bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mountain gazelle optimisation‐based 3DOF‐FOPID‐virtual inertia controller for frequency control of low inertia microgrid
Authors: Santra Swapan , De Mala 
Published: August 2023

AbstractThe primary objective of the authors is to design a new robust and improved virtual inertia controller (VIC) for renewable energy dominated inverter interfaced low inertia microgrid (LIMG). Increasing penetration of inertia‐less renewable generation in microgrid leads to increased frequency deviation during and after a disturbance. To improve the frequency response of the LIMG, conventional VIC added with different second stage and third stage controllers are proposed in existing works. Higher degree‐of‐freedom (DOF) PID controller synchronised with fractional‐order (FO) operators are used with conventional VIC controllers. These controllers work in addition with conventional VIC and the multi‐stage controllers make the system more complex. To reduce the number of controller stages and, subsequently, reduce cost and complexity of the system, a single stage 3DOF‐FOPID controller is proposed to mitigate the frequency deviation after a disturbance in a LIMG. Performance of the proposed single stage controller is compared with that of the existing controllers to establish the advantages of the proposed controller. The parameters of the proposed 3DOF‐FOPID controller are optimised by Mountain Gazelle Optimsation. The robustness of this controller is also tested for random load fluctuation and renewable power variations in presence of system non‐linearities.
Preparation and performance improvement of a dual‐component microcapsule self‐healing system for silicone rubber insulating material
Authors: Li Mingzhe , Cao Bin , Yin Fanghui , Mei Hongwei , Wang Liming 
Published: August 2023

AbstractA key factor limiting the development of silicone rubber insulating materials is their short service life in harsh operating environments. The establishment of a self‐healing system is desired to improve the long‐term operational performance of silicone rubber and effectively extend its service life. In this study, poly(urea‐formaldehyde) microcapsules containing polydimethylsiloxane healing agents are prepared and a dual‐component microcapsule self‐healing system for silicone rubber material is established and optimised. In addition, the key factors affecting the self‐healing performance (such as microcapsule size, rupture rate, and microcapsule doping amount) are investigated. It is found that when SYLGARDTM 184 is used as the healing agent and matrix, with a 4:6 (A:B) doping ratio of dual‐component microcapsules and 20 phr doping amount, the self‐healing efficiency is approximately 51%. Furthermore, increasing the microcapsule rupture rate and the interface strength between the healing agent and the matrix can effectively improve the self‐healing performance. The relationships between key factors and self‐healing performance are theoretically revealed. Based on the relationships, the microcapsule self‐healing system can be appropriately designed. The results of this paper can provide a reference for the development of long‐life silicone rubber insulating materials.
Electro‐optical spiking neural networks using an enhanced optical axon with pulse amplitude modulation and automatic gain controller
Authors: Uleru George‐Iulian , Hulea Mircea , Younus Othman Isam , Ghassemlooy Zabih , Rajbhandari Sujan 
Published: August 2023

AbstractVisible light communication can be leveraged to establish a wireless link between neurons in spiking networks even when neural areas are in relative motions. In electro‐optical spiking neural networks (SNN), parallel transmission is often achieved through wavelength division multiplexing (WDM). However, WDM can be prohibitive in certain applications due to the need for multiple narrow‐band transmitters and receivers with optical bandpass filters. Instead of WDM, an alternative approach of using non‐orthogonal multiple access is explored (NOMA) with a pulse amplitude modulation (PAM) scheme in optical axons to enable parallel neural paths in an SNN. To evaluate NOMA with PAM, the authors implement an electro‐optical SNN that controls the force of two anthropomorphic fingers actuated by the shape memory alloy‐based actuators. An optical reference channel is used to dynamically adjust the optical receiver's gain to improve the receiver's decoding performance. Experimental results demonstrate that the electro‐optical SNN can maintain control over the fingers and hold an object under varying channel conditions. Hence, the proposed system offers robustness against dynamic optical channels induced by the relative motion of neurons.
Extraction of the significant wave height from synthetic HF radar data acquired on a floating platform
Authors: Hashemi Sepideh , Shahidi Reza , Gill Eric W. 
Published: August 2023

