Search Results (398)

Social inclusion has become a widely shared normative commitment across research on education, health and development, yet its enactment often remains uneven and difficult to sustain. This concept paper develops the idea of “Lost in Inclusion” to examine how inclusion is translated, unsettled and partially lost as it moves across theory, method and territory. Drawing on practice vignettes from four years of a transdisciplinary community-based research project in rural Bolivia, the article explores three sites of tension: the encounter between epistemic openness and academic demands for certainty; the translation of participation into intervention-oriented action; and the recognition of children and young people as knowledge producers. Rather than treating barriers as pre-existing obstacles, the article argues that barriers may emerge through the very practices intended to enact inclusion. It distinguishes between where inclusion gets lost, as inclusive commitments become legible within institutional and methodological logics, and how researchers and partners may become lost in the pursuit of inclusion itself when its normative force makes tensions difficult to question. The article proposes a relational and ethically reflexive approach to inclusion-oriented research, in which success depends less on eliminating tension than on protecting the conditions that keep participation and recognition open to negotiation. Full article

The safe and stable operation of coal-fired generating units is of critical strategic importance for ensuring the reliable supply of power systems. However, the fault evolution of industrial thermal systems exhibits the characteristics of strong nonlinearity and a long incubation period, coupled with the extreme scarcity of key fault samples (Few-shot) in actual production, which severely limits the engineering application of traditional data-driven diagnostic methods. Existing deep learning models, which are highly dependent on massive and balanced labeled data, not only struggle to overcome the overfitting bottleneck in scenarios with scarce fault samples, but also frequently introduce severe label noise (Label Noise) by ignoring the physical incubation period of faults, resulting in the degradation of the model’s decision boundary. To address the above challenges, this paper proposes a novel fault diagnosis framework integrating multi-scale spatiotemporal feature engineering and the Tabular Prior-Data Fitted Network (TabPFN). Starting from the physical mechanism of the system, this paper develops a dynamic label cleaning strategy based on multivariate statistical deviation, which accurately defines the fault divergence point to eliminate the noise in the incubation period. The constructed multi-scale spatiotemporal feature engineering integrating first-order difference and sliding window statistics can effectively map the transient mutation and steady-state evolution trend of the system. The introduced pre-trained TabPFN model based on the Transformer architecture, relying on its Bayesian inference capability and in-context learning (In-Context Learning) mechanism, can realize parameter-tuning-free and efficient classification for scarce samples. Experiments based on high-fidelity dynamic simulation data from GE Steam Power show that under the strict setting of limiting the training set to only 2000 samples, the proposed method achieves a comprehensive diagnostic accuracy of up to 99.29% and an F1-score of 0.9929 for seven typical operating conditions. Multi-dimensional comparative experiments and ablation studies confirm that the proposed framework comprehensively outperforms six mainstream baseline models, including XGBoost and SVM, in terms of precision, recall, and anti-interference robustness, and also delivers outstanding performance when benchmarked against deep learning models. This provides a brand-new theoretical perspective and technical paradigm for equipment health management in the context of industrial big data. Full article

In semi-arid Ghana, girls bear primary responsibility for household water collection in communities without on-premises access, a burden intensified by climate change associated with rising temperatures and frequent droughts. Yet little is known about how this responsibility shapes their embodied health and well-being. Guided by a political ecology of health framework, this qualitative study examined how water insecurity shapes girls’ physical, psychosocial, and neurological health in two communities, Wechiau and Kandue, in Ghana’s Upper West Region. We conducted in-depth interviews with nineteen purposively selected girls aged 10 to 19 and analyzed the transcripts thematically. Four interconnected themes emerged: structural and political determinants of water access, environmental and ecological conditions, gendered social relations, and embodied health consequences. Financial barriers, governance failures, and infrastructure deficits force girls into long queues and repeated trips, a burden that falls disproportionately on girls under cultural norms exempting boys from water duties. This gendered allocation of labour produces gendered health harm, whereby girls sustain musculoskeletal pain and injury carrying heavy loads over hazardous terrain, while chronic time loss disrupts sleep, schooling, and psychological well-being. Girls with pre-existing conditions such as epilepsy face the most acute risk, as water fetching directly exacerbates their vulnerability to seizures. These findings show that addressing this crisis requires more than infrastructure investment. Coordinated public–private investment in water access must be paired with social protection for vulnerable households, gender-responsive water policy, and rainwater-harvesting strategies that confront the structural and gendered inequities placing this burden on girls. Full article

