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Actively responding to climate change and accelerating the realization of carbon emission reduction are the common goals of global sustainable development. Hydrogen energy, as a "zero carbon emission" secondary energy, is playing an increasingly important role on the world energy stage. Hydrogen energy infrastructure (focusing on hydrogen refueling stations) is a key infrastructure in the transition to a hydrogen economy. However, the indicators that influence the location selection of hydrogen refueling stations (HRSs) are specific and have complex relationships, and current research still lacks a comprehensive analysis of its location selection decision-making indicators. Based on this research gap, this study proposes a hybrid fuzzy decision-making approach to determine the relationship and degree of influence between indicators. Firstly, this study identified 18 decision indicators that influence location selection from four dimensions: policy, economy, society, and environment. Subsequently, semi-structured interviews were used to initially assess the relationship between indicators. Finally, by using interpretive structural modeling and the fuzzy decision-making trial and evaluation laboratory (ISM-FDEMATEL), a 7-level structural model is established, which clarifies the complex relationship between indicators and the individual attributes of each indicator. The research results show that the indicators of the policy dimension have a more significant influencing degree than the other three dimensions and have a greater influence on the entire indicator system; the indicators of the economic dimension have a higher influencing degree and are easily influenced by other indicators. These findings will benefit policymakers and practitioners to prioritize and manage their resources effectively and efficiently and provide a valuable reference for advancing the sustainable development of the infrastructure for hydrogen energy. [ABSTRACT FROM AUTHOR]

Copyright of Hudební Výchova is the property of Charles University Prague, Faculty of Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

How galaxies replenish their gas supply in order to sustain star formation, is a research topic of many of the new and upcoming neutral atomic hydrogen (Hi) surveys on the SKA precursor instruments. This replenishment, or accretion, of gas in the form of Hi is likely to occur at column densities one or two orders of magnitude below previous observational limits and it has, so far, not been unambiguously detected. We present recent deep Hi observations of NGC 5068, an isolated nearby star-forming galaxy observed by MeerKAT as part of the MHONGOOSE survey. This survey is the deepest Hi survey of nearby galaxies until the advent of the SKA and is reaching column densities of resolution. The combination of the resolution and sensitivity of the MeerKAT HI data have revealed "fingers" of low column density gas extending out from the main Hi disk of the galaxy. While the origin of these fingers remains a mystery for now, the dynamics of the main galaxy disk and the outer disk in which the fingers are located, as well as the morphology of the fingers, does not seem to suggest a previous merger event. [ABSTRACT FROM AUTHOR]

The limitations of visual inspection and interpretation of Infrared (IR) spectra from either laboratories or astronomical observations to cover a wide range of molecular types motivated us to apply a mathematical approach via a matching method called spectra similarity to identify the carriers of UIE bands in a sample of astronomical objects. The reliability of such a purely mathematical approach in targeting the correct molecular structures was tested by applying two sets of experimental IR spectra against the large pool of theoretical IR databases composed of 300,000 organic, organometallic, and, inorganic molecules. Spectra similarity calculations show that the second-row elements C, H, N, and O dominate the elemental composition of HD 44179, M 82, NGC 7023, NGC 7027, and Orion bar. In addition, the signatures of third-row elements S and Si were also identified. Our preliminary results confirm the signatures of complex organic molecules in interstellar environments. These are composed of benzene rings, five-membered rings, and side chain aliphatic groups in the cited astronomical sources. In some cases, the similarity approach suggested the fully aliphatic structures as the origin of UIE carriers. [ABSTRACT FROM AUTHOR]

Copyright of Energy Chemical Industry is the property of Energy Chemical Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Social responsibility strategies are indispensable for the sustainable development of the Belt and Road Initiative (BRI). Nonetheless, the application of social responsibility (SR) policies in such mega infrastructure projects remains a pressing concern since a number of barriers impede the effective integration of SR practices. Therefore, this paper seeks to identify these barriers and determine the interrelationships among them. A list of barriers was first identified from a literature review and expert consultation. Subsequently, a survey was designed to collect experts' views on the interrelations among these barriers. The Fuzzy DEMATEL method was employed to analyze these barriers' causal relationships and interdependencies. Subsequently, the ISM approach was used to develop a hierarchical structure and establish the driving and dependence relationships among them. The classification of barriers, based on driving power and dependence power, was accomplished using the MICMAC analysis. The results reveal that barriers such as "The diverse institutions, cultures, and social conditions among BRI countries", "Lack of robust social responsibility laws and regulations in the host countries", "Lack of stringent and legally binding BRI policies and guidelines governing social responsibility", "The diverse environmental and social frameworks and standards among BRI countries", "The diverse international, national, and private funds for BRI projects", and "Lack of customer awareness and knowledge of CSR" are the most critical barriers and have the greatest influence on social responsibility implementation. Identifying these key barriers and their interrelationships will assist decision-makers, policymakers, and other stakeholders involved in BRI mega infrastructure projects in minimizing or overcoming them, hence increasing the chances of successfully integrating social responsibility practices within these projects. [ABSTRACT FROM AUTHOR]

