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Abstract Pulmonary artery hypertension (PAH) is a chronic vascular disease defined by the elevation of pulmonary vascular resistance and mean pulmonary artery pressure, which arises due to pulmonary vascular remodelling. Prior research has already established a link between the autonomic nervous system (ANS) and PAH. Therefore, the rebalancing of the ANS offers a promising approach for the treatment of PAH. The process of rebalancing involves two key aspects: inhibiting an overactive sympathetic nervous system and fortifying the impaired parasympathetic nervous system through pharmacological or interventional procedures. However, the understanding of the precise mechanisms involved in neuromodulation, whether achieved through medication or intervention, remains insufficient. This limited understanding hinders our ability to determine the appropriate timing and scope of such treatment. This review aims to integrate the findings from clinical and mechanistic studies on ANS rebalancing as a treatment approach for PAH, with the ultimate goal of identifying a path to enhance the safety and efficacy of neuromodulation therapy and improve the prognosis of PAH.

To reduce the emission of fine particulate matter (PM) from gasoline engines, a gasoline particulate filter (GPF) filtration model that considers the fractal characteristics of both particulate matter and filters is designed and its filtration characteristics are analyzed based on several GPF filtration models. Based on the GPF filtration mechanism, a new mathematical model of fiber efficiency filtration is established after correcting the classical filtration formula based on correcting the particle size of particles and considering the relationship between the porosity of the fiber filter and the fractal dimension of the filter. Simulation of the filtration efficiency of the established mathematical filtration model is carried out to obtain visual filtration efficiency curves. The filtration efficiency and pressure drop of the model are analyzed, and the effects of parameters such as particle fractal dimension, filter fractal dimension, filter thickness, flow rate, and fiber diameter on filtration efficiency are also comprehensively analyzed. The results show that when the fractal dimension of particles is in the range of 1.8-2.8, the most penetrating particle size decreases with the increase of the fractal dimension of particles. When the particle size is small, the effect of fractal dimension on filtration efficiency is not obvious. When the particle size is larger than the most penetrating particle range, the larger the fractal dimension, the filtration efficiency increases obviously. Considering the relationship between the fractal dimension of the filter body and the porosity and the influence of each on the filtration efficiency, the optimal fractal dimension of the filter body should be between 1.82 and 1.925, and the thickness of the filter has some influence on the filtration efficiency. The slower the gas flow rate is, the better the trapping effect will be. However, considering the problem of automobile emission, a lower exhaust speed can be chosen. Increasing the fiber diameter can improve the trapping efficiency of the model, but will reduce the porosity of the trapping collective. Therefore, the fiber diameter should not be too large under the combined action.

Substations play a crucial role in the proper operation of power systems. Online fault diagnosis of substation equipment is critical for improving the safety and intelligence of power systems. Detecting the target equipment from an infrared image of substation equipment constitutes a pivotal step in online fault diagnosis. To address the challenges of missed detection, false detection, and low detection accuracy in the infrared image object detection in substation equipment, this paper proposes an infrared image object detection algorithm for substation equipment based on an improved YOLOv8n. Firstly, the DCNC2f module is built by combining deformable convolution with the C2f module, and the C2f module in the backbone is replaced by the DCNC2f module to enhance the ability of the model to extract relevant equipment features. Subsequently, the multi-scale convolutional attention module is introduced to improve the ability of the model to capture multi-scale information and enhance detection accuracy. The experimental results on the infrared image dataset of the substation equipment demonstrate that the improved YOLOv8n model achieves mAP@0.5 and mAP@0.5:0.95 of 92.7% and 68.5%, respectively, representing a 2.6% and 3.9% improvement over the baseline model. The improved model significantly enhances object detection accuracy and exhibits superior performance in infrared image object detection in substation equipment. [ABSTRACT FROM AUTHOR]

