Sustainable Network Research Articles

Reverse Logistics Network Model of Dual-Channel Recycling Boxes Based on Genetic Algorithm Optimization: A Multi-Objective and Uncertain Environment Perspective

In the context of carbon neutrality, plastic ban, and green development, this paper aims to maximize the comprehensive interest of manufacturers in building a sustainable logistic network. It proposes a reverse logistics network model of a dual-channel model with multiple objectives and a random environment for the construction of recycling boxes projects and uses a Stackelberg game to develop pricing strategies for dual-channel recyclers and a genetic algorithm to optimize reverse logistics networks. This paper found the following: multi-objective evaluation is more conducive to sustainable development; when consumers are price-sensitive, a stronger rebate can significantly increase revenue; online platform recyclers should invest more in marketing than traditional recyclers; retailers’ willingness to cooperate in recycling has a significant impact on the overall benefits; the impact of government subsidies is marginal and needs to be controlled to a certain extent; the impact of green credit is insignificant, and the strength of commercial banks’ scrutiny of companies has little effect; an increase in environmental uncertainty within a certain range can lead to an overall loss of benefits, and an excessive impact can be out of line with extreme values. It provides a basis for decision-making on strategies for enterprises to build a logistics network of recycling boxes, government subsidies, green credit from banks, and dual-channel recyclers.

Designing a Resilient–Sustainable Supply Chain Network of Age-Differentiated Blood Platelets Using Vertical–Horizontal Transshipment and Grey Wolf Optimizer

Blood platelets are a typical instance of perishable age-differentiated products with a shelf life of five days (on average), which may lead to significant wastage of some collected samples. At the same time, a shortage of platelets may also be observed because of emergency demands and the limited number of donors, especially during disasters such as wars and the COVID-19 pandemic. Therefore, developing an efficient blood platelet supply chain management model is highly necessary to reduce shortage and wastage. In this research, an integrated resilient-sustainable supply chain network of perishable age-differentiated platelets considering vertical and horizontal transshipment is designed. In order to achieve sustainability, economic cost, social cost (shortage), and environmental cost (wastage) are taken into account. A reactive resilient strategy utilizing lateral transshipment between hospitals is adopted to make the blood platelet supply chain powerful against shortage and disruption risks. The presented model is solved using a metaheuristic based on a local search-empowered grey wolf optimizer. The obtained results demonstrate the efficiency of the proposed vertical-horizontal transshipment model in reducing total economic cost, shortage, and wastage by 3.61%, 30.1%, and 18.8%, respectively.

A Survey of Symbiotic Radio: Methodologies, Applications, and Future Directions.

The sixth generation (6G) wireless technology aims to achieve global connectivity with environmentally sustainable networks to improve the overall quality of life. The driving force behind these networks is the rapid evolution of the Internet of Things (IoT), which has led to a proliferation of wireless applications across various domains through the massive deployment of IoT devices. The major challenge is to support these devices with limited radio spectrum and energy-efficient communication. Symbiotic radio (SRad) technology is a promising solution that enables cooperative resource-sharing among radio systems through symbiotic relationships. By fostering mutualistic and competitive resource sharing, SRad technology enables the achievement of both common and individual objectives among the different systems. It is a cutting-edge approach that allows for the creation of new paradigms and efficient resource sharing and management. In this article, we present a detailed survey of SRad with the goal of offering valuable insights for future research and applications. To achieve this, we delve into the fundamental concepts of SRad technology, including radio symbiosis and its symbiotic relationships for coexistence and resource sharing among radio systems. We then review the state-of-the-art methodologies in-depth and introduce potential applications. Finally, we identify and discuss the open challenges and future research directions in this field.

