Search Results (17,900)

Background/Objectives: Dental diseases represent a great problem for oral health care, and early diagnosis is essential to reduce the risk of complications. Panoramic radiographs provide a detailed perspective of dental structures that is suitable for automated diagnostic methods. This paper aims to investigate the use of an advanced deep learning (DL) model for the multiclass classification of diseases at the sub-diagnosis level using panoramic radiographs to resolve the inconsistencies and skewed classes in the dataset. Methods: To classify and test the models, rich data of 10,580 high-quality panoramic radiographs, initially annotated in 93 classes and subsequently improved to 35 consolidated classes, was used. We applied extensive preprocessing techniques like class consolidation, mislabeled entry correction, redundancy removal and augmentation to reduce the ratio of class imbalance from 2560:1 to 61:1. Five modern convolutional neural network (CNN) architectures—InceptionV3, EfficientNetV2, DenseNet121, ResNet50, and VGG16—were assessed with respect to five metrics: accuracy, mean average precision (mAP), precision, recall, and F1-score. Results: InceptionV3 achieved the best performance with a 97.51% accuracy rate and a mAP of 96.61%, thus confirming its superior ability for diagnosing a wide range of dental conditions. The EfficientNetV2 and DenseNet121 models achieved accuracies of 97.04% and 96.70%, respectively, indicating strong classification performance. ResNet50 and VGG16 also yielded competitive accuracy values comparable to these models. Conclusions: Overall, the results show that deep learning models are successful in dental disease classification, especially the model with the highest accuracy, InceptionV3. New insights and clinical applications will be realized from a further study into dataset expansion, ensemble learning strategies, and the application of explainable artificial intelligence techniques. The findings provide a starting point for implementing automated diagnostic systems for dental diagnosis with greater efficiency, accuracy, and clinical utility in the deployment of oral healthcare. Full article

Airport aprons are complex, multi-node operational hubs frequently affected by queue congestion resulting from control handovers, taxi conflicts, and external factors. To enable proactive congestion management, we propose a new and accurate method for apron queue length prediction. The core of our approach is a multi-queue network model in which queues are systematically divided by control position and taxi direction. This framework, which applies the Fluid Flow Approximation and is calibrated with historical data, effectively captures the dynamics of multi-node traffic flow. In a validation case study at Beijing Daxing International Airport (ZBAD), the model achieved high accuracy, with the mean absolute error of queue length prediction averaging 0.5 aircraft. The results demonstrate the model’s ability to characterize queue dynamics on a minute-level scale across a full day. Full article

Background: The Balance Error Scoring System (BESS) is the most practiced static postural balance assessment tool, which relies on visual observation, and has been adopted as the gold standard in the clinic and field. However, the BESS can lead to missed and inaccurate diagnoses—because of its low inter-rater reliability and limited sensitivity—by missing subtle balance deficits, particularly in the athletic population. Smartphone technology using motion sensors may act as an alternative option for providing quantitative feedback to healthcare clinicians when performing balance assessments. The primary aim of this study was to explore the discriminative validity of an alternative novel smartphone-based cloud system to measure balance remotely in soccer athletes with and without hip pain. Methods: This is an exploratory cross-sectional study. A total of 64 Australian soccer athletes (128 hips, 28% females) between 18 and 40 years completed single and tandem stance balance tests that were scored using the modified BESS test and quantified using the smartphone device attached to their lower back. An Exploratory Factor Analysis (EFA) and a Clustered Receiver Operating Characteristic (ROC) using an Area Under the Curve (AUC) were used to explore the discriminative validity between the smartphone sensor system and the modified BESS test. A Linear Mixed-Effects Analysis of Covariance (ANCOVA) was used to determine any statistical differences in static balance measures between individuals with and without hip-related pain. Results: EFA revealed that the first factor primarily captured variance related to smartphone measurements, while the second factor was associated with modified BESS test scores. The ROC and the AUC showed that the smartphone sway measurements in the anterior–posterior and mediolateral directions during single-leg stance had an acceptable to excellent level of accuracy in distinguishing between individuals with and without hip-related pain (AUC = 0.72–0.80). Linear Mixed-Effects ANCOVA analysis found that individuals with hip-related pain had significantly less single-leg balance variability and magnitude in the anteroposterior and mediolateral directions compared to individuals without hip-related pain (p < 0.05). Conclusion: Due to the ability of smartphone technology to discriminate between individuals with and without hip-related pain during single-leg static balance tasks, it is recommended to use the technology in addition to the modified BESS test to optimise a clinician-led assessment and to further guide clinical balance decision-making. While the study supports smartphone technology as a method to assess static balance, its use in measuring balance during dynamic movements needs further research. Full article

