Search Results (13,935)

Background: Reducing salt in bread is considered a straightforward, cost-effective public health intervention and is implemented in several countries, either voluntarily or through legislation. A Memorandum of Understanding (MoU) was signed in Greece in 2016, setting a voluntary maximum salt content of 1.2% in artisanal bread. This study aimed to evaluate the effectiveness of the MoU and assessed the potential impact of reducing salt in bread on overall salt intake, using the MoU target and the relevant WHO global sodium benchmark. Methods: Artisanal bread samples (n = 253) randomly collected from different parts of Greece in 2024 were analyzed for salt content and compared with samples collected in 2012 (n = 220). Salt intake from bread was estimated using data from the Hellenic National Nutrition and Health Survey (HNNHS), and modeling scenarios were conducted. Results: The MoU and related voluntary awareness activities were ineffective as a strategy to reduce salt in bread. The mean salt content in bread in 2024 was 1.41 (0.30)%, representing a 6.8% increase compared to 1.32 (0.31)% in 2012. Only 19.4% of samples in 2024 contained ≤1.2% salt, compared to 31.8% in 2012. Full MoU compliance would enable an additional 3.1% of Greek bread consumers, currently exceeding 5 g in their daily salt intake from foods alone, to reduce their intake to below 5 g. This would rise to 6.2% if the WHO sodium benchmark was implemented. Conclusions: A mandatory salt limit, aligned with the WHO global benchmark, is urgently needed to support national reformulation strategies. This work can contribute to European and international discussions on food reformulation. Full article

The global spread of micro- and nanoplastics (MNPs) has emerged as an environmental and medical concern, with growing evidence of their role in cardiovascular disease (CVD). These particles, originating from the degradation of larger plastics and consumer products, can be ingested or inhaled, cross biological barriers, and accumulate in human tissues, including blood, myocardium, and atherosclerotic plaques. Experimental and clinical studies suggest that MNPs contribute to CVD through multiple mechanisms: activation of systemic inflammation and inflammasomes, oxidative stress, endothelial dysfunction, prothrombotic activity, and direct myocardial injury, ultimately promoting fibrosis and impaired contractility. Epidemiological data further indicate that populations exposed to higher plastic pollution or with pre-existing cardiovascular risk factors may be particularly vulnerable. Taken together, these findings identify MNPs as a potential novel environmental cardiovascular risk factor. Advancing detection methods, mechanistic research, and public health strategies will be essential to mitigate their impact and reduce plastic-related cardiovascular burden. Full article

Investigating the pore structure and understanding the relationship between pore characteristics and mechanical properties are crucial to research in the study of cement mortar. At present, the segmentation of large-scale concrete pores is mainly conducted using traditional algorithms or software, which are time-consuming and operate in a semi-automated manner. However, the application of these methods faces challenges when analyzing large-scale rock pores due to factors such as a lack of data, artifacts, and inconsistent contrast. In this study, six series of cement mortars were subjected to real-time CT scanning under uniaxial loading (RT-CT) to collect real-time three-dimensional data on the evolution of pore structures during loading. To address issues such as artifacts and inconsistent contrast, a new augmentation method was proposed to overcome artifacts and enhance contrast consistency. Finally, the augmented dataset was utilized for training, and the Fast R-CNN algorithm served as the framework for developing the pore recognition model. The results indicate that the improved algorithm demonstrates enhanced convergence and greater accuracy in pore segmentation. A mathematical model is developed to relate uniaxial compressive strength (UCS) to pore fractal dimension and porosity, based on pore segmentation analysis. The fractal dimensions evolution of each specimen is consistent with the progressive failure indicated by the strain-stress curve. Under uniaxial loading, specimens with a 4:1 cement–sand ratio exhibited peak strength. The incorporation of fractals improved particle contact, thereby facilitating the formation of the skeletal structure. These efforts contribute to improving the identification of the deformation of cement mortars. Full article

