Search Results (35,117)

Acid-etched fracture conductivity testing (AFCT) is a key technique for evaluating the effectiveness of acid fracturing, but the existing test apparatus still presents many problems. One of these problems is the sudden expansion at the connection part, which leads to flow instabilities. To address the limitations of the current experimental apparatus, a new structure for the conductive chamber was developed. By using computational fluid dynamics methods, a numerical model of the fluid domain, was established. A numerical parameter sensitivity analysis was subsequently conducted, with the diverging angle ranging from 12° to 22°, the buffering section length ranging from 10 to 35 mm, and the inner diameter of the injecting pipe ranging from 2 to 7 mm. Considering factors such as the fluid flow characteristics, material costs, and processing costs, the recommended structural parameters for the conductive chamber were determined as follows: a diverging angle of 16°, a buffering section length of 30 mm, and an injecting pipe inner diameter of 5 mm. A simple preliminary experimental comparison between the existing and new structures was conducted; the experimental results primarily show a more uniform etching pattern when using the new structure under simulated conditions. This study provides a scientific basis for upgrading and improving AFCT apparatus. The new geometry may reduce hydraulic interference and potentially improve test reliability. Full article

Rural architectural heritage landscapes are complex socio-spatial systems in which built heritage, cultural landscape values, local use continuity, and environmental constraints interact. This study evaluates conservation priorities and controlled-use suitability in Akşahap, a rural heritage settlement in Antalya Province, Türkiye. Conservation plan data, building inventories, environmental and accessibility datasets, and field observations were integrated at building, parcel, and raster levels using geographic information systems (GIS), multi-criteria decision analysis (MCDA), and the analytic hierarchy process (AHP). Criterion weights were derived from pairwise comparisons completed by a multidisciplinary panel of 10 experts. The analytical framework integrates the adaptive reuse capacity index (ARCI), threshold pressure index (TPI), memory continuity coefficient (MCC), and other spatial criteria within an enhanced spatial suitability (ESS) model. Model plausibility and internal consistency were assessed against the documented spatial and conservation structure of the settlement, while robustness was examined through sensitivity analysis of the AHP weights. Out of 134 historic buildings, 51.5% are in poor or ruined/derelict condition, including 16.4% classified specifically as ruined. Classes A–B comprise 38% of the study area, whereas 62% falls within conservation-priority Classes C–E. The framework provides a conservation-oriented spatial decision structure for distinguishing areas where controlled intervention may be compatible with heritage conservation from areas where conservation pressures should take precedence. Full article

Seafloor topography (ST) has important significance for earth science research, marine resource exploration and underwater navigation. The conventional ST inversion methods are limited by linear approximation and poor small-scale topographic feature prediction. This study proposes a novel Wavelet-Assisted and High-Frequency Enhancement Neural Network (WAHFENN), an architecture integrating discrete wavelet transform (DWT), low-frequency retainment module (LFRM) and high-frequency enhancement module (HFEM) to enhance bathymetry prediction accuracy and capture small-scale topographic features. We apply the WAHFENN to predict the ST in a local area of the South China Sea (SCS). The results demonstrate that the WAHFENN model achieves a standard deviation (STD) of 50.66 m against shipborne single-beam check points, outperforming the topo_27.1 and SDUST2023BCO models by 31.46% and 28.49%, and surpassing the conventional Smith and Sandwell (SAS) method, gravity-geological method (GGM), and convolutional neural network (CNN) method by 78.23 m, 65.18 m, and 4.4 m, respectively. The WAHFENN model achieves a STD of 103.80 m against shipborne multibeam bathymetry data, representing improvements of 38.75%, 25.16%, and 15.58% over the SAS, GGM, and CNN models, respectively. The topographic detail comparisons and power spectral density analysis demonstrate that the WAHFENN model has the potential to outperform conventional methods in identifying small-scale topographic features. Full article