For many years, entrepreneurs were considered the change agents of their societies. They use their initiative and innovative minds to solve problems and create value. In the aftermath of the digital transformation era, a new group of entrepreneurs have emerged who are called transformational entrepreneurs. They use various digital platforms to create value. Surprisingly, despite their importance, they have not been sufficiently investigated. Therefore, this research scrutinizes the elements affecting transformational entrepreneurship in digital platforms. To do so, the authors have considered a two-phase method. First, interpretive structural modeling (ISM) and Matrices d'Impacts Croises Multiplication Appliqué a Un Classement (MICMAC) are used to suggest a model. ISM is a qualitative method to reach a visualized hierarchical structure. Then, four unsupervised machine learning algorithms are used to ensure the accuracy of the proposed model. The findings reveal that transformational leadership could mediate the relationship between the entrepreneurial mindset and thinking and digital transformation, interdisciplinary approaches, value creation logic, and technology diffusion. The GMM in the full type, however, has the best accuracy among the various covariance types, with an accuracy of 0.895. From the practical point of view, this paper provides important insights for practitioners, entrepreneurs, and public actors to help them develop transformational entrepreneurship skills. The results could also serve as a guideline for companies regarding how to manage the consequences of a crisis such as a pandemic. The findings also provide significant insight for higher education policymakers. [ABSTRACT FROM AUTHOR]

Organisations are increasingly creating inter-organisational ecosystem partnerships to innovate openly. Despite effective knowledge management significantly supporting ecosystem infrastructures, empirical insights into the importance of and interdependencies between conditions for successful knowledge exchange across ecosystem contexts remain unexplored within existing literature. This study implements a mixed-method approach to ascertain which conditions are responsible for knowledge transfer success across innovation ecosystems. Interpretive Structural Modelling was employed to analyse questionnaires with key ecosystem stakeholders, in order to impose a hierarchical structure upon the conditions. The configurational nature of these conditions, and their combinations into solutions for success was ascertained through analysing semi-structured interviews using fuzzy-set Qualitative Comparative Analysis. Results reveal multiple, mutually exclusive pathways to knowledge transfer success, grouped into three solution types, increasing understanding of the interrelated nature of the knowledge transfer conditions. Limitations and implications for future research are provided. [ABSTRACT FROM AUTHOR]

The decline in the productivity of the national sugarcane commodity has become an issue in the last ten years. When domestic demand for sugar is increasing, the decline in productivity indicates a crisis in sugarcane production. At the same time, the involved institutions and strategic programs to encourage sugarcane production have not been well identified. The research aims to determine the institutions that are expected to play a role and the strategic programs that are prioritized in increasing sugarcane production in Southeast Sulawesi, Indonesia. This study uses Interpretative Structural Modeling (ISM) to formulate complexity, hierarchy (levels) and classification among elements. The results showed that of the 14 sub-elements of the acting institutions studied, five institutions were key actors for increasing production, namely banking institutions; universities; agricultural, plantation and fisheries offices; industry, trade, cooperatives, & Small and Medium Enterprises (SMEs') offices; and private companies. Then, from the 13 sub-elements of the strategic programs, there are five strategic programs that also serve as the key programs, namely extension/assisting farmers; addition of plantation labor; providing access to capital; variations of profit-sharing and buying systems of sugarcane; and accuracy of time and adequacy of the supply of raw materials to the mills. This means that these institutions must play an important role, as well as strategic programs that must be carried out effectively to address the issue of sugarcane production in the country. Thus, researchers recommend involving all stakeholders in conducting strategic programs with the key actors as the leading driving institutions.

