Spatial Analysis of Bioclimatic Vulnerability of Kermanshah Facing Heat Wave by Hossein Yavari, Mstafa Karampour, Dariush Yarahmadi

“…The land surface temperature of Kermanshah city was extracted by applying a single channel algorithm on the thermal bands of ،TIRS sensor of the Landsat 8 platform for July 9, 2015. …”
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Multistable Physical Neural Networks by Ben‐Haim Eran, Givli Sefi, Or Yizhar, Gat D. Amir

“…Building on these maps, both global and local algorithms for training multistable PNNs are implemented. …”
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Efficient Deep Learning Models Revolutionize Doctor’s Training for Point-of-Care Ultrasound by Hsin-Hung Chou, Yi-Chung Chang, Wan-Ching Lien, Lung-Chun Lin, Xing-Zheng Lin, Ting-En Hsu, Yueh-Ping Liu, Li Liu, Yen-Ting Chan, Feng-Sen Kuan

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Exploring the possibilities of MADDPG for UAV swarm control by simulating in Pac-Man environment by Artem Novikov, Sergiy Yakovlev, Ivan Gushchin

“…This study highlights its adaptability and ability to outperform traditional rule-based methods in dynamic and complex environments. Future research should focus on comparing the effectiveness of MADDPG-trained models with multi-agent algorithms, such as Expectimax, Alpha-Beta Pruning, and Monte Carlo Tree Search (MCTS), to further understand the advantages and limitations of learning-based approaches compared with traditional decision-making methods in collaborative and adversarial UAV operations. …”
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Navigating the complexities of AI and digital governance: the 5W1H framework by S. Matthew Liao, Iskandar Haykel, Katherine Cheung, Taylor Matalon

“…This paper introduces a ''5W1H'' framework to systematically analyze AI governance proposals through six key questions: What should be regulated (data, algorithms, sectors, or risk levels), Why regulate (ethics, legal compliance, market failures, or national interests), Who should regulate (industry, government, or public stakeholders), When regulation should occur (upstream, downstream, or lifecycle approaches), Where it should take place (local, national, or international levels), and How it should be enacted (hard versus soft regulation). …”
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Can Fire Season Type Serve as a Critical Factor in Fire Regime Classification System in China? by Huijuan Li, Sumei Zhang, Xugang Lian, Yuan Zhang, Fengfeng Zhao

“…By using data normalization and the k-means clustering algorithm, the study area was divided into five types of FR zones (FR 1–5) with significant differences. …”
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An Analysis on the Applicability of Meta-Heuristic Searching Techniques for Automated Test Data Generation in Automatic Programming Assessment by Musa et al.

“…Several recent MHST are included in the comparative evaluation combining both the local and global search algorithms ranging from the year of 2000 until 2018. …”
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Dose enhancement effects of different-sized nanoparticles on tumors and surrounding tissues using Geant4 track structure simulation by Tuğrul Taylan

“…The nanoparticles can be localized within tumor tissues, providing enhanced protection to normal tissues during radiation therapy while achieving significant dose enhancement within tumors. …”
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An experimental optimization environment for developing an intracycle pitch control in cross flow turbines by Stefan Hoerner, Roberto Leidhold, Shokoofeh Abbaszadeh, Karla Ruiz-Hussmann, Timo Bennecke, Zhao Zhao, Paul Joedecke, Christian-Thoralf Weber, Pierre-Luc Delafin, Cyrille Bonamy, Yves Delannoy

“…A superimposed governing system performs an experimental optimization process. Evolutionary algorithms are to be employed to solve two objectives: (1) maximizing the efficiency and (2) minimizing the structural loads. …”
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Leveraging AI for Resilient Urban Disaster Management in India by Naikade Kshtij, Dharangutti Yogesh, Hangirgekar Ramni Sachin

“…A comparative study of India's approach vis-à-vis Singapore's integrated AI environmental monitoring system, which reduced the severity of disasters by 45% because of early detection, brings to light serious implementation gaps in Indian cities. In this study, researchers showed that the use of AI algorithms together with monitoring environmental parameters would enhance the processes of disaster detection and management. …”
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Utilising artificial intelligence in prehospital emergency care systems in low- and middle-income countries: a scoping review by Odhran Mallon, Odhran Mallon, Odhran Mallon, Freddy Lippert, Freddy Lippert, Willem Stassen, Marcus Eng Hock Ong, Caitlin Dolkart, Caitlin Dolkart, Thomas Krafft, Eva Pilot

“…Overall, machine learning algorithms outperformed other comparator methods when they were used in all but two studies.DiscussionLimitations included only analysing articles published in English. …”
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Random Undersampled Digital Elevation Model Super-Resolution Based on Terrain Feature-Aware Deep Learning Network by Ziqiang Huo, Meng Xi, Jingyi He, Zhengjian Li, Jiabao Wen

“…Meanwhile, the general spatial interpolation algorithms based on deep learning usually have low model complexity and lack the specific designed loss function, which usually leads to significant interpolation errors. …”
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Equitable and effective clinical guidance development and dissemination: trauma aims to lead the way by Deborah M Stein, Elliott R Haut, Eileen M Bulger, Vanessa P Ho, Andres M Rubiano, Ashley Farley, Lacey N LaGrone, Danielle J Wilson, Maria Michaels, Meghan B Lane-Fall, Michael A Person, Linda Reinhart