An enhanced opportunistic rank-based parent node selection for sustainable & smart IoT networks

The Internet of Things (IoT) is a network of interconnected devices that includes low-end devices (sensors) and high-end devices (servers). The routing protocol used the Low-Power and Lossy Networks (RPL) protocol, which was designed to collect data in Low-Power and Lossy Networks (LLN) efficiently and reliably. The RPL rank property specifies how sensor nodes are placed in Destination Oriented Directed Acyclic Graphs (DODAG) based on an Objective Function (OF). The OF includes information such as the Expected Transmission Count (ETX) and packet delivery rate. The rank property aids in routing path optimization, reducing control overhead, and maintaining a loop-free topology by using rank-based data path validation. However, it causes many issues, such as optimal parent selection, next-hop node selection, and network instability. We proposed an Enhanced Opportunistic Rank-based Parent Node Selection for Sustainable and Smart IoT Networks to address these issues. The optimal parent node is determined by forecasting the expected energy of each node using Received Signal Strength (RSS) and an enhanced reinforcement learning algorithm. The proposed method addresses the issue of selecting the next-hop neighbor node and improves routing stability. Furthermore, when a large number of new nodes try to join the sustainable IoT-based smart cities, the proposed technique provides optimal load balance.

Deep Q-learning multiple networks based dynamic spectrum access with energy harvesting for green cognitive radio network

In cognitive radio (CR), dynamic spectrum access (DSA) can be used for secondary user (SU) to improve its spectrum efficiency under the limited spectrum resources. However, the SU has to consume more energy to sense the state of primary user (PU) before spectrum access. Therefore, high spectrum efficiency and lower energy consumption have become the requirements of CR. In this paper, we propose a sustainable green CR network combining DSA and energy harvesting, which can perform intelligent spectrum sensing, spectrum access and energy harvesting by a reward mechanism of deep Q-learning multiple networks (DQMN). The DQMN model consisting of multiple neural networks is designed to learn multiple learning objectives, including spectrum sensing, channel selection, communication power and access mode in DSA. A sleep mechanism is introduced to optimize the spectrum access mode and further reduce the energy consumption. The simulation results show that the DQMN-based DSA model can autonomously sense the channel state, harvest the energy, and then select the appropriate access strategy. Due to the energy harvesting and sleep mechanism, the proposed system model shows lower energy consumption and higher average effective throughput than the traditional models.

Experimental demonstration of a metro area network with terabit-capable sliceable bit-rate-variable transceivers using directly modulated VCSELs and coherent detection

Disaggregation in optical networks is particularly relevant to be considered for the deployment of 5G services and towards the support of 6G. Particularly in the metro area network (MAN), this is especially crucial, as is the adoption of suitable photonic technologies enabling dense integration to design a sustainable network architecture. Furthermore, to dynamically allocate the ever-increasing traffic, supporting multiterabit capacity, an optimal usage of the available resources by properly exploiting the multiple dimensions (including the spectral and spatial ones), with programmable and adaptive data plane solutions, is key. In this work, we assess the capabilities of a disaggregated MAN that relies on new photonic devices, node architectures, and sliceable bandwidth/bit-rate-variable transceivers, approaching wavelength division multiplexing and space division multiplexing. A hierarchical network topology is attained and the feasibility of a cross-hierarchy optical continuum is demonstrated. In fact, we experimentally demonstrate the successful transmission of multiterabits/second capacity across multiple nodes corresponding to different hierarchy levels that have different implementation schemes and support different technologies. For the top tier hierarchy level nodes, we demonstrate the transmission of up to 8 × 11 = 88 spatial/spectral channels, for a total capacity of 1.676 Tb/s, employing a node architecture able to handle up to 2560 spatial/spectral channels at different aggregation levels and granularities.

Bioenergy production from agro-livestock waste under uncertainty: a sustainable network design

Bioenergy production from agro-livestock waste under uncertainty: a sustainable network design

A discourse and content analysis of representation in the mainstream media of the South African National Health Insurance policy from 2011 to 2019

