In 1993, biologist Margie Profet captured the attention of the popular press with the publication of her radical thesis: menstruation has a function. Traditional theories, she claims, typically view menstruation as a functionless by-product of cyclic flux. The details of Profet's functional account are similarly radical: she argues that menstruation has been naturally selected to defend the female reproductive tract from sperm-borne pathogens. There are a number of weaknesses in Profet's evolutionary analysis. However, I focus on a set of pragmatic (...) problems that arise prior to any details of her evolutionary account. In arguing for the importance of pragmatic considerations, I draw from the linguistic analyses of Nelson Goodman. I conclude that critical investigation of the evolutionary details of Profet's pathogen defense account will be more feasible if and when biologists more frequently feature the female system of pathogen defense in their inductive generalisations. The system needs to be better entrenched before its functional components, such as menstruation, can be thoroughly investigated. (shrink)

In assessing the appropriateness of a scientific community's research effort, Solomon considers a number of "decision vectors," divided into the empirical and non-empirical. Value judgments get sorted as non-empirical vectors. By way of contrast, I introduce Anderson's discussion of the evidential role of value judgments. Like Anderson, I argue that value judgments are empirical in the relevant sense. I argue further that Solomon's decision matrix needs to be reconceptualized: the distinction should not be between the empirical vs. non-empirical, but between (...) the relevant vs. irrelevant. Whether particular value judgments are relevant or not is an empirical question, to be decided on a case-by-case basis. (shrink)

: The relationship between facts and values—in particular, naturalism and normativity—poses an ongoing challenge for feminist science studies. Some have argued that the fact/value holism of W.V. Quine's naturalized epistemology holds promise. I argue that Quinean epistemology, while appropriately naturalized, might weaken the normative force of feminist claims. I then show that Quinean epistemic themes are unnecessary for feminist science studies. The empirical nature of our work provides us with all the naturalized normativity we need.

I have argued that political values are beliefs informed, more or less well, by the evidence of experience and that, where relevant and well-supported by evidence, the inclusion of political values in scientific theorizing can increase the objectivity of research. The position I endorse has been called the “values-as-evidence” approach. In this essay I respond to three kinds of resistance to this approach, using examples of feminist political values. Solomon questions whether values are beliefs that can be tested, Alcoff argues (...) that even if our values are beliefs that can be tested, testing them might not be desirable because doing so assigns these important values a contingency that weakens their normative force, and Yap argues that the approach is too idealistic in its articulation of the role of evidence in our political deliberations. In response, I discuss the ways that values can be tested, I analyze the evidential strength of feminist values in science, and I argue that the evidence-based nature of these values is neither a weakness nor an idealization. Problems with political values affecting science properly concern the dogmatic ways that evaluative beliefs are sometimes held—a problem that arises with dogmatism toward descriptive beliefs as well. I conclude that scientists, as with the rest of us, ought to adopt a pragmatically-inclined appreciation of the fallible, inductive process by which we gather evidence in support of any of our beliefs, whether they are described as evaluative or descriptive. (shrink)

In lieu of an abstract here is the first paragraph: -/- I mean the subtitle of my essay both as praise for the clarity with which Elizabeth Anderson writes about what is at stake in debates about values in science, and as a promise to outline an even more direct route to the heart of the matter. I begin with a quick review of the steps in Anderson’s argument that seem necessary and, indeed, laudable, followed by a brief discussion of (...) those steps in her argument that we might fruitfully skip. The hope is that we will arrive at roughly the same location but with a firmer foothold. (shrink)

Feminist theorists have shown that knowledge is embodied in ways that make a difference in science. Intemann properly endorses feminist standpoint theory over Longino’s empiricism, insofar as the former better addresses embodiment. I argue that a pragmatist analysis further improves standpoint theory: Pragmatism avoids the radical subjectivity that otherwise leaves us unable to account for our ability to share scientific knowledge across bodies of different kinds; and it allows us to argue for the inclusion, not just of the knowledge produced (...) from marginalised bodies, but of the marginalised themselves. (shrink)

While feminist epistemologists have made important contributions to the deconstruction of the traditional representationalist model, some elements of the Cartesian legacy remain. For example, relativism continues to play a role in the underdetermination thesis used by Longino and Keller. Both argue that because scientific theories are underdetermined by evidence, theory choice must be relative to interpretive frameworks. Utilizing Davidson's philosophy of language, I offer a nonrepresentationalist alternative to suggest how relativism can be more fully avoided.

