Hidden Markov Model-Based Video Recognition for Sports by Zhiyuan Wang, Chongyuan Bi, Songhui You, Junjie Yao

“…A multistream residual neural network is used to extract features from video data of three modalities, and the extracted feature vectors are fed into the attention mechanism network, then, the more critical information for video recognition is selected from a large amount of spatiotemporal information, further learning the temporal dependencies existing between consecutive video frames, and finally fusing the multistream network outputs to obtain the final prediction category. By training and optimizing the model in an end-to-end manner, recognition accuracies of 92.7% and 64.4% are achieved on the dataset, respectively.…”
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Correlations between Texture Profile Analysis and Sensory Evaluation of Cured Largemouth Bass Meat (Micropterus salmoides) by Meijin Li, Jiajun Huang, Yanan Chen, Xiaoyan Zu, Guangquan Xiong, Hailan Li

“…These results provide a basis for predicting the quality of large mouth bass cured products.…”
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Determination of the Peak and Residual Shear Strengths of the Sandwich Material in Slopes by Qijun Hu, Qijie Cai, Leping He, Xiaoqiang Yang, Tao Ye, Rendan Shi

“…The numerical model is built based on these parameters and both the peak and residual shear strengths can be predicted from the model. The prediction results show that the peak and residual internal friction angle are 19.36° and 14.61°, while the peak and residual cohesion are 22.33 kPa and 2.73 kPa, respectively. …”
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Stress Dependency of Creep Response for Glass/Epoxy Composite at Nonlinear and Linear Viscoelastic Behavior by N. Nosrati, A. Zabett, S. Sahebian

“…Stress dependency of creep stages (primary, secondary, and tertiary) of neat epoxy was evaluated at this range of temperature. The prediction results of composite at room temperature show that the raising of the stress levels leads to the acceleration of viscoelastic strain values, but the creep compliance does not present any dependency. …”
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Gap Structure of the Overdoped Iron-Pnictide Superconductor Ba(Fe0.942Ni0.058)2As2: A Low-Temperature Specific-Heat Study by Gang Mu, Bo Gao, Xiaoming Xie, Yoichi Tanabe, Jingtao Xu, Jiazhen Wu, Katsumi Tanigaki

“…Our observation is similar to that observed in the Co-doped system in our previous work and is consistent with the theoretical prediction for a superconductor with line nodes in the energy gap.…”
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Influence of screw angle and position on the extraction dynamics of Cross-Laminated Timber: an empirical analysis and comparative modeling study by Jia Lei, Xinran Li, Xinmeng Wang, Feibin Wang, Shuo Wang, Xin An, Meng Gong, Zeli Que

“…The ratios of experimental capacities to model predictions were 1.26–1.49 for the Blaß and Uibel (B&U) model and 1.36–1.56 for Eurocode 5 (EC5). …”
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Enhanced Landslide Susceptibility Assessment in Western Sichuan Utilizing DCGAN-Generated Samples by Yuanxin Tong, Hongxia Luo, Zili Qin, Hua Xia, Xinyao Zhou

“…In-depth validation based on large landslide image data demonstrates the superiority of the DCGAN-ResNet, achieving a remarkable landslide prediction accuracy of 91.31%. Consequently, the generation of supplementary samples via the DCGAN is an effective strategy for enhancing the performance of machine learning models in LSA, underscoring the promise of this methodology in advancing early landslide warning systems in western Sichuan.…”
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Development and Validation of an Age-Specific Lower Extremity Finite Element Model for Simulating Pedestrian Accidents by Jing Huang, Yongcheng Long, Yu Yan, Lin Hu

“…The development of age-specific lower extremity models will lead to an improved understanding of the pedestrian lower extremity injury mechanisms and injury risk prediction for the whole population in vehicle-pedestrian collision accidents.…”
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Calculation of Spotting Particles Maximum Distance in Idealised Forest Fire Scenarios by José C. F. Pereira, José M. C. Pereira, André L. A. Leite, Duarte M. S. Albuquerque

“…For the cases of single and four torching trees the model predicts the maximum distances consistently but for slightly different particle diameter.…”
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Assessment of Accuracy of COSMIC and KOMPSAT Radio Occultation Temperature and Pressure by Descalzo Karl Philippe A., Macalalad Ernest P.

