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• The TM capture is first reformulated to nonlinear quadratic optimal control. • The high-efficiency NMPC with robustness and optimality is proposed for TM capture. • The mixed state and control constraints are integrated into the robust optimal control. • The proposed method guarantees the computational efficiency. This paper presents a nonlinear high-efficiency model predictive control (NHMPC) with constraints designed for the test mass (TM) capture phase of the drag-free satellite about gravitational wave observatory. To avoid collisions between test mass and satellite cavity, TM capture is the essential technology for drag-free satellite. Test mass is located inside an electrostatic suspension and locked by a clamp mechanism initially. The test masses are released with high initial offsets and velocities when the mechanism is retracted. The purpose of this phase is to guarantee the TM to be positioned at the cage center and attitude aligned with the local cage frame. Due to the low actuation authority of electrostatic suspension along with critical initial offsets and velocities, it is a challenging task to design the attitude and translation control schemes with simultaneous consideration of system performance and energy consumption. For the problem given above, the TM capture can be reformulated into a nonlinear quadratic optimal control problem with the state and input constraints. A nonlinear model predictive control (NMPC) structure is also designed to handle the noises by forming a closed loop. This control framework can realize optimality and robustness in a compromise. To improve the speed of solving high-dimensional nonlinear optimal control online for MPC, the indirect Chebyshev pseudospectral method with constraints is employed. The convergence and stability of the control loop are analyzed. Simulation examples show that the feasibility and performance of the designed control loop are verified compared with the existing methods and computation times in the millisecond range can be achieved. [ABSTRACT FROM AUTHOR]

Internet of Things and growing popularity of connected devices are increasing the demand for wearables applications, which is driving the development of ultra-low power secure systems. Thus, having a system running at low voltage with low power consumption introduces the use of ambient energy harvesting. Besides, batteries have several limitations reducing their compatibility with wearables, including periodical need for recharge, limited life-time, heavy and large form factor and non-eco-friendly life-cycle. Therefore ambient energy harvesting technology gives a promising alternative for self-powered wearables. The integration of these solutions on the same silicon support is a real technological challenge. This paper proposes a multi-harvesting fully-integrated system architecture combining AC and DC sources. Based on a MATLAB/Simulink model, the proposed system allows the calibration of an optimal solution. It is designed and simulated in 40 nm CMOS process, demonstrating the use of an AC source as secondary input to start a harvesting system with very low DC source as main source. • Ambient energy harvesting technology gives a promising alternative for self-powered wearables. • Multi-harvesting fully-integrated system architecture combining AC and DC sources. • Energy harvesting system level modeling using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

A small applied electric field is particularly crucial in the practical application of dielectric ceramic capacitors, since it means a longer lifetime of the capacitors in practical energy storage applications. Based on the traditional ferroelectric BaTiO 3 , the (1- x)(Ba 0.6 Na 0.2 Bi 0.2)TiO 3 - x NaNbO 3 medium-entropy material is designed in this paper, which correlates configuration entropy with energy storage performance. The findings demonstrate that the BNBT-0.15NN ceramic synchronously achieves high energy storage density (2.95 J/cm3) and the energy storage efficiency (95.2%) at 180 kV/cm when the configuration entropy rises to 1.43R. The idea of medium-entropy energy storage under low electric field is proposed for the first time, opening up a new avenue for research into the preparation of high energy storage dielectric ceramics via exploring medium-entropy composition. [ABSTRACT FROM AUTHOR]

The paper puts forward an intelligent approach that deals with the computation of an optimal path with collision avoidance for the stick-carrying twin moving from a pre-assumed start position to a predefined goal position. It has been solved through the efficient implementation of modified cuckoo search, sine cosine algorithm, and particle swarm optimization to design a hybrid algorithm aimed at using the communal advantages of the search and position update ability of these algorithms. The benefits are realized by integrating the egg-laying behavior of the cuckoo species to achieve an efficient global search strategy with modified parameters, local search strategy of particle swarm optimization, and greedy approach of sine cosine algorithm. The proposed algorithm is validated using 10 standard benchmark functions, computer simulation using C language, and real robot platform using Epuck robot to illustrate minimal time, shortest distance, collision avoidance, path smoothness, synchronized action, and reduced energy usage in terms of the path traveled, execution time, the number of steps, and the number of turns in the static as well as the dynamic environment. [ABSTRACT FROM AUTHOR]

Recently-developed twisted multifilament artificial muscles are an intelligent material or device storing energy and outputing work under external stimuli. These muscles are fabricated by a twist-insertion procedure and share a two-step process in triggering rotation or contraction, that is volumetric expansion and tension-torsion coupling. The energy conversion efficiency of thermal actuation for the artificial muscles made of carbon nanotubes has attracted increasing attention due to the giant tensile stroke, record energy density, and easy controllability. In the present paper, the thermal actuation processes of artificial muscles are modeled by numerical simulations, in which the energy conversion efficiency is expressed as the ratio of work output to heat input. The Cosserat curve theory is introduced to analyze the geometric effect of cross-sectional shapes on the tensile TMFAM. The abilities of axial elastic deformation and stretching stable for the CNTBs with typical shapes are quantified. The work output and energy conversion efficiency are then evaluated, offering the opportunity to elevate the mechanical properties of artificial muscles. The strategy to improve the muscle performance is also outlined through their dependence on the geometrical and material parameters. [ABSTRACT FROM AUTHOR]

Abstract The thin-film electrode has been regarded as one of the desirable options for the vanadium redox flow battery. However, most of the thin-film electrodes developed to date suffer from high mass transport resistance and deliver unsatisfactory performance. In this work, we proposed a dual-layer thin-film electrode, consisting of a backing layer and a catalyst layer, for flow batteries. The backing layer with larger pores is adapted to improve the concentration distribution as well as to provide mechanical support while the catalyst layer with smaller pores provides sufficient active sites. The electrospun fiber mat is adopted as catalyst layer and backing layers in the study include carbon paper, carbon cloth and graphite felt. It is showed that carbon cloth supported electrospun fiber mat electrode demonstrates an energy efficiency of 76.1% at the current density of 240 mA cm−2, which is much higher that achieved by conventional electrospun fiber mat electrodes (61.9%). After optimizations of flow field designs and key operational parameters, the energy efficiency of battery with dual-layer structured electrodes can reach as high as 80.2% at a current density of 240 mA cm−2. In addition, the present battery can stably operate for more than 800 cycles without obvious decay. Highlights • A dual-layer thin-film electrode was proposed for VRFBs. • The battery performance was much improved with the dual-layer electrode. • The roles of backing layer and catalyst layer were identified. • The effects of backing layer, flow field and flow rates were disclosed. • The battery can operate stably over 800 cycles at 240 mA cm−2. [ABSTRACT FROM AUTHOR]

In this paper, a real-time chemi-thermomechanical pulping (CTMP) process was investigated, where opportunities were explored to increase energy consumption efficiency with a guarantee of expected pulp properties as a priority. Due to the process instrumentation limitations, such as the lack of advanced regulatory controllers and optimization units, the advanced process controllers and real-time optimization strategies can hardly be implemented in the existing CTMP processes. Therefore, a novel visualization-based process evaluation methodology is proposed to aid the mill operators in making further decisions to improve the process operations. Additionally, to determine the desirable pulp properties for production, an inferential sensor model is developed that estimates the handsheet properties from pulp properties. Also, to assess the model prediction accuracy in real-time, a reliability index model is designed. The proposed methodologies have been validated on a practical CTMP process, and the results show the satisfactory performance of the system. • Developed a strategy to evaluate the CTMP performance using visualization methodology. • A novel inferential sensor model is implemented to determine the desirable pulp properties. • A reliability index model classifying reliability levels and assessing model accuracy is developed. • The proposed model adapts to model updates, uncertainties, and computation resource limitations. [ABSTRACT FROM AUTHOR]

