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The simulation of the rainfall-runoff process in the watershed has particular importance for a better understanding of hydrologic issues, water resources management, river engineering, flood control structures, and flood storage. In this study, to simulate the rainfall-runoff process, rainfall and discharge data were used in the period 1997-2017. After data qualitative control, rainfall, and discharge delays were determined using the coefficients of autocorrelation, partial autocorrelation, and cross-correlation in R Studio software. Then, the effective parameters and the optimum combination were determined by the Gamma test method and used to implement the model under three different scenarios in MATLAB software. Gamma test results showed that today's precipitation parameters, precipitation of the previous day, discharge of the previous day, and discharge of two days ago have the greatest effect on the outflow of the basin. Also, the Pt Qt-1 and Pt Pt-1 Qt-1 Qt-2 Qt-3 combinations were selected as the most suitable input combinations for modeling. The results of the modeling showed that in the support vector machine model, the Radial Base kernel Function (RBF) has a better performance than multiple and linear kernels. Also, the performance of the Artificial Neural Network model (ANN) is better than the Support Vector Machine model (SVM) with Radial Base kernel Function (RBF).

Background: The present study aimed to evaluate the sperm chromatin quality and rate of apoptosis in formaldehyde (FA)-exposed mice and the possible protective effects of curcumin (Cur) on the fertility potential of spermatozoa.Methods: Twenty-four adult male NMRI mice were randomly divided into three groups: Group Ι (control), group ΙΙ (Sham) received Cur solvent (2 ml/day, intraperitoneal), group ΙΙΙ received FA (10 mg/kg, intraperitoneal), and group ΙV received FA+Cur (100 mg/kg, intraperitoneal). After 35 days, the spermatozoa from right cauda epididymis were analyzed using Chromomycin A3 (CMA3) and TUNEL staining.Result: Regarding CMA3 and TUNEL tests, the data revealed a significant increase (p

Protozoan parasites of the genus Theileria are tick-borne parasites that have been found in many species of mammals. More than a dozen species of Theileria have been found in cattle, water buffalo, sheep, and goats. Theileria orientalis is a non-pathogenic blood protozoan parasite that was detected and identified during a regular investigation of piroplasmida infection in indigenous cattle in the spring of 2019 in Northern Provinces of Iran. In total, 92 blood samples were collected from different areas of Guilan and Mazandaran Provinces, Iran during the spring. The Giemsa stained blood smears did not show any parasitic infection; however, T. orientalis was identified by 18S rRNA gene polymerase chain reaction (PCR) and DNA sequencing. The specific sequenced DNA for T. orientalis was registered in GenBank under the accession number MN453385. The partial 18S rRNA gene sequence of the obtained DNA showed 100% nucleotide identity with reference sequences for the T. orientalis that have been registered from Europe, Africa, and Asia. Additionally, molecular phylogenetic studies have shown that T. orientalis Iran GC98-01 isolate belongs to nonpathogenic T. orientalis type 3 (buffeli). In this study, the indigenous Bos indicus cattle were detected as asymptomatic carriers of Theileria spp. infection. Here, we identified and genotyped T. orientalis for the first time as T. orientalis type 3 (buffeli) in Iran using molecular phylogenetic analysis and registered the 18S rRNA gene sequence of the T. orientalis GC98-01 isolate in GenBank. Moreover, rare T. annulata infection was detected in cattle using semi-nested PCR in Mazandaran (Miankaleh peninsula). The T. orientalis can be differentiated from other Theileria and Babesia haemoprotozoan parasites by specific molecular assays.

Chemoselective arylation of 5-aminopyrazoles was performed through oxidative formation of orthoquinones from catechols catalyzed by Myceliophthora thermophila laccase (Novozym 51003), and subsequently nucleophilic attack of 5-aminopyrazole to the catechol intermediates. The C-4 arylated products were obtained under extremely mild conditions without the need for amine protection or halogenation of the substrates. From this method, 10 derivatives with moderate to good efficiency (42–94%) were prepared. [ABSTRACT FROM AUTHOR]

Off-site steel construction (OSC) is an emerging alternative to traditional on-site methods that offers advantages such as reduced waste, improved quality, and faster delivery. However, OSC also requires robust monitoring and control mechanisms within fabrication shops to ensure the timely and cost-effective completion of projects. This study presents the development and application of a machine learning (ML) model to estimate the cost performance index (CPI), a pivotal indicator of project progress and performance, by considering influential factors that affect OSC projects. A comprehensive analysis of data from 56 OSC projects fabricated in a steel parts manufacturing facility between 2020 and 2022 was conducted. The investigation focused on four key features: project weight, project utilization type, project connection type, and material supply method, examining their impact on CPI. The data set was meticulously preprocessed, and three ML algorithms—support vector machine (SVM), gradient boosting (GB), and decision tree (DT)—were employed to model CPI. Model performance was evaluated and compared using metrics including root mean square error, accuracy, and R-squared. The findings demonstrated that GB excelled in CPI prediction, achieving an accuracy rate of 91%. This research underscores the utility of ML as a valuable tool for monitoring and controlling off-site steel construction projects. It also provides insights into the factors that influence CPI and suggests ways to optimize them for better project outcomes. Furthermore, the study contributes to the literature on OSC by exploring the relationship between project characteristics and performance indicators, which can help practitioners improve their decision-making and planning processes. The study also discusses the limitations and challenges of applying ML models to OSC data, such as data availability, quality, and consistency. [ABSTRACT FROM AUTHOR]

