bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards the Flexible Distribution Networks Design Using the Reliability Performance Metric
Authors: Shushpanov Ilia , Suslov Konstantin , Ilyushin Pavel , Sidorov Denis N.
Published: September 2021

At present, the entire world is moving towards digitalization, including in the electric power industry. Digitalization is in its heyday and a lot of articles and reports are devoted to this topic. At the same time, the least digitalized of the electrical networks are distribution networks that account for a very large share in electric power systems. The article proposes a methodology for creating a flexible distribution network based on the use of digital technology. Additionally, we elaborate a methodology with the identification and collection of the necessary information to create digital networks, develop ways to adapt the required equipment, and suggest methods of recognition of some short circuits. Furthermore, we address the issue of reliability of the information obtained from digital devices, develop a technique for arranging the devices to cover the entire network as required to improve the power system protection of electrical power distribution networks. The above measures make it possible to ensure the flexibility of the active distribution network, as well as to adjust the parameters of the actuation of power system protection depending on changes in external conditions and in the event of emergencies. We propose a technique for controlling the distribution network, based on the factoring-in of the type of damage during an emergency in real time, as well as a technique for arranging the measuring devices and the creation of an information and communication network. We provide recommendations for the design and operation of electric power distribution networks with digital network control technology.
Special Issue “Innovation, Policy, and Regulation in Electricity Markets”
Authors: Kiesling Lynne L. , Meeus Leonardo , Pollitt Michael G.
Published: September 2021

The rise of intermittent renewable energy generation, the coming mass penetration of electric vehicles and moves to decarbonise the gas grid are leading to widespread innovation experiments within electricity systems and their associated markets [...]
Effect of Microwave Pulses on the Morphology and Development of Spark-Ignited Flame Kernel
Authors: Cheng Xiaobei , Zhang Xinhua , Wang Zhaowen , Wu Huimin , Wang Zhaowu , Chen Jyh-Yuan
Published: September 2021

Microwave-assisted spark ignition (MAI) is a promising way to enhance the ignition performance of engines under lean conditions. To understand the effect of microwave-induced flow during MAI, the development and morphology of spark-ignited methane-air flame kernel under various microwave pulse parameters are experimentally studied. Experiments are conducted in a constant volume combustion chamber, and flame development is recorded through a high-speed shadowgraph method. Flame area and deformation index are adopted to evaluate the flame characteristic. Results show that increasing the microwave pulse energy from 0 to 150 mJ exhibits a threshold process for expanding the flame kernel area under 0.2 MPa ambient pressure. When the pulse energy is below the threshold of 90 mJ, the microwave enhancing efficiency is much lower than that beyond the threshold. Increasing microwave pulse repetition frequency (PRF) changes the flow on flame surface and raises the absorption efficiency for microwave energy, and thus helps to improve the MAI performance under higher pressures. Hence, 1 kHz pulses cause more obvious flame deformation than those with higher PRF pulses under 0.2 MPa, while this tendency is reversed as the ambient pressure increases to 0.6 MPa. Besides, microwave pulses of different repetition frequencies lead to different flame kernel morphology, implying the various regimes behind the interaction between a microwave and spark kernel.
Remediation Strategies for Performance Anxiety across Sex, Sport and Stage: Identifying Common Approaches and a Unified Cognitive Model
Authors: Rowland David L. , Moyle Gene , Cooper Stewart E.
Published: September 2021

Strategies for addressing anxiety-related decrements in performance have been implemented across a variety of domains, including Sex, Sport, and Stage. In this review, we (1) iterate the dominant anxiety-related remediation strategies within each of these domains; (2) identify over-lapping and domain-specific strategies; and (3) attempt to unify the conceptualization of performance-related anxiety across these three areas under the information-processing framework of the Reflective/deliberative—Impulsive/automatic Model (RIM). Despite both diversity and similarity in remediation approaches across domains, we found that many strategies appear to share the common goal of maintaining a dominant automatic style of information processing in high performance demand situations. We then describe how various remediation strategies might hypothetically fit within the RIM framework and its subcomponents, identifying each intervention as falling into one or more broad categories related to achieving and/or maintaining dominance in automatic information processing. We conclude by affirming the benefit of adopting a unifying information-processing framework for the conceptualization of performance-related anxiety, as a way of both guiding future cross- and inter- disciplinary research and elucidating effective remediation models that share common pathways/mechanisms to improved performance.
“Covid Just Amplified the Cracks of the System”: Working as a Frontline Health Worker during the COVID-19 Pandemic
Authors: Willis Karen , Ezer Paulina , Lewis Sophie , Bismark Marie , Smallwood Natasha
Published: September 2021

