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Nanobody (Nb) is derived from the variable domain of heavy-chain antibody (HCAb), naturally displaying notable properties like nano-scale size, exceptional stability, high specificity, low immunogenicity, and cryptic epitope accessibility. These features contribute to its great therapeutic potential as a valuable research tool across diverse diseases, especially autoimmune diseases (AIDs). Caplacizumab (Cablivi®) is the first nanobody drug approved for treating acquired thrombotic thrombocytopenic purpura (aTTP). This review summarizes the biomolecular structure, usage of Nb as a foundation of recombinant constructs, and biochemical properties of nanobodies. As attractive therapeutic candidates, many clinical trials of Nbs have been conducted, elucidating potential therapeutic strategies for AIDs. Therefore, the preclinical development and application of Nbs in AIDs are emphasized throughout this review.

Abstract In the process of natural gas extraction, the phenomenon of liquid loading will affect the efficiency of gas well extraction and reduce the life of the well. Compared with conventional drainage gas extraction technology, the jet pump can not only reduce the bottom back pressure and ensure the stable production of gas reservoirs but also promote the final recovery rate. Since the jet pump relies on the interaction between fluid particles to transfer energy, the energy loss is large and the efficiency is low. To maximize the advantages of the gas‐driven jet pump, this study innovatively combines a static mixer with an annular jet pump. Utilizing the cyclonic effect produced by the static mixer, the original gas‐liquid axial motion is transformed into a stronger vortex motion, and the liquid droplets are changed into a liquid film that is easier to carry, which significantly improves the discharge efficiency of the jet pump. This study uses a combination of numerical simulation and experimental analysis to compare the associated effects of the new annular jet pump (NAJP) and the conventional annular jet pump (CAJP) on the liquid‐carrying performance of gas wells in terms of cyclonic effect, droplet breakage ratio, and pump efficiency. The results show that, compared with CAJP, NAJP increases the mass flow rate of the sucked fluid. The droplet breakage ratio increases by 15.4% year‐on‐year, while the critical liquid‐carrying flow rate is reduced by about 10.7%, resulting in a maximum pumping efficiency of 37%, an increase of about 30.7% year‐on‐year. At the same time, the reduction of the energy coefficient means lower energy consumption. In summary, NAJP is better than CAJP in terms of liquid‐carrying effect and efficiency.

Public transit has been continuously developing because of advocacy for low-carbon living, and concerns about its safety have gained prominence. The various factors that constitute the bus operating environment are extremely complex. Although existing research on operational security is crucial, previous studies often fail to fully represent this complexity. In this study, a novel method was proposed to identify the risk factors for bus operations based on a Bayesian network. Our research was based on monitoring data from the public transit system. First, the Tabu Search algorithm was applied to identify the optimal structure of the Bayesian network with the Bayesian Information Criterion. Second, the network parameters were calculated using bus monitoring data based on Bayesian Parameter Estimation. Finally, reasoning was conducted through prediction and diagnosis in the network. Additionally, the most probable explanation of bus operation spatial risk was identified. The results indicated that factors such as speed, traffic volume, isolation measures, intersections, bus stops, and lanes had a significant effect on the spatial risk of bus operation. In conclusion, the study findings can help avert dangers and support decision-making for the operation and management of public transit in metropolitan areas to enhance daily public transit safety.

Abstract Different microorganisms can produce different proteases, which can adapt to different industrial requirements such as pH, temperature, and pressure. Salt-tolerant proteases (STPs) from microorganisms exhibit higher salt tolerance, wider adaptability, and more efficient catalytic ability under extreme conditions compared to conventional proteases. These unique enzymes hold great promise for applications in various industries including food, medicine, environmental protection, agriculture, detergents, dyes, and others. Scientific studies on microbial-derived STPs have been widely reported, but there has been little systematic review of microbial-derived STPs and their application in high-salt conventional soybean fermentable foods. This review presents the STP-producing microbial species and their selection methods, and summarizes and analyzes the salt tolerance mechanisms of the microorganisms. It also outlines various techniques for the isolation and purification of STPs from microorganisms and discusses the salt tolerance mechanisms of STPs. Furthermore, this review demonstrates the contribution of modern biotechnology in the screening of novel microbial-derived STPs and their improvement in salt tolerance. It highlights the potential applications and commercial value of salt-tolerant microorganisms and STPs in high-salt traditional soy fermented foods. The review ends with concluding remarks on the challenges and future directions for microbial-derived STPs. This review provides valuable insights into the separation, purification, performance enhancement, and application of microbial-derived STPs in traditional fermented foods. Graphical Abstract

This study focuses on the calcined coal gangue (CCG)-blended cements containing Stöber nano-SiO2 (SNS) particles. The effects of SNS particles on the workability, hydration behaviour, mechanical properties and microstructure evolution of the blended cements were comprehensively investigated at curing ages ranging from 1 to 28 d. The hydration behaviour was studied via isothermal calorimetry test, X-ray diffraction (XRD) and thermogravimetric (TG) tests. The microstructural evolution was studied using mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM). The results show that the incorporation of SNS led to a significant reduction in fluidity, particularly at an SNS content of 3%. The SNS significantly increased the compressive strength of the CCG-blended cement at all curing ages, and the optimum SNS content was found to be 2%. SNS significantly accelerated not only the early cement hydration but also the pozzolanic reaction of CCG at later curing ages, resulting in a decrease in portlandite, as evidenced by the isothermal calorimetry, XRD and TG analysis. Microstructural analysis shows that the incorporation of SNS effectively refined the pore structure of the CCG-blended cement, resulting in the formation of a dense microstructure. All these beneficial effects of SNS provides advantages in the development of the compressive strength of the CCG-blended cement at all curing ages.

