Analyzing topics in social media for improving digital twinning based product development

Digital twinning enables manufacturers to create digital representations of physical entities,thus implementing virtual simulations for product development.Previous efforts of digital twinning neglect the decisive consumer feedback in product development stages,failing to cover the gap between physical and digital spaces.This work mines real-world consumer feedbacks through social media topics,which is significant to product development.We specifically analyze the prevalent time of a product topic,giving an insight into both consumer attention and the widely-discussed time of a product.The primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset distribution.Therefore,these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse topics.To this end,this work combines deep learning and survival analysis to predict the prevalent time of topics.We propose a specialized deep survival model which consists of two modules.The first module enriches input covariates by incorporating latent features of the time-varying text,and the second module fully captures the temporal pattern of a rumor by a recurrent network structure.Moreover,a specific loss function different from regular survival models is proposed to achieve a more reasonable prediction.Extensive experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.

A Video Captioning Method by Semantic Topic-Guided Generation

In the video captioning methods based on an encoder-decoder,limited visual features are extracted by an encoder,and a natural sentence of the video content is generated using a decoder.However,this kind ofmethod is dependent on a single video input source and few visual labels,and there is a problem with semantic alignment between video contents and generated natural sentences,which are not suitable for accurately comprehending and describing the video contents.To address this issue,this paper proposes a video captioning method by semantic topic-guided generation.First,a 3D convolutional neural network is utilized to extract the spatiotemporal features of videos during the encoding.Then,the semantic topics of video data are extracted using the visual labels retrieved from similar video data.In the decoding,a decoder is constructed by combining a novel Enhance-TopK sampling algorithm with a Generative Pre-trained Transformer-2 deep neural network,which decreases the influence of“deviation”in the semantic mapping process between videos and texts by jointly decoding a baseline and semantic topics of video contents.During this process,the designed Enhance-TopK sampling algorithm can alleviate a long-tail problem by dynamically adjusting the probability distribution of the predicted words.Finally,the experiments are conducted on two publicly used Microsoft Research Video Description andMicrosoft Research-Video to Text datasets.The experimental results demonstrate that the proposed method outperforms several state-of-art approaches.Specifically,the performance indicators Bilingual Evaluation Understudy,Metric for Evaluation of Translation with Explicit Ordering,Recall Oriented Understudy for Gisting Evaluation-longest common subsequence,and Consensus-based Image Description Evaluation of the proposed method are improved by 1.2%,0.1%,0.3%,and 2.4% on the Microsoft Research Video Description dataset,and 0.1%,1.0%,0.1%,and 2.8% on the Microsoft Research-Video to Text dataset,respectively,compared with the existing video captioning methods.As a result,the proposed method can generate video captioning that is more closely aligned with human natural language expression habits.

Pain perception enhancement in consecutive secondeye phacoemulsification cataract surgeries under topical anesthesia

Cataract is the main cause of visual impairment and blindness worldwide while the only effective cure for cataract is still surgery.Consecutive phacoemulsification under topical anesthesia has been the routine procedure for cataract surgery.However,patients often grumbled that they felt more painful during the second-eye surgery compared to the first-eye surgery.The intraoperative pain experience has negative influence on satisfaction and willingness for second-eye cataract surgery of patients with bilateral cataracts.Intraoperative ocular pain is a complicated process induced by the nociceptors activation in the peripheral nervous system.Immunological,neuropsychological,and pharmacological factors work together in the enhancement of intraoperative pain.Accumulating published literatures have focused on the pain enhancement during the secondeye phacoemulsification surgeries.In this review,we searched PubMed database for articles associated with pain perception differences between consecutive cataract surgeries published up to Feb.1,2024.We summarized the recent research progress in mechanisms and interventions for pain perception enhancement in consecutive secondeye phacoemulsification cataract surgeries.This review aimed to provide novel insights into strategies for improving patients’intraoperative experience in second-eye cataract surgeries.

