bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Era in Engineering Plastics: Compatibility and Perspectives of Sustainable Alipharomatic Poly(ethylene terephthalate)/Poly(ethylene 2,5-furandicarboxylate) Blends
Authors: Papageorgiou Dimitrios G. , Tsetsou Irini , Ioannidis Raphael O. , Nikolaidis George N. , Exarhopoulos Stylianos , Kasmi Nejib , Bikiaris Dimitrios N. , Achilias Dimitris S. , Papageorgiou George Z.
Published: March 2021

The industrialisation of poly(ethylene 2,5-furandicarboxylate) for total replacement of poly(ethylene terephthalate) in the polyester market is under question. Preparation of high-performing polymer blends is a well-established strategy for tuning the properties of certain homopolymers and create tailor-made materials to meet the demands for a number of applications. In this work, the structure, thermal properties and the miscibility of a series of poly(ethylene terephthalate)/poly(ethylene 2,5-furandicarboxylate) (PET/PEF) blends have been studied. A number of thermal treatments were followed in order to examine the thermal transitions, their dynamic state and the miscibility characteristics for each blend composition. Based on their glass transition temperatures and melting behaviour the PET/PEF blends are miscible at high and low poly(ethylene terephthalate) (PET) contents, while partial miscibility was observed at intermediate compositions. The multiple melting was studied and their melting point depression was analysed with the Flory-Huggins theory. In an attempt to further improve miscibility, reactive blending was also investigated.
Rotational Particle Separation in Solutions: Micropolar Fluid Theory Approach
Authors: Shelukhin Vladimir
Published: March 2021

We develop a new mathematical model for rotational sedimentation of particles for steady flows of a viscoplastic granular fluid in a concentric-cylinder Couette geometry when rotation of the Couette cell inner cylinder is prescribed. We treat the suspension as a micro-polar fluid. The model is validated by comparison with known data of measurement. Within the proposed theory, we prove that sedimentation occurs due to particles’ rotation and rotational diffusion.
Microstructured Macromaterials Based on IPN Microgels
Authors: Nasimova Irina Rashitovna , Rudyak Vladimir Yurievich , Doroganov Anton Pavlovich , Kharitonova Elena Petrovna , Kozhunova Elena Yurievna
Published: March 2021

This study investigates the formation of microstructured macromaterials from thermo- and pH-sensitive microgels based on interpenetrating networks of poly-N-isopropylacrylamide (PNIPAM) and polyacrylic acid (PAA). Macromaterials are produced as a result of the deposition of microgel particles and subsequent crosslinking of polyacrylic acid subnetworks to each other due to the formation of the anhydride bonds during annealing. Since both PNIPAM and PAA are environment-sensitive polymers, one can expect that their conformational state during material development will affect its resulting properties. Thus, the influence of conditions of preparation for annealing (pH of the solution, the temperature of preliminary drying) on the swelling behavior, pH- and thermosensitivity, and macromaterial inner structure was investigated. In parallel, the study of the effect of the relative conformations of the IPN microgel subnetworks on the formation of macromaterials was carried out by the computer simulations method. It was shown that the properties of the prepared macromaterials strongly depend both on the temperature and pH of the PNIPAM-PAA IPN microgel dispersions. This opens up new opportunities to obtain materials with pre-chosen characteristics and environmental sensitivity.
Ontology-Based Process Modelling-with Examples of Physical Topologies
Authors: Preisig Heinz A
Published: March 2021

