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Corporate green innovation is the crucial driving force to promote green development and realize the construction goal of "Beautiful China." Meanwhile, the development of Fintech creates a more favorable external environment for corporate green innovation. Using the panel data of China's Digital Financial Inclusion Index and Energy Poverty Index at provincial level from 2011 to 2020, this paper studies the influence of Fintech on corporate green innovation by the evidence from Chinese heavily polluting enterprises. Based on stepwise regression, this paper further examines the mediating role of energy poverty, including energy consumption level, energy consumption capacity, and energy consumption structure, in the relationship between Fintech and corporate green innovation. The results show that (1) Fintech contributes to improving the level of green innovation of heavily polluting enterprises; (2) energy poverty acts as a mediator in the process of Fintech's influence on corporate green innovation; (3) Fintech can promote green innovation of heavily polluting enterprises by improving the level of regional energy consumption level, but it fails to influence corporate green innovation through energy consumption capacity and energy consumption structure. These results provide governments and companies with implications on facilitating corporate green innovation so as to further promote green development. [ABSTRACT FROM AUTHOR]

Computation offloading and service migration are two major research hotspots in the mobile edge computing (MEC) environment. However, in the existing MEC architecture, the idle computing resources of offsite edge servers are not fully utilized, which leads to the problem of high overall system time and energy costs. In this paper, we propose a multi-edge collaborative computation offloading strategy for this problem. The strategy analyzes and calculates the energy consumption and latency cost of task execution for local terminals, edge servers and central cloud, constructs a computation offloading model with the weighted sum of latency and energy consumption as the optimization objective, and then solves the model using an improved genetic algorithm to obtain the best computation offloading decision. On the other hand, the mobility of users in the MEC environment leads to service migration, which leads to unbalanced load on the edge servers and network congestion, etc. This paper proposes an energy threshold-based task migration strategy. The strategy analyzes the time and energy consumption of service execution and data transmission, designs an edge server selection algorithm based on the energy consumption threshold, constructs a service migration model, and finally solves the optimal service migration strategy by improving the genetic algorithm. Experimental results show that the multi-edge collaborative computation offloading strategy proposed can significantly improve the performance of data transfer cost, energy consumption, and task completion time. The proposed migration strategy based on energy consumption threshold can significantly improve the performance of mobile server energy consumption, service completion time, and data transfer energy consumption. [ABSTRACT FROM AUTHOR]

A method (Ankerfors and Lindström in Method for providing nanocellulose comprising modified cellulose fibers, ) was employed to physically attach anionic carboxymethyl cellulose (CMC) chains onto wood pulp, upon which it was fibrillated by a microfluidizer-type homogenizer at high applied pressures and at dilute conditions [<3 % (w/w)]. It was found that the CMC-modified pulp can be fibrillated at the same consistencies as many of the commercially available NFC products. The NFC manufacturing process was also deemed to be energy efficient, as it lacked the need for mechanical pre-treatment, which is often a prerequisite for the production of many existing NFC systems. The CMC-based NFC was studied with respect to the rheological characteristics, and was also characterized using AFM-imaging. Further, The NFC was made into films, and its tensile strength was determined together with its barrier properties. In general, the rheological characteristics (viscosity and storage modulus) together with the tensile strength and oxygen barrier properties of the films were improved with increasing the number of passes through the microfluidizer. The fibrillated CMC-modified pulp was found to be as efficient as other NFC systems when employed as dry strength additive. The employment of the investigated material, which can be produced at acceptable costs and through environmentally benign and industrially relevant processes can, hence, potentially lead to significant future savings in the energy consumption levels in the paper and cardboard manufacturing processes, which have been recognized as major application areas of NFC products. [ABSTRACT FROM AUTHOR]

In this paper, the optimum design considering main operating points (MOPs) of the electric vehicle (EV) traction motor is conducted to reduce the torque ripple and improve the fuel economy. MOPs of the traction motor can be determined by considering ratios of number of operating points or considering energy consumption ratios based on the EV simulation. Specifically, the optimum design is conducted with the aim of minimizing the torque ripples at MOPs determined by considering ratios of number of operating points (RNOMOPs) and maximizing the inverter-motor system efficiencies at MOPs determined by considering energy consumption ratios (ECRMOPs) to simultaneously reduce the torque ripple and improve the fuel economy. To verify the effectiveness of the optimum design considering MOPs proposed in this paper, torque ripples at RNOMOPs and the fuel economies according to the initial model and the optimum model are compared. As a result, torque ripples of the optimum model at RNOMOPs are remarkably decreased by 58.84 %p and 57.77 %p, respectively, and the fuel economy according to the optimum model is improved by 1.72 %. Conclusively, the effectiveness of the optimum design considering MOPs of the EV traction motor for the torque ripple reduction and the fuel economy improvement is verified. [ABSTRACT FROM AUTHOR]

It can be observed from the existing energy literature the previous papers investigating the influence of renewables consumption on GDP for the USA commonly ignore structural breaks in the US economy. Hence, the purpose of this paper is to examine the impact of renewable energy consumption on economic growth for the USA using quarterly data over the period 1977Q1-2019Q3 through unit root and cointegration methods based on different approaches in modelling structural breaks. Our empirical evidence confirms that all unit root tests give similar outputs and show all the variables become stationary at 1st differences. Besides, cointegration tests show highly different results in terms of the statistical significance of the coefficients. For instance, the cointegration test without structural breaks indicates that renewable energy consumption has no impact on economic growth. With sharp structural breaks in the cointegration approach, there is no cointegration between the variables. The empirical findings of the cointegration test with sharp and gradual breaks imply that renewable energy consumption has positive effects on economic growth. This paper discusses the implications of the empirical findings. [ABSTRACT FROM AUTHOR]

In this paper, an electric servo actuator implementation scheme with an energy recovery link is proposed to address the potential energy being wasted when a load falls. The new electric servo actuator recovers the potential energy dissipated during the load-falling progress by using an accumulator. The accumulator can also provide extra thrust when the load is lifted. Moreover, a dynamic model is established based on the theory of power bond graphs to analyze the dynamic and energy consumption characteristics of the new electric servo actuator. The simulation results show that the new electric servo actuator can significantly reduce the required motor power and effectively recover the potential energy of the load. This achievement is very significant for improving the energy efficiency of electric servo actuators and mitigating global warming. In addition, the modeling method used in this paper has important reference significance for the energy consumption analysis of other mechanical systems with complex energy domains. [ABSTRACT FROM AUTHOR]

