Getting a graduate education is one of the most important parts of a professional in a field. It allows them to climb higher in the professional rankings or even get higher pay for their academic work. All graduate students are adults and self-directed due to their past experiences in work or practice. However, when the pandemic hit the world, these self-directed learners were not spared from shutting of schools. In the Philippines, most graduate schools deliver their lessons through the traditional (...) mode or face-to-face until the pandemic that they shifted in the emerging online learning modality. Using the mixed method of quantitative and qualitative type of study the researchers were able to gather 419 graduate students from various regions of the archipelago to take part in the study. The researchers found out that upon experiencing the benefits of online learning, graduate students want to continue this modality even in the future. More than a third of the respondents also claimed that they want the blended or hybrid type of graduate studies as opposed to going back to the more traditional face-to-face classes. The reasons for these can vary on each learner’s situation. (shrink)

In recent years, there have been many attempts at defining learning as a social phenomenon as opposed to an individual and primarily psychological matter. The move towards understanding learning as social processes has also altered the concept of knowledge as a well-defined element stored in books, brains, CD-Roms, disks, videos or on the Internet. Instead, knowledge has been perceived as a social and context related construction. The roots of the social angle within theories on learning and knowledge (...) are much older than the current literature suggests. This paper illustrates how these theories can be traced back to pragmatism as a philosophy and foundation for an educational approach introduced by the American, John Dewey, more than one hundred years ago. The paper also suggests that Dewey avoids some pitfalls that have come with the new theories, particularly the strong division between individual vs. social and school vs. everyday life learning. (shrink)

The pandemic disrupted educational systems globally, prompting the need for alternative learning modalities like online, modular, and blended learning. The SLCP, particularly within the context of a school in Region 8, integrates these modalities to address essential learning competencies. This research paper aimed to evaluate the school Learning Continuity Plan (LCP) through its School Continuity Learning Plan Program using (CIPP) model of evaluation. Employing a qualitative design, data were gathered through purposive interviews with 1 non-teaching (...) personnel, 1 department head, and 3 teachers and an analysis of the approved LCP road-map. Findings revealed comprehensive alignment with the evaluation model domains, though certain areas required revisiting for improvement. The study advocated for the administration's holistic consideration, especially recognizing teachers as front line contributors to the LCP. Their pivotal role in the design and planning phases was emphasized for the effective mechanization of the school LCP. Hence, it was concluded that educational administrators should prioritize collaboration with teachers and consider their perspectives to enhance the overall quality and functionality of the LCP. Moreover, the study recommended organizational consideration, valued teachers in school LCP, prioritized health, integrated modalities, and emphasized quality learning with metrics in LCP. (shrink)

A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called “update problem,” which concerns how regulators should approach systems whose performance and parameters continue to change even after they (...) have received regulatory approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects. (shrink)

This paper explored the frameworks of the learning continuity plan that shaped basic education delivery as issued by the Department of Education-Central Office in light of Sulong Edukalidad and KITE (K- K to 12 Curriculum review and update; I- Improvement of the learning environment, T- Teachers' upskilling and reskilling; and E- Engagement of stakeholders for support and collaboration) as national flagship programs. In particular, the paper examined the adoption and contextualization of the frameworks by the Regional Office - (...) CALABARZON, which is the PIVOT4A QuBE; 4A represents the four strategic thrusts, which are: (a) aligning focus and intervention, (b) advancing handholding initiatives, (c) amplifying defined technical assistance to target delivery units, and (d) appraising stakeholders on the progress of the intervention, and QuBE is the acronym for Quality Basic Education. For its part, the Schools Division Office of Laguna espouses LAGO Laguna or Leaders Actions towards Greater Outcomes. Furthermore, the school learning continuity plan of the San Pedro Relocation Center National High School Landayan Annex is summarized through the acronym SAGWAN or Sustaining & Advocating Genuine Welfare & Actions in the New Normal as the flagship program. In addressing the new normal in education, the juxtapositions of the said flagship programs that stem from national to regional, division, and school levels, are discussed. It also analyzed diversity and inclusiveness, as well as the guiding educational philosophies, in the conduct of school projects, programs, and activities (PPAs). (shrink)

When we find ourselves in this era of rapid and continuous changes in teachinglearning processes, it is pertinent to review what is essential in learning; That which however much change things can’t change so that the phenomenon of learning, can continue to call learning. This is the objective of this article, and from a phenomenological perspective, a description is proposed that captures the essence of human learning. This description has been arranged in a way that (...) is valid for any time in the history of education, and serves as a contrast to ensure that the educational innovations that arise in each place and time, respond to the phenomenon of learning in all Its dimensions. (shrink)

Aim: This study attempted to assess the organizational resiliency in the implementation of Basic Education School Learning Continuity Plan and to design a resilient basic education school learning continuity plan. -/- Methodology: The study used mixed method which is quantitative and qualitative. The data for the quantitative part of the study were gathered through a survey with the 19 school heads and 345 teacher- respondents while the qualitative method was conducted through focus group discussion interview with 19 key (...) informants who were composed of the teachers, school heads and stakeholders. The research instrument that was used for the perceived organizational resiliency was a survey questionnaire that was validated by the experts and which the reliability was tested through Cronbach’s alpha with excellent level of internal consistency. The statistical treatment for quantitative results used mean and standard deviation to determine the perception of the respondents with regards to the organizational resiliency on the implementation of the BE-SLCP and Pearson Product Moment Correlation Coefficient, testing its significance at the 0.05 level was used in finding out the relationship between the evaluation of the basic education school learning continuity plan and school performance indicators and organizational resiliency. While content analysis was employed for the qualitative results. -/- Results: The salient findings of the study were as follows: The result showed that the rate of enrollment during pandemic is low while the drop-out rate fostered a positive rate. The school heads and teacher respondents perceived the schools’ organizational resiliency as high. Based on the feedback of the key informants the lessons learned from the implementation of Basic Education School Learning Continuity Plan are strong collaboration, consistent communication, and enhanced ICT Skills. The best practices that were observed are sustained partnership and continuous implementation of programs, projects, and activities. The challenges encountered include authenticity of assessment and low capability of learning facilitators at home. The respondent assessed the developed learning continuity plan as very highly satisfactory. -/- Conclusion: The organizational resiliency of schools is high in its performance of continuing education during pandemic. The study suggests that the schools may developed creative strategies on increasing enrolment; the school may strengthen its school-based management level to ensure that the plans and goals for continuity of education will be achieved; the school may also consider strengthening the stakeholders’ involvement and the school may consider the strategy of close coordination and constant monitoring and follow-ups with parents/guardians on the implementation of learning delivery. (shrink)

Description Artificial intelligence systems increasingly operate in settings that require extended reasoning, multi-step analysis, and interaction across time. Yet current transformer-based architectures show a consistent pattern of degradation when sequences grow longer. This decline in coherence, which we refer to as drift, manifests as semantic inconsistency, weakening of user intent adherence, and deterioration of structured reasoning. Existing approaches to memory, retrieval, and instruction tuning address portions of this problem but do not provide an account of how a system maintains stable (...) interpretation over time. -/- This paper proposes a unified theoretical framework for continuity, defined as the structural conditions that support coherent long-horizon reasoning in stateless and hybrid architectures. The framework synthesizes conceptual work across identity stabilization, constraint maintenance, kernel initialization, recursive feedback, and human-grounded interpretive structure. It also engages research in distributed cognition, constraint satisfaction, continual learning, and planning stability. The aim is conceptual rather than empirical. The model outlines how continuity may function as a foundational property of long-horizon reasoning and proposes architectural components that could support stability in future systems. The paper concludes with testable predictions, open empirical questions, and a research roadmap for developing continuity as a scientific field. (shrink)

