Explore the vast range of titles available.

\"Using the most extensive and currently available survey opinion data, this book empirically supports the argument that Latinos have emerged as a convergent panethnic political group, beyond the individual national origin identities dating to the time of the 1990 Latino National Political Survey when Mexican-Americans, Puerto Ricans, and Cuban Americans were treated conceptually as politically distinct groups. Replete with data and supplemented by an extensive online resource, this book offers scholars, students, and sophisticated general readers evidence and inspiration for understanding the dynamics of Latino politics in the US today\"-- Provided by publisher.

A combination of genetic susceptibility and environmental exposure is thought to cause inflammatory bowel disease (IBD), but the non-genetic component remains poorly characterized. We therefore undertook a search for environmental variables and gene-environment interactions associated with future IBD diagnosis in a large UK cohort. Using self-report and electronic health records, we identified 1946 Crohn’s disease (CD) and 3715 ulcerative colitis (UC) patients after quality control in the UK Biobank. Based on prior literature and biological plausibility , we tested 38 candidate environmental variables for association with CD, UC, and overall IBD using Cox proportional hazard regressions. We also tested whether these variables interacted with polygenic risk in predicting disease, following up significant (FDR < 0.05) results with tests for SNP-environment associations. We performed robustness analyses on all significant results. As in previous reports, appendectomy protected against UC, smoking (both current and previous) elevated risk for CD, current smoking protected against UC, and previous smoking imparted a risk for UC. Childhood antibiotic use associated with IBD, as did sun exposure during the winter. Socioeconomic deprivation was conferred a risk for IBD, CD, and UC. We uncovered negative interactions between polygenic risk and previous oral contraceptive use for IBD and UC. Polygenic risk also interacted negatively with previous smoking in predicting UC. There were no individually significant SNP-environment interactions. Thus, for a limited set of environmental variables, there was strong evidence of association with IBD diagnosis in the UK Biobank, and interaction with polygenic risk was minimal.

Natural disasters are common challenges faced by Asian countries and seriously threaten people’s lives and social stability. Therefore, more adequate regional cooperation is needed to jointly respond. As Taiwan has actively promoted the people-centered New Southbound Policy (NSP) since 2016, various initiatives and plans to strengthen social resilience and common interests have been implemented one after another. Among them, the NSP cooperation with specific focus on disaster prevention and HADR is of specific importance. The initiative is a positive and pragmatic move to promote partnership between Taiwan and its neighboring countries. This article delineates the common challenges and threats facing the Asian region - natural disasters. It - with the analysis of how the NSP serves as a facilitator for Taiwan’s cooperation with partner countries in disaster prevention thereby fostering forge resilient partnerships. In fact, academic research on this critical topic remains relatively scarce. Since the NSP’s cooperation on disaster preparedness encompasses the cross-sectoral partnership, mirroring the essence of the identical to the P-P-P-P modality. This aspect merits thorough investigation and deeper exploration, it is indeed worthy of in-depth investigation. Hence, this article is structured into four parts. The first part works as the introduction, the second part delves into introduces the rationale behind the NSP, focusing on that is, the P-P-P-P cross-sectoral partnership., while the third part addresses how the

For a satellite navigation system, binary spreading codes with good autocor-relation and cross-correlation properties are critical for ensuring precise synchronization and tracking with minimal intrasystem interference. In this paper, we demonstrate that multiple instances of the spreading code design problem found in the literature may be cast as binary-constrained convex optimization problems. This approach enables new optimization methods that can exploit the convex structure of the problem. We demonstrate this approach using a block coordinate descent (BCD) method, which applies a convexity-exploiting branch-and-bound method to perform the block updates. With minimal tuning, the BCD method was able to identify Global Positioning System codes with better mean-squared correlation performance compared with the Gold codes and codes derived from a recently introduced natural evolution strategy.

Optimizing the correlation properties of spreading codes is critical for minimizing inter-channel interference in satellite navigation systems. By improving the codes’ correlation sidelobes, we can enhance navigation performance while minimizing the required spreading code lengths. In the case of low-earth orbit (LEO) satellite navigation, shorter code lengths (on the order of a hundred) are preferred due to their ability to achieve fast signal acquisition. Additionally, the relatively high signal-to-noise ratio in LEO systems reduces the need for longer spreading codes to mitigate inter-channel interference. In this work, we propose a two-stage block coordinate descent (BCD) method which optimizes the codes’ correlation properties while enforcing the autocorrelation sidelobe zero property. In each iteration of the BCD method, we solve a mixed-integer convex program over a block of 25 binary variables. Our method is applicable to spreading code families of arbitrary sizes and lengths, and we demonstrate its effectiveness for a problem with 66 length-127 codes and a problem with 130 length-257 codes.

Nowadays, the amount of wed data is increasing at a rapid speed, which presents a serious challenge to the web monitoring. Text sentiment analysis, an important research topic in the area of natural language processing, is a crucial task in the web monitoring area. The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data. Deep learning is a hot research topic of the artificial intelligence in the recent years. By now, several research groups have studied the sentiment analysis of English texts using deep learning methods. In contrary, relatively few works have so far considered the Chinese text sentiment analysis toward this direction. In this paper, a method for analyzing the Chinese text sentiment is proposed based on the convolutional neural network (CNN) in deep learning in order to improve the analysis accuracy. The feature values of the CNN after the training process are nonuniformly distributed. In order to overcome this problem, a method for normalizing the feature values is proposed. Moreover, the dimensions of the text features are optimized through simulations. Finally, a method for updating the learning rate in the training process of the CNN is presented in order to achieve better performances. Experiment results on the typical datasets indicate that the accuracy of the proposed method can be improved compared with that of the traditional supervised machine learning methods, e.g., the support vector machine method.

