Zakia Akter¹, Sumona Akter Jannat², Sheikh Md. Shibly¹, Afroza Sultana¹, Amdadul Hoque Amran¹, Joairia Hossain Faria¹, Sabina Yeasmin¹, Md. Parvez Anwar^1,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.075524 - 30 January 2026

Abstract Seaweed extract contains plant growth regulators and bio-stimulants that enhance plant growth and development. In Bangladesh, winter rice (Boro rice) in the nursery bed often shows poor seed emergence and weak seedling growth due to low temperature. This problem can be addressed by using seaweed extract as a seed priming agent and bio-stimulant. The objective of this study was to evaluate the effectiveness of seaweed extract (Crop Plus) on seed emergence, seedling growth, and vigor of winter rice in the nursery. Two experiments were conducted at Bangladesh Agricultural University using BRRI dhan89. The laboratory experiment… More >

Eun Hae Lee^1,2,3, Minjae Choi^4,5, Hanul Park^3,6, Joon Hee Han^3,6,7, Sujeong Yu^3,8, Joshua Kirabo Sempungu^1,2,3,6, Inbae Sohn^4,6, Yo Han Lee^3,6,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.073735 - 28 January 2026

Abstract Background: That Central and Eastern Europe and Central Asia (CEECA) experienced a major mortality crisis in the 1990s is a well-established finding, with most analyses focusing on singular causes like alcohol-related deaths. However, the utility of the integrated “deaths of despair” framework, which views alcohol, drug, and suicide deaths as a unified socio-economic phenomenon, remains under-explored in this context. Crucially, the long-term evolution of the composition of despair within the region remains a largely unexplored area of inquiry. Therefore, this study aims to analyze the long-term trends, changing composition, and regional heterogeneity of deaths from despair… More >

Yuyang Nie^1,2,#, Kunkun Jiang^2,3,#, Tianci Wang⁴, Cong Liu^1,2, Kangli Du^1,2, Yuxian Cao², Guofeng Qu^2,*, Lijia Hou^2,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.073592 - 28 January 2026

Abstract Background: Amid the global rise in adolescent sedentary behavior and psychological distress, extant research has largely focused on variable-level associations, neglecting symptom-level interactions. This study applies network analysis, aims to delineate the interconnections among sedentary time, social support, social exclusion, and psychological distress in Chinese students, and to identify core and bridge symptoms to inform targeted interventions. Methods: This study employed a cross-sectional design to investigate the complex relationships among sedentary behavior, social support, social exclusion, and psychological distress among Chinese students. The research involved 459 high school and university students, using network analysis and mediation… More >

Yue Cui¹, Ziqing Yang², Hao Gao^1,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.072986 - 28 January 2026

Abstract Background: In the Chinese context, the impact of short video applications on the psychological well-being of older adults is contested. While often examined through a pathological lens of addiction, this perspective may overlook paradoxical, context-dependent positive outcomes. Therefore, the main objective of this study is to challenge the traditional Compensatory Internet Use Theory by proposing and testing a chained mediation model that explores a paradoxical pathway from social support to life satisfaction via problematic social media use. Methods: Data were collected between July and August 2025 via the Credamo online survey platform, yielding 384 valid responses… More >

Ramazan İnci^1,*, Davut Açar², Osman Tayyar Çelik³, Yunus Tunç⁴

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.072072 - 28 January 2026

Abstract Background: Bullying during adolescence is shaped by numerous psychosocial factors such as family dynamics, attachment, and peer relationships. This study aims to examine parental acceptance-rejection, attachment styles, and social exclusion factors as key psychosocial variables predicting bullying behavior in adolescents. Methods: In a cross-sectional study conducted with 349 high school students in Hakkari, Türkiye. Data were collected using the Olweus Bullying Scale, the Parental Acceptance-Rejection Scale, the Social Exclusion Scale, and the Three-Dimensional Attachment Styles Scale. Independent samples t-tests, one-way ANOVAs, Pearson correlations, and hierarchical regression analyses were performed. Results: Research findings reveal that peer bullying varies… More >

Jimin Chae¹, Youngbin Lym^2,*, Geiguen Shin^2,3,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.071482 - 28 January 2026

Abstract Background: Social connection is widely recognized as a protective determinant of health, yet its direct and indirect effects on mental health remain underexplored. This study examines the relationship between social connection and mental health, focusing on the mediating role of quality of life (QoL) and the moderating effect of regional differences. Methods: We analyzed data from the 2019 Korean Community Health Survey, comprising 229,099 adults. Mental health was assessed through validated measures of depressive symptoms and psychological well-being. Social connection was measured using indicators of interpersonal ties and community participation, and QoL was assessed via self-reported… 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 >

Moein Tosan^1,*, Vahid Nourani^2,3, Ozgur Kisi^4,5,6, Yongqiang Zhang⁷, Sameh A. Kantoush⁸, Mekonnen Gebremichael⁹, Ruhollah Taghizadeh-Mehrjardi¹⁰, Jinhui Jeanne Huang¹¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074768 - 29 January 2026

Abstract The integration of machine learning (ML) into geohazard assessment has successfully instigated a paradigm shift, leading to the production of models that possess a level of predictive accuracy previously considered unattainable. However, the black-box nature of these systems presents a significant barrier, hindering their operational adoption, regulatory approval, and full scientific validation. This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence (XAI) as applied to geohazard science (GeoXAI), a domain that aims to resolve the long-standing trade-off between model performance and interpretability. A rigorous synthesis of 87 foundational… 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 >

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 >