Search Results (5,492)

Background/Objectives: Ankylosing Spinal Disorders (ASDs) encompass a heterogeneous group of rheumatic diseases characterized by progressive ankylosis of the axial skeleton, including Ankylosing Spondylitis (AS), Diffuse Idiopathic Skeletal Hyperostosis (DISH), and Non-Radiographic Axial Spondyloarthritis (nr-AxSpA). Spinal ankylosis profoundly alters the biomechanical properties of the vertebral column, transforming it into a rigid long-bone equivalent and dramatically increasing fracture risk even after low-energy trauma. Once a fracture occurs, the long lever arm created by the ankylosed segments generates enormous mechanical stress at the fracture site, making surgical stabilization mandatory in the vast majority of cases. Long posterior instrumentation is the treatment of choice; however, no consensus exists regarding the optimal number of instrumented levels. The aim of this study is to clinically and radiologically evaluate long posterior instrumentation in the 3 + 3 (3 proximal and 3 caudal screws), 3 + 2 (3 proximal and 2 caudal screws), or 2 + 2 (2 proximal and 2 caudal screws) configuration for the treatment of traumatic ASD thoracolumbar vertebral fractures, in terms of implant failure, infection rate, and mortality. Methods: Between 2018 and 2023, 65 consecutive patients with ASD-related thoracolumbar vertebral fractures were treated at our institution. After applying pre-defined inclusion and exclusion criteria, 37 patients were enrolled. Patients were retrospectively divided into three groups according to the posterior arthrodesis configuration (notation indicates number of instrumented vertebral levels proximal + distal to the fracture: 3 + 3, 3 + 2, or 2 + 2). Radiological outcomes were assessed for loosening, screw cut-out, and implant breakage. Infection and mortality rates within 3 months from surgery were evaluated as secondary endpoints. Statistical analysis was performed using the Fisher exact test (significance set at p < 0.05). Results: Thirty-seven patients (28 males and 9 females; mean age 77 ± 7.3 years) were included, with a mean follow-up of 30 ± 5.3 months. Instrumentation configurations were as follows: 23 (3 + 3), 5 (3 + 2), and 9 (2 + 2). Three implant failures (8.1%) and four infections (10.8%) were recorded. Eleven patients died within 3 months of surgery. A statistically significant difference was found between instrumentation length and mechanical complications (p = 0.0468), while no significant difference was observed for infection (p = 1) or mortality rate (p = 0.137). Conclusions: In this exploratory retrospective cohort, the 3 + 3 configuration was associated with the lowest observed rate of implant failure in ASD thoracolumbar fractures, suggesting a potential mechanical advantage over shorter constructs that warrants confirmation in larger prospective studies. No significant correlation was found between instrumentation length and infection rate or early mortality. Prospective, multicentre studies with larger cohorts are warranted to establish definitive guidelines for instrumentation length in this challenging patient population. Full article

Background/Objectives: Type 1 diabetes mellitus (T1DM) in primary school children presents unique challenges due to developmental dependence on adults, limited self-care abilities, and the need for continuous medical supervision. These factors may adversely affect health-related quality of life (HRQoL), particularly in early educational settings. Although diabetes-specific HRQoL assessment is essential for comprehensive pediatric diabetes care, no validated Croatian language instrument has previously been available. This preliminary study aimed to evaluate the initial validity, reliability, and factor structure of the Croatian version of the Pediatric Quality of Life Inventory (PedsQL) 3.2 Diabetes Module. Methods: This cross-sectional preliminary study was conducted in a clinical pediatric diabetes care setting in Croatia and included 70 children with T1DM, aged 7–14 years, and their parents or caregivers, recruited using convenience sampling. HRQoL was assessed using the Croatian versions of the PedsQL 4.0 Generic Core Scales and the PedsQL 3.2 Diabetes Module, which was translated using a forward-backward translation procedure. Reliability was evaluated using Cronbach’s α and test–retest reliability using the intraclass correlation coefficient (ICC). Construct validity was examined using Spearman’s correlation analysis and exploratory factor analysis. Results: Children with T1DM reported higher overall HRQoL than parent proxy reports, except for the Diabetes symptoms scale of the PedsQL 3.2 Diabetes Module and the School functioning scale of the PedsQL 4.0 Generic Core Scales. Internal consistency was satisfactory across all scales (Cronbach’s α = 0.71–0.85). Spearman’s correlations between subscales and total scores were strong (ρ = 0.61–0.92). Test–retest reliability was excellent (ICC = 0.982–0.996). Exploratory factor analysis supported construct validity: Bartlett’s test of sphericity was significant for both child (χ² = 1398.57, p < 0.001) and parent reports (χ² = 1302.74, p < 0.001), and the Kaiser–Meyer–Olkin measure indicated acceptable sampling adequacy (child = 0.65; parent = 0.68). Extracted factors explained 66.30% of the variance in child reports and 61.80% in parent reports, with factor loadings ranging from 0.41 to 0.89 and 0.41 to 0.85, respectively. Conclusions: The Croatian version of the PedsQL 3.2 Diabetes Module is an initial valid, reliable, and feasible instrument for assessing diabetes-specific HRQoL in Croatian primary school children with T1DM. Its use may support systematic HRQoL monitoring and improve family-centered pediatric diabetes care. Full article

