bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Fabrication of a High-Speed Atomic Force Microscope Scan-Head
Authors: Otieno Luke Oduor , Alunda Bernard Ouma , Kim Jaehyun , Lee Yong Joong
Published: January 2021

A high-speed atomic force microscope (HS-AFM) requires a specialized set of hardware and software and therefore improving video-rate HS-AFMs for general applications is an ongoing process. To improve the imaging rate of an AFM, all components have to be carefully redesigned since the slowest component determines the overall bandwidth of the instrument. In this work, we present a design of a compact HS-AFM scan-head featuring minimal loading on the Z-scanner. Using a custom-programmed controller and a high-speed lateral scanner, we demonstrate its working by obtaining topographic images of Blu-ray disk data tracks in contact- and tapping-modes. Images acquired using a contact-mode cantilever with a natural frequency of 60 kHz in constant deflection mode show good tracking of topography at 400 Hz. In constant height mode, tracking of topography is demonstrated at rates up to 1.9 kHz for the scan size of 1μm×1μm with 100×100 pixels.
A Parallel Computing Approach to Spatial Neighboring Analysis of Large Amounts of Terrain Data Using Spark
Authors: Zhang Jianbo , Ye Zhuangzhuang , Zheng Kai
Published: January 2021

Spatial neighboring analysis is an indispensable part of geo-raster spatial analysis. In the big data era, high-resolution raster data offer us abundant and valuable information, and also bring enormous computational challenges to the existing focal statistics algorithms. Simply employing the in-memory computing framework Spark to serve such applications might incur performance issues due to its lack of native support for spatial data. In this article, we present a Spark-based parallel computing approach for the focal algorithms of neighboring analysis. This approach implements efficient manipulation of large amounts of terrain data through three steps: (1) partitioning a raster digital elevation model (DEM) file into multiple square tile files by adopting a tile-based multifile storing strategy suitable for the Hadoop Distributed File System (HDFS), (2) performing the quintessential slope algorithm on these tile files using a dynamic calculation window (DCW) computing strategy, and (3) writing back and merging the calculation results into a whole raster file. Experiments with the digital elevation data of Australia show that the proposed computing approach can effectively improve the parallel performance of focal statistics algorithms. The results also show that the approach has almost the same calculation accuracy as that of ArcGIS. The proposed approach also exhibits good scalability when the number of Spark executors in clusters is increased.
Automatic Lung Segmentation on Chest X-rays Using Self-Attention Deep Neural Network
Authors: Kim Minki , Lee Byoung-Dai
Published: January 2021

Accurate identification of the boundaries of organs or abnormal objects (e.g., tumors) in medical images is important in surgical planning and in the diagnosis and prognosis of diseases. In this study, we propose a deep learning-based method to segment lung areas in chest X-rays. The novel aspect of the proposed method is the self-attention module, where the outputs of the channel and spatial attention modules are combined to generate attention maps, with each highlighting those regions of feature maps that correspond to “what” and “where” to attend in the learning process, respectively. Thereafter, the attention maps are multiplied element-wise with the input feature map, and the intermediate results are added to the input feature map again for residual learning. Using X-ray images collected from public datasets for training and evaluation, we applied the proposed attention modules to U-Net for segmentation of lung areas and conducted experiments while changing the locations of the attention modules in the baseline network. The experimental results showed that our method achieved comparable or better performance than the existing medical image segmentation networks in terms of Dice score when the proposed attention modules were placed in lower layers of both the contracting and expanding paths of U-Net.
Self-Powered Flexible Sour Sensor for Detecting Ascorbic Acid Concentration Based on Triboelectrification/Enzymatic-Reaction Coupling Effect
Authors: Zhao Tianming , Wang Qi , Du An
Published: January 2021

Artificial sensory substitution systems can mimic human sensory organs through replacing the sensing process of a defective sensory receptor and transmitting the sensing signal into the nervous system. Here, we report a self-powered flexible gustation sour sensor for detecting ascorbic acid concentration. The material system comprises of Na2C2O4-Ppy with AAO modification, PDMS and Cu wire mesh. The working mechanism is contributed to the triboelectrification/enzymatic-reaction coupling effect, and the device can collect weak energy from body movements and directly output triboelectric current without any external power-units. The triboelectric output is affected by AA concentration, and the response is up to 34.82% against 15.625 mM/L of AA solution. Furthermore, a practical application in detecting ascorbic acid concentration of different drinks has been demonstrated. This work can encourage the development of wearable flexible electronics and this self-powered sour sensor has the potential that can be acted as a kind of gustatory receptors to build electronic tongues.
Step Length Measurements Using the Received Signal Strength Indicator
Authors: Yang Zanru , Tran Le Chung , Safaei Farzad
Published: January 2021

