When Sartre arrives late to meet Pierre at a local establishment, he discovers not merely that Pierre is absent, but Pierre’s absence, where this depends, or so Sartre notoriously supposes, on a frustrated expectation that Pierre would be seen at that place. Many philosophers have railed against this view, taking it to entail a treatment of the ontology of absence that Richard Gale describes as ‘attitudinal’ – one whereby absences are thought to ontologically depend on psychological attitudes. In this paper, (...) I aim to make Sartre’s intuition respectable. What Sartre perceives is an absential location, only the boundaries of which are circumscribed by what Sartre is doing at that place: meeting Pierre. I explain how this Sartrean view, though not specifically attributable to Sartre, nonetheless honours some of the phenomenological data described, if a little opaquely, in Being and Nothingness. (shrink)

I offer a reading of Murdoch's conception of concrete universality as it appears in 'The Idea of Perfection', the first essay in the Sovereignty of Good. I show that it has British Idealist overtones that are inflected by Wittgenstein, a thought I try to illuminate by drawing an analogy with Wittgenstein's discussion of the metre stick in Paris in Philosophical Investigations §50. In the last part of the paper, I appeal to the work of Murdoch's erstwhile tutor Donald MacKinnon to (...) respond to an objection to my strategy, which I draw from Murdoch's later work, Metaphysics as a Guide to Morals. (shrink)

This paper expands on the notion of "moral conversion" (advanced by Bernard Lonergan but underdeveloped in his work) by developing a typology that uses two "cross-hatching" criteria. First, it distinguishes between moral conversions that have to do with a person's relation to moral obligation, good and evil, and between moral conversions that have to do with how a person regards the question of happiness and the meaning of life. Secondly, it distinguishes between conversions regarding the _content_ (what is good/evil or (...) the meaning of life?), regarding _attitude_ (becoming committed to doing good or to the meaning-seeking process), and regarding _coherence_ (conforming one's actual life to what is understood to be right/wrong or meaning-giving). Combining both criteria leaves us with six types of moral conversion. Each type is then illustrated and supported with concrete real-life narratives. I conclude with an attempt at a definition of moral conversion that includes all types. The article is a summary of Chapter 5 of my dissertation (2008) "Narratives of Hope: A Philosophical Study of Moral Conversion.". (shrink)

Anscombe’s published writings, lectures and notes on sensation point toward a sophisticated critique of sense-data, representationalist and direct realist theories of perception (in both their historical and contemporary forms), and a novel analysis of the concept of sensation. Her philosophy of perception begins with the traditional question, ‘What are the objects of sensation?’, but the response is a grammatical rather than ontological enquiry. What, she asks, are the characteristics of the grammatical object of sensation verbs? Anscombe’s answer is: sensation verbs (...) take ‘intentional objects’, where an ‘intentional object’ is a description which has the characteristics of the concept of intention—characteristics elucidated in great detail in her Intention. This simple manoeuvre allows Anscombe to reject two opposing positions in the philosophy of perception - that the objects of sensation are sense-data and that the objects of sensation are tables and chairs (etc). Both views, she argues, fail to recognise the grammatical fact that the verbs of sensation take intentional objects. This chapter outlines the case for a grammatical methodology in philosophy of perception, by framing an analysis of the ways in which the sense-data and direct-realist theories of perception arise out of the theoretical demands that are generated when the grammar of the concept of sensation is left unexplored. Along the way, it will be shown that Anscombe’s philosophy of perception should not be read as fore-running contemporary representationalism, and that reading it as such in fact runs counter not only to the letter but the spirit of Ancombe’s proposal. (shrink)

J. R. R. Tolkien’s four functions of fantasy stories, as developed in his Andrew Lang lecture “On Fairy Stories” (1939), have become a key conceptual tool for discussing human beings’ attraction to fantasy stories, particularly when attempting to push the analysis beyond the literary into the aesthetic, and beyond the aesthetic into the existential. Applying this interpretive key to an analysis of the expanding genre of post-apocalyptic fiction reveals that post-apocalyptic stories, despite superficial differences, are surprisingly close to fairy stories (...) in their aesthetic core and orientation, and that post-apocalyptic stories are well-suited to fulfill—albeit with their own distinctive aesthetic feel—the functions of Enchantment, Recovery, Escape and Consolation. Furthermore, in exploring the function of Consolation, a number of motifs come to surface that suggest the presence of a religious dimension common to many of the most celebrated exponents of the genre. (shrink)