The current approaches to study the coupling mechanism between economy and environment seem to fall into self-circulation, failed to reveal "economy–environment interdependence". In this context, an exogenous variable is introduced into these models for better understanding the relationship. Benzo[a]pyrene (Bap) environment inventories in different cities in China were estimated by fugacity model based on Bap concentration data collected from previous studies. An extended model, stochastic impacts by regression on population, affluence and technology (STIRPAT), generated from original IPAT (a model that expresses the idea that environmental impact (I) is the product of three factors: population (P), affluence (A) and technology (T)) was used to study the relationship between Bap environment inventory and anthropogenic factors including population, vehicle amount, affluence and energy consumption. A ridge regression has been applied to optimize the model parameters. Overall, there exist good relationships between Bap inventories and economic factors. Specifically, population and affluence are the most significant factors that influence Bap inventory. Rise of the scale of population and vehicle amount increases the Bap inventory, as growth of GDP per capita and the efficiency of energy utilization have inhibitory effect on Bap inventory. Apparently, the influences of anthropogenic factors on Bap inventory are different between north and south China. Bap inventory in north China is more sensitive to the change of population and energy consumption, while is highly correlated to GDP per capita in south China. Further, according to social-economic development prediction, with assist of the result from STIRPAT model, we find out that Bap inventory drop 31% from 2001 to 2020 in China. Our estimates are comparable with the published data and confirmed that Bap can be regarded as a chemical indicator of social-economic development in China. Image 1 • An extended STIRPAT model derived from original IPAT was developed. • Population and affluence are the key factors that influence Bap inventory. • Advance of affluence and technology level curbs Bap environmental exposure. • We predict Bap environmental inventory drop 31% from 2001 to 2020 in China. • Bap can be used as an indicator of social-economic development in China. [ABSTRACT FROM AUTHOR]

• A dynamic safety corridor approach strategy is developed with approach window and straight path. • The safe approach trajectory is planned to optimize transfer time and fuel consumption. • An emergency evacuation strategy is designed and the safety velocity is analyzed. • Motion precision and convergence time are ensured based on coupling control of orbit and attitude. The on-orbit service technology is expected to play an important role in space debris removal missions. In order to capture and remove space debris objects, servicing satellites need to have the ability of mission planning, to quickly approach space debris objects in limited time, to avoid possible collisions and other safety issues, and to have the ability of emergency suspension and evacuation in the event of sudden accidents. The mission planning of safe approach and emergency evacuation to large slow-rotating space debris is studied in this paper. Firstly, the relative orbit and attitude dynamics model of servicing satellite and space debris object is established. Then, a dynamic safety corridor approach strategy is proposed according to the motion characteristics of large slow-rotating space debris and the optimal safe approach trajectory is planned based on particle swarm algorithm. For the purpose of instant response to the possible accidents during the approach, an emergency evacuation strategy is proposed, while the safety velocity and the emergency evacuation time are analyzed. The relative orbit and attitude coupled control method is designed based on global terminal sliding mode control. The stability of this control method is proved by Lyapunov argument as well as the error convergence time is analyzed. Finally, the effectiveness of the mission planning and control method is verified by numerical simulations. The results of this paper can provide a preliminary technical exploration for the approach and capture strategy of large slow-rotating space debris. [ABSTRACT FROM AUTHOR]

• A novel technique to trace Sun location upon the lunar sky is proposed. • LOLA databases are used to recreate the lunar topography. • Harvesting solar energy from multi-sites on the Moon south pole is proposed. • The multi-site strategy removes the need of energy storage system. • Algorithm to reproduce time series illumination profiles of the Moon. Recently, space organizations have considered the Moon to host lunar bases. Such bases require power and energy to function. However, the efficient and safe use of the energy resources on the Moon is a huge challenge. Space photovoltaic (PV) power systems are appealing technologies due to their maturity and high solar energy availability at some locations on the Moon. The effectiveness of these PV systems depends on their selenographic location, which might necessitate the deployment of energy storage technologies to cover the base's energy demand. Some analysts have proposed the installation of PV modules on kilometers-tall towers near the lunar poles to harvest more solar energy and limit the need for energy storage systems (ESSs). Alternatively, this paper proposes to harvest the energy from multiple sites in the lunar South Pole region using a novel technique to compute the Sun illumination profile and the LOLA topographic databases to compute the terrain elevations. The proposed algorithm seeks the most optimal configuration of sites and tower heights to minimize the longest night period and total distance between the sites. This study assesses groups of 1 to 6 sites assuming the use of towers having heights of 10, 100, and 500 [m]. The time horizon for the analysis is one Axial Precession Cycle, which is approximately 18.6 years. According to the results, a system of two sites with a separation of 42.05 [km] and towers of 500 [m] height has a maximum darkness period of only 3 [h] while another solution proposes a system of three sites with towers of 10 [m] that removes the need of EES (solar eclipse periods by the Earth are not considered). The proposed technique is suitable for engineering applications, such as base planning and operation management. [ABSTRACT FROM AUTHOR]

Approximate Computing techniques are finding a central role in modern applications, by optimizing architectures to relax some computation but with a constrained inaccuracy. In many applications, the FFT algorithm is invariably applied and there is a need for approximate low energy hardware solutions to the FFT. The paper thus proposes an approximate, fixed-point, in-place, shared memory architecture for FFT. It is well observed that the energy at FFT I/Os is not strictly contiguous, hence the proposed FFT exploits this window to tune the error. The proposed FFT and its associated butterfly unit is constructed to efficiently incorporate approximate Toom–Cook multiplication. As said, a supporting function in error correction based on the sparsity patterns, is a feature of this design. The design synthesized at 32 n m shows on average, a 48.6% and 52.8% improvement in consumption of area and energy, respectively, with as less error as 0.1% with pruning. • Approximate Computing can exploit error resilience of the applications to reduce chip resources. • FFT is a fundamental block on chip and approximating FFT can accelerate many applications. • The proposed FFT is based on approximate Toom-Cook multiplication that has a reduced number of sub-multiplications. • Pre-computing the evaluations in Toom-Cook multiplication of the twiddle factors in the butterfly provides additional savings. • This design allows a secondary mode of operation in FFT to trade-off sparsity in the FFT outputs with the error in the final FFT sequence. [ABSTRACT FROM AUTHOR]

Nowadays, system-on-chip (SoC) integrates more processing cores and IPs with network-on-chip (NoC) for satisfying today's demands, including low power consumption and high performance, without affecting the elasticity and scalability. When the on-chip processing elements (PEs) are increased, burst traffic will be generated from memory and cache accesses. As a result, the packet transmission latency is increased, reducing the system's speed performance. This paper proposes a new hybrid Base delta Neighbourhood Indexing Sequence (BΔ-NIS) based compression method for compressing the network traffic. The previous solutions for data compression often contain incompressible packets for certain data patterns. The proposed method uses Neighbourhood Indexing Sequence (NIS) algorithm to determine the optimal codeword for uncompressible chunks of delta compression approach using zeros and ones based traversals. Because of the dynamic nature of BΔ-NIS, it is highly effective and compresses all chunks in the packets efficiently. Moreover, the concept of dynamic header flit extension is introduced in the proposed compression method, which provide unlimited scalability. The proposed BΔ-NIS algorithm is robust to packet losses and leads to significant power saving. The simulation results show that the proposed BΔ-NIS reduces the hardware overhead and overtakes the existing data compression techniques by attaining a mean compression ratio of 0.73. In addition, BΔ-NIS accomplishes 51% latency reduction compared to the baseline router (without any compression). It also minimizes the power consumption of the de/compression modules by 70.14% and 20.8% as compared to the NoΔ and FlitZip respectively. • To propose an effective data compression technique for on-chip communication network, which reduces the data traffic overhead, loss of data packet, delay, and energy consumption by enhancing the delta compression method using NIS. Due to reduced operations, the proposed BΔ-NIS compressor utilizes the router's bandwidth and links without consuming more power. • To reduce the network latency by exploiting the neighbourhood information on the BΔ-NIS compressor through the consideration of zero's and one's traversals. • To increase the compression ratio by identifying data patterns within a flit and an optimal codeword for uncompressible packets of the delta compression method. • To give high level of confidence in terms of compression ratio by storing the metadata of the proposed BΔ-NIS compressor in the unused portion of the header flit. [ABSTRACT FROM AUTHOR]

