Repetitive Information Research Articles

Examining the Views of non-e-Tutoring Students on Potential Benefits and Drawbacks of Incorporating e-Tutoring into Their Learning Experience

In recent years, technology integration in education has transformed the learning environment in online institutions. One technological advancement that has gained popularity is e-tutoring which offers personalised academic support to students through online platforms. While e-tutoring has become well-known and has been adopted to promote collaborative learning, there are still students who do not use these services for various reasons. This study explored the perspectives of students who do not utilize e-tutoring services in online education settings, shedding light on their perceptions. A quantitative research approach with a descriptive design was adopted to gather and analyse data on the perspectives of non-e-tutoring students. Online questionnaires were employed as the primary data collection method, allowing for the efficient collection of data from many respondents. The data was analysed using the Statistical Package for the Social Sciences (SPSS). Ethical concepts such as informed consent, anonymity of responses and protection of respondents against harm were maintained. Findings indicate that non-e-tutoring students perceive a sense of control over their own pace of learning, suggesting a preference for self-directed learning and the ability to tailor their educational experience to their individual needs and learning styles. They also perceive a lack of clarity in the roles of e-tutors, leading to uncertainty about their responsibilities. In terms of communication, students feel overwhelmed by the volume of announcements and find repetitive information frustrating. Additionally, some students face challenges with their internet connection and associated cost, which can hinder their participation in online activities. Notably, a desire for peer interactions, teamwork, and a sense of belonging in a community highlights the importance of social aspects in online learning. This study recommends that students seek alternate support systems by contacting academics and facilitators for guidance and clarification. Developing self-directed learning skills is essential, empowering students to take charge of their learning through setting objectives, creating their study plans, and utilising resources. For higher education institutions (HEIs), it was recommended that they ensure that a variety of support services are available to cater to the needs of all students, including non-e-tutoring students.

Slow feature‐constrained decomposition autoencoder: Application to process anomaly detection and localization

SummaryDetecting anomalies in manufacturing processes is crucial for ensuring safety. However, noise significantly undermines the reliability of data‐driven anomaly detection models. To address this challenge, we propose a slow feature‐constrained decomposition autoencoder (SFC‐DAE) for anomaly detection in noisy scenarios. Considering that the process can exhibit both long‐term trends and periodic properties, the process data is decomposed into trends and cycles. The repetitive information is mitigated by slicing and randomly masking certain trends and cycles. Dependencies among slices are constructed to extract intrinsic information, while high‐frequency noise is reduced using a slow feature‐constrained loss. Anomalies are detected and localized through a reconstruction error strategy. The effectiveness of SFC‐DAE is demonstrated using data from a sugar factory and a secure water treatment system.

The role of everolimus in metastatic breast cancer and possibilities of moving forward-a narrative review.

In hormone-receptor-positive (HR+) and human epidermal growth factor receptor 2-negative (HER2-) metastatic breast cancer endocrine-based therapies are preferred over chemotherapy. One of the treatment options is the combination of everolimus with exemestane or other endocrine drug in later lines mainly based on progression-free survival (PFS) results of the phase 3 BOLERO-2 trial. Altogether, clinical trials did not prove an overall survival (OS) benefit and considerable side effects hampered its application in the day-by-day practice. In recent years CDK4/6-inhibitors became a first-choice combination partner to the endocrine treatment, everolimus still has a place within the treatment armamentarium. Although everolimus is a targeted drug, there is no accepted predictive biomarker and further patient selection is not possible. However, several directions can be defined how to optimally use everolimus. For update information on everolimus treatment in breast cancer I have performed a literature search. I used the keywords "breast cancer" and "everolimus" and extended the search in PubMed from 01/01/2014 to 10/02/2023. I considered all phase 3 trials, the phase 1-2 trials with not repetitive information, studies with biomarker results and I also checked review articles to identify potential relevant other clinical trial reports. I also have made a search in clinicaltrials.gov for recently completed and ongoing trials. I summarized the search results in this concise and brief report focusing on main trial results and ongoing research with everolimus. The most promising research directions seem to be further investigations for useable predictive biomarkers, for combinations with other targeted drugs (even in a triple combination) and for the feasibility of pharmacologically guided dosing method.

