Institute Of Electrical And Electronics Engineers Research Articles

Determining optimal paths of virtual links in Avionics Full-Duplex Switched Ethernet networks using modified ant colony optimization algorithm

The Aeronautical Radio Incorporated (ARINC) 664 Avionics Full Duplex Switched Ethernet (AFDX) data bus has been developed based on the Ethernet standard Institute of Electrical and Electronics Engineers (IEEE) 802.3, including redundancy, guaranteed bandwidth, and deterministic timing of transmit interval time to meet the increasing amounts of data exchange needs among aircraft equipment while reducing the harness weight. Isolated data tunnels, called Virtual Links (VLs), are established to route Ethernet frames from a single source to one or more destinations using end systems and switches that contain a static routing mechanism specific to ARINC 664 AFDX. A limited number of algorithmic approaches are presented in the literature to route VLs; however, they are proven in uncomplicated topologies. Depending on the technological development, the node amount consisting of End Systems (ESs), Switches, and Remote Data Concentrators (RDCs) in the aircraft data bus structure will increase enormously. Bandwidth utilization (BWU), bandwidth flexibility limit for further extensions, port usage, total harness weight, latency, and redundancy should be considered in addition to number of nodes to determine VLs’ route paths in ARINC 664 AFDX networks. In such cases, determining optimal paths of VLs in ARINC 664 AFDX networks becomes a challenging problem to solve with the existing methods in the literature. This paper proposes an Ant Colony Optimization (ACO) based algorithm to determine optimal VL path routes in the complex topology of ARINC 664 AFDX networks considering multi-effective parameters. Experimental simulations are executed to confirm the validity of the presented algorithm in different case studies and show its applicability in the industry. The obtained results of the proposed algorithm outperform conventional algorithms existing in the literature in terms of bandwidth usage depending on predefined expansion capacity and solution time. The proposed algorithm also introduces novelty by considering the redundancy, total harness weight, and delay limit, which is deficient in the existing literature.

PEREGRINATIONS ON THE STUDY OF THE OPTICAL PROPERTIES OF NONSPHERICAL PARTICLES

PEREGRINATIONS ON THE STUDY OF THE OPTICAL PROPERTIES OF NONSPHERICAL PARTICLES

Methods for the Clinical Validation of Digital Endpoints: Protocol for a Scoping Review Abstract.

Clinical trials often use digital technologies to collect data continuously outside the clinic and use the derived digital endpoints as trial endpoints. Digital endpoints are also being developed to support diagnosis, monitoring, or therapeutic interventions in clinical care. However, clinical validation stands as a significant challenge, as there are no specific guidelines orienting the validation of digital endpoints. This paper presents the protocol for a scoping review that aims to map the existing methods for the clinical validation of digital endpoints. The scoping review will comprise searches from the electronic literature databases MEDLINE (PubMed), Scopus (including conference proceedings), Embase, IEEE (Institute of Electrical and Electronics Engineers) Xplore, ACM (Association for Computing Machinery) Digital Library, CENTRAL (Cochrane Central Register of Controlled Trials), Web of Science Core Collection (including conference proceedings), and Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports. We will also include various sources of gray literature with search terms related to digital endpoints. The methodology will adhere to the Joanna Briggs Institute Scoping Review and the Guidance for Conducting Systematic Scoping Reviews. A search for reviews on the existing evidence related to this topic was conducted and has shown that no such review was previously undertaken. This review will provide a systematic assessment of the literature on methods for the clinical validation of digital endpoints and highlight any potential need for harmonization or reporting of methods. The results will include the methods for the clinical validation of digital endpoints according to device, digital endpoint, and clinical application goal of digital endpoints. The study started in January 2023 and is expected to end by December 2023, with results to be published in a peer-reviewed journal. A scoping review of methodologies that validate digital endpoints is necessary. This review will be unique in its breadth since it will comprise digital endpoints collected from several devices and not focus on a specific disease area. The results of our work should help guide researchers in choosing validation methods, identify potential gaps in the literature, or inform the development of novel methods to optimize the clinical validation of digital endpoints. Resolving these gaps is the key to presenting evidence in a consistent way to regulators and other parties and obtaining regulatory acceptance of digital endpoints for patient benefit. PRR1-10.2196/47119.

