Half of the drug offenders incarcerated in the United States are black, even though whites and blacks use and sell drugs at the same rate, and blacks make up only 13 percent of the population. Noncomparativists about retributive justice see nothing wrong with this picture; for them, an offender’s desert is insensitive to facts about other offenders. By contrast, comparativists about retributive justice assert that facts about others can partially determine an offender’s desert. Not surprisingly, comparativists, especially comparative egalitarians, contend (...) that differential punishment is retributively unjust. I agree with this assessment, but take issue with the reasons egalitarians cite in its favor. In this paper, I argue that differential punishment violates retributive justice because it contributes to structural racial oppression. Over the course of developing and defending this claim, I identify the shortcomings of both comparative egalitarianism and respectarianism, which is the most popular and plausible brand of noncomparativism. (shrink)

Few problems in the philosophy of evolutionary biology are more widely disseminated and discussed than the charge of Darwinian evolution being a tautology. The history is long and complex, and the issues are many, and despite the problem routinely being dismissed as an introductory-level issue, based on misunderstandings of evolution, it seems that few agree on what exactly these misunderstandings consist of. In this paper, I will try to comprehensively review the history and the issues. Then, I will try to (...) present the following “solution”, or, one might say, “dissolution”, of the problem, and consider the wider implications of formal, or schematic, explanations in science: yes, the principle of natural selection is a tautology, and so what? It is a promissory note for actual, physical, explanations in particular cases, and is none the worse for that. This is not a new argument, of course, but it does point up the importance of formal schematic models in science. (shrink)

The aim of this paper is to evaluate the critical success factors and investigate the benefits that might be gained once implementing Electronic Customer Relationship Management at HEI from employee perspective. The study conducted at Al Quds Open University in Palestine and data collected from (300) employee through a questionnaire which consist of four variables. A number of statistical tools were intended for hypotheses testing and data analysis, including Spearman correlation coefficient for Validity, reliability correlation using Cronbach’s alpha, and Frequency (...) and Descriptive analysis. The overall findings of the current study show that all the features were important for staff and it was critical success factors, at the same time, websites were providing all the features discussed by the theory whereas staff showed their willingness to use those features if provided. It is also discovered that implementing Electronic Customer Relationship Management can cause staff retention, were provided efficiently and needed to be improved. Research limitations: The survey findings were based on QOU employee in Palestine, UAE and KSA branches not included in the study. (shrink)

Essay on Transcendental Philosophy presents the first English translation of Salomon Maimon's principal work, originally published in Berlin in 1790. In this book, Maimon seeks to further the revolution in philosophy wrought by Kant's Critique of Pure Reason by establishing a new foundation for transcendental philosophy in the idea of difference. Kant judged Maimon to be his most profound critic, and the Essay went on to have a decisive influence on the course of post-Kantian German Idealism. A more recent admirer (...) was Gilles Deleuze who drew on Maimon's Essay in constructing his own philosophy of difference. This long-overdue translation makes Maimon's brilliant analysis and criticism of Kant's philosophy accessible to an English readership for the first time. The text includes a comprehensive introduction, a glossary, translators' notes, a bibliography of writings on Maimon and an index. It also includes translations of correspondence between Maimon and Kant and a letter Maimon wrote to a Berlin journal clarifying the philosophical position of the essay, all of which bring the book's context alive for the modern reader. (shrink)

Abstract: Online book review platforms generate vast user data, making accurate rating prediction crucial for personalized recommendations. This research explores neural networks as simple models for predicting book ratings without complex algorithms. Our novel approach uses neural networks to predict ratings solely from user-book interactions, eliminating manual feature engineering. The model processes data, learns patterns, and predicts ratings. We discuss data preprocessing, neural network design, and training techniques. Real-world data experiments show the model's effectiveness, surpassing traditional methods. This research can (...) enhance user experience, book catalog organization, and aid publishers, simplifying recommendation processes and providing tailored suggestions based on user preferences. (shrink)

Contemporary republicanism is characterized by three main ideas: free persons, who are not subject to the arbitrary power of others; free states, which try to protect their citizens from such power without exercising it themselves; and vigilant citizenship, as a means to limit states to their protective role. This book advances an economic model of such republicanism that is ideologically centre-left. It demands an exit-oriented state interventionism, one that would require an activist government to enhance competition and resource exit from (...) dominating relationships within markets of all types, be they marital, labour, or residential. The substantial intervention and redistribution necessitated by such an approach make it leftist, while its tendency to work with rather than against the grain of markets makes it centrist. It is, in short, a contribution to progressive republican political thought, but one that is less social-democratic than market-democratic in character. (shrink)

