Search Results (13,289)

Requirements Engineering and Management (REM) tools play a significant role in ensuring project compliance and efficiency. Automotive engineering must comply with regulatory standards, requiring detailed documentation, rigorous testing, and solid traceability. Despite their importance, REM tools are underexplored from the usability and user experience perspective (UX), even though poor usability can hinder development workflows across stakeholder teams. This study presents a case study of heuristic usability evaluation of IBM DOORS Next Generation, conducted with expert evaluators, using Nielsen’s 10 Usability Heuristics as an evaluation framework. The identified issues were analyzed in terms of impacted heuristics and severity ratings. Additionally, we underwent a Large Language Model (LLM)-based heuristic evaluation, using ChatGPT-5, prompted with the same heuristic set and static screenshots. The LLM detected several issues overlapping with human findings (32%), as well as new ones (23%); therefore, 55% of its outputs are considered valid and 45% are unconfirmed. This highlights both the potential and limitations of AI-driven usability assessment. Overall, the findings underscore the usability challenges of REM tools and suggest that LLMs may serve as complementary evaluators, accelerating early-stage heuristic inspections in safety-critical engineering environments. Full article

Wind-assisted ship propulsion (WASP) has gained considerable interest as a means of reducing fuel consumption and Greenhouse Gas (GHG) emissions, with further benefits when combined with weather-optimized routing. This study employs and extends a National Technical University of Athens (NTUA) weather-routing optimization tool to more realistically assess WASP performance through integrated modeling. The original tool minimized fuel consumption using forecasted weather data and a physics-based performance model. A previous extension to account for the WASP effect introduced a 1-Degree Of Freedom (DOF) model that accounted only for longitudinal hydrodynamic and aerodynamic forces, estimating the reduced main-engine power required to maintain speed in given conditions. The current study incorporates a 3-DOF model that includes side forces and yaw moments, capturing resulting drift and rudder deflection effects. A Kamsarmax bulk carrier equipped with suction sails served as the case study. Initial simulations across various operating and weather conditions compared the two models. The 1-DOF model predicted fuel-saving potential up to 26% for the tested apparent wind speed and the range of possible headings, whereas the 3-DOF model indicated that transverse effects reduce WASP benefits by 2–7%. Differences in Main Engine (ME) power estimates between the two models reached up to 7% Maximum Continuous Rating (MCR) depending on the speed of wind. The study then applied both models within a weather-routing optimization framework to assess whether the optimal routes produced by each model differ and to quantify performance losses. It was found that the revised optimal route derived from the 3-DOF model improved total Fuel Oil Consumption (FOC) savings by 1.25% compared with the route optimized using the 1-DOF model when both were evaluated with the 3-DOF model. Full article

A kinetic and thermodynamic multi-phase model has been developed to describe the adsorption of gases on ice surfaces and their subsequent diffusional loss into the bulk ice phase. This model comprises a gas phase, a solid surface, a sub-surface layer, and the ice bulk. The processes represented include gas adsorption on the surface, solvation into the sub-surface layer, and diffusion in the ice bulk. It is assumed that the gases dissolve according to Henry’s law, while the surface concentration follows the Langmuir adsorption equilibrium. The flux of molecules from the sub-surface layer into the ice bulk is treated according to Fick’s second law. Kinetic and thermodynamic quantities as applicable to the uptake of small carbonyl compounds on ice surfaces at temperatures around 200 K have been used to perform model calculations and corresponding sensitivity tests. The primary application in this study is acetic acid. The model simulations are applied by fitting the experimental data obtained from coated-wall flow-systems (CWFT) measurements, with the best curve-fit solutions providing reliable estimations of kinetic parameters. Over the temperature range from 190 to 220 K, the estimated desorption coefficient, k_des, varies from 0.02 to 1.35, while adsorption rate coefficient, k_ads, ranges from 3.92 and 4.17, and the estimated diffusion coefficient, D, changes by more than two orders of magnitude, increasing from 0.03 to 13.0. Sensitivity analyses confirm that this parameter estimation approach is robust and consistent with underlying physicochemical processes. It is shown that for shorter exposure times the loss of molecules from the gas phase is caused exclusively by adsorption onto the surface and solvation into the sub-surface layer. Diffusional loss into the bulk, on the other hand, is only important at longer exposure times. The model is a useful tool for elucidating surface and bulk process kinetic parameters, such as adsorption and desorption rate constants, solution and segregation rates, and diffusion coefficients, as well as the estimation of thermodynamic quantities, such as Langmuir and Henry constants and the ice film thickness. Full article

