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The aim of this study is to analyze the clinical characteristics and outcomes of Chinese patients with progressive multifocal leukoencephalopathy (PML) who were treated with programmed cell death protein 1 (PD1) blockade therapies. We retrospectively analyzed patients who were admitted to our hospital between October 1, 2020, and October 1, 2022, diagnosed with PML and treated with PD1 blockade therapies. Four patients with PML who were treated with PD1 blockade therapies were identified. All patients were male, and their ages ranged from 19 to 54 years old. One patient (Case 2) exhibited mild pleocytosis, while three patients (Cases 2–4) had markedly reduced T lymphocyte cell counts prior to treatment. The time interval between symptom onset and treatment initiation ranged from six to 54 weeks. All patients received pembrolizumab treatment, with a total of two to four doses administered. Three patients who responded to pembrolizumab treatment showed clinical improvement starting around 8 weeks after the initiation of therapy. Although one patient did not show clinical improvement, they ultimately survived until the last follow-up. None of the patients in this study exhibited immune-related adverse events or immune reconstitution inflammatory syndrome. PD1 blockade appears to be a promising novel therapeutic option for PML; additional prospective studies are necessary to confirm its efficacy. [ABSTRACT FROM AUTHOR]

Background: The early accurate diagnoses for autoimmune encephalitis (AE) and infectious encephalitis (IE) are essential since the treatments for them are different. This study aims to discover some specific and sensitive biomarkers to distinguish AE from IE at early stage to give specific treatments for good outcomes. Results: We compared the host gene expression profiles and microbial diversities of cerebrospinal fluid (CSF) from 41 patients with IE and 18 patients with AE through meta-transcriptomic sequencing. Significant differences were found in host gene expression profiles and microbial diversities in CSF between patients with AE and patients with IE. The most significantly upregulated genes in patients with IE were enriched in pathways related with immune response such as neutrophil degranulation, antigen processing and presentation and adaptive immune system. In contrast, those upregulated genes in patients with AE were mainly involved in sensory organ development such as olfactory transduction, as well as synaptic transmission and signaling. Based on the differentially expressed genes, a classifier consisting of 5 host genes showed outstanding performance with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.95. Conclusions: This study provides a promising classifier and is the first to investigate transcriptomic signatures for differentiating AE from IE by using meta-transcriptomic next-generation sequencing technology. [ABSTRACT FROM AUTHOR]

An accurate evaluation of the dust accumulation on photovoltaic (PV) panels enables the development of cleaning plans and improves the grid connection security of PV power stations. In this paper, a novel image enhancement algorithm is proposed to evaluate the dust accumulation on PV panels. An atmospheric scattering model was used to analyze the difference in the image characteristics of clean and dusty PV panels. A model was proposed to describe the relationship between the model coefficient and the dust level by minimizing the pixel difference between the images of the soiled and clean PV panels. An experiment system was designed to acquire the images of clean and soiled PV panels with different dust levels. The model parameters were determined by data fitting, and the model's generalization ability was improved by adding a noise item. The results show that the model can estimate the dust level on PV panels with an accuracy of 83.78%. The average root mean square error (RMSE) of samples 1–4 is 1.67, and the error range is 2–3.5 g/m 2. In addition, the proposed method has higher accuracy than the optical attenuation and weighing methods, with a mean absolute error (MAE) of 1.61 g/m 2. The method is simple and reliable for intelligent cleaning of PV panels. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic (PV) power stations are installed in high-elevation locations and various configurations. Traditional manual cleaning methods suffer from low cleaning quality, low efficiency, and high water consumption, making it difficult to achieve consistent cleaning. This paper proposes a novel water-free cleaning robot for dust removal from PV panels of distributed PV systems in water-scarce areas. A force analysis is conducted, and safe working conditions are considered to ensure the robot can safely clean PV panels installed at a large angle. The material, structure, and sealing device of the robot are optimized based on the carrying capacity of the PV system and the operating efficiency. A combined dust removal system consisting of a rolling brush and negative pressure is developed to prevent dust raising during cleaning. Experiments are carried out on a 2-kW distributed PV system on the roof of a university in Northeast China to verify the effectiveness of the negative pressure adsorption system and the obstacle crossing and cleaning abilities of the robot. The results show that the water-free cleaning robot can effectively remove dust from the panels. The average dust cleaning rate is 92.46%, and the increase rate of the PV efficiency ranges from 11.06% to 49.53%. In addition, the robot has a small volume and weight and is more suitable than manual or mechanical cleaning for dust removal from PV panels of distributed PV systems in water-scarce areas. • A large amount of dust deposition will seriously reduce the efficiency of PV power generation. • A novel water-free cleaning robot is designed for distributed PV power station. • A method of rolling brush and negative pressure for dust collecting is proposed. • To improve the obstacle-surmounting ability of cleaning robot, a sealing device is designed. [ABSTRACT FROM AUTHOR]

This article deals with the application of well-known (G′/G)-expansion to investigate the travelling wave solutions of nonlinear evolution problems including the Boussinesq equation, Klein–Gordon equation, and sine-Gordon equation as these problems appear frequently in mathematical physics. The beauty of the suggested method is to transform the highly nonlinear evolution equation into a system of nonlinear algebraic equations by means of trial solution and auxiliary equation. It is found that the presented approach is simple, efficient, has a less computational cost, and produced rational trigonometric solutions. In order to investigate the novel results, various simulations have been executed. It is renowned that all solutions are in the form of soliton with a single hump and singular which is travelling as time increases gradually. It travels as time travel with the same shape. Moreover, as A2 decreases the amplitude of the solutions decreases. A comparative study illustrates that some of the obtained solution matches with the existing results against particular values of parameters and various new travelling wave solution attained the first time. The method seems more appropriate by means of a computational work. It can also be extended to demonstrate the behavior of other physical models of physical nature. [ABSTRACT FROM AUTHOR]

COVID-19-associated cerebellar ataxia has rarely been reported and its clinical characteristics remain understudied. This study aims to report patients with COVID-19-associated cerebellar ataxia from our institution. COVID-19-associated cerebellar ataxia was diagnosed based on the prodromal COVID-19 infection and the exclusion of other causes. This study provides a summary of the patients’ clinical presentations, neuroimaging features, and the results of anti-cerebellar antibody examinations. Our study included 11 patients and 4 were male. The median onset age was 38 years. Five patients also demonstrated signs of encephalopathy. Brain magnetic resonance imaging (MRI) was either unremarkable (n = 6) or showed bilateral cerebellar lesions (n = 5), which were typically transient, although brain atrophy could be observed later in the disease course. Anti-Homer-3 and anti-Yo antibodies were each detected in one patient, respectively. All patients received immunotherapy and nine improved. Compared with the late-onset group, individuals who exhibited ataxia earlier following COVID-19 onset (interval<5 days) were significantly younger [median age 18 (15.5–31) vs. 53.5 (44-64.8) years, p = 0.009] and more likely to present with encephalopathy (5/5 vs. 0/6, p = 0.002).They also experienced more severe symptoms [median modified Rankin scale (mRS) score at zenith 5 (5–5) vs. 2 (1.75–2.75), p = 0.017] and had a less favorable prognosis [median mRS score at the last follow-up 4 (2–5) vs. 1 (0-1.25), p = 0.009]. COVID-19-associated cerebellar ataxia can appear with encephalopathy. Brain MRI may show transient bilateral cerebellar lesions and brain atrophy later. Patients who exhibited ataxia earlier following COVID-19 were younger, had more severe symptoms and poorer outcomes. [ABSTRACT FROM AUTHOR]