Industrial digitalization increasingly requires automated tools capable of extracting structured knowledge from complex engineering documentation, such as Piping and Instrumentation Diagrams (P&IDs). This work proposes an integrated framework that combines object detection and Large Language Models (LLMs) for automated Knowledge Graph (KG) generation. The approach enables the transformation of unstructured P&ID schematics into machine-interpretable representations, supporting data-driven analysis and decision-making. A modular pipeline is developed, including image pre-processing, symbol detection via a YOLO-based model, and identification of semantic relations between schematic elements using LLMs. The proposal also includes the definition of a reference ontology, which is exploited for the construction of the KG, and a diagram dataset designed to test the performance of the object detection model. The KG generation procedure achieves strong results in terms of image reconstruction across a wide set of industrial schematics, while also preserving the semantic integrity and completeness of the original diagrams. The proposed method represents a significant step toward the digitalization of industrial knowledge, bridging traditional engineering documentation and semantic-based technologies. Full article

The proliferation of generative artificial intelligence (GenAI) has precipitated an urgent, global reassessment of how higher education evaluates critical thinking, creative agency, and academic integrity. However, scholarly and institutional responses remain fragmented across cultural contexts, impeding the development of robust, flexible, and discipline-adaptable assessment frameworks. Responding to the imperative to move beyond the traditional standardized assessment paradigm, this study conducts a comparative discourse analysis of 5368 academic articles in Anglophone/Western scholarly discourse (Web of Science, WoS) and Chinese (CNKI). Using LDA topic modeling and Word2Vec-enhanced semantic analysis, the study identifies two divergent orientations: an Anglophone/Western discourse that frames AI as an instrument for cognitive augmentation, efficiency optimization, and functional human–AI collaboration; and a Chinese discourse that emphasizes epistemic sovereignty, the reconstruction of creative subjectivity, and systemic institutional rebuilding against technological alienation. These pathways are mapped onto a tripartite framework of Tools, Creative Subjectivity, and Organizational Ecosystems. The findings demonstrate that AI integration is culturally embedded rather than technically determined, carrying profound implications for assessment validity, academic integrity policy, and equitable access to AI-enhanced learning. The study synthesizes these insights into a culturally responsive assessment framework that redirects evaluation from standardized, product-centric outputs toward process-oriented, transparent, and ethically governed human–AI co-authorship. By centering critical autonomy, AI literacy, and epistemological diversity, this framework offers actionable strategies for inclusive assessment redesign, institutional policy development, and sustainable competency cultivation in the GenAI era. Full article

