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AbstractThe purpose of this paper is to develop a conceptual framework to describe facilitators’ use of power in facilitation practice. Facilitators are mainly, in theory as well as in practice, described as neutral power sharers. This reductive understanding of the role of facilitators is problematic as it hides the influence that these practitioners have over the process and outcome of collaborative governance. Analyzing two Swedish collaborative governance processes, we develop a framework that sheds light on facilitators’ use of power. The framework includes three alternative power moves that facilitators can make: sequencing, framing and concluding. Facilitators’ attempts to make these moves are, in the framework, located on a continuum between authority and argumentation in the following positions: authority, tempered authority, tempered argumentation and argumentation. This paper contributes to the facilitation and collaborative governance literatures by providing a conceptual framework applicable for further research into facilitators’ use of power, as well as for developing facilitation handbooks and training programmes. [ABSTRACT FROM AUTHOR]

Big data analytics (BDA) has been introduced in the past few years in most industries as a factor capable of revolutionizing their operations by offering significant efficiency opportunities and benefits. To compete in this digital age, businesses must adopt a client-centric service model, founded on data delivering continuous value and achieving optimal performance, whilst also upgrading their own decision-making and reporting processes. This article aims to explore how UK FM organizations are currently capitalizing on BDA to drive innovation and 'added value' in their operations. The objective is to shed light on the initial BDA adoption efforts within the UK's FM sector, particularly capturing the benefits experienced by FM organizations in relation to customer value and improved decision-making processes. Drawing upon exploratory sequential research including a qualitative stage with 12 semi-structured interviews and an industry-wide questionnaire survey with 52 responses, a novel fifteen-variable model for BDA outcomes was developed. Exploratory Factor Analysis (EFA) and a Higher-Order model using Partial Least Square Structural modelling (PLS-SEM) were used to validate the scale. The EFA output generated three dimensions with 14 items. The dimensions included Improved client value, FM business operations added value, and Improved efficiency added value. Furthermore, the results of PLS-SEM confirmed the validity of the scale items and the reflective–formative measurement model. The findings suggest that the contemporary digitization trend offers the FM service the unique opportunity to develop a smarter, client-centric strategy resulting in more personalized services and stronger customer relationships. Furthermore, efficient resource management and planning powered by analytics and data-driven insights emerge as a key driver for competitive differentiation in the field. As one of the first studies to develop and validate scale items measuring specific dimensions of BDA adoption outcomes, the study makes significant contributions to the literature. [ABSTRACT FROM AUTHOR]

This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems. [ABSTRACT FROM AUTHOR]

In communication technologies, device-to-device (D2D) communication is essential for resource management and power control, which are major research concerns nowadays. D2D resource allocation involves dividing vital resources, such as time, power, and spectrum, among several devices. Each device can connect to other devices via one or more frequency channels. D2D communication shares the cellular user resources, while signal power transmission causes interference to the users who share the same channel. So, there is a need to control the power of the D2D device to prevent interference. For proper power control and optimization of multi-channel D2D communication, which is a challenging task, we proposed a deep learning approach incorporating a hybrid resource allocation framework. This framework aims to increase the sum rate of D2D user equipment (DUE) while considering quality of service (QoS) factors like limiting interference to cellular user equipment (CUE) and guaranteeing individual DUE rates above a certain threshold. The proposed resource allocation scheme combines two methods, namely a metaheuristic hybrid particle swarm Cauchy approach to African vulture optimization (HPSCAV) and a modified long short-term memory (MLSTM) based approach. The HPSCAV scheme helps to ensure that the QoS constraints are met, while the MLSTM-based approach is utilized for efficient resource allocation by optimizing the power and improving it with HPSCAV. Simulation results validate that the proposed model achieved better performance in various metrics such as system capacity, power consumption, spectral efficiency (SE), and energy efficiency (EE). [ABSTRACT FROM AUTHOR]

Renewable resources are a crucial component in power system research within contemporary intelligent power systems. It is imperative that they are given meticulous consideration. Microgrids (MGs) have emerged as a solution to facilitate the integration of renewable energy sources on a large scale. The incorporation of power production innovations that exhibit high levels of unpredictability in their generation would have a notable impact on the management of power resource planning within microgrids. Therefore, it is imperative to implement efficient power management mechanisms in microgrids. This study introduces a scheduling arrangement for a microgrid (MG) that includes a solar power unit (PV), which operates on a day-ahead basis. This paper explores the effects of various weather variables on the power generated by photovoltaic units and the optimum scheduling of microgrids. The present study employs the modified fluid search optimization algorithm to address the challenge of optimum management of energy in a microgrid connected to the grid that is characterized by a high level of unpredictability. The simulation results of the study indicate that the implementation of a photovoltaic model in an actual setting enhances the precision of the system for managing energy and reduces the overall operational costs of the interconnected microgrid. The algorithm under focus has been evaluated on a standard MG. The method under consideration has been executed on the MATLAB/Simulink computational environment and its efficacy has been evaluated. [ABSTRACT FROM AUTHOR]