Background: Encephalitis associated with antibodies against alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) is an extremely rare type of antibody-mediated encephalitis. This research aims to investigate the clinical characteristics and prognosis of anti-AMPAR encephalitis.Methods: This retrospective study enrolled nine patients with anti-AMPAR encephalitis. Demographic information, clinical manifestations, laboratory and radiological findings, treatment and response were collected and analyzed. These patients were followed up with an average period of 72 weeks to gather prognostic information.Results: Nine patients (7 females and 2 males) were enrolled with a mean age at disease onset of 59 years old. Three clinical pictures, including limbic encephalitis (n = 7; 78%), pure amnesia (n = 1; 11%) and fulminant encephalitis (n = 1; 11%) were identified. New symptoms of dysphagia and deafness were identified in the clinical spectrum of anti-AMPAR encephalitis. All patients had positive blood AMPAR antibodies, and six of them (67%) had paired positive antibodies in cerebrospinal fluid (CSF). Brain magnetic resonance imaging (MRI) was abnormal in 75% of the patients with no specific patterns recognized. Six patients (67%) had tumors, including lung cancers and thymomas. After immunotherapy and oncotherapy, partial improvement of neurological symptoms was observed among all 6 patients with available records during their hospitalization. After a mean follow-up of 72 weeks, 3 patients had marked decrease of modified Rankin Scale (mRS) score, 1 patient had unchanged mRS score, 4 patients died and the other one was lost.Conclusions: Anti-AMPAR encephalitis mainly presents as limbic encephalitis, and is paraneoplastic in 67% of cases. Thus, intensive screening for tumors is recommended for all anti-AMPAR patients. Although patients showed a good short-term therapeutic response, the overall prognosis was not satisfactory. [ABSTRACT FROM AUTHOR]

The monolithic radial transmission line (MRTL) is a key component in tens of MA-class pulsed power accelerators, which connects the multiparallel modules of the prime pulsed power sources to the central vacuum insulator stack and magnetically insulated transmission line (stack-MITL system). The choice of electrical parameters of MRTL is essential for the entire accelerator design, which would bring great influences on the peak output current and the power couple efficiency. In Phys. Rev. Special Topics-Accel. Beams, 2015, 18(10): 110401, some semianalytical formulae have been proposed to optimize the MRTL impedance that maximizes the peak current or peak power on the stack-MITL system. However, the formulae are only valid under several assumptions that are thought to be unpractical. In this article, an analytic model of the accelerator is developed to optimize the electrical parameters of MRTL. Analytical expressions of the output voltage and current at the terminal of MRTL are deduced. By simplifying the analytic model, mathematical expressions of the optimal MRTL output impedance that maximizes the peak current or peak power delivered from the prime power sources to the stack-MITL system are obtained. The factors that contribute to the optimal impedance are analyzed comprehensively with short-circuit load and Z-pinch load. Conclusions are beneficial to design the pulsed power accelerator in the future. [ABSTRACT FROM AUTHOR]

Dust accumulation at the border of photovoltaic (PV) panels significantly reduces the power, safety, and economy of PV power generation. However, no quantitative model of the power generation of PV panels with border dust exists. A piecewise single-diode model (SDM) is used to establish a PV power generation model that considers border dust accumulation. The influence of the dust concentration and dust area on the model parameters is investigated. A numerical solution based on a dynamic initial value is proposed to obtain the voltage and power of PV cells at a fixed current. An experimental platform for border dust deposition is built to verify the model performance. The influence of border dust on the power generation of PV panels is discussed. The results show that border dust accumulation significantly reduces the power generation of PV cells. The power loss is 60.13 W (63.30 W) when the border on the PV panel's short (long) side is covered with 36 g/m 2 of sandy soil. Moreover, the impact of dust on the PV power generation differs for dust in different locations. The power loss increases with an increase in the dust concentration and dust area when the dust is on the border of the PV panel's short side. In contrast, due to bypass diode protection, when dust is on the long side of the PV panel, the power loss will increase and then stabilize as the dust concentration increases. The dust type also affects the PV power generation ability. At the same dust level and location, the power loss is the largest for cement, followed by sandy soil and red soil. • A PV power generation model that considers border dust accumulation is established. • New model reveals significant power reduction in PV panels due to border dust accumulation. • Experiments show dust type and location greatly impact power generation. • Dust on short side of panel causes more power loss than on long side. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitors (ICIs) have made a breakthrough in cancer therapy. Patients treated with ICIs may develop immune‐related adverse events (irAEs) due to the upregulated activity of the immune system. With the increasing use of ICIs, irAEs are more frequently reported and have become important challenges in many patients. Neurological irAEs, which include encephalitis, myelitis, aseptic meningitis, peripheral neuropathy, myasthenia gravis, and myositis, consist of a distinct group of neuroinflammatory disorders. These disorders are rare but frequently severe with high mortality and morbidity. Patients with neurological irAEs are generally responsive to immune‐modulating therapy. Therefore, early recognition and treatment are the most important ways to improve their prognosis. Here, we provide a comprehensive review of neurological irAEs associated with ICIs and focus on these affecting central nervous systems. [ABSTRACT FROM AUTHOR]

Pseudorabies virus (PRV) is known to cause severe encephalitis in juvenile pigs and various non-native hosts; recent evidences suggest that PRV might cause encephalitis in humans. In a multicenter cohort study in China, next-generation sequencing of cerebrospinal fluid (CSF) was performed to detect pathogens in all patients with clinically suspected central nervous system infections. This study involved all the patients whose CSF samples were positive for PRV-DNA; their clinical features were evaluated, and species-specific PCR and serological tests were sequentially applied for validation. Among the 472 patients tested from June 1, 2016, to December 1, 2018, six were positive for PRV-DNA, which were partially validated by PCR and serological tests. Additionally, we retrospectively examined another case with similar clinical and neuroimaging appearance and detected the presence of PRV-DNA. These patients had similar clinical manifestations, including a rapid progression of panencephalitis, and similar neuroimaging features of symmetric lesions in the basal ganglia and bilateral hemispheres. Six of the patients were engaged in occupations connected with swine production. PRV infection should be suspected in patients with rapidly progressive panencephalitis and characteristic neuroimaging features, especially with exposure to swine. [ABSTRACT FROM AUTHOR]

[Display omitted] • Solar irradiation of the nanocomposite allows the photogenerations of H 2 O 2. • The nanocomposite is able to adsorb and concurrently photodegrade ciprofloxacin. • E. coli growth is inhibited by the nanocomposite under solar irradiation. • Upconverting nanoparticles do not leach from the polymer matrix. Solar-driven photocatalysis is of great interest in terms of a sustainable use of energy and its application in wastewater treatment. The UV light-driven photogeneration of H 2 O 2 by solar irradiation is an advanced strategy for the treatment of bacteria and recalcitrant pollutants in wastewater, but suffers from low efficiencies. In this work, a solar-driven multifunctional nanocomposite consisting of Tm3+ upconverting nanoparticles, poly(vinyl alcohol), poly(acrylic acid) and hydroxylated sulfonated poly(ether ether ketone) was prepared. The components were crosslinked via a heating treatment at 170 °C, resulting in a non-leaching porous material. This nanocomposite exhibited excellent adsorption ability (89 % in 150 min) toward a 100 mg/L ciprofloxacin aqueous solution and proved to photodegrade it (50 %) upon 4 h artificial solar irradiation, exploiting photon upconversion processes. Moreover, an 80 % bactericidal effect against E. coli was registered upon sunlight irradiation. Altogether, these results suggest the feasibility of a solar-driven wastewater treatment based on upconverting nanoparticles. [ABSTRACT FROM AUTHOR]