Mind the Gap: Understanding Coverage Breaks of Newly Launched Engineering and Computer Science Journals in Core Databases

Journal coverage is an important factor to consider when evaluating a database. In this study, we counted the number of articles (including early access articles) in newly-launched journals (2018-2022) from two major association publishers in engineering and computer science: the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM). We then compared these numbers with the number of articles from these journals in Web of Science (WOS) Core Collection, Scopus, and Google Scholar (GS). Results indicated that GS had higher percentages for coverage for all the journals that were assessed and outperformed WOS and Scopus in terms of how fast newly-launched journals were indexed. Findings from this study will help librarians evaluate each database in reference, instruction, and collection development.

Enhancing distribution generator impact mitigation using an adaptive protection scheme based on modified pelican optimization algorithm and active database management system

AbstractThis paper addresses the challenge of protecting electrical networks in the presence of distribution generators (DGs). The use of DGs affects fault currents, leading to miscoordination between protection relays and causing constraints on network reliability. To tackle this issue, the authors propose an adaptive protection scheme (APS) based on a modified pelican optimization algorithm (MPOA) and active database management system (ADBMS). The APS coordinates directional overcurrent relays and distance relays, while the MPOA simulates a pelican mating strategy and includes a modified internal equation. The proposed APS is further upgraded with ADBMS to save system resources by storing relay settings in the database and calling them when the state of DGs changes without running optimization algorithms. The effectiveness of the proposed APS is validated on the Institute of Electrical and Electronics Engineers (IEEE) eight‐bus test system and the IEEE 14‐bus distribution network. Results indicate that the APS can effectively protect electrical networks in the presence of DGs, while the ADBMS upgrade saves system resources.

Assessment of Background Radiation Around Telecommunication Masts of the Major Network Service Providers in Kaduna South, Kaduna State, Nigeria

This work assessed the electromagnetic radiation from telecommunication masts of major service providers (AIRTEL, ETISALAT, GLO, and MTN) around residential areas in Kaduna South, Nigeria. The Radio Frequency Electromagnetic Force (RF EMF) strength meter of serial number (480836) and a measuring tape were used to obtain readings away from the masts. The electric field strengths E, magnetic field strengths H, and power densities were measured at 0 m, 100 m, 200 m, 300 m, 400 m, and 500 m away from the mast base in V/m, A/m, and W/m2, respectively. The power densities measured were: 0.000091 W/m2 for Airtel, 0.000195 W/m2 for Etisalat, 0.000488 W/m2 for GLO, and 0.421352 W/m2 for MTN. These readings were compared with the standard valve (4.5 W/m2) recommended by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the 6.05 W/m2 recommended by the Institute of Electrical and Electronic Engineers (IEEE) for exposure limits of radio frequency waves and the Federal Communications Commission (FCC) and were discovered to be below the recommended values. The radiological health effects, absorbed radiation dose rates, the annual effective dose equivalent (AEDE), and the excess lifetime cancer risks (ELCR) were evaluated. The values of absorbed dose rates were 0.000091 Gys-1 for Airtel, 0.000195333 Gys-1 for Etisalat, 0.000488333 Gys-1 for GLO, and 0.421352 Gys-1 for MTN, which were observed to be far lower than the world permissible value of 0.02472 Gys-1, except that of MTN, which is higher than the permissible value. However, the estimated annual effective dose equivalents (AEDE) were found to be higher than the ICNIRP permissible limits of 1.00 mSvy-1 for the public, which implies that the base stations do pose radiological risks in the long term. Also, the excess lifetime cancer risk for the stations’ users was all above the 0.29 × 10-3 per Sievert world recommended value by the United Nations Scientific Committee on the Effects of Atomic Radiations, UNSCEAR (2018), and Audu et al., (2019). This, therefore, suggests there is a high probability of the base station workers and residents within 500 m of the base mast to develop radiation-related illnesses over a long time. Regular radiological monitoring of all the base stations is recommended.

