Search Results (57,498)

This paper proposes an efficient and easily applicable offline method to compensate for the air magnetic flux in the characterization of soft magnetic materials using the Epstein Frame. The proposed approach does not necessitate the incorporation of additional components into the experimental setup or the implementation of complex methodologies for the evaluation of the surface of the Epstein coil. The proposed strategy has undergone rigorous evaluation for three distinct Fe-Si commercial stripes, encompassing Non-Oriented Grain (NOG) and Oriented Grain (OG) specimens with varying thicknesses and Silicon percentages. In any case, given the independence of the proposed procedure from the material nature, it can be applied to each variety of soft magnetic material. The results obtained have been presented in the form of magnetization curves and magnetic losses across a broad spectrum of frequencies. A comparison of these results with the data provided by manufacturers has been conducted, thereby substantiating the efficacy of the proposed approach despite its apparent simplicity compared with those proposed by the standard. The findings underscore the paramount importance of implementing compensation techniques to circumvent persistent errors in the assessment of soft magnetic materials’ performance, particularly in scenarios characterized by elevated magnetic density flux values. This imperative is of primary significance for the development of accurate magnetic materials models and for the prediction of power system behavior in fault conditions. Full article

Fracture in concrete is inherently a multi-stage process, yet traditional three-stage frameworks do not explicitly distinguish between micro-crack development and macro-crack propagation, particularly under varying ligament length conditions. The influence of ligament length (notch-to-depth ratios of 0.0, 0.2, 0.3, 0.4, and 0.5) on the crack propagation characteristics in notched concrete beams under three-point bending is investigated. Three-dimensional digital image correlation (3D DIC) was employed to monitor full-field displacement and strain, enabling the evaluation of key fracture parameters including horizontal displacement, crack mouth opening displacement (CMOD), horizontal strain, fracture process zone (FPZ) length, macro-crack length, and total fracture zone length. A high-magnification industrial camera (100×) was simultaneously used for real-time observation of the notch tip. Based on the evolution of these parameters, the fracture process was divided into four distinct stages: linear elastic stage, micro-crack initiation and propagation stage, macro-crack initiation and propagation stage, and complete failure stage. The industrial camera observations confirmed macro-crack initiation at approximately 60% of the post-peak load, validating the proposed four-stage division. Quantitative results show that increasing the notch depth ratio from 0.0 to 0.5 reduces the peak load by approximately 30–40% and decreases the nominal stress proportionally. The FPZ was found to be fully developed at the 60% post-peak load threshold, after which it diminished as macro-crack propagation dominated. Aggregate bridging, crack deflection, and crack branching were consistently identified as the primary toughening mechanisms governing the ligament effect. The crack propagation mechanisms in the four stages are controlled by the combined effects of front free boundary effect, stress concentration effect, ligament effect, and back free boundary effect. These findings provide a refined understanding of concrete fracture that can inform the safety assessment and design of concrete bending members in infrastructure construction. Full article

The growing adoption of cycling as part of the transition toward sustainable urban mobility, driven by climate change concerns and increasing congestion, has heightened the need to ensure cyclist safety in metropolitan areas. This study proposes an integrated spatio-temporal analytical framework to examine the evolution of reported bicycle–vehicle injury accidents in the Lisbon Metropolitan Area (LMA). The framework combines Geographic Information Systems (GIS)-based spatial statistics with Emerging Hotspot Analysis (EHA) to identify and track changes in accident clustering over time, across pre-, during-, and post-COVID-19 containment periods. This study contributes by applying Emerging Hotspot Analysis to bicycle accident data, an approach still largely unexplored, and by proposing a sequential and integrated framework that links traditional spatial analysis methods with dynamic hotspot detection and machine learning techniques, enabling a shift from static pattern identification to enhanced interpretation of evolving accident occurrence patterns and hotspot dynamics. Results reveal evidence of spatial consolidation and changing hotspot distributions over time, with emerging hotspots increasingly located in suburban transition zones and at the edges of existing cycling infrastructure. These patterns may reflect changes in mobility demand and infrastructure provision, although the absence of exposure data prevents a direct assessment of this relationship. Complementary analysis using forest-based machine learning models identifies key factors associated with hotspot formation and accident severity, including crash type, temporal patterns (e.g., day of the week), and environmental conditions such as slope and lighting. These findings highlight the value of combining spatio-temporal analysis with predictive modelling to support data-driven urban planning and targeted safety interventions. Lisbon provides a relevant case study for cities undergoing similar transitions toward sustainable transport systems. Full article

