Macroeconomic Determinants of Investment Decisions for Medium and Large Enterprises in Poland’s Manufacturing Sector by Adam Andrzej Zając, Michał Wielechowski, Krzysztof Smoleń, Dariusz Karaś

“…Cointegration analysis and the Vector Error Correction Model (VECM) are employed to identify macroeconomic indicators that consistently impact the propensity to invest in specific sectors, while also assessing the presence of investment seasonality. …”
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The Impact of Stokvel and Banking Sector Efficiency: an Econometrics Model using (ARDL) Approach to Cointegration by Ngcobo, Lindiwe

“…The negative and statistically significant coefficient of the error correction model (ECM) also confirmed the prevalence of a causal relationship between stokvel and banking sector efficiency. …”
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Empirical Model of Equatorial ElectroJet (EEJ) Using Long‐Term Observations From the Indian Sector by S. Tulasi Ram, M. Ankita, B. Nilam, S. Gurubaran, Manoj Nair, Gopi K. Seemala, P. S. Brahmanandam, A. P. Dimri

“…The modeled monthly mean variations of EEJ field at ground exhibit excellent correlation of 0.96 with the observations with the root mean square error <5 nT. …”
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Climate Change Risk, Performance, and Value Added in Agricultural Sector by Ramin Amani, Zanko Ghorbani, Zana Mozaffari

“…One of the pathways towards achieving sustainable development involves the advancement of the agricultural sector, a vital economic segment. Progress in almost all sectors of economy, even the industry sector is closely correlated to growth in agriculture. …”
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The role of feed-in tariffs in encouraging insurance companies to invest in renewables by Serhiy Lyeonov, Artem Artyukhov, Laura Bokenchina, Diana Sitenko, Yuliia Yehorova, Maksym Zhytar, Alla Moroz

“…The results reveal that greater insurance sector assets positively correlate with higher renewable energy consumption, with a coefficient of 0.143 (p &lt; 0.01) in the fixed effects model. …”
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Investigating the Relationship between Air Pollutants and Meteorological Parameters in the Agricultural Sector of Mazandaran Province Using Logistic Regression by Majid Ghorbani, Abolfazl Mahmoodi, Mohammad Khaledi

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Assessment of the possibility of forecasting hydrometeorological extremes using the solar activity index by Miskevich I.V., Kotova E.I.

“…The paper presents the results of research of correlation fields of the relationship between Wolf number and hydrometeorological parameters (air and water temperature, annual precipitation, snow cover height) for a number of points in the western sector of the Russian Arctic (Norilsk, Indiga, Unsky Mayak, Kholmogory). …”
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Bias correction methods for simulated precipitation in the Brazilian Legal Amazon by Fabrício Daniel dos Santos Silva, Helber Barros Gomes, Claudia Priscila Wanzeler da Costa, Antônio Vasconcelos Nogueira Neto, Ismael Guidson Farias de Freitas, Mário Henrique Guilherme dos Santos Vanderlei, Maria Cristina Lemos da Silva, Rafaela Lisboa Costa, Jean Sousa dos Reis, Vânia dos Santos Franco, Ana Paula Paes dos Santos, Ivan Saraiva, Rodrigo Lins da Rocha Júnior, Jório Bezerra Cabral Júnior, Helder José Farias da Silva, Edmir dos Santos Jesus, Douglas Batista da Silva Ferreira, Renata Gonçalves Tedeschi

“…Among all months, REG-PCR achieved superior correlation and lower errors in 8 out of 12 months (February, March, April, July, September, October, November, and December). …”
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A new high-resolution hydrodynamic model for the coastal Beaufort Sea in the Arctic Ocean: model construction and evaluation by J. Blake Clark, J. Blake Clark, Wesley Moses, Ahmed El-Habashi, Maria Tzortziou, Kyle J. Turner, Hisatomo Waga, Hisatomo Waga, Steven Ackelson, Jonathan Sherman, Jonathan Sherman

“…The aquatic environment of the coastal Arctic is rapidly changing, and understanding how this change will affect the coastal ocean is critical across sectors. To address this, a three-dimensional (3-D) hydrodynamic model was constructed, spanning the coastal Beaufort Sea from −153° to −142° W, explicitly including river delta channels and lagoons, and extending to the continental shelf. …”
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Modeling Factors Influencing Project Financing Risk by Gholamreza Sharafi, Kiamars Fathi Hafashjani, Faegh Ahmadi

