bluuebunny/crossref_metadata_2025 huggingface dataset:
Increasing the Income of Chili Farmers by Implementation of Internal Company Institution
Authors: Kristiana Dwi , Mukson Mukson , Santoso Siswanto Imam
Published: January 2025

Chili is an important commodity for daily consumption, and its demand has the potential to increase as the population grows. The centralized location of production and the uncertainty of production quantities cause price disparities, reflecting poor supply management. This study aims to describe the corporate institutions of chili farming, analyze institutional performance, and factors that affect income of farmers. The method used is descriptive quantitative with a sample of 77 respondents taken with the Slovin formula at Gapoktan Bina Tani and Gapoktan Sumber Jaya in Blora. Data were collected through interviews with questionnaires, and multiple linear regression analysis was used to determine influential factors. The results showed that the institutional strengthening program variable (X1), the partnership expansion program (X2), and the economic scale improvement program (X3) had a high average value, meaning that the majority of respondents gave positive responses to the programs implemented. Factors that have a significant effect on chili farming income are variables of education level, length of farming, and amount of production, while variables of age, land area, and company performance have no significant effect on chili farming income. This conclusion indicates the need for institutional development to improve the welfare of chili farmers. Keywords: Chilli, Corporation, Farmer, Income, Institution.
Implementing a Flipped Classroom Approach Assisted by Edpuzzle Video Annotations to Improve Students’ Cognitive Abilities on Global Warming Topics
Authors: Meilani Hasna Azka , Rusnayati Heni , Danawan Agus
Published: January 2025

This study was prompted by students’ low cognitive abilities in solving physics problems, particularly those related to global warming. The research aimed to determine the extent to which a flipped classroom strategy assisted by Edpuzzle video annotations could enhance students’ cognitive abilities. A quasi-experimental design with a control group pretest-posttest approach was employed. The sample comprised 70 tenth-grade students from public high schools in Bandung City. Data were collected through essay-based cognitive tests, an instrument assessing the feasibility of video annotation media, and classroom observation sheets. The n-gain formula was used for data analysis. The results showed a moderate improvement in students’ cognitive abilities. Both the video annotation media and the flipped classroom strategy were considered feasible, with nearly all planned activities successfully implemented. Overall, integrating a flipped classroom approach with Edpuzzle video annotations proves effective in improving students’ cognitive abilities on global warming topics.
Gehalt und Karriere im Projektmanagement 2024
Authors: Deutsche Gesellschaft für Projektmanagement e. V. GPM
Published: January 2025

Die achte Gehalts- und Karrierestudie für Projektmanagerinnen und Projektmanager im deutschsprachigen Raum gilt als die einflussreichste Erhebung in der Projektmanagement-Praxis in Europa. Seit der letzten Erhebung 2019 haben weitreichende soziale, ökonomische und politische Entwicklungen für große Veränderungen gesorgt.
Die Ergebnisse der Studie zeigen nicht nur, wie sich Gehälter und Karrierechancen in Deutschland, Österreich und der Schweiz seit 2019 entwickelt haben, sondern beleuchten auch aktuelle Trends und Herausforderungen. Neben den traditionellen Themen wurde in dieser Studie ein besonderer Fokus auf die Anwendung Künstlicher Intelligenz (KI) im Projektmanagement gelegt.
Die Studie liefert wertvolle Erkenntnisse, sowohl für Projektmanagerinnen und Projektmanager, die ihre Karriere strategisch planen, als auch für Unternehmen, die sich in einem zunehmend wettbewerbsintensiven Umfeld positionieren wollen.
Feasibility study on preloaded flexible conical plain bearings as wind turbine main bearings
Authors: Euler Jan , Jacobs Georg , Jakobs Timm , Röder Julian
Published: January 2025

Wind turbines (WT) are a key technology towards acarbon neutral energy production worldwide. Currentlyabout 30 % of the Levelized Cost of Electricity (LCoE)consist of service and maintenance. Critical components are the main roller bearings which have a failureprobability between 15 and 30 % within 20 years. Mainbearing replacements are expensive maintenance procedures because an external crane is needed whichcosts about $250.000 per day for offshore WTs. Hence,a discussed countermeasure within the wind industryis to use segmented plain bearings in future WTs. These types of bearings allow an up-tower sub-componentwise replacement of faulty parts without the need todismantle the whole drivetrain. Therefore, the Chair for Wind Power Drives (CWD) developed, tested andvalidated a segmented conical plain bearing conceptfor the use as a main bearing for WTs.
To function properly common plain bearings need aminimum clearance to allow the formation of a convergent lubrication gab. This initial clearance negativelyinfluences run-out, as the shaft can move freely within the limits of the clearance. As WT drivetrain concepts become more integrated, main bearings for WTs need to fulfill higher requirements regarding the allowable run-out of the main shaft. Therefore, a fundamental design challenge arises for plain bearings as rotor main bearings. One approach to reduce run-out for roller main bearings is to use preloaded tapered roller bear ings. For common plain bearings however preloading is not possible. However, the concept of preloading was successfully transferred to the flexible conical plain bearing concept developed at the CWD and the main shaft run-out severely reduced. In this work the feasibility of a preloaded flexible conical plain bearing as a WT rotor main bearing is evaluated and the advantages and disadvantages contrasted.
Verschleißverhalten von additiv gefertigten Kunststoff-Kunststoff-Gleitpaarungen
Authors: Harden Felix , Schädel Birgit , Kral Roland , Siebert Leonard , Adelung Rainer , Jacobs Olaf
Published: January 2025

