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In an era of increasing energy demands and environmental concerns, optimizing energy consumption within buildings is crucial. Despite the vast improvements in HVAC and lighting systems, plug loads remain an under-studied area for enhancing building energy efficiency. This paper studies smart plug active operating mode detection, plug-level load forecasting, and plug scheduling methodologies. This research leverages a unique dataset from the University of California, San Diego, consisting of readings from over 150 smart plugs in several office buildings for more than a year, notably during the post-Covid era. This dataset is made publicly available. A comprehensive literature review on plug, i.e. , appliances operating mode detection is presented. Novel unsupervised learning approaches are applied to identify plug operating modes. A pipeline integrating the detected modes with forecasting and scheduling is developed, aiming at building energy consumption reduction. Our findings offer valuable insights and promising results into smart plug management for energy-efficient buildings. • A novel year-long dataset of power readings from 169 office smart plugs is introduced. • The first review of plug load mode detection methods and terminology is presented. • The paper proposes an unsupervised method to identify plugs load operating modes. • The proposed smart plug scheduling pipeline reduces energy consumption by 20%. [ABSTRACT FROM AUTHOR]

This paper examines the pervasive barriers hindering the widespread adoption of energy efficiency measures across various sectors. It categorizes these barriers into economic barriers, high upfront costs, organizational challenges, such as technical expertise and behavioral barriers, namely risk aversion and framing. To address these barriers, a comprehensive review of energy efficiency policies is conducted. These policies include incentives, coercive instruments, award systems, university industry collaboration, and technical support. To review energy efficiency barriers and policies, a methodological approach integrating non-structured snowball sampling with targeted literature review techniques was devised. Finally, the paper underlines that the success of these policies is contingent upon the active involvement, the collaboration, and the feedback of businesses, ensuring the feasibility of these policies. Their proactive engagement is indispensable in tackling energy efficiency barriers and driving the implementation of energy-efficient technologies, thus paving the way for a more sustainable and competitive businesses. [ABSTRACT FROM AUTHOR]

This paper examines the role of energy efficiency in enhancing corporate sustainability and competitiveness. Employing a systematic literature review methodology, we analyze the financial advantages, corporate sustainability alignment, and crisis resilience associated with energy efficiency measures in business operations. The results highlight the significant cost savings, improved profitability, and enhanced competitiveness resulting from energy-efficient practices. Furthermore, we elucidate how energy efficiency initiatives align with broader corporate sustainability objectives, reduce environmental impact, and promote corporate social responsibility. Additionally, the paper discusses how energy efficiency enhances organizational resilience by bolstering preparedness for crises and promoting long-term sustainability and adaptability. The main implications of our findings underscore the importance of prioritizing energy efficiency as a strategic imperative for businesses seeking to achieve sustainability goals while maintaining competitiveness in a rapidly evolving global landscape. [ABSTRACT FROM AUTHOR]

The paper presents an original method for synthesizing high-density composite ceramics with a Mo 2 C/Mo 3 Co 3 C composition using the technology of high-speed Spark Plasma Sintering (SPS) of a mechanically activated powder mixture of Mo 2 C-10 wt%Co. The activated homogeneous starting mixture was obtained by wet milling/activation of the initial powders of Mo 2 C and Co in an anhydrous isopropanol medium. The ceramic samples were consolidated by SPS under vacuum (10−5 atm.) at 1000, 1100, 1150, and 1200 °C with a heating rate of 87.3 °C·min−1 under constant external uniaxial pressing pressure of 50 MPa. The sintering kinetics were studied, the twofold nature of sintering was established, and the temperature range of active densification was determined. The ceramic synthesis is accompanied by a reactive interaction of the components, resulting in the formation of the Mo 3 Co 3 C phase and a change in the crystal lattice of the non-stoichiometric Mo 2 C x compound. The ceramic obtained in the temperature interval of the isothermal sintering stage of 1150–1200 °C is homogeneous in the distribution of Mo and Co, consists of a Mo 2 C/Mo 3 Co 3 C phase mixture, has a density value of 98.13 ± 0.5 % theoretical density, and has an average Vickers hardness value of ∼1526 HV. Increasing the sintering temperature to 1200 °C results in the formation of almost monolithic structures in the samples, but also induces the formation of large defects due to the collective consolidation of pores within the bulk material and increases the susceptibility of the ceramic to cracking. The paper presents preliminary results from electrochemical testing. The proposed method for synthesizing Mo 2 C/Mo 3 Co 3 C ceramics has potential for use in optimizing the composition and production methods of electrode materials for the realization of active components in heterostructured electrodes used for hydrogen evolution. [ABSTRACT FROM AUTHOR]

Aerogels, with their exceptional properties such as high insulation performance, lightness, and eco-friendliness, present immense potential for use in the construction industry. This paper reviews the progress in research and application of various types of aerogels in construction, highlighting notable case studies demonstrating energy efficiency gains and sustainability impacts. It delves into the lifecycle of aerogels, discussing environmental implications at each stage. Despite their potential, aerogels face certain limitations including production complexity, brittleness, and high cost which hinder their widespread adoption. The paper further discusses these challenges and highlights the need for continuous research and development efforts to overcome them, with a view to achieving more scalable production, enhanced mechanical properties, and cost reduction. Looking forward, the paper discusses the expansive horizon of aerogel research, emphasizing the potential of smart aerogels, their integration with renewable energy systems, and exploring the possibility of recycling or creating bio-based aerogels to enhance their overall sustainability. While still in early stages, aerogels promise to be a major contributor to the future of construction, leading to more sustainable and efficient built environments. [ABSTRACT FROM AUTHOR]

Energy consumption forecasting plays a pivotal role in modern resource management and sustainable development. This paper presents a concise overview of state-of-the-art techniques and methodologies employed in the field of energy consumption forecasting, with a particular emphasis on the application of Machine Learning (ML) models. The paper surveys recent advancements, addresses key challenges, and identifies promising directions for future research in this critical domain. By examining the current landscape of energy consumption forecasting through the lens of machine learning, this review aims to offer researchers and practitioners valuable insights and guidance for enhancing the accuracy and efficiency of energy consumption pattern prediction. [ABSTRACT FROM AUTHOR]

Anion exchange membrane water electrolysis is a novel technology that is expected to produce hydrogen more efficiently than alkaline water electrolysis and without noble metals, which are required in proton exchange membrane water electrolysis. The latter aspect presents a decisive advantage in times of rising material availability concerns. However, resulting from the early stage of development, different materials are considered for use in anion exchange membrane water electrolysis. This study presents a methodology for a comparative analysis of different electrodes regarding their energy demand and material criticality. The energy demand is determined by measuring polarization curves, while the material criticality is quantified using the GeoPolRisk midpoint and SH2E methods. Based on energy demand and material criticality, a multi-criteria optimization was performed to identify the materials that present the best compromise. The results show that the multi-criteria optimization of different electrodes in terms of energy demand and material criticality provides valuable insights for the evaluation of catalysts and can be used to guide sustainable catalyst development. Among the considered materials, the combination of nickel-molybdenum on carbon paper for the cathode and a stainless steel mesh for the anode represent the optimum compromise when considering energy demand and material criticality equivalently essential. [Display omitted] • Methodology for simultaneous optimization of energy demand and material criticality. • Comparative study on electrode materials for anion exchange membrane electrolysis. • Stainless steel materials balance energy demand and material criticality for anode. • On the cathode side, carbon materials present the optimum compromise. • Pt's high material criticality lead to uncompetitive multi-criteria performance. [ABSTRACT FROM AUTHOR]

