University of Shahrood

• Multi-objective optimization applied to achieve NZEB in Prague. • Integration of passive, active, and renewable strategies. • Energy savings drop from 70 % to 32 % under future climates. • Emphasis on adaptive, resilient building designs. • NZEB strategies need regular optimization for future climates. This study applies a Multi-Objective Optimization (MOO) framework to enhance the energy performance of a typical high-rise residential building in Prague and assess its resilience under future climate change scenarios. A total of 18 passive strategies—including external wall and roof insulation (0.08–0.20 m), optimized Window-to-Wall Ratios (10–90 % by orientation), triple low-E glazing, and dynamic shading overhangs—were evaluated alongside two active strategies (heating and cooling COP optimization) and three renewable strategies (roof-mounted photovoltaic (PV) panels with tilt, spacing, and orientation optimization). Results show that integrating these strategies can reduce annual energy demand by up to 68.7 % under current climate conditions and by 56 %, 42 %, and 32 % in the 2020s, 2050s, and 2080s, respectively. The optimized configuration maintained acceptable indoor comfort, with Predicted Percentage Dissatisfied (PPD) values reduced by up to 18 % compared to the baseline. PV panels generated 53,875 kWh annually under current conditions, covering 69 % of thermal and electrical demand, though coverage declined under future climate scenarios due to rising cooling loads. These findings demonstrate that a comprehensive integration of passive, active, and renewable strategies can significantly improve building performance and provide a resilient pathway toward Near-Zero Energy Buildings (NZEBs) under changing climatic conditions.

Lentil (Lens culinaris Medik.) is an important legume crop cultivated predominantly under dryland conditions where water deficit stress frequently limits yield. Understanding key yield-related and physiological traits that influence grain production under drought is essential for developing effective mitigation strategies. This study aimed to identify these traits and to evaluate the potential of growth regulators and bacterial inoculation to alleviate the effects of water stress in lentils. Field studies were conducted as a factorial experiment over two consecutive years using combinations of Pseudomonas fluorescens seed inoculation and foliar application of spermidine (0, 0.5, and 1 mM) and melatonin (0, 50, and 100 μM). Analysis of variance revealed significant effects for all treatments and their interactions on agronomic traits, yield components, and physiological parameters. Stepwise regression analysis showed that 100-seed weight, number of seeds per plant, and stem dry weight had the greatest direct positive effects on seed yield, whereas leaf dry weight had a negative effect. Among physiological traits, soluble sugars, photosynthetic pigments, and antioxidant enzyme activity were positively associated with yield, whereas total phenols, flavonoids, and malondialdehyde were major negative contributors. The combined application of 100 μM melatonin and 1 mM spermidine significantly improved sugar accumulation, photosynthetic pigment content, and antioxidant enzyme activity while reducing oxidative damage indicators. The integration of biochemical and statistical analyses effectively identified key traits driving lentil yield under drought stress. Foliar application of melatonin and spermidine also shows promise as an agronomic practice to improve lentil tolerance and productivity in water-limited environments. © 2026 Society of Chemical Industry.

A multi-objective optimization framework is developed for designing a hybrid renewable energy system (HRES) for Hormuz Island, integrating photovoltaic, wind, lithium-ion battery, proton-exchange-membrane electrolyzer-tank-fuel-cell, and a standby diesel generator. Real meteorological and demand data are employed to minimize the Levelized Cost of Energy (LCOE) and Net Present Cost (NPC) while maximizing the Renewable Energy Fraction (REF) and system resilience. The optimization integrates multiple conflicting techno-economic and environmental objectives through a TOPSIS-guided multi-criteria framework, where a scalar closeness coefficient is used as the fitness function within the search process, without explicit Pareto-front construction. Results indicate that, compared with the diesel baseline, the optimized configuration achieves an LCOE of 0.139 USD kWh −1 , a 38.6 % reduction in NPC, a REF of 87 %, and nearly 89 % GHG mitigation. The discount rate exhibits the highest sensitivity, inducing ± 14.8 % variability in NPC and ± 9.2 % in LCOE, followed by battery CAPEX (±9.5 % NPC) and PV CAPEX (±7.1 %). Resilience evaluation under 10 % PV and 20 % hydrogen-storage perturbations confine the Loss-of-Load Probability to ≤ 2 %, confirming robust operation under adverse climatic fluctuations. Life-cycle assessment demonstrates approximately 20 % reduction in CO 2 -equivalent emissions, while techno-economic analysis indicates about 15 % reduction in total energy cost relative to conventional diesel supply. The proposed configuration provides a replicable blueprint for off-grid, climate-vulnerable islands seeking reliable and low-carbon electrification pathways.

