Practical Applications Research Articles

What does “urgency” mean when prioritizing cancer treatment? Results from a qualitative study with German oncologists and other experts during the COVID-19 pandemic

PurposeCancer care in Germany during the COVID-19 pandemic was affected by resource scarcity and the necessity to prioritize medical measures. This study explores ethical criteria for prioritization and their application in cancer practices from the perspective of German oncologists and other experts.MethodsWe conducted fourteen semi-structured interviews with German oncologists between February and July 2021 and fed findings of interviews and additional data on prioritizing cancer care into four structured group discussions, in January and February 2022, with 22 experts from medicine, nursing, law, ethics, health services research and health insurance. Interviews and group discussions were digitally recorded, transcribed verbatim and analyzed using qualitative content analysis.ResultsNarratives of the participants focus on “urgency” as most acceptable criterion for prioritization in cancer care. Patients who are considered curable and those with a high level of suffering, were given a high degree of “urgency.” However, further analysis indicates that the “urgency” criterion needs to be further distinguished according to at least three different dimensions: “urgency” to (1) prevent imminent harm to life, (2) prevent future harm to life and (3) alleviate suffering. In addition, “urgency” is modulated by the “success,” which can be reached by means of an intervention, and the “likelihood” of reaching that success.ConclusionOur analysis indicates that while “urgency” is a well-established criterion, its operationalization in the context of oncology is challenging. We argue that combined conceptual and clinical analyses are necessary for a sound application of the “urgency” criterion to prioritization in cancer care.

Near surface camera informed agricultural land monitoring for climate smart agriculture

Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.

Impact of a biostimulant enriched in betalain degradation products on ROS Signaling, Proline Accumulation, and Phytohormone Homeostasis

This study investigates the potential of a biostimulant derived from Selenicereus undatus peel waste and enriched in betalain degradation products (BDP), to influence Arabidopsis thaliana seedling development. Notably, lower BDP concentrations enhanced seedling development, while higher dosages exhibited adverse effects. Assessment of mitochondrial activity in both seeds and purified organelles showed that the tested biostimulant did not affect mitochondrial activity or integrity, highlighting its independence from mitochondrial performance. Mechanistically, BDP-enriched biostimulant modulated ROS-signaling, diminishing H2O2 by regulating the enzymatic activity and gene expression of SOD, CAT, GPX, and GR. Particularly, analyzing their different isoform via qRT-PCR, the primary cellular compartment where detoxification occurred were identified. Furthermore, biostimulant was able to influence proline-accumulation, altering both the expression of metabolism (PC5S, P5CR and OAT) and catabolism (PDH and P5CDH) related genes. Finally, the BDP-enriched biostimulant altered phytohormone levels, mainly affecting ABA/ABA-glu, tZea/tZea-rib, and tZea/IAA. Concerning GAs, the increase in GA4 and GA7 suggested an involvement of GA13ox, a hypothesis encouraged by qRT-PCR analysis. In summary, this study underscores the potential of BDP-based biostimulant as sustainable promoters of plant growth, influencing critical regulatory pathways during germination. Further research is necessary to explore their extensive applications in agricultural practices.

Near surface camera informed agricultural land monitoring for climate smart agriculture

Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.

Cosmetic Shop Sentiment Analysis on TikTok Shop Using the Support Vector Machine Method

User reviews are crucial in today's digital world for determining a product's quality. Nevertheless, these remarks are frequently disorganized and erratic, which confuses people and makes it challenging for them to make wise purchases. The erratic character of these reviews breeds uncertainty and makes determining a product's actual value more difficult. To help consumers more effectively evaluate and select products on platforms such as TikTok Shop, this study uses sentiment analysis tools. It hopes to accomplish this by improving the overall shopping experience and empowering customers to make more confident and informed selections. This research aims to assist consumers in evaluating and selecting products on TikTok Shop, an online shopping platform, by employing sentiment analysis techniques that help consumers make more informed decisions. In this study, a total of 500 comments from TikTok Shop users were collected as data. 350 comments have been set aside for training and 150 comments were set aside for testing. Data was gathered employing scraping, an automated process that makes use of the Python library's Selenium module to retrieve data from the internet. We employed the Support Vector Machine approach, an efficient machine learning tool for text classification, to assess the comments. 121 comments were categorized as having positive sentiment and 29 as having negative sentiment based on the test results. The system successfully recommended the "Ourluxbeauty" cosmetics store as a shop with many positive sentiments, indicating a recommendation level of 0.7 on the positive sentiment scale. The system's accuracy was measured using a Confusion Matrix, resulting in an accuracy rate of 78% and an inaccuracy rate of 22%. This demonstrates that the system can accurately classify comment sentiments and has significant potential for application in e-commerce practices to enhance the online shopping experience.

