bluuebunny/crossref_metadata_2025 huggingface dataset:
Enabling Real-Time On-Chip Audio Super Resolution for Bone-Conduction Microphones
Authors: Li Yuang , Wang Yuntao , Liu Xin , Shi Yuanchun , Patel Shwetak , Shih Shao-Fu
Published: December 2022

Voice communication using an air-conduction microphone in noisy environments suffers from the degradation of speech audibility. Bone-conduction microphones (BCM) are robust against ambient noises but suffer from limited effective bandwidth due to their sensing mechanism. Although existing audio super-resolution algorithms can recover the high-frequency loss to achieve high-fidelity audio, they require considerably more computational resources than is available in low-power hearable devices. This paper proposes the first-ever real-time on-chip speech audio super-resolution system for BCM. To accomplish this, we built and compared a series of lightweight audio super-resolution deep-learning models. Among all these models, ATS-UNet was the most cost-efficient because the proposed novel Audio Temporal Shift Module (ATSM) reduces the network’s dimensionality while maintaining sufficient temporal features from speech audio. Then, we quantized and deployed the ATS-UNet to low-end ARM micro-controller units for a real-time embedded prototype. The evaluation results show that our system achieved real-time inference speed on Cortex-M7 and higher quality compared with the baseline audio super-resolution method. Finally, we conducted a user study with ten experts and ten amateur listeners to evaluate our method’s effectiveness to human ears. Both groups perceived a significantly higher speech quality with our method when compared to the solutions with the original BCM or air-conduction microphone with cutting-edge noise-reduction algorithms.
A 40 MHz 11-Bit ENOB Delta Sigma ADC for Communication and Acquisition Systems
Authors: Fakhoury Hussein , Jabbour Chadi , Nguyen Van-Tam
Published: December 2022

This paper describes a Delta Sigma ADC IC that embeds a 5th-order Continuous-Time Delta Sigma modulator with 40 MHz signal bandwidth, a low ripple 20 to 80 MS/s variable-rate digital decimation filter, a bandgap voltage reference, and high-speed CML buffers on a single die. The ADC also integrates on-chip calibrations for RC time-constant variation and quantizer offset. The chip was fabricated in a 1P7M 65 nm CMOS process. Clocked at 640 MHz, the Continuous-Time Delta Sigma modulator achieves 11-bit ENOB and 76.5 dBc THD up to 40 MHz of signal bandwidth while consuming 82.3 mW.
Research on Aviation Safety Prediction Based on Variable Selection and LSTM
Authors: Zeng Hang , Guo Jiansheng , Zhang Hongmei , Ren Bo , Wu Jiangnan
Published: December 2022

Accurate prediction of aviation safety levels is significant for the efficient early warning and prevention of incidents. However, the causal mechanism and temporal character of aviation accidents are complex and not fully understood, which increases the operation cost of accurate aviation safety prediction. This paper adopts an innovative statistical method involving a least absolute shrinkage and selection operator (LASSO) and long short-term memory (LSTM). We compiled and calculated 138 monthly aviation insecure events collected from the Aviation Safety Reporting System (ASRS) and took minor accidents as the predictor. Firstly, this paper introduced the group variables and the weight matrix into LASSO to realize the adaptive variable selection. Furthermore, it took the selected variable into multistep stacked LSTM (MSSLSTM) to predict the monthly accidents in 2020. Finally, the proposed method was compared with multiple existing variable selection and prediction methods. The results demonstrate that the RMSE (root mean square error) of the MSSLSTM is reduced by 41.98%, compared with the original model; on the other hand, the key variable selected by the adaptive spare group lasso (ADSGL) can reduce the elapsed time by 42.67% (13 s). This shows that aviation safety prediction based on ADSGL and MSSLSTM can improve the prediction efficiency of the model while keeping excellent generalization ability and robustness.
An Efficient Dehazing Algorithm Based on the Fusion of Transformer and Convolutional Neural Network
Authors: Xu Jun , Chen Zi-Xuan , Luo Hao , Lu Zhe-Ming
Published: December 2022

The purpose of image dehazing is to remove the interference from weather factors in degraded images and enhance the clarity and color saturation of images to maximize the restoration of useful features. Single image dehazing is one of the most important tasks in the field of image restoration. In recent years, due to the progress of deep learning, single image dehazing has made great progress. With the success of Transformer in advanced computer vision tasks, some research studies also began to apply Transformer to image dehazing tasks and obtained surprising results. However, both the deconvolution-neural-network-based dehazing algorithm and Transformer based dehazing algorithm magnify their advantages and disadvantages separately. Therefore, this paper proposes a novel Transformer–Convolution fusion dehazing network (TCFDN), which uses Transformer’s global modeling ability and convolutional neural network’s local modeling ability to improve the dehazing ability. In the Transformer–Convolution fusion dehazing network, the classic self-encoder structure is used. This paper proposes a Transformer–Convolution hybrid layer, which uses an adaptive fusion strategy to make full use of the Swin-Transformer and convolutional neural network to extract and reconstruct image features. On the basis of previous research, this layer further improves the ability of the network to remove haze. A series of contrast experiments and ablation experiments not only proved that the Transformer–Convolution fusion dehazing network proposed in this paper exceeded the more advanced dehazing algorithm, but also provided solid and powerful evidence for the basic theory on which it depends.
A Flexible Pressure Sensor with a Mesh Structure Formed by Lost Hair for Human Epidermal Pulse Wave Monitoring
Authors: Wang Xue , Feng Zhiping , Li Peng , Wang Luna , Chen Liang , Wu Yufen , Yang Jin
Published: December 2022