Evaluating the effectiveness of various remediation therapies on the engineering properties of oil-contaminated soils

This study was embarked upon to ascertain the effectiveness of using physical and biological remediation procedures to amend the impact of oil pollution on soil engineering properties. Soil contaminated with crude oil at a mix ratio of 20:1 (percentage by weight), was remediated with charcoal (C2), charcoal + seaweed extract + groundnut plant (C3), and charcoal + wood ash + groundnut plant (C4). During the procedure, the soil’s total petroleum hydrocarbon (TPH) level was determined through American Public Health Association (APHA) approved procedures; while the geotechnical (maximum dry density “MDD”, optimal moisture content “OMC, and California bearing ratio “CBR”) and electrical properties (electrical conductivity “EC”, dielectric constant “ɛʹ, and electrical resistivity “ρ”) were measured in accordance with American Society for Testing and Materials (ASTM) and Institute of Electrical and Electronics Engineers (IEEE) approved procedures. The results depicted that the TPH, ɛʹ and ρ-values of the contaminated soil samples declined by 56.71%, 82.01% and 72.80%; 31.65%, 36.85% and 19.92%; and 56.32%, 62.03% and 60.27%, respectively in the C2, C3 and C4 samples after the remediation program; while the soil EC increased by 103.45%, 145.68% and 135.34% in the C2, C3 and C4 samples, respectively. Likewise, the C2, C3 and C4 samples CBR, MDD and OMC values increased by 29.49%, 34.75% and 50.30%; 7.87%, 11.02% and 4.72; and 12.02%, 28.01% and 40.38%, respectively at the end of the experimental period. These findings portrayed that hybridized remediation therapies are better remediating techniques in remediating contaminated soils; hence sustaining the integrity of the soils’ geotechnical and electrical properties.

A weighted-sum chaotic sparrow search algorithm for interdisciplinary feature selection and data classification

In today’s data-driven digital culture, there is a critical demand for optimized solutions that essentially reduce operating expenses while attempting to increase productivity. The amount of memory and processing time that can be used to process enormous volumes of data are subject to a number of limitations. This would undoubtedly be more of a problem if a dataset contained redundant and uninteresting information. For instance, many datasets contain a number of non-informative features that primarily deceive a given classification algorithm. In order to tackle this, researchers have been developing a variety of feature selection (FS) techniques that aim to eliminate unnecessary information from the raw datasets before putting them in front of a machine learning (ML) algorithm. Meta-heuristic optimization algorithms are often a solid choice to solve NP-hard problems like FS. In this study, we present a wrapper FS technique based on the sparrow search algorithm (SSA), a type of meta-heuristic. SSA is a swarm intelligence (SI) method that stands out because of its quick convergence and improved stability. SSA does have some drawbacks, like lower swarm diversity and weak exploration ability in late iterations, like the majority of SI algorithms. So, using ten chaotic maps, we try to ameliorate SSA in three ways: (i) the initial swarm generation; (ii) the substitution of two random variables in SSA; and (iii) clamping the sparrows crossing the search range. As a result, we get CSSA, a chaotic form of SSA. Extensive comparisons show CSSA to be superior in terms of swarm diversity and convergence speed in solving various representative functions from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) benchmark set. Furthermore, experimental analysis of CSSA on eighteen interdisciplinary, multi-scale ML datasets from the University of California Irvine (UCI) data repository, as well as three high-dimensional microarray datasets, demonstrates that CSSA outperforms twelve state-of-the-art algorithms in a classification task based on FS discipline. Finally, a 5%-significance-level statistical post-hoc analysis based on Wilcoxon’s signed-rank test, Friedman’s rank test, and Nemenyi’s test confirms CSSA’s significance in terms of overall fitness, classification accuracy, selected feature size, computational time, convergence trace, and stability.

Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms.

