Search Results (1,546)

In magnetometer, accelerometer, and rate gyroscope (MARG) attitude and heading reference systems, accelerometers and magnetometers are susceptible to external acceleration and soft/hard magnetic anomalies, which reduce the attitude estimation accuracy. To address this problem, a sequential adaptive Kalman filter algorithm based on the geophysical field is proposed for anti-interference MARG attitude estimation. By establishing the linear system model based on the gravitational field and geomagnetic field, the singularity and coupling in other system models are avoided. Additionally, the sequential Sage–Husa adaptive strategy is employed to estimate the measurement noise parameters in real time by a specific force and magnetic vector, which suppresses the impact of external acceleration and the soft/hard magnetic anomalies. To verify the effectiveness and advancement of the proposed algorithm, a series of anti-interference experiments were designed. Experimental results show that, compared with the geophysical-field-based Kalman filter algorithm without an adaptive strategy, the proposed improved algorithm reduces the yaw maximum error by over 94% and inclination maximum error by over 21%, which improves the MARG attitude estimation robustness and makes this algorithm superior to the existing three adaptive strategies and two algorithms. Full article

Most studies link transport infrastructure to urbanization using mode-specific stocks or absolute scale, leaving the interactive effects of land transport infrastructure (LTI) and their stage-dependent effects underexplored. To fill this gap, this study examines the interactive effects of relative improvements in LTI on the relationship between urbanization and economic development. Using panel data covering 31 Chinese provinces (2000–2022) and the Gompertz function to model urbanization dynamics and the income elasticities. The modeled urbanization growth rate approaches zero near RMB 600,000 per capita, consistent with a saturation level of 12.2; the peak income elasticity is 1.39 at about RMB 90,000. Results by subsample reveal that high-accessibility roads support early urbanization, while rising incomes see conventional rail, high-mobility roads, and high-speed rail sequentially supporting more sustained urban growth. A mid MPA (balanced high-accessibility and high-mobility roads) fosters more advanced and sustained urbanization, whereas high MPA is detrimental to urbanization. Interaction analysis indicates that prioritizing high-accessibility roads and conventional rail best supports urbanization works at low income; conventional rail and high-mobility roads at mid income; and high-mobility roads and high-speed rail at high income; elasticity patterns mirror these stages. The estimates indicate that a configuration of low RPA and high HPM is linked to lower urbanization. These findings highlight the dynamic and stage-dependent impacts of LTI on urbanization, emphasizing that transport infrastructure strategies should be tailored to different stages of economic development to achieve faster and more sustainable urban growth. Full article

The purpose of this study was to assess workers’ perceptions, experiences, and strategies related to financial health, with the goal of identifying and validating a model of financial resilience aligned with theoretical and empirical fit criteria. A sequential quantitative approach was employed, combining exploratory factor analysis (EFA) to uncover latent dimensions, followed by confirmatory factor analysis (CFA) to validate the resulting structure. This dual methodology was designed to ensure both empirical robustness and theoretical coherence. The study used a non-experimental, cross-sectional design and drew on survey data from 311 employees of a sugar company in San Juan Bautista Tuxtepec, Oaxaca, selected through a non-probabilistic self-selection sampling method. The instrument, based on existing models was administered electronically. Internal consistency was assessed using Cronbach’s alpha (α), McDonald’s omega (ω), composite reliability (CR), and average variance extracted (AVE), while multivariate normality was also examined. Findings reveal that financial resilience encompasses not only recovery from financial shocks but also proactive financial behaviors such as budgeting, long-term saving, and responsible debt management. Respondents emphasized the role of credit history, insurance access, and perceived financial autonomy in promoting both financial stability and emotional well-being. These results contribute to the theoretical conceptualization of financial resilience and have practical implications for policy and financial education with a preventive and mental health-oriented perspective. Full article

Counting and listing triangles in graphs is a fundamental task in network analysis, supporting applications such as community detection, clustering coefficient computation, k-truss decomposition, and triangle centrality. We introduce the cover-edge set, a novel concept that eliminates unnecessary edges during triangle enumeration, thereby improving efficiency. This compact cover-edge set is rapidly constructed using a breadth-first search (BFS) strategy. Using this concept, we develop both sequential and parallel triangle-counting algorithms and conduct comprehensive comparisons with state-of-the-art methods. We also design a benchmarking framework to evaluate our sequential and parallel algorithms in a systematic and reproducible manner. Extensive experiments on the latest Intel Xeon 8480+ processor reveal clear performance differences among algorithms, demonstrate the benefits of various optimization strategies, and show how graph characteristics, such as diameter and degree distribution, affect algorithm performance. Our source code is available on GitHub. Full article

