Search Results (8,221)

Purpose: This pilot study aimed to evaluate how deviations in X-ray tube head angulation and digital sensor alignment affect the radiographic measurement of the profile angle in CAD-CAM abutments. Materials and Methods: A mandibular model was used with five implant positions (central, buccal, and lingual offsets). Custom CAD-CAM abutments were designed with identical bucco-lingual direction contours and varying mesio-distal asymmetry for the corresponding implant positions. Periapical radiographs were acquired under controlled conditions by systematically varying vertical tube angulation, horizontal tube angulation, and horizontal sensor rotation from 0° to 20° in 5° increments for each parameter. Profile angles, interthread distances, and proximal overlaps were measured and compared with baseline STL data. Results: Profile angle measurements were significantly affected by both X-ray tube and sensor deviations. Horizontal tube angulation produced the greatest profile angle distortion, particularly in buccally positioned implants. Vertical x-ray tube angulations beyond 15° led to progressive underestimation of profile angles, while horizontal tube head rotation introduced asymmetric mesial–distal variation. Sensor rotation also caused marked interthread elongation, in some cases exceeding 100%, despite vertical projection being maintained. Profile angle deviations greater than 5° occurred in multiple conditions. Conclusions: X-ray tube angulation and sensor alignment influence the reliability of profile angle measurements. Radiographs with > 10% interthread elongation or crown overlap may be inaccurate and warrant re-acquisition. Special attention is needed when imaging buccally positioned implants. Full article

Background/Objectives: Multidrug-resistant (MDR) bacterial colonization poses a significant risk for subsequent infections, especially within hospital environments. Healthcare workers can inadvertently transmit these MDR bacteria to vulnerable patients, exacerbating the problem. This study aimed to determine the colonization rates of MDR bacteria among patients and healthcare workers in a rural Ethiopian hospital with limited resources. Methods: Between 26 May and 6 June 2024, nasal, rectal, vagino-rectal exudate, and stool samples were collected from patients (n = 78) and healthcare workers (n = 11) at Gambo General Hospital (Oromia Region, Ethiopia). Samples were cultured on chromogenic media selective for methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus spp. (VRE), and carbapenemase-producing Enterobacteriaceae (CPE). Bacterial identification was performed using MALDI-TOF mass spectrometry (Bruker), antimicrobial susceptibility testing using the MicroScan WalkAway system (Beckman Coulter), and genotypic characterization with the MDR Direct Flow Chip kit (Vitro). Results: MRSA nasal colonization was detected in 43.3% of patients (13/30; 95% CI: 27.4–60.8%) and 27.3% of healthcare workers (3/11; 95% CI: 6.0–61.0%) (p = 0.73). Rectal (or stool) colonization by MDR bacteria was significantly higher in pediatric patients (85.0%, 17/20; 95% CI: 62.1–96.8%) than in adults (14.3%, 4/28; 95% CI: 5.7–31.5%) (p < 0.001). Notably, a high proportion of pediatric patients harbored Escherichia coli strains co-producing NDM carbapenemase and CTX-M ESBL, and VRE strains were also predominantly isolated in this group. Conclusions: This study reveals a concerningly high prevalence of MRSA and MDR Enterobacteriaceae, especially among children at Gambo Hospital. The VRE prevalence was also substantially elevated compared to other studies. These findings underscore the urgent need for strengthened infection control measures and antimicrobial stewardship programs within the hospital setting. Full article

Background/Objectives: Neck pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage, affecting the cervical region. It represents one of the leading causes of disability, with a prevalence of 30%. Transcranial direct current stimulation (tDCS) is a non-invasive electrotherapy technique that enables direct modulation of cortical excitability. It involves the application of a low-intensity electrical current to the scalp, targeting the central nervous system. The aim of this study was to analyze the effects of tDCS on functionality, pain, mobility, and pressure pain threshold in patients with chronic nonspecific neck pain. Methods: Thirty participants (18–60 years) were selected to receive ten treatment sessions over a four-week period using tDCS (CG = 15) or transcutaneous electrical nerve stimulation (TENS) (CG = 15), with the following various related variables evaluated: functionality (Neck Disability Index), pain intensity (NPRS), cervical range of motion (ROM), and pressure pain threshold (PPT). Assessments were conducted at baseline, post-treatment, one month, and three months after the intervention. Results: The within-group analysis revealed statistically significant improvements for both groups at post-treatment, one-month follow-up, and three-month follow-up. Conclusions: The comparison between groups shows favorable changes in the tDCS group for PPT measurements. Full article

