This study investigates the stability of three-dimensional (3D) tunnel roofs with varied burial depth in saturated rock strata following the Hoek–Brown (HB) failure criterion. Within the framework of limit analysis, a 3D kinematic collapse mechanism for tunnel roofs, incorporating the existence of pore water pressure, is developed, and corresponding stability indices are formulated. Numerical methods are utilized to determine the optimal solutions of these indices. A comprehensive parametric study evaluates the influence of 3D geometric characteristics, HB parameters, and burial depth on tunnel stability. Results demonstrate the evolution of tunnel-roof stability as the critical depth-to-span ratio C/R increases from shallow- to deep-buried conditions; for the parameter combinations examined in this study, the critical C/R separating the two mechanisms ranges approximately from 0.15 to 2.0, depending on the rock-mass properties, pore-pressure condition, and tunnel geometry. Furthermore, stability charts correlating supporting pressure and factor of safety (FoS) in saturated strata are proposed to offer practical design guidance. Findings indicate that neglecting pore water pressure may significantly underestimate the structural stability, thereby emphasizing the necessity of incorporating hydrogeological effects in tunnel design. The proposed stability assessment framework provides theoretical support for the safe development of deep underground spaces under complex geological conditions, including deep energy exploitation, underground storage facilities, and related geotechnical engineering applications.
This paper proposes a location error-tolerant data embedding technique for display-to-camera (D2C) communication systems. The method is designed to enable robust data transmission while maintaining high image fidelity, facilitating simultaneous digital content display and data communication. To address the common issue of alignment and localization inaccuracies in D2C systems, the proposed approach defines specific regions of interest to ensure robustness against object detection model location errors. The architecture employs an expanding and contracting network structure for the encoder to achieve seamless data integration, while the decoder utilizes a computationally efficient “thin” structure for rapid data extraction. To improve performance in diverse environments, various distortion models were integrated into the system training. The system’s effectiveness was evaluated by measuring the bit error rate under conditions of Gaussian noise, blur, simulated localization errors, and real-world distortions. Image quality was validated using peak signal-to-noise ratio and the structural similarity index measure. The results indicate that the proposed technique maintains superior image quality and achieves reliable data recovery even in the presence of significant localization errors. These findings suggest that the approach provides a stable and effective solution for practical mobile-based D2C communication.
Constructed wetlands are promising nature-based solutions for mitigating environmental pollution, able to retain suspended solids as microplastic (MP) particles. This study aims (i) to identify potential sources and transport pathways of MPs in the Tancat de la Pipa constructed wetland (Spain), using a conceptual source-attribution approach based on spatial distribution, particle characteristics, catchment characteristics, hydrological connections, and the available literature and official information, as well as (ii) to evaluate the effectiveness of the wetland in retaining MPs within the investigated size range. Sampling was conducted at six locations representing two inlet canals, two internal sites, and two outlet points of the wetland towards Albufera Lake. Higher MP abundances were recorded at the inlet, while lower concentrations at internal and outlet sites indicated partial retention, with apparent removal efficiencies ranging from 24% to 50%, depending on the sampling campaign. Blue and transparent MPs predominated, while polyethylene (47%) and polyester (18%) were the dominant polymers, followed by polymethyl methacrylate (10%), polystyrene (8%), and polypropylene (7%). The spatial distribution and particle characteristics, together with catchment land use and hydrological connections, indicated potential contributions from mixed sources, including packaging, agricultural activities, wastewater, traffic runoff, fisheries, and industrial activities. These findings provide an initial assessment of potential MP sources and transport pathways in a Mediterranean wetland and indicate partial retention of MPs within the investigated size range, supporting the need for longer-term and source-specific monitoring to improve source attribution and evaluate seasonal variability.
Deep neural networks (DNNs) have achieved remarkable success in medical image classification, yet their performance remains sensitive to dataset size. Knowledge distillation (KD) alleviates this issue by transferring knowledge from a high-capacity teacher to a lightweight student. However, conventional KD relies on a static teacher, while adaptive teacher updating may improve performance on the student-learning data while reducing retention of knowledge acquired during teacher pretraining. To address these limitations, we propose an Anti-forgetting Adaptive Teacher-driven Knowledge Distillation framework (A2T-KD), which aims to balance teacher adaptation and pretraining-knowledge retention. The proposed framework integrates three modules: MITR for cross-epoch representation consistency, DSDO for prediction-space decoupling and class discriminability, and SGKD for feature- and logit-level knowledge transfer. Across nine medical imaging datasets, A2T-KD achieved higher mean values than the fixed-teacher Vanilla KD baseline in 30 of 36 dataset–metric comparisons. It also exhibited the lowest pretraining-set ACC degradation among the evaluated teacher-update baselines on all nine datasets, supporting the intended balance between teacher adaptation and pretraining-knowledge retention under the evaluated settings.