Background/Objectives: The Korean Survey for Cancer Survivorship (KSCS) aims to comprehensively assess cancer survivors’ health behaviors, quality of life (QoL), and socioeconomic challenges. This study evaluated the feasibility of the KSCS protocol and identified key factors influencing psychological health and QoL among cancer survivors. Methods: The nationwide survey targeted survivors diagnosed with breast, colorectal, liver, lung, stomach, prostate, and gynecological cancers who had completed active treatment within 1 to 10 years. The respondents were given the option to participate in the survey either online or in-person. The questionnaire has 229 questions, including internationally validated tools such as the EQ-5D-3L, PHQ-9, and GAD-7. Results: A total of 983 cancer survivors completed the survey (92.7% online, 8.3% in-person) and were categorized by post-diagnosis duration. Survivors diagnosed within 1–3 years reported higher rates of moderate-severe depression (11.4% vs. 8.3%), moderate-severe anxiety (5.9% vs. 5.1%), and poorest QoL (63.0% vs. 50.9%) compared to those diagnosed more than 5 years ago. Employment changes, such as loss of job, change of workplace, or work leave, were significantly associated with worse health outcomes, including higher rates of moderate-severe depression (OR = 4.39; 95% CI 2.43–7.96), moderate-severe anxiety (OR = 3.63; 95% CI 1.68–0.88), and having extreme QoL problems (OR = 6.37; 95% CI 2.03–20.00). Conclusions: The KSCS protocol is feasible for nationwide implementation and provides comprehensive data on health, psychological, and socioeconomic challenges among cancer survivors. Preliminary findings highlight employment’s critical role in cancer survivors’ well-being and the need for survivorship care that integrates socioeconomic and clinical factors. Full article

Musculoskeletal disorders (MSDs) remain one of the most persistent occupational health challenges in the U.S. construction industry, where physically demanding tasks such as heavy lifting, kneeling, and working in awkward postures contribute to elevated injury rates. This study aims to identify significant job-related determinants of MSDs in construction-sector occupations. By integrating publicly available datasets from the Survey of Occupational Injuries and Illnesses (SOII) and the Occupational Information Network (O*NET) datasets, a stepwise multiple regression analysis was conducted on 344 occupation-condition observations representing 86 construction occupations, yielding a final model that explained 49% of the variance. Ten significant predictors of MSD events were identified and classified as either risk amplifiers or mitigators. Amplifiers included factors such as exposure to noise, disease, hazardous conditions, and time pressure, all of which heightened MSD risk, while mitigators—such as reduced cramped-space exposure and regulated work environments—were associated with lower risk. MSDs resulting from sprains, strains, or tears accounted for 62.8% of all cases, frequently leading to days away from work (36.3%) or job restrictions (26.5%). The findings underscore that ergonomic risk in construction extends beyond physical strain to include scheduling, equipment design, and work organization. These results provide actionable insights for employers and safety professionals to redesign tools, optimize task rotation, and implement realistic work pacing strategies, ultimately reducing MSD incidence and improving productivity in this high-risk sector. Full article

