Navigating risk: Analyzing the effects of climate change and political connections on bank financing access for the agricultural sector by Silviana Pebruary, Hadi Ismanto, Fatchur Rohman, Dwi Falihaturrohman

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Integrating knowledge management with smart technologies in public pharmaceutical organizations by Muhanad Mahmoud, Talaat Shma, Adel Aziz, Abdelrehim Awad

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Optimizing Bundle Block Adjustment for High-Overlap Small-Format Multi-Head Camera Systems by T. Bannakulpiphat, T. Bannakulpiphat, W. Karel, C. Ressl, N. Pfeifer

“…Furthermore, analysis of the RMS error suggests that by adding the oblique images to the nadir image block and removing the 2-fold tie points, vertical accuracy improved considerably, while planimetric accuracy remained consistent.…”
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Techno-Economic Viability of Fuel Cell Emulator Toward Sustainable Energy Systems: A Survey by M. S. Priya, S. Saravanan, K. R. M. Vijaya Chandrakala, Umashankar Subramaniam, Sivakumar Selvam

“…It explores fuel cell (FC) emulator evolution, categorizing them into pseudo, mathematical modelling, and electronic types, ensuring reliable performance comparable to real FC systems and assessing accuracy through the margin of error. The paper discusses control strategies, designing fuel cell emulators, and implementation techniques, addressing FC degradation issues and covering fault detection while suggesting solutions. …”
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Improved lightweight DeepLabV3+ for bare rock extraction from high-resolution UAV imagery by Pengde Lai, Chao Lv, Lv Zhou, Shengxiong Yang, Jiao Xu, Qiulin Dong, Meilin He

“…Results show the following: (1) When MobileNetV2 is used as the backbone of DeepLabV3+, the Accuracy, F1 score, and MIoU reach 97.39 %, 78.91 %, and 82.11 %, respectively, outperforming VGG16, Xception, SqueezeNet, and traditional segmentation models. (2) Applying the lightweight DeepLabV3+ model to bare rock identification in orthophoto imagery of the study area results in a bare rock rate error of approximately 5 %, demonstrating the practical applicability of the model. (3) After the introduction of the attention mechanism, the model's Recall, F1 score, and MIoU increased by 14.00 %, 8.37 %, and 5.62 %, respectively, remarkably enhancing identification completeness and boundary accuracy. …”
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Generative Adversarial Networks for Climate-Sensitive Urban Morphology: An Integration of Pix2Pix and the Cycle Generative Adversarial Network by Mo Wang, Ziheng Xiong, Jiayu Zhao, Shiqi Zhou, Yuankai Wang, Rana Muhammad Adnan Ikram, Lie Wang, Soon Keat Tan

“…Additionally, CycleGAN-enhanced outputs exhibited a 35% reduction in relative error compared to Pix2Pix-generated images, significantly improving edge preservation and urban feature accuracy. …”
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Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance Analysis by Stefano Rodinò, Giuseppe Rota, Matteo Chiodo, Antonio Corigliano, Carmine Maletta

“…A PRISMA-guided literature review (2003–2025) identified 24 studies, which were categorized by control architectures (hybrid AI-linear, pure AI, adaptive, and model predictive control) and evaluated through quantitative metrics, including Root Mean Square Error (RMSE%) and a weighted scoring system for experimental rigor. …”
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Reliability of a VO2-derived running-speed threshold in trained men by Marialaura Espaillat, David M. Falkins, Brandi Antonio, David H. Fukuda, Jeffrey R. Stout

“…Reliability statistics included ICC(2,1), standard error of measurement (SEM), coefficient of variation (CV), and minimal detectable change at 95% confidence (MDC95).Results RSVO₂ showed excellent reliability: ICC(2,1) = 0.96 (95% CI 0.89–0.99); SEM = 0.35 km·h−1; CV = 3.0%; MDC95 = 0.98 km·h−1. …”
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Development and Evaluation of Machine Learning Models for Air-to-Land Temperature Conversion Using the Newly Established Kunlun Mountain Gradient Observation System by Yongkang Li, Qing He, Yongqiang Liu, Amina Maituerdi, Yang Yan, Jiao Tan

“…The results demonstrated that the CatBoost algorithm outperformed the others under complex terrain and climatic conditions, achieving a coefficient of determination (R²) of 0.997 and the lowest root mean square error (RMSE) of 0.627 °C, indicating superior robustness and accuracy. …”
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On the parametric analytics to investigate hole quality characteristics during helical milling of carbon fiber reinforced plastics stacks by Mehdi Tlija, Nimra Naeem, Mohammad Pervez Mughal, Kiran Mughal, Saad Ullah, Muhammad Sana, Anamta Khan

“…Analysis of variance is used to study the impact of input variables on the hole quality aspects that include delamination damage, length of uncut fiber, circularity error, and surface roughness by using the Taguchi design of experiment. …”
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RivAIr: A custom-designed UAV-based sensor for real-time water area segmentation and surface velocity estimation by Marco La Salandra, Rosa Colacicco, Simone Panza, Giovanni Fumai, Pierfrancesco Dellino, Domenico Capolongo