A new technology is much more required in this fast-growing communication world due to inability to bear the segments by the conventional wiring-based transmitting strategies and the coordinated dimensions of System on Chip (SoC). The new technology could manage the increasing enduring failures due to diminishing technology parameters. To solve the above problem, an advanced technical paradigm Network-on-Chip (NoC) was created to keep pace with the correspondence requests on this high demanding network frame. In this paper, we have dealt with Fault Tolerance Topology Design (FTTD) along with Corvus Seek Algorithm (CoSA) and Look Advanced Propinquity Enlightened Routing (LA-PER) that can tolerate the single continuous failures in joiners of NoC for specific applications. The proposed work generates fault-tolerant topology with two substitute paths between the transmitting Processing Elements (PEs) using proposed Routing Element architecture for the applications. The FTTD carefully selects the shortest substitute path with CoSA and LA-PER that led to energy reduction and minimal latency utilization. The aim of the method is to optimize the communication with less energy utilization and latency, tested with different multimedia applications against different topologies. The proposed method's outcomes exhibited the reduction in energy and latency an extent of 19.15% and 35.29%. • The proposed work generates fault-tolerant topology with two substitute paths between the transmitting Processing Elements (PEs) using proposed Routing Element architecture for the applications. • The Routing Element(RE) architecture using Look advanced Propinquity Enlightened Routing (LA-PER) for shortest path generation. • The proposed design of Fault Tolerance topology for ASNoC uses Corvus seek algorithm (CoSA) that is based on crow search algorithm. • The proposed algorithms run in Vivado 15.1 HLS and implemented in Zynq7000 series xc7z020clg484-1chip. [ABSTRACT FROM AUTHOR]

Networks on chips (NoCs) are a concept inspired by computer networks for constructing multiprocessor systems that can handle communication across processing cores. One of the most critical applications of NOC is efficient nonstop routing. Different routes exist in these networks to get from one node to another; thus, a function that can assist in determining the optimum route to the target should be available. This paper uses a new hybrid method called Sc ored R egional congestion aware and D ICA (ScRD) to select a better output channel and increase NOC performance. After applying the ScRD algorithm, the traffic packets are examined by an analyzer, which determines if the NoC traffic is local or non-local based on the number of hops. Therefore, if the traffic is local, a scoring mechanism will select a better output channel; otherwise, the best output channel will be chosen using DICA or RCA selection functions, depending on the system state and the introduced parameter. Finally, Nirgam simulation was used to test the suggested method under various traffic conditions and selection criteria. The simulation results demonstrated that the strategy outperformed delay time, throughput, and energy consumption alternatives. It reduced packet delay by 27.10% and increased throughput by 10%. When these two factors were considered, energy consumption dropped by 6.86%. Also, the synthesis results showed that the hardware cost of the proposed approach is 1.2% lower than the two basic methods. • It introduces a new output selection strategy called ScRD based on parameters that affect NoC performance. • It introduces a new method to reduce traffic based on the proposed approach of using a traffic analyzer, in which the type of traffic is determined, and then the best selection function related to the type of traffic is used. • It introduces a router architecture for energy consumption analysis, which checks energy consumption in standby mode for wormhole routers and virtual channels. Also, it provides a solution to reduce energy consumption. • It compares the simulation results with the RTL implementation. • Analyzes the hardware overhead for the proposed solution. [ABSTRACT FROM AUTHOR]

Sodium niobate (NaNbO 3) ceramics are commonly investigated for use as energy storage ceramics because of their excellent properties. NaNbO 3 ceramics are modified mainly by doping with a Bi-based composite perovskite, that is, by the nonequivalent doping of Bi3+ at the A site of the NaNbO 3 ceramic. In addition, the high volatility of Bi at high temperatures increases the defects in the ceramics. This paper provides a new idea of doping modification of sodium NaNbO 3 -based energy storage ceramics. Here, (1− x)NaNbO 3 – x Sr(Mg 1/3 Nb 2/3)O 3 (x = 0.17, 0.20, 0.23, 0.26) ceramics were prepared by doping NaNbO 3 with an Sr-based composite perovskite. Compared with Bi-based composite perovskite, Sr-based composite perovskite doping of NaNbO 3 ceramics can also obtained good energy storage properties: a total energy storage density of 4.28 J/cm3 and an energy storage efficiency of 89.3%. In addition, the ceramics exhibited good frequency stability (2–200 Hz) and a high charge/discharge rate (1.06 μs). [ABSTRACT FROM AUTHOR]

[Display omitted] • Electro permanent magnet is proposed for magnetocaloric refrigeration. • Electro permanent magnet is numerically and experimentally evaluated. • Its energy efficiency above 80% is reached with magnetic energy recovery. • Magnetic field can be switched between min and max up to 50 times per second. • Fast switching of magnetic field can be used with thermal switches. Magnetocaloric energy conversion represents an alternative to existing refrigeration, heat pump and energy harvesting technologies. A crucial part of a magnetocaloric device concerns the magnetic field source. It uses mainly rare-earth materials and consists of moving parts and a drive system while displaying a limited energy efficiency and unavailability of fast and variable control of the magnetic field. Recent advances in efficient heat transfer for high-frequency magnetic cooling call for new developments of magnetic field sources that can operate with high efficiency at high frequencies. We report the concept of an electro-permanent magnetic (EPM) field source that efficiently recovers magnetic energy. In contrast to existing magnets, it allows very well-controlled operation without any moving parts. The main objective of this paper is to present a numerical and experimental study in which such an EPM was designed, built and tested. An extensive numerical investigation of the proposed design was carried out in terms of various geometrical and operating parameters. One of the design variations was built and experimentally evaluated for its energy efficiency and temperature increase at various operating frequencies. We demonstrate an energy efficiency of these magnets of over 80% and operation with frequencies up to 50 Hz, which is crucial for future high-power-density and high-frequency magnetocaloric devices. Considering high energy efficiency at high operating frequencies, such EPMs would allow for miniaturization, making them a viable option for future compact magnetocaloric devices. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multimodal self-supervised architecture for energy-efficient audio-visual (AV) speech enhancement that integrates Graph Neural Networks with canonical correlation analysis (CCA-GNN). The proposed approach lays its foundations on a state-of-the-art CCA-GNN that learns representative embeddings by maximizing the correlation between pairs of augmented views of the same input while decorrelating disconnected features. The key idea of the conventional CCA-GNN involves discarding augmentation-variant information and preserving augmentation-invariant information while preventing capturing of redundant information. Our proposed AV CCA-GNN model deals with multimodal representation learning context. Specifically, our model improves contextual AV speech processing by maximizing canonical correlation from augmented views of the same channel and canonical correlation from audio and visual embeddings. In addition, it proposes a positional node encoding that considers a prior-frame sequence distance instead of a feature-space representation when computing the node's nearest neighbors, introducing temporal information in the embeddings through the neighborhood's connectivity. Experiments conducted on the benchmark ChiME3 dataset show that our proposed prior frame-based AV CCA-GNN ensures a better feature learning in the temporal context, leading to more energy-efficient speech reconstruction than state-of-the-art CCA-GNN and multilayer perceptron. • Demonstrates a GNN-CCA application for energy-efficient AV speech enhancement. • Proposes a novel GNN-CCA model for self-supervised fusion of correlated features. • Proposes the prior-frame positional encoding to model temporal information in graphs. • Outperforms baselines considering both audio reconstruction and energy efficiency. [ABSTRACT FROM AUTHOR]

Extant research has highlighted the pros and cons of the use of (a) competition and (b) positive (peer) pressure as two mechanisms for invoking peer effects to bring about long-term behavior change. However, these mechanisms have been studied separately and in different contexts. In this paper, we compare the differential impacts of competition versus positive pressure-induced pro-environmental behavior change in the same target population. This study spans a seven-year horizon between 2013 and 2019 (pre-COVID) during which the workforce of about 2500 employees in the Stephan P. Clark Government Center in Miami, Florida, was split into four groups - one control and three treatment groups. Each treatment group was subjected to (a) a healthy inter-group competition through tracking, benchmarking, and comparing monthly group level electricity saving, and (b) positive pressure through the appointment of champions who encouraged pro-environmental behavior among their group members. Studying these mechanisms concurrently in the same population allows for a nuanced comparison of their differential impacts on pro-environmental behavior change. • In this paper, we compare the differential impacts of competition-induced spillover and supervision-induced peer pressure in affecting pro-environmental behavior change in the same target over a seven-year analysis horizon. • The study spans 2013-2019 during which the workforce of about 2500 employees in a building was split into four groups (one control and three treatment) and was subjected to both peer pressure and healthy competition. • The study spans 2013-2019 during which the workforce of about 2500 employees in a building was split into four groups (one control and three treatment) and was subjected to both peer pressure and healthy competition. • These results have implications for the design of pro-environment campaigns that could leverage peer effects through fostering an environment of intragroup pride and healthy inter-group rivalry sprinkled with intermittent supervisory interventions for making long-term impacts. [ABSTRACT FROM AUTHOR]

