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This research aims to establish a systematic framework for parametric identification of materials undergoing high temperatures and high strain rates. While advanced testing equipment, such as the Gleeble physical simulator, can produce controlled measurements of specimens under various conditions, significant challenges remain in determining the parameters of constitutive relations. Temperature gradients inevitably arise during Gleeble testing, leading to nonuniform strain distribution caused by complex thermal–mechanical coupling. Although finite element analysis of Gleeble testing can be performed, such simulations are computationally expensive, making brute-force optimization to minimize the difference between experimental data and finite element simulation across the parametric space infeasible. Furthermore, since the related constitutive relations are semi-empirical in nature, the ground truth of the constitutive parameters is generally unknown. In this context, a single-objective optimization based on a number of testing conditions may yield biased results or become trapped in local minima. In this research, we employ finite element analysis simulating Gleeble operation as the foundation, leveraging a suite of computational intelligence tools to address these challenges. We first develop a multi-response Gaussian process surrogate model, trained using a relatively small amount of finite element data, to rapidly emulate the forward analysis. We then implement a multi-objective optimization approach using simulated annealing to individually minimize the differences between experimental results and emulations under various testing conditions. AISI 9310 steel and the Johnson–Cook model are adopted for methodological demonstration. The development of the finite element model, Gaussian process surrogate model, and inverse optimization is detailed, and the results obtained are discussed. This framework can be extended to the parametric identification of other materials and heat treatment conditions using Gleeble testing. [ABSTRACT FROM AUTHOR]

For improving the accuracy of wellbore trajectory prediction, especially in the build section, a new prediction model based on long short-term memory (LSTM) network was proposed. At the same time, the model was built by Python language and TensorFlow library. The well inclination and azimuth angle were predicted by the LSTM model. Moreover, the prediction accuracy of the LSTM was compared with that of the minimum curvature method. The results show that the average prediction error of the LSTM is much 50% lower than the minimum curvature method, and the proposed model in this article is more consistent with the actual drilling data. This method does not rely on any assumption of the path shapes and geometries. In addition, the model proposed in this article is easy to use and convenient for the engineering field application without the derivation of mathematical formulas. [ABSTRACT FROM AUTHOR]

Aiming at the problem caused by the fact that traditional on-board charging systems (OBC) usually use a single form of AC input, this research suggests a suitable isolated OBC for single-phase and three-phase power sources. It adopts a two-stage structure. The front stage can switch between different input modes. Using a single-phase AC input mode, the front stage is a totem pole bridgeless cascade Boost circuit for power factor correction (PFC). It can be switched to a three-phase six-switch PFC circuit in a three-phase AC input mode. A full-bridge LLC resonant converter serves as the rear stage. The system adopts a double closed-loop control strategy and a mixed control mode that consists of pulse width modulation (PWM) and pulse frequency modulation (PFM). In either the single-phase or three-phase AC input mode, the front stage PFC circuit can output a 700 V DC bus voltage, and keep the input current and input voltage in phase. It is possible to achieve soft switching in the rear stage resonant converter, and to output a wide range of voltage to charge the power battery. According to a theoretical analysis, a system simulation model is built and an experimental prototype of the OBC is produced. The accuracies of the simulation and the theoretical analysis are confirmed by experimental findings. [ABSTRACT FROM AUTHOR]

ABSTRACT The on‐board DC/DC converter proposed in this research consists of a Buck converter and a three‐phase interleaved LLC resonant converter. This paper analyzes the average current and output current ripple characteristics of the rear‐stage converter in addition to explaining the topology and working principles of the front‐stage Buck converter and the rear‐stage three‐phase interleaved LLC resonant converter. To establish a single‐phase fundamental equivalent circuit for the rear‐stage converter, apply the fundamental wave analysis method, and it is essential for comprehending the impact of relevant system parameters on voltage gain characteristics and resonant operating regions. Conducting research on the two‐stage on‐board DC/DC converter's control strategy involves establishing small‐signal circuit models for both front and rear stage converters, derivation of parameters for double closed‐loop control, and determination of the control strategy employing phase‐shifted current sharing for the rear‐stage converter in cases where significant tolerance exists in resonant components. The simulation for a two‐stage on‐board DC/DC converter results indicates that under various conditions of resonant component tolerance, the converter is capable of achieving balanced phase currents and demonstrates minimal output current ripple prior to capacitor filtering. A 1.5‐kW experimental prototype has been fabricated, and the experimental findings indicate that the output voltage remains stable at 13.8 V despite fluctuations in input voltage. The highest efficiency of the prototype is about 96.27%, and the utilization of phase‐shifting current balancing control results in a notable reduction in output current ripple. This provides more evidence that the design of a two‐stage on‐board DC/DC converter is correct. [ABSTRACT FROM AUTHOR]

This paper focuses on the integration technology of electric vehicle motor driving system and on-board charging system. The front-stage bi-directional AC/DC converter time-multiplexes a three-phase bridge full-control circuit into a staggered parallel totem pole power factor correction circuit and a three-phase inverter circuit, which enables the on-board charging system to have a motor control function. It can form a bidirectional AC/DC converter which can realize the function of motor control. The rear-stage DC/DC converter can realize the energy exchange between power supply and power battery or power battery and motor through dual active bridge structure. The working characteristics of the front-stage circuit and the soft switching range of the rear-stage circuit are studied. The mathematical model of the front-stage circuit and the rear-stage circuit are established by small signal analysis, and the control strategy of the front-stage double-closed loop and the rear-stage double-loop competition is adopted. The PI controller parameters are designed by using the mathematical model of the system, and the stability of the compensated system is verified by the open-loop amplitude-frequency characteristic curve. The simulation model and experimental platform are established. The charging mode is verified by the electronic load and high voltage DC source simulate the power battery. The driving mode is verified by using the 5 kW permanent magnet synchronous motor. The simulation and experimental results verify the feasibility of the system to achieve charging and driving functions. [ABSTRACT FROM AUTHOR]

Rolling bearing is a critical component of machinery that has been widely applied in manufacturing, transportation, aerospace, and power and energy industries. The timely and accurate bearing fault detection thus is of vital importance. Computational data-driven deep learning has recently become a prevailing approach for bearing fault detection. Despite the progress of the deep learning approach, the deep learning performance is hinged upon the size of labeled data, the acquisition of which is expensive in actual implementation. Unlabeled data, on the other hand, are inexpensive. In this research, we develop a new semi-supervised learning method built upon the autoencoder to fully utilize a large amount of unlabeled data together with limited labeled data to enhance fault detection performance. Compared with the state-of-the-art semi-supervised learning methods, this proposed method can be more conveniently implemented with fewer hyperparameters to be tuned. In this method, a joint loss is established to account for the effects of labeled and unlabeled data, which is subsequently used to direct the backpropagation training. Systematic case studies using the Case Western Reserve University (CWRU) rolling bearing dataset are carried out, in which the effectiveness of this new method is verified by comparing it with other well-established baseline methods. Specifically, nearly all emulation runs using the proposed methodology can lead to around 2%–5% accuracy increase, indicating its robustness in performance enhancement. [ABSTRACT FROM AUTHOR]

ABSTRACT In photovoltaic (PV) power generation systems, to obtain the best energy conversion efficiency, effective DC‐DC converters must balance the solar panels output voltage with the voltage on the DC bus. The converter is designed to have low voltage stresses on each device, and it has the characteristics of a wide input range and high gain. The following actions were done to confirm that the proposed converter had a number of features appropriate for PV systems and an experimental verification platform was established based on theoretical analysis and small‐signal modeling. The input range of the scaled‐down experimental prototype is 40–80 V, with a rated output of 400 V and a rated power of 200 W. The experiment confirms the accuracy and feasibility of the theoretical analysis for the converter. [ABSTRACT FROM AUTHOR]

