Hierarchical Classification Research Articles

'One of the greatest injustices of our time': The impact of social representations of modern slavery in the UK-A mixed methods approach.

This study aimed to examine how social representations of modern slavery and immigration become entangled in newspaper media. 2672 UK newspaper articles were collated from 2013 to 2022 and analysed using Content Analysis (Descendant Hierarchical Classification) and Critical Discourse Analysis (CDA). Two themes and corresponding extracts were identified from the content analysis output and analysed using CDA allowing for the exploration of the role of the hegemonic social representations to understanding how discourses of modern slavery are reproduced through the othering in relation to ethnicity and migration. The two themes that emerged were 'Leading the way in tackling slavery' and 'Making claims.' The themes combined showed how the media and political discourse positioned the UK government as leading the way in tackling modern slavery. Simultaneously, discourses criminalized migrants and constructed them as the source of social harm, and the obstacles to resolving the social injustices were those opposing anti-immigration legislation. These two contradictory positions have implications in how modern slavery is understood by the public. More worryingly, it could lead to silence from victims due to the threat of being criminalized.

Experiences and perspectives on rapid-test diagnosis of tuberculosis, histoplasmosis and cryptococcosis in people with advanced HIV/AIDS disease in Porto Alegre, Brazil.

The rapid diagnosis of opportunistic infections (OIs) is critical for improving the health outcomes of people living with HIV/AIDS (PLWHA). This study aimed to describe the feasibility of implementing a package for the rapid diagnosis of tuberculosis, histoplasmosis, and cryptococcosis in patients with advanced HIV/AIDS disease in Porto Alegre, Brazil. The research involved two focus groups with health professionals, four in-depth interviews with healthcare managers, and twelve interviews with PLWHA. The corpus was analyzed using Descending Hierarchical Classification (DHC). The study found that the rapid test diagnosis intervention was generally well-received by patients and health professionals, improving diagnosis and treatment outcomes. However, it also identified several areas for improvement, including the need for expanded psychosocial support and enhanced coordination between health services. The findings have important implications for the development and implementation of policies and programs aimed at enhancing the diagnosis and treatment of OIs among PLWHA with advanced diseases. Further research should explore social determinants of HIV/AIDS mortality to offer valuable insights into improving prevention and treatment strategies. By prioritizing patient-centered care and improving coordination between health services, policymakers and health professionals can improve the health outcomes of PLWHA with advanced disease in Porto Alegre and other similar settings.

Incremental healthcare costs of the simple SOF measure of phenotypic frailty in community-dwelling older adults.

Frailty defined by the Cardiovascular Health Study (CHS) phenotype is associated with higher healthcare expenditures in community-dwelling Medicare beneficiaries after accounting for claims-based cost indicators. However, frailty assessment using the CHS phenotype is often not feasible in routine clinical practice. We evaluated whether frailty identified by the simple Study of Osteoporotic Fractures (SOF) phenotype is associated with subsequent incremental costs after accounting for claims-derived cost indicators. Prospective study utilizing data from four cohort studies of older adults linked with Medicare claims composed of 8264 community-dwelling fee-for-service beneficiaries (4389 women, 3875 men). SOF Frailty Phenotype (three components: weight loss, poor energy, and inability to rise from chair five times without using arms) and CHS Frailty Phenotype (operationalized using five components) derived from cohort data. Participants were classified as robust, prefrail, or frail using each phenotype. Multimorbidity index (CMS Hierarchical Conditions Categories score) and Kim frailty indicator (approximating the deficit accumulation index) derived from claims. Annualized total and sector-specific healthcare costs ascertained for 36 months after frailty assessment. Average annualized total healthcare costs (2023 US dollars) were $15,021 in women and $15,711 in men. After accounting for claims-based multimorbidity and frailty indicators, average incremental costs of SOF phenotypic frailty (two or three components) versus robust (none) were $7142 in women and $5961 in men, only modestly lower than incremental costs of CHS phenotypic frailty ($9422 in women, $6479 in men). SOF phenotypic frailty in both sexes was associated with higher subsequent expenditures in the inpatient, skilled nursing facility, and home healthcare sectors. As observed with CHS phenotypic frailty, SOF phenotypic frailty is associated with higher subsequent total and sector-specific expenditures after accounting for claims-derived indicators. The parsimonious SOF phenotype can be readily assessed in space-constrained and time-limited practice settings to improve identification of older adults at high risk of costly care.

