Search Results (1,636)

Background/Objectives: The growing number of older adults with complex healthcare needs increases demand for homecare services, while a shrinking workforce often lacks skills for advanced tasks. Digital health is seen as a promising tool to address these challenges. This study explored Swedish homecare professionals’ perceptions of the potential use of digital twins in daily work. Methods: Four focus group discussions were conducted with 31 homecare professionals; two groups each in one urban/rural and one rural municipality. Data were analyzed using inductive content analysis. Results: Three main themes emerged: (i) Perceptions of digital twins as support for older adults, including needs-based, individualized care and proactive support enabling preventive measures; (ii) Perceptions of digital twins as support for professionals, including a better work environment through streamlined tasks and flows and enhanced planning and assessment; and (iii) Concerns about digital twins, focusing on ethical and social issues and limited understanding, which were related to monitoring aspects, the importance of physical visits, and how the technology works. Conclusions: Digital twins are perceived by professionals to have the potential to improve homecare services by supporting both older adults and professionals; however, further research is needed to address concerns and practical implications. Full article

The main objective of this research was to evaluate the seasonal variability of PM₁₀-bound polycyclic aromatic hydrocarbons (PAHs), their sources, and analyse their health impacts We confirmduring the COVID-19 pandemic period. The chemical composition of PM₁₀ in terms of PAH content was carried out using the gas chromatography-mass spectrometry (GC-MS) technique. PM₁₀ samples were collected in Krakow from 2020 to 2021. A total of 92 samples of particulate matter (PM₁₀ fraction) were analysed. The analyses contained 16 basic PAHs identified by the United States Environmental Protection Agency (U.S. EPA) as the most harmful. The information obtained on the concentrations of PAHs was used to determine the profiles of pollution sources, exposure profiles, and the values of toxic equivalency factors recommended by the EPA: mutagenic equivalent to B[a]P (ang. mutagenic equivalent, MEQ), toxic equivalent to B[a]P (ang. toxic equivalent, TEQ), and carcinogenic equivalent to 2,3,7,8-tetrachlorodibenzo-p-dioxin (ang. carcinogenic equivalent, CEQ). In Kraków, heavy PAHs accounted for over 90% of the total PAHs detected in the PM₁₀ samples. In addition, air trajectory frequency analysis was performed to obtain information on the possibility of transporting pollutants from selected areas in the vicinity of the studied site. Interpreting the trajectory results provided information on the nature of air pollution sources. Analysis of Kraków’s air mass trajectory showed that the highest daily concentration of PM₁₀ in the air flow was from the southwest and east for days. Full article

Accurate traffic flow forecasting is a fundamental component of Intelligent Transportation Systems and proactive urban mobility management. However, the inherent complexity of urban traffic flow, characterized by non-stationary dynamics and multi-scale temporal dependencies, poses significant modeling challenges. Existing spatio-temporal models often struggle to reconcile the discrepancy between static physical road constraints and highly dynamic, state-dependent spatial correlations, while their reliance on fixed temporal receptive fields limits the capacity to disentangle overlapping periodicities and stochastic fluctuations. To bridge these gaps, this study proposes a novel Multi-scale Graph-Decoupling Spatial–temporal Network (MS-GSTN). MS-GSTN leverages a Hierarchical Moving Average decomposition module to recursively partition raw traffic flow signals into constituent patterns across diverse temporal resolutions, ranging from systemic daily trends to high-frequency transients. Subsequently, a Tri-graph Spatio-temporal Fusion module synergistically models scale-specific dependencies by integrating an adaptive temporal graph, a static spatial graph, and a data-driven dynamic spatial graph within a unified architecture. Extensive experiments on four large-scale real-world benchmark datasets demonstrate that MS-GSTN consistently achieves superior forecasting accuracy compared to representative state-of-the-art models. Quantitatively, the proposed framework yields an overall reduction in Mean Absolute Error of up to 6.2% and maintains enhanced stability across multiple forecasting horizons. Visualization analysis further confirms that MS-GSTN effectively identifies scale-dependent spatial couplings, revealing that long-term traffic flow trends propagate through global network connectivity while short-term variations are governed by localized interactions. Full article

