In this paper, a new Intervention Meditation approach is outlined by the current author. To overcome lack of clarity in defining meditation, a new universal Operational definition of meditation is proposed with elaborative explanation. The new meditation approach has multitude of deeper concepts from Bhagavad Gita and modern psychology and the expert / Guru of the proposed method must be a qualified and experienced before getting trained on this approach specifically. This paper elevates meditation from a casual approach level to (...) a separate systematic branch of psychology level. (shrink)

In this paper current author proposes a need for studying intricate differences between psychological aspects, factors, cognitions, affect, behaviour and related dynamics. It can be promoted as a separate branch within the domain of Psychology as Intricacy Psychology. Case history taking for the new Intervention Meditation of current author is proposed in line with capturing such intricacies of the client. The current also proposes to study the intricacies belonging to ‘Self’ as an important factor within his new Intervention Meditation approach. (...) View of Self in line with current author’s new Intervention Meditation is also outlined. This paper is at the initial stage of the series of papers planned to develop his new Intervention Meditation approach which is entirely different from all other meditations in vogue. (shrink)

Individuals come across extreme Uncertainty situations, feeling rounded–up 360 degrees, as if trapped inside Chakra Vyuha (wheel form strategy). For uncertainty is often related to future, the concept of Future Time perspective (FTP) is relied upon for attaining positive states. But FTP is a critical factor which works both positively and negatively depending on other factors intervening. For politicians and businessmen such states of being trapped in Chakra Vyuha, can mean end of their political life or business life. In the (...) current paper, the author describes the introspection of a Subject while he attempted to alter his Future Time perspective. This introspective report further gave clue for Merged Time Perspective or Seamless Time Horizon instead of FTP and also that Regulatory Focus and Resistance To Change need to be combined, coalesced and treated on a single continuum. Current author also has proposed a therapy outline to help businessmen and politicians to come out of devastating situations in which they feel rounded – up by opponents 360 degrees. (shrink)

Deep learning, a transformative force in computational biology, has reshaped biological data analysis and interpretation terrain. This review delves into the multifaceted role of deep knowledge in this field, exploring its historical roots, inherent advantages, and persistent challenges. It investigates explicitly its application in two pivotal domains: DNA sequence classification, where it has been used to identify disease-causing mutations, and protein structure prediction from sequence data, where it has enabled the accurate determination of protein tertiary structures. Moreover, it offers a (...) glimpse into the future trajectory of this dynamic field, sparking intrigue and excitement about the potential of deep learning. Deep learning, a powerful tool in computational biology, can be traced back to the inception of 'threshold logic,' a fusion of algorithmic principles and mathematical frameworks devised to mimic cognitive processes. This seminal breakthrough, which introduced the concept of a threshold function that could be used to model complex decision-making processes, paved the way for deep learning to decipher intricate patterns embedded within vast biological datasets. Deep learning presents many benefits, including precise disease diagnosis, novel drug discovery, and personalized medicine facilitation. Its prowess lies in handling vast, intricate datasets while enhancing generalization capabilities, a testament to its evolution and development that we can appreciate. (shrink)

This research paper explores effective governance strategies aimed at ensuring consistency and compliance within Business Rules Management Systems (BRMS). As organizations increasingly rely on BRMS to streamline decision-making processes, the need for robust governance becomes paramount. The abstract delves into the challenges posed by evolving business environments and complex regulatory landscapes, emphasizing the significance of maintaining rule consistency and compliance. The paper investigates diverse governance models and their applicability in promoting adherence to business rules across organizational units. Additionally, it addresses (...) the role of technology in facilitating governance mechanisms, including version control, audit trails, and automated monitoring. The study contributes valuable insights to both academia and practitioners by offering a nuanced understanding of governance strategies crucial for fostering reliability, uniformity, and compliance within BRMS. (shrink)

The purpose of this work is to compare the ways in which relational databases—more especially, Microsoft SQL Server—and non-relational databases—MongoDB—handle unstructured data in JSON format. Although a lot of study has been done comparing different database management systems in terms of security, performance, and other aspects, little is known about how these databases are evaluated according to the type of data they handle. The main goal of this study is to examine the various features that relational and non-relational databases provide (...) for handling JSON data. We will conduct a number of tests to do this, making sure that the relevant data doesn't need to be normalised. To draw a conclusion, the outcomes of these studies will thereafter be examined. (shrink)

