Regional Characteristics Research Articles

Pattern of situation of tuberculosis in the Islamic republic of Afghanistan

Purpose of the study: to study the incidence of tuberculosis depending on gender, age and regional characteristics over the past 6 years (2017-2022) in the Islamic Republic of Afghanistan. Material and methods: Data from the official statistics of the National Tuberculosis Program of Afghanistan, as well as the WHO assessment of the incidence of tuberculosis in the Islamic Republic of Afghanistan over the past 6 years (2017-2022) were analyzed. Results: In the Islamic Republic of Afghanistan, the situation with tuberculosis remains a cause for concern. Over the past 6 years, the difference in the frequency of detection of new cases of tuberculosis between the WHO estimate and official statistics ranges from 26.7% to 36.9%. It should be noted that in 2017-2019, the detection of tuberculosis improved slightly; in 2019-2020, when the COVID-19 pandemic occurred, the detection of new cases of tuberculosis decreased, and in 2020-2022 it increased again.. Over the past 6 years More women than men suffer from tuberculosis. The difference in incidence is 4490 cases in 2017, 7936 in 2018, 10990 in 2019, 4230 in 2020, 12453 in 2021 and 5690 cases in 2022 and is significant. In 2022, as in other years, more (40%) were diagnosed with tuberculosis in young people aged 15-34 years. Moreover, about 11% of new cases (5562 out of 51654 cases) were registered in the age group 0-4 years, which should be paid special attention to. The most serious situation regarding the incidence of tuberculosis is in the Khost, Nangarhar and Nimroz regions, which are 350, 324 and 315 per 100 thousand population, respectively. The best situation for tuberculosis incidence is in the Panjshir, Maidan-Wardak and Bamiyan regions, which are 47, 72 and 83 per 100,000 population, respectively. Conclusions: The difference in the incidence of new cases of tuberculosis between the WHO estimate and official statistics ranges from 26.7% to 36.9%. In 2019-2020, when the COVID-19 pandemic occurred, the number of new cases of tuberculosis decreased, and in 2020-2022 it increased again. Women are more prone to tuberculosis than men. 40% of new cases of tuberculosis are registered among young people aged 15-34 years, about 11% of new cases are registered in the age group 0-4 years. Among the 34 provinces of Afghanistan, the worst situation is in the provinces of Khost, Nangarhar and Nimroz, and the best situation for tuberculosis is in the provinces of Panjshir, Maidan Wardak and Bamiyan.

Shallow Reverse Moderate Earthquakes in the Weiyuan Shale Gas Field, Sichuan Basin, China, Related to Hydraulic Fracturing

Abstract The Weiyuan shale gas field in the stable southern Sichuan basin, China, has experienced increasing seismicity since systematic hydraulic fracturing (HF) operations in 2015. Three Mw≥4.4 shallow earthquakes occurred in the Weiyuan area between September 2019 and February 2020, yet their seismogenic faults, rupture models, and relationship with HF are unknown. In this study, we first obtain the high-resolution coseismic deformation fields of these three events and then invert their slip distribution. The result shows that all three events are shallow high-dip reverse events under the contractional Weiyuan anticline environment with peak slips of 158, 68, and 34 cm and at depths of 4, 3, and 1.6 km, respectively. The spatial relationship between seismogenic faults, horizontal wells, as well as geological data reveals that pore-pressure diffusion due to the HF may be the main mechanism of the 8 September 2019 and the 18 December 2019 events, whereas the 16 February 2020 event may be attributed to the poroelastic stress perturbation caused by the HF. Our study highlights that HF activities and regional geological characteristics jointly influence the properties of earthquakes in the Sichuan basin.