In this paper, portable transceivers with micro-controllers and radio frequency modules are developed to measure the received signal strength, path loss, and thus the distance between the human ankles for both indoor and outdoor environments. By comparing the experimental results and the theoretical model, a path loss model between transceivers attached to the subject’s ankles is derived. With the developed experimental path loss model, the step length can be measured relatively accurately, despite the imperfections of hardware devices, with the distance errors of a centimeter level. This paper, therefore, helps address the need for a distance measurement method that has fewer health concerns, is accurate, and is less affected by occlusions and confined spaces. Our findings possibly lay a foundation for some important applications, such as the measurement of gait speed and localization of the human body parts, in wireless body area networks.
Quaternion Model of Higher-Order Rotating Polarization Wave Modulation for High Data Rate M2M LPWAN Communication
Authors: Ahmad Zaid , Hashim Shaiful Jahari , Rokhani Fakhrul Zaman , Al-Haddad Syed Abul Rahman , Sali Aduwati , Takei Ken
Published: January 2021

With growing interest in Industry 4.0, machine-to-machine communication (M2M) will become the key enabler for low-power wide area networks (LPWANs) in connecting machines and sensor nodes distributed across a distance in the industrial environment. The choice of modulation and diversity techniques, and the selection of spectrum (licensed/unlicensed) will impact and influence the requirements of wireless M2M systems. Link reliability is one of the most important requirements for LPWAN deployment in industrial scenarios. Rotating Polarization Wave (RPW) system has been recently proposed as an LPWAN solution for reliable M2M communication in high clutter environment and it deploys BPSK modulation with polarization diversity (PD). This paper proposes a new multi-level Rotating Polarization Phase-Shift Keying (RP-MPSK) modulation to provide high data rate and energy efficiency. A novel quaternion model for RPW system (Q-RPW) is also proposed to reduce the complexity in modeling, simulation, and implementation. Results using Q-RPW model show that RP-MPSK modulation offers a high diversity gain over BPSK with second-order diversity. Bit error rate (BER) performance of RP-MPSK modulation compared against other LPWAN modulation like MPSK, FSK and QAM has shown high reliability and substantial improvement in SNR. To overcome the degradation in error performance caused by the proposed higher-order modulation, sampling rates are recommended based on BER performance. BER performance of RP-MPSK under multipath and interference conditions is also investigated.
Development of Non Expensive Technologies for Precise Maneuvering of Completely Autonomous Unmanned Aerial Vehicles
Authors: Bigazzi Luca , Gherardini Stefano , Innocenti Giacomo , Basso Michele
Published: January 2021

In this paper, solutions for precise maneuvering of an autonomous small (e.g., 350-class) Unmanned Aerial Vehicles (UAVs) are designed and implemented from smart modifications of non expensive mass market technologies. The considered class of vehicles suffers from light load, and, therefore, only a limited amount of sensors and computing devices can be installed on-board. Then, to make the prototype capable of moving autonomously along a fixed trajectory, a “cyber-pilot”, able on demand to replace the human operator, has been implemented on an embedded control board. This cyber-pilot overrides the commands thanks to a custom hardware signal mixer. The drone is able to localize itself in the environment without ground assistance by using a camera possibly mounted on a 3 Degrees Of Freedom (DOF) gimbal suspension. A computer vision system elaborates the video stream pointing out land markers with known absolute position and orientation. This information is fused with accelerations from a 6-DOF Inertial Measurement Unit (IMU) to generate a “virtual sensor” which provides refined estimates of the pose, the absolute position, the speed and the angular velocities of the drone. Due to the importance of this sensor, several fusion strategies have been investigated. The resulting data are, finally, fed to a control algorithm featuring a number of uncoupled digital PID controllers which work to bring to zero the displacement from the desired trajectory.
Determining Sonication Effect on E. coli in Liquid Egg, Egg Yolk and Albumen and Inspecting Structural Property Changes by Near-Infrared Spectra
Authors: Nagy David , Felfoldi Jozsef , Taczmanne Bruckner Andrea , Mohacsi-Farkas Csilla , Bodor Zsanett , Kertesz Istvan , Nemeth Csaba , Zsom-Muha Viktoria
Published: January 2021

