Search Results (12)

Background/Objectives: The aim of this study was to evaluate the possible effects of shock wave (SW) application for the noninvasive treatment of impacted ureteral stones on the pericalcular ureteral tissue in terms of changes in ureteral wall thickness. Methods: A total of 114 patients with impacted proximal ureteral stones underwent ESWL at our department. Patient- and stone-related parameters (size, density, and location); radiological parameters, including ureteral wall thickness (UWT); and degree of hydronephrosis were assessed on NCCT images before and shortly after the procedure. The possible effects of applying high-energy shock waves to the pericalcular ureteral tissue were evaluated according to changes in ureteral wall thickness. A comparative evaluation was conducted based on the number of sessions and the outcomes of SWL. Results: The mean value of UWT after the first session of stone management decreased significantly when compared to the mean pre-operative value (p < 0.005). This was also noted after the second treatment session, after which the mean UWT was significantly lower than the pre-operative value (p < 0.005). However, the difference in the mean UWT before and after three sessions of SWL was not significant (p = 0.104). A detailed evaluation of these values in all groups revealed that although the decrease in the mean UWT in cases with a successful outcome was significant (p < 0.005), the change in these values was not significant in cases for which the treatment was unsuccessful (partial disintegration of the stone or no disintegration at all) (p = 0.145). Conclusions: Application of SW in patients with impacted upper ureteral stones may not have a detrimental effect on the ureteral wall or compromise a secondary procedure if the stones are successfully disintegrated and passed after one or two sessions. However, in the case of resistant stones, an increased number of sessions and more SWs may induce such adverse effects, warranting further evaluation in future studies. Full article

Aim: The aim of this study was to evaluate the patient-, stone-, and anatomy-related factors that may influence the impaction status of proximal ureteral stones, with a particular focus on the ureteral wall thickness (UWT) as a radiologic surrogate marker of chronic irritation. Materials and Methods: A total of 115 adult patients who underwent ureteroscopic treatment for impacted proximal ureteral stones between January 2021 and December 2023 were retrospectively analyzed. The demographic data, comorbidities, stone characteristics (volume, location, and HU value), and anatomical parameters (hydronephrosis grade, proximal ureteral diameter, and UWT) were evaluated using non-contrast computed tomography (NCCT). The correlations between the UWT and both patient- and stone-related variables were assessed using linear and logistic regression analyses. Results: The mean patient age was 45.3 ± 13.8 years, with a male-to-female ratio of 2.11. A significant positive correlation was observed between the UWT and hydronephrosis grade (p = 0.002), presence of comorbidities such as hypertension or diabetes mellitus (p = 0.005), and stone volume (p = 0.005). A larger stone diameter and length were also significantly associated with increased UWT (p = 0.014 and p = 0.005, respectively). However, no statistically significant correlation was found between the UWT and stone density (p = 0.614) or the duration of stone presence (p = 0.987). Conclusions: Increased ureteral wall thickness appears to be a strong indicator of stone impaction severity and is positively associated with hydronephrosis, comorbid conditions, and stone size. These findings support the potential clinical utility of UWT in preoperative planning and treatment selection for impacted upper ureteral stones. Further prospective studies are warranted to validate these observations and explore their implications for procedural success and complication risk. Full article

Background and Objectives: Ureteral kinking may hinder endoscopic access and reduce the success of ureteroscopic lithotripsy (URSL). This study evaluated whether kinking can be predicted preoperatively using non-contrast computed tomography (CT) by introducing a novel metric—Maximum Horizontal Ureteral Displacement (HUDmax)—and assessed its predictive value for procedural success. Materials and Methods: Data from 1261 patients who underwent URSL for a single ureteral stone were retrospectively analyzed. Patients were categorized into two groups based on whether the stone could be reached using a semirigid ureteroscope. Propensity score matching (1:2) was performed based on stone size and location, resulting in two matched cohorts: Group 1—Semirigid Inaccessible (SRI, n = 72), and Group 2—Semirigid Accessible (SRA, n = 144). Stone characteristics, ureteral wall thickness (UWT), and HUDmax were evaluated. Correlations between HUDmax and surgical parameters were analyzed, and the predictive value of HUDmax was assessed using receiver operating characteristic (ROC) analysis. Results: The SRI group showed significantly higher HUDmax values (median 2.36 mm vs. 1.2 mm, p < 0.0001). Semirigid access failure necessitated conversion to flexible ureteroscopy in all SRI cases, compared to 15% in the SRA group (p < 0.0001). Stone-free rates were significantly lower in the SRI group (45% vs. 82%, p < 0.0001), and the use of a double-J stent or nephrostomy placement was more frequent. Operative times were also longer in the SRI group (55 vs. 42 min, p < 0.0001). HUDmax correlated positively with operative time (r = 0.258, p = 0.005) but not with stone size, density, UWT, or hydronephrosis. ROC analysis showed HUDmax strongly predicted semirigid access failure (AUC: 0.805; cutoff: 1.58 mm), and moderately predicted stone-free status (AUC: 0.697; cutoff: 1.68 mm). Conclusions: Severe ureteral kinking constitutes a significant anatomical obstacle to the success of semirigid URSL. This study is the first to demonstrate that clinically relevant kinking can be predicted preoperatively using a non-contrast imaging modality, via the novel HUDmax parameter. Full article