Revamping Image-Recipe Cross-Modal Retrieval with Dual Cross Attention Encoders

The image-recipe cross-modal retrieval task, which retrieves the relevant recipes according to food images and vice versa, is now attracting widespread attention. There are two main challenges for image-recipe cross-modal retrieval task. Firstly, a recipe’s different components (words in a sentence, sentences in an entity, and entities in a recipe) have different weight values. If a recipe’s different components own the same weight, the recipe embeddings cannot pay more attention to the important components. As a result, the important components make less contribution to the retrieval task. Secondly, the food images have obvious properties of locality and only the local food regions matter. There are still difficulties in enhancing the discriminative local region features in the food images. To address these two problems, we propose a novel framework named Dual Cross Attention Encoders for Cross-modal Food Retrieval (DCA-Food). The proposed framework consists of a hierarchical cross attention recipe encoder (HCARE) and a cross attention image encoder (CAIE). HCARE consists of three types of cross attention modules to capture the important words in a sentence, the important sentences in an entity and the important entities in a recipe, respectively. CAIE extracts global and local region features. Then, it calculates cross attention between them to enhance the discriminative local features in the food images. We conduct the ablation studies to validate our design choices. Our proposed approach outperforms the existing approaches by a large margin on the Recipe1M dataset. Specifically, we improve the R@1 performance by +2.7 and +1.9 on the 1k and 10k testing sets, respectively.

Analysis on the Evolution Characteristics of Rural Tourism Public Service System from the Perspective of Digitalization—Empirical Evidence from the Silk Road Economic Belt

The integration of digitalization with public tourism services has emerged as a new model for the development of the rural tourism public services. The Silk Road Economic Belt is a crucial area for the future development of the tourism industry. Exploring the coupling of the digitalization and the development trends of the rural tourism public services is significant for advancing rural tourism in this region. This study applies Complex Adaptive Systems theory and Synergy theory to the development of the digitization of rural tourism public services, clarifying the structure of the digitization of the rural tourism public service system. Based on this, a collaborative development evaluation system for the digitization of rural tourism public services is constructed, focusing on the Silk Road Economic Belt. By using the SMI-P evaluation model, ArcGIS spatial visualization, Gini coefficient, and kernel density methods, the collaborative development of the digitization of the rural tourism public service system was measured and analyzed in this paper. The findings reveal the following: (1) The collaborative development of the digitization of the rural tourism public service system in the Silk Road Economic Belt shows a gradual upward trend, yet the overall level of the collaboration is transitioning from a state of imbalance. (2) Compared to the Southwest, the Northwest exhibits a lower overall level of collaboration, with growing disparities in collaborative development indices among provinces, although there is a trend toward higher levels of aggregation. (3) The development of the rural tourism public services is trending toward a favorable state of multi-dimensional collaboration, characterized by significant regional features, including multi-polarization and unbalanced development. Finally, optimization paths for the digital development of the rural tourism public services were proposed in this study, which can provide guidance for improving the construction of the digitization of the rural tourism public service system and exploring clear development pathways.

The role of geographic distances in green industrial pilot policies evaluation: A sustainability transition perspective.

Green industrial policies (GIPs) aim to promote the adoption ofgreen technology within a sustainability framework. While previous evaluations of GIP have focused more on the policy itself and the impacts within the policy boundaries, this paper further introduces the geographical factor to analyze the impact of different spatial geographic distances on implementing GIP. By integrating geographic distances into a spatial difference-in-differences analysis to assess the effects of municipal green industry policies (referred to as green industry pilot policies in this paper) enacted in 11 cities in China from 2006 to 2010, we find that these policies not only improve environmental and economic outcomes in targeted regions but also have spillover effects that may affect neighboring areas negatively, highlighting the importance of geographic considerations. The effectiveness of green industrial policies varies across cities, influenced by local socio-technical systems and regional characteristics such as infrastructure and information technology. These findings suggest that policy impacts are complex and multifaceted, requiring a comprehensive understanding of geographic interdependencies. By incorporating geographic factors, this research contributes to sustainability transition theory by offering insights into the spatial and temporal dynamics of socio-technical systems. The results underscore the need for policymakers to consider spatial-temporal aspects and potential secondary effects on adjacent regions when designing and implementing green industrial policies.

