bluuebunny/crossref_metadata_2025 huggingface dataset:
Registration and Fusion of Close-Range Multimodal Wheat Images in Field Conditions
Authors: Dandrifosse Sébastien , Carlier Alexis , Dumont Benjamin , Mercatoris Benoît
Published: April 2021

Multimodal images fusion has the potential to enrich the information gathered by multi-sensor plant phenotyping platforms. Fusion of images from multiple sources is, however, hampered by the technical lock of image registration. The aim of this paper is to provide a solution to the registration and fusion of multimodal wheat images in field conditions and at close range. Eight registration methods were tested on nadir wheat images acquired by a pair of red, green and blue (RGB) cameras, a thermal camera and a multispectral camera array. The most accurate method, relying on a local transformation, aligned the images with an average error of 2 mm but was not reliable for thermal images. More generally, the suggested registration method and the preprocesses necessary before fusion (plant mask erosion, pixel intensity averaging) would depend on the application. As a consequence, the main output of this study was to identify four registration-fusion strategies: (i) the REAL-TIME strategy solely based on the cameras’ positions, (ii) the FAST strategy suitable for all types of images tested, (iii) and (iv) the ACCURATE and HIGHLY ACCURATE strategies handling local distortion but unable to deal with images of very different natures. These suggestions are, however, limited to the methods compared in this study. Further research should investigate how recent cutting-edge registration methods would perform on the specific case of wheat canopy.
Inconsistency among Landsat Sensors in Land Surface Mapping: A Comprehensive Investigation Based on Simulation
Authors: Chen Feng , Wang Chenxing , Zhang Yuansheng , Yi Zhenshi , Fan Qiancong , Liu Lin , Song Yuejun
Published: April 2021

Comprehensive investigations on the between-sensor comparability among Landsat sensors have been relatively limited compared with the increasing use of multi-temporal Landsat records in time series analyses. More seriously, the sensor-related difference has not always been considered in applications. Accordingly, comparisons were conducted among all Landsat sensors available currently, including Multispectral Scanner (MSS), Thematic Mappers (TM), Enhanced Thematic Mappers (ETM+), and Operational Land Imager (OLI)) in land cover mapping, based on a collection of synthesized, multispectral data. Compared to TM, OLI showed obvious between-sensor differences in channel reflectance, especially over the near infrared (NIR) and shortwave infrared (SWIR) channels, and presented positive bias in vegetation spectral indices. OLI did not always outperform TM and ETM+ in classification, which related to the methods used. Furthermore, the channels over SWIR of TM and its successors contributed largely to enhancement of inter-class separability and to improvement of classification. Currently, the inclusion of MSS data is confronted with significant challenges regarding the consistency of surface mapping. Considering the inconsistency among the Landsat sensors, it is applicable to generate a consistent time series of spectral indices through proper transformation models. Meanwhile, it suggests the generation of specific class(es) based on interest instead of including all classes simultaneously.
An Auto-Focus Method of Microscope for the Surface Structure of Transparent Materials under Transmission Illumination
Authors: Liao Yang , Xiong Yonghua , Yang Yunhong
Published: April 2021

This paper is concerned with auto-focus of microscopes for the surface structure of transparent materials under transmission illumination, where two distinct focus states appear in the focusing process and the focus position is located between the two states with the local minimum of sharpness. Please note that most existing results are derived for one focus state with the global maximum value of sharpness, they cannot provide a feasible solution to this particular problem. In this paper, an auto-focus method is developed for such a specific situation with two focus states. Firstly, a focus state recognition model, which is essentially an image classification model based on a deep convolution neural network, is established to identify the focus states of the microscopy system. Then, an endpoint search algorithm which is an evolutionary algorithm based on differential evolution is designed to obtain the positions of the two endpoints of the region where the real focus position is located, by updating the parameters according to the focus states. At last, a region search algorithm is devised to locate the focus position. The experimental results show that our method can achieve auto-focus rapidly and accurately for such a specific situation with two focus states.
Through-Wall UWB Radar Based on Sparse Deconvolution with Arctangent Regularization for Locating Human Subjects
Authors: Rittiplang Artit , Phasukkit Pattarapong
Published: April 2021

A common problem in through-wall radar is reflected signals much attenuated by wall and environmental noise. The reflected signal is a convolution product of a wavelet and an unknown object time series. This paper aims to extract the object time series from a noisy receiving signal of through-wall ultrawideband (UWB) radar by sparse deconvolution based on arctangent regularization. Arctangent regularization is one of the suitably nonconvex regularizations that can provide a reliable solution and more accuracy, compared with convex regularizations. An iterative technique for this deconvolution problem is derived by the majorization–minimization (MM) approach so that the problem can be solved efficiently. In the various experiments, sparse deconvolution with the arctangent regularization can identify human positions from the noisy received signals of through- wall UWB radar. Although the proposed method is an odd concept, the interest of this paper is in applying sparse deconvolution, based on arctangent regularization with an S-band UWB radar, to provide a more accurate detection of a human position behind a concrete wall.
CovidArray: A Microarray-Based Assay with High Sensitivity for the Detection of Sars-Cov-2 in Nasopharyngeal Swabs
Authors: Damin Francesco , Galbiati Silvia , Gagliardi Stella , Cereda Cristina , Dragoni Francesca , Fenizia Claudio , Savasi Valeria , Sola Laura , Chiari Marcella
Published: April 2021