AbstractThe usual procedure for extracting ocean surface information from data acquired from a high‐frequency surface wave radar transmitting from a floating platform is to first compensate for the motion of the antenna in the acquired motion‐contaminated Doppler spectrum and then extract the ocean wave parameters from the motion‐compensated result. The authors propose a new real‐time method to estimate the significant wave height directly from the antenna's received electric field in the time‐domain without requiring prior knowledge of the motion parameters or performing motion compensation. Based on the relation between the ocean surface displacement and the received electric field, this method calculates the significant wave height from the windowed variance of the upper envelope of the received electric field. A preliminary calibration is required, which can be carried out either by the deployment of a wave buoy or by analysing the data over a time period during which the sea state varies. The results from this simple proposed technique show that it may be used to estimate the significant wave height with a root‐mean‐square error (RMSE) between 10 and 14 cm over a wide range of significant wave height values.
Forecasting weather‐related power outages using weighted logistic regression
Authors: Sharma Vinayak , Hong Tao , Cecchi Valentina , Hofmann Alex , Lee Ji Yoon 
Published: August 2023

AbstractWeather is a key driving factor of power outages. In this article, a methodology to forecast weather‐related power distribution outages one day‐ahead on an hourly basis is presented. A solution to address the data imbalance issue is proposed, where only a small portion of the data represents the hours impacted by outages, in the form of a weighted logistic regression model. Data imbalance is a key modelling challenge for small and rural electric utilities. The weights for outage and non‐outage hours are determined by the reciprocals of their corresponding number of hours. To demonstrate the effectiveness of the proposed model, two case studies using data from a small electric utility company in the United States are presented. One case study analyses the weather‐related outages aggregated up to the city level. The other case study is based on the distribution substation level, which has rarely been tackled in the outage prediction literature. Compared with two variants of ordinary logistic regression with equal weights, the proposed model shows superior performance in terms of geometric mean.
Novel Modal Resonance Proximity Region between photovoltaic and multi‐machine subsystems under inertia and damping variations
Authors: Li Xiaojun , Liang Jifeng , Ding Jiong , Wang Guoyan , Yu Tengkai , Zhang Rui 
Published: August 2023

AbstractA Modal Resonance Proximity Region (MRPR) is proposed is to analyse dynamic interactions between photovoltaic (PV) converters and synchronous generators in a multi‐machine system. The MRPR, delimited by the intersection of open‐loop modes' possible positions from two subsystems, indicates that two open‐loop modes would encounter each other in this region and modal resonances occur. Also, an indicator is utilised to assess the strength of such dynamic interaction. On top of MRPR and indicator, PV control parameters are optimised to avoid the possibly occurring modal resonances. As shown in time‐domain simulation, the proposed MRPR can precisely capture the occurring region for potential modal resonances and such potential modal resonances can be significantly mitigated by the MRPR and indicator‐based PV control parameter optimisation.
Oscillation suppression strategy for modular multi‐level converter grid‐connected system based on virtual damping in modulation link
Authors: Li Jialong , Liu Junliang , Du Xiong , Wei Ze , Fan Lijuan , Li Hongxin , Cheng Zhuo 
Published: August 2023

AbstractModular multilevel converters (MMC) are widely used in power systems. However, relevant studies and reports show that MMC connected with the grid may cause oscillation problems. When the bridge arm resistance is large enough, the operating conditions will be stable because of overdamping in the system. According to this idea, a virtual damping strategy is proposed, which is equivalent to increasing the resistance of the bridge arm without any actual power loss. Meanwhile, from the perspective of the impedance‐based analysis method, it is explained that this virtual damping control strategy can improve the stability margin of the system to realize the suppression of oscillation. In order to verify the effectiveness of the control method, the MMC simulation model is established under Matlab/Simulink. The simulation results show that the proposed virtual damping control can effectively suppress the MMC oscillation problem.
A method to detect internal leakage of hydraulic cylinder by combining data augmentation and multiscale residual CNN
Authors: He Qingchuan , Ruan Huiqi , Pan Jun , Lyu Xiaotian 
Published: August 2023