Exploration of the Extended World of the 13 Characters in Weiliaozi

In addition to the Establishing the first unified empire in East Asia XiHuang (始皇) and HanFei (韩非) active political and military activities, WeiLiao (尉缭) had a great impact on the unification of the six countries of Qin in the late Zhanguo Period with his political purpose and concept of "XingDe (刑德)". This weiliao's military and political rule and its theory were the metric development that later brought about the legalization of territory and system and the perfection of Qin (秦) 18's legal system, which was the driving force for the development of science, technology and civilization. If can grasp the background of the pivotal figures and events in the history of the times and their causes, and rediscover the literature and artifacts with regional characteristics as a scene of a long history, the regions and their boundaries that can be seen in the whole map of the 21st century are not only the territorial boundaries divided and staged. As an integrated media of the behavior product of the human community, its epistemological objects, whether paper or digital, have a great impact on the globalization (the whole area) of the global village.

Relationship Between Participation of In-Hospital Cardiac Rehabilitation and Regional Characteristics in Japan - Insight From the Japanese Registry of All Cardiac and Vascular Diseases and the Diagnosis Procedure Combination.

The influence of various regional backgrounds on participation in cardiac rehabilitation (CR) remains underexplored. We investigated the regional characteristics that potentially promote CR participation. This was a nationwide cross-sectional cohort study using the Japanese Registry of All Cardiac and Vascular Diseases and the Diagnosis Procedure Combination. This study included a cohort of 2.7 million inpatients hospitalized between April 2012 and March 2021. The CR participation rate for each hospital was calculated as the percentage of eligible patients who underwent CR during their admission. Among all hospitals, those that do not perform CR were defined as No-CR hospitals. The remaining hospitals were categorized into High- and Low-CR hospitals based on the median level of the CR participation rate (41.5%). High-CR hospitals had significantly smaller medical service areas (P<0.0001), a higher number of physicians per population (P<0.0001), higher air temperature (P=0.02), and fewer primary industry workers (P=0.005) than the other 2 groups. Logistic regression analyses revealed that a lower consumer price index was a significant regional factor that characterized High-CR hospitals, and a lower population aged ≥65 years was a factor approaching significance that characterized the region where High-CR hospitals are located. High-CR hospitals were found in regions with a lower consumer price index and a trend towards a lower population aged ≥65 years.

Aeromagnetic Data Analysis for Sustainable Structural Mapping of the Missiakat Al Jukh Area in the Central Eastern Desert: Enhancing Resource Exploration with Minimal Environmental Impact

This study integrates aeromagnetic data with geological information to develop a consistent interpretation of both shallow and deep structural frameworks at various depths in the Missiakat Al Jukh area, located in the Central Eastern Desert, Egypt. The research begins by processing reduced-to-the-north magnetic pole (RTP) anomalies, using Fast Fourier Transformation (FFT) techniques to distinguish between local residual structures and broader regional features. This multi-scale approach enables a more detailed understanding of the geological complexity in the region, revealing its subsurface structures. Advanced geophysical methods such as upward continuation, Euler deconvolution, source parameter imaging (SPI), and global particle swarm optimization (GPSO) were applied to further refine the determination of structural depths, offering critical insights into the distribution and orientation of geological features at varying depths. The study reveals dominant structural orientations aligned in the NNW-SSE, ENE-WSW, north–south, and east–west directions, reflecting the region’s complex tectonic history. This research is of great importance in terms of sustainability. By delivering detailed subsurface maps and providing more accurate depth estimates of basement rocks (between 0.6 and 1.3 km), it contributes to sustainable resource exploration in the region. A better understanding of the geological structure helps minimize the environmental impact of exploration by reducing unnecessary drilling and concentrating efforts on areas with higher potential. Additionally, the use of non-invasive geophysical techniques supports the transition toward more environmentally conscious exploration practices. The integration of these advanced methods promotes a more sustainable approach to mineral and resource extraction, which is crucial for balancing economic growth with environmental preservation in geologically sensitive areas. Ultimately, this work provides a thorough geological interpretation that not only aids future exploration efforts but also aligns with the global push for sustainable and eco-friendly resource management.

