This book provides a critical reflection on automated science and addresses the question whether the computational tools we developed in last decades are changing the way we humans do science. More concretely: Can machines replace scientists in crucial aspects of scientific practice? The contributors to this book rethink and refine some of the main concepts by which science is understood, drawing a fascinating picture of the developments we expect over the next decades of human-machine co-evolution. The (...) volume covers examples from various fields and areas, such as molecular biology, climate modeling, clinical medicine, and artificial intelligence. The explosion of technological tools and drivers for scientific research calls for a renewed understanding of the human character of science. This book aims precisely to contribute to such a renewed understanding of science. (shrink)

Artificial intelligence (AI) technologies are used in various domains of human activities, and one of these domains is scientific research. Now, researchers in many scientific areas try to apply AI technologies to their research and automate it. These researchers claim that the ‘automation of science’ will liberate people from non-creative tasks in scientific research, and radically change the overall state of science and technology so that large-scale innovation results. As I see it, the automation of science is (...) remarkable in another respect: since science is one of the most distinctive human activities, the tendency of automating it prompts us to reconsider the aspects of our humanity itself. One of these aspects is concerned with human creativity, on which this article focuses. In this article, I address two questions concerning the automation of science: first, ‘Can AI makes creative discovery?’; second, ‘What implications may the automation of science have on science and society?’. Scientific discovery is said to be one of the most creative phases of scientific research. I show that, though there are no reasons in principle why AI could not make creative discovery, we do not at present have enough knowledge how to realize it. If the automation of science nevertheless proceeds, the cultural values science as a creative activity has might be reduced, and it might alter the state of scientific community and its relationship with society in some undesirable way. Therefore, I conclude, we need to specify desirable ways of introducing AI technologies into science and devise measures against demerits of automating science. (shrink)

In our paper, we reassess the role of non-epistemic values in scientific practice by drawing lessons from machine learning and the automation of science. Due to several influential arguments (e.g., Rudner 1953, or Longino 1990), traditional philosophy of science has largely converged on the view that non-epistemic values are necessary for the justification of scientific claims. Recently, renewed support of this view has been made by appealing to the No Free Lunch theorems in the context of machine learning—that (...) there is no universally optimal machine learning algorithm (Dotan 2020). The argument claims that the No Free Lunch theorems entail that epistemic values are insufficient for discriminating hypotheses. In the negative part of our paper, we critique Dotan’s argument. We argue that NFL theorems do not entail that ‘all hypotheses have the same average expected error’. We also discuss what NFL theorems do in fact say about inductive inference and theory choice. In the positive part, we argue that the possibility of ‘value-free science’ (understood as a science that involves only epistemic values in the context of justification) seems to be supported by developments in the automation of science. For example, Robot Scientist presented in King et al. (2004) can automatically propose hypotheses, design and conduct experiments, and interpret the results. We argue that in these systems, only epistemic values are involved in hypothesis construction and testing. (shrink)

Certain characteristics make machine learning a powerful tool for processing large amounts of data, and also particularly unsuitable for explanatory purposes. There are worries that its increasing use in science may sideline the explanatory goals of research. We analyze the key characteristics of ML that might have implications for the future directions in scientific research: epistemic opacity and the ‘theory-agnostic’ modeling. These characteristics are further analyzed in a comparison of ML with the traditional statistical methods, in order to demonstrate (...) what it is specifically that makes ML methodology substantially unsuitable for reaching explanations. The analysis is given broader philosophical context by connecting it with the views on the role of prediction and explanation in science, their relationship, and the value of explanation. We proceed to show, first, that ML disrupts the relationship between prediction and explanation commonly understood as a functional relationship. Then we show that the value of explanation is not exhausted in purely predictive functions, but rather has a ubiquitously recognized value for both science and everyday life. We then invoke two hypothetical scenarios with different degrees of automatization of science, which help test our intuitions on the role of explanation in science. The main question we address is whether ML will reorient or otherwise impact our standard explanatory practice. We conclude with a prognosis that ML would diversify science into purely predictively oriented research based on ML-like techniques and, on the other hand, remaining faithful to anthropocentric research focused on the search for explanation. (shrink)

Important decisions that impact humans lives, livelihoods, and the natural environment are increasingly being automated. Delegating tasks to so-called automated decision-making systems can improve efficiency and enable new solutions. However, these benefits are coupled with ethical challenges. For example, ADMS may produce discriminatory outcomes, violate individual privacy, and undermine human self-determination. New governance mechanisms are thus needed that help organisations design and deploy ADMS in ways that are ethical, while enabling society to reap the full economic and social (...) benefits of automation. In this article, we consider the feasibility and efficacy of ethics-based auditing as a governance mechanism that allows organisations to validate claims made about their ADMS. Building on previous work, we define EBA as a structured process whereby an entity’s present or past behaviour is assessed for consistency with relevant principles or norms. We then offer three contributions to the existing literature. First, we provide a theoretical explanation of how EBA can contribute to good governance by promoting procedural regularity and transparency. Second, we propose seven criteria for how to design and implement EBA procedures successfully. Third, we identify and discuss the conceptual, technical, social, economic, organisational, and institutional constraints associated with EBA. We conclude that EBA should be considered an integral component of multifaced approaches to managing the ethical risks posed by ADMS. (shrink)

With the increasing reliance on technology-based sports officiating, ensuring accuracy and fairness in decision-making is critical. Traditional video refereeing methods often suffer from human subjectivity and limitations in object detection. This paper presents an automated refereeing system utilizing Fuzzy C-Means (FCM) clustering to segment images from sports videos. The proposed system effectively identifies players, ball positions, and boundary lines, thereby enabling more accurate and consistent decisions. By integrating FCM-based segmentation, the system significantly enhances object detection precision, reduces human error, (...) and improves the reliability of referee judgments. Experimental results demonstrate that the proposed approach substantially improves officiating accuracy in dynamic sports environments. The key innovation of this research lies in applying FCM clustering for real-time competition analysis, thereby increasing the efficiency and adaptability of automated refereeing systems across various sports. (shrink)

This project focuses on improving vehicle damage assessment efficiency and user experience through the development of an AI-powered web application. The system is designed to streamline workflows for users by automating tasks such as damage detection, severity classification, and repair cost estimation. It leverages Convolutional Neural Networks (CNN) and Detectron2 to accurately identify and segment damaged vehicle parts, while providing users with detailed results, including estimated repair costs derived from a predefined dataset. The application further enhances efficiency by eliminating manual (...) inspections, reducing processing time, and improving accuracy. Built with Flask for backend development, it ensures seamless data handling and scalability. Ultimately, the integration of advanced AI and data analytics improves decision-making and operational outcomes, offering a reliable solution for insurance automation, automobile services, and fleet management. (shrink)