T-SRE: Transformer-based semantic Relation extraction for contextual paraphrased plagiarism detection

Plagiarism has become a pervasive issue in academics and professionals to safeguard academic integrity and intellectual property rights. The escalating sophistication of plagiarized content through semantic manipulation and structural reorganization poses significant challenges to existing detection systems that rely primarily on lexical similarity measures. The proposed T-SRE (Transformer-based Semantic Relation Extraction), a novel framework addresses the limitations of traditional n-gram and string-matching approaches by leveraging deep semantic analysis. The proposed framework combines Dependency Parsing (DP) for syntactic relationship mapping and Named Entity Recognition (NER) for contextual entity identification, augmented by a transformer-based neural network that captures long-range contextual dependencies. This learning methodology incorporates three key components: a position-aware word reordering algorithm, Levenshtein distance metric for structural similarity, and contextual word embeddings for semantic preservation detection. The proposed T-SRE enhances text structure recognition by combining position-aware reordering with semantic preservation through ensemble learning. The system implements a hierarchical classification scheme that quantifies plagiarism severity through a four-tier taxonomy: heavy, low, non-plagiarized and verbatim copy. The Udacity benchmark dataset showcases the model’s superior detection capabilities, achieving 92% precision, 89% recall, and an F1-score of 90.5%, particularly in lightweight textual modifications.The framework achieves a granularity score of 1.28, outperforming existing approaches.

A Signal Generation Fault Diagnosis Method for Planetary Reducer of High‐Pressure Grinding Roll

ABSTRACTThis paper proposes a signal generation fault diagnosis method for the challenge of insufficient training samples in the planetary reducer of high‐pressure grinding roll (HPGR). The expression of the vibration response signal is derived, and a fault vibration signal model is established for the planetary reducer of HPGR. To generate the expanded samples, an adaptive fox optimization (AFO) algorithm is employed for optimizing the parameters of the fault vibration signal model, so that the simulated signal matches the measured signal. Before that, the measured vibration signal of the planetary reducer of HPGR is preprocessed by a tunable Q‐factor wavelet transform (TQWT). Finally, the residual convolution neural and long short‐term memory network with attention mechanism (ResCNN‐LSTM‐ATT) is utilized for feature extraction from collected signals, and the hierarchical fault classifier is developed for the fault classification. The experimental results show that the AFO algorithm has better performance, and the signals generated by the optimized fault vibration signal model are in good agreement with the measured signals. The classification accuracy of normal signals reaches 99.70%, which shows good diagnosis accuracy in both known fault classification and unknown fault identification. Compared to other methods, the proposed method has better diagnostic accuracy.

Ecosystem service indicators on military-managed drylands in the Western United States.

Lands devoted to military use are globally important for the production of ecosystem services and for the conservation of biodiversity. The United States has one of the largest military land estates in the world, and most of these areas occur in water-limited landscapes. Despite many of these areas receiving intense or sustained disturbance from military training activities, the structure and function of ecosystems contained within their boundaries continue to provide critical benefits to people across spatial scales. The land owned and managed by the Department of Defense is subject to regulation across local, state, and federal governing bodies, constraining and shaping both how land management is conducted and how ecosystem services are prioritized. Here, we explored the supply of ecosystem services from military lands in dryland areas of the United States using key indicators of ecosystem services: biodiversity estimates derived from range maps, ecosystem productivity estimates from satellite observations, and spatially explicit, hierarchical ecosystem classifications. Additionally, we utilized content analysis of the environmental management plans of these areas to describe the unique set of demands and regulatory constraints on these areas. We found that the US military land estate in drylands contains many types of ecosystems and provides a large and diverse supply of ecosystem services, comparable to the sum of services from public lands in these areas. Additionally, the degree to which the ecosystem services concept is captured in environmental management plans is strongly shaped by the language of the governing legislation that mandated the use of environmental management plans in these areas, although these plans do not explicitly address land management using the concept of ecosystem services. Collectively, our findings suggest that military use and management of land represents an important source of ecosystem services, that military land use can be considered a cultural ecosystem service unto itself, and that top-down regulation can affect how these services are identified and valued. Our work highlights the need for the research and conservation communities to quantify ecosystem services from individual military installations so that both services and biodiversity can be safeguarded in an era of military conflict across the globe.

