In this paper, we argue that our moral concern for future well-being should reduce over time due to important practical considerations about how humans interact with spacetime. After surveying several of these considerations (around equality, special duties, existential contingency, and overlapping moral concern) we develop a set of core principles that can both explain their moral significance and highlight why this is inherently bound up with our relationship with spacetime. These relate to the equitable distribution of (1) moral concern in (...) a universe that we can only causally affect in one temporal direction; (2) access to the benefits from using spacetime as a resource; and (3) the burdens of care given (1) and (2). We conclude by considering the practical implications of our argument and find that, while it is often assumed that a preference for present over future well-being weakens the case for existential risk mitigation, this likely is not the case. (shrink)

Abstract: This research employs a Random Forest classification model to predict and assess obesity and cardiovascular disease (CVD) risk based on a comprehensive dataset collected from individuals in Mexico, Peru, and Colombia. The dataset comprises 17 attributes, including information on eating habits, physical condition, gender, age, height, and weight. The study focuses on classifying individuals into different health risk categories using machine learning algorithms. Our Random Forest model achieved remarkable performance with an accuracy, F1-score, recall, and precision all reaching 97.23%. (...) The model's success is attributed to its ability to effectively leverage attributes related to eating habits, physical condition, and demographic factors. Feature importance analysis reveals key factors contributing to accurate predictions. This paper contributes to the field of health analytics by demonstrating the effectiveness of Random Forest in predicting and categorizing health risks. The insights gained from this research have the potential to inform personalized health interventions and contribute to the advancement of precision health strategies. Furthermore, the study highlights the importance of leveraging machine learning techniques for health risk assessment in diverse populations. (shrink)

In this paper we aim to demonstrate the enormous ethical complexity that is prevalent in child obesity cases. This complexity, we argue, favors a cautious approach. Against those perhaps inclined to blame neglectful parents, we argue that laying the blame for child obesity at the feet of parents is simplistic once the broader context is taken into account. We also show that parents not only enjoy important relational prerogatives worth defending, but that children, too, are beneficiaries of that relationship in (...) ways difficult to match elsewhere. Finally, against the backdrop of growing public concern and pressure to intervene earlier in the life cycle, we examine the perhaps unintended stigmatizing effects that labeling and intervention can have and consider a number of risks and potential harms occasioned by state interventions in these cases. (shrink)

The long unbearable sufferings in the past and agonies experienced in some future timelines in which a malevolent AI could torture people for some idiosyncratic reasons (s-risks) is a significant moral problem. Such events either already happened or will happen in causally disconnected regions of the multiverse and thus it seems unlikely that we can do anything about it. However, at least one pure theoretic way to cure past sufferings exists. If we assume that there is no stable substrate (...) of personal identity and thus a copy equals original, then by creating many copies of the next observer-moment of a person in pain in which he stops suffer, we could create indexical uncertainty in her future location and thus effectively steal her consciousness from her initial location and immediately relieve her sufferings. However, to accomplish this for people who have already died, we need to perform this operation for all possible people thus requiring enormous amounts of computations. Such computation could be performed by the future benevolent AI of Galactic scale. Many such AIs could cooperate acausally by distributing parts of the work between them via quantum randomness. To ensure their success, they need to outnumber all possible evil AIs by orders of magnitude, and thus they need to convert most of the available matter into computronium in all universes where they exist and cooperate acausally across the whole multiverse. Another option for curing past suffering is the use of wormhole time-travel to send a nanobot in the past which will, after a period of secret replication, collect the data about people and secretly upload them when their suffering becomes unbearable. (shrink)

This article assesses how autonomy and machine learning impact the existential risk of nuclear war. It situates the problem of cyber security, which proceeds by stealth, within the larger context of nuclear deterrence, which is effective when it functions with transparency and credibility. Cyber vulnerabilities poses new weaknesses to the strategic stability provided by nuclear deterrence. This article offers best practices for the use of computer and information technologies integrated into nuclear weapons systems. Focusing on nuclear command and control, avoiding (...) autonomy and machine learning is recommended as one means to reduce the existential risk of unintended nuclear conflict. (shrink)

Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained and validated, (...) achieving an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting of three (...) layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, (...) recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. (shrink)

Risk Management Strategies and Investment Behaviors are considered important factors in the investing activities of the retail investors. This study seeks to determine the relationship between Risk Management Strategies and Investment Behavior of Generation Z retail investors. The study is a correlational research and purposive sampling was used to select the respondents for this study. Cochran’s formula was utilized to determine the total sample size or total number of respondents. Spearman’s Rank-Order Correlation was employed to assess the significant relationship of (...) Risk Management Strategies and Investment Behaviors. Based on the results, it showed that risk management strategies have a significant relationship with one or more of the investment behaviors. Risk avoidance has a significant relationship with confirmation bias. Risk reduction has a significant relationship with all of the investment behaviors such as overconfidence, loss-aversion, recency bias, and confirmation bias. Risk transfer shows a significant relationship with only loss-aversion and confirmation bias. And lastly, risk acceptance also has a significant relationship with all of the investment behaviors. This study recommends exploring other types of behavioral biases under cognitive bias and emotional bias to determine their correlation with risk management strategies. Also, broadening the sample size, demographic scope, and geographic scope of the study can improve the generalizability of the study and provide a more comprehensive understanding of the relationship between investment behavior and risk management strategies. The researchers also recommend to utilize interviews and focus group discussions to gain an in-depth understanding of the underlying values and attitudes concerning investment behavior. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, (...) recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be made, like adding wearable device data in real-time or investigating deep learning techniques. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by (...) training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)

