The University of Zanjan (ZNU) (Persian: دانشگاه زنجان Dāneshgāh-e Zanjan) is located in Zanjan, Iran. It was founded in 1975 and organized in four faculties. Nowadays it is one of the largest universities of the country with a community of over 10,000 students. At the 2006 census, population of the nearby urban area (Zanjan city which is about 5 km away) was over 400s and the nearby rural area was only 125.
In this paper, we present the distributed dislocation technique (DDT) to calculate mode I/II stress intensity factors (SIFs) as well as electric displacement intensity factor (EDIF) in a piezoelectric structural layer that contain multiple curved cracks. By applying the Fourier transform, the governing electro-elastic equations are formulated in terms of the Burgers vectors bx, by and electric dislocation bϕ. Furthermore, stress analysis in the layer without crack under concentrated electromechanical loading is carried out. Solving the partial differential equations results in a system of Cauchy-type singular integral equations. These equations are solved numerically using the collocation method. The numerical solution of these equations yields dislocation densities on a crack face which are used to determine field intensity factors. The DDT represents an effective approach for solving plane crack problems with high precision. Its effectiveness extends to complex crack geometries and systems containing multiple curved cracks. The results have been validated by comparison with analytical solutions in other available papers. Numerical results focusing on the influence of geometry of curved cracks, loading combination parameters, thickness of layer, and interaction between cracks on field intensity factors.
Global climate change and subsequent increase in temperature and dry periods are expected to affect agricultural productivity and food security. Here, we modeled the impact of future climate on water yield and rice productivity across a range of elevations in two watersheds in Northern Iran using the AquaCrop model. After evaluating 19 Atmosphere-ocean general circulation models (AOGCMs) from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report (AR5) using statistical metrics, the models with better performance at six stations were selected in predicting historical air temperature (T) and precipitation (P). Our results showed that, despite a general increase in minimum and max air temperature during near future period (2020–2040), spring months had the highest increase in Tmax, while the lowest increase occurred during the winter months for all the stations. The greatest change in precipitation was observed in summer months. Increases in the future T and P were predicted to be larger at higher altitudes. The annual trend of rice yield was increasing based on RCP4.5 scenario and a gradually increasing pattern in rice yield was revealed from the lowlands to uplands. Under the RCP8.5 scenario, the annual rice yield in most areas was predicted to decline in the future, except for the upland area. In the lowland areas, most of the AOGCMs predicted a decrease in the water productivity, but an increase in water productivity was observed in the upland areas. The information on water productivity would help devise strategic water management plans during periods of both water shortage and excess under a changing climate.
The belief in a just world has been shown to serve adaptive psychological functions and has been linked to better subjective well-being. Cognitive emotion regulation strategies, such as positive reappraisal and putting adverse events into perspective, can likewise support subjective well-being by helping individuals reframe emotionally challenging experiences. The present study examined the concurrent relationships between belief in a just world, cognitive emotion regulation strategies, and subjective well-being for the first time in higher education within individualistic versus collectivist cultures. A total of N = 1,051 university students participated in an online survey conducted in two countries. Structural equation modeling indicated a different understanding of the psychological constructs for this educational context within different cultures. However, in both samples, consistent positive relationships emerged between belief in a just world, adaptive emotion regulation strategies, and subjective well-being. In contrast, three maladaptive strategies demonstrated either negative or no relationships with belief in a just world and well-being. These findings suggest that belief in a just world and adaptive regulation strategies may jointly support psychological adjustment across cultural contexts. The observed empirical patterns align with theoretical expectations and offer initial cross-cultural insights into the interplay of justice beliefs, emotion regulation, and well-being. These results may inform culturally sensitive interventions aimed at enhancing well-being in university settings.
Afshari sheep, a prominent fat-tailed Iranian breed, exhibits a unique capacity for tail fat deposition, serving as an adaptive energy reserve in fluctuating nutritional environments. This study aimed to identify novel genetic variants in five key candidate genes (PPARG, FABP4, FASN, SCD, and DGAT1) associated with fat metabolism in 133 Afshari & times; Booroola Merino backcross lambs. Using PCR amplification and Sanger sequencing, five novel variants were discovered: two intronic SNPs in PPARG (G>A and C>T with variant allele frequencies of 0.16 and 0.33, respectively), one intronic SNP in FABP4 (T > G; frequency 0.20), a missense mutation in exon 34 of FASN (C>A; p.Thr2002Asn; frequency 0.33), and a 5 ' UTR variant in DGAT1 (C>A; frequency 0.40) overlapping a GC-box motif. No variants were detected in the SCD gene. Bioinformatics analyses, including multiple sequence alignment, protein structure modeling, and stability prediction, suggested that the FASN missense mutation may reduce protein stability, while the DGAT1 5 ' UTR variant may influence transcriptional regulation. Additionally, QTL mapping supported the association of FASN and DGAT1 variants with fat deposition traits. These findings provide genome-informed insights into the genetic architecture of fat metabolism in sheep and highlight potential molecular markers for future breeding programs targeting fat-tail morphology.
Irrigation networks in arid and semi-arid regions face water deficits and distribution losses, making reliable operation a challenge. This study investigates the Dez distributary E1R1 (Iran) with three pool-controlled reaches (Ch1–Ch3) and six turnouts (TO1–TO6) using operator records and Irrigation Conveyance System Simulation (ICSS) scenarios. Objectives are to infer an operator-consistent reward using Maximum Entropy Inverse Reinforcement Learning (MEIRL), derive gate-control policies from the recovered reward, and evaluate performance using indicators. The framework couples MEIRL with an ICSS Saint–Venant simulator, learning from expert state–action demonstrations and testing the policy across scenarios. Unlike canal-control reinforcement learning that prescribes or tunes a reward (e.g., fuzzy SARSA), this approach infers a canal-specific reward from expert behavior, yielding an auditable preference model for policy derivation and validation. Water-level regulation is assessed via maximum and integral absolute error (MAE, IAE), while delivery service is assessed via Molden–Gates indicators: efficiency (MPF), equity (MPE), and dependability (MPD). After learning, MAE falls below 5
