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Acute alcohol intoxication impairs cognitive and psychomotor abilities leading to various public health hazards such as road traffic accidents and alcohol-related violence. Intoxicated individuals are usually identified by measuring their blood alcohol concentration (BAC) using breathalysers that are expensive and labour-intensive. In this paper, we developed the Audio-based Deep Learning Algorithm to Identify Alcohol Inebriation (ADLAIA) that can instantly predict an individual's intoxication status based on a 12-second recording of their speech.ADLAIA was trained on a publicly available German Alcohol Language Corpus that comprises a total of 12,360 audio clips of inebriated and sober speakers (total of 162, aged 21-64, 47.7% female). ADLAIA's performance was determined by computing the unweighted average recall (UAR) and accuracy of inebriation prediction.ADLAIA was able to identify inebriated speakers-with BAC of 0.05% or higher-with an UAR of 68.09% and accuracy of 67.67%. ADLAIA had a higher performance (UAR of 75.7%) in identifying intoxicated speakers (BAC0.12%).Being able to identify intoxicated individuals solely based on their speech, ADLAIA could be integrated in mobile applications and used in environments (such as bars, sports stadiums) to get instantaneous results about inebriation status of individuals.

Three highly pathogenic avian influenza (H5N8) viruses were detected in the migratory bird Podiceps nigricollis in Northwest China in June 2021. Phylogenetic analysis indicated that these H5N8 isolates belonged to clade 2.3.4.4b, which were highly homologous to strains isolated in China and South Korea. In this study, H5N8 virus infection in Podiceps nigricollis was detected using Oxford Nanopore Technologies sequencing technology and caused pathological changes in multiple organs.

SnO2 was the most promising ETL. Systematical investigation of the doping elements from same main group and the same period into SnO2 found that the K doped SnO2 based PSCs has low defect density and high conductivity, and achieved a PCE of 20.92%.

For a graph G and a family of graphs $\mathcal {F}$ , the Turán number ${\mathrm {ex}}(G,\mathcal {F})$ is the maximum number of edges an $\mathcal {F}$ -free subgraph of G can have. We prove that ${\mathrm {ex}}(G,\mathcal {F})\ge {\mathrm {ex}}(K_r, \mathcal {F})$ if the chromatic number of G is r and $\mathcal {F}$ is a family of connected graphs. This result answers a question raised by Briggs and Cox [‘Inverting the Turán problem’, Discrete Math.342(7) (2019), 1865–1884] about the inverse Turán number for all connected graphs.

Purpose Fully automatic operation (FAO) metro system is the trend of urban rail in modern cities. This study aims to develop a service quality scale for measuring passenger service perception in the context of FAO metro system. Design/methodology/approach Based on the interviews and the dimensions proposed in the previous service quality scales of the public transport sector, this study develops a multidimension service quality scale. Face-to-face interviews and surveys are conducted to obtain the riding experience and satisfaction of passengers in the metro stations of Beijing Subway Yanfang Line and Beijing Daxing International Airport Express that are FAO metro lines. Reliability, validity and nomological validity of our scale are demonstrated. Findings Six dimensions (i.e. tangibles, information, safety, efficiency, empathy and innovation) and 22 items are included in the scale through quantitative analysis. It is found that all of the six dimensions have a significant relationship with passenger satisfaction. Especially information and innovation are the more important dimensions of FAO metro system service quality. Originality/value This study contributes to the service quality literature in the public transport sector taking the FAO metro system as the object. The findings may provide a guide for the FAO metro operators to evaluate and improve their service quality.

