14 research outputs found
Spatial Assessment of Soil Erosion Risk Using RUSLE Embedded in GIS Environment: A Case Study of Jhelum River Watershed
No full textThe watershed area of the Mangla Reservoir spans across the Himalayan region of India and Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly terrain and relatively thin vegetation cover, which significantly increases the river’s sediment output, especially during the monsoon season, leading to a decline in the reservoir’s storage capacity. This work assesses the soil erosion risk in the Jhelum River watershed (Azad Jammu and Kashmir (AJ&K), Pakistan) using the Revised Universal Soil Loss Equation of (RUSLE). The RUSLE components, including the conservation support or erosion control practice factor (P), soil erodibility factor (K), slope length and slope steepness factor (LS), rainfall erosivity factor (R), and crop cover factor (C), were integrated to compute soil erosion. Soil erosion risk and intensity maps were generated by computing the RUSLE parameters, which were then integrated with physical factors such as terrain units, elevation, slope, and land uses/cover to examine how these factors affect the spatial patterns of soil erosion loss. The 2021 rainfall data were utilized to compute the rainfall erosivity factor (R), and the soil erodibility (K) map was created using the world surface soil map prepared by the Food and Agriculture Organization (FAO). The slope length and slope steepness factor (LS) were generated in the highly rough terrain using Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). The analysis revealed that the primary land use in the watershed was cultivated land, accounting for 27% of the area, and slopes of 30% or higher were present across two-thirds of the watershed. By multiplying the five variables, the study determined that the annual average soil loss was 23.47 t ha−1 yr−1. In areas with dense mixed forest cover, soil erosion rates ranged from 0.23 t ha−1 yr−1 to 25 t ha−1 yr−1. The findings indicated that 55.18% of the research area has a low erosion risk, 18.62% has a medium erosion risk, 13.66% has a high risk, and 11.6% has a very high erosion risk. The study’s findings will provide guidelines to policy/decision makers for better management of the Mangla watershed
Design of a CMOS Differential Operational Transresistance Amplifier in 90 nm CMOS Technology
No full textIn this paper, a CMOS differential operational transresistance amplifier (OTRA) is presented. The amplifier is designed and implemented in a standard umc90-nm CMOS technology. The differential OTRA provides wider bandwidth at high gain. It also shows much better rise and fall time and exhibits a very good input current dynamic range of 50 to 50 μA. The OTRA can be used in many analog VLSI applications. The presented amplifier has high gain bandwidth product of 617.6 THz Ω. The total power dissipation of the presented amplifier is also very low and it is 0.21 mW
Spatial Assessment of Soil Erosion Risk Using RUSLE Embedded in GIS Environment: A Case Study of Jhelum River Watershed
No full textThe watershed area of the Mangla Reservoir spans across the Himalayan region of India and
Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly
terrain and relatively thin vegetation cover, which significantly increases the river’s sediment output,
especially during the monsoon season, leading to a decline in the reservoir’s storage capacity. This
work assesses the soil erosion risk in the Jhelum River watershed (Azad Jammu and Kashmir (AJ&K),
Pakistan) using the Revised Universal Soil Loss Equation of (RUSLE). The RUSLE components,
including the conservation support or erosion control practice factor (P), soil erodibility factor (K),
slope length and slope steepness factor (LS), rainfall erosivity factor (R), and crop cover factor (C),
were integrated to compute soil erosion. Soil erosion risk and intensity maps were generated by
computing the RUSLE parameters, which were then integrated with physical factors such as terrain
units, elevation, slope, and land uses/cover to examine how these factors affect the spatial patterns
of soil erosion loss. The 2021 rainfall data were utilized to compute the rainfall erosivity factor
(R), and the soil erodibility (K) map was created using the world surface soil map prepared by the
Food and Agriculture Organization (FAO). The slope length and slope steepness factor (LS) were
generated in the highly rough terrain using Shuttle Radar Topography Mission Digital Elevation
Model (SRTM DEM). The analysis revealed that the primary land use in the watershed was cultivated
land, accounting for 27% of the area, and slopes of 30% or higher were present across two-thirds
of the watershed. By multiplying the five variables, the study determined that the annual average
soil loss was 23.47 t ha−1 yr−1
. In areas with dense mixed forest cover, soil erosion rates ranged
from 0.23 t ha−1 yr−1
to 25 t ha−1 yr−1
. The findings indicated that 55.18% of the research area has a
low erosion risk, 18.62% has a medium erosion risk, 13.66% has a high risk, and 11.6% has a very
high erosion risk. The study’s findings will provide guidelines to policy/decision makers for better
management of the Mangla watershed
Quantifying Confidence in Diabetic Retinopathy Diagnosis: A Comparative XAI Study of Deep Learning and Bayesian Neural Networks
No full textDiabetic Retinopathy remains the primary microvascular complication of diabetes and a leading cause of irreversible blindness globally. While deep learning models offer high diagnostic accuracy, their widespread clinical integration is profoundly limited by two fundamental, unresolved deficiencies in previous literature: the absence of comprehensive, fair comparative analysis across diverse architectures and the pervasive lack of transparent, quantifiable prediction confidence necessary for clinical acceptance. This study directly addresses these challenges by presenting a highly optimized and rigorous comparative evaluation of three powerful models: the high-capacity EfficientNetB0, the computationally efficient MobileNetV3Small, and a novel Custom Bayesian Neural Network (BNN) framework. Through robust methodology, all models achieved exceptional generalization, stabilizing with impressive final F1-Score > 0.91. The Custom BNN demonstrated clear superiority as the most reliable diagnostic tool, securing the highest Accuracy 0.9294 and F1-score 0.9289 on the objective test set. Most significantly, this work delivers a breakthrough in safety assurance by integrating sophisticated Explainable AI (XAI) and probabilistic modeling: Grad-CAM and Local Interpretable Model-agnostic Explanations (LIME) confirmed anatomically grounded decision-making, while the BNN uniquely provides quantifiable uncertainty metrics, offering a crucial 95% confidence interval (CI) for every diagnosis. These results validate a new generation of high-performance models, led by a transparent BNN architecture, that are ready for implementation to deliver reliable, trusted, and efficient Diabetic Retinopathy screening solutions worldwide
