Hierarchical Pattern Research Articles

Self-supervised learning for accurately modelling hierarchical evolutionary patterns of cerebrovasculature

Cerebrovascular abnormalities are critical indicators of stroke and neurodegenerative diseases like Alzheimer’s disease (AD). Understanding the normal evolution of brain vessels is essential for detecting early deviations and enabling timely interventions. Here, for the first time, we proposed a pipeline exploring the joint evolution of cortical volumes (CVs) and arterial volumes (AVs) in a large cohort of 2841 individuals. Using advanced deep learning for vessel segmentation, we built normative models of CVs and AVs across spatially hierarchical brain regions. We found that while AVs generally decline with age, distinct trends appear in regions like the circle of Willis. Comparing healthy individuals with those affected by AD or stroke, we identified significant reductions in both CVs and AVs, wherein patients with AD showing the most severe impact. Our findings reveal gender-specific effects and provide critical insights into how these conditions alter brain structure, potentially guiding future clinical assessments and interventions.

Identification and Analysis of Peak Population Migration Patterns in the Beijing-Tianjin-Hebei Region Based on Complex Network Theory

Abstract. As a vital economic and population agglomeration area in China, the Beijing-Tianjin-Hebei region has attracted considerable attention regarding population migration issues. Population migration not only directly affects the population size and structure of cities within the region but also has profound implications for economic development, social stability, and urban planning. This study extends the analysis to the "Spring Festival Travel Rush" period, using population migration data from January to March in the years 2020 to 2022 as the basis. By employing complex network theory and relevant statistical indicators, the study explores the peak period population migration patterns and their variations in the Beijing-Tianjin-Hebei region. The research firstly identifies significant changes in population migration patterns in the Beijing-Tianjin-Hebei region around 2021 and analyzes the changing patterns. Secondly, network statistical indicators indicate hierarchical population migration patterns based on city tiers. Furthermore, cities with unique migration patterns are identified. Finally, based on an in-depth exploration of population migration patterns and their variations in the Beijing-Tianjin-Hebei region, the study provides suggestions and prospects for future urban planning and development, offering important references for addressing the challenges posed by population migration in the region.

Mathematical Evaluation of Deep Learning Architecture with Feature Fusion for Cervical Cancer Detection Classification

Cervical cancer is still one of the main reasons women die around the world, which shows how important it is to have accurate and quick ways to diagnose the disease. This study looks into how to improve the accuracy of finding cervical cancer using a deep learning-based classification system that combines features. In this mixed classic feature extraction methods, like Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT), with deep learning architectures, like Graph Convolutional Networks (GCNs) and Capsule Networks. Each of these added its own benefits to the fusion model. Both HOG and SIFT are good at capturing important texture and structure features, even when the image conditions change. GCNs are used to learn relationship data between picture areas. This helps get a better sense of how things fit together in space, which is very important for medical imaging. Capsule Networks, on the other hand, let you understand the subtleties of hierarchical patterns in pictures of cervical cells. They do this by looking at spatial hierarchies and rotational changes that are very important for finding cancer. Our suggested fusion model combines these multi-level features, taking advantage of how the best features of deep learning and standard learning methods work together. Validation on standard cervical cancer image datasets showed that this fusion approach was much more accurate and precise at classifying than individual methods. Quantitative testing showed that the fusion model had higher sensitivity and specificity, which shows that it can reduce the number of fake negatives and positives, which is very important in clinical diagnosis. Our results show that the feature fusion model not only gives us a strong and accurate way to classify cervical cancer, but it also lays the groundwork for more uses in other complicated areas of medical imaging.

Programmable adhesion through triangular and hierarchical cuts in metamaterial adhesives.

