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Abstract Nowadays, power system protection is increasingly important because of the growing number of customers and the pressing need for timely fault resolution and relay operations. This paper addresses the non‐linear nature of the objective function in the optimal coordination of directional overcurrent relays (DOCRs) by employing a quadratic Taylor series expansion around an operating point, converting the problem into a quadratically constrained quadratic programming problem, ensuring a global optimal solution with increased computational efficiency. Additionally, the quadratic constraints are converted into second‐order cone constraints for compatibility with the CPLEX solver. Using the least square method, the operating point values are determined and further fine‐tuned using iterations with the DOCR operation times. The IEEE 3‐bus, IEEE 8‐bus, and IEEE 14‐bus test systems are used to test the method, which shows higher improvement rates in reducing DOCR operation times and enhancing cooperation than conventional and metaheuristic methods. The simulation results verify the numerical superiority of the method in optimizing the protection system's efficiency while obtaining rapid and accurate solutions. The proposed method was tested on IEEE 3‐bus, 8‐bus, and 14‐bus systems, optimizing relay operating times to 0.87, 2.96, and 7.05 s, respectively, demonstrating the method's efficiency over conventional approaches.

Abstract Protecting DC microgrids (DCMGs) from faults is critical due to the rapid current changes that occur in milliseconds. However, ensuring fast and accurate protection in DCMGs is more challenging than in AC systems. This study proposes a novel protection algorithm using traveling waves (TWs) for fault detection and localization. The high‐order synchrosqueezing transform (FSSTH) is applied to precisely identify TWs at the relay location. FSSTH offers a sharp time–frequency representation, enhancing the accuracy and speed of fault detection. This method can accurately detect transient phenomena like TWs in DCMGs, even with noise and variable fault resistance. By using the spectral envelope with FSSTH, ridges in time–frequency representations are extracted, improving fault diagnosis. The approach differentiates external from internal faults and recognizes fault direction by assessing TW polarity. Testing on two different DCMGs showed this algorithm's high efficiency and accuracy, with fault location errors ranging from 1 to 50 meters in low‐voltage and 13 to 64 meters in medium‐voltage DCMGs, even under challenging conditions like high resistance (up to 500 Ω) and low signal‐to‐noise ratio (5 dB). These results demonstrate the method's superior accuracy and robustness compared to existing techniques.