To investigate the influence of Zn-alloying on the microstructure and tensile mechanical properties of Mg–6Bi alloy after hot extrusion, a new ternary Mg–6Bi–3Zn alloy was prepared by extrusion at 300 °C. The microstructures, texture, dynamic precipitates and tensile mechanical behaviors of the extruded alloy were characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD), electron backscattered diffraction (EBSD) and a material testing machine at room temperature. After extrusion, the Mg–6Bi–3Zn alloy possesses a bimodal microstructure with elongated large unrecrystallized (unDRXed) grains and fine dynamic recrystallized (DRXed) grains. In addition, non-basal 2_1>//ED, 8_3>//ED and 2_1>//ED textures are observed within DRXed grains due to the Zn addition, leading to texture weakening in the extruded Mg–6Bi–3Zn alloy. Zn addition facilitates the dynamic precipitation behavior, leading to a 12.2% area fraction of Mg3Bi2 precipitates with an average size of 39.2 nm. Furthermore, incorporation of Zn atoms in Mg3Bi2 phases and segregation of Zn at the grain boundary are found. The extruded Mg–6Bi–3Zn alloy exhibits a tensile strength of 336 ± 7.1 MPa and a yield strength of 290 ± 5.5 MPa, as well as an elongation of 11.5%. Therefore, Zn addition is beneficial to enhance strength and keep good ductility for the extruded Mg–6Bi–3Zn alloy.

The fusion of global contextual information with local cropped block details is crucial for segmenting ultra-high resolution images. In this study, A novel fusion mechanism termed global–local deep fusion (GL-Deep Fusion) is introduced, based on an enhanced transformer architecture that efficiently integrates global contextual information and local details. Specifically, we propose the global–local synthesis networks (GLSNet), a dual-branch network where one branch processes the entire original image, while the other branch handles cropped local patches as input. The feature fusion of different branches in GLSNet is achieved through GL-Deep Fusion, significantly enhancing the accuracy of ultra-high resolution image segmentation. Identifying tiny overlapping items is a task where the model excels, demonstrating its particular effectiveness. To optimize GPU memory utilization, a dual-branch architecture was meticulously designed. This architecture proficiently leverages the features it extracts and seamlessly integrates them into the enhanced transformer framework of GL-Deep Fusion. Benchmarks on the DeepGlobe and Vaihingen datasets demonstrate the efficiency and accuracy of the proposed model. It significantly reduces GPU memory usage by 24.1% on the DeepGlobe dataset, enhancing segmentation accuracy by 0.8% over the baseline model. On the Vaihingen dataset, our model delivers a Mean F1 score of 90.2% and achieves a mIoU of 90.9%, highlighting its exceptional memory efficiency and segmentation precision.

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.

Proton exchange membrane fuel cells have attracted widespread attention due to their cleanliness and high energy density, but the performance degradation during operation greatly limits their commercialization. Therefore, the reliable degradation prediction of fuel cell performance is of great significance. The recovery phenomenon of the reversible voltage loss that occurs during the operation of fuel cells has posed great difficulties for model training and prediction. Moreover, the models may easily and erroneously learn the combined trends in the recovery of reversible voltage loss and performance degradation. To address this issue, this paper employs the Transformer model to predict the performance degradation of fuel cells. By utilizing the unique self-attention structure and masking mechanism of the Transformer model, the signal for the recovery of the reversible voltage loss is adopted as the input for the model to avoid interference from information before voltage recovery on subsequent predictions. Experimental results show that the model has the highest prediction accuracy at various prediction starting points. Meanwhile, it can predict the accelerated performance degradation of fuel cells, which has positive implications for health management.

Abstract The study of the onset characteristics of the free piston Stirling generator (FPSG) is essential for its development. In this study, the onset characteristics of the FPSG were discussed based on a thermodynamic model using the Routh stability criterion. First, the onset conditions of three types of FPSG, alpha, beta, and gamma, were analyzed. Then an experimental and numerical study of a 100 W beta‐type FPSG prototype was conducted. The impacts on the onset temperatures of spring stiffness, charging pressure, and external load were further explored. The results demonstrate that no matter how the parameters are altered, alpha‐type FPSG cannot start up. Whether beta and gamma type FPSG can start up depends on the design and operating parameters. For the prototype, as the displacer spring stiffness increases, the onset temperature rises first and then drops. When the displacer spring stiffness is 19.2 N/mm, the corresponding onset temperature is the lowest of 489 K. The FPSG is easier to start up at a higher external load and higher charging pressure. The experimental results of the impacts of the spring stiffness, charging pressure, and external load on the onset temperature agree well with the calculated results, with errors ranging from 1.7% to 9.12%. The proposed approach can play a guiding significance in studying FPSG onset conditions.

