Heterogeneous Acceleration for 5G New Radio Channel Modelling Using FPGAs and GPUs

L'abstract è presente nell'allegato / the abstract is in the attachmen

Can Common Stocks Provide Hedge against Inflation? Evidence from SAARC Countries

The theory says that if stocks provide an effective hedge against inflation then the effect of expected inflation should be compensated in the form of nominal stock return. As Fisher Hypothesis (1930) concluded that nominal expected return on a security is a function of expected inflation rate as well as expected real interest rate. Bodie (1976) worked on Fisher Hypothesis and found that actual nominal return depends on expected and unexpected inflation rates and also it depends on expected and unexpected nominal returns. According to Geske and Roll (1983) a positive relationship exists between stock returns and inflation, based on the assumption that securities represent claims on real assets. When there is an increase in rate of inflation, it is expected that prices of real assets will also rise, thereby improving the value of securities representing a claim on such real assets. We found that various studies in this area reported against the hypothesis, showing a negative relationship between the two. However, certain other studies support the theory asserting that the relationship existing between stock returns and inflation is positive. While the negative relationship between inflation and stock return is against the theory, negative results have led to formation of hypothesis such as tax augmented hypothesis. The tax augmented hypothesis states that when we deduct tax from the stock returns, their relationship with inflation tends to get negative as the quantum and rate of taxes also rise along with inflation. This hypothesis also opines that initial researcher did not consider the tax impact when they were empirically testing the relationship between stock returns and inflation

Characterization of sorghum germplasm for various morphological and fodder yield parameters

This study was performed to evaluate and characterize 24 sorghum accessions for various morphological and fodder yield parameters. The germplasm displayed considerable variability for leaf color, midrib color, panicle shape, days to 50% flowering, leaf area, flag leaf area, plant height and green fodder yield, while differences of smaller magnitude were observed for number of leaves and tillers plant-1. Genotype Fsd -sorghum was mature early with minimum days to maturity (63) while maximum plant height (232 cm) was observed for Acc.1692. Moreover, Acc.1827 exhibited maximum leaf area (447 cm2) and the highest green fodder yield at 50% maturity (58 t ha-1) was recorded for Acc. 1763. The results of this study indicate that significant genetic diversity exists among the sorghum accessions. The genetic potential of Fsd-sorghum, accessions 1692, 1827 and 1763 can be exploited in future sorghum breeding programs. Further, these genotypes are recommended for commercial cultivation to meet the fodder needs of the country.Keywords: Fodder, Sorghum bicolor, accession

CUDA-Optimized GPU Acceleration of 3GPP 3D Channel Model Simulations for 5G Network Planning

Simulation of massive multiple-input multiple-output (MIMO) channel models is becoming increasingly important for testing and validation of fifth-generation new radio (5G NR) wireless networks and beyond. However, simulation performance tends to be limited when modeling a large number of antenna elements combined with a complex and realistic representation of propagation conditions. In this paper, we propose an efficient implementation of a 3rd Generation Partnership Project (3GPP) three-dimensional (3D) channel model, specifically designed for graphics processing unit (GPU) platforms, with the goal of minimizing the computational time required for channel simulation. The channel model is highly parameterized to encompass a wide range of configurations required for real-world optimized 5G NR network deployments. We use several compute unified device architecture (CUDA)-based optimization techniques to exploit the parallelism and memory hierarchy of the GPU. Experimental data show that the developed system achieves an overall speedup of about 240× compared to the original C++ model executed on an Intel processor. Compared to a design previously accelerated on a datacenter-class field programmable gate array (FPGA), the GPU design has 33.3 % higher single precision performance, but for 7.5 % higher power consumption. The proposed GPU accelerator can provide fast and accurate channel simulations for 5G NR network planning and optimization

Survey of medicinal plants and patterns of knowledge in district Swabi/ Khyber Pakhtunkhwa, Pakistan

To investigate and document the indigenous knowledge on the medicinal flora of the native communities in Swabi district, Pakistan. A field survey was carried out and data was based on semi-structured interviews, group discussions, and questionnaire from 8 villages. 180 local individual of local communities were interviewed and collected data were analyzed by quantitative and descriptive index. During the survey, 81 species belonging to 45 families were reported. Solanaceae was the leading family with 7 species. Herb (48%) was the dominant plant habit and leaf (24%) was the most preferred used part for indigenous medicine. Moreover, decoction (28.93%) was the most prestigious method. According to the result, the highest use value was documented for Jaundice ailment (1.00-0.81). Besides, 11 plants added to the endangered species list. Local experts of Swabi district practice a huge variety of ethnomedicinal plants in treating a wide spectrum of disorders, especially those plants used to cure jaundice. Our finding suggest that the pharmacological potential across some of these plants has been therapeutically validated however still need to explore the pharmacological properties of other species. Hence, the present investigation, aside from being a source of new insight for ethnobotanical and pharmacological cure of many disorders, might contribute to upgrade the sustainability, conservation, and management of medicinal flora in the Bachai Sikandari, district Swabi

Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection

Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model’s resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications

FPGA Acceleration of 3GPP Channel Model Emulator for 5G New Radio

The channel model is by far the most computing intensive part of the link level simulations of multiple-input and multiple-output (MIMO) fifth-generation new radio (5G NR) communication systems. Simulation effort further increases when using more realistic geometry-based channel models, such as the three-dimensional spatial channel model (3D-SCM). Channel emulation is used for functional and performance verification of such models in the network planning phase. These models use multiple finite impulse response (FIR) filters and have a very high degree of parallelism which can be exploited for accelerated execution on Field Programmable Gate Array (FPGA) and Graphics Processing Unit (GPU) platforms. This paper proposes an efficient re-configurable implementation of the 3rd generation partnership project (3GPP) 3D-SCM on FPGAs using a design flow based on high-level synthesis (HLS). It studies the effect of various HLS optimization techniques on the total latency and hardware resource utilization on Xilinx Alveo U280 and Intel Arria 10GX 1150 high-performance FPGAs, using in both cases the commercial HLS tools of the producer. The channel model accuracy is preserved using double precision floating point arithmetic. This work analyzes in detail the effort to target the FPGA platforms using HLS tools, both in terms of common parallelization effort (shared by both FPGAs), and in terms of platform-specific effort, different for Xilinx and Intel FPGAs. Compared to the baseline general-purpose central processing unit (CPU) implementation, the achieved speedups are 65X and 95X using the Xilinx UltraScale+ and Intel Arria FPGA platform respectively, when using a Double Data Rate (DDR) memory interface. The FPGA-based designs also achieved ~3X better performance compared to a similar technology node NVIDIA GeForce GTX 1070 GPU, while consuming ~4X less energy. The FPGA implementation speedup improves up to 173X over the CPU baseline when using the Xilinx UltraRAM (URAM) and High-Bandwidth Memory (HBM) resources, also achieving 6X lower latency and 12X lower energy consumption than the GPU implementation

Exploring transfer learning in chest radiographic images within the interplay between COVID-19 and diabetes

The intricate relationship between COVID-19 and diabetes has garnered increasing attention within the medical community. Emerging evidence suggests that individuals with diabetes may experience heightened vulnerability to COVID-19 and, in some cases, develop diabetes as a post-complication following the viral infection. Additionally, it has been observed that patients taking cough medicine containing steroids may face an elevated risk of developing diabetes, further underscoring the complex interplay between these health factors. Based on previous research, we implemented deep-learning models to diagnose the infection via chest x-ray images in coronavirus patients. Three Thousand (3000) x-rays of the chest are collected through freely available resources. A council-certified radiologist discovered images demonstrating the presence of COVID-19 disease. Inception-v3, ShuffleNet, Inception-ResNet-v2, and NASNet-Large, four standard convoluted neural networks, were trained by applying transfer learning on 2,440 chest x-rays from the dataset for examining COVID-19 disease in the pulmonary radiographic images examined. The results depicted a sensitivity rate of 98 % (98%) and a specificity rate of almost nightly percent (90%) while testing those models with the remaining 2080 images. In addition to the ratios of model sensitivity and specificity, in the receptor operating characteristics (ROC) graph, we have visually shown the precision vs. recall curve, the confusion metrics of each classification model, and a detailed quantitative analysis for COVID-19 detection. An automatic approach is also implemented to reconstruct the thermal maps and overlay them on the lung areas that might be affected by COVID-19. The same was proven true when interpreted by our accredited radiologist. Although the findings are encouraging, more research on a broader range of COVID-19 images must be carried out to achieve higher accuracy values. The data collection, concept implementations (in MATLAB 2021a), and assessments are accessible to the testing group

Transformational leadership and employees' performance: the mediating role of employees' commitment in private banking sectors in Pakistan

In this competitive era, organisations face issues related to leadership styles in maintaining employee performance and commitment. Leadership, like transformational leadership, motivates employees to achieve the target for an organisation. However, employee performance and commitment are behavioural factors that help them achieve organisational goals. These factors develop emotional attachments like loyalty and faithfulness among the employees towards the organisations. This study examines the relationship between transformational leadership, employee commitment, and employee performance in a developing country like Pakistan. Although, in the twenty-first century, issues related to leadership styles have given new ways for researchers to further insight into employee performance and commitment study. However, this paper aims to determine the impact of transformational leadership on employee performance by mediating the role of employee commitment in the private banking sector. The cross-sectional and descriptive survey was used in the data collection; 466 employees of the banks participated in the study. The research indicates that transformational leadership positively impacts employee performance and commitment. The findings show that transformational leadership has a positive effect on employees' performance and employee commitment