Ultrasound Detection Acupuncture Needling Training: Description of the Method

Acupuncture is unique to Chinese medicine and is widely used in practice. In order to avoid the complication of pneumothorax due to needle puncture of the lungs, we developed an ultrasound detection acupuncture (UDA) approach to measure the safe needle depth to improve patient safety. This study established a UDA training program and recruited trainees in our hospital to validate the effectiveness of the program. The trainees attended an eight-hour course, and practiced their skills using an acupuncture simulator model of GB21. Pre- and post-test data were analyzed. The level of satisfaction of the trainees was assessed by interview. In total, 16 trainees completed the course. Kendall’s coefficient for the program was 0.82, and the average CVI was 0.98, showing good reliability and validity. Trainees exhibited significant improvement in terms of reduction of the incidence of pneumothorax after completing the course (P < 0.05), and the trainees were satisfied with the teaching of the ultrasound skill. Feedback from interviews showed that use of ultrasound to measure the safe needle depth may improve the mastery of acupuncture point GB21 and reduce the fear of causing pneumothorax

Asymptotically Optimal Energy Efficient Offloading Policies in Multi-Access Edge Computing Systems with Task Handover

We study energy-efficient offloading strategies in a large-scale MEC system with heterogeneous mobile users and network components. The system is considered with enabled user-task handovers that capture the mobility of various mobile users. We focus on a long-run objective and online algorithms that are applicable to realistic systems. The problem is significantly complicated by the large problem size, the heterogeneity of user tasks and network components, and the mobility of the users, for which conventional optimizers cannot reach optimum with a reasonable amount of computational and storage power. We formulate the problem in the vein of the restless multi-armed bandit process that enables the decomposition of high-dimensional state spaces and then achieves near-optimal algorithms applicable to realistically large problems in an online manner. Following the restless bandit technique, we propose two offloading policies by prioritizing the least marginal costs of selecting the corresponding computing and communication resources in the edge and cloud networks. This coincides with selecting the resources with the highest energy efficiency. Both policies are scalable to the offloading problem with a great potential to achieve proved asymptotic optimality - approach optimality as the problem size tends to infinity. With extensive numerical simulations, the proposed policies are demonstrated to clearly outperform baseline policies with respect to power conservation and robust to the tested heavy-tailed lifespan distributions of the offloaded tasks.Comment: 15 pages, 22 figure

Understanding the Effects of Colleague Participation and Public Cause Proximity on Employee Volunteering Intentions: The Moderating Role of Power Distance

Many organizations encourage their employees to participate in charitable activities as part of their corporate social responsibility strategies. As a result, there has been an increased research interest in employee volunteering behavior. However, while previous research on employee volunteering decisions has focused on both individual-level and organizational-level factors, there has been less focus on peer involvement and volunteer cause proximity. To go some way to filling this research area, this paper conducted two studies to examine the possible effects of colleague participation, colleague position and public cause proximity on employee volunteering intentions. Study 1 found that colleague participation and public cause proximity had significant effects on employee volunteering, and Study 2 found that power distance played a moderating role in the relationship between colleague position and employee volunteering. This study contributes to theoretical research on employee volunteering and provides some information to assist firms retain engaged volunteers

A Sparse Smoothing Newton Method for Solving Discrete Optimal Transport Problems

The discrete optimal transport (OT) problem, which offers an effective computational tool for comparing two discrete probability distributions, has recently attracted much attention and played essential roles in many modern applications. This paper proposes to solve the discrete OT problem by applying a squared smoothing Newton method via the Huber smoothing function for solving the corresponding KKT system directly. The proposed algorithm admits appealing convergence properties and is able to take advantage of the solution sparsity to greatly reduce computational costs. Moreover, the algorithm can be extended to solve problems with similar structures including the Wasserstein barycenter (WB) problem with fixed supports. To verify the practical performance of the proposed method, we conduct extensive numerical experiments to solve a large set of discrete OT and WB benchmark problems. Our numerical results show that the proposed method is efficient compared to state-of-the-art linear programming (LP) solvers. Moreover, the proposed method consumes less memory than existing LP solvers, which demonstrates the potential usage of our algorithm for solving large-scale OT and WB problems.Comment: 29 pages, 17 figure

Ethyl 3-[2-(p-tolyl­carbamothio­yl)hydrazinyl­idene]butano­ate

The title compound, C14H19N3O2S, was obtained from a condensation reaction of N-(p-tol­yl)hydrazinecarbothio­amide and ethyl acetoacetate. The mol­ecule assumes an E configuration; the thio­semicarbazide and ester groups are located on the opposite sides of the C=N bond. The almost planar thio­semicarbazide unit (r.m.s. deviation = 0.0130 Å) is tilted at a dihedral angle of 49.54 (12)° with respect to the benzene ring. Inter­molecular N—H⋯N and N—H⋯S hydrogen bonding stabilizes the crystal structure. The eth­oxy group of the ester unit is disordered over two positions, with a site-occupancy ratio of 0.680 (10):0.320 (10)

Revising regularisation with linear approximation term for compressive sensing improvement

In this Letter, the authors propose a novel revised regularisation to improve the performance of compressive sensing (CS) reconstruction. They suppose that a specific regularisation term is insufficient to accommodate the prior information of CS while it can be improved by further imposing a linear approximation term. They also prove that the revised regularisation is substantially equivalent to the CS preprocessing methods. They conduct extensive experiments on various CS algorithms, which show the effectiveness of their revised regularisation

BRST-antifield-treatment of metric-affine gravity

The metric-affine gauge theory of gravity provides a broad framework in which gauge theories of gravity can be formulated. In this article we fit metric-affine gravity into the covariant BRST--antifield formalism in order to obtain gauge fixed quantum actions. As an example the gauge fixing of a general two-dimensional model of metric-affine gravity is worked out explicitly. The result is shown to contain the gauge fixed action of the bosonic string in conformal gauge as a special case.Comment: 19 pages LATEX, to appear in Phys. Rev.