This study comprehensively evaluates the Beidou navigation satellite system's PPP-B2b service for real-time positioning and zenith total delay (ZTD) estimation. Despite its operational status, PPP-b2b's effective coverage is underexplored. We define this coverage by analysing satellite visibility and geometry and evaluating ZTD and positioning accuracy at 48 stations. Results show PPP-B2b achieves ZTD Errors of 10–20 mm in China and 15–30 mm in Europe, with kinematic experiments in China and Japan showing consistent accuracy within 20 mm. This study highlights PPP-b2b's potential for enhancing atmospheric monitoring and precise positioning in its effective regions. [ABSTRACT FROM AUTHOR]

This study investigates a novel approach for assessing the health status of rotating machinery transmission systems by analyzing the dynamic degradation of bearings. The proposed method generates multi-dimensional data by creating virtual states and constructs a multi-dimensional model using virtual state-space in conjunction with mechanism model analysis. Innovatively, the Hammerstein–Wiener (HW) modeling technique from control theory is applied to identify these dynamic multi-dimensional models. The modeling experiments are performed, focusing on the model's input and output types, the selection of nonlinear module estimators, the configuration of linear module transfer functions, and condition transfer. Dynamic degradation response signals are generated, and the method is validated using four widely recognized databases consisting of accurate measurement signals collected by vibration sensors. Experimental results demonstrated that the model achieved a modeling accuracy of 99% for multiple bearings under various conditions. The effectiveness of this dynamic modeling method is further confirmed through comparative experimental data and signal images. This approach offers a novel reference for evaluating the health status of transmission systems. [ABSTRACT FROM AUTHOR]

Background: Plaque psoriasis is relatively straightforward to identify. When diagnostic concerns arise in atypical cases, a biopsy is needed. It is widely accepted that the Munro microabscess and the spongiform pustule of Kogoj are diagnostic pathological features. However, the diagnostic dilemma is likely to arise in cases without these specific pathological changes and typical clinical features. This study aimed to investigate clinical and pathological clues in distinguishing atypical plaque psoriasis from its mimics. Methods: We evaluated the clinicopathological features of 20 cases of atypical plaque psoriasis and 40 cases of psoriasis mimics as controls including pityriasis rosea (n = 10), pityriasis lichenoides chronica (n = 8), and subacute dermatitis (n = 22). Results: A retrospective analysis of the clinicopathological characteristics of patients with atypical plaque psoriasis and controls was performed. Pathologically, there were significant differences between the two groups in the types of parakeratosis (P = 0.046), epidermal capture of extravasated erythrocytes (P = 0.011), focal basal liquefied degeneration (P = 0.017), types of inflammatory cells (P = 0.000), and depth of inflammation (P = 0.000). Clinically, we found the presence of scales and crusts was significantly different between the two groups. Conclusion: This study offers insight into the clinicopathological features of atypical plaque psoriasis. These differential diagnostic features, compared with its mimics, are proposed to assist the clinician in the diagnosis and treatment of atypical plaque psoriasis. [ABSTRACT FROM AUTHOR]

In this work, we explore the Boltzmann equation with Fermi-Dirac statistics (briefly, BFD equation), which models the dilute gases when quantum effects are taking into consideration. Specifically, we firstly establish the uniform energy estimate and construct the global-in-time classical solutions of the scaled BFD equations under small assumption on the initial data. Then we use this uniform estimate to derive the acoustic limit from the scaled BFD equations within the context of classical solutions. Compared with our companion article Jiang and Zhou (2024) [23] , our uniform estimate in Knudsen number in this paper is crucial and hard to be obtained for the compressible Euler scaling and the acoustic limit is global-in-time, rather than almost global-in-time. [ABSTRACT FROM AUTHOR]

This study investigates the influence of mixed cascade failures on the resilience of manufacturing enterprise supply chain networks, which are susceptible to disruptions from upstream and downstream enterprises due to external uncertainties. We propose a single-layer directed network model to examine mixed cascade failures and analyze the effectiveness of protection and recovery strategies for improving network resilience through extensive simulation experiments. The numerical analysis yields the following key findings: (i) Under targeted attacks, the Erdős–Rényi (ER) network exhibits marginally enhanced robustness, whereas the Barabási–Albert (BA) network proves to be more resilient against random attacks. (ii) Effectively mitigating failure risk and reducing the likelihood of failure can be accomplished by increasing the upper load capacity limit and decreasing the lower load capacity limit for manufacturing firms. Furthermore, a comprehensive approach to protection and recovery measures strengthens the stability of supply chain networks in the face of external risks. (iii) Constraints, such as epidemic control measures and infrastructure damage, may impede the recovery process of manufacturing firms' supply chains when addressing external uncertainties. [ABSTRACT FROM AUTHOR]

As a significant branch of production scheduling problem, the Flexibility Job Shop Scheduling Problem (FJSP) is a typical NP-hard problem. Most conventional flexible workshop scheduling primarily focuses on performance aspects involving production efficiency such as time and quality. In recent years, due to increased energy costs and environmental pollution, 'low-carbon scheduling' has garnered attention as a new scheduling paradigm among scholars and engineers. This paper investigates a low-carbon flexible Job shop scheduling problem, proposing a Grey Wolf Optimization algorithm (SC-GWO), aiming to minimize the sum of carbon emission costs and makespan costs. This algorithm employs the Grey Wolf Algorithm (GWO) as the fundamental optimization method, adaptively choosing between global and local searches based on the dispersion degree of individuals. Firstly, integrating the Sine Cosine Algorithm (SCA), the sinusoidal cosine search mechanism is applied to GWO to enhance its local search capability. Secondly, a new leader selection mechanism is introduced to prevent leaders from falling into local optima, thus improving the algorithm's global exploration capability. Utilizing a nonlinear convergence factor strategy controls the global exploration and local exploitation capabilities in different algorithm stages, enhancing optimization accuracy and accelerating convergence, achieving a dynamic balance between the two. Finally, validation of the SC-GWO algorithm's ability to solve low-carbon scheduling problems in flexible job shop scheduling instances is conducted. Experimental results demonstrate the superior performance of SC-GWO in solving low-carbon flexible workshop scheduling instances. Comparative experiments against four other advanced algorithms on 22 classic benchmark test functions confirm SC-GWO's better convergence. Through standard test functions like Bandimarte instances applied to solve FJSP, experimental results showcase the excellent optimization performance of SC-GWO. Compared to HGWO and GWO, the makespan time is reduced by 22.25% and 39.27%, respectively. The proposed SC-GWO algorithm demonstrates favorable solving effects on flexible job shop scheduling instances, meeting actual production scheduling needs. [ABSTRACT FROM AUTHOR]

Texture deterioration of meat products upon high‐temperature sterilization is a pressing issue in the meat industry. This study evaluated the effect of different thermal sterilization temperatures on the textural and juiciness of ready‐to‐eat (RTE) chicken breast. In this study, by dynamically monitoring the texture and juiciness of chicken meat products during the process of thermal sterilization, it has been observed that excessively high sterilization temperatures (above 100°C) significantly diminish the shear force, springiness and water‐holding capacity of the products. Furthermore, from the perspective of myofibrillar protein degradation, molecular mechanisms have been elucidated, unveiling that the thermal sterilization treatment at 121°C/10 min triggers the degradation of myosin heavy chains and F‐actin, disrupting the lattice arrangement of myofilaments, compromising the integrity of sarcomeres, and resulting in an increase of approximately 40.66% in the myofibrillar fragmentation index, thus diminishing the quality characteristics of the products. This study unravels the underlying mechanisms governing the dynamic changes in quality of chicken meat products during the process of thermal sterilization, thereby providing theoretical guidance for the development of high‐quality chicken products. [ABSTRACT FROM AUTHOR]

