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Inorganic halide perovskite-based compounds of A 3 BX 3 have been gaining huge attention during the development of high-efficiency solar cells (SC) in recent years. This paper, investigating the physical and chemical properties could unveil its further potential to be used as absorber materials in advanced multijunction photovoltaics. The structural, optical, and electronic characterization of different compositional compounds of A 3 BX 3 (A=Ca/Sr, B P/As, X=I/Br) perovskites are examined thoroughly to be used as absorber in high performance photovoltaics. The DFT study reveals direct bandgaps of 1.93 (2.60) eV, 1.58 (2.14) eV, 1.96 (2.63) eV, 1.58 (2.16) eV, 1.52 (2.30) eV, 1.26 (1.94) eV, 1.53 (2.32) eV, and 1.26 (1.96) eV at Γ for the Ca 3 AsBr 3 , Ca 3 AsI 3 , Ca 3 PBr 3 , Ca 3 PI 3 , Sr 3 AsBr 3 , Sr 3 AsI 3 , Sr 3 PBr 3 , and Sr 3 PI 3 perovskites using PBE (HSE) methods with optical absorption of (2−5)x104 (a. u.) in the optical region (< 3.2 eV). Subsequently, the potential of the A 3 BX 3 absorber has been investigated and analyzed at varying layer thickness, defect densities, and doping concentrations with SnS 2 electron transport layer (ETL) layer using FTO/SnS 2 /A 3 BX 3 /Ni heterostructure with current density-voltage (J-V) characteristics and corresponding quantum efficiency (QE) observation. The power conversion efficiency (PCE) of 13.52, 20.88, 13.66, 22.47, 20.83, 28.16, 20.77, and 27.32% were achieved in Ca 3 AsBr 3 , Ca 3 AsI 3 , Ca 3 PBr 3 , Ca 3 PI 3 , Sr 3 AsBr 3 , Sr 3 AsI 3 , Sr 3 PBr 3 , and Sr 3 PI 3 absorber-based heterojunction SC. Notably, the highest PV performance revealed a PCE over 28% with a V OC of 0.913 V, J SC of 35.75 mA/cm−3, and FF of 86.26% obtained in the Sr 3 AsI 3 absorber SCs. Thus, these detailed studies on the physical and optoelectronic properties of eight (8) different possible absorber-based configurations including their PV applications unveil the huge potential of A 3 BX 3 (i.e., Sr 3 AsI 3) and provide resources for the practical cell development of inorganic perovskites-based high efficiency SC over 28%. • The structural, electronic, and optical properties of novel inorganic cubic perovskites of A 3 BX 3 (A=Ca, Sr, B P, As, X=I, Br) were investigated in detail using FP-DFT. • The potential of A 3 BX 3 absorber has been investigated and analyzed at varying layer thickness, defect densities, and doping concentrations with SnS 2 electron transport layer (ETL) systematically. • The photovoltaic performance of A 3 BX 3 absorber based structure were investigated via SCAPS-1D. • The maximum power conversion efficiency was found 28% with V OC 0.913 V of, J SC of 35.75 mA/cm−3, and FF of 86.26% due to Sr 3 AsI 3 absorber. [ABSTRACT FROM AUTHOR]

This response mainly deals with the scientific description of the comments on the structure of the published compound (Bi 1/2 K 1/2)(Fe 1/2 W 1/2)O 3. The newly designed (Bi 1/2 K 1/2)(Fe 1/2 W 1/2)O 3 compound was prepared using the solid-state reaction method. The ingredients were taken to satisfy the required stoichiometry/chemical composition to form a single-phase compound. The lattice parameters and structure is strongly affected by K and W doped elements. The structural analysis is supported by the results of other experimental techniques (dielectric study, Raman characterization, Hysteresis loop, etc.). [ABSTRACT FROM AUTHOR]

The nanocrystalline metallic bilayer structure can effectively enhance the strength of Cu alloy while maintaining excellent electrical conductivity. Based on the microstructure evolution of Cu/Ag alloy observed by transmission electron microscope (TEM), molecular dynamics (MD) simulations of Cu/Ag alloy with nanocrystalline metallic bilayer structure were established. The mechanical properties and deformation mechanisms of nanocrystalline Cu/Ag-bilayer alloys were investigated at different temperatures. A mixed Hall-Petch (H-P) relationship was observed between the ultimate stress and the increased layer thickness. In this paper, the deformation mechanism changes from dislocation accumulation at the Cu-Ag interface to dislocation through the Cu-Ag interface. In addition, the data analysis results of elastic modulus showed an approximately linear decrease trend with increasing temperature, and the deformation mechanism shifted from dislocation motion to a combination of dislocation motion and grain boundaries (GBs) diffusion. These findings provide guidance for the design of high magnetic field facility (HMFF) water-cooled magnets and very large-scale integration (VLSI) lead frames. • The deformation behaviour of the nanocrystalline Cu/Ag-bilayer alloy at different thicknesses (4–6 nm) and temperatures (1–800 K) is investigated. • The layer thickness of Cu/Ag-bilayer alloy conforms to the mixed H-P relationship. The mechanism changes from dislocation pile-up. • As temperatures increases, the deformation mechanism transformed from dislocation emission to a combination of dislocation motion and the high-temperature softening. [ABSTRACT FROM AUTHOR]