The accumulation of space debris is an ever-increasing problem. Systems that can reliably de-orbit (i.e., decrease the time to re-entry for) an inactive resident space object (RSO) are highly sought after. In this paper, we investigate the feasibility of utilizing the forces owing to the interaction of Earth's magnetic field with a permanent magnet that has been attached to an RSO in low Earth orbit. We consider an actively controlled permanent magnet that is optimally oriented to remove energy from the system, and the most favorable magnet-to-RSO mass ratio. We show that, even under these best-case assumptions, the interaction between a permanent magnet and Earth's magnetic field is not a viable means of de-orbiting an RSO. [Display omitted] • The interaction between a permanent magnet and Earth's magnetic field can be used to remove energy from an object in orbit. • The orientation of the permanent magnet can be controlled to optimize the energy loss. • This optimal orbital energy loss is not sufficient to de-orbit the object in a reasonable amount of time. • Interaction between a permanent magnet and Earth's magnetic field is therefore not a viable means for de-orbiting space debris. [ABSTRACT FROM AUTHOR]

This paper studies the prescribed-time relative motion control problem of spacecraft formation flying under input saturation. Using prescribed-time theory, a prescribed-time sliding mode is designed such that the states on the sliding mode converge to the equilibrium in the prescribed time. Based on the prescribed-time sliding mode, a prescribed-time relative motion tracking controller is developed, which guarantees fast formation maneuvers with feasible fuel consumption and strong robustness under input saturation. Furthermore, a simulation example is carried out to verify the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Mixed-mode ventilation system (MMVS) can significantly decrease the energy consumption of Egyptian office buildings while meeting indoor air quality requirements and thermal comfort of occupancy. This paper studies the effect of using mixed-mode ventilation system (MMVS) in achieving both energy efficiency and thermal comfort in office buildings. The research specifically focuses on investigating the energy-saving potential and thermal performance of existing office buildings in Egypt by merging natural ventilation (NV) with mechanical ventilation (MV) to reach an effective mixed-mode ventilation system (MMVS). Additionally, an energy performance simulation was used to study how to achieve thermal comfort and the building's indoor temperature. Firstly, a selected office building in Cairo was used as a case study, and its thermal performance was studied by simulation. Secondly, site measurements were conducted to record the real thermal performance of the tested office building. These measurements were used for the validation phase. The validation demonstrated a satisfactory agreement between the building simulation and measurement data for air temperature (T) and relative humidity (RH). Thirdly, building energy simulations and analysis were used for testing the energy performance and thermal comfort of the tested scenarios that have different alternatives in the operation settings. The results revealed that it is possible to increase the thermal comfort hours by merging natural ventilation and applying the mixed-mode ventilation system only by changing the operation settings on the MMVS performance. The building's annual energy consumption was reduced by approximately 27 % compared with the use of HVAC in the building. Natural Ventilation hours increased by approximately 27 % to reach 427 hours (h) and the HVAC hours decreased by 17 % to reach 898 hours (h). [ABSTRACT FROM AUTHOR]

This paper proposes an improved dual-phased framework for energy-efficient, cluster-based routing in Wireless Sensor Networks (WSNs). It addresses the critical challenge of balancing energy consumption with reliable network performance. Cluster-based routing is a crucial parameter for WSN operational efficiency, especially in applications demanding minimal energy use and dependable data transmission. The proposed framework integrates two advanced meta-heuristic algorithms: Sailfish Optimization (SFO) and Spotted Hyena Optimization (SHO). This combined approach leverages SFO's rapid exploration for efficient clustering and optimal Cluster Head (CH) selection. Additionally, SHO's refined exploitation capabilities optimize efficient routing paths. This innovative methodology significantly improves network performance metrics like energy efficiency, network lifetime, and Packet Delivery Ratio (PDR). The originality of this work lies in the dual-phased optimization strategy. It distinctively outperforms traditional single-algorithm based approaches by employing a unique hybrid optimization approach, offering greater originality and value. Experimental simulations demonstrate that the proposed framework outperforms several popular algorithms in terms of key performance metrics. This makes it a valuable contribution to the field and an efficient solution for diverse applications. [ABSTRACT FROM AUTHOR]

Decarbonizing the building sector to achieve sustainable development goals (SDGs) is a complex issue that poses significant challenges to the scientific community. In this context, the current paper aims to assess the viability of employing incinerated by-products as a sustainable mineral resource to produce sustainable geopolymers. To this end, the study focuses on the formulation of geopolymer binders using fly ash with various proportions (ranging from 0 % to 50 %) of metakaolin (MK), investigating their physico-mechanical, microstructural analysis as well as their sealing capacity. The results indicate that incorporating 30 % MK not only preserves good workability but also significantly improves mechanical strength, ensuring long-term durability by increasing bulk density and concurrently reducing water absorption and harmful porosity. Moreover, the results of the Toxicity Characteristic Leaching Procedure (TCLP) demonstrated that the released contaminants remained within the regulatory limits established by the USEPA. The geopolymer binder developed exhibited a significant reduction of 77.23 % and 57.92 % in terms of carbon dioxide equivalent (CO 2 -e) emissions and energy demand compared to conventional cement. This study introduces a novel approach to promoting a circular economy within the building sector. [Display omitted] [ABSTRACT FROM AUTHOR]

Space-based solar power systems (SSPS) envision the usage of focused microwave beams to transfer power from space to ground. To supply a large amount of power and to be able to focus a microwave beam efficiently, a large infrastructure must be assembled in orbit, thus, making the implementation of SSPS an arduous task. In the history of SSPS designs, aside from economical obstacles, technical obstacles such as large ohmic losses due to long DC lines in the structure, large rotary joints to decouple beam pointing and solar power collection, heating problems from power lines converging into a single point, and complex control mechanisms, manufacture, and assembly are present. A simpler implementation using multiple foldable thin film membrane satellite modules is proposed as a feasible design, born from the advancement of thin film electronics. Its economical feasibility is achieved through the reduced weight, large surface area, and membrane's ability to be stowed. The thin film SSPS combines advantageous properties of past designs such as a simple structure, simple control mechanism, and a distributed power collection and transfer architecture avoiding the ohmic loss and heating problems. However, due to the thin film membrane hosting both the antenna and the solar cells, competition occurs between maximum solar power collection and high efficiency power transfer capabilities. In this paper, we propose a framework to analyze the performance of the thin film SSPS solar cell-antenna integration design in terms of the wireless power transfer efficiency and fuel consumption, from which we identified the trade-offs on designing and operating the station. We used this framework to analyze three different antenna element designs in combination with three different attitude control goals, and found out that the key aspects of the antenna design are beam width, radiation efficiency, and efficient solar cell integration. [ABSTRACT FROM AUTHOR]