Scientific assessment of sustainable development potential (SDP) and analysis of spatial heterogeneity mechanisms of traditional villages are crucial for promoting the synergy between cultural heritage conservation and rural revitalization strategies. With an emphasis on traditional villages in the Cantonese region, this study develops a thorough evaluation methodology that combines spatial analysis and multi-criteria decision-making. It aims to (1) systematically reveal the spatial differentiation characteristics of sustainable development potential; (2) develop and validate a combined weighting method that effectively integrates both subjective and objective weights; and (3) identify key driving factors and their interaction mechanisms influencing the formation of this potential. To achieve these objectives, the research sequentially conducted the following steps: First, an evaluation indicator system encompassing socioeconomic, cultural, ecological, and infrastructural dimensions was developed. Second, the Analytic Hierarchy Process and the Entropy Weight Method were employed to calculate subjective and objective weights, respectively, followed by integration of these weights using a combined weighting model. Subsequently, the potential assessment results were incorporated into a Geographic Information System, and spatial autocorrelation analysis was applied to identify agglomeration patterns. Finally, the Geographical Detector model was utilized to quantitatively analyze the explanatory power of various influencing factors and their interactions on the spatial heterogeneity of potential. The main findings are as follows: First, the sustainable development potential of traditional Cantonese villages exhibits a significant “core–periphery” spatial structure, forming a high-potential corridor in the Zhongshan–Jiangmen–Foshan border area, while peripheral areas generally display “low–low” agglomeration characteristics. Second, the combined weighting model effectively reconciled 81.0% of case discrepancies, significantly improving assessment consistency (Kappa coefficient above 0.85). Third, we identified economic income (q = 0.661) and ecological baseline (q = 0.616) were identified as key driving factors. Interaction detection revealed that the interaction between economic income and transportation accessibility had the strongest explanatory power (q = 0.742), followed by the synergistic effect between ecological baseline and architectural heritage (q = 0.716), highlighting the characteristic of multi-factor synergistic driving. The quantitative and spatially explicit evaluation framework established in this study not only provides methodological innovation for research on the sustainable development of traditional villages but also offers a scientific basis for formulating regionally differentiated revitalization strategies. The research findings hold significant theoretical and practical importance for achieving a positive interaction between the conservation and development of traditional villages. Full article

For numerous years, researchers have extensively explored real parameter single-objective optimization by evolutionary computation. Among the various types of evolutionary algorithms, Differential Evolution (DE) performs outstandingly. Recently, the academic community has began concerning itself with long-term search. IMODE is a good DE algorithm for long-term search. The algorithm is based on two primary mutation strategies and one secondary. Within the population, the control ratio of each mutation strategy is determined by their respective performance outcomes. Sequential Quadratic Programming (SQP), an iterative method for continuous optimization, is employed on the best individual in the final stage of IMODE at a dynamic probability as a local search method. Based on the DE algorithm, we propose Differential Evolution with Secondary Mutation Strategies (SMSDE). In the proposed algorithm, more secondary mutation strategies are added, in addition to the original one used in IMODE. In each generation, just one of the secondary mutation strategies is activated based on history performance to cooperate with the two primary mutation strategies. In addition, at a dynamic probability, SQP is now called not only for the best individual in the final stage, but also for the worst individual among old ones in each generation. The experimental results demonstrate that SMSDE performs better than a number of state-of-the-art algorithms, including IMODE. Full article

Background/Objectives: Patients in palliative care often experience multifaceted forms of suffering that extend beyond physical symptoms, including existential distress, loss of meaning, and emotional pain. Ketamine-assisted psychotherapy (KAP) has emerged as a promising intervention for alleviating such complex forms of suffering, yet models specifically tailored to palliative populations remain scarce. This narrative review synthesizes current evidence on ketamine’s neurobiological, psychological, and experiential effects relevant to end-of-life care, and presents a novel, time-limited KAP model designed for use in palliative settings. Methods: Drawing from both biochemical and psychedelic paradigms, the review integrates findings from neuroscience, phenomenology, and clinical practice. In particular, it incorporates a dual-level experiential framework informed by recent models distinguishing ketamine’s differential effects on self-processing networks: the Salience Network (SN), related to embodied self-awareness, and the Default Mode Network (DMN), associated with narrative self-construction. This neurophenomenological perspective underpins the rationale for using two distinct dosing sessions. Results: The article proposes a short-course, time-limited KAP model that integrates preparatory and integrative psychotherapy, two ketamine dosing sessions (one low-dose and one moderate-dose), concurrent psychotherapy, goals of care discussion (GOCD), and optional pharmacological optimization. The model emphasizes psychological safety, meaning-making, and patient-centered care. The sequential dosing strategy leverages ketamine’s unique pharmacology and experiential profile to address both bodily and narrative dimensions of end-of-life distress. Conclusions: This dissociative-psychedelic model offers a compassionate, pragmatic, and theoretically grounded approach to relieving psychological and existential suffering in palliative care. By integrating neurobiological insights with psychotherapeutic processes, it provides a flexible and patient-centered framework for enhancing meaning, emotional resolution, and quality of life at the end of life. Further research is needed to evaluate its clinical feasibility, safety, and therapeutic efficacy. Full article

