Designing a mathematical model of the biomass supply chain to build a power plant despite disruption

The importance of mass biology has increased due to pollution caused by biomass burial, the profitability of biomass energy, and the demand for energy in the supply chain network. The goal of this research is to design a model for the biomass supply chain network with an economic and ecological approach to reduce costs and carbon emissions. Research gaps have been addressed, which include determining desired and undesired process outputs, along with simultaneously examining material supply disruptions and final product demand. The mathematical model used is a mixed-integer linear programming model. The primary objective is to minimize costs, and the secondary objective is to minimize carbon emissions. To address this in a single-target function under uncertainty, the fuzzy TH mathematical model has been employed. Uncertainty and disruptions have been studied through scenario building. The model's validation includes a case study in Fars province, where the findings justify the construction of four power plants. The proposed model improved the accuracy of electricity production predictions by 2.1 percent. An analysis and sensitivity study was performed on the TH method's parameters and changes in customer demand values according to predictions. The results show that the proposed model performs well, achieving cost-effectiveness through the integration of economic and ecological approaches. It also successfully reduces greenhouse gas emissions, enhances energy security and stability, and demonstrates a positive impact. Introduction More than 70 thousand tons of biomass waste are produced in Iran daily. These waste products result in the generation of methane gas and carbon dioxide, leading to severe air pollution and climate changes in the country. Given that 14% of Iran's electricity production comes from hydropower, and the nation is grappling with drought, electricity generation has decreased, leading to government-imposed power cuts, particularly in industrial areas. To address the need for biomass resource investment in energy production, the main challenge is the absence of an optimization model for the biomass supply chain that encompasses all relevant factors. Hence, this research aims to design a flexible optimization model for the biomass supply chain, offering insights to investors on how to produce energy with reduced costs and lower carbon emissions. Key research gaps identified are as follows: 1-Simultaneously addressing uncertainty arising from disruptions in the first two levels of the supply chain, encompassing biomass supply from raw materials, and examining the fourth level - the customer level - by defining scenarios. 2- Innovatively considering capacity levels in the context of the biomass supply chain, a subject not widely explored before. 3- Focusing on the production of bioenergy in conjunction with by-products. 4- Deliberating on the definition of desired outputs at separation centers. 5- Highlighting the importance of considering undesired outputs at separation centers. 6- Proposing a stochastic-probabilistic-fuzzy planning approach to enhance flexibility, particularly in managing risks and operational disruptions. Research Method This network encounters two types of uncertainty, both of which cause disruptions. Consequently, four scenarios have been devised to address these disruptions: 1- The scenario involving reduced raw material supply due to drought's impact. 2- The scenario in which electricity demand decreases in response to specific conditions. 3- The scenario where both of the aforementioned scenarios occur simultaneously. 4- A scenario without any disturbances. As a result, a resilient model has been developed to manage disturbances while ensuring environmental sustainability. The proposed model is a mixed-integer linear programming mathematical model with two objective functions: cost minimization and carbon emission minimization. The model is solved using the exact solution method in conjunction with Gomes software. To address function targeting under uncertainty, the fuzzy TH mathematical model has been employed. The model's validation has been examined through a case study in Fars province. Findings Several findings have emerged from the study: The construction of four power plants is recommended, each to be located at one of the ten proposed sites, with each having a different capacity. The proposal includes the establishment of four biomass separation centers. Different types of biomass are utilized in the power plants in varying proportions. Biomass transportation involves three types of transporters with capacities of ten tons, fifteen tons, and twenty tons. The quantity of these transporters varies across different separation centers and power plants. Electricity is supplied to six different applicants. The quantity of fertilizer produced varies according to different scenarios and time periods. The sensitivity analysis reveals that increasing the coefficient of the first objective function results in a decrease in the values of the first objective function. Conversely, decreasing the coefficient of the second objective function simultaneously leads to an increase in the value of the second objective function. Conclusion The model designed for this purpose is a sustainable development model that encompasses two of the three sustainability aspects, namely, the reduction of greenhouse gas emissions and the minimization of economic costs. Therefore, it is a resilient model that employs a scenario-based approach to address various forms of uncertainty. In the case of this study, raw materials were procured from nine out of ten biomass supply centers, indicating resilience in terms of biomass supply. The model optimally allocates resources among the supply chain members to minimize greenhouse gas emissions while also considering cost-effectiveness. The inclusion of favorable and unfavorable outputs in the model impacts the annual electricity production of each power plant. Without these variables, the model would overestimate electricity production. Sensitivity analysis reveals the trade-off between objective functions, confirming the model's correct and logical performance. Therefore, the model's validity is established. It is recommended that, in further development of this model, specific travel times for trucks between locations be included in the model

Effectiveness of Cognitive Behavioral Therapy on Spiritual Well-Being and Emotional Intelligence of the Elderly Mourners

Objective: Grief is one of the most painful experiences of the humans after linking emotions. In the literature of trauma, grief and mourning can be seen on many topics. Intervention and treatment of grief seems necessary as the period of mourning is prolonged. Thus, this study aimed at understanding the effectiveness of cognitive behavioral therapy on spiritual well-being and emotional intelligence in the elderly bereavement. Method: This was an experimental study with pre-and posttest design, and control group. The population of this study was the elderly mourners in city of Ardabil in 15-2014. After conducting clinical interviews and diagnostic tests using the sampling method, 30 elderly mourners selected. Spiritual Well-Being questionnaire and Emotional Intelligence questionnaire were used for data collection. The questionnaire and pretest-posttest were used in this study. Data were analyzed using multivariate analysis of covariance. Results: The results of the data analysis revealed that cognitive behavioral therapy increased spiritual well-being and emotional intelligence of the mourners was not significantly different between the 2 groups (P0.05). Conclusion: Method of cognitive behavioral therapy helps confront the emotional drain and grief acceptance, increasing the spiritual well-being and emotional intelligence of the elderly bereavement.