Message-transmission energy expenditure dominates battery lifetime in a Wireless Sensor Network (WSN). This paper newly combines network coding with a brokered WSN architecture to decrease the number of messages by means of message aggregation. It also facilitates low-latency delivery of critical messages and improves the overall energy efficiency of a WSN. Sensor nodes are arranged into subgroups, in each of which a broker separates messages into High Priority (HP) and Best Effort (BE) queues. Both arriving HP and BE messages are separately aggregated through network coding and, according to priority, are forwarded to the next broker or eventually to a data sink, where they are decoded. Service differentiation, together with network coding, prolongs the lifetime of the network by reducing the amount of energy consumed in brokers and increases message throughput by reducing waiting times at intermediate brokers. Best-effort message latency was reduced message as well as for high-priority messages. Without network coding all WSN nodes had run out of energy, whereas with the network-coded approach, twenty percent of the sensor nodes were still alive. This compares with some prior research which provides one or more of low message latency, increased message throughput, reduced WSN energy consumption, and prioritized queueing but not all these features together. [ABSTRACT FROM AUTHOR]

Transportation is the key carbon emission source after energy supply and industrial production. Under the vision of carbon peak and carbon neutralization, the pressure of reducing carbon emissions in transportation will be greater in the future. This paper constructs a model that takes transportation carbon emission as the main target and freight transportation utility value as the auxiliary target. The constructed model satisfies the constraints of freight turnover in the whole society, freight economic and social benefits, and the ecological constraints of the freight system. With MATLAB, the freight turnover of roadways, railways, and waterways (excluding ocean transportation) in 2030 is solved by using the adaptive genetic algorithm. The results indicate that (I) compared with the current freight structure of China, the roadway freight sharing rate in 2030 will decrease by 8.07%, and the railway freight sharing rate and the waterway freight sharing rate (excluding ocean transportation) will increase by 0.93% and 7.13%, respectively. (II) After optimization, the energy consumption and carbon emission are reduced by 42,471,500 tons (10.3%) and 91,379,400 tons (10.2%) of standard coal, respectively. (III) The adaptive genetic algorithm outperforms the traditional genetic algorithm in terms of convergence speed and accuracy. (IV) As the weight coefficient of carbon emission increases, the utility value of freight transportation consistently decreases, and the sensitivity increases. Meanwhile, as the carbon emission weight coefficient increases, carbon emission keeps decreasing, and the sensitivity decreases. [ABSTRACT FROM AUTHOR]

There is a growing research literature focused on ‘invisible energy policy’ that explores the complex links between policies in non-energy sectors and energy demand. Invisible energy policies are those that do not include energy as a visible policy objective but still pose implications for energy demand that are largely unrecognised in non-energy organisational settings. Within this innovative area of analysis, to date, little attention has been paid to how ‘energy’ features in the discourse of non-energy contexts. This paper makes a distinctive contribution by examining how energy issues, such as energy demand, travel, and energy skills, do or do not feature in the policy and wider strategy of a non-energy policy setting. The research focuses on the content of policies and other documents in the non-energy policy context of UK secondary schooling, using a case study approach. It aimed to identify how energy surfaces and/or remains absent in different types of documentary evidence at the school. The textual analysis shows how openings for energy to surface as a concern at the school are often heavily bounded in specific policy areas, such as sustainability and education. It also foregrounds the lack of recognition in the sample for how school operations and strategy can produce demand for energy more fundamentally. It argues that these openings for energy to surface can provide points of negotiation to discuss the more fundamental energy impacts of policy. The paper concludes by reflecting on the analysis’ implications for research on ‘invisible energy policies’ and low-carbon transitions. [ABSTRACT FROM AUTHOR]

As a major part of global development governance, G20 countries account for 80% of global carbon emissions. In order to achieve the goal of "carbon neutrality" proposed by the United Nations, it is important to compare and analyze the drivers of carbon emissions in G20 countries and provide recommendations for their carbon emission reduction. Based on the data of 17 G20 countries in the EORA database, this paper compares the drivers of carbon emissions of each country from 1990-2021 using weighted average structural decomposition and K-mean model. This paper focuses on four drivers, including carbon emission intensity, final demand structure, export structure, and production structure. Carbon emission intensity and final demand structure are the main factors of carbon emission reduction, and the other two factors have little influence. Among the G20 countries, the UK is in the first category because it does the best job on the four factors of carbon emissions, yet Italy is in the last category because it does not take full advantage of the four factors. Therefore, improving supply energy efficiency and adjusting demand, export, and industrial structure have become important tools for countries to transform and achieve carbon neutrality. [ABSTRACT FROM AUTHOR]

With the development of urbanization, the problem of the disintegrated between industry and city became more prominent, exploring the reasons. The efficiency of new-type industry has been the crucial factor in city-industry integration. This paper constructs the measurement index system of new-type urbanization via DEA-BCC methodology, starting from the quality of urbanization to analyze the efficiency of urbanization. This paper chooses the total energy consumption, general public budget expenditure, and the proportion of employment personnel in the tertiary industry in all urban units as input variables. The total retail sales of consumer goods, urbanization rate, average annual concentration of pm2.5 (popW), and built-up area as output variables. This paper uses DEA method to measure the comprehensive efficiency value, technical efficiency value, and scale efficiency value of new urbanization in Shanghai, and analyzes the influencing factors of urbanization efficiency. The results show the following: (1) The overall level of comprehensive efficiency value, technical efficiency value, and scale efficiency of Shanghai's new-type urbanization are relatively high, especially the technical efficiency basically stays at a high level. The overall trend of scale efficiency and comprehensive efficiency is consistent, and the comprehensive efficiency is greatly influenced by scale efficiency. (2) The technical efficiency of urbanization in Shanghai is close to the optimal, and there is little space for further increasing technological input to improve the comprehensive efficiency of new-type urbanization. The scale efficiency is slightly lower than the technical efficiency, and there is still some space for optimization. (3) In terms of urbanization input indicators, Shanghai's total energy consumption and general public budget input were too much in the early years, which led to the reduction of urbanization efficiency, and the situation has been improved in recent years. In terms of the output index of urbanization, increasing the total retail sales of social consumer goods and the output of built-up area can make the urbanization efficiency of Shanghai reach the optimal efficiency. [ABSTRACT FROM AUTHOR]

Fog computing has been introduced in recent years to extend cloud services to the network edge in order to minimize network delay and network congestion and overcome cloud computing limitations. However, several challenges are yet to be addressed in order to achieve the full benefits of the fog-IoT paradigm for low latency applications. The foremost crucial challenge is designing efficient resource management schemes capable of maximizing the throughput and minimizing the delay of IoT application. In this paper, we propose a QoS-aware greedy edge placement scheme that defines the way in which application modules are distributed across fog devices in order to minimize the end-to-end latency of real-time IoT applications. The proposed scheme is composed of two stages: a greedy-delay minimizing application module selection stage and a greedy-delay minimizing application module placement stage. The first stage aims to reduce the end-to-end latency by determining the order in which the application modules are placed across fog devices to maintain high QoS of real-time applications. The second stage uses the depth first search algorithm to select the fog node that meets application modules' processing, storage and bandwidth requirements. To evaluate the performance of the proposed scheme intelligent surveillance through distributed camera networks application was used. The application modules are mapped onto fog nodes connected in different configurations. The experimental results demonstrate that the proposed scheme provides high QoS by reducing the end-to-end latency, the network usage and the energy consumption. [ABSTRACT FROM AUTHOR]