This work explores the philosophical implications of moral conversion: the fact that, at some point in their lives, people may change their deep-seated convictions, attitudes and patterns of action regarding moral matters in rather unexpected and surprising ways. The fact of moral conversion and the common characteristics of the process are established through the analysis of a compilation of stories of moral conversion from various sources and settings. This analysis yields the definition of conversion as an “existential change” in the (...) person, and six classes of moral conversion are identified. Turning to the philosophical implications of moral conversion, the work examines its possible bearing, first, on the free choice/determinism debate, proposing that a study of moral conversion is of interest to this discussion because the unpredictability of moral conversion challenges determinism, and because during the process the subject often undergoes certain experiences that may be revealing of free choice. Second, the work examines the implications of moral conversion for the debate on internalism and externalism, proposing that the fact of moral conversion supports an internalist view of morality, i.e., that the intelligible content of moral norms, as understood by the moral agent, is one of the central operative factors in the agent’s adopting (and then living by) the new moral standards. (shrink)

I consider the first part of Marty’s Raum und Zeit, which treats of both the nature of space and spatial perception. I begin by sketching two charges that Marty raises against Kantian and Brentanian conceptions of space (and spatial perception) respectively, before detailing what I take to be a characteristically Martyan picture of space perception, though set against the backdrop of contemporary philosophy of perception. Marty has it that spatial relations are non-real but existent, causally inert relations that are grounded (...) in space, which is itself non-real but existent. Objects do not inhere in space in the way properties inhere in substances. Rather, there is a ‘non-real’ relation of ‘fulfillment’ (Erfüllung) that holds between objects and places in space, which itself subsists. I consider whether any contemporary philosophy of perception is equipped to make sense of Martyan space perception and I suggest that the most promising conception is Naïve Realism. I then outline a difficulty for this theoretical translation. Naïve Realism is a direct theory of perception whereby S is said to perceive O just in case S stands in a psychological relation of acquaintance with O, where this relation is both non-representational and explanatorily primitive. For Marty however, all relations are non-real and, insofar as they are grounded, are neither fundamental, nor brute or primitive in an explanatory sense. I close by detailing what I thereby take a distinctively Martyan form of Naïve Realism to involve. The central theoretical tenet that phenomenal character is fundamentally constituted by worldly objects is preserved; but the manifestly relational structure of the acquaintance relation, construed in particular as a relation of awareness, is treated as derivative. I make headway in spelling out the latter claim by bringing Marty into fleeting conversation with another Thomist - G.E.M. Anscombe. (shrink)

This paper involves constructive exegesis. I consider the contrast between morality and art as sketched in Philippa Foot's 1972 paper of the same name, ‘Morality and Art’. I then consider how her views might have shifted against the background of the conceptual landscape afforded by Natural Goodness, though the topic of the relation of art and morality is not explicitly explored in that work. The method is to set out some textual fragments from Natural Goodness that can be arranged for (...) a tentative Footian ‘aesthetics’. I bring them into conversation with some ideas from Iris Murdoch to elucidate what I think the import may be, for Foot, of depicting human form. (shrink)

Saunders Mac Lane famously remarked that "Bourbaki just missed" formulating adjoints in a 1948 appendix (written no doubt by Pierre Samuel) to an early draft of Algebre--which then had to wait until Daniel Kan's 1958 paper on adjoint functors. But Mac Lane was using the orthodox treatment of adjoints that only contemplates the object-to-object morphisms within a category, i.e., homomorphisms. When Samuel's treatment is reconsidered in view of the treatment of adjoints using heteromorphisms or hets (object-to-object morphisms between objects in (...) different categories), then he, in effect, isolated the concept of a left representation solving a universal mapping problem. When dualized to obtain the concept of a right representation, the two halves only need to be united to obtain an adjunction. Thus Samuel was only a now-simple dualization away for formulating adjoints in 1948. Apparently, Bodo Pareigis' 1970 text was the first and perhaps only text to give the heterodox "new characterization" (i.e., heteromorphic treatment) of adjoints. Orthodox category theory uses various relatively artificial devices to avoid formally recognizing hets--even though hets are routinely used by the working mathematician. Finally we consider a "philosophical" question as to whether the most important concept in category theory is the notion of an adjunction or the notion of a representation giving a universal mapping property (where adjunctions arise as the special case of a bi-representation of dual universal mapping problems). (shrink)

Mac Cumhaill and Wiseman’s book about the formative years of four influential female philosophers is well-researched and timely, appearing shortly after Lipscomb’s (2022) on the same topic. They describe the lives of Elizabeth Anscombe, Philippa Foot, Mary Midgley and Iris Murdoch from 1938 to 1956, that is, from the last pre-war term at Oxford, where all four took a BA, to the term in which Anscombe defended her famous objection to "Mr. Truman’s Degree" at Oxford’s general assembly. Using a wide (...) range of sources, the authors paint a vivid picture of how the war shaped British universities and thereby the philosophical education of these four students. (shrink)

In this commentary on Yashar Saghai's article "Salvaging the Concept of Nudge" (JME 2013) I discuss his distinction between a 'prod' (which is 'substantially controlling') and a 'nudge' (which is ‘substantially non-controlling’).

