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Multi class photoplethysmography-based deep model for cardiovascular disease classification

Background cardiovascular disease is the leading global cause of mortality. Photoplethysmography (PPG), widely embedded in consumer wearables, offers a scalable diagnostic modality. However, prior approaches are often constrained by handcrafted features, binary classification, and poor generalizability, limiting their clinical impact. Methods A deep hierarchical convolutional neural network (CNN) was designed to extract both morphological and rhythmic characteristics directly from raw photoplethysmography (PPG) signals. The architecture employs progressively structured convolutional filter hierarchies to capture multi-scale signal features. To enhance signal stability and training efficiency, a dual-stage normalization strategy was implemented, consisting of Z-score standardization followed by Min–Max scaling. In addition, batch normalization and dropout regularization were incorporated to improve model generalization and reduce the risk of overfitting. The proposed framework was trained and evaluated on a multi-source dataset comprising 612 patients and 2,448 annotated PPG segments distributed across six diagnostic classes: atrial fibrillation (AF), heart failure (HF), acute coronary syndrome (ACS), cerebral vascular accident (CVA), deep vein thrombosis (DVT), and normal sinus rhythm (NSR). Results The model achieved an overall accuracy of 93.48%, a macro-average F1-score of 0.9386, and a Cohen’s Kappa of 0.8968, indicating “almost perfect” agreement. AF and HF were detected with flawless precision and recall (1.000), while ACS achieved high sensitivity (recall 0.964). Errors were primarily confined to physiologically related conditions (e.g., ACS vs. CVA). Inference efficiency was demonstrated with <5 ms per segment on consumer-grade hardware, confirming feasibility for real-time applications. Conclusion The proposed framework advances beyond lightweight but underpowered or overly complex models by combining representational depth with computational efficiency. Limitations include the need for external, multi-center validation and explainability integration. This study establishes a robust foundation for PPG-based, multi-class cardiovascular diagnostics, supporting clinical decision support and next-generation wearable health technologies.

Simulation research on evacuation of public buildings based on BIM technology and fuzzy algorithm

Abstract Large public buildings are characterized by high occupancy and complex functions, which can easily lead to issues such as congestion in evacuation routes and disorderly crowd behavior in the event of a fire. Conducting analyses and simulation assessments of evacuation mechanisms in fire scenarios is of great significance for improving building fire safety standards and emergency management capabilities. At present, there are significant gaps in the existing research on evacuation simulation for public buildings: Most studies rely on fixed parameter assumptions and fail to effectively quantify the influence of subjective factors such as psychological factors, safety awareness, and social roles on evacuation behavior. Moreover, the combination of BIM technology and evacuation simulation mostly focuses on the presentation of spatial geometric information, lacking a deep integration with quantitative methods for quantifying the subjective behavior of personnel, resulting in insufficient authenticity and predictive reliability of evacuation simulations, and making it difficult to precisely support fire protection design and emergency decision-making. In response to this research gap, this study has established an integrated framework that quantifies subjective human-related factors, maps them to key behavioral parameters through fuzzy inference, and couples them with BIM-based fire and evacuation simulations to provide a verifiable linkage between fire scene constraints, human behavior, and evacuation outcomes. This paper employs fuzzy logic theory together with Pyrosim and Pathfinder to investigate the effects of human-related subjective factors and fire scene conditions on fire evacuation safety. A questionnaire survey was conducted to examine how psychological factors, safety awareness, and social roles of pedestrians influence evacuation behavior. Through the reliability and validity test of the valid questionnaire data and the spearman correlation analysis, it is found that there is a significant positive correlation between safety awareness, psychological factors, social roles and the evacuation behavior. Based on fuzzy rules, the domains and membership functions of the linguistic variables representing these factors are defined, enabling the quantification of the influencing factors and the calculation of the initial evacuation speed. Finally, a BIM model was established and applied to the evacuation simulation of a large shopping mall project in Southwest China to verify the feasibility of the fuzzy algorithm and the safety of the evacuation design. This research innovatively combines fuzzy algorithms with BIM technology, making up for the deficiencies of existing studies in the quantification of subjective factors of personnel and the deep integration of BIM technology. It provides a more scientific calculation plan and data for the study of public building evacuation, and offers reference basis for fire protection design, personnel allocation, emergency plan formulation, and rescue operations.