Despite the successful applications of the remote sensing image in agriculture, meteorology, and geography, its relatively low spatial resolution is hindering the further applications. Super-resolution technology is introduced to conquer such a dilemma. It is a challenging task due to the variations in object size and textures in remote sensing images. To address that problem, we present SymSwin, a super-resolution model based on the Swin transformer aimed to capture a multi-scale context. The symmetric multi-scale window (SyMW) mechanism is proposed and integrated in the backbone, which is capable of perceiving features with various sizes. First, the SyMW mechanism is proposed to capture discriminative contextual features from multi-scale presentations using corresponding attentive window size. Subsequently, a cross-receptive field-adaptive attention (CRAA) module is introduced to model the relations among multi-scale contexts and to realize adaptive fusion. Furthermore, RS data exhibit poor spatial resolution, leading to insufficient visual information when merely spatial supervision is applied. Therefore, a U-shape wavelet transform (UWT) loss is proposed to facilitate the training process from the frequency domain. Extensive experiments demonstrate that our method achieves superior performance in both quantitative metrics and visual quality compared with existing algorithms. Full article

Intense convection is often accompanied by high-frequency lightning and is highly prone to producing heavy rainfall, strong winds, hail, and tornadoes, frequently resulting in significant damage and loss of life. It is necessary to understand the mechanisms and meteorological conditions of intense convection. This study utilizes the Thunderstorm Feature Dataset from 2010–2018 to analyze the characteristics of thunderstorms with extreme lightning activity (TELAs), defined as thunderstorms whose lightning frequency ranks in the top 1%. Four regions with relatively high thunderstorm activity were selected for analysis: Northeast China (NEC), North China (NC), South China (SC), and the Tibetan Plateau (TP). In NEC, TELAs primarily occur just west of upper-level westerly troughs (UWT), including cold vortices. In NC, TELAs are mainly associated with UWT and subtropical highs (STH). In SC, TELAs are related to frontal systems, easterly waves, tropical cyclones, and STH. In TP, TELAs are generated by TP vortices. Before the TELA process, vertically integrated moisture divergence (VIMD) and convective available potential energy (CAPE) show the most notable anomalies. Except for the TP, TELAs are typically located between centers of anomalies with positive and negative geopotential height (500 hPa) and near centers of anomalies with positive CAPE and negative VIMD, accompanied by notable increases in surface temperature and wind speed. These findings offer a valuable reference for the early warning and forecasting of intense convection. Full article

Natural secondary forests play a crucial role in global ecological security, climate change mitigation, and biodiversity conservation. However, accurately delineating individual tree crowns and identifying tree species in dense natural secondary forests remains a challenge. This study combines deep learning with traditional image segmentation methods to improve individual tree crown detection and species classification. The approach utilizes hyperspectral, unmanned aerial vehicle laser scanning data, and ground survey data from Maoershan Forest Farm in Heilongjiang Province, China. The study consists of two main processes: (1) combining semantic segmentation algorithms (U-Net and Deeplab V3 Plus) with watershed transform (WTS) for tree crown detection (U-WTS and D-WTS algorithms); (2) resampling the original images to different pixel densities (16 × 16, 32 × 32, and 64 × 64 pixels) and inputting them into five 3D-CNN models (ResNet10, ResNet18, ResNet34, ResNet50, VGG16). For tree species classification, the MSFB combined with the CNN models were used. The results show that the U-WTS algorithm achieved a recall of 0.809, precision of 0.885, and an F-score of 0.845. ResNet18 with a pixel density of 64 × 64 pixels achieved the highest overall accuracy (OA) of 0.916, an improvement of 0.049 over the original images. After incorporating MSFB, the OA improved by approximately 0.04 across all models, with only a 6% increase in model parameters. Notably, the floating-point operations (FLOPs) of ResNet18 + MSFB were only one-eighth of those of ResNet18 with 64 × 64 pixels, while achieving similar accuracy (OA: 0.912 vs. 0.916). This framework offers a scalable solution for large-scale tree species distribution mapping and forest resource inventories. Full article

