Assessment of Economic Management of Land Resources to Enhance Food Security by Dinara Tleshpayeva, Natalia Bondarenko, Mikhail Leontev, Galina Mashentseva, Julia Plaksa, Andrey Zharov, Diana Stepanova, Agzhan Karbozova

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High-quality DNA extraction for Desmodium gangeticum: A medicinal shrub of Shivalik Himalayas by Shiwani Singh, Maneesh S. Bhandari, Neeraj Dwivedi

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Vegetation optical depth as a key predictor for fire risk escalation by Dinuka Kankanige, Yi Y. Liu, Ashish Sharma

“…This study investigates whether vegetation parameters can be utilized in fire risk prediction in the absence of fire weather information, and how they can be utilized to effectively reflect on the fire risk increment from a minimum point, which is the concern in bushfire occurrence. Using the McArthur Forest Fire Danger Index (FFDI) as a measure of fire danger, a clear association with the satellite-observed vegetation optical depth (VOD) was noted for segments illustrating risk increment. …”
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Parametric Design and Mechanical Characterization of a Selective Laser Sintering Additively Manufactured Biomimetic Ribbed Dome Inspired by the Chorion of Lepidopteran Eggs by Alexandros Efstathiadis, Ioanna Symeonidou, Emmanouil K. Tzimtzimis, Dimitrios Avtzis, Konstantinos Tsongas, Dimitrios Tzetzis

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Comparison of Conservation Strategies for California Channel Island Oak (Quercus tomentella) Using Climate Suitability Predicted From Genomic Data by Alayna Mead, Sorel Fitz‐Gibbon, John Knapp, Victoria L. Sork

“…We compare the impact of these approaches on predicted maladaptation to climate using Gradient Forest. We also introduce a climate suitability index to identify optimal pairs of seed sources and planting sites for approaches involving assisted gene flow. …”
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Bioinformatics and Deep Learning Approach to Discover Food-Derived Active Ingredients for Alzheimer’s Disease Therapy by Junyu Zhou, Chen Li, Yong Kwan Kim, Sunmin Park

“…We employed the bioinformatics-integrated deep neural analysis of NCs for Disease Discovery (BioDeepNat) application in the data collected from chemical databases. Random forest regression models were utilized to predict the IC₅₀ (pIC₅₀) values of ligands interacting with AD-related target proteins, including acetylcholinesterase (<i>AChE</i>), amyloid precursor protein (<i>APP</i>), beta-secretase 1 (<i>BACE1</i>), microtubule-associated protein tau (<i>MAPT</i>), presenilin-1 (<i>PSEN1</i>), tumor necrosis factor (<i>TNF</i>)<i>-α</i>, and valosin-containing protein (<i>VCP</i>). …”
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Variations in carbon stocks across traditional and improved agroforestry in reference to agroforestry and households’ characteristics in Southeastern Ethiopia by Boja Tilinti, Mesele Negash, Zebene Asfaw, Teshale Woldeamanuel

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Do meteorological variables impact air quality differently across urbanization gradients? A case study of Kaohsiung, Taiwan, China by Bohan Wu, Shuang Zhao, Yuxiang Liu, Chunyan Zhang

“…The results revealed that: (1) Suburban areas exhibited significantly better air quality than urban and near-urban areas, with annual AQI values of 59.58 in Meinong (outskirts), 67.86 in Renwu (suburbs area), and 76.73 in Qianjin (urban area), showing a progressive improvement in air quality from urban to suburban areas, primarily due to lower levels of urbanization and abundant forest resources; (2) Temperature and relative humidity emerged as key meteorological variables influencing AQI, with Granger causality tests indicating that temperature significantly affects AQI, especially in urban areas. …”
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Identifying impact factors on the communications and telecommunications sector using ensemble machine learning methods by Badykova Idelia, Biktimirova Kamilla

“…Using the Python programming language, the following models were built: linear regression, Ridge, Bagging, RandomForestRegressor, GradientBoostingRegressor and XGBRegressor. …”
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Ethnobotanical Study on Wild Edible Plants in Metema District, Amhara Regional State, Ethiopia by Getinet Masresha, Yirgalem Melkamu, Getnet Chekole Walle

“…For sustainable utilization, conservation, value addition, and market linkage practices shall be strengthened to improve the livelihoods of local people and sustainable forest management.…”
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Identification of ubiquitination-related key biomarkers and immune infiltration in Crohn’s disease by bioinformatics analysis and machine learning by Wei Chen, Zeyan Xu, Haitao Sun, Wen Feng, Zhenhua Huang

