Search Results (2,756)

In complex process systems, accurate real-time anomaly detection is essential to ensure operational safety and reliability. This study proposes a novel detection method that combines information granulation with kernel principal component analysis (KPCA). Here, information granulation is introduced as a general framework, with the principle of justifiable granularity (PJG) adopted as the specific implementation. Time series data are first granulated using PJG to extract compact features that preserve local dynamics. The KPCA model, equipped with a radial basis function kernel, is then applied to capture nonlinear correlations and construct monitoring statistics including $T^2$ and SPE. Thresholds are derived from training data and used for online anomaly detection. The method is evaluated on the Tennessee Eastman process and Continuous Stirred Tank Reactor datasets, covering various types of faults. Experimental results demonstrate that the proposed method achieves a near-zero false alarm rate below 1% and maintains a missed detection rate under 6%, highlighting its effectiveness and robustness across different fault scenarios and industrial datasets. Full article

Sleep is a critical physiological process for the mental and physiological restoration of people. The air conditioning usually serves as a common approach to maintain or improve sleep quality. However, available data are still limited regarding the actual sleep quality under different air conditioning modes, which leads to insufficient evidence to support the optimization of the temperature control strategies of air conditioners. To address this gap, an online questionnaire survey was carried out to identify the adjustments of air conditioners during nocturnal sleep, as well as the subjective sleep quality of residents in the summer. A total of 571 valid responses were collected from participants across various age groups, genders, and climatic regions in China through the online surveys that considered several aspects of sleep and air conditioner usage. Pearson’s Chi-square test was used to detect the differences between items in surveys. The results indicated that 74.6% of respondents used air conditioners to regulate their sleep environments in summer, with a preferred temperature of approximately 26 °C. Gender difference had a limited contribution to the adjusting behaviors of air conditioners (χ² = 3.83, p = 0.281), while age played a significant role (χ² = 20.06, p = 0.018). On the contrary, sleep-related adjusting behaviors of the air conditioner were more influenced by subjective factors such as concerns about being awakened by cold or heat. Nonetheless, over 50% of respondents reported experiencing thermal disturbances during sleep, including awakenings by either cold or heat, regardless of the adjustments (χ² = 20.3, p = 0.002). Furthermore, 68.7% of respondents reported their preference for dynamic temperature adjustments during sleep. Findings revealed that the age and subjective aspects were critical for the adjusting behaviors of air conditioners during sleep, and the dynamic air conditioning control was preferred more by users. This study provided empirical evidence to support the optimization of air conditioning modes and the development of adaptive, dynamical sleeping air conditioning systems. Full article

Common polymeric materials (neat polymers) are quite well known, and their properties are often available in appropriate material databases. However, material data, e.g., rheological data, for materials such as polymer blends, polymer composites (including wood plastic composites), and filled plastics are simply lacking in material databases. This paper addresses the problem of determining viscosity curves for one of the most widely used advanced polymeric materials: wood plastic composites. Studies were conducted in laboratory and production settings, i.e., on-line. Laboratory tests were conducted in two ways: on the basis of classical rheometric measurements, i.e., High-Pressure Capillary Rheometry (HPCR), and on the basis of Melt Flow Index (MFI) measurements, also including tests based on a limited number of measurement points. Tests in production conditions, i.e., on-line, were conducted during the extrusion process using the measurement of the process output (material flow rate) and pressure in a specialized extrusion die. The test results (viscosity curves) obtained from Melt Flow Index (MFI) measurements and on-line measurements were presented and evaluated against the background of the results (viscosity curves) obtained from classical capillary rheometry measurements (HPCR). Due to the lack of rheological data of wood plastic composites in available databases, in-house research methods based on the two-point viscosity curve determination in the plastometric (MFI) tests and the tests under production conditions, that is, on-line, have been proposed. The two-point method, based on the power law model, is quick and easy to implement, and allows for solving many polymer processing issues analytically. On-line tests have the significant advantage of being conducted under the actual flow conditions of the tested material, rather than under laboratory conditions, as is the case with rheometric and plastometric tests, which do not take into account the processing history of the tested material. The issues of rheology and modeling of wood plastic composite processing, e.g., extrusion and injection molding, which have not yet been resolved and require practical solutions, were also discussed. The results of this part of the study (viscosity curves and models) will be used in the second part of the study to evaluate the impact of rheological testing methods and rheological models on the accuracy of process modeling (extrusion). Full article