Pressure injuries are increasing worldwide, and there has been no significant improvement in preventing them. This study is aimed at reviewing and evaluating the studies related to the prediction model to identify the risks of pressure injuries in adult hospitalized patients using machine learning algorithms. In addition, it provides evidence that the prediction models identified the risks of pressure injuries earlier. The systematic review has been utilized to review the articles that discussed constructing a prediction model of pressure injuries using machine learning in hospitalized adult patients. The search was conducted in the databases Cumulative Index to Nursing and Allied Health Literature (CINAHIL), PubMed, Science Direct, the Institute of Electrical and Electronics Engineers (IEEE), Cochrane, and Google Scholar. The inclusion criteria included studies constructing a prediction model for adult hospitalized patients. Twenty-seven articles were included in the study. The defects in the current method of identifying risks of pressure injury led health scientists and nursing leaders to look for a new methodology that helps identify all risk factors and predict pressure injury earlier, before the skin changes or harms the patients. The paper critically analyzes the current prediction models and guides future directions and motivations.

PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN-LSTM.

Researchers commonly use continuous noninvasive blood-pressure measurement (cNIBP) based on photoplethysmography (PPG) signals to monitor blood pressure conveniently. However, the performance of the system still needs to be improved. Accuracy and precision in blood-pressure measurements are critical factors in diagnosing and managing patients' health conditions. Therefore, we propose a convolutional long short-term memory neural network (CNN-LSTM) with grid search ability, which provides a robust blood-pressure estimation system by extracting meaningful information from PPG signals and reducing the complexity of hyperparameter optimization in the proposed model. The multiparameter intelligent monitoring for intensive care III (MIMIC III) dataset obtained PPG and arterial-blood-pressure (ABP) signals. We obtained 75,226 signal segments, with 60,180 signals allocated for training data, 12,030 signals allocated for the validation set, and 15,045 signals allocated for the test data. During training, we applied five-fold cross-validation with a grid-search method to select the best model and determine the optimal hyperparameter settings. The optimized configuration of the CNN-LSTM layers consisted of five convolutional layers, one long short-term memory (LSTM) layer, and two fully connected layers for blood-pressure estimation. This study successfully achieved good accuracy in assessing both systolic blood pressure (SBP) and diastolic blood pressure (DBP) by calculating the standard deviation (SD) and the mean absolute error (MAE), resulting in values of 7.89 ± 3.79 and 5.34 ± 2.89 mmHg, respectively. The optimal configuration of the CNN-LSTM provided satisfactory performance according to the standards set by the British Hypertension Society (BHS), the Association for the Advancement of Medical Instrumentation (AAMI), and the Institute of Electrical and Electronics Engineers (IEEE) for blood-pressure monitoring devices.

Surmounting Barriers to Healthcare Data and Information: International Case Studies

Objective: In this article, the authors review the progress in healthcare interoperability from 2010 to 2023 from an international perspective. Countries covered here include the Republic of China (Taiwan), the Republic of Croatia, the Republic of Estonia, and the Commonwealth of Australia. Interoperability, in the context of this paper, is “the ability to share information across time and space from multiple devices, sources, and organizations,” as defined by the Institute of Electrical and Electronic Engineers (IEEE). This is followed by recommendations for future work toward improving the standardization of heterogeneous data in the healthcare setting. Methodology: A literature review was conducted on established interoperability standards and systems in healthcare based on information obtained from journal publications, government, academy reports, published materials, and publicly available websites. Examples of specific interoperability efforts and an evaluation of their feasibility were conducted at three levels of healthcare interoperability, as defined by the National Academy of Medicine: 1) inter-facility (macro-tier) interoperability, 2) intra-facility (meso-tier) interoperability, and 3) Point-of-Care (micro-tier) interoperability. The authors assessed the adoption levels of each standard by looking at factors that support or limit its systemic adoption. Estimations on the number of users—medical professionals and patients—for each system were made in instances where verifiable data are available. Results: Presented here is an evaluation of four interoperability parameters: 1) device/equipment interoperability, 2) compatibility issues, 3) involved organizations, and 4) migration and conversion issues. Conclusions: Despite many parallel ongoing efforts to improve the standardization of healthcare information in the mobile devices, Internet of Things (IoT), and electronic health records (HER) sectors, there remains space for improvement. The recent development of the Trusted Exchange Framework and Common Agreement (TEFCA) framework has significantly reduced the friction of data exchange in many healthcare contexts. In addition, funding architectures for mediating data between separate healthcare organizations, or middleware architectures, may also be an effective strategy for consolidating healthcare data and improving information exchange.

