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

Selgelid and Koplin’s article ‘Kidney Sales and the Burden of Proof’ (K&S 2019) presents a series of detailed and persuasive arguments, intended to demolish my own arguments against the prohibition of organ selling. And perhaps they might succeed, if the case described by the authors were anything like the one I actually make. However, notwithstanding the extensive quotations and the detailed explanations of the way I supposedly argue, this account of my position comprehensively mistakes both the conclusions I reach and (...) the arguments I give for them. -/- I know that there are around many misconceptions about my views on this subject, but I have always hoped they could not survive a reading of what I had actually written. I have just—after a gap of many years—looked again at the two most recent of the texts Koplin and Selgelid refer to, and it goes without saying that I can see various things I could now do better; but I do still find these misinterpretations hard to understand. And since anyone with nothing to go on but this article would reasonably conclude that the original texts were not worth reading, I am grateful to the editors for the opportunity to try to set the record straight. -/- I presume not many readers would be interested in a detailed comparative commentary on the texts, showing where this account gets my intentions wrong. I shall try instead to explain how what I do mean—and what I think I say—diverges from what is said here, and then go on to a brief outline of what my arguments and conclusions really are. I hope this may also give some sense of why, for all the opposition I have encountered since I was first drawn into this debate, I persist in thinking that the work I have been doing is important not only for this topic but for analysis in practical ethics more generally. (shrink)

When Mill published The Subjection of Women in 1869 he wanted to replace the domination of one sex by the other laws based on ‘a principle of perfect equality’. It is widely complained, however, that even advanced countries have still failed to achieve equality between the sexes. Power and wealth and influence are still overwhelmingly in the hands of men. But equalities of these kinds are not the ones required by the principle of equality that Mill had in mind; and, (...) furthermore, a principle that demanded them would actually be incompatible with Mill’s. The conclusion is not, however, that social policies dealing with men and women are all they should be. It is just that although the fundamental problems of feminism could be – and to a considerable extent still can be – expressed in terms of requirements for justice and equality, we have now reached a stage where concentrating on these ideas can distort the real problems, and may actually impede the kind of progress that is needed. (shrink)

This is a one page handout presenting some objections A.R. Radcliffe-Brown makes to Frazer on rites and Frazer's evolutionism.

Diane Vaughan’s popular book, The Challenger Launch Decision: Risky Technology, Culture and Deviance at NASA, advances a thesis that I termed the “cog in the machine manifesto”: since the Challenger disaster was the result of the determined, mechanistic movement of the parts of the organizational system; once the mechanism was set in motion, the disaster was inevitable, and could not have been prevented. In order to expose the fallacies of the cog in the machine manifesto, I consider an alternative (...) umbrella thesis and four sub-theses. The umbrella thesis proposes that the causes of the Challenger disaster were the lack of an overall 'safety first' ethos, and irresponsible choice and poor decision-making practices in the lead up to and including the final risky decision. The four sub-theses are as follows: (1) the widespread justification that one always faces inherent risk when one operates in the realm of risky technology (a thesis foreshadowed by Vaughan’s book) is an illicit and misleading justification when one considers that there is no binding necessity to employ the risky technology; (2) the real culprit is not so much "risky technology," which locates the risk in the technology, but "risky assessment," which locates the risk in the decision to employ technology while not basing that decision on known safe designs and continued use on performance data; (3) the decision to choose an Orbiter without a passenger and crew abort system is proof in and of itself of a lack of a "safety first" management priority; (4) an analysis of the fateful eve of the Challenger launch teleconference launch decision makes it impossible to construe it as a banal, routine and inevitable organizational mistake. A careful examination of the “cog in the machine manifesto” and similar frameworks that purport to explain moral disasters will reveal that the language of risky technology, organizational complexity, and inevitable accidents is deeply pernicious and should thus be avoided at all costs. (shrink)

Susanne Langer was a student at Radcliffe College between 1916 and 1926---a highly transitional period in the history of American philosophy. Intellectual generalists such as William James, John Dewey, and Josiah Royce had dominated philosophical debates at the turn of the century but the academic landscape gradually started to shift in the years after World War I. Many scholars of the new generation adopted a more piecemeal approach to philosophy---solving clearly delineated, technical puzzles using the so-called “method of logical (...) analysis”. Especially at Harvard, the intellectual climate rapidly changed. The department hired several philosophers who had contributed to the development of symbolic logic---H. M. Sheffer, C. I. Lewis, and A. N. Whitehead---and Harvard quickly began to be viewed as a central hub for analytic philosophy in the United States. This chapter contextualizes Langer’s earliest work by reading it through the lens of this shifting academic environment. Though Harvard did not allow women to take its courses until 1943, Langer is one of the most significant fruits of this period. Her dissertation “A Logical Analysis of Meaning” and her first publications are all illustrations of the approach that came to dictate the American philosophical conversation. By exploring the increased focus on the logical-analytic method and Langer’s attempts to expand the new approach to what she later called “non-discursive” symbolisms, I situate her publications in the intellectual context of the 1920s. (shrink)

