A new method for determining factors Influencing productivity of deep coalbed methane vertical cluster wells by HUANG Li, XIONG Xianyue, WANG Feng, SUN Xiongwei, ZHANG Yixin, ZHAO Longmei, SHI Shi, ZHANG Wen, ZHAO Haoyang, JI Liang, DENG Lin

“…The dominant factors causing productivity differences are still uncertain, which hinders productivity improvement. To further evaluate the primary factors controlling productivity, a new method for assessing productivity-influencing factors was developed, based on the production dynamic parameters of 36 vertical cluster wells and using neural networks to predict bottom-hole flowing pressure. …”
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Advances in gait research related to Alzheimer’s disease by Shuding Yan, Xiaoping Yun, Qiang Liu, Zhenmei Hong, Yufan Chen, Shuijing Zhang

“…In the future, it is hoped that with the results of clinical manifestations, neuroimaging, and electrophysiology, simple and objective gait analysis results can be used as an alternative biomarker for cognitive decline to diagnose dementia early.Research objectiveThis research offers a comprehensive scoping review of the contemporary landscape of clinical gait evaluation. It delineates the pertinent concepts of gait analysis and machine learning in AD and elucidates the intricate interplay between gait patterns and cognitive status.MethodsA comprehensive literature search was conducted within PubMed for all articles published until march 18, 2024, using a set of keywords, including “machine learning and gait “and “gait and Alzheimer.” original articles that met the selection criteria were included.Results and significanceA strong correlation exists between autonomous gait and cognitive attributes, necessitating further investigation into the selective interplay between gait and mental factors. …”
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A dual-adaptive stochastic reinforcement chimp optimization algorithm for fire detection and multidimensional problem solving by Ziyang Zhang, Lingye Tan, Diego Martín, Leren Qian, Mohammad Khishe, Pradeep Jangir

“…We evaluate TASR-CHOA against 4 categorical optimization techniques as well as 18 top IEEE CEC-BC algorithms. …”
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Fast2Vec, a modified model of FastText that enhances semantic analysis in topic evolution by Ayu Pertiwi, Azhari Azhari, Sri Mulyana

“…Evaluation metrics include Spearman and Pearson correlation coefficients to assess the alignment with human judgments. …”
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Masked and Noise-Masked Multimodal Brain Tumor Image Segmentation Using SegFormer and Shared Encoder Framework by K. Hemalatha, P. R. Vishnu Vardhan, Alfred Dharmaraj Aravindraj, S. Hari Hara Sudhan

“…Additionally, noise-masking introduces a controlled Gaussian noise into the MRI images creating random variations in the pixel intensities and thereby encouraging the model to learn the invariant and essential patterns in MRI images. Evaluation on Brain Tumor Segmentation (BraTS2020) challenge dataset demonstrates that MNMS outperforms conventional convolutional neural network (CNN) based methods, achieving superior accuracy and Dice Similarity Coefficient (DSC) scores. …”
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Attention-Driven Hybrid Ensemble Approach With Bayesian Optimization for Accurate Energy Forecasting in Jeju Island’s Renewable Energy System by Muhammad Ali Iqbal, Joon-Min Gil, Soo Kyun Kim

“…The combination of fluctuating consumer demand patterns and high variability across different energy sources presents significant challenges in maintaining a reliable balance between supply and demand. …”
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Programmed Cell Death-Ligand 1 Expression and Clinical Outcomes Among Patients with Resected, Early-Stage Non-Small Cell Lung Cancer: A Real-World Study by Parneet K. Cheema, Iqra Syed, Femida Gwadry-Sridhar, Muhammad Rakibuz-Zaman, Robin Sachdeva, Alec Pencz, Luna Zhan, Katrina Hueniken, Devalben Patel, Karmugi Balaratnam, Khaleeq Khan, Benjamin Grant, Brandon S. Sheffield, M. Elizabeth O. Locke, Daniel Moldaver, Mary Kate Shanahan, Geoffrey Liu, M. Sara Kuruvilla

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Seismic rockfall risk assessments and mitigation strategies for transportation infrastructure in high-risk regions by Kelechi Nnaji Eze, Oluwadamilare Olamide Ilesanmi, Godspower Charles Igah, Adefemi Quddus Abidola, Fega Eric Ojefia, Adedamola Martins Adekoya

“…Infrastructure resistance depends strongly on having effective procedures to assess hazards and implement protective strategies because these events show unpredictable patterns. This paper reviews contemporary rockfall hazard evaluation methods while studying risk assessment structures and presenting examples of quake-proof mitigation practices in transportation systems. …”
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Autonomous scheduling mechanism based on energy awareness for improving resource allocation in serverless IoT edge by Mohsen Ghorbian, Mostafa Ghobaei-Arani, Leila Esmaeili

“…The efficiency of the proposed approach was evaluated utilizing three separate load distribution patterns (exponential, Poisson, and exponential-Poisson), and the results indicate the prevention of energy waste and an average reduction of 1.66% in energy consumption. …”
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How well are implementation strategies and target healthcare professional behaviors reported? A secondary analysis of 204 implementation trials using the TIDieR checklist and AACTT... by Charlene Weight, Billy Vinette, Rachael Laritz, Meagan Mooney, Sonia A. Castiglione, Marc-André Maheu-Cadotte, Nikolas Argiropoulos, Kristin Konnyu, Christine E. Cassidy, Simonne E. Collins, Sonia Semenic, Nicola Straiton, Sandy Middleton, Natalie Taylor, Marie-Pierre Gagnon, Shuang Liang, Laura Crump, Olivia Di Lalla, Talia Meyers, Daniel N. Elakpa, Guillaume Fontaine