Metastatic Patterns: Insights from Japanese Pathological Autopsy Registry Analysis.

Understanding the metastatic patterns is crucial for the treatment of malignancies. This study aimed to identify the characteristic organ metastases of primary malignancies, including rare malignancies, and classify them according to their metastatic patterns. We extracted data on primary malignancies and organ metastases from the Annual of Pathological Autopsy Cases in Japan recorded in 1993-2021. Autopsy findings of the primary and metastatic organs in patients with malignancy were recorded on an organ-by-organ basis. The metastatic frequency (number of metastases per autopsy) and the proportion (percentage of organs with metastases out of the total in a primary malignancy) for 48 organ metastasis sites across 76 primary malignancies were calculated. Metastatic patterns were classified into hierarchical and non-hierarchical clustering classifications based on the standard proportion of organ metastases. A total of 332,195 autopsy cases and 810,206 organ metastases were analyzed. The metastatic frequency of all malignancies was 2.44. Malignancies of the placenta, eye, and ovary showed a higher propensity for metastasis, whereas central nervous system malignancies showed a lower tendency. Metastasis site was a characteristic of each malignancy, with a particularly high proportion of lung metastasis in parathyroid malignancy and bone metastasis in prostate malignancy. In the hierarchical and non-hierarchical cluster methods, brain, lung, liver, bone, peritoneum, and hematolymphoid organ were key metastatic sites, and this factor divided primary malignancies into seven categories. The unweighted kappa coefficient comparing the two classification methods was 0.84 (95% confidence interval: 0.75-0.93). The proportion of metastatic organs was influenced by anatomical location and/or organ specificity of the primary malignancies. Our study provides a comprehensive overview of the patterns and frequencies of metastatic organ sites associated with 76 primary malignancies. Our findings will provide useful information for future research and clinical practice.

Les systèmes agroforestiers du domaine soudanien du Togo : historique et facteurs explicatifs de la dynamique évolutive

The aim of this study is to analyse the evolutionary trajectories of these agroforestry systems in order to identify the main drivers of change that influence their resilient and sustainable management in the Sudanian zone of Togo. Specifically, the aim is to: analyse the main changes that have occurred since 1960; characterise the evolutionary trajectories; and determine the drivers of change that could help to understand the evolution of these systems. The data were collected using a retrospective and prospective diagnostic survey of a sample of farmers, including resource persons, following the Historical-Sociological Investigation Method by means of semi-structured interviews. A Principal Component Analysis coupled with an Ascending Hierarchical Classification was used to study the main stages in the development of these systems. Despite having adopted various measures to promote their adaptability, profound changes have marked the evolution of these systems, testing their limits in the face of these challenges. In this context of adaptability, the results reveal two major transition models: a ‘model of transition from indigenous agricultural systems to extensive and sustainable systems’, dominant in ecological zone I with 47.44% of the systems surveyed, and an ‘agro-ecological transition model towards sustainable, innovative and resilient systems’, dominant in ecological zone II with 52.56% of the said systems. In this duality of agroforestry system management models, it is essential to analyse the evolutionary trajectories and drivers of change in order to draw up appropriate agricultural and environmental policies, with a view to the possible transplantation of these trajectories for sustainable and resilient agriculture both within and between ecological zones.