Reductionism and splitting application domain into disciplines and identify the smallest required model-granules, termed ”basic entity” combined with systematic construction of the behaviour equations for the basic entities, yields a systematic approach to process modelling. We do not aim toward a single modelling domain, but we enable to address specific application domains and object inheritance. We start with reductionism and demonstrate how the basic entities are depending on the targeted application domain. We use directed graphs to capture process models, and we introduce a new concept, which we call ”tokens” that enables us to extend the context beyond physical systems. The network representation is hierarchical so as to capture complex systems. The interacting basic entities are defined in the leave nodes of the hierarchy, making the overall model the interacting networks in the leave nodes. Multi-disciplinary and multi-scale models result in a network of networks. We identify two distinct network communication ports, namely ports that exchange tokens and ports that transfer information of tokens in accumulators. An ontology captures the structural elements and the applicable rules and defines the syntax to establish the behaviour equations. Linking the behaviours to the basic entities defines the alphabet of a graphical language. We use this graphical language to represent processes, which has proven to be efficient and valuable. A set of three examples demonstrates the power of the graphical language. The Process Modelling framework (ProMo) implements the ontology-centred approach to process modelling and uses the graphical language to construct process models.
Water Resources, Forced Migration and Tensions with Host Communities in the Nigerian Part of the Lake Chad Basin
Authors: Kamta Frederic Noel , Schilling Janpeter , Scheffran Jürgen
Published: March 2021

The Lake Chad region is facing a nexus of interconnected problems including fragility, violent conflict, forced displacement, and scarcity of water and other resources, further aggravated by climate change. Focusing on northeast Nigeria, this study aims to answer the following questions: (1) What role does access to water and farming play in out-migration and return in northeast Nigeria? (2) What is the potential of tensions between internally displaced persons (IDPs) and host communities? Data for this study were collected between March and May 2019 by interviewing 304 local residents and IDPs in northeast Nigeria, as well as experts on migration, environmental, humanitarian and conflict-related issues in research centers and governmental institutions in Abuja. Given the pronounced water scarcity in the region, the results show that between 47% and 95% of rural community members interviewed in northeast Nigeria would be willing to migrate in cases of water scarcity. At the time of study, only 2.5% to 7% of respondents had migrated previously in response to water scarcity, indicating that insecurity and conflict were, however, more relevant drivers of displacement. Regarding our second research question, we find a potential for tensions between IDPs and host communities, as 85% of the interviewed host community members oppose the presence of the IDPs. Hence, measures are needed to improve relations between the two groups. In order to avoid a future scenario where water scarcity becomes a significant driver of migration, efficient management of water resources is paramount. Such action would not only address the issue of migration, but also strengthen the resilience of communities in northern Nigeria.
Multi AGV Coordination Tolerant to Communication Failures
Authors: Matos Diogo , Costa Pedro , Lima José , Costa Paulo
Published: March 2021

Most path planning algorithms used presently in multi-robot systems are based on offline planning. The Timed Enhanced A* (TEA*) algorithm gives the possibility of planning in real time, rather than planning in advance, by using a temporal estimation of the robot’s positions at any given time. In this article, the implementation of a control system for multi-robot applications that operate in environments where communication faults can occur and where entire sections of the environment may not have any connection to the communication network will be presented. This system uses the TEA* to plan multiple robot paths and a supervision system to control communications. The supervision system supervises the communication with the robots and checks whether the robot’s movements are synchronized. The implemented system allowed the creation and execution of paths for the robots that were both safe and kept the temporal efficiency of the TEA* algorithm. Using the Simtwo2020 simulation software, capable of simulating movement dynamics and the Lazarus development environment, it was possible to simulate the execution of several different missions by the implemented system and analyze their results.
Satellite Imagery for Monitoring and Mapping Soil Chromium Pollution in a Mine Waste Dump
Authors: Khosravi Vahid , Ardejani Faramarz Doulati , Gholizadeh Asa , Saberioon Mohammadmehdi
Published: March 2021