Heat treatment is necessary for GW63K (Mg-6Gd-3Y-0.5Zr, wt%) alloy fabricated by wire are additive manufacturing (WAAM) to improve the mechanical properties before final applications. Considering the unique solidification process and microstructure of WAAMed GW63K alloys, the heat treatment parameters must be different from the cast or forged ones. Thus, the present paper investigated the influence of solution temperature and time on the microstructure and mechanical properties of WAAMed GW63K, as well as the aging temperature and time. It was found that the as-deposited alloy exhibited fine equiaxed α-Mg grains with an average size of 13 μm and few eutectic phase Mg 24 (Gd, Y) 5 around the grains. The proper temperature of solution treatment was 480℃ and the proper time was 0.5 h, lower and shorter than cast ones. After solution treatment, the average size of α-Mg grains almost remained the same and nearly all of the eutectic phase dissolved. To exert precipitation strengthening to a larger extent, we conduct aging treatment at 175°C, 200°C, and 225°C and draw their age-hardening curves. Besides, a superior strength /ductility trade-off was obtained peak aging at 200°C for 80 h, with the ultimate tensile strength of 337±8 MPa, yielding strength of 231±2 MPa, and elongation of 8.9±1.0%. This outstanding strength can be accounted for by the refinement strengthening of fine microstructure inherited from as-deposited GW63K alloy and precipitation strengthening from dense β′ precipitates with a remarkable plate aspect ratio. Besides, suitable β′ precipitates could support a favorable work hardening effect and compatible deformation capability. • The Mg-6Gd-3Y-0.5Zr alloy with low rare earth content fabricated by WAAM has obtained fine microstructure. • The refinement of microstructure would be retained under an optimal solution treatment (480°C×0.5 h). • A superior strength /ductility trade-off with UTS of 337±8 MPa, YS of 231±2 MPa and EL of 8.9±2.0% was obtained. [ABSTRACT FROM AUTHOR]

The rapid development of lithium-ion batteries (LIBs) has been a great driving force for the progress of human society. The continuous development of electric vehicles, power grids, electronic products and other industries all have an increasing demand for high energy density batteries. The primary factor limiting the development of LIBs that have high energy and low cost is the cathode material. In the existing cathode materials, the Lithium-rich (Li-rich) cathodes compensate for capacity by redox reactions involving anions and cations, which have become the ideal cathode materials for the future LIBs due to their ultra-high specific capacity and energy density. However, the problems such as structural deterioration and lattice oxygen evolution during the cycle of Li-rich cathodes have seriously hindered the commercialization of Li-rich cathodes. Therefore, this paper has discussed the factors leading to the degradation of the electrochemical performance of Li-rich cathodes from the aspects of electron energy state and microstructure. Meanwhile, it also discussed the latest development in the modification and analysis of Li-rich cathodes including multifunctional surfaces, gradient doping and high entropy doping matters, in order to provide useful insights into the future preparation and modification of these cathodes. [ABSTRACT FROM AUTHOR]

March algorithms are widely used in Memory Built-In Self-Test (MBIST) on-chip memory testing, providing linear test complexities that reduce the test time and cost. However, studies show that March algorithms with complexities lower than 18N have poor coverages of faults that have emerged with the advent of the nanometer process technologies and are more relevant to nowadays memories. New March AZ1 and March AZ2 algorithms, with 13N and 14N complexities, respectively, were introduced to provide optimum coverage of those faults and to produce a shorter test than an 18N-complexity test algorithm with a lesser area overhead, thus reducing chip manufacturing costs. This paper presents the implementation and validation of MBIST controllers that applied the March AZ1 and March AZ2 algorithms in a Field-Programmable Gate Array (FPGA) device. They were implemented in the Intel Max 10 DE10-Lite FPGA Development Board. A test generator was built in FPGA, as an alternative to the external tester, to provide test vectors required in initiating the test on the memory model using the implemented MBIST. The FPGA experimental tests demonstrated that they function correctly as the expected test sequences were observed. In addition, their fault detection abilities were also validated through tests on a fault-injected memory model, which shows that the implemented March AZ1 and March AZ2 provide 80.6% and 83.3% coverage of the intended faults, respectively, which outperform any other existing 14N-complexity March algorithms. [ABSTRACT FROM AUTHOR]

