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Background The technical and tactical diagnosis of table tennis is extremely important in the preparation for competition which is complicated by an apparent nonlinear relationship between athletes’ performance and their sports quality. The neural network model provides a high nonlinear dynamic processing ability and fitting accuracy that may assist in the diagnosis of table tennis players’ technical and tactical skill. The main purpose of this study was to establish a technical and tactical diagnosis model of table tennis matches based on a neural network to analyze the influence of athletes’ techniques and tactics on the competition results. Methods A three-layer Back Propagation (BP) neural network model for table tennis match diagnosis were established. A Double Three-Phase evaluation method produced 30 indices that were closely related to winning table tennis matches. A data sample of 100 table tennis matches was used to establish the diagnostic model ( n  = 70) and evaluate the predictive ability of the model ( n  = 30). Results The technical and tactical diagnosis model of table tennis matches based on BP neural network had a high-level of prediction accuracy (up to 99.997%) and highly efficient in fitting ( R 2  = 0.99). Specifically, the technical and tactical diagnosis results indicated that the scoring rate of the fourth stroke of Harimoto had the greatest influence on the winning probability. Conclusion The technical and tactical diagnosis model of table tennis matches based on BP neural network was highly accurate and efficiently fit. It appears that the use of the model can calculate athletes’ technical and tactical indices and their influence on the probability of winning table tennis matches. This, in turn, can provide a valuable tool for formulating player’s targeted training plans.

Artificial intelligence (AI) is constrained by the high energy consumption of von Neumann architectures and the limited scalability of traditional silicon‐based synapses. Two‐dimensional (2D) van der Waals (vdW) materials, with their atomic‐scale thickness, tunable electronic properties, and ease of heterogeneous integration, offer a promising platform for next‐generation neuromorphic hardware. Here, the authors report a vdW floating‐gate transistor (BP/POx/WSe2) with a high on‐off current ratio (≈105) and a large memory window (73 V), benefitting from the optimized interface band alignment via 2D heterostructure engineering. Key synaptic functionalities are demonstrated, including short‐term plasticity (STP), long‐term plasticity (LTP), and electro‐optical dependent plasticity, short‐term paired‐pulse facilitation (PPF), and long‐term potentiation/depression (LTP/D). Notably, the device mimics human visual memory under optical stimuli while achieving ultralow energy consumption (10 pJ per synaptic event), outperforming most reported photoelectronic synaptic devices. Furthermore, a two‐path convolutional neural network (CNN) is introduced that synergistically merges optical and electronic inputs, which enables efficient feature extraction and weight updating, and achieves 96.9% accuracy in the Labeled Faces in the Wild (LFW) face recognition task. The work presents a promising approach for neuromorphic electronics, paving the way for energy‐efficient vision processing in edge AI applications. In this paper, a novel floating gate transistor (BP/POx/WSe2) is developed, which enables rich synaptic functionality under optoelectronic conditions and can mimic human visual memory. By introducing a two‐path convolutional neural network that synergistically fuses optical and electronic inputs, it can achieve efficient feature extraction and weight updating, and achieve 96.9% accuracy in the (LFW) face recognition task.

•A novel method was developed to simultaneously record SCE and EEG signals.•The method's validity was evaluated using a classical resting-state study design.•Spectral characteristics in resting state were delineated for SCE and EEG signals.•Spectral power of delta and alpha oscillations was able to predict EC/EO behaviors.•Top-down regulation from the brain to spinal cord was found in delta oscillations. The brain and the spinal cord together make up the central nervous system (CNS). The functions of the human brain have been the focus of neuroscience research for a long time. However, the spinal cord is largely ignored, and the functional interaction of these two parts of the CNS is only partly understood. This study developed a novel method to simultaneously record spinal cord electrophysiology (SCE) and electroencephalography (EEG) signals and validated its performance using a classical resting-state study design with two experimental conditions: eyes-closed (EC) and eyes-open (EO). We recruited nine postherpetic neuralgia patients implanted with a spinal cord stimulator, which was modified to record SCE signals simultaneously with EEG signals. For both EEG and SCE, similar differences were found in delta- and alpha-band oscillations between the EC and EO conditions, and the spectral power of these frequency bands was able to predict EC/EO behaviors. Moreover, causal connectivity analysis suggested a top-down regulation in delta-band oscillations from the brain to the spinal cord. Altogether, this study demonstrates the validity of simultaneous SCE-EEG recording and shows that the novel method is a valuable tool to investigate the brain-spinal interaction. With this method, we can better unite knowledge about the brain and the spinal cord for a deeper understanding of the functions of the whole CNS.