Background/Objectives: Pain and pain-related conditions are considered a global health and financial burden. In order to improve pain management, pain intensity assessment, and pain diagnosis, various biomarkers have been proposed. Since their clinical utility is not proven yet, the aim of this umbrella review is to synthesize existing evidence of all types of pain biomarkers available. Methods: Systematic searches were conducted in PubMed, Scopus, and the Cochrane Library from inception to 2 June 2025. Eligible studies were systematic reviews and meta-analyses examining any clinical, biochemical, genetic, neurophysiological, or imaging biomarker related to pain. The screening of studies, data extraction, and assessment of methodological quality using the AMSTAR-2 tool were conducted by two independent reviewers. Findings were summarized narratively. Results: A total of 49 systematic reviews and meta-analyses were included. Most reviews were rated as low or critically low quality. Inflammatory biomarkers (CRP, IL-6, TNF-α) reported the most consistent associations with chronic musculoskeletal pain, while neuroimaging and EEG measures reflected central nervous system alterations. Proteomic multi-protein panels demonstrated exploratory diagnostic potential, particularly for fibromyalgia, but lacked clinical validation. Evidence for genetic, hormonal, metabolic, neurochemical, and tissue-specific biomarkers was inconsistent and methodologically limited, supporting mechanistic rather than clinical inference. Conclusions: No single biomarker has achieved clinical validation for chronic pain, but several biomarker classes show promise. Future implications include high-quality longitudinal studies, standardized protocols, and multidimensional biomarker panels. Full article

Background: Osteoporosis and osteopenia are prevalent bone diseases characterized by reduced bone mineral density (BMD) and an increased risk of fractures, particularly in postmenopausal women. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, it has limitations regarding accessibility, cost, and predictive capacity for fracture risk. Machine learning (ML) approaches offer an opportunity to develop automated and more accurate diagnostic models by incorporating both BMD values and clinical variables. Method: This study retrospectively analyzed BMD data from 142 postmenopausal women, classified into 3 diagnostic groups: normal, osteopenia, and osteoporosis. Various supervised ML algorithms—including Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and Artificial Neural Networks (ANN)—were applied. Feature selection techniques such as ANOVA, CHI2, MRMR, and Kruskal–Wallis were used to enhance model performance, reduce dimensionality, and improve interpretability. Model performance was evaluated using 10-fold cross-validation based on accuracy, true positive rate (TPR), false negative rate (FNR), and AUC values. Results: Among all models and feature selection combinations, SVM with ANOVA-selected features achieved the highest classification accuracy (94.30%) and 100% TPR for the normal class. Feature sets based on traditional diagnostic regions (L1–L4, femoral neck, total femur) also showed high accuracy (up to 90.70%) but were generally outperformed by statistically selected features. CHI2 and MRMR methods also yielded robust results, particularly when paired with SVM and k-NN classifiers. The results highlight the effectiveness of combining statistical feature selection with ML to enhance diagnostic precision for osteoporosis and osteopenia. Conclusions: Machine learning algorithms, when integrated with data-driven feature selection strategies, provide a promising framework for automated classification of osteoporosis and osteopenia based on BMD data. ANOVA emerged as the most effective feature selection method, yielding superior accuracy across all classifiers. These findings support the integration of ML-based decision support tools into clinical workflows to facilitate early diagnosis and personalized treatment planning. Future studies should explore more diverse and larger datasets, incorporating genetic, lifestyle, and hormonal factors for further model enhancement. Full article

Monitoring construction-phase settlement of estuary-crossing tunnels founded on coastal soft soils is critical for risk management, yet dense in situ measurements are often unavailable along linear corridors. This study uses Sentinel-1A ascending SAR imagery (65 scenes, September 2022–August 2025) to retrieve time-series deformation along the Sanya Estuary Channel tunnel (China) using Permanent Scatterer InSAR (PS-InSAR) and Small Baseline Subset InSAR (SBAS-InSAR). The two approaches reveal a consistent subsidence hotspot at Tunnel Section D (DK0+000–DK0+330), while most of the corridor remains within ±5 mm/a. The line-of-sight deformation rates range from −24 to 17.7 mm/year (PS-InSAR) and −29.9 to 18.7 mm/a (SBAS-InSAR). Time-series analysis at representative points in Section D indicates a maximum cumulative settlement of −75.7 mm and a clear acceleration after May 2023. By integrating the deformation results with geological reports, construction logs and rainfall records, we infer that compressible marine clays and interbedded sand/aquifer zones control the hotspot, whereas excavation/dewatering and rainfall-related groundwater fluctuations further promote consolidation. The results provide a practical basis for subsidence risk screening and monitoring prioritization for estuary-crossing infrastructure in coastal soft-soil settings. From a sustainability perspective, the proposed joint PS–SBAS InSAR framework provides a scalable and cost-effective tool for continuous deformation surveillance, supporting preventive maintenance and risk-informed management of urban underground infrastructure. Full article

