bluuebunny/crossref_metadata_2025_split huggingface dataset:
Citral, a major component of lemon myrtle (<i>Backhousia citriodora</i>) essential oil, inhibits adipogenesis in 3T3-L1 preadipocytes during mitotic clonal expansion
Authors: Choi Yae Rim , Kyung Park Min , Lee Ae Sin , Kim Young-Suk , Kim Min Jung 
Published: January 2025

Lemon myrtle essential oil (LMEO), a popular food-flavoring and perfume, has anti-inflammatory and antioxidant properties, and contains citral in abundance. Citral-enriched essential oils exhibit anti-obesity effects; however, the effect of LMEO on adipogenesis and the precise mechanism of citral have not been elucidated. This study evaluated the anti-adipogenic activity of commercial LMEO and its components, geranial, neral and citral (a mixture of geranial and neral), in 3T3-L1 cell differentiation and identified the anti-adipogenic mechanism of citral. Treatment of 3T3-L1 preadipocytes with various concentrations of LMEO, neral, geranial, and citral for 24 h and 48 h showed that LMEO (12.5–25 μg/mL) inhibited 3-isobutyl-1-methylxanthine, dexamethasone, and insulin (MDI)-induced differentiation of preadipocytes. Citral, accounting for 88.67% of total compounds in LMEO, also suppressed MDI-induced differentiation and expression of adipogenic transcription factors (PPARγ, C/EBPα, and Fabp4). Additionally, citral inhibited the early stages of differentiation, particularly mitotic clonal expansion (MCE), by deactivating cell cycle-related factors (cyclins and cyclin-dependent kinases) and arresting the cell cycle at G0/G1 phase, which was caused by inhibiting upstream signaling pathways, especially PI3K/AKT pathway. Therefore, regulation of early stages of adipogenesis, especially MCE, is a key mechanism underlying the anti-adipogenic activity of citral. LMEO and citral can be potentially used as anti-obesity agents.
Characterization of biochemical traits, volatile compounds, and sensorial attributes of pistachio (<i>Pistacia vera</i> L.) nuts and oil as affected by regulated deficit irrigation and roasting
Authors: Abidi Walid , Akrimi Rawaa , Rizzo Valeria , Cincotta Fabrizio , Merlino Maria , Muratore Giuseppe 
Published: January 2025

Prolonged drought poses a critical challenge for pistachio cultivation in Mediterranean regions. Water-saving strategies and selecting adapted cultivars can help maintain sustainable production. This study was conducted in the experimental orchard of the Regional Center of Agricultural Research of Sidi Bouzid, Tunisia. Four pistachio cultivars (Mateur, Elguetar, Kerman, and Ohadi) grafted on P. atlantica rootstock were used to investigate the impact of regulated deficit irrigation (RDI) on yield, nut composition, volatile compounds, phenolic compounds, fatty acid profile, and sensory analysis. Three irrigation treatments were applied: control (T0) received 100% crop evapotranspiration (ETc) during all stages of nut development; RDI treatment (T1) received 50% ETc during stages I and II of nut development, followed by 100% ETc during stage III; and stressed treatment (T2) received 50% ETc during two growing seasons (2017 and 2018). Pistachio nuts were immersed in a solution of NaCl (5% w/v) for 5 min and then introduced in an oven for 2 h at 120ºC to obtain ‘roasted’ pistachio for volatile and sensorial analysis. Results showed that RDI saved 20% of irrigation water during stages I and II of nut development while maintaining similar yield, nut composition, and fatty acid profile as that of control. Moreover, the volatile compounds content and oil yield were enhanced under drought stress. Roasting enhanced the perception of volatile compounds, especially nitrogenous ones. Cultivars Mateur and Ohadi showed high sensory quality, with Ohadi achieving the highest consumer satisfaction despite lower yield and dehiscence rate. Cultivar Mateur was suitable for high-density planting in semiarid environments. Overall, RDI proved effective for increasing water use efficiency without compromising pistachio quality, suggesting its suitability for arid and semiarid orchards.
Taraxasterol from edible plants inhibits breast cancer progression by targeting the miRNA-140-5p/OGT axis: Implications for functional food-based therapies
Authors: Hu Yanjun , Mao Jiefei 
Published: January 2025