When gathering optical satellite pictures, light reflected from the surface due to water vapor, snow, fog, haze, and more tiny particles in the environment is generally seen as a gap in the propagation process. Haze has a greater number of suspended particles like aerosols and water droplets. These particles have absorption effects and scattering in the light. Although haze translucency grants a chance for image restoration, a well-organized and broadly relevant haze removal procedure for holding several hazes is quite an extensive provocation. To address this issue, this paper proposed an Optimal Contrast Limited Adaptive Histogram Equalization (OCLAHE) to capture further intricate features and patterns connected to haze, enabling more accurate haze detection and removal. The deeper network can learn complicated descriptions and recognize between hazy and nonhazy regions with higher precision. The proposed method is validated in I-Haze and O-Haze datasets, and its performance is quantified by various performance metrics such as MSE, SSIM, PSNR, WPSNR, and Running time. The experimental consequences demonstrate that the developed model performs better than other techniques and attains an MSE from both datasets as 0.0054 and 0.0051. Overall, the proposed model amends the accuracy and reliability of haze detection in images. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic parameters lightning search algorithm (DP-LSA) is proposed for optimal operation management of electric vehicle (EV) parking lots (PLs) and distributed generations (DGs) in a microgrid (MG). The MG comprises distributed generation sources such as diesel generators and fuel cells. The batteries of electric vehicles parked in the PL are also used for energy storage. A proposed objective function combines operating, maintenance, and replacement costs, considering inflation, motivation, and annual growth. Simulations are done in two scenarios, and the proposed algorithm results are compared with particle swarm optimization (PSO) algorithm results. Simulation results prove the better performance of the proposed LSA algorithm than the PSO algorithm in reducing the microgrid operation cost. [ABSTRACT FROM AUTHOR]

In this paper, we construct a deep framework for full-reference image quality assessment (FR-IQA) by combining convolution and self-attention features; this approach effectively uses multiscale features to mimic the image evaluation in human eyes. We achieve the integration of information from local to global. First, convolutional neural network (CNN) and encoder of Swin transformer are used to extract multiscale paired features. Second, for each type of features, we fuse them after converting them to a fixed number of channels. For the fused features, we use the square of the difference between two pixel values at the corresponding channel positions to represent the features of the distortion degree. Then, we introduce a recurrent neural network (RNN) to capture the global features. Finally, for the two types of features, we use the full connection (FC) layer to regress two scores and then add the weights to compute the ultimate perceived score. By training and testing on publicly available FR-IQA datasets, experimental results further validate the superiority of our approach. [ABSTRACT FROM AUTHOR]

A charge and discharge management scheme is proposed. The stored electric energy in distributed storage devices will converge to the consistent. The consistency of the stored electric energy of the devices helps to maintain the maximum load capacity and maximum consumption capacity of distributed storage devices. The charging and discharging process is constructed as a time-varying optimization problem, and the proposed algorithm can respond to the time-varying parameters of the distributed storage devices in real time. The time-varying neurodynamic algorithms can obtain time-varying optimal solution trajectories to give the optimal charging and discharging strategy in real time. In addition, the proposed approach in this paper focuses on the privacy protection of device data. Each device can calculate the power of discharging or charging by communicating with the partially connected nodes. Numerical simulations of the proposed scheme in the paper are given to verify the effectiveness of the scheme. Numerical simulations show that our scheme can make the electric energy stored in each storage device converge and maintain the maximum load capacity or maximum consumption capacity of the whole distributed storage device. [ABSTRACT FROM AUTHOR]

Confronted with the fast social development, the speed of garbage generation has increased. Therefore, from every aspect, especially staying green and healthy, it is urgent and vital to ensure accurate and stable garbage classification. In the field of garbage classification, household garbage has a large volume and the classification process still requires manual intervention. Besides, most of the current studies focus on industrial waste and medical waste, while the existing studies on household garbage are not effective enough. Furthermore, these studies are mostly trained and validated in self-built datasets, making the experimental results insufficiently representative and severely limiting the development of research. Aiming to facilitate the study of household garbage classification, this paper proposes a simple yet effective network model (FConvNet) to improve classification performance. In this paper, an effective convolution block with fused convolution is designed to enhance the potential correlation of the features in the spatial dimension, and then a lightweight symmetric structure is constructed to infer more information about the feature representation. The experiments on public datasets have justified the effectiveness of FConvNet with accuracy rates of 95%, 95% and 97%, achieving better performance on public datasets compared to baselines. Additionally, an online system based on the WeChat mini program framework is designed to provide convenient household garbage classification services and enable the collection of uploaded images to perform model optimizations. [ABSTRACT FROM AUTHOR]

The computational intelligence-based digital forecasting for social systems has been a novel tendency. This work takes forecasting of regional economy as the problem scenario, and introduces radial basis function neural network (RBFNN) to deal with this concern. Hence, an RBFNN-based fast forecasting model for regional economy is constructed in this paper. First, the economic flow data are encoded into sequential format, and RBFNN is employed to establish a sequential forecasting model that fits flow data. In addition, a fuzzy clustering method is further utilized to complete missing data in flow sequence, in order to improve the data quality for model calculation. In simulation procedure, the proposal is implemented on real economic operation data for assessment, and mean absolute error (MAE) is introduced to measure the forecasting performance. Several typical regression-based forecasting methods are introduced as the baseline. The experimental results show that MAE of the proposal is about 20%–30% lower than that of baseline methods, showing a better forecasting performance. [ABSTRACT FROM AUTHOR]