AbstractDeveloping a method to detect internal leakage in hydraulic cylinder, which is used for Electro‐Hydrostatic Actuators (EHA), is important to prevent serious malfunctions for aircrafts. At present, the internal leakage in an EHA cannot be accurately detected only using operational data. This paper proposed a convolutional neural networks (CNN) based method to detect internal leakage in hydraulic cylinder according to the relationship between operational state parameters of EHA and leakage in the hydraulic cylinder. A method was presented to align multi‐source signals with different forms by using the motor current as a benchmark. Because the number of monitoring signals are relatively small, a feedforward neural network (FFNN) based data augment method is proposed to increase parameters of input data set. A general method on how to detect internal leakage by combining signals alignment, data augmentation and multiscale residual CNN was proposed. The experimental results show that the proposed method can be used to accurately detect internal leakage in a hydraulic cylinder operating under non‐stationary load and velocity conditions, and the detection accuracy reached 99.8%.
Free device location independent WiFi‐based localisation using received signal strength indicator and channel state information
Authors: Abuhoureyah Fahd , Yan Chiew Wong , Bin Mohd Isira Ahmad Sadhiqin , Al‐Andoli Mohammed 
Published: August 2023

AbstractThe trajectory localisation of human activities using signal analytics has become a reality due to the widespread use of advanced signal processing systems. Device‐free localisation using WiFi devices is prevalent, and the received signal strength indicator (RSSI) and channel state information (CSI) signals offer additional benefits. However, radio frequency (RF) localisation is highly dependent on the environment, so updating fingerprint data is necessary by changing the environment. This work presents Fine‐grained Indoor Detection and Angular Radar for recognising and locating humans using a multipath trajectory reflections system that does not require training. It estimates location using a probabilistic approach that considers changes in CSI and RSSI across multiple nodes, generating an informative dataset that reflects the current trajectory and status of the location. The presented method extracts data from clustered Raspberry Pi 4B and Nexmon. The method exhibits a versatile real‐time location‐tracking solution by utilising the distinctive properties of RF signals. This technology has significant implications for various applications, including human medical monitoring, gaming, smart cities, and optimising building layouts to improve efficiency. The model demonstrates location‐independent localisation with up to 80% accuracy in mapping trajectories at any location. The findings indicate that the proposed model is effective and reliable for indoor localisation and activity tracking, making it a promising solution for implementation in real‐world environments.
Portfolio benefits of taxonomy orientated and renewable European electric utilities
Authors: Cauthorn Thomas , Klein Christian , Remme Leonard , Zwergel Bernhard 
Published: August 2023

AbstractThis paper investigates carbon and energy mix risk in the equity prices of EU-Taxonomy orientated and renewable European electric utility companies. We calculate carbon intensity and energy mix factors to measure possible carbon and energy mix premia while investigating the performance of portfolios of EU-Taxonomy orientated and renewable European electric utilities. We use a unique dataset to extend the three-factor model presented by Fama and French (1993) and find evidence of a positive renewable energy mix premium for portfolios of EU-Taxonomy orientated firms and firms with a high level of renewable energy in the energy mix. A positive low-carbon premium is also found for these same portfolios. Lastly, based on the three-factor model, an EU-Taxonomy orientated portfolio outperforms both a non-orientated portfolio and a non-reporting portfolio while a renewable energy portfolio outperforms a conventional energy portfolio. Our results are important for regulators, investors and European electric utilities in assessing the impact environmental regulations have on a firm’s cost of capital.
Household practices and determinants of solid waste segregation in Addis Ababa city, Ethiopia
Authors: Adefris Worku , Damene Shimeles , Satyal Poshendra 
Published: August 2023

