bluuebunny/crossref_metadata_2025 huggingface dataset:
Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies
Authors: Huang Faming , Tao Siyu , Li Deying , Lian Zhipeng , Catani Filippo , Huang Jinsong , Li Kailong , Zhang Chuhong
Published: September 2022

Landslides are affected not only by their own environmental factors, but also by the neighborhood environmental factors and the landslide clustering effect, which are represented as the neighborhood characteristics of modelling spatial datasets in landslide susceptibility prediction (LSP). This study aims to innovatively explore the neighborhood characteristics of landslide spatial datasets for reducing the LSP uncertainty. Neighborhood environmental factors were acquired and managed by remote sensing (RS) and the geographic information system (GIS), then used to represent the influence of landslide neighborhood environmental factors. The landslide aggregation index (LAI) was proposed to represent the landslide clustering effect in GIS. Taking Chongyi County, China, as example, and using the hydrological slope unit as the mapping unit, 12 environmental factors including elevation, slope, aspect, profile curvature, plan curvature, topographic relief, lithology, gully density, annual average rainfall, NDVI, NDBI, and road density were selected. Next, the support vector machine (SVM) and random forest (RF) were selected to perform LSP considering the neighborhood characteristics of landslide spatial datasets based on hydrologic slope units. Meanwhile, a grid-based model was also established for comparison. Finally, the LSP uncertainties were analyzed from the prediction accuracy and the distribution patterns of landslide susceptibility indexes (LSIs). Results showed that the improved frequency ratio method using LAI and neighborhood environmental factors can effectively ensure the LSP accuracy, and it was significantly higher than the LSP results without considering the neighborhood conditions. Furthermore, the Wilcoxon rank test in nonparametric test indicates that the neighborhood characteristics of spatial datasets had a great positive influence on the LSP performance.
Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape
Authors: Aghababaei Masoumeh , Ebrahimi Ataollah , Naghipour Ali Asghar , Asadi Esmaeil , Pérez-Suay Adrián , Morata Miguel , Garcia Jose Luis , Rivera Caicedo Juan Pablo , Verrelst Jochem
Published: September 2022

Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible . To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification algorithms (MLCAs) together into a single intuitive user-friendly graphical user interface (GUI). To fill this gap and to facilitate and automate the usage of MLCAs, here we present a novel GUI software package that allows systematically training, validating, and applying pixel-based MLCA models to remote sensing imagery. The so-called MLCA toolbox has been integrated within ARTMO’s software framework developed in Matlab which implements most of the state-of-the-art methods in the machine learning community. To demonstrate its utility, we chose a heterogeneous case study scene, a landscape in Southwest Iran to map PTs. In this area, four main PTs were identified, consisting of shrub land, grass land, semi-shrub land, and shrub land–grass land vegetation. Having developed 21 MLCAs using the same training and validation, datasets led to varying accuracy results. Gaussian process classifier (GPC) was validated as the top-performing classifier, with an overall accuracy (OA) of 90%. GPC follows a Laplace approximation to the Gaussian likelihood under the supervised classification framework, emerging as a very competitive alternative to common MLCAs. Random forests resulted in the second-best performance with an OA of 86%. Two other types of ensemble-learning algorithms, i.e., tree-ensemble learning (bagging) and decision tree (with error-correcting output codes), yielded an OA of 83% and 82%, respectively. Following, thirteen classifiers reported OA between 70% and 80%, and the remaining four classifiers reported an OA below 70%. We conclude that GPC substantially outperformed all classifiers, and thus, provides enormous potential for the classification of a diversity of land-cover types. In addition, its probabilistic formulation provides valuable band ranking information, as well as associated predictive variance at a pixel level. Nevertheless, as these are supervised (data-driven) classifiers, performances depend on the entered training data, meaning that an assessment of all MLCAs is crucial for any application. Our analysis demonstrated the efficacy of ARTMO’s MLCA toolbox for an automated evaluation of the classifiers and subsequent thematic mapping.
Monocular-Based Pose Estimation Based on Fiducial Markers for Space Robotic Capture Operations in GEO
Authors: Opromolla Roberto , Vela Claudio , Nocerino Alessia , Lombardi Carlo
Published: September 2022

This paper tackles the problem of spacecraft relative navigation to support the reach and capture of a passively cooperative space target using a chaser platform equipped with a robotic arm in the frame of future operations such as On Orbit Servicing and Active Debris Removal. Specifically, it presents a pose determination architecture based on monocular cameras to deal with a space target in GEO equipped with retro-reflective and black-and-white fiducial markers. The proposed architecture covers the entire processing pipeline, i.e., starting from markers’ detection and identification up to pose estimation by solving the Perspective-n-Points problem with a customized implementation of the Levenberg–Marquardt algorithm. It is designed to obtain relative position and attitude measurements of the target’s main body with respect to the chaser, as well as of the robotic arm’s end effector with respect to the selected grasping point. The design of the configuration of fiducial markers to be installed on the target’s approach face to support the pose determination task is also described. A performance assessment is carried out by means of numerical simulations using the Planet and Asteroid Natural Scene Generation Utility tool to produce realistic synthetic images of the target. The proposed approach robustness is evaluated against variable illumination scenarios and considering different uncertainty levels in the knowledge of initial conditions and camera intrinsic parameters.
Using SincNet for Learning Pathological Voice Disorders
Authors: Hung Chao-Hsiang , Wang Syu-Siang , Wang Chi-Te , Fang Shih-Hau
Published: September 2022

