Search Results (6,886)

Heart rate variability (HRV), which quantitatively characterizes fluctuations in beat-to-beat intervals, serves as a critical indicator of cardiovascular and autonomic nervous system health. The inherent ability of non-contact methods to eliminate the need for subject contact effectively mitigates user burden and facilitates scalable long-term monitoring, thus attracting considerable research interest in non-contact HRV sensing. In this study, we propose a novel algorithm for HRV extraction utilizing FMCW millimeter-wave radar. First, we developed a calibration-free 3D target positioning module that captures subjects’ micro-motion signals through the integration of digital beamforming, moving target indication filtering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering techniques. Second, we established separate phase-based mathematical models for respiratory and cardiac vibrations to enable systematic signal separation. Third, we implemented the Second Order Spectral Sparse Separation Algorithm Using Lagrangian Multipliers, thereby achieving robust heartbeat extraction in the presence of respiratory movements and noise. Heartbeat events are identified via peak detection on the recovered cardiac signal, from which inter-beat intervals and HRV metrics are subsequently derived. Compared to state-of-the-art algorithms and traditional filter bank approaches, the proposed method demonstrated an over 50% reduction in average IBI (Inter-Beat Interval) estimation error, while maintaining consistent accuracy across all test scenarios. However, it should be noted that the method is currently applicable only to scenarios with limited subject movement and has been validated in offline mode, but a discussion addressing these two issues is provided at the end. Full article

Understanding morphological variation and asymmetry in avian limbs provides essential insights into functional anatomy, locomotor behavior, and developmental stability. In this study, we investigated shape and size variation in the feet of quails (Coturnix coturnix) using two-dimensional geometric morphometric methods. A total of 233 animals were analyzed, representing both the left and right feet of male and female individuals. Nine homologous fixed landmarks were digitized on each foot, and configurations were subjected to Generalized Procrustes Analysis, followed by mirroring of right-side landmarks to ensure consistent orientation. Statistical analyses revealed no significant sexual dimorphism in either foot shape or centroid size. Principal Component Analysis indicated that the main shape variation was distributed individually rather than by sex and primarily affected the relative positions of toes and claws. Procrustes ANOVA confirmed that differences between sexes were not greater than expected by chance. Directional and fluctuating asymmetry were evaluated using a bilateral symmetry model to assess bilateral asymmetry. Directional asymmetry indicated consistent left–right differences, while fluctuating asymmetry reflected individual-level developmental instability and comprised the main source of variation. These findings provide a detailed morphological baseline for quail foot structure and highlight the importance of considering asymmetry in studies of avian functional morphology. The approach may also be a reference for future research into developmental stress, locomotor adaptation, or species-specific anatomical patterns. Full article

This study offers a comprehensive petrophysical evaluation and reservoir characterization of the Srikail Gas Field, situated on the Tripura Uplift in the east-central Bengal Basin. Utilizing well log data from four wells (Srikail-1 to Srikail-4), the analysis targets the Bhuban and Bokabil formations of the Surma Group. Standard log suites, including gamma ray, spontaneous potential, caliper, resistivity, neutron, density, and sonic logs, were interpreted using both manual techniques and digital analysis through software. Key petrophysical properties, including shale volume, effective porosity, fluid saturations, permeability, and bulk volume of water, were estimated using a combination of empirical modeling and automated interpretation workflows. Cross-plot methodologies were applied to assist in reservoir evaluation. The study integrated both qualitative and quantitative approaches to characterize each reservoir unit in detail. Results demonstrate significant heterogeneities in reservoir quality across the field. While some intervals exhibit favorable properties suitable for commercial gas production, others are characterized by high carbonate content, poor porosity, and very low permeability (Sand Cwith.05 to 0.08 mD), indicative of tight to semi-conventional reservoirs. The most productive zones, identified as the D sands, are cleaner sands with excellent permeability (102 mD to 355 mD). In contrast, deeper intervals generally exhibit tighter characteristics, with DST-derived permeability values ranging from 0.6 to 0.01 mD. The study recommends integrating core analysis, advanced petrophysical modeling, and 3D seismic interpretation with well log data to enhance reservoir delineation in the Srikail Gas Field. This combined approach would reduce uncertainties, improve input parameter accuracy, and offer a more comprehensive understanding of the Bhuban Formation’s heterogeneity, ultimately supporting more effective reservoir evaluation and hydrocarbon recovery planning. Full article