miR-124 is a significantly up-regulated miRNA in peripheral blood collected from piglets infected with Salmonella Typhimurium, suggesting that it may play an important role in Salmonella pathogenesis. This study focused on the transcriptomic analysis of peripheral blood mononuclear cells (PBMCs) isolated from miR-124 sponge and Salmonella Typhimurium-treated piglets, and trying to investigate the function of miR-124 in Salmonella infection. The transcriptome profiling analysis revealed that 2778 genes in miR-124 sponge + Salmonella Typhimurium treatment versus control, 2271 genes in Salmonella Typhimurium treatment versus control, and 1301 genes in miR-124 sponge + Salmonella Typhimurium versus Salmonella Typhimurium treatment, were differentially expressed, respectively (FDR 0.05 and fold change 2.0). Pathway analysis indicated that the MAPK signaling pathway, Ribosome pathway, and T-cell receptor signaling pathway were the most significantly enriched pathway in differentially expressed genes between miR-124 sponge + Salmonella Typhimurium and Salmonella Typhimurium along treatment (FDR 0.05). Reporter assays and electrophoretic mobility shift assays showed that miR-124 is a crucial regulatory factor that targets IQ motif containing GTPase-activating protein 2 (IQGAP2). Cell culture experiment indicated that miR-124 attenuated the Salmonella Typhimurium-mediated activation of CDC42 and RAC1 (P 0.05). Cultured PBMCs treated with miR-124 and IQGAP2-siRNA had higher intracellular Salmonella count than control samples, particularly 12 h post-infection (P 0.05). Immunofluorescence analysis revealed that miR-124 treatment reduced the percentage of LAMP-1-positive phagosomes. The miR-124 could be an important regulator for IQGAP2/Rho GTPase pathway in Salmonella Typhimurium-infected PBMCs, and this pathway could be a target for Salmonella that support its infection in PBMCs in piglets.

Seeing alcohol in media has been demonstrated to increase alcohol craving, impulsive decision-making, and hazardous drinking. Due to the exponential growth of (social) media use it is important to develop algorithms to quantify alcohol exposure efficiently in electronic images. In this article, we describe the development of an improved version of the Alcoholic Beverage Identification Deep Learning Algorithm (ABIDLA), called ABIDLA2.ABIDLA2 was trained on 191,286 images downloaded from Google Image Search results (based on search terms) and Bing Image Search results. In Task-1, ABIDLA2 identified images as containing one of eight beverage categories (beer/cider cup, beer/cider bottle, beer/cider can, wine, champagne, cocktails, whiskey/cognac/brandy, other images). In Task-2, ABIDLA2 made a binary classification between images containing an "alcoholic beverage" or "other". An ablation study was performed to determine which techniques improved algorithm performance.ABIDLA2 was most accurate in identifying Whiskey/Cognac/Brandy (88.1%) followed by Beer/Cider Can (80.5%), Beer/Cider Bottle (78.3%), and Wine (77.8%). Its overall accuracy was 77.0% (Task-1) and 87.7% (Task-2). Even the identification of the least accurate beverage category (Champagne, 64.5%) was more than five times higher than random chance (12.5% = 1/8 categories). The implementation of balanced data sampler to address class skewness and the use of self-training to make use of a large, secondary, weakly labeled dataset particularly improved overall algorithm performance.With extended capabilities and a higher accuracy, ABIDLA2 outperforms its predecessor and enables the screening of any kind of electronic media rapidly to estimate the quantity of alcohol exposure. Quantifying alcohol exposure automatically through algorithms like ABIDLA2 is important because viewing images of alcoholic beverages in media tends to increase alcohol consumption and related harms.

Wurfbainia villosa is a well-known medicinal and edible plant that is widely cultivated in the Lingnan region of China. Its dried fruits (called Fructus Amomi) are broadly used in traditional Chinese medicine for curing gastrointestinal diseases and are rich in volatile terpenoids. Here, we report a high-quality chromosome-level genome assembly of W. villosa with a total size of approximately 2.80 Gb, 42 588 protein-coding genes, and a very high percentage of repetitive sequences (87.23%). Genome analysis showed that W. villosa likely experienced a recent whole-genome duplication event prior to the W. villosa-Zingiber officinale divergence (approximately 11 million years ago), and a recent burst of long terminal repeat insertions afterward. The W. villosa genome enabled the identification of 17 genes involved in the terpenoid skeleton biosynthesis pathway and 66 terpene synthase (TPS) genes. We found that tandem duplication events have an important contribution to the expansion of WvTPSs, which likely drove the production of volatile terpenoids. In addition, functional characterization of 18 WvTPSs, focusing on the TPS-a and TPS-b subfamilies, showed that most of these WvTPSs are multi-product TPS and are predominantly expressed in seeds. The present study provides insights into the genome evolution and the molecular basis of the volatile terpenoids diversity in W. villosa. The genome sequence also represents valuable resources for the functional gene research and molecular breeding of W. villosa.