Characterization of the agriculture-livestock-environment integration system (sia2e) in the Maradi region along the north-center-south gradient

In Niger in general and in the Maradi region in particular, the management of space and natural resources leads to problems for agricultural and livestock farms. This study was conducted in the Maradi region in order to characterize the agriculture-livestock-environment integration system at the farm level in the Maradi region along the gradient of North (Dakoro), Center (Guidan Roumdji), South (Madarounfa). The methodology used to achieve this objective consisted of surveys of 300 households in the Maradi region, which were then analysed using Hierarchical Ascending Classification (HAC) and Principal Component Analysis (PCA). The results allowed us to distinguish three groups of farm sizes. Group 1 farms are characterized by large farms with an age greater than 41 years, a total cultivated area greater than 1 ha, and a large number of livestock greater than 3 UTF. These farmers are more represented in Dakoro (57%) and Guidan Roumdji (38%) and are more involved in Agriculture-Livestock-Environment than the other groups. As for group 2 farms, they are more numerous than group 1 farms in Guidan Roumdji (49%) and Madarounfa (49%). These farms have a cultivated area > 3 ha and a number of dependents between 5 and 14 peoples. The farms in group 3 are the least old and less numerous than those in groups 1 and 2, with very low values in Dakoro. This study categorized farmers in the Maradi region into three groups according to age, field area, household size and number of animals. It shows that agriculture-livestock-environment integration is practised by all the farms surveyed, but at different levels. Finally, particular attention was paid to the contribution of this agriculture-livestock-environment integration to improving soil fertility, household income and food and nutritional security.

Automatic knee osteoarthritis severity grading based on X-ray images using a hierarchical classification method

BackgroundThis study aims to develop a hierarchical classification method to automatically assess the severity of knee osteoarthritis (KOA).MethodsThis retrospective study recruited 4074 patients. Clinical diagnostic indicators and clinical diagnostic processes were applied to develop a hierarchical classification method that involved four sub-task classifications. These four sub-task classifications were the classification of Kellgren-Lawrence (KL) grade 0–2 and KL grade 3–4, KL grade 3 and KL grade 4, KL grade 0 and KL grade 1–2, and KL grade 1 and KL grade 2, respectively. To extract the features of clinical diagnostic indicators, four U-Net models were first used to segment the total joint space (TJS), the lateral joint space (LJS), the medial joint space (MJS), and osteophytes, respectively. Based on the segmentation result of TJS, the region of knee subchondral bone was generated. Then, geometric features were extracted based on segmentation results of the LJS, MJS, TJS, and osteophytes, while radiomic features were extracted from the knee subchondral bone. Finally, the geometric features, radiomic features, and combination of geometric features and radiomic features were used to construct the geometric model, radiomic model, and combined model in KL grading, respectively. A strict decision strategy was used to evaluate the performance of the hierarchical classification method in all X-ray images of testing cohort.ResultsThe U-Net models achieved relatively satisfying performances in the segmentation of the TJS, the LJS, the MJS, and the osteophytes with the dice similarity coefficient of 0.88, 0.86, 0.88, and 0.64 respectively. The combined models achieved the best performance in KL grading. The accuracy of combined models was 98.50%, 81.65%, 82.07%, and 74.10% in the classification of KL grade 0–2 and KL grade 3–4, KL grade 3 and KL grade 4, KL grade 0 and KL grade 1–2, and KL grade 1 and KL grade 2, respectively. For all X-ray images of the testing cohort, the accuracy of the hierarchical classification method was 65.98%.ConclusionThe hierarchical classification method developed in the current study is a feasible approach to assess the severity of KOA.

Geothermal resources of Hungary: a play-based review

In this paper we review the geothermal resources of Hungary using the geothermal play concept and its scale-dependent hierarchical categorization. The Pannonian Basin belongs to an extensional domain play type, with two main plays: the porous Neogene basin-fill sediments and the fractured–karstified Paleozoic and Mesozoic basement rocks. Both plays can be divided into hydrostatic and overpressured play segments. The most widely utilized play level in the hydrostatic segment of the porous Neogene sediment play comprises laterally extended, highly permeable sandstone layers storing low-salinity thermal water with average flow rate of c. 900 l min −1 and temperatures between 50 and 100°C in the depth range of 1–2 km. The other prosperous play level is represented by the fractured–karstified Mesozoic carbonates in the hydrostatic segment of the basement play having elevated temperatures (90–120°C) and several thousand l min −1 flow rates. The play level related to fractured Paleozoic rocks is less important due to its limited extent. The overpressured segment of the basement play may have high flow rates but it is still an untapped resource as exploitation is limited by high overpressure, high temperatures (170–200°C) and highly saline hot waters.