Background/Objectives: SA001, a mofetil-ester prodrug of rebamipide, was developed to enhance gastrointestinal absorption and systemic exposure, which was confirmed in a prior Phase 1 study. Given the limited efficacy of current symptomatic therapies for primary Sjögren’s syndrome (pSS), this trial aimed to assess whether the improved bioavailability of SA001 could translate into clinical benefits. Methods: This multicenter, randomized, double-blind, placebo-controlled Phase 2a study enrolled adults who met the 2016 ACR–EULAR criteria for pSS. The participants were randomly assigned to one of four groups: SA001 at 360, 720, or 1080 mg/day (administered twice daily for 8 weeks) or placebo. Exploratory ocular assessments included tear break-up time, ocular surface staining, the Schirmer test, and the Standard Patient Evaluation of Eye Dryness. Oral endpoints included unstimulated whole salivary flow and the Xerostomia Inventory. Anti-SSA(Ro) antibodies were assessed both quantitatively and qualitatively. Safety evaluations comprised adverse events (AEs), ophthalmic examinations, laboratory tests, and vital signs. The efficacy outcomes were exploratory, and this study was not powered to formally test efficacy hypotheses. Results: Twenty-eight women (mean age 58.54 ± 9.29 years; range 41–75 years) were enrolled in this study and randomly assigned to one of the study groups. SA001 showed no statistically significant improvements versus placebo in ocular or oral endpoints, and no consistent dose–response relationship was observed. The anti-SSA(Ro) findings did not differ meaningfully across the groups. SA001 was generally well-tolerated, with infrequent, mostly mild-to-moderate AEs; however, one serious AE occurred in the placebo group. No clinically relevant ophthalmic or laboratory safety signals were detected. Conclusions: Despite the fact that markedly increased systemic exposure has been demonstrated previously, SA001 did not improve the dryness outcomes in pSS. These findings suggest that systemic exposure alone may be insufficient in established glandular disease and highlight the need for tissue-exposure-driven strategies and biomarker-informed patient selection in future studies. Predefined primary efficacy endpoints and objective, gland-proximal measures of target engagement (e.g., standardized salivary gland ultrasonography and salivary or tear fluid biomarker assessments) may help to better interpret local pharmacodynamic activity and the likelihood of a clinically meaningful benefit. Full article

Accurate traffic flow prediction is a core component of intelligent transportation systems, supporting proactive traffic management, resource optimization, and sustainable urban mobility. However, urban traffic networks exhibit heterogeneous multi-scale periodic patterns and time-varying spatial interactions among road segments, which are not sufficiently captured by many existing spatio-temporal forecasting models. To address this limitation, this paper proposes PDR-STGCN (Periodicity-Aware Dynamic Relational Spatio-Temporal Graph Convolutional Network), an enhanced STGCN framework that jointly models multi-scale periodicity and dynamically evolving spatial dependencies for traffic flow prediction. Specifically, a periodicity-aware embedding module is designed to capture heterogeneous temporal cycles (e.g., daily and weekly patterns) and emphasize dominant social rhythms in traffic systems. In addition, a dynamic relational graph construction module adaptively learns time-varying spatial interactions among road nodes, enabling the model to reflect evolving traffic states. Spatio-temporal feature fusion and prediction are achieved through an attention-based Bidirectional Long Short-Term Memory (BiLSTM) network integrated with graph convolution operations. Extensive experiments are conducted on three datasets, including Metro Traffic Los Angeles (METR-LA), Performance Measurement System Bay Area (PEMS-BAY), and a real-world traffic dataset from Guizhou, China. Experimental results demonstrate that PDR-STGCN consistently outperforms state-of-the-art baseline models. For next-hour traffic forecasting, the proposed model achieves average reductions of 16.50% in RMSE, 9.00% in MAE, and 0.34% in MAPE compared with the second-best baseline. Beyond improved prediction accuracy, PDR-STGCN reveals latent spatio-temporal evolution patterns and dynamic interaction mechanisms, providing interpretable insights for traffic system analysis, simulation, and AI-driven decision-making in urban transportation networks. Full article

