For children, the collateral damage of the COVID-19 pandemic response has been considerable. In this paper, we use the framework of evidence-based medicine to argue that child abuse is another negative side effect of COVID-19 lockdowns. While it was certain that school closures would have profound social and economic costs, it remains uncertain whether they have any effect on COVID-19 transmission. There is emerging evidence that lockdowns significantly worsened child abuse on a global scale. Low-income and middle-income countries are particularly (...) vulnerable to increases in child abuse. The best available external evidence from systematic research during the pandemic demonstrates an increase in the risk of child maltreatment, an increase in child maltreatment hospitalisations and a concerning decrease in official child maltreatment referrals. The paradoxical phenomenon of increased hospitalisations and decreased reports is unlikely to be explained by a genuine decrease in child abuse. We conclude that lockdowns have an unacceptably high risk of negative side effects for children, as evidenced by child abuse, the true extent of which appears to be masked by lockdown-related disruptions to schools and other surveillance systems. The desire for a sense of security may be a tempting bias towards emphasising the resilience of children, but it is ethically problematic to push children towards abuse in the name of public health. It is our view that the collateral damage of prolonged school closures for society’s most vulnerable members is a powerful ethical consideration against any pandemic response which involves their use. (shrink)

Understanding academic gender gaps is difficult because gender-imbalanced fields differ across many features, limiting researchers’ ability to systematically study candidate causes. In the present preregistered research, we isolate two potential explanations—brilliance beliefs and fixed versus growth intelligence mindsets—by comparing two fields that have inverse gender gaps and historic and topical overlap: philosophy and psychology. Many more men than women study philosophy and vice versa in psychology, with disparities emerging during undergraduate studies. No prior work has examined the contributions of both (...) self-perceptions of brilliance and fixed versus growth mindsets on choice of major among undergraduate students. We assessed field-specific brilliance beliefs, brilliance beliefs about self, and mindsets, cross-sectionally in 467 undergraduates enrolled in philosophy and psychology classes at universities in the United States and Canada via both in-person and online questionnaires. We found support for the brilliance beliefs about the self, but not mindset, explanation. Brilliance beliefs about oneself predicted women’s but not men’s choice of major. Women who believed they were less brilliant were more likely to study psychology (perceived to require low brilliance) over philosophy (perceived to require high brilliance). Findings further indicated that fixed versus growth mindsets did not differ by gender and were not associated with major. Together, these results suggest that internalized essentialist beliefs about the gendered nature of brilliance are uniquely important to understanding why men and women pursue training in different academic fields. (shrink)

Feelings of belonging are integral in people’s choice of what career to pursue. Women and men are disproportionately represented across careers, starting with academic training. The present research focuses on two fields that are similar in their history and subject matter but feature inverse gender gaps—psychology (more women than men) and philosophy (more men than women)—to investigate how theorized explanations for academic gender gaps contribute to feelings of belonging. Specifically, we simultaneously model the relative contribution of theoretically relevant individual differences (...) (empathizing, systematizing, and intellectual combativeness) as well as life goals (prioritization of family, money, and status) to feelings of belonging and majoring in psychology or philosophy. We find that men report higher intellectual combativeness than women, and intellectual combativeness predicts feelings of belonging and majoring in philosophy over psychology. Although systematizing and empathizing are predictive of belonging and, in turn, majoring in psychology and philosophy, respectively, when other factors are taken into account, women and men do not differ in empathizing and systematizing. Women, more than men, report prioritizing having a family, wealth, and status in choosing a career, and these directly or indirectly feed into feelings of belonging and majoring in psychology, in contrast to prior theory. Together, these findings suggest that students’ perceptions of their own combativeness and the extent to which they desire money and status play essential roles in women’s feeling they belong in psychology and men’s feeling they belong in philosophy. (shrink)

