Resource-based destination competitiveness evaluation using analytic hierarchy process (AHP): The case study of West Virginia

This study aimed to evaluate West Virginia\u27s resource-based tourism competitiveness in relation to its neighboring competitors using analytic hierarchy process (AHP). The study also sought to investigate the utility of AHP in destination competitiveness evaluation. Ten executive directors from West Virginia\u27s Convention and Visitors Bureaus (CVBs) and 891 visitors to West Virginia participated in this study. Findings revealed that West Virginia performed well on availability of adventure-based activities, nature-based activities, and had a competitive edge on hospitality and friendliness of residents, safety and security, and value for money in shopping items in relation to competing destinations. AHP was shown to be a reliable tool to evaluate destination competitiveness. Theoretical and managerial implications and future research suggestions are discussed

A Contrastive Study of Temporal-spatial Metaphor between Chinese and Americans

Psychological time orientation includes past, present and future. Spatial-temporal metaphor tends to be used for understanding the abstract temporal concept. Chinese adopt “Time-moving Metaphor”, in which the future is in the back and the past is in the front. In Americans’ “Ego-moving Metaphor”, front is assigned to the future and back to the past. The paper makes a contrastive study of Static Past-oriented Chinese and Dynamic Future-oriented Americans. The difference is closely related to the ideology, cultural tradition and language system

Based on Multi-sensor Information Fusion Algorithm of TPMS Research

AbstractIn the paper are presented algorithms of TPMS (Tire Pressure Monitoring System) based on multi-sensor information fusion. A Unified mathematical models of information fusion are constructed and three algorithms are used to deal with, which include algorithm based on Bayesian, algorithm based on the relative distance (an improved algorithm of bayesian theory of evidence), algorithm based on multi-sensor weighted fusion. The calculating results shows that the algorithm based on d-s evidence theory of multisensor fusion method better than the algorithm the based on information fusion method or the bayesian method

Inequalities and Duality in Gene Coexpression Networks of HIV-1 Infection Revealed by the Combination of the Double-Connectivity Approach and the Gini's Method

The symbiosis (Sym) and pathogenesis (Pat) is a duality problem of microbial infection, including HIV/AIDS. Statistical analysis of inequalities and duality in gene coexpression networks (GCNs) of HIV-1 infection may gain novel insights into AIDS. In this study, we focused on analysis of GCNs of uninfected subjects and HIV-1-infected patients at three different stages of viral infection based on data deposited in the GEO database of NCBI. The inequalities and duality in these GCNs were analyzed by the combination of the double-connectivity (DC) approach and the Gini's method. DC analysis reveals that there are significant differences between positive and negative connectivity in HIV-1 stage-specific GCNs. The inequality measures of negative connectivity and edge weight are changed more significantly than those of positive connectivity and edge weight in GCNs from the HIV-1 uninfected to the AIDS stages. With the permutation test method, we identified a set of genes with significant changes in the inequality and duality measure of edge weight. Functional analysis shows that these genes are highly enriched for the immune system, which plays an essential role in the Sym-Pat duality (SPD) of microbial infections. Understanding of the SPD problems of HIV-1 infection may provide novel intervention strategies for AIDS

Microbial communities and arsenic biogeochemistry at the outflow of an alkaline sulfide-rich hot spring.

Alkaline sulfide-rich hot springs provide a unique environment for microbial community and arsenic (As) biogeochemistry. In this study, a representative alkaline sulfide-rich hot spring, Zimeiquan in the Tengchong geothermal area, was chosen to study arsenic geochemistry and microbial community using Illumina MiSeq sequencing. Over 0.26 million 16S rRNA sequence reads were obtained from 5-paired parallel water and sediment samples along the hot spring's outflow channel. High ratios of As(V)/AsSum (total combined arsenate and arsenite concentrations) (0.59-0.78), coupled with high sulfide (up to 5.87 mg/L), were present in the hot spring's pools, which suggested As(III) oxidation occurred. Along the outflow channel, AsSum increased from 5.45 to 13.86 μmol/L, and the combined sulfide and sulfate concentrations increased from 292.02 to 364.28 μmol/L. These increases were primarily attributed to thioarsenic transformation. Temperature, sulfide, As and dissolved oxygen significantly shaped the microbial communities between not only the pools and downstream samples, but also water and sediment samples. Results implied that the upstream Thermocrinis was responsible for the transformation of thioarsenic to As(III) and the downstream Thermus contributed to derived As(III) oxidation. This study improves our understanding of microbially-mediated As transformation in alkaline sulfide-rich hot springs

MovePose: A High-performance Human Pose Estimation Algorithm on Mobile and Edge Devices

We present MovePose, an optimized lightweight convolutional neural network designed specifically for real-time body pose estimation on CPU-based mobile devices. The current solutions do not provide satisfactory accuracy and speed for human posture estimation, and MovePose addresses this gap. It aims to maintain real-time performance while improving the accuracy of human posture estimation for mobile devices. The network produces 17 keypoints for each individual at a rate exceeding 11 frames per second, making it suitable for real-time applications such as fitness tracking, sign language interpretation, and advanced mobile human posture estimation. Our MovePose algorithm has attained an Mean Average Precision (mAP) score of 67.7 on the COCO \cite{cocodata} validation dataset. The MovePose algorithm displayed efficiency with a performance of 69+ frames per second (fps) when run on an Intel i9-10920x CPU. Additionally, it showcased an increased performance of 452+ fps on an NVIDIA RTX3090 GPU. On an Android phone equipped with a Snapdragon 8 + 4G processor, the fps reached above 11. To enhance accuracy, we incorporated three techniques: deconvolution, large kernel convolution, and coordinate classification methods. Compared to basic upsampling, deconvolution is trainable, improves model capacity, and enhances the receptive field. Large kernel convolution strengthens these properties at a decreased computational cost. In summary, MovePose provides high accuracy and real-time performance, marking it a potential tool for a variety of applications, including those focused on mobile-side human posture estimation. The code and models for this algorithm will be made publicly accessible