This study systematically compared the growth performance, nutrient composition, accumulation and speciation of selenium (Se), and in vitro bioaccessibility in yellow mealworm (Tenebrio molitor L.) larvae, which were reared on substrates supplemented with selenite (Se⁴⁺) and selenate (Se⁶⁺) at concentrations of 0, 5, 10, and 20 mg/kg over 28 days. The results showed that high Se concentrations (≥10 mg/kg) significantly reduced larval biomass, with Se⁶⁺ having a slightly stronger inhibitory effect than Se⁴⁺. The mealworms effectively accumulated Se in a dose- and form-dependent manner. Peak total Se concentrations were observed on day 14, after which there was a decline, suggesting the presence of potential elimination mechanisms, such as moulting. The bioaccumulation factors (BAFs) were all below 1, indicating its limited enrichment capacity for both Se⁴⁺ and Se⁶⁺. Nutrient composition was altered, with both Se forms stimulating crude protein and polysaccharide synthesis while inhibiting fat accumulation. Mineral content (Mg, Fe, Zn) was also modulated, with differences observed between the Se⁴⁺ and Se⁶⁺ treatments. Notably, mealworms exhibited a remarkable ability to biotransform inorganic Se into organic forms, with organic Se proportions exceeding 79% in all treatments. Selenate was more efficiently bio-converted, yielding a higher proportion of organic Se. In vitro gastrointestinal digestion revealed significantly higher Se bioaccessibility from Se⁶⁺-treated mealworms (up to 85.12%) than from Se⁴⁺-treated ones (up to 60.67%). Analysis of the bioaccessible fraction by Se speciation identified SeCys₂ as the dominant compound (>92% of the detected species), with much lower levels of SeMet. Trace amounts of unmetabolised Se⁶⁺ were only detected in the Se⁶⁺-exposed groups. These findings highlight T. molitor as an efficient bioreactor for producing bioaccessible, organically bound Se, primarily as SeCys₂, with Se⁶⁺ being the more favourable precursor for generating a high-quality, bioavailable source of Se for potential use in feed or food. Full article

In order to explore the main regulatory genes and related pathways of growth traits, transcriptome sequencing was performed on the hypothalamus, pituitary, and liver tissues of 12-month-old greater amberjack (Seriola dumerili) with different growth rates. In total, 504 (118 up- and 386 down-regulated), 556 (283 up- and 273 down-regulated), and 699 (224 up- and 475 down-regulated) differentially expressed genes (DEGs) were identified in the hypothalamus, pituitary, and liver tissues, respectively. GO and KEGG pathway analyses revealed significant differences in the expression of several genes involved in growth, metabolism, and immune-related pathways. The mRNA expression levels of genes related to growth (ghrh, ghra, igf1), cell proliferation (fgf19, fgfr4, mapk8b, map2k4b, and map4k3), and lipid metabolism (acsl5, dgat2, lipeb, cyp7a1, and fabp10a) were up-regulated in the fast-growing (FG) group, while the cartl and sst1.1 were down-regulated. Conversely, genes associated with glycolysis (fbp1a, pklr, pgm2), citrate cycle (aclya, idh1), and immune-related pathways (irf1b, cxcl10, tnfb, lysg, ifi44, mapk11, and mapk12b) were up-regulated in the slow-growing (SG) group. These findings indicate that the FG exhibited greater lipid metabolism capacity and cell proliferation ability, while the SG expended additional energy to cope with environmental stress, with hindered growth during immune response. This study enhances our understanding of the genetic mechanisms underlying differences in growth rates and provides essential gene resources for future growth-related molecular breeding programs in greater amberjack. Full article