Vor dem Hintergrund der rasant steigenden Bedeutung der additiven Fertigung in der industriellen Anwendung und der sich daraus ergebenden Freiheit in Design und Materialkombinationen, sind additiv gefertigte Kunststoff-Kunststoff-Gleitpaarungen von zunehmendem Interesse. Idealerweise sollten maßgeschneiderte Compounds zum Einsatz kommen, um das volle Potential von additiv gefertigten Kunststoff-Kunststoff-Gleitpaarungen auszuschöpfen. In Anbetracht der additiven Fertigungsverfahren spielen jedoch nicht nur die Materialpaarungen, sondern auch ein Verständnis des Einflusses unterschiedlicher Druckparameter eine entscheidende Rolle. Diese Arbeit stellt eine erste, mit Hilfe des Kugel-Prisma-Tribometers ermittelte Verschleißdatenbasis für kommerziell erhältliche Tribo-Werkstoffe dar. Davon ausgehend werden die Einflüsse von Druckorientierung und Materialpaarungen auf das Verschleißverhalten untersucht. Des Weiteren wird deren wechselseitige Abhängigkeit diskutiert. Ergänzend werden die Besonderheiten und Herausforderungen der Prüfkörperherstellung sowie der Verschleißmessung betrachtet.
La realización del derecho humano a la alimentación adecuada en contextos de inflación. Un estudio en consumidores urbanos argentinos
Authors: Bonet Ana María , Nessier María Celeste , Zapata María Elisa , Marconi Agustina , Boragnio Aldana
Published: January 2025

Objetivo: estudiar de qué manera el aumento de precio afecta la realización de los diferentes aspectos del derecho humano a la alimentación adecuada (DHA) en personas de diferente nivel educativo. Metodología: estudio descriptivo observacional de corte transversal aplicando una encuesta digital autoadministrada a 3.592 adultos. Se realizó un análisis reflexivo de los resultados de la encuesta tomando como criterio de análisis los aspectos del DHA. Resultados: la inflación incide en la alimentación de las personas con menor así como con mayor nivel educativo. Los aspectos primero y segundo del DHA se vieron más afectados en las personas con menor nivel educativo. Las personas con mayor nivel educativo vieron más comprometido el segundo y el tercer nivel. Limitaciones: se detecta como limitación el alcance del estudio a personas con acceso a internet. También el formato de difusión de la encuesta a través de asociaciones académicas y organizaciones no gubernamentales que concentró el estudio en personas vinculadas a estos círculos. Conclusiones: los sectores con menor nivel educativo se encuentran expuestos a una mayor vulnerabilidad alimentaria, que llega a afectar el mínimo de existencia. La inflación afecta los aspectos nutricional y socioambiental del DHA tanto de personas con bajo como con alto nivel educativo, aunque de diferente manera. La realización efectiva del DHA en tiempos de inflación amerita la consideración integral de este derecho en sus diferentes aspectos o niveles.
Vacunación infantil en México: situación actual y su impacto a la salud
Authors: Álvarez-Contreras Cristina , Flores-Navarro María Fernanda , López-Tirado Melissa López-Tirado , Martínez-Trujillo Sandra , Monge-Hernández Nicole , Pérez-Bartutis Gretel Susana
Published: January 2025

La vacunación es una herramienta de salud pública cuyo objetivo es disminuir la incidencia de enfermedades, principalmente infecciosas. Mundialmente se han establecido esquemas de vacunación con el fin de disminuir o erradicar enfermedades y proteger a la población. Sin embargo, en México los esquemas de vacunación no son completados debido a diferentes factores, como la falta de cobertura y la desinformación tanto del personal de salud como de la población en general. Entre las poblaciones afectadas se encuentran los infantes de 0 a 12 años, un sector muy vulnerable, por lo que el propósito de este artículo de divulgación es exponer la situación actual de la vacunación infantil y sus implicaciones en la salud de este sector poblacional.
Childhood vaccination in Mexico: current situation and its impact on health
Abstract
Vaccination is a public health tool whose objective is to reduce the incidence of diseases, mainly infectious diseases. Vaccination schedules have been established worldwide in order to reduce or eradicate diseases and protect the population. However, in Mexico, vaccination schedules are not completed due to different factors, such as lack of coverage and misinformation among both health personnel and the general population. Among the affected populations are children from 0 to 12 years old, a very vulnerable sector, so the purpose of this article is to present the current situation of childhood vaccination and its implications for the health of this population sector.
Games as Windows and Remedies to Modern Society: A Qualified Defense of Agonistic Encounters
Authors: López Frías Francisco Javier
Published: January 2025

In this article, I explore connections among sport, competition, and capitalist society by drawing from works in the philosophy of sport and critical theory. Initially, I identify games, especially sports, as fundamental activities in contemporary life. In particular, I claim that the glorification of victory in sporting contests permeates the attitude toward competition of individuals in capitalist societies. Thus, sports can be understood as windows to observe the “achievement ethos” inherent in capitalism. Subsequently, I explore both philosophical victory-centric and mutualistic views of sporting competition. What follows from this examination is that competition itself is not inherently negative. It becomes detrimental when pursued at any cost. In alignment with Robert L. Simon’s sport mutualism, I advocate that competition may yield positive moral and social outcomes when approached as a “mutual quest for excellence.” To conclude, I trace this positive, mutualistic conception of competition back to ancient Greek and post-Hellenic reflections on agōn and excellence.
Le mouvement vivant. Critique du corps machine
Authors: Félix François
Published: January 2025

