Allocation method of coupled PV-energy storage-charging station in hybrid AC/DC distribution networks balanced with economics and resilience

The hybrid AC/DC distribution network has become a research hotspot because of the wide access to multiple sources and loads. Meanwhile, extreme disasters in the planning period cause huge losses to the hybrid AC/DC distribution networks. A coupled PV-energy storage-charging station (PV-ES-CS) is an efficient use form of local DC energy sources that can provide significant power restoration during recovery periods. However, over investment will happen if too many PV-ES-CSs are installed. Therefore, it is important to determine the optimal numbers and locations of PV-ES-CS in hybrid AC/DC distribution networks balanced with economics and resilience. Firstly, the advantages of PV-ES-CS in normal operation and extreme disasters are analysed and the payment function is quantified accurately. Secondly, a bi-level optimal allocation model of PV-ES-CS in hybrid AC/DC distribution networks is established. In this model, the payment function using Nash equilibrium to balance economics and resilience is addressed at the upper-level, and the typical scenarios are simulated, and the optimal results are obtained using the genetic algorithm in lower level. Finally, a series of examples are analysed, which demonstrate the necessity of balancing economics and resilience, and advantages of DC lines in network restoration after disasters

Validation and reliability test of Chinese language patient-reported impact of symptoms in schizophrenia scale

BackgroundPatient-reported outcomes, or subjective evaluations directly reflecting the patient’s views, feelings, and judgments, are now being used to evaluate the outcomes of care and treatment of people with schizophrenia. In this study, we used an updated tool, the patient-reported impact of symptoms in schizophrenia scale (PRISS), translated into Chinese languages to assess the subjective experiences of schizophrenia patients.ObjectiveThis study aimed to test the psychometrics of the Chinese languages PRISS (CL-PRISS).MethodThis study used the Chinese version of PRISS (CL-PRISS), acquired from the harmonized English-language version. A total of 280 patients enrolled in this study were asked to complete the CL-PRISS, the positive and negative syndrome scale (PANSS), and the World Health Organization Disability Assessment Schedule (WHO-DAS). Construct and concurrent validity was tested using the confirmatory factor analysis (CFA) and Spearman correlation coefficient, respectively. The reliability of CL-PRISS was tested using Cronbach’s α coefficient and the internal correlation coefficient.ResultsConfirmatory factor analysis (CFA) analysis demonstrated three major factors in CL_PRISS: the first factor is productive experiences, the second factor is affective-negative, and the third factor experiences. The factor loadings between items and factors ranged from 0.436 to 0.899 (RMSEA = 0.029, TLI = 0.940, CFI = 0.921). The correlation coefficient between the CL_PRISS and PANSS was 0.845, and between the CL-PRISS and WHO-DAS was 0.886. The ICC of the total CL_PRISS was 0.913, and Cronbach’s α was 0.903.ConclusionThe Chinese version of the PRISS (CL_PRISS) can be effectively used for assessing the subjective experience of Chinese patients with schizophrenia

Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper

DeePMD-kit v2: A software package for Deep Potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.Comment: 51 pages, 2 figure

Metabolic risk factors of cognitive impairment in young women with major psychiatric disorder

BackgroundCognitive performance improves clinical outcomes of patients with major psychiatric disorder (MPD), but is impaired by hyperglycemia. Psychotropic agents often induce metabolism syndrome (MetS). The identification of modifiable metabolic risk factors of cognitive impairment may enable targeted improvements of patient care.ObjectiveTo investigate the relationship between MetS and cognitive impairment in young women with MPD, and to explore risk factors.MethodsWe retrospectively studied women of 18–34 years of age receiving psychotropic medications for first-onset schizophrenia (SCH), bipolar disorder (BP), or major depressive disorder (MDD). Data were obtained at four time points: presentation but before psychotropic medication; 4–8 and 8–12 weeks of psychotropic therapy; and enrollment. MATRICS Consensus Cognitive Battery, (MCCB)—based Global Deficit Scores were used to assess cognitive impairment. Multiple logistic analysis was used to calculate risk factors. Multivariate models were used to investigate factors associated with cognitive impairment.ResultsWe evaluated 2,864 participants. Cognitive impairment was observed in 61.94% of study participants, and was most prevalent among patients with BP (69.38%). HbA1c within the 8–12 week-treatment interval was the most significant risk factor and highest in BP. Factors in SCH included pre-treatment waist circumference and elevated triglycerides during the 8–12 weeks treatment interval. Cumulative dosages of antipsychotics, antidepressants, and valproate were associated with cognitive impairment in all MPD subgroups, although lithium demonstrated a protect effect (all P < 0.001).ConclusionsCognitive impairment was associated with elevated HbA1c and cumulative medication dosages. Pre-treatment waist circumference and triglyceride level at 8–12 weeks were risk factors in SCH. Monitoring these indices may inform treatment revisions to improve clinical outcomes