Dual-Band Nested Circularly Polarized Antenna Array for 5G Automotive Satellite Communications
No full textCurrently, 5G low-earth orbit satellite communications offer enhanced wireless coverage beyond the reach of 5G terrestrial networks, with important implications, particularly for automated and connected vehicles. Such wireless automotive mass-market applications demand well-designed compact user equipment antenna terminals offering non-terrestrial jointly with terrestrial communications. The antenna should be low-profile, conformal, and meet specific parameter values for gain and operational frequency bandwidth, tailored to the intended applications, in line with the aesthetic design requirements of passenger cars. This work presents an original concept for a dual-band nested circularly polarized automotive user terminal that operates at the S-band frequencies around 3.5 GHz and Ka-band frequencies around 28 GHz, namely within the 5G new-radio bands n78 and n257, respectively. The proposed terminal is designed to be integrated into the plastic components of a passenger vehicle. The arrays consist of 2 × 2 aperture-coupled corner-truncated microstrip slot patch antenna elements for the n78 band and of 4 × 4 single-layer edge-truncated microstrip circular slot patch antenna elements for the n257 band. The embedded arrays offer, across the two bands, respectively, 9.9 and 13.7 dBi measured realized gain and 3-dB axial ratio bandwidths of 100 and 1500 MHz for the n78 and n257 bands along the broadside direction. Detailed link budget calculations anticipate uplink data rates of 21 and 6 Mbit/s, respectively, deeming it suitable for various automotive mobility and Internet-of-Things applications
Design of high efficiency wireless power transmission system at low resonant frequency
No full textThis paper presents a novel design of a wireless power transmission system which transfers an appreciable amount of electrical power wirelessly using low resonant frequency, with an excellent efficiency, and has a very low cost implementation. The designs of induction coils at both source and receiver sides are also presented in this paper. The mechanism for power transmission is through electro-magnetic induction. Also an immense knowledge of electronics was applied in order to design the source and receiver between which this transfer took place. In order to realize this method an AC-AC converter, and AC-DC rectifier were used at source and receiver sides respectively along with the resonant circuits. The work was carried out by the experimental setup and results demonstrate that proposed system design can successfully transfer the amount of power that can be used in many practical applications.</p
Quantifying Confidence in Diabetic Retinopathy Diagnosis: A Comparative XAI Study of Deep Learning and Bayesian Neural Networks
No full textDiabetic Retinopathy remains the primary microvascular complication of diabetes and a leading cause of irreversible blindness globally. While deep learning models offer high diagnostic accuracy, their widespread clinical integration is profoundly limited by two fundamental, unresolved deficiencies in previous literature: the absence of comprehensive, fair comparative analysis across diverse architectures and the pervasive lack of transparent, quantifiable prediction confidence necessary for clinical acceptance. This study directly addresses these challenges by presenting a highly optimized and rigorous comparative evaluation of three powerful models: the high-capacity EfficientNetB0, the computationally efficient MobileNetV3Small, and a novel Custom Bayesian Neural Network (BNN) framework. Through robust methodology, all models achieved exceptional generalization, stabilizing with impressive final F1-Score > 0.91. The Custom BNN demonstrated clear superiority as the most reliable diagnostic tool, securing the highest Accuracy 0.9294 and F1-score 0.9289 on the objective test set. Most significantly, this work delivers a breakthrough in safety assurance by integrating sophisticated Explainable AI (XAI) and probabilistic modeling: Grad-CAM and Local Interpretable Model-agnostic Explanations (LIME) confirmed anatomically grounded decision-making, while the BNN uniquely provides quantifiable uncertainty metrics, offering a crucial 95% confidence interval (CI) for every diagnosis. These results validate a new generation of high-performance models, led by a transparent BNN architecture, that are ready for implementation to deliver reliable, trusted, and efficient Diabetic Retinopathy screening solutions worldwide
Effects of Variation of Axial Load on Seismic Performance of Shear Deficient RC Exterior BCJs
No full textAbstract The focus of this paper is to investigate the effect of column axial load levels on the performance of shear deficient reinforced concrete beam column joints (BCJs) under monotonic and cyclic loading. The problem of interaction between shear stress in BCJ and axial load on column has been addressed in this work by initially postulating a mechanistic model and substantiated by an experimental test program. This was achieved by conducting appropriate tests on seven BCJ sub-assemblies subjected to monotonic and reversed cyclic loading, with varying levels of the column axial load. Experimental results were further validated using a finite element model in an ABAQUS environment. The effect of variation of compressive strength of concrete was considered in a subsequent parametric study, in order to obtain sufficient data, and utilized to develop a new shear strength model for BCJs which includes influences of all the important parameters required to predict the shear strength of BCJs. The results showed that column axial load affects the seismic performance of BCJs significantly. Experimental results demonstrated that at initial stages of loading, increase in axial load enhances the shear capacity of the joint and reduces its ductility. However, when the column axial load/axial strength ratio increases to about 0.6–0.7, shear strength starts to decrease rapidly, leading to pure axial failure of the joint. The magnitude of axial load/axial capacity ratio also dictates the failure mode and development of crack patterns in BCJs. Results of reverse cyclic tests on BCJs showed that high value of axial load/axial capacity ratio increases the initial stiffness of BCJ but rate of stiffness degradation is accelerated after peak strength attenuation