Les modèles théoriques, on le sait, sont parfois de véritables obstacles épistémologiques. Tel a particulièrement été le cas de l’arc réflexe. Destiné tout d’abord à rendre compte des mouvements involontaires, il a peu à peu été élevé au rang de modèle explicatif du mouvement en général, animal et humain. Les problèmes que ce schéma théorique rencontre sont cependant nombreux : en dissociant comme il le fait l’occasion du mouvement d’avec sa réalisation, en réduisant l’organisme à une série de processus spécifiques séparés et programmés, il néglige le rapport actif de l’être vivant à son milieu, appauvrit donc la réalité corporelle et psychique vécue, et s’avère ainsi inapte à ressaisir l’insertion fondamentale de l’homme dans son monde. Un modèle inadéquat auquel pourtant le mariage aujourd’hui de l’homme et de la machine redonne une préoccupante actualité.
Pengaruh Kinerja Penyuluh Pertanian Terhadap Perilaku Usahatani Petani di Desa Sindulang, Kecamatan Cimanggung, Kabupaten Sumedang
Authors: Nisa Milhatin , Charina Anne
Published: January 2025

Agricultural extension workers are change agents who interact directly with farmers, particularly regarding their behavior in cultivation activities. The success of agricultural extension programs largely depends on the quality of the extension workers' performance in fulfilling their responsibilities. This study aims to identify farmers' perceptions of agricultural extension workers' performance and the impact of this performance on farmers' farming behavior. The research was conducted in Sindulang Village, Cimanggung Subdistrict, Sumedang Regency, West Java Province, involving 71 vegetable farmer respondents. This study uses a quantitative design with a survey technique through questionnaires. Data analysis was performed using simple linear regression analysis. The results show that, according to farmers' perceptions, the performance of agricultural extension workers is categorized as high in terms of responsiveness, responsibility, accountability, and service quality. Based on the hypothesis testing analysis and t-value, it is known that the performance of agricultural extension workers has a significant and positive influence on farmers' farming behavior. Additionally, the coefficient of determination indicates that the independent variable, which is the performance of extension workers, can explain the variation that occurs in the dependent variable, namely farmers' farming behavior. Agricultural extension workers should evaluate the success of their programs and strategies and ensure that each extension activity provides equal benefits to all parties.
Analysis of Factors that Influence Consumer Loyalty in Purchasing Vegetables at the Panorama Spill Market in Bengkulu City
Authors: Indriani Desy , Irnad Irnad , Sumantri Bambang
Published: January 2025

This study aims to analyze the level of loyalty and what factors influence the loyalty of vegetable consumers at The Panorama Spill Market, Bengkulu City. This research was conducted during June-July 2024. The data collection methods used are secondary data and primary data. Where primary data is obtained from direct interviews with 100 traders (virtucio formula). The data analysis method used is structural equation modeling (SEM) analysis to answer the problem formulation. The results showed that the level of consumer satisfaction in purchasing vegetables in the panorama spill market of Bengkulu City had a satisfied category. The level of consumer loyalty in purchasing vegetables in the panorama spill market of Bengkulu City has a loyal category. The results of the direct analysis show that price and product quality affect customer satisfaction and customer satisfaction and service quality affect product loyalty. In addition, the indirect effect states that price and product quality affect consumer loyalty through customer satisfaction.
Ancaman Keberlanjutan Koperasi Akibat Permasalahan Pakan pada Kelompok Peternak atau Koperasi Sapi Perah di Indonesia
Authors: Sulistyati Marina , Saputra Arif Ragil , Herlina Linda , Yunasaf Unang , Nurlina Lilis , Alim Syahirul , Mauludin Mohamad Ali
Published: January 2025

Cooperatives and farmer groups in Indonesia's dairy sector are facing several serious challenges that can threaten their sustainability. One of the main issues is the availability and quality of livestock feed. This literature study aims to analyze and summarize various aspects of the threats to the sustainability of cooperatives and farmer groups caused by feed-related problems in the context of dairy cattle in Indonesia. The research method employed in this study is literature review. This literature study investigates issues related to livestock feed, such as inadequate feed availability, feed prices, and the quality of feed that can affect the productivity and health of dairy cattle. The results of the literature study indicate that uncertainty in feed supply, rising feed prices, and the lack of access to high-quality feed for farmers are the primary threats to the continued operation of cooperatives and farmer groups. Therefore, cooperatives must have a strategy to anticipate threats related to feed issues in order to maintain the sustainability of the cooperative.
Analisis Faktor-faktor yang Memengaruhi Akses Kredit dan Dampaknya Terhadap Produksi Bawang Merah di Kabupaten Manggarai Menggunakan Model Seleksi Heckman
Authors: Utami Dewanti Risa , Prasetyo Kunandar , Cordanis Astried Priscilla
Published: January 2025

Shallot is an agricultural product that contributes greatly to the Indonesian economy. One of the main problems in shallot farming is the low ability of farmers to access credit. The purpose of this study is to determine the factors that influence access to credit and understand the impact of credit on shallot production in Manggarai Regency. This study was conducted in Reok District, Manggarai Regency. The study site was specifically chosen taking into account that Reok District is the only shallot development area in Manggarai Regency. The sampling technique in this study used proportional quota sampling. The sample consisted of 50 farmers, consisting of 30 farmers who did not have access to credit, and 20 farmers who had accessed credit. The data analysis method used in this study is the Heckman selection model. The analysis of the Heckman selection model consists of two phases of analysis, namely the selection equation and the outcome equation. Based on the results of the analysis of the Heckman selection model, it is found that the factors that have a real impact on the credit availability of shallot farmers in Manggarai Regency are age, education, land area, number of household members and membership of farmer groups. farmers. In the second phase (outcome's equation), it is known that shallot production is significantly affected by labor input, pesticides and the amount of credit.  Research findings indicate a positive correlation between enhanced credit access and increased shallot production. Facilitating credit access for farmers can be a significant policy intervention to stimulate shallot production in Manggarai Regency. Therefore, initiatives are necessary to promote credit accessibility, especially from formal financial institutions.
Análise comparativa de expansores maxilares empregados em pacientes com fissura labiopalatina
Authors: Alves Adyson Herbert Correia , Solidônio Rachel Brazuna , Sales Samara Menezes , Salgado Alexandre Daher Yunes , Carvalho Matheus Lopes , Fontenele Ana Letícia Daniel , Ribeiro Lara Leal , Silvestre Francisbênia Alves , Lomonaco Diego , Moreira Madiana Magalhães
Published: January 2025