Surmounting Barriers to Healthcare Data and Information: Cases in Point, the U.S. Experience

Objective: The authors review the progress in healthcare interoperability from 2010 to 2023 in the United States. Interoperability, in the context of this paper, is “the ability to share information across time and space from multiple devices, sources, and organizations”, as defined by the IEEE (Institute of Electrical and Electronic Engineers). This is followed by recommendations for future work toward improving the standardization of heterogeneous data in the healthcare setting. Methodology: A literature review was conducted on established interoperability standards and systems in healthcare based on information obtained from journal publications, government, academy reports, published materials, as well as publicly available websites. Examples of specific interoperability efforts and an evaluation of their feasibility were conducted at three levels of healthcare interoperability, as defined by the National Academy of Medicine: 1) inter-facility (macro-tier) interoperability, 2) intra-facility (meso-tier) interoperability, and 3) Point-of-Care (micro-tier) interoperability. An evaluation of four interoperability parameters: 1) device/equipment interoperability, 2) compatibility issues, 3) involved organizations, and 4) migration and conversion issues are presented. The evaluation assessed the adoption levels of each standard by looking at factors that support or limit its systemic adoption. Estimations on the number of users—medical professionals and patients—for each system were made in instances where verifiable data were available. Results: This review reveals that… Conclusions: Despite many parallel ongoing efforts to improve the standardization of healthcare information, in the mobile devices, Internet of Things (IoT), and electronic health record (HER) sectors, there remains space for improvement. The recent development of the Trusted Exchange Framework and Common Agreement (TEFCA) greatly reduces the friction of data exchange in many healthcare contexts. In addition, funding architectures for mediating data between separate healthcare organizations, or middleware architectures, might also be an effective strategy for consolidating healthcare data and improving information exchange.

Pragmatic Approaches to Interoperability – Surmounting Barriers to Healthcare Data and Information Across Organizations and Political Boundaries

Objective This paper provides a review of the landscape of interoperability efforts in healthcare from 2010 to 2023, in the US and abroad. Interoperability, in the context of this paper, is “the ability to share information across time and space from multiple devices, sources, and organizations”, as defined by the IEEE (Institute of Electrical and Electronic Engineers). This review is followed by recommendations for future work toward improving the standardization of heterogeneous data in the healthcare setting. Methodology and Results A literature review was conducted on established interoperability standards and systems in healthcare, based on information obtained from journal publications, government, academy reports, published materials, and publicly available websites. The review emphasizes four interoperability parameters: device/equipment interoperability, compatibility issues, involved organizations, and migration and conversion issues. It evaluates adoption levels for each standard, evaluating factors supporting and/or limiting systemic adoption. Estimations on the number of users – both medical professionals and patients – for each system were made in instances where verifiable data were available. Examples of specific interoperability efforts and an evaluation of their feasibility were conducted at three levels of healthcare interoperability, as defined by the National Academy of Medicine: Inter-facility (macro-tier) interoperability, Intra-facility (meso-tier) interoperability, and Point-of-care (micro-tier) interoperability. Conclusions Despite many parallel ongoing efforts to improve the standardization of healthcare information, in the mobile devices, IoT, and EHR sectors, there is still more space for improvement. The recent development of the TEFCA framework has greatly reduced the friction of data exchange in many healthcare contexts. In addition, funding architectures for mediating data between separate healthcare organizations, or middleware architectures, may also be an effective strategy for consolidating healthcare data and improving information exchange.

The Principles of Hearable Photoplethysmography Analysis and Applications in Physiological Monitoring-A Review.