Metamaterial design approaches, which integrate structural elements into material systems, enable the control of uncommon behaviours by decoupling local and global properties. Leveraging this conceptual framework, metamaterial adhesives incorporate nonlinear cut architectures into adhesive films to achieve unique combinations of adhesion capacity, release, and spatial tunability by controlling how cracks propagate forward and in reverse directions during separation. Here, metamaterial adhesive designs are explored with triangular cut features while integrating hierarchical and secondary cut patterns among primary nonlinear cuts. Both cut geometry and secondary cut features tune adhesive force capacity and energy of separation. Importantly, the size and spacing of cut features must be designed around a critical length scale. When secondary cut features are greater than a critical length, cracks can be steered in multiple directions, going both forward and backwards within a primary attachment element. This control over crack dynamics enhances the work of separation by a factor of 1.5, while maintaining the peel force relative to a primary cut. If hierarchical cut features are too small or too compliant, they interact and do not distinctly modify crack behaviour. This work highlights the importance of adhesive length scales and stiffness for crack control and attachment characteristics in adhesive films.This article is part of the theme issue 'Origami/Kirigami-inspired structures: from fundamentals to applications'.

Object Detection Using Capsule Neural Network: An Overview

Appropriate remedies for tasks like language translation, object identification, object segmentation, picture recognition, and natural language processing are needed for today's computer vision tasks. This paper explores the use of Capsule neural Networks (Caps Nets) in object identification and offers a thorough analysis of their developments and uses. It presents an analysis of the transformative impact of Caps Nets on object identification tasks. This distinctive the architecture of neural network is presented as an alternative to convolutional neural networks (CNNs). The paper highlights that how Caps Nets are unique in capturing spatial correlations and hierarchical patterns in visualization data by analyzing the fundamental ideas of the technology. Additionally, it demonstrates how Caps net out perform CNNs in terms of generalization, interpretability, and last in the resistance to spatial distortions, confirming their goodness at object detection. By integrating the Caps networks with the latest scientific findings and advances, the paper shows the current status and potential future paths for object detection methods that use this leading neural network architecture.

D-MHGCN: An End-to-End Individual Behavioral Prediction Model Using Dual Multi-Hop Graph Convolutional Network.

Predicting individual behavior is a crucial area of research in neuroscience. Graph Neural Networks (GNNs), as powerful tools for extracting graph-structured features, are increasingly being utilized in various functional connectivity (FC) based behavioral prediction tasks. However, current predictive models primarily focus on enhancing GNNs' ability to extract features from FC networks while neglecting the importance of upstream individual network construction quality. This oversight results in constructed functional networks that fail to adequately represent individual behavioral capacity, thereby affecting the subsequent prediction accuracy. To address this issue, we proposed a new GNN-based behavioral prediction framework, named Dual Multi-Hop Graph Convolutional Network (D-MHGCN). Through the joint training of two GCNs, this framework integrates individual functional network construction and behavioral prediction into a unified optimization model. It allows the model to dynamically adjust the individual functional cortical parcellation according to the downstream tasks, thus creating task-aware, individual-specific FCNs that largely enhance its ability to predict behavior scores. Additionally, we employed multi-hop graph convolution layers instead of traditional single-hop methods in GCN to capture complex hierarchical connectivity patterns in brain networks. Our experimental evaluations, conducted on the large, public Human Connectome Project dataset, demonstrate that our proposed method outperforms existing methods in various behavioral prediction tasks. Moreover, it produces more functionally homogeneous cortical parcellation, showcasing its practical utility and effectiveness. Our work not only enhances the accuracy of individual behavioral prediction but also provides deeper insights into the neural mechanisms underlying individual differences in behavior.

Multi-Cultural and Local Wisdom-based Curriculum Development Model in the Independent Campus Learning Program to Improve Graduates' Micro Skills

