Doing Pre-operative Investigations in Emergency Department; a Clinical Audit

Introduction: Pre-operative investigations for emergency surgical patients differ between centers. Following established guidelines can reduce unnecessary investigation, cost of treatment and hospital stay. The present audit was carried out to evaluate the condition of doing pre-operative investigations for three common surgical emergencies compared to National Institute for Health and Care Excellence (NICE) guidelines and local criteria.Methods: A retrospective clinical audit of acute-appendicitis, abscess and hernia patients admitted to the emergency department was carried out over a one-year period from July 2014 to July 2015. Data of laboratory investigations, their indication, cost and duration of hospital stay was collected and compared with NICE-guidelines.Results: A total of 201 patients were admitted to the emergency department during the audit period. These included 77(38.3%) cases of acute-appendicitis, 112 (55.7%) cases of abscesses, and 12 (6%) cases of hernia. Investigations not indicated by NICE-guidelines included 42 (20.9%) full blood counts, 29 (14.4%) random blood sugars, 26 (12.9%) urea tests, 4 (2%) chest x-rays, 13 (6.5%) electrocardiographs, and 58 (28.9%) urine analyses. These cost 25,675 Rupees (245.46 Dollars) in unnecessary investigation costs and 65.7 days of additional hospital stay.Conclusions: Unnecessary investigations for emergency surgical patients can be reduced by following NICE-guidelines. This will reduce workload on emergency services, treatment costs and the length of hospital stay

Health monitoring in proactive reliability management of deteriorating concrete bridges

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Crowd Modeling using Temporal Association Rules

Understanding crowd behavior has attracted tremendous attention from researchers over the years. In this work, we propose an unsupervised approach for crowd scene modeling and anomaly detection using association rules mining. Using object tracklets, we identify events occurring in the scene, demonstrated by the paths or routes objects take while traversing the scene. Allen\u27s interval-based temporal logic is used to extract frequent temporal patterns from the scene. Temporal association rules are generated from these frequent temporal patterns. Our goal is to understand the scene grammar, which is encoded in both the spatial and spatio-temporal patterns. We perform anomaly detection and test the method on a well-known public data

Can Temperature be Used as a Predictor of Data Traffic? A Real Network Big Data Analysis

The proliferation of mobile devices and big data has made it possible to understand the human movements and forecasts of precise and intelligent short and long-term data consumption of services like call, sms, or internet data which has interesting and promising applications in modern cellular networks. Human nature and moods are known to be synonymous with the physical attributes of mother nature such as temperature. The change in those physical features affects the human routines and activities such as cellular data consumptions. The future of telecommunication lies in the exploration of heap of information and data available to companies and inferring the valuable results through extensive analysis. In this paper, we analyze three main traits of cellular activity: sms, call, and internet. This paper investigates whether the relationship between the temperature and the cellular data consumption exits or not. This work introduces a novel approach to identify the strength of relationship between the temperature and cellular activity (sms, call, internet) and discuss the methods to quantify the relationship using correlation method. The real network CDR big data set - Milano Grid data set is used to analyze the behavior of the cellular activity with respect to temperature

An Algorithm: Optimal Homotopy Asymptotic Method for Solutions of Systems of Second-Order Boundary Value Problems

Optimal homotopy asymptotic method (OHAM) is proposed to solve linear and nonlinear systems of second-order boundary value problems. OHAM yields exact solutions in just single iteration depending upon the choice of selecting some part of or complete forcing function. Otherwise, it delivers numerical solutions in excellent agreement with exact solutions. Moreover, this procedure does not entail any discretization, linearization, or small perturbations and therefore reduces the computations a lot. Some examples are presented to establish the strength and applicability of this method. The results reveal that the method is very effective, straightforward, and simple to handle systems of boundary value problems

Smart and Secure CAV Networks Empowered by AI-Enabled Blockchain: Next Frontier for Intelligent Safe-Driving Assessment