Experimental and computational models for intracardiac flow analysis with blood speckle imaging

Intracardiac flow analysis aims to evaluate blood flow patterns and associated parameters for the assessment of cardiac function. However, there is limited understanding as to how flow parameters are influenced by various sources, such as pressure upstream/downstream and cardiac chamber compliance. The objective of this study was to investigate experimental and computational tissue-mimicking models to be used alongside 2D Blood Speckle Imaging for intracardiac flow analysis. Two geometries, an axisymmetric swell and idealized left ventricle, were utilized. As an initial parameter of interest, the pressure-drop across each geometry was determined from tissue-mimicking phantoms using direct pressure measurements, blood speckle imaging, and 3D computational fluid dynamics simulations with fluid-structure interaction. The results indicate limited quantitative agreement between direct measurements, 2D blood speckle imaging, and 3D computational fluid dynamics, with qualitative agreement capturing a consistent shape of the pressure drop curve between methods. Additionally, the importance of phantom design is demonstrated due to the likely impact of gel thickness on flow patterns and their associated measurements. The findings of this study indicate that future work focusing on the optimization of BSI settings and increasing model complexity with the inclusion of cardiac valves and patient-specific geometries are still required. These models may then allow for further tuning of variables to better understand their effect on various intracardiac flow parameters, and ultimately their clinical applicability.

Horizon scanning for European wild pollinators identifies world-leading legislation as a key opportunity for pollinators

Abstract Wild insect pollinators contribute significantly to agricultural productivity, biodiversity, and ecosystem functioning. Wild pollinators are increasingly affected by multiple interacting stressors. Proactively identifying emerging risks and feasible mitigation strategies will be critical to ensuring the long-term stability of wild pollinators biodiversity and pollination services. We conducted the first continental scale horizon scan focused on wild pollinators in Europe. A structured Delphi-based approach was used to identify emerging issues that may have significant implications for wild pollinators over the coming decade. Ten priority issues were identified, including both potential risks and opportunities. For the first time in a pollinator-focused horizon scan, legislation was identified as a key opportunity, with the European Union Nature Restoration Regulation recognised for its potential to influence pollinator conservation through mandatory restoration and monitoring targets. In contrast, political developments such as the rise of populist parties and post-truth discourse may impede policy implementation. Several issues relating to pesticide use were also identified, including developments in RNA interference technologies and precision application methods, which may reduce non-target impacts if risks are appropriately assessed. These findings provide a foundation for further research and policy evaluation in support of pollinator conservation under changing environmental and political conditions.

Lived experience of cognitive-communication changes for people with acquired brain injury and familiar communication partners: A qualitative evidence synthesis

Background and objectives Cognitive-communication disorder (CCD) is common after acquired brain injury (ABI), reported in about two-thirds of people who sustain an injury. Quantitative studies have found that the disorder can negatively impact a person’s ability to socially re-integrate into the community, return to work or education and achieve a good quality of life. However, little is known about how the disorder impacts people with ABI and the family. Therefore, the aim of this qualitative evidence synthesis was to provide a detailed exploration of the lived experience of CCD for people with ABI and their family members. Methods A systematic literature search was conducted across eight databases (CINAHL Ultimate, PsycINFO, PsycARTICLES, Medline, EMBASE, AMED, Scopus, PubMed) to August 2025. Studies were included if they reported on people with ABI who present with CCD (or similar term) and/or familiar communication partners whereby the impact of the disorder was described. Relevant data were extracted, and studies were critically appraised using the Critical Appraisal Skills Programme (CASP) qualitative checklist and the confidence of the findings was assessed using GRADE-CERQual tool. The final included studies were synthesised using thematic analysis. Results 13 articles met the eligibility criteria and reported on 103 people with ABI with CCD and 66 familiar communication partners including spouses, parents, friends, carers, siblings and children. Methodologies comprised interviews (n = 10), focus groups (n = 1), spoken discourse samples (n = 1) and online survey (n = 1). Eight main analytic themes were identified centred around the experiences of both people with ABI: (1) communicating is not easy; (2) lack of awareness and feeling tired; (3) anxiety, embarrassment and isolation; (4) connecting with others; and (5) participation and identity; and their familiar communication partner: (6) adjusting to giving increased support; (7) emotional toll of supporting; (8) relationship and life role changes. Conclusions This review highlights the broad and unique impacts of CCD for both people with ABI and their familiar communication partners. People with ABI require tolerance to manage their communication difficulties; and communication partners require education, support and training to manage the change in relationship. These findings underpin the need for interventions to include partners in rehabilitation and for therapists to consider the diverse needs of people with ABI including emotions, relationships, social participation and changes to identity.