Predicting progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) requires models that represent both regional brain abnormalities and their evolution across repeated examinations. We developed a longitudinal graph neural network that integrates structural magnetic resonance imaging, FDG-PET, regional imaging biomarkers, and clinical covariates across irregular follow-up visits. The study included 614 participants with baseline MCI from the Alzheimer’s Disease Neuroimaging Initiative: 218 converters to AD within five years and 396 non-converters, with 2438 eligible longitudinal visits. Each visit was represented as an 82-node brain graph based on the Desikan–Killiany atlas. Node features combined a 128-dimensional multimodal convolutional embedding with four regional biomarkers. Graph-attention layers modelled spatial dependencies, a node-wise gated recurrent unit modelled longitudinal dependencies, and masked temporal self-attention aggregated variable-length visit sequences. Participants were divided at the subject level into development and held-out test sets, and hyperparameters were selected by five-fold cross-validation within the development set. On the held-out test set of 123 participants, the model reached an area under the receiver operating characteristic curve of 0.859 (95% CI 0.795–0.915), balanced accuracy of 0.805 (95% CI 0.736–0.862), sensitivity of 0.781, and specificity of 0.832. The AUC was numerically higher than that of the strongest baseline, a CNN–GRU sequence model, which reached 0.832 (95% CI 0.758–0.894); the paired AUC difference was 0.027 (95% CI 0.009–0.098), the unadjusted DeLong p-value was 0.026, and the Holm-adjusted p-value was 0.052, which was not significant at the conventional 0.05 threshold after correction for multiple comparisons. In ablation experiments, removing temporal modelling reduced the AUC to 0.818, and removing the spatial graph structure reduced it to 0.808, the two largest reductions observed. Integrated-gradient analysis placed the highest importance on hippocampal and entorhinal regions. Combining graph-based spatial modelling with recurrent longitudinal reasoning was associated with higher discrimination than sequence modelling alone, though this difference was not statistically significant after correction for multiple comparisons. Validation was restricted to a single research cohort (ADNI), and no independent external dataset was used; prospective external validation on an independent cohort is required before the model can be considered for clinical use Because FDG-PET was unavailable for 19.3% of visits, we report the headline result separately from a sensitivity analysis restricted to participants with complete FDG-PET at every visit (development set cross-validated AUC 0.891 vs. 0.874 for the full cohort with masked missing FDG-PET); multimodal performance should be read as cohort-dependent rather than as a single unconditional figure. Full article

This study presents a starch-modified calcined-ZnAl layered double-hydroxide (S-C-ZnAl-LDH) nanocomposite as a multifunctional nanofiller for poly(vinylidene fluoride) (PVDF) efficiently performing, simultaneously, ultrafiltration membrane filtration and efficient adsorbent for the recovery of Acid Blue dye-contaminated water. The synergistic effects of starch modification and thermal activation on nanofiller structure, interfacial compatibility, and membrane performance were systematically investigated through a comparison with pristine ZnAl-LDH, calcined ZnAl-LDH, starch-modified ZnAl-LDH, and calcined starch-modified ZnAl-LDH. SEM, TEM, and XRD analyses confirmed the formation of hierarchical layered nanosheet architectures with a uniform dispersion of crystalline ZnAl domains within a partially amorphous starch matrix, promoting enhanced polymer–nanofiller interfacial interactions Adsorption performance was influenced by solution pH, initial dye concentration, and temperature. Nonlinear kinetic analysis showed that the PFO model described the kinetic data better. However, the overall kinetic modeling findings suggest that Acid Blue 92 adsorption is governed by a combination of physicochemical interactions, suggesting a complex adsorption mechanism was involved. The starch-modified nanocomposite exhibited excellent regeneration stability, retaining approximately 88–90% of its adsorption capacity after five adsorption–desorption cycles. More importantly, the incorporation of S-C-ZnAl-LDH into PVDF membranes significantly enhanced membrane functionality, increasing water flux and permeance by 42.9% and 25%, respectively, while improving Acid Blue rejection by 35.7% to approximately 98%. These improvements are attributed to enhanced membrane hydrophilicity, optimized nanofiller dispersion, and favorable polymer–filler interfacial interactions that facilitate water transport while maintaining high separation efficiency. This work demonstrates an effective strategy for integrating renewable bio-based modifiers with layered nanomaterials to engineer advanced polymeric films exhibiting enhanced permeability, selectivity, durability, and reusability, providing a sustainable platform for multifunctional membrane technologies in water purification and environmental protection. Full article