Gallium (Ga) is a critical element in developing renewable energy generation and energy efficient systems. The supply of Ga is at risk and needed recycling technologies for its availability in future. This study demonstrated the recovery of Ga3+ from low gallium concentrated wafer fabrication industry wastewaters using the siderophores desferrioxamine B (DFOB) and desferrioxamine E (DFOE). The complexation of Ga3+ by DFOB and DFOE was through hydroxamate group as demonstrated by infrared spectroscopy, nuclear magnetic resonance and density functional theory calculations. The high selectivity of DFOB/E towards Ga3+ was observed due to the formation of highly stable complex. Indeed, due to the formation of such high stability complex, the DFOB and DFOE were able to successfully complex 100% Ga in the two different process water from wafer fabrication industry. For the recovery of the siderophores, a high rate of decomplexation of Ga (>90%) was achieved upon addition of 6 times excess of ethylenediaminetetraacetic acid (EDTA) at pH of 3.5. More than 95% of Ga-DFOB and Ga-DFOE complex were recovered with purity (% of Ga moles in comparison to total moles of metals) of 69.8 and 92.9%, respectively by application of a C18 reversed-phase chromatography column. This study, for the first time, demonstrated a technical solution to the recovery of Ga3+ from the low concentrated wastewater based on siderophores and reversed-phase chromatography. A German patent application had been filed for this technology. Image 1 • DFOB and DFOE can selectively complex Ga3+ from its low concentrated real wastewaters. • Reversed-phase HPLC could recover more than 90% of Ga and regenerated DFOB/E. • Ga purity in recovered Ga-DFOB and Ga-DFOE complexes was 69.8 and 92.9%. [ABSTRACT FROM AUTHOR]

• The process of automation could hinder political trust in government. • The impact results from unemployment, higher down-side risk and lower group participation. • The performance-based legitimacy structure in a developmental state may hinder its political legitimacy when promoting development. • The government should focus more on citizens' trust when pursuing technological development. • Social security is crucial for governance faced with fast technological advancement. The side effects of automation on the economy have been discussed frequently, but little is known regarding its political consequences. This paper examines the causal effect that automation induces on political costs for the local government. By combining the national individual-level panel data of political trust with the prefecture-level robot exposure rate in China, we find that the development of automation would reduce individuals' political trust in the Chinese local government. Furthermore, we explore the channels through which the automation process could affect political trust, namely the risk of unemployment, intensified pessimism about local government, higher downside risk, and declining organization participation. This paper provides empirical evidence for the impact of automation and the source of political legitimacy, emphasizing the crucial role of governments in coping with technological progress and making good use of endogenous creative destruction. [Display omitted] [ABSTRACT FROM AUTHOR]

Background Brucellosis is the most common zoonotic infection in the world. Brucellosis with nervous system involvement is known as ‘neurobrucellosis’ (NB). The diagnosis of NB is difficult because its clinical manifestations are non-specific and the sensitivity of routine culture tests is low. Methods Next-generation sequencing (NGS) of cerebrospinal fluid (CSF) was used to detect pathogens in patients with clinically suspected central nervous system (CNS) infections at a tertiary referral center in China between June 1, 2016 and June 1, 2017. The clinical characteristics and NGS results of patients with the diagnosis of NB were reviewed in this study. Results Four patients were rapidly diagnosed with NB using NGS of the CSF in patients with clinically suspected CNS infections, although the clinical manifestations varied dramatically between these patients. NGS of the CSF revealed that the sequence reads identified that corresponded to Brucella species ranged from 11 to 104, with genomic coverage ranging from 0.043% to 0.4%. Rapid diagnosis led to prompt treatment with the appropriate antibiotics. Conclusions This study demonstrates the power of NGS of the CSF coupled with a bioinformatic pipeline in the diagnosis of NB. [ABSTRACT FROM AUTHOR]

• The self-healing properties of the STC-SMA composite bridge deck pavement were investigated. • Interlayer failure and recovery mechanism of rigid-flexible composite pavement was discussed. • Correlation between the healing indexes of the monotonic test and the cyclic test was analyzed. By conducting the monotonic and cyclic direct shear tests under different healing conditions, the interlayer performance and healing properties of the STC (Super Toughness Concrete)-SMA (Stone Matrix Asphalt) were investigated. Healing properties of the STC-SMA with two different asphalt binders under various healing conditions were assessed. Two different indexes were used to evaluate the healing efficiency of the STC-SMA in both the monotonic and cyclic direct shear tests. The findings indicate that the failure pattern between the STC and SMA is that asphalt is stripped out of the aggregates. The interlayer shear resistance is restored to different degrees under different healing conditions, and extending the healing time or increasing the healing temperature under a certain range promotes the healing rate of the asphalt mixture. Healing efficiency of the STC-SMA interlayer decreases gradually as the healing time increases. Interlayer healing performance of the specimen using the SBS (Styrene-Butadiene-Styrene)-modified asphalt is superior to those of high viscosity asphalt. Results of the monotonic direct shear test is less sensitive to healing conditions than the cyclic direct shear test. Healing conditions significantly affect the adhesion strength of asphalt binder and aggregates, which is directly relate to the decrease rate of the shear stiffness modulus of the healed STC-SMA specimen. For the same asphalt, there is a high correlation between the healing indexes of the monotonic direct shear test and the healing indexes of the cyclic direct shear test. [ABSTRACT FROM AUTHOR]

Background: Mycophenolate mofetil (MMF) is frequently used in the treatment of neurological autoimmune disorders. However, its effect on the relapse risk in anti-leucine-rich glioma–inactivated protein 1 (anti-LGI1) encephalitis is not well studied. Methods: In this prospective observational cohort study, anti-LGI1 encephalitis patients were grouped according to MMF treatment status (MMF and non-MMF groups). The primary outcome was relapse after disease onset. Results: A total of 83 patients were included, with a median onset age of 60 years. Fifty-four patients were men (65.1%). The MMF group comprised 28 patients and the non-MMF group comprised 55. Median follow-up from symptom onset was 26 months. Relapse occurred in 43 patients (51.8%). Median modified Rankin scale (mRS) score at enrollment was significantly higher in the MMF group than the non-MMF group (3 vs. 2; p = 0.001). Median mRS score at last follow-up was comparable between groups (1 vs. zero; p = 0.184). Both MMF treatment (HR 0.463; 95% CI, 0.231–0.929; p = 0.030) and cognitive impairment at enrollment (HR 3.391; 95% CI, 1.041–11.044; p = 0.043) were independent predictors of relapse. Starting immunotherapy before development of cognitive impairment trended towards reducing relapse risk. Outcome at last follow-up was good (mRS score 0–2) in all patients except for one in the non-MMF group. Adverse events associated with MMF treatment were mild and transient. Conclusion: Although the outcome of anti-LGI1 encephalitis patients is generally favorable, relapse is common, especially in those with cognitive impairment. MMF treatment is well-tolerated and can significantly reduce the risk of relapse. [ABSTRACT FROM AUTHOR]

The gas gap of a multi-gap gas switch can be classified as trigger and self-breakdown gaps based on the breakdown condition. A two-gap gas switch consisting of a trigger gap and a self-breakdown gap is developed to independently study the breakdown characteristics of these two types of switch gaps. Trigger experiments for the switch are conducted under various trigger voltage rise rates and different working coefficients. The experimental results indicate that the trigger gap has significantly more jitter than the self-breakdown gap, and the overall performance of the gas switch is determined primarily by the trigger gap. A novel pre-ionization structure with disks is implemented into the two-gap gas switch, considerably decreasing the breakdown delay of the trigger gap and reducing the jitter to a quarter or even less compared to that without pre-ionization. A calculation model of the breakdown time delay for the trigger gap is provided based on the foundational development of the avalanche. The probability distribution of the time required for the initial electron generation is derived in the absence of pre-ionization. The calculated breakdown time delay agrees well with the experimental results in cases with and without pre-ionization under most trigger settings. The method and principle of calculating the breakdown time delay can analyze the collapse of a gas gap with different electrode configurations (quasi-uniform or uniform electrical fields) and various gas media under a nanosecond pulse voltage. [ABSTRACT FROM AUTHOR]