In the Independent Campus Learning Program [Merdeka Belajar Kampus Merdeka (MBKM)], the 5-1-2 learning pattern is used in the undergraduate program. In implementing this pattern, curriculum coherence is needed between study programs and between institutions. A model for developing a curriculum based on local wisdom and multiculturalism was created to improve the micro-skills of program graduates. A model for developing a curriculum based on local wisdom and multiculturalism was created to improve the micro-skills of program graduates. A model for developing a curriculum based on local wisdom and multiculturalism was created to improve the micro-skills of program graduates. In order to attain the objective, a hierarchical pattern of relationships is employed, beginning with the central construct, primary dimension, and indicator. A structural Equation Model (SEM) is used to analyse the relationship between variables, while an inductive approach is used for exploratory purposes to identify factors. In order to evaluate the dependability of the model, a study was conducted at two different universities in Indonesia: Sultan Ageng Tirtayasa University in Serang, Banten Province, and Galuh Ciamis University in West Java Province. The study involved testing the coherence of the curriculum of various programs at both universities and evaluating the micro-skills of graduates who had participated in the MBKM program. Samples from the 4 study programs implementing MBKM at the two universities were used to test curriculum coherence. Then, to determine the increase in graduates' micro-skills, a sample of 70 students was used. The results of the analysis show that curriculum development has a direct effect on improving graduates' micro-skills. In developing the curriculum, three factors strengthen micro skills improvement: development of knowledge, responsiveness to technological developments, and relevance to needs.

Rank Size Rule Analysis on the Urban Population Distribution of Indonesia for the Year 2020

The rank-size rule which is known as Zipf``s law is an interesting research topic in the field of urban and regional studies. Conclusions are drawn based on the parameters that examine people and their economic activities in a particular city. this study aims to assess the unique urban development and population distribution of Indonesia by taking into account the complex socioeconomic system and geographically different archipelago. This study uses demographical data from various cities in Indonesia to understand the hierarchal pattern of population distribution in urban areas. Furthermore, Indonesia is an interesting case study regarding urban economics since it has a vast geographical area and contains different cultures and economic activities. The main objective of this research is to understand the population distribution and to evaluate the accuracy of the rank-size rule model in this country. This research also aimed to identify the main factors that deviate the population distribution from the rank-size rule distribution. It is particularly important to develop practical policies on the urban development issues. The minor variations of population distribution in Indonesia from the rank-size rule model are a good tool for decision-makers to allocate resources, develop infrastructure, and contribute to sustainable urban expansion. In addition, this study is significant for understanding how the urban centers developed in Indonesia and how they interact with broader socio-economic perspectives which is essential for further urban development and wise decision-making.

EHPR: Learning evolutionary hierarchy perception representation based on quaternion for temporal knowledge graph completion

Research on temporal knowledge graphs garners attention due to the intricate connection between facts and dynamic temporal factors. However, existing research uses timestamp as auxiliary data for representation learning and directly integrate it into facts, resulting in the inability to capture the intrinsic connections between relations under time evolution. To handle these challenges, we propose the Evolutionary Hierarchy Perception Representation (EHPR), which first leverages the Hamilton product to perform rotational transformations on relation and entity over time, aiming to learn temporal relation and temporal entity with close interactions with time information. Later, EHPR is divided into two modules: (a) Rotating the head entity towards the tail entity using temporal relation through Hamilton product to model complex patterns with quaternion rotation capabilities. (b) Adopting an evolutionary hierarchical factor to capture the differences in modulus distribution between the temporal head entity and the temporal tail entity, aiming to manage the evolutionary hierarchical information between different temporal entities. In this way, EHPR not only utilizes the rich quaternion rotation capabilities to model various relation patterns but also further enables modeling of evolutionary hierarchical patterns through evolutionary hierarchy factors. Experiments show that EHPR achieves remarkable performance on six mature benchmarks compared to state-of-the-art models. Furthermore, we successfully transferred the core idea of EHPR into complex embeddings, showcasing the framework's adaptability. Compared to complex embedding models, EHPR also demonstrates stronger expressive abilities with the Hamilton operator, surpassing the performance of complex Hermitian operator.

Insight into the initiation and propagation mechanism of desiccation cracking in clayey soil from DEM simulations

Desiccation cracking, a common natural phenomenon, significantly affects the mechanical and hydraulic properties of clayey soils. This study employs 3D Discrete Element Method (DEM) to investigate the initiation and propagation mechanisms of soil desiccation cracking. By integrating water evaporation at the soil surface and water transportation between soil grains into a traditional DEM model, we unveil a sequential and hierarchical pattern in the development of desiccation cracks. Initially, micro cracks proliferate and coalesce in regions of soil defects characterized by the lowest coordination numbers, leading to the genesis of primary cracks. Subsequently, a complex network of cracks propagates, governed by the interplay between cracking and the redistribution of the tensile stress field. As desiccation advances, the detachment of soil from the bottom wall and an increase in soil strength contribute to the stabilization of the crack network. Notably, the hierarchical nature of desiccation cracking is predominantly shaped by initial soil defects and subsequently refined by stress concentration within the soil clods, correlating with changes in the length-to-height ratio of the soil clods.