HAGCN: A hybrid-order brain network-based graph convolution learning framework with multi-head attention for brain disorder classification

Brain networks provide essential insights in diagnosing many brain disorders, such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). However, current works often fail to fully capture the potential interaction characteristics of inter-regional functional connectivity within the brain network. To address this limitation, we propose a Hybrid-Attention Graph Convolution Network (HAGCN) for brain disorder classification. The learning framework designs a hybrid-order effective network (graph) for representing the lower-order and higher-order spatial interaction relations between brain regions, proposes a prior graph based on statistical tests to eliminate the noisy edges effectively, presents a dual-channel graph convolution module for simultaneously capturing information flow features of each brain region in out-degree and in-degree directions, and implement a multi-head attention module to focus and discern the significant features from the multidimensional interaction relations for improving the expressiveness of the model. Experiments on ADHD-200 and ABIDE-I datasets show the effectiveness and advantages of our proposed method, achieving the highest average accuracy values of 77.95 % and 78.17 %, respectively. The results demonstrate that integrating multidimensional and directional interaction characteristics can enhance the network’s capacity and improve learning efficiency. Additionally, our approach identifies and analyzes several key brain regions linked to brain disorders.

Socioeconomic factors and COVID-19 mortality in immune-mediated rheumatic diseases patients: regional analysis from Argentina, Mexico and Brazil

BackgroundSARS-CoV-2 infection has become a major international issue, not only from a medical point of view, but also social, economic and political. Most of the available information comes from the United States, Europe, and China, where the population and the socioeconomic status are very different from Latin American countries. This study evaluates the effect of regional socioeconomic characteristics on mortality due SARS-CoV-2 infection in patients with immune-mediated rheumatic diseases (IMRD) from Argentina, Mexico and Brazil.MethodsData from three national registries, SAR-COVID (Argentina), CMR-COVID (Mexico) and ReumaCoV-Brasil (Brazil), were combined. Adult IMRD patients with SARS-CoV-2 infection were recruited. National data for each province/state, including population density, number of physicians per inhabitant, income, unemployment, GINI index, Municipal Human Development Index (MHDI), stringency index, vaccination rate and most frequent viral strains per period were assessed as risk factors for mortality due to COVID-19.ResultsA total of 4744 patients were included, 2534 (53.4%) from SAR-COVID, 1166 (24.6%) from CMRCOVID and 1044 (22.0%) from ReumaCoV-Brasil. Mortality due to COVID-19 was 5.4%. In the multivariable analysis, higher number of physicians per 1000 inhabitants and being infected during the vaccination period of each country were associated with lower mortality. After adjustment for socioeconomic factors, there was no association with country of residence and mortality.ConclusionThese findings corroborate the complex interplay between socioeconomic factors, rheumatic disease activity, and regional disparities as determinants of death due to COVID-19 in Argentina, Brazil and Mexico. Thus, this research provides valuable insights for guiding public health policies and clinical practice in the ongoing fight against the COVID-19 pandemic.

Study of Pattern of Craniofacial Injuries in Road Traffic Accident Cases Autopsied in Midnapore Medical College

Background: The objective of this study was to investigate the pattern of craniofacial injuries in victims of fatal road traffic accidents, observed through autopsies conducted at Midnapore Medical College, West Midnapore from January 2021 to December 2023. Methods: It is a retrospective, cross- sectional observational study. Data was collected by thorough analysis of the injuries documented in post-mortem reports of all the alleged road traffic accident cases where victim sustained Cranio- facial injuries within a time span of 3 years, from 1st January 2021 to 31st December, 2023. Data was analysed using Microsoft Excel. Conclusion: The study highlights the necessity of context-specific interventions to address the unique injury patterns observed in West Midnapore's road traffic accidents. Improving road infrastructure, raising traffic safety awareness, and promoting protective measures like seat belts and helmets can reduce severe craniofacial injuries. The prevalence of maxillofacial fractures underscores the face's vulnerability, with long-term physical and psychological impacts. Effective prevention strategies, such as strict traffic regulations and awareness campaigns on the dangers of distracted or impaired driving, are essential. Despite limitations, the study provides valuable insights into craniofacial injury patterns in fatal accidents, stressing the need for enhanced road safety, stronger traffic laws, and better post-accident care. Interdisciplinary collaboration among healthcare professionals, law enforcement, and policymakers is crucial for developing comprehensive strategies to manage and prevent these injuries. By examining regional injury characteristics and comparing them with other studies, this research contributes to the broader understanding of craniofacial trauma, offering significant implications for improving road safety, trauma care, and forensic practices.