Respiratory viral co-infections by viruses such as influenza virus, SARS-CoV-2, and respiratory syncytial virus (RSV) are a significant clinical issue in high-risk populations such as children, elderly patients, and immunocompromised individuals. Sequential and simultaneous co-infections exacerbate disease severity, leading to acute respiratory distress syndrome (ARDS), prolonged hospitalization, and increased mortality. Molecular and immunological interactions are complex, context-dependent, and largely unknown. Experimental models of infection that accurately mimic human respiratory physiology are required for the study of viral dynamics, virus–virus interactions, and virus–host interactions. This review outlines a range of complex in vitro and ex vivo models, including organoids, air–liquid interface cultures, lung-on-a-chip platforms, and in vivo animal models, highlighting their ability to simulate the complexity of respiratory co-infections and their limitations. The field has developed significantly, despite challenges like variability across viral strains, timing of infection, and non-standardization of models. Integration of multi-omics technologies and application of highly translational models such as non-human primates and lung-on-a-chip technology are promising avenues to uncover the molecular determinants of co-infection and guide development of targeted therapeutic strategies. Interrelatedness of experimental models and clinical outcomes is highly critical to improve prevention and treatment of respiratory viral co-infections mainly among high-risk populations. Full article

Phosphatidylserine (PS) holds considerable importance in both the food and medical sectors; however, its biosynthesis is critically dependent on phospholipase D (PLD). The practical application of PLD is constrained by pronounced side reactions in its free form and by reduced selectivity when immobilized. To address these challenges, this study employed a sequential strategy involving bioimprinting to hyperactivate PLD, followed by microencapsulation via ionotropic gelation within an alginate–chitosan matrix. This approach induced conformational rigidification, enabling PLD to maintain its hyperactivated state in aqueous environments. Under optimal conditions, the encapsulation efficiency reached 78.56%, and the enzyme activity recovery achieved 105.27%. The immobilized bioimprinted PLD demonstrated exceptional catalytic performance, achieving a 94.68% PS yield within 20 min, which significantly surpassed that of free PLD (85.82% in 150 min) and non-imprinted immobilized PLD (90.34% in 60 min). This represents 7.27-fold and 2.14-fold efficiency improvements, respectively. Furthermore, the biocatalyst exhibited outstanding storage stability, thermal stability, and reusability (77.53% yield after 8 cycles). To our knowledge, this is the first report combining bioimprinting with alginate-chitosan microencapsulation via ionotropic gelation, which yielded remarkably enhanced PLD activity. These findings highlight the strong potential of this method for efficient PS production. Full article

Background: Sepsis is a heterogeneous syndrome in which patients with similar clinical presentations at admission may exhibit markedly different treatment responses and outcomes, suggesting that comparable macroscopic features can conceal profoundly distinct perfusion and hemodynamic states. Aim: This study aimed to characterize the hemodynamic profile of patients with community-acquired sepsis, assess its correlation with macro-hemodynamic indices, compare fluid responders with non-responders, and explore the prognostic value of early identification of a feature consistent with distributive shock. Methods: A prospective observational pilot study was conducted in the Intermediate Medical Care Unit (IMCU) of Ospedale Alto Vicentino (Santorso, Italy), September 2024–May 2025. 115 consecutive adults with community-acquired sepsis underwent NICaS^® bioimpedance assessment at IMCU admission. Sepsis was diagnosed at IMCU admission as suspected/confirmed infection plus an acute increase in total Sequential Organ Failure Assessment (SOFA) ≥ 2 points. Hemodynamic indices were analyzed in relation to the Sequential Organ Failure Assessment (SOFA) score and mean arterial pressure (MAP), fluid responsiveness, and 30-day mortality. Results: Hemodynamics were heterogeneous across patients and within SOFA strata. SOFA showed no correlation with SV, SI, CO, or CI; weak inverse associations for TPR (r = −0.198, p = 0.034) and TPRI (r = −0.241, p = 0.009) were observed. MAP did not correlate with SV, SI, CO, or CI, but correlated positively with TPR (r = 0.461) and TPRI (r = 0.547) and with CPI (ρ = 0.550), all p < 0.001. A distributive profile was present in 21.7% (25/115), increasing with higher SOFA (p = 0.033); only 20% of those with this profile had MAP < 65 mmHg at admission. Fluid non-responders (27.8%) had lower resistance and higher CI (4.1 vs. 3.4 L/min/m²; p = 0.015). The distributive profile was not associated with 30-day mortality (log-rank p = 0.808). Conclusions: In IMCU patients with community-acquired sepsis, macro-indices (SOFA, MAP) correlate poorly with the underlying hemodynamic state. Early noninvasive profiling reveals within-SOFA circulatory heterogeneity and may support operational, individualized resuscitation strategies; these pilot findings are hypothesis-generating and warrant prospective interventional testing. Full article

