Search Results (4,830)

Aging populations have intensified the demand for thermally comfortable and energy-efficient housing, particularly for elderly residents whose diminished thermoregulatory capacity renders them disproportionately vulnerable to indoor temperature fluctuations. Existing senior apartments in cold-climate regions frequently fail to meet age-specific thermal comfort standards, yet systematic retrofit optimization frameworks explicitly tailored to elderly occupants remain scarce. This study presents a data-driven multi-objective optimization framework for building envelope retrofitting, which is validated using on-site temperature measurements from a representative 1980s brick–concrete senior apartment building in Beijing. The framework integrates Latin Hypercube Sampling (LHS) for design space exploration, a Long Short-Term Memory (LSTM) surrogate model for simultaneous prediction of three performance objectives, and Non-dominated Sorting Genetic Algorithm II (NSGA-II) for Pareto-optimal solution generation, with final selection performed via a weighted Mahalanobis distance-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Optimization targets—annual energy consumption, indoor thermal discomfort hours, and retrofit cost—are parameterized using the age-sensitive comfort thresholds specified in GB 50340-2016. The LSTM surrogate achieved R² values of 0.91–0.93 across all objectives with training–testing differences below 0.02. The optimal retrofit package—Polyvinyl Chloride (PVC) Low Emissivity (Low-E) double-glazed windows (5 + 6A + 5), glass fiber roof insulation (65.25 mm), and Extruded Polystyrene (XPS) external wall insulation (65.39 mm)—reduces annual energy consumption by 47.1% (from 40,867 to 21,626 kWh) and annual thermal discomfort hours by 62.4% (from 2454 °C·h to 923 °C·h). SHapley Additive exPlanations (SHAP)-based sensitivity analysis further identifies wall U-value and roof thickness as the dominant performance drivers. A reproducible and computationally efficient pathway is provided by the proposed framework for evidence-based envelope retrofit decision-making in existing senior residential buildings. Full article

Wheelchair users face significant mobility limitations related to both medical issues (e.g., musculoskeletal strain, pressure ulcers) and urban accessibility challenges. This pilot study introduces a sensor system integrating an inertial measurement unit (IMU), GPS (Global Positioning System), and a pressure-measuring seat to monitor distance travelled, speed, and posture in relation to real-world conditions. Seven participants navigated an approximately 800-metre outdoor course, divided into 13 sections, while real-time data were recorded. The results showed an average speed of 1.24 ± 0.41 m/s with peak speeds of up to 2.67 m/s. The centre of pressure on the seat fluctuated by an average of 25 mm in the x and y directions (left-right: COPx, back-forward: COPy). The data for average speed, COPx, and COPy showed significant differences between most of the 13 sections, with large, very large, and huge effect sizes. Comparing the speed, COPx, and COPy data with respect to distance travelled, and correlating them between the seven participants by applying the rank-sum method to the mean R² and calculating Kendall’s W, revealed that speed, COPx, and COPy were influenced by course conditions (R² medians between 0.013 and 0.499; W = 0.7857, strong agreement; χ²p = 0.0281). Small R² values indicate more individualised participant behaviour, while large R² values highlight the stronger influence of course conditions on the parameters. This non-invasive and cost-effective system provides objective motion data that can be used for future research in wheelchair design and rehabilitation strategies. Despite its advantages, this study was limited to able-bodied participants, so further clinical trials with individuals with mobility impairments are needed. Full article