The integration of Robotic Process Automation (RPA) into Human Resources (HR) functions represents a significant advancement in organizational efficiency and effectiveness. RPA technology automates repetitive and rule-based tasks, allowing HR professionals to focus on strategic initiatives that add value to the organization. This paper provides a comprehensive introduction to RPA within HR, detailing its applications, benefits, implementation strategies, and how its principles can be transferred to other domains such as finance. By examining the transformative potential of RPA, organizations can better (...) prepare for the future of work, where automation and human ingenuity coexist to drive success. (shrink)

The increasing complexity of modern networks has amplified the challenges associated with ensuring robust and scalable security. With the rapid evolution of cyber threats, traditional methods of network security management are often inadequate, leading to inefficiencies and vulnerabilities. Automation has emerged as a transformative approach to streamline network operations, enhance security postures, and reduce the margin of human error. This study explores the integration of Ansible, a powerful open-source automation tool, into network security workflows to deliver a comprehensive framework for (...) secure network automation. This research begins by examining the limitations of conventional network security management, emphasizing the time-intensive and error-prone nature of manual configurations. It highlights how automation, specifically using Ansible, addresses these issues by enabling consistent, repeatable, and scalable processes. Ansible’s ability to seamlessly integrate with diverse networking devices and platforms makes it an ideal choice for automating complex network environments. The methodology involves the development of automated playbooks for various security tasks, including firewall rule management, vulnerability scanning, configuration compliance checks, and incident response. By leveraging Ansible's declarative language, these playbooks are designed to be user-friendly and adaptable, ensuring ease of deployment across networks of varying sizes and complexities. The study also incorporates real-world use cases to demonstrate the tool's effectiveness in mitigating security risks while significantly reducing administrative overhead. Furthermore, the paper evaluates the impact of automation on key performance metrics such as response time to security incidents, accuracy of configurations, and overall system uptime. The findings indicate that integrating Ansible into network security workflows not only enhances operational efficiency but also fortifies security by minimizing misconfigurations and improving threat response times. These results underscore the importance of adopting automation as a critical component of modern network security strategies. In addition to technical insights, the study addresses potential challenges, such as initial implementation costs, skill gaps, and the need for continuous updates to automation scripts to align with evolving security requirements. It provides recommendations for organizations to overcome these obstacles, including investing in employee training and adopting iterative implementation strategies. The research concludes by affirming the transformative potential of automation tools like Ansible in revolutionizing network security management. By reducing reliance on manual processes, organizations can focus their efforts on proactive threat hunting and strategic planning, thereby fostering a more resilient security infrastructure. This paper serves as a comprehensive guide for network administrators and IT professionals seeking to leverage Ansible for secure network automation, providing both theoretical insights and practical solutions to enhance network security in an increasingly dynamic cyber landscape. (shrink)

Governments and social scientists are increasingly developing machine learning methods to automate the process of identifying terrorists in real time and predict future attacks. However, current operationalizations of “terrorist”’ in artificial intelligence are difficult to justify given three issues that remain neglected: insufficient construct legitimacy, insufficient criterion validity, and insufficient construct validity. I conclude that machine learning methods should be at most used for the identification of singular individuals deemed terrorists and not for identifying possible terrorists from some more general (...) class, nor to predict terrorist attacks more broadly, given intolerably high risks that result from such approaches. (shrink)

The Automated Title Verification System is an advanced solution designed to streamline the validation of new title submissions by ensuring their uniqueness and compliance with regulatory guidelines. Leveraging Natural Language Processing (NLP) and phonetic algorithms such as Soundex and Metaphone, the system efficiently checks titles against an extensive database of over 160,000 entries to prevent duplicates, misleading combinations, or the use of disallowed words. By automating the verification process, the system eliminates the inefficiencies and inconsistencies of manual review, providing (...) instant feedback to users. Key features include similarity scoring, rule enforcement (e.g., blocking titles that modify existing ones with periodicity indicators), and a user-friendly interface. The solution significantly reduces processing time and human error while enhancing scalability for large volumes of submissions. Future enhancements aim to incorporate AI-driven contextual analysis, blockchain for immutable records, and multilingual support, further solidifying its role as a robust tool for publishers, media houses, and regulatory authorities. (shrink)

The project Automated Document Classification using YOLOv3 and Tkinter for Real-Time Segmentation presents an innovative solution for automating the extraction of visual content from PDF documents using advanced computer vision techniques. Leveraging the power of the YOLOv3 object detection model, combined with pdf2image, OpenCV, and Tkinter, the system efficiently converts PDF pages into high-resolution images, detects and labels objects within them, and presents the results through a user-friendly graphical interface. This approach significantly reduces manual labor and error in tasks (...) like document annotation, visual data extraction, and image analysis. The system's ability to handle complex layouts and detect multiple object classes demonstrates its robustness and scalability. It proves especially beneficial in fields such as legal document processing, educational content digitization, and intelligent archival systems. With further enhancements like OCR integration, cloud deployment, and support for semantic understanding, the solution has strong potential to evolve into a powerful AI driven document analysis platform. (shrink)

The Examination Automation Management System (EAMS) is developed to modernize and simplify the exam processes in educational institutions. Traditional exam management often involves a lot of manual work, which can lead to errors, delays, and inefficiencies. EAMS addresses these challenges by automating critical tasks such as exam scheduling, seating arrangements, invigilation duty allocation, and hall ticket generation. The system offers a userfriendly platform for administrators, faculty, and students, making the examination process smoother, faster, and more reliable. By centralizing all exam-related (...) activities and providing real-time updates and notifications, EAMS improves communication, reduces manual errors, ensures better data security, and saves valuable time and resources. This project demonstrates how automation can enhance the transparency, efficiency, and overall management of examinations, setting a strong foundation for future advancements like AI-driven monitoring and online exams. (shrink)

The early and accurate detection of plant diseases plays a vital role in minimizing crop damage and enhancing agricultural productivity. Traditional methods for identifying plant diseases—such as manual observation and expert consultations—are often time-consuming, costly, and reliant on the availability of skilled personnel. To overcome these limitations, this study presents an automated system for plant disease detection using advanced machine learning techniques. The proposed framework utilizes convolutional neural networks (CNNs), specifically pretrained models like ResNet and SqueezeNet, to analyze images (...) of plant leaves and classify them as healthy or diseased. The system begins with the collection and preprocessing of a diverse dataset comprising both healthy and infected leaf samples. Image augmentation techniques such as rotation, scaling, and flipping are applied to increase dataset variability and enhance model robustness. Subsequently, feature extraction is conducted through CNN-based models that capture color, shape, and texture-based characteristics from the leaf images. Transfer learning is employed to finetune the models on the specific dataset, leveraging previously learned features for improved accuracy. The performance of the trained models is evaluated using key metrics including accuracy, precision, recall, and F1- score. Experimental results indicate a high degree of classification accuracy and generalization capability on unseen data. The final model is deployed through a user-friendly interface that can be integrated into mobile or web-based applications, allowing farmers to upload leaf images and receive instant disease diagnosis and possible treatment recommendations. The proposed system holds significant promise for real-world applications, particularly in regions with limited access to agricultural experts. Moreover, its scalability and adaptability pave the way for future extensions to other crops and disease types. This research underscores the transformative potential of machine learning in precision agriculture, offering a cost-effective, scalable, and efficient solution for early plant disease detection. (shrink)