A new coronavirus (SARS-CoV-2) caused the current coronavirus disease (Covid-19) epidemic. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is used as the gold standard for clinical detection of SARS-CoV-2. Under ideal conditions, RT-qPCR Covid-19 assays have analytical sensitivity and specificity greater than 95%. However, when the sample panel is enlarged including asymptomatic individuals, the sensitivity decreases and false negatives are reported. Moreover, RT-qPCR requires up to 3–6 h with most of the time involved in RNA extraction from swab samples. We introduce CovidArray, a microarray-based assay, to detect SARS-CoV-2 markers N1 and N2 in the nasopharyngeal swabs. The method is based on solid-phase hybridization of fluorescently-labeled amplicons upon RNA extraction and reverse transcription. This approach combines the physical-optical properties of the silicon substrate with the surface chemistry used to coat the substrate to obtain a diagnostic tool of great sensitivity. Furthermore, we used an innovative approach, RNAGEM, to extract and purify viral RNA in less than 15 min. We correctly assigned 12 nasopharyngeal swabs, previously analyzed by RT-qPCR. Thanks to the CovidArray sensitivity we were able to identify a false-negative sample. CovidArray is the first DNA microarray-based assay to detect viral genes in the swabs. Its high sensitivity and the innovative viral RNA extraction by RNAGEM allows the reduction of both the amount of false-negative results and the total analysis time to about 2 h.
A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications
Authors: Tropea Mauro , Arieta Angelo , De Rango Floriano , Pupo Francesco
Published: April 2021

Vehicle positioning is becoming an important issue related to Intelligent Transportation Systems (ITSs). Novel vehicles and autonomous vehicles need to be localized under different weather conditions and it is important to have a reliable positioning system to track vehicles. Satellite navigation systems can be a key technology in providing global coverage and providing localization services through many satellite constellations such as GPS, GLONASS, Galileo and so forth. However, the modeling of positioning and localization systems under different weather conditions is not a trivial objective especially considering different factors such as receiver sensitivity, dynamic weather conditions, propagation delay and so forth. This paper focuses on the use of simulators for performing different kinds of tests on Global Navigation Satellite System (GNSS) systems in order to reduce the cost of the positioning testing under different techniques or models. Simulation driven approach, combined with some specific hardware equipment such as receivers and transmitters can characterize a more realistic scenario and the simulation can consider other aspects that could be complex to really test. In this work, the main contribution is the introduction of the Troposphere Collins model in a GNSS simulator for VANET applications, the GPS-SDR-SIM software. The use of the Collins model in the simulator allows to improve the accuracy of the simulation experiments throughout the reduction of the receiver errors.
Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
Authors: Bok Dominik , O’Hagan Daniel , Knott Peter
Published: April 2021

Radar detection and track building performance is an essential part of a radar system. A high realized coherent integration gain often contributes to an improved performance. This is essential to the successful detection and tracking of weak moving targets. However, the actual movement within the coherent processing interval can introduce range walk effects. The processing will then result in range and Doppler frequency resolutions that become finer than a single moving point scatterer’s spread over range and—often not considered—over Doppler frequency. In particular for a wide instantaneous bandwidth, the impact on the achievable integration gain can become severe already for a constant effective velocity. Therefore, high desired integration gains as required in passive radar are not easily achieved against relatively fast moving targets. The main intent of this article is to present the movement effects on a classical range-Doppler analysis to give an insight on the achievable performance and to quantify otherwise appearing degradations. Interestingly, a classical analysis of experimental datasets evaluated from a DVB-T based passive radar measurement campaign even resolved the fluctuation of a target response within the instantaneously processed bandwidth. The findings strengthen the need for advanced processing methods that can at least partly address individual implications of fast moving targets in real-time applications properly.
Metal-Oxide Based Nanomaterials: Synthesis, Characterization and Their Applications in Electrical and Electrochemical Sensors
Authors: Fazio Enza , Spadaro Salvatore , Corsaro Carmelo , Neri Giulia , Leonardi Salvatore Gianluca , Neri Fortunato , Lavanya Nehru , Sekar Chinnathambi , Donato Nicola , Neri Giovanni
Published: April 2021