Evolutionary applications of game theory present one of the most pedagogically accessible varieties of genuine, contemporary theoretical biology. We present here Oyun (OY-oon, http://charlespence.net/oyun), a program designed to run iterated prisoner’s dilemma tournaments, competitions between prisoner’s dilemma strategies developed by the students themselves. Using this software, students are able to readily design and tweak their own strategies, and to see how they fare both in round-robin tournaments and in “evolutionary” tournaments, where the scores in a given “generation” directly determine contribution (...) to the population in the next generation. Oyun is freely available, runs on Windows, Mac, and Linux computers, and the process of creating new prisoner’s dilemma strategies is both easy to teach and easy for students to grasp. We illustrate with two interesting examples taken from actual use of Oyun in the classroom. (shrink)

The paper offers a philosophically infused analysis of Cormac McCarthy’s The Road. The main idea is that McCarthy’s novel is primarily a statement on the meaning of life. Once this idea is argued for and endorsed, by using a parallel between The Road and a 19th century Hungarian dramatic poem, The Tragedy of Man, the paper goes on to argue that the most plausible – although admittedly not the only possible – interpretation of The Road is that it advocates (...) a religious account of the meaning of life that is able to accommodate all other possible interpretations of how the question of meaning figures in the novel. (shrink)

-/- The closing paragraph of Cormac McCarthy’s The Road hums with mystery. Some find it suggestive of renewal, though only vaguely. Others contend that it does little to ameliorate the novel’s pessimism. Still others find it offers both lamentation and hopefulness, while some pass it over in silence. As an admirer with a taste for puzzle solving, here I offer a new interpretation revealing a surprisingly optimistic denouement.

The paper offers a philosophically infused analysis of Cormac McCarthy’s The Road. The main idea is that McCarthy’s novel is primarily a statement on the meaning of life. Once this idea is argued for and endorsed, by using a parallel between The Road and a 19th century Hungarian dramatic poem, The Tragedy of Man, the paper goes on to argue that the most plausible – although admittedly not the only possible – interpretation of The Road is that it advocates (...) a religious account of the meaning of life that uses what I call a practical conception of God (that borrows some of its aspects from Kant’s philosophy of religion). (shrink)

MacKay and Robinson (2016) claim that some legal procedures that regulate organ donations (VAC, opt-in, opt-out) bypass people's rational capacities and thus are “potentially morally worse than MAC”, which only employs a very mild form of coercion. We provide a critique of their argumentation and defend the opposite thesis: MAC is potentially morally worse than the three other options.

On September 7, 2008 the executive administration of American President George W. Bush announced that his government would take over the giant mortgage finance companies Fannie Mae and Freddie Mac, costing the citizens $200 billion. One week later, the 160 year-old American investment bank Lehman Brothers filed for the largest bankruptcy in U.S. history. What would soon be known worldwide as “the financial crisis” had begun. In response to that crisis, less than a month later, on October 3, 2008, the (...) United States Congress passed the Emergency Economic Stabilization Act of 2008, which established the Troubled Asset Relief Program and authorized the use of $700 billion in taxpayer funds to bail out the banking and finance industry. As a result, the U.S. Treasury reports that the total bailout gave Bank of America $45 billion, Citigroup Bank $45 billion, AIG Bank $40 billion, J. P Morgan $25 billion, Wells Fargo $25 billion, General Motors $10.4 billion, Goldman Sachs $10 billion, Morgan Stanley $10 billion, GMAC $5 billion, and Chrysler corporation, a mere $4 billion. This response to a perceived crisis was not limited to the United States. On October 12, 2008—in one day alone—the United Kingdom coughed up the equivalent of €679 billion in bank relief. And in Germany, Der Spiegel reported on December 23, 2008 “The German government whipped its €480 billion bank bailout package through parliament in record time.". (shrink)

This is my (Subhasis Chattopadhyay's) draft of PhD pre-submission. Dr. Scriver has (had) put it up online in her blog and I found it today, that is 1:06 pm, 28th May, 2017. I am grateful to her since intellectual ideas can otherwise be hijacked. She has done a wonderful editorial job.