Frequent failures in wind turbines underscore the critical need for accurate and efficient online monitoring and early warning systems to detect abnormal conditions. Given the complexity of monitoring numerous components individually, subsystem-level monitoring emerges as a practical and effective alternative. Among all subsystems, the gearbox is particularly critical due to its high failure rate and prolonged downtime. However, achieving both efficiency and accuracy in gearbox condition monitoring remains a significant challenge. To tackle this issue, we present a novel adaptive condition monitoring method specifically for wind turbine gearbox. The approach begins with adaptive feature selection based on correlation analysis, through which a quantitative indicator is defined. With the utilization the selected features, graph-based data representations are constructed, and a self-supervised contrastive residual graph neural network is developed for effective data mining. For online monitoring, a health index is derived using distance metrics in a multidimensional feature space, and statistical process control is employed to determine failure thresholds. This framework enables real-time condition tracking and early warning of potential faults. Validation using SCADA data and maintenance records from two wind farms demonstrates that the proposed method can issue early warnings of abnormalities 30 to 40 h in advance, with anomaly detection accuracy and F1 score both exceeding 90%. This highlights its effectiveness, practicality, and strong potential for real-world deployment in wind turbine monitoring applications. Full article

In today’s digital ecosystem, where millions of users interact with diverse online services and generate vast amounts of textual, transactional, and behavioral data, ensuring the trustworthiness of this information has become a critical challenge. Low-quality data—manifesting as incompleteness, inconsistency, duplication, or noise—not only undermines analytics and machine learning models but also exposes unsuspecting users to unreliable services, compromised authentication mechanisms, and biased decision-making processes. Traditional data quality assessment methods, largely based on manual inspection or rigid rule-based validation, cannot cope with the scale, heterogeneity, and velocity of modern data streams. To address this gap, we propose DQMAF (Data Quality Modeling and Assessment Framework), a generalized machine learning–driven approach that systematically profiles, evaluates, and classifies data quality to protect end-users and enhance the reliability of Internet services. DQMAF introduces an automated profiling mechanism that measures multiple dimensions of data quality—completeness, consistency, accuracy, and structural conformity—and aggregates them into interpretable quality scores. Records are then categorized into high, medium, and low quality, enabling downstream systems to filter or adapt their behavior accordingly. A distinctive strength of DQMAF lies in integrating profiling with supervised machine learning models, producing scalable and reusable quality assessments applicable across domains such as social media, healthcare, IoT, and e-commerce. The framework incorporates modular preprocessing, feature engineering, and classification components using Decision Trees, Random Forest, XGBoost, AdaBoost, and CatBoost to balance performance and interpretability. We validate DQMAF on a publicly available Airbnb dataset, showing its effectiveness in detecting and classifying data issues with high accuracy. The results highlight its scalability and adaptability for real-world big data pipelines, supporting user protection, document and text-based classification, and proactive data governance while improving trust in analytics and AI-driven applications. Full article

Temporal network diffusion models play a crucial role in healthcare, information technology, and machine learning, enabling the analysis of dynamic event-based processes such as disease spread, information propagation, and behavioral diffusion. This study addresses the challenge of reconstructing temporal network diffusion events in real time under conditions of missing and evolving data. A novel non-parametric reconstruction method by simple weights differentiationis proposed to enhance source detection robustness with provable improved error bounds. The approach introduces adaptive cost adjustments, dynamically reducing high-risk source penalties and enabling bounded detours to mitigate errors introduced by missing edges. Theoretical analysis establishes enhanced upper bounds on false positives caused by detouring, while a stepwise evaluation of dynamic costs minimizes redundant solutions, resulting in robust Steiner tree reconstructions. Empirical validation on three real-world datasets demonstrates a 5% improvement in Matthews correlation coefficient (MCC), a twofold reduction in redundant sources, and a 50% decrease in source variance. These results confirm the effectiveness of the proposed method in accurately reconstructing temporal network diffusion while improving stability and reliability in both offline and online settings. Full article