Data silos and topological incompatibility between building information modeling (BIM) geometric models and finite element method (FEM) analysis models in transportation infrastructure engineering represent critical bottlenecks that impede real-time digital twin analysis and the intelligent transformation of the industry. Based on a critical review of existing BIM-to-FEM conversion methods and their limitations, this study proposes a “BIM-FEM” seamless conversion and dynamic twin mapping method that integrates parametric modeling with finite element meshing, with modeling and repair time reduced from 16 h to 3 h, and the maximum element aspect ratio improved from 84.78 to 16.59. In terms of geometric topology, we propose a collaborative construction method in which finite element hexahedral meshing rules drive BIM parametric modeling in reverse. By regularizing the decomposition of axis lines and cross-sectional feature points of linear transportation structures and optimizing their topology, we achieve fully automated hexahedral meshing without topological errors. In terms of mechanical analysis, an “offline pre-solution, online superposition” computational order-reduction model is proposed. This reduces the high-dimensional full-range finite element solution of dynamic traffic loads to a dot product operation between the influence line matrix and real-time load vectors, enabling sub-second computational response under high-concurrency dynamic traffic conditions—specifically, single-point mapping takes less than 0.27 ms, incremental updates are controlled within 0.2 s. In terms of spatiotemporal mapping and system applications, a high-fidelity “FEM-BIM” mapping mechanism based on inverse isoparametric transformation and AABB (Axis-Aligned Bounding Box) spatial indexing has been established, supporting real-time rendering of 3D cloud maps on the web and digital twin applications in engineering. Applications of this method in real-world bridge engineering digital twin systems have demonstrated its ability to perform automatic structural safety assessments and health condition predictions with an overall computation time reduction of approximately 73% compared to conventional approaches. This addresses the shortcoming of traditional structural health monitoring—which emphasizes sensor-based identification over mechanistic evaluation—and provides a viable path for intelligent, precise management and maintenance of transportation infrastructure throughout its entire life cycle. Full article

Background: Periodontal disease has been increasingly associated with several systemic conditions, including diabetes mellitus. During pregnancy, both periodontitis and diabetes mellitus—including gestational and pre-existing forms (type 1 and type 2)—share common inflammatory and metabolic pathways that may influence maternal and neonatal outcomes. However, the consistency and strength of this association remain under debate. Objective: Our objective was to synthesize the available evidence from systematic reviews and meta-analyses on the association between maternal periodontal disease and diabetes mellitus during pregnancy, as well as its relationship with adverse pregnancy outcomes. Methods: An overview of systematic reviews was conducted following PRISMA recommendations. A comprehensive search was performed in PubMed, Scopus, Web of Science, and Cochrane CENTRAL from inception to the search date. A total of 161 records were identified, with 23 articles assessed in full text. Finally, nine systematic reviews were included, seven of which incorporated meta-analyses. Methodological quality was evaluated using AMSTAR 2, and risk of bias was assessed with ROBIS. Due to heterogeneity, a narrative synthesis was conducted. The degree of overlap among reviews was quantified using the corrected covered area (CCA). Results: The evidence consistently suggests a positive association between periodontal disease and diabetes mellitus during pregnancy, with increased risk reported in multiple meta-analyses, particularly for gestational diabetes mellitus. Periodontitis was also associated with adverse pregnancy outcomes, including preeclampsia, preterm birth, and low birth weight, although findings were not entirely homogeneous. The methodological quality of the included reviews was variable, predominantly low or critically low. A high level of overlap among reviews was observed (CCA = 15.2%). Conclusions: Current evidence supports an association between maternal periodontal disease, diabetes mellitus during pregnancy, and adverse maternal-fetal outcomes. However, these findings should be interpreted with caution due to methodological limitations and study overlap. Periodontal assessment in prenatal care and oral health education may be preventive strategies. Further studies are needed to clarify causality and evaluate these interventions. Full article