Inter-turn short circuit (ITSC) is a frequent fault of interior permanent magnet synchronous motors (IPMSM). If ITSC faults are not promptly monitored, it may result in secondary faults or even cause extensive damage to the entire motor. To enhance the reliability of IPMSMs, this paper introduces a fault diagnosis method specifically designed for identifying ITSC faults in IPMSMs. The sparse coefficients of phase current and torque are solved by clustering shrinkage stage orthogonal matching tracking (CcStOMP) in the greedy tracking algorithm.The CcStOMP algorithm can extract multiple target atoms at one time, which greatly improves the iterative efficiency. The multiple features are utilized as input parameters for constructing the random forest classifier. The constructed random forest model is used to diagnose ITSC faults with the results showing that the random forest model has a diagnostic accuracy of 98.61% using all features, and the diagnostic accuracy of selecting three of the most important features is still as high as 97.91%. The random forest classification model has excellent robustness that maintains high classification accuracy despite the reduction of feature vectors, which is a great advantage compared to other classification algorithms. The combination of greedy tracing and the random forest is not only a fast diagnostic model but also a model with good generalisation and anti-interference capability. This non-invasive method is applicable to monitoring and detecting failures in industrial PMSMs.

Abstract Using enzymes to hydrolyze and recycle poly(ethylene terephthalate) (PET) is an attractive eco-friendly approach to manage the ever-increasing PET wastes, while one major challenge to realize the commercial application of enzyme-based PET degradation is to establish large-scale production methods to produce PET hydrolytic enzyme. To achieve this goal, we exploited the industrial strain Pichia pastoris to express a PET hydrolytic enzyme from Caldimonas taiwanensis termed CtPL-DM. In contrast to the protein expressed in Escherichia coli, CtPL-DM expressed in P. pastoris is inactive in PET degradation. Structural analysis indicates that a putative N-glycosylation site N181 could restrain the conformational change of a substrate-binding Trp and hamper the enzyme action. We thus constructed N181A to remove the N-glycosylation and found that the PET hydrolytic activity of this variant was restored. The performance of N181A was further enhanced via molecular engineering. These results are of valuable in terms of PET hydrolytic enzyme production in industrial strains in the future. Graphical Abstract

The utilization of calcined coal gangue (CCG) and limestone for the preparation of blended cement is an efficient approach to address the issue of coal gangue disposal. However, the compressive strength development of blended cement is slow, particularly at high substitution levels of CCG. Therefore, this study aimed to promote the hydration and mechanical properties of the calcined coal gangue–limestone blended cements by increasing the curing temperature. In this study, the samples were cured at two different temperatures, namely 20 and 40 °C. The four groups of samples contained 15 wt.%, 30 wt.%, 45 wt.% and 60 wt.% cement substitutions using CCG and limestone (2:1 mass ratio). The compressive strength, hydration and microstructure were investigated at the ages of 1 to 28 d. X-ray diffraction (XRD) and thermogravimetry (TG) were used to study the hydration behavior of samples. Mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM) were used to determine the microstructure of the samples. The results indicate that an increase in curing temperature significantly promotes the compressive strength of the calcined coal gangue–limestone blended cements from 1 to 28 d. The microstructural analysis indicates that increasing the curing temperature not only promotes cement hydration but also facilitates the reaction of CCG, which precipitated more hydrates such as C-A-S-H gel, Hc and Mc. These hydrates are conducive to refining the pore structures and densifying the microstructure, which sufficiently explains the enhanced compressive strength of the calcined coal gangue–limestone blended cements.

The extruded Mg–6Bi alloy and Mg–6Bi–1Ag alloy subjected to extrusion at 300 °C with the extrusion ratio of 25:1 and die-exit speed of 2 m/min were used to investigate microstructure characteristics and mechanical behavior. The experimental results demonstrate that the bimodal microstructure, composed of coarse dynamic unrecrystallized (unDRXed) grains and fine dynamic recrystallized (DRXed) grains, was obtained after extrusion. The Ag addition can obviously promote dynamic recrystallization and average grain size. It also indicates that the dynamic precipitation is significantly promoted by Ag addition during extrusion, obtaining more fraction of the Mg3Bi2 precipitates. Moreover, the extruded Mg–6Bi–1Ag alloy has a high tensile yield strength of 304 ± 2.0 MPa, which is increased by 19% compared to the extruded Mg–6Bi alloy, and elongation of 11.0 ± 1.7%, almost the same as 11.9 ± 0.9% of the extruded Mg–6Bi alloy. This result also shows that the extruded Mg–6Bi–1Ag alloy exhibits better strain hardening capacity. Therefore, Ag exhibits an effective role in promoting dynamic recrystallization and dynamic precipitation, resulting in the enhancement of strength and strain hardening capacity of the extruded Mg–6Bi–1Ag alloy, as well as keeping high ductility.

The labor cost of the current network operation and maintenance is high and the efficiency is low.How to quickly and accurately identify the type of network interference, and improve the work efficiency of the maintenance personnel has become an urgent problem to be solved.An intelligent interference identification method of improved deep residual network (ResNet) was studied.The interference data was collected and preprocessedby connecting with the communication interference data interface of the operator's northbound network management.The type of itwaslabelled and corrected by the current network experts to form an offline interference data set.Then the interference frequency domain information was used to generate the interference spectrum waveform image, and performed image processing and data processing for different interference types.After that, the traditional Res Net algorithm was improved according to the business characteristics to extract the features of single interference type.The features were weighted in the compound interference type to identify any type of interference.Finally, the interference data was identified online by importing the trained model, which effectively improved the accuracy and efficiency of interference identification.

