Analyzing energy consumption of nature-inspired optimization algorithms

Nature-Inspired Optimization (NIO) algorithms have become prevalent to address a variety of optimization problems in real-world applications because of their simplicity, flexibility, and effectiveness. Some application areas of NIO algorithms are telecommunications, image processing, engineering design, vehicle routing, etc. This study presents a critical analysis of energy consumption and their corresponding carbon footprint for four popular NIO algorithms. Microsoft Joulemeter is employed for measuring the energy consumption during the runtime of each algorithm, while the corresponding carbon footprint of each algorithm is calculated based on the UK DEFRA guide. The results of this study evidence that each algorithm demonstrates different energy consumption behaviors to achieve the same goal. In addition, a one-way Analysis of Variance (ANOVA) test is conducted, which shows that the average energy consumption of each algorithm is significantly different from each other. This study will help guide software engineers and practitioners in their selection of an energy-efficient NIO algorithm. As for future work, more NIO algorithms and their variants can be considered for energy consumption analysis to identify the greenest NIO algorithms amongst them all. In addition, future work can also be considered to ascertain possible relationships between NIO algorithms and the energy usage of hardware resources of different CPU architectures

Brokerage System for Integration of LrWPAN Technologies

The prevalent demand for remote data sharing and connectivity has catalysed the development of many wireless network technologies. However, low-power and low-rate wireless network technologies have emerged as the preferred choice (due to cheap procurement and maintenance cost, efficiency, and adaptability). Currently, these groups of wireless networks are adopted in homes, health, and business sectors. The increase in existing WSNs has resulted in the incompatibility of wireless network protocols and poses a problem that results in high acquisition or maintenance costs, increased complexity, reliability inadequacies in some instances, lack of uniformity within similar standards, and high energy consumption. To address this problem, we develop a novel machine-to-machine software-based brokerage application (known as JosNet) for interoperability and integration between Bluetooth LE, Zigbee, and Thread wireless network technologies. JosNet allows one network protocol to exchange data packets or commands with each other. In this paper, we present a novel working network brokerage model for a one-to-one network protocol to communication (e.g., from Zigbee to Bluetooth) or one-to-many network protocol communication (e.g., from Bluetooth to Zigbee, Thread, etc.) to securely send messages in a large-scale routing process for short or long-range connections. We also present a large-scale implementation of JosNet using a routing table for large areas. The results show an industry standard performance for end-to-end latency time and throughput

Analysis and Prediction Model of Fuel Consumption and Carbon Dioxide Emissions of Light-Duty Vehicles

Due to the alarming rate of climate change, fuel consumption and emission estimates are critical in determining the effects of materials and stringent emission control strategies. In this research, an analytical and predictive study has been conducted using the Government of Canada dataset, containing 4973 light-duty vehicles observed from 2017 to 2021, delivering a comparative view of different brands and vehicle models by their fuel consumption and carbon dioxide emissions. Based on the findings of the statistical data analysis, this study makes evidence-based recommendations to both vehicle users and producers to reduce their environmental impacts. Additionally, Convolutional Neural Networks (CNN) and various regression models have been built to estimate fuel consumption and carbon dioxide emissions for future vehicle designs. This study reveals that the Univariate Polynomial Regression model is the best model for predictions from one vehicle feature input, with up to 98.6% accuracy. Multiple Linear Regression and Multivariate Polynomial Regression are good models for predictions from multiple vehicle feature inputs, with approximately 75% accuracy. Convolutional Neural Network is also a promising method for prediction because of its stable and high accuracy of around 70%. The results contribute to the quantifying process of energy cost and air pollution caused by transportation, followed by proposing relevant recommendations for both vehicle users and producers. Future research should aim towards developing higher performance models and larger datasets for building APIs and applications

Integrating the HFACS Framework and Fuzzy Cognitive Mapping for In-Flight Startle Causality Analysis

This paper discusses the challenge of modeling in-flight startle causality as a precursor to enabling the development of suitable mitigating flight training paradigms. The article presents an overview of aviation human factors and their depiction in fuzzy cognitive maps (FCMs), based on the Human Factors Analysis and Classification System (HFACS) framework. The approach exemplifies system modeling with agents (causal factors), which showcase the problem space's characteristics as fuzzy cognitive map elements (concepts). The FCM prototype enables four essential functions: explanatory, predictive, reflective, and strategic. This utility of fuzzy cognitive maps is due to their flexibility, objective representation, and effectiveness at capturing a broad understanding of a highly dynamic construct. Such dynamism is true of in-flight startle causality. On the other hand, FCMs can help to highlight potential distortions and limitations of use case representation to enhance future flight training paradigms