This study aimed to characterize the metabolomic profile of aqueous extracts of Hypericum perforatum (St. John’s wort) commercial samples using ¹H NMR spectroscopy, in order to evaluate their chemical composition as consumed in infusion form. Seven retail samples were extracted with water under conditions simulating tea preparation and analyzed using ¹H NMR. Metabolite identification and quantification were performed with Chenomx^® software, followed by multivariate statistical analyses including PCA and OPLS-DA. A total of 37 metabolites were identified, predominantly primary metabolites such as sugars (glucose, fructose), amino acids (glycine, asparagine), short-chain organic acids (malic, citric, and acetic acids), secondary metabolites (trigonelline, chlorogenic acid) and metabolism products (methanol, ethanol, acetic acid and acetone). Notably, key bioactive compounds traditionally associated with H. perforatum, such as hypericin and hyperforin, were not detected under the experimental conditions employed. Multivariate analyses revealed significant variability among samples, suggesting differences in composition likely related to origin, processing, or potential adulteration. These findings demonstrate that aqueous preparations of St. John’s wort differ substantially from extracts used in clinical trials, being dominated by primary metabolism rather than specialized bioactive compounds. This has important implications for the interpretation of its therapeutic effects when consumed as tea. Furthermore, the study highlights the utility of NMR-based metabolomics as a robust tool for quality assessment and authentication of herbal products. Full article

Standalone use of EAHEs for room cooling is a passive and nature-based alternative to air conditioning technology that can be used to mitigate the increase in electricity and GWP-refrigerant consumption associated with cooling in buildings. EAHEs replacing air conditioning is documented in the technical and research literature. However, for office-room cooling, EAHEs are mostly employed as a support to air conditioning systems for precooling outdoor air. The larger cooling loads and the stricter design conditions commonly used in the sizing of office rooms prevent the most commonly investigated EAHE typologies from operating effectively in standalone cooling mode. To assess the feasibility of alternative typologies, such as large-diameter EAHEs, tools that are capable of modeling the complexity of the coupled heat and moisture transfer between air and soil are particularly valuable. For detailed assessments, researchers typically turn to advanced commercial tools; however, developments in free and open-source scientific programming languages that combine symbolic computation packages with efficient numerical solvers of partial differential equations allow analyses at reduced cost that are comparable to those from commercial tools. This paper shows how one such programming language can be used to study the coupled heat and moisture transfer problem in EAHEs. Starting from the symbolic form of the mathematical problem, the numerical implementation is described and validated with monitoring data from an existing large-diameter EAHE. Using the validated computational model, the paper proceeds to study the sensitivity of load removal in EAHEs operating in standalone and precooling cooling modes, highlighting fundamental differences between both operating modes, identifying the most relevant design parameters and providing guidance on the conditions under which an EAHE enables self-sufficient cooling of office buildings. The results show how, for a hot and dry climate, standalone EAHEs with large diameters (∼1 m), buried at depths larger than 3 m, allow the removal of up to 20 kWh/m² of room sensible cooling loads, a level that is consistent with the cooling demand of low-energy office buildings. Full article