In this study, liquid egg, albumen, and egg yolk were artificially inoculated with E. coli. Ultrasound equipment (20/40 kHz, 180/300 W; 30/45/60 min) with a circulation cooling system was used to lower the colony forming units (CFU) of E. coli samples. Frequency, absorbed power, energy dose, and duration of sonication showed a significant impact on E. coli with 0.5 log CFU/mL in albumen, 0.7 log CFU/mL in yolk and 0.5 log CFU/mL decrease at 40 kHz and 6.9 W absorbed power level. Significant linear correlation (p < 0.001) was observed between the energy dose of sonication and the decrease of E. coli. The results showed that sonication can be a useful tool as a supplementary method to reduce the number of microorganism in egg products. With near-infrared (NIR) spectra analysis we were able to detect the structural changes of the egg samples, due to ultrasonic treatment. Principal component analysis (PCA) showed that sonication can alter C–H, C–N, –OH and N–H bonds in egg. The aquagrams showed that sonication can alter the properties of H2O structure in egg products. The observed data showed that the absorbance of free water (1412 nm), water molecules with one (1440 nm), two (1462 nm), three (1472 nm) and four (1488 nm) hydrogen bonds, water solvation shell (1452 nm) and strongly bonded water (1512 nm) of the egg samples have been changed during ultrasonic treatment.
DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería
Authors: Lupión Marcos , Medina-Quero Javier , Sanjuan Juan F. , Ortigosa Pilar M.
Published: January 2021

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.
The Sensor Hub for Detecting the Developmental Characteristics in Reading in Children on a White vs. Colored Background/Colored Overlays
Authors: Jakovljević Tamara , Janković Milica M. , Savić Andrej M. , Soldatović Ivan , Todorović Petar , Jere Jakulin Tadeja , Papa Gregor , Ković Vanja
Published: January 2021

This study investigated the influence of white vs. 12 background and overlay colors on the reading process in twenty-four school-age children. Previous research reported that colors could affect reading skills as an important factor in the emotional and physiological state of the body. The aim of the study was to assess developmental differences between second and third grade students of an elementary school, and to evaluate differences in electroencephalography (EEG), ocular, electrodermal activities (EDA) and heart rate variability (HRV). Our findings showed a decreasing trend with age regarding EEG power bands (Alpha, Beta, Delta, Theta) and lower scores of reading duration and eye-tracking measures in younger children compared to older children. As shown in the results, HRV parameters showed higher scores in 12 background and overlay colors among second than third grade students, which is linearly correlated to the level of stress and is readable from EDA measures as well. Our study showed the calming effect on second graders of turquoise and blue background colors. Considering other colors separately for each parameter, we assumed that there are no systematic differences in reading duration, EEG power band, eye-tracking and EDA measures.
Prediction of Motion Intentions as a Novel Method of Upper Limb Rehabilitation Support
Authors: Lewandowski Bogusz , Wudarczyk Sławomir , Sperzyński Przemysław , Bałchanowski Jacek
Published: January 2021

This article is devoted to the novel method of upper limb rehabilitation support using a dedicated mechatronic system. The mechatronic rehabilitation system’s main advantages are the repeatability of the process and the ability to measure key features and the progress of the therapy. In addition, the assisted therapy standard is the same for each patient. The new method proposed in this article is based on the prediction of the patient’s intentions, understood as the intentions to perform a movement that would be not normally possible due to the patient’s limited motor functions. Determining those intentions is realized based on a comparative analysis of measured kinematic (range of motion, angular velocities, and accelerations) and dynamic parameter values, as well as external loads resulting from the interaction of patients. Appropriate procedures were implemented in the control system, for which verification was conducted via experiments. The aim of the research in the article was to examine whether it is possible to sense the movement intentions of a patient during exercises, using only measured load parameters and kinematic parameters of the movement. In this study, the construction of a mechatronic system prototype equipped with sensory grip to measure the external loads, control algorithms, and the description of experimental studies were presented. The experimental studies of the mechanism were aimed at the verification of the proper operation of the system and were not a clinical trial.
Mesh Denoising via Adaptive Consistent Neighborhood
Authors: Guo Mingqiang , Song Zhenzhen , Han Chengde , Zhong Saishang , Lv Ruina , Liu Zheng
Published: January 2021

In this paper, we propose a novel guided normal filtering followed by vertex updating for mesh denoising. We introduce a two-stage scheme to construct adaptive consistent neighborhoods for guided normal filtering. In the first stage, we newly design a consistency measurement to select a coarse consistent neighborhood for each face in a patch-shift manner. In this step, the selected consistent neighborhoods may still contain some features. Then, a graph-cut based scheme is iteratively performed for constructing different adaptive neighborhoods to match the corresponding local shapes of the mesh. The constructed local neighborhoods in this step, known as the adaptive consistent neighborhoods, can avoid containing any geometric features. By using the constructed adaptive consistent neighborhoods, we compute a more accurate guide normal field to match the underlying surface, which will improve the results of the guide normal filtering. With the help of the adaptive consistent neighborhoods, our guided normal filtering can preserve geometric features well, and is robust against complex shapes of surfaces. Intensive experiments on various meshes show the superiority of our method visually and quantitatively.
Evaluation of a Dielectric-Only Transmitarray for Generating Multi-Focusing Near-Field Spots Using a Cluster of Feeds in the Ka-Band
Authors: Vaquero Álvaro F. , Pino Marcos Rodríguez , Arrebola Manuel , Matos Sérgio A. , Costa Jorge R. , Fernandes Carlos A.
Published: January 2021