Urban streets profiling with coupled spatio-temporal characteristics and topological information from the biking perspective

Urban street profiling is the spatio-temporal pattern discovery of street-level urban areas, which plays a vital role in understanding urban structures and dynamics. Due to the natural topology and various geographic characteristics on the streets, it is necessary to combine multi-dimensional spatio-temporal information to understand different profiles of streets. This research aims to develop a street profiling framework according to the coupled characteristics of streets. At the start, a bidirected dual graph and a spatial weighted graph embedding method were used to solve the street representation. Then, the street profiles can be extracted by clustering embedding vectors of streets and feature importance analysis. As the case study, we employed the bike trajectories and street view images in Xiamen, China to depict the geographic attributes of streets. The results can reveal nine spatio-temporal street profiles from the biking perspective, including three spatial distribution patterns and two spatial semantic patterns. Urban streets in the study area show a significant hierarchical pattern because of locations and the spatial lags of the biking behaviors. Meanwhile, the spatio-temporal characteristics of biking behaviors are the main factors of street profiles, though the street environment attributes participate in over half the number of profile types. We further evaluated the profiling ability of the proposed framework and the importance of urban street profiles using coupled characteristics. Overall, this study explored the profiling method for coupling static and dynamic characteristics of urban streets. The profiling results also help understand street usage and experiences by bikers, which have a practical value on the human-oriented classification of streets and further urban development from a geographic view.

Architected Carbon Structures for Energy Storage

Carbon materials, owing to their excellent electrical conductivity, tailor-ability, inexpensiveness, and versatility, have been extensively studied as electrode materials for energy storage devices, including batteries and supercapacitors. In this talk, I will discuss how to combine conventional materials/chemical synthesis with 3D printing techniques to develop architected carbon electrode materials for supercapacitors. The capacitance of carbon-based supercapacitor electrodes has remained at a mediocre level between 100 and 200 F/g for decades. Until recently, a new family of carbon materials termed hierarchical porous carbons has pushed the capacitance to new benchmark values beyond 300 F/g and has revitalized the exploration of carbon materials for supercapacitors. We have recently developed some hierarchical porous carbon areogels contain different scales of pores inter-connected together and assembled in hierarchical patterns, through a combination of freeze drying, template, chemical etching and 3D printing. These porous carbons have an open porous structure, providing an extremely large and accessible surface area. They can be used as electric double layer materials that enable ultrafast charging/discharging and retain outstanding capacitive performance even at ultralow temperatures (-70 °C). They can serve as 3D conductive scaffolds/current collectors to support pseudo-capacitive materials with ultrahigh mass loadings. These findings exemplified how to use a deterministic structure to improve the rate capability of supercapacitors and their capacitances at ultrahigh current densities and mass loadings and ultralow temperatures, which are long-standing challenges for SCs.

Exploring the Experiences of Community-Dwelling Older Adults on Using Wearable Monitoring Devices With Regular Support From Community Health Workers, Nurses, and Social Workers: Qualitative Descriptive Study.

