Text classification in fair competition law violations using deep learning

IntroductionEnsuring fair competition through manual review is a complex undertaking. This paper introduces the utilization of Long Short-Term Memory (LSTM) neural networks and TextCNN to establish a text classifier for classifying and reviewing normative documents.MethodsThe experimental dataset used consists of policy measure samples provided by the antitrust division of the Guangdong Market Supervision Administration. We conduct a comparative analysis of the performance of LSTM and TextCNN classification models.ResultsIn three classification experiments conducted without an enhanced experimental dataset, the LSTM classifier achieved an accuracy of 95.74%, while the TextCNN classifier achieved an accuracy of 92.7% on the test set. Conversely, in three classification experiments utilizing an enhanced experimental dataset, the LSTM classifier demonstrated an accuracy of 96.36%, and the TextCNN classifier achieved an accuracy of 96.19% on the test set.DiscussionThe experimental results highlight the effectiveness of LSTM and TextCNN in classifying and reviewing normative documents. The superior accuracy achieved with the enhanced experimental dataset underscores the potential of these models in real-world applications, particularly in tasks involving fair competition review

Targeting Trop2 in solid tumors: a look into structures and novel epitopes

Trophoblast cell surface antigen 2 (Trop2) exhibits limited expression in normal tissues but is over-expressed across various solid tumors. The effectiveness of anti-Trop2 antibody-drug conjugate (ADC) in managing breast cancer validates Trop2 as a promising therapeutic target for cancer treatment. However, excessive toxicity and a low response rate of ADCs pose ongoing challenges. Safer and more effective strategies should be developed for Trop2-positive cancers. The dynamic structural attributes and the oligomeric assembly of Trop2 present formidable obstacles to the progression of innovative targeted therapeutics. In this review, we summarize recent advancements in understanding Trop2’s structure and provide an overview of the epitope characteristics of Trop2-targeted agents. Furthermore, we discuss the correlation between anti-Trop2 agents’ epitopes and their respective functions, particularly emphasizing their efficacy and specificity in targeted therapies

GWAS Analysis and QTL Identification of Fiber Quality Traits and Yield Components in Upland Cotton Using Enriched High-Density SNP Markers

It is of great importance to identify quantitative trait loci (QTL) controlling fiber quality traits and yield components for future marker-assisted selection (MAS) and candidate gene function identifications. In this study, two kinds of traits in 231 F6:8 recombinant inbred lines (RILs), derived from an intraspecific cross between Xinluzao24, a cultivar with elite fiber quality, and Lumianyan28, a cultivar with wide adaptability and high yield potential, were measured in nine environments. This RIL population was genotyped by 122 SSR and 4729 SNP markers, which were also used to construct the genetic map. The map covered 2477.99 cM of hirsutum genome, with an average marker interval of 0.51 cM between adjacent markers. As a result, a total of 134 QTLs for fiber quality traits and 122 QTLs for yield components were detected, with 2.18–24.45 and 1.68–28.27% proportions of the phenotypic variance explained by each QTL, respectively. Among these QTLs, 57 were detected in at least two environments, named stable QTLs. A total of 209 and 139 quantitative trait nucleotides (QTNs) were associated with fiber quality traits and yield components by four multilocus genome-wide association studies methods, respectively. Among these QTNs, 74 were detected by at least two algorithms or in two environments. The candidate genes harbored by 57 stable QTLs were compared with the ones associated with QTN, and 35 common candidate genes were found. Among these common candidate genes, four were possibly “pleiotropic.” This study provided important information for MAS and candidate gene functional studies

Strain engineered nanomembranes as anodes for lithium ion batteries

Lithium ion batteries (LIBs) have attracted considerable interest due to their wide range of applications, such as portable electronics, electric vehicles (EVs) and aerospace applications. Particularly, the emergence of a variety of nanostructured materials has driven the development of LIBs towards the next generation, which is featured with high specific energy and large power density. Herein, rolled-up nanotechnology is introduced for the design of strain-released materials as anodes of LIBs. Upon this approach, self-rolled nanostructures can be elegantly combined with different functional materials and form a tubular shape by relaxing the intrinsic strain, thus allowing for enhanced tolerance towards stress cracking. In addition, the hollow tube center efficiently facilitates electrolyte mass flow and accommodates volume variation during cycling. In this context, such structures are promising candidates for electrode materials of LIBs to potentially address their intrinsic issues. This work focuses on the development of superior structures of Si and SnO2 for LIBs based on the rolled-up nanotech. Specifically, Si is the most promising substitute for graphite anodes due to its abundance and high theoretical gravimetric capacity. Combined with the C material, a Si/C self-wound nanomembrane structure is firstly realized. Benefiting from a strain-released tubular shape, the bilayer self-rolled structures exhibit an enhanced electrochemical behavior over commercial Si microparticles. Remarkably, this behavior is further improved by introducing a double-sided carbon coating to form a C/Si/C self-rolled structure. With SnO2 as active material, an intriguing sandwich-stacked structure is studied. Furthermore, this novel structure, with a minimized strain energy due to strain release, exposes more active sites for the electrochemical reactions, and also provides additional channels for fast ion diffusion and electron transport. The electrochemical characterization and morphology evolution reveal the excellent cycling performance and stability of such structures