Objective: Compassion has been associated with eudaimonia and prosocial behavior, and has been regarded as a virtue, both historically and cross-culturally. However, the psychological study of compassion has been limited to laboratory settings and/or standard survey assessments. Here, we use an experience sampling method (ESM) to compare naturalistic assessments of compassion with standard assessments, and to examine compassion, its variability, and associations with eudaimonia and prosocial behavior. -/- Methods: Participants took a survey which included standard assessments of compassion and eudaimonia. (...) Then, over four days, they were repeatedly asked about their level of compassion, eudaimonia, and situational factors within the moments of daily life. Finally, prosocial behavior was tested using the Dual Gamble Task and an opportunity to donate task winnings. -/- Results: Analyses revealed within-person associations between ESM compassion and eudaimonia. ESM compassion also predicted eudaimonia at the next ESM time point. While not impervious to situational factors, considerable consistency was observed in ESM compassion in comparison with eudaimonia. Further, ESM compassion along with eudaimonia predicted donating behavior. Standard assessments did not. -/- Conclusion: Consistent with virtue theory, some individual’s reports displayed a probabilistic tendency toward compassion, and ESM compassion predicted ESM eudaimonia and prosocial behavior toward those in need. (shrink)

At the end of 2017, Kristen Roupenian’s short story, Cat Person, went viral. Published at the height of the #MeToo movement, it depicted a ‘toxic date’ and a disturbing sexual encounter between Margot, a college student, and Robert, an older man she meets at work. The story was widely viewed as a relatable denunciation of women’s powerlessness and routine victimization. In this paper, I push against this common reading. I propose an alternative feminist interpretation through the lens of Simone (...) de Beauvoir’s notion of narcissism: a form of alienation that consists in making oneself both the subject and the ultimate project of one’s life. Framing Margot as a narcissist casts her as engaging, not in subtly coerced, undesired sex, but rather in sex that is desired in a tragically alienated way. I argue that Beauvoir’s notion of narcissism is an important tool for feminists today – well beyond the interpretation of Cat Person. It presses us to see systematic subordination not just as something done to women, but also as something women do to themselves. This in turn highlights the neglected role of self-transformation as a key aspect of feminist political resistance. (shrink)

Concerns of moral distress in health care have never been more relevant. In her book, Health Care Ethics Through the Lens of Moral Distress, Kristen Jones-Bonofiglio provides a comprehensive review of the effects of moral distress on providers and health care delivery, while highlighting the complexities of making ethical decisions in practice. Jones-Bonofiglio’s thoroughness and use of interdisciplinary, historical, and cultural scholarship makes this book an excellent introductory resource on moral distress for health care providers and researchers alike.

During emerging public health crises, both policymakers and members of the public are looking to scientific experts to provide guidance. Even in cases where there are significant uncertainties, there is pressure for experts to “speak with one voice” to avoid confusion, allow officials to make evidence-based decisions rapidly, and encourage public support for such decisions. This can lead experts to engage in masking of information about the state of the science or regarding assumptions involved in policy recommendations. Although experts might (...) have good reasons for masking disagreements, uncertainties, or assumptions when offering policy advice, we argue that this strategy can result in epistemic harms. Using the case of the COVID-19 pandemic, we show that public health authorities masked two types of information necessary for laypersons to evaluate public health recommendations: (1) experts’ disagreements about the scientific evidence and (2) the role of values in making inferences from the science to policy positions. We contend that this resulted in epistemic harms against laypeople that provide a pro tanto case against masking information. We further argue that when the science is in flux and policies need to be implemented despite significant uncertainties, there is an all-things-considered case against masking the types of information discussed. (shrink)

Abstract: The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, necessitating accurate and timely forecasting of cases for effective mitigation strategies. In this research paper, we present a novel approach to predict COVID-19 cases using Artificial Neural Networks (ANNs), harnessing the power of machine learning for epidemiological forecasting. Our ANNs-based forecasting model has demonstrated remarkable efficacy, achieving an impressive accuracy rate of 97.87%. This achievement underscores the potential of ANNs in providing precise and data-driven insights into the dynamics (...) of the pandemic. However, this paper underscores the critical importance of a comprehensive evaluation beyond accuracy, including metrics such as sensitivity, specificity, and the area under the ROC curve (AUC-ROC), to assess the model's performance robustness. The research paper offers detailed insights into the architecture of the ANN model, encompassing critical hyperparameters, data preprocessing techniques, and regularization strategies employed to optimize model accuracy. Ethical considerations surrounding data privacy and potential biases within the COVID-19 dataset are also addressed. While the achieved accuracy is a significant milestone, this study underscores the dynamic and evolving nature of the pandemic, necessitating continuous model refinement and validation. Furthermore, it emphasizes the importance of considering false positives and false negatives in the context of public health decision-making. In conclusion, this research contributes to the arsenal of tools available for pandemic management by showcasing the potential of ANNs in COVID-19 case forecasting. It encourages ongoing exploration and adaptation of predictive models to enhance their applicability in real-world public health scenarios, ultimately contributing to more effective pandemic control and response efforts. (shrink)