Blockchain technology, with its peer-to-peer trading feature, influences the management of energy consumption by offering the potential to transform transparency, efficiency, and sustainability within the energy sector. Nonetheless, there is a need to develop analytical decision-making models tailored for managing peer-to-peer energy transactions to improve energy resilience. Therefore, the purpose of this paper is to address the research question: How can energy distribution systems be protected via blockchain technology to enhance energy resilience and mitigate vulnerabilities to disruptions ? This paper employs a conceptual research model design and a mathematical decision-making model to address the research question by capturing the peer-to-peer trading capability of blockchain technology. The theory of planned behavior provides theoretical explanations for the proposed model. The sample includes longitudinal energy consumption data from 2015 to 2023 in Texas. The findings indicate a significant improvement in energy efficiency along with a considerable decrease in total electricity consumption. Post hoc analysis results reveal that the seasonal autoregressive integrated moving average algorithm is effective as a reliable input for the proposed mathematical model. The significant implications are to implement blockchain-based smart grids in which energy systems become more resilient to disruptions, as the peer-to-peer capability enables users to trade energy. The proposed model suggests that energy will be used more efficiently and effectively. This paper contributes to prior works by introducing a mathematical model that captures the trading behavior of energy consumers. Moreover, this paper proposes the SARIMA algorithm to predict energy demand. • A mathematical peer-to-peer trading model is presented to improve resilience. • A blockchain-based smart grid is presented for consumer energy consumption. • Five machine learning algorithms are presented in energy demand management. • Longitudinal energy consumption data of Texas is used in the model. • Theoretical support is provided using the theory of planned behavior. [ABSTRACT FROM AUTHOR]

This paper investigates the performance and fuel-saving effect of a velocity control algorithm on modern 50 cc scooters (Euro 5). The European Parliament has adopted major CO 2 emission reductions by 2030. But modern combustion-powered scooters are inefficiently restricted and emit unnecessary amounts of CO 2. Replacing the original restriction method with the system presented in this paper, the engine's operating point is being improved significantly. A Throttle-by-Wire-System senses the rider's throttle command and manipulates the throttle valve. A redundant wheel speed sensor measures the precise vehicle velocity using the Hall effect. The entire system is managed by a central ECU, executing the actual velocity control, fail-safe functions, power supply and handling inputs/outputs. For velocity control, an adaptive PI-controller has been simulated, virtually tuned and implemented, limiting the max. velocity regulated by legal constraints (45 km/h). In this way, the environmentally harmful restrictors used today can be bypassed. By implementing a human–machine interface, including a virtual dashboard, the system is capable of interfacing with the rider. For evaluation purposes a measurement box has been developed, logging vehicle orientation, system/control variables and engine parameters. A Peugeot Kisbee 50 4T (Euro 5) is serving as test vehicle. Finally, the system has been evaluated regarding performance and fuel efficiency both through simulation and road testing. Fuel savings of 13.6% in real-world test scenarios were achieved while maintaining vehicle performance. • Efficiency is increased and the throttle valve opening is reduced by 50%. • The controlled air supply minimizes the fuel injected by up to 25%. • Significant improvements of fuel consumption and CO 2 emissions by 14%. • Consistent performance is demonstrated by using energy more effectively. [ABSTRACT FROM AUTHOR]

Since General Secretary Xi Jinping pledged the "30·60" targets to the world, the importance of pollution reduction and carbon reduction has become increasingly prominent. The previous focus on the pollution reduction effects of emission trading systems, while overlooking their carbon reduction impacts, as well as the limitations of research methods and scope, is no longer suitable for the needs of the new era. Therefore, this paper constructs a CGE model to study the synergistic effects, simulating the synergistic emission reduction effects of sulfur dioxide emission trading policies, and comes to the following conclusions: After the implementation of the sulfur dioxide emission trading mechanism, under different sulfur price scenarios, the synergistic reduction amounts of SO 2 and CO 2 vary greatly. As the sulfur price increases, the reduction amount becomes larger. In the five sulfur price scenarios set in this paper, the maximum reduction of SO 2 can reach about 111,400 tons, and the maximum for CO 2 is about 399 million tons. The CO 2 reduction rate is approximately 1.0767 times that of SO 2. This indicates that the sulfur dioxide emission trading policy is a very effective path for synergistic pollution and carbon reduction, with good reduction effects. In addition, sulfur dioxide emission trading will reduce the consumption of fossil energy, decrease industry output, raise product prices, cause GDP loss, and lead to reduced social welfare to varying degrees. [ABSTRACT FROM AUTHOR]

Energy development in industrial parks promotes both industrial and inter-park economies. However, as the number of industrial parks increases and the economic level of the parks rises, energy consumption is also increasing rapidly. The rapid expansion of inter-park trading markets is accompanied by a surge in carbon emissions, making industrial parks increasingly significant contributors to carbon emissions within the industrial sector. In response to this environmental challenge, this paper proposes an integrated trading approach within industrial parks that considers carbon constraints. By implementing a spot trading method for surplus industrial energy, the goal is to mitigate the carbon emission pressure within these parks. Firstly, the paper conducts an analysis of the characteristics of combined heat and power units in industrial parks. It introduces an integrated analysis method within the target region, consolidating information from various park units to enhance flexibility in energy utilization. Secondly, an energy storage model is established. This model efficiently leverages energy storage capacity to balance fluctuations in energy supply and demand within industrial parks, thereby alleviating carbon emission pressure. Finally, the study and analysis of an industrial park in Liaoning Province were conducted using the Yalmip + Gurobi commercial software on the MATLAB platform. Compared with the traditional industrial park, the carbon dioxide emission of the industrial park is reduced by 91.49t, and the energy cost is saved by 11.2 million RMB. Numerical results from instances indicate that this method surpasses traditional trading approaches, aligning with the rigorous standards of scientific research. • An energy storage model is proposed to address the shortage of energy storage in waste heat trading in industrial parks. • A coordinated scheduling program is presented to reduce energy scheduling imbalances between multiple campuses. • The carbon-constrained energy storage device is integrated to improve the decarbonization capacity. [ABSTRACT FROM AUTHOR]

In an aero-engine rotor system, a blade-casing rub-impact fault can lead to a frequency modulation phenomenon of the vibration signal. This paper discusses the extraction of the modulation information in the fault signal using the variational nonlinear chirp mode decomposition (VNCMD) method to detect early rub-impact faults. The regularities of frequency modulation are revealed in both the simulated and experimental blade-casing rubbing signals. According to the frequency modulation law, an empirical formula is summarized to judge the rub-impact fault type. Then, we propose an evaluation index to judge the fault severity based on the energy theory by using the advantage of the VNCMD method that can reconstruct the decomposed fault signal. Finally, the fault severity in the simulated and experimental signals with different parameters is compared. The results show the correctness and reliability of this evaluation index. The research in this paper provides a theoretical basis for the detection and judgment of the rubbing severity of blade-casing rub-impact faults. • The time-frequency modulation rules of different rub-impact faults are found and summarized. • An empirical formula used to judge the type of rubbing is summarized in the early stage of the rub-impact fault. • An evaluation index for judging the fault severity is proposed based on energy theory. [ABSTRACT FROM AUTHOR]

This study aims to investigate the factors of renewable energy consumption in the fifth Technology Revolution with specific evidence from ASEAN countries. The study applies an autoregressive distributed lag (ARDL) approach. The paper also uses the unrestricted fixed and random panel data methods. The manuscript identifies significant factors such as technological innovation, governmental policies, public awareness, electricity consumption, country population, foreign direct investment inflows, imports, exports, and economic growth that have influenced the renewable energy sector in these nations. This study also investigates the determinants that influence renewable energy in the context of international integration and the fifth technology revolution in ASEAN countries. The research analyzes data spanning two decades, from 2000 to 2022, through a combination of econometric modeling and machine learning techniques. Preliminary findings indicate that technological innovation, government policies, and public awareness significantly affect renewable energy consumption in these countries. The empirical results offer insights for policymakers and stakeholders in leveraging technological advancements for sustainable energy development. The findings from this study on the determinants of renewable consumption in the context of the Fifth Technology Revolution in ASEAN countries also have several important policy implications for the sustainable development goal. • This study aims to investigate the factors of renewable energy consumption in the fifth Technology Revolution. • The study uses panel data analysis, and the paper identifies significant factors that have influenced the renewable energy sector. [ABSTRACT FROM AUTHOR]