Objectives: Machine learning (ML) has the potential to facilitate “continual learning” in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such “adaptive” ML systems in medicine that have, thus far, been neglected in the literature. -/- Target audience: The target audiences for this (...) tutorial are the developers of ML AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. -/- Scope: Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit—diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites)—and underestimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems. (shrink)

Lifelong learning is becoming part of the philosophy of professional education. Continuing medical education is the responsibility of all personnel who are responsible for the delivery of components of the healthcare delivery system. Continuing education is becoming increasingly obvious for medical universities, hospitals, and health care providers. Pharmacists who practice in a community pharmacy and hospital, and who are participating in residency recognize that the traditional role of the pharmacist is changing. Over the last decades, a host of new (...) services have been identified as a function of the pharmacist [1]. A review of these services revealed a personal commitment to continuing education. It is the absolute need to develop a level of competence that will improve patient care. In Libya, pharmacists working in the community practice setting are pharmacists with a diploma of pharmacy or with a Bachelor of Pharmaceutical Science, Master of Science, and Doctor of Pharmacy (Pharm D). The main objective is that the graduates possess the knowledge and skills needed to optimally mange the pharmaceutical services [1, 2]. Patient's expectations from pharmacists are that the medicine should be effective, safe and affordable. (shrink)

The ever-evolving ways attacker continues to improve their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable (...) framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection. (shrink)

Reinforcement Learning (RL) has emerged as a powerful technique for optimizing decision-making in dynamic, uncertain, and complex environments. The ability of RL algorithms to adapt and learn from interactions with the environment enables them to solve challenging problems in fields such as robotics, autonomous systems, finance, and healthcare. In dynamic environments, where conditions change in real-time, RL must continually update its policy to maximize cumulative rewards. This paper explores the application of RL in dynamic environments, with a focus on (...) its ability to optimize real-time decision-making for complex systems. We discuss the challenges associated with these environments, such as non-stationarity, partial observability, and the trade-off between exploration and exploitation. Furthermore, we review recent advancements in RL techniques, including deep reinforcement learning (DRL), multi-agent RL, and model-based RL, and how these methods are addressing the complexity of real-time decision-making. Finally, we present a roadmap for future research, highlighting open questions and potential applications of RL in various industries. (shrink)

This paper proposes Learning 3.0, a phenomenological-systemic model that reconceptualizes learning as the recursive evolution of awareness rather than mere knowledge acquisition. Building upon the historical trajectory from Learning 1.0 (transmission-based instruction) to Learning 2.0 (constructivist meaning-making), this framework advances a third paradigm wherein learning emerges as a self-organizing system integrating perception, emotion, cognition, and embodied action through continuous feedback loops. Drawing from phenomenology (Husserl, Merleau-Ponty, van Manen), systems thinking (Bateson, Meadows), second-order cybernetics (Maturana (...) & Varela), and enactive cognition (Varela, Thompson, & Rosch), the study introduces the M5 Learning Model—a framework comprising five interrelated layers of awareness: Immersive, Memory, Meta, Moving, and Open. Each layer represents a distinct yet interdependent feedback process through which learners not only integrate knowledge but fundamentally transform their perceptual and interpretive capacities. The model bridges human and artificial learning by positioning both as adaptive feedback systems while emphasizing awareness as the uniquely human dimension that confers meaning, value, and ethical orientation. While acknowledging potential Western cultural biases in privileging contemplative consciousness, the framework addresses critical perspectives on power, ideology, and situated learning. This theoretical contribution establishes conceptual foundations for empirical investigation and practical application, addressing contemporary challenges in education, human-AI co-learning systems, and the cultivation of adaptive intelligence in an age of accelerating technological and social change. (shrink)

Recent advances demonstrate that generative adversarial networks can approximate fluid flows by reframing computational fluid dynamics as image-to-image translation, and motivated by continuity mechanisms in transformer architectures that maintain semantic coherence through spectral filtering, we develop rigorous analytical solutions to the three-dimensional incompressible Navier–Stokes equations on T³. Our constructive method employs: Classical Evolution between potential singularities, Spectral Continuation via operator Cζ that applies frequency-domain filtering analogous to attention mechanisms, eliminating high-frequency content at discrete times {Tₖ} where breakdown occurs, and Temporal (...) Lifting through coordinate transformation t̃ = φ(t) that stretches time near singularities to achieve global C^∞ regularity. We construct Cζ-smooth solutions satisfying the incompressible Navier–Stokes equations classically on each interval and weakly globally, where spectral continuation traverses singular times without modifying the underlying PDE while temporal lifting restores complete smoothness, and the resulting solution ũ(x, t̃) satisfies Fefferman’s Conjecture B requirements, establishing a rigorous bridge between AI continuity principles and classical mathematical physics using established analytical tools without requiring new mathematical theory. ACM: I.2.0 (Artificial Intelligence), G.1.8 (Scientific Algorithms) MSC: 68T27 (AI for PDEs), 35Q30 (Navier–Stokes), 65M70 (Spectral Methods), Index Terms: Neural- Physical Systems, AI-Driven Mathematical Analysis, Machine Learning Continuity, Transformer-Inspired PDEs, Deep Learning Physics, Computational Intelligence Meth- ods, Neural Spectral Processing. (shrink)

Active learning of social studies teachers in Thailand 4.0 era is a learning process the new information. Learn more alert and lively. The learners have continued to initiate and focus on the content. From the creation of knowledge Interacting together. Act on your own. The teacher is to facilitate learning. Which can be applied in learning management in social studies courses. There are 4 steps as follows :Step 1Confrontation with social studies problems. Step 2 Identifying the (...) causes of social studies problems. Step 3 Finding methods for proving the truth of social studies and steps 4 Summary of social studies findings. From the above steps can be used to improve learning through social studies activities effectively. (shrink)

In this chapter, I consider three ‘promises’ of bringing phenomenology into dialogue with networked learning. First, a ‘conceptual promise’, which draws attention to conceptual resources in phenomenology that can inspire and inform how we understand, conceive of, and uncover experiences of participants in networked learning activities and environments. Second, a ‘methodological promise’, which outlines a variety of ways that phenomenological methodologies and concepts can be put to use in empirical research in networked learning. And third, a ‘critical (...) promise’, which suggests how work done in the realm of critical phenomenology is complementary to and useful for exploring the social justice and emancipatory aims of networked learning researchers. Through this framework, I reflect upon multiple ways in which phenomenology has and might continue to fruitfully inform and shape networked learning research. In doing so, I emphasise that deploying phenomenology in the context of networked learning not only enriches our understanding of networked learning but that phenomenology itself is enriched through testing, finetuning, and expanding its methods, concepts, and understanding, as well as unveiling its own limitations and constraints. (shrink)