Deep learning techniques such as convolutional neural networks (CNN) have been successfully applied to identify pathological voices. However, the major disadvantage of using these advanced models is the lack of interpretability in explaining the predicted outcomes. This drawback further introduces a bottleneck for promoting the classification or detection of voice-disorder systems, especially in this pandemic period. In this paper, we proposed using a series of learnable sinc functions to replace the very first layer of a commonly used CNN to develop an explainable SincNet system for classifying or detecting pathological voices. The applied sinc filters, a front-end signal processor in SincNet, are critical for constructing the meaningful layer and are directly used to extract the acoustic features for following networks to generate high-level voice information. We conducted our tests on three different Far Eastern Memorial Hospital voice datasets. From our evaluations, the proposed approach achieves the highest 7%–accuracy and 9%–sensitivity improvements from conventional methods and thus demonstrates superior performance in predicting input pathological waveforms of the SincNet system. More importantly, we intended to give possible explanations between the system output and the first-layer extracted speech features based on our evaluated results.
Transmitter and Receiver Circuits for a High-Speed Polymer Fiber-Based PAM-4 Communication Link
Authors: Strömbeck Frida , Bao Mingquan , He Zhongxia Simon , Zirath Herbert
Published: September 2022

A high data rate RF-DAC and a power detector (PD) are designed and fabricated in a 250 nm indium phosphide (InP) double heterojunction bipolar transistor (DHBT) technology. A communication link using the Tx-Rx over polymer microwave fiber (PMF) is measured. The link consists of a pulse amplitude modulation (PAM) modulator and a PD as a demodulator, as well as a one-meter-long dielectric waveguide. The working frequency range of the complete link is verified to be 110–150 GHz. The peak output power of the PAM modulator is 5 dBm, and it has a −3 dB bandwidth of 43 GHz. The PD consists of a parallel connected common emitter configured transistor and a common base configured transistor to suppress the odd-order harmonics at the PD’s output, as well as a stacked transistor to amplify the output signal. Tx and Rx chips, including pads, occupy a total area of only 0.83 mm2. The PMF link can support a PAM-4 signal with 22 Gbps data transmission, and a PAM-2 signal with 30 Gbps data transmission, with a bit error rate (BER) of <10−12, with demodulation performed in real time. Furthermore, the energy efficiency for the link (Tx + Rx) is 4.1 pJ/bit, using digital data input and receiving PAM-2 output (5.6 pJ/bit for PAM-4).
Mechanic-Electric-Thermal Directly Coupling Simulation Method of Lamb Wave under Temperature Effect
Authors: Yang Xiaofei , Xue Zhaopeng , Zheng Hui , Qiu Lei , Xiong Ke
Published: September 2022

Lamb Wave (LW)-based structural health monitoring method is promising, but its main obstacle is damage assessment in varying environments. LW simulation based on piezoelectric transducers (referred to as PZTs) is an efficient and low-cost method. This paper proposes a multiphysics simulation method of LW propagation with the PZTs under temperature effect. The effect of temperature on LW propagation is considered from two aspects. On the one hand, temperature affects the material parameters of the structure, the adhesive layers and the PZTs. On the other hand, it is considered that the thermal stress caused by the inconsistency of thermal expansion coefficients among the structure, the adhesive layers, and the PZTs affect the piezoelectric constant of the PZTs. Based on the COMSOL Multiphysics, the mechanic–electric–thermal directly coupling simulation model under temperature effect is established. The simulation model consists of two steps. In the first step, the thermal-mechanic coupling is carried out to calculate the thermal stress, and the thermal stress effect is introduced into the piezoelectric constant model. In the second step, mechanic–electric coupling is carried out to simulate LW propagation, which considers the piezoelectric effect of the PZTs for the LW excitation and reception. The simulation results at −20 °C to 60 °C are obtained and compared to the experiment. The results show that the A0 and S0 mode of simulation signals match well with the experimental measurements. Additionally, the effect of temperature on LW propagation is consistent between simulation and experiment; that is, the amplitude increases, and the phase velocity decreases with the increment of temperature.
A Nonlinear Finite-Time Robust Differential Game Guidance Law
Authors: Xi Axing , Cai Yuanli
Published: September 2022

