Search Results (13)

Amine transaminases (ATA) are critical players in producing non-canonical amino acids, essential building blocks in pharmaceuticals and fine chemicals. Significant progress has been made in discovering and engineering enzymes in this field, enhancing their use in organic synthesis. However, challenges such as co-factor regeneration, substrate, and product inhibition remain significant limitations to widespread industrial enzyme application. (Chemo-)enzymatic cascades offer efficient and environmentally friendly pathways for synthesizing amino acids, reducing the need for multiple synthesis steps and saving the purification of intermediates. This review focuses specifically on the synthesis of non-canonical amino acids, emphasizing the use of enzymatic and chemoenzymatic cascades involving ATA. Full article

In recent years, northeastern Iran, particularly Khorasan Razavi province, has experienced wind erosion and dust storms, although large-scale studies are limited. To assess wind patterns, sand drift, and dust events, hourly wind data were analyzed using Fryberger’s method, along with trend analysis through the Mann–Kendall and Sen’s slope tests. Additionally, MODIS satellite data and Google Earth Engine helped identify event frequency and spatial patterns. The results show that east (12%) and southeast winds (9.6%) are the most frequent, with an average annual wind speed of 4.39 knots. Sand drift potential (DP = 96, RDP = 21.6) indicates sand movement from southeast to northwest, with a multi-directional wind system (unidirectional index of 0.22). The results of the AOD index show that the amount of dust in the north and northwest part is more than other locations, and more than 500 events with dust has been registered over the last two decades. These findings suggest that policymakers should monitor these trends to mitigate the environmental and infrastructural damage caused by blowing sand. Full article

The construction sector in Iran faces substantial inefficiencies, including high material wastage, posing environmental and economic risks. This study investigated the adoption of Lean Construction (LC) practices and AI/IoT technologies in Iran’s public construction sector using a mixed-methods approach. This research examined the organizational, technical, and infrastructural factors across four key provinces—Tehran, Isfahan, Khorasan Razavi, and Fars—and employed fuzzy logic to address the uncertainties in adoption decisions. Data from 28 key stakeholder interviews were analyzed using Python 3.9, with libraries such as Pandas 1.3.3, NumPy 1.21.2, and skfuzzy 0.4.2 for the statistical analysis and NVivo 12 for the thematic coding. The analysis revealed that organizational readiness and leadership support were the critical drivers of adoption, particularly in Isfahan and Khorasan Razavi, which exhibited the highest adoption likelihood scores (0.5000). Tehran and Fars showed slightly lower scores due to regulatory barriers and financial limitations. The findings highlight the need for targeted leadership training, regulatory reforms, and infrastructure investments to accelerate the adoption of these technologies. This study aligned with the Sustainable Development Goals (SDG 9: Industry, Innovation, and Infrastructure and SDG 11: Sustainable Cities and Communities) by offering practical recommendations for advancing sustainable practices in Iran’s construction sector. The insights provided have broader implications for other developing economies facing similar challenges, contributing to global efforts toward sustainable development. Full article

The transition from vernacular architectural patterns to current architecture in rural Iran has led to various socio-cultural and environmental problems in the last decade. This study explores the nature of this transition, which has been overlooked in the studies of vernacular architecture in Iran. Furthermore, this article contributes to the ongoing academic debate on the decline and transformation of vernacular architectural patterns in the context of modernization. It analyzes the forces behind the decline and rise of vernacular settlements in a case study area, the Salami region of the Khaf district in Iran’s Khorasan Razavi province, by exploring how it is possible to reinterpret vernacular architectural patterns in the context of current architecture to utilize the new developments in rural Iran not as an obstacle but as an opportunity for improvement. To this end, this article explores vernacular architectural patterns in a case study area in Iran, supported by socio-cultural aspects and the environmental conditions of the region. This study conducts architectural and anthropological fieldwork on three vernacular houses in a case study area and uses participant observation and informal interview methods to understand the people and their interaction with their built environment. The findings of this article thus contradict previous studies on learning from vernacular architecture by shedding light on vernacular architecture in Iran as a system by demonstrating the inextricable links between different vernacular architectural patterns. Therefore, this study argues that to draw lessons from vernacular architectural patterns for current architecture in rural Iran, it is necessary to limit its communication concerning people’s contemporary needs but not to neglect this communication completely. Full article

