Search Results (44,670)

Purpose: This study provides a critical examination of the literature on applying artificial intelligence (AI) and Extended Reality (XR) in restaurant settings and related foodservice operations. It focuses on how AI and XE influence consumer nutrition awareness and decision-making about food choices, and their implications for customer satisfaction, loyalty, and service delivery in foodservice environments. Design/methodology/approach: The study adopts a systematic literature review (SLR) approach following the PRISMA method. An initial search identified over 3900 academic papers published between 2016 and 2025. Studies were selected on the basis of predetermined inclusion and exclusion criteria, and 26 peer-reviewed articles were analyzed. The review provides a conceptual synthesis and develops propositions for practical applications and future research directions. Findings: The review reveals a shift from static systems that rely on optimization, toward adaptive and user-centered solutions that are behavior-oriented. AI applications predominate in the case of calorie tracking, personalized recommendations, and menu planning. Though deployment of XR technologies (e.g., AR and VR) is less prevalent, they offer potential for immersive, and real-time interventions. A key distinction emerges between studies demonstrating empirical effectiveness (e.g., improved understanding and healthier choices) and those focused on technical and/or conceptual developments. To date, there has been limited validation of behavioral impacts in foodservice settings. Originality: This study offers a theory-informed conceptualization of AI and XR applications in restaurant and foodservice contexts by integrating three perspectives: hospitality (menus and dining experience), nutrition (dietary awareness and healthier choices), and human–technology interaction (technology acceptance and user engagement). The study reconceptualizes AI- and XR-enabled systems as behavioral intervention tools and outlines a focused research agenda for advancing nutritional communication in foodservice environments. Full article

This paper provides a systematic review of recent applications of NLP methods for analyzing traffic crash reports, with a focus on estimating crash severity, crash duration, and crash causation. The review covers prior research using probabilistic topic modeling methods such as LDA, STM, and hierarchical Dirichlet processes in addition to research using transformer-based language models, which include encoder-based models like BERT and PubMedBERT as well as decoder-based models like GPT, GPT2, ChatGPT, GPT-3, and LLaMA. The review starts with a systematic literature selection process with predefined inclusion criteria. We categorize the reviewed studies into the following application areas: crash severity prediction, risk factor identification in crashes, and road safety analysis. The results show several complementary advantages of using different NLP techniques to achieve different analytical goals. Topic models allow for interpretable and exploratory pattern discovery, while encoder models are well-suited for structured prediction problems. Decoder models have the additional flexibility to perform zero-shot and few-shot reasoning, which makes them useful for reasoning about under-sampled or under-reported data. Across the literature, hybrid methods that combine text and structured data outperform individual methods in terms of prediction accuracy and broad applicability. Challenges across the literature include class imbalance, lack of standardization in preprocessing and evaluation methods, and the tradeoff between prediction accuracy and interpretability of prediction models. These findings highlight the importance of aligning model selection with data availability and operational constraints, pointing toward future research directions in hybrid modeling frameworks, standardized evaluation protocols, and real-world deployment of NLP-driven traffic safety systems. Full article

Direct methanol fuel cells (DMFCs) offer significant advantages including high energy density and rapid refueling, making them promising power sources for portable electronic products. However, their practical application, particularly in passive systems, is hindered by critical mass transport limitations: water flooding in the cathode and CO₂ bubble blockage in the anode. Herein, a novel dual-gradient patterned wettability current collector (CC) was designed to alleviate this mass transport impedance. The design uniquely integrates wedge-shaped gradients with surface energy gradients to create a unified, self-driven mechanism for efficient water and CO₂ bubble transport at both electrodes. A mathematical model was developed to quantitatively evaluate the effects of the dual-gradient structure. The results confirm that water removal is enhanced when the cathode current collector features a hydrophobic periphery with a dual-gradient patterned wettability interior on the gas-diffusion-layer side and a fully hydrophilic air-side surface, whereas an inverted pattern facilitates anode CO₂ removal. Optimal fabrication parameters on 316 L stainless steel were established by investigating laser scanning conditions and low-surface-energy agent concentrations. The experimental results show that the passive DMFCs incorporating the optimized current collectors delivered marked performance improvements. At 1 mol·L⁻¹ methanol, the novel anode and cathode current collectors increased peak power density by 15.6% and 14.5%, respectively. Electrochemical impedance spectroscopy revealed a 31.4% and 31.9% reduction in mass transfer resistance of the cell with novel anode and cathode current collectors, respectively, confirming improved gas–liquid self-driven efficiency. Furthermore, the new cells exhibited substantially enhanced long-term stability over 18 h of continuous discharge, attributed to the robust wettability achieved via laser–silane modification. Overall, these findings suggest that the proposed dual-gradient wettability design is a promising method for improving internal mass transport, potentially supporting the development of more robust passive DMFCs. Full article