Abstract: Sign language recognition is one of the most rapidly expanding fields of study today. Many new technologies have been developed in recent years in the fields of artificial intelligence the sign language-based communication is valuable to not only deaf and dumb community, but also beneficial for individuals suffering from Autism, downs Syndrome, Apraxia of Speech for correspondence. The biggest problem faced by people with hearing disabilities is the people's lack of understanding of their requirements. In this paper we try (...) to fill this gap. By trying to translate sign language using artificial intelligence algorithms, we focused in this paper using transfer learning technique based on deep learning by utilizing a MobileNet algorithm and compared it with the previous paper results[10a], where we get in the Mobilenet algorithm on the degree of Accuracy 93,48% but the VGG16 the accuracy was 100% For the same number of images (43500 in the dataset in size 64*64 pixel ) and the same data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch . (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)

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)

Philosophers of science are increasingly interested in engaging with scientific communities, policy makers, and members of the public; however, the nature of this engagement has not been systematically examined. Instead of delineating a specific kind of engaged philosophy of science, as previous accounts have done, this article draws on literature from outside the discipline to develop a framework for analyzing different forms of broadly engaged philosophy of science according to two key dimensions: social interaction and epistemic integration. Clarifying the many (...) forms of engagement available to philosophers of science can advance future scholarship on engagement and promote more strategic engagement efforts. (shrink)

Historically, republicans were of different minds about markets: some, such as Rousseau, reviled them, while others, like Adam Smith, praised them. The recent republican resurgence has revived this issue. Classical liberals such as Gerald Gaus contend that neo-republicanism is inherently hostile to markets, while neo-republicans like Richard Dagger and Philip Pettit reject this characterization—though with less enthusiasm than one might expect. I argue here that the right republican attitude toward competitive markets is celebratory rather than acquiescent and that republicanism demands (...) such markets for the same reason it requires the rule of law: because both are essential institutions for protecting individuals from arbitrary interference. I reveal how competition restrains—and in the limit, even eradicates—market power and thereby helps us realize “market freedom,” i.e., freedom as non-domination in the context of economic exchange. Finally, I show that such freedom necessitates “Anglo-Nordic” economic policies. (shrink)

Contemporary republicans have adopted a less-than-charitable attitude toward private beneficence, especially when it is directed to the poor, worrying that rich patrons may be in a position to exercise arbitrary power over their impoverished clients. These concerns have led them to support impartial public provision by way of state welfare programs, including an unconditional basic income (UBI). In contrast to this administrative model of public welfare, I will propose a competitive model in which the state regulates and subsidizes a decentralized (...) and nonstatist provision of support for the poor. This model will fix the historically objectionable features of private provision by having the state prevent collusion among private charities, deliver information to recipients about alternative sources of assistance, and give substantial grants to charities as well as tax incentives and vouchers to donors. I will contend that such an approach would do a better job of minimizing domination of the poor than traditional welfare states and may prove more politically feasible than a UBI, at least in the near term in certain national contexts. (shrink)

In the longstanding debate between liberals and libertarians over the morality of redistributive labor taxation, liberals such as John Rawls and Ronald Dworkin have consistently taken the position that such taxation is perfectly compatible with individual liberty, whereas libertarians such as Robert Nozick and Murray Rothbard have adopted the (very) contrary position that such taxation is tantamount to slavery. In this paper, I argue that the debate over redistributive labor taxation can be usefully reconstituted as a debate over the incidents (...) (or components) of self-ownership, with liberals arguing for a narrow definition of the concept and libertarians arguing for a broad one. By using what Alan Ryan has called the “language of proprietorship,” I pinpoint precisely the source of the disagreement between liberals and libertarians and assess the relative strengths of their arguments. I also show that the respective definitions of self-ownership used by liberals and libertarians are deeply problematic—though for strikingly different reasons. (shrink)

This article criticizes radical labor republicanism on republican grounds. I show that its demand for universal workplace democracy via workers’ cooperatives conflicts with republican freedom along three different dimensions: first, freedom to choose an occupation…and not to choose one; second, freedom within the very cooperatives that workers are to democratically govern; and third, freedom within the newly proletarian state. In the conclusion, I ask whether these criticisms apply, at least in part, to the more modest, incrementalist strand of labor republicanism. (...) To the extent that they do, delaboring republicanism might be the best response. (shrink)