Flexible pressure sensors with the capability of monitoring human vital signs show broad application prospects in personalized healthcare. In this work, a hair-based flexible pressure sensor (HBPS) consisting of lost hair and polymer films was proposed for the continuous monitoring of the human epidermal arterial pulse waveform. A macroscale mesh structure formed by lost hair provides a simplified spacer that endows the triboelectric-based flexible pressure sensor with sufficient contact–separation space. Based on this mesh structure design, the hair-based flexible pressure sensor can respond to the slight pressure change caused by an object with 5 mg weight and hold a stable output voltage under 1–30 Hz external pressure excitation. Additionally, the hair-based flexible pressure sensor showed great sensitivity (0.9 V/kPa) and decent stability after 4500 cycles of operation. Given these compelling features, the HBPS can successfully measure the human epidermal arterial pulses with obvious details at different arteries. The proposed HBPS can also be used to monitor the pulse signals of different subjects. Furthermore, the three different pulse wave transmission time (PTT) values (PTT-foot, PTT-middle, and PTT-peak) can be obtained by simultaneously monitoring human pulse and electrocardiogram signals, which has enormous application potential for assessing cardiovascular system health.
Development of a New Electrochemical Sensor Based on Molecularly Imprinted Biopolymer for Determination of 4,4′-Methylene Diphenyl Diamine
Authors: Ghaani Masoud , Büyüktaş Duygu , Carullo Daniele , Farris Stefano
Published: December 2022

A new molecularly imprinted electrochemical sensor was proposed to determine 4,4′-methylene diphenyl diamine (MDA) using molecularly imprinted polymer–multiwalled carbon nanotubes modified glassy carbon electrode (MIP/MWCNTs/GCE). GCE was coated by MWCNTs (MWCNTs/GCE) because of their antifouling qualities and in order to improve the sensor sensitivity. To make the whole sensor, a polymeric film made up of chitosan nanoparticles was electrodeposited by the cyclic voltammetry method on the surface of MWCNTs/GCE in the presence of MDA as a template. Different parameters such as scan cycles, elution time, incubation time, molar ratio of template molecules to functional monomers, and pH were optimized to increase the performance of the MIP sensor. With a detection limit of 15 nM, a linear response to MDA was seen in the concentration range of 0.5–100 µM. The imprinting factor (IF) of the proposed sensor was also calculated at around 3.66, demonstrating the extremely high recognition performance of a MIP/MWCNT-modified electrode. Moreover, the sensor exhibited good reproducibility and selectivity. Finally, the proposed sensor was efficiently used to determine MDA in real samples with satisfactory recoveries ranging from 94.10% to 106.76%.
Comparison of Characteristics of a ZnO Gas Sensor Using a Low-Dimensional Carbon Allotrope
Authors: Lee Jihoon , Park Jaebum , Huh Jeung-Soo
Published: December 2022

Owing to the increasing construction of new buildings, the increase in the emission of formaldehyde and volatile organic compounds, which are emitted as indoor air pollutants, is causing adverse effects on the human body, including life-threatening diseases such as cancer. A gas sensor was fabricated and used to measure and monitor this phenomenon. An alumina substrate with Au, Pt, and Zn layers formed on the electrode was used for the gas sensor fabrication, which was then classified into two types, A and B, representing the graphene spin coating before and after the heat treatment, respectively. Ultrasonication was performed in a 0.01 M aqueous solution, and the variation in the sensing accuracy of the target gas with the operating temperature and conditions was investigated. As a result, compared to the ZnO sensor showing excellent sensing characteristics at 350 °C, it exhibited excellent sensing characteristics even at a low temperature of 150 °C, 200 °C, and 250 °C.
Intelligent Reflecting Surface-Assisted Physical Layer Key Generation with Deep Learning in MIMO Systems
Authors: Liu Shengjie , Wei Guo , He Haoyu , Wang Hao , Chen Yanru , Hu Dasha , Jiang Yuming , Chen Liangyin
Published: December 2022