There is a growing consensus among philosophers of science that core parts of the scientific process involve non-epistemic values. This undermines the traditional foundation for public trust in science. In this article I consider two proposals for justifying public trust in value-laden science. According to the first, scientists can promote trust by being transparent about their value choices. On the second, trust requires that the values of a scientist align with the values of an individual member of the public. I (...) argue that neither of these proposals work and suggest an alternative that does better. When scientists must appeal to values in the course of their research, they should appeal to democratic values: the values of the public or its representatives. (shrink)

This project focuses on the development of an intraday trading bot designed to predict stock price movements and execute trades based on advanced machine learning algorithms. The bot utilizes a combination of ARIMA (Autoregressive Integrated Moving Average), Support Vector Regression (SVR), and Random Forest models to forecast short-term price trends. In addition, it incorporates technical indicators such as Simple Moving Averages (SMA) and Exponential Moving Averages (EMA) to enhance prediction accuracy. The model leverages historical stock data obtained from Yahoo Finance, (...) using features like price, volume, and momentum indicators. The bot’s architecture is structured with a base class for stock prediction, allowing for easy integration and extension of different models. The goal of this project is to automate intraday trading strategies by providing realtime, data-driven decision-making, with a focus on maximizing profit and minimizing risk. (shrink)

Philosophers of science now broadly agree that doing good science involves making non-epistemic value judgments. I call attention to two very different normative standards which can be used to evaluate such judgments: standards grounded in ethics and standards grounded in political philosophy. Though this distinction has not previously been highlighted, I show that the values in science literature contain arguments of each type. I conclude by explaining why this distinction is important. Seeking to determine whether some value-laden determination meets substantive (...) ethical standards is a very different endeavor from seeking to determine if it is politically legitimate. (shrink)

This article critically analyzes Rawls’s attitude toward envy. In A Theory of Justice, Rawls is predominantly concerned with the threat that class envy poses to political stability. Yet he also briefly discusses the kind of envy that individuals experience toward their social peers, which he calls particular envy, and which I refer to as peer envy. He quickly concludes, however, that particular envy would not present a serious risk to the stability of his just society. In this article, I contest (...) this claim by arguing that the principles that structure Rawls’s ideal society are likely to exacerbate particular envy to a politically risky extent. Section 1 reconstructs his account of envy, giving special attention to his belief that competition kindles envy between peers. Section 2 then examines the way in which Rawls often endorses rivalry within the body politic. I argue that the society governed by justice as fairness is, on account of this rivalry, likely to generate a politically problematic degree of particular envy. In Section 3, I invoke ancient Greece as an example of a society that was, as a result of its intense competitiveness, often imperiled by dangerously elevated levels of peer envy. I then survey the key institutional mechanisms by which the ancient Greeks sought to manage this hazardous emotion. It turns out, however, that most of these mechanisms would be unavailable to Rawls insofar as they starkly contravene his principles of justice. I conclude that if Rawlsians wish to establish a society that fosters rivalry, they would do well to reflect on the means by which peer envy can be effectively harnessed. (shrink)

Looks into evaluation of information provision probability from different sources, based on use of linguistic variables. Formation of functions appurtenant for its unclear variables provides for adoption of decisions by the decision maker, in conditions of nonprobabilistic equivocation. The development of market relations in Ukraine increases the independence and responsibility of enterprises in justifying and making management decisions that ensure their effective, competitive activities. As a result of the analysis, it is determined that the condition of economic facilities can be (...) described and determined by the decision-maker, in the presence of the necessary information. The confidence of the decision-maker in the information received is different and the decisions made have a correspondingly different level of information risk. It is important to substantiate the procedure for assessing the numerical extent of information risk in decision-making based on the information obtained in conditions of uncertainty. The use of a linguistic variable in the processing of expert data presented in the form of a matrix of binary relations of values of the membership function, which allowed to move to further processing of knowledge to support decision-making in the management of industrial, commercial, financial and other activities. As a mathematical model for estimating the numerical measure of information risk when making decisions based on the information obtained in conditions of non-stochastic uncertainty, a model has been developed to model natural language uncertainties, which differs from existing ones by formalizing knowledge taking into account uncertainty of input information. Making such a clear decision in a fuzzy environment has appropriate values of effectiveness and risk. The paper proposes all the functions and accessories of indicators of both quantitative nature and qualitative nature to bring their values in the field of definition to one scale. Then the indicator of the effectiveness of decision-making will be a measure of the clarity of the cross-section of fuzzy subsets, which correspond to the introduced indicators of information risk. The condition of economic facilities can be described and determined by the decision-maker, if the necessary information is available. Decision-making on thenumerical measure of information risk must be determined by a set of basic indicators, which can be both quantitative and qualitative in nature. Predictive values of indicators should be determined in conditions of nonstochastic uncertainty. In this case, the indicators of a quantitative nature can be determined by fuzzy triangular numbers, which implement a high level of confidence in the subjective judgments of experts. Indicators of qualitative nature should be presented in linguistic variables. The values of the indicators of qualitative nature that are predicted must be considered for all fuzzy variable terms-sets of linguistic variables introduced into consideration. For any fuzzy variable, the introduction to the consideration of a clear set of values as carriers of the α-level of its membership function allows to reduce to a single interpretation of the predicted values of indicators of quantitative and qualitative nature in terms of non-stochastic uncertainty. (shrink)

