Semidiscrete vortex solitons

We demonstrate a possibility of the creation of stable optical solitons combining one continuous and one discrete coordinate, with embedded vorticity, in an array of planar waveguides with intrinsic cubic-quintic nonlinearity. The same system may be realized in terms of the spatiotemporal light propagation in an array of tunnel-coupled optical fibers with the cubic-quintic nonlinearity. In contrast with zero-vorticity states, semidiscrete vortex solitons do not exist without the quintic term in the nonlinearity. Two types of the solitons, \emph{viz.}, intersite- and onsite-centered ones (IC and OC, respectively), with even and odd numbers $N$ of actually excited sites in the discrete direction, are identified. We consider the modes carrying the embedded vorticity $S=1$ and $2$. In accordance with their symmetry, the vortex solitons of the OC type exhibit an intrinsic core, while the IC solitons with a small $N$ may have a coreless structure. Facilitating their creation in the experiment, the modes reported in the present work may be much more compact states than their counterparts considered in other systems, and they feature strong anisotropy. They can be set in motion in the discrete direction, provided that the coupling constant exceeds a certain minimum value. Collisions between moving vortex solitons are considered too.Comment: 14 pages, 12 figures, 74 References,Published on Advanced Photonics Researc

WPS-Dataset: A benchmark for wood plate segmentation in bark removal processing

Using deep learning methods is a promising approach to improving bark removal efficiency and enhancing the quality of wood products. However, the lack of publicly available datasets for wood plate segmentation in bark removal processing poses challenges for researchers in this field. To address this issue, a benchmark for wood plate segmentation in bark removal processing named WPS-dataset is proposed in this study, which consists of 4863 images. We designed an image acquisition device and assembled it on a bark removal equipment to capture images in real industrial settings. We evaluated the WPS-dataset using six typical segmentation models. The models effectively learn and understand the WPS-dataset characteristics during training, resulting in high performance and accuracy in wood plate segmentation tasks. We believe that our dataset can lay a solid foundation for future research in bark removal processing and contribute to advancements in this field

Effectiveness of virtual team learning in entrepreneurship education: a survey study

This study examines the effectiveness of virtual team learning for entrepreneurship competence in the Chinese higher education sector. Related research on the effectiveness of virtual team learning is sparse, especially in the area of entrepreneurship education. We assumed four hypotheses to analyze two sorts of relationships: one between input, respondents’ demographics or characteristics, and mediators, namely virtual teamwork, virtual taskwork, information and communication technology; the other between mediators and output, thus the effectiveness of entrepreneurship education. An online survey was carried out to collect respondents’ perceptions of virtual team learning in entrepreneurship education from teamwork, taskwork, and information and communication technology aspects, considering respondents’ demographics or characteristics. By explaining factors of the team process, the findings show that virtual teamwork, taskwork, and information and communication technology positively affect the entrepreneurial outcome of virtual team learning. Additionally, individual characteristics, including gender, education degree, education field, entrepreneurial family history, and prior entrepreneurial experience have different effects on three elements of virtual teams. The applied model provides a holistic perspective on virtual team learning and explains the association between three sectors. These findings may provide an empirical basis for making decisions in the design and development of entrepreneurship learning and teaching offerings

Experimental investigation on dynamic behaviour of heavy-haul railway track induced by heavy axle load

The damage to the track structure and the influence to the line deformation have greatly deteriorated with the increase of the axle load compared with that of the ordinary trains. However, there is a paucity of experimental research on the dynamic influence of the heavier haul freight trains on the railway tracks. The objective of this study is to investigate the dynamic behaviour of heavy-haul railway track induced by heavy axle load by field experimental tests. The wheel–rail dynamic force, the track structure dynamic deformation and the track vibration behaviour are measured and analysed when the train operates in the speed range from 10 to 75 km/h and the axle load of vehicles varies from 21 to 30 t. Comparisons between the results for the axle conditions of 25 and 30 t are made in this paper to reveal the axle load effects. It is demonstrated that part of the indicators reflecting the dynamic behaviour of the railway track increases approximately linearly with the train running speed and axle load, while others are influenced negligibly

Nanobubbles in water and wastewater treatment systems:Small bubbles making big difference

Since the discovery of nanobubbles (NBs) in 1994, NBs have been attracting growing attention for their fascinating properties and have been studied for application in various environmental fields, including water and wastewater treatment. However, despite the intensive research efforts on NBs' fundamental properties, especially in the past five years, controversies and disagreements in the published literature have hindered their practical implementation. So far, reviews of NB research have mainly focused on NBs' role in specific treatment processes or general applications, highlighting proof-of-concept and success stories primarily at the laboratory scale. As such, there lacks a rigorous review that authenticates NBs' potential beyond the bench scale. This review aims to provide a comprehensive and up-to-date analysis of the recent progress in NB research in the field of water and wastewater treatment at different scales, along with identifying and discussing the challenges and prospects of the technology. Herein, we systematically analyze (1) the fundamental properties of NBs and their relevancy to water treatment processes, (2) recent advances in NB applications for various treatment processes beyond the lab scale, including over 20 pilot and full-scale case studies, (3) a preliminary economic consideration of NB-integrated treatment processes (the case of NB-flotation), and (4) existing controversies in NBs research and the outlook for future research. This review is organized with the aim to provide readers with a step-by-step understanding of the subject matter while highlighting key insights as well as knowledge gaps requiring research to advance the use of NBs in the wastewater treatment industry