Objetivo: Realizar uma revisão integrativa de literatura para comparar os efeitos causados por diferentes tipos de expansores maxilares em pacientes com fissura labiopalatina (FLP). Métodos: Foi realizada uma revisão nas bases de dados PubMed e Biblioteca Virtual em Saúde (BVS), no período de 2016 a 2023, na língua inglesa e com os descritores “Cleft Palate” e “Palatal Expansion Technique”. Resultados: Após exclusão de duplicatas e leitura dos títulos e resumos, 12 artigos preencheram os critérios de elegibilidade para leitura na íntegra. Expansores Haas/Hyrax, mini-Hyrax, tipo Leque, expansor com abertura diferencial (EDO) ou ósseo palatino suportado foram utilizados para expansão maxilar rápida e Quadrihélice para  expansão maxilar lenta. Em geral, Hyrax e Quadrihélice podem ser igualmente indicados. EDO promoveu maior aumento na largura intercanina do que intermolar e o expansor palatino ósseo suportado produziu expansão esquelética alveolar sem efeitos dentários adversos. Considerações finais: Este trabalho compara vários aparelhos destinados à expansão maxilar rápida ou lenta, sendo vantajosa uma maior expansão maxilar em região anterior do que posterior em pacientes com FLP. É essencial que o ortodontista analise, para cada caso clínico, os efeitos causados pelos expansores maxilares, visando atingir resultados eficazes e minimizar possíveis efeitos adversos.
O parto domiciliar planejado a partir da assistência prestada pelo enfermeiro
Authors: Matos Stefhany Cristini , Molin Rossano Sartori Dal
Published: January 2025

RESUMO
Objetivo: Identificar os benefícios e os desafios do parto domiciliar planejado (PDP) a partir da assistência realizada pelo enfermeiro.  Métodos: Revisão integrativa da literatura de caráter analítico acerca dos benefícios e desafios do PDP. Foram realizadas etapas de amostragem, categorização e avaliação dos artigos, discussão e interpretação desses resultados. Utilizou-se estudos das bases de dados BVS (Biblioteca Virtual em Saúde) e PubMed, publicados a partir de 2019, disponíveis de forma gratuita e na íntegra; por meio dos descritores: Parto Domiciliar, Assistência ao Parto, Enfermagem Obstétrica e Autonomia Profissional, associados aos operadores booleanos “AND” e “OR”. Os conceitos e definições dos autores pesquisados foram preservados, com devida citação e referência. Resultados: A busca nas bases de dados BVS e PubMed, após aplicação dos critérios de inclusão e exclusão, resultou em apenas 7 artigos, evidenciando os benefícios e desafios do PDP, associados a assistência de enfermagem. Considerações finais: O parto planejado em casa oferece benefícios significativos, porém enfrenta desafios, como preconceitos sociais e a falta de políticas públicas que restringem seu acesso. Isso enfatiza a relevância da autonomia do enfermeiro obstetra na promoção de uma assistência qualificada e centrada na mulher.
Palavras-chave: Parto Domiciliar; Assistência ao Parto, Enfermagem Obstétrica, Autonomia Profissional.
فاعلية برنامج قائم على التعليم المتمايز لتدريس مادة الفقه في تنمية مهارات التفكير التأملي لدى طلاب الصف الثاني الثانوي بمحافظة صنعاء
Authors: الأشول سليم محسن صالح , الجلال محمد أحمد , العفيف حميد فرحان
Published: January 2025

هدف البحث إلى الكشف عن فاعلية التعليم المتمايز لتدريس مادة الفقه في تنمية مهارات التفكير التأملي لدى طلاب الصف الثاني ثانوي بمحافظة صنعاء، واستخدم الباحث المنهجين الوصفي أسلوب البناء والتطوير والمنهج شبه التجريبي؛ لمناسبتهما لطبيعة البحث، وتمثلت الأدوات في قائمة مهارات التفكير التأملي وتكونت من (25) مؤشرًا تندرج تحت (5) مهارات أساسية، واختبار مهارات التفكير التأملي مكونًا من (30) سؤالًا، وتكونت عينة البحث من (80) طالبًا بالصف الثاني الثانوي بمحافظة صنعاء، تم تقسيمهم إلى مجموعتين متساويتين ومتكافئتين، تجريبية وضابطة، درست المجموعة التجريبية باستخدام البرنامج المقترح القائم على استراتيجية التعليم المتمايز في الفصل الثاني من العام الدراسي 2023/ 2024م، ودرست الضابطة بالطريقة المعتادة، وتوصل البحث إلى العديد من النتائج أهمها: التوصل إلى قائمة بمهارات التفكير التأملي. وجود فروق ذات دلالة إحصائية عند مستوى دلالة (0.000) بين متوسطي درجات أفراد المجموعتين التجريبية والضابطة في التطبيق البعدي لاختبار مهارات التفكير التأملي، لصالح أفراد المجموعة التجريبية الذين تعلموا وفقًا لاستراتيجية التعليم المتمايز. وبناءً عليه فقد تم تقديم التوصيات الآتية: الإفادة من البرنامج التدريبي الذي أعده البحث الحالي في تعليم الطلبة في صفوف ومقررات أخرى. وضرورة إدراج استراتيجية التعليم المتمايز ضمن الاستراتيجيات المستخدمة في إعداد معلمي التربية الإسلامية في كليات التربية، وتدريبهم على كيفية استخدامها.
دراسة تحليلية لأسئلة الاختبارات التحصيلية ومدى قياس الكفايات المستهدفة في مادة اللغة العربية بالمرحلة المتوسطة
Authors: الحازمي خالد أحمد
Published: January 2025

