Measuring Optical Scattering in Relation to Coatings on Crystalline X-Ray Scintillator Screens by Matthias Diez, Simon Zabler

“…Correcting artificial intensities that stem from scattered light, therefore, is of primary interest and demands quantitative measurements. While numerous methods have been developed to reduce X-ray scattering artifacts, fewer methods deal with optical scattering. …”
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GenAI and effective reading among university students: Prospects, challenges, and future directions by George Matto, Jaffar Msafiri Ponera, Valeria Kyumana

“…Continuous advancements in the fields of science and technology have led to the emergence of innovative technologies such as Generative Artificial Intelligence (GenAI). While there have been increased use of GenAI among university students, some scholars relate its use with negative impacts regarding students reading habits while others relate it with positive. …”
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Structured Summarization of League of Legends Match Data Optimized for Large Language Model Input by Jooyoung Kim, Wonkyung Lee, Jungwoon Park

“…By systematically summarizing structured match information—including match overviews, player and team statistics, timeline summaries, and algorithmically selected key events—the LoL-MDC significantly reduces the data size from approximately 80,000 tokens to under 2000 tokens while retaining analytical value. …”
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LCD: IDENTIFYING INFLUENTIAL NODES IN COMPLEX NETWORKS WITH LAYERED CLUSTERING AND DEGREE by Abdulhakeem Othman Mohammed

“… Identifying influential nodes in networks is a key challenge in understanding how information spreads. While numerous algorithms have been proposed in the literature, many struggle with either limited spreading efficiency or high computational complexity. …”
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Visceral adiposity index as a predictor of metabolic dysfunction-associated steatotic liver disease: a cross-sectional study by Tuo Zhou, Xiang Ding, Linjie Chen, Qianxiong Huang, Linfang He

“…Weighted multivariable regression models, subgroup analyses, and machine learning algorithms were used to evaluate associations and predictive performance. …”
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Effective Parallel Processing Social Media Analytics Framework by Ravindra Kumar Singh, Harsh Kumar Verma

“…In recent years lots of research were conducted and various machine learning algorithms were developed around the processing of data to achieve higher accuracy while reducing the processing time is still challenging. …”
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Emerging technologies in finance: challenges for a sustainable finance by Dhouha Nefla, Sana Jellouli

“…The analysis highlights the dual nature of emerging technologies: while they enhance financial efficiency, transparency, and inclusion, and offer significant opportunities to advance sustainable finance, they also introduce risks such as cybersecurity threats, algorithmic bias, regulatory challenges, and critical barriers to long-term sustainability. …”
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Picture Fuzzy Concept Lattice Models for Knowledge Structure Analysis by Chen Zhang, Zengtai Gong

“…Existing research results mainly focus on classical knowledge spaces, while insufficient attention has been paid to the uncertainty of data in practical problems. …”
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Intelligent Processing Methods by Veronika V. Tolmanova, Denis A. Andrikov

“…These methods, together with modern algorithms and computer models, allow extracting valuable information from huge volumes of raw data, as well as analyzing and forecasting various phenomena and trends. …”
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The battle for TNCs in Latin America: navigating policy trends and sociotechnical controversies in the regulation of ride-hailing platforms by Ronald Sáenz-Leandro

“…Complementing this quantitative regulatory mapping with qualitative insights from semi-structured interviews with regional scholars, we identify significant variability in regulatory stringency and priorities among countries. Findings reveal that while policy frameworks predominantly emphasize market access and innovation-driven consumer protection measures, other crucial sociotechnical controversies, such as driver misclassification, environmental impacts, algorithmic transparency, and data-sharing obligations, remain marginal or neglected. …”
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Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS. by Manas Gaur, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonathan Beich, Jyotishman Pathak, Amit Sheth

“…In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). …”
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A Methodological Study on Expanding the Microarchitectural Design Space of Melt Electrowriting by Kai Cao, Yi He, Anni Wang, Yunpeng Wang, Junyi Song, Jun Zhong, Qisheng Chen, Rongwu Wang

“…ABSTRACT Melt electrowriting (MEW), an advanced additive manufacturing technique for fabricating microfibers, has demonstrated significant potential in tissue engineering applications. While MEW shares many similarities with fused filament fabrication (FFF), the conventional slice‐filling algorithms used in FFF are ill‐suited for MEW, as they fail to address the requirements for continuous toolpaths and diverse microarchitectures. …”
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Methodological Integration of Machine Learning and Geospatial Analysis for PM10 Pollution Mapping by Kalid Hassen Yasin, Muaz Ismael Yasin, Anteneh Derribew Iguala, Tadele Bedo Gelete, Erana Kebede

“…The study contributes valuable insights for implementing scalable pollution prediction systems in resource-constrained urban environments while acknowledging interpretability challenges inherent to complex ML models. • Preprocessing of spatial data from various sources, incorporating the handling of missing/abnormal data, analysis, and normalization • Implementation of the three ML algorithms with rigorous hyperparameter tuning, model validation, and performance assessment • Mapping PM10 Hotspots on the Gradient Direction and Distance from the City Center…”
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Machine Learning for Predicting Bank Stability: The Role of Income Diversification in European Banking by Karim Farag, Loubna Ali, Noah Cheruiyot Mutai, Rabia Luqman, Ahmed Mahmoud, Nol Krasniqi

“…Some advocate for diversification, while others argue that its importance should not be overstated. …”
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Mathematical Principles for Modeling Transmission Dynamics of Transport Facilities by O. S. Rukteshel, Al. M. Zakharik, An. M. Zakharik, U. M. Zakharik, S. V. Gusarov

“…Diagrams of the algorithms that realize the mentioned methods are given in the paper.…”
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Leveraging machine learning to evaluate the effect of raw materials on the compressive strength of ultra-high-performance concrete by Mohamed Abdellatief, G. Murali, Saurav Dixit

“…Feature importance analysis revealed that steel fiber content, curing age, and silica fume content are the most significant factors affecting CS, while quartz powder and limestone powder showed minimal influence. …”
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Contribution of hydrogeological, well logs and machine learning in predicting the aquifer hydraulic properties in arid regions: a case study of Nubian Sandstone aquifer, Farafra Oa... by Ahmed Nosair, Muhammad Nabih, Ahmed Bakry

“…In contrast, the LR model showed acceptable performance, while the SVM model had comparatively lower correlation. …”
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Graph neural processes for molecules: an evaluation on docking scores and strategies to improve generalization by Miguel García-Ortegón, Srijit Seal, Carl Rasmussen, Andreas Bender, Sergio Bacallado

“…So far, most studies of NPs have focused on low-dimensional datasets of highly-correlated tasks. While these homogeneous datasets are useful for benchmarking, they may not be representative of realistic transfer learning. …”
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Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

“…This paper presents a novel, data-driven approach for predicting Building Energy Ratings (BER) in urban environments, using advanced Machine Learning (ML) algorithms. Focusing on Dublin, we integrate diverse geospatial datasets with building-specific and neighbourhood-scale features to classify BER. …”
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Probing the Pitfalls: Understanding SVD’s Shortcomings in Language Model Compression by Сергей Александрович Плетенев

“…However, targeted compression approaches, such as selectively compressing the most redundant parts of the model or weighting algorithms, mitigated these negative effects. Conclusion:  These results demonstrate that factorization, when used properly, can significantly reduce computational requirements while preserving the core linguistic capabilities of large language models. …”
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