Rural women in developing countries often face significant barriers to employment, income generation, and access to productive resources. In Iran, medicinal plants (MPs) cultivation offers a sustainable pathway toward economic empowerment and livelihood resilience. This study develops and prioritizes strategies to enhance the livelihood sustainability of rural women through MPs cultivation using an integrated SWOT (Strengths - Weaknesses - Opportunities - Threats)-Fuzzy AHP (Analytic Hierarchy Process)-TOWS (Threats- Opportunities - Weaknesses- Strengths) model. Data collected from experts in agricultural and rural development sectors were analyzed to evaluate internal and external strategic factors. The strategic space analysis revealed that internal strengths (0.473) outweighed weaknesses (0.128), while external opportunities (0.325) surpassed threats (0.092), indicating that the favorable strategic space (O + S = 0.798) dominated the risky space (T + W = 0.220). Twelve strategies were formulated and prioritized, among which two emerged as most critical: (1) economic empowerment of rural women through home-based MPs processing enterprises and (2) promotion of greenhouse-based cultivation as a sustainable alternative to wild harvesting. The findings highlight the importance of leveraging indigenous knowledge, improving branding and packaging, and strengthening institutional support to achieve sustainable rural livelihoods. The proposed hybrid framework provides a replicable analytical tool for policymakers to design context-specific interventions linking women's empowerment, biodiversity conservation, and rural economic sustainability.

Natural hazards have led to livelihood insecurity, poverty, and infrastructure destruction in arid regions. It is essential to enhance the farmers' resilience to current and future hazards. In this respect, understanding adaptive strategies that increase the farmers' resilience is imperative. The present qualitative research was conducted to identify adaptive strategies for farmers of the Sistan Plain (Iran) to enhance their resilience to hazards. Data were collected through in-depth and semi-structured interviews with experts, observation, and field notes. The results revealed drought, dust storms, and floods as the major hazards farmers face in this area. The adaptive strategies to drought could be divided into technical-agricultural management, water resources management, livelihood, and legal-infrastructure factors. In addition, strategies for enhancing farmers' resilience to floods included infrastructure, technical, and livelihood management strategies. Also, the findings indicated that dealing with the dust storms requires applying technical, economic, legal, and infrastructure strategies. Risk management, good water governance and water diplomacy, accelerating economic growth, developing non-farm and small-scale enterprises, building capacity and empowering rural families, and strengthening infrastructures can enhance resilience to natural hazards in Sistan Plain.

The purpose of this research is to identify and prioritize the challenges of sustainable food security in Iran. The most important problem of the research is the lack of in-depth identification of sustainable food security challenges and their prioritization in Iran. The method of this research is qualitative method. This research has been conducted from June 2022 to April 2023 to provide a comprehensive, and applied model to understand the challenges through Grounded Theory (GT) and Analytic Hierarchy Process (AHP) in Khouzestan province, Iran. The data were collected by performing focus group and brain storming session with the 35 agricultural experts. Data analysis was done using MAXQDA12 software in three steps: open, axial and selective coding. The key results of this study by GT identified 34 initial codes and seven main challenges were categorized and prioritized using AHP technique were: low efficiency and weakness in developing appropriate consumption methods, lack of facility support and empowerment for the development of agricultural products, failure to monitor production diversity and proper cultivation pattern, little attention to the criteria of sustainable agricultural development in production, lack of attention to the development of transformation and complementary industries and the production of healthy products with a knowledge-based approach, unfavorable economic conditions for access and continued consumption of healthy food and lack of necessary export platforms and lack of monitoring of illegal imports. Finally, suitable recommendations were presented to overcome the existing challenges.

