Central South University

Li-ion battery (LIB) packs have been widely used in the vehicle industry. The abnormality detection and localization of battery systems are receiving more and more attention. In this paper, the spatiotemporal entropy is proposed to detect and locate thermal abnormalities of LIB packs. Based on the Karhunen-Loève (KL) decomposition, the spatial entropy and temporal entropy can be constructed from different scales, and then appropriately integrated into the comprehensive spatiotemporal entropy. The kernel density estimation is employed to derive the detection threshold of the spatiotemporal entropy, based on which the abnormality detection can be achieved. The entropy contribution function is designed for abnormality localization based on the spatial basis function (SBF) variations in different modes. The physical meaning of the spatiotemporal entropy is explained from the perspective of the system disorder degree, energy concentration, and information theory. Experiments on the Li-ion battery pack under different fault conditions demonstrate that the proposed method can timely detect and precisely locate the abnormal cells at the early stage.

Nonlinear time-varying systems complicate the analysis and controller design compared to nonlinear time-invariant systems. Single-phase differential grid-tied inverter is a classic nonlinear time-varying system. In this article, an input-output linearization (IOL) based coordinate transformation and a nonlinear power decoupling control scheme are proposed for the single-phase differential grid-tied inverter. Because of the transformation, the nonlinear periodic time-varying system is transformed into a nonlinear time-invariant one. Based on the nonlinear time-invariant model, a nonlinear power decoupling control scheme based on the idea similar to back-stepping control is designed, which realizes good grid current control and power decoupling. Furthermore, the small-signal stability of the inverter is proved by eigenvalue analysis method in the linear time-invariant (LTI) framework. Experiment results validate the effectiveness of the proposed control strategy and stability analysis.

The modular design can effectively improve the circuit extensibility and control flexibility of equalizers for long series-connected battery strings. This paper proposes a modular battery equalizer based on phase-shift modulation. The LC resonant converters with synchronous rectification are employed as the automatic cell-level equalizer to realize any-cells-to-any-cells (AC2AC) equalization and soft-switching operation, which enhances the balancing speed and efficiency. Different cell-level equalizers are connected with auxiliary windings. The fast equalization between modules is achieved by adjusting the phase shift of driving signals applying for the cell-level equalizers. A prototype with three modules, each consisting of three series cells, is built and tested. Experimental and comparative results confirm the validity and characteristics of the proposed modular equalizer.

Ground faults can be caused by abnormal grounding of several different modules in the electrical traction drive system, leading to vulnerable train operation. Therefore, rapid and accurate diagnosis of ground faults is of great significance to improve safety and reliability of electric locomotives and electric multiple units (EMU). This paper proposes a mechanism and data hybrid driven method to diagnose the ground faults. Firstly, according to ground fault mechanism analysis, characteristic variables are constructed and several fault features are extracted by using signals selected from existing control system. Secondly, according to historical data, the probability membership function associated with fault features and typical ground fault types is established offline by fuzzy logic theory. Then, the probability information of fault features is fused using Dempster-Shafer (D-S) evidence theory and a decision-making method based on the basic probability number is applied to classify faults. Finally, field experiments are performed and the results show that compared with canonical correlation analysis (CCA) based method and on-board fault diagnosis method, the proposed method has better performance in the presence of large measurement noise and transient condition changes, and it is also promising for practical applications.

Due to the interconnected characteristics between subsystems and the strong correlation within subsystems, the monitoring of plant-wide processes has become a challenging problem, especially for tandem plant-wide processes that exist in various industrial fields, such as petrochemicals, metallurgy and sewage treatment. In this paper, a novel spatio-temporal monitoring method is proposed for the hot strip mill process, a typical tandem industrial process. Firstly, the plant-wide process is divided into different sub-blocks based on the tandem structure. Then, a distributed conditional variational recurrent autoencoder (CVRAE)-based process monitoring method is proposed to build the local latent variable model of each subsystem using relevant dynamic features extracted from the previous subsystem. The latent distributions and reconstructed errors are used to design local monitoring statistics for local process monitoring. A global monitoring statistic is established by deep support vector data description (SVDD) to monitor the whole process. Finally, the effectiveness and superiority of the proposed method are demonstrated by a hot strip mill (HSM) process case, which shows better monitoring performance compared to the existing methods.