Chinese Title Generation for Short Videos: Dataset, Metric and Algorithm.

Previous work for video captioning aims to objectively describe the video content but the captions lack human interest and attractiveness, limiting its practical application scenarios. The intention of video title generation (video titling) is to produce attractive titles, but there is a lack of benchmarks. This work offers CREATE, the first large-scale Chinese shoRt vidEo retrievAl and Title gEneration dataset, to assist research and applications in video titling, video captioning, and video retrieval in Chinese. CREATE comprises a high-quality labeled 210 K dataset and two web-scale 3 M and 10 M pre-training datasets, covering 51 categories, 50K+ tags, 537K+ manually annotated titles and captions, and 10M+ short videos with original video information. This work presents ACTEr, a unique Attractiveness-Consensus-based Title Evaluation, to objectively evaluate the quality of video title generation. This metric measures the semantic correlation between the candidate (model-generated title) and references (manual-labeled titles) and introduces attractive consensus weights to assess the attractiveness and relevance of the video title. Accordingly, this work proposes a novel multi-modal ALignment WIth Generation model, ALWIG, as one strong baseline to aid future model development. With the help of a tag-driven video-text alignment module and a GPT-based generation module, this model achieves video titling, captioning, and retrieval simultaneously. We believe that the release of the CREATE dataset, ACTEr metric, and ALWIG model will encourage in-depth research on the analysis and creation of Chinese short videos.

Balancing Feedback for Optimal Learning: Evaluative and Descriptive Methods in English Language Classrooms

This study explores the impact of evaluative and descriptive feedback on student learning in English language classrooms. It distinguishes between the two feedback types—evaluative, which is further divided into rewarding and punishing, and descriptive, which focuses on specific performance aspects. The study is based on observations of classroom interactions and interviews with an English teacher who employs both feedback types to enhance student engagement and educational outcomes. Evaluative feedback in the study is shown to affect students' emotional responses and behavioral adjustments, with rewarding feedback boosting morale and motivation, while punishing feedback serves to correct behavior and ensure classroom discipline. Conversely, descriptive feedback, as used by the teacher, involves detailed explanations and discussions with students about their performances, encouraging deeper cognitive processing and active participation in the learning process. The findings suggest that a balanced approach, utilizing both evaluative and descriptive feedback, can significantly improve students’understanding and retention of the material. This paper argues for the strategic use of feedback to foster a more dynamic and effective learning environment, highlighting the nuances of feedback application in educational practices.

The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature.

Artificial intelligence (AI) has emerged as a revolutionary technology with several applications across different dental fields, including pedodontics. This systematic review has the objective to catalog and explore the various uses of artificial intelligence in pediatric dentistry. A thorough exploration of scientific databases was carried out to identify studies addressing the usage of AI in pediatric dentistry until December 2023 in the Embase, Scopus, PubMed, and Web of Science databases by two researchers, S.L.R. and A.L.G. From a pool of 1301 articles, only 64 met the predefined criteria and were considered for inclusion in this review. From the data retrieved, it was possible to provide a narrative discussion of the potential implications of AI in the specialized area of pediatric dentistry. The use of AI algorithms and machine learning techniques has shown promising results in several applications of daily dental pediatric practice, including the following: (1) assisting the diagnostic and recognizing processes of early signs of dental pathologies, (2) enhancing orthodontic diagnosis by automating cephalometric tracing and estimating growth and development, (3) assisting and educating children to develop appropriate behavior for dental hygiene. AI holds significant potential in transforming clinical practice, improving patient outcomes, and elevating the standards of care in pediatric patients. Future directions may involve developing cloud-based platforms for data integration and sharing, leveraging large datasets for improved predictive results, and expanding AI applications for the pediatric population.

