Search Results (2,784)

Purpose: This paper aimed to identify dosimetric, clinical, and CT-based densitometric predictors of radiation-induced pulmonary events in breast cancer patients treated with moderately hypofractionated radiotherapy. Materials and Methods: A single-institution cohort of 1172 consecutive patients treated with 3D conformal whole-breast radiotherapy (40 Gy/15 fractions) before 2017 was analyzed. Ipsilateral lung DVHs and CT densitometry metrics were extracted. Clinical variables and cardiac calcification (CAC) scores (Agatston_score, CAC_volume, Max_HU_Heart) were included. Univariable and multivariable logistic regressions were performed; collinearity was assessed via Spearman correlation and VIF. Optimal thresholds were derived using the Youden index. Internal validation used bootstrap resampling. Results: After a median follow-up of 6.5 years, 18 patients developed moderate/severe pulmonary events. The univariable analysis showed associations with lung densitometric features (median/mean HU, 10th percentile, the lung volume with HU < −850 (V850)), V37 Gy, lung volume, and CAC scores. Lower lung HU values and larger lung volumes were linked to higher risk. The best models combined V850 (or lung volume) with a CAC metric. The model including V850 > 175 cc and continuous Max_HU_Heart achieved an optimism-corrected AUC of 0.68, with good fit and calibration (Hosmer–Lemeshow p = 0.33, R² = 0.847). Conclusions: The baseline cardiopulmonary status, captured by lung and heart densitometry, predicts pulmonary toxicity better than dosimetry. V850 > 175 cc was associated with a 4-fold higher risk, consistent with air trapping, known as a marker of emphysema. Full article

Fast and scalable lithium-ion cell diagnostics require measurements that are shorter and simpler than full impedance analysis, yet richer and more interpretable than single scalar resistance indicators or raw waveform classification alone. This paper introduces a practical recovery stamp screening method in which short post-load voltage recovery intervals after pulse and pulse–multisine excitation are treated as compact diagnostic events, rather than as single resistance-like indices or parameter identification segments. For this purpose, a constrained two-timescale relaxation model is introduced to retain fast and slower recovery contributions in a low-dimensional form. Using laboratory measurements on two lithium-ion pouch cell families based on nickel manganese cobalt oxide (NMC)/graphite and LiFePO₄/graphite chemistry, each retained load removal event is converted into a signed, current-normalized recovery curve and parameterized by the proposed model. The fitted parameters provide a compact, physics-informed recovery state, while the resampled local waveform preserves transition morphology and short-time relaxation structure that are not fully retained by compact variables alone. These two inputs are evaluated separately and jointly in ordered event sequences under a reference-centered binary screening formulation. The curated dataset comprises 48 original recovery events. Local label-preserving augmentation is applied as training-side regularization, yielding 490 event instances and 230 event sequences. A scalar recovery-amplitude baseline has reached balanced accuracies of 0.833 without and 0.929 with operating context, whereas the best deep learning result is obtained only when fitted variables and waveform are combined. In that setting, TimesNet has reached a median validation balanced accuracy of 0.938. These findings show that post-load polarization recovery contains diagnostically useful information beyond scalar amplitude measures and can support rapid, interpretable reference-deviation screening. Full article

Count outcomes are commonly analyzed using Poisson regression, but empirical data often exhibit overdispersion, excess ties, heaping, or other departures from the Poisson distribution. This paper evaluates a rank-Poisson transformation, denoted poisrank, designed to map observed counts onto Poisson quantiles before fitting a Poisson regression model. Our goal is to test whether a rank-Poisson transformation offers a useful general-purpose strategy when count data do not satisfy Poisson assumptions. Using an empirical example and a Monte Carlo simulation study with Poisson, overdispersed, rounded, and gapped count distributions, we compared Poisson regression on raw counts, Poisson regression after the poisrank transformation, quasi-Poisson regression, and additional comparison approaches. Although the transformation made the marginal distribution more similar to a Poisson distribution, it generally did not outperform standard alternatives for inference. In particular, quasi-Poisson regression more consistently maintained appropriate rejection rates with overdispersion whereas poisrank tended to be conservative and often reduced power. These findings suggest that the rank-Poisson transformation is better understood as an exploratory robustness device than as a preferred replacement for established count-data methods. Full article

