Search Results (1,554)

Background: Beverage intake is a consequential yet underappreciated driver of health in Mediterranean settings. Comparative evidence for Spain and Italy based on harmonised measures is scarce. This study addresses that gap by profiling beverage portfolios and their sociodemographic correlates in parallel adult samples from both countries. Methods: We conducted a cross-sectional analysis of adults in Spain (n = 483) and Italy (n = 403) using aligned, validated instruments (NutSo-HH; NutSo-HH-Ita). Outcomes were water (Wtr), sugar-sweetened soft drinks (Sfd), juice (Juc), energy drinks (End), coffee (Cff), alcohol (Alc), and episodes of intoxication (Gtd). Associations were assessed via non-parametric tests, multivariable linear models, and an EBIC-selected Gaussian graphical model (GGM). Main results: Italians reported higher Alc and Gtd; Spaniards reported higher Sfd and Juc. Wtr was comparable across countries, and Cff differences were marginal. Age and sex emerged as the most consistent correlates (older age and male sex with higher Alc; younger age with higher Sfd), whereas education and income were not stable determinants. The GGM suggested behavioural clustering of Sfd–Juc–End, with weak partial correlations for other beverages after adjustment. Implications: Distinct country profiles imply differentiated priorities. In Spain, interventions could prioritise reducing sugar-sweetened beverage intake among younger adults through age-targeted primary care counselling, mandatory water (and unsweetened milk) availability in schools, tiered excise taxes on sugar-sweetened drinks, and restrictions on child- and youth-directed marketing of high-sugar beverages. In Italy, primary care and community health services could routinely screen adults for risky alcohol use and deliver brief, culturally attuned advice that promotes lower-risk patterns of wine consumption during meals. Given the cross-sectional design, self-report measures, and non-probabilistic sampling, findings should be interpreted as context-sensitive markers rather than causal determinants; nevertheless, they highlight concrete prevention approaches and regulatory levers for each country’s beverage-related health risks. Full article

This paper presents a comparative analysis of pedestrian behavior and perceived safety among university students at two signalized intersections near the campus premises of the Aristotle University of Thessaloniki, Greece. Although both intersections include pedestrian crosswalks and traffic lights, one permits vehicle left turns during pedestrian phases via flashing yellow arrows, while the other restricts all vehicle movement. Two questionnaire-based surveys (n₁ = 304 and n₂ = 303) recorded demographic information, crossing behavior, perceived risk, and preferred safety interventions. Results indicate that the intersection permitting vehicle conflict is associated with significantly lower levels of perceived safety and higher instances of risk-taking, such as crossing “at any time”. Conversely, the vehicle-restricted intersection fosters greater compliance with pedestrian signals and a stronger sense of security. Key factors influencing crossing decisions included vehicle speed, signal duration, pedestrian group presence, and urgency. Respondents prioritized safety improvements such as pedestrian countdown timers, enhanced signage, and enforcement cameras. These findings underscore the critical role of signal phasing in shaping pedestrian behavior and safety perceptions. Evidence-based recommendations are offered to urban planners and policymakers to enhance pedestrian safety through targeted infrastructure upgrades and enforcement strategies. Full article

Current research on acoustic encoding of emotional content suggests that there are universal cues, allowing for decoding within and across taxa. This is particularly important for human–animal relationships, wherein domestic animals are supposed to be particularly efficient in decoding human emotions. Here we investigated whether the decoding of the emotional content in human voices shared universal acoustic properties, or whether it could be influenced by experience. Emotional human voices were presented to two populations of horses, in which behavioral, cardiac, and brain responses were measured. The two populations differed in their living and working conditions: one population lived in naturalistic conditions (stable social groups in pastures) and were ridden occasionally for outdoor trail riding with one to a few different riders, while the other was kept in more restricted conditions (individual stalls) and participated in riding lessons involving many different riders. Assessment of the horses’ welfare state (animal-based measures) and their relationships with humans, performed independently of the playback experiments, revealed that the populations differed in both aspects. Whereas both populations appeared to react to the angry human voice, the population with the best welfare state and relationship with humans showed little differentiation between the different emotional voices and exhibited low behavioral reactions. On the contrary, the other population showed high behavioral and cardiac reactions to all negative voices. Brain responses also differed, with the first population showing higher responses (increased gamma, i.e., excitation) for the happy voice and the second for fear and anger (increased theta, i.e., alarm). Thus, animals’ affective state and past experiences appear very influential for their perception of (cross-taxa) acoustic emotional cues. Full article