HISTÓRIA DA SOCIOLOGIA: O DESENVOLVIMENTO DA SOCIOLOGIA I -/- A SOCIOLOGIA BRASILEIRA -/- HISTORY OF SOCIOLOGY: THE DEVELOPMENT OF SOCIOLOGY I -/- THE BRAZILIAN SOCIOLOGY -/- Emanuel Isaque Cordeiro da Silva – IFPE-BJ, CAP-UFPE e UFRPE. E-mails: [email protected] e [email protected] WhatsApp: (82)9.8143-8399. -/- PREMISSA -/- Como na França de Émile Durkheim, os primeiros passos da Sociologia no Brasil, em termos institucionais, ocorreram a partir de iniciativas para a inclusão dessa disciplina no ensino secundário (hoje, ensino médio). A primeira tentativa ocorreu (...) em 1890, logo após a proclamação da República, com a reforma educacional de Benjamin Constant, que defendia o ensino laico em todos os níveis. O ensino secundário tinha por objetivo a formação intelectual dos jovens fora do contexto religioso, então predominante. Mas, sem nunca ter sido de fato incluída nos currículos escolares, a Sociologia foi expurgada pela Reforma Epitácio Pessoa, em 1901. Somente em 1925 a Sociologia retornou ao currículo do ensino secundário por meio da Reforma de Rocha Vaz, que tinha os mesmos objetivos da de Benjamin Constant. Em decorrência dessa reforma, o Colégio Pedro II, na cidade do Rio de Janeiro, implantou a Sociologia regularmente no seu currículo. Em 1928, a disciplina foi introduzida nas escolas de vários estados brasileiros, notadamente em São Paulo, Rio de Janeiro e Pernambuco. -/- Desde 1925, podem-se destacar alguns intelectuais que contribuíram para o ensino de Sociologia no ensino secundário, lecionando e escrevendo manuais para esse nível: Fernando de Azevedo (1894-1974), Gilberto Freyre (1900-1987), Carneiro Leão (1887-1966) e Delgado de Carvalho (1884-1980), em São Paulo, Rio de Janeiro e Recife. Eles tinham por objetivo preparar intelectualmente os jovens das elites dirigentes, elevando o conhecimento daqueles que chegavam às escolas médias. Esses autores, em sua maioria, foram influenciados pela Sociologia desenvolvida na Europa e nos Estados Unidos. -/- Em 1931, a reforma de Francisco Campos, no governo de Getúlio Vargas, introduziu a Sociologia nos cursos preparatórios ao ensino superior nas faculdades de Direito, Ciências Médicas, Engenharia e Arquitetura, além de mantê-la nos cursos normais (de formação de professores). -/- No final do Estado Novo, em 1942, o governo de Getúlio Vargas, através de seu ministro da Educação, Gustavo Capanema, implantou a Reforma Capanema, que excluiu a Sociologia do ensino secundário brasileiro. Assim, a Sociologia fez parte do currículo do ensino secundário por 18 anos (1925-1942). Ela, contudo, permaneceu como matéria obrigatória do currículo das Escolas Normais, de nível secundário, destinadas à formação de professores do antigo ensino primário (o que corresponderia aos atuais 2º a 5º anos). -/- 1. A SOCIOLOGIA NO ENSINO SUPERIOR: INÍCIO DO SÉCULO XX -/- Nas primeiras décadas do século XX, a Sociologia no ensino superior estava presente de forma embrionária em vários estados brasileiros (Pernambuco, Rio de Janeiro, São Paulo) e principalmente nos cursos de Direito. Desenvolveu-se de forma mais consistente, contudo, nos estados que passavam por um processo de industrialização e urbanização crescente desde o final da década de 1910. -/- Essas transformações da estrutura econômica e social repercutiram nas esferas culturais e educacionais, propiciando um crescente interesse pelos estudos científicos da realidade social da época. -/- Em decorrência disso, foram publicados importantes trabalhos que contribuíram para o desenvolvimento das ciências sociais no Brasil. Uma das preocupações dos pensadores daquele período era a busca do entendimento do Brasil por meio de seus componentes históricos, tendo por base as muitas vertentes europeias e estadunidenses das ciências humanas. -/- Esses pensadores defendiam a ideia de que havia uma ligação entre o passado colonial e a configuração social em que viviam. Para eles, esse condicionamento do passado deveria ser superado para que o país saísse do atraso. As preocupações desses pensadores giravam em torno do futuro do Brasil e das possibilidades de mudança social e das resistências a elas. Entre outros, podem ser citados: Francisco José de Oliveira Vianna (1883 -1951), Caio da Silva Prado Júnior (1907-1990), Sérgio Buarque de Holanda (1902-1982), Gilberto Freyre (1900-1987) e Fernando de Azevedo (1894-1974). -/- Pode-se afirmar que foi entre as décadas de 1920 e 1940 que a Sociologia fincou alicerces no Brasil. Nesse período, procuraram-se definir mais claramente as fronteiras entre a Sociologia e as áreas do conhecimento afins, como a Literatura, a História e a Geografia, e institucionalizou-se o curso de Sociologia e/ou Ciências Sociais em faculdades e universidades. Foram fundadas a Escola Livre de Sociologia e Política (ELSP) em São Paulo, em 1933, e as Universidades de São Paulo (USP) e do Distrito Federal (UDF), esta no Rio de Janeiro, respectivamente em 1934 e 1935, nas quais foram instituídos cursos de Ciências Sociais. -/- Vários professores estrangeiros vieram ao Brasil para trabalhar nessas universidades e contribuíram de modo significativo para o desenvolvimento da Sociologia no Brasil. Entre outros, podem ser citados: Donald Pierson (1900-1995), Alfred Radcliffe-Brown (1881-1955), Claude Lévi -Strauss (1908-2009), Georges Gurvitch (1894-1965), Roger Bastide (1898-1974), Charles Morazé (1913-2003) e Paul-Arbousse Bastide (1901-1985). -/- A revista Sociologia, da Escola Livre de Sociologia e Política, foi um exemplo da produção sociológica da época. Criada em 1939 e publicada até 1981, em São Paulo, constituiu um verdadeiro marco de estudo, pesquisa e divulgação das Ciências Sociais no Brasil. -/- 1.1 De 1940 a 1960: disseminação da