Alzheimer’s disease (AD) is increasingly recognized as a biological continuum characterized by early neuropathological and molecular changes that precede the onset of clinical symptoms. Fluid biomarkers have transformed the diagnostic landscape by enabling the in vivo detection of core AD pathologies, particularly amyloid-β deposition and tau-related neurodegeneration. Despite the rapid expansion of candidate biomarkers, however, only a limited number have successfully translated into clinical practice. Discovery-phase approaches, primarily driven by mass spectrometry-based proteomics, enable the unbiased identification of novel biomarker candidates across multiple biological pathways. Research-phase methods, including immunoassays such as enzyme-linked immunosorbent assay (ELISA), electrochemiluminescence immunoassays (ECLIA), microfluidic platforms, and ultrasensitive technologies such as single-molecule array (SIMOA), support analytical and clinical validation in well-characterized cohorts. Clinical implementation has been advanced by fully automated platforms, including Lumipulse and Elecsys, which have obtained regulatory approval for cerebrospinal fluid biomarkers and, more recently, blood-based biomarkers. These developments represent a paradigm shift toward minimally invasive and scalable diagnostic strategies that may reduce dependence on neuroimaging techniques. Nevertheless, major challenges remain, including assay standardization, inter-platform variability, demonstration of clinical utility, and barriers to widespread clinical adoption. This review provides a comprehensive overview of analytical methods used to measure AD fluid biomarkers in cerebrospinal fluid and plasma, structured according to the biomarker development pipeline from discovery to clinical implementation. Overall, the review highlights a fit-for-purpose approach to biomarker development and emphasizes the complementary roles of diverse analytical technologies across the different phases of biomarker translation. Full article

STEM learning unfolds over many years. It is shaped by changing contexts, transitions, and occasional breaks. However, much of the existing work still focuses on single stages or isolated factors. This article introduces the E³ Framework. Its purpose is to provide a language for examining why some STEM trajectories endure, why others fade, and what kinds of ecological alignment allow learning to remain viable in the flow of real life. Based on a systemic approach, we aim to explain how STEM participation is preserved over time. This framework describes stability as the result of interactions among three ecological domains: resources, regulation, and time. We identify five key functions—robustness, regulatory re-alignment, renewal, informational persistence, and environmental fit. These functions show how engagement holds steady or recovers as circumstances shift. The E³ Framework offers a way to analyze how supports, feedback loops, and time-related structures either come together or fall apart. We provide simple design guidelines and matrices to show how educators and policymakers can better support STEM trajectories. Full article

Rooftop photovoltaic (PV) potential assessments have advanced significantly through high-resolution geospatial methods. However, most studies remain focused on well-planned urban environments and primarily consider geometric or radiative factors, often neglecting material constraints and deployment realism in heterogeneous cities of the Global South. This study addresses these gaps by developing an automated LiDAR- and GIS-based methodology to estimate rooftop PV potential in Cartagena, Colombia, explicitly integrating cadastral constraints, geometric feasibility, and roof material exclusion. The workflow combines LiDAR-derived elevation data, parcel-based segmentation, slope and aspect filtering, and post-processing techniques to identify PV-suitable rooftops, validated against 482 manually delineated polygons. The optimal configuration (45° slope threshold; 0.25 m buffer) achieved RMSE values of 6.79° (slope) and 20.95° (aspect). A geometry-constrained panel fitting algorithm estimated 3,599,631 panels across 146,091 rooftops, representing 7.06 km² of suitable area. Compared to simple area-based methods, this approach reduced capacity estimates by approximately 15.3%, demonstrating the importance of geometric realism. A key contribution is the integration of asbestos-cement (AC) roof exclusion, which reduced suitable rooftop area by ~65%, resulting in a final capacity of 1,281,202 panels. Estimated annual generation decreased from 1891.9 GWh/year to 673.4 GWh/year, equivalent to supplying 53.4–126.8% of Cartagena’s households. The proposed methodology provides a scalable framework for realistic urban PV assessment and introduces a dual-purpose planning tool that enables authorities to both prioritize solar deployment and identify areas requiring roof remediation, supporting safer and more controlled energy transitions in developing-country cities. Full article