In recent years, relaxor ferroelectric single crystals have attracted people's attention and research because of their excellent piezoelectric properties at morphotropic phase boundary(MPB). The ternary system Pb(In1/2Nb1/2)O 3 -Pb(Mg1/3Nb2/3)O 3 -PbTiO 3 (PIN-PMN-PT) crystals have higher Curie temperature and coercive field, which have the opportunity to be applied in high-temperature devices. The crystals of the rhombohedral phase have better stability, but their performance is not as good as that of the MPB component crystals. In this paper, the piezoelectric properties of rhombohedral PIN-PMN-28PT single crystals are improved by Sm3+ doping. The piezoelectric coefficient d 33 after AC polarization along [001] direction can reach 3125pC/N, which is 60% higher than that of undoped crystals. Meanwhile, the crystal exhibits excellent optical performance after DC polarization along [110] direction. The crystal has an effective electro-optic coefficient of 386 pm/V at room temperature and a stable frequency-dependent characteristic, which is expected to be an ideal material for electro-optic devices. [ABSTRACT FROM AUTHOR]

The article presents the discussion on new hybrid system combining ceria nanoparticles and mesenchymal stem cell nanovesicles to effectively treat rheumatoid arthritis by reducing symptoms and promoting regulatory T cells in a collagen-induced arthritis model. Topics include the challenges of rheumatoid arthritis, the role of reactive oxygen species in disease progression; and the therapeutic potential of nanotechnology for immune regulation.

Wearable glucose biosensors enable noninvasive glucose monitoring, thereby enhancing blood glucose management. In this work, we present a wearable biosensor based on carbon black nanoparticles (CBNPs) for glucose detection in human sweat. The biosensor consists of CBNPs, Prussian blue (PB), glucose oxidase, chitosan, and Nafion. The fabricated biosensor has a linear range of 5 µM to 1250 µM, sensitivity of 14.64 µA mM−1 cm−2, and a low detection potential (−0.05 V, vs. Ag/AgCl). The detection limit for glucose was calculated as 4.83 µM. This reusable biosensor has good selectivity and stability and exhibits a good response to glucose in real sweat. These results demonstrate the potential of our CBNP-based biosensor for monitoring blood glucose in human sweat. [ABSTRACT FROM AUTHOR]

The addition of zinc can induce the formation of polymetallic (Zn-Ni-Co) carbonate hydroxides/hydroxides (MCHs/M(OH) 2) heterostructure nanosheet array with rich defect structures on Ni foam as a superior cathode (ZNC/NF) for alkaline aqueous rechargeable Zn-based batteries (AAZBs). The as-made binder-free cathode offers an extremely high mass loading of 9.2 mg cm−2, exhibiting excellent areal capacities and energy densities. [Display omitted] Developing high mass loading cathodes with high capacity and durable life cycles is greatly worthwhile and challenging for alkaline aqueous rechargeable Zn-based batteries (AAZBs). Herein, we demonstrate an efficient zinc-induced strategy to rationally develop Zn-Ni-Co carbonate hydroxides/hydroxides heterostructure nanosheet array with an extremely high mass loading of 9.2 mg cm−2 on Ni foam (ZNC/NF) as such a superior cathode for AAZBs. It is discovered that Ni-Co hydroxide nanowires can be transformed into Zn-Ni-Co carbonate hydroxides/hydroxides heterostructure nanosheet with rich defect structures after the introduction of Zn during the synthetic process. The formed heterostructures and rich defect structures can enhance ion and electron transfer efficiency, thus ensuring the excellent electrochemical performance under high loading condition. Consequently, the ZNC/NF//Zn battery shows an outstanding areal capacity of 2.1 mAh cm−2 at 5 mA cm−2, with an ultrahigh energy density of 3.6 mWh cm−2. Moreover, the battery can still retain a high capacity of 0.42 mAh cm−2 after 5000 cycles at 50 mA cm−2, suggesting strong long-term cycling stability. This research enables pave the way for the rational design and manufacture of advanced electrode materials with large mass loadings. [ABSTRACT FROM AUTHOR]

Minimising the friction force of the archwire‐bracket sliding contact is crucial for achieving a high‐quality orthodontic treatment. Micro‐dimples and micro‐grooves textures were successfully produced on the two slot surfaces of the stainless steel orthodontic brackets with the laser surface texturing technology. The effect of surface texture on reducing the friction and wear behaviour of the stainless steel archwire‐bracket sliding contact combinations in artificial saliva environment was investigated. Friction coefficients of less than 0.15 and mild wear were obtained by using the three rows micro‐grooves textured bracket with a width of 60 μm and a spacing of 150 μm. The friction and wear performance was further enhanced with the addition of orthodontic wax materials. It was strongly argued that the synergetic effects of the fabricated micro‐grooves texture and the added orthodontic wax materials leading to the outstanding friction and wear behaviour of the archwire‐bracket sliding contacts. [ABSTRACT FROM AUTHOR]

This paper justifies the compressible Euler and acoustic limits from quantum Boltzmann equation with Fermi–Dirac statistics rigorously. By employing Hilbert expansion, in particular analyzing the nonlinear implicit transformation between the classical form of compressible Euler equations and the one obtained directly from BFD, and some new type of Grad–Caflisch type decay estimate of the linearized collision operator, we establish the compressible Euler limit from scaled BFD equation, which was formally derived by Zakrevskiy in (Kinetic models in the near-equilibrium regime. Thesis at Polytechnique, 2015) by moment method. Consequently, the acoustic limit is obtained in optimal scaling with respect to Knudsen number. [ABSTRACT FROM AUTHOR]

Introduction: The left partial anomalous pulmonary vein connection is a rare congenital heart disease, especially with intact atrial septum. Now we reported a case of the left superior pulmonary vein drainage to left innominate vein through a vertical vein, and corrected with video assisted thoracoscopy. Case presentation: A-59-years old man diagnosed left anomalous partial pulmonary vein connection with presentation of short breathiness and palpation, and diagnosed with computer tomography pulmonary angiography. The operation was carried out under video assisted thoracoscopy with one manipulation incision and one observational incision, the vertical vein was dissected and anastomosis with left atrial appendage. The patients recovered smoothly and postoperative CTPA showed anastomosis ostium was unobstructed. Conclusion: The left lateral thoracotomy and video assisted thoracoscopic surgery is a feasible for correction of left PAPVC with intact interatrial septum without using CPB. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to explore the efficacy and safety of tailored therapy guided by genotypic resistance in the first‐line treatment of Helicobacter pylori (H. pylori) infection in treatment‐naive patients. Methods: Gastric mucosal specimens were taken during gastroscopy, and main mutations of clarithromycin‐ and levofloxacin‐resistant genes were detected by polymerase chain reaction (PCR). Sensitive antibiotics were selected individually for treating H. pylori infection with tailored bismuth‐containing quadruple therapy (BQT) consisting of esomeprazole 20 mg twice daily, bismuth potassium citrate 220 mg twice daily, amoxicillin 1 g twice daily, and clarithromycin 500 mg twice daily, or levofloxacin 500 mg once daily, or metronidazole 400 mg four times daily. Safety and patient compliance were assessed 1–3 days after eradication. Treatment outcome was evaluated by urea breath test 4–8 weeks after eradication. Results: One hundred and thirty‐two treatment‐naive patients with H. pylori infection were included. PCR results suggested resistance rates of 47.7% and 34.9% for clarithromycin and levofloxacin, respectively, and a dual resistance rate of 18.2%. Eradication rates of tailored BQT were 87.1% and 95.8% by intention‐to‐treat (ITT) analysis and per‐protocol (PP) analysis, respectively. There was no statistically significant difference in the efficacy of 7‐day clarithromycin‐containing, 7‐day levofloxacin‐containing, and 14‐day full‐dose metronidazole‐containing BQT (ITT analysis: P = 0.488; PP analysis: P = 0.833). The incidence of adverse events was 19.7%, and patient compliance was 97.7%. Conclusion: Tailored BQT guided by genotypic resistance can achieve satisfactory efficacy, safety, and patient compliance in the first‐line treatment of H. pylori infection. [ABSTRACT FROM AUTHOR]