Food is the basic material basis for human survival. Sudden food safety event risks mainly derive from accidental or natural food safety risks, poor food storage environments, and inefficient government regulation policies. The factor identification of sudden food safety risks is the key to controlling such risks. Therefore, the efficient and scientific identification of risk sources and types will be very important in managing sudden food safety risks. In this study, 16 sudden food safety event risk factors were identified through a literature review, and their interactive relationships were clarified using an interpretive structural model (ISM). Then, the weights of influencing factors were calculated through the analytic hierarchy process (AHP), and the combined weight of indices was determined. Results show that the 16 sudden food safety event risk factors can be divided into four levels. The quality standard for food safety (S5) and food storage (S14) is at the bottom layer of risks of sudden food safety events (the first-layer index weight is 36.899%). The judgment matrices at the four levels passed the consistency check. The influence weight of the factor "whether it contains transgenic raw materials" (S9) ranks second (the total weight is 18.151%). This index system for sudden food safety event risk factors is highly effective, with good operability for managing sudden food safety event risks. The obtained conclusions are important reference values for identifying the factors influencing food safety risk management, determining the emphasis of food safety supervision, realizing food risk prevention and control, and strengthening and guaranteeing the food safety level.

A compact self-isolated four-element MIMO antenna is introduced for use in ISM systems and WLAN applications in the 2.4 GHz frequency band. The proposed design consists of four printed dipoles so four integrated baluns are responsible for feeding these antennas. By choosing an appropriate arrangement of the elements next to each other and using the feature of self-isolated antennas, great isolation is achieved in the first step. Finally, by utilizing a printed decoupling component in the space between the dipoles, the value of the envelope correlation coefficient (ECC) parameter comes very close to zero for the MIMO antenna. The major part of the isolation in this design is the responsibility of the self-isolated technique, and adding the decoupling element at the last stage of the design is to complete the antenna isolation process. The fabricated prototype of the proposed MIMO antenna operating at 2.25-2.87 GHz frequency band reaches the measured port-to-port isolation of better than 19 dB, ECC of less than 0.0009, the peak gain of 8.6 dBi with total dimensions of $1.11\lambda _{0}\times 1.11 \lambda _{0}\times 0.34\lambda _{0}$ . The numerical and experimental results approve the excellent MIMO performance for 2.4 GHz applications.

The industrial revolution has gone through four revolutions, this fourth one is Industry 4.0 or I4.0, which aims to improve the performance of companies by moving towards digitalization and relying on the internet of things technology IOT and the cyber physical system CPS. Lean-PLM is a system that has become a pillar of Industry 4.0 because of the benefits it brings: productivity improvement, performance, waste reduction. The objective of our study is to examine the correlation between Lean/Industry 4.0 and PLM/I4.0 and to present an implementation model using the ISM (Interpretive Structural Modelling of Critical Success Factor) method of Lean-PLM in Industry 4.0. The results obtained from this research show that Lean can be associated with Industry 4. 0, which gives us Lean 4.0, PLM is a pillar of Industry 4.0 and finally the success of the Lean-PLM model in Industry 4.0 is based on the support and commitment of top management”, “Big Data (BD)”, “Change Management” and “Professional Training and Development (PTD)”.

The developments of the fourth industrial revolution have caused changes in all areas of society, including production. The changes in production caused by the fourth industrial revolution have also resulted in fundamental changes in the supply chain and have converted it to supply chain 4.0. Organisations must be receptive to supply chain 4.0 to maintain their competitive advantage. Therefore, this study aimed to investigate the relationships among supply chain 4.0 technologies so that, by learning and understanding these connections, industries can pave the way for the implementation of these technologies in their supply chains and use them in problem-solving. The literature review was used to identify the supply chain 4.0 technologies, and the Delphi technique was applied to extract them, including the Internet of Things (IoT), cyber-physical systems, cloud computing, big data, blockchain, artificial intelligence, Radio-frequency Identification (RFID), augmented reality, virtual reality, and simulation. The relationships of supply chain 4.0 technologies were examined using the DEMATEL technique and based on interpretive structural modelling (ISM), their deployment map was drawn. The type of technologies was determined using the MICMAC method. The MICMAC analysis found that the artificial intelligence technology is independent and, based on the findings through the DEMATEL technique, this technology is related to simulation, which belongs to the first level of the interpretive structural modelling technique, and IoT, cloud computing, big data, and blockchain technologies, which are at the second level. Based on the ISM method, RFID, virtual reality, augmented reality and simulation technologies are located at the first level; IoT, cyber-physical systems, cloud computing, big data and blockchain technologies are situated in the second level; and artificial intelligence technology belongs to the third level. According to the related literature, few studies have been conducted on the issues of supply chain 4.0 and the technologies that affect it.