Human brain cortex acts as a rich inspiration source for constructing efficient artificial cognitive systems. In this paper, we investigate to incorporate multiple brain-inspired computing paradigms for compact, fast and high-accuracy neuromorphic hardware implementation. We propose the TripleBrain hardware core that tightly combines three common brain-inspired factors: the spike-based processing and plasticity, the self-organizing map (SOM) mechanism and the reinforcement learning scheme, to improve object recognition accuracy and processing throughput, while keeping low resource costs. The proposed hardware core is fully event-driven to mitigate unnecessary operations, and enables various on-chip learning rules (including the proposed SOM-STDPR-STDP rule and the R-SOM-STDP rule regarded as the two variants of our TripleBrain learning rule) with different accuracy-latency tradeoffs to satisfy user requirements. An FPGA prototype of the neuromorphic core was implemented and elaborately tested. It realized high-speed learning (1349 frame/s) and inference (2698 frame/s), and obtained comparably high recognition accuracies of 95.10%, 80.89%, 100%, 94.94%, 82.32%, 100% and 97.93% on the MNIST, ETH-80, ORL-10, Yale-10, N-MNIST, Poker-DVS and Posture-DVS datasets, respectively, while only consuming 4146 (7.59%) slices, 32 (3.56%) DSPs and 131 (24.04%) Block RAMs on a Xilinx Zynq-7045 FPGA chip. Our neuromorphic core is very attractive for real-time resource-limited edge intelligent systems.

Due to the merits of high rigidity and good dynamics, hybrid machine tools have been gradually applied to efficient machining of thin-walled workpiece with complex geometries. However, the discontinuity of tangential component of toolpath in hybrid machine tools may cause velocity fluctuations, leading to poor surface quality of workpiece. In this paper, a novel 5-axis hybrid machine tool is taken as an example to demonstrate a smooth toolpath interpolation method. First, an adaptive acceleration and deceleration control algorithm is presented to realize the smooth transition between two constrained velocity points. Second, a spline curve-based interpolation algorithm is proposed to realize the smoothness of the trajectory. Meanwhile, a parameter synchronization method is proposed to ensure the synchronization of the interpolated tool-axis vector and the interpolated tool tip. Thirdly, an inverse kinematic analysis is conducted based on an inverse position solution model and a velocity mapping model. Finally, a set of machining tests on S-shape workpiece in line with the ISO standard is carried out to verify the effectiveness of the proposed smooth toolpath interpolation method.

The photothermoelectric effect, directly converting light energy into electrical energy, shows promising prospects in self-powered broad-band optical detection, which can extend to various applications, such as sensing, optoelectronic communications, and wide-temperature-range measurements. However, the low photosensitivity, narrow-band response, and rapid performance degeneration under continuous illumination restrict its broad application. Herein, we propose a simple bottom-up strategy to manipulate nanowires (NWs) into a well-defined multilayer Te-Ag

Summary Background Pediatric thoracoscopic lobectomy is a technically challenging procedure that may result in better pain control, better cosmetic results, and shorter hospital stay. However, data describing the learning curve of total thoracoscopic lobectomy (TTL) have yet to be obtained. To evaluate our learning curve for TTL in children, we reviewed the safety and efficiency of our initial experiences with TTL in pediatric patients with congenital lung malformation. Methods This was a retrospective study of all pediatric patients undergoing TTL between March 2011 and January 2017. Cumulative summation (CUSUM) analysis of operative time (OT) was used. Results One hundred patients were retrospectively analyzed and chronologically divided into three phases: the ascending (A), plateau (B), and descending (C) phases of CUSUM of OT. Phases A, B, and C comprised 35, 22, and 43 cases, respectively. OT decreased significantly from phases A to B (P = 0.035) and B to C (P = 0.019). Age and weight of patients both reduced significantly from phase A to B (p = 0.017 and p = 0.012, respectively), while the two measures did not vary from phase B to C (p = 0.987 and p = 0.874, respectively). Chest tube duration and length of hospital stay had similar trend. All complications occurred in five cases in phase A (5/35). Six cases were converted to open surgery (6%). Four conversions occurred within phase A and two in phase C (4/35 vs 2/43, p = 0.490). There were no mortalities. Conclusions Repeated standardized training plays a role in overcoming the learning curve for thoracoscopic lobectomy in children, and CUSUMOT indicates that a learning curve of approximately 57 cases is required in our institute.