هدفت الدراسة إلى تحليل أسئلة الاختبارات التحصيلية، والتعرف على مدى قياسها للكفايات المستهدفة في مادة اللغة العربية للمرحلة المتوسطة، وقد تكون مجتمع الدراسة من نماذج أسئلة الاختبارات التحصيلية للفصل الدراسي الأول للعام الدراسي 1443ه، وكان عددها (36) نموذجا، تم تحليلها بواسطة بطاقة تحليل صممها الباحث معتمدا على جدول المواصفات والأوزان النسبية للاختبار التحصيلي للفصل الدراسي الأول للعام 1443ه الصادر عن قسم اللغة العربية بإدارة تعليم جدة، وقد أظهرت النتائج أن الأسئلة التي طرحت لقياس كفايات مكون فهم المقروء جاءت بمستوى منخفض وبنسبة(41%)، وجاءت الفقرات التي تقيس كفايات مكون الوظيفة النحوية بمستوى متوفر بدرجة متوسطة ونسبة (48%)، وجاءت الأسئلة التي تقيس كفايات مكون الصنف اللغوي بنسبة(37%) ومستوى منخفض، وجاءت الأسئلة التي تقيس كفايات مكون الأسلوب اللغوي بمستوى متوفر بدرجة متوسطة ونسبة (41%)، وجاءت الأسئلة التي تقيس كفايات مكون الرسم الإملائي بمستوى متوفر بدرجة متوسطة ونسبة (51%)، وجاءت الأسئلة التي تقيس كفايات مكون الرسم الكتابي ومستوى متوفر بدرجة متوسطة بنسبة(48%). كما أظهرت النتائج أن مدى قياس الكفايات المستهدفة على مستوى الصفوف جاءت كالتالي: الصف الثالث متوسط، جاء مستوى توفر متوسط بنسبة (44.05%)، وجاء مستوى توفر الأسئلة التي تقيس الكفايات المستهدفة في الصف الأول في المرتبة الثانية بنسبة (44.4%) ومستوى متوسط، وجاء توفر أسئلة قياس الكفايات المستهدفة في الصف الثاني المتوسط في المرتبة الأخيرة كأدنى نسبة وهي (41.2%) ومتوسط أداء في مستوى متوسط.
مدى تضمين مهارات استشراف المستقبل في محتوى مقرر الذكاء الاصطناعي للمرحلة الثانوية بالمملكة العربية السعودية
Authors: العروي أميرة صالح , المحمدي نجوى عطيان
Published: January 2025

هدفت الدراسة إلى معرفة درجة تضمين مهارات استشراف المستقبل في محتوى مقرر الذكاء الاصطناعي للمرحلة الثانوية في المملكة العربية السعودية. ولتحقيق أهداف الدراسة أتبعت الباحثتين المنهج الوصفي التحليلي بأسلوب تحليل المحتوى لملائمته لطبيعة البحث، حيث قامت الباحثتان بتحليل محتوى مقرر الذكاء الاصطناعي الذي يدرسه طلاب وطالبات الصف الثالث ثانوي في المرحلة الثانوية (مسار علوم الحاسب والهندسة)، لطبعة العام الدراسي 1445 ه- الموافق2023م، باستخدام بطاقة تحليل محتوى مشتملة على (4) مهارات رئيسة تضمنت على (27) مؤشرا لقياس درجة التضمين، توزعت في أربعة مهارات رئيسة لمهارات استشراف المستقبل وهي: مهارة إثارة الخيال، مهارة حل المشكلات، ومهارة اتخاذ القرار، ومهارة المقارنة بين الأراء المختلفة، وأظهرت النتائج أن تضمين مهارات استشراف المستقبل في مقرر الذكاء الاصطناعي للمرحلة الثانوية (مسار علوم الحاسب والهندسة) بالمملكة العربية السعودية كان بنسبة (52.29%) وبدرجة تضمين متوسطة وفي ضوء نتائج الدراسة أوصت الباحثتان في تطوير محتوى مقرر الذكاء الاصطناعي المملكة العربية السعودية في ضوء مهارات استشراف المستقبل نظرا لأهمية هذه المهارات حيث تساهم في جعل الفرد متكيف مع حاضره ومستعد لتصميم وصناعة مستقبله، فالمستقبل ملك لمن يستعد له.
Development of Parallel On-the-Fly Crystal Algorithm for Reaction Discovery in Large and Complex Molecular Systems
Authors: Pandey Ankit , Costa Gustavo J. , Mushfiq Alam , Poirier Bill , Liang Ruibin
Published: January 2025