Hydraulic fracturing of unconventional reservoirs requires accurate fracture monitoring for treatment optimization. Low-frequency distributed acoustic sensing (LF-DAS) in neighbor wells provides dense strain-rate observations, but gauge-length averaging limits spatial resolution and merges closely spaced fracture features. This study formulates gauge-length averaging as an explicit convolution operator and develops a regularized inversion method combining Tikhonov smoothing, a recursive prior, and L-curve parameter selection, supported by a semi-analytical multi-fracture forward model. On a synthetic benchmark, the method advances the effective resolution from the 10 m gauge-length scale to the 1 m sample-spacing scale, recovering fracture count in all hit-window time slices (versus 32% for raw data), achieving Pearson correlation of 0.80 versus 0.29, with peak-position error reduced by 47%. Noise-sensitivity analysis indicates a practical SNR floor near 20 dB, and Wiener-filter comparison confirms 1.5–2.7× correlation and 1.5–2.3× peak-count advantages across tested noise levels. Field application to HFTS-2 B1H stages 22 and 23 reveals previously hidden tensile features consistent with higher local fracture density. With per-stage processing in seconds and no extra sensing hardware, the method is well suited for near-real-time deployment. Full article

To support low-cost, non-destructive crop growth monitoring, this study systematically compared different vegetation indices, voxel sizes, and camera angles using a point cloud voxelization approach combined with a vegetation index weighted canopy volume index (CVM_VI) to assess aboveground biomass (AGB) in winter oilseed rape (Brassica napus L.). Field experiments were conducted from 2021 to 2024 at the Yangma Experimental Base of the Chengdu Academy of Agricultural and Forestry Sciences. Red, green, blue (RGB) imagery of oilseed rape was acquired using an unmanned aerial vehicle (UAV) during the following five key growth stages: seedling, bolting, flowering, podding, and maturity. Collected images were processed to generate point clouds, which were subsequently voxelized at four resolutions (0.03, 0.05, 0.07, and 0.1 m). CVM_VI was constructed by integrating vegetation indices (VIs) derived from the RGB data and the voxelized canopy structural information. Regression models were established between the CVM_VI values and field-measured AGB to estimate biomass. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative error (RE). There were strong correlations (r > 0.80) between the estimated and measured AGB across all voxelization treatments throughout the growth period. Among the 20 VIs tested, regression methods based on the blue green ratio index (BGI), color intensity index, blue red ratio index, vegetative index, and green red ratio index consistently showed superior estimation performance across three consecutive years, demonstrating their good applicability for estimating AGB in oilseed rape under varying agronomic conditions (different varieties, densities, and sowing dates). The cubic regression model CVM_BGI performed best under a 45° UAV camera angle, with the highest R² and lowest RMSE and RE (2021–2022: R² = 0.864, RMSE = 2414.18 kg/ha, RE = 14.8%; 2022–2023: R² = 0.754, RMSE = 2550.53 kg/ha, RE = 14.9%; 2023–2024: R² = 0.863, RMSE = 1953.61 kg/ha, RE = 22.9%). Since the estimation performance showed negligible differences among voxel sizes, and the 0.1–m voxel offered the smallest data volume and shortest analysis time, the CVM_BGI model with a 0.1–m voxel was selected as the preferred approach, providing a practical balance between estimation performance and processing demand. These findings highlight the application potential of point cloud voxelization technology for crop biomass estimation. This study proposes a novel, non-destructive, and efficient framework for estimating field crop AGB using low-cost UAV RGB imagery, facilitating the wider adoption of UAV technology in practical agricultural production. Full article