(1) Background--In 2021, the forest chief system was fully implemented nationwide. Under this background, how to embed the forest-chief system into the forest resources management system in the state-owned forest areas and fully realize the comprehensive efficiency of forest supervision and protection policies has become an important issue in the forest areas at present. (2) Methods--Based on the field investigation of some typical forestry enterprises in the key state-owned forest areas, this paper systematically analyzed the forest resources management practice after the reform in the key state-owned forest areas at the present stage, summarized the new challenges faced by the forest resources management mechanism after the implementation of the forest chief system, and on this basis, put forward policy suggestions to help the forest chief system to be embedded in the forest resources management system in the key state-owned forest areas. (3) Results--First, the forest chief system was carried out in the key state-owned forest areas, and the state-owned forest industry enterprises were incorporated into the local forest chief system through the organization of personnel. Second, the managers in different state-owned forest industry enterprises served as forest chiefs in different forms. Among them, the enterprise managers of China Longjiang Forest Industry Group Co., Ltd., Yichun Forest Industry Group and Daxing' anling Forestry Group participated in the forest chief system by serving as deputy forest chief of their respective counties (cities or districts). The managers of Jilin Forest Industry Group Co., Ltd.and Changbai Mountain Forest Industry Group served as the general forest chief of their respective forestry bureaus in accordance with the county-level forest chief system. As one of the six major areas of responsibility under the forest chief system in Inner Mongolia Autonomous Region, Inner Mongolia Forest Industry Group appointed deputy forest chief to manage forest resources. (4) Conclusions and Discussions--The implementation of forest chief system in the key state-owned forest areas faces a series of challenges. First, in the process of realizing the policy goal of forest chief system in the key state-owned forest areas, the forest resources management in the forest areas needs to be systematically integrated in the goal setting. Second, under the background of the overall implementation of forest chief system, the forest resources management in the key state-owned forest areas has a pattern of "dual-track and parallel". Third, the coordination mechanism of forest resources management under the forest chief system in the key state-owned forest areas is not perfect, including the government-enterprises coordination mechanism and the social participation mechanism. Based on the above analysis, the following policy suggestions are put forward. First, promote the integration of forest resources management objectives based on the reality of the key state-owned forest areas. Second, develop a list of forest chief powers to clarify the boundaries of responsibilities and powers of forest resources management under the forest chief system. Third, accelerate the establishment of the working mechanism of forest resources management under the forest chief system. In short, when promoting the forest resources management under the forest chief system in the key state-owned forest areas, the appeals of the central government, local governments, forest industry enterprises, forest residents and other parties must be comprehensively considered, the work objectives of the forest chief system must be clearly defined, the work content of the forest chief system must be enriched, and the objectives of integrating ecological benefits, social benefits and economic benefits must be formulated. [ABSTRACT FROM AUTHOR]

Long sequence time-series forecasting (LSTF) problems, such as weather forecasting, stock market forecasting, and power resource management, are widespread in the real world. The LSTF problem requires a model with high prediction accuracy. Recent studies have shown that the transformer model architecture is the most promising model structure for LSTF problems compared with other model architectures. The transformer model has the property of permutation equivalence, which leads to the importance of sequence position encoding, an essential process in model training. Currently, the continuous dynamics models constructed for position encoding using the neural differential equations (neural ODEs) method can model sequence position information well. However, we have found that there are some limitations when neural ODEs are applied to the LSTF problem, including the time cost problem, the baseline drift problem, and the information loss problem; thus, neural ODEs cannot be directly applied to the LSTF problem. To address this problem, we design a binary position encoding-based regularization model for long sequence time-series prediction, named Seformer, which has the following structure: 1) The binary position encoding mechanism, including intrablock and interblock position encoding. For intrablock position encoding, we design a simple ODE method by discretizing the continuum dynamics model, which reduces the time cost required to compute neural ODEs while maintaining their dynamics properties to the maximum extent. In interblock position encoding, a chunked recursive form is adopted to alleviate the baseline drift problem caused by eigenvalue explosion. 2) Information transfer regularization mechanism: By regularizing the model intermediate hidden variables as well as the encoder-decoder connection variables, we can reduce information loss during the model training process while ensuring the smoothness of the position information. Extensive experimental results obtained on six large-scale datasets show a consistent improvement in our approach over the baselines. [ABSTRACT FROM AUTHOR]

Device‐to‐Device (D2D) communication is one of the leading technologies which works under the aegis of fifth generation (5G) communication for the Internet of Things in Healthcare. In D2D communication, power control with resource allocation is the most challenging area for the research to make them suitable for IoT devices. In the present work, power control with resource allocation is done by using the Linear pricing game power allocation method. First, a mathematical min‐function based problem is proposed whilst taking the interference, data rate and minimum power requirement as a constraint to ensure the resource and power allocation. In the next phase, a linear pricing game (LPG) method is utilized to propose the LPG power control with resource allocation (L‐PaRAG). Mathematical analysis and theorem ensure the minimum cost function with global stability for an optimal solution of the proposed problem. Numerical analysis and results show that the proposed algorithm gives the best results for power control and resource management whilst managing the interference to maintain the quality of services (QoS) in D2D communication. Optimized power can be utilized for the communication between the base station and D2D users, especially, during disaster management. [ABSTRACT FROM AUTHOR]

The sixth generation (6G) mobile communication system is expected to meet the different service needs of modern communication scenarios. Heterogeneous networks (HetNets) have received a lot of attention in recent years due to their potential as a novel structure for evolutionary networks. When compared to homogeneous networks, HetNets provide more potential for spatial spectrum reuse and higher quality of service (QoS). However, effective resource management (RM) solutions are essential to prevent interference and accomplish spectrum sharing due to mutual interference. This paper presents a comprehensive review of resource management in 6G HetNets. The study aims to give crucial background on HetNets to aid in the creation of more effective methods in this field of study. First, a detailed examination of recent work is presented in resource management aspects such as power allocation, user association, mode selection, and spectrum allocation. Second, we identify the most severe challenges associated with the current resource management methods and propose suitable solutions. Finally, several open issues and emerging areas of research are highlighted. [ABSTRACT FROM AUTHOR]

The CPC Central Committee and the State Council attach great importance to agricultural scientific and technological innovation. The "bottleneck" problem in agriculture has become a national strategy. Collaborative innovation is an effective way to solve the "bottleneck" problem. It faces the main battlefield of modern agricultural construction, the resolution of major problems facing the development of agricultural industry and the breakthrough of core technology. The construction and development of agriculture and rural areas put higher demands on scientific and technological innovation. As an important organizational form of agricultural science and technology innovation, collaborative innovation will play a pivotal role in the construction of innovative countries in China, and it is also of great significance for the construction of modern agricultural research institutes. Starting from the policy background of building agricultural and rural collaborative innovation infrastructure and the research status at home and abroad, this paper deeply analyzed the bottleneck problems faced by China' s agricultural scientific and technological innovation, such as agricultural scientific research big data, high-performance computing, original algorithm model, data application innovation and data security protection, and built an emerging agricultural collaborative innovation infrastructure of "data" + "algorithm" + "computing power" based on advanced information technologies such as artificial intelligence (AI), blockchain, cloud computing and big data. Furthermore, a collaborative innovation platform for agricultural and rural scientific research was constructed to serve the interdisciplinary integration. The platform simplified and speed up the iteration and flow of AI models and data, and implemented large-scale heterogeneous computing resource scheduling technology, multi-model data fusion method and friendly development support environment. The platform was dedicated to assist researchers in different disciplines to deeply integrate data, models, algorithms and computing power resource management. It created an out-of-the-box interactive cloud development environment, which was applied to the teaching, scientific research, scientific data sets and algorithm research and other scenarios, and formed an ecological closed-loop of artificial intelligence integration with multiple disciplines. It was of great strategic significance to enhance the original innovation ability in China's agricultural and rural fields, realize the leap in key fields, ensure the long-term development of science and technology, and transform from a large agricultural country to an agricultural power. [ABSTRACT FROM AUTHOR]