“…Suggestions include (1): professional societies collaborate rather than compete for guideline development.(2) Design primary clinical research for implementation, and where relevant, with guideline development in mind.(3) Select clinical topics for guideline development through systematic prioritization, with an emphasis on patient-centered outcomes.(4) Develop guideline authorship groups with a focus on transparency, equity of opportunity and diversity of representation.(5) Establish a plan for regular review and updating and provide the date the guideline was last updated for transparency.(6) Integrate options for adapting the guideline to local resources and needs at the time of development.(7) Make guidelines available on a platform that allows for open feedback and utilization tracking.(8) Improve discoverability of guidelines.(9) Optimize user-experience with a focus on inclusion of bedside-ready, mobile-friendly infographics, tables or algorithms when feasible.(10) Use open access and open licenses.(11) Disseminate clinical guidance via comprehensive and equitable communication channels. …”
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Accuracy Evaluation of Multi-Source Precipitation Data in Mountain Flood Simulation in Guoning Village， Xiahe County by Wubin HUANG, Jing FU, Runxia GUO, Junxia ZHANG, Yu LEI

“…From 22:00 on September 6， 2023 to 04:00 （Beijing Time） on September 7， Xiahe County in Gansu Province experienced severe convective weather， with short-term heavy rainfall in some areas， causing flash floods in Guoning Village， Xiahe County， resulting in casualties.In this study， the characteristics of Radar Quantitative Precipitation Estimation （Radar-QPE）， FengYun 4B Quantitative Precipitation Estimation （FY4B-QPE）， and CMA Multi-source Precipitation Analysis （CMPA） precipitation products were contrastive analyzed based on meteorological station observations.These precipitation data were used to drive the hydrodynamic hydrological model and evaluate the effect of different precipitation data in the flash flood simulation.The results showed that： （1） Among the 12-hour cumulative precipitation amounts， CMPA demonstrated higher accuracy in terms of the position of large value areas and differences in local precipitation levels； Radar-QPE was closer to AWS （Automatic Weather Station） in terms of cumulative precipitation level but showed significant differences in spatial distribution； FY4B-QPE overestimated the cumulative precipitation level by 33.8%.（2） In terms of hourly distribution， CMPA was most similar to AWS in terms of temporal evolution， spatial distribution， and precipitation level； Radar-QPE's peak values were smaller， and the peak times were lagged， with negative deviations in precipitation being dominant； FY4B-QPE's peak values and peak times were consistent with reality， but there were deviations in the start and end times of precipitation， with positive deviations in precipitation being dominant.（3） In the hydrological simulation study， CMPA， Radar-QPE， and FY4B-QPE all overestimated water levels， but the timing of water level peaks was more consistent with AWS.CMPA performed best in terms of RMSE （Root Mean Square Error）， NSE （Nash Efficiency Coefficient）， and Bias （Relative Deviation）， followed by Radar-QPE， and FY4B-QPE performed relatively poorly.Although existing site-observed precipitation cannot fully meet the needs of research and early warning for small and medium scale mountain floods， the high precision of CMPA data could effectively supplement the deficiencies of traditional meteorological observation stations to some extent.Meanwhile， the algorithms and accuracy of Radar-QPE and FY4B-QPE needed to be further improved and enhanced.…”
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The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis. by Toviah Moldwin, Li Shay Azran, Idan Segev

“…Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rules to enable artificial neural networks to effectively perform both supervised and unsupervised learning tasks. …”
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An IoT-enabled AI system for real-time crop prediction using soil and weather data in precision agriculture by MD Shaifullah Sharafat, Nilavro Das Kabya, Rahimul Islam Emu, Mehrab Uddin Ahmed, Jakaria Chowdhury Onik, Mohammad Aminul Islam, Riasat Khan

“…A user survey involving farmers, agricultural researchers, and students assessed the usability, accuracy, and reliability of the system. …”
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Machine learning-based prediction and classification of seawater intrusion in the hyper-arid coastal aquifer of Fujairah, UAE by Assaad Kassem, Ahmed Sefelnasr, Abdel Azim Ebraheem, Luqman Ali, Faisal Baig, Mohsen Sherif

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Insights from UKCTOCS for design, conduct and analyses of large randomised controlled trials by Usha Menon, Aleksandra Gentry-Maharaj, Matthew Burnell, Sophia Apostolidou, Andy Ryan, Jatinderpal K Kalsi, Naveena Singh, Lesley Fallowfield, Alistair J McGuire, Stuart Campbell, Steven J Skates, Anne Dawnay, Mahesh Parmar, Ian J Jacobs

“…The long-term follow-up UKCTOCS (2015 20) was supported by National Institute for Health and Care Research (NIHR HTA grant 16/46/01), Cancer Research UK, and The Eve Appeal. …”
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Machine learning applications in forecasting patient satisfaction and clinical outcomes after carpal tunnel release: a retrospective study by Zohreh Manoochehri, Sara Manoochehri, Seyed Reza Bagheri, Alireza Abdi, Ehsan Alimohammadi

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Root-Zone Salinity in Irrigated Arid Farmland: Revealing Driving Mechanisms of Dynamic Changes in China’s Manas River Basin over 20 Years by Guang Yang, Xuejin Qiao, Qiang Zuo, Jianchu Shi, Xun Wu, Alon Ben-Gal

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