Denial of services (DoS) attacks exist in wind integrated power system. DoS attacks can cause network-induced delay and packages loss in information transmission. Meanwhile, considering the parameter perturbation of controller and system model uncertainty in wind integrated power system, these may cause the system dynamic performances degradation or even instability. Based on the above considerations, the joint non-fragile automatic generation robust control of wind integrated power system under DoS attacks is studied in this paper. In order to ensure the expected system performance and more effectively utilize the limited network communication resources under DoS attacks, a novel dynamic multi-event driven mechanism based joint non-fragile H ∞ automatic generation control method is proposed. By constructing a suitable Lyapunov-Krasovskii functional and utilizing the Shur complement lemma to handle nonlinear matrix inequality, the sufficient conditions are derived to guarantee the asymptotic stability of wind integrated power system under DoS attacks. Furthermore, the performance of the proposed non-fragile regulator is demonstrated through a four-area wind integrated power system to show the feasibility and applicability. The analysis result indicates that the proposed scheme provides stronger robustness, higher wind energy utilization efficiency and more efficient communication mechanism. [Display omitted] • A multi-event driven mechanism (MEDM) based joint non-fragile control scheme is derived. • The wind-integrated power system model based on MEDM is established. • The MEDM is integrated into the joint non-fragile controller design under DoS attacks. • The MEDM based joint non-fragile H ∞ automatic generation control strategy is proposed. • It provides stronger robustness and higher wind energy utilization efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the Enhanced Whale Optimization Algorithm (EWA) to optimize dependent task offloading in a multi-edge cloud computing environment. Our proposed algorithm aims to identify the most suitable offloading scenario for dependent tasks, focusing on minimizing total processing latency, energy consumption, and associated costs. We operate within a system comprising many decentralized Mobile Edge Computing servers (MECs) and a centralized cloud server. Two novel improvement operations, namely Frame Shifting (FS) and Load Redistribution Strategy (LRS), are introduced to enhance the performance of the whale algorithm. Through simulation, our results demonstrate the superior performance of EWA. Specifically, compared to the Whale Optimization Algorithm (WOA), EWA achieves a remarkable reduction in latency by 22.84%, a substantial decrease in energy consumption by 78.28%, and a notable reduction in cost usage by 61.47%. These outcomes underscore the efficacy and practical significance of the proposed EWA in addressing the challenges posed by dependent task offloading in the multi-edge cloud computing landscape. [ABSTRACT FROM AUTHOR]

Closed-form models are important because they offer more design insights, require less computing time and prevent convergence problems in modeling compliant mechanisms. However, most of the available closed-form models, e.g., beam constraint models, are only appropriate for beam flexures within the intermediate deflection range. In this paper, a method for developing closed-form models based on the chained beam constraint model through the combined use of strain energy and Castigliano's first theorem is proposed. A closed-form model for beam flexures with a large prediction range is derived using this method. By modeling simple beams and compliant mechanisms using the proposed closed-form model, it is shown that the model has a significantly broader prediction range than the beam constraint model and is more efficient than the chained beam constraint model. The large prediction range and high efficiency of the proposed closed-form model make it potentially useful for optimizing compliant mechanisms. • A method to develop closed-form models for beam flexures is proposed. • A closed-form model for CBCM with 3 discretized elements is obtained. • Closed-form load-displacement relations for beam flexures are obtained. • The proposed molded is demonstrated to reduce the computational time. [ABSTRACT FROM AUTHOR]

• Novel damped conformable fractional accumulation method. • Prediction of energy related series with the uncertainties. • Design of damped conformable fractional grey bernoulli model. • Improved prediction accuracy, robustness and applicability. • Prediction of world coal consumption and production in 2025–2026. Energy resources, such as oil, coal are important to people's life and the development of the economy. Predicting the energy consumption and production with uncertainties can help the government and policymakers make the reasonable energy strategy and give constructive guidance. In this paper, a novel damped conformable fractional accumulation operator with two parameters are proposed to have control of both accumulation generation and new information involvement. The new accumulation can be adjusted from linear approximation to high order accumulation. A structure adaptive grey Bernoulli model based on proposed accumulation method is established, with the parameters optimized by particle swarm optimization algorithm. For testifying the effectiveness and the robustness of the model, two validation cases and two application cases are used to build the prediction model of diverse series with unexpected trends or fluctuations in energy fields. Compared with other existing fractional grey models and non-grey models, the proposed model has best prediction accuracy, better model stability and greatly improved applicability. The global coal consumption by industry is predicted as 30.85×106 TJ in 2025 and 30.81×106 TJ in 2026. The world coal production could be 182.93–187.73 exajoules in 2025 and 180.85–182.16 exajoules in 2026. [ABSTRACT FROM AUTHOR]

This paper explores the feasibility of utilizing lead-free multilayer piezo stacks, particularly focusing on Zr-doped 0.95(Bi 0. 5 Na 0.5)TiO 3 -0.05(BaTiO 3) piezoceramics, for energy harvesting. Various lead-free BNT-BT disk samples with different Zr concentrations undergo structural, microstructural, and electrical characterizations. The results illustrate that Zr-doping improves piezoelectric constitutive parameters, notably at a 2.0 mol% concentration. Electromechanical tests were conducted on multilayer piezo stacks, involving variations in electric load, mechanical load, frequency, and number of stacked disks. These tests demonstrate the ability of the multilayer transducer to produce up to 1.5 V (peak) under particular conditions: a 250 Hz frequency, a 10 N mechanical load, and an open circuit electric load, utilizing a stack comprising 5 disks. Employing an analytical mathematical model, an effective d 33 e f f of the system is determined, yielding values ranging from 588 to 625 pC/N for a stack composed of 5 disks. These findings highlight the considerable potential of lead-free multilayer piezo stacks in energy harvesting applications, offering crucial insights for the development of sustainable and eco-friendly energy solutions. [ABSTRACT FROM AUTHOR]

In this paper, a novel Model Predictive Control (MPC) technique for multi-satellite formation flying geometry acquisition and maintenance in high-drag environment is presented. The proposed MPC relies on a linearized and convexified quasi-nonsingular Relative Orbital Elements (ROE) model based on state transition matrices propagation, allowing to include the effect of perturbations in the prediction to optimize fuel efficiency and tracking accuracy. The formation is controlled with respect to a non-decaying orbiting point to perform absolute and relative station keeping simultaneously. For this purpose, a new dedicated plant matrix to include drag effects on ROE in the propagation is derived and validated with respect to numerical results. In all simulations, the satellites are assumed to be equipped with a single low-thrust propulsion unit, therefore, specific constraints are included in the controller to obtain a feasible solution in a real operational scenario. Moreover, a collision avoidance constraint is added in case of close proximity operations exploiting a linear mapping between the set of ROE and cartesian coordinates expressed in the Local-Vertical-Local-Horizontal (LVLH) reference frame. The controller response is simulated in several realistic mission contexts with a high-fidelity orbital propagator and the results are validated for fuel efficiency by comparing them to similar approaches available in literature and to optimal solutions obtained respectively with a direct single shooting algorithm and with a closed-form impulsive formulation. [ABSTRACT FROM AUTHOR]

