Picture fuzzy (PF) sets are extremely reasonable to represent the uncertain, imprecise, and inconsistent information that exists in scientific and engineering fields. To meet decision makers' preference selection, the operational flexibility of aggregation operators shows its importance in dealing with the flexible decision-making problems in the PF environment. With assistance from Aczel-Alsina operations, we introduce the aggregation strategies of PFNs. We initially broaden the Aczel-Alsina norms to PF situations and present a few new operations of PFNs in view of which we build up a few new PF aggregation operators, for instance, the PF Aczel-Alsina weighted geometric, order weighted geometric, and hybrid weighted geometric operators. Furthermore, a decision support approach has been developed using the proposed aggregation operators under the PF environment. In this method, the aggregated results of each evaluated alternative are determined, and their score values are obtained. Then, all alternatives were ranked in decreasing order, and the best one was determined based on the highest score value. An illustrative example related to mango production is presented to investigate the most influential factor that resulted in mango production minimization. Finally, a comparison study was conducted on the proposed decision support method and the existing relative techniques. The result shows that the proposed method can overcome the insufficiency of lacking decision flexibility in the existing MAGDM method by the PF weighted geometric aggregation operators.

The agricultural sector faces challenges such as water scarcity, energy inefficiency, and declining productivity, particularly in arid regions. Traditional irrigation methods contribute to resource depletion and environmental impacts. Solar-powered smart irrigation systems integrate precision irrigation with renewable energy, improving water use and productivity. In Pakistan, where agriculture contributes 19% of gross domestic product and employs 40% of the workforce, these challenges are severe, especially in water-scarce areas like the Cholistan Desert. This study examines the impact of solar-powered smart irrigation on agricultural productivity, water conservation, and energy efficiency in the Cholistan Desert. Using a quantitative cross-sectional design, data were collected from 384 farmers via structured questionnaires. Statistical analyses, including multiple linear regression, paired sample t-tests, and Structural Equation Modeling (SEM), were conducted. Findings show significant improvements in crop yield (from 3.0 to 4.8 tons/hectare) and reductions in water and energy consumption. Regression analysis highlighted strong positive effects on yield and efficiency, while SEM confirmed reduced environmental impact and operational costs. The study concludes that solar-powered irrigation enhances productivity, conserves resources, and promotes sustainability. Policymakers should provide financial incentives, invest in renewable infrastructure, and implement training programs to support adoption. Collaborative efforts are essential for sustainable agriculture in arid regions.

Background: Climate change significantly impacts global well-being, with rural and agricultural communities, particularly women, bearing a disproportionate burden. In Pakistan’s Malakand Division, women face increased mental health challenges due to environmental stressors such as temperature rise, extreme weather, and environmental degradation. These stressors are expected to exacerbate issues like stress, anxiety, and depression. Understanding their effects on rural women’s mental health is crucial for developing effective intervention strategies.Methodology: This study employs quantitative methodologies to assess the impact of climate change on the mental health of rural women in Malakand Division, focusing on Dir Upper, Dir Lower, and Shangla districts. A cross-sectional design was used, with a sample size of 600 women selected through multistage cluster sampling for geographic representation. Data were collected using structured questionnaires addressing stress, anxiety, and community dynamics. Data were analyzed using multiple regression, structural equation modeling (SEM), ANOVA, and logistic regression.Results: The results revealed that climate change factors—temperature increase (β = 0.42, p < 0.01), extreme weather events (β = 0.36, p < 0.01), precipitation changes (β = 0.31, p < 0.05), and environmental degradation (β = 0.28, p < 0.05)—significantly impacted rural women’s mental health. High levels of stress (72%), anxiety (68%), and depression (56%) were reported. Social support (β = -0.45, p < 0.01), community cohesion (β = -0.37, p < 0.05), access to resources (β = -0.39, p < 0.01), and cultural norms (β = -0.33, p < 0.05) were key factors mitigating the effects of climate stress. Gender disparities were evident, with women showing higher mental health challenges compared to men in similar conditions.Conclusion: The study concludes that climate change significantly exacerbates mental health issues for rural women. It highlights the need for gender-sensitive, community-based interventions that address both climate adaptation and mental health. Strengthening community resilience, improving access to resources, and investing in healthcare and education are vital for enhancing well-being in the face of climate change.

Introduction: Mental health stigma remains a major barrier to accessing psychiatric care worldwide, with pronounced effects in culturally traditional societies such as Peshawar, Pakistan. In the Pashtun cultural context, the code of Pashtunwali—an honor-based system—shapes social attitudes and behaviors, potentially influencing mental health help-seeking patterns. This study examines how cultural narratives, social norms, and stigma interact to affect help-seeking behavior in this sociocultural setting.Methods: A cross-sectional survey was conducted among a stratified random sample of 400 adults aged 19 years and above in Peshawar. Data were collected using culturally validated instruments, including the Mental Illness Stigma Scale (MISS) and a Social Norms Scale. Bivariate analyses employed simple linear regression and binary logistic regression to examine individual relationships between variables. Multivariate analyses, including multiple linear regression and Structural Equation Modeling (SEM), were used to assess combined effects and mediation pathways.Results: Cultural narratives had a positive impact on help-seeking behavior, explaining 42% of its variance. Stigma showed a significant negative association, decreasing help-seeking likelihood by 26% for each unit increase. Social norms demonstrated a positive association with help-seeking behavior and indirectly reduced stigma. Collectively, these variables accounted for 68% of the variance in help-seeking likelihood.Discussion: The findings highlight the pivotal role of culturally resonant narratives and supportive social norms rooted in Pashtunwali in improving mental health service utilization. Addressing stigma while reinforcing positive cultural frameworks can substantially enhance help-seeking behavior in Peshawar and similar sociocultural contexts.

Climate change is a major global challenge affecting migration patterns, particularly in coastal communities vulnerable to sea-level rise, flooding, and extreme weather. Pakistan, with its extensive coastline and diverse environmental conditions, faces significant climate-induced migration issues, especially in Karachi, Thatta, Gwadar, Badin, and Muzaffargarh. This study aims to investigate the impact of climate change on migration patterns in these five selected regions of Pakistan. By analyzing climate variables and socio-economic factors, the research seeks to provide a localized understanding of how climate change drives population movements. A cross-sectional survey design was employed to gather data from 350 participants across these regions. Stratified random sampling ensured representation from each area, and data were collected using a structured questionnaire administered online. Statistical analyses included multiple linear regression, logistic regression, and structural equation modeling (SEM). This study found a strong positive relationship between climate change variables (sea level rise, temperature increases, and flooding) and migration patterns. Both direct impacts of climate change and indirect socio-economic factors influenced the likelihood of migration. The SEM analysis revealed that climate awareness partially mediates the relationship between climate change and migration. In conclusion, climate change significantly drives migration in Pakistan’s coastal communities, with both direct environmental impacts and socio-economic conditions playing crucial roles. Enhanced climate awareness and comprehensive adaptation strategies are essential. Policies should focus on climate resilience through infrastructure improvements, early warning systems, and socio-economic support programs. Strengthening education and economic opportunities is vital to build community resilience and effectively manage climate-induced migration.