Walkability measurement has expanded from a small set of generic neighbourhood-level proxies into a heterogeneous field of indices, scored audits, perceived-environment scales, route-based instruments, and context-specific assessment instruments. This expansion has improved the sensitivity of walkability research, but it has also made it increasingly difficult to judge which instruments are appropriate for different users, walking purposes, and urban or environmental contexts. This review addresses this problem by comparing operational walkability instruments through an adapted walking-needs framework derived from the Expanded Hierarchy of Walking Needs (HoWN). Publications from 1990 to 2025 were identified through a structured search and screening workflow covering general walkability measurement, population-sensitive instruments, purpose-specific instruments, climate- and exposure-sensitive instruments, and urban-form-specific instruments. After eligibility assessment and consolidation, 46 walkability instruments were retained for comparative analysis. For this review, the framework is adapted from a behavioural hierarchy into an instrument-level comparative structure, with feasibility re-specified as basic environmental passability. The instruments are then compared across five tiers: feasibility, accessibility, safety, comfort, and pleasurability. The review shows that walkability measurement remains strongly concentrated in lower-order and functionally measurable dimensions, especially pedestrian infrastructure, destination access, connectivity, and traffic safety. By contrast, comfort, pleasurability, environmental exposure, personal security, and user-specific constraints are less consistently formalised and often appear only in specialised instruments. Population-, purpose-, climate-, and urban-form-sensitive instruments do not merely add indicators; they alter which walking needs become foundational in specific assessment scenarios. This review contributes a fit-for-purpose comparative logic for walkability measurement. It shows how a shared walking-needs framework can be used to diagnose coverage imbalance, identify scenario-specific threshold conditions, and guide the selection, adaptation, and transfer of instruments across different users, walking purposes, environmental exposures, and urban forms. Full article

The rapid expansion of photovoltaic (PV) generation has increased the need for educational and experimental platforms that allow students and researchers to study the dynamics, control strategies, and power conversion stages of grid-connected PV systems under realistic operating conditions. Although Hardware-in-the-Loop (HIL) simulation is widely used to validate power electronic converters and control algorithms, many existing platforms rely on specialized real-time simulators that limit their accessibility in academic environments. This paper presents the design and implementation of a cost-effective HIL simulation platform for grid-connected PV systems intended for research and training applications. The proposed system integrates real hardware under test within a real-time environment that emulates PV array behavior and grid conditions, combining Controller Hardware-in-the-Loop (CHIL) and Power Hardware-in-the-Loop (PHIL) techniques. A Texas Instruments C2000 microcontroller is used as the real-time digital simulator, providing an accessible alternative to conventional real-time simulation platforms. The platform architecture, the real-time PV emulator, and the experimental implementation are described and validated through simulation and experimental results. Finally, guided laboratory practices are presented to support hands-on training in PV systems and power electronics. Full article

Ultrasound (US) imaging is widely used to guide minimally invasive procedures such as percutaneous nephrolithotomy (PCNL), while electromagnetic (EM) tracking can complement US guidance by providing line-of-sight-independent instrument localization. However, US probes may distort the EM tracking field in a probe-dependent manner. This study characterized probe-induced EM interference for a conventional 3D/4D phased-array probe and a handheld wireless probe. Three experiments were conducted using an EM tracking system: spatial mapping of interference along each probe body, assessment of probe–sensor separation for the handheld probe, and evaluation of probe-induced tracking deviations in a simulated EM-guided PCNL setup with tracked needle and catheter sensors. EM-US calibration was then performed using low-interference sensor positions. The phased-array probe produced minimal disturbance, maintaining submillimetric positional and subdegree orientational precision across tested modes. Compared with the evaluated phased-array probe, the evaluated handheld wireless probe generated stronger, spatially localized interference, requiring ≥75 mm positional and ≥50 mm orientational separation to recover baseline precision. In the PCNL simulation, the phased-array probe maintained tracking stability, whereas the handheld probe introduced localized deviations. Both probes produced RMS calibration residuals below 1 mm under controlled conditions. These results provide device-specific baseline measurements and a workflow for probe-dependent interference assessment and sensor-placement optimization in EM-US navigation. Full article