Multi-building multi-energy virtual power plants (MB-ME-VPPs) show great promise for the aggregation and coordination of distributed flexible resources across multiple integrated energy buildings to participate in electricity markets. However, a significant challenge arises when managing the energy of MB-ME-VPPs, especially since buildings can dynamically join or depart during the aggregation phase. Traditional model-based optimization methods face difficulties in obtaining accurate mathematical models of individual buildings, and may also raise privacy concerns. In contrast, model-free multi-agent reinforcement learning (MARL) methods offer a promising alternative by allowing agents to learn their control policies through interactions with their environments. Nevertheless, conventional MARL methods are normally applied in static multi-agent environments, where the number and identity of agents remain fixed and predetermined. Consequently, these conventional MARL methods lack the ability to adapt to the dynamic behaviors of agents joining or leaving the environment. To this end, this paper proposes a novel approach named MAT-Adapt, embedding the multi-agent transformer with a parallel adapter module, to address the dynamic participation issue in MB-ME-VPP energy management. Firstly, it formulates the coordination of building agents as a sequential modeling process and leverages the representational capabilities of the attention mechanism from the multi-agent transformer technique. Secondly, it introduces a parallel adapter module called AdaptMLP to enhance adaptability during the dynamic participation phase, efficiently reducing the need for extensive fine-tuning of model parameters. Simulations on the IEEE 33-bus distributional electricity market with 3 to 9 multi-energy buildings show the superior performance of our proposed MAT-Adapt method in facilitating efficient coordination of dynamically participating buildings within the context of the MB-ME-VPP. In comparison to the conventional MADDPG and MAPPO methods training from scratch, the proposed MAT-Adapt method demonstrates its superior adaptability, achieving 0.75–0.91 normalized rewards in new state conditions within 5% of training episodes, while MADDPG and MAPPO can only reach 0.11–0.43 within the same timeframe. Furthermore, the proposed MAT-Adapt method exhibits its strong generalization performance by evaluating the dynamic participation of various building types and regions. • A multi-energy buildings VPP framework is formulated with dynamic building participating. • A transformer-based multi-agent reinforcement learning (MARL) approach is proposed. • A novel network structure is proposed for rapidly adapt to dynamic participating configurations. • Key pattern of operation adaption to dynamic building configurations is investigated in 3 scenarios. • Economic operation, reduced network line losses, and environmental benefits are investigated. [ABSTRACT FROM AUTHOR]

Efficient power management is fundamental for organizations and systems to conserve resources, reduce costs, and improve environmental sustainability. To achieve the mentioned purposes, it is vital to perceive power consumption patterns and trends to make informed decisions about energy usage. By evaluating power consumption, inefficiencies and areas for improvement can be identified. In this regard, this paper presents a comprehensive study to predict power usage in a non-residential building HVAC system. The study employs the integration of machine learning models and optimization algorithms to achieve accurate predictions. The results demonstrate the effectiveness of the approach, with the CatBoost-AO model depicting superior performance in comparison with other models across a range of statistical evaluation metrics including NSE, JSD, RMSE, MAPE, VAF, KLD, as well as R2 and runtime. Remarkably, the CatBoost-AO model achieves the maximum R2 value of 0.91, indicating strong predictive capability. Overall, the presented study highlights the potential of employing machine learning and optimization techniques to improve power management and resource efficiency in HVAC systems, contributing to more sustainable and cost-effective operations in non-residential buildings. • Study conducts power consumption predictions using a comparative approach. • CatBoost and AdaBoost models integrated with optimizers for enhanced performance. • Aquila Optimizer, Arithmetic Optimization Algorithm, and Parallel Particle Swarm Optimization are utilized. • Hybrid models evaluated using metrics like NSE, JSD, RMSE, and R2. • CatBoost-AO model excels, achieving an R2 of 0.91 in testing, outperforming other models. [ABSTRACT FROM AUTHOR]

Summary: The efficiency and security of WSNs are improved by designing multiple lightweight cryptographic techniques and a 6LoWPAN protocol, depending on resource utilization and a reasonable amount of energy. This is still caused by constraints such as power resource management, authentication, key management, and flexibility protocols. To address these challenges, a novel approach called the modified gray wolf‐based chameleon swarm (MGW‐CS) algorithm is presented to select the cluster heads from WSN nodes. The MGW‐CS algorithm is an amalgamation of both improved gray wolf optimizer and chameleon swarm algorithm. During cluster head selection, the QoS parameters such as network lifetime, energy efficiency, and throughput and packet delivery ratio are improved and optimized. By presenting a lightweight and flexible cryptographic model to manage encryption complexities, the encryption parameters are automatically selected based on the currently available resources of each sensor node for data encryption. Among various WSN nodes, data exchanging and secure communication are established with the development of novel authentication and a lightweight key management model. The proposed model is implemented using the NS‐2 software. The proposed methodology offers a packet delivery rate of 98.6% for a total of 500 nodes when compared to the different state‐of‐art techniques. [ABSTRACT FROM AUTHOR]

The authors investigate the adaptive resource allocation problem for cognitive power line communication system. The dynamic optimisation model is employed to maximise the energy‐aware network utility, which is defined as the difference of the weighted network throughput and the power consumption. Then, the authors decompose the stochastic optimisation into a traffic management subproblem and a joint subchannel and power allocation subproblem. Since the joint subchannel and power allocation subproblem is a mixed integer non‐linear optimisation, a continuity relaxation and Lagrange dual method is proposed to obtain the optimal solution of the mixed integer non‐linear optimisation subproblem. After that, the authors develop an adaptive power line communication resource allocation algorithm to cope with the power line communication network dynamics only according to the current network state information. Finally, we derive a trade‐off mechanism between network utility and network delay, in which the network delay is proportional to the control parameter V and the network utility is proportional to the parameter 1/V$1/V$. Simulation results not only show the effectiveness but also verify the theoretical analysis of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Irrigation is a critical component of agriculture that consumes both power and water. As a result, intelligent irrigation systems that can manage the utilized resources are required. Using the Lagrange Multiplier approach, an optimal strategy for managing water and power resources is proposed in this work. It is employed because of its high accuracy solution and low hardware complexity. In the agricultural area, the optimization issue is phrased as a trade-off between water and power consumption. In order to test the proposed method, MATLAB software is used in two case studies. The obtained results confirmed the claimed efficiency and optimality of power and water resource management. [ABSTRACT FROM AUTHOR]

