bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing story writing through collaborative work: Case study in a Colombian educational institution (2022)
Authors: Osorío-Marín Gerardo , Mejía-Ríos Jennifer , Díaz-Téllez Ángel Saúl
Published: July 2024

The integration of collaborative work in educational processes has proven to be an effective strategy in various fields of knowledge. This study focuses on the writing of stories within the Filadelfia Educational Institution in Caldas, Colombia, with the objective of analyzing the transformations generated by collaborative work in the writing of stories and the development of students' narrative skills. Such an objective is assumed to be an opportunity for the understanding and application of narrative theory and the basic principles of writing as a communication system. A qualitative route was adopted, involving 40 students from the institution. Data were collected through direct observation, field diaries, and narrative analysis of the texts produced by the participants. The selection of the students was carried out considering an intentional sample with writing skills for the school stage as criteria, and there was informed consent for the research. The findings indicate that students not only improved in their ability to apply the basic principles of writing and story theory, but they were also motivated to actively participate in the reading and writing processes. Collaborative work facilitated the collective construction of knowledge, improved communication between students and teachers, and promoted a dynamic of meaningful and participatory learning. This study concludes that collaborative work is a valuable pedagogical strategy to improve story writing. The results suggest that encouraging interaction and knowledge construction in groups can have a significant positive impact on learning narrative writing. These findings have important implications for the teaching of literature and writing in educational contexts, suggesting the need to integrate collaborative approaches into the curriculum.
Lion head colorful tie-dye at Foshan city, Guangdong Province: National representative and revitalizing self-identity in the process of chinesenization
Authors: Ji Xinyu , Sirisuk Metta
Published: July 2024

This hybrid study combines qualitative and quantitative data collection, analysis, and synthesis. Using this method, we can understand the Lion Head Colorful Tie-dye culture from different angles and levels, improving the validity and credibility of the research. The objectives of this study are: 1) to study Foshan lion head colored tie-dye from the perspective of cultural identity; 2) to study and analyze the process of Foshan lion head Colorful Tie-dye becoming National Representative; 3) to study and analyze the role of Foshan lion head Colorful Tie-dye in the revitalization of self-identity in the process of Chinesenization. The study considered data collected from academic literature, data collected in the field, and questionnaire and semistructured interview data for overseas and ethnic Chinese. It was conducted using the concepts of national symbolism and the revitalization of self-identity. The research results show that the meaning, social relations, and cultural status of the lion head color tie are constantly changing with the changes in different periods. Whether in daily life or major ceremonies, Foshan Lion Head continues to build self-identity and cultural identity. In the context of globalization and with the acceleration of the process of Chinesenization, the phenomenon of Foshan lion heads being colored as a national symbol came into being, and they continued to show their identity to the outside world, gradually forming a revitalization of their self-identity. This research will provide new perspectives and methods for cultural studies, cross-cultural communication, and the study of the Chinesenization process, which can better understand the connotation and significance of Chinese traditional culture, promote cultural understanding and exchanges, and contribute to promoting cultural diversity and cross-cultural communication. Moreover, cultural equality plays an essential role.
The influence of Istighfar Dhikr on brain wave activity: An EEG-based study on anxiety management
Authors: Sumarti Heni , Tasyakuranti Alvania Nabila , Zulfikah Putri , Edison Rizki Edmi
Published: July 2024

In the domain of leadership, individuals are not immune to the psychological ramifications of their roles, with anxiety being a notable byproduct of cerebral processes. Within the Islamic faith, the practice of istighfar, or seeking forgiveness, is promulgated as a remedial technique to mitigate such anxieties. To evaluate the impact of istighfar on cerebral functions quantitatively and impartially, this investigation employed electroencephalography (EEG) to monitor neural activity. Engaging 24 young adults, evenly distributed across genders, this study meticulously recorded neural oscillations amidst induced stress and subsequent istighfar invocation. Advanced analytical methods, including power spectral density and logistic binary regression, were applied to discern the influence of istighfar on neural wave patterns. The results illuminated a gender-based divergence in neural dominance post istighfar: males exhibited alpha wave activity, whereas females showed a pronounced shift in beta wave activity. The implications of this study are threefold: first, it establishes EEG as a reliable instrument for the objective measurement of neural activity alterations in anxious states; second, it corroborates the efficacy of istighfar in modulating neural wave dominance, thereby contributing to anxiety reduction; and third, it unveils gender-specific neural response patterns to istighfar. This inquiry not only augments the corpus of knowledge regarding the interplay of spirituality and neurology but also endorses istighfar as a potentially valuable intervention for anxiety management, thus bridging the gap between contemplative traditions and contemporary scientific validation.
Evaluating the levels of C-reactive protein (CRP) among post vaccinated subjects of COVID-19 vaccine: A cohort study
Authors: Barthwal Shilpa , Angel Bennet , Angel Annette , Joshi Vinod , Praveen Uppu , Bhardwaj Urmila Devi , M. Shareef B. , Khaneja Poorna , Joshi Ramesh , Dheer Monika , Kumari Khushbu , Singh Neha , Sharma Bhawna , Maqbool Peer Nuzhat , Khan Ambreen Shafaat , Chitransh Aarya , Chauhan Reshu , Sailo Lalmalsawmi
Published: July 2024