The parallel on-the-fly Crystal algorithm is a new, efficient global search algorithm that has found utility in exploring the single-state potential energy surfaces and conical intersection seam spaces of a wide range of molecules in the gas phase. Despite its major developments, its application to complex molecular systems, especially in the condensed phase, remains challenging due to the high dimensionality of the configurational space. In this work, we address this challenge and extend its applicability to the reaction discovery of large, complex molecular photoswitches in the condensed phase. This is achieved by improved search algorithms that facilitate the exploration of a comparatively large Crystal configurational subspace while relaxing the other degrees of freedom. The new Crystal algorithm is applied to bilirubin and a next-generation class of molecular photoswitches in the vacuum and the aqueous solution environment in a quantum mechanics/molecular mechanics (QM/MM) setting. We designed an automatic and systematic workflow to discover previously unreported minima and low-energy reaction pathways and analyze them with a focus on the low-energy spectrum. Our findings reveal the algorithm's effectiveness in quickly exploring the configuration space, uncovering new product minima that are kinetically accessible, which provides new insights into the intricate chemical reactivities of these molecules. The results underscore the promising potential of parallelized global exploration methods in reaction discovery in biomolecular systems.
Collective Intelligence of Specialized Language Models Guides Realization of de novo Chemical Synthesis
Authors: Li Haote , Sarkar Sumon , Lu Wenxin , Loftus Patrick , Qiu Tianyin , Shee Yu , Cuomo Abbigayle , Webster John-Paul , Kelly H. Ray , Manee Vidhyadhar , Sreekumar Sanil , Buono Frederic , Crabtree Robert , Newhouse Timothy , Batista Victor
Published: January 2025

While hundreds of thousands of new chemical reactions are reported annually, efficient use of this vast collection of synthetic knowledge remains a persistent challenge in modern chemistry. Recent applications of large language models (LLMs) have shown promise, but systems that reliably work for de novo compounds and molecular transformations have remained elusive. Here we introduce MOSAIC (Multiple Optimized Specialists for AI-Driven Chemical Prediction), a computational framework that enables chemists to harness the collective knowledge of millions of reaction protocols. In contrast to existing approaches relying on agentic models, MOSAIC leverages the open-source Llama3.1-8B-instruct architecture. By training 2,489 specialized chemical experts on Voronoi-clustered reaction spaces, we establish a scalable paradigm that delivers reproducible and human-readable experimental protocols for complex syntheses. Experimental validation demonstrates MOSAIC's ability to predict and execute previously unreported transformations, including challenging reactions via Buchwald-Hartwig amination, Suzuki coupling, and olefin metathesis. We validate this approach through the successful synthesis of over 35 novel compounds spanning pharmaceuticals, materials, agrochemicals, and cosmetics. This framework establishes a new relationship between computational and experimental chemistry, providing a foundation for accelerated chemical discovery across disciplines.
Design of Application Framework for Vital Monitoring Mobile-Based System
Authors: Rizky Ananda Muhammad , Faisal Mohammad Reza , Herteno Rudy , Nugroho Radityo Adi , Abadi Friska
Published: January 2025

In the realm of modern healthcare, continuous monitoring can leverage the affordable wearable devices available on the market to manage costs. However, these devices face several limitations, such as restricted access for other parties, including nurses and doctors, and the need for redevelopment to integrate new devices for data accessibility. This study addresses these challenges by establish an application framework tailored for mobile-based systems, by ensuring accessibility by external parties. The research contribution is encompassing two key aspects: the potential implementation of Feature-Oriented Domain Analysis (FODA) in the domain of mobile-based vital sign monitoring, particularly in the absence of prior studies addressing the same context, and the identification reusable (frozen spots) and adaptable components (hot spots), providing guidance for the development of mobile-based vital sign monitoring. FODA is utilized during the analysis activity. Design patterns are then implemented when creating class diagrams in the design activity. This study finding reveal 7 primary features and 18 sub-features essential that must be incorporated into the application framework. The framework includes 5 hot spots and 7 frozen spots, with the implementation of Strategy and Filter design patterns. In conclusion, the developed application framework represents a significant advancement in vital sign monitoring, particularly within mobile-based systems. This study emphasizing limitations in analysis and design phases. In future research, the focus will shift to the construction and stabilization phases, all crucial for refining the framework. Implementing framework in actual applications can aid in developing vital sign monitoring systems and potentially improving healthcare outcomes.
Performance of an AIOT-Particle Device for Air Quality and Environmental Data Prediction in Salatiga Area Using ARIMA Model
Authors: Kurniawan Johanes Dian , Trihandaru Suryasatriya , Parhusip Hanna Arini
Published: January 2025

This study introduces the AIOT-Particle, a compact device designed for comprehensive air quality and environmental monitoring in Tegalrejo, Salatiga, Indonesia. Addressing the need for real-time, multi-parameter environmental data, the device simultaneously tracks PM1.0, PM2.5, temperature, humidity, pressure, and altitude, utilizing a built-in data fusion algorithm to ensure accurate and coherent data collection. Air pollution standards classify air quality as "good" (0–50), "moderate" (51–100), "unhealthy" (101-200), "very unhealthy" (201-300), and "hazardous" (>300). The research contribution is the development and validation of the AIOT-Particle using the ARIMA model for precise environmental monitoring. The methods involved deploying the device in Salatiga and applying the ARIMA model to analyze the collected data for accuracy. The results demonstrated promising accuracy: for PM1.0, the RMSE was 8.13 with an MAE of 6.04; for PM2.5, the RMSE was 6.60 with an MAE of 4.49. Environmental data analysis showed an RMSE of 0.74 for temperature (MAE 0.43), 2.11 for humidity (MAE 1.36), 0.25 for pressure (MAE 0.19), and 2.18 for altitude (MAE 1.70). These findings highlight the device's potential to enhance environmental surveillance and public health assessments, advance the understanding of air quality dynamics, and support targeted interventions to mitigate environmental risks. The novelty of this study lies in the integration of multiple environmental parameters into a single monitoring device, validated for accuracy using the ARIMA model.
Study of Indirect Vector Control Induction Motor Based on Takagi Sugeno Type Fuzzy Logic on Rotational Speed Control Primary Surveillance Radar
Authors: Setiawan Paulus , Dharmawan Muchamad Wizdan , Santoso Prasidananto Nur , Pratiwi Elisabeth Anna , Dinaryanto Okto
Published: January 2025