This research paper investigates the transformative applications of data visualization techniques in the realm of financial crime detection, with a specific emphasis on addressing the challenges posed by credit card fraud and money laundering. The abstract explores the intricate landscape of visualizing financial data to uncover patterns, anomalies, and potential illicit activities. Through a comprehensive review of existing methodologies and case studies, the paper illuminates the pivotal role data visualization plays in enhancing the efficiency and accuracy of fraud detection systems. (...) By synergizing advanced visualization tools with machine learning algorithms, the study aims to provide insights into how financial institutions can bolster their defenses against evolving threats. Ethical considerations, usability, and the real-world impact of data visualization in combating financial crime are also scrutinized. This research contributes to the evolving discourse on leveraging visualization technologies to fortify financial systems against illicit activities, fostering a proactive and responsive approach to safeguarding economic ecosystems. (shrink)

This paper examines how predictive analytics enhances risk management in financial institutions. Advanced tools like machine learning and statistical modeling help predict risks, identify trends, and implement strategies to prevent losses by analyzing historical and real-time data. It covers the use of predictive analytics for credit risk, market risk, operational risk, and fraud detection, with practical case studies. Additionally, it discusses challenges, ethical issues, and prospects in this field.

In the context of mid-level professional writing jobs, we examine the productivity effects of a generative artificial intelligence technology, namely the assistive chatbot ChatGPT. We used ChatGPT to randomly expose half of the 444 college-educated professionals to occupation-specific, incentive-based writing tasks in a preregistered online experiment. Our results show that ChatGPT considerably increases average productivity: output quality improves by 0.4 standard deviations and task completion time drops by 0.8 standard deviations. By compressing the production distribution, ChatGPT also lessens worker inequality, (...) mainly helping individuals with lesser starting abilities. ChatGPT shifts the focus of activities from rough-drafting to idea generation and editing, thereby replacing worker effort rather than enhancing worker skills. Furthermore, exposure to ChatGPT increases job satisfaction and self-efficacy while also heightening both concerns and excitement about automation technologies. (shrink)

Through an examination of Stack Overflow posts, this study explores Docker-related subjects and difficulties in order to pinpoint common problems and patterns in Docker development. We created an extensive dataset of Docker conversations by utilising both tag-based and content-based filtering techniques. These conversations were categorised using Latent Dirichlet Allocation (LDA) topic modelling, which showed that the most common theme is Application Development, which includes topics like Framework Management, Coding Problems, Data Transfer, and Docker-specific frameworks. Because of Docker's strong market presence, (...) developers are very interested in using it to build and manage apps, as evidenced by this dominant category. The study highlights important areas of interest and difficulty within the Docker community and offers insightful information on the many technical and operational problems faced by Docker developers. These findings offer guidance for future research, tool development, and educational resources, aiming to address the evolving needs of Docker practitioners and enhance the overall development experience. (shrink)

In the current digital environment, cyberattacks continue to pose a serious risk and difficulty. Internet of Things (IoT) devices are becoming more and more vulnerable due to security problems like ransomware, malware, poor encryption, and IoT botnets. These flaws may result in ransom demands, data tampering, illegal access, and system risks. Creating strong cybersecurity procedures for contemporary smart environments is essential to resolving these problems. This strategy uses proactive network traffic monitoring to spot any dangers in the Internet of Things (...) ecosystem. Our approach is to improve Smart Environments' security and awareness of potential threats. Two IoT gateways were used to examine the effectiveness and performance of a deep neural network (DNN) model. The results were promising: the model caused an average increase of less than 30 kb/s in network bandwidth and a mere 2% rise in CPU usage. Additionally, memory and power consumption were minimal, with 0.42 GB and 0.2 GB of memory usage for NVIDIA Jetson and Raspberry Pi devices, respectively, and an average 13.5% increase in power consumption per device. The machine learning models achieved nearly 93% detection accuracy and a 92% F1 score on the datasets used. Our framework demonstrates an effective and efficient method for detecting malware and attacks in Smart Environments. (shrink)

Over the last few years, personal health monitoring wearable devices have emerged as innovative applications of Artificial Intelligence (AI) in the healthcare industry as they help in real time analysis and prediction of health standardized check-ups and health management. To navigate through the current trends, new technologies and developments, the prospects are as follows: The article also gives a logical look at the state of the art of such devices, enumerating the advantages and drawbacks, as well as outlining the main (...) ethical issues. In terms of method, it also discusses different AI approaches, data acquisition procedures, and those integrating systems used in the context of health monitoring. The finding and implication section discusses the findings of the current trend and the possibility of improvement in application and user acceptance. The last section provides a prognosis on what the area of personal health monitoring might look like in the future, as well as the position of AI in the optimisation of patient experience. (shrink)