Aim: With the intensification of population aging, the public health challenges posed by multimorbidity have become increasingly severe. This study employs bibliometric analysis to elucidate research hotspots and trends in the field of multimorbidity against the backdrop of global aging. The immediate aim is to systematically map the intellectual landscape and evolving patterns in multimorbidity research. The ultimate long-term aim is to provide a scientific basis for optimizing chronic disease prevention systems and guiding future research directions. Methods: The study adopted the descriptive research method and employed a bibliometric approach, analyzing 8129 publications related to multimorbidity from the Web of Science Core Collection. Using CiteSpace, we constructed and visualized several knowledge structures, including collaboration networks, keyword co-occurrence networks, burst detection maps, and co-citation networks within the multimorbidity research domain. Results: The analysis included 8129 articles from 2004 to 2024, published across 1042 journals, with contributions from 740 countries/regions, 33,931 institutions, and 40,788 authors. The five most frequently occurring keywords were prevalence, health, older adult, mortality, and risk. The top five contributing countries globally were the United States, the United Kingdom, Germany, China, and Spain. Five pivotal research trajectories delineate the intellectual architecture of this field: ① Evolution of Disease Cluster Management: Initial investigations (2013–2014) prioritized disease cluster coordination within general practice settings, particularly cardiovascular comorbidity management through primary care protocols and self-management strategies. ② Paradigm Shifts in Health Impact Assessment: Multimorbidity outcome research demonstrated sequential transitions—from physical disability evaluation (2013) to mental health consequences like depression (2016), culminating in current emphasis on holistic health indicators including frailty syndromes (2015–2019). ③ Expansion of Risk Factor Exploration: Analytical frameworks evolved from singular physical activity metrics (2014) toward comprehensive lifestyle-related determinants encompassing behavioral and environmental dimensions (2021). ④ Emergence of Polypharmacy Scholarship: Medication optimization studies emerged as a distinct research stream since 2016, addressing therapeutic complexities in multimorbidity management. ⑤ Frontier Investigations: Cutting-edge directions (2019–2021) feature cardiometabolic multimorbidity patterns and their dementia correlations, signaling novel interdisciplinary interfaces. Conclusions: The prevalence of multimorbidity is on the rise globally, particularly in older populations. Therefore, it is essential to prioritize the prevention of cardiometabolic conditions in older adults and to provide them with appropriate and effective health services, including disease risk monitoring and community-based chronic disease care. Full article

Speech recognition based on Electroencephalogram (EEG) has attracted considerable attention due to its potential in communication and rehabilitation. Existing methods typically process spatial and temporal features with sequential, parallel, or constrained feature fusion strategies. However, the intricate cross-relationships between spatial and temporal features remain underexplored. To address these limitations, we propose a spatial–temporal coupled cross-attention mechanism through a hierarchical network, named STCCA. The proposed STCCA consists of three key components: local feature extraction module (LFEM), coupled cross-attention (CCA) fusion module, and global feature extraction module (GFEM). The LFEM employs CNNs to extract local temporal and spatial features, while the CCA fusion module leverages a dual-directional attention mechanism to establish deep interactions between temporal and spatial features. The GFEM uses multi-head self-attention layers to model long-range dependencies and extract global features comprehensively. STCCA is validated on three EEG-based speech datasets, achieving accuracies of 45.45%, 25.91%, and 29.07%, corresponding to improvements of 1.95%, 3.98%, and 1.98% over the comparison models. Full article