The rapid deterioration of road infrastructure requires accurate and efficient methods for detecting pavement distresses. Unmanned Aerial Vehicles (UAVs) have emerged as a reliable alternative to conventional inspection techniques, enabling high-resolution data acquisition and improved operational safety. This study investigates the application of the Smart Oblique Capture (SOC) technique for pavement inspection through a systematic calibration of UAV flight parameters, including Ground Sample Distance (GSD), frontal and lateral overlap, camera tilt angle, and flight pattern. A structured experimental campaign was conducted, comprising 135 parameter combinations evaluated across three independent scenarios, resulting in a total of 405 UAV flights. The analysis focused on assessing the impact of these parameters on the visual quality of two-dimensional pavement reconstructions and processing efficiency. The results show that a configuration consisting of a 0.5 cm/pixel GSD, 70% frontal overlap, 80% lateral overlap, and a 70° camera tilt angle achieves the best balance between reconstruction quality and computational cost. Furthermore, the findings indicate that Smart Oblique Capture does not provide a statistically significant improvement in reconstruction quality for linear infrastructure compared to conventional oblique configurations, despite requiring a higher number of images and longer processing times. Overall, the results demonstrate that flight parameter calibration plays a more critical role than the adoption of advanced acquisition strategies such as Smart Oblique Capture. This study provides practical and reproducible guidelines for UAV-based pavement inspection, supporting efficient data acquisition while minimizing redundant information and unnecessary computational costs in infrastructure monitoring workflows. Full article

The construction industry is one of the major sources of carbon emissions, and green retrofitting of buildings is an effective pathway to promoting sustainable development in the sector. However, existing research and implementation strategies often struggle to reconcile the needs of governments, businesses, and residents. Therefore, this study proposes a comprehensive research framework that employs bibliometric and text analysis methods to examine implementation barriers in retrofitting projects across four dimensions: policy, cost, technology, and resident satisfaction. The results indicate that retrofitting costs are the primary factor, while technology is a secondary factor. Furthermore, existing policies feature vague technical standards, insufficient incentives, and a lack of differentiation. Conflicts of interest and challenges regarding cost allocation persist throughout the renovation life cycle. Decision-support tools and renovation technologies face limitations and issues regarding applicability. Residents face constraints from multiple factors, including their knowledge base and economic capacity. Based on these findings, the government urgently needs to improve a differentiated policy system and encourage technological R&D and knowledge dissemination. Enterprises must actively respond to policies and optimize their technologies and management practices. Residents need to enhance their energy-saving awareness, participate in retrofitting efforts, and improve their energy consumption behaviors. Full article

Laser fiber-based collimation straightness measurement can eliminate the intrinsic drift of the laser source while offering a simple configuration and simultaneous measurement of straightness in two orthogonal directions. As a high-precision optoelectronic sensing method, it has been widely used for the measurement of straightness, parallelism, perpendicularity, and multi-degree-of-freedom geometric errors. However, two common issues remain in practical applications. One is the nonlinear response of the four-quadrant detector, the core position-sensitive sensor, which is caused by detector nonuniformity and the quasi-Gaussian distribution of the spot. The other is the degradation of measurement performance by atmospheric inhomogeneity and air turbulence along the optical path, particularly in long-distance measurements. To address these issues, a two-dimensional planar calibration method is first proposed to replace conventional one-dimensional linear calibration. A polynomial surface-fitting model is introduced to correct the nonlinear response and inter-axis coupling errors of the four-quadrant photoelectric sensor. Simulation and experimental results show that the proposed method significantly reduces the standard deviation of calibration residuals and improves measurement accuracy. In addition, based on our previously developed common-path beam-drift digital compensation method, comparative experiments were carried out on double-pass common-path and single-pass optical configurations employing corner-cube retroreflectors, and theoretical simulations were performed to analyze the influence of air-turbulence disturbances on measurement stability. Both theoretical and experimental results show that the double-pass common-path configuration exhibits more pronounced temporal drift. Therefore, a real-time digital compensation method for beam drift in long-distance single-pass common-path measurements is proposed. Experimental results demonstrate that the proposed method effectively suppresses drift induced by environmental air turbulence and thereby improving the accuracy and stability of long-travel geometric-error and related straightness measurement for machine-tool linear axes. Full article