AbstractWith the advent of Internet technologies, traditional word-of-mouth communication has transformed into electronic word-of-mouth communication or EWOM. The influence of EWOM in the hospitality industry is very strong. Hospitality services such as hotels and restaurants cannot be evaluated before consumption. Therefore, buying intangible products and services is more risky, so customers rely more on word of mouth. This study was conducted with the aim of identifying the factors affecting the presentation of EWOM in the Instagram social network in the restaurant industry and providing a local model of these factors. The research approach is quantitative. The model was tested by distributing questionnaires among 384 Instagram users, and the relationships between variables were analyzed by Smart PLS 3 and Spss 26 software. The findings show that restaurant service, brand reputation, and individual, technological, and social factors are effective in electronic word-of-mouth advertising in the restaurant industry on the Instagram social network.IntroductionSocial network sites (SNS’s) are virtual communities that allow people to connect with each other by creating profiles, sharing, and uploading photos and statuses. Due to the participatory and social nature of SNS, consumer conversations about brands are known as word of mouth (WOM) (Chou and Kim, 2011). Word-of-mouth communication through Internet-based technologies is known as electronic word-of-mouth (EWOM) advertising (Jung and Jang, 2011; Henning Tora et al., 2004). EWOM has a very strong impact on the hospitality industry because intangible products, such as restaurant services, cannot be evaluated before consumption. As a result, buying intangible products and services carries more risk, so customers are more dependent on the interpersonal influence of word-of-mouth advertising (Jung and Jang, 2011). With the increasing importance of EWOM, understanding customer behavior from doing it and identifying the factors influencing its dissemination by customers has become more beneficial for managers, especially professional marketers. Most academic studies in this field have examined the effectiveness of EWOM communications and the process by which this factor influences consumer purchasing decisions. Although previous research has examined the drivers of EWOM releases, social networking sites have not been considered and the issue of consumers' intention to do so on social networking sites in the restaurant industry has received little attention in the literature. Therefore, the present study has identified the factors affecting the publication of EWOM by Instagram users in relation to the restaurant industry. As a result of this research, the answer to the question is what are the factors influencing the presentation of EWOM on Instagram in the restaurant industry?Case studyThe aim of this study is to design and present a conceptual model for the factors affecting EWOM among Instagram social network users in the restaurant industry.Materials and Methodologyin this research we seek to provide a local model of factors affecting word of mouth in the restaurant industry in social networks, In the quantitative phase of the research, survey research was used to test the obtained model in the qualitative section and to examine the relationships between variables. Due to the infinity of the statistical population, the statistical sample for the distribution of the questionnaire using the Cochran's formula was estimated 384 people. As a result, 396 electronic questionnaires were randomly distributed among Instagram users, and at the end, 384 correct questionnaires were analyzed. In order to evaluate the validity of the research tool was used by experts. For data analysis, the least squares method has been done using SPSS26 and Smart PLS3 software.Discussion and ResultsThe purpose of this study was to provide an EWOM model on the Instagram as a social network site in the restaurant industry. In this study, taking into account the initial studies and opinions of the interviewees and analyzing the collected data, the data model based on the paradigm model of Strauss and Corbin was obtained using the grounded theory.In a quantitative phase, the hypotheses extracted from the model were tested and all 24 hypotheses were confirmed. Comparison of research findings with other studies in this field shows the effect of food price and quality on restaurant word of mouth on social networks. Consistent with Tan and et al. (2016) The effect of staff attitude on restaurant word-of-mouth advertising on the Instagram social network is consistent with Jung and Jang (2011) studies confirming the positive effect of being seen by virtual friends on restaurant word of mouth, with Kim and et al. (2016) is similar.The positive effect of self efficacy on restaurant word of mouth on social media is consistent with the findings of Hennig-Thurau et al. (2011).Conclusion Based on the results obtained; Restaurant services (quality and price of food, staff attitude, restaurant environment and restaurant diversity), brand reputation, personal factors (self efficacy t and visibility) have a positive and significant effect on restaurant recommendations to others and are the main reasons for consumer participation in EWOM. Individual factors (altruism, expression of feelings, curiosity about the new restaurant experience, appreciation of restaurants and lifestyle), technological and social factors also have a positive and significant effect on restaurant sharing on Instagram. Sharing a restaurant (story, post, comment, mention and restaurant tagging) by consumers has a positive and significant effect on brand awareness, brand loyalty, intention to buy others and restaurant promotion.

Social network analysis is able to relate the status of the social system and the structures of relationships between stakeholders and social well-being, with reference to specific criteria and measurement of quantitative and mathematical indicators. Therefore, the present study was conducted with the aim of achieving a correct management plan based on social monitoring in the form of relationships between greenspace actors in Tehran. In this research, the status of the social network of greenspace activists in Tehran was investigated based on the links between information exchange and participation. The results showed that the level of cohesion and social capital in the social network of local stakeholders in this area is weak to moderate and the stability of the network was estimated as moderate. Also, based on the QAP index, it can be said that the degree of correlation between the two networks of information exchange and participation among actors in the region has a high coefficient. The findings of this study can be used in line with principled policies in sustainable management of greenspaces in Tehran and for planning, decision-making and, ultimately, success in this field of inquiry. In this regard, the need to strengthen the links of information exchange and participation among stakeholders seems necessary to strengthen social capital to achieve a successful management in terms of sustainable management of urban greenspaces in this area.

With the help of a hydrothermal approach in this study, we could provide flower-like nanostructures (NSs) of zinc oxide (ZnO) doped with Tb (FL-NS Tb3+/ZnO). Then, FL-NS Tb3+/ZnO morphology was investigated by energy-dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), X-ray powder diffraction (XRD), and map analysis. The results revealed higher activity centers and porosity of this nanocomposite, which were followed by acceptable electrochemical function. Hence, it can be utilized for fabricating an electrochemical sensor with an appropriate response for the simultaneous determination of kynurenic acid (KYN) and tryptophan (TRP). However, as compared with the modified carbon paste electrode (FL-NS Tb3+/ZnO/CPE), the bare carbon paste electrode (BCPE) exhibited a weak response toward KYN and TRP but the modified electrode was followed by a high current response for KYN and TRP at a potential 0.35 and 0.809 V. Therefore, cyclic voltammetry (CV) was applied in optimal experimental conditions to study the electrochemical behaviors of KYN and TRP over the surface of the proposed modified electrode. Moreover, we used differential pulse voltammetry (DPV) for quantitative measurements. It was found that this new modified electrode linearly ranged from 0.001 to 700.0 μM, with detection limits of 0.34 nM and 0.22 nM for KYN and TRP, respectively. In addition, KYN and TRP in real samples can be analyzed by this sensor, with a recovery of 97.75%−103.6% for the spiked KYN and TRP in real samples.