Levels of C-Reactive Protein (CRP) serve as indicators of inflammation and disease severity. During the pandemic of COVID-19, many severe patients had shown increased levels of CRP. Few studies have also indicated role of CRP among subjects vaccinated by COVID-19 vaccines. In this present study, we report study of levels of CRP among subjects vaccinated by Covishield and Covaxin. The study was made under five categories of post vaccinated subjects viz; Not Infected-Non Vaccinated; Infected-Vaccinated-Reinfected; Post Vaccinated Infected; Infected-Vaccinated and Non Infected-Vaccinated. Their blood was drawn and serum separated for the study. The Patient Information Sheet and Patient consent form was filled prior to collection of blood sample. CRP detection was done using the ELISA based assay (hs-CRP ELISA kit, DBC, Inc). A total of 88 individuals were studied. It was observed that out of these 5 categories maximum increase in CRP levels were seen in individuals who were Noninfected and Vaccinated. This suggests the increased CRP levels among Vaccinated subjects leading to possibility of immune malfunction post vaccination.
Enhancing participant satisfaction in educational programs for poverty reduction: Insights from a regional study in Vietnam
Authors: Nguyen Hai Trung , Dang Dung Huu
Published: July 2024

This comprehensive review explores the intricate relationship between education and poverty reduction, addressing the global landscape, challenges, and transformative potential of educational interventions. With more than nine percent of the global population enduring extreme poverty, understanding the nuanced dynamics of poverty becomes paramount. Education has emerged as a pivotal catalyst for sustainable change, empowering individuals with knowledge and skills to break cycles of deprivation. However, persistent challenges such as limited access to quality education and socioeconomic disparities underscore the need for innovative and inclusive approaches. This research investigates participant satisfaction within an educational program aimed at poverty reduction, with a primary focus on the "knowledge of poverty reduction" category, which constitutes 61% of the educational content. The study assessed participant satisfaction levels in three distinct regions: Ha Long, Van Don, and Ba Che in Quang Ninh Province, Vietnam. The results revealed that the highest level of satisfaction, "Strongly Satisfied," was reported by 50% of participants overall, indicating that a substantial majority expressed high contentment with group educational activities. This endorsement suggested that the program not only met but also exceeded the expectations of a significant portion of participants. These findings underscore the regional nuances in participant satisfaction and emphasize the importance of tailored strategies for effective educational programs in poverty reduction. The study contributes valuable insights to the field, informing future endeavors to address diverse regional needs and preferences.
Genome-wide association study reveals polymorphisms associated with total number born in pigs in mexican Landrace-Yorkshire sows
Authors: Duifhuis Rivera Theodor , Lemus-Flores Clemente , Fernández López J. C. , Valdovinos Miguel Angel Ayala , Chipres David Román Sánchez , García Jorge Galindo , Núñez Fernando Grageola
Published: July 2024

Genome-wide association studies (GWAS) are based on the field of animal genetics, and have the power to detect mutations, facilitating the identification of diverse productive traits. This study aimed to elucidate how single-nucleotide polymorphisms (SNPs) affect the number of piglets born alive (NBA) per litter. Forty-eight interbreed Landrace-Yorkshire sows, of which 33 sows had high estimated breeding value (HEBV) scores and 15 sows had low estimated breeding value (LEBV) from two different production locations in western Mexico were genotyped by microarrays. In total, nine SNPs were considered to be associated with NBA traits. These loci were located on chromosomes 3, 9, 11, 12, and 17. This study identified a statistically important marker (rs81283526), which was located in the ABCC1 gene on chromosome 3, potentially suggesting an association with the number of piglets born alive trait in Mexican Landrace-Yorkshire sows. The marker-assisted selection (MAS) approach using the signals described in this study could be used to apply them towards enhancing pig breeding programs.
A qualitative study on the structure, functions and roles of social work in some public hospitals in Vietnam
Authors: Dang Ly , Minh Bui
Published: July 2024

Social work in hospitals around the world has a history of over 100 years. However, systematic social work in hospitals in Vietnam has only been conducted for the past 10 years. The development of social work in general and hospital social work in particular depends on various factors, resulting in differences in the roles of social work in practice among different types of hospitals and between countries with different socioeconomic contexts. This qualitative study examines the structure, functions and actual roles of social work in public hospitals in Vietnam in comparison with other countries. This study was conducted in 2021, 44 in-depth interviews were conducted with key stakeholders who develop and implement social work activities in 07 hospitals in Vietnam. The research focus on analysing the administrative structure and the functions/roles of hospital social workers, finding out barriers that undermine the development of hospital social work in Vietnam. The research outline and tools were approved by a professional evaluation board of Vietnam National University (VNU Hanoi). We conducted a defense to attain an official permission from the Ethical Research Council in National Children’s hospital, one of the main research sites and obtained certificate 393/BVNTW-VNCSKTE dated March 02, 2021. With its centralized model, hospital social work conducts three groups of functions, including: Supporting function, Cooperation function and other independent functions. It is interesting that while social work often incurs costs on hospital operations in other countries, it generates financial resources for hospitals in Vietnam. This is also the reason for the rapid development of hospital social work in Vietnam over the past 10 years.
Does disruptive technology and AI (Artificial Intelligence) influence logistics management?
Authors: Ramasamy Indradevi , Natarajan Sathya , Sathyamoorthy Vinod Kumar P.
Published: July 2024

