Islamic Azad University, Tehran

The rapid increase in global cooling demand, particularly in regions with high solar potential, has emphasized the urgent need for sustainable and electricity‑independent refrigeration technologies. In response to this challenge, this work proposes a novel solar‑assisted single‑effect Lithium Bromide–Water (LiBr/H₂O) absorption refrigeration system incorporating a supersonic ejector and a triple‑layer solar thermal storage unit. The design aims to maximize energy recovery and reduce operating cost through combined thermodynamic and thermoeconomic optimization. Governing mass and energy conservation equations are established and solved using the Engineering Equation Solver (EES). Energy, exergy, and cost assessments are performed for both ejector‑assisted and conventional configurations to quantify improvements in the Coefficient of Performance (COP), exergy efficiency, and component cost rate under various solar irradiation and generator temperatures. Results reveal that ejector integration enhances COP by 12.7% and exergy efficiency by 11.3%, while reducing total investment cost by 9% compared to the baseline cycle. The optimized configuration achieves coefficient of performance of 0.74 and solar coefficient of performance of 0.58 under solar irradiation of 973 W/m², confirming marked enhancements in thermodynamic efficiency, cost effectiveness, and overall system sustainability.

The aim of this systematic review and meta-analysis was to evaluate comprehensively the therapeutic potential of Abelmoschus esculentus (okra) supplementation across the diabetes spectrum of key metabolic risk factors. A search was conducted in PubMed, Scopus, Web of Science, EMBASE and the Cochrane Library, up to 23 July 2025, to identify randomized controlled trials evaluating the effects of okra supplementation on metabolic risk factors in diabetes. Fourteen randomized controlled trials published between 2020 and 2025, including a total of 836 participants, were analysed. Okra supplementation led to significant reductions in 2h postprandial glucose [weighted mean difference (WMD)=-22.39mg/dL, 95% confidence interval (CI): -41.39 to -3.38; P=0.021], fasting blood sugar (WMD=-23.66mg/dL, 95% CI: -34.20 to -13.12; P<0.001), glycosylated haemoglobin (WMD=-0.30%, 95% CI: -0.59 to -0.02; P=0.034), homeostatic model assessment for insulin resistance (WMD=-0.59 units, 95% CI: -1.01 to -0.18; P=0.005), low-density lipoprotein cholesterol (WMD=-8.55mg/dL, 95% CI: -14.42 to -2.68; P=0.004) and total cholesterol (WMD=-12.58mg/dL, 95% CI: -22.78 to -2.37; P=0.016) levels. The certainty of evidence was very low for most outcomes, except for diastolic blood pressure and glycosylated haemoglobin, which were rated as low. Regarding methodological quality, six trials were rated as good, two as fair and six as poor. Okra supplementation might improve glycaemic control and lipid profiles, indicating its potential as a complementary approach in diabetes management. Despite limitations from small and heterogeneous trials, these findings support future research on optimal dosing, safety and personalized applications in metabolic disease management.

Abstract This paper resolves the previously open problem of determining the metric dimension of the zero-divisor graph $\Gamma (R)$ for the Boolean ring $R = (\mathbb {Z}_2)^n$ . The unique structure of this graph, characterised by its diameter of $3$ and lack of common neighbours, has hindered all standard approaches. We introduce a novel combinatorial method that constructs an explicit resolving set. Consequently, we provide a precise formula for $\dim _m(\Gamma ((\mathbb {Z}_2)^n))$ , closing a notable gap in the literature on metric dimensions of zero-divisor graphs. As an application, we compute the metric dimension of a zero-divisor graph of a ring with a Boolean factor.

This research explores the dual role of artificial intelligence (AI) and its narratives in shaping environmental, social, and governance (ESG) performance in China's retail industry. Drawing on the dynamic capabilities view and signaling theory, we conceptualize AI as both a substantive technological resource and a symbolic communicative mechanism, with green innovation serving as a mediating process. Using panel data of 2306 A-share listed retail firms from 2020 to 2024 (9710 firm-year observations), we employ system generalized method of moments (GMM) estimations to address dynamic panel bias, endogeneity, and unobserved heterogeneity. The findings reveal that both AI technology and AI narrative have a positive impact on ESG performance, with AI technology exerting a stronger substantive effect and AI narratives functioning as credible signals that enhance legitimacy. Moreover, green innovation significantly improves ESG outcomes and partially mediates the artificial intelligence - ESG performance relationship. Robustness checks using alternative measures, estimators, subsamples, and placebo tests confirm the stability of the findings. This research contributes to the literature by linking digital transformation and sustainability, highlighting the interplay between substantive and symbolic mechanisms, and providing insights from an emerging economy context. The findings offer theoretical, managerial, and policy implications for leveraging AI to advance sustainable development in retailing.

Background: Many efforts have been made to combine the diverse capabilities of different plant species into a unique plant to increase the quantity and quality of the food product.Accordingly, scientists succeeded in producing triticale as a new pathogen by using a cross between wheat (Triticum spp.) and rye (Secale cereale), which aims to increase the ability of wheat, as one of the most important food sources among grains in the world.It was to withstand harsh environmental conditions such as drought stress.Various studies show that this grain has a high potential to be used as a multipurpose product for direct human use or as a fodder product.Therefore, triticale can be considered a potential product with special genetic conditions, whose yield is still far from its potential.According to scientists of breeding science, creating diversity, whether natural or synthetic, in agricultural products and selecting genotypes with the highest yields and stability in different environments are among the main goals of studies in the field of breeding.It is also reported that the stress tolerance of triticale genotypes is usually higher than wheat genotypes, triticale is less affected by stress conditions, and its yield will be higher than wheat.Resistance to drought stress is a complex process that includes a network of plant responses at the physiological and molecular levels that have not yet been properly discovered and understood.However, creating diversity, selecting genotypes, and studying different traits will help scientists in this direction.In the current study, different triticale genotypes produced by domestic scientists were cultivated and tested under different irrigation conditions to consider the possibility of introducing new cultivars resistant to drought stress and changing environmental conditions.In addition, the relationship between morphological and agronomic traits related to seed yield was evaluated in this research using some advanced statistical methods to find possible traits suitable for indirect selection.The amount of different genetic, phenotypic, and environmental indicators was also investigated to examine the effect of the environment and genetics on the traits.Methods: To reflect the effect of water deficit on triticale and the probability of screening some suitable genotypes tolerant to drought stress, a study was carried out on nine triticale genotypes under four irrigation regimes during two years.These genotypes included Senabad, Pag, Juanillo, ET-85-4, In each year, four different irrigation regimes were applied with interruption of irrigation in three stages, including the flowering stage, the seed milky stage, and the seed pulp stage, along with the control condition.In each year, a split-plot design based on a randomized complete block design with three blocks (replication) was used every two years of the experiment (the growing season 2018-2019) in the research station located in the research complex of the Zarghan Agriculture and Natural Resources Research and Training Center, Fars, Iran.Different traits, including plant height, leaf angle, leaf weight, total dry matter, spike length, spike weight, spike number, grain number, straw yield, harvest index, and grain yield, were measured for all applied genotypes in this study.The data obtained from this experiment were first subjected to the composite analysis of variance, and year variance, environmental variance, genotypic variance, phenotypic variance, and test error variance were estimated based on these calculations.The analysis was performed in SAS-9.4-M6software using a programming code stored on the GitHub website.Results: The results showed that a lower number of irrigation and earlier withholding of water from the triticale plants can lead to a high decrease in the productivity of triticale genotypes.