There is a stable growth in aquaculture production to avoid seafood scarcity. The usage of eco-friendly feed additives is not only associated with aquatic animal health but also reduces the risk of deleterious effects to the environment and consumers. Aquaculture researchers are seeking dietary solutions to improve the growth performance and yield of target organisms. A wide range of naturally derived compounds such as probiotics, prebiotics, synbiotics, complex carbohydrates, nutritional factors, herbs, hormones, vitamins, and cytokines was utilized as immunostimulants in aquaculture. The use of polysaccharides derived from natural resources, such as alginate, agar, laminarin, carrageenan, fucoidan, chitin, and chitosan, as supplementary feed in aquaculture species has been reported. Polysaccharides are prebiotic substances which are enhancing the immunity, disease resistance and growth of aquatic animals. Further, chitin (CT), chitosan (CTS) and chitooligosaccharides (COS) were recognized for their biodegradable properties and unique biological functions. The dietary effects of CT, CTS and COS at different inclusion levels on growth performance, immune response and gut microbiota in aquaculture species has been reviewed. The safety regulations, challenges and future outlooks of CT, CTS and COS in aquatic animals have been discussed in this review.

The development of cobalt-based supported catalysts with high PMS catalytic activity and stability by adjusting the composition of the support is highly desirable yet remains scarce. In the work, a series of catalysts (Co 2 AlO 4 / Al 2 O 3-xSiO 2) were prepared by impregnation and high-temperature calcination using Al 2 O 3-xSiO 2 with a low Si-Al ratio as the support. Measurement techniques such as XRD, XPS, UV-DRS, FTIR, BET, SEM and HRTEM were used to characterize textural and chemical properties (ratio of Co 3+ /Co 2+ , specific surface area, pore size, pore volume, etc.). The ratio of Co 3+ /Co 2+ and pore volume of Co 2 AlO 4 /Al 2 O 3-xSiO 2 can be turned by controlling the ratio of Si to Al, which are closely related to the catalytic performance and reusability of the catalysts. The optimized catalyst (Co 2 AlO 4 /Al 2 O 3-0.25SiO 2) can completely degrade 10 mg/L p-nitrophenol (PNP) in 40 min in the pH range of 3-9 with excellent reusability. The effects of several reaction parameters (i.e., PMS dosage, Co 2 AlO 4 /Al 2 O 3-0.25SiO 2 dosage, reaction temperature, initial pH value, and inorganic ions) on PNP removal were comprehensively investigated. Sulfate radical (SO 4 • −) and singlet oxygen (1 O 2) are making a major contribution to the degradation of PNP. Moreover, a millimeter-scale catalyst (CoSiAl-0.25/Al 2 O 3 pellet) was prepared by sol adsorption and high-temperature calcination method, which maintained high oxidation activity after treatment of 18 L wastewater (PNP of 10 mg/L) in a continuous flow process. The method is simple and easy to operate on a large scale, providing a new perspective on the design and preparation of cobalt-aluminum spinel catalysts for activated PMS.

1) Extreme temperatures promote allergic asthma development in mice; 2) Extreme temperatures induce airway and lung damage in the asthmatic mouse model; 3) Exposure to temperature extremes, particularly high temperature, worsened airway hyperresponsiveness (AHR) and exacerbated allergic asthma.

Background: Despite mounting evidence connecting outdoor air pollution with otitis media (OM), the role of air pollutant(s) exposure during which critical window(s) on childhood OM remains unknown. Objectives: We sought to identify the key air pollutant(s) and critical window(s) associated with the onset and recurrent attacks of OM in kindgarteners. Methods: A combined cross-sectional and retrospective cohort study involving 8,689 preschoolers aged 3 to 6 years was performed in Changsha, China. From 2013 to 2020, data on air pollutants were collected from ambient air quality monitoring stations in Changsha, and the exposure concentration to each child at their home address was calculated using the inverse distance weighted (IDW) method. The relationship between air pollution and OM in kindergarteners was studied using multiple logistic regression models. Results: Childhood lifetime OM was associated with PM2.5, SO2 and NO2, with ORs (95% CI) of 1.43 (1.19-1.71), 1.18 (1.01-1.37) and 1.18 (1.00-1.39) by per IQR increase in utero exposure and with PM2.5, PM2.5-10 and PM10, with ORs = 1.15 (1.00-1.32), 1.25 (1.13-1.40) and 1.49 (1.28-1.74) for entire post-natal exposure, respectively. The 2nd trimester in utero and the post-natal period, especially the 1st year, were key exposure time windows to PM2.5 and PM10 associated with lifetime OM and the onset of OM. Similarly, the 4th gestational month was a critical window for all pollutants except CO exposure in relation to lifetime OM and OM onset, but not recurrent OM attacks. PM2.5 exposure during the nine gestational months and PM10 exposure during the first three years had cumulative effects on OM development. Our subgroup analysis revealed that certain children were more susceptible to the OM risk posed by air pollution. Conclusions: Early-life exposure to air pollution, particularly PM2.5 during the middle of gestation and PM10 during the early post-natal period, was associated with childhood OM.