The Internet of Things (IoT) has evolved into the Artificial Intelligence of Things (AIoT), where intelligent data processing complements large-scale connectivity across applications ranging from smart homes to industrial automation. However, its rapid expansion and the increasing adoption of AI have led to growing environmental concerns, particularly increased energy consumption and electronic waste. These issues highlight the importance of Green AIoT practices, which extend Green IoT by combining energy-efficient communication, computing, and intelligent resource management to achieve energy-efficient and sustainable AIoT operation. This paper presents a comprehensive survey of techniques aimed at improving the energy efficiency and sustainability of Green AIoT systems. The focus is placed on networking aspects, particularly machine-to-machine (M2M) communications and wireless sensor networks (WSNs), alongside the roles of computing infrastructures, data centers, and energy-efficient processor architectures. The survey further examines how AI-assisted techniques, including TinyML, edge AI, and intelligent computation offloading, complement traditional Green IoT mechanisms to reduce energy consumption. Key approaches such as low-power communication protocols, energy-efficient data processing, data compression, smart energy management, and energy harvesting are reviewed and compared. Furthermore, the paper summarizes representative state-of-the-art solutions with quantitative insights and discusses open challenges and future research directions toward environmentally sustainable AIoT systems.
This study addresses the challenge of the mechanical behavior of fractured rock masses in cold regions under freeze–thaw-fatigue coupling. Uniaxial step-incremental cyclic loading tests were conducted on double-cracked red sandstone subjected to different numbers of freeze–thaw cycles to reveal the damage evolution laws. Based on the variable-order fractional derivative theory, the traditional Nishihara model was improved by replacing the Abel dashpot in the viscoplastic component with a variable-order fractional dashpot, thereby establishing a fatigue deformation constitutive model that accounts for freeze–thaw damage. The novelty of the model lies in coupling the variable-order fractional viscoplastic element with stepwise cyclic loading equivalence, fatigue-threshold-controlled deformation, and freeze–thaw damage degradation, rather than merely replacing the dashpot in the classical Nishihara framework. The experimental results indicate that freeze–thaw cycles accelerate macroscopic damage of the rock mass. As the number of freeze–thaw cycles increases, the crack initiation stress, dilatancy stress, and peak stress decrease in a stepwise manner. The rock specimens exhibit significant softening characteristics, accompanied by intensified dilatancy, propagation of secondary cracks at the tips of pre-existing flaws, and a transition of the failure mode towards shear failure. By analyzing the mean stress versus axial deformation curves of the last two stages of cyclic loading, the fatigue threshold stress ratio of freeze–thaw damaged double-cracked sandstone was determined, confirming that freeze–thaw damage reduces the fatigue strength of the rock mass.
This paper investigates the relationship between the benefits achieved by predictive maintenance implementation and the related costs incurred intentionally or not. Specifically, we seek to establish an evaluation method for predictive maintenance that includes prediction model performance, which will better indicate that a predictive maintenance strategy built on the model will succeed in providing benefits compared to a pre-existing maintenance strategy. This will help justifying the costs and complexities of implementing new maintenance procedures and ensure positive financial outcomes. We demonstrate this method with a case study based on scheduled maintenance of an offshore wind farm where a declarative modeling approach is used to simulate service costs and operations, while a deep learning model provides insights on imminent downtime events. The results of this case study show that predictive maintenance is only profitable under specific conditions, such as limited service resources and high prediction model performance, and a profitable performance threshold for the underlying model is obtained.