As an important supplement to ground computing, edge computing can effectively alleviate the computational burden on ground systems. In the context of integrating edge computing with low-Earth-orbit satellite networks, this paper proposes an edge computing task offloading algorithm based on distributed multi-agent deep reinforcement learning (DMADRL) to address the challenges of task offloading, including low transmission rates, low task completion rates, and high latency. Firstly, a Ground–UAV–LEO (GUL) three-layer architecture is constructed to improve offloading transmission rate. Secondly, the task offloading problem is decomposed into two sub-problems: offloading decisions and resource allocation. The former is addressed using a distributed multi-agent deep Q-network, where the problem is formulated as a Markov decision process. The Q-value estimation is iteratively optimized through the online and target networks, enabling the agent to make autonomous decisions based on ground and satellite load conditions, utilize the experience replay buffer to store samples, and achieve global optimization via global reward feedback. The latter employs the gradient descent method to dynamically update the allocation strategy based on the accumulated task data volume and the remaining resources, while adjusting the allocation through iterative convergence error feedback. Simulation results demonstrate that the proposed algorithm increases the average transmission rate by 21.7%, enhances the average task completion rate by at least 22.63% compared with benchmark algorithms, and reduces the average task processing latency by at least 11.32%, thereby significantly improving overall system performance. Full article

The rapid expansion of online education has generated large volumes of learner interaction data, highlighting the need for intelligent systems capable of transforming this information into personalized guidance. Educational Recommender Systems (ERS) represent a key application of big data analytics and machine learning, offering adaptive learning pathways that respond to diverse student needs. For widespread adoption, these systems must align with pedagogical principles while ensuring transparency, interpretability, and seamless integration into Learning Management Systems (LMS). This paper introduces a comprehensive framework and implementation of an ERS designed for platforms such as Moodle. The system integrates big data processing pipelines to support scalability, real-time interaction, and multi-layered personalization, including data collection, preprocessing, recommendation generation, and retrieval. A detailed use case demonstrates its deployment in a real educational environment, underlining both technical feasibility and pedagogical value. Finally, the paper discusses challenges such as data sparsity, learner model complexity, and evaluation of effectiveness, offering directions for future research at the intersection of big data technologies and digital education. By bridging theoretical models with operational platforms, this work contributes to sustainable and data-driven personalization in online learning ecosystems. Full article

Urban streetscapes are among the most frequently encountered spatial environments in daily life, and their restorative visual features have a significant impact on well-being. Although existing studies have revealed the relationship between streetscape environments and perceived restorativeness, there remains a lack of scalable, data-driven methods for quantifying such perception at the street level. This study proposes an interpretable and replicable framework for predicting streetscape restorativeness by integrating semantic segmentation, perceptual evaluation, and machine learning techniques. Taking Liwan District of Guangzhou as a case study, street-view images (SVIs) were collected and processed using the Mask2Former model to extract the following five key visual metrics: greenness, openness, enclosure, walkability, and imageability. Based on the Perceived Restorativeness Scale (PRS), an online questionnaire was designed from four dimensions (fascination, being away, compatibility, and extent) to score a random sample of images. A random forest model was then trained to predict the perceptual levels of the full dataset, followed by K-means clustering to identify spatial distribution patterns. The results revealed that there were significant differences in visual characteristics among high, medium, and low restorativeness street types. The proposed framework enables scalable, data-driven evaluation of perceived restorativeness across diverse urban streetscapes. By embedding perceptual metrics into large-scale urban analysis, the framework offers a replicable and efficient approach for identifying streets with low restorative potential—thus providing urban planners and policymakers with a novel tool for prioritizing street-level renewal, improving public well-being, and supporting perception-oriented urban design without the need for labor-intensive fieldwork. Full article