Pure, mixed and doped metal oxides (MOX) have attracted great interest for the development of electrical and electrochemical sensors since they are cheaper, faster, easier to operate and capable of online analysis and real-time identification. This review focuses on highly sensitive chemoresistive type sensors based on doped-SnO2, RhO, ZnO-Ca, Smx-CoFe2−xO4 semiconductors used to detect toxic gases (H2, CO, NO2) and volatile organic compounds (VOCs) (e.g., acetone, ethanol) in monitoring of gaseous markers in the breath of patients with specific pathologies and for environmental pollution control. Interesting results about the monitoring of biochemical substances as dopamine, epinephrine, serotonin and glucose have been also reported using electrochemical sensors based on hybrid MOX nanocomposite modified glassy carbon and screen-printed carbon electrodes. The fundamental sensing mechanisms and commercial limitations of the MOX-based electrical and electrochemical sensors are discussed providing research directions to bridge the existing gap between new sensing concepts and real-world analytical applications.
Bias Voltage Dependence of Sensing Characteristics in Tunneling Magnetoresistance Sensors
Authors: Wiśniowski Piotr , Nawrocki Maciej , Wrona Jerzy , Cardoso Susana , Freitas Paulo. P.
Published: April 2021

One of the characteristic features of tunneling magnetoresistance (TMR) sensors is a strong influence of bias voltage on tunneling current. Since fundamental sensing characteristics of the sensors are primarily determined by the tunneling current, the bias voltage should impact these characteristics. Previous research has indeed showed the influence of the bias voltage on the magnetic field detection and sensitivity. However, the effect has not been investigated for nonlinearity and hysteresis and the influence of bias voltage polarity has not yet been addressed. Therefore, this paper systematically investigates the dependence of field sensitivity, nonlinearity, hysteresis and magnetic field detection of CoFeB/MgO/CoFeB-based magnetoresistance sensors on bias voltage magnitude and polarity. The sensitivity and field detection of all sensors improved significantly with the bias, whereas the nonlinearity and hysteresis deteriorated. The sensitivity increased considerably (up to 32 times) and linearly with bias up to 0.6 V. The field detection also decreased substantially (up 3.9 times) with bias and exhibited the minimum values for the same magnitude under both polarities. Significant and linear increases with bias were also observed for nonlinearity (up to 26 times) and hysteresis (up to 33 times). Moreover, not only the voltage magnitude but also the polarity had a significant effect on the sensing characteristics. This significant, linear and simultaneous effect of improvement and deterioration of the sensing characteristics with bias indicates that both bias voltage magnitude and polarity are key factors in the control and modification of these characteristics.
Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography
Authors: Prats-Boluda Gema , Pastor-Tronch Julio , Garcia-Casado Javier , Monfort-Ortíz Rogelio , Perales Marín Alfredo , Diago Vicente , Roca Prats Alba , Ye-Lin Yiyao
Published: April 2021

Preterm birth is the leading cause of death in newborns and the survivors are prone to health complications. Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy. The current methods used in clinical practice to diagnose preterm labor, the Bishop score or cervical length, have high negative predictive values but not positive ones. In this work we analyzed the performance of computationally efficient classification algorithms, based on electrohysterographic recordings (EHG), such as random forest (RF), extreme learning machine (ELM) and K-nearest neighbors (KNN) for imminent labor (<7 days) prediction in women with TPL, using the 50th or 10th–90th percentiles of temporal, spectral and nonlinear EHG parameters with and without obstetric data inputs. Two criteria were assessed for the classifier design: F1-score and sensitivity. RFF1_2 and ELMF1_2 provided the highest F1-score values in the validation dataset, (88.17 ± 8.34% and 90.2 ± 4.43%) with the 50th percentile of EHG and obstetric inputs. ELMF1_2 outperformed RFF1_2 in sensitivity, being similar to those of ELMSens (sensitivity optimization). The 10th–90th percentiles did not provide a significant improvement over the 50th percentile. KNN performance was highly sensitive to the input dataset, with a high generalization capability.
Stochastic-Biomechanic Modeling and Recognition of Human Movement Primitives, in Industry, Using Wearables
Authors: Olivas-Padilla Brenda Elizabeth , Manitsaris Sotiris , Menychtas Dimitrios , Glushkova Alina
Published: April 2021

In industry, ergonomists apply heuristic methods to determine workers’ exposure to ergonomic risks; however, current methods are limited to evaluating postures or measuring the duration and frequency of professional tasks. The work described here aims to deepen ergonomic analysis by using joint angles computed from inertial sensors to model the dynamics of professional movements and the collaboration between joints. This work is based on the hypothesis that with these models, it is possible to forecast workers’ posture and identify the joints contributing to the motion, which can later be used for ergonomic risk prevention. The modeling was based on the Gesture Operational Model, which uses autoregressive models to learn the dynamics of the joints by assuming associations between them. Euler angles were used for training to avoid forecasting errors such as bone stretching and invalid skeleton configurations, which commonly occur with models trained with joint positions. The statistical significance of the assumptions of each model was computed to determine the joints most involved in the movements. The forecasting performance of the models was evaluated, and the selection of joints was validated, by achieving a high gesture recognition performance. Finally, a sensitivity analysis was conducted to investigate the response of the system to disturbances and their effect on the posture.
Control of Excitation of Cladding Modes by Tapering an Insertion of Special Fiber
Authors: Bakurov Diomid D. , Ivanov Oleg V.
Published: April 2021