The energy transition towards sustainable resources is more urgent than ever given the environmental and geopolitical challenges. Being one of the major energy users, cities need to understand their energy sector to accomplish its transition, by means of data. However, data are not easily accessible and have their own challenges. This paper presents a joint effort between researchers, city representatives and industry to provide an urban system service that supports research, accelerates urban innovation, and involves the community. An energy data portal, "NRGYHUB", has been developed, where hourly data from thousands of energy meters are available. These meters were collected from neighborhoods in the city of Västerås, Sweden, and they measure electrical and heating energy. In addition, the data are complemented by geometrical and non-geometrical information of the buildings, as well as demographic statistics of the areas. The paper describes the process of data collection, preprocessing, and visualization, in addition to the main challenges and limitations of the project. This dataset can be used for energy use benchmarking, prediction, and analysis. • The creation and implementation of an energy data portal is detailed and made available on nrgyhub.mdu.se. • Legal and licensing issues in data sharing are explored, offering insights for those interested in Sweden and beyond • By showcasing real-world problems, the impact and role of this data sharing initiative are demonstrated. [ABSTRACT FROM AUTHOR]

• This paper proposes the research on dimension reduction for visualization of simplified security region of integrated energy system considering renewable energy access. • The imbalance between energy demand and supply caused by the renewable energy uncertainty is defined as a special N-1 fault. • An interval security region model of integrated energy system is constructed, according to the N-1 safety constraints of equipment and pipeline. • An engineering simplification approach for interval security region model is proposed, in order to quantify the security boundary, security distance, and interval total supply capability. • A dimension reduction observation method based on observation variable optimization is proposed, which can enlarge the volume unaffected by renewable energy uncertainty. This paper proposes the research on dimension reduction for visualization of simplified security region of integrated energy system considering renewable energy access. Reason: Integrated energy system is conducive to achieving low-carbon transition and efficient utilization of energy. As the penetration of renewable energy increases, the security of integrated energy system faces severe challenges. It holds significance for comprehensively studying N-1 security test method in the operation scenario of integrated energy system, the impact of renewable energy on the IES security region model, and the optimal selection for observation variables. Methods: Firstly, a model for interval security region of integrated energy system is constructed, according to the N-1 safety constraints of energy hub key equipment and pipeline. The imbalance between energy demand and supply caused by the renewable energy uncertainty is defined as a special N-1 fault. Then, an engineering simplification approach for interval security region model is proposed, in order to quantify the security boundary, security distance, and interval total supply capability. On the basis, a dimension reduction observation method based on observation variable optimization is proposed, the selection of observation variables help to expand the volume of area unaffected by renewable energy uncertainty. Results: To the existed problem in current research, an example is presented to verify the effectiveness of proposed method, which brings forward N-1 security test method in the operation scenario of integrated energy system, analyzes the impact of renewable energy on the security region model, and realizes the optimal selection for observation variables of simplified security region. Conclusions: Interval security region of integrated energy system can intuitively estimate the security status of operating points and observe the security boundaries. The simplified model can satisfy the needs of calculating precision. Optimal selection of observation variables is necessary, and the volume of area unaffected by renewable energy uncertainty can be effectively increased. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are widely used in modern AI systems, and their dedicated accelerators have become a promising option for edge scenarios due to the energy efficiency and high performance. Since the DNN model requires significant storage and computation resources, various energy efficient DNN accelerators or algorithms have been proposed for edge devices. Many quantization algorithms for efficient DNN training have been proposed, where the weights in the DNN layers are quantified to small/zero values, therefore requiring much fewer effective bits, i.e., low-precision bits in both arithmetic and storage units. Such sparsity can be leveraged to remove non-effective bits and reduce the design cost, however at the cost of accuracy degradation, as some key operations still demand a higher precision. Therefore, in this paper, we propose a universal mixed-precision DNN accelerator architecture that can simultaneously support mixed-precision DNN arithmetic operations. A big–little core controller based on RISC-V is implemented to effectively control the datapath, and assign the arithmetic operations to full precision and low precision process units, respectively. Experimental results show that, with the proposed designs, we can save 16% chip area and 45.2% DRAM access compared with the state-of-the-art design. [Display omitted] • A mixed precision structure to process different effective bit-width weights separately. • An MCU contains two big–little RISC-V cores to control the accelerator and peripherals. • A fine-grain control flow to control the queuing operations with low hardware amount. [ABSTRACT FROM AUTHOR]

Data compression is essential to reduce high storage and communication costs for a wide range of systems and applications. Canonical Huffman coding plays a pivotal role for several compression standards. This paper presents bit-parallel static and dynamic canonical Huffman decoder implementations using an optimized lookup table approach on a fine-grain many-core processor array and an Intel FPGA. The decoder implementation results are compared with an Intel i7-4850HQ and a massively parallel Nvidia GT 750M GPU executing the corpus benchmarks: Calgary, Canterbury, Artificial, and Large. The many-core implementations achieve a scaled throughput per chip area that is 891× and 7× greater on average than the i7 and GT 750M respectively. Moreover, the many-core implementations result in a scaled energy efficiency (compressed bits decoded per energy) that is 149.5×, 3.9×, and 2.5× greater on average than the i7, GT 750M, and Intel FPGA respectively. In addition, the optimized lookup-table-based static canonical Huffman decoder on the Intel FPGA yields performance and energy efficiency improvements of 2.1× and 3.68× respectively on average compared to a dynamic canonical Huffman decoder at a 17% cost in compression ratio. • The optimized look-up table approach speeds up the canonical Huffman decoding. • Static decoder executes faster than the dynamic decoder at the cost of compression ratio. • Many-core array implementation outperforms GPU, CPU, and FPGA in terms of area and energy efficiency. [ABSTRACT FROM AUTHOR]

Network-on-Chip (NoC) is the state of the art on-chip interconnection network for packet based communication. NoCs can offer low packet latency, high bandwidth, high throughput with minimum area, better energy efficiency and fault tolerance. Routers are the basic building blocks of the NoCs. In this paper, we present the design of a Congestion Aware Router for NoC which is then implemented using Vivado HLS. The router is then used to develop a scalable NoC based on mesh topology. Using the NoC as a test bed we carry out simulations and estimate performance metrics like latency, waiting time and total packets handled for various configurations of NoC. Provisions to alter parameters like buffer depth, packet size, packet injection interval and traffic are also added. Further, we propose a simple mechanism for detecting congestion at the router. The congestion metric is then used to adapt the XY dimension order routing into a Congestion Aware minimal adaptive X/Y routing strategy with very low hardware overhead. The proposed routing method is compared against conventional XY DOR, GCA routing and RCS based routing algorithms for different parameter variations. The results show that the proposed routing method can reduce packet latency for different traffic patterns at medium packet injection rates. • A Vivado HLS implementation of NoC Simulator. • Performance metrics like latency, waiting time and total packets are estimated. • Effect of traffic pattern, size of the mesh and packet size is carried out. • Proposed routing algorithm has been compared with GCA and RCS algorithms. • HLS NoC simulator results are compared against state-of-the-art Booksim simulator. [ABSTRACT FROM AUTHOR]

• A holistic overview of commercial building automation systems on cyber security. • Review hardware and software architectures of building automation systems. • Review building automation system protocol-specific vulnerabilities. • Identify typical cyber-attack detection and defense approaches at three levels. • Categorize cyber resilient control strategies for buildings under cyber attacks. Modern Building Automation Systems (BASs), as the brain that enable the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as the cloud, to enable low-cost remote management, optimized automation via outsourced cloud analytics, and increased building-grid integrations. As smart buildings move towards open communication technologies, providing access to BASs through the building's intranet, or even remotely through the Internet, has become a common practice. However, increased connectivity and accessibility come with increased cyber security threats. BASs were historically developed as closed environments with limited cyber-security considerations. As a result, BASs in many buildings are vulnerable to cyber-attacks that may cause adverse consequences, such as occupant discomfort, excessive energy usage, and unexpected equipment downtime. Therefore, there is a strong need to advance the state-of-the-art in cyber-physical security for BASs and provide practical solutions for attack mitigation in buildings. However, an inclusive and systematic review of BAS vulnerabilities, potential cyber-attacks with impact assessment, detection & defense approaches, and cyber resilient control strategies is currently lacking in the literature. This review paper fills the gap by providing a comprehensive up-to-date review of cyber-physical security for BASs at three levels in commercial buildings: management level, automation level, and field level. The general BASs vulnerabilities and protocol-specific vulnerabilities for the four dominant BAS protocols (i.e., BACnet, KNX, LonWorks, and Modbus) are reviewed, followed by a discussion on four attack targets and seven potential attack scenarios. The impact of cyber-attacks on BASs is summarized as signal corruption, signal delaying, and signal blocking. The typical cyber-attack detection and defense approaches are identified at the three levels. Cyber resilient control strategies for BASs under attack are categorized into passive and active resilient control schemes. Open challenges and future opportunities are finally discussed. [ABSTRACT FROM AUTHOR]