In this paper, I explore a noted empirical link between regret and insomnia. Drawing on Brian O’Shaughnessy analysis of wakeful consciousness, I sketch three candidate ways of excavating a conceptual connection. Regret involves a certain kind of temporal orientation that, for O’Shaughnessy, only the state of wakefulness makes possible. Regret involves mental activity – it is productive of and precipitates patterns of counterfactual thought and imagining. Further, picking up a cue from Bernard Williams’ celebrated conception of agent-regret in ‘Moral Luck’, (...) sometimes the mental activity that occurs in regret is affective and expressive – as I argue, it is expressive of the wish that things were otherwise were satisfied now. Finally, I take up the puzzle as to why occurrent regret is not merely incompatible with sleep – insofar as it necessitates wakefulness - but may preclude falling asleep and I offer a tentative solution, focusing on the self-conscious character of occurrent regret. (shrink)

I introduce a perceptual phenomenon so far overlooked in the philosophical literature: ‘visual evanescence’. ‘Evanescent’ objects are those that due to their structured visible appearances have a tendency to vanish or evanesce from sight at certain places and for certain ‘biologically apt’ perceivers. Paradigmatically evanescent objects are those associated with certain forms of animal camouflage. I show that reflection on visual evanescence helps create conceptual room for a treatment of looks statements not explicit in the contemporary literature, one which takes (...) its cue from the relatively neglected philosophy of perception of G.E.M. Anscombe. In sensory contexts in which evanescence is known to have occurred, the use of ‘looks invisible’ seems to be intelligible, a fact that sensationalist and objectivist treatments of the nature of looks, as I call them, cannot obviously accommodate - at least, as I explain, when coupled with what Cora Diamond (1981) describes as a ‘natural’ understanding of nonsense. Focusing on a number of imaginative scenarios in which the statement ‘looks invisible’ is permissible, I oppose these ontological treatments with the grammatical approach I draw from Anscombe and in doing so I uncover a degree of unity across the cases I consider: something can intelligibly be said to ‘look invisible’ if one can see or appears to see the entirety of the region that the ‘invisible’ thing occupies. The region ‘looks empty’ but is full. (shrink)

In 2016 Beziau, introduce a more restricted concept of paraconsistency, namely the genuine paraconsistency. He calls genuine paraconsistent logic those logic rejecting φ, ¬φ |- ψ and |- ¬(φ ∧ ¬φ). In that paper the author analyzes, among the three-valued logics, which of these logics satisfy this property. If we consider multiple-conclusion consequence relations, the dual properties of those above mentioned are: |- φ, ¬φ, and ¬(ψ ∨ ¬ψ) |- . We call genuine paracomplete logics those rejecting the mentioned properties. (...) We present here an analysis of the three-valued genuine paracomplete logics. (shrink)

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

Abstract Heart Attack is the Cardiovascular Disease (CVD) which causes the most deaths among CVDs. We collected a dataset from Kaggle website. In this paper, we propose an ANN model for the predicting whether a patient has a heart attack or not that. The dataset set consists of 9 features with 1000 samples. We split the dataset into training, validation, and testing. After training and validating the proposed model, we tested it with testing dataset. The proposed model reached an accuracy (...) of 98.01% on the Heart Disease Dataset. (shrink)

heart attack analysis & prediction dataset is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. (...) A neural network model with four layers (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 88% and an average error of 0078296. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

Abstract: Classifying sea animals is an important problem in marine biology and ecology as it enables the accurate identification and monitoring of species populations, which is crucial for understanding and protecting marine ecosystems. This paper addresses the problem of classifying 19 different sea animals using convolutional neural networks (CNNs). The proposed solution is to use a pretrained MobileNetV2 model, which is a lightweight and efficient CNN architecture, and fine-tune it on a dataset of sea animals. The results of the study (...) show that the fine-tuned MobileNetV2 model is able to achieve an accuracy of 85% on the classification task, which is competitive with the state-of-the-art methods. Additionally, the study also found that data augmentation and regularization techniques such as dropout were important for preventing overfitting and improving the performance of the model. This study demonstrates the potential of using pretrained models and efficient CNN architectures for classifying sea animals, and it provides insights into the challenges and best practices for solving this important problem in marine biology and ecology. (shrink)