Background: The modern pharmacist is no longer just a person who prepares medicines, but has become an important health care provider involved in public health, including pharmaceutical care, prevention, chronic disease management and digital health. Such a transition requires the need for professional independence, supported by effective regulation. Deontological codes guarantee this independence and duties but variability in legal frameworks across Europe limits consistent innovation in pharmaceutical care. Objectives: The study analyses the deontological framework for pharmacist professional independence across 30 European jurisdictions and evaluates how effectively each national framework protects independence as a precondition for the development of pharmaceutical care, taking into account professional mobility and changes in healthcare system requirements. Methods: A comparative analysis examined deontological codes and professional ethics from 27 EU countries, the UK, Norway, and Switzerland. Data were collected using a 14-criterion protocol on topics like legal status, professional independence, conscientious objection, confidentiality, advertising, inter-professional relations, competition, errors, telepharmacy, digitalization, continuity of care, professional development, salary regulation, pharmaceutical services, and incompatibilities. These criteria were organized into five thematic clusters for comparison. Results: Three regulation models were identified regarding the legal status of deontological codes: (a) incorporation into primary health legislation, (b) delegation to professional bodies to establish binding corporatist codes, and (c) implementation of soft-law professional standards. While all models share an ethical focus, emphasizing patient priority and confidentiality, they differ considerably in terms of legal bindingness, sanctions, and structural safeguards of professional independence—such as pharmacists’ salaries, ownership rights, and incompatibilities. Telepharmacy is governed by different regulatory frameworks, whereas the application of AI in pharmacy practice has not yet been incorporated into existing deontological codes. Conclusions: The current deontological framework affirms professional independence but offers uneven protection; its effectiveness relies more on the legal structure, enforceability, and safeguards than on explicitly enshrined independence. EU-wide harmonization is neither legally feasible nor essential; instead, a shared minimum standard advocated by professional organizations could facilitate European coordination. The digital and AI gap is the most urgent area for deontological updates. Full article

Keyword-based semantic search performs poorly on complex knowledge repositories such as System Dynamics model databases, where users know the behavior they want to simulate but not the structural vocabulary needed to retrieve it. We replace the static search field with an interactive web-based conversational agent, built as a Retrieval-Augmented Generation architecture in which the system asks context-aware clarifying questions to narrow the search scope across multiple turns. The architecture was evaluated in an ablation study of 37 benchmark scenarios over a curated corpus of 63 models, comparing six retrieval strategies against an expert semantic baseline using Precision@5, Recall@5, MRR@5, nDCG@5 and Hit@5. Conversational refinement raised mean nDCG@5 from 0.1066 to 0.4422 and Hit@5 from 0.1892 to 0.5946. The improvement over the broad-intent baseline is significant on nDCG@5 and MRR@5 under Holm-corrected Wilcoxon signed-rank tests. This comparison aggregates the clarification exchange with the additional user input it elicits. A separate condition that bypasses the generative rewriting step bounds the contribution of that step. A residual gap to the expert semantic baseline (nDCG@5 = 0.5750) remains and is significant on MRR@5. Lexical BM25 applied to expert queries outperformed dense retrieval on every metric (nDCG@5 = 0.8053), showing that sparse matching retains a decisive advantage where the structural vocabulary is exact. We conclude that conversational elicitation is an effective mechanism for narrowing the expertise gap in this domain, and that the lexical results motivate pairing it with hybrid sparse–dense retrieval. Full article