In the case of China, there is a need of green energy system to boost growth and environment. However, the current growth of urbanization is putting high pressure on energy system through financial capital. Hence, it is a need to develop such path through renewable energy consumption, capital development, and urbanization to enhance the performance of development and environment. Therefore, this paper contributes to the literature by adding the asymmetries between renewable energy, urbanization, economic growth, and capital investment by covering the period from 1970 to 2021. For this purpose, we apply the nonlinear autoregressive distributed lag model to find the nonlinearities between the variables under study. The findings confirm the asymmetric short- and long-term relationships between the variables. In this sense, capitalization shows the short- and long-term asymmetric impacts on renewable energy consumption. In addition, urbanization and economic growth have long-term asymmetric and positive impacts on renewable energy consumption. At last, this paper provides applicable and practical policy implications for China. [ABSTRACT FROM AUTHOR]

Based on the background of carbon neutrality targets, panel data of 30 Chinese provinces during 2000–2019 were selected in this paper to explore the nonlinear correlation mechanism among transportation, tourism development, renewable and non-renewable energy consumption and economic growth. Firstly, by employing a fixed effects model as the base model, we confirmed the existence of correlations among these variables. And the nonlinear moderating effects were also verified by setting interaction terms in the model. Secondly, we further discussed the characteristics of the nonlinear relationship among the variables by employing a threshold effects model. Our results demonstrated that the effect of transportation on economic growth is positive and significant if and only if renewable energy consumption surpasses a certain threshold. Meanwhile, we also found that the effect of tourism development on economic growth is positive when non-renewable energy consumption is under a certain threshold. Finally, our study indicated that transportation maintains a positive and significant effect on economic growth within any threshold of tourism development, and the positive effect gradually increases as the threshold rises. The findings of this paper suggested that for China, the contribution of transportation and tourism to economic growth can be expanded by further expanding the substitution of renewable energy for non-renewable energy. [ABSTRACT FROM AUTHOR]

New energy strategies are crucial to address energy and environmental issues, but the energy consumption transition may also affect firm behavior with unintended economic consequences. Using data from A-share listed companies from 2010 to 2019, this paper investigates the impact of energy consumption structure transformation on firms' total factor productivity (TFP) using China's new energy demonstration city (NEDC) policy as a shock. It is found that the NEDC reduces firms' TFP by about 6.4%. This conclusion still holds after a series of robustness and endogeneity tests. According to the channel analysis, NEDC reduces the efficiency of firms' resource allocation and innovation, resulting in efficiency losses. Furthermore, differences in firms' ownership and geographical location make the impact of NEDC on TFP heterogeneous. For example, the hindering effect of NEDC on TFP is more pronounced in private firms and firms in regions with lower marketization. This paper shows that the promotion and application of new energy may have certain economic costs. To better balance the benefits and costs of new energy strategies, the government and other relevant departments should increase policy flexibility and perfection. [ABSTRACT FROM AUTHOR]

Background: The Association of Southeast Asian Nations (ASEAN) relies mainly on fossil fuels in their energy supply, leading to higher CO2 emissions, pollution, and further environmental degradation. This paper uses the panel vector autoregressive and the Granger non-causality test in the heterogeneous panels, together with long-run estimation techniques, to examine the dynamic link among energy consumption, economic growth, and carbon emissions with the focus on renewable energy for the ASEAN countries in the past three decades. Results: The findings from this paper indicate that carbon emissions are associated with energy consumption. In contrast, renewable energy usage reduces CO2 emissions, improving environmental quality. Economic growth is associated with increased energy consumption and carbon emissions in the ASEAN countries. The findings also indicate that the effects of energy consumption on economic growth are more significant than those of renewable energy in ASEAN. When considered together, these findings form a vicious circle regarding the energy–growth–emission nexus for the ASEAN economies. In addition, a bidirectional Granger causality among energy consumption, economic growth, CO2 emissions and renewable energy usage is confirmed. Conclusions: Renewable energy has emerged as an important viable option for the ASEAN nations to achieve their dual objectives of enhanced economic growth, reduced CO2 emission, leading to improved environmental quality. [ABSTRACT FROM AUTHOR]

Grinding, a critical precision machining process for difficult-to-cut alloys, has undergone continual technological advancements to improve machining efficiency. However, the sustainability of this process is gaining heightened attention due to significant challenges associated with the substantial specific grinding energy and the extensive heat generated when working with difficult-to-cut alloys, renowned for their exceptional physical and mechanical properties. In response to these challenges, the widespread application of massive coolant in manufacturing industries to dissipate grinding heat has led to complex post-cleaning and disposal processes. This, in turn, has resulted in issues such as large energy consumption, a considerable carbon footprint, and concerns related to worker health and safety, which have become the main factors that restrict the development of grinding technology. This paper provides a holistic review of sustainability in grinding difficult-to-cut alloys, encompassing current trends and future directions. The examination extends to developing grinding technologies explicitly tailored for these alloys, comprehensively evaluating their sustainability performance. Additionally, the exploration delves into innovative sustainable technologies, such as heat pipe/oscillating heat pipe grinding wheels, minimum quantity lubrication, cryogenic cooling, and others. These groundbreaking technologies aim to reduce dependence on hazardous coolants, minimizing energy and resource consumption and carbon emissions associated with coolant-related or subsequent disposal processes. The essence of these technologies lies in their potential to revolutionize traditional grinding practices, presenting environmentally friendly alternatives. Finally, future development trends and research directions are put forward to pursue the current limitation of sustainable grinding for difficult-to-cut alloys. This paper can guide future research and development efforts toward more environmentally friendly grinding operations by understanding the current state of sustainable grinding and identifying emerging trends. [ABSTRACT FROM AUTHOR]

This paper investigates the persistence of shocks on non-renewable and renewable energy consumption for 15 leading countries by renewable energy consumption, over the period 1980–2018. For this aim, we apply Christopoulos and Leon-Ledesma (J Int Money Finance 29(6):1076–1093, 2010) Fourier ADF unit root test which structural breaks are included by using a trigonometric function to allow for smooth temporary mean changes rather than jump functions. In application, we do not need to specify the numbers, locations and the forms of the structural breaks a priori. The results give that shocks on non-renewable energy consumption per capita are persistent for 13 countries except Sweden and the United States; shocks on renewable energy consumption per capita are persistent for 12 countries except Canada, Sweden and the United Kingdom. Overall, we conclude that persistent policy implications on non-renewable energy consumption are more effective tool than transitory policy stances for 13 countries except Sweden and the United States whereas persistent policy implications on renewable energy consumption are more effective for 12 countries except Canada, Sweden and the United Kingdom. In other words, since the energy consumption returns to its trend path quickly, any policy will not be effective on non-renewable energy consumption for Sweden and the United States, and also on renewable energy consumption for Canada, Sweden and the United Kingdom. [ABSTRACT FROM AUTHOR]