Sociologia na universidade -/- A partir do final da Segunda Guerra Mundial até meados da década de 1960, disseminaram-se as Faculdades de Filosofia, Ciências e Letras no Brasil, em universidades ou fora delas, e a Sociologia passou a fazer parte do currículo dos cursos de Ciências Sociais ou a apresentar-se como disciplina obrigatória em outros cursos. -/- Uma nova geração de cientistas sociais passou a ter presença marcante no Brasil. Entre eles, podem ser citados: Florestan Fernandes (1920-1995), Antonio Candido (1918-2017), Maria Isaura Pereira de Queiroz (1918-2018), Juarez Rubens Brandão Lopes (1925-2011), Alberto Guerreiro Ramos (1915-1982), Luiz Aguiar da Costa Pinto (1920-2002) e Hélio Jaguaribe (1923-2018), que influenciaram muitos cientistas sociais em todo o território nacional. -/- Os principais temas de pesquisas, análises e discussões nesse período foram: imigração e colonização, estudos de comunidades, educação, folclore, questão rural e urbana, teoria e método das Ciências Sociais, estratificação e mobilidade sociais e sociologia da arte e da literatura. Cabe um destaque para o tema das relações étnico-raciais envolvendo a questão do negro no Brasil, graças a um projeto de pesquisa financiado pela Unesco. -/- Nesse período, a Sociologia tornou-se disciplina hegemônica no quadro das Ciências Sociais no Brasil, e a primeira a formar uma “escola” ou uma “tradição” em São Paulo, tendo em Florestan Fernandes um dos seus mentores. -/- 1.2 De 1960 a 1980: consolidação da Sociologia no Brasil -/- Mesmo sob a ditadura civil-militar no Brasil, a partir de 1964, a Sociologia começou a se expandir, principalmente nos grandes centros urbanos, e a se relacionar com outros campos das ciências humanas. As discussões sobre o processo de industrialização crescente no país foram o centro das atenções. -/- Um dos temas de discussão era o desenvolvimento, incluindo as teorias da dependência e da modernização. Outros temas de debates e pesquisas da época foram o trabalho industrial e o sindicalismo, a formação da classe trabalhadora, a urbanização crescente e as transformações no campo, os problemas da marginalidade social, a presença do capital estrangeiro e a indústria nacional. A questão educacional também esteve presente, pois de alguma forma os problemas sociais estavam vinculados à precariedade do setor. Foram bastante debatidos, ainda, o autoritarismo, principalmente depois do golpe militar de 1964, e o planejamento, criando-se uma interface com a ciência política. Além disso, destacaram-se, nesse momento, os estudos sobre a América Latina. -/- Em razão desses debates, foram incluídas nas universidades disciplinas como: Sociologia do Desenvolvimento, Sociologia Urbana, Sociologia Rural, Sociologia Industrial e do Trabalho (incluindo a questão sindical), Sociologia do Planejamento, Sociologia da Educação e da Juventude e Sociologia da Arte e Literatura. -/- Muitos foram os estudiosos que, em diferentes áreas do pensamento sociológico, desenvolveram pesquisas nessas décadas. Alguns dos que formaram a segunda geração de sociólogos no Brasil são: Octavio Ianni (1926-2004), Marialice M. Foracchi (1929-1971), Fernando Henrique Cardoso (1931-), Leôncio Martins Rodrigues (1934-), Heleieth Saffioti (1934-2010), Maurício Tragtenberg (1929-1998), Francisco de Oliveira (1933-), Luiz Pereira (1933-1985), Luiz Eduardo W. Wanderley (1935-), José de Souza Martins (1938-), Gabriel Cohn (1938-), Roberto Schwarz (1938-), Elide Rugai Bastos, Luiz Werneck Vianna (1938-) e Simon Schwartzman (1939-). -/- 1.3 Diversificação da Sociologia no Brasil -/- A partir da década de 1980, ampliam-se os cursos de graduação e pós-graduação (mestrado e doutorado) em Ciências Sociais e, em particular, de Sociologia em todo o território nacional, elevando o nível, em número e em qualidade, das pesquisas e do ensino da área. Os estudos sociológicos passaram a ser mais específicos, ocorrendo uma fragmentação dos temas/objetos, como: violência, gênero e sexualidade, corpo e saúde, religião, cotidiano, comunicação e informação, indústria cultural, representações sociais, consumo, cidadania, direitos humanos, questão ambiental, globalização, ciência e tecnologia, urbanização, juventude, família, trabalho, classes e mobilidade social, questões étnico-raciais, Estado e sociedade civil. -/- Novas gerações de sociólogos se formam nas universidades em quase todos os estados da federação, e há centros de ensino e pesquisa na maioria das universidades, disseminando-se assim a formação de sociólogos, sob diversas influências teóricas. A formação em Sociologia, que havia se estabelecido a partir de São Paulo e Rio de Janeiro, na década de 1930, expandiu-se para todo o território nacional em pouco mais de 50 anos. -/- 1.4 O retorno da Sociologia ao ensino médio -/- Oficialmente extinta do currículo do ensino médio em 1942, a Sociologia voltou a marcar presença em um ou outro estado da federação, de modo intermitente, a partir da década de 1980, com os primeiros ensaios de democratização da sociedade brasileira, conforme diz o sociólogo brasileiro Amaury C. Moraes, na esteira de mudanças na legislação educacional realizadas pelo governo ditatorial pós-1964. A Lei n° 7.044/82 flexibilizou o ensino médio (então 2º grau) e profissionalizante, abrindo espaço para a presença da Sociologia, não em caráter obrigatório, mas por escolha das escolas. Nesse sentido, ela começou a ter uma presença crescente nos currículos escolares. -/- Aqui cabe citar as Orientações Curriculares para o ensino médio, (p. 104-105). -/- Há uma interpretação corrente que (...) deve ser bem avaliada criticamente; ela afirma que a presença ou a ausência da Sociologia no currículo está vinculada a contextos democráticos ou