The second most frequent cause of tumor-related mortality in women is malignant breast cancer (BC). The exploration of new therapeutic agents and related mechanisms is crucial for BC research. Numerous investigations have demonstrated that taraxasterol (TAX) has antitumor effects, but the role of TAX in BC and the corresponding mechanisms are unknown. In this investigation, after treating BC cells with TAX, the proportion of apoptosis was measured using flow cytometry, the Transwell® was used to measure cell invasion, and the wound-healing assay was used to measure cell migration. It was discovered that TAX promoted the apoptosis of BC cells and inhibited BC cell migration and invasion. In addition, according to the results of in vivo tests, tumor growth was suppressed by TAX. Mechanistically, O-GlcNAc transferase (OGT) expression was indirectly reduced by TAX through the up-regulation of miRNA-140-5p expression. According to this theory, TAX blocks the growth and migration of BC by controlling the miRNA-140-5p/OGT axis.
Desafios e perspectivas do descarte de resíduos sólidos ao desenvolvimento regional da Amazônia
Authors: Pereira Keliane da Cruz , Monteiro Geane Ribeiro Silva , Araújo Júnior Antônio Carlos Ribeiro 
Published: January 2025

A presente pesquisa trata do sistema de armazenamento de resíduos sólidos no município de Pacaraima, município situado no extremo norte de Roraima, propondo-se a (i) verificar como é realizado a coleta e armazenamento de resíduos sólidos; (ii) descrever possíveis implicações ambientais e sociais que a precariedade no armazenamento de resíduos sólidos pode acarretar ao município e aos seus munícipes, e neste sentido; (iii) identificar a existência de planos ou ações de intervenção do poder público quanto à desativação do lixão e a construção de um aterro sanitário. Este trabalho constitui-se em um estudo de caso com: a pesquisa in loco mediada pela observação, registro fotográfico e realização de entrevista semiestruturadas com representante municipal, bem como, entrevista com migrantes catadores de recicláveis no local. Desta forma, constatou-se que os resíduos coletados na cidade são depositados a céu aberto. Além de um local aberto, o lixão não possui nenhum tipo de identificação quanto à finalidade da área, nem cercas de proteção que impeçam o acesso, atraindo animais e migrantes em busca da manutenção de sua sobrevivência.
O efeito do luxo sobre a diversidade arbórea das praças públicas na Cidade de Boa Vista – RR
Authors: Lima Gilneide Rodrigues , Pequeno Pedro Aurélio Costa Lima 
Published: January 2025

Resumo
O urbanismo provoca conflitos entre organização socioeconômica e conservação da biodiversidade, resultando em um número limitado de espécies arbóreas nativas nas cidades. A diversidade arbórea nativas é importante pelo conforto térmico que reduz a concentração de calor e para os pássaros que as utilizam para alimentação e nidificação, não serem capazes de completar o ciclo e deixam de ocorrer na região. Um padrão observado em algumas cidades do mundo, foi denominado efeito do luxo, devido a diversidade de plantas exóticas estar associada aos bairros mais ricos em relação aos bairros mais pobres. A condição socioeconômica é comumente correlacionada com o efeito do luxo, mostrando que os benefícios de biodiversidade não serem compartilhados equitativamente pela sociedade. O efeito do luxo pode ser modificado, quando a relação entre a socioeconomia e a biodiversidade for influenciada pelos legados de uso da terra, que modulam as espécies influenciadas pela riqueza, à medida que os bairros envelhecem, evidenciando o efeito do legado. Diante disso, o propósito deste estudo foi analisar a ocorrência do efeito luxo nas praças públicas de Boa Vista, avaliando o quanto as variáveis preditoras: valor de venda do metro quadrado da área dos imóveis, distância linear do centro da cidade, área do bairro, tamanho da população humana, refletiram sobre as variáveis preditoras: número de praças e o número de espécies arbóreas por praças nos bairros de Boa Vista. Os resultados mostraram relações positivas e negativas entre as variáveis, refutando o efeito do luxo e validando o efeito do legado.
Palavras-chave: Efeito do luxo. Diversidade arbórea. Praça pública. Efeito do legado.
Design of Cost-Effective Steel Surface Defect Detector Based on Deep Learning
Authors: Lee Jundong , Kim Hwan Seog , Wan Ju Yong , Kim Byoungwook , Suh Sangmin 
Published: January 2025