The purpose of the research was to designing an agricultural extension education model to support household food security in Khouzestan province, Iran. To achieve the purpose of the research, a mixed quantitative and qualitative research method was used. The statistical sample in the qualitative stage, which determined by theoretical saturation of the data, was 30 key informants and agricultural extension experts of Khuzestan province. The statistical sample of the quantitative part was determined based on Cochran’s formula of 110 extension experts. Grounded theory method was used for data analysis in the qualitative stage and structural equation model was used in the quantitative stage. Based on the ground theory method, causal conditions, axial phenomenon, intervening conditions, contextual conditions, strategies and consequences, were identified, and a paradigm model was developed. Then, based on the structural equation model method, the SEM model was formulated and validated. Based on the results, 69

The purpose of this research was to analyze the effective factors of sustainable agricultural development in Khuzestan Province, Iran, through qualitative method. To achieve the objectives of the research, semi-structured interviews and brainstorming techniques were used to collect data, and triangulation was used to evaluate the validity of qualitative findings. In order to implement the qualitative method, three types of open, axial, and selective coding were used. MAXqda12 software was used to analyze the collected data. Based on the qualitative analysis, 127 initial codes with 1,785 repetitions with 42 sub-categories in 4 main categories were identified: Strength, Weakness, Threats and Opportunities. By strengthening strengths and taking advantage of opportunities, we can reduce weaknesses and get rid of threats. Using the obtained results by planners will pave the way for sustainable agricultural development in Khuzestan Province, Iran.

Droughts, floods, and dust storms have increased the vulnerability of farm families in developing countries. Well-timed and effective interventions could reduce vulnerability to natural hazards. However, the adoption of adaptive strategies is significantly influenced by the farmers' viewpoints about the causes and effects of natural hazards and their perceived capacity to deal with these crises. This study employed the Q methodology to investigate farmers' viewpoints on vulnerability and adaptation to natural hazards in the Sistan Plain, Iran. The Q-sort procedure was conducted with the participation of 29 farmers, and the qmethod package in R software was used to analyze the Q-factor. The results revealed four types of perceptions regarding vulnerability and adaptation to natural hazards: passive-oriented, pragmatic activists, consequentialists, and change-averse. Different viewpoints demonstrate that farmers have divergent opinions regarding vulnerability and adaptation to natural hazards. Identifying these viewpoints can provide useful information for policymaking and the scaling up of specific adaptive strategies for each group, which can eventually enhance farmers’ resilience to natural disasters.

Brands are among the most valuable assets of agricultural businesses. Geographical branding can play a fundamental role in national and international markets by creating a competitive identity. On the other hand, orchard owners in a certain geographical region can understand the status of a product’s supply chain. Nonetheless, few studies have focused on how branding can influence the status of a product’s supply chain. Thus, the present study aimed to analyze the effect of geographical branding on improving the apple supply chain. The research is an applied study in terms of the goal, conducted by the survey methodology. Data were collected by distributing 360 questionnaires among apple orchard owners in Damavand County sampled by simple randomization. Cochran’s formula estimated the sample size. The research instrument was a research-made questionnaire. Data were analyzed by structural equation modeling. According to the results, special brand value, brand loyalty, brand image, brand attitude, brand experience, brand purchasing intention, and brand identity were the components found to improve the efficiency of the Apple supply chain significantly.

Dust storms are among the major environmental problems. Most governments have failed to manage this recurring phenomenon. The present study investigated the farmers’ resilience to dust storms and the factors reducing their vulnerability to these catastrophic events. To this end, a mixed-methods research approach, including grounded theory and survey research, was performed. Qualitative findings were analyzed using Atlas. ti 9 software and the factors affecting farmers’ resilience and vulnerability to dust were identified through the application of SmartPLS3 software. Qualitative findings disclosed that agricultural education and extension services, financial supports, and health care, political, and legal supports can enhance the resilience of farmers to dust storms. However, among the five dimensions of resilience to dust storms, including access to basic services, adaptive capacity, assets, social safety nets, and stability, assets and access to basic services were among the major indicators of resilience. Additionally, health care, political, and legal supports, agricultural education and extension services, and financial supports were the main determinants of resilience to dust storms. Furthermore, quantitative analysis revealed low, medium, and high vulnerabilities to dust storms. Assets and access to basic services were the major drivers of farmers’ vulnerability to dust storms. Providing institutional, educational, financial, and health care supports can enhance farmers’ resilience to dust storms. Also, poverty alleviation and capacity-building policies can empower vulnerable groups and reduce their reliance on climate-sensitive income resources.

