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Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful. [ABSTRACT FROM AUTHOR]

The paper presents a bibliometric analysis from 2014 to 2020 of the emerging and engaging field of quantum computing called Quantum Machine Learning (QML). The study discusses the analysis results from the comprehensive high indexed databases worldwide such as Institute of Electrical and Electronics Engineers (IEEE), Scopus, Web of Science (WOS), Google Scholar and the Association for Computing Machinery (ACM). Tools like iMapbuilder, IBM and SPSS Statistics are used to provide meaningful insights and flawless representations of the extracted data. There has been little research to provide a macroscopic overview of renowned authors, subject areas, funding agencies and patent applications related to Quantum Clustering (QC). The result and analysis of this study show an interesting fact, most researchers are now aware of quantum technology from the past few years. The purpose of bibliometric and patentometric analysis papers is to figure out the importance and utility of the QC research area. Most of the countries are taking an initiative to seek attention towards QC but the analysis shows that China and the US are leading. The survey revealed that the maximum numbers of publications of QC are from Physics and Astronomy followed by Computer Science. [ABSTRACT FROM AUTHOR]

A large number of renewable distributed generation (DG) systems connect to the distribution network, affecting the structure of the traditional distribution network and forming an active distribution network (ADN). Although the accelerating grid penetration of DGs brings significant challenges to the distribution network operation, the islanded operation capability of DGs provides a flexible solution to the self‐healing of ADNs from faults. To ensure that ADN can quickly recover and reconfigure in the event of a fault and continue to maintain safe, economical, and reliable operation, this paper proposes a dynamic microgrid formation method for ADNs combined with the dynamic network reconfiguration and intentional islanding operation of DGs. An optimization model is designed to represent the proposed self‐healing method, maximizing the load restoration and minimizing the DG cost, line network loss, and voltage excursion simultaneously. A binary hybrid optimization solver is applied to pursue the optimal self‐healing schedules from the optimization model. The self‐healing method is evaluated on the Institute of Electrical and Electronics Engineers (IEEE) 33‐node system and the IEEE 123‐node system, which indicate its rationality and effectiveness fully verified. By optimizing the on and off states of the normally open switches and the on‐grid and off‐grid operation states of DGs, ADNs not only get healed with minimum load curtailment, but also achieve minimal DG generation cost, network loss, and node voltage deviation. In addition, compared with traditional solvers, the proposed solver has a slightly higher computational time than the corresponding solver. [ABSTRACT FROM AUTHOR]

Background: The impact of the pandemic caused by the coronavirus (SARS-CoV-2), causing the disease COVID-19, has brought losses to the world in terms of deaths, economic and health problems. The expected return of the public to activities adapted to the new health situation led to discussions about the use of vaccination and its effects. However, the demand for proof of vaccination showed how inconsistent, unregistered, and uncontrolled this health process is with current technologies. Despite the proven effectiveness of vaccines in reducing infection rates, mortality, and morbidity, there are still doubts about their use in preventing certain infections and injuries, as well as the use of digital medical records for identification at public events and disease prevention. Therefore, this review aims to analyze the use of digital immunization cards in disease prevention in general.Methods: A systematic review of Science, PubMed/MEDLINE, LILACS /BSV, CINALH, and IEEE and Xplore was performed using PRISMA guidelines. The authors summarized the studies conducted over the last decade on the impacts of prophylaxis by control through immunization cards. Studies were selected using the following terms: Vaccination; Mobile Applications; Health Smarts Cards; Immunization Programs; Vaccination Coverage. For data analysis, we used Mendeley, Excel, RStudio, and Bibliometrix software among others.Results: A total of 1828 publications were found. After applying eligibility criteria (Articles published in Portuguese, Spanish or English in the last 10 years). Studies that only dealt with paper or physical records were excluded, as well as studies that were not linked to their country's health Department, as a possibility of bias exists with these types of information). After removing duplicates and applying filters 1 and 2, we included 18 studies in this review. This resulted in 18 papers that met our priori inclusion criteria; it was found that the most relevant sources were from the databases of the Institute of Electrical and Electronics Engineers (IEEE).Conclusions: Considering the selected studies, we found that scientific evidence and epidemiological surveillance are essential tools to characterize the efficiency and effectiveness of immunization passport protection intervention and to ethically justify them. Technological development of digital vaccine passports can assist in vaccination programs and positively impact disease prophylaxis. [ABSTRACT FROM AUTHOR]

Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers' experiences, tend to be error-prone and lack precision across vast farming areas, especially in data-scarce regions. Recent advancements in data collection, notably through high-resolution sensors and the use of deep learning (DL), have significantly increased the accuracy and breadth of agricultural data, providing better support for policymakers and administrators. In our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting, underscoring its growing significance in enhancing yield predictions. Our approach enabled us to identify 92 relevant studies across four major scientific databases: the Directory of Open Access Journals (DOAJ), the Institute of Electrical and Electronics Engineers (IEEE), the Multidisciplinary Digital Publishing Institute (MDPI), and ScienceDirect. These studies, all empirical research published in the last eight years, met stringent selection criteria, including empirical validity, methodological clarity, and a minimum quality score, ensuring their rigorous research standards and relevance. Our in-depth analysis of these papers aimed to synthesize insights on the crops studied, DL models utilized, key input data types, and the specific challenges and prerequisites for accurate DL-based yield forecasting. Our findings reveal that convolutional neural networks and Long Short-Term Memory are the dominant deep learning architectures in crop yield prediction, with a focus on cereals like wheat (Triticum aestivum) and corn (Zea mays). Many studies leverage satellite imagery, but there is a growing trend towards using Unmanned Aerial Vehicles (UAVs) for data collection. Our review synthesizes global research, suggests future directions, and highlights key studies, acknowledging that results may vary across different databases and emphasizing the need for continual updates due to the evolving nature of the field. [ABSTRACT FROM AUTHOR]

The importance of flexible alternating current transmission system (FACTS) devices is increasing day by day as they provide compensation for power systems. The installation of such devices solves many issues regarding the stability and power transferability of power systems. However, for optimizing the performance of FACTS devices, the location and sizes of FACTS devices must be selected very carefully. On the other side, the utilization of optimization techniques is increasing daily, as it is capable of solving nonlinear, random problems by its stochastic nature. As a result, it became a reliable tactic for solving stochastic problems almost in every aspect of our lives. Therefore, it is adopted for solving power system problems, like, FACTS devices allocation, distributed generators allocation, determining component's parameters, and more. In this paper, the optimal size and location of several FACTS devices are selected to achieve the following single objective functions separately: fuel cost minimization and power losses minimization, then combining the mentioned objective function into one multiobjective function for gross cost minimization. The power system optimized is the Institute of Electrical and Electronics Engineers 30‐bus standard system, and the FACTS devices installed are static VAR compensator, thyristor control series compensator, and thyristor‐controlled phase shifter, while the optimization is performed using different conventional and recent optimization algorithms, including a newly developed algorithm, typically Leader Walrus Optimization Algorithm "LWaOA," making a comparison among such algorithms to investigate the algorithm that possesses the best performance. The results of the optimization processes show the superiority of LWaOA. [ABSTRACT FROM AUTHOR]

This 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. [ABSTRACT FROM AUTHOR]

Reducing loss and providing a secure power supply for customers have always been the main goal of distribution systems planners. Recently, the development of competitive electricity markets and responsive loads participation in such markets together with the integration of regional energy networks (RENs) have resulted in creating some challenges for RENs' owners to increase operating efficiency, and reduce investment costs. In this paper, to overcome these problems, a new modeling for coordinated planning of distribution networks and RENs in the presence of responsive loads is proposed and to achieve the optimal solution of the problem, the genetic algorithm is used. Operation uncertainties due to the installation of wind turbines and photovoltaic resources in RENs are considered. The probability‐tree method is used to generate and model operation scenarios and their variable production, respectively. To validate and confirm the efficiency of the proposed model, numerical studies were applied on a 25‐bus Institute of Electrical and Electronics Engineers standard distribution network, including two RENs equipped with responsive loads. The results show the fact that the flexibility of the demand side in the electrical sector due to the activity of responsive loads causes to increase in the installation capacity of energy resources and storages. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Reliable communications are essential to provide intelligent services to connected cars. For operational services, connected vehicles in VANET (Vehicle Ad hoc Networks) regularly transfer large amounts of data related to vehicular safety. Similarly, V2X (Vehicle‐to‐Everything) communications include vehicles exchanging information with each other and with infrastructure that is, Vehicle‐to‐Infrastructure (V2I) and Vehicle‐to‐Vehicle (V2V). This paper has analyzed the performance of IEEE 802.11p and 5G test network in a collaborative manner under realistic conditions. For performance analysis the exchange of safety‐critical road weather and traffic information has been performed to enhance the traffic safety and traffic efficiency in the domain of intelligent transportation system (ITS). The vehicular connectivity is provided in V2V and V2I scenarios by utilizing short‐range IEEE 802.11 standard or cellular approaches, such as the 5G network. Here, we consider combining these technologies in a cooperative manner to exploit jointly their advantages. In this cooperative heterogeneous network, the IEEE 802.11p supports safety‐related pilot use cases while the provision of non‐safety‐related pilot use cases are supported by the 5G test network. The performance analysis revealed that the IEEE 802.11p performs quite reasonably well with restricted mobility in contrast of 5G test network in a collaborative manner to avoid road accidents. [ABSTRACT FROM AUTHOR]