Abstract: Ensuring the proactive detection of transaction risks is paramount for financial institutions, particularly in the context of managing credit scores. In this study, we compare different machine learning algorithms to effectively and efficiently. The algorithms used in this study were: MLogisticRegressionCV, ExtraTreeClassifier,LGBMClassifier,AdaBoostClassifier, GradientBoostingClassifier,Perceptron,RandomForestClassifier,KNeighborsClassifier,BaggingClassifier, DecisionTreeClassifier, CalibratedClassifierCV, LabelPropagation, Deep Learning. The dataset was collected from Kaggle depository. It consists of 164 rows and 8 columns. The best classifier with unbalanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%,Recall100.0% and the F1-score (...) 100.0%. However, the best classifier with balanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%, Recall 100.0% and the F1-score 100.0%. (shrink)

Desire satisfaction theorists and attitudinal-happiness theorists of well-being are committed to correcting the psychological attitudes upon which their theories are built. However, it is not often recognized that some of the attitudes in need of correction are evaluative attitudes. Moreover, it is hard to know how to correct for poor evaluative attitudes in ways that respect the traditional commitment to the authority of the individual subject's evaluative perspective. L. W. Sumner has proposed an autonomy-as-authenticity requirement to perform this task, but (...) this article argues that it cannot do the job. Sumner's proposal focuses on the social origins of our values and overlooks the deep psychological roots of other evaluative attitudes that are just as problematic for welfare. If subjective theories of welfare are to be at all plausible they may need to abandon or modify their traditional commitment to the authority of the individual subject. (shrink)

Abstract: A corn crop typically refers to a large-scale cultivation of corn (also known as maize) for commercial purposes such as food production, animal feed, and industrial uses. Corn is one of the most widely grown crops in the world, and it is a major staple food for many cultures. Corn crops are grown in various regions of the world with different climates, soil types, and farming practices. In the United States, for example, the Midwest is known as the "Corn (...) Belt" due to its extensive corn production. Corn crops can be grown using a variety of methods, including conventional tillage, no-till farming, and the use of genetically modified crops. Due to its importance, there is a need for discovering the corn diseases and treating them. The aim of this study is to propose a deep learning model for the classification of corn leaf diseases based on Convolutional Neural Network. A model for classifying maize diseases was developed using a dataset for classifying Corn diseases that contains 4 classes of disease. The dataset was collected from Keggel website. The proposed model was trained, validated, and tested. The F1-score (99.83%), Recall (99.83%), precision (99.83%), Accuracy (99.83%). (shrink)

Abstract: This study introduces a machine learning-based model for predicting student performance using a comprehensive dataset derived from educational sources, encompassing 15 key features and comprising 62,631 student samples. Our five-layer neural network demonstrated remarkable performance, achieving an accuracy of 89.14% and an average error of 0.000715, underscoring its effectiveness in predicting student outcomes. Crucially, this research identifies pivotal determinants of student success, including factors such as socio-economic background, prior academic history, study habits, and attendance patterns, shedding light on the (...) nuanced dynamics of student performance. The key influential features identified in this study offer valuable insights into the complex factors shaping student achievement. These insights are vital for educators, policymakers, and institutions seeking to enhance educational outcomes and promote equitable access to quality education. This research provides a data-driven foundation for proactive interventions, personalized learning strategies, and support systems, ultimately contributing to improved student performance and academic success. The high accuracy of the predictive model and the feature analysis it provides empower decision-makers in the education sector. This model holds significant potential for applications in student performance monitoring, early intervention, and tailored educational strategies. By adopting a data-driven approach, this work advances the field of educational analytics and contributes to the goal of fostering student success and educational equity. (shrink)

The purpose of the article is to reveal the scientific approach that substantiates the impact of the creation of strategic alliances (SA) on the digital transformation of business and the development of their innovative power based on identified success factors. The aim was achieved using the following methods: abstract logic and typification (for classification of SA's success factors), generalization (to determine the peculiarities of SA's influence on their innovation development), analytical and ranking method (to determine the relationship between the dynamics (...) creating of SA and the degree of acceleration of digital transformation), expert evaluation (to determine the degree of influence of SA success factors by business areas). Implementing a business-integrated approach to understanding digital transformation will accelerate implementation of innovation and organizational change, identifies future prospects for market, customer and business relationships, highlighting the importance of researching the factors that ensure their success. An alternative vision of SA’s success factors is suggested, which are determined by the possibilities the organization of partnerships and organizational culture, integration of business sectors, compatibility of management goals, external relevance, obtaining synergies. Innovation is considered by the authors as an integration factor that reconciles all groups of SA success factors, giving them the necessary focus to solve business problems. The results of the study show that the creation of SA has a significant impact on accelerating and changes priority of digital transformation of business areas involved in strategic partnerships, and the impact of SA on the development of their innovation power is crucial. (shrink)