The purpose of this research is identifying and prioritize the challenges of sustainable food security in Iran. This research has been conducted from June 2022 to April 2023 to provide a comprehensive, and applied model to understand the challenges through Grounded Theory (GT) and Analytic Hierarchy Process (AHP) in Khouzestan province, Iran. This research is an applied type of research. The data were collected by performing focus group and brain storming techniques with the 35 agricultural experts in this field. Data analysis was done by using MAXQDA12 software in three steps: open, axial and selective coding. The results of this study by GT identified 34 initial codes and seven main following challenges were categorized and by AHP prioritized: low production efficiency and lack of attention to extension of the favorable consumption pattern, lack of facility support and empowerment for the development of agricultural products, failure to monitor production diversity and proper cultivation pattern, little attention to the criteria of sustainable agricultural development in production, lack of attention to the development of transformation and complementary industries and the production of healthy products with a knowledge-based approach, unfavorable economic conditions for access and continued consumption of healthy food and lack of necessary export platforms and lack of monitoring of illegal imports. Finally applied recommendations were presented to reduce the existing challenges.

The research aimed to identify the challenges of developing and implementing a climate-smart agriculture (CSA)-based curriculum in Iran’s agricultural vocational schools. It was exploratory descriptive-analytical in nature and applied in goal, in which data were collected with the library and deep interview method. The research methodology was based on grounded theory. The statistical population was composed of 16 researchers, authors, managers, and experts of the Office of Textbook Compilation of the Organization for Educational Research and Planning and the Research Center of Educational Studies. The participants were selected by the homogenous purposive sampling method. The interview with the target population continued until it reached theoretical saturation. Data were analyzed using the content analysis method. The data collected in the interviews were subjected to open, axial, and selective coding, which resulted in deriving 119 concepts and 28 categories. The results revealed a seven-dimension structure composed of the challenges related to determining educational goals, trainees, trainers’ professional process, teaching methods, curriculum content selection and organization, curriculum implementation, and curriculum appraisal. The results can help the experts in the Office of Textbook Compilation experts adopt smarter policies and solutions to solve the challenges of developing and implementing a CSA-based curriculum in agricultural vocational schools in Iran.

This research aimed to explore the empowerment of trainees of agricultural schools for the development of their professional performance. It was a survey study. The statistical population was composed of all trainees studying in agricultural schools in Iran in the 2020-2021 educational year, amounting to 1,119 students, out of whom 169 trainees were sampled by simple randomization. Due to the COVID-19 pandemic and the closure of the schools, the questionnaires were sent and received by e-mail from the provinces of Tehran, Khuzestan, Fars, Qazvin, Mazandaran, and Semnan. Data were analyzed by the structural equation method using Smart PLS3. Based on the results, the educational content, educational process, management process, technical trainer development, and supply of space, equipment, and technology in agricultural schools have positive and significant effects on the empowerment of trainees in these schools. The standardized path coefficients revealed that the educational content directly accounted for 67.2% of the variance in the trainees' empowerment. Also, 39.9, 31.1, 30.2, and 29.8% of the variance in the Iranian trainee's empowerment were captured by the educational process, management process, technical trainer development, and the supply of space, equipment, and technology, respectively.

This research aimed to ascertain the prerequisites for the advancement of the slow food movement in Iran. Employing both quantitative and qualitative methods, it adopted a descriptive and survey-oriented design. Semi-structured interviews were conducted with 15 experts well-versed in the extension of slow food, employing a snowball sampling technique. The interview data underwent coding and analysis employing open coding, axial coding, and selective coding methods. The study encompassed experts and managers in agricultural extension and education across the nation. For statistical analysis, a structural equation model and confirmatory factor analysis were employed, utilizing SMART PLS 3 and SPSS 26 software. The goodness-of-fit index (GoF) was utilized to evaluate the comprehensive validity of the research model. From a qualitative perspective, six primary facets of the slow food model emerged: 1. Extension strategies in harmony with slow food principles; 2. Methods of extending the slow food movement; 3. Supportive policies for slow food propagation; 4. Intervening conditions; 5. Causal conditions (triggers and applications) of the slow food paradigm; and 6. Outcomes resulting from the adoption of the slow food ethos. These facets collectively comprised a total of 38 sub-components. Through analysis of the structural equation model, key facets with substantial operational weight and significant influence on the promotion of slow food were identified. These prominent components encompass disease prevention, the organization of festivals and exhibitions, the revision of laws, the shaping of individuals’ lifestyles, the enhancement of food tourism capacity, and the optimization of human resources.