Utilizing the metaphor of the palimpsest, this paper looks at layering processes at work in the natural world and human perceptions of it, paying particular attention to the manifestations of the past visible in constructions in and of the landscape. History is made of constant reformations, in which pieces of the past make up the present. The palimpsest offers a useful tool for discussing a trans-temporal landscape. The layers of landscape construction go beyond the literal geological construction, encompassing human pasts (...) and presents as well. Engaging the palimpsestuous layers at work in physical and textual landscape construction allows us to place human endeavours into more expansive notions of temporality. (shrink)

The article entitled “Cultural evolution in Vietnam’s early 20th century: A Bayesian networks analysis of Hanoi Franco-Chinese house designs” investigates how diverse cultures and religious creeds influenced the architecture of Hanoi in the early 20th century.

Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which (...) helps in protecting them from developing deadly blood clots. A tomato classification approach is presented with a data set containing approximately 5,266 images with 7 species belonging to tomatoes. The Neural Network Algorithms (CNN), a deep learning technique applied widely in image recognition, is used for this task. (shrink)

Abstract: Colon cancer is a prevalent and life-threatening disease, necessitating accurate and timely diagnosis for effective treatment and improved patient outcomes. This research paper presents the development of a knowledge-based system for diagnosing colon cancer using the CLIPS language. Knowledge-based systems offer the potential to assist healthcare professionals in making informed diagnoses by leveraging expert knowledge and reasoning mechanisms. The methodology involves acquiring and structuring medical knowledge specific to colon cancer, followed by the implementation of a knowledge- based system using (...) CLIPS. The system's design integrates relevant medical databases and employs advanced reasoning mechanisms to facilitate accurate diagnosis. Evaluation results demonstrate the system's effectiveness, with high accuracy and sensitivity rates compared to existing methods. The findings highlight the potential of knowledge-based systems in improving clinical practice and patient care. The research contributes to the growing field of medical informatics by demonstrating the feasibility and effectiveness of CLIPS-based knowledge systems for colon cancer diagnosis. Future research can focus on enhancing system performance and expanding its scope to encompass other types of cancer diagnoses. (shrink)

This research paper presents the development and evaluation of an expert system for diagnosing kidney diseases. The expert system utilizes a decision-making tree approach and is implemented using the CLIPS and Delphi frameworks. The system's accuracy in diagnosing kidney diseases and user satisfaction were evaluated. The results demonstrate the effectiveness of the expert system in providing accurate diagnoses and high user satisfaction.

Vertigo is a common symptom that can result from various underlying diseases and conditions, ranging from benign to severe. Accurate and timely diagnosis of the cause of vertigo is crucial for appropriate management and treatment. In this research, we propose the development of an expert system for vertigo diseases diagnosis, utilizing artificial intelligence (AI) and the proposed Expert System which was produced to help assist healthcare professionals in diagnosing the cause of vertigo based on a patient's symptoms, medical history, and (...) other relevant clinical information. The proposed expert system presents an overview about vertigo diseases are given, the cause of disease is outlined and the treatment or recommendation of disease whenever possible is given out. CLIPS language was used for designing and implementing the proposed expert system. The potential of the proposed expert system lies in its ability to enhance the accuracy and efficiency of vertigo diagnosis, as well as assist in the proper referral and management of patients. (shrink)

In this chapter we use methods of corpus linguistics to investigate the ways in which mathematicians describe their work as explanatory in their research papers. We analyse use of the words explain/explanation (and various related words and expressions) in a large corpus of texts containing research papers in mathematics and in physical sciences, comparing this with their use in corpora of general, day-to-day English. We find that although mathematicians do use this family of words, such use is considerably less prevalent (...) in mathematics papers than in physics papers or in general English. Furthermore, we find that the proportion with which mathematicians use expressions related to ‘explaining why’ and ‘explaining how’ is significantly different to the equivalent proportion in physics and in general English. We discuss possible accounts for these differences. (shrink)

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...) large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%. (shrink)

