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Railroad is one of the primary modes to transport hazardous materials (hazmat) in North America. For instance, Canadian railroads carried around 50 million tons of hazmat in 2018. Given the inherent danger of trains carrying hazmat, this study aimed at exploring a novel way towards mitigation of the associated risk. This study sought to investigate whether proper rail track infrastructure investment can mitigate the risk from hazmat shipments. To this end, a methodology was developed and then applied to the Canadian railroad network. The proposed three-step methodology captured the differing perspectives of rail carriers and regulatory agencies, and entailed (1) ascertaining the risk-level of various yards and links in the given railroad network, (2) specifying potential candidates for infrastructure investment, and (3) finding the optimum set of investment decisions. The proposed methodology was then applied to the Canadian railroad network to demonstrate that significant risk-reduction can be achieved by adding alternative rail-links around the riskiest locations (i.e. the network hot-spots), and also to show that risk-reduction function is non-linear with non-monotonous behavior. The study showed the possibility of significant hazmat risk reduction through alternative rail-links that could take traffic away from the network hot-spots. The methodology and the results from the Canadian case can be used by railroad companies and policy makers to estimate the value of potentially risk-reducing infrastructure investments.

Suspended sediment load is an indicator of erosion in watersheds. Therefore, accurately estimating the daily suspended sediment load (DSSL) is an important issue in river engineering. In this research, Artificial Neural Networks (ANN), Genetic Expression Programming (GEP) intelligent models, and the traditional Sediment rating curve (SRC) model were used to estimate DSSL in the Kasilian and Rood Zard watersheds in Iran. The input data to the models included instantaneous flow discharge (Q), average daily flow discharge (Qi), average daily flow discharge with a delay of three days (Qi-1,Qi-2,Qi-3), average daily precipitation (Pi), and average daily precipitation with a delay of three days (Pi-1,Pi-2,Pi-3); the output data was DSSL. In this research, the selforganizing map (SOM) artificial neural network was used for data clustering, and gamma test (GT) methods were used to obtain the best combination of input variables to intelligent models. The results showed that the best models for estimating DSSL in the Kasilian and Rood Zard watersheds were respectively the ANN model with an activation function of tangent sigmoid with the best combination of input variables (Qi-1,Qi-2,Qi-3,Pi,Pi-1,Pi-2,Pi-3) and the GEP model with the input variables Qi,Qi-1,Qi-2,Pi,Pi-1,Pi-2,Pi-3. The statistical values of the ANN-tangent sigmoid model for the Kasilian watershed were MAE=231.4 (ton day-1), RMSE=578.6 (ton day-1), NSE =0.98, and R²=0.98; these values for the GEP model in the Rood Zard watershed were MAE=475.7 (ton day-1), RMSE=1671.9 (ton day-1), NSE=0.99, and R²=0.99. The SRC model in the Kasilian watershed with R²=0.34 and NSE=0.08 and the Rood Zard watershed with R²=0.59 and NSE=-0.11 showed the low accuracy of this model in estimating DSSL. [ABSTRACT FROM AUTHOR]

The changing and intensifying landscape of global, national, and local disaster risks, driven by socio-political, environmental, and technological shifts, underscores the critical need for risk assessment by international agencies and governments. The Risk Matrix, introduced in 1995, has been widely used for risk assessment in different contexts, lauded for its simplicity and effectiveness. This model relies on the core risk components of consequence and likelihood, making it a favored tool for risk managers. To enhance the precision of risk assessment, various adaptations and extensions of the risk matrix have emerged; while some indirectly address resilience aspects, none explicitly integrate resilience into the matrix. This paper explores the risk matrix and its extensions, advocating for the inclusion of resilience in risk assessment. It introduces an empirical approach to quantify resilience, through a survey targeting small and medium-sized businesses in Southern Ontario, Canada. By developing two types of risk matrices--one with resilience considerations and one without--our work demonstrates how resilience alters risk prioritization, highlighting the importance of preparedness. This research underscores the pivotal role of resilience in risk assessment and urges its explicit integration into risk matrices to enhance accuracy and efficacy. Through practical examples and empirical data, the paper builds a compelling case for the central role of resilience in modern risk assessment practices. [ABSTRACT FROM AUTHOR]

