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This paper aims to investigate an improved control method and digital signal processor-based (DSP-based) digital implementation of three-phase standalone inverter. The proposed method is performance developed of the proportional-resonant controller (PRC) with harmonic injection technique, aiming to improve load voltages quality under different loads, especially nonlinear loads. The advanced proposed multi-loop controller is consisted of current harmonic loops for suppressing odd harmonic, which are analyzed in discrete-time domain. Besides, the voltage loop is also used to compensate the output capacitor voltage.

The proposed method can effectively enlarge output voltage stability with low total harmonics distortion and improve the dynamic transient response. The other advantage of the proposed PRC is the injection of the selective harmonic without any additional calculation compensator.

The method is given the opportunity to be controlled exactly all harmful outputs with high-quality voltage referenced of the standalone inverter. The proposed method is implemented using a DSP processor (TMS320F28335) and is verified on the 10 kVA three-phase standalone inverter prototype.

The proposed method is performance developed of the PRC with harmonic injection technique, aiming to improve load voltages quality under different loads, especially nonlinear loads.

This paper aims to propose a novel integrated control method (ICM) for high-power-density non-inverting interleaved buck-boost DC-DC converter. To achieve high power conversion by conventional single phase DC-DC converter, inductor value must be increased. This converter is not suitable for industrial and high-power applications as large inductor value will increase the inductor current ripple. Thus, two-phase non-inverting interleaved buck-boost DC-DC converter is proposed.

The proposed ICM approach is based on the theory of integrated dynamic modeling of continuous conduction mode (CCM), discontinuous conduction mode and synchronizing parallel operation mode. In addition, it involves the output voltage controller with inner current loop (inductor current controller) to make a fair balancing between two stages. To ensure fast transient performance, proposed digital ICM is implemented based on a TMS320F28335 digital signal microprocessor.

The results verify the effectiveness of the proposed ICM algorithm to achieve high voltage regulating (under 0.01 per cent), very low inductor current ripple (for boost is 1.96 per cent, for buck is 1.1) and fair input current balance between two stages (unbalancing current less than 0.5A).

The proposed new ICM design procedure is developed satisfactorily to ensure fast transient response even under high load variation and the solving R right-half-plane HP zeros of the CCM. In addition, the proposed method can equally divide the input current of stages and stable different parallel operation modes with large input voltage variations.

The high prevalence of hypertension is a significant concern worldwide. A decrease in salt intake is a well-known strategy to control hypertension and reduce the risk of cardiovascular diseases. Little is known about the factors influencing salt intake behaviors in settings such as Iran where consumption of salt is higher than the recommended amount. The purpose of this study was to find out what factors affect three salt intake behaviors among women: adding salt when cooking, adding salt to prepared food at the table and purchasing salty foods.

A community-wide cross-sectional survey of 516 women was conducted in Ilam, Iran. Participants were recruited using the cluster random sampling method. The survey included behaviors and variables of an expanded theory of planned behavior. The hypothesized relationships were investigated using structural equation modeling.

Perceived behavioral control (PBC) (p < 0.001) and habit (p = 0.01) appeared to be significant factors of adding salt when cooking. Determinants of adding salt to food at the table were intention (p < 0.001), PBC (p < 0.001), habit (p < 0.001). Also, PBC (p < 0.001), intention (p < 0.001), habit (p < 0.001) were identified as predictors of the purchase of salty foods. The results did not support the moderating role of Hedonic feeling to salt on the relation between intention and behaviors.

This study would be applicable to develop salt reduction interventions. Because altering the hedonic response to food is difficult, the lack of its moderating role could be a promising finding for developing salt reduction interventions.

This paper aims to optimize the interactions of businesses located within industrial clusters (ICs) by using a supply-demand hub in ICs (SDHIC) as a conjoint provider of logistics and depository facilities for small- and medium-sized enterprises (SMEs) as producers, where all of these interactions are under supervision of a third-party logistics provider (3PL).

To evaluate the values of SDHIC, three mathematical models are proposed, optimally solved via GAMS and then compared. Also, a “linear relaxation-based heuristic” procedure is proposed to yield a feasible initial solution within a significant shorter computational time. To illustrate the values of SDHIC, comprehensive calculations over a case study and generated sets of instances are conducted, including several sensitivity analysis.

The experimental results demonstrate the efficiency of SDHIC for SMEs via combining batches and integrating the holding space of inventories, while the outcomes of the case study are aligned with those obtained from random sample examples, which confirms the trueness of used parameters and reveals the applicability of using SDHIC in real world. Finally, several interesting managerial implications for practitioners are extracted and presented.