Relaxor ferroelectric single crystal Pb(In1/2Nb1/2)O3–Pb(Mg1/3Nb2/3)O3–PbTiO3 (PIN–PMN–PT) has received extensive research and attention due to its excellent piezoelectric, electromechanical, and other properties. In particular, tetragonal relaxor ferroelectric single crystals have shown greater application potential in high‐temperature devices due to their higher Curie temperature and better stability. However, the low piezoelectric performance of tetragonal crystals also greatly limits its application, and research on its optical properties is not sufficient. In this paper, the tetragonal phase Sm–PIN–PMN–PT single crystal was obtained by the Bridgman method, and its piezoelectric and electro‐optical properties are studied. At the same time, we gave a complete set of piezoelectric, dielectric, and elastic coefficient matrices. The results show that the crystal has excellent piezoelectric and electromechanical properties. The piezoelectric coefficient can reach 905pC/N, the electromechanical coupling coefficient k33 is 85%, and the kt is 72%. The transmittance of the sample polarized along [001] direction can reach 66%. The effective electro‐optical coefficient γc is 60 pm/V at room temperature and can reach 86 pm/V at 70°C. It has a wide operating temperature range and stable performance with frequency variation, which has certain reference significance for application in new electro‐optical devices. [ABSTRACT FROM AUTHOR]

An extended (2+1)-dimensional shallow water wave (SWW) model which can describe the evolution of nonlinear shallow water wave propagation in two spatial and temporal coordinates, is systematically studied. The multi-linear variable separation approach is addressed to the extended (2+1)-dimensional SWW equation. The variable separation solution consisting of two arbitrary functions is obtained, by assumption, from a specific ansatz. By selecting these two arbitrary functions as the exponential and trigonometric forms, resonant dromion, lump, and solitoff solutions are derived. Meanwhile, some novel fission and fusion phenomena including the semifoldons, peakons, lump, dromions, and periodic waves are studied with graphical and analytical methods. The results can be used to enhance the variety of the dynamics of the nonlinear wave fields related by engineering and mathematical physics. [ABSTRACT FROM AUTHOR]

The Daming Temple, built during 457–464 C.E., is one of the developing ancient temple heritage spaces located in Yangzhou city, P. R. China. Over the past 60 years, variation in visitors' spatial perception along the tour routes in the temple has occurred. This research attempts to reveal the changes in visitors' visual perception along the spatial sequences at 3 different times (i.e., 1962, 1973 and 2022). A quantitative GIS-based method, which includes analysing the distribution of visitors' spatial preferences and spatial configuration, is proposed. Digital landscape tools and quantitative estimation methods are used, including mapping within Rhinoceros software, the kernel density estimation (KDE) method within ArcGIS software and spatial syntax analysis within DepthMap software. Extracted geodata from 500 photographs of the heritage space taken by volunteer visitors are analysed within the GIS environment. Values of the mean depth (MD) at both levels of visibility and accessibility are calculated within the visibility graph analysis (VGA) model. Comparisons between the visual preferences of the visitors and the spatial configuration along the spatial sequence are conducted. The results indicate that the spatial sequence has a significant impact on visitors' visual preferences and tour routes. The phenomenon of spatial sequence among dynamic temporal variations and the effects of narrative spaces along the spatial sequence are highlighted and explained, which reveal the relationship between visitors' geospatial preference and the spatial configuration of the temple. Some suggestions are put forwards for further studies on the revitalisation and management of East Asian ancient temple heritage spaces. [ABSTRACT FROM AUTHOR]

Population aging and migration are two important phenomena in the process of social development in China, which have a significant impact on industrial structure upgrading. This study explores the moderating effect of the population migration on the population aging impacting the industrial structure upgrading based on Chinese provincial panel data from 2000 to 2021. The results demonstrate that the population aging has become a development trend of China's population, and it has a significantly hinder industrial structure upgrading. Furthermore, the population migration resulting diffusion and convergence of economic factors has a meaningful moderating effects on the population aging impacting the industrial structure upgrading. Our study suggests that a reasonable and orderly population migration is critical in achieving stable and sustainable industrial structure upgrading, especially in the context of the China's population aging. [ABSTRACT FROM AUTHOR]

• A new bearing prognosis framework is built upon the time-domain signal preprocessing and physics-based wavelet neural network (WNN). • Empirical mode decomposition (EMD) and statistical time series analysis are adopted as performance multipliers of WNN. • The framework enables the physical interpretation of bearing fault diagnosis and yields excellent predictive performance. • Comprehensive case studies using multiple datasets demonstrate the feasibility of the framework. Rolling bearings are important components in rotating machinery in various industries. Conducting intelligent prognostics for bearing remaining useful life (RUL) prediction plays an important role in the health management and reliability assessment of these machinery systems. While the bearing fault prognosis using measured system response through machine learning techniques has attracted significant attention and demonstrated promising potential, completely data-driven approaches face some challenges in understanding complex domains with data efficient learning. In this research, we develop an integral bearing fault prognosis framework informed by the well-designed time-domain signal preprocessing to conduct the bearing RUL prediction. This framework is built upon physical feature-oriented signal preprocessing and an associated wavelet neural network (WNN). Sequential procedures of time-domain analyses are proposed to extract the physical features of bearing degradation. Empirical mode decomposition (EMD) is specifically chosen owing to its capability of handling bearing fault signals that are nonstationary with underlying nonlinearities. The WNN is a new class of neural networks that combines the classic neural network and wavelet analysis. Here in this research a WNN model built upon B-spline mother wavelet to suit the preceding EMD-based signal preprocessing is constructed to process the bearing degradation features extracted and then identify their correlation with bearing RUL. The combination of EMD-based signal preprocessing and B-spline mother wavelet in WNN enables the network to learn the input-output correlation in a physical sense. Case studies formulated upon multiple bearing datasets and multiple benchmark methods are carried out to systematically validate the proposed framework. The results consistently demonstrate prediction accuracy and performance robustness. [ABSTRACT FROM AUTHOR]

The Qaidam Basin is one of the major coal‐bearing basins in northeastern China with the coal seams mainly preserved in the Lower–Middle Jurassic. The Middle Jurassic sedimentary environment, sequence stratigraphy, paleoclimate, and coal accumulation in the Qaidam Basin have been extensively studied, while the sedimentary characteristics and depositional evolution of the Lower Jurassic coal‐bearing series are less well known. We systematically analysed the facies association, depositional system, basin evolution, and coal accumulation of the Early Jurassic Pliensbachian‐aged coal‐bearing series from the Dameigou section in the northern Qaidam Basin. Five lithological types have been identified, ranging from conglomerates, sandstones, siltstones, and mudstones to combustible organic rocks, which were further subdivided into 12 lithofacies. These lithofacies were grouped into four facies associations, representing the braided fluvial, meandering fluvial, lacustrine, and meandering fluvial‐lacustrine transition deposits, respectively. The Pliensbachian Stage mainly developed as a fluvial‐lacustrine depositional system and an evolution history from braided fluvial to meandering fluvial, then to lacustrine, and finally back to meandering fluvial was reconstructed. We suggest that climate change is the major controlling factor for depositional evolution during the Pliensbachian. The transition of the depositional environment from fluvial to lacustrine in the study area can be attributed to the rapid climatic change induced by volcanism at this interval. [ABSTRACT FROM AUTHOR]