Digital twin technology is widely promoted as a transformative step for precision livestock farming, yet no fully realized, engineering-grade digital twins are deployed in commercial dairy or poultry systems today. This work establishes the current state of knowledge on dairy and poultry digital twins by synthesizing evidence through systematic database searches, thematic evidence mapping and critical analysis of validation gaps, carbon accounting and adoption barriers. Existing platforms are better described as near-digital-twin systems with partial sensing and modelling, digital-twin-inspired prototypes, simulation frameworks or decision-support tools that are often labelled as twins despite lacking continuous synchronization and closed-loop control. This distinction matters because the empirical foundation supporting many claims remains limited. Three critical gaps emerge: life-cycle carbon impacts of digital infrastructures are rarely quantified even as sustainability benefits are frequently asserted; field-validated improvements in feed efficiency, particularly in poultry feed conversion ratios, are scarce and inconsistent; and systematic reporting of failure rates, downtime and technology abandonment is almost absent, leaving uncertainties about long-term reliability. Adoption barriers persist across technical, economic and social dimensions, including rural connectivity limitations, sensor durability challenges, capital and operating costs, and farmer concerns regarding data rights, transparency and trust. Progress for cows and chickens will require rigorous validation in commercial environments, integration of mechanistic and statistical modelling, open and modular architectures and governance structures that support biological, economic and environmental accountability whilst ensuring that system intelligence is worth its material and energy cost. Full article

Background: Perianal infections (PIs) are a serious threat in patients with acute myeloid leukemia (AML). While CPX-351 is designed to reduce gastrointestinal toxicity, its impact on the incidence of PIs is unknown. This study aims to evaluate the incidence and characteristics of PIs in a cohort of CPX-351-treated AML patients. Methods: We enrolled 22 adult patients diagnosed with secondary AML receiving CPX-351 between May 2020 and July 2025 at Policlinico Tor Vergata Hospital. Statistical analysis used descriptive statistics and multivariate analysis. Results: The incidence of PIs in the cohort was 31.8%. Microbiological cultures from the lesions commonly yielded Klebsiella pneumoniae and Enterococcus species. The development of a PI was associated with a significantly longer hospital stay (mean, 49.6 vs. 37.7 days; p = 0.034). An increased odds ratio of having PIs was noted for mucositis and positive rectal swabs (17.961, p = 0.062; 5.554, p = 0.391, respectively), with two patients (28.5%) having a positive pre-infection swab for Klebsiella pneumoniae. Surgical intervention was guided by patient pain levels and hematological criteria. Surgical patients had significantly higher pain levels (p = 0.001) and a platelet count greater than 20 × 10⁹/L (p = 0.028). All patients were alive at 30 days, with low rates of septic shock (14.2%, n = 1) and no infection-related mortality or recurrence. Conclusions: Despite CPX-351’s known reduced gastrointestinal toxicity, our study showed a significantly higher incidence of PIs compared to literature data. While the outcomes were favorable, PIs led to prolonged hospitalization. Routine rectal swab surveillance could be a valuable tool for risk stratification and preemptive strategies. Full article