Physical layer secret key generation (PLKG) is a promising technology for establishing effective secret keys. Current works for PLKG mostly study key generation schemes in ideal communication environments with little or even no signal interference. In terms of this issue, exploiting the reconfigurable intelligent reflecting surface (IRS) to assist PLKG has caused an increasing interest. Most IRS-assisted PLKG schemes focus on the single-input-single-output (SISO), which is limited in future communications with multi-input-multi-output (MIMO). However, MIMO could bring a serious overhead of channel reciprocity extraction. To fill the gap, this paper proposes a novel low-overhead IRS-assisted PLKG scheme with deep learning in the MIMO communications environments. We first combine the direct channel and the reflecting channel established by the IRS to construct the channel response function, and we propose a theoretically optimal interaction matrix to approach the optimal achievable rate. Then we design a channel reciprocity-learning neural network with an IRS introduced (IRS-CRNet), which is exploited to extract the channel reciprocity in time division duplexing (TDD) systems. Moreover, a PLKG scheme based on the IRS-CRNet is proposed. Final simulation results verify the performance of the PLKG scheme based on the IRS-CRNet in terms of key generation rate, key error rate and randomness.
Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium
Authors: Lemenkova Polina , De Plaen Raphaël , Lecocq Thomas , Debeir Olivier
Published: December 2022

Archived seismograms recorded in the 20th century present a valuable source of information for monitoring earthquake activity. However, old data, which are only available as scanned paper-based images should be digitised and converted from raster to vector format prior to reuse for geophysical modelling. Seismograms have special characteristics and specific featuresrecorded by a seismometer and encrypted in the images: signal trace lines, minute time gaps, timing and wave amplitudes. This information should be recognised and interpreted automatically when processing archives of seismograms containing large collections of data. The objective was to automatically digitise historical seismograms obtained from the archives of the Royal Observatory of Belgium (ROB). The images were originallyrecorded by the Galitzine seismometer in 1954 in Uccle seismic station, Belgium. A dataset included 145 TIFF images which required automatic approach of data processing. Software for digitising seismograms are limited and many have disadvantages. We applied the DigitSeis for machine-based vectorisation and reported here a full workflowof data processing. This included pattern recognition, classification, digitising, corrections and converting TIFFs to the digital vector format. The generated contours of signals were presented as time series and converted into digital format (mat files) which indicated information on ground motion signals contained in analog seismograms. We performed the quality control of the digitised traces in Python to evaluate the discriminating functionality of seismic signals by DigitSeis. We shown a robust approach of DigitSeis as a powerful toolset for processing analog seismic signals. The graphical visualisation of signal traces and analysis of the performed vectorisation results shown that the algorithms of data processing performed accurately and can be recommended in similar applications of seismic signal processing in future related works in geophysical research.
An Integrated Methodology for Bibliometric Analysis: A Case Study of Internet of Things in Healthcare Applications
Authors: Ullah Rahmat , Asghar Ikram , Griffiths Mark G.
Published: December 2022

This paper presents an integrated and easy methodology for bibliometric analysis. The proposed methodology is evaluated on recent research activities to highlight the role of the Internet of Things in healthcare applications. Different tools are used for bibliometric studies to explore the breadth and depth of different research areas. However, these Methods consider only the Web of Science or Scopus data for bibliometric analysis. Furthermore, bibliometric analysis has not been fully utilised to examine the capabilities of the Internet of Things for medical devices and their applications. There is a need for an easy methodology to use for a single integrated analysis of data from many sources rather than just the Web of Science or Scopus. A few bibliometric studies merge the Web of Science and Scopus to conduct a single integrated piece of research. This paper presents a methodology that could be used for a single bibliometric analysis across multiple databases. Three freely available tools, Excel, Perish or Publish and the R package Bibliometrix, are used for the purpose. The proposed bibliometric methodology is evaluated for studies related to the Internet of Medical Things (IoMT) and its applications in healthcare settings. An inclusion/exclusion criterion is developed to explore relevant studies from the seven largest databases, including Scopus, Web of Science, IEEE, ACM digital library, PubMed, Science Direct and Google Scholar. The study focuses on factors such as the number of publications, citations per paper, collaborative research output, h-Index, primary research and healthcare application areas. Data for this study are collected from the seven largest academic databases for 2012 to 2022 related to IoMT and their applications in healthcare. The bibliometric data analysis generated different research themes within IoMT technologies and their applications in healthcare research. The study has also identified significant research areas in this field. The leading research countries and their contributions are another output from the data analysis. Finally, future research directions are proposed for researchers to explore this area in further detail.
Impact Evaluation of Cyberattacks on Connected and Automated Vehicles in Mixed Traffic Flow and Its Resilient and Robust Control Strategy
Authors: Wang Ting , Tu Meiting , Lyu Hao , Li Ye , Orfila Olivier , Zou Guojian , Gruyer Dominique
Published: December 2022

Connected and automated vehicles (CAVs) present significant potential for improving road safety and mitigating traffic congestion for the future mobility system. However, cooperative driving vehicles are more vulnerable to cyberattacks when communicating with each other, which will introduce a new threat to the transportation system. In order to guarantee safety aspects, it is also necessary to ensure a high level of information quality for CAV. To the best of our knowledge, this is the first investigation on the impacts of cyberattacks on CAV in mixed traffic (large vehicles, medium vehicles, and small vehicles) from the perspective of vehicle dynamics. The paper aims to explore the influence of cyberattacks on the evolution of CAV mixed traffic flow and propose a resilient and robust control strategy (RRCS) to alleviate the threat of cyberattacks. First, we propose a CAV mixed traffic car-following model considering cyberattacks based on the Intelligent Driver Model (IDM). Furthermore, a RRCS for cyberattacks is developed by setting the acceleration control switch and its impacts on the mixed traffic flow are explored in different cyberattack types. Finally, sensitivity analyses are conducted in different platoon compositions, vehicle distributions, and cyberattack intensities. The results show that the proposed RRCS of cyberattacks is robust and can resist the negative threats of cyberattacks on the CAV platoon, thereby providing a theoretical basis for restoring the stability and improving the safety of the CAV.
Estimating Ground Reaction Forces from Two-Dimensional Pose Data: A Biomechanics-Based Comparison of AlphaPose, BlazePose, and OpenPose
Authors: Mundt Marion , Born Zachery , Goldacre Molly , Alderson Jacqueline
Published: December 2022

