Minimum Mean Research Articles

Re-locative guided search optimized self-sparse attention enabled deep learning decoder for quantum error correction

Heavy hexagonal coding is a type of quantum error-correcting coding in which the edges and vertices of a low-degree graph are assigned auxiliary and physical qubits. While many topological code decoders have been presented, it is still difficult to construct the optimal decoder due to leakage errors and qubit collision. Therefore, this research proposes a Re-locative Guided Search optimized self-sparse attention-enabled convolutional Neural Network with Long Short-Term Memory (RlGS2-DCNTM) for performing effective error correction in quantum codes. The integration of the self-sparse attention mechanism in the proposed model increases the feature learning ability of the model to selectively focus on informative regions of the input codes. In addition, the use of statistical features computes the statistical properties of the input, thus aiding the model to perform complex tasks effectively. For model tuning, this research utilizes the RIGS nature-inspired algorithm that mimics the re-locative, foraging, and hunting strategies, which avoids local optima problems and improves the convergence speed of the RlGS2-DCNTM for Quantum error correction. When compared with other methods, the proposed RlGS2-DCNTM algorithm offers superior efficacy with a Minimum Mean Squared Error (MSE) of 4.26, Root Mean Squared Error of 2.06, Mean Absolute Error of 1.14 and a maximum correlation and of 0.96 and 0.92 respectively, which shows that the proposed model is highly suitable for real-time error decoding tasks.

Physiological and cerebral hemodynamic changes during routine nursing procedures for very preterm infants.

Care procedures for preterm infants can induce stress that may disrupt homeostasis, possibly altering cerebral perfusion or oxygenation. We evaluated the physiological and cerebral oxygenation changes during the routine care of very preterm infants. We analyzed the changes in heart and respiratory rates and in systemic and regional cerebral oxygen saturation of 27 very preterm infants, defining three care periods of 5 min each: 30 min before care, 30 min during care, and 30 min after care. Mean maximum and minimum values for each parameter during the defined care periods were compared by analysis of variance (ANOVA) for repeated measures. The mean heart rate was significantly higher during (160 ± 8 bpm) than before and after care (151 ± 21 and 151 ± 6 bpm, respectively). The mean respiratory rate decreased during care and increased afterward: 44 ± 2.2, 40.6 ± 3.2, and 46.7 ± 3.4 cycles/min, respectively (p < 0.05). The mean regional cerebral oxygen and systemic saturation did not vary significantly. Mean minimum and maximum values for each parameter varied during and after care as compared with before care (all p < 0.01). The mean minimum cerebral and systemic saturation was lower after care than before care: 59 ± 8 % versus 63 ± 8 % and 83 ± 3 % versus 90 ± 7 %, respectively (p < 0.05). During routine care procedures for very preterm infants, the change in physiological parameters suggested an autonomic stress reaction. Cerebral desaturation may occur during and after the care of such infants and call for specific attention to better support the physiological and cerebral well-being of these infants during standard care procedures.

Concomitant Cartilage Procedures With Meniscal Allograft Transplantation Do Not Substantially Alter Failure or Survival Rates Relative to Meniscal Allograft Transplantation Without Cartilage Procedures: A Systematic Review.

