Search Results (16,199)

Background: Three-dimensional surface imaging is widely used in breast surgery. Recently, smartphone-based approaches have emerged. This investigation examines whether smartphone-based three-dimensional surface imaging provides clinically acceptable data in terms of accuracy when compared to a validated reference tool. Methods: Three-dimensional surface models were generated for 40 patients who underwent breast reconstruction surgery using the Vectra H2 (Canfield Scientific, Fairfield, NJ, USA) and the LiDAR sensor of an iPhone 15 Pro in conjunction with photogrammetry. The generated surface models were superimposed using CloudCompare’s ICP algorithm, followed by 14 linear surface-to-surface measurements to assess agreement between the three-dimensional surface models. Statistical methods included absolute error calculation, paired t-test, Bland–Altman analysis, and Intra-Class Correlation Coefficients to evaluate intra- and inter-rater reliability. Results: The average landmark-to-landmark deviation between smartphone-based and Vectra-based surface models was M = 2.997 mm (SD = 1.897 mm). No statistical differences were found in 13 of the 14 measurements for intra-rater comparison and in 12 of the 14 for inter-rater comparison. The Intra-Class Correlation Coefficient for intra-rater reliability of the iPhone was good, ranging from 0.873 to 0.993. Intra-Class Correlation Coefficient values indicated good reliability, ranging from 0.873 to 0.993 (intra-rater) and 0.845 to 0.992 (inter-rater). Bland–Altman analyses confirmed moderate to reliable agreement in 13 of 14 measurements. Conclusions: Smartphone-based three-dimensional surface imaging presents promising possibilities for breast assessment. However, it may not yet be suitable for highly detailed breast assessments requiring accuracy below the 3 mm threshold. Full article

Probable Maximum Storms (PMS) are synthetic design storms represented by idealized hyetographs. They play a critical role in assessing extreme rainfall events over extended durations and are widely applied in the hydraulic design of infrastructure such as dams, culverts, and bridges. PMS provide essential input for estimating Probable Maximum Floods (PMF), vital for analyzing worst-case flood scenarios with the potential to cause catastrophic loss of life and property. Despite their importance, the estimation of design storms at ungauged locations, particularly across synoptic scales, remains a major scientific and engineering challenge. This study addresses this gap by utilizing the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) dataset, which provides near-global estimated precipitation coverage. IMERG’s 24 h design storm hyetographs (expressed as cumulative percentage of precipitation throughout a 24 h period) were modeled and compared with similar reference data from NOAA Atlas 14 across twenty-eight regions and seven larger zones covering most of the conterminous United States (CONUS). Across the regions, the average root mean square error (RMSE) was 3.7%, with a mean relative bias (RB) of 1.4%. The mean normalized storm loading index (NSLI) from NOAA Atlas 14 was −7.7%, indicating that 57.7% of the total precipitation was received during the first 12 h of the storm, whereas IMERG storms exhibited a mean NSLI of −4.1%, suggesting they are also frontloaded but to a lesser extent. Across the broader zones, the mean RMSE was 4.8% and the mean RB was 1.1%. The mean NSLI values were −9.7% for NOAA Atlas 14 and −5.7% for IMERG, again indicating that IMERG storms are less frontloaded. When design storm families were estimated corresponding with different degrees of frontloading (corresponding to the 10, 20, …, 90% deciles of NSLI), the 40th to 60th percentile range exhibited the strongest agreement between IMERG and NOAA Atlas 14 hyetographs. Full article

This study investigates the energy consumption and losses associated with the gas production process in a condensate gas reservoir-type gas storage system. The energy consumption linked to each unit and key equipment was determined by HYSYS simulation, followed by a sensitivity analysis and exergy analysis. The findings reveal that the condensate oil stabilization tower is the primary energy-consuming equipment, responsible for 70.61% of the total energy consumption (3.82 × 10⁵ kJ·h⁻¹/1%). The temperature of the condensate reboiler is identified as the most significant influencing factor. Furthermore, the equipment exhibiting the highest exergy loss is the J-T valve (1.2 × 10⁷ kJ·h⁻¹), which contributes to 25.23% of the total loss. Consequently, to mitigate energy consumption in the gas production system, it is crucial to control the temperature of the condensate oil reboiler. Enhancing efficiency will rely on recovering the pressure energy loss associated with the J-T valve. The field gas gathering system lacks sub-unit energy consumption measurement and flow measurement for key process fluids. This study can provide methodological and data references for optimizing the operation of this condensate oil–gas reservoir-type storage facility. Full article

