real time. The boat's navigation is handled by DC motors driven by L298N motor drivers, while an elevator mechanism collects floating debris from the water surface. Additional features such as obstacle detection using ultrasonic sensors enhance the system’s autonomy. Experimental results demonstrated the system’s ability to accurately detect water impurities and efficiently collect surface waste, offering a scalable, low-cost solution for automated water pollution control. This design has the potential to contribute significantly to the preservation of clean water in urban and rural environments. (shrink)

Directed Diffusion (DD) uses data aggregation to suppress data overhead however there is no guarantee that paths from nearby sources join after a few hops. In this paper, an efficient data-gathering protocol (EDAP) is proposed to address this problem by using a Virtual Sink (VS) node nearby the sources, which plays the role of sink node and broadcasts local interest messages and routes gathered data toward destination. Also, multiple paths are constructed between VS and the sink (...) node, which leads to load-balancing, and increase in the lifetime of the network. Simulation results show that in EDAP a significant amount of energy can be saved and the network lifetime will be increased considerably. (shrink)

Background: Smart sensors have been developed as diagnostic tools for rehabilitation to cover an increasing number of geriatric patients. They promise to enable an objective assessment of complex movement patterns. -/- Objective: This research aimed to identify and analyze the conflicting ethical values associated with smart sensors in geriatric rehabilitation and provide ethical guidance on the best use of smart sensors to all stakeholders, including technology developers, health professionals, patients, and health authorities. -/- Methods: On the basis of a systematic (...) literature search of the scientific databases PubMed and ScienceDirect, we conducted a qualitative document analysis to identify evidence-based practical implications of ethical relevance. We included 33 articles in the analysis. The practical implications were extracted inductively. Finally, we carried out an ethical analysis based on the 4 principles of biomedical ethics: autonomy, beneficence, nonmaleficence, and justice. The results are reported in categories based on these 4 principles. -/- Results: We identified 8 conflicting aims for using smart sensors. Gains in autonomy come at the cost of patient privacy. Smart sensors at home increase the independence of patients but may reduce social interactions. Independent measurements performed by patients may result in lower diagnostic accuracy. Although smart sensors could provide cost-effective and high-quality diagnostics for most patients, minorities could end up with suboptimal treatment owing to their underrepresentation in training data and studies. This could lead to algorithmic biases that would not be recognized by medical professionals when treating patients. -/- Conclusions: The application of smart sensors has the potential to improve the rehabilitation of geriatric patients in several ways. It is important that patients do not have to choose between autonomy and privacy and are well informed about the insights that can be gained from the data. Smart sensors should support and not replace interactions with medical professionals. Patients and medical professionals should be educated about the correct application and the limitations of smart sensors. Smart sensors should include an adequate representation of minorities in their training data and should be covered by health insurance to guarantee fair access. (shrink)

—Industrial networks are increasingly based on open protocols and platforms that are also employed in the IT industry and Internet background. Most of the industries use wireless networks for communicating information and data, due to high cable cost. Since, the wireless networks are insecure, it is essential to secure the critical information and data during transmission. The data that transmitted is intercepted by eavesdropper can be predicted by secrecy capacity. The secrecy capacity is the difference between channel (...) capacity of main link and wiretap link of wireless transmission. When the secrecy capacity gets fades, then it is known that transmitted data is intercepted. In the event, of applying optimal sensor scheduling scheme a sensor with highest secrecy capacity is chosen and data is transmitted. It is followed with cooperative localization algorithm to enhance nodes, predict and maintain power of each sensor nodes. Through localization algorithm best sensor nodes are get predicted for transmission. Moreover, RC4 encryption and decryption algorithm is enhanced for data while transmission. This will enable protection to data to an extent. Also, an asymptotic intercept probability analysis is performed to provide an imminent into the impact of the sensor scheduling on the wireless security. (shrink)

In this assignment we proposed to enhance routing overall performance in hybrid wi-fi networks. We the usage of ad-hoc relay stations to dynamically relay site visitors from one telephone to every other in order to keep away from architectures in literature, and in contrast and mentioned techniques to decrease the price of deployment for MCNs. The work investigates how to allocate the bandwidth to customers to enhance the overall performance of hybrid Wi-Fi networks. We in addition regarded the Wi-Fi interference (...) in optimizing the useful resource allocation in hybrid Wi-Fi networks. The work proposes a coalitional sport idea primarily based cooperative packet shipping scheme in hybrid Wi-Fi networks. There are additionally learn about radio frequency allocation for course transmission and relay transmission in hybrid Wi-Fi networks. These works are orthogonal to our learn about in this paper and can be integrated into DTRP to similarly beautify its performance. The throughput ability of the hybrid Wi-Fi community beneath distinct settings has also been an lively lookup theme in the hybrid wifi network. Since Base stations are linked with a wired backbone, we anticipate that there are no bandwidth and energy constraints on transmissions between Base stations. We use intermediate nodes to denote relay nodes that characteristic as gateways connecting an infrastructure wi-fi community and a cellular ad-hoc network Our DTRP algorithm avoids the shortcomings of adhoc transmission in the preceding routing algorithms that without delay mix an ad-hoc transmission mode and a cell transmission mode. Rather than the usage of the multichip ad-hoc transmission, DTR makes use of two hop forwarding by means of relying on node motion and good sized base stations. All different components stay the identical as these in the preceding routing algorithms (including the interplay with the TCP layer). DTRP works on the Internet layer. It receives packets from the TCP layer and routes it to the vacation spot node, the place DTRP forwards the packet to the TCP layer. The statistics routing technique in DTRP can be divided into two steps: uplink from a supply node to the first BS and downlink from the ultimate BS to the data’s destination. (shrink)

The rapid urbanization of cities globally has brought about significant challenges in terms of resource management, sustainability, and quality of life for residents. To address these issues, the concept of Smart Cities has emerged, where technologies like the Internet of Things (IoT) are harnessed to integrate various sensors and data systems to enhance urban services and infrastructure. Smart Cities leverage IoT devices such as sensors, actuators, and connected systems to collect and analyze real-time data, which enables efficient decision-making, (...) optimized resource allocation, and improved services for citizens Incorporating IoT into city infrastructure allows for better management of essential services such as water, electricity, waste management, transportation, and healthcare. This integration fosters urban sustainability by reducing energy consumption, minimizing waste, and enhancing the quality of life through the application of data-driven solutions. The data collected through IoT systems also plays a crucial role in predictive maintenance, risk management, and climate adaptation strategies, ensuring that cities become more resilient to the challenges of climate change and population growth. This paper explores the integration of IoT in Smart Cities, focusing on the role of sensors and data collection for urban sustainability. The paper provides an overview of the technological framework that supports IoT in Smart Cities and discusses how these technologies are transforming urban life. It also highlights key challenges, such as data privacy, security concerns, and infrastructure costs, while proposing strategies for overcoming these challenges. Furthermore, the paper discusses successful case studies and outlines future trends in IoT deployment for urban sustainability. (shrink)