Background: COVID-19 vaccination has demonstrated a favorable safety profile in children; however, factors associated with the occurrence of adverse events (AEs) following immunization remain insufficiently characterized. This study aimed to identify the demographic and clinical factors associated with AEs following COVID-19 vaccination in children aged 5–11 years. Methods: We conducted an ambispective observational cohort study in Madrid, Spain, including children aged 5–11 years vaccinated with the 10 µg formulation of BNT162b2 (Comirnaty^®, Pfizer-BioNTech). Data were obtained from vaccination registries, electronic health records, and structured telephone interviews with parents or legal guardians. Demographic characteristics, anthropometric variables, previous SARS-CoV-2 infection, medical history, and concomitant medication use were evaluated. Univariable analyses and multivariable logistic regression were performed to identify factors independently associated with the occurrence of suspected AEs. Results: A total of 2026 children were included. Suspected AEs were reported in 759 participants (37.5%). In univariable analyses, greater weight, height, pre-existing medical conditions, concomitant medication use, clinically vulnerable status, and previous SARS-CoV-2 infection were significantly associated with AEs. In the multivariable model, previous SARS-CoV-2 infection (OR 4.07, 95% CI 3.13–5.30), number of concomitant medications (OR 7.77, 95% CI 5.22–11.56), number of pre-existing medical conditions (OR 1.86, 95% CI 1.62–2.14), and height (OR 1.03, 95% CI 1.01–1.04) remained independently associated with AEs. Age, sex, body mass index, and clinically vulnerable status were not independently associated with AE occurrence. Conclusions: In this large real-world pediatric cohort, previous SARS-CoV-2 infection, comorbidity burden, and concomitant medication use were independently associated with adverse events following COVID-19 vaccination. These findings suggest that underlying clinical complexity may be a more important determinant of post-vaccination reactogenicity than demographic characteristics and support the value of individualized pre-vaccination assessment in pediatric populations. Full article

Background: Health assessment in custodial settings represents a core medico-legal function, particularly when clinical changes may influence detention compatibility and judicial decisions. Objective documentation of disease progression during incarceration is essential to ensure respect for the principle of healthcare equivalence and the protection of fundamental rights. Methods: A retrospective descriptive medico-legal analysis was conducted on 37 male inmates referred for forensic evaluation. Standardized assessments included anamnesis, physical and psychiatric examination, evaluation of medical records, and comparative evaluation of pre-detention and detention-period health status. Results: Pre-existing conditions accounted for 73% of pathologies, while 27% developed during imprisonment. Clinical deterioration occurred in 35.1% of inmates (13 cases). Psychiatric disorders were frequent (27%, 10 inmates), predominantly depressive. Conclusions: The findings indicate that imprisonment may constitute a setting that contributes to the progression of medical and psychiatric conditions, highlighting structural and organizational challenges within penitentiary healthcare. Strengthening standardized medico-legal assessment protocols, improving multidisciplinary collaboration, and ensuring continuity of care between prison and community health services are crucial steps toward protecting inmates’ health. This study provides an exploratory assessment of health conditions in a cohort of inmates within the Italian correctional system, emphasizing the central role of healthcare in custodial settings. Since many Italian correctional facilities are equipped with well-developed healthcare services and appropriate diagnostic and therapeutic resources, numerous acute and chronic medical conditions can be effectively managed within the prison setting, ensuring continuity of care while limiting the need for transfer to external healthcare facilities. Full article