A liquid-sealed single-mode–no-core–single-mode (SNS) structure fiber temperature sensor based on polyvinyl alcohol (PVA) partial replacement coating is proposed. Using a liquid-sealed glass capillary structure, the PVA solution is introduced into the SNS structure and avoids its influence by environmental humidity. Temperature can be obtained by measuring the shift of the multimode interference spectrum, which is affected by the thermal optical effect of the PVA solution. Through theoretical simulation of the sensor, the optimal NCF fiber length and coating stripped length are obtained by comprehensively considering the transmitted loss and output spectrum signal-to-noise ratio (SNR). The optimal PVA solution concentration is selected by measuring the thermo-optic coefficient (TOC) and refractive index (RI). Based on the theoretical optimization results, a PVA solution-coated SNS fiber optic temperature sensor is experimentally fabricated, and temperature-sensing characteristics are measured within −3.6 to 73.2 °C. The experimental results show that the sensor has a high sensitivity (nm/°C, maximum is 21.713 nm/°C) and has a resolution of 10⁻³ °C. ${\lambda }_{dip}$ has a stable negative linear relationship with temperature, and the correlation coefficient of the fitting curve exceeds 95%. The temperature cycling experiment and long-term stability test show that the temperature sensor has good repeatability and stability. The experimental results also show the nonlinear relationship between the temperature measurement range and sensitivity, clarify the important factors affecting the response performance of fiber temperature sensors, and provide important reference values for optical fiber temperature sensors. Full article

Deepfake audio refers to speech that has been synthetically generated or altered through advanced neural network techniques, often with a degree of realism sufficient to convincingly imitate genuine human voices. As these manipulations become increasingly indistinguishable from authentic recordings, they present significant threats to security, undermine media integrity, and challenge the reliability of digital authentication systems. In this study, a robust detection framework is proposed, which leverages the power of self-supervised learning (SSL) and attention-based modeling to identify deepfake audio samples. Specifically, audio features are extracted from input speech using two powerful pretrained SSL models: HuBERT-Large and WavLM-Large. These distinctive features are then integrated through an Attentional Multi-Feature Fusion (AMFF) mechanism. The fused features are subsequently classified using a NeXt-Time Delay Neural Network (NeXt-TDNN) model enhanced with Efficient Channel Attention (ECA), enabling improved temporal and channel-wise feature discrimination. Experimental results show that the proposed method achieves a 0.42% EER and 0.01 min-tDCF on ASVspoof 2019 LA, a 1.01% EER on ASVspoof 2019 PA, and a pooled 6.56% EER on the cross-channel ASVspoof 2021 LA evaluation, thus highlighting its effectiveness for real-world deepfake detection scenarios. Furthermore, on the ASVspoof 5 dataset, the method achieved a 7.23% EER, outperforming strong baselines and demonstrating strong generalization ability. Moreover, the macro-averaged F1-score of 96.01% and balanced accuracy of 99.06% were obtained on the ASVspoof 2019 LA dataset, while the proposed method achieved a macro-averaged F1-score of 98.70% and balanced accuracy of 98.90% on the ASVspoof 2019 PA dataset. On the highly challenging ASVspoof 5 dataset, which includes crowdsourced, non-studio-quality audio, and novel adversarial attacks, the proposed method achieves macro-averaged metrics exceeding 92%, with a precision of 92.07%, a recall of 92.63%, an F1-measure of 92.35%, and a balanced accuracy of 92.63%. Full article