Background/Objectives: Virtual reality (VR) is increasingly used to reduce pain during pediatric needle procedures, but its effectiveness may vary by developmental stage and gender. This study evaluated whether immersive VR reduces venipuncture pain in children and adolescents and examined parent–patient agreement and gender-specific response patterns. Methods: A prospective nonrandomized clinical study was conducted within a hospital-based pediatric venipuncture service using an alternating 1:1 allocation sequence. Participants aged 4–18 years underwent venipuncture with either VR (n = 49) or standard care (n = 29). Procedural pain was measured using the Faces Pain Scale–Revised (FPS-R) with independent parent ratings. Analysis of covariance (ANCOVA) compared post-procedural FPS-R scores while adjusting for baseline pain. Exploratory age and gender-specific analyses were also performed. Results: VR led to a clear reduction in pain for children, even after adjusting for baseline scores (3.55 vs. 4.73; p = 0.003). Adolescents, however, reported similarly low pain in both groups (2.81 vs. 2.79; p = 0.60), and several mentioned that the PEGI 3 content felt too young for them, which likely limited how engaged they were. Among children, girls showed the most noticeable drop in pain, which matches the subgroup’s adjusted significance (p = 0.011). Parent–patient agreement was stronger in children (r ≈ 0.7–0.8) than in adolescents (r ≈ 0.4–0.5), and VR did not change this pattern. Most participants said they would choose VR again for future procedures. Conclusions: Immersive VR helped reduce venipuncture pain in children but had little effect in adolescents, underscoring the need for age-appropriate or more interactive VR content for older patients. Overall, these findings support using VR selectively as a distraction tool that fits the developmental needs of pediatric groups. Full article

Groundwater vulnerability assessments serve as essential tools for sustainable groundwater management, particularly in regions with intensive anthropogenic activities. However, improving the objectivity and predictive reliability of vulnerability assessment frameworks remains a critical scientific challenge in groundwater science, especially for coastal aquifer systems characterized by strong heterogeneity and complex hydrogeological processes. The traditional DRASTIC model is a widely recognized method but suffers from subjectivity in assigning parameter ratings and weights, often leading to arbitrary and potentially inaccurate vulnerability maps. This limitation also restricts its applicability in areas with complex hydrogeological conditions. To enhance the accuracy and adaptability of the traditional DRASTIC model, a hybrid PSO-BP-DRASTIC framework was developed and applied it to a coastal city in China. Specifically, the model employs a backpropagation neural network (BP-NN) to optimize indicator weights and integrates the particle swarm optimization (PSO) algorithm to refine the initial weights and thresholds of the BP-NN. By introducing a data-driven and globally optimized weighting mechanism, the proposed framework effectively overcomes the inherent subjectivity of conventional empirical weighting schemes. Using ten-fold cross-validation and observed nitrate concentration data, the traditional DRASTIC, BP-DRASTIC, and PSO-BP-DRASTIC models were systematically validated and compared. The results demonstrate that (1) the PSO-BP-DRASTIC model achieved the highest classification accuracy on the test set, the highest stability across ten-fold cross-validation, and the strongest correlation with the nitrate concentrations; (2) the importance analysis identified the aquifer thickness and depth to the groundwater table as the most influential factors affecting groundwater vulnerability in Yantai; and (3) the spatial assessments revealed that high-vulnerability zones (7.85% of the total area) are primarily located in regions with intensive agricultural activities and high aquifer permeability. The hybrid PSO-BP-DRASTIC model effectively mitigates the subjectivity of the traditional DRASTIC method and the local optimum issues inherent in BP-NNs, significantly improving the assessment accuracy, stability, and objectivity. From a scientific perspective, this study demonstrates the feasibility of integrating swarm intelligence and neural learning into groundwater vulnerability assessment, providing a transferable and high-precision methodological paradigm for data-driven hydrogeological risk evaluation. This novel hybrid model provides a reliable scientific basis for the reasonable assessment of groundwater vulnerability. Moreover, these findings highlight the importance of integrating a hybrid optimization strategy into the traditional DRASTIC model to enhance its feasibility in coastal cities and other regions with complex hydrogeological conditions. Full article