Concern on rice production in Indonesia is escalating since the production of rice tends to be levelling off for more than a decade. Soil degradation in lowland is among the main challenges. Soil encounters continuous decreasing organic matter and organic carbon content because the biomass from the rice plants was not returned to the land sufficiently. Most of the biomasses went out of the land or were burned by rice producers since there was not enough time to decompose the biomass before returning into the soil of lowland or it need additional cost to do so. Most farmers preferred to return the rice straw after burning, but it will not improve the condition. Otherwise, burning of rice straw caused air pollution since it released at least three kg particulate matter consisting of CO, SO2, CO2, and ash. Using interpretive structural modeling (ISM) this paper is trying to find the important factors with sequential solution. The result shows that encouraging rice producers to return rice straw into soil (RRIS) should start with Provision of Immediate Decomposing Technology and assistance program and Credit and Assistance Program.

The purpose of this study was interpretive structural modeling of the factors affecting supply chain optimization in the electricity industry. In this study, using a mixed method, a qualitative-quantitative approach was employed to model and verify the factors affecting supply chain optimization. To this end, through content analysis and thematic analysis using Novo software to form thematic networks, a qualitative model of factors influencing supply chain optimality was designed. In order to formulate a basic pattern of the factors affecting supply chain optimality, 23 experts in the electricity industry were selected through purposeful sampling method. In the quantitative section of the study, interpretive structural modeling (ISM) approach and MICMAC analysis were used. The results showed that the initial framework of supply chain optimality model has six dimensions including supply chain performance, supply chain management, supply chain risk management, supply chain quality management, production and operation, and supply chain stability. In the ISM graph, the variables were categorized at different levels, in which the "agency" and "strategy" factors were the highest and "customer satisfaction" was the lowest factor. After MICMAC analysis, the variables were categorized in three groups of independent variables with 11 variables, with four variables and dependent variables with 10 variables. No variables were included in the PA.

Manufacturing is one of the biggest drivers of a country’s economic growth. Nevertheless, due to globalization and flourishing consumer markets, the technological influx in manufacturing evolution poses a significant threat to climate change. To deal with the situation, green manufacturing came forward to play a vital role in lowering the impact of mass production on the global environment. The qualitative research based on expert opinion is used to have viewpoints for the implementation of green manufacturing based on green supply chain manufacturing (GSCMEs) enablers. The study, in this regard, focuses on exploring the key enablers adopted by the manufacturers to embrace green practices by using framework based on Interpretative Structural Modelling and Cross-Impact Multiplication Applied to Classification (MICMAC) analysis. Results indicate that economic constraints and the regulatory framework have high driving power and less dependency power. Researchers provide managers with a new outlook on the future towards building an eco-friendly supply chain and gaining a competitive edge over their competitors.

Abstract Based on 2.5-Dimensional (2.5D) frequency selective surface (FSS) structures, a new miniaturized frequency selective surface structure is proposed in this work to operate as a bandreject filter in the 2.4 GHz (2400 to 2483.5 MHz) and 5 GHz (5150 to 5350 MHz and 5470 to 5725 MHz) ISM (Industrial, Scientific, and Medical) bands. The FSS has the metallization layer formed by a square loop with an internal cross loop, with each geometry being responsible for a rejection frequency range. The main characteristics of the structure are discussed, emphasizing the resonance frequency variation as a function of the increasing number of vias. Numerical results are obtained through simulations using the commercial software ANSYS HFSS. In addition, by analyzing numerical results for structures with different numbers of vias, it was possible to obtain expressions for calculating the first resonance frequency for each geometry. The cell size of the 2.5D FSS of the proposed cell is only 0.0883 λ0, where λ0 corresponds to the free-space wavelength of the lower resonance frequency. The FSS presents dual polarization, angular stability for angles up to 50° and its simulated response presented two resonant frequencies, the first at 2.65 GHz and the second at 5.34 GHz, with relative bandwidths of 38.11% and 35.96%, respectively. For comparison and validation purposes, the prototype is fabricated and measured. A very good agreement between the simulation and measurement results is observed.