“…Key genes were selected by combining hub genes from the protein-protein interaction (PPI) network with feature genes identified by Lasso and Random Forest (RF) algorithms. Additionally, the correlation between key genes and immune infiltration was assessed, and Gene Set Enrichment Analysis (GSEA) of key genes was conducted. …”
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Productivity and socioeconomic sustainability of Bubalus bubalis in the western lowlands of Venezuela by Carlos Alberto Calles Navas, Verena Torres Cardenas

“…In contrast, buffalo farming requires forests. However, to convince farmers to apply this type of livestock; it was necessary to demonstrate its greater profitability. …”
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Análisis histórico de la explotación maderera en las serranías del Pacífico Central de Costa Rica by Eugenio González-J.

“…[Conclusión] La colonización temprana de la región, la existencia de infraestructura, alta demanda de productos forestales en el ámbito nacional e internacional, y la disponibilidad de recursos forestales en una zona donde convergen el bosque seco tropical, el bosque nuboso y el bosque húmedo tropical, y las condiciones edafoclimáticas que facilitaron también el desarrollo de cultivos agrícolas, favorecieron posiblemente el cambio de uso del suelo y, por ende, la explotación forestal de la región. …”
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Projected range overlap between the predator Teretrius nigrescens and the invasive stored product pest Prostephanus truncatus expands under climate change by Rachel R. Harman, William R. Morrison, III, Alison R. Gerken

“…The larger grain borer, Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae), is a forest-dwelling destructive pest of stored corn and cassava native to Central America and invasive in Africa. …”
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Predicting breast cancer recurrence using deep learning by Deepa Kumari, Mutyala Venkata Sai Subhash Naidu, Subhrakanta Panda, Jabez Christopher

“…Utilizing the Wisconsin Diagnostic Breast Cancer and Wisconsin Prognostic Breast Cancer datasets, the framework integrates multiple deep learning architectures- Multi layer Perceptron (MLP), Visual Geometry Group (VGG), Residual Network (ResNet), and Extreme Inception (Xception)-with traditional machine learning models such as Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR). This hybridization leads to the creation of 16 robust models that enhance interpretability, facilitate generalization, and effectively manage challenges related to small datasets, class imbalance, and data preprocessing. …”
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Entomofauna asociada a la polinización del cultivo de cacao nativo, San Ignacio Moxos, Beni, Bolivia by Ángel Fernando Copa Bazán, Alfredo Matareco Maza, Betty Flores Llampa

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Sulfur Hexafluoride (SF6) versus Perfluoropropane (C3F8) in the Intraoperative Management of Macular Holes: A Systematic Review and Meta-Analysis by Idan Hecht, Michael Mimouni, Eytan Z. Blumenthal, Yoreh Barak

“…Publications up to October 2018 that focused on macular hole surgery in terms of primary closure, complications, and clinical outcomes were included. Forest plots were created using a weighted summary of proportion meta-analysis. …”
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A novel hybrid inception-xception convolutional neural network for efficient plant disease classification and detection by Wasswa Shafik, Ali Tufail, Chandratilak Liyanage De Silva, Rosyzie Anna Awg Haji Mohd Apong

“…To assess the presented IX-CNN model performance, different classifiers, namely, support vector machine (SVM), decision tree (DT) and random forest (RF), were used. The experiments used six datasets, including PlantVillage, Turkey Disease, Plant Doc, Rice Disease, RoCole, and NLB datasets. …”
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Multimodal machine learning for analysing multifactorial causes of disease—The case of childhood overweight and obesity in Mexico by Rosario Silva Sepulveda, Magnus Boman, Magnus Boman

“…The supervised pipeline employed four methods: Linear classifier with Elastic Net regularisation, k-Nearest Neighbour, Decision Tree, and Random Forest. The unsupervised pipeline used traditional methods with k-Means and hierarchical clustering, with the optimal number of clusters calculated to be k = 2.ResultsThe decision tree classifier in the supervised early fusion approach produced the best quantitative results. …”
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The Use of Novel Alginate Capsules in a Monitoring System for <i>Drosophila suzukii</i> in a Cherry Orchard in the Region of La Araucanía, Chile by Marcelo Lizama, Fernando Manuel Alves-Santos, Luis Manuel Navas-Gracia, Daniel Martínez-Cisterna, Cristian Medina, Ramón Rebolledo, Manuel Chacón-Fuentes, Leonardo Bardehle

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