Many people are worried about the harmful effects of climate change but nevertheless enjoy some activities that contribute to the emission of greenhouse gas (driving, flying, eating meat, etc.), the main cause of climate change. How should such people make choices between engaging in and refraining from enjoyable greenhouse-gas-emitting activities? In this chapter, we look at the answer provided by decision theory. Some scholars think that the right answer is given by interactive decision theory, or game theory; and moreover think (...) that since private climate decisions are instances of the prisoner’s dilemma, one rationally should engage in these activities provided that one enjoys them. Others think that the right answer is given by expected utility theory, the best-known version of individual decision theory under risk and uncertainty. In this chapter, we review these different answers, with a special focus on the latter answer and the debate it has generated. (shrink)

Abstract: Background: Coronary artery disease remains the leading cause of morbidity and mortality Worldwide. Previous reviews pointed that nursing interventions are beneficial for coronary artery patients. However, most interventions focused on education and counselling, but not consistent with the outcome set; still did not consider patient’s coronary artery disease risky characteristics. Related studies in China also difficult to find. Therefore this study was conducted to investigate kinds of nursing interventions delivered to coronary artery patients and match them with patient’s risk (...) factors of coronary artery disease. Results of this study were expected to add new knowledge that will alert nurses to consider coronary artery risk factors which in turn might enable the development of appropriate approaches to improve patient’s wellbeing hence reduce frequent coronary artery morbidity and mortality. Methods: A descriptive, cross-sectional, retrospective design using clinical case notes was employed. Study was undertaken in coronary care wards at the teaching hospital in China from November 2017 to September 2018. Structured-literature supported self-designed questionnaire was utilized for data collection. Chi square (χ2) test and multivariate logistic regression for adjusted odds ratio with 95% confidence interval were used to compare the relationship among independent (patient’s risk coronary artery disease factors) and dependent (nursing interventions) categorical variables. Ethical permission was granted accordingly. Results: A total of 300 coronary artery patients’ case notes were audited with mean age 63±11.2 years. Of these 175 (58.3%) were males. 126(42%) were smoking and 224(74.7%) were hypertensive. More evidence based nursing interventions than education and counselling were found to be delivered to these patients. “Administer coronary artery disease medication and their instructions” was mostly delivered to many patients 291(97%) while “counsel to cope with stress” was the least one 60 (20.0%). Three of eight nursing interventions delivered significantly matched with three or all of these patient’s coronary artery risk variables (age, smoking, hypertension and diabetes) (p < 0.05 and/or < 0.01) with Adjusted odds ratio (95% CI) within their significant ranges. Conclusion: This study delivers valuable insight that, nurses in the studied teaching hospital delivered beneficial evidence based nursing interventions to patients with coronary artery disease which significantly matched with their risk factors of coronary artery illness. However, care for stress was low hence needs improvement. Furthermore, research is needed to get consistency of nursing interventions with patient’s end point clinical outcomes for further appraisal of nursing efforts in caring CAD patients . (shrink)

The co-evolutionary concept of Three-modal stable evolutionary strategy of Homo sapiens is developed. The concept based on the principle of evolutionary complementarity of anthropogenesis: value of evolutionary risk and evolutionary path of human evolution are defined by descriptive (evolutionary efficiency) and creative-teleological (evolutionary correctly) parameters simultaneously, that cannot be instrumental reduced to others ones. Resulting volume of both parameters define the trends of biological, social, cultural and techno-rationalistic human evolution by two gear mechanism ˗ gene-cultural co-evolution and techno- humanitarian balance. (...) The resultant each of them can estimated by the ratio of socio-psychological predispositions of humanization/dehumanization in mentality. Explanatory model and methodology of evaluation of creatively teleological evolutionary risk component of NBIC technological complex is proposed. Integral part of the model is evolutionary semantics (time-varying semantic code, the compliance of the biological, socio-cultural and techno-rationalist adaptive modules of human stable evolutionary strategy). (shrink)

This paper provides a retrospective and prospective overview of TU Delft’s approach to engineering ethics education. For over twenty years, the Ethics and Philosophy of Technology Section at TU Delft has been at the forefront of engineering ethics education, offering education to a wide range of engineering and design students. The approach developed at TU Delft is deeply informed by the research of the Section, which is centered around Responsible Research and Innovation, Design for Values, and Risk Ethics. These theoretical (...) approaches are premised on the notion that technologies are inherently value-laden, and as such contain the possibility of fostering or hindering moral values. Each of these approaches encourages students to take a proactive attitude with respect to their projects and profession, thinking creatively about – and taking responsibility for – how to both prevent harm and do good via the technologies they help develop. To explain how this is put into practice, this paper sketches a brief history of ethics teaching at TU Delft, outlines current activities, and presents future plans for Bachelor and Master’s level engineering ethics education at TU Delft. (shrink)