Automation testing has become an integral part of modern software development, significantly improving efficiency, reducing human error, and enhancing testing accuracy. Over the last seven years, significant advancements have been made in automation testing optimization, focusing on enhancing the effectiveness of testing procedures and optimizing resource allocation. This paper provides a comprehensive review of the recent techniques and trends in automation testing optimization. The review highlights the evolution of automation testing strategies, investigates novel optimization methods, identifies datasets commonly used in (...) research, and discusses the emerging trends that are shaping the future of automation testing. A comparative analysis of various optimization models and their performance is also presented, leading to the identification of the most effective approaches in current research. The paper concludes with insights into the future of automation testing optimization, exploring areas of potential improvement and innovation. (shrink)

The purpose of this research is to design a system using machine learning to automate sign language gesture recognition and facilitate unproblematic interaction between the hearing/speech impaired community and society as a whole. This research works towards solving the communication disability faced by the deaf and mute community through a computer vision framework for recognizing hand gestures and mapping them to relevant text or speech. The work employs a rich dataset of static American Sign Language (ASL) hand signs and uses (...) Convolutional Neural Networks (CNNs) for efficient feature extraction and gesture classification. The suggested model is trained and evaluated on a huge collection of labeled gesture images and exhibits high accuracy with robust generalization to novel data. One of the primary contributions of the work is the implementation of the trained model into an interactive, realtime graphical user interface developed with Python's Tkinter library. The interface captures live input from a webcam, processes each frame to identify gestures, and shows the identified character on the screen. The results of the evaluation confirm the ability of CNNs to accurately identify ASL alphabets with little delay. The proposed system presents an effective and scalable approach towards real-time sign language interpretation, supporting inclusiveness and accessibility of the disabled community. The present research also creates a scope for extension in development, i.e., inclusion of dynamic gesture detection and portability to mobile or embedded platforms to provide enhanced use in real-life situations. (shrink)

This thesis examines the prospects for mechanical procedures that can identify true, complete, universal, first-order logical theories on the basis of a complete enumeration of true atomic sentences. A sense of identification is defined that is more general than those which are usually studied in the learning theoretic and inductive inference literature. Some identification algorithms based on confirmation relations familiar in the philosophy of science are presented. Each of these algorithms is shown to identify all purely universal theories without (...) function symbols. It is demonstrated that no procedure can solve this universal theory inference problem in the more usual senses of identification. The question of efficiency for theory inference systems is addressed, and some definitions of limiting complexity are examined. It is shown that several aspects of obvious strategies for solving the universal theory inference problem are NP-hard. Finally, some non-worst case heuristic search strategies are examined in light of these NP-completeness results. These strategies are based upon an isomorphism between clausal entailments of a certain class and partition lattices, and are applicable to the improvement of earlier work on language acquisition and logical inductive inference. (shrink)

The identification of disease on the plant is a very important key to prevent a heavy loss of yield and the quantity of agricultural product. The symptoms can be observed on the parts of the plants such as leaf, stems, lesions and fruits. The leaf shows the symptoms by changing color, showing the spots on it. This identification of the disease is done by manual observation and pathogen detection which can consume more time and may prove costly. The aim of (...) the project is to identify and classify the disease accurately from the leaf images. The steps required in the process are Preprocessing, Training and Identification. For identification of disease features of leaf such as major axis, minor axis etc. are extracted from leaf and given to classifier for classification. In our project we have used cassava leaf for identifying its disease. We have used algorithms namely SqueezeNet as existing and ResNet-50 as proposed system in terms of accuracy. From the results its proved that ResNet-50 works better than SqueezeNet. The tool we have used for the detection of leaf disease is MATLAB. (shrink)

: In recent years, artificial intelligence (AI) has become a critical enabler of innovation in supply chain and logistics management. By leveraging AI capabilities, enterprises can automate key processes, optimize operations, and make data-driven decisions that lead to enhanced efficiency, reduced costs, and improved customer satisfaction. Microsoft Azure provides a comprehensive suite of AI-driven tools and services designed to streamline and automate various aspects of supply chain and logistics operations. This paper explores how Azure's AI tools are reshaping the landscape (...) of supply chain management, focusing on key areas such as demand forecasting, inventory management, route optimization, predictive maintenance, and real-time monitoring. Furthermore, the paper delves into the benefits, challenges, and implementation strategies for integrating Azure AI solutions within enterprise ecosystems, with realworld case studies demonstrating successful AI-powered transformations. (shrink)

The rapid expansion of digital data has made metadata crucial in organizing, managing, and retrieving information effectively. Machine learning (ML) offers powerful tools to automate and enhance metadata processing, leading to improved accuracy, scalability, and efficiency. This paper explores how ML algorithms are applied to metadata extraction, classification, annotation, and enrichment. We review current research, examine the methodologies employed, and present a comparative analysis of techniques. Our findings suggest that supervised learning models, especially deep learning architectures, outperform traditional rule-based systems (...) in most scenarios. However, challenges remain in terms of interpretability, bias, and data quality. (shrink)

: As organizations continue to embrace cloud computing, the migration of workloads, applications, and data from on-premises environments to the cloud remains one of the most critical tasks. However, migrating to the cloud is often seen as a daunting task due to the complexity, the risk of downtime, and the potential for service disruptions. Artificial Intelligence (AI) and automation have emerged as transformative technologies for cloud migration, providing solutions that enable a seamless, efficient, and zero-downtime transition. This paper explores the (...) role of AI in automating the cloud migration process, detailing how machine learning (ML) and AI-powered tools can optimize migration planning, reduce errors, and ensure continuous service availability. It highlights the key benefits of AI in workload assessment, data transfer, and post-migration optimization. Through the integration of advanced AI models, automation tools, and real-time analytics, organizations can achieve a smooth cloud migration process with minimal disruption to their business operations. The paper also examines real-world case studies where AI-powered cloud migration has been successfully implemented. (shrink)

Foundation models, particularly large language models (LLMs), are transforming how data engineering tasks are automated across domains. This paper explores the use of LLMs to automate the creation, management, and optimization of Extract-Transform-Load (ETL) pipelines in a domain-agnostic manner. We provide conceptual frameworks and practical strategies for integrating foundation models into the ETL lifecycle, and we highlight use cases where such models (via platforms like Amazon Bedrock) generate pipeline code, enhance data transformation quality, and adapt pipeline execution. Through a (...) review of related work and an illustrative implementation, we demonstrate that LLMs can translate natural language specifications into pipeline workflows, assist in data harmonization and quality control, and optimize pipeline performance with minimal human intervention. We discuss evaluation metrics for these LLM-driven pipelines and examine the challenges of reliability, scalability, and governance. The results indicate that foundational models offer a promising avenue to drastically reduce development time and improve the robustness of data pipelines, suggesting a paradigm shift in AI-assisted data engineering. (shrink)

This research paper presents the use of Playwright, JavaScript & Cucumber to perform scalable and efficient functional automation, comparing to traditional tools. The paper shows that Playwright reduces test execution time, improves cross browser support, and gets the testing process more efficient, applying quantitative analysis, geographical distribution, and case studies. It finds Playwright to transform how software quality is built, loading faster releases, robust security validations, and pleasurable user experiences in a variety of environments.