Background/Objectives: Accurate urinary bladder (UB) volume measurement is essential for diagnosing urinary retention, evaluating post-void residuals, and guiding catheterization decisions. Conventional 2D ultrasound and automated non-visual bladder scanners can be limited by operator variability and systematic errors. Three-dimensional (3D) ultrasound may improve accuracy and reproducibility, but data on handheld, semi-automated devices remain scarce. This study aimed to compare the accuracy, reproducibility, and feasibility of a handheld 3D ultrasound device versus conventional 2D ultrasound for UB volume estimation, using measured voided volume as the reference standard. Methods: Fifty-three healthy male volunteers (mean age 19.6 ± 2.0 years) underwent bladder volume assessment by two novice operators using both methods: 2D ultrasound (manual caliper-based) and handheld 3D ultrasound device (Butterfly iQ). Each operator performed two repeated measurements per method. True voided volume was recorded immediately after scanning. Accuracy was assessed using median differences, percentage error, and Bland–Altman analysis. Intra- and inter-operator reproducibility were evaluated with intraclass correlation coefficients (ICC). Results: Both methods significantly underestimated bladder volume (p < 0.001). The 3D method demonstrated higher accuracy, with a median percentage error of −11.2% to −12.0%, versus −27.6% to −36.7% for 2D. The mean bias ranged from −64.9 mL to −72.3 mL for 3D, compared to −137.4 mL to −191.6 mL for 2D. Intra-operator reproducibility was excellent for all methods (ICC > 0.96). Inter-operator agreement was higher for 3D (ICC = 0.977; bias 7.3 mL) than for 2D (ICC = 0.927; bias −54.2 mL). All scans were completed successfully; however, the 3D device occasionally faced technical errors in large bladder volumes. Conclusions: Handheld 3D ultrasound yielded greater accuracy and inter-operator consistency than conventional 2D ultrasound in healthy adults, with minimal operator input. Both methods underestimated true volume, indicating the need for clinical consideration when interpreting measurements. These findings support broader clinical adoption of handheld 3D ultrasound, particularly in settings with variable sonographic expertise, while highlighting the need for validation in elderly and pathological populations. Full article

Calibration of power equipment has become an essential task in modern power systems. This paper proposes a distributed remote calibration prototype based on a cloud–edge–end architecture by integrating intelligent sensing, Internet of Things (IoT) communication, and edge computing technologies. The prototype employs a high-precision frequency-to-voltage conversion module leveraging satellite signals to address traceability and value transmission challenges in remote calibration, thereby ensuring reliability and stability throughout the process. Additionally, an environmental monitoring module tracks parameters such as temperature, humidity, and electromagnetic interference. Combined with video surveillance and optical character recognition (OCR), this enables intelligent, end-to-end recording and automated data extraction during calibration. Furthermore, a cloud-edge task scheduling algorithm is implemented to offload computational tasks to edge nodes, maximizing resource utilization within the cloud–edge collaborative system and enhancing service quality. The proposed prototype extends existing cloud–edge collaboration frameworks by incorporating calibration instruments and sensing devices into the network, thereby improving the intelligence and accuracy of remote calibration across multiple layers. Furthermore, this approach facilitates synchronized communication and calibration operations across symmetrically deployed remote facilities and reference devices, providing solid technical support to ensure that measurement equipment meets the required precision and performance criteria. Full article

Accurate spatial data is essential for the effective planning and restoration of rural streets, which are linear elements within settlements. This study evaluates the applicability of digital street models to landscape architecture, focusing on the precision and efficiency of three data acquisition methods: terrestrial laser scanning (TLS), aerial photogrammetry using an unmanned aerial vehicle (UAV), and close-range photogrammetry (CRP) using a smartphone. TLS was used as the reference method due to its high local geometric accuracy, while UAV and CRP were assessed as low-cost alternatives. We conducted field data collection, digital model processing, and a comparative analysis of accuracy, cost, and time requirements. TLS achieved high precision, with 85% of measured points within ±0.5 cm; however, it produced data gaps due to scanning obstacles. UAV-derived models demonstrated 93% agreement with TLS and offered more complete coverage, making it a more efficient option for overall mapping. CRP models showed only 34% compliance with TLS but provided superior texture detail. However, their limited geometric accuracy and risk of deformation constrain their use in visualizing specific elements. Among the low-cost methods, the UAV is the most suitable for generating models usable in GIS and CAD environments. A combined approach—using a UAV for accurate geometry and CRP for detailed textures—offers a cost-effective strategy for enhancing model quality in landscape architectural applications. Full article

The transportation sector is witnessing a paradigm shift toward more sustainable and efficient propulsion systems, with a particular focus on public transportation vehicles such as buses. In this context, hybrid powertrains combining internal combustion engines with electric propulsion systems have emerged as prominent contenders due to their ability to offer significant fuel savings and CO₂ emission reductions compared to conventional diesel powertrains. In this study, the simulation of a complete hybrid bus vehicle is carried out to evaluate the impact of two different hybrid powertrain architectures compared to the diesel reference one. The selected vehicle is a 12 m city bus that performs typical urban driving routes represented by real measured driving cycles. First, the vehicle model was developed using a state-of-the-art diesel powertrain (internal combustion engine) and validated against literature data. This model facilitates a comprehensive evaluation of system efficiency, fuel consumption, and CO₂ emissions while incorporating the effects of driving cycle variability. Subsequently, two different hybrid configurations (parallel P1 and series) are implemented in the model and compared to predict the relative energy consumption and environmental impact, highlighting advantages and challenges. Full article