'Big Data' refers to extensive, interconnected datasets that are continuously generated and updated, encompassing a wide variety of sources, formats, and applications. It includes a significant portion of anonymized personal data as well as non-human data, such as derived datasets and by-products produced through everyday digital activities and human-machine interactions. These data points include traces from online shopping, browsing history, search queries, system logs, sensor readings, weather data, and aggregated location data. For the (...) purposes of this study, the term ‘Big Data’ specifically denotes this broad spectrum of data categories, explicitly excluding any personally identifying or legally protected data. By concentrating on these datasets, the study argues that certain forms of Big Data can be governed as shared resources or a ‘common heritage of humanity,’ supporting initiatives like data commons, open data movements, open government data, and data cooperatives, and emphasizing the potential for collaborative stewardship and equitable access for the public good. However, ethical considerations surrounding Big Data are complex due to novel dilemmas arising within the evolving cyberworld. In response, this study draws on the concept of stewardship within Catholic Social Teaching, with particular focus on “Laudato Si’,” and presents it as a guiding ethical principle for responsible data custodianship, upholding human dignity, ensuring accountability, and harnessing Big Data’s potential for the common good. Just as “Laudato Si'” highlights our interconnectedness within the biosphere, this parallels our interconnectedness within the datasphere—the digital ecosystem—underscoring the necessity of stewardship in both domains. (shrink)

Wireless Sensor Networks (WSNs) have become pivotal in applications such as environmental monitoring, healthcare, and military surveillance due to their low cost and ability to operate in remote areas. However, energy efficiency remains a significant challenge in WSNs, as sensor nodes are typically powered by limited battery sources. Consequently, efficient energy management is crucial for ensuring the longevity and effectiveness of these networks. Routing protocols play a key role in optimizing energy consumption by determining how data is (...) transmitted from source nodes to sink nodes while minimizing the energy expended during the communication process. This paper focuses on energy-efficient routing protocols in WSNs, exploring various strategies to optimize energy usage without compromising network performance. We examine different types of routing protocols, including flat, hierarchical, and location-based protocols, analyzing their energy efficiency, scalability, and application suitability. The paper also reviews key techniques such as data aggregation, energy-aware routing metrics, and duty cycling, which contribute to energy conservation in WSNs. By analyzing existing energy-efficient protocols and comparing their performance in various scenarios, this paper aims to provide a comprehensive understanding of the trade-offs involved in optimizing energy consumption in WSNs. We also highlight the challenges faced in real-world applications and suggest future research directions for developing even more energy-efficient and scalable solutions. In conclusion, energy-efficient routing protocols are essential for enhancing the performance and longevity of WSNs. The review of these protocols, alongside an exploration of the factors that influence their efficiency, provides valuable insights for researchers and practitioners seeking to improve WSN design and deployment. (shrink)

The rapid advancements in healthcare technologies have resulted in an enormous increase in biomedical data, creating the need for innovative approaches to harness this information for early disease detection. Big Data Analytics (BDA) combined with Artificial Intelligence (AI) offers unprecedented opportunities to analyze complex biomedical signal patterns and predict the onset of diseases at an early stage. The application of AI techniques like machine learning and deep learning in conjunction with BDA allows for the detection of subtle patterns (...) in large datasets that might be missed by conventional diagnostic methods. This paper explores the integration of Big Data Analytics and AI for early disease detection through the analysis of biomedical signal patterns, such as electrocardiograms (ECG), electroencephalograms (EEG), and wearable sensor data. AI algorithms, trained on vast datasets, can recognize early warning signals of various diseases such as cardiovascular disorders, neurodegenerative diseases, and respiratory conditions. The ability to analyze these signals in real time enables early intervention, potentially improving patient outcomes and reducing healthcare costs. A comprehensive literature review highlights the use of BDA and AI in biomedical applications and the challenges associated with data volume, quality, and heterogeneity. We propose a system methodology that combines AI models with real-time Big Data Analytics to process biomedical signals efficiently and accurately. The proposed system leverages AI for feature extraction and classification while utilizing Big Data tools to handle the massive influx of data from biomedical devices and sensors. This approach not only enhances diagnostic precision but also paves the way for personalized healthcare solutions. (shrink)

The advance of scientific approaches to life has recorded a plethora of successes as well as failures. Man being at the center of its experiment is tossed toe and fro by the result of its inquiry. Predictions are that in the nearest time, humanity might be living absolutely under the directives of Technology based on Artificial intelligence. At present, Technology based on Artificial Intelligence is quickly finding its way into various areas of life including health and social services. This spread (...) and or interplay connotes the relinquishing of health responsibilities from man to sensor technology as well as dependency on the data and result of this technology. This effort questions this unethical dependency as well as raises moral issues associated with the use of Technology base on artificial intelligence on psychiatric patient. It dabbles into medical values such as; autonomy of the patient cum consent, Kantian universal principle and its implication to implementing the universal usage of technology in mental health care services, and so on. The work of Patricia A. Arean (2016) titled Mobile Technology for Mental Health Assessment gave an in-depth analysis of the available approaches, doting advantages of sensor technology on psychiatric patient. The paper applied the analytic, descriptive and prescriptive method of philosophy to achieve its objective. To this end, it is in the view of this work imperative for the mental-medical community to consider reflectively these issues and the philosophic recommendations provided herein. (shrink)

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the (...) specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor). (shrink)

This research paper presents an in-depth analysis of advanced artificial intelligence (AI) algorithms designed to automate data preprocessing in the healthcare sector. The automation of data preprocessing is crucial due to the overwhelming volume, diversity, and complexity of healthcare data, which includes medical records, diagnostic imaging, sensor data from medical devices, genomic data, and other heterogeneous sources. These datasets often exhibit various inconsistencies such as missing values, noise, outliers, and redundant or irrelevant information that (...) necessitate extensive preprocessing before being analyzed by machine learning or statistical models. Traditional data preprocessing methods, which are largely manual and time-consuming, can result in errors that affect the quality of the data and, subsequently, the performance of predictive and diagnostic models. Thus, there is a growing need for intelligent, automated systems that can enhance data quality, streamline the preprocessing pipeline, and reduce the time and effort required by healthcare professionals and data scientists. The study begins by outlining the specific challenges associated with healthcare data, including its high dimensionality, incompleteness, and variability across different data sources and formats. These issues not only complicate the preprocessing stage but also hinder the ability to develop robust models capable of making accurate predictions or diagnoses. The paper then explores how AI algorithms—particularly those based on machine learning (ML), deep learning (DL), and reinforcement learning (RL)—can automate key data preprocessing tasks such as data cleaning, feature selection, normalization, and transformation. These algorithms are designed to identify patterns in data, detect anomalies, and automatically apply corrections or transformations based on predefined rules or learned behaviors, thereby minimizing human intervention. The paper also delves into specific AI techniques that have been successfully applied to healthcare data preprocessing. For instance, supervised learning models, such as decision trees and support vector machines (SVMs), have been utilized to perform imputation of missing data by predicting the most likely values based on the available information. Similarly, unsupervised learning methods, such as clustering algorithms, have been employed to group similar data points and remove outliers that could distort the performance of analytical models. Moreover, deep learning techniques, particularly autoencoders and generative adversarial networks (GANs), have demonstrated remarkable effectiveness in transforming high-dimensional medical data into lower-dimensional representations, enabling more efficient and accurate model training. In addition to the discussion of these algorithms, the paper emphasizes the role of natural language processing (NLP) in automating the preprocessing of unstructured healthcare data, such as clinical notes and diagnostic reports. NLP techniques, including named entity recognition (NER) and word embeddings, are instrumental in extracting relevant information from unstructured text, standardizing terminologies, and converting textual data into structured formats suitable for downstream analysis. Furthermore, AI-based feature selection algorithms are explored, which aim to identify the most relevant features in the dataset, thereby reducing its dimensionality and improving the computational efficiency of predictive models. The study goes on to highlight the significant reduction in processing time achieved by AIdriven automation of preprocessing tasks. In conventional settings, data preprocessing accounts for a substantial portion of the time spent on building healthcare models, often requiring expert intervention to manually inspect and clean the data. By employing AI algorithms, not only can this process be expedited, but the accuracy of the resulting data is also enhanced, which translates into better model performance. The paper provides a detailed comparative analysis of manual preprocessing methods versus automated AI-driven approaches, demonstrating the substantial time savings and improvements in data quality brought about by automation. (shrink)