Algorithms for noise reduction that use the translation invariant wavelet transform indirectly are spatially selective filtering algorithms in the wavelet domain. These algorithms use the undecimated wavelet transform to accurately determine the coefficients corresponding to the contours in the images, these being processed differently from the other wavelet coefficients. The use of the undecimated wavelet transform in image noise reduction applications leads not only to an improvement in terms of Mean Square Error (MSE), but also in terms of the content quality of the processed images. In the case of noise reduction procedures by truncation of wavelet coefficients, artifacts appear, especially in the approximation of singularities, due to some pseudo-Gibbs phenomena. These artifacts, which appear locally, are troublesome in the case of object recognition applications from images acquired in conditions of nonuniform illumination and low contrast. In this work we propose a method of feature extractor based on undecimated wavelet transform (UWT) and local binary pattern (LBP). The results obtained on images acquired from drones in adverse conditions show promising results in terms of accuracy. The authors show that the displacement-invariant wavelet transform is an very good method of compression and noise reduction in signals. Full article

Boat acceleration profiles can provide valuable information for coaches and practitioners to make meaningful technical interventions and monitor the determinants of success in rowing. Previous studies have used simple feature detection methods to identify key phases within individual strokes, such as drive onset, drive time, drive offset and stroke time. However, based on skill level, technique or boat class, the hull acceleration profile can differ, making robust feature detection more challenging. The current study’s purpose is to employ the undecimated wavelet transform (UWT) technique to detect individual features in the stroke acceleration profile from a single rowing hull-mounted accelerometer. In this investigation, the temporal and kinematic values obtained using the AdMos^TM sensor in conjunction with the UWT processing approach were strongly correlated with the comparative measures of the Peach™ instrumented oarlock system. The measures for stroke time displayed very strong agreeability between the systems for all boat classes, with ICC values of 0.993, 0.963 and 0.954 for the W8+, W4− and W1x boats, respectively. Similarly, the drive time was also very consistent, with strong to very strong agreeability, producing ICC values of 0.937, 0.901 and 0.881 for the W8+, W4− and W1x boat classes. Further, a Bland–Altman analysis displayed little to no bias between the AdMos^TM-derived and Peach™ measures, indicating that there were no systematic discrepancies between signals. This single-sensor solution could form the basis for a simple, cost-effective and accessible alternative to multi-sensor instrumented systems for the determination of sub-stroke kinematic phases. Full article

(1) Background: Wilms tumor (WT) treated preoperatively is cured in over 90% of cases. However, how long preoperative chemotherapy can be given is unknown. (2) Methods: 2561/3030 patients with WT (age < 18 years) treated between 1989 and 2022 according to SIOP-9/GPOH, SIOP-93-01/GPOH, and SIOP-2001/GPOH are retrospectively analyzed to assess the risk of time to surgery (TTS) for relapse-free survival (RFS) and overall survival (OS). (3) Results: TTS was calculated for all surgeries, with the mean being 39 days (38.5 ± 12.5) for unilateral tumors (UWT) and 70 days (69.9 ± 32.7) for bilateral disease (BWT). Relapse occurred in 347 patients, of which 63 (2.5%) were local, 199 (7.8%) were metastatic, and 85 (3.3%) were combined. Moreover, 184 patients (7.2%) died, 152 (5.9%) due to tumor progression. In UWT, recurrences and mortality are independent of TTS. For BWT without metastases at diagnosis, the incidence of recurrence is less than 18% up to 120 days and increases to 29% after 120 days, and to 60% after 150 days. The risk of relapse (Hazard Ratio) adjusted for age, local stage, and histological risk group increases to 2.87 after 120 days (CI 1.19–7.95, p = 0.022) and to 4.62 after 150 days (CI 1.17–18.26, p = 0.029). In metastatic BWT, no influence of TTS is detected. (4) Conclusions: The length of preoperative chemotherapy has no negative impact on RFS or OS in UWT. In BWT without metastatic disease, surgery should be performed before day 120, as the risk of recurrence increases significantly thereafter. Full article