Disruptive technologies like AI, automation, and IoT are transforming logistics and distribution management, driving efficiency, cost savings, and improved customer experiences. This empirical investigation investigates the role of these technologies in optimizing operations, enhancing decision-making, and building resilient supply chains. The study analyzes the impact of disruptive technologies on various aspects of logistics, including inventory management, route optimization, predictive maintenance, and last-mile delivery. By leveraging real-time data and advanced analytics, AI algorithms enable accurate demand forecasting, automated replenishment systems, and personalized delivery options, leading to increased efficiency and customer satisfaction. The integration of IoT sensors facilitates real-time tracking of shipments and environmental conditions, enhancing visibility and enabling proactive issue resolution. However, ethical considerations surrounding AI, workforce transformation, and cybersecurity pose challenges that require careful navigation. Study survey was conducted among 216 people from logistics and distribution sector to know “role of disruptive technology and artificial intelligence in effective logistics and distribution management.” The study concludes that there is a significant impact of disruptive technology and artificial intelligence on logistics and distribution management.
Harsh punishment and aggressive behavior in vietnamese adolescents: The role of gender of parents and adolescents
Authors: Vu Ba Tuan , Tran Tu Thi Cam , Vu Hoa Le , Dam Anh Thi Van , Nguyen Thuy Quynh
Published: July 2024

Recent studies have highlighted the detrimental effects of harsh punishment on psychopathology development in Vietnam, predominantly focusing on the father's role. However, there remains a gap in understanding how harsh punishment impacts children based on parental gender and child gender differences. The current study aims to address this gap by examining the influence of parental gender on harsh punishment in Vietnamese parents and assessing the association between paternal and maternal harsh punishment and aggressive behavior in boys and girls. There were a total of 1011 Vietnamese adolescents (48.2% girls and Mage = 12.9, SD = 1.16, range = 11.0 – 16.1) taking part in the study. Results suggested that fathers and mothers exhibited similar levels of harsh punishment in their parenting. Additionally, harsh paternal punishment is linked equally to aggressive behavior among boys and girls. In contrast, maternal harsh punishment is linked more to aggressive behavior in boys than to aggressive behavior in girls. The findings underscore the widespread use of harsh punishment in both Vietnamese fathers and mothers and highlight that harsh paternal punishment is more related to aggressive behavior in children compared to harsh maternal punishment.
Relationship between Knowledge and Working Period with Compliance with the Use of Pesticide Spray PPE by Farmers in Triyagan Village
Authors: Susanto Agus , Syahrotun Nisa Suratna Farhana , Sari Yulia
Published: July 2024

In 2019, there were 147 cases of agricultural pesticide poisoning in Indonesia. Preventing pesticide poisoning requires a high knowledge of personal protective equipment (PPE) and extensive work experience among farmers. This helps farmers become more skilled and aware of the risks, making them more careful and compliant with PPE usage. This study aims to determine the relationship between knowledge and years of service with compliance in using PPE when spraying pesticides. The research is analytical and observational with a cross-sectional design. Respondents were 57 farmers from Triyagan Village, all members of farmer groups. Total sampling was used to select participants. The research instruments included a questionnaire to assess farmers' knowledge and years of service and an observation sheet to evaluate PPE compliance. Data were analyzed using the gamma test and logistic regression. Bivariate analysis with the gamma test revealed a significant relationship between knowledge and years of service with PPE compliance among farmers in Triyagan Village (p = 0.033; r = 0.525; p = 0.037; r = 0.402). Multivariate analysis with logistic regression further confirmed the significant relationship, indicating that knowledge had a greater influence on PPE compliance than years of service (p = 0.033; OR = 3.319; p = 0.037; OR = 1.708). In conclusion, knowledge and tenure are significantly related to PPE compliance among farmers in Triyagan Village. Enhanced knowledge appears to be the critical factor in properly using PPE when spraying pesticides.
The Effect of Motored Houses on Coliform Pollution in Marine Waters and Its Impact On Public Health, Jayapura City
Authors: Simbiak Herlin Mienatha , Tania Imoliana Wilhelmina , Krismono Krismono , Rahardjo Djoko
Published: July 2024