AbstractSolid waste segregation plays a critical role in effective waste management; however, the practice remains at a low level in developing countries like Ethiopia. Despite the persistent nature of the problem, there are limited studies to date that can provide sufficient empirical evidence to support appropriate efforts by policy makers and practitioners, particularly in the context of the developing world. Therefore, the main objective of this study was to analyze household practices and determinants of solid waste segregation in the urban areas of Addis Ababa, the capital city of Ethiopia. To achieve this objective, data were generated through a household survey, focus group discussions, key informant interviews, and field observations. The collected quantitative data were cleaned, encoded, and statistically analyzed using descriptive statistics in SPSS, while thematic analysis was undertaken to evaluate and describe the qualitative data. The data analysis revealed that only 21.3% of respondents reported frequent solid waste segregation, while about half (45.5%) segregated solid waste rarely. Conversely, a considerable proportion (28.7%) of the respondents reported not segregating solid waste, and the remaining 4.5% of respondents were unsure about the practice. This implies that only one-fifth of the total sampled respondents actually implement solid waste segregation practices at the household level. The chi-square test showed that respondents’ awareness/training (P = 0.000) and use of social organizations to discuss waste management (P = 0.001) are significantly associated with the practice of solid waste segregation. This highlights the need to focus on awareness-raising efforts among the general public in order to improve the knowledge, attitudes, and behaviors of individual households and residents toward solid waste segregation practices. Additionally, enabling policies, sufficient infrastructure, and incentive mechanisms can also help enhance wider adoption of the practices.
Pistachios as a recovery food following downhill running exercise in recreational team‐sport individuals
Authors: Philpott Jordan , Kern Mark , Hooshmand Shirin , Carson Imogene , Rayo Vernon , North Elise , Okamoto Lauren , O’Neil Timothy , Hong Mee Young , Liu Changqi , Dreczkowski Gillian , Rodriguez‐Sanchez Nidia , Witard Oliver C. , Galloway Stuart D. 
Published: August 2023

ABSTRACTWe aimed to investigate the impact of pistachio nut consumption on muscle soreness and function following exercise‐induced muscle damage. Using a randomised cross‐over design, male team‐sport players (n = 18) performed a 40‐minute downhill treadmill run to induce muscle damage, which was conducted after 2‐wks of consuming either control (CON, water), a standard dose of daily pistachios (STD, 42.5 g/d) or a higher dose of daily pistachios (HIGH, 85 g/d). Lower limb muscle soreness (visual analogue scale), muscle function (maximal voluntary isokinetic torque and vertical jump), and blood markers of muscle damage/inflammation (creatine kinase, C‐reactive protein, myoglobin, superoxide dismutase) were measured pre (baseline) and post (24, 48, and 72 h) exercise. No trial order effects were observed for any outcome measurement across trials. Mean quadriceps soreness (non‐dominant leg) during exercise recovery was reduced (p < 0.05) in HIGH vs. CON (mean difference (95%CI): 13(1–25) mm). Change in soreness in the dominant quadriceps was not different between HIGH vs. CON (p = 0.06; mean difference (95%CI): 13(−1 to 26 mm)). No main effects of time or trial were observed for mean soreness of hamstrings, or on isokinetic torque of knee extensors or knee flexors, during recovery. Serum creatine kinase concentration peaked at 24 h post‐damage (mean(SEM): 763(158)µg/L) from baseline (300(87)µg/L), but had returned to baseline by 72 h post (398(80)µg/L) exercise in all trials, with no trial or trial × time interaction evident. These data suggest that high dose pistachio nut ingestion may provide some alleviation of muscle soreness, but no effect on muscle function, following modest muscle damage.
The accuracy of screening instruments for sarcopenia: a diagnostic systematic review and meta-analysis
Authors: Huang Li , Shu Xiaoyu , Ge Ning , Gao Langli , Xu Ping , Zhang Yu , Chen Yu , Yue Jirong , Wu Chenkai 
Published: August 2023

Abstract

Objective
This review aimed to summarise the diagnostic accuracy of screening tools for sarcopenia.

Methods
We conducted a systematic review along with a critical appraisal of published studies on screening tools for sarcopenia. We assessed the measurement properties of screening instruments using the consensus-based standards for selecting health measurement instruments (COSMIN) checklist. We evaluated the risk bias of the included studies using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. The diagnostic test accuracy of instruments for sarcopenia was reported using sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR).

