Application of Deep Learning for Stock Prediction Within the Framework of Portfolio Optimization in Quantitative Trading by Xiaoyu Qin

“… This paper proposes a method for stock prediction and portfolio optimization as a part of quantitative trading based on a combination of Bi-RNN and a modified snake optimization algorithm (MSOA) to build optimal portfolios and outperform conventional models and benchmarks. Methods/Analysis: We employ the Bi-RNN model, which processes historical stock data in both forward and backward directions to unveil intricate temporal dependencies. …”
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Predictive optimization in automotive supply chains: a BiLSTM-Attention and reinforcement learning approach by Asmae Amellal, Issam Amellal, Mohammed Rida Ech-charrat, Hamid Seghiouer

“…Focusing on Moroccan automobile companies, we utilized Enterprise Resource Planning (ERP) system data to forecast customer behavior using a BiLSTM model enhanced with an Attention mechanism. …”
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Study protocol for a Prospective Observational study of Safety Threats and Adverse events in Trauma (PrO-STAT): a pilot study at a level-1 trauma centre in Canada by Melissa McGowan, Charles Keown-Stoneman, Brodie Nolan, Teodor Grantcharov, Eliane M Shore, Anisa Nazir

“…A synchronised data capture and analysis platform will comprehensively assess AEs, errors and human and environmental factors during trauma resuscitations. …”
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Physical Layer Evaluation on IEEE 802.11p With Different Configurations in NLOS Scenarios for V2V Communications by Shuting Guo, Daniel N. Aloi, Jia Li, Hongmei Zhao

“…Firstly, to break the limitation of partial physical layer (PHY) evaluation, extensive PHY metrics, which include the packet error rate (PER), packet reception ratio (PRR), output packet inter-arrival time (IAT), and output effective data rate, are adequately employed to fulfill complete PHY evaluation. …”
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Bioimpedance assessment method based on back propagation neural network for irreversible electroporation of liver tissue by Chengjiang Wang, Yuchi Zhang, Fulai Lin, Zhuoqun Li, Zhuomin Ping, Yujia Shi, Yunfei Chen, Mengbo Yu, Wenyu Qin, Yiyin Rong, Jian Zhuang, Yi Lyu, Fenggang Ren

“…The model yielded acceptable prediction results with a root mean square error (RMSE) of 7.33, mean absolute percentage error (MAPE) of 8.62%, and coefficient of determination (R 2) of 0.82. …”
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Fluidization expansion of novel generation dense medium and flow regime transition in gas-solid separation fluidized bed by Chenyang Zhou, Yuemin Zhao, Chengguo Liu, Yanjiao Li, Zhonglin Gao, Xuchen Fan, Tatiana Aleksandrova, Chenlong Duan

“…A quantitative criterion is proposed to identify the transition point. Based on the error analysis, the available data in the literature and the present work gave an overall in 5 × 10−5 error range compared to the prediction data. …”
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Water Level Variation Monitoring in East Lake, Wuhan Based on Satellite Altimetry by LIU Huo-sheng, WANG Hai-hong, YU Qian-hui, LU Liang, QIN Peng-cheng, LIU Yi-bing

“…[Results] (1) Statistical analysis of pulse peakiness and waveform width from the lake surface altimetry echoes revealed that approximately 50% of East Lake’s waveforms exhibited specular reflections with distinct sharp peaks, while about 30% displayed complex shapes containing two or more peaks. (2) The results of accuracy validation using the on-site measured data of water levels showed that the 50% threshold retracking method achieved optimal performance, with a root mean square error (RMSE) of 0.108 m and a correlation coefficient of 0.87. (3) Based on the 50% threshold retracking method, and using Jason-3 data, the water level time series of East Lake from September 2017 to February 2022 was established. …”
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The impact of knowledge management on business performance with emphasis on the role of accounting information quality (Case study: financial institutions listed on the Tehran Capi... by Rahele Mashaykhi, Ahmad Pifeh, Hamed Ahmadzade

“…In this study, after drawing the conceptual model, data analysis was performed using structural equation modeling with a partial least squares approach and through SEM-PLS and SPSS24 software. …”
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Evaluation of the performance of ERA5, ERA5-Land and MERRA-2 reanalysis to estimate snow depth over a mountainous semi-arid region in Iran by Faezehsadat Majidi, Samaneh Sabetghadam, Maryam Gharaylou, Reza Rezaian

“…A comparison was conducted using SND data from synoptic stations within the study area. …”
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DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial–temporal fusion attention by Zheng Chen, Quan Qian