HIV remains a significant public health challenge in sub-Saharan Africa (SSA), with vulnerable communities disproportionately affected and further marginalised by the COVID-19 pandemic. HIV self-testing (HIVST) has emerged as a transformative, empowering tool to bridge testing gaps and promote health equity. This study examined post-COVID-19 strategies for leveraging HIVST to empower vulnerable populations and advance health promotion in SSA. Analysis was performed using secondary Demographic and Health Survey (DHS) data (2015–2022) collected across 24 SSA countries. In addition, qualitative interviews were conducted with female sex workers in Port Harcourt, Nigeria (18–31 May 2023). The study adopted an explanatory sequential mixed-methods design. Quantitative analysis using complex sample logistic regression revealed low awareness (16.3%) and uptake (2.5%) of HIVST among the 594,639 respondents. Key predictors of uptake included higher education (aOR, 7.36; 95% CI, 6.62–8.18), wealth (richest quintile aOR, 3.28; 95% CI, 2.95–3.65), and knowledge of HIV transmission (aOR, 33.43; 95% CI, 11.03–101.24). Thematic analysis highlighted privacy, autonomy, and convenience as key benefits, while cost, stigma, and fear of testing alone were major barriers. The participants emphasised peer-led outreach and integration of HIVST into public health systems as effective strategies. The findings were integrated interpretively, linking macro-level testing disparities with community-level experiences to inform post-pandemic policy and programme design. The study concludes that HIVST holds strong potential to empower marginalised groups and strengthen community-driven HIV prevention post-COVID-19, but success will depend on equity-driven policies and sustainable implementation frameworks, guided by affordability and community participation. Full article

This study focuses on human awareness, a critical component in human–robot interaction, particularly within agricultural environments where interactions are enriched by complex contextual information. The main objective is identifying human activities occurring during collaborative harvesting tasks involving humans and robots. To achieve this, we propose a novel and lightweight deep learning model, named 1D-ResNeXt, designed explicitly for recognizing activities in agriculture-related human–robot collaboration. The model is built as an end-to-end architecture incorporating feature fusion and a multi-kernel convolutional block strategy. It utilizes residual connections and a split–transform–merge mechanism to mitigate performance degradation and reduce model complexity by limiting the number of trainable parameters. Sensor data were collected from twenty individuals with five wearable devices placed on different body parts. Each sensor was embedded with tri-axial accelerometers, gyroscopes, and magnetometers. Under real field conditions, the participants performed several sub-tasks commonly associated with agricultural labor, such as lifting and carrying loads. Before classification, the raw sensor signals were pre-processed to eliminate noise. The cleaned time-series data were then input into the proposed deep learning network for sequential pattern recognition. Experimental results showed that the chest-mounted sensor achieved the highest F1-score of 99.86%, outperforming other sensor placements and combinations. An analysis of temporal window sizes (0.5, 1.0, 1.5, and 2.0 s) demonstrated that the 0.5 s window provided the best recognition performance, indicating that key activity features in agriculture can be captured over short intervals. Moreover, a comprehensive evaluation of sensor modalities revealed that multimodal fusion of accelerometer, gyroscope, and magnetometer data yielded the best accuracy at 99.92%. The combination of accelerometer and gyroscope data offered an optimal compromise, achieving 99.49% accuracy while maintaining lower system complexity. These findings highlight the importance of strategic sensor placement and data fusion in enhancing activity recognition performance while reducing the need for extensive data and computational resources. This work contributes to developing intelligent, efficient, and adaptive collaborative systems, offering promising applications in agriculture and beyond, with improved safety, cost-efficiency, and real-time operational capability. Full article

Power transformers are critical assets in modern power grids, where failures can lead to significant operational disruptions and financial losses. Dissolved Gas Analysis (DGA) is a key sensor-based technique widely used for condition monitoring, but traditional diagnostic approaches rely on deterministic thresholds that overlook uncertainty in degradation dynamics. This paper proposes a probabilistic framework for Prognostics and Health Management (PHM) of power transformers, integrating self-adaptive Auto Regressive Integrated Moving Average modeling with a probabilistic reformulation of Duval’s graphical methods. The framework enables automated estimation of fault types and failure likelihood directly from DGA sensor data, without requiring labeled datasets or expert-defined rules. Dissolved gas dynamics are forecasted using time-series models with residual-based uncertainty quantification, allowing probabilistic fault inference from predicted gas trends without assuming deterministic persistence of a specific fault type. A sequential pipeline is developed for real-time fault tracking and reliability assessment, aligned with IEC, IEEE, and CIGRE standards. Two case studies validate the method: one involving gas loss in an experimental setup and another examining thermal degradation in a 345 kV transformer. Results show that the framework improves diagnostic reliability, supports early fault detection, and enhances predictive maintenance strategies. By combining probabilistic modeling, time-series forecasting, and sensor-based diagnostic inference, this work contributes a practical and interpretable PHM solution for sensor-enabled monitoring environments in modern power grids. Full article