Timely recognition and addressing of concomitant cartilage damage at the time of meniscal allograft transplantation (MAT) is critical to warrant future success. However, there remains a scarcity of data comparing outcomes between MAT with and without cartilage procedures. To compare patient-reported outcomes and rates of complications, failures, reoperations, and graft survivorship after MAT with concomitant cartilage procedures (MAT/Cart) and MAT without (MAT/NoCart). Systematic review; Level of evidence, 4. A literature search was performed according to the 2020 PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-analyses) using the Scopus, PubMed, and Embase computerized databases from inception to January 7, 2024. Human clinical studies with levels of evidence 1 to 4 were included that evaluated patient-reported outcomes, postoperative complications, failures, reoperations, and graft survivorship with a minimum mean follow-up of 2 years. Study quality was assessed using the Methodological Index for Non-randomized Studies criteria and Modified Coleman Methodology Score. Twenty-six studies from 2006 to 2024 consisting of 1031 patients were included. Thirteen studies (n = 367) reported on MAT/Cart with a mean patient age of 37.6 years and mean follow-up of 72.9 months. Thirteen studies (n = 665) reported on MAT/NoCart procedures in patients with a mean age of 33.6 years and mean follow-up of 58.6 months. Postoperatively, both study groups had improved clinical outcomes, with International Knee Documentation Committee scores ranging from 55.3 to 74.4 in the MAT/Cart group versus 61.7 to 89.8 in the MAT/NoCart group and Lysholm scores from 62.5 to 85.9 versus 72 to 92.4, respectively. The incidence of failure ranged from 0% to 33% after MAT/Cart versus 3.8% to 43.7% after MAT/NoCart. All included patients either had well-aligned lower extremities in the coronal plane, within 3° to 5° of neutral on the affected side, or underwent correction via an osteotomy before or during the MAT procedure. Subsequent surgery not related to failure was higher in the MAT/Cart group (range, 11.8%-83.3%) as compared with the MAT/NoCart group (range, 4.3%-30.8%). The mean survival rates after MAT/Cart ranged from 86.2% to 100% at 2 years, 75% to 97.9% at 5 years, and 70% to 85% at 10 years. The mean survival rates after MAT/NoCart ranged from 83.5% to 93% at 2 years, 82.6% to 85% at 5 years, and 55% to 90% at 10 years. Decreased range of motion and arthrofibrosis were the most frequently reported complications in each group. In patients with normal lower limb coronal plane alignment, performing cartilage restoration procedures in combination with MAT does not substantially alter clinical outcomes or complication, failure, and survival rates relative to isolated MAT.

Temporal dynamics hidden within species baselines – multifaceted decline of a lichen bioindicator (Bryoria fuscescens)

Abstract Rapid advances in species distribution modelling have been facilitated by open availability of ‘big data’ and powerful statistical methods. A key consideration remains the time window over which field recorded occurrence data are sampled to develop a baseline species distribution. Too narrow, and distributions are incomplete and affected by sampling bias, too broad and distributions may fail to meet an assumption of equilibrium, having been affected by dynamic change across a range of different predictors. Lichens are a case in point; being diverse, functionally important and the subject of bioclimatic modelling for conservation assessment, they are nevertheless a specialist taxonomic group that is comparatively less well recorded compared to birds, mammals or vascular plants, for example. In this study, we examined the distribution of the ‘hair-lichen’ Bryoria fuscescens, based on UK record data. We partitioned records into sub-decadal periods (1970s, 1990s, 2010s), and accounting for recording effort, we compared these distributions to three predictors: an historical reconstruction of two different pollutants (sulphur dioxide and nitrogen deposition), and the climate (minimum mean temperature). We asked whether the strength of evidence for the effect of environmental predictors on Bryoria fuscescens distribution varied among the different decades, while also considering a potential for lag-effects. We show that a Bryoria fuscescens distribution that appears static, is dynamic when referenced against patterns of field recording effort. Climate was consistently important in explaining Bryoria fuscescens distribution, which was also affected by the changing pattern of pollution over time. This included a lag-effect of peak sulphur dioxide in the 1970s, and accrued effects of nitrogen deposition that strengthen over time. Overall, we conclude that Bryoria fuscescens has undergone a long-term decline in extent over the last six decades, caused by complex multivariate effects of air pollution, probably combined with climate warming. The ability to resolve these trends for assessment against future conservation targets depends critically on maintaining field identification skills and a sufficiently robust recording effort.

A novel framework to predict transversal and shear parameters of unidirectional composites by combining experimental, numerical and machine learning methods

AbstractThis work presents a new method to predict the transversal and shear properties of unidirectional composites (UD) through combining the experimental, numerical and machine learning methods. The experimental results proved the complexity and difficulty of explaining primary factors affecting the mechanical properties of UD. The representative unit cell model was then created to generate 500 virtual samples for machine learning. The results show that the back propagation neural network model (BP) is the most suitable for predicting the mechanical properties of UD, with an accuracy of 98% within a 2% error. The minimum mean square and absolute errors are 1.09E‐3 and 1.15E‐5, respectively. It is proved that the interface has significant influences on all mechanical properties of UD and shear modulus of composite in 12 directions (Gc12) of UD is affected by all input parameters through the optimized BP model. Due to the wide coverage of input data, the proposed BP model is universal and can be adopted to predict transversal and shear properties of UD made from different kinds of fibers.Highlights Interface has influences on all parameters of unidirectional composites. Shear properties of unidirectional composites along 12 directions are intricated. Machine learning can predict mechanical properties of unidirectional composites. Specific samples are beneficial to improve the predicted accuracy.