“…The results demonstrate that our framework significantly improves prediction accuracy, with a mean absolute error more than 30% lower than other methods. Further analysis demonstrated the robustness of DGL-STFA across various battery life stages, including early, mid, and end-of-life phases. …”
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A hybrid model for short-term offshore wind power prediction combining Kepler optimization algorithm with variational mode decomposition and stochastic configuration networks by Bingbing Yu, Yonggang Wang, Jun Wang, Yuanchu Ma, Wenpeng Li, Weigang Zheng

“…Finally, a multi-seasonal and multi-scenario wind power forecasting analysis is conducted by using an actual data set from an offshore wind farm in China. …”
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Determination of the enthalpy of evaporation of pentaerythritol esters of various structures using gas chromatographic retention characteristics by Yu. F. Ivanova, V. V. Emelyanov, S. V. Levanova, Yu. N. Telnov

“…The enthalpies of evaporation calculated based on the enthalpies of sorption and logarithmic retention indices within the limits of error of the correlation dependencies coincide with the literature data and the values predicted by the quantitative structure–property relationship method. …”
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Integration of Aerial Mapping using UAV and Low-cost Backpack LiDAR for Biomass and Carbon Stock Estimation Calculation by Q. P. A. N. Ila, M. N. Cahyadi, H. H. Handayani, A. B. Raharjo, R. Mardiyanto, I. W. Farid, D. Saptarini, E. E. Saratoga

“…The analysis showed that the backpack LiDAR had an RMSE error of 0.793 meters and a standard deviation of 0.30332 cm for DBH. …”
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Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy by Chenlong Fan, Ying Liu, Tao Cui, Mengmeng Qiao, Yang Yu, Weijun Xie, Yuping Huang

“…Various preprocessing techniques, including Savitzky−Golay (S−G), multiplicative scatter correction (MSC), standard normal variate (SNV), and the first derivative (1D), were employed to preprocess the raw spectral data. Near-infrared spectral data from different varieties of maize grain powder were collected, and quantitative analysis of protein content was conducted using Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) models. …”
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Fusing satellite imagery and ground-based observations for PM2.5 air pollution modeling in Iran using a deep learning approach by Zohreh Sohrabi, Jamshid Maleki

“…We utilized satellite data, ground-based observations, and meteorological parameters as input features. …”
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A Real-Time Approach for Assessing Rodent Engagement in a Nose-Poking Go/No-Go Behavioral Task Using ArUco Markers by Thomas Smith, Trevor Smith, Fareeha Faruk, Mihai Bendea, Shreya Kumara, Jeffrey Capadona, Ana Hernandez-Reynoso, Joseph Pancrazio

“…In short, this protocol involves detailed instructions for building a suitable behavioral chamber, installing and configuring all required software packages, constructing and attaching an ArUco marker pattern to a rat, running the behavioral software to track marker positions, and analyzing the engagement data for determining optimal task durations. These methods provide a robust framework for real-time behavioral analysis without the need for extensive training data or high-end computational resources. …”
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Unsupervised Feature Representation Based on Deep Boltzmann Machine for Seizure Detection by Tengzi Liu, Muhammad Zohaib Hassan Shah, Xucun Yan, Dongping Yang

“…Since EEG data are heavily under-represented, supervised learning techniques are not always practical, particularly when the data is not sufficiently labelled. …”
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Genomic Prediction of Milk Fat Percentage Among Crossbred Cattle in the Indian Subcontinent by Raghavendran Vadivel Balasubramanian, Murali Nagarajan, Marimuthu Swaminathan, Raja Angamuthu, Muralidharan Jaganadhan, Saravanan Ramasamy, Malarmathi Muthusamy, Thiruvenkadan Aranganoor Kannan, Sunday Olusola Peters

“…Genetic analysis involved 1478 animals genotyped for 49,911 SNPs after applying a rigorous quality control process, and imputation improved the accuracy of genomic data, boosting allele frequency correlation from 0.594 to 0.882. …”
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Clinical Decision Support System for Liver Fibrosis Prediction in Hepatitis Patients: A Case Comparison of Two Soft Computing Techniques by Shaker El-Sappagh, Farman Ali, Amjad Ali, Abdeltawab Hendawi, Farid A. Badria, Doug Young Suh

“…The ANFIS model is designed using trial and error based on the analysis of various experiments. …”
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Modeling residue formation from crude oil oxidation using tree-based machine learning approaches by Mohammad-Reza Mohammadi, Seyyed-Mohammad-Mehdi Hosseini, Behnam Amiri-Ramsheh, Saptarshi Kar, Ali Abedi, Abdolhossein Hemmati-Sarapardeh, Ahmad Mohaddespour

“…In this work, the thermo-oxidative profiles and residue formation of crude oils during thermogravimetric analysis (TGA) were modeled using 3075 experimental data points from 18 crude oils with API gravities ranging from 5 to 42. …”
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