Dust accumulation reduces the energy conversion efficiency of photovoltaic (PV) panels and their safe operation. In this paper, a novel dust concentration and energy conversion efficiency (DC-ECE) model is proposed to estimate the effect of dust accumulation on PV generation performance. The effects of wind, particle flow, and dust deposition on the PV panels are investigated by computational fluid dynamics (CFD). The realizable k - ϵ model and discrete phase model (DPM) are used to predict the turbulent flow and dust deposition, respectively. An on-site wind tunnel experiment is designed to verify the effectiveness of the CFD simulation. Moreover, an expression of the deposition rate is developed to analyze the effects of wind speed and particle diameter on dust concentration. The results indicate that the spiral vortex of the dust particles becomes more pronounced and gradually diffuses as the installation angle increases. The length and angle of the vortex at the rear of the PV panel reach the maximum at a wind speed of 3.9 m/s and an installation angle of 45°. When the dust particle diameter is less than 120 μ m, the wind speed has the largest impact on the conversion efficiency of the PV panels, and the conversion efficiency has a linear relationship with the deposition time. The conversion efficiency loss increases with an increase in the dust particle diameter and the wind speed. The maximum conversion efficiency loss of the PV panels is 72.9%. • Dust deposition on the performance of PV panels are numerically investigated. • A novel dust concentration and energy conversion efficiency model is established. • A wind flow simulation experiment with PV panels in a wind tunnel is designed. [ABSTRACT FROM AUTHOR]

Uneven dust accumulation can significantly influence the thermal balance between different regions of photovoltaic (PV) panels, leading to a sharp decrease in power generation efficiency and service life. In this paper, a new identification method for uneven dust accumulation on the surface of PV panels is developed to analyze the dust state (concentration and distribution) quantitatively. First, a novel deep residual neural network (DRNN) is proposed to obtain the regional dust concentration. The residual elements in the model are connected in the form of skipping layers to reduce the order of the weight matrices and improve the network flexibility and the feature learning accuracy. Second, an image preprocessing method is designed to classify the dust accumulation. It includes transformation and correction, removal of the silver grid, nonlinear interpolation, equivalent segmentation, and clustering. A new error evaluation method, error loop, is proposed to analyze the consistency between the measured and the experimental results. The results show that the DRNN has better prediction accuracy than other methods. The R2 and mean absolute error (MAE) of the DRNN are 78.7% and 3.67, respectively. In addition, three conditions are used to verify the performance of the identification method for determining uneven dust accumulation. The average evaluation coefficients of the error loop are 1.19, 0.77, and 1.10, respectively, meeting the design requirements. The proposed method can provide theoretical support for the intelligent operation and maintenance of PV systems. • An identification method for the uneven dust of PV panels is proposed. • A DRNN network architecture of free identity mapping is established. • A multiscale error evaluation method (error loop) is presented. • The model is verified by experiments and error loop evaluation. [ABSTRACT FROM AUTHOR]

In the past 5 years, the positivity rate of autoimmune encephalitis antibody panels has significantly decreased in patients with clinically suspected encephalitis in an encephalitis center in China. Furthermore, the spectrum of patients with autoantibodies related to autoimmune encephalitis has changed significantly, exhibiting a decreased percentage of patients with anti- N -methyl- d -aspartate receptor antibodies and an increased percentage of patients with infrequently observed autoantibodies. Meanwhile, a small but non-negligible proportion of patients with autoantibodies against cell surface and synaptic proteins exhibited positivity for more than one autoantibody. • The positivity rate of autoimmune encephalitis antibody panels has decreased significantly. • The percentage of patients with anti-NMDAR antibodies has decreased significantly in the past 5 years. • A small proportion of patients with autoimmune encephalitis exhibited positivity for more than one autoantibody. [ABSTRACT FROM AUTHOR]

A novel analysis method for energy efficiency loss is proposed in this paper, which is used to evaluate the effect of dust accumulation in PV system, quantitatively. Considering the nonlinear power generation characteristic of PV panels at low irradiance (below 200–300 W/m2), the coupling model of dust concentration and photoelectric conversion efficiency (DC-PCE) is established. To determine the selection range of unknown parameters (saturation irradiance, influence coefficient of dust accumulation) in the model, a PV power generation experiment with six representative dust pollutants (ash, laterite, stone dust, sandy, coal powder and cement) is designed. According to experimental fitting data, the saturation irradiance and corresponding standard deviation of influence coefficient for the six pollutants are given. Eventually, the effectiveness of the model is verified by using ash in natural conditions. Results show that the proposed model has excellent generalization ability and the accuracy is 83.12%. It offers a simple and reliable tool for evaluating the degree of dust accumulation on site. • A novel analysis method for energy efficiency loss is proposed. • The coupling model of dust concentration and photoelectric conversion efficiency (DC-PCE) is established. • The model can consider the characteristics of dust particles and the actual power generation at low irradiance. • The model is verified by ash in outdoor conditions. [ABSTRACT FROM AUTHOR]

As a new type of energy which is developing vigorously in China, nuclear energy has been widely concerned in all aspects. The circulating water system in the nuclear power plant takes water from seawater, cools the steam engine through the condenser, and then carries waste heat from the outlet to the sea. If the temperature of the outlet is too high, it will not only cause the temperature rise near the water surface of the atmosphere and the ground layer near the shore, but also affect the ecological environment inside the ocean. In this paper, a model based on the echo state network with variable memory length (VML-ESN) is proposed to predict outlet temperature of the nuclear power plant. It can get memory according to the different input autocorrelation characteristic length to adjust the status update equation. The simulation results show that compared with ESN, Leaky-ESN, and Twi-ESN, the proposed model has better prediction performance, with a MAPE of 3.42%. In addition, when the reservoir size is 40, the error of VML-ESN is smaller than that of other models. [ABSTRACT FROM AUTHOR]

• Proposed a spatial–temporal prediction model for PV power generation that combines data-driven and deep learning. • The ABCGRU network based on BiConvGRU, self-attention mechanism and encoder-decoder structure is established. • Pearson correlation coefficient, the normalized Euclidean distance and the Shape-based distance (SBD) analysis based on cross-correlation are used to analyze spatial-temporal characteristics. • The model is verified by four regional and urban datasets. Effective utilization of spatial–temporal information can improve the accuracy of ultra-short-term prediction of power generation from distributed photovoltaic (PV) stations in the region. This paper introduces an ultra-short-term spatial–temporal prediction model for distributed PV power generation, blending data-driven methodology with deep learning technique. The model integrates a self-attention mechanism (SA), a Bi-directional Convolutional Gated Recurrent Unit (BiConvGRU), and an encoder-decoder structure, called ABCGRU. The spatial–temporal attributes of PV power generation can be effectively utilized to accurately predict the output of PV power stations at different locations. Firstly, this paper proposes a 2D distributed PV measurement frame approach considering the spatial–temporal properties of PV power. The combination of Pearson correlation coefficient, the normalized Euclidean distance, the Shape-based distance (SBD) analysis based on cross-correlation and geographic distance reduces the input dimensionality. Secondly, to better capture the spatial–temporal patterns within the 2D distributed PV measurement frame, this paper proposes the ABCGRU model. Finally, the predictive performance of the model is verified through experiments. On the Birmingham dataset, the relative absolute error (RAE) for single-step (15 min) prediction is 0.13, and the average RAE for multi-step (30–60 min) prediction is about four times higher than ConvGRU. The single-step prediction RAE of Little Rock and New Orleans datasets is about 3–4 times higher than ConvGRU. In the comparison between the same series of models, the 4-layer ABCGRU has the highest accuracy. Moreover, the effectiveness of data dimensionality reduction was verified through experimental comparison. The RAE for single-step prediction on the Datong dataset is 0.0048. [ABSTRACT FROM AUTHOR]