In today’s rapidly evolving financial landscape, the adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies, coupled with the deployment of Business Rules Management Systems (BRMS), has transformed how financial transactions are conducted, monitored, and secured. With fraud, particularly in check deposit transactions, becoming increasingly sophisticated, financial institutions are turning to AI and ML to enhance their risk management strategies. This paper explores the integration of AI-driven models and business rules in financial transactions, focusing on their application in fraud (...) detection and prevention. We also examine the associated challenges, such as data privacy, ethical concerns, and the need for regulatory frameworks, while outlining the opportunities that lie ahead for future financial innovation. (shrink)

This research paper explores the dynamic integration of artificial intelligence (AI) and machine learning (ML) to enhance Business Rules Management Systems (BRMS) for the facilitation of agile business strategies. In the evolving landscape of digital enterprises, the demand for adaptive and responsive decision-making processes is paramount. The abstract investigates the symbiotic relationship between AI, ML, and BRMS, elucidating their combined potential to empower organizations in crafting agile and resilient business strategies. The study delves into the mechanisms by which AI and (...) ML augment traditional BRMS, offering predictive insights, optimizing decision rules, and fostering real-time adaptability. Through a comprehensive analysis, the research aims to provide valuable insights into the transformative capabilities of this integrated approach, shedding light on its implications for business agility, competitiveness, and strategic innovation. (shrink)

A CONSTRUCTIVE CRITIQUE OF THE FOUNDATIONS OF PHILOSOPHY: Abstract of the Ph.D. Dissertation By Ramesh N. Patel June, 1970 -/- Philosophy pursues a rational explication of our understanding, experiences, and values in terms of objective truth and reality. Conspicuously, its view of rationality has been rigid and preconceived. Application of the preconceived reason in the explication of the essential features of our world fails and issues in a network of dialectical tangles. These artificially created tangles pose a unique intellectual (...) challenge, but the perennial failure to resolve them limits the intellectual response of philosophers to remaining caught in the tangles, or to taking intuitively favorable sides, or to simply denouncing the tangles as meaningless. Each is understandable. -/- Superficially, traditional philosophical reason looks like a correct tool for the explication of our world of common sense, but its applications to the world result in logical tangles showing its inadequacy. The philosopher’s faith in such reason ends up in one of two positions: abandoning our world in the interests of such reason, or embodying itself in the world through a special form of reason. Plato took the first and Aristotle took the second position. My analysis shows that Plato failed to explicate the relation between his world of Forms and our world of particulars, and, more importantly, between the Forms and the intuitive reason which grasps them. Aristotle, in my analysis, cannot conceal that the law of noncontradiction is a mere exhortation and has no descriptive necessity with respect to thought, things, or meaning. We also find such dialectical difficulties in Augustine, Hume and Strawson. -/- “Microphilosophical feasibilism” is our proposal for the best treatment, if not the definitive solution, of these problems. Instead of separating philosophical method into logic, epistemology, axiology, and metaphysics on the one side, and partitioning the projected results of philosophizing into validity, truth, valuation, and reality on the other, we need to start philosophizing with a general distinction between “method employed” and “intended result.” This distinction is not pre-colored by claims to ultimate verities. It is a direct aid to understanding. I consider the basic inevitable paradox that a philosopher should explain not only our understanding of experience but also his philosophizing itself. The circularity between “method employed” and “intended result” needs to be admitted as an initial fact about reason rather than circumvented by an implausibly claimed capacity of philosophical reason to formally transcend everything including this circularity. -/- Presuppositions are reduced to a minimum inevitable number. Being our primary microphilosophical criteria, they are reason, experience, and value in their minimal sense. They cannot be plausibly substituted or further reduced. They cannot be preferred one or more to others arbitrarily. We show how, in microphilosophy they coincide or synchronize. Jointly and basically, they form the minimum method employed. The initial, basic, intended result is called minimum feasibility. The fundamental circularity between method and result leaves no way out but to ground self-reference in such a manner that method and result “logico-genetically” coincide. Two extreme situations are avoided: “the logical zero-situation” where nothing is true or real, and “the credulous open situation” where everything is true or real. Finding a mean manifests a basic value. If the meaning of truth and reality is to be preserved, the basic value must have a clear impact and express descriptive rather than revisionary reason. -/- Several other stringent demands are raised as conditions which microphilosophy should fulfil to achieve feasibility. The final, joint, outcome of both “method” and “result,” in the thesis of microphilosophical feasibilism, is formulated thus: Self refers freely and symbolically to itself, own person, own body, material bodies, other persons’ bodies, other persons, and other selves. -/- Ramesh N. Patel Doctor of Philosophy in Philosophy conferred in June, 1970 University of New Mexico, Albuquerque, N.M. (shrink)