An anchored house, which stands above seawater, is common in North Jayapura and South Jayapura. Wooden foundations support these houses. People frequently contract waterborne diseases when they use or consume water that is contaminated with pathogenic bacteria, such as coliform bacteria from human feces. In these areas, high levels of coliform bacteria can have an impact on water quality and public health. This study looks into how anchored houses affect total coliform contamination, the number of cases of waterborne disease (WBD), and the link between demographic factors, water quality, and WBD cases. We use the Most Probable Number (MPN) method to detect total coliform bacteria in marine waters. It consists of an initial test using LBS and LBD media for prediction, followed by a second test using Brilliant Green Lactose Bile Broth (BGLBB) media for confirmation. We also use observation and interview methods to identify WBD cases in anchored housing in North Jayapura and South Jayapura districts. North Jayapura has the highest coliform bacteria contamination in marine waters, with an average of 25.7 MPN/100 mL. Similarly, the highest contamination in clean water is in North Jayapura, averaging around 347.6 MPN/100 mL. The demographic characteristics in both districts are mostly men aged 18–65 years with high school education and occupations such as housewives and fishermen. The South Jayapura sub-district has the highest incidence of WBD. Anchored houses have no significant effect on water quality or the incidence of WBD, and there is no correlation between water quality and WBD incidence.
The Relationship of Noise Intensity and Hypertension in Communities Living Around Kendal Regency Railways
Authors: Istiqomah Istiqomah , Setiani Onny , Sulistiyani Sulistiyani , Budiyono Budiyono
Published: July 2024

Railway traffic contributes significantly to noise pollution in urban areas, adversely affecting human health. This study focuses on the impact of train noise on hypertension among residents living near the Kendal Regency railroad tracks. With 105 participants residing within 0–30 meters from the tracks, aged 25–64, the study utilized purposive sampling to select 50 individuals. The average noise intensity measured was 73.1975 dBA, exceeding the permissible limit of 70 dBA. While the mean systolic and diastolic blood pressures indicated pre-hypertension, statistical analysis revealed a significant association between age and hypertension incidence (p = 0.042), with those over 40 facing a 4.5 times higher risk. However, no significant association was found between noise intensity and hypertension incidence (p = 0.292), systolic (p = 0.312), or diastolic blood pressure (p = 0.729). Additionally, factors such as distance from home, length of stay, rest, BMI, water consumption, and physical activity showed no significant association with hypertension incidence (p > 0.05). These findings suggest that while age is crucial in hypertension risk, noise intensity from railway traffic may not be a primary contributing factor. Further research is warranted to explore additional variables impacting hypertension in railway-adjacent communities.
Extract Ethanol of Tempuyung (Sonchus arvensis) Leaves as Anti-Hyperuricemia: In Vitro Studies
Authors: Parisa Nita , Hidayat Rachmat , Fatmawati Fatmawati
Published: July 2024

Hyperuricemia is a condition where there is an increase in blood uric acid levels above 7 mg/dL in men and 6 mg/dl in women. One of the enzymes that plays a role in hyperuricemia is xanthine oxidase which can inhibit uric acid synthesis. Tempuyung leaves (Sonchus avernis) are one of the plants that have antihyperuricemia properties. This study aims to determine the effect of ethanol extract of tempuyung leaves on xanthine oxidase in vitro. Tempuyung leaves come from Palembang, South Sumatra, Indonesia, and are macerated with 96% ethanol until a thick extract is obtained. The xanthine oxidase inhibition test was carried out on tempuyung leaf extract and Allopurinol with respective concentrations of 6.25; 12.5; 25; and 50 ppm and followed by absorption measurements using UV-V spectrophotometry at a wavelength of 293 nm and determining the IC50 value. The research results showed that the ethanol extract of daun tempuyung leaves had an IC50 value of 23.37 ppm, higher than allopurinol of 17.16 ppm which was used as a reference. These results classify the ethanol extract of daun tempuyung leaves as a strong inhibitor of the xanthin oxidase enzyme. Therefore, the ethanol extract of daun tempuyung leaves has the potential to be anti hyperuricemic.
Examining Transit Service Improvements with Internet-of-Things (IoT): A Disparity Analysis
Authors: Chandra Shailesh , Valencia Robert , Oruganti Vamsi Krishna
Published: July 2024

Literature shows that poor service reliability of transit often leads to uncertain waiting times at transit stations, diminishing their popularity and usage, and this particularly affects low-income populations more likely to rely heavily on transit. This research delves into an in-depth analysis of the inequality assessment of the Los Angeles (LA) Metro Rail lines, with a particular focus on evaluating the potential impacts of integrating Internet of Things (IoT) technology. This study investigates how IoT could influence service connectivity, accessibility, and the existing disparities in transit services. The primary goal of this study is to assess how the implementation of IoT technology could enhance the connectivity and accessibility of LA Metro Rail services, and whether such technological advancements could contribute to mitigating service inequality. The research methodology encompasses a comprehensive analysis of connectivity and accessibility values across all LA Metro Rail lines for the years 2015, 2017, and 2019. This approach allows for evaluating the potential impact of IoT technology on service connectivity and accessibility for inequality. The study considers several factors, including potential ridership based on low-income populations’ proximity to transit stations and the integration process of IoT technologies within the rail service infrastructure. The study reveals that IoT implementation could significantly improve service accessibility across all rail lines, indicating a positive trend in technological advancements enhancing public transit. However, the impact on reducing service inequality is inconsistent. Notably, Rail Line B would experience fluctuating accessibility with IoT, while Rail Line C showed persistent inequality under IoT deployment scenarios. These findings suggest that while IoT could hold promise for enhancing service quality, its effectiveness is subject to a range of influencing factors, scheduled frequency and stops at stations for the transit rail lines as well as the presence of low-income populations near these stations.
Нарощування видобутку вуглеводнів у межах Крукеницької западини Більче-Волицької зони Передкарпатського прогину
Authors: Fedoryshyn D. D. , Trubenko O. M. , Fedoryshyn S. D. , Mykhailovskyi I. Z. , Sulyma O. D.
Published: July 2024

