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Objective: To compare the mean VSS score of intra lesional Verapamil Hydrochloride vs Triamcinolone Acetonide in treatment of keloids/ hypertrophic scars. Study type, settings & duration: This non randomized controlled trial was conducted in Sheikh Zayed Hospital, Rahim Yar Khan from July 2019 to June 2020.   Methodology: In this study the cases of either gender and age more than 12 years were included irrespective of duration of keloid via non probability consecutive sampling. A total of 100 cases were included. The cases were divided into two equal groups by lottery method. The cases in group A were treated with intralesional Triamcinolone (40mg/ml) while group B was treated with intralesional Verapamil (2.5mg/ml).In both groups, session was repeated every 3 weeks for a maximum of 8 sessions or until complete flattening of the scar. Patients were accessed on the basis of Vancouver scar scale (VSS). VSS score was compared between both groups pre and post treatment. Results: The mean age in group A and B was 22.16±9.01 vs. 22.68±8.93 years respectively (p =0.49). The mean duration of keloid or scar was 6.58±1.54 months in group A and 6.46±1.42 in group B with p =0.31.VSS score after treatment was 2.41±1.19 vs 1.65±0.93 with respect to change in Pliability score and it was 1.61±0.81 vs 1.25±0.72 in terms of height in Verapamil vs Triamcinolone group with p values of 0.05 and 0.04 respectively. Conclusion: There is statistically significant difference in Triamcinolone group with respect to change in Pliability score and height as compared to...

Pests in rice fields not only affect the yield and quality of rice but also cause serious ecological and environmental problems due to the heavy reliance on pesticides. Since various pests have irregular and changeable shapes, small sizes, and complex backgrounds, field rice pest detection is an essential prerequisite and challenge for the precise control of pests in the field. A multiscale aggregated vision MambaU-Net (MAVM-UNet) model for rice pest detection is constructed. The model consists of four main modules, Visual State Space (VSS), multiscale VSS (MSVSS), Channel-Aware VSS (CAVSS), and multiscale attention aggregation (MSAA), where VSS is used as the basic module for capturing context information, MSVSS is used to capture and aggregate fine-grained multiscale feature of field rice pest images, CAVSS is added into Skip connection to select the critical channel representations of the encoder and decoder, and MSAA is added in the bottleneck layer to integrate the pest features of different layers of the encoder. Combining MSAA and CAVSS can capture the low-level details and high-level semantics and dynamically adjust the contributions of features at different scales; for example, the slender legs and antennae of pests rely on fine-grained features, while the large body of pests relies on coarse-grained features. A large number of experimental results on the rice pest image subset of the IP102 dataset show that MAVM-UNet is superior to the state-of-the-art models, with PA and MIoU of 82.07% and 81.48%, respectively. The proposed model provides important guidance for the monitoring and control of pests in rice fields. The codes are available at https://github.com/ZengsihaoNB666/mavmunet.git .

To improve the signal-to-noise ratio (SNR) of vibration signals in a phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, a principal component analysis variable step-size normalized least mean square (PCA-VSS-NLMS) denoising method was proposed in this study. First, the mathematical principle of the PCA-VSS-NLMS algorithm was constructed. This algorithm can adjust the input signal to achieve the best filter effect. Second, the effectiveness of the algorithm was verified via simulation, and the simulation results show that compared with the wavelet denoising (WD), Wiener filtering, variational mode decomposition (VMD), and variable step-size normalized least mean square (VSS-NLMS) algorithms, the PCA-VSS-NLMS algorithm can improve the SNR to 30.68 dB when the initial SNR is −1.23 dB. Finally, the PCA-VSS-NLMS algorithm was embedded into the built Φ-OTDR system, an 11.22 km fiber was measured, and PZT was added at 10.19–10.24 km to impose multiple sets of fixed-frequency disturbances. The experimental results show that the SNR of the vibration signal is 8.77 dB at 100 Hz and 0.07 s, and the SNR is improved to 26.17 dB after PCA-VSS-NLMS filtering; thus, the SNR is improved by 17.40 dB. This method can improve the SNR of the system’s position information without the need to change the existing hardware conditions, and it provides a new scheme for the detection and recognition of long-distance vibration signals.

This study introduces a novel Variable Sample Size Exponentially Weighted Moving Average (VSS-EWMA) control chart for monitoring the coefficient of variation, termed as Dynamic Adaptive CV (DACV) chart. Tailored for dynamic production settings where both the process mean and variability are subject to change, the proposed chart integrates an adaptive sampling strategy within the EWMA framework, allowing real-time adjustment of sample size in response to process conditions. Comparative analysis with the conventional Fixed Sample Size EWMA (FEWMA) chart reveals that DACV chart exhibits enhanced sensitivity in detecting small to moderate shifts in variability. Its performance is rigorously evaluated using Average Run Length (ARL), Standard Deviation of Run Length (SDRL), and run-length percentiles. Visualizations through heat maps further affirm its robustness across a wide range of shift magnitudes and smoothing parameters. A real-world application using semiconductor manufacturing data demonstrates the practical utility of DACV chart, underscoring its potential in contemporary quality monitoring systems.

In this study, wet oxidation of excess sludge from the caprolactam wastewater treatment process was investigated. The effects of reaction conditions, including sludge concentration, the amount of sludge, reaction temperature and time and the oxygen supply amount, were discussed. The highest removal rates of chemical oxygen demand (COD) and volatile suspended solid (VSS) that can be attained at 78.6 and 89.3% were acquired separately under 260 °C for 60 min with an initial oxygen gas pressure of 1.3 MPa. The sludge was hydrolyzed and oxidized under hydrothermal conditions, producing small molecule organic acids, such as acetic, formic and oxalic acids, as the main products, which could be used as carbon sources for wastewater treatment. These results indicated that wet oxidation provides a favourable and feasible alternative method for the treatment of excess sludge from the coal chemical industry.

