Carbendazim-chitosan and copper- and cobalt-fusarium nanoparticles biological activity against potato root rot disease caused by Rhizoctonia solani by Gehad M.M. Abd El-Wahab, Yasser I. Khedr, Sanaa A. Masoud, Atef M.K. Nassar

“…Management strategies of potato fungal diseases rely mainly on using conventional fungicides that could cause risks to humans. Therefore, implementing environmentally friendly control strategies would be crucial. …”
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Techno-Economic Analysis of Lignin-Containing Micro- and Nano-Fibrillated Cellulose for Lightweight Linerboard Packaging by Heather Starkey, Maria Gonzalez, Hasan Jameel, Lokendra Pal

“…This study developed the first model to evaluate changes in steam consumption and other process parameters on a paper machine when incorporating lignin-containing micro- and nano-fibrillated cellulose (LMNFC) as a dry-strength additive, as well as its economic effects. Significant operational differences were observed in steam consumption, dissolved solids in the sewer stream, and production rates when implementing LMNFC in different scenarios. …”
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Exploring the injury severity of unlicensed powered two- and three-wheeler drivers in two-vehicle crashes in China by Peixiang Xu, Fulu Wei, Dong Guo, Yongqing Guo, Lizu Sun, Chuan Liu, Bin Zhou

“…Additionally, factors such as drunk driving, fatigued driving, and being an unlicensed driver over the age of 53 notably elevate the risk of serious injury or death, with unlicensed motorcyclists being disproportionately affected. …”
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Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters by S. Aishwarya, P. C. Siddalingaswamy, Krishnaraj Chadaga

“…Multiple ML models were implemented, including Random Forest Classifier, Logistic Regression, Decision Tree Classifier, K-Nearest Neighbors, CatBoost Classifier, and an Artificial Neural Network. …”
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Interpretable Prediction of a Decentralized Smart Grid Based on Machine Learning and Explainable Artificial Intelligence by Ahmet Cifci

“…A four-node star network implementing the decentralized smart grid control (DSGC) concept was investigated, and a dataset based on simulations of this network was used. …”
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Predicting Employee Attrition: XAI-Powered Models for Managerial Decision-Making by İrem Tanyıldızı Baydili, Burak Tasci

“…Future work should validate generalizability across diverse industries and develop lightweight, real-time implementations.…”
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Machine Learning Unveils the Impacts of Key Elements and Their Interaction on the Ambient-Temperature Tensile Properties of Cast Titanium Aluminides Employing SHAP Analysis by Shiqiu Liu, Li Liang

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Initial Stand Volume and Residual Live Trees Drive Deadwood Carbon Stocks in Fire and Harvest Disturbed Boreal Forests at North‐Central Alberta by Richard Osei, Charles A. Nock

“…We also determined whether their relative effects are consistent across deadwood types (snags, CWD) and disturbance regimes using generalized additive mixed models with study site as random factor in all cases. …”
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Interpretable Dual-Channel Convolutional Neural Networks for Lithology Identification Based on Multisource Remote Sensing Data by Sijian Wu, Yue Liu

“…The model adopts a parallel dual-channel structure to extract spectral and spatial features simultaneously, thus implementing lithology identification in remote sensing images. …”
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Rethinking the measurements and predictors of environmental degradation in Ethiopia: Predicting long-term impacts using a kernel-based machine learning approach by Tesfaye Etensa, Tekie Alemu, Mengesha Yayo

“…The relationships among these predictors are complex, often nonlinear, non-additive, and include reverse causality, making it difficult for traditional econometric models to capture them. …”
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Explainable artificial intelligence for predicting medical students’ performance in comprehensive assessments by Haniye Mastour, Toktam Dehghani, Ehsan Moradi, Saeid Eslami

“…In this framework, SHapley Additive exPlanations (SHAP) provided granular insights into model logic by identifying high-impact courses as dominant predictors of success and individualized risk profiles. …”
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WILD TURKEY HARVEST MANAGEMENT: PLANNING FOR THE FUTURE by William M. Healy, Shawn M. Powell

“…Conceptual models used to implement these strategies differ from those used for many other game and fish species in that turkey hunting mortality is assumed to be additive to natural mortality, recruitment is assumed to be independent of population density, and populations are characterized by annual fluctuations that may approach ±50% of the long‐term mean. …”
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Effect of organic acid concentration in lubricant on tribological characteristics of friction couple by V. E. Burlakova, E. G. Drogan

“…Herewith, a selective transfer and a wearless friction regime are implemented under friction of the brass 59–steel 40X couple. …”
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A Prototype Unit of a Distributed Sensor System for Ecological Monitoring by E. A. Sevryukova, E. A. Volkova, V. A. Doroshenko, A. V. Solodkov

“…The receiver and transmitter of the NB-IoT standard were implemented on the Xilinx Zedboard evaluation board. …”
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Assessment of COVID-19 pandemic-related detrimental impact on the population of nuclear city: two-year results by Mikhail V. Osipov, Evgeny P. Fomin

“…Conclusion — The analyses revealed significant impact of the COVID-19 pandemic on the overall excess mortality in the nuclear city population in 2020 and 2021 implemented in both direct and indirect way. The population size was a major significant risk factor confounding the overall mortality. …”
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Prediction of undernutrition and identification of its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms. by Md Merajul Islam, Nobab Md Shoukot Jahan Kibria, Sujit Kumar, Dulal Chandra Roy, Md Rezaul Karim

“…The models' performance was evaluated through accuracy and area under the curve (AUC). Additionally, SHapley Additive exPlanations (SHAP) were employed to illustrate the influencing predictors of undernutrition.…”
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Mobile Application and Machine Learning-Driven Scheme for Intelligent Diabetes Progression Analysis and Management Using Multiple Risk Factors by Huaiyan Jiang, Han Wang, Ting Pan, Yuhang Liu, Peiguang Jing, Yu Liu

“…We developed a stacking model combining eXtreme Gradient Boosting (XGBoost), Support Vector Classifier (SVC), Extra Trees (ET), and K-Nearest Neighbors (KNN) to explore the impact of various influencing factors on HbA1c dynamics, which achieved a classification accuracy of 94.23%. Additionally, we applied SHapley Additive exPlanations (SHAP) to visualize the contributions of risk factors to HbA1c dynamics, thus clarifying the differential impacts of these factors on diabetes progression. …”
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Matrix-Based ACO for Solving Parametric Problems Using Heterogeneous Reconfigurable Computers and SIMD Accelerators by Vladimir Sudakov, Yuri Titov

“…To solve the problem of stagnation of the method without a priori information about the system, a new probabilistic formula for choosing the parameter value is proposed, based on the additive convolution of the number of pheromone weights and the number of visits to the vertex. …”
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Effect modifiers of the temperature-mortality association for general and older adults population of Brazil’s metropolitan areas by Cristiane Aschidamini, Antônio Carlos Monteiro Ponce de Leon

“…Effects of this association were estimated for each group in 42 locations using a generalized additive model combined with the nonlinear distributed lag model. …”
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Assessing the Relationship between COVID-19, Air Quality, and Meteorological Variables: A Case Study of Dhaka City in Bangladesh by Md Sariful Islam, Mizanur Rahman, Tanmoy Roy Tusher, Shimul Roy, Mohammad Arfar Razi

“…Therefore, our results suggest that an effective public health intervention measures should be implemented to slowdown the spreading of COVID-19.…”
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