Нарощування видобутку вуглеводнів в Україні передбачає створення і впровадження у виробництво нових перспективних методико-технологічних засобів петрофізичного забезпечення та супроводу буріння пошукових площ, які базуються на сучасних досягненнях науки і техніки. Аналіз геологічної будови літолого-стратиграфічних товщ неогенової системи Крукеницької западини Більче-Волицької зони Передкарпатського прогину підтвердив, що в кожному окремо взятому випадку є свої особливості як у складі будови матриці гірської породи, так і в умовах її утворення. За результатами аналізу та оцінки якості інтерпретації фізичних параметрів геофізичних досліджень складнопобудованих порід-колекторів неогенових відкладів встановлено вплив на показники електричних, радіоактивних та акустичних вимірювань мінерального складу типу глинистості, температури і пластового тиску. Електрометричні методи, які входять у типовий комплекс ГДС, мають свої обмеження при реєстрації значень питомого електричного опору, зокрема недостатню роздільну здатність та неоднозначність оцінки характеру насичення при дослідженні складнопобудованих порід-колекторів.У процесі оцінки характеру насичення складнопобудованих порід-колекторів неогенових відкладів виявлено наявність водонасичених порід у покрівлі газонасичених пластів без суцільної перемички, що також обумовлює обводнення пластів продуктивних горизонтів, таким чином відбувається зниження видобутку газу і конденсату. Піщані породи нижньодашавської підсвіти характеризуються, крім нормального перешарування, наявністю скупчень вуглеводнів у лінзовидних пастках, з різноманітними петрофізичними параметрами. Врахування впливу тонкошаруватості неогенових відкладів на зареєстровані параметри методів геофізичних досліджень свердловин дасть змогу підвищити їх інформативність та ефективність під час виділення газонасичених порід-колекторів складної будови.
How to classify domain entities into top-level ontology concepts using large language models
Authors: Lopes Alcides , Carbonera Joel , Rodrigues Fabricio , Garcia Luan , Abel Mara
Published: July 2024

Classifying domain entities into their respective top-level ontology concepts is a complex problem that typically demands manual analysis and deep expertise in the domain of interest and ontology engineering. Using an efficient approach to classify domain entities enhances data integration, interoperability, and the semantic clarity of ontologies, which are crucial for structured knowledge representation and modeling. Based on this, our main motivation is to help an ontology engineer with an automated approach to classify domain entities into top-level ontology concepts using informal definitions of these domain entities during the ontology development process. In this context, we hypothesize that the informal definitions encapsulate semantic information crucial for associating domain entities with specific top-level ontology concepts. Our approach leverages state-of-the-art language models to explore our hypothesis across multiple languages and informal definitions from different knowledge resources. In order to evaluate our proposal, we extracted multi-label datasets from the alignment of the OntoWordNet ontology and the BabelNet semantic network, covering the entire structure of the Dolce-Lite-Plus top-level ontology from most generic to most specific concepts. These datasets contain several different textual representation approaches of domain entities, including terms, example sentences, and informal definitions. Our experiments conducted 3 study cases, investigating the effectiveness of our proposal across different textual representation approaches, languages, and knowledge resources. We demonstrate that the best results are achieved using a classification pipeline with a K-Nearest Neighbor (KNN) method to classify the embedding representation of informal definitions from the Mistral large language model. The findings underscore the potential of informal definitions in reflecting top-level ontology concepts and point towards developing automated tools that could significantly aid ontology engineers during the ontology development process.
Development of neuro-Behcet’s disease in a patient with operable HER2-positive breast cancer during neoadjuvant chemotherapy: A case report
Authors: Nakai Taketo , Seki Hirohito , Makino Akitsugu , Kadota Yoshie , Kaburagi Takuji , Okumura Takehiro , Karahashi Tsuyoshi
Published: July 2024