Hybrid parking space prediction model: integrating ARIMA, Long short-term memory (LSTM), and backpropagation neural network (BPNN) for smart city development

Parking space prediction is a significant aspect of smart cities. It is essential for addressing traffic congestion challenges and low parking availability in urban areas. The present research mainly focuses on proposing a novel scalable hybrid model for accurately predicting parking space. The proposed model works in two phases: in first phase, auto-regressive integrated moving average (ARIMA) and long short-term memory (LSTM) models are integrated. Further, in second phase, backpropagation neural network (BPNN) is used to improve the accuracy of parking space prediction by reducing number of errors. The model utilizes the ARIMA model for handling linear values and the LSTM model for targeting non-linear values of the dataset. The Melbourne Internet of Things (IoT) based dataset, is used for implementing the proposed hybrid model. It consists of the data collected from the sensors that are employed in smart parking areas of the city. Before analysis, data was pre-processed to remove noise from the dataset and real time information collected from different sensors to predict the results accurately. The proposed hybrid model achieves the minimum mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) values of 0.32, 0.48, and 0.56, respectively. Further, to verify the generalizability of the proposed hybrid model, it is also implemented on the Harvard IoT-based dataset. It achieves the minimum MSE, MAE, and RMSE values of 0.31, 0.47, and 0.56, respectively. Therefore, the proposed hybrid model outperforms both datasets by achieving minimum error, even when compared with the performance of other existing models. The proposed hybrid model can potentially improve parking space prediction, contributing to sustainable and economical smart cities and enhancing the quality of life for citizens.

A Novel Codebook Based CSI Reconstruction Algorithm in FDD Massive MIMO Systems

ABSTRACTChannel state information (CSI) plays a critical role for beamforming technology in frequency division duplex (FDD) massive multiple‐input and multiple‐output (MIMO) systems. In order to improve the beamforming accuracy, we present a novel codebook based CSI reconstruction algorithm to enhance CSI feedback performance, thereby improving communication quality. Based on the existing codebook feedback mechanism, initially, we leverage channel temporal correlation to smooth the precoding matrix using a sliding window approach, where both Type I and Type II codebooks are considered. Subsequently, a minimum mean square error (MMSE) based modification technique is introduced to modify the reconstruction result. The proposed CSI reconstruction method incorporates an adjustable weighting parameter within the sliding window, rendering it adaptable to channels with different user speeds, such as high‐speed train communication channel. Moreover, our algorithm entirely operates at the base station (BS). Therefore, there is no additional feedback overhead compared with traditional codebook based CSI feedback methods. Numerical results present the proposed algorithm outperforms baselines for beamforming.

DOP118 Correlation between psoasic index and diaphragmatic index ultrasound in the evaluation of sarcopenia in a cohort of patients with inflammatory bowel disease: a study in a tertiary referral center