Atmospheric ammonia (NH₃) is an important alkaline precursor of secondary inorganic aerosols, but its variability in livestock- and agriculture-influenced peri-urban environments remains poorly constrained. In this study, atmospheric NH₃ was measured at a peri-urban site in Chuncheon, South Korea and its variability was examined in relation to micro-meteorology, source direction, and surface–atmosphere exchange. Mean NH₃ concentrations were 200.8 ± 91.1 ppb during the April campaign, 83.0 ± 34.4 ppb during the May campaign, and 27.9 ± 16.2 ppb during the December campaign, indicating higher NH₃ levels during the April and May campaigns than during the December campaign. Campaign-specific correlation analyses showed that the relationships between NH₃ and micrometeorological variables differed among the observation periods, with robust associations observed in April and May but not in December. Moreover, the higher NH₃ concentration in the April campaign than in the May campaign, despite the lower mean temperature, indicates that the observed variability was not controlled by temperature alone. Conditional probability function analysis showed that elevated NH₃ concentrations in the April and May campaigns were mainly associated with southwesterly winds, suggesting the influence of nearby livestock and agricultural sources. The Penman–Monteith-derived latent heat flux further showed that daytime NH₃ enhancement coincided with evaporative surface-exchange conditions potentially favorable for volatilization, although it did not directly quantify manure-derived NH₃ emissions. In contrast, the December campaign showed lower NH₃ concentrations, weaker source-directional patterns, and limited latent heat flux influence, suggesting suppressed volatilization and intermittent local accumulation under stable conditions. These results indicate that the conditions associated with short-term NH₃ variability differed among the selected campaigns, reflecting complementary influences of source direction and campaign-specific micrometeorological and surface-exchange conditions, highlighting the need for concurrent gas- and particle-phase measurements to assess potential implications for PM_2.5 formation. Full article

There are a wide range of possible applications for the collection and analysis of statistical data in sports, although their potential has not yet been fully exploited. This study focuses on the areas in which cognitive computing systems can offer advantages for sports organizations. Furthermore, it explores the extent to which media companies can use artificial intelligence to evaluate unstructured data and provide better services. Six semi-structured interviews with experts from the fields of sports, media, and information technology were evaluated using qualitative content analysis. This revealed the need for companies in both industries to adapt to rapidly changing market conditions. The speed of decision-making can be increased by collecting and analyzing large amounts of data in real time. Furthermore, relationships can be derived that were previously hidden due to the cognitive limitations of the human brain. Based on the analysis of all dimensions of athletic ability and the facets of a player’s character, team performance can be improved. In addition, it is possible to assess the extent to which an athlete’s character is compatible with a team and with which teammates he or she is likely to be a better or worse fit. Media companies are enabled to provide sports organizations with insights from the use of cognitive applications and are transforming from media companies to service providers. Full article

Introduction: Restrictive diets are increasingly encountered in pediatric practice and may be adopted voluntarily or prescribed for medical conditions. Although they can support normal growth when appropriately planned, exclusion of nutritionally important foods may increase the risk of nutrient inadequacy. This narrative review examined nutritional deficiencies and laboratory monitoring in children following plant-based, food-allergy elimination, gluten-free, ketogenic, and protein-restricted diets for inherited metabolic disorders. Methods: Targeted searches of PubMed, Scopus, and Web of Science were conducted through 30 June 2026 using pediatric, diet-specific, nutritional-status, and biomarker terms. Because this was a narrative review, the literature was selected and synthesized qualitatively rather than through a formal systematic-screening process; no fixed study count, duplicate independent screening, or formal risk-of-bias assessment was performed. Professional guidelines and position papers were prioritized when discussing monitoring considerations, while pediatric studies were used to describe dietary intake, biochemical findings, clinically manifest deficiency, and growth outcomes. Results: Nutritional risks differed according to the foods or nutrients restricted. Vitamin B12 and iron were major concerns in plant-based diets, whereas cow’s milk and multiple-food elimination increased the risk of inadequate calcium, vitamin D, iodine, protein, and energy intake. Gluten-free diets were commonly associated with low fiber, iron, folate, and B-vitamin intake, particularly when refined, non-fortified products predominated. Ketogenic dietary therapy required attention to selenium, vitamin D, bone-related minerals, carnitine in selected patients, and linear growth. In phenylketonuria and related disorders, nutritional adequacy depended strongly on protein-substitute adherence and appropriate provision of essential amino acids and micronutrients. Across all dietary patterns, laboratory results required interpretation in relation to dietary intake, growth, supplementation, inflammation, medication, and the underlying condition. Across dietary patterns, inadequate intake, biochemical abnormalities, clinically manifest deficiency, and impaired growth were considered related but distinct outcomes. Conclusions: Nutritional monitoring should be individualized and based on the actual dietary restriction and clinical risk. The principal contribution of this review is a practical, risk-based framework that links the specific dietary restriction and adequacy of replacement foods with growth, symptoms, supplementation, and targeted laboratory biomarkers. Full article