Energy efficient permutation flowshop scheduling problem (PFSP) is widely studied to reduce the energy consumption in manufacturing systems. Most traditional studies make the premise that setup time is a component of processing time. However, this assumption does not satisfy the small batches, more varieties manufacturing models criterion. Therefore, energy efficient PFSP with setup times thus becomes a pressing problem that needs to be resolved. The contribution of this paper includes: (1) a permutation flowshop scheduling problem (PFSP) mathematical model by considering energy consumed and setup time by each machine in the system; (2) a grey-wolf algorithm optimization with variable neighborhood search (VNS) method (GWO_VNS) is proposed for the multi-objective PFSP model; (3) solved 9 benchmarks problems of Taillard (1993) [8] for the minimization of flowtime (FT) and energy consumption (EC). The performance of the proposed GWO_VNS algorithm is evaluated on the benchmark problems selected from the published literature [15]. It is noted that the proposed algorithm performed better on both the objectives i.e., FT and EC minimization. Overall, the proposed algorithm achieved 33.25% and 30.56% average improvement in FT and EC minimization respectively on benchmark problems. [ABSTRACT FROM AUTHOR]

This paper focuses on the monitoring and prediction of the energy consumption of IT equipment to make informed decisions in terms of energy efficiency. The challenge with current monitoring systems lies in their specialization, scalability and integration complexities. To overcome these challenges, we propose a monitoring and prediction system for energy consumption of IT equipment. The proposed solution combines an adaptable, cost-effiective and energy-Efficient embedded device with open source software and a service-oriented architecture (SOA), which offers flexibility and integration capabilities, facilitating the easy inclusion of several workstation working from different environments. Several traditional Linear Regression (LR) models were evaluated prediction models using a temporal window of hour taking into account several features. As a result of the LR models evaluation, it is established that the Bayesian Ridge model was the best model since it presented the lowest error and the highest coefficient of determination. Finally, two approaches were evaluated to predict energy consumption: a Kernel Density Estimation (KDE)-based mechanism proposed to generate predictor variables in order to predict future energy using the best LR model, and a KDE-based energy model. Numerical results show that the energy prediction using KDE for the energy measurements provides lower time response than the LR based mechanism for the available dataset. [ABSTRACT FROM AUTHOR]

• Theoretical prediction tools have guidance for magnesium alloy development. • Synergistic effects of commonly used methods on strength and thermal conductivity. • Proposed principles of preferred elements in thermal conductivity Mg alloys. As highly integrated circuits continue to advance, accompanied by a growing demand for energy efficiency and weight reduction, materials are confronted with mounting challenges pertaining to thermal conductivity and lightweight properties. By virtue of numerous intrinsic mechanisms, as a result, the thermal conductivity and mechanical properties of the Mg alloys are often inversely related, which becomes a bottleneck limiting the application of Mg alloys. Based on several effective modification methods to improve the thermal conductivity of Mg alloys, this paper describes the law of how they affect the mechanical properties, and clearly indicates that peak aging treatment is one of the best ways to simultaneously enhance an alloy's thermal conductivity and mechanical properties. As the most frequently used Mg alloy, cast alloys exhibit substantial potential for achieving high thermal conductivity. Moreover, recent reports indicate that hot deformation can significantly improve the mechanical properties while maintaining, and potentially slightly enhancing, the alloy's thermal conductivity. This presents a meaningful way to develop Mg alloys for applications in the field of small-volume heat dissipation components that require high strength. This comprehensive review begins by outlining standard testing and prediction methods, followed by the theoretical models used to predict thermal conductivity, and then explores the primary influencing factors affecting thermal conductivity. The review summarizes the current development status of Mg alloys, focusing on the quest for alloys that offer both high thermal conductivity and high strength. It concludes by providing insights into forthcoming prospects and challenges within this field. [ABSTRACT FROM AUTHOR]

Measuring the sustainability performance of an organisation is a rather complex task. This study aims at analysing a broad number of performance indicators related to the economic, environmental, and social pillars of the Triple Bottom Line (TBL) framework and evaluates their applicability toward the development of a sustainable performance measurement system (SPMS) of projects. To this purpose, an analysis of the annual sustainability reports of the listed EURO STOXX Sustainability 40 companies was conducted, identifying the sustainability indicators incorporated in each individual report, and classifying them according to the three pillars of the TBL framework. The analysis of the reports yielded 67 economic indicators, 109 environmental, 130 social, and 38 additional ones, mainly combining the latter two categories, giving 344 unique indicators in total. The proposed system, based on empirical evidence, recommends that selected indicators for an SPMS for projects should relate to predefined subcategories. These are costs, efficiency, and shareholder return for the economic dimension; emissions, energy, paper, resource consumption, waste, and water for the environmental dimension; diversity & equality, ethical behaviour, external commitment, health & safety, training & development, and working conditions for the social dimension. [ABSTRACT FROM AUTHOR]

Electricity is establishing ground as a means of energy, and its proportion will continue to rise in the next generations. Home energy usage is expected to increase by more than 40% in the next 20 years. Therefore, to compensate for demand requirements, proper planning and strategies are needed to improve home energy management systems (HEMs). One of the crucial aspects of HEMS are proper load forecasting and scheduling of energy utilization. Energy management systems depend heavily on precise forecasting and scheduling. Considering this scenario, this article was divided into two parts. Firstly, this article gives a thorough analysis of forecasting models in HEMs with the primary goal of determining whichever model is most appropriate in a given situation. Moreover, for optimal utilization of scheduling strategies in HEMs, the current literature has discussed a number of scheduling optimization approaches. Therefore, secondly in this article, these approaches will be examined thoroughly to develop effective operating scheduling and to make wise judgments regarding usage of these techniques in HEMs. Finally, this paper also presents the future technical advancements and research gaps in load forecasting and scheduling and how they affect HEMs activities in the near future. [ABSTRACT FROM AUTHOR]

To address the limitations of 5 G, 6 G wireless networks are envisaged to provide sub-millisecond latency, ultra-high connection density, extremely high data rates, better coverage, reliability, availability, etc., for cloud computing, Internet-of-Everything, and cyber-physical systems. However, avoiding several trust-related issues in the design of the wireless networks may delay the goal. In recent years, Blockchain has become a promising technology that can transform various fields by providing innovative and profitable solutions. Blockchain's intrinsic features, such as immutability, decentralization, anonymity, and transparency, can corroborate trust among isolated networks to secure access control, authentication, better integrity, secrecy, and effectual resource sharing. This paper paints a holistic picture of 6 G wireless networks and Blockchain, including architecture, Blockchain-assisted 6 G services, deployment, 6 G-assisted Blockchain service models, consensus mechanisms for Blockchain-as-a-Service, 6 G-assisted mining and consensus, and others. It also sheds light on the integration possibilities of Blockchain and 6 G, its potential benefits, and the effects of consensus mechanisms on 6 G concerning security, scalability, and energy consumption, to name a few. The existing 6 G projects and applications have also been summarized, along with the discussion on research and projects to standardize 6 G. Various research directions for Blockchain, 6 G, and their integration have also been explored and reported. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0, achieving both energy efficiency and robust security in Cyber-Physical Systems (CPSs) presents a significant challenge because of the resource requirements and complexity of these systems. This paper presents a novel method to integrate energy efficiency and robust security measures in CPSs. We propose the integration of anomaly detection techniques into the CPSs, to facilitate self-adaptation to changing conditions and threats, thereby enhancing system flexibility and reliability while also optimizing energy consumption. Our approach enhances the flexibility and reliability of CPSs by integrating Deep Reinforcement Learning (DRL) into the MAPE-K (Monitor-Analyze-Plan-Execute with Knowledge) control loop. This integration not only streamlines anomaly detection but also optimizes energy consumption, ensuring efficient and effective management of critical system functions. The outcome is a marked improvement in the adaptive decision-making capabilities of CPSs, leading to heightened security and better energy efficiency across various sectors and applications. This study significantly advances sustainable industrial practices within the Industry 4.0 paradigm, emphasizing the development of CPSs that excel in both energy efficiency and robust security. [ABSTRACT FROM AUTHOR]