Self‐care behavior prevention of COVID‐19 in the general population based on Pender Health Promotion Model: A cross‐sectional study

Abstract Background and Aims Coronavirus with its sudden and widespread outbreak has obviously imposed devastating consequences in various aspects of human life. The purpose of this study was to determine the predictive value of Pender's Health Promotion Model (HPM) structures in self‐care preventive behavior against coronavirus disease 2019 (COVID‐19) among the general population of Ardabil, Iran. Methods The present retrospective descriptive‐correlational study was conducted on citizens of Ardabil aged 18 years and over in 2021. After dividing the city of Ardabil into four parts, 50 people from each area of the city and a total of 200 people were selected through the available sampling method through social media. Data collection tools included a demographic profile, perceived self‐efficacy scale, perceived emotional questionnaire, perceived social support questionnaire, perceived benefits and barriers questionnaire, researcher‐made COVID‐19 self‐care questionnaire, and commitment to action questionnaire based on Pender's HPM structures in an online manner. Data were analyzed by Amos 22 software and using structural equation modeling. Results According to the results, direct path analysis to COVID‐19 self‐care behavior indicated that the variables of perceived self‐efficacy (β = 0.18, p < 0.01), interpersonal effects (β = 0.19, p < 0.01), positive emotion (β = 0.15, p < 0.05) and perceived benefits (β = 0.20, p < 0.01) are able to significantly predict self‐care behaviors. Moreover, the bootstrapping test results in the indirect path analysis demonstrated that the variables of perceived self‐efficacy (95% confidence interval [CI], 0.012, 0.066), perceived social support (95% CI, 0.002, 0.026), and perceived barriers (95% CI, −0.019, −0.002) and benefits (95% CI, 0.001, 0. 015) through the mediator variable of commitment to action are able to significantly predict COVID‐19 self‐care behavior. Conclusions Based on the findings of the present study, it can be claimed that the proposed model of COVID‐19 self‐care behavior has an acceptable fitness in the general population. This model can be used in developing educational programs and intervention techniques to modify people's attitudes and behaviors

Providing a health‐promotion behaviors model in elderly: Psychological capital, perceived social support, and attitudes toward death with mediating role of cognitive emotion regulation strategies

Abstract Background and Aims The aim of this study was to present a health‐promotion behaviors model in the elderly based on psychological capital, social support, and attitudes toward death mediated with mediating role of emotional cognitive regulation strategies in the elderly in Ardabil. Methods The research method was correlational which was done by the path analysis method. The statistical population of the study consisted of all elderly people in Ardabil in 2020, from which 250 people were selected by convenient sampling method and were investigated with research tools including Health‐Promotion Lifestyle Profile (1998), attitudes toward death profile (1994), Psychological Capitals (2007), social support (1988) and Cognitive Emotion Regulation (2001). Data were analyzed by Amos‐24 software and using structural equation modeling. Results The results showed that psychological capital, social support, and attitude towards death directly affect health‐promotion behaviors and also indirectly improve them through cognitive emotion regulation strategies. These results can have a significant impact on promoting health and improving the quality of life of the elderly population. Conclusions Based on the findings of the present study, it can be claimed that the proposed model for the health of the elderly has an acceptable fitness and this model can be used in developing educational programs and intervention techniques to improve the health of this group of people

The Relationship Between Workplace Spirituality, Job Satisfaction and Attitude Toward Death Among the Staff of the Emergency Department

Background: The present study was carried out aimed to examine the relationship between workplace spirituality and job satisfaction. It also aimed to determine the intermediary role of attitude toward death among emergency department staff in Shiraz city. Materials and Methods: In this descriptive and correlation study, the statistical population included 90 staff of the Shiraz Emergency department who were selected through simple random sampling in 2016-2017. Spirituality questionnaires in the workplace, life satisfaction and attitude towards death were used in this research, and the data were analyzed by SPSS and EMOS software. Results: The results of the data analysis indicated that workplace spirituality had a negative effect (&beta;=-0.32, P=0.001) on neutral acceptance and a positive effect (&beta;=0.21, P=0.03) on active acceptance and life satisfaction (&beta;=0.19, P=0.05). Furthermore, out of the attitudes toward death scales, only neutral acceptance could play a mediating role between workplace spirituality and life satisfaction (&beta;=-0.27, P=0.00). Model fitting indicators show that the model has a good fit (X2=0.86, GFA=0.97, CFI=0.99). Conclusion: The results show that workplace spirituality is compatible with life satisfaction and the mediating role of attitude toward death among the staff of the emergency department