This study addressed the issue of insufficient activity in CALB lipase during the catalytic synthesis of key chiral intermediates for moxifloxacin. A structure-guided protein engineering strategy was employed to systematically modify its functional domains. Through molecular dynamics simulations of CALB-I189K, multiple regions exhibiting high conformational flexibility were preliminarily identified. Subsequently, by integrating 3D structural alignment with active site pocket distance analysis, the functionally most critical region (143–146) was selected. A site-directed saturation mutation library was constructed specifically targeting this region. Building upon the previously reported CALB-I189K, a mutant I189K/L144R/A146K was ultimately obtained through high-throughput screening combined with chiral HPLC validation. This mutant maintains excellent stereoselectivity (E = 206.52) while enhancing catalytic efficiency (k_cat/Κ_m) to 273.73 min⁻¹·mM⁻¹, approximately 4.5-fold that of I189K. At a substrate concentration of 1 M, it achieves 50% conversion within 2.6 h, demonstrating kinetic resolution capabilities approaching industrial standards. Molecular simulation analysis indicates that the L144R and A146K mutations synergistically enhance catalytic performance primarily by optimizing spatial distances between catalytic residues. This study not only provides a high-performance catalyst for the efficient biosynthesis of moxifloxacin chiral intermediates but also offers new insights for enzyme rational design based on dynamic structural information. Full article

Hydrogen refueling stations (HRSs) are a critical enabling infrastructure for fuel cell electric vehicles (FCEVs), yet their deployment in dense metropolitan areas often faces a dual challenge: limited travel-time accessibility for users and low public acceptance driven by perceived safety risks. This study develops an integrated, city-scale framework to quantify HRS accessibility and resident acceptance and to identify expansion priorities for Seoul, South Korea. We combine (i) an online perception survey of 1000 adult residents (October 2024) capturing environmental awareness, perceived safety, siting preferences, and willingness-to-travel distance; (ii) spatial demand data on FCEV registrations by administrative dong (n = 2443 vehicles, 2022); and (iii) network-based travel-time analysis using the Seoul road network and the current HRS supply (n = 10, 2024). Accessibility is evaluated under three travel-time thresholds (10, 15, and 20 min), with service-area delineation and demand-weighted underserved-area diagnosis. Candidate expansion sites are generated and screened using operational and regulatory constraints (e.g., site area and proximity to protected facilities), followed by a p-median location-allocation optimization to select five additional sites that minimize demand-weighted travel impedance. Results indicate that, under the 20 min threshold (7.7 km at an average operating speed of 23.1 km/h), 50 of 425 dongs (11.8%) and 244 of 2443 FCEVs (10.0%) are outside the baseline service coverage. After adding five sites (total n = 15), underserved dongs decrease to 5 (1.2%) and underserved FCEVs to 26 (1.1%) for the 20 min threshold, with consistent improvements across shorter thresholds. Survey responses further reveal that only 12.5% of respondents perceive HRSs as safe, while 46.5% report a maximum willingness-to-travel distance of up to 5 km, underscoring the need for both accessibility enhancement and risk-aware communication. The proposed workflow offers a transparent, reproducible approach to support equitable and risk-informed HRS planning by jointly considering network accessibility, demand distribution, and social acceptance, thereby contributing to sustainable urban mobility, low-carbon transport transition, and socially acceptable hydrogen infrastructure deployment. Beyond local accessibility improvement, the study is framed in the broader context of sustainability, as equitable and socially acceptable hydrogen refueling infrastructure can support low-carbon urban transport transitions and more resilient metropolitan energy-mobility systems. Full article

When artificial satellites disintegrate in orbit due to collisions, explosions, or anti-satellite tests, they generate a large amount of space debris. These debris move in orbit through formation-like flight patterns, which we refer to as debris clusters. Compared to constellation satellites in formation flight, debris clusters have closer mutual distances and higher spatial density. To address this phenomenon, this paper proposes a detection method for satellite breakup events through a two-layer clustering strategy using the DBSCAN algorithm in three-dimensional spatiotemporal domains. The method provides a systematic approach for identifying and characterizing breakup debris through orbital backpropagation and density analysis, serving as a screening tool for potential breakup events. Using the 2009 Cosmos 2251-Iridium 33 collision breakup event as a validation case, experimental results demonstrate that our two-layer clustering approach achieves good clustering accuracy (98.92%) and a positive recall rate (93.20%), with a temporal resolution of 5 s and spatial precision of 21.68 km. The methodology was further applied to analyze the 2024 Intelsat 33E breakup event, successfully identifying sparse debris clusters from Intelsat 33E. The detected breakup time showed an 8 min deviation from the Space-track.org official report. Full article

