Search Results (85)

Background/Objective: Precise preprocedural planning is essential for the safety and efficacy of structural heart interventions. Conventional imaging modalities, while informative, do not allow for direct and accurate visualization, limiting procedural predictability. We aimed to develop and validate a high-resolution micro-computed tomography (microCT)-based reverse modeling workflow that integrates digital reconstructions of metallic cardiac devices into patient imaging datasets, enabling accurate, patient-specific virtual simulation for procedural planning. Methods: Clinical-grade transcatheter heart valves, septal defect occluders, patent ductus arteriosus occluders, left atrial appendage closure devices, and coronary stents were scanned using microCT (36.9 μm resolution). Agreement was assessed by intra-class correlation coefficients (ICC) and Bland–Altman analyses. Device geometries were reconstructed into 3D stereolithography files and virtually implanted within multislice CT datasets using dedicated software. Results: Devices were successfully reverse-modeled with high geometric fidelity, showing negligible dimensional deviations from manufacturer specifications (mean ΔDistance range: −0.20 to +0.20 mm). Simulated measurements demonstrated excellent concordance with postprocedural imaging (ICC 0.90–0.96). The workflow accurately predicted clinically relevant parameters such as valve-to-coronary distances and implantation depths. Notably, preprocedural simulation identified a case at high risk of coronary obstruction, confirmed clinically and managed successfully. Conclusions: The microCT-based reverse modeling workflow offers a rapid, reproducible, and clinically relevant method for patient-specific simulation in structural heart interventions. By preserving anatomical fidelity and providing detailed device–tissue spatial visualization, this approach enhances preprocedural planning accuracy, risk stratification, and procedural safety. Its resource-efficient digital nature facilitates broad adoption and iterative simulation. Full article

This article presents a novel methodology for evaluating the safety of forklift operations in intralogistics systems using a multi-user simulation model integrated with virtual reality (MUSM-VR). Set against the backdrop of persistent safety challenges in warehouse environments, particularly for inexperienced operators, the study addresses the need for proactive safety assessment tools. The authors develop a simulation framework within the FlexSim 24.2 environment, enhanced by proprietary VR and server integration libraries, enabling interactive, immersive testing of warehouse layouts and operational scenarios. Through literature review and analysis of risk factors, the methodology incorporates human, infrastructural, organizational, and technical dimensions of forklift safety. A case study involving inexperienced participants demonstrates the model’s capability to identify high-risk areas, assess operator behavior, and evaluate the impact of visibility and speed parameters on collision risk. Results highlight the effectiveness of MUSM-VR in pinpointing hazardous intersections and inform design recommendations such as optimal speed limits and layout modifications. The study concludes that MUSM-VR not only facilitates early-stage safety analysis but also supports ergonomic design, operator training, and iterative testing of preventive measures, aligning with Industry 4.0 and 5.0 paradigms. The integration of immersive simulation into design and safety workflows marks a significant advancement in intralogistics system development. Full article

Cold-climate urban neighborhoods face mounting energy and thermal risks from extreme weather and power outages, creating trade-offs between different resilience capacities and objectives. This study develops a scalable, data-driven decision-making tool to support early-stage prioritization of resilience strategies at both the building component and neighborhood levels. A database of 48 active and passive strategies was systematically linked to 14 resilience objectives, reflecting energy- and thermally oriented capacities. Each strategy–objective pair was qualitatively assessed through a literature review and translated into probability distributions. Monte Carlo simulations (10,000 iterations) were performed to generate possible outcomes and several scores were calculated. Comparative scenario analysis—spanning holistic, short-term, long-term, energy-oriented, and thermally oriented perspectives—highlighted distinct adoption patterns. Active energy strategies, such as ESS, decentralized RES, microgrids, and CHP, consistently achieved the highest adoption (A) scores across levels and scenarios. Several passive measures, including green roofs, natural ventilation with passive heat recovery, and responsive glazing, also demonstrated strong multi-objective performance and outage resilience. A case study application integrated stakeholder-specific objective weightings, revealing convergent strategies suitable for immediate adoption and divergent ones requiring negotiation. This tool provides an adaptable probabilistic foundation for evaluating resilience strategies under uncertainty. Full article

