Search Results (28,126)

The increasing complexity of modern food production demands advanced solutions for quality control (QC), safety monitoring, and process optimization. This review systematically explores recent advancements in machine learning (ML) for QC across six domains: Food Quality Applications; Defect Detection and Visual Inspection Systems; Ingredient Optimization and Nutritional Assessment; Packaging—Sensors and Predictive QC; Supply Chain—Traceability and Transparency and Food Industry Efficiency; and Industry 4.0 Models. Following a PRISMA-based methodology, a structured search of the Scopus database using thematic Boolean keywords identified 124 peer-reviewed publications (2005–2025), from which 25 studies were selected based on predefined inclusion and exclusion criteria, methodological rigor, and innovation. Neural networks dominated the reviewed approaches, with ensemble learning as a secondary method, and supervised learning prevailing across tasks. Emerging trends include hyperspectral imaging, sensor fusion, explainable AI, and blockchain-enabled traceability. Limitations in current research include domain coverage biases, data scarcity, and underexplored unsupervised and hybrid methods. Real-world implementation challenges involve integration with legacy systems, regulatory compliance, scalability, and cost–benefit trade-offs. The novelty of this review lies in combining a transparent PRISMA approach, a six-domain thematic framework, and Industry 4.0/5.0 integration, providing cross-domain insights and a roadmap for robust, transparent, and adaptive QC systems in the food industry. Full article

Background and Objective: The aim of this study was to evaluate the effects of enamel matrix protein, platelet-rich fibrin (PRF), and bone graft on new bone formation beyond the skeletal system by creating calvarial bone defects in rats. The effects were assessed using histopathological and immunohistochemical analyses. Materials and Methods: In this study, calvarial bone defects were created in male Sprague Dawley rats weighing 500–550 g. The animals were randomly divided into seven groups: Control (n = 13), Emdogain (EMD, n = 13), Emdogain + Bone Graft (EMD + BG, n = 13), Platelet-Rich Fibrin (PRF, n = 13), PRF + Bone Graft (PRF + BG, n = 13), Bone Graft (BG, n = 13), and PRF + Emdogain + Bone Graft (PRF + EMD + BG, n = 13). An additional group of 36 rats was used for PRF preparation. Titanium domes were placed on the calvarial bone defects, and the animals were sacrificed after three months. Bone samples were evaluated histopathologically for new bone formation, numbers of osteoblasts and osteoclasts, angiogenesis, and fibrosis. Immunohistochemical analysis of bone formation was performed using OPG and RANKL staining kits. Data were analyzed statistically. Results: The PRF group showed a significantly higher level of moderate new bone formation compared with the PRF + BG, EMD + BG, and PRF + EMD + BG groups (p ≤ 0.05). No significant differences were observed among the groups in terms of fibrosis or angiogenesis (p > 0.05). Similarly, OPG and RANKL levels, as well as the OPG/RANKL ratio, did not differ significantly between groups (p > 0.05). Conclusions: Based on the findings of this study, the combined use of Emdogain, PRF, and bone graft appears to have beneficial effects on enhancing bone formation in calvarial defects. Full article

Camelids are increasingly recognized as important livestock species. They are valuable sources of meat, fiber, and milk. Despite their growing popularity, many aspects of their reproductive physiology and pathology remain unclear. Their reproductive performance is reported to be low in many countries. Advances in camelid veterinary care have identified several disorders, some of which are species-specific. This article describes an approach to and the diagnosis of infertility and subfertility cases in alpacas, llamas, and camels referred to the authors over the past 35 years. Ultrasonography, endometrial cytology, and biopsy are the primary diagnostic tools for practitioners. However, laparoscopy, hysteroscopy, and cytogenetics are indicated for cases referred to theriogenologists. The incidence of congenital and acquired reproductive disorders is presented. A high incidence of congenital defects of the reproductive tract is found in South American camelids, which raises concerns about animal welfare. Acquired disorders are similar to those described in other species. Endometritis and endometrosis are major disorders contributing to infertility and early pregnancy loss. However, studies on uterine defense mechanisms and the pathogenesis of these disorders are lacking. Hydrobursitis, a common cause of infertility in dromedary camels, warrants further research. The implications of some contagious diseases (tuberculosis, campylobacteriosis, and brucellosis) in female infertility are discussed. These findings emphasize the importance of including camelid medicine in veterinary education to ensure a high standard of care for this species. Full article

