Search Results (21,706)

Currently, the applications of Large Language Models (LLMs) have expanded to diverse areas, from code generation to the medical diagnosis of various pathologies. This work aims to explore what an LLM can achieve using information from CFD simulations of turbulent flow in a manifold, and to determine whether users or students can employ it as a guide for conducting this type of analysis. Through a case study, it is intended to investigate the following aspects of an LLM: (1) the type of information it handles regarding the behavior of turbulent flow within a manifold, (2) whether it identifies the boundary conditions necessary to perform a CFD simulation in a manifold, (3) its capacity to provide recommendations for improving CFD simulations based on the results obtained, (4) whether it can predict the results of CFD simulations based on previous results, and (5) whether users or students can use it as a guiding tool for performing CFD simulations. Among the findings, it was discovered that the LLM used has sufficient information on turbulent flows within a manifold and can make recommendations to improve the results of CFD simulations. It was also identified that LLM offers a user-friendly environment and that it is possible to predict CFD simulation results by varying the manifold boundary conditions. On the other hand, the LLM’s prediction model trained on CFD simulation data yielded RMSEs of 0.029 m/s for flow velocity and 0.33 °C for temperature, and R² values of 0.999 for flow velocity and 0.998 for temperature. Full article

Digital technologies are reshaping how tourists and hospitality consumers search for, personalize, interpret, and share experiences. This study examines customer value co-creation (VCC) as a mechanism linking digital-age participation with consumer outcomes in tourism and hospitality. A PRISMA 2020-guided meta-analysis was conducted using Scopus, Web of Science Core Collection, and Hospitality & Tourism Complete. Forty peer-reviewed studies met the eligibility criteria. Random-effects models synthesized unadjusted correlations between VCC and its main antecedents and outcomes. VCC was positively associated with customer engagement, perceived innovation, and sustainability/CSR-related perceptions. On the outcome side, the strongest and most mature associations were observed for satisfaction (r = 0.64), loyalty (r = 0.61), and perceived value (r = 0.52). Extended outcomes, including experience evaluations, well-being, image, and equity-related indicators, were also positive on average but less empirically mature. High heterogeneity and wide prediction intervals show that VCC is better understood as a context-dependent mechanism rather than a universally strong predictor. Exploratory evidence suggests that digitally intensive service environments may strengthen the VCC–loyalty association. Although the evidence base is not cultural-tourism-specific, the findings are relevant to cultural and heritage settings where digital touchpoints can support interpretation, perceived authenticity, symbolic meaning, and post-visit advocacy. Full article

Critical infrastructure restoration (CIR) is a disaster-management and sustainability challenge because prolonged disruption of energy, water, transport, communications, healthcare, and public-administration services can amplify social, economic, and environmental losses. This PRISMA 2020-reported systematic review synthesizes post-2016 scientific literature and official policy, legal, standards, and technical documents on CIR and AI decision support. The review identified 55 records, removed 1 duplicate, excluded 1 ineligible record, and retained 53 core sources for qualitative synthesis, including 31 scholarly publications and 22 official documents. Manual screening was used; no automated screening or AI-assisted exclusion tools were applied. The results are organized around four research questions covering regulatory frameworks, recovery practices, supporting systems, and AI model families. The synthesis shows that CIR is shaped by layered governance through NIS2, the CER Directive, the AI Act, and national measures; by operational recovery practices such as continuity planning, cyber crisis coordination, interdependency mapping, and model-supported restoration; by digital platforms including SCADA/ICS, IoT sensing, GIS/common operating pictures, decision-support systems, simulation environments, and digital twins; and by AI methods ranging from classical machine learning and computer vision to reinforcement learning and generative assistants. However, evidence maturity remains uneven, with many AI applications still simulation-based, sector-specific, or weakly validated in real restoration settings. The review contributes an integrated CIR-oriented framework showing that AI creates practical value when embedded in interoperable, human-supervised, regulation-aware, and empirically validated restoration architectures that support sustainable service continuity rather than isolated automation. Full article