Despite the prevalence of the Yo-Yo Intermittent Recovery Tests Level 1 (YYIRTL1) and Level 2 (YYIRTL2) in elite soccer, knowledge surrounding their association and prediction of match performance is limited. This study investigated the association between respective tests and match running performance in male professional soccer players. High-intensity (HIR), high-speed (HSR), and sprinting (SPR) running distances were collected using a global positioning system from eleven professional male players who completed the YYIRTL1 and YYIRTL2. Associations between match performance and the YYIRT were assessed using correlational analyses, and the predictability of the YYIRT with match performance was assessed using univariate linear regression analyses. Strong correlations were found between YYIRTL1 and both HIR (r = 0.79) and HSR (r = 0.73). A moderate correlation was observed between YYIRTL2 and HIR (r = 0.42) and a weak correlation was observed between YYIRTL2 and HSR (r = 0.12). No correlation was observed between YYIRTL1 and SPR (r = 0.07) and a moderate, negative correlation was observed between YYIRTL2 and SPR (r = −0.21). Univariate regression analyses suggested that YYIRTL1 explained 63% of HIR variance, which YYIRTl2 did not, and that neither test suggested significant predictive ability in HSR or SPR. The YYIRTL1 is strongly associated with, and may predict, in-game HIR in Canadian male professional soccer players. Full article

Background/Objectives: Triple-negative breast cancer (TNBC) is an aggressive subtype associated with a poor prognosis and limited treatment options. Inflammatory and hematological biomarkers have emerged as potential tools for disease characterization, particularly in low-resource settings. Methods: This cross-sectional analytical study was conducted between July 2022 and February 2024 at Dalal Jamm Hospital in Dakar, Senegal, and included 120 women: 40 with TNBC, 40 with hormone-dependent breast cancer (HDBC), and 40 healthy controls. Blood samples were collected at diagnosis before any treatment to measure complete blood counts and C-reactive protein (CRP) levels. Inflammatory ratios—neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR)—were calculated. Results: TNBC patients displayed a distinct inflammatory profile characterized by elevated neutrophil counts, CRP, NLR, and MLR, as well as reduced lymphocyte and basophil percentages compared to healthy controls. NLR > 1.12 demonstrated strong discriminatory ability (AUC = 0.847; sensitivity 90%; specificity 65%). Differences between TNBC and HDBC were less pronounced, except for CRP and basophil levels. Multivariate analysis confirmed independent associations of elevated NLR, CRP, and neutrophils with TNBC. Conclusions: These findings provide new insights into the inflammatory and hematological characteristics of TNBC in this population and support further investigation of accessible biomarkers for early disease stratification in similar settings. Full article

This study assesses the efficacy of the Physics Communication Project (PCP) in bolstering students’ confidence, particularly in teamwork and presentation abilities. The PCP is a group-work exercise for first year students taking Physics 1, the introductory course for all physics degrees at the University of Glasgow. It is designed to encourage and improve team and communication skills. The study delves into the PCP’s influence on student perceptions and experiences by employing χ²-analysis to look for statistically significant differences in quantitative data and the General Inductive Method to identify key themes in qualitative data. Findings reveal that students were initially apprehensive about public speaking. However, there was a significant improvement in students’ confidence levels in teamwork and presentations following participation in the PCP, with qualitative data emphasising the benefits of teamwork enhancements. Additionally, the PCP fosters community among participants, enhancing their academic journey beyond mere skill acquisition. Moreover, the PCP is vital in addressing gender disparities in confidence levels, particularly in presentations. Initially, women displayed notably lower confidence than men, but post-project, their confidence aligned with that of men, indicating substantial growth among female participants. Full article