A novel agent‐based online prediction method is presented in this paper, to predict the status of both transient and short‐term voltage (STV) stability, against fault occurrence. In the proposed method, the trajectories of the relative frequency deviation (ΔF) and the power imbalance (ΔP) are estimated by employing the third‐degree polynomial curve fitting method. By tracking the estimated trajectories on ΔF–ΔP plane and checking some simple defined rules, an early prediction of both transient and STV instability is achieved in an organized multi‐agent system (MAS). In order to evaluate the performance of the proposed algorithm, the method has been tested on IEEE 39‐bus system, IEEE 118‐bus system and IEEE Nordic test system. Based on the obtained results, the proposed algorithm has an overall accuracy of 99.5% under symmetrical and asymmetrical faults, PMU measurement error, different operating points and topological changes. [ABSTRACT FROM AUTHOR]

The power grid is going through significant changes with the introduction of renewable energy sources and the incorporation of smart grid technologies. These rapid advancements necessitate new models and analyses to keep up with the various emergent phenomena they induce. A major prerequisite of such work is the acquisition of well-constructed and accurate network datasets for the power grid infrastructure. In this paper, we propose a robust, scalable framework to synthesize power distribution networks that resemble their physical counterparts for a given region. We use openly available information about interdependent road and building infrastructures to construct the networks. In contrast to prior work based on network statistics, we incorporate engineering and economic constraints to create the networks. Additionally, we provide a framework to create ensembles of power distribution networks to generate multiple possible instances of the network for a given region.The comprehensive dataset consists of nodes with attributes, such as geocoordinates; type of node (residence, transformer, or substation); and edges with attributes, such as geometry, type of line (feeder lines, primary or secondary), and line parameters. For validation, we provide detailed comparisons of the generated networks with actual distribution networks.The generated datasets represent realistic test systems (as compared with standard test cases published by Institute of Electrical and Electronics Engineers (IEEE)) that can be used by network scientists to analyze complex events in power grids and to perform detailed sensitivity and statistical analyses over ensembles of networks. [ABSTRACT FROM AUTHOR]

Child abuse refers to any form of maltreatment, including physical abuse, sexual abuse, emotional abuse, and neglect that occurs to children under 18 years of age. Digital games and virtual reality (VR) can be used as beneficial solutions for dealing with child maltreatment concerns. This study aimed to present a conceptual framework for showing the applications of these technologies in managing child abuse. The framework is developed in two stages: (1) a scoping review to gather digital games and VR applications for child abuse issues through the search in PubMed, Ovid (APA PsycInfo), Scopus, Web of Science, ProQuest, Institute of Electrical and Electronics Engineers (IEEE), Cochrane Database of Systematic Reviews, and grey literature and (2) developing a conceptual framework based on the review results and validating it by 12 experts. The proposed conceptual framework shows that digital games and VR have been used for six main topics: (1) medical education, (2) prevention, (3) screening, (4) diagnosis, (5) treatment, and (6) forensic medicine in response to child abuse issues. Studies have more focused on child sexual abuse prevention, behavioral monitoring of sexual offenders in forensic medicine, and knowledge or performance assessment of students in medical education. Serious games (SGs), computer simulation, and immersive VR were common technologies for children, students, and forensic medicine, respectively. The experts believe the combination of immersive features of VR with SGs can further encourage user engagement. It appears that digital games and VR can play a positive role in child abuse management. Given the extensive capabilities of these technologies, further studies are needed to show all their potential applications for child abuse problems. [ABSTRACT FROM AUTHOR]

