Social Media and Men’s Health: Separating Science from Speculation in Andrology by Michael George, Vaibhav Modgil, Ian Pearce, Theodora Stasinou

“…Social media algorithms reinforce consumption patterns by continuously delivering content based on user engagement: the more an individual interacts with a topic or creator, the more related content appears, creating a self-sustaining feedback loop; this principle underpins the concept of virality and risks the rapid dissemination of inaccuracies online. …”
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Sparse Convolution FPGA Accelerator Based on Multi-Bank Hash Selection by Jia Xu, Han Pu, Dong Wang

“…However, many computing devices that claim high computational power still struggle to execute neural network algorithms with optimal efficiency, low latency, and minimal power consumption. Consequently, there remains significant potential for further exploration into improving the efficiency, latency, and power consumption of neural network accelerators across diverse computational scenarios. …”
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Enhancing Underground Thermal Environments in Cairo: The Role of Subway Entrance Geometry in Optimizing Natural Ventilation by Omar Mohamed Abdelaziz, Xu Cui, Xiaozheng Sun

“…The primary objective is to identify optimal entrance configurations that maximize natural airflow, thereby improving passenger comfort and reducing energy consumption. Focusing on the newly constructed segments of the Cairo subway, the research employs a mixed-methods approach that integrates computational fluid dynamics (CFD) simulations with a questionnaire survey to evaluate interactions between various entrance designs and urban wind flow patterns. …”
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Thermal Footprint of the Urbanization Process: Analyzing the Heat Effects of the Urbanization Index (UI) on the Local Climate Zone (LCZ) and Land Surface Temperature (LST) over Two... by Nadia Falah, Jaime Solis-Guzman, Nahid Falah

“…This phenomenon changes the pattern of the local climate zone (LCZ) and significantly affects the climate, vegetation dynamics, energy consumption, water resources, and public health. …”
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From COVID-19 Pandemic Pivot to Progress: Blended Work as a Pathway to Greener African Cities by Patrick Gad Iradukunda, Arnaud Iradukunda, Pierre Gashema, Emile Ngabo, Jean de Dieu Tuyishime, Tumusime Musafiri, Thérèse Umuhoza, Ulysse Uwayo, Enos Moyo, Richard Habimana, Claude Mambo Muvunyi, Tafadzwa Dzinamarira

“…Notably, the shift to working from home (WFH) during the pandemic improved individual well-being and reduced office energy consumption and commuting-related emissions. Robust digital infrastructure and organizational and sustainable policies are essential to fully leverage blended work for urban sustainability. …”
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Synergistic Non-Intrusive Load Monitoring: Dual-Model Training and Inference for Improved Load Disaggregation Prediction by Mazen Bouchur, Andreas Reinhardt

“…During disaggregation, our scheme selects the model that is closest to the appliance’s power consumption pattern, thereby leveraging the most fitting model for each specific instance. …”
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Nairobi motorcycle transit comparison dataset: Fuel vs. electric vehicle performance tracking (2023)Mendeley Data by Alois Mbutura, Martin Kitetu, Halloran Stratford, M.J. Booysen

“…Researchers can leverage the data to explore operational differences between ICE and electric motorcycles, evaluate economic impacts on riders, or model energy consumption trends.…”
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A personalized approach to understanding food cravings and intake: a study protocol by Saša Zorjan, Sašo Karakatič, Marina Horvat, Satja Mulej Bratec, Živa Krajnc

“…Machine learning techniques will be employed to uncover distinct participant clusters, characterized by unique patterns of food consumption and weight changes over six months. …”
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Deciphering city-level residential AMI data: An unsupervised data mining framework and case study by Han Li, Miguel Heleno, Kaiyu Sun, Wanni Zhang, Luis Rodriguez Garcia, Tianzhen Hong

“…Key findings highlight the substantial impact of the COVID-19 pandemic on residential energy use, uncover patterns like intraday load variations, weekly consumption trends, and annual weather sensitivity. …”
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Advanced Energy Management for Residential Buildings Optimizing Costs and Efficiency Through Thermal Energy Storage and Predictive Control by Ricardo Felez, Jesus Felez