Intergenerational contact is a key component of the informal support systems that contribute to the wellbeing of older adults. As societies age and migration patterns diversify family structures, understanding how contact is sustained across generations becomes increasingly relevant for health equity and the sustainability of care systems. In this study, we conceptualise sustainability not in environmental terms but as social and health-system sustainability—that is, the long-term ability of families and care systems to maintain intergenerational ties, ensure equitable access to support, and remain resilient under demographic and social pressures. Drawing on theories of intergenerational solidarity and social capital, this study situates contact as both a resource for individual wellbeing and a pillar of care sustainability in diverse societies. We examine the frequency of contact between parents and adult children among adults aged 50 and above, comparing migrant and non-migrant populations across 25 European countries. Using data from Waves 7, 8, and the COVID-19 wave of the Survey of Health, Ageing and Retirement in Europe (SHARE), we assess both in-person and digital forms of contact before and during the pandemic. Results show that older migrants have less frequent contact with parents but more frequent contact with children than do non-migrants, with similar patterns attested across repeated cross-sections (2017, 2019, 2021). The strong contact observed in each cross-section, facilitated by digital tools, implies resilient family ties under public-health stress. However, resilience is uneven: weaker contact with parents among migrant populations reflects structural barriers such as visa restrictions, caregiving responsibilities, discrimination, language barriers, and unequal digital access. Moreover, differences in access and proficiency with digital tools suggest that digital contact did not compensate equally across groups. These findings underscore the importance of sustainable and inclusive strategies in ageing and health policy. Specifically, targeted digital literacy programmes for older migrants, policies supporting transnational caregiving, affordable internet access, mobility solutions, and anti-discrimination measures in family visitation are crucial to reducing inequities. Full article

The health and productivity of rabbits are highly sensitive to the environmental conditions within the rabbit house, particularly to fluctuations and deviations in temperature, relative humidity, and carbon dioxide (CO₂) concentration. However, owing to the thermal inertia and residual evaporation effects inherent in ventilation and cooling systems, environmental changes often exhibit delayed responses, rendering real-time control inadequate. Accurate prediction of key environmental parameters is indispensable for formulating effective environmental control strategies, as it enables consideration of their future dynamics and thereby enhances the rationality of regulation in rabbit farming. Existing prediction models often exhibit unsatisfactory accuracy and weak generalization, which restricts the incorporation of prediction into effective environmental control strategies. To address these limitations, summer indoor and outdoor environmental data were collected from rabbit houses in Nanping, Fujian; Jiyuan, Henan; and Qingyang, Gansu, China—three climatically distinct regions—forming three datasets. Based on these datasets, a multi-parameter time-series prediction model, Patch and Cross-Attention Enhanced Transformer for Rabbit House Prediction (PatchCrossFormer-RHP), is introduced, integrating patching and attention mechanisms. The model partitions the sequences of rabbit house temperature, relative humidity, and CO₂ concentration into patches and incorporates auxiliary parameters, such as indoor air velocity and outdoor temperature and humidity, to enhance feature representation. Furthermore, it applies cross-attention with differentiated encoding to disentangle multi-parameter relationships and improve predictive performance. This study used the Fujian dataset as the primary benchmark. On this dataset, PatchCrossFormer-RHP achieved root mean square error (RMSE) values of 0.290 °C, 1.554%, and 38.837 ppm for rabbit house temperature, humidity, and CO₂ concentration, respectively, with corresponding R² values of 0.963, 0.956, and 0.838, consistently outperforming RNN, GRU, and LSTM. Transfer experiments with single- and multi-source pretraining followed by fine-tuning on Fujian demonstrated that strong cross-regional generalization can be achieved with only limited target-domain data. Full article