In e-learning systems, even though the automatic detection of learning styles is considered the key element in the adaptation process, it does not represent the main goal of this process at all. Indeed, to accomplish the task of adaptation, it is also necessary to be able to automatically select the learning objects according to the detected styles. The classification techniques are the most used techniques to automatically select the learning objects by processing data derived from learning object metadata. By using these classification techniques, considerable results are obtained via several approaches and consist of mapping the learning objects into different teaching strategies and then mapping these strategies into the identified learning styles. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they do not directly map learning object metadata elements to learning style dimensions. Moreover, they do not consider the fuzzy nature of learning objects. Indeed, any learning object can be suitable for different learning styles at varying degrees of suitability. This highlights the need to find a way to remedy this shortcoming. Our work is part of the automatic selection of learning objects. So, we will propose an approach that uses the fuzzy classification technique to select learning objects based on learning styles. In this approach, the metadata of each learning object that complies with the Institute of Electrical and Electronics Engineers (IEEE) standard are stored in a database as an Extensible Markup Language (XML) file. The Fuzzy C Means algorithm is used, on one hand, to assign fuzzy suitability rates to the stored learning objects and, on the other hand, to cluster them into the Felder and Silverman learning styles model categories. The experiment results show the performance of our approach. [ABSTRACT FROM AUTHOR]

An accurate prediction of fetal birth weight is crucial in ensuring safe delivery without health complications for the mother and baby. The uncertainty surrounding the fetus's birth situation, including its weight range, can lead to significant risks for both mother and baby. As there is a standard birth weight range, if the fetus exceeds or falls below this range, it can result in considerable health problems. Although ultrasound imaging is commonly used to predict fetal weight, it does not always provide accurate readings, which may lead to unnecessary decisions such as early delivery and cesarian section. Besides that, no supporting system is available to predict the weight range in Saudi Arabia. Therefore, leveraging the available technologies to build a system that can serve as a second opinion for doctors and health professionals is essential. Machine learning (ML) offers significant advantages to numerous fields and can address various issues. As such, this study aims to utilize ML techniques to build a predictive model to predict the birthweight range of infants into low, normal, or high. For this purpose, two datasets were used: one from King Fahd University Hospital (KFHU), Saudi Arabia, and another publicly available dataset from the Institute of Electrical and Electronics Engineers (IEEE) data port. KFUH's best result was obtained with the Extra Trees model, achieving an accuracy, precision, recall, and F1-score of 98%, with a specificity of 99%. On the other hand, using the Random Forest model, the IEEE dataset attained an accuracy, precision, recall, and F1-score of 96%, respectively, with a specificity of 98%. These results suggest that the proposed ML system can provide reliable predictions, which could be of significant value for doctors and health professionals in Saudi Arabia. [ABSTRACT FROM AUTHOR]

The increasing prevalence and severity of wildfires, severe storms, and cyberattacks is driving the introduction of numerous microgrids to improve resilience locally. While distributed energy resources (DERs), such as small-scale wind and solar photovoltaics with storage, will be major components in future microgrids, today, the majority of microgrids are backed up with fossil-fuel-based generators. Small modular reactors (SMRs) can form synergistic mix with DERs due to their ability to provide baseload and flexible power. The heat produced by SMRs can also fulfill the heating needs of microgrid consumers. This paper discusses an operational scheme based on distributed control of flexible power assets to strengthen the operational resilience of SMR-DER integrated-energy microgrids. A framework is developed to assess the operational resilience of SMR-DER microgrids in terms of system adaptive real-power capacity quantified as a response area metric (RAM). Month-long simulation results are shown with a microgrid developed in a modified Institute of Electrical and Electronics Engineers (IEEE)-30 bus system. The RAM values calculated along the operational simulation reflect the system resilience in real time and can be used to supervise the microgrid operation and reactor's autonomous control. [ABSTRACT FROM AUTHOR]