The vehicle routing problem is one of the most often studied optimization problems. In this study, an improved artificial bee colony (ABC) algorithm is proposed which is structured specifically to address the capacitated vehicle routing problem (CVRP), a significant challenge in combinatorial optimization. The proposed algorithm integrates several novel features to improve its performance on CVRP instances. These include a unique initialization strategy that spreads non-repeated customers during initialization, an innovative dual group strategy in the employed bee phase, and an improved scout bee phase with perturbation techniques. Additionally, the proposed algorithm effectively handles infeasible solutions using a novel penalty function formula. The Chebyshev Minkowski’s distance is utilized for route evaluation, enhancing spatial relationship representation over traditional Euclidean distances. Moreover, the classical CVRP model is extended by introducing new variables and constraints to capture changing demand at each customer location within a route. The algorithm’s efficiency was extensively evaluated using benchmark data sets comprising 73 instances from data sets A, B, and P sourced from the VRP instances library site. This rigorous testing enables comprehensive assessments and meaningful comparisons with other algorithms. Overall, the proposed ABC algorithm offers a promising solution for addressing CVRP challenges, providing advancements in solution quality, robustness, and adaptability. Finally, the optimal results are compared in terms of their statistical significance using Friedman and Wilcoxon rank tests with three well-known optimizer algorithms in the literature. Full article

Background/Objectives: Comparative effects of oral second-generation H1-antihistamines vary by rhinitis category, outcome, and treatment period. We assessed evidence structure, estimability, and differentiation among regimens. Methods: PubMed, Embase, Web of Science Core Collection, CENTRAL, WHO ICTRP, ClinicalTrials.gov, and the EU Clinical Trials Register were searched on 1–3 July 2026 for records through 30 June 2026. Eligible studies were natural-exposure randomized trials. Continuous outcomes were analyzed using covariance-aware generalized least squares, and binary harms were analyzed using Bayesian arm-level models. RoB 2, CINeMA, and ROB-MEN were applied in duplicate. Registration was retrospective. Results: Sixty-one reports generated 66 report–randomization entries from 64 cohorts (34,831 participants); 35 model-ready randomizations informed nine structures. In seasonal allergic rhinitis, six of seven TNSS estimates, two of three ocular estimates, and four RQLQ estimates favored active treatment. Any-adverse-event estimates were imprecise. Under the primary prior, cetirizine yielded a WDAE OR of 0.25 (95% CrI 0.08–0.66), but predictive classification was prior-sensitive. Of 34 continuous active–active comparisons, seven were directly informed and 27 indirect-only. All 147 estimable binary active–active contrasts were reported as secondary derivations: 141 CrIs included one and six excluded one, all indirect-only and not formally appraised with CINeMA. Six of 14 regimen-level nodes contributed only to tolerability structures, precluding balanced regimen-specific benefit–harm assessment. All 89 estimable comparisons within the defined CINeMA appraisal scope were rated very low; two non-estimable effects were not evaluable. Conclusions: Predominantly placebo-anchored and sparse evidence established neither a reliable regimen hierarchy nor therapeutic equivalence. Findings were hypothesis-generating, and somnolence remained an evidence gap. No external funding supported conduct; NEXUS PEOPLE, S.L. funded the APC. OSF registration was retrospective: doi:10.17605/OSF.IO/DPT5N. Full article

Robust traffic sign detection is essential for reliable autonomous driving systems; however, detection performance can be substantially affected by adverse environmental conditions. Although simulation-based approaches are widely used to evaluate robustness, the extent to which simulated weather reproduces real-world environmental effects remains insufficiently understood. This study presents a controlled, comparative evaluation methodology for assessing a YOLOv8-based traffic sign detection model across four environmental conditions: clear (sunny), simulated rain, real-world rain, and simulated snow. The same road segment, camera configuration, and predefined set of 55 traffic-sign instances were maintained across the evaluated conditions, enabling interpretable comparisons while minimizing scene-level variability. Detection performance was assessed using representative detection confidence (RDC) at the traffic-sign-instance level, along with image-quality metrics such as sharpness, saturation, and intensity. Mean RDC was highest under clear conditions (0.825), followed descriptively by simulated snow (0.647), real-world rain (0.408), and simulated rain (0.395). However, simulated and real-world rain did not differ significantly in RDC (Holm-adjusted p = 0.770), while the Friedman test indicated a significant overall difference among conditions (χ²(3) = 98.767, p < 0.001; Kendall’s W = 0.599). Image-quality analysis further revealed substantial differences between rainfall conditions in successfully detected traffic-sign regions, particularly in Sharpness and Mean Saturation. Overall, the findings demonstrate that simulated weather can produce traffic-sign-level detector responses that are statistically comparable to those observed under independently recorded real-world rainfall, while producing substantially different image-level characteristics. The results support the use of controlled weather simulation as a complementary evaluation approach, alongside real-world validation, to investigate the environmental robustness of camera-based traffic-sign detection systems. Full article