The adoption of computer vision pose estimation approaches, used to identify keypoint locations which are intended to reflect the necessary anatomical landmarks relied upon by biomechanists for musculoskeletal modelling, has gained increasing traction in recent years. This uptake has been further accelerated by keypoint use as inputs into machine learning models used to estimate biomechanical parameters such as ground reaction forces (GRFs) in the absence of instrumentation required for direct measurement. This study first aimed to investigate the keypoint detection rate of three open-source pose estimation models (AlphaPose, BlazePose, and OpenPose) across varying movements, camera views, and trial lengths. Second, this study aimed to assess the suitability and interchangeability of keypoints detected by each pose estimation model when used as inputs into machine learning models for the estimation of GRFs. The keypoint detection rate of BlazePose was distinctly lower than that of AlphaPose and OpenPose. All pose estimation models achieved a high keypoint detection rate at the centre of an image frame and a lower detection rate in the true sagittal plane camera field of view, compared with slightly anteriorly or posteriorly located quasi-sagittal plane camera views. The three-dimensional ground reaction force, instantaneous loading rate, and peak force for running could be estimated using the keypoints of all three pose estimation models. However, only AlphaPose and OpenPose keypoints could be used interchangeably with a machine learning model trained to estimate GRFs based on AlphaPose keypoints resulting in a high estimation accuracy when OpenPose keypoints were used as inputs and vice versa. The findings of this study highlight the need for further evaluation of computer vision-based pose estimation models for application in biomechanical human modelling, and the limitations of machine learning-based GRF estimation models that rely on 2D keypoints. This is of particular relevance given that machine learning models informing athlete monitoring guidelines are being developed for application related to athlete well-being.
Predicting Site Energy Usage Intensity Using Machine Learning Models
Authors: Ngnamsie Njimbouom Soualihou , Lee Kwonwoo , Lee Hyun , Kim Jeongdong
Published: December 2022

Climate change is a shift in nature yet a devastating phenomenon, mainly caused by human activities, sometimes with the intent to generate usable energy required in humankind’s daily life. Addressing this alarming issue requires an urge for energy consumption evaluation. Predicting energy consumption is essential for determining what factors affect a site’s energy usage and in turn, making actionable suggestions to reduce wasteful energy consumption. Recently, a rising number of researchers have applied machine learning in various fields, such as wind turbine performance prediction, energy consumption prediction, thermal behavior analysis, and more. In this research study, using data publicly made available by the Women in Data Science (WiDS) Datathon 2022 (contains data on building characteristics and information collected by sensors), after appropriate data preparation, we experimented four main machine learning methods (random forest (RF), gradient boost decision tree (GBDT), support vector regressor (SVR), and decision tree for regression (DT)). The most performant model was selected using evaluation metrics: root mean square error (RMSE) and mean absolute error (MAE). The reported results proved the robustness of the proposed concept in capturing the insight and hidden patterns in the dataset, and effectively predicting the energy usage of buildings.
Oral Bioavailability Enhancement of Vancomycin Hydrochloride with Cationic Nanocarrier (Leciplex): Optimization, In Vitro, Ex Vivo, and In Vivo Studies
Authors: Abdellatif Menna M. , Ahmed Sara Mohamed , El-Nabarawi Mohamed A. , Teaima Mahmoud
Published: December 2022

To explore the performance of the cationic nanocarrier leciplex (LPX) in escalating the oral bioavailability of vancomycin hydrochloride (VAN) by promoting its intestinal permeability. With the aid of a D-optimal design, the effect of numerous factors, including lipid molar ratio, cationic surfactant molar ratio, cationic surfactant type, and lipid type, on LPX characteristics, including entrapment efficacy (EE%), particle size (P.S.), polydispersity index (P.I.), zeta potential value (Z.P.), and steady-state flux (Jss) were assessed. The optimized formula was further evaluated in terms of morphology, ex vivo permeation, stability, cytotoxicity, and in vivo pharmacokinetic study. The optimized formula was spherical-shaped with an E.E. of 85.2 ± 0.95%, a P.S. of 52.74 ± 0.91 nm, a P.I. of 0.21 ± 0.02, a Z.P. of + 60.8 ± 1.75 mV, and a Jss of 175.03 ± 1.68 µg/cm2/h. Furthermore, the formula increased the intestinal permeability of VAN by 2.3-fold compared to the drug solution. Additionally, the formula was stable, revealed good mucoadhesive properties, and was well tolerated for oral administration. The in vivo pharmacokinetic study demonstrated that the VAN Cmax increased by 2.99-folds and AUC0-12 by 3.41-folds compared to the drug solution. These outcomes proved the potentiality of LPX in increasing the oral bioavailability of poorly absorbed drugs.
Medicalization of Sexuality and Trans Situations: Evolutions and Transformations
Authors: Giami Alain
Published: December 2022