Securing safe-driving for connected and autonomous vehicles (CAVs) continues to be a widespread concern despite various sophisticated functions delivered by artificial intelligence for in-vehicle devices. Besides, diverse malicious network attacks become ubiquitous along with the worldwide implementation of the Internet of Vehicles, which exposes a range of reliability and privacy threats for managing data in CAV networks. Combined with the fact that the capability of existing CAVs in handling intensive computation tasks is limited, this implies a need for designing an efficient assessment system to guarantee autonomous driving safety without compromising data security. Motivated by this, in this article, we propose a novel framework, namely Blockchain-enabled intElligent Safe-driving assessmenT (BEST), that offers a smart and reliable approach for conducting safe driving supervision while protecting vehicular information. Specifically, a promising solution that exploits a long short-term memory model is introduced to assess the safety level of the moving CAVs. Then, we investigate how a distributed blockchain obtains adequate trustworthiness and robustness for CAV data by adopting a byzantine fault tolerance-based delegated proof-of-stake consensus mechanism. Simulation results demonstrate that our presented BEST gains better data credibility with a higher prediction accuracy for vehicular safety assessment when compared with existing schemes. Finally, we discuss several open challenges that need to be addressed in future CAV networks.Comment: 8 pages, 6 figures. This paper has been accepted for publication by IEEE Networ

Advances in Halloysite Nanotubes (HNTs)-Based Mixed-Matrix Membranes for CO2 Capture

Membrane technology promises a highly economical and efficient solution for CO2 separation. Many polymeric membranes have been reported in the past for the separation of gases specially to remove CO2 from natural gas and low-pressure flue-gas streams. The performance of membranes can be tailored by dispersing nanofillers in a polymeric matrix to produce mixed-matrix membranes (MMMs). This not only adds mechanical strength to membranes but also reduces compaction of the polymeric layer at high pressure and maintains high performance. Halloysite nanotubes (HNTs) gained attention in gas separation technology and due to their tubular structure have been used in a variety of applications in biomedical, coating, composite, and electronic industries. However, very little but conclusive literature and reviews are available to indicate that functionalized and non-functionalized HNTs can improve the performance of MMMs for efficient CO2 capture. The current status and gaps for potential applications of HNTs-based membranes for gas separation are identified and reviewed

Emotional Intelligence and Role Conflict a Bond of Converse Relationship: Evidence from the Hospitals Sector of the Health Industry

This research study aimed at finding the convers relationship of emotional intelligence and role conflict at the hospitals of Peshawar, KPK, Pakistan. Emotional intelligence is considered the best coping strategy to deal with work stress, while role conflict is the most common practicing stressor that contributestothe stressful condition of a person. Hospitals are the most crowded and overburdened sector of any nation that is heavily dependent on multiple roles of nurses, doctors, and medical staff. The objective of this research was to see the effect of emotional intelligence in dealing with role conflict of the nurses and medical staff of three big hospitals in the city. The results show a significant inverse relationship between emotional intelligence and role conflict. The P-Value (0.000), T-statistics (16), R-Square (0.32) and path coefficients (-0.56)show highly significant results of underline relationships. For these findings, SmartPLS 3.0 was used to analyze the response of 359 nurses and medical staff employing stratified sampling and systematic random sampling techniques on a five-point scale of adapted questionnaires

Intelligent resource management for eMBB and URLLC in 5G and beyond wireless networks

In the era of 5G and beyond wireless networks, the simultaneous support of enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) poses significant challenges in managing radio resources efficiently. By leveraging the puncturing technique, we propose an intelligent resource management framework for meeting the strict latency and reliability requirement of URLLC services and the high data rate for eMBB services. In particular, a semi-supervised learning and deep reinforcement learning (DRL) based architecture is proposed to manage the resources intelligently. We decompose the optimization problem into two subproblems: 1) resource block allocation (RBA) strategy for eMBB slice, and 2) URLLC scheduling. Through extensive simulations and performance evaluations, we demonstrate the effectiveness of the proposed technique in optimizing resource utilization, minimizing latency for URLLC users, and maximizing the throughput for eMBB services. Simulation findings demonstrate that the proposed methodology can ensure the URLLC reliability requirements while maintaining higher average sum rate for eMBB and higher convergence rate. The proposed framework paves the way for the efficient coexistence of diverse services, enabling wireless network operators to optimize resource allocation, improve user experience, and meet the specific requirements of eMBB and URLLC applications