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The iolinid mite Coccalicus clavatus Willmann, 1952 was collected during a survey from the soil of Gilanegharb, Kermanshah, Iran in 2022. This species was first described in the family Alycidae by Willmann in 1952 and then André and Uusitalo transferred it to the family Iolinidae in 2006, accompanied by transferring species from the genus Paratydaeolus to the genus Coccalicus. Some challenges about species characters of this mite are discussed in this paper and the synonymy of C. lukoschusi and C. clavatus is proposed. The genus consists of 12 species with clavate trichobothria, and is related to various habitats like soil, stored products, under barks, on leaves of different plants, and on birds. They have been reported from Europe, Antarctica and North America and this is the first record of the Coccalicus mites from Asia. [ABSTRACT FROM AUTHOR]

Background: A suicide attempt is a major societal problem because it imposes high costs on societies worldwide. This paper analyses the spatiotemporal clustering of suicide attempt in Kermanshah, Iran from 2006-14. Methods: This study draws on 18,333 individuals (7,234 males and 11,097 females) who attempted suicide across the Kermanshah province. Data was collected from the records of individuals registered in hospitals across the Kermanshah province between 2006 and 2014. Mean Center, Standard Deviational Ellipse (SDE), Moran's I and Kernel Density Estimation (KDE) in Arc/GIS10.6 software were used for the analysis of the spatial distribution of suicide attempt, while the chi-squared test in SPSS was used to examine the different demographic variables between groups within/outside spatial clusters of suicide. Results: The results show that a total of 18,331 suicide attempts (39.46% male and 60.53% female) were reported between 2006 and 2014 in the Kermanshah province. The spatial pattern of suicide attempts was clustered in 16 clusters (6 high clusters and 10 low clusters) and statistically significant differences were found within and outside the hotspots of suicide attempts. Most hot spots were formed in and around cities. Younger people were at a greater risk. The rate of suicide attempts reduced in illiterate people and increased in people with university degrees. Unmarried people were associated with a higher risk of suicide attempt than was married status for both males and females. Conclusion: The results of this study could help public health practitioners and policymakers in Iran prioritize resources and target efforts for suicide attempt prevention. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a severe ongoing health crisisworldwide. Studying the socio-economic impacts of COVID-19 can help policymakers develop successful pandemic management plans. This paper focuses on the spatial epidemiology of COVID-19 among different social classes in the Kermanshah metropolis, Iran. This cross-sectional study uses the data of people infected with COVID-19 in the Kermanshah metropolis in 2020, acquired from the official COVID-19 Registry of Kermanshah. The results show that 2013 people were infected with COVID-19 (male = 1164 and female = 849). The mean age of the patients was 45 ± 18.69. The Moran's I show that COVID-19 in different social classes was clustered across the neighbourhoods in the Kermanshah metropolis. The mean ages of men and women were 44.51 ± 18.62 and 45.69 ± 18.76, respectively. Importantly, COVID-19 was highly prevalent in the middle-class groups. Age group comparisons indicate that older people were the most infected in poorer neighbourhoods. In the middle-classtheage group of 0–14 years and in the rich neighbourhoods the age group of 15–64 years were the most exposed to the disease. The findings of this study suggest that older people and lower socioeconomic classes should be prioritised while developing and implementing preventative programs for COVID-19 and similar pandemics. [ABSTRACT FROM AUTHOR]