A Hybrid Dual Stream ProbSparse Self-Attention Network for spatial–temporal photovoltaic power forecasting

Growing energy demand and increasing environmental challenges underscore the importance of precise forecasts for photovoltaic (PV) operations in renewable energy generation systems. At this stage, it is mainstream to combine both temporal and spatial factors to forecast PV power generation. However, there are fewer studies that consider factors at very large spatial scales. This paper proposes Hybrid Dual Stream ProbSparse Self-Attention Network (HDSPAN), a novel spatial–temporal photovoltaic power forecasting network architecture that can solve the above limitations. The model implements an encoder–decoder approach that extracts the required spatial–temporal information through a dual stream distilling mechanism. In addition, the ProbSparse self-attention mechanism is employed to improve model efficiency and reduce repetitive and redundant information processing. The hyperparameters are optimized using Tree-structured Patzen estimator to improve forecasting outcomes. This paper demonstrates the effectiveness of spatial–temporal PV forecasting by using ERA5 reanalyzed PV data as a case study. Our results show that the HDSPAN model achieves a 10% higher forecasting accuracy compared to the baseline models, significantly advancing PV power forecasting.

A periodic-modulation-oriented noise resistant correlation method for industrial fault diagnostics of rotating machinery under the circumstances of limited system signal availability

The periodical impulses caused by localized defects of components are the vital characteristic information for fault detection and diagnosis of rotating machines. In recent years, multitudinous spectrum analysis-based signal processing methods have been developed and authenticated as the powerful tools for excavating fault-related repetitive transients from the measured complex signals. Nonetheless, in practice, their applications can be severely confined by the constraints of limited system signal availability and incomplete information extraction under intricate noise interferences. To tackle the aforementioned issues, this paper proposes a periodic-modulation-oriented noise resistant correlation (PMONRC) method for target period detection and fault diagnosis of rotating machinery. Firstly, the envelope of raw signal is obtained via a novel sequential procedure of signal element-wise squaring, spectral Gini index-guided adaptive low-pass filtering, and signal element-wise square root computation, to highlight the modulated wave component that is more likely to be related to the potential fault-induced periods. Subsequently, a series of sub-signals, which can encode the fault-related repetitive information and enhance noise resistance, are constructed utilizing the envelope signal. Based upon the envelope signal and the obtained sub-signals, a weighted envelope noise resistant correlation function can be derived with the assistance of the L-moment ratio-based indicator and Sigmoid transformation. Finally, the specific fault type of the rotating machinery can be identified and affirmed accordingly. The proposed PMONRC method, which is nonparametric and completely adaptive to the signal being processed itself, overcomes the deficiencies of spectral analysis-based approaches, and is applicable for the engineering circumstances of system signal limitation and low signal-to-noise ratio (SNR), possessing immense practical merit. Both simulation analyses and experimental validations profoundly demonstrate that the proposed method is superior to other existing state-of-the-art time-domain correlation methods. Moreover, as an attempt as well as exemplar to apply this method, the PMONRC-based incipient fault diagnostic results of rolling bearing data from the well-known experimental platform PRONOSTIA are presented and discussed as well, to further elucidate the effectiveness and practical engineering significance of the proposed method.

An Analysis of Subtitle Translation of Chang 'an from the Perspective of Skopos Theory

Movies are carriers of culture and have become an indispensable means of cultural dissemination in today's society. The demand for subtitle translation is also constantly increasing. Subtitle translation in films is constrained by time, space, and culture; therefore, subtitles should be concise and easy to understand. This article analyzed and evaluated the subtitle translation of the Chinese film "Chang 'an" based on three rules of Skopos theory: skopos rules, intratextual coherence and fidelity rule. The translation purpose of the translator is to help the audience have a better viewing experience and accurately convey the information of Chinese subtitles through a concise English translation. When translating Chinese to English subtitles, translators should omit repetitive or plot-irrelevant information, add conjunctions to make the translation smooth and coherent and transform cultural differences.