The study aims to assess a sustainable green financial environment by exploring the underlying structure of monetary seismic aftershocks of the COVID-19 pandemic. This study is qualitative and uses a review of literature, primary data collection methods, and qualitative analysis techniques as the study's overall design. The data is collected by one-to-one interview using a matrix style questionnaire from a panel of experts based on the purposive sampling technique. Interpretive structural modeling (ISM) combined with Matrices' Impacts Cruise's Multiplication Appliquée a UN Classement (MICMAC) is used for assessment, modeling, and analysis of data. The monetary aftershocks, namely, "more cash in hand required," "decreased travel costs," "shift to more certain or fixed revenue streams," "lower rent costs," "more zealous monitoring of cash collection cycle," and "decreased entertainment costs," occupy level I (top of the model being least critical shocks), and "tedious regulations" occupy level VIII (bottom of the model being the most vital). Other aftershocks form the middle of the model being moderate critical. Analysis of MICMAC shows that monetary seismic aftershocks high fees for assistance regarding SOPs, tedious regulations, and more downtime due to pandemic alerts are independent. This study addresses the core issue from within the aftermath of the COVID-19 pandemic. It provides new important information regarding the structure of a sustainable green financial environment that is useful for economists, financial analysts, commercial and central bankers, accountants and finance managers from the organization's public/and private sectors, local and international community, and researchers of the domain. It provides an informative structural model and classification of critical aftershocks. There are specific data/methodological/resource-related limitations of the study. The study's data are collected from a focus group; the study's methodology is qualitative and indicates relations among variables that do not quantify the associations. The study is a typical initiative of academic researchers with limited financial/physical resources; therefore, the generalizability of the study results is accordingly limited. The study is based on original, essential data and innovatively and creatively approaches the problem. It provides a unique model of an unprecedented phenomenon for reverberating the sustainable green financial environment. [ABSTRACT FROM AUTHOR]

The study aims to assess and analyze the social outcomes of the COVID-19 pandemic. The study uses the discourse of comprehensive literature review to identify the outcomes, Interpretive Structural Modeling (ISM) for developing a structural model and Matrices' Impacts Cruise's Multiplication Appliquée a UN Classement (MICMAC) for analysis, classification of societal outcomes, and corroboration of results of ISM. Data from fifteen experts was collected through a survey questionnaire. As a result of the literature review, a list of sixteen outcomes was generated and verified by a panel of experts. Results of ISM revealed that the outcomes, namely, "emotional instability," "mental health self-harm," loneliness reduced recreational activities, obesity, and "increased screen time" come at the top of the model; therefore, they are less vital outcomes, whereas the most significant outcome which is at the bottom of the model is "employment instability"; hence it has a major impact on the society. The remaining outcomes fall in the middle of the model, so they have a moderate-severe impact. Results of MICMAC validate the findings of ISM. Overall findings of the study reveal that "employment instability" is the crucial social outcome of the COVID-19 pandemic. It is an original attempt based on real-time data, which is helpful for society at large, researchers, the international community, and policymakers because this study provides a lot of new information about the phenomenon. The study includes understanding society at large, policymakers, and researchers by illustrating the complex relations and simplifying the connections among a wide range of social outcomes of COVID-19. [ABSTRACT FROM AUTHOR]

This research aims to determine the barrier to implementing GSCM practices by wood-based furniture SMEs at Jepara Regency, Central Java Province, Indonesia. This research uses a literature review to identify the relevant barrier. Then, this research uses the integration of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) to find the cause and effect and hierarchical structures among barriers to implementing GSCM practices. Based on a literature review, this research identified six characteristics as a barrier. The result indicated that, according to the DEMATEL-ISM integration analysis, lack of top management involvement (B5), cost and financial constraints (B4), lack of knowledge (B3), and lack of technological support (B2) fall into the cause group with the most significant causality and also lie in the root layer. [ABSTRACT FROM AUTHOR]

The number of devices connected to the internet has grown exponentially through the last decade, making IoT and connection worldwide more possible every year, enabling an incredible number of applications. This calls for better and more efficient methods for designing wireless devices. An efficient and small antenna is needed to ensure connectivity, range, and battery life. Under these circumstances, antenna booster technology is proposed, which uses a tiny component called an antenna booster to excite currents in the ground plane of the IoT device. This allows the antenna booster to be as small as 12 mm × 3 mm × 2.4 mm, representing only ~λ/30 at 863 MHz. The antenna booster is matched across the frequency range using a matching network. The paper compares an antenna booster and a monopole antenna regarding bandwidth for a design using a 120 mm × 60 mm ground plane in the 863 MHz to 928 MHz frequency range. Afterwards, the same designs are analysed when the ground plane size changes from 20 mm × 30 mm to 200 mm × 200 mm using steps of 10 mm to determine which approach can be reused across 53.8% of the ground planes with S11 < −6 dB without making any changes to the antenna system; for contrast, the monopole antenna can be reused only 4.6%. In addition, the antenna booster features better total efficiencies of up to 2.3 dB. A physical prototype with the antenna booster validates the numerical analysis. [ABSTRACT FROM AUTHOR]