During the rainy season and bad weather, strong winds blowing at an airport can cause dynamic changes in performance of the primary surveillance radar (PSR) antenna which is driven by an induction motor (IM). Changes in dynamic performance that occur in this IM can be in the form of changes in PSR rotation speed, changes in torque values, and changes in stator current values. In this article, we propose the application of the Takagi Sugeno method to fuzzy logic indirect vector control of IM as a solution that can reduce changes in the dynamic performance of motor as PSR drivers during bad weather. The contribution of this research is the application of the Takagi Sugeno method in a fuzzy inference system (FIS), where this fuzzy logic control system replaces the conventional proportional integral (PI) controller for indirect vector control IM. Takagi Sugeno method is computationally efficient and works well with optimization and adaptive techniques, which makes it very attractive in control problems, particularly for dynamic nonlinear systems. Takagi Sugeno type FIS uses weighted average to compute the crisp output, so the Sugeno’s output membership functions are either linear or constant. Furthermore, Takagi Sugeno method has better processing time since the weighted average replace the time consuming defuzzification process. The results obtained after simulation in MATLAB Simulink environment showed that fuzzy logic using the Takagi Sugeno method which is used as a substitute controller for indirect vector control can provide better performance when compared to conventional PI controllers. These results can be seen from the response values of rotor rotation speed, electromagnetic torque, and stator current. Overall, this research provides discourse on improving the dynamic performance of IM through the application of the Takagi Sugeno fuzzy logic indirect vector control method.
Word Embedding Feature for Improvement Machine Learning Performance in Sentiment Analysis Disney Plus Hotstar Comments
Authors: Jasmir Jasmir , Nurhadi Nurhadi , Rohaini Eni , Pahlevi B M Riza , Pardamean Simanjuntak Daniel Sintong
Published: January 2025

In this research we apply several machine learning methods and word embedding features to process social media data, specifically comments on the Disney Plus Hotstar application. The word embedding features used include Word2Vec, GloVe, and FastText. Our aim is to evaluate the impact of these features on the classification performance of machine learning methods such as Naive Bayes (NB), K-Nearest Neighbor (KNN), and Random Forest (RF). NB is very simple and efficient and very sensitive to feature selection. Meanwhile, KNN is known for its weaknesses such as biased k values, overly complex computations, memory limitations, and ignoring irrelevant attributes. Then RF has a weakness, namely that the evaluation value can change significantly with just a slight change in the data. Feature selection in text classification is crucial for enhancing scalability, efficiency, and accuracy. Our testing results indicate that KNN achieved the highest accuracy both before and after feature selection. The FastText feature led to the highest performance for KNN, yielding balanced accuracy, precision, recall, and F1-score values.
Random Search-Based Parameter Optimization on Binary Classifiers for Software Defect Prediction
Authors: Ali Misbah , Azam Muhammad Sohaib , Shahzad Tariq
Published: January 2025

Machine learning classifiers consist of a set of parameters. The efficiency of these classifiers in the context of software defect prediction is greatly impacted by the parameters chosen to execute the classifiers. These parameters can be optimized to achieve more accurate results. In this research, the efficiency of binary classifiers for software defect prediction is analyzed through parameter optimization using random search technique. Three heterogeneous binary classifiers i.e., Decision tree, Support vector machine, and Naïve Bayes are selected to examine the results of parameter optimization. The experiments were performed on seven publicly available NASA Datasets. The dataset was split into 70-30 proportions with class preservation. To evaluate the performance; five statistical measures have been implemented i.e., precision, recall, F-Measure, the area under the curve (AUC), and accuracy. The findings of the research revealed that there is significant improvement in accuracy for each classifier. On average, decision tree improved from 88.1% to 95.4%; support vector machine enhanced the accuracy from 94.3% to 99.9%. While Naïve Bayes showed an accuracy boost from 74.9% to 85.3%. This research contributes to the field of machine learning by presenting comparative analysis of accuracy improvements using default parameters and optimized parameters through random search. The results presented that he performance of binary classifiers in the context of software prediction can be enhanced to a great extent by employing parameter optimization using random search.
Analysis Kernel and Feature: Impact on Classification Performance on Speech Emotion Using Machine Learning
Authors: Gondohanindijo Jutono , Noersasongko Edi , Pujiono Pujiono , Muljono Muljono
Published: January 2025

The main objective of this study is to test the machine learning kernel's selection against the characteristics of the data set used, resulting in good classification performance. The goal of speech emotion recognition is to improve computers' ability to detect and process human emotions in order to improve their ability to respond to interactions between people and computers. It can be applied to feedback on talks, including sentimental or emotional content, as well as the detection of human mental health. One field of data mining work is Speech Emotion Recognition. One of the important things in data mining research is to determine the selection of the kernel Classifier, know the characteristics of datasets, perform Engineering Features and combine features and Corpus Datasets to obtain high accuracy. The research uses analysis and comparison methods using private and public datasets to detect speech emotions. Experimental analysis was done on the characteristics of datasets, selection of kernel classifiers, pre-processing, feature and corpus datasets fusion. Understanding the selection of a classifier kernel that matches the characteristics of the dataset, engineering features and the merger of features and datasets are the contributions of this investigation to improving the accuracy of the classification of speech emotion data. For models with the selection of kernels that match the characteristics of their datasets, this study gave an increase in accuracy of 12.30% for the private dataset and 14.80% for the public dataset, with accuracies of 100.00% and 74.80% respectively. Combining features and public datasets provides an increase in accuracy of 33.62% with an accuracy of 73.95%.
Reducing Overfitting in Neural Networks for Text Classification Using Kaggle's IMDB Movie Reviews Dataset
Authors: Poningsih Poningsih , Windarto Agus Perdana , Alkhairi Putrama
Published: January 2025