Background/Objectives: Patients with eosinophilic chronic obstructive pulmonary disease (COPD) often remain symptomatic despite optimized triple inhaled therapy. Dupilumab is a fully human monoclonal antibody that blocks the IL-4 receptor alpha subunit, thereby inhibiting IL-4 and IL-13 signaling. Evidence from randomized trials supports dupilumab for add-on treatment of type 2-high COPD, but data referring to short-term effectiveness in clinical practice are quite limited. Methods: We conducted an observational, compassionate-use study enrolling 13 consecutive outpatients with eosinophilic COPD (blood eosinophils ≥ 300 cells/µL) receiving add-on biologic therapy with dupilumab 300 mg every two weeks. Clinical (CAT, mMRC), functional (spirometry and body plethysmography), and inflammatory parameters (blood eosinophils/basophils, fibrinogen, FeNO) were evaluated at baseline and after four weeks of treatment. Safety was monitored after injection in a clinical setting, as well as via weekly phone follow-up. Results: Participants (84.6% male; mean age 67.08 ± 11.42 years) experienced rapid and clinically meaningful improvements at four weeks. CAT score decreased from baseline 21.40 ± 6.22 to 14.00 ± 5.58 (p < 0.001) and mMRC scale from 2.90 ± 0.73 to 1.80 ± 0.63 (p < 0.0001), respectively. Pre-bronchodilator FEV₁ increased from baseline 1.35 ± 0.65 L to 1.59 ± 0.84 L (p < 0.05), and FVC from 2.36 ± 0.92 L to 2.83 ± 1.11 L (p < 0.01). A marked lung deflation was observed: indeed, residual volume declined from baseline 4.17 ± 1.98 L to 3.47 ± 2.07 L (p < 0.05), with a concomitant reduction in specific effective airway resistance (from baseline 3.15 ± 1.77 to 2.43 ± 1.44 kPa·s; p < 0.05) associated with significant increases in mid-expiratory flow (FEF₂₅₋₇₅: from baseline 0.62 ± 0.38 to 0.86 ± 0.71 L/s; p < 0.05) and peak expiratory flow (3.80 ± 1.40 to 4.48 ± 1.79 L/s; p < 0.01). Type 2 inflammatory biomarkers changed as follows: blood eosinophil count fell from baseline 390.0 ± 43.75 to 190.0 ± 65.47 cells/µL (p < 0.001); blood basophil number decreased from baseline 37.50 ± 13.89 to 26.25 ± 13.02 cells/µL (p < 0.001); plasma fibrinogen lowered from baseline 388.4 ± 54.81 to 334.9 ± 72.36 mg/dL (p < 0.01); FeNO levels dropped from baseline 23.95 ± 18.10 to 14.00 ± 2.04 ppb (p < 0.0001). Dupilumab was well tolerated, and no treatment-related serious adverse events or discontinuations were detected. Conclusions: Within an exploratory context of daily medical activity referring to eosinophilic COPD already treated with maximal inhaled therapy, we found relevant therapeutic effects of a four-week add-on treatment with dupilumab. In particular, our patients manifested rapid improvements in symptoms, airflow limitation, and lung hyperinflation, paralleled by significant decrements of type 2 inflammatory signatures. Such encouraging results were associated with a favorable short-term safety profile. However, larger and longer studies are necessary to corroborate these preliminary findings. Full article

