Optimization Design and Test Analysis of Rice Electric Binder Knotter Based on ADAMS by Difa Bao, Jufei Wang, Zhi Liang, Chongcheng Chen, Wuxiong Weng, Shuhe Zheng, Jinbo Ren

“…Based on the ADAMS software, a simulation model of the knotter operation was constructed. Using the Box–Behnken design (BBD) method and response surface analysis of variance, a regression prediction model for knotter operation evaluation indicators was established, and the multi-objective optimization of the knotter’s operation quality was performed. …”
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Climate-responsive vector control strategies for Aedes albopictus by Jesús Bellver-Arnau, Laura Blanco-Sierra, Santi Escartin, Simone Mariani, Frederic Bartumeus

“…Methods In this study, we employed a metapopulation model to simulate Ae. albopictus population dynamics and dispersal, optimizing the timing and spatial allocation of larvicidal treatments. …”
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Remote sensing perspective in exploring the spatiotemporal variation characteristics and post-disaster recovery of ecological environment quality, a case study of the 2010 Ms7.1 Yu... by Yaohui Liu, Yu Lin, Wenyi Liu, Jie Zhou, Jin Wang

“…Then, we analyzed the spatiotemporal characteristics of Yushu’s EEQ from 2001 to 2020 and explored spatial autocorrelation relationships. Last, the driving mechanism of EEQ recovery in Yushu was analyzed using the GeoDetector model. …”
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Vegetation greening does not significantly enhance ecosystem resilience in the Northern Hemisphere by Jingjing Zhang, Xingming Hao, Yongchang Liu, Xuewei Li, Qixiang Liang, Fan Sun, Mengtao Ci, Yupeng Li

“…Greening is asynchronous with ecosystem resilience in the context of vegetation restoration, thus highlighting the uncertainty in predicting the future sustainability of ecosystems. …”
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The Samples and Binary Fractions of Red Supergiants in M31 and M33 by the HST Observations by Min Dai, Shu Wang, Biwei Jiang, Ying Li

“…These results are in good agreement with predictions from the Binary Population and Spectral Synthesis binary evolution model.…”
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Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning by Jonne Pohjankukka, Timo A. Räsänen, Timo P. Pitkänen, Arttu Kivimäki, Ville Mäkinen, Tapio Väänänen, Jouni Lerssi, Aura Salmivaara, Maarit Middleton

“…Additionally, we provided an approach for evaluating spatial prediction uncertainty based on the models’ internal prediction agreement. …”
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Spatiotemporal analysis of Anopheles gambiae larval sites and malaria transmission in Djilakh, Central Senegal by Assane Ndiaye, Camille Morlighem, Aminata Niang Diène, Moussa Kane, Abdoulaye Diallo, Fassiatou Tairou, Mohamed Abderemane Nourdine, Pape Cheikh Sarr, Lassana Konaté, Ousmane Faye, Oumar Gaye, El Hadji Amadou Niang, Catherine Linard, Ousmane Sy

“…Abstract Background The progress made against malaria has resulted in a nationwide reduction of the disease burden in Senegal. The observed overall low transmission levels are, however, marked by an important spatial heterogeneity with hotspots subsisting in several parts of the country. …”
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Forest cover restoration analysis using remote sensing and machine learning in central Malawi by Jabulani Nyengere, Precious Masuku, Sylvester Chikabvumbwa, Weston Mwase, Msaiwale Kathewera, Allena Laura Njala, Wilson Tchongwe, Isaac Tchuwa, Tiwonge I Mzumara, Chikondi Chisenga, Wilfred Kadewa, Emmanuel Chinkaka, Harineck Tholo

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Characterising the spatio-temporal patterns of water quality parameters in the cradle of humankind world heritage site using Sentinel-2 and random forest regressor by Sinesipho Ngamile, Sinesipho Ngamile, Mahlatse Kganyago, Sabelo Madonsela, Sabelo Madonsela, Vuyelwa Mvandaba

“…IntroductionWater quality assessment is essential for monitoring and managing freshwater resources, particularly in ecologically and culturally significant areas like the Cradle of Humankind World Heritage Site (COHWHS). This study aimed to predict and map the spatio-temporal patterns of both optically and non-optically active water quality parameters within small inland water bodies located in the COHWHS.MethodsHigh-resolution Sentinel-2 Multispectral Instrument (MSI) satellite data and two random forest models (Model 1 [consisting of sensitive spectral bands] and Model 2 [consisting of spectral bands + indices]) were used alongside In-situ measurements of chlorophyll-a, suspended solids, dissolved oxygen (DO), pH, Temperature, and electrical conductivity (EC) were integrated to establish empirical relationships and assess spatial variability across high-flow and low-flow conditions.ResultsThe results indicated that DO could be predicted with the highest accuracy under low-flow conditions, followed by EC. …”
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Machine learning for the rElapse risk eValuation in acute biliary pancreatitis: The deep learning MINERVA study protocol by Mauro Podda, Adolfo Pisanu, Gianluca Pellino, Adriano De Simone, Lucio Selvaggi, Valentina Murzi, Eleonora Locci, Matteo Rottoli, Giacomo Calini, Stefano Cardelli, Fausto Catena, Carlo Vallicelli, Raffaele Bova, Gabriele Vigutto, Fabrizio D’Acapito, Giorgio Ercolani, Leonardo Solaini, Alan Biloslavo, Paola Germani, Camilla Colutta, Savino Occhionorelli, Domenico Lacavalla, Maria Grazia Sibilla, Stefano Olmi, Matteo Uccelli, Alberto Oldani, Alessio Giordano, Tommaso Guagni, Davina Perini, Francesco Pata, Bruno Nardo, Daniele Paglione, Giusi Franco, Matteo Donadon, Marcello Di Martino, Dario Bruzzese, Daniela Pacella