Supporting Children with Autism Using Visual Arts

The current study aims to develop a special program for both instructors and educators to train children with autism via art while also promoting their positive behaviors and social requirements. To achieve these goals, an experimental study was carried out at the Centre of Community Services for Autism, drawing on previous research in the field of art therapy. The study concentrated on the usage of images and their visual composition from the linguistic, cognitive, emotional, motor, and social perspectives. The study's findings demonstrated that the image, its simulation, and its practical application all play important roles in promoting specific developmental features of children with autism. Keywords: Visual Arts, Image, Arts Therapy, Children with Autism

Reducing the Impact of Time Evolution on Source Code Authorship Attribution via Domain Adaptation

Source code authorship attribution is an important problem in practical applications such as plagiarism detection, software forensics, and copyright disputes. Recent studies show that existing methods for source code authorship attribution can be significantly affected by time evolution, leading to a decrease in attribution accuracy year by year. To alleviate the problem of Deep Learning (DL)-based source code authorship attribution degrading in accuracy due to time evolution, we propose a new framework called Time D omain A daptation (TimeDA) by adding new feature extractors to the original DL-based code attribution framework that enhances the learning ability of the original model on source domain features without requiring new or more source data. Moreover, we employ a centroid-based pseudo-labeling strategy using neighborhood clustering entropy for adaptive learning to improve the robustness of DL-based code authorship attribution. Experimental results show that TimeDA can significantly enhance the robustness of DL-based source code authorship attribution to time evolution, with an average improvement of 8.7% on the Java dataset and 5.2% on the C++ dataset. In addition, our TimeDA benefits from employing the centroid-based pseudo-labeling strategy, which significantly reduced the model training time by 87.3% compared to traditional unsupervised domain adaptive methods.

Elements of Timed Pattern Matching

The rise of machine learning and cloud technologies has led to a remarkable influx of data within modern cyber-physical systems. However, extracting meaningful information from this data has become a significant challenge due to its volume and complexity. Timed pattern matching has emerged as a powerful specification-based runtime verification and temporal data analysis technique to address this challenge. In this paper, we provide a comprehensive tutorial on timed pattern matching that ranges from the underlying algebra and pattern specification languages to performance analyses and practical case studies. Analogous to textual pattern matching, timed pattern matching is the task of finding all time periods within temporal behaviors of cyber-physical systems that match a predefined pattern. Originally we introduced and solved several variants of the problem using the name of match sets, which has evolved into the concept of timed relations over the past decade. Here we first formalize and present the algebra of timed relations as a standalone mathematical tool to solve the pattern matching problem of timed pattern specifications. In particular, we show how to use the algebra of timed relations to solve the pattern matching problem for timed regular expressions and metric compass logic in a unified manner. We experimentally demonstrate that our timed pattern matching approach performs and scales well in practice. We further provide in-depth insights into the similarities and fundamental differences between monitoring and matching problems as well as regular expressions and temporal logic formulas. Finally, we illustrate the practical application of timed pattern matching through two case studies, which show how to extract structured information from temporal datasets obtained via simulations or real-world observations. These results and examples show that timed pattern matching is a rigorous and efficient technique in developing and analyzing cyber-physical systems.

A Review of Abstraction Methods Toward Verifying Neural Networks

Neural networks as a machine learning technique are increasingly deployed in various domains. Despite their performance and their continuous improvement, the deployment of neural networks in safety-critical systems, in particular for autonomous mobility, remains restricted. This is mainly due to the lack of (formal) specifications and verification methods and tools that allow for having sufficient confidence in the behavior of the neural-network-based functions. Recent years have seen neural network verification getting more attention; many verification methods were proposed, yet the practical applicability of these methods to real-world neural network models remains limited. The main challenge of neural network verification methods is related to the computational complexity and the large size of neural networks pertaining to complex functions. As a consequence, applying abstraction methods for neural network verification purposes is seen as a promising mean to cope with such issues. The aim of abstraction is to build an abstract model by omitting some irrelevant details or some details that are not highly impacting w.r.t some considered features. Thus, the verification process is made faster and easier while preserving, to some extent, the relevant behavior regarding the properties to be examined on the original model. In this article, we review both the abstraction techniques for activation functions and model size reduction approaches, with a particular focus on the latter. The review primarily discusses the application of abstraction techniques on feed-forward neural networks and explores the potential for applying abstraction to other types of neural networks. Throughout the article, we present the main idea of each approach and then discuss its respective advantages and limitations in detail. Finally, we provide some insights and guidelines to improve the discussed methods.