Device‐to‐device (D2D) communication plays a vital role in communication technologies with resource management and power control, which are the major issues for researchers in the era of 2020. In 5G networks, machine learning algorithms play a crucial role to manage these issues. In the proposed work, the problem is formulated for the uplink underlaid model of D2D communication. The Lyapunov optimization technique is utilized with a combination of machine learning techniques for resource allocation in D2D communication. First, the maximization of uplink and overall system capacity is formulated with resource management, which guarantees the signal to interference noise ratio for the D2D users. The optimization is a mixed integer non‐linear problem which uses the Lyapunov optimization method to optimize the bit error rate value and iterative algorithm to optimize the power value with different constraints. After attaining the optimized value, the support vector machine technique is utilized to ensure the spectral efficiency of an overall system in autonomous mode. Simulation results show that the proposed method provides higher reliability and power efficiency with higher system capacity in comparison to prevailing technologies. [ABSTRACT FROM AUTHOR]

Machine-to-machine (M2M) communication technology is an emerging technology that can connect smart wireless devices. The most obvious feature of M2M is that the communication between devices does not require human intervention. Therefore, ensuring the low-energy consumption of M2M devices is a necessary condition for prolonging the survival time of their devices. This paper first considers the coexistence of M2M and H2H scenarios. Aimed at the energy efficiency of M2M equipment and the channel capacity of H2H equipment, a multiobjective problem is constructed for joint spectrum and power resource management, and a weighted Chebyshev algorithm is proposed to solve this problem. Secondly, in view of the additional interference problems caused by the introduction of M2M communication, the intercell cooperative link selection algorithm is used to optimize its resilience. The effectiveness of the algorithm is proven by simulation results. [ABSTRACT FROM AUTHOR]

To gain high energy efficiency and low latency, fog computing can be considered as a promising enabling technology for supporting future IoT networks. Exponentially increasing data rate demand of smart IoT devices in the fifth‐Generation (5G) networks require new resource management and power allocation schemes. Fog computing involves the deployment of data storage devices (fog nodes) near the proximity of IoT nodes. The fog nodes collaborate to process requests generated from users. These nodes can store data in their storage capacity and supports in achieving quality‐of‐service (QoS) requirements. A joint node association and energy efficiency maximization problem is formulated in this article. The proposed problem is a non‐linear concave programming problem under cache size, association, and optimal power allocation constraints. A mesh adaptive direct search algorithm (MADS) is used to solve the formulated problem to find the sub‐optimal results. The efficient performance of the algorithm can be seen in comparison with more complex algorithms namely, outer approximation algorithm and exhaustive search algorithm. Extensive simulations are done to observe the detailed performance analysis of the IoT‐Fog network. As a result, energy‐efficient resource allocation is done under cache and QoS constraints with very little complexity and low computational power and delays. [ABSTRACT FROM AUTHOR]

To urge power supply companies to effectively fulfill their political, economic and social responsibilities, and to achieve effective mobilization of demand-side resources and efficient management of power distribution resources, the sensitivity evaluation and the classification of power users were investigated. The sensitivity characteristics of power users were analyzed, and the definition of power user sensitivity was proposed. A power user sensitivity evaluation index system was established based on three factors of politics, economy and society, and the evaluation index matrix was established. The quantitative and qualitative index data preprocessing methods were provided. The power user sensitivity evaluation algorithms were introduced based on analytic hierarchy process（AHP）, fuzzy C-means（FCM） clustering algorithm and comprehensive evaluation method combining the two methods. The evaluation algorithm was applied to a provincial science and technology core area, and the sensitivities of industry and commerce in the area users, residents and public service users were evaluated. The results show that the sensitivity levels of each user can be obtained by the proposed method, which can provide reference for the planning and construction of network distribution in the region [ABSTRACT FROM AUTHOR]

This study presents a novel Hunger Game Search and Niching Chimp Optimization Algorithms (HGS-NChOA) for optimizing grid-connected desalination plants powered by renewable energy. The primary innovation of this study is the significant advantages provided by the HGS-NChOA method, particularly in terms of reducing the cost of freshwater production and mitigating greenhouse gas emissions. Key outcomes reveal the HGS-NChOA's superiority in reducing freshwater production costs and greenhouse gas emissions. Notably, the desalination unit capacity decreased from 10.4 m³ to 8.5 m³ , with a cost reduction of 0.223 $/m³ in the PV-battery storage-wind turbine system. Experimental results show a 59% and 49% decrease in computation time for the PV-battery and PV-hydrogen systems, respectively. Sensitivity analysis highlights the significant impact of solar irradiation on investment costs. Overall, HGS-NChOA demonstrates enhanced efficiency and economic viability in managing grid-connected, renewable energy-powered desalination facilities. Sensitivity analysis showed that solar radiation has a more significant impact on investment costs compared to wind speed, with hourly solar radiation fluctuations affecting water production costs by 17.09% to 19.56%. Additionally, the study indicates that integrating a diesel generator into the system can further reduce costs and greenhouse gas emissions, proving HGS-NChOA's versatility in optimizing hybrid energy systems. Statistical analysis using metrics like Inverted Generational Distance (IGD) and Maximum Spread (MaxS) demonstrated the proposed method's superior convergence and diversity compared to well-known multi-objective algorithms like MOPSO, MOEA/D, and MOGWO-PSO. • Proposing a grid-connected integrated renewable energy and desalination plant. • Applying niching concept to standard ChOA. • Introducing a unique multi-objective hybrid optimization technique: HGS–NChOA. • Modifying specific criteria to affect investment cost by sensitivity analysis. [ABSTRACT FROM AUTHOR]