With the wide application of airdrop technology in rescue activities in civil and aerospace fields, the importance of accurate airdrop is increasing. This work comprehensively analyzes the interactive mechanisms among multiple models affecting airdrops, including wind field distribution, drag force effect, and the parachute opening process. By integrating key parameters across various dimensions of these models, a multidimensional parameter dynamic evolution (MPDE) target prediction method for aerial delivery parachutes in radar-detected wind fields is proposed, and the Runge–Kutta method is applied to dynamically solve for the final landing point of the target. In order to verify the performance of the method, this work carries out field airdrop experiments based on the radar-measured meteorological data. To evaluate the impact of model input errors on prediction methods, this work analyzes the influence mechanism of the wind field detection error on the airdrop prediction method via the Relative Gain Array (RGA) and verifies the analytical results using the numerical simulation method. The experimental results indicate that the optimized MPDE method exhibits higher accuracy than the widely used linear airdrop target prediction method, with the accuracy improved by 52.03%. Additionally, under wind field detection errors, the linear prediction method demonstrates stronger robustness. The airdrop error shows a trigonometric relationship with the angle between the synthetic wind direction and the heading, and the phase of the function will shift according to the difference in errors. The sensitivity of the MPDE method to wind field errors is positively correlated with the size of its object parachute area. Full article

This study conducts a comparative evaluation of Turkiye’s carbon emission profile from both sectoral and global perspectives. Utilizing 2022 data from 76 countries, it applies two widely recognized multi-criteria decision-making (MCDM) methods: MEREC, for determining objective weights of criteria, and PROMETHEE II, for ranking countries based on these criteria. All data used in the analysis were obtained from the World Bank, a globally recognized and credible statistical source. The study evaluates seven criteria, including carbon emissions from the energy, transport, industry, and residential sectors, along with GDP-related indicators. The results indicate that Turkiye’s carbon emissions, particularly from industry, transport, and energy, are substantially higher than the global average. Moreover, countries with higher levels of industrialization generally rank lower in environmental performance, highlighting a direct relationship between industrial activity and increased carbon emissions. According to PROMETHEE II rankings, Turkiye falls into the lower-middle tier among the assessed countries. In light of these findings, the study suggests that Turkiye should implement targeted, sector-specific policy measures to reduce emissions. The research aims to provide policymakers with a structured, data-driven framework that aligns with the country’s broader sustainable development goals. MEREC was selected for its ability to produce unbiased criterion weights, while PROMETHEE II was chosen for its capacity to deliver clear and meaningful comparative rankings, making both methods highly suitable for evaluating environmental performance. This study also offers a broader analysis of how selected countries compare in terms of their carbon emissions. As carbon emissions remain one of the most pressing environmental challenges in the context of global warming and climate change, ranking countries based on emission levels serves both to support scientific inquiry and to increase international awareness. By relying on recent 2022 data, the study offers a timely snapshot of the global carbon emission landscape. Alongside its contribution to public awareness, the findings are expected to support policymakers in developing effective environmental strategies. Ultimately, this research contributes to the academic literature and lays a foundation for more sustainable environmental policy development. Full article

Accurately hindcasting waves in semi-enclosed, fetch-limited basins remains challenging for reanalysis models, which tend to underestimate storm peaks near the coast. We developed interpretable ML models for Rijeka Bay (northern Adriatic) using only wind observations from two land-based wind stations to predict buoy H_m0 measurements spanning 2009–2011 (testing) and 2019–2021 (training and validation). The tested tree-based models included Random Forest, XGBoost, and Explainable Boosting Machine. This study introduces a novel approach in the literature by employing weighted schemes and feature engineering to enhance the predictive performance of interpretable, low-complexity machine learning models in hindcasting waves. Representing wind direction as sine–cosine components generally reduced RMSE and BIAS relative to traditional speed–direction inputs, while an exponential sample weight scheme that emphasized storm waves halved extreme H_m0 underprediction without inflating overall RMSE. The best-performing model, a Random Forest model, achieved an RMSE of 0.096 m and a correlation of 0.855 on the unseen test set—30% lower overall RMSE and 50% lower extreme wave RMSE than the MEDSEA and COEXMED hindcasts. Additionally, the underprediction was reduced by 90% compared to these reanalysis models. The method offers a computationally lightweight, transferable supplement to numerical wave guidance for coastal engineering and harbor operations. Full article