Calf disbudding is a routine practice in the dairy industry to prevent horn growth and reduce the risk of injury to animals and handlers. However, growing public concern about animal welfare has raised questions about the acceptability of common disbudding methods. This study explored public perceptions of caustic paste and hot-iron disbudding using a mixed methods approach. Quantitative survey analyses captured measurable trends while iterative qualitative analysis explored the underlying reasons behind participant’s attitudes. A convenience sample with a total of 511 Texas resident participants completed a 44-item online survey that included demographic questions, the Animal Attitude Scale (AAS-10), and image-based evaluations of the two techniques. Quantitative analysis using factor analysis and multiple regression revealed that concern for animal welfare and justification for animal use were the most significant predictors (p < 0.001) of method acceptability, with caustic paste generally viewed as more humane. Qualitative responses reinforced these results, identifying themes of animal suffering, ethical concerns, and a widespread lack of public knowledge. While caustic paste was preferred, skepticism toward hot-iron disbudding was more pronounced among low-income participants. Nonetheless, when properly performed with pain control, hot-iron disbudding is often considered a more controlled and welfare-conscious method due to faster healing times and reduced risk of injury to other animals from paste exposure. These findings underscore the need for consumer education and transparent communication from the dairy industry. Full article

The misuse of drone technology poses significant risks to public and personal safety, emphasizing the need for accurate and efficient aerial target detection to prevent detection failures due to randomly distributed airborne targets and mitigate interference from undesired directions. Unlike conventional beam-synthesis techniques that often require either a large number of array elements or iterative numerical optimization, the proposed method analytically derives the excitation distribution by solving a newly formulated weighted-constraint problem, thereby fully accounting for mutual coupling between elements and ensuring both computational efficiency and design accuracy. In this communication, a 10 × 4 planar microstrip antenna array with a wide flat-top beam and low sidelobe is designed based on the extended method of maximum power transmission efficiency. The optimized distribution of excitations for the antenna array, which achieves a shaped beam with uniform gain over the desired angular range while suppressing sidelobe levels (SLLs) outside the shaped region, is derived by analytically solving a newly formulated weighted constraint problem. To reduce the number of antenna elements and enhance radiation characteristics, the inter-element spacings in the E-plane and H-plane are set to 0.55 λ₀ and 0.75 λ₀, where λ₀ is the free-space wavelength at 3.5 GHz. Measurement results indicate that the flat-top beam in the E-plane has a wide half-power beamwidth (HPBW) of 51.2° and a low SLL of −30.1 dB, while the beam in the H-plane has a narrow HPBW of 20.1° and a low SLL of −30.5 dB, thereby demonstrating its capability in aerial target detection and airborne tracking applications. Full article

Healthcare professionals (namely, occupational therapists) face ergonomic risk factors that may lead to work-related musculoskeletal disorders (WRMSD). Ergonomic assessments are crucial to mitigate this occupational issue. Wearable devices are a potential solution for such assessments, providing continuous measurement of biomechanical and physiological parameters. Ergo4workers (E4W) is a mobile application designed to integrate data from independent wearable sensors—motion capture system, surface electromyography, force platform, and smartwatch—to provide an overview of the posture and workload of occupational therapists. It can help identify poor work practices and raise awareness about ergonomic risk factors. This paper describes the development of E4W by following a User-Centred Design (UCD) approach. The initial stage focused on specifying the context of use in collaboration with six occupational therapists. Then the app was implemented using WordPress. Three iterations of the UCD cycle were performed. The usability test of prototype 1 was carried out in a laboratory environment, while the others were tested in a real healthcare work environment. The Cognitive Walkthrough was applied in the usability tests of prototypes 1 and 2. The System Usability Scale evaluated prototype 3. Results evidenced positive feedback, reflecting an easy-to-use and intuitive smartphone app that does not interfere with daily work activities. Full article