BackgroundMedia is a crucial factor in shaping public opinion and setting policy agendas. There is limited research on the role of media in health policy processes in low- and middle-income countries. This study profiles South Africa as a case example, currently in the process of implementing a major health policy reform, National Health Insurance (NHI).MethodsA descriptive, mixed methods study was conducted in five sequential phases. Evidence was gathered through a scoping review of secondary literature; discourse analysis of global policy documents on universal health coverage and South African NHI policy documents; and a content and discourse analysis of South African print and online media texts focused on NHI. Representations within media were analysed and dominant discourses that might influence the policy process were identified.ResultsDiscourses of ‘health as a global public good’ and ‘neoliberalism’ were identified in global and national policy documents. Similar neoliberal discourse was identified within SA media. Unique discourses were identified within SA media relating to biopolitics and corruption. Media representations revealed political and ideological contestation which was not as present in the global and national policy documents. Media representations did not mirror the lived reality of most of the South African population. The discourses identified influence the policy process and hinder public participation in these processes. They reinforce social hierarchy and power structures in South Africa, and might reinforce current inequalities in the health system, with negative repercussions for access to health care.ConclusionsThere is a need to understand mainstream media as part of a people-centred health system, particularly in the context of universal health coverage reforms such as NHI. Harmful media representations should be counter-acted. This requires the formation of collaborative and sustainable networks of policy actors to develop strategies on how to leverage media within health policy to support policy processes, build public trust and social cohesion, and ultimately decrease inequalities and increase access to health care. Research should be undertaken to explore media in other diverse formats and languages, and in other contexts, particularly low- and middle-income countries, to further understand media’s role in health policy processes.

Multi-objective combinatorial optimization analysis of the recycling of retired new energy electric vehicle power batteries in a sustainable dynamic reverse logistics network.

The recycling of retired new energy vehicle power batteries produces economic benefits and promotes the sustainable development of environment and society. However, few attentions have been paid to the design and optimization of sustainable reverse logistics network for the recycling of retired power batteries. To this end, we develop a six-level sustainable dynamic reverse logistics network model from the perspectives of economy, environment, and society. We solve the multi-objective combinatorial optimization model to explore the layout of the sustainable reverse logistics network for retired new energy vehicle power batteries recycling. A case study is implemented to verify the effectiveness of the proposed model. The results show that (a) the facility nodes near the front of the network fluctuate more by opening and closing; (b) the dynamic reverse logistics network is superior to its static counterpart; and (c) cooperation cost changes affect the transaction volume between third-party and cooperative enterprises and total network cost.

UNIVERSITIES AS THE SUSTAINABILITY CENTERS: EVIDENCES FROM THE STUDENTS’ SURVEY DATA

Summary. The study was conducted within the framework of The German-Ukrainian University Technology Transfer Internship Program 2021 (GUTT.INUP 2021). The GUTT. INUP 2021 program was aimed at the exchange of pedagogical and research experience between universities in Germany and Ukraine, as well as at mutual acquaintance with the latest scientific achievements of German and Ukrainian scientists, including in the field of sustainable development. The University of Würzburg (Germany) has extensive experience in joining new member universities to the existing Bavarian Network for Sustainability in Higher Education, as well as in organizing and managing student activities in areas related to the implementation of a sustainable development strategy. At present, environmental education, the cultivation of an environmental outlook, and the involvement of students in the activities of universities aimed at implementing the basic principles of environmental sustainability as an important component of a sustainable development strategy are very relevant in the context of global climate change. Using the experience of the University of Würzburg, as well as the practice of other European universities in this field, our team conducted a survey among students from different faculties to collect their perceptions and opinions on sustainable development in higher education and environmental sustainability issues. Based on the results of the survey, we proposed a roadmap for the Ivan Franko National University of Lviv as a future center for sustainable development, including activities aimed at achieving environmental sustainability goals in the context of the sustainable development strategy. Keywords: universities, environmental education, students’ activities, environmental sustainability, sustainable development

The Existence Of Truck Driver In The Spirit Of Work

The role of truck drivers in sustainable transportation and logistics in a sustainable supply chain network is particularly noteworthy. The driver work system is very different from the employees in the company. Therefore, this research was conducted to analyze the effect of work passion, strength against frustration, quality of survival, and team spirit on the morale of truck drivers in their delivery activities. This study used a quantitative approach with 173 truck drivers participating as respondents. Testing using SEM analysis with the SmartPLS 3.0 application. The results showed that work passion, strength against frustration, quality of endurance, and group spirit each had a unidirectional relationship with morale, meaning that the higher the passion for work, the strength against frustration, the quality of survival, and the group spirit of truck drivers, the higher the morale will increase. Based on the coefficient value, it can be seen that the quality of survival is a predictor that has a dominant influence on morale, which is indicated by the highest coefficient of 0.288.

