RFID Application of Smart Grid for Asset Management

RFID technology research has resolved practical application issues of the power industry such as assets management, working environment control, and vehicle networking. Also it provides technical reserves for the convergence of ERP and CPS. With the development of RFID and location-based services technology, RFID is converging with a variety of sensing, communication, and information technologies. Indoor positioning applications are under rapid development. Micromanagement environment of the assets is a useful practice for the RFID and positioning. In this paper, the model for RFID applications has been analyzed in the microenvironment management of the data center and electric vehicle batteries, and the optimization scheme of enterprise asset management is also proposed

FedSC:A Sidechain-Enhanced Edge Computing Framework for 6G IoT Multiple Scenarios

The imminent deployment of sixth-generation (6G) wireless communication systems promises new opportunities and challenges for model training using data from edge devices in the Internet of Things (IoT). However, current research has yet to fully address the efficiency and scalability challenges arising from the extensive connectivity of edge devices across various scenarios. The presence of malicious devices further intensifies system uncertainty during large-scale data interactions and model training, making it difficult for a single model to effectively manage the complexities introduced by heterogeneous devices and dynamic network conditions. To overcome these challenges, we propose FedSC, an innovative edge computing framework that leverages side-chain technology for efficient edge node management and employs federated learning to enable robust cross-device and cross-scenario model interactions. To accelerate the multi-model aggregation process, we introduce an asynchronous cross-domain iterative algorithm (ACDI) based on smart contracts. Additionally, to mitigate the impact of malicious and inactive nodes, we propose a robust consensus algorithm and a committee mechanism for leader node election based on contribution value. Experimental results demonstrate that the proposed FedSC achieves a 3.2% and 44.23% accuracy improvement on i.i.d. and non-i.i.d. dataset, respectively, along with a remarkable latency reduction of 256.51%, compared to FedAvg. Our work is conducive to the training of multiple models in different IoT scenarios, utilizing substantial amounts of IoT device data and facilitating collaboration between models. Furthermore, it enables the provision of fundamental services to diverse applications in 6G

Evaluation of the MODS Culture Technique for the Diagnosis of Tuberculous Meningitis

Tuberculous meningitis (TBM) is a devastating condition. The rapid instigation of appropraite chemotherapy is vital to reduce morbidity and mortality. However rapid diagnosis remains elusive; smear microscopy has extremely low sensitivity on cerebrospinal fluid (CSF) in most laboratories and PCR requires expertise with advanced infrastructure and has sensitivity of only around 60% under optimal conditions. Neither technique allows for the microbiological isolation of M. tuberculosis and subsequent drug susceptibility testing. We evaluated the recently developed microscopic observation drug susceptibility (MODS) assay format for speed and accuracy in diagnosing TBM.Two hundred and thirty consecutive CSF samples collected from 156 patients clinically suspected of TBM on presentation at a tertiary referal hospital in Vietnam were enrolled into the study over a five month period and tested by Ziehl-Neelsen (ZN) smear, MODS, Mycobacterial growth Indicator tube (MGIT) and Lowenstein-Jensen (LJ) culture. Sixty-one samples were from patients already on TB therapy for >1day and 19 samples were excluded due to untraceable patient records. One hundred and fifty samples from 137 newly presenting patients remained. Forty-two percent (n = 57/137) of patients were deemed to have TBM by clinical diagnostic and microbiological criteria (excluding MODS). Sensitivity by patient against clinical gold standard for ZN smear, MODS MGIT and LJ were 52.6%, 64.9%, 70.2% and 70.2%, respectively. Specificity of all microbiological techniques was 100%. Positive and negative predictive values for MODS were 100% and 78.7%, respectively for HIV infected patients and 100% and 82.1% for HIV negative patients. The median time to positive was 6 days (interquartile range 5-7), significantly faster than MGIT at 15.5 days (interquartile range 12-24), and LJ at 24 days (interquartile range 18-35 days) (P<0.01).We have shown MODS to be a sensitive, rapid technique for the diagnosis of TBM with high sensitivity, ease of performance and low cost (0.53 USD/sample)

MIMO-DoAnet: Multi-channel Input and Multiple Outputs DoA Network with Unknown Number of Sound Sources

Recent neural network based Direction of Arrival (DoA) estimation algorithms have performed well on unknown number of sound sources scenarios. These algorithms are usually achieved by mapping the multi-channel audio input to the single output (i.e. overall spatial pseudo-spectrum (SPS) of all sources), that is called MISO. However, such MISO algorithms strongly depend on empirical threshold setting and the angle assumption that the angles between the sound sources are greater than a fixed angle. To address these limitations, we propose a novel multi-channel input and multiple outputs DoA network called MIMO-DoAnet. Unlike the general MISO algorithms, MIMO-DoAnet predicts the SPS coding of each sound source with the help of the informative spatial covariance matrix. By doing so, the threshold task of detecting the number of sound sources becomes an easier task of detecting whether there is a sound source in each output, and the serious interaction between sound sources disappears during inference stage. Experimental results show that MIMO-DoAnet achieves relative 18.6% and absolute 13.3%, relative 34.4% and absolute 20.2% F1 score improvement compared with the MISO baseline system in 3, 4 sources scenes. The results also demonstrate MIMO-DoAnet alleviates the threshold setting problem and solves the angle assumption problem effectively.Comment: Accepted by Interspeech 202

Association between prospective registration and overall reporting and methodological quality of systematic reviews: a meta-epidemiological study

