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Supply chain (SC) environment is surrounded by risk variables. This issue is regarded as an emerging and strategic problem which must be resolved by SC executives. The ability to measuring green supplier’s performance and affecting risk variables to demonstrating effective suppliers list has a potential contribution to be investigated. This paper aims to develop a decision-making model to assess green suppliers under legislation and risk factors. This leads to fewer disruptions in managing the SC and its impact to further improvement. It also presents research concepts forming a new approach for identification, prediction and understating relationship of supply risk.

At primal stage, different risk factors that influence green suppliers’ performance are indicated and their relationship is analyzed using decision-making trial and evaluation laboratory (DEMATEL) method. At the same time, failure mode and effect analysis is used to determine risk rating of each supplier. Finally, the evaluation based on distance from average solution (EDAS) method ranks suppliers and several comparisons and analysis are performed to test the stability of the results. The approaches include comparison to technique for order performance by similarity to ideal solution, multi-attributive border approximation area comparison, Vlse Kriterijumska Optimizacija I Kompromisno Resenje and complex proportional assessment methods, followed by analysis of rank reversal, weight sensitivity analysis and effect of dynamic metrics.

A real-time case study on green supplier selection (GSS) problem of a reputed construction company of Spain has been presented to demonstrate the practical aspects of the proposed method. In practice, though organizations are aware of various risks from local and global suppliers, it is difficult to incorporate these risk factors for ranking the suppliers. This real-case application shows the evaluation and incorporation of risk factors into the supplier selection model.

The proposed multi-criteria decision model quantitatively aids managers in selecting green suppliers considering risk factors.

A new model has been developed to present a sound mathematical model for solving GSS problems which considers the interaction between the supplier selection risk factors by proposing an integrated analytical approach for selecting green suppliers strategically consisting of DEMATEL, FMEA and EDAS methods.

This paper aims to develop high-pass (HP) negative group delay (NGD) investigation based on three-port lumped circuit. The main particularity of the proposed three-port passive topology is the consideration of only a single circuit element represented by a capacitor.

The methodology of the paper is to consider the S-matrix equivalent model derived from admittance matrix approach. So, an S-matrix equivalent model of a three-port circuit topology is established from admittance matrix approach. The frequency-dependent basic expressions are explored to perform the HP-NGD analysis. Then, the existence condition of HP-NGD function type is analytically demonstrated. The specific characteristics and synthesis equations of HP-NGD circuit with respect to the desired optimal NGD value are established.

After computing the frequency expressions to perform the HP-NGD analysis, this study demonstrated the existence condition of HP-NGD function type analytically. The validity of the HP-NGD theory is verified by a prototype of three-port circuit. The proof-of-concept (POC) single capacitor three-port circuit presents an NGD response and characteristics from analytical calculation and simulation is in very good correlation.

An innovative theory of HP-NGD three-port circuit is studied. The proposed HP-NGD topology is constituted by only a single capacitor. After the topological description, the S-matrix model is established from the Y-matrix by means of Kirchhoff voltage law and Kirchhoff current law equations. A POC of single capacitor three-port circuit was designed and simulated with a commercial tool. Then, a prototype with a surface-mounted device component was fabricated and tested. As expected, simulation and measurement results in very good agreement with the calculated model show the feasibility of the HP-NGD behavior. This work is compared to other NGD-type function with diverse number of ports and components.

Hardgrove grindability index (HGI) is an important physical parameter used to demonstrate the relative hardness of coal particles. Modeling of HGI based on coal conventional properties is a quite complicated procedure. The paper aims to develop a new accurate model for prediction of HGI that is called optimized evolutionary neural network (OPENN).

The procedure for generation of the proposed OPENN predictive model was performed in two stages. In the first stage, as the high dimensionality involved in the input space, a correlation-based feature selection (CFS) algorithm was used to select the most important influencing variables for HGI prediction. In the second stage, a combination of differential evolution (DE) and biography-based optimization (BBO) algorithms as a global search method were applied to evolve weights of a multi-layer perception neural network.

The proposed OPENN was examined and compared with other typical models using a wide range of Kentucky coal samples. The testing results showed that the accuracy of the proposed OPENN model is significantly better than the other typical models and can be considered as a promising alternative for HGI prediction.

As HGI test is relatively expensive procedure, there is an economical interest on HGI modeling based on coal conventional properties (proximate, ultimate and petrography); the proposed OPENN model to estimate HGI would be a valuable and practical tool for coal industry.