Forest height is a key structural parameter for evaluating ecological functions, biodiversity, and carbon dynamics. While LiDAR and Synthetic Aperture Radar (SAR) provide vertical structure information, their large-scale use is restricted by sparse sampling (LiDAR) and temporal decorrelation (SAR). Optical remote sensing offers complementary spectral information but lacks direct height retrieval. To address these limitations, we developed a multi-modal framework integrating GEDI waveform LiDAR, Sentinel-1 SAR (InSAR and PolSAR), and Sentinel-2 multispectral data, combined with machine learning, to estimate forest canopy height across Jiangsu Province, China. GEDI L2A footprints were used as training labels, and a suite of structural and spectral features was extracted from SAR, GEDI, and Sentinel-2 data as input variables for canopy height estimation. The performance of two ensemble algorithms, Random Forest (RF) and Gradient Tree Boosting (GTB) for canopy height estimation, was evaluated through stratified five-fold cross-validation. RF consistently outperformed GTB, with the integration of SAR, GEDI, and optical features achieving the best accuracy (R² = 0.708, RMSE = 2.564 m). The results demonstrate that InSAR features substantially enhance sensitivity to vertical heterogeneity, improving forest height estimation accuracy. These findings highlight the advantage of incorporating SAR, particularly InSAR with optical data, in enhancing sensitivity to vertical heterogeneity and improving the performance of RF and GTB in estimating forest height. The framework we proposed is scalable to other regions and has the potential to contribute to global sustainable forest monitoring initiatives. Full article

The desire to improve the safety of navigation, especially in restricted and very busy areas like the straits, leads researchers to evaluate possible uses of Artificial Intelligence as an alternative to the traditional probabilistic methods. This is possible thanks to the large amount of available AIS data generated by ships in transit. In this work, a Machine Learning algorithm (Classification Decision Tree) was trained with eight features coming from AIS data of the Strait of Messina (Italy), with the aim of carrying out a two-class classification of the single AIS data to find anomalies in ship transits that could compromise navigation safety. Since anomalous events are relatively rare, compared to the large amount of information related to the normal navigation situations, the challenge of this work was to obtain an artificial dataset with the aim of simulating the possible anomalous navigation conditions for the Strait investigated, known the active risk mitigation means one. For this reason, the dataset containing abnormal events was obtained simulating different risk scenarios. A hyperparameters tuning with a Bayesian optimization approach and a 5-fold cross validation have been performed to improve the quality of the model and a large dataset has been tested. The accuracy of both validation and test phases is <99.5% and <95.9%, respectively. This can make it possible to identify anomalous navigation conditions in real time, in order to quickly classify possible conditions of risk. The method can be used as a Decision Support Tool by the authority in order to improve the capacity of the single operator to identify the possible risk situation inside the Strait of Messina. Full article

Synthetic Aperture Radar (SAR) three-dimensional (3D) imaging has considerable potential in disaster monitoring and topographic mapping. Conventional 3D SAR imaging techniques for unmanned aerial vehicle (UAV) formations require rigorously regulated vertical or linear flight trajectories to maintain signal coherence. In practice, however, restricted collaboration precision among UAVs frequently prevents adherence to these trajectories, resulting in blurred scattering characteristics and degraded 3D localization accuracy. To address this, a 3D reconstruction technique based on horizontal-cross configurations is proposed, which establishes a new theoretical framework. This approach reduces stringent flight restrictions by transforming the requirement for vertical baselines into geometric flexibility in the horizontal plane. For dual-UAV subsystems, a geometric inversion algorithm is developed for initial scattering center localization. For multi-UAV systems, a multi-aspect fusion algorithm is proposed; it extends the dual-UAV inversion method and incorporates basis transformation theory to achieve coherent integration of multi-platform radar observations. Numerical simulations demonstrate an 80% reduction in implementation costs compared to tomographic SAR (TomoSAR), along with a 1.7-fold improvement in elevation resolution over conventional beamforming (CBF), confirming the framework’s effectiveness. This work presents a systematic horizontal-cross framework for SAR 3D reconstruction, offering a practical solution for UAV-based imaging in complex environments. Full article