Temperature compensation is the main measure to solve the problem that the detection accuracy of non-dispersive infrared CO2 gas sensor is affected by temperature. As the measurement accuracy of the non-dispersive infrared CO2 gas sensor is easily affected by the ambient temperature, this article analyzes the reasons why the sensor is affected by temperature, and proposes a temperature compensation method that integrates the Whale Algorithm (WOA) and BP neural network. The whale algorithm is used to optimize the weights and thresholds of the BP neural network to build a temperature compensation model for the non-dispersive infrared CO2 gas sensor and compare the superiority with the traditional BP neural network model and particle swarm optimization (PSO) BP neural network model. The experimental results show that the temperature compensation model error of WOA-BP algorithm is lower than 30 ppm, and the average absolute error percentage is 3.86%, which is far better than BP neural network and PSO-BP neural network, and effectively reduces the influence of temperature on the accuracy of the sensor. [ABSTRACT FROM AUTHOR]

Background18F-Flutemetamol (18F-FMM) PET/CT imaging has been widely applied to the diagnosis of mild cognitive impairment and Alzheimer's disease.PurposeThis study aims to explore the distributions of 18F-FMM in different brain regions of mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients and the factors that may affect the combination of 18F-FMM, evaluating the use of 18F-FMM PET/CT imaging in the diagnosis of MCI and AD patients.MethodsFrom July 2020 to April 2021, 24 subjects with MCI (12 males, 12 females, aged of (70.54±9.27) years old), 25 subjects with AD (11 males, 14 females, aged (70.56±7.30) years old) and 9 subjects as normal controls (NC, 2 males, 7 females, aged (57.67±9.00) years old) were recruited from Shanghai. The 18F-FMM PET/CT brain imaging of all subjects were qualitatively and semi-quantitatively analyzed by visual evaluation and standardized uptake value ratio (SUVR), respectively. Student's t-test, one-way analysis of variance and rank sum test were used for the comparison of quantitative variables. Pearson's chi-squared test and Fisher's exact test were used for the comparison of qualitative variables. The diagnostic efficiency was obtained by ROC curve. Multiple linear regression analysis was used to analyze the factors that might affect the combination of 18F-FMM.ResultsThe positive rate of visual assessment was significantly different in NC, MCI and AD groups (χ2=26.85, p < 0.001), and significant differences existed statistically in SUVR of all brain regions among the three groups (H value: 11.54~19.34, F value: 7.43, 11.48, all p < 0.01). The mean values of SUVR in the whole brain of the three groups were statistically different (Z value: -3.87, -2.79, -2.45, all p < 0.016 7). Compared with MCI group, the positive rate in occipital lobe, temporal lobe, parietal lobe and posterior cingulate gyrus was statistically significant (χ2=7.53~10.89, all p < 0.01). With respect to NC, SUVR of all brain regions was lower than that in AD, while SUVR of temporal lobe, frontal lobe and precuneu were lower than that in MCI. Meanwhile, SUVR of occipital lobe, temporal lobe, frontal lobe and parietal lobe in MCI were lower than that in AD. Receiver operating characteristic (ROC) curves showed that the SUVR value of temporal lobe and the mean value of SUVR in the whole brain had the ability to distinguish NC, MCI and AD in sequence. Multiple linear regression analysis showed that cognitive level was significantly negatively correlated with 18F-FMM SUVR in different brain regions.Conclusion18F-FMM PET/CT brain imaging could reflect the cognitive level of middle-aged and elderly people intuitively and has clear diagnostic value for MCI and AD.