Feasibility of drug delivery to the eye’s posterior segment by topical instillation of PLGA nanoparticles

We investigated the delivery of drugs to the posterior segment of the eye by non-invasive topical instillation using submicron-sized poly(D,L-lactide-co-glycolide)(PLGA) nanoparticles(NPs). Surface-modified PLGA NPs were developed to improve the drug delivery efficiency to the retina and were administered as topical eye drops to mice. Chitosan(CS) and glycol chitosan(GCS), which are mucoadhesive polymers, and polysorbate 80(P80) were used as surface modifiers, and have been reported to increase the association of NPs with cells.Coumarin-6 was used as a model drug and fluorescent marker, and after ocular administration of PLGA NP eye drops, the fluorescence intensity of coumarin-6 was observed in the retina. The fluorescence image analysis indicated that there are several possible routes to the retina and fates of PLGA NPs in ocular tissue, and that these pathways involved the corneal,non-corneal, or uveal routes. Delivery to the mouse retina segments after topical administration was increased by surface modification with CS, GCS, or P80. Surface-modified PLGA NPs are a promising method for retinal drug delivery via topical instillation.

TG-SMR:AText Summarization Algorithm Based on Topic and Graph Models

Recently,automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization.However,most of the computing methods that are used in real systems are based on graph models,which are characterized by their simplicity and stability.Thus,this paper proposes an improved extractive text summarization algorithm based on both topic and graph models.The methodology of this work consists of two stages.First,the well-known TextRank algorithm is analyzed and its shortcomings are investigated.Then,an improved method is proposed with a new computational model of sentence weights.The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods.Finally,through experiments on the DUC2004 and DUC2006 datasets,our proposed improved graph model algorithm TG-SMR(Topic Graph-Summarizer)is compared to other text summarization systems.The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores.It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.

Water-responsive gel extends drug retention and facilitates skin penetration for curcumin topical delivery against psoriasis

Psoriasis is a chronic inflammatory skin disease characterized by erythema,scaling,and skin thickening.Topical drug application is recommended as the first-line treatment.Many formulation strategies have been developed and explored for enhanced topical psoriasis treatment.However,these preparations usually have low viscosity and limited retention on the skin surface,resulting in low drug delivery efficiency and poor patient satisfaction.In this study,we developed the first water-responsive gel(WRG),which has a distinct water-triggered liquid-to-gel phase transition property.Specifically,WRG was kept in a solution state in the absence of water,and the addition of water induced an immediate phase transition and resulted in a high viscosity gel.Curcumin was used as a model drug to investigate the potential of WRG in topical drug delivery against psoriasis.In vitro and in vivo data showed that WRG formulation could not only extend skin retention but also facilitate the drug permeating across the skin.In a mouse model of psoriasis,curcumin loaded WRG(CUR-WRG)effectively ameliorated the symptoms of psoriasis and exerted a potent anti-psoriasis effect by extending drug retention and facilitating drug penetration.Further mechanism study demonstrated that the anti-hyperplasia,anti-inflammation,anti-angiogenesis,anti-oxidation,and immunomodulation properties of curcumin were amplified by enhanced topical drug delivery efficiency.Notably,neglectable local or systemic toxicity was observed for CUR-WRG application.This study suggests that WRG is a promising formulation for topically psoriasis treatment.

Topic Controlled Steganography via Graph-to-Text Generation

Generation-based linguistic steganography is a popular research area of information hiding.The text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention to.However,in the course of our experiment,we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text,which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases,and that the topic of generated texts is uncontrollable,so there is still room for improvement in concealment.In this paper,we propose a topic-controlled steganography method which is guided by graph-to-text generation.The proposed model can automatically generate steganographic texts carrying secret messages from knowledge graphs,and the topic of the generated texts is controllable.We also provide a graph path coding method with corresponding detailed algorithms for graph-to-text generation.Different from traditional linguistic steganography methods,we encode the secret information during graph path coding rather than using conditional probability.We test our method in different aspects and compare it with other text generative steganographic methods.The experimental results show that the model proposed in this paper can effectively improve the quality of the generated text and significantly improve the concealment of steganographic text.

Research on high-performance English translation based on topic model

Retelling extraction is an important branch of Natural Language Processing(NLP),and high-quality retelling resources are very helpful to improve the performance of machine translation.However,traditional methods based on the bilingual parallel corpus often ignore the document background in the process of retelling acquisition and application.In order to solve this problem,we introduce topic model information into the translation mode and propose a topic-based statistical machine translation method to improve the translation performance.In this method,Probabilistic Latent Semantic Analysis(PLSA)is used to obtains the co-occurrence relationship between words and documents by the hybrid matrix decomposition.Then we design a decoder to simplify the decoding process.Experiments show that the proposed method can effectively improve the accuracy of translation.