A transmitarray antenna is evaluated to generate a multi-focusing spot area in the Fresnel region of the antenna in the Ka-band. The antenna is designed to focus the radiated field at a certain point using a central feeding configuration. The number of feeds is increased to create as many focusing spots as feeds. The feeds are placed along an arc defined in the principal planes of the transmitarray, radiating independent near-field spots and providing a solution with a wide-angle spot scanning without an antenna displacement and a high isolation between feeds. To validate this concept, a transmitarray based on dielectric-only cells is designed and simulated under full-wave conditions. Then, this design is manufactured using a 3D printing technique, and the prototype is measured in a planar acquisition range. Measurements are performed for different feed positions in order to validate the multi-focusing capability of the antenna. Measurements and simulations show a high agreement and validate the proposed design technique.
Hip Positioning and Sitting Posture Recognition Based on Human Sitting Pressure Image
Authors: Wan Qilong , Zhao Haiming , Li Jie , Xu Peng
Published: January 2021

Bad sitting posture is harmful to human health. Intelligent sitting posture recognition algorithm can remind people to correct their sitting posture. In this paper, a sitting pressure image acquisition system was designed. With the system, we innovatively proposed a hip positioning algorithm based on hip templates. The average deviation of the algorithm for hip positioning is 1.306 pixels (the equivalent distance is 1.50 cm), and the proportion of the maximum positioning deviation less than three pixels is 94.1%. Statistics show that the algorithm works relatively well for different subjects. At the same time, the algorithm can not only effectively locate the hip position with a small rotation angle (0°–15°), but also has certain adaptability to the sitting posture with a medium rotation angle (15°–30°) or a large rotation angle (30°–45°). Using the hip positioning algorithm, the regional pressure values of the left hip, right hip and caudal vertebrae are effectively extracted as the features, and support vector machine (SVM) with polynomial kernel is used to classify the four types of sitting postures, with a classification accuracy of up to 89.6%.
Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis
Authors: Cuadrado Javier , Michaud Florian , Lugrís Urbano , Pérez Soto Manuel
Published: January 2021

Optical motion capture is currently the most popular method for acquiring motion data in biomechanical applications. However, it presents a number of problems that make the process difficult and inefficient, such as marker occlusions and unwanted reflections. In addition, the obtained trajectories must be numerically differentiated twice in time in order to get the accelerations. Since the trajectories are normally noisy, they need to be filtered first, and the selection of the optimal amount of filtering is not trivial. In this work, an extended Kalman filter (EKF) that manages marker occlusions and undesired reflections in a robust way is presented. A preliminary test with inertial measurement units (IMUs) is carried out to determine their local reference frames. Then, the gait analysis of a healthy subject is performed using optical markers and IMUs simultaneously. The filtering parameters used in the optical motion capture process are tuned in order to achieve good correlation between the obtained accelerations and those measured by the IMUs. The results show that the EKF provides a robust and efficient method for optical system-based motion analysis, and that the availability of accelerations measured by inertial sensors can be very helpful for the adjustment of the filters.
Sensor and Component Fault Detection and Diagnosis for Hydraulic Machinery Integrating LSTM Autoencoder Detector and Diagnostic Classifiers
Authors: Mallak Ahlam , Fathi Madjid
Published: January 2021

Anomaly occurrences in hydraulic machinery might lead to massive system shut down, jeopardizing the safety of the machinery and its surrounding human operator(s) and environment, and the severe economic implications following the faults and their associated damage. Hydraulics are mostly placed in ruthless environments, where they are consistently vulnerable to many faults. Hence, not only are the machines and their components prone to anomalies, but also the sensors attached to them, which monitor and report their health and behavioral changes. In this work, a comprehensive applicational analysis of anomalies in hydraulic systems extracted from a hydraulic test rig was thoroughly achieved. First, we provided a combination of a new architecture of LSTM autoencoders and supervised machine and deep learning methodologies, to perform two separate stages of fault detection and diagnosis. The two phases were condensed by—the detection phase using the LSTM autoencoder. Followed by the fault diagnosis phase represented by the classification schema. The previously mentioned framework was applied to both component and sensor faults in hydraulic systems, deployed in the form of two in-depth applicational experiments. Moreover, a thorough literature review of related work from the past decade, for autoencoders related fault detection and diagnosis in hydraulic systems, was successfully conducted in this study.
Shallow Graph Convolutional Network for Skeleton-Based Action Recognition
Authors: Yang Wenjie , Zhang Jianlin , Cai Jingju , Xu Zhiyong
Published: January 2021