The COVID-19 pandemic has intensified existing workplace stresses and created new challenges for people working on the healthcare frontline, including rapid workplace changes and increasing uncertainty at work, along with fear of contracting the virus. The purpose of this study is to examine the workplace challenges described by Australian frontline health workers during the pandemic. Drawing on a national online anonymous survey of 9518 healthcare workers, we analysed free-text responses to the question: “What did you find to be the main challenges that you faced during the pandemic?” A qualitative content analysis was undertaken. We identified five key themes relating to participant experiences of workplace challenges. These were: Work-life imbalance; Following orders or caring for patients; Unpredictability, disruption, and inconsistency at work; The right to be safe at work; and (Un)preparedness in the workplace. We argue that working during the COVID-19 pandemic illuminated existing occupational health and safety issues that have serious implications for job satisfaction, health workforce retention, and ultimately, patient care.
Simple Disposable Odor Identification Tests for Predicting SARS-CoV-2 Positivity
Authors: Ziuzia-Januszewska Laura , Dobrzyński Paweł , Ślączka Krzysztof , Ciszek Jaromir , Krawiec Łukasz , Wierzba Waldemar , Zaczyński Artur
Published: September 2021

Olfactory dysfunction (OD) is a common manifestation of COVID-19 and may be useful for screening. Survey-based olfactory evaluation tends to underestimate the prevalence of OD, while psychophysical olfactory testing during a pandemic has the disadvantage of being time consuming, expensive, and requiring standardized laboratory settings. We aimed to develop a quick, simple, affordable, and reliable test to objectively assess the prevalence and diagnostic accuracy of OD in COVID-19. The olfactory function of 64 COVID-19 inpatients and 34 controls was evaluated using a questionnaire and a simple disposable odor identification test (SDOIT) developed for this study. Four SDOIT models were assessed: 10-SDOIT, 9-SDOIT, 8-SDOIT, and 4-SDOIT, with 10, 9, 8 and 4 samples, respectively. We found a high frequency of self-reported OD in COVID-19 patients, with 32.8% and 42.2% reporting current and recent OD, respectively. Different SDOIT models revealed smell impairment in 54.7–64.1% of COVID-19 patients. The combination of either 10-SDOIT results and self-reported OD, or 8-SDOIT results and self-reported OD, were the best predictors of COVID-19, both with an AUC value of 0.87 (0.85 and 0.86 for the age-matched subjects). OD is a common symptom of COVID-19. A combination of self-reported smell deterioration and OD psychophysically evaluated using SDOIT appears to be a good predictor of COVID-19.
Nutrition, Physical Activity, and New Technology Programs on Obesity Prevention in Primary Education: A Systematic Review
Authors: Navidad Lorenzo , Padial-Ruz Rosario , González Mar Cepero
Published: September 2021

Early acquisition of healthy lifestyle habits is crucial for good adult health. For this reason, the primary stage of education is a critical period to implement educational policies in this regard. The aim of this review is to compile the published evidence regarding school interventions at the primary stage aimed at preventing obesity, and which integrate as part of their action plan two features: an improvement in knowledge or nutrition habits and the promotion of physical activity (PA), and the use of new information and communications technologies (ICT) to do this. The method used for this review is the searching of different databases for publications that include these criteria. The results show beneficial effects of such interventions in improved eating habits and increased PA. The effect on BMI is limited, and the use of ICT can be of help at a motivational level for the maintenance and fulfilment of the health objectives. However, studies of this type in elementary school are very limited, so it would be necessary to continue researching on this line. In conclusion, this review demonstrates the suitability of carrying out mixed interventions (improved nutrition and PA) together with the use of new technologies to improve health and prevent obesity at an early age.
Seasonal Variation, Chemical Composition, and PMF-Derived Sources Identification of Traffic-Related PM1, PM2.5, and PM2.5–10 in the Air Quality Management Region of Žilina, Slovakia
Authors: Jandacka Dusan , Durcanska Daniela
Published: September 2021

Particulate matter (PM) air pollution in the urban environment is mainly related to the presence of potential sources throughout the year. Road transport is one of the most important sources of PM in the urban environment, because it directly affects pedestrians. PM measurements were performed in the city of Žilina, Slovakia, at various road-traffic-related measurement stations over the course of several years. This paper evaluates changes in the concentration of the fine fraction (PM2.5), the ultrafine fraction (PM1), and the coarse fraction (PM2.5–10) over time. PM concentrations were measured by reference gravimetric method. Significant changes in PM concentrations over time due to the diversification of pollution sources and other, secondary factors can be observed from the analysis of the measured data. PM samples were subjected to chemical analysis inductively coupled plasma mass spectrometry (ICP-MS) to determine the concentrations of elements (Mg, Al, Ca, Cr, Cu, Fe, Cd, Sb, Ba, Pb, Ni, and Zn). The seasonal variation of elements was evaluated, and the sources of PM2.5, PM1, and PM2.5–10 were estimated using principal component analysis (PCA) and positive matrix factorization (PMF). PM2.5 (maximum concentration of 148.95 µg/m3 over 24 h) and PM1 (maximum concentration of 110.51 µg/m3 over 24 h) showed the highest concentrations during the heating season, together with the elements Cd, Pb, and Zn, which showed a significant presence in these fractions. On the other hand, PM2.5–10 (maximum concentration of 38.17 µg/m3 over 24 h) was significantly related to the elements Cu, Sb, Ba, Ca, Cr, Fe, Mg, and Al. High correlation coefficients (r ≥ 0.8) were found for the elements Mg, Ca, Fe, Al, Cd, Pb, and Zn in the PM1 fraction, Cd, Pb, and Zn in PM2.5, and Ba, Sb, Fe, Cu, Cr, Mg, Al, and Ca in PM2.5–10. Using PMF analysis, three major sources of PM (abrasion from tires and brakes, road dust resuspension/winter salting, and combustion processes) were identified for the PM2.5 and PM1 fractions, as well as for the coarse PM2.5–10 fraction. This study reveals the importance of non-exhaust PM emissions in the urban environment.
Correction: Colindres et al. Migrant Agricultural Workers’ Health, Safety and Access to Protections: A Descriptive Survey Identifying Structural Gaps and Vulnerabilities in the Interior of British Columbia, Canada. Int. J. Environ. Res. Public Health 2021, 18, 3696
Authors: Colindres Carlos , Cohen Amy , Caxaj C. Susana
Published: September 2021