In this study, the fatigue behavior in high-speed train bogie frames and mounted traction motors was investigated through dynamic stress measurements and vibration analysis. A spectrum synthesis method was developed to integrate multipoint random vibrations from the bogie frame into a unified excitation spectrum for motor fatigue assessment. The results demonstrate that fatigue damage in the bogie frame progresses linearly with increasing speed, with critical stress concentrations being identified at the motor base weld seams (41.4 MPa equivalent stress at 400 km/h). Traction motor vibration spectra were found to deviate substantially from IEC 61373 standards, leading to higher fatigue damage that follows an exponential growth pattern relative to speed increases. The proposed methodology provides direct experimental validation of component-specific fatigue mechanisms under operational loading conditions. Full article

Background/Objectives: Anorexia nervosa (AN) is a severe disorder with limited treatment efficacy. This interim analysis aimed to assess the preliminary efficacy and safety of transcranial direct current stimulation (tDCS) in reducing core AN symptoms, stress, depression, low self-esteem, and BMI in adolescent females, to determine the rationale for continuing the study. Methods: A single-center, randomized, double-blind, placebo-controlled trial included 20 adolescent females with AN assigned to an active tDCS group (n = 10) or a sham group (n = 10). The intervention involved 30 sessions over three weeks, targeting the dorsolateral prefrontal cortex. Outcomes were assessed at baseline, post-treatment, and follow-up using the Eating Attitudes Test (EAT-26) for eating disorder symptoms, the Perceived Stress Scale (PSS-10) for stress, the Beck Depression Inventory (BDI) for depression, the Rosenberg Self-Esteem Scale (SES) for self-esteem, and body mass index (BMI) measurements. Safety and tolerability were assessed using the tDCS Side Effects Questionnaire. Results: Eating disorder symptoms significantly decreased in the active tDCS group at study end (p = 0.003) and follow-up (p = 0.02), while no significant changes were observed in the sham group. Although BMI increased more in the active group (13.78%) than in the sham group (7.31%), this difference was not statistically significant (p = 0.10). Conclusions: Adverse effects were mild and transient, with no serious safety concerns reported. Based on the results of this interim analysis, the study will proceed due to promising efficacy outcomes and good treatment tolerability. Full article

The thermal environment problem is one of the main focuses of current urban environment research. At present, there are various methods used in urban space thermal environment (USTE) research. As a simulation method to quantify the USTE, the urban weather generator (UWG) has undergone great development and achieved many progressive results. It is necessary to establish and review its current research status by synthesizing UWG multi-scale applications. This review adopts a literature review approach, leveraging the Web of Science Core Collection to obtain previous relevant publications from 2010 to 2025 using “urban weather generator” and “thermal environment” as keywords. The literature is categorized by research themes, including model development, parameter optimization, and application cases. Through innovative analyses of spatio-temporal-scale classification, parameter optimization, the integration of anthropogenic heat emissions, and the multi-domain simulation potential of the UWG, this review synthesizes the application outcomes of the UWG model in multi-scale research, addressing gaps in current urban climate studies. The paper aims to elaborate and analyze the model’s current research status considering the following six aspects. First, the basic parameters in UWG simulation are introduced, including the data and parameter determination settings used in such simulations. Secondly, we introduce the simulation model and its basic principles, the simulation process, and the main steps of this process. Third, we classify and define UWG simulations of spatial thermal environments at different time scales and spatial scales. Fourth, regarding how to improve the accuracy of the UWG model, the deterministic parameters and uncertainty parameters settings are analyzed, respectively. Then, the impacts of anthropogenic heat during the simulation process are also discussed. Fifth, the applications of the UWG model in some major fields and its possible future development directions are addressed. Finally, the existing problems are summarized, the future development trends are prospected, and research on possible expected mitigation measures for the USTE is described. Full article