Background: Current guidelines endorse the integration of exercise into cancer care. The diagnosis of cancer and its treatment, however, may introduce factors that make exercise engagement difficult, especially for individuals with advanced stages of disease. In this paper, we describe the baseline screening and triage process implemented for the Alberta Cancer Exercise (ACE) hybrid effectiveness-implementation study and share findings that highlight the multifaceted complexity of the process and the direct role of the clinical exercise physiologist (CEP). Methods: ACE was a hybrid effectiveness-implementation study examining the benefit of 12-week cancer-specific community-based exercise program. The ACE screening process was developed by integrating evidence-based guidelines with oncology rehabilitation expertise to ensure safe and standardized participation across cancer populations. The screening process involved four steps: (1) a pre-screen for high-risk cancers, (2) completion of a cancer-specific intake form and the Physical Activity Readiness Questionnaire for Everyone (PAR-Q+), (3) a CEP-led interview to further evaluate cancer status, cancer-related symptoms and other health issues (performed in-person or by phone), and (4) a baseline fitness assessment that included measurement of vital signs. Results: A total of 2596 individuals registered and underwent prescreening for ACE with 2570 (86.6%) consenting to participate. After full screening including the baseline fitness testing, 209 participants (8.1%) were identified as requiring further medical clearance. Of these, 191 (91.4%) had either a high-risk cancer, metastatic disease or were in the palliative end-stage of cancer, and 161 (84.3%) reported cancer-related symptoms potentially affecting their ability to exercise. In total, 806 (31.4%) participants were triaged to CEP-supervised in-person programming, 1754 (68.2%) participants to ACE community programming, and 8 (0.3%) specifically to virtual programming (post-COVID-19 option). Conclusions: The findings highlight the complexity and challenges of the screening and triage process, and the value of a highly trained CEP-led iterative approach that included the application of clinical reasoning. Full article

Conventional geostress methods of measurement cannot reveal an accurate geostress field distribution in an engineering area, limited by both cost and prevailing geological conditions. This study introduces an improved LSTM–Attention neural network for in situ stress field inversion. By integrating long short-term memory (LSTM) networks—which capture temporal dependencies in sequential data with attention mechanisms that emphasize critical features, the proposed method addresses inherent non-linearity and discontinuity challenges in deep subsurface stress field inversion. The integrated LSTM and multi-head attention architecture extracts temporal features and weights critical information within ground stress field data. Through iterative refinement via optimizers and loss functions, this framework successfully inverts stress boundary conditions while mitigating overfitting risks. The inversion of the stress field around a hydropower station indicates that the proposed method allows accurate inversion of distribution of the geostress field; the inversion values of the maximum principal stress, intermediate principal stress, and minimum principal stress conform to those measured. This study provides a new method for accurately and reliably inverting the stress field for deep engineering geological surveys and rock mass engineering design, which has significant scientific value and engineering application prospects. The rockburst risk of chambers is evaluated according to the stress field, which shows that locations with a burial depth of 274.3 m are at moderate to weak risk of rockburst. Full article

This study investigates the concentrations of essential and trace elements (Ni, Cu, Fe, Zn, Mn, and Al) in packaged ice cream samples collected from markets in Kırşehir province, located in Central Anatolia, Turkey, aiming to assess potential health risks associated with their consumption. Among the detected trace elements, Al (3.21–16.6 mg/kg) and Fe (2.03–24.0 mg/kg) had the highest concentrations, followed by Zn (0.56–3.00 mg/kg), Ni (0.84–4.84 mg/kg), Cu (1.15–3.46 mg/kg), and Mn (0.18–1.56 mg/kg). To explore the relationships between trace elements and identify possible contamination sources, chemometric approaches including principal component analysis, correlation matrices, and hierarchical cluster analysis (Ward’s method) were applied. Human health risk assessment was conducted by calculating Estimated Daily Intake (EDI), Target Hazard Quotient (THQ), Hazard Index (HI), and Carcinogenic Risk (CR), with uncertainty evaluated through Monte Carlo Simulation (10,000 iterations). HI values above 1 in children and adults indicate that trace element exposure through ice cream consumption may pose a health risk. High Al-THQ and Ni-CR values in children may require stricter monitoring and regulatory measures in case of long-term and regular consumption. Full article