Performance measurement of a Resilient-Sustainable Supply Chain through fuzzy multi-criteria techniques

New trends and approaches related to environmental protection and social welfare; changes have been implemented that adapt supply chains (SC) to meet the requirements of modern production processes. On the other hand, there is a lack of proposals that integrate the sustainability and resilience approaches in the measurement of performance in supply chains despite being two elements widely studied separately. In the literature, no studies have been found that define the impact that resilience and sustainability have together on the performance of the supply chain, this being a limitation for decision makers to establish strategies that positively impact resilient-sustainable supply chains (RSSC). Therefore, the proposal of this work is focused on developing a method that combines multicriteria analysis techniques with fuzzy logic that determines the levels of performance in resilient-sustainable supply chains. The state of the art in terms of resilience and sustainability in supply chains was analyzed, which led to the definition of performance criteria in RSSC, continuing with the definition of the relationship of these performance elements through the tool known as DEMATEL. Then, the use of the “analytical network process” method is proposed, which determines the weighting of each selected performance criterion. The results show that the social dimension is the most influential and important within this framework. Working conditions and social responsibility have the greatest influence on the SCP. This study established the main attributes of the SC that decision makers should consider when designing and planning a resilient and sustainable network, as well as their influence relationships in the SCP.

Metaheuristic optimizers to solve multi-echelon sustainable fresh seafood supply chain network design problem: A case of shrimp products

Seafood products are sought-after among communities all over the globe and are the main sources of essential nutrition for humans. Recently, the seafood supply chain networks have encountered obstacles that new sustainability regulations and the pandemic have brought forward. In this study, a novel supply chain network is developed for fresh seafood, considering sustainability aspects, to ideally balance the financial aspect of the network while enhancing the recycling of waste products. Moreover, four metaheuristics are employed to conquer the computational complexity of exact solution methods. To evaluate the performance of the algorithms in addressing the complexity of the proposed seafood supply chain model, some numerical examples in three different scales are designed. The obtained results from metaheuristic optimizers are assessed based on five effective measures. To facilitate the statistical analysis process, each measure is normalized using the relative deviation index indicator. According to the results obtained from the implementation of metaheuristic algorithms, it can be concluded that the multi-objective grey wolf and multi-objective golden eagle optimizers outperform the other two solution methods in terms of quality of solutions. Therefore, they can be applied efficiently in solving real-world seafood supply chain network problems.

Batteryless NB-IoT prototype for bidirectional communication powered by ambient light

IoT use-cases aim to provide high-quality services where IoT infrastructures are always linked with the Internet. They require large scale deployment of small devices acting as sensors and actuators in different places. These devices need to be energy efficient and run sustainably. The maintenance of such large scale devices is difficult if they are powered by batteries that need to be replaced after some time. Moreover, disposing of these batteries also pollutes the soil and contaminates groundwater. Narrowband IoT (NB-IoT) is one of the long-range technologies that supports connectivity even in deep indoor closed areas such as underground parking lots, elevators, and tunnels. Therefore, in this article, the suitability of an NB-IoT device or User Equipment (UE) to be powered without any batteries to enable a long-term sustainable maintenance-free IoT network is demonstrated. We develop a prototype using a small photovoltaic solar panel for indoor light harvesting using sunlight or an artificial light bulb to power the NB-IoT UE. Due to the unpredictability of energy harvesting, the batteryless UE can experience frequent switches between the ON and OFF states. Therefore, we evaluate the performance of the solution where the UE communicates either uni- (uplink only) or bi-directionally (uplink and downlink) for different capacitor sizes at different artificial light intensities. We also evaluate the solution under dynamic harvesting power using natural light during daytime. The experimental results conclude that the batteryless NB-IoT UE supports a transmission interval of 60 s without experiencing any restarts when harvesting 6 mW or more, which can be harvested by placing a 6 W artificial light bulb at a distance of 60 cm or less.