In the original article [...]
Interventions to Improve Medication Adherence in Patients with Schizophrenia or Bipolar Disorders: A Systematic Review and Meta-Analysis
Authors: Loots Elke , Goossens Eva , Vanwesemael Toke , Morrens Manuel , Van Rompaey Bart , Dilles Tinne
Published: September 2021

Adherence to prescribed medication regimes improves outcomes for patients with severe mental illness such as schizophrenia or bipolar disorders. The aim of this systematic review and meta-analysis was to compare the effectiveness among interventions to improve medication adherence in patients with schizophrenia or bipolar disorders. Literature published in the last decade was searched for interventions studies to improve adherence in patients with schizophrenia or a bipolar disorder. Interventions were categorised on the basis of type, and the context and effectiveness of the interventions were described. Two review authors independently extracted and assessed data, following criteria outlined by the Cochrane Handbook for Systematic Reviews of Interventions. The GRADEPro (McMaster University, 2020, Ontario, Canada) was used for assessing the quality of the evidence. Twenty-three publications met the selection criteria. Different types of interventions aiming to improve adherence were tested: educational, behavioural, family-based, technological, or a combination of previous types. Meta-analysis could be performed for 10 interventions. When considered separately by subgroups on the basis of intervention type, no significant differences were found in adherence among interventions (p = 0.29; I2 = 19.9%). This review concluded that successful interventions used a combination of behavioural and educational approaches that seem easy to implement in daily practice.
The Implementation of National Dementia Plans: A Multiple-Case Study on Denmark, Germany, and Italy
Authors: Boeree Nadia Céline , Zoller Claudia , Huijsman Robbert
Published: September 2021

In a theory-driven, qualitative, multiple-case study, we examined the implementation of national dementia plans (NDPs) in Denmark, Germany, and Italy to determine how stakeholders involved in implementing the NDPs evaluate contextual factors in light of the World Health Organization’s seven action areas of dementia-focused policy. To analyse the NDPs, we used a driver diagram of large-scale change and conducted both document analysis and semi-structured interviews, after which we performed three-way open coding to analyse the methods. The results show that the implementation of NDPs has increased awareness of dementia in all three countries by positioning the disease as a national public health concern. The study also reveals that resources, the use of change theory, and the monitoring of implementation and fragmentation are crucial factors to consider for countries when implementing NDPs. Although stakeholders find the NDPs useful, many challenges remain for their successful implementation due to the highly limited means for implementation and evaluation. Moreover, present NDPs all lack a communication plan that encompasses all layers of society to address ways of achieving change. Patients with dementia, and their informal caregivers, should be included more in the design, implementation, and evaluation of NDPs in order to improve their usefulness and effectiveness.
Universal Properties and Specificities of the β2-Adrenergic Receptor-Gs Protein Complex Activation Mechanism Revealed by All-Atom Molecular Dynamics Simulations
Authors: Mitra Argha , Sarkar Arijit , Borics Attila
Published: September 2021

G protein-coupled receptors (GPCRs) are transmembrane proteins of high pharmacological relevance. It has been proposed that their activity is linked to structurally distinct, dynamically interconverting functional states and the process of activation relies on an interconnecting network of conformational switches in the transmembrane domain. However, it is yet to be uncovered how ligands with different extents of functional effect exert their actions. According to our recent hypothesis, based on indirect observations and the literature data, the transmission of the external stimulus to the intracellular surface is accompanied by the shift of macroscopic polarization in the transmembrane domain, furnished by concerted movements of highly conserved polar motifs and the rearrangement of polar species. In this follow-up study, we have examined the β2-adrenergic receptor (β2AR) to see if our hypothesis drawn from an extensive study of the μ-opioid receptor (MOP) is fundamental and directly transferable to other class A GPCRs. We have found that there are some general similarities between the two receptors, in agreement with previous studies, and there are some receptor-specific differences that could be associated with different signaling pathways.
Butyrate Protects Pancreatic Beta Cells from Cytokine-Induced Dysfunction
Authors: Prause Michala , Pedersen Signe Schultz , Tsonkova Violeta , Qiao Min , Billestrup Nils
Published: September 2021