Wind farm performance monitoring has traditionally relied on deterministic models, such as power curves or machine learning approaches, which often fail to account for farm-wide behavior and the uncertainty quantification necessary for the reliable detection of underperformance. To overcome these limitations, we propose a probabilistic methodology for turbine-level active power prediction and uncertainty estimation using high-frequency SCADA data and farm-wide autoregressive information. The method leverages a Stochastic Variational Gaussian Process with a Linear Model of Coregionalization, incorporating physical models like manufacturer power curves as mean functions and enabling flexible modeling of active power and its associated variance. The approach was validated on a wind farm in the Belgian North Sea comprising over 40 turbines, using only 15 days of data for training. The results demonstrate that the proposed method improves predictive accuracy over the manufacturer’s power curve, achieving a reduction in error measurements of around 1%. Improvements of around 5% were seen in dominant wind directions (200°–300°) using 2 and 3 Latent GPs, with similar improvements observed on the test set. The model also successfully reconstructs wake effects, with Energy Ratio estimates closely matching SCADA-derived values, and provides meaningful uncertainty estimates and posterior turbine correlations. These results demonstrate that the methodology enables interpretable, data-efficient, and uncertainty-aware turbine-level power predictions, suitable for advanced wind farm monitoring and control applications, enabling a more sensitive underperformance detection. Full article

Reproductive health literacy (RHL) is essential to women’s ability to make informed reproductive health (RH) decisions and is a key determinant of RH outcomes. Resettled refugee women often experience poorer RH outcomes, yet there is limited research on their RHL and its influence on RH decision-making. This scoping review aims to (1) to evaluate existing methods for measuring RHL among resettled refugee women and (2) to characterize the relationship between RHL, RH decision-making, behavior, and outcomes among refugee women residing in high-income countries. A search of peer-reviewed literature published in English found limited direct measurement of RHL. Measurement methods were primarily qualitative or based on unvalidated survey instruments, limiting comparability and generalizability. The current methodologies do not adequately capture RH knowledge or RHL proficiency. A range of additional factors were found to influence RH decision-making and behavior, supporting the need for a means to accurately measure RHL. Further quantitative research is needed to clarify the extent to which RHL and knowledge influence RH behavior and outcomes. The development of a culturally relevant, validated RHL instrument that integrates knowledge and contextual influences would support healthcare providers and public health agents in serving and designing effective interventions for refugee women post-resettlement. Full article

Objectives: New smartphone-based methods for measuring cervical spine range of motion (CS-ROM) and posture are emerging. The purpose of this study was to assess the reliability and validity of three such methods in patients with non-specific chronic neck pain (NSCNP). Methods: The within-day test–retest reliability of CS-ROM and forward head posture (craniovertebral angle-CVA) was examined in 45 patients with NSCNP. CS-ROM was simultaneously measured with an accelerometer sensor (KFORCE Sens^®) and a mobile phone device (iHandy and Compass apps), testing the accuracy of each and the parallel-forms reliability between the two methods. For construct validity, correlations of CS-ROM with demographics, lifestyle, and other cervical and thoracic spine biomechanically based measures were examined in 90 patients with NSCNP. Male–female differences were also explored. Results: Both methods were reliable, with measurements concurring between the two devices in all six movement directions (intraclass correlation coefficient/ICC = 0.90–0.99, standard error of the measurement/SEM = 0.54–3.09°). Male–female differences were only noted for two CS-ROM measures and CVA. Significant associations were documented: (a) between the six CS-ROM measures (R = 0.22–0.54, p < 0.05), (b) participants’ age with five out of six CS-ROM measures (R = 0.23–0.40, p < 0.05) and CVA (R = 0.21, p < 0.05), (c) CVA with two out of six CS-ROM measures (extension R = 0.29, p = 0.005 and left-side flexion R = 0.21, p < 0.05), body mass (R = −0.39, p < 0.001), body mass index (R = −0.52, p < 0.001), and chest wall expansion (R = 0.24–0.29, p < 0.05). Significantly lower forward head posture was noted in subjects with a high level of physical activity relative to those with a low level of physical activity. Conclusions: The reliability of both CS-ROM methods was excellent. Reductions in CS-ROM and increases in CVA were age-dependent in NSCNP. The significant relationship identified between CVA and CWE possibly signifies interconnections between NSCNP and the biomechanical aspect of dysfunctional breathing. Full article

