Biochemical disturbances in schizophrenia — a «window of opportunity» by T. V. Zhilyaeva, A. S. Piatoikina, E. D. Kasyanov, G. V. Rukavishnikov, I. V. Semennov, O. V. Kostina, A. S. Blagonravova, G. E. Mazo

“…Materials and methods: in the blood serum of 125 patients with schizophrenia and 95 healthy volunteers the levels of folate and cobalamin (B12) (chemiluminescent immunoassay on microparticles), homocysteine (HC, enzymatic analysis), tetrahydrobiopterin (BH4, competitive enzyme immunoassay, ELISA), reduced glutathione (GSH, spectrophotometry with Ellman’s reagent), interleukin-6 (IL-6, ELISA based on a three-stage «sandwich version») and C-reactive protein (CRP, immunoturbodimetric method) were evaluated.   Results: In patients with schizophrenia the level of all studied serum biochemical markers, except for B12, was significantly different compared to healthy volunteers. …”
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Comparison of Surface Current Measurement Between Compact and Square-Array Ocean Radar by Yu-Hsuan Huang, Chia-Yan Cheng

“…In Taiwan, the Taiwan Ocean Research Institute (TORI) has established a data quality management mechanism based on international standards for data filtering, noise reduction, and outlier detection, improving the accuracy and stability of radar-derived velocity measurements.To eliminate the effects caused by different center frequencies between MABT and KNTN, this study used the same algorithms and parameter settings as much as possible in all steps, from Doppler spectra processing to radial velocity calculation, ensuring the comparability of the data. …”
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Strategy for Optimizing Vitamin B12 Production in <i>Pseudomonas putida</i> KT2440 Using Metabolic Modeling by Thomaz Satuye Prieto-de Lima, Keilor Rojas-Jimenez, Christopher Vaglio

“…<b>Methods</b>: We curated and modified a genome-scale metabolic model of <i>Pseudomonas putida</i> KT2440 and evaluated different strategies to optimize vitamin B₁₂ production using the knockin and OptGene algorithms from the COBRA Toolbox. …”
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FTIR-Based Microplastic Classification: A Comprehensive Study on Normalization and ML Techniques by Octavio Villegas-Camacho, Iván Francisco-Valencia, Roberto Alejo-Eleuterio, Everardo Efrén Granda-Gutiérrez, Sonia Martínez-Gallegos, Daniel Villanueva-Vásquez

“…Furthermore, the impact of different normalization techniques (Min-Max, Max-Abs, Sum of Squares, and Z-Score) on classification accuracy was evaluated. …”
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Fine-Tuning EEG Channel Utilization for Emotionally Stimulated Biometric Authentication by Chetan Rakshe, Christy Bobby Thomas, Mohanavelu Kalathe, Vanteemar S. Sreeraj, Ganesan Venkatasubramanian, Deepesh Kumar, A. Amalin Prince, Jac Fredo Agastinose Ronickom

“…In this study, we propose a novel pipeline to evaluate electroencephalography (EEG)-based biometric system across different emotional states and optimize critical brain regions using machine learning algorithms. …”
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Characterizing the Inter-Core Qubit Traffic in Large-Scale Quantum Modular Architectures by Sahar Ben Rached, Isaac Lopez Agudo, Santiago Rodrigo, Medina Bandic, Artur Garcia-Saez, Sebastian Feld, Hans van Someren, Eduard Alarcon, Carmen G. Almudever, Sergi Abadal

“…Based on the showcased results, we conclude on the parallelization and scalability of presented algorithms, qualitatively evaluate the resource requirements as we scale circuit sizes, and lay the foundations of application-oriented benchmarking of large-scale multi-core architectures.…”
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Protecting Industrial Control Systems From Shodan Exploitation Through Advanced Traffic Analysis by Sayed Reza Ghazinour Naeini, Alireza Shameli-Sendi, Masoume Jabbarifar

“…Several machine learning algorithms were evaluated, including Random Forest, Support Vector Machine, Logistic Regression, and Gradient Boosting. …”
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Cost-Efficient RSSI-Based Indoor Proximity Positioning, for Large/Complex Museum Exhibition Spaces by Panos I. Philippopoulos, Kostas N. Koutrakis, Efstathios D. Tsafaras, Evangelia G. Papadopoulou, Dimitrios Sigalas, Nikolaos D. Tselikas, Stefanos Ougiaroglou, Costas Vassilakis

“…A total 15 methods/algorithms were evaluated against prediction accuracy across 20 RSSI datasets, incorporating diverse hall cell allocations and visitor movement patterns. …”
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AI’s role in transforming learning environments: a review of collaborative approaches and innovations by Dwi Mariyono, Akmal Nur Alif Hd