In this paper, a robust differential game guidance law is proposed for the nonlinear zero-sum system with unknown dynamics and external disturbances. First, the continuous-time nonlinear zero-sum differential game problem is transformed into solving the nonlinear Hamilton–Jacobi–Isaacs equation, a time-varying cost function is developed to reflect the fixed terminal time, and the robust guidance law is developed to compensate for the external disturbance. Then, a novel neural network identifier is designed to approximate the unknown nonlinear dynamics with online weight tuning. Subsequently, an online critic neural network approximator is presented to estimate the cost function, and time-varying activation functions are considered to deal with the fixed final time problem. An adaptive weight tuning law is given, where two additional terms are added to ensure the stability of the closed-loop nonlinear system and so as to meet the terminal cost at a fixed final time. Furthermore, the uniform ultimate boundedness of the closed-loop system and the critic neural network weights estimation error are proven based upon the Lyapunov approach. Finally, some simulation results are presented to demonstrate the effectiveness of the proposed robust differential game guidance law for nonlinear interception.
The Method of Creel Positioning Based on Monocular Vision
Authors: Tu Jiajia , Han Sijie , Sun Lei , Shi Weimin , Dai Ning
Published: September 2022

Automatic replacement of bobbins on the frame is one of the core problems that must be solved in the textile industry. In the process of changing the bobbin, it is faced with problems such as the position offset of the bobbin, the loosening and deformation of the bobbin, which will lead to the failure of replacing the bobbin. Therefore, it is necessary to initialize the creel coordinates regularly, also considering the labor intensity and poor reliability of manual positioning. This paper proposes an automatic creel positioning method based on monocular vision. Firstly, the industrial camera was installed on the drum changing manipulator controlled by the truss system, and each yarn frame in the yarn area was inspected. Secondly, the end face image of the creel was collected at a reasonable distance, and the collected images were transmitted to the computer in real time through the Ethernet bus. Thirdly, the center coordinates (x, y) and radius r of the creel were marked by the improved Hough circle detection algorithm. Finally, the coordinate deviation of the creel was calculated and transmitted to the system controller to realize deviation correction. Before the test, the creel positioning markers were specially designed and the camera was calibrated. Thus, the influence of image complex background, creel end roughness, reflection and other factors can be reduced, and the image processing speed and positioning accuracy can be improved. The results show that the positioning effect of this method is fine when the distance between the center of the camera lens and the center of the end face of the creel is 170~190 mm. Moreover, when the distance is 190 mm, the positioning effect is the best, with an average error of only 0.51 mm. In addition, the deviation between the center coordinate and the radius of the end face of the marker is also very small, which is better than the requirements of bobbin yarn changing accuracy.
N-Step Pre-Training and Décalcomanie Data Augmentation for Micro-Expression Recognition
Authors: Lee Chaehyeon , Hong Jiuk , Jung Heechul
Published: September 2022

Facial expressions are divided into micro- and macro-expressions. Micro-expressions are low-intensity emotions presented for a short moment of about 0.25 s, whereas macro-expressions last up to 4 s. To derive micro-expressions, participants are asked to suppress their emotions as much as possible while watching emotion-inducing videos. However, it is a challenging process, and the number of samples collected tends to be less than those of macro-expressions. Because training models with insufficient data may lead to decreased performance, this study proposes two ways to solve the problem of insufficient data for micro-expression training. The first method involves N-step pre-training, which performs multiple transfer learning from action recognition datasets to those in the facial domain. Second, we propose Décalcomanie data augmentation, which is based on facial symmetry, to create a composite image by cutting and pasting both faces around their center lines. The results show that the proposed methods can successfully overcome the data shortage problem and achieve high performance.
Over-Two-Octave Supercontinuum Generation of Light-Carrying Orbital Angular Momentum in Germania-Doped Ring-Core Fiber
Authors: Yang Jian , Wang Yingning , Fang Yuxi , Geng Wenpu , Zhao Wenqian , Bao Changjing , Ren Yongxiong , Wang Zhi , Liu Yange , Pan Zhongqi , Yue Yang
Published: September 2022

In this paper, we design a silica-cladded Germania-doped ring-core fiber (RCF) that supports orbital angular momentum (OAM) modes. By optimizing the fiber structure parameters, the RCF possesses a near-zero flat dispersion with a total variation of <±30 ps/nm/km over 1770 nm bandwidth from 1040 to 2810 nm for the OAM1,1 mode. A beyond-two-octave supercontinuum spectrum of the OAM1,1 mode is generated numerically by launching a 40 fs 120 kW pulse train centered at 1400 nm into a 12 cm long designed 50 mol% Ge-doped fiber, which covers 2130 nm bandwidth from 630 nm to 2760 nm at −40 dB of power level. This design can serve as an efficient way to extend the spectral coverage of beams carrying OAM modes for various applications.
IoT-Based Fish Farm Water Quality Monitoring System
Authors: Chen Chiung-Hsing , Wu Yi-Chen , Zhang Jia-Xiang , Chen Ying-Hsiu
Published: September 2022