Background: To investigate baseline MNV characteristics in Optical Coherence Tomography Angiography (OCTA) and its impact on therapeutic needs and visual acuity after 5 years in initially therapy-naïve eyes. Methods: A retrospective study of 43 therapy-naïve eyes with neovascular AMD (nAMD). OCTA was performed at baseline and all eyes were observed for 5 years. MNV architecture was characterized by area, total vessel length, flow density and fractal dimension. These variables were tested for correlation with the number of administered intravitreal injections (IVIs) and best-corrected visual outcome (BCVA) after 5 years of treatment. Results: Mean follow-up time was 4.97 ± 0.21 years. Area and total vessel length of MNVs were significantly associated with a higher number of administered IVIs after 5 years (p < 0.05), flow density significantly correlated with fewer IVIs (p < 0.05). Fractal dimension showed a tendency to more IVIs (p = 0.056) after 5 years. Flow density at baseline correlated with a better BCVA (p < 0.05). In contrast, MNV area size, total vessel length and fractal dimension did not show any correlation to BCVA after 5 years (p > 0.05). Conclusions: Specific features of MNV architecture such as area, total vessel length and flow density can predict long-term treatment requirement and visual outcome. Further studies using deep learning algorithms are necessary to explore the usage of these findings in daily practice. Full article

In the effort to mitigate climate change, the Marine Hydrokinetic (MHK) energy from ocean currents emerges as an important renewable source due to its large potential, although it remains underexploited. In the Southwestern Tropical Atlantic, surface potentials linked to the North Brazil Current (NBC) are known, but the subsurface North Brazil Undercurrent (NBUC) remained unquantified. This study addressed this gap by applying a two-step approach using more than 20 years of high-resolution (1/12°) climatological and daily reanalysis data to estimate current power density (CPD) throughout the water column along the Brazilian shelf (4° N–12° S), with focus on energetic hotspots where maximum CPD exceeds 1000 W m⁻². The climatological analysis revealed 12 persistent hotspots (H1–H12). Daily analyses show highly energetic but seasonally variable surface hotspots north of 4° S linked to the NBC (H4–H12; >885 W·m⁻²) and weaker but more stable subsurface hotspots south of 4° S associated with the NBUC at depths of 130–266 m (H1–H3; 831–808 W·m⁻²). These patterns are likely influenced by flow–topography interactions along the continental margin. Overall, subsurface resources exhibit greater reliability than surface counterparts, highlighting the importance of incorporating subsurface dynamics in future MHK assessments and development along the Brazilian margin. Full article

Solar-driven irrigation, a promising clean technology for agricultural water conservation, is constrained by mismatched photovoltaic (PV) pump outflow and irrigation demand, alongside unstable PV output. While compressed-air energy storage (CAES) shows mitigation potential, existing studies lack systematic explorations of pump water-lifting characteristics and supply capacity under coupled meteorological and air pressure effects, limiting its practical promotion. This study focuses on a solar-coupled compressed-air energy storage regulated sprinkler irrigation system (CAES-SPSI). Integrating experimental and theoretical methods, it establishes dynamic flow models for three DC diaphragm pumps considering combined PV output and outlet back pressure, introduces pressure loss and drop coefficients to construct a nozzle pressure dynamic model via calibration and iteration, and conducts a 1-hectare corn field case study. The results indicate the following: pump flow increases with PV power and decreases with outlet pressure (model deviation < 9.24%); nozzle pressure in pulse spraying shows logarithmic decline; CAES-SPSI operates 10 h/d, with hourly water-lifting capacity of 0.317–1.01 m³/h and daily cumulation of 6.71 m³; and the low-intensity and long-duration mode extends irrigation time, maintaining total volume and optimal soil moisture. This study innovatively incorporates dynamic air pressure potential energy into meteorological-PV coupling analysis, providing a universal method for quantifying pump flow changes, clarifying CAES-SPSI’s water–energy coupling mechanism, and offering a design basis for its agricultural application feasibility. Full article