Background: Dysregulation of vascular homeostasis can induce cardiovascular diseases and increase global mortality rates. Although lineage tracing studies have confirmed the pivotal role of modulated vascular smooth muscle cells (VSMCs) in the progression of pathological vascular remodeling, the underlying mechanisms are still unclear. Methods: The expression of Tudor-SN was determined in VSMCs of artery stenosis, PDGF-BB-treated VSMCs and atherosclerotic plaque. Loss- and gain-of-function approaches were used to explore the role of Tudor-SN in the modulation of VSMCs phenotype both in vivo and in vitro. Results: In this study, we demonstrate that Tudor-SN expression is significantly elevated in injury-induced arteries, atherosclerotic plaques, and PDGF-BB-stimulated VSMCs. Tudor-SN deficiency attenuates, but overexpression aggravates the synthetic phenotypic switching of VSMCs and pathological vascular remodeling. Loss of Tudor-SN also reduces atherosclerotic plaque formation and increases plaque stability. Mechanistically, PTEN, the major regulator of the MAPK and PI3K-AKT signaling pathways, plays a vital role in Tudor-SN-mediated regulation on proliferation and migration of VSMCs. Tudor-SN facilitates the polyubiquitination and degradation of PTEN via NEDD4-1, thus exacerbating vascular remodeling under pathological conditions. BpV (HOpic), a specific inhibitor of PTEN, not only counteracts the protective effect of Tudor-SN deficiency on proliferation and migration of VSMCs, but also abrogates the negative effect of carotid artery injury-induced vascular remodeling in mice. Conclusions: Our findings reveal that Tudor-SN deficiency significantly ameliorated pathological vascular remodeling by reducing NEDD4-1-dependent PTEN polyubiquitination, suggesting that Tudor-SN may be a novel target for preventing vascular diseases. [ABSTRACT FROM AUTHOR]

Objective: To summarize the clinical characteristics and prognosis of febrile infection-related epilepsy syndrome with claustrum lesions (FIRES-C). Method: Clinical data of FIRES-C patients were collected retrospectively. The study reviewed and analyzed their clinical manifestations, treatment strategies, and prognosis. Result: Twenty patients were enrolled, including 13 females and 7 males, with a median onset age of 20.5 years. All patients developed seizures after fever, with a median interval of 5 days. Brain MRI showed symmetric lesions in the claustrum in all patients. The median interval from seizure onset to abnormal MRI signals detection was 12.5 days. All patients had negative results for comprehensive tests of neurotropic viruses and antineuronal autoantibodies. Seventy percent of cases had been previously empirically diagnosed with autoimmune encephalitis or viral encephalitis before. All patients received anti-seizure medicine. Eleven patients (55%) received antiviral therapy. All patients received immunotherapy, including glucocorticoids (100%), intravenous immunoglobulin (IVIg) (65%), plasma exchange (PLEX) (10%), tocilizumab (10%), rituximab (5%), and cyclophosphamide (5%). Sixty percent of patients received long-term immunotherapy (≥ 3 months). The median follow-up was 11.5 months;60% of patients were diagnosed with refractory epilepsy. Conclusion: Bilateral claustrum lesion on MRI is a distinctive neuroimage feature for FIRES, which may serve as an indication for the initial clinical assessments. FIRES-C should be classified as a type of inflammatory encephalopathy characterized by a monophasic nature. Some FIRES-C patients respond to immunotherapy and antiseizure treatments but most experience refractory epilepsy as a long-term outcome. [ABSTRACT FROM AUTHOR]

Background: Diagnosis and monitoring of leptomeningeal malignancy remain challenging, and are usually based on neurological, radiological, cerebrospinal fluid (CSF) and pathological findings. This study aimed to investigate the diagnostic performance of CSF metagenomic next-generation sequencing (mNGS) and chromosome copy number variations (CNVs) analysis in the detection of leptomeningeal malignancy. Methods: Of the 51 patients included in the study, 34 patients were diagnosed with leptomeningeal malignancies, and 17 patients were diagnosed with central nervous system (CNS) inflammatory diseases. The Sayk's spontaneous cell sedimentation technique was employed for CSF cytology. And a well-designed approach utilizing the CSF mNGS-CNVs technique was explored for early diagnosis of leptomeningeal malignancy. Results: In the tumor group, 28 patients were positive for CSF cytology, and 24 patients were positive for CSF mNGS-CNVs. Sensitivity and specificity of CSF cytology were 82.35% (95% CI: 66.83-92.61%) and 94.12% (95% CI: 69.24-99.69%). In comparison, sensitivity and specificity of CSF mNGS-CNV were 70.59% (95% CI: 52.33-84.29%) and 100% (95% CI: 77.08-100%). There was no significant difference in diagnostic consistency between CSF cytology and mNGS-CNVs (p = 0.18, kappa = 0.650). Conclusions: CSF mNGS-CNVs tend to have higher specificity compared with traditional cytology and can be used as a complementary diagnostic method for patients with leptomeningeal malignancies. [ABSTRACT FROM AUTHOR]

The interplay between bacteria and their host influences the homeostasis of the human immune microenvironment, and this reciprocal interaction also affects the process of tissue damage repair. A variety of immunomodulatory commensal bacteria reside in the body, capable of delivering membrane vesicles (MVs) to host cells to regulate the local immune microenvironment. This research revealed, for the initial time, the significant enhancement of mucosal and cutaneous wound healing by MVs secreted by the human commensal Lactobacillus reuteri (RMVs) through modulation of the inflammatory environment in wound tissue. Local administration of RMVs reduces the proportion of pro‐inflammatory macrophages in inflamed tissues and mitigates the level of local inflammation, thereby facilitating the healing of oral mucosa and cutaneous wounds. The elevated oxidative stress levels in activated pro‐inflammatory macrophages can be modulated by RMVs, resulting in phenotypic transformation of macrophages. Furthermore, 3‐hydroxypropionaldehyde present in RMVs can decrease the mitochondrial permeability of macrophages and stabilize the mitochondrial membrane potential, thereby promoting the conversion of macrophages to an anti‐inflammatory phenotype. This study pioneers the significance of commensal bacterial MVs in tissue injury repair and presents a novel concept for the repair of tissue damage. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to investigate the factors influencing relapse and prognosis in patients with primary autoimmune cerebellar ataxia (PACA), an area previously not well understood. Methods: This prospective cohort study included patients who satisfied the modified diagnostic criteria of PACA. A modified Rankin scale score ≤ 2 at the last follow-up was defined as a favorable prognosis. Cox and Logistic regression were utilized to identify relapsing and prognostic factors, respectively. Results: A total of 68 patients were included and 35.3% were male. The median onset age was 42.9 years (IQR 22.1–54.0). Neuronal autoantibodies were detected in 33 (50.8%) patients. Of the 65 patients who received first-line immunotherapy, 55 (84.6%) were responsive and 10 (15.4%) were not. Responsiveness to first-line immunotherapy emerged as an independent factor for favorable prognosis (HR 16.762; 95% CI 2.877–97.655; p = 0.002), as did the absence of peripheral neuropathy/radiculopathy (HR 14.286; 95% CI 2.41–83.333; p = 0.003). Relapses occurred in 19 (27.9%) patients. Onset age ≤ 43 years (HR 5.245; 95% CI 1.499–18.35; p = 0.009), presence of peripheral neuropathy/radiculopathy (HR 4.280; 95% CI 1.622–11.298; p = 0.003) and elevated cerebrospinal fluid (CSF) protein concentration (HR 3.443; 95% CI 1.083–10.951; p = 0.036) were statistically significant relapsing factors. Conclusion: This study identified younger onset age, presence of peripheral neuropathy/radiculopathy and elevated CSF protein concentration as relapsing factors, and absence of peripheral neuropathy/radiculopathy and responsiveness to first-line immunotherapy as independent factors for favorable prognosis in PACA patients. These findings may guide individualized treatment strategies and potentially improve patient outcomes. [ABSTRACT FROM AUTHOR]