Background and Objectives: Type 2 diabetes mellitus (T2DM) remains a significant public health problem across Gulf Cooperation Council (GCC) nations because of advancements in urbanization alongside behavioural lifestyle changes and genetic predispositions. Specific self-management methods are fundamental in T2DM management because they provide better glycaemic control and decrease complications. Achieving a synthesis of updated evidence about self-management strategies and patient perception within GCC nations represents the primary objective of this narrative review. Materials and Methods: The studies included in the present review were retrieved from the Web of Science, Scopus, Medline, Saudi Digital Library, and Embase. We included peer-reviewed studies that were published from January 2020 to March 2025. The selected studies measured the self-management practices of adult T2DM patients by examining medication adherence, dietary patterns, blood glucose monitoring, and treatment barriers. Results: Research data indicate that patients demonstrate different levels of self-care management behaviours, where medication compliance is fair, but dietary patterns and physical activities remain areas of concern. High levels of knowledge deficits, cultural elements, and economic background substantially impact patients’ self-management practices. Patients indicate their need for enhanced and personalized care, better connections with healthcare providers, and interventions that consider their cultural backgrounds. Conclusions: Patients throughout the GCC region encounter ongoing difficulties that prevent them from performing their best at self-management, even though advanced healthcare facilities exist in this region. Therefore, it is critical to develop culturally sensitive patient-centered care, individualized educational programs, and adopt supportive digital solutions to enhance diabetes-related self-care management. Full article

As China advances toward its “Dual Carbon” goals, clarifying the role of the digital economy (DE) in reducing urban carbon emissions is of growing importance. This study uses panel data from 274 Chinese prefecture-level cities (2011–2022) and applies benchmark regression, the Spatial Durbin Model (SDM), two-regime SDM, threshold analysis, and mediation effect modeling to examine the impact of the DE on carbon emission intensity (CEI) and its spatial spillover effects. Results show that the DE significantly reduces CEI through both direct and indirect channels. Spatial analysis reveals that the DE’s spillover effect is most pronounced within a 500 km range. Regionally, the DE has a stronger inhibitory effect on CEI in eastern and western regions, while its effect in the central region is weaker or even reversed, likely due to reliance on carbon-intensive industries. Resource-based cities exhibit stronger spatial spillovers than non-resource-based ones, suggesting greater potential for DE-driven low-carbon transitions. A threshold effect is also identified at a DE index value of 0.0326, beyond which the marginal benefits decline. Pathway analysis indicates that while the DE improves production efficiency, it does not significantly promote green, high-value-added transformation, partially masking its carbon reduction effects. These findings highlight the need for tailored regional strategies to enhance the low-carbon potential of the DE. Full article

Using the digital economy as a new carrier to drive innovation has become an indispensable strategic support for the progress of countries, while environmental protection is a necessary condition to ensure its sustainable development. A multi-transition framework is proposed in this article with the purpose of revealing the relations between the digital economy and regional innovation under the indirect effects of environmental performance; a mediating model and threshold effect model are used to examine empirical results considering the panel data at Chinese provincial levels. Moreover, the heterogeneity and the robustness also verified the multi-transition effect. The research findings are as follows: First, the digital economy can significantly enhance regional innovation capacity, which also shows obvious dimensional and geographical heterogeneities. Second, a mediating effect of the digital economy on regional innovation under environmental performance is considered, and environmental performance has an increasingly significant role in fostering regional innovation as the digital economy continues to grow, indicating that improving environmental performance is an essential strategy for achieving the steady release of innovation dividends. Furthermore, the above results are provided with heterogeneity and robustness under PM_2.5, COVID-19, and regional innovation quantiles. Therefore, focusing on regional innovation via environmental performance is necessary, and policymakers and professionals should pay more attention to further stimulating regional innovation potential. Full article