This article explores the evolution of the definition and the process of medicalization of sexuality during the second half of the 20th century. After a review and discussion of the notion of medicalization, the application of this notion to a few examples is discussed, including the emergence of sexuality, the demedicalization of homosexuality, the treatment of “sexual disorders”, the prevention of HIV infection, and the gender-affirmation pathways for transgender and gender diverse (TGD) people. The analysis of these situations—in the light of the notion of medicalization—allows us to better understand the multiple facets of this notion. In particular, we observe processes of medicalization and demedicalization, depathologization, and pharmacologization. The notion of medicalization of sexuality appears here as a useful concept for understanding the conceptualization and treatment of diversities in the field of sexuality and gender.
Modeling of Fuzzy Cognitive Maps with a Metaheuristics-Based Rainfall Prediction System
Authors: Al Duhayyim Mesfer , Mohamed Heba G. , Alzahrani Jaber S. , Alabdan Rana , Mousa Mohamed , Zamani Abu Sarwar , Yaseen Ishfaq , Alsaid Mohamed Ibrahim
Published: December 2022

Rainfall prediction remains a hot research topic in smart city environments. Precise rainfall prediction in smart cities becomes essential for planning security measures before construction and transportation activities, flight operations, water reservoir systems, and agricultural tasks. Precise rainfall forecasting now becomes more complex than before because of extreme climatic changes. Machine learning (ML) approaches can forecast rainfall by deriving hidden patterns from historic meteorological datasets. Selecting a suitable classification method for forecasting has become a tough job. This article introduces the Fuzzy Cognitive Maps with a Metaheuristics-based Rainfall Prediction System (FCMM-RPS) technique. The intention of the FCMM-RPS technique is to predict rainfall automatically and efficiently. To accomplish this, the presented FCMM-RPS technique primarily pre-processes the rainfall data to make it compatible. In addition, the presented FCMM-RPS technique predicts rainfall using the FCM model. To enhance the rainfall prediction outcomes of the FCM model, the parameter optimization process is performed using a modified butterfly optimization algorithm (MBOA). The performance assessment of the FCMM-RPS technique is tested on a rainfall dataset. A widespread comparison study highlights the improvements of the FCMM-RPS technique in the rainfall forecasting process compared to existing techniques with a maximum accuracy of 94.22%.
Experiments and Modeling of the Attenuation of the Dynamic Elastic Modulus of Saturated Red Clay under Cyclic Loading
Authors: Ma Hongyan , Zhuang Yuanlong , Chen Liangliang , Lv Mengxi , Xu Song
Published: December 2022

Due to the long-term effect of cyclic traffic loads, the accumulated deformation of red clay subgrades is increasing, strength is decreasing, and problems such as uneven settlement are occurring. In order to improve the stability and durability of red clay subgrade, this paper analyzes the effects of cyclic stress ratios and compactness on the development of the dynamic elastic modulus of saturated red clay. This is achieved through dynamic triaxial tests on red clay in the Qingyuan County, Zhejiang Province, China. The results show that the saturated red clay has an attenuation threshold cyclic stress ratio under cyclic loading. The attenuation threshold cyclic stress ratio prediction models were constructed based on the confining pressure and compactness, respectively. By introducing the concept of a relative cyclic stress ratio, the prediction model of the dynamic elastic modulus of saturated red clay (as based on cyclic vibration times and close cyclic stress ratio) is constructed. The model realizes the prediction of the dynamic elastic modulus of red clay under arbitrary cyclic vibration times via the initial cyclic stress ratio, compactness, and other parameters. It provides a theoretical basis for rationally evaluating the dynamic stability of red clay subgrade under cyclic loading.
A Meta-Regression Analysis of Hunters’ Valuations of Recreational Hunting
Authors: Gren Ing-Marie , Kerr Geoffrey
Published: December 2022

Hunters’ valuations of recreational hunting have been estimated by a large number of location-specific studies since the early 1970s, but to date there has been no systematic assessment of this research at the global scale. The present study performed a meta-analysis of 80 studies with 588 value-per-day estimates. The assessment showed a high concentration of studies pertaining to the valuation of deer and the valuation of hunting in the USA. The average value was USD 69 /hunting day in 2020 prices, but the variation was large, ranging from USD 4 to 325 /hunting day. The statistical performance of alternative mixed-effect models explaining the estimated value differences was tested with different hunting attributes (targeted game animal); context variables (income/capita, population density, year of study, region of application); and study characteristics (valuation method, publication outlet). The results showed that the type of game animal, income per capita, and valuation method had significant effects on estimated values. The predictive power was high for all models, supporting the application of the meta-analysis results to guide the management of hunting where primary valuation studies have not been undertaken, in particular outside the USA.
Soft-Switching Smart Transformer Design and Application for Photovoltaic Integrated Smart City Power Distribution
Authors: Esenboğa Burak , Demirdelen Tuğçe
Published: December 2022