autoritários, respectivamente. No entanto, se se observar bem, pelo menos em dois períodos isso não se confirma, ou se teria de rever o caráter do ensino de Sociologia para entender sua presença ou ausência. Entre 1931 e 1942, especialmente após 1937, a Sociologia está presente e é obrigatória no currículo em um período que abrange um governo que começa com esperanças democratizantes e logo se tinge de autoritarismo, assumindo sua vocação ditatorial mais adiante. Em outro momento, em plena democracia, o sentido do veto do Presidente da República (2001) à inclusão da Sociologia como disciplina obrigatória traz uma certa dificuldade para essa hipótese. O que se entende é que nem sempre a Sociologia teve um caráter crítico e transformador, funcionando muitas vezes como um discurso conservador, integrador e até cívico – como aparece nos primeiros manuais da disciplina. Não se pode esquecer que a Sociologia chegou ao Brasil de mãos dadas com o positivismo. No caso recente, deve-se entender que a ausência da disciplina se prende mais a tensões ou escaramuças pedagógico-administrativas que propriamente a algum conteúdo ideológico mais explícito (BRASIL/MEC, 2006). -/- Paralelamente, algumas associações de sociólogos, em diferentes estados brasileiros – mas principalmente em São Paulo –, iniciaram um movimento pela volta da Sociologia ao ensino médio, almejando a ampliação de espaço de trabalho para os formandos dos cursos de Ciências Sociais. Apesar de não haver a obrigatoriedade da disciplina, os cursos de Ciências Sociais continuavam a formar professores de Sociologia. Em alguns estados brasileiros, ao longo dos anos, foram abertos concursos para professores de Sociologia. -/- Com a Constituição de 1988 e a consequente formulação das constituições estaduais, sociólogos em todo o país passaram a reivindicar a presença da Sociologia no ensino médio. Assim, leis estaduais de alguns estados e as constituições estaduais do Rio de Janeiro e de Minas Gerais tornaram a Sociologia obrigatória no ensino médio. -/- A partir do ano 2000, organizações representativas de sociólogos (sindicatos e associações de sociólogos e de cientistas sociais) de várias tendências, assim como pequenos grupos nas universidades do país, começaram a desenvolver um movimento em defesa da obrigatoriedade do ensino da Sociologia no nível médio, considerando que os conteúdos dessa disciplina contribuíam para melhor formação do jovem estudante. -/- O movimento estendeu-se ao início do século XXI, conquistando o apoio de instituições de ensino, associações científicas, intelectuais, sindicatos e associações de categorias profissionais. Finalmente, pela Lei n° 11.684, de 2 de junho de 2008, que alterou a Lei de Diretrizes e Bases da Educação Nacional, a Sociologia retornou oficial e obrigatoriamente ao currículo do ensino médio brasileiro. -/- A título de conclusão, e ainda refletindo sobre a Sociologia e sua contribuição para a formação dos estudantes, é importante destacar o que as Orientações Curriculares para o ensino médio afirmam com muita clareza: -/- A presença da Sociologia no currículo do ensino médio tem provocado muita discussão. Além dessa justificativa que se tornou slogan ou clichê – “formar o cidadão crítico” –, entende-se que haja outras mais objetivas decorrentes da concretude com que a Sociologia pode contribuir para a formação do jovem brasileiro, quer aproximando esse jovem de uma linguagem especial que a Sociologia oferece, quer sistematizando os debates em torno de temas de importância dados pela tradição ou pela contemporaneidade. A Sociologia, como espaço de realização das Ciências Sociais na escola média, pode oferecer ao aluno, além de informações próprias do campo dessas ciências, resultados das pesquisas as mais diversas, que acabam modificando as concepções de mundo, a economia, a sociedade e o outro, isto é, o diferente – de outra cultura, “tribo”, país etc. Traz também modos de pensar (...) ou a reconstrução e desconstrução de modos de pensar. É possível, observando as teorias sociológicas, compreender os elementos da argumentação – lógicos e empíricos – que justificam um modo de ser de uma sociedade, classe, grupo social e mesmo com unidade. Isso em termos sincrônicos ou diacrônicos, de hoje ou de ontem. -/- Um papel central que o pensamento sociológico realiza é a desnaturalização das concepções ou explicações dos fenômenos sociais. Há uma tendência sempre recorrente a se explicarem as relações sociais, as instituições, os modos de vida, as ações humanas, coletivas ou individuais, a estrutura social, a organização política etc. com argumentos naturalizadores. Primeiro, perde -se de vista a historicidade desses fenômenos, isto é, que nem sempre foram assim; segundo, que certas mudanças ou continuidades históricas decorrem de decisões, e essas, de interesses, ou seja, de razões objetivas e humanas, não sendo fruto de tendências naturais (BRASIL/MEC, 2006). -/- REFERENCIAL TEÓRICO -/- BRASIL. Secretaria de Educação Básica. Orientações curriculares para o ensino médio: ciências humanas e suas tecnologias. Brasília: Ministério da Educação/Secretaria de Educação Básica, 2006. -/- FILHO, E. D. L. A sociologia no Brasil: história, teorias e desafios. Sociologias , Porto Alegre, n. 14, p. 376-437, dezembro de 2005. -/- MARTINS, C. B.; MICELI, S. (Org.). Sociologia brasileira hoje. São Paulo: ateliê editorial, 2017. -/- SILVA, A. Sociologia em movimento. 2ª ed. São Paulo: Moderna, 2016. -/- -/- SBS - Sociedade Brasileira de Sociologia PUCRS - PPG em Ciências Sociais Avenida Ipiranga, 6681 - Partenon CEP: 90619-900 - Porto Alegre, RS [email protected] -/- Estimule a criatividade, respeite o direito autoral. -/- ©2019 - Emanuel Isaque Cordeiro da Silva. (shrink)

