Epizootiological and Epidemiological Situation on Leptospirosis in the Russian Federation over the Period of 2013–2022 and the Forecast for 2023 by D. V. Trankvilevsky, E. Yu. Kiseleva, V. M. Korzun, N. V. Breneva, Yu. A. Verzhutskaya, I. D. Zarva, O. N. Skudareva, S. V. Balakhonov

“…When studying the material from small mammals using bacteriological, immunological and molecular-biological methods, Leptospira circulation was detected in 52 entities of the Russian Federation, in all federal districts. …”
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The burden of attention deficit hyperactivity disorder and incidence rate forecast in China from 1990 to 2021 by Ningyu Li, Ningyu Li, Junqiang Zhao, Junqiang Zhao, Junqiang Zhao, Junqiang Zhao, Fujun Zhou

“…ObjectiveTo analyze the temporal trends and future projections of attention-deficit/hyperactivity disorder (ADHD) burden among children and adolescents in China from 1990 to 2021, and to identify age-, period-, and cohort-specific drivers of disease progression.MethodsUsing data from the Global Burden of Disease Study 2021, we conducted joinpoint regression to detect trend transitions in ADHD incidence and age-standardized rates. …”
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A Consistency-Aware Hybrid Static–Dynamic Multivariate Network for Forecasting Industrial Key Performance Indicators by Jiahui Long, Xiang Jia, Bingyi Li, Lin Zhu, Miao Wang

“…Extensive experiments on both synthetic and real-world radar detection datasets demonstrated that CHSDM-Net achieved significant improvements compared with existing methods. …”
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Financial risk forecasting with RGCT-prerisk: a relational graph and cross-temporal contrastive pretraining framework by Liyu Chen, Xiangwei Fan

“…Abstract Financial risk forecasting is critical for the early detection of corporate distress, yet traditional methods and recent deep learning models exhibit notable limitations. …”
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Forecasting Technology Trends through the Gap Between Science and Technology: The Case of Software as an E-Commerce Service by Tugrul Daim, Esraa Bukhari, Dana Bakry, James VanHuis, Haydar Yalcin, Xiaoli Wang

“…For that, it is important to turn to methods that are used for technology forecasting. …”
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Mathematical Evaluation of Classical and Quantum Predictive Models Applied to PM2.5 Forecasting in Urban Environments by Jesús Cáceres-Tello, José Javier Galán-Hernández

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Analyses of burned area of forest by adaptive neuro-fuzzy approach by Jasmina Dedić, Srđan Jović, Jelena Đokić

“…The ANFIS process was implemented to detect the dominant factors which affect the forecasting of the burned area of forest.…”
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Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection by Paolo Fazzini, Giuseppe La Tona, Matteo Diez, Maria Carmela Di Piazza

“…VMDMS enables a selective detection process, identifying modes across channels that synergistically enhance forecasting accuracy. …”
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A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo Extrapolation by Jinliang Yao, Feifan Xu, Zheng Qian, Zhipeng Cai

“…Through experiments on a radar dataset from Shanghai, China, the results show that our proposed method obtains higher Probability of Detection (POD), Critical Success Index (CSI), Heidke Skill Score (HSS), and lower False Alarm Rate(FAR).…”
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Effectiveness and trend forecasting of tuberculosis diagnosis after the introduction of GeneXpert in a city in south-eastern Brazil. by Thaís Zamboni Berra, Dulce Gomes, Antônio Carlos Vieira Ramos, Yan Mathias Alves, Alexandre Tadashi Inomata Bruce, Luiz Henrique Arroyo, Felipe Lima Dos Santos, Ludmilla Leideanne Limirio Souza, Juliane de Almeida Crispim, Ricardo Alexandre Arcêncio

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Maize tasseling date forecast from canopy height time series estimated by UAV LiDAR data by Yadong Liu, Chenwei Nie, Liang Li, Lei Shi, Shuaibing Liu, Fei Nan, Minghan Cheng, Xun Yu, Yi Bai, Xiao Jia, Liming Li, Yali Bai, Dameng Yin, Xiuliang Jin

“…This study proposed an approach to timely identify and forecast the maize TD. We obtained RGB and light detection and ranging (LiDAR) data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments. …”
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Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study by Xin Xiong, Linghui Xiang, Litao Chang, Irene XY Wu, Shuzhen Deng

“…ConclusionsOur study developed model IBE to predict the incidence of mumps in Yunnan province, offering a potential tool for early detection of mumps outbreaks. The performance of model IBE underscores the potential of integrating search engine data and environmental factors to enhance mumps incidence forecasting. …”
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A transferable machine learning model for real-time forecast of epidemic dynamics and pre-trigger event warning by Enpei Chen, Xiong Yu

“…Abstract Wastewater-based epidemiology (WBE) is emerging as an effective tool to provide early warnings of potential disease outbreaks within communities through detecting the presence of pathogens in wastewater before clinical cases are reported. …”
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Comparison of early forecasts of the incidence of thyroid cancer in residents of the Russian Federation after the Chernobyl accident with observational data by I. A. Zvonova, M. I. Balonov

“…The method for assessing doses in the thyroid gland is based on the results of monitoring in May-June 1986 of radioiodine in the environment, food and in the body of residents. …”
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MICROALBUMINURIA AS A FORECAST FACTOR FOR CARDIO-VASCULAR COMPLICATIONS AND A MARKER OF THERAPY EFFICACY IN PATIENTS WITH ARTERIAL HYPERTENSION by L. A. Haisheva, L. I. Katelnitskaya

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Forecast of the morphology and occurrence conditions of complex chromite ore bodies (on example of Labogeyskoe-2 site, Polar Urals) by T. P. Morozova, E. V. Karelina, V. E. Markov

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A Novel Energy Control Digital Twin System with a Resource-Aware Optimal Forecasting Model Selection Scheme by Jin-Woo Kwon, Anwar Rubab, Won-Tae Kim

“…Utilizing real-world LPG consumption data from 887 sensors, the proposed system achieves forecasting accuracy comparable to previous methods while reducing latency by up to 19 times in low-resource settings.…”
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Forecasting monthly runoff in a glacierized catchment: A comparison of extreme gradient boosting (XGBoost) and deep learning models. by Mohammed Majeed Hameed, Adil Masood, Aadil Hamid, Ahmed Elbeltagi, Siti Fatin Mohd Razali, Ali Salem

“…Given the significant autocorrelation in runoff time series data, which may hinder the evaluation of prediction models, a novel statistical method is employed to assess the effectiveness of forecasting models in detecting turning points in the runoff data. …”
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Multi-Agent Deep Reinforcement Learning for Integrated Demand Forecasting and Inventory Optimization in Sensor-Enabled Retail Supply Chains by Yongbin Yang, Mengdie Wang, Jiyuan Wang, Pan Li, Mengjie Zhou

“…While existing approaches employ statistical and machine learning methods for demand forecasting, they often fail to capture complex temporal dependencies and lack the ability to simultaneously optimize inventory decisions. …”
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Earthquake Prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis by Cosmina-Mihaela Rosca, Adrian Stancu

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