Oridonin ameliorates depressive-like behaviors induced by chronic unpredictable mild stress in mice via TXNIP/NLRP3 signaling pathway

Purpose: To investigate the effect and mechanism of oridonin in chronic unpredictable mild stress (CUMS)-induced depressive-like behaviors. Methods: CUMS was established using 6-week stress stimuli, including feed/water deprivation, night lighting, inverted light/dark cycle, and tail clamping. Depressive behaviors were analyzed using the sucrose preference test, forced swim test (FST), and tail suspension test (TST). Locomotor activity was analyzed using the open field test (OFT) while inflammatory cytokines were analyzed by enzyme-linked immunosorbent assay. The activation of the TXNIP/NLRP3 signaling pathway was evaluated by western blot. Results: Sucrose consumption of CUMS-treated mice was significantly decreased, while immobility times of the FST (control vs. CUMS, ~50 to 150 s; p < 0.01) and TST (Control vs. CUMS, ~50 to 130 s; p < 0.01) were increased; oridonin significantly reversed these effects. Spontaneous locomotor activities (crossing, rearing, and grooming) measured in the OFT were decreased after the CUMS procedure, and oridonin increased these activities (p < 0.01 vs. CUMS). Oridonin decreased the production of tumor necrosis factor alpha, interleukin (IL)-1β, IL-6, and monocyte chemoattractant protein-1 in the hippocampus of CUMS-treated mice and significantly inhibited activation of the TXNIP/NLRP3 pathway induced by CUMS. Conclusion: Oridonin ameliorates depressive-like behaviors in mice induced by CUMS, partly via TXNIP/NLRP3 signaling pathway. Thus, the findings provide evidence for the potential application of oridonin in depression therapy

Machine learning for differentiating between pancreatobiliary-type and intestinal-type periampullary carcinomas based on CT imaging and clinical findings.

PurposeTo develop a diagnostic model for distinguishing pancreatobiliary-type and intestinal-type periampullary adenocarcinomas using preoperative contrast-enhanced computed tomography (CT) findings combined with clinical characteristics.MethodsThis retrospective study included 140 patients with periampullary adenocarcinoma who underwent preoperative enhanced CT, including pancreaticobiliary (N = 100) and intestinal (N = 40) types. They were randomly assigned to the training or internal validation set in an 8:2 ratio. Additionally, an independent external cohort of 28 patients was enrolled. Various CT features of the periampullary region were evaluated and data from clinical and laboratory tests were collected. Five machine learning classifiers were developed to identify the histologic type of periampullary adenocarcinoma, including logistic regression, random forest, multi-layer perceptron, light gradient boosting, and eXtreme gradient boosting (XGBoost).ResultsAll machine learning classifiers except multi-layer perceptron used achieved good performance in distinguishing pancreatobiliary-type and intestinal-type adenocarcinomas, with the area under the curve (AUC) ranging from 0.75 to 0.98. The AUC values of the XGBoost classifier in the training set, internal validation set and external validation set are 0.98, 0.89 and 0.84 respectively. The enhancement degree of tumor, the growth pattern of tumor, and carbohydrate antigen 19-9 were the most important factors in the model.ConclusionMachine learning models combining CT with clinical features can serve as a noninvasive tool to differentiate the histological subtypes of periampullary adenocarcinoma, in particular using the XGBoost classifier

First Isolation and Multilocus Sequence Typing of <i>Brucella canis</i> from a Subclinically Infected Pet Dog in China

Canine brucellosis, a worldwide zoonotic disease, is mainly caused by Brucella canis. In the present study, we isolated a Brucella strain (CD3) from a subclinically infected pet dog in Sichuan Province, Southwestern China. Classical biotyping methods and molecular biological tests (BCSP31 and BcSS PCR) proved that the strain belonged to B. canis. Furthermore, B. canis CD3 and another two B. canis strains (WJ5 and YA4), which were all isolated from pet dogs in Sichuan, were genotyped using multilocus sequence typing (MLST). Our results showed that the three B. canis strains were identified as the same sequence type (ST21). The present study is the first to report B. canis strain from a subclinically infected pet dog in China, indicating a potential threat to public health posed by subclinical infections in pet dogs. We suggest that screening for B. canis should be incorporated into routine medical examination of pet dogs and other companion animals in areas with a history of animal or human brucellosis