A Novel Strategy for Extracting Richer Semantic Information Based on Fault Detection in Power Transmission Lines.

With the development of the smart grid, the traditional defect detection methods in transmission lines are gradually shifted to the combination of robots or drones and deep learning technology to realize the automatic detection of defects, avoiding the risks and computational costs of manual detection. Lightweight embedded devices such as drones and robots belong to small devices with limited computational resources, while deep learning mostly relies on deep neural networks with huge computational resources. And semantic features of deep networks are richer, which are also critical for accurately classifying morphologically similar defects for detection, helping to identify differences and classify transmission line components. Therefore, we propose a method to obtain advanced semantic features even in shallow networks. Combined with transfer learning, we change the image features (e.g., position and edge connectivity) under self-supervised learning during pre-training. This allows the pre-trained model to learn potential semantic feature representations rather than relying on low-level features. The pre-trained model then directs a shallow network to extract rich semantic features for downstream tasks. In addition, we introduce a category semantic fusion module (CSFM) to enhance feature fusion by utilizing channel attention to capture global and local information lost during compression and extraction. This module helps to obtain more category semantic information. Our experiments on a self-created transmission line defect dataset show the superiority of modifying low-level image information during pre-training when adjusting the number of network layers and embedding of the CSFM. The strategy demonstrates generalization on the publicly available PASCAL VOC dataset. Finally, compared with state-of-the-art methods on the synthetic fog insulator dataset (SFID), the strategy achieves comparable performance with much smaller network depths.

The Emerging Role of Police in Facilitating Psychiatric Evaluation Since the 2013 Implementation of the First Chinese Mental Health Law.

In 2013, China's first Mental Health Law (MHL) took effect, with the goal of better protecting patients' rights. Under the law the police, with appropriate training, rather than family members, employers or medical staff sent from a hospital, are the ones who bring persons in behavioral crises to medical facilities for psychiatric assessment for possible involuntary hospitalization. We examined the proportion and distinctive characteristics of persons brought to psychiatric emergency services (PES) by the police since the implementation of MHL. We used medical records to document demographic and clinical characteristics of all persons evaluated at the PES of the Guangzhou Psychiatric Hospital, the largest psychiatric hospital in China's fourth largest city, from April 2017 to August 2017. Bivariate and multivariate statistical analyses were performed to identify characteristics of patients brought to the PES by the police. Among 1515 PES visits, 166 (11.0%) were brought by the police as compared to virtually none in the years before the law took effect. Compared to non-police referrals, police referrals were associated with male gender, age greater than 30, more documented violent behavior, greater likelihood of having been restrained, and higher rates of hospital admission after assessment. Assessed risk of suicidality and diagnoses of substance use disorder were not significantly associated with police referral. A modest but increased and noteworthy proportion of patients evaluated at the PES after implementations of China's MHL were brought by the police, especially those with violent behavior requiring restraint and hospitalization resulting from mental illness.

IKULDAS: An Improved kNN-Based UHF RFID Indoor Localization Algorithm for Directional Radiation Scenario.

Ultra high frequency radio frequency identification (UHF RFID)-based indoor localization technology has been a competitive candidate for context-awareness services. Previous works mainly utilize a simplified Friis transmission equation for simulating/rectifying received signal strength indicator (RSSI) values, in which the directional radiation of tag antenna and reader antenna was not fully considered, leading to unfavorable performance degradation. Moreover, a k-nearest neighbor (kNN) algorithm is widely used in existing systems, whereas the selection of an appropriate k value remains a critical issue. To solve such problems, this paper presents an improved kNN-based indoor localization algorithm for a directional radiation scenario, IKULDAS. Based on the gain features of dipole antenna and patch antenna, a novel RSSI estimation model is first established. By introducing the inclination angle and rotation angle to characterize the antenna postures, the gains of tag antenna and reader antenna referring to direct path and reflection paths are re-expressed. Then, three strategies are proposed and embedded into typical kNN for improving the localization performance. In IKULDAS, the optimal single fixed rotation angle is introduced for filtering a superior measurement and an NJW-based algorithm is advised for extracting nearest-neighbor reference tags. Furthermore, a dynamic mapping mechanism is proposed to accelerate the tracking process. Simulation results show that IKULDAS achieves a higher positioning accuracy and lower time consumption compared to other typical algorithms.

Anti-Obesity and Hypoglycemic Effects of Poncirus trifoliata L. Extracts in High-Fat Diet C57BL/6 Mice.