Smart object recommendation based on topic learning and joint features in the social internet of things

With the extensive integration of the Internet,social networks and the internet of things,the social internet of things has increasingly become a significant research issue.In the social internet of things application scenario,one of the greatest challenges is how to accurately recommend or match smart objects for users with massive resources.Although a variety of recommendation algorithms have been employed in this field,they ignore the massive text resources in the social internet of things,which can effectively improve the effect of recommendation.In this paper,a smart object recommendation approach named object recommendation based on topic learning and joint features is proposed.The proposed approach extracts and calculates topics and service relevant features of texts related to smart objects and introduces the“thing-thing”relationship information in the internet of things to improve the effect of recommendation.Experiments show that the proposed approach enables higher accuracy compared to the existing recommendation methods.

ESG Discourse Analysis Through BERTopic: Comparing News Articles and Academic Papers

Environmental,social,and governance(ESG)factors are critical in achieving sustainability in business management and are used as values aiming to enhance corporate value.Recently,non-financial indicators have been considered as important for the actual valuation of corporations,thus analyzing natural language data related to ESG is essential.Several previous studies limited their focus to specific countries or have not used big data.Past methodologies are insufficient for obtaining potential insights into the best practices to leverage ESG.To address this problem,in this study,the authors used data from two platforms:LexisNexis,a platform that provides media monitoring,and Web of Science,a platform that provides scientific papers.These big data were analyzed by topic modeling.Topic modeling can derive hidden semantic structures within the text.Through this process,it is possible to collect information on public and academic sentiment.The authors explored data from a text-mining perspective using bidirectional encoder representations from transformers topic(BERTopic)—a state-of-the-art topic-modeling technique.In addition,changes in subject patterns over time were considered using dynamic topic modeling.As a result,concepts proposed in an international organization such as the United Nations(UN)have been discussed in academia,and the media have formed a variety of agendas.

Topic Modelling and Sentimental Analysis of Students’Reviews

Globally,educational institutions have reported a dramatic shift to online learning in an effort to contain the COVID-19 pandemic.The fundamental concern has been the continuance of education.As a result,several novel solutions have been developed to address technical and pedagogical issues.However,these were not the only difficulties that students faced.The implemented solutions involved the operation of the educational process with less regard for students’changing circumstances,which obliged them to study from home.Students should be asked to provide a full list of their concerns.As a result,student reflections,including those from Saudi Arabia,have been analysed to identify obstacles encountered during the COVID-19 pandemic.However,most of the analyses relied on closed-ended questions,which limited student involvement.To delve into students’responses,this study used open-ended questions,a qualitative method(content analysis),a quantitative method(topic modelling),and a sentimental analysis.This study also looked at students’emotional states during and after the COVID-19 pandemic.In terms of determining trends in students’input,the results showed that quantitative and qualitative methods produced similar outcomes.Students had unfavourable sentiments about studying during COVID-19 and positive sentiments about the face-to-face study.Furthermore,topic modelling has revealed that the majority of difficulties are more related to the environment(home)and social life.Students were less accepting of online learning.As a result,it is possible to conclude that face-to-face study still attracts students and provides benefits that online study cannot,such as social interaction and effective eye-to-eye communication.

Topic-Aware Abstractive Summarization Based on Heterogeneous Graph Attention Networks for Chinese Complaint Reports

Automatic text summarization(ATS)plays a significant role in Natural Language Processing(NLP).Abstractive summarization produces summaries by identifying and compressing the most important information in a document.However,there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics.In particular,Chinese complaint reports,generated by urban complainers and collected by government employees,describe existing resident problems in daily life.Meanwhile,the reflected problems are required to respond speedily.Therefore,automatic summarization tasks for these reports have been developed.However,similar to traditional summarization models,the generated summaries still exist problems of informativeness and conciseness.To address these issues and generate suitably informative and less redundant summaries,a topic-based abstractive summarization method is proposed to obtain global and local features.Additionally,a heterogeneous graph of the original document is constructed using word-level and topic-level features.Experiments and analyses on public review datasets(Yelp and Amazon)and our constructed dataset(Chinese complaint reports)show that the proposed framework effectively improves the performance of the abstractive summarization model for Chinese complaint reports.

