Advancements and challenges in inverse lithography technology: a review of artificial intelligence-based approaches by Yixin Yang, Kexuan Liu, Yunhui Gao, Chen Wang, Liangcai Cao

“…The review tracks recent advancements of ILT in model improvement and algorithmic efficiency. Challenges such as extended computational runtimes and mask-writing complexities are summarized, with potential solutions discussed. …”
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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

“…Gradient Boosting consistently outperformed other algorithms, achieving the highest F1-scores across all programmable logic controllers (PLC) models. …”
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WU Hongchun1, ZHENG Youqi1, CAO Liangzhi1, DU Xianan1, WANG Xuesong2, ZU Tiejun1, LIU Zhouyu1, HE Qingming1, CHEN Ronghua1, GE Li1, YANG Rui2, GAO Xinzhao2, WANG Shixi2... by WU Hongchun1, ZHENG Youqi1, CAO Liangzhi1, DU Xianan1, WANG Xuesong2, ZU Tiejun1, LIU Zhouyu1, HE Qingming1, CHEN Ronghua1, GE Li1, YANG Rui2, GAO Xinzhao2, WANG Shixi2, A Reai2

“…The findings suggest that employing advanced numerical simulation algorithms can circumvent the inherent shortcomings of existing methods and software in terms of theoretical models, addressing the issue of computational accuracy that fails to meet engineering requirements due to model defects. …”
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Machine learning for the prediction of mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis by Xiangui Lv, Daiqiang Liu, Xinwei Chen, Lvlin Chen, Xiaohui Wang, Xiaomei Xu, Lin Chen, Chao Huang

“…Results A total of 8 studies were included, with a total of 53 predictive models and 17 machine learning algorithms used. Meta-analysis using a random effects model showed that the overall C index in the training set was 0.81 (95% CI: 0.78–0.84), sensitivity was 0.39 (0.32–0.47), and specificity was 0.92 (95% CI: 0.89–0.95). …”
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Optimization and prediction of corporate credit rating through advanced feature selection based on AI and deep learning by Jumanah Ahmed Darwish

“…Our findings reveal that feature selection leads to substantial improvements in both classification accuracy and model efficiency, with XGBoost emerging as the top performer, closely followed by RF. …”
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Advancing Spike Sorting Through Gradient‐Based Preprocessing and Nonlinear Reduction With Agglomerative Clustering by Mohammad Amin Lotfi, Fatemeh Zareayan Jahromy, Mohammad Reza Daliri

“…In the challenging portion of the dataset, our models demonstrated a 12% improvement in accuracy. …”
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Village-level climate and weather variability, mediated by village-level crop yield, is associated with linear growth in children in Uganda by Vernon M Chinchilli, Paddy Ssentongo, Djibril M Ba, Claudio Fronterre

“…Normalised rainfall anomaly and total precipitation were derived from the African Rainfall Estimation Algorithm Version 2 product. Linear regression models were used to associate total annual precipitation and anomalies with height-for-age z-scores and to explore the mediating role of crop yield qualitatively. …”
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A XGBoost-Based Prediction Method for Meat Sheep Transport Stress Using Wearable Photoelectric Sensors and Infrared Thermometry by Ruiqin Ma, Runqing Chen, Buwen Liang, Xinxing Li

“…The results show that SVC and GBDT algorithms are more effective and the overall model classification accuracy reached 86.44% and 91.53%. …”
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An Unmanned Delivery Vehicle Path-Planning Method Based on Point-Graph Joint Embedding and Dual Decoders by Jiale Cheng, Zhiwei Ni, Wentao Liu, Qian Chen, Rui Yan

“…Hence, a path-planning model is presented to tackle the routing dilemma of UDVs in logistics. …”
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Numerical Simulation of Earthquake Impacts on Marine Structures: A Comprehensive Review by Adel Kabi, Jersson X. Leon-Medina, Francesc Pozo