The rapid expansion of industrialization and the increase in energy demand make energy resource management necessary in various industries. Creating digital twins from available streaming data offers a practical way to enhance the efficiency of manufacturing systems. Digital twins allow for detailed analysis of key metrics, such as the energy consumption of complete manufacturing systems. Utilizing digital twins can greatly assist in improving manufacturing processes for reduced energy consumption. In this paper, we investigate the data requirements for the generation of energy-oriented digital twins of manufacturing systems. For this purpose, we developed a case study to illustrate and outline the necessary data requirements for a manufacturing system to enable the collection of data that can be used to extract energy-oriented simulation models. We, subsequently, analyze and generalize our findings that can be applied to a broader context. [ABSTRACT FROM AUTHOR]

Clustering and routing algorithms are critical to collecting and processing data sensed by nodes of wireless sensor networks deployed in various missions including environmental monitoring, tracking, and rescue operations. In this paper, a clustering algorithm is presented for wireless sensor networks with balanced cluster size resulting in energy efficiency. The algorithm is based on forming clusters using topology of the network and routing aggregated data from each cluster head to the base station. The cluster formation phase involves using tree-based traversals on the underlying sensor network graph. Using simulations, it is demonstrated that clusters of similar sizes are obtained for networks of varying sensor radii. It is demonstrated that the time to death of the first node of this clustering algorithm is superior to well-known clustering algorithm for random networks. [ABSTRACT FROM AUTHOR]

Hydrothermal carbonization (HTC) is a promising process for the upgrading of wet biomass residues. Models of HTC processes, in particular at continuous pilot-scale, are needed to move HTC from lab-scale to industrial scale. This study presents a process model for mild HTC, dewatering and conversion to intermediate energy carriers (bio-pellets and biogas for power and/or heat production) of three wet biomass residue streams: paper sludge, olive pomace and orange peels, based on lab- and pilot-scale experiments. In addition, the process energy efficiency and feedstock utilization of the HTC process is calculated and compared with conventional treatment options for the chosen residues, i.e., direct anaerobic digestion (olive pomace, orange peels) or combustion after conventional dewatering (paper sludge). The process model indicates that the HTC pilot-scale process is much more efficient in terms of feedstock utilization to produce heat and/or power than the reference scenarios. The process energy efficiency of the HTC process (pilot-scale) was calculated to be 26 %, 63 % and 40 % for paper sludge, olive pomace and orange peel feedstocks, respectively. For all feedstocks, both the solid and liquid-generated products are equally important for improving the overall process energy efficiency. This study demonstrates the potential benefits of HTC processes for upgrading wet biomass waste streams based on continuous pilot-scale data. • Hydrothermal carbonization successfully upgraded wet biomass residues for energy. • Hydrothermal carbonization was more efficient for heat/power than reference cases. • Solid and liquid products are both important to improve process energy efficiency. [ABSTRACT FROM AUTHOR]

In this research paper, we propose a novel hybrid deep learning approach, SSA-CNN-LSTM, for forecasting solar power generation. The approach combines Singular Spectrum Analysis (SSA), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks to leverage temporal and spatial dependencies in real-time greenhouse solar power generation data. Through a comprehensive comparative analysis, SSA-CNN-LSTM is compared against three established models, CNN-LSTM, SSA-CNN, and SSA-LSTM, employing real solar power generation data over a two-year period. The findings prominently demonstrate SSA-CNN-LSTM's exceptional performance, particularly in the 1-hour ahead prediction horizon. With an hour-ahead Mean Absolute Error (MAE) of 0.1202, SSA-CNN-LSTM surpasses the forecast precision of CNN-LSTM (0.6269), SSA-CNN (0.2354), and SSA-LSTM (0.2049). This excellence extends to the 2-hour-ahead forecast, where SSA-CNN-LSTM maintains its superiority with an MAE of 0.1400. In the day-ahead forecast, SSA-CNN-LSTM upholds its competitiveness, demonstrating an MAE of 0.1774. These outcomes underscore the immense potential of SSA-CNN-LSTM as a formidable tool for precise solar power forecasting. The model's effectiveness empowers greenhouse operators and energy management systems to optimize resource allocation, ultimately fostering elevated energy efficiency and overall greenhouse productivity. [ABSTRACT FROM AUTHOR]

This research paper presents a novel technique for improving load-shedding in DC microgrids by incorporating a binary number generator (BNG). Load shedding, a controlled strategy utilized to maintain equilibrium between power supply and demand within a microgrid when the aggregate demand surpasses generated power, is widely employed. Nevertheless, conventional load-shedding methods frequently encounter challenges, notably over-shedding and voltage deviations, particularly in scenarios involving numerous controllable loads. To overcome these challenges, an enhanced algorithm is proposed, which integrates the BNG technique. Simulation results conclusively demonstrate the effectiveness of the proposed algorithm in ensuring microgrid stability during periods of supply-demand imbalances. [ABSTRACT FROM AUTHOR]

Cloud computing technology provides access on demand to virtualized resources, services, and applications via a distributed network. In cloud data centers effective energy utilization is a critical concern in today's technology-driven world. Cloud data centers (CDC) are massive facilities that host and manage an enormous amount of data and computing resources. This article addresses the growing significance and energy-intensive nature of data centers. Due to the rapid growth of cloud computing, it offers on-demand access to resources globally and leads to substantial power consumption and carbon impact on the environment. They consume substantial amounts of energy, and optimizing their energy utilization is essential for reducing operational costs, minimizing environmental impact, and ensuring sustainable growth. To combat this, efficient energy-saving approaches using machine learning methods have been researched. ML methods hold great potential for enhancing energy efficiency in CDCs by analysing data, detecting patterns, and optimizing resource usage. The focus areas include CPU usage prediction, overload finding, underload estimation, selection, migration, and relocation of VMs to attain resources and improve energy utilization. The paper compares energy-saving results achieved by different machine learning techniques in data centers to minimize energy usage and meet service level agreements (SLA). The machine learning approaches reduce energy consumption from 1.6% to 88.5% compared to the benchmark approach mentioned, considering various settings and parameters. [ABSTRACT FROM AUTHOR]