Potential of Tai Chi chuan on promoting cognitive functions in Chinese elders : a systematic review

Objective: Tai Chi Chuan might have some potential for promoting cognitive functions in older adults due to its unique features compared to simple exercise such as stretching and running. This project aims to investigate the effectiveness of Tai Chi Chuan in promoting cognitive functions in the older population. Methods: A literature search using specific keywords was conducted in the database PubMed, MEDLINE, Web of Science, and Wanfang Data (Chinese) to identify original studies that a) used interventional study design, b) included cognitive outcome measurements, and c) enrolled elders without serious medical conditions. A systematic review was then conducted to synthesize relevant results. Results: A total of 12studies were identified. The results were synthesized and analyzed The most significant effect of Tai Chi was observed in apparent slowing of clinical dementia progression, but this could be a result of publication bias. Compared to previous reviews, this project did detect some potential of Tai Chi Chuan on promoting cognitive functions, but the results on executive functions, memory or language ability were not consistent. Small sample sizes, varied participant baseline data and various Tai Chi and control interventions might contribute to this result. Discussion: Intervention and cognitive outcome measurements were not consistent in individual studies. And most studies only had acceptable methodological quality. Disparities in results were present when cognitive functions were assessed with different tests. Further rigorously designed studies with larger sample sizes are needed to get a more definitive conclusion. Conclusion: Tai Chi Chuan might be effective as a behavioral intervention to contain age-related cognitive declines in Chinese communities due to relatively low cost, safer practice, cultural acceptability and socially involvement. Larger, better controlled studies are needed.published_or_final_versionPublic HealthMasterMaster of Public Healt

Deep-Learning-Based Wireless Visual Sensor System for Shiitake Mushroom Sorting

The shiitake mushroom is the second-largest edible mushroom in the world, with a high nutritional and medicinal value. The surface texture of shiitake mushrooms can be quite different due to different growing environments, consequently leading to fluctuating market prices. To maximize the economic profit of the mushroom industry, it is necessary to sort the harvested mushrooms according to their qualities. This paper aimed to develop a deep-learning-based wireless visual sensor system for shiitake mushroom sorting, in which the visual detection was realized by the collection of images and cooperative transmission with the help of visual sensors and Wi-Fi modules, respectively. The model training process was achieved using Vision Transformer, then three data-augmentation methods, which were Random Erasing, RandAugment, and Label Smoothing, were applied under the premise of a small sample dataset. The training result of the final model turned out nearly perfect, with an accuracy rate reaching 99.2%. Meanwhile, the actual mushroom-sorting work using the developed system obtained an accuracy of 98.53%, with an 8.7 ms processing time for every single image. The results showed that the system could efficiently complete the sorting of shiitake mushrooms with a stable and high accuracy. In addition, the system could be extended for other sorting tasks based on visual features. It is also possible to combine binocular vision and multisensor technology with the current system to deal with sorting work that requires a higher accuracy and minor feature identification

Research on Efficient Multi-Behavior Recommendation Method Fused with Graph Neural Network

Currently, most recommendation algorithms only use a single type of user behavior information to predict the target behavior. However, when browsing and selecting items, users generate other types of behavior information, which is important, but often not analyzed or modeled by traditional recommendation algorithms. This study aims to design a multi-behavior recommendation algorithm based on graph neural networks by analyzing multiple types of behavior information in users’ product purchasing process, to fully utilize multiple types of user behavior information. The algorithm models users, items, and user behavior in multiple dimensions by incorporating attention mechanisms and multi-behavior learning into graph neural networks, and solves the problem of imbalanced user behavior weights from the perspective of multi-task loss optimization. After experimental verification, we proposed that the multi-behavior graph attention network (MGAT) algorithm has better performance compared to four other classical recommendation algorithms on the Beibei and Taobao datasets. The results demonstrate that the multi-behavior recommendation algorithm based on graph neural networks has practicality in fully utilizing multiple types of user information, and can solve the problem of imbalanced user behavior weights to some extent

Characterization of the complete chloroplast genome of medical plant Curculigo orchioides Gaertn. (Amaryllidaceae)

Curculigo orchioides Gaertn. distributed in subtropical regions of Asia including southern China and India. The plant is used as a traditional medicine in China for the treatment of menorrhagia, osteoporosis, and other gynecological problems. The complete chloroplast genome was reported in this study using the Illumina NovaSeq platform. The whole genome of this species was 157,472 bp in length, with a total GC content of 37.44%. The large single copy (LSC) was 86,507 bp, the small single copy (SSC) was 16,867 bp, and both of the two inverted repeats (IRs) were 27,049 bp, respectively. A total of 132 unique genes were identified, among which are 86 protein-coding genes, 38 tRNA genes and 8 rRNA genes. The phylogenetic analysis revealed that C. orchioides was highly clustered with C. capitulata. Our study will provide useful fundamental data for further phylogenetic and evolutionary studies of C. orchioides