Today’s food industry across the world is facing never-ending difficulties in meeting customers’ expectations of safe and quality food. Such of them include issues to do with contamination, adulteration, and ensuring quality standards of the products when manufactured in large quantities and distributed to different areas. This has called for new solutions, which have come in the form of Artificial Intelligence (AI), which offers solutions to challenges in food safety and quality control in detection, monitoring and management. The present deployment (...) of AI in this field will also be discussed in this paper by virtue of machine learning algorithms, computer vision, industrial robots, and the IoT. With the help of AI, the safety and quality of food products can be improved as well as the food industry can obtain more accurate and real-time data for analysis, maintenance, and risk assessment. The application of AI systems in food safety and quality assurance provides not only reliability and decreased chances of human mistakes but efficiency as well. This paper aims to systematically summarize the current research achievements, methods, and application practices of AI in food safety and quality control. In effect, this paper presents a qualitative case analysis and research evidence that demonstrate the opportunities and risks arising from the implementation of AI technologies in the food sector. The analysis goes further considering the future trends and possible advancements oriented to creating a clear and prospective route for the researchers and specialists in the spheres of AI application for food safety and quality control. (shrink)

This research paper delves into the realm of Financial Network Analytics, unraveling the opportunities and challenges it presents in the domains of systemic risk management and investment analysis. The study navigates the intricate web of interconnected financial entities, employing advanced analytics techniques to uncover meaningful insights. Through a comprehensive exploration, the paper identifies opportunities for leveraging financial network data to enhance systemic risk detection mechanisms and refine investment strategies. Simultaneously, it addresses the inherent challenges, such as data complexity and dynamic (...) network structures, offering nuanced perspectives on their implications. The findings contribute to the evolving landscape of financial analytics, providing a foundation for the development of robust tools and methodologies that can aid financial institutions and investors in navigating the complexities of modern financial networks. (shrink)

Because of advanced automation, data exchange, and cyber-physical systems, traditional project risk management strategies are losing their effectiveness in the era of Industry 4.0. This study investigates how integrating Industry 4.0 technology into project risk management frameworks can have real-world implications. Using technologies like artificial intelligence (AI), big data analytics, and the Internet of Things (IoT), it proposes a new framework that enhances risk identification, assessment, and mitigation methods. In order to demonstrate how Industry 4.0 is transforming risk management practices, (...) this article examines real-world case studies and industry implementations. The framework addresses significant opportunities and challenges to provide practitioners with useful insights for adapting to the evolving technological context. The findings suggest that incorporating Industry 4.0 technologies can significantly improve risk management efficiency and project outcomes, offering a pathway for organizations to navigate the complexities of modern project environments. (shrink)

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into the research landscape has transforming almost every extending field, including pharmaceutical research. The idea of drug discovery itself is very conventional and has long been criticized for being overly lengthy and expensive, which sometimes may take more than 10 years and billions of dollars to develop a certain drug. AI and ML formulate the future of the drug discovery process by using big data to provide preliminary drug candidates more (...) effectively. This paper overviews the innovations defined by AI and ML in the field of drug discovery, major achievements, techniques, and use cases. Additionally, we explore how AI algorithms can enter biological data, inspect drug-target relations, determine optimal drug design, and potentially recompose famous drugs. This means through using big data with the help of AI in the process of research, previously undisclosed patterns that help in developing effective treatments for patients are found. The paper also addresses the related issues and limitations in applying AI in this domain, namely, data quality issues, the interpretability of AI solutions, and some ethical concerns. Herein, to provide a concrete foundation to the concepts mentioned so far, we discuss the different AI applications and case studies in drug discovery from a survey of the available literature. The article also provides information regarding the methodologies used in AI-enabled drug discovery like deep learning, reinforcement learning, and natural language processing. Moreover, we compare the use of conventional and artificial intelligence methods, while demonstrating what is good and what maybe in both. The results section offers a review and an integration of the most recent objectives and recommendations for subsequent research instruction. Therefore, despite these prohibitive AI and ML forecasts for drug discovery improvement, continuous interaction between computational scientists, biologists, and regulatory authorities is functional to fully unlock this potential. (shrink)

This comprehensive article explores the convergence of cloud computing and Java technologies, highlighting their synergistic relationship in modern software development. It examines key concepts in cloud computing, including virtualization, microservices architecture, CI/CD practices, and serverless computing. The article also discusses various cloud service models (SaaS, PaaS, and IaaS), their applications, and essential tools and technologies for cloud development. A financial institution's infrastructure modernization case study illustrates the practical implementation of these concepts, demonstrating how cloud technologies and Java can enhance agility, (...) reliability, cost-efficiency, and innovation in traditional industries. (shrink)

