The Impact of Low-Cost Carriers on inbound Tourism of Thailand

Outbound tourism primarily concerns air travel due to the limited options for transportation. Many studies related to aviation and tourism have been conducted. However, most studies are in the US and European context, with only a few cases having been studied in Asia. The purpose of this research is to focus on the development of Low-Cost carriers (LCCs) in major Asian nations. Particularly, it aims to study the influence of LCCs on Thailands tourism. To investigate if LCCs contribute to an increase in Thailands tourism demand, multiple regression analysis with key indicators is employed using monthly data from January 2013 to December 2017. The findings reveal that LCCs have a positive impact on Thailands tourism. Furthermore, LCCs are the main factor in Thailands tourism demand. It is necessary to consider the future development of LCCs and whether LCCs can play an important role in the tourism supply chain in the future

IBVC: Interpolation-driven B-frame Video Compression

Learned B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction. However, previous learned approaches often directly extend neural P-frame codecs to B-frame relying on bi-directional optical-flow estimation or video frame interpolation. They suffer from inaccurate quantized motions and inefficient motion compensation. To address these issues, we propose a simple yet effective structure called Interpolation-driven B-frame Video Compression (IBVC). Our approach only involves two major operations: video frame interpolation and artifact reduction compression. IBVC introduces a bit-rate free MEMC based on interpolation, which avoids optical-flow quantization and additional compression distortions. Later, to reduce duplicate bit-rate consumption and focus on unaligned artifacts, a residual guided masking encoder is deployed to adaptively select the meaningful contexts with interpolated multi-scale dependencies. In addition, a conditional spatio-temporal decoder is proposed to eliminate location errors and artifacts instead of using MEMC coding in other methods. The experimental results on B-frame coding demonstrate that IBVC has significant improvements compared to the relevant state-of-the-art methods. Meanwhile, our approach can save bit rates compared with the random access (RA) configuration of H.266 (VTM). The code will be available at https://github.com/ruhig6/IBVC.Comment: Submitted to IEEE TCSV

JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation

Video frame interpolation (VFI) aims to generate predictive frames by warping learnable motions from the bidirectional historical references. Most existing works utilize spatio-temporal semantic information extractor to realize motion estimation and interpolation modeling. However, they insufficiently consider the real mechanistic rationality of generated middle motions. In this paper, we reformulate VFI as a Joint Non-linear Motion Regression (JNMR) strategy to model the complicated motions of inter-frame. Specifically, the motion trajectory between the target frame and the multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. ConvLSTM is adopted to construct this joint distribution of complete motions in temporal dimension. Moreover, the feature learning network is designed to optimize for the joint regression modeling. A coarse-to-fine synthesis enhancement module is also conducted to learn visual dynamics at different resolutions through repetitive regression and interpolation. Experimental results on VFI show that the effectiveness and significant improvement of joint motion regression compared with the state-of-the-art methods. The code is available at https://github.com/ruhig6/JNMR.Comment: Accepted by IEEE Transactions on Image Processing (TIP

Integration of transcriptomics and metabolomics reveals the responses of the maternal circulation and maternal-fetal interface to LPS-induced preterm birth in mice

BackgroundTerm birth (TB) and preterm birth (PTB) are characterized by uterine contractions, rupture of the chorioamniotic membrane, decidual activation, and other physiological and pathological changes. In this study, we hypothesize that inflammation can cause changes in mRNA expression and metabolic stability in the placenta, decidua, chorioamniotic membrane, uterus and peripheral blood, ultimately leading to PTB.MethodsTo comprehensively assess the effects of inflammation on mRNA expression and metabolite production in different tissues of pregnancy, we used a mouse PTB model by intraperitoneally injecting lipopolysaccharide (LPS) and integrated transcriptomics and metabolomics studies.ResultsOur analysis identified 152 common differentially expressed genes (DEGs) and 8 common differentially expressed metabolites (DEMs) in the placenta, decidua, chorioamniotic membrane, uterus, and peripheral blood, or placenta and uterus after LPS injection, respectively. Our bioinformatics analysis revealed significant enrichment of the NOD-like receptor signaling pathway (mmu04621), TNF signaling pathway (mmu04668), IL-17 signaling pathway (mmu04657), and NF-kappa B signaling pathway in the transcriptomics of different tissues, and Hormone synthesis, Lysosome, NOD-like receptor signaling pathway, and Protein digest and absorption pathway in metabolomics. Moreover, we found that several upstream regulators and master regulators, including STAT1, STAT3, and NFKB1, were altered after exposure to inflammation in the different tissues. Interaction network analysis of transcriptomics and metabolomics DEGs and DEMs also revealed functional changes in mice intraperitoneally injected with LPS.ConclusionsOverall, our study identified significant and biologically relevant alterations in the placenta, decidua, chorioamniotic membrane, uterus, peripheral blood transcriptome and the placenta and uterus metabolome in mice exposed to LPS. Thus, a comprehensive analysis of different pregnancy tissues in mice intraperitoneally injected with LPS by combining transcriptomics and metabolomics may help to systematically understand the local and systemic changes associated with PTB caused by inflammation

