Umar Khan

In the field of liquid composite moulding (LCM) simulations, a long-standing assumption has dominated–the belief in constant resin viscosity. While effective in many cases, this assumption may not hold for the infusion process, which lasts for an extended period. This impacts the mechanical properties of the cured epoxy, which are crucial for load transfer in polymer structures. The majority of epoxy resins operate on a bipartite foundation, wherein their viscosity undergoes dynamic alterations during the process of cross-linking. Temperature and cross-linking intricately interact, with elevated temperatures initially reducing viscosity due to kinetic energy but later increasing it as cross-linking accelerates. This interplay significantly influences the efficiency of the infusion process, especially in large and intricate moulds. This article explores the significant temperature dependence of epoxy resin viscosity, proposing an accurate model rooted in its non-linear evolution. This model aligns with empirical evidence, offering insights into determining the optimal starting temperature for efficient mould filling. This study presents an advanced infusion model that extends existing non-linear dual-split viscosity approaches by incorporating the experimental validation of viscosity variations. Unlike previous models that primarily focus on theoretical or numerical frameworks, this work integrates experimental insights to optimize infusion temperature for efficient resin infusion in large and complex parts. Building on these findings, a novel mould-filling technique is proposed to enhance efficiency and reduce material waste.

This paper presents a unique control system to regulate power exchanges and load bus voltage in a networked microgrid (NMG) system comprising AC and DC microgrids. During the islanding of a microgrid in this NMG system, load voltage and power balance can get disturbed. A control system and associated converter and inverter control methods are presented to rectify these issues. An efficient model predictive control (MPC) method, which gives a tracking error of 50% lower than a conventional proportional-integral (PI) controller, is used to control multiple inverters in the NMG system. Simulation studies are conducted to test the NMG in islanding and load change scenarios. With the help of these studies, it is verified that the MPC-controlled inverters can provide better tracking accuracy in achieving desired power flows in the NMG system.

Background: Hepatocellular carcinoma (HCC) is among the most prevalent cancers and the second leading cause of cancer-related mortality globally. Objective: The aim was to investigate the sensitivity of sorafenib administration after TACE in advanced HCC patients. Methods: From June 2021 to June 2022, 36 patients from Hayatabad Medical Complex Peshawar were enrolled in this retrospective research. Based on the correct state of liver activity, sorafenib medication was started within two weeks of finishing TACE treatment. Throughout each follow-up, the liver and kidney function, adverse effects, and medicinal impact were assessed using the Modified response assessment criteria in solid tumors (mRECIST). Results: The mean overall survival (mOS) and mean time to progress (mTTP) were 11.5 months and 7.5 months, respectively. Patients with one or more localised hepatic lesions were respectively 18 months and 12 months old (2 = 4.613, P = 0.0314). No patients had a full response, three had partial responses, 11 had stable disease (SD), and 22 had progressive illness (PD), as per the mRECIST. The disease control rate (DCR) was 38.89% (14/36) and the response rate was 8.33% (3/36) for both. Hand-foot skin irritation (21 incidences, 58.3%), and diarrhoea (17 incidences, 47.22%) were the predominant adverse effects.

Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients’ sensory data is performed to obtain highly accurate predictions of the patients’ sensory data (patients’ vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring.

The term partial discharge (PD) refers to a partial bridging of insulating material between electrodes that sustain an electric field in high-voltage (HV) systems. Long-term PD activity can lead to catastrophic failures of HV systems resulting in economic, energy and even human life losses. Such failures and losses can be avoided by continuously monitoring PD activity. Existing techniques used for PD localization including time of arrival (TOA) and time difference of arrival (TDOA), are complicated and expensive because they require time synchronization. In this paper, a novel received signal strength (RSS) based localization algorithm is proposed. The reason that RSS is favoured in this research is that it does not require clock synchronization and it only requires the energy of the received signal rather than the PD pulse itself. A comparison was made between RSS based algorithms including a proposed algorithm, the ratio and search and the least squares algorithm to locate a PD source for nine different positions. The performance of the algorithms was evaluated by using two field scenarios based on seven and eight receiving nodes, respectively. The mean localization error calculated for two-field-trial scenarios show, respectively, 1.80 m and 1.76 m for the proposed algorithm for all nine positions, which is the lowest of the three algorithms.