Soccer is practiced by professionals, amateurs, and recreational players. The physical assessment tools used by professionals are rarely available in recreational settings. Given the widespread participation and potential health benefits of soccer activity, it becomes essential to identify simple and accessible indicators that can help to characterize physical ability in non-professional players. This cross-sectional observational work explores which health status and lifestyle indices can be useful to estimate physical ability in recreational male soccer players when field testing is not feasible. Sixty-six participants volunteered in the study. Five performance field tests were conducted, and a related overall physical ability index (KPI_tot) was defined, while a questionnaire was developed to investigate nine BIO_Indices (BMI, age, physical activity level, job, alcohol consumption, smoking habits, sports career, occurring injuries, medical history). Data for the selected performance tests are reported for the recruited recreational athletes. KPI_tot was estimated from BIO_Indices, using a stepwise backward regression. The selected model, named SOCCER_Index, incorporates six out of nine BIO_Indices, excluding smoking habits, sports career, and medical history (R² = 0.536). In conclusion, with a simple questionnaire, an estimate of soccer players’ physical ability can be obtained. Further data collection is needed to obtain a more generalizable and robust SOCCER_Index. Full article

This study aimed to assess maternal health profiles related to diet, pregnancy, and breastfeeding practices among 437 mothers with children under 24 months in a rural village in Madagascar, and to examine their association with maternal educational attainment using interviews and anthropometric data. Bivariate statistical analyses were performed to explore associations between maternal education level and all studied variables. Multivariate analyses were also conducted but did not yield reliable results and are therefore not presented. The findings showed that higher maternal education was strongly associated with better socioeconomic conditions; improved access to essential resources like food, clean water, and healthcare facilities; and greater dietary diversity. More educated women reported consuming a wider range of foods, reflecting better nutritional quality and potential benefits for maternal health. In contrast, education level did not significantly affect pregnancy-related care or breastfeeding practices as recommended by the WHO. This suggests that while education enhances women’s ability to access and choose nutritious diets, broader cultural or systemic factors may shape maternal care behaviors. Women with higher educational attainment had greater access to diverse and sufficient diets, which may contribute to improved maternal nutritional status. Sustainable interventions aimed at improving women’s education and nutritional literacy are needed to support informed dietary choices and improve maternal and child health outcomes. Full article

Bicuspid Aortic Valve (BAV) disease is recognized as the most common congenital heart condition and is frequently associated with complex valvular and aortic disorders. Cardiovascular computed tomography (CT) has become essential for diagnosing BAV, planning procedures, and evaluating patients after treatment. This is largely due to CT’s high spatial resolution and its ability to perform volume imaging effectively. This review provides an up-to-date overview of the increasing role of cardiovascular CT in the management of bicuspid aortic valve (BAV). It covers various aspects, including BAV morphology, optimal sizing for transcatheter aortic valve replacement (TAVR), and post-procedural monitoring. We highlight significant innovations, such as supra-annular sizing techniques and artificial intelligence (AI)-guided analysis, that position CT at the nexus of anatomy, function, and targeted treatment. Additionally, we address controversies concerning inconsistencies in sizing algorithms, recent classification challenges, and radiation exposure. Future development areas include AI predictive tools, radiomic phenotyping, and CT-guided precision medicine. This synthesis aims to provide clinicians and researchers with a high-level guide to the clinical integration of cardiovascular CT and its future in the BAV population. This review provides the most current, comprehensive synthesis on the pivotal role of cardiovascular CT in BAV management, offering a roadmap for integrating advanced imaging into clinical practice and guiding future research priorities. Full article

Networked systems operating under uncertainty require decision making frameworks capable of balancing nominal efficiency and robustness against correlated risks. In this work, we study a distributionally robust weighted dominating set problem as a system-level model for robust network design, where node selection decisions are affected by uncertainty in costs and their correlation structure. We formulate the problem as a bi-objective optimization model that simultaneously minimizes the expected price and a risk measure derived from mean–covariance ambiguity. Rather than proposing new optimization algorithms, we conduct a systematic, methodological, and computational analysis of classical multiobjective solution approaches within this nonconvex and combinatorial setting. In particular, we compare weighted-sum, lexicographic, and $\epsilon$-constraint methods, highlighting their ability to reveal different structural properties of the Pareto Frontier. Our numerical results demonstrate that the methods that use scalarization allow us to obtain only partial insights for networked systems where robustness is inherent. However, the $\epsilon$-constraint method is highly efficient in recovering the full set of Pareto-optimal solutions. Once obtained, the Pareto Frontier exposes non-supported solutions and disruptive changes in its form. Notice that the latter is directly related to different configurations of dominating sets which are induced by the uncertainties. Consequently, these observations allow us to select from different subsets of relevant operating conditions for robust network designs that are significantly different for a decision maker. Full article