Economic development relies on access to electrical energy, which is crucial for society's growth. However, power shortages are challenging due to non-renewable energy depletion, unregulated use, and a lack of new energy sources. Ethiopia's Debre Markos distribution network experiences over 800 h of power outages annually, causing financial losses and resource waste on diesel generators (DGs) for backup use. To tackle these concerns, the present study suggests a hybrid power generation system, which combines solar and biogas resources, and integrates Superconducting Magnetic Energy Storage (SMES) and Pumped Hydro Energy Storage (PHES) technologies into the system. The study also thoroughly analyzes the current and anticipated demand connected to the distribution network using a backward/forward sweep load flow analysis method. The results indicate that the total power loss has reached its absolute maximum, and the voltage profiles of the networks have dropped below the minimal numerical values recommended by the Institute of Electrical and Electronics Engineers (IEEE) standards (i.e., 0.95–1.025 p.u.). After reviewing the current distribution network's operation, additional steps were taken to improve its effectiveness, using metaheuristic optimization techniques to account for various objective functions and constraints. In the results section, it is demonstrated that the whale optimization algorithm (WOA) outperforms other metaheuristic optimization techniques across three important objective functions: financial, reliability, and greenhouse gas (GHG) emissions. This comparison is based on the capability of the natural selection whale optimization algorithm (NSWOA) to achieve the best possible values for four significant metrics: Cost of Energy (COE), Net Present Cost (NPC), Loss of Power Supply Probability (LPSP), and GHG Emissions. The NSWOA achieved optimal values for these metrics, namely 0.0812 €/kWh, 3.0017 × 106 €, 0.00875, and 7.3679 × 106 kg reduced, respectively. This is attributable to their thorough economic, reliability, and environmental evaluation. Finally, the forward/backward sweep load flow analysis employed during the proposed system's integration significantly reduced the impact of new energy resources on the distribution network. This was evident in the reduction of total power losses from 470.78 to 18.54 kW and voltage deviation from 6.95 to 0.35 p.u., as well as the voltage profile of the distribution system being swung between 1 and 1.0234 p.u., which now comply with the standards set by the IEEE. Besides, a comparison of the cost and GHG emission efficiency of the proposed hybrid system with existing (grid + DGs) and alternative (only DGs) scenarios was done. The findings showed that, among the scenarios examined, the proposed system is the most economical and produces the least amount of GHG emissions. [ABSTRACT FROM AUTHOR]

Non-linear and dynamic systems analysis of human movement has recently become increasingly widespread with the intention of better reflecting how complexity affects the adaptability of motor systems, especially after a stroke. The main objective of this scoping review was to summarize the non-linear measures used in the analysis of kinetic, kinematic, and EMG data of human movement after stroke. PRISMA-ScR guidelines were followed, establishing the eligibility criteria, the population, the concept, and the contextual framework. The examined studies were published between 1 January 2013 and 12 April 2023, in English or Portuguese, and were indexed in the databases selected for this research: PubMed®, Web of Science®, Institute of Electrical and Electronics Engineers®, Science Direct® and Google Scholar®. In total, 14 of the 763 articles met the inclusion criteria. The non-linear measures identified included entropy (n = 11), fractal analysis (n = 1), the short-term local divergence exponent (n = 1), the maximum Floquet multiplier (n = 1), and the Lyapunov exponent (n = 1). These studies focused on different motor tasks: reaching to grasp (n = 2), reaching to point (n = 1), arm tracking (n = 2), elbow flexion (n = 5), elbow extension (n = 1), wrist and finger extension upward (lifting) (n = 1), knee extension (n = 1), and walking (n = 4). When studying the complexity of human movement in chronic post-stroke adults, entropy measures, particularly sample entropy, were preferred. Kinematic assessment was mainly performed using motion capture systems, with a focus on joint angles of the upper limbs. [ABSTRACT FROM AUTHOR]

The use of renewable energy has become a necessity to generate electricity, and is taking the place of conventional coal-fired power, as it has clear economic and environmental benefits. The purpose of this paper is to quantify the comprehensive benefits of hydropower in reducing wind power fluctuation in a hybrid coal-fired/hydro/wind power system. To achieve this, a wind–hydropower generation system must be established, and its complementary characteristic is also illustrated based on the resultant wind speed. Then, based on Institute of Electrical and Electronics Engineers (IEEE) 14 Bus Power System, coal-fired generation is introduced to model a final hybrid integrated power system. The comprehensive benefit evaluation method, including the power consumption profit, the peak load profit, the energy conservation profit, the guide vane fatigue cost, the maintenance cost, and the start-stop cost, is presented to assess the join operation benefits of the wind/hydropower/coal-fired integrated system. The complementary investigation shows that the hydropower system has an excellent effect on suppressing wind power fluctuation, although the fatigue damage of hydraulic guide vanes is a little severe for the time scale of hours, compared with that of seconds. The quantified benefit shows that the increased power consumption profit, the increased guide vane fatigue loss cost, the decreased peak-load profit, and the increased energy conservation profit are RMB 1262.43 million/year, RMB 0.37 million/year, RMB 333.97 million/year and RMB 0.54 million/year, with the wind and hydropower ratio increasing from 16% to 25%, which directly leads to the annual total revenue increasing to RMB 143.2673 million. The implementation of this paper can contribute to making more efficient use of wind-hydro power in conventional electricity production. [ABSTRACT FROM AUTHOR]