“…Employing model predictive control (MPC) and integrating real-time data on energy prices, weather conditions, and consumption patterns, the system minimizes energy costs, while maintaining indoor thermal comfort. …”
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Unleashing the Power of NVMe in Cloud: A Complete Software-Defined Emulation Study by Mahdi Siamaki, Bardia Safaei

“…Accordingly, this study leverages NVMeVirt, a powerful software-defined NVMe emulator, to investigate the impact of various workload characteristics on critical performance metrics. …”
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The Impact of Daylight Saving Time on the Energy Efficiency of Buildings: A Bibliometric and General Review by Ivo Araújo, Leonel J. R. Nunes, David Patíño Vilas, António Curado

“…Although DST primarily aims to optimize energy consumption by leveraging natural daylight, much attention has focused on its broader societal effects. …”
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Transforming Building Energy Management: Sparse, Interpretable, and Transparent Hybrid Machine Learning for Probabilistic Classification and Predictive Energy Modelling by Yiping Meng, Yiming Sun, Sergio Rodriguez, Binxia Xue

“…This study introduces a hybrid machine learning framework grounded in Sparse, Interpretable, and Transparent (SIT) modelling to enhance building energy management. Leveraging the REFIT Smart Home Dataset, the framework integrates occupancy pattern analysis, appliance-level energy prediction, and probabilistic uncertainty quantification. …”
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Assessing climate change impacts on water footprint of crop production and crop yield: a case study of the Wu River basin, Taiwan by Guan-Zhou Lin, Li-Chi Chiang

“…Variation in rainfall patterns impact blue-green water consumption and directly affect crop yields, with projected reductions averaging 18% for rice, 41% for sweet potatoes, and 45% for oranges. …”
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Evaluation of Socially and Culturally Coordinated Development in Cities of Yangtze River Economic Belt and Its Spatial Correlation by Zhenzhen Yi, Xianzhong Cao, Liuting Qin

“…This study suggests the need to enhance the development of the Yangtze River’s culture, promote the development of cultural industry clusters, foster the integration of various business models, leverage scientific and educational resources, optimise the cultural consumption market, and achieve the coordinated development of the social and cultural sectors, thereby enabling the Yangtze River Economic Belt to play a greater role in Chinese-style modernisation.…”
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Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms by Silviu Gresoi, Grigore Stamatescu, Ioana Făgărășan

“…By analyzing historical consumption patterns, anomaly detection techniques, and geospatial data, the proposed system enhances fraud detection capabilities across both smart and non-smart grids. …”
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Enhancing Spreading Factor Assignment in LoRaWAN with a Geometric Distribution Approach for Practical Node Distributions by Phanupong Tempiem, Rardchawadee Silapunt

“…Comparative analyses were performed against the uniform random node pattern and existing algorithms, including the ADR, EXPLoRa, QCVM, and SD. …”
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GAN-Based Driver’s Head Motion Using Millimeter-Wave Radar Sensor by Hong Nhung Nguyen, Yong-Hwa Kim

“…This study leverages a compact millimeter-wave radar to monitor driver head movements, providing a non-intrusive method to assess driver focus. …”
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Multi-Microgrid Optimization With Electric Vehicle Mobile Energy Storage Considering Travel Characteristics by Xiaoyi Zhang, Jie Ma, Zheng Yang, Xiang Zhang, Yishuo Qiao, Yudong Du, Zhiwei Li

“…To address the economic challenges posed by the integration of a large number of electric vehicles (EVs) into microgrids, while leveraging their mobile energy storage (MES) capabilities and accounting for the impact of EV users’ travel patterns on charging and discharging behaviors, a microgrid scheduling model is proposed that incorporates the MES characteristics of EVs under user travel habits. …”
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Deep Learning for Telecom Self-Optimized Networks: Benefits and Implications by Farah Alhaqui, Iyad Lahsen-Cherif, Mariam Elkhechafi, Ahmed Elkhadimi

“…This study introduces an efficient traffic prediction approach that combines an innovative data partitioning strategy to capture spatial dependencies with Long Short-Term Memory (LSTM) networks to model temporal patterns. Leveraging real traffic data from a leading Moroccan telecom operator, the proposed model accurately forecasts future traffic patterns and their geographic distribution, achieving an absolute prediction error of less than 15 GB. …”
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