Green consumption has become an important part of an environmentally friendly society. Policy guidance can effectively promote green consumption. Green consumption policies are extensive because of the diversity of green consumption objects. However, the various objects are not completely isolated, and policy must have a certain level of consistency. Therefore, from the perspective of green consumption objects, this paper first uses the text mining method to mine 308 green consumption policies in China, analyzing the objects covered in China's green consumption policies. Then, the evaluation system of green consumption policy consistency was built, seven key policies were selected, and the PMC-index method was used to measure policy consistency. The results show that (i) the main objectives of China's green consumption policy are the promotion of green products and the efficient use of energy. Green services and green recycling have been subject to fewer policy efforts. (ii) In terms of the links involved in green consumption, China pays more attention to the supply and the consumption chain and less attention to the recycling chain. (iii) The quantitative evaluation results of the seven typical policies show that the policy consistency is good but not perfect. The policy shortcomings are mainly reflected in the incentive methods. Therefore, it is recommended to further improve the green consumption objectives and enrich the means and methods of green consumption guidance. [ABSTRACT FROM AUTHOR]

This paper reviews the literature on (potential) green recovery measures in the context of the global financial crisis and the COVID-19 pandemic, focusing on their macroeconomic effects. We find that spending for renewables and energy efficiency is particularly promising with regard to macroeconomic impacts. Moreover, the empirical evidence suggests that green recovery measures are associated with larger macroeconomic effects compared to conventional non-green recovery spending. We also derive lessons learned with regard to open questions and issues as well as accompanying framework conditions which could enhance a macroeconomically successful implementation of green recovery measures. [ABSTRACT FROM AUTHOR]

The majority of Europe's building stock consists of facilities built before 2001, presenting a substantial opportunity for energy efficiency improvements during their operation and maintenance phase. Digitalizing these buildings with digital twin technology can significantly enhance their energy efficiency. Reviewing the applications and trends of digital twins in this context is beneficial to understand the current state of the art and the specific challenges encountered when applying this technology to older buildings. This study focuses on the application of digital twins in building operations and maintenance (O & M), emphasizing energy efficiency throughout the building lifetime. A systematic process to select 21 pertinent use-case studies was performed, complemented by an analysis of six enterprise-level digital twin solutions. This was followed by an overview of general characteristics, thematic classification, detailed individual study analyses, and a comparison of digital twin solutions with commercial tools. Five main applications of digital twins were identified and examined: component monitoring, anomaly detection, operational optimization, predictive maintenance and simulation of alternative scenarios. The paper highlights challenges like the reliance on Building Information Modeling (BIM) and the need for robust data acquisition systems. These limitations hinder the implementation of digital twins, in particular in existing buildings with no digital information available. It concludes with future research directions emphasizing the development of methods not solely reliant on BIM data, integration challenges, and potential enhancements through AI and machine learning applications. [ABSTRACT FROM AUTHOR]

In this paper, a schmitt trigger-based single-sided 7T stable SRAM is proposed for ultra-low energy and near-threshold operation, which supports a bit interleaving scheme. The proposed ST-7T SRAM design improves the WSNM (Write Static Noise Margin) and RSNM (Read Static Noise Margin) and consumes less energy. Moreover, obtain high read strength by utilizing a single-sided ST (schmitt trigger) inverter. Furthermore, write ability is also enhanced by applying an ST inverter write assist scheme (Negative VWWL technique), which can limit the tripping voltage of the ST inverter circuit. The PST-7T (Proposed ST-7T) circuit minimizes the read power by 49.96%, write power by 39.27%, and leakage power by 39.17% compared to conventional-6T SRAM. The RSNM and WSNM of the proposed SRAM circuit are enhanced by 66.28% and 18.97% compared to conventional-6T SRAM. The write energy and read energy utilization are also lowered by 14.87% and 14.19% compared to the SE7T SRAM cell. [ABSTRACT FROM AUTHOR]

This literature review presents a comprehensive review of life Cycle Assessmernt (LCA), as an emerging tool in the field of cultural heritage research and demonstrate how this tool could be useful to support the development of green heritage science into an environmentally responsible field of scientific endeavour. LCA is a standardised, structured, comprehensive, international environmental assessment tool and a rapidly evolving field of research that leverages and harmonises efforts across many sectors to inform environmentally-friendly solutions and choices. LCA has been growing in importance as an evidence-based tool in the field of heritage science, being used as a decision-support tool at micro level (typically for questions related to specific products/processes) and macro levels (e.g. strategies, scenarios, and policy options). This review explores applications of LCA (and the complementary Life Cycle Cost Assessment (LCC), and Social Life Cycle Assessment (S-LCA)) to a wide array of conservation and preservation actions. The paper also examines challenges associated with the application of these life cycle-based methods in heritage science, in order to put forward a set of recommendations to guide the domain of heritage science towards greener and more sustainable practices and impacts. Based on a review of the principles of green chemistry, green analytical chemistry, green engineering, and nature conservation, the paper also attempts to formulate nine principles of green heritage science, taking into account the complexity of research challenges and the environmental and socio-economic sustainability. [ABSTRACT FROM AUTHOR]

In underground coal mine, the routing protocol in Wireless Sensor Network (WSN) based on fog computing can effectively achieve combination the monitoring task with the computing task, and provide the correct data forwarding path to meet the requirements of the aggregation and transmission of sensed information. However, the energy efficiency is still taken into account, especially, the unbalance of energy consumption. 5G is a technical system of high frequency and low frequency mixing, with characteristics of large capacity, low energy consumption and low cost. With the formal freeze on 5G NSA standards, 5G networks are one step closer to our lives. In this paper, a centralized non-uniform clustering routing protocol C-EEUC based on the residual energy and communication cost. The C-EEUC protocol considers all nodes as candidate cluster heads in the clustering stage and defines a weight matrix P. The value of the matrix elements takes into account the residual energy of nodes and the cost of communication between nodes and cluster heads, selected as the basis for the cluster head. When selecting a cluster head, each time a node with the largest weight is selected from a set of candidate cluster heads, other candidate cluster heads within the competition range abandon competition, and then updates the candidate cluster head set. Experimental results show that the protocol optimized in this paper can effectively extend the network life cycle. [ABSTRACT FROM AUTHOR]