With the rapid growth of electric vehicles, charging demand at highway service areas has increased sharply, while insufficient charging facilities have intensified the mismatch between supply and demand. Existing studies on photovoltaic–energy storage–charging systems mainly focus on urban scenarios and rarely consider the spatiotemporal characteristics of long-distance highway travel. To address this gap, this study proposes a capacity planning method for photovoltaic–energy storage–charging systems in highway service areas. EV charging load is simulated using a Monte Carlo approach considering travel characteristics and state of charge, while an M/M/c queuing model is used to quantify user waiting time. A multi-objective optimization model considering system costs and waiting-time costs is solved using a multi-objective genetic algorithm, and the Pareto solutions are ranked by VIKOR. Under the normal-load scenario, the optimized configuration yields a weighted average waiting time of 5.06 min and reduces the maximum waiting time from 52 min to 11.83 min, with a construction and maintenance cost of RMB 2.5758 million. Under the high-load scenario, the corresponding values are 5.35 min, 12.12 min, and RMB 3.3078 million, respectively. The results show that the proposed method can adapt system capacity to different traffic demand levels while maintaining charging service quality.
To address the mismatch between solid conveying efficiency and melting efficiency in grooved barrel single-screw extruders (SSEs), the effects of barrel groove and screw channel structural parameters on the melting start point and melting length were systematically investigated based on the groove-channel coupled melting (GCCM) theory. Three extruder configurations—smooth barrel, spiral-grooved IKV (Institut für Kunststoffverarbeitung), and GCCM—were designed and tested on a hydraulically driven clamshell barrel SSE platform with a screw diameter of 45 mm and a length-to-diameter ratio of 30:1. Low-density polyethylene (LDPE) grade 607 was used as the model material. The results demonstrate that increasing the barrel groove depth shifts both the melting start point and melting length downstream, whereas the groove width has negligible effects. A minimum melting start point is achieved at a groove pitch of 4D. At a screw speed of 30 r/min, the GCCM extruder equipped with a BARR barrier screw achieves a melting start point 24.0% earlier than the IKV extruder and a melting length shortened to 62.7% of the IKV value, representing reductions of 27–35% and 21–31% compared to reported barrier screw and Maddox screw melting lengths, respectively. The actual throughput reaches 93.7–95.7% of the theoretical solid conveying throughput, with specific energy consumption only 8.5% higher than the IKV extruder and throughput fluctuation reduced to 23.7%. Complete melting is achieved at a melting zone temperature of only 110 °C, confirming the dominant role of internal frictional heat. This study provides systematic experimental data and theoretical guidance for the structural optimization of grooved barrel SSEs.
Integrating distributed energy resources (DERs) via Virtual Power Plants (VPPs) faces challenges like renewable intermittency, communication scheduling uncertainties, and high data collection costs. While existing studies often overlook practical implementation efficiency, this paper proposes a VPP scheduling framework integrated with communication optimization. First, a communication-scheduling model is established to quantify the impact of network uncertainties on revenue. Second, an equipment pre-allocation strategy based on historical data clustering is presented to lower trial-and-error costs and algorithm complexity. Finally, a global optimization algorithm achieves time-segmented collaborative optimization of equipment access, reducing network switching frequency while balancing packet loss, transmission delay, and operational revenue. Simulation results demonstrate that the proposed strategy reduces VPP scheduling revenue loss by approximately 23.6% compared with the traditional greedy algorithm. Furthermore, when evaluated against classic metaheuristic baseline algorithms such as PSO under identical forecasting conditions, Network-Aware FA (NAFA) effectively escapes local optima and achieves the lowest revenue loss, strongly validating the economic efficiency, algorithmic superiority and scheduling reliability of the proposed framework.
Data silos and topological incompatibility between building information modeling (BIM) geometric models and finite element method (FEM) analysis models in transportation infrastructure engineering represent critical bottlenecks that impede real-time digital twin analysis and the intelligent transformation of the industry. Based on a critical review of existing BIM-to-FEM conversion methods and their limitations, this study proposes a “BIM-FEM” seamless conversion and dynamic twin mapping method that integrates parametric modeling with finite element meshing, with modeling and repair time reduced from 16 h to 3 h, and the maximum element aspect ratio improved from 84.78 to 16.59. In terms of geometric topology, we propose a collaborative construction method in which finite element hexahedral meshing rules drive BIM parametric modeling in reverse. By regularizing the decomposition of axis lines and cross-sectional feature points of linear transportation structures and optimizing their topology, we achieve fully automated hexahedral meshing without topological errors. In terms of mechanical analysis, an “offline pre-solution, online superposition” computational order-reduction model is proposed. This reduces the high-dimensional full-range finite element solution of dynamic traffic loads to a dot product operation between the influence line matrix and real-time load vectors, enabling sub-second computational response under high-concurrency dynamic traffic conditions—specifically, single-point mapping takes less than 0.27 ms, incremental updates are controlled within 0.2 s. In terms of spatiotemporal mapping and system applications, a high-fidelity “FEM-BIM” mapping mechanism based on inverse isoparametric transformation and AABB (Axis-Aligned Bounding Box) spatial indexing has been established, supporting real-time rendering of 3D cloud maps on the web and digital twin applications in engineering. Applications of this method in real-world bridge engineering digital twin systems have demonstrated its ability to perform automatic structural safety assessments and health condition predictions with an overall computation time reduction of approximately 73% compared to conventional approaches. This addresses the shortcoming of traditional structural health monitoring—which emphasizes sensor-based identification over mechanistic evaluation—and provides a viable path for intelligent, precise management and maintenance of transportation infrastructure throughout its entire life cycle.