There is an urgent need to develop novel and high-performance catalysts for chlorinated volatile organic compound oxidation as a co-benefit of NOx. In this work, HSiW/CeO2 was used for chlorobenzene (CB) oxidation as a co-benefit of NOx reduction and the inhibition mechanism of NH3 was explored. CB oxidation over HSiW/CeO2 primarily followed the Mars–van–Krevelen mechanism and the Eley-Rideal mechanism, and the CB oxidation rate was influenced by the concentrations of surface adsorbed CB, Ce4+ ions, lattice oxygen species, gaseous CB, and surface adsorbed oxygen species. NH3 not only strongly inhibited CB adsorption onto HSiW/CeO2, but also noticeably decreased the amount of lattice oxygen species; hence, NH3 had a detrimental effect on the Mars–van–Krevelen mechanism. Meanwhile, NH3 caused a decrease in the amount of oxygen species adsorbed on HSiW/CeO2, which hindered the Eley-Rideal mechanism of CB oxidation. Hence, NH3 significantly hindered CB oxidation over HSiW/CeO2. This suggests that the removal of NOx and CB over this catalyst operated more like a two-stage process rather than a synergistic one. Therefore, to achieve simultaneous NOx and CB removal, it would be more meaningful to focus on improving the performances of HSiW/CeO2 for NOx reduction and CB oxidation separately.

Along with the advancement of modern control technology, electric vehicles have gradually become the leading force in the development of the automobile industry. Interior permanent magnet synchronous motors (IPMSMs) have gradually become a mainstream electric vehicle drive component due to their high power density and excellent controllability. This type of motor can form a high-precision closed-loop control system with position sensors, which improves the overall performance. However, the operating conditions are complicated, and position sensor failure can easily occur, which reduces the stability of the motor system. For the fault-tolerant control problem of position sensor failure, pulse high-frequency voltage injection and an improved sliding mode observer (SMO) are employed to achieve the sensorless control of both the low-speed and medium-high-speed sections of the electric motor. Combining the advantages of high-frequency voltage injection in the low-speed section with a high accuracy of position estimation and the advantages of improved SMO in the high-speed section with low computational volume, the composite sensorless control strategy is proposed for IPMSM in the full-speed section. Secondly, an improved SMO using a segmented composite function as the control function is proposed to improve the robustness of the system. It is proposed to address the issue of the smooth transition between the low-speed section and the medium- and high-speed section by using linear weighting. Finally, in order to validate the accuracy of the sensorless control technology, a rapid control prototype of IPMSMs based on Speedgoat is constructed by using a semi-physical simulation system. The efficacy of the control algorithms in the low-speed section, high-speed section and transition interval is verified.

Nowadays, massive photovoltaic power stations are being integrated into grid networks. However, a large number of photovoltaic facilities are located in special areas, which presents difficulties in management. Unmanned Aerial Vehicle (UAV)-assisted data collection will be a prospective solution for photovoltaic systems. In this paper, based on Deep Reinforcement Learning (DRL), we propose a UAV-assisted scheme, which could be used in scenarios without awareness of sensor nodes’ (SNs) precise locations and has better universality. The optimized data collection work was formulated as a Markov Decision Process (MDP), and the approximate optimal policy was found by Deep Q-Learning (DQN). The simulation results show efficiency and convergence and demonstrate the effectiveness of the proposed scheme compared with other benchmarks.

Steel slag is the waste slag generated after steel smelting, which has cementitious activity. However, untreated steel slag can damage the integrity of steel slag concrete due to its harmful expansion. This study prepared porous aggregates by mixing powdered steel slag, fly ash, and cement and carbonated them with CO2 under high pressure conditions (0.2 MPa). The effect of carbonation on the performance of steel slag aggregate was studied using volume stability and crushing value. The effect of different carbonation conditions on the products was studied using X-ray diffraction (XRD) and thermogravimetric (TG) analyses, and the carbon sequestration efficiency of steel slag under different treatment methods was quantitatively evaluated. The research results indicate that untreated steel slag was almost completely destroyed and lost its strength after autoclave curing. With the increase in temperature and carbonation time, the performance of steel slag aggregate gradually improved and the pulverization rate, expansion rate, and crushing value gradually decreased. According to the experimental results of XRD and TG, it was found that the reaction between f-CaO (free CaO) and CO2 in steel slag generated CaCO3, filling the pores inside the aggregate, which was the internal reason for the improvement of aggregate performance. After comparison, the best carbonation method was maintained at 55 °C for 72 h. After carbonation, the steel slag aggregate had a pulverization rate of 2.4%, an expansion rate of 0.23%, a crushing value of 23%, and a carbon sequestration efficiency of 11.27% per unit weight of aggregate.

As a typical brittle material, the tensile strength of concrete is much lower than its compressive strength. The main failure mode of concrete buildings under explosive and impact loading is spalling, so it is crucial to understand the dynamic tensile performance of concrete. This paper presents an experimental study on the dynamic tensile strength of steel-fiber-reinforced self-compacting concrete (SFRSCC). Specimens of two different self-compacting concrete (SCC) mixes (C40 and C60) and four different fiber volume fractions (0.5%, 1.0%, 1.5%, and 2.0%) are fabricated. Dynamic tensile strengths of SFRSCC are obtained using a modified Hopkinson bar system. The relationships between the dynamic tensile strength of the corresponding SCC mix, the quasi-static compressive strength, and the fiber volume fraction are discussed. An empirical equation is proposed. It is shown that SFRSCC with high compressive strength has higher dynamic tensile strength than low-strength SFRSCC for the same fiber content, and the dynamic tensile strength of SFRSCC possesses an approximately linear relation with the fiber volume fraction. The mechanism underlying this fiber-reinforcement effect is investigated.