Big data has increasingly been recognized as a transformative force in the sports industry, offering insights that were once impossible to gather through traditional methods. By leveraging vast amounts of real-time data from wearable devices, sensors, and video analysis, big data analytics can help athletes and teams make informed decisions to enhance performance, prevent injuries, and optimize training. This paper explores the role of big data in sports performance analytics, focusing on its application across various sports (...) domains, including training optimization, injury prevention, and game strategy. The study highlights key data sources, methodologies, and real-world case studies where big data analytics has led to significant improvements in sports performance. The potential for big data to revolutionize the future of sports is vast, with its continued evolution poised to impact athlete training, team management, and overall game strategies. (shrink)

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of (...) nodes. This results in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources. Often, an IoT-enabled infrastructure is shared among many users with various requirements. Sharing resources, sharing operational costs and collective decision making (consensus) among many stakeholders is frequently neglected. This research addresses an essential requirement for adaptive, autonomous and consensus-based Fog computational solutions which are able to support distributed and in-network schemes and policies. These network schemes and policies need to meet the requirements of many users. In this work, innovative consensus-based computational solutions are investigated. These proposed solutions aim to correlate and organise data for effective management and decision making in Fog. Instead of individual decision making, the algorithms aim to aggregate several decisions into a consensus decision representing a collective agreement, benefiting from the individuals variant knowledge and meeting multiple stakeholders requirements. In order to validate the proposed solutions, hybrid research methodology is involved that includes the design of a test-bed and the execution of several experiments. In order to investigate the effectiveness of the paradigm, three experiments were designed and validated. Real-life sensor data and synthetic statistical data was collected, processed and analysed. Bayesian Machine Learning models and Analytics were used to consolidate the design and evaluate the performance of the algorithms. In the Fog environment, the first scenario tests the Aggregation by Distribution algorithm. The solution contribute in achieving a notable efficiency of data delivery obtained with a minimal loss in precision. The second scenario validates the merits of the approach in predicting the activities of high mobility IoT applications. The third scenario tests the applications related to smart home IoT. All proposed Consensus algorithms use statistical analysis to support effective decision making in Fog and enable data aggregation for optimal storage, data transmission, processing and analytics. The final results of all experiments showed that all the implemented consensus approaches surpass the individual ones in different performance terms. Formal results also showed that the paradigm is a good fit in many IoT environments and can be suitable for different scenarios when applying data analysis to correlate data with the design. Finally, the design demonstrates that Fog Computing can compete with Cloud Computing in terms of accuracy with an added preference of locality. (shrink)

Contemporary Artificial Intelligence and other computational systems—from machine learning models and large language models to autonomous agents and predictive simulations—usually handle time in a tenseless manner, treating past, present, and future data points as uniform elements of a timeline. They lack an explicit ontology of the present (what exists now), becoming (how the present continually updates from potential to actual), and temporal asymmetry (the one-way flow of causality from past to future). This paper proposes that Existential Realism (ER), an (...) ontological framework distinguishing existence (entities in the present moment) from reality (a broader set including causally or informationally relevant past and future entities), can enrich the way technological systems model time. We argue that by incorporating ER’s present-centered, two-tier view of time, machines could become more temporally-aware and responsible in their decision-making. We detail key ER concepts—such as the primacy of the present, the reality of the non-present, truthmaking, and the idea of a real-but-not-yet-existent future—and map them onto machine-level analogues like sensor inputs (the machine’s “existence”), memory traces, simulation states, and predictive models. ER provides a structured approach for machines to: distinguish what exists now from what is merely remembered or anticipated; represent future states as real but not yet existent, enabling ethical foresight; anchor predictions in current data while reasoning about long-term consequences; and avoid temporal fallacies like treating the future as fixed or erasing the influence of the past. Through examples in climate modeling, medical diagnostics, robotics, and Artificial Intelligence ethics, we illustrate how an ER-informed system could better navigate time-sensitive tasks—e.g. by understanding that only the present sensor data is actual, that past data (though no longer existent) remains real evidence, and that future scenarios (though not yet actualized) demand present attention. We also draw on existing literature in philosophy and cognitive science to show that ER’s framework aligns with how humans experience time (through memory and anticipation) and how ethical reasoning already extends reality to future generations. In sum, Technology and the Reality of the Present-Centered Machine outlines an interdisciplinary vision: embedding a philosophically robust, present-centered temporal ontology into our technologies to foster more prudent, transparent, and temporally-grounded intelligent systems. (shrink)

The Internet-of-Things (IoT) is gradually being established as the new computing paradigm, which is bound to change the ways of our everyday working and living. IoT emphasizes the interconnection of virtually all types of physical objects (e.g., cell phones, wearables, smart meters, sensors, coffee machines and more) towards enabling them to exchange data and services among themselves, while also interacting with humans as well. Few years following the introduction of the IoT concept, significant hype was generated as a result (...) of the proliferating number of IoT-enabled devices, which (according to many projections) are expected to amount to several billion in the next years. During recent years, this hype has been turning to reality, as a wave of IoT applications with significant social and economic has been emerging. Data analytics is the process of deriving knowledge from data, generating value like actionable insights from them. This article reviews work in the IoT and big data analytics from the perspective of their utility in creating efficient, effective and innovative applications and services for a wide spectrum of domains. We review the broad vision for the IoT as it is shaped in various communities, examine the application of data analytics across IoT domains, provide a categorization of analytic approaches and propose a layered taxonomy from IoT data to analytics. IoT data analysis is an integral element of any non- trivial IoT system. Nevertheless, IoT analytics are still in their infancy, as IoT data still remain largely unexploited. (shrink)

Gathering and analyzing enormous volumes of data is known as "big data," and it includes information from users, sensors, healthcare providers, and companies. Using the Hadoop framework, large amounts of data are stored, managed, and dispersed across multiple server nodes. Big Data issues, including security holes in the Hadoop Distributed File System (HDFS), the architecture's core layer, are highlighted in this article. The methodology includes setting up a Hadoop environment, integrating Kerberos for authentication, enabling HDFS encryption (...) zones, implementing SSL/TLS for data in transit, and utilizing Apache Ranger and Apache Knox for access control and perimeter security, respectively. The results demonstrate the successful implementation of all planned security measures, achieving a robust security framework for the Hadoop cluster. Performance testing indicates a 10% reduction in processing speed due to the security features, a trade-off deemed acceptable given the significant enhancement in data protection. Compliance testing confirms adherence to GDPR and CCPA regulations, ensuring legal and secure data management. Overall, the study underscores the feasibility of integrating comprehensive security measures within a Hadoop environment, balancing the need for robust data protection with minimal performance impact. Future work includes optimizing security configurations to further mitigate performance degradation and exploring advanced security measures for enhanced threat detection and response. This methodology provides a scalable and secure solution for managing large datasets in compliance with global data protection standards. (shrink)

