Dilfuza Jabborova^1,2,3,*, Khurshid Sulaymanov¹, Muzafar Jabborov⁴, Nayan Ahmed⁵, Tatiana Minkina⁶, Olga Biryukova⁶, Nasir Mehmood^6,*, Vishnu D. Rajput⁶

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.072577 - 30 January 2026

Abstract The combined effects of macronutrients (Nitrogen, Phosphorus, and Potassium-N, P, K) and micronutrient fertilization on turmeric yield, soil enzymatic activity, microbial biomass, and nutrient dynamics remains poorly understood, despite their significance for sustainable soil fertility management and optimizing crop productivity across diverse agroecosystems. To investigate, a net house experiment on sandy loam Haplic Chernozem was conducted to 03 fertilizer regimes, viz. N₇₅P₅₀K₅₀ kg ha⁻¹ (T-2), N₁₂₅P₁₀₀K₁₀₀ kg ha⁻¹ (T-3), and N₁₀₀P₇₅K₇₅ + B₃Zn₆Fe₆ kg ha⁻¹ (T-4). Furthermore, the influence of these treatments was systematically assessed on soil nutrient status (N, P, K), enzymatic activities (alkaline phosphomonoesterase, dehydrogenase, fluorescein diacetate… More >

Huaruo Chen^1,2, Gefan Wang¹, Hancai Qiu¹, Hui Ma¹, Zhentao Peng¹, Ruihan Liu³, Feng Xu^4,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.073911 - 28 January 2026

Abstract Background: As an important indicator of subjective well-being (SWB), decent work is a key guarantee for the sustainable development of teachers and their psychological health and work quality. Faced with the rapid development of artificial intelligence and the global labor market, vocational college teachers are facing challenges such as workload pressure and limited career development, which may harm their well-being. This study aims to localize the measurement method of decent work in Chinese vocational education based on the theory of the Psychology of Working Theory, and explore the relationship mechanism between organizational support, career adaptability, decent… More >

Jinpan Wang^1,#, Yue Yuan^2,#,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.071212 - 28 January 2026

Abstract Background: Childhood leukemia, a malignant proliferative disorder of the hematopoietic system and the most common childhood cancer, poses a significant threat to the lives and health of affected children. For parents, a leukemia diagnosis in their child is a profoundly traumatic event. As primary caregivers, they endure immense psychological distress and caregiving stress throughout the prolonged and demanding treatment process, which can adversely affect their own well-being and caregiving capacity. However, the psychological mechanisms, such as the role of mindfulness, linking caregiver stress to parental coping strategies remain underexplored, and evidence-based interventions to support these parents… More >

Mong-Fong Horng^1,#, Thanh-Lam Nguyen^1,#, Thanh-Tuan Nguyen^2,*, Chin-Shiuh Shieh^1,*, Lan-Yuen Guo³, Chen-Fu Hung⁴, Chun-Chih Lo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075064 - 29 January 2026

Abstract The global population is rapidly expanding, driving an increasing demand for intelligent healthcare systems. Artificial intelligence (AI) applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend. Among these applications, mouth motion tracking and mouth-state detection represent an important direction, providing valuable support for diagnosing neuromuscular disorders such as dysphagia, Bell’s palsy, and Parkinson’s disease. In this study, we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices. The proposed system integrates the Facial… More >

Fatima Al-Quayed^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074799 - 29 January 2026

Abstract The rapid proliferation of Internet of Things (IoT) devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative, distributed architectural solutions. This paper proposes FE-ACS (Fog-Edge Adaptive Cybersecurity System), a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge, fog, and cloud layers to optimize security efficacy, latency, and privacy. Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923, while maintaining significantly lower end-to-end latency (18.7 ms) compared to cloud-centric (152.3 ms) and fog-only (34.5… More >

Kang-Jia Wang¹, Hongzhi Zhang², Runsheng Lin^3,*, Jiabin Li⁴, Xiao-Yong Wang^1,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074787 - 29 January 2026

Abstract Conventional low-carbon concrete design approaches have often overlooked carbonation durability and the progressive loss of cover caused by surface scaling, both of which can increase the long-term risk of reinforcement corrosion. To address these limitations, this study proposes an improved design framework for low-carbon slag concrete that simultaneously incorporates carbonation durability and cover scaling effects into the mix proportioning process. Based on experimental data, a linear predictive model was developed to estimate the 28-day compressive strength of slag concrete, achieving a correlation coefficient of R = 0.87711 and a root mean square error (RMSE) of… More >

Menwa Alshammeri^1,2,*, Noshina Tariq³, NZ Jhanji^4,5, Mamoona Humayun⁶, Muhammad Attique Khan⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074627 - 29 January 2026

Abstract Artificial Intelligence (AI) is changing healthcare by helping with diagnosis. However, for doctors to trust AI tools, they need to be both accurate and easy to understand. In this study, we created a new machine learning system for the early detection of Autism Spectrum Disorder (ASD) in children. Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning. For this, we combined several different models, including Random Forest, XGBoost, and Neural Networks, into a single, more powerful framework. We used two different types More >

Zhuofan Li¹, Daniel H. Pak², James S. Duncan², Liang Liang³, Minliang Liu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073757 - 29 January 2026

Abstract Patient-specific finite element analysis (FEA) is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo. However, inverse material property identification using FEA, which requires iteratively solving nonlinear hyperelasticity problems, is computationally expensive which limits the ability to provide timely patient-specific insights to clinicians. In this study, we present an inverse material parameter identification strategy that integrates deep neural networks (DNNs) with FEA, namely inverse DNN-FEA. In this framework, a DNN encodes the spatial distribution of material parameters and effectively regularizes the inverse solution, which aims to reduce susceptibility to local optima… More >

Amel Ksibi^*, Hatoon Albadah, Ghadah Aldehim, Manel Ayadi

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073533 - 29 January 2026

Abstract Sustainable energy systems will entail a change in the carbon intensity projections, which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions. The present article outlines the TransCarbonNet, a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory (Bi-LSTM) network to forecast the carbon intensity of the grid several days. The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data; hence, it is able to give… More >

Jihoon Moon^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.071052 - 29 January 2026

Abstract Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data, a challenge that becomes especially pronounced when commissioning new facilities where operational records are scarce. This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such “cold-start” forecasting problems. It primarily covers three interrelated domains—solar photovoltaic (PV), wind power, and electrical load forecasting—where data scarcity and operational variability are most critical, while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective. To this end, we examined… More >