The aim of this study was to understand the female swimmers’ annual performance progression and stability between 10 and 18 years across swimming distances and techniques. Data from female Portuguese Top-50 rankings in the short-course pool was extracted from an open access database (swimrankings.net). Performances were grouped by distances (50-, 100- and 200 m) and techniques (freestyle, backstroke, breaststroke and butterfly), totalizing 12 events as performance metrics. A total of 343 swimmers and 3087 performances distributed by nine consecutive competitive seasons were retrospectively assessed. The mean and normative stability were computed for tracking performance trends, while reporting the year-to-year percentage improvement. The differences across distances and techniques were tested with a linear mixed-effects model using intraclass correlation coefficient (ICC). The performance progression was characterized by marked improvements during the early ages (up to 13% yearly) and an emerging plateau around the 15–16 years. The stability patterns varied between events, with the backstroke technique (ICC = 0.13) demonstrating greater consistency of individual differences on developmental trajectories, whereas shorter races (i.e., 50 m; ICC = 0.15) tended to be more stable than 100 m or 200 m (ICC = 0.12). It can be concluded that female swimmers’ performance stabilizes at the 15–16 years of age. Despite reduced differences, the backstroke technique and short distances seem to show a slightly more stable trend in progressing from childhood to adulthood. Full article

Flood damage assessment remains challenging, as conventional flood risk management mainly relies on hydraulic hazard maps that do not explicitly reproduce observed damage patterns. Recent advances in remote sensing and machine learning (ML) enable the integration of environmental and socio-economic data with historical impact information to improve flood damage modeling. This study proposes an explainable machine learning framework for flood damage susceptibility mapping, using observed institutional damage records from the 2011 and 2013 flood events combined with 17 geospatial flood risk factors (FRFs) representing hazard, exposure, and vulnerability. This approach enables the capture of non-linear relationships between flood damage and FRFs. For comparison purposes, the same framework was also applied using hydraulically modeled flood extents corresponding to return periods of 30, 200, and 500 years. The framework was tested along the Basilicata Ionian coast in southern Italy, a Mediterranean region characterized by complex geomorphology, intense rainfall events, and recurrent flood impacts. An eXtreme Gradient Boosting (XGBoost) model was trained using 17 FRFs related to hazard, exposure, and vulnerability at a spatial resolution of 20 m. The model achieved high performance with an accuracy of 0.988, an F1-score for the minority class of 0.860, and an ROC-AUC (test) of 0.996. High to very high flood damage probability was predicted in approximately 4.1% of the study area, mainly in low-lying floodplains near river corridors and infrastructure. SHAP-based explainability analysis revealed that damage susceptibility was predominantly driven by hazard and exposure factors: Drainage density (17.10%), Railway distance (16.33%), and Elevation (15.42%), extreme precipitation (Max rainfall, 10.66%) and Street distance (7.51%), with socio-economic vulnerability contributing less than 4%. The observed damage target exhibited clear threshold-like patterns (e.g., sharp risk increases below ~25/35 m elevation or within ~150/200 m of road infrastructure), contrasting with the smoother, continuous gradients produced by hydraulic scenarios. This analysis identified the most influential predictors and their response ranges. The proposed framework complements hydraulic hazard mapping by explicitly modeling observed flood damage, supporting flood risk assessment in flood-prone coastal regions. Full article

Methane (CH₄) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH₄ emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH₄ emissions at the facility-level. Near-field quantification of methane (CH₄) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH₄ emissions to controlled single-point emissions between 0.4 and 5.2 kg CH₄ h⁻¹. Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain. Full article