The paper presents a hybrid intelligent expert diagnostic system (HIESD) of power transformer (PT) subsystems realized on the basis of integration of measuring and computing hardware and software complexes into a single functional architecture. HIESD performs online diagnostics of four main subsystems of PT: 1—insulating (liquid and solid insulation); 2—electromagnetic (windings, magnetic conductor); 3—voltage regulation; and 4—high-voltage inputs. Computational complexes and modules of the system are connected with the real object of power grids, 110/10 kV substation, which interact with each other and contain a relational database of retrospective offline data of the PT “life cycle” (including test and measurement results), supplemented by online monitoring data of the main subsystems, corrected by high-precision test measurements; analytical complex, in which the work of calculation modules of the operational state of PT subsystems is supplemented by predictive analytics and machine learning modules; and a knowledge base, sections of which are regularly updated and supplemented. The system architecture is tested at industrial facilities in terms of online transformer diagnostics based on dissolved gas analysis (DGA) data. Additionally, a theoretical model of diagnostics based on the electromagnetic characteristics of the transformer, which takes into account distorted and nonlinear modes of its operation, is presented. The scientific significance of the work consists of the presentation of the following new provisions: Methodology and algorithm for diagnostics of electromagnetic parameters of ST, taking into account nonlinearity and non-sinusoidality of winding currents and voltages; formation of optimal client–service architecture of training models of hybrid system based on the processes of data storage and management; and modification of the moth–flame algorithm to optimize the smoothing coefficient in the process of training a probabilistic neural network Full article

E-commerce is quickly increasing purchasing behavior across the globe, but little is known about how psychological paradigms underscore online buying intentions for locally essential items as nomadic local foods. The primary goal of this research is to examine the effects of some important psychological constructs and motivational values on predicting consumers’ intention to purchase nomadic and local foods via online e-commerce platforms, such as Ashayershop. This study followed the Theory of Planned Behavior (TPB) and looked at direct and mediated effects of attitudes, perceived behavioral control, and subjective norms on intention to purchase. Structural Equation Modeling (SEM) was conducted, based on data collected from a representative sample of consumers who were familiar with online shopping for local foods. The results highlight that attitude towards online shopping for local foods was the strongest direct predictor of intention to purchase (β = 0.383, T = 9.487, p < 0.001). Perceived behavioral control (β = 0.220, T = 5.316, p < 0.001), hedonic value (β = 0.213, T = 4.907, p < 0.001), utilitarian value (β = 0.187, T = 3.719, p < 0.001), and subjective norms (β = 0.149, T = 3.493, p < 0.001), received a significant positive effect on intention. In addition, hedonic and utilitarian values bountifully mediated the relation between psychological antecedents (attitudes, perceived behavioral control, and subjective norms) and purchase intention. For instance, attitude indirect effect via hedonic value was β = 0.080 (T = 3.783, p < 0.01), and indirect effect via utilitarian value was β = 0.040 (T = 3.058, p < 0.01), indicating the importance of these values as mediators. This research makes a contribution to the literature by showing that motivational values serve as not only an outcome but also as cognitive–affective mediators in the behavioral process thus expanding the TPB in the context of digital food markets. In general, these results provide valuable insights to e-commerce platforms and policymakers who desire to promote consumer engagement with products stemming from culture and tradition on line by developing new integrated strategies that address the cognitive, emotional, and social components. Full article

Background: As the United States population becomes increasingly diverse, the representation of minorities in health professions is critical to addressing health disparities. Few investigations have been conducted among students enrolled in the first semester of the nursing program, a vulnerable and adjustment period for most nursing majors. Thus, this study examined the association between reading comprehension and science aptitude on student retention and standardized test scores. Method: A cross-sectional repeated measures study was conducted to investigate the outcomes from a compendium of programmatic interventions implemented among n = 80 nursing students enrolled in the first semester of a pre-licensure baccalaureate nursing program in one Hispanic-serving institution. These interventions included the Weaver™ reading online program, case studies, NCLEX-type practice tests, test-taking skills, and peer-mentoring. Data collection was conducted in Spring 2024. Multivariate statistical analysis was used to determine predictors associated with student retention and standardized test scores. An independent t-test was used to examine any significant difference in the reading comprehension level among the cohort’s participants. A qualitative investigation using thematic analysis was conducted to understand students’ experiences with the programmatic interventions. Results: Students’ baseline reding comprehension level was significantly associated with failure in the first semester of the nursing program (β = −0.815; SE = 0.349; Wald = 5.444; p < 0.05). End-of-term reading comprehension level was significantly associated with end-of-course HESI score in the Foundations in Nursing course (β = 26.768; SE = 10.049; Beta = 0.445; p < 0.05) while science GPA was significantly associated with end-of-course HESI score for Health Assessment (β = 3.022; SE = 1.315; Beta = 0.434; p < 0.05. Cohort retention was 75%. The independent t-test result indicated a significant difference in reading level was found between those who dropped out from the cohort (M = 4.23, SE = 0.173 and those who did not (M = 5.15, SE = 0.188), t (68) = −3.037, p < 0.01. A reading level of grade 10 and above was associated with student progression to the next semester (M = 10.16, SE = 0.375, t (70) = −0.560, p < 0.05. Although the participants found the reading comprehension modules tedious, test-taking strategies, applying the nursing process in case studies, and the expertise of a nurse educator, who understood the learning needs of first-semester students, were perceived as critical to academic success. Conclusions: Reading comprehension and science aptitude are essential to students’ early success in the nursing program. Addressing gaps in reading comprehension and science aptitude before admission to a nursing program would increase chances of success in the early stages of a nursing major. Full article