An Ni25Co20Cu10Fe25Mn20 high-entropy alloy (HEA) was produced using mechanical alloying. Similar to equiatomic NiCoCuFeMn alloy, a face-centered cubic single-phase nanocrystalline HEA was obtained after 28-h milling. The lattice parameter of this new HEA (0.360 nm) was slightly smaller than that of equiatomic alloy (0.361 nm). The crystallite size of the final product was ~ 9 nm, significantly finer than the values reported for the equiatomic HEAs. According to the analysis of the HEA powder particles' deformation behavior, the system operated in a ductile-brittle mode. The thermal behavior of prepared alloy, characterized by differential thermal analysis, was similar from that of equiatomic NiCoCuFeMn alloy. Similar to the equiatomic NiCoCuFeMn, the sluggish diffusion effect was not true for the Ni25Co20Cu10Fe25Mn20 HEA. Studying the magnetic behavior of Ni25Co20Cu10Fe25Mn20 HEA, using vibrating sample magnetometry, confirmed a soft magnetic behavior for this material like an equiatomic alloy. [ABSTRACT FROM AUTHOR]

IntroductionLandslide is an important geological hazard and one of the natural disasters that are constantly happening around the world. The factors that cause landslides are numerous and complex. Today, due to the importance of landslides and the effects of not paying attention to this issue, risk prevention has become an important tool in land use planning and management. Given the sensitivity and importance of this issue, the preparation of zoning maps of landslide sensitivity is very important and should be considered. These maps show landslide-prone areas and safe areas. Effective use of the landslide map can reduce the potential damage to the event and thus avoid many hazards. Landslides are natural events, but they can become dangerous and cause casualties and damage to man-made and natural structures.Materials and MethodsThe study area is the Neka Watershed located in the east of Mazandaran Province. To study landslides in this study, from nine maps including land use, slope, geology, slope direction, land curvature, distance from faults, communication routes, sewage from the river, and rainfall were selected and each map was extracted to produce the final landslide map. Finally, the maps were finalized in GIS software and the final map was prepared using the fuzzy logic method. In the fuzzy model, each of the pixels in the map is given a value between zero and one. To perform fuzzy in ArcGIS software the fuzzy Membership tool was applied. In this research, the Shannon entropy method was also used. Finally, the landslide map was extracted and evaluated. According to the final map and its studies, landslide-sensitive areas were identified.Results and DiscussionVarious factors, in relation to each other and in relation to local characteristics, cause domain instability. Instability factors with different contributions to the occurrence of mass movements, especially in the occurrence of landslides. To study landslides in this study, from 9 maps including land use, slope, geology, slope direction, land curvature, distance from the fault, communication paths (sewage from the road), sewage from the river and rainfall, and the map of each separately The final landslide was extracted to prepare the map. The maps were prepared in the final GIS software and the final map was prepared using the fuzzy logic method. The fuzzy model was performed using Arc GIS software and each of the pixels in the map was given a value between zero and one. The Shannon entropy method was used in this study. Finally, the landslide map was extracted and evaluated. According to the final map and its studies, landslide-sensitive areas were identified.ConclusionBased on the knowledge-based approach, the evaluation of several parameters such as geology, slope, land cover, slope direction, land curvature, rainfall, distance to flow, distance to road, and distance to fault were overlayed and the landslide map was extracted. A numerical scale (1-5) from very high to very low impact was used. Areas with high and very high sensitivity have been recorded in areas without vegetation and with high slopes and high rainfall. Sub-watersheds N2 and N1 are ranked 1st and 2nd, respectively, in terms of high landslide potential. The reason for the high intensity of landslides in these two sub-watersheds is low vegetation and a high slope.

Landslides are one of the major types of slope instability which cause enormous financial losses and casualties; therefore, it is imperative to consider them in the study of geomorphology, erosion and sedimentation in important watershed. The aim of the present study was to investigate and rank the landslide susceptibility using a random forest method in the Sadat Mahalleh Watershed, Sari. Elevation from the sea level, geology, land use, slope (degree, direction, shape and length), distance from linear elements such as faults, streams and roads, normalized difference vegetation index and slope form were considered as effective parameter in the landslide occurrence. Zoning landslide sensitivity was achieved by coding in the R and GIS10.3 soft wares. In order to determine the weight of each of the factors and classes effective in zoning the landslide sensitivity and validation of landslide potential prediction maps, the abundance ratio method and the receiver operating characteristics (ROC) were used. The results indicated that among the effective factors, vegetative cover and land use, had the most paramount impact on the landslide occurrence in the study area. A landslide sensitivity map was prepared using the random forest method by dividing into five class, namely: very low (3.85%), low (5.38%), moderate (23.08%), high (50%) and very high (7.69%) sensitivities. Validation of the results of the landslide sensitivity zoning maps, using the relative factor characteristics index diagram, indicated that the area under the curve (AUC) for random forest method was 0.709 and its standard error was 0.10. It is reasonable to claim that the forest method provided an acceptable accuracy for mapping the landslide sensitivity. A landslide sensitivity map provides prone complete and accurate information for natural resource managers to detect the landslide areas.

Evidence suggests that mortality attributed to noncommunicable diseases (NCDs) will increase from 38 million in 2012 to 52 million by 2030. The recent epidemiological data show that these diseases are increasing in low- and middle-income countries so that about 80% of all deaths of NCDs occurred in low- and middle-income countries. It has been estimated that an insufficient level of physical activity leads to a large share of the burden of these diseases. Evidence suggests that the rate of insufficient levels of physical activity in low- and middle-income countries has increased over the past 15 years. The authorities and policymakers must be advocated with consistent evidence from low- and middle-income countries on productivity loss and increased healthcare costs due to the absence or insufficient levels of physical activity. It is also necessary to include physical activity across all policies to prevent possible escalation of the economic burden related to physical inactivity in the near future.