Philosophers of science, particularly those working on science and values, often talk about the need for science to be socially responsible. However, what this means is not clear. In this paper, we review the contributions of philosophers of science to the debate over socially responsible science and explore the dimensions that a fruitful account of socially responsible science should address. Our review shows that offering a comprehensive account is difficult. We contend that broad calls for socially responsible science that fail (...) to attend to relevant dimensions are not the solution, as they preclude meaningful changes to research institutions and practices. We conclude that narrower, more explicit accounts are more likely to lead to substantive transformation. (shrink)

Critics have recently argued that reliabilists face trade-off problems, forcing them to condone intuitively unjustified beliefs when they generate lots of true belief further downstream. What these critics overlook is that reliabilism entails that there areside-constraintson belief-formation, on account of which there are some things you should not believe, even if doing so would have very good epistemic consequences. However, we argue that by embracing side-constraints the reliabilist faces a dilemma: she can either hold on to reliabilism, and with it (...) aforementioned side-constraints, but then needs to explain why we should allow the pursuit of justification to get in the way of the acquisition of true belief; or she can deny that there are side-constraints – and in effect give up on reliabilism. We'll suggest that anyone moved by the considerations that likely attract people to reliabilism in the first place – the idea the true belief is good, and as such should be promoted – should go for the second horn, and instead pursue a form ofepistemic utilitarianism. (shrink)

This introduction to the Journal of Business Research special issue on anti-consumption briefly defines and highlights the importance of anticonsumption research, provides an overview of the latest studies in the area, and suggests an agenda for future research on anti-consumption.

Abstract: In this study, we explore the possibility of classifying the plant species. We collected the plant species from Kaggle website. This dataset encompasses 544 samples, encompassing 136 distinct plant species. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing plant Species classification accuracy and efficiency. This research explores plant Species classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 544 entries, we develop and evaluate a neural network model. Our neural network, (...) featuring a single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.002. Beyond performance metrics, we delve into the intricacies of plant Species classification through feature importance analysis. The most influential features— Vegsout, durflow, semiros, pdias, begflow, wind, leafy, autopoll and insects— uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring plant safety, and bolstering global trade. (shrink)

Accurate assessment and comprehension of smoking behavior are pivotal for elucidating associated health risks and formulating effective public health strategies. In this study, we introduce an innovative approach to predict and analyze smoking prevalence using an artificial neural network (ANN) model. Leveraging a comprehensive dataset spanning multiple years and geographic regions, our model incorporates various features, including demographic data, economic indicators, and tobacco control policies. This research investigates smoking trends with a specific focus on gender-based analyses. These findings are pivotal (...) for enhancing public health initiatives aimed at mitigating tobacco use, assessing associated health risks, and mitigating smoking-related diseases. (shrink)

Abstract: As artificial intelligence (AI) technologies become increasingly integrated into various sectors, ethical considerations surrounding their development and deployment have become paramount. This paper explores the multifaceted ethical landscape of AI, focusing on key challenges such as bias, transparency, privacy, and accountability. It examines how these issues manifest in AI systems and their impact on society. The paper also evaluates current approaches and solutions aimed at mitigating these ethical concerns, including regulatory frameworks, ethical guidelines, and best practices for AI design. (...) By providing a comprehensive analysis of these challenges and solutions, this paper aims to contribute to the ongoing discourse on responsible AI development and implementation, advocating for a balance between technological innovation and ethical integrity. (shrink)

This article argues, against contemporary experimentalist criticism, that conceptual analysis has epistemic value, with a structure that encourages the development of interesting hypotheses which are of the right form to be valuable in diverse areas of philosophy. The article shows, by analysis of the Gettier programme, that conceptual analysis shares the proofs and refutations form Lakatos identified in mathematics. Upon discovery of a counterexample, this structure aids the search for a replacement hypothesis. The search is guided by heuristics. The heuristics (...) of conceptual analysis are similar to those in other interesting areas of scholarship, and so hypotheses generated by it are of the right form to be applicable to diverse areas. The article shows that the explanationist criterion in epistemology was developed and applied in this way. The epistemic value of conceptual analysis is oblique because it contributes not towards the main purpose of conceptual analysis but towards the reliable development of epistemically valuable hypotheses in philosophy and scholarship. (shrink)

The current study is conducted to examine the following research questions: 1) How are the father’s interactions with children regarding waste recycling associated with the children’s recycling behaviors? 2) How are the mother’s interactions with children regarding waste recycling associated with the children’s recycling behaviors?