Considering the productivity and profits of manufacturers, defects in steel products must be detected very carefully. Traditionally, machine learning-based methods such as support vector machines (SVM) have been widely used for steel surface defect detection (SDD). These machine learning methods rely on expert parameters, so their performance is susceptible to these parameters. Recently, deep learning-based methods independent of these expert parameters have been developed. The most popular of these uses is transfer learning, which allows for efficient learning by applying a previously trained model to a new problem. This approach can overcome existing methods' limitations and provide more accurate and efficient SDD solutions. In order to design the transfer learning-based SDD, input images should be modified and resized, which could cause input image distortion. Moreover, the transfer learning-based models generally have many layers and weights. This paper is motivated by the following questions. 1) Is the transfer learning-based model the best method for SDD? 2) What is the smallest model without compromising performance? This paper proposes a dedicated neural network for steel surface defect detection to answer the above questions. In addition, for cheap neural networks, the initially designed neural network is gradually reduced by monitoring the performances. We achieved a maximum f1-score of 0.978 and a minimum AUC of 0.995 from the experimental results.
A Comparative Study on the Performance of Algorithms on Different AI Platforms
Authors: Lee Youngseok , Cho Jungwon 
Published: January 2025

To understand the basic concepts and principles of artificial intelligence (AI) and to solve problems using AI, it is necessary to use various platforms. Among AI machine-learning (ML) models, the prediction algorithm is a basic AI model that can be used in various fields, such as for predicting weather, grades, product prices, and population, and is likely to be used to gain a basic understanding of AI. Many educational AI platforms implement prediction algorithms to help understand these AI models. In this study, prediction algorithms were implemented using the following AI platforms: Orange3, Entry, and Python to learn the temperature data in the Seoul area of Korea using a linear regression model, predict the value of temperature change, and evaluate the performance of the prediction algorithm for each platform. Additionally, to understand machine learning classification models and develop effective teaching methods, we conducted a prototype test to compare and analyze each platform's photo classification methods and performance. As a result of the comparison, Python exhibited the best performance, followed by Orange3 and Entry, with differences in accuracy and predicted values. To understand AI, it is necessary to understand the reliability of AI models and use an appropriate platform that considers the development level of the learner. In the future, we aim to research different ways to efficiently understand AI by comparing and analyzing its performance using various AI models.
Artificial Intelligence Models for Flowing Bottomhole Pressure Estimation: State-of-the-Art and Proposed Future Research Directions
Authors: Agwu Okorie Ekwe , Alatefi Saad , Alkouh Ahmad , Azim Reda 
Published: January 2025

Flowing bottom hole pressure (FBHP) is an important parameter during evaluation of conventional and unconventional oil and gas resources and is mainly used for production optimization, calculation of productivity index and assessment of well performance. FBHP in an oil well is a multifaceted parameter that has a number of factors affecting it. It is characterized by high stochasticity, non-linearity and non-stationarity. Traditionally, production engineers rely on physics-based models and empirical correlations calibrated with measurements from offset wells to calculate FBHP. However, in recent times, there has been a significant shift towards the use of cutting-edge artificial intelligence (AI) algorithms. The present study is designed to provide a historical account of past and present models developed with AI algorithms for estimating FBHP. To achieve this, a deep bibliographic survey was conducted using various peer reviewed journals and relevant oil and gas conference papers. The results of the review have been presented in tables to avoid ambiguity. To make the review novel, the merits and demerits of each of the AI models for FBHP prediction are highlighted and discussed in detail. In this direction 54 models were isolated from the literature. The findings indicate that artificial neural network is the preferred algorithm by several researchers. However, the transparency and interpretability issues associated with the neural network algorithm has propelled researchers to explore the possibility of deploying physics informed machine learning techniques to model FBHP. This review would serve as a valuable reference for production engineers seeking information on AI models for FBHP estimation.
Leveraging Random Forest Algorithm for Enhanced Lead Conversion and Customer Retention
Authors: Tan Xin Yi , Lim Siew Mooi , Lim Tong Ming , Tan Chi Wee , Husaini Noor Aida 
Published: January 2025