Usage of topical insulin for the treatment of diabetic keratopathy,including corneal epithelial defects

BACKGROUND Diabetic keratopathy(DK)occurs in 46%-64%of patients with diabetes and requires serious attention.In patients with diabetes,the healing of corneal epithelial defects or ulcers takes longer than in patients without diabetes.Insulin is an effective factor in wound healing.The ability of systemic insulin to rapidly heal burn wounds has been reported for nearly a century,but only a few studies have been performed on the effects of topical insulin(TI)on the eye.Treatment with TI is effective in treating DK.AIM To review clinical and experimental animal studies providing evidence for the efficacy of TI to heal corneal wounds.METHODS National and international databases,including PubMed and Scopus,were searched using relevant keywords,and additional manual searches were conducted to assess the effectiveness of TI application on corneal wound healing.Journal articles published from January 1,2000 to December 1,2022 were examined.The relevancy of the identified citations was checked against predetermined eligibility standards,and relevant articles were extracted and reviewed.RESULTS A total of eight articles were found relevant to be discussed in this review,including four animal studies and four clinical studies.According to the studies conducted,TI is effective for corneal re-epithelialization in patients with diabetes based on corneal wound size and healing rate.CONCLUSION Available animal and clinical studies have shown that TI promotes corneal wound healing by several mechanisms.The use of TI was not associated with adverse effects in any of the published cases.Further studies are needed to enhance our knowledge and understanding of TI in the healing of DK.

Research on the Chinese Path to Modernization in History,Key Topics,and Outlook:A Cite Space-based Bibliometric Analysis

In the speech delivered at the centenary celebration of the Communist Party of China(CPC),Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,made important remarks on the“Chinese path to modernization.”This term represents the latest achievement China has scored in adapting Marxism to the Chinese context and the needs of the times.It has set the course as China embarks on a new journey of building a strong socialist country with Chinese characteristics and achieving national rejuvenation.Drawing on CiteSpace,we conducted a visualized bibliometric analysis of literature on the Chinese path to modernization by searching the CNKI database using subject terms such as“Chinese modernization,Chinese path to modernization,and Chinese-style modernization.”The findings reveal that:(a)Research on the Chinese path to modernization has gone through three stages:initial establishment,pioneering exploration,and comprehensive in-depth development.(b)Existing literature has covered the four key topics associated with the Chinese path to modernization,namely its essence,goal,methodology,and pioneering achievements.(c)Future research may focus on building up China’s strength in agriculture,developing the digital economy,modernizing China’s system and capacity for governance,and establishing a unique socialist discourse system for Chinese modernization.

Multi-label Emotion Classification of COVID–19 Tweets with Deep Learning and Topic Modelling

The COVID-19 pandemic has become one of the severe diseases in recent years.As it majorly affects the common livelihood of people across the universe,it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak.The public opinions are been shared enormously in microblogging med-ia like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics,sports,entertainment etc.,This work presents a combination of Intensity Based Emotion Classification Convolution Neural Net-work(IBEC-CNN)model and Non-negative Matrix Factorization(NMF)for detecting and analyzing the different topics discussed in the COVID-19 tweets as well the intensity of the emotional content of those tweets.The topics were identified using NMF and the emotions are classified using pretrained IBEC-CNN,based on predefined intensity scores.The research aimed at identifying the emotions in the Indian tweets related to COVID-19 and producing a list of topics discussed by the users during the COVID-19 pandemic.Using the Twitter Application Programming Interface(Twitter API),huge numbers of COVID-19 tweets are retrieved during January and July 2020.The extracted tweets are ana-lyzed for emotions fear,joy,sadness and trust with proposed Intensity Based Emotion Classification Convolution Neural Network(IBEC-CNN)model which is pretrained.The classified tweets are given an intensity score varies from 1 to 3,with 1 being low intensity for the emotion,2 being the moderate and 3 being the high intensity.To identify the topics in the tweets and the themes of those topics,Non-negative Matrix Factorization(NMF)has been employed.Analysis of emotions of COVID-19 tweets has identified,that the count of positive tweets is more than that of count of negative tweets during the period considered and the negative tweets related to COVID-19 is less than 5%.Also,more than 75%nega-tive tweets expressed sadness,fear are of low intensity.A qualitative analysis has also been conducted and the topics detected are grouped into themes such as eco-nomic impacts,case reports,treatments,entertainment and vaccination.The results of analysis show that the issues related to the pandemic are expressed dif-ferent emotions in twitter which helps in interpreting the public insights during the pandemic and these results are beneficial for planning the dissemination of factual health statistics to build the trust of the people.The performance comparison shows that the proposed IBEC-CNN model outperforms the conventional models and achieved 83.71%accuracy.The%of COVID-19 tweets that discussed the different topics vary from 7.45%to 26.43%on topics economy,Statistics on cases,Government/Politics,Entertainment,Lockdown,Treatments and Virtual Events.The least number of tweets discussed on politics/government on the other hand the tweets discussed most about treatments.