Atmospheric pressure plasma jet (APPJ) is a promising technique for the sterilization of pathogenic microorganisms in an ambient environment. In this work, a helium-APPJ was generated by double dielectric barrier discharge and applied to the sterilization of model microorganism in air and water. Discharge characteristics (including waveform and frequency of applied voltage), jet properties (such as feed gas flow rate, jet length, thermal effect, and optic emission spectra), and sterilization performance (in terms of clear/sterilized area, size of plaques, and sterilization efficiency) were investigated. Homogeneous helium plasma jet was generated in an energy-efficient way (18 kHz, 6 kV, 0.08 W) with a 19 mm jet and limited heating. The He-APPJ achieved good sterilization performances within very short treatment time (as short as 30 s). For surface sterilization, the area of clear zone and size of the plaque were 1809 mm2 and 48 mm, respectively, within 5 min treatment. For water sterilization, 99.8% sterilization efficiency was achieved within 5 min treatment. The optic emission spectra suggest that active species such as excited molecules, ions, and radicals were produced in the He-APPJ. The as-produced active species played important roles in the sterilization process.

In medical emergency scenarios, conventional single‐parameter monitoring cannot fully assess patient conditions, and current technologies lack intelligent emergency‐state recognition, which delays timely treatment. To overcome these challenges, this study develops a high‐performance flexible thermoelectric textile‐based wearable system that generates a 139.7 mV open‐circuit voltage at ΔT = 30 K. To prevent skin‐thermoelectric contact from distorting sensor readings, a Kirigami spacer is integrated to ensure proper air gaps while maintaining flexibility and breathability. By integrating optical cardiac sensing, the system establishes a thermoelectric‐optical system for wireless real‐time cooperative monitoring of both respiration and cardiac activity. The research systematically investigates the effects of airflow velocity on thermoelectric output, leading to the development of an innovative dynamic airflow‐thermoelectric response theory. This theory enables speech recognition through airflow variations with 98% accuracy. Additionally, the study creates a multi‐source fusion recognition model that combines respiratory patterns, speech‐airflow, and cardiac signals to identify emergency states with 98% accuracy. These advances supplement the theoretical understanding of thermoelectric responses to physiological activities while providing decision support for emergency conditions, demonstrating considerable potential for clinical application. For insufficient medical emergency monitoring, this study develops a wearable system with flexible thermoelectric textiles and thermoelectric‐optical sensing. It establishes a dynamic airflow‐thermoelectric response theory (enabling 98% speech recognition) and a multi‐source fusion model for 98% emergency state recognition, aiding clinical decision‐making.

Reliable machine learning techniques have vast potential in assisting clinical decision-making, including applications in bioinformatics and medical imaging analysis. However, AI-driven medical research is often limited by data scarcity, data quality, and the black-box nature of machine learning models. Thus, there is an urgent need for reliable surrogate models to overcome these challenges, enabling accurate learning from small datasets to guide clinical diagnosis. Here, we conducted a retrospective observational clinical study and proposed a data-driven predictive model that estimates mean pulmonary artery pressure (mPAP) based on individual patient clinical diagnostic features, enabling accurate assessment of pulmonary hypertension. Furthermore, we innovatively incorporate CMR-related features into the disease evaluation framework. Compared to traditional invasive measurement methods, this framework can not only accurately predict a patient’s mPAP using easily accessible noninvasive physiological features but also incorporate uncertainty quantification to extract qualitative patterns, aiding clinical diagnosis.