Innovation network is an autocatalytic network; however, there are several factors that affect the process and result. In the beginning, the innovation is very difficult due to certain blocked factors. Based on the machine learning network resource fusion algorithm, this paper conducts research on the innovation process and innovation driving force based on autocatalytic network. An autocatalytic network is introduced to explain the innovation process, including the evolutionary mechanism, evolution law, and the critical property of system innovation. It is concluded that the key of innovation is to unlock some key effect factors in core or find the key factors that connect different innovation. At last, the evolution law and the critical point are given, which could make us make sound and timely decisions. [ABSTRACT FROM AUTHOR]

This paper studies multi-agent deep reinforcement learning (MADRL) based resource allocation methods for multi-cell wireless powered communication networks (WPCNs) where multiple hybrid access points (H-APs) wirelessly charge energy-limited users to collect data from them. We design a distributed reinforcement learning strategy where H-APs individually determine time and power allocation variables. Unlike traditional centralized optimization algorithms which require global information collected at a central unit, the proposed MADRL technique models an H-AP as an agent producing its action based only on its own locally observable states. Numerical results verify that the proposed approach can achieve comparable performance of the centralized algorithms. [ABSTRACT FROM AUTHOR]

Climate change is intensifying glacier melting and lake development in High Mountain Asia (HMA), which could increase glacial lake outburst flood hazards and impact water resource and hydroelectric power management. However, quantification of variability in size and type of glacial lakes at high resolution has been incomplete in HMA. Here, we developed a HMA Glacial Lake Inventory (Hi-MAG) database to characterize the annual coverage of glacial lakes from 2008 to 2017 at 30 m resolution using Landsat satellite imagery. It is noted that a rapid increase in lake number and moderate area expansion was influenced by a large population of small glacial lake (= 0.04 km²), and faster growth in lake number occurred above 5300 m elevation. Proglacial lake dominated areas showed significant lake area expansion, while unconnected lake dominated areas exhibited stability or slight reduction. Small glacial lakes accounted for approximately 15% of the lake area in Eastern Hindu Kush, Western Himalaya, Northern/Western Tien Shan, and Gangdise Mountains, but contributed > 50 % of lake area expansion in these regions over a decade. Our results demonstrate proglacial lakes are a main contributor while small glacial lakes are an overlooked element to recent lake evolution in HMA. Regional geographic variability of debris cover, together with trends in warming and precipitation over the past few decades, largely explain the current distribution of supra- and proglacial lake area across HMA. The Hi-MAG database are available at: https://doi.org/10.5281/zenodo.3700282, it can be used for studies on glacier-climate-lake interactions, glacio-hydrologic models, glacial lake outburst floods and potential downstream risks and water resources. [ABSTRACT FROM AUTHOR]

With the implementation of device-to-device (D2D) communication in primary cellular networks, there will be notable benefits such as increase in cellular capacity, energy efficiency and spectral efficiency. The interference to the cellular user equipment (CUE), due to D2D pairs is reduced with proper strategies on resource allocation and transmit power assignment to the D2D pairs. This leads to healthier sum rate and battery life of D2D pairs. In this paper, we propose a distance-based interference reduction and power management scheme to reduce the interference on CUEs and evolved NodeB (eNB) and thus improve the sum rate of D2D pairs. The power allocation is based on the D2D pairs distance to the CUEs and eNB, which is estimated based on Haversine formula. The transmit power of D2D transmitter is effectively manipulated with respect to the transmit power of CUEs. The system sum rate, total transmit power of D2D pairs and the battery life of D2D user equipment are analyzed and validated through simulation. [ABSTRACT FROM AUTHOR]

Based on the assumption that the water, energy and food sectors are linked, the nexus concept drives research into the integration of governance systems. Nexus governance requires actors to engage across policy domains, governance levels and public, private and civic spheres. There is a need for a better understanding of how stakeholders navigate the inter‐organisational networks constituting a given nexus. In this article, we present a three‐dimensional relational framework and corresponding methods for investigating nexus governance. Drawing on mixed‐methods network research with organisations implementing water, energy, agricultural and environmental policies in the Upper Blue Nile basin of Ethiopia, we demonstrate how our framework for investigating network structure, relations and narratives can inform a relational understanding of nexus governance. [ABSTRACT FROM AUTHOR]

The issue of energy has emerged as one of the greatest challenges facing mankind. In an industrial perspective, the development of site utility systems (generally combined heat and power (CHP) systems) for the generation and management of utilities provides a great potential source for energy savings. However, in most industrial sites, a master–slave relationship usually governs this kind of system and limits the potential operating capacity of CHP. To improve the decision-making process, Agha et al. (2010. Integrated production and utility system approach for optimising industrial unit operation. Energy, 35, 611–627) have proposed an integrated approach that carries out simultaneous and consistent scheduling of batch production plants and site utility systems. The modelling of the problem relies on a mixed integer linear programming (MILP) formulation. Nevertheless, although it is a powerful mathematical tool, it still remains difficult to use for non-expert engineers. In this framework, a graphical formalism based on existing representations (STN, RTN) has been developed: the extended resource task network (ERTN). Combined with an efficient and generic MILP formulation, it permits various kinds of industrial problems, including production and consumption of utility flows to be modelled homogenously. This paper focuses on the semantic elements of the ERTN formalism and illustrates their use through representative examples. [ABSTRACT FROM PUBLISHER]

This article introduces the dynamical failure assessment method, which is a new kind of strategy for active failure management for complex physical engineered systems such as nuclear power plants. This method aims at evaluating, in regards to system objectives, residual capabilities of faulty components without trying to get a completely precise model of these components. Then, it anticipates reconfiguration possibilities by proposing solutions including faulty components. Several requirements are put forward in this article in order to perform DFA analysis. This article details consequently a co-operative modelling framework where a functional architecture is interacting with architecture of components architecture composed of several dynamical behaviour models representing dynamical interactions between system components. These dynamical models represent mixed hybrid information (discrete transitions and continuous flows) based on hybrid formalism. A two-tank system connected to a heat exchanger is used as an illustration. [ABSTRACT FROM AUTHOR]