Introduction: Stiff-Person Syndrome (SPS) is a rare autoimmune neurological disorder characterized by progressive muscle rigidity and painful spasms, primarily affecting axial and proximal musculature. Its diagnosis can be challenging due to clinical overlap with other neurological conditions such as spasticity, dystonia, or functional movement disorders. The detection of antibodies against glutamic acid decarboxylase (anti-GAD) is a key biomarker that supports diagnosis. Methods: Two clinical cases of patients with manifestations consistent with classic SPS are described, and were evaluated in a specialized neurology service. Both patients underwent detailed clinical assessment, complementary studies, and serum testing for anti-GAD antibodies. Results: Both patients presented with progressive rigidity and fluctuating muscle spasms, predominantly involving axial musculature. After an extensive diagnostic workup, elevated anti-GAD antibody titers were documented in both cases, confirming the diagnosis of classic SPS. Treatment with medications enhancing GABAergic neurotransmission was associated with significant clinical improvement, evidenced by reduced rigidity and the decreased frequency of spasms. Conclusions: These cases highlight the importance of considering SPS in the differential diagnosis of progressive rigidity syndromes. Identification of anti-GAD antibodies is essential for diagnostic confirmation, and treatment that aims to enhance GABAergic neurotransmission can significantly improve symptoms and patient functionality. Full article

Background: Energy dissipation reflects the viscoelastic behavior of biological tissues and plays a key role in arterial elastic recoil and diastolic flow support. In the native aorta, efficient storage and release of mechanical energy are essential for maintaining ventriculo–aortic coupling. The energy dissipation ratio (EDR) quantifies the proportion of mechanical energy lost during a loading–unloading cycle and may provide insight into the biomechanical performance of aortic substitutes. Bovine pericardial patches (BPPs) are widely used in cardiovascular surgery for arterial reconstruction, patch angioplasty, and tubular replacement. Today, EDR has not been systematically investigated in BPPs. Methods: Forty glutaraldehyde-treated BPPs from four commercial manufacturers (n = 10 per supplier) were subjected to low-cycle fatigue testing using a uniaxial tensile system under controlled physiological conditions (37 °C). Standardized bone-shaped specimens were tested at progressive strain percentage levels. Thickness, EDR, and the percentage of specimens failing to reach progressively higher strain levels were evaluated from stress–strain hysteresis loops. Results: BPPs thickness ranged from 0.253 to 0.608 mm, with no significant differences among most groups. For the 10% strain, all BPPs reached the target deformation and demonstrated comparable EDR values. In detail, the mean EDR was 21.40 ± 7.02% for Edwards Lifesciences, 24.95 ± 6.80% for Supple Peri-Guard (Baxter), 24.99 ± 6.04% for Xenosure (LeMaitre), and 23.37 ± 6.12% for Invengenx–Tisgenx, with no statistically significant intergroup differences (p > 0.05). For the 20% strain, only 18.75% of specimens remained structurally intact, and variability increased. At 30% strain, structural failure occurred in nearly all samples. No significant orientation-dependent differences were observed. Conclusions: Commercially available BPPs exhibit similar biomechanical behavior under moderate deformation. However, tolerance to higher strain is limited. EDR analysis provides a clinically relevant parameter to assess elastic performance and may contribute to optimizing aortic substitute selection. Full article