This paper presents the development, implementation, and evaluation of a Random Forest-based Social Lead Scoring Model to address key business challenges in lead generation, customer retention, and optimization of lead management processes. The main goal is to create a strong, data-driven tool to precisely estimate lead conversion probabilities to guide better marketing and sales strategy decision-making. The model uses social metrics and past lead data to estimate conversion probabilities for every lead. The Tkinter library created a user-friendly interface that allows straightforward usage for non-technical business professionals. This model includes two fundamental functions calculate_probability and predict_conversion– which offer practical and pragmatic insights. During the development phase, a thorough cross-valuation was conducted by the model trained on a large dataset, including several lead characteristics, to decrease overfitting and improve the model's predictive performance. Thus, the model scored 89.46%, which is higher than that of conventional lead-scoring techniques. However, there is still room for development, specifically in enhancing its predictive power and reducing overfitting risks on different datasets, although this model has great accuracy. The results highlight the need for data-driven strategies in raising conversion rates and show the possibilities of machine learning in lead management optimization. Including extra data sources and investigating advanced technologies such as deep learning should be conducted by future studies to improve model performance further. The increased accuracy in predictive analytics will give companies a competitive edge in their operations.
Educação para a Sexualidade no Brasil: Avaliação de Objetivos de Ensino em Documentos Oficiais
Authors: Storrer Giovanna , Saltori Giovana Cordeiro , Müller Talissa Palma , De Luca Gabriel Gomes 
Published: January 2025

O objetivo deste estudo foi avaliar as características de objetivos de ensino da Educação para a Sexualidade nos Parâmetros Curriculares Nacionais (PCNs) e na Base Nacional Comum Curricular (BNCC). Para tanto, foram identificadas em cada um dos documentos unidades de objetivos relativas à Educação para a Sexualidade e elas foram avaliadas com base nos critérios: natureza, clareza e contexto do objetivo e dimensão da sexualidade. Nos PCNs foram encontrados 26 objetivos: 81% foram classificados como comportamentos; 73% são claros; quanto ao contexto, 81% são externos à instituição escolar; e as dimensões da sexualidade prevalentes são a Psicológica e a Micropolítica, com 35% cada. Na BNCC foram identificados 25 objetivos: 36% foram classificados como comportamentos; 76% são claros; 24% são externos à instituição escolar; e as dimensões prevalentes são a Preventiva e a Micropolítica, com 28% cada. Os objetivos dos PCNs, embora expressos de forma menos clara, correspondem a comportamentos a serem apresentados pelo/a aluno/a em seus contextos de vida. Averiguou-se retrocessos na BNCC no que diz respeito à descrição de falsos objetivos de ensino, limitados ao contexto de sala de aula, e prevalência da dimensão preventiva da sexualidade. Argumenta-se a necessidade de propor comportamentos-objetivo socialmente relevantes de modo que a Educação Para a Sexualidade esteja mais alinhada ao compromisso com o pleno desenvolvimento do sujeito, visto que se trata de um direito humano fundamental. Palavras-chave: Educação Sexual, BNCC, PCNs, formulação de objetivos de ensino, programação de ensino.
Inteligencia artificial y la gestión del talento humano en Chihuahua, su impacto y consecuencias
Authors: Hernandez Morales Hector Humberto , Arroyo Ávila José René , Alvídrez Díaz María del Rosario de Fátima 
Published: January 2025