Non-cooperative sensing using signals of opportunity traditionally requires an explicit reference signal for target detection and localization. This paper introduces a reference-free sensing framework in which target geometry is inferred directly from the received waveform rather than by comparison with an acquired or reconstructed illuminator signal. The proposed framework is implemented using the Ranging, Detection, Imaging, Communications, Approach, and Landing (RaDICAL) architecture, which combines a hybrid Dish–Sparse Uniform Circular Array (SUCA) receiver with Starlink downlink transmissions as spaceborne illuminators of opportunity. Deterministic Multifrequency Dither (DMD) applied across the SUCA elements transforms spatial diversity into unique composite waveform signatures. A unified electromagnetic and signal-processing model is developed that combines spherical-wave propagation, parabolic focusing, deterministic multifrequency modulation, and QR-based waveform-domain hypothesis testing for direct target localization. Numerical simulations together with link-budget analysis demonstrate the feasibility of the proposed approach. Single-dwell detection of 0 dBsm targets is achieved at physical signal-to-noise ratios near 0 dB, while near-unity detection probability is obtained above 10 dB SNR under controlled false-alarm conditions. The results demonstrate that commercial Starlink LEO communication satellites can serve as practical illuminators of opportunity for reference-free non-cooperative sensing without requiring acquisition or reconstruction of the transmitted illuminator waveform. Full article

As exhaust emission standards continue to tighten, evaporative emissions have attracted increasing attention, while information on motorcycles, particularly older models, remains limited. In this study, seven China II and China III motorcycles with engine displacement between 50 and 150 mL were tested using a sealed housing for evaporative determination (SHED), including standardized hot soak and 1 h diurnal breathing loss (DBL) measurements; an additional non-replicated 12 h laboratory-reproduced temperature-profile test was conducted as an exploratory comparison. Across the displacement-matched China II and China III motorcycles, DBL decreased from 6.17–11.12 g to 0.33–0.57 g, corresponding to pairwise reductions of approximately 91–97%, while hot soak loss (HSL) decreased from approximately 1.2–1.6 g to 0.07–0.29 g, corresponding to reductions of approximately 82–95%. These differences coincided with multiple evaporative-emission-control upgrades introduced with China III, including activated-carbon canisters, improved fuel-system sealing, and closed-crankcase ventilation; their individual contributions could not be isolated. In a vehicle-specific comparison between two 150 China III motorcycles, the electronic fuel injection (EFI) equipped motorcycle exhibited 66.9% lower DBL than the carburetor-equipped motorcycle, although this single comparison cannot establish a general EFI effect. The exploratory 12 h test also yielded higher cumulative DBL than the standardized 1 h procedure; however, because test duration, temperature amplitude, and temperature-change rate differed simultaneously, the individual contribution of these factors could not be separated. Full article