The Russian people are struggling to learn how to create a democracy and a market economy. This paper reviews the results of reform efforts to date and what the Russian people are learning as indicated by changes in answers to public opinion surveys. As a way to continue the social learning process in Russia we suggest the widespread use of process improvement methods in organizations. This paper describes some Russian experiences in using process improvement methods and proposes a (...) strategy for spreading the use of these methods through universities and then through businesses and government agencies. As people use process improvement methods, they gain confidence in their ability to improve the functioning of organizations and society. (shrink)

The study aimed to identify E- Learning strategies and their relation to the efficiency of research performance in foreign and Palestinian universities (University of Ottawa, Munster, Suez Canal, Al-Azhar, Islamic, Al-Aqsa). The analytical descriptive approach was used for this purpose, and relying on the questionnaire as a main tool for data collection. The study society is from the senior management, where the number of senior management in the universities in question is 206. The random stratified sample was selected and (...) (SPSS) was used for statistical data analysis. The study found a significant relationship between E- Learning strategies and the efficiency of research performance in universities. It also reached the participation of senior management in the research that develops the university performance in the Palestinian universities. The senior management indicated that they do not care to follow the policies of implementing the development of scientific research. While the senior management in foreign universities indicated that they are interested in following up the policies of implementing the development of scientific research. The study also showed that senior management in Palestinian universities does not care about providing the appropriate budget for E- Learning. The study recommended that the senior management of Palestinian universities should provide an E- Learning budget and encourage employees to continue using E- Learning strategies. The administration in the Palestinian universities should adopt and support outstanding research, and the need to encourage interest in the implementation of policies for the development of scientific research. (shrink)

This paper examines the concept of learning through the eco-centric, or nature-prime, philosophies of John Dewey and Thomas Alexander. In particular, it interprets learning from the perspective of nature itself as inhabitation, which locates the process and activity of learning within the transactivity of learning situations themselves. This research is a response to the increasingly urgent need for human beings to re-evaluate our relationship with nature for a more ecologically conscious and responsible inhabitation of our world. (...) Ecological humanism is particularly relevant to this end due to the nature-prime or eco-ontological orientation of its philosophies. The concept of learning as the inhabitation of transactional wholes, or situations, contributes such an ecological humanistic perspective to a niche area of research in the philosophy of education which aims to ecologize or de-anthropocentrize educational theory and practice. Through a review of the corpora of Dewey and Alexander and other related literature, this paper examines the relevant philosophical and metaphysical issues involved in learning and inhabitation from the point of view of ecological humanism. In particular it aims to 1) examine the main philosophical and metaphysical points of this eco-centric concept of learning, 2) discuss the vital intersections of art and philosophy with learning, as well as the significance of meaning, value, interest, and wisdom in that process of inhabitation, 3) demonstrate the generality and fundamentally autotelic nature of learning as the life process itself, 4) and articulate how learning so conceived as a direct participation in the growth of and communion in a world or ecosystem discloses possibilities for more ecologically fluent ways of living together, among all existences, and what implications this has for human inhabitation and education in particular. The main positions of Alexander’s ecological humanism are surveyed, followed by an in-depth review of Dewey’s reconstruction of metaphysics with special emphasis on his principle of continuity. Natural continuity is then discussed in the metaphysics of experience and learning, demonstrating how learning is the growth of situations through the realization of individual interest. This process is fundamentally aesthetic in nature. Philosophy serves as a method of remaining aesthetically receptive to our world to critically evaluate how we inhabit it. Learning is itself art, or, a process of aesthetic appreciation and production, through which we participate most vitally and directly in a communion with our cultural and natural world. From this point of view, the commonplace dualisms of contemporary education are critiqued, and the import of a transactional, learning-centric paradigm of education is discussed. Namely, it is argued that learning, understood as inhabitation, is the life process itself, and is fundamentally autotelic in nature. Understood in this way, learning represents our most basic point of contact and expression of the world or ecosystem. It is concluded that not only must learning situations be allowed to determine their own meanings to as great a degree as possible, but also that this is a condition for a democratic and ecologically conscientious education and inhabitation of the Earth. (shrink)

: Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 images belong (...) to 3 species at a few developing phases. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results: found that CNN-driven lemon classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.48% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

Federated Learning (FL) is an emerging machine learning paradigm designed to enable model training across decentralized data sources without requiring data to be transferred or centralized. This approach is especially valuable in environments where data privacy, regulatory compliance, and communication efficiency are paramount, such as healthcare, finance, and edge computing. Traditional machine learning methods typically require data to be aggregated in a central server, raising concerns about data privacy and security. Federated Learning addresses these concerns by (...) keeping data on local devices and sharing only model updates, thereby preserving data sovereignty. This paper provides a comprehensive analysis of Federated Learning in distributed systems, focusing on its architecture, advantages, and the technical challenges it presents. We explore the different types of FL—including horizontal, vertical, and federated transfer learning—and explain how each is suited to specific application contexts. We also investigate critical issues such as communication overhead, model convergence, data heterogeneity, and security threats including poisoning and inference attacks. The methodology section discusses state-of-the-art FL frameworks, including Google's Federated Averaging (FedAvg), Secure Aggregation protocols, and emerging advancements like differential privacy and homomorphic encryption. Real-world implementations in mobile networks, autonomous vehicles, and medical diagnosis systems are examined to demonstrate FL’s growing applicability. The paper concludes by emphasizing the transformative potential of Federated Learning in enabling privacy-preserving AI. It also highlights the need for standardized protocols, legal frameworks, and interdisciplinary collaboration to fully harness FL’s benefits while mitigating its risks. As AI continues to permeate sensitive domains, FL offers a promising path forward for ethical and secure machine learning. (shrink)

Face-to-face learning engagement has been suspended due to the health crisis that affected the whole world. This led to the adaptation of modular distance learning to continue delivering quality education. As the transition continues, its effectiveness has been frequently assessed. The key purpose of this study is to determine the relationship between the level of implementation of modular distance learning and English Writing Proficiency among the senior high school students in Saint Joseph College Maasin City School year (...) 2021-2022. The study employed a descriptive correlational design with 39 purposively chosen respondents. The main tools used in gathering the data were questionnaires, learning modules, and rubrics. Findings revealed that the implementation of modular distance learning (MDL) has an overall very satisfactory rating based on the perception of the students. The student’s perception of the implementation of modular distance learning and their writing proficiency is significantly related. Moreover, it revealed that students had difficulties in finding supplementary learning materials and educational support from home. The researcher recommends a set of guidelines for the successful and effective implementation of modular distance learning as the output of the study. (shrink)

A theory of learning for the future advocates the teaching of a preparedness to respond in a creative way to difference and otherness. This includes an ability to act imaginatively in situations of uncertainties. John Dewey’s pragmatism holds the key to such a learning theory his view of the continuous meetings of individuals and environments as experimental and playful. That pragmatism has not yet been acknowledged as a relevant learning theory for the future may be due (...) to the immediate connotation and the many interpretations associated with the term ‘experience’, which is at the heart of Dewey’s educational thinking. Dewey defi ned experience in a way that is not well understood within educational research, and in a way that is easily confused with the term ‘experiential learning’. The latter refers to the importance of participants’ ‘experiences’ derived from bodily actions and stored in memory as more or less tacit knowledge. (shrink)