An atrial septal defect (ASD) is the most common congenital heart defect (CHD) diagnosed in adulthood. It is characterized by significant anatomical heterogeneity and complications that evolve over time. While often asymptomatic in children, the signs of adverse effects of ASD increase with age, including a greater risk of heart failure, stroke, atrial fibrillation (AF), and reduced life expectancy. ASD is traditionally considered a right-heart lesion due to long-term complications such as arrhythmias, right-sided heart failure, thromboembolism, and, in a subset of patients, pulmonary arterial hypertension (PAH). The pathophysiology of atrial shunts also affects the left heart due to volume overload and adverse ventriculo-ventricular interaction. Early diagnosis of interatrial septal anomalies is essential to prevent hemodynamic consequences and/or thromboembolic events. Electrocardiographic (ECG) findings play a crucial role in this early diagnosis. This narrative review aims to update clinicians on the latest evidence regarding the pathophysiological link between ASD and cardiac rhythm disorders, the nuances of optimal diagnostics, treatment options (surgical, interventional, pharmacological), and the need for long-term follow-up for patients with ASD. The review will determine the risk of conduction disorders compared to a healthy population and to compare the prevalences of conduction disorders, mortality, and pacemaker use in patients with closed ASDs versus those with open ASDs. Full article

This study investigates the uniaxial compression failure of magnesium-based wood-like material (MWM) prisms (100 × 100 × 300 mm³) using digital image correlation (DIC). The results revealed an average compressive strength of 8.76 MPa and a dominant failure mode with Y-shaped or inclined penetrating cracks. A novel piecewise constitutive model was established, combining a quartic polynomial and a rational fraction, demonstrating high fitting accuracy. Critically, the proportional limit was identified to be very low (20–35% of peak stress), attributed to early-stage damage from fiber–matrix interfacial defects. DIC analysis quantitatively distinguished dual crack initiation modes, pure mode I (occurring at ≈100% peak load) and mixed mode I/II (initiating earlier at 90.02% peak load), demonstrating that tensile shear coupling accelerates failure. These findings provide critical mechanistic insights and a reliable model for optimizing MWM in sustainable construction. Future work will explore the material’s behavior under multiaxial loading. Full article

Enamel hypoplasia (EH) is a stress marker commonly used in bioarcheological research to investigate health during growth. However, its analysis in contemporary samples allows for additional avenues of research, including comparison with medical records. The aim of the present research is to explore the influence of biological sex and socioeconomic status on the distribution of EH and examine the factors that contribute to the development of this defect. In this perspective, analysis of dentition was conducted on 132 individuals, with known information about age, biological sex, nationality, medical records, and socioeconomic status. Statistical analysis was conducted using Fisher’s test and the chi-square test. As a result, EH was observed more frequently among individuals from disadvantaged backgrounds, while a significant association was observed with socioeconomic status, evidencing a strong association between EH presence and structural vulnerability (chi-square, p = 0.04). The frequency of EH between sexes was not significant; however, a higher frequency was observed among males (chi-square, p = 0.94). We hypothesize that the impact of female biological buffering might be reduced in the European sample, as this result aligns with background information of the context. These results align with the research hypotheses and reinforce the multifactorial etiology of EH. Full article

This in vitro study evaluated the fracture resistance of metal–ceramic crowns repaired with milled hybrid resin, printed hybrid resin, lithium disilicate, and direct composite resin. One hundred crowns were fabricated, fractured under controlled loading, and 80 with standardized defects were randomly assigned to four groups (n = 20). Repairs were performed using CAD/CAM onlays or direct composite, followed by compressive testing until fracture. Mean fracture resistance values ranged from 1858.95 N to 1997 N across all groups, exceeding typical posterior occlusal forces (700–900 N). No statistically significant differences were found among groups (p = 0.200). Most failures were cohesive. These results indicate that both digital (milled and printed) and direct techniques offer sufficient strength to serve as minimally invasive and cost-effective alternatives to full crown replacement. Although limited by the in vitro design, this study supports the applicability of modern repair approaches in daily practice. Full article

This study investigates the effects of Ar ion irradiation on the mechanical properties and microstructure of SA508 Grade 3 Class 1 and Class 2 reactor pressure vessel steels. Three different fluence levels of Ar ion irradiation were applied to simulate accelerated irradiation damage conditions. Charpy impact and tensile tests conducted before and after irradiation showed no significant changes in bulk mechanical properties. Stopping and Range of Ions in Matter (SRIM) and Transport of Ions in Matter (TRIM) simulations revealed that Ar ion irradiation produces a shallow penetration depth of approximately 2.5 µm, highlighting the limitations of conventional macro-mechanical testing for evaluating irradiation effects in such a thin surface layer. To overcome this limitation, nano-indentation tests were performed, revealing a clear increase in indentation hardness after irradiation. Transmission electron microscopy (TEM) analysis using STEM–BF imaging confirmed a higher density of irradiation-induced defects in the irradiated specimens. The findings demonstrate that while macro-mechanical properties remain largely unaffected, micro-scale testing methods such as nano-indentation are essential for assessing irradiation-induced hardening in shallowly damaged layers, providing insight into the behavior of SA508 reactor pressure vessel steels under accelerated irradiation conditions. Full article