The low fertility of plinthosols is a major constraint on agricultural production, largely due to the presence of plinthite, which restricts the availability of water and nutrients. This study aimed to simulate the growth and yield of grain maize on a loosened plinthosol amended with termite mound (from Macrotermes falciger) material in the Lubumbashi region. A 660-hectare perimeter was established, subdivided into ten maize blocks (B1–B10) and a control block (B0), which received the same management practices as the other blocks except for subsoiling and termite mound amendment. The APSIM model was used for simulations. The leaf area index (LAI) was estimated from Sentinel-2 imagery via Google Earth Engine, using the Simple Ratio (SR) spectral index, and integrated into APSIM alongside agro-environmental variables. Model performance was assessed using cross-validation (2/3 calibration, 1/3 validation) based on the coefficient of determination (R²), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE). Results revealed a temporal LAI dynamic consistent with maize phenology. Simulated LAI matched observations closely (R² = 0.85 − 0.93; NSE = 0.50 − 0.77; RMSE = 0.29 − 0.40 m² m⁻²). Maize grain yield was also well predicted (R² = 0.91; NSE > 0.80; RMSE < 0.50 t ha⁻¹). Simulated yields reproduced the observed contrast between treated and control blocks: 10.4 t ha⁻¹ (B4, 2023–2024) versus 4.1 t ha⁻¹ (B0). These findings highlight the usefulness of combining remote sensing and biophysical modeling to optimize soil management and improve crop productivity under limiting conditions. Full article

Background: Energy drink [ED] consumption is common among young adults and has been linked to adverse health effects and risky behaviors. This study compared medical and non-medical university students to assess whether health education influences knowledge, consumption, and attitudes toward EDs. Although medical and non-medical students are not minors, their opinions on the national ban on EDs sales to individuals under 18 provide valuable insight into attitudes toward regulation. Material and Methods: A cross-sectional online survey was conducted among 871 students (42.1% medical, 57.9% non-medical). The questionnaire assessed demographics, ED consumption, knowledge, motivations, and regulatory attitudes. It was pilot-tested on 30 students to ensure clarity, and internal consistency was confirmed (Cronbach’s α = 0.78 for knowledge; α = 0.81 for attitudes). Non-parametric tests (Mann–Whitney U, Kruskal–Wallis) and chi-square analyses compared groups. Results: Participants’ mean age was 22.1 years; most were female (73.2%). Medical students demonstrated significantly better knowledge of ED ingredients (simple sugars, B vitamins, L-carnitine, electrolytes; p < 0.01) and adverse effects (e.g., irritability, dizziness, nausea; p < 0.05). However, ED consumption frequency did not differ between medical and non-medical students. The main reasons for ED use were energy and concentration; social motives were less frequent. Female students more often supported the ban on ED sales to minors and additional advertising restrictions (p < 0.001), while overall confidence in enforcement was low. Conclusions: Despite greater awareness, medical students consume EDs at rates comparable to non-medical students. Educating medical students on safe caffeine use is crucial, since shift work may promote stimulant intake. Combining targeted education with stronger enforcement could enhance the impact of regulatory policies and reduce risky consumption among young adults. Full article