Semantically redundant training data removal and deep model classification performance: A study with chest X-rays

Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data. A common understanding is that its performance scales up with the amount of training data. However, the data must also exhibit variety to enable improved learning. In medical imaging data, semantic redundancy, which is the presence of similar or repetitive information, can occur due to the presence of multiple images that have highly similar presentations for the disease of interest. Also, the common use of augmentation methods to generate variety in DL training could limit performance when indiscriminately applied to such data. We hypothesize that semantic redundancy would therefore tend to lower performance and limit generalizability to unseen data and question its impact on classifier performance even with large data. We propose an entropy-based sample scoring approach to identify and remove semantically redundant training data and demonstrate using the publicly available NIH chest X-ray dataset that the model trained on the resulting informative subset of training data significantly outperforms the model trained on the full training set, during both internal (recall: 0.7164 vs 0.6597, p<0.05) and external testing (recall: 0.3185 vs 0.2589, p<0.05). Our findings emphasize the importance of information-oriented training sample selection as opposed to the conventional practice of using all available training data.

잠재적 고객만족 개선 지수(PCSI Index)를 활용한 학습관리 시스템(LMS)의 품질만족 인식에 관한 연구

This paper aims to analyze the satisfaction and perception of university students regarding the quality of Learning Management Systems (LMS) utilized in mobile environments. The analysis is conducted through Timko's Customer Satisfaction Coefficient and the PCSI Index. To achieve this goal, the definitions and concepts of smart learning and LMS are examined, and a survey on the quality satisfaction of LMS functionalities based on the Kano model is conducted among 127 university students. Through this process, Critical considerations for improving the quality of LMS in mobile environments are analyzed, including the prevention of information misperception errors, acquisition of course-related materials, and avoidance of repetitive unnecessary information. It is hoped that the directional insights derived from this study will serve as a basis for the development and design of specific LMS systems.

Evaluation of the Competitiveness of China’s Coastal Container Ports In the Context of the Maritime Silk Road

A scientific and reasonable assessment of port competitiveness will facilitate a comprehensive understanding of the current development of Chinese container ports along the Maritime Silk Road. Considering the network attributes of ports and the repetitive information among indicators, this paper proposes a novel port competitiveness evaluation and grading model based on entropy-improved catastrophe progression method. On the basis of a directed and weighted shipping network, the “dominance” indicator is innovatively introduced. The evaluation system including land-oriented competitiveness, sea-oriented competitiveness, hinterland support capacity and production and operation capacity provides a comprehensive assessment of port competitiveness. It is found that: land-oriented and sea-oriented competitiveness have the greatest influence on port competitiveness. A pattern of “One core, two wings, and four ports’ coordination” has been developed among coastal ports in China. The competition among ports in the Bohai Rim region is fierce, and production and operation ability of ports in the southeast coast needs to be strengthened. Fuzhou port and Rizhao port have high hinterland support and high development potential. The results of this paper can provide decision support for the development and upgrading of China’s coastal ports under the Maritime Silk Road initiative.

Doctors’ perceptions of using their digital twins in patient care

Recent Artificial Intelligence (AI) advancements have facilitated tools capable of generating digital twins of real human faces and voices for interactive communication. In this research, we explore utilizing Digital Twins of Doctors (DTDs) in healthcare because using a doctor’s identity can provide benefits like enhancing the credibility of the health information delivered using computers. DTDs are computer-controlled AI-generated digital replicas of doctors that closely resemble their characteristics. However, there exist limitations, including the social implications of using a doctor’s identity, potential negative impacts on doctor–patient communication, and liability concerns. To ensure a comprehensive understanding of DTD usage in healthcare before widespread adoption, systematic research is essential. As a step towards this direction, in this qualitative research, we report findings from 13 semi-structured interviews with doctors. Our findings indicate that doctors believe DTDs offer benefits by saving doctors’ time through the efficient delivery of repetitive information and personalizing patient care. Moreover, while using a doctor’s identity can enhance credibility, it also raises concerns about using a doctor’s identity to spread potential misinformation. These findings contribute by informing future researchers about doctors’ perspectives on utilizing DTDs in healthcare, guiding the development of effective implementation strategies for responsible DTD integration into healthcare.