“…However, challenges persist in areas such as computational efficiency and algorithmic limitations. Key findings highlight the ability of these models to accurately simulate primary forces during seismic events and secondary effects, such as wave-induced loads. …”
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Design and accuracy evaluation of AI orthognathic surgery plates by LIU Zhikai, XU Chunwei, ZHU Zhaokun, LIU Yao, LUO En

“…[Methods:] This research established an AI algorithm for orthognathic surgical splints and obtained the available automatic design program. …”
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MASIP: A Methodology for Assets Selection in Investment Portfolios by José Purata-Aldaz, Juan Frausto-Solís, Guadalupe Castilla-Valdez, Javier González-Barbosa, Juan Paulo Sánchez Hernández

“…This paper proposes a Methodology for Assets Selection in Investment Portfolios (MASIP) focused on creating investment portfolios using heuristic algorithms based on the Markowitz and Sharpe models. …”
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A Real-Time Signal Measurement System Using FPGA-Based Deep Learning Accelerators and Microwave Photonic by Longlong Zhang, Tong Zhou, Jie Yang, Yin Li, Zhiwen Zhang, Xiang Hu, Yuanxi Peng

“…The results show that the time of inference saves 28–31% for the DNN model and 71–73% for the LSTM + DNN model compared to running on graphic processing unit (GPU). …”
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Impacts of noise and interference on the bit error rate of the FBMC-OQAM modulation scheme in 5G systems by A. Androon, O. V. Tikhonova

“…The graphical and numerical data obtained through computer modeling demonstrates improved BER in 5G networks using FBMC-OQAM. …”
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On the Potential of a New Generation of Magnetometers for MEG: A Beamformer Simulation Study. by Elena Boto, Richard Bowtell, Peter Krüger, T Mark Fromhold, Peter G Morris, Sofie S Meyer, Gareth R Barnes, Matthew J Brookes

“…However in practice, this will require both improved hardware and modelling algorithms.…”
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The role of physical metallurgical relationships in enhancing alloy properties prediction and design: A case study on Q&P steel by Yong Li, Hua Li, Chenchong Wang, Pedro Eduardo Jose Rivera‐Diaz‐del‐Castillo

“…To address this, this study proposes a novel framework that combines PM knowledge graphs (PMKGs) with graph neural networks (GNNs) to predict the tensile properties of quenching and partitioning steels, using genetic algorithms for dual‐objective optimization. Compared to traditional artificial intelligence (AI) models, this framework shows significant advantages in predicting ultimate tensile strength (UTS) and total elongation (TEL) with higher accuracy and stability. …”
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Adopting an Open-Source Processing Strategy for LiDAR Drone Data Analysis in Under-Canopy Archaeological Sites: A Case Study of Torre Castiglione (Apulia) by Nicodemo Abate, Roberto Goffredo, Giorgia Dato, Antonio Minervino Amodio, Antonio Loperte, Alessia Frisetti, Gabriele Ciccone, Sara Elettra Zaia, Maria Sileo, Rosa Lasaponara, Nicola Masini

“…Key steps include training the classifier, generating digital terrain models, digital feature models, and digital surface models, and enhancing the visibility of archaeological features. …”
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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System by Safdar Hussain Javed, Maaz Bin Ahmad, Muhammad Asif, Waseem Akram, Khalid Mahmood, Ashok Kumar Das, Sachin Shetty

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A DSP–FPGA Heterogeneous Accelerator for On-Board Pose Estimation of Non-Cooperative Targets by Qiuyu Song, Kai Liu, Shangrong Li, Mengyuan Wang, Junyi Wang

“…Instruction-level control supports model generalization, while a weight concatenation strategy improves resource utilization during convolution. …”
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Performance Evaluation of Neighbors-Based Learning Methods for Network Intrusion Detection System by Ngoc Thien Nguyen

“…In this study, we apply data preprocessing techniques such as Random Under Sampling to balance the dataset and Robust Scaler to reduce the effect of outliers, thereby improving model performance. Three classification algorithms are implemented, including k-Nearest Neighbors (k-NN), Radius Nearest Classifier (RNC), and Nearest Centroid Classifier (NCC), with the goal of evaluating their effectiveness in detecting cyber-attacks. …”
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