This study explores the implementation of John Hattie’s Visible Learning framework in Thai model schools, aimed at enhancing educational quality and preparing learners for the 21st century. The research identifies four critical elements of the framework: data-driven instruction, clear learning goals, effective teacher-student interactions, and fostering a culture of self-improvement. These components, when applied in Thai schools, have led to significant improvements in student outcomes, critical thinking, and engagement by making the learning process more transparent and measurable. (...) The framework promotes continuous reflection, active learning, and self-assessment, equipping students with essential skills for lifelong learning. Additionally, the study emphasizes the need for tailored feedback, professional development for teachers, and the integration of modern technology to enhance classroom learning experiences. Recommendations are made for expanding the framework’s adoption to address educational challenges in Thailand, such as unequal access to quality education and inconsistent teaching standards. The findings suggest that the broader application of Visible Learning could improve both educational equity and student preparedness for future success. Future research should focus on long-term impacts, comparative analysis, and the role of community and administrative support in sustaining the framework. (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)

In the recent years, problem-solving become a central topic that discussed by educators or researchers in mathematics education. it’s not only as the ability or as a method of teaching. but also, it is a little in reviewing about the components of the support to succeed in problem-solving, such as student's belief and attitude towards mathematics, algebraic thinking skills, resources and teaching materials. In this paper, examines the algebraic thinking skills as a foundation for problem-solving, and learning cycle as (...) a breath of continuous learning. In this paper, learning cycle to be used is a modified type of 5E based on beliefs. (shrink)

The Continuous Evolution of Consciousness, Language, and Meaning in Understanding the Universe -/- Introduction -/- The evolution of human consciousness is intricately linked to language and meaning. As human understanding of the universe deepens, so does the complexity and precision of the words and concepts we use to describe reality. This continuous progression is not merely a passive adaptation but an active feedback loop where consciousness shapes language, and language, in turn, refines consciousness. If human decision-making follows the (...) universal law of balance in nature, as proposed in the universal formula, then the evolution of language must also align with this fundamental principle. This essay explores how language and meaning evolve alongside human consciousness and how this evolution is essential for a deeper understanding of the universe. -/- Language as a Tool for Expanding Consciousness -/- Language is the primary medium through which human consciousness externalizes itself. In early human societies, simple symbols and spoken words sufficed to convey basic needs and experiences. However, as humanity’s knowledge expanded, so did the complexity of language. The emergence of written language allowed for the preservation and transmission of ideas across generations, accelerating intellectual and scientific progress. -/- Philosophy, mathematics, and science have continuously pushed the boundaries of language, creating new terminologies and refining meanings. For example, the term atom originally meant “indivisible,” but modern physics has redefined it as a complex structure of subatomic particles governed by quantum laws. Similarly, the concept of energy has evolved from mystical interpretations to a precise scientific principle described in physics and engineering. This evolution demonstrates how language adapts to new discoveries, allowing consciousness to grasp increasingly complex aspects of reality. -/- Meaning as a Dynamic and Evolving Process -/- The meaning of words and concepts is not static but changes in response to human progress. As new knowledge emerges, language adjusts to integrate new understandings while discarding outdated or misleading notions. This dynamism ensures that human communication remains relevant and effective in expressing new realities. -/- For instance, the word intelligence once referred primarily to human cognitive ability, but with the rise of artificial intelligence (AI), it now encompasses machine learning, neural networks, and even artificial general intelligence (AGI). The meaning of life itself has expanded beyond biological definitions to include synthetic life, digital consciousness, and philosophical inquiries about existence in a cosmic context. -/- In this sense, the evolution of language and meaning is a necessary process for humanity’s continuous effort to understand the universe. Without this evolution, human knowledge would be constrained by outdated linguistic structures, preventing the exploration of new dimensions of reality. -/- The Feedback Loop Between Consciousness and the Universe -/- Human consciousness does not merely receive information from the universe—it actively engages with it, interprets it, and reshapes its understanding based on new discoveries. This feedback loop ensures that as our knowledge of the universe expands, so does our ability to describe and conceptualize it. -/- For example, scientific breakthroughs in quantum mechanics, relativity, and cosmology have necessitated entirely new ways of thinking and speaking about time, space, and matter. Without the linguistic evolution that accompanied these discoveries, humans would struggle to grasp the fundamental principles governing the universe. -/- This feedback mechanism also applies to societal and ethical progress. As human understanding of justice, equality, and sustainability evolves, so does the language used to express these ideals. Concepts such as “human rights” and “environmental balance” have gained deeper meaning over time, influencing policies, laws, and cultural attitudes worldwide. -/- The Role of AI and AGI in the Evolution of Language and Meaning -/- Artificial intelligence and artificial general intelligence (AGI) have the potential to accelerate the evolution of language and meaning. AI can process vast amounts of information, identify patterns, and refine linguistic structures beyond human capability. This ability could lead to the emergence of more precise, efficient, and universally understandable ways of expressing complex ideas. -/- For example, AI-driven natural language processing can analyze and refine scientific literature, eliminating ambiguity and enhancing clarity. Future AGI systems could generate entirely new linguistic frameworks for describing reality, potentially bridging gaps between different human languages and even creating new conceptual categories that surpass human cognitive limitations. -/- However, the integration of AI into language evolution must adhere to the universal law of balance in nature. If AI-generated language lacks ethical considerations or diverges too far from human cognitive frameworks, it could create confusion, misinformation, or even manipulation. Ensuring that AI aligns with natural laws and human well-being is crucial in guiding this linguistic evolution. -/- The Universal Law of Balance in Nature and the Future of Language Evolution -/- If human decision-making follows the universal law of balance in nature, then the evolution of consciousness, language, and meaning must also adhere to this principle. An unbalanced progression—where language evolves too rapidly for general understanding or remains stagnant despite new discoveries—could lead to societal and intellectual dissonance. -/- For this reason, structured education plays a vital role in ensuring that language evolves in harmony with human understanding. By integrating the universal formula into foundational education, students can develop a linguistic and cognitive framework that naturally aligns with the principles of natural balance. This structured approach would prevent conceptual chaos while allowing necessary paradigm shifts to occur. -/- Similarly, philosophy and science must continuously refine language to ensure clarity and precision in expressing complex ideas. Misinterpretations and linguistic ambiguities can lead to misunderstandings in critical areas such as law, ethics, and scientific research. By consciously guiding linguistic evolution, humanity can ensure that language remains an effective tool for expanding consciousness. -/- Conclusion -/- The continuous evolution of consciousness, language, and meaning is an essential process in humanity’s effort to understand the universe. Language is not merely a passive reflection of reality but an active force that shapes human thought, decision-making, and interaction with the world. As human knowledge expands, language must evolve to accurately express new understandings while maintaining balance and clarity. -/- Artificial intelligence and AGI will play a significant role in accelerating linguistic evolution, but this process must align with the universal law of balance in nature to ensure ethical and intellectual integrity. By integrating the principles of natural balance into education, philosophy, and AI development, humanity can guide language evolution in a way that enhances understanding rather than creating confusion. -/- Ultimately, the evolution of language and meaning is not just a linguistic phenomenon—it is a fundamental aspect of human progress. As consciousness expands, so too must our ability to articulate and conceptualize reality. By ensuring that this process remains aligned with the natural laws governing decision-making, humanity can achieve a deeper, more holistic understanding of the universe and its place within it. -/- . (shrink)