• Customary authorities have persisted in many post-colonial developing countries. • Customary authorities have comparative local resource management advantages. • Customary authorities need to be held accountable to their people. The role of customary authorities in decentralized natural resource management is a subject of debate among conservation scientists and practitioners. This review article provides aggregate insights into what literature says. More of published articles on the subject matter are supportive of customary authorities playing roles in decentralized natural resource management for reasons such as their local legitimacy, effectiveness and efficiency in regulating resource use. The articles that are not supportive justified their stance with the assertion that customary authorities are not democratic and not accountable local governance institutions. Considering that customary authorities have comparative resource regulation advantages, the call to involve them in the ongoing decentralization of natural resource management is reasonable. However, tailored arrangements need to be put in place to ensure that customary authorities are locally accountable in their exercise of decentralized resource management powers. [ABSTRACT FROM AUTHOR]

• Water is scarce in the nine coal-fired energy bases of the north and west China. • The water footprint of coal and power production in the nine bases is evaluated. • The virtual water flow along with the energy products transfer is also analyzed. • Water stress in the energy bases is keeping increasing in the past and future. • The comprehensive countermeasures are proposed to ensure water and energy security. In the past decades, the nine major energy bases in north and west China produced increasing amounts of coal and coal-fired power to meet rapidly growing energy demand. However, the poor water resources condition has become a bottleneck for sustainable development. In this study, the water footprint of coal and power production is evaluated, and the flow of virtual water arising from the transport of energy products is quantified. The results show that total coal and power production increased continuously from 2006 to 2015. The annual total water footprint was as much as 1.29 billion m3; it showed an initially increasing trend that peaked in 2013, after which it decreased. After 2013, the water footprint decreased primarily as a result of improvements in water-saving technologies. The results also indicate that increasing amounts of virtual water (as much as 0.87 billion m3 in 2015) embedded in energy products flowed from the north and west water deficient areas to the southeast water-rich areas of China, causing severe water scarcity in the major energy producing regions. Unfortunately, based on our estimates, water stress will continuously increase during the 13th Five-Year Plan period (2016–2020). This study finds that water-saving measures alone are not sufficient to relieve the water shortage and to guarantee water security. Comprehensive countermeasures, including an adjustment to the industrial structure, the physical transfer of water, regulation of the virtual water trade and policy and planning changes, are also necessary. [ABSTRACT FROM AUTHOR]

Electrical power control and management is the key towards improving the efficiency and security of energy supply. Proper management would help in safely diversifying energy supplies, reducing energy consumption during peak load, and improving grid integration condition and reliability. Therefore, this study presents a novel real-time and smart power management system for monitoring and controlling power flow between substations, distributed renewable sources, and loads towards optimising energy consumption and balancing electric power supply. The concept and architecture of the proposed system are discussed in this study within the context of the smart grid principle. The proposed energy management system does not require any changes in the traditional substation. Simulation results and implementation are conducted to demonstrate the validity of the proposed system. [ABSTRACT FROM AUTHOR]

Over the last few years, nexus-thinking has become a buzzword in urban research and practice. This also applies to recent claims of greater integration or coordination of urban infrastructures that have traditionally been managed separately and have been unbundled. The idea is to better address their growing sociotechnical complexity, their externalities and their operation within an urban system of systems. This article introduces a collection of case studies aimed at critically appraising how concepts of nexus and infrastructure integration have become guiding visions for the development of green, resilient or smart cities. It assesses how concepts of nexus and calls for higher interconnectivity and 'co-management' within and across infrastructure domains often forestall more politically informed discussions and downplay potential risks and institutional restrictions. Based on an urban political and sociotechnical approach, the introduction to this special issue centres around four major research gaps: 1) the tensions between calls for infrastructure re-bundling and the urban trends and realities driven by infrastructure restructuring since the 1990s; 2) the existing boundary work in cities and urban stakeholders' practices in bringing fragmented urban infrastructures together; 3) the politics involved in infrastructural and urban change and in aligning urban infrastructures that often defy managerial rhetoric of resource efficiency, smartness and resilience; and 4) the spatialities at play in infrastructural reconfigurations that selectively promote specific spaces and scales of metabolic autonomy, system operation (and failure), networked interconnectivities and system regulation. We conclude by outlining directions for future research. [ABSTRACT FROM AUTHOR]

The integration of infrastructure domains and resource flows such as electricity, heat, water and waste increasingly gains currency in strategies to achieve more resource-efficient, smart and resilient cities. While widely discussed concepts of a nexus of resource systems, such as energy–water–food, aim at a more optimised and integrative management of resource flows, this article investigates how infrastructure integration is accomplished through the establishment of new interfaces and junctions between formerly separated systems. In particular, it focuses on households as an arena where different urban infrastructures intersect and different kinds of sometimes contradicting demands are imposed to co-manage these infrastructures, such as in the case of own electricity generation from photovoltaics along with the charging of electric cars and the management of household energy consumption. The installation of meters and the constant monitoring of resource use and consumption feedback to household members is regarded as a crucial element in such a transition towards more sustainable urban infrastructures. Empirically, the article studies the introduction of hot tap water meters in urban households in Sweden and the resistance and reactions of these households to such a metering regime. Our study shows how meters as new junctions between energy suppliers and users but also between separate infrastructures of electricity, hot tap water and room heating become contested political terrains which are linked to broader socio-political questions of urban change. In contrast to system management perspectives, such an 'inside-out' approach rather lends itself to context-sensitive and navigational governance approaches of infrastructure integration. [ABSTRACT FROM AUTHOR]

In the Adriatic-Ionian (ADRION) area, cultural borders and political rifts caused a lack of cooperation and development as well as a weak application of EU policies. This, combined with small dimensions & infrastructural limits of the ADRION ports, made them lose their historical mission as places of exchanges, and suffer from low modernization rate, inadequate smartness level and unsolved issues related to sustainability and urban regeneration needs. On the other hand, ADRION ports are still complex ecosystems, offering the perfect substrate for becoming again actors of the development in this area. In this framework, the project PoWER: "Ports as Driving Wheels of Entrepreneurial Realm", funded under the INTERREG V B ADRION Programme 1st call, aims to support the evolution of ports into Innovation Hubs, able to act as new "transmission belts" between ports and regions, and to exploit their untapped entrepreneurial potential. To do so, an action-based foresight methodology is proposed, used to smoothen the transition of a total of six (6) ADRION ports into such 'Innovation Hubs'. This methodology encompasses five (5) interconnected steps, starting at a highest-level planning and ending in the strategic design of a common transition framework. The tools used to support the ports' transition into innovation hubs do not differ from the common tools used in any other foresight activity, but are highly adjusted to the maritime sector. To this extent, an investigation to set the benchmark was conducted, whose results will be used to nurture a foresight process especially focused on the creation of reference scenarios for orienting the PoWER transition approach. Such framework does thus not only set the ground for a smoother transition of the ADRION ports, but will also support them to predict, pursue, and prepare for the potential opportunities, as well as threats, that the future holds. [ABSTRACT FROM AUTHOR]