The Temane gas field, the first producing natural gas field in Mozambique, remains a key supplier to southern Mozambique and the South African market. In recent years, however, the field has experienced an accelerated production decline, raising concerns regarding its long-term supply sustainability. Between 2020 and 2024, gas production decreased by approximately 25%, motivating a comprehensive reserve assessment to quantify the remaining potential and support informed reservoir management. This study applied three modern rate transient analysis (RTA) methods (Blasingame, normalized rate–cumulative, and flowing material balance) to twenty years of daily production data from thirteen producing wells across three reservoirs (G-9A, G-9B, and TEast) on a per-well basis. The RTA methods yielded consistent estimates, indicating an original gas-in-place value of 1576.38 Bscf, a remaining gas-in-place value of 503.37 Bscf, an estimated ultimate recovery of 1405.25 Bscf, and a field-average recovery factor of 76.35%. Reservoir-level recovery factors are estimated at 79% for G-9A, 74.92% for G-9B, and 58.01% for TEast. Despite the high depletion level, the magnitude of the observed production decline is not fully explained by reservoir exhaustion alone, suggesting that the field retains significant remaining recovery potential. Full article

Rating curves are derived from the combined measurement of water levels and discharges in rivers. This curve is used to convert observed water levels into flow rates, thereby generating discharge time series. Traditionally, rating curves are computed using exponential regression, which often neglects the underlying hydraulic conditions. Consequently, such curves may provide reasonable estimates of average flow but become unreliable under extreme conditions (e.g., high water levels). This research proposes a strategy for estimating discharge at high water levels using hydraulic modelling to support designers and practitioners in interpreting the upper range of the stage–discharge relationship. The methodology was applied to assess the rating curve for high flows in the Magdalena River at the Magangué reach (Bolívar, Colombia). Daily discharge records from 1974 to 2023 were analysed. The maximum historical discharge recorded was 11,127 m³/s (in 2010), while the mean annual peak discharge was 7904 m³/s. The proposed methodology yielded Manning’s roughness coefficients ranging from 0.046 to 0.052 and achieved satisfactory performance, with a Nash–Sutcliffe Efficiency (NSE) of 0.99. Results demonstrated that the traditional regression-based method tends to underestimate maximum discharges relative to a properly calibrated upper section of the rating curve. The analysis revealed systematic underestimation by the conventional approach, with discrepancies of up to 4.2% in determining maximum discharges. These findings emphasise the importance of incorporating hydraulic modelling to refine rating curves for high-flow conditions, thereby improving the reliability of design discharges. Full article

The transition toward intelligent and sustainable power systems requires practical schemes to integrate industrial demand flexibility into short-term operation, particularly in emerging electricity markets. This paper proposes an integrated framework that combines data-driven flexibility characterisation with a bi-level optimisation model for an industrial demand-side aggregator participating in the short-term balancing market. Flexibility is identified from AMI data and process information of large consumers, yielding around 2 MW of interruptible load and 3 MW of reducible load over a 24 h horizon. At the upper level, the aggregator maximises its profit by submitting flexibility offers; at the lower level, the system operator minimises balancing costs by co-optimising thermal generation and activated flexibility. The problem is formulated as a mixed-integer linear programming model and is evaluated on a real subtransmission and distribution network of a local utility in Ecuador, with ex-post power flow validation in DIgSILENT PowerFactory. Numerical results show that, despite the limited flexible capacity, the aggregator reduces the maximum energy price from USD/MWh 172.32 to 139.59 (about 19%), generating a daily revenue of USD 2475.15. From a network perspective, demand flexibility eliminates undervoltage at the most critical bus (from 0.93 to 1.03 p.u.) without creating overvoltages, while line loadings remain below 50% in all cases and total daily technical losses decrease from 89.46 to 89.10 MWh (about 0.4%). These results highlight both the potential and current limitations of industrial demand flexibility in short-term markets. Full article