The preservation of twentieth-century reinforced concrete buildings increasingly requires the integration of structural engineering assessment with heritage conservation principles. Although the deterioration mechanisms of reinforced concrete in marine environments have been extensively investigated, relatively few studies have examined how advanced material degradation affects the technical feasibility of preserving modern reinforced concrete heritage structures. This study addresses this gap through the structural assessment of the Patras Port Authority Building (OLPA), a reinforced concrete building constructed in the early 1970s and exposed for more than five decades to an aggressive coastal environment, providing the engineering basis for determining whether a complete code-based structural assessment is justified in accordance with KAN.EPE. and EN ISO 13822. A comprehensive inspection and testing program was carried out, including visual inspection, crack mapping, concrete core testing, carbonation-depth measurements, pH determination, chloride-content analysis, half-cell potential measurements, electrical resistivity measurements, and selective exposure of reinforcement. The engineering assessment revealed extensive deterioration of the structural system, including low concrete strength (approximately C8/10), carbonation exceeding the concrete cover, pH values between 7 and 8, chloride concentrations ranging from 0.0377% to 0.8975% by cement mass, and severe reinforcement corrosion. The measured average cross-sectional loss reached 34.5% for longitudinal reinforcement and 65.6% for transverse reinforcement (stirrups), accompanied by significant reductions in mechanical properties and ductility. It should be noted that concrete samples for chloride determination were collected at depths well beyond the reinforcement level. Additional deficiencies associated with inadequate confinement reinforcement, outdated seismic detailing, previous earthquake damage, cracking in columns and shear walls, and uncertainty regarding the geometry and condition of the foundation system further increase structural vulnerability. The engineering assessment indicates that the combined effects of long-term environmental exposure, corrosion-induced deterioration, obsolete design provisions, and existing structural deficiencies substantially reduce the reliability and seismic performance of the load-bearing system. Within this context, the study examines the implications of advanced deterioration for the preservation of reinforced concrete heritage buildings and proposes an integrated assessment framework that combines structural safety, durability, material integrity, intervention feasibility, and heritage significance. The proposed approach contributes to a more comprehensive engineering-based methodology for evaluating preservation strategies for aging reinforced concrete buildings exposed to aggressive marine environments. These findings also raise important concerns regarding the technical feasibility of preserving ageing reinforced concrete buildings located in highly seismic regions, where ensuring structural safety may require the introduction of new load-bearing elements together with the replacement of a substantial portion of the already deteriorated original material. Full article

Tumor cell invasion is a critical step in local tumor progression, recurrence, and metastasis. Conventional two-dimensional migration assays and many existing three-dimensional invasion models often assess cell migration, invasion into the extracellular matrix and matrix degradation as separate endpoints, although these processes are tightly coupled in vivo. Therefore, robust and reproducible in vitro models are needed to investigate tumor cell invasion under defined extracellular matrix conditions. We developed an agarose-based three-dimensional invasion assay, termed the Freiburg 3D invasion assay, for the simultaneous analysis of tumor cell migration, invasion, and extracellular matrix degradation. The system consists of a 2.8% agarose matrix containing defined microcavities connected by a common loading channel. Tumor cells are seeded into these microcavities, where they form compact cell aggregates. The cavities are subsequently filled with collagen type I or extracellular matrix gel. After polymerization, the matrix-containing agarose strips are transferred into parking pockets, cultured for several days, and monitored by microscopy. Invasion distance, single-cell migration, and ECM-cleared area are quantified from serial microscopic images using image analysis software. The system distinguished weakly invasive MCF7 breast cancer cells from highly invasive MDA-MB-231 cells. In addition, treatment with a protease inhibitor and irradiation reduced tumor cell invasion and extracellular matrix remodeling, demonstrating the suitability of the assay for pharmacological and radiation-response studies. The Freiburg 3D invasion assay provides a practical and reproducible three-dimensional in vitro model for analyzing tumor cell invasion and protease-associated extracellular matrix degradation. Full article