User identity linkage, which aims to link identities of a natural person across different social platforms, has attracted increasing research interest recently. Existing approaches usually first embed the identities as deterministic vectors in a shared latent space, and then learn a classifier based on the available annotations. However, the formation and characteristics of real-world social platforms are full of uncertainties, which makes these deterministic embedding based methods sub-optimal. Besides, semi-supervised models utilize the unlabeled data to help capture the intrinsic data distribution. However, the existing semi-supervised linkage methods heavily rely on the heuristically defined similarity measurements to incorporate the innate closeness between labeled and unlabeled samples. Such manually designed assumptions may not be consistent with the actual linkage signals and further introduce the noises. To address the mentioned limitations, in this paper we propose a novel Noise-aware Semi-supervised Variational User Identity Linkage (NSVUIL) model. Specifically, we first propose a novel supervised linkage module to incorporate the available annotations. Each social identity is represented by a Gaussian distribution in the Wasserstein space to simultaneously preserve the fine-grained social profiles and model the uncertainty of identities. Then, a noise-aware self-learning module is designed to faithfully augment the few available annotations, which is capable of filtering noises from the pseudo-labels generated by the supervised module. The filtered reliable candidates are added into the labeled set to provide enhanced training guidance for the next training iteration. Empirically, we evaluate the NSVUIL model over multiple real-world datasets, and the experimental results demonstrate its superiority.

Accurate wind power curves (WPCs) are crucial for wind energy development and utilization, e.g., wind power forecasting and wind turbine condition monitoring. In the era of big data, large-scale datasets make the training of power curve models inefficient, especially for kernel-based models. Furthermore, most models do not take into account the error characteristics of WPC modeling. In this study, a large-scale generalized kernel-based regression model is proposed to solve the above problem. First, a generalized loss function, which can model both symmetric and asymmetric error distributions, is designed for model training. Then, the Nyström technique is employed to get the approximate kernel matrix, based on which an eigenvalue-based kernel regression framework is constructed. Next, a large-scale generalized kernel-based regression model is developed with model parameters tuned using the alternating direction method of multipliers. Before WPC modeling, a three-step data processing method based on isolation forest is designed to process missing data, irrational data, and outliers in the collected data. The WPC modeling results on four large-scale wind datasets demonstrate that the proposed model generates accurate WPCs with high efficiency. Furthermore, the effect of turbulence intensity on WPC modeling and the effectiveness of LSGKRM with multivariate inputs are also verified.

For a conventional wireless power transfer (WPT), two-stage structure is one of the most common topologies, of which the efficiency will be dramatically decreased by a cascaded dc-dc converter. In this work, a partial power processing (PPP) structu-re for WPT system is proposed to improve the system efficiency and reduce output ripple. The receiver is divided into the main receiver and the auxiliary receiver. Most of the power is trans-ferred to the load through the main receiver, while the auxiliary receiver is used to regulate the output voltage. As a result, the switching loss and device stress of the dc-dc cascaded at receiver can be significantly reduced. Besides, the optimal switching phase can be obtained by analyzing the output waveforms of two receiv-ers to minimize the output ripple. A 500-W prototype is built to validate the feasibility of the proposed system. With the same output capacitor, controlling the switching phase can reduce the output ripple by 36% compared with that without the control. The processing power of the proposed system is from 4% to 29% when the input voltage changes from 190 V to 250 V. The efficiency is improved by 2.4% compared with the two-stage structure under the same input and output conditions.