Freud proposes that in unconscious processing, logical connections are also (heavily) based upon phonological similarities. Repressed concerns, for example, would also be expressed by way of phonologic ambiguity. In order to investigate a possible unconscious influence of phonological similarity, 31 participants were submitted to a tachistoscopic subliminal priming experiment, with prime and target presented at 1ms. In the experimental condition, the prime and one of the 2 targets were phonological reversed forms of each other, though graphemically dissimilar (e.g., “nice” and (...) “sign”); in the control condition the targets were pseudo-randomly attributed to primes to which they don’t belong. The experimental task was to “blindly” pick the choice most similar to the prime. ERPs were measured with a focus on the N320, which is known to react selectively to phonological mismatch in supraliminal visual word presentations. The N320 amplitude-effects at the electrodes on the midline and at the left of the brain significantly predicted the participants’ net behavioral choices more than half a second later, while their subjective experience is one of arbitrariness. Moreover, the social desirability score (SDS) significantly correlates with both the behavioral and the N320 brain responses of the participants. It is proposed that in participants with low SDS the phonological target induces an expected reduction of N320 and this increases their probability to pick this target. In contrast, high defensive participants have a perplexed brain reaction upon the phonological target, with a negatively peaking N320 as compared to control and this leads them to avoid this target more often. Social desirability, which is understood as reflecting defensiveness, might also manifest itself as a defense against the (energy-consuming) ambiguity of language. The specificity of this study is that all of this is happening totally out of awareness and at the level of very elementary linguistic distinctions. (shrink)

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the insurance industry by optimizing risk assessment, fraud detection, and customer service. However, the rapid adoption of these technologies raises significant ethical concerns, particularly regarding bias and fairness. This chapter explores the ethical challenges of using AI and ML in insurance risk management, focusing on bias mitigation and fairness enhancement strategies. By analyzing real-world case studies, regulatory frameworks, and technical methodologies, this chapter aims to provide a roadmap for developing ethical AI/ML (...) systems in the insurance sector. It highlights the importance of transparency, accountability, and inclusivity in ensuring equitable outcomes. (shrink)

The increasing complexity and demands of next-generation networks necessitate the integration of Artificial Intelligence (AI) to enhance their efficiency and security. This article explores advancements in AI-driven communication systems, focusing on optimizing network performance, ensuring robust security measures, and addressing the challenges of scalability and real-time adaptability. By analyzing case studies, emerging technologies, and recent research, this study highlights AI's transformative potential in redefining communication systems for future applications.

The Unified Medical Language System and the Gene Ontology are among the most widely used terminology resources in the biomedical domain. However, when we evaluate them in the light of simple principles for wellconstructed ontologies we find a number of characteristic inadequacies. Employing the theory of granular partitions, a new approach to the understanding of ontologies and of the relationships ontologies bear to instances in reality, we provide an application of this theory in relation to an example drawn from the (...) context of the pathophysiology of hypertension. This exercise is designed to demonstrate how, by taking ontological principles into account we can create more realistic biomedical ontologies which will also bring advantages in terms of efficiency and robustness of associated software applications. (shrink)

In today’s competitive job market, job seekers often struggle to find suitable employment due to limited support and fragmented resources across various platforms. The PGRKAM Web Application addresses these challenges by centralizing employment data into a single, user-friendly platform that enhances job search efficiency. The application consolidates job listings from multiple sources, offers real-time notifications, and provides personalized job recommendations. It is structured with a two-tiered service model: a free version offering access to job listings and essential search features, and (...) a subscription-based version providing advanced support, including performance analysis, personalized training suggestions, and a structured feedback system to continuously improve employability. Optimized for web access and designed to support regional economic growth, the PGRKAM Web Application empowers job seekers with relevant, real-time data, ultimately increasing their chances of securing meaningful employment in Punjab. (shrink)

An explicit formal-ontological representation of entities existing at multiple levels of granularity is an urgent requirement for biomedical information processing. We discuss some fundamental principles which can form a basis for such a representation. We also comment on some of the implicit treatments of granularity in currently available ontologies and terminologies (GO, FMA, SNOMED CT).