The clustering algorithm is an effective method for developing energy efficiency routing protocol for wireless sensor networks (WSNs). In clustered WSNs, cluster heads must handle high traffic, thus consuming more energy. Therefore, forming balanced clusters and selecting optimal cluster heads are significant challenges. The paper proposes a sector clustering algorithm based on K-means called KMSC. KMSC improves efficiency and balances the cluster size by employing symmetric dividing sectors in conjunction with K-means. For the selection of cluster heads (CHs), KMSC uses the residual energy and distance to calculate the weight of the node, then selects the node with the highest weight as CH. A hybrid single-hop and multi-hop communication is utilized to reduce long-distance transmissions. Furthermore, the impact of the number of sectors, the threshold for clustering, and the network size on the performance of KMSC has been explored. The simulation results show that KMSC outperforms EECPK-means, K-means, TSC, LSC, and SEECP in terms of FND, HND, and LND. [ABSTRACT FROM AUTHOR]

DC microgrids are integral to smart grids, enhancing grid reliability, power quality, and energy efficiency while enabling individual grid independence. They combine distributed and renewable energy sources, reducing overall energy consumption. High-gain DC–DC converters are crucial for elevating voltages from low-voltage DC sources like solar panels and wind turbines in DC microgrids. This paper introduces a non-isolated DC–DC converter designed to achieve ultra-high step-up (UHSU) voltage conversion utilizing a two-winding coupled inductor (CI). The propounded UHSU configuration achieves a substantial voltage increase by employing low duty cycles and a decreased turn ratio for the CI, resulting in a smaller core size. Moreover, this UHSU circuit incorporates soft-switching capabilities for both power switches and diodes, enhancing its efficiency. By keeping the voltage stress on the switches low, the design minimizes losses and improves overall efficiency. The operational modes are thoroughly analyzed, and comparisons with other topologies are presented to demonstrate the effectiveness of the proffered UHSU circuit. Finally, the performance of the UHSU circuit is validated through the construction and testing of a 150-W laboratory prototype operating at a switching frequency of 50 kHz, with Vin = 20 V and Vout = 300 V. [ABSTRACT FROM AUTHOR]

Energy efficiency and physical-layer security are crucial considerations in the advancement of mobile edge computing systems. This paper addresses the trade-off between secure-reliability and energy consumption in finite blocklength (FBL) communications. Specifically, we examine a three-node scenario involving a user, a legitimate edge computing server, and an eavesdropper, where the user offloads sensitive data to the edge server while facing potential eavesdropping threats. We propose an optimization framework aimed at minimizing energy consumption while ensuring secure-reliability by decomposing the problem into manageable subproblems. By demonstrating the convexity of the objective function concerning the variables, we establish the existence of an optimal parameter selection for the problem. This implies that practical optimization of parameters can significantly enhance system performance. Our numerical results demonstrate that the application of FBL regime and retransmission mechanism can effectively reduce the energy consumption of the system while ensuring secure-reliability. For the quantitative analyses, the retransmission mechanism is 33.1% better than no retransmission, and the FBL regime is 13.1% better than infinite blocklength (IBL) coding. [ABSTRACT FROM AUTHOR]

Controller placement issue (CPI) in software-defined networks (SDNs) describes the controllers' number, location, and assigning of forwarding devices to controllers. Recently, mathematical formulations and algorithms have been proposed to solve various problems in SDNs and software-defined wide area networks. The comprehensive literature review can be divided into four groups according to objectives: (i) minimizing latency between forwarding devices or switches and their corresponding controllers, and minimizing latency between controllers, (ii) improving network resilience and stability, (iii) minimizing energy consumption and installation costs and (iv) using multi-objective approaches. In addition to the objectives of each research, the importance of this paper is to examine the CPI in terms of reducing the network search space in order to optimally place the controller and how to assign switches to the controllers. In this paper, first the mathematical formulations in previous studies will be examined and then, for solving CPI, the existing algorithms will be discussed. Different classifications of CPIs and related formulas/algorithms, descriptions, advantages and disadvantages will be separately provided. A comprehensive comparison of proposed approaches with their advantages and disadvantages in the summarized tables will be provided. Also, a comparative discussion of different statistics of CPIs will be presented in terms of some technical features such as objective-oriented problems and parameters in four categories, estimated environments, and efficient estimating factors in CPIs. Finally, we explained the future studies' challenges, problems related to CPIs, ideas and following orientations in this field. [ABSTRACT FROM AUTHOR]

Fog computing and the Internet of Things (IoT) have revolutionized healthcare monitoring systems, enabling real-time health data collection and transmission while overcoming cloud computing limitations. However, efficiently selecting fog nodes for application modules with varying deadline requirements and ensuring adherence to quality of service (QoS) criteria pose significant challenges due to resource constraints and device limitations. In this paper, we present a novel two-layered hierarchical design for fog devices, leveraging cluster aggregation to optimize the selection of fog nodes for healthcare applications. We introduce three efficient algorithms to minimize system latency and reduce energy consumption in fog computing environments. Our proposed model is rigorously evaluated using the iFogSim toolkit and compared with cloud-based and latency-aware model [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology.19, 2018, 10.1145/3186592]. In four distinct network topologies, our model exhibits an average latency reduction of at least 87% and energy consumption reduction of at least 76% when compared to the Cloud-based model. Similarly, when compared to the Latency-aware model proposed in [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology. 19, 2018, 10.1145/3186592], our model showcases a minimum reduction of 43% in average latency and 27% in energy consumption. Our contribution lies in addressing the complexity of selecting fog nodes for application modules with diverse deadline requirements, while ensuring QoS. This work advances the field of real-time healthcare monitoring systems, promising substantial improvements in efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

The relationship between energy consumption, environmental pollution, and economic growth has been the subject of intense research over the past few decades. However, empirical evidence remains scarce and with mixed results. This paper throws a new light on the nonlinear impact of environmental degradation on economic growth depending on the level of energy intensity. This study provides an original approach in the context of Malaysia by utilizing an innovative threshold regression approach suggested by Hansen (Econometrica 68(3):575–603, 2000), which allows the data to endogenously divided into two regimes with an exogenous threshold variable. We find strong evidence that environmental degradation has a nonlinear effect on economic growth in Malaysia. In particular, CO2 has a negative effect on growth, but only after the primary energy intensity reaches a threshold level of 91.33 tons of oil equivalent (TOE)/GDP and the final energy intensity reaches 51.56 TOE/GDP, respectively. Our results are robust in terms of ecological footprint. Several policy implications are highlighted which aim to encourage sustainable energy use for sustainable growth in the future. [ABSTRACT FROM AUTHOR]

A large volume of solar energy dissemination in a supply grid originates extreme variations in the load, resulting in a duck-form load arc that can cause stability issues. Also, the cost of energy consumption is found to vary between the off-peak and peak loads observed in the duck-shaped load curve. Accurate load forecast and demand response program is a key task for duck curve management in a distribution structure. Hence, this work proposes a Demand Response (DR) program using deep learning neural networks namely Long Short-Term Memory (LSTM). The proposed DR program is implemented in a modified 12 bus radial distribution network for duck curve management, where voltage stability is taken care of simultaneously minimizing the electricity cost in an energetic pricing environment. LSTM is used for forecasting the load and linear programming is used for load shedding. Therefore, this paper resolves the dual aims of flattening the duck-shaped load arc and minimizing electricity costs by combining them into a single objective function. [ABSTRACT FROM AUTHOR]