A method for controlling the excitation of cladding modes by tapering special fiber insertions made of SM450 and coreless fibers is proposed. The coupling coefficients between the core mode and the cladding modes of the tapered fiber insertion are calculated. For the calculation, changes in the effective refractive indices and phases of the fiber core and in the cladding modes upon tapering are found. The field distribution of the core mode of the standard fiber transmitted through fiber insertion is obtained. The transmission characteristics of insertions of SM450 and coreless fibers during tapering are simulated and compared with the experiment. The possibility of controlling the transmission and excitation of various cladding modes is confirmed experimentally.
Health-BlockEdge: Blockchain-Edge Framework for Reliable Low-Latency Digital Healthcare Applications
Authors: Ejaz Muneeb , Kumar Tanesh , Kovacevic Ivana , Ylianttila Mika , Harjula Erkki
Published: April 2021

The rapid evolution of technology allows the healthcare sector to adopt intelligent, context-aware, secure, and ubiquitous healthcare services. Together with the global trend of an aging population, it has become highly important to propose value-creating, yet cost-efficient digital solutions for healthcare systems. These solutions should provide effective means of healthcare services in both the hospital and home care scenarios. In this paper, we focused on the latter case, where the goal was to provide easy-to-use, reliable, and secure remote monitoring and aid for elderly persons at their home. We proposed a framework to integrate the capabilities of edge computing and blockchain technology to address some of the key requirements of smart remote healthcare systems, such as long operating times, low cost, resilience to network problems, security, and trust in highly dynamic network conditions. In order to assess the feasibility of our approach, we evaluated the performance of our framework in terms of latency, power consumption, network utilization, and computational load, compared to a scenario where no blockchain was used.
VirtualCPR: Virtual Reality Mobile Application for Training in Cardiopulmonary Resuscitation Techniques
Authors: García Fierros Francisco Javier , Moreno Escobar Jesús Jaime , Sepúlveda Cervantes Gabriel , Morales Matamoros Oswaldo , Tejeida Padilla Ricardo
Published: April 2021

Deaths due to heart diseases are a leading cause of death in Mexico. Cardiovascular diseases are considered a public health problem because they produce cardiorespiratory arrests. During an arrest, cardiac and/or respiratory activity stops. A cardiorespiratory arrest is rapidly fatal without a quick and efficient intervention. As a response to this problem, the VirtualCPR system was designed in the present work. VirtualCPR is a mobile virtual reality application to support learning and practicing of basic techniques of cardiopulmonary resuscitation (CPR) for experts or non-experts in CPR. VirtualCPR implements an interactive virtual scenario with the user, which is visible by means of employment of virtual reality lenses. User’s interactions, with our proposal, are by a portable force sensor for integration with training mannequins, whose development is based on an application for the Android platform. Furthermore, this proposal integrates medical knowledge in first aid, related to the basic CPR for adults using only the hands, as well as technological knowledge, related to development of simulations on a mobile virtual reality platform by three main processes: (i) force measurement and conversion, (ii) data transmission and (iii) simulation of a virtual scenario. An experiment by means of a multifactorial analysis of variance was designed considering four factors for a CPR session: (i) previous training in CPR, (ii) frequency of compressions, (iii) presence of auditory suggestions and (iv) presence of color indicator. Our findings point out that the more previous training in CPR a user of the VirtualCPR system has, the greater the percentage of correct compressions obtained from a virtual CPR session. Setting the rate to 100 or 150 compressions per minute, turning on or off the auditory suggestions and turning the color indicator on or off during the session have no significant effect on the results obtained by the user.
Robust Global Motion Estimation for Video Stabilization Based on Improved K-Means Clustering and Superpixel
Authors: Wu Rouwan , Xu Zhiyong , Zhang Jianlin , Zhang Lihong
Published: April 2021