Typhoons in summer and cold snaps during winter in Taiwan often cause huge aquaculture losses. Simultaneously, the lack of human resources is a problem. Therefore, we used wireless transmission technology with various sensors to transmit the temperature, pH value, dissolved oxygen, water level, and life expectancy of the sensor in the fish farm to the server. The integrated data are transmitted to mobile devices through the Internet of Things, enabling administrators to monitor the water quality in a fish farm through mobile devices. Because the current pH sensors cannot be submerged in the liquid for a long time for measurements, human resources and time are required to take the instrument to each fish farm for testing at a fixed time. Therefore, a robotic arm was developed to complete automatic measurement and maintenance actions. We designed this arm with a programmable logic controller, a single chip combined with a wireless transmission module, and an embedded system. This system is divided into control, measurement, server, and mobility. The intelligent measurement equipment designed in this study can work 24 h per day, which effectively reduces the losses caused by personnel, material resources, and data errors.
Research on a Defecation Pre-Warning Algorithm for the Disabled Elderly Based on a Semi-Supervised Generative Adversarial Network
Authors: Zou Yanbiao , Wu Shenghong , Zhang Tie , Yang Yuanhang
Published: September 2022

The elderly population in China is continuously increasing, and the disabled account for a large proportion of the elderly population. An effective solution is urgently needed for incontinence among disabled elderly people. Compared with disposable adult diapers, artificial sphincter implantation and medication for incontinence, the defecation pre-warning method is more flexible and convenient. However, due to the complex human physiology and individual differences, its development is limited. Based on the aging trend of the population and clinical needs, this paper proposes a bowel sound acquisition system and a defecation pre-warning method and system based on a semi-supervised generative adversarial network. A network model was established to predict defecation using bowel sounds. The experimental results show that the proposed method can effectively classify bowel sounds with or without defecation tendency, and the accuracy reached 94.4%.
Development and Validation of a Tunable Diode Laser Absorption Spectroscopy System for Hot Gas Flow and Small-Scale Flame Measurement
Authors: Tu Ran , Gu Junqing , Zeng Yi , Zhou Xuejin , Yang Kai , Jing Jiaojiao , Miao Zhihong , Yang Jianhong
Published: September 2022

TDLAS (tunable diode laser absorption spectroscopy) is an important gas analysis method that can be employed to obtain characteristic parameters non-invasively by the infrared absorption spectra of tracer molecules such as CH4, H2O and O2. In this study, a portable H2O-based TDLAS system with a dual optical path was developed with the aim of assessing the combustion characteristics of flammable gases. Firstly, a calculation method of gas characteristics including temperature and velocity combining absorption spectra and a HITRAN database was provided. Secondly, to calibrate and validate this TDLAS system precisely, a pressure vessel and a shock tube were introduced innovatively to generate static or steady flow fields with preset constant temperatures, pressures, or velocities. Static tests within environment pressures up to 2 MPa and steady flow field tests with temperatures up to 1600 K and flow velocities up to 950 m/s were performed for verification. It was proved that this system can provide an accurate values for high temperature and velocity gas flows. Finally, an experimental investigation of CH4/air flames was conducted to test the effectiveness of the system when applied to small diffusion flames. This TDLAS system gave satisfactory flame temperature and velocity data owing to the dual optical path design and high frequency scanning, which compensated for scale effects and pulsation of the flame. This work demonstrates a valuable new approach to thermal hazard analysis in specific environments.
Evaluating the Typical Day-to-Day Variability of WHOOP-Derived Heart Rate Variability in Olympic Water Polo Athletes
Authors: Bellenger Clint R. , Miller Dean , Halson Shona L. , Roach Gregory D. , Maclennan Michael , Sargent Charli
Published: September 2022

Heart rate (HR) and HR variability (HRV) can be used to infer readiness to perform exercise in athletic populations. Advancements in the photoplethysmography technology of wearable devices such as WHOOP allow for the frequent and convenient measurement of HR and HRV, and therefore enhanced application in athletes. However, it is important that the reliability of such technology is acceptable prior to its application in practical settings. Eleven elite male water polo players (age 28.8 ± 5.3 years [mean ± standard deviation]; height 190.3 ± 3.8 cm; body mass 95.0 ± 6.9 kg; international matches 117.9 ± 92.1) collected their HR and HRV daily via a WHOOP strap (WHOOP 3.0, CB Rank, Boston, MA, USA) over 16 weeks ahead of the 2021 Tokyo Olympic Games. The WHOOP strap quantified HR and HRV via wrist-based photoplethysmography during overnight sleep periods. The weekly (i.e., 7-day) coefficient of variation in lnRMSSD (lnRMSSDCV) and HR (HRCV) was calculated as a measure of day-to-day variability in lnRMSSD and HR, and presented as a mean of the entire recording period. The mean weekly lnRMSSDCV and HRCV over the 16-week period was 5.4 ± 0.7% (mean ± 95% confidence intervals) and 7.6 ± 1.3%, respectively. The day-to-day variability in WHOOP-derived lnRMSSD and HR is within or below the range of day-to-day variability in alternative lnRMSSD (~3–13%) and HR (~10–11%) assessment protocols, indicating that the assessment of HR and HRV by WHOOP does not introduce any more variability than that which is naturally present in these variables.
Development of Prediction Models for Soil Nitrogen Management Based on Electrical Conductivity and Moisture Content
Authors: Mirzakhaninafchi Hasan , Mani Indra , Hasan Murtaza , Nafchi Ali Mirzakhani , Parray Roaf Ahmad , Kumar Dinesh
Published: September 2022