BackgroundSubjective cognitive decline (SCD) and mild cognitive impairment (MCI) are a transitional state between normal aging and mild dementia, and most of them have potential Alzheimer's Disease (AD) pathology. Studying the pathophysiological characteristics of SCD and MCI is helpful to the early diagnosis of AD.PurposeThis study aims to investigate the brain perfusion differences among SCD, MCI and AD, and to evaluate the application value of brain perfusion SPECT/CT imaging combined with NeuroGamTM software in these diseases.MethodsFrom July 2020 to April 2021, 15 patients with SCD, 29 patients with MCI and 36 patients with AD were collected from several general hospitals in Shanghai. All subjects underwent neuropsychological test and 99mTc-ECD brain perfusion SPECT/CT imaging. The perfusion levels of Brodmann areas (BA) in the three groups were semi-quantitatively analyzed by NeuroGamTM, and compared with normal database. Student's t-test, one-way analysis of variance and rank sum test were used for the comparison of quantitative variables. Pearson's chi-squared test was used for the comparison of qualitative variables.ResultsThere is no significant difference in BA 4~11, 17~25, 28, 31~32, 36~40 and 44~47 between SCD group and MCI group. The mean perfusion values of BA 8-right(R), 20-left(L), 21R, 23L, 23R, 24L, 31L, 38L, 38R and 45R in SCD group are significantly higher than those in AD group (p

Byline: Juan. Yang, Xiaohui. Zhao, Haijing. Sui, Xueyuan. Liu Background: The prevalence of mild cognitive impairment (MCI), herein China, was without involving the suburban communities, where the awareness of MCI [...]

Aiming at the problem of wind curtailment caused by the lack of system flexibility, an optimal scheduling strategy for improving the flexibility of the electricity-gas- heat interconnection system by the coordinated operation of the gas network management and storage characteristics and the heat storage characteristics of the heat network is proposed. Firstly, the influence of gas network storage and heat network storage on improving flexibility is analyzed respectively. Then, an electricity-gas-heat interconnected system scheduling model considering the dynamic characteristics of gas network management and t the delay characteristics of heat storage in heat network, which greatly improves the imbalance between supply and demand of flexibility in space and time brought by the anti-peak regulation characteristics of wind power to the system. Finally, the IEEE-24 nodes power system, Belgium's 20-node natural gas network and 6- node thermal network are used, for example, analysis. The results show that the proposed scheduling scheme improves the flexibility of the system while its operating cost and wind curtailment cost are the lowest, which promotes the consumption of wind power. [ABSTRACT FROM AUTHOR]

The main reducer is the main component of the whole vehicle, and its main function is to realize deceleration and torque increase. For Baha racing car, in order to improve the dynamic performance of the whole car, the main reducer is designed from the aspects of layout, transmission ratio distribution, shift mode, overall size and shell structure. Calculate the transmission ratio range of reducer according to the performance parameters of transmission parts, and verify the rationality of transmission ratio; Then determine the parameters of gear according to the transmission ratio and related parameters, and finally design the parameters of gear according to the transmission ratio and related parameters, and finally design the parameters of other parts of reducer. Based on the determined parameters, 3D modeling software UG is used to build 3D models of various parts of the reducer, and finite element analysis software ANSYS is used to simulate and analyze the parts to check whether the comprehensive mechanical properties meet the requirements. In this paper, the design of the main reducer realizes the comprehensive design of small size, light weight, reasonable transmission ratio distribution, high reliability, shifting gears during driving, and the comprehensive mechanical properties also meet the requirements.

This study focuses on a small agricultural diesel engine fueled with B20 (20% biodiesel and 80% diesel by volume) blend fuel in a plateau area. The combustion chamber's structural parameters and fuel injection angle were taken as variables at peak torque conditions. First, a full factorial design was used for experimental design. Second, the back-propagation (BP) neural network was employed to predict the indicated thermal efficiency and the indicated specific NOx emission. Third, the non-dominated sorting genetic algorithm-II (NSGA-II) was utilized to optimize the indicated thermal efficiency and the indicated specific NOx emission. Finally, the technique for order of preference by similarity to ideal solution (TOPSIS) method was applied to obtain optimal solutions, and a three-dimensional numerical simulation was conducted to verify the optimization results. The optimization results indicate that the shape characteristics of the combustion chamber have a certain influence on the engine's performance. The optimized design significantly reduces NOx emissions, by 22.83%, compared to the original engine, whilst maintaining the engine's performance. [ABSTRACT FROM AUTHOR]