Recent technical advances in Wide-Area Measurement Systems (WAMS) have made it possible to use a combination of measured signals from remote locations to design centralized control. However, the transmission delay of remote signals and changes in the power system operating point are significant issues in the operation of the Wide-Area Damping Controller (WADC). Regarding issues and uncertainties in the power system, mixed H_2/H_∞ synthesis has been proposed for wide-area robust controller design. In this paper, an adaptive wide-area robust controller for Thyristor Controlled Series Capacitors (TCSC) is presented to improve the inter-area oscillation damping in multi-machine power systems in which the time-varying delays in feedback signal time are taken into account. So, an Adaptive Delay Compensator (ADC) is used to compensate for the delay of receiving remote signals. Despite the nonlinearity of the power system, the changes in the operating point, and the presence of time-varying delays, the proposed scheme shows robust performance in damping the low-frequency oscillations. The efficiency of the proposed control system in the presence of TCSC is shown through simulation results that show its superiority over the conventional control system. The simulation of the paper is carried out on the Four-Machine Two-Area test system and 10-machine, 39-bus New England power system.

AbstractThis study aimed to design a model of job empowerment of rural female-headed households under supervision of the Welfare Organization in Tehran province. Statistical population included all rural female-headed households under supervision of the Welfare Organization in Tehran province, comprising 495 women who 216 women were selected. The sampling method was stratified random sampling proportional to the size of towns of Tehran Province which towns were considered as the classes of sampling. A questionnaire designed by researcher was used to collect the data. The results showed that commitment and responsibility, economic factors, technical skills, psychological skills, social factors, creativity and solving problem, communication skills and grouping work, information and special knowledge, and educational factors had the most effects on main structure, respectively. Based on findings, it can be concluded that considered variables for job empowerment in the studied women were reliable and it can be used to investigate of the rural female-headed households under supervision of the Welfare Organization.

Nowadays, power system protection is increasingly important because of the growing number of customers and the pressing need for timely fault resolution and relay operations. This paper addresses the non‐linear nature of the objective function in the optimal coordination of directional overcurrent relays (DOCRs) by employing a quadratic Taylor series expansion around an operating point, converting the problem into a quadratically constrained quadratic programming problem, ensuring a global optimal solution with increased computational efficiency. Additionally, the quadratic constraints are converted into second‐order cone constraints for compatibility with the CPLEX solver. Using the least square method, the operating point values are determined and further fine‐tuned using iterations with the DOCR operation times. The IEEE 3‐bus, IEEE 8‐bus, and IEEE 14‐bus test systems are used to test the method, which shows higher improvement rates in reducing DOCR operation times and enhancing cooperation than conventional and metaheuristic methods. The simulation results verify the numerical superiority of the method in optimizing the protection system's efficiency while obtaining rapid and accurate solutions. The proposed method was tested on IEEE 3‐bus, 8‐bus, and 14‐bus systems, optimizing relay operating times to 0.87, 2.96, and 7.05 s, respectively, demonstrating the method's efficiency over conventional approaches. [ABSTRACT FROM AUTHOR]

Protecting DC microgrids (DCMGs) from faults is critical due to the rapid current changes that occur in milliseconds. However, ensuring fast and accurate protection in DCMGs is more challenging than in AC systems. This study proposes a novel protection algorithm using traveling waves (TWs) for fault detection and localization. The high‐order synchrosqueezing transform (FSSTH) is applied to precisely identify TWs at the relay location. FSSTH offers a sharp time–frequency representation, enhancing the accuracy and speed of fault detection. This method can accurately detect transient phenomena like TWs in DCMGs, even with noise and variable fault resistance. By using the spectral envelope with FSSTH, ridges in time–frequency representations are extracted, improving fault diagnosis. The approach differentiates external from internal faults and recognizes fault direction by assessing TW polarity. Testing on two different DCMGs showed this algorithm's high efficiency and accuracy, with fault location errors ranging from 1 to 50 meters in low‐voltage and 13 to 64 meters in medium‐voltage DCMGs, even under challenging conditions like high resistance (up to 500 Ω) and low signal‐to‐noise ratio (5 dB). These results demonstrate the method's superior accuracy and robustness compared to existing techniques. [ABSTRACT FROM AUTHOR]