In this contribution, we analyze and discuss the recently published Active Faults Greece (AFG) database highlighting how poorly constrained and inaccurate practices could impact the overall results. The analysis of the 3814 fault traces and the 892 individual faults proposed in the AFG emphasizes several critical aspects (such as lacking major geomorphological evidence, fault length exaggeration, improper reporting of blind faults, mapping of fault traces without precision, tracing and characterization of faults without field control, proposal of fault traces differing from well-mapped historical earthquake ruptures, omission and misjudgment of the literature, etc.). All these issues effectively invalidate the AFG, discredit the results presented to the public, and introduce a strong bias in any possible estimate of seismic hazard. Furthermore, we outline the criticalities caused by misleading, uncertain, or disputed data transmitted to administrators, professionals, and the public in general, who do not have the capacity or sufficient knowledge and expertise for carefully verifying the applicability of the product for seismic hazard assessment analyses. For secure fault data definition, the remote sensing/geomorphological approach at the base of the AFG could certainly be useful, but definitely not sufficient. Indeed, for the compilation of internationally recognized databases of seismogenic sources and active fault traces, the systematic and critical analysis of historical and/or instrumental seismicity, palaeoseismological, morphotectonic, geodetic, remote sensing, shallow geophysical techniques, and structural investigations would be required. Full article

This paper presents a proof-of-concept investigation into a novel hermetically sealed tunable-medium Extrinsic Fabry–Pérot Interferometer (EFPI) temperature sensor architecture. A series of tuneable-sensitivity EFPI temperature sensors is demonstrated, comprising a large-diameter fused silica diaphragm with a 800 $\mathrm{m}$ diameter, significantly exceeding conventional designs (typically ∼125 $\mathrm{m}$), with polished diaphragm thicknesses ranging from 28 to 49 $\mathrm{m}$, housed in hermetically sealed rigid melting point capillaries with a 1.8 mm internal diameter. By exploiting thermally induced pressure differentials generated by a tunable Krytox GPL 105 oil/air fill fraction within the sealed rigid cavity, the sensors demonstrate a continuously tuneable sensitivity design space spanning 0.45 to 190 $\mathrm{n}\mathrm{m}/\mathrm{K}$. An exact nonlinear thermal pressure model is derived and validated, replacing the linearised approximation which is shown to be inapplicable at fill fractions approaching unity. The low-sensitivity configuration (0.45 $\mathrm{n}\mathrm{m}/\mathrm{K}$) was characterised at the National Standards Authority of Ireland (NSAI) National Metrology Laboratory against ITS-90 fixed points: the Triple Point of Water (273.16 K) and the Gallium Fixed Point (302.9146 K), with traceability to the International Temperature Scale of 1990 (ITS-90), yielding an instrument-limited resolution of $<1.1$ mK, consistent with the metrological validation environment. The high-sensitivity configurations (21 and 190 $\mathrm{n}\mathrm{m}/\mathrm{K}$) were characterised on a laboratory bench, achieving instrument-limited theoretical resolutions of <24 $\mathrm{K}$ and $<2.6$$\mathrm{K}$ respectively, pending future metrological validation. The 190 $\mathrm{n}\mathrm{m}/\mathrm{K}$ sensitivity represents an improvement of approximately 21.7× over the closest directly comparable prior Citationutilised fusion splicing and manual polishing. Future development priorities include metrological validation of the high-sensitivity configurations, long-term stability characterisation, thermal cycling, and progression towards an all-glass hermetically sealed construction. Full article

The Fifth Industrial Revolution (Industry 5.0) requires human-centric mechanisms that preserve the integrity, reproducibility, and interpretability of AI-driven decisions in smart manufacturing. Injection molding generates heterogeneous, imbalanced, and weakly labeled process data, posing reliability and integrity risks to data-driven quality control. This study proposes an integrity-verified and reproducibility-instrumented secure machine learning framework for operating-regime analysis in injection molding that integrates (i) SHA-256-based data-integrity verification at ingestion, (ii) Pearson correlation-based feature selection, and (iii) a Gaussian Mixture Model (GMM) under a passive-adversary threat model with Transport Layer Security (TLS)-secured transmission. Evaluated on real industrial data (n = 6719 cycles, seven process variables), correlation-based feature selection retained four non-redundant variables and improved the GMM Silhouette Score from 0.274 ± 0.075 (all features) to 0.323 ± 0.014 (95% CI [0.318, 0.329]), a +18.2% relative improvement (paired t(29) = 3.39, p = 0.002; Cohen’s d = 0.62; Wilcoxon p = 0.022), while lowering the Davies–Bouldin Index from 1.63 to 1.17. The Silhouette standard deviation of 0.014 over 30 seeds meets the σ ≤ 0.02 reproducibility target. The GMM resolves four interpretable operating regimes—one low-load regime consistent with nominal operation and three elevated-load regimes (left-side, right-side, and bilateral)—with operator-readable per-variable signatures. Relative to hard-partition and projection baselines, the GMM is not Silhouette-optimal but provides an interpretable, generative regime model that meets the σ ≤ 0.02 reproducibility target. The framework operationalizes human-centric manufacturing security as measurable integrity, reproducibility, and interpretability. Full article