Yield Estimation of Wheat Using Cropland Masks from European Common Agrarian Policy: Comparing the Performance of Enhanced Vegetation Index 2, Normalized Difference Vegetation Index, and MERIS Terrestrial Chlorophyll Index in Spanish Nomenclature of Territorial Units for Statistics Level 2 Regions

Monitoring wheat yield and production is essential for ensuring global food security. Remote sensing can be used to achieve it due to its ability to provide global, comprehensive, synoptic, and repetitive information in near real-time. This study used the 2006–2016 Normalized Difference Vegetation Index (NVDI) and Enhanced Vegetation Index 2 (EVI2) time series at a 250 m spatial resolution and 2006–2011 MERIS Terrestrial Chlorophyll Index (MTCI) time series at a 300 m spatial resolution. The post-maximum period for pixels containing wheat was selected based on the EU’s Common Agrarian Policy (CAP) and Corine Land Cover (CLC) data. It was correlated with yield and production values from governmental statistics (GS) of the largest Nomenclature of Territorial Units for Statistics level 2 (NUTS-2) wheat producers in Spain and for Spain overall. The selection of wheat masks was crucial for the accuracy of the models, with CAP masks offering greater forecasting capability. Models using CLC produced R2 values between 0.45 and 0.7, while those using CAP outperformed the former with R2 values of 0.9 throughout Spain. Production models outperformed yield models, and MTCI was the vegetation index (VI) that provided the greatest R2 value of 0.94. However, model accuracy was heavily conditioned by the precision of input data, where anomalies were detected in some NUTS-2.

"It Bothered Me": The Mental Burden of COVID-19 Media Reports on Community-Dwelling Elderly People.

Background and Objectives: Elderly people may have difficulties understanding the quality and quantity of information about the COVID-19 epidemic, which can put an additional mental strain on their health and well-being. The purpose of this study was to explore the processing of COVID-19 information among older people. Materials and Methods: A qualitative study was carried out in summer 2021. The sampling was based on the snowball method. This approach allowed us to communicate with the next potential participants relatively freely and without reservations. Two female researchers (both MD, PhD) conducted the interviews. All interviews were held in Serbian. The data were analyzed using qualitative content analysis. Results: The interviews were conducted with 13 participants (average age 71 years). The analysis of qualitative content suggested that four topics could be identified: (1) sources of information, (2) information interest and need, (3) reporting of information and (4) suggestions for better reporting. The participants were troubled by the excess of information, repetitive information about death tolls, unqualified people in media discussing the pandemic and inconsistent reporting. These features caused the participants to feel the psychological burden in processing all the pieces of information. Conclusions: The elderly people in Serbia followed mainstream media to get information about COVID-19; however, they perceived a variety of problems with reporting, which made the understanding of the information difficult and psychologically burdensome. These findings should be taken into consideration when delivering health-related information to elderly people.

Research and Evaluation of a Cyberchondria Severity Scale in a Chinese Context.

Cyberchondria is a problematic or unhelpful behavior pattern that describes excessive or repetitive online health-related information searching related to an enhanced level of health anxiety. Such internet-derived medical anxiety can manifest itself in different ways across cultures. This study explores the unique nature of cyberchondria in the context of Chinese culture, identifying the risk factors for the condition and the possible negative outcomes. An exploratory factor analysis (EFA) was used to explore whether the structure of the Chinese version of the Cyberchondria Severity Scale (C-CSS) is different from that in western context. Subsequently, a confirmatory factor analysis (CFA) was used to verify the model fit of the C-CSS. Finally, a series of regression analysis were used to test the relationship between cyberchondria and its antecedent variables and consequence variables in Chinese context. Retained 18 items and revised to 3 dimensions (Negative Effects, Excessiveness and Reassurance Seeking), the Chinese version of the Cyberchondria Severity Scale (C-CSS) was developed. In the context of China, the three antecedents were also effective predictors of cyberchondria, and C-CSS is also related to theoretically relevant outcomes. This study initially demonstrated the validity, reliability and applicability of C-CSS to assess the severity of cyberchondria among Chinese undergraduates.