The price of carbon emission rights in the market fluctuates greatly due to various factors from economy, finance, and climate. For an enterprise that needs to conduct carbon trading activities, it is extremely necessary to fully grasp the price of carbon trading in the future period. Thus in this paper, a deep neural network-based assistive decision model for financial risk prediction in carbon trading market is proposed for this purpose. Specifically, the dynamic risk spillover effects of domestic and international carbon trading markets are studied, and a frontier time-varying model is utilized to measure the risk spillover effects. Then, the deep neural network is used for quantitative research and to construct an intelligent decision scheme that outputs financial risk prediction results. Four perspectives, energy price, climate environment, carbon market price, and macroeconomics, are selected as the input to analyze the influence factors of carbon emission rights price. Finally, several linear regression models are adopted as the baseline methods for comparison, and experimental results show that the proposed method can achieve better prediction performance compared with baseline methods. [ABSTRACT FROM AUTHOR]

Several hardware Trojan (HT) detection techniques are available today to ensure the security of hardware systems. However, the existing pre-silicon HT detection techniques have problems such as difficulties in capturing HT path features and poor applicability. To address these challenges, this paper proposes a gate-level HT detection scheme based on a deep learning model. We parse the circuit gate-level netlist and develop an algorithm to extract circuit path sentences based on the signal propagation rule. Path sentences consisting of gate names are extracted as experimental datasets. We apply the theory of natural language processing (NLP) to the task of HT detection and use three neural networks to filter the length of path sentences. Then, based on the deep learning model text convolutional neural network (TextCNN), we propose PS-TextCNN for HT detection. Our approach is verified on seven benchmark circuits of the RS232-series and eight benchmark circuits of the s-series. We achieve an average true positive rate (TPR) of 88.9%. The TPR of the RS232-series reaches a high score of 99.5%. The TPR of the s-series is 79.5%, which is significantly higher than that of the existing gate-level HT detection techniques. [ABSTRACT FROM AUTHOR]

Since the leakage currents are dramatically increasing while the MOS transistors scale down to deep-submicron processes, the I – V characteristic of the channel is varied unintentionally. As a result, the analog integrated circuit (IC) designs based on it (at above 100-nm technologies) will malfunction. In this paper, a novel low-leakage double-body MOSFET (LLDB-M), as a circuit-level scheme in answering to the need for the current-mode analog IC design in deep-submicron processes, is proposed. In this technique, the sub-threshold and gate-oxide tunneling leakage currents (as two major parts) are reduced via lowering the gate-source voltage and increasing the threshold voltage of the MOS transistor. An LLDB-M consists of two typical transistors — the first one is the main transistor and the latter implements the shift-voltage to reduce the gate-source voltage and floor of the channel leakage currents. The drain, gate, and body of the main transistor as well as the body and source of the second one, organize the terminals of an LLDB-M. The proposed LLDB-M transistors are replaced with large-leakage transistors in a translinear loop in the weak inversion region and then the commonly-used circuits such-as the current mirror, one-quadrant multiplier-divider (at both up-down and stack topologies), the true RMS-DC converter, and the log-domain low-pass-filter are designed. The effectiveness and performance of the proposed LLDB-M technique and circuits are evaluated using HSPICE software in 22-nm BSIM4 CMOS process and Cadence Virtuoso tool in 65-nm TSMC CMOS technology. Post-layout simulation results show that the proposed circuit-level method by considerably reducing leakage currents could ensure the effective function of the current-mode analog IC designs in deep-submicron technologies. Also, comprehensive simulations have been performed to prove the robustness and reliability of the proposed circuits against process, voltage and temperature (PVT) changes. [ABSTRACT FROM AUTHOR]

This paper presents a novel jitter reduction technique for a voltage-controlled ring oscillator (VCRO). This technique is helpful in employing VCRO-based circuits like Analog to Digital Converter (ADC), Phase Locked Loop (PLL) and time-based circuits whose performance is severely degraded by high jitter. In the proposed technique, the jitter is extracted using Excess Phase Discrimination (EPD) block and is transformed into Pulse Width Modulated (PWM) signal, which is then fed into the Phase Compensator (PC) block. This block reduces the jitter by about half its value after enabling the Single Ended Injection Locking (SEIL). The proposed work is designed in SCL 180 nm CMOS technology at 1.8 V supply. On enabling the proposed circuit, the Root Mean Square (RMS) jitter reduces from 5.94 to 2.58 ps (about 56%) for VCRO running at 58 MHz and consumes a total power of 375.84 μ W. The overall performance of the proposed circuit does not vary much in the post-layout simulations, hence demonstrating the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) techniques leverage the justice system in terms of effectiveness and efficiency. AI-empowered multi-label crime classification can facilitate the precise and expedient categorization of various legal documents. Multi-label classification within the justice system has an indispensable role in achieving accurate legal categorization that invigorates case analysis, optimizes resource distribution and refines the contours of legal processes. To support this critical function, this paper proposes a temporal convolutional network (TCN)-bidirectional encoder representations from transformers (BERT)-coupled model for multi-label crime classification. The proposed method fuses the temporal formation and semantic information in the model to obtain a high-quality result. The experimental results show that the proposed method achieved the best accuracy in comparison to existing methods on a public dataset. [ABSTRACT FROM AUTHOR]