Abstract Qualitative analysis of water resources is one of the most widely used topics in water resources research today. Researchers use various analysis methods of water parameters to achieve the desired goals in this field. This research uses artificial intelligence (AI), learning machine (LM), data mining, and mathematical techniques to simulate water behavior and estimate its parametric changes. The proposed model used in this study was a Self-adaptive Extreme learning machine (SAELM) to estimate hydrogeological parameters of the Meghan wetland located in Markazi province in Iran. In addition, SAELM simulation results were compared to Least square support vector machine (LSSVM), Multiple linear regression (MLR), and Adaptive Neuro-fuzzy inference system (ANFIS) models. The simulated parameters were Electrical Conductivity (EC), Total Dissolved Solids (TDS), Groundwater Level (GWL), and salinity. This information was related to sampling for 175 months in the study area. Finally, after simulation operation, four models were introduced as superior models. Mentioned exceptional models were SAELM in GWL modeling, SAELM in modeling the EC, MLR in salinity simulation, and LSSVM in the simulation of TDS parameters. Moreover, by five approaches, the models' performance was evaluated. Suggested strategies were performance evaluation by statistical indicators, Wilson score method uncertainty analysis (WSMUA), response & correlation plots, discrepancy ratio charts, and distribution error diagrams. Based on statistical indicators, the SAELMGWL model was the most accurate model with RMSE, MAPE, and R 2 indices equal to 0.1496, 0.0043, and 0.9933, respectively. The ANFIS model had the worst results in simulation.

Abstract Despite the adverse effects of late spring frost on the ecological and economic impact on agriculture, the management of these events are poorly investigated. Therefore, the grounded theory was used for extracting the livelihood stabilization model of orchardists on the shores of Lake Urmia against late spring frosts. The statistical population of this study in the qualitative part included 22 experts in the field of horticulture. In the quantitative study, they included all orchardists in the villages along with Lake Urmia, comprising 341 people, of which 236 were randomly selected. By carefully examining and linking the concepts and subcategories, the most important subcategories that were included six subcategories. The results of all calculated goodness-of-fit indexes (chi-square [P > 0.01], RMSEA = 0.000, NFI = 0.95, CFI = 0.91, and GFI = 0.92) showed the model provided an excellent fit for the data. Standardized regression coefficients showed that interfering factors had the greatest impact on the livelihood of orchardists against late spring frosts (β = 0.47). Also, the background factors (β = 0.43) and causal factors (β = 0.39) were found to affect the livelihood of the orchardists in encountering late spring frost. The results of multiple square correlations of dependent variables also indicated that about 41% of the livelihood variance of small-scale orchardists was explained by the predictor variables (i.e. causal conditions, background factors and interfering factors). The findings of this study can assist policymakers, planners and relevant managers while paying more attention to orchardists in the region, so as to support decisions in order to achieve sustainable development.

Abstract Considering the cooperative sector capabilities in organizing rural people, primarily focusing on empowering manpower and adopting a sustainable approach to basic resources (water and soil), village-cooperative initiative has emerged as a new concept by registering and forming 4565 new cooperatives in Iran’s rural regions. The present research which was conducted in two qualitative and quantitative phases designed a new model by integrating the new village-cooperative approach along with the sustainable livelihood’s framework theory. The study sample of the qualitative phase included 32 theorists of the village-cooperative initiative. To analyze the data, the grounded theory and three-step process of open coding, axial coding and selective coding were used using MAXqda18 software. Finally, a paradigm model was designed whose most important components included causal conditions (shocks and seasonal conditions), contextual conditions (trends and governmental support), intervening conditions (control and market development), central categories (sustainable livelihoods with a village- cooperative approach), strategies (coping and adaptation) and consequences (creating jobs, establishing cooperatives, supporting smart agriculture and strengthening social capital). The statistical quantitative phase population consisted of cooperatives members in the village-cooperative initiative (N=405), being selected through Cochran's formula with proportional sampling method summing up to198 people from 5 provinces. The data collection tool was a researcher-made questionnaire whose validity and reliability were confirmed. The Bayesian structural equation modeling was used to analyze the data. The results of the research quantitative phase showed that the variables of financial capital, physical capital, social capital, coping and adaptation strategies, human capital and natural capital were the most effective variables on village-cooperative initiative members’ sustainable livelihoods, respectively. Finally, a hybrid model based on the qualitative and quantitative studies was designed and suggestions were made; for instance, the creation of suitable grounds for off-farm activities such as rural industries and ecotourism.