Background: This study aims to establish standardized reference values for myocardial T1 and T2 relaxation times in a clinically and imaging-defined real-world patient cohort, evaluating their variability in relation to age, sex, and comorbidities. By identifying key physiological and pathological influences, this investigation seeks to enhance CMR-based myocardial mapping for improved differentiation between normal and pathological myocardial conditions. Methods: This retrospective observational study analyzed T1 and T2 relaxation times using CMR at 1.5 Tesla in a cohort of 491 subjects. T1 and T2 times were measured using MOLLI and GRASE sequences, and statistical analyses assessed intra- and interindividual variations, including the influence of age, sex, and comorbidities, to establish reference values and improve myocardial tissue characterization. Results: T1 and T2 relaxation times were analyzed in 291 and 200 participants, respectively. The mean global T1 time was 1004.7 ± 49.8 ms, with no significant differences between age groups (p = 0.81) or sexes (p = 0.58). However, atrial fibrillation (AF) and mitral regurgitation (MR) were associated with significantly prolonged T1 times (p < 0.05). The mean global T2 time was 67.4 ± 8.6 ms, with age-related prolongation (p < 0.05), but no sex differences (p = 0.46). Comorbidities did not significantly influence T2 times, except for NYHA Class III–IV patients, who exhibited prolonged T2 values (p < 0.05). Conclusions: Standardized T1 and T2 reference values are essential to improve diagnostic accuracy and risk stratification in CMR-based myocardial tissue characterization. Future research should focus on multicenter validation, AI-driven analysis, and the development of age- and comorbidity-adjusted normative databases to enhance individualized cardiovascular care. Full article

Natural radionuclides may occur in groundwater and pose health risks when present in elevated concentrations. This study evaluates the quality of shallow groundwater in Nysa County (SW Poland) based on the activity concentrations of natural radionuclides radon (²²²Rn) and radium (²²⁶Ra) and estimates the associated radiological risk from water ingestion. Twenty-three groundwater samples were collected from private wells located within two distinct geological units: the Fore-Sudetic Block and the Opole Trough. Activity concentrations of ²²²Rn and ²²⁶Ra were measured using the liquid scintillation counting method. A spatial distribution model for ²²²Rn was developed using inverse distance weighting in QGIS. Local hydrogeochemical background levels were determined using the Q-Dixon test, interquartile range, and Shapiro–Wilk normality test. The background ranged from 2.6 to 3.9 Bq·L⁻¹ in the Opole Trough and from 0 to 10.7 Bq·L⁻¹ in the Fore-Sudetic Block. The lower detection limit (0.05 Bq·L⁻¹) for ²²⁶Ra activity concentration measurements was not exceeded. Effective dose rates were calculated in accordance with the recommendations of the International Commission on Radiological Protection and United Nations Scientific Committee on the Effects of Atomic Radiation. Doses ranged from <1 µSv to over 120 µSv·y⁻¹. Although all samples met national regulatory standards (≤1 mSv·y⁻¹), the World Health Organization reference level (0.1 mSv·y⁻¹) was exceeded in two cases. The results support the need for the radiological monitoring of unregulated private wells and provide a scientific basis for the refinement of legal frameworks and health protection strategies. Full article

Extra-osseous calcification refers to the pathological deposition of calcium salts in soft tissues. Its most recognized forms affect the cardiovascular system, leading to vascular and heart valve calcifications. This process is active and regulated, involving the phenotype transition of resident cells into osteo/chondrogenic lineage. Chronic kidney disease (CKD) patients frequently suffer from vascular and other soft tissue calcification. OsteoSense dyes are fluorescent imaging agents developed to visualize calcium deposits during bone formation. In addition to its application in bone physiology, it has been used to detect vascular smooth muscle cell calcification in vitro and to evaluate calcification ex vivo. Here, we investigated CKD-associated soft tissue calcification by applying OsteoSense in vivo. CKD was induced by a diet containing adenine and elevated phosphate. OsteoSense (80 nmol/kg body weight) was injected intravenously through the retro-orbital venous sinus 18 h before the measurement on an IVIS Spectrum In Vivo Imaging System. OsteoSense staining detected calcium deposition in the aorta, kidney, heart, lung, and liver in CKD mice. On the other hand, no calcification occurred in the brain, eye, or spleen. OsteoSense positivity in the calcified soft tissues in CKD mice was associated with increased mRNA levels of osteo/chondrogenic transcription factors. Our findings demonstrate that OsteoSense is a sensitive and effective tool for detecting soft tissue calcification in vivo, and may be particularly valuable for studies of CKD-related ectopic calcification. Full article