Currently, elevator group control systems containing multiple elevators are used in high-rise buildings, where elevator groups are centrally dispatched according to a set dispatching plan to provide vertical transportation services for the passengers. However, if these elevators work independently, once passengers send out a call signal, multiple elevators will respond to the same request at the same time, and the elevator group will repeatedly go back and forth, which seriously affects the system operation efficiency and also increases energy consumption, so it is a key issue to solve the dispatching problem in the elevator group control system. Elevator group control scheduling is an NP-hard problem with explosive combination characteristics. In this paper, a multi-objective model is formulated based on the criteria of average passenger travel time, average waiting time and system energy consumption for the scheduling problem. Then the particle swarm optimization algorithm is proposed to solve the scheduling problem. Finally, the performance of the proposed algorithm is compared with the genetic algorithm-based elevator group control scheduling, the simulation results show that the proposed algorithm with fast convergence while the waiting time of passengers is significantly decreased. [ABSTRACT FROM AUTHOR]

In this paper, a novel image reconstruction method for printmaking is proposed via the combination of nonlinear diffusion filtering and convolution neural network. Nonlinear diffusion filtering based on a partial differential equation abstracts the input image to extract color features and texture features of images by nonlinear structured tensors. The image color features and texture features are combined to obtain a feature vector, and the feature vector is fed into the model as the input data. Then, the pre-trained deep learning model VGG-19 is utilized as the backbone network for further feature representation, and the extracted feature maps of each layer of the VGG19 model can be visualized for image reconstruction. The quality of the reconstructed images of prints is improved with the powerful feature extraction and combination ability of neural networks, which has the advantages of fast reconstruction speed and high quality of reconstructed images. Finally, the simulation experiments can demonstrate that the proposed reconstruction approach can achieve relatively ideal reconstruction performance in scenes of printmaking. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries degradation can be classified into capacity reduction and increase of the internal resistance. Accurate estimation of the degradation is important to determine the economic value and the remaining useful life of the battery. This paper proposes a method which analyzes the transient voltage response at the early stage of discharge, starting from the fully charged state. An equivalent circuit composed by a series resistance and a Warburg element is adopted to fit the transient voltage. The Warburg element is modeled as a transmission line composed by a ladder of resistances and capacitances. The method is validated with data obtained before and after aging cycles performed at 1.4 C charge and 2.0 C discharge rates, at 25 °C, 10 °C and −5 °C. The loss of cyclable lithium is deemed to be the main reason behind the battery degradation at all the tested temperatures. The dependency of the Warburg parameter to the remaining cell capacity is the same regardless of the aging temperature, suggesting that estimation of the Warburg parameter provides information about the state of health of the cell. On the other hand, the relationship between the capacity reduction and the increase of resistance has a temperature-dependent slope with low increase at lower temperatures where lithium plating is likely to occur. Accordingly, a relatively low resistance increase is deemed to be the sign of lithium plating. [Display omitted] • Analysis of degradation based on the voltage response at the early stage of discharge. • The equivalent circuit includes a resistance (R s) and a Warburg element (W). • The method is validated on cells aged with fast charging at 25 °C, 10 °C, −5 °C. • W provides a measure of the capacity and R s is an indicator of possible lithium plating. [ABSTRACT FROM AUTHOR]

Oxygen electrodes play a vital role in realizing high-performance solid oxide cells (SOCs) with high roundtrip efficiency, large-scale capacity, and long-term stability energy storage. This paper constructs Ti-doped strontium cobaltite perovskites (SrCo 1-x Ti x O 3-δ , SCTx, x = 0.2, 0.3, 0.4, 0.5) as newly stable oxygen electrodes for SOCs. The changes of oxygen vacancy in SCTx with Ti-doped concentration are investigated by X-ray photoelectron spectroscopy (XPS). The results show that the content of oxygen vacancy decreases with the enrichment of Ti doping. The area-specific resistance (ASR) of the symmetrical cell with SrCo 0.8 Ti 0.2 O 3-δ (SCT2) electrode is only 0.013 Ω cm2 at 850 °C in air. The single cell with SCT2 oxygen electrode achieves the peak power density of 1.313 W cm−2 at 850 °C under humidified H 2 conditions in fuel cells mode, and the current density reaches 2.95 A cm−2 under an electrolytic voltage of 1.9 V at 850 °C for CO 2 electrolysis. The Density functional theory (DFT) calculations confirm that the oxygen vacancy formation energy is the lowest when the ratio of Ti to Co is 2:8, verifying the superior property of SCT2. This study comes up with a creation for developing high-stability and high-activity oxygen electrode materials that can be used for energy storage systems. • Constructed SrCo 1-x Ti x O 3-δ as newly stable oxygen electrodes for SOCs. • The peak power density achieved 1.313 W cm−2 at 850 °C with SCT2 electrode. • The cyclic reversible stability of the single cell reached over 100h. • The SCT2 performed advanced activity for the low oxygen vacancy formation energy. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing demand for electrical vehicles, which are more energy efficient and environmentally friendly than their traditional counterpart. One of the crucial aspects of electrical systems is energy storage. A promising option for energy storage is Lithium-ion batteries (LIBs) due to their high energy density, longer life cycles, and faster charging when compared to other batteries. The disadvantage of employing LIBs is that they are more difficult to maintain because they are more prone to failure, and they are greatly dependent on the system's thermal behavior, which has led to an emphasis in research on battery thermal management systems (BTMSs). The BTMS aims to address this issue by limiting the temperature fluctuation of the battery cells to maintain the average temperature within recommended limits throughout the charging and discharging processes. There has been a continuous interest in nanofluids and phase change materials (PCMs) because of their unique properties and their potential thermal application as a cooling medium for high-density batteries. Recent studies have aimed to use different active and passive methods with varying materials and cooling system geometries to achieve the highest possible thermal performance. The current review aims to outline the recent studies on thermal management systems for LIBs. Three main sections have been presented in this paper. The first section reviewed studies on nanofluids in different BTMSs, the other one highlighted techniques adopted to overcome the low thermal conductivity drawback in PCMs for BTMs, and the last one focuses on the effects of combining both cooling approaches. We believe that the present review paper provides useful information that could be a significant step toward developing high-performance BTMS. • A summary of the most recent research on BTMS of LIBs. • Nanofluids and PCM-based BTMS attract significant interest in LIBs. • PCM with carbon additives enhance LIB pack temperature uniformity. • Incorporation of fins has demonstrated effectiveness in improving BTMS performance. • Metal foams with PCMs enhance BTMS performance via improved thermal conductivity. [ABSTRACT FROM AUTHOR]