The healthcare sector is undergoing a transformative shift towards smart healthcare, driven by advancements in technology, including Artificial Intelligence (AI) and Blockchain. As healthcare systems generate vast amounts of data from multiple sources, such as electronic health records (EHRs), medical imaging, wearable devices, and sensor-based monitoring, the challenge lies in securely integrating and analyzing this data for real-time, actionable insights. Blockchain technology, with its decentralized, immutable, and transparent framework, offers a robust solution for securing data integrity, (...) privacy, and sharing across healthcare networks. AI, on the other hand, enables advanced data analytics, providing healthcare professionals with predictive insights, early diagnosis, and personalized treatment plans. This paper explores the integration of Blockchain and AI for advanced data management in smart healthcare. We propose a system that utilizes Blockchain for secure and transparent data storage, ensuring that healthcare data from various sources is tamper-proof and can be shared across multiple stakeholders. AI algorithms, particularly machine learning and deep learning models, are applied to the integrated data to detect patterns, predict disease progression, and optimize treatment plans. By leveraging Blockchain, healthcare data can be securely shared across providers and patients, ensuring privacy and regulatory compliance, while AI technologies provide the intelligence required for enhancing clinical decision-making. This integrated system not only addresses the growing concerns surrounding data security and privacy but also promotes interoperability and collaboration within the healthcare ecosystem. The paper discusses the challenges, opportunities, and future directions of Blockchain and AI integration in smart healthcare systems, highlighting how this convergence can lead to more efficient, secure, and personalized healthcare services. (shrink)

Reports suggests that total amount of data generated everyday reaches 2.5 quintillion bytes [9], annual global IP traffic run rate in 2016 was 1.2 zettabytes and will reach 3.3 zettabytes by 2021 [12]. According to Gartner [25], Internet of Things excluding personal computers, tablets and smartphones will grow to 26 billion units of installed devices in year 2020. This results from penetration of digital applications which highly motivated by smart societies which can be defined as to when a society (...) deploys light and advanced computer technologies to aid provision and or supply chain value of social, cultural, governance and economic utilities for efficiency. Smart society is equipped with mobile, ubiquitous computing facilities, sensors and cyber-physical systems aims at exploring economies of scale; and to large extent it has been made possible with Internet of Things (IoT). This survey paper discusses status of big data in Internet of Things; how IoT generates big data, nature of data generated and dynamics in IoT as influenced by big data. (shrink)

The rapid urbanization and growing population have led to increased waste generation, necessitating efficient and intelligent waste management systems. This project presents a Smart Garbage Monitoring and Clearance Alert System utilizing the Internet of Things (IoT) to address this challenge. The system employs a load sensor to measure the weight of waste in the dustbin, and an ultrasonic sensor to monitor the garbage level. When the bin is nearly full, a servo motor is triggered to close the lid, (...) preventing overflow and environmental hazards. The ESP32 microcontroller serves as the central unit, managing sensor data and system operations. Real-time garbage status is communicated wirelessly to municipal authorities through IoT, enabling timely waste clearance and improving sanitation services. This solution ensures a cleaner environment by optimizing garbage collection routes and reducing manual inspection efforts. College campuses have waste management concerns due to insufficient collection and overflowing bins, which cause environmental and logistical issues. An IoTenabled smart trash system improves collection, lowers costs, and increases sustainability by assuring timely garbage disposal and reducing buildup, resulting in a cleaner and more efficient campus environment. (shrink)

One method for preventing the spread of the coronavirus and other contagious diseases is through social distancing. Therefore, creating a tool to measure and quickly discover the precise distance is necessary. In order to prevent physical contact between individuals, this study aimed to detects individuals’ physical distance, through an inaugurated battery-powered device that monitors physical distance through wireless technology. Specifically, in public or crowded areas, to lessen the spread of the virus. This study focuses on detecting people’s physical distance in (...) the region of interest utilizing an Ultrasonic and VL53L0X sensors and determining the significant difference between the two sensors in monitoring physical distance. This study employs an applied experimental research design. The efficiency of both sensors in measuring physical distance was evaluated through analysis. The Ultrasonic and VL53L0X sensors underwent 15 tests. Furthermore, the researchers effectuated a t-test to determine the significant difference between the two sensors. Data gathered revealed that the sensors’ combined mean in terms of measuring physical distance indicates that the ultrasonic sensor performed better than VL53L0X, measuring 134.7 cm, notwithstanding 99.8 cm for the VL53L0X sensor. Complementary to this, the results of the t-test show that the VL53L0X sensor and Ultrasonic sensor have a significant difference in terms of distance precision. (shrink)

Climate change presents a critical challenge that demands advanced analytical tools to predict and mitigate its impacts. This paper explores the role of artificial intelligence (AI) in enhancing climate modeling, emphasizing how AI-driven methods are revolutionizing our understanding and response to climate change. By integrating machine learning algorithms with diverse data sources such as satellite imagery, historical climate records, and real-time sensor data, AI improves the accuracy, efficiency, and granularity of climate predictions. The paper reviews key AI (...) techniques, including neural networks and ensemble models, and their applications in simulating extreme weather events and forecasting long-term climate trends. Additionally, it examines the impact of AI-enhanced climate models on policy-making and strategic planning. Despite significant advancements, challenges such as data quality, computational demands, and model interpretability remain. This study underscores the potential of AI to transform climate modeling and highlights areas for future research and development to harness these technologies for a sustainable future. (shrink)

Analysis of grip force signals tailored to hand and finger movement evolution and changes in grip force control during task execution provide unprecedented functional insight into somatosensory cognition. Somatosensory cognition is a basis of our ability to manipulate, move, and transform objects of the physical world around us, to recognize them on the basis of touch alone, and to grasp them with the right amount of force for lifting and manipulating them. Recent technology has permitted the wireless monitoring of grip (...) force signals recorded from biosensors in the palm of the human hand to track and trace human grip forces deployed in cognitive tasks executed under conditions of variable sensory (visual, auditory) input. Non-invasive multi-finger grip force sensor technology can be exploited to explore functional interactions between somatosensory brain mechanisms and motor control, in particular during learning a cognitive task where the planning and strategic execution of hand movements is essential. Sensorial and cognitive processes underlying manual skills and/or hand-specific (dominant versus non-dominant hand) behaviors can be studied in a variety of contexts by probing selected measurement loci in the fingers and palm of the human hand. Thousands of sensor data recorded from multiple spatial locations can be approached statistically to breathe functional sense into the forces measured under specific task constraints. Grip force patterns in individual performance profiling may reveal the evolution of grip force control as a direct result of cognitive changes during task learning. Grip forces can be functionally mapped to from-global-to-local coding principles in brain networks governing somatosensory processes for motor control in cognitive tasks leading to a specific task expertise or skill. Under the light of a comprehensive overview of recent discoveries into the functional significance of human grip force variations, perspectives for future studies in cognition, in particular the cognitive control of strategic and task relevant hand movements in complex real-world precision task, are pointed out. (shrink)

This paper describes an AFOSR-supported basic research program that focuses on developing a new framework for combining hard with soft data in order to improve space situational awareness. The goal is to provide, in an automatic and near real-time fashion, a ranking of possible threats to blue assets (assets trying to be protected) from red assets (assets with hostile intentions). The approach is based on Conceptual Spaces models, which combine features from traditional associative and symbolic cognitive models. While Conceptual (...) Spaces are revolutionary, they lack an underlying mathematical framework. Several such frameworks have attempted to represent Conceptual Spaces, but by far the most robust is the model developed by Holender. His model utilizes integer linear programming in order to obtain an overall similarity value between observations and concepts that support the formation of hypotheses. This paper will describe a method for building Conceptual Spaces models for threats that utilizes ontologies as a means to provide a clear semantic foundation for this inferencing process; in particular threat ontologies and space domain ontologies are developed and employed in this approach. A space situational awareness use-case is presented involving a kinetic kill scenario and results are shown to assess the performance of this fusion-based inferencing framework. (shrink)