Blockchain technology has become a game changer in sectors like accounting and auditing. Its usage is still restricted due to a lack of insight into what drives people to adopt it for financial services like accounting and auditing. This research delves into the factors that influence the adoption of blockchain systems in accounting and auditing services by utilizing an enhanced edition of the Theory of Planned Behavior. In this study, alongside the previously established elements like Attitude, subjective norm, and Perceived Behavioral Control, self-perception and personal moral values are included to reflect how identity and ethics impact decision-making processes. Data were gathered via an online survey (N = 751) conducted on the Prolific platform, and the hypotheses were tested using Structural Equation Modeling. The hypotheses were examined through the Structural Equation Modeling method. The findings indicate that each of the five predictors plays a significant role in influencing Behavioral Intention, with personal moral values being the influential factor followed by subjective norm and Perceived Behavioral Control. Attitude plays an important role in shaping adoption choices and showcases the complexity involved in such decisions. As such, it is crucial to take into account ethical factors when encouraging the use of blockchain technology. This study adds to the existing knowledge of the Theory of Planned Behavior framework, offering insights for companies aiming to boost the implementation of blockchain systems in professional settings. Future research avenues and real-world implications are explored with an emphasis placed on developing targeted strategies that align technological adoption with personal values and organizational objectives. Full article

In this study, we propose a novel adaptive algorithm for approximate nearest neighbor (ANN) search, based on the inverted file (IVF) index (cluster-based index) and online query complexity classification. The concept of the classical IVF search implemented in vector databases is as follows: all data vectors are divided into clusters, and each cluster is assigned to its central point (centroid). For an ANN search query, the closest centroids are determined, and the further search continues in the corresponding clusters only. In our study, the complexity of each query is assessed and classified with the use of results of an initial trial search in a limited number of clusters. Based on this classification, the algorithm dynamically determines the presumably sufficient number of clusters which is sufficient to achieve the desired Recall value, thereby improving vector search efficiency. Our experiments show that such a complexity classifier can be built with the use of a single feature, and we propose an algorithm for its training. We studied the impact of various features on the query processing and discovered a strong dependence on the number of clusters that contains at least one nearest neighbor (productive clusters). The new algorithm is designed to be implemented on top of the IVF search which is a well-known algorithm for approximate nearest neighbor search and uses existing IVF indexes that are widely used in the most popular vector database management systems, such as pgvector. The results obtained demonstrate a significant increase in the speed of nearest neighbor search (up to 35%) while maintaining a high Recall rate of 0.99. Additionally, the search algorithm is deterministic, which might be extremely important for tasks where the reproducibility of results plays a crucial role. The developed algorithm has been tested on datasets of varying sizes up to one billion data vectors. Full article

The aim of the study was to explore the psychosocial perceptions that young adults have regarding gender-based violence, including those based on their personal experiences, and to highlight perceptions related to social media and how its use might be connected to gender-based violence. The participants were 40 university students from Northwestern Italy with an average age of 21.8 years (range: 19–25); 50% were women. Sampling was non-probabilistic and followed a purposive convenience strategy. Semi-structured interviews were conducted online and audio-recorded, and data were analyzed using the reflective thematic approach. The results revealed that young adults are very aware, at a theoretical level, of “offline” physical, psychological, and verbal gender-based violence and its effects, while they do not give much consideration to online violence, despite often being victims of it, as revealed by their accounts, for example, through unsolicited explicit images or persistent harassment on social media. Therefore, the results of this research highlight the need to develop primary prevention programs focused on increasing awareness and providing young people with more tools to identify when they have been victims of violence, both online and offline, and to process the emotional experiences associated with such events. Full article