Life expectancy prediction, a pivotal facet of public health and policy formulation, has witnessed remarkable advancements owing to the integration of neural network models and comprehensive datasets. In this research, we present an innovative approach to forecasting life expectancy in diverse countries. Leveraging a neural network architecture, our model was trained on a dataset comprising 22 distinct features, acquired from Kaggle, and encompassing key health indicators, socioeconomic metrics, and cultural attributes. The model demonstrated exceptional predictive accuracy, attaining an impressive 99.27% (...) and an average error of 0.0034, underscoring the potency of deep learning in tackling complex, real-world challenges. Furthermore, our study delved into feature importance analysis, identifying critical determinants such as HIV/AIDS prevalence, income composition of resources, and socioeconomic status that significantly influence life expectancy. These findings provide actionable insights for healthcare policymakers and practitioners, emphasizing the importance of addressing health disparities, promoting economic development, and implementing targeted interventions. This research bridges the gap between data-driven methodologies and global health, offering a robust predictive tool and enriching our understanding of the multifaceted dynamics that shape life expectancy across nations. (shrink)

Vigorous debate over the moral propriety of cognitive enhancement exists, but the views of the public have been largely absent from the discussion. To address this gap in our knowledge, four experiments were carried out with contrastive vignettes in order to obtain quantitative data on public attitudes towards cognitive enhancement. The data collected suggest that the public is sensitive to and capable of understanding the four cardinal concerns identified by neuroethicists, and tend to cautiously accept cognitive enhancement even as they (...) recognize its potential perils. The public is biopolitically moderate, endorses both meritocratic principles and the intrinsic value of hard work, and appears to be sensitive to the salient moral issues raised in the debate. Taken together, these data suggest that public attitudes toward enhancement are sufficiently sophisticated to merit inclusion in policy deliberations, especially if we seek to align public sentiment and policy. (shrink)

To improve oneself and grow professionally, career development has been found to be crucial, as it serves as a roadmap for the professional growth of employees. However, a barrier, known as the glass ceiling, hinders the progress of employees, especially those in the LGBTQIA+ community. This study explores the impact of the glass ceiling on the career development of selected LGBTQIA+ individuals, shedding light on the barriers faced by this community in the workplace. The researchers used a qualitative multiple-case study (...) and selected ten (10) LGBTQIA+ participants from large enterprises via the judgmental sampling technique. Through thematic analysis, psychological impact, workplace discomfort, lack of recognition, and career progress challenges emerged as the impacts of the glass ceiling on the participants. Strategies for overcoming glass ceiling barriers, such as resilience, self-reflection, and community support, are identified. Thus, the study found that glass ceilings are really evident in the context of businesses. With this, the proposed advocacy plan in the study suggests interventions like workplace inclusion training, mentorship networks, and seminars on employee rights. Furthermore, it emphasizes the call for ongoing research and advocacy efforts to dismantle barriers and cultivate workplaces that not only embrace diversity but also provide equitable opportunities for career growth. (shrink)

Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict effect of oxygen consumption of thylakoid membranes (chloroplasts) from spinach after inhibition. A number of factors were identified that may affect of oxygen consumption of thylakoid membranes from spinach. Factors such as curve, herbicide, dose, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some inhibition of photosynthesis in farms. (...) The evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the effect of oxygen consumption of thylakoid membranes (chloroplasts) from spinach after inhibition with 100% accuracy. (shrink)

Abstract: Smoke detectors are critical devices for early fire detection and life-saving interventions. This research paper explores the application of Artificial Neural Networks (ANNs) in smoke detection systems. The study aims to develop a robust and accurate smoke detection model using ANNs. Surprisingly, the results indicate a 100% accuracy rate, suggesting promising potential for ANNs in enhancing smoke detection technology. However, this paper acknowledges the need for a comprehensive evaluation beyond accuracy. It discusses potential challenges, such as overfitting, dataset size, (...) and class imbalance, and presents strategies employed to address these issues. Additionally, the paper emphasizes the importance of alternative evaluation metrics, including precision, recall, and F1-score, to assess the model's performance comprehensively. Detailed information regarding the model architecture, data preprocessing, and hyperparameter tuning is provided to enhance the reproducibility of the research. The study also discusses ethical considerations and the implications of false positives and false negatives in smoke detection applications. While achieving 100% accuracy is a notable accomplishment, this paper calls for cautious interpretation and further validation in real-world settings. It concludes by highlighting areas for future research and improvement in smoke detection technology, emphasizing the need for practical deployment and the reduction of false alarms. (shrink)