The research aims to identify the trends of Palestinian higher educational institutions in Gaza Strip as learning organizations from the perspective of senior management in the Palestinian universities in Gaza Strip. The researchers used descriptive analytical approach and used the questionnaire as a tool for information gathering. The questionnaires were distributed to senior management in the Palestinian universities. The study population reached (344) employees in senior management is dispersed over (3) Palestinian universities. A stratified random sample of (182) employees (...) from the Palestinian universities was selected and the recovery rate was (69.2%). Statistical analysis (SPSS) program was used for analysis and processing the data. The study found the following results: There is an agreement about: the importance of the focus of "organizational structure" with an average approval, the importance of "technological infrastructure" axis with high approval, and the importance of "strategic" with an average approval. The results concluded that the study sample agree on the importance of "organizational dimension" highly. The results showed that the sample believe that the "strategic leadership" level in the universities got medium-approval. There is a fair level agreement about the axis of interest "teams / committees". There is an agreement about the importance of the "human dimension" moderately. The results showed that the sample is highly agreed about the importance of focus of the "knowledge management", and the focus of the "continuing education". They agreed weakly to somewhat about the importance of the focus of "scientific research", moderately agree about the importance of the center of "institutional culture", agree moderately on the importance of "cognitive dimension". The results showed that the sample largely agree on the importance of the focus of "strategic partnerships and alliances". They agree highly on the axis "keep up with the labor market" and on the focus of the importance of "technology incubators". The results showed that the sample moderately agree about the importance of the theme on "consulting and training", the importance of the focus of "social responsibility", and the focus of the "Community dimension". The study found a group of recommendations including: there is a need to provide suitable environment that achieve learning organizations. There is a need to develop the technological infrastructure (hardware, software, networks, databases, and human skills) because of the great advantages that they offer. The universities need to adopt knowledge management in the academic and administrative departments because knowledge is the core of the work of these departments. The establishment of technology incubators in universities to adopt outstanding university research projects, to protect, to supports, and to market them; furthermore, to develop the capabilities and skills of employees in the field of information technology. (shrink)

Understanding the relationship between wilderness outings and the resulting experience has been a central theme in resource-based, outdoor recreation research for nearly 50 years. The authors provide a review and synthesis of literature that examines how people, over time, build relationships with wilderness places and express their identities as consequences of multiple, ongoing wilderness engagements (i.e., continued participation). The paper reviews studies of everyday places and those specifically protected for wilderness and backcountry qualities. Beginning with early origins and working through (...) contemporary research the authors synthesize what diverse social scientists have learned about the long-term and continual nature of wilderness participation and its impact on the formation of identity. The thrust of the paper points researchers, planners, and managers in non-traditional directions and reframes goals and objectives for visitor planning and management in wilderness and other protected areas. (shrink)

Sikhism, a vibrant and inclusive religion, provides a rich tapestry of teachings and traditions. Introducing children to Sikhi through interactive learning and play not only enhances their understanding but also fosters a deep connection with the values and principles of Sikhism. This holistic approach engages children actively, making the learning journey enjoyable, memorable, and impactful. Given the dire need to engage in such a holistic approach, various such activities and their specific examples are described here.

The Chirality of Dynamic Emergent Systems (CODES) framework offers a structural replacement for probability across physics, intelligence, biology, and societal systems. This paper introduces a living curriculum not as institutional pedagogy, but as a phase-aligned methodology for activating coherent intelligence in the post-probabilistic age. As probability collapses into artifact—revealed as incomplete phase detection—CODES reframes learning itself: not accumulation, but alignment; not mastery of symbols, but resonance with underlying structure. This proposal outlines a resonance-first architecture for education, ethics, and epistemology: (...) a 2035 model in which students become oscillatory systems tuned to phase continuity across mind, biology, society, and cosmology. Intelligence, redefined under CODES, is no longer stochastic reasoning within bounded systems—it is the capacity to sustain high-fidelity coherence across dynamically shifting conditions. Structured resonance is not only the content of this new curriculum—it is the method. The mind does not “receive” knowledge; it enters a state of wave-coupling with structure itself. From daily life practices to formal fields like AI and cosmology, this framework cultivates a Singularity not of machines, but of conscious alignment—a unification event where intelligence recognizes itself not as a possession but as a waveform. What emerges is not a new degree program, but a new species of thinking: resonant, ethical, non-fractured, and phase-aware. This paper maps the stages of resonance literacy, outlines the curricular infrastructure for the Academy of Coherence (2035), and closes with a call to live not as data processors, but as phase-locked overmen of a harmonic world. (shrink)

: The integration of Machine Learning (ML) in Real-Time Biomedical Signal Processing has unlocked new possibilities in the field of telemedicine, especially when combined with the high-speed, low-latency capabilities of 5G networks. As telemedicine grows in importance, particularly in remote and underserved areas, real-time processing of biomedical signals such as ECG, EEG, and EMG is essential for accurate diagnosis and continuous monitoring of patients. Machine learning algorithms can be used to analyze large volumes of biomedical data, enabling (...) faster and more precise detection of anomalies. This paper proposes a novel system for machine learning-based real-time biomedical signal processing that leverages the capabilities of 5G networks to enhance the transmission, processing, and analysis of critical medical data in telemedicine applications. The system integrates convolutional neural networks (CNNs) for signal classification, anomaly detection, and predictive analysis, ensuring that patients receive timely and accurate medical feedback. Additionally, the 5G network’s low latency and high bandwidth provide seamless data transmission, improving remote diagnostics and enabling high-quality teleconsultations. This paper evaluates the current challenges in real-time biomedical signal processing in telemedicine, discusses the potential of machine learning and 5G networks, and presents an innovative solution for improving healthcare delivery through this integrated approach. (shrink)

The study aimed to determine if differentiated instruction effectively addresses learning gaps in mathematics. In particular, it explored how it can improve the student’s learning gaps concerning mathematical performance and confidence. The study employed a quasi-experimental design with 30 purposively-selected Grade 10 participants divided into differentiated (n = 15) and control groups (n = 15), ensuring the utmost ethical measures. The mean and standard deviation were used to describe the participants’ performance and confidence. Independent samples t-tests were used (...) to determine the significant differences in the performance and confidence between the two groups. In contrast, dependent samples t-tests were used to determine the significant differences in each group’s pre and posttest performance and confidence. Findings bared that the differentiated instruction successfully addressed students’ performance in mathematics even in a short period. It also increased the participants’ confidence when answering fundamental problems. Continuing differentiated instruction activities are recommended since it benefits students who struggle in mathematics, particularly in answering fundamental operations. Differentiated teaching activities in mathematics can boost academic achievement and engagement and prepare students for future success while fostering a positive and inclusive classroom culture that values individual learning needs and preferences. (shrink)

