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To enhance the efficacy of multimedia quantum processing and diminish processing overhead, an advanced multimedia quantum representation model and quantum video display framework are devised. A range of framework processing operators are also developed, including an image color compensation operator, a bit plane inversion operator, and a frame displacement operator. In addition, to address image security issues, two quantum image operations have been proposed: color transformation operation and pixel blending operation. The research results indicated that the grayscale cost of the framework designed in this study was 33.8, the color cost was 40.5, and the total cost was 574 δ. In terms of color, the distribution of image elements in the red, green, and blue (RGB) channels was more balanced. In summary, the quantum video display framework has significant advantages in processing efficiency and image security. Compared to previous studies, the model proposed in this study exhibits higher processing speed and better processing quality in the face of complex image and video data. It effectively addresses the limitations of existing processing techniques while addressing emerging image security issues.

The carbon–nitrogen bond is one of the most prevalent chemical bonds in natural and artificial molecules, as many naturally existing organic molecules, pharmaceuticals, agrochemicals, and functional materials contain at least one nitrogen atom. Radical decarboxylative carbon–nitrogen bond formation from readily available carboxylic acids and their derivatives has emerged as an attractive and valuable tool in modern synthetic chemistry. The promising achievements in this research topic have been demonstrated via utilizing this strategy in the synthesis of complex natural products. In this review, we will cover carbon–nitrogen bond formation via radical decarboxylation of carboxylic acids, Barton esters, MPDOC esters, N–hydroxyphthalimide esters (NHP esters), oxime esters, aryliodine(III) dicarboxylates, and others, respectively. This review aims to bring readers a comprehensive survey of the development in this rapidly expanding field. We hope that this review will emphasize the knowledge, highlight the proposed mechanisms, and further disclose the fascinating features in modern synthetic applications.

Human identification based on motion capture data has received signification attentions for its wide applications in authentication and surveillance systems. The optical motion capture system (OMCS) can dynamically capture the high-precision three-dimensional locations of optical trackers that are implemented on a human body, but its potential in applications on gait recognition has not been studied in existing works. On the other hand, a typical OMCS can only support one player one time, which limits its capability and efficiency. In this paper, our goals are investigating the performance of OMCS-based gait recognition performance, and realizing gait recognition in OMCS such that it can support multiple players at the same time. We develop a gait recognition method based on decision fusion, and it includes the following four steps: feature extraction, unreliable feature calibration, classification of single motion frame, and decision fusion of multiple motion frame. We use kernel extreme learning machine (KELM) for single motion classification, and in particular we propose a reliability weighted sum (RWS) decision fusion method to combine the fuzzy decisions of the motion frames. We demonstrate the performance of the proposed method by using walking gait data collected from 76 participants, and results show that KELM significantly outperforms support vector machine (SVM) and random forest in the single motion frame classification task, and demonstrate that the proposed RWS decision fusion rule can achieve better fusion accuracy compared with conventional fusion rules. Our results also show that, with 10 motion trackers that are implemented on lower body locations, the proposed method can achieve 100% validation accuracy with less than 50 gait motion frames.

Conventional mobile robots employ LIDAR for indoor global positioning and navigation, thus having strict requirements for the ground environment. Under the complicated ground conditions in the greenhouse, the accumulative error of odometer (ODOM) that arises from wheel slip is easy to occur during the long-time operation of the robot, which decreases the accuracy of robot positioning and mapping. To solve the above problem, an integrated positioning system based on UWB (ultra-wideband)/IMU (inertial measurement unit)/ODOM/LIDAR is proposed. First, UWB/IMU/ODOM is integrated by the Extended Kalman Filter (EKF) algorithm to obtain the estimated positioning information. Second, LIDAR is integrated with the established two-dimensional (2D) map by the Adaptive Monte Carlo Localization (AMCL) algorithm to achieve the global positioning of the robot. As indicated by the experiments, the integrated positioning system based on UWB/IMU/ODOM/LIDAR effectively reduced the positioning accumulative error of the robot in the greenhouse environment. At the three moving speeds, including 0.3 m/s, 0.5 m/s, and 0.7 m/s, the maximum lateral error is lower than 0.1 m, and the maximum lateral root mean square error (RMSE) reaches 0.04 m. For global positioning, the RMSEs of the x-axis direction, the y-axis direction, and the overall positioning are estimated as 0.092, 0.069, and 0.079 m, respectively, and the average positioning time of the system is obtained as 72.1 ms. This was sufficient for robot operation in greenhouse situations that need precise positioning and navigation.