The supply chain performance measurement system (SCPMS) is considered an integral and important part of supply chain management (SCM) for an effective and efficient supply chain (SC). The healthier and more flexible SCPMS is based on the enablers from which the SC metrics are taken. The identification of such enablers must be aligned with the strategic objectives of the organization and mapped to the SC measurement objectives. Hence, this study identifies a set of enablers and ranks them. Interpretive structural modeling (ISM)-based methodology is used in the current study to model the SCPMS implementation enablers. MICMAC analysis is further employed to categorize and comprehend the importance of each SCPMS enabler. ISM offers relationship modeling of SCPMS enablers, whereas MICMAC helps with classifying them into four categories. The Delphi method is then used to validate the ISM model. The SCPMS variable is also used in prioritization using stepwise weight assessment ratio analysis (SWARA). Practicing managers may benefit when developing and installing the SCPMS to satisfy the requirements of supply chain 4.0 for Industry 4.0 (I4.0). They will also be able to recognize and allocate resources while implementing SCPMS. The prioritization using SWARA provides an important ranking according to its importance. The SCPMS enablers of 'top management support', 'SC 4.0 performance measurement awareness', and 'managerial readiness' are found to be significant. [ABSTRACT FROM AUTHOR]

Copyright of Industrial Minerals & Processing / Huagong Kuangwu yu Jiagong is the property of Industrial Minerals & Processing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The coronavirus (COVID-19) pandemic has witnessed a significant loss for farming in India due to restrictions on movement, limited social interactions and labor shortage. In this scenario, Artificial Intelligence (AI) could act as a catalyst for helping the farmers to continue with their farming. This study undertakes an analysis of the applications and benefits of AI in agri-food supply chain, while highlights the challenges facing the adoption of AI. Data were obtained from 543 farmers in Odisha (India) through a survey, and then interpreted using "Interpretive Structural Modelling (ISM)"; MICMAC; and "Step-Wise-Assessment and Ratio-Analysis (SWARA)". Response time and accuracy level; lack of standardization; availability of support for big data; big data support; implementation costs; flexibility; lack of contextual awareness; job-losses; affordability issues; shortage of infrastructure; unwillingness of farmers; and AI safety-related issues are some challenges facing the AI adoption in agri-food supply chain. Implications were drawn for farmers and policy makers. [ABSTRACT FROM AUTHOR]

Context: this paper describes the detection of wide binary and multiple central stars (CSs) of Galactic planetary nebulae (PNe) using the most up-to-date data available from the Gaia Data Release 3 (Gaia DR3). Aims: the objective of this study is to benefit from the Gaia DR3's reliable measurements of parallax and proper motion to reveal the binary, ternary, and quadruple CSs of planetary nebulae. Methods: in our search for the binary and multiple CSs in the Gaia archive, we adopted the criteria provided in the literature to ensure that the CS and its partner(s) have comparable parallaxes and proper motions. Results: we have detected a total of 45 binary and multiple stellar systems coupled with the CSs of PNe. Based on the standard error of the parallax, this sample was divided into four categories: highest probable, probable, possible, and uncertain systems, which comprise 18, 8, 10, and 9 objects, respectively. Except for A 35, NGC 246 and IC 3568, the radial velocities of the CSs' companions are unknown for our PNe sample. The radial velocity measurements of these three companion stars are comparable to their linked CSs. The results show the detection of a highly probable quadruple system, and a probable quadruple system: NGC6853 and PHRJ1129-6012, respectively. In addition, we found one highly probable (Fr 2-42), one probable (M 1-58) and two possible (IC 2553 and PHR J1123-6030) ternary CS systems. The results further show that the primary components of eight wide and very wide binary systems are actually close binary systems. Moreover, the masses of the components of nine PN binary and multiple wide systems were calculated. [ABSTRACT FROM AUTHOR]

Exploring the determinants of residents' willingness to pay (WTP) for water quality improvements is essential for protecting the water quality and formulating effective governance policies. This paper applies the double-bounded dichotomous contingent valuation method (CVM) to determine residents' WTP for water quality improvements, calculate the average annual willingness of Haikou residents to pay for water quality improvements, and explore the factors influencing residents' WTP for water quality improvements. Interpretive structural modeling (ISM) is then used to further analyze the association and hierarchy between the influencing factors. The results show that (1) Residents are willing to pay an average of 28.42 RMB/household/year for water quality improvements, and the total annual value of water quality improvements in Haikou is at least 5.58 × 108 RMB; (2) age, education level, income level, minor family member and government trust are the main factors that significantly (p < 0.05) influence urban residents' WTP; (3) among the factors identified as significantly affecting WTP, the deeper root factors include educational attainment, age, and ultimately, the willingness of residents to participate in water quality improvements through indirect factors (government trust, income level, presence of minors). This study allows an in-depth and detailed understanding of the determinants of residents' WTP for water quality improvements and provides a practical and relevant guide to water quality improvements in the tropics. [ABSTRACT FROM AUTHOR]