Abstract Background Sarcopenia is prevalent in patients with inflammatory bowel disease(IBD) and impacts surgical and therapeutic outcomes; thus, effective diagnostic tools are needed to assess muscle mass and function in IBD population.The ileo-psoas muscle(IP) muscle has already been used in the literature for the assessment of sarcopenia, however, as it is a deep muscle and it needs experienced operators for a proper measurement. Therefore, diaphragm muscle (DM) more superficial and easily assessed, was examined to check its correlation with IP. The aim of this work was to evaluated the correlation of positivity SARC-F score with ultrasound (US) measure of the IP and DM. Methods A total of 57 IBD patients with significant suspected sarcopenia with SARC-F score positive ≥4, were enrolled. IP and DM US were detected in the enrolled patients. The transverse diameter of the IP was evaluated. Moreover, we calculated the US psoas/height ratio as the average of the psoas diameter divided by the patient's height (mm/m). For DM, diameters during expiration and during inspiration were assessed, then diaphragmatic expansion was calculated. We calculated the diaphragm/height ratio (mm/m) as well. The correlation between IP and DM ratio was researched by using the Pearson correlation test. Age and sex were taken into consideration as confounders in a linear regression model, using the psoas/height ratio and these confounders as independent variables and the diaphragm index as dependent variable. Results The mean of the three US IP measured diameters was 28.40 mm, the psoas/height ratio was 16,62 mm/m. The mean maximum inspiration DM diameter was 20.4 mm (± 5 mm), the mean minimum expiration MD diameter was 10.7 mm (± 3,5 mm). The mean difference between the two diameters was 9.7 mm (± 3,4 mm). The diaphragm/height ratio was 0.59 mm/m (±0.21 mm/m). A significant correlation was identified between IP and DM ratio, with a Pearson coefficient r = 0.3568 (p-value &amp;lt;0.05). A consistent association was found between the two indices, with diaphragm/height ratio increasing linearly with the psoas/height ratio (OR: 0.018; p-value: 0.008). Multivariate analysis showed that age and sex were not significantly associated with the diaphragm/height ratio (p-value &amp;gt;0.05). Conclusion The findings of this study emphasize the potential of muscle US as a reliable diagnostic tool for assessing sarcopenia in IBD patients.IP US is confirmed to correlate with sarcopenia.DM can be used more easily and correlates with IP.This research could take a significant implications for disease management in IBD patients.

Long-term outcomes of second-generation drug-eluting stents versus coronary artery bypass graft: a systematic review and meta-analysis of randomized trials and multivariable adjusted data.

Long-term data are limited comparing percutaneous coronary intervention (PCI) with second-generation drug-eluting stents (S-DES) vs coronary artery bypass grafting (CABG) in patients with complex coronary artery disease (CAD). We aimed to conduct a systematic review and meta-analysis comparing these interventions in patients with left main or multivessel CAD. We systematically reviewed PubMed, Embase, and Cochrane for studies that compared PCI with S-DES and CABG in patients with left main or multivessel CAD with a minimum mean follow-up period of 3 years. Outcomes of interest were all-cause mortality, myocardial infarction (MI), stroke, and major adverse cardiovascular events (MACE). We extracted data from observational studies as multivariable-adjusted or propensity score-matched hazard ratio to minimize confounding and pooled hazard ratios with 95% confidence intervals (CIs) using a random effects model in Review Manager 5.4.1. We included 10 studies, 3 of which were randomized controlled trials, comprising a total of 26964 patients, of whom 14928 underwent PCI. As compared with CABG, S-DES had a significantly higher risk of all-cause mortality (hazard ratio 1.35; 95% CI 1.22-1.49; P < 0.001; I2 = 0%), MACE (hazard ratio 1.27; 95% CI 1.08-1.50; P = 0.005; I2 = 67%), and MI (hazard ratio 2.43; 95% CI 1.80-3.28; P < 0.01; I2 = 0%). There was no significant difference between groups in incidence of stroke. In this meta-analysis of patients with complex CAD, PCI with S-DES was associated with an increased risk of mortality, MI, and MACE compared with CABG.

Spatiotemporal Dynamics of Drought in the Huai River Basin (2012–2018): Analyzing Patterns Through Hydrological Simulation and Geospatial Methods

As climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, including the standardized precipitation index (SPI), standardized soil moisture index (SSMI), and Standardized Streamflow Index (SSFI), to comprehensively investigate the spatiotemporal characteristics of drought in the Huai River Basin, China, from 2012 to 2018. The simulation performance of the WRF-Hydro model was evaluated by comparing model outputs with reanalysis data at the regional scale and site observational data at the site scale, respectively. Our results demonstrate that the model showed a correlation coefficient of 0.74, a bias of −0.29, and a root mean square error of 2.66% when compared with reanalysis data in the 0–10 cm soil layer. Against the six observational sites, the model achieved a maximum correlation coefficient of 0.81, a minimum bias of −0.54, and a minimum root mean square error of 3.12%. The simulation results at both regional and site scales demonstrate that the model achieves high accuracy in simulating soil moisture in this basin. The analysis of SPI, SSMI, and SSFI from 2012 to 2018 shows that the summer months rarely experience drought, and droughts predominantly occurred in December, January, and February in the Huai River Basin. Moreover, we found that the drought characteristics in this basin have significant seasonal and interannual variability and spatial heterogeneity. On the one hand, the middle and southern parts of the basin experience more frequent and severe agricultural droughts compared to the northern regions. On the other hand, we identified a time–lag relationship among meteorological, agricultural, and hydrological droughts, uncovering interactions and propagation mechanisms across different drought types in this basin. Finally, we concluded that the WRF-Hydro model can provide highly accurate soil moisture simulation results and can be used to assess the spatiotemporal variations in regional drought events and the propagation mechanisms between different types of droughts.

Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption.

Energy is integral to the socio-economic development of every country. This development leads to a rapid increase in the demand for energy consumption. However, due to the constraints and costs associated with energy generation resources, it has become crucial for both energy generation companies and consumers to predict energy consumption well in advance. Forecasting energy needs through accurate predictions enables companies and customers to make informed decisions, enhancing the efficiency of both energy generation and consumption. In this context, energy generation companies and consumers seek a model capable of forecasting energy consumption both in the short term and the long term. Traditional models for energy prediction focus on either short-term or long-term accuracy, often failing to optimize both simultaneously. Therefore, this research proposes a novel hybrid model employing Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) to simultaneously predict both short-term and long-term residential energy consumption with enhanced accuracy measures. The proposed model is capable of capturing complex temporal and spatial features to predict short-term and long-term energy consumption. CNNs discover patterns in data, LSTM identifies long-term dependencies and sequential patterns and Bi-LSTM identifies complex temporal relations within the data. Experimental evaluations expressed that the proposed model outperformed with a minimum Mean Square Error (MSE) of 0.00035 and Mean Absolute Error (MAE) of 0.0057. Additionally, the proposed hybrid model is compared with existing state-of-the-art models, demonstrating its superior performance in both short-term and long-term energy consumption predictions.

The Influence of Edaphic and Climatic Factors on the Morphophysiological Behavior of Young Argan Plants Cultivated in Orchards: A Comparative Analysis of Three Regions in Southwest Morocco

Argania spinosa (L.) Skeels is a unique endemic species in Morocco, renowned for its ecological characteristics and socio-economic importance. In Morocco, recent years have seen an exacerbation of the harmful effects of climate change, leading to an alarming decline in the natural regeneration of this species in its original habitats. It seems that the only viable solution lies in the domestication of this genetic heritage. This study marks the first in-depth investigation of the impact of various climatic and edaphic factors on the morphological and physiological traits of Argania spinosa young plants, assessed in six separate orchards and observed over four seasons (March 2022 (Winter), June 2022 (Summer), November 2022 (Autumn), and March 2023 (Winter)). A climatic assessment was carried out at each site, including measurements of rainfall, maximum and minimum temperatures, mean temperature, air temperature, and wind speed. The soil was analyzed for the pH, electrical conductivity (EC), water content, limestone (CaCO3), Kjeldahl nitrogen (N), available phosphorus (P2O5), organic matter (OM), and carbon/nitrogen ratio (C/N). To gain a better understanding of the morphophysiological characteristics of young argan seedlings, we carried out various observations, such as measuring the height and diameter of aerial parts, and the water content of leaves (WCL) and branches (WCB), quantifying chlorophyll (mg/m2) and leaf area. The results revealed a significant impact of edaphic and climatic factors on the morphophysiological parameters of young argan trees. Results revealed significant correlations of young argan plants between edaphic and climatic factors and morphophysiological parameters. The Tamjloujt site, characterized by protective vegetation cover, showed optimal growth conditions with the highest leaf and branch water content (46.89 ± 4.06% and 37.76 ± 3.51%, respectively), maximum height growth (91.33 ± 28.68 mm), trunk diameter (24.85 ± 3.78 mm), and leaf surface area (69.33 ± 19.28 mm2) during Summer 2022. The Saharan zone of Laqsabi exhibited peak chlorophyll concentrations (506.9 ± 92.25 mg/m2) during Autumn 2022, due to high temperatures. The mountainous environment of Imoulass negatively impacted plant growth (mean height: 52.61 ± 12.37 mm; diameter: 6.46 ± 1.57 mm) due to harsh climatic and edaphic conditions. This research provides vital knowledge regarding the environmental factors influencing the establishment of young argan plants within the Argan Biosphere Reserve. This contributes to the development of more effective domestication strategies and the restoration of agroecosystems. The aim is to use this knowledge to promote the rehabilitation and sustainability of argan agroecosystems.