In recent years, the control of devices “by the power of the mind” has become a very controversial topic but has also been very well researched in the field of state-of-the-art gadgets, such as smartphones, laptops, tablets and even smart TVs, and also in medicine, to be used by people with disabilities for whom these technologies may be the only way to communicate with the outside world. It is well known that BCI control is a skill and can be improved through practice and training. This paper aims to improve and diversify signal processing methods for the implementation of a brain-computer interface (BCI) based on neurological phenomena recorded during motor tasks using motor imagery (MI). The aim of the research is to extract, select and classify the characteristics of electroencephalogram (EEG) signals, which are based on sensorimotor rhythms, for the implementation of BCI systems. This article investigates systems based on brain-computer interfaces, especially those that use the electroencephalogram as a method of acquisition of MI tasks. The purpose of this article is to allow users to manipulate quadcopter virtual structures (external, robotic objects) simply through brain activity, correlated with certain mental tasks using undecimal transformation (UWT) to reduce noise, Independent Component Analysis (ICA) together with determination coefficient (r²) and, for classification, a hybrid neural network consisting of Radial Basis Functions (RBF) and a multilayer perceptron–recurrent network (MLP–RNN), obtaining a classification accuracy of 95.5%. Following the tests performed, it can be stated that the use of biopotentials in human–computer interfaces is a viable method for applications in the field of BCI. The results presented show that BCI training can produce a rapid change in behavioral performance and cognitive properties. If more than one training session is used, the results may be beneficial for increasing poor cognitive performance. To achieve this goal, three steps were taken: understanding the functioning of BCI systems and the neurological phenomena involved; acquiring EEG signals based on sensorimotor rhythms recorded during MI tasks; applying and optimizing extraction methods, selecting and classifying characteristics using neuronal networks. Full article

The article presents a research in the field of complex sensing, detection, and recovery of communications networks applications and hardware, in case of failures, maloperations, or unauthorized intrusions. A case study, based on Davis AI engine operation versus human maintenance operation is performed on the efficiency of artificial intelligence agents in detecting faulty operation, in the context of growing complexity of communications networks, and the perspective of future development of internet of things, big data, smart cities, and connected vehicles. (*). In the second part of the article, a new solution is proposed for the detection of applications faults or unauthorized intrusions in traffic of communications networks. The first objective of the proposed method is to propose an approach for predicting time series. This approach is based on a multi-resolution decomposition of the signals employing the undecimate wavelet transform (UWT). The second approach for assessing traffic flow is based on the analysis of long-range dependence (LRD) (for this case, a long-term dependence). Estimating the degree of long-range dependence is performed by estimating the Hurst parameter of the analyzed time series. This is a relatively new statistical concept in communications traffic analysis and can be implemented using UWT. This property has important implications for network performance, design, and sizing. The presence of long-range dependency in network traffic is assumed to have a significant impact on network performance, and the occurrence of LRD can be the result of faults that occur during certain periods. The strategy chosen for this purpose is based on long-term dependence on traffic, and for the prediction of faults occurrence, a predictive control model (MPC) is proposed, combined with a neural network with radial function (RBF). It is demonstrated via simulations that, in the case of communications traffic, time location is the most important feature of the proposed algorithm. Full article

This study uses an optimization approach representation and numerical solution for the variable viscosity and non-linear Boussinesq effects on the free convection over a vertical truncated cone in porous media. The surface of the vertical truncated cone is maintained at uniform wall temperature and uniform wall concentration (UWT/UWC). The viscosity of the fluid varies inversely to a linear function of the temperature. The partial differential equation is transformed into a non-similar equation and solved by Keller box method (KBM). Compared with previously published articles, the results are considered to be very consistent. Numerical results for the local Nusselt number and local Sherwood number with the six parameters (1) dimensionless streamwise coordinate ξ, (2) buoyancy ratio N, (3) Lewis number Le, (4) viscosity-variation parameter ${\mathsf{\theta }}_{\mathrm{r}}$ , (5) non-linear temperature parameter ${\mathsf{\delta }}_1$ , and (6) non-linear concentration parameter ${\mathsf{\delta }}_2$ are expressed in figures and tables. The Taguchi method was used to predict the best point of the maxima of the local Nusselt (Sherwood) number of 3.8636 (5.1156), resulting in ξ (4), N (10), Le (0.5), ${\mathsf{\theta }}_{\mathrm{r}}$ (−2), ${\mathsf{\delta }}_1$ (2), ${\mathsf{\delta }}_2$ (2) and ξ (4), N (10), Le (2), ${\mathsf{\theta }}_{\mathrm{r}}$ (−2), ${\mathsf{\delta }}_1$ (2), ${\mathsf{\delta }}_2$ (2), respectively. Full article