“…MethodsTo explore these dynamics, this study employs an objective-oriented approach, leveraging a descriptive-correlational method for data collection. Central to this methodology is the utilization of Structural Equation Modeling (SEM), a powerful analytical tool that allows for the examination of complex relationships among variables. …”
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Physics-based parameters selection and machine learning driven prediction of pool boiling bubble departure diameter by Uzair Sajjad, Yu-Hao Chu, Haseeb Yaqoob, Akash Sengupta, Hafiz Muhammad Ali, Khalid Hamid, Wei-Mon Yan

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Evaluation of models of enteric methane emissions in finishing steers by J. Vargas, M. Swenson, A.K. Schilling-Hazlett, I.A. Reis, C. Velasquez, E.C. Martins, L. Sitorski, L.M. Campos, P.H.V. Carvalho, K.R. Stackhouse-Lawson, S.E. Place

“…Seventy-two equations were compared based on the mean square prediction error (MSPE), the decomposition of the root MSPE (RMSPE), and the concordance correlation coefficient (CCC). …”
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Presenting the Model of Effective Factors on Smart Governance in the Country by Ghaem Gheiravani, Mohammad Montazeri, Shams Sadat Zahedi

“…If this value is equal to or greater than 0.4, it confirms that the variance between the structure and its indicators is greater than the variance of the size error of that structure, and the reliability of that measurement model is acceptable. …”
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Enhancing Efficiency: Halton Draws in the Generalized True Random Effects Model by David H. Bernstein

“…Furthermore, increasing the number of Halton draws either improves or has no detrimental impact on correlation, mean squared error, relative bias, and upward bias for persistent, transient, and total technical efficiency. …”
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Development of Machine Learning Models for Sandface Pressure Prediction in Oil Well by Lorraine P. Oliveira, Raul M. Foronda, Alexandre V. Grillo, Brunno F. dos Santos

“…The best-performing model achieved an ??2 score of 0.982 and a Mean Squared Error (MSE) of 4.878 × 10?…”
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Constitutive model of metal rubber material considering plastic accumulation behavior by FENG Zhipeng, YANG Fang, FU Hailong, AI Shigang, WANG Yue, YUAN Liangyang

“…The result demonstrates that the plasticity-considered model has the smaller fitting errors with experimental data, compared to cantilever beam models and porous models, which reflects its superior accuracy in data analysis compared to traditional models. …”
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Hybridized Deep Learning Model for Perfobond Rib Shear Strength Connector Prediction by Jamal Abdulrazzaq Khalaf, Abeer A. Majeed, Mohammed Suleman Aldlemy, Zainab Hasan Ali, Ahmed W. Al Zand, S. Adarsh, Aissa Bouaissi, Mohammed Majeed Hameed, Zaher Mundher Yaseen

“…In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient (R = 0.96) and lowest value root mean square error (RMSE = 0.03936 KN). …”
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Examining the Effects of Intersectoral Uncertainty Transmission Using a Time-Varying Model by Hamidreza Hamidi, Mirfeiz Fallah Shams, Hosein Jahangirnia, Mojgan Safa

“…To examine changes in cross-sectoral uncertainty contagion, the time-varying parameter vector autoregression model (TVP-VAR) is used with monthly data from January 2008 to December 2020. …”
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Iron Ore Information Extraction Based on CNN-LSTM Composite Deep Learning Model by Haili Chen, Mengxiang Xia, Yaping Zhang, Ruonan Zhao, Bingran Song, Yang Bai

“…According to the model&#x2019;s results, the particle size classification accuracy is 91.67%, the F1 score is 0.92, the coefficient of determination (R2) for the water content regression is 0.89023, the mean squared error (MSE) is 0.00082, the root mean square error (RMSE) is 0.02872, and the mean absolute error (MAE) is 0.01558. …”
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Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete by Muhammad Izhar Shah, Shazim Ali Memon, Muhammad Sohaib Khan Niazi, Muhammad Nasir Amin, Fahid Aslam, Muhammad Faisal Javed

“…Concrete specimens with varying proportion of SCBA were prepared in the laboratory, and results were used for model validation. The performance of the developed models was then evaluated by statistical criteria and error assessment tests. …”
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