The accurate measurement of a crack width in concrete infrastructure is essential for structural safety assessment and maintenance. However, existing image-based methods either suffer from overestimation in complex geometries or are computationally inefficient. This paper proposes a novel hybrid approach combining a fast skeleton-pruning algorithm and a crack-width measurement technique called edge-OrthoBoundary (EOB). The skeleton-pruning algorithm prunes the skeleton, viewed as the longest branch in a tree structure, using a depth-first search (DFS) approach. Additionally, an intersection removal algorithm based on dilation replaces the midpoint circle algorithm to segment the crack skeleton into computable parts. The EOB method combines the OrthoBoundary and edge shortest distance (ESD) techniques, effectively correcting the propagation direction of the skeleton points while accounting for their width. The validation of real cracks shows the skeleton-pruning algorithm’s effectiveness, eliminating the need for a specified threshold and reducing time complexity. Experimental results with both actual and synthetic cracks demonstrate that the EOB method achieves the smallest RMS, MAE, and R values, confirming its accuracy and stability compared to the orthogonal projection (OP), OrthoBoundary, and ESD methods. Full article

Marine mollusk shells are a promising renewable source of calcium-based materials, offering a sustainable alternative for their synthesis. In this study, five types of marine shells—Chamelea gallina, Mya arenaria, Rapana venosa, Mytilus edulis, and Pecten maximus—were calcined at 900 °C for 2 h. The resulting powders were characterized by XRD, FTIR, SEM, PSD, and zeta potential analyses. XRD confirmed the dominant presence of CaO, with residual calcite and portlandite. FTIR spectra supported these findings, indicating the decomposition of carbonate phases and the formation of Ca–O bonds. SEM imaging revealed species-specific microstructures ranging from lamellar and wrinkled textures to compact aggregates, while particle size distributions varied from 15 to 37 μm. Thermogravimetric analysis revealed a two-step decomposition process for all samples, with significant species-dependent differences in mass loss and conversion efficiency, highlighting the influence of biogenic origin on the thermal stability and CaO yield of the resulting powders. Zeta potential measurements showed low colloidal stability, with the best performance found in Rapana venosa and Pecten maximus calcinated samples. Antibacterial activity was evaluated using a direct contact method against Escherichia coli and Enterococcus faecalis. All samples exhibited complete inactivation of E. coli, regardless of exposure time, while E. faecalis required prolonged contact (3.3 h) for full inhibition. The results highlight the potential of biogenic CaCO₃ and CaO powders as functional, antimicrobial materials suitable for environmental and biomedical applications. This study underscores the viability of marine shell waste valorization within a circular economy framework. Full article

Background: Asparaginase (ASPase) plays an important role in the therapy of acute lymphoblastic leukemia (ALL). Serum ASPase activity (SAA) can be modified and even abolished by host immune responses; therefore, current treatment guidelines recommend to monitor SAA during treatment administration. The SAA monitoring schedule needs to be carefully planned to reduce the number of samples without hampering the possibility of measuring pharmacokinetics (PK) parameters in individual patients. Complex modelling approaches, not easily applicable in common practice, have been applied in previous studies to estimate ASPase PK parameters. This study aimed to estimate PK parameters by using a simplified approach suitable for real-world settings with limited sampling. Methods: Our study was based on 434 patients treated in Italy within the AIEOP-BFM ALL 2009 trial. During the induction phase, patients received two doses of pegylated ASPase and were monitored with blood sampling at five time points, including time 0. PK parameters were estimated by using the individually available SAA measurements with simple modifications of the classical non-compartmental PK analysis. We also took the opportunity to develop and validate a series of limited sampling models to predict ASPase exposure. Results: During the induction phase, average ASPase activity at day 7 was 1380 IU/L after the first dose and 1948 IU/L after the second dose; therapeutic SAA levels (>100 IU/L) were maintained until day 33 in 90.1% of patients. The average AUC and clearance were 46,937 IU/L × day and 0.114 L/day/m², respectively. The database was analyzed for possible associations of PK parameters with biological characteristics of the patients, finding only a limited dependence on sex, age and risk score; however, these differences were not sufficient to allow any dose or schedule adjustments. Thereafter the possibility of further sampling reduction by using simple linear models to estimate the AUC was also explored. The most simple model required only two samplings 7 days after each ASPase dose, with the AUC being proportional to the sum of the two measured activities A(7) and A(21), calculated by the formula AUC = 14.1 × [A(7) + A(21)]. This model predicts the AUC with 6% average error and 35% maximum error compared to the AUC estimated with all available measures. Conclusions: Our study demonstrates the feasibility of a direct estimation of PK parameters in a real-life situation with limited and variable blood sampling schedules and also offers a simplified method and formulae easily applicable in clinical practice while maintaining a reliable pharmacokinetic monitoring. Full article