BACKGROUND: Neuro-Behcet’s disease (NBD) is a variant of Behcet’s disease (BD). To our knowledge, there have been no previous reports on concurrent NBD in breast cancer patients undergoing chemotherapy. CASE PRESENTATION: Our patient had a history of BD and was asymptomatic. She was diagnosed with human epidermal growth factor receptor 2-positive breast cancer by core needle biopsy and was administered neoadjuvant chemotherapy. After four courses, in addition to the aggravation of the existing adverse events, headache, fever, dysarthria, and muscle weakness in the upper left and lower extremities appeared. On admission, she was diagnosed with acute NBD, and steroid therapy was initiated. After her symptoms improved gradually, she was discharged. Then, she underwent mastectomy and axillary lymph node dissection for breast cancer. Trastuzumab and pertuzumab plus tamoxifen were administered postoperatively. Two years postoperatively, no recurrence of breast cancer and NBD was noted. CONCLUSION: When chemotherapy is administered to breast cancer patients with a history of BD, it is necessary to select chemotherapy with as few adverse events as possible and to continue with treatment while paying attention to the risk of NBD.
Tomato maturity stage prediction based on vision transformer and deep convolution neural networks
Authors: Nahak Pradeep , Pratihar Dilip Kumar , Deb Alok Kanti
Published: July 2024

Automated assessment of tomato crop maturity is vital for improving agricultural productivity and reducing food waste. Traditionally, farmers have relied on visual inspection and manual assessment to predict tomato maturity, which is prone to human error and time-consuming. Computer vision and deep learning automate this process by analysing visual characteristics, enabling data-driven harvest decisions, optimising quality, and reducing waste for sustainable and efficient agriculture. This research demonstrates deep learning models accurately classifying tomato maturity stages using computer vision techniques, utilising a novel dataset of 4,353 tomato images. The Vision Transformer (ViT) model exhibited superior performance in classifying tomatoes into three ripeness categories (immature, mature, and partially mature), achieving a remarkable testing accuracy of 98.67% and the Convolution neural network (CNN) models, including EfficientNetB1, EfficientNetB5, EfficientNetB7, InceptionV3, ResNet50, and VGG16, achieved testing accuracies of 88.52%, 89.84%, 91.16%, 90.94%, 93.15%, and 92.27%, respectively, when tested with unseen data. ViT significantly surpassed the performance of CNN models. This research highlights the potential for deploying ViT in agricultural environments to monitor tomato maturity stages and packaging facilities smartly. Transformer-based systems could substantially reduce food waste and improve producer profits and productivity by optimising fruit harvest time and sorting decisions.
RELASI KRISTEN DAN ISLAM DALAM KERANGKA MODERASI BERAGAMA DI DESA HOYANE, KECAMATAN SEKO, LUWU UTARA
Authors: Rumbi Frans Paillin , Paonganan Semar
Published: July 2024

In this research, we use the notion of religious moderation to examine the relationship between Christians and Muslims in Hoyane village, Seko District, North Luwu. The perspectives of Christians are highlighted in this study. In the research locus, information was gathered via interviews and observation. To find out their opinions on dealing with Muslims, we spoke with leaders in the Christian community. The study's findings demonstrate that the existence of tabligh congregations strains ties between religions. Nonetheless, Christians attempt to live with this by maintaining their relationships with Muslims. Acknowledge the growing religious consciousness among Muslims and cultivate a welcoming and tolerant mindset. They also reinforce interfaith cooperation and unity through a cultural approach.
Revitalisasi Peran Keluarga Tumbuhkan Toleransi Sejak Dini: Teladan Kisah Luqman
Authors: Juhri Muhammad Alan
Published: July 2024

Momok intoleransi tampak semakin mengancam setelah didapati sederet kasus yang melibatkan anak-anak. Mulai dari ujaran kebencian (hate speech), penyerangan individu dan rumah ibadah umat agama lain, bahkan hingga melakukan aksi terorisme berupa penembakan dan pengeboman. Kondisi mengenaskan ini mesti segera diberantas mengingat anak-anak sebagai tunas bangsa yang akan memperjuangkan keharmonisan bangsa ke depan. Dengan menggali keteladanan kisah Luqman (QS Luqman: 15), tulisan ini menegaskan bahwa nilai-nilai toleransi sangat perlu ditumbuhkan sejak usia dini. Peran keluarga sebagai madrasatul ula perlu direvitalisasi. Mulai dari pembekalan, pembiasaan, keteladanan, hingga pengawasan perlu diimplementasikan sebagai langkah-langkah praktis yang ditawarkan dalam tulisan ini.
KAJIAN KOMPARATIF AL-QUR’AN DAN WEDA TENTANG MODERASI BERAGAMA
Authors: Pramana M. Syarif Adi , Noor Muhammad Fauzi
Published: July 2024