Considered a global public health priority, sepsis is characterized by life-threatening organ dysfunction caused by a dysregulated host response to infection. Its heterogeneous clinical presentation arises, in part, from pre-existing chronic conditions such as hypertension, metabolic syndrome, diabetes, alcohol exposure, psychosocial stress, and periodontitis, which induce persistent systemic changes even before the infectious event. This narrative review synthesizes evidence from experimental models and clinical studies to clarify the molecular and immunological mechanisms by which chronic conditions influence the septic state. We discuss how these conditions converge on common pathophysiological mechanisms, including low-grade chronic inflammation, oxidative stress, endothelial and mitochondrial dysfunction, and changes in the microbiota and neuroimmune regulation. Pathways such as TLR-NF-κB signaling and the NLRP3 inflammasome are maintained in a basal state of activation, lowering the threshold for hyperinflammatory responses and increasing the risk of multiple organ dysfunction syndrome. In conclusion, understanding these phenotypes can guide the identification of biomarkers and the development of personalized therapeutic strategies, thereby moving beyond one-size-fits-all approaches to the management of sepsis and septic shock. Full article

Objectives: To analyze adolescent suicide rates during the COVID-19 pandemic, examine the relationship between bullying, cyberbullying, and suicidal behavior, and identify evidence-based prevention strategies. Methods: A systematic review of 26 peer-reviewed articles (published between 2020 and 2025) was conducted across five databases. Studies were appraised for quality using STROBE and SRQR guidelines. Results: The pandemic significantly impacted adolescent mental health, increasing depression, anxiety, and suicidal ideation, particularly among females and vulnerable populations. Notably, while traditional bullying decreased during school closures (78.2% reduction), cyberbullying reports saw a substantial increase (with a 264.4% spike documented in national helpline data). Key risk factors included social isolation, excessive social media use, pre-existing mental health conditions, and low socioeconomic status. Protective factors included family support and access to mental health services. Conclusions: COVID-19 created a significant adolescent mental health crisis. The shift to cyberbullying represents a critical emerging threat. Effective prevention requires a multitiered approach including universal screening, telehealth, and social–emotional learning programs while addressing the persistent shortage of mental health professionals. Full article

Controlled bridge simulation and laboratory damage identification require models that can simulate structural responses and infer localized stiffness loss from limited measurements. Existing Fourier Neural Operator (FNO)-based vehicle–bridge interaction (VBI) models provide efficient surrogates for these mappings, but the global Fourier representation can smooth localized damage transitions and introduce boundary-related errors for finite-span bridge responses. This study adapts the Wavelet Neural Operator (WNO) to the VBI setting and develops the Vehicle–Bridge Interaction Wavelet Neural Operator (VIWNO), an application-oriented framework for wavelet-domain operator learning between structural response fields and damage fields. VIWNO is pre-trained on a numerical VBI finite-element dataset (VBI-FE) and fine-tuned using only healthy-state measurements from a scaled VBI experimental dataset (VBI-EXP), before being evaluated on unseen laboratory damage scenarios. Under the controlled VBI-FE setting, where bridge, vehicle, speed, and measured road-profile parameters are fixed and the main variation is the damage field, VIWNO reduces forward response errors by 20–30% and inverse damage-estimation errors by 26–32% relative to the FNO-based Vehicle–Bridge Interaction Neural Operator (VINO) baseline. Additional morphology and operating-condition stress tests show that the error increases under sharper damage fields and perturbed VBI conditions, but VIWNO remains more accurate than VINO and the added convolutional or frequency-domain baselines in the tested cases. On VBI-EXP, projection-only healthy-state fine-tuning reduces intact false-damage levels and yields sharper damage estimates than VINO under both displacement and acceleration inputs. Stability checks over five initializations and repeated vehicle passages show limited variation in the reported inverse metrics. These results support the feasibility of wavelet-domain neural operators for calibrated VBI simulation and scaled laboratory damage identification, while field-scale bridge health monitoring still requires validation under broader traffic, environmental, support, and damage-morphology variability. Full article