Heart rate variability (HRV) is an important indicator of autonomic nervous system activity and can be used for the identification of affective states. The development of remote Photoplethysmography (rPPG) technology has made it possible to measure pulse rate variability (PRV) using a camera without any sensor-skin contact, which is highly correlated to HRV, thus, enabling contactless assessment of emotional states. In this study, we employed ten machine learning techniques to identify emotions using camera-based PRV features. Our experimental results show that the best classification model achieved a coordination correlation coefficient of 0.34 for value recognition and 0.36 for arousal recognition. The rPPG-based measurement has demonstrated promising results in detecting HAHV (high-arousal high-valence) emotions with high accuracy. Furthermore, for emotions with less noticeable variations, such as sadness, the rPPG-based measure outperformed the baseline deep network for facial expression analysis. • Features for emotion recognition were extracted from videos using rPPG. • Recognition based on rPPG shows correlation with annotation. • The rPPG-based approach is effective in detecting positive emotions. • RPPG outperforms the recognition of sad emotions based on facial expressions. [ABSTRACT FROM AUTHOR]

• The vibration suppression study of the plate structure based on the nonlinear piezoelectric shunt damping is proposed. • The modified Ritz method and complexification-averaging method are adopted to obtain dynamic responses of the system. • The effects of the negative capacitance and nonlinear capacitance on the vibration suppression performance are discussed. This paper proposes a nonlinear piezoelectric shunt damping circuit to mitigate the vibration of the plate structures subjected to the aerodynamic and harmonic excitations. The nonlinear piezoelectric shunt damping circuit consisting of the piezoelectric patch, inductor, resistor, negative capacitor and nonlinear capacitor is considered. Firstly, the governing equations of the electro-mechanical coupling nonlinear system are formulated based on the energy method and modified Ritz method. Furthermore, the complexification-averaging method combined with pseudo arc-length continuation method is adopted to analyze the steady-state response of the coupled system with aerodynamic and harmonic forces. A considerable number of numerical cases are performed to validate the proposed model and the time domain and frequency domain dynamic responses of the plate structures with and without control are further compared to validate the effectiveness of the proposed control strategy. Meanwhile, the energy transfer analysis between the plate structure and external piezoelectric shunt damping circuit is investigated. Finally, the effects of key parameters of the nonlinear piezoelectric shunt damping circuit including the negative capacitance and nonlinear capacitance on the vibration suppression performance of the piezoelectric shunt damping are discussed. [ABSTRACT FROM AUTHOR]

Fault diagnosis of rolling bearings becomes an important research subject, where the data-driven deep learning-based techniques have been extensively exploited. While the state-of-the-art research has shown the substantial progresses in bearing fault diagnosis, they mostly were implemented upon the hypothesis that the location of bearing prone to failure already is known. Nevertheless, in actual practice many rolling bearings are installed in a complex machinery system, any of which is likely subject to fault. As such, fault diagnosis essentially is a process to achieve both fault localization and identification, which results in many fault scenarios to be handled. This will significantly degrade the fault diagnosis performance using conventional deep learning analysis. In this research, we aim to develop a new deep learning framework to address abovementioned challenge. We particularly design a hierarchical deep learning framework consisting of multiple sequentially deployed deep learning models built upon the transfer learning. This can improve the learning adequacy for a high-dimensional problem with many fault scenarios involved even under limited dataset, thereby enhancing the fault diagnosis performance. Without the prior knowledge regarding the fault location, this methodology is greatly favored by the sensor/data fusion which takes full advantage of the enriched pivot fault-related features in the measurements acquired from different accelerometers. Systematic case studies using the publicly accessible experimental rolling bearing dataset are carried out to validate this new methodology. [ABSTRACT FROM AUTHOR]

The figure a shown the schematic diagram of the pre-crosslinking rod-coating technique. First, GO and PPD underwent a crosslinking reaction for 180 min at 60℃ using DMF as the solvent to generate amide bonds, resulting in the formation of a pre-crosslinked GO-PPD solution (2 mg/ml). The main reaction involved the epoxy ring-opening reaction between the amine in PPD and the epoxy group in GO, resulting in a hydroxyl group and secondary amine. [Display omitted] • A large-area GO NF membrane was prepared by Pre-Crosslinking Rod Coating Technique. • The obtained GO-PPD NF membrane showed superior 99% rejection rate for dyes. • The permeability of GO-PPD NF membrane was 10 times that of non-crosslinking GO NF membrane. In existing separation membranes, it is difficult to quickly produce large-area graphene oxide (GO) nanofiltration membranes with high permeability and high rejection, which is the bottleneck of industrialization. In this study, a pre-crosslinking rod-coating technique is reported. A GO-P-Phenylenediamine (PPD) suspension was obtained by chemically crosslinking GO and PPD for 180 min. After scraping and coating with a Mayer rod, the ultra-thin GO-PPD nanofiltration membrane with an area of 400 cm2 and a thickness of 40 nm was prepared in 30 s. The PPD formed an amide bond with GO to improve its stability. It also increased the layer spacing of GO membrane, which could improve the permeability. The prepared GO nanofiltration membrane had a 99 % rejection rate for dyes such as methylene blue, crystal violet, and Congo red. Meanwhile, the permeation flux reached to 42 LMH/bar, which was 10 times that of the GO membrane without PPD crosslinking, and it still maintained excellent stability under strongly acidic and basic conditions. This work successfully solved the problems of GO nanofiltration membranes, including the large-area fabrication, high permeability and high rejection. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to determine the incidence and severity of symptoms of patients with cervical cancer within 6 months after radiotherapy and chemotherapy, form a symptom burden report, evaluate the distribution characteristics of symptoms, identify symptom clusters, and provide a basis for clinical doctors and nurses to improve the symptom management of patients with cervical cancer after radiotherapy and chemotherapy. Methods: The patients with cervical cancer within 6 months after radiotherapy and chemotherapy were recruited to investigate their symptom burden. Exploratory factor analysis was used to identify symptom clusters. Results: A total of 250 patients participated in the study. The study found that the most common symptom among the 40 symptoms was fatigue, and the most serious symptom was nocturia. Based on the occurrence rate and severity of symptoms, nine symptom clusters were identified, including psycho–emotion-related symptom cluster, pain–disturbed sleep-related symptom cluster, menopausal symptom cluster, tinnitus–dizziness-related symptom cluster, urinary-related symptom cluster, dry mouth–bitter taste-related symptom cluster, intestinal-related symptom cluster, memory loss–numbness-related symptom cluster, and emaciation-related symptom cluster. The three most serious symptom clusters are pain–disturbed sleep-related symptom cluster, urinary-related symptom cluster, and memory loss–numbness-related symptom cluster. Conclusion: The symptoms of patients with cervical cancer within 6 months after radiotherapy and chemotherapy are complex, and nine symptom clusters can be identified according to the incidence and severity of symptoms. We can find the potential biological mechanism of each symptom cluster through the discussion of previous mechanism research and clinical research. The number of symptom clusters and the number of symptoms within the symptom cluster are closely related to the symptom evaluation scale selected for the study. Therefore, the symptom cluster study urgently needs a targeted symptom evaluation scale that can comprehensively reflect the patient’s condition. [ABSTRACT FROM AUTHOR]