This article engages with two fundamental attitudes toward animals who are used for human consumption: attention and ironic detachment. Taken as polarities linked with animal consumption and the refusal thereof, I discuss how these two attitudes are shaped and manifested during moments of encounter with the animals in question. Starting from a striking photograph from the Lychee and Dog Meat Festival in China, I explore the embodiment of these attitudes in the “gaze” of human participants during the encounter with animals (...) who are going to be killed for food. (shrink)

A liberal antinomy of parenting exists: strong liberal intuitions militate in favor of both denying special resources to parenting projects (on grounds of project-neutrality) and granting them (on grounds of respect for personhood). I show that we can reconcile these two claims by rejecting a premise common to both--viz. that liberalism is necessarily committed to extensive procreative liberties--and limiting procreation and subsequent parenting to adults who meet certain psychological and especially financial criteria. I also defend this argument, which provides a (...) Kantian complement to utilitarian arguments for restricting procreation, against a variety of objections. (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)

Abstract: The Knowledge-Based System for Diagnosing Breast Cancer aims to support medical students in enhancing their education regarding diagnosis and counseling. The system facilitates the analysis of biopsy images under a microscope, determination of tumor type, selection of appropriate treatment methods, and identification of disease-related questions. According to the Ministry of Health's annual report in Gaza, there were 7,069 cases of breast cancer between 2009 and 2014, with 1,502 cases reported in 2014. In an era dominated by visual information, where (...) 65% of the population are visual learners, the Knowledge-Based System serves as an effective tool to aid long-term information retention by utilizing visual and textual resources. The system features a user-friendly interface, enabling students to diagnose the disease and expand their understanding. Furthermore, it can be accessed via smartphones. The Knowledge-Based System comprises three primary components: Firstly, it provides specific patient-oriented questions to familiarize students with the questioning process. Secondly, it facilitates the analysis of biopsy sample images to determine their integrity and identify potential infections. Lastly, the system incorporates animated videos illustrating various approaches for treating breast cancer. (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)

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)

Rawls offers three arguments for the priority of liberty in Theory, two of which share a common error: the belief that once we have shown the instrumental value of the basic liberties for some essential purpose (e.g., securing self-respect), we have automatically shown the reason for their lexical priority. The third argument, however, does not share this error and can be reconstructed along Kantian lines: beginning with the Kantian conception of autonomy endorsed by Rawls in section 40 of Theory, we (...) can explain our highest-order interest in rationality, justify the lexical priority of all basic liberties, and reinterpret Rawls’ threshold condition for the application of the priority of liberty. Perhaps unsurprisingly, this Kantian reconstruction will not work within the radically different framework of Political Liberalism. (shrink)

t: Wireless Sensor Networks (WSNs) have become increasingly prevalent in various applications, ranging from environmental monitoring to smart cities. However, the limited energy resources of sensor nodes pose significant challenges in maintaining network longevity and data transmission efficiency. Duty-cycled WSNs, where sensor nodes alternate between active and sleep states to conserve energy, offer a solution to these challenges but introduce new complexities in data transmission.

As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising the integrity and confidentiality of (...) the data. The protocol leverages a hybrid cryptographic approach, combining symmetric and asymmetric encryption, to ensure robust protection against unauthorized access while maintaining low computational overhead. Furthermore, the implementation of an optimized keyword indexing mechanism facilitates fast and accurate search operations, making the protocol well-suited for real-time IoT applications. Extensive experiments were conducted using various IoT datasets to evaluate the performance of the proposed protocol in terms of encryption speed, search efficiency, and overall security. The results demonstrate that the optimized encryption protocol significantly outperforms existing methods, offering a scalable solution that meets the stringent requirements of IoT systems. This research contributes to the field by providing a practical and secure encryption solution that addresses the unique challenges of IoT environments, paving the way for more secure and efficient IoT networks. (shrink)

In this paper, I use a comparative analysis of mysticism in Zen and the Abrahamic faiths to formulate a phenomenological account of mysticism “as such.” I argue that, while Zen Buddhism is distinct from other forms of mystical experience in important ways, it can still be fit into a general phenomenological category of mystical experience. First, I explicate the phenomenological accounts of mysticism provided by Anthony Steinbock and Angela Bello. Second, I offer an account of Zen mysticism which both coheres (...) with and problematizes these accounts, arguing that Zen demonstrates the inadequacy of these accounts as descriptions of mysticism as a universal religious category. Lastly, I use this investigation to propose that Zen mysticism does generally cohere with the mystical experiences of other religions, but only if we devise a new formula for speaking phenomenologically about mystical experience as such which captures this phenomenon in all of its manifestations. (shrink)

