Search results

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

Research shows peer health education programs on university campuses can support students in pursuing sustainable health-related behavior changes. However, few programs deliver peer health education through a nondiet, weight-inclusive framework. Research shows that health educators who challenge the status quo of diet culture and weight-focused health interventions may face unique challenges when sharing this perspective with others. Thus, the purpose of this study was to examine the experiences of peer educators who provided critical health education by introducing a nondiet, weight-inclusive approach to health.

Five health coaches from a university health coaching program at a mid-sized southwestern university participated in a semi-structured interview. The data were analyzed through interpretative phenomenological analysis.

Peer educators faced numerous challenges when introducing nondiet, weight-inclusive approaches such as lacking credibility as a peer to challenge weight-centric messages, feeling conflicted about honoring clients’ autonomy when clients are resistant to a weight-inclusive approach and feeling uncomfortable when discussing client vulnerabilities. Peer educators also identified several strategies that helped them navigate these challenges such as being intentional with social media, using motivational interviewing to unpack clients’ concerns about weight, and seeking group supervision.

Given the reality that health coaches will face challenges sharing weight-inclusive health approaches, educators and supervisors should explicitly incorporate strategies and training methods to help peer health coaches prepare for and cope with such challenges. More research is also needed to examine effective ways to introduce weight-inclusive approaches to college students.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

The stress concentration factor (SCF) is commonly utilized to assess the fatigue life of a tubular T-joint in offshore structures. Parametric equations derived from experimental testing and finite element analysis (FEA) are utilized to estimate the SCF efficiently. The mathematical equations provide the SCF at the crown and saddle of tubular T-joints for various load scenarios. Offshore structures are subjected to a wide range of stresses from all directions, and the hotspot stress might occur anywhere along the brace. It is critical to incorporate stress distribution since using the single-point SCF equation can lead to inaccurate hotspot stress and fatigue life estimates. As far as we know, there are no equations available to determine the SCF around the axis of the brace.

A mathematical model based on the training weights and biases of artificial neural networks (ANNs) is presented to predict SCF. 625 FEA simulations were conducted to obtain SCF data to train the ANN.

Using real data, this ANN was used to create mathematical formulas for determining the SCF. The equations can calculate the SCF with a percentage error of less than 6%.

Engineers in practice can use the equations to compute the hotspot stress precisely and rapidly, thereby minimizing risks linked to fatigue failure of offshore structures and assuring their longevity and reliability. Our research contributes to enhancing the safety and reliability of offshore structures by facilitating more precise assessments of stress distribution.

Precisely determining the SCF for the fatigue life of offshore structures reduces the potential hazards associated with fatigue failure, thereby guaranteeing their longevity and reliability. The present study offers a systematic approach for using FEA and ANN to calculate the stress distribution along the weld toe and the SCF in T-joints since ANNs are better at approximating complex phenomena than standard data fitting techniques. Once a database of parametric equations is available, it can be used to rapidly approximate the SCF, unlike experimentation, which is costly and FEA, which is time consuming.

Obesity and associated morbidity and mortality are major challenges for people with severe mental illness, particularly in secure (forensic) mental health care (patients who have committed a crime or have threatening behaviour). This study aims to explore experiences of weight management in secure mental health settings.

This study used a mixed-methods approach, involving thematic analysis. A survey was delivered to secure mental health-care staff in a National Health Service (NHS) mental health trust in Northern England. Focus groups were conducted with current and former patients, carers and staff in the same trust and semi-structured interviews were undertaken with staff in a second NHS mental health trust.

The survey received 79 responses and nine focus groups and 11 interviews were undertaken. Two overarching topics were identified: the contrasting perspectives expressed by different stakeholder groups, and the importance of a whole system approach. In addition, seven themes were highlighted, namely: medication, sedentary behaviour, patient motivation, catered food and alternatives, role of staff, and service delivery.

Secure care delivers a potentially “obesogenic environment", conducive to excessive weight gain. In future, complex interventions engaging wide-ranging stakeholders are likely to be needed, with linked longitudinal studies to evaluate feasibility and impact.

To the best of the authors’ knowledge, this is the first study to involve current patients, former patients, carers and multidisciplinary staff across two large NHS trusts, in a mixed-methods approach investigating weight management in secure mental health services. People with lived experience of secure services are under-represented in research and their contribution is therefore of particular importance.

Land value varies at a micro level depending on the location’s economic, geographical and political determinants. The purpose of this study is to present a comprehensive assessment of the determinants affecting land value in the Indian city of Thiruvananthapuram in the state of Kerala.

The global influence of the identified 20 explanatory variables on land value is measured using the traditional hedonic price modeling approach. The localized spatial variations of the influencing parameters are examined using the non-parametric regression method, geographically weighted regression. This study used advertised land value prices collected from Web sources and screened through field surveys.

Global regression results indicate that access to transportation facilities, commercial establishments, crime sources, wetland classification and disaster history has the strongest influence on land value in the study area. Local regression results demonstrate that the factors influencing land value are not stationary in the study area. Most variables have a different influence in Kazhakootam and the residential areas than in the central business district region.

This study confirms findings from previous studies and provides additional evidence in the spatial dynamics of land value creation. It is to be noted that advanced modeling approaches used in the research have not received much attention in Indian property valuation studies. The outcomes of this study have important implications for the property value fixation of urban Kerala. The regional variation of land value within an urban agglomeration shows the need for a localized method for land value calculation.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

The current study aims to explore the determinants of user intentions towards fitness YouTube channels, employing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Uses and Gratifications Theory (UGT) as theoretical frameworks.

Symmetric and asymmetric analyses were employed for data analysis, utilizing partial least squares-structural equation modeling (PLS-SEM) for symmetric analysis and fuzzy-set qualitative comparative analysis (fsQCA) for asymmetric analysis.

The study revealed significant impacts of most UTAUT2 determinants and all UGT determinants on user intentions. Additionally, the fsQCA results supported the concept of equifinality, indicating that various configurations of causal combinations can predict a high level of behavioral intention. These findings underscore the significance of comprehending user motivations and factors related to technology and social media in the context of maintaining or increasing followership and viewership for fitness content providers.

The findings suggest that individuals with high expectations and facilitating conditions, as per UTAUT, and heightened hedonic and socializing motivations, in line with UGT, are more inclined to follow fitness YouTube channels. This study offers valuable insights for fitness content creators and marketers navigating the complexities of the digital age.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.