Can virtual reality predict body part discomfort and performance of people in realistic world for assembling tasks?

This paper presents our work on relationship of evaluation results between virtual environment (VE) and realistic environment (RE) for assembling tasks. Evaluation results consist of subjective results (BPD and RPE) and objective results (posture and physical performance). Same tasks were performed with same experimental configurations and evaluation results were measured in RE and VE respectively. Then these evaluation results were compared. Slight difference of posture between VE and RE was found but not great difference of effect on people according to conventional ergonomics posture assessment method. Correlation of BPD and performance results between VE and RE are found by linear regression method. Moreover, results of BPD, physical performance, and RPE in VE are higher than that in RE with significant difference. Furthermore, these results indicates that subjects feel more discomfort and fatigue in VE than RE because of additional effort required in VE

A novel approach for determining fatigue resistances of different muscle groups in static cases

In ergonomics and biomechanics, muscle fatigue models based on maximum endurance time (MET) models are often used to integrate fatigue effect into ergonomic and biomechanical application. However, due to the empirical principle of those MET models, the disadvantages of this method are: 1) the MET models cannot reveal the muscle physiology background very well; 2) there is no general formation for those MET models to predict MET. In this paper, a theoretical MET model is extended from a simple muscle fatigue model with consideration of the external load and maximum voluntary contraction in passive static exertion cases. The universal availability of the extended MET model is analyzed in comparison to 24 existing empirical MET models. Using mathematical regression method, 21 of the 24 MET models have intraclass correlations over 0.9, which means the extended MET model could replace the existing MET models in a general and computationally efficient way. In addition, an important parameter, fatigability (or fatigue resistance) of different muscle groups, could be calculated via the mathematical regression approach. Its mean value and its standard deviation are useful for predicting MET values of a given population during static operations. The possible reasons influencing the fatigue resistance were classified and discussed, and it is still a very challenging work to find out the quantitative relationship between the fatigue resistance and the influencing factors

Numerical calculation of wing-bending moment with real-time strain monitoring by FBG modulation

This paper presents an application of Structural Health Monitoring System based on Fiber Bragg Grating sensors (FBGs) dedicated to wing-bending moment. A numerical calculation of bending moment is proposed to the application of real-time wing-bending moment monitoring. With the advantage of anti-electromagnetic interference, small size and light weight, Fiber Bragg grating (FBG) sensors have been applied in structural health monitoring system (SHMS). An experiment was performed in full-scale fatigue test of an aircraft, and the wings of aircraft were subjected to specific loading conditions, and the strain data was collected by FBGs’ demodulation. The relationship matrix K between the strain and the wing-bending moment was established. It is a new approach for the wing-bending moment real-time monitoring with the simple FBG strain collection modulation

On sumsets involving kkth power residues

In this paper, we study some topics concerning the additive decompostions of the set $D_k$ of all $k$th power residues modulo a prime $p$. For example, we prove that $$\lim_{x\rightarrow+\infty}\frac{B(x)}{\pi(x)}=0,$$ where $\pi(x)$ is the number of primes $\le x$ and $B(x)$ denotes the cardinality of the set $$\{p\le x: p\equiv1\pmod k; D_k\ \text{has a non-trivial 2-additive decomposition}\}.$

Correlation of Chimerism with Acute Graft-versus-Host Disease in Rats following Liver Transplantation

The accurate diagnosis of acute graft-versus-host disease following liver transplantation (LTx-aGVHD) has been hampered. Chimerism appears in the majority of recipients after LT and its significance in the diagnosis of LTx-aGVHD has not been clearly established. To demonstrate the significance of chimerism on the diagnosis of LTx-aGVHD, we compared the change of chimerism in syngeneic LT recipients, semiallogeneic LT recipients, and LTx-aGVHD induced recipients. Chimerism in PBMCs following sex-mismatched LT was identified by real-time PCR based on a rat Y-chromosome-specific primer. All recipients in semiallogeneic group grew in a normal pattern. However, when 4 × 108 donor splenocytes were transferred simultaneously during LT, the morbidity of lethal aGVHD was 100%. The chimerism appeared slightly higher in the semiallogeneic group than in the syngeneic LT group, but the difference was not significant. However, when the recipients developed lethal aGVHD after LT, chimerism in the PBMCs increased progressively, and even at an early time, a significant increase in chimerism was observed. In conclusion, high level chimerism correlated well with LTx-aGVHD, and detection of chimerism soon after transplantation may be of value in the diagnosis of LTx-aGVHD prior to the onset of symptoms

Single transverse-spin asymmetry for DD-meson production in semi-inclusive deep inelastic scattering

We study the single-transverse spin asymmetry for open charm production in the semi-inclusive lepton-hadron deep inelastic scattering. We calculate the asymmetry in terms of the QCD collinear factorization approach for $D$ mesons at high enough $P_{h\perp}$, and find that the asymmetry is proportional to the twist-three tri-gluon correlation function in the proton. With a simple model for the tri-gluon correlation function, we estimate the asymmetry for both COMPASS and eRHIC kinematics, and discuss the possibilities of extracting the tri-gluon correlation function in these experiments.Comment: 13 pages, 7 figure

Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation

Advanced recommender systems usually involve multiple domains (scenarios or categories) for various marketing strategies, and users interact with them to satisfy their diverse demands. The goal of multi-domain recommendation is to improve the recommendation performance of all domains simultaneously. Conventional graph neural network based methods usually deal with each domain separately, or train a shared model for serving all domains. The former fails to leverage users' cross-domain behaviors, making the behavior sparseness issue a great obstacle. The latter learns shared user representation with respect to all domains, which neglects users' domain-specific preferences. These shortcomings greatly limit their performance in multi-domain recommendation. To tackle the limitations, an appropriate way is to learn from multi-domain user feedbacks and obtain separate user representations to characterize their domain-specific preferences. In this paper we propose $\mathsf{H^3Trans}$, a hierarchical hypergraph network based correlative preference transfer framework for multi-domain recommendation. $\mathsf{H^3Trans}$ represents multi-domain feedbacks into a unified graph to help preference transfer via taking full advantage of users' multi-domain behaviors. We incorporate two hyperedge-based modules, namely dynamic item transfer module (Hyper-I) and adaptive user aggregation module (Hyper-U). Hyper-I extracts correlative information from multi-domain user-item feedbacks for eliminating domain discrepancy of item representations. Hyper-U aggregates users' scattered preferences in multiple domains and further exploits the high-order (not only pair-wise) connections among them to learn user representations. Experimental results on both public datasets and large-scale production datasets verify the superiority of $\mathsf{H^3Trans}$ for multi-domain recommendation.Comment: Work in progres