Obtaining accurate global motion is a crucial step for video stabilization. This paper proposes a robust and simple method to implement global motion estimation. We don’t extend the framework of 2D video stabilization but add a “plug and play” module to motion estimation based on feature points. Firstly, simple linear iterative clustering (SLIC) pre-segmentation is used to obtain superpixels of the video frame, clustering is performed according to the superpixel centroid motion vector and cluster center with large value is eliminated. Secondly, in order to obtain accurate global motion estimation, an improved K-means clustering is proposed. We match the feature points of the remaining superpixels between two adjacent frames, establish a feature points’ motion vector space, and use improved K-means clustering for clustering. Finally, the richest cluster is being retained, and the global motion is obtained by homography transformation. Our proposed method has been verified on different types of videos and has efficient performance than traditional approaches. The stabilization video has an average improvement of 0.24 in the structural similarity index than the original video and 0.1 higher than the traditional method.
A Pixel Design of a Branching Ultra-Highspeed Image Sensor
Authors: Ngo Nguyen Hoai , Shimonomura Kazuhiro , Ando Taeko , Shimura Takayoshi , Watanabe Heiji , Takehara Kohsei , Nguyen Anh Quang , Charbon Edoardo , Etoh Takeharu Goji
Published: April 2021

A burst image sensor named Hanabi, meaning fireworks in Japanese, includes a branching CCD and multiple CMOS readout circuits. The sensor is backside-illuminated with a light/charge guide pipe to minimize the temporal resolution by suppressing the horizontal motion of signal carriers. On the front side, the pixel has a guide gate at the center, branching to six first-branching gates, each bifurcating to second-branching gates, and finally connected to 12 (=6×2) floating diffusions. The signals are either read out after an image capture operation to replay 12 to 48 consecutive images, or continuously transferred to a memory chip stacked on the front side of the sensor chip and converted to digital signals. A CCD burst image sensor enables a noiseless signal transfer from a photodiode to the in-situ storage even at very high frame rates. However, the pixel count conflicts with the frame count due to the large pixel size for the relatively large in-pixel CCD memory elements. A CMOS burst image sensor can use small trench-type capacitors for memory elements, instead of CCD channels. However, the transfer noise from a floating diffusion to the memory element increases in proportion to the square root of the frame rate. The Hanabi chip overcomes the compromise between these pros and cons.
Measurement-Based Domain Parameter Optimization in Electrical Impedance Tomography Imaging
Authors: Dusek Jan , Mikulka Jan
Published: April 2021

This paper discusses the optimization of domain parameters in electrical impedance tomography-based imaging. Precise image reconstruction requires accurate, well-correlated physical and numerical finite element method (FEM) models; thus, we employed the Nelder–Mead algorithm and a complete electrode model to evaluate the individual parameters, including the initial conductivity, electrode misplacement, and shape deformation. The optimization process was designed to calculate the parameters of the numerical model before the image reconstruction. The models were verified via simulation and experimental measurement with single source current patterns. The impact of the optimization on the above parameters was reflected in the applied image reconstruction process, where the conductivity error dropped by 6.16% and 11.58% in adjacent and opposite driving, respectively. In the shape deformation, the inhomogeneity area ratio increased by 11.0% and 48.9%; the imprecise placement of the 6th electrode was successfully optimized with adjacent driving; the conductivity error dropped by 12.69%; and the inhomogeneity localization exhibited a rise of 66.7%. The opposite driving option produces undesired duality resulting from the measurement pattern. The designed optimization process proved to be suitable for correlating the numerical and the physical models, and it also enabled us to eliminate imaging uncertainties and artifacts.
A Redundancy Metric Set within Possibility Theory for Multi-Sensor Systems
Authors: Holst Christoph-Alexander , Lohweg Volker
Published: April 2021

In intelligent technical multi-sensor systems, information is often at least partly redundant—either by design or inherently due to the dynamic processes of the observed system. If sensors are known to be redundant, (i) information processing can be engineered to be more robust against sensor failures, (ii) failures themselves can be detected more easily, and (iii) computational costs can be reduced. This contribution proposes a metric which quantifies the degree of redundancy between sensors. It is set within the possibility theory. Information coming from sensors in technical and cyber–physical systems are often imprecise, incomplete, biased, or affected by noise. Relations between information of sensors are often only spurious. In short, sensors are not fully reliable. The proposed metric adopts the ability of possibility theory to model incompleteness and imprecision exceptionally well. The focus is on avoiding the detection of spurious redundancy. This article defines redundancy in the context of possibilistic information, specifies requirements towards a redundancy metric, details the information processing, and evaluates the metric qualitatively on information coming from three technical datasets.
Sensing with Polarized LIDAR in Degraded Visibility Conditions Due to Fog and Low Clouds
Authors: Ronen Ayala , Agassi Eyal , Yaron Ofer
Published: April 2021