Due to the complex principle of transverse thermoelectric conversion and the specific preparation technique, there are few studies on the three-dimensional structure of artificially tilted multilayer thermoelectric devices (ATMTDs), which also lags the development of ATMTDs for applications. It is necessary to optimize the three-dimensional structure of ATMTDs to satisfy the requirements of practical applications in thermal sensors, power generation, refrigeration, and so on. In this paper, Co/Bi 0.5 Sb 1.5 Te 3 ATMTDs are selected as the research object. A COMSOL Multiphysics simulation analysis and experimental validation are used to establish the relationship between the structure and its thermal sensing, power generation, and cooling performance. Results indicate that increasing the length significantly improves the thermal response voltage. Increasing the length and width enhances output power while increasing the height of the ATMTD improves the thermoelectric conversion efficiency. The effective cooling temperature improves when the heat transfer area decreases or the height increases. Based on these findings, flexible thin-film thermal sensors, high-efficiency waste heat power generation tubular devices, and high-efficiency refrigeration pyramidal devices are designed. The results will lead to effective optimization and promote the multi-field application of ATMTDs. • The relationship between the structure and performance of ATMTD was established. • Increasing the device's length significantly improved the thermal response voltage. • The output power of the ATMTD was proportional to its length and width. • The effective cooling temperature was improved when the cold end area was reduced. • Based on the research results, three new ATMTDs were designed. [ABSTRACT FROM AUTHOR]

Manganese oxide is a promising material due to its low cost, abundant reserves, and environmental friendliness. However, low cycling performance has been restricting the manganese oxide cathode materials on the road to commercialization. In this paper, Mn 2 O 3 microspheres with a porous structure are prepared by hydrothermal process and then compounded with low dimensional and small-scale carbon dots (CDs), and the obtained Mn 2 O 3 /CDs materials are used to assemble aqueous zinc ion batteries. X-ray Diffraction (XRD) and Scanning Electron Microscope (SEM) are used to characterize its structure and morphology, which prove that the composite with CDs do not change the structure of the original material. The morphology of microspheres can increase the surface area of electrolyte-electrode interface, which is beneficial to ion embedding into cathode materials. Compared with pure Mn 2 O 3 , the composite with CDs content of 3% (MC3) has the best electrochemical performance (the discharge specific capacity is 252.7 mAh·g−1 at 0.1 A g−1). Moreover, the reversible capacity of the Mn 2 O 3 /CDs composites is improved due to the inhibition of the dissolution of Mn by the highly conductive CDs. The specific discharge capacity of the Mn 2 O 3 /CDs composites after 300 cycles is 102.8 mAh·g−1, which is 2.3 times higher than that of the pure Mn 2 O 3. • Mn 2 O 3 /CDs porous microspheres prepared by hydrothermal method improves cycling performance. • CDs improves the specific capacity and cycle life of Mn 2 O 3 cathode. • The reaction kinetics of high rate performance of Mn 2 O 3 /CDs is investigated. [ABSTRACT FROM AUTHOR]

The thermal management system (TMS) in electric vehicles (EVs) is a comprehensive system that integrates an air conditioning system for the cabin, a temperature control system for the battery, and a cooling system for the motor. The currently used PI control strategy can only meet the basic TMS functions and cause high energy consumption. In this paper, we present a novel model predictive control (MPC) strategy for the TMS to optimize operational performance in real time. Different from the independent PI control for the individual components, MPC can predict future operation conditions and provide the optimal operating inputs in advance. A complete control-oriented model for MPC is developed, and the MPC strategy is designed to minimize the total power consumption of the TMS under the control-oriented model and constraints. The evaluation is carried out under several cases including the fixed ambient temperature, realistic ambient temperature, and different vehicle speeds. The results showed that the novel MPC strategy saved energy consumption by 5.9%–10.3% in these cases when compared to the PI strategy, demonstrating the effectiveness and feasibility of the proposed MPC control. • Proposed a model predictive control strategy for thermal management systems in EVs. • Developed a control-oriented model based on data-driven methods and physical principles. • Minimizes total power consumption while maintaining cabin and battery temperatures. • Results demonstrate the effectiveness of the MPC strategy under various conditions. [ABSTRACT FROM AUTHOR]