A micro-grid (MG) is regarded as a localized small-scale power supply system designed for providing electrical power for a remote community, which may be connected to the grid. The paper considers six participants' sites in a remote community. The peak energy consumption patterns for these participants must be different to ensure coalition for mutual benefits and this would facilitate efficient utilization of energy resources. This community concerned may include rural communities having participants such as schools, housing estates, industrial sites, etc. The propose approach would ensure that many participants and their needs are captured and optimized profit of the participants would ensure mutual benefits. In islanded MG, most of the cost/profit distributions in energy trading problems are done based on the self-interest of the participants. Some consider a fair (acceptable or reasonable) cost/profit distribution using the cooperative game theory (CGT) based on the Nash bargaining solution (NBS). However, this method leads to a high degree of dissatisfaction. To improve the fairness and mutual benefits for all MG participants, a new flexible approach is developed. Specifically, CGT which uses a generalized NBS (GNBS) has been proposed, which relaxes certain axiom of the NBS to ensure that fairness of game theory is achieved. The approach allows the profit of the MG participants to be shared using a mechanism based on the negotiation power (i.e. weighted fairness) so that the MG would attain an improved economic outcome. Empirical evaluation of the approach indicates that overall profits using CGT is higher when compared with an independent approach. Moreover, different in negotiation power is achieved in which higher profit is allocated to any participant based on participant's mutual agreement in achieving fair profit distribution. A robust optimization algorithm called Teaching-Learning-Based-Optimization (TLBO) is developed to obtain optimal results. The heuristics algorithms are used in testing the effectiveness of TLBO, which confirm that the TLBO is indeed powerful and robust enough to give quality solutions in solving energy-trading problems. [ABSTRACT FROM AUTHOR]

This paper investigates the position and attitude (pose) tracking control of a rigid body with uncertain mass and inertia. By virtue of the dual quaternion description, a six-degree-of-freedom (6-DOF) relative motion model is established in an integrated and compact way. Then an adaptive pose tracking controller is proposed based on the immersion and invariance (I&I) theory, where the key integrability obstacle is overcome by the dynamic scaling technique. Compared with the existing filter system-based method that constructs a filter system to circumvent the integrability obstacle, the dynamic scaling-based method is directly designed based on the original system, which is capable of retaining all benefits of the I&I design with much lower dimension dynamic extensions. Comparisons with the filter system-based I&I method and the classical certainty equivalent method are first carried out from the theoretical analyses, which elucidate benefits of the proposed method in lower control demands. Then the comparative simulations are conducted to verify the features and effectiveness of the proposed dynamic scaling-based I&I adaptive control scheme. • In the framework of dual quaternions, a six-degree-of-freedom adaptive pose tracking controller is proposed based on the immersion and invariance. • Substantial dimension reduction and structure simplification are achieved by using the dynamic scaling technique. • Ideal closed-loop performances are guaranteed to be recovered by the proposed adaptive controller. • Control demands and energy consumptions are barely affected by the adaptive gain and can always be kept close to those in the ideal case. • Thorough theoretical analyses are presented to elucidate the benefits of the proposed adaptive controller. [ABSTRACT FROM AUTHOR]

Minimizing energy consumption is one of the most interesting issues in recent industrial production. Energy-efficient scheduling is a promising approach to reducing energy consumption in manufacturing systems. However, energy-efficient scheduling is a problem of great interest, especially for the Distributed permutation Flow-Shop Manufacturing Problem (DPFSMP). This paper addresses the critical challenge of energy efficiency in the industry of DPFSMP optimization, where task processing times depend closely on their position in the production line. The objective is to minimize the total energy consumption (TEC) for all machines in distributed workshops. To solve this problem, we propose two efficient constructive algorithms for energy-efficient scheduling of this type of problem with speed-dependent tasks: the Nawaz Enscore and Ham (NEH) based algorithm and the Greedy Random Adaptive Search Procedure (GRASP) based algorithm. We evaluate the performance of the two proposed algorithms on a set of benchmark problems to identify the one with the best performance. The results show that the (NEH) algorithm outperforms the GRASP algorithm in terms of energy efficiency and solution quality. In fact, the proposed algorithms can be used by manufacturers to reduce energy consumption in their production systems. This can help manufacturers save money, reduce environmental impact and improve overall sustainability. [ABSTRACT FROM AUTHOR]

Faced with the growing demand for energy worldwide, fossil fuels, like any other resource available in finite quantities, are in the process of disappearing since their production cannot increase indefinitely. On the other hand, Russia's war with Ukraine is driving up fossil fuel prices, which in turn is impacting price increases at several levels. Added to this is the need to limit global warming, which is largely due to CO2 emissions from the consumption of fossil fuels. Faced with all these constraints, it is essential that the world looks to other sources of energy and that Morocco, in particular, can limit its energy dependence on fossil fuel imports by ensuring an internal energy supply; the best solution appears to be the development of biofuel and in particular, biodiesel. Due to their high energy content and compatibility with gasoline supply infrastructure, biodiesel may be utilized in motor vehicles. To achieve the optimal utilization of biodiesel in internal combustion engines, it is first required to choose the best feedstock that will provide both increased engine performance and economic viability. In this perspective, this paper provides a summary of the climate and energy situation in the world and in Morocco in particular, and then addresses the interest of biodiesel and its production. [ABSTRACT FROM AUTHOR]

The energy demand has increased rapidly worldwide due to the depletion of fossil fuels. Recently, the rate of energy consumption has become unsustainable, providing a strong incentive for many countries to pursue renewable energy sources. By 2030, Oman's national energy strategy aims to generate 20% of its electricity from renewable sources. In the same context, Oman has the target to join the front rank of countries that aim to capitalize on their renewable resources to produce green hydrogen for export to the global market and domestic utilization. This study investigates the prospects for creating green hydrogen using solar energy in Oman's Dhofar region. The key obstacles to solar-to-hydrogen generation are discussed in this paper. [ABSTRACT FROM AUTHOR]

Blockchain is a decentralized digital ledger technology that offers transparency, security, and immutability by recording and verifying transactions across multiple computers. However, it faces limitations such as scalability issues, high energy consumption, slow transaction validation, expanding storage requirements. Understanding these limitations is crucial for evaluating blockchain's suitability for specific use cases and finding solutions to overcome these challenges. Recognizing these limitations is imperative for assessing blockchain's applicability and devising solutions. This paper introduces a hybrid mechanism, ShardedScale, designed to enhance scalability, throughput, and system performance while mitigating time, gas, and ether consumption. The proposed ShardedScale approach combines various techniques including Proof of Work (PoW), Dynamic On-demand Proof of Stake (DPOS), Inter Planetary File System (IPFS), and sharding. ShardedScale achieved a remarkable 40% reduction in transaction confirmation times, a substantial 60% improvement in throughput, and demonstrated cost-effectiveness with a 30% reduction in gas price consumption and a 25% decrease in ethers consumption. These findings signify a significant step towards addressing challenges faced by traditional blockchain systems, providing valuable insights for future designs in blockchain technology. [ABSTRACT FROM AUTHOR]