The National Cancer Institute’s Thesaurus (NCIT) has been created with the goal of providing a controlled vocabulary which can be used by specialists in the various sub-domains of oncology. It is intended to be used for purposes of annotation in ways designed to ensure the integration of data and information deriving from these various sub-domains, and thus to support more powerful cross-domain inferences. In order to evaluate its suitability for this purpose, we examined the NCIT’s treatment of the kinds of (...) entities which are fundamental to an ontology of colon carcinoma. We here describe the problems we uncovered concerning classification, synonymy, relations and definitions, and we draw conclusions for the work needed to establish the NCIT as a reference ontology for the cancer domain in the future. (shrink)

As the systematic revolution converges computing technologies with Artificial Intelligence (AI) applications, various industries have been transformed, ranging from healthcare to finance. This paper presents an extensive review of the progress achieved through this synergy, alongside the challenges and possible future perspectives. The methodology used in this study is a literature review supported by a theoretical model illustrating the integration of AI with computing. The findings reveal that computational capabilities, data processing speeds, and AI model efficiencies have drastically improved. However, (...) significant roadblocks persist, particularly in data privacy, ethical considerations, and hardware limitations. These challenges underscore the need for addressing the field’s boundless potential for breakthroughs. Existing hurdles, such as the lack of robust regulations, call for timely intervention. As we move forward, this paper emphasizes the necessity for ongoing innovation and the development of comprehensive policies to navigate the evolving landscape. Ultimately, our conclusion highlights the critical importance of preparing for these challenges to fully realize the transformative potential of the AI and computing synergy. (shrink)

Predictive analytics, empowered by generative AI and cloud technologies, has the potential to revolutionize educational practices by facilitating early intervention and proactive support for students. This chapter explores the integration of predictive analytics in educational settings, focusing on how datadriven insights can identify at- risk students and tailor interventions to their specific needs. By leveraging generative AI algorithms, educators can analyze vast amounts of data, including academic performance, engagement levels, and socio- emotional factors, to predict potential challenges before they escalate. (...) The chapter highlights case studies that demonstrate successful implementations of predictive analytics in schools and universities, showcasing improved student outcomes, enhanced engagement, and more personalized learning experiences. Additionally, it discusses the ethical considerations and challenges associated with data privacy and bias in AI systems. (shrink)

1. Over the last 50 years or so, and especially over the last few decades, the U.S. and many other societies have undergone a large, rapid, and broad decline in anti-gay attitudes. The magnitude, s...

that can serve as a foundation for more refined ontologies in the field of proteomics. Standard data sources classify proteins in terms of just one or two specific aspects. Thus SCOP (Structural Classification of Proteins) is described as classifying proteins on the basis of structural features; SWISSPROT annotates proteins on the basis of their structure and of parameters like post-translational modifications. Such data sources are connected to each other by pairwise term-to-term mappings. However, there are obstacles which stand in the (...) way of combining them together to form a robust meta-classification of the needed sort. We discuss some formal ontological principles which should be taken into account within the existing datasources in order to make such a metaclassification possible, taking into account also the Gene Ontology (GO) and its application to the annotation of proteins. (shrink)

Arguments attempting to debunk moral beliefs, by showing they are unjustified, have tended to be global, targeting all moral beliefs or a large set of them. Popular debunking arguments point to various factors purportedly influencing moral beliefs, from evolutionary pressures, to automatic and emotionally-driven processes, to framing effects. We show that these sweeping arguments face a debunker’s dilemma: either the relevant factor is not a main basis for belief or it does not render the relevant beliefs unjustified. Empirical debunking arguments (...) in ethics can avoid this predicament, but only if they are refocused on highly selective classes of moral belief. Experimental data can combine with familiar consistency reasoning to reveal that like cases are not being treated alike. Selective debunking arguments are unlikely to yield sweeping sceptical conclusions, but they can lead to rational moral change. (shrink)

Can reasoning improve moral judgments and lead to moral progress? Pessimistic answers to this question are often based on caricatures of reasoning, weak scientific evidence, and flawed interpretations of solid evidence. In support of optimism, we discuss three forms of moral reasoning (principle reasoning, consistency reasoning, and social proof) that can spur progressive changes in attitudes and behavior on a variety of issues, such as charitable giving, gay rights, and meat consumption. We conclude that moral reasoning, particularly when embedded in (...) social networks with mutual trust and respect, is integral to moral progress. (shrink)

Kant interpreters are divided on the question of whether determinate cognition plays a role in the harmony of the faculties in aesthetic judgement. I provide a ‘non-cognitive’ interpretation that allows Kant’s statements regarding judgements of natural beauty to cohere such that determinate cognition need not be taken to perform any role in such judgements. I argue that, in aesthetic harmony, judgement privileges the free activity of the imagination over the cognizing function of the understanding for the purpose of unifying the (...) object, although the free imagination cannot violate the obscure concepts and principles of ordinary common sense. (shrink)