Hand Gesture Recognition (HGR) is a vital technology that enables intuitive human–computer interaction in various domains, including augmented reality, smart environments, and assistive systems. Achieving both high accuracy and real-time performance remains challenging due to the complexity of hand dynamics, individual morphological variations, and computational limitations. This paper presents a lightweight and efficient skeleton-based HGR framework that addresses these challenges through an optimized multi-stream Convolutional Neural Network (CNN) architecture and a trainable ensemble tuner. Dynamic 3D gestures are transformed into structured, noise-minimized 2D spatiotemporal representations via enhanced data-level fusion, supporting robust classification across diverse spatial perspectives. The ensemble tuner strengthens semantic relationships between streams and improves recognition accuracy. Unlike existing solutions that rely on high-end hardware, the proposed framework achieves real-time inference on consumer-grade devices without compromising accuracy. Experimental validation across five benchmark datasets (SHREC2017, DHG1428, FPHA, LMDHG, and CNR) confirms consistent or superior performance with reduced computational overhead. Additional validation on the SBU Kinect Interaction Dataset highlights generalization potential for broader Human Action Recognition (HAR) tasks. This advancement bridges the gap between efficiency and accuracy, supporting scalable deployment in AR/VR, mobile computing, interactive gaming, and resource-constrained environments. Full article

Cryptorchidism in piglets, characterized by undescended testicles, causes economic losses and reduces consumer acceptance. Hyperprolific sows (HPS) have been hypothesized to produce a higher incidence of cryptorchid offspring. This study investigated the incidence of cryptorchidism in piglets born to HPS and its association with piglet birth weight and litter size in an observational study. Data from 276 litters (144 Landrace × Yorkshire sows; 4003 piglets) were analyzed. Sows were classified by genetic line (conventional: 68 litters; HPS: 208 litters) and parity (primiparous: 144; second parity: 132). At first parity, all gilts were inseminated with semen from a phenotypically unilateral cryptorchid Duroc boar, whereas at second parity, semen from three normal Duroc boars, which were full siblings, was used. The Landrace × Yorkshire HPS line produced more piglets per litter than the conventional Landrace × Yorkshire line (16.5 ± 0.3 vs. 12.4 ± 0.6; p < 0.001). Cryptorchidism occurred in 25.7% (37/144) of litters inseminated with semen from the cryptorchid boar, compared with 3.8% (5/132) of litters inseminated with semen from normal boars (p < 0.001). In total, 42 sows produced at least one cryptorchid piglet across both parities. Among affected sows (n = 42), the average number of cryptorchid piglets per litter was 1.3 ± 0.6 (range: 1–3). In the HPS line, cryptorchidism was detected in 24.1% (26/108) of litters, compared with 30.6% (11/36) in the conventional line (p = 0.441). HPS piglets had lower birth weights than conventional piglets (1.14 ± 0.01 vs. 1.30 ± 0.02 kg; p < 0.001). In the HPS line, litters with cryptorchid piglets had lower birth weights than those without (1.11 ± 0.02 vs. 1.18 ± 0.01 kg; p = 0.012), whereas no difference was observed in the conventional line (1.31 ± 0.04 vs. 1.28 ± 0.02 kg; p = 0.917). Litter size did not differ between litters with and without cryptorchid piglets in either genetic line. In conclusion, the lower average birth weight in cryptorchid litters of the HPS line, but not in conventional lines, suggests that HPS breeds may influence cryptorchidism incidence. These findings highlight the need to optimize fetal growth especially in the HPS to reduce this risk. Full article

Background/Objectives: Dementia is a growing public health concern in rapidly aging Japan. Dietary factors, including dairy products, have been proposed as modifiable influences on cognitive health, although findings across studies remain inconsistent. This study aimed to examine the association between habitual cheese consumption and incident dementia in a large, population-based cohort of older Japanese adults, and to provide epidemiological evidence regarding its potential preventive role in populations with low baseline dairy intake. Methods: We analyzed data from the Japan Gerontological Evaluation Study (JAGES) 2019–2022 cohort, linking survey responses to long-term care insurance (LTCI) certification records. Participants aged ≥65 years without prior LTCI certification were included. Cheese consumption was assessed at baseline and categorized as ≥1 time/week vs. non-consumers. Propensity score matching (PSM) was applied on sociodemographic and health-related covariates. Cox proportional hazards models estimated hazard ratios (HRs) for incident dementia over three years. Results: After PSM, 7914 participants were analyzed (3957 consumers; 3957 non-consumers). Baseline covariates were well-balanced. Over 3 years, 134 consumers (3.4%) and 176 non-consumers (4.5%) developed dementia, corresponding to an absolute risk difference of 1.06 percentage points. Cheese consumption was associated with a lower hazard of dementia (HR = 0.76, 95% CI 0.60–0.95, p = 0.015). Conclusions: Habitual cheese consumption (≥1 time/week) was modestly associated with a reduced 3-year incidence of dementia in older Japanese adults. While the absolute risk reduction was small, these findings are consistent with prior observational evidence linking dairy intake to cognitive health. Further research is warranted to clarify dose–response relationships, cheese subtypes, and underlying mechanisms. Full article