Barriers and Facilitators Affecting Access to Health Care for People With Syphilis: Protocol for a Scoping Review.

Syphilis is a systemic, preventable, and curable infection caused by the bacterium Treponema pallidum. Despite being treatable, syphilis continues to have a high incidence, with a resurgence observed even in countries with strong health surveillance systems. This highlights the need to understand the various strategies used globally to improve access to care for individuals with syphilis. This scoping review aims to identify and map the barriers and facilitators affecting access to health care for people with syphilis. This scoping review will follow the methodology outlined by the Joanna Briggs Institute. The search will be conducted across several databases, including PubMed/MEDLINE, Scopus, Embase, LILACS (Virtual Health Library), and CINAHL (EBSCO). In addition, sources of unpublished studies or gray literature will be explored. Studies focusing on access to health care for individuals with syphilis will be included, regardless of geographic location, country, or language. Two independent reviewers will assess the results, and data will be extracted using a tool specifically developed for this review. The extracted quantitative data will be presented in tables and analyzed using descending hierarchical classification, represented by a class dendrogram. Barriers and facilitators will be categorized into dimensions of access. Database searching began in October 2024. Full-text screening and review are expected to be completed in December 2024. Data extraction and analysis are expected to be completed by February 2025, and the final report will be completed in March 2025. The findings of this scoping review, guided by this protocol, will elucidate the main barriers and facilitators that affect access to syphilis treatment. This study may contribute to the practices of health professionals, managers, and the academic community, and provide relevant information for the population. Open Science Framework Registries osf.io/kpsab; https://osf.io/kpsab. PRR1-10.2196/63561.

Contribution of agromorphological and qualitative traits to the study of the diversity of the caprifig (Ficus carica L.) in traditional agroecosystems in northern Morocco

Morocco is founded to be as one of the substantial leading fig tree producers. Caprification plays a crucial role in fig production regions as the primary factor that significantly influences both the yield and quality of fig production. Our study focuses on characterizing caprifig varieties in the Bni Ahmed region (northern Morocco) and emphasizing their phenotypic potential in the context of climate change. Using 34 agromorphological and qualitative traits (IPGRI), the study examined 40 caprifig trees belonging to four local varieties of caprifig. The varietal characterization was completed by the collecting and analyzing fruit and leaf samples in the laboratory. Qualitative traits indicated morphological variability among the varieties investigated. All quantitative parameters, except fruit skin thickness, showed significant differences. Analysis of simple correlation coefficients indicated strong relationships between the measured variables. The analysis of the simple correlation coefficients revealed strong correlations between the measured variables. A remarkable phenotypic variety was identified among the caprifig accessions studied. The principal component analysis showed that the first two components explain 60% of the total variability. Weight, fruit dimensions, neck diameter, fruit pulp thickness, number of Blastophaga, length, width, leaf area, and central lobe length contribute significantly to the total variation. Analysis of the Hierarchical Ascending Classification revealed the existence of three varieties: L’morr, l’louizi, and l’hlou. The variety L’morr has shown a very high potential in terms of fruit characters and pollinating insects. Thus, a genetic analysis can confirm the agromorphological results and breeding programs should consider these unknown genetic resources, such as the L’morr variety, to provide better quality caprifigs.

Hierarchical multi-label classification model for science and technology news based on heterogeneous graph semantic enhancement

In the context of high-quality economic development, technological innovation has emerged as a fundamental driver of socio-economic progress. The consequent proliferation of science and technology news, which acts as a vital medium for disseminating technological advancements and policy changes, has attracted considerable attention from technology management agencies and innovation organizations. Nevertheless, online science and technology news has historically exhibited characteristics such as limited scale, disorderliness, and multi-dimensionality, which is extremely inconvenient for users of deep application. While single-label classification techniques can effectively categorize textual information, they face challenges in leading science and technology news classification due to a lack of a hierarchical knowledge framework and insufficient capacity to reveal knowledge integration features. This study proposes a hierarchical multi-label classification model for science and technology news, enhanced by heterogeneous graph semantics. The model captures multi-dimensional themes and hierarchical structural features within science and technology news through a hierarchical transmission module. It integrates graph convolutional networks to extract node information and hierarchical relationships from heterogeneous graphs, while also incorporating prior knowledge from domain knowledge graphs to address data scarcity. This approach enhances the understanding and classification capabilities of the semantics of science and technology news. Experimental results demonstrate that the model achieves precision, recall, and F1 scores of 84.21%, 88.89%, and 86.49%, respectively, significantly surpassing baseline models. This research presents an innovative solution for hierarchical multi-label classification tasks, demonstrating significant application potential in addressing data scarcity and complex thematic classification challenges.