Personalized federated learning (pFL) aims to address data heterogeneity by training client-specific models. However, it faces two critical challenges under few-shot conditions. First, existing methods often overlook the hierarchical structure of neural representations, limiting their ability to balance generalization and personalization. Second, recent approaches incorporate representation-level inductive biases that typically rely on rigid assumptions, such as fixed perturbation patterns or compact class clusters, making them vulnerable to distribution shifts in federated environments. To overcome these limitations, we propose pFedH2A, a novel hierarchical framework incorporating brain-inspired mechanisms, tailored for personalized federated learning in few-shot scenarios. First, we design a dual-branch hypernetwork (DHN) that employs two structurally distinct branches to generate aggregation weights. Each branch is biased toward capturing either low-level shared features or high-level personalized representations, enabling fine-grained personalization by mimicking the brain’s division of perceptual and representational processing. Second, we introduce a relation-aware module that learns an adaptive similarity function for each client, supporting few-shot classification by measuring whether a pair of samples belongs to the same class without relying on rigid prototype assumptions. Extensive experiments on public image classification datasets demonstrate that pFedH2A outperforms existing pFL baselines under few-shot scenarios, validating its effectiveness. Full article

The performance of a printed square split-ring resonator as a sensor for quantifying nanoparticle concentrations in PVDF-based nanocomposites was evaluated at UHF frequencies. The sensing mechanism was based on the frequency response of parameter S₂₁, observing the shift in the resonant frequency and a variation in S₂₁ level, while samples were placed on the ring split and compared to the sensor without a sample. Experiments with samples of PVDF-based nanocomposites combined with different concentrations of both MoS₂ and MXenes, ranging from 0.01% to 0.2%, were conducted. In general, considering both types of samples studied, it was observed that, as the concentration increases, S₂₁ (dB) increases from −6.35 to −6 dB. At the same time, the resonance frequency in the S₂₁ plot went from 500.4 to 498.25 MHz. Although the concentrations and their variations were relatively low, shifts in the resonance frequency of S₂₁ were evident, demonstrating the ability of the sensor to detect low concentrations and variations of MoS₂ and MXenes, being the detection of samples with higher concentrations feasible as future work, and concluding that the sensor had a relatively acceptable performance. In this study, MXenes were the concentrations that produced more noticeable shifts in the resonance frequency of S₂₁. Likewise, characterizations based on SEM and TEM were performed to corroborate the ones at UHF frequencies. Full article

As IoT environments continue to grow in scale and complexity, the increasing number of interconnected sensors and devices makes end-to-end system behaviour progressively harder to understand. Process mining offers strong potential to address this challenge by transforming sensor-driven event data into interpretable insights at the process level. Yet, current tools are typically designed for business processes, not sensor-driven IoT workflows, which raises questions about their suitability in the IoT context. This discrepancy is evident in existing comparative studies, which often rely on feature checklists, rarely consider usability and interaction effort, or fail to evaluate support for domain-specific analytical tasks. This study introduces a structured evaluation methodology that combines a functional capability assessment derived from vendor materials with a task-based evaluation grounded in 13 representative questions from an IoT-oriented smart factory scenario, focusing on clarity, ease of use, and the ability to address context-specific analytical needs. The results highlight notable strengths and trade-offs among the investigated tools, demonstrating substantial variation in usability, effort, and analytical coverage, and showing that no single tool fully supports the breadth of process-intelligence needs in IoT contexts. The proposed methodology provides a replicable foundation for evaluating process mining tools in domain-specific settings and supports more informed tool selection for IoT-driven analytical workflows. Full article