What makes science trustworthy to the public? This chapter examines one proposed answer: the trustworthiness of science is based at least in part on its independence from the idiosyncratic values, interests, and ideas of individual scientists. That is, science is trustworthy to the extent that following the scientific process would result in the same conclusions, regardless of the particular scientists involved. We analyze this "idiosyncrasy-free ideal" for science by looking at philosophical debates about inductive risk, focusing on two recent proposals (...) which offer different methods of avoiding idiosyncrasy: the high epistemic standards proposal and the democratic values proposal. (shrink)

This forum contribution addresses two major themes in de Goede’s original essay on ‘Financial security’: (1) the relationship between stable markets and the proverbial ‘security dilemma’; and (2) the development of new decision-technologies to address risk in the post-World War II period. Its argument is that the confluence of these two themes through rational choice theory represents a fundamental re-evaluation of the security dilemma and its relationship to the rule of law governing market relations, ushering in an era of perpetual (...) physical and financial insecurity. (shrink)

The world's leading cause of morbidity and death is cardiovascular diseases (CVD), which makes early detection essential for successful treatments. This study investigates how optimization techniques can be used with machine learning (ML) algorithms to forecast cardiovascular illnesses more accurately. ML models can evaluate enormous datasets by utilizing data-driven techniques, finding trends and risk factors that conventional methods can miss. In order to increase prediction accuracy, this study focuses on adopting different machine learning algorithms, including Decision Trees, Random Forest, Support (...) Vector Machines, and Neural Networks, that have been tuned using strategies including hyper parameter selection, crossvalidation, and feature selection. (shrink)

Modern warehouse operations are increasingly confronted with critical inventory management issues such as stockouts, overstocking, and delays in restocking—all of which stem largely from the inefficiencies of manual inventory systems. To address these challenges, this project proposes a comprehensive smart warehouse management solution that combines real-time inventory tracking with advanced predictive analytics and intelligent stock alert mechanisms. Central to this system is a user-friendly web application that empowers both administrators and customers to manage products efficiently, monitor stock levels dynamically, and (...) automate reordering processes. The solution leverages ARIMA-based time series forecasting models to analyze historical inventory and sales data, enabling the system to accurately predict future stock requirements. This predictive capability ensures proactive inventory replenishment, minimizes the risk of supply chain disruptions, and enhances overall operational reliability. Additionally, the platform incorporates visual dashboards that display key insights such as product demand patterns, sales trends, and inventory turnover rates, supporting data-driven decision-making. By automating critical inventory functions, the system significantly reduces human error and administrative overhead. It enhances transparency and responsiveness throughout the supply chain, ensuring that stock levels are always optimized to meet customer demand without unnecessary surplus. The integration of intelligent alerts provides timely notifications for low-stock or high- demand items, enabling swift corrective actions. In summary, the proposed smart warehouse management system transforms conventional inventory handling into a proactive, intelligent, and efficient process. It offers a scalable and adaptive framework that not only addresses current warehouse challenges but also supports future growth and digital transformation initiatives in inventory management. (shrink)

Cloud computing has revolutionized the way businesses store and process data, offering flexibility, scalability, and cost efficiency. However, with these benefits come significant security risks. Cloud environments introduce unique vulnerabilities that organizations must address to ensure data protection and maintain compliance. This paper explores common cloud security vulnerabilities, methods for identifying risks, and strategies for mitigating them. By reviewing existing literature and best practices, the study emphasizes the importance of proactive security measures in preventing data breaches, unauthorized access, (...) and service disruptions. (shrink)

Abstract. In this article we developed scientific and applied foundations of commercialization of the nature-resource potential of anthropogenic objects, on the example of exhausted mines. It is determined that the category of “anthropogenic object” can be considered in a narrow-applied sense, as specific anthropogenic objects to ensure the target needs, and in a broad theoretical sense, meaning everything that is created and changed by human influence, that is the objects of both artificial and natural origin. It was determined that problems (...) of commercialization of the natural-resource potential of anthropogenic objects are most often considered by researchers for specific objects, without having complex methodological coverage from the point of view of combining environmental, technical, economic and managerial components. When studying the substantiation of the scientific base, the authors confirmed the feasibility of the commercialization of natural-resource potential of anthropogenic objects on the example of a number of theoretical scientific studies in reclamation, reconstruction, recreation, remediation, restoration of biological productivity and economic value of land disturbed by economic activity. The considered examples of exhausted mines in the 21st century in the USA, Canada, Germany, Romania, and Poland indicate a wide range of opportunities for their commercialization. The study of the potential for commercialization of exhausted mines in the post-Soviet countries testified to the underused reserves for the commercialization of their nature-resource potential and their high potential for further development. The authors proposed the identification of anthropogenic objects on the basic livelihood spheres of society. There were identified the main system (natural, biological, technical, economic, social, managerial) and structural (subjects, trends, threats, risks, problems, challenges) factors of diagnosing the state of an anthropogenic object. A set of measures has been developed for commercialization of an anthropogenic object in functional and production activities, product policy, financial and investment spheres, pricing and sales policies, promotion, management and determination of property rights. Recommendations were provided on optimizing the management decision-making process based on a set of positivistic development principles, methods, and management functions. The study allows international organizations, state and local authorities, territorial communities, owners and potential investors to see new opportunities and make mutually beneficial decisions on the rational use of the nature-resource potential of anthropogenic objects. (shrink)