Pancreatic beta cell dysfunction caused by metabolic and inflammatory stress contributes to the development of type 2 diabetes (T2D). Butyrate, produced by the gut microbiota, has shown beneficial effects on glucose metabolism in animals and humans and may directly affect beta cell function, but the mechanisms are poorly described. The aim of this study was to investigate the effect of butyrate on cytokine-induced beta cell dysfunction in vitro. Mouse islets, rat INS-1E, and human EndoC-βH1 beta cells were exposed long-term to non-cytotoxic concentrations of cytokines and/or butyrate to resemble the slow onset of inflammation in T2D. Beta cell function was assessed by glucose-stimulated insulin secretion (GSIS), gene expression by qPCR and RNA-sequencing, and proliferation by incorporation of EdU into newly synthesized DNA. Butyrate protected beta cells from cytokine-induced impairment of GSIS and insulin content in the three beta cell models. Beta cell proliferation was reduced by both cytokines and butyrate. Expressions of the beta cell specific genes Ins, MafA, and Ucn3 reduced by the cytokine IL-1β were not affected by butyrate. In contrast, butyrate upregulated the expression of secretion/transport-related genes and downregulated inflammatory genes induced by IL-1β in mouse islets. In summary, butyrate prevents pro-inflammatory cytokine-induced beta cell dysfunction.
PPARγ and TGFβ—Major Regulators of Metabolism, Inflammation, and Fibrosis in the Lungs and Kidneys
Authors: Kökény Gábor , Calvier Laurent , Hansmann Georg
Published: September 2021

Peroxisome proliferator-activated receptor gamma (PPARγ) is a type II nuclear receptor, initially recognized in adipose tissue for its role in fatty acid storage and glucose metabolism. It promotes lipid uptake and adipogenesis by increasing insulin sensitivity and adiponectin release. Later, PPARγ was implicated in cardiac development and in critical conditions such as pulmonary arterial hypertension (PAH) and kidney failure. Recently, a cluster of different papers linked PPARγ signaling with another superfamily, the transforming growth factor beta (TGFβ), and its receptors, all of which play a major role in PAH and kidney failure. TGFβ is a multifunctional cytokine that drives inflammation, fibrosis, and cell differentiation while PPARγ activation reverses these adverse events in many models. Such opposite biological effects emphasize the delicate balance and complex crosstalk between PPARγ and TGFβ. Based on solid experimental and clinical evidence, the present review summarizes connections and their implications for PAH and kidney failure, highlighting the similarities and differences between lung and kidney mechanisms as well as discussing the therapeutic potential of PPARγ agonist pioglitazone.
Estimating Fuel Moisture in Grasslands Using UAV-Mounted Infrared and Visible Light Sensors
Authors: Barber Nastassia , Alvarado Ernesto , Kane Van R. , Mell William E. , Moskal L. Monika
Published: September 2021

Predicting wildfire behavior is a complex task that has historically relied on empirical models. Physics-based fire models could improve predictions and have broad applicability, but these models require more detailed inputs, including spatially explicit estimates of fuel characteristics. One of the most critical of these characteristics is fuel moisture. Obtaining moisture measurements with traditional destructive sampling techniques can be prohibitively time-consuming and extremely limited in spatial resolution. This study seeks to assess how effectively moisture in grasses can be estimated using reflectance in six wavelengths in the visible and infrared ranges. One hundred twenty 1 m-square field samples were collected in a western Washington grassland as well as overhead imagery in six wavelengths for the same area. Predictive models of vegetation moisture using existing vegetation indices and components from principal component analysis of the wavelengths were generated and compared. The best model, a linear model based on principal components and biomass, showed modest predictive power (r² = 0.45). This model performed better for the plots with both dominant grass species pooled than it did for each species individually. The presence of this correlation, especially given the limited moisture range of this study, suggests that further research using samples across the entire fire season could potentially produce effective models for estimating moisture in this type of ecosystem using unmanned aerial vehicles, even when more than one major species of grass is present. This approach would be a fast and flexible approach compared to traditional moisture measurements.
Improving Attitude Estimation Using Gaussian-Process-Regression-Based Magnetic Field Maps
Authors: Kuevor Prince E. , Cutler James W. , Atkins Ella M.
Published: September 2021

Magnetometers measure the local magnetic field and are present in most modern inertial measurement units (IMUs). Readings from magnetometers are used to identify Earth’s Magnetic North outdoors, but are often ignored during indoor experiments since the magnetic field does not behave how most expect. This paper presents methods to create, validate, and visualize three-dimensional magnetic field maps to expand the use of magnetic fields as a sensing modality for navigation. The utility of these maps is measured in their ability to accurately represent the magnetic field and to enable dynamic attitude estimation. In experiments with motion capture truth data, a small multicopter with three-axis inertial measurements, including magnetometer, traversed five flight profiles distinctly exciting roll, pitch, and yaw motion to provide interesting trajectories for attitude estimation. Indoor experimental results were compared to those outdoors to emphasize how spatial variation in the magnetic field drives the need for our mapping techniques. Our work presents a new way of visualizing 3D magnetic fields, which allows users to better reason about the magnetic field in their workspace. Next, we show that magnetic field maps generated from coverage patterns are generally more accurate, but training such maps using observations from desired flight paths is sufficient in the vicinity of these paths. All training sets were interpolated using Gaussian process regression (GPR), which yielded maps with <1 μT of error when interpolating between and extrapolating outside of observed locations. Finally, we validated the utility of our GPR-based maps in enabling attitude estimates in regions of high magnetic field spatial variation with experimental data.
Identification of Damage on Sluice Hoist Beams Using Local Mode Evoked by Swept Frequency Excitation
Authors: Wei Qingyang , Xu Hao , Li Yifei , Chen Li , Novák Drahomír , Cui Li , Cao Maosen
Published: September 2021