The straightforward strategy of building a chiral C-O bond directly on a general carbon radical center is challenging and stereocontrol of the reactions of open-chain hydrocarbon radicals remains a largely unsolved problem. Advance in this elementary step will spur the development of asymmetric radical C-O bond construction. Herein, we report a copper-catalyzed regioselective and enantioselective carboesterification of substituted dienes using alkyl diacyl peroxides as the source of both the carbon and oxygen substituents. The participation of external acids in this reaction substantially extends its applicability and leads to structurally diverse allylic ester products. This work represents the advance in the key elementary reaction of intermolecular enantioselective construction of C-O bond on open-chain hydrocarbon radicals and may lead to the discovery of other asymmetric radical reactions. Stereocontrol of C–O bond formation from a carbon-based radical is very difficult due to the rapid inversion of the carbon radical. Here the authors present a method to form chiral esters from conjugated dienes with copper and chiral PyBox ligands, likely proceeding via an allylic radical.

Background Nitzschia closterium f. minutissima is a commonly available diatom that plays important roles in marine aquaculture. It was originally classified as Nitzschia (Bacillariaceae, Bacillariophyta) but is currently regarded as a heterotypic synonym of Phaeodactylum tricornutum . The aim of this study was to obtain the draft genome of the marine microalga N. closterium f. minutissima to understand its phylogenetic placement and evolutionary specialization. Given that the ornate hierarchical silicified cell walls (frustules) of diatoms have immense applications in nanotechnology for biomedical fields, biosensors and optoelectric devices, transcriptomic data were generated by using reference genome-based read mapping to identify significantly differentially expressed genes and elucidate the molecular processes involved in diatom biosilicification. Results In this study, we generated 13.81 Gb of pass reads from the PromethION sequencer. The draft genome of N. closterium f. minutissima has a total length of 29.28 Mb, and contains 28 contigs with an N50 value of 1.23 Mb. The GC content was 48.55%, and approximately 18.36% of the genome assembly contained repeat sequences. Gene annotation revealed 9,132 protein-coding genes. The results of comparative genomic analysis showed that N. closterium f. minutissima was clustered as a sister lineage of Phaeodactylum tricornutum and the divergence time between them was estimated to be approximately 17.2 million years ago (Mya). CAFF analysis demonstrated that 220 gene families that significantly changed were unique to N. closterium f. minutissima and that 154 were specific to P. tricornutum , moreover, only 26 gene families overlapped between these two species. A total of 818 DEGs in response to silicon were identified in N. closterium f. minutissima through RNA sequencing, these genes are involved in various molecular processes such as transcription regulator activity. Several genes encoding proteins, including silicon transporters, heat shock factors, methyltransferases, ankyrin repeat domains, cGMP-mediated signaling pathways-related proteins, cytoskeleton-associated proteins, polyamines, glycoproteins and saturated fatty acids may contribute to the formation of frustules in N. closterium f. minutissima. Conclusions Here, we described a draft genome of N. closterium f. minutissima and compared it with those of eight other diatoms, which provided new insight into its evolutionary features. Transcriptome analysis to identify DEGs in response to silicon will help to elucidate the underlying molecular mechanism of diatom biosilicification in N. closterium f. minutissima.

The advent of deep learning has significantly propelled the utilization of neural networks for Synthetic Aperture Radar (SAR) ship detection in recent years. However, there are two main obstacles in SAR detection. Challenge 1: The multiscale nature of SAR ships. Challenge 2: The influence of intricate near-shore environments and the interference of clutter noise in offshore areas, especially affecting small-ship detection. Existing neural network-based approaches attempt to tackle these challenges, yet they often fall short in effectively addressing small-ship detection across multiple scales and complex backgrounds simultaneously. To overcome these challenges, we propose a novel network called SwinT-FRM-ShipNet. Our method introduces an integrated feature extractor, Swin-T-YOLOv5l, which combines Swin Transformer and YOLOv5l. The extractor is designed to highlight the differences between the complex background and the target by encoding both local and global information. Additionally, a feature pyramid IEFR-FPN, consisting of the Information Enhancement Module (IEM) and the Feature Refinement Module (FRM), is proposed to enrich the flow of spatial contextual information, fuse multiresolution features, and refine representations of small and multiscale ships. Furthermore, we introduce recursive gated convolutional prediction heads (GCPH) to explore the potential of high-order spatial interactions and add a larger-sized prediction head to focus on small ships. Experimental results demonstrate the superior performance of our method compared to mainstream approaches on the SSDD and SAR-Ship-Dataset. Our method achieves an F1 score, mAP0.5, and mAP0.5:0.95 of 96.5% (+0.9), 98.2% (+1.0%), and 75.4% (+3.3%), respectively, surpassing the most competitive algorithms.