Highlights • The savings made by efficient allocation are higher than using consolidation with migration techniques. • The host efficiency metric, used by GREEEN500 list, may not estimate the energy efficiency of a virtualised server, accurately. • Frequent migration of a particular VM has impact on datacenter's energy efficiency and workload's performance. • The policies such as MMT and CMCR could decrease the total number of migrations, however, they do not avoid repeatable migrations of a particular VM. • There is a trade-off between energy efficiency and performance; that varies with respect to the total number of migrations. Abstract In clouds, placement of on-demand applications on heterogeneous machines, has turned out to be a crucial research problem, particularly, in terms of performance and energy consumption. Techniques like Dynamic Voltage and Frequency Scaling (DVFS), processor speed adjustment and features such as turning off displays, activating sleep modes, etc. are only useful for decreasing the energy consumption of a single machine, at marginal loss in performance. They cannot be used to achieve significant power optimization in High Performance Computing (HPC) systems such as grids, and cloud datacenters; because power saved by scaling down the processor voltage is far less than switching off a machine. Resource management, using dynamic consolidation of VMs, allows cloud service providers to optimize resource usability, performance and decrease power consumption. This paper investigates various resource management techniques, and suggests several heuristic approaches to optimise energy consumption and performance in elastic datacenters. Using real workload datasets, our evaluation suggests that a combination of the proposed VM allocation and consolidation with migration control technique could save approximately 1.96%–9.38% energy, and improve 0.32%–5.96% performance, as compared to its closest rivals. [ABSTRACT FROM AUTHOR]

Abstract Ecological performance of an energy supply chain (ESC) is an important yet understudied field, which differs from the traditional manufacturing supply chain. This research proposes a novel two-stage data envelopment analysis (DEA) model that employs frontier-shift methodology to measure ESC ecological efficiency when carbon permits are tradable. The proposed frontier-shift DEA (FSDEA) approach uses the carbon trading quantitative information to determine to whom the trade should be made and how much to trade. In addition to estimating ESC efficiency performance, this research also stipulates the efforts required for a particular decision-making units (DMU) to become efficient and discuss the benefits that derived from the proposed exchange. It also reveals why certain DMUs are more likely involved in trading than others. Under the two-stage ESC, this research shows that either stage can improve the overall efficiency, which can be optimally derived if and only if both stages obtain the optimal solutions. In an application study of cities in one of the Chinese provinces, the proposed FSDEA model compares each city's potential percentage efficiency improvement with minimal possible efforts (carbon reduction), which differs from existing DEA models that rank solely by efficiency scores. Results show that on average the amount of carbon credits a city should trade to its competitors is 392.21 tons, resulting in a 6.61% improvement in efficiency. Among cities, Xuancheng and Fuyang is ranked in the first and the last place, respectively, according to their effectiveness in potential efficiency improvement with possible reduction efforts. Our findings offer valuable insights into ESC efficiency and help businesses make advantageous ecological decisions. Highlights • A two-stage frontier-shift DEA model is proposed. • The model is used to measure energy supply chains' ecological performances. • The model considers that carbon permits are tradable. • The ecological efficiency of 16 cities in Anhui, China are examined. [ABSTRACT FROM AUTHOR]

This article deals with the optimization of energy resource management of industrial districts, with the aim of minimizing customer energy expenses. A model of the district is employed, whose optimization gives rise to a nonlinear constrained optimization problem. Here the focus is on its numerical solution. Two different methods are considered: a sequential linear programming method and a particle swarm optimization method. Efficient implementations of both approaches are devised and the results of the tests performed on several energetic districts are reported, including a real case study. [ABSTRACT FROM AUTHOR]

This article presents the major challenges faced by the state for tackling energy poverty as one of the barriers to sustainable development not only of the energy sector but also of the society. The article presents the scope of energy poverty in Bulgaria, the underlying factors, the consequences, and the trends of development in the last three years until 2016. The article also provides a brief guide to tackling the challenges of combating energy poverty. [ABSTRACT FROM AUTHOR]

The significant integration of variable energy resources in power systems requires the consideration of greater operational details in capacity expansion planning processes. In hydrothermal systems, this motivates a more thorough assessment of the flexibility that hydroelectric reservoirs may provide to cope with variability. This work proposes a stochastic programming model for capacity expansion planning that considers representative days with hourly resolution and uncertainty in yearly water inflows. This allows capturing high resolution operational details, such as load and renewable profile chronologies, ramping constraints, and optimal reservoir management. In addition, long-term scenarios in the multi-year scale are included to obtain investment plans that yield reliable operations under extreme conditions, such as water inflow reduction due to climate change. The Progressive Hedging Algorithm is applied to decompose the problem on a long-term scenario basis. Computational experiments on an actual power system show that the use of representative days significantly outperforms traditional load blocks to assess the flexibility that reservoir hydroelectric plants provide to the system, enabling an economic and reliable integration of variable resources. The results also illustrate the impacts of considering extreme long-term scenarios in the obtained investment plans. [ABSTRACT FROM AUTHOR]

This article discusses the role of private nonprofit organization on the possession and development of fissionable materials for the production of atomic energy. The Joint Congressional Committee on Atomic Energy intends to hold public hearings on the subject of the development of nuclear power for industrial use. A general agreement stated that if adequate research and development programs are undertaken, nuclear power can be developed so that the source of energy can and will ultimately make a major contribution to the world's energy requirements.