Over the past three decades, Taiwan has been struggling to gain an advantage and develop its role in Asia. This island has strived to balance its asymmetric relationship with China by engaging in regional integration in Southeast Asia and beyond. In the 1990s, the Taiwan government initiated the first wave of its Go South Policy aimed at building links at business and government levels with that region. The institutional and social legacy of the Go South Policy contributed to the making of the New Southbound Policy (NSP) which was proposed toward the end of 2015. This paper will unpack Taiwan's presence in Southeast Asia by highlighting the international socialization process of the NSP and Taiwan's strategic interaction with the region. It consists of four sections: the first section introduces the concept of international socialization. The second section discusses the positioning of Taiwan's previous Go South policies. Starting with the shift from a mentality of ＂Taiwanese Asia＂ (Taiwan de yazhou, 臺灣的亞洲) to one of ＂Asian Taiwan＂ (Yazhou de Taiwan, 亞洲的臺灣), it describes in detail how Taiwan's successive southward engagement initiatives have blended into the international socialization processes in the region. The third section highlights the relationships the policy's key actors and stakeholders, including transnational actors, are establishing with their counterparts in Southeast Asia and the new social linkages that are currently being promoted. This includes the activities of Taiwanese residents in Southeast Asia and Southeast Asian migrants in Taiwan. The paper concludes by summarizing Taiwan's international socialization in Asia.

Driven by the concepts of digital twin and metaverse, constructing a high-fidelity, semantic-rich, and interactive digital copy of the physical world has become a key issue in the field of surveying, mapping, and geographic information. However, in typical complex landforms such as urban canyons and mountainous forest areas, the single-sensor data acquisition methods (such as UAV oblique images and lidar) has inherent information blind spots and accuracy bottlenecks. Traditional data fusion approaches predominantly focus on shallow geometric alignment and splicing at the geometric level, ignoring the heterogeneity of different data sources in semantic connotations, leads to common problems such as geometric distortion, detail loss, and semantic inconsistency in the fusion products. To break through this dilemma, this paper proposes an adaptive fusion framework for multi-source data of complex landforms (SAAF-Net) with deeply coupled semantic information. Centered on computer vision, this framework constructs a full-link technical process from raw data to high-precision semantic 3D models: Two-stream parallel semantic parsing: A two-stream deep semantic segmentation network for images and point clouds (based on SegFormer and PointNeXt) is designed to achieve fine-grained classification of scene features (the average intersection over union mIoU exceeds 90%), providing high-dimensional semantic priors for fusion. Semantic-guided cross-source registration: A semantic weighted iterative closest point algorithm (SW-ICP) is proposed. By constraining the corresponding point search space through cross-source semantic consistency and combining with the significance weighting of local geometric structures, the robustness problem of heterogeneous data registration is solved. Neural adaptive fusion modeling: A multi-factor driven neural network model is constructed to dynamically evaluate the confidence of data sources under different semantic categories and observation conditions, achieving the optimal fusion of pixel-level elevation and texture. Experiments in the city center and mountainous forest areas show that compared with mainstream methods, the root mean square error (RMSE) of SAAF-Net is reduced by 35% − 48%, and the completeness is improved to over 99%. Especially, the reconstruction quality in building edges, vegetation-covered areas, and light-shadow areas is significantly improved.with a substantial enhancement in visual realism. This research provides theoretical and technical support for the construction of a high-precision 3D base for digital twin cities.

Background Frailty status among critically ill patients with acute kidney injury (AKI) is not well described despite its importance for prognostication and informed decision-making on life-sustaining therapies. In this study, we aim to describe the epidemiology of frailty in a cohort of older critically ill patients with severe AKI, the outcomes of patients with pre-existing frailty before AKI and the factors associated with a worsening frailty status among survivors. Methods This was a secondary analysis of a prospective multicentre observational study that enrolled older (age > 65 years) critically ill patients with AKI. The clinical frailty scale (CFS) score was captured at baseline, at 6 months and at 12 months among survivors. Frailty was defined as a CFS score of ≥ 5. Demographic, clinical and physiological variables associated with frailty as baseline were described. Multivariable Cox proportional hazard models were constructed to describe the association between frailty and 90-day mortality. Demographic and clinical factors associated with worsening frailty status at 6 months and 12 months were described using multivariable logistic regression analysis and multistate models. Results Among the 462 patients in our cohort, median (IQR) baseline CFS score was 4 (3–5), with 141 (31%) patients considered frail. Pre-existing frailty was associated with greater hazard of 90-day mortality (59% ( n  = 83) for frail vs. 31% ( n  = 100) for non-frail; adjusted hazards ratio [HR] 1.49; 95% CI 1.11–2.01, p  = 0.008). At 6 months, 68 patients (28% of survivors) were frail. Of these, 57% ( n  = 39) were not classified as frail at baseline. Between 6 and 12 months of follow-up, 9 (4% of survivors) patients transitioned from a frail to a not frail status while 10 (4% of survivors) patients became frail and 11 (5% of survivors) patients died. In multivariable analysis, age was independently associated with worsening CFS score from baseline to 6 months (adjusted odds ratio [OR] 1.08; 95% CI 1.03–1.13, p  = 0.003). Conclusions Pre-existing frailty is an independent risk factor for mortality among older critically ill patients with severe AKI. A substantial proportion of survivors experience declining function and worsened frailty status within one year.