New comprehensive mean estimation using regression-cum-exponential type estimator: application with neutrosophic data

When the point estimator is used to estimate population parameters, it provides a single value. In such a scenario, the neutrosophic method is beneficial for estimating the parameters of interest in sampling theory as it yields interval estimates where the parameter value mainly originates. Neutrosophic statistics focuses on uncertain or imprecise data. In this article, we suggest a new enhanced neutrosophic class of estimators to estimate the population mean. The properties (bias and mean squared error) are derived from the first-degree approximation. The suggested estimators are useful when working with uncertain, unclear, neutrosophic-type data. The best possible values of the defining scalars characterizing constants and the minimum neutrosophic mean squared error (MSE) for the suggested estimators are determined for these ideal values. Neutrosophic estimators outperform their classical counterparts because the existing estimated interval includes the minimum MSE when estimating the population mean. We use a simulation study and a real dataset from the Islamabad Stock Exchange. Variations in parameter and estimator combinations are reflected in the MSE values. From the numerical results, the estimators , , and have substantially higher MSE values, suggesting more significant estimation error. The estimators (i=1, 2, 3, 4, and 5) show better accuracy performance with relatively minimum MSE values. The numerical outcome shows that the suggested classes of estimators perform well as compared to the existing estimators.

Coordinate Transformation Using the Least Squares Method: A Case Study on Trabzon, Türkiye

The ED50 datum uses the Hayford ellipsoid and the Greenwich meridian as the prime meridian. In Turkey, the ED50 datum with the Hayford ellipsoid was used until 2001, after which the ITRF datum with the GRS80 ellipsoid became the standard. To utilize coordinate systems across different datums, these systems must be associated and transformed accordingly. This study determines 2D transformation parameters for converting the ED50 datum to the ITRF-96 datum. For this transformation, common points in both the ED50 and ITRF systems were selected in the Maçka and Ortahisar districts of Trabzon province. The two-dimensional similarity (Helmert) and Affine transformation methods were applied to these common points to establish the relationship between the two systems within the study area. Statistical data was gathered to evaluate the performance of the transformation parameters and process. Additionally, test points within the study areas were selected, and their ED50 datum coordinates were transformed to ITRF-96 coordinates using these parameters. The differences between the transformed coordinates and the current coordinates of these test points were analyzed, with root mean square error (RMSE), minimum and maximum errors, and mean absolute error (MAE) values calculated. In this study, a total of 197 points were used in Değirmendere, and 106 points were used in Maçka. It was observed that the sensitivity parameters of the transformation in the Maçka region are better than those in Değirmendere, potentially due to the number of common points. When comparing the Affine and Similarity transformations, the similarity transformation yielded significantly better results. All calculations were conducted using MATLAB R2022b software.

Design and Implementation of Least Mean Square Adaptive Filter Using Verilog

Adaptive filters are ability of adaptation to an unknown environment. These filters have been used widely because of its capable of operating in an unknown system and low power implementation of hardware. Adaptive filters have great range of signal processing and control operations for the tracking time variations of input statistics and Robust to the noise immunity. These filters used various Areas like Noise cancelling (interface cancelling), system identification, inverse modelling and echo predication. Adaptive filters structures have the adaptive algorithms to perform the time variations of the input statics and Robust to the noise cancelling. The most popular algorithm is LMS (Least Mean Square) it produces the least mean square of error signal in the adaptive filter to minimize noise power. Adaptive filter structures follow the two algorithms RLS and LMS. RLS algorithms excellent performance with increased complexity and the filter coefficients that minimize waited linear least squares cost function relating to the input signals. It requires infinite memory for error signal. LMS algorithms are simplest to understand and describe the hardware of the system compare to the RLS (Recursive Least Square). LMS algorithms are follows the stochastic gradient descent method to minimize the error signal and de-nosing task. It estimates the gradient vector from the input data and LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which leads to minimum mean square error. It doesn’t require correlation functions for calculations. The main aim of the project is to design the LMS algorithm based adaptive filter using Verilog HDL to reduce the power consumption, hardware complexity and improving the noise cancelling for the adaptive filter on the FGPA boards. An important challenge in the LMS adaptive filters design implementation of structural model in the Verilog HDL for image processing to target the noise cancelling, power and hardware complexity. Tool use for implementation the structural model of LMS filter is vivado tool and Xilinx software for FPGA board implementation.