States in the Middle East and North Africa (MENA) have historically leaned towards conservative, reactionary models of drugs policy. The combination of authoritarian forms of government, whether dynastic monarchies (Morocco, Jordan and Persian Gulf countries), semi-military republics (Syria, Egypt and Algeria) or religiously sanctioned republics (Iran), with the strong influence of Islamic law and norms, has signified that the region has enforced strict forms of drug prohibitions. For that matter, the region is home to cultural and social norms that are less permissive than in other regions of the world: for instance, with regard to premarital sex, homosexuality, clothing, alcoholic drinks and freedom of expression. This image of the MENA region is often overplayed by media commentators and Western scholars, especially in the field of drugs policy. The almost total absence of studies of drugs policy or drugs history in the MENA, excluding works in epidemiology, speaks well about the oblivion to which the region has been relegated over the last decades. The chapter provides first a background on the main questions regarding MENA drugs policy, looking at the historical developments in drug regulations and drug trends. Then, it discusses the current policies that operate across the region and, if pertinent, the prospects of policy development. When necessary, the argument refers to contextual elements that have influenced the direction in national and transitional drugs policy. Conscious of the fact that the MENA is as vast and diverse as a continent, I have opted to focus on three paradigmatic cases (Iran, Lebanon and Saudi Arabia), which provide an adequate geographical and thematic coverage of the MENA drugs policy. The three cases cover different aspects of today’s MENA drugs policy spectrum, from draconian repressive measures to progressive harm reduction programmes. Taken in their geographical dimension one can appreciate the variety and difference that exists within the MENA region, therefore supporting one of the key objectives of this chapter, which is to provide a nuanced analysis of drugs policy against the grain of homogenising and culturally reductive approaches.

This study aims to investigate the feasibility of proposed microgrid (MG) that comprises photovoltaic, wind turbines, battery energy storage and diesel generator to supply a residential building in Grindelwald which is chosen as the test location.

Three operational configurations were used to run the proposed MG. In the first configuration, the electric energy can be vended and procured utterly between the main-grid and MG. In the second configuration, the energy trade was performed within 15 kWh as the maximum allowable limit of energy to purchase and sell. In the third configuration, the system performance in the stand-alone operation mode was investigated. A whale optimization technique is used to determine the optimal size of MG in all proposed configurations. The cost of energy (COE) and other measures are used to evaluate the system performance.

The obtained results revealed that the first configuration is the most beneficial with COE of 0.253$/KWh and reliable 100%. Furthermore, the whale optimization algorithm is sufficiently feasible as compared to other techniques to apply in the applications of MG.

The value of the proposed research is to investigate to what extend the integration between MG and main-grid is beneficial economically and technically. As opposed to previous research studies that have focused predominantly only on the optimal size of MG.

Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO₂) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO₂ consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO₂ consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019.

This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model.

After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process.

The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.

The purpose of this study is to frame the dimensions of the external institutional environment, namely, cultural-cognitive, normative and regulative dimensions as the main actors in the organisational field. More precisely, Libyan commercial banks have been identified as empirical evidence, to identify constraints of the institutional environment governing the behaviour and decision-making of commercial banks, when adopting Islamic financial transactions.

A questionnaire has been designed for 14 Libyan commercial banks which is distributed to the Board of Directors, managers, directors of departments, and personnel. The exploratory factor analysis (EFA) and the measurement model by using the first-order and second-order confirmatory factor analysis (CFA) have been applied as essential steps to embody the conceptual framework and test the research hypotheses.

The results of the EFA indicated sufficient correlation among the dimensions of the external environment. The CFA supported this study’s hypotheses. The modelling showed that the cultural-cognitive, normative and regulative dimensions are institutional constraints impeding Libyan commercial banks’ adoption of Islamic financial transactions. Interestingly, the findings of the CFA align with the EFA findings in supporting the conceptual framework of the research. They portrayed that the cultural-cognitive dimension has been identified by explicit and implicit cognition.

This study systematically embodies the dimensions of the external institutional environment, namely, cultural-cognitive, normative and regulative dimensions, as the main factors in the organisational field to be conceptually rich lenses to investigate social considerations to reinforce institutional thought broadly. The results of this study were consistent with extant Islamic financial literature, reflecting symmetry and similarity across commercial banks, particularly at the first stage of adopting Islamic financial transactions.

Active flow control on the NACA 0024 airfoil defined as suction-injection jet at the chord-based Reynolds number of 1.5 × 1e + 5 is studied.