In today's data-driven business environment, organizations increasingly rely on advanced analytics and decision support systems to gain a competitive edge. Azure AI and Machine Learning (ML) provide powerful tools for predictive analytics, enabling businesses to forecast trends, optimize processes, and make more informed decisions. By leveraging the capabilities of Microsoft Azure, businesses can integrate AI and ML into their decision-making processes, enhancing productivity and improving strategic outcomes. This paper explores how Azure's AI and ML tools can be applied to predictive (...) analytics and decision support systems, with a focus on real-time decision-making, risk mitigation, and operational efficiency. We will discuss the key Azure services like Azure Machine Learning, Azure Cognitive Services, and Azure Databricks, as well as real-world use cases that highlight their impact on business performance. The paper will also highlight the challenges associated with implementing these technologies and suggest best practices for organizations aiming to integrate AI and ML into their systems. (shrink)

Natural Language Processing (NLP), a subset of Artificial Intelligence (AI), has emerged as a powerful tool in analyzing human language to detect underlying mental health issues such as depression and suicidal tendencies. By leveraging techniques in machine learning and deep learning, NLP systems can process and interpret textual data from diverse sources like social media, forums, and online journals to identify patterns and linguistic markers associated with mental health conditions. These markers include the use of emotionally charged words, changes in (...) writing style, expressions of hopelessness, isolation, or distress, which are critical indicators of a person's psychological state. The process typically involves data preprocessing, feature extraction using models such as TF-IDF or word embeddings (e.g., Word2Vec, GloVe), and classification using supervised learning techniques or deep learning models like BERT or LSTM. The application of NLP for mental health detection is particularly impactful in healthcare and public safety. It enables early identification and intervention for at-risk individuals by providing clinicians and support organizations with actionable insights from large volumes of textual content. On social media platforms, it facilitates automated monitoring to detect crisis situations and initiate timely support. Additionally, in educational institutions and workplaces, such systems can promote mental well-being by identifying signs of distress and enabling appropriate counseling or outreach efforts. However, this field faces notable challenges, including data privacy concerns, ethical considerations, the risk of false positives or misdiagnosis, and ensuring the cultural and linguistic diversity of datasets to avoid bias. Ongoing research aims to improve model accuracy and interpretability while integrating multimodal data for more robust detection. The goal is to develop responsible and sensitive AI systems that assist in understanding and supporting mental health, ultimately contributing to a safer, more empathetic digital environment. (shrink)

The continuous growth of healthcare data has made it essential to develop efficient systems that not only alert healthcare providers but also visualize patient data in a comprehensible way. This study introduces a Health Alert System integrated with Report Visualization powered by Data Analytics to improve patient monitoring and alerting mechanisms. By leveraging real-time data from wearable sensors and hospital records, the system generates health alerts based on deviations from normal parameters. The proposed system combines predictive analytics and historical data (...) to flag potential emergencies before they occur. The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care. (shrink)

The rapid adoption of telemedicine has transformed healthcare delivery, especially during the COVID-19 pandemic. This digital shift has enabled medical professionals to offer consultations and manage patients remotely, ensuring continuity of care while reducing exposure risks. However, the integration of telemedicine has presented both opportunities and challenges for doctors, particularly in terms of their work-life balance. This paper explores the digital adaptation of doctors in public and private hospitals concerning telemedicine practices and its impact on their work-life harmony. The (...) study highlights the advantages telemedicine offers, such as increased flexibility, reduced commuting time, and better time management. It also examines the challenges doctors face, including the demands of always being digitally accessible, the learning curve of new technologies, and the difficulty in maintaining boundaries between personal and professional life. Using both quantitative and qualitative data, this research compares the telemedicine adaptation experiences of doctors in public versus private hospitals, considering factors such as hospital infrastructure, support systems, and patient load. (shrink)

Metaphors for describing the introduction, impacts, and management of non-native species are numerous and often quite outspoken. Policy-makers have adopted increasingly disputed metaphorical terms from scientific discourse. We performed a critical analysis of the use of strong metaphors in reporting scientific findings to policy-makers. Our analysis shows that perceptions of harm, invasiveness or nativeness are dynamic and inevitably display multiple narratives in science, policy or management. Improving our awareness of multiple expert and stakeholder narratives that exist in the context of (...) non-native species management, as well as metaphorical alternatives, is critical. (shrink)

Arguably, ethical guidelines and medical laws on medical confidentiality have fallen short of extrapolating the methodology for dealing with potential ethical complexities in its practice. This drawback has made it difficult for physicians to prevent harm from occurring if it has not yet happened, remove harm if it has already taken place, or minimise harm if it is unavoidable. Therefore, this article attempts to outline certain principles in the form of a framework to govern the management of confidential information in (...) light of Islamic legal maxims. This effort is meant to help health workers to deal with conflicting situations, with reasonable care given to those at risk, while maintaining therapeutic trust and professionalism. The notable significance of this framework is that it is structured after identifying the potential harms in the protection and disclosure of medical confidentiality in selected cases from patient's, doctor's and third party's perspectives and addressing each of them with due importance. (shrink)