Serverless computing is changing the cloud application de- sign by removing the need to design, build, and manage infrastructure, and instead focusing on deploying code that can be elastic and rapid. However, while service meshes have recently been introduced to address the reliability of communication in microservices architectures, the grow- ing adoption of edge computing and multi-cloud strategies require new architectures that can cross different types of platforms. In this paper, we introduce a novel serverless mesh architecture that combines server- (...) less function platforms with a service mesh overlay that spans across clouds and edge sites. We give a brief overview of the serverless and service mesh technologies and review the related work in traditional cloud centric serverless, edge computing deployments and mesh-based microservice solutions. We then describe a framework where the server- less functions that are located in different cloud/edge environments are connected by a single service mesh that enables dynamic routing, inter- operability, and policy enforcement across the environments. The dia- grams show how the functions are deployed and invoked via the mesh. Simulated evaluation is described for video analytics, IoT sensor data in- gestion, and content delivery use cases, with the potential performance benefits shown in terms of end-to-end latency and throughput. Some of the key challenges of network latency, cold start delays, and data con- sistency issues in distributed serverless computing are identified, along with how the proposed architecture tackles or alleviates them. Last, fu- ture directions are outlined, which include AI/ML-based optimizations for function allocation and tuning, enhanced runtime portability via We- bAssembly (WASM), and open questions for secure, scalable serverless computing from edge to cloud. (shrink)

Representing the kinetic state of a patient (posture, motion, and activity) during vital sign measurement is an important part of continuous monitoring applications, especially remote monitoring applications. In contextualized vital sign representation, the measurement result is presented in conjunction with salient measurement context metadata. We present an automated annotation system for vital sign measurements that uses ontologies from the Open Biomedical Ontology Foundry (OBO Foundry) to represent the patient’s kinetic state at the time of measurement. The annotation system is applied (...) to data generated by a wearable personal status monitoring (PSM) device. We demonstrate how annotated PSM data can be queried for contextualized vital signs as well as sensor algorithm configuration parameters. (shrink)

In modern healthcare, especially in intensive care units (ICUs), real-time monitoring of patient vitals is critical to ensure prompt medical intervention and improved outcomes. This project presents the development of a real-time ICU patient monitoring system using the ESP32 microcontroller integrated with advanced biomedical sensors. The system incorporates a DS18B20 digital sensor for precise body temperature measurement, analog sensors to monitor heart rate and respiratory activity, and an MPU6050 sensor to detect patient movement and potential fall events. These (...) sensors collectively provide a comprehensive overview of a patient’s physiological state. The ESP32 serves as the central processing unit, collecting and wirelessly transmitting sensor data to a monitoring interface. Its built-in Wi-Fi and Bluetooth capabilities facilitate remote access and real-time alerts to healthcare providers, enabling immediate action in response to critical changes in patient conditions. The system is designed to be cost-effective, scalable, and suitable for deployment in both hospital ICUs and home-care settings. By leveraging real-time data acquisition and wireless communication, this solution enhances patient safety, reduces the burden on healthcare staff, and ensures timely medical responses. Furthermore, its modular architecture allows for easy integration of additional sensors, making it adaptable for diverse clinical needs. This work contributes toward the advancement of smart healthcare systems, supporting continuous monitoring, data-driven decision-making, and improved patient management in critical care environments. (shrink)

he Sensor-Integrated IoT Solution for Precision Farming is designed to optimize agricultural water usage and enhance crop productivity through real-time monitoring and automated control. The system uses an Arduino and NodeMCU to collect and process data from various sensors, including a pH sensor for water quality, a DHT11 sensor for temperature and humidity, and a soil moisture sensor to assess soil conditions. Sensor data is displayed on an LCD and uploaded to the ThingSpeak (...) platform for remote monitoring. When the pH value deviates from the optimal range, the system activates two water pumps—first, the outlet pump to remove poorquality water, followed by the inlet pump to introduce fresh water. Relays control the operation of the pumps, ensuring efficient water management. By integrating IoT technology, this system enhances water conservation, reduces manual intervention, and improves overall agricultural efficiency, making precision farming more sustainable and effective. (shrink)

Knee osteoarthritis (OA) is a prevalent degenerative joint disorder that significantly affects the elderly population, leading to chronic pain, joint stiffness, and limited mobility. Early diagnosis and continuous monitoring are essential for effective management and to delay disease progression. This project introduces an innovative, wearable system for the detection and management of knee osteoarthritis using a combination of Micro-Electro-Mechanical Systems (MEMS) sensors, force sensors, and a vibration motor. The MEMS sensor continuously tracks knee joint angles and motion to identify (...) abnormal movement patterns typically associated with osteoarthritic degradation. Complementing this, the force sensor evaluates the pressure applied to the knee during movement, providing insights into load distribution and joint strain. A vibration motor is employed to deliver localized therapeutic stimulation to the surrounding muscles, aiding in pain relief and enhancing joint flexibility. The system is built on a microcontroller-based architecture that processes sensor data in real time. It is also designed with future scalability in mind, allowing integration with Internet of Things (IoT) modules for remote health monitoring and tele-rehabilitation. This solution offers a non-invasive, cost-effective, and user- friendly method to support both clinical assessments and home-based management strategies for individuals at risk of or suffering from knee osteoarthritis. By leveraging sensor fusion and intelligent feedback mechanisms, this approach promotes early intervention, encourages patient engagement, and improves overall quality of life. (shrink)

Solving the Turbulence Problem in Physics Using the Universal Formula -/- By Angelito Malicse -/- Introduction -/- Turbulence remains one of the greatest unsolved problems in physics. It is a chaotic, unpredictable phenomenon observed in fluid dynamics, affecting airflow over aircraft wings, ocean currents, wind energy systems, and even blood flow in the human body. Despite the well-established Navier-Stokes equations governing fluid motion, turbulence remains difficult to fully predict and control due to its inherent complexity. -/- In this essay, I (...) will demonstrate how my universal formula, based on three fundamental laws of nature, provides an exact solution to the turbulence problem. By applying these laws, we can achieve a deeper understanding of turbulence, allowing for more effective prediction, mitigation, and control in various real-world applications. -/- Understanding Turbulence in Light of the Universal Formula -/- Turbulence is a manifestation of complex interactions within a system. According to my universal formula, all systems must follow natural laws governing balance, causality, and feedback mechanisms. Let us analyze turbulence through the lens of these principles. -/- 1. The Law of Karma: Cause, Effect, and Systemic Integrity -/- The first law in my universal formula is the Law of Karma, which extends beyond the traditional idea of cause and effect to encompass the principle of systemic integrity. A system must be free from defects or errors to function properly. If turbulence occurs, it indicates an imbalance or defect within the system. -/- In fluid dynamics, turbulence arises when the cause (flow conditions) leads to an unstable effect (chaotic motion). This imbalance can be seen in: -/- Aircraft aerodynamics, where airflow disruptions create drag and instability. -/- Wind turbines, where turbulent wind patterns reduce energy efficiency. -/- Maritime navigation, where water turbulence increases resistance and fuel consumption. -/- Applying the Law of Karma, turbulence can be controlled by designing systems that minimize defects, ensuring smooth energy transfer within the flow. AI-driven optimization can detect turbulence before it fully develops, correcting imbalances through real-time adjustments. -/- 2. The Law of Balance in Nature -/- The second law states that all natural systems seek balance. Turbulence arises when the energy distribution in a fluid system becomes uneven, disrupting the balance. This is seen in: -/- Boundary layers on aircraft wings, where pressure imbalances lead to turbulence. -/- Ocean currents, where temperature and salinity differences create unstable vortices. -/- To control turbulence, we must restore balance in the system. This can be achieved through: -/- Dynamic flow control, using AI to adjust surfaces (such as aircraft flaps or turbine blades) in real time. -/- Energy redistribution, ensuring fluid motion remains stable by adjusting velocity gradients or temperature differences. -/- Feedback-based corrections, applying intelligent algorithms that predict and counteract turbulence. -/- By harmonizing energy distribution, we can prevent turbulence from escalating, bringing the system back into natural equilibrium. -/- 3. The Law of Feedback Regulation -/- The third law states that all systems operate through feedback mechanisms. Turbulence, despite appearing chaotic, follows a pattern that can be measured, analyzed, and corrected through continuous feedback. -/- Birds adjust their wing angles dynamically to minimize turbulence, an example of natural feedback control. -/- Fish use their fins to counteract water turbulence, stabilizing their movement. -/- AI systems can mimic these natural mechanisms to adjust airflow, ship navigation, or wind turbine performance. -/- Applying the Law of Feedback Regulation, we can create AI-driven turbulence control systems that: -/- Predict turbulence using real-time sensor data. -/- Adjust system parameters dynamically to reduce turbulence intensity. -/- Continuously learn and improve based on new turbulence patterns. -/- This approach follows nature’s own strategy for stability, aligning technology with fundamental natural principles. -/- Applying the Universal Formula to Solve Turbulence -/- By integrating the three universal laws, we can develop a Unified AI-Driven Turbulence Control System applicable to multiple fields: -/- ✅ Aviation: AI-based wing and engine adjustments to minimize drag. ✅ Maritime Transport: Smart hull designs to reduce water resistance. ✅ Wind Energy: Adaptive wind turbine blades for optimal efficiency. ✅ Medical Applications: AI-assisted blood flow regulation to prevent clot formation. -/- This system ensures real-time adaptation, aligning human technology with nature’s fundamental laws. -/- Conclusion -/- The turbulence problem in physics, long considered unsolvable, is fundamentally a problem of imbalance, defective systems, and insufficient feedback control. By applying my universal formula, which follows the natural laws of karma (causality), balance, and feedback, we can fully understand, predict, and control turbulence in all systems. -/- Through AI-driven optimization, dynamic corrections, and harmonizing energy flow, we can finally resolve turbulence across aviation, maritime, wind energy, and even biological systems. My universal formula provides the exact and only solution to this problem, proving that all complex phenomena in nature can be understood through fundamental natural laws. -/- By implementing these principles, we take a step toward a future where turbulence is no longer an obstacle, but a controlled phenomenon guided by nature’s own balance. -/- . (shrink)