Educational assessment relies on well-constructed test items to measure student learning accurately, yet traditional item development is time-consuming and demands specialized psychometric expertise. Automatic item generation (AIG) offers template-based scalability, and recent large language model (LLM) advances promise to democratize item creation. However, fully automated approaches risk introducing factual errors, bias, and uneven difficulty. To address these challenges, we propose and evaluate a hybrid human-in-the-loop (HITL) framework for AIG that combines psychometric rigor with the linguistic flexibility of LLMs. In a Spring 2025 case study at Franklin University Switzerland, the instructor collaborated with ChatGPT (o4-mini-high) to generate parallel exam variants for two undergraduate business courses: Quantitative Reasoning and Data Mining. The instructor began by defining “radical” and “incidental” parameters to guide the model. Through iterative cycles of prompt, review, and refinement, the instructor validated content accuracy, calibrated difficulty, and mitigated bias. All interactions (including prompt templates, AI outputs, and human edits) were systematically documented, creating a transparent audit trail. Our findings demonstrate that a HITL approach to AIG can produce diverse, psychometrically equivalent exam forms with reduced development time, while preserving item validity and fairness, and potentially reducing cheating. This offers a replicable pathway for harnessing LLMs in educational measurement without sacrificing quality, equity, or accountability. Full article

Background/Objectives: The development of hypophosphatemia has been associated with intravenous iron products, with the rate of hypophosphatemia found to be higher with ferric carboxymaltose. This consensus statement provides clinical guidance on the risk of hypophosphatemia development with ferric carboxymaltose and the approaches for management. To develop consensus recommendations regarding the clinical implications of hypophosphatemia after the administration of ferric carboxymaltose, the assessment of patient risk profile, and recommended approaches for risk reduction. Methods: Consensus statements were developed from an in-person meeting of specialists with expertise in iron pathophysiology and iron therapy and further supplemented with literature review. The multidisciplinary expert panel comprised global iron specialists spanning anesthesiology, cardiology, gastroenterology, obstetrics/gynecology, hematology, nephrology, and iron molecular biology. Structured discussions were held in an in-person meeting to gather expert opinion on the evidence base regarding intravenous iron and hypophosphatemia. Consolidated summary opinions underwent further iterations of panel review to form consensus recommendation statements. Results: The expert panel developed the following consensus statements: (1) Routine serum phosphate level measurement is not recommended for low-risk patients before or after treatment with ferric carboxymaltose, as most cases of hypophosphatemia that occur following the administration of ferric carboxymaltose are asymptomatic and transient; (2) patients receiving ferric carboxymaltose should be assessed for the degree of risk for developing symptomatic or severe hypophosphatemia prior to administration; (3) monitoring serum phosphate is recommended for patients at an increased risk for developing low serum phosphate or who require repeated courses of ferric carboxymaltose treatment at higher doses; (4) prophylactic oral phosphorus after ferric carboxymaltose is unlikely to effectively elevate phosphate and is not recommended for routine clinical practice; and (5) hypophosphatemic osteomalacia is rare and the risk of development after the administration of ferric carboxymaltose, in particular single infusion, is low. Conclusions: Hypophosphatemia following ferric carboxymaltose is predominantly asymptomatic and transient. Individuals at higher risk for developing hypophosphatemia with ferric carboxymaltose treatment include those who receive multiple infusions, higher cumulative doses, or long-term iron treatment or who have underlying clinical risk factors. These consensus statements provide structured guidance on the risk of hypophosphatemia with ferric carboxymaltose and the approaches to clinical management. Full article