A study was conducted with the goal of developing an algorithm for use in sensors to monitor available soil N. For this purpose, three different soils were selected. The soils were studied for electrical conductivity (EC) at four different moisture levels and four levels of N. The selection of moisture levels was based on optimum moisture levels between tillage moisture and field capacity. The results revealed a significant relationship between electrical conductivity and moisture level of the soil as well as between electrical conductivity and soil N content. Based on these relations, a polynomial model was developed between the EC of each selected soil sample and moisture content as well as N levels. The regression model for moisture content-based EC determination had coefficients of determination of 0.985, 0.988, and 0.981 for clay loam, sandy loam, and sandy loam soils, respectively. Similarly, the regression model for N content-based EC determination had coefficients of determination of 0.9832, 0.9, and 0.99 for clay loam, sandy loam, and sandy loam soils, respectively. An algorithm developed using a polynomial relationship between the EC of each selected soil sample at all moisture and N levels can be used to develop a sensor for site-specific N application.
Design of a Multimodal Oculometric Sensor Contact Lens
Authors: de Bougrenet de la Tocnaye Jean-Louis , Nourrit Vincent , Lahuec Cyril
Published: September 2022

Oculometric data, such as gaze direction, pupil size and accommodative change, play a key role nowadays in the analysis of cognitive load and attentional activities, in particular with the development of Integrated Visual Augmentation Systems in many application domains, such as health, defense and industry. Such measurements are most frequently obtained by different devices, most of them requiring steady eye and body positions and controlled lighting conditions. Recent advances in smart contact lens (SCL) technology have demonstrated the ability to achieve highly reliable and accurate measurements, preserving user mobility, for instance in measuring gaze direction. In this paper, we discuss how these three key functions can be implemented and combined in the same SCL, considering the limited volume and energy consumption constraints. Some technical options are discussed and compared in terms of their ability to be implemented, taking advantage of recent developments in the field.
Explainable Malware Detection System Using Transformers-Based Transfer Learning and Multi-Model Visual Representation
Authors: Ullah Farhan , Alsirhani Amjad , Alshahrani Mohammed Mujib , Alomari Abdullah , Naeem Hamad , Shah Syed Aziz
Published: September 2022

Android has become the leading mobile ecosystem because of its accessibility and adaptability. It has also become the primary target of widespread malicious apps. This situation needs the immediate implementation of an effective malware detection system. In this study, an explainable malware detection system was proposed using transfer learning and malware visual features. For effective malware detection, our technique leverages both textual and visual features. First, a pre-trained model called the Bidirectional Encoder Representations from Transformers (BERT) model was designed to extract the trained textual features. Second, the malware-to-image conversion algorithm was proposed to transform the network byte streams into a visual representation. In addition, the FAST (Features from Accelerated Segment Test) extractor and BRIEF (Binary Robust Independent Elementary Features) descriptor were used to efficiently extract and mark important features. Third, the trained and texture features were combined and balanced using the Synthetic Minority Over-Sampling (SMOTE) method; then, the CNN network was used to mine the deep features. The balanced features were then input into the ensemble model for efficient malware classification and detection. The proposed method was analyzed extensively using two public datasets, CICMalDroid 2020 and CIC-InvesAndMal2019. To explain and validate the proposed methodology, an interpretable artificial intelligence (AI) experiment was conducted.
Development and Application of Gas Production Measurement System of Coal-Rock under Temperature–Pressure Coupling
Authors: Ma Chunde , Wang Zihe , Xu Jiaqing , Tan Guanshuang , Lv Zhihai , Zhu Quanqi
Published: September 2022

In this study, a measurement system for gas generation of coal-rock under temperature–pressure coupling was developed by adding gas extraction, collection, and flow-monitoring devices to the original stainless-steel liquid seepage pipeline of an MTS-815 rock triaxial testing machine, which can be used to study the production mechanism of coalbed methane in a real geological environment. The system has the functions of axial loading, confining pressure loading, continuous heating, gas gathering, etc., and has the advantages of good air tightness, high accuracy and stability, long-term loading and heating, and controllable single variables. The preliminary test for the gas production of anthracite in the Shaanxi Formation of the Qinshui Basin under temperature–pressure coupling was carried out by the developed test system. The results show that the test system can provide accurate and effective measurement means for the study of gas production by coal-rock deformation and is expected to provide effective help for the control and exploitation of coalbed methane.
A Feasibility Study of a Traffic Supervision System Based on 5G Communication
Authors: Tengg Allan , Stolz Michael , Hillebrand Joachim
Published: September 2022

