Robust design of bicycle infrastructure networks by Christoph Steinacker, Mads Paulsen, Malte Schröder, Jeppe Rich

“…In this paper, we approach the problem from two perspectives: direct optimization methods, which generate near-optimal solutions using operations research techniques, and conceptual heuristics, which offer intuitive and scalable algorithms grounded in network science. …”
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Capacity Estimation and Knee Point Prediction Using Electrochemical Impedance Spectroscopy for Lithium Metal Battery Degradation via Machine Learning by Qianli Si, Shoichi Matsuda, Yasunobu Ando, Toshiyuki Momma, Yoshitaka Tateyama

“…To reduce data complexity and improve model efficiency, the input by selecting specific frequency points based on SHAP values is further optimized. …”
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The Design and Data Analysis of an Underwater Seismic Wave System by Dawei Xiao, Qin Zhu, Jingzhuo Zhang, Taotao Xie, Qing Ji

“…The host computer performs the collaborative optimization of multi-modal hardware architecture and adaptive signal processing algorithms, enabling the detection of ship targets in oceanic environments. …”
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Designing and implementing a Web-based real time routing service for crisis management (a case study for district 11 of Tehran) by javad sadidi, seyed hassan hosseini sajedi

“…Timing framework associated with catastrophes is one of the most important issues in crisis management. In such cases, being immediate has a considerable importance and web based real-time routing service as an important tool has a significant role in relief operations improvement. …”
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AI Applications for Chronic Condition Self-Management: Scoping Review by Misun Hwang, Yaguang Zheng, Youmin Cho, Yun Jiang

“…Conversational AI (21/66, 32%) and multiple machine learning algorithms (16/66, 24%) were the most used AI technologies. …”
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Reliable Event Detection via Multiple Edge Computing on Streaming Traffic Social Data by Yipeng Ji, Jingyi Wang, Yan Niu, Hongyuan Ma

“…Then, we utilize graph neural networks to perform semi-supervised learning on HIN to obtain the optimal meta-path weights. We also develop Binary Sample Graph Convolutional Neural Network (BS-GCN) and Binary Sample Graph Attention Network (BS-GAT) to improve the reliability of graph neural network models based on the characteristics of traffic event detection and design an incremental clustering algorithm based on event similarity to implement streaming social traffic event detection. …”
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Errors in the diagnosis of types of diabetes mellitus: causes and prevention strategies (literature review and own research results) by K.I. Gerush, N.V. Pashkovska, O.Z. Ukrainets

“…This is due to the increasing heterogeneity of DM, blurring of the boundaries between its types, atypical disease course, the decreased diagnostic value of the essential criteria for DM types (age, presence of metabolic syndrome signs, ketosis, dependency on insulin therapy), presence of comorbid conditions, and limited availability of diagnostic tests to specify the type of diabetes. To optimize diagnosis and prevent diagnostic errors, we have developed a Telegram bot DiaType based on a multilevel algorithm for the differential diagnosis of various types of DM. …”
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Proposed Comprehensive Methodology Integrated with Explainable Artificial Intelligence for Prediction of Possible Biomarkers in Metabolomics Panel of Plasma Samples for Breast Canc... by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…The SHapley Additive Descriptions (SHAP) analysis evaluated the optimal prediction model for interpretability. <i>Results</i>: The RF algorithm showed improved accuracy (0.963 ± 0.043) and sensitivity (0.977 ± 0.051); however, LightGBM achieved the highest ROC AUC (0.983 ± 0.028). …”
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Guaranteed efficient energy estimation of quantum many-body Hamiltonians using ShadowGrouping by Alexander Gresch, Martin Kliesch

“…They are helpful for identifying measurement settings that improve the energy estimate the most. This task constitutes an NP-hard problem. …”
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Research and Analysis of Out-of-Tolerance Problem in Mold Processing Based on Intelligence by Juxian Wang