According to recent research, metabolic‐associated fatty liver disease (MAFLD) has emerged as an important underlying etiology of hepatocellular carcinoma (HCC). However, the molecular mechanism of MAFLD‐HCC is still unclear. Tumor necrosis factor receptor‐associated factor 2 (TRAF2) is the key molecule to mediate the signal of inflammatory NF‐κB pathway. This study aims to investigate the potential dysregulation of TRAF2 and its biological function in MAFLD‐HCC. Huh7 TRAF2−/− demonstrated increased tumor formation ability compared to huh7 TRAF2+/+ when stimulated with transforming growth factor‐β (TGF‐β). The decisive role of TGF‐β in the development of MAFLD‐HCC was confirmed through the specific depletion of TGF‐β receptor II gene in the hepatocytes (Tgfbr2ΔHep) of mice. In TRAF2−/− cells treated with TGF‐β, both the glycolysis rate and lipid synthesis were enhanced. We proved the signal of the mechanistic target of rapamycin complex 1 (mTORC1) could be activated in the presence of TGF‐β, and was enhanced in TRAF2−/− cells. The coimmunoprecipitation (co‐IP) experiments revealed that TRAF2 fortified the Smurf2‐mediated ubiquitination degradation of AXIN1. Hence, TRAF2 depletion resulted in increased Smad7 degradation induced by AXIN1, thus promoting the TGF‐β signal. We also discovered that PLX‐4720 could bind with AXIN1 and restrained the tumor proliferation of TRAF2−/− in mice fed with high‐fat diet (HFD). Our findings indicate that TRAF2 plays a significant role in the pathogenesis of MAFLD‐HCC. The reduction of TRAF2 expression leads to the enhancement of the TGF‐β‐mTORC1 pathway by facilitating AXIN1‐mediated Smad7 degradation. [ABSTRACT FROM AUTHOR]

Specular highlight detection is an essential task with various applications in computer vision. This paper aims to detect specular highlights in single high-resolution images using deep learning while avoiding excessive GPU memory consumption. To achieve this, we present a high-resolution specular highlight detection dataset with manual annotations of specular highlights. Given our dataset, we propose a patch-level bidirectional refinement network for high-resolution specular highlight detection. The main idea is to utilize both the pathway from small-scale patch to large-scale patch and its reverse pathway to progressively refine the detection results of adjacent-scale specular highlight patches. Moreover, based on our detection network, we propose a modified inpainting framework for specular highlight removal as an application. Lastly, we provide ten potential research directions for specular highlight detection, inspiring researchers for further study. [ABSTRACT FROM AUTHOR]

The development of infrastructures in a sustainable manner ensures the preservation of human life. It means that societies should have constant access to clean energy and water resources. The focus on nexus planning and operation including renewable energy sources has covered the requirements for the development of sustainable integrated systems to provide secure energy and water while minimizing carbon footprint. However, the uncertainty of renewable energy resources challenges the operation of such integrated nexuses. In this regard, this paper proposes a risk-constrained operation of a sustainable energy-water nexus with the aim to reduce operational costs as well as carbon dioxide (CO2) emissions amount (t CO2). To supply the energy and water demands, a combination of renewable energy sources and desalination systems are considered. The downside risk constraint is proposed to investigate the impacts of risks of uncertainties associated with renewable energy production and electricity consumption as well as water demands. Electrical and water storage infrastructures are involved to increase the flexibility of scheduling and ensure affordability and sustainability. The problem is modeled as a mixed-integer programming problem resulting in optimal decision-making to manage the energy/water resources and storage systems to provide clean and safe energy and water with the minimum CO2 emission. For the emission sector, the carbon capturing and storing (CCS) technology is proposed to capture the CO2 defusing by the energy sector and resolve the carbon-related issues. To restrict carbon production, both CO2 taxes and cap policies are examined and the results showed that the latter has a considerable effect to incentivize the decision-maker to use CCS technology. [ABSTRACT FROM AUTHOR]

Autoimmune cerebellar ataxia (ACA) is an important and potentially treatable cause of sporadic cerebellar syndrome, but studies with large sample size are limited. This study reported a large ACA series in China and described its etiology and clinical characteristics. We reviewed all ACA patients from our hospital (2013–2021) and analyzed their clinical and paraclinical features, treatment, and outcome. ACA subtypes investigated included paraneoplastic cerebellar degeneration (PCD), primary autoimmune cerebellar ataxia (PACA), anti-glutamate decarboxylase (GAD)–associated cerebellar ataxia, opsoclonus-myoclonus syndrome (OMS), Miller Fisher syndrome (MFS), and ACA-associated with autoimmune encephalitis. A total of 127 patients were identified and 40.9% were male. The median onset age was 47.0 years. Gait ataxia was the most prevalent feature followed by limb ataxia, dizziness, and dysarthria/dysphagia. Extracerebellar manifestations included pyramidal signs (28.3%) and peripheral neuropathy/radiculopathy (15.0%). ACA subtypes were PCD (30.7%), PACA (37.8%), ACA associated with autoimmune encephalitis (12.6%), anti-GAD-associated ACA (8.7%), MFS (7.1%), and OMS (3.1%). Neuronal antibodies were positive in 67.7% of patients. Brain magnetic resonance imaging was unremarkable (55.7%) or showed atrophy (18.3%) or abnormal signal intensity (26.1%, most of which was extracerebellar). Although most patients received immunotherapy, the modified Rankin scale at last follow-up was ≤ 2 in only 47.3% patients. Thirteen patients died and 24 relapsed. Compared with PACA, PCD patients were older and had poorer outcome. This study illustrates the heterogeneity in the clinical features of ACA and suggests the importance of neuronal antibody testing in ACA diagnosis. PCD and PACA are the dominant ACA subtypes, and the former has a less favorable prognosis. [ABSTRACT FROM AUTHOR]

Objective: To describe the clinical and radiological characteristics of anti‐metabotropic glutamate receptor 5 (mGluR5) encephalitis. Methods: We reviewed the clinical data of five patients with anti‐mGluR5 encephalitis, and performed a literature review. Results: The five cases included a 52‐year‐old man who developed a biphasic course of anti‐mGluR5 encephalitis after herpes simplex encephalitis, a 22‐year‐old woman who showed bilateral basal ganglia lesions on brain magnetic resonance imaging (MRI), and a 36‐year‐old man with mixed aphasia and generalized tonic‐clonic seizures, a 51‐year‐old man presented with personality changes, hallucinations, delusions, sleeping disorders and a 58‐year‐old man with short‐term memory deficits and absence seizures.. There are 16 reported cases of anti‐mGluR5 encephalitis worldwide. Of all 21 patients, with a median onset age of 35 years old, the main neurological symptoms were cognitive impairment (85.7%, 18/21), psychiatric or behavior problems (76.2%, 16/21), seizures (57.1%, 12/21), sleeping disorders (52.4%, 11/21), different degrees of decreased consciousness (42.9%, 9/21), and movement disorders (23.8%, 5/21). Brain MRI was normal in 11 of 21 patients. Lesions of the limbic lobes were presented in 5 patients, while involvement of other extralimbic regions was also reported. Seven of 21 (33.3%) cases were combined with tumors. Elevated white blood cell counts or specific oligoclonal IgG bands in the cerebrospinal fluid were found in 18 of 21 patients, with marked improvements observed after immunotherapy. Discussion: Patients with anti‐mGluR5 encephalitis typically present with diffuse, rather than purely limbic, encephalitis. Anti‐mGluR5 encephalitis can be triggered by herpes simplex encephalitis. The risk of a combined tumor may be reduced in anti‐mGluR5 encephalitis patients. [ABSTRACT FROM AUTHOR]