Integrated immune, apoptotic and mitochondrial gene dysregulation in Long COVID and their association with symptom burden at 10 months post-infection

Individual work performance questionnaire: Translation and validation in Chinese

The Individual Work Performance Questionnaire (IWPQ) serves as a recognized multidimensional instrument employed for the assessment of work performance, covering task performance, contextual performance, and counterproductive work behavior. Although extensively utilized, limited research has explored its psychometric attributes within the organizational framework in China. This study aims to bridge this research void by executing a comprehensive validation investigation with a sample of 833 Chinese workers. Multiple models including the three-factor model, higher-order model, and bi-order model demonstrating strong psychometric properties. Among these, the three-factor model was chosen for more detailed examination. The initial step involved conducting confirmatory factor analysis using AMOS, evaluating factors such as normality, factor loadings, reliability, common method bias, and overall model adequacy. Subsequently, a multigroup confirmatory factor analysis was performed to investigate measurement equivalence among subgroups based on gender. Following this, structural equation modeling in SmartPLS was utilized to assess criterion-related validity by examining the correlation between overall work performance and accomplishment as gauged by Seligman’s PERMA framework. The findings indicated robust psychometric characteristics, with factor loadings surpassing 0.70, high reliability and convergent validity (CR > 0.70 and AVE > 0.50), and adequate model suitability (RMSEA < 0.05). Assessment of measurement invariance validated the stability of the tripartite structure across genders, as evidenced by RMSEA values meeting criteria for both male and female cohorts. Criterion validity assessment unveiled a substantial positive correlation between overall individual work performance and accomplishment (β = 0.511, p < 0.001), denoting a noteworthy predictive capacity. These findings establish the IWPQ as a reliable and conceptually grounded instrument suitable for assessing individual work performance in Chinese organizational contexts.

Insights into protein synthesis dynamics of gilts from the same genetic background and age differing in protein deposition

Abstract Protein synthesis in Low and High protein deposition (PD) gilts, exploring regulatory pathways within the same genetic background and age were studied. Gilts in Low (157 g/d) and High (219 g/d) PD groups underwent jugular vein cannulation to assess insulin, IGF-I and glucose postprandial responses to the same nutrient intake. L[1- 13 C]valine administration enabled measuring protein synthesis rate and efficiency. Results showed 94% greater ( P  < 0.05) fractional synthesis rates in the longissimus dorsi and tended ( P  = 0.10) to a greater (11%) absolute synthesis rate in the liver of High PD gilts. High PD gilts tended ( P  = 0.10) to be more sensitive to insulin. Transcriptomics analyses in muscle identified 67 up-regulated and 102 down-regulated unique genes. Among the up-regulated genes, four olfactory receptors (OR4L1, OR5D13, OR6B2, OR10R2) and one ribosomal protein (RPS15A) present the highest fold-changes in High vs. Low PD gilts. Functional analyses identified six enriched gene ontology terms relate to muscle development, three to protein metabolism and four to signaling pathways. Rap1 signaling and regulation of actin cytoskeleton were over-represented KEGG pathways. High PD gilts exhibit greater protein synthesis and efficiency of protein synthesis, with transcriptomic evidence suggesting changes in the glucose/insulin metabolism and reduced muscle protein degradation.