The aim of the study was to analyse the epidemiological characteristics of confirmed COVID-19 cases in the Tumbes region between March 2020 and December 2022. Method: An ecological design with a descriptive approach was used for the study population, which consisted of 51,421 confirmed cases of COVID-19 residing in the provinces of Contralmirante Villar, Tumbes, and Zarumilla in the department of Tumbes. Confirmed cases of COVID-19 reported in SISOVID by the Ministry of Health and the registry of confirmed cases of COVID-19 in all age groups were recorded. The sociodemographic data collected was supplemented with information available on the web platform of the National Institute of Statistics and Informatics (INEI). The data were organized into databases and analyzed using software such RStudio (versión 2024.12.1), applying epidemiological indicators (incidence, prevalence, mortality, and lethality). The epidemiological data were organized and analyzed in a database prepared in Microsoft Excel to consolidate the information through data filtering and organization. Results: Among the main findings, a decrease in incidence was observed between 2021 (7840/100,000 inhabitants) and 2022 (5721/100,000 inhabitants), although the cumulative case rate increased from 15% to 20%. The mortality rate fell significantly from 2.57 per 1000 inhabitants in 2021 to 0.33 in 2022. The highest fatality rates were concentrated among people over 60 years of age with comorbidities, especially men. Conclusion: The pandemic was unevenly distributed, affecting adults between 30 and 59 years of age, populations with pre- existing conditions, and densely populated urban areas the most. The geographic distribution of cases helped identify areas warranting priority for improving the health response. It is recommended to strengthen epidemiological surveillance, improve access to health services in densely populated areas, and prioritise care for vulnerable groups such as older adults and indigenous peoples. Full article

Background/Objectives: Healthcare institutional betrayal (HIB) is a betrayal trauma that occurs when a healthcare organization or system perpetrates acts of wrongdoing against or fails to act to protect an individual who depends upon that system for care and services. Known consequences of experiencing acts of HIB include increased healthcare provider and system distrust and anticipated healthcare avoidance, highlighting the public health implications of unaddressed HIB. Little is known about actions that healthcare stakeholders can take to repair HIB. Thus, this study experimentally tested the effects of receiving one of two reparative actions following a healthcare encounter containing HIB (empathic apology vs. organizational change) performed by one of two healthcare system stakeholders: healthcare provider or system administrator. Methods: Residual HIB perceptions, trust, expectations for future healthcare, and intentions to avoid future care were assessed post-repair conditions. Initially, undergraduate participants (N = 198) were asked to imagine themselves experiencing a common healthcare scenario which included HIB. After post-HIB encounter baseline measurements, participants were then randomly assigned to one of four conditions (three with HIB repair actions vs. one control). Participants receiving any type of HIB repair reported significantly lower residual HIB, increased positive expectations for future healthcare, and greater trust in healthcare post-repair, with effect sizes ranging from small to large. Results: Generally, two HIB repair conditions (organizational change to repair HIB and healthcare provider apology repair) outperformed the healthcare administrator apology condition; all repair conditions outperformed the control condition. Conclusions: Our finding that specific actions can facilitate post-HIB recovery is clinically meaningful. Medical professionals and healthcare administrators need to address patients’ past negative experiences with healthcare and take action to repair pre-existing HIB to improve patients’ ongoing and future healthcare experiences. Full article

This longitudinal cohort study examined acute and mid-term mental health impacts among 4346 survivors of the 2018 Camp Fire in Northern California using a retrospective survey that asked participants to report symptoms at one, three, and six months post-wildfire. Forty-one percent of participants reported at least one mental health symptom three weeks after the wildfire. Mental health symptoms declined over the six-month follow-up. The mean number of mental health symptoms was 3.7 (95% CI, 3.3–4.1) at three weeks, 3.3 (3.0–3.7) at one month, 3.1 (2.8–3.4) at three months, and 2.7 (2.5–3.1) at six months. Among survivors reporting one or more symptoms, severe home damage was associated with a 1.6-fold higher number of mental health symptoms, preexisting mental health conditions and job loss due to the wildfire were each associated with 1.2-fold increase, and prior physical disabilities with a 1.1-fold increase. Loss of a family member or friend and living alone were also associated with a higher number of mental health symptoms, whereas both younger age (<18 years) and male sex were associated with fewer symptoms. Wildfire exposure is associated with increased mental health risks, particularly among those with preexisting conditions and significant fire-related losses. Full article