Objective: Numerous variables are involved in green supply chain management and investigating any intervening variable can influence a diverse set of others. Blockchain, as an emerging technology of the fourth generation of the industry, comes with a variety of distinctive characteristics. The analysis of the integration between blockchain and green supply chain management will be highly intricate. Therefore, it is essential to establish a model to comprehend it effectively. Methods: This study was carried out in two phases. In the first phase, the required information was collected, extracted, analyzed, and combined through a systematic review with a focus on the key questions. After identifying the primary existing sources, based on the entry conditions, some items were removed according to the determined conditions. Finally, 18 articles were thoroughly reviewed, and the variables were extracted. In the second phase, the validation, refinement, and examination of the relationship among variables were discussed through questionnaires. The combined method of DEMATEL and Interpretive Structural Modeling (ISM) were used. Results: Variables from the green supply chain, likely to be influenced by blockchain were extracted, refined, and modeled in 10 levels, ranging from the most influential to the least influential ones. Environment-related variables, along with business viability, are the most influential. Conclusion: According to the achieved model and the degree of influence and impact of the variables, the main driver for moving towards green practices and utilizing blockchain include "the need to obtain environmental certification (government pressure)," "buying green products (inclination)," and "giving importance to the social responsibility of the green company." The outcome also depends on "business continuity" and environmental variables. An important aspect regarding the relationship between blockchain and the green supply chain is the location of the variables influenced by blockchain in the interpretive structural model. These variables were placed in the middle levels of the model, indicating that before implementing blockchain technology, it is crucial to assess the influencing variables and determine their value. The assessment can determine whether the establishment or non-establishment of blockchain technology is justified. If these variables indicate the need for changes in the intermediate variables of the model, blockchain technology can be used as a tool or enabler in the supply chain to drive the desired changes based on the specified influencing variables. Ultimately, as the intermediate variables in the model change, the dependent or influencing variables in the model can also be affected. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Trypanosomosis is responsible for the death of 3 million heads of cattle yearly, with 50 million animals at risk in sub-Saharan Africa. The problem of trypanosomosis is still far from being solved due to the fact that trypanosomes affect multiple hosts, widespread trypanocidal drug resistance and antigenic variation displayed by trypanosomes. Trypanocidal drugs: Isometamidium chloride (ISM) and Diminazene aceturate (DA) are the most widely used drugs for control animal trypanosomosis in Ethiopia. However, there is growing concern that their future effectiveness may be severely reduced by widespread drug resistance. Because it is very unlikely that new trypanosomal drugs will be released on to the market in the future, it is essential to maintain the efficacy of the currently available drugs. So proper detection methods of drug resistance by test in ruminants, in mice, in vitro, molecular tests and xenodiagnoses and followed by the right techniques on the delay of the development of drug resistance like reduction in the number of treatments, avoidance of under dosage, change of drugs, use of national drug police and if once resistance present allowing integrated control measures such as reducing vector numbers to reduce the number of drug treatments was great importance. [ABSTRACT FROM AUTHOR]

Radio activity in AGN (Active Galactic Nuclei) produce feedback on the host galaxy via the impact of the relativistic jets on the circumnuclear gas. Although radio jets can reach up to several times the optical radius of the host galaxy, in this review we focus on the observation of the feedback deposited locally in the central region of the host galaxies, in the form of outflows due to the jet-gas interaction. We begin by discussing how galaxy mergers and interactions are the most favored scenario for triggering radio AGN after gas accretion to the nuclear supermassive black hole and star formation enhancement in the nuclear region, observed in particular in the most luminous sources. We then discuss observational signatures of the process of jet-gas coupling, in particular the resulting outflows and their effects on the host galaxy. These include the presence of shock signatures and the detection of outflows not only along the radio jet but perpendicular to it in many sources. Although most of the studies are done via the observation of ionized gas, molecular gas is also being increasingly observed in outflow, contributing to the bulk of the mass outflow rate. Even though most radio sources present outflow kinetic powers that do not reach 1 % L b o l , and thus do not seem to provide an immediate impact on the host galaxy, they act to heat the ISM gas, preventing star formation, slowing the galaxy mass build-up process and limiting the stellar mass growth, in a "maintenance mode" feedback. [ABSTRACT FROM AUTHOR]