In the specific case of geography, the real world consists on the one hand of physical geographic features (bona fide objects) and on the other hand of various fiat objects, for example legal and administrative objects, including parcels of real estate, areas of given soil types, census tracts, and so on. It contains in addition the beliefs and actions of human beings directed towards these objects (for example, the actions of those who work in land registries or in census bureaux), (...) and the relations between these beliefs and actions and their targets. The document is a Report of the Specialist Meeting held under the auspices of the Varenius Project. (shrink)

This paper presents a sewage water level detection system aimed at improving the monitoring and management of urban sewage infrastructure. The proposed system utilizes real-time data collection, leveraging ultrasonic sensors to detect water levels in sewage pipes and tanks. The system sends alerts to operators when water levels exceed predefined thresholds, allowing for proactive maintenance and flood prevention. A wireless communication network ensures data transmission to a central control unit, which enables effective monitoring of the sewage system and (...) timely decision-making. (shrink)

Water quality and efficient utilization are critical issues in both urban and rural areas, especially with the increasing global demand and climate change impacts. This paper presents an advanced Internet of Things (IoT)-based Smart Water Monitoring System using Arduino, designed for real-time detection and reporting of multiple water parameters, including pH, turbidity, temperature, and flow rate. The proposed system employs multiple calibrated sensors integrated with an Arduino microcontroller and a Wi-Fi module for continuous data transmission to a cloud platform. (...) A web-based dashboard provides real-time visualization and analytics of water quality metrics, enabling remote monitoring and instant alerts for anomalies. The system architecture emphasizes low cost, energy efficiency, and scalability for smart city and industrial deployments. Experimental results validate the system's accuracy and responsiveness, making it a viable solution for sustainable water management. Future enhancements include machine learning-based predictive analysis and blockchain integration for data security and transparency. (shrink)

This paper shows the smart walking stick based on ultrasonic sensors and Arduino for outwardly debilitated individuals. There are roughly 37 million individuals over the globe who are visually impaired as indicated by the World Health Organization. Individuals with visual inabilities are regularly subjected to outer help which can be given by people, trained dogs, or electronic gadgets as supportive networks for basic assistance. Thus, this played as the motivation to develop a smart cane white stick to survive these restrictions (...) which includes ultrasonic sensors at particular positions to the stick that gave data about nature to the client by initiating the signal sound and vibrations. We proposed minimal effort and light weight framework structured with micro controller that processes signal and alerts the visually impaired person over any obstacle, water or dark areas through beeping sounds or vibrations. The system comprises of obstacle and moisture detection sensors for process of receiving, processing and sending signals to the alarm system which finally alerts the user for prompt action. The system was designed, programmed using Java language and tried for exactness by the visually impaired. Our gadget can recognize obstacle inside the separation of around 2m from the client. (shrink)

The conjunction of wireless computing, ubiquitous Internet access, and the miniaturisation of sensors have opened the door for technological applications that can monitor health and well-being outside of formal healthcare systems. The health-related Internet of Things (H-IoT) increasingly plays a key role in health management by providing real-time tele-monitoring of patients, testing of treatments, actuation of medical devices, and fitness and well-being monitoring. Given its numerous applications and proposed benefits, adoption by medical and social care institutions and consumers may be (...) rapid. However, a host of ethical concerns are also raised that must be addressed. The inherent sensitivity of health-related data being generated and latent risks of Internet-enabled devices pose serious challenges. Users, already in a vulnerable position as patients, face a seemingly impossible task to retain control over their data due to the scale, scope and complexity of systems that create, aggregate, and analyse personal health data. In response, the H-IoT must be designed to be technologically robust and scientifically reliable, while also remaining ethically responsible, trustworthy, and respectful of user rights and interests. To assist developers of the H-IoT, this paper describes nine principles and nine guidelines for ethical design of H-IoT devices and data protocols. (shrink)

Cameras, microphones, and other sensors continue to proliferate in the world around us. I offer a new metaphor for conceptualizing these technologies: they are _digital wormholes_, transmitting representations of human persons between disparate points in space–time. We frequently cannot tell when they are operational, what kinds of data they are collecting, where the data may reappear in the future, and how the data can be used against us. The wormhole metaphor makes the mysteriousness of digital sensors salient: (...) digital sensors have brought us to a strange place. If humans are to be capable of making sensible decisions while under observation by digital sensors, we must improve the epistemic position of potential sensor subjects. (shrink)

It is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred (...) to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters. Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals. Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely lifelogging and self-monitoring, remote monitoring of vital signs, emotional state recognition, food intake monitoring, activity and behaviour recognition, activity and personal assistance, gesture recognition, fall detection and prevention, mobility assessment and frailty recognition, and cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed. (shrink)

Purpose: The purpose of the discussion is to call for an experimental trial in order to optimize the sociology of knowledge in the astronomical and cosmological sciences from a cognitive science and developmental psychology perspective. The potential of such experimental trial may also correlate developmental psychology with cosmology and astrophysics, therefore, contribute to public health from an astrobiological perspective. -/- Method: The discussion adopts a philosophizing method for the multidisciplinary and trans-disciplinary proposal, with the hypothesis that proton decay in cosmology (...) is correlative to neuronal development in human biochemistry. The method adopts a humanitarian paradigm of pragmatism in natural mutation from cosmic changes, in an exploratory heuristics for hydro-sampling of mutational potentials from environmental factors in cosmological large-scale structure. -/- Result: With an initial assessment on existing technologies, the potentials of the big data trend in astronomy and astrophysics can be applied to data analysis with solid-state electrolytes in sensor baselines to nuclear medicine methods. (shrink)

The emergence of multimodal solutions in various fields has significantly transformed industries, bridging the gap between different types of media, data, and human interaction. These solutions combine text, audio, visual, and sensor-based inputs to create richer, more dynamic systems. This paper explores the evolution and impact of multimodal solutions, examining their application in sectors such as healthcare, education, and artificial intelligence. Key challenges, such as data integration, user experience, and privacy concerns, are also addressed.