Foot-and-mouth disease (FMD) is endemic in Iran and associated with a large impact on the livestock industry. Livestock mobility is recognized as one of the most important risk factors for FMD and other infectious livestock diseases’ introduction and dissemination. The description of temporal and spatial aspects of livestock mobility networks in Iran provides insights into FMD epidemiology. It also assists in the formulation of recommendations to mitigate the risk of animal disease transmission through livestock movements. This study is the first spatiotemporal description of official/registered cattle, sheep, goat, and camel movements in Iran, using records related to the period from March 2020 to August 2021 extracted from the Iran Veterinary Organization (IVO) Quarantine system. It shows that the static networks drawn by the movements of small ruminants, cattle, and camels to farms or slaughterhouses are complex and highlights the predominance of a few provinces and towns. In particular, the results show that Razavi Khorasan and West Azerbaijan provinces, major provinces for the Iranian livestock sector, are provinces where significant volumes of small ruminants and cattle are moved (from, to, or within) and, therefore, should be prioritized for targeted and timely risk reduction interventions. This study produces some of the necessary inputs for the risk assessment of FMD and similar transboundary animal diseases (TADs) spread within Iran, which is needed to regularly update the national risk-based control strategy for FMD and other TADs. Full article

The interaction between genotypes and environments plays an important role in selecting superior genotypes for target locations. The main objectives of the present study were to analyze the effect of the genotype-by-environment interaction (GEI) and identify superior, newly developed, and promising barley genotypes for cold regions in Iran. For these purposes, a set of genotypes obtained from breeding programs for cold climates in Iran, along with two reference genotypes, were investigated at eight research stations (Tabriz, Ardabil, Arak, Miandoab, Mashhad, Jolge Rokh, Karaj, and Hamadan) during two consecutive growing seasons (2019–2020 and 2020–2021). The results of the freezing test (LT₅₀) showed that most of the tested genotypes had significant cold tolerance at the seedling stage. Based on the additive main effect and multiplicative interaction (AMMI) analysis, environment (E) and GEI effects explained 49.44% and 16.55% of the total variation in grain yield, respectively. Using AMMI1 and AMMI2 models, G2 and G20 were found to be superior genotypes in terms of grain yield and stability. Moreover, AMMI-based stability parameters considered the G20 genotype to be the ideal genotype. A two-plot analysis of the genotype-by-environment interaction (GGE) biplot showed that the 16 experimental environments were grouped into 2 mega-environments. Of the test environments, ARK1 and KAJ2 had the highest discriminating power and representativeness ability, and these were identified as ideal environments for testing advanced genotypes for yield and stability performance during early barley breeding practices in cold areas in Iran. In conclusion, both AMMI and GGE biplot models identified several superior genotypes, among which G20, with a high average yield relative to the overall average yield and the lowest IPC1 score, was found to have high yield stability and is recommended for inclusion in breeding programs for cold climates in Iran. Full article

In the present study, kashk samples were collected from two regions of Iran, the Fars (Abadeh) and Razavi Khorasan (Kalat) provinces. Fifteen bacteria were isolated and physiological and biochemical assays were performed. After identification to the genus level, eight isolates were identified as lactic acid bacteria (LAB) and subjected to molecular identification and probiotic properties assays. The results revealed that the isolates were Enterococcus faecium KKP 3772 (KF1), Enterococcus faecium C1 (KF2), Pediococcus pentosaceus H11 (KF3), Pediococcus pentosaceus VNK-1 (KK4), Lactococcus lactis RSg (KK1), Enterococcus faecalis P190052 (KK2), Enterococcus mundtii CECT972T (KK3), and Lactiplantibacillus plantarum PM411 (KK5). Only the numbers of L. lactis RSg (KK1) and Lpb. Plantarum PM411 (KK5) decreased to below 9 Log CFU/mL after acidic conditions (pH = 3) and showed weak antibacterial activity. Enterococcus mundtii CECT972T (KK3) and E. faecium C1(KF2) were highly susceptible to bile salts, while P. pentosaceus VNK-1 (KK4) and P. pentosaceus H11 (KF3) showed the highest resistance. All of the isolates were resistant to tetracycline and sensitive to chloramphenicol and gentamicin. The antimicrobial activity of P. pentosaceus VNK-1 (KK4) and P. pentosaceus H11 (KF3) was higher than other isolates and consequently, their inhibition zones were larger. The adhesion capabilities of LAB isolates to intestinal epithelial cells were evaluated by examining the auto-aggregation factor and cell surface hydrophobicity. The highest and lowest cell surface hydrophobicity and auto-aggregation were obtained from P. pentosaceus VNK-1 (KK4) and E. mundtii CECT972T (KK3), respectively. In general, P. pentosaceus VNK-1 (KK4) and P. pentosaceus H11 (KF3) have shown better probiotic properties as compared to other isolates. Full article