This study aims to predict NOx (i.e. NO + NO 2) emissions at the selective catalytic reduction inlet from coal-fired boilers. It develops a lightweight predictive model that can be adapted to the generation system dominated by renewable energy with fluctuating power. The random forest algorithm was first used to select the input variables from the auxiliary variables. The maximum information coefficient algorithm was then used to estimate the time delay between the input variables and the NOx emissions. Afterwards, to overcome the "channel collapse" issue in convolutional neural networks (CNNs), a Channel Equalization block (CE-Block) was proposed to activate the suppressed channels, and a lightweight network, referred to as CE-CNN, was designed by fusing a CNN with the CE-Block. Finally, a dynamic NOx emissions prediction model was developed employing the CE-CNN, and the model was evaluated using real data from a 600 MW down-fired boiler. The results showed that the dimensionality reduction policy decreased the model training time by 21.74 %, and the analysis of the time delay decreased the RMSE by 7.31 %. Compared to the baseline model 3-layer CNN, the proposed model achieves performance improvements of 20.97 %, 22.11 %, and 3.48 % in terms of RMSE, MAE, and R 2, respectively. [Display omitted] • A simple but effective regression method employing channel equalization CNN (CE-CNN). • CE-CNN improves the accuracy of predicting NOx emissions from coal-fired boilers. • The lightweight network is designed to address the "channel collapse" issue. • MIC is applied to estimate the time delay between the input and the target variables. • RF is employed to prioritize manipulated variables for industrial non-linear data. [ABSTRACT FROM AUTHOR]

[Display omitted] • A frame-based vacuuming photovoltaic cleaning robot was designed. • Optimization of brush helix angle using a discrete element model. • Simulation of the vacuuming process using a coupled simulation model. • Setting up a regression equation to solve for the optimal parameters. Photovoltaic (PV) module surface ash cleaning has been a difficult problem in the PV power generation industry. This paper designs a PV module cleaning robot for use in complex scenarios, designs the walking mechanism and cleaning mechanism of the cleaning robot, and completes the optimization of the structural parameters. The mechanical model of the roller brush to sweep dust was established, the influence of the spiral angle of the roller brush on the motion trajectory of dust particles was analyzed, and the helix angle of the symmetrical spiral roller brush was optimized by Engineering Discrete Element Method (EDEM) software, and the helix angle of the symmetrical spiral roller brush was determined to be 20°. The critical structural parameters of the dust transport pipeline were optimized by combining the computational fluid dynamics-discrete element model (CFD-DEM) with statistical methods. Finally, the performance test was carried out, and the test results showed that the cleaning robot could complete the cleaning of PV modules when the module inclination angle was 20–50°, and the average improvement rate of PV module power generation power was 10.6 %, 16.4 %, and 27.8 % when the ash density was 3.04 g/m2, 7.31 g/m2, and 10.36 g/m2, respectively. [ABSTRACT FROM AUTHOR]

The present study provides a simulation of a battery pack (BCK) comprising lithium-ion battery cells positioned within an air channel utilizing Finite Element Method (FEM). A tube containing a flowing liquid, which serves the purpose of cooling BCK, sits at center of the BCK. Laminar nanofluids (NFs) or water flow enters tube, and air flows inside channel. A transient investigation is conducted on BCK temperature, heat transfer coefficient (HTC), and air and liquid outlet temperatures by varying airflow rate (AFR). The results demonstrate that raising airflow decreases the BCK's average temperature (TAE) and maximum temperature (TMM). The T AE of the BCK is more sensitive to changes in flow rate. Furthermore, as the AFR increases, the HTC increases because liquid and air leaving the outlet are cooled. Using NFs instead of water in the tube causes the average and T MM of the BCK to decrease and the HTC to enhance. The results show that The TMM and the TAE of the BCK are increased until the first 25 min and then are reduced. Using NFs instead of water reduces the outlet air and water temperature when AFR is low. Using NFs for battery cooling typically results in better performance than water. [ABSTRACT FROM AUTHOR]

In the current study we report a novel autoantibody against Purkinje cells in a patient with primary autoimmune cerebellar ataxia (PACA) associated with Sjogren's syndrome (SS). Tissue-based indirect immunofluorescence assay (TBA) of the patient's serum and cerebrospinal fluid (CSF) revealed IgG antibody to Purkinje cells and the granular layer of the rat cerebellum. Rab6A was identified as autoantigen by mass spectrometry (MS) and Western blotting, and the interactions between Rab6A or its homologous Rab6B and autoantibody in patient serum were verified by recombinant cell-based assay (CBA) and neutralization experiments. This autoantibody may represent a novel biomarker in the diagnosis of PACA. [Display omitted] • We report a novel autoantibody against Purkinje cells in a patient with primary autoimmune cerebellar ataxia (PACA) associated with Sjogren's syndrome (SS). • We identified an autoantibody against Rab6A/Rab6B. • Our finding may represent a novel biomarker in the diagnosis of PACA. • The role played by the antibody in the mechanism of PACA warrants further study. [ABSTRACT FROM AUTHOR]

A novel channel-selection convolutional neural network (CS-CNN) is proposed to predict NOx emission from coal-fired boilers under steady-state and transient load conditions. First, a new channel-selection convolutional layer (CS-CL) is presented to replace regular convolutional layer (RCL). The CS-CL evaluates the channel importance of the input variables, selects the Top-C important channels and releases the hyperparameters of the remaining low-importance channels, thus contributing to maximize the utilization of the parameter resources of the model. The advantages of using CS-CLs are the preservation of the great majority of manipulated variables involved in combustion control among the input variables and the prevention of the model overfitting problem due to the redundancy of input variables. Second, a sliding window-based preprocessing method is applied to the historical data of the boiler which is divided into four-dimensional (4D) tensors. Then, comparative tests are performed on long short-term memory (LSTM) model, baseline CNN and CS-CNN using the historical data of a 670 MW boiler. The results of tests showed that CS-CNN has higher prediction performance. Finally, in order to increase the interpretability of the deep learning black box model, this study analyzes the working mechanism of the CS-CNN through ablation analysis and visualization of model parameters. • Channel importance assessment of input variables by channel selection convolutional layer. • Data-driven model for complete burner manipulation variables. • Novel convolutional neural network for predicting NOx emission from coal-fired boilers. • Model visualization analysis of convolutional neural network for industrial time series data. [ABSTRACT FROM AUTHOR]