Overfitting presents a significant challenge in developing text classification models using neural networks, as it occurs when models learn too much from the training data, including noise and specific details, resulting in poor performance on new, unseen data. This study addresses this issue by exploring overfitting reduction techniques to enhance the generalization of neural networks in text classification tasks using the IMDB movie review dataset from Kaggle. The research aims to provide insights into effective methods to reduce overfitting, thereby improving the performance and reliability of text classification models in practical applications. The methodology involves developing two LSTM neural network models: a standard model without overfitting reduction techniques and an enhanced model incorporating dropout and early stopping. The IMDB dataset is preprocessed to convert reviews into sequences suitable for input into the LSTM models. Both models are trained, and their performances are compared using various metrics. The model without overfitting reduction techniques shows a test loss of 0.4724 and a test accuracy of 86.81%. Its precision, recall, and F1-score for classifying negative reviews are 0.91, 0.82, and 0.86, respectively, and for positive reviews are 0.84, 0.92, and 0.87. The enhanced model, incorporating dropout and early stopping, demonstrates improved performance with a lower test loss of 0.2807 and a higher test accuracy of 88.61%. For negative reviews, its precision, recall, and F1-score are 0.92, 0.84, and 0.88, and for positive reviews are 0.86, 0.93, and 0.89. Overall, the enhanced model achieves better metrics, with an accuracy of 89%, and macro and weighted averages for precision, recall, and F1-score all at 0.89. The applying overfitting reduction techniques significantly enhances the model's performance.
Performance of Lexical Resource and Manual Labeling on Long Short-Term Memory Model for Text Classification
Authors: Hayaty Mardhiya , Pratama Aqsal Harris
Published: January 2025

Data labeling is a critical aspect of sentiment analysis that requires assigning labels to text data to reflect the sentiment expressed. Traditional methods of data labeling involve manual annotation by human annotators, which can be both time-consuming and costly when handling large volumes of text data. Automation of the data labeling process can be achieved through the utilization of lexicon resources, which consist of pre-labeled dictionaries or databases of words and phrases in sentiment information. The contribution of this study is an evaluation of the performance of lexicon resources in document labeling. The evaluation aims to provide insight into the accuracy of using lexicon resources and inform future research. In this study, a publicly available dataset was utilized and labeled as negative, neutral, and positive. To generate new labels, a lexicon resource such as VADER, AFINN, SentiWordNet, and Liu & Hu was employed. An LSTM model was then trained using the newly generated labels. The performance of the trained model was evaluated by testing it on data that had been manually labeled. The study found manual labeling led to highest accuracy of 0.79, 0.80, and 0.80 for training, validation, and testing respectively. This is likely due to manual creation of test data labels, enabling the model to learn and capture balanced patterns. Models using lexicon resources (VADER and AFINN) had lower accuracy of 0.54 and 0.56. SentiWordNet had lowest accuracy among all models with 0.49, and Liu&Hu model had the lowest testing score of 0.26. Our research indicates that lexicon resources alone are not sufficient for sentiment data labeling as they are dependent on pre-defined dictionaries and may not fully capture the context of words within a sentence, thus, manual labeling is necessary to complement lexicon-based methods to achieve better result.
Evaluating Sampling Techniques for Healthcare Insurance Fraud Detection in Imbalanced Dataset
Authors: Lopo Joanito Agili , Hartomo Kristoko Dwi
Published: January 2025

Detecting fraud in the healthcare insurance dataset is challenging due to severe class imbalance, where fraud cases are rare compared to non-fraud cases. Various techniques have been applied to address this problem, such as oversampling and undersampling methods. However, there is a lack of comparison and evaluation of these sampling methods. Therefore, the research contribution of this study is to conduct a comprehensive evaluation of the different sampling methods in different class distributions, utilizing multiple evaluation metrics, including , , , Precision, and Recall. In addition, a model evaluation approach be proposed to address the issue of inconsistent scores in different metrics. This study employs a real-world dataset with the XGBoost algorithm utilized alongside widely used data sampling techniques such as Random Oversampling and Undersampling, SMOTE, and Instance Hardness Threshold. Results indicate that Random Oversampling and Undersampling perform well in the 50% distribution, while SMOTE and Instance Hardness Threshold methods are more effective in the 70% distribution. Instance Hardness Threshold performs best in the 90% distribution. The 70% distribution is more robust with the SMOTE and Instance Hardness Threshold, particularly in the consistent score in different metrics, although they have longer computation times. These models consistently performed well across all evaluation metrics, indicating their ability to generalize to new unseen data in both the minority and majority classes. The study also identifies key features such as costs, diagnosis codes, type of healthcare service, gender, and severity level of diseases, which are important for accurate healthcare insurance fraud detection. These findings could be valuable for healthcare providers to make informed decisions with lower risks. A well-performing fraud detection model ensures the accurate classification of fraud and non-fraud cases. The findings also can be used by healthcare insurance providers to develop more effective fraud detection and prevention strategies.