As a global vibration characteristic, natural frequency often suffers from insufficient sensitivity to structural damage, which is associated with local variations of structural material or geometric properties. Such a drawback is particularly significant when dealing with the large scale and complexity of sluice structural systems. To this end, a damage detection method in sluice hoist beams is proposed that relies on the utilization of the local primary frequency (LPF), which is obtained based on the swept frequency excitation (SFE) technique and local resonance response band (LRRB) selection. Using this method, the local mode of the target sluice hoist beam can be effectively excited, while the vibrations of other components in the system are suppressed. As a result, the damage will cause a significant shift in the LPF of the sluice hoist beam at the local mode. A damage index was constructed to quantitatively reflect the damage degree of the sluice hoist beam. The accuracy and reliability of the proposed method were verified on a three-dimensional finite element model of a sluice system, with the noise resistance increased from 0.05 to 0.2 based on the hammer impact method. The proposed method exhibits promising potential for damage detection in complex structural systems.
TOF-Based Fast Self-Positioning Algorithm for UWB Mobile Base Stations
Authors: Han Yuxiang , Zhang Xiaoming , Lai Zhengxi , Geng Yuchen
Published: September 2021

To solve the problem of heavy workload and high cost when acquiring the position of Ultra-Wideband (UWB) mobile base stations in sports fields, a fast self-positioning algorithm for UWB mobile base stations algorithm based on Time of Flight (TOF) is proposed. First, according to the layout of the base stations in the sports field, the local coordinate system is determined, and an equation based on the ranging information between the base stations is established; the Least Square method is used to calculate the coordinates of each base station, and the Newton Iteration method is used to converge the positioning results. Then the origin and propagation law of positioning error, as well as the method of reducing the positioning error are analyzed. The simulation data and experimental results show that the average positioning accuracy of the mobile base station is within 0.05 m, which meets the expected accuracy of the base station position measurement. Compared with traditional manual measurement methods, base station self-positioning can effectively save deployment time and reduce workload.
A 77-dB Dynamic-Range Analog Front-End for Fine-Dust Detection Systems with Dual-Mode Ultra-Low Noise TIA
Authors: Rad Reza E. , Hejazi Arash , Asl Seyed-Ali H. , Shehzad Khuram , Verma Deeksha , Kim SungJin , Rikan Behnam S. , Pu YoungGun , Kim Joon Tae , Hwang Keum Cheol , Yang Youngoo , Lee Kang-Yoon
Published: September 2021

This paper presents an analog front-end for fine-dust detection systems with a 77-dB-wide dynamic range and a dual-mode ultra-low noise TIA with 142-dBΩ towards the maximum gain. The required high sensitivity of the analog signal conditioning path dictates having a high sensitivity at the front-end while the Input-Referred Noise (IRN) is kept low. Therefore, a TIA with a high sensitivity to detected current bio-signals is provided by a photodiode module. The analog front end is formed by the TIA, a DC-Offset Cancellation (DCOC) circuit, a Single-to-Differential Amplifier (SDA), and two Programmable Gain Amplifiers (PGAs). Gain adjustment is implemented by a coarse-gain-step using selective loads with four different gain values and fine-gain steps by 42 dB dynamic range during 16 fine steps. The settling time of the TIA is compensated using a capacitive compensation which is applied for the last stage. An off-state circuitry is proposed to avoid any off-current leakage. This TIA is designed in a 0.18 µm standard CMOS technology. Post-layout simulations show a high gain operation with a 67 dB dynamic range, input-referred noise, less than 600 fA/√Hz in low frequencies, and less than 27 fA/√Hz at 20 kHz, a minimum detectable current signal of 4 pA, and a 2.71 mW power consumption. After measuring the full path of the analog signal conditioning path, the experimental results of the fabricated chip show a maximum gain of 142 dB for the TIA. The Single-to-Differential Amplifier delivers a differential waveform with a unity gain. The PGA1 and PGA2 show a maximum gain of 6.7 dB and 6.3 dB, respectively. The full-path analog front-end shows a wide dynamic range of up to 77 dB in the measurement results.
Automated Detection and Removal of Cardiac and Pulse Interferences from Neonatal EEG Signals
Authors: Tamburro Gabriella , Croce Pierpaolo , Zappasodi Filippo , Comani Silvia
Published: September 2021