Smart city power distributions have become promising technologies to meet the demand for energy in developed countries. However, increase in smart grids causes several power quality problems on the smart grid, in particular, current and voltage harmonic distortions, sudden voltage sag and swells, fault current, and isolation deterioration. Smart transformers are potential solutions to improve the power quality on the electric grid. They present energy efficiency, ensure grid reliability and power flow control, voltage regulation, bidirectional power flow, fault current limiting, harmonic blocking, and galvanic isolation. Therefore, this paper offers an optimal selection of a three-stage (AC-DC-DC-AC) smart transformer model and power control strategy for solar PV power plant integrated smart grids. The topology of the rectifier, isolated bidirectional converter, and inverter has soft-switching features. This enables low conduction loss, low electromagnetic interference (EMI), high efficiency, achievable zero-voltage switching for converters, and zero-current switching for electrical auxiliary systems. Operation strategies of the proposed ST, PWM control, voltage, and current control between converters, including a medium-voltage (MV) high-frequency transformer to realize a 10 kVA, 450 Vdc to 220 Vdc, or 220 Vac ST, are presented. Significantly, the ST prototype achieves 96.7% conversion efficiency thanks to its control strategy, even under unstable power generation conditions from the solar PV plant. Experimental results obtained on the 344 Vac 10.4 A load current validates the dv/dt rate 6.8 kV/us. The dynamic and experimental results of the proposed bidirectional smart transformer demonstrate the success in preventing power quality problems for photovoltaic integrated smart city power distribution.
Evaluation and Prediction of Land Use Ecological Security in the Kashgar Region Based on Grid GIS
Authors: Ran Hong , Ma Yonggang , Xu Zhonglin
Published: December 2022

Predicting trends in land use/cover change (LUCC) and assessing future ecological security (ES) can help effectively balance regional ecological conservation and economic development. In this study, a land use simulation model was used to predict the spatial-temporal variation in land use in Kashgar in 2030 under three scenarios (inertial development, agricultural land protection, and forest and rangeland conservation). Subsequently, by combining the pressure–state–response model and predicted land use, the ES of the Kashgar region in 2000, 2010, and 2020 were evaluated. We found that (1) only agricultural land and built-up land in the Kashgar region increased from 2000–2020 by 4174.79 and 434.44 km2, respectively. (2) The area of the region belonging to a generally safe level or above decreased from 38.36% in 2000 to 36.89% in 2010 and then increased to 37.71% in 2020. (3) In 2030, the ES status under the three scenarios will be improved, among which the agricultural land protection scenario corresponds to the best ES status. This research is valuable for deeply understanding the interaction mechanism between LUCC and ES in typical artificial oases in arid areas of the Kashgar region and sustaining relatively stable internal structures and socioeconomic activities in the arid oasis of the Kashgar region.
Influence of Human Biology and Health (HBH) Teaching–Learning Process on Students’ Conceptions of the COVID-19 Vaccine
Authors: Anastácio Zélia Caçador , Da Silva Thayná Champe , Antão Celeste Meirinho , Braide Andrea Stopiglia Guedes , Battisti Iara Denise Endruweit , Carvalho Graça S.
Published: December 2022

The COVID-19 pandemic created the need for universal vaccination. This study aimed to compare university students’ (pre-service teachers) conceptions who had already learned the immune system and vaccination topics in the “Human Biology and Health (HBH)” curricular unit with those who had not yet taken part in it. It also intended to verify the influence of secondary school background, perception of one’s own health, feeling at risk for COVID-19 and their own experience with it and scientific knowledge related to SARS-CoV-2 vaccination. It was a cross-sectional study with a mixed methodology for data analysis. A questionnaire was applied online to a sample of 102 university students. Results show that students who had already taken the subject on the immune system and vaccination had more acceptable conceptions about the vaccine and wanted to be vaccinated but not in the initial moment of the national vaccination process. The fear of adverse reactions seemed to be the major hesitancy factor. Furthermore, students’ argumentation showed that their conceptions progressed towards more socio-scientific reasoning.
A Longitudinal Study of EFL Teacher Agency and Sustainable Identity Development: A Positioning Theory Perspective
Authors: Wu Xinxin
Published: December 2022