The aim of this study was to evaluate the effect of incorporating button mushroom (Agaricus bisporus) powder (5% and 10%, w/w) and two expansion methods (deep-fat frying and Fmicrowaving) on the nutritional, bioactive, sensory, and physical properties of third-generation snacks. Mushroom addition increased the contents of protein, raw fiber, ash and polyphenols compounds, particularly caffeic acid and chlorogenic acid derivatives. The highest nutritional value was observed in microwave-expanded snacks containing 10% mushroom powder, which showed increased protein (4.59%), ash (2.5%) and raw fiber (3.31%) contents combined with very low fat level (0.14%) Microwave expansion promoted better retention of bioactive compounds with the highest total polyphenol content reaching 195.48 mg/kg. Instrumental sensory analyses revealed that mushroom addition intensified bitter and metallic taste attributes and enhanced roasted and earthy aroma notes associated with increased levels of pyrazines, phenols, alcohols, and acids. Moreover, mushroom incorporation reduced expansion at higher inclusion levels, altered texture, and caused a darker color. Overall, dried mushroom powder proved to be an effective potential functional ingredient that improved the nutritional and antioxidant value of third-generation snacks, while microwave expansion offered superior retention of bioactive compounds and more favorable physical characteristics. Full article

Introduction: Traditional instruments for measuring lifelong learning of health professionals fail to capture digital-age learning, creating a critical measurement disconnect. To address this gap, we developed a 16-item Self-Pursuits, Aspirations, and Knowledge Endeavors in the Digital Era (SPArKED) instrument. Methods: To gather validity evidence for SPArKED, a cross-sectional survey was deployed to health professional students (n = 558). The survey questionnaire included: SPArKED, Jefferson scale of lifelong learning for students in health professions, basic psychological needs satisfaction scale, and human–computer trust scale assessing students’ trust in generative technology to support lifelong learning. Exploratory factor analysis (EFA) and correlation analysis were performed. Results: The EFA of the SPArKED revealed a three-component structure: networked learning, i-learning (individual mastery), and AI-powered learning, together explaining 55% of the total variance. The SPArKED demonstrated good internal consistency (α = 0.86) and convergent validity with the Jefferson scale of lifelong learning (r = 0.75). The correlations between SPArKED and psychological needs satisfaction scores were moderately high: autonomy (r = 0.50), competence (r = 0.48), and relatedness (r = 0.51). SPArKED had a higher correlation with students’ trust in generative technology to support lifelong learning than the Jefferson scale (r = 0.52 and r = 0.32, respectively). Conclusions: Compared to the Jefferson scale, the SPArKED instrument appears to better capture digital-age learning behaviors among students in health professions. By assessing these evolving behaviors in learners, education programs can better guide future health practitioners in developing desired lifelong learning competencies and digital literacies. Future research should gather validity evidence for SPArKED across diverse learner samples and educational stages, informing a critical re-assessment of established instruments in the rapidly evolving learning landscape. Full article