LIDAR (Light Detection and Ranging) sensors are one of the leading technologies that are widely considered for autonomous navigation. However, foggy and cloudy conditions might pose a serious problem for a wide adoption of their use. Polarization is a well-known mechanism often applied to improve sensors’ performance in a dense atmosphere, but is still not commonly applied, to the best of our knowledge, in self-navigated devices. This article explores this issue, both theoretically and experimentally, and focuses on the dependence of the expected performance on the atmospheric interference type. We introduce a model which combines the well-known LIDAR equation with Stocks vectors and the Mueller matrix formulations in order to assess the magnitudes of the true target signal loss as well as the excess signal that arises from the scattering medium radiance, by considering the polarization state of the E–M (Electro-Magnetic) waves. Our analysis shows that using the polarization state may recover some of the poor performance of such systems for autonomous platforms in low visibility conditions, but it depends on the atmospheric medium type. This conclusion is supported by measurements held inside an aerosol chamber within a well-controlled and monitored artificial degraded visibility atmospheric environment. The presented analysis tool can be used for the optimization of design and trade-off analysis of LIDAR systems, which allow us to achieve the best performance for self-navigation in all weather conditions.
Validity and Reliability of the Inertial Measurement Unit for Barbell Velocity Assessments: A Systematic Review
Authors: Clemente Filipe Manuel , Akyildiz Zeki , Pino-Ortega José , Rico-González Markel
Published: April 2021

The use of inertial measurement unit (IMU) has become popular in sports assessment. In the case of velocity-based training (VBT), there is a need to measure barbell velocity in each repetition. The use of IMUs may make the monitoring process easier; however, its validity and reliability should be established. Thus, this systematic review aimed to (1) identify and summarize studies that have examined the validity of wearable wireless IMUs for measuring barbell velocity and (2) identify and summarize studies that have examined the reliability of IMUs for measuring barbell velocity. A systematic review of Cochrane Library, EBSCO, PubMed, Scielo, Scopus, SPORTDiscus, and Web of Science databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 161 studies initially identified, 22 were fully reviewed, and their outcome measures were extracted and analyzed. Among the eight different IMU models, seven can be considered valid and reliable for measuring barbell velocity. The great majority of IMUs used for measuring barbell velocity in linear trajectories are valid and reliable, and thus can be used by coaches for external load monitoring.
Dynamically Controlling Offloading Thresholds in Fog Systems
Authors: Alenizi Faten , Rana Omer
Published: April 2021

Fog computing is a potential solution to overcome the shortcomings of cloud-based processing of IoT tasks. These drawbacks can include high latency, location awareness, and security—attributed to the distance between IoT devices and cloud-hosted servers. Although fog computing has evolved as a solution to address these challenges, it is known for having limited resources that need to be effectively utilized, or its advantages could be lost. Computational offloading and resource management are critical to be able to benefit from fog computing systems. We introduce a dynamic, online, offloading scheme that involves the execution of delay-sensitive tasks. This paper proposes an architecture of a fog node able to adjust its offloading threshold dynamically (i.e., the criteria by which a fog node decides whether tasks should be offloaded rather than executed locally) using two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC). These algorithms seek to minimize overall delay, maximize throughput, and minimize energy consumption at the fog layer. Compared to other benchmarks, our approach could reduce latency by up to 95%, improve throughput by 71%, and reduce energy consumption by up to 67% in fog nodes.
Design and Simulation Investigation of Si3N4 Photonics Circuits for Wideband On-Chip Optical Gas Sensing around 2 µm Optical Wavelength
Authors: Koompai Natnicha , Chaisakul Papichaya , Limsuwan Pichet , Le Roux Xavier , Vivien Laurent , Marris-Morini Delphine
Published: April 2021

We theoretically explore the potential of Si3N4 on SiO2 waveguide platform toward a wideband spectroscopic detection around the optical wavelength of 2 μm. The design of Si3N4 on SiO2 waveguide architectures consisting of a Si3N4 slot waveguide for a wideband on-chip spectroscopic sensing around 2 μm, and a Si3N4 multi-mode interferometer (MMI)-based coupler for light coupling from classical strip waveguide into the identified Si3N4 slot waveguides over a wide spectral range are investigated. We found that a Si3N4 on SiO2 slot waveguide structure can be designed for using as optical interaction part over a spectral range of interest, and the MMI structure can be used to enable broadband optical coupling from a strip to the slot waveguide for wideband multi-gas on-chip spectroscopic sensing. Reasons for the operating spectral range of the system are discussed.
A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning
Authors: Kaluarachchi Tharindu , Reis Andrew , Nanayakkara Suranga
Published: April 2021

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.
Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network
Authors: Kim Bubryur , Yuvaraj N. , Sri Preethaa K. R. , Hu Gang , Lee Dong-Eun
Published: April 2021