This study reports on a longitudinal narrative case study of Lin, a tertiary level EFL teacher in China, exploring how she exercised her agency and achieved sustainable professional identity development in different phases of her career. Based on narrative data primarily from three rounds of semi-structured interviews and other sources of data, and drawing on the positioning theory, this research was designed to uncover the complex relationships between agency and professional identity transformation in an English teacher’s sustainable professional development trajectory. The analysis of Lin’s narratives was organised into three stages: the initial stage of negotiating exploited and marginalised teacher identities, then becoming a student again, and, finally, reconstructing professional teacher and researcher identities. The findings suggest the significant role of consistent individual agency in an EFL teacher’s negotiation and reconstruction of professional identities. The results also support the existing literature in revealing that identity is not a static or fixed notion, but rather a dynamic and ongoing process that is affected by a range of internal and external factors. The research also shows the vital role of teachers’ emotions and emotion regulation in the agency enactment and identity development. This study has important implications for language teacher agency and identity research. In particular, it may shed light on language teachers’ sustainable professional development, which is crucial in ensuring the pursuit of sustainable development in education and many other sustainable development goals.
How the Internet Affects China’s Green Consumption Development: Empirical Research Based on Baidu Index Data
Authors: Peng Jinyan , Li Kai , Gao Yingpeng
Published: December 2022

Green consumption falls short in China’s overall green economic and social transformation. There is a consensus to activate the potential of green consumption through the Internet. However, limited by the availability of green consumption data, few empirical studies focus on the impact of the Internet on green consumption. Based on the 2016–2020 Baidu Index of green consumption in 31 provinces and cities in China, this paper uses a fixed effect model with Driscoll-Kraay standard errors to assess the impact of the Internet on China’s green consumption development and its impact mechanism. The results showed that improved Internet access capability and the development of the e-government play a significant role in promoting green consumption. The former helps to reduce the cost of consumers’ information acquisition, while the latter helps to strengthen market supervision and improve the market environment. E-commerce has a significant negative impact on the development of green consumption, mainly because the existing e-commerce model is not conducive to the development of consumer trust. The impact of digital payment is not significant. Therefore, to achieve multi-governance and improve the Internet by activating green consumption, government departments need to promote the innovation of marketing models and strengthen the platform’s supervision.
Selection of Active Microorganism Strains Isolated from a Naturally Salty Lake for the Investigation of Different Microbial Potentials
Authors: Tekebayeva Zhanar , Temirbekova Aliya , Bazarkhankyzy Aidana , Bissenova Gulmira , Abzhalelov Akhan , Tynybayeva Indira , Temirkhanov Aslan , Askarova Nurgul , Mkilima Timoth , Sarmurzina Zinigul
Published: December 2022

The biological variety of aquatic ecosystems is significantly impacted by the chemical and microbiological composition of water bodies, and there is strong reciprocal feedback between these two factors, especially for reservoirs, which can and do have a significant impact on neighboring ecosystems. Today there is an acute need to identify the most effective and economically feasible methods for cleaning and restoring water bodies. Therefore, the aim of the study was to find strains of microorganisms which are capable of biodegrading such problematic pollutants as insoluble phosphates and excess nitrogenous compounds and at the same time, are capable of suppressing the bacterial composition in reservoir waters. In the course of the research, a number of the most active strains of microorganisms were isolated from lake water samples. Five isolates were obtained from salt water and the isolates were then identified using morphological, and biochemical techniques, as well as the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). In addition to the characterization and identification of the isolates, the species-specific levels of phosphate solubilizing, and nitrifying activities were also established. Thus, all isolated strains were studied and characterized, and their influence on the content of minerals such as phosphorus and nitrogen, which are important for living organisms in water, was studied. In summary, the Pseudomonas Extrem-Orientalis isolate was observed to be highly effective in solubilizing phosphates, nitrifying, and had the greatest antagonistic capacity among the investigated isolates. The information gleaned from the study’s findings helps raise more awareness in the field of microbiology and water treatment in general. The findings offer promise for the development of biopreparations with bioremediation capabilities for cleaning polluted water bodies of pollutants from various sources.
Prioritising Maintenance Work Orders in a Thermal Power Plant: A Multicriteria Model Application
Authors: de Andrade Sidney Jose Meireles , Pinheiro Plácido Rogério , Narciso Glauber Jean Alves , Pimentel Neto José Tarcisio , da Silva Bandeira João Pedro , de Andrade Vinicius Sales , de França Moraes Cayo Cid
Published: December 2022

Maintenance is one of the most rapidly expanding activities in the industrial environment, since its application is no longer limited to simple, regular fixes. In the case of thermal power plants maintenance is essential, since they only operate when the National Electric System Operator wants them to complement the production from renewable sources such as hydro, wind, and solar. To limit the frequency of failures that result in generation unavailability, the operation team performs daily inspections to evaluate the equipment’s condition and the risks to the generating process. If an anomaly is found, the maintenance team will create service notes to address it. This research aims to demonstrate how the method Measuring Attractiveness by a Category-Based Evaluation Technique (Macbeth) can be applied to the development of a multiple-criterion model to support decision making in ordering the criticality of systems in thermal plant operational inspection routes to propose new methodologies for routine execution to increase the operation team’s productivity. According to the results of the judgement matrix, the recommended ordering enabled a strategy for the performance of the current operational routes by redefining the criticality, periodicity, routing, and resources utilised, hence preserving the plant’s reliability. According to the results, the proposed ranking will enable a new strategy for integrated maintenance planning, redefining the criticality of service orders according to the judgement based on criteria and subcriteria, thereby allowing the application of resources appropriately and focusing on what is more important to maintain the thermal power plant’s continuity and operational safety.
Plot-Scale Runoff Generation and Sediment Loss on Different Forest and Other Land Floors at a Karst Yellow Soil Region in Southwest China
Authors: Peng Ruiwen , Deng Han , Li Ruoshuang , Li Yiqiu , Yang Guangbin , Deng Ou
Published: December 2022