The LTAR Grazing Land Common Experiment at Platte River High Plains Aquifer.

The Long-Term Agroecosystem Research (LTAR) network of the United States Department of Agriculture (USDA) consists presently of 18 sites within the contiguous United States that are managed by the Agricultural Research Service (ARS) and its partners. The LTAR network focuses on developing national strategies for more efficient, resilient, and profitable agricultural production systems, improved environmental quality, and enhanced rural prosperity. The Platte River High Plains Aquifer (PRHPA) LTAR site is managed jointly by the University of Nebraska-Lincoln (UNL) and USDA-ARS and is one of the LTAR sites that conduct research on both integrated cropping and grazing systems. The PRHPA region encompasses multiple land resource areas and diverse agricultural production systems. The PRHPA sites, predominantly located in eastern Nebraska, are designated as an integrated system focused specifically on the region's dominant production practices of row crop (corn and soybean), managed pastures, and beef cattle production. Here, we focus on C3 cool-season smooth bromegrass (Bromus inermis Leyss.) pasture grazing systems under prevailing and alternative management practices for the region. The sites evaluate continuous and rotational grazing with and without pasture fertilization (prevailing practices). In an additional treatment, cattle are supplemented with dry distillers grains plus solubles, while manure supplies fertilization (alternative practice). Main measurements at the site evaluate plant and animal productivity, forage quality, greenhouse gas fluxes, and soil physical, chemical, and biological properties. This paper describes the regional characteristics of the PRHPA site, ongoing LTAR research related to pasture and livestock production, stakeholder engagement, and future research plans.

The regional features of digitalization in public administration: experience of the Orel region

Relevance. Russian regions are characterized by varying degrees of readiness for the introduction of digital technologies into the management system, this process often takes place formally and does not imply a significant change in the format of interaction between the population and business, on the one hand, and regional executive authorities, on the other. The study of the specifics of digitalization of public administration will allow to identify "bottlenecks" for improving measures to support digital transformation at the level of the subject of the Russian Federation, to identify and replicate successful experience.The purpose of the article is predetermined by its topic and is to determine the regional features of the digitalization of public administration in the Orel region.Objectives. To achieve this goal, the theoretical, methodological, organizational and regulatory foundations of digitalization of public administration are considered, a comparative analysis of key indicators of digitalization in this area is carried out, the opportunities and threats of digitalization are determined, taking into account the specifics of the socio-economic development of the region under study.Methodology. The materials for the study were official statistics presented on the official websites of the Federal State Statistics Service of Russia, compiled by constituent entities of the Russian Federation in the “Information Society” section. The main research methods are statistical analysis of data series, comparative analysis, SWOT analysis.Results. Based on the results of the study, it was revealed that the basic conditions for the digital transformation of management in the Oryol region have been formed: relevant regional strategies and programs have been developed and are being implemented, benchmarks for their implementation have been established, the system of authorities is functioning and the infrastructure necessary for successful digitalization is being developed. At the same time, there is no list of specific measures necessary for the integration of state regional information systems into federal super services, measures for personnel and financial support for digitalization processes, increasing the level of digital literacy of the population, and intensifying outreach work among the population. Solving these problems will improve the efficiency of the digital transformation of public administration in the Orel region.Conclusions. Regional specifics determine the effectiveness of the digital transformation of public administration in the constituent entities of the Russian Federation, and therefore regional programs should not only repeat federal initiatives, but also adapt them taking into account the territorial characteristics of socio-economic development.

Detecting Amyloid Positivity Using Morphometric Magnetic ResonanceImaging.