At present, autonomous driving vehicles are designed in an ego-vehicle manner. The vehicles gather information from their on-board sensors, build an environment model from it and plan their movement based on this model. Mobile network connections are used for non-mission-critical tasks and maintenance only. In this paper, we propose a connected autonomous driving system, where self-driving vehicles exchange data with a so-called road supervisor. All vehicles under supervision provide their current position, velocity and other valuable data. Using the received information, the supervisor provides a recommended trajectory for every vehicle, coordinated with all other vehicles. Since the supervisor has a much better overview of the situation on the road, more elaborate decisions, compared to each individual autonomous vehicle planning for itself, are possible. Experiments show that our approach works efficiently and safely when running our road supervisor on top of a popular traffic simulator. Furthermore, we show the feasibility of offloading the trajectory planning task into the network when using ultra-low-latency 5G networks.
Low-Light Image Enhancement Using Photometric Alignment with Hierarchy Pyramid Network
Authors: Ye Jing , Chen Xintao , Qiu Changzhen , Zhang Zhiyong
Published: September 2022

Low-light image enhancement can effectively assist high-level vision tasks that often fail in poor illumination conditions. Most previous data-driven methods, however, implemented enhancement directly from severely degraded low-light images that may provide undesirable enhancement results, including blurred detail, intensive noise, and distorted color. In this paper, inspired by a coarse-to-fine strategy, we propose an end-to-end image-level alignment with pixel-wise perceptual information enhancement pipeline for low-light image enhancement. A coarse adaptive global photometric alignment sub-network is constructed to reduce style differences, which facilitates improving illumination and revealing under-exposure area information. After the learned aligned image, a hierarchy pyramid enhancement sub-network is used to optimize image quality, which helps to remove amplified noise and enhance the local detail of low-light images. We also propose a multi-residual cascade attention block (MRCAB) that involves channel split and concatenation strategy, polarized self-attention mechanism, which leads to high-resolution reconstruction images in perceptual quality. Extensive experiments have demonstrated the effectiveness of our method on various datasets and significantly outperformed other state-of-the-art methods in detail and color reproduction.
COVID-19 and Microcredit: Dissecting an NGO’s Training, Financial Support, and Women Empowerment Programmes
Authors: Dotsey Senyo
Published: September 2022

This paper reports the findings from a microcredit (financial inclusion) scheme that has been operated by a non-governmental organization since 2012 in a local community in Ghana, and sustained through the COVID-19 pandemic. It first examines microfinance, women’s empowerment and third-sector organizational dynamics. It then provides an overview of microfinance in Ghana within the context of the COVID-19 pandemic, followed by dissecting the organization’s microcredit, training and women’s empowerment programmes. The following part documents the findings, with brief concluding thoughts and policy implications appearing in the last section. It is argued here that financial schemes, particularly those operated by third-sector organizations, can play a significant role in helping women in particular to deal with the secondary effects of COVID-19 by providing improved non-financial services and easy access to microfinance at low, sustainable interest rates. These findings have implications for policy formulation and sustainable development.
Use of Twitter among College Students for Academics: A Mixed-Methods Approach
Authors: Amiruzzaman Stefanie , Amiruzzaman Md
Published: September 2022

For almost a decade, Twitter use and its impact on students’ academic performance have been explored by many researchers. Despite growing scholarly interest, studies have been mostly quantitative in nature. The findings of previous studies are conflicting; thus, an in-depth study is needed to determine how and what impacts college students’ academic performance (i.e., GPA) when they spend time on Twitter. The purpose of this study was to understand the effects of Twitter use on college students’ academic performance. The present study shows that individual analysis techniques, such as quantitative or qualitative tools, are not enough to understand the underlying relationship. Therefore, a mixed-method approach (i.e., correlation and discourse analysis) was used to analyze the research data. Undergraduate students responded (N = 498) to a set of items along with some open-ended questions (n = 121). The results of this study indicate that how students use Twitter matters more than the amount of time they spend using it for their studies.
“These Girls Never Give Statements”: Anti-Trafficking Interventions and “Victim-Witness Testimony” in India
Authors: Ramachandran Vibhuti
Published: September 2022

Framing sex trafficking as primarily a law enforcement and criminal justice issue, the U.S. State Department funds global South NGOs to work with the Indian legal system to strengthen prosecutions of sex trafficking cases. Though rescuing sex workers and training them to testify against alleged traffickers is key to these interventions, and though rescued sex workers do sometimes testify, my ethnographic research and interviews with NGOs, legal actors, and sex workers in India revealed that this is a rare occurrence. This article explores the reasons behind this reported pattern, as well as the challenges faced by those who do testify. Through these findings, it critically examines the possibilities and limitations of the prosecutorial focus of U.S.-driven, NGO-mediated anti-trafficking interventions. It situates anti-trafficking interventions centered on “victim-witness testimony” in the Indian socio-legal context, demonstrating how prosecution is shaped by a range of factors, circumstances, and contingencies involving foreign-funded NGOs, the procedures, political economy and culture of the Indian legal system, individual legal actors’ motivations, and rescued sex workers’ complex subjectivities, experiences, choices, and perceptions of justice. It draws upon and contextualizes these findings to challenge prevalent assumptions about the victimhood of global South sex workers, about global South legal systems necessarily lacking resources and commitment, and about anti-trafficking solutions rooted in criminal justice incontrovertibly benefiting survivors of sex trafficking.
Introduction to Special Issue “Gender, Politics, and Everyday Life: Power, Resistance, and Representation”
Authors: Hutchinson Dawn , Underwood Lori
Published: September 2022