Abstract: Artificial Intelligence (AI) is rapidly becoming a cornerstone of modern healthcare, offering unprecedented capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper explores the transformative impact of AI on the healthcare sector, examining how it enhances patient outcomes, improves the efficiency of medical practices, and introduces new ethical and operational challenges. By analyzing current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, this paper highlights the significant advancements AI has brought to the (...) field. However, the integration of AI also raises concerns related to data privacy, algorithmic bias, and the displacement of healthcare professionals. This study provides a comprehensive overview of these developments, offering insights into the future potential of AI in revolutionizing healthcare while addressing the ethical considerations that must accompany its adoption. The findings suggest that while AI holds the promise of transforming healthcare delivery, careful consideration of its implications is essential to ensure equitable and effective implementation. (shrink)

Through the idea of the sublime, Kant articulated a type of aesthetic judgement whereby one experiences the limits of cognition and representation. The result of this, for Kant, is the demonstration and cultivation of our moral nature. Lyotard reframes the idea of the sublime in terms of post-modernity through his development of the idea of the event. The experience of the event is roughly equivalent to the experience of the sublime. Crucially though, the experience of the event, unlike the sublime, (...) does not foster individual morality; rather, it points to injustices. (shrink)

Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. Additionally, (...) it discusses challenges like data privacy, barriers to technology adoption, and the ethical implications of AI in farming. Integrating AI into agricultural practices holds the promise of greater efficiency and sustainability, paving the way for future innovations. (shrink)

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

Clive Walter Ingram Pearson was an Australian Existentialist and Religious Studies philosopher who spent his entire profesional career in the University of Auckland Philosophy Department. He was an idiosyncratic teacher and thinker who had a major influence on several contemporary ANZAC philosophers, as well as many hundreds of his students. This article is a tribute to him.

Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. Additionally, the (...) paper addresses the challenges faced in AI-driven medical imaging, including data quality, model interpretability, and ethical considerations. By examining recent advancements and real-world case studies, this paper provides insights into the current state of AI in medical imaging and its potential future directions. The findings highlight the ongoing evolution of AI technologies and their crucial role in advancing medical diagnostics and treatment strategies. (shrink)

Abstract: Early detection of breast cancer plays a pivotal role in improving patient prognosis and reducing mortality rates. Recent technological advancements have significantly enhanced the accuracy and effectiveness of breast cancer screening methods. This paper explores the latest innovations in early detection, including the evolution of digital mammography, the impact of 3D mammography (tomosynthesis), and the use of advanced imaging techniques such as molecular imaging and MRI. Furthermore, the integration of artificial intelligence (AI) in diagnostic tools is discussed, highlighting how (...) machine learning algorithms are refining imaging analyses and reducing diagnostic errors. Advances in genetic screening, liquid biopsies, and biomarkers are also examined, showcasing their potential in identifying high-risk individuals and enabling personalized treatment plans. This paper provides insights into the future of breast cancer detection, outlining both the opportunities and challenges that lie ahead in adopting these innovative technologies to improve patient outcomes. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into leadership practices is rapidly transforming organizational dynamics and decision-making processes. This paper explores the ways in which AI enhances leadership effectiveness by providing data- driven insights, optimizing decision-making, and automating routine tasks. Additionally, it examines the challenges leaders face when adopting AI, including ethical considerations, potential biases in AI systems, and the need for upskilling. By analyzing current applications of AI in leadership and discussing future trends, this study aims to offer a (...) comprehensive overview of the opportunities AI presents for effective leadership, as well as the strategies required to address its associated challenges. (shrink)