Topic model for long document extractive summarization with sentence-level features and dynamic memory unit

The Transformer-based summarization models rely solely on the attention mechanism for document encoding, making it difficult to accurately capture long-range dependencies in long documents due to the presence of attention redundancy. To address this issue, we propose an extractive summarization framework guided by a topic model (TopicSum) that utilizes a heterogeneous graph neural network to leverage the topic information as document-level features during the sentence selection process, thereby capturing the long-range dependencies among sentences. The sentence-level features in this topic model align with the basic unit of the extractive summarization task. Additionally, a memory mechanism is employed to dynamically store and update the memory module, reducing the potential of repetitive information guiding sentence selection. We evaluated the model on three large document datasets, namely Pubmed, arXiv, and GovReport, and achieved significantly higher Rouge scores than previous works, including extractive and abstractive models. Furthermore, our experiments demonstrate that recent highly regarded large language models such as ChatGPT are insufficient to handle the long document summarization task directly. The proposed approach in this paper exhibits sufficient competitiveness in terms of both generation quality and deployment conditions.

RESTful API žiniatinklio paslaugų diegimas duomenų administravimui skirtinguose serveriuose

The article analyzes the model developed during the INOSTART program project that improves the process of uploading and updating information on the company's web pages. This system will help the company to quickly change existing, often repetitive information in all its locations, simplify its management process, provide access to its management to persons without IT and WordPress knowledge, and solve the most frequent problems of multilingual support. Keywords: data management, administration, RESTful API, system.

Prediction and classification of sEMG-based pinch force between different fingers

The movement of intelligent EMG-driven prosthesis mainly relies on the synergy of different fingers to achieve function of grasping objects. The paper proposes a novel scheme for force prediction and movement classification about pinch between different fingers based on surface electromyography (sEMG) using machine learning. The pinch force and sEMG signals are recorded synchronously by a data acquisition device. Eight features are extracted, which are proven to have better performance in the estimation of sEMG-to-force. We present a novel feature selection method that uses the one-dimensional time series similarity assessment based on Manhattan distance to eliminate the repetitive information between features. Three optimal features carrying less repetitive information are retained. Seven machine learning algorithms are used to predict force strength. The results show that the Long Short Term Memory (LSTM) has the best performance of force prediction, achieving a R2 of 0.9517 and RMSE of 3.2723. The paper proposes a novel method of converting EMG feature sequences to the normalized gray image in order to classify the finger movement. Five classifiers based on image feature extraction and the Convolutional Neural Network (CNN) are developed respectively. The experimental results indicate that the CNN performs best, achieving an accuracy of 97.66%. In this way, it not only realizes the accurate force prediction, but also realizes the movement classification between different fingers. The proposed methodology has the potential to realize simultaneous force and movement control of prosthetic hand.

Neural-Network-Assisted Packet Accelerators for Internet of Things Network Systems

Major device nodes within the internet of things (IoT) system collects and store information in bit forms of 0’s and 1’s regardless of its repetition. The nodes do not possess the capability of processing redundant data information except for outright rejection/replacement of packets of similar sizes. This becomes a research problem since high volumes of packet redundancy are prevalent owing to repetitive information. Many optimal solutions have been provided to reprocess redundant packets and to store them in edge server for accessibility by other connected IoT systems and networks servers. To do so, major IoT platforms implements tier-based network layers which primarily aid seamless communication among nodes. These network layers perform near-similar tasks of guaranteeing packet sensing and exchange although at often-higher energy requirement. To mitigate the energy concerns, packets are clustered and compressed, allowing exchange of essential information only. But the approach continues to present heavy packet-losses and/or redundancies. In this paper, two-tier layered network | where packet exchange is conducted at the top layer in order to lower energy consumption and promote system-reliability is investigated. All packets are first segregated into multiple clusters using the Voronoi cell-based correlation cluster formation (VC3F) technique. Cluster heads (CHs) are identified by their multi-path (M-Score) value, thus, assuming sole responsibility of redundant packet removal within each cluster. The redundant packets are then moved to the edge-tier layer using optimized multi-objective flower pollination (MO-FPO) routing, and finally processed using hybrid models of novel fast-fully connected neural network (F2CNN) accelerator and Lempel-Ziv-Welch (LZW) data compression. The F2CNN and LZW models are harmonized to further collectively explore potential benefits of both models. These benefits include the neural capability to work on the sensitivity level of the packets determined by the packet classification and validation approaches. The system is evaluated with detailed experimental investigations where higher system throughput, packet delivery ratio (PDR), end-to-end delay and system reliability are corroborated.

Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery

Simultaneous localization and mapping (SLAM), as a key task of unmanned vehicles for construction machinery, is of great significance for later path planning and control. Construction tasks in the engineering field are mostly carried out in bridges, tunnels, open fields, etc. The prominent features of these environments are high scene similarity, few geometric features, and large-scale repetitive texture information, which is prone to sensor detection degradation. This leads to positioning drift and map building failure. The traditional method of motion estimation and 3D reconstruction uses a single sensor, which lacks enough information, has poor adaptability to the environment, and cannot guarantee good positioning accuracy and robustness in complex environments. Currently, the strategy of multi-sensor fusion is proven to be an effective solution and is widely studied. This paper proposes a SLAM framework that integrates LiDAR, IMU, and camera. It tightly couples the texture information observed by camera, the geometric information scanned by LiDAR, and the measured value of IMU, allowing visual-inertial odometry (VIO) and LiDAR-inertial odometry (LIO) common implementation. The LIO subsystem extracts point cloud features and matches them with the global map. The obtained pose estimation can be used for the initialization of the VIO subsystem. The VIO system uses direct method to minimize the photometric error and IMU measurement error between images to estimate the pose of the robot and the geometric structure of the scene. The two subsystems assist each other to perform pose estimation, and can operate normally even when any subsystem fails. A factor graph is used to combine all constraints to achieve global pose optimization. Keyframe and sliding window strategies are used to ensure real-time performance. Through real-vehicle testing, the system can perform incremental and real-time state estimation and reconstruct a dense 3D point cloud map, which can effectively solve the problems of positioning drift and mapping failure in the lack of geometric features or challenging construction environments.

The Role of Intracoronary Imaging for the Management of Calcified Lesions.

Interventional cardiologists in everyday practice are often confronted with calcified coronary lesions indicated for percutaneous transluminal coronary angioplasty (PTCA). PTCA of calcified lesions is associated with diverse technical challenges resulting in suboptimal coronary stenting and adverse long-term clinical outcomes. Angiography itself offers limited information regarding coronary calcification, and the adjuvant use of intracoronary imaging such as intravascular ultrasound (IVUS) and Optical Coherence Tomography (OCT) can guide the treatment of calcified coronary lesions, optimizing the different stages of the procedure. This review offers a description of why, when, and how to use intracoronary imaging for PTCA of calcified coronary lesions in order to obtain the most favorable results. We used the PubMed and Google Scholar databases to search for relevant articles. Keywords were calcified coronary lesions, intracoronary imaging, IVUS, OCT, coronary calcium modification techniques, PTCA, and artificial intelligence in intracoronary imaging. A total of 192 articles were identified. Ninety-one were excluded because of repetitive or non-important information.

A Hybrid Continuous-Time Dynamic Graph Representation Learning Model by Exploring Both Temporal and Repetitive Information

Recently, dynamic graph representation learning has attracted more and more attention from both academic and industrial communities due to its capabilities of capturing different real-world phenomena. For a dynamic graph represented as a sequence of timestamped events, there are two kinds of evolutionary essences: temporal and repetitive information. At present, the temporal information of interactions (e.g., timestamps) have been deeply explored. However, as another vital nature of dynamic graphs, the repetitive information of interactions between two nodes is neglected, which may lead to inaccurate node representation. To address this issue, we propose a novel continuous-time dynamic graph representation learning model, which consists of a node-level-memory based module, a historical high-order neighborhood based vertical aggregation module and a repetitive-topological information based horizontal aggregation module. In particular, to characterize the evolving pattern of the repetitive information of interactions between a pair of nodes, we put forward a repetitive-interaction based attention mechanism to integrate the two key attributes (i.e., the content and the number of interactions) of repetitive interactions at different moments, based on the insight that the repetitive behaviors of nodes are widespread and essential. We conduct extensive experiments including future link prediction tasks (for transductive and inductive learning) and dynamic node classification task, and results on three real-life dynamic graph datasets demonstrate that the proposed method significantly outperforms state-of-the-art baselines, for both observed nodes and new ones.