OVERVIEW OF LANGUAGE CONTACT PHENOMENON FROM THE LINGUISTS' PERSPECTIVES

The phenomenon of language contact occurs due to the influence of social-linguistic factors, community dynamics, and international interactions. Consequently, it is a social phenomenon, a cultural encounter where languages coexist and mutually influence each other within a communicative community. Language contact contributes to the formation of diglossia, bilingualism, and borrowed words; phenomena of convergence, and interference; and the emergence of pidgin and creole. This article provides a comprehensive analysis and synthesis of language contact from the perspectives of Vietnamese and Korean linguists, based on data from Vietnamese and Korean. The research results can be preliminary applied to conceptualize the phenomenon of language contact, contributing to the expansion of studies on language transformation and effective application in language teaching and learning practices.

Passionate About Esports: Esports Players’ Motivation to Participate in and Watch Esports Events

Alderfer’s ERG (i.e., existence, relatedness, and growth) theory of motivation (1969) was adopted in this study to analyze individuals’ motivations for engaging in esports. This study investigated the relatively new field of esports viewership and participation by concentrating on the motivating factors behind esports consumption to establish whether esports viewership and participation are distinct markets that stand alone or are comparable to or complement each other for consumption. The study was conducted using qualitative methods consisting of semistructured focus-group interviews. The transcript was coded using open, axial, and selective coding to develop themes fitting within the ERG theory. The current study found similarities and unique findings in esports participation and consumption motivation factors under the ERG groups. Practical applications are proposed for employing the results of the study to further marketing and development efforts in this field.

Prediction of Plant Ubiquitylation Proteins and Sites by Fusing Multiple Features

Introduction: Protein ubiquitylation is an important post-translational modification (PTM), which is considered to be one of the most important processes regulating cell function and various diseases. Therefore, accurate prediction of ubiquitylation proteins and their PTM sites is of great significance for the study of basic biological processes and the development of related drugs. Researchers have developed some large-scale computational methods to predict ubiquitylation sites, but there is still much room for improvement. Much of the research related to ubiquitylation is cross-species while the life pattern is diversified, and the prediction method always shows its specificity in practical application. This study just aims at the issue of plants and has constructed computational methods for identifying ubiquitylation protein and ubiquitylation sites. Method: In this work, we constructed two predictive models to identify plant ubiquitylation proteins and sites. First, in the ubiquitylation proteins prediction model, in order to better reflect protein sequence information and obtain better prediction results, the KNN scoring matrix model based on functional domain Gene Ontology (GO) annotation and word embedding model, i.e. Skip-Gram and Continuous Bag of Words (CBOW), are used to extract the features, and the light gradient boosting machine (LGBM) is selected as the ubiquitylation proteins prediction engine. Results: As a result, accuracy (ACC), Precision, recall rate (Recall), F1_score and AUC are respectively 85.12%, 80.96%, 72.80%, 76.37% and 0.9193 in the 10-fold cross-validations on independent dataset. In the ubiquitylation sites prediction model, Skip-Gram, CBOW and enhanced amino acid composition (EAAC) feature extraction codes were used to extract protein sequence fragment features, and the predicted results on training and independent test data have also achieved good performance. Conclusion: In a word, the comparison results demonstrate that our models have a decided advantage in predicting ubiquitylation proteins and sites, and it may provide useful insights for studying the mechanisms and modulation of ubiquitination pathways

Fast Incomplete Multi-View Clustering With View-Independent Anchors.

Multi-view clustering (MVC) methods aim to exploit consistent and complementary information among each view and achieve encouraging performance improvement than single-view counterparts. In practical applications, it is common to obtain instances with partially available information, raising researches of incomplete multi-view clustering (IMC) issues. Recently, several fast IMC methods have been proposed to process the large-scale partial data. Though with considerable acceleration, these methods seek view-shared anchors and ignore specific information among single views. To tackle the above issue, we propose a fast IMC with view-independent anchors (FIMVC-VIA) method in this article. Specifically, we learn individual anchors based on the diversity of distribution among each incomplete view and construct a unified anchor graph following the principle of consistent clustering structure. By constructing an anchor graph instead of pairwise full graph, the time and space complexities of our proposed FIMVC-VIA are proven to be linearly related to the number of samples, which can efficiently solve the large-scale task. The experiment performed on benchmarks with different missing rate illustrates the improvement in complexity and effectiveness of our method compared with other IMC methods. Our code is publicly available at https://github.com/Tracesource/ FIMVC-VIA.