This paper introduces and explores a novel subclass $\mathcal{R}_{\Sigma}^{h,p}(\lambda,\gamma)$ of bi-univalent functions within the open unit disk $\mathbb{U}$. We establish upper bounds for the second, third, and fourth coefficients of functions belonging to this class. Furthermore, we derive estimates for the Fekete-Szegö problem in this context. The results, presented in this study extend and enhance several recent works by earlier authors.

Abstract Two-dimensional (2D) materials have gained significant attention for their atomic-scale thickness, extensive surface area, and outstanding mechanical properties, positioning them as promising nanoadditives to improve lubricant performance in diverse industrial fields. In this work, molybdenum disulfide (MoS 2 )/ reduced graphene oxide (RGO) nanocomposites were successfully synthesized using a hydrothermal route with varying graphene oxide (GO) weight percentages relative to ammonium heptamolybdate tetrahydrate (2%, 5%, 10%, and 15%). MoS 2 nanocomposites exhibited coexistence of tetragonal (1 T) and hexagonal (2H) phases with well-aligned nanosheets on the RGO surface, as confirmed by multiple characterization techniques. The tribological properties of SN 500 base oil containing intrinsic MoS 2 and MoS 2 /RGO nanocomposites were evaluated using a four-ball friction tester at 1200 rpm under a load of 200 N. The nanocomposite containing 10 wt% GO demonstrated superior friction reduction compared to MoS 2 and other composites, achieving a significant 32.06% improvement in antifriction performance relative to the base oil. Further studies on the load and temperature effects showed the lowest friction coefficient at ambient temperature under a 200 N load for this sample. These findings highlight the strong potential of MoS 2 /RGO nanocomposites as efficient nanoadditives for enhancing lubrication, indicating their promise for use in advanced high-performance industrial lubricants.

Counts in epidemiology often deviate from equidispersion and exhibit spatial, temporal, and nonlinear structure that the Poisson model cannot accommodate. We introduce a gamma-count structured additive regression model that strategically integrates penalized complexity priors in two critical aspects: (i) a principled penalized complexity prior on the dispersion parameter of the gamma-count distribution, which naturally shrinks toward the base Poisson model when the data support equidispersion, and (ii) scale-dependent penalized complexity hyperpriors on the smoothing variances for nonlinear, spatial, and temporal effects. By formulating the model within a latent Gaussian framework, we enable efficient approximate Bayesian inference through integrated nested Laplace approximations. Simulation studies across under-, equi-, and over-dispersed regimes show that the penalized complexity prior for dispersion parameter combined with scale-dependent hyperpriors yields accurate estimation of dispersion and smooth effects, favorable predictive scores, and robust inference. In empirical applications to larynx cancer mortality in Germany, COVID-19 incidence in Georgia (USA), and lung and bronchus cancer in Iowa (USA), the gamma-count structured additive regression model exhibits competitive or enhanced fit relative to Poisson and negative binomial counterparts, while elucidating interpretable nonlinear and spatial structures. This framework delivers robust, spatially resolved estimates of disease burden in the presence of non-equidispersion, thereby facilitating evidence-based resource allocation, epidemiological surveillance, and monitoring of health disparities, contributing to Sustainable Development Goals (SDGs) 3 (Good Health and Well-Being) and 10 (Reduced Inequalities). For geographically targeted analyses, it further supports informed decision-making in urban and community planning, aligning with SDG 11 (Sustainable Cities and Communities).

Multiple Sclerosis (MS) and Myelitis are serious inflammatory spinal cord disorders with overlapping clinical symptoms and radiological characteristics, making accurate differentiation challenging yet clinically essential. Early and precise diagnosis is critical for guiding treatment strategies and improving patient outcomes. In this study, we propose KhayyamNet, a novel hybrid deep learning architecture designed to fuse complementary local and global representations for the accurate diagnosis of MS and Myelitis using spinal MRI. To improve robustness and generalization capability, a comprehensive preprocessing strategy including data augmentation and intensity normalization is also applied to reduce noise and address data variability. The proposed architecture combines three complementary deep learning models for feature extraction composed of Xception for high-level semantic features, Convolutional Neural Networks (CNNs) for fine-grained local patterns, and Vision Transformers (ViTs) for global contextual representations via attention mechanisms. Extracted features are then fused and refined using the Minimum Redundancy Maximum Relevance (MRMR) algorithm to eliminate redundancy and retain the most informative signals. Finally, a Random Forest (RF) classifier utilizes the optimized feature set to achieve accurate and robust differentiation between MS, Myelitis, and control spinal MRIs. Experimental results demonstrate that KhayyamNet outperforms existing methods by achieving an average classification accuracy of 98.15±0.80%. This framework demonstrates promising performance for the automated analysis of spinal MRIs and shows potential to assist in the differentiation of MS and Myelitis. While these findings highlight the potential of KhayyamNet for automated MRI interpretation, its evaluation is limited to a single-center dataset, and further validation on external multi-center data is required.