Electrical cardiac and pulsatile interference is very difficult to remove from electroencephalographic (EEG) signals, especially if recorded in neonates, for which a small number of EEG channels is used. Several methods were proposed, including Blind Source Separation (BSS) methods that required the use of artificial cardiac-related signals to improve the separation of artefactual components. To optimize the separation of cardiac-related artefactual components, we propose a method based on Independent Component Analysis (ICA) that exploits specific features of the real electrocardiographic (ECG) signals that were simultaneously recorded with the neonatal EEG. A total of forty EEG segments from 19-channel neonatal EEG recordings with and without seizures were used to test and validate the performance of our method. We observed a significant reduction in the number of independent components (ICs) containing cardiac-related interferences, with a consequent improvement in the automated classification of the separated ICs. The comparison with the expert labeling of the ICs separately containing electrical cardiac and pulsatile interference led to an accuracy = 0.99, a false omission rate = 0.01 and a sensitivity = 0.93, outperforming existing methods. Furthermore, we verified that true brain activity was preserved in neonatal EEG signals reconstructed after the removal of artefactual ICs, demonstrating the effectiveness of our method and its safe applicability in a clinical context.
Sensorless Adaptive Voltage Control for Classical DC-DC Converters Feeding Unknown Loads: A Generalized PI Passivity-Based Approach
Authors: Gil-González Walter , Montoya Oscar Danilo , Restrepo Carlos , Hernández Jesus C.
Published: September 2021

The problem of voltage regulation in unknown constant resistive loads is addressed in this paper from the nonlinear control point of view for second-order DC-DC converters. The converters’ topologies analyzed are: (i) buck converter, (ii) boost converter, (iii) buck-boost converter, and (iv) non-inverting buck-boost converter. The averaging modeling method is used to model these converters, representing all these converter topologies with a generalized port-Controlled Hamiltonian (PCH) representation. The PCH representation shows that the second-order DC-DC converters exhibit a general bilinear structure which permits to design of a passivity-based controller with PI actions that ensures the asymptotic stability in the sense of Lyapunov. A linear estimator based on an integral estimator that allows reducing the number of current sensors required in the control implementation stage is used to determine the value of the unknown resistive load. The main advantage of this load estimator is that it ensures exponential convergence to the estimated variable. Numerical simulations and experimental validations show that the PI passivity-based control allows voltage regulation with first-order behavior, while the classical PI controller produces oscillations in the controlled variable, significantly when the load varies.
Evidence for the Effectiveness of Feedback from Wearable Inertial Sensors during Work-Related Activities: A Scoping Review
Authors: Lee Roger , James Carole , Edwards Suzi , Skinner Geoff , Young Jodi L. , Snodgrass Suzanne J.
Published: September 2021

Background: Wearable inertial sensor technology (WIST) systems provide feedback, aiming to modify aberrant postures and movements. The literature on the effects of feedback from WIST during work or work-related activities has not been previously summarised. This review examines the effectiveness of feedback on upper body kinematics during work or work-related activities, along with the wearability and a quantification of the kinematics of the related device. Methods: The Cinahl, Cochrane, Embase, Medline, Scopus, Sportdiscus and Google Scholar databases were searched, including reports from January 2005 to July 2021. The included studies were summarised descriptively and the evidence was assessed. Results: Fourteen included studies demonstrated a ‘limited’ level of evidence supporting posture and/or movement behaviour improvements using WIST feedback, with no improvements in pain. One study assessed wearability and another two investigated comfort. Studies used tri-axial accelerometers or IMU integration (n = 5 studies). Visual and/or vibrotactile feedback was mostly used. Most studies had a risk of bias, lacked detail for methodological reproducibility and displayed inconsistent reporting of sensor technology, with validation provided only in one study. Thus, we have proposed a minimum ‘Technology and Design Checklist’ for reporting. Conclusions: Our findings suggest that WIST may improve posture, though not pain; however, the quality of the studies limits the strength of this conclusion. Wearability evaluations are needed for the translation of WIST outcomes. Minimum reporting standards for WIST should be followed to ensure methodological reproducibility.
Development of Low-Cost Wireless Sensing System for Smart Ultra-High Performance Concrete
Authors: Le Huy-Viet , Kim Tae-Uk , Khan Suleman , Park Jun-Young , Park Jong-Woong , Kim Seung-Eock , Jang Yun , Kim Dong-Joo
Published: September 2021

This study proposes the development of a wireless sensor system integrated with smart ultra-high performance concrete (UHPC) for sensing and transmitting changes in stress and damage occurrence in real-time. The smart UHPC, which has the self-sensing ability, comprises steel fibers, fine steel slag aggregates (FSSAs), and multiwall carbon nanotubes (MWCNTs) as functional fillers. The proposed wireless sensing system used a low-cost microcontroller unit (MCU) and two-probe resistance sensing circuit to capture change in electrical resistance of self-sensing UHPC due to external stress. For wireless transmission, the developed wireless sensing system used Bluetooth low energy (BLE) beacon for low-power and multi-channel data transmission. For experimental validation of the proposed smart UHPC, two types of specimens for tensile and compression tests were fabricated. In the laboratory test, using a universal testing machine, the change in electrical resistivity was measured and compared with a reference DC resistance meter. The proposed wireless sensing system showed decreased electrical resistance under compressive and tensile load. The fractional change in resistivity (FCR) was monitored at 39.2% under the maximum compressive stress and 12.35% per crack under the maximum compressive stress tension. The electrical resistance changes in both compression and tension showed similar behavior, measured by a DC meter and validated the developed integration of wireless sensing system and smart UHPC.
An Embeddable Algorithm for Automatic Garbage Detection Based on Complex Marine Environment
Authors: Deng Hongjie , Ergu Daji , Liu Fangyao , Ma Bo , Cai Ying
Published: September 2021