Not long ago, hearables paved the way for biosensing, fitness, and healthcare monitoring. Smart earbuds today are not only producing sound but also monitoring vital signs. Reliable determination of cardiovascular and pulmonary system information can explore the use of hearables for physiological monitoring. Recent research shows that photoplethysmography (PPG) signals not only contain details on oxygen saturation level (SPO2) but also carry more physiological information including pulse rate, respiration rate, blood pressure, and arterial-related information. The analysis of the PPG signal from the ear has proven to be reliable and accurate in the research setting. (1) Background: The present integrative review explores the existing literature on an in-ear PPG signal and its application. This review aims to identify the current technology and usage of in-ear PPG and existing evidence on in-ear PPG in physiological monitoring. This review also analyzes in-ear (PPG) measurement configuration and principle, waveform characteristics, processing technology, and feature extraction characteristics. (2) Methods: We performed a comprehensive search to discover relevant in-ear PPG articles published until December 2022. The following electronic databases: Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Scopus, Web of Science, and PubMed were utilized to conduct the studies addressing the evidence of in-ear PPG in physiological monitoring. (3) Results: Fourteen studies were identified but nine studies were finalized. Eight studies were on different principles and configurations of hearable PPG, and eight studies were on processing technology and feature extraction and its evidence in in-ear physiological monitoring. We also highlighted the limitations and challenges of using in-ear PPG in physiological monitoring. (4) Conclusions: The available evidence has revealed the future of in-ear PPG in physiological monitoring. We have also analyzed the potential limitation and challenges that in-ear PPG will face in processing the signal.

Literature Review of Automated Grading Systems Utilizing MRI for Neuroforaminal Stenosis.

Cervical neural foraminal stenosis is a common and debilitating condition affecting people between the ages 40-60. Although it is established that MRI is the best method of scanning the neural foramen, the question remains whether there is a role for three-dimensional MRIs and whether it is possible to develop a computer-aided automated grading system to establish the degree of clinically relevant cervical foraminal stenosis. The study's objective is to conduct a literature review of existing or recently developed automated grading systems for the cervical neural foramen, including volumetric MRI evaluations of the foramen. A systematic search of Cochrane Library, Cochrane Clinical Trials, Ovid MEDLINE, EMBASE, CINAHL, ACM Digital Library and Institute of Electrical and Electronics Engineers (IEEE), and Web of Science was performed for reports examining automated systems and volumetric scanning foraminal stenosis published before 31.07.2021. 3971 articles were identified of which 8 were included in the study. The automated grading systems of the neural foramen focus largely on the lumbar spine with elements that may be applicable to the cervical spine. Although there are established studies on the automated grading of the lumbar spine, it is uncertain whether any of these are reproducible in the cervical spine. Visual grading systems for the cervical spine demonstrate good inter-reader reliability between radiologists and clinicians. The Park visual grading method shows strong inter-reader reliability across radiologists and clinicians despite the limited data on the correlation with neurological symptoms or surgical outcome. There is scope for further development of an automated grading system for cervical foraminal stenosis to improve the speed and consistency of image interpretation.

Evaluation of inactivation of bovine coronavirus by low-level radiofrequency irradiation

Inactivation of influenza A virus by radiofrequency (RF) energy exposure at levels near Institute of Electrical and Electronics Engineers (IEEE) safety thresholds has been reported. The authors hypothesized that this inactivation was through a structure-resonant energy transfer mechanism. If this hypothesis is confirmed, such a technology could be used to prevent transmission of virus in occupied public spaces where RF irradiation of surfaces could be performed at scale. The present study aims to both replicate and expand the previous work by investigating the neutralization of bovine coronavirus (BCoV), a surrogate of SARS-CoV-2, by RF radiation in 6–12 GHz range. Results showed an appreciable reduction in BCoV infectivity (up to 77%) due to RF exposure to certain frequencies, but failed to generate enough reduction to be considered clinically significant.

1991 to 2019: The rise of machine interpreting research

Conceived as a scientometric study, this paper searches for understanding the research status of machine interpreting on the IEEE (Institute of Electrical and Electronics Engineers) database from 1991 to 2019. Documents were analyzed considering a series of measures such as most prominent academic institutions and countries that investigate machine interpreting, citation, co-authorship, keywords co-occurrence, reference coupling, and textual-based analysis retrieved from the documents’ titles and abstracts. Through VOSviewer software and its tools for data collecting and visualization, machine interpreting research in the analyzed corpus focuses on three main concerns: machine translation, speech synthesis, and Japanese language.