Ten substituted quinobenzothiazinium salts were tested for their ability to inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). All the compounds inhibited AChE in the IC₅₀ range of 0.03–0.658 µM, with 5,8,10-trimethyl-12H-quinolino[3,4-b][1,4]benzothiazin-5-ium chloride (3d) being the most potent inhibitor, with an IC₅₀ value significantly better than that of the clinically used rivastigmine and galantamine and comparable to that of tacrine and donepezil. The IC₅₀ values for BChE inhibition ranged from 0.34 to 4.25 µM; 5,9-dimethyl-12H-quinolino[3,4-b][1,4]benzothiazin-5-ium chloride (3b) exhibited the strongest BChE inhibitory activity and in general, all the investigated compounds were more potent inhibitors than rivastigmine and galantamine. Based on the calculated selectivity index values, they are rather preferential inhibitors of AChE. Cytotoxicity tests performed on normal human dermal fibroblasts (HFF-1) did not demonstrate any significant cytotoxicity under the tested conditions. The distance-oriented structure distribution for the studied molecules was related with the activity data using principal component analysis and hierarchical clustering analysis. (SAR)-based evaluation is reported to predict activity cliffs using a similarity–activity landscape index for the AChE inhibitory response values. Moreover, direct protein-mediated in silico methods were utilized to identify factors that may be relevant for quantitative (Q)SAR modeling. In practice, target-oriented molecular docking was used to organize the spatial distribution of the ligand property space for the anti-AChE system. In general, this series of alkylated quinobenzothiazinium salts with potent inhibitory activity against cholinesterases fulfills Lipinski’s rule of five based on in silico predictions and is also expected to have high absorption in the human gastrointestinal tract. All active derivatives are also expected to penetrate the blood–brain barrier, making them promising compounds for further research and possible use in Alzheimer’s disease therapy. Full article

This study focuses on the production and characterization of polyvinyl alcohol (PVA)-chitosan (CS)-based nanoparticles loaded with vitamin E (VitE) and ephedrine (Ep) via electrospraying for intranasal drug delivery in narcolepsy treatment. The nanoparticles were successfully synthesized using optimized parameters (15.5 kV voltage, 0.3 mL/h flow rate, 25 G needle size, and 14 cm distance). Scanning electron microscopy (SEM) analysis confirmed the formation of spherical particles with an average size of 350–500 nm, while energy-dispersive X-ray spectroscopy (EDS) mapping revealed a homogeneous elemental distribution with oxygen (51.74%), silicon (24.48%), carbon (6.47%), zinc (6.08%), and aluminum (3.82%). Fourier-transform infrared (FTIR) spectra demonstrated the successful encapsulation of VitE and Ep through characteristic peaks at 3285 cm⁻¹ (OH stretching), 1731 cm⁻¹ (C=O stretching), and 1086 cm⁻¹ (C-O-C stretching). In vitro drug release analysis indicated a controlled and sustained release profile, with cumulative VitE and Ep release reaching 78.6% and 84.3%, respectively, over 48 h in phosphate-buffered saline (PBS, pH 7.4). Antioxidant activity assessment using the DPPH assay confirmed an R² value of 18.84 µg/mL, demonstrating significant free radical scavenging potential. The antibacterial activity, tested via the disk diffusion method, exhibited inhibition zones of 18.31 ± 5.8 mm (E. coli) and 21.51 ± 1.57 mm (S. aureus), confirming strong antimicrobial properties. These findings suggest that the developed electrosprayed PVA/CS nanoparticles loaded with VitE and Ep offer a promising intranasal delivery system with enhanced bioavailability, controlled release, antioxidant capacity, and antibacterial properties, making them a viable candidate for narcolepsy treatment. Full article