Genes are thought to have evolved from long-lived and multiply-interactive molecules in the early stages of the origins of life. However, at that stage there were no replicators, and the distinction between interactors and replicators did not yet apply. Nevertheless, the process of evolution that proceeded from initial autocatalytic hypercycles to full organisms was a Darwinian process of selection of favourable variants. We distinguish therefore between Neo-Darwinian evolution and the related Weismannian and Central Dogma divisions, on the one hand, and (...) the more generic category of Darwinian evolution on the other. We argue that Hull’s and Dawkins’ replicator/interactor distinction of entities is a sufficient, but not necessary, condition for Darwinian evolution to take place. We conceive the origin of genes as a separation between different types of molecules in a thermodynamic state space, and employ a notion of reproducers. (shrink)

A common claim in the philosophy-of-race literature is that the unearned benefits of whiteness can by themselves burden their recipients with special antiracist obligations, i.e., that these benefits can impose duties unilaterally, without the mediation of their recipients’ wills, and that these duties go beyond our general antiracist duties, which derive from our common liberal-democratic citizenship and shared humanity. I will argue against this claim, though I acknowledge that there may be duties that follow from these benefits when they are (...) combined with the affirmation or assertion of, assent to, or even mere acquiescence in white identity. Without such ratification, however, there will be no basis for either special white duties or a distinctively white guilt. (shrink)

This chapter sets forth an interpretation of public reason that appeals to our central capabilities as human beings. I argue that appealing to central human capabilities and to the related idea of respect for threshold capabilities is the best way to understand public reason. My defense of this position advances stepwise: first, I consider a central alternative to a capability account, which regards public reason as a matter of contracting; next, I describe central concerns with contract views and show how (...) a capability view can avoid them; lastly, I consider extending public reason beyond national borders and test the two views in this context. (shrink)

Recent work in the philosophy of emotion focuses on challenging dualistic conceptualizations. Three of the most obvious dualisms are the following: 1. emotion opposes reason; 2. emotion is subjective, while reason is objective; 3. emotion lies internal to the subject, while reason is external. With challenges to these dualisms, one of the more interesting questions that has surfaced is the idea of emotional appropriateness in a particular context. Here, consider a widely held belief in the United States associates racialized groups (...) with specific emotions—most notably African American women with anger. Clearly these emotional attributions are essentialistic--and hence racist. But this response is too easy. The acknowledgement that emotion lies both internally in the subject and externally in the world opens the possibility of attributing an emotional temperament to a population group. Yet if an identity group has an emotional temperament, can they be accountable for emotionally appropriate behavior? (shrink)

Philosophers have given sustained attention to the controversial possibility of (legal) markets in transplantable human organs. Most of this discussion has focused on whether such markets would enhance or diminish autonomy, understood in either the personal sense or the Kantian moral sense. What this discussion has lacked is any consideration of the relationship between self-ownership and such markets. This paper examines the implications of the most prominent and defensible conception of self-ownership--control self-ownership (CSO)--for both market and nonmarket organ-allocation mechanisms. The (...) paper contends that CSO rules out a large set of nonmarket mechanisms, including escheatage ("presumed consent"), compensated takings of organs, and restricted gifting. It also argues that CSO, if accompanied by an economistic concern for welfare, can underwrite varying types of markets in human organs, ranging from mutual-insurance pools to inter vivos (i.e., live donor) organ sales. (shrink)

The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework (...) achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

The producers of Automobile products innovated a new thought of designing the two- wheelers in such a way to attract the women. Today most of the women prefer to travel through two-wheelers. A wide variety of two-wheelers of all category light- weighted, medium – weighted and heavy weighted vehicles have been introduced in the market. The objective of the study is to know the preference and satisfaction of women consumers over two-wheelers and the various aspects, which determines the purchase or (...) buying behavior and to know the expectations of women consumers over two-wheelers. The sample size of the study was conducted in Tiruchirappalli city with 75 respondents through Non-Probability Random Sampling Method. (shrink)

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)

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)