Non-farm activities are a means of livelihood stabilization and are regarded as a sustainable approach to bringing balance to the economic, social, cultural, and environmental dimensions of sustainable livelihood. The main purpose of this study was to develop strategies for stabilizing the livelihood of smallholder farmers through non-farm activities using a combined SWOT-AHP-TOWS model. The results of analyzing the strategic space for developing strategies for stabilizing the livelihood of smallholders through non-farm activities revealed that the strengths (0.391) were more than the weaknesses (0.276) in the internal space and that the opportunities (0.195) were more than the threats (0.138) in the external space. Also, it was found that the internal challenges (S + W = 0.667) were more important than the external challenges (O + T = 0.33) in developing livelihood stabilization strategies. Further, the results showed that the beneficial space (O + S = 0.586) dominated the risky space (T + W = 0.414). Eventually, 20 strategies were developed among which the most important ones were "establishing and developing greenhouse cultivation based on the crop patterns considering the relative advantages of the villages" and "establishing microcredit foundations and funds to support the youth in getting involved in rural non-farm businesses." In general, the results can provide new insights into the stabilization of the livelihood of smallholders through non-farm activities.

The growing use of wireless technologies in power systems has raised concerns about cybersecurity, particularly regarding GPS spoofing attacks (GSAs). These attacks manipulate GPS data, leading to modifications in the phase angle of phasor measurement units (PMUs). In this paper, a Deep-learning GPS-Spoofing Counteraction (DLGSC) algorithm is proposed, utilizing PMU data for GSA detection and PMU data correction. The algorithm incorporates a recurrent neural network (RNN) and a set of long short-term memory (LSTM) units separately, for signal correction after attack detection. Unlike existing methods that struggle with simultaneous attacks or they are static methods, DLGSC tackles these challenges by leveraging deep learning techniques. By selecting appropriate features for GSA detection, DLGSC achieves accurate results. The algorithm is evaluated on standard IEEE 14-bus and IEEE 39-bus power systems, and its performance is compared to statistical, dynamic, and Deep Learning (DL) methods in the literature. Additionally, an experimental setup is designed to validate the algorithm in a laboratory environment. Results demonstrate the easy-implementable DLGSC algorithm's satisfactory real-time performance in various scenarios, such as load variations and noise, achieving over 98% accuracy. Notably, DLGSC is cable of detecting multiple GSAs on different PMUs.

The GPS is vulnerable to GPS spoofing attack (GSA), which leads to disorder in time and position results of the GPS receiver. In power grids, phasor measurement units (PMUs) use GPS to build time-tagged measurements, so they are susceptible to this attack. As a result of this attack, sampling time and phase angle of the PMU measurements change. In this paper, a neural network GPS spoofing detection (NNGSD) with employing PMU data from the dynamic power system is presented to detect GSAs. Numerical results in different conditions show the real-time performance of the proposed detection method.

Wheeled Mobile Robots (WMR) are often used to move and work in outdoor environments, often unstructured. The robots' operation in these environments was disrupted due to the ground's unevenness, and slippage occurs. Slipping compared to moving wheel robots at normal levels causes large accumulated errors in the robot's position. Therefore, diagnosing the amount of wheel slip is necessary to improve the control of robot movement. This paper presents an LPV controller with an LPV observer that allows the robot to track the path if there is uncertainty due to the kinematic model's linearization and the occurrence of an unknown longitudinal slip. To evaluate the performance of the proposed method, the controller and observer are designed using the linear matrix inequality (LMI) approach and are simulated in MATLAB software, and superior tracking results are presented.