“…The quality of these results shows potential in COSMIC and KOMPSAT as high-quality applications for weather prediction.…”
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Structure-guided identification of mitogen-activated protein kinase-1 inhibitors towards anticancer therapeutics. by Md Nayab Sulaimani, Shazia Ahmed, Farah Anjum, Taj Mohammad, Anas Shamsi, Ravins Dohare, Md Imtaiyaz Hassan

“…Subsequently, the selected hits underwent rigorous screening that included the identification of potential pan-assay interference compounds (PAINS), ADMET evaluation, and prediction of pharmacological activities using PASS analysis. …”
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Levels of mastering theoretical knowledge of higher mathematics in computerized testing by Sergey G. Kal’ney, Natalia N. Konsevich, Ekaterina S. Migunova, Elena V. Chaykina

“…In particular, when discussing some tasks with the student during the exam, the teachers questions usually do not have a test form and it is more difficult to predict the success of the students performance in this case. …”
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Growth Differentiation Factor 15 and Risk of Death in Haemodialysis Patients by Christelle Calen, Seraina Von Moos, Pietro Cippà, Alexandre Mebazaa, Mattia Arrigo

“…The performance of circulating GDF-15 to predict long-term mortality has an area under the ROC curve of 0.76, P = 0.028. …”
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Situation Awareness-Based Safety Assessment Method for Human–Autonomy Interaction Process Considering Anchoring and Omission Biases by Shengkui Zeng, Qidong You, Jianbin Guo, Haiyang Che

“…In SAB-TDM, SA perception and comprehension affected by anchoring bias are quantified with the Adaptive Control of Thought-Rational (ACT-R) theory and Anchoring Adjustment Model (AAM); behavioral utility prediction affected by omission bias is quantified with Prospect Theory. …”
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Morphometric Assessment of the Piriform Aperture and Its Clinical and Forensic Applications by Beryl Shitandi Ominde, Joyce Ekeme Ikubor, Jennifer Efe Jaiyeoba-Ojigho, Orovwoghene Faithful Omoro, Patrick Sunday Igbigbi

“…The percentage accuracy for correct sex prediction was assessed using the discriminant function analysis. …”
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Comparison of molecular analysis results determined by next-generation sequencing to immunohistochemical indicators and clinicopathological parameters in prostate adenocarcinomas by Merve Çırak Balta, İbrahim Halil Erdoğdu, Esin Oktay, Nil Çulhac

“…Conclusion: Regarding prognosis prediction and treatment, identifying the molecular changes in genes playing roles in prostate cancer with next-generation sequencing is very important.…”
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In Silico Analysis on the Interaction of Haloacid Dehalogenase from Bacillus cereus IndB1 with 2-Chloroalkanoic Acid Substrates by Enny Ratnaningsih, Saepulloh Saepulloh

“…Structure prediction revealed that Bcfd1 protein consist of two domains. …”
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A Mathematical Model for the Within-Host Dynamics of Malaria Parasite with Adaptive Immune Responses by Jemal Muhammed Ahmed, Getachew Tashome Tilahun, Shambel Tadesse Degefa

“…Mathematical analysis of epidemics is crucial for long-term disease prediction and helps to guide decision-makers in terms of public health policy. …”
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Comparative Analysis of All-Sky Luminous Efficacy Models Based on Calculated and Measured Solar Radiation Data of Four Worldwide Cities by Donghyun Seo

“…It was found that all the evaluated models provide good predictions (over 0.96 R2 value) for both global and diffuse illuminance. …”
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Local kernel renormalization as a mechanism for feature learning in overparametrized convolutional neural networks by R. Aiudi, R. Pacelli, P. Baglioni, A. Vezzani, R. Burioni, P. Rotondo

“…Remarkably, we identify a completely different form of kernel renormalization: whereas the kernel of the fully-connected architecture is just globally renormalized by a single scalar parameter, the convolutional kernel undergoes a local renormalization, meaning that the network can select the local components that will contribute to the final prediction in a data-dependent way. This finding highlights a simple mechanism for feature learning that can take place in overparametrized shallow convolutional neural networks, but not in shallow fully-connected architectures or in locally connected neural networks without weight sharing.…”
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