Early detection of amyloid-β (Aβ) positivity is essential for an accurate diagnosis and treatment of Alzheimer's disease (AD), but it is currently costly and/or invasive. We aimed to classify Aβ positivity (Aβ+) using morphometric features from magnetic resonance imaging (MRI), a more accessible and non-invasive technique, in two clinical population scenarios: one containing AD, mild cognitive impairment (MCI) and cognitively normal (CN) subjects, and another only cognitively impaired subjects (AD and MCI). Demographic, cognitive (Mini-Mental State Examination [MMSE] scores), regional morphometry MRI (volumes, areas, and thicknesses), and derived morphometric graph theory (GT) features from all subjects (302 Aβ+, age: 73.3±7.2, 150 male; 246 Aβ-, age: 71.1±7.1, 131 male) were combined in different feature sets. We implemented a machine learning workflow to find the best Aβ+ classification model. In an AD+MCI+CN population scenario, the best-performing model selected 120 features (107 GT features, 12 regional morphometric features and the MMSE total score) and achieved a negative predictive value (NPVadj) of 68.4%, and a balanced accuracy (BAC) of 66.9%. In a AD+MCI scenario, the best model obtained NPVadj of 71.6%, and BAC of 70.7%, using 180 regional morphometric features (98 volumes, 52 areas and 29 thicknesses from temporal, parietal, and frontal brain regions). Although with currently limited clinical applicability, regional MRI morphometric features have clinical usefulness potential for detecting Aβ status, which may be augmented by a combination with cognitive data when cognitively normal subjects make up a substantial part of the population presenting for diagnosis.

Key Issues and Challenges in Establishing Regionally Differentiated Water Quality Standard in China

The goal of environmental management in China is transitioning from pollution control to the improvement of ecological quality. The establishment of regional differential water quality standards can better adapt to the ecological characteristics and development needs of different regions. Given the shortcomings of current water quality standards and the needs of technological development, this study analyzed the causes and influencing factors of regional differences in water quality standards and summarized China's regional variations in areas such as the characteristics of receiving water bodies, social attributes, climate conditions, physicochemical properties of water, and aquatic biotic populations. It also examined the impact of regional characteristics on the assessment of biological toxicity. Based on these analyses, the study identified key scientific questions that may arise in the development of regional differential water quality standards in China and outlined future critical research directions in this field. These directions include developing theories and methods for determining the effluent dilution coefficient, establishing methods for identifying regional key water quality indicators and assessing their impact on water quality standards, determining regional characteristic species and their influence on water quality standards, and formulating theories and methods for categorizing different regions across the country. The aim of the study was to provide a reference for the scientific establishment of water quality standards in China.

Pengaruh Nilai Iklan Smartphone terhadap Niat Beli Produk

This study examines the influence of smartphone advertising value on product purchase intentions. This research uses quantitative methods by distributing online questionnaires to 151 Indonesian citizens without specific regional characteristics. The results of the analysis using regression analysis show that informativeness, entertainment and incentives are proven to have a positive effect on advertising value. Meanwhile, credibility and irritation were not proven to have a positive effect on advertising value. And finally, advertising value is also not proven to have a positive effect on purchase intention

Local and regional enhancements of CH4, CO, and CO2 inferred from TCCON column measurements

Abstract. In this study, we demonstrate the utility of available correlative measurements of carbon species to identify regional and local air mass characteristics as well as their associated source types. In particular, we combine different regression techniques and enhancement ratio algorithms with carbon monoxide (CO), carbon dioxide (CO2), and methane (CH4) total column abundance from 11 sites of the Total Carbon Column Observing Network (TCCON) to infer relative contributions of regional and local sources to each of these sites. The enhancement ratios provide a viable alternative to univariate measures of relationships between the trace gases that are insufficient in capturing source-type and transport signatures. Regional enhancements are estimated from the difference between bivariate regressions across a specific time window of observed total abundance of these species (BERr for bulk enhancement regression ratio) and inferred anomalies (AERr for anomaly enhancement regression ratio) associated with a site-specific background. Since BERr and AERr represent the bulk and local species enhancement ratio, respectively, its difference simply represents the site-specific regional component of these ratios. We can then compare these enhancements for CO2 and CH4 with CO to differentiate between combustion and non-combustion air masses. Our results show that while the regional and local influences in enhancements vary across sites, dominant characteristics are found to be consistent with previous studies over these sites and with bottom-up anthropogenic and fire emission inventories. The site in Pasadena shows a dominant local influence (&gt; 60 %) across all species enhancement ratios, which appear to come from a mixture of biospheric and combustion activities. In contrast, Anmyeondo shows more regionally influenced (&gt; 60 %) air masses associated with high-temperature and/or biofuel combustion activities. Ascension Island appears to only show a large regional influence (&gt; 80 %) on CO / CO2 and CO / CH4, which is indicative of transported and combustion-related CO from the nearby African region, consistent with a sharp rise in column CO (3.51 ± 0.43 % ppb yr−1) at this site. These methods have important applications to source analysis using spaceborne column retrievals of these species.