Significant morphological transformations resulting from open-pit mining activities always present major problems with site safety and slope stability. This study investigates an active marble quarry in Dinar, Türkiye by combining geospatial analysis and photogrammetry based on unmanned aerial vehicles (UAV). Acquired in 2024 and 2025, high-resolution images were combined with dense point clouds produced by Structure from Motion (SfM) methods. Iterative Closest Point (ICP) registration (RMSE = 2.09 cm) and Multiscale Model-to-Model Cloud Comparison (M3C2) analysis was used to quantify the surface changes. The study found a volumetric increase of 7744.04 m³ in the dump zones accompanied by an excavation loss of 8359.72 m³, so producing a net difference of almost 615.68 m³. Surface risk factors were evaluated holistically using a variety of morphometric criteria. These measures covered surface variation in several respects: their degree of homogeneity, presence of any unevenness or texture, verticality, planarity, and linearity. Surface variation > 0.20, roughness > 0.15, and verticality > 0.25 help one to identify zones of increased instability. Point cloud modeling derived from UAVs and GIS-based spatial analysis were integrated to show that morphological anomalies are spatially correlated with possible failure zones. Full article

Background/Objectives: Walking speed (WS) is associated with morbidity and mortality. This study sought to investigate the associations between WS and hospitalization among patients with stable cardiovascular disease (CVD) and analyze how changes in WS impact all-cause hospitalization during exercise interventions. Methods: Of the 3328 patients in the ITER registry, 2871 (aged 65 ± 11 years) were analyzed. WS was measured using the 1 km treadmill walking test (1 km-TWT). Hospitalization was evaluated after one and three years according to the baseline WS tertiles. Additionally, 1465 patients were re-evaluated three years after the baseline, categorized into SlowWS and FastWS groups, and subsequently associated with changes in WS (worsening or low, moderate, and high improvements), generating six joint categories. Hospitalization was re-assessed during the fourth and sixth years after the baseline. The associations between WS and all-cause and CVD hospitalization were examined using Cox proportional hazard models, adjusting for demographic and clinical confounders. Results: A higher baseline WS was inversely associated with one-year hospitalization, with a 42% lower risk of all-cause hospitalization (95% CI: 0.51, 0.66) and a 38% lower risk of cardiovascular-related events (95% CI: 0.45, 0.86) compared to those in slower patients. Significant but mitigated magnitudes were observed for three-year hospitalization. A similar trend resulted in WS changes over time. Interestingly, the six-year risk in the SlowWS-high group was a 43% (95% CI: 0.45, 0.74) lower risk, which was comparable to that in the FastWS-low patients. Conclusions: The 1 km-TWT effectively predicts hospitalization among cardiac outpatients and is a valuable educational tool for exercise-based interventions in secondary prevention. These findings emphasize the efficacy of exercise-based programs, highlighting the importance of promoting exercise in long-term CVD management. Full article

Background/Objectives: Malnutrition is a key contributor to poor outcomes in older adults with fragility fractures, increasing the risk of complications, functional decline, prolonged hospital stays, mortality, and healthcare costs. Substantial evidence limited to hip fracture supports early, interdisciplinary nutrition care. However, global audits reveal that most hip fracture patients do not receive recommended interventions. This quality improvement (QI) project aimed to co-create and test a pathway and toolkit to help apply evidence-based nutrition care in different fragility fracture settings globally. Methods: The SIMPLER Pathway and toolkit (SIMPLER) were developed through a multiphase, co-creation QI initiative (2018–2025), guided by the Knowledge-to-Action framework. Global experts and clinical teams synthesized evidence, identified the “know-do” gap, and adapted SIMPLER to context through iterative action–reflection cycles. The Model for Improvement guided team building, goal setting, testing changes, and measuring outcomes at pilot sites. Results: Over 100 co-creation activities between 2018 and 2025 engaged staff and patients to shape and refine SIMPLER. A global clinician survey (n = 308, 46 countries), two bi-national audits (n = 965, 63 hospitals), and qualitative interviews (n = 15) confirmed a widespread evidence-practice gap. The pathway and toolkit were pilot-tested in five hospitals across four countries, with action–reflection cycles enabling continuous refinement of prioritized nutrition improvements tailored to the local context. Following endorsement in late 2024, 46 healthcare services in 23 countries have formally committed to implementing SIMPLER. Conclusions: The SIMPLER Nutrition Pathway provides a scalable, adaptable framework to support the delivery of evidence-based nutrition care in fragility fracture settings. A global evaluation is underway. Full article