Temperature uniformity and temperature fluctuation are two important parameters of refrigerator and have significant effects on fresh-keeping and cold-storage technology. The influencing factors of temperature distribution attract more attention in refrigerator factory. However, the definitions of temperature uniformity and temperature fluctuation are still ambiguous, and present characterization methods cannot accurately describe the temperature field of refrigerator. This paper proposed a novel dual characterization method to reveal the temperature uniformity and fluctuation (TUF) and presented a new arrangement strategy of temperature measuring elements in refrigerator. Results show the proposed method can simultaneously and visibly characterize the TUF behavior. The slope and area of curves in temperature radar chart reveal the TUF distribution clearly. The first layer of refrigerator shows a greater temperature fluctuation, especially when the compressor is working. Refrigerators with different volume have different TUF phenomena. Adding a cold storage module on the top of refrigerator can notably improve TUF feature and make the internal temperature closer to target temperature. The mechanism of TUF change was discussed from heat transfer and cascaded energy utilization. The proposed dual characterization method can reveal temperature distribution with space and time simultaneously and help improve the temperature field of cold-heat preservation to save energy. • Propose the new methodology of temperature fluctuation and temperature uniformity. • Reveal temperature fluctuation and uniformity visually by the sharpness and enclosed area of curve in temperature radar chart. • Present a 27-point strategy to measure temperature field of refrigerator accurately. • Improve the temperature fluctuation and uniformity of refrigerator by 20%∼75% through adding a cold storage module. [ABSTRACT FROM AUTHOR]

As the usage of information and communication technology (ICT) solution have been exploded in the last decades, the research community exhibits a deficiency in terms of methodologies for accurately quantifying the energy consumption of individual ICT components. This paper presents a novel methodology aimed at addressing the existing research gap by offering a comprehensive analysis of energy consumption ranges for ICT components, measured in Watt. The methodology being proposed involves the classification of ICT components into distinct classes, namely sensors, gateways, servers-platforms, and databases, while these classes are further subdivided into sub-classes. The calculation of energy consumption places emphasis on particular components within each sub-class, encompassing CPUs, RAM, GPUs, hard drives, network switches, cooling systems, and communication units. Compared with existing green ICT standards where mainly focused on strategy-related recommendations, the methodology utilises manufacturers' specifications for standard operational procedures in order to compute energy consumption. The objective of the methodology is to extend the current research and provide countable energy consumption attributes of various component types, that are used in the ICT architecture, supporting the decision-making process of architects regarding environmentally sustainable and high-performance device selection. This study concludes with the development of a tree diagram for calculating the energy consumption of an ICT system. [ABSTRACT FROM AUTHOR]

Improving energy efficiency in public passenger transport is key to achieving sustainability goals. In many developing countries, public passenger transport relies heavily on fossil fuels, which necessitates a strong focus on reducing emissions from energy consumption. Surprisingly, few studies have addressed this issue in the context of daily operations, with most research focusing on operational and infrastructural changes. This paper aims to fill this gap by introducing detailed energy consumption variables into a public transport simulation model using SUMO, a tool known for its seamless integration with Python. The development of two Python scripts underpins this endeavour. The first script facilitates the automatic generation of scenarios, allowing the manipulation of parameters such as station spacing, line length, passenger capacity and waiting times, among others. The second script is dedicated to estimating energy consumption for each scenario. Together, these tools allow us to calculate average energy consumption for different scenarios and modes of public transport. As a result, they provide valuable insights for the formulation of public policies aimed at saving energy in different public transport environments and lay the foundation for the integration and development of intelligent transport systems and simulation-based digital twins. [ABSTRACT FROM AUTHOR]

Welding is a widespread process requiring substantial amount of energy and generating high carbon emissions, so calculating and reducing its carbon footprint is crucial to make it more sustainable. This paper compares welding methods (RLW, GMAW) regarding their carbon emissions and provides guidelines to identify Carbon Intensive Elements (C.I.E.). A mathematical model is employed for theoretical calculations and Life Cycle Assessment for analytical ones. Carbon emissions are normalized per welding parameters, showing that RLW is more carbon efficient than GMAW. Based on the approach used and produced results, a strategical framework developed as a roadmap to identify the most C.I.E. [ABSTRACT FROM AUTHOR]

This paper provides a method for predicting the energy efficiency of buildings using artificial intelligence tools. The scopes is twofold: prediction of the levels of the heating load and cooling load of buildings. A feature of this research is the performance of intellectual analysis in conditions of a limited amount of data when solving the stated tasks. An improved method of augmentation and prediction (input-doubling method) is proposed by processing data within each cluster of the studied dataset. The selection of the latter occurs due to the use of the fast and easy-to-implement k-means method. Next, a prediction is made using the input-doubling method within each separate cluster. The simulation of the method was performed on a real-world dataset of 768 observations. The proposed approach was found to have a high prediction accuracy in the absence of overfitting and high generalization properties of the improved method. Comparison with existing methods showed an increase in accuracy by 40-46% (MSE) compared to SVR with rbf kernel, which is the basis for the improved method, and by 5-12% (MSE) compared to the closest existing hierarchical predictor. [ABSTRACT FROM AUTHOR]

Considering the sensitivity of the tailless full-wing configuration unmanned aerial vehicle to disturbance and the strong nonlinearity and coupling caused by the manipulation combining the propeller and rudder, a finite-time terminal sliding mode controller with compound compensation is proposed in this paper to ensure stable attitude control. Based on singular perturbation theory, the inner loop sliding mode controller is designed so that the supremum of the convergence time of the rotational angular velocity tracking error in the sliding phase can be directly determined by the control parameters without requiring the initial state, and the outer loop sliding mode controller is designed so that the sliding surface function of the attitude angle tracking error has a fast convergence speed when it is far from and close to the origin. The compound compensation design further addresses the manipulation nonlinearity and disturbance sensitivity of the research object. Based on the minimum Euclidean norm optimization, the designed control allocator achieves command decoupling and reduces the desired control command. Through comprehensive verification, the proposed controller shows superior control performance and low energy consumption and exhibits strong robustness in the cruising flight envelope. • The convergence time supremum of the rotational angular velocity tracking error in the sliding phase can be determined. • The compound compensation design deals with the manipulation nonlinearity and disturbance sensitivity. • The existence and uniqueness of the minimal Euclidean norm solution of the desired control command have been proven. • The proposed controller has strong robustness in the flight envelope. [ABSTRACT FROM AUTHOR]

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user's movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user's mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user's mobility traces, avoiding the random generation of users' traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices' batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider. [ABSTRACT FROM AUTHOR]

Buildings have significantly contributed to high energy demand (30.3%) and GHG emissions (26.1%) worldwide. Consequently, many developed countries have set carbon-neutral targets for 2050, mandating that all new constructions thereafter be designed as net-zero emission buildings (NZEBs). To achieve this goal, one of the most practical sources of renewable energy, solar power, is employed in this study to estimate the economic feasibility of implementing NZEBs in the non-residential sector of the United States. While past studies have typically estimated the economic payback period of solar power technology by assuming fixed values for "PV energy conversion rates (%)" and "investment costs ($USD)", this paper carefully investigated the economic feasibility of "solar-based NZEBs" by precisely tracking these dynamic future changes in PV panels. These techno-economic variables were carefully predicted using a statistical technique known as the five-parameter logistic (5 PL) function. The results show that Site 4 is the only solar region suitable for implementing "PV-integrated NZEBs", reaching a payback period of 8.44 years in 2040 (Scenario 1). Furthermore, with small technological improvements in PV energy efficiency (%), buildings located in Site 3 and Site 4 can meet the net-zero emission target with payback periods of less than 10 years in 2034 (Scenario 2). Finally, starting in 2044, the "PV-integrated system" will have a payback period of approximately 7 years in most study locations, even without federal support for the solar investment tax credit (SITC). Accordingly, federal support for solar power generation will be more effective if the SITC rate is gradually reduced by 2.5% annually starting in 2033. In conclusion, this paper suggests the economic feasibility of implementing solar-based NZEBs in the United States non-residential sector by considering the synergetic effect of technological improvement and cost reduction in PV-integrated systems. • This study predicted the cost and efficiency of PV panels to estimate the economic feasibility of solar-based NZEBs. • The cost reduction and technical advancement in PV panels can remarkably improve the economics of solar power. • The 5 PL function is systematically modeled to predict the total investment cost and efficiency of PV panels. • In most study locations, solar-based NZEBs are projected to achieve a 7-year payback period beginning in 2044. • The federal support for the SITC rate (%) is recommended to be gradually lowered starting in 2033. [ABSTRACT FROM AUTHOR]