Background: Self-efficacy is an important psychological factor for healthy aging, but how long-term sport participation builds self-efficacy in older adults is not fully understood. This study explored how playing tennis for many years shapes self-efficacy in older South Korean adults and identified the key mechanisms. Methods: In-depth interviews were conducted with 11 older male adults (aged 65–75 years) who had played tennis for 15–25 years and remained active at least twice per week. Participants were recruited from tennis clubs in South Korea through purposive sampling. Data were analyzed using Braun and Clarke’s reflexive thematic analysis. Results: Four themes emerged. (1) Mastery Through Progressive Achievement: gradual skill development and competitive success were perceived to support confidence that extended beyond the tennis court; (2) Social Embeddedness and Collective Efficacy: peer encouragement and observing similar others succeed were described as supporting participants’ belief in their own capabilities; (3) Physical Vitality as Confidence Foundation: sustained physical fitness and functional independence derived from tennis participation were perceived to support broader self-confidence in daily life; and (4) Mental Resilience and Cognitive Engagement: the strategic demands of tennis and its stress-relieving effects may contribute to psychological resilience and a continued sense of purpose in later life. Conclusions: Long-term tennis participation was perceived to support self-efficacy through multiple interconnected pathways consistent with Bandura’s social cognitive theory. These findings suggest that structured, community-based tennis programs may contribute to supporting psychological well-being and promoting healthy aging in rapidly aging societies. Full article

This study investigates the removal of Cd²⁺ ions from aqueous solutions using hard magnetic strontium hexaferrite (SrFe₁₂O₁₉) nanoparticles synthesized via the Pechini method, with an average particle size of 116 nm. The material was successfully obtained at a relatively low calcination temperature of 900 °C. The crystalline structure of the hexaferrite particles was investigated by X-ray diffraction, confirming SrFe₁₂O₁₉ crystalline structure. The powder samples were also characterized by Fourier transform infrared spectroscopy (FTIR). The morphology and size distribution were studied using scanning electron microscopy (SEM). Furthermore, the magnetic properties of strontium hexaferrite contribute significantly to adsorption and removal processes, primarily by acting as a recoverable magnetic adsorbent. The ferromagnetic material, with its high saturation magnetization and coercivity, responds rapidly to external magnets, facilitating the removal of contaminants and maintaining its magnetic characteristics even in complex chemical environments. For this purpose, its magnetic behavior was also studied using vibrating sample magnetometry (VSM). The experimental adsorption results were successfully modeled using PFO (pseudo—first—order) and PSO (pseudo—second—order) along with Freundlich and Langmuir isotherms, to fit the experimental adsorption data of the Cd(II) salt from the 0.1 and 0.2 mg samples at room temperature for two quantities of strontium hexaferrite at times ranging from 2.5 to 60 min. The results indicate that the strontium hexaferrite nanoparticles exhibited a 90% removal efficiency, which was the highest value. Additionally, the strontium hexaferrite can be magnetically recovered along with the adsorbed cadmium, representing a more efficient way to remediate water. Full article

Background: Problematic smartphone use has been increasingly reported among university students, including nursing students, yet the availability of brief, culturally appropriate, and psychometrically sound assessment instruments in Greece remains limited. Aim: This study aimed to translate and culturally adapt the Greek version of the Smartphone Addiction Scale—Short Version (SAS-SV) and to evaluate its psychometric properties, including internal structure, reliability, and convergent validity with the Mobile Phone Problem Use Scale-10, among nursing students in Greece. Methods: In a cross-sectional study, nursing students from multiple departments across Greece (N = 331) completed the Greek SAS-SV, distributed online via official university forums, student groups, and institutional social media pages, between September 2025 and November 2025. We conducted exploratory and confirmatory factor analyses using polychoric correlations, examined convergent validity, performed exploratory comparisons across demographic characteristics, and estimated the reliability of the SAS-SV. Results: Confirmatory factor analysis was consistent with a one-factor structure and showed acceptable model fit. Internal consistency was high (Cronbach’s α was 0.862 and McDonald’s omega was 0.891), with supportive evidence of convergent validity through its correlation with the MPPUS-10 (Spearman’s ρ = 0.772, p < 0.001). Conclusions: The Greek SAS-SV showed acceptable psychometric properties among nursing students and seems appropriate for research purposes in Greece. Full article