Porcine enteric coronaviruses (CoVs), including swine acute diarrhea syndrome coronavirus (SADS-CoV), porcine epidemic diarrhea virus (PEDV), porcine deltacoronavirus (PDCoV), and porcine transmissible gastroenteritis virus (TGEV), are major pathogens causing porcine viral diarrhea syndrome (VDS), which brings significant economic losses to the swine industry; distinguishing between these clinically similar viruses has become a serious challenge. We developed a highly specific and interference-resistant porcine CoV multiplex digital PCR (dPCR) assay. The assay exhibited robust anti-interference capabilities, as the concentrations of the four viruses did not affect their accurate quantification. The coefficients of variation (CV%) of intra-batch and inter-batch repeatability for all target viruses were less than 11%. The limit of quantification (LoQ) of this dPCR assay reached 7.5 copies/reaction for each target, and it was one order of magnitude more sensitive than qPCR. The limits of detection (LoD) for SADS-CoV, PEDV, PDCoV, and TGEV were 2.72, 3.00, 3.56, and 3.19 copies/reaction, respectively. A total of 408 known samples were used for validation tests, and the results were highly consistent with the known conditions, showing a compliance rate of 97–100%. The diagnostic specificity (Dsp) of the method was 99–100%. In conclusion, the developed multiplex dPCR assay is highly suitable for early detection and quarantine in four porcine CoVs. The results indicate that this dPCR method is characterized by high specificity, anti-interference capabilities, repeatability, and high sensitivity. It also demonstrates a high compliance rate and diagnostic specificity in sample detection. This multiplex dPCR will contribute to the control of porcine enteric CoV-caused VDS and provide clues for subsequent research. Full article

Background: Stroke survivors and their family caregivers face substantial burdens resulting from a complex interplay of clinical, functional, and psychosocial factors. Community-based resources and social networking are critical for alleviating caregiver burden and improving outcomes for both caregivers and stroke survivors. Objectives: To assess the Saudi population’s readiness to use social platforms for stroke survivors’ recovery and caregiver support. Methods: A cross-sectional descriptive design was employed between March and June 2025 to collect data from community-dwelling individuals using sociodemographic and Technology Readiness Index (TRI) questionnaires. Results: A convenience sample of 576 participants was enrolled in this study. Overall, the participants showed a high level of technology readiness, with a total TRI mean score of M = 3.65, SD = 0.86. Optimism had the highest mean score, followed by innovativeness, insecurity, and discomfort. Significant differences in technology readiness were identified based on age, educational level, employment status, familiarity with modern technology, and healthcare provider status. Conclusions: This study demonstrates a high level of technological readiness across the Saudi population, indicating strong potential for integrating social connection platforms into stroke recovery and caregiver support. These findings align with Saudi Arabia’s Vision 2030 goals for digital transformation in the healthcare sector. Future research should focus on conducting feasibility studies to better understand the desirable features of e-health services and digital solutions within the Saudi community. Full article

The reuse of Waste Foundry Sand (WFS) in construction remains constrained by fragmented research, unclear regulatory pathways, and inconsistent assessments of environmental safety and material performance. This study introduces a novel decision-making framework that systematically integrates mechanical performance metrics and leachate toxicity data to classify WFS into three categories: Approved, Reusable with Treatment, or Rejected. The framework is based on a bibliometric analysis of 822 publications and a meta-analysis of 45 experimental mix designs and 30 peer-reviewed leachate studies. Normalized compressive strength (NSR), water-to-cement (w/c) ratio, and heavy metal leachate concentrations are used as screening criteria. Thresholds are benchmarked against regulatory limits from the United States Environmental Protection Agency (EPA), the European Union Landfill Directive, and South Africa’s National Waste Standards. Validation using field data from a foundry in Gauteng Province, South Africa, confirms the framework’s practicality and adaptability. Results indicate that over 80 percent of WFS samples comply with environmental thresholds, and mixes with 10-to-30 percent WFS substitution often outperform control specimens in terms of compressive strength. However, leachate exceedances for cobalt and lead in certain chemically bonded sands highlight the need for batch-specific evaluation and potential treatment. The proposed framework supports data-driven, transparent reuse decisions that enhance environmental compliance and promote circular material flows in the built environment. Future work should focus on digital implementation, life-cycle monitoring, and expanding the framework to other industrial byproducts. Full article