Artificial intelligence (AI) is constrained by the high energy consumption of von Neumann architectures and the limited scalability of traditional silicon‐based synapses. Two‐dimensional (2D) van der Waals (vdW) materials, with their atomic‐scale thickness, tunable electronic properties, and ease of heterogeneous integration, offer a promising platform for next‐generation neuromorphic hardware. Here, the authors report a vdW floating‐gate transistor (BP/PO x /WSe 2 ) with a high on‐off current ratio (≈10 5 ) and a large memory window (73 V), benefitting from the optimized interface band alignment via 2D heterostructure engineering. Key synaptic functionalities are demonstrated, including short‐term plasticity (STP), long‐term plasticity (LTP), and electro‐optical dependent plasticity, short‐term paired‐pulse facilitation (PPF), and long‐term potentiation/depression (LTP/D). Notably, the device mimics human visual memory under optical stimuli while achieving ultralow energy consumption (10 pJ per synaptic event), outperforming most reported photoelectronic synaptic devices. Furthermore, a two‐path convolutional neural network (CNN) is introduced that synergistically merges optical and electronic inputs, which enables efficient feature extraction and weight updating, and achieves 96.9% accuracy in the Labeled Faces in the Wild (LFW) face recognition task. The work presents a promising approach for neuromorphic electronics, paving the way for energy‐efficient vision processing in edge AI applications.

Working women in Shanghai are a high-risk group of suffering work stress and burnout. Women have been found to be affected by work-family conflicts, which results in lower health-related quality of life (HRQoL), higher job stress, and burnout. This study evaluated the potential physical activity and counselling intervention effects on health outcomes of working women in Shanghai. Participants were randomly recruited from eight communities of Shanghai using the stratified cluster sampling method. A total of 121 female workers took part in this study, who were randomly divided into three groups: a control group and two intervention groups (individual-based and group-based intervention). The first intervention involved a moderate physical activity program and an individual based counselling intervention, while the second included the same physical activity program, but with a group counselling approach. Both interventions lasted 12 weeks. Subjective perceptions of work stress, burnout, and HRQoL were measured before and after the intervention. In the control group, the HRQoL value decreased after the intervention, with the mean value falling from 91.59 to 87.10, while there was no significant difference found between participants for stress (p = 0.752) and burnout (p = 0.622) before and after the intervention. After the intervention, the value of stress and burnout decreased, and the value of HRQoL increased in the two intervention groups. At the intervention’s completion, there were significant differences compared between the two intervention groups and the control group separately regarding changes in burnout and HRQoL (all p = 0.000). For stress, the group-based intervention group exhibited a significant difference compared to the control group (p = 0.000), while the individual-based intervention group did not (p = 0.128). A Physical activity and counselling intervention delivered either in a group or individual format could reduce stress, burnout, and improve HRQoL of working women in Shanghai, and the group interventions were potentially more effective than those targeted at individuals.

To evaluate and summarize the best available evidence on pain assessment and management after hemorrhoidal surgery, thereby providing an evidence-based foundation for the future development of clinical practice guidelines. Literature was systematically retrieved using the \"6S\" evidence model, a hierarchical framework designed to prioritize the highest quality synthesized evidence. Searches were conducted across key databases (PubMed, Embase, Cochrane Library) from inception to February 1, 2025. Two independent reviewers performed screening, data extraction, and quality appraisal, with discrepancies resolved by consensus or a third reviewer. A total of 22 studies were included, comprising 1 clinical decision, 3 guidelines, 7 expert consensuses, 3 evidence summaries, and 8 systematic reviews. These yielded 29 key evidence statements across seven domains: principles of pain management, pain assessment, pharmacological and non-pharmacological interventions, Traditional Chinese Medicine (TCM), wound cleansing and dressing selection, and health education. This study consolidates 29 evidence-based recommendations from seven clinical domains essential for post-hemorrhoidectomy pain management. These findings directly inform clinical practice guidelines through the establishment of standardized frameworks for validated pain assessment tools, core pain management principles, multimodal analgesia protocols integrating both pharmacological and non-pharmacological interventions, TCM therapeutic approaches, evidence-based wound cleansing and dressing selection standards, and structured health education programs, thereby enabling evidence-driven clinical standardization to improve outcomes.