Locations prone to landslides must be identified and mapped to prevent landslide-related damage and casualties. Machine learning approaches have proven effective for such tasks and have thus been widely applied. However, owing to the rapid development of data-driven approaches, deep learning methods that can exhibit enhanced prediction accuracies have not been fully evaluated. Several researchers have compared different methods without optimizing them, whereas others optimized a single method using different algorithms and compared them. In this study, the performances of different fully optimized methods for landslide susceptibility mapping within the landslide-prone Kermanshah province of Iran were compared. The models, i.e., convolutional neural networks (CNNs), deep neural networks (DNNs), and support vector machine (SVM) frameworks were developed using 14 conditioning factors and a landslide inventory containing 110 historical landslide points. The models were optimized to maximize the area under the receiver operating characteristic curve (AUC), while maintaining their stability. The results showed that the CNN (accuracy = 0.88, root mean square error (RMSE) = 0.37220, and AUC = 0.88) outperformed the DNN (accuracy = 0.79, RMSE = 0.40364, and AUC = 0.82) and SVM (accuracy = 0.80, RMSE = 0.42827, and AUC = 0.80) models using the same testing dataset. Moreover, the CNN model exhibiting the highest robustness among the three models, given its smallest AUC difference between the training and testing datasets. Notably, the dataset used in this study had a low spatial accuracy and limited sample points, and thus, the CNN approach can be considered useful for susceptibility assessment in other landslide-prone regions worldwide, particularly areas with poor data quality and quantity. The most important conditioning factors for all models were rainfall and the distances from roads and drainages. [ABSTRACT FROM AUTHOR]

This study aims at providing the spatial contours of frequency-content parameters of ground motion records obtained during the 2017 Mw 7.3 Ezgeleh–Kermanshah earthquake in Iran. For this purpose, three scalar frequency-content parameters named the mean period (T m) , the average spectral period (T ave) , and the smoothed spectral predominant period (T 0) are calculated. Sixty earthquake records with the closest site-to-source distances in the range of 5–330 km were used to make several high-resolution frequency-content maps for the study area. In addition, a new practical framework is presented to estimate soil condition maps based on a simplified ground motion records analyzing which could be used in rapid loss evaluation after earthquakes. As a new idea, the main features of the horizontal-to-vertical spectral ratio curves including amplification amplitude and peak frequency that can identify resonance effect are automatically considered in the proposed regional soil class map. The consistency between frequency-content maps with the anticipated soil condition map is discussed. Prepared maps showed that very long period range extends in the northwest–southeast direction, far from the epicenter. [ABSTRACT FROM AUTHOR]

One of the causes of premature deaths in different parts of the world and Iran during the Qajar era was the spread of infectious diseases in the 19th century. Meanwhile, in general, Iran did not have favorable health conditions. Kermanshah, as one of the main highways in the west of the country, the gateway to Mesopotamia and the most important pilgrimage route of Shiite Iran, has been one of the busiest travel, commercial and pilgrimage routes, and hence, was exposed to the spread of various infectious disease The purpose of this research is to investigate the prevalence of infectious diseases in Kermanshah during the Qajar era and its effects on the population change and death. The research method is historical and descriptive-analytical; documentary methods have been used to collect the information. The results and achievements of the research showed that the outbreak of infectious diseases, especially cholera, plague, and typhoid, occurred many times in Kermanshah and was one of the important factors in the significant decrease in the population of this state. The results of this research have shown that the ignorance, illiteracy, and superstition, the low level of medical knowledge, the inappropriate and insecure roads, the poor performance of the king and the local rulers in making appropriate decisions to prevent the spread of such diseases, had a significant impact on the extent of the casualties and the population decline. [ABSTRACT FROM AUTHOR]

Christ's thorn (Paliurus spina‐christi Mill.) tree is an important and valuable forestry species and has significant potential in afforestation and reforestation systems. In recent years, dieback symptoms were observed on Paliurus spina‐christi trees in Gilan‐e Gharb region of Kermanshah province, west Iran. During 2020 to 2021, Christ's thorn trees that exhibit branch dieback symptoms were sampled for fungal isolations. The pathogenicity tests, biochemical assay of growth, acid production on creatine sucrose agar (CREA) medium and morphological and molecular investigations (ITS and β‐tubulin regions) identified Paecilomyces formosus as a dieback causal agent. This is the first report of P. formosus pathogenicity on Paliurus spina‐christi trees. Furthermore, disease severity was assayed on 11 tree species under three different temperatures 15, 25 and 35°C. Disease severity caused by all isolates on detached branches was greater at 35°C than at 15 and 25°C. This study presents the host range of this pathogen and showed that these potential hosts are prone to this pathogen under high temperatures, which forest trees experienced in recent decades. [ABSTRACT FROM AUTHOR]

