A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters

Current LTE network is faced with a plethora of Configuration and Optimization Parameters (COPs), both hard and soft, that are adjusted manually to manage the network and provide better Quality of Experience (QoE). With 5G in view, the number of these COPs are expected to reach 2000 per site, making their manual tuning for finding the optimal combination of these parameters, an impossible fleet. Alongside these thousands of COPs is the anticipated network densification in emerging networks which exacerbates the burden of the network operators in managing and optimizing the network. Hence, we propose a machine learning-based framework combined with a heuristic technique to discover the optimal combination of two pertinent COPs used in mobility, Cell Individual Offset (CIO) and Handover Margin (HOM), that maximizes a specific Key Performance Indicator (KPI) such as mean Signal to Interference and Noise Ratio (SINR) of all the connected users. The first part of the framework leverages the power of machine learning to predict the KPI of interest given several different combinations of CIO and HOM. The resulting predictions are then fed into Genetic Algorithm (GA) which searches for the best combination of the two mentioned parameters that yield the maximum mean SINR for all users. Performance of the framework is also evaluated using several machine learning techniques, with CatBoost algorithm yielding the best prediction performance. Meanwhile, GA is able to reveal the optimal parameter setting combination more efficiently and with three orders of magnitude faster convergence time in comparison to brute force approach

A Review of ICD Anti-Tachycardia Therapy Programming with Generic Programming for Primary and Secondary Prevention

Intracardiac defibrillator plays a pivotal role in preventing sudden cardiac death; however, inappropriate shock delivery remains an important source of morbidity and mortality. Advancements in device technology along with various shock reduction strategies play a key role in reducing inappropriate and unnecessary shocks. Anti-tachycardia pacing (ATP) is the first-line therapy prior to shock delivery. Several trials have validated the efficacy of ATP for both slow and fast ventricular tachycardia without significant increase in occurrence of arrhythmia-related syncope. In addition, trials also support that therapy for non-sustained tachycardia can be prevented by higher programmed zones and prolonged intervals to detect without higher risk of syncope. With this perspective, authors employ a customized programming for both primary and secondary prevention to reduce inappropriate therapies or unnecessary therapies, in particular, progression to shock but allow for spontaneous termination at slower ventricular tachycardia rates. The programming was instituted at the time of device implantation or at follow up

3D Printing in Pharmaceutical Sector: An Overview

The pharmaceutical industry is moving ahead at a rapid pace. Modern technology has enabled the development of novel dosage forms for targeted therapy. However, the fabrication of novel dosage forms at industrial scale is limited and the industry still runs on conventional drug delivery systems, especially modified tablets. The introduction of 3D printing technology in the pharmaceutical industry has opened new horizons in the research and development of printed materials and devices. The main benefits of 3D printing technology lie in the production of small batches of medicines, each with tailored dosages, shapes, sizes, and release characteristics. The manufacture of medicines in this way may finally lead to the concept of personalized medicines becoming a reality. This chapter provides an overview of how 3D printed technology has extended from initial unit operations to developed final products

Hardware realization and PID control of multi-degree of freedom articulated robotic arm

A robotic manipulator is the most important component in an industrial environment for autonomous execution of tasks. Given the repoted fact that a PID (Proportional-Integral-Derivative) will continue to be the main workhorse in the automation sector, the present paper deals with designing and realizing this control law. A custom-developed pseudo-industrial platform AUTAREP (AUTonomous Articulated Robotic Educational Platform) centered on a 6DOF (Six Degree of Freedom) manipulator is considered. The derived kinematic and dynamic models of the arm form the basis of MATLAB-based control simulation. The control law after discretization is also implemented on embedded hardware. When subject to various inputs, result of trajectory tracking in the form of output responses, demonstrate superior performance in transient as well as steady state. The stability and convergent behavior of the outputs is also observed, thus highlighting efficacy of proposed approach

Utilization of Ionic Organic Polymer to Improve Performance and Properties of Problematic Soils

Problematic soils with high compressibility and low shear strength are often treated with traditional chemical stabilizing additives such as cement and lime to improve their engineering properties. Polymers were employed to improve and reinforce a variety of material qualities in a wide range of applications. The use of polymer SBS (stabilizer base stabilizer) to improve the characteristics of problematic soils is discussed in this research. Two types of soils were used. The first type, "soil A Burj," is collapsible soil and was collected from Burj El-Arab city, while the second type, "soil B Dam," is fine sand and was obtained from Damietta city. The untreated and treated samples were subjected to sieve analysis, hydrometer, liquid limits, standard compaction, collapse potential (CP), direct shear, California Bearing Ratio (CBR) testing, and SEM, TEM, chemical, and microstructural analysis tests. Three different polymer SBS concentrations in water were used (1:300, 1:150, and 1:10). The results showed that by adding the polymer SBS, LL and OWC decreased exhibiting more plastic behavior compared to the non-treated samples. Also, the CP decreased with adding the polymer SBS, and the degree of collapsibility was enhanced from trouble to moderate trouble condition. The shear strength, internal friction angle, and CBR value were also improved. In summary, the best results were produced when a polymer ratio of 1:150 was used and a curing time of at least 28 days was provided. Doi: 10.28991/CEJ-2023-09-12-05 Full Text: PD

Neuromorphic AI Empowered Root Cause Analysis of Faults in Emerging Networks

Mobile cellular network operators spend nearly a quarter of their revenue on network maintenance and management. A significant portion of that budget is spent on resolving faults diagnosed in the system that disrupt or degrade cellular services. Historically, the operations to detect, diagnose and resolve issues were carried out by human experts. However, with diversifying cell types, increased complexity and growing cell density, this methodology is becoming less viable, both technically and financially. To cope with this problem, in recent years, research on self-healing solutions has gained significant momentum. One of the most desirable features of the self-healing paradigm is automated fault diagnosis. While several fault detection and diagnosis machine learning models have been proposed recently, these schemes have one common tenancy of relying on human expert contribution for fault diagnosis and prediction in one way or another. In this paper, we propose an AI-based fault diagnosis solution that offers a key step towards a completely automated self-healing system without requiring human expert input. The proposed solution leverages Random Forests classifier, Convolutional Neural Network and neuromorphic based deep learning model which uses RSRP map images of faults generated. We compare the performance of the proposed solution against state-of-the-art solution in literature that mostly use Naive Bayes models, while considering seven different fault types. Results show that neuromorphic computing model achieves high classification accuracy as compared to the other models even with relatively small training dat

The Potential Role of Nitric Oxide in Halting Cancer Progression Through Chemoprevention

Nitric oxide (NO) in general plays a beneficial physiological role as a vasorelaxant and the role of NO is decided by its concentration present in physiological environments. NO either facilitates cancer-promoting characters or act as an anti-cancer agent. The dilemma in this regard still remains unanswered. This review summarizes the recent information on NO and its role in carcinogenesis and tumor progression, as well as dietary chemopreventive agents which have NO-modulating properties with safe cytotoxic profile. Understanding the molecular mechanisms and cross-talk modulating NO effect by these chemopreventive agents can allow us to develop better therapeutic strategies for cancer treatment