This paper discusses how frequency‐stable operational decision‐making of asynchronous grids with intensive high‐capacity HVDC tie‐lines, can be achieved leveraging a novel total transfer capability (TTC) evaluation approach. This problem is formulated as a hierarchical optimization model with multiple decision‐makers, in which the master problem is solved by the coordinator to determine the base case maximum acceptable transmission power of each HVDC tie‐line, while the sending‐ and receiving‐end grid's local TSO each autonomously solves a frequency constrained optimal power flow sub‐problem. Coordinated fast and slow frequency arrest strategies are fully integrated into the TTC assessment framework, which not only ensure that last‐resort protections are not triggered following the HVDC bi‐pole block contingency, but also take advantage of the potential control capability of the sending‐ and receiving‐end grids and would therefore increase the TTC of HVDC tie‐lines. A decentralized algorithm based on accelerated analytical target cascading is developed to solve the formulated model. Case studies on a modified two‐area RTS‐96 system and a practical large‐scale system demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

With the adoption of gas‐fired units (GFU), the interaction between the electricity and gas systems has been intensified. The failure of the gas sources may lead to the insufficiency of the gas supply to the GFUs, and further result in the electricity supply shortage, threatening reliabilities of electricity and gas systems. However, compared with the electric power flow, the dynamics of the gas flow are much slower. Most of the existing studies evaluated the reliabilities of integrated electricity and gas systems (IEGS) without considering the slower dynamics of gas flow, which are not fully accurate in the short‐term. This paper proposes a short‐term reliability evaluation technique for IEGS considering the gas flow dynamics. Firstly, the short‐term reliability models of gas sources, GFUs, and gas compressors are developed. Then, the multi‐stage contingency management scheme is proposed, where gas flow dynamics are analysed for determining the time‐varying load curtailments of electricity and gas. Moreover, a time‐sequential Monte Carlo simulation technique is developed with the finite‐difference scheme to tackle the gas flow dynamics during the short‐term reliability evaluation. Finally, the proposed reliability evaluation technique is validated using an integrated IEEE reliability test system and the practical Belgium gas transmission system. [ABSTRACT FROM AUTHOR]

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. • Propose new algorithm for virtual links routing in highly complex networks. • Considering multiple constraints of avionics industry for optimal routing. • New method determines faster optimal routes with less processing power. • Detecting and removing unnecessary network elements for reducing overall cost/weight. • Proposed algorithm eliminates merger and integration problems in large networks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Infusion Pumps (IP) are medical devices that were developed in the 1960s and generate fluid flow at pressures higher than that of normal blood pressure. Various hospital sectors make use of them, and they have become indispensable in therapies requiring continuity and precision in the administration of medication and/or food. As they are classified Class III (high risk) equipment, their maintenance is crucial for proper performance of the device, as well as patient and operator safety. The principal consideration of the pump is the volume infused, and the device demands great attention to detail when being calibrated. A lack of necessary care with this equipment can lead to uncertainty in volume and precision during the administration of substances. Because of this, it is essential to evaluate its reliability, to prevent possible failures at time of execution. This control aims at the quality of the intended infusion result, becoming an indication of quality. Methods: This systematic review summarizes studies done over the last 10 years (2011 to December 2021) that address the reliability and accuracy of hospital infusion pumps, in order to identify planning of maintenance and/or other techniques used in management of the equipment. The Prisma method was applied and the databases utilized were Embase, MEDLINE/Pubmed, Web of Science, Scopus, IEEE Xplore, and Science Direct. In addition, similar reviews were studied in Prospero and the Cochrane Library. For data analysis, softwares such as Mendeley, Excel, RStudio, and VOSviewer were used, and Robvis helped in plotting risk of bias results for studies performed with Cochrane tools. Results: The six databases selected produced 824 studies. After applying eligibility criteria (inclusion and exclusion), removing duplicates, and applying filters 1 and 2, 15 studies were included in the present review. It was found that the most relevant sources came from the Institute of Electrical and Electronics Engineers (IEEE) and that the most relevant keywords revolved around the terms ("device failure", "infusion pumps", "adverse effects", "complications", etc.). These results made clear that there remains substantial room for improvement as it relates to the study of accuracy and reliability of infusion. Conclusions: We verified that the reliability and precision analysis of hospital infusion pumps need to be performed in a more detailed and consistent way. New developments, considering the model and IP specification, are intended, clearly explaining the adopted methodology. [ABSTRACT FROM AUTHOR]