Graph convolutional networks (GCNs) have brought considerable improvement to the skeleton-based action recognition task. Existing GCN-based methods usually use the fixed spatial graph size among all the layers. It severely affects the model’s abilities to exploit the global and semantic discriminative information due to the limits of receptive fields. Furthermore, the fixed graph size would cause many redundancies in the representation of actions, which is inefficient for the model. The redundancies could also hinder the model from focusing on beneficial features. To address those issues, we proposed a plug-and-play channel adaptive merging module (CAMM) specific for the human skeleton graph, which can merge the vertices from the same part of the skeleton graph adaptively and efficiently. The merge weights are different across the channels, so every channel has its flexibility to integrate the joints. Then, we build a novel shallow graph convolutional network (SGCN) based on the module, which achieves state-of-the-art performance with less computational cost. Experimental results on NTU-RGB+D and Kinetics-Skeleton illustrates the superiority of our methods.
An Experimental Study on Condition Diagnosis for Thrust Bearings in Oscillating Water Column Type Wave Power Systems
Authors: Kim Tae-Wook , Oh Jaewon , Min Cheonhong , Hwang Se-Yun , Kim Min-Seok , Lee Jang-Hyun
Published: January 2021

In order to utilize wave energy, various wave power systems are being actively researched and developed and interest in them is increasing. To maximize the operational efficiency, it is very important to monitor and maintain the fault of components of the system. In recent years, interest in the management cost, high reliability and facility utilization of such systems has increased. In this regard, fault diagnosis technology including fault factor analysis and fault reproduction is drawing attention as an important main technology. Therefore, in this study, to reproduce and monitor the faults of a wave power system, firstly, the failure mode of the system was analyzed using FMEA analysis. Secondly, according to the derived failure mode and effect, the thrust bearing was selected as a target for fault reproduction and a test equipment bench was constructed. Finally, with the vibration data obtained by conducting the tests, the vibration spectrum was analyzed to extract the features of the data for each operating status; the data was classified by applying the three machine learning algorithms: naïve Bayes (NB), k-nearest neighbor (k-NN), and multi-layer perceptron (MLP). The criteria for determining the fault were derived. It is estimated that a more efficient fault diagnosis is possible by using the standard and fault monitoring method of this study.
A Multilane Tracking Algorithm Using IPDA with Intensity Feature
Authors: Akbari Behzad , Thiyagalingam Jeyan , Lee Richard , Thia Kirubarajan
Published: January 2021

Detection of multiple lane markings on road surfaces is an important aspect of autonomous vehicles. Although a number of approaches have been proposed to detect lanes, detecting multiple lane markings, particularly across a large number of frames and under varying lighting conditions, in a consistent manner is still a challenging problem. In this paper, we propose a novel approach for detecting multiple lanes across a large number of frames and under various lighting conditions. Instead of resorting to the conventional approach of processing each frame to detect lanes, we treat the overall problem as a multitarget tracking problem across space and time using the integrated probabilistic data association filter (IPDAF) as our basis filter. We use the intensity of the pixels as an augmented feature to correctly group multiple lane markings using the Hough transform. By representing these extracted lane markings as splines, we then identify a set of control points, which becomes a set of targets to be tracked over a period of time, and thus across a large number of frames. We evaluate our approach on two different fronts, covering both model- and machine-learning-based approaches, using two different datasets, namely the Caltech and TuSimple lane detection datasets, respectively. When tested against model-based approach, the proposed approach can offer as much as 5%, 12%, and 3% improvements on the true positive, false positive, and false positives per frame rates compared to the best alternative approach, respectively. When compared against a state-of-the-art machine learning technique, particularly against a supervised learning method, the proposed approach offers 57%, 31%, 4%, and 9× improvements on the false positive, false negative, accuracy, and frame rates. Furthemore, the proposed approach retains the explainability, or in other words, the cause of actions of the proposed approach can easily be understood or explained.
Ratiometric Detection of Mercury (II) Ions in Living Cells Using Fluorescent Probe Based on Bis(styryl) Dye and Azadithia-15-Crown-5 Ether Receptor
Authors: Panchenko Pavel , Efremenko Anastasija , Feofanov Alexey , Ustimova Mariya , Fedorov Yuri , Fedorova Olga
Published: January 2021