“…AI’s ability to enhance learning outcomes is evident, yet concerns around algorithmic bias, data privacy and the digital divide must be addressed to ensure equitable access to AI-powered education worldwide. …”
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Enhanced Spring Wheat Soil Plant Analysis Development (SPAD) Estimation in Hetao Irrigation District: Integrating Leaf Area Index (LAI) Under Variable Irrigation Conditions by Qiang Wu, Dingyi Hou, Min Xie, Qi Gao, Mengyuan Li, Shuiyuan Hao, Chao Cui, Keke Fan, Yu Zhang, Yongping Zhang

“…While integrating canopy structural parameters with spectral data represents a promising solution, systematic investigation of this approach throughout the entire growth cycle of spring wheat under different irrigation regimes remains limited. This study evaluated three machine learning algorithms (Random Forest, Support Vector Regression, and Multi-Layer Perceptron) for SPAD estimation in spring wheat cultivated in the Hetao Irrigation District. …”
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Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study by Wei Wang, Xiang Chen, Licong Xu, Kai Huang, Shuang Zhao, Yong Wang

“…Compared with existing advanced algorithms, this system is more accurate in identifying PPSDs. …”
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Climate change will alter Amazonian bumblebees’ distribution, but effects are species-specific by Patrícia Nunes-Silva, André Luis Acosta, Rafael Cabral Borges, Breno Magalhães Freitas, Ricardo Caliari Oliveira, Tereza Cristina Giannini, Vera Lucia Imperatriz-Fonseca

“…An ensemble modeling approach combining five different algorithms was used to predict areas of stability, habitat loss, and potential range expansion.ResultsBy 2060, B. brevivillus is projected to lose 41.6% of its current suitable habitat, with significant reductions in northern and coastal regions. …”
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Improving chlorophyll-a estimation using Sentinel-2 data: a comparative analysis of augmented datasets by Jinmyeong Lee, Do Hyuck Kwon, Heewon Jeong, Gibeom Nam, Euiho Hwang, Jin Hwi Kim, Kyung Hwa Cho, Hyo Gyeom Kim

“…The synergetic impacts of rapid urbanization and climate change contribute to the unprecedented occurrence of severe algal blooms, which require sufficient high-concentration data for successful model training. In this study, we evaluated the feasibility of integrating datasets from two different watersheds to estimate chlorophyll-a (Chl-a) concentrations using machine learning models with Sentinel-2 imagery. …”
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Integrating Low-Altitude Remote Sensing and Variable-Rate Sprayer Systems for Enhanced Cassava Crop Management by Pongpith Tuenpusa, Grianggai Samseemoung, Peeyush Soni, Thirapong Kuankhamnuan, Waraphan Sarasureeporn, Warinthon Poonsri, Apirat Pinthong

“…The image processing algorithms and calibration methods used were deemed acceptable, with drones equipped with variable-rate sprayer systems outperforming RC helicopters in overall quality. …”
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Distilling knowledge from graph neural networks trained on cell graphs to non-neural student models by Vasundhara Acharya, Bülent Yener, Gillian Beamer

“…Even non-neural models can learn from a neural network teacher. We evaluated our approach across varying dataset complexities, including the presence or absence of distribution shifts, varying degrees of imbalance, and different levels of graph complexity for training GNNs. …”
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Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

“…These methods depend on empirical relationships and constants that can differ between basins. This study proposes an effective data-driven approach that utilizes machine learning algorithms to forecast reservoir pore pressure. …”
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A machine-learning approach for predicting butyrate production by microbial consortia using metabolic network information by Claudia Silva-Andrade, Sergio Hernández, Pedro Saa, Ernesto Perez-Rueda, Daniel Garrido, Alberto J. Martin

“…The performance of the algorithms was evaluated using k-fold cross-validation and new experimental data, displaying a Pearson correlation coefficient exceeding 0.75 for the predicted and observed butyrate production in two bacteria consortia. …”
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SPA-Net: An Offset-Free Proposal Network for Individual Tree Segmentation from TLS Data by Yunjie Zhu, Zhihao Wang, Qiaolin Ye, Lifeng Pang, Qian Wang, Xiaolong Zheng, Chunhua Hu

“…SPA-Net was rigorously validated on two different forest datasets. On both BaiMa and Hong-Tes Lake datasets, the approach demonstrated superior performance compared to several contemporary segmentation approaches evaluated under the same conditions. …”
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Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction by Zhengbiao Hu, Shuangshan Bu, Kai Wang, Qianqian Cao, Huanhuan Zheng, Jie Yang, Shanshan Chen, Yuemeng Wu, Wenkai Ren, Chenlei He

“…In this study, multiple machine learning algorithms were used to develop a non-invasive and improved model for the diagnosis of IgAN. …”
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Advancing road maintenance with EfficientDet-based pothole monitoring by Jenefa Archpaul, Taurshia Antony, Kuriakose Bessy Mani, Kumar Edward Naveen Vijaya, Lincy Archpaul

“…This diversity enables the model to generalize effectively across different conditions. Our experimental evaluations demonstrate that the EfficientDet-based model achieves an impressive average precision of 0.90 and a robust recall of 0.92, highlighting its capacity for accurate and swift pothole detection-an essential component for improving road maintenance. …”
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