Background: Tuberculosis (TB) remains a leading cause of global morbidity and mortality, yet its impact extends far beyond microbiological cure. Many TB survivors experience persistent structural lung damage or functional impairment consistent with post-TB lung disease, while growing evidence highlights long-term non-respiratory health outcomes. Synthesizing evidence across lung and non- respiratory health outcomes, along with their risk factors, is critical to inform long-term TB care. Methods: We conducted a scoping review including systematic reviews reporting on post-TB long-term health outcomes. A search was performed in PubMed/MEDLINE on 27 July 2025, using terms related to “tuberculosis,” “systematic review,” “meta-analysis,” “sequelae” and “long-term health outcomes,” without language restrictions. Results: Nine systematic reviews met inclusion criteria. Most focused on pulmonary outcomes and consistently demonstrated that TB is associated with chronic airflow obstruction, reduced lung function, and an increased long-term risk of lung cancer, although residual confounding from environmental, clinical, and socioeconomic factors cannot be excluded. While younger adults are more prone to developing COPD after TB in high TB burden settings, older individuals face a higher risk of broader post-TB lung sequelae. Evidence suggested that TB survivors are at increased risk of non-respiratory complications. HIV co-infection, low CD4 counts, older age, pre-existing hepatitis, prior TB treatment, and hypoalbuminemia were associated with post-TB liver injury, while baseline hearing impairment and HIV co-infection increased the likelihood of post-TB hearing loss. TB was also linked to elevated risk of several non-pulmonary cancers, including oesophageal, cervical, hematological, pancreatic, and gastric malignancies, with the highest risk within the first year after TB diagnosis and persisting, though attenuated, in subsequent years. Conclusion: TB should be viewed as a chronic condition with enduring lung and non-respiratory health outcomes. TB survivors face increased risks of COPD, lung cancer, and a range of non-respiratory health outcomes, including hepatic and auditory complications, particularly among high-risk groups, such as those living with HIV infection, along with baseline hearing and hepatic impairment. Public health programmes must extend care beyond microbiological cure to include integrated, post-TB long-term monitoring of lung, hepatic and hearing across all ages, including malignancy surveillance, after baseline assessments to identify high-risk TB survivors. Future research should also elucidate risk factors for post-TB malignancy, and clarify the relationship between neurological, renal, and musculoskeletal sequelae and TB to inform evidence-based TB survivorship care. Full article

Artificial intelligence (AI) has emerged as a transformative tool for plant health monitoring, offering new opportunities for scalable, timely, and data-driven pest and disease management in agriculture. This review provides a comprehensive synthesis of AI-based methods for pest and plant disease detection, systematically organizing existing literature across sensing modalities, learning paradigms, and deployment scales. We distinguish between population-level pest monitoring, plant-centric visual inspection, and field-scale surveillance, as well as between post-symptomatic disease recognition and pre-symptomatic detection enabled by spectral imaging technologies. Beyond summarizing recent advances, this work places strong emphasis on critical analysis, discussing fundamental limitations related to data scarcity, domain shift, generalization under field conditions, and the challenge of disentangling biotic from abiotic stress factors. The review further examines the distinction between correlation-driven AI predictions and causal disease understanding, positioning AI as a complementary decision-support tool alongside established diagnostic methods. Building on these insights, we outline key future research directions, including multimodal sensor fusion, explainable and trustworthy AI, edge-based deployment for real-time monitoring, and the development of foundation models for unified agricultural intelligence. This review aims to serve as both an accessible entry point and a critical reference for advancing AI-driven plant health management. Full article