Use of decongestants may disrupt cell signaling pathways that control Tbx gene expression, leading to hypoplastic left heart syndrome

Hypoplastic left heart syndrome (HLHS) collectively refers to a range of congenital heart defects, all involving some degree of left ventricular hypoplasia, or underdevelopment of the left ventricle. Additionally, HLHS often involves coarctation of the aorta, and can also include hypoplasia of the ascending aorta, as well as mitral and/or aortic valve stenosis or atresia. HLHS is extremely rare, as it has been reported to occur in only 1 in 5000 live births each year. The cause of HLHS is currently unknown, however much research is being done to discover how and why these defects occur. HLHS is known to be familially inherited in some instances and is also associated with many well-characterized genetic disorders, including Holt-Oram syndrome, Turner’s syndrome, Noonan syndrome, Smith-Lemli-Opitz syndrome, as well as trisomies 13, 18, and 21. Additionally, an autosomal recessive pattern of inheritance has been found amongst some siblings, however, no specific genes have been implicated. Incidence of HLHS also varies significantly in certain geographical regions and some studies have found a seasonal correlation in HLHS, indicating a possible environmental cause

Multi-Network Latency Prediction for IoT and WSNs

The domain of Multi-Network Latency Prediction for IoT and Wireless Sensor Networks (WSNs) confronts significant challenges. However, continuous research efforts and progress in areas such as machine learning, edge computing, security technologies, and hybrid modelling are actively influencing the closure of identified gaps. Effectively addressing the inherent complexities in this field will play a crucial role in unlocking the full potential of latency prediction systems within the dynamic and diverse landscape of the Internet of Things (IoT). Using linear interpolation and extrapolation algorithms, the study explores the use of multi-network real-time end-to-end latency data for precise prediction. This approach has significantly improved network performance through throughput and response time optimization. The findings indicate prediction accuracy, with the majority of experimental connection pairs achieving over 95% accuracy, and within a 70% to 95% accuracy range. This research provides tangible evidence that data packet and end-to-end latency time predictions for heterogeneous low-rate and low-power WSNs, facilitated by a localized database, can substantially enhance network performance, and minimize latency. Our proposed JosNet model simplifies and streamlines WSN prediction by employing linear interpolation and extrapolation techniques. The research findings also underscore the potential of this approach to revolutionize the management and control of data packets in WSNs, paving the way for more efficient and responsive wireless sensor networks

Audit of an Organisation’s ICT Systems for Flexible Working

This research entails an audit of the ICT systems within an organisation to determine the environmental impact of flexible working on the organisation’s carbon footprint. The study reviews current issues and methodologies in the green ICT sector before providing an overview of the research process. Questionnaires and observations are employed for the investigation on employee working habits. A number of energy consumption measuring tools such as Joulemeter, Powermeter, and SusteIT are used to audit energy consumption of laptops, monitors and phones used by the organisation. This research reveals that working from home has a lower carbon footprint than working in the office primarily due to commuting-related energy consumption. Approximately 20% of the organisation’s staff work from home. The organisation’s annual carbon footprint is 31,509kg of CO2 emissions taking into consideration IT equipment and travel-related emissions. The recommendation is to allow more staff to work from home with given guidelines on the responsible handling of IT equipment in order to reduce their energy consumption. It is recommended that further study be undertaken in order to gain a detailed carbon footprint report

Investigation of a UK Financial Organisation’s Green Computing Strategy

This study involves an investigation on the Green ICT strategy of a financial organization. The baseline for the Green ICT strategy implementation is elicited via a semi-structured interview and assessed using a bespoke tool developed for a SURF Maturity Model driven framework. This framework encompasses Green ICT strategy, Greening of ICT in the organization and Greening of operations in ICT. The results of the study reveal that the overall baseline score is 1.8 out of 5.0 which is a relatively low score. However, the overall target level set for organization is 3.0 out of 5.0 accompanied by a roadmap and action plan (with several key action objectives) that covers a 5-year timeframe to bridge the gap between the baseline and the target. An IT representation from the organization provides some feedback on the action plan that leads to several amendments relating to cloud technology and a written business case for promoting a Green ICT strategy