“…The model includes the following steps: the spatial transformation of variables using kernel Principal Component Analysis (kPCA), the creation of 2D images from transformed data, the application of convolutional filters, max-pooling, flattening, and final risk prediction via a fully connected layer. …”
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A novel on-line dual sensing system for soil property measurement and mapping by Rukayat Afolake Oladipupo, Muhammad Abdul Munnaf, Parsat Sanganta, Ajit Borundia, Abdul Mounem Mouazen

“…Partial least squares regression models for vis-NIRS sensor were calibrated and validated, while a linear regression model was established for validation of the ISE sensor. …”
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Relative Humidity and Air Temperature Characteristics and Their Drivers in Africa Tropics by Isaac Kwesi Nooni, Faustin Katchele Ogou, Abdoul Aziz Saidou Chaibou, Samuel Koranteng Fianko, Thomas Atta-Darkwa, Nana Agyemang Prempeh

“…Semi-arid regions in the Sahelian–Guinean belt recorded moderate TEMP (25–30 °C) and RH (30–60%), while the Guinean coastal belt and Congo Basin experienced cooler, more humid conditions (TEMP < 20 °C, RH (60–90%). (2) Trend analysis using Mann–Kendal and Sen slope estimator analysis revealed spatial heterogeneity, with increasing TEMP and deceasing RH trends varying by region and season. (3) The warming rate was higher in arid and semi-arid areas, with seasonal rates exceeding annual averages (0.18 °C decade⁻¹). …”
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Impact of Extreme Climate Indices on Vegetation Dynamics in the Qinghai–Tibet Plateau: A Comprehensive Analysis Utilizing Long-Term Dataset by Hanchen Duan, Beiying Huang, Shulin Liu, Jianjun Guo, Jinlong Zhang

“…Both correlation analysis and random forest methodologies underscore the impact of extreme climate indices on vegetation variations, with the random forest model exhibiting superior capability in capturing nonlinear relationships. …”
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ThermalGS: Dynamic 3D Thermal Reconstruction with Gaussian Splatting by Yuxiang Liu, Xi Chen, Shen Yan, Zeyu Cui, Huaxin Xiao, Yu Liu, Maojun Zhang

“…Thermal infrared (TIR) images capture temperature in a non-invasive manner, making them valuable for generating 3D models that reflect the spatial distribution of thermal properties within a scene. …”
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A Near-Real-Time Operational Live Fuel Moisture Content (LFMC) Product to Support Decision-Making at the National Level by Akli Benali, Giuseppe Baldassarre, Carlos Loureiro, Florian Briquemont, Paulo M. Fernandes, Carlos Rossa, Rui Figueira

“…The model predicted well the temporal LFMC variability across most of the sampling sites. …”
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Deep Learning Approach for Estimating Workability of Self-Compacting Concrete from Mixing Image Sequences by Zhongcong Ding, Xuehui An

“…We propose a deep learning approach to better utilize the spatial and temporal information obtained from image sequences of the self-compacting concrete- (SCC-) mixing process to recover SCC characteristics in terms of the predicted slump flow value (SF) and V-funnel flow time (VF). …”
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Indigenous‐Led Analysis of Important Subsistence Species Response to Resource Extraction by Kathleen A. Carroll, Fabian Grey, Nicholas Anderson, Nelson Anderson, Jason T. Fisher

“…Here, the Indigenous authors designed the study and processed remote camera trap data, then sought out Western scientists to generate generalized linear models to evaluate moose habitat use and spatial‐numerical responses to possible stressors in north‐central Alberta, including fire, harvest, oil and gas extraction, and other disturbances. …”
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Loss Function Optimization Method and Unsupervised Extraction Approach D-DBSCAN for Improving the Moving Target Perception of 3D Imaging Sonar by Jingfeng Yu, Aigen Huang, Zhongju Sun, Rui Huang, Gao Huang, Qianchuan Zhao

“…By assigning different weights to data points in different density regions, the model pays more attention to accurate predictions in a sparse area, resulting in a 6.939 improvement in mIOU for semantic segmentation tasks, while lakebed mIOU achieved a high score of 99.28. …”
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Long short-term memory exploitation of satellite gravimetry to infer floods by Omid Memarian Sorkhabi, Joseph Awange

“…Flood forecasting is a vital segment of disaster risk management in that it contributes to the prediction of the magnitude, occurrence, duration and timing of floods. …”
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The application of unoccupied aerial systems (UAS) for monitoring intertidal oyster density and abundance by Jenny Bueno, Sarah E. Lester, Joshua L. Breithaupt, Sandra Brooke

“…These findings allowed us to extract clusters from geospatial products and predict live oyster abundance and spatial density on 138 reefs covering 138 382 m2 over two locations. …”
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