Light Detection and Ranging (LiDAR) three-dimensional (3D) object detection degrades under point sparsity, outliers, coordinate noise, and calibration drift, yet detector evaluation remains largely limited to clean benchmarks. This study focuses on sensing robustness rather than detector redesign. We introduce Bounded Graph Conditioning (BGC)—a deterministic pre-voxelization front-end that applies k-nearest-neighbor (kNN) neighborhood averaging with bounded residual correction upstream of an unchanged detector backbone. BGC is evaluated together with a reproducible sensor-degradation stress protocol and a risk-constrained operating-boundary analysis. Experiments on KITTI with PointPillars, SECOND, and Voxel R-CNN show that BGC most clearly improves retained detection quality and feasible operating coverage under strong noise and strong outlier stress; gains under other degradation types are smaller and backbone-dependent. In the primary score-level box-disjoint calibration/test evaluation on SECOND, maximum feasible coverage at a target risk bound of $0.2$ improves from $0.0754$ to $0.1374$ under strong noise ($\sigma =0.10$ m) and from $0.1323$ to $0.1591$ under strong outliers ($p=0.10$); a cross-backbone check on Voxel R-CNN confirms the same direction ($0.1860\to 0.2864$). Comparison with traditional filtering (SOR and ROR) reveals complementary strengths across fault types. A range-adaptive BGC variant that adjusts parameters per distance bin further improves performance under mixed unknown faults, spherical-coordinate noise, and on a dataset-matched nuScenes validation (adaptive BGC mAP/NDS: $0.2687/0.4493$ vs. baseline $0.2471/0.3846$ under strong noise). Severe translation drift collapses all configurations to full rejection, exposing an explicit sensing boundary beyond the reach of local conditioning. These results support BGC as a practical sensor-side robustness enhancement under the studied degradation protocol, with conditional rather than universal applicability across backbones and fault types. Full article

Effect-size estimation for Mood’s median test has received relatively little methodological attention despite the test’s widespread use in robust and nonparametric analysis. This study evaluates four candidate effect-size estimators: the median absolute deviation-based estimator (Delta–MAD), the probability of superiority (PS), Cramér’s V, and a newly proposed bootstrap-standardized median difference (Delta-Boot) across simulation settings involving normal data with equal variances, log-normal skewness, and heteroscedasticity with a twofold variance difference. Under equal variances, PS achieved the highest classification accuracy for moderate and large effects, with Delta–MAD and Delta–Boot close behind and Cramér’s V performing worst. Performance under log-normal skewness was nearly unchanged, demonstrating the robustness of median- and rank-based methods to heavy right-tailed distributions. Notably, Delta–Boot began to show improved performance for moderate effect sizes in the log-normal setting. Under heteroscedasticity, estimator behaviour diverged sharply: PS remained highly effective for distinguishing no and large effects but showed reduced accuracy for moderate effects due to its sensitivity to spread differences; Cramér’s V degraded substantially across all effect sizes; and the two median-standardized estimators—especially Delta–Boot—were more resilient, stabilizing more rapidly with increasing sample size and achieving the highest accuracy for moderate and large shifts at larger n. These patterns indicate that PS (or Delta–MAD) is most appropriate when variances are equal or nearly so, whereas Delta–Boot provides the most reliable performance in settings where variance imbalance is likely. Finally, a real-world application to fasting glucose data from the 2024 WHO STEPS survey in Trinidad and Tobago illustrates the practical utility of these approaches. Full article

Additive manufacturing (AM) has transformed traditional manufacturing by enabling the fabrication of complex geometries and functional components that are difficult or impossible to produce using conventional techniques. Recent advancements have expanded AM capabilities through the integration of multi-material systems, allowing for enhanced performance, customisation, and functionality of manufactured parts. Despite rapid development, there is a limited consolidated understanding of the processes, material combinations, and practical implications of multi-material additive manufacturing (MMAM) across different application domains. This study aims to provide a comprehensive overview of general additive manufacturing processes, with a particular focus on the evolution and implementation of multi-material fabrication techniques. The review draws upon publicly available scientific literature to analyse various AM technologies, material pairing strategies, and process parameters. Comparative analysis is conducted between the additive and conventional manufacturing approaches to highlight advantages and limitations. The findings reveal significant progress in material compatibility, interface bonding, and process integration, enabling the production of multifunctional and performance-optimised components. Diverse applications are identified across aerospace, biomedical, and industrial sectors. MMAM represents a critical advancement in modern manufacturing, offering expanded design freedom and functional integration. Continued research is essential to address the remaining challenges in material compatibility, scalability, and process standardisation. Full article

Asymmetric Cascaded Multilevel Inverters (ACMLIs) have emerged as a prominent solution for medium- and high-power applications due to their ability to provide an increased number of output voltage levels with fewer power switches. However, maintaining low total harmonic distortion (THD) and ensuring robust stability under varying operating conditions remain significant challenges. This study experimentally validates a voltage-feedback Proportional-Resonant (PR) control strategy for a seven-level ACMLI. Unlike conventional current-feedback methods, the proposed approach directly regulates the output voltage, providing superior harmonic suppression and enhanced steady-state accuracy. The stability and dynamic performance of the controller were theoretically analyzed using Bode diagrams and root locus methods, and further verified through the MATLAB Curve Fitting Tool (CFT) with a high correlation (R² = 0.9989). Experimental results demonstrate that the integration of the PR controller significantly improves power quality, reducing the current THD from 6.55% to 3.68% and the voltage THD to 2.94%. These findings confirm that the system fully complies with IEEE 519 standards and outperforms several existing strategies in the literature. The results establish the voltage-feedback PR control as a robust, high-precision, and practical alternative for power quality-oriented multilevel inverter applications in modern energy systems. Full article