In this article, we investigate the problem of seeking Nash equilibrium (NE) in multiagent systems within cooperation–competition networks. Each agent aims to optimize a total cost function that accounts for its own interests as well as those of its cooperators, considering both cooperative and noncooperative interactions with other agents. It is worth noting that, unlike in existing N-coalition games, the agents in this study only have knowledge of whether they are cooperative or noncooperative with their neighboring agents; they do not have information about non-neighboring agents who might be cooperators. As a result, due to potential disconnections in the communication topology within the cluster, it is not possible to consider the entire cluster as a virtual player to optimize its objective functions. To address this issue, we developed an algorithm using the singular perturbation technique, which divides the system into two distinct timescales. We propose a novel estimation algorithm to estimate the total cost function of disconnected subnetworks within the fast system. In the slow system, the search for NE is based on a gradient algorithm, while the Lyapunov stability theory is utilized to analyze the convergence of the algorithms. Furthermore, we extend the problem to accommodate scenarios where multiple subnetworks exist within the network. Numerical simulations are conducted to demonstrate their capability for resolving the noncooperative game problem in cooperation–competition networks.

The transformer-less split-capacitor four-wire current source inverter (CSI) is effective to reduce leakage current, but its effect is closely related to the impedance of the neutral wire and common mode voltage. The leakage current can be almost eliminated when the impedance of the neutral wire is zero, but the zero impedance condition does not hold in practice. To further suppress the leakage current, a new carrier-based modulation scheme with reduced common mode (CM) voltage is proposed in this paper. The resulting CM voltage (peak-to-peak) is half that of the conventional modulation scheme. Moreover, the conducted interference is also effectively reduced. The experimental results validate the effectiveness of the proposed modulation scheme.

Objective: This study aimed to review relevant literature and develop a pictorial action plan (PAP) to enhance self-management among older patients with chronic obstructive pulmonary disease (COPD). Methods: In Stage 1, an integrative review was conducted to identify key elements of respiratory self-management action plans. In Stage 2, cartoon pictograms with plain descriptions were designed. In Stage 3, the PAP was validated by 40 older patients with COPD and an expert panel. Results: While the eight included studies demonstrated positive effects on knowledge and quality of life, key elements identified included: traffic light motif, plain and explicit language, and several action plan topics. The final PAP comprises three traffic light-coloured zones and 24 pictograms that introduce self-management strategies for normal, decreasing, and severely decreased airflow. After revising the cartoon characters, all of the pictograms received guessability ratings above 70% and acceptable mean translucency ratings. Discussion: The integrative review provides evidence about the effectiveness and key elements of PAPs. The PAP developed was found to be valid and feasible for use among older patients with chronic respiratory conditions. Practice implications: This study offered an example of translating evidence into patient education practice to enhance self-management in older patients with COPD.

Single abrasive particles ultrasonic vibration grinding serves as the foundation for investigating the ultrasonic vibration grinding process. This paper proposes an innovative semi-analytical model, known as the ultrasonic vibration grinding heat transfer analysis (UVGHTA) model, which accurately predicts the temperature field in single abrasive particles ultrasonic vibration grinding with complex motion-induced heat sources. Firstly, a Gaussian-shaped heat source model is established for the grinding zone. Then, the alternating direction implicit (ADI) scheme of the finite difference method is employed to solve the heat conduction partial differential equations with ultrasonic heat source load boundary conditions. During the numerical iteration process, a synchronized additional method for heat sources is introduced to incorporate the temperature rise caused by the continuously moving heat source into the calculations, resulting in a complete semi-analytical predictive model that accurately simulates the dynamic temperature field in ultrasonic vibration grinding. Finally, the temperature field calculation results of the proposed model are compared with the finite element software calculation results and the experimentally measured temperature values for verification. This study addresses the challenge of predicting the temperature field in single abrasive particles ultrasonic grinding and provides a new approach to predicting the work surface temperature field in the heat transfer process involving multi-dimensional motion-induced time-varying heat sources.