Cell Phone Radiation Exposure Limits and Engineering Solutions

In the 1990s, the Institute of Electrical and Electronics Engineers (IEEE) restricted its risk assessment for human exposure to radiofrequency radiation (RFR) in seven ways: (1) Inappropriate focus on heat, ignoring sub-thermal effects. (2) Reliance on exposure experiments performed over very short times. (3) Overlooking time/amplitude characteristics of RFR signals. (4) Ignoring carcinogenicity, hypersensitivity, and other health conditions connected with RFR. (5) Measuring cellphone Specific Absorption Rates (SAR) at arbitrary distances from the head. (6) Averaging SAR doses at volumetric/mass scales irrelevant to health. (7) Using unrealistic simulations for cell phone SAR estimations. Low-cost software and hardware modifications are proposed here for cellular phone RFR exposure mitigation: (1) inhibiting RFR emissions in contact with the body, (2) use of antenna patterns reducing the Percent of Power absorbed in the Head (PPHead) and body and increasing the Percent of Power Radiated for communications (PPR), and (3) automated protocol-based reductions of the number of RFR emissions, their duration, or integrated dose. These inexpensive measures do not fundamentally alter cell phone functions or communications quality. A health threat is scientifically documented at many levels and acknowledged by industries. Yet mitigation of RFR exposures to users does not appear as a priority with most cell phone manufacturers.

A Systematic Literature Review of Automation Quality of Service in Computer Networks: Research Trends, Datasets, and Methods

The article is a systematic literature review of the use of automation for quality of service (QoS) in computer networks. It summarizes the research trends, datasets, and methods used in the field and provides an overview of the current state of the art. The focus of the review is on the use of automation for QoS management and improvement in computer networks, including the use of machine learning, artificial intelligence, and other computational techniques. The review highlights the need for further research and development in this area and provides insights into future directions for the field. The review covered a wide range of studies, including research papers and conference proceedings, and involved a comprehensive database search of the Scopus database covering journals and proceedings such as the Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery (ACM) Digital Library, Springer, and ScienceDirect databases between 2017 and September 2022. From these databases, 1856 metadata were found, which eventually became seventy-three metadata after going through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol.

Mobile Application to identify and recognize emotions for children with autism: A systematic review

IntroductionEmotions are a vital component of human interaction. Children with Autism Spectrum Disorder (ASD) face severe difficulties in sensing and interpreting the emotions of others, as well as responding emotionally appropriately. Developers are producing many mobile applications to assist ASD children in improving their facial expression detection and reaction abilities and increasing their independence.ObjectiveThis systematic review aims to explore the mobile application in helping children with ASD to identify and express their feeling.MethodsThe inclusion and exclusion articles for our analysis were mapped using the PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analysis diagram. The studies were retrieved from the following four databases: Google Scholar, Scopus, Association for Computing Machinery (ACM), and Institute of Electrical and Electronics Engineers (IEEE). Additionally, two screening processes were used to determine relevant literature. Reading the title and abstract was the initial step, followed by reading the complete content. Finally, the authors display the results using a narrative synthesis.ResultsFrom four electronic databases, we retrieved 659 articles. six studies that met our inclusion criteria were included in the systematic review. More details about inclusion and exclusion criteria can be found in the Eligibility criteria.ConclusionThis systematic review sheds light on current research that employed mobile applications to improve emotion detection and expression in children with ASD. This smartphone application has the potential to empower autistic children by assisting them in expressing their emotions and enhancing their ability to recognize emotions. However, it is currently deemed essential to assess the effectiveness of mobile applications for remediation through more rigorous methodological research. For example, most included studies were quantitative and focused on statical measurements. However, there is an immediate need for more incredible research in this area to include qualitative research and to consider large samples, control groups and placebo, prolonged treatment durations, and follow-up to see whether improvements are sustainable and to ensure the effectiveness of applications.