Wind tunnel testing techniques are the main research tools for evaluating the wind loadings of buildings. They are significant in designing structurally safe and comfortable buildings. The wind tunnel pressure measurement technique using pressure sensors is significant for assessing the cladding pressures of buildings. However, some pressure sensors usually fail and cause loss of data, which are difficult to restore. In the literature, numerous techniques are implemented for imputing the single instance data values and data imputation for multiple instantaneous time intervals with accurate predictions needs to be addressed. Thus, the data imputation capacity of machine learning models is used to predict the missing wind pressure data for tall buildings in this study. A generative adversarial imputation network (GAIN) is proposed to predict the pressure coefficients at various instantaneous time intervals on tall buildings. The proposed model is validated by comparing the performance of GAIN with that of the K-nearest neighbor and multiple imputations by chained equation models. The experimental results show that the GAIN model provides the best fit, achieving more accurate predictions with the minimum average variance and minimum average standard deviation. The average mean-squared error for all four sides of the building was the minimum (0.016), and the average R-squared error was the maximum (0.961). The proposed model can ensure the health and prolonged existence of a structure based on wind environment.
Bandwidth Optimization of a Textile PIFA with DGS Using Characteristic Mode Analysis
Authors: Elias Bashar Bahaa Qas , Soh Ping Jack , Al-Hadi Azremi Abdullah , Akkaraekthalin Prayoot , Vandenbosch Guy A. E.
Published: April 2021

This work presents the design and optimization of an antenna with defected ground structure (DGS) using characteristic mode analysis (CMA) to enhance bandwidth. This DGS is integrated with a rectangular patch with circular meandered rings (RPCMR) in a wearable format fully using textiles for wireless body area network (WBAN) application. For this integration process, both CMA and the method of moments (MoM) were applied using the same electromagnetic simulation software. This work characterizes and estimates the final shape and dimensions of the DGS using the CMA method, aimed at enhancing antenna bandwidth. The optimization of the dimensions and shape of the DGS is simplified, as the influence of the substrates and excitation is first excluded. This optimizes the required time and resources in the design process, in contrast to the conventional optimization approaches made using full wave “trial and error” simulations on a complete antenna structure. To validate the performance of the antenna on the body, the specific absorption rate is studied. Simulated and measured results indicate that the proposed antenna meets the requirements of wideband on-body operation.
Occupational Conditions in Brazilian Modern Rural Slave Labour
Authors: Garreto Gairo , Baptista J. Santos , Mota Antônia
Published: April 2021

Work in rural properties in conditions similar to slavery remains a reality in many countries, including Brazil. The Brazilian State characterises contemporary slave labour as a condition of freedom restriction for paying off debt, served by intensive working hours or inadequate working conditions related to Occupational Safety and Health. This study highlights the working conditions in rural slavery in Brazil, based on the Occupational Safety and Health perspective. The study was carried out based on a sample of Inspection Reports of the Governmental Authority to Combat Modern Slavery. A random sample of 42 reports was collected and analysed, describing the working conditions of 392 rural workers characterised as labour analogous to slavery. The analysis strategy was carried out from an Occupational Safety and Health standpoint. Data sets were identified and selected, grouped into five categories: Work routine; General health conditions; Manual machines and tools; Environmental conditions. Widespread exposure of Neo-enslaved workers to stressful working hours was found, in addition to severe Occupational Safety and Health problems. The occurrence of all these characteristics simultaneously was the most observed phenomenon among the sample, which demonstrates that it is possible to identify cases of modern slavery from an Occupational Safety and Health perspective.
How a Lack of Green in the Residential Environment Lowers the Life Satisfaction of City Dwellers and Increases Their Willingness to Relocate
Authors: Kley Stefanie , Dovbishchuk Tetiana
Published: April 2021

This paper investigates whether various forms of green spaces in the residential environment are associated with city dwellers’ life satisfaction and their willingness to relocate. Previous research on different forms of green spaces in the residential environment as a direct source of life satisfaction is scarce, and we know little about whether green spaces affect the decision to relocate. We address these topics with a two-equation model that estimates respondents’ considerations to relocate while accounting for life satisfaction. With this strategy, we are able to test which aspects of residential greenery (window view, green environment, green yard, own garden, and balcony) are associated with one or both outcomes, controlling for life-course events and demographic characteristics. The data come from a primary survey conducted in two large German cities, Cologne and Hamburg, in 2020/21 (N = 1886). The results show that not having green elements in the window view, not having a green yard, and—exclusively for parents—not having a garden increase the likelihood of considering residential relocation. Not having a balcony and not having a garden are directly associated with decreased life satisfaction, and decreased life satisfaction triggers the willingness to relocate.
Organizations’ Management of the COVID-19 Pandemic: A Scoping Review of Business Articles
Authors: Boiral Olivier , Brotherton Marie-Christine , Rivaud Léo , Guillaumie Laurence
Published: April 2021