Snake encountersin human-populated and wildlife areas pose significant threats to public safety and biodiversity. Each year, many incidents result in snakebites, often leading to serious injuries or fatalities, and frequently result in harm to snakes due to fear-driven responses. Traditional methods forsnake detection, such as visual observation, are typically slow and can lead to delayed or inaccurate responses, increasing the risks associated with human-snake encounters. This study presents a Real-Time Snake Detection and Alert System that employs advanced artificial intelligence (...) (AI) and machine learning (ML) techniques, specifically using YOLO (You Only Look Once), to accurately identify the presence ofsnakes in pre-recorded videos or static images. Oursystem processes these visual inputs to determine if a snake is present, providing an immediate, automated alarm to alert individuals nearby if a snake is detected. This system isintended to be a rapid-response solution for detecting snakesin images or video files, enhancing safety in snake-prone areas by increasing awareness of potential threats without requiring constant live monitoring or additional notifications to authorities. (shrink)

Techno-ethics is the area in the philosophy of technology which deals with emerging robotic and digital AI technologies. In the last decade, a new techno-ethical challenge has emerged: Autonomous Weapon Systems (AWS), defensive and offensive (the article deals only with the latter). Such AI-operated lethal machines of various forms (aerial, marine, continental) raise substantial ethical concerns. Interestingly, the topic of AWS was almost not treated in Jewish law and its research. This article thus proposes an introductory ethical-halakhic perspective on AWS, (...) in the Israeli context. The article has seven sections. Section 1 defines AWS and the main ethical concerns it evokes, while providing elementary definitions and distinctions. §2 locates AWS within the realm of Jewish laws of war (hilkhot-ẓava), which recognize the right for self-defense, as well as the status of universally accepted moral norms. §3 unfolds pragmatic ethical premises of a humane techno-ethics, which are required for the identifying AWS as a moral question: I. Relationality; II. Technology is not (completely) neutral; III. The fallaciousness of transhumanism. It is argued that these premises are compatible with halakhic tradition. §4 investigates the question of the morality of AWS, within the field of military AI ethics. It is clarified why the standard categories of war-ethics (ad bello, in bellum) do not capture the singular ethical problem of AWS, which pertains to the operation of military means, rather than their human targets. It is argued that reductive perception of the human mind is misleading about the feasibility of ‘ethical robots’, capable of independent moral discretion. To provide a thick examination of human agency from the perspective of Jewish tradition, §5 explores two stories from the biblical book of Samuel (the murder of Nob’s priests and of Uriah). The lessons about the significance of moral agency within the pubic-political military sphere are made explicit, as well as the possible costs resulting from the loss of human agency in the case of AWS. Given that realpolitik considerations are basic in halakhah, §6 considers some possible contemporary socio-political implications of the AWS, that may risk the sustainability of the democratic project. §7 concludes by pointing out humane contributions of Jewish law to contemporary techno-ethics. (shrink)

The continuous growth of healthcare data has made it essential to develop efficient systems that not only alert healthcare providers but also visualize patient data in a comprehensible way. This study introduces a Health Alert System integrated with Report Visualization powered by Data Analytics to improve patient monitoring and alerting mechanisms. By leveraging real-time data from wearable sensors and hospital records, the system generates health alerts based on deviations from normal parameters. The proposed system combines predictive analytics and historical data (...) to flag potential emergencies before they occur. The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care. (shrink)

This month, the AISDL Team was glad to see its continuing effort to raise the voice for conserving wildlife appearing in Pacific Conservation Biology (published by CSIRO/the Australian Academy of Science). The article stipulates the need for weaving humane values with scientific information, leveraging the sociocultural power to harmonize humans with nature. The article articulates the coauthors’ idea of building a funding source to contribute to the nature conservation cause by investing in some listed stocks. Technically, stocks we intend to (...) invest in must possess some key features of (i) having significant growth potential, (ii) containing little environmental risk of potential moral-practical gaps in their CSR missions, and (iii) showing a certain degree of “relevance” with AISDL’s fundamentals. (shrink)

Nobody claims to be a proponent of white feminism, but according to the critique presented in this article, many in fact are. I argue that feminism that does not take multiple axes of oppression into account is bad in three ways: it strategically undermines solidarity between women; it risks inconsistency by advocating justice and equality for some women but not all; and it impedes the ultimate function of feminism function by employing epistemological “master’s tools” that stand in antithesis to (...) feminist emancipatory work. In investigating ethnocentrism in feminism, I develop the idea of latent ethnocentrism, which occupies the space between meaning that is generated from reference to the self and overt racism. I identify an epistemological prong in the ethnocentrism charge against feminism, where I draw on bell hooks’ interlocking axes-model of oppression to answer why the ethnocentrism problem is important for feminism and what its underlying epistemological causes are. I draw on Uma Narayan’s destabilization of cultural dualisms to argue that they do not serve emancipatory agendas. There is a mutually constitutive relation between language that informs our understanding, on the one hand, and the political agendas that produce this language to sustain the male and the western norm as center, on the other hand. I call this circular and reciprocally reinforcing mechanism the episteme-politic. I conclude that the ethnocentrism problem is not merely an issue of strategy or feminist consistency but of shooting oneself in the foot by uncritically accepting patriarchal concepts for feminist politics. (shrink)

