Practical Applications Research Articles

الحلول شبه التحليلية لمعادلات فيشر الكسرية للزمن عبر الطريقة التكرارية الجديدة

إحدى الطرق الفعالة لحل المعادلات التفاضلية الجزئية غير الخطية ذات المشتقات الكسرية هي الطريقة التكرارية لتحويل سومودو الجديدة (NSTIM). إنه يبرع في حل الألغاز الرياضية الصعبة ويقدم معلومات ثاقبة حول سلوك معادلات فيشر ذات الكسر الزمني. الطريقة، التي تستخدم مشتقات كابوتو الحسية وWolfram في Mathematica، موثوقة وسهلة الاستخدام وتعطي تصويرًا مرئيًا للحل. أظهرت النتائج التحليلية أن الطريقة المقترحة فعالة وبسيطة في توليد حلول دقيقة لمعادلات فيشر الكسرية للزمن. أصبحت النتائج أكثر موثوقية وقابلة للتطبيق من خلال تضمين مشتقات كابوتو الحسية. تعتمد النمذجة الرياضية على فعالية وبساطة منهج NSTIM لحل معادلات فيشر ذات الكسر الزمني لأنها تتيح حلولاً دقيقة دون استخدام الكثير من قوة المعالجة. يعد نهج NSTIM أداة مفيدة للباحثين في مجموعة متنوعة من المجالات لأنه يوفر أيضًا إطارًا مرنًا يمكن تعديله بسهولة مع المعادلات التفاضلية الكسرية الأخرى. أصبح من الممكن الآن فحص ديناميكيات وسلوك الأنظمة المعقدة التي تحكمها معادلات فيشر الكسرية الزمنية بكفاءة وموثوقية، مما يفتح طرقًا بحثية جديدة. إن القدرة على حل معادلات فيشر ذات الكسور الزمنية بكفاءة وموثوقية باستخدام نهج NSTIM لها آثار مهمة على مجالات مختلفة مثل الديناميات السكانية والبيولوجيا الرياضية وعلم الأوبئة. يمكن للباحثين الآن تحليل انتشار الأمراض أو دراسة الديناميكيات السكانية للأنواع بدقة أعلى وجهد حسابي أقل. يمهد هذا التقدم في حل المعادلات التفاضلية الكسرية الطريق لرؤى أعمق حول سلوك وأنماط الأنظمة المعقدة، مما يؤدي في نهاية المطاف إلى تعزيز الفهم العلمي وتقديم إمكانيات جديدة للتطبيقات العملية.

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.

Oxygen Vacancy and Bandgap Simultaneous Modulation to Achieve High Lithiophilicity and Mechanical Strength of Lithium Metal Anodes.

Metal oxides with conversion and alloying mechanisms are more competitive in suppressing lithium dendrites. However, it is difficult to simultaneously regulate the conversion and alloying reactions. Herein, conversion and alloying reactions are regulated by modulation of the zinc oxide bandgap and oxygen vacancies. State-of-the-art advanced characterization techniques from a microcosmic to a macrocosmic viewpoint, including neutron diffraction, synchrotron X-ray absorption spectroscopy, synchrotron X-ray microtomography, nanoindentation, and ultrasonic C-scan demonstrated the electrochemical gain benefit from plentiful oxygen vacancies and low bandgaps due to doping strategies. In addition, high mechanical strength 3D morphology and abundant mesopores assist in the uniform distribution of lithium ions. Consequently, the best-performed ZnO-2 offers impressive electrochemical properties, including symmetric Li cells with 2000h and full cells with 81% capacity retention after 600 cycles. In addition to providing a promising strategy for improving the lithiophilicity and mechanical strength of metal oxide anodes, this work also sheds light on lithium metal batteries for practical applications.

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.