Effectiveness of topical oxygen therapy in wound healing for patients with diabetic foot ulcer

Objectives:Non-healing wounds have been one of the major challenges in health care because of increased morbidity,especially for those who have diabetes mellitus.Numerous regimens are being innovated to produce an evidence-based practice that would minimize complications and promote healing.Topical oxygen therapy is an innovation in wound care that has been considered influential in the wound healing process.This intervention aims to increase the oxygen concentration in the affected limb to promote wound healing.Methods:This research applied an experimental design that targeted a total of 60 adult patients aged 45–64 years with diabetic foot ulcers.A randomized systematic sampling technique was used to allow equal chances and prevent bias.In total,30 patients in the control group received usual care for diabetic foot ulcers,and the remaining 30 patients in the experimental group received topical oxygen therapy together with standard care for diabetic foot ulcers.Subjects were assessed using the Wagner-Meggitt Wound Classification System.Results:The result proved that there was a significant difference in the wound grade of patients in the experimental group after the application of the usual wound care plus the topical oxygen therapy using Friedman's test.The control and experimental groups were compared using Mann–Whitney statistical analyses,and the results showed that there was a significant difference between the control and experimental groups after the application of topical oxygen therapy.Conclusions:Topical oxygen therapy was demonstrated to be effective to aid in the wound healing process of patients with diabetic foot ulcers.Fur ther research was recommended to improve the application of topical oxygen therapy to patients with chronic wounds and promote the wound healing process.

Topic PageRank——一种基于主题的搜索引擎

通过研究传统的超链分析算法PageRank及其改进算法Hilltop和TSPR的不足,提出了一种新的改进的方法Topic PageRank。这种算法是对每一个页面进行页面分类,然后根据分类的结果分别对每一个主题进行页面等级计算,因此,每一个页面对不同的主题将呈现出不同的页面等级得分,能更加准确地反映出页面的重要性。

CICS环境下基于网络图的Topic Maps知识平台研究

在对CICS环境下知识管理的需求和网络图能力的分析基础上,提出了利用Topic Maps技术构造CIS环境下统一、独立的知识平台的思想.围绕工程网络图中的知识转换问题,提出了T和M两阶段变换方法.同时,提出了TM与DBMS系统集成方案,通过TM数据的动态更新来适应CICS生产知识的时变性.该方案可用于CICS环境下的其他领域,从而构造覆盖整个CICS的统一的TM平台.

基于“Topic”的语义信息组织技术研究——以DITA和Topic Maps为中心

基于“Topic”的语义信息组织与图书馆学的经典Subject理论密不可分，可将其视为较为抽象化的Subject理论在语义Web环境下的一种“演化”和实用化的主题技术。文章首先解读Subject和Topic的概念，并概述两种基于Topic的国际标准技术，即TopicMaps和DITA；其次，简要描述TopicMaps的技术和应用，并重点介绍在图书馆界鲜为人知的DITA技术及其应用；再次，对基于“Topic”的语义信息组织方法予以总结。

Topic Maps与学科知识管理

提出了用主题地图(TopicMaps)来管理学科知识的思想,分析了利用主题地图来建立学科知识结构的优越性。以软件工程专业的几个知识单元为实例,说明了用主题地图导航软件工程知识库的具体方法。用主题地图处理器Omnigator实现了软件工程学科知识的在线索引字典。