‘We need to be supported so that we are able to also provide better care’ Well-being and self-care needs among health workers providing HIV care to children and adolescents in Africa: Qualitative findings from 12 high HIV-prevalence African countries

Frontline healthcare workers providing HIV services to children, adolescents and their families in Africa face significant stressors and well-being related challenges. These in turn may also impact their ability to provide high-quality, compassionate care. Despite healthcare providers’ central role in the delivery of care and support for others, there is limited research exploring their well-being and self-care. This study examined the well-being and self-care-related challenges and needs among healthcare workers providing HIV services to children, adolescents and their caregivers in Africa. A qualitative study design was employed, including participatory priority-setting and focus group discussions with 801 providers across 24 sites in 12 high HIV-prevalence African countries. Data were thematically analysed and Orem’s Theory of Self-Care and the Self-Care Matrix were employed to guide the interpretation and organization of findings. The study identified four major themes. First, participants described burnout and personal struggles as well-being related challenges. Second, they described the belief that addressing these challenges was important for their own well-being, as well as the well-being of their patients. Third, awareness of healthcare worker well-being, alongside effective communication are important contributors to a healthier workforce. Lastly, several interventions were suggested, including fostering teamwork, providing education, and offering psychosocial support within the workplace. Findings emphasize the critical need for tailored interventions that can inform future practices and strategies for healthcare worker well-being. Ultimately, these interventions are crucial for better supporting healthcare providers, enhancing their well-being, and addressing the ongoing challenges in HIV care to children and adolescents in Africa.

Overcoming single model bias through GRACE and multi model data reveals Iran water storage depletion drivers

Abstract Rapid changes in terrestrial water storage (TWS) pose serious threats to water security in arid and semi-arid regions such as Iran. However, the inherent uncertainties of individual hydrological models hinder robust assessments of the respective impacts of natural variability and anthropogenic influence on water storage dynamics in these areas. To address this issue, our study integrates GRACE/GRACE-FO satellite gravity data, five mainstream hydrological models (GLDAS-Noah, GLDAS-VIC, GLDAS-CLSM, ERA5, and WGHM), and GPM global precipitation data. Four observational datasets related to precipitation, runoff, and evapotranspiration were derived, and 64 different hydrological model combinations were constructed. These combinations were comprehensively evaluated against Mascon products as a benchmark. Ultimately, the model combination with the best fitting performance was selected for spatial and temporal variation analysis and attribution analysis. The findings reveal that: (1) The model combination constructed using ERA5-derived evapotranspiration and runoff data, combined with precipitation data from VIC/CLSM, exhibits the highest consistency with Mascon data. (2) In densely populated northern and southwestern regions, the natural water flux shows significant upward trends. Nevertheless, TWSA declines there because anthropogenic extraction (captured in the net residual) outweighs the natural increase, causing groundwater discharge to surface systems and amplifying evapotranspiration and runoff losses. (3) In sparsely populated arid central regions, TWSA remains relatively stable, with an average annual natural water anomaly change rate of approximately + 0.01 cm/yr, primarily due to the offsetting effects of precipitation and evapotranspiration. This study systematically evaluates the applicability of a multi-model approach in arid regions. The integrated hydrological modeling framework developed herein provides a methodology for selecting model configurations and quantifying associated uncertainties in water storage assessment, thereby offering a scientific basis for sustainable water resource management in arid environments.

Gender, status, and team interaction: A microdynamic exploration of wearable sensor data across 11 research groups

Diversity research is increasingly moving beyond a static focus on linear relationships between team-level diversity attributes and outcomes toward a dynamic, configurational perspective on team processes. Recent developments emphasise the structural dimensions of interpersonal relations within teams and how dyadic relationships, mutual perceptions, and behaviours shape team-level outcomes over time. Drawing on real-world organisational data, we apply a hierarchical Dynamic Actor Network Model to examine the role of gender homophily, professional status, and gender-based status cues in face-to-face interactions across 11 R&D teams. Our analysis of wearable sensor data reveals nuanced patterns that challenge any clear-cut diversity effects within teams. Although mixed‑gender interactions are generally more common, the interplay between professional status and gender‑based status cues changes across organisational contexts. Professional status shows no clear effect on interaction frequency, whereas gender‑based status effects are observed in research laboratories but not in private companies. These findings underscore the continued relevance of demographic attributes and associated status dynamics, while highlighting the value of a configurational and temporally sensitive approach to understanding team interaction.