While children’s experiences of online risks and harm is a growing area of research in New Zealand, public discussion on the matter has largely been informed by mainstream media’s fixation on the dangers of technology. At best, debate on risks online has relied on overseas evidence. However, insights reflecting the New Zealand context and based on representative data are still needed to guide policy discussion, create awareness, and inform the implementation of prevention and support programmes for children. This (...) research report presents findings from a quantitative study regarding different aspects related to risks and online safety. It looks at the online experiences that children find bothersome and upsetting and explores the hurtful behaviours they encounter or engage in, both online and in person. Evidence regarding exposure to different types of potentially harmful online content is also presented. Another relevant contribution is the insights related to excessive internet use.The findings presented in this report are based on data from 2,061 New Zealand children aged 9-17. We hope the findings will contribute to the development of policies, practices and services designed to support New Zealand children to safely take advantage of the opportunities available to them online. (shrink)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized through (...) techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

This thesis concerns our moral and epistemic responsibilities regarding our concepts. I argue that certain concepts can be morally, epistemically, or socially problematic. This is particularly concerning with regard to our concepts of social kinds, which may have both descriptive and evaluative aspects. Being ignorant of certain concepts, or possessing mistaken conceptions, can be problematic for similar reasons, and contributes to various forms of epistemic injustice. I defend an expanded view of a type of epistemic injustice known as ‘hermeneutical injustice’, (...) where widespread conceptual ignorance puts members of marginalized groups at risk of their distinctive and important experiences lacking intelligible interpretations. Together, I call the use of problematic concepts or the ignorance of appropriate concepts ‘conceptual incapacities’. I discuss the conditions under which we may be responsible for our conceptual incapacities on several major theories of responsibility, developing my own account of responsibility in the process, according to which we are responsible for something just in case it was caused by one of our reasons-responsive constitutive psychological traits. However, I argue that regardless of whether we are responsible for something, we may still be required to take responsibility for it. Whether or not we are responsible for our conceptual incapacities, we are required to reflect critically upon them in a variety of scenarios that throw our use of those concepts into question. I consider the method of conceptual engineering — the philosophical critique and revision of concepts — as one way we might take responsibility for our concepts, or at least, defer that duty to experts. But, this top-down model of conceptual revision is insufficient. Using a pragmatist model of the social epistemology of morality, I argue that conceptual inquiry is a social endeavour in which we are all required to participate, to some degree. (shrink)

Migraine is a complex neurological disorder characterized by recurrent moderate to severe headaches, accompanied by additional symptoms such as nausea, sensitivity to light and sound, and visual disturbances. Accurate and timely diagnosis of migraines is crucial for effective management and treatment. However, the diverse range of symptoms and overlapping characteristics with other headache disorders pose challenges in the diagnostic process. In this research, we propose the development of an expert system for migraine diagnosis using artificial intelligence and the CLIPS (C (...) Language Integrated Production System) framework. The expert system utilizes a rule-based inference engine to analyze patient-reported symptoms and provide reliable diagnoses or probability scores indicating the likelihood of migraine. The knowledge base of the expert system is designed based on expert knowledge obtained from medical professionals specializing in migraines. The collected knowledge is translated into a structured format suitable for the CLIPS inference engine, incorporating rules and facts to represent the diagnostic criteria and associated symptoms. The system prompts users to provide relevant information about their symptoms, medical history, and potential triggers. It applies the defined rules and facts to evaluate the likelihood of migraine and generate accurate diagnoses or probability scores. Preliminary evaluation results demonstrate the potential of the expert system as a valuable tool for diagnosing migraines. A dataset of anonymized patient records with confirmed migraine cases was used to test the system. The diagnoses generated by the expert system were compared against the known diagnoses, and a high level of accuracy was observed, with 90% of cases correctly diagnosed as migraines. These results highlight the effectiveness and reliability of the system in assisting medical professionals in the diagnosis of migraines. The proposed expert system offers several advantages for migraine diagnosis. It leverages the collective knowledge and expertise of experienced migraine specialists, providing a standardized and consistent approach to diagnosis. The system can handle large amounts of patient data and effectively analyse complex relationships between symptoms, risk factors, and diagnostic criteria. Furthermore, it offers real-time feedback and recommendations, supporting medical professionals in their clinical decision-making process. Future work involves refining the expert system based on feedback from medical experts, expanding the knowledge base to encompass a wider range of symptoms and risk factors, and conducting further evaluations to enhance its accuracy and applicability in clinical settings. The development of an expert system for migraine diagnosis has the potential to improve the diagnostic process, leading to more effective management and treatment strategies for individuals suffering from migraines. (shrink)

Abstract: The convergence of nanotechnology and artificial intelligence (AI) represents a transformative frontier in modern science, with the potential to revolutionize multiple industries, particularly healthcare. Nanotechnology enables the manipulation of matter at the atomic and molecular scale, while AI offers sophisticated data analysis, pattern recognition, and decision-making capabilities. This paper explores the synergies between these two fields, focusing on their impact on medical diagnostics, targeted drug delivery, and personalized treatments. By leveraging AI's predictive power and nanotechnology's precision, healthcare can achieve (...) unprecedented levels of accuracy in disease detection and treatment, leading to improved patient outcomes. Beyond healthcare, this convergence extends to environmental monitoring, smart materials, and manufacturing, offering solutions to some of the world’s most pressing challenges. The paper also discusses the ethical considerations and potential risks of integrating AI with nanotechnology, emphasizing the need for responsible innovation. Together, AI and nanotechnology hold the promise of reshaping industries and improving quality of life on a global . (shrink)