Desalination is set to become a major source of drinking water in several Middle Eastern countries over the coming decades. Solar distillation is a simple power-independent method of water desalination, which can be carried out in active or passive modes. This study is among the first attempts to investigate the possibility of desalinating brackish groundwater resources under the threat of saltwater intrusion in the southern areas of Razavi Khorasan province in Iran. For this purpose, a pilot solar distillation unit was constructed to analyze the effects of the unit orientation, depth of the water pool, atmospheric conditions, input salinity, and flow continuity on the solar distillation performance. The results showed that the unit exhibited the highest efficiency when it had a 3 cm deep water pool. It was oriented facing southward while operating a continuous flow for at least 3 days under sunny weather conditions. It was found that among the studied parameters, the unit orientation and pool depth had the greatest impact on the water production performance for this type of water desalination system. Conversely, the water production efficiency was not very sensitive to the input salinity level. Overall, the solar distillation technology was able to reduce the salinity by 99.7% and the hardness by 94.7%. Full article

In this research, high-order shape functions commonly used in different finite element implementations are investigated with a special focus on their applicability in the semi-analytical finite element (SAFE) method being applied to wave propagation problems. Hierarchical shape functions (p-version of the finite element method), Lagrange polynomials defined over non-equidistant nodes (spectral element method), and non-uniform rational B-splines (isogeometric analysis) are implemented in an in-house SAFE code, along with different refinement strategies such as h-, p-, and k-refinement. Since the numerical analysis of wave propagation is computationally quite challenging, high-order shape functions and local mesh refinement techniques are required to increase the accuracy of the solution, while at the same time decreasing the computational costs. The obtained results reveal that employing a suitable high-order basis in combination with one of the mentioned mesh refinement techniques has a notable effect on the performance of the SAFE method. This point becomes especially beneficial when dealing with applications in the areas of structural health monitoring or material property identification, where a model problem has to be solved repeatedly. Full article

It is evident that relations between political conditions and community development have become sophisticated in recent years. More people now live in urbanized areas, and this ongoing urbanization has various ramifications. Many countries are facing swift urban transformation which alters their regional development patterns. Urban sprawl, migration and rural depopulation, regional inequalities, increasing urban poverty, and social injustice are some of these emerging problems. Assessing regional development for identifying the aforementioned predicaments is really imperative and related to the United Nations Sustainable Development Goal (SDG) 11. However, there are limited studies that focus on the assessment of regional sustainable development at both national and regional scales, simultaneously. Thus, this study aims to fill the gap by developing a robust method that can assess and compare the level of sustainability in various regions and at varying scales. This helps to identify areas where urgent prevention or mitigation strategies and action plans are required. In this study, we strived to evaluate Iran’s regions and Tehran’s provinces based on sustainability indicators. To end this, the authors use factor analysis and F’ANP model in both assessments. The results of the study show that Tehran Province was the most developed province, and its F’ANP result was 2.006. Tehran is 10% more sustainable than the third region in the country which is Khorasan Razavi. Isfahan and Khorasan Razavi provinces were in the next in rank with scores of 1.984 and 1.8, respectively. At the bottom of the list, the northern Khorasan, Ilam, and Kohkiloye-Boyerahmad provinces were in the lowest ranked in terms of access to sustainability indices. It is patently obvious that Iran suffers from uneven development, and the majority of border provinces have moderate or bad situations. This uneven development also intensifies migration to Tehran, which already has one-sixth of Iran’s population which has led todeteriorating social inequity and environmental injustice, nationally. The results of the regional assessment of Tehran also show that there is uneven development in Tehran Province. Tehran County is twice as good and sustainable as 68 percent of the counties in this region. The F’ANP result for Tehran County was 0.580, and it has been ranked first over a period due to the exceptional number of facilities in this region. Tehran County became 20% more sustainable during this decade. After Tehran, Firoozkoh and Damavand counties were in the next ranks with scores of 0.389 and 0.343, respectively. Qarchak, Ghods, and Baharestan counties were the weakest based on the sustainability indices, and their F’ANP results were below 0.2. Full article