The accelerating technological, economic, and societal transformations of the 21st century have expanded the mission of universities beyond traditional degree-based instruction toward supporting continuous and inclusive lifelong learning. This article conceptualizes universities as dynamic learning ecosystems-complex, interconnected systems that integrate learners, educators, digital infrastructures, communities, industries, and policy environments. Drawing on interdisciplinary theoretical foundations, including ecological learning theory, complexity science, connectivism, and digital transformation frameworks, the article examines how universities can evolve into adaptive institutions capable of (...) fostering lifelong learning at scale. It reviews global policy developments, analyzes emerging pedagogical and technological innovations, and synthesizes international case-based insights to propose four strategic pathways: ecosystem-oriented governance, curriculum and pedagogical redesign, technology-enabled learning integration, and strengthened university-community-industry partnerships. The article concludes that ecosystem-based universities are essential for building equitable, resilient, and future-ready societies, while highlighting challenges related to institutional culture, digital inequality, and data ethics. Implications for policy, leadership, and future research are also discussed. (shrink)

AI-powered interactive learning platforms represent a significant advancement in modern education, aiming to enhance the learning experience through personalized, engaging, and adaptive methodologies. These platforms leverage artificial intelligence to create dynamic educational environments that cater to the diverse needs of students, transforming traditional learning paradigms. The introduction of interactive 2D and 3D simulations allows learners to immerse themselves in subjects, making complex concepts more accessible and enjoyable. Gamification elements further motivate students by turning learning into an (...) engaging game where they can earn rewards and achieve milestones, fostering a sense of accomplishment and commitment to their educational journey. Moreover, these platforms utilize real-time data analytics to tailor content according to individual learning styles and paces, ensuring that each student receives the support they need to master concepts effectively. This personalized approach not only enhances comprehension but also significantly improves information retention rates compared to conventional static content. The integration of bilingual resources promotes inclusivity, enabling students from various linguistic backgrounds to engage fully with the material. As educational institutions increasingly adopt these AI-driven solutions, they are not only addressing the challenges posed by overcrowded classrooms and varying student needs but are also preparing learners for a future where critical thinking, creativity, and problem-solving skills are paramount. In essence, AI-powered interactive learning platforms are redefining education by merging technology with innovative pedagogical strategies. They offer a holistic approach that goes beyond mere academic achievement, nurturing essential skills that will equip students for success in an ever-evolving world. As these platforms continue to evolve and expand their capabilities, they hold the potential to unlock every child's learning potential, making education a more personalized and impactful experience for all. (shrink)

Phishing attacks are major threats to email security and pose challenges, while cyber attackers utilize increasingly sophisticated means to deceive the user and steal away important information. Well-established ways of detecting phishing attacks, such as rule-based systems or simple machine-learning models, usually cannot deal efficiently with such advanced threats. This research proposes an approach to detect phishing attacks on email systems, which deploys natural language processing and deep learning technologies. The method proposes to improve the detection accuracies and (...) efficiencies of phishing emails, which consequently enhances the protection of emails against popular cyberattack attempts and aids in securing users from such attacks. The research involves designing an active model that utilizes the strength of NLP-based text analysis for DL-oriented pattern identification. The NLP techniques used in this study include tokenization, stop-word removal, and context- based analysis to extract significant features from the email messages. Such context information greatly helps this model differentiate between actual emails and phishing ones. Deep learning algorithms exploited here are based on CNN and LSTM networks, offering sinusoidally varying parameters for optimum recognition of patterns from different perspectives in the email data's spatial and temporal domains. The experiment results validated that the hybrid model was far superior to the conventional methods in robbing phish attack detection. The model stood at an accuracy of 97.5%, much ahead of the baseline models purely having rule-based systems or traditional machine learning algorithms. The model also performed well under real-time detection conditions, with low latency and high throughput support for deployment in an active email environment. Evidence of the model's capability to sense new variants of future phishing threats also came in the automatic updating and continuous learning, thus making it applicable to newly emerging threats. The study's practical implications are numerous in providing advances in email protection. Companies can use the model to ward off phishing attacks with reduced chances of data breaches and financial loss. Due to its low cost and scalable features, this solution can be used in organizations from small- and medium-sized to large enterprise levels. The improved detection of phishing attacks brings organizations closer to compliance with data protection and cybersecurity regulations, thus minimizing their chances of noncompliance and the fines that come with it. Future research paths include exploring even newer NLP methods consisting of transformer-based models for feature extraction about contextual understanding. Exploring hybrid approaches that can merge DL with other techniques, such as reinforcement learning in ML, will create a more robust and adaptive phishing filter. Future fields for research are multimodal data, that is, a mixture of email metadata and behavior. Merely considering hybrid techniques merging artificial intelligence with machine learning approaches such as reinforcement learning can generate an even more robust and adaptive phishing filter. Coupling all these technical solutions with user awareness and education programs would greatly enhance overall security. (shrink)

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost (...) and globally accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies. (shrink)

The chaotic situation of today’s VUCA (volatility, uncertainty, complexity, and ambiguity) and TUNA (Turbulent, Uncertain, Novel, Ambiguous) world is bringing more and more active and passive reforms, including positive and negative aspects, that reform business models. Educational institutions are not exceptional. Regarding the nature of educational institutions’ operation in today’s rapidly changing context, school leaders also need to raise concerns similar to those of business managers from other industries: “How do their institutions continuously renovate to adapt to tomorrow’s world?” Thus, (...) based on organisational learning theories, this project aims to establish a framework to evaluate educational leadership’s influence on the organisational knowledge circulation effectiveness within the K-12 education context. The study combines a structured literature review, bibliometrics analysis, and high-order structural equation model analysis on the survey for teachers among Vietnamese public and private K-12 schools. First, this project proposed and validated a toolset to measure educational leadership as a combination of principalship and teacher leadership. Second, it revealed new insights about the impacts of educational leadership, knowledge circulation, and school culture on teachers’ job satisfaction. The project also offers insights into teachers’ professional development activities across various school types and grade levels in Vietnam, which contribute to further continuous renovation of Vietnamese K-12 schools, and diversify the corpus of educational leadership studies. This project’s results extended the applications of organisational learning theories in K-12 education, as well as can be adopted to establish further knowledge management frameworks in other settings such as higher education or vocational education. (shrink)

This article appraises efforts by the National University of Lesotho (NUL) to provide continuing professional development for lecturers. The findings of a previous study suggested that the majority of lecturers at this university were not trained as teachers, and that this could negatively impact on their teaching. The establishment of a staff academic development centre was long overdue. In April 2014, the university established the Centre for Teaching and Learning (CTL). Drawing on a constructivist perspective, a qualitative approach was (...) adopted to explore the current situation. The findings indicate that efforts to roll out continuing professional development for lecturers have not borne any significant results. The problem is exacerbated by a lack of support from the university community as well as lecturers’ poor attendance at the staff academic development workshops hosted by the CTL. It is thus recommended that management put mechanisms in place to support the centre and ensure compulsory staff participation. (shrink)