Drug–drug interaction extraction based on multimodal feature fusion by Transformer and BiGRU

Understanding drug–drug interactions (DDIs) plays a vital role in the fields of drug disease treatment, drug development, preventing medical error, and controlling health care-costs. Extracting potential from biomedical corpora is a major complement of existing DDIs. Most existing DDI extraction (DDIE) methods do not consider the graph and structure of drug molecules, which can improve the performance of DDIE. Considering the different advantages of bi-directional gated recurrent units (BiGRU), Transformer, and attention mechanisms in DDIE tasks, a multimodal feature fusion model combining BiGRU and Transformer (BiGGT) is here constructed for DDIE. In BiGGT, the vector embeddings of medical corpora, drug molecule topology graphs, and structure are conducted by Word2vec, Mol2vec, and GCN, respectively. BiGRU and multi-head self-attention (MHSA) are integrated into Transformer to extract the local–global contextual DDIE features, which is important for DDIE. The extensive experiment results on the DDIExtraction 2013 shared task dataset show that the BiGGT-based DDIE method outperforms state-of-the-art DDIE approaches with a precision of 78.22%. BiGGT expands the application of multimodal deep learning in the field of multimodal DDIE

p-Norm Broad Learning for Negative Emotion Classification in Social Networks

Negative emotion classification refers to the automatic classification of negative emotion of texts in social networks. Most existing methods are based on deep learning models, facing challenges such as complex structures and too many hyperparameters. To meet these challenges, in this paper, we propose a method for negative emotion classification utilizing a Robustly Optimized BERT Pretraining Approach (RoBERTa) and p-norm Broad Learning (p-BL). Specifically, there are mainly three contributions in this paper. Firstly, we fine-tune the RoBERTa to adapt it to the task of negative emotion classification. Then, we employ the fine-tuned RoBERTa to extract features of original texts and generate sentence vectors. Secondly, we adopt p-BL to construct a classifier and then predict negative emotions of texts using the classifier. Compared with deep learning models, p-BL has advantages such as a simple structure that is only 3-layer and fewer parameters to be trained. Moreover, it can suppress the adverse effects of more outliers and noise in data by flexibly changing the value of p. Thirdly, we conduct extensive experiments on the public datasets, and the experimental results show that our proposed method outperforms the baseline methods on the tested datasets

Short-term effects of ophthalmic topical 0.01% atropine on the ocular surface, pupil size, and subsequent subjective quality of vision in young myopic Chinese adults

BackgroundDaily use of low concentrations of atropine is recommended for children undergoing myopia control therapy. While the benefits of controlling myopia progression have been confirmed, the potential unwanted side effects on the ocular surface, pupil size, and quality of vision following the administration of 0.01% atropine have not been investigated.ObjectiveThis single-arm, self-control study aimed to investigate the short-term effects of 0.01% atropine topical eye drop (He Eye Hospital Co., Ltd., Shenyang, China) on pupil size and subjective quality of vision in participants with myopia. Each 3 mL vial of eye drops contains atropine (0.01%), sodium chloride (0.9%), and benzalkonium chloride (0.005%) in an aqueous solution.MethodsThirty-three adults (66 eyes) were recruited for the study. The mean age of the participants recruited for this study was 24.91 ± 3.36 years. This study is registered with Clinical Trials.gov (NCT06071260). Assessments were performed at baseline and 10 h, 14 h, and 18 h following the administration of 0.01% topical atropine drop (TAD). Mesopic pupil diameter (MPD), photopic pupil diameter (PPD), higher order aberration (HOA), non-invasive tear breakup time (NITBUT), tear meniscus height (TMH), tear film lipid layer (TFLL), and Redness score (RS). Subjective assessments included the quality of vision (QoV) and the ocular surface disease index (OSDI) questionnaires.ResultsFollowing the use of 0.01% atropine, PPD significantly increased at all the time points (p < 0.001); MPD increased significantly at 10 h and 14 h (p < 0.001 and p < 0.05, respectively). A decrease in TMH and an increase in the OSDI questionnaire scores were observed up to 10 and 14 h, respectively, after using atropine (p < 0.001). Glare (p = 0.004 at 10 h and p = 0.003 at 14 h), blurred vision (p < 0.0001 at 10 h and p = 0.035 at 14 h), and focusing difficulties (p < 0.0001 at 10 h and p < 0.0001 at 14 h) were significantly higher at both 10 h and 14 h after using atropine. No significant changes were observed in the HOA, NITBUT, and RS scores (all p > 0.05) at all time points.ConclusionDecreased TMH, dry eye symptoms, and visual symptoms will likely persist overnight but often diminish within 18 h after using 0.01% atropine eye drops