Advances in AI are powering increasingly precise and widespread computational propaganda, posing serious threats to national security. The military and intelligence communities are starting to discuss ways to engage in this space, but the path forward is still unclear. These developments raise pressing ethical questions, about which existing ethics frameworks are silent. Understanding these challenges through the lens of “cognitive security,” we argue, offers a promising approach.

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)

The Automated Data Analytics System leverages Generative AI, specifically OpenAI’s GPT-4, to empower non-technical users to interact with enterprise data through natural language queries. Seamlessly integrated with a Snowflake data warehouse and built on a Streamlit-based user interface, the tool automatically translates user input into SQL queries, executes them, and delivers structured insights along with dynamic visualizations. This eliminates the need for manual SQL writing and custom development, significantly lowering the technical barriers traditionally associated with data analysis. The intuitive (...) interface enables business analysts and decision-makers to independently explore data, identify trends, and generate real-time reports without relying on developers or data teams. By automating the entire analytics workflow from natural language query generation to result visualization the platform simplifies data exploration, enhances user autonomy, and fosters a data-driven culture across the organization. As a result, businesses can make faster, smarter decisions and extract actionable insights from raw data with minimal effort and no coding required. (shrink)

The modernization of electrical power systems is rapidly evolving through the integration of artificial intelligence (AI) and automation-enhanced instrumentation. These technologies are essential to meet the growing demand for grid reliability, resilience, and efficiency amidst the complexities introduced by renewable energy integration, distributed generation, and real-time grid management. This research paper investigates the multifaceted role of AI and automation in transforming electrical instrumentation, focusing on their impact on system diagnostics, fault prediction, load forecasting, and dynamic stability. By combining intelligent sensors, (...) automated control systems, and machine learning algorithms, modern electrical power networks are achieving unprecedented levels of situational awareness and operational agility. Real-time data from phasor measurement units (PMUs), smart meters, and intelligent electronic devices (IEDs) feed into AI models capable of detecting anomalies, optimizing load distribution, and supporting self-healing grid mechanisms. This paradigm shift enhances the ability of utilities to prevent outages, minimize maintenance costs, and ensure grid security. This paper also identifies key challenges associated with this transformation, including data interoperability, cybersecurity threats, model transparency, and the need for skilled personnel. Through a review of recent advancements, case studies, and implementation frameworks, the paper highlights current gaps and outlines future research directions. The findings suggest that while AI and automation bring remarkable opportunities, their successful implementation in electrical power systems requires a holistic approach—balancing technical innovation with regulatory compliance, ethical considerations, and infrastructure upgrades. This study provides a roadmap for utilities and policymakers aiming to harness the power of smart instrumentation to build resilient, adaptive, and future-ready power grids. (shrink)

This essay redefines Microsoft Power BI as not merely a business intelligence tool, but as a cognitive prosthesis and an epistemic infrastructure that influences management cognition, attention, and the generation of organisational knowledge. Although traditional literature regards Power BI mainly as a visualisation or decision-support instrument, this study employs a critical conceptual and metascientific perspective to analyse how the platform pre-defines legitimate evidence, influences organisational reasoning, and reallocates interpretive labour within knowledge-driven organisations. The analysis puts up three connected arguments. To (...) begin with, Power BI acts like a cognitive prosthesis by automating basic operations like trend identification, forecasting, and comparison measurements. This frees managers from low-level duties while also making them dependent on its dashboards and algorithmic logic. Second, it works as a micro-technology of cognitive capitalism by capturing, directing, and turning management attention into a resource of economic and organisational value. Third, the platform incorporates epistemic authority into its visualisations and metrics, gently regulating the generation of organisational knowledge and influencing the ethical and political aspects of managerial decision-making. By integrating perspectives from cognitive science, critical theory, science and technology studies, philosophy of technology, and political economy, the article illustrates that the automation of cognition is inherently biased, resulting in epistemic constriction, ethical challenges, and technocratic validation masquerading as “data-driven” objectivity. The research enhances metascientific discourse by elucidating the infrastructure of knowledge production within organisations and advocating for reflexive literacy in the utilisation of data analytics platforms. It emphasises the ethical, epistemological, and organisational implications of cognitive prostheses such as Power BI and promotes a more critical approach to the technologies that increasingly shape managerial cognition and decision making. (shrink)

Home management and controlling have seen a great introduction to network that enabled digital technology, especially in recent decades. For the purpose of home automation, this technique offers an exciting capability to enhance the connectivity of equipment within the home. Also, with the rapid expansion of the Internet, there are potentials that added to the remote control and monitoring of such network-enabled devices. In this paper, we had been designed and implemented a fully manageable and secure smart home automation system (...) based on a cloud computing system with an ESP Arduino system. The security of home had been improved by adding a complete camera system with a GSM communication technique to connect the Arduino output data to an external specified number if there is no internet provider. We used three sensors for temperature, gas, and motion measurements. The ESP8226 Wi-Fi device programmed the sensors to maintain the sensors measurements and transfer them to the cloud server database which is programmed to the web server via Appatshy and Mysql formats. The system implemented with high time response so that all readings updated and appeared spontaneously. The designed system should be effective, a secure, and rapid response real-time smart home system should be achieved. (shrink)