Inverse-Free DZNN Models for Solving Time-Dependent Linear System via High-Precision Linear Six-Step Method.

Time-dependent linear system (TDLS) is usually encountered in scientific research, which is the mathematical formulation of many practical applications. Different from conventional inverse-need models, by utilizing zeroing neural network (ZNN) method twice, an inverse-free continuous ZNN (CZNN) model is developed for solving TDLS. For conveniently practical use, a discrete model is naturally desired. Superior to conventional discretization methods, a general linear six-step (LSS) method with the seventh-order precision and five variable parameters is proposed for the first time. Constraints about five variable parameters are theoretically analyzed to guarantee the efficacy of the general LSS method. Within constraints, 12 specific LSS methods are further developed. Aided with the general LSS method, an inverse-free discrete ZNN (DZNN) is proposed and termed DZNN-LSS model, and its precision is greatly improved compared with conventional discrete models. For comparison, three conventional discretization methods are also utilized to generate DZNN models. Detailed theoretical analyses are provided to prove the efficacy of relevant models. In addition, a specific TDLS example is considered to show the effectiveness and superiority of the DZNN-LSS model. More than that, applications to manipulator control and sound source localization are conducted to illustrate the applicability of the DZNN-LSS model.

A Comparative and Applied Study of Qur'anic and Biblical Teachings on the Principles of Commerce and Business

This study examines the ethical principles of commerce and business as outlined in the Qur'an and the Bible, providing a comparative analysis of their teachings and their application in modern business practices. The research explores key themes including honesty in trade, ethical treatment of workers, prohibition of usury, promotion of charity and social responsibility, and their implications for contemporary economic systems. Through detailed case studies of businesses implementing Qur'anic and Biblical principles, the study evaluates the outcomes and challenges of integrating religious values into corporate governance. It synthesizes findings from primary sources—Qur'anic verses and Biblical passages—as well as secondary sources such as scholarly articles and commentaries. The significance of this study lies in its contribution to understanding how religious teachings can inform and guide ethical business conduct, fostering trust, sustainability, and social responsibility. Recommendations for future research and practice highlight opportunities for further exploration and implementation of these principles in diverse cultural and regulatory contexts.

E2SCNet: Efficient Multiobjective Evolutionary Automatic Search for Remote Sensing Image Scene Classification Network Architecture.

Remote sensing image scene classification methods based on deep learning have been widely studied and discussed. However, most of the network architectures are directly reliant on natural image processing methods and are fixed. A few studies have focused on automatic search mechanisms, but they cannot weigh the interpretation accuracy and the parameter quantity for practical application. As a result, automatic global search methods based on multiobjective evolutionary computation have more advantages. However, in the ranking process, the network individuals with large parameter quantities are easy to eliminate, but a higher accuracy may be obtained after full training. In addition, evolutionary neural architecture search methods often take several days. In this article, in order to solve the above concerns, we propose an efficient multiobjective evolutionary automatic search framework for remote sensing image scene classification deep learning network architectures (E2SCNet). In E2SCNet, eight kinds of lightweight operators are used to build a diversified search space, and the coding connection mode is flexible. In the search process, a large model retention mechanism is implemented through two-step multiobjective modeling and evolutionary search, where one step involves the "parameter quantity and accuracy," and the other step involves the "parameter quantity and accuracy growth quantity." Moreover, a super network is constructed to share the weight in the process of individual network evaluation and promote the search speed. The effectiveness of E2SCNet is proven by comparison with several networks designed by human experts and networks obtained by gradient and evolutionary computing-based search methods.

Finite element stress analysis of nano copper-based anti-friction coating for improved threaded connections in petroleum drilling tools

Threaded connections are commonly used in connecting petroleum drilling tools (PDTs). In this study, we use nano copper (NaCU) and epoxy-resin binder to prepare a NaCU-based anti-friction coating (NaCU-AFC). Furthermore, we use finite element analysis (FEA) to validate torque and pressure distribution on the contact tooth surface of threaded joints of PDTs. The results demonstrate that the NaCU-AFC can effectively reduce torque by 35.66%, and significantly improve anti-sticking performance, thereby extending the service life of PDTs. In summary, this study provides valuable insights for practical applications in coating processes for PDTs.