This study presents a non-explosive method for simulating blast loading on reinforced concrete (RC) slabs. The method involves using a newly developed blast simulator to apply a speedy impact load on the slab, which generates a pressure wave similar to that of an actual blast. Both experimental and numerical simulations were carried out to evaluate the effectiveness of the method. The experimental results showed that the non-explosive method can produce a pressure wave with a peak pressure and duration analogous to those of an actual blast. The numerical simulations also showed good agreement with the experimental results. Additionally, parameter studies were conducted to evaluate the effects of the rubber shape, the impact velocity, the bottom thickness, and the upper thickness on the impact loading. The results indicate that pyramidal rubber is more suitable as an impact cushion for simulating blast loading than planar rubber. The impact velocity has the widest range of regulation for peak pressure and impulse. As the velocity increases from 12.76 to 23.41 m/s, the corresponding range of values for peak pressure is 6.457 to 17.108 MPa, and for impulse, it is 8.573 to 14.151 MPa∙ms. The variation in the upper thickness of the pyramidal rubber has a more positive effect on the impact load than the bottom thickness. With the upper thickness increasing from 30 mm to 130 mm, the peak pressure decreased by 59.01%, and the impulse increased by 16.64%. Meanwhile, when the bottom part’s thickness increased from 30 mm to 130 mm, the peak pressure decreased by 44.59%, and the impulse increased by 11.01%. The proposed method provides a safe and cost-effective alternative to traditional explosive methods for simulating blast loading on RC slabs.

Triboelectric Nanogenerators (TENGs) have been regarded as an effective method to solve the energy problem since they were first demonstrated in 2012. Due to their high-power generation and low cost, TENGs have been widely applied in the fields of energy, security, biomedicine, the environment, and so on. For now, many researchers are focusing on contact electrification (CE) and surface modification in order to discover the fundamental CE mechanism and approaches to further enhance the performance of TENG devices. In this work, we employ gamma radiation (γ ray) to induce surface modifications on the dielectric materials (polymers in this work) and study its influence on CE. It is found that, due to the high energy of γ ray, some chemical bonds in polymers are destroyed and reformed. This changes the electron density of the polymer molecule, and thus varies the electron transfer ability of the dielectric materials. Afterward, polytetrafluoroethylene (PTFE) and polyvinyl chloride (PVC) become more negative, and polyethylene terephthalate (PET) become the opposite. Kapton, in particular, remains stable even after 1 MGy-dose radiation. This study reconfirms that electron transfer is the dominant process for polymers-related CE. It also suggests that triboelectric nanogenerators could be fabricated with materials possessing a high anti-radiation ability, and used for sensing or energy generation in space or other environments where radiation exists.

Este trabalho tem como intuito refletir sobre uma modalidade narrativa amplamente difundida na contemporaneidade, a autobiografia. Interessa-nos, neste sentido, sua vertente literária, as autobiografias que galgam o status de arte. Claro está que não se trata de um gênero estanque ou limitado às formas convencionais, sendo que um de nossos propósitos é verificar as soluções estéticas de renovação deste objeto. Como corpus de análise, elegemos o romance A Favor do Vento, de Duca Leindecker, músico com larga e bem sucedida carreira, principalmente como líder da banda Cidadão Quem, e escritor de três livros, com boa aceitação do público leitor. Julgamos que A Favor do Vento configura-se como uma autobiografia não convencional, pelo uso de artifícios que manejam constantemente a ação de cifrar e decifrar, dando ao leitor relevante tarefa na efetivação do texto literário. A avaliação das possibilidades estéticas utilizadas nas narrativas em que autor e personagem compartilham o mesmo contexto axiológico é, sem dúvida, uma reflexão pungente na contemporaneidade, e nosso propósito aqui é modestamente contribuir na construção deste panorama tão vário.

Huangjiu lees (HL) is a byproduct in Chinese Huangjiu production with various nutrient and biological functional components. Without efficient treatment, it could cause environmental issues and bioresource wasting. Existing dominant recovery approaches focus on large-scale disposal, but they ignore the application of high-value components. This study discusses the advantages and limitations of existing resourcing approaches, such as feed, food and biogas biological production, considering the efficiency and value of HL resourcing. The extraction of functional components as a suggestion for HL cascade utilization is pointed out. This study is expected to promote the application of HL resourcing.

The stress-distribution angle is an important parameter for the design of retaining walls and foundation beds and has a non-negligible role in the rationality of engineering design. There is a lack of research on stress distribution in reinforced earth-retaining walls under cyclic loading. In order to study the stress distribution characteristics of geogrid-reinforced soil-retaining walls (GRSW) under cyclic loading, the stress distribution characteristics of GRSW under a different number of load cycles were analyzed by field tests, and the effects of the length of reinforcement, the friction coefficient of the reinforcement–soil interface and the modulus of reinforcement on the stress distribution characteristics of GRSW were analyzed by numerical simulation. The results show that, with the increase in the number of load cycles, the vertical dynamic earth pressure shows a noticeable decreasing trend from high to low along the wall height. The vertical dynamic earth pressure increases first and then decreases along the length of reinforcement. When the number of load cycles increases to more than 100,000, the stress-distribution angle of the GRSW does not change much, the upper part remains at 35~79°, and the middle part remains at 47~68°. The influence depth of stress distribution in GRSW is about 1.13 times the wall height. The interfacial friction coefficient of reinforced soil has a superior influence on the stress distribution in GRSW, followed by the length and modulus of reinforcement.