The conference theme this year was “Gender, Politics, and Everyday Life: Power, Resistance and Representation” [...]
Expert and Diffuse Design of a Sustainable Circular Economy in Two German Circular Roadmap Projects
Authors: Melles Gavin , Wölfel Christian , Krzywinski Jens , Opeskin Lenard
Published: September 2022

According to sustainability transitions theory, socio-technical change requires a convergence of politics, social change, technology, and niche innovations. Recently, a circular economy has been proposed as the engine of such change in the EU New Green Deal and Germany. Mainstream circular economy emphasizes the closing of material loops as the way to ensure green growth, and there is a key role for design to achieve such change. According to reports, however, the global appetite for a circular economy remains limited and critics have pointed to several contradictions between the rhetoric and reality of the circular economy and sustainable development. In addition, current formulations of circular economy misrepresent the plurality of discourses for a sustainable circular economy and the role of expert and diffuse circular design. In this study, we employ the recently articulated ten principles for a sustainable circular economy and society to analyze two contrasting circular roadmap projects in Germany, which reflect two contrasting technical and reformist circular discourses, and understandings of the role of design. We find that there are narrow and broad interpretations of design inherent in these circular policies as well as the exemplification of the difference between a technical circular economy and reformist circular society discourses. The practical applied value of this analysis is that the framework can be employed to analyze other policies.
Applications of Big Data in Media Organizations
Authors: Veglis Andreas , Saridou Theodora , Panagiotidis Kosmas , Karypidou Christina , Kotenidis Efthimis
Published: September 2022

The exploitation of data in the media industry has always played a significant role. This is especially evident today, since data (and in many cases big data) are generated through various activities that relate to the production and also consumption of news. This paper attempts to highlight the importance of big data utilization in the media industry. Specifically, it discusses cases of big data exploitation, such as media content consumption and management, data journalism production, social content utilization, and participatory journalism applications. The study also examines the changes that big data has introduced in all stages of the journalism practice, from news production to news distribution, by utilizing the available tools. Finally, it discusses new developments that relate to semantic web (Web 3.0) technologies, which have already started to be adopted by media organizations around the world.
Do Primary School Children Benefit from Drop-Jump Training with Different Schedules of Augmented Feedback about the Jump Height?
Authors: Leukel Christian , Karoß Sabine , Gräßlin Florian , Nicolaus Jürgen , Gollhofer Albert
Published: September 2022

In children, the training of jumps leads to improved jumping and running performance. Augmented feedback about the jump height is known to facilitate performance improvements in adults. In the present study, the impact of augmented feedback on jumping performance was investigated in 4th grade primary school children executing drop-jump training for 8 weeks (24 sessions, 3 times/week). Ten children (eight males, two females, aged 9.6 ± 0.3 years), received feedback for 8 weeks, and 11 children (nine males, two females, aged 9.5 ± 0.2 years) received feedback only during the last 4 weeks. Drop-jumps training was integrated in physical education classes. Drop-jump and countermovement-jump heights were improved after 24 training sessions (p < 0.01 for both types of jumps in both groups). Ground contact times of drop-jumps were quite long (>200 ms) and not altered by training, and the reactive strength index of drop-jumps was between 0.75 and 1.5 in most children. Augmented feedback did not facilitate jumping performance like in previous studies with adult participants. In contrast, withholding augmented feedback during the first 4 weeks of training was associated with a reduction in jumping performance (p < 0.01 for drop-jumps, p < 0.05 for countermovement-jumps). Finally, improvements did not transfer to functional motor tasks containing jumps. According to the costs and outcomes we do not recommend drop-jump training with augmented feedback about the jump height for 4th grade physical education classes.
Wide-Pulse High-Frequency Neuromuscular Electrical Stimulation Evokes Greater Relative Force in Women Than in Men: A Pilot Study
Authors: Ye Xin , Gockel Nathan , Vala Daniel , Devoe Teagan , Brodoff Patrick , Gaza Victor , Umali Vinz , Walker Hayden
Published: September 2022