This paper presents the development of a highly sensitive gas detection system based on a resonant photoacoustic cell for detecting dissolved gases in transformer oil. A simulation model of the resonant photoacoustic cell was studied and optimized the buffer chamber volume while ensuring signal enhancement. The volume of the photoacoustic cell was reduced by about 80% compared to the classical model. A resonant photoacoustic cell was then fabricated based on the optimized simulation optimization. The dual-resonance photoacoustic system was constructed by combining the resonant PA cell with a handmade cantilever fiber acoustic sensor. The system's sensitivity was further improved by using an erbium-doped fiber amplifier, wavelength modulation, and harmonic detection technology. The experimental results showed that the system achieved a detection limit of 6 ppb and an excellent linear range under 1000 ppm for C2H2 gas. The developed gas detection system has potential applications for monitoring the condition of power transformers in power grids. [ABSTRACT FROM AUTHOR]

The development of mineral prospectivity mapping (MPM), which aims to outline and prioritize mineral exploration targets, has been spurred by advances in data-driven machine learning algorithms. Supervised data-driven MPM is a typical few-shot task, suffering from a scarcity of labeled data, the over-fitting of models and an uncertainty of predictions. The main objective of this contribution is to propose a robust framework of few-shot learning (FSL), combining data augmentation and transfer learning to enable the generation of prospectivity models with excellent predictive efficiency and low uncertainty. The mineral systems approach was used to transfer a conceptual mineral system into mappable exploration criteria. Synthetic minority over-sampling technique (SMOTE) was employed to augment and balance the labeled dataset, allowing for model pre-training with the large synthetic training dataset of a source domain. The knowledge derived from pre-trained models was then transferred to the target domain by fine-tuning, and the prospectivity model was generated in light of over-fitting and uncertainty assessments. The proposed FSL framework was applied to tungsten prospectivity mapping in southern Jiangxi Province. The results indicated that the SMOTE-ed balanced dataset boosted the classification accuracy in the training process. The FSL models yielded an arch-shaped prediction point pattern which was favorable for focusing potential targets with high probability and low uncertainty. The FSL models achieved a high predictive performance (test AUC = 0.9172) and the lowest quantitative over-fitting value compared to the models derived from the benchmark algorithms of random forest and support vector machine. Four levels of potential targeting zones, considering both predictive efficiency and uncertainty, were extracted from the resulting FSL prospectivity map. The final high-potential and low-risk exploration targets only cover 4.27% of the area, but capture 41.53% of known tungsten deposits, thus achieving a superior predictive performance. This study highlights the capability of FSL framework to control over-fitting and generate high-confidence exploration targets with low levels of uncertainty. [ABSTRACT FROM AUTHOR]

A computationally effective and physically accurate metamodeling approach is demonstrated to analyze, under uncertainties, the spring-in angle deformation for composite manufacturing processes. Various uncertainties are inevitably present in this manufacturing process due to the heterogeneous thermo-mechanical properties of the composite materials. Analysis of uncertainty propagation using the direct Monte Carlo approach is computationally prohibitive, which calls for the employment of machine learning techniques and surrogate models or metamodels such as Gaussian processes (GP). While these approaches are promising, tuning model parameters and optimizing the hyperparameters are essential to predictive modeling performance. So far, most existing approaches rely on empirical experience through trial and error. Randomly selecting these hyperparameters results in excessive computational cost and poor convergence results. A nature-inspired methodology has been developed to guide the GP in selecting and optimizing the hyperparameters for the uncertainty propagation analysis of composite manufacturing processes. An improved firefly algorithm (iFA) takes account of the environmental factor. It disregards the contribution of a constant attractiveness factor, which in turn accelerates the convergence rate at the early stages of the generation and boosts the immunity of the proposed algorithm. The proposed methodology enabled selection of the proper combination of the factors for the GP and showed its merits over other state-of-the-art deterministic/metaheuristic algorithms, which is further confirmed by various nonparametric, multiple comparison tests. [ABSTRACT FROM AUTHOR]

The effects of thermal processing on the texture and collagen properties of ready‐to‐eat chicken claws were investigated and the relationship between the texture of chicken claws and the collagen properties of connective tissue was further discussed. The texture, sensory and collagen characteristics (content, solubility, and structure), and the degradation of insoluble collagen of chicken claws underwent thermal processing at 90°C/30 min, 100°C/30 min, 115°C/15 min, and 121°C/10 min were measured. The results showed that thermal processing significantly reduced the textural parameters (hardness, springiness, cohesiveness, chewiness, and resilience) of chicken claws, and the textural deterioration intensified with the increase in temperature. Thermal processing aggravated the degradation of collagen fibers in cooked chicken claws, resulting in decreased insoluble collagen content and increased collagen solubility. Moreover, higher thermal processing temperature produced more soluble collagen components composed of low‐molecular weight peptides. The textural deterioration of chicken claws caused by thermal processing was related to the decrease of insoluble collagen content and the increase of content of soluble collagen composed of low‐molecular weight peptides. On the one hand, the decreased of insoluble collagen content led to the weakening of connective tissue. On the other hand, low‐molecular weight peptides hindered the cooling renaturation of soluble collagen, leading to the formation of gels with poor mechanical properties. [ABSTRACT FROM AUTHOR]

Fast motion video sequence processing is quite difficult. In order to improve the effect of fast motion video sequence processing, this paper improves the traditional codebook model algorithm and proposes an improved codebook model algorithm. Moreover, this paper analyzes and summarizes the development and application of background modeling-based methods in moving target detection, and points out the applicability and limitations of traditional methods, which lays the foundation for the further research of moving target detection based on background modeling in the complex background. In addition, this paper analyzes the characteristics of fast motion videos, and combines deep learning algorithms to improve the feature recognition effect of fast motion video sequences. Finally, this paper verifies the effect of this method through experimental research. Through experimental research, we know that the improved algorithm proposed in this paper can realize effective processing of fast motion video, and can improve the feature recognition effect of motion video frames. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-output port on-board charging system based on a CLLC resonant converter, which can realize the function of simultaneously charging high-voltage power batteries and low-voltage batteries. The system topology is composed of a front-stage PFC and a latter-stage CLLC resonant converter. The system uses a three-port transformer to couple the resonant converter, the high-voltage charging circuit, and the low-voltage charging circuit. The system also realizes soft switching through resonant components. According to the design requirements of the charging system, the control strategy of the dual-output ports is determined by analyzing the working mode and gain characteristics of the system and the dual closed-loop control structure is designed to realize the functions of power factor correction and wide-range voltage regulation. Simulation and experimental results verify the correctness of theoretical analysis and structural design. [ABSTRACT FROM AUTHOR]

To study the clinical and prognostic features of non-B non-C alpha-fetoprotein (AFP)(-)-hepatocellular carcinoma (HCC) (NBNC-AFP(-)-HCC) and the relationship between the prognostic features of HCC and hepatitis B virus surface antigen (HBsAg) status and AFP. We enrolled 227 patients who underwent hepatic resection for HCC between January 1998 and December 2007 at Sun Yat-sen University Cancer Center, all of whom were diagnosed with HCC by pathology. All patients were stratified into one of four groups (B-AFP(+)-HCC, B-AFP(-)-HCC, NBNC-AFP(+)-HCC, and NBNC-AFP(-)-HCC) according to AFP levels and HBsAg status. The clinicopathologic and survival characteristics of NBNC-AFP(-)-HCC patients were compared with those of all other three groups. Out of the 105 NBNC-HCC patients, 43 patients (40.9%) had AFP-negative HCC. There were some differences in factors between the B-AFP(+) and NBNC-AFP(-) patients, such as age, body mass index (BMI), diabetes, and ALT (P < 0.05). On univariate analysis, tumour size, secondary tumour, and portal invasion were prognostic factors for overall survival (OS) and disease-free survival (DFS) (P < 0.05). Cox multivariate regression analysis suggested that tumour size and tumour number (P < 0.05) were independent predictors. In addition, compared with the B-AFP(+)-HCC, B-AFP(-)-HCC, and NBNC-AFP(+)-HCC groups, the NBNC-AFP(-)-HCC patients had the best DFS (P < 0.05). Compared with the B-AFP(+)-HCC and NBNC-AFP(+)-HCC groups, the NBNC-AFP(-)-HCC patients had better OS (P < 0.05), and survival rates were similar to those of B-AFP(-)-HCC patients. NBNC-AFP(-)-HCC patients had a relatively favourable prognosis. It can serve as a useful marker in predicting the risk of tumour recurrence in the early stages. [ABSTRACT FROM AUTHOR]