Effect of salicylic acid application on growth, production, fungal decay, and overall quality of strawberry

Salicylic acid (SA), a plant growth regulator, induces various physiological and metabolic processes that affect plant growth and development. A study conducted at PMAS-Arid Agriculture University in 2017-2018 and 2018-2019 examined the preharvest application of SA on the growth and fruit quality of strawberry (cv. Chandler). A 4 mM concentration of SA was found to be the most effective, improving plant height (14.60 cm), average number of leaves per plant (5.27), leaf area (68.40 cm²), and average fruit weight (13.53 g). In terms of postharvest parameters, SA-treated strawberries showed higher total soluble solids (5.10 °Brix), ascorbic acid content (12.41 mg/100 ml of juice), and total sugar content (6.54%). A similar trend was observed in the activity of antioxidant enzymes (SOD, POD, and CAT) as well as physiological parameters such as photosynthetic rate, transpiration rate, and stomatal conductance. The SA treatments helped maintain the overall quality of the strawberries and also reduced damage caused by fungal decay. The data revealed that the minimum mean fungal decay (4.28%) was recorded after 4 days of storage, while the maximum percentage of fungal decay (20.38%) was observed after 12 days of storage. Therefore, foliar application of SA at 4 mM is recommended as the most effective treatment for enhancing strawberry growth and fruit quality. The study explains how SA can be used to preserve the quality of strawberries before harvest. An additional goal of this research is to provide growers with detailed information about the influence of SA on strawberry crop growth and production.

Can all variations within the unified mask-based beamformer framework achieve identical peak extraction performance?

This study investigates mask-based beamformers (BFs), which estimate filters for target sound extraction (TSE) using time-frequency masks. Although multiple mask-based BFs have been proposed, no consensus has been reached on which one offers the best target-extraction performance. Previously, we found that maximum signal-to-noise ratio and minimum mean square error (MSE) BFs can achieve the same extraction performance as the theoretical upper-bound performance, with each BF containing a different optimal mask. However, two issues remained unsolved: only two BFs were covered, excluding the minimum variance distortionless response BF; and ideal scaling (IS) was employed to ideally adjust the output scale, which is not applicable to realistic scenarios. To address these issues, this study proposes a unified framework for mask-based BFs comprising two processes: filter estimation that can cover all possible BFs and scaling applicable to realistic scenarios by employing a mask to generate a scaling reference. Based on the operators and covariance matrices used in BF formulas, all possible BFs can be classified into 12 variations, including two new ones. Optimal masks for both processes are obtained by minimizing the MSE between the target and BF output. The experimental results using the CHiME-4 dataset suggested that 1) all 12 variations can achieve the theoretical upper-bound performance, and 2) mask-based scaling can behave like IS, even when constraining the temporal mean of a non-negative mask to one. These results can be explained by considering the practical parameter count of the masks. These findings contribute to 1) designing a TSE system, 2) improving scaling accuracy through mask-based scaling, and 3) estimating the extraction performance of a BF.

Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming.

Reconfigurable Intelligent Surface (RIS) panels have garnered significant attention with the emergence of next-generation network technologies. This paper proposes a novel data-driven approach that leverages Light Detecting and Ranging (LiDAR) sensors to enhance user localization and beamforming in RIS-assisted networks. Integrating LiDAR sensors into the network will be instrumental, offering high-speed and precise 3D mapping capabilities, even in low light or adverse weather conditions. LiDAR data facilitate user localization, enabling the determination of optimal RIS coefficients. Our approach extends a Graph Neural Network (GNN) by integrating LiDAR-captured user locations as inputs. This extension enables the GNN to effectively learn the mapping from received pilots to optimal beamformers and reflection coefficients to maximize the RIS-assisted sumrate among multiple users. The permutation-equivariant and -invariant properties of the GNN proved advantageous in efficiently handling the LiDAR data. Our simulation results demonstrated significant improvements in sum rates compared with conventional methods. Specifically, including locations improved on excluding locations by up to 25% and outperformed the Linear Minimum Mean Squared Error (LMMSE) channel estimation by up to 85% with varying downlink power and 98% with varying pilot lengths, and showed a remarkable 190% increase with varying downlink power compared with scenarios excluding the RIS.