With the rapid development of autonomous vehicles and intelligent transportation systems, vehicle detection and distance estimation have become critical technologies for ensuring driving safety. However, real-world in-vehicle environments impose strict constraints on computational resources, making it impractical to deploy high-end GPUs. This implies that even highly accurate algorithms, if unable to run in real time on embedded platforms, cannot fully meet practical application demands. Although existing deep learning-based detection and stereo vision methods achieve state-of-the-art accuracy on public datasets, they often rely heavily on massive computational power and large-scale annotated data. Their high computational requirements and limited cross-scenario generalization capabilities restrict their feasibility in real-time vehicle-mounted applications. On the other hand, traditional algorithms such as Semi-Global Block Matching (SGBM) are advantageous in terms of computational efficiency and cross-scenario adaptability, but when used alone, their accuracy and robustness remain insufficient for safety-critical applications. Therefore, the motivation of this study is to develop a stereo vision-based collision warning system that achieves robustness, real-time performance, and computational efficiency. Our method is specifically designed for resource-constrained in-vehicle platforms, integrating a lightweight YOLOv8n detector with SGBM-based depth estimation. This approach enables real-time performance under limited resources, providing a more practical solution compared to conventional deep learning models and offering strong potential for real-world engineering applications. Full article

Background: Previous studies have shown that a diet with inflammatory and antioxidant properties can alter the risk of cognitive impairment. There are few studies using a large sample of the Chinese population. The specific relationship between inflammation, an antioxidant diet, and cognitive impairment remains unclear, and the potential impact of metabolic disorders remains to be determined. Methods: This is a cross-sectional study, with data from the China Health and Nutrition Survey (CHNS) in 2018. Individual and combined effects of the dietary inflammation index (DII) and composite dietary antioxidant index (CDAI) on cognitive impairment were assessed by binary logistic regression models. Nonlinear correlations and the inflection point were explored using restricted cubic splines (RCSs), and the mediation effects of triglyceride glucose–body mass index (TyG-BMI) were explored in greater depth using causal mediation analysis. Results: An increased CDAI was associated with a significantly decreased risk of cognitive impairment, at 0.68 (95%CI: 0.499–0.928). Contrary to this, the DII was positively associated with the risk of cognitive impairment, at 1.289 (95%CI: 1.03–1.613). The joint effects of the DII and CDAI indicated the minimal hazard effects on the risk of cognitive (0.787 (95%CI: 0.622–0.995)) impairment in subjects with low_DII + high_CDAI when compared with those with high_DII + low_CDAI. Furthermore, a significant nonlinear relationship was found between the CDAI and the risk of cognitive impairment, exhibiting an “L”-shaped curve (p-overall = 0.001, p-nonlinear = 0.007). However, no evidence was found for a nonlinear relationship between the DII and the risk of cognitive impairment. The mediation analysis did not reveal a mediating effect of TyG-BMI on the association between the CDAI and DII scores and the risk of cognitive impairment. Conclusions: Findings revealed that the CDAI could mitigate the adverse consequences of the DII on cognitive decline, which offers new insights into preventing early cognitive impairment through dietary intervention. Full article