Rapid urban development, increasing impermeable surfaces, poor drainage system and changes in extreme precipitations are the most important factors that nowadays lead to increased urban flooding and it has become an urban problem. Urban flood mapping and its use in making an urban development plan can reduce flood damages and losses. Constantly producing urban flood hazard maps using models that rely on the availability of detailed hydraulic-hydrological data is a major challenge especially in developing countries. In this study, urban flood hazard map was produced with limited data using three machine learning models: Genetic Algorithm Rule-Set Production, Maximum Entropy (MaxEnt), Random Forest (RF) and Naïve Bayes for Kermanshah city, Iran. The flood hazard predicting factors used in modeling were: slope, land use, precipitation, distance to river, distance to channel, curve number (CN) and elevation. Flood inventory map was produced based on available reports and field surveys, that 117 flooded points and 163 non-flooded points were identified. Models performance was evaluated based on area under the receiver-operator characteristic curve (AUC-ROC), Kappa statistic and hits and miss analysis. The results show that RF model (AUC-ROC = 99.5%, Kappa = 98%, Accuracy = 90%, Success ratio = 99%, Threat score = 90% and Heidke skill score = 98%) performed better than other models. The results also showed that distance to canal, land use and CN have shown more contribution among others for modeling the flood and precipitation had the least effect among other factors. The findings show that machine learning methods can be a good alternative to distributed models to predict urban flood-prone areas where there are lack of detailed hydraulic and hydrological data. [ABSTRACT FROM AUTHOR]

Background: Examining various problems after disasters is important for the affected people. Managing humanitarian aid and donations among the affected people is considered as one of the most important problems after disasters. Therefore, the present study aimed to evaluate the challenges and barriers of humanitarian aid management in 2017 Kermanshah Earthquake.Methods: This study was conducted from November 2017 to January 2018, with qualitative case study design. The population included 21 people including 6 humanitarian aid manager, 6 volunteers, 4 aid workers, and 5 affected people. The data were collected through semi-structured interviews and purposeful sampling, which continued until saturating the data. The strategies recommended by Guba were used for evaluating the trustworthiness of the data. The data was analyzed with conventional content analysis method according to method suggested by Graneheim and Lundman.Results: Based on the results, two themes, nine categories, and 19 sub-categories were identified considering the challenges and barriers of humanitarian aid and donors' management during the 2017 Kermanshah Earthquake. The categories included education, command and coordination, communication and information, rules, security, traffic and overcrowding, assessment, providing system, and cultural setting. Also, two themes including managerial and structural barriers were extracted.Conclusion: Adopting an effective management and appropriate policies with respect to humanitarian aid and modifying structural and managerial barriers can improve the performance and management of humanitarian aid. [ABSTRACT FROM AUTHOR]

Levels of some essential and toxic heavy metals such as lead, cadmium, chromium, nickel, zinc and copper in cereals and agricultural products obtained from the markets in Kermanshah city, west Iran, were determined by inductively coupled plasma-optical emission spectrometry (ICP-OES). The average concentrations for lead and cadmium in some cereals were higher than the maximum levels set by the Codex Alimentarius. A potential human health risk assessment was conducted by calculating estimated weekly intake (EWI) of the metals from eating cereals and comparison of these values with provisional tolerable weekly intake (PTWI) values. In combination with recent cereal consumption data, the EWIs of heavy metals were calculated for the Kermanshah population. EWI data for the studied metals through cereal consumption were lower than the PTWI values. Cr, Ni, Zn and Cu levels in all samples analysed were within the ranges reported for similar cereals from various parts of the world. [ABSTRACT FROM PUBLISHER]