Compared to conventional signalised intersections, roundabouts are increasingly recognised for their ability to improve traffic safety and operational efficiency. However, accurately modelling their complex traffic dynamics remains challenging, particularly in multilane configurations characterised by lane-changing manoeuvres and gap-acceptance interactions. This study presents a behaviour-driven microscopic simulation framework based on agent-based modelling (ABM) for evaluating roundabout performance under varying geometric and traffic demand conditions. In the proposed framework, each vehicle is represented as an autonomous agent capable of route selection, yielding, lane-changing, and speed adaptation according to predefined behavioural rules. This enables a detailed representation of local traffic interactions and operational conflicts that are not fully captured by traditional aggregate traffic models. The simulation environment is used to analyse idealised one-, two-, and three-lane roundabout configurations and to assess the operational impact of targeted geometric modifications. The proposed methodology is further validated using real-world traffic data collected from the Brașov Central Roundabout, Romania. Simulation results demonstrate that the ABM framework can realistically reproduce traffic throughput, average speed, number of stops, and travel time under high traffic demand conditions. Furthermore, the introduction of a channelised right-turn lane resulted in measurable operational improvements, including increased average speed and reduced delay. The findings highlight the applicability of agent-based simulation as a decision-support tool for roundabout design, traffic management, and infrastructure optimisation, contributing to safer and more efficient urban mobility systems. Full article

The present pilot study examined the feasibility, acceptability, and preliminary effectiveness of a VR-based social skills intervention for children with Autism Spectrum Disorder (ASD). Five participants aged 7–17 years participated in a six-session intervention protocol, with each session lasting approximately 45 min. The study employed a randomized AB/BA crossover design, in which participants completed two sessions under an Active condition involving instructional prompts, corrective feedback, and positive reinforcement, and two sessions under a Neutral condition without prompts or reinforcement. Following the intervention phases, participants completed a Generalization session in a novel virtual environment and a Follow-up session to assess skill retention over time. The intervention incorporated two interactive VR scenarios: (a) a simulated classroom environment, where a teacher-avatar engaged participants in structured question-and-answer activities, and (b) a virtual school playground, where participants interacted with peer avatars in social communication tasks designed to promote conversational engagement, turn-taking, and social reciprocity. All avatar interactions and trial progressions were controlled by the therapist through a dedicated monitoring interface, ensuring standardized implementation across participants. Evaluation was based on qualitative observation, analysis of recorded video material, and brief post-intervention parent interviews. Preliminary findings suggest that the intervention was feasible, well accepted by participants, and associated with improvements in social communication performance, supporting the potential of VR as a complementary tool for social skills training in children with ASD. Full article

Background/Objectives: Deep learning approaches have demonstrated strong performance in breast histopathology image classification; however, reliable generalization across heterogeneous acquisition environments remains challenging due to domain shift. In clinical practice, missed malignant cases are particularly critical because they may directly affect diagnostic decisions and patient outcomes. This study systematically investigates the behavior of modern deep learning architectures and adaptation strategies under realistic cross-domain conditions, with particular emphasis on malignant case detection and false-negative reduction. Methods: Three modern architectures—ConvNeXt-Tiny, Swin-Tiny, and MaxViT-Tiny—were initially trained on a large-scale breast histopathology dataset and subsequently evaluated on the BreaKHis dataset using strict patient-level separation to avoid information leakage. Three transfer settings were investigated: direct zero-shot transfer, head-only adaptation, and full fine-tuning. Performance was evaluated independently across four magnification levels (40×, 100×, 200×, and 400×) using accuracy, precision, sensitivity, F1-score, ROC–AUC, PR–AUC, and false-negative rates. Results: Direct zero-shot transfer produced substantial performance degradation across all architectures, with mean false-negative rates ranging from 75.85% to 90.11%, highlighting the limited transferability of source-domain representations under heterogeneous acquisition conditions. Both adaptation strategies substantially improved performance and reduced missed malignant cases to below 10%. Swin-Tiny under head-only adaptation achieved the most favorable malignant detection profile, reaching a mean sensitivity of 97.36% while reducing the average false-negative rate to 2.64%. In contrast, MaxViT-Tiny achieved the highest mean ROC–AUC value (0.849) after full fine-tuning, although this did not correspond to the lowest false-negative burden. Conclusions: The findings demonstrate that maximizing global discrimination performance does not necessarily correspond to optimal malignant detection under cross-domain conditions. Sensitivity and missed-case analysis provide complementary information beyond conventional discrimination metrics and may support more informed model assessment. Furthermore, the proposed methodology provides a reproducible framework for investigating adaptation performance in AI-assisted breast histopathology systems. Full article