“…Validated along the Basento River (Basilicata, Italy) under controlled conditions, the optimal configuration of RivAIr achieved a 90% confidence score in water surface area detection and velocity estimation with a −3% error relative to in-situ measurements, showing strong consistency with in-situ flow observations. …”
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Psychometric properties of screening tools for mild cognitive impairment in older adults based on COSMIN guidelines: a systematic review by Shasha Wen, Dongmei Cheng, Nana Zhao, Xinyu Chen, Xianying Lu, Yue Li, Huanle Liu, Jing Gao, Chaoming Hou, Ran Xu

“…No data were found on cross-cultural validity/measurement invariance, measurement error, or responsiveness. The final three instruments, AV-MoCA, HKBC, and Qmci-G, received class A recommendations and were recommended for use. …”
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IMERG-Like Precipitation Retrieval From Geo-Kompsat-2A Observations Using Conditional Generative Adversarial Networks by Kyung-Hoon Han, Jaehoon Jeong, Sungwook Hong

“…The results demonstrated a probability of detection of 0.607, a critical success index of 0.482, a root-mean-square error of 0.759 mm/h, and a correlation coefficient of 0.671. …”
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Unmanned Aerial Vehicle–Unmanned Ground Vehicle Centric Visual Semantic Simultaneous Localization and Mapping Framework with Remote Interaction for Dynamic Scenarios by Chang Liu, Yang Zhang, Liqun Ma, Yong Huang, Keyan Liu, Guangwei Wang

“…In dynamic scenarios, the localization accuracy attains an average absolute pose error (APE) of 0.1275 m. This outperforms state–of–the–art methods like Dynamic–VINS (0.211 m) and ORB–SLAM3 (0.148 m) on the EuRoC MAV Dataset. …”
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Minimum Audible Angle in 3rd-Order Ambisonics in Horizontal Plane for Different Ambisonic Decoders by Katarzyna Sochaczewska, Karolina Prawda, Paweł Małecki, Magdalena Piotrowska, Jerzy Wiciak

“…The findings of this study provide valuable insights for spatial audio applications based on ambisonic technology.…”
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Using machine learning to predict the rupture risk of multiple intracranial aneurysms by Junqiang Feng, Chunyi Wang, Yu Wang, He Liu, He Liu

“…The model demonstrated preferable predication performance in unruptured aneurysms (TNR: 0.837) but showed limitations in predicting ruptured aneurysms (TPR: 0.644). Error analysis revealed that the model’s lower TPR may be attributed to the small sample size and dataset imbalance. …”
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Point-to-Interval Prediction Method for Key Soil Property Contents Utilizing Multi-Source Spectral Data by Shuyan Liu, Dongyan Huang, Lili Fu, Shengxian Wu, Yanlei Xu, Yibing Chen, Qinglai Zhao

“…For point predictions, metrics such as the coefficient of determination (R²) and error metrics demonstrated significant enhancements compared to predictions based solely on single-source spectral data. …”
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Linear Active Disturbance Rejection Control-Based Voltage Controller for Buck and Boost DC/DC Converters in DC Distribution Grids by Asimenia Korompili, Oemer Ekin, Marija Stevic, Veit Hagenmeyer, Antonello Monti

“…This provides more degrees-of-freedom in the design of the voltage controller, beyond the design of the common L-ADRC formulation based on the bandwidth of the linear extended state observer and the scaling factor of a proportional error feedback controller. In addition, the physical significance of the converter’s states allows the integration of additional control functions, relying on the electrical quantities of the converter, for the enhancement of the performance of the voltage controller. …”
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Predicting Oncological and Functional Outcomes by Nephrectomy Type for T1 Renal Tumors Using Machine Learning Models by Dongrul Shin, Maisy Song, Jungyo Suh, Cheryn Song

“…Model performance for recurrence prediction was evaluated with area under the curve receiver operating characteristic, area under the precision-recall curve, and log-loss, while eGFR prediction was assessed using root mean square error (RMSE) and R2. Results Of the 823 patients, 463 (56.3%) had T1a tumors and 487 (59.2%) underwent PN. …”
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Neural network-based snow depth retrieval from AMSR-2 brightness temperatures using ICESat-2 measurement as ground truth by Sunny Sun-Mack, Yongxiang Hu, Xiaomei Lu, Yan Chen, Ali Omar

“…The trained NN was then applied to AMSR-2 clear-sky wide-swath observations for the 2018–2019 and 2019–2020 Arctic winters, generating daily snow depth estimates across Arctic sea ice.ResultsValidation against independent ICESat-2 data showed strong performance: the NN-based AMSR-2 snow depth retrievals had a near-zero bias and a root mean square error (RMSE) of 10 cm. Further validation using (a) instantaneous matchups, (b) daily geolocation comparisons, and (c) monthly Arctic-wide averages confirmed consistent results. …”
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