Long Term Evolution (LTE) is the next step (fourth generation) mobile radio communication technology that succeeds the HSPA 3GPP standardization body. LTE is expected to be the most competitive radio technology in the future to provide high-data-rate transmission, low latency, improved service and reduced costs. As known, mobile phone traffic is divided into two parts: an uplink and a downlink. This paper presents the LTE two duplexing modes: LTE-TDD (Time Division Duplexing) and LTE-FDD(Frequency Division Duplexing).

/pdf/lte-fdd-vs-lte-tdd-from-a-qos-perspective-4h143vum7s.pdf

LTE FDD vs LTE TDD from a Qos Perspective

This paper describes comparative analysis of the performance between three scheduling algorithm, Round Robin, Proportional Fair, and Maximum C / I based on Monte Carlo simulation method on the Release 8 LTE network using case studies of Bandung city Simulations performed with radio planning software Atoll 321 From the test results, Proportional Fair scheduling algorithm outperformed the other two algorithms in downlink parameters, the cumulated throughput, fairness and the average BLER

Scheduling techniques in release 8 LTE network

Downlink scheduling is one of the essential operations when it comes to improving the quality of service in Long Term Evolution (LTE) With an increasing user base, there will be an extensive challenge in resource provisioning too A review of existing approaches shows that there is a significant possibility of improvement in this regard, and hence, the proposed manuscript presents a benchmarking model for addressing the issues associated with Best-Channel Quality Indicator (CQI), Round Robin, and Hybrid Automatic Repeat Request (HARQ) The outcome shows HARQ scheduling to offer better performance in higher throughput, higher fairness, and lower delay over different test cases

/pdf/simplified-framework-for-benchmarking-standard-downlink-enrb3dwzx0.pdf

Simplified Framework for Benchmarking Standard Downlink Scheduler over Long Term Evolution

Cancer is one of the dangerous leading causes of mortality and morbidity in the world. There are many treatments for cancer such as, immunotherapy, radiotherapy, surgery, and chemotherapy. The cancer treatments such as therapies kills the normal cells leads to severe side effects. Scientists looking for new strategies to eliminate the cancerous cells. Exosomes play key role in cancer therapy, it is nanometer sized lipid bilayer secreted from various types of cells, like blood, breast milk, saliva, pancreatic juice, cerebral spinal fluid, and peritoneal fluids. Exosomes carry bio active molecules such as pro teins, lipids and RNA’s. Exosomes serving biological carrier for some drugs, microRNA, Inc RNA and antibodies. The components of exosomes play a key role in metastasis, regulating growth hormone, angiogenesis in cancer development process, and as a prognostic marker in tumor patients. Due to resistance of drug, specific targeting difficulty and self-renewal capacity of cancer stem cells contributes cancer treatment relapse. To overcome the difficulties, nano technology-based drug delivery is used to get better treatment for cancer. Exosomes play role in cancer therapy because they are nontoxic, nonimmunogenic, and can be engaged to robust delivery capacity and targeting specificity. The role of exosomes in cancer, focusing on proteins and noncoding RNA; the interaction between exosomes and tumor micro environment, the mechanism the epithelial-mesenchymal transition

https://doi.org/10.47583/ijpsrr.2022.v77i02.002

A Review on Role of Exosomes in Cancer Therapy

Experimental Study on Forced Convective Heat Transfer Enhancement Using Water-based ZnO Nanofluid

The fertility of a soil is governed by potential of Hydrogen (pH) value of the soil. This research paper presents a novel approach for predicting the pH value of a soil by using RGB (Red, Green, Blue) values of an image. The study uti-lizes machine learning techniques to develop a model that can accurately predict the soil pH based on the colour information captured in an image of the soil. The model was trained with a dataset containing RGB and corresponding pH value as the attributes and tested using a variety of images. Results show that the proposed model is able to predict soil pH with minimal error, demonstrating the potential for using image analysis as a practical and efﬁcient method for soil pH determination in agriculture and soil science. With the available dataset, various regression approaches have been implemented to predict the soil pH value, and eventually the experimental results shows that the polynomial regression is the most effective method as the data is not linear for analysing this dataset.

https://rspsciencehub.com/article_23731_2a60349e13b0cde1920fdd8f1dc6952e.pdf

Srinivasa R K

Papers

Simplified Framework for Benchmarking Standard Downlink Scheduler over Long Term Evolution

A Review on Role of Exosomes in Cancer Therapy

Experimental Study on Forced Convective Heat Transfer Enhancement Using Water-based ZnO Nanofluid

An Efficient Regression Method To Predict Soil pH Using RGB Values

Encryption with access policy and cloud data selection for secure and energy-efficient cloud computing