a meta-analysis of the 5-year efficacy and safety

Background The objective of this study was to compare the efficacy and safety of taxane (docetaxel or paclitaxel), cisplatin, and fluorouracil (Tax-PF) with cisplatin plus fluorouracil (PF) regimen by a meta-analysis of data retrieved from the literature. Methods Seven randomized clinical trials were identified, which included patients with advanced head and neck cancer who underwent induction chemotherapy with either a Tax-PF or PF protocol. The outcomes included the 3-year and 5-year overall survival (OS) and progression-free survival (PFS), overall response rate (ORR) and different types of adverse events. Results The 3-year OS rate (HR: 1.14; 95% CI: 1.03 to 1.25; P = 0.008), 3-year PFS rate (HR: 1.24; 95% CI: 1.08 to 1.43; P = 0.002), 5-year OS rate (HR: 1.30; 95% CI, 1.09 to 1.55;P = 0.003), 5-year PFS rate (HR: 1.39; 95% CI, 1.14 to 1.70; P = 0.001) and ORR to chemotherapy (OR 1.66; 95% CI, 1.35 to 2.05; P < 0.001) of the patients in the Tax-PF group were statistically superior to those in the PF group. In terms of toxicities, the incidence of febrile neutropenia (OR 2.36; 95% CI, 1.62 to 3.46; P < 0.001), alopecia (OR 8.22; 95% CI, 3.99 to 16.92; P < 0.001), diarrhea (OR 1.57; 95% CI, 1.05 to 2.36; P = 0.03) and leukopenia (OR 2.79; 95% CI, 1.86 to 4.21; P < 0.001) was higher in the Tax-PF group. Conclusion The Tax-PF induction chemotherapy improved PFS and OS, and the ORR was better as compared to PF- based therapy regimens at the cost of a higher incidence of adverse events

Understanding In-Context Learning from Repetitions

This paper explores the elusive mechanism underpinning in-context learning in Large Language Models (LLMs). Our work provides a novel perspective by examining in-context learning via the lens of surface repetitions. We quantitatively investigate the role of surface features in text generation, and empirically establish the existence of \emph{token co-occurrence reinforcement}, a principle that strengthens the relationship between two tokens based on their contextual co-occurrences. By investigating the dual impacts of these features, our research illuminates the internal workings of in-context learning and expounds on the reasons for its failures. This paper provides an essential contribution to the understanding of in-context learning and its potential limitations, providing a fresh perspective on this exciting capability

Method of Reservoir Optimal Operation Based on Improved Simulated Annealing Genetic Algorithm

According to the specific circumstances of Wanjiazhai Reservoir, establish a reservoir optimal scheduling nonlinear mathematical model with a maximum generation capacity target, this paper uses an improved simulated annealing genetic algorithm to solve the model. The algorithm is in view of the defects of the traditional simulated annealing genetic algorithm to improve the algorithm from three aspects: introducing the niche technology, using adaptive crossover and mutation strategy, using the elitist strategy during the selection. Through examples are calculated and compared with the traditional simulated annealing genetic algorithm, the improved algorithm effectively overcomes the stagnation phenomenon, to enhance the global search ability. Its optimization performance is better than that of the traditional simulated annealing genetic algorithm

Object-Oriented Classification of Hyperspectral Remote Sensing Images Based on Genetic Algorithm and Support Vector Machine

This paper proposes a method of reducing dimensions based on genetic algorithm and object-oriented classification based on support vector machine (SVM). The basic idea is subspace decomposition of hyperspectral images at first, then selecting suitable bands in each subspace by using genetic algorithm and putting all selected bands of each subspace together. Furthermore, the hyperspectral image is segmented into a series of objects and then the spectral features and spatial features of objects in the selected bands are extracted. Finally, SVM classification is used according to features of the objects. The algorithm proposed is more effective and superior in dimension reduction and classification of hyperspectral image