Ensuring the effective removal of airborne particles is essential for maintaining indoor air quality, particularly in environments with limited ventilation. This study examines how ion polarity regime, voltage, and relative humidity influence aerosol particle removal in a controlled, room-sized chamber (35.8 m³) using a custom-built air ionizer. Experiments were conducted under stagnant and ventilated conditions (0.5 h⁻¹) while varying ionizer polarity (positive, negative, bipolar, alternating), voltage (6 kV, 10 kV), humidity (40%, 70%), and aerosol type (incense smoke, nebulized KCl). Positive and negative unipolar ionization achieved over 90% removal within 60 min, with decay rates of 0.04–0.05 min⁻¹, half-lives of 13–17 min, and clean air delivery rates (CADR) of 60–90 m³ h⁻¹. Bipolar ionization was less efficient due to ion-ion recombination, yielding CADR values below 25 m³ h⁻¹, while alternating polarity improved deposition (40–70 m³ h⁻¹) by reducing recombination losses. Relative humidity had a minimal influence on unipolar performance but moderated efficiency in bipolar and alternating modes. Under low ventilation, unipolar negative ionization sustained high removal (96.7%), while ozone remained below the detection limits of the methods used. These findings indicate that ion polarity control and field strength strongly influence particle removal and that unipolar or alternating-polarity operation can provide effective particle removal under controlled chamber conditions, including a low-ventilation case of 0.5 h⁻¹. Full article

Real-time binaural beat synthesis in dynamic acoustic environments is challenged by carrier non-stationarity, interaural phase discontinuities, and processing delay in conventional digital signal processing pipelines. This study proposes a predictive dual-stage neural framework for phase-coherent auditory synthesis under non-stationary acoustic conditions. The framework decouples real-time carrier estimation from phase-coherent signal generation through two specialized modules. An intelligent acoustic sensing module (AI-1) estimates time-varying carrier information across harmonic, fluctuating, and broadband acoustic profiles using a causal neural front-end with an adaptive confidence-driven strategy. A predictive phase-coherent generator (AI-2) then forecasts short-horizon carrier trajectories and drives a discrete-time phase accumulator to maintain continuous phase evolution during binaural beat embedding. Objective evaluation under multiple acoustic profiles and noise conditions shows that the proposed framework maintains strong phase continuity, with a Phase Coherence Factor greater than 0.91, and low artifact levels, with a Signal-to-Artifact Ratio greater than 39.8 dB, under the evaluated conditions. Additional comparisons with conventional DSP baselines, stronger classical F0 estimators, a lightweight neural F0 tracker, and component-wise ablation variants further demonstrate that the performance improvement arises from the combination of adaptive carrier estimation and predictive phase-coherent actuation, rather than from carrier estimation alone. Hardware profiling shows a combined INT8 inference time of 2.4 ms per frame on a resource-constrained Raspberry Pi Zero 2W-class edge device. Importantly, this inference time and the sub-millisecond phase-accumulator resolution should not be interpreted as sub-millisecond end-to-end physical audio latency. The complete system still includes buffering, framing, neural inference, and output processing delay; the proposed method instead reduces effective phase-boundary misalignment through short-horizon predictive compensation. These results support the proposed framework as a lightweight engineering solution for real-time phase-continuous auditory synthesis in dynamic listening environments. The reported PCF and SAR values should be interpreted as signal-level indicators of phase continuity and artifact suppression, rather than as evidence of listener comfort, perceptual preference, or neurophysiological efficacy. Full article

Orographic cirrus clouds frequently occur over mountainous regions and can influence the radiative balance of the upper troposphere, yet their characteristics and regional variability remain insufficiently understood on a global scale. In this study, we investigate the occurrence, vertical structure, and microphysical and optical properties of orographic cirrus over four major mountainous regions, namely the Rocky Mountains, the Andes, the Alps, and the Himalayas, using the Identification and Classification of Cirrus (IC-CIR) framework together with satellite observations from MODIS, CloudSat, and CALIPSO. The results reveal clear regional differences in both occurrence and structure. Cloud cover is higher over the Himalayas and the Alps and lower over the Andes, while seasonal variability is strongest over the Himalayas and the Alps and weakest over the Andes. In terms of vertical structure, cirrus over the Andes reaches higher cloud tops and exhibits a bimodal distribution. The Andes also show smaller values of ice water path, optical depth, and cirrus reflectance. These results provide a unified comparison of orographic cirrus clouds across four representative major mountainous regions and highlight substantial regional differences in their characteristics and potential radiative influence under different topographic and dynamical environments. Full article