Integrating machine learning and multi-criteria decision analysis for health risk management in water distribution networks

Abstract Leakages and breaks in water distribution networks (WDNs) cause significant water losses and pose health risks due to pathogen intrusion. The Water Safety Plan (WSP), developed by the World Health Organization (WHO), provides a comprehensive framework for identifying, assessing, and controlling risks within water supply systems. This study demonstrates the application of the WSP framework through a case study of a WDN in Sweden. Pipe break probabilities were estimated using three classification models: Logistic regression, random forest, and extreme gradient boosting (XGBoost), while hydraulic and health consequences were evaluated using hydraulic modelling and Quantitative Microbial Risk Assessment (QMRA) to quantify the overall health risk. A Multi-Criteria Decision Analysis (MCDA) approach, specifically the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was utilized to prioritize risk mitigation strategies through breakage and leakage control measures. The proposed approach integrates predictive modelling, consequence evaluation, and decision analysis, offering a structured method for water utilities in prioritizing interventions and improving the overall safety and reliability of WDNs.

Venous and arterial thromboembolic events after COVID-19 during the Omicron period in three European countries

Abstract Patients with earlier SARS-CoV-2 variants are at increased risk of venous and arterial thromboembolic (VTE, ATE) events. Here we aimed to contextualise the incidence of thromboembolic events among patients with COVID-19 during the Omicron period. We conducted a population-based cohort study using electronic health records from the UK (CPRD GOLD), the Netherlands (IPCI), and Spain (SIDIAP) within the DARWIN EU ® network. Two cohorts were included: a pre-pandemic population (2017–2019) and individuals infected with SARS-CoV-2 during the Omicron-dominant period. We estimated incidence rates (IRs) of VTE, ATE, and other cardiovascular events at 30-, 60-, 90-, and 180-days post-infection. Crude incidence rate ratios (IRRs) and age-sex standardized incidence ratios (SIRs) were calculated relative to the pre-pandemic cohort. Analyses were stratified by prior infection, vaccination status, and immunocompromised status. In total, we included over 7.6 million individuals (CPRD GOLD: 5.28 M; IPCI: 1.59 M; SIDIAP: 0.75 M) in the general population cohort, and about 0.8 million individuals (CPRD GOLD: 248,847; IPCI: 330,200; SIDIAP: 200,563) in the COVID-19 Omicron cohort. Crude IRs varied by outcome and data source. For VTE, IRs per 100,000 person-years were 136 [95%CI 131–141] in SIDIAP, 167 [164–169] in CPRD GOLD, and 264 [259–270] in IPCI. Elevated SIRs for VTE and ATE were observed following SARS-CoV-2 infection, highest within 30 days and persisting up to 180 days. In CPRD GOLD, the VTE SIR was 3.61 [2.45–5.53] at 30 days, decreasing to 1.88 [1.52–2.34] at 180 days. Higher SIRs were observed among immunocompromised individuals and those without prior infection. Our findings indicate that among individuals diagnosed with SARS-CoV-2 infection during the Omicron-dominant period, observed rates of thromboembolic events exceeded expected background incidence, particularly in the early post-infection period.

Threonine sulfation: a rare post translational modification in insect adipokinetic hormones

Abstract Peptides of the adipokinetic hormone family are responsible for metabolic roles in insects, regulating release of energy metabolites from the fat body. We report on adipokinetic hormone octapeptide sequences bearing a rarely identified post-translational modification (sulfation of a threonine residue) in two beetle subfamilies (Cetoniinae and Dynastinae) of the large superfamily of Scarabaeoidea (dung beetles, rhinoceros beetles and flower beetles), and in a bug species (family Coreidae). In the cetonids Pachnoda sinuata , Dicronorhina derbyana derbyana , Tropinota hirta , Protaetia cuprea , Cetonia aurata and Oxytherea funesta sulfated Pacsi-AKH is found (pQINLTsTGW amide), while sulfated Penid-AKH (pQVNISsTGW amide) occurs in the dynastid beetles Pentodon idiota , Xylotrupes gideon and Syrichthodontus spurius. Sulfated Schgr-AKH-II (pQLNFSsTGW amide) is found in the twig wilter Holopterna alata . Sequence elucidation was achieved by mass spectrometry, however, due to the labile nature of the sulfate group under mass spectrometric conditions, the modified amino acid could not be easily identified. Edman degradation and comparative mass spectrometry evaluations with synthetic sulfopeptide standards were therefore employed for sequence validation. This type of sulfation was previously only reported present on the protein backbone of very few proteins from vertebrates (including humans), a mollusc and a protozoan parasite.