The stage separation process, though often completed within one second, plays a critical role in determining the overall success and safety of the launch mission. The process of host stage separation is simulated to study the flow field evolution and the impact on the lower-stage. Overset mesh is utilized together with a novel adaptive mesh refinement sensor for the purpose of adapting to the relative motion. A third-order scheme is adopted in spatial discretization, and the simulation results fit well with the experiment data. The results show that the initial shockwave oscillated back and forth in the cavity of the lower-stage, leading to sustained oscillations in the forces of the lower-stage. Based on the monitor data, the force acting on the lower-stage exhibits five phases. Compared with former research, a longer interstage results in two more obvious oscillation phases. The pressure distribution on the forward dome of the lower-stage is also studied. Full article

The in-service assessment of marine propulsion engines requires more than nominal rating comparison because operating severity is shaped by propeller demand, resistance growth, air-path response, and thermal state. This study develops a quantitative benchmarking method for the regime-dependent performance assessment of a low-speed two-stroke Wärtsilä 6RT-flex58T-D engine installed on a 31,000 DWT multi-purpose container vessel. The method integrates certified sea-trial measurements, endurance-test records, manufacturer load-diagram constraints, and a 15% service-margin projection within one reference framework. Three representative regimes are evaluated: a measured light-running baseline (SR1), a measured thermally stabilised sustained regime (SR2), and a projected heavy-running regime derived from the baseline using a 15% sea-margin assumption (R2). Comparison is performed using indicators of operating-point position, shaft torque, propeller-law consistency, selected air-path and thermal variables, load-diagram proximity, and corrected specific fuel oil consumption where available. The SR1 baseline followed the fitted propeller law with deviations not exceeding 1.18%, confirming a coherent light-running reference. In SR2, corrected SFOC decreased from 174.4 to 172.0 g/kWh, while the exhaust temperature before turbine increased from 359 °C to 435 °C, and the corresponding thermal margin decreased from 156 °C to 80 °C. Under the +15% service-margin projection, the required shaft power at the 100% trial point increased from 12,046.0 to 13,852.9 kW, exceeding the 13,560 kW installation MCR by 2.2%, with corresponding 15% increases in torque and BMEP. These results demonstrate that measured baseline operation, sustained-load severity, and projected heavy-running demand can be distinguished quantitatively within one installation-specific load-diagram-based benchmarking framework. Full article

Air knives are extensively employed in many cold rolling or tin plate production lines for drying purposes. Generally, these systems are oversized, resulting in excessive energy consumption, a consequence of insufficient understanding of their performance. Considering this deficiency, an empirical exploration was initiated to analyze the functionality of an air knife oriented perpendicularly to a given surface. Given the scarcity of information within the current body of literature, particular emphasis was placed on the regions affected by the finite dimensions of the device. Impingement pressure distributions were measured at the midspan plane and planes parallel to the midspan but extending beyond the projection of the air knife. The midspan impingement pressure profile aligned with the established bell-shaped distribution, whereas the outcomes beyond the air knife’s projection conformed to an analytically fitted similarity principle. Consequently, the mathematical formulations introduced in this study facilitate the mapping of the impingement pressure within the whole impingement plane, encompassing areas influenced by the finite length of the air knife, thereby representing the innovative contribution of this research. Full article