• A model is proposed for short-term power demand forecasting. • The method is a hybrid of ELM and ANFIS networks. • The weight coefficients of the procedure are determined by an enhanced bioinspired algorithm. • The algorithm is called Developed Parasitism-Predation Algorithm. One of the most crucial steps in comprehensively planning the proper and efficient use of energy (optimisation of consumption) and appropriate management of resources to fulfil the power demand is accurately forecasting the energy demand (optimisation of production). This study presents the modelling of an ideal technique for short-term power demand prediction as a novel hybrid approach comprising two distinct methods, namely the Elman neural network (ELM) and adaptive network–based fuzzy inference system (ANFIS). The hybrid approach outperforms the conventional individual methods because it can eliminate the drawbacks of the individual methods while retaining their advantages, particularly considering that ELM and ANFIS can handle non-linear data. The weight coefficients of the procedures in the proposed hybrid approach were determined using a novel, enhanced bioinspired algorithm, called the improved parasitism–predation algorithm, to achieve better accuracy. The simulation results demonstrated the superiority of the proposed approach over other state-of-the-art approaches including the independent ELM and ANFIS. [ABSTRACT FROM AUTHOR]

The technology of power electronic systems has diversified into industrial, commercial, and residential areas. Developing a strategy to improve the performance of the electrical energy of an electric vehicle (EV) requires an analysis of the model that describes it. EVs are complex mechatronic systems described by nonlinear models and, therefore, its study is not an easy task. It can improve the performance of a battery bank by creating new batteries that allow for greater storage or by developing a management energy system. This article shows the development of a power supply management system based on fuzzy logic for an electric vehicle, in order to minimize the total energy consumption and optimize the battery bank. The experimental result is shown using the fuzzy controller under standard operating conditions. An increase in battery performance and overall performance of energy consumption is shown. Speed signals acquired show improvements in some dynamic, such as overshoot, settling time, and steady-state error parameters. It is shown that this fuzzy controller increases the overall energy efficiency of the vehicle. [ABSTRACT FROM AUTHOR]

One will not understand the brain without an integrated exploration of structure and function, these attributes being two sides of the same coin: together they form the currency of biological computation. Accordingly, biologically realistic models require the re-creation of the architecture of the cellular components in which biochemical reactions are contained. We describe here a process of reconstructing a functional oligocellular assembly that is responsible for energy supply management in the brain and creating a computational model of the associated biochemical and biophysical processes. The reactions that underwrite thought are both constrained by and take advantage of brain morphologies pertaining to neurons, astrocytes and the blood vessels that deliver oxygen, glucose and other nutrients. Each component of this neuro-glio-vasculature ensemble (NGV) carries-out delegated tasks, as the dynamics of this system provide for each cell-type its own energy requirements while including mechanisms that allow cooperative energy transfers. Our process for recreating the ultrastructure of cellular components and modeling the reactions that describe energy flow uses an amalgam of state-of the-art techniques, including digital reconstructions of electron micrographs, advanced data analysis tools, computational simulations and in silico visualization software. While we demonstrate this process with the NGV, it is equally well adapted to any cellular system for integrating multimodal cellular data in a coherent framework. [ABSTRACT FROM AUTHOR]

Within the Climate and Energy Directive, the European Commission has set three main targets: reduction of GHG emissions, increase a share of renewable resources, and improvements in energy efficiency. This paper introduces needs and solutions for capacity building in cross-border regions of Slovenia-Croatia-Hungary in order to better implement energy efficiency and renewable resources potentials. It was identified that a lack of knowledge by the stakeholders represents a main obstacle for implementing more environmental and economic acceptable energy solutions on a local and regional levels. To address the competences gaps by the stakeholders, online training modules were designed and implemented. The results of trainings revealed the best evaluation scores, and consequently improved knowledge and competencies in Hungary, followed by Croatia and Slovenia. [ABSTRACT FROM AUTHOR]

A spatial-temporal model is proposed for optimal integrated water and energy resource management in urban areas, considering daily surplus output from residential grid-connected rooftop photovoltaics as an energy source for sustainable supply. The model addresses optimal investment and operational decisions of a desalination-based water supply system driven by surplus photovoltaic output and grid electricity. The two-level mixed integer linear programming model considers demands, systems configuration, resources capacity and electricity tariffs and gives the solution such that the highest compatibility with available renewable energy is achieved. The model is then applied to Perth, Australia and solved for three operational scenarios. The results show, for a given year, hourly (flexible) basis scenario leads to $9 521 425 and $18 673 545 economic benefits over seasonal (semi-flexible) and yearly (fixed) basis scenarios, respectively. They also indicate 19.9% better economic performance in terms of annualised unit cost of water production over existing Southern seawater desalination plant in Perth. Additionally, it is shown that the seasonal change on the optimal solutions mainly corresponds to the share of each energy resource to meet water-related energy demand. Finally, the results indicate higher sensitivity to the variation of the photovoltaic installation density compared to financial rate. [ABSTRACT FROM AUTHOR]

Based on the 873 questionnaires collected from six cities in four provinces in China, we made a quantitative analysis of different types of consumers' expectations for government low-carbon subsidies by using the SPSS. The results indicate that: (1) Significant differences exist in the 'expectations for government subsidies on low-carbon products' from different types of consumers; there are significant differences among consumers with different monthly income, educational attainment and age on the 'expectations for government subsidies on low-carbon products', but the difference is not significant for the consumers in different regions or gender. (2) Monthly income and educational attainment exert significant influences on consumer 'expectations for government subsidies on low-carbon products', and the influence of monthly income is the biggest. Finally, we put forward policy recommendations accordingly. [ABSTRACT FROM AUTHOR]

Large-scale development of urban rail transit has attracted attention owing to its sizable energy consumption. Energy-efficient planning can enhance the distribution and utilization of limited energy resources. This study proposes a two-stage urban rail transit operation planning approach comprising running time allocation and regenerative energy utilization to save energy consumption. The proposed models and algorithms holistically deal with inter-station running time synergy which utilizes surplus running time to achieve minimum energy consumption. They also implement the hauling and braking synergy of multiple trains in multiple trips, with adjustments to departure intervals and dwelling times, to maximize the regenerative energy real-time utilization rate. The algorithms utilize chromosomes in genetic algorithm to represent possible operation stage combinations, conduct feasible direction iterations to facilitate surplus-time effective allocations, and maximize the derived overlap time for operation synergy of trains under the precondition of energy-efficient train movements between stations. A case study of the metro line demonstrates that considerable energy saving is achievable through the proposed planning approach. [ABSTRACT FROM AUTHOR]