This paper sets mathematics among the sciences, despite not being empirical, because it studies relations of various sorts, like the sciences. Each empirical science studies the relations among objects, which relations determining which science. The mathematical science studies relations as such, regardless of what those relations may be or be among, how relations themselves are related. This places it at the extreme among the sciences with no objects of its own (A Subject with no Object, by J.P. Burgess and G. (...) Rosen). Examples are discussed. The historical development of written mathematics from algorithms on clay tablets to theorems with proofs is said to show that application of algorithms to specific problems and theorems to scientific objects is like allegorical interpretation. Mathematics fits into the modern scientific context because the sciences, beginning with Galileo, have been constructed in imitation of mathematics. Viewing mathematics this way does not solve any ontological problems, but it does show how mathematics avoids them. Epistemological problems, insuperable for object realism, are simplified. For example, we have access to relations among any objects that we can consider objectively, first physical objects and then mathematical objects of greater and greater abstraction. (shrink)

Recently, a number of philosophers have argued that we can and should “consequentialize” non-consequentialist moral theories, putting them into a consequentialist framework. I argue that these philosophers, usually treated as a group, in fact offer three separate arguments, two of which are incompatible. I show that none represent significant threats to a committed non-consequentialist, and that the literature has suffered due to a failure to distinguish these arguments. I conclude by showing that the failure of the consequentializers’ arguments has implications (...) for disciplines, such as economics, logic, decision theory, and linguistics, which sometimes use a consequentialist structure to represent non-consequentialist ethical theories. (shrink)

It aimed to identify the strategic creativity in Islamic banks in Palestine between reality and implementation. The study adopted the descriptive analytical approach. A questionnaire was designed as a tool for the study. The study community consisted of all employees in Islamic banks from the top and middle management and the study has been applied to the Palestinian Islamic bank and the Arab Islamic Bank. The comprehensive inventory method was used, given the small size of the study sample, as questionnaires (...) were distributed to (175) employees, and a number of (5) categories were chosen from each branch of the bank(general manager, deputy general manager, director Branch, department head, department manager). (164) questionnaires have been used Recovered with a recovery rate of (93.71%). The study showed a number of results, the most important of which is the availability of dimensions of strategic innovation at a high level in Islamic banks in Palestine with a relative weight of (82.22%). In addition, that there are no differences between the averages estimates about the reality of the study variables in Islamic banks due to (gender, age group, educational qualification, number of years of service, job title). The study also presented a set of recommendations, including that the bank should provide the environment and the appropriate climate for employees to invest their intellectual energy, urge them to strategic creativity, and deal fairly with all creative ideas regardless of their source. (shrink)

This article brings an account of reasonable expectations to bear on the question of when unauthorized immigrants have a right to be regularized—that is, to be formally guaranteed freedom from the threat of deportation. Contrary to the current literature, which implicitly relies on a flawed understanding of reasonable expectations, this article argues that only those unauthorized immigrants who have both been tacitly permitted by the state despite lacking formal authorization and have remained long enough to develop deep social roots in (...) the state have a right to regularization. (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)

In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of data generated by modern (...) applications. By leveraging algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method efficiently distributes data across multiple paths, ensuring balanced network utilization. The combination of these algorithms with multipath routing significantly reduces congestion and improves overall network performance. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with 97.50 (...) % accuracy. The factor of DriveTrain has the most influence on MPG_City evaluation. Similar studies can be carried out for the evaluation of other characteristics of cars. (shrink)

Consumer behavior analytics has become a pivotal aspect for businesses to understand and predict customer preferences and actions. The advent of machine learning (ML) algorithms has revolutionized this field by providing sophisticated tools for data analysis, enabling businesses to make data-driven decisions. However, the effectiveness of these ML algorithms significantly hinges on the optimization techniques employed, which can enhance model accuracy and efficiency. This paper explores the application of various optimization techniques in consumer behaviour analytics using machine learning algorithms. By (...) focusing on the optimization of key parameters, the study aims to improve the predictive power of models and reduce computational costs. (shrink)