Indonesia is famous for its diverse tribes, languages, and beliefs, a fact that can not be disputed by the people of Indonesia. They will not separate the diversity in Indonesia from the conflicts that arise in people's lives caused by various factors. Then religious moderation is one of the efforts or middle ground that occurs in the lives of Indonesian people to maintain the integrity and harmony that has long been built. In addition, it also prevents extreme and radical thoughts and exaggerated attitudes that arise in social life, both in the social and spiritual spheres. This article uses a qualitative method, library research with an analysis comparing the text of the Qur'an with the Vedas, which discusses religious moderation. The results in this paper state that the teachings of Islam and Hinduism, when talking about religious moderation, it turns out that many have in common, which ultimately leads to several important points: no one religious teaching wants division in it, does not force to embrace the same teachings, build peace and happiness in life, even united in living life in the world as a whole society because everything is one family that has the same purpose in life in the world.
Determining Legal Relevance with LLMs using Relevance Chain Prompting
Authors: Bilgin Onur , Licato John
Published: July 2024

In legal reasoning, part of determining whether evidence should be admissible in court requires assessing its relevance to the case, often formalized as its probative value---the degree to which its being true or false proves a fact in issue. However, determining probative value is an imprecise process and must often rely on consideration of arguments for and against the probative value of a fact. Can generative language models be of use in generating or assessing such arguments? In this work, we introduce relevance chain prompting, a new prompting method that enables large language models to reason about the relevance of evidence to a given fact and uses measures of chain strength. We explore different methods for scoring a relevance chain grounded in the idea of probative value. Additionally, we evaluate the outputs of large language models with ROSCOE metrics and compare the results to chain-of-thought prompting. We test the prompting methods on a dataset created from the Legal Evidence Retrieval dataset. After postprocessing with the ROSCOE metrics, our method outperforms chain-of-thought prompting.
On Clustering in Qualitative Spatial and Temporal Reasoning
Authors: Boukontar Abderrahmane , Condotta Jean-François , Salhi Yakoub
Published: July 2024

Our understanding of the world is intricately linked to both the spatial arrangement of objects and the timing of events. Knowledge-dependent systems employ mechanisms like Qualitative Spatial and Temporal Reasoning (QSTR) to effectively process and interpret this information. This article explores application of QSTR in data clustering, offering several contributions. These include introducing a formal clustering framework for qualitative data, implementing a satisfiability encoding to compute a clustering, introducing two appropriate distance measures for Qualitative Relation Networks, and experimentally validating through adaptations of k-means and Hierarchical Agglomerative Clustering algorithms.
Game Theoretic Analysis of the Feedback Loop Caused by Widely Available Computer Estimation on Market Values
Authors: Silaghi Viorel , Alssadi Zobaida , Mathew Ben , Alotaibi Majed , Alqarni Ali , Silaghi Marius
Published: July 2024

Public availability of computer generated predictions can change the markets and its impact is here investigated with a game theoretic approach.Real estate inflation is not a new phenomenon but its consistent and almost monotonous persistence over unusually many years, coinciding with new prominence of public estimation information from successful Mass Real Estate Estimators (\MREE{}) already caused various independent research organizations to investigate potential links.What we model is a repetitive theoretical game between the MREEs and the home owners, where each player has secret information and expertise.In contrast to competing results, for our model and simulations a restriction of MREE-style price estimation availability to opt-in properties may help partially reduce an inflationary pressure.
Twitter User Account Classification to Gain Insights into Communication Dynamics and Public Awareness During Tampa Bay's Red Tide Events
Authors: Skripnikov Andrey , Roy Tania , Neffati Fehmi , Adkins Melvin , Beck Marcus
Published: July 2024

This study presents an innovative approach to analyzing environmental challenges, focusing on the localized impacts of toxic algal blooms, specifically the dinoflagellate Karenia brevis on Florida's Gulf Coast, commonly known as "red tide". Despite the extensive influence of social media in public discourse, its potential in environmental awareness remains largely untapped. Our research exploits Twitter data to examine communication trends and public understanding of red tide issues in the Tampa Bay area from 2018 to 2022. For that study period, we collected 63K tweets from 30K accounts that mentioned terms related to red tide. Our methodology involves a tiered labeling process to obtain over 15K labeled accounts. In the initial tier, we employ predefined dictionaries for account groups to establish preliminary class designations, streamlining the subsequent labeling tiers, one of which is aided by preliminary machine learning classification. Having used several text classification algorithms and feature preprocessing approaches, Support Vector Machine with Bidirectional Encoder Representations from Transformers (BERT) yielded the best cross-validation performance in both accuracy (90%) and versatility (unweighted F1 score of 0.67). Lastly, we creatively leveraged the Term Frequency-Inverse Document Frequency (TF-IDF) method to study the terms that most distinguish each user category from the rest.
Preference Reasoning Under Partial Alternatives
Authors: Huelsman Michael
Published: July 2024

Selecting a single alternative among many is a key cognitive task. Many formal approaches for solving this problem have been explored, but these approaches consider having, or eliciting, complete preference information. As a natural extension, work has also been done to consider situations where complete preference information is unknown. What has not been studied is the effect of incomplete information wrt the alternatives an agent may select from. This work focuses on the computational problems that arise when preference information is known, but alternatives are only partially specified, such as when one is searching online classified ads. We both define these problems and specify some general computational complexity results. While the complexity of the defined problems are not tightly bound, we do provide a case study which demonstrates how partial alternatives affect different preference representations differently.
Predicting Solar Energy Output On Meteorological Time-Series Data Using Machine Learning
Authors: Harrison Caleb , Tubuntoeng Phadungsak , Liu Xudong
Published: July 2024

