The Research of Users’ Continuance Intention in Relationship-Based Virtual Communities from the Perspective of Quality

With the increasing number of virtual communities, it was challenging to retain existing users and encourage their continued participation in recent years. A research framework to investigate virtual community users\u27 continuance intention was proposed from the perspective of quality. Based on the empirical study, it was found that information and system quality directly affected functional benefits and social benefits, which ultimately determined users’ continuance intention to get and to provide information. Furthermore, by modeling information quality and system quality as multifaceted constructs, the results revealed key quality concerns in relationship-based virtual communities. The conclusions had a certain significance on theoretical research and management practice

Effect of specimen thicknesses on water absorption and flexural strength of CFRP laminates subjected to water or alkaline solution immersion

In this paper, an experimental research was undertaken to investigate the effect of specimen thicknesses on water absorptions and flexural strengths of wet lay-up CFRP laminates subjected to distilled water or alkaline solution immersion up to 180 days. Test results showed that the water uptake and flexural strength retention of CFRP laminates were significantly affected by the adopted specimen thickness. For the same aging time, the water uptake of CFRP laminates decreased in the early stage of immersion and increased in the later stage of immersion with the increase of specimen thickness. Meanwhile, the flexural strength retention generally increased as specimen thickness increased. In addition, a new thickness-based accelerated method for hygrothermal aging test of CFRP laminates was proposed. The accelerated factors of the water uptake and flexural strength retention of CFRP laminates were theoretically deduced. The proposed analytical model of accelerated factors was verified with current test data, and then applied to predict long-term properties of CFRP laminates. Compared with the traditional temperature-based accelerated method, the new thickness-based accelerated method is much easier to apply to predict long-term properties of CFRP laminates with good accuracy

Pd-Doped SnO 2

Methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2C2) are important fault characteristic hydrocarbon gases dissolved in power transformer oil. Online monitoring these gaseous components and their generation rates can present the operational state of power transformer timely and effectively. Gas sensing technology is the most sticky and tricky point in online monitoring system. In this paper, pure and Pd-doped SnO2 nanoparticles were synthesized by hydrothermal method and characterized by X-ray powder diffraction, field-emission scanning electron microscopy, and energy dispersive X-ray spectroscopy, respectively. The gas sensors were fabricated by side-heated preparation, and their gas sensing properties against CH4, C2H6, C2H4, and C2H2 were measured. Pd doping increases the electric conductance of the prepared SnO2 sensors and improves their gas sensing performances to hydrocarbon gases. In addition based on the frontier molecular orbital theory, the highest occupied molecular orbital energy and the lowest unoccupied molecular orbital energy were calculated. Calculation results demonstrate that C2H4 has the highest occupied molecular orbital energy among CH4, C2H6, C2H4, and C2H2, which promotes charge transfer in gas sensing process, and SnO2 surfaces capture a relatively larger amount of electric charge from adsorbed C2H4

INFLUENCE OF SHOOT FORCE ON MOTION VARIABILITY OF TOP SNOOKER PLAYERS

The purpose of this study was to determine the influence of shooting with different force on motion variability of top snooker players. Six professional male players shot with hard force shot (HF) and soft force shot (SF), then coefficient of multiple correlations (CMC) and coefficient of variation (CV) were calculated. In SF, flexion-extension (p=.045) and adduction-abduction (p=.001) of shoulder showed higher CMCs than HF and adduction-abduction (p=.042) of shoulder showed lower CV than HF. In SF, flexion-extension of wrist showed higher CMC (p=.035) and lower CV (p=.030) than HF and adduction-abduction of wrist showed higher CMC (p=.039) and lower CV (p=.036) than HF. There was no difference in CMC and CV of cue. Thus hard force shot might increase motion variability of upper limbs

An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks

This paper aims to develop a novel model to assess the risk factors of maritime supply chains by incorporating a fuzzy belief rule approach with Bayesian networks. The new model, compared to traditional risk analysis methods, has the capability of improving result accuracy under a high uncertainty in risk data. A real case of a world leading container shipping company is investigated, and the research results reveal that among the most significant risk factors are transportation of dangerous goods, fluctuation of fuel price, fierce competition, unattractive markets, and change of exchange rates in sequence. Such findings will provide useful insights for accident prevention

THE POTENTIAL PHASE FOR HAMSTRING MUSCLE STRAIN INJURIES DURING OVERGROUND SPRINTING

The purpose of this study was to examine the potential for hamstring injury during overground sprinting by investigating hamstring muscle strain. Twenty males and 20 females with sprint training experience participated this study. Isokinetic strength data, three-dimensional kinematic data in a hamstring isokinetic test, and kinematic and ground reaction forces data in a sprinting test were collected for each participant. The muscle strains and muscle elongation velocity of hamstring, lower extremity joint torques and power were determined. Hamstring muscle strains reach peaks during the late swing phase (89.2% - 90.6% gait cycle). The peak muscle strains of biceps long head and semitendinosus were greater than that of semimembranosus (p = 0.002 and p = 0.029). The potential for hamstring muscle strain injury may occur during late swing phase of overground sprinting. Biceps long head and semitendinosus may be at higher risk for muscle strain injury compared to semimembranosus

GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection

Detecting out-of-distribution (OOD) examples is crucial to guarantee the reliability and safety of deep neural networks in real-world settings. In this paper, we offer an innovative perspective on quantifying the disparities between in-distribution (ID) and OOD data -- analyzing the uncertainty that arises when models attempt to explain their predictive decisions. This perspective is motivated by our observation that gradient-based attribution methods encounter challenges in assigning feature importance to OOD data, thereby yielding divergent explanation patterns. Consequently, we investigate how attribution gradients lead to uncertain explanation outcomes and introduce two forms of abnormalities for OOD detection: the zero-deflation abnormality and the channel-wise average abnormality. We then propose GAIA, a simple and effective approach that incorporates Gradient Abnormality Inspection and Aggregation. The effectiveness of GAIA is validated on both commonly utilized (CIFAR) and large-scale (ImageNet-1k) benchmarks. Specifically, GAIA reduces the average FPR95 by 23.10% on CIFAR10 and by 45.41% on CIFAR100 compared to advanced post-hoc methods.Comment: Accepted by NeurIPS202

FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models

Few-shot class-incremental learning (FSCIL) aims to mitigate the catastrophic forgetting issue when a model is incrementally trained on limited data. While the Contrastive Vision-Language Pre-Training (CLIP) model has been effective in addressing 2D few/zero-shot learning tasks, its direct application to 3D FSCIL faces limitations. These limitations arise from feature space misalignment and significant noise in real-world scanned 3D data. To address these challenges, we introduce two novel components: the Redundant Feature Eliminator (RFE) and the Spatial Noise Compensator (SNC). RFE aligns the feature spaces of input point clouds and their embeddings by performing a unique dimensionality reduction on the feature space of pre-trained models (PTMs), effectively eliminating redundant information without compromising semantic integrity. On the other hand, SNC is a graph-based 3D model designed to capture robust geometric information within point clouds, thereby augmenting the knowledge lost due to projection, particularly when processing real-world scanned data. Considering the imbalance in existing 3D datasets, we also propose new evaluation metrics that offer a more nuanced assessment of a 3D FSCIL model. Traditional accuracy metrics are proved to be biased; thus, our metrics focus on the model's proficiency in learning new classes while maintaining the balance between old and new classes. Experimental results on both established 3D FSCIL benchmarks and our dataset demonstrate that our approach significantly outperforms existing state-of-the-art methods