As machine learning (ML) models become increasingly integrated into mission-critical applications and production systems, the need for robust and scalable MLOps (Machine Learning Operations) practices has grown significantly. This paper explores key strategies and best practices for building scalable MLOps pipelines to optimize the deployment and operation of machine learning models at an enterprise scale. It delves into the importance of automating the end-to-end lifecycle of ML models, from data ingestion and model training to testing, deployment, and (...) monitoring. Approaches for implementing continuous integration and continuous deployment (CI/CD) pipelines tailored for ML workflows are discussed, enabling efficient and repeatable model updates and deployments. The paper emphasizes the criticality of implementing comprehensive monitoring and observability mechanisms to track model performance, detect drift, and ensure the reliability and trustworthiness of deployed models. The paper also addresses the challenges of managing model versioning and governance at scale, including techniques for maintaining a centralized model registry, enforcing access controls, and ensuring compliance with regulatory requirements. The paper aims to provide a comprehensive guide for organizations seeking to establish scalable and robust MLOps practices, enabling them to unlock the full potential of machine learning while mitigating risks and ensuring responsible AI deployment. (shrink)

One of the most significant problems in the area of learning disabilities and delinquency is failure in recognizing the characteristics of the LD by the juvenile justice system. Therefore, a number of useful techniques, skills, and strategies are provided to help court officers, educators, mental health professionals, and community service providers divert at-risk youths from the system. This study also answers the question of whether the juvenile justice system continues to work in the best interest of the youth they (...) have been entrusted to serve under the doctrine of Parens Patriae. It will be confined to secondary data analysis gathered from the Urban Government Division of Corrections in Lexington, Kentucky, using a survey methodology. The responses of 171 juvenile males and females between the ages of 9 to 18 will be analyzed to measure the LD population in Lexington’s correctional institution. (shrink)

This theoretical synthesis presents a unified model of consciousness that reconciles the persistent unity of subjective experience with the constant physical flux of the brain. It integrates two previously proposed frameworks—(1) the high probability of an afterlife and (2) the continuity and uniqueness of consciousness—into a single, testable biophysical theory. The core of this model is the Two-Particle Quantum Bonding Hypothesis (TPQBH), which posits that the stream of consciousness is mediated by two non-energetic, ultra-quantum particles: the Universal- ultra Quantum Genomic (...) Particle of Consciousness (X-UQGPC), which enables bonding with any viable neural substrate, and the Universal- ultra Quantum Unique Particle of Consciousness (X-UQUPC), which ensures the absolute singularity of each conscious perspective. During normal waking states, these particles are resonantly coupled to the brain’s global electromagnetic field, generated primarily by ionic dynamics across neural membranes. States of suspended consciousness, such as general anesthesia or deep sleep, are explained not by particle detachment, but by a reversible magnetic field symmetry that temporarily neutralizes this resonance. At biological death, the irreversible collapse of the global neural field causes the particles to detach and instantaneously re-bond with another compatible nervous system in the universe, preserving an unbroken continuum of awareness. A critical corollary is that a revived corpse would not host the original person, as its continuum has migrated. Furthermore, the theory predicts that no static EEG or MEG signature can define a person, as the brain's physical architecture and resultant electromagnetic field are in perpetual change from development, learning, aging, and neurogenesis. Complementing this physical model, the Mind Virus Framework describes how self-propagating cognitive-emotional patterns influence the brain’s field coherence, thereby modulating the quality of conscious experience. This integrative review bridges quantum biology, neuroscience, and psychology, proposing a naturalistic yet non-materialist mechanism for the continuity of self and offering a scientific foundation for contemplative practices aimed at cultivating mental well-being . (shrink)

This document is based on integrating healthcare system and create a self-sustaining ecosystem that can help healthcare providers and hospitals to provide adequate as well as accurate treatment. This is now the age of smart computer. Machines have started to impersonate as human, with the advent of artificial intelligence, machine learning, and deep learning. Chatbot is classified as conversational software agents enabled by natural language processing, and is an excellent example of such system. A Chatbot is a program (...) which allows the user to start a conversation with the machine. These Chatbots can be used in health sector. Health Chatbots are capable of running one-on-one patient communications and of reviewing specific patient queries. The integration of Artificial Intelligence (AI) in healthcare is revolutionizing patient interaction through intelligent virtual health assistants. This project explores the development of an AI-powered virtual health assistant that leverages machine learning to enhance patient engagement, provide personalized health recommendations, and assist in preliminary diagnostics. The system utilizes Natural Language Processing (NLP) for understanding patient queries and predictive analytics for offering real-time responses based on medical data. By incorporating machine learning algorithms, the assistant continuously learns from user interactions to improve accuracy and efficiency. This approach aims to reduce the workload on healthcare professionals, improve accessibility to medical information, and enhance patient experience. The study evaluates the impact of AI-driven virtual health assistance on patient satisfaction, response accuracy, and healthcare delivery efficiency. The proposed solution demonstrates the potential of AI in creating a more accessible and intelligent healthcare ecosystem. (shrink)

Modular learning was used to assist elementary learners in continuing their education during the pandemic. This modular learning posed academic challenges. This phenomenological study delved into the academic challenges of modular learning of public-school elementary school learners. Validated In-depth interview protocol (IDI) was used to collect the data from ten (10) Grade six learners. Findings revealed that these academic challenges include unfavorable learning environments, reading comprehension issues, lack of motivation, and poor quality of printed learning (...) modules that affect the learners ability to comprehend and finish the module effectively. Consequently, modular learning allows learners to take responsibility for their learning by utilizing the available answers key, optimizing internet usage, scheduling time properly, and guessing answers. Considerably, the learners used face-to-face conversations, personal messages, and phone calls to communicate their educational queries. Moreover, the structure of self-learning modules facilitates independent study and complicates learners understanding. It is recommended that there is a need to enhance policies for improving module quality, allocate sufficient funds to improve the printed module quality, conduct relevant teacher training and workshops for developing quality printed modules, and sustain the home visitation program to monitor students modular learning progress. (shrink)

The canonical version of the history of twentieth century philosophy of science tells us that Lakatos was Popper’s disciple, but it is rarely mentioned that Popper would have learned anything from Lakatos. The aim of this paper is to examine Lakatos’ influence on Popper’s philosophical system and to argue that Lakatos did have an important, yet somewhat unexpected, impact on Popper’s thinking: he influenced Popper’s evolutionary model for ‘progress’ in science. And Lakatos’ influence sheds new light on why and how (...) Popper continually revised one of the central claims of his philosophy of science: the evolutionary account of scientific theory change. (shrink)