For the first time, a core drain current model based on surface potential without any implicit functions is developed for beta-phase gallium oxide (β-Ga2O3) power metal-oxide-semiconductor field-effect transistors (MOSFETs). The surface potential solution is analytically deduced by solving the Poisson equation with appropriate simplification assumptions in accumulation, partial-depletion, and full-depletion modes. Then, the drain current expression is analytically derived from the Pao–Sah integral as a function of the mobile charge density obtained from the surface potential at the source and drain terminals. In addition, nonlinear resistors in the source/drain access region are considered. It continuously predicts the characteristics of β-Ga2O3 power MOSFETs in all operation modes, including accumulation mode, partial-depletion mode, and full-depletion mode. Furthermore, the validity of the model is verified by comparing the results of the model with the numerical simulations carried out with the technology computer-aided design (TCAD) tool ATLAS Device Simulator from Silvaco. Good agreement between the proposed model and TCAD simulations is shown for β-Ga2O3 power MOSFETs with different intrinsic channel lengths, channel doping concentrations, and channel thicknesses. Ultimately, the Gummel symmetry test and the harmonic balance simulation test are performed to validate the model's robustness and convergence. [ABSTRACT FROM AUTHOR]

Machinery condition monitoring is a crucial application to enable condition-based predictive maintenance, realizing smart manufacturing in the context of the Industry 4.0 revolution. Machine learning that is built upon data correlation has recently become mainstream for machinery fault diagnosis. To boost the fault diagnosis performance of the machine learning methods, extracting high-quality fault-related features from raw time-series response signals is the key. In this research, we propose a signal processing-based feature extraction method, the so-called recurrence plot (RP), to evaluate the hidden dynamic characteristics of the vibration signals collected from the machinery system that is subject to fault. To further examine the properties of RP, the recurrence quantification analysis (RQA) is employed to quantitatively measure the patterns in the RP. The main strength of the proposed methodology lies in its excellent capability in handling the nonstationary signals with strong system nonlinearity and ambient noise, thereby yielding high-quality features with a high degree of separation in terms of the fault condition. A novel quantitative metric based on the silhouette is established to thoroughly assess the feature quality, providing systematic guidance for machinery condition monitoring. Comprehensive case studies using publicly accessible gear and bearing fault datasets are carried out to validate the proposed methodology. The enhanced performance of the proposed methodology also is highlighted by comparing it with other benchmark methods. [ABSTRACT FROM AUTHOR]

A radialized nanostructure catalyst self-assembled by MnOOH/MnO 2 nanosheets anchored with Pt single atoms (Pt SA -MnOOH/MnO 2) is developed by thermally assisted two-step electrochemical method. It implements real-time HCHO-to-CO 2 conversion (∼85.7%) and long-term continuous HCHO removal (∼98%) for 100 h in 15 ppm HCHO atmosphere at 25 °C at the first time, by the collaborative action of active hydroxyl and active oxygen species. [Display omitted] • Pt SA -MnOOH/MnO 2 catalyst is developed by thermally assisted two-step electrodeposition. • The catalyst shows the unique 3D radialized nanostructure. • It implements enhanced HCHO-to-CO 2 conversion (∼85.7%) in 15 ppm HCHO atmosphere at 25 °C. • In the dynamic test, the catalyst achieves long-term continuous HCHO removal (∼98%) for 100 h. • The synergistic action between Pt SA (O*) and MnOOH (OH*) for enhancing CO 2 conversion in indoor formaldehyde degradation. Maintaining high activity during prolonged catalysis is always the pursuit in catalytic degradation of organic pollutants. For indoor formaldehyde (HCHO) degradation, the accumulation of intermediates is the major factor limiting the conversion of HCHO to final product CO 2 (HCHO-to-CO 2 conversion) and long-lasting catalysis. Herein, a three-dimensional radialized nanostructure catalyst self-assembled by MnOOH/MnO 2 nanosheets anchored with Pt single atoms (Pt SA -MnOOH/MnO 2 with a trace platinum loading amount of 0.09%) is developed by thermally assisted two-step electrochemical method, which achieves enhanced CO 2 production in catalytic HCHO degradation at the room temperature by the collaborative action of active hydroxyl (OH*) and active oxygen species (O 2 *). By boosting intermediates' decomposing, the catalyst implements real-time HCHO-to-CO 2 conversion (∼85.7%) and long-term continuous HCHO removal (∼98%) during 100 h in a 15 ppm HCHO atmosphere at 25 °C under a weight hourly space velocity of 30000 mL/g cat ∙h. Density functional theory calculation shows that the formation energy of O 2 * from O 2 over Pt SA -MnOOH/MnO 2 is nearly half lower than that over Pt-MnO 2 catalyst. And decomposing accumulated intermediates gives the credit to OH* species sustainably generated by the combined action of MnOOH and O 2 *. The synergistic action between Pt SA and MnOOH contributes to the continuous production of O 2 * and OH* for enhancing CO 2 production in indoor catalytic formaldehyde degradation. [ABSTRACT FROM AUTHOR]

A radialized nanostructure catalyst self-assembled by MnOOH/MnO 2 nanosheets anchored with Pt single atoms (Pt SA -MnOOH/MnO 2) is developed by thermally assisted two-step electrochemical method. It implements real-time HCHO-to-CO 2 conversion (∼85.7%) and long-term continuous HCHO removal (∼98%) for 100 h in 15 ppm HCHO atmosphere at 25 °C at the first time, by the collaborative action of active hydroxyl and active oxygen species. [Display omitted] • Pt SA -MnOOH/MnO 2 catalyst is developed by thermally assisted two-step electrodeposition. • The catalyst shows the unique 3D radialized nanostructure. • It implements enhanced HCHO-to-CO 2 conversion (∼85.7%) in 15 ppm HCHO atmosphere at 25 °C. • In the dynamic test, the catalyst achieves long-term continuous HCHO removal (∼98%) for 100 h. • The synergistic action between Pt SA (O*) and MnOOH (OH*) for enhancing CO 2 conversion in indoor formaldehyde degradation. Maintaining high activity during prolonged catalysis is always the pursuit in catalytic degradation of organic pollutants. For indoor formaldehyde (HCHO) degradation, the accumulation of intermediates is the major factor limiting the conversion of HCHO to final product CO 2 (HCHO-to-CO 2 conversion) and long-lasting catalysis. Herein, a three-dimensional radialized nanostructure catalyst self-assembled by MnOOH/MnO 2 nanosheets anchored with Pt single atoms (Pt SA -MnOOH/MnO 2 with a trace platinum loading amount of 0.09%) is developed by thermally assisted two-step electrochemical method, which achieves enhanced CO 2 production in catalytic HCHO degradation at the room temperature by the collaborative action of active hydroxyl (OH*) and active oxygen species (O 2 *). By boosting intermediates' decomposing, the catalyst implements real-time HCHO-to-CO 2 conversion (∼85.7%) and long-term continuous HCHO removal (∼98%) during 100 h in a 15 ppm HCHO atmosphere at 25 °C under a weight hourly space velocity of 30000 mL/g cat ∙h. Density functional theory calculation shows that the formation energy of O 2 * from O 2 over Pt SA -MnOOH/MnO 2 is nearly half lower than that over Pt-MnO 2 catalyst. And decomposing accumulated intermediates gives the credit to OH* species sustainably generated by the combined action of MnOOH and O 2 *. The synergistic action between Pt SA and MnOOH contributes to the continuous production of O 2 * and OH* for enhancing CO 2 production in indoor catalytic formaldehyde degradation. [ABSTRACT FROM AUTHOR]