Spatiotemporal Analysis of High-Quality Development and Coordination in Cities Along the Lower Yellow River

The current urban development in cities along the Lower Yellow River is in tension regarding human–land relations. To achieve the goals of ecological protection and high-quality development (HQD), it is urgent to scientifically measure and analyse the region’s function development and development coordination (DC). This study focuses on cities along the Lower Yellow River, constructs a three-dimensional HQD assessment framework based on urban functions through multiple remote sensing data, and evaluates DCs by feature classification. The results show the following: (1) The HQD of the study area shows a trend of decreasing and then increasing during 2000–2020 and reaches its highest level at the end. HQD shows a spatial trend of decreasing from south to north and from east to west. (2) The overall agricultural function of the study area declined slightly; the ecological function declined first and then increased, with the highest value occurring in 2000; and the urban function increased steadily and improved significantly after 2015. (3) DCs under different administrative levels are polarised, with high-level DCs exhibiting a spatial leader effect. (4) Urban development preferences in the study area are divergent, and the functional type with the highest share under different administrative scales is agro-ecological, which is mainly influenced by differences in natural base. This study reveals the characteristics of HQD and functional changes in cities along the Lower Yellow River, combined with a hierarchical classification of DCs and the types of development preferences, providing a reference for the formulation of spatial governance strategies.

Copy number variation heterogeneity reveals biological inconsistency in hierarchical cancer classifications

Cancers are heterogeneous diseases with unifying features of abnormal and consuming cell growth, where the deregulation of normal cellular functions is initiated by the accumulation of genomic mutations in cells of - potentially - any organ. At diagnosis malignancies typically present with patterns of somatic genome variants on diverse levels of heterogeneity. Among the different types of genomic alterations, copy number variants (CNV) represent a distinct, near-ubiquitous class of structural variants. Cancer classifications are foundational for patient care and oncology research. Terminologies such as the National Cancer Institute Thesaurus provide large sets of hierarchical cancer classification vocabularies and promote data interoperability and ontology-driven computational analysis. To find out how categorical classifications correspond to genomic observations, we conducted a meta-analysis of inter-sample genomic heterogeneity for classification hierarchies on CNV profiles from 97,142 individual samples across 512 cancer entities, and evaluated recurring CNV signatures across diagnostic subsets. Our results highlight specific biological mechanisms across cancer entities with the potential for improvement of patient stratification and future enhancement of cancer classification systems and provide some indications for cooperative genomic events across distinct clinical entities.

Optimizing Hierarchical Condition Category-Risk Adjustment Factor Management in Population Health Using Rapid Process Improvement Methods.

Centers for Medicare & Medicaid Services provides reimbursement through Hierarchical Condition Category (HCC) coding. Medical systems strive toward risk adjustment optimization, often implementing costly chart review processes. Previously, our organization implementing countermeasures through workflows was complex and performed in silos. Our goal was to put in place HCC-Risk Adjustment Factor (RAF) improvement tools to optimize HCC-RAF management in Population Health using rapid process improvement methods. In this quality improvement analysis (IRB#806198), we used Lean methodology to develop tools and implement streamlined processes for providers to manage, document, and code high-risk HCC conditions. Rather than applying costly countermeasures, Transformational Healthcare conducted a Rapid Process Improvement Workshop (RPIW), with workgroups implementing proposed changes, to improve processes. Each of these tools was embedded in standard work, for teams to use in practice. Tools included the development of RPIW-inspired work groups, a Provider Education website, tip sheets, clinical champions, trainings, audits, practice alerts, smart phrases, schedule view tools, severity scores, reports, dashboards, on-screen decision-support tools, coding expertise, and HCC standard work. Quantitatively, Year 1 showed enterprise HCC-RAF scores improved by 4.1%. We were able to develop tools for providers and team members to allow for more optimized pathways. Although quantitatively we realized an improvement in enterprise HCC-RAF score, our overall aim was to improve process flow and limit waste. Leveraging Lean improvement methods for the collective design of tools has supported culture change. In the end, we found that providers are indeed willing to adopt these newly built tools. These tools have optimized operations, allowing providers to work smarter, not harder.