Background/Objectives: Chronic liver diseases, including metabolic dysfunction-associated steatohepatitis (MASH) and chronic viral hepatitis (CVH), are major global health concerns due to their potential progression to cirrhosis, liver failure, and hepatocellular carcinoma. Because liver biopsy, despite meeting the diagnostic gold standard, is invasive and associated with complications, non-invasive fibrosis assessment tools have been increasingly recommended in clinical practice. This study aimed to compare the diagnostic performance of several non-invasive fibrosis markers (ARR, APRI, FI, FIB-4, API, NFS, BARD) and transient elastography in detecting advanced liver fibrosis (F4) in patients with MASH and CVH. Methods: This retrospective study included 237 adult patients (77 MASH, 160 CVH) who underwent liver biopsy between 2017 and 2025 at the University Clinical Center of Serbia. CVH included chronic hepatitis B (CHB) and C (CHC). Patients were evaluated using serum fibrosis indices and TE, and results were compared to histological staging (F0–F4). ROC analysis assessed diagnostic performance. Results: Cirrhosis (F4) was more common in CVH than MASH (p < 0.001). In MASH, NFS (AUROC 0.931), FIB-4 (0.915), BARD (0.872), and APRI (0.878) showed high diagnostic accuracy for F4. In CHC, APRI (0.931), FIB-4 (0.863), and TE (0.938) had strong performance, while in CHB, TE (0.987) outperformed FIB-4 (0.821). Sensitivity and specificity varied by test and cohort, with TE consistently yielding the best results where available. Conclusions: Non-invasive methods, particularly NFS and FIB-4 for MASH and TE for CVH, effectively identify advanced fibrosis. Their application could significantly reduce the need for biopsy, especially in high-risk groups. TE demonstrated superior accuracy, but access limitations highlight the continued relevance of serum-based scores. Full article

Somatostatin is a potent endocrine regulator and neurotransmitter, exerting predominantly inhibitory effects in different tissues of the body, via G-protein coupled receptors. Five such specific receptors have been identified, with different effects and tissue distribution. The multifaceted actions and effects of somatostatin make it useful as a potential therapeutical means in various pathologies; however, in clinical practice, somatostatin analogues, namely octreotide, lanreotide and pasireotide, are commonly used instead, due to their increased half-life and increased receptor selectivity, with pasireotide showing a more extensive receptor binding profile and high affinity for somatotastin receptor (SSTR) 5, which may prove effective in cases of resistance to first-generation analogues. Apart from their many uses in neoplastic pathologies, somatostatin analogues represent viable treatment choices in some ocular pathologies, congenital hyperinsulinism, gastrointestinal bleedings and portal hypertension, acute pancreatitis, and dumping syndrome. They have also been used in some cases, with varying degrees of success, in patients with post-surgical gastrointestinal and lymphatic fistulas, refractory chronic diarrhoea and polycystic kidney disease; many applications in paediatric patients have also been documented. The aim of this review is to present the applications of somatostatin and its analogues as alternative or second-line therapies, along with insights into their effectiveness and future potential. Full article

Proton exchange membrane fuel cell vehicles (PEMFCVs) have attracted widespread attention in recent years. However, there are many challenges existing in the development, such as the durability and economy of the fuel cell system (FCS). In this investigation, a fuzzy energy management strategy based on Grey Bernoulli Prediction (FEMS-GBP) is proposed to mitigate these two issues. Grey Bernoulli Prediction (GBP) is used to predict the FCS short-term future power demand with a low calculation amount, which is suitable for real-time on-board applications in PEMFCVs. Therefore, FEMS-GBP can proactively adjust FCS output power to reduce large load change times during PEMFCV operation, thereby improving FCS durability. Fuzzy control is employed to accomplish the energy management task between the FCS and the battery for better fuel economy. Numerical simulations and experiments under different vehicle driving cycles are carried out to evaluate the performance of FEMS-GBP. By comparing it with two other conventional energy management strategies, FEMS-GBP is demonstrated to be feasible and effective, as it achieves favorable performance in balancing durability and economy, especially under practical driving conditions. Full article