This chapter’s main topic revolves around Davidson’s account of radical interpretation and the concept of triangulation as a necessary feature of communication and the formation of beliefs. There are two important implications of this model of belief formation for feminists studying the effects of social location on knowledge production generally, and the production of scientific knowledge in particular. The first is Davidson’s argument that whatever there is to the meaning of any of our beliefs must be available from the radical (...) interpreter’s external, third-person perspective. The second important implication of triangulation is that Davidson’s model is a holistic one that shows that there is no substantive difference in the triangulation process by which we form beliefs concerning basic descriptive features of the world and beliefs concerning evaluative features of the world. (shrink)

The works of the later Wittgenstein resonate with aspects of the pragmatist tradition in American philosophy. Davidson’s work is similarly informed. We argue that because of their association with the pragmatist tradition, their work can be put to use by philosophers interested in social justice issues, including, for example, feminism, and critical race theory. Philosophers concerned with social justice continue to struggle between the extremes of an untenable foundationalism and a radical relativism. Given their holistic understanding of knowledge, meaning and (...) communication, the work of Wittgenstein and Davidson is particularly suited to dissolving the foundationalist/relativist dichotomy. We explore how this and other features of their work facilitates philosophy for social change. (shrink)

In Clough’s reply paper to me (http://wp.me/p1Bfg0-1aN), she laments how feminist calls for diversity within scientific communities are inadvertently sidelined by our shared feminist empiricist prescriptions. She offers a novel justification for diversity within epistemic communities and challenges me to accept this addendum to my prior prescriptions for biomedical research communities (Goldenberg 2013) on the grounds that they are consistent with the epistemic commitments that I already endorse. In this response, I evaluate and accept her challenge.

While most of healthcare research and practice fully endorses evidence-based healthcare, a minority view borrows popular themes from philosophy of science like underdetermination and value-ladenness to question the legitimacy of the evidence-based movement’s philosophical underpinnings. While the feminist origins go unacknowledged, those critics adopt a feminist reading of the “gap argument” to challenge the perceived objectivism of evidence-based practice. From there, the critics seem to despair over the “subjective elements” that values introduce to clinical reasoning, demonstrating that they do not (...) subscribe to feminist science studies’ normative program——where contextual values can enable good science and justified decisions. In this paper, I investigate why it is that the critics of evidence-based medicine adopt feminist science’s characterization of the problem but resist the productive solutions offered by those same theorists. I suggest that the common feminist empiricist appeal to idealized epistemic communities is impractical for those working within the current biomedical context and instead offer an alternate stream of feminist research into the empirical content of values (found in the work of Elizabeth Anderson and Sharyn Clough) as a more helpful recourse for facilitating the important task of legitimate and justified clinical decision-making. I use a case study on clinical decision-making to illustrate the fruitfulness of the latter feminist empiricist framework. -/- See response by Sharyn Clough: http://wp.me/p1Bfg0-1aN See reply by Maya Goldenberg: http://wp.me/p1Bfg0-1oY. (shrink)

Racist beliefs express value judgments. According to an influential view, value judgments are subjective, and not amenable to rational adjudication. In contrast, we argue that the value judgments expressed in, for example, racist beliefs, are false and objectively so. Our account combines a naturalized, philosophical account of meaning inspired by Donald Davidson, with a prominent social-psychological theory of values pioneered by the social-psychologist Milton Rokeach. We use this interdisciplinary approach to show that, just as with beliefs expressing descriptive judgments, beliefs (...) expressing value judgments have empirical content, or can be inferentially linked to beliefs that do; the truth or falsity of that content can be objectively assigned; and that assignment is amenable to rational assessment. While versions of this objective view of value judgments have been defended by moral realists of various metaphysical stripes, our argument has the virtue of appealing, instead, to accounts that are as naturalistically informed as possible. And, unlike the influential subjective view of value judgments, and racist beliefs more particularly, our arguments are better able to account for instances where rational, persuasive strategies have been effective in reducing the ubiquity of racism in American culture. (shrink)