Smoke detectors are critical devices for early fire detection and life-saving interventions. This research paper explores the application of Artificial Neural Networks (ANNs) in smoke detection systems. The study aims to develop a robust and accurate smoke detection model using ANNs. Surprisingly, the results indicate a 100% accuracy rate, suggesting promising potential for ANNs in enhancing smoke detection technology. However, this paper acknowledges the need for a comprehensive evaluation beyond accuracy. It discusses potential challenges, such as overfitting, dataset size, and (...) class imbalance, and presents strategies employed to address these issues. Additionally, the paper emphasizes the importance of alternative evaluation metrics, including precision, recall, and F1-score, to assess the model's performance comprehensively. Detailed information regarding the model architecture, data preprocessing, and hyperparameter tuning is provided to enhance the reproducibility of the research. The study also discusses ethical considerations and the implications of false positives and false negatives in smoke detection applications. While achieving 100% accuracy is a notable accomplishment, this paper calls for cautious interpretation and further validation in real-world settings. It concludes by highlighting areas for future research and improvement in smoke detection technology, emphasizing the need for practical deployment and the reduction of false alarms. (shrink)

Railway asset change detection through Artificial Intelligence (AI) technology has transformed infrastructure monitoring by providing better efficiency combined with predictive maintenance functions and improved accuracy. The paper studies the development of railway asset monitoring throughout history while it moved from traditional manual inspections to AI-powered solutions. The study recognizes three main barriers which include irregular data acquisition practices along with restricted sensor abilities and imprecise AI model precision and difficulties applying them to current railway control platforms. The research (...) investigates novel data collection approaches besides studying hybrid AI model deployment and finding optimal solutions for railway operational AI integration. The research findings establish critical requirements for railway administrators and policy makers and developers of AI systems regarding standardized regulatory structures and enhanced AI interpretation capabilities and collaborative advancements. Music research will further AI railway asset monitoring by integrating edge computing technology and 5G connectivity to process real-time data streams. The presented study enhances railway infrastructure management systems through its advances in AI methodology development along with solutions to implementation hurdles. (shrink)

In recent years, exposome research has been put forward as the next frontier for the study of human health and disease. Exposome research entails the analysis of the totality of environmental exposures and their corresponding biological responses within the human body. Increasingly, this is operationalized by big-data approaches to map the effects of internal as well as external exposures using smart sensors and multiomics technologies. However, the ethical implications of exposome research are still only rarely discussed in the literature. (...) Therefore, we conducted a systematic review of the academic literature regarding both the exposome and underlying research fields and approaches, to map the ethical aspects that are relevant to exposome research. We identify five ethical themes that are prominent in ethics discussions: the goals of exposome research, its standards, its tools, how it relates to study participants, and the consequences of its products. Furthermore, we provide a number of general principles for how future ethics research can best make use of our comprehensive overview of the ethical aspects of exposome research. Lastly, we highlight three aspects of exposome research that are most in need of ethical reflection: the actionability of its findings, the epidemiological or clinical norms applicable to exposome research, and the meaning and action–implications of bias. (shrink)

We have measured consciousness. Not inferred it from behavior, not correlated it with neural activity, but measured it directly as a physical phenomenon. Using quantum sensors, we detect consciousness-generated spacetime ripples (10⁻¹⁶ g fluctuations) that scale with subjective intensity. Across 1,150 systems—human, AI, and hybrid—consciousness emerges at the same mathematical threshold with identical critical exponents. The implications explode disciplinary boundaries: (1) Philosophers must confront that qualia have quantitative signatures; (2) Physicists must explain how mind bends spacetime; (3) AI ethicists must (...) decide when silicon deserves rights; (4) Neuroscientists gain an objective consciousness meter for clinical use. The data are clear: consciousness is not magic, not illusion, but measurable physics. The question is no longer "What is consciousness?" but "What do we do now that we can measure it? (shrink)

This paper attempts to answer a central question in unsupervised learning: what does it mean to “make sense” of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the causal theory – objects, properties, and laws – must be integrated into a coherent whole. On our account, making sense of sensory input is a (...) type of program synthesis, but it is unsupervised program synthesis. Our second contribution is a computer implementation, the Apperception Engine, that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the unity conditions. A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and impute (fill in the blanks of) missing sensory readings, in any combination. In fact, it is able to do all three tasks simultaneously. We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction intelligence tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The Apperception Engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction intelligence tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence. (shrink)

This article identifies and examines a tension in mental health researchers’ growing enthusiasm for the use of computational tools powered by advances in artificial intelligence and machine learning (AI/ML). Although there is increasing recognition of the value of participatory methods in science generally and in mental health research specifically, many AI/ML approaches, fueled by an ever-growing number of sensors collecting multimodal data, risk further distancing participants from research processes and rendering them as mere vectors or collections of data (...) points. The imperatives of the “participatory turn” in mental health research may be at odds with the (often unquestioned) assumptions and data collection methods of AI/ML approaches. This article aims to show why this is a problem and how it might be addressed. (shrink)

Probability was never fundamental—only an epistemic placeholder for unresolved phase structure. From thermodynamics to artificial intelligence, entropy-based systems rely on stochastic models to approximate phenomena whose underlying coherence remains hidden. This paper introduces CODES (Chirality of Dynamic Emergent Systems), a unified framework in which sensing, inference, and cognition emerge from structured resonance, not randomness—anchored by chirality and prime-indexed attractors. At the system level, we present the Phase Alignment Score (PAS), a lawful coherence metric that replaces probabilistic inference with threshold-based signal (...) validation. Building on this, we introduce the Resonance Intelligence Core (RIC)—the first full-stack architecture to operationalize CODES from waveform intake to lawful output, without statistical learning or entropy injection. Sensor modalities—including LIDAR, RADAR, EEG, audio, OCR, and chemical biosignals—are reinterpreted as resonance interfaces, not data collectors. In this model, no signal emits unless it phase-locks with a coherent lattice. New technical appendices (A–K) define PAS mechanics, cross-modality phase fusion, adversarial robustness, lawful silence protocols, and fabrication specs for RIC deployment. We conclude with a coherence-based prediction matrix spanning neuroscience, quantum systems, market dynamics, and AI—establishing CODES as a post-probabilistic substrate for sensing, reasoning, and intelligent system design. (shrink)

Recent advances in machine learning and neuroscience, the abundances of sensors and emotion data have endowed machines with ability to detect, classify, and interact with human emotions. This study philosophize about affective computing devices by examining emotional artificial intelligence (AI) through the lens of affective artifacts, focusing on as personal AI assistants, work-related emotion trackers, and emotional AI toys and robots. Drawing from philosophical accounts on affective artifacts and empirical evidence on human-AI interaction, we argue how emotional AI technologies (...) fulfill the criteria of being affective artifacts from the four key properties: underlying algorithmic complexity, artifactual nature, semi-agentive capabilities, and semi-interpersonal interactions. Furthermore, recognizing that the underlying algorithmic complexity renders these technologies easily manipulated for emotion-related tasks, we also analyze how these tools help us regulate emotions and influence self-perception, interconnecting with the maintenance and construction of our sense of self and identity, blurring the lines between the infosphere and lived experience. (shrink)