The Aedes aegypti mosquito plays an important role in the spread of diseases, including epidemic ones, such as dengue fever, Zika virus disease, yellow fever, and chikungunya disease. To control the population of Ae.aegypti, we transferred an HR3 RNAi fragment into the microalgae Chlamydomonas, which serves as food for Ae.aegypti larvae. Results showed that the HR3 RNAi transgenic algal strains were lethal to Ae.aegypti. The integumentary system of larvae fed with HR3 RNAi transgenic algal strains was severely damaged. Muscles of the larvae were unevenly distributed and disordered, and their midgut showed disintegration of the intestinal cavity. RNA-Seq results demonstrated that on the 4th day of inoculation with the transgenic algae, the abundance of early expressed genes in the 20E signal transduction pathway of larvae fed with the HR3 RNAi transgenic algal strain significantly reduced. These genes include E74, E75, E93, and 20E receptor complex EcR/USP and FTZ-F1 gene regulated by HR3. In later experiments, a scale test of 300 Ae.aegypti eggs per group was carried out for 30 days, and the survival rate of Ae.aegypti fed with the HR3 RNAi transgenic strain was only 1.3%. These results indicate that the HR3 RNAi transgenic strain exerts obvious insecticidal effect.

The emergence of new technologies, such as artificial intelligence (AI), may manifest as technology panic in some people, including adolescents who may be particularly vulnerable to new technologies (the use of AI can lead to AI dependence, which can threaten mental health). While the relationship between AI dependence and mental health is a growing topic, the few existing studies are mainly cross-sectional and use qualitative approaches, failing to find a longitudinal relationship between them. Based on the framework of technology dependence, this study aimed to determine the prevalence of experiencing AI dependence, to examine the cross-lagged effects between mental health problems (anxiety/depression) and AI dependence and to explore the mediating role of AI use motivations. A two-wave cohort program with 3843 adolescents (Male = 1848, = 13.21 ± 2.55) was used with a cross-lagged panel model and a half-longitudinal mediation model. 17.14% of the adolescents experienced AI dependence at T1, and 24.19% experienced dependence at T2. Only mental health problems positively predicted subsequent AI dependence, not vice versa. For AI use motivation, escape motivation and social motivation mediated the relationship between mental health problems and AI dependence whereas entertainment motivation and instrumental motivation did not. Excessive panic about AI dependence is currently unnecessary, and AI has promising applications in alleviating emotional problems in adolescents. Innovation in AI is rapid, and more research is needed to confirm and evaluate the impact of AI use on adolescents' mental health and the implications and future directions are discussed.

Objective Catheter-associated urinary tract infections (CAUTIs) are common healthcare-related infections in intensive care units (ICUs). This study investigated the risk factors for CAUTIs in critically ill patients. Methods This study was a single-centre, retrospective, matched case-control study of patients undergoing indwelling catheterization in the ICU from December 1, 2016, to October 31, 2021. Patients with catheterizations were matched 1:4 with controls that were hospitalized in the ICU during the same period (with a difference in admission time of no more than two months). Results CAUTI occurred in 18 of 403 patients, with an infection rate of 3.7/per 1000 catheter days. Repeat catheterization of the urinary catheter (OR = 10.09) and days of antibiotic use (OR = 0.13) were independent risk factors for CAUTI ( P  < 0.05). A total of 31 pathogen strains were detected in urine samples from 18 CAUTI patients. The main pathogens were Gram-positive bacteria ( n  = 13, 41.9%), fungi ( n  = 10, 32.3%) and Gram-negative bacteria ( n  = 7, 22.6%). CAUTI was associated with an increase in hospitalization days by 26 days and an increase in total hospitalization cost of ¥160,000 ( P  < 0.001). Conclusion CAUTIs pose an economic and health burden for ICU patients. Repeat catheterization and longer use of antibiotics are to be avoided as much as possible.