The advent of generative artificial intelligence (AI) in the financial technology (FinTech) sector has created unprecedented opportunities for automating and enhancing financial advisory systems. This research focuses on the application of generative AI to develop automated financial advisory platforms, integrating natural language processing (NLP) and reinforcement learning (RL) for the formulation of personalized investment strategies. Traditional financial advisory models, often characterized by manual processes, human bias, and limited scalability, are increasingly unable to meet the demands of a fast-paced and (...) diverse investment landscape. In response, AI-driven systems present a transformative approach, leveraging the power of generative models to process vast amounts of data and provide real-time, tailored financial recommendations to both retail and institutional investors. This study delves into the technical mechanisms underpinning the integration of generative models with NLP and RL frameworks. Generative models, including variational autoencoders (VAEs) and generative adversarial networks (GANs), play a critical role in simulating complex financial scenarios and generating investment strategies that reflect dynamic market conditions. By synthesizing vast amounts of historical market data, these models create high-dimensional representations of financial environments, which are then used to train reinforcement learning agents. The RL agents learn optimal investment strategies through continuous interaction with these simulated environments, dynamically adjusting to new market information and user preferences. This ability to simulate and optimize investment decisions allows for more sophisticated, personalized strategies, as compared to conventional rule-based systems. Natural language processing enhances the system by enabling it to process unstructured data from various sources, including financial news, reports, and social media, which can significantly impact market trends. NLP models, particularly transformer-based architectures like BERT and GPT, are employed to extract, interpret, and summarize relevant textual information in real-time, feeding it into the generative and RL models. This integration allows the financial advisory system to understand and respond to both quantitative and qualitative factors affecting financial markets. Moreover, the NLP component supports direct interaction between the AI-driven system and users, facilitating personalized communication and user-specific strategy recommendations. This two-way communication is pivotal in enhancing customer engagement, as users can input preferences, risk tolerance, and financial goals, which the system continuously refines and incorporates into its investment strategy formulation. Reinforcement learning plays a pivotal role in the adaptive learning process, allowing the system to improve its decision-making over time by receiving feedback from the environment, such as market performance and user satisfaction. Specifically, model-free RL approaches like Q-learning and policy gradient methods are applied to optimize investment strategies. These approaches enable the system to evaluate multiple potential actions in real-time and select those with the highest expected return, given the current market state and individual user profile. Over time, the RL agent learns to maximize cumulative returns by balancing exploration of new strategies with the exploitation of known profitable actions. By leveraging RL in tandem with generative models, the system can autonomously adjust its strategy in response to changing market conditions and user requirements, thereby delivering a highly customized investment plan that evolves with the investor’s financial landscape. The potential of these AI-driven advisory systems lies not only in their technical sophistication but also in their ability to democratize financial planning. Traditionally, high-quality financial advisory services have been accessible primarily to affluent individuals or large institutions due to the high cost of personalized financial advice. By automating the advisory process through AI, these systems can provide personalized financial planning at scale, making high-quality investment strategies accessible to a broader range of users, including those with limited financial literacy or smaller investment portfolios. This democratization of financial services is particularly significant in the context of retail investors, who can now access sophisticated financial insights and recommendations that were previously reserved for institutional clients. Furthermore, this paper explores the broader implications of AI-driven financial advisory systems on investor behavior and decision-making. By providing real-time, data-driven insights and personalized investment strategies, these systems have the potential to mitigate common cognitive biases in financial decision-making, such as overconfidence, loss aversion, and herd behavior. Through continuous learning and adaptation, AI-driven systems can guide users towards more rational, objective investment decisions, potentially improving overall financial outcomes for both retail and institutional investors. However, the paper also addresses the challenges associated with the deployment of AI in financial advisory systems, including issues of data privacy, algorithmic transparency, and the need for robust regulatory frameworks to ensure the ethical and responsible use of AI in financial decision-making. (shrink)

The time window between the disclosure of a new cyber vulnerability and its use by cybercriminals has been getting smaller and smaller over time. Recent episodes, such as the Log4j vulnerability, exemplify this well. Within hours after the exploit being released, attackers started scanning the internet looking for vulnerable hosts to deploy threats like crypto currency miners and ransom ware on vulnerable systems. Thus, it becomes imperative for the cybersecurity defense strategy to detect threats and their capabilities as early as (...) possible to maximize the success of prevention actions. Although crucial, discovering new threats is a challenging activity for security analysts due to the immense volume of data and information sources to be analyzed for signs that a threat is emerging. In this sense, we present a framework for automatic identification and profiling of emerging threats using Twitter messages as a source of events and MITRE ATT&CK as a source of knowledge for threat characterization. The framework comprises three main parts: identification of cyber threats and their names; profiling the identified threat in terms of its intentions or goals by employing two machine learning layers to filter and classify tweets; and alarm generation based on the threat’s risk . The main contribution of our work is the approach to characterize or profile the identified threats in terms of their intentions or goals, providing additional context on the threat and avenues for mitigation. In our experiments, the profiling stage reached an F1 score of 77% in correctly profiling discovered threats. (shrink)

I am very used to strange books and special people, but Hawkins stands out due to his use of a simple technique for testing muscle tension as a key to the “truth” of any kind of statement whatsoever—i.e., not just to whether the person being tested believes it, but whether it is really true! What is well known is that people will show automatic, unconscious physiological and psychological responses to just about anything they are exposed to—images, sounds, touch, odors, ideas, (...) people. So, muscle reading to find out their true feelings is not radical at all, unlike using it as a dousing stick (more muscle reading) to do “paranormal science”. -/- Hawkins describes the use of decreasing tension in the muscles of an arm in response to increases in cognitive load thus causing the arm to drop in response to the constant pressure of someone’s fingers. He seems unaware that there is a long established and vast ongoing research effort in social psychology referred to by such phrases as ‘implicit cognition’, ‘automaticity’ etc., and that his use of ‘kinesiology’ is one tiny section. In addition to muscle tone (infrequently used) social psychologists measure EEG, galvanic skin response and most frequently verbal responses to words, sentences, images or situations at times varying from seconds to months after the stimulus. Many, such as Bargh and Wegner, take the results to mean we are automatons who learn and act largely without awareness via S1 (automated System 1) and many others such as Kihlstrom and Shanks say these studies are flawed and we are creatures of S2 (deliberative System 2). Though Hawkins seems to have no idea, as in other areas of the descriptive psychology of higher order thought, the situation regarding “automaticity” is still as chaotic as it was when Wittgenstein described the reasons for the sterility and barrenness of psychology in the 30’s. Nevertheless, this book is an easy read and some therapists and spiritual teachers may find it of use. -/- Those wishing a comprehensive up to date framework for human behavior from the modern two systems view may consult my book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle’ 2nd ed (2019). Those interested in more of my writings may see ‘Talking Monkeys--Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet--Articles and Reviews 2006-2019 3rd ed (2019), The Logical Structure of Human Behavior (2019), and Suicidal Utopian Delusions in the 21st Century 4th ed (2019) . (shrink)

Social innovations are usually understood as new ideas, initiatives, or solutions that make it possible to meet the challenges of societies in fields such as social security, education, employment, culture, health, environment, housing, and economic development. On the one hand, many citizen science activities serve to achieve scientific as well as social and educational goals. Thus, these actions are opening an arena for introducing social innovations. On the other hand, some social innovations are further developed, adapted, or altered after (...) the involvement of scientist-supervised citizens in research and with the use of the citizen science tools and methods such as action research, crowdsourcing, and community-based participatory research. Such approaches are increasingly recognized as crucial for gathering data, addressing community needs, and creating engagement and cooperation between citizens and professional scientists. However, there are also various barriers to both citizen science and social innovation. For example, management, quality and protection of data, funding difficulties, non-recognition of citizens’ contributions, and limited inclusion of innovative research approaches in public policies. In this volume, we open theoretical as well as empirically-based discussion, including examples, practices, and case studies of at least three types of relations between citizen science and social innovation: domination of the citizen science features over social innovation aspects; domination of the social innovation features over the citizen science aspects; and the ways to achieve balance and integration between the social innovation and citizen science features. Each of these relationships highlights factors that influence the development of the main scales of sustainability of innovations in the practice. These innovations are contributing to a new paradigm of learning and sharing knowledge as well as interactions and socio-psychological development of participants. Also, there are factors that influence the development of platforms, ecosystems, and sustainability of innovations such as broad use of the information and communications technologies including robotics and automation; emerging healthcare and health promotion models; advancements in the development and governance of smart, green, inclusive and age-friendly cities and communities; new online learning centers; agri-food, cohousing or mobility platforms; and engagement of citizens into co-creation or co-production of services delivered by public, private, non-governmental organizations as well as non-formal entities. (shrink)