This paper proposes a robust design approach based on the Design for Six Sigma (DFSS), to promote the robustness of our previous model-free-adaptive-control-based (MFAC-based) energy management strategy (EMS) for the plug-in hybrid electric vehicles (PHEVs) in real-time application. First, the multi-island genetic algorithm (MIGA) is employed for a deterministic design of the MFAC-based EMS, and the Monte Carlo simulation (MCS) is utilized to evaluate the sigma level of the strategy with the deterministic design results. Second, a DFSS framework is formulated to reinforce the robustness of the MFAC-based EMS, in which the velocity and the vehicle mass are considered external disturbances whilst the terminal state of charge (SOC) of the battery and the fuel consumption (FC) are conducted as responses. In addition, real-time SOC constraints are incorporated into Pontryagin’s minimum principle (PMP) to confine the fluctuation of battery SOC in MFAC-based EMS to make it closer to the solution of the dynamic programming (DP). Finally, the effectiveness of the robust design results is assessed by contrasting with other strategies for various combined driving cycles (including velocity, vehicle mass, and road slope). The comparisons demonstrate the remarkable promotion of the robust design in terms of the energy-saving potential and the performance against external disturbance. The average improvement of the FCs can reach up to a considerable 19.66% and 9.79% in contrast to the charge-depleting and charge-sustaining (CD-CS) strategy as well as the deterministic design of MFAC-based EMS. In particular, the energy-saving performance is comparable to DP, where there is only a gap of −1.68%.

To investigate the effects of limestone powder and gypsum on the early hydration of ye’elimite, the hydration behavior of C4A3S¯-LP-CaSO4·2H2O-H2O systems are researched. The hydration behavior of systems are researched by employing isothermal calorimetry, XRD technique and chemical shrinkage. The thermodynamic modelling method is employed to predict the equilibrium phase assemblages. The results show that the system with 5 wt.% LP has a comparable hydration heat evolution to limestone powder-free systems. Limestone powder can take part in the reaction to produce monocarboaluminate in the system with M-value (molar ratio of gypsum to ye’elimite) of 1, but monocarboaluminate is not found in the system with M-value of 2. The level off time of chemical shrinkage shortens with the increase of limestone powder dosage. Thermodynamic modelling results show that monocarboaluminate is no longer formed in all systems when M-value exceeds 1.27, which corresponds to the XRD results. This study can provide theoretical guidance for the rational utilization of limestone powder in calcium sulphoaluminate cement.

Abstract Aqueous rechargeable Zn/MnO2 zinc-ion batteries (ZIBs) are reviving recently due to their low cost, non-toxicity, and natural abundance. However, their energy storage mechanism remains controversial due to their complicated electrochemical reactions. Meanwhile, to achieve satisfactory cyclic stability and rate performance of the Zn/MnO2 ZIBs, Mn2+ is introduced in the electrolyte (e.g., ZnSO4 solution), which leads to more complicated reactions inside the ZIBs systems. Herein, based on comprehensive analysis methods including electrochemical analysis and Pourbaix diagram, we provide novel insights into the energy storage mechanism of Zn/MnO2 batteries in the presence of Mn2+. A complex series of electrochemical reactions with the co-participation of Zn2+, H+, Mn2+, SO4 2−, and OH− were revealed. During the first discharge process, co-insertion of Zn2+ and H+ promotes the transformation of MnO2 into Zn x MnO4, MnOOH, and Mn2O3, accompanying with increased electrolyte pH and the formation of ZnSO4·3Zn(OH)2·5H2O. During the subsequent charge process, Zn x MnO4, MnOOH, and Mn2O3 revert to α-MnO2 with the extraction of Zn2+ and H+, while ZnSO4·3Zn(OH)2·5H2O reacts with Mn2+ to form ZnMn3O7·3H2O. In the following charge/discharge processes, besides aforementioned electrochemical reactions, Zn2+ reversibly insert into/extract from α-MnO2, Zn x MnO4, and ZnMn3O7·3H2O hosts; ZnSO4·3Zn(OH)2·5H2O, Zn2Mn3O8, and ZnMn2O4 convert mutually with the participation of Mn2+. This work is believed to provide theoretical guidance for further research on high-performance ZIBs.

Model compression makes it possible to deploy face detection models on devices with limited computing resources. Edge–cloud collaborative inference, as a new paradigm of neural network inference, can significantly reduce neural network inference latency. Inspired by these two techniques, this paper adopts a two-step acceleration strategy for the CenterNet model. Firstly, the model pruning method is used to prune the convolutional layer and the deconvolutional layer to obtain a preliminary acceleration effect. Secondly, the neural network is segmented by the optimizer to make full use of the computing resources on the edge and the cloud to further accelerate the inference of the neural network. In the first strategy, we achieve a 62.12% reduction in inference latency compared to the state-of-the-art object detection model Blazeface. Additionally, with a two-step speedup strategy, our method is only 26.5% of the baseline when the bandwidth is 500 kbps.