Future projection of drought vulnerability is vital for northern provinces of Iran, including North Khorasan, Khorasan-Razavi, and South Khorasan, due to the highly dependent of their economy on agriculture. The study is motivated by the fact that no research has been conducted to project the future Drought Vulnerability Index (DVI). DVI consist of three components of exposure, sensitivity, and adaptation capacity. More exposure levels of drought, higher sensitivity value, and lower adaptation capacity lead to a higher amount of vulnerability. Combined ERA-Interim-observation meteorological data, CMIP5 models under RCP4.5 and RCP8.5 scenarios, and national census data are used to estimate DVI in the past and future periods. CanESM2, GFDL-ESM2M, and CNRM-CM5 General Circulation Model (GCM) are selected from CMIP5 based on Taylor diagram results. The delta-change technique was selected for statistical downscaling of GCM outputs because it is most widely used. The study period is regarded as 1986–2005 as observation and four future 20-years periods during 2021–2100. Results indicated that the dissipation of the class of “very low” vulnerability is eminent in the near future period of 2021–2040 under the RCP4.5 scenario, and all provinces would experience a new worse class of “very high” vulnerability at 2081–2100, both under RCP4.5 and RCP8.5 scenarios. Full article

The spatial distribution of precipitation is one of the most important climatic variables used in geographic and environmental studies. However, when there is a lack of full coverage of meteorological stations, precipitation estimations are necessary to interpolate precipitation for larger areas. The purpose of this research was to find the best interpolation method for precipitation mapping in the partly densely populated Khorasan Razavi province of northeastern Iran. To achieve this, we compared five methods by applying average precipitation data from 97 rain gauge stations in that province for a period of 20 years (1994–2014): Inverse Distance Weighting, Radial Basis Functions (Completely Regularized Spline, Spline with Tension, Multiquadric, Inverse Multiquadric, Thin Plate Spline), Kriging (Simple, Ordinary, Universal), Co-Kriging (Simple, Ordinary, Universal) with an auxiliary elevation parameter, and non-linear Regression. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R²) were used to determine the best-performing method of precipitation interpolation. Our study shows that Ordinary Co-Kriging with an auxiliary elevation parameter was the best method for determining the distribution of annual precipitation for this region, showing the highest coefficient of determination of 0.46% between estimated and observed values. Therefore, the application of this method of precipitation mapping would form a mandatory base for regional planning and policy making in the arid to semi-arid Khorasan Razavi province during the future. Full article

Soil phosphorus (P) is a vital but limited element which is usually leached from the soil via the drainage process. Soil phosphorus as a soluble substance can be delivered through agricultural fields by runoff or soil loss. It is one of the most essential nutrients that affect the sustainability of crops as well as the energy transfer for living organisms. Therefore, an accurate simulation of soil phosphorus, which is considered as a point source pollutant in elevated contents, must be performed. Considering a crucial issue for a sustainable soil and water management, an effective soil phosphorus assessment in the current research was conducted with the aim of examining the capability of five different wavelet-based data-driven models: gene expression programming (GEP), neural networks (NN), random forest (RF), multivariate adaptive regression spline (MARS), and support vector machine (SVM) in modeling soil phosphorus (P). In order to achieve this goal, several parameters, including soil pH, organic carbon (OC), clay content, and soil P data, were collected from different regions of the Neyshabur plain, Khorasan-e-Razavi Province (Northeast Iran). First, a discrete wavelet transform (DWT) was applied to the pH, OC, and clay as the inputs and their subcomponents were utilized in the applied data-driven techniques. Statistical Gamma test was also used for identifying which effective soil parameter is able to influence soil P. The applied methods were assessed through 10-fold cross-validation scenarios. Our results demonstrated that the wavelet–GEP (WGEP) model outperformed the other models with respect to various validations, such as correlation coefficient (R), scatter index (SI), and Nash–Sutcliffe coefficient (NS) criteria. The GEP model improved the accuracy of the MARS, RF, SVM, and NN models with respect to SI-NS (By comparing the SI values of the GEP model with other models namely MARS, RF, SVM, and NN, the outputs of GEP showed more accuracy by 35%, 30%, 40%, 50%, respectively. Similarly, the results of the GEP outperformed the other models by 3.1%, 2.3%, 4.3%, and 7.6%, comparing their NS values.) by 35%-3.1%, 30%-2.3%, 40%-4.3%, and 50%-7.6%, respectively. Full article