Fact/value holism has become commonplace in philosophy of science, especially in feminist literature. However, that facts are bearers of empirical content, while values are not, remains a firmly-held distinction. I support a more thorough-going holism: both facts and values can function as empirical claims, related in a seamless, semantic web. I address a counterexample from Kourany where facts and values seem importantly discontinuous, namely, the simultaneous support by the Nazis of scientifically sound cancer research and morally unsound political policies. I (...) conclude that even by the criteria available at the time, Nazi cancer research was empirically weak, and the weaknesses in their research are continuous with their moral failures in just the ways predicted by the holism I support. (shrink)

The hygiene hypothesis offers an explanation for the correlation, well-established in the industrialized nations of North and West, between increased hygiene and sanitation, and increased rates of asthma and allergies. Recent studies have added to the scope of the hypothesis, showing a link between decreased exposure to certain bacteria and parasitic worms, and increased rates of depression and intestinal auto- immune disorders, respectively. What remains less often discussed in the research on these links is that women have higher rates than (...) men of asthma and allergies, as well as many auto-immune disorders, and also depression. The current paper introduces a feminist understanding of gender socialization to the epidemiological and immunological picture. That standards of cleanliness are generally higher for girls than boys, especially under the age of five when children are more likely to be under close adult supervision, is a robust phenomenon in industrialized nations, and some research points to a cross- cultural pattern. I conclude that, insofar as the hygiene hypothesis successfully identifies standards of hygiene and sanitation as mediators of immune health, then attention to the relevant patterns of gender socialization is important. The review also makes clear that adding a feminist analysis of gender socialization to the hygiene hypothesis helps explain variation in morbidity rates not addressed by other sources and responds to a number of outstanding puzzles in current research. Alternative explanations for the sex differences in the relevant morbidity rates are also discussed (e.g., the effects of estrogens). Finally, new sources of evidence for the hygiene hypothesis are suggested in the form of cross-cultural and other natural experiments. (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)

Heart palpitations, while often benign, can sometimes be indicative of severe underlying conditions requiring immediate intervention. Accurate and swift diagnosis thus remains a clinical priority. "A CLIPS-Based Expert System for Heart Palpitations Diagnosis" represents a novel approach to addressing this challenge, harnessing the power of artificial intelligence and rule-based expert systems. Specifically, this system applies a suite of 7 if-then rules to evaluate potential heart palpitations causes and assign one of three outcomes: 1) A confirmed diagnosis of heart palpitations, 2) (...) A suspected link to cardiovascular diseases, and 3) A possible association with anxiety or stress disorders. The expert system offers an intuitive user interface, allowing for seamless symptom input and instant diagnosis based on user-provided information. This paper explores the various phases of this expert system's lifecycle, including design, implementation, and evaluation. Furthermore, the study situates the system within the broader discourse on rule-based expert systems for heart palpitations diagnosis, critically analyzing their efficiency, potential pitfalls, and ongoing challenges. Through this research, the value of integrating rule-based expert systems in clinical diagnostic processes is highlighted, illustrating its capacity to enhance diagnostic accuracy and patient outcomes. (shrink)

A long series of studies in social psychology have shown that the explanations people give for their own behaviors are fundamentally different from the explanations they give for the behaviors of others. Still, a great deal of uncertainty remains about precisely what sorts of differences one finds here. We offer a new approach to addressing the problem. Specifically, we distinguish between two levels of representation ─ the level of linguistic structure (which consists of the actual series of words used in (...) the explanation) and the level of conceptual structure (which consists of the concepts these words are used to express). We then formulate and test hypotheses both about self-other differences in conceptual structure and about self-other differences in the mapping from conceptual structure to linguistic structure. (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)

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)

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)

Abstract. Intelligent Tutoring System Builder (ITSB) is an authoring tool designed and developed to aid teachers in constructing intelligent tutoring systems in a multidisciplinary fields. The teacher is needed to create a set of pedagogical fundamentals, which, in line, are inured to automatically build up a broad tutor framework and construct an intelligent tutoring system. In this paper an explanation of the theory and the architecture of the tool is outlined. A presentation of several system components, the requirements of the (...) different components, integration of these components in ITSB tool are shown. Furthermore, implanting of requirements, cognitive principle, and common design fundamentals in the tool to ease the use of teachers. A variety of design matters, an example of building an intelligent tutoring system for teaching Java language using ITSB tool and an evaluation are presented. (shrink)

The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective data in (...) other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors’ rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs. (shrink)

This paper critically examines coincidence arguments and evolutionary debunking arguments against non-naturalist realism in metaethics. It advances a version of these arguments that goes roughly like this: Given a non-naturalist, realist metaethic, it would be cosmically coincidental if our first order normative beliefs were true. This coincidence undermines any prima facie justification enjoyed by those beliefs.