Yellow soils developed in limestone weathering materials are representative on Guizhou Plateau, Southwest China. As one of the most important cultivated soils in Guizhou Province, karst yellow soils are generally thin and can be significantly damaged by mild soil erosion. This work used the structural equation model (SEM) to analyze the influence of various factors on runoff generation and sediment loss based on a long time series (2015–2020) of natural rainfall-runoff data and soil erosion data collected from 18 runoff plots in the karst yellow soil region of Southwest China, which reflects the erosion dynamics under natural conditions. Slope runoff plots are the most popular and efficient way to collect data on runoff generation and soil erosion. The findings show that: (1) There were 139 rainfall-runoff occurrences between 2015 and 2020, with moderate rain (10–25 mm) and heavy rain (25–50 mm) events making up the majority. Due to its high frequency and prolonged duration, heavy rain had the greatest impact on the overall rainfall erosivity R value (R). (2) Mean surface runoff (SR) values ranged from 17.37 mm to 133.90 mm, while mean sediment loss rates (SLR) ranged from 1.36 t·ha−1·a−1 to 23.49 t·ha−1·a−1. SR and SLR can be successfully reduced by the forest floor. Broadleaf forest, coniferous forest, mixed forest, and orchard had mean SR values of 19.33%, 12.97%, 16.10%, and 33.38% of fallow land, respectively, and had mean SLR values of 6.90%, 5.79%, 6.34%, and 12.64% of fallow land, respectively. (3) SR generation at the plot scale was substantially linked with 30-min maximum rainfall intensity (I30), while vegetation coverage (VC) and antecedent soil water content (ASW) showed negative direct inference on SR and runoff sediment concentration (RSC) according to SEM analysis. The VC and ASW showed the highest indirect impact on SLR. This study will serve as a scientific reference for the water and soil erosion management in karst yellow soil region and serve as a scientific guidance for regional land use in Southwest China.
Economical Di-Rhamnolipids Biosynthesis by Non-Pathogenic Burkholderia thailandensis E264 Using Post-Consumption Food Waste in a Biorefinery Approach
Authors: Kumar Rajat , Johnravindar Davidraj , Wong Jonathan W. C. , Patria Raffel Dharma , Kaur Guneet
Published: December 2022

Rhamnolipids (RLs) are one of the most promising eco-friendly green alternatives to commercially viable fossil fuel-based surfactants. However, the current bioprocess practices cannot meet the required affordability, quantity, and biocompatibility within an industrially relevant framework. To circumvent these issues, our study aims to develop a sustainable biorefinery approach using post-consumption food waste as a second-generation feedstock. In-depth substrate screening revealed that food waste hydrolysate (FWH) was rich in readily assimilable carbohydrates, volatile fatty acids, and amino acids. The fermentative valorization of FWH as a sole carbon and energy source with Burkholderis thailandensis E264 in a bioreactor showed active RLs biosynthesis of up to 0.6–0.8 g/L (34–40 mg/g FWH) in a short duration (72 h). In terms of the kinetic parameters, the FWH-RLs outperformed other supplemented pure/waste streams. Interestingly, the recovered RLs had a long chain length, with Rha-Rha-C12-C14 being the predominant isoform and exhibiting a strong emulsification ability (E24, 54.6%). To the best of our knowledge, this study is the first to prove bioreactor-level RLs production and their abundance in food waste. Moreover, the feasibility of this developed process could propel next-generation biosurfactants, lower waste burdens, and increase the industrial applicability of RLs, thereby significantly contributing to the development of a circular bioeconomy.
The Use of Mobile Phones and Other Unsafe Behavior While Cycling in the Metropolitan Area of Mexico City
Authors: Santos-Reyes Jaime , Pastenes-Medina Yareli , Padilla-Pérez Diego
Published: December 2022

Unsafe behavior while driving contributes to road accidents. The paper addresses cyclists’ risky behavior by employing a questionnaire-based survey to a sample of n = 1136 in the metropolitan area of Mexico City. The main results are as follows: (a) 31.4% and 24.2% of the participants use a mobile phone for talking and text messaging while cycling, respectively, with males engaging in these unsafe acts more often than females; (b) a high percentage of participants are most likely to communicate with their parents, through either talking (48.9%) or text messaging (39.6%); (c) regarding the use of mobile phones for talking/texting (along with social network and gender) as predictors of a crash/fall while cycling, it was found that a one SD change in the frequency of talking while cycling increased the odds of a crash/fall by a factor of 1.198, as did a one SD increase in the frequency of texting by 1.232, while gender contributed to the outcome but not the individuals to whom cyclists talk or text; (d) cycling “without holding the handlebars” contributed significantly to the outcome. An education campaign or legislation enforcement (or both) may be needed to prevent single-bicycle crashes related to this unsafe act.