Abstract: In recent years, the outbreak of various poultry diseases has posed a significant threat to the global poultry industry. Therefore, the accurate and timely detection of chicken diseases is critical to reduce economic losses and prevent the spread of diseases. In this study, we propose a method for classifying chicken diseases using a convolutional neural network (CNN). The proposed method involves preprocessing the chicken images, building and training a CNN model, and evaluating the performance of the model. The dataset (...) used in this study comprises chicken images of three different diseases: Newcastle disease, avian influenza, and infectious bursal disease. The proposed method achieved an overall F1-score of 99.04% in classifying the chicken diseases. The results demonstrate the effectiveness of using CNN in classifying chicken diseases and provide a promising approach for early detection and diagnosis of poultry diseases. (shrink)

Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this elusive feat. (...) Through rigorous experimentation and data-driven approaches, we seek to determine the viability of AI in the realm of lottery predictions. Our findings reveal both the promise and limitations of AI in this context, shedding light on the complexities of lottery data and the potential need for quantum computing as a last resort. (shrink)

Abstract: The sinking of the RMS Titanic in 1912 remains a poignant historical event that continues to captivate our collective imagination. In this research paper, we delve into the realm of data-driven analysis by applying Artificial Neural Networks (ANNs) to predict the chances of survival for passengers aboard the Titanic. Our study leverages a comprehensive dataset encompassing passenger information, demographics, and cabin class, providing a unique opportunity to explore the complex interplay of factors influencing survival outcomes. Our ANN-based predictive model (...) achieved an accuracy rate of 78%, shedding light on the underlying patterns within the data. However, this paper underscores the multifaceted nature of survival prediction and acknowledges several challenges. These include missing data imputation, feature engineering, and the inherent noise within historical datasets. We further discuss the importance of model evaluation metrics, showcasing not only accuracy but also precision, recall, and F1-score as essential indicators of predictive performance. The research paper meticulously outlines the architecture of the ANN model, emphasizing the key hyperparameters, activation functions, and regularization techniques employed in model development. Additionally, we address ethical considerations related to data handling and potential biases within the dataset. While our achieved accuracy is a notable achievement, this study emphasizes the importance of interpreting the results with due caution. It underscores the need for a holistic perspective that considers the practical implications of false positives and false negatives, especially in the context of a disaster scenario. In conclusion, this research contributes to our understanding of survival prediction in historical events like the Titanic disaster using modern machine learning techniques. It calls for further exploration and refinement of predictive models, with a focus on enhancing the interpretability and generalizability of such models to real-world applications. (shrink)

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

Abstract: Context: grapevine leaves are an important agricultural product that is used in many Middle Eastern dishes. The species from which the grapevine leaf originates can differ in terms of both taste and price. Method: In this study, we build a deep learning model to tackle the problem of grape leaf classification. 500 images were used (100 for each species) that were then increased to 10,000 using data augmentation methods. Convolutional Neural Network (CNN) algorithms were applied to build this model (...) specifically using the pre-trained model on top of the VGG16 architecture. Then, dense layers were added to classify the output of the Convolutional layers and classify outputs to the five classes (species) the leaf belonged to. Results: It was found that feature extraction without fine-tuning the convolutional layers yielded poor results, about 86% accuracy, while training the whole network along with some data preprocessing gave the best results, about 99.45% accuracy on the testing dataset. Conclusions: The proposed CNN model is an effective one for the problem of classification of grape leaf species. (shrink)

Water quality assessment is critical for environmental sustainability and public health. This research employs neural networks to predict water quality, utilizing a dataset of 21 diverse features, including metals, chemicals, and biological indicators. With 8000 samples, our neural network model, consisting of four layers, achieved an impressive 94.22% accuracy with an average error of 0.031. Feature importance analysis revealed arsenic, perchlorate, cadmium, and others as pivotal factors in water quality prediction. This study offers a valuable contribution to enhancing water quality (...) monitoring and decision-making for stakeholders and policymakers. (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)

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: The aim behind analyzing Google Stock Prices dataset is to get a fair idea about the relationships between the multiple attributes a day might have, such as: the opening price for each day, the volume of trading for each day. With over a hundred thousand days of trading data, there are some patterns that can help in predicting the future prices. We proposed an Artificial Neural Network (ANN) model for predicting the closing prices for future days. The prediction is (...) based on these features (date, opening price, highest price, lowest price, volume), which were used as input variables and (closing price) as output variable for our ANN model. Our model was created, trained, and validated using data set in JNN environment, which its title is “Google-Stock-Prices”. The model evaluation showed that the ANN model is able to predict correctly 99.48% of the validation samples. (shrink)

Abstract: Climate change presents a critical challenge that demands advanced analytical tools to predict and mitigate its impacts. This paper explores the role of artificial intelligence (AI) in enhancing climate modeling, emphasizing how AI-driven methods are revolutionizing our understanding and response to climate change. By integrating machine learning algorithms with diverse data sources such as satellite imagery, historical climate records, and real-time sensor data, AI improves the accuracy, efficiency, and granularity of climate predictions. The paper reviews key AI techniques, including (...) neural networks and ensemble models, and their applications in simulating extreme weather events and forecasting long-term climate trends. Additionally, it examines the impact of AI-enhanced climate models on policy-making and strategic planning. Despite significant advancements, challenges such as data quality, computational demands, and model interpretability remain. This study underscores the potential of AI to transform climate modeling and highlights areas for future research and development to harness these technologies for a sustainable future. (shrink)