Background/Objectives: This research presents a novel analytical method for breast tumor characterization and tissue classification by leveraging intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) combined with hyperspectral imaging techniques and deep learning. Traditionally, dynamic contrast-enhanced MRI (DCE-MRI) is employed for breast tumor diagnosis, but it involves gadolinium-based contrast agents, which carry potential health risks. IVIM imaging extends conventional diffusion-weighted imaging (DWI) by explicitly separating the signal decay into components representing true molecular diffusion ($D$) and microcirculation of capillary blood (pseudo-diffusion or $D*$). This separation allows for a more comprehensive, non-invasive assessment of tissue characteristics without the need for contrast agents, thereby offering a safer alternative for breast cancer diagnosis. The primary purpose of this study was to evaluate different methods for breast tumor characterization using IVIM-DWI data treated as hyperspectral image stacks. Dice similarity coefficients and Jaccard indices were specifically used to evaluate the spatial segmentation accuracy of tumor boundaries, confirmed by experienced physicians on dynamic contrast-enhanced MRI (DCE-MRI), emphasizing detailed tumor characterization rather than binary diagnosis of cancer. Methods: The data source for this study consisted of breast MRI scans obtained from 22 patients diagnosed with mass-type breast cancer, resulting in 22 distinct mass tumor cases analyzed. MR images were acquired using a 3T MRI system (Discovery MR750 3.0 Tesla, GE Healthcare, Chicago, IL, USA) with axial IVIM sequences and a bipolar pulsed gradient spin echo sequence. Multiple b-values ranging from 0 to 2500 s/mm² were utilized, specifically thirteen original b-values (0, 15, 30, 45, 60, 100, 200, 400, 600, 1000, 1500, 2000, and 2500 s/mm²), with the last four b-value images replicated once for a total of 17 bands used in the analysis. The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). The comparisons were assessed by evaluating the similarity of the detection results from each method to ground truth tumor areas, which were manually drawn on DCE-MRI images and confirmed by experienced physicians. Similarity was quantitatively measured using the Dice similarity coefficient and the Jaccard index. Additionally, the performance of the detectors was evaluated using 3D-ROC analysis and its derived criteria (AUC_OD, AUC_TD, AUC_BS, AUC_TD−BS, AUC_ODP, AUC_SNPR). Results: The findings objectively demonstrated that the DNN method achieved superior performance in breast tumor detection compared to KCEM and I-KCEM. Specifically, the DNN yielded a Dice similarity coefficient of 86.56% and a Jaccard index of 76.30%, whereas KCEM achieved 78.49% (Dice) and 64.60% (Jaccard), and I-KCEM achieved 78.55% (Dice) and 61.37% (Jaccard). Evaluation using 3D-ROC analysis also indicated that the DNN was the best detector based on metrics like target detection rate and overall effectiveness. The DNN model further exhibited the capability to identify tumor heterogeneity, differentiating high- and low-cellularity regions. Quantitative parameters, including apparent diffusion coefficient ($ADC$), pure diffusion coefficient ($D$), pseudo-diffusion coefficient ($D*$), and perfusion fraction ($PF$), were calculated and analyzed, providing insights into the diffusion characteristics of different breast tissues. Analysis of signal intensity decay curves generated from these parameters further illustrated distinct diffusion patterns and confirmed that high cellularity tumor regions showed greater water molecule confinement compared to low cellularity regions. Conclusions: This study highlights the potential of combining IVIM-DWI, hyperspectral imaging techniques, and deep learning as a robust, safe, and effective non-invasive diagnostic tool for breast cancer, offering a valuable alternative to contrast-enhanced methods by providing detailed information about tissue microstructure and heterogeneity without the need for contrast agents. Full article