The European Green Deal and similar strategies seek to improve sustainability of the agricultural sector via public support programmes. It is important to assess the costs of sustainable energy use in agriculture by exploiting the shadow price approach. However, the earlier literature often ignored the fact that shadow price analysis may be sensitive to the assumed direction of optimization. This paper seeks to disentangle the major patterns in energy-related greenhouse gas (GHG) emission performance in the selected European Union countries by assuming different optimization directions. The country-level data are used to construct the environmental production technology by means of the data envelopment analysis. The different directional output distance functions (aggregate, unit and radial) for the weak disposability data envelopment analysis models are used to quantify the shadow prices of the energy-relevant GHG emission and construct the marginal abatement cost curves. The results indicate spatial and temporal variation in the environmental performance that can be addressed by adjusting the support programmes. [ABSTRACT FROM AUTHOR]

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. [ABSTRACT FROM AUTHOR]

Renewable energies, such as solar and wind, traditionally suffer from temporal incongruity. Society's energy demand peaks occur at different times of day than the electricity generation potential of a photovoltaic panel or, often, a wind turbine. Heat demand, in particular, is subject to a significant mismatch between the availability of heat (in the summer) and the need for heat (in the winter). Thus, a future energy system design should incorporate underground thermal energy storage (UTES) to avoid this temporal mismatch and emphasize thermal applications. Such a basis of design would introduce new methods of energy arbitrage, encourage the adoption of geothermal systems, and decrease the carbon intensity of society. UTES techniques are becoming increasingly sophisticated. These methods of storage can range from simple seasonal storage for residential structures in a grouted borehole array (BTES), to aquifer thermal energy storage (ATES), deep reservoir storage (RTES) in basins, among others. The method that each of these techniques shares is the use of the earth as a storage medium. UTES can also be characterized for electricity production, but this work largely explores applications in heating and cooling, further limited in scope to sensible heat storage (SHS). Heating and cooling processes—residential, commercial, and industrial—make up large fractions of energy demand in North America. This is also true of other locales. With the increasing concerns of climate change, exacerbated by anthropogenic greenhouse gas emissions, developers and municipal planners are strategizing to decarbonize building heating and cooling at district scales. This review covers the integration of UTES techniques with thermal energy network (TEN) technology across large districts. Though storage has long been in use for conventional district heating networks, designs are rapidly innovating, indicating broader applications of UTES integration with a TEN is advantageous from both an efficiency and economic perspective. This rapid innovation indicates the need for the integrated review offered in this paper. [ABSTRACT FROM AUTHOR]

Electric impulse drilling (EID) technology has great industrialization potential since it has unique advantages in crushing hard rocks of deep formation. To study the mechanism of dielectric breakdown is of great significance for accelerating the practical application of EID technology. In this paper, the dielectric breakdown damage model (DBDM) is established based on the heterogeneous granite model. On the basis of DBDM, the whole process of absolute dielectric breakdown (ADB) is studied, including the formation of the plasma channel and the variation of the electric field. Then, the mechanism of regional dielectric breakdown (RDB) development to ADB is studied, and the influence of peak voltage, electrode spacing and the parameters of a high-voltage electric pulse (HVEP) is discussed. Besides, the efficiency of two dielectric breakdown modes is evaluated. It is found that dielectric breakdown will only occur in the domain where the strength of the local electric field formed by HVEP exceeds the critical breakdown field strength of rock. RDB will occur when the pulse duration is too short or the peak voltage is too low. It is worth noting that RDB can be developed into ADB under multi-HVEPs. Moreover, under the same voltage, there is almost no difference between the failure volume caused by RDB under multi-HVEPs and ADB under a single HVEP. However, the energy consumption of the RDB under multi-HVEPs decreases exponentially with increasing voltage and is always higher than that of the ADB under a single HVEP. When designing the supporting tools and bits of EID technology, the life of insulation, the characteristics of formation, and energy utilization should be fully considered. The related research in this paper aims to provide reference and guidance for the application of EID technology in drilling engineering. Highlights: The variation of the electric field, the change of minerals and the generation of plasma channel in granite during dielectric breakdown is studied. The effect of the characteristics of high-voltage electric pulse (HVEP) on the dielectric breakdown in granite is studied. The mechanism and rock-breaking efficiency of absolute dielectric breakdown (ADB) and regional dielectric breakdown (RDB) are explored. [ABSTRACT FROM AUTHOR]

With the development of distribution generation (DG) technology, large amount of renewable energy connected to the microgrid, which has a significant impact on the consumption of renewable energy. The nonlinear load connected to the microgrid leads to the reduction of power quality, and the line impedance between the distribution generation and the point of common coupling (PCC) is different, resulting in the harmonic current cannot be reasonably shared, and the transmission of redundant information brings enormous pressure to communication system. To solve these problems, this paper proposed a microgrid harmonic power-sharing method based on an event-triggered consensus algorithm. Firstly, the hierarchical coordination control architecture of the microgrid is established. Then, when designing distributed frequency and harmonic power controllers based on event-triggered consensus, the impact of communication network time delay on system performance is considered. The status information is exchanged between distributed controllers only when the event is triggered, and the actual value is replaced by the estimated value at other times, which can effectively reduce the transmission of redundant information. Finally, this paper uses the Lyapunov method to prove the stability of the proposed control method, analyzes the event-triggered interval, and avoids the Zeno phenomenon. The effectiveness of the proposed power-sharing method is verified by the hardware-in-the-loop experiment. [ABSTRACT FROM AUTHOR]

Scene understanding is crucial for mission planning and monitoring in autonomous systems. Existing approaches have attached importance to understanding structured scenes such as indoor and urban environment. However, for diverse unstructured scenes filled with multiple bending-sloping surfaces, understanding them remains an unsettled issue. Compared to methods through three-dimensional (3D) point clouds and information fusion techniques, monocular understanding enjoys the advantages of less energy consumption and less memory intensive. In this paper, we propose a new methodology to understand multiple bending-sloping surfaces in diverse scenes and establish their 3D reconstruction from a monocular camera. Bending-sloping angle projections are extracted. Through their subclusters and combinations, potential arched surfaces are estimated. Through geometric constraints of integrity and orientation, an arched scene is approximated in 3D reconstruction. Using geometric inference, no prior training is needed, and the approach requires neither the camera being calibrated nor the camera internal parameters. By comparing results to the ground truth, the percentage of incorrectly classified pixels was evaluated. The results proved that the methodology is successful in understanding multiple bending-sloping surfaces, meeting scene understanding requirements in diverse unstructured environments. [ABSTRACT FROM AUTHOR]