Hydraulic structures in navigation–hydropower hubs exhibit complex deformation governed by coupled hydraulic, thermal, and long-term effects, making reliable deformation monitoring challenging. Conventional statistical models are prone to multicollinearity-induced overfitting and elevated false alarm rates, while existing temperature components inadequately characterize delayed thermal responses under large reservoir-level fluctuations. To address these issues, this study proposes an SMT-β deformation monitoring model that integrates separated modeling technology (SMT) with a beta distribution-based temperature formulation. Within a physics-guided sequential modeling framework, long-term deformation trends are first extracted using ICEEMDAN, followed by the development of a beta distribution-based temperature component to characterize lagged thermal effects during stable reservoir-level periods. The hydraulic load component is subsequently identified from the residual deformation, and the total deformation is reconstructed through linear superposition of physically interpretable components. Validation using monitoring data from a 300 m-class super-high arch dam demonstrates that the proposed model achieved the highest prediction accuracy (RMSE = 0.318 mm), reduced the overfitting coefficient to 0.48, and eliminated false alarms. The proposed framework effectively mitigates multicollinearity-induced overfitting while improving the physical interpretability of deformation monitoring, offering a robust and extensible approach for hydraulic structures subjected to complex environmental loads.
Single-axis solar tracker arrays are commonly installed within the lower atmospheric boundary layer. Their wind loads are governed not only by the module tilt angle, row-to-row sheltering and wind-direction angle but also by the inflow turbulence intensity. To clarify the variation in PV array wind loads under different turbulent environments, large-eddy simulation (LES) was used to investigate the mean pressure coefficient, the standard deviation pressure coefficient and the flow field of a multi-row tracker array subjected to three inflow turbulence intensities. The model scale was 1:240, with a chord length C = 0.02 m, a ground clearance h = 0.0375 m and a row spacing of 0.047 m. The target turbulent inflow was generated in ANSYS Fluent using the narrowband synthesis random flow generation (NSRFG) method, and the inlet spectra, mean-velocity profile and turbulence-intensity profile were verified in an empty domain. Turbulence intensity had a limited overall effect on the mean pressure coefficient but slightly increased its chordwise non-uniformity. In contrast, it markedly increased the standard deviation of the wind pressure coefficient while making its chordwise distribution more uniform. The outer trackers showed greater non-uniformity in both the mean pressure coefficient and the standard deviation pressure coefficient than the inner trackers. Increasing the module tilt angle strengthened the sheltering effect on standard deviation wind loads. Oblique wind weakened the sheltering effect but generally produced greater mean wind loads and wind load standard deviations at the leeward-end modules. Instantaneous vorticity fields showed that a high turbulence intensity weakened periodic vortex shedding behind the modules. These results provide a basis for evaluating the combined effects of the turbulence intensity, tilt angle and wind-direction angle in the wind-resistant design of PV trackers.