• Combining hydrodynamic and acoustic cavitation significantly enhances the energy-efficient processing of cellulose fibers. • Achieving maximum efficiency requires extensive optimization through multiphysical simulations and experimental investigations to understand the interaction of physical phenomena. • Effective sonicator design requires tuning of sonotrodes, signals, structural geometry, fluid volumes, flow conditions, and nozzle design to achieve synchronized resonance amplification at high power. • Cavitation treatment gives a notably improved fiber properties at specific concentrations, temperatures when combined with proper acoustic and hydrodynamic cavitation intensities. The cellulose pulp refining process is crucial for achieving high-quality paper characteristics. This research aims to enhance energy efficiency while maintaining good fiber quality using hydrodynamic and acoustic cavitation (HAC). Experiments were conducted with an in-house developed flow-through sonicator combined with a novel Venturi nozzle for hydrodynamic cavitation. The Venturi design was determined by analytical modeling and verified by CFD simulation with multi-phase turbulence models to balance cavitation intensity and turbulence against the acoustic cavitation effect. Experimental evaluation of two batches of CTMP fibers, pre-processed in different ways, showed significant improvements in paper strength and fiber properties. The best results for Batch 1 (HC and LC) were obtained with 386 kWh/bdt for AC and 350 kWh/bdt for HC (60 °C, 2 % concentration). The tensile strength index increased by 26 %, and the TEA-index, related to freeness, increased by 55 %. HAC treatment (750 kWh/bdt, 70 °C, 1.5 % concentration) of the less refined Batch2 (HC) yielded results better than the Batch 1 reference. These findings confirm the energy-efficient potential of the sonicator concept compared to traditional industrial processes. The conclusion is that HAC-refining of softwood pulp requires a proper balance between hydrodynamic and acoustic cavitation intensities. Both fiber concentration by weight and temperature are critical for an energy-efficient process. [Display omitted] [ABSTRACT FROM AUTHOR]

Harmonizing the cleanliness and economics of energy is an effective means of integrating economic growth with carbon peaking goals, and realizing sustainable development in China. However, due to the existence of the energy trilemma, a worthwhile research question is how to fulfill the carbon peaking commitment while guaranteeing China's stable national development. Based on this, this paper not only discusses the nonlinear relationship between clean energy consumption transition and economic growth through threshold regression, but also simulates several scenarios of energy transition dynamics with different growth rates by using Oracle Crystal Ball software to forecast the future trend of China's carbon emissions up to 2030, aiming to find a transition scenario that can make the peak of carbon emissions appear without significant economic burden, so as to put forward countermeasures and suggestions to avoid falling into the energy trilemma in the process of fulfilling the commitment of carbon peaking. This paper finds that: firstly, there is an inverted U-shaped non-linear relationship between clean energy consumption transition and economic growth, with the inflection point of the inverted U-shape being 0.595; and secondly, only when energy transition exceeds 0.535 in 2030, the peak of carbon emissions (about 12,922.28 Mt) will occur. In summary, this paper argues that China should steadily advance energy transition, avoiding either too fast or too slow a pace (bring clean energy consumption transition to within the 0.535–0.595 range by 2030), in order to secure economic growth while honoring its commitment to peak carbon. Therefore, China should prudently design a coal exit strategy to avoid falling into the energy trilemma when promoting energy transition. Moreover, if China wants to further fulfill its commitment to carbon neutrality by 2060, it needs to continue to deepen the construction of green financial markets in order to better balance the relationship between energy cleanliness and economy. [ABSTRACT FROM AUTHOR]

Climate emergency is widely acknowledged. However, our institutions are struggling to find new intervention types to accelerate the transition. This paper analyses this struggle by combining agency theories and discursive approaches to study transition intermediaries, i.e, organizations seeking to foster the transition activities of others. The internal meaning structures of intermediaries are described as interlocking shared action frames, i.e., beliefs and meanings underpining their activities. These frames are characterized through the definition of pairs of contrasting frame elements along eleven framing dimensions. This conceptualization allows for a semi-quantitative mapping of internal structures. The method is developped and illustrated with the in-depth case study of a Swiss regional utility department running an energy efficiency programme. Analysing a series of exploratory workshops, we find that established frames revolve around technology-oriented, managerial approaches to the transition, ultimately narrowing the range of imaginable interventions. While these are well-studied shortcomings of energy efficiency centred approaches, further observations suggest that these frames underpin the perception of intervention impacts, helping keep staff and recipients involved. To strike a balance between energy saving targets and transformative ambitions, this paper suggests revising programme evaluation logics and reframing technological solutions as responses, rather than substitutes, to practice changes. [Display omitted] • A discursive approach is used to explore the agency of transition intermediaries. • Internal meaning structures are described as interlocking shared agency frames. • A utility department running an energy efficiency programme is taken as case study. • An established perspective revolves around technical, managerial approaches. • Emergent perspectives tend to be linked this coherent established perspective. [ABSTRACT FROM AUTHOR]

• This paper studies the new problem, EVs' provision of flexible ramping products (FRPs). • The dispatching capability of EV fleets is disclosed through the feasible operating region in variable spaces. • A cloud-edge collaborated market structure for the electricity market with FRPs is proposed to facilitate a large amount of EVAs integrated with protected privacy. • A distributed optimal model combining the EVA's bidding subproblem and the market clearing subproblem is established. • The distributed optimal model is solved based on the modified analytical target cascading (ATC), in which the convergence performance is improved through heuristic constraints. • Time-coupled multi-period (TCMP) operation mode is implemented in the market to maximize the dispatching potential of EV fleets. More flexible ramping service is required due to the increase of renewable power generation in power systems. Electric vehicles (EVs) could provide such flexible ramping products (FRPs) at low cost while participating in the electricity market through aggregation. However, EVs' dispatching capability cannot be fully utilized without the right incentives. This paper addresses a distributed optimal model developed between EV aggregators (EVAs) and the independent system operator (ISO). To make such concept, a cloud-edge collaborated market structure is adopted. At the edge level, EVAs assess the dispatching capability and solve the market bidding subproblem. At the cloud level, ISO solves the market clearing subproblem considering system economy and security. The overall problem is solved by the analytical target cascading (ATC) method. Heuristic constraints are also introduced into the model to improve convergence performance. The model is tested on a modified IEEE 30-bus system. Results demonstrate that the proposed method can incentivize EVAs with different owners to shift load and provide FRPs accurately, meanwhile reducing the cost and increasing the consumption of renewable energy effectively. [ABSTRACT FROM AUTHOR]