This study investigates the dynamics of brick-and-mortar versus click-and-mortar microbusinesses, focusing on the strengths, weaknesses, opportunities, and threats (SWOT) within the local context of Balayan. Ten purposively sampled microbusiness entrepreneurs were examined using the SWOT method. The findings underscore the significance of product assessment, communication, and customer experiences in cultivating trust. Sensory experiences and competitive pricing emerge as strengths, while challenges such as poor sales necessitate strategic interventions. External factors, particularly technological advancements, exert influence on the retail landscape. Key strategies (...) include competitive pricing, flexibility, and an understanding of customer behavior. The study highlights growth potential in tailored experiences but acknowledges the risks posed by e-commerce competition. Additionally, a SWOT strategic plan is developed to leverage internal and external strengths and opportunities while mitigating weaknesses and threats. (shrink)

Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in dvance regarding: on-going construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. In (...) our proposed system we propose a new forecasting method that uses a Convolutional Neural Network monthly rainfall for a selected location. In our proposed system we are going to forecast the rainfall result based on the mean square error, mean absolute error and root mean square error, which we get in train and test of the dataset based deep learning technique. (shrink)

Abstract: Vegetables are an essential component of a healthy diet and play a critical role in promoting overall health and well- being. Vegetables are rich in important vitamins and minerals, including vitamin C, folate, potassium, and iron. They also provide fiber, which helps maintain digestive health and prevent chronic diseases. We are proposing a deep learning model for the classification of vegetables. A dataset was collected from Kaggle depository for Vegetable with 15000 images for 15 different classes. The data was (...) preprocessed, normalized and split into three sets (train, valid, test). The proposed model was trained and validated using the train and valid sets and accuracy of both training and validation was very high. For the evaluation of the proposed model we utilized these metrics: accuracy, F1-score, precision, Recall and time required for testing. We then tested the proposed model using the test set. The result of the testing was accuracy (99.95%), F1-score (99.95%), precision (99.95%), Recall (99.95%) and time required for testing the test set was 1.38 seconds. (shrink)

That human beings have the potential for rationality and the ability to cultivate it is a fact of human nature. But to value rationality and its subsidiary character dispositions - impartiality, intellectual discrimination, foresight, deliberation, prudence, self-reflection, self-control - is another matter entirely. -/- I am going to take it as a given that if a person's freedom to act on her impulses and gratify her desires is constrained by the existence of others' equal, or more powerful, conflicting impulses and (...) desires, then she will need the character dispositions of rationality to survive. The more circumscribed one's freedom and power, the more essential to survival and flourishing the character dispositions of rationality and the spirit may become. (shrink)

Due to the COVID-19 Pandemic, several changes have been forcibly made and observed in various fields and areas of society, one of which include the field of education; the foundation of the formation of intellect and knowledge. After two years of studying indoors and private educational institutions holding virtual classes, the time has finally come for students to be re- adjusted once more to the blended mode of learning; a combination of virtual and in-person classes. Thus, this study aimed to (...) investigate the development of students’ study habits and how these habits could potentially affect the students’ academic achievement or performance. The study utilized a descriptivecorrelational design to examine the impact of study habits to the academic performance of 137 senior high school students. The study made use of the Palsane and Sharma’s Study Habits Inventory, or also known as PSSHI, and using this instrument, the study has been able to procure sufficient results that will answer the present study’s problems. Using linear regression analysis, the present study found that there was a significance between the study habits and academic performance of the senior high school students, thus the study’s null hypothesis was eventually rejected. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, identifying the (...) presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

Abstract: The Spotify Status Dataset is a valuable resource that provides real-time insights into the operational status and performance of Spotify, a popular music streaming platform. This dataset contains a wide array of information related to server uptime, user activity, service disruptions, and more, serving as a critical tool for both Spotify's internal monitoring and the broader data analysis community. As digital services like Spotify continue to play a central role in music consumption, understanding the platform's status becomes crucial for (...) ensuring a seamless user experience. This dataset offers numerous research and analysis opportunities, whether you're interested in exploring service availability trends or investigating user behavior patterns during service disruptions. This study highlights the significance of the Spotify Status Dataset, its potential applications, and the valuable insights it can offer to various stakeholders, from Spotify engineers working to enhance service reliability to researchers studying the effects of downtime on user engagement. It invites readers on a journey through this dataset to uncover the secrets of Spotify's operational status and its impact on music enthusiasts worldwide. (shrink)

