Did Turkey Experience Reductions in Air Pollution During The Covid-19 Lockdown and Partial Lockdown? by İsmet Yener, Kazım Onur Demirarslan

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Assesment of Vegetation Cover Status in Dry Lands of The Sudan Using Social and Terrestrial Data by Mohammed Hamed Mohammed, Suzan Abdelrahman Hamad, Hassan Elnour Adam

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Genetic diversity, phylogenetic relationships and systematic revision of some Delimini (Gastropoda, Clausiliidae) from Mid-Mediterranean areas: new insights and biogeographic consi... by M. Colomba, S. Reale, A. Gregorini, S. Scibetta, A. Reitano, R. Viviano, W. De Mattia, F. Liberto, S. Surdo, I. Sparacio

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Waterbirds habitat mapping using unmanned aerial vehicle in Belawan Mangrove Ecosystems, North Sumatera, Indonesia by Thoha Achmad Siddik, Fauziyah Laely Nur, Jumilawaty Erni, Marwan Mochammad Akbar

“…The results of mapping using UAVs obtained a mangrove forest waterbird habitat covering an area of 1,01 hectares. …”
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An Automatic Recognition Method of Microseismic Signals Based on S Transformation and Improved Gaussian Mixture Model by Kaikai Wang, Chun’an Tang, Ke Ma, Xintang Wang, Qiang Li

“…The identification accuracy is as high as 94%, and its recognition effect is superior to other recognition models (such as traditional Gaussian Mixture Model based on Expectation-Maximum (EM-GMM), Backpropagation (BP) neural network, Random Forests (RF), Bayes (Bayes) methods, and Logistic Regression (LR) method). …”
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Application of Artificial Intelligence in Landslide Susceptibility Assessment: Review of Recent Progress by Muratbek Kudaibergenov, Serik Nurakynov, Berik Iskakov, Gulnara Iskaliyeva, Yelaman Maksum, Elmira Orynbassarova, Bakytzhan Akhmetov, Nurmakhambet Sydyk

“…Among models, random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and multilayer perception (MLP) are used as the baseline to compare any new model introduced to develop LSM. …”
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Performance evaluation and comparative analysis of different machine learning algorithms in predicting postnatal care utilization: Evidence from the ethiopian demographic and healt... by Daniel Niguse Mamo, Agmasie Damtew Walle, Eden Ketema Woldekidan, Jibril Bashir Adem, Yosef Haile Gebremariam, Meron Asmamaw Alemayehu, Ermias Bekele Enyew, Shimels Derso Kebede

“…From fifteen models, the MLP Classifier (f1 score = 0.9548, AUC = 0.99), Random Forest Classifier (f1 score = 0.9543, AUC = 0.98), and Bagging Classifier (f1 score = 0.9498, AUC = 0.98) performed excellently, with a strong ability to differentiate between classes. …”
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Self-directed learning versus traditional didactic learning in undergraduate medical education: a systemic review and meta-analysis by Jasmine Aulakh, Hana Wahab, Christine Richards, Satesh Bidaisee, Prakash V. A. K Ramdass

“…Key words used were “self-directed learning” AND “undergraduate medical education.” Forest plots were generated with the Open Meta-analyst Software, comparing SDL and TDL. …”
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Optimum Conditions and LC-ESI-MS Analysis of Phenolic Rich Extract from Eucalyptus marginata L. under Maceration and Ultrasound-Assisted Extraction Methods Using Response Surface M... by Soumaya Hasni, Ghayth Rigane, Hanene Ghazghazi, Hajer Riguene, Amir Bouallegue, Olfa Khedher, Moufida A. Oueslati, Ridha Ben Salem

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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Predicting nighttime black ice using atmospheric data for efficient winter road maintenance patrols by Jinhwan Jang

“…In this context, the present study investigates machine learning techniques, including Random Forest, CatBoost, and Deep Neural Networks, for forecasting nighttime icing on rural highways in Korea. …”
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Effects of Land-Use Dynamics on Soil Organic Carbon and Total Nitrogen Stock, Western Ethiopia by Yitayh Leul, Mohammed Assen, Shimeles Damene, Asmamaw Legass

“…Soil organic carbon (SOC) and total nitrogen (TN) stock are key indicators of soil quality in tropical regions; however, their status is often degraded, especially due to massive deforestation in natural forest areas associated with extensive agricultural land use. …”
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Des semis et des clones by Malou Delplancke, Yildiz Aumeeruddy-Thomas

“…Whether it is by natural regeneration in forests, controlled sowing in fields or vegetative propagation by grafting (telqem) in gardens, almonds are characterized by a management of their seeds (diversification by sexual reproduction) and clones (identical reproduction by vegetative propagation) in a spatial and temporal framework within territories. …”
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Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy by Min Liang, Min Liang, Fuyuan Luo

“…Predictive modeling was performed using advanced machine learning algorithms, including XGBoost, Multilayer Perceptron, K-Nearest Neighbor, and Random Forest. Additionally, traditional models, such as logistic regression and AJCC staging, were employed for comparison. …”
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How does playfulness (re)frame the world? Evidence for selective cognitive and behavioral redirecting in times of adversity by Xiangyou Shen, Zoe Crawley

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Construction of the Giant Panda National Park corridor and restoration of edible bamboo: A case study of from the Chengdu area region by Xing Wang, Yu-Ting Yang, Yan Wu, Yu Xie, Pei Tu, Mei-Ling Liu, Mei-Jiao Zhang, Tao Lu

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Multiple PM Low-Cost Sensors, Multiple Seasons’ Data, and Multiple Calibration Models by S Srishti, Pratyush Agrawal, Padmavati Kulkarni, Hrishikesh Chandra Gautam, Meenakshi Kushwaha, V. Sreekanth

“…The ML models included (i) Decision Tree, (ii) Random Forest (RF), (iii) eXtreme Gradient Boosting, and (iv) Support Vector Regression (SVR). …”
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Monitoring Urban Expansion and Loss of Agriculture on the North Coast of West Java Province, Indonesia, Using Google Earth Engine and Intensity Analysis by Laju Gandharum, Djoko Mulyo Hartono, Asep Karsidi, Mubariq Ahmad

“…Landsat data and a robust random forest (RF) classifier available in GEE were chosen for producing LULC maps. …”
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Effect of Fertilization with Ash from Biomass Combustion on the Fatty Acid Composition of Winter Rapeseed Oil by Ewa Szpunar-Krok, Anna Wondołowska-Grabowska

“…The response to this challenge is a three-year field experiment (2018–2021) where the effect of fertilization with ash from forest biomass (approx. 70%) and agricultural biomass (approx. 30%), and soil type (Gleyic Chernozem and Haplic Luvisol), on the fatty acid (FA) profile of winter rape seeds (<i>Brassica napus</i> L. ssp. …”
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Conflits armés et environnement by Al–Hamandou Dorsouma, Michel-André Bouchard

“…From degradation of natural resources, such as water, agricultural land, forest and biodiversity to collateral environmental damages such as oil spills, and finally to the collapse of environmental governance, environmental impacts of conflicts may seriously affect post conflict rehabilitation and reconstruction and may sustain conditions of personal civil unrest afterwards. …”
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