In this paper, a first-order Euler semi-implicit finite element scheme is proposed for solving the two-dimensional incompressible magnetohydrodynamics flows with variable density numerically. The proposed FEM algorithm is unconditionally stable at the full discrete level, which is a key issue in designing efficient algorithms for the multi-physical field problems. When the time step size τ and the mesh size h both are sufficiently small, optimal spatial error estimates in L 2 -norm are presented for the density, velocity and magnetic field by using energy techniques based on uniform regularities of solutions to the time-discrete scheme and the method of mathematical induction. Finally, numerical results are given to confirm the theoretical analysis. [ABSTRACT FROM AUTHOR]

We investigate the error of the (semidiscrete) Galerkin method applied to a semilinear subdiffusion equation in the presence of nonsmooth initial data. The diffusion coefficient is allowed to depend on time. We use the energy method to find optimal bounds on the error under weak natural assumptions on the diffusivity. First, we prove the result for the linear problem and then use the "frozen nonlinearity" technique coupled with various generalizations of Grönwall inequality to carry the result to the semilinear case. The paper ends with numerical illustrations supporting the theoretical results. [ABSTRACT FROM AUTHOR]

• The relationship between the buoyancy and gravity is analyzed when the thermal effects are considered. • Three station-keeping scenarios are proposed and compared for the stratospheric airship. • The significance of pressure control and altitude control is revealed by the calculation and analysis. • An effective way to specify the helium's mass is presented when the thermal effects are considered. • The airspeed control is shown as an efficient way to cut down the energy consumption during the long-endurance flight. The ability to achieve long-endurance station-keeping flights makes stratospheric airships desirable platforms for the provision of communication and surveillance services. To maintain long-endurance flights, it is necessary to consider the problem of energy consumption. In this paper, we discuss long-endurance flight scenarios of stratospheric airships in the presence of thermal effects. The balance between buoyancy and gravity is influenced by thermal effects during the diurnal cycle. We perform a theoretical analysis based on the helium's mass, pressure differential, and altitude as the main factors. To verify the effectiveness of the control over the pressure differential and the altitude, three long-endurance flight scenarios are proposed and compared. Then, the corresponding optimization problems are constructed to determine the energy-minimum flight. Finally, further efforts are made to reduce energy consumption. The realization and limitations of two strategies for improvement are analyzed. A comparison with other scenarios shows the effectiveness of energy conservation. The study in this paper thus provides a reference for station-keeping applications of stratospheric airships. [ABSTRACT FROM AUTHOR]

Transistor threshold voltage (V t) scaling causes higher power consumption by increasing the subthreshold leakage and short-circuit currents in CMOS circuits. Leakage currents are significant contributors to the overall power consumption of digital systems-on-chip as threshold voltage, channel length, and gate oxide thickness are reduced with CMOS technology scaling. A new dual-pullup/dual-pulldown (DPU/DPD) repeater is proposed in this paper for higher energy efficiency in low-voltage and low-frequency applications. The standby mode leakage power consumption is reduced by 59.11% with the proposed clock tree as compared to the conventional 3 level H-tree operating with a power supply voltage of 1.0V in a 45 nm CMOS technology. The short-circuit currents are suppressed by selectively employing high-V t transistors in the repeaters. The clock network with the proposed buffer lowers the active mode energy consumption by up to 24.91% as compared to a conventional clock tree under equal silicon area constraint. Post layout results reveal that the statistical spread of clock skew in the DPU/DPD H-tree is also 20.60% lower than the conventional H-tree network. • A novel dual-pullup/dual-pulldown (DPU/DPD) repeater is presented for low leakage and high frequency clock distribution. • The minimum operating voltage of a clock tree is determined for achieving the lowest total energy consumption while satisfying the clock frequency and signal transition time (10% of clock cycle time) requirements at the leaves. • The influences of leakage currents on the total energy consumption of the clock trees designed with three different repeaters (conventional CMOS inverter, previously published SPLIT-IO, and the new DPU/DPD) are benchmarked at various supply voltages and frequencies. • The leakage power reduction provided with the DPU/DPD buffers is evaluated. • The impact of process parameter fluctuations on the clock skew of the novel clock distribution network is also characterized in this paper. [ABSTRACT FROM AUTHOR]

• The thruster performance and glide-slope constraint are considered. • A novel time-varying feedback guidance and its incremental form are proposed. • The guidance laws are near-fuel-optimal and can avoid subsurface flight. • The trajectory can be set as a straight line. • An optimal problem is constructed to satisfy the glide-slope constraint. Taking into account the thruster performance (total thrust magnitude constraint and control acceleration deviation) and glide-slope constraint, this paper presents two types of new feedback guidance strategies for Mars pinpoint soft landing, which are near-fuel-optimal and robust to disturbance. A novel and simple time-varying feedback guidance law is proposed at first. Next, this guidance law is extended to the incremental guidance to improve the robustness and ability to deal with the thruster performance and disturbance. An advantage of these two guidance schemes is that they can avoid subsurface flight owing to the monotonic convergence of position and velocity. Moreover, an exciting feature of these two guidance laws is that the trajectory of the vehicle can be set as a straight line by choosing the guidance laws parameters. Finally, an optimal problem is constructed and solved based on this feature to obtain the guidance laws parameters to satisfy the glide-slope constraint. Its robustness, fuel efficiency, and performance in dealing with acceleration deviation and thruster constraint are demonstrated for different simulation scenarios, especially in comparison with zero-effort-miss/zero-effort-velocity feedback guidance law. [ABSTRACT FROM AUTHOR]

• Novel and simpler detumbling control strategy is proposed. • Fuel consumption estimation is compared with the traditional control method. • The effectiveness, validity, stability of the proposed control strategy are demonstrated. Tether-net is known as a popular method to actively remove space debris. After successfully capturing a space debris object by a net, a tethered system consisting of a chaser satellite, a main tether and a target is formed. Since the most urgent and important task after capture is to avoid collision between the chaser and the tumbling target, detumbling of the target via the force by the main tether has to be performed. In this paper, we propose a novel and simple detumbling strategy to effectively stabilize the tethered system by controlling the motion of the chaser. Comparing to a previously proposed tension-based detumbling strategy, this newly proposed velocity-based method is simpler and more practical. Moreover, the fuel consumption of the proposed method is only 1% that of the tension-based method for investigated scenarios. With the proposed detumbling strategy, we not only analyze the influence of the system configuration on the detumbling effectiveness, but also analyze the detumbling capability of the method, concluding that the velocity-based detumbling strategy is able to detumble a spinning target up to 1.5 rad/s under a specific configuration. In addition, the proposed method is found to be suitable for both light and massive target detumbling. [ABSTRACT FROM AUTHOR]

• The coupling relationship between trajectory and battery is considered. • Trajectory-battery integrated design is proposed for spacecraft. • Electric Pump-Fed Engines are applied to low-Earth orbital maneuvers. • Advantages of the electric pump system in the structural mass are revealed. This paper proposes a trajectory-battery integrated design of the electric pump-fed (EPF) engine and demonstrates its application feasibility to orbital maneuvers in low-Earth orbit (LEO). Based on the mass model that comprises the main components of the engine, the advantages of the electric pump system in terms of the structural mass, addition of total impulse and change in velocity that the engine can provide are demonstrated compared with those of the traditional pressure-gas system. The performances of four typical battery cells are assessed to select the suitable cell for the application cases. Besides, the applications of the trajectory-battery integrated design to the specific orbital maneuvers are investigated. Impulsive and continuous control strategies are considered based on the EPF engine limited by the light/umbra condition and charge time. The optimal transfer trajectories considering the charging processes of the battery involved in orbital maneuvers are demonstrated. For the impulsive thrust, the limitation of the battery performance with regard to the effectiveness of the Hohmann transfer is given. For the continuous thrust, the orbital control problem has been turned into an optimization problem and the optimal fuel consumption is obtained using the combinatorial numerical optimization. The simulations confirm the superiority of the EPF engine based on its significant reduction of the structural mass and validate the application feasibility to orbital maneuvers in LEO. [ABSTRACT FROM AUTHOR]