“…According to the numerical simulation results, the reasoning mechanism of an intelligent design system including instance and rule knowledge is designed, and an uncertain knowledge reasoning algorithm based on the reliability factor is proposed to improve the accuracy of mold size and reduce the overtolerance of the mold.…”
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Fingerprint Classification Based on Multilayer Extreme Learning Machines by Axel Quinteros, David Zabala-Blanco

“…In this study, we introduce, for the first time, the use of a multilayer extreme learning machine (M-ELM) for fingerprint classification, aiming to improve training efficiency. A comparative analysis is conducted with CNNs and unbalanced extreme learning machines (W-ELMs), as these represent the most influential methodologies in the literature. …”
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An Enhanced Framework for Assessing Pluvial Flooding Risk with Integrated Dynamic Population Vulnerability at Urban Scale by Xinyi Shu, Chenlei Ye, Zongxue Xu, Ruting Liao, Pengyue Song, Silong Zhang

“…This study uses Jinan, a typical foothill plain city in Shandong Province, as a case study to compare the performance of differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) in calibrating the SWMM. …”
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Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge. by Yeonuk Kim, Monica Garcia, T Andrew Black, Mark S Johnson

“…We found a strong correlation (r = 0.93) between the sensitivity of ET estimates to machine-learned parameters and model error (root-mean-square error; RMSE), indicating that reduced sensitivity minimizes error propagation and improves performance. Notably, the most accurate hybrid model (RMSE = 17.8 W m-2 in energy unit) utilized a novel empirical parameter, which is relatively stable due to land-atmosphere equilibrium, outperforming both the pure ML model and hybrid models requiring conventional parameters (e.g., surface conductance). …”
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Data-driven approach to mid-latitude coherent scatter radar data classification by Berngardt Oleg

“…Based on 2021 data, a solution of the problem of automatic data classification is presented without their labeling by an expert and without postulating the number of classes. The algorithm automatically labels the data, determines the optimal number of signal classes observed by the radars, and trains a two-layer classifying neural network of an extremely simple structure. …”
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Refined Assessment Method of Offshore Wind Resources Based on Interpolation Method by Wenchuan Meng, Zaimin Yang, Zhi Rao, Shuang Li, Xin Lin, Jingkang Peng, Yuwei Cao, Yingquan Chen

“…To enhance the prediction accuracy of offshore wind speed, this study employs an interpolation algorithm to improve spatial resolution based on the ERA5 reanalysis dataset. …”
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Breast Tumor-Like-Masses Segmentation From Scattering Images Obtained With an Ultrahigh-Sensitivity Talbot-Lau Interferometer Using Convolutional Neural Networks by Ionut-Cristian Ciobanu, Nicoleta Safca, Elena Anghel, Dan Popescu

“…Future work will focus on optimizing CNN architecture and expanding the dataset to improve the segmentation of small tumor-like masses.…”
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Method of Diagnostics of Operation Modes of Individual Heat Supply Units, Allowing to Detect Pre-Emergency Situations at an Early Stage by Dvortsevoy A.I., Borush O.V., Khoreva V.A., Yakovina I.N.

“…This was confirmed by the "Elbow Method", which determined the optimal number, which made it possible to significantly improve the forecasting of emergency modes. …”
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Solar panel fault diagnosis based on the intelligentrecursive method by Saadat Boulanouar, Fengal Boualem

“…It guarantees the optimal functioning of solar panels, maximizing energy production and improving return on investment. …”
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Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers by Vivek Singh, Shikha Chaganti, Matthias Siebert, Sowmya Rajesh, Andrei Puiu, Raj Gopalan, Jamie Gramz, Dorin Comaniciu, Ali Kamen

“…For most screening programs, age and clinical risk factors such as family history are part of the initial risk stratification algorithm. …”
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Real-time temperature prediction of large-scale lithium battery module driven by data based on few measurement points by Jiajie HAN, Qingyang YUAN, Yu LI, Bo ZHANG, Ke XUE, Tian LAN

“…This slight error increase is attributed to the complex heat transfer dynamics near cooling interfaces, which pose challenges for most data-driven models. This study highlights the effectiveness of the Gappy POD algorithm in managing the thermal dynamics of large-scale energy storage systems in real time. …”
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