This project studies the conversion of CO2 into fuels and chemicals in a dielectric barrier discharge (DBD) reactor. CO2, H2 and CH4 have been used as reactants, and special attention has been paid on understanding the plasma-catalytic synergy when a catalyst is placed in a plasma discharge. CO2 and CH4 are major greenhouse gases, responsible for the global greenhouse effect and climate change. The overall aim of this project is to initiate CO2 hydrogenation and biogas reforming at ambient temperature and atmospheric pressure by using plasma-catalysis. In this project, non-thermal plasma has been generated in a DBD reactor with and without a packed-bed of catalyst, enabling the CO2 conversion to be investigated under three conditions: Plasma alone, thermal catalysis and plasma-catalysis. Transitional metal catalysts such as Cu, Co, Mn, and Ni supported on Al2O3 and SiO2 have been screened, and their performance in the CO2 hydrogenation and biogas reforming have been compared under the three conditions. The synergy between non-thermal plasma and catalysts has been clearly identified. The effects of a catalyst’s properties and operational parameters on the reactions have also been studied. The project starts by the investigation of CO2 hydrogenation with H2. Results showed that reverse water-gas shift reaction and CO2 methanation were dominant in the plasma CO2 hydrogenation process. Compared to plasma CO2 hydrogenation without a catalyst, the combination of plasma with Cu/Al2O3, Mn/Al2O3 and Cu-Mn/Al2O3 catalysts enhanced the conversion of CO2 by 6.7% to 36%. The Mn/Al2O3 catalyst showed the best catalytic activity, as it increased the CO yield by 114% and the energy efficiency of CO production by 116%. The Ni/Al2O3 was even better than the Mn/Al2O3 catalyst, while its presence in the DBD reactor has clearly demonstrated a plasma-catalytic synergy at low temperatures. In addition, the introduction of argon in the reaction has enhanced the conversion of CO2, the yield of CO and CH4 and the energy efficiency of the plasma process. The formation of metastable argon (Ar*) in the plasma has created new reaction-routes which made a significant contribution to the enhanced CO2 conversion and CH4 yield. Biogas reforming has also been initiated at ambient temperatures by non-thermal plasma. The combination of plasma with the Co/Al2O3, Cu/Al2O3, Mn/Al2O3 and Ni/Al2O3 catalysts significantly enhanced CH4 conversion and showed a plasma-catalytic synergy for CH4 conversion and overall energy efficiency of the process. The best CH4 conversion of 19.6% and syngas production have been achieved over the Ni/Al2O3 catalyst at a discharge power of 7.5 W and a gas flow rate of 50 ml min-1 . Moreover, the addition of K-promoter into the catalyst has further improved the performance of the Ni/Al2O3 catalyst.A conclusion of the findings of this project and outlook for further work is presented in Chapter seven, where it is concluded that non-thermal plasma has initiated the CO2 hydrogenation and biogas reforming at lower temperatures, comparing with thermal catalytic processes. The combination of plasma and catalyst has further improved the performance of the hydrogenation processes, in terms of conversion, yield, and energy efficiency, while significant synergy between DBD plasma and catalysts has been observed. By upgrading the catalyst and adjusting the operational parameters (e.g. molar ratio of feed gas, preparation method of catalyst, composition of catalyst, and promoters), the plasma-catalytic CO2 hydrogenation and biogas reforming processes can be further optimised.