Background: Non-alcoholic Fatty Liver Disease (NAFLD) is growing more prevalent worldwide. Although non-invasive diagnostic approaches such as conventional ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy, their efficacy has been called into doubt. Artificial Intelligence (AI) is now combined with traditional diagnostic processes to improve the performance of non-invasive approaches. Objective: This study explores how well various AI methods function and perform on ultrasound (US) images to diagnose and quantify non-alcoholic fatty liver disease. Methodology: A systematic review was conducted to achieve this objective. Five science bibliographic databases were searched, including PubMed, Association for Computing Machinery ACM Digital Library, Institute of Electrical and Electronics Engineers IEEE Xplore, Scopus, and Google Scholar. Only peer-reviewed English articles, conferences, theses, and book chapters were included. Data from studies were synthesized using narrative methodologies per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Results: Forty-nine studies were included in the systematic review. According to the qualitative analysis, AI significantly enhanced the diagnosis of NAFLD, Non-Alcoholic Steatohepatitis (NASH), and liver fibrosis. In addition, modalities, image acquisition, feature extraction and selection, data management, and classifiers were assessed and compared in terms of performance measures (i.e., accuracy, sensitivity, and specificity). Conclusion: AI-supported systems show potential performance increases in detecting and quantifying steatosis, NASH, and liver fibrosis in NAFLD patients. Before real-world implementation, prospective studies with direct comparisons of AI-assisted modalities and conventional techniques are necessary. [ABSTRACT FROM AUTHOR]

• HFA has been inspired the efforts of human society to become felicity. • People in the community are divided into: Elite, disciple, and ordinary. • Parts of the HFA are as: 1) move to the elite 2) personal search 3) revolution. The present paper introduces a new evolutionary algorithm to solve optimization issues called the human felicity algorithm (HFA). The main idea of the HFA has been inspired the efforts of human society to become felicity. Because the change in human felicity is possible, only through a change in human thought, in the proposed algorithm, the objective function is human felicity in society and the search space optimization is the human thought in society. Like other optimization algorithms, the human felicity algorithm starts solving the problem by an initial random population. Then the population is divided into three categories of elites, disciples, and ordinary people. Afterward, people in human society change their thoughts by 3 ways: 1- being influenced by elites 2- personal experiences 3- sudden and drastic changes of thoughts for various reasons, such as war and famine. To test the capability of the proposed algorithm, the standard test functions of IEEE (Institute of Electrical and Electronics Engineers) Congress on Evolutionary Computation (CEC) 2014, CEC 2019, and CEC2020 with different dimensions have been used. In addition, to evaluate the ability of the proposed algorithm to solve various real-world complex engineering problems, 4 standard classic engineering optimization problems are examined and compared. To test the ability of the proposed algorithm in hyper-parameters tuning, the five-database classification was simulated using the Support Vector Machines (SVM). The simulation of the proposed algorithm on different test functions shows its high ability to solve complex problems with many local optimal points. For statistical analysis, powerful nonparametric methods such as Friedman and Nemenyi post-Hoc test for statistical analysis have been used. Comparing the HFA with some other optimization algorithms demonstrates its better performance in solving complicated multidimensional problems with the lower iteration. [ABSTRACT FROM AUTHOR]

With the gradual opening and rapid development of power markets, large-scale multi-area interconnected power systems (LMIPSs) have become an inevitable pattern. The traditional centralized economic dispatch optimization method has the disadvantages of slow calculation speed, easy exposure of private equipment information, and considers only one cost objective. This paper introduces the distributed concept into the multi-objective grey wolf optimizer (MOGWO) to mitigate these deficiencies; then proposes the distributed MOGWO (DMOGWO). When the DMOGWO solves the LMIPS problems, the sub-problems of each area are optimized independently, and the overall optimization can be realized by sharing only part of the boundary bus information between areas. Case studies are carried out in two cases of the Institute of Electrical and Electronics Engineers (IEEE) 39-bus and 118-bus systems. The results show that when solving the multi-objective economic dispatch in LMIPS, compared with centralized optimization, the proposed DMOGWO can effectively ensure the privacy of information, the obtained objective values are smaller, and the performance test is better. • Distributed multi-objective optimization problems are considered. • Distributed multi-objective economic dispatch framework is built. • A distributed multi-objective grey wolf optimizer is proposed. • The optimizer fills the gap of distributed intelligent methods for optimization. • Optimal unit cost and carbon emissions are obtained by the optimizer simultaneously. [ABSTRACT FROM AUTHOR]

