Search Results (22,687)

The review evaluates current clinical and epidemiological evidence regarding the use of plant-derived compounds in pediatric practice. Data from randomized controlled trials indicate symptomatic efficacy of selected agents—particularly in acute respiratory infections—alongside generally favorable safety profiles when standardized preparations are used. Emerging research also explores applications in neurodevelopmental disorders, gastrointestinal conditions, and dermatology, and as supportive therapy in pediatric oncology. However, variability in product quality, limited pediatric-specific trials, potential toxicity, and regulatory inconsistencies remain significant challenges. The integration of phytotherapy into pediatric care therefore requires rigorous study design, careful safety monitoring, and clear quality standards to ensure an evidence-based risk–benefit balance. Full article

The global maritime sector is undergoing rapid transformation, creating an urgent need to align digital port technologies with a sustainable development framework. However, existing research on smart ports and the blue economy is fragmented and predominantly driven by deterministic approaches that overlook systemic complexity and uncertainty. This study develops a smart port system model grounded in blue economy principles, using a Bayesian network to analyze causal relationships among operational, environmental, and governance variables under uncertainty. The model incorporates key factors including port operational efficiency, logistics reliability, environmental compliance systems, coastal employment, and regulatory enforcement. The findings indicate that operational and logistical factors are the primary drivers of the system, while environmental and socioeconomic variables strongly shape sustainability outcomes. Scenario analysis shows that coordinated interventions targeting these key variables generate the greatest improvements in Smart Port–Blue Economy integration. Sensitivity analysis further identifies coastal economic output, regional competitiveness, and marine ecosystem health as the most responsive outcome variables. The research offers lessons for policymakers to enhance port management by integrating logistics and technological considerations with blue economy principles to design adaptive and resilient policies, particularly in island regions. Full article

Orthotropic laminates are highly desirable in composite structures because they eliminate bending–twisting coupling, simplify structural behavior, improve analytical predictability, and facilitate structural design, optimization, and certification. However, achieving fully orthotropic behavior in laminated composites remains challenging because conventional laminate architectures generally retain stiffness couplings arising from anisotropic ply orientations and stacking-sequence effects. This work introduces the Tailored Antisymmetric Composite (TAC) concept, a laminate architecture that provides the closest practical approximation to full orthotropy while preserving broad stiffness-tailoring capability and manufacturability. TAC laminates are constructed from tailored antisymmetric sublaminates that enforce D₁₆ = D₂₆ = 0 while maintaining extremely small extension–bending coupling terms B₁₆ and B₂₆. Representative TAC and symmetric Quad laminates were compared analytically, statistically, and experimentally. Monte Carlo simulations comprising 100,000 realizations with realistic ±0.1° AFP/ATL fiber-orientation deviations showed that the distributions of the extension–bending coupling terms \(B_{16}^*\) and \(B_{26}^*\) remained nearly indistinguishable for both laminate architectures, with probability-density overlap coefficients between 0.87 and 0.98. In contrast, the bending–twisting coupling terms \(D_{16}^*\) and \(D_{26}^*\) were 140–600 times lower in TAC laminates than in the corresponding Quad laminates, and their statistical distributions exhibited complete separation. Experimental measurements of post-cure warpage confirmed that TAC laminates achieved dimensional stability comparable to symmetric Quad laminates while exhibiting lower variability. These results demonstrate that TAC laminates combine exact elimination of bending–twisting coupling with negligible extension–bending coupling, statistical robustness to realistic manufacturing variability, and excellent dimensional stability, establishing TAC as a practical and systematic route toward full orthotropy in laminated composite structures. Full article