This research examines the water and energy performance in wet cooling towers and identifies methods for enhancing efficiency and minimizing water and energy consumption by exploring methods to enhance system performance. A three-dimensional transient model, developed using MATLAB's open-source code software, was utilized to simulate the cooling tower's behaviour under various operating conditions. This research focuses on precise simulation of cooling tower behavior and demand modeling aided by regression. The model's accuracy was validated through experimental measurements in diverse environmental conditions. The key parameter and performance that is investigated in this paper is the temperature profile of the cooling tower, in which the performance algorithm and proposed methodologies are anchored in the operational temperature. The experimental results led to operational solutions for enhancing cooling tower performance. For winter conditions, the recommended action involves closing the upper part of the cooling tower and activating the two side fans. Specific approaches are suggested for mid-season and summer scenarios, focusing on make-up water consumption and ambient air temperature control, respectively. In addition, results indicated a close alignment between the model and the actual system, with discrepancies of less than 2% in energy consumption and 5% in water consumption. Analysis of proposed productivity enhancements and changes in supply policies indicated significant potential for energy and water conservation in wet cooling towers. Implementing these solutions could lead to an estimated annual reduction of 44% in water consumption and 4.2% in energy consumption. [ABSTRACT FROM AUTHOR]

High voltage AC/DC cables require thicker insulation to ensure excellent insulation performance. Selecting the appropriate crosslinker to design the crosslink and crystalline structure of XLPE is key to the insulating properties. Although the crystallinity of XLPE has been extensively studied by using X-ray diffraction and differential scanning calorimeters, few researchers have designed in situ experiments to observe its structure. In this paper, we have created an in situ observational experiment to monitor the structural changes of XLPE insulation. XLPE formed by DTAP crosslinker produces lower gas byproducts, which can effectively reduce the degassing time and energy consumption. The test results and COMSOL simulations indicate that the XLPE formed by straight-chain alkane crosslinkers has better mechanical, thermal and electrical insulating properties than cyclic alkane crosslinkers. Our work is of great significance for selecting suitable crosslinker to control the structure of XLPE and determining the production conditions for high-voltage cables. [ABSTRACT FROM AUTHOR]

The digital economy has played a vital role in changing economic and social development patterns and has had a multifaceted impact on carbon neutrality. This paper strives to contribute to the existing research by analyzing the relationship between the digital economy and electricity consumption intensity and providing a comprehensive understanding of the mechanisms that influence the digital economy and energy consumption, particularly focusing on electricity consumption intensity through the construction of a theoretical model. To fulfill this objective, this study introduces the development level of the digital economy into the production function of representative power enterprise, constructs a partial theoretical equilibrium analysis framework, calculates provincial digital economy development indices for Chinese provinces from 2013–2021 using the CRITIC method, and examines the impact of the digital economy on power consumption intensity. The results show that the digital economy can reduce the intensity of power consumption. A unit standard deviation increase in the digital economy development level reduces power consumption intensity by 0.1008 standard deviations. The study also conducts a mechanism analysis with economic growth as the mediating variable, which shows that the digital economy promotes economic growth and reduces electricity consumption intensity. The moderating effect of the digital economy on power consumption is further examined using local government fiscal pressure as the moderating variable. The results show that the impact of the digital economy on reducing power consumption intensity decreases with the increase of government fiscal pressure. This study not only contributes to practical applications but also enhances our comprehension of the interplay between the digital economy and power consumption. On the one hand, the development of the digital economy can reduce power consumption, thus reducing carbon emissions and achieving low-carbon growth. On the other hand, central governments can promote the development of the digital economy by reducing the fiscal pressure on local governments through transfer payments. [ABSTRACT FROM AUTHOR]

The unflinching upsurge in global warming has put the world into a state of disarray, and policymakers are not sure of what the future holds for the ecosystem. To get the global environment back on track of sustainability, efforts are relentlessly emanating from both developed and developing economies to identify the hindering and enhancing factors. Given the preceding narratives, this paper investigates the tripartite impacts of renewable and nonrenewable energy, trade openness, and institutions on sustainable environment in 54 African countries from 1996 to 2019. The study employs the two-step system generalized methods of moment (SYS-GMM) in estimating the effects of the regressors on four indicators of sustainable environment. Six findings are prominent from the study. First, nonrenewable energy (NRE) and trade openness (TO) hinder sustainable environment, while renewable energy enhances it. Second, the unconditional impacts of institutions drive the path to a sustainable environment. Third, the interaction of institutions with NRE and TO hinders sustainable environment. However, after conducting institutional enhancement estimations, the interactive terms became supportive of sustainable environment. Fourth, the thresholds are policy-relevant across the specifications. Specifically, a 31.11% utilization of nonrenewable energy and 43.75% openness to trade contribute to a sustainable environment. Sixth, the impacts of economic growth and its square empirically support the inverted U-shaped EKC. This study suggests the adoption of carbon-taxing on nonrenewable energy products, price-subsidization for renewable energy goods and services, and institutional quality reforms as pathways towards achieving a sustainable environment in Africa. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are considered as a promising method to provide service for Internet of Remote Things (IoRT). The incorporation of non-orthogonal multiple access (NOMA) into UAV-assisted networks provides a possibility for massive IoRT devices access. In this paper, we investigate the downlink transmission UAV-assisted wireless communication system based on NOMA in IoRT networks, where UAVs fly vertically to provide services for IoRT devices. In order to transmit as much data as possible, we aim to maximize the energy efficiency (EE) of the UAV by jointly optimizing NOMA user scheduling, UAV trajectory and power allocation. Due to the coupling between variables, the derived problem is mixed integer optimization and non-convex. To solve this challenging problem, we first decouple the variables and split the original problem into three subproblems, i.e., user scheduling, trajectory planning, and power allocation. Then, a double-loop iterative algorithm is proposed to solve the corresponding subproblems based on the Dinkelbach method and successive convex approximation (SCA) techniques. Finally, simulation results give some insights e.g., NOMA outperforms OMA in both spectral efficiency and energy efficiency and the proposed NOMA-based EE-oriented scheme is approximate three times energy efficient than benchmark schemes. [ABSTRACT FROM AUTHOR]

With the rapid development of industrial IoT technology, a growing number of intelligent devices are being deployed in smart factories to digitally upgrade the manufacturing industry. The increasing number of intelligent devices brings a huge task request. Fog computing, which is an emerging distributed computing paradigm, is widely applied to process the device data generated in smart manufacturing. However, as fog nodes are resource limited and geographically widely distributed limitations, proper fog node placement strategies are critical to enhance the service performance of fog computing systems. In this paper, we study the problem of fog node placement in smart factories and divide it into two scenarios, fixed device and mobile device fog node placement, depending on the mobility of the devices. The fog node placement model and objective function are built in the two scenarios, and two improved heuristic algorithms are proposed to obtain the most optimal placement scheme. In addition, we perform simulation experiments based on existing intelligent production line prototype platforms and devices to evaluate the performance of the proposed algorithms. The IGA reduces latency by an average of 586.7 - 1089 ms over the benchmark algorithm, saving 18.3 - 39 % in energy consumption. The total latency of IMOA is reduced by 59.8 - 68.5 % , and the maximum latency is reduced by 48.8 - 69.2 % . The experimental results show that the proposed algorithms outperform other benchmark algorithms in terms of task response time and energy consumption. [ABSTRACT FROM AUTHOR]