The European Middle Pleistocene represents a critical spatiotemporal interval in human evolution, marked by increasing morphological variability and ongoing debate regarding the evolutionary processes leading to the emergence of Neandertals. In particular, it remains unclear whether this variability reflects the coexistence of multiple evolutionary lineages within Europe or structured variation within a single, evolving lineage. Within this context, the site of Bilzingsleben (Thuringia, Germany) provides a key contribution to discussions of European Middle Pleistocene population structure. This study presents a detailed morphological assessment of the Bilzingsleben E7 mandibular fragment, integrating qualitative anatomical observations with quantitative analyses of discrete characters. The Bilzingsleben mandible was examined directly and evaluated within a broad comparative framework including European Middle Pleistocene hominins, Neandertals, and selected African and Asian specimens. Multivariate analyses, including Principal Coordinates Analysis (PCoA) and neighbor-joining cluster analysis based on Gower distances, were used to explore patterns of morphological affinity. Qualitative analysis indicates that the Bilzingsleben mandible exhibits a mosaic combination of predominantly primitive features—such as multiple mental foramina, marked lateral relief of the corpus, and a weakly developed submandibular fossa—together with a limited number of incipiently derived traits, including posterior extension of the corpus and a downward orientation of the digastric fossae. Quantitative results consistently place Bilzingsleben within the morphological variability of European Middle Pleistocene hominins but outside the compact Neandertal cluster. In the PCoA, Bilzingsleben occupies an intermediate (PCo1) and peripheral position (PCo2), contrasting with more centrally positioned specimens such as Mauer. Taken together, these results support an interpretation of Bilzingsleben as part of a European Middle Pleistocene set of populations exhibiting mosaic morphology, rather than considering Bilzingsleben as evidence for a distinct evolutionary lineage. When integrated with evidence from other anatomical elements from Bilzingsleben, the mandibular morphology supports interpreting this population within the broader evolutionary context of the Neandertal lineage. Full article

This study investigates bolted connections in open-section glass fiber-reinforced polymer (GFRP) composite cross-arms for 34.5 kV energy distribution towers. Six GFRP angle sections (L50 × 5 to L120 × 12) were tested under tensile loading using a constant edge distance-to-bolt diameter ratio (e/d = 5), and the connection performance was evaluated based on general maximum and deformation-based criteria (4% and 1 mm hole elongation). Connection capacities ranged from 14.65 to 36.68 kN for single-bolt configurations. Results from multi-bolt connections tests indicated strong influence of bolt layout on connection performance. The highest load capacities of 46.45 kN and 45.93 kN were obtained, respectively, with the two-row bolt configuration and staggered configuration. Comparison of the measured load capacities with ASCE/SEI 74-23 predictions revealed significant discrepancies depending on the assumed failure mode of the connection. A simplified finite element model was developed to predict load–displacement response, capturing initial stiffness and overall trends with reasonable agreement, particularly for connections exhibiting similar failure modes. The findings provide a reliable basis for selecting appropriate bolted connection details in open-section GFRP cross-arm systems. Full article

In this study, Delrin^® (POM) polymer was reinforced with 15 wt.% chopped ramie fiber (RF) to develop a sustainable composite, which was injection-molded and machined using abrasive waterjet machining (AWJM). SEM revealed a skin-core morphology with flow-induced RF alignment and small voids at bundle crossovers, indicating interfacial adhesion. A Taguchi L₉ (3³) design evaluated waterjet pressure (WJP: 100–300 MPa), traverse speed (TS: 100–200 mm/min), and stand-off distance (SoD: 1–3 mm) on kerf width (KW) and surface roughness (SR). Increasing WJP from 100 to 300 MPa lowered mean SR from 6.23 to 4.80 µm (23% reduction) and KW from 1.31 to 1.07 mm (reduction of 18%); enlarging SoD from 1 to 3 mm raised SR from 4.98 to 5.55 µm (an 11% increase) and KW from 1.12 to 1.20 mm (a of 7% increase); and raising TS from 100 to 200 mm/min narrowed KW from 1.24 to 1.11 mm (a 10.5% reduction) with a modest SR decrease from 5.45 to 5.28 µm. ANOVA confirmed WJP as the dominant factor for SR (79.8%), as well as a significant SoD (18.3%). For KW, the influence of WJP (68.8%) was substantial, followed by TS (19.9%) and SoD (11%). Linear models captured the trends well (SR: R² = 88.29%; KW: R² = 93.36%). A desirability-based multi-response optimizer yielded ideal conditions for TS (200 mm/min), WJP (300 MPa), and SoD (1 mm), predicting a KW of 0.94 mm and an SR of 4.1567 µm. Confirmation tests produced a KW (0.970 ± 0.01 mm) and SR (4.27 ± 0.05 µm), which are within 3.19% and 2.73% of the predicted values, validating the DoE regression approach. Full article