Some of the managerial and practical implications are optimizing interactions of businesses involved in a supply chain of an IC containing some customers, suppliers and manufacturers and rectifying the present noteworthy gaps pertaining to the previously published research via using real assumptions and merging upstream and downstream of the supply chain through centralizing on storage of raw materials (supply echelon) and finished products (demand echelon) at the same place simultaneously to challenge a classic concept in which supply and demand echelons were being separately planned regarding their inventory management and logistics activities and showing the positive consequences of such challenge, showing the performance improvement of the proposed model compared to the classic model, by increasing the storing cost of raw materials and finished products, considering some disadvantages of using SDHIC and showing the usefulness of SDHIC in total, presenting some applied findings according to the obtained results of sensitivity analysis.

The key contributions of this paper to the literature are suggesting a new applied mathematical methodology to the supply chain (SC) of ICs by means of a conjoint provider of warehousing activities called SDHIC, comparing the new proposed model with the two classic ones and showing the proposed model’s dominancy, showing the helpful outcomes of collaborating 3PL with the SMEs in a cluster, proposing a “linear relaxation-based heuristic” procedure to yield a feasible initial solution within a significant shorter computational time and minimizing total supply chain costs of such IC by optimum application of facilities, lands and labor.

The purpose of this paper is to develop an Internet of Things (IoT)-based framework for supply chain (SC) performance measurement and real-time decision alignment. The aims of the proposed model are to optimize the performance indicator based on integrated supply chain operations reference metrics.

The SC multi-dimensional structure is modeled by multi-objective optimization methods. The operational presented model considers important SC features thoroughly such as multi-echelons, several suppliers, several manufacturers and several products during multiple periods. A multi-objective mathematical programming model is then developed to yield the operational decisions with Pareto efficient performance values and solved using a well-known meta-heuristic algorithm, i.e., non-dominated sorting genetic algorithm II. Afterward, Technique for Order of Preference by Similarity to Ideal Solution method is used to determine the best operational solution based on the strategic decision maker’s idea.

This paper proposes a dynamic integrated solution for three main problems: strategic decisions in high level, operational decisions in low level and alignment of these two decision levels.

The authors propose a human intelligence-based process for high level decision and machine intelligence-based decision support systems for low level decision using a novel approach. High level and low level decisions are aligned by a machine intelligence model as well. The presented framework is based on change detection, event driven planning and real-time decision alignment.

This paper aims to study the impact of existence and lack of discount on the relationships between one manufacturer and one retailer under the cooperative and the non-cooperative games and the members’ profits are compared.

In the first approach, the manufacturer’s price function is constant, and in the second approach, this price function is a decreasing function with respect to lot size. These approaches are modeled through three games structure, including two Stackelberg games and one cooperative game.

Some numerical instances comprising sensitivity analysis are provided, and then the members’ profits in different scenarios are compared. This paper reveals that in the presented models, whether the members are inclined to change their profits.

This paper presents a tool of decision-making for the type of relationships of members in two different circumstances, and an approach is also presented to maximize the members’ profit.

In this paper, the relationships between one manufacturer and one retailer are studied under six different circumstances, where pricing, cooperative advertising and inventory cost are considered simultaneously. Also, a different model is presented to make a balance in individual profits and gain more profit for each member compared to the cooperative and non-cooperative game.

In this paper, a multi-objective multi-echelon supply-distribution model is proposed to optimize interactions of entities located within an Industrial Cluster (IC) including small- and medium-sized enterprises (SMEs), using a third-party logistics provider (3PL)-managed supply-demand hub in industrial cluster (SDHIC) as a specific public provider of warehousing and logistics services.

The three considered objectives are minimizing the total logistics costs, maximizing the rate of demand satisfaction and maximizing the quality of delivery. Because some parameters such as “demand of customers” are naturally fuzzy because of incompleteness and/or inaccessibility of the needed information, the triangular fuzzy number is applied for all fuzzy parameters to handle this difficulty. The proposed model is primarily changed into a correspondent supplementary crisp model. To solve such a model, a revised multi-choice goal programming (RMCGP) approach is then used with the purpose of finding a compromise solution.

Experimental results demonstrate that all enterprises involved in such a supply chain benefit with several advantages using SDHIC by consolidating shipments and merging the storage space of inventories. The applicability of the presented model is shown by conducting these experiments over an applied industrial case study.