Atmospheric scattering caused by fog degrades image quality and significantly reduces the reliability of computer vision systems. Existing dehazing studies have mainly evaluated dehazing performance using pixel-level metrics such as PSNR and SSIM. However, these metrics do not fully reflect the actual impact of dehazing on downstream object detection performance. Therefore, this paper treats image dehazing as a preprocessing step for object detection in foggy environments and analyzes its effect using standard object detection evaluation metrics. The experimental results demonstrate that, under three fog-density conditions, $\beta =0.005$, $0.010$, and $0.020$, images processed by the DL-U-Net-based dehazing method achieved higher $\mathrm{mAP}@0.5$ values than the corresponding original hazy images, with relative improvements of $+0.39\%$, $+6.60\%$, and $+13.37\%$, respectively. Furthermore, under the dense fog condition of $\beta =0.020$, Recall improved more substantially than Precision. These results indicate that, as fog density increases, dehazing preprocessing becomes more effective in restoring object structural information, reducing missed detections, and enhancing downstream object detection performance. Full article

One of the fundamental recommendations for sustainable agricultural practices is protecting soil biodiversity by increasing the use of organic fertilizers and substrates. According to EU regulations, certain animal by-products (including horn shavings) may be used as crop fertilizers; however, insufficient information is available on the impact of this fertilizer substrate on the soil environment. This study was conducted to determine the effects of annual soil application of horn shavings on selected characteristics of Lumbricidae communities and physicochemical properties of the soil. Experimental plots had the following treatments of cattle horn shavings (CHS): CHS100 (100%; 1.3 t·ha⁻¹; equivalent to 161 kg N/ha), CHS75 (75%; 0.98 t·ha⁻¹), CHS50 (50%; 0.65 t·ha⁻¹), and SL (control without fertilization). After 2 years of application, an electrical method was used to collect earthworms over the following 3 years. Earthworms found belonged to five species representing three ecological groups: Dendrobaena octaedra, Dendrodrilus rubidus tenuis, Lumbricus rubellus, Aporrectodea caliginosa, and Lumbricus terrestris. Significantly higher values of earthworm metrics were demonstrated between the plot with the highest fertilization (CHS100) and the plots with lower horn shavings additions (abundance: CHS100 > CHS75 and CHS50 by a mean of 43.2%; biomass: CHS100 > CHS75 and CHS50 by a mean of 43%). Species richness was not affected but an increase in CHS application led to a greater biodiversity index. CHS treatments affected selected soil parameters to varying degrees, with soil moisture having the greatest influence on the given earthworm traits. Cattle horn shavings used as a fertilizer are a positive promoter of earthworms in soils and further research in this area may be warranted. Full article

Single-use polypropylene (PP) food containers represent a rapidly growing waste stream characterized by compositional heterogeneity and microstructural defects. Conventional reactive compatibilization using isotactic maleic anhydride-grafted PP (iPP-g-MA) provides rigid crystalline anchoring but lacks the interfacial flexibility to accommodate complex micro-defects. Herein, we propose a defect-tolerant compatibilization strategy by developing a binary iPP-g-MA/aPP-g-MA masterbatch for real post-consumer rPP derived from food-service containers. The amorphous aPP-g-MA component is proposed to provide a compliant interfacial environment that accommodates stress concentrations associated with microscale defects, whereas the iPP-g-MA component contributes crystalline anchoring with the recycled PP matrix. This soft/hard interfacial architecture is supported by grafting-degree analysis, GPC, XRD, DSC crystallization behavior, and SEM fracture morphology. The 1:1 mass-ratio binary formulation shows a marked improvement in elongation at break to 200%, representing a 203% increase relative to the single-component iMA system. The notched Charpy impact strength is enhanced to 8.98 kJ m⁻², while tensile strength is retained at 20.9 MPa within the typical strength–ductility trade-off of polymer toughening. TGA shows no premature degradation within the melt-processing window, indicating adequate thermal stability for melt reprocessing. This study provides a compositionally tunable, data-supported route for high-value upcycling of heterogeneous post-consumer polyolefins. From an application viewpoint, the improved ductility-impact balance makes the material relevant to injection-moulded semi-structural products such as storage crates, appliance housings, and automotive interior panels. Full article