Recently, the augmented complex least mean square (ACLMS) algorithm has been shown to be an effective frequency estimator in unbalanced three-phase power systems. Its main idea is to reformulate the three-phase voltages using a widely linear complex-valued model by Clarke’s-transformation whose parameters are updated according to least squares. Since the derivation of the ACLMS is based on the -norm, it cannot work properly in an impulsive noise environment, which is commonly encountered in smart grids. In this paper, a generalized version of ACLMS, referred to as improved ACLMS (IACLMS) is developed to provide robust and accurate parameter estimation in the presence of heavy-tailed noise, which replaces the -norm by the -norm with . The IACLMS is solved using the steepest descent gradient, whose updates are influenced by the step size. Therefore, a new variable step size IACLMS (VSS-IACLMS) algorithm is devised to guarantee the convergence rate and accuracy. Computer simulations demonstrate that compared to the ACLMS algorithm, the proposed IACLMS and VSS-IACLMS algorithms exhibit stronger robustness and faster convergence speed. The VSS-IACLMS is superior to IACLMS as its mean square error performance can reach the Cramér-Rao lower bound.

The tendency to use less-invasive laser-based methods with far more obvious effectiveness has been taken into consideration today for improvement of burning scars. The present study thus aimed to assess the efficacy of two laser-based techniques including pulsed dye laser (PDL) and ablative fractional CO2 laser (AFCL) and its combination on improving different aspects of burning scars regardless of the types of scar as hypertrophic or keloid scars. This randomized single-blinded clinical trial was performed on patients suffering hypertrophic or keloid burning scars. The patients were randomly assigned into three groups scheduling for treatment with PDL alone, AFCL alone, or its combination. All patients were visited before and 40 days after the last treatment session, and their scars were assessed. In all groups, significant improvement was revealed in the Vancouver scar scale (VSS) score, the color of scar, vascular bed in the scar, the and height of scar and its pliability; however, the improvement in each item was more highlighted in the group receiving a combination therapy with PDL and AFCL techniques. In this regard, the highest improvement was found in vascular bed and pliability in the combination therapy group as compared to other groups. Although the superiority of the combined group was not statistically significant, due to the high percentage of improvement in total VSS and most of its indicators, it can be clinically significant. The efficacy of the treatment protocols was different considering subgroups of mature and immature scars (less than 1 year), so that more improvement in pliability of scar, vascularity, and color of scar was found in the group scheduling for PDL + AFCL as compared to those who were treated with PDL alone in immature scar group but not in mature scar group. Combined treatment can be much more effective in improving the appearance and pathological characteristics of scars than each individual treatment. This effectiveness can be seen mainly in immature scars.

Infrared and visible image fusion (vis-ir) enhances diagnostic accuracy in medical imaging and biological analysis. Existing CNN-based and Transformer-based methods face computational inefficiencies in modeling global dependencies. The author proposes VSS-SpatioNet, a lightweight architecture that replaces self-attention in Transformers with a Visual State Space (VSS) module for efficient dependency modeling. The framework employs an asymmetric encoder-decoder with a multi-scale autoencoder and a novel VSS-Spatial (VS) fusion block for local-global feature integration. Evaluations on TNO, Harvard Medical, and RoadScene datasets demonstrate superior performance. On TNO, VSS-SpatioNet achieves state-of-the-art Entropy (En = 7.0058) and Mutual Information (MI = 14.0116), outperforming 12 benchmark methods. For RoadScene, it attains gradient-based fusion performance ( =0.5712), Piella’s metric ( =0.7926), and average gradient (AG = 5.2994), surpassing prior works. On Harvard Medical, the VS strategy improves Mean Gradient by 18.7% (0.0224 vs. 0.0198) against FusionGAN, validating enhanced feature preservation. Results confirm the framework’s efficacy in medical applications, particularly precise tissue characterization.

Stroke has emerged as the primary contributor to morbidity and mortality in patients undergoing treatment with Left Ventricular Assist Devices (LVADs), possibly arising from the turbulent flow and elevated wall shear stresses generated in these devices. A minimally invasive LVAD (LifeheART) has been proposed to address these issues, employing an intra-aortic location and a shaftless impeller design. The current study uses Particle Image Velocimetry (PIV) flow visualization, carried out in a Cardiovascular Mock Circulation Loop (CMCL), to identify the velocity distribution at the pump outlet in order to validate the developed CFD model. Subsequently, the model evaluates the blood shear stress distribution and blood damage index. The results showed that the calculated viscous shear stress (VSS) and the blood damage index of the LifeheART prototype is significantly lower than the published data for current clinically available devices, confirming the potential utility of the new design to improve patient outcomes.

Visual secret sharing (VSS), also called visual cryptography, allows a secret image to be divided into several shares and the secret image can only be recovered through the collaboration of the shares. Due to high security and low computational complexity, VSS has been widely used in secure communications. In this paper, we propose the first turtle shell (TS)-based VSS scheme that shares the secret information into two meaningful shares. Firstly, a TS reference matrix is established. Then, secret information is hidden in a cover image to construct two meaningful shares with the help of TS reference matrix. The hidden secret information can be extracted without distortion if both shares are gathered and the cover image can be correctly restored by solving the location conflict problem. Moreover, an invalid share can easily be detected by the proposed scheme. Experimental results indicate that the proposed scheme provides excellent visual quality and is superior to state-of-the-art VSS schemes.