Solar energy production using photovoltaic (PV) systems is increasingly popular as a source of renewable energy for numerous applications. However, there is a main challenge with solar energy, namely, the unpredictability of its energy output. Therefore, accurate short-term predicting of the power output for PV systems is essential for effective decision making in power grid management. To this end, this paper focuses on training selected machine learning models, both traditional regression models and deep recurrent neural networks, to accurately predict solar energy output on meteorological time-series data from the Alice Springs solar farm in Australia. These machine learning models include linear regression, gated recurrent unit, recurrent neural network, long short-term memory, and random forest regression. The results of these tests showed that simple ensemble methods can outperform powerful single models and that hyperparameter tuning can greatly improve the performance of a model
Multimodal and Explainable Android Adware Classification Techniques
Authors: Ayinde Momoreoluwa , Islam Sheikh Rabiul , Dehzangi Iman , Lisa Fahmida Tasnim
Published: July 2024

With the widespread availability of adware masquerading as useful apps, there is an increasing need for robust security measures to identify adware. The identification of adware as a malware is a challenging task, as it often appears benign despite its malicious intent in the background. In this study, we propose an approach to classify adware on Android devices using data from multiple modalities. The focus is particularly on the classification of Airpush and Dowgin adware. Our proposed method uses both tabular and grayscale image data, and a feedforward neural network architecture to build a multimodal deep learning model that achieves a 95% prediction accuracy. Additionally, we incorporate Explainable AI (XAI) to enhance the interpretability of the results. The efficiency of our proposed approach is showcased through its ability to classify adware instances in an explainable manner, highlighting its significance not only in adware classification but also in fortifying against the evolving challenges posed by adware.
Evolution of Norms for a Trustworthy AI Society and Our Responsibilities and Roles
Authors: Lim JiHun , Seo Jeongeun , Kwon HunYeong
Published: July 2024

Artificial Intelligence (AI) holds promising opportunities for enhancing human life, yet its pervasive influence on society also introduces unintended negative consequences. Throughout the 2010s, a growing recognition of ethical concerns in AI led to the formulation of principles emphasizing ethical research and development. Various global entities have since endeavored to establish norms for AI ethics. Recently, beyond the declaration of ethical principles, concrete practical guides and codes of conduct are emerging, and in some countries, they are developing into specific legal regulatory discussions. This paper provides a detailed examination of the evolutionary trajectory of AI norms and further explores the responsibilities and roles of diverse stakeholder groups, including developers, suppliers, and users, in ensuring the trustworthiness of AI. The analysis aims to move beyond abstract ethical principles to actionable norms, emphasizing the need for a holistic societal response.
Matching-based Coalition Formation for Multi-robot Task Assignment Under Partial Uncertainty
Authors: Latham Brenden , Ufimtsev Vladimir , Dutta Ayan
Published: July 2024

In this paper, we study the multi-robot coalition formation problem for instantaneous task allocation, where a group of robots needs to be allocated to a set of tasks to execute optimally. One robot might not be enough to complete a given task, so forming teams to complete these tasks becomes necessary. In many real-world scenarios, the robots might have noisy localization. Due to this, cost calculations for robot-to-task assignments become uncertain. However, a small amount of resources might be available to accurately localize a subset of these robots. To this end, we propose a bipartite graph matching-based task allocation strategy (centralized and distributed versions) that gracefully handles the uncertainty arising from cost calculations using an interval-based technique while leveraging the fact that a small number of robots might be localized on demand using an external system such as drones. We have tested the proposed technique in simulation. Results show that our approach is moderately fast -- scales up to 100 robots and 50 tasks in 0.85 sec. (distributed solution) while gracefully handling partial uncertainty.
Enhanced Multi-Class Detection of Fake News
Authors: Li Chih-yuan , Chun Soon Ae , Geller James
Published: July 2024

The spread of fake news has emerged as a critical challenge in the digital era. Confusion and conflict can arise if people mistake fake news for real news. Thus, advanced detection methodologies are desired. This paper aims to identify fake news, while addressing the issue of class imbalances. We employ multi-class fake news detection, an advanced methodology beyond traditional binary classification. We highlight CNN’s better performance over the baseline BERT model in the literature, with improvements in accuracy, precision, recall, and F1-Score. We uniquely experimented with four model variants: CNN and BERT with both trainable embeddings and BERT embeddings. Our experiment demonstrates CNN's effectiveness in identifying text patterns. To address class imbalances, we experimented with three different balancing methods. Our study includes fine-tuning ChatGPT for multi-class classification. The result indicates notable limitations in ChatGPT's automated classification, which highlights the complexities of AI-based categorization. Our findings demonstrate the CNN model's efficiency and effectiveness, and show the intricacies of fake news detection. These insights confirm the need for advanced AI methodologies in combating misleading information.