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)

As sustainable tourism gains increasing attention, understanding the factors that influence travelers' motivation to engage with sustainable travel agents becomes crucial. This study investigates the moderating effect of social media usage on the relationship between the perceived value of websites and motivational factors for sustainable travel agents. The study proposes that social media usage acts as a moderator in shaping the relationship between the perceived value of websites and motivational factors. This study has utilized smart tourism. Therefore, independent variable motivation (...) in terms of informativeness gratification and gratification acted as moderating variables. This study has utilized an online questionnaire adapted from a standardized questionnaire based on the literature review. Moreover, a total of 146 managers were surveyed in which they were required to respond to 55 items that elicited the 7 variables social media use, Propensity, perceived website value, motivating factors, entertainment gratification, informativeness gratification, sustainable smart tourism. The data were analyzed by SPSS and PLS-SEM. The findings indicated that relevance and importance of social media use and sustainable smart tourism. The result revealed that 7 hypotheses had a significant relationship. 3 hypotheses did not show a moderating effect. Overall, this study extends the understanding of sustainable smart tourism. These findings will benefit travel agents’ managers in Malaysia to better understand the role of social media use and utilize it towards developing sustainable sustainable smart tourism. (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)

The aim behind analyzing the Goodreads dataset is to get a fair idea about the relationships between the multiple attributes a book might have, such as: the aggregate rating of each book, the trend of the authors over the years and books with numerous languages. With over a hundred thousand ratings, there are books which just tend to become popular as each day seems to pass. We proposed an Artificial Neural Network (ANN) model for predicting the overall rating of books. (...) The prediction is based on these features (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which were used as input variables and (average_rating) as output variable for our ANN model. Our model were created, trained, and validated using data set in JNN environment, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.78% of the validation samples. (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)

In lieu of an abstract, here is a brief excerpt of the content:130 Feminist Studies 46, no. 1. © 2020 by Feminist Studies, Inc. Laura Bisaillon, Alana Cattapan, Annelieke Driessen, Esther van Duin, Shannon Spruit, Lorena Anton, and Nancy S. Jecker Doing Academia Differently: “I Needed Self-Help Less Than I Needed a Fair Society” A great deal of harm is being done by belief in the virtuousness of work. — Bertrand Russell, “In Praise of Idleness” We are committed to doing (...) academia in particular ways, and not in others. — Brocher Foundation Feminist Collective I had decided that becoming a university professor might be a good fit; friends and colleagues had suggested that I would be valuable to a university, so I prepared for a restructuring of my family life.1 I read Tokarczyk and Fay’s Working-Class Women in the Academy and Babcock and Laschever’s Women Don’t Ask to gather understandings of the milieu I was entering. I now see that my preparations had not shined sufficient light on the social and structural factors that organize the contemporary university.... As it turns out, the restructuring of our lives within the working conditions, priorities, and rationalities of this professional milieu triggered unanticipated and painful effects.... Had Mountz published her article “Women on the Edge” before I decided on a career migration into the university system and, had I been fortunate enough to discover it, I would have made other career choices.2 1. The authors, who describe themselves as the Brocher Foundation Feminist Collective, indicate their personal narratives in this text using italics. 2. Michelle M. Tokarczyk and Elizabeth A. Fay, eds., Working-Class Women in the Academy: Laborers in the Knowledge Factory (Amherst: University of Massachusetts Press, 1993); Linda Babcock and Sara Laschever, Women Don’t Ask: Negotiation and the Gender Divide (Princeton, NJ: Princeton University Bisaillon et al. 131 Introduction: Personal, Professional, and Political We are seven women who met at the Brocher Foundation in Hermance, Switzerland, in July 2017, where we had been selected to spend a few months as research fellows. This competitively awarded opportunity is open to persons fluent in English, from graduate students to senior scholars, with or without academic appointment, whose scholarship advances knowledge about the ethical, legal, and social implications of health and biotechnologies.3 When we found ourselves on the shores of Lake Leman with the gift of time, we began opening up to each other in unexpected, compelling ways. Whatever our vantage point in the hierarchy of the university system, we discovered that there was remarkable overlap in our experiences as women in academia. When we began writing, our authoring team was composed of two graduate students, four faculty members at the assistant professor rank, and one full professor, residing in Canada, the Netherlands, Romania, the United Kingdom, and the United States. Our plural subject position led us to underexplored and imaginative responses to broader social issues, which we share in this article as the “Brocher Foundation Feminist Collective.” We analyze our experiences from our standpoint as women health researchers laboring in the academy. Our similarities and differences were exciting entry points for discussion and analysis: we are all of eastern or western European descent. We have different countries of birth, mother tongues, ages, disciplinary training and career trajectories, relationships to chronic illness and disability, marital status, and experiences with parenthood. We bring the troubles and contradictions we experience as academics into conversation with each other. We seek to productively illustrate and emphasize that the tensions we see around us, and also feel within us, arise as a result of how the academic system is organized, produced, and sustained across time, place, and space. Press, 2003); Alison Mountz, “Women on the Edge: Workplace Stress at Universities in North America,” The Canadian Geographer 60, no. 2 (2016): 205–18. 3. We experienced this fellowship as a valuable opportunity. But we also recognized that our access to it was mediated by social, economic, or other obligations. As countries in the global north continue to tighten their national border regimes, marginalized academics, especially those from the global South, were less likely to succeed in getting a Swiss visa or securing relevant temporal and economic resources. 132 Bisaillon et al. The promise here, we posit... (shrink)