This study aimed to examine the potential sex differences in wide-pulse high-frequency neuromuscular electrical stimulation (WPHF NMES)-evoked force. Twenty-two subjects (10 women) completed this study. Prior to the stimulation, the visual analogue scale (VAS) for discomfort and the rating of perceived exertion (RPE) were measured, followed by the isometric strength of the dominant elbow flexor muscles. The subjects then completed ten, 10-s on 10-s off WPHF NMES (pulse width: 1 ms, frequency: 100 Hz) at maximum tolerable intensities. The subjects’ RPE was recorded after each set, and the VAS was measured following the last stimulation. The stimulation induced significant increase in discomfort for both sexes, with women having greater discomfort than men (men: 22.4 ± 14.9 mm, women: 39.7 ± 12.7 mm). The stimulation amplitude was significantly greater in men than in women (men: 16.2 ± 6.3 mA, women: 12.0 ± 4.5 mA). For the evoked force, only the relative NMES-evoked force was found greater in women than in men (men: 8.96 ± 6.51%, women: 17.08 ± 12.61%). In conclusion, even at the maximum tolerable intensity, WPHF NMES evoked larger relative elbow flexion force in women than in men, with women experiencing greater discomfort.
A Qualitative Study of Legacy Systems Modernisation for Citizen-Centric Digital Government
Authors: Abu Bakar Humairath , Razali Rozilawati , Jambari Dian Indrayani
Published: September 2022

Legacy systems are valuable assets in most public sector agencies that have been in use for a long time. These systems support government service delivery to the citizens and maintain vital public administration functions and data. However, legacy systems are often related to technical difficulties that impede innovation efforts. The maintenance of the systems has become challenging and incompatible with the demands of digital transformation in the public sector. Due to their importance, the systems cannot be easily discarded. Rebuilding the old systems from scratch entails a long development timeline, high cost, and the loss of critical service functionalities. These circumstances encourage the public sector agencies to implement the modernisation of legacy systems. However, the modernisation effort for legacy systems in the public sector is not straightforward. Besides technical aspects, it should also consider non-technical aspects, including the requirements of the new era of citizen-centric digital government. In order to achieve this aspiration, a complete strategy must be developed to serve as a guide for government agencies. Hence, the purpose of this study is to develop a comprehensive guideline for the public sector. The research has been developed using a qualitative methodology that incorporates the theoretical and empirical phases. The theoretical phase was conducted through a literature review of previous studies related to the research topic. The empirical phase in the public sector was implemented and analysed using phenomenology and grounded theory methods. A total of 19 informants were involved in the individual and focus group interviews conducted. The study results revealed that human, process, product, and organisation aspects as well as the related characteristics of the citizen-centric influence the legacy systems modernisation in the era of digital government. The findings contribute as a complete guideline for the public sector agencies in modernising the legacy systems in line with the citizen-centric digital government vision.
Implications of the Emergence of Autonomous Vehicles and Shared Autonomous Vehicles: A Budapest Perspective
Authors: Shatanawi Mohamad , Mészáros Ferenc
Published: September 2022

The introduction of autonomous vehicles (AVs) and shared autonomous vehicles (SAVs) is projected to enhance network performance and accessibility. The future share distribution of AV and SAV is not yet apparent, nor is which of these two future transport modes will become dominant. Therefore, this research deploys a simulation-based dynamic traffic assignment using Visum software to investigate the impact of varying the share distribution of AVs and SAVs on Budapest’s network performance and consumer surplus in three projected future traffic scenarios for the years 2030 and 2050 compared to the Base scenario for 2020. The three future scenarios are presented and characterized by different penetration rates of AVs and SAVs to reflect the uncertainty in the market share of these future cars as follows: Mix-Traffic scenario for 2030, and AV-Focused and SAV-Focused scenarios for 2050. The results revealed that the emergence of AVs and SAVs would improve the overall network performance, and better performance was observed with increasing the share distribution of SAVs. Similarly, the consumer surplus increased in all future scenarios, especially with increasing the share distribution of AVs. Consequently, the advent of AVs and SAVs will improve traffic performance and increase consumer surplus, benefiting road users and authorities.
Performance Evaluation of Solar Chimney Power Plants with Bayburt Stone and Basalt on the Ground as Natural Energy Storage Material
Authors: Cuce Pinar Mert , Cuce Erdem , Alshahrani Saad , Saboor Shaik , Sen Harun , Veza Ibham , Saleel C. Ahamed
Published: September 2022

This research examines the effect of using Bayburt stone or basalt as an energy storage unit in SCPPs. The effect of using low-cost materials on the system performance is evaluated. Based on the Manzanares pilot plant (MPP), a 3D CFD model was created. Geometric parameters were kept constant in simulations performed with ANSYS FLUENT engineering commercial software. In addition to DO (discrete coordinates) for the radiation model, the solar ray-tracing algorithm (SRTA) and the RNG k-e turbulence model (RNGTM) were solved, coupled, and the outputs of the system were evaluated at outdoor temperatures of 290 and 300 K. The temperature and velocity distributions, as well as power outputs (PO) of the system by using Bayburt stone and basalt as ground material, are compared for different outdoor temperatures and solar radiation conditions. It is understood that the use of both materials contributes to the performance of the system at a similar rate and can be used economically. It is noticed that the plant gives a PO of approximately 41,636 kW with both storage materials at a radiation intensity of 800 W/m2 and an outdoor temperature of 300 K. It is seen that the outdoor temperature affects the temperature rise in the plant, which is higher at 290 K.