Air pollution from particulate matter (PM2.5 and PM10) poses a serious public health risk in urban environments, particularly in areas with heavy vehicular traffic. Against this backdrop, the present study proposes an Internet of Things (IoT)-based system designed to support air quality monitoring and evidence-based decision-making regarding PM2.5 and PM10 concentrations, integrating low-cost sensors with a machine learning prediction module. The study follows an experimental-applied design with a quantitative–comparative approach. Its scientific contribution is organized around an integrated IoT-ML framework addressing a concrete gap in the literature: the lack of local empirical evidence regarding which family of machine learning algorithms delivers the greatest accuracy, stability, and computational efficiency for particulate matter forecasting in mid-altitude urban environments using low-cost sensors. On one hand, the framework proposes and deploys a four-node IoT network for continuous PM2.5 and PM10 monitoring in high-traffic urban microenvironments—representing one of the first sustained deployments with low-cost, high-temporal-resolution sensors (10-minute intervals) in Arequipa, Peru. On the other hand, the study presents the most extensive benchmarking reported in the local literature: a systematic evaluation of 44 machine learning algorithms under homogeneous experimental conditions, covering classical statistical models, traditional machine learning techniques, deep learning architectures, and hybrid approaches, along with an analysis of ensemble learning strategies using Ridge stacking and K-Fold cross-validation. This unified comparative analysis—applying consistent metrics (MAE, RMSE, R², and MAPE), the same prediction horizon, and a shared dataset—provides replicable empirical evidence that had not previously been reported for the urban context of Arequipa. The results show that traditional statistical models perform poorly overall, while tree-based and boosting algorithms consistently achieve R² values above 0.90 for both pollutants. Ensemble models, particularly stacking with Ridge regression and cross-validation, yielded the strongest overall performance, demonstrating greater robustness and prediction stability. Explainability criteria were also incorporated, enabling an assessment of each base model’s individual contribution and identifying the variables most relevant to the prediction process. The methodological contribution provides future researchers with a rigorous reference framework for algorithm selection in environmental IoT systems. Taken together, the findings demonstrate that combining low-cost IoT networks with advanced machine learning and ensemble learning techniques constitutes an effective, scalable, and cost-efficient alternative for air quality monitoring, predictive analysis, and the support of informed mitigation strategies in urban environments. Full article

As a hundred-kilowatt-class advanced electric propulsion technology, the Variable Specific Impulse Magnetoplasma Rocket (VASIMR) holds immense potential for deep space exploration. During the ion cyclotron resonance heating (ICRH) process of VASIMR, the resonance of high-mass, low-charge-state ions demands exceptionally strong background magnetic fields; its reliance on strong magnetic fields imposes stringent thermal control requirements on superconducting magnets, significantly driving up the volume and mass penalties of the system. To address this challenge, this study explores a low-magnetic-field VASIMR architecture utilizing a light gas, Neon (Ne), as the propellant. We systematically investigate the thrust performance and the evolution of ion energy under the multivariable coupling of pre-ionization power, ICRH power, and the magnetic field topology within the resonance zone. The results demonstrate the technical and engineering feasibility of the low-magnetic-field and light-propellant propulsion scheme. Specifically, the thrust gains corresponding to the pre-ionization and ICRH power are approximately 2.2 mN/100 W and 10.3 mN/500 W, respectively. Furthermore, optimizing the magnetic field topology significantly enhances the ion energy absorption efficiency in the resonance zone, yielding a thrust improvement of 25.3 mN. This study achieves a significant reduction in the background magnetic field strength compared to conventional VASIMR, elucidating the multi-regime control mechanisms of the low-field VASIMR. These findings lay a robust theoretical and experimental basis for future lightweight designs and performance leaps. Full article

The objective of this study is to recover the transient out-of-plane displacement field of clamped circular plates subjected to blast loading using a single high-speed camera, as a low-cost alternative to stereo Digital Image Correlation (DIC) for the specific class of axisymmetrical structural responses of circular plates. The dynamic response of thin metal plates to blast loading is a fundamental problem in protective structural design, traditionally investigated through DIC. Although it provides full-field displacement measurements with high spatial resolution, it requires stereo camera arrangements, controlled illumination, speckle pattern preparation, and elaborate calibration procedures that significantly increase experimental cost and complexity. This study introduces a monocular optical method applicable to axisymmetrically defined material testing applications, such as the response of circularly supported isotropic plates under a uniform impulsive load, to recover the transient out-of-plane displacement field without using DIC. Clamped circular aluminum plates are subjected to blast loading generated by PG-3 charges of variable mass detonated at the closed end of a shock tube, with the exposed face matching the tube cross-section so as to enforce axisymmetric pressure load. A diametral reference line marked on the rear face of each specimen was recorded by a single high-speed camera, and a perspective correction derived from the axisymmetric deformed geometry was then applied to reconstruct the time-resolved displacement profile along the diameter. The permanent post-test deformed shape of each plate was subsequently digitized through 3D scanning and used as ground truth to validate the optical reconstruction. The reconstructed profiles closely matched the scans: for the conventional responses the root-mean-square error was 1.251 mm with a normalized mean residual of 6.57% (Case A) and 1.793 mm (9.20%, Case B), while for the anomalous counterintuitive response it was 1.043 mm (14.93%, Case C). Symmetry can thus be exploited as an active measurement principle to obtain quantitative blast-response data with substantially reduced experimental burden and without specialized stereo-optical instrumentation. Full article