In the modern digital era, cloud storage has become an indispensable service due to its scalability, accessibility, and cost-effectiveness. However, with the vast amount of sensitive information stored on cloud platforms, ensuring data security and privacy remains a critical challenge. Traditional encryption techniques, while secure, often hinder efficient data retrieval, especially when using keyword searches. To address this, attribute-based keyword search (ABKS) offers a promising solution by allowing secure, fine-grained access control and efficient keyword searches over encrypted data. This paper (...) delves into the integration of optimization techniques within ABKS to enhance search efficiency and data security in cloud storage environments. We explore various optimization strategies, such as index compression, query processing enhancement, and encryption optimization, which aim to reduce computational overhead while maintaining robust security measures. Through a comprehensive analysis, the paper illustrates how these techniques can significantly improve the performance of cloud storage systems, ensuring both security and usability. Experimental results demonstrate that optimized ABKS not only accelerates search queries but also reduces storage costs, making it a viable solution for modern cloud storage challenges. Future research directions include exploring advanced machine learning algorithms for predictive search optimizations and further improving the resilience of ABKS against emerging security threats. (shrink)

In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm optimization, and simulated (...) annealing. By focusing on both resource utilization and SLA compliance, the proposed approach aims to reduce latency, improve throughput, and maximize overall system efficiency. (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)

As these opening quotes acknowledge, the Prisoner’s Dilemma (PD) represents a core puzzle within the formal mathematics of game theory.3 Its rise in conspicuity is evident figure 2.1 above demonstrating a relatively steady rise in incidences of the phrase’s usage between 1960 to 1995, with a stable presence persisting into the twenty first century. This famous two-person “game,” with a stock narrative cast in terms of two prisoners who each independently must choose whether to remain silent or speak, each advancing (...) self-interest at the expense of the other and thereby achieving a mutually suboptimal outcome, mires any social interaction it is applied to into perplexity. The logic of this game proves the inverse of Adam Smith’s invisible hand: individuals acting on self interest will achieve a mutually suboptimal outcome. However, as this chapter illuminates, the assumptions underlying game theory drive this conclusion. (shrink)

The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyberattacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational overhead. By leveraging (...) advanced optimization techniques such as Grid Search and Particle Swarm Optimization (PSO), the proposed SVM-based IDS achieves superior classification results. (shrink)

Being a great jurist and influential scholar with seminal works on hadith, theology, biography and jurisprudence, al-Nawawī’s (d. 1277) views on epidemics are of great significance in these days of the pandemic. This article explores his views and explanations vis-à-vis epidemic to find his perspectives on balance between qadr (predestination) and ḥadhar (precaution) by conducting a content analysis of his various texts. In this article, the authors mainly referred to his texts Sharaḥ Muslim, Riyaḍ al-Ṣāliḥīn, al-Majmūʿ Sharaḥ Muhadhdhab, Rawḍat al-Ṭālibīn (...) and al- Adhkār al-Muntakhab. This study substantiates that in the view of al-Nawawī, Islam postulates a balanced position between taking precaution and faith in Allah’s decrees in dealing with the situations of an epidemic. Thus, it holistically complements the concepts of qadr and hadhar to guide people towards leading a faithful and safe life in the trying times of epidemic. (shrink)

This review offers an overview of Sandis's book and raises a few questions about it.

The research methodology involves data preprocessing, feature engineering, model training, and performance evaluation. We employ optimization methods such as Genetic Algorithms and Grid Search to fine-tune model parameters, ensuring robust and generalizable models. The dataset used includes patient medical records, with features like age, blood pressure, cholesterol levels, and lifestyle habits serving as inputs for the ML models. Evaluation metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC), assess the model's predictive power.

Background: Community-based education (CBE) involves educating the head (cognitive), heart (affective), and the hand (practical) by utilizing tools that enable us to broaden and interrogate our value systems. This article reports on the use of virtue ethics (VE) theory for understanding the principles that create, maintain and sustain a socially accountable community placement programme for undergraduate medical students. Our research questions driving this secondary analysis were; what are the goods which are internal to the successful practice of CBE in medicine, (...) and what are the virtues that are likely to promote and sustain them? -/- Methods: We conducted a secondary theoretically informed thematic analysis of the primary data based on MacIntyre’s virtue ethics theory as the conceptual framework. -/- Results: Virtue ethics is an ethical approach that emphasizes the role of character and virtue in shaping moral behavior; when individuals engage in practices (such as CBE), goods internal to those practices (such as a collaborative attitude) strengthen the practices themselves, but also augment those individuals’ virtues, and that of their community (such as empathy). We identified several goods that are internal to the practice of CBE and accompanying virtues as important for the development, implementation and sustainability of a socially accountable community placement programme. A service-oriented mind-set, a deep understanding of community needs, a transformed mind, and a collaborative approach emerged as goods internal to the practice of a socially accountable CBE. The virtues needed to sustain the identified internal goods included empathy and compassion, connectedness, accountability, engagement [sustained relationship], cooperation, perseverance, and willingness to be an agent of change. -/- Conclusion: This study found that MacIntyre’s virtue ethics theory provided a useful theoretical lens for understanding the principles that create, maintain and sustain CBE practice. (shrink)