Road alignment creates unequal shortwave-radiation inputs on opposite embankment slopes, whereas the way in which overlying fill height redistributes this contrast among embankment layers remains unclear. This study aimed to quantify how road-axis azimuth controls transverse heat input and how upper fill height redistributes this input among the upper fill, rock-filled target layer, and permafrost foundation of a representative G219 rock-filled ventilated embankment. A two-dimensional transient enthalpy-based heat-transfer model, incorporating a smoothed phase-change treatment and coupled convection–shortwave boundary conditions, was used to compare three azimuths (0∘, 45∘, and 90∘), three upper fill heights (0.5, 1.5, and 3.5 m), and cold-year, reference-climate, and warm-year boundaries. Three counterfactual scenarios were used to separate the effects of target-layer equivalent thermal properties and exposed-surface optical conditions, while continuous simulations over 0∘–165∘ verified the representativeness of the formal azimuths. The common-domain response at 45∘ reached 67.9%–70.1% of that at 90∘, indicating a continuous transition from near-symmetric to strong transverse heating input. Under the thermal–optical scenario at 90∘, increasing fill height increased common-domain asymmetry by factors of 2.69–2.89; however, the target-layer response consistently peaked at 1.5 m, whereas the foundation-layer response decreased. The upper-fill-to-target-layer peak lag also increased with fill height. These patterns persisted across climate boundaries and prescribed thermal–optical–conductivity perturbations. Within the controlled scenarios, road alignment set the transverse heat input, whereas fill height regulated its within-profile transfer and layer-specific thermal response. The results support comparative thermal assessment of embankments with a similar upper-fill–rock-filled-target-layer–permafrost-foundation sequence, rather than design of an optimum fill height for a specific site.
Recently, consumers have shown increasing interest not only in healthier food but also in products labelled as “free-from”. Among these, foods free of substances that can trigger allergic reactions in hypersensitive individuals, such as nickel, are of relevance. In this context, a two-year study was conducted in a soilless strawberry production system managed to minimise nickel inputs, to assess the feasibility of producing fruits complying with the “nickel-free” limit of 0.01 mg kg−1. The experiment involved two strawberry cultivars (‘Asia’ and ‘Arianna’) and two plant types for each cultivar (tray plants and cold-stored A+ plants), grown in a 100% perlite substrate. Productive parameters, organoleptic quality, nutritional traits, and nickel content in fruits were evaluated. The aim was to determine differences among cultivars and plant types in yield, fruit quality, and nutritional value, and to verify whether the plant type affects nickel transfer to the fruit. Yield was highest in ‘Asia’ cold-stored plants, while ‘Arianna’ tray plants showed the best qualitative profile. Fruit nickel exceeded the limit in all combinations in the first autumn, but thereafter remained below it only in tray plants, showing that plant material choice, rather than the inert substrate alone, determines the production of nickel-free fruits.
Network-level congestion assessment requires segment states to be aggregated across roads that differ in length, observed flow, capacity, and functional role. In data-limited settings, however, traffic agencies may only have road class, segment length, traffic flow, capacity, and average speed, and the effect of the aggregation rule is rarely made explicit. This study formulates a segment-to-network congestion assessment problem for such settings. Road segments are classified with road-class-specific speed thresholds under three definitions—severe-only, moderate-or-worse, and mild-or-worse—and six benchmark rules are compared under identical segment classifications. A class-function-weighted ratio is then proposed to combine within-class congested mileage with each road class’s share of observed hourly flow–length exposure. The method is evaluated on 31 segments in a 39.615 km urban road network using a single audited data table. Under the severe-only definition, the six benchmark ratios range from 7.01% to 12.38%, whereas the proposed ratio is 9.98%. The proposed ratio is 32.89% for moderate-or-worse congestion and 94.92% for mild-or-worse congestion. Joint 5% and 10% perturbations in both directions of the cutoff defining each outcome produce ranges of 7.63–9.98%, 28.19–42.71%, and 91.17–98.14%, respectively. These results show that network diagnosis depends materially on both the congestion definition and aggregation rule. The proposed method is more interpretable for decisions where road-class function and auditable aggregation are important, while the numerical findings remain specific to the retained case data.
Space-constrained articulated mechanisms integrated into mechatronic assemblies require rigorous kinematic characterisation during preliminary design, since spatial limitations and the avoidance of kinematic locking in the transient regime constrain the admissible geometry. An original planar guidance-and-retraction linkage is analysed, composed of three fixed joints, a linear hydraulic actuator, a rigid block of six interconnected elements, and two guiding links that suppress the out-of-plane degrees of freedom; the retractable landing gear of a light training aircraft is adopted as the application case. All mobile joints are expressed in closed form as explicit functions of a single input parameter, the actuator length, without decomposition into Assur groups or iterative compatibility equations. The analytical positions, velocities and accelerations were cross-verified against two independent packages, Linkage v.3.16.14 and GIM v.2025.4; deviations remain below 0.25% of the amplitude of each quantity, with velocity root-mean-square deviations below 0.16 mm/s. The guiding dyad reaches neither dead-centre over the stroke, although its link lengths are treated as preliminary because the 35° minimum transmission-angle margin is not maintained over the first 2.2% of the stroke, for which a corrective dimensioning is provided. The closed-form characterisation provides a basis for subsequent structural, dynamic and experimental analysis.