Background: During adolescence, the brain is undergoing anatomical and physiological maturation processes with changes to cognitive development. However, in adolescents with phenylketonuria (PKU), executive function and mental health are adversely affected by high blood phenylalanine (Phe) levels. Objectives: We aim to describe clinical outcomes in adolescents with PKU. Methods: Cross-sectional questionnaires were performed on quality of life (EuroQol “EQ-5D-5L” questionnaire), anxiety and depression (Hospital Anxiety and Depression Scale- HADS) and food neophobia in a single PKU centre. Retrospective data on metabolic control (previous 12 months) and medical history, and current data on anthropometry, dietary treatment, and comorbidities were collected. Results: In total, 33 adolescents with PKU participated with a mean age of 13.5 ± 1.3 y (16 boys, 17 girls). All were on a Phe restricted diet, with 3 also prescribed sapropterin. Questionnaires were self-completed by n = 25/33 (76%) adolescents. A mean of 36 ± 26 blood Phe spots were performed over 12 months. There was a mean of 83% of blood Phe < 600 µmol/L and 49% < 360 µmol/L. In total, 39% (n = 13/33) of adolescents were overweight/obese, 18% (n = 6/33) overweight and 21% (n = 7/33) obese. Medical history documented mental health disorders (anxiety/depression) in 7 cases, low mood, suicidal thoughts and self-harming in 5, and neuro diversity in 4 (autism and/or attention-deficit hyperactivity disorder (ADHD). In the HADS questionnaire, 12% (n = 3/25) of adolescents scored borderline abnormal for anxiety and 12% (n = 3/25) abnormal for depression. Mean anxiety scores for females were almost twice as high compared to males. No significant correlation was found between blood Phe and anxiety/depression scores. However, higher Phe levels showed a trend towards reduced enjoyment and emotional responsiveness, including lower scores on measures of pleasure, cheerfulness, and emotional reactivity. Although not statistically significant, these findings suggest a potential subtle association between poorer metabolic control and mood disturbances in adolescents with PKU. No food neophobia was identified in our study. Conclusions: Adolescents with PKU presented with high levels of depression and anxiety. Long term studies focusing on quality of life and neurocognition even when achieving the PKU European guidelines are necessary. Different therapeutic options are needed to improve the outcome of patients with PKU. Full article

This paper presents a novel continuous-wave Doppler RADAR system for real-time speed measurement of moving objects, implemented using software-defined radio (SDR). Unlike traditional high-cost solutions typically found in research centers or specialized laboratories, this prototype offers a low-cost, compact, and easily deployable platform that lowers the entry barrier for experimentation and research. Operating within the 70 MHz–6 GHz range, SDR enables highly flexible signal processing; in this implementation, a 5.5 GHz carrier is selected to improve the detection precision by exploiting its reduced bandwidth for more accurate observation of frequency shifts. The carrier is modulated with a 2 kHz signal, and Doppler frequency deviations induced by object motion are processed to calculate velocity. Using a Welch spectral estimator, the system effectively reduces noise and extracts the Doppler frequency with high reliability. The prototype achieves speed measurements up to 196.36 km/h with approximately 2% error in the 0–100 km/h range, confirming its suitability for road traffic monitoring. A key innovation of this work is its single-antenna cross-polarized configuration, which simplifies hardware requirements while maintaining measurement accuracy. Furthermore, the system’s portability and open-access design make it ideal for in-vehicle applications, enabling direct deployment for automotive testing, driver-assistance research, and educational demonstrations. All design files and implementation details are openly shared, eliminating patent restrictions and encouraging adoption in low-resource academic and research environments. Full article

Food insecurity is a pervasive public health issue in the United States. While food pantries attempt to alleviate this issue, their effectiveness is limited by structural and logistical barriers that affect service accessibility. Transportation is a frequently underexamined barrier for individuals trying to access food aid. The purpose of this study is to assess the interplay of client- and pantry-level characteristics and their influence on food aid accessibility across several transportation modalities. This cross-sectional survey study collected data from 430 food pantry clients concerning their demographics, transportation methods, and perceptions of transportation barriers. Pantry characteristics were also collected focusing on transportation infrastructure and operational policies. Individual and grouped comparisons were made between transportation methods in relation to pantry visitation, with those walking, biking, and taking a bus to the pantry grouped to compare to those taking a car. Higher food insecurity score, smaller household size, single relationship status, and race were independently associated with increased odds of walking, biking, or taking a bus to the pantry. Having closer bus stops, more bus lines, and no monthly use limits were independently associated with increased odds of walking, biking, or taking a bus to the pantry. Several characteristics were associated with specific transportation modalities when accessing food aid. Our results are particularly concerning given the increased food insecurity and additional vulnerabilities seen in those who walk, bike, or take the bus to the pantry. Transportation disadvantage may be ameliorated by less restrictive pantry use policies and more robust public transit. Full article