Distinct inhibitory connectivity motifs could trigger distinct forms of anticipation in the retinal network

Abstract Motion is an important feature of visual scenes and retinal neuronal circuits selectively signal different motion features. It has been shown that the retina can extrapolate the position of a moving object, thereby compensating sensory transmission delays and enabling signal processing in real-time. Amacrine cells, the inhibitory interneurons of the retina, play essential roles in such computations although their precise function remain unclear. Here, we computationally explore the potential effects of two different inhibitory connectivity motifs on the retina’s response to moving objects, in a simplified model of the retina: feed-forward and recurrent feed-back inhibition. In this model, both motifs can account for motion anticipation with two different mechanisms. Feed-forward inhibition truncates motion responses and shifts peak responses forward via subtractive inhibition, whereas recurrent feed-back coupling evokes excitatory and inhibitory waves with different phases that interfere and shift the response peak. A key difference between the two mechanisms is how the anticipatory peak shift scales with the speed of a moving object. Motion prediction with feed-forward circuits monotonically decreases with increasing speeds, while recurrent feed-back coupling induces tuning curves that exhibit a preferred speed for which motion prediction is maximal.

Shared pathogens among honey bees, wild bees and hoverflies in mountain ecosystems

Crop establishment-irrigation interactions driven rice (Oryza sativa L.) performance and water productivity

A one-hour delayed school start improves sleep, sleepiness and inhibitory control in early adolescents in a randomized controlled trial

Abstract Early school start times (SST) force adolescents to wake earlier than biologically preferred, creating chronic sleep debt. While evidence supports delaying SSTs, few controlled studies have used objective measures to assess their impact. We conducted a controlled intervention in a French boarding school to evaluate the effects of a one-hour delay in SST on sleep (N = 50) and cognitive functioning (N = 73) in early adolescents (age 12.8 years [11.7–14.2], 66% girls). After a baseline period with 8 a.m. starts (T0), four classes were randomized: half remained at the early schedule (Control-SST), half switched to 9 a.m. (Delayed-SST). Sleep measured by actigraphy, cognitive performance and mental health were assessed at baseline (T0) and 6 months later (T1). Between T0 and T1, total sleep time decreased in the Control-SST group, whereas the Delayed-SST group showed a modest, non-significant increase. This resulted at T1 in a 26-min between-group difference (Cohen’s d = 0.93, p  = 0.007). Sleep onset time did not differ between groups. Sleepiness decreased in Delayed-SST but increased in Control-SST (d = -0.52, p  = 0.042). Inhibitory control improved in Delayed-SST compared to Control-SST (d = -0.79, p  = 0.001), with trends toward better sustained attention (d = -0.40, p  = 0.051). Delaying SST by one hour appears to mitigate the progressive reduction in sleep duration commonly observed during adolescence, and benefits both cognitive functioning and sleepiness outcomes in early adolescence.

Emergence of power laws in hierarchical dynamics on multi-level graphs

Abstract Power law distributions are widely recognized in complex systems as indicative of underlying complexity in interaction networks and critical macroscopic behavior. Previous studies have emphasized the importance of network structure and dynamics in understanding the emergence of such statistical patterns and predicting extreme events. In this study, we investigate the emergence of power law behavior in delay distributions within a multi-level hierarchical network of agents governed by priority rules. Using railway systems as case study, we model the dynamics of high-speed and local trains agents assigned distinct priority levels. By introducing stochastic fluctuations into scheduled travel times, derived from empirical data, we observe that local trains exhibit a markedly higher incidence of larger delays than high-speed trains. We propose a queue-based dynamical model, calibrated using Italian railway data, and validate our findings through comparative analysis with Italian and German datasets. The model reproduces the empirically observed power law exponent associated with the Italian local train delays. Furthermore, we analyze the influence of operational policies, such as priority assignment and delay compensation thresholds, finding their effects both in data and in the model. These results underscore the capacity of simple hierarchical structures and rule-based dynamics to generate complex statistical behaviors without intricate interaction networks.

An ARIMAX model integrating Baidu index for improved hand, foot, and mouth disease incidence prediction