The present study investigated the possible anti-obesity and hypoglycemic effects of Poncirus trifoliata L. extracts. Mature fruit were divided into flavedo (PF) and juice sacs (PJ), and extracts from them were tested on C57BL/6 mice fed a high-fat diet (HFD) for thirteen weeks. Both fruit extracts (40 mg/kg body weight, respectively) showed anti-obesity and hypoglycemic effects. Consumption of PF and PJ extracts reduced body weight by 9.21% and 20.27%, respectively. Liver and adipose weights, fasting glucose, serum triglyceride (TG), and low density lipoprotein cholesterol (LDL-c) levels decreased significantly, while serum high density lipoprotein cholesterol (HDL-c) and oral glucose tolerance levels increased significantly in response to two fruit extracts. These effects were due in part to the modulation of serum insulin, leptin, and adiponectin. Furthermore, transcript levels of fatty acid synthase (FAS) and stearoyl-CoA desaturase 1 (SCD1) were reduced while those of carnitine palmitoyltransferase 1α (CPT1α) and insulin receptor substrate 2 (IRS2) were increased in the liver of C57BL/6 mice, which might be an important mechanism affecting lipid and glucose metabolism. Taken together, P. trifoliata fruit can be potentially used to prevent or treat obesity and associated metabolic disorders.

Purification of Flavonoids from Chinese Bayberry (Morella rubra Sieb. et Zucc.) Fruit Extracts and α-Glucosidase Inhibitory Activities of Different Fractionations.

Chinese bayberry (Morella rubra Sieb. et Zucc.) fruit have a diverse flavonoid composition responsible for the various medicinal activities, including anti-diabetes. In the present study, efficient simultaneous purification of four flavonoid glycosides, i.e., cyanidin-3-O-glucoside (1), myricetin-3-O-rhamnoside (2), quercetin-3-O-galactoside (3), quercetin-3-O-rhamnoside (4), from Chinese bayberry pulp was established by the combination of solid phase extract (SPE) by C18 Sep-Pak(®) cartridge column chromatography and semi-preparative HPLC (Prep-HPLC), which was followed by HPLC and LC-MS identification. The purified flavonoid glycosides, as well as different fractions of fruit extracts of six bayberry cultivars, were investigated for α-glucosidase inhibitory activities. The flavonol extracts (50% methanol elution fraction) of six cultivars showed strong α-glucosidase inhibitory activities (IC50 = 15.4-69.5 µg/mL), which were higher than that of positive control acarbose (IC50 = 383.2 µg/mL). Four purified compounds 1-4 exerted α-glucosidase inhibitory activities, with IC50 values of 1444.3 µg/mL, 418.8 µg/mL, 556.4 µg/mL, and 491.8 µg/mL, respectively. Such results may provide important evidence for the potential anti-diabetic activity of different cultivars of Chinese bayberry fruit and the possible bioactive compounds involved.

[Hypoglycemic and hypolipidemic effects of quercetin and its glycosides].

Quercetin and its glycosides are important flavonols in traditional herbal drugs and plant-derived food, and they have diverse hiological activities such as antioxidant, anticarcinogenic, anti-inflammatory, hypoglycemic and hypolipidemic activities. Numerous studies have demonstrated that quercetin and its glycosides were effective in the prevention and treatment of non-infectious chronic disease such as diabetes, obesity, and hyperlipidemia. They can regulate glucose and lipid metaholism through different mechanisms. They can decrease blood glucose via protecting pancreatic/p cells or/and improving insulin sensitivity. Also, they have lipid-lowering effects, which may be the result of regulation of lipid catabolism or/and anabolism. Their distributions, as well as the hypoglycemic and hypolipidemic effects are reviewed in this paper. In addition, further bioactivities as well as their dose-activity relationship, structure-activity relationship, bioavailability, and future clinical application of quercetin and its glycosides are discussed and proposed.

Analytical Separated Neuro-Space Mapping Modeling Method of Power Transistor.

An analytically separated neuro-space mapping (Neuro-SM) model of power transistors is proposed in this paper. Two separated mapping networks are introduced into the new model to improve the characteristics of the DC and AC, avoiding interference of the internal parameters in neural networks. Novel analytical formulations are derived to develop effective combinations between the mapping networks and the coarse model. In addition, an advanced training approach with simple sensitivity analysis expressions is proposed to accelerate the optimization process. The flexible transformation of terminal signals in the proposed model allows existing models to exceed their current capabilities, addressing accuracy limitations. The modeling experiment for the measurement data of laterally diffused metal-oxide-semiconductor transistors demonstrates that the novel method accurately represents the characteristics of the DC and AC of transistors with a simple structure and efficient training process.

Improved Empirical Formula Modeling Method Using Neuro-Space Mapping for Coupled Microstrip Lines.

In this paper, an improved empirical formula modeling method using neuro-space mapping (Neuro-SM) for coupled microstrip lines is proposed. Empirical formulas with correction values are used for the coarse model, avoiding a slow trial-and-error process. The proposed model uses mapping neural networks (MNNs), including both geometric variables and frequency variables to improve accuracy with fewer variables. Additionally, an advanced method incorporating simple sensitivity analysis expressions into the training process is proposed to accelerate the optimization process. The experimental results show that the proposed model with its simple structure and an effective training process can accurately reflect the performance of coupled microstrip lines. The proposed model is more compatible than models in existing simulation software.

Room-Temperature CMOS Monolithic Resonant Triple-Band Terahertz Thermal Detector.