Low Earth orbit (LEO) satellites are increasingly required to process and securely stream video data for remote sensing, surveillance, and onboard perception applications. However, the strict constraints of onboard computing capability, power budget, and thermal dissipation make conventional encryption schemes difficult to apply to real-time video streaming tasks. To address this challenge, this paper proposes a low-complexity real-time video encryption algorithm for resource-constrained LEO satellites. The proposed method integrates selective encryption with a lightweight permutation–diffusion mechanism to reduce computational overhead while maintaining effective protection of continuous video streams. To enhance security, a chaotic pseudo-random sequence generator is employed to improve encryption randomness, and a dynamic key scheduling strategy is introduced to increase temporal key variability and strengthen resistance to statistical and differential attacks across successive frames. The algorithm is further designed for efficient deployment on embedded onboard platforms with limited hardware resources. Experimental results show that the proposed method achieves favorable performance in encryption speed, computational complexity, information entropy, adjacent pixel correlation, and differential attack resistance. Compared with conventional full-encryption methods, the proposed algorithm offers a more balanced trade-off between security and real-time efficiency, demonstrating its potential for secure video streaming in resource-constrained LEO satellite systems. Full article

Background/Objectives: Diet quality among university students is influenced by multiple behavioural and lifestyle factors, yet limited research has examined how cannabis use relates to overall diet quality within this population. This study examined the association between cannabis use and diet quality among university students and assessed whether this relationship was explained by behavioural, contextual, and psychological factors. Methods: A cross-sectional online survey was distributed to all registered students at a large Canadian university in January 2026. Diet quality was assessed using the Canadian Food Intake Screener (CFIS), and past 30-day cannabis use was examined as the primary exposure. Hierarchical multiple linear regression models were conducted sequentially, adjusting for demographic characteristics, health behaviours, mental health variables, living arrangements, meal skipping, and other substance use. Results: Among 1581 survey respondents, 1467 participants were included in the fully adjusted regression analyses. Past 30-day cannabis use was reported by 33.7% of participants. In demographic-adjusted analyses, cannabis use was associated with lower diet quality scores (B = −0.81, p < 0.01). This association remained statistically significant following adjustment for health behaviours, mental health variables, and living arrangements. However, after adjustment for meal skipping, the association between cannabis use and diet quality was attenuated and no longer statistically significant (B = −0.44, p = 0.09). Meal skipping emerged as one of the strongest behavioural correlates of lower diet quality. Additional adjustment for other substance use did not materially alter findings. Conclusions: Cannabis use was initially associated with lower diet quality among university students; however, this association was attenuated after accounting for broader behavioural factors, particularly meal skipping. Given the cross-sectional design, these findings do not establish whether cannabis use influences dietary behaviours or whether meal skipping represents a pathway linking cannabis use and diet quality. These findings highlight the importance of considering diet quality within a broader behavioural framework and suggest that eating patterns represent an important correlate of diet quality among university students. Full article

Rotational friction welding offers an adhesive-free approach for producing wood dowel joints, but pull-out performance and process consistency are strongly affected by the welding parameters. This study investigated the effects of the hole-to-dowel diameter ratio, rotational speed, and plunging rate on rotationally friction-welded beech (Fagus sylvatica L.) dowel joints. An L9 orthogonal design was combined with supplementary testing, curve-based validity assessment, post-peak analysis, post-pull-out surface imaging, and independent validation. Range analysis ranked the parameter effects as plunging rate, hole-to-dowel diameter ratio, and rotational speed. Type III analysis of variance confirmed significant effects of the hole-to-dowel diameter ratio and plunging rate, whereas rotational speed was not significant within 1600–2000 rpm. The predicted combination was a ratio of 0.80, 1800 rpm, and 14 mm·s⁻¹. The validation group reached 2567.22 N, 34.96% above T3, but its coefficient of variation of 35.93% showed that considerable variability remained. All joints failed by complete dowel withdrawal; the exposed dowel surfaces indicated mixed interfacial separation, sliding, and localized wood-fiber tearing. Darkened regions occurred at different speed levels, without consistent evidence of extensive burning at 2000 rpm. High-capacity joints also showed more abrupt post-peak degradation, indicating a trade-off between capacity, consistency, and failure suddenness. Full article