Background: The differential diagnosis of respiratory distress syndrome (RDS) and congenital pneumonia (CP) in newborns remains a complex clinical challenge due to the similarity in their clinical manifestations and their potential to coexist. Objective: We aimed to determine differential diagnostic predictors of RDS and CP in newborns by using mathematical modeling and machine learning methods. Methods: A retrospective cohort study was conducted; de-identified medical records of 244 newborns (97 with RDS and 143 with CP) were collected to assess clinical, anamnestic, laboratory, and instrumental data by applying multiple regression analysis, ROC analysis, logistic regression models, and Random Forest. Results: Patients with CP presented with a more severe condition at admission (57.1% vs. 23.3%; p = 0.023), required mechanical ventilation (MV) more frequently (22.4% vs. 8.2%; p = 0.004), and were more often transferred to the intensive care unit (ICU) (77.3% vs. 55.7%; p = 0.001). They further had lower hemoglobin levels (151 ± 28 g/L vs. 164 ± 31 g/L; p = 0.001) and red blood cell counts (p = 0.021). Regression analysis demonstrated that the severity of the condition and the presence of cerebral ischemia were dependent on hemoglobin levels in the case of CP, while gestational age played a determining role in RDS. The machine learning models achieved an accuracy of 0.69 and an area under the curve (AUC) of 0.82 (Random Forest). The key predictors for differential diagnosis of RDS were low gestational age, hyperbilirubinemia, and congenital heart defects, while for CP, they were hemoglobin < 151 g/L, lymphocytes < 4.8 × 10³/μL, oxygen saturation < 90–91%, and cerebral ischemia. Conclusions: The use of mathematical modeling methods made it possible to identify prognostically significant predictors for the differential diagnosis of RDS and CP. The resulting models are best viewed as proof-of-concept tools for hypothesis generation and future research, as external validation is necessary before they can be considered for clinical use. Full article

Urban logistics supports retail, public services, healthcare, construction, and household consumption, but delivery decisions often fail to account for the social costs imposed on congested roads, curbside space, air quality, noise exposure, safety, and public-space use. This study addresses the decision-maker mismatch that arises when the vehicle operator physically generates an externality while receivers, shippers, platforms, building managers, or consumers control delivery timing, shipment fragmentation, service level, fleet choice, and receiving conditions. It develops a Pigouvian policy framework that integrates dynamic road-user charging, curbside pricing, emission-based instruments, off-hour delivery incentives, clean-vehicle support, consolidation incentives, and revenue recycling. The study combines a structured narrative synthesis, decision-maker mapping, an illustrative Korean urban logistics scenario, cost–benefit comparison, and deterministic sensitivity screening. Under the stated scenario assumptions, a carrier-only peak charge reduces monetized daily external costs by only 1.3%, whereas a combined road-and-curbside package reduces them by 12.5%, and an integrated Pigouvian package reduces them by 23.6%. Sensitivity results preserve this ranking. The paper contributes a transferable policy design architecture for internalizing urban logistics externalities while maintaining freight functionality and stakeholder acceptability. Full article

This article presents a structured spectral-stability assessment of an nJ-class ultrafast fiber laser generating femtosecond pulses with an approximate pulse duration of 115 fs, based on an ensemble of 61 consecutively acquired optical spectra. The study is motivated by the practical need to extract reliable short-sequence stability information from routine compact-spectrometer exports without requiring a separate pulse-diagnostic instrument at the initial assessment stage. For each spectrum, peak wavelength, centroid wavelength, FWHM bandwidth, integrated spectral area, correlation with the ensemble mean spectrum, and RMS deviation were calculated. Principal component analysis (PCA) was then applied to reduce the full spectral ensemble into a compact diagnostic space and to identify the dominant modes of residual spectral variation. The analyzed spectra yielded a peak wavelength of 775.31 ± 0.19 nm, a FWHM bandwidth of 7.95 ± 0.20 nm, an integrated spectral area of 10.43 ± 0.42 a.u.·nm, and a correlation with the mean spectrum of 0.99957 ± 0.00019, confirming a highly repeatable spectral envelope. PCA showed that PC1 and PC2 explained 66.50% and 12.60% of the variance, respectively, while PC3 contributed only 1.90%, indicating that the measured variability was weak and largely low-dimensional. These results demonstrate that consecutively exported optical spectra can provide a defensible and physically interpretable short-sequence stability assessment of ultrafast femtosecond fiber lasers, offering a practical route for routine monitoring, early-stage diagnostics, and future integration with simultaneous temporal and spectral characterization. Full article