Results
We screened 7,120 titles and abstracts; 42 studies including five screening tools for sarcopenia were included. The overall study quality assessed by the QUADAS-2 tool was moderate to good. Of the five screening tools, three instruments had specificities ≥85%: 92% [95% confidence interval (CI): 63–99%] for the SARC-F modified version, 87% (95% CI: 82–90%) for the SARC-F and 85% (95% CI: 77–90%) for the Ishii score. Three tools had sensitivity ≥75%, namely, MSRA 82% (95% CI: 69–90%), Ishii score 79% (95% CI: 62–89%) and U-TEST 76%. PLR higher than 5.0 were present for the Ishii score and SARC-F modified versions; the Ishii score also had the best NLR of 0.25 of all scales.

Conclusion
The MSRA and Ishii score had excellent sensitivity for sarcopenia screening at an early stage; SARC-F modified versions and Ishii score had superior specificity for sarcopenia diagnosis.
Characterization of inhalation exposures at a wildfire incident during the Wildland Firefighter Exposure and Health Effects (WFFEHE) Study
Authors: Navarro Kathleen M , Fent Kenneth , Mayer Alexander C , Brueck Scott E , Toennis Christine , Law Brandon , Meadows Juliana , Sammons Deborah , Brown Skylar 
Published: August 2023

Abstract
Wildland firefighters (WFFs) are exposed to many inhalation hazards working in the wildland fire environment. To assess occupational exposures and acute and subacute health effects among WFFs, the wildland firefighter exposure and health effects study collected data for a 2-year repeated measures study. This manuscript describes the exposure assessment from one Interagency Hotshot Crew (N = 19) conducted at a wildfire incident. Exposures to benzene, toluene, ethylbenzene, xylene isomers, formaldehyde, acetaldehyde, and naphthalene were measured through personal air sampling each work shift. Biological monitoring was done for creatinine-adjusted levoglucosan in urine pre- and post-shift. For 3 days sampling at the wildfire incident, benzene, toluene, ethylbenzene, xylene isomers (m and p, and o) exposure was highest on day 1 (geometric mean [GM] = 0.015, 0.042, 0.10, 0.42, and 0.15 ppm, respectively) when WFFs were not exposed to smoke but used chainsaws to remove vegetation and prepare fire suppression breaks. Exposure to formaldehyde and acetaldehyde was highest on day 2 (GM = 0.03 and 0.036 ppm, respectively) when the WFFs conducted a firing operation and were directly exposed to wildfire smoke. The greatest difference of pre- and post-shift levoglucosan concentrations were observed on day 3 (pre-shift: 9.7 and post-shift: 47 μg/mg creatinine) after WFFs conducted mop up (returned to partially burned area to extinguish any smoldering vegetation). Overall, 65% of paired samples (across all sample days) showed a post-shift increase in urinary levoglucosan and 5 firefighters were exposed to benzene at concentrations at or above the National Institute for Occupational Safety and Health (NIOSH) recommended exposure limit. Our findings further demonstrate that exposure to inhalation hazards is one of many risks that wildland firefighters experience while suppressing wildfires.
A fast and globally optimal solution for RNA-seq quantification
Authors: Yi Huiguang , Lin Yanling , Chang Qing , Jin Wenfei 
Published: August 2023

Abstract
Alignment-based RNA-seq quantification methods typically involve a time-consuming alignment process prior to estimating transcript abundances. In contrast, alignment-free RNA-seq quantification methods bypass this step, resulting in significant speed improvements. Existing alignment-free methods rely on the Expectation–Maximization (EM) algorithm for estimating transcript abundances. However, EM algorithms only guarantee locally optimal solutions, leaving room for further accuracy improvement by finding a globally optimal solution. In this study, we present TQSLE, the first alignment-free RNA-seq quantification method that provides a globally optimal solution for transcript abundances estimation. TQSLE adopts a two-step approach: first, it constructs a k-mer frequency matrix A for the reference transcriptome and a k-mer frequency vector b for the RNA-seq reads; then, it directly estimates transcript abundances by solving the linear equation ATAx = ATb. We evaluated the performance of TQSLE using simulated and real RNA-seq data sets and observed that, despite comparable speed to other alignment-free methods, TQSLE outperforms them in terms of accuracy. TQSLE is freely available at https://github.com/yhg926/TQSLE.