Mango is an economically significant fruit crop cultivated in various tropical and subtropical regions around the world. However, the productivity and quality of mangoes can be severely impacted by a range of pests. This research paper introduces an innovative approach to identify mango pests using an expert system. The expert system integrates knowledge from entomology and plants to provide accurate identification of common mango pests. The paper outlines the development and implementation of the expert system using Clips shell, which utilizes (...) rule-based techniques to analyze symptoms associated with mango pests. By inputting observed symptoms, such as leaf spots, fruit damage, leaf mild and stem cankers the system can accurately identify the specific pest species affecting the mango tree. For each pest, the paper discusses their specific symptoms and Favorable conditions that affect the mango plant. By understanding the identification and symptoms of these pests, mango growers and agricultural professionals can implement appropriate pest management strategies to mitigate the economic losses caused by these insects. The research presented in this paper aims to contribute to the body of knowledge on mango pest management, facilitating sustainable mango production and ensuring the availability of high-quality mangoes in global markets. (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)

This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed to enhance (...) model generalizability. (shrink)

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

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

Papaya is a tropical fruit with a green cover, yellow pulp, and a taste between mango and cantaloupe, having commercial importance because of its high nutritive and medicinal value. The process of sorting papaya fruit based on maturely is one of the processes that greatly determine the mature of papaya fruit that will be sold to consumers. The manual grading of papaya fruit based on human visual perception is time-consuming and destructive. The objective of this paper is to the status (...) classification of papaya fruits if it's mature or partially matured or unmatured. A deep learning technique that was extensively applied to image recognition was used. The trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. Classification model of VGG16 achieved a 100% accuracy and 112 seconds of training time. (shrink)

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

Abstract: As artificial intelligence (AI) technologies rapidly evolve and permeate various aspects of society, the need for effective regulation and governance has become increasingly critical. This paper explores the current landscape of AI regulation, examining existing frameworks and their efficacy in addressing the unique challenges posed by AI. Key issues such as ensuring compliance, mitigating biases, and maintaining transparency are analyzed. The paper also delves into ethical considerations surrounding AI governance, emphasizing the importance of fairness and accountability. Through case studies (...) of different regulatory approaches and their outcomes, the paper highlights both successes and areas for improvement. Finally, it offers insights into future directions for AI regulation, advocating for innovative policies and enhanced international cooperation to ensure that AI technologies are developed and deployed responsibly. This comprehensive analysis aims to contribute to the ongoing discourse on creating robust regulatory mechanisms that can keep pace with the rapid advancement of AI. (shrink)

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

Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, our investigation (...) identifies the most influential features in breast cancer diagnosis, shedding light on the key determinants of malignancy. Notably, we find that factors such as fractal dimension_se, symmetry worst, compactness_worst, symmetry_se, and smoothness_se play pivotal roles in distinguishing between benign and malignant cases. This research contributes to the ongoing efforts to enhance breast cancer diagnosis, providing valuable insights into feature importance and showcasing the potential of neural networks in medical applications. Our findings have implications for improving early detection and treatment strategies, ultimately contributing to improved patient outcomes. (shrink)

Web page phishing is a type of phishing attack that targets websites. In a web page phishing attack, the attacker creates a fake website that looks like a legitimate website, such as a bank or credit card company website. The attacker then sends a fraudulent message to the victim, which contains a link to the fake website. When the victim clicks on the link, they are taken to the fake website and tricked into entering their personal information.Web page phishing attacks (...) are a serious threat to online security. They can be very effective, as they often look very convincing. To protect yourself from web page phishing attacks, you should be suspicious of any emails or messages that ask for your personal information. Do not click on links in emails or messages from unknown senders. If you are unsure whether a website is legitimate, do not enter your personal information. Therefore, in this study, we present a novel approach to detect whether a web page is phishing or legitimate using a neural network model. (shrink)

Abstract: Heart attacks, or myocardial infarctions, are a leading cause of mortality worldwide. Early prediction and accurate analysis of potential risk factors play a crucial role in preventing heart attacks and improving patient outcomes. In this study, we conduct a comprehensive review of datasets related to heart attack analysis and prediction. We begin by examining the various types of datasets available for heart attack research, encompassing clinical, demographic, and physiological data. These datasets originate from diverse sources, including hospitals, research institutions, (...) and public health agencies. Our analysis aims to identify common features, data quality, and potential biases in these datasets. Next, we explore the predictive modeling techniques employed in heart attack prediction. Machine learning algorithms, such as decision trees, support vector machines, and deep neural networks, have gained prominence in predicting heart attacks. We discuss the strengths and limitations of these methods and highlight recent advancements in predictive modeling. Furthermore, we delve into the critical risk factors associated with heart attacks. Factors such as age, gender, hypertension, diabetes, and cholesterol levels are examined for their significance in predicting cardiac events. We also investigate the role of lifestyle factors, including smoking, diet, and physical activity, in heart attack risk assessment. Additionally, this review addresses the importance of data preprocessing and feature engineering in improving prediction accuracy. Feature selection methods, missing data handling, and data scaling techniques are discussed to enhance the robustness of heart attack prediction models. In conclusion, this comprehensive dataset review provides valuable insights into the state of heart attack analysis and prediction. It serves as a resource for researchers and healthcare professionals seeking to better understand the datasets available for heart attack research and the methods employed for accurate prediction. Ultimately, our efforts in dataset analysis and predictive modeling contribute to the advancement of preventive cardiology and the reduction of heart attack-related morbidity and mortality. (shrink)