The objective of this study was to analyze the early COVID-19 crisis management practices implemented in organizations based on a scoping review of relevant business articles published on this issue in newspapers and magazines between March and May 2020. In total, after applying inclusion and exclusion criteria on 2707 potentially relevant articles, 246 articles describing organizational initiatives to manage COVID-19 were selected and analyzed in detail. The results of this study highlight the opportunities and threats arising from the pandemic as well as the most innovative measures put in place, particularly in the areas of health, human resources management (HRM), work organization, social and environmental responsibility, and crisis management. The description of the main practices identified and their illustration through various examples show the importance of corporate sustainability in managing the pandemic and demonstrate the cross-cutting nature of this crisis, which affects most corporate functions simultaneously. This study also makes it possible to identify certain leaders’ approaches that can be considered exemplary or, in contrast, that should be avoided, while highlighting the paradoxes and difficulties of assessing corporate social responsibility in times of crisis.
Sustainability Assessment of Pasture-Based Dairy Sheep Systems: A Multidisciplinary and Multiscale Approach
Authors: Barron Luis Javier R. , Andonegi Aitor , Gamboa Gonzalo , Garmendia Eneko , García Oihana , Aldai Noelia , Aldezabal Arantza
Published: April 2021

This article describes a novel methodological approach for the integrated sustainability assessment of pasture-based dairy sheep systems. Most studies on livestock system sustainability focus on animal production, farm profitability, and mitigation strategies of greenhouse gas emissions. However, recent research indicates that pasture-based livestock farming also contributes positively to rural areas, and the associated increase in plant diversity promotes ecosystem functioning and services in natural and managed grasslands. Likewise, little attention has focused on how pasture-based livestock systems affect soil carbon changes, biodiversity, and ecotoxicity. Furthermore, the quality and safety of food products, particularly sheep milk and cheese, and socioeconomic issues such as cultural heritage and consumer behavior are often neglected in livestock system sustainability assessments. To improve the analysis of sustainability and adaptation strategies of livestock systems, we suggest a holistic approach that integrates indicators from diverse disciplines with complementary methods and models capable of capturing the complexity of these systems at multiple scales. A multidisciplinary perspective generates new indicators to identify critical trade-offs and synergies related to the resilience of dairy sheep livestock systems. A multiscale approach provides insights on the effects of socioeconomic and environmental changes associated with current dairy sheep grazing systems across multiple scales. The combined approach will facilitate the development and progressive implementation of novel management strategies needed to adapt pasture-based dairy sheep farms to changing conditions under future socioeconomic and environmental scenarios.
Impact of Selected Intelligent Systems in Logistics on the Creation of a Sustainable Market Position of Manufacturing Companies in Poland in the Context of Industry 4.0
Authors: Stanisławski Robert , Szymonik Andrzej
Published: April 2021

Within the broadly understood concept of Industry 4.0, many intelligent systems featuring artificial intelligence are used. Their importance in recent years has been growing. That can be attributed to a number of notable advantages these systems provide, including the possibility of expanding the manufacturer’s offer, greater customization of products, which facilitates reaching niche markets, as well as better internal and/or external communication (between the company and the consumer). Six such systems are brought into focus: The Internet of Things, Data Base, Cloud Computing, Blockchain, SCADA (Supervisory Control and Data Acquisition), and SMAC (Social, Mobile, Analytics, Cloud). The aim of the study was to assess their relevance and impact on the market position of enterprises in Poland. The analysis is based on the research carried out by the authors in 2020 on a sample of production companies that use logistic supply chains in their operations. The conclusions are to some extent surprising, e.g., the companies do not believe that implementing intelligence systems gives them significant market advantage nor that the positive impact of these systems depends directly on the number of systems used.
Boon or Bane: Effect of Adjacent YIMBY or NIMBY Facilities on the Benefit Evaluation of Open Spaces or Cropland
Authors: Wu Wun-Jheng , Wu Pei-Ing , Liou Je-Liang
Published: April 2021

This is the first study to comprehensively evaluate the benefit of urban open spaces and cropland with different adjacent public facilities seen as locally undesirable (“not in my backyard”, NIMBY) or desirable (“yes in my backyard”, YIMBY). The total benefit increases or decreases for urban open space and cropland with adjacent NIMBY or YIMBY facilities in a municipality in Taiwan. The results show that for the city as a whole, the current arrangement of NIMBY and YIMBY in different zones decreases the total benefit of urban open spaces in highly urbanized zones and increases the total damage to cropland in extremely rural zones. This indicates a need to avoid further installing NIMBY or YIMBY facilities in already occupied urban open spaces. The results also demonstrate that locating NIMBY or YIMBY facilities near cropland fails to highlight the benefit of YIMBY facilities and magnifies opposition to NIMBY facilities. For individual housing units, the total damage is 1.87% of the average housing price for cropland-type open space with adjacent NIMBY or YIMBY facilities, and the total benefit is 7.43% of the average housing price for urban-type open space in a highly urbanized area. In contrast, the total benefit for open space with adjacent NIMBY or YIMBY facilities is a 2.95–13.80% increase in the average housing price for areas with mixed urban open space and cropland.