This paper advocates for a general policy of penal leniency: judges should often sentence offenders to a punishment less severe than initially preferred. The argument’s keystone is the relatively uncontroversial Minimal Invasion Principle (MIP). MIP says that when more than one course of action satisfies a state’s legitimate aim, only the least invasive is permissibly pursued. I contend that MIP applies in two common sentencing situations. In the first, all sentences within a statutorily specified range are equally proportionate. Here MIP (...) applies directly. In the second, judges reasonably believe that one of the sentences within the range is the most proportionate, but can’t identify it with any certainty. In these cases of sentencing uncertainty, judges must be indifferent between their preferred sentence and a softer one, and this indifference triggers MIP. MIP thus frequently mandates some degree of leniency. I conclude with some comments on statistical uncertainty. (shrink)

Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as Genetic Algorithms (...) (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. (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)

The integration of Augmented Reality (AR) and the Internet of Things (IoT) within smart homes represents a significant leap forward in home automation and user experience. This paper explores a comprehensive framework that combines these technologies to create an immersive, efficient, and user-friendly smart home environment. By utilizing AR, users can interact with and control IoT-enabled devices in real time through visual and intuitive interfaces. This integration not only simplifies the management of home systems but also enhances the overall user (...) experience by providing a seamless blend of the physical and digital worlds. (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)

The Genetic Ranking Optimization Algorithm (GROA) is used to rank phishing content based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time. The classification system was evaluated using benchmark phishing datasets, and the results demonstrated a significant improvement in detection accuracy and reduced false (...) positives. (shrink)

Capital punishment—the legally authorized killing of a criminal offender by an agent of the state for the commission of a crime—stands in special need of moral justification. This is because execution is a particularly severe punishment. Execution is different in kind from monetary and custodial penalties in an obvious way: execution causes the death of an offender. While fines and incarceration set back some of one’s interests, death eliminates the possibility of setting and pursuing ends. While fines and incarceration narrow (...) one’s routes to happiness, death eliminates its possibility. Not surprisingly, there is much debate about the moral permissibility of capital punishment. This entry maps the terrain of this debate. The first section discusses justifications of the death penalty as they appear in major theories of punishment. The second section surveys moral objections to execution that apply to most justifications. The third addresses procedural criticisms, which do not target the morality of execution so much as the justice of its implementation. (shrink)

Abstract Charles Darwin, in his discussions with Asa Gray and in his published works, doubted whether God could so arrange it that exactly the desired contingent events would occur to cause particular outcomes by natural selection. In this paper, I argue that even a limited or neo-Leibnizian deity could have chosen a world that satisfied some arbitrary set of goals or functions in its outcomes and thus answer Darwin's conundrum. In more general terms, this supports the consistency of natural selection (...) with providentialism, and makes “theistic evolutionism” a coherent position to hold. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...) behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. T. (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)

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)

This article defends the conclusion that the soccer World Cup in Qatar should not be boycotted by rich countries from the Global North. This conclusion is underpinned by considerations about the economic background conditions in guest workers’ home countries. Three arguments are considered for the view that the World Cup should be boycotted. It is argued that each of these arguments is unsound. Section 7 contains a discussion of an argument for a boycott that centers on the process through which (...) Qatar was awarded the World Cup. This argument is rejected. The article lists several ways in which guest workers in Qatar are wronged, but this fact about how guest workers are treated does not justify a boycott by rich countries from the Global North. (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)

This comparison helps in identifying which model performs better in the task of facial reconstruction from distorted images. Visualizing the results in the form of a graph provides a clear and concise way to understand the comparative performance of the algorithms. The ultimate goal of this project is to develop a system that can accurately reconstruct distorted faces, which can be invaluable in identifying accident victims or assisting in medical treatments. By providing a reliable method for facial reconstruction, this technology (...) can potentially save lives and improve outcomes for individuals involved in accidents. (shrink)

The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. Extensive simulations are conducted using synthetic and real-world datasets to evaluate the performance of the proposed algorithm. The results demonstrate that the optimized load balancing approach outperforms traditional algorithms in terms of SLA compliance, resource utilization, and energy efficiency. The findings suggest that the integration of optimization techniques into load balancing algorithms can significantly enhance (...) the operational efficiency of data centers, paving the way for future advancements in autonomous and self-optimizing data centers. (shrink)

The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that no single server (...) holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. The implementation of this framework is evaluated on multiple cloud platforms, demonstrating its effectiveness in safeguarding data with minimal performance impact. Future enhancements may include integrating machine learning algorithms to predict and adapt to new security threats in real time, further solidifying the reliability of cloud storage solutions. (shrink)