Weathering and oxidation of sulphide minerals in mine wastes release toxic elements in surrounding environments. As an alternative to traditional sampling and chemical analysis methods, the capability of proximal and remote sensing techniques was investigated in this study to predict Chromium (Cr) concentration in 120 soil samples collected from a dumpsite in Sarcheshmeh copper mine, Iran. The samples’ mineralogy and Cr concentration were determined and were then subjected to laboratory reflectance spectroscopy in the range of Visible–Near Infrared–Shortwave Infrared (VNIR–SWIR: 350–2500 nm). The raw spectra were pre-processed using Savitzky-Golay First-Derivative (SG-FD) and Savitzky-Golay Second-Derivative (SG-SD) algorithms. The important wavelengths were determined using Partial Least Squares Regression (PLSR) coefficients and Genetic Algorithm (GA). Artificial Neural Networks (ANN), Stepwise Multiple Linear Regression (SMLR) and PLSR data mining methods were applied to the selected spectral variables to assess Cr concentration. The developed models were then applied to the selected bands of Aster, Hyperion, Sentinel-2A, and Landsat 8-OLI satellite images of the area. Afterwards, rasters obtained from the best prediction model were segmented using a binary fitness function. According to the outputs of the laboratory reflectance spectroscopy, the highest prediction accuracy was obtained using ANN applied to the SD pre-processed spectra with R2 = 0.91, RMSE = 8.73 mg/kg and RPD = 2.76. SD-ANN also showed an acceptable performance on mapping the spatial distribution of Cr using the ordinary kriging technique. Using satellite images, SD-SMLR provided the best prediction models with R2 values of 0.61 and 0.53 for Hyperion and Sentinel-2A, respectively. This led to the higher visual similarity of the segmented Hyperion and Sentinel-2A images with the Cr distribution map. This study’s findings indicated that applying the best prediction models obtained by spectroscopy to the selected wavebands of Hyperion and Sentinel-2A satellite imagery could be considered a promising technique for rapid, cost-effective and eco-friendly assessment of Cr concentration in highly heterogeneous mining areas.
The Quality of Remote Sensing Optical Images from Acquisition to Users
Authors: Selva Massimo
Published: March 2021

The need to observe and characterize the environment leads to a constant increase of the spatial, spectral, and radiometric resolution of new optical sensors [...]
Quality Assessment of TanDEM-X DEMs, SRTM and ASTER GDEM on Selected Chinese Sites
Authors: Han Haijiao , Zeng Qiming , Jiao Jian
Published: March 2021

Digital elevation models (DEMs) are the basic data of science and engineering technology research. SRTM and ASTER GDEM are currently widely used global DEMs, and TanDEM-X DEM, released in 2016, has attracted users’ attention due to its unprecedented accuracy. These global datasets are often used for local applications and the quality of DEMs affects the results of applications. Many researchers have assessed and compared the quality of global DEMs on a local scale. To provide some additional insights on quality assessment of 12- and 30-m resolution TanDEM-X DEMs, 30-m resolution ASTER GDEM and 30-m resolution SRTM, this study assessed differences’ performance in relation to not only geographical features but also the ways in which DEMs have been created on selected Chinese sites, taking ICESat/GLAS points with 14-cm absolute vertical accuracy but size of 70-m diameter and 12-m resolution TanDEM-X DEM with less than 10-m absolute vertical accuracy as the reference data for comprehensive quality evaluation. When comparing the three 30-m DEMs with the reference DEM, an improved Least Z-Difference (LZD) method was applied for co-registration between models, and Quantile–Quantile (Q-Q) plot was used to identify if the DEM errors follow a normal distribution to help choose proper statistical indicators accordingly. The results show that: (1) TanDEM-X DEMs have the best overall quality, followed by SRTM. ASTER GDEM has the worst quality. The 12-m TanDEM-X DEM has significant advantages in describing terrain details. (2) The quality of DEM has a strong relationship with slope, aspect and land cover. However, the relationship between aspect and vertical quality weakens after data co-registration. The quality of DEMs gets higher with the increasing number of images used in the fusion process. The quality in where slopes opposite to the radar beam is the worst for SRTM, which could provide a new perspective for quality assessment of SRTM and other DEMs whose incidence angle files are available. (3) Systematic deviations can reduce the vertical quality of DEM. The differences have non-normal distribution even after co-registration. For researchers who want to know the quality of a DEM in order to use it in further applications, they should pay more attention to the terrain factors and land cover in their study areas and the ways in which the DEM has been created.
Precipitation Drives the NDVI Distribution on the Tibetan Plateau While High Warming Rates May Intensify Its Ecological Droughts
Authors: Jiao Kewei , Gao Jiangbo , Liu Zhihua
Published: March 2021

Climate change has significantly affected the ecosystem of the Tibetan Plateau. There, temperature rises and altered precipitation patterns have led to notable changes in its vegetation growth processes and vegetation cover features. Yet current research still pays relatively little attention to the regional climatic determinants and response patterns