This project focuses on developing a community-based reporting and monitoring tool to improve women’s safety in colleges and universities. The tool allows users to anonymously report unsafe incidents, harassment, or suspicious activities, with features for immediate alerting to campus authorities and support groups. It integrates GPS tracking for emergencies and data visualization to monitor safety trends. Built using Java, XML, and Android Studio, the platform fosters collaboration between students, faculty, and administrators, encouraging proactive safety measures. By enabling discreet reporting and (...) real-time monitoring, the tool aims to create a safer campus environment for women. (shrink)

We provide a methodology for the creation of ontological partitions in biomedicine and we test the methodology via an application to the phenomenon of blood pressure. An ontology of blood pressure must do justice to the complex networks of intersecting pathways in the organism by which blood pressure is regulated. To this end it must deal not only with the anatomical structures and physiological processes involved in such regulation but also with the relations between these at different levels of granularity. (...) For this purpose our ontology offers a variety of distinct partitions � of substances, processes and functions � and integrates these together within a single framework via transitive networks of part-whole and dependence relations among the entities in each of these categories. The paper concludes with a comparison of this methodology with the approaches of GOTM, KEGG, DIP and BIND and provides an outline of how the methodology is currently being applied in the field of biomedical database integration. (shrink)

The International Classification of Functioning, Disability and Health provides a classification of human bodily functions, which, while exhibiting non-conformance to many formal ontological principles, provides an insight into which basic functions such a classification should include. Its evaluation is an important first step towards such an adequate ontology of this domain. Presented at the 13th Annual North American WHO Collaborating Center Conference on the ICF, 2007.

Wild Wise Weird: The Kingfisher Story Collection is an imaginative anthology that blends whimsical, wise, and strange tales. Filled with adventures, life lessons, and unique insights, it offers engaging and thought-provoking stories for readers of all ages. Perfect for fans of fantasy and the surreal, this collection captivates and inspires.

The theory of granular partitions (TGP) is a new approach to the understanding of ontologies and other classificatory systems. The paper explores the use of this new theory in the treatment of task-based clinical guidelines as a means for better understanding the relations between different clinical tasks, both within the framework of a single guideline and between related guidelines. We used as our starting point a DAML+OIL-based ontology for the WHO guideline for hypertension management, comparing this with related guidelines and (...) attempting to show that TGP provides a flexible and highly expressive basis for the manipulation of ontologies of a sort which might be useful in providing more adequate Computer Interpretable Guideline Models (CIGMs) in the future. (shrink)

Samuel Scheffler has argued that people value tradition for its own sake because they view it as accumulated experience, and as playing an important role in forming their personal integrity, structuring their lives, and providing them with a sense of belonging. These reasons, according to Scheffler, are de facto justifications that people offer for choosing to act on purely traditional grounds. In this essay, I argue that these de facto reasons must be supplemented if they are to be seen as (...) de jure rational grounds for valuing tradition for its own sake. Such a supplementation would open up the possibility of articulating a coherent theory of moderate cosmopolitanism. (shrink)

Evidence-based medicine relies on the execution of clinical practice guidelines and protocols. A great deal of of effort has been invested in the development of various tools which automate the representation and execution of the recommendations contained within such guidelines and protocols by creating Computer Interpretable Guideline Models (CIGMs). Context-based task ontologies (CTOs), based on standard terminology systems like UMLS, form one of the core components of such a model. We have created DAML+OIL-based CTOs for the tasks mentioned in the (...) WHO guideline for hypertension management, drawing comparisons also with other related guidelines. The advantages of CTOs include: contextualization of ontologies, providing ontologies tailored to specific aspects of the phenomena of interest, dividing the complexity involved in creating ontologies into different levels, providing a methodology by means of which the task recommendations contained within guidelines can be integrated into the clinical practices of a health care set-up. (shrink)

Security is a major concern on the internet, and Distributed Denial of Service (DDoS) attacks are a significant threat. These attacks overwhelm network resources and use up bandwidth, making it difficult for legitimate users to access services. One challenge in dealing with DDoS attacks is telling the difference between a sudden increase in traffic from real users, known as a "flash crowd," and actual attack traffic. This paper looks at different existing solutions for detecting DDoS attacks and explains how these (...) methods help reduce their impact. As more businesses move to cloud-based services, the risk of DDoS attacks has increased. These attacks can seriously disrupt organizations by overloading their network resources and making important services unavailable. Traditional defense methods, like firewalls, are no longer enough to handle the scale and complexity of modern DDoS attacks. To address this issue, We propose a new approach that utilizes the implementation of deep learning models to detect DDoS attacks in cloud environments. Our method uses various features from network traffic to train machine learning algorithms, aiming to effectively identify DDoS attacks in real-time with high accuracy. (shrink)