In this study, we report a low-temperature approach involving a combination of a sol–gel hydrothermal method and spark plasma sintering (SPS) for the fabrication of cubic phase ZrW2−xMoxO8 (0.00 ≤ x ≤ 2.00) bulk ceramics. The cubic-ZrW2−xMoxO8 (0.00 ≤ x ≤ 1.50) bulk ceramics were successfully synthesized within a temperature range of 623–923 K in a very short amount of time (6–7 min), which is several hundred degrees lower than the typical solid-state approach. Meanwhile, scanning electron microscopy and density measurements revealed that the cubic-ZrW2−xMoxO8 (0.00 ≤ x ≤ 1.50) bulk ceramics were densified to more than 90%. X-ray diffraction (XRD) results revealed that the cubic phase ZrW2−xMoxO8 (0.00 ≤ x ≤ 1.5) bulk ceramics, as well as the sol–gel-hydrothermally synthesized ZrW2−xMoxO7(OH)2·2H2O precursors correspond to their respective pure single phases. The bulk ceramics demonstrated negative thermal expansion characteristics, and the coefficients of negative thermal expansion were shown to be tunable in cubic-ZrW2−xMoxO8 bulk ceramics with respect to x value and sintering temperature. The cubic-ZrW2−xMoxO8 solid solution can thus have potential applications in electronic devices such as heat sinks that require regulation of thermal expansion.

Barium titanate (BT)-based ceramics are one of the promising piezoelectric materials for environment-friendly electro-mechanical transformation. However, high performance materials are often sintered at high temperatures, resulting in volatile components and increased energy consumption. Here, 0.82Ba(Ti0.89Sn0.11)O3-(0.18-x)(Ba0.7Ca0.3)TiO3-xBiFeO3 (BTS-BCT-xBF) piezoelectric ceramics were prepared by microwave sintering (MWS) method, and the structure and properties were emphatically studied, aiming to reveal the regulatory mechanism of MWS on the structure and properties. Compared with conventional solid sintering (CS), the phase structure presents a similar evolution in MWS ceramics as a function of BF, while the more refined grain size and the denser structure are observed in MWS ceramics. The electrical properties (e.g., d33, εr, tan δ, etc.) of MWS ceramics are superior to the CS ceramics owing to the refined grain size and denser microstructure. It is worth noting that the energy storage performance (e.g., energy storage density, energy storage efficiency) significantly outperformed expectations due to the slender hysteresis loop resulting from the smaller grain and high cubic phase. Therefore, the MWS sintering mechanism can further drive practical application of BT-based ceramics.

A rolling element signal has a long transmission path in the acquisition process. The fault feature of the rolling element signal is more difficult to be extracted. Therefore, a novel weak fault feature extraction method using optimized variational mode decomposition with kurtosis mean (KMVMD) and maximum correlated kurtosis deconvolution based on power spectrum entropy and grid search (PGMCKD), namely KMVMD-PGMCKD, is proposed. In the proposed KMVMD-PGMCKD method, a VMD with kurtosis mean (KMVMD) is proposed. Then an adaptive parameter selection method based on power spectrum entropy and grid search for MCKD, namely PGMCKD, is proposed to determine the deconvolution period T and filter order L. The complementary advantages of the KMVMD and PGMCKD are integrated to construct a novel weak fault feature extraction model (KMVMD-PGMCKD). Finally, the power spectrum is employed to deal with the obtained signal by KMVMD-PGMCKD to effectively implement feature extraction. Bearing rolling element signals of Case Western Reserve University and actual rolling element data are selected to prove the validity of the KMVMD-PGMCKD. The experiment results show that the KMVMD-PGMCKD can effectively extract the fault features of bearing rolling elements and accurately diagnose weak faults under variable working conditions.

Address is a structured description used to identify a specific place or point of interest, and it provides an effective way to locate people or objects. The standardization of Chinese place name and address occupies an important position in the construction of a smart city. Traditional address specification technology often adopts methods based on text similarity or rule bases, which cannot handle complex, missing, and redundant address information well. This paper transforms the task of address standardization into calculating the similarity of address pairs, and proposes a contrast learning address matching model based on the attention-Bi-LSTM-CNN network (ABLC). First of all, ABLC use the Trie syntax tree algorithm to extract Chinese address elements. Next, based on the basic idea of contrast learning, a hybrid neural network is applied to learn the semantic information in the address. Finally, Manhattan distance is calculated as the similarity of the two addresses. Experiments on the self-constructed dataset with data augmentation demonstrate that the proposed model has better stability and performance compared with other baselines.

The safety of electric vehicles (EVs) has aroused widespread concern and attention. As the core component of an EV, the power battery directly affects the performance and safety. In order to improve the safety of power batteries, the internal failure mechanism and behavior characteristics of internal short circuit (ISC) and thermal runaway (TR) in extreme cases need to be tested and studied. The safety of lithium ion batteries (LIBs) has become a research hotspot for many scholars. With unreasonable misuse or abuse of lithium ion batteries, it is easy to cause internal short circuits, resulting in thermal runaway, which poses a great threat to the safety of the whole vehicle. This comprehensive review aims to describe the research progress of safety testing methods and technologies of lithium ion batteries under conditions of mechanical, electrical, and thermal abuse, and presents existing problems and future research directions.