Bis(styryl) dye 1 bearing N-phenylazadithia-15-crown-5 ether receptor has been evaluated as a ratiometric fluorescent chemosensor for mercury (II) ions in living cells. In aqueous solution, probe 1 selectively responds to the presence of Hg2+ via the changes in the emission intensity as well as in the emission band shape, which is a result of formation of the complex with 1:1 metal to ligand ratio (dissociation constant 0.56 ± 0.15 µM). The sensing mechanism is based on the interplay between the RET (resonance energy transfer) and ICT (intramolecular charge transfer) interactions occurring upon the UV/Vis (380 or 405 nm) photoexcitation of both styryl chromophores in probe 1. Bio-imaging studies revealed that the yellow (500–600 nm) to red (600–730 nm) fluorescence intensity ratio decreased from 4.4 ± 0.2 to 1.43 ± 0.10 when cells were exposed to increasing concentration of mercury (II) ions enabling ratiometric quantification of intracellular Hg2+ concentration in the 37 nM–1 μM range.
Particle Filtering Based Remaining Useful Life Prediction for Electromagnetic Coil Insulation
Authors: Guo Haifeng , Xu Aidong , Wang Kai , Sun Yue , Han Xiaojia , Hong Seung Ho , Yu Mengmeng
Published: January 2021

Electromagnetic coils are one of the key components of many systems. Their insulation failure can have severe effects on the systems in which coils are used. This paper focuses on insulation degradation monitoring and remaining useful life (RUL) prediction of electromagnetic coils. First, insulation degradation characteristics are extracted from coil high-frequency electrical parameters. Second, health indicator is defined based on insulation degradation characteristics to indicate the health degree of coil insulation. Finally, an insulation degradation model is constructed, and coil insulation RUL prediction is performed by particle filtering. Thermal accelerated degradation experiments are performed to validate the RUL prediction performance. The proposed method presents opportunities for predictive maintenance of systems that incorporate coils.
Maintaining Social Distancing during the COVID-19 Outbreak
Authors: Goniewicz Krzysztof , Khorram-Manesh Amir
Published: January 2021

The analyses of the European Centre for Disease Prevention and Control and an increasing amount of scientific research show that the spread of SARS-CoV-2 may be limited by maintaining social distancing, appropriate hand hygiene, and following the basic principles of prophylaxis. While simulating models applied to social distancing have all been tested and evaluated, the implementation of its guidelines in different traditions and cultures has not been discussed sufficiently. Consequently, applying social distancing guidelines alone may not be enough to contain the spread of the coronavirus. This brief report aims to clarify the role of cultural and behavioral differences in the diverse outcomes of COVID-19 management.
An Advanced Control Technique for Power Quality Improvement of Grid-Tied Multilevel Inverter
Authors: Jahan Sumaya , Biswas Shuvra Prokash , Hosain Md. Kamal , Islam Md. Rabiul , Haq Safa , Kouzani Abbas Z. , Mahmud M A Parvez
Published: January 2021

The use of different control techniques has become very popular for controlling the performance of grid-connected photovoltaic (PV) systems. Although the proportional-integral (PI) control technique is very popular, there are some difficulties such as less stability, slow dynamic response, low reference tracking capability, and lower output power quality in solar PV applications. In this paper, a robust, fast, and dynamic proportional-integral resonance controller with a harmonic and lead compensator (PIR + HC + LC) is proposed to control the current of a 15-level neutral-point-clamped (NPC) multilevel inverter. The proposed controlled is basically a proportional-integral resonance (PIR) controller with the feedback of a harmonic compensator and a lead compensator. The performance of the proposed controller is analyzed in a MATLAB/Simulink environment. The simulation result represents admirable performance in terms of stability, sudden load change response, fault handling capability, reference tracking capability, and total harmonic distortion (THD) than those of the existing controllers. The responses of the inverter and grid outlets under different conditions are also analyzed. The harmonic compensator decreases the lower order harmonics of grid voltage and current, and the lead compensator provides the phase lead. It is expected that the proposed controller is a dynamic aspirant in the grid-connected PV system.
Predicting Bicycle-on-Board Transit Choice in a University Environment
Authors: Rybarczyk Greg , Shaker Richard R.
Published: January 2021

Bicycles-on-board (BoB) transit is a popular travel demand management (TDM) tool across many U.S. cities and universities, yet research on this mode within a university environment remains minimal. The purpose of this research is to investigate how personal and neighborhood factors influence this travel choice in a university setting. Relying on attitudinal data from a stated preference survey, this study examined the effect of personal characteristics and seven key neighborhood conditions on the willingness to utilize BoB for the “first mile” of the journey to campus. The study used exploratory spatial data analysis (ESDA), a discrete choice modeling framework, and geovisualizations to understand the likelihood of choosing this mode among a university population in Flint, Michigan, USA. The results revealed that the majority of constituents were not interested in BoB, aside from a cluster near the commercial business district. Also of note was that long commutes, and reduced access to parks and bicycle facilities dissuaded people from choosing this mode. Surprisingly, a neighborhood’s walkability or bikeability had no effect on respondent’s interest in using BoB. Lastly, the geovisualizations showcased where localized interventions may effectively increase this mode choice in the future.
Sustainable Recycling of Electric Arc Furnace Steel Slag as Aggregate in Concrete: Effects on the Environmental and Technical Performance
Authors: Diotti Alessandra , Cominoli Luca , Galvin Adela Perèz , Sorlini Sabrina , Plizzari Giovanni
Published: January 2021