The use of wearable monitoring devices (WMDs), such as smartwatches, is advancing support and care for community-dwelling older adults across the globe. Despite existing evidence of the importance of WMDs in preventing problems and promoting health, significant concerns remain about the decline in use after a period of time, which warrant an understanding of how older adults experience the devices. This study aims to explore and describe the experiences of community-dwelling older adults after receiving our interventional program, which included the use of a smartwatch with support from a community health workers, nurses, and social workers, including the challenges that they experienced while using the device, the perceived benefits, and strategies to promote their sustained use of the device. We used a qualitative descriptive approach in this study. Older adults who had taken part in an interventional study involving the use of smartwatches and who were receiving regular health and social support were invited to participate in focus group discussions at the end of the trial. Purposive sampling was used to recruit potential participants. Older adults who agreed to participate were assigned to focus groups based on their community. The focus group discussions were facilitated and moderated by 2 members of the research team. All discussions were recorded and transcribed verbatim. We used the constant comparison analytical approach to analyze the focus group data. A total of 22 participants assigned to 6 focus groups participated in the study. The experiences of community-dwelling older adults emerged as (1) challenges associated with the use of WMDs, (2) the perceived benefits of using the WMDs, and (3) strategies to promote the use of WMDs. In addition, the findings also demonstrate a hierarchical pattern of health-seeking behaviors by older adults: seeking assistance first from older adult volunteers, then from social workers, and finally from nurses. Ongoing use of the WMDs is potentially possible, but it is important to ensure the availability of technical support, maintain active professional follow-ups by nurses and social workers, and include older adult volunteers to support other older adults in such programs.

Cretaceous cyclic peritidal carbonates of the Apulia Carbonate Platform (Apulia, southern Italy) in a hierarchical sequence‐stratigraphic perspective: A case study from the Murge area (the Giovinazzo sea‐cliff section)

AbstractCretaceous cyclic peritidal carbonates form the bulk of the Apulia Region in Italy and represent the vestiges of the Apulia Carbonate Platform. To show from a sequence stratigraphic perspective the architecture of peritidal carbonates, the 17 m thick Albian Giovinazzo sea‐cliff section was studied at a centimetre detail, aiming to: (i) describe cyclic facies organisation in beds and bedsets; (ii) reconstruct the relative sea‐level curve and its evolution over time; (iii) interpret the long‐term evolution of the accommodation space in terms of sequence stratigraphy. The hierarchical stacking pattern of facies in beds and bedsets reveals Milankovitch cyclicity. As a working hypothesis, elementary sequences are assumed to represent the precession cycle (ca 20 kyr) and small‐scale and medium‐scale sequences the short (ca 100 kyr) and long (ca 400 kyr) eccentricity cycles, respectively. Four different types of elementary sequences (condensed, catch‐down, catch‐up and give‐up) are recognised and interpreted in terms of relative sea‐level changes to reconstruct the relative sea‐level curve of the entire succession. The envelope of the reconstructed relative sea‐level curve is used to represent the long‐term accommodation change on the platform, which covers a time span of approximately 1.8 Myr. Most of this time was spent in subaerial exposure, as approximately 1.2 Myr was predicted to be condensed in a stratigraphic interval encompassing both the sequence‐boundary zone/falling stage deposits and the lowstand deposits. Moreover, it was interpreted that about two‐third of the total thickness of the succession was formed in only 280 kyr and consisted of both transgressive and maximum‐flooding deposits. The main implication of this study is that unconformities do not necessarily correspond to single surfaces but, rather, to very amalgamated intervals or unconformity zones. Moreover, based on biostratigraphic constraints, there is a correlation between the unconformity zone of the studied succession and the third‐order KAl4 sequence boundary of the Cretaceous eustatic cycle chart.

TELS: Learning time-evolving information and latent semantics using dual quaternion for temporal knowledge graph completion

In temporal knowledge graphs (TKGs), the status of facts is intricately tied to the dynamic and precise nature of temporal factors. Existing research merely treats time as supplementary information, without considering the latent semantic changes caused by the positional changes of entities within specific relations in a temporal context. Furthermore, due to the coarse granularity of timestamps in existing TKGs, the number of multiple relations pattern among entities significantly increases, limiting model performance. This paper proposes a Time-Evolving Information and Latent Semantics model (TELS), which represents facts as dual quaternion embeddings to provide a compact and elegant representation. Specifically, we use timestamp dual quaternions, transforming the entity and relation into temporal entity and temporal relation through dual quaternion multiplication. Besides, we introduce semantic-aware dual quaternion to capture the latent semantics arising from the positional changes of entities within specific relations. Next, TELS consists of two parts: (a) We use semantic-aware dual quaternions to perform transformations on head entity and tail entity respectively through dual quaternion multiplication. Next, we utilize temporal relation to transform head entity to tail entity through dual quaternion multiplication. (b) We adopt an evolutionary hierarchical factor to encapsulate the differences in modulus distribution between the temporal head entity and temporal tail entity. In this way, TELS not only uses dual quaternions to handle key patterns and multiple relations pattern, but also handles evolutionary hierarchical patterns by capturing the modulus distribution differences between temporal entities. Meanwhile, TELS learns semantic-aware dual quaternion embeddings to capture the latent semantics endowed by relations to entities. Empirically, TELS can boost the performance over seven temporal knowledge graph benchmarks.