Pressure ulcers are a serious problem that affects more than one million patients worldwide each year. These ulcers often occur when patients have limited mobility and cannot change positions in bed on their own. Although there are guidelines for the care and transportation of high-risk patients and products for the care of these patients have been developed and marketed, the rate of pressure ulcers continues to rise. This paper presents a prototype control pressure ulcer platform. This system controls the standard threshold pressure level by receiving feedback from the patient and bed pressure status. The proposed system has been designed and implemented. Also, mechanical and electronic structures are modeled and, a suitable controller is designed for optimal performance. The performance of the system has been proven by simulations performed.

In nonlinear dynamical systems such as complex and high-order systems, it is difficult to analytically compute the Lyapunov function considering the nonlinear dynamics. Hence, in such cases, numerical methods offer viable alternatives. This paper presents a new approach to construct appropriate Lyapunov functions based on evolutionary theory and biological mathematics, using the multi-objective genetic algorithm (MO-GA), which is a meta-heuristic algorithm for nonlinear dynamical systems. The use of genetic algorithm allows for direct generation of initial data from the Lyapunov theory, leading to the selection and construction of suitable candidates in the shortest time possible. The proposed method has been implemented and validated through several examples.

ABSTRACT Background Trauma is the third leading cause of death in the world and the first cause of death among people younger than 44 years. In traumatic patients, especially those who are injured early in the day, arterial blood gas (ABG) is considered a golden standard because it can provide physicians with important information such as detecting the extent of internal injury, especially in the lung. However, measuring these gases by laboratory methods is a time-consuming task in addition to the difficulty of sampling the patient. The equipment needed to measure these gases is also expensive, which is why most hospitals do not have this equipment. Therefore, estimating these gases without clinical trials can save the lives of traumatic patients and accelerate their recovery. Methods In this study, a method based on artificial neural networks for the aim of estimation and prediction of arterial blood gas is presented by collecting information about 2280 traumatic patients. In the proposed method, by training a feed-forward backpropagation neural network (FBPNN), the neural network can only predict the amount of these gases from the patient’s initial information. The proposed method has been implemented in MATLAB software, and the collected data have tested its accuracy, and its results are presented. Results The results show 87.92% accuracy in predicting arterial blood gas. The predicted arterial blood gases included PH, PCO2, and HCO3, which reported accuracy of 99.06%, 80.27%, and 84.43%, respectively. Therefore, the proposed method has relatively good accuracy in predicting arterial blood gas. Conclusions Given that this is the first study to predict arterial blood gas using initial patient information(systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate (PR), respiratory rate (RR), and age), and based on the results, the proposed method could be a useful tool in assisting hospital and laboratory specialists, to be used.

Context: An effort to predict the final condition of patients is one of the purposes of many studies; since it enables the treatment system to provide the necessary facilities in the best possible time and prevent wasting time and energy as well as increasing patient mortality. Research purpose: This study was purposed to investigate the correlation between arterial blood gas (ABG) and patient mortality and design a system to predict the final patients' condition. Method: In this study, a method has been proposed to identify dynamic systems to estimate the final condition of trauma patients and predict their death or survival probability during treatment or being confined in the medical center. The proposed method by using the information of patients' arterial blood gases identifies a linear model indicating the correlation between these gases and the patients' final condition. This method is based on system identification using ARX model simulated in MATLAB and its results are presented. Results: Data of 2802 patients (365 deaths and 2437 survivors) with an average age of 37.87 years old and GCS average of 9.27 including 470 female and 2332 male patients were studied. The designed structure was tested with 62.57% accuracy to be able to predict patient mortality. Therefore, it can be stated that the proposed method has a good accuracy in predicting the final patients' condition based on dynamic analysis. Discussion and Conclusion: It is unavoidable mortality due to accidents and severe injuries. Also, it is important to predict the death probability based on data from the early hours of the onset of trauma in patients; since it takes time to collect the data of patient's condition. Therefore, it is very important to find reliable methods to measure the patients' condition and predict the mortality. The study of these methods has always been considered by physicians due to its high importance. This study has almost been able to meet physicians' need by providing a method based on the study of dynamics and dynamic relationships discussing arterial and mortal blood gases.