Background/Objectives: The rapid expansion of New Approach Methodologies (NAMs) is transforming oral biopharmaceutics by offering mechanistically rich, human-relevant tools that can reduce reliance on animal testing while improving translational confidence. Regulatory agencies, including the Food and Drug Administration (FDA) and the European Medicines Agency (EMA), are increasingly open to NAM-generated evidence, provided that methods are fit-for-purpose and scientifically justified. This review synthesizes current advances and evaluates how NAMs can be integrated across drug-development stages to enhance the prediction of oral absorption, formulation performance, and regulatory decision-making. Methods: A comprehensive literature review was conducted across classical and emerging methodologies, including in vitro permeability and solubility models, organoids, organ-on-a-chip (OoC) systems, machine learning frameworks, and mechanistic approaches such as the physiologically based pharmacokinetic (PBPK) and biopharmaceutics (PBBM) models. Emphasis was placed on physiological relevance, predictive performance, validation status, and regulatory applicability. Results: Classical tools remain essential for the Biopharmaceutics Classification System (BCS)-based biowaivers and risk-based assessments, yet they often lack physiological fidelity. NAMs provide enhanced representation of intestinal architecture, hydrodynamics, transporter activity, and metabolism. Organoids and microphysiological systems generate high-quality permeability and metabolic data, while computational NAMs enable scalable prediction of ADME properties and formulation behavior. When integrated into PBPK/PBBM models, these methods have great potential in predicting in vivo performance in humans. Evidence demonstrates that NAMs can refine, reduce, and, in specific contexts, replace animal studies without compromising scientific rigor. Conclusions: NAMs complement, rather than displace, classical biopharmaceutic tools, enabling a more mechanistic, human-centered, and ethically responsible framework for drug development. Their effective implementation will depend on continued validation, standardization, and regulatory harmonization as the field transitions toward fully NAM-supported biopharmaceutical assessment. Full article

The flipped classroom model is increasingly recognised as a viable alternative to traditional teaching methods; however, its effectiveness largely depends on factors such as instructional design, implementation strategies, and the specific educational context. The current literature does not adequately address specific strategies for teachers to implement the flipped classroom model in practice. Therefore, the purpose of this study was twofold: first, to design a teaching sequence as a practical product that can be used to deliver lessons, and second, to assess the effectiveness of the teaching sequence as a tool for enhancing knowledge development in geometrical optics. The participants were third-year physical science students (N = 93) enrolled in a Bachelor of Education degree programme, who took a geometrical optics course lasting one semester. The methodology employed was design-based research, and this article provides a detailed description of the first iteration, including how the teaching sequence evolved over four years. The initial results obtained from tests performed during and after the implementation of the initial iteration of the teaching sequence showed that the teaching sequence was more effective in enhancing students’ recall of facts and basic concepts than in promoting their ability to explain ideas or concepts and apply that knowledge to new situations. The teaching sequence was refined over four years, suggesting that while the flipped classroom model is a viable tool in physics teacher education, it is not a one-size-fits-all solution. Instead, a continuously evolving, context-sensitive design is necessary. Full article

Enterprise spatial data supply chains (SDSCs) increasingly support high-stakes decision-making; yet, the provenance in operational geospatial systems is often fragmented across metadata records, workflow logs, and application-specific formats. This limits traceability, reproducibility, auditability, and fitness-for-purpose assessment, particularly when organisations need to explain how spatial products were created, with which parameters, spatial references, and dependencies. This study proposes the Enterprise Spatial Data Provenance Knowledge Infrastructure (ESDPKI), a standards-aligned framework that treats provenance as enterprise knowledge infrastructure rather than passive metadata. Using a design science research approach, the study synthesised the literature-derived requirements, standards-based interoperability constraints, and representative spatial data supply chain workflows to develop four artefacts: a six-layer reference architecture, the GeoPROV minimal semantic profile, a validation-gated ingestion and governance mechanism, and a reproducible evaluation blueprint with service-level objectives. Together, these artefacts support provenance capture, semantic normalisation, validation, queryable lineage, catalogue linkage, and policy-aware disclosure across enterprise environments. The resulting design makes geospatial operations, parameters, geometry, and coordinate reference system context machine-actionable, enabling lineage tracing, impact analysis, discovery-time fitness-for-purpose assessment, and stronger governance at scale. ESDPKI therefore provides a coherent architectural pathway for operationalising trustworthy, explainable, and scalable spatial provenance in enterprise settings. Full article