In this paper we argue that dissociative identity disorder (DID) is best interpreted as a causal model of a (possible) post-traumatic psychological process, as a mechanical model of an abnormal psychological condition. From this perspective we examine and criticize the evidential status of DID, and we demonstrate that there is really no good reason to believe that anyone has ever suffered from DID so understood. This is so because the proponents of DID violate basic methodological principles of good causal modeling. (...) When every ounce of your concentration is fixed upon blasting a winged pig out of the sky, you do not question its species' ontological status. James Morrow, City of Truth (1990). (shrink)

This study presents a neural network-based model for predicting smoke potential in a specific area using a Kaggle-derived dataset with 15 environmental features and 62,631 samples. Our five-layer neural network achieved an accuracy of 89.14% and an average error of 0.000715, demonstrating its effectiveness. Key influential features, including temperature, humidity, crude ethanol, pressure, NC1.0, NC2.5, SCNT, and PM2.5, were identified, providing insights into smoke occurrence. This research aids in proactive smoke mitigation and public health protection. The model's accuracy and feature (...) analysis empower decision-makers, with potential applications in real-time smoke event monitoring and preparedness strategies. This work contributes to the field of air quality forecasting and environmental stewardship, offering a data-driven approach to address smoke-related challenges and enhance community well-being. (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)

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)

Abstract: Rice, as a paramount staple crop worldwide, sustains billions of lives. Precise classification of rice types holds immense agricultural, nutritional, and economic significance. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing rice type classification accuracy and efficiency. This research explores rice type classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 18,188 entries and key rice grain attributes, we develop and evaluate a neural network model. Our neural network, featuring a (...) single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.000001. Beyond performance metrics, we delve into the intricacies of rice type classification through feature importance analysis. The most influential features—'id,' 'Area,' 'MajorAxisLength,' 'MinorAxisLength,' 'Eccentricity,' 'ConvexArea,' 'EquivDiameter,' 'Extent,' 'Perimeter,' 'Roundness,' and 'AspectRatio'—uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring food safety, and bolstering global trade. (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)

Abstract: This research delves into the utilization of Artificial Neural Networks (ANNs) as a powerful tool for predicting the overall ratings of books by leveraging a diverse set of attributes. To achieve this, we employ a comprehensive dataset sourced from Goodreads, enabling us to thoroughly examine the intricate connections between the different attributes of books and the ratings they receive from readers. In our investigation, we meticulously scrutinize how attributes such as genre, author, page count, publication year, and reader reviews (...) influence a book's overall rating. Through rigorous analysis and experimentation, we construct an advanced ANN model tailored for predictive analysis in the realm of book ratings. The outcomes of our study reveal the remarkable potential of ANNs in this domain. The ANN model exhibits an impressive level of accuracy when it comes to forecasting book ratings, underlining the efficacy and promise of artificial neural networks in enhancing our understanding and prediction of book evaluations. This research opens up new avenues for leveraging machine learning techniques to gain deeper insights into the dynamics of book ratings and reader preferences. (shrink)

The theoretical foundations of climate science have received little attention from philosophers thus far, despite a number of outstanding issues. We provide a brief, non-technical overview of several of these issues – related to theorizing about climates, climate change, internal variability and more – and attempt to make preliminary progress in addressing some of them. In doing so, we hope to open a new thread of discussion in the emerging area of philosophy of climate science, focused on theoretical foundations.