Soil salinization poses a significant threat to sustainable agricultural development and ecological security worldwide, resulting in considerable crop losses annually. The advent of drone-based hyperspectral remote sensing offers promising solutions for monitoring soil salinity, due to its high spatial resolution and versatile data acquisition capabilities. However, soil moisture alters both the scattering and absorption characteristics of electromagnetic radiation, thereby modifying soil spectral reflectance and masking salinity-related diagnostic spectral features, which can reduce the accuracy of conventional salinity estimation models. This study evaluates six spectral transformation methods—raw reflectance data (Ref), first derivative (FDR), Piecewise Direct Standardization (PDS), Orthogonal Signal Correction (OSC), FDR + PDS, and FDR + OSC—in conjunction with three machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). A Stacking ensemble model integrating these base learners was further developed to improve soil salinity inversion under moisture interference. The results demonstrated that the Stacking model achieved the highest accuracy and stability among the evaluated models. Additional comparisons with XGBoost and Random Forest (RF) further confirmed the competitive performance of the proposed Stacking framework. The FDR + OSC–Stacking combination achieved the best validation performance, with $R_p^2$ = 0.87, RMSEP = 0.67 mS·cm⁻¹, and RPD = 2.93. Compared with the Ref–Stacking model, $R_p^2$ increased by 0.32 (from 0.55 to 0.87), while RMSEP decreased by 0.58 mS·cm⁻¹ (from 1.25 to 0.67 mS·cm⁻¹). The results showed that PDS had limited effectiveness in correcting moisture-related spectral variation, whereas OSC more effectively mitigated moisture interference while preserving spectral information relevant to salinity estimation. Among the machine learning models evaluated, the Stacking ensemble model achieved better predictive performance than MLP, SVR, and KNN. Furthermore, the FDR + OSC–Stacking combination provided the best performance among the evaluated modeling frameworks and was successfully applied to UAV hyperspectral imagery for spatial mapping of EC_1:5. These findings demonstrate the potential of combining appropriate spectral correction with Stacking for UAV-based soil salinity assessment and provide useful technical support for site-specific salinity management in precision agriculture. Full article

Mediterranean grasslands can contribute to soil greenhouse gas emissions, but their flux dynamics remain poorly constrained because of strong temporal, seasonal, and management-related variability. This study compared the effects of grazing management, stage-wise variation across seasons, and diurnal variability on soil carbon dioxide (CO₂) and nitrous oxide (N₂O) fluxes under rotational grazing (RG) and continuous grazing (CG). High-frequency monitoring captured flux patterns across non-grazed and grazed stages in spring ryegrass–clover and summer teff trials. Generalized linear models identified soil temperature as the environmental variable most strongly and consistently associated with CO₂ and N₂O flux variability (p < 0.001). Segmented regression showed significant CO₂ temperature breakpoints (~32–34 °C) under both systems, while N₂O showed a sharp threshold (~30 °C) only under CG. Soil CO₂ flux was significantly higher under RG than CG throughout the spring trial (p < 0.05). In the summer trial, CG showed higher CO₂ and N₂O fluxes than RG early on (p < 0.001 for both), with no difference by the end of the trial. N₂O did not differ between systems for most of the spring trial. Afternoon fluxes generally exceeded morning fluxes for both gases (p < 0.05). These are stage-specific, context-dependent instantaneous fluxes from a single-site, one-year comparison rather than cumulative seasonal budgets. Overall, the relative effects of rotational and continuous grazing on soil CO₂ and N₂O fluxes changed across sampling stages, demonstrating that grazing-related soil GHG dynamics in this Mediterranean forage system are primarily context dependent rather than governed by a consistently superior grazing strategy. Full article

The present study examined the factor structure, measurement invariance across sex and language, concurrent and discriminant validity and achievement-related criterion validity of the eight-item mathematics self-concept scale from TIMSS 2023 in a sample of 4463 eighth-grade students in Hong Kong. Cronbach’s alpha was high (α = 0.86) and a confirmatory factor model accounting for wording-related method variance fit the data substantially better than a strictly unidimensional model. Equivalence testing revealed that the mathematics self-concept scale achieved strict measurement invariance across both sex and language groups. Correlations provided strong concurrent/convergent evidence with liking mathematics (r = 0.59) and moderate evidence with valuing mathematics (r = 0.36), discriminant evidence through a small association with science self-concept (r = 0.20), and achievement-related criterion evidence through a positive association with mathematics achievement (B = 10.65) using a multilevel modeling approach. Findings support the mathematics self-concept scale’s practical utility for describing students’ perceived mathematical competence and for making cross-group comparisons while cautioning against overreliance on a strict unidimensional structure that ignores negative wording effects. Implications, limitations and future directions of the present study are discussed. Full article