• A new conformable fractional opposite-direction accumulation is proposed. • The CFGNOM (1,1) model is more reliable and effective than other fractional grey models. • The application idea of a grey model for emergencies is accurate. • The CFGNOM (1,1) model is used to predict the per capita primary energy consumption of South and Central America. To better reflect the principle of new information priority, this paper proposes a new conformable fractional opposite-direction accumulation operator and constructs a new grey prediction model, the CFGNOM (1,1) model. Three cases are used to verify the effectiveness of the CFGNOM (1,1) model, and a grey modeling prediction idea for emergencies is proposed. The CFGNOM (1,1) model is used to predict the per capita primary energy consumption of South and Central America over the next three years. The following conclusions can be drawn. First, the new conformable fractional opposite-direction accumulation method is effective and reasonable and can make full use of the information contained in the latest data to form new sequences. Second, compared with other existing fractional grey models and the BP and SVM models, the CFGNOM (1,1) model has higher prediction accuracy and can make better use of new information for modeling. Third, the application idea of a grey model for emergencies is accurate and can also be applied to the future economic and social prediction affected by the pandemic situation. Fourth, the prediction results show that the per capita energy consumption of South and Central America in 2021 is 49.9–52.7 gigajoules, the per capita energy consumption in 2022 is 48.4–51.8 gigajoules, and the per capita energy consumption in 2023 is 46.8–50.1 gigajoules. [ABSTRACT FROM AUTHOR]

Continuously growing demands due to the predictable faults or abnormal events of the flexible production line are becoming a challenge to easily cause the energy waste, which needs to accurately predict the real-time energy consumption of the actual production process. By reviewing the related works about prediction model approach and energy consumption analysis in the actual production application, it is necessary to overcome two main challenges: optimal prediction selection and internal relation mining of energy consumption under different equipment working conditions. To deal with the existing challenges, the paper proposes a novel attention-based Long and Short-Term Memory (LSTM) approach to predict energy consumption with temporal series under various equipment working conditions. This method offers forecasting support to form a complete prediction framework for the automatic Miniature Circuit Breaker (MCB) production line, which can be used to predict the specific working condition based on attention-based LSTM approach. By comparing various prediction approaches for energy consumption under the working condition prediction with various equipment of different production line, the Fully Connected Neutral (FCN) network approach can achieve a high accuracy and effectiveness based on MAE, R2 and MSE evaluations. [ABSTRACT FROM AUTHOR]

An energy functional describes the equilibrium state of a system. In this work, we present a novel technique, Functional Optimization using Neural Networks (FONN), for minimizing the system's energy. FONN utilizes neural networks to process information at discrete grid points, considering their interactions with neighboring grid points, to update the state of the system. The training process involves formulating a loss function based on the system's energy, and with the help of multiple fine-tuning steps, the method employs a progressive energy reduction technique that decreases the energy in multiple steps. FONN's effectiveness is demonstrated across various problems, including the minimization of the heat and Lyapunov energy. Moreover, the paper explores the minimization of the elastic bending energy with an area constraint. [ABSTRACT FROM AUTHOR]

Energy and water are interdependent. Water is essential for energy production, and energy is indispensable for water processes. This paper develops a Stackelberg game based bi-level multi-objective model for optimal water allocation under uncertainties from an energy-water nexus perspective, which considers energy-related water consumption and water-related energy production. It handles the hierarchical decision-making problem between the river basin authority and the subareas, as well as the tradeoffs between social equality and ecological damage. The upper level of the model makes schemes on water allocation for multiple subareas, and the lower level decides on water allocation for different types of power generation. To generate reasonable and practical strategies for water allocation, ɛ -constraint method and KKT conditions are used. A case study of Minjiang River, China is given to demonstrate the applicability and practicality. Scenarios analysis is also conducted to demonstrate the effectiveness of the model and the effects of the control parameter changes. The results indicate that the river basin authority would allocate larger water resources to the subareas with higher energy demand. It is concluded that the proposed allocation method is advantageous to the subareas with higher water utilization efficiency. The proposed bi-level model demonstrates its reasonableness and superiority to the single-level model. Policy implications are finally provided to support the sustainable development of energy-water nexus. • A bi-level model for water allocation in energy-water nexus system is proposed. • The hierarchical decision sequence between the river basin and subareas is solved. • The tradeoffs between social equality and ecological damage are made. • Optimal water allocation for multiple subareas and energy resources are quantified. • Uncertainties in decision-making environment are handled by fuzzy set theory. [ABSTRACT FROM AUTHOR]

• Recent advances in discovery and performance prediction of energy storage materials relying on ML. • An overview of the current status and dilemmas of ML databases commonly used in energy storage materials. • ML in energy storage material discovery and performance prediction: typical applications and examples. • Dilemmas to be faced in the development of ML-assisted or led energy storage materials, and corresponding prospects. Energy storage material is one of the critical materials in modern life. However, due to the difficulty of material development, the existing mainstream batteries still use the materials system developed decades ago. Machine learning (ML) is rapidly changing the paradigm of energy storage material discovery and performance prediction due to its ability to solve complex problems efficiently and automatically. Various excellent works are constantly emerging in the field of ML assisted or dominated development of energy storage material, such as exploring of new materials, studying of battery performance, investigating of battery aging mechanism. In this paper, we methodically review recent advances in discovery and performance prediction of energy storage materials relying on ML. After a brief introduction to the general workflow of ML, we provide an overview of the current status and dilemmas of ML databases commonly used in energy storage materials. The typical applications and examples of ML to the finding of novel energy storage materials and the performance forecasting of electrode and electrolyte materials. Furthermore, we explore the dilemmas that will be faced in the development of applied ML-assisted or dominated energy storage materials and propose a corresponding outlook. This review systematically summarizes the current development of ML-assisted energy storage materials research, which is expected to point the way for its further development. [ABSTRACT FROM AUTHOR]

Since a renewable energy is connected to a high-altitude integrated energy system (HAIES), challenges arise for system operation. Shared energy storage as a jointly operated energy hub for multi-integrated energy system (IES) can effectively improve the economy and flexibility of the system. This paper proposes a joint day-ahead and intra-day scheduling strategy for a HAIES considering a shared composite energy storage operator (SCESO) and profit clearing scheme. First, a HAIES structure suitable for residential, industrial, and commercial applications is built based on combined oxygen supply and integrated demand response (IDR). An SCESO is then proposed to provide multiple energy sharing services for HAIES cluster, including electricity–oxygen–hydrogen. Accordingly, HAIESs and SCESOs are aggregated to create a HAIES cluster energy sharing framework. Second, during the intra-day scheduling stage, which is based on real-time forecast data that considers multiple source–load–charge uncertainties, the flexible adjustment capabilities of each unit of the framework are used to achieve intra-day energy balance and ensure the safe operation of the framework. Furthermore, a profit clearing scheme is proposed, introducing different energy contribution quantification methods for different energy cooperation modes. Finally, day-ahead and intra-day simulation results under multiple scenarios are analyzed. The simulation results demonstrate that the framework is optimal in terms of economy, adaptability to different RE outputs and RE uncertainties, and energy storage utilization efficiency. • It proposes a new HAIES and SCESO energy cooperation framework. • An intra-day scheduling strategy for the framework is proposed. • A profit clearing scheme is constructed. [ABSTRACT FROM AUTHOR]

Renewable Energy Communities (RECs) are being deployed all around the World as a technically feasible solution to decreasing users' dependence on fossil fuels. Several demonstration facilities have shown their potential to provide final consumers with clean energy and all associated environmental benefits. However, the economic evaluation of these systems as a whole set is more complex than evaluating generation technologies individually, which can be considered a barrier, and it may be more complicated to calculate its uncertainty with precision. This paper deals with this challenge and adapts a model for the evaluation of the global Levelized Cost of Electricity (LCOE) of a polygeneration microgrid to the characteristics of a typical REC, allowing the assessment of the distribution of the LCOE depending on the uncertainty of the input parameters. Thanks to its simple analytical formulation, the proposed model, that can be used for any combination of technologies (both renewable and conventional), provides relevant information on uncertainty propagation in a symbolic way that avoids the need to run numerical simulations or make assumptions on the distribution of the random input parameters. A case study has been presented, considering a typical small electrical REC with photovoltaic plants and micro wind turbines. Although the model can be defined to any market, as a representative example, it has been evaluated according to the current Spanish and Italian regulations, which are analyzed in depth with reference to the scientific literature. Results show that uncertainties in parameter estimates give rise to a very large scatter in the LCOE, pointing out a set of quantities whose role is crucial for a reliable estimate, among which electricity purchase and selling prices, yearly power load, and self-consumption / virtually-shared energy rates stand out. [Display omitted] • The global LCOE for a typical REC has been defined. • The uncertainty propagation in the LCOE formulation has been studied. • The proposed model has been applied to an electrical polygeneration REC. • As an example, the Spanish and Italian regulatory frameworks have been considered. • The LCOE of RECs is mainly sensitive to the electricity price and the power load. [ABSTRACT FROM AUTHOR]