Background and Objectives: Japan’s aging population faces growing challenges related to oral frailty, a condition characterized by the decline of oral function associated with physical and nutritional deterioration. Impaired oral function contributes to reduced chewing, swallowing, and saliva secretion, leading to poor appetite and frailty progression. Ninjin’yoeito (NYT), a traditional Kampo formula, has been clinically used to improve systemic weakness and oral symptoms. This study aimed to evaluate the efficacy and safety of NYT in improving oral health among elderly individuals with impaired oral function. Materials and Methods: In this open-label prospective study, patients received NYT daily for 12 weeks. Assessments included oral symptom scores, mucosal moisture, repetitive saliva swallowing tests (RSST), gustatory function by visual analogue scale (VAS), an 11-item oral questionnaire, and immune profiling by flow cytometry. Safety was assessed through hematological and biochemical tests. Results: Symptom scores decreased from 8.27 at baseline to 3.64 at 12 weeks (p = 0.006), while oral condition scores improved from 5.09 to 1.36 (p = 0.006). Mucosal moisture increased (25.1 to 28.1, p = 0.03), and RSST frequency improved (2.18 to 4.55, p = 0.046). Questionnaire scores declined from 5.1 to 2.0 (p < 0.001). VAS-taste was unchanged overall (p = 0.21) but improved in low baseline patients. Laboratory findings showed no adverse changes, with favorable lipid trends. Immune analysis revealed a decline in NKG2D expression (p = 0.02), whereas other activating and inhibitory markers remained stable. Conclusions: NYT was well tolerated and associated with gradual improvements in oral and physical symptoms among elderly individuals with impaired oral function. These findings provide preliminary evidence supporting the feasibility of Kampo-based approaches for maintaining oral health in aging populations and warrant further validation in larger controlled trials. Full article

Biogas production plays a key role in the development of renewable energy systems; however, forecasting biomethane yield remains challenging due to the nonlinear nature of anaerobic digestion. The objective of this study was to develop a predictive model based on Radial Basis Function Neural Networks (RBF-NN) to approximate biomethane production using operational data from the Przybroda biogas plant in Poland. Two separate models were constructed: (1) the relationship between process temperature and daily methane production, and (2) the relationship between methane fraction and total biogas flow. Both models were trained using Gaussian activation functions, individually adjusted neuron parameters, and a zero-level correction algorithm. The developed RBF-NN models demonstrated high approximation accuracy. For the temperature-based model, root mean square error (RMSE) decreased from 531 m³ CH₄·day⁻¹ to 52 m³ CH₄·day⁻¹, while for the methane-fraction model, RMSE decreased from 244 m³ CH₄·day⁻¹ to 27 m³ CH₄·day⁻¹. The determination coefficients reached R² = 0.99 for both models. These results confirm that RBF-NN provides an effective and flexible tool for modeling complex nonlinear dependencies in anaerobic digestion, even when only limited datasets are available, and can support real-time monitoring and optimization in biogas plant operations. Full article

Understanding how cultivated xerophytic shrubs physiologically regulate canopy water loss under anomalously wet conditions is crucial for predicting ecohydrological responses and for providing practical guidance in landscape restoration under the ongoing warming–wetting trend on the northern Loess Plateau. This study tested hypotheses concerning the hierarchy of atmospheric and soil-water controls on canopy transpiration (E_c), stomatal conductance (g_s), the strength of canopy–atmosphere coupling, and species-specific soil-water sensitivities and water-use strategies in Caragana korshinskii and Salix psammophila. Concurrent measurements of branch-level sap flow, meteorological variables, and soil water content (SWC) at multiple depths were conducted in two adjacent stands during the wet season of a climatically wet year (July–September 2017). Meteorological factors, particularly vapor pressure deficit (VPD), were the dominant drivers of daily E_c and g_s, whereas SWC exerted secondary but species-specific influences. Both shrubs were strongly coupled to the atmosphere, with consistently low decoupling coefficients (Ω ≈ 0.11–0.15) on daily scales. C. korshinskii maintained stable water use through access to deeper soil, whereas S. psammophila responded sensitively to fluctuations in shallow SWC. These contrasting patterns indicate depth-partitioned water-use strategies and a context-dependent continuum between isohydric and anisohydric behavior rather than fixed species traits. The findings support improved parameterization of shrub water use in ecohydrological models, more effective water-use management, and informed species selection and nursery practices for landscape restoration in semi-arid regions experiencing warming–wetting climatic shifts. Full article