Accurate and reliable characterization of current transformer (CT) performance is essential for maintaining grid stability and power quality in modern electrical networks. CT measurements are key to effective monitoring of harmonic distortions, supporting regulatory compliance and ensuring the safe operation of the grid. This paper addresses a method for the characterization of CTs across an extended frequency range from 50 Hz up to 150 kHz, driven by increasing power quality issues introduced by renewable energy installations and non-linear loads. Traditional CT calibration approaches involve measurement setups that offer ppm-level uncertainty but are complex to operate and limited in practical frequency range. To simplify and expand calibration capabilities, a calibration system employing a sampling ammeter (power analyzer) was developed, enabling the direct measurement of CT secondary currents of an unknown CT and a reference CT without any further auxiliary equipment. The resulting expanded magnitude ratio uncertainties for the wideband CT calibration system are 10 ppm ($k=2$) up to 10 kHz and less than 120 ppm from 10 kHz to 150 kHz; these uncertainties do not include the uncertainty of the reference CT. Additionally, the operational conditions and setup design choices, such as instrument warm-up duration, grounding methods, measurement shunt selection, and cable type, were evaluated for their impact on measurement uncertainty and repeatability. The results highlight the significance of minimizing parasitic impedances at higher frequencies and maintaining consistent testing conditions. The developed calibration setup provides a robust foundation for future standardization efforts and practical guidance to characterize CT performance in the increasingly important supraharmonic frequency range. Full article

Energy conservation and carbon reduction are critical in energy policies. Therefore, numerous energy-saving methods, such as the introduction of new technologies and the replacement of outdated equipment, have been proposed. To determine whether these methods are effective in energy conservation and carbon reduction, scientific validation is required. The most common validation method is energy baseline. An energy baseline refers to the use of data measured before energy-saving improvements. It is used to construct a mathematical model that describes energy consumption. Using the baseline, the energy consumption during the baseline period after improvements is calculated. By subtracting the measured consumption from the value, the amount of energy saved is estimated. Traditionally, linear regression is used to establish energy baseline prediction. However, linear regression has limitations with complex energy data. Therefore, we used deep learning models to handle nonlinear data in the air compression system for comparative analysis. The developed long-short-term memory (LSTM) model showed superior capabilities for processing nonlinear data, aligning with the actual data distribution, and reducing errors. Compared with linear regression models, the LSTM model reduced uncertainty, risk, and cost by 40.3%. Full article

Crop aeroponic cultivation still faces issues such as insufficient precision in water supply control and scientifically-based irrigation scheduling. To address this challenge, the present study aims to establish a precision irrigation protocol adapted to the characteristics of crop aeroponic cultivation. Using coriander (Coriandrum sativum L.) as the experimental subject, crop water requirements were estimated utilizing both the FAO56 P-M equation and its revised form. The RMSE between the water requirement measured values and the calculated values using the P-M formula is 2.12 mm, the MAE is 2.0 mm, and the MAPE is 14.29%. The RMSE between the water requirement measured values and the calculated values using the revised P-M formula is 0.88 mm, the MAE is 0.82 mm, and the MAPE is 5.78%. The results indicate that the water requirement values calculated using the revised P-M formula are closer to the measured values. For model development, this study used coriander evapotranspiration as a basis. Major environmental variables influencing water requirement were selected as input features, and the daily reference water requirement served as the output. Three modeling approaches were implemented: Random Forest (RF), Bagging, and M5P Model Tree algorithms. The results indicate that, in comparing various input combinations (C1: air temperature, relative humidity, atmospheric pressure, wind speed, radiation, photoperiod; C2: air temperature, relative humidity, wind speed, radiation; C3: air temperature, relative humidity, radiation), the RF model based on C1 input demonstrated superior performance with RMSE = 0.121 mm/d, MAE = 0.134 mm/d, MAPE = 2.123%, and R² = 0.971. It significantly outperforms the RF models with other input combinations, as well as the Bagging and M5P models across all input scenarios, in terms of convergence rate, determination coefficient, and comprehensive performance. Its predictions aligned more closely with observed data, showing enhanced accuracy and adaptability. This optimized prediction model demonstrates particular suitability for forecasting water requirements in aeroponic coriander production and provides theoretical support for efficient, intelligent water-saving management in crop aeroponic cultivation. Full article