Ear diseases, if not diagnosed and treated promptly, can lead to severe health complications, including chronic pain, infection, and permanent hearing loss. Early detection is crucial to preventing these outcomes and ensuring effective treatment. However, traditional diagnostic methods for ear diseases typically depend on manual examination of the ear by experienced medical professionals using otoscopes. These methods are not only timeconsuming and resource-intensive but can also be highly subjective, often leading to inconsistent or inaccurate diagnoses, especially in under-resourced or remote (...) areas. To address these challenges, this project introduces an automated, deep learning-based system for the detection of ear infections. By leveraging the power of Convolutional Neural Networks (CNNs), the system is capable of analyzing medical images of the ear and classifying them into two categories: Normal or Infected. The approach involves training a CNN model on a labeled dataset of ear images, allowing it to learn complex patterns and features associated with various ear conditions. Once trained, the system can accept new ear images as input, process them through the CNN model, and output a diagnosis within seconds. This not only significantly reduces the time required for diagnosis but also enhances accuracy and consistency, minimizing the risk of human error. The proposed system is designed to support medical professionals by acting as a diagnostic aid, particularly in regions with limited access to specialized healthcare services. Ultimately, this project aims to demonstrate how artificial intelligence and deep learning can be harnessed to improve medical diagnostics, reduce workload for healthcare workers, and contribute to better patient outcomes by facilitating early and accurate detection of ear infections. (shrink)

This paper proposes a structural and philosophical framework for the automation of scientific research within the broader development of Global Artificial Intelligence (GAI). It introduces two complementary modes of automation: Artificial Research by Application, which categorizes empirical data into structured conceptual models, and Artificial Research by Deduction, which constructs logical hypotheses and inferential chains based on internal cognitive architectures. Together, these modes enable AI systems to carry out autonomous scientific processes—including modeling, experimentation, reasoning, and self-correction—across various domains. The paper presents (...) a three-stage architecture (application, replication, and auto-replication) that underpins this process and defines how AI systems evolve from passive data tools to active generators of theoretical knowledge. Within the framework of GAI, the automation of research is not only a technological advancement but a philosophical redefinition of epistemology, agency, and the structure of scientific inquiry. The implications for ethics, methodology, and human-AI collaboration are explored, particularly as AI begins to replicate and surpass traditional forms of human reasoning. (shrink)

The shift to Microservices architecture and Application Programming Interface (API) - first development has transformed the landscape of software engineering, empowering development teams to create highly scalable, modular systems with agile, independent service deployment. However, the complexities of distributed architectures present unique challenges that traditional testing methodologies are often ill-equipped to address. These include managing inter-service dependencies, handling asynchronous communications, and ensuring data consistency across distributed nodes, all of which necessitate advanced testing strategies. This paper explores AI-enhanced testing strategies specifically (...) designed for Microservices and APIs, harnessing the power of machine learning, intelligent test generation, and anomaly detection. By leveraging machine learning models trained on production data, AI-driven approaches dynamically generate high-fidelity test cases and prioritize high-risk interactions, thereby optimizing test coverage and reducing test cycle duration. Additionally, intelligent test generation replicates real-world usage scenarios, creating adaptive tests that evolve with application changes. AI-powered anomaly detection adds a crucial layer of oversight, identifying deviations from expected behavior across interconnected services and flagging potential faults before they impact production. Furthermore, self-healing test mechanisms driven by AI continuously adjust and update test configurations as APIs evolve, maintaining relevance in high-speed CI/CD environments. This paper demonstrates how AI-driven testing elevates testing precision, enhances fault detection, and enables robust quality assurance in complex, API-driven systems. (shrink)

"Challenges and Issues of Modern Science" serve as an in-depth exploration of contemporary trends, sustainable development initiatives, and global concerns within the realm of scientific inquiry. The papers represent a collaborative effort by experts, researchers, and practitioners from various disciplines to address the multifaceted challenges facing modern science. Through in-depth analysis, empirical studies, and theoretical frameworks, the contributions in these proceedings aim to shed light on emerging trends, propose innovative solutions, and foster dialogue on critical issues shaping the (...) future of science and technology. By encapsulating the latest research findings and insights, the proceedings serve as a valuable resource for scholars, policymakers, and industry professionals, contributing to the advancement of knowledge and the sustainable development of our global community. Scope covers a wide range of topics and disciplines related to modern science. It includes but is not limited to Aerospace Vehicles, Engines, Energy, and Thermotechnics, Innovative Technologies in Mechanical Engineering, Structural and Functional Materials, Integrated Automation Technologies and Robotics, Control Systems, Telecommunications and Navigation, Information Technology and Cybersecurity, Project Management and Leadership, and Ecology, industrial, and environmental safety. The journal welcomes empirical studies, theoretical frameworks, and practical applications that contribute to the understanding and resolution of challenges in contemporary science. (shrink)

This book explores how robotics and artificial intelligence can enhance human lives but also have unsettling “dark sides.” It examines expanding forms of negativity and anxiety about robots, AI, and autonomous vehicles as our human environments are reengineered for intelligent military and security systems and for optimal workplace and domestic operations. It focuses on the impacts of initiatives to make robot interactions more humanlike and less creepy. It analyzes the emerging resistances against these entities in the wake of omnipresent AI (...) applications. It unpacks efforts by developers to have ethical and social influences on robotics and AI, and confronts the AI hype that is designed to shield the entities from criticism. The book draws from science fiction, dramaturgical, ethical, and legal literatures as well as current research agendas of corporations. Engineers, implementers, and researchers have often encountered users' fears and aggressive actions against intelligent entities, especially in the wake of deaths of humans by robots and autonomous vehicles. The book is an invaluable resource for developers and researchers in the field, as well as curious readers who want to play proactive roles in shaping future technologies. (shrink)

This paper investigates the transformative role of data science in predictive insurance, focusing on its applications in risk profiling and customer retention strategies. Through advanced statistical techniques, machine learning models, and big data analytics, insurance companies can refine their risk assessment processes, enhancing the accuracy of policy pricing and underwriting. The research delves into various data sources, including customer demographics, transaction histories, and behavioral data, to create predictive models that estimate risk more effectively. Furthermore, the paper explores how these (...) models contribute to customer retention by identifying at-risk clients and enabling personalized retention strategies. Case studies from industry leaders highlight the effectiveness of these models in optimizing marketing efforts, improving customer satisfaction, and reducing churn. The study also examines challenges such as data privacy concerns, model transparency, and regulatory compliance, while proposing solutions to overcome these hurdles. Finally, the paper offers insights into the future of predictive insurance, emphasizing the growing importance of artificial intelligence, automation, and real-time analytics in the sector’s evolution. (shrink)