Recent intelligent diagnostic algorithms for industrial practice have achieved impressive results. However, due to safety considerations, complex environments and deployment cost constraints in real industrial production, there are still some challenges in the form of edge computing as a vehicle. Therefore, this work proposes a fault diagnosis and distance localization model and deploys the model on edge computing devices for real-time fault diagnosis and distance localization. Lightweight is achieved by modifying the embedding module of the Transformer module as well as self-attention (MFLOPs: 0.487, Params: 6.148 k). Simultaneously, the introduction of D dformable C o n colution in self-attention and multidimensional output in decoding achieves strong robustness (95.92% of accuracy and ± 1.32 cm of error in the noise environments with SNR of −10 dB to 10 dB). Finally, this method was deployed on two edge computing devices for real-time diagnosis (FPS: 34 and 38). As the first attempt to explore the feasibility of deploying fault diagnosis and distance location strategies in edge computing devices, this work not only provides a novel solution for the challenge in industrial practice, but also is expected to promote the process of advanced information processing technology from theoretical design to industrial practice. [ABSTRACT FROM AUTHOR]

The use of phase change materials (PCMs) in building envelopes is a promising energy efficiency technology. Nevertheless, it is difficult to design a PCM wall for annual thermal regulation since the outdoor environment varies significantly in different seasons. To figure out a design strategy for the PCM walls, three wall specimens with different assemblies were designed and developed to test the thermal performance in summer and winter. A mathematical model was established and validated using experimental data. The thermal performance of the proposed walls was tested, simulated and evaluated using internal surface temperature, thermal inertia and daily heat fluxes compared with a reference wall. The internal surface temperature amplitude was reduced by maximum of 0.73 °C by 21.4% during summer and 0.88 °C by 23.9% during winter. The thermal inertial which represents the ability of the wall to resist the change in temperature was enhanced from 1.24 to 2.0 by 60.3%. The annual average energy savings by using PCMs were 17.7%, 20.2% and 23.1% when the PCM spheres were placed at the first, second, and third layer, respectively. The optimal position of PCM spheres was the third layer for summer, fourth layer for winter, and third layer for annual application. • Three wall specimens with different assemblies were designed, developed, and tested. • A mathematical model was established and validated using experimental data. • The internal surface temperature amplitude was reduced by maximum of 0.88 °C. • The thermal inertial was enhanced from 1.24 to 2.0 by 60.3% using PCM. • A design strategy was proposed to dfigure out the optimal position for PCM. [ABSTRACT FROM AUTHOR]

Abstract Evidence suggesting an association between ozone exposure and stroke risk remains inconsistent; variations in the distributions of susceptibilities of the study populations may explain some of it. We examined the hypothesis in a general Chinese population. During 2013–2015, 1356 first-ever stroke events were selected from a large representative sample, the China National Stroke Screening Survey (CNSSS) database; daily maximal 8-hour ozone concentrations were obtained from spatiotemporally interpolated estimates of in-situ observations over China. We conducted a time-stratified case-crossover design to assess associations between stroke risk and ambient ozone exposure. Next, potential effect modifiers were identified using interaction analyses. Final, a well-established approach was applied to estimate individual-level susceptibility (i.e., the individual-specific effect given a certain combination of multiple effect-modifiers) and its probability distribution among all the CNSSS participants (n = 1,292,010). With adjustments for temperature, relative humidity and ambient fine particulate matter exposure, a 10-μg/m3 increment in mean ozone levels 2–3 day prior to symptom onset was associated with a 3.0% change in stroke risk (95% confidence interval: −1.2%, 7.3%). This association was statistically significantly enhanced by male gender, rural residence and low vegetable and fruit consumption. The subgroup results suggested that a fraction of the population might be considerably affected by ozone, regardless of the insignificant association in average level. The analysis of susceptibility distribution further indicated that the ozone-stroke association was statistically significantly positive in 14% of the general population. Susceptibility to ozone-related stroke significantly varied among Chinese adults. Characterizing individual-level susceptibility reveals the complexity underlying the weak average effect of ozone, and supports to plan subpopulation-specific interventions to mitigate the stroke risk. Graphical abstract Unlabelled Image Highlights • Probability distribution of ozone-related stroke susceptibility remained unknown. • We included a representative sample of 1,292,010 Chinese adults nationwide. • We explored 14 individual-level potential modifiers of ozone-stroke associations. • Susceptibility to ozone-related stroke varied largely among the entire population. • Acute effects of ozone on stroke were significant only in specific subpopulations. [ABSTRACT FROM AUTHOR]

The accurate spatial-temporal prediction of photovoltaic (PV) power generation helps the power system dispatching department to make reasonable dispatching plans. In this paper, a robust spatial-temporal prediction model for PV power generation based on a denoising autoencoder (DAE) combining Gramian angular summation fields (GASF) and convolutional neural network (CNN) is proposed. First, the downscaling and modeling of power data use Pearson correlation to reduce inaccurate and inefficient predictions due to large-scale sparse data. Second, the model training data are encoded with noise reduction using a denoising autoencoder. Finally, the performance of the proposed model is experimentally verified. The results show that the model still performs well when the data exist in different degrees of contamination, with an average mean square error (MSE) of 71.58. Experiments based on China and the USA PV power generation datasets with mean absolute error (MAE) of 4.26 and 3.96, respectively. [ABSTRACT FROM AUTHOR]

The selective catalytic reduction (SCR) denitrification efficiency of coal-fired boilers is used to assess the clean production of power plants. Accurate predictions of NOx emissions at the SCR inlet can help improve the denitrification efficiency. However, the generation and distribution of NOx in a furnace involves complex combustion, thermodynamics, and fluid mechanics processes. The physical-based method for NOx emission models is complex and requires expensive computational resources. To address these issues, deep learning was employed to develop a prediction approach based on the random forest (RF) algorithm and lightweight convolutional neural network (CNN). To avoid curse of dimensionality, the random forest algorithm was utilized to select the importance of the candidate variables. The selected variables were in line with the boiler NOx generation and combustion control trends. The lightweight CNN was developed to build the model that predicts NOx emissions. The key was that a Cross-Channel Communication (C3)-Block was introduced into the lightweight CNN to resolve the issue in which there was no communication between feature maps in the same convolutional layer. This model was named C3–CNN. The predictive effectiveness of the C3–CNN-based model was assessed on the real historical dataset of a 600 MW down-fired boiler. The experimental results indicated that the proposed method reduced the model depth of the CNN (saved 3 convolutional layers, 1.5 times parameters, and 1 times FLOPs than 6-layer CNN) while also ensuring the model prediction accuracy (RMSE = 13.52704 ± 0.21036 mg/m3, MAE = 9.89313 ± 0.18288 mg/m3, R 2 = 0.93337 ± 0.00222), which was suitable for the online optimization of industrial pollutant control and may assist cleaner production. [Display omitted] • A novel regression approach based on convolutional neural networks (C3–CNN) is proposed. • C3–CNN can make NOx emission predicts for coal-fired boilers. • C3–CNN improves efficiency and accuracy of NOx emission predicts under steady-state and transient conditions. • C3–CNN's lightweight framework encourages cross-channel communication between feature maps of the same convolutional layer. • Random forest algorithm is utilized to prioritize the model's manipulated variables for high-dimensional industrial variables. [ABSTRACT FROM AUTHOR]

Anti-protein kinase Cgamma (anti-PKCγ) antibodies are rare onconeural antibodies associated with paraneoplastic cerebellar degeneration (PCD). To date, only two patients with PCD and anti-PKCγ antibodies have been reported. Here, we report a Chinese patient with PCD and anti-PKCγ antibodies. Screening for tumor revealed lymphoepithelial carcinoma in tonsil. The patient's symptoms improved gradually after radiotherapy for the lymphoepithelial carcinoma and intravenous immunoglobulin immunotherapy. • This is the third patient with paraneoplastic cerebellar degeneration (PCD) and anti-PKCγ antibodies. • We suggest that immune-mediated ataxia should be considered in the context of sporadic adult onset ataxia, and comprehensive screening for anti-neural antibodies. • Tumor removal with early initiation of immunotherapy may be a beneficial treatment strategy. [ABSTRACT FROM AUTHOR]