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Wi-FiAG: Fine-Grained Abnormal Gait Recognition via CNN-BiGRU with Attention Mechanism from Wi-Fi CSI by Anming Dong, Jiahao Zhang, Wendong Xu, Jia Jia, Shanshan Yun, Jiguo Yu

“…We construct an abnormal gait dataset comprising seven distinct gait classes to train and evaluate the proposed network. Experimental results demonstrate that the proposed method achieves an average recognition accuracy of 95%, surpassing classical baseline models by at least 2%.…”
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Identification and Interpretation of Cultural Landscape Field from the Integral Conservation Perspective by Zhitong LIANG, Yan DU, Haiyan XIA

“…The actors involved in the overall operation and their relational network are highly generalized into an ontological model of the CLF, which consists of the subject, object, place, and the three-dimensional relationships among them. …”
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Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry by PNV Srinivasa Rao, PVY Jayasree

“…The selected data set is pre-processed and feature selection for the optimization for the improvement in accuracy, and automation decision making the framework of the convolution neural network along with the ensemble boosted tree classifier developed is optimized using the jellyfish optimization and Recurrent Neural Network and Long Short-Term Memory (RNN-LSTM) model for the recognition of patterns and numerical vectors in the real-world data after processing of output then it is sent back as the input for the recurrent network to make the decision in the shipbuilding process. …”
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Analyzing the performance of biomedical time-series segmentation with electrophysiology data by Richard Redina, Jakub Hejc, Marina Filipenska, Zdenek Starek

“…We compared a rule-based method, a support vector machine (SVM), fully convolutional semantic neural network (UNet), region proposal network (Faster R-CNN), and recurrent neural network for electrocardiographic signals (DENS-ECG). …”
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Identification of PRKCQ-AS1 as a Keratinocyte-Derived Exosomal lncRNA That Promotes Th17 Differentiation and IL-17 secretion in Psoriasis Through Bioinformatics, Machine L... by Gao P, Gao X, Lin L, Zhang M, Luo D, Chen C, Li Y, He Y, Liu X, Shi C, Yang R

“…This study investigates key exosomal ncRNAs regulating the Th17/IL-17 axis in psoriasis and their mechanisms.Methods: We integrated bulk RNA sequencing datasets from the GEO database to construct and evaluate exosome-related patterns. Subsequently, exosome-related ncRNAs in psoriasis lesions were identified primarily through weighted gene co-expression network analysis and five machine learning algorithms. …”
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Effectiveness of unilateral lower-limb exoskeleton robot on balance and gait recovery and neuroplasticity in patients with subacute stroke: a randomized controlled trial by Congcong Huo, Guangjian Shao, Tiandi Chen, Wenhao Li, Jue Wang, Hui Xie, Yan Wang, Zengyong Li, Pengyuan Zheng, Liguo Li, Luya Li

“…In addition, the cortical activation pattern related to robot-assisted training was measured before and after intervention via functional near-infrared spectroscopy. …”
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A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization by Mădălina Duceac (Covrig), Călin Gheorghe Buzea, Alina Pleșea-Condratovici, Lucian Eva, Letiția Doina Duceac, Marius Gabriel Dabija, Bogdan Costăchescu, Eva Maria Elkan, Cristian Guțu, Doina Carina Voinescu

“…We trained a deep neural network, random forest, and an autoencoder (trained only on non-recurrence cases) to model baseline and anomalous patterns. …”
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Clinical innovation research model for fundus diseases: 6 elements, 3 ones by WEN Feng, ZHANG Xiongze, LI Miaoling, GAN Yuhong, SU Yongyue

“…The "3 Ones"pathway embodies hierarchical research progression - transitioning from individual phenomena (point observations) to population-level patterns (linear correlations), ultimately constructing a multidimensional disease cognition network(planar integration). …”
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Nitrogen content estimation of apple trees based on simulated satellite remote sensing data by Meixuan Li, Xicun Zhu, Xicun Zhu, Xinyang Yu, Cheng Li, Dongyun Xu, Ling Wang, Dong Lv, Yuyang Ma

“…The support vector machine model constructed based on Sentinel-2 satellite simulated data was the optimal nitrogen content inversion model, with an average R² value of 0.81 and an average RMSE value of 0.15 for training sets across different phenological periods, and an average R² value of 0.61 and an average RMSE value of 0.23 for validation sets.DiscussionThis study systematically evaluated the applicability and accuracy differences of multi-source satellite data for estimating nitrogen content in apple trees, and clarified the variation patterns of nitrogen-sensitive spectral bands and optimal modeling strategies across key phenological stages. …”
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Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica by Kaushlesh Singh Shakya, Azadeh Alavi, Julie Porteous, Priti Khatri, Amit Laddi, Manojkumar Jaiswal, Vinay Kumar

“…Additionally, fine-tuning the Inception-ResNet-v2 network on these enriched features reduces retraining costs when new unlabeled data becomes available. t-distributed stochastic neighbor embedding (t-SNE) is employed for effective feature representation through manifold learning, capturing complex patterns that previous methods might miss. …”
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