Terrestrial laser scanning (TLS) represents an efficient method for acquiring spatial data in urban environments, while the quality of resulting geometric outputs is significantly influenced by subsequent point cloud processing. This article focuses on analyzing the accuracy of automatic building footprint extraction from classified TLS point clouds, with an emphasis on the role of data cleaning methods. The study area is located in the city center of Žiar nad Hronom, where urban structures were monitored using TLS. For detailed analysis, three objects were selected—an apartment building, a garage, and an industrial building—representing different levels of geometric complexity. To simulate realistic processing conditions, classification results obtained from different software (Leica Cyclone 3DR, Trimble RealWorks, and LiDAR360) were used. Their quality was evaluated using standard metrics such as Precision, Recall, and F1-score. These classifications also served as input scenarios containing typical errors, such as point clusters, vegetation near buildings, or misclassified terrain elements. Subsequently, selected point cloud cleaning methods were applied to these datasets, specifically statistical outlier removal, noise filter, and label connected components. The accuracy of the extracted building footprints was evaluated by comparison with reference data obtained from geodetic measurements. The results show that automatic classification alone is not sufficient to achieve accurate building footprints, and that data cleaning plays a decisive role. For example, in the case of the apartment building, statistical filtering reduced the area from 1052 m² to approximately 854 m² (reference value: 706 m²) and significantly improved positional accuracy (centroid shift reduced from 0.455 m to 0.077 m). Similarly, for the industrial building, the area was reduced from 215 m² to approximately 165 m² (reference: 148 m²) while maintaining the correct number of corner points. In contrast, noise filter method proved to be less reliable, as removing up to 25–30% of points often did not lead to improvements in footprint geometry. The results highlight the importance of systematic point cloud cleaning as a key step in automated building footprint extraction and demonstrate that a properly selected combination of methods can significantly improve accuracy even in noisy datasets. The article also provides practical guidance for efficient TLS data processing in geoinformatics applications. Full article

Array Gm-APD LiDAR is highly vulnerable to strong backscattering caused by dynamic smoke. Conventional depth imaging methods cannot rapidly identify the smoke occlusion state, which greatly reduces the target recovery quality of the reconstructed depth image. To solve this problem, this paper presents a non-parametric algorithm for rapid smoke detection and depth imaging for array Gm-APD LiDAR. The proposed method does not rely on parameter estimation of the echo model. Instead, it determines the presence of smoke occlusion by calculating the Pearson correlation coefficient between the echo signal obtained from the superposition of all array pixels and the instrument response function. In this way, the method rapidly identifies smoke interference in a single depth image, performs fast denoising, and reconstructs the depth image. In a dynamic smoke environment with an average attenuation length of no more than 5.1, the proposed algorithm achieves 100% accuracy in occlusion discrimination based on 250 frames of array data. When the smoke occlusion rate reaches 96% and the average attenuation length is 2.29, the method obtains a target recovery of 0.71, which is 86.8% higher than that of the conventional algorithm. These results indicate that the proposed method has strong practical value for array Gm-APD LiDAR, especially for high-speed depth imaging in harsh atmospheric environments with severe obscuration. Full article

With the continuous implementation of the national dual carbon target and the refined control of operating costs in civil buildings, the issue of cleaning and regenerating high-consumption air filter materials in civil buildings has become a hot research topic. This study took rGO air filter material as the research object from the perspective of commercial cost optimization and, using water as the cleaning medium, compared and analyzed the changes in filtration efficiency, airflow resistance, comprehensive performance, and full dimension economy during five cycles of regeneration using water cleaning and ultrasonic cleaning methods. The results showed that ultrasonic cleaning can better maintain the microscopic morphology and structural integrity of the rGO filter, exhibiting more stable filtration performance and slower performance attenuation during repeated regeneration. After the first cleaning, the filtration effectiveness following water cleaning was higher than that following ultrasonic cleaning, with filtration efficiencies 1.21%, 0.18%, and 1.11% higher for PM₁₀, PM_2.5, and PM_1.0, respectively. After the 2nd to 5th cleaning cycles, the filtration efficiency following ultrasonic cleaning was higher than that following water cleaning, with increases of 3.79%, 2.18%, 2.20%, and 6.49% for PM₁₀; 3.20%, 1.22%, 2.96%, and 3.25% for PM_2.5; and 1.90%, 2.02%, 2.02%, and 6.21% for PM_1.0, respectively. The counting filtration efficiency of the ultrasonic cleaning method is relatively high for particle sizes roughly between 0.35 and 2.5 μm, while the difference between large particles is small. The filtration resistance value of the water cleaning method is higher than that of the ultrasonic cleaning method. The QF of the ultrasonic cleaning is always higher than that of the water cleaning method. After five washes, the QF values of PM₁₀, PM_2.5, and PM_1.0 under the ultrasonic cleaning method were 2.26, 2.04, and 2.37 times higher, respectively, than those under the water washing cleaning method. When the replacement frequency is the same, the cost of using ultrasonic cleaning is lower than that of water cleaning. It can effectively reduce the operating costs and asset replacement costs of the fresh air system and is more suitable for the landing and long-term cost control needs of large-scale civil construction projects. Therefore, it is recommended that ultrasonic cleaning be used to recycle rGO air filter materials. These findings provide reference value for the large-scale use of rGO air filter materials and the creation of low-carbon indoor environments. Full article