Vision-based displacement measurement has become increasingly popular in bridge health assessment. However, this technology faces challenge that a complicated power-consuming supply system must be installed in the darkness, making it unable to meet the needs of low energy consumption and low cost. This paper proposes a bridge displacement measurement method using power-free target and thermography to address it. First, a thermography measurement system for bridge displacement in the darkness is developed. The power-free target with a hollowed-out circular feature is adopted to form the contrast to the background, and graphene is coated on the target surface to enhance the infrared emissivity and contrast further. Second, a sub-pixel elliptic center positioning algorithm based on thermography enhancement and data optimization is proposed to improve the accuracy and the ability to resist occlusion. The feasibility of the method is verified by laboratory tests and bridge field tests. When the measurement distance is within 15 m, the results meet the engineering error requirements of 5%, with the prospect of popularization in small and medium-sized bridges displacement measurement. • A bridge displacement measurement method in the darkness using thermography is proposed for the first time to address the power supply problem in bridge measurement. • A thermography measurement system for bridge displacement is developed and power-free target with graphene coated is adopted to form the contrast to the background. • A two-step parameter optimization strategy for elliptic center positioning is proposed. [ABSTRACT FROM AUTHOR]

Textile and cosmetic industries generate large amounts of dye effluents requiring treatment before discharge. This wastewater contains high levels of reactive dyes, low to none-biodegradable materials and chemical residues. Technically, dye wastewater is characterised by high chemical and biological oxygen demand. Biological, physical and pressure-driven membrane processes have been extensively used in textile wastewater treatment plants. However, these technologies are characterised by process complexity and are often costly. Also, process efficiency is not achieved in cost-effective biochemical and physical treatment processes. Membrane distillation (MD) emerged as a promising technology harnessing challenges faced by pressure-driven membrane processes. To ensure high cost-effectiveness, the MD can be operated by solar energy or low-grade waste heat. Herein, the MD purification of dye wastewater is comprehensively and yet concisely discussed. This involved research advancement in MD processes towards removal of dyes from industrial effluents. Also, challenges faced by this process with a specific focus on fouling are reviewed. Current literature mainly tested MD setups in the laboratory scale suggesting a deep need of further optimization of membrane and module designs in near future, especially for textile wastewater treatment. There is a need to deliver customized high-porosity hydrophobic membrane design with the appropriate thickness and module configuration to reduce concentration and temperature polarization (CP and TP). Also, energy loss should be minimized while increasing dye rejection and permeate flux. Although laboratory experiments remain pivotal in optimizing the MD process for treating dye wastewater, the nature of their time intensity poses a challenge. Given the multitude of parameters involved in MD process optimization, artificial intelligence (AI) methodologies present a promising avenue for assistance. Thus, AI-driven algorithms have the potential to enhance overall process efficiency, cutting down on time, fine-tuning parameters, and driving cost reductions. However, achieving an optimal balance between efficiency enhancements and financial outlays is a complex process. Finally, this paper suggests a research direction for the development of effective synthetic and natural dye removal from industrially discharged wastewater. [Display omitted] • Discharge of dye effluent requires robust and cost-effective treatment processes. • Membrane distillation emerged as a promising tool towards dye remediation. • The current study reviewed research advancement towards environmental dye remediation. • However, fouling and thermal efficiency of MD remain a major challenge. • The reviewed study suggested optimization of membrane and module designs for effective dye remediation. [ABSTRACT FROM AUTHOR]

The battery electric vehicle (BEV) industry, commanding over half of the global market share in China, has garnered significant attention in response to escalating concerns about climate change. NCM (LiNi x Co y Mn z O 2) batteries, prized for their high energy density, are pivotal in this industry. However, their production relies on critical metals, posing environmental challenges. Recycling processes aim to address this issue by recovering critical metals from end-of-life batteries. The industry is updating from NCM 111 to NCM 811 batteries to enhance energy density and reduce costs, but the key factors influencing the material flows and environmental impacts remain uncertain. This paper aims to identify the key factors contributing to the potential differences in material flows and environmental impacts introduced by the NCM battery upgrading in China. Following the new energy vehicle sales projections in China until 2035, a dynamic material flow analysis was employed to calculate material demands, recovered amounts, and substitution rates for various upgrading scenarios. The study reveals that the majority of retired batteries until 2035 were sold between 2022 and 2029. Material substitution rates prove to be more sensitive to the upgrading scenario than the lifetime and growth rate of BEVs. Faster NCM 811 penetration results in over 2.5 times more saved cobalt demand compared to additional nickel demand. A life cycle assessment of NCM batteries reveals a 39% lower global warming impact for NCM 811 compared to NCM 111, highlighting environmental benefits from increased energy density and reduced cobalt consumption. The energy density improvement contributed 85%, while the alteration in material composition contributed 15% to the global warming reduction in NCM 811. Recycling emerges as a crucial strategy in mitigating environmental impacts across all categories, particularly addressing eutrophication, previously deemed challenging due to the difficulty in recovering Li 2 CO 3. [ABSTRACT FROM AUTHOR]

[Display omitted] Ghana has been a pioneer in energy efficiency in the sub-Saharan region, becoming the first country to introduce minimum energy performance standards and energy labelling for lighting, air conditioners and refrigerating appliances in 2008. It also introduced an innovative and not yet widely adopted policy of banning the import of used air conditioners and refrigerating appliances, mostly from Europe. While the impact of this policy package has already been assessed in terms of energy savings in Ghana, it is still unclear whether the import ban on used products makes sense in terms of environmental impact. Ghana's ban may seem surprising given that current European product policies in the context of the Green Deal aim to extend the lifetime of products and encourage their reuse to make them more sustainable. In this paper, different scenarios covering new and used refrigerating appliances as well as different end-of-life management options were examined in a life cycle analysis. The results show that exporting used refrigerators from Europe to West Africa increases their environmental impact per functional unit in many categories, even if it extends the lifetime of the product. The informal end-of-life treatment of refrigerators by open burning has a massive impact on human toxicity. As energy consumption during the use phase is the main determinant of most environmental impacts, energy efficiency policies (minimum energy efficiency performance standards and labelling) combined with an import ban on used refrigerating appliances are the best approach to reduce these impacts. It is therefore recommended that the Ghanaian policy package be replicated in other countries in the region, but also that exporting countries take action, as the fight against environmental dumping is a shared responsibility. • Used refrigerators exported to West Africa increase greenhouse gas emissions. • Energy efficiency reduces the environmental impact of refrigerators. • Import ban on used refrigerating appliances is justified. • Informal end-of-life treatment of refrigerators has a major impact on human toxicity. [ABSTRACT FROM AUTHOR]

Continuously growing demands due to the predictable faults or abnormal events of the flexible production line are becoming a challenge to easily cause the energy waste, which needs to accurately predict the real-time energy consumption of the actual production process. By reviewing the related works about prediction model approach and energy consumption analysis in the actual production application, it is necessary to overcome two main challenges: optimal prediction selection and internal relation mining of energy consumption under different equipment working conditions. To deal with the existing challenges, the paper proposes a novel attention-based Long and Short-Term Memory (LSTM) approach to predict energy consumption with temporal series under various equipment working conditions. This method offers forecasting support to form a complete prediction framework for the automatic Miniature Circuit Breaker (MCB) production line, which can be used to predict the specific working condition based on attention-based LSTM approach. By comparing various prediction approaches for energy consumption under the working condition prediction with various equipment of different production line, the Fully Connected Neutral (FCN) network approach can achieve a high accuracy and effectiveness based on MAE, R2 and MSE evaluations. [ABSTRACT FROM AUTHOR]

An energy functional describes the equilibrium state of a system. In this work, we present a novel technique, Functional Optimization using Neural Networks (FONN), for minimizing the system's energy. FONN utilizes neural networks to process information at discrete grid points, considering their interactions with neighboring grid points, to update the state of the system. The training process involves formulating a loss function based on the system's energy, and with the help of multiple fine-tuning steps, the method employs a progressive energy reduction technique that decreases the energy in multiple steps. FONN's effectiveness is demonstrated across various problems, including the minimization of the heat and Lyapunov energy. Moreover, the paper explores the minimization of the elastic bending energy with an area constraint. [ABSTRACT FROM AUTHOR]