The three-dimensional incompressible unsteady Reynolds-averaged Navier–Stokes equations with the SST k-ω turbulence model are used to study the effects of coflow-jet (CFJ) on the dynamic and static stall phenomena. CFJ implementation is conducted with several momentum coefficients to investigate their turnover. Furthermore, the current work intends to analyze the CFJ performance by varying the Reynolds number and jet momentum coefficient and comparing all states to the baseline airfoil, which has not been studied in prior research investigations.

It is observed that at the momentum coefficient (Cµ) of 0.06, the lift coefficients at low attack angles (up to a = 15) dramatically increase. Furthermore, the dynamic stall at the given Reynolds number and with the lowered frequency of 0.15 is explored. In the instance of Cµ = 0.07, the lift coefficient curve does not show a noticeable stall feature compared to Cµ = 0.05, suggesting that a more powerful stronger jet can entirely control the dynamic stall.

Furthermore, the current work intends to analyze the CFJ performance by varying the jet momentum coefficient and comparing all states to the baseline airfoil, which has not been studied in prior research investigations.

Noncommunicable diseases (NCDs) count for over 80% of premature death worldwide. More than 76% of the total burden of diseases in Iran is devoted to NCDs. In line with the World Health Organization action plan, Iran has developed its national action plan that led to establishment of the National Committee for Prevention and Control of NCDs (INCDC), whose aim is 30% mortality reduction attributed to NCDs by 2030. The stewardship of health system is the cornerstone of performing and sustaining meaningful actions toward prevention and control of NCDs. The literature is tiny on how to materialize the stewardship and governance of health system. The purpose of this article is to report the findings of a national study that aimed to identity functions and subfunctions of stewardship of NCDs and its related risk factors in Iran.

This is a qualitative study. The authors conducted interviews with 18 purposefully selected interviewees until the authors reached saturation. Thematic content analysis was used for analysis and MAXQDA 10 was employed for data management. The difficulty of coordinating with interviewers and health policymakers in the field slowed the process of research progress.

The authors identified seven themes and categorized them as main functions for appropriate stewardship of NCDs in Iran, including intelligence generation; strategic framework; evidence-based policies/decisions; system design; resource allocation/development; capacity-building and enforcement/alignment; and categorized them as important.

The seven themes presented as stewardship functions include concepts and practical examples of the experiences and performance of leading countries in the field of NCDs control that can help policymakers and health managers for better descion-making.

Iran adopted its national action plan in 2015 and WHO selected Iran as a fast-track country in 2017. The study confirmed that to achieve the global targets, appropriate and contextual stewardship for any specific setting is fundamental. Iran needs to improve its stewardship for prevention and control of NCDs and implement its national action plan. Therefore, the functions and policies outlined in this article for the proper performance of NCDs can improve more meaningful practices in this area in Iran and many other countries.

The implementation of circular economy strategies is one of the central objectives of several governments seeking a transition toward a sustainable development. Circular economy in agriculture deals with the production of agricultural commodities making an efficient use of resources and avoiding unnecessary waste and carbon emission generation. Disruptions in the production and supply of critical agricultural products can have serious negative repercussions for firms and consumers of the food supply chain. In recent decades, disruptions generated by natural disasters such as hurricanes, thunderstorms and floods have greatly impacted social communities and industrial sectors. Supply chain risks approaches are seen to contribute key elements to address the impacts of natural disaster toward the implementation of circular economy in agriculture, helping to prevent collapses in the production and supply of food. The purpose of this paper is to study and identify flood risk drivers and their effects on the sustainability of an agriculture supply chain in connection with a circular economy strategy. By using an extended Step-wise Weight Assessment Ratio Analysis method combined with a multi-criteria decision analysis, the most essential flood drivers with a degree of importance are reported here. Then, the authors propose an Evaluation of Data based on average ASsessment method, to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas. The application of this research lies within the framework of a real agricultural project founded by the European Commission Scientific Section, called RUC-APS.

The authors use management science-based tools to address circular economy in agriculture. The authors propose a multi-criteria-based methodology to assess the risks of flooding in crops areas. To validate the proposed methodology, a case example from Spain is discussed to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas.

The proposed multi-criteria methodology confirmed a successful application to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas. Organizations and firms in the agricultural business can use the methodology to identify risks drivers and to detect the best projects to mitigate the highest impacts of flooding risks in crops areas.

The authors use supply chain risks approaches to address the impacts of natural disaster on the implementation of circular economy in agriculture. The authors propose a robust multi-criteria-based methodology to assess the risks of flooding in crops areas and we used to determine the best mitigating projects to face flooding risks on crop areas.