High altitude platforms (HAPs) have been considered a promising paradigm to assist mobile edge computing (MEC) in wide-area internet of things (IoT) scenarios due to their wide coverage, large payload capacity, and excellent channel quality. In this paper, a HAP-assisted MEC system is investigated, where the HAP is deployed as an edge server to provide computation offloading services for IoT terminal devices (TDs) with limited local computing capability. Offloading is enabled by uplink and downlink communications between TDs and HAP in a clustered non-orthogonal multiple access (C-NOMA) manner. Considering potential economic benefits and expected environmental impact, we aim to minimize system energy consumption, subject to the constraints on maximum tolerable delay, transmit power budget, and uplink and downlink decoding power. To this end, an iterative algorithm based on relaxation and successive convex approximation methods is proposed, which efficiently solves this challenging non-convex problem by alternately optimizing power allocation and HAP deployment. Numerical results show that the proposed scheme significantly reduces the system energy consumption compared to the other benchmark scheme. It is also shown that the system energy consumption is indirectly effected by the decoding order in the uplink and downlink C-NOMA. [ABSTRACT FROM AUTHOR]

Since non-orthogonal multiple access (NOMA) has become a promising technology to enhance network energy efficiency (EE) thanks to its low latency,high reliability and massive connectivity, the optimization of EE in NOMA-based uplink heterogeneous networks (HetNets) is considered in this paper. The optimization problem for EE maximization is formulated as a combination of user association (base station selection, sub-channel allocation) and power control, which is hardly tractable. To address it, a user association algorithm satisfied the many-to-one relationship between mobile users, base stations and sub-channels is firstly proposed based on matching theory. Then, an iterative power control algorithm evolved from fractional programming and difference of convex programming is presented by proving the concavity of the sum-rate of sub-channels. Finally, simulation results are shown that NOMA-based HetNets equipped with our designed algorithms yield much higher EE than that with the compared schemes. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, the expensive and scarce resources are energy and frequency spectrum. To overcome this scarcity of spectrum, cognitive radio has been introduced in WSNs. The licensed band is utilized by the primary users in cognitive radio whereas the secondary users can use the licensed channels. For improving the network lifetime, overall network scalability, and energy consumption, the clustering technique is used to group the sensor nodes into clusters. Clustering algorithms must be energy efficient because of the difficulties in replacing or recharging the batteries of nodes. While designing the algorithms, more constraints result in the clustering for CRSN as the dynamic spectrum access has been addressed. We propose an energy-efficient fuzzy clustering and congestion control algorithm (EFCCA) in this paper to improve energy efficiency. With the consideration of spectrum availability, queue length, and residual energy, the election of cluster head contributes to the energy efficiency of the algorithm. The active Queue Management algorithm is used to monitor and control the congestion rate. The proposed EFCCA's performance evaluates with the help of comparison with other clustering methods and it shows enhanced performance in energy efficiency and lifetime based on the outcomes of experimental investigation. [ABSTRACT FROM AUTHOR]

Flying ad hoc network (FANET) is a new technology, which creates a self-organized wireless network containing unmanned aerial vehicles (UAVs). In FANET, routing protocols deal with important challenges due to limited energy, frequent link failures, high mobility of UAVs, and limited communication range of UAVs. Thus, a suitable path is always essential to transmit data between UAVs. In this paper, a local filtering-based energy-aware routing scheme (LFEAR) is proposed for FANETs. LFEAR improves the template of the route request (RREQ) packet by adding three fields, namely the energy, reliable distance, and movement similarity of the relevant route to create stable and energy-efficient paths. In the routing process, LFEAR presents a local filtering construction technique to avoid the broadcasting storm issue. This filter limits the broadcasting range of RREQs in the network. Accordingly, only UAVs inside this local filtered area can rebroadcast RREQs and other UAVs must eliminate these packets. After ending the route discovery process, the destination begins the route selection phase and extracts information about each discovered route, including the number of hops, route energy, reliable distance, and movement similarity. Then, the destination node calculates a score for each path based on the extracted information, selects the route with the highest score, and sends a route reply (RREP) packet to the source node through this route. Finally, the simulation process of LFEAR is performed using the NS2 simulator, and two simulation scenarios, namely change in network density and change in the speed of UAVs, are defined to evaluate network performance. In the first scenario, LFEAR improves energy consumption, packet delivery rate, network lifespan, and delay by 1.33%, 1.77%, 6.74%, and 1.71%, while its routing overhead is about 16.51% more than EARVRT. In the second scenario, LFEAR optimizes energy consumption and network lifetime by 5.55% and 5.67%, respectively. However, its performance in terms of routing overhead, packet delivery rate, and delay is 23%, 2.29%, and 6.67% weaker than EARVRT, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock's direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions. [ABSTRACT FROM AUTHOR]

In recent years, the widespread adoption of wireless sensor networks (WSN) has resulted in the growing integration of the internet of things (IoT). However, WSN encounters limitations related to energy and sensor node lifespan, making the development of an efficient routing protocol a critical concern. Cluster technology offers a promising solution to this challenge. This study introduces a novel cluster routing protocol for WSN. The system selects cluster heads and relay nodes utilizing the multi-strategy fusion snake optimizer (MSSO) and employs the minimum spanning tree algorithm for inter-cluster routing planning, thereby extending the system's lifecycle and conserving network energy. In pursuit of an optimal clustering scheme, the paper also introduces tactics involving dynamic parameter updating, adaptive alpha mutation, and bi-directional search optimization within MSSO. These techniques significantly increase the algorithm convergence speed and expand the available search space. Furthermore, a novel efficient clustering routing model for WSN is presented. The model generates different objective functions for selecting cluster heads and relay nodes, considering factors such as location, energy, base station distance, intra-cluster compactness, inter-cluster separation, and other relevant criteria. When selecting cluster heads, the fuzzy c-means (FCM) algorithm is integrated into MSSO to improve the optimization performance of the algorithm. When planning inter-cluster routing, the next hop node is selected for the relay node based on distance, residual energy, and direction.The experimental results demonstrate that the proposed protocol reduces energy consumption by at least 26.64% compared to other cluster routing protocols including LEACH, ESO, EEWC, GWO, and EECHS-ISSADE. Additionally, it increases the network lifetime of WSN by at least 25.84%, extends the stable period by at least 52.43%, and boosts the network throughput by at least 40.99%. [ABSTRACT FROM AUTHOR]

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively. [ABSTRACT FROM AUTHOR]