The Life Insurance sector grapples with distinct challenges, balancing multifaceted operational workflows, rigorous compliance standards, and the surging demand for swift software deployment. Conventional Quality Assurance (QA) paradigms often fall short in delivering the responsiveness required by today's digital transformation efforts. This paper examines how Artificial Intelligence (AI)-enhanced test automation frameworks can substantially truncate time-to-market while ensuring software excellence. By leveraging advanced predictive analytics, strategic test case prioritization, and integrated Continuous Integration/Continuous Delivery (CI/CD) processes, Life Insurance enterprises can accelerate software (...) launch cycles with improved dependability, thereby meeting the ever-evolving expectations of the marketplace and regulatory entities. (shrink)

I am very used to strange books and special people but Hawkins stands out due to his use of a simple technique for testing muscle tension as a key to the “truth” of any kind of statement whatsoever—i.e., not just to whether the person being tested believes it, but whether it is really true! What is well known is that people will show automatic, unconscious physiological and psychological responses to just about anything they are exposed to—images, sounds, touch, odors, ideas, (...) people. So muscle reading to find out their true feelings is not radical at all, unlike using it as a dousing stick (more muscle reading) to do “paranormal science”. Hawkins describes the use of decreasing tension in the muscles of an arm in response to increases in cognitive load thus causing the arm to drop in response to the constant pressure of someone’s fingers. He seems unaware that there is a long established and vast ongoing research effort in social psychology referred to by such phrases as ‘implicit cognition’, ‘automaticity’ etc., and that his use of ‘kinesiology’ is one tiny section. In addition to muscle tone (infrequently used) social psychologists measure EEG, galvanic skin response and most frequently verbal responses to words, sentences, images or situations at times varying from seconds to months after the stimulus. Many, such as Bargh and Wegner, take the results to mean we are automatons who learn and act largely without awareness via S1 and many others such as Kihlstrom and Shanks say these studies are flawed and we are creatures of S2. Though Hawkins seems to have no idea, as in other areas of the descriptive psychology of higher order thought, the situation regarding “automaticity” is still as chaotic as it was when Wittgenstein described the reasons for the sterility and barrenness of psychology in the 30’s. Nevertheless, this book is an easy read and some therapists and spiritual teachers may find it of use. -/- Those wishing a comprehensive up to date framework for human behavior from the modern two systems view may consult my article The Logical Structure of Philosophy, Psychology, Mind and Language as Revealed in Wittgenstein and Searle 59p(2016). For all my articles on Wittgenstein and Searle see my e-book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Wittgenstein and Searle 367p (2016). Those interested in all my writings in their most recent versions may consult my e-book Philosophy, Human Nature and the Collapse of Civilization - Articles and Reviews 2006-2016 662p (2016). -/- All of my papers and books have now been published in revised versions both in ebooks and in printed books. -/- Talking Monkeys: Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet - Articles and Reviews 2006-2017 (2017) Amazon ASIN # B071HVC7YP. -/- The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle--Articles and Reviews 2006-2016 (2017) Amazon ASIN # B071P1RP1B. -/- Suicidal Utopian Delusions in the 21st century: Philosophy, Human Nature and the Collapse of Civilization - Articles and Reviews 2006-2017 (2017) Amazon ASIN # B0711R5LGX . (shrink)

The fundamental purpose of software testing is to develop new test case sets that demonstrate the software product's deficiencies. Upon preparation of the test cases, the Test Oracle delineates the expected program behavior for each scenario. The application's correct functioning and its properties will be assessed by prioritizing test cases and running its components, which delineate inputs, actions, and outputs. The prioritization methods include initial ordering, random ordering, and reverse ranking based on fault detection capabilities. software application development often used (...) a test suite, which was less well recognized as a suite for validating software correctness. Each test case set in the suite had distinct instructions and goals based on the system and its configuration that were evaluated. This article presents a Generative AI artificial neural network model for automated software testing based on particle swarm optimization. Generative AI is the method by which computers use existing data, including text, audio/video files, images, and code, to produce new material. An artificial neural network (ANN) is a complex adaptive system capable of altering its internal structure in response to the information it processes. Precisely manipulate the connection and its weight to achieve optimal accuracy. The PSO is a heuristic, population-based global optimization method. (shrink)

As the newest, most savvy technology domain, Large Language Models (LLMs) are the technological force which is now revolutionizing Automated Code Review and Software Quality Assurance, making the context aware analysis, adaptive learning, and the intelligent recommendations. However, traditional code review methods are not only time consuming, but also prone to human error and are not scalable. The use of LLMs in software engineering allows for the detection of defects, optimization of performance and the maintenance of improve mental quality. (...) In this study, we examine the path of automated code review, LLM capabilities and the effect they have on software quality. LLMs clearly have great advantages, but accuracy is still a concern, false positives abound, biases lurk, and computation can be a constraint. The research also identifies areas of future advancement on the hybrid AI-human review systems as well as improved LLM architectures that understand the code significantly better for more robust reviews. (shrink)

Philosophy of science is expanding via the introduction of new digital data and tools for their analysis. The data comprise digitized published books and journal articles, as well as heretofore unpublished material such as images, archival text, notebooks, meeting notes, and programs. The growth in available data is matched by the extensive development of automated analysis tools. The variety of data sources and tools can be overwhelming. In this article, we survey the state of digital work in the (...) philosophy of science, showing what kinds of questions can be answered and how one can go about answering them. (shrink)

The “Challenges and Issues of Modern Science” collection comprises scientific research on relevant topics related to the latest advancements in various fields of science. Emphasis is placed on developing aerospace technology, thermodynamics and energy, mechanical engineering, materials science and technologies, automation, electronics and telecommunications, information technology, project management, ecology, and industrial and environmental safety. It can be helpful for professionals in the respective fields, scientists, educators, and students. The presented material will help readers expand their knowledge of (...) diverse approaches to solving current scientific and practical issues. The papers are published in the author's edition. До збірника «Виклики та проблеми сучасної науки» увійшли наукові праці з актуальних тем, що пов’язані з найновішими досягненнями в різних галузях науки. Акцентується увага на розвитку аерокосмічної техніки, термодинаміки та енергетики, машинобудування, матеріалознавства та технологій, автоматизації, електроніки та телекомунікацій, інформаційних технологій, управління проектами, екології, промислової та екологічної безпеки. Може бути корисним для фахівців у відповідних галузях, науковців, викладачів та студентів. Поданий матеріал допоможе читачам розширити знання про різноманітні підходи до розв’язання актуальних науково-практичних задач. Матеріали публікуються в авторській редакції. (shrink)