This paper presents a comprehensive study on automated defect detection in complex structures using phased array ultrasonic testing data, focusing on both traditional signal processing and advanced deep learning methods. As a non-AI baseline, the well-known signal-to-noise ratio algorithm was improved by introducing automatic depth gate calculation using derivative analysis and eliminated the need for manual parameter tuning. Even though this method demonstrates robust flaw indication, it faces difficulties for automatic defect detection in highly noisy data or in cases with large pore zones. Considering this, multiple DL architectures—including fully connected networks, convolutional neural networks, and a novel Convolutional Attention Temporal Transformer for Sequences—are developed and trained on diverse datasets comprising simulated CIVA data and real-world data files from welded and composite specimens. Experimental results show that while the FCN architecture is limited in its ability to model dependencies, the CNN achieves a strong performance with a test accuracy of 94.9%, effectively capturing local features from PAUT signals. The CATT-S model, which integrates a convolutional feature extractor with a self-attention mechanism, consistently outperforms the other baselines by effectively modeling both fine-grained signal morphology and long-range inter-beam dependencies. Achieving a remarkable accuracy of 99.4% and a strong F1-score of 0.905 on experimental data, this integrated approach demonstrates significant practical potential for improving the reliability and efficiency of NDT in complex, heterogeneous materials. Full article

This study advances toward establishing the theoretical foundations of Industry 6.0 by developing a comprehensive framework that integrates artificial intelligence (AI), decentralized control systems, and cyber–physical production environments for intelligent, sustainable, and adaptive manufacturing. The research employs a tri-modal methodology (deductive, inductive, and abductive reasoning) to construct a theoretical architecture grounded in five interdependent constructs: advanced technology integration, decentralized organizational structures, mass customization and sustainability strategies, cultural transformation, and innovation enhancement. Unlike prior conceptualizations of Industry 6.0, the proposed framework explicitly emphasizes the cyclical feedback between innovation and organizational design, as well as the role of cultural transformation as a binding element across technological, organizational, and strategic domains. The resulting framework demonstrates that AI-driven decentralized control systems constitute the cornerstone of Industry 6.0, enabling autonomous real-time decision-making, predictive zero-defect manufacturing, and strategic organizational agility through distributed intelligent control architectures. This work contributes foundational theory and actionable guidance for transitioning from centralized control paradigms to AI-driven distributed intelligent manufacturing control systems, establishing a conceptual foundation for the emerging Industry 6.0 paradigm. Full article

Undetected cracks in railroad wheels pose significant safety and economic risks, while current inspection methods are limited by cost, coverage, or contact requirements. This study explores the use of passive, air-coupled ultrasonic acoustic (UA) sensors for detecting wheel damage on stationary or moving wheels. Two controlled datasets of wheelsets, one with clear damage and another with early, service-induced defects, were tested using hammer impacts. An automated system identified high-energy bursts and extracted features in both time and frequency domains, such as decay rate, spectral centroid, and entropy. The results demonstrate the effectiveness of UAE (ultrasonic acoustic emission) techniques through Kernel Density Estimation (KDE) visualization, hypothesis testing with effect sizes, and Receiver Operating Characteristic (ROC) analysis. The decay rate consistently proved to be the most effective discriminator, achieving near-perfect classification of severely damaged wheels and maintaining meaningful separation for early defects. Spectral features provided additional information but were less decisive. The frequency spectrum characteristics were effective across both axial and radial sensor orientations, with ultrasonic frequencies (20–80 kHz) offering higher spectral fidelity than sonic frequencies (1–20 kHz). This work establishes a validated “ground-truth” signature essential for developing a practical wayside detection system. The findings guide a targeted engineering approach to physically isolate this known signature from ambient noise and develop advanced models for reliable in-motion detection. Full article