Multiband terahertz (THz) detectors show great application potential in imaging, spectroscopy, and sensing fields. Thermal detectors have become a promising choice because they could sense THz radiations on the whole spectrum. This paper demonstrates the operation principle, module designs with in-depth theoretical analysis, and experimental validation of a room-temperature CMOS monolithic resonant triple-band THz thermal detector. The detector, which consists of a compact triple-band octagonal ring antenna and a sensitive proportional to absolute temperature (PTAT) sensor, has virtues of room-temperature operation, low cost, easy integration, and mass production. Good experimental results are obtained at 0.91 THz, 2.58 THz, and 4.2 THz with maximum responsivities of 32.6 V/W, 43.2 V/W, and 40 V/W, respectively, as well as NEPs of 1.28 µW/Hz0.5, 2.19 µW/Hz0.5, and 2.37 µW/Hz0.5, respectively, providing great potential for multiband THz sensing and imaging systems.

A Mesh Space Mapping Modeling Method with Mesh Deformation for Microwave Components.

In this study, a low-cost space mapping (SM) modeling method with mesh deformation is proposed for microwave components. In this approach, the coarse-mesh model with mesh deformation is developed as the coarse model, and the fine-mesh model is simulated as the fine model. The SM technique establishes the mapping relationship between the coarse-mesh model and the fine-mesh model. This approach enables us to combine the computational efficiency of the coarse model with the accuracy of the fine model. The automatic mesh deformation technology is embedded in the coarse model to avoid the discontinuous change in the electromagnetic response. The proposed model consisting of the coarse model and two mapping modules can represent the features of the fine model more accurately, and predict the electromagnetic response of microwave components quickly. The proposed mesh SM modeling technique is applied to the four-pole waveguide filter. The value for the training and test errors in the proposed model is less than 1%, which is lower than that for the ANN models and the existing SM models trained with the same data. Compared with HFSS software, the proposed model can save about 70% CPU time in predicting a set of 100 data. The results show that the proposed method achieves a good modeling accuracy and efficiency with few training data and a low computational cost.

Mobile-based program improves healthy eating of ulcerative colitis patients: A pilot study.

Background: Dietary management plays a crucial role in the treatment of patients with ulcerative colitis (UC). While various e-services provide dietary advice, the long-term dietary management requires continuous monitoring and dynamic adjustment to accommodate the evolving nature of the disease and meet the patients' nutritional needs. Consequently, the development of a novel dietary management tool that incorporates diet tracking, personalized nutritional feedback, and evidence-based advice becomes imperative. This study aims to address this need by developing a WeChat applet called "HealthyGut" specifically designed for the dietary management of UC patients, and evaluate its feasibility, acceptability, and preliminary efficacy. Methods: A total of 134 UC patients were equally allocated into the intervention group (receiving a 12-week mobile-based dietary management via HealthyGut) and control group (receiving a paper-based food diary and routine advice). The feasibility outcomes were recruitment, retention, engagement, satisfaction, and acceptability in the intervention group. Dietary intakes were effective outcomes. Results: Both groups had satisfactory retention rates (89.6% and 77.6%, respectively). The System Usability Scale in the intervention group yielded "good usability" with a mean score of 79.63 (SD 7.39), and all participants reported good user experiences and perceived benefits after using HealthyGut. At week 12, intervention responders reported significantly higher daily energy intake than control group (Z = -3.089, p = 0.002). Conclusions and Implications: The results display that HealthyGut as a dietary management tool is feasible and accepted by UC patients, and it may help them make healthier food choices. Larger sample studies should be considered in the future.

Network Pharmacology-Based and Experimental Identification of the Effects of Paeoniflorin on Major Depressive Disorder.

Objective: Major depressive disorder (MDD) is one of the most common psychiatric disorders, the diagnosis and treatment of MDD are major clinical issues. However, there is a lack of effective biomarkers and drugs diagnosis and therapeutics of MDD. In the present study, bioinformatics analysis combined with an experimental verification strategy was used to identify biomarkers and paeoniflorin targets for MDD diagnosis and treatment. Methods: Based on network pharmacology, we obtained potential targets and pathways of paeoniflorin as an antidepressant through multiple databases. We then constructed a protein-protein interaction network and performed enrichment analyses. According to the results, we performed in vivo and in vitro experimental validation. Results: The results showed that paeoniflorin may exert an antidepressant effect by regulating cell inflammation, synaptic function, NF-κB signaling pathway, and intestinal inflammation. Conclusion: NPM1, HSPA8, HSPA5, HNRNPU, and TNF are the targets of paeoniflorin treatment. In addition, we demonstrated that paeoniflorin inhibits inflammatory cytokine production via the p38MAPK/NF-κB pathway and has neuroprotective effects on the synaptic structure. Our findings provide valuable evidence for the diagnosis and treatment of MDD.

Draft Genome Sequence of Lactobacillus brevis Strain 3M004, a Probiotic with Potential Quorum-Sensing Regulation.

We present here the draft genome sequence of Lactobacillus brevis strain 3M004, a probiotic that has potential for regulating quorum sensing. The strain was obtained from a type of aquafeed. The assembly consists of 2,459,326 bp and contains 33 contigs, with a G+C content of 45.10%.