Introduction: Over consumption of salt adversely affects health and is associated with some diseases. Salt over consumption has been reported to be higher in Asian countries including Iran. This research aimed to identify the facilitative factors of reduced salt consumption among 20–65 year-old women to develop effective educational interventions. Methods: This research used qualitative content analysis. We included 42 (31 women aged 20–65 years residing in Hamidia, Yazd and 11 healthcare providers) using Purposeful sampling method. Data were collected through focal group discussions (three groups of 6) and in-depth semi-structured individual interviews with 24 Participants. Data analysis was done using Graneheim and Lundman's approach. Results: Motivators and facilitators were extracted. The former consisted of sub-categories: physical fitness and healthcare, obtaining others' approval, family cooperation and support, internal motivators, contextual motivators and healthcare staff support. Sub-categories of the latter were gaining more information from different instructions, willingness to acquire self-protection skills and nutritional advice. These factors affected to reduce the salt intake among women. Conclusion: In the light of the qualitative results of motivating and facilitating factors, it is essential to enhance internal motivators and increase access to information via different communicative channels in community including schools, medical care providers and public associations. It is also necessary to pave the way for more family and medical staff support, provide appropriate educational and advertising programs, raise women's awareness and change their attitude and behavior with this concern. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is believed to be an impressive factor in hydrological process, agricultural productivity, and monitoring dangerous outcomes of climate changes. The present study aimed to monitor and recognize the pattern of spatial and temporal variation of SMAP soil moisture in five subcatchments of Simineh-Zarrineh catchment in northwest of Iran from 2015 to 2017. Precipitation data of 35 meteorological stations and 287 soil moisture points were derived from the SMAP and used to monitor SM variations in the time scale. The results indicated that the SM variations are subject to precipitation variations throughout the monthly scale in the catchment. In all the seasons of this period, SM was found to have a decreasing trend from north to south of the catchment. On the contrary, no such constant pattern was observed from east to west. Oscillation SM patterns in this period were completely coordinated with the precipitation pattern. The determination coefficient between monthly SMAP soil moisture and precipitation for each subcatchment was 0.9, 0.83, 0.7, 0.84, and 0.71 for Bokan, Saqqez, Takab, Saeinqaleh, and Miandoab subcatchments, respectively. Spatial variability of standard deviation for SM values was used to find the amount of deviation from the average value during dry and wet seasons. The results revealed that in the seasonal scale, northwestern (0.067 to 0.069 cm³.cm³) and eastern parts (0.057 to 0.061 cm³.cm³) of the study area had higher values of the SM standard deviations in autumn. Additionally, according to the findings, a high value of standard deviation was observed in autumn because of irregular precipitation events and fluctuation of the temperature. [ABSTRACT FROM AUTHOR]

Background and Objective: Metabolic syndrome, especially prediabetes, is one of the most common health problems due to incomplete glucose metabolism that has a direct relationship with lifestyle. This study was conducted to determine the factors that enable and nurture physical activity in women with prediabetes based on the PEN-3 model. Material and Method: This descriptive-analytical study was conducted on 41 prediabetic women aged 30–65 years and 9 service providers in health centers. Data were collected from a semi-structured individual interview based on the PEN-3 model. The Graneheim and Lundman method was used to analyze the data. Results: During data analysis, two main themes that include enabling factors and nurturing factors in the domain of physical activity and 6 classes including enablers (positive, negative, and existential) and nurturers (positive, negative, and existential) were extracted from the data content. Conclusion: By determining enablers and nurturers, the providers of services can facilitate the participation of prediabetic women in physical activity by applying positive social and structural effects and also to eliminate negative environmental conditions. [ABSTRACT FROM AUTHOR]