In this paper, a novel adaptive smooth disturbance observer-based fast finite-time adaptive backstepping control scheme is presented for the attitude tracking of the 3-DOF helicopter system subject to compound disturbances. First, an adaptive smooth disturbance observer (ASDO) is proposed to estimate the composite disturbance, which owns the characteristics of smooth output, fast finite-time convergence, and adaptability to the disturbance of unknown derivative boundary. Then, a finite-time backstepping control protocol is construct to drive the elevation and pitch angles to track reference trajectories. To tackle the "explosion of complexity" and "singularity" problems in the conventional backstepping design framework, a fast finite-time command filter (FFTCF) is utilized to estimate the virtual control signal and its derivative. Moreover, a fractional power-based auxiliary dynamic system is introduced to compensate the error caused by the FFTCF estimation. Furthermore, an improved fractional power-based adaptive law with the $\sigma $-modification term is designed to attenuate the observer approximation error, such that the tracking performance is further enhanced. In terms of the fast finite-time stability theory, the signals of the closed-loop system are all fast finite-time bounded while the attitude tracking errors can fast converge to a sufficiently small region of the origin in finite time. Finally, a contrastive numerical simulation is carried out to validate the effectiveness and superiority of the designed control scheme., Comment: 26 pages,9 figures

In the past decade, automatic product description generation for e-commerce have witnessed significant advancement. As the services provided by e-commerce platforms become diverse, it is necessary to dynamically adapt the patterns of descriptions generated. The selling point of products is an important type of product description for which the length should be as short as possible while still conveying key information. In addition, this kind of product description should be eye-catching to the readers. Currently, product selling points are normally written by human experts. Thus, the creation and maintenance of these contents incur high costs. These costs can be significantly reduced if product selling points can be automatically generated by machines. In this paper, we report our experience developing and deploying the Intelligent Online Selling Point Extraction (IOSPE) system to serve the recommendation system in the JD.com e-commerce platform. Since July 2020, IOSPE has become a core service for 62 key categories of products (covering more than 4 million products). So far, it has generated more than 1.1 billion selling points, thereby significantly scaling up the selling point creation operation and saving human labour. These IOSPE generated selling points have increased the click-through rate (CTR) by 1.89% and the average duration the customers spent on the products by more than 2.03% compared to the previous practice, which are significant improvements for such a large-scale e-commerce platform.

In-Situ state heat conduction in drilling workpiece results in time-varying temperature field, which affects the final machining accuracy. From the joint action of many factors, such as geometry, material, and thermal convection characteristics, it is difficult to accurately model and describe the time-varying characteristics of the temperature field. Therefore, the time-varying model of the In-Situ state temperature field is analyzed in this paper, which lays a foundation for establishing a high-precision multi-physical field model. First, following the geometric characteristics of the drilling hole and the three-dimensional heat transfer mechanism, the heat transfer model of the drilled part is established, and the relationship between the temperature field and the physical parameters of the workpiece is characterized. Then, combining radial and axial unsteady heat conduction mechanisms, the dimensionless temperature calculation method is adopted to realize the construction of a temperature time-varying model. After that, the model parameters are revised according to the In-Situ state evolution process of the checkpoints. Finally, the experiments are carried out to calculate the credibility of the model. The results show that the prediction model is consistent with the experimental data, and the credibility of non-room temperature dynamic interval is 99.23%, which can effectively reflect the real temperature field conduction process.