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)

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

By deploying the methodology of Judith Butler's notion of performativity, this article intends to understand the possibility of the concept of queerness beyond the possibilities of gender studies and queer theory and to develop a concept transcending the limits of identity. It is undeniable that Foucault's concept of disciplinarity is one of the major precursors of the notion of performativity, which is a more focused tool for what Foucault broadly devised. Both thinkers explain how the subject is a construction by (...) power. They explain how bodies are marked, assigned, and manipulated and expose the banality with which these operations take one for granted. It is through unearthing the disciplinary aspects of performativity in Butler and foreseeing the performative aspects of disciplinarity in Foucault that this article finds its methodological perspective. It is notable that disciplinarity and, as its extension, performativity ultimately exposes the underlying ontology of identity as a 'truth effect' rather than as an apriori truth. The paper critically analyses the artist's identity and the artistic discourse to unravel the queerness or an underlying plurality of aesthetic experience using performativity as a formative tool. (shrink)

Abstract: Tomatoes are among the most popular vegetables in the world due to their frequent use in many dishes, which fall into many varieties in common and traditional foods, and due to their rich ingredients such as vitamins and minerals, so they are frequently used on a daily basis, When we focus our attention on this vegetable, we must also focus and take into consideration the diseases that affect this vegetable, a deep learning model that classifies tomato diseases has been (...) proposed. The aim of this paper is to diagnose tomato diseases based on a dataset containing 11000 picture and 11 classes. The model gave the final accuracy test with Accuracy (99.87%), F1-score (99.87%), Recall (99.87%), and Precision (99.88%) in an estimated time 1.36 second, as with these results it proved its effectiveness and good ability in the classification of the testing data. (shrink)

Abstract: Forests are areas with a high density of trees, and they play a vital role in the health of the planet. They provide a habitat for a wide variety of plant and animal species, and they help to regulate the climate by absorbing carbon dioxide from the atmosphere. While in 2010, the world had 3.92Gha of forest cover, covering 30% of its land area, in 2019, there was a loss of forest cover of 24.2Mha according to the Global Forest (...) Watch institute. Discovery and classification depend on human experience and effort, so the error in the results of this process can lead to forest fires and disasters. Therefore, deep learning algorithms from artificial intelligence and machine learning sciences have been applied to help specialists avoid false or inaccurate diagnoses when detecting Forest fires in images using a pre-trained convolutional neural network called VGG16. The model was customized to fit the Forest fires classification and then applied to a dataset consisting of (14,000) of the Forests collected from the Kaggle depository. We trained, validated, and tested the modified VGG16 model. The proposed VGG16 model obtained Precision (99.96%), Recall (99.96%), and F1-Score (99.96%). (shrink)

Abstract: Accurate stock price prediction is essential for informed investment decisions and financial planning. In this research, we introduce an innovative approach to forecast stock prices using an Artificial Neural Network (ANN). Our dataset, consisting of 5582 samples and 6 features, including historical price data and technical indicators, was sourced from Yahoo Finance. The proposed ANN model, composed of four layers (1 input, 1 hidden, 1 output), underwent rigorous training and validation, yielding remarkable results with an accuracy of 99.84% and (...) an average error of 0.005. Additionally, we conducted a feature importance analysis, identifying the key drivers of stock price prediction, such as "High," "Low," "Open," "Volume"," and "Date." This study not only offers a highly accurate stock price prediction model but also contributes valuable insights into the influential factors affecting stock prices, enhancing the field of financial analysis and investment decision-making that are suitable for the task. (shrink)

ABSTRACTAccording to Nietzsche, both modern individuals and societies are pathologically fragmented. In this paper, I examine how he proposes we combat this affliction in his Untimely Meditations. I argue that he advocates a dual struggle involving both instrumental domination and eradication. On these grounds, I claim the following: 1. pace a growing number of commentators, we cannot categorise the species of conflict he endorses in the Untimely Meditations as agonistic; and 2. this conflict is better understood as analogous to the (...) species of struggle that Schopenhauer describes in his account of purposive organisation. (shrink)