Increasing the surface area of active materials is a crucial approach to enhance the capacity of supercapacitors. In this study, we present an interdigitated supercapacitor featuring a double-layer active material prepared using a sacrificial layer method. The replacement of the insulating substrate with a gel electrolyte facilitates ion motion between the active electrodes. Finite element analysis (FEA) and electrochemical experiments confirm that the utilization of double-layer active material can significantly improve the capacity (1.5–1.85 times higher than traditional structures). The electrochemical performance of the three active materials has been significantly improved after using the double-layer active material structure, which proves that the method in this paper has strong material compatibility. Furthermore, an interdigital supercapacitor (ISC) is fabricated using electrodes with double-layer active material through the sacrificial layer method and its electrochemical performance is evaluated. At a scan rate of 10 mV·s-1, ISC exhibits an impressive area-specific capacitance as high as 68.4 mF·cm-2. Remarkably, ISC can maintain stable power output even under extreme deformation and fatigue testing conditions. This strategy involving interdigitated supercapacitors with double-layer active materials prepared via the sacrificial layer method presents a promising solution for flexible energy storage devices. • Use sacrificial layer method to remove the substrate. • A double-layer active material structure with increased capacity is proposed. • Replace the insulating substrate with a gel electrolyte. [ABSTRACT FROM AUTHOR]

In the backdrop of the carbon neutrality, lithium-ion batteries are being extensively employed in electric vehicles (EVs) and energy storage stations (ESSs). Extremely harsh conditions, such as vehicle to grid (V2G), peak-valley regulation and frequency regulation, seriously accelerate the life degradation. Consequently, developing long-life batteries for typical scenarios has emerged as a crucial focus for future endeavors. This paper concentrates on the fundamental scientific inquiries and comprehensively reviews aspects including the urgent requirements, degradation mechanisms, design methods, durability modeling and management approaches for long-life batteries. Firstly, it underscores the significance and urgency of developing long-life batteries under the typical EV and ESS scenarios. Subsequently, we delve into the long-life battery design methods concerning degradation mechanism suppression. Following this, the degradation modeling and advanced management strategies for achieving long-life batteries are elucidated. Lastly, facing the existing challenges and future opportunities, we provide new insights and perspectives to promote the development and practical application of long-life lithium-ion batteries. • A comprehensive review of the key issues towards long-life battery is presented. • The pressing long-life demands of EV and ESS typical application is highlighted. • A critical review of degradation mechanism and modeling is emphasized. • Challenges and solutions of long-life battery design and management are discussed. • Prospects of long-life battery are illustrated. [ABSTRACT FROM AUTHOR]

We propose a model of two identical autonomous quantum refrigerators that interact through their qubits coupled to cold thermal baths. We consider the same specific disposition for each refrigerator related to a three-level system coupled to different thermal baths and, in turn, to a qubit as the object to be cooled. By coupling the two systems, we show that under certain conditions on the model parameters, the devices can operate in the regime of an independent autonomous quantum refrigerator or be subject to an adjustment of the thermodynamic performances. We further investigate the correlations induced by the interaction between the two refrigerators, and illustrate their behavior with respect to their thermodynamic performances. We have found that adjusting the different interaction constants of the model can lead to an evolution of the quantum correlations of the two machines that is inversely related to the behavior of the thermodynamic quantities of both refrigerators. However, this is not necessarily the case when the temperatures of the model are considered as the reference parameters. [ABSTRACT FROM AUTHOR]

Theoretical investigations of electron-impact ionisation for carbon dioxide (CO2) in low to intermediate energy ranges are presented. In addition to the energy range, we have estimated triple differential cross-section (TDCS) in coplanar and perpendicular plane geometry. We have calculated the TDCS for electron impact using the modified distorted wave formalism up to the second order. Post-collision interaction effects have also been introduced to describe the collision dynamics accurately. There is a good correlation between the distorted-wave Born approximation results and the experimental results in terms of forward and backward scattering regions. [ABSTRACT FROM AUTHOR]

The paper investigates the validity and reliability of the blind-bolt repair method for repairing delaminated composite aircraft panels. The delaminated specimens are prepared by inserting Teflon films during the manufacturing process to simulate interlayer damage. Subsequently, these specimens are repaired using the blind-bolt method. Modal and uniaxial compression tests are conducted to quantitatively evaluate the natural frequency, mode shape and load-bearing strength of both delaminated and bolt-repaired specimens. Digital image correlation and ultrasonic phased array techniques are employed to characterize buckling instability and damage evolution of specimens. The results reveal that the natural frequency and compressive buckling strength of delaminated specimens significantly decrease. The mode shape also changes nonlinearly with the stiffness reduction. This variation is proportional to the size and the quantity of delaminations. The blind-bolt repair method effectively restores the vibration and mechanical properties of the delaminated composite structure by reconnecting separated sub-laminates. A repair tolerance of 20 mm–60 mm is recommended for a single blind-bolt. When the delamination length is 35 mm, the repair efficiency for the critical buckling load and the ultimate load is the highest, at 58.3% and 64.4%, respectively. [Display omitted] [ABSTRACT FROM AUTHOR]