The polarization and depolarization current (PDC) measurement has been widely used to assess the aging degree of cross-linked polyethylene (XLPE) cables. However, the aging type identification of XLPE cables has not been fully considered. In this study we conduct PDC measurement on thermal-aged and water tree-aged XLPE cable samples respectively, upon which a graphical approach to assess the insulation condition and distinguish the aging type of XLPE cables is proposed. First, to designate the evaluation indexes of the insulation condition, multiple PDC measurement based dielectric parameters are considered. Then, to reflect the sensitivity of each evaluation index to the condition of XLPE cable, a combined weighting method based on the group fuzzy analytic hierarchy process (GFAHP) and entropy method (EM) is introduced. Furthermore, the improved radar chart method (RCM) based graphical approach is proposed to comprehensively and intuitively characterize the evaluation indexes. Finally, the aging degree of the XLPE cable samples is quantitatively assessed by calculating the area of the polygon in the chart. Simultaneously, utilizing the position of the areal coordinate and the shape characteristic parameter of the polygon, the thermal-aged and water tree-aged samples are effectively distinguished. [ABSTRACT FROM AUTHOR]

Circulating monocytes are precursors of both tissue macrophages and dendritic cells, and they can infiltrate the central nervous system (CNS) where they transform into bone marrow‐derived macrophages (BMDMs). BMDMs play essential roles in various CNS diseases, thus modulating BMDMs might be a way to treat these disorders because there are currently no efficient therapeutic methods available for most of these neurological diseases. Moreover, BMDMs can serve as promising gene delivery vehicles following bone marrow transplantation for otherwise incurable genetic CNS diseases. Understanding the distinct roles that BMDMs play in CNS diseases and their potential as gene delivery vehicles may provide new insights and opportunities for using BMDMs as therapeutic targets or delivery vehicles. This review attempts to comprehensively summarize the neurological diseases that might be treated by modulating BMDMs or by delivering gene therapies via BMDMs after bone marrow transplantation. [ABSTRACT FROM AUTHOR]

As one of the effective measures to reduce energy consumption of high-speed railway, train operation control has gained much attention because of its cost-effectiveness. Conventional approaches to eco-driving generally rely on single-mass train models and lack a thorough investigation into the vehicle cruising process, which can overlook real-world operation conditions and hinder further improvements in energy efficiency. To address these issues, this paper proposes a comprehensive framework for optimizing the cruising speed of high-speed trains (HSTs) based on track conditions to reduce energy consumed by motion resistances (ECMR). Firstly, a multibody dynamics simulation model of the HST is developed and validated to accurately evaluate ECMR under a range of operation scenarios. Simulation results show that the impact of speed on ECMR varies considerably according to track characteristics. Subsequently, a neural network is constructed to derive the regressive relationship between operation conditions and ECMR. On this basis, an optimization model for the cruising speed under different track conditions is formulated to minimize the ECMR of a railway segment with a given operation time. Finally, the effectiveness of the model is validated through case studies, which demonstrate that ECMR can be reduced by up to 27.85% compared to traditional control strategies. The proposed optimization method can be integrated with current train speed profiles to further enhance energy efficiency. This framework has broad applicability for addressing engineering problems related to train operation optimization. [Display omitted] • Energy consumed by motion resistances (ECMR) of high-speed trains (HSTs) is studied. • A dynamic model of the HST is developed to characterize ECMR under various scenarios. • A strong correlation between speed and track properties on ECMR is observed. • A unified framework is built to optimize the cruising speed for energy conservation. • Up to 27.85% of ECMR can be reduced through the cruising speed optimization. [ABSTRACT FROM AUTHOR]

With the widespread implementation of battery energy storage systems (BESSs), significant attention has been focused on issues involving electrical safety. The series arc hazard caused by loose connectors between batteries has become a serious problem. However, research findings for the evolution process of the series arc and the related hazard principle are still unclear. Therefore, in this study we focus on the series arc at the negative terminal of a 20 Ah prismatic lithium-ion battery, establish an experimental platform for the arc, and conduct research on the hazards process. Our results indicate that the arc can induce the thermal failure of the battery when the power supply voltage is 300 V and the circuit current is 15 A. Through a battery voltage analysis, computed tomography scans, and jellyroll disassembly, we uncover the evolution process and hazard laws of series arcs and clarify the failure pathways of arc-induced battery faults. The hotspots formed by arc melt the casing and cause electrolyte leakage. In addition, the heat transfer from the battery terminal to the jellyroll induces separator melting and internal short circuits in batteries. These cause an internal short circuit between the anode and the cathode, as well as combustion of the leaked electrolyte, which give rise to distinct thermal runaway behavior under different states of charge. By comparing runaway behavior with failures triggered by external heating, we clarify that the series arc is a novel risk factor that induces failure. This study addresses the gap in research related to arc effects on battery safety. This is crucial to the development of safe battery systems that do not present arc hazards. [Display omitted] • Heat transfer from arc leads to separator melting and internal short circuits. • Localized hotspots of arcs cause casing melting and electrolyte leakage. • Internal short circuit induces electrode failure and arc ignites electrolyte combustion. • State of charge is a crucial factor influencing the behavior of thermal runaway. [ABSTRACT FROM AUTHOR]

The application of nanotip-induced local electric fields has been demonstrated to effectively tailor the microenvironment and electrochemical properties of catalysts, sparking significant interest in this field. Modifying the electronic structure at active sites, enhancing the concentration of electrolytes and reactants nearby the active center, and leveraging the strong proximity effect generated by the nanotip-induced local electric field have resulted in notable enhancements in electrocatalytic activity, selectivity, and durability. This review offers a comprehensive and structured overview of the latest progress in harnessing the tip effect in electrocatalysis, emphasizing the advantages of the local electric field influenced by the tip effect. Furthermore, it delves into the fundamental principles and origins of the electric field induced by the tip effect. Detailed conversations on applying these principles to enhance the electrocatalytic performance are also provided. The purpose of this review is to present researchers with a fresh concept and direction for further elevating electrocatalytic performance. [Display omitted] [ABSTRACT FROM AUTHOR]

A multi-image encryption technique combining a four-dimensional chaotic system and multi-layer embedding is proposed because images are easily attacked from the outside during network transmission and that traditional image encryption algorithms have limitations such as long encryption time, low ciphertext image security, and poor information diffusion when a single image is encrypted multiple times. algorithm first creates a four-dimensional chaotic system to produce chaotic sequences; the system’s positive Lyapunov exponent indicates that it has chaotic properties in four directions; then, multiple images are processed using multi-layer embedding and chaotic sequences to create a composite image. Next, block scrambling and variable parameter Arnold’s scrambling are combined to perform block sorting and pixel disorder on the composite image. Lastly, the composite image is processed in separate blocks to execute various encryption techniques. To obtain the ciphertext image, several blocks are finally treated using either same-or-different-or diffusion processing. Simulation experiments show that this algorithm has a faster encryption time, better security and information diffusion in the encrypted image, a maximum correlation value of -\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$-$$\end{document}0.00244 between adjacent pixels in the ciphertext image, and UACI of 33.4663% and 99.6086%, respectively (the theoretical values are 0, 99.61% and 33.46%) [ABSTRACT FROM AUTHOR]