This study identifies global factors conditioning the global problematics of the direction of social development. Global threats were evaluated and defined as dangerous processes, phenomena, and situations that cause harm to health, safety, well-being, and the lives of all humanity, and require removal. The essence of global risks was defined. These risks were defined as events or conditions that may cause a significant negative effect for several countries or spheres within a strategic period if they occur. Global problems (...) were conceptualized. These problems were defined as phenomena, matters, and situations that are not completely understandable, interesting, actual, require solving and regulation, and in addition do not have unified solutions. Current global challenges were analyzed, defined as strategic guidelines that cause transformational changes and are receiving attention from humanity. Global trends were defined as courses of social development. The composition and role of global actors were described, and defined as international organizations, leading states, regional organizations, powerful corporations, institutional investors, large cities, and well-known personalities. The directions of positive courses of social development were developed by means of using the authors’ mechanism of solving the global problematics. (shrink)

This article argues that Lara Buchak’s risk-weighted expected utility (REU) theory fails to offer a true alternative to expected utility theory. Under commonly held assumptions about dynamic choice and the framing of decision problems, rational agents are guided by their attitudes to temporally extended courses of action. If so, REU theory makes approximately the same recommendations as expected utility theory. Being more permissive about dynamic choice or framing, however, undermines the theory’s claim to capturing a steady choice disposition in the (...) face of risk. I argue that this poses a challenge to alternatives to expected utility theory more generally. (shrink)

Abstract: Artificial Intelligence (AI) has rapidly advanced, offering significant potential to transform business decision-making. This paper delves into how AI can be harnessed to enhance strategic decision-making within business contexts. It investigates the integration of AI-driven analytics, predictive modeling, and automation, emphasizing their role in improving decision accuracy and operational efficiency. By examining current applications and case studies, the paper underscores the opportunities AI offers, including improved data insights, risk management, and personalized customer experiences. It also addresses the challenges businesses (...) face when adopting AI, such as data privacy concerns, integration difficulties, and the need for skilled professionals. The paper concludes by offering recommendations for successfully implementing AI solutions to maximize benefits while mitigating potential challenges. (shrink)

Many people are worried about the harmful effects of climate change but nevertheless enjoy some activities that contribute to the emission of greenhouse gas (driving, flying, eating meat, etc.), the main cause of climate change. How should such people make choices between engaging in and refraining from enjoyable greenhouse-gas-emitting activities? In this chapter we look at the answer provided by decision theory. Some scholars think that the right answer is given by interactive decision theory, or game theory; and moreover think (...) that since private climate decisions are instances of the prisoner’s dilemma, one rationally should engage in these activities provided that one enjoys them. Others think that the right answer is given by expected utility theory, the best-known version of individual decision theory under risk and uncertainty. In this chapter we review these different answers, with a special focus on the latter answer and the debate it has generated. (shrink)

Many parents cite intimacy as one of their reasons for deciding to educate at home. It seems intuitively obvious that home education is conducive to intimacy because of the increased time families spend together. Yet what is not clear is whether intimacy can provide justification for one’s decision to home educate. To see whether this is so, we introduce the concept of ‘attentive parenting’, which encompasses a set of family characteristics, and we examine whether and under what conditions attentive parents (...) risk loss of intimacy by sending their children to school; or, alternatively, whether they can avoid this risk by educating children at home. What we will determine is whether families who exhibit the specified characteristics are prima facie justified in educating their children at home under the conditions of interest. We argue that, for attentive parents, home education not only promotes greater intimacy, but also provides insurance against the loss of intimacy that may occur under certain conditions when children attend schools. (shrink)

Abstract; As artificial intelligence (AI) technologies become increasingly integrated into various facets of society, their impact on human rights has garnered significant attention. This paper examines the intersection of AI and human rights, focusing on key issues such as privacy, bias, surveillance, access, and accountability. AI systems, while offering remarkable advancements in efficiency and capability, also pose risks to individual privacy and can perpetuate existing biases, leading to potential discrimination. The use of AI in surveillance raises ethical concerns about (...) the balance between security and civil liberties. Additionally, disparities in access to AI technologies can exacerbate inequalities, affecting human rights across different populations. This paper explores these challenges and proposes frameworks for ensuring that AI development and deployment adhere to human rights principles. By analyzing current practices and policies, the study aims to provide actionable recommendations for creating a more equitable and responsible AI landscape. (shrink)

This chapter discusses three types of sociable robots for older adults: robotic caregivers ; robotic friends ; and sex robots. The central argument holds that society ought to make reasonable efforts to provide these types of robots and that under certain conditions, omitting such support not only harms older adults but poses threats to their dignity. The argument proceeds stepwise. First, the chapter establishes that assisting care-dependent older adults to perform activities of daily living is integral to respecting dignity. Here, (...) the argument establishes the vital role that carebots-of-the-future might play in aged societies as the supply of working age adults falls shy of demand. Next, the chapter extends this analysis to designing friendbots for socially isolated older adults. The argument holds that reasonable efforts to provide access to friendbots for socially isolated adults is also a future societal responsibility. Finally, the chapter applies similar reasoning to show that societies ought to make reasonable efforts to support sexual capabilities for older adults who want to be sexual but are bereft of sex partners. The argument draws on capability accounts of justice to show that when central human capacities, such as bodily integrity; intimate relationships; and the use of senses, imagination and thought, are at risk of falling below a threshold level, society should intercede. (shrink)