The term circular economy (CE) refers to a new idea that transforms the old idea of the linear economy, which created the take-make-distribution-disposal-disposal model to take-makedistribution-consume back. When a product reaches its final stage, it will not be discarded; instead, it will be sent back in a form or quality different from the original. The 6R principles: reduce, reuse, recycle, recover, redesign, and remanufacture, are considered during the return process. Several challenges must be overcome for a circular economy to be implemented. However, no one has yet identified and analyzed the difficulties that hinder the implementation of a circular economy in Indonesia, especially in Jepara Regency. This study aims to identify and analyze the difficulties that hinder the implementation of a circular economy in waste management and make plans to overcome them. Five respondents from various stakeholder groups participated in this research, including local government, academics, and nongovernmental organizations. The questionnaire was processed using the Interpretive Structural Modeling (ISM) technique to identify the main obstacles. The results show that the critical factor inhibiting the implementation of a circular economy is the price for building a waste collection, storage, processing, and disposal (B8) system and government initiatives to decide on implementation (B2). Furthermore, based on respondents' assessments, the cost of collecting waste (B16) and the practice of disposing of hazardous waste (B1) waste that is not regulated is at the second level. Waste management requires a model that follows the circular economy principle to answer these inhibiting factors. The waste bank model is the most appropriate to apply to ensure the circular economy concept is running. [ABSTRACT FROM AUTHOR]

This research introduces the design and simulation of a monopole antenna with a simple ground plane, specifically developed for dual-band communication. The antenna consists of a radiating patch, feed line, and ground plane, and it was fabricated on an FR-4 substrate. The operating frequency bands of the antenna cover 2.5-2.7 GHz and 3.9-4.6 GHz. By carefully selecting the dimensions, the antenna achieves resonance frequencies at 2.6 GHz and 4.2 GHz, with corresponding bandwidths of 200 MHz and 700 MHz. The research provided excellent impedance matching for the antenna, as indicated by the measured return loss (S11) values of -29.35 dB and -22.48 dB, surpassing the required criterion of S11 < -10 dB. The compact size of the antenna, measuring 12×24×1.6 mm2, makes it highly suitable for applications with space limitations. The antenna performance was extensively analyzed through simulations utilizing High-Frequency Structure Simulator (HFSS) software. The results validate its effectiveness for two specific frequency bands:2.6 GHz for mobile broadband service and 4.2 GHz for various applications such as cordless phones, wireless audio devices, and industrial, Scientific, and Medical Networks (ISM). This research lays a solid foundation for future developments in dual-band communication antennas, offering potential benefits in terms of connectivity and communication efficiency. [ABSTRACT FROM AUTHOR]

Mid-air gestures as a new form of human–computer interaction has a wide range of satisfaction factors, for which the primary and secondary relationships and hierarchical relationships between factors are unclear. By examining usability definitions, collecting satisfaction questionnaires and user interviews, 30 observed variables were obtained and a scale was developed. A total of 310 valid questionnaires were collected, and six latent variables were summarized through factor analysis. The matrix quantitative analysis of latent variables based on interpretative structural model theory was used to construct a hierarchical model of influencing factors of satisfaction with mid-air gestures. The study shows that the influencing factors of mid-air gesture satisfaction can be divided into three levels. The first layer of attractiveness is the direct influencing factor on the surface and the goal of mid-air gesture design. In the second layer, Simplicity and Efficiency, Simplicity and Tiredness, and Tiredness and Friendliness interact with each other. Simplicity positively affects Friendliness, and Efficiency positively affects Tiredness. The third layer, Intuitiveness is the root layer influencing factor, which affects Simplicity. This study provides a theoretical basis for the design of mid-air gesture so that it can be designed and selected more objectively. [ABSTRACT FROM AUTHOR]

Massive urbanization and resource scarcity are global phenomena for which researchers around the world see urban transformation into a smart city as the most viable solution. Yet urban planners face many hurdles in transforming cities. These include economic factors such as the high cost of infrastructure development and maintenance, social paradigms, governance, and environmental issues that limit government ambitions. To design an optimal strategy for creating a smart city, the various barriers must be identified and prioritized. The various barriers to the successful implementation of the smart city mission were identified using an extensive literature review and expert opinions. To understand the complex interdependencies among these barriers, the interpretive structural modeling method (ISM) was used. MICMAC analyzed the ISM model to classify the barriers. Indian cities vary greatly in terms of infrastructure, size, population, and available facilities. Therefore, the impact of the barriers and their interdependencies also vary. The author has extended the study and used the fuzzy approach MICMAC to improve the model by incorporating the variations observed when the city category changes. This study will help urban developers and planners to identify the relationship between barriers and design strategic planning to make the city smarter. [ABSTRACT FROM AUTHOR]