The growing energy demand and environmental consciousness provoke the conventional single-objective optimization framework no longer satisfies new power system planning and control requirements. The number of optimization objectives being considered in power system optimization is increasing, necessitating the development of many-objective Optimal Power Flow (OPF) problems and the development of solution methods. In this paper, the fitness functions for the Many-Objective OPF (MaO-OPF) problem have been formulated, and a new Many-Objective Gradient-Based Optimizer (MaOGBO) based on reference point strategy is proposed to solve the MaO-OPF problem. The objectives of the MaO-OPF problem is to minimize the Reactive Power Loss (RPL), Active Power Loss (APL), Voltage Magnitude Deviation (VMD), Voltage Stability Indicator (VSI), Total Emission (TE), and the Total Fuel Cost (TFC) by satisfying different complex equality and inequality constraints. In the proposed MaOGBO, a reference point strategy is employed to acquire evenly spread Pareto -optimal solutions in each objective space. In order to improve the effectiveness of Pareto solutions, a mixed-multi-constraint handling approach is also implemented. In order to deal with the complex non-linear constraints, the process utilizes both the repair technique and penalty function. Besides, a fuzzy-based membership function strategy is also applied to locate the Best Compromise Solution (BCS) from the Pareto -optimal analytical solution. In order to validate the effectiveness of the proposed MaOGBO, DTLZ and MaF benchmark test suites are considered. In addition, a standard Institute of Electrical and Electronics Engineers (IEEE) bus test systems, such as IEEE-30/IEEE-57/IEEE-118 with different case studies, are also considered to assess the performance of the proposed algorithm. The obtained results are compared with other state-of-the-art algorithms, and the proposed MaOGBO proves the superiority over other competitors for most of the selected problems, including MaO-OPF. Finally, the proposed MaOGBO is also validated on large-scale Algerian 59-bus power systems and proved its superiority in handling realistic systems. This research is further supported up with extra online service and guidance at https://premkumarmanoharan.wixsite.com/mysite. • MaOGBO is developed using a reference-point mechanism to obtain the solution diversity and faster convergence. • MaOGBO is combined with a multi-constraint handling mechanism and fuzzy membership-based best compromise solution strategy. • The performance of the MaOGBO is validated on 22 benchmark test suites and three test frameworks of MaO-OPF problems, and also validated on large scale Algerian 59-bus real-time system. • Performance metrics, such as GD, IGD, SP, HV, and RT are calculated for all selected problems. • Three different statistical significance tests are carried out to assess the performance of MaOGBO. [ABSTRACT FROM AUTHOR]

With the large‐scale increasing demand for passenger transportation, the alternating current (AC) 25 kV traction power supply system (TPSS) of electrified railway is introduced into the construction of urban rail transit in China instead of the direct current (DC) TPSS. In order to suppress the negative sequence current and cancel the neutral section at the exit of traction substation of the AC TPSS, a novel AC integrated power supply system (IPSS) of the urban rail transit based on single‐phase static var generator (SVG) is proposed, which the capacity of SVGs can be further reduced. The topology of the novel AC IPSS is introduced. By defining the constraint factors of negative sequence current and reactive power compensated, the comprehensive compensation model of negative sequence current and reactive power of the novel system is established based on the corresponding standards, which the full compensation and satisfactory compensation can be achieved. The method of determining the compensation scheme and the control strategy are proposed. Taking the actual data as an example, the effectiveness of the compensation scheme is proved. In addition, the correctness of the control strategy proposed is proved by simulation and experiment. [ABSTRACT FROM AUTHOR]

Optimal transmission network reconfiguration is an important means to improve the economy and reliability of power grid. However, the traditional optimal transmission switching only considers the transmission line switching strategy and some approximate power flow models are adopted to make it tractable, such as linearisation or convex relaxation ones. In this article, first, a comprehensive network reconfiguration model considering both the line switching strategy and the bus splitting strategy inside substations is established. This optimal network reconfiguration incorporating AC power flow equations is an intractable mixed integer non‐linear programming problem. The model is first relaxed to a mixed integer second‐order cone programming to get the initial solution and an extended feasibility recovery algorithm is developed to obtain the solution satisfying the AC power flow equations. Numerical tests show that the proposed method can reduce the total generation cost and guarantee the system security while the baseline methods cannot obtain feasible solution in most cases. [ABSTRACT FROM AUTHOR]