An electrochemical sensor for the detection of uric acid was constructed using cobalt oxide-modified porous carbon@multi-walled carbon nanotube (MWCNTs) composite material for the modification of the electrode. Firstly, ZIF-67 is generated on carbon nanotubes using the surfactant cetylammonium bromide (CTAB) as template. The vesicles generated by CTAB act as nucleation sites for the in situ growth of ZIF-67. Then, cobalt oxide-modified porous carbon was obtained after high-temperature carbonization of ZIF-67, leading to the formation of cobalt oxide-modified porous carbon@MWCNT composite materials. Co-N and Co-O active sites on the composite material can improve the oxidation of uric acid on the electrode surface, leading to enhanced sensitivity and selectivity for uric acid detection. The sensor has a good linear range from 1 to 40 μM for uric acid detection with a detection limit of 0.09 μM. The sensor was utilized for determination of uric acid in actual serum samples. Graphical abstract

The influences of tensile and compressive stresses on stress relaxation behavior and mechanical properties of 2219 aluminum alloy have been systematically studied with different initial stress levels at 165℃. Under the same initial stress level and ageing time, tensile stress relaxation is greater than that in compression. The stress relaxation difference between them mainly originates from the primary relaxation stage, while the steady-state relaxation stage is basically identical. With the increase of stress level, the yield strength of tensile-stress-relaxation aged sample augments gradually, and the compressive stress sample fluctuates slightly. The corresponding deformation and strengthening mechanisms are revealed through transmission electron microscopy observation and analysis. When the initial stress is 100MPa, the sample in compression has larger diameter and volume fraction of the mixed GP I and GP II precipitates with unobvious stress- oriented effect. These microstructural features in compression lead to higher yield strength and smaller stress relaxation rate than the tensile sample. Nevertheless, at the high initial stress of 170MPa, massive dislocations and main strengthening θ’precipitates in tensile sample result in stronger yield strength and rapider stress relaxation as compared with compressive sample.

Tumor metastasis significantly impacts the prognosis of non-small cell lung cancer (NSCLC) patients, with lymph node (LN) metastasis being the most common and early form of spread. With the development of adjuvant immunotherapy, increasing attention has been paid to the tumor-draining lymph nodes（TDLN) in early-stage NSCLC, especially tumor-metastatic lymph nodes, which provides poor prognostic information but has potential benefits in adjuvant treatment. We showed the remodeled immune environment in TDLNs through using TCR-seq to analyse 24 primary lung cancer tissues and 134 LNs from 24 lung cancer patients with or without LN metastasis. Additionally, we characterized the spatial profiling of immunocytes and tumor cells in TDLNs and primary tumor sites through using multi-IHC. We found the remodeled immune environment in TDLNs through analyzing primary lung cancer tissues and LNs from NSCLC patients with or without LN metastasis. Considering the intricate communication between tumor and immunocytes, we further subdivided TDLNs, revealing that metastasis-negative LNs from LN-metastatic patients (MNLN) exhibited greater immune activation, exhaustion, and memory in comparison to both metastasis-positive LNs (MPLN) and TDLNs from non-LN-metastatic patients (NMLN). Our data indicate that LN metastasis facilitated tumor-specific antigen presentation in TDLNs and induces T cell priming, while existing tumor cells generate an immune-suppressive environment in MPLNs through multiple mechanisms. These findings contribute to a comprehensive understanding of the immunological mechanisms through which LN metastasis influences tumor progression and plays a role in immunotherapy for NSCLC patients.

Fenugreek (Trigonella foenum-graecum) is a leguminous plant that is consumed as a spice to improve the flavor of food around the world. Fenugreek has been extensively cited to hold medicinal properties such as hypo-cholesterolemic, anti-carcinogenic, antidiabetic, immunological activity, and as carrier of potential antioxidants. Aside from its therapeutic uses as an alternate and complementary medicine, Fenugreek has been in use as a stabilizing, emulsifying, and binding ingredient in foodstuff and in manufacturing numerous processed foods. Fenugreek leaves and seeds are extremely valuable and featured with important phytochemicals such as amino acids, steroidal saponins, carbohydrates, alkaloids, and other organic and inorganic substances and minerals of human health significance. Fenugreek seeds and leaves can be consumed either fresh or cooked as a spice, culinary ingredient, flavoring agent, and or preservative. In this chapter, we will discuss the possible uses of fenugreek as a nutraceutical ingredient and as a functional food.