The aim of this research work was the evaluation of the feasibility to utilize industrial by-products, such as electric arc furnace steel slags, for sustainable concrete production. The paper evaluated the environmental and mechanical properties of steel slags and concrete, respectively. Specifically, the release of contaminants from steel slags was investigated by leaching test and the properties of fresh and hardened concrete were evaluated for a concrete mixture designed with a partial substitution (30%) of natural coarse aggregates with electric arc furnace steel slags. The results show that the concentrations of pollutants were lower than the legal limits imposed by the Ministerial Decree 186/2006 and the addition of steel slag can enhance the mechanical performance of concrete. The compressive strength of cubic specimens was also measured after different cycles of alternate wetting–drying. The steel slag incorporation results in a stiffness comparable to that of a traditional concrete. Overall, the mechanical and leaching characterization has shown that the reuse of electric arc furnace steel slags for sustainable concrete production is feasible and reliable.
Urban Living Labs and Citizen Science: From Innovation and Science towards Policy Impacts
Authors: Veeckman Carina , Temmerman Laura
Published: January 2021

City governments are currently exploring different participatory mechanisms in order to meet the growing demand in society for deliberate decision-making. Through developments in sensing technology, data processing and visualization, citizen science is emerging as a powerful tool for the general public to participate in scientific research that informs policy. Citizen science can be used as an approach in Urban Living Labs, whereby public and private stakeholders are involved in innovation and data collection processes together with citizens. However, there is currently little synergy between citizen science and Urban Living Labs, and how science, innovation and policy can be interoperable. Therefore, this article conducts a comparative case study analysis on the participatory processes and related outputs, outcomes and impacts of the FloodCitiSense Urban Living Labs (Brussels, Birmingham, Rotterdam). These initiatives developed an early warning system for urban flooding through the collection and analysis of crowdsourced information. Data on the participatory processes were collected through 11 in-depth interviews and evaluated on its effectiveness in achieving policy outcomes. The discussion reflects on best practices in incorporating citizen science in Urban Living Labs based on the experienced opportunities and challenges in FloodCitiSense.
Governing Transitions towards Sustainable Agriculture—Taking Stock of an Emerging Field of Research
Authors: Melchior Inga C. , Newig Jens
Published: January 2021

The need for fundamental changes in the way humans interact with nature is now widely acknowledged in order to achieve sustainable development. Agriculture figures prominently in this quest, being both a major driver and a major threat to global sustainability. Agricultural systems typically have co-evolved with other societal structures—retailers, land management, technology, consumer habits, and environmental and agricultural law—and can therefore well be described as socio-technical regimes in the sense of the sustainability transitions literature. This paper aims to give an overview of the emerging field of governing transitions to sustainability agriculture and the topics and trends covered, focusing on how agricultural transitions are being governed through a variety of actors and at a variety of levels. We conduct a systematic review of 153 articles published before the year 2019. We identify two main perspectives: papers that analyse the status quo in farming practices and reasons for lock-in, and papers that explore potential transition pathways and their governance. Predominantly, papers study (local) niche developments and discuss governance options for upscaling, rather than actual regime change. Seven distinct perspectives emerge from our reading of the selected articles: application of theoretical perspectives from the literature on socio-technical transitions; governance and regulation; knowledge and learning; concrete approaches to reduce the environmental impact of agricultural systems; urbanisation, urban agriculture, and local food networks; the role of agri-food businesses; as well as the role of gender. While a variety of local case studies shows potential for small-scale changes that might be transferable to other regions and higher levels of governance, it generally appears that more integrative, comparative work and perhaps more coherence in conceptual approaches would benefit the currently highly fragmented field.
Economic and Social Factors That Predict Readmission for Mental Health and Drug Abuse Patients
Authors: Bui Quang “Neo” , Moriuchi Emi
Published: January 2021

