A method for short-term wind power forecasting under extreme weather conditions based on meteorological factor interpretability and hybrid deep learning algorithms by Bo Wang, Shu Wang, Zheng Wang, Yingying Zheng, Xin Li

“…This model combines the CNN and BiLSTM network to capture local features and long-term temporal trends complementarily and further enhances the learning of key temporal features through an AM, finally outputting the forecasting results. Through case analysis on a wind power dataset in Liaoning, China, the experimental results show that compared with traditional forecasting methods, the proposed framework reduces the mean absolute error (MAE) by 52.29% and increases R2 by 0.0625 under windy conditions, and reduces the MAE by 64.29% and increases R2 by 0.0462 under low-temperature weather. …”
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Inclusion of young people with disabilities in the future of work: forecasting workplace, labour market and community-based strategies through an online and accessible Delphi surve... by Arif Jetha, Kathleen A Martin Ginis, Emile Tompa, Monique A M Gignac, Dwayne van Eerd, Kay Nasir

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Global burden of pneumoconiosis attributable to occupational particulate matter, gasses, and fumes from 1990~2021 and forecasting the future trends: a population-based study by Mao Qingsong, Ruijie Xiao, Wenqi Yang, Xinyi Wang, Yu-zhe Kong

“…Furthermore, an ARIMA model was employed to forecast future trends of pneumoconiosis up to 2050.ResultThe year 2021 saw pneumoconiosis from occupational particulate matter, gasses, and fumes account for roughly 4,775 deaths and 117.80 thousand disability-adjusted life years (DALYs). …”
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Forecasting Western Corn Rootworm (<i>Diabrotica virgifera virgifera</i> LeConte) Density and Non-Chemical Control of Larvae: A Practical Review by Levente Vörös, Rita Ábrahám, Wogene Solomon, Gyula Pinke

“…In this review, we carried out an extensive literature analysis on methods for forecasting WCRs and evaluated the practical applicability of the latest non-chemical control methods targeting its larvae. …”
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Day-ahead photovoltaic power generation forecasting with the HWGC-WPD-LSTM hybrid model assisted by wavelet packet decomposition and improved similar day method by Ruxue Bai, Jinsong Li, Jinsong Liu, Yuetao Shi, Suoying He, Wei Wei

“…Four independent LSTM networks are then applied to these sub-sequences to forecast output, which are then reconstructed to derive the ultimate forecast outcome for solar photovoltaics. …”
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Modeling and computing experiment in the problem of predicting the per capita monetary income of the population based on the methods of correlation and regression analysis by Angelina Vladimirovna Vaskina, Victoria Igorevna Naats

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RISKS IN STRATEGIC PLANNING AND IN THE BUDGETARY SPHERE: FEATURES OF LEGAL REGULATION by LOGVENCHEVA A.O.

“…In addition, to the introduction and conclusion, the proposed scientific work includes two parts, which are respectively devoted to the analysis of the legal basis for establishing risks in strategic planning documents and in the Budget Code of the Russian Federation. …”
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Sustainable development of the Republic of Bashkortostan of the Russian Federation: current state and prospects by Yu. K. Akchulpanov

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Copula-based operation strategy for green hydrogen production systems considering coordination with energy storage systems by Sungwoo Kang, Seungmin Jung, Dongwon Lee, Yeuntae Yoo, Gilsoo Jang

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A Hybrid AI Framework for Enhanced Stock Movement Prediction: Integrating ARIMA, RNN, and LightGBM Models by Adel Alarbi, Wagdi Khalifa, Ahmad Alzubi

Subjects: “…stock market forecasting…”
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Editorial: Applications of machine learning in volcanology by Pierdomenico Romano, Bellina Di Lieto, Silvia Scarpetta, Carmen Benítez, Yosuke Aoki

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COMPARISON OF FORECASTING VIOLENCE CASES NUMBER AGAINST WOMEN AND CHILDREN USING DOUBLE EXPONENTIAL SMOOTHING (DES) AND AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) METHODS by Siti Nur Fadilah, Putroue Keumala Intan, Wika Dianita Utami

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Discrete Time Series Forecasting in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part II: Are Hive Weight and In-Hive Temperature Seasonal and Colony-Specific? by Vladimir A. Kulyukin, Aleksey V. Kulyukin, William G. Meikle

“…We performed a systematic autoregressive integrated moving average (ARIMA) time series analysis to answer three fundamental questions: (a) Does seasonality matter in the ARIMA forecasting of hive weight and in-hive temperature? …”
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Enhancing microgrid forecasting accuracy with SAQ-MTCLSTM: A self-adjusting quantized multi-task ConvLSTM for optimized solar power and load demand predictions by Ehtisham Lodhi, Nadia Dahmani, Syed Muhammad Salman Bukhari, Sujan Gyawali, Sanjog Thapa, Lin Qiu, Muhammad Hamza Zafar, Naureen Akhtar

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STUDY, FORECAST AND CONTROLLED SEISMIC HAZARD REDUCTION IN THE IDENTIFIED SEGMENTS OF THE MAIN FAULTS BY CYCLIC INJECTION OF FLUID THROUGH DEEP MULTI-BRANCH DIRECTIONALLY INCLINED... by V. V. Ruzhich, A. G. Vakhromeev, S. A. Sverkunov, V. M. Ivanishin, R. H. Akchurin, E. A. Levina

“…The forecast emphasizes the detection of places for 1–11-year earthquake generation cycles.A comprehensive analysis of the collected information made it possible to substantiate the conclusion about an opportunity to prevent earthquake damage by using hydrodynamic damping of seismically hazardous fault segments. …”
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THE STUDY OF THE EVOLUTION OF THE RESIDENT POPULATION IN ROMANIA ACCORDING TO NATURAL MOVEMENT by NADIA ELENA STOICUȚA

“…At the end of the article, forecasts were made for a period of four years.…”
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Prediksi Nilai Inflasi Post Covid 19 di Indonesia by Ajeng Wahyuni

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Could Kı̄lauea's 2020 Post Caldera‐Forming Eruption Have Been Anticipated? by Paul Segall, Kyle Anderson, Taiyi A. Wang

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Symmetries or Asymmetries: How MSCI Index Advanced European Markets’ Exchange Rates Respond to Macro-Economic Fundamentals by Mosab I. Tabash, Muhammad AsadUllah, Quratulain Siddiq, Marwan Mansour, Linda Nalini Daniel, Mujeeb Saif Mohsen Al-Absy

“…Previous studies suggest that the existence of asymmetrical relationships justifies the selection of NARDL for empirical analysis. This study makes a contribution to the existing literature, as it proves that forecasting via NARDL is also robust for analysis. …”
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Deep learning approaches for estimating maximum wall deflection in excavations with inconsistent clay stratigraphy by Vinh V. Le, HongGiang Nguyen, Nguyen Huu Ngu

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