El máximo potencial de la inteligencia artificial (IA) se logra fusionando la creatividad y la empatía del talento humano, con la innovación tecnológica. El objetivo del estudio fue analizar la forma en que se puede integrar la inteligencia artificial (IA) en la gestión del talento humano, con especial atención a las pequeñas y medianas empresas (PyMEs) en Chihuahua, Chih. México. Fue una investigación cualitativa, transeccional-descriptiva, con fuerte enfoque documental. Se enmarca en el contexto de la Industria 4.0, caracterizada por la adopción de tecnologías avanzadas como el Internet de las Cosas (IoT), el Big Data y las medidas de ciberseguridad, que han transformado los procesos de negocio; a pesar de los desafíos relacionados con recursos limitados, barreras culturales y altos costos de implementación, la inteligencia artificial (IA) es una herramienta estratégica para optimizar la eficiencia y optimizar la toma de decisiones. Entre los principales resultados, se destaca que la integración conjunta entre tecnología y talento humano es clave para promover la competitividad y la sostenibilidad, destacando la importancia de la capacitación de los empleados y el diseño de estrategias que equilibren la innovación tecnológica y la estabilidad laboral.

The maximum potential of artificial intelligence (AI) is achieved by merging the creativity and empathy of human talent with technological innovation. The aim of the study was to analyze how artificial intelligence (AI) can be integrated into human talent management, with special attention to small and medium-sized enterprises (SMEs) in Chihuahua, Chih. Mexico. It was a qualitative, cross-sectional-descriptive research, with a strong documentary focus. It is framed in the context of Industry 4.0, characterized by the adoption of advanced technologies such as the Internet of Things (IoT), Big Data, and cybersecurity measures, which have transformed business processes; despite the challenges related to limited resources, cultural barriers and high implementation costs, AI is a strategic tool to optimize efficiency and optimize decision making. Among the main results, it is highlighted that the joint integration between technology and human talent is key to promoting competitiveness and sustainability, highlighting the importance of employee training and the design of strategies that balance technological innovation and job stability.
Dégradation des Terres Agricoles par Salinisation dans la Vallée Fossile du Sine : Cas de la Commune de Niakhar (Région de Fatick)
Authors: Sene François Ngor , Fall Aïdara C. A. Lamine , Sane Yancouba 
Published: January 2025

La salinisation des terres, phénomène mondial qui affecte particulièrement les zones sous influence fluviomarine, constitue une contrainte majeure pour l’agriculture. Cette étude vise à comprendre le processus de dégradation des terres agricoles par salinisation dans la vallée fossile du Sine, notamment dans la commune de Niakhar, région de Fatick. En combinant l’analyse de la conductivité électrique des sols, la cartographie basée sur des indices de télédétection (NDSI, SI, BI et ISN issus des images Landsat 7 et 8) et des enquêtes socioéconomiques, les résultats montrent que la salinisation est principalement provoquée par la remontée capillaire et l’intrusion des eaux salées du bras de mer du Sine Saloum. Entre 2000 et 2021, la superficie des terres salées a augmenté de 42,56 %, tandis que la végétation et les zones cultures ont respectivement diminué de 49,79 % et 2,39 %. La salinité des sols varie significativement selon les types de tannes (nus, herbacés et arbustifs). Ces résultats soulignent la nécessité de stratégies de gestion durable des terres pour limiter l’impact de la salinisation, préserver les écosystèmes agricoles et soutenir l’élaboration de politiques adaptées aux zones vulnérables.

Land salinization, a global phenomenon particularly affecting areas under river-marine influence, poses a significant challenge to agriculture. This study examines the mechanisms of agricultural land degradation through salinization in the fossil valley of the Sine, specifically within the commune of Niakhar in the Fatick region. By integrating soil electrical conductivity analysis, mapping based on remote sensing indices (NDSI, SI, BI, and ISN derived from Landsat 7 and 8 imagery), and socioeconomic surveys, our results reveal that salinization is primarily caused by capillary rise and the intrusion of saline water from the Sine Saloum estuary. Between 2000 and 2021, saline land areas increased by 42,56%, while vegetation cover and cultivated areas decreased by 49,79% and 2,39%, respectively. Significant variability in soil salinity was observed across different types of tannes (bare, herbaceous, and shrubby). These results highlight the pressing need for sustainable land management strategies to mitigate salinization, safeguard agricultural ecosystems, and inform policy frameworks in vulnerable regions.