Metabolome mapping and DNA fingerprinting of Tribulus terrestris from three different Egyptian populations with the assessment of their morphological characters and antioxidant activity

Tribulus terrestris L. is a cosmopolitan annual herb that grows under various ecological conditions, reflecting the variation in its morphological, chemical, and biological features. T. terrestris was collected from three different populations (I, II, III) in Egypt and subjected to comparative investigation to evaluate the morphological, metabolomic, and genetic variations among these populations. Additionally, the antioxidant activity of the collected specimens was assessed. Significant variations in vegetative morphology were observed among the three populations. Detailed microscopic examination revealed unique traits in the foliar epidermal and pollen grains characteristics of each population. LC-ESI-MS/MS analysis of aqueous methanol extracts led to the tentative identification of 101 compounds across 17 different chemical classes from which flavonoids are predominant and represented by 28 compounds; Polymethoxylated flavones were reported for the first time in the three investigated extracts. GC/FID analysis of diethyl ether extracts identified 12 saturated and 8 unsaturated fatty acids, with linolenic acid being the major component, constituting 52.21 %, 65.71 %, and 49.52 % in populations I, II and III, respectively. The DNA fingerprinting showed variations with 54.54 % polymorphism in ISSR markers, confirming the existence of two distinct genotypes among the studied T. terrestris plants. Furthermore, the extracts from the three populations exhibited weak antioxidant activity against DPPH. The obtained data revealed considerable morphological, metabolomic, and genetic diversity among the three populations of T. terrestris. These findings suggest treating T. terrestris (I) as a distinct variety of T. terrestris and enhancing the understanding of the plant's diversity, which is essential for its effective application in traditional and modern medicinal practices.

Development of a near-infrared ratiometric fluorescent probe for real-time detection of HClO in real samples, oxidative stress model, and ferroptosis model

During the COVID-19 epidemic, hypochlorous acid (HClO) was frequently utilized for indoor and outdoor disinfection. The sensitive and selective detection of HClO puts forward high requirements for food safety. Furthermore, HClO is crucial for physiological and pathological activities. However, excessive HClO is harmful to the environment, animals, and humans. Therefore, it is imperative to construct a highly selective and sensitive HClO detection method to determine the safety of food and water, as well as the role of physiological and pathological processes. Herein, we successfully developed a near-infrared (NIR) ratiometric fluorescent probe FLD, which had high practicality and wide application range and was utilized to detect HClO in real samples and organisms. Probe FLD had the advantages of simple synthesis, high selectivity and sensitivity, significant colorimetric change, and low detection limit. Probe FLD was loaded onto test strips to create a single, reasonably priced solid sensor that enabled the visual detection of HClO. The experiments with real samples showed that the probe had good accuracy and precision. Importantly, the measurement of HClO levels in oxidative stress and ferroptosis models was successfully accomplished. Overall, probe FLD’s outstanding qualities make it a good prospect for application in environmental monitoring, food safety, and biology.

Modified space suspension bentonite-based powder for suppression dust explosion in moist conditions: Experimental testing and validations

Bentonite, a non-metallic mineral mainly composed of montmorillonite, has been found to be effective in preventing explosion propagations. However, it is prone to absorbing water, leading to the issue of solidifying into lumps (caking problems) and reducing its ability to entrain in the pressure front of an explosion for stopping propagation. In this paper, three different dispersion modification technologies for bentonite are proposed. The corresponding modified bentonite samples are prepared and treated in various moist environments to assess their moisture absorption properties. Subsequently, space suspension testing is performed using a designed dispersion test device. Additionally, pipeline explosion suppression tests are conducted for different samples. The flame length, flame development time and flame peak velocity of various modified bentonite-based powders are measured. Through comparisons, the optimal modified explosion suppression bentonite used in mines is selected. It is determined that the use of calcium-based bentonite with a cation exchange capacity 0.75 times modified by bisdodecyldimethylammonium chloride for insertion exhibits better dispersion effects. Such modified bentonite-based powder can also demonstrate effective explosion suppressions and holds potential for widespread application in practices of explosion controls in the future.

Quantitative assessment of irrigation water and organic/inorganic amendment on biometric growth profiles of Abelmoschus esculentus and Solanum lycopersicum and their varieties

ABSTRACT In recent decades, the use of chemical fertilizers has been recklessly provoked to meet the increased food needs of the rapidly growing population. However, there is some disagreement about the use of chemical fertilizers in agriculture. Hence, the appropriate nitrogen, phosphate, and potassium ratios must be determined before their application in agricultural practices. This study explored three distinct sources of nutrients to support healthy seed germination and reduce nutrient loss: chemical fertilizers, vermicompost, and nutrient-laden irrigation water supply. A sustainable, affordable, and green petri plate seed germination experiment was used to analyze the biometric growth patterns of two plant species (Abelmoschus esculentus and Solanum lycopersicum). To quantify the effects of different irrigation water sources (groundwater, river water), their combinations with chemical fertilizers and vermicompost (3 ton/ha), multivariate statistical methods such as correlation, principal component analysis, and deep neural networks were used. The purpose of this research was to find the optimal nutrient delivery technique for encouraging healthy plant growth while minimizing the environmental stress of excessive nutrient application.