With the continuous development of artificial intelligence, embedding object detection algorithms into autonomous underwater detectors for marine garbage cleanup has become an emerging application area. Considering the complexity of the marine environment and the low resolution of the images taken by underwater detectors, this paper proposes an improved algorithm based on Mask R-CNN, with the aim of achieving high accuracy marine garbage detection and instance segmentation. First, the idea of dilated convolution is introduced in the Feature Pyramid Network to enhance feature extraction ability for small objects. Secondly, the spatial-channel attention mechanism is used to make features learn adaptively. It can effectively focus attention on detection objects. Third, the re-scoring branch is added to improve the accuracy of instance segmentation by scoring the predicted masks based on the method of Generalized Intersection over Union. Finally, we train the proposed algorithm in this paper on the Transcan dataset, evaluating its effectiveness by various metrics and comparing it with existing algorithms. The experimental results show that compared to the baseline provided by the Transcan dataset, the algorithm in this paper improves the mAP indexes on the two tasks of garbage detection and instance segmentation by 9.6 and 5.0, respectively, which significantly improves the algorithm performance. Thus, it can be better applied in the marine environment and achieve high precision object detection and instance segmentation.
Virtual Reality-Incorporated Horse Riding Simulator to Improve Motor Function and Balance in Children with Cerebral Palsy: A Pilot Study
Authors: Chang Hyun Jung , Jung Yong Gi , Park Young Sook , O Se Hwi , Kim Da Hye , Kim Chang Woo
Published: September 2021

The horse riding simulator (HRS) reportedly has a beneficial effect on motor function and balance in children with cerebral palsy (CP). However, by itself, the HRS is not a sufficient source of challenge and motivation for children. To address this issue, we combined the HRS with virtual reality (VR) to promote somatosensory stimulation and motivation. Sixteen children (ages: 5–17 years) with CP and presenting Gross Motor Function Classification System (GMFCS) levels I–IV were enrolled in the study. Using a head-mounted display and controllers, interventions were carried out over 30-min periods (two rides lasting 12 min each, along with a six-min rest period) twice a week over a period of eight weeks (16 sessions in aggregate). The Pediatric Balance Scale (PBS), Gross Motor Function measure (GMFM)-88, and GMFM-66 scores of each participant were measured before and after the interventions. Statistically significant improvements were observed in the PBS, GMFM-66, the total GMFM-88 scores, and those corresponding to dimensions D and E of GMFM-88 after the intervention (p < 0.05). This study demonstrates that VR-incorporated HRS is effective in improving motor function and balance in children with CP and that its incorporation in conventional PT programs could yield beneficial results.
Capacitively-Coupled ECG and Respiration for Sleep–Wake Prediction and Risk Detection in Sleep Apnea Patients
Authors: Huysmans Dorien , Castro Ivan , Borzée Pascal , Patel Aakash , Torfs Tom , Buyse Bertien , Testelmans Dries , Van Huffel Sabine , Varon Carolina
Published: September 2021

Obstructive sleep apnea (OSA) patients would strongly benefit from comfortable home diagnosis, during which detection of wakefulness is essential. Therefore, capacitively-coupled electrocardiogram (ccECG) and bioimpedance (ccBioZ) sensors were used to record the sleep of suspected OSA patients, in parallel with polysomnography (PSG). The three objectives were quality assessment of the unobtrusive signals during sleep, prediction of sleep–wake using ccECG and ccBioZ, and detection of high-risk OSA patients. First, signal quality indicators (SQIs) determined the data coverage of ccECG and ccBioZ. Then, a multimodal convolutional neural network (CNN) for sleep–wake prediction was tested on these preprocessed ccECG and ccBioZ data. Finally, two indices derived from this prediction detected patients at risk. The data included 187 PSG recordings of suspected OSA patients, 36 (dataset “Test”) of which were recorded simultaneously with PSG, ccECG, and ccBioZ. As a result, two improvements were made compared to prior studies. First, the ccBioZ signal coverage increased significantly due to adaptation of the acquisition system. Secondly, the utility of the sleep–wake classifier increased as it became a unimodal network only requiring respiratory input. This was achieved by using data augmentation during training. Sleep–wake prediction on “Test” using PSG respiration resulted in a Cohen’s kappa (κ) of 0.39 and using ccBioZ in κ = 0.23. The OSA risk model identified severe OSA patients with a κ of 0.61 for PSG respiration and κ of 0.39 using ccBioZ (accuracy of 80.6% and 69.4%, respectively). This study is one of the first to perform sleep–wake staging on capacitively-coupled respiratory signals in suspected OSA patients and to detect high risk OSA patients based on ccBioZ. The technology and the proposed framework could be applied in multi-night follow-up of OSA patients.
Denial-of-Service Attack on IEC 61850-Based Substation Automation System: A Crucial Cyber Threat towards Smart Substation Pathways
Authors: Ashraf Suleman , Shawon Mohammad H. , Khalid Haris M. , Muyeen S. M.
Published: September 2021