A Field-Programmable Gate Array-Based Adaptive Sleep Posture Analysis Accelerator for Real-Time Monitoring.

This research presents a sleep posture monitoring system designed to assist the elderly and patient attendees. Monitoring sleep posture in real time is challenging, and this approach introduces hardware-based edge computation methods. Initially, we detected the postures using minimally optimized sensing modules and fusion techniques. This was achieved based on subject (human) data at standard and adaptive levels using posture-learning processing elements (PEs). Intermittent posture evaluation was performed with respect to static and adaptive PEs. The final stage was accomplished using the learned subject posture data versus the real-time posture data using posture classification. An FPGA-based Hierarchical Binary Classifier (HBC) algorithm was developed to learn and evaluate sleep posture in real time. The IoT and display devices were used to communicate the monitored posture to attendant/support services. Posture learning and analysis were developed using customized, reconfigurable VLSI architectures for sensor fusion, control, and communication modules in static and adaptive scenarios. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed Board-based field-programmable gate array (FPGA) Xilinx board was used for experimental validation.

LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars

With the increasing amount of astronomical observation data, it is an inevitable trend to use artificial intelligence methods for automatic analysis and identification of light curves for full samples. However, data sets covering all known classes of variable stars that meet all research needs are not yet available. There is still a lack of standard training data sets specifically designed for any type of light-curve classification, but existing light-curve training sets or data sets cannot be directly merged into a large collection. Based on the open data sets of the All-Sky Automated Survey for SuperNovae, Gaia, and Zwicky Transient Facility, we construct a compatible light-curve data set named LEAVES for automated recognition of variable stars, which can be used for training and testing new classification algorithms. The data set contains a total of 977,953 variable and 134,592 nonvariable light curves, in which the supported variables are divided into six superclasses and nine subclasses. We validate the compatibility of the data set through experiments and employ it to train a hierarchical random forest classifier, which achieves a weighted average F1-score of 0.95 for seven-class classification and 0.93 for 10-class classification. Experimental results prove that the classifier is more compatible than the classifier established based on a single band and a single survey, and has wider applicability while ensuring classification accuracy, which means it can be directly applied to different data types with only a relatively small loss in performance compared to a dedicated model.

Revictimization of women victims of intimate partner violence in the French press: A social representations approach.

This study aims to analyze how the press portrays cases of revictimization experienced by women in France, through the lens of social representation theory. An exploratory, descriptive study was conducted using a corpus of 157 online press articles. A total of 187,773 words and 5,240 segments were analyzed using Iramuteq version 7, employing top-down hierarchical classification and lexical similarity analysis. The analysis identified four classes: 1. Violent incidents (25.5%); 2. Political actions against violence (26%); 3. Institutional revictimization (26.6%) and 4. Violence and breaking the silence (21.9%). The findings illustrate how media portrayals contribute to the construction of social representations surrounding revictimization. Class 1 reveals a focus on extreme cases of violence, such as femicides, and highlights a potential gender bias in media reporting through the omission of terms like 'femicide'. Class 2 demonstrates the influence of ideological perspectives on the portrayal of political measures against violence, with conservative outlets framing such actions within traditional values, while progressive newspapers advocate for systemic reform. Class 3 shows a gap in the conceptualization of institutional revictimization in the French press compared to other countries, revealing how conservative portrayals can minimize systemic issues and perpetuate harmful stereotypes. Class 4 shows the role of female journalists in addressing various forms of violence, despite the limited impact of the #MeToo movement in recent coverage. Collectively, these findings elucidate how media representations shape public attitudes and institutional responses to gender-based violence through the lens of social representations.