Smoke detection is a practical approach for early identification of wildfires and mitigating hazards that affect ecosystems, infrastructure, property, and the community. The existing deep learning (DL) object detection methods (e.g., Detection Transformer (DETR)) have demonstrated significant potential for early awareness of these events. However, their precision is influenced by the low visual salience of smoke and the reliability of the annotation, and collecting real-world and reliable datasets with precise annotations is a labor-intensive and time-consuming process. To address this challenge, we propose a weakly supervised Transformer-based approach with a teacher–student architecture designed explicitly for smoke detection while reducing the need for extensive labeling efforts. In the proposed approach, an expert model serves as the teacher, guiding the student model to learn from a variety of data annotations, including bounding boxes, point labels, and unlabeled images. This adaptability reduces the dependency on exhaustive manual annotation. The proposed approach integrates a Deformable-DETR backbone with a modified loss function to enhance the detection pipeline by improving spatial reasoning, supporting multi-scale feature learning, and facilitating a deeper understanding of the global context. The experimental results demonstrate performance comparable to, and in some cases exceeding, that of fully supervised models, including DETR and YOLOv8. Moreover, this study expands the existing datasets to offer a more comprehensive resource for the research community. Full article

Analyzing accident reports to absorb past experiences is crucial for construction site safety. Current methods of processing textual accident reports are time-consuming and labor-intensive. This research applied the LDA topic model to analyze construction accident reports, successfully identifying five main types of accidents: Falls from Height (23.5%), Struck-by and Contact Injuries (22.4%), Slips, Trips, and Falls (21.8%), Hot Work & Vehicle Hazards (18.1%), and Lifting and Machinery Accidents (14.2%). By mining the rich contextual details within unstructured textual descriptions, this research revealed that environmental factors constituted the most prevalent category of contributing causes, followed by human factors. Further analysis traced the root causes to deficiencies in management systems, particularly poor task planning and inadequate training. The LDA model demonstrated superior effectiveness in extracting interpretable topics directly mappable to engineering knowledge and uncovering these latent factors from large-scale, decade-spanning textual data at low computational cost. The findings offer transformative perspectives for improving construction site safety by prioritizing environmental control and management system enhancement. The main theoretical contributions of this research are threefold. First, it demonstrates the efficacy of LDA topic modeling as a powerful tool for extracting interpretable and actionable knowledge from large-scale, unstructured textual safety data, aligning with the growing interest in data-driven safety management in the construction sector. Second, it provides large-scale, empirical evidence that challenges the traditional dogma of “human factor dominance” by systematically quantifying the critical role of environmental and managerial root causes. Third, it presents a transparent, data-driven protocol for transitioning from topic identification to causal analysis, moving from assertion to evidence. Future work should focus on integrating multi-dimensional data for comprehensive accident analysis. Full article

Embodied energy flows link production systems with the energy sector, reflecting dependencies and structural risks under globalization and regional coordination. Guangdong, China’s most manufacturing-intensive, open, and energy-consuming province, is a central hub in both global value chains and domestic production networks, playing a pivotal role in national energy security. Understanding Guangdong’s embodied energy flows is essential for revealing the transmission of energy across multi-level spatial systems and the resilience of China’s energy infrastructure. This study integrates international (EXIOBASE) and Chinese inter-provincial input–output data to build a province-level nested global MRIO model, combined with Structural Path Analysis (SPA), to characterize Guangdong’s manufacturing embodied energy flows in domestic and international dual circulation from 2002 to 2017. Our findings confirm Guangdong’s pivotal bridging role in embodied energy transfers. First, flows are dual-directional and dominated by international transfers. Second, energy efficiency has improved, narrowing the intensity gap between export- and domestic-oriented industries. Third, flows have diversified spatially from concentration in developed regions toward developing regions, with domestic inter-provincial flows more dispersed. Finally, embodied energy remains highly concentrated across sectors, with leading industries shifting from labor- and capital-intensive to capital- and technology-intensive sectors. This research offers vital empirical evidence and policy reference for enhancing national energy security and optimizing spatial energy allocation. Full article