The Evolution of Modern Science and Its Rapid Progress in the Last Century -/- Science, as a structured and systematic pursuit of knowledge, has evolved significantly over the past 482 years since the Scientific Revolution in the 16th century. The development of the scientific method, rooted in observation, experimentation, and reasoning, laid the foundation for modern science. While early scientific discoveries took centuries to accumulate, the last 100 years have seen an unprecedented acceleration in scientific progress. This (...) rapid advancement is due to key factors such as technological innovation, increased collaboration, and improved access to knowledge. -/- The Birth of Modern Science (1543 Onward) -/- The Scientific Revolution (16th-17th century) marked the beginning of modern scientific methodology. In 1543, Nicolaus Copernicus published De revolutionibus orbium coelestium, proposing a heliocentric model of the solar system, which challenged long-held geocentric beliefs. This era saw contributions from Galileo Galilei, who improved the telescope and proved heliocentrism through astronomical observations, and Isaac Newton, who formulated the laws of motion and gravity. These discoveries established a foundation for physics, astronomy, and mathematics, guiding scientific thought for centuries. -/- In the 18th and 19th centuries, science expanded further, with breakthroughs in chemistry (Antoine Lavoisier’s work on oxygen), biology (Charles Darwin’s theory of evolution), and electromagnetism (Michael Faraday’s discoveries). These advancements paved the way for modern physics, medicine, and engineering. -/- The Explosive Growth of Science in the Last 100 Years (1925-2025) -/- The 20th and 21st centuries have witnessed the fastest growth in scientific knowledge in human history. The rate of discovery has increased exponentially due to various technological and societal factors. -/- 1. Technological Advancements -/- The development of computers and digital technology revolutionized scientific research. The ability to store, analyze, and simulate complex data has enabled faster experimentation and theory development. -/- The Invention of particle accelerators led to breakthroughs in quantum physics, such as the discovery of subatomic particles (e.g., the Higgs boson in 2012). -/- The Human Genome Project (completed in 2003) mapped human DNA, transforming genetics and medicine. -/- 2. Scientific Collaboration and Globalization -/- Unlike earlier centuries when scientists worked in isolation, the last 100 years have seen an unprecedented level of collaboration across countries. -/- Organizations like NASA, CERN, and WHO have pooled resources and expertise, leading to breakthroughs such as space exploration and vaccines. -/- The internet and digital communication have enabled real-time exchange of scientific knowledge, accelerating progress. -/- 3. Medical and Biological Advancements -/- The discovery of antibiotics (penicillin in 1928) revolutionized medicine, dramatically increasing human lifespan. -/- Advancements in vaccines and medical imaging (such as MRI and CT scans) have improved healthcare worldwide. -/- The development of mRNA vaccine technology (used for COVID-19 vaccines in 2020) showcases how modern science can respond to global challenges quickly. -/- 4. Physics and Space Exploration -/- Einstein’s theory of relativity (1905-1915) redefined our understanding of space and time, influencing modern astrophysics. -/- The moon landing (1969) and the exploration of Mars and deep space have expanded human knowledge beyond Earth. -/- The James Webb Space Telescope (launched in 2021) is uncovering new insights into the origins of the universe. -/- 5. Artificial Intelligence and Automation -/- The rise of AI and machine learning has accelerated scientific discoveries by processing massive datasets quickly. -/- AI is now used in medicine, climate research, and even scientific hypothesis generation. -/- Why Science is Progressing Faster Now -/- Several factors contribute to the rapid acceleration of science in the last 100 years: -/- Exponential Growth of Knowledge: Each new discovery builds upon previous ones, creating a snowball effect. -/- Access to Information: The internet and digital libraries provide instant access to scientific literature. -/- Increased Funding and Research Institutions: Governments and private companies invest heavily in research and development. -/- Automation and AI: AI helps analyze data, simulate experiments, and accelerate discoveries. -/- Interdisciplinary Approaches: Modern science integrates multiple fields (e.g., biology + AI = bioinformatics), leading to faster innovation. -/- Conclusion -/- Science has come a long way from its structured beginnings during the Scientific Revolution to the rapidly evolving landscape of the 21st century. While it took centuries to develop early scientific principles, the last 100 years have seen an extraordinary acceleration in discoveries and technological advancements. The combined effects of global collaboration, digital technology, and artificial intelligence ensure that science will continue to progress at an ever-increasing rate, shaping the future of humanity. -/- This remarkable growth in science underscores the power of human curiosity and innovation, proving that knowledge, when pursued systematically, has no limits. -/- . (shrink)

The integration of Artificial Intelligence (AI) into software development has transformed traditional methodologies, making development cycles more efficient and reducing manual intervention. AI-driven tools and techniques enhance various stages of the Software Development Lifecycle (SDLC), from requirement analysis to deployment and maintenance. This paper explores the role of AI in automating software engineering tasks, discusses the impact of intelligent automation on efficiency, and presents real-world applications and future research directions.

We introduce here evoText, a new tool for automated analysis of the literature in the biological sciences. evoText contains a database of hundreds of thousands of journal articles and an array of analysis tools for generating quantitative data on the nature and history of life science, especially ecology and evolutionary biology. This article describes the features of evoText, presents a variety of examples of the kinds of analyses that evoText can run, and offers a brief tutorial describing how (...) to use it. (shrink)

This article offers a philosophical and methodological analysis of the phenomenon of artificial intelligence within the context of transformations in contemporary scientific knowledge. Particular attention is given to the changing conceptions of the subject, the nature of truth, the structure of knowledge, and the ontology of digital reality. The study explores how the algorithmization of thought, the automation of analytical processes, and the expansion of hybrid cognitive systems are influencing philosophical perspectives on subjectivity, rationality, and the limits of scientific understanding. (...) To address these issues, the article employs both anthropological and analytical approaches. A critical examination is conducted of current theoretical frameworks that interpret the shifting cognitive status of the subject in conditions shaped by digital simulation and the functional substitution of human thinking. The analysis further suggests that digital reality is emerging as a distinct ontological domain, in which knowledge arises through the interaction of informational structures rather than through direct representation of the physical world. Consequently, there is a pressing need to revise the methodological foundations of the philosophy of science. This revision should incorporate a humanistic critique of digital technologies and promote critical reflection on the boundaries of delegating intelligence to machine-based systems. Future research should focus on the development of ontological models of hybrid cognition, the exploration of cognitive symbiosis between humans and artificial intelligence, and the re-evaluation of the scientific method in the context of automated knowledge generation. -/- . (shrink)

The change in the organization of science education over the past fifty years is quickly recalled. Being its cultural bound the lack of a conception of the foundation of science, the multiple innovations have resulted as temporary improvements without a clear direction, apart from the technocratic goal of an automation of learning processes. The discovery of two dichotomies as the foundations of science suggests a pluralist conception of science, and hence the need to entirely renew (...) class='Hi'>science education, in particular by introducing the notions of incommensurability and radical variations in meaning of the basic notions. This change was already anticipated by several proposals within each subject-matter. (shrink)