One of the basic strategies to strengthen the sustainable livelihood of local communities against environmental crises, including climate change, is to increase the resilience of these communities against disruption and chaos created in ecological systems. therefore The present research was conducted with the goal of gardeners' sustainable livelihood resilience model to climate change in Hamadan province. The research method was descriptive-correlational and causal-relational. The statistical population included the gardeners of Hamedan province (N=53785). The sample size was 380 people using the Krejcie and Morgan table, and the stage sampling method was used. In this research, the situation of sustainable livelihood resilience model of gardeners in Hamedan province in the face of climate change was analyzed by using questionnaire tool and using statistical software SPSS22 and SMART PLS3 and the reliability and validity of the model were confirmed according to the calculated indicators. In total, based on the average of 3.22 out of 5 (sd=1.01), it can be concluded that the livelihood resilience of gardeners in the face of climate change in Hamedan province is evaluated at an average level. The results of Structural Equation Modeling (SEM) indicate that environmental norms (β=0.691) and adaptation capacity with climate fluctuations (β=0.606) have a positive and significant effect on the resilience of sustainable livelihoods, the environmental attitude variable It has a positive and significant effect on environmental beliefs (β=0.617), place attachment has a positive and significant effect on adaptation capacity (β=0.610) and the environmental attitude of gardeners in the face of climate change (β=0.567). Also, based on the results, the variable of environmental awareness has a positive and significant effect on adaptation capacity with climate fluctuations (β=0.536) and the variable of environmental beliefs also has a positive and significant effect on environmental norms in the face of climate change (β=0.667). In total, based on the findings, 0.72 of the changes in the dependent variable of sustainable livelihood resilience are directly or indirectly explained by the independent variables of environmental attitude, environmental awareness, environmental beliefs, environmental norms, place attachment and adaptation capacity. The results of this research can be useful and practical for decision-makers in the field of agriculture, especially horticulture, from the point of view of environmental psychology. The findings of this research can be used for policy making and planning and decision-making in order to develop resilience and stabilize the livelihood of gardeners in Hamedan province in the face of climate change. [ABSTRACT FROM AUTHOR]

This study presents a new method based on three types of deep learning-based models (DLM) for estimation of water parameters. The DLM models were recurrent neural networks (RNN), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The study areas were the Colorado River basin in the United States and the Mighan Wetland in Iran. The electrical conductivity (EC), dissolved oxygen (DO), total dissolved solids (TDS), chloride ions (Cl), and river flow rate (debi) were simulated by the DLM models. The Wilson score (WS) uncertainty analysis results for Colorado modelling showed that LSTMdebi, RNNDO, and RNNEC were the best models in simulating due to having the lowest errors (Mean ei equal to 0.36, −1.50, and −0.59), respectively. Finally, the highest value of the R2 index, 0.998, was achieved by the LSTM model in modelling the debi parameter, and 0.996 in EC modelling, in the Mighan Wetland. [ABSTRACT FROM AUTHOR]

Background: The present study was conducted to provide an upto-date understanding of clinical applications in balance capability, as well as delivering a review of pieces of literature on Virtual Reality (VR) on balance control in stroke survivors. Methods: Databases including PubMed, Cochrane Library, and Pedro were searched for published papers from 2010 to 2020 with the terms "Game-based rehabilitation", "balance training", "virtual reality", "stroke", "neurorehabilitation", and "virtual environment". We evaluated the effect of VR on balance improvement after a stroke. Results: 33 articles describing results following the use of VR on balance in patients with stroke that met our inclusion criteria were found. Among these studies, two studies described the results in acute, eight in subacute, twenty-two in chronic stroke patients, and one study included both chronic and subacute stroke patients. The results indicated that balance can be improved with VR. Conclusion: The results of this study strengthen the idea that VR training has the potential to become an effective adjacent to routine rehabilitation treatments for improving balance status post-stroke. However, conducting a randomized control trial that incorporates all three stroke phases with an appropriate study setting is necessary to achieve an integrated clinical protocol. [ABSTRACT FROM AUTHOR]