As we enter the era of big data, we have to face big data generated by industrial systems that are massive, diverse, high-speed, and variability. In order to effectively deal with big data possessing these characteristics, deep learning technology has been widely used. However, the existing methods require great human involvement that is heavily depend on domain expertise and may thus be non-representative and biased from task to similar task, so for a wide variety of prognostic and health management (PHM) tasks, how to apply the developed deep learning algorithms to similar tasks to reduce the amount of development and data collection costs has become an urgent problem. Based on the idea of transfer learning and the structures of deep learning PHM algorithms, this paper proposes two transfer strategies via transferring different elements of deep learning PHM algorithms, analyzes the possible transfer scenarios in practical application, and proposes transfer strategies applicable in each scenario. At the end of this paper, the deep learning algorithm of bearing fault diagnosis based on convolutional neural networks (CNN) is transferred based on the proposed method, which was carried out under different working conditions and for different objects, respectively. The experiments verify the value and effectiveness of the proposed method and give the best choice of transfer strategy.

The next generation internet security architectures were analyzed and the characteristics and security challenges of the existing network were studied. The security goals of reconfigurable network were defined, and the“security substrate”based security management and control mechanism of reconfigurable network was presented. The definition of “security substrate”was given and the corresponding reconfigurable networks security model was illustrated. The construction of security services with multilevel security structure and security service establishment process via “security substrate”were put forward, which were key components of the security management and control architecture of reconfigurable network.

A new elevated-temperature high-strength Mg−4Er−2Y−3Zn−0.4Mn (wt %) alloy was developed by semi-continuous casting, solid solution treatment, and hot extrusion. W phase (Mg3(Er,Y)2Zn3) with fcc structure, long period stacking ordered phases with 18R (Mg10(Er,Y)1Zn1) and 14H (Mg12(Er,Y)1Zn1) structures, and basal plane stacking faults (SFs) was formed in the as-cast alloy, mainly due to the alloy component of (Er + Y)/Zn = 1:1 and Er/Y = 1:1 (at %). After solid solution treatment and hot extrusion, the novel microstructure feature formed in as-extruded alloy is the high number-density nanospaced basal plane SFs throughout all the dynamically recrystallized (DRXed) and un-DRXed grains, which has not been previously reported. The as-extruded alloy exhibits superior tensile properties from room temperature to 300 °C. The tensile yield strength can be maintained above 250 MPa at 300 °C. The excellent elevated-temperature strength is mainly ascribed to the formation of nanospaced basal plane SFs throughout the whole Mg matrix, fine DRXed grains ~2 μm in size, and strongly basal-textured un-DRXed grains with profuse substructures. The results provide new opportunities for the development of deformed Mg alloys with satisfactory mechanical properties for high-temperature services.

Average grain size is usually used to describe a polycrystalline medium; however, many investigations demonstrate the grain-size distribution has a measurable effect on most of mechanical properties. This paper addresses the experimental quantification for the effects of grain-size distribution on attenuation in α-titanium alloy by laser ultrasonics. Microstructures with different mean grain sizes of 26–49 μm are obtained via annealing at 800 °C for different holding times, having an approximately log-normal distribution of grain sizes. Experimental measurements were examined by using two different theoretical models: (i) the classical Rokhlin’s model considering a single mean grain size, and (ii) the improved Turner’s model incorporating a log-normal distribution of grain sizes in the attenuation evaluation. Quantitative agreement between the experiment and the latter model was found in the Rayleigh and the Rayleigh-to-stochastic transition regions. A larger attenuation level was exhibited than the classical theoretical prediction considering a single mean grain size, and the frequency dependence of attenuation reduced from a classical fourth power to an approximately second power due to a greater probability of large grains than the assumed Poisson statistics. The provided results would help support the use of laser ultrasound technology for the non-destructive evaluation of grain size distribution in polycrystalline materials.

Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurate short-term load forecast (STLF). In recent years, deep learning approaches provide better performance to predict electrical load in real world cases. The convolutional neural network (CNN) can extract the local trend and capture the same pattern, and the long short-term memory (LSTM) is proposed to learn the relationship in time steps. In this paper, a new deep neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy. The proposed model was tested in a real-world case, and detailed experiments were conducted to validate its practicality and stability. The forecasting performance of the proposed model was compared with the LSTM model and the CNN model. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used as the evaluation indexes. The experimental results demonstrate that the proposed model can achieve better and stable performance in STLF.

In recent years, concrete-filled steel tubular (CFST) bridges have been widely used in bridge construction. However, interface disengaging is one of the bridges’ most common defects. It affects not only the confinement of the steel tube to the core concrete, but also the bearing capacity and the structure durability. In order to solve the problem of disengaging at the interface of steel tube-confined concrete and ensure the integrity of the structure, a new method based on a distributed temperature detection system is proposed to identify disengaging at the interface between steel tube and concrete. Through the analysis of experiments and numerical simulation, it was found that when the structure surface was heated, the temperature of the void area was higher than that of the solid area. The temperature of the maximum void height was the highest, the temperature increase rate was the fastest, and the cooling rate was the slowest. The boundary of the void suffered from sudden changes in temperature. The more serious the void, the greater was the temperature difference between the void and the solid. After changing the heating direction, the temperature distribution could still be identified. This study achieved simplicity and efficiency with respect to the detection of interface separation.