Complex diseases often involve distributed interactions among biological regions, physiological systems, imaging phenotypes, and clinical variables that are not fully captured by anatomical proximity, isolated biomarkers, or conventional feature-based representations. In oncology, neuroimaging, critical care, and systems medicine, distant or apparently separate biomedical sectors may show strong statistical or functional coupling associated with multimodal imaging signatures, inflammatory responses, metabolic constraints, treatment-induced changes, or shared disease-state organization. In this work, we introduce a proof-of-concept relational graph framework for representing such candidate hidden connectivity in terms of correlation-induced accessibility bridges. The novelty of the framework is that it does not treat biomedical correlation, graph distance, and network connectivity as separate descriptors but explicitly couples non-factorizable inter-sector correlation to localized accessibility compression in an emergent disease-state geometry. The proposed framework represents a biomedical system as a weighted relational graph in which nodes correspond to clinically relevant entities, such as tissue regions, imaging-derived features, biomarker modules, physiological variables, or disease states, while weighted edges encode constraints on functional, statistical, or pathological accessibility. Within this structure, coarse-grained biomedical sectors are defined as organized subsystems, and non-factorizable coupling between sectors is quantified using mutual-information-type measures. Candidate biomedical bridges are then defined operationally as localized, high-gain reductions in effective inter-sector accessibility distance. We introduce explicit coupling rules linking sector-level correlation to bridge-specific accessibility compression, including an effective distance-compression model and an ensemble-based formulation. Numerical proof-of-concept simulations on randomized modular graph ensembles show that increasing correlation strength systematically reduces effective inter-sector distance and increases bridge gain. The strongest compression occurs when correlation modulates a designated bridge architecture, exceeding the effects observed under random non-bridge or generic inter-sector modulation. These simulations are not intended to validate a disease-specific biological mechanism but to test whether the proposed correlation–compression rule produces bridge-specific effects distinguishable from null graph perturbations. The resulting structures should not be interpreted as physical anatomical tunnels or direct causal pathways unless supported by additional biological evidence. Rather, they represent correlation-induced accessibility bridges: localized, high-gain routes in a patient- or disease-specific relational geometry. The framework may therefore provide a theoretical and computational basis for prioritizing candidate hidden connectivity patterns in radiomics, multimodal prognosis, physiological deterioration, recurrence modeling, and systems-level disease networks. Full article

Efficient removal of stones and soil from harvested hazelnuts remains a critical challenge in postharvest processing, especially in regions where mechanization is limited. There is a growing need to optimize cleaning systems to improve grain quality, reduce labor, and support scalable operations. This study investigates the optimization of air velocity, feed rate, drop distance, and impurity mixture in a horizontal wind tunnel pneumatic separation system designed for hazelnut postharvest cleaning. Using both classical statistical analysis and Random Forest (RF) modeling, the performance metrics, grain purity, grain loss, and net contaminant removal, were evaluated across variable settings. The results reveal significant influences of air velocity and drop distance on cleaning efficiency, with optimal performance achieved at 25 m/s, 500 kg/h, and a 60–70 cm drop range. Machine learning models achieved high predictive accuracy (R² > 0.9), confirming their utility for performance forecasting. This integrated approach offers robust recommendations for machine parameter settings, supporting mechanized cleaning solutions to enhance efficiency and reduce manual labor in hazelnut production. Full article

Fixed-bottom or floating offshore structures are the foundations, platforms, and associated infrastructure that allow for oil and gas production systems, offshore wind turbines, and cabling. The remote nature of these structures and the harsh environment with high variability in wind, waves, currents, and weather make construction activity very difficult and unpredictable; the cost of variation in the schedule can lead to high construction vessel and personnel costs. The adoption of artificial intelligence using trends observed in historical data can help achieve more accurate construction costs and schedule predictions, reducing the capital expenditure cost of installation. A resource-significant activity, sometimes called a resource-critical activity or high-resource-demand activity, is an activity on a construction or project schedule that consumes a disproportionately large share of one or more resources compared with others. Plant Design Modelling (PDM) is a digital process that creates and manages a detailed 3D model of a building’s physical and functional characteristics and semantic information, such as cost and schedule. PDM serves as a single source of truth for multidisciplinary activities and, therefore, serves as a rich data source for various construction applications, including project scheduling and cost estimation. Neural networks (NNs), a subset of machine learning algorithms inspired by the human brain, excel at identifying patterns in complex datasets and making predictions, such as forecasting costs based on non-linear relationships and historical trends. Data from an offshore structure modification project were extracted from Aveva’s Everything PDM, focusing on installation activities to create a dataset for machine learning model training. The structured data extracted exhibit non-linear patterns; therefore, linear, regularised linear, robust linear, and the ensemble (tree-based) models and supervised neural network models with varied architecture and hyperparameter values were evaluated and compared. The best performance was obtained using the deep-optimised ANN model. The result obtained is consistent with previous studies. The neural network models show a superior ability to predict the non-linear nature of offshore construction activities’ time. Full article