This article presents the design, implementation, and comparative evaluation of two Unmanned Aerial Vehicles (UAV)-based architectures for automated road data acquisition and processing. The first system uses Arduino Portenta H7 boards to perform real-time inference at the edge, reducing connectivity dependency. The second employs ESP32-CAM modules that transmit raw data for remote server-side processing. We experimentally validated and compared both systems in terms of power consumption, latency, and detection accuracy. Results show that the Portenta-based system consumes 36.2% less energy and achieves lower end-to-end latency (10,114 ms vs. 11,032 ms), making it suitable for connectivity-constrained scenarios. Conversely, the ESP32-CAM system is simpler to deploy and allows rapid model updates at the server. These findings provide a reference for Intelligent Transportation System (ITS) deployments requiring scalable, energy-efficient, and flexible road monitoring solutions. Full article

In this study, we investigated the spatial and temporal dynamics of pork carcass prices across European Union Member States, focusing on the influence of wheat prices and population income levels between 2014 and 2023. Our analysis revealed that both input costs (reflected by wheat price fluctuations) and income-driven demand factors exert significant and spatially correlated effects on pork carcass prices. The results demonstrate the existence of spatial interdependencies among neighboring countries, indicating that price changes in one region may propagate through the broader European market. By integrating spatial econometric techniques within a panel data framework, this research provides empirical evidence of the interconnected nature of EU agricultural markets, advancing the existing literature by demonstrating how input markets and consumer income dynamics jointly shape price behavior within an integrated regional economy. Our findings contribute to a deeper understanding of price transmission mechanisms in the livestock sector and offer valuable insights for policymakers seeking to enhance market efficiency and resilience within the Common Agricultural Policy context. Full article

As part of urban sustainable food strategies, reducing land and emission footprints motivates interest in edible insects (EI) as a sustainable protein source. However, research on the determinants of young consumers’ acceptance and willingness to pay for insect-based foods in Central and Eastern Europe remains limited. This study assessed whether Polish students are willing to pay more for foods containing EI when production is environmentally friendly. The analysis focused on identifying socio-demographic and product-related factors influencing willingness to pay a higher price. Data were collected in November 2023 through a nationwide Computer-Assisted Web Interview (CAWI) conducted via Google Forms among 947 Polish university students. A logistic regression model was applied to determine socio-demographic predictors, while exploratory factor analysis was used to identify latent dimensions of product attributes and food categories. Results revealed that gender and place of residence significantly affected willingness to pay, with women and urban residents showing higher readiness. Attributes related to convenience, availability, sensory appeal, health and nutrition claims, and CO₂ reduction benefits were the strongest positive correlates. The findings suggest pragmatic pathways for introducing insect-based foods into sustainable urban food systems and highlight the role of education in fostering environmentally responsible consumer behavior. Full article

Pavement condition assessment is a fundamental aspect of airport pavement management systems (APMS) for ensuring safe and efficient airport operations. However, conventional methods, which rely on extensive on-site inspections and complex calculations, are often time-consuming and resource-intensive. In response, Industry 4.0 has introduced machine learning (ML) as a powerful tool to streamline these processes. This study explores five ML algorithms (Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM)) for predicting the Pavement Condition Index (PCI). Using basic alphanumeric distress data from three international airports, this study predicts both numerical PCI values (on a 0–100 scale) and categorical PCI values (3 and 7 condition classes). To address data imbalance, random oversampling (SMOTE—Synthetic Minority Oversampling Technique) and undersampling (RUS) were used. This study fills a critical knowledge gap by identifying the most effective algorithms for both numerical and categorical PCI determination, with a particular focus on validating class-based predictions using relatively small data samples. The results demonstrate that ML algorithms, particularly Random Forest, are highly effective at predicting both the numerical and the three-class PCI for the original database. However, accurate prediction of the seven-class PCI required the application of oversampling techniques, indicating that a larger, more balanced database is necessary for this detailed classification. Using 10-fold cross-validation, the successful models achieved excellent performance, yielding Kappa statistics between 0.88 and 0.93, an error rate of less than 7.17%, and an area under the ROC curve greater than 0.93. The approach not only significantly reduces the complexity and time required for PCI calculation, but it also makes the technology accessible, enabling resource-limited airports and smaller management entities to adopt advanced pavement management practices. Full article