A novel FFT-based computational framework for coupling non-local elastic-plastic damage model is proposed in this paper, which is employed to accurately simulate the transverse tensile behaviors of unidirectional (UD) CF/PEEK composite materials under different temperatures. To address the distinct material properties exhibited by different microscopic constituents within the composite, two approaches are employed: (i) an integral-type regularized traction-separation damage model is applied to the interphase debonding, and (ii) an implicit gradient regularization technique for Lemaitre-type damage (matrix cracking) of the PEEK. By coupling the non-local damage model capable of accurately characterizing different failure modes within composites with an FFT computational framework, accurate predictions of the transverse tensile performance of UD-CF/PEEK composites at different temperature conditions can be achieved. Furthermore, the influence of non-local feature parameters and interphase mechanical properties on the composite's mechanical behavior is thoroughly discussed. Comparisons with experimental results affirm the accurate prediction of the transverse tensile behavior of UD-CF/PEEK composites through the proposed computational framework. Ultimately, based on this framework, the transverse tensile modulus and strength of UD-CF/PEEK composites, considering different fiber volume fractions and temperatures, are predicted, successfully demonstrating the effectiveness and applicability of the proposed approach in forecasting the mechanical behavior of composites. [Display omitted] • A novel computational framework combining two non-local damage models with FFT method has been developed to accurately predict the complex failure mechanisms within composite materials. • The proposed computational framework, as presented in this study, demonstrated remarkable accuracy in predicting the transverse tensile properties of UD-CF/PEEK composites. • This research successfully extended the presented computational framework to high temperature environments, predicting changes in failure mechanisms under high-temperature conditions. [ABSTRACT FROM AUTHOR]

Basalt fiber responds well to the global concept of environmental protection due to its attractive superiorities of green and excellent mechanical performance. However, the ultra-low interfacial adhesion strength of fiber-to-matrix deeply restricts its wide application. Moreover, attempts to utilize natural- or bio-materials on basalt fiber towards growth in interfacial adhesion remains in its infancy, if feasible, it would make green materials shine brilliantly. Herein, we install citric acid modified β-cyclodextrin (β-CD) and polyethyleneimine (PEI) on basalt fiber via dipping approach for the first time, which results in 76.9% increment in fiber-to-epoxy adhesion over control fiber. Unique inner hydrophobic and outer hydrophilic conical cavity architecture of β-CD endows interphase with dense ring hydrogen bond band "core"- covalent/ionic bonding "shell" multiple interactions that balance stress transfer and energy dissipation. Additionally, "rigid" β-CD and "flexible" PEI function as crack deflection and stress distribution for gaining stronger interphase of basalt fiber composites. This paper serves to provide a new exploitation for advancing green and high-performance composites. [Display omitted] [ABSTRACT FROM AUTHOR]

• IMF deposition is crucial for beef quality and economic value. • Epigenetic mechanisms transform beef-cattle IMF research globally. • Our paper synthesizes epigenetic-IMF associations in cattle. • We explore environmental factors impacting IMF. • This paper emphasizes DNA methylation, histone modifications, and non-coding RNAs in IMF regulation. Intramuscular fat (IMF) deposition profoundly influences meat quality and economic value in beef cattle production. Meanwhile, contemporary developments in epigenetics have opened new outlooks for understanding the molecular basics of IMF regulation, and it has become a key area of research for world scholars. Therefore, the aim of this paper was to provide insight and synthesis into the intricate relationship between epigenetic mechanisms and IMF deposition in beef cattle. The methodology involves a thorough analysis of existing literature, including pertinent books, academic journals, and online resources, to provide a comprehensive overview of the role of epigenetic studies in IMF deposition in beef cattle. This review summarizes the contemporary studies in epigenetic mechanisms in IMF regulation, high-resolution epigenomic mapping, single-cell epigenomics, multi-omics integration, epigenome editing approaches, longitudinal studies in cattle growth, environmental epigenetics, machine learning in epigenetics, ethical and regulatory considerations, and translation to industry practices from perspectives of IMF deposition in beef cattle. Moreover, this paper highlights DNA methylation, histone modifications, acetylation, phosphorylation, ubiquitylation, non-coding RNAs, DNA hydroxymethylation, epigenetic readers, writers, and erasers, chromatin immunoprecipitation followed by sequencing, whole genome bisulfite sequencing, epigenome-wide association studies, and their profound impact on the expression of crucial genes governing adipogenesis and lipid metabolism. Nutrition and stress also have significant influences on epigenetic modifications and IMF deposition. The key findings underscore the pivotal role of epigenetic studies in understanding and enhancing IMF deposition in beef cattle, with implications for precision livestock farming and ethical livestock management. In conclusion, this review highlights the crucial significance of epigenetic pathways and environmental factors in affecting IMF deposition in beef cattle, providing insightful information for improving the economics and meat quality of cattle production. [ABSTRACT FROM AUTHOR]