This study evaluated the efficiency and potential risks associated with three clinical tools for removing cement-retained implant-supported prostheses: Magnetic Mallet, sliding hammer, and Coronaflex. The tests consisted of: cementation of three-unit bridge models onto titanium abutments with different geometries using Zinc Oxide non-eugenol or Zinc Phosphate cement. Seven different geometries of three-unit bridges were tested; therefore, a total of 7 bridges × 2 luting agents × 3 tools were combined in a full factorial analysis. Five test replicates were performed for each combination, resulting in a total of 5 × 7 × 2 × 3 = 210 retrieval tests. The 70 tests regarding the Coronaflex were taken from a previously conducted experiment on the topic, using the same dental bridge models and the same experimental conditions. Efficiency was assessed by the percentage of successful removals and the maximum force recorded with a piezoelectric load cell. For temporary cementations, the sliding hammer achieved the highest retrieval rate, while the Magnetic Mallet demonstrated comparable efficiency with lower forces. Coronaflex showed lower success rates and higher forces than Magnetic Mallet. For permanent cementations, most bridges were not removable, and attempts with the sliding hammer occasionally resulted in abutment screw damage. Within the limitations of this study, the Magnetic Mallet appears to be an effective option for removing bridges cemented with temporary cement, potentially in combination with a sliding hammer for highly retentive geometries. Zinc phosphate cement should be avoided when retrievability is desired, except for abutments with very low retention capability. Full article

Intermittent claudication (IC) is the most frequent symptomatic manifestation of lower-extremity peripheral artery disease (PAD). Supervised exercise therapy (SET) and endovascular revascularization (ER) are established treatments, but their relative and combined effects on health-related quality of life (HRQoL) remain. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) comparing SET, ER, and ER+SET, with HRQoL as the primary outcome. Methods: Following PRISMA 2020, PubMed, Embase, and CENTRAL were used in December 2024. Eligible RCTs enrolled with IC (excluding critical limb-threatening ischemia) and reported validated HRQoL outcomes at ≥3 months. Two reviewers independently extracted data and assessed risk of bias using the Cochrane RoB 2.0 tool. Random-effects meta-analyses pooled standardized mean differences (SMDs) for HRQoL and mean differences (MDs) for walking distance. Results: Five RCTs (n = 728) were included. Compared with optimal medical therapy, both SET and ER improved HRQoL and walking distance. At 12 months, no significant effect was observed between SET and ER (SMD 0.02; 95% CI: −0.18 to 0.22). ER+SET was superior to SET alone (SMD 0.35; 95% CI: 0.12–0.57). Beyond 24 months, improvements were sustained with SET but attenuated with ER, accompanied by higher reintervention rates in ER-containing arms (approximately 20–30% by 2 years). Adverse events were rare (<1%). Conclusions: Given moderate-certainty evidence (GRADE), SET should remain the first-line therapy for intermittent claudication because it provides durable improvements in patient-centered outcomes with minimal harm. Endovascular revascularization (ER) can provide faster symptom relief, but its long-term benefits are constrained by restenosis and repeat procedures, particularly in femoropopliteal disease. Full article

Driver distraction, particularly mobile phone use while driving, remains one of the leading causes of road traffic incidents worldwide. In response to this issue and leveraging recent technological advances and increased access to intelligent systems, this research presents the development of an application running on an intelligent embedded architecture for the automatic detection of mobile phone use by drivers, integrating computer vision, inertial sensing, and edge computing. The system, based on the YOLOv8n model deployed on a Jetson Xavier NX 16Gb—Nvidia, combines real-time inference with an inertial activation mechanism and cloud storage via Firebase Firestore, enabling event capture and traceability through a lightweight web-based HMI interface. The proposed solution achieved an overall accuracy of 81%, an inference rate of 12.8 FPS, and an average power consumption of 8.4 W, demonstrating a balanced trade-off between performance, energy efficiency, and thermal stability. Experimental tests under diverse driving scenarios validated the effectiveness of the system, with its best performance recorded during daytime driving—83.3% correct detections—attributed to stable illumination and enhanced edge discriminability. These results confirm the feasibility of embedded artificial intelligence systems as effective tools for preventing driver distraction and advancing intelligent road safety. Full article