Background/Objectives: As survival improves in congenital heart defects (CHD), psychosocial support—particularly during crises—has become increasingly important. We examined how concerns of CHD patients and their relatives evolved during the Coronavirus Disease 2019 (COVID-19) pandemic, focusing on the influence of role (patient vs. relative), gender, and CHD complexity. Methods: The German National Register for Congenital Heart Defects (NRCHD) conducted two nationwide online surveys in April 2020 (Survey 1) and April 2021 (Survey 2). Free-text responses were analyzed using Mayring’s summarizing content analysis. Categories were coded per respondent (present/absent) for exploratory comparisons by year, role, sex, and CHD complexity. Analyses were cross-sectional and descriptive (p-values unadjusted). Results: In survey 1, 15.9%, and in survey 2, 19.3% of respondents provided qualitative information. In 2020, dominant themes included general COVID-19 information (37.3%), lack of CHD-specific information (30.4%), worry (24.1%), fear (23.2%), isolation (21.4%), and uncertainty (21.2%). By 2021, concerns shifted toward vaccination (24.1%) and vaccination prioritization (23.4%), while information gaps (21.8%) and fear (21.0%) persisted. Significant year-to-year changes included decreases in general information needs, concern, isolation, and uncertainty, and increases in prioritization (all p < 0.01). Relatives consistently reported higher psychological burden than patients (p ≤ 0.01). Conclusions: Concerns moved from early fear/uncertainty to vaccination and prioritization one year later, with persistent information needs across subgroups. Clear CHD-specific communication, caregiver-inclusive psychosocial support, and crisis-resilient care pathways (including telemedicine) are essential for this vulnerable population. Full article

The complex environment along railway lines, characterized by low imaging quality, strong background interference, and densely distributed small objects, causes existing detection models to suffer from low accuracy in practical applications. To tackle these challenges, this study aims to develop a robust and lightweight insulator detection model specifically optimized for these challenging railway scenarios. To this end, we release a dedicated comprehensive dataset named complexRailway that covers typical railway scenarios to address the limitations of existing insulator datasets, such as the lack of small-scale objects in high-interference backgrounds. On this basis, we present CutP5-LargeKernelAttention-SIoU (C5LS), an improved YOLOv8 variant with three key improvements: (1) optimized YOLOv8’s detection head by removing the P5 branch to improve feature extraction for small- and medium-sized targets while reducing computational redundancy, (2) integrating a lightweight Large Separable Kernel Attention (LSKA) module to expand the receptive field and improve contextual modeling, (3) and replacing CIoU with SIoU loss to refine localization accuracy and accelerate convergence. Experimental results demonstrate that it reaches 94.7% in mAP@0.5 and 65.5% in mAP@0.5–0.95, outperforming the baseline model by 1.9% and 3.5%, respectively. With an inference speed of 104 FPS and a model size of 13.9 MB, the model balances high precision and lightweight deployment. By providing stable and accurate insulator detection, C5LS not only offers reliable spatial positioning basis for subsequent defect identification but also builds an efficient and feasible intelligent monitoring solution for these failure-prone insulators, thereby effectively enhancing the operational safety and maintenance efficiency of the railway power system. Full article

In Liquid Composite Molding (LCM), the high variability present in reinforcement properties such as permeability creates additional challenges during the injection process, such as void formation. Although improved injection strategy designs can mitigate the formation of defects, these processes can benefit from real-time process monitoring and control to adapt the injection conditions when needed. In this study, a machine vision algorithm is proposed, with the objective of detecting and tracking both fluid flow and bubbles in an LCM setup. In this preliminary design, 3D-printed porous geometries are used to mimic the architecture of textile reinforcements. The results confirm the applicability of the proposed approach, as the detection and tracking of the objects of interest is possible, without the need to incur in elaborate experimental preparations, such as coloring the fluid to increase contrast, or complex lighting conditions. Additionally, the proposed approach allowed for the formulation of a new dimensionless number to characterize bubble transport efficiency, offering a quantitative metric for evaluating void transport dynamics. This research underscores the potential of data-driven approaches in addressing manufacturing challenges in LCM by reducing the overall process monitoring complexity, as well as using the acquired reliable data to develop robust, data-driven models that offer new understanding of process dynamics and contribute to improving manufacturing efficiency. Full article

Aluminum alloys under long-term service or repetitive stress are prone to small fatigue cracks (FCs) with arbitrary orientations, necessitating eddy current probes with focused magnetic fields and directional selectivity for reliable detection. This study presents a flexible printed circuit board (FPCB) probe with a double-layer planar excitation coil and a double-layer differential receiving coil. The excitation coil employs a reverse-wound design to enhance magnetic field directionality and focusing, while the differential receiving coil improves sensitivity and suppresses common-mode noise. The probe is optimized by adjusting the excitation coil overlap and the excitation–receiving coil angles to maximize eddy current concentration and detection signals. Finite element simulations and experiments confirm the system’s effectiveness in detecting surface cracks of varying sizes and orientations. To further characterize these defects, two time-domain features are extracted: the peak-to-peak value ($\Delta$P), reflecting amplitude variations associated with defect size and orientation, and the signal width ($\Delta$W), primarily correlated with defect angle. However, substantial overlap in their value ranges for defects with different parameters means that these features alone cannot identify which specific parameter has changed, making prior defect classification using a Transformer-based approach necessary for accurate quantitative analysis. The proposed method demonstrates reliable performance and clear interpretability for defect evaluation in aluminum components. Full article