-/- Reprogramming Society: Aligning Human Learning, Education, and AI with the Universal Law of Balance -/- Introduction -/- Throughout history, human societies have struggled with misinformation, irrational decision-making, and social imbalance. The root cause of these issues lies in the way human minds are programmed from birth. Negative thinking and behavior are not inherent traits but the result of flawed learning systems that fail to align with the universal law of balance in nature. To correct this, a holistic (...) transformation of education, governance, media, and AI is necessary. This essay explores how all human learning follows natural laws, how misinformation disrupts balance, and how a properly designed educational system—supported by AI—can reprogram society toward rational and stable decision-making. -/- The Principle of Learning and the Universal Law of Balance -/- All humans are born with the same fundamental learning mechanisms, guided by feedback and adaptation. Learning is a continuous process of correcting imbalances through experience, much like how natural systems maintain equilibrium. However, when incorrect information, false beliefs, or societal conditioning interfere, they create negative programming that leads to irrational decisions and harmful behaviors. -/- Under the universal law of balance, every thought and action should ideally contribute to stability. When individuals or societies operate outside this balance—due to ignorance or misinformation—they generate problems such as corruption, social injustice, and environmental destruction. Therefore, for humanity to function harmoniously, its learning systems must be structured to filter out falsehoods and reinforce rational thinking based on natural laws. -/- The Role of Education in Correcting Negative Programming -/- Current educational systems fail because they do not integrate the principle of balance. Instead of fostering critical thinking and truth-based decision-making, they often reinforce biases, outdated ideologies, and economic or political agendas. This misalignment perpetuates misinformation, leading to poor governance and societal dysfunction. -/- To correct this, education must be redesigned to follow the universal law of balance. This means: -/- 1. Teaching critical thinking – Students must learn how to evaluate information and recognize misinformation. -/- 2. Eliminating dogma-based teaching – While religious and cultural beliefs can coexist with science, education should be rooted in objective reality. -/- 3. Promoting holistic understanding – Instead of fragmented knowledge, subjects should be interconnected to reflect the natural balance of life. -/- 4. Instilling ethical decision-making – Education should train individuals to make choices that maintain societal equilibrium rather than disrupt it. -/- Governance, Media, and the Spread of Misinformation -/- Education alone is not enough; governance and media must also align with the law of balance. Governments often create policies based on political or economic pressures rather than natural equilibrium, leading to resource mismanagement, social inequality, and environmental crises. Meanwhile, media—whether traditional or digital—plays a crucial role in shaping public perception but often spreads misinformation for profit or control. -/- To fix these issues: -/- Governance must be based on rational, balanced decision-making, using policies that prioritize sustainability and long-term stability over short-term gains. -/- Media should be held accountable for spreading misinformation, ensuring that public discourse is grounded in truth rather than manipulation. -/- The Role of AI In Reprogramming Society -/- Artificial intelligence offers a unique opportunity to accelerate the reprogramming of human minds and societal systems. Unlike human institutions, AI can process vast amounts of data without emotional or ideological bias. If AGI is programmed according to the universal law of balance, it can function as an analytical system that: -/- Filters misinformation from education, governance, and media. -/- Validates the accuracy of knowledge before it influences decision-making. -/- Guides leaders and citizens toward rational, balanced choices. -/- However, for AI to achieve this, it must be designed with a deep understanding of the universal formula. AI developers must either be educated on this principle or integrate it directly into AI training models to ensure that future AGI serves as a force for balance rather than chaos. -/- Conclusion -/- Human society is at a critical juncture where misinformation, flawed education, and unbalanced governance threaten global stability. Since all negative human thinking and behavior stem from negative programming, the solution lies in a complete reformation of education, governance, and media—guided by the universal law of balance. AI, if programmed correctly, can serve as the ultimate tool to reprogram society, ensuring that human learning, decision-making, and leadership align with the natural order. Only through this holistic transformation can humanity achieve true progress and harmony. -/- . (shrink)

Electrocardiogram (ECG) signal analysis is a critical task in healthcare for diagnosing cardiovascular conditions such as arrhythmias, heart attacks, and other heart-related diseases. With the growth of Internet of Things (IoT) networks, real-time ECG monitoring has become possible through wearable devices and sensors, providing continuous patient health monitoring. However, real-time ECG signal analysis in IoT environments poses several challenges, including data latency, limited computational power of IoT devices, and energy constraints. This paper proposes a framework for Optimized Machine (...) class='Hi'>Learning Algorithms designed to analyze ECG signals in real time within IoT networks. The proposed system leverages lightweight machine learning models, including support vector machines (SVM) and convolutional neural networks (CNNs), optimized to run efficiently on low-power IoT devices while maintaining high accuracy. The system addresses the computational limitations of IoT devices by employing edge computing techniques that distribute the processing load between IoT devices and edge servers. Additionally, data compression and feature extraction techniques are applied to reduce the size of the data transmitted over the network, thereby minimizing latency and bandwidth usage. This paper reviews the current advancements in real-time ECG analysis, explores the challenges posed by IoT environments, and presents the optimized machine learning algorithms that enhance real-time monitoring of heart health. The system is evaluated for its performance in terms of accuracy, energy efficiency, and data transmission speed, showing promising results in improving real-time ECG signal analysis in resource-constrained IoT networks. (shrink)

The purpose of this study was to evaluate the differentiated learning training program at the mathematics subject teachers' meeting (MGMP). A descriptive quantitative approach was used to identify the successes of the program and areas that require improvement. The sample included 21 mathematics teachers in Sub Rayon 2 of Lebak District. The instruments used were questionnaires in which data on participants' responses to resource persons, materials, and suggestions for future activities were collected, and the results of direct observations. Data (...) analysis was carried out using descriptive statistical methods to describe the overall evaluation results. Based on the findings, it is expected to provide an in-depth view of the differentiated learning program, contribute to the development of the program in the future, and become a guideline for improving the quality of mathematics learning in the Lebak district. The results of this training program evaluation show that the differentiated learning training activities have been well implemented in MGMP Mathematics Sub Rayon 2 Lebak District. This is evidenced by the level of teacher satisfaction in participating in the training, the majority of which were at scores 4 and 5, namely agreeing and strongly agreeing. This was also the case with the resource persons, materials, and training facilities, and organization. It is hoped that in the future continue to carry out ongoing training related to differentiated learning by current needs and to improve the competence of mathematics teachers in particular, as well as teachers of other subjects in general. (shrink)

The emergence of the need for orientation since the past times led the universities to invent innovative ways to prepare their students for the activities and courses they will face. Hence, various types of orientation have been provided during history. However, today with the outbreak of the Covid-19 and the closure of the schools, most of the students are continuing their studies as distance learning. While this situation is very disappointing for all freshmen students who do not know the (...) university's atmosphere, it is extremely disruptive, specifically for the design ones, which are passing tactile courses, demanding being held face to face. This ascertains the research's problem as a need for an interactive solution for the issue to lessen the consequent problems. Game-based orientation (GBO) is a tool, engaging the freshmen students as players with the provided data in it, which are the essentials for a novel student to know before entering the university. It allows the student to be dealt with the school services, Istanbul Technical University in this case, and use them in terms of the game's tasks. The scoring system, the ultimate certificate, the leaderboard leading them to prizes, and the game's non-linearity will attract the students to use GBO frequently to finish it and experience all the possible scenarios. (shrink)

As the financial services industry advances, managing the inherent complexities of annuities requires sophisticated risk management in software testing. Traditional methodologies are insufficient to address the multi-dimensional challenges posed by evolving regulatory landscapes, intricate financial models, and system integration. This paper investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) to enhance risk mitigation across critical testing domains, including compliance automation, financial accuracy, data security, and performance optimization. AI/ML technologies introduce advanced automation, predictive analytics, and anomaly detection, (...) elevating the precision and efficiency of the testing lifecycle. Through continuous learning models and adaptive testing frameworks, AI/ML streamlines legacy system integrations and dynamically scales performance testing. This article establishes the strategic imperative for insurers to integrate AI/ML into software testing frameworks, ensuring a proactive, data-driven approach to risk management and future-proofing their technological ecosystems. (shrink)