Phosphates pollution, primarily from agricultural runoff and wastewater discharge, is a major contributor to water eutrophication, adversely affecting aquatic ecosystems. This study reports the synthesis, characterization, and phosphates adsorption performance of a MgAlFe-layered double hydroxide (MgAlFe-LDH) with a 2:1:1 cationic ratio. The material was prepared via co-precipitation and characterized using digital microscopy, XRD, BET, XPS, and FTIR. Adsorption experiments were conducted at pH 3 and 9 to investigate equilibrium, kinetics, and reusability. The MgAlFe-LDH exhibited a high maximum adsorption capacity (q_max ≈ 215 mg/g) largely independent of pH, with adsorption well described by the Langmuir model. Kinetic studies revealed a pseudo-first-order mechanism, indicating that adsorption is dominated by surface diffusion and electrostatic interactions. Phosphate removal occurs through a dual mechanism involving rapid electrostatic attraction at protonated surface sites and slower ion exchange in the LDH interlayers. The material retained over 75% of its adsorption capacity after five consecutive adsorption–desorption cycles, highlighting its potential for sustainable phosphate recovery. Overall, the MgAlFe-LDH represents a promising, reusable adsorbent for phosphorus removal from wastewater, supporting circular economy strategies. Full article

The digital economy is a key driver of industrial upgrading and regional growth. Focusing on Gansu Province—an under-represented, less-developed region in northwest China—this study constructs a multidimensional digital economy index (DEI) for 2009–2023 under a unified normalisation and weighting scheme. Two complementary MCDA approaches—entropy-weighted TOPSIS and SESP-SPOTIS—are implemented on the same 0–1 normalised indicators. Robustness is assessed using COMSAM sensitivity analysis and is benchmarked against a PCA reference. The empirical analysis then estimates log-elasticity models linking modern logistics production (MLP) and the DEI to the provincial GDP and sectoral value added, with inferences based on White heteroskedasticity–robust standard errors and bootstrap confidence intervals. Results show a steady rise in the DEI with a temporary dip in 2021 and recovery thereafter. MLP is positively and significantly associated with GDP and value added in the primary, secondary, and tertiary sectors. The DEI is positively and significantly associated with GDP, the primary sector, and the tertiary sector, but its effect is not statistically significant for the secondary sector, indicating a manufacturing digitalisation gap relative to services. Cross-method agreement and narrow sensitivity bands support the stability of these findings. Policy implications include continued investment in digital infrastructure and accessibility, targeted acceleration of manufacturing digitalisation, and the development of a “digital agriculture–smart logistics–green development” pathway to foster high-quality, sustainable regional growth. Full article

This paper presents the development and implementation of a shopfloor visualization-oriented digitalization framework for heterogeneous industrial equipment, aimed to enhance sustainable performance in manufacturing environments. The proposed solution addresses a critical challenge in modern industry: the integration of legacy and modern equipment into a unified, real-time monitoring and control system. In this paper, a modular and scalable architecture that enables data acquisition from equipment with varying communication protocols and technological maturity was designed and implemented, utilizing Industrial Internet of Things (IIoT) gateways, protocol converters, and Open Platform Communications Unified Architecture (OPC UA). A key contribution of this work is the integration of various data sources into a centralized visualization platform that supports real-time monitoring, anomaly detection, and performance analytics. By visualizing operational parameters—including energy consumption, machine efficiency, and environmental indicators—the system facilitates data-driven decision-making and supports predictive maintenance strategies. The implementation was validated in a real industrial setting, where the solution significantly improved transparency, reduced downtime, and contributed to measurable energy efficiency gains. This research demonstrates that visualization-oriented digitalization not only enables interoperability among heterogeneous assets, but also acts as a catalyst for achieving sustainability goals. The developed methodology and tools provide a replicable model for manufacturing organizations seeking to transition toward Industry 4.0 in a resource-efficient and future-proof manner. Full article

This study examines patterns of artificial intelligence (AI) adoption in Ecuadorian micro and small enterprises (MSEs), with an emphasis on functional diversity across value chain activities. Based on a cross-sectional dataset of 781 enterprises and an entropy-based model, it assesses internal variability in AI use and explores its relationship with strategic perception and dynamic capabilities. The findings reveal predominant partial adoption, alongside high functional entropy in sectors such as mining and services, suggesting an ongoing phase of technological experimentation. However, a significant gap emerges between perceived strategic use and actual functional configurations—especially among microenterprises—indicating a misalignment between intent and organizational capacity. Barriers to adoption include limited technical skills, high costs, infrastructure constraints, and cultural resistance, yet over 70% of non-adopters express future adoption intentions. Regional analysis identifies both the Andean Highlands and Coastal regions as “innovative,” although with distinct profiles of digital maturity. While microenterprises focus on accessible tools (e.g., chatbots), small enterprises engage in data analytics and automation. Correlation analyses reveal no significant relationship between functional diversity and strategic value or capability development, underscoring the importance of qualitative organizational factors. While primarily descriptive, the entropy-based approach provides a robust diagnostic baseline that can be complemented by multivariate or qualitative methods to uncover causal mechanisms and strengthen policy implications. The proposed framework offers a replicable and adaptable tool for characterizing AI integration and informing differentiated support policies, with relevance for Ecuador and other emerging economies facing fragmented digital transformation. Full article