African ontological discourse revolves around a few principles, the interrelatedness of being, what is variously interpreted as communalism, ubuntu, Holism, communitarianism etc. This is the view that every being in the world, animate and inanimate are interconnected into a whole. This makes it possible for African environmental attitude to claim to be holistic. Since we are one, we care for each other, humans care for animals, plants, and mountains not because of what to gain from them but because we are (...) the same and harming the river is same as harming oneself. The weakness of seeing African environmental ethics as only holistic is that it is not African enough as the paper will argue. The second principle in African ontological discourse is the human being. This principle has made scholars like Callicott and even some African scholars to describe African environmental ethics as anthropocentric. The paper also argues that branding African environmental ethics anthropocentric is not African enough. This is because Africans live in an interconnected world, comprising both the living, the dead and nature. Humans are only one privileged part of the whole and this is because of her obligatory role to nature and the world as a result of her capabilities. Through the method of analysis, the paper argues that a plausible African environmental ethics will be one that will blend the holistic nature of the African ontology and its pride of place given to humans. It will be discovered that obligatory anthropoholism can comfortably blend these two principles without necessarily being anthropocentric. (shrink)

Consumer behavior analytics has become a pivotal aspect for businesses to understand and predict customer preferences and actions. The advent of machine learning (ML) algorithms has revolutionized this field by providing sophisticated tools for data analysis, enabling businesses to make data-driven decisions. However, the effectiveness of these ML algorithms significantly hinges on the optimization techniques employed, which can enhance model accuracy and efficiency. This paper explores the application of various optimization techniques in consumer behaviour analytics using machine learning algorithms. By (...) focusing on the optimization of key parameters, the study aims to improve the predictive power of models and reduce computational costs. (shrink)

Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies that provide (...) personalized feedback and interventions based on detected emotional states. Our approach surpasses traditional machine learning methods, demonstrating superior performance in real-time applications. (shrink)

Although social scientists have identified diverse behavioral patterns among children from dissimilarly structured families, marketing scholars have progressed little in relating family structure to consumption-related decisions. In particular, the roles played by members of single-mother families—which may include live-in grandparents, mother’s unmarried partner, and step-father with or without step-sibling(s)—may affect children’s influence on consumption-related decisions. For example, to offset a parental authority dynamic introduced by a new stepfather, the work-related constraints imposed on a breadwinning mother, or the imposition of adult-level (...) household responsibilities on children, single-mother families may attend more to their children’s product preferences. -/- Without a profile that includes socio-economic, behavioral, and psychological aspects, efficient and socially responsible marketing to single-mother households is compromised. Relative to dual-parent families, single-mother families tend to have fewer resources and less buying power, children who consume more materialistic and compulsively, and children who more strongly influence decision making for both own-use and family-use products. Timely research would ensure that these and other tendencies now differentiate single-mother from dual-parent families in ways that marketers should address. Hence, our threefold goal is (1) to consolidate and highlight gaps in existing theory applied to studying children’s influence on consumption-related decision making in single-mother families, and (2) to propose a hybrid framework that merges two theories conducive to such research, and (3) to identify promising research propositions for future research. (shrink)

Brought to you by: Crop X editors: Jonathan Alexander Perez, Harmony Smith, Corinne Tendorf, David Turturo, and Derek Rahn Williams. Faculty Advisor: David Turturo; Crop X team included: Chaimae Alehyane, Zachary S. Casey, Suzanna Brinez, Jacob Brown, Elizabeth George, Francisco Javier Muniz Ituarte, Brodey Myers. -/- Credits: Huckabee College of Architecture; Graphic Designers: Studio BLDG (Blossom Liu + Danny Gray); English Editor: Luke Studebaker; Spanish Translator: Jessie Forbes; Printer: Die Keure. Cover Photo: Derek Williams. -/- Generously supported by the Graham (...) Foundation for Advanced Studies in the Fine Arts and the Huckabee College of Architecture, Texas Tech University. (shrink)