Malaria is a life-threatening disease spread to humans by some types of mosquitoes. It is mostly found in tropical countries. It is preventable and curable. The infection is caused by a parasite and does not spread from person to person. Symptoms can be mild or life-threatening. Mild symptoms are fever, chills and headache. Severe symptoms include fatigue, confusion, seizures, and difficulty breathing. Infants, children under 5 years, pregnant women, travelers and people with HIV or AIDS are at higher risk of (...) severe infection. Discovering this disease is a good step for proper and correct treatment. Determining the treatment with high accuracy depends on the method used in the diagnosis. Correctly, expert systems can greatly help to avoid spreading this disease. The expert system diagnoses malaria disease correctly to facilitate doctors to find the correct treatment based on the appropriate diagnosis. Objectives: An expert system has been established based on CLIPS to diagnose malaria disease. (shrink)

Job motivation remains an area of concern among researchers due to the rising issues of poor or lack of motivation among workers. This refers to one’s personal will or drives to perform a task at work. Meanwhile, job satisfaction refers to an employee’s sense of fulfillment with his or her work experience. Therefore, the current study utilized the descriptive- correlational research design to investigate the impact of job motivation on the job satisfaction of accountants. To gather essential data and achieve (...) the objectives of the study, Multidimensional Work Motivational Scale (MWMS) and Minnesota Satisfaction Questionnaire (MSQ) were administered to the target respondents. The collected data were then analyzed using a Multiple Regression Analysis; findings revealed that job motivation has a significant impact on the job satisfaction of accountants. Additionally, the results of this study were carefully evaluated and discussed, and recommendations were made to benefit the stakeholders of the study and contribute to the existing body of knowledge. (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)

This study aims to investigate the relationship between job burnout and employee performance in the feed industry during the ASF outbreak. Further, the researchers employed a descriptive-correlational research design in order to analyze the acquired data and produce pertinent findings. Thus, the researchers gathered data from one hundred two (102) feed industry employees. The Maslach Burnout Inventory (MBI) and Individual Work Performance Questionnaire (IWPQ) were employed to ascertain the extent of job burnout experienced by the respondents and evaluate employee performance, (...) respectively. Based on the statistical analysis, there is a significant relationship between job burnout and employee performance. Moreover, the study's findings were thoroughly analyzed and discussed. (shrink)

Abstract Alzheimer's disease has spread insanely throughout the world. Early detection and intervention are essential to improve the chances of a positive outcome. This study presents a new method to predict a person's likelihood of developing Alzheimer's using a neural network model. The dataset includes 373 samples with 10 features, such as Group,M/F,Age,EDUC, SES,MMSE,CDR ,eTIV,nWBV,Oldpeak,ASF.. A four-layer neural network model (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 98.10% and an average error (...) of 0.01. This study provides a valuable tool for early detection and intervention of Alzheimer's, thus contributing to the field of health and medicine. (shrink)

This study sought to investigate the Human Resource and Management (HRM) Program of a preschool hereinafter referred to as the Early Years Learning Center (EYLC) in Mandaluyong City for purposes of this research study. This is a qualitative case study that delved particularly into the issue of employee retention, especially of seasoned teachers and staff of the subject learning center. It used the interview method to generate an in-depth analysis as it revisited its HRM Program. To triangulate the data gathered, (...) documents from websites and social media as well as artifacts were also utilized. There is a dearth of research on the Human Resource and Management Program in preschool education. Most studies are from India and China. In terms of the HRM Program in education, SWOT analysis was frequently used to help improve the program. There were negligible sources that used the balanced scorecard at the preschool level. Hence, a combination of the SWOT analysis and the Balanced Scorecard method were utilized - the SWOT Analysis to determine the problems the learning center faces vis-à-vis its HRM Program, and the balanced scorecard in the search for a solution or strategy to help solve the problem. Based on the study, these combined tools proved to be effective in revisiting the HRM Program of schools. After interviewing the Directress of EYLC, findings disclosed that to be able to improve the retention of employees in the learning center, the focus should be given to the hiring policies and procedures keeping in mind the job security of its targeted employees. This brings forward concerns over advertising schemes utilizing different venues and platforms as well as issues involving the provision of seasoned employees with a retirement package, comprehensive Health Management Organization (HMO), and a continuing professional development program. (shrink)