According to the United Nations, curtailing the rise of mental illness and drug abuse has been an important goal for sustainable development of member states. In the United States, reducing readmission rates for mental health and drug abuse patients is critical, given the rising health care costs and a strained health care system. This study aims to examine economic and social factors that predict readmission likelihood for mental health and drug abuse patients in the state of New York. Patient admission data of 25,846 mental health patients and 32,702 drug abuse patients with multiple visits in New York hospitals in 2015 were examined. Findings show that economic factors like income level and payment type impact readmission rates differently: The poorest patients were less likely to get readmitted while patients with higher incomes were likely to experience drug relapse. Regarding social factors, mental health patients who lived in neighborhoods with high social capital were less likely to be readmitted, but drug abuse patients in similar areas were more likely to be readmitted. The findings show that policy-makers and hospital administrators need to approach readmission rates differently for each group of patients.
Cooperatives and Social Capital: A Narrative Literature Review and Directions for Future Research
Authors: Saz-Gil Isabel , Bretos Ignacio , Díaz-Foncea Millán
Published: January 2021

How cooperatives generate and absorb social capital has attracted a great deal of attention due to the fact that they are collective organizations owned and democratically managed by their members, and, accordingly, are argued to be closely linked to the nature and dynamics of social capital. However, the extant literature and knowledge on the relationship between cooperatives and social capital remain unstructured and fragmented. This paper aims to provide a narrative literature review that integrates both sides of the relationship between cooperatives and social capital. On the one hand, one side involves how cooperatives create internal social capital and spread it in their immediate environment, and, on the other hand, it involves how the presence of social capital promotes the creation and development of cooperatives. In addition, our theoretical framework integrates the dark side of social capital, that is, how the lack of trust, reciprocal relationships, transparency, and other social capital components can lead to failure of the cooperative. On the basis of this review, we define a research agenda that synthesizes key trends and promising research avenues for further advancement of theoretical and empirical insights about the relationship between cooperatives and social capital, placing particular emphasis on rural and agricultural cooperatives.
Feasibility Analysis of the Sustainability of the Tres Palos Coastal Lagoon: A Multifactorial Approach
Authors: Casarrubias-Jaimez Ana I. , Juárez-López Ana Laura , Rosas-Acevedo José Luis , Reyes-Umaña Maximino , Rodríguez-Herrera América Libertad , Ramos-Quintana Fernando
Published: January 2021

We studied the cultural eutrophication process assessments of coastal lagoons by considering sociopolitical, socioeconomic, demographic, technological, and cultural factors, which represent indirect drivers exerting effects on the eutrophication process, causing changes on the trophic status. Multifactorial interactions in eutrophication processes make understanding their complex dynamics difficult, leading to unreliable assessments and, consequently, to unsustainable management actions. This, in turn, hinders the feasibility of coastal lagoon sustainability. We propose a method based on the evaluation of pathways derived from a multifactorial network, which represents the eutrophication process, with the aim of determining the feasibility of the sustainability of the Tres Palos coastal lagoon. Our findings revealed that most of the evaluations of relationships belonging to pathways were unfeasible due to reasons such as: there was no evidence of the existence of public policies, technological skills, and cultural factors; there was a lack of data related to human settlements around the lagoon and river, industrial waste, agricultural practices, and tourism. The preceding shortcomings hinder the feasibility of coastal lagoon sustainability under study. We suggest that assessments of cultural eutrophication processes that overlook sociopolitical, socioeconomic, technological, and cultural factors are limited and inadequate for supporting the feasibility of sustainable coastal lagoons.
Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers
Authors: Bajirao Tarate Suryakant , Kumar Pravendra , Kumar Manish , Elbeltagi Ahmed , Kuriqi Alban
Published: January 2021

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.
A Distance-Based AHP-DEA Super-Efficiency Approach for Selecting an Electric Bike Sharing System Provider: One Step Closer to Sustainability and a Win–Win Effect for All Target Groups
Authors: Bajec Patricija , Tuljak-Suban Danijela , Zalokar Eva
Published: January 2021

Existing research on electric bike sharing systems (e-BSS) emphasises the importance of the sustainability of the systems and the need to respect the views of all stakeholders when planning e-BSS. However, this research overlooks the fact that the sustainability of e-BSS depends to a large extent on the skills and knowledge of the parties who select an electric bike provider, which in most cases is the investor in the e-BSS. There is no previous paper that provides support for investors in (1) defining a set of criteria for selecting a provider that takes into account all of the three domains of sustainability (economic, social, and environmental) and (2) developing a tool that best meets sustainability standards on the one hand and the needs and requirements of all stakeholders (including e-bike users and investors) on the other hand. A distance-based analytic hierarchy process/data envelopment analysis (AHP-DEA) super-efficiency approach was proposed and applied to adapt DEA to the needs of predefined groups by using slack variables. The approach takes into account the fact that not all outputs have a positive impact on the final outcome; the approach also allows decision-makers to define the hierarchical structure of the importance of the criteria directly based on the responses of the selected group. A case study in Slovenia illustrated the application of the approach.