Objective: To investigate the differences in main characteristics, reporting and methodological quality between prospectively registered and non-registered systematic reviews. Methods: PubMed was searched to identify systematic reviews of randomized controlled trials published in 2015 in English. After title and abstract screening, potentially relevant reviews were divided into three groups: registered non-Cochrane reviews, Cochrane reviews, and non-registered reviews. For each group, random number tables were generated in Microsoft Excel, and the first 50 eligible studies from each group were randomly selected. Data of interest from systematic reviews were extracted. Regression analyses were conducted to explore the association between total R-AMSTAR or PRISMA scores and the selected characteristics of systematic reviews. Results: The conducting and reporting of literature search in registered reviews were superior to non-registered reviews. Differences in nine of the 11 R-AMSTAR items were statistically significant between registered and non-registered reviews. The total R-AMSTAR score of registered reviews was higher than non-registered reviews (MD=4.82, 95%CI: 3.70, 5.94). Sensitivity analysis by excluding the registration related item presented similar result (MD=4.34, 95%CI: 3.28, 5.40). Total PRISMA scores of registered reviews were significantly higher than non-registered reviews (all reviews: MD=1.47, 95%CI: 0.64-2.30; non-Cochrane reviews: MD=1.49, 95%CI: 0.56-2.42). However, the difference in the total PRISMA score was no longer statistically significant after excluding the item related to registration (item 5). Regression analyses showed similar results. Conclusions: Prospective registration may at least indirectly improve the overall methodological quality of systematic reviews, although its impact on the overall reporting quality was not significant

Locate and Beamform: Two-dimensional Locating All-neural Beamformer for Multi-channel Speech Separation

Recently, stunning improvements on multi-channel speech separation have been achieved by neural beamformers when direction information is available. However, most of them neglect to utilize speaker's 2-dimensional (2D) location cues contained in mixture signal, which limits the performance when two sources come from close directions. In this paper, we propose an end-to-end beamforming network for 2D location guided speech separation merely given mixture signal. It first estimates discriminable direction and 2D location cues, which imply directions the sources come from in multi views of microphones and their 2D coordinates. These cues are then integrated into location-aware neural beamformer, thus allowing accurate reconstruction of two sources' speech signals. Experiments show that our proposed model not only achieves a comprehensive decent improvement compared to baseline systems, but avoids inferior performance on spatial overlapping cases.Comment: Accepted by Interspeech 2023. arXiv admin note: substantial text overlap with arXiv:2212.0340

InstructDET: Diversifying Referring Object Detection with Generalized Instructions

We propose InstructDET, a data-centric method for referring object detection (ROD) that localizes target objects based on user instructions. While deriving from referring expressions (REC), the instructions we leverage are greatly diversified to encompass common user intentions related to object detection. For one image, we produce tremendous instructions that refer to every single object and different combinations of multiple objects. Each instruction and its corresponding object bounding boxes (bbxs) constitute one training data pair. In order to encompass common detection expressions, we involve emerging vision-language model (VLM) and large language model (LLM) to generate instructions guided by text prompts and object bbxs, as the generalizations of foundation models are effective to produce human-like expressions (e.g., describing object property, category, and relationship). We name our constructed dataset as InDET. It contains images, bbxs and generalized instructions that are from foundation models. Our InDET is developed from existing REC datasets and object detection datasets, with the expanding potential that any image with object bbxs can be incorporated through using our InstructDET method. By using our InDET dataset, we show that a conventional ROD model surpasses existing methods on standard REC datasets and our InDET test set. Our data-centric method InstructDET, with automatic data expansion by leveraging foundation models, directs a promising field that ROD can be greatly diversified to execute common object detection instructions.Comment: 29 pages (include Appendix) Published in ICL

Motor Deficits and Decreased Striatal Dopamine Receptor 2 Binding Activity in the Striatum-Specific Dyt1 Conditional Knockout Mice

DYT1 early-onset generalized dystonia is a hyperkinetic movement disorder caused by mutations in DYT1 (TOR1A), which codes for torsinA. Recently, significant progress has been made in studying pathophysiology of DYT1 dystonia using targeted mouse models. Dyt1 ΔGAG heterozygous knock-in (KI) and Dyt1 knock-down (KD) mice exhibit motor deficits and alterations of striatal dopamine metabolisms, while Dyt1 knockout (KO) and Dyt1 ΔGAG homozygous KI mice show abnormal nuclear envelopes and neonatal lethality. However, it has not been clear whether motor deficits and striatal abnormality are caused by Dyt1 mutation in the striatum itself or the end results of abnormal signals from other brain regions. To identify the brain region that contributes to these phenotypes, we made a striatum-specific Dyt1 conditional knockout (Dyt1 sKO) mouse. Dyt1 sKO mice exhibited motor deficits and reduced striatal dopamine receptor 2 (D2R) binding activity, whereas they did not exhibit significant alteration of striatal monoamine contents. Furthermore, we also found normal nuclear envelope structure in striatal medium spiny neurons (MSNs) of an adult Dyt1 sKO mouse and cerebral cortical neurons in cerebral cortex-specific Dyt1 conditional knockout (Dyt1 cKO) mice. The results suggest that the loss of striatal torsinA alone is sufficient to produce motor deficits, and that this effect may be mediated, at least in part, through changes in D2R function in the basal ganglia circuit

Methanosarcinaceae and acetate-oxidizing pathways dominate in high-rate thermophilic anaerobic digestion of waste-activated sludge

This study investigated the process of high-rate, high-temperature methanogenesis to enable very-high-volume loading during anaerobic digestion of waste-activated sludge. Reducing the hydraulic retention time (HRT) from 15 to 20 days in mesophilic digestion down to 3 days was achievable at a thermophilic temperature (55°C) with stable digester performance and methanogenic activity. A volatile solids (VS) destruction efficiency of 33 to 35% was achieved on waste-activated sludge, comparable to that obtained via mesophilic processes with low organic acid levels