In recent years, machine learning (ML) has been increasingly used in many fields, including medicine. Magnetic resonance imaging (MRI) is a non-invasive and effective diagnostic technique; however, manual image analysis is time-consuming and prone to human variability. In response, ML models have been developed to support MRI analysis, particularly in segmentation and classification tasks. This work presents an updated narrative review of ML applications in brain MRI, with a focus on tumor classification and segmentation. A literature search was conducted in PubMed and Scopus databases and Mendeley Catalog (MC)—a publicly accessible bibliographic catalog linked to Elsevier’s Scopus indexing system—covering the period from January 2020 to April 2025. The included studies focused on patients with primary or secondary brain neoplasms and applied machine learning techniques to MRI data for classification or segmentation purposes. Only original research articles written in English and reporting model validation were considered. Studies using animal models, non-imaging data, lacking proper validation, or without accessible full texts (e.g., abstract-only records or publications unavailable through institutional access) were excluded. In total, 108 studies met all inclusion criteria and were analyzed qualitatively. In general, models based on convolutional neural networks (CNNs) were found to dominate current research due to their ability to extract spatial features directly from imaging data. Reported classification accuracies ranged from 95% to 99%, while Dice coefficients for segmentation tasks varied between 0.83 and 0.94. Hybrid architectures (e.g., CNN-SVM, CNN-LSTM) achieved strong results in both classification and segmentation tasks, with accuracies above 95% and Dice scores around 0.90. Transformer-based models, such as the Swin Transformer, reached the highest performance, up to 99.9%. Despite high reported accuracy, challenges remain regarding overfitting, generalization to real-world clinical data, and lack of standardized evaluation protocols. Transfer learning and data augmentation were frequently applied to mitigate limited data availability, while radiomics-based models introduced new avenues for personalized diagnostics. ML has demonstrated substantial potential in enhancing brain MRI analysis and supporting clinical decision-making. Nevertheless, further progress requires rigorous clinical validation, methodological standardization, and comparative benchmarking to bridge the gap between research settings and practical deployment. Full article

Background/Objectives: Binaural beat audio has gained popularity as a non-invasive tool to promote relaxation and enhance cognitive performance, though empirical support has been inconsistent. We developed a novel algorithm integrating real-time electroencephalography (EEG) feedback to dynamically tailor binaural beats to induce relaxed brain states. This study aimed to examine the efficacy and feasibility of this algorithm in a clinical trial. Methods: In a randomized, double-blinded, sham-controlled crossover trial, 25 healthy adults completed two 30 min sessions (EEG-guided intervention versus sham). EEG (Fp1) was recorded using a consumer-grade single-electrode headset, with auditory stimulation adjusted in real time based on EEG data. Outcomes included EEG frequency profiles, stop signal reaction time (SSRT), and novelty encoding task performance. Results: The intervention rapidly reduced dominant EEG frequency in all participants, with 100% achieving <8 Hz and 96% achieving <4 Hz within median 7.4 and 9.0 min, respectively. Compared to the sham, the intervention was associated with an faster novelty encoding reaction time (p = 0.039, dz = −0.225) and trends towards improved SSRT (p = 0.098, dz = −0.209), increased boundary separation in stop trials (p = 0.065, dz = 0.350), and improved inhibitory drift rate (p = 0.067, dz = 0.452) within the limits of the exploratory nature of these findings. Twenty-four (96%) participants reached a target level of <4 Hz with the intervention, while none reached this level with the sham. Conclusions: Real-time EEG-guided binaural beats may rapidly induce low-frequency brain states while potentially preserving or enhancing aspects of executive function. These findings support the feasibility of personalized, closed-loop auditory entrainment for promoting “relaxed alertness.” The results are preliminary and hypothesis-generating, warranting larger, multi-channel EEG studies in ecologically valid contexts. Full article