The generation of the mix-based expansion of modern power grids has urged the utilization of digital infrastructures. The introduction of Substation Automation Systems (SAS), advanced networks and communication technologies have drastically increased the complexity of the power system, which could prone the entire power network to hackers. The exploitation of the cyber security vulnerabilities by an attacker may result in devastating consequences and can leave millions of people in severe power outage. To resolve this issue, this paper presents a network model developed in OPNET that has been subjected to various Denial of Service (DoS) attacks to demonstrate cyber security aspect of an international electrotechnical commission (IEC) 61850 based digital substations. The attack scenarios have exhibited significant increases in the system delay and the prevention of messages, i.e., Generic Object-Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV), from being transmitted within an acceptable time frame. In addition to that, it may cause malfunction of the devices such as unresponsiveness of Intelligent Electronic Devices (IEDs), which could eventually lead to catastrophic scenarios, especially under different fault conditions. The simulation results of this work focus on the DoS attack made on SAS. A detailed set of rigorous case studies have been conducted to demonstrate the effects of these attacks.
The Influence of Target Properties on the Accuracy of Reflectorless Distance Measurements
Authors: Gmitrowicz-Iwan Joanna , Myszura Magdalena , Olenderek Tomasz , Ligęza Sławomir , Olenderek Heronim
Published: September 2021

Recent years have brought dynamic developments in surveying equipment and techniques. These include reflectorless electromagnetic distance measurement (RL EDM), which is used in a range of devices, especially total stations. Studies concerning the influence of the reflecting surface on the accuracy of RL EDM tend to focus on the colour of the measurement surface, while the influence of the density and thickness of materials is usually neglected. Therefore, this study undertook to examine 53 samples representing various materials of dissimilar features: colour, type of surface and density. The results show that dark and mat surfaces cause higher RL EDM errors than bright, gloss materials. Nonetheless, 76% of the results were in compliance with equipment specifications. Moreover, it was found that the density of the samples had significant impact on the overall accuracy. RL EDM to EPS (expanded polystyrene sheets, low-density material, commonly called Styrofoam) involved a significantly higher error rate. It demonstrates that total station measurements and laser scanning should be performed cautiously, especially with regard to materials of low density (e.g., EPS) and on short distances, where the value of relative error is high.
Gait Parameters in Healthy Preschool and School Children Assessed Using Wireless Inertial Sensor
Authors: Gieysztor Ewa , Kowal Mateusz , Paprocka-Borowicz Małgorzata
Published: September 2021

Background: The objective gait assessment in children has become more popular. Basis parameters for comparison during the examination are advisable. Objectives: The study aim was to investigate the typical gait parameters of healthy preschool and school children, using a wireless inertial sensor as the reference for atypical gait. The additional aim was to compare the specific gait parameters in the younger and older group of children. Methods: One hundred and sixty-one children’s gait parameters were evaluated by a G-Walk BTS G-SENSOR smart analyzer. The children were walking barefoot, at a self-selected speed, on a five-meter walkway, and they turned around and go back twice. Results: Age significantly influences most of the spatiotemporal parameters. The support phase becomes shorter with age. Accordingly, the swing phase becomes longer with age. The results also show that older children need shorter double support and have longer single support. Moreover, the pelvic tilt symmetry index is higher with increasing age. In each age division, the smallest variation in all gait parameters within the oldest group of examined children was observed. A comparison between the left and right side gait parameters shows the higher difference in boys than in girls. A significant difference was calculated in the pelvic obliquity symmetry index. Girls had significantly more symmetrical obliquity than boys. Conclusions: the research indicates the basic parameters of typical children’s gait, which may be a reference to atypical gait in the case of trauma or disability.
A Novel High-Q Dual-Mass MEMS Tuning Fork Gyroscope Based on 3D Wafer-Level Packaging
Authors: Xu Pengfei , Si Chaowei , He Yurong , Wei Zhenyu , Jia Lu , Han Guowei , Ning Jin , Yang Fuhua
Published: September 2021

Tuning fork gyroscopes (TFGs) are promising for potential high-precision applications. This work proposes and experimentally demonstrates a novel high-Q dual-mass tuning fork microelectromechanical system (MEMS) gyroscope utilizing three-dimensional (3D) packaging techniques. Except for two symmetrically decoupled proof masses (PM) with synchronization structures, a symmetrically decoupled lever structure is designed to force the antiparallel, antiphase drive mode motion and eliminate low frequency spurious modes. Thermoelastic damping (TED) and anchor loss are greatly reduced by the linearly coupled, momentum- and torque-balanced antiphase sense mode. Moreover, a novel 3D packaging technique is used to realize high Q-factors. A composite substrate encapsulation cap, fabricated by through-silicon-via (TSV) and glass-in-silicon (GIS) reflow processes, is anodically bonded to the wafer-scale sensing structures. A self-developed control circuit is adopted to realize loop control and characterize gyroscope performances. It is shown that a high-reliability electrical connection, together with a high air impermeability package, can be fulfilled with this 3D packaging technique. Furthermore, the Q-factors of the drive and sense modes reach up to 51,947 and 49,249, respectively. This TFG realizes a wide measurement range of ±1800 °/s and a high resolution of 0.1°/s with a scale factor nonlinearity of 720 ppm after automatic mode matching. In addition, long-term zero-rate output (ZRO) drift can be effectively suppressed by temperature compensation, inducing a small angle random walk (ARW) of 0.923°/√h and a low bias instability (BI) of 9.270°/h.