Bird migration is an awe-inspiring phenomenon that causes massive global shifts in bird distributions twice a year. To understand the evolution of this phenomenon, it is crucial to know the mortality costs of these journeys. Extreme weather-related events can lead to abnormally high mortality rates among migratory birds, while high mercury concentration may reduce the survival of songbirds in the field, especially for the long-distance migrant insectivores. Yet the specific vulnerability factors remain largely unknown. We collected the opportunistic dead Eastern Red-rumped Swallows (Cecropis daurica) in a mass-mortality event caused by a cold wave in autumn migration in Southern China. Mercury concentration in their tail feathers is 0.57 ± 0.37 µg g⁻¹, lower than the established toxicity threshold for birds. The claws’ hydrogen stable isotopic (δD) values ranged from −116 to −78 ‰, with a mean of (−88.00 ± 8.22) ‰. Stable hydrogen isotopes indicated broad origins for the Eastern Red-rumped Swallows, ranging from ~30° N to ~62° N and ~10° E to ~150° E. Considering subspecies ranges, most of the dead swallows likely came from their almost furthest breeding sites. Our results indicated the primary cause of the mass-mortality event was likely fatigue or starvation resulting from long-distance flight during an extreme cold wave. Mercury exposure may not be the main direct cause of death. Full article

Two-dimensional Ti₃C₂O₂ MXene has emerged as a promising electrode material for non-enzymatic glucose sensing due to its metallic conductivity and biocompatibility. However, the atomic-scale sensing mechanism remains unclear. This DFT study uses the PBE functional with the D3(BJ) dispersion correction to elucidate glucose–MXene interactions under idealized vacuum conditions. Pristine Ti₃C₂O₂ shows metallic behavior with a density of states of about 8.2 states per electron volt at the Fermi level, dominated by Ti 3d states. β-d-glucose adsorbs onto the surface through hydrogen bonding, with an adsorption energy of −0.82 eV at a separation distance of 2.8 angstroms. Bader analysis indicates a transfer of about 0.15 electrons from MXene to glucose, resulting in a Fermi level shift of about −0.15 eV and an 18% reduction in the density of states at the Fermi level. These changes correspond to an estimated sensitivity of approximately 0.6 μA mM⁻¹ cm⁻² and a detection limit of about 17 µM, consistent with reported experimental performance of MXene-based sensors. Comparative adsorption calculations for common sweat interferents yield −0.45 eV for lactate and −0.25 eV for urea, indicating weaker interfacial affinity than glucose; these values reflect thermodynamic binding strength and possible surface occupation rather than definitive electrochemical selectivity, which additionally depends on redox potential, electron-transfer kinetics, and operating bias. We acknowledge three main limitations: first, the model considers only pure oxygen termination rather than mixed oxygen, hydroxyl, and fluorine terminations; second, the calculations are performed under vacuum rather than in aqueous conditions; third, the study is based on static zero kelvin structures rather than finite temperature dynamics. Despite these idealizations, the results provide baseline mechanistic insights to support rational design of MXene-based glucose sensors. Full article