Bidirectional Long Short-Term Memory Development for Aircraft Trajectory Prediction Applications to the UAS-S4 Ehécatl

The rapid advancement of unmanned aerial systems in various civilian roles necessitates improved safety measures during their operation. A key aspect of enhancing safety is effective collision avoidance, which is based on conflict detection and is greatly aided by accurate trajectory prediction. This paper represents a novel data-driven trajectory prediction methodology based on applying the Long Short-Term Memory (LSTM) prediction algorithm to the UAS-S4 Ehécatl. An LSTM model was designed as the baseline and then developed into a Staked LSTM to better capture complex and hierarchical temporal trajectory patterns. Next, the Bidirectional LSTM was developed for a better understanding of the contextual trajectories from both its past and future data points, and to provide a more comprehensive temporal perspective that could enhance its accuracy. LSTM-based models were evaluated in terms of mean absolute percentage errors. The results reveal the superiority of the Bidirectional LSTM, as it could predict UAS-S4 trajectories more accurately than the Stacked LSTM. Moreover, the developed Bidirectional LSTM was compared with other state-of-the-art deep neural networks aimed at aircraft trajectory prediction. Promising results confirmed that Bidirectional LSTM exhibits the most stable MAPE across all prediction horizons.

"All You Need Is Love" a Social Network Approach to Understanding Attachment Networks in Adulthood.

This study examined five dimensions of attachment network structure in a large sample of adults (n = 930, 57% female) between 24 to 80 years of age. We employed a newly validated diagrammatic measure, the web-based hierarchical mapping technique (WHMT), to measure the attachment strength to participants' five closest relationships and the physical distance to and between network members. Our first aim was to replicate existing research on the composition of adult attachment networks, exploring variations in network patterns by age, romantic status, and parental status. Our second aim was to explore four new network dimensions, including physical distance to network members, hierarchical patterns, centrality, and density. The results replicated previous work on network composition, highlighting the pivotal role of romantic partners as primary attachment figures through adulthood. The analysis of the new network dimensions revealed a clear divide between adults in romantic relationships and those who are not. Compared to the single adults, the adults in romantic relationships were more hierarchical in their attachment preferences, reported lower emotional connection to friends and parents, and lived farther from their network, which was also more geographically dispersed. In other words, romantically involved adults put more of their attachment eggs in one basket. The results also showed that the older adults tended to live further away from their attachment network and had a more geographically dispersed network compared to the younger adults.

Revealing multi-scale spatial synergy of mega-city region from a human mobility perspective

ABSTRACT Spatial synergy is strengthened integration and connection between cities in a mega-city region, transcending administrative boundaries. The central flow theory suggests that the mega-city regions are formed by the interconnected flows of people across cities, making the spatial synergy can be measured by assessing the aggregation and intensity of flows and interactions between cities and regions. Human mobility data, such as mobile phone data and social media check-ins, enable the tracking of human movements, thus facilitating the transition of central flow theory from theoretical constructs to empirical research. To this end, this study presents an alternative data-driven framework to reveal the multi-scale spatial synergy of mega-city regions from a human mobility perspective. It uncovers homogeneously spatial communities with high inter-city integration using community detection. Strong internal spatial connections of 2.13 billion mobility are filtered using network backbone extraction. An experiment in the Pearl River Delta (PRD), China, demonstrates a multi-scale and multi-core hierarchical spatial synergy in the PRD region. The detailed findings are as follows: (1) Three cities attract the majority of human mobility. Mobility distance is short in urban centers and long in suburban areas. (2) The spatial integration pattern shows the detected communities reveal the hierarchical integration pattern with three main integrated regions: Guangzhou-Foshan-Zhaoqing, Shenzhen-Dongguan-Huizhou, and Zhuhai-Zhongshan-Jiangmen. (3) The spatial connection pattern illustrates the close ties of 9 cities and three core cities, including Guangzhou, Shenzhen, and Foshan. These results provide a human-centric understanding of urban synergy and deeper insights into central flow theory, which supports cooperative development in mega-city regions.