Yin-Yang Staining Technique to Create a Nonstained Internal Limiting Membrane Flap to Cover Large Idiopathic Macular Holes.

Purpose: To describe a modified technique for negative and positive (Yin-Yang) staining of the internal limiting membrane (ILM) to create a nonstained ILM flap that covers large idiopathic macular holes (MHs). Methods: Consecutive patients with large idiopathic MHs (>400 μm) were prospectively included in the study. After the central vitreous was removed, a droplet of triamcinolone acetonide was injected, covering the MH and surrounding area. Subsequently, indocyanine green (ICG) was injected to stain the outer area of the ILM, followed by creation of a flap from the temporal stained area. Finally, the nonstained ILM flap was inverted to cover the MH. The main outcomes included the best-corrected visual acuity (BCVA), macular contour, and integrity of the outer retina. Results: This study comprised 31 patients (31 eyes). Of the eyes, 28 (90.3%) achieved primary MH closure. The mean minimum linear diameter and base diameter of the MH was 593 ± 119 μm and 1082 ± 242 μm, respectively. At the 6-month follow-up, 12 eyes (38.7%) and 9 eyes (29.0%) had regained a U-shaped or V-shaped macular contour, respectively. In addition, the mean logMAR BCVA improved from 1.06 ± 0.30 preoperatively to 0.56 ± 0.31 (P < .001). Twenty-one eyes (67.7%) and 16 eyes (51.6%) had regained integrity of the external limiting membrane and ellipsoid zone, respectively, at the 6-month follow-up. Conclusions: The modified ILM staining technique using triamcinolone acetonide and ICG sequentially is a safe and effective method of creating a nonstained ILM flap that covers large MHs and prevents the foveal area from coming into direct contact with ICG.

Spatio-temporal rainfall variability and trends using a Kriging-interpolation and Innovative trend analysis approach: the case of Wolaita zone, south Ethiopia

Climate change is one of the worst environmental issues, with a negative impact on most developing countries across the globe and in some regions, including Ethiopia. This study seeks to establish the temporal and spatial changes in rainfall for the period 1987–2021. In this study, ordinary statistical measures, such as the mean and coefficient of variation, precipitation concentration index (PCI), and standardized anomaly index (SAI), were applied to investigate the rainfall variation. Concerning the estimation of the spatiotemporal distribution and magnitude of changes in, non-parametric Mann-Kendall (MK) tests, Sen’s slope estimator, and innovative trend analysis (ITA) were also conducted in ArcGIS 10.8 environment and XLSTAT/R. The study showed significant fluctuations in rainfall in the Wolaita zone, with minimum mean annual rainfall in 1997 and maximum rainfall in 2003. All but the Belg season had more negative seasonal anomalies than positive ones. The annual rainfall for each of the AEZs in different parts of the country ranged from one another; it was 969.03 mm in a southeast lowland AEZ and 1648.75 mm in the northwest highland AEZ. Rainfall was not uniformly distributed throughout the year and study area; the highland AEZs received more rainfall in the Belg and Kermit seasons than in the lowland seasons. In the ordinary kriging results, the extent of variability in the CV of the mean annual rainfall for each zone was identifiable. The southwest lowland AEZ produced a CV of 24.22% with a decrease in rainfall amounts, and the northeast highland AEZ produced a CV of 31.63% of the rainfall distribution amounts. This area includes lowland and highland AEZs of the northeastern part of the study area’s rainfall, which is moderately distributed by PCI. This information is helpful when attempting to associate development and cropping systems with temporal and spatial climatic patterns with respect to rainfall for agro-climatological activities and projects or flood regulatory measures.