Consumers are increasingly willing to choose more sustainable products, driven by affordability and sustainability considerations. However, they often face difficulties in understanding the multitude of product certifications and identifying “greenwashing” marketing claims. This highlights the need for a clear and harmonized sustainability scoring system that allows consumers to benchmark products. Sustainability encompasses three key pillars: environmental, social, and economic. Accurately scoring a product’s sustainability requires addressing a wide range of criteria within these pillars, introducing significant complexity. This study proposes a multicriteria methodology for scoring the sustainability of apparel products into an A to E label. The approach combines a life cycle assessment covering environmental impacts from “farm-to-gate”, with a social evaluation based on country-level social key performance indicators (KPIs) and factory-specific data aligned with the International Labour Organization (ILO). Additionally, the sustainability score incorporates the impact of product durability, as longer-lasting products can reduce environmental footprint and costs for consumers. The methodology is defined and validated through a case study of a white T-shirt produced with 50% recycled cotton and 50% organic cotton. The results demonstrate the comprehensive assessment of the T-shirt’s environmental and social impacts, providing a detailed sustainability score, highlighting the role of recyclability. This comprehensive sustainability scoring system aims to provide consumers with a clear, harmonized, and reliable assessment of product sustainability, empowering everyone to make informed purchasing decisions aligned with their values. It will also enable brands and retailers to calculate the sustainability score of their products, including in the scope of digital product passport, provided they can ensure traceability and transparency along the supply chain. Full article

Background/Objectives: To compare patient-reported outcome measures (PROMs) between digital and conventional impression techniques for implant-supported fixed dental prostheses (iFDPs). Methods: A systematic literature search was conducted in PubMed, Embase, Scopus, and the Cochrane Library databases up to June 2025, following PRISMA guidelines. Human clinical studies reporting PROMs between digital and conventional impression techniques for iFDPs were included. Studies using structured, but not necessarily validated, questionnaires were eligible. Two reviewers independently performed study selection, data extraction, and risk of bias assessment. Where possible, meta-analyses were conducted using a random-effects model to pool comparable outcomes across studies using mean differences (MD) or standardized mean differences (SMD) with 95% confidence intervals (CIs). Results: Out of 1784 records screened, eighteen studies were included. Most studies showed that digital impressions were associated with higher patient satisfaction, compared to conventional impressions. Ten studies contributed data to at least one outcome; pooled analyses included the following: overall satisfaction (k = 5), comfort (k = 7), gagging/nausea (k = 5), esthetic satisfaction (k = 2), unpleasant taste (k = 5), anxiety (k = 5), discomfort (k = 2), pain (k = 5), and overall discomfort (k = 5). Digital impressions were significantly favored (p < 0.05) for anxiety (MD = 13.3, 95% CI: −22 to −4.5), nausea (MD = −26.4, 95% CI −46.8 to −6.0), bad taste (MD = −34.8, 95% CI −58.3 to −11.3), discomfort (SMD = −2.24, 95% CI −3.51 to −0.98), comfort (SMD = 1.77, 95% CI: 0.60 to 2.94), perceived procedure time (SMD = 0.96; 95% CI 0.29 to 1.62), and overall satisfaction (SMD = 0.55; 95% CI 0.01 to 1.09). No statistically significant differences were found for pain or esthetic evaluation. Substantial between-study heterogeneity was observed among the included studies. Conclusions: Current evidence indicates that digital impression workflows enhance the overall patient experience for implant-supported fixed restorations, especially in domains linked to comfort and procedural efficiency. These findings support PROM-informed personalization of impression workflows: screening for gagging, anxiety, or intolerance to impression materials could guide patient-tailored use of intraoral scanning while acknowledging no consistent advantage for pain or esthetic perception. Full article