Characterization of Endophytic Entomopathogenic Fungal Isolates: Growth Dynamics, Viability, Density, and Morphological Attributes

Aims: This study aims to comprehensively characterize endophytic entomopathogenic fungal isolates, focusing on their growth dynamics, viability, density, and morphological attributes, with implications for biocontrol strategies against insect pests. Study Design: The research tracked radial growth rates of fungal isolates over 21 days, conducted conidial viability assessments at 24 and 48 hours, measured conidial density, and performed morphological analysis to identify distinct characteristics. Methodology: Endophytic entomopathogenic fungal isolates, including Beauveria bassiana and Lecanicillium lecanii, were subjected to radial growth rate tracking, viability assessments, density measurements, and morphological analysis. Statistical analysis was employed to identify significant variations among isolates and time intervals. Results: Significant variations in radial growth rates were observed among isolates, with Beauveria bassiana isolate ChBb exhibiting a rate of 3.66 mm/day, followed by B. bassiana isolate NaBb with 3.92 mm/day, and Lecanicillium lecanii isolate KmLl with 3.06 mm/day. Conidial viability assessments revealed temporal dynamics, with NaBb maintaining the highest viability at both 24 and 48 hours. Additionally, significant differences in conidial density were noted, with NaBb exhibiting the highest density followed by ChBb and KmLl. Morphological analysis unveiled distinct characteristics among isolates, including differences in conidia length, width, and length-to-width ratio. Conclusion: The findings underscore the potential of Beauveria bassiana and Lecanicillium lecanii isolates in biocontrol strategies against insect pests, highlighting their varying growth dynamics, viability, density, and morphological attributes. These insights contribute to the understanding of entomopathogenic fungi and their applications in sustainable pest management practices.

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.

Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis

Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a deep learning with a multilayer neural network (the NMTLR model) for predicting the survival rate of patients with Primary Hepatocellular Carcinoma. HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and fivefold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. The study included 2197 HCC patients, randomly divided into a training cohort (70%, n = 1537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model demonstrated enhanced accuracy and well-calibrated survival estimates, achieving an Area Under the Curve (AUC) of 0.824 for 1-year survival predictions, 0.813 for 3-year, and 0.803 for 5-year survival rates. This model's superior calibration and discriminative ability enhance its utility for clinical prognostication in Primary Hepatocellular Carcinoma. We deployed the NMTLR model as a web application for clinical practice. The NMTLR model have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with primary liver cancer.

What's your experience?: A duoethnographic dialogue to advance disability inclusion in medical education

AbstractBackgroundAlthough disability inclusion in medical education is gaining interest internationally, scholarship and policy recommendations on this topic largely hail from the US, Canada, Australia and the UK. Existing scholarship, while calling for medical education to enact cultural and attitudinal change related to disability, has yet to exemplify how educators might critically examine their understandings.ApproachAs two medical educators and researchers, one based in New Zealand and the other based in Saudi Arabia, we took a duoethnographic approach to explore tensions, possibilities and assumptions regarding disability and disability inclusion in medical education. Through a year‐long synchronous and asynchronous dialogue, we examined our experiences in relation to literature from critical disability studies and disability inclusion in medical education.FindingsWe present recurrent themes from our dialogue. We consider what disability means, explore definitions and models of disability in our contexts, as well as our lived curriculum of disability. We grapple with the applicability of disability inclusion practices across borders. We explore the complexity of supporting access without a clear roadmap, while recognising educators' potential in this work. Finally, we recognise that, if disability is relational, we have the power and responsibility to address ableism in medical education. Throughout, we return to the importance of local consultation with disabled people (learners, physicians) to better understand how services ought to be oriented.ConclusionDuoethnographic dialogue is a fruitful approach to critically examine understandings of disability with others and represents a necessary start to work in education that seeks to advance justice. We share possible actions to take the work forward beyond dialogue and suggest that readers engage in such dialogues with others in their own contexts.

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.