It’s in Our DNA: Leadership Perspectives on Institutionalizing STEM Success in an Alliance

This study investigated how institutional leaders within an alliance navigate and use their agency to cultivate organizational change to support the success of underrepresented racial minority (URM) science, technology, engineering, and math (STEM) students. As part of this study, we partnered with the Illinois Louis Stokes Alliance for Minority Participation (ILSAMP), a signature program of the National Science Foundation (NSF), to explore our research question. The phenomenon of interest is the institutional leaders’ perceptions of their agency and their organization’s efforts to engage in the Alliance and support URM STEM student success through the various initiatives. The research team conducted 20 semi-structured interviews with institutional leaders and faculty at 11 public and private institutions as part of the STEM alliance. We utilized Braun and Clarke’s (2006) six-phased thematic analysis to identify patterns of meaning within the data that respond to the research question. Findings revealed a leadership perspective that frames approaches to STEM initiatives becoming a part of an institution’s fabric. The contribution of this study relates to the illumination of the tension between institutional leaders' agency to make change sustainable versus structural and leadership networks inhibiting STEM success efforts.

Artificial intelligence enabled energy aware clustering technique for sustainable wireless communication systems

Green energy is one of the effective solutions for future sustainability and finds use in several application areas. Sustainable communication aims to decrease energy utilization of the system and increase resource utilization to a maximum extent. On sustainable wireless sensor network (WSN), the restricted battery capacities of the sensors necessitate energy efficient protocols to accomplish sustainable communication. Earlier works reported that clustering is one of the effective techniques employed to accomplish energy efficiency. Since clustering is treated as an NP hard optimization problem, meta-heuristic optimization algorithms are executed. In this aspect, this study designs an artificial intelligence enabled energy aware clustering scheme for sustainable wireless communication system, named AIEAWC-SWCS technique. The major intention of the AIEAWC-SWCS technique is for attaining sustainable energy management in WSN. To accomplish this, the AIEAWC-SWCS model derives a novel lens oppositional based learning (LOBL) with deer hunting optimization algorithm (DHOA) for optimum selection of cluster heads (CHs) and construct clusters. The proposed AIEAWC-SWCS model computes a suitability purpose with four variables namely energy competence, cluster density, inter-cluster coldness, and intra-cluster distance. The nodes with maximum fitness values have the ability to become CHs and perform sustainable communication. The AIEAWC-SWCS approach demonstrates a minimum MPLR of 2.67 percent with 200–100 base stations. It is guaranteed to be able to carry out WSN sustainable energy management. The experimental validation of the AIEAWC-SWCS model is tested in various features and the comparative analysis shows the potential of the AIEAWC-SWCS model in accomplishing sustainable communication.

Nanofibrillar biochar from industrial waste as hosting network for transition metal dichalcogenides. Novel sustainable 1D/2D nanocomposites for electrochemical sensing

Industrial wastes have become elective sustainable sources to obtain materials for electronic/electroanalytical purposes; on the other hand, easy and green strategies to include semiconductor 2D graphene-like materials in conductive networks are highly required.In this work, 1D/2D nanocomposites (NCs) based on nanofibrillar biochar (BH) from paper industry waste and transition metal dichalcogenides (TMDs: MoS2, WS2, MoSe2, and WSe2), were prepared in water via liquid phase exfoliation (LPE) using sodium cholate as bioderived surfactant. The TMD amount in the NCs has been carefully optimized, searching for the best compromise between electron transfer ability and electroanalytical performances. Four different water-dispersed BH-TMD NCs have been selected and comprehensively studied from the electrochemical point of view and morphologically characterized. The BH-TMDs potentiality have been demonstrated in model solutions and real samples towards different analytes of biological and agri-food interest. The most performing NCs have been selected and used for the simultaneous determination of the neurotransmitters dopamine (DP) and serotonin (SR), and the flavonoids quercetin (QR) and rutin (RT), obtaining good linearity (R2 ≥ 0.9956) with limits of detection ranging from 10 to 200 nM. Reproducible quantitative recovery values (90–112%, RSD ≤6%, n = 3) were obtained analyzing simultaneously DP and SR in synthetic biological fluid and drugs, and QR and RT in food supplements, proving the usability of the proposed materials for real analyses.This work proves that BH-nanofibers act as a sustainable conductive hosting network for 2D-TMDs, allowing full exploit their electroanalytical potential. The proposed BH-TMD NCs represent a sustainable, affordable, and captivating opportunity for the electrochemical and (bio)sensoristic field.