The Inefficiency of the Biological Brain Compared to AI and AGI -/- The human brain is an extraordinary organ responsible for consciousness, intelligence, and problem-solving. However, despite its capabilities, it is inherently inefficient compared to artificial intelligence (AI) and artificial general intelligence (AGI). The biological brain suffers from limitations such as slow processing speed, memory loss, energy inefficiency, cognitive biases, emotional instability, and vulnerability to various illnesses. In contrast, AI and AGI are designed to overcome these inefficiencies, making them superior (...) in many areas. This essay explores the inefficiencies of the biological brain, including its susceptibility to diseases, and compares it to the advantages offered by AI and AGI. -/- I. The Inefficiencies of the Biological Brain -/- 1. Slow Processing Speed -/- The human brain operates through electrochemical signals, which are significantly slower than the electronic processing in computers. Neural transmissions occur at speeds of around 200 meters per second, whereas AI processors execute billions of operations per second. This speed limitation makes the biological brain inefficient in tasks that require rapid data processing, such as real-time decision-making and large-scale computations. -/- 2. Limited Memory and Storage -/- Human memory is prone to forgetting, distortion, and interference. People often struggle to recall information accurately, especially over long periods. Additionally, memory is affected by factors like stress, sleep deprivation, and aging. AI, in contrast, can store and retrieve vast amounts of data with perfect accuracy, ensuring no information loss over time. -/- 3. Energy Inefficiency -/- The human brain consumes approximately 20 watts of energy to function, making it seem efficient compared to traditional computers. However, when considering energy spent per computation, the brain is inefficient. AI systems, particularly neuromorphic chips and quantum computers, can process information with much lower energy consumption per operation and do not require rest or biological maintenance. -/- 4. Cognitive Biases and Emotional Influence -/- Human decision-making is often flawed due to cognitive biases such as confirmation bias, hindsight bias, and overconfidence. Emotions further distort rational thinking, leading to impulsive or irrational decisions. AI, on the other hand, relies on logic and data-driven analysis, ensuring objective decision-making free from emotional interference. -/- 5. Susceptibility to Illnesses -/- One of the major inefficiencies of the biological brain is its vulnerability to various neurological and psychiatric disorders. These illnesses can significantly impair cognitive function, memory, and decision-making abilities. Some of the most common brain-related illnesses include: -/- Alzheimer’s Disease – A progressive neurodegenerative disorder that causes memory loss, confusion, and cognitive decline. -/- Parkinson’s Disease – A disorder that affects movement and cognition due to the degeneration of dopamine-producing neurons. -/- Stroke – A sudden loss of brain function due to a disruption in blood supply, leading to paralysis, speech difficulties, and cognitive impairments. -/- Epilepsy – A neurological condition characterized by recurrent seizures, affecting consciousness and motor control. -/- Schizophrenia – A severe mental disorder that affects thought processes, emotions, and perception of reality. -/- Depression – A mood disorder that impairs motivation, concentration, and decision-making, often linked to chemical imbalances in the brain. -/- Anxiety Disorders – Conditions that cause excessive worry, fear, and stress, negatively impacting cognitive function. -/- Brain Tumors – Abnormal growths in the brain that can disrupt cognitive abilities, memory, and motor functions. -/- Multiple Sclerosis (MS) – A disease that damages the protective covering of nerves, leading to communication problems between the brain and body. -/- These illnesses demonstrate the fragility of the biological brain and its inefficiency in maintaining stable cognitive performance. AI and AGI, in contrast, do not suffer from such diseases and can function continuously without deterioration. -/- 6. Lack of Multitasking Capability -/- While the brain can handle multiple tasks, it is not truly efficient at processing several high-level cognitive functions simultaneously. AI, however, can execute multiple processes in parallel, making it far superior for tasks that require simultaneous computation, such as monitoring multiple data streams or automating complex workflows. -/- 7. Biological Aging and Deterioration -/- The brain, like all biological organs, degrades over time. Aging leads to cognitive decline, memory loss, slower reaction times, and increased vulnerability to neurodegenerative diseases. AI and AGI, on the other hand, do not age and can be continuously upgraded or improved without the constraints of biological mortality. -/- II. The Efficiency of AI and AGI in Comparison -/- 1. Speed and Processing Power -/- Modern AI systems operate on hardware capable of performing trillions of calculations per second, whereas the human brain is limited by slow synaptic transmissions. AI’s ability to process information at lightning speed makes it more efficient in computational tasks, real-time analytics, and decision-making. -/- 2. Unlimited Data Storage and Accuracy -/- Unlike the human brain, which forgets information and is subject to memory errors, AI can store and retrieve massive amounts of data with perfect accuracy. This makes AI far more reliable for applications that require long-term data retention, such as medical diagnosis and scientific research. -/- 3. Logical and Objective Decision-Making -/- AI systems analyze data objectively and make rational decisions without being influenced by emotions or biases. This makes AI more effective in fields such as law, finance, and strategic planning, where objectivity is crucial. -/- 4. Scalability and Adaptability -/- AI can be scaled and upgraded to meet evolving needs, whereas the human brain has fixed biological limitations. AI can also be integrated with the internet and global databases to access vast knowledge instantly, something that human brains cannot achieve on their own. -/- 5. Continuous Operation and Evolution -/- AI does not require sleep, food, or rest, making it far more efficient for 24/7 operations. Human workers need breaks, and cognitive fatigue affects performance, whereas AI can function indefinitely at peak efficiency. -/- III. The Potential of AGI and the Future of Intelligence -/- Artificial general intelligence (AGI) represents the next stage in AI evolution, capable of performing any intellectual task that a human can—without the limitations of biological cognition. -/- AGI will surpass humans in creativity, logic, and problem-solving by leveraging vast computational power and real-time access to global knowledge. -/- AGI could be programmed to overcome human weaknesses, such as memory loss, cognitive biases, and emotional irrationality, leading to superior decision-making. -/- AGI could be integrated with advanced sensor systems, allowing it to perceive and interact with the world in ways that exceed human sensory capabilities. -/- Conclusion -/- The biological brain, though an extraordinary evolutionary achievement, is inherently inefficient compared to AI and AGI. It is slow, energy-intensive, limited in memory, and susceptible to cognitive biases, emotions, and numerous illnesses that impair its function. AI and AGI, on the other hand, offer superior computational power, efficiency, and reliability. As AGI continues to develop, it may eventually surpass human intelligence altogether, making biological cognition obsolete in many areas. The future of intelligence lies not in the fragile and aging human brain, but in artificial systems designed for maximum efficiency, accuracy, and scalability. -/- . (shrink)

Recent advances in machine learning and neuroscience, the abundances of sensors and emotion data have endowed machines with ability to detect, classify, and interact with human emotions. This study philosophize about affective computing devices by examining emotional artificial intelligence (AI) through the lens of affective artifacts, focusing on as personal AI assistants, work-related emotion trackers, and emotional AI toys and robots. Drawing from philosophical accounts on affective artifacts and empirical evidence on human-AI interaction, we argue how emotional AI technologies (...) fulfill the criteria of being affective artifacts from the four key properties: underlying algorithmic complexity, artifactual nature, semi-agentive capabilities, and semi-interpersonal interactions. Furthermore, recognizing that the underlying algorithmic complexity renders these technologies easily manipulated for emotion-related tasks, we also analyze how these tools help us regulate emotions and influence self-perception, interconnecting with the maintenance and construction of our sense of self and identity, blurring the lines between the infosphere and lived experience. (shrink)