The main contributions of this research are proposing a practical mathematical approach to the supply chain of ICs using a specific public warehouse managed by a 3PL, called SDHIC, bridging the existing gaps with respect to the already published researches in this area by applying real-world assumptions such as uncertainty; optimizing the interactions of involved entities in the supply chain of an IC, comprising suppliers, SMEs as manufacturers and customers; minimizing the total incurred logistics costs to such a system through optimum usage of lands, facilities, labors, etc. and boosting the satisfaction of customers through maximizing the service level criteria, illustrating the positive consequences of cooperation of 3PL with the SMEs/manufacturers in an IC, showing the applicability of the adopted approach by implementing it on an applied industrial instance.

Due to the limitations of conventional urban planning, it is essential to develop novel techniques of urban futruing. This paper aimed to use the scenario technique to create four plausible narratives of the future of Isfahan. Also, the authors described the problems of city foresight in the Global South.

This paper chronicles the Schwartzian steps taken to build explorative scenarios of Isfahan City in Iran in 2040. After using a STEEPV (Social, Technological, Environmental, Economic, Political, Value) analysis, the authors prioritized the collected variables by combining influence diagrams, the iceberg metaphor and an expert-based survey. Once the key uncertainties were derived, four scenarios were developed and discussed.

Through thematic analysis of the official visions of Isfahan’s future and the juxtaposition of these narratives with insight yielded in the scenario-development process, the paper concludes that the Northernness of the prevailing urban imaginaries, uncritical mimetic benchmarking, depoliticization of urban futures and the decorative reductionistic visions colonize urban futures in Isfahan/Iran. Critical/deconstructive city foresight and application of discomfort/ignorance criteria in the generation of scenarios can improve the rigor and quality of city foresight in the Global South.

The application of city foresight in the Global South has been limited. The paper is a step toward bridging this gap and providing some recommendations on how city foresight in the Global South might differ from its counterparts in the Global North.

Human resources are one of the most important and effective elements for companies. In other words, employees are a competitive advantage. This issue is more vital in the supply chains and production systems, because of high need for manpower in the different specification. Therefore, manpower planning is an important, essential and complex task. The purpose of this paper is to present a manpower planning model for production departments. The authors consider workforce with individual and hierarchical skills with skill substitution in the planning. Assuming workforce demand as a factor of uncertainty, a two-stage stochastic model is proposed.

To solve the proposed mixed-integer model in the real-world cases and large-scale problems, a Benders’ decomposition algorithm is introduced. Some test instances are solved, with scenarios generated by Monte Carlo method. For some test instances, to find the number of suitable scenarios, the authors use the sample average approximation method and to generate scenarios, the authors use Latin hypercube sampling method.

The results show a reasonable performance in terms of both quality and solution time. Finally, the paper concludes with some analysis of the results and suggestions for further research.

Researchers have attracted to other uncertainty factors such as costs and products demand in the literature, and have little attention to workforce demand as an uncertainty factor. Furthermore, most of the time, researchers assume that there is no difference between the education level and skill, while they are not necessarily equivalent. Hence, this paper enters these elements into decision making.

This research aims to monitor vegetation indices to assess drought in paddy rice fields in Mazandaran, Iran, and propose the best index to predict rice yield.

A three-step methodology is applied. First, the paddy rice fields are mapped by using three satellite-based datasets, namely SRTM DEM, Landsat8 TOA and MYD11A2. Second, the maps of indices are extracted using MODIS. And finally, the trend of indices over rice-growing seasons is extracted and compared with the rice yield data.

Rice paddies maps and vegetation indices maps are provided. Vegetation Health Index (VHI) combining average Temperature Condition Index (TCI) and minimum Vegetation Condition Index (VCI), and also VHI combining TCI_min and VCI_min are found to be the most proper indices to predict rice yield.

The results serve as a guideline for policy-makers and practitioners in the agro-food industry to (1) support sustainable agriculture and food safety in terms of rice production; (2) help balance the supply and demand sides of the rice market and move towards SDG2; (3) use yield prediction in the rice supply chain management, pricing and trade flows management; and (4) assess drought risk in index-based insurances.

This study, as one of the first research assessing and mapping vegetation indices for rice paddies in northern Iran, particularly contributes to (1) extracting the map of paddy rice fields in Mazandaran Province by using satellite-based data on cloud-computing technology in the Google Earth Engine platform; (2) providing the map of VCI and TCI for the period 2010–2019 based on MODIS data and (3) specifying the best index to describe rice yield through proposing different calculation methods for VHI.