The Impact of Cooperative Behavior between Social Organizations during the COVID-19 Pandemic Outbreak in Shanghai: A Simulation Approach.

In 2022, a new outbreak of the COVID-19 pandemic created considerable challenges for the Shanghai public health system. However, conventional prevention and control strategies, which only rely on formal organizations, inefficiently decrease the number of infections. Thus, a multi-organization management mode is needed for pandemic prevention. In this paper, we applied a stochastic actor-oriented model (SAOM) to analyze how these social organizations cooperate with others and further identify the mechanism that drives them to create a reliable and sustainable cooperative relationship network from the perspective of social network analysis. The model allowed us to assess the effects of the actor’s attributes, the network structure, and dynamic cooperative behavior in RSiena with longitudinal data collected from 220 participants in 19 social organizations. The results indicated that the number of cooperative relationships increased during the pandemic, from 44 to 162, which means the network between social organizations became more reliable. Furthermore, all the hypotheses set in four sub-models were significant (t-ratio < 0.1, overall max t-ratio < 0.25, and e/s > 2). Additionally, the estimated values showed that four factors played a positive role in forming the cooperative relationship network, i.e., all except the “same age group effect (−1.02)”. The results also indicated that the social organizations tend to build relationships with more active actors in the community in every time period. This paper is of great significance regarding the innovation of public health system management and the improvement of Chinese grassroots governance.

Sustainable network design of bioenergies generation based on municipal solid waste (MSW) management under uncertainty

Municipal solid waste in a circular economy will be used as a resource, and the design of a product-oriented waste management network is essential. Considering the significant added value of bioenergy, it is one of the most important outputs of the waste management network that the production of a diverse range of them needs further study. Focusing on this issue, a sustainable waste management network was designed to optimally extract various bioenergies by considering all three dimensions of economic, environmental, and social sustainability under uncertain conditions. The proposed model in this research for the sustainable municipal solid waste network was a multi-objective possibilistic mixed-integer non-linear programming. For dealing with uncertainty in this network, a combination of methods based on interactive fuzzy programming was used. The model was implemented after collecting data from Arad Kooh, Iran as a case study using the General Algebraic Modeling System (GAMS) software. The results of the proposed solution method were the amount of bioenergies generated by treatment technologies and the launched technologies throughout the defined time interval. The validation results of the proposed solution method confirm the research results.

A novel separate chance-constrained programming model to design a sustainable medical ventilator supply chain network during the Covid-19 pandemic

&lt;p style='text-indent:20px;'&gt;Providing new models or designing sustainable networks in recent studies represents a growing trend. However, there is still a gap in the simultaneous modeling of the three dimensions of sustainability in the electronic medical device supply chain (SC). In this paper, a novel hybrid chance-constrained programming and cost function model is presented for a green and sustainable closed-loop medical ventilator SC network design. To bring the problem closer to reality, a wide range of parameters including all cost parameters, demands, the upper bound of the released &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ co_2 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, and the minimum percentage of the units of product to be disposed and collected from a customer and to be dismantled and shipped from DCs are modeled as uncertain along with the normal probability distribution. The problem was first formulated into the framework of a bi-objective stochastic mixed-integer linear programming (MILP) model; then, it was reformulated into a tri-objective deterministic mixed-integer nonlinear programming (MINLP) one. In order to model the environmental sustainability dimension, in addition to handling the total greenhouse gas emissions, the total waste products were also controlled. The efficiency and applicability of the proposed model were tested in an Iranian medical ventilator production and distribution network. For sensitivity analyses, the effect of some critical parameters on the values of the objective functions was carefully examined. Finally, valuable managerial insights into the challenges of companies during the COVID-19 pandemic were presented. Numerical results showed that with the increase in the number of customers in the COVID-19 crisis, social responsibility could improve cost mean by up to 8%.&lt;/p&gt;