Epidemiology of Trichomonas vaginalis infection in the Middle East and North Africa: systematic review, meta-analyses, and meta-regressions

Trichomoniasis, caused by the parasite Trichomonas vaginalis (TV), remains an underappreciated sexually transmitted infection (STI), primarily due to inadequate understanding of its epidemiology and public health implications. This study aimed to characterize TV epidemiology in the Middle East and North Africa (MENA). Systematic review and analysis of evidence sourced from international, regional, and national databases were conducted. Findings were reported following PRISMA guidelines. Random-effects meta-analyses and meta-regressions were performed to determine pooled mean prevalence, investigate associations with prevalence, and identify sources of between-study heterogeneity. The review identified 263 relevant publications, encompassing 462TV prevalence measures. The pooled mean TV prevalence was estimated as follows: 4.7% (95% CI: 3.9-5.6%) in the general population of women, 17.2%(95% CI: 5.4-33.6%) among intermediate-risk populations, 10.3% (95% CI: 6.2-15.3%) among female sex workers, 13.9% (95% CI: 12.3-15.6%) among symptomatic women, 7.4% (95% CI: 1.9-15.5%) among infertility clinic attendees, 2.3% (95% CI: 0.1-6.3%) among women with miscarriages or ectopic pregnancies, and 1.6%(95% CI: 0.8-2.7%) among STI clinic attendees. Limited data were found for men. Multivariable meta-regressions explained >40% of the prevalence variation, unveiling a hierarchical prevalence pattern by population type, an inverse correlation with national income, and a prevalence decline at a rate of 1% per calendar year. Despite conservative sexual norms, MENA has a substantial TV prevalence, comparable to the global TV prevalence. The unexpectedly high prevalence of this curable infection may, in part, be attributed to limited access to and underutilization of STI screening and treatment services. This work was supported by the Qatar Research, Development, and Innovation Council [ARG01-0522-230273] and by the Biomedical Research Program at Weill Cornell Medicine-Qatar.

Evaluation of regional comprehensive development efficiency under low-carbon policy: based on optimized DDF-GML combined with unsupervised clustering method

In the study of urban development, it is very important to evaluate the influence of production factors reasonably and efficiently for the region to achieve efficient development. The principal aim of this investigation is to amalgamate the conventional measurement model characterized by robust interpretability with the non-parametric model characterized by limited interpretability, thereby enhancing the precision of research outcomes. Towards this objective, the study employs an optimized directional distance function integrated with a global Malmquist–Luenberger index to formulate a comprehensive total factor productivity measurement framework. In elucidating the homogeneous attributes of regions, departing from prior methodologies reliant on manual or direct algorithmic partitioning, this paper employs the K-means clustering algorithm for index discernment, abstracting the concept of K-means clustering centroids to encapsulate regional homogeneity, thereby delineating results through the visualization of regional development potential maps and the evolution of centroid-based clustering trend maps. The findings of the investigation illuminate common patterns of change across disparate regions, proposing a strategy for leveraging regional resource endowments towards a cohesive framework, thereby transcending constraints imposed by production efficiency limitations. Amidst the backdrop of the COVID-19 pandemic, this study draws upon provincial-level data spanning from 2000 to 2018 in China. The conclusive analytical outcomes underscore the pivotal role of energy factors in regional development efficiency, particularly within high-potential development regions, followed by the capital and labor factors. Concurrently, the study discerns a discernible hierarchical pattern among areas of development potential, which exhibits correlation with factor mobility dynamics.