Agritourism farm-stay guesthouses represent a burgeoning sector of rural tourism, wherein locally produced food serves as the primary experiential attraction. This study examines tourist perceptions regarding food quality, sensory characteristics, sustainability awareness, loyalty indicators, and comparative evaluations at three farm-stay guesthouses in Caraș-Severin County, Romania, located at distinct altitudes: lowland (Sacu, 154 m a.s.l.), hill (Văliug, 550 m a.s.l.), and mountain (Cozia, 1130 m a.s.l.). Altitude in this study marks three distinct settings—lowland, hill, mountain—rather than functioning as a tested independent variable. The results show that tourists evaluated all three guesthouses similarly, with no statistically significant differences across zones. The comparative design was a way of asking whether own-farm food quality perceptions hold across different agritourism contexts, not a test of what altitude does to those perceptions. A structured questionnaire (n = 650) was distributed to guests following an informed consent protocol. Four latent constructs were operationalised: food quality (FQ; Cronbach’s α = 0.593), sensory characteristics (SCs; α = 0.596), sustainability perception (SP; α = 0.393), and comparison with non-farm establishments (CF; α = 0.621). Overall gastronomic satisfaction was particularly high (mean = 4.71 ± 0.62 on a 1–5 Likert scale), and the average overall score was 9.44 ± 1.01 out of 10. Multiple regression accounted for 7.5% of the satisfaction variance (R² = 0.075; F(4,643) = 13.09, p < 0.001), with sensory characteristics (β = 0.232, p < 0.001) and sustainability perception (β = 0.088, p = 0.020) serving as significant predictors. Food origin transparency substantially impacted satisfaction (ANOVA: F(3,646) = 4.964, p = 0.002): visitors who received thorough provenance explanations were more satisfied (mean = 4.77) than those who received no information (mean = 4.57). Among the 569 respondents with prior non-farm experience, 85.2% rated farm-stay cuisine as superior to non-farm alternatives overall. Food quality perceptions in these three Caraș-Severin guesthouses are uniformly high regardless of altitude. What separates more satisfied guests from less satisfied ones is not the measurable quality of the product but whether the host explained where it came from. Full article

Private investment is critical for post-conflict economic recovery, yet evidence on how specific fiscal policy instruments, such as taxation, borrowing composition, and expenditure types, affect domestic and foreign investment in a post-conflict set-up remains limited. This study examines whether disaggregated fiscal policies are associated with crowding in or out private investment in Rwanda, a post-conflict economy characterized by constrained fiscal space, shallow credit markets, and evolving institutions. Using a Vector Error Correction Model (VECM), on quarterly data spanning 1996 Q1–2024 Q4, the analysis captures long- and short-run dynamics between disaggregated fiscal variables, institutional quality, and private investment. The results indicate that direct taxes and domestically financed debt are negatively associated with both domestic and foreign private investment. Externally financed capital spending, on the other hand, is associated with a crowding-in effect, stimulating both local and foreign investment. Lagged measures of institutional quality also enhance investment outcomes, highlighting the conditional role of government in shaping fiscal transmission. These findings demonstrate that fiscal effects are instrument-specific, depending on funding sources and composition, and mediated by institutional and macroeconomic conditions. By integrating disaggregated fiscal analysis with institutional context, this study provides empirically grounded insights for designing fiscal strategies that support private sector-led recovery and sustainable growth in post-conflict and resource-constrained economies. Full article

Reinforced concrete beams with a fiber-reinforced polymer (FRP) plate bonded to their soffit, known as FRP-plated RC beams, commonly fail due to premature debonding of the FRP plate, limiting the utilization of the FRP strength. Inclined U-jacketing has been demonstrated to be effective as the end anchorage for mitigating debonding failures. The mechanism by which the inclined U-jacketing mitigates debonding failure remains unclear, and no design approach has been developed. Therefore, the present study has been conducted to investigate the mitigating effects of the key parameters of the inclined U-jacket through a series of four-point bending tests and systematic modeling. The test results indicate that both the inclination angle and the chamfer radius significantly affected the bond behavior of inclined U-jacket-to-concrete joints. Compared with the 45° configuration, reducing the inclination angle to 30° increased the peak load and peak displacement by 85.4% and 81.6%, respectively. In contrast, the effect of U-jacket side height became negligible once an effective bonded height had been reached, as increasing the side height from 75 mm to 120 mm changed the peak load by only 2.17%. In addition, a pre-peak parameter identification framework based on a power-function-type cohesive element constitutive relationship was proposed and validated. By analyzing the power-function parameters, namely the coefficient a and exponent b, the influences of U-jacket geometric variables on interfacial mechanical behavior were quantitatively characterized. The proposed approach provides experimentally verifiable parameterization to support the optimized design of inclined U-jacket anchorage systems. Full article