Kiln–House Isomorphism and Cultural Isomerism in the Pavilions of the Yuci Area: The Xiang-Ming Pavilion as an Example

The pavilion is a time-honored architectural form in the Chinese silhouette with strong regional characteristics. Its appearance and technical means are often adaptively combined according to the characteristics of local architecture. The “kiln–house isomorphism” is a unique construction technology of the Shanxi construction type in China. Therefore, the “kiln–house isomorphism” is generally adopted for the construction of pavilions in Shanxi. This study focuses on Xiaonanzhuang Village, Yuci District, Jinzhong City, Shanxi Province. Taking the Xiang-ming Pavilion, the core building of the village, as an example, we analyze the architectural characteristics of the “kiln–house isomorphism” in the pavilion in this area, describe the general construction rules of the region, and conduct a deep investigation of the five “cultural isomerism” factors of the pavilions in the region, namely, geomancy, etiquette and music, beliefs, clans, and cultivation and study. The results of this research will enrich the regional knowledge of such pavilions and add new objects for the protection of local architectural heritage, providing a theoretical basis for the contemporary adaptive reuse of pavilions in the Yuci area from a cultural perspective.

Bi-VLGM: Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation

AbstractMedical reports containing specific diagnostic results and additional information not present in medical images can be effectively employed to assist image understanding tasks, and the modality gap between vision and language can be bridged by vision-language matching (VLM). However, current vision-language models distort the intra-model relation and only include class information in reports that is insufficient for segmentation task. In this paper, we introduce a novel Bi-level class-severity-aware Vision-Language Graph Matching (Bi-VLGM) for text guided medical image segmentation, composed of a word-level VLGM module and a sentence-level VLGM module, to exploit the class-severity-aware relation among visual-textual features. In word-level VLGM, to mitigate the distorted intra-modal relation during VLM, we reformulate VLM as graph matching problem and introduce a vision-language graph matching (VLGM) to exploit the high-order relation among visual-textual features. Then, we perform VLGM between the local features for each class region and class-aware prompts to bridge their gap. In sentence-level VLGM, to provide disease severity information for segmentation task, we introduce a severity-aware prompting to quantify the severity level of disease lesion, and perform VLGM between the global features and the severity-aware prompts. By exploiting the relation between the local (global) and class (severity) features, the segmentation model can include the class-aware and severity-aware information to promote segmentation performance. Extensive experiments proved the effectiveness of our method and its superiority to existing methods. The source code will be released.

Analysis and prediction of mesoscale eddy kinetic energy variations in the Kuroshio extension

The Kuroshio Extension (KE) region, a crucial area in the Northwest Pacific Ocean, exhibits eddy kinetic energy with various scales of periodicity. Understanding how to extract its characteristic features and analyze and predict their periodic correlations has become vital for studying the regulatory mechanisms of eddy kinetic energy in the KE region. This paper first introduces a mesoscale eddy hybrid identification algorithm based on the flow field vector and the closed flow field. Using this algorithm, we gather mesoscale eddy identification data from the KE region to extract the monthly average series of five typical features of the KE region. Subsequently, wavelet theory is applied to analyze the cycles of these main features, identifying the common cycles of the KE region as the primary focus for analyzing the vorticity kinetic energy. This analysis includes cycle correlations with globally recognized indices, and it predicts these correlations. Further analysis of the main characteristic cycles through wavelet theory reveals that the KE region's eddy kinetic energy is significantly influenced by solar activity over long periods and by the North Pacific ocean-atmosphere interaction over shorter, interannual periods. Finally, this paper introduces a W-LSTM (Wavelet Decomposition based Long Short-term Memory Networks) prediction model based on wavelet decomposition for the KE region, covering January 2023–December 2023. The model demonstrates its effectiveness, achieving a Root Mean Square Error (RMSE) of 0.2530 and a correlation coefficient of 0.8259 between the predicted data and the actual observations.