The Gene Ontology is an important tool for the representation and processing of information about gene products and functions. It provides controlled vocabularies for the designations of cellular components, molecular functions, and biological processes used in the annotation of genes and gene products. These constitute three separate ontologies, of cellular components), molecular functions and biological processes, respectively. The question we address here is: how are the terms in these three separate ontologies related to each other? We use statistical methods and (...) formal ontological principles as a first step towards finding answers to this question. (shrink)

Recent work on the quality assurance of the Gene Ontology (GO, Gene Ontology Consortium 2004) from the perspective of both linguistic and ontological organization has made it clear that GO lacks the kind of formalism needed to support logic-based reasoning. At the same time it is no less clear that GO has proven itself to be an excellent terminological resource that can serve to combine together a variety of biomedical database and information systems. Given the strengths of GO, it is (...) worth investigating whether, by overcoming some of its weaknesses from the point of view of formal-ontological principles, we might not be able to enhance a version of GO which can come even closer to serving the needs of the various communities of biomedical researchers and practitioners. It is accepted that clinical and bioinformatics need to find common ground if the results of data-intensive biomedical research are to be harvested to the full. It is also widely accepted that no single method will be sufficient to create the needed common framework. We believe that the principles-based approach to life-science data integration and knowledge representation must be one of the methods applied. Indeed in dealing with the ontological representation of carcinomas, and specifically of colon carcinomas, we have established that, had GO (and related biomedical ontologies) followed some of the basic formal-ontological principles we have identified (Smith et al. 2004, Ceusters et al. 2004), then the effort required to navigate successfully between clinical and bioinformatics systems would have been reduced. We point here to the sources of ontologically-related errors in GO, and also provide arguments as to why and how such errors need to be resolved. (shrink)

Ontological principles are needed in order to bridge the gap between medical and biological information in a robust and computable fashion. This is essential in order to draw inferences across the levels of granularity which span medicine and biology, an example of which include the understanding of the roles of tumor markers in the development and progress of carcinoma. Such information integration is also important for the integration of genomics information with the information contained in the electronic patient records in (...) such a way that real time conclusions can be drawn. In this paper we describe a large multi-granular datasource built by using ontological principles and focusing on the case of colon carcinoma. (shrink)

It is widely understood that protein functions can be exhaustively described in terms of no single parameter, whether this be amino acid sequence or the three-dimensional structure of the underlying protein molecule. This means that a number of different attributes must be used to create an ontology of protein functions. Certainly much of the required information is already stored in databases such as Swiss-Prot, Protein Data Bank, SCOP and MIPS. But the latter have been developed for different purposes and the (...) separate data-structures which they employ are not conducive to the needed data integration. When we attempt to classify the entities in the domain of proteins, we find ourselves faced with a number of cross-cutting principles of classification. Our question here is: how can we bring together these separate taxonomies in order to describe protein functions? Our proposed answer is: via a careful top-level ontological analysis of the relevant principles of classification, combined with a new framework for the simultaneous manipulation of classifications constructed for different purposes. (shrink)

This paper explores the intersection between Advaita Vedanta's non-dualistic conception of consciousness and the evolving field of quantum physics, offering a dialogue between ancient metaphysical insights and modern scientific thought. Advaita Vedanta, an Indian philosophical tradition, posits that consciousness is the fundamental reality, transcending the material world, which it views as an illusion, or Maya. Similarly, contemporary developments in quantum physics challenge the classical materialistic worldview by revealing that reality, at its most fundamental level, is interconnected, probabilistic, and observer-dependent. This (...) philosophical dialogue aims to bridge these two domains by examining how quantum phenomena, such as wave-particle duality, non-locality, and the observer effect, resonate with Advaita’s concept of Brahman as the ultimate, non-dual consciousness. By exploring parallels between these seemingly disparate fields, this paper questions the limitations of materialistic interpretations of consciousness and proposes that quantum physics could offer a scientific framework to support Advaita’s assertion that consciousness is the substratum of reality. The discussion further investigates how the observer effect in quantum mechanics might align with the Advaitic principle that reality is shaped by consciousness. This study contributes to contemporary debates in the philosophy of mind and philosophy of science by proposing a unified model of consciousness that transcends dualistic divisions between mind and matter, advancing the possibility of a deeper metaphysical understanding of the universe. (shrink)