Cathinone and its synthetic derivatives are among the most popular drugs worldwide. However, the literature provides data on the medicinal and cytotoxic potential of some of these compounds. These data are extremely limited due to the need to obtain additional permits for laboratory studies. Consequently, the therapeutic potential of cathinones may not have been fully explored. Furthermore, the literature provides data on the reduction or reversal of undesirable biological properties of drugs encapsulated in a bio-compatible carrier and administered through targeted therapy. The current study presents preliminary theoretical and experimental tests for further research on target cathinone–graphene–oxide complexes. A non-psychotropic cathinone model—o-fluorophenylacetic acid—was used. The NMR properties (chemical shifts, spin–spin coupling constants, and T₁ relaxation times) of graphene oxide–F-derivative complexes were measured at an acidic and neutral pH. To analyze the structure and stability of the possible complexes in different environments, molecular modelling was performed with simplified graphene oxide models using density functional theory. Experimental data were compared with theoretical values, and the most stable structures that may account for the observed spectral properties of the studied complexes were presented. The obtained data indicate a stronger tendency towards the formation and stabilization of GO-2-fluorophenylacetic acid complexes in a neutral environment. Full article

Essential amino acids and unsaturated fatty acids are key nutritional indicators. The human body preferentially absorbs these compounds, which have beneficial effects on health. In aquatic ecosystems, plankton communities serve as the primary food source for aquatic organisms, playing a crucial role in shaping their nutritional composition. In this study, we collected populations of Macrobrachium nipponense and corresponding water samples from ten distinct geographical locations across China. These sites included five freshwater resources and five saline–alkaline water resources. This study measured the ionic composition and plankton community structure of water samples, and analyzed the nutritional components of M. nipponense, aiming to identify indicator taxa linked to the nutritional value in this species. The results show significant differences in both nutritional components and plankton community structures between freshwater and saline–alkaline environments. This suggests a correlation between specific plankton taxa and the nutritional value of M. nipponense. Using relative sequence abundance data from metabarcoding, linear discriminant analysis effect size (LEfSe) analysis identified six plankton indicator taxa at the genus level. Their abundance differed significantly between the two habitat types. The saline–alkaline region had three associated taxa: Cyclotella, Brachionus, and Chaetoceros. In contrast, Arctodiaptomus, Cryptomonas, and Limnoithona were identified as indicator taxa for freshwater regions. Redundancy analysis (RDA) and Pearson correlation analysis revealed that, with the exception of the SY site, the abundance of Chaetoceros and Brachionus in saline–alkaline waters tracked with levels of K⁺, Ca²⁺, and HCO₃⁻. Meanwhile, at the SZ site, plankton community richness rose with CO₃²⁻. Furthermore, the potential correlations between plankton indicator taxa and the formation of specific nutritional components in M. nipponense were explored. These findings highlight the complex interactions among ionic composition, plankton indicator taxa, and nutritional value in M. nipponense. Ultimately, this study can support the development of artificial techniques to regulate the nutritional components of this commercially important species. Full article

As new energy vehicles and energy storage systems become more common, safety accidents caused by lithium-ion batteries overheating have become more of a concern. Early detection based on distinctive gases (such as H₂ and CO) can give an earlier warning than typical monitoring methods like temperature, voltage, or impedance. Nonetheless, attaining high-precision identification in intricate mixed-gas settings continues to be difficult because of the considerable cross-sensitivity of metal oxide semiconductor (MOS) gas sensors. This research presents an ISA-LSTM-TCN multi-task learning model utilizing an enhanced spatial attention mechanism for the swift identification and concentration forecasting of distinctive gases during lithium-ion battery thermal runaway. The model improves key feature extraction and anti-noise performance by combining the long-term temporal modeling ability of the Long Short-Term Memory (LSTM) network with the multi-scale feature extraction ability of the Temporal Convolutional Network (TCN). It also adds an Improved Spatial Attention (ISA) module with a residual multiplication structure. Moreover, in a multi-task learning framework, joint optimization of gas categorization and concentration regression is facilitated using a hard parameter-sharing method. Tests using a built MOS sensor array dataset show that the model is 99.23% accurate at classifying gases and that the $R^2$ values for predicting H₂ and CO concentrations are 0.9510 and 0.8400, respectively. Tests on public datasets and in different noisy environments show that the model is even better at generalizing and is more robust. The results show that the suggested method allows for quick, accurate detection of thermal runaway gases. This makes it an effective and smart way to monitor battery safety warning systems. Full article