This study formulates panel furniture production as a flexible job shop scheduling problem (FJSP) with constraints on transport resources under varying production conditions. An adaptive large neighborhood search (ALNS) method guided by a heterogeneous graph neural network (HeteroGNN), termed HeteroGNN-ALNS, is developed to balance completion time, waiting time, and workload during the production process. The current scheduling state in ALNS is represented as a heterogeneous graph, where panel jobs and production resources are modeled as different node types, and assignment, transport, and sequence information is represented by different edge types. A search state vector is also introduced to describe the current search process. The HeteroGNN is trained using an actor–critic method to guide neighborhood operator selection and destroy set construction in ALNS. Experiments are conducted under four production conditions and five job scales. The results show that HeteroGNN-ALNS achieves better overall scheduling performance than dispatching rules and representative search methods. Statistical and ablation analyses further verify the effectiveness of the proposed method.
With the rise of artificial intelligence (AI), an increasing number of AI-based diagnostic tools are being developed. Before clinical implementation, these tools must be validated against existing gold standards. This requires trials that quantify the agreement between AI predictions and reference measurements. However, designing such agreement studies poses methodological challenges that differ substantially from classical superiority trials. This paper aims to provide statistical methods for assessing diagnostic agreement. Methods were categorized according to the measurement scale of the data (nominal, ordinal, continuous)—with a separate group for methods that apply across several scales—and according to the number of raters or measurements involved. A decision tree is provided as a simplified educational framework for method selection rather than as a general method-selection algorithm: design features such as repeated measurements, clustering, spectrum effects, dependence between raters, and an imperfect reference method are not encoded in it and are discussed separately, together with the circularity and confounding issues specific to the validation of AI-based tools. For each method, we summarized assumptions, appropriate use cases, interpretation of results, and available open-source software for sample size calculation and analysis, and we illustrate the sample size calculations in three fully worked examples covering binary, ordinal, and continuous outcomes. We further distinguish conditional inference about one fixed, frozen model version from the broader generalization to a class of algorithms or to future model versions, which require additional sources of algorithmic and dataset variability to be represented in the design and analysis. We conclude by outlining open methodological questions—including Bayesian approaches to agreement estimation, methods for complex AI outputs, agreement models for clustered and repeated-measures designs, and the limited software support for Gwet’s AC1/AC2 sample size planning—that warrant further work as diagnostic technologies and statistical methodology continue to evolve.
Plant-based beverages (PBBs) can be fortified to contribute to vitamin D intake. The nutritional composition of vitamin D-fortified PBBs (FPBBs) and consumer use of PBBs are examined through a three-phase technique that included a 2010–2025 national registry retrospective study (vitamin D FPBBs = 167), an on-shelf market survey (vitamin D FPBBs = 111), and a consumer survey on 225 subjects. Coconut-based PBBs had the highest vitamin D mean (0.84 μg/100 mL) in the registry database, although in the market survey oat-based PBBs had the highest vitamin D average (0.91 μg/100 mL). The compositional heterogeneity of FPBB matrices was confirmed by non-parametric Kruskal–Wallis tests across five broader botanical categories (soy, nuts, coconut, grains, blended), for carbohydrate content (χ2 = 52.43, df = 4, p < 0.001), protein content (χ2 = 55.85, df = 4, p < 0.001), lipid content (χ2 = 26.94, df = 4, p < 0.001), and calorie content (χ2 = 34.31, df = 4, p < 0.001). In contrast, vitamin D content did not differ significantly across these categories (χ2 = 8.21, df = 4, p = 0.084). Dietary preferences were the main drivers of daily use (48.3% of consumers), while the subgroup driven by general health benefits or vitamin D fortification tended to consume these products mainly weekly (33.3%) or occasionally (42.8%). The mean serving size reported by respondents was 157.14 mL. Since vitamin D status was not biochemically assessed, this study does not evaluate whether current consumption addresses population-level vitamin D status. These findings highlight the need for front-of-pack labelling on nutritional density and fortification.