A pineapple is a tropical plant with eatable leafy foods most monetarily critical plant in the family Bromeliaceous. The pineapple is native to South America, where it has been developed for a long time. The acquaintance of the pineapple with Europe in the seventeenth century made it a critical social symbol of extravagance. Since the 1820s, pineapple has been industrially filled in nurseries and numerous tropical manors. Further, it is the third most significant tropical natural product in world creation. In (...) the twentieth century, Hawaii was a prevailing maker of pineapples, particularly for the US, be that as it may, by 2016, Costa Rica, Brazil, and the Philippines represented almost 33% of the world's creation of pineapples. In this paper, machine learning based approach is presented for identifying type pineapple with a dataset that contains 1,311images use 946 images for training, 197 images for validation and 168 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a heldout test set. (shrink)

This study aims to identify the strengthening of the global entrepreneurial orientation practice for employees at the University of Palestine, where the researchers used the descriptive and analytical approach, through a questionnaire distributed to a sample of employees at the University of Palestine, where the size of the study population is (234) employees and the sample size is (117) Employees, of whom (90) employees responded. The study found a set of results, the most important of which are: that there is (...) a good degree of global entrepreneurial orientation for employees, as the percentage reached (70.25%), and the results also showed that there are no statistically significant differences in the smart university transformation and their impact on the global entrepreneurial trend according to personal and organizational data. The study presented a set of recommendations, the most important of which are: The need to work to enhance the global entrepreneurial orientation of the university's employees. (shrink)

In recent years, there has been a heated debate about how to interpret findings that seem to show that humans rapidly and automatically calculate the visual perspectives of others. In the current study, we investigated the question of whether automatic interference effects found in the dot-perspective task (Samson, Apperly, Braithwaite, Andrews, & Bodley Scott, 2010) are the product of domain-specific perspective-taking processes or of domain-general “submentalizing” processes (Heyes, 2014). Previous attempts to address this question have done so by implementing inanimate (...) controls, such as arrows, as stimuli. The rationale for this is that submentalizing processes that respond to directionality should be engaged by such stimuli, whereas domain-specific perspective-taking mechanisms, if they exist, should not. These previous attempts have been limited, however, by the implied intentionality of the stimuli they have used (e.g. arrows), which may have invited participants to imbue them with perspectival agency. Drawing inspiration from “novel entity” paradigms from infant gaze-following research, we designed a version of the dot-perspective task that allowed us to precisely control whether a central stimulus was viewed as animate or inanimate. Across four experiments, we found no evidence that automatic “perspective-taking” effects in the dot-perspective task are modulated by beliefs about the animacy of the central stimulus. Our results also suggest that these effects may be due to the task-switching elements of the dot-perspective paradigm, rather than automatic directional orienting. Together, these results indicate that neither the perspective-taking nor the standard submentalizing interpretations of the dot-perspective task are fully correct. (shrink)

In this article, it has been aimed to examine Nietzsche’s main critique towards different understandings of morals in his era. With this criticism, it is aimed to integrally understand the opinions -which are articulated directly or metaphorically- towards morals which have been encountered. In here, while keeping in mind the difference between the concepts of immoralism and amoralism, Nietzsche’s views are interpreted. Being parallel to that aim mentioned above, it has been investigated if there is a thinking in Nietzsche which (...) can be seen as a kind of understanding of morals (maybe as the understanding of morals without morals) or not. And finally, it has been thought that if Nietzsche’s views on morals have any value for the 21st century and upcoming 22nd century human or not, whether there’s something left to learn for this human from his critiques or not. (shrink)

Climate change temperature prediction plays a crucial role in effective environmental planning. This study introduces an innovative approach that harnesses the power of Artificial Neural Networks (ANNs) within the Just Neural Network (JustNN) framework to enhance temperature forecasting in the context of climate change. By leveraging historical climate data, our model achieves exceptional accuracy, redefining the landscape of temperature prediction without intricate preprocessing. This model sets a new standard for precise temperature forecasting in the context of climate change. Moreover, our (...) research provides valuable insights into the pivotal factors influencing temperature variations, making significant contributions to environmental science and climate mitigation strategies. (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)

This study employs a neural network to analyze the connection between smoking, drinking, and various health-related factors using a dataset of 5148 samples. Achieving an impressive 99.94% accuracy and an average training error of 0.0016, the model identifies influential factors such as serum aminotransferases, serum creatinine, sex, weight, and triglyceride levels. These findings enhance our understanding of lifestyle choices and their impact on health. This research underscores the potential of machine learning in studying complex health phenomena.

Heart failure 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 918 samples with 11 features, such as age, sex, chest pain type, resting blood pressure, 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 and 1 output) was trained on the dataset and achieved an accuracy of 90% and an average error of 0.009. 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)

As a contribution to a wider discussion on moral discernment in theological anthropology, this paper seeks to answer the question “What is the impact of mental illness on an individual’s ability to make moral decisions?” Written from a clinical psychiatric perspective, it considers recent contributions from psychology, neuropsychology and imaging technology. It notes that the popular conception that mental illness necessarily robs an individual of moral responsibility is largely unfounded. Most people who suffer from mental health problems do not lose (...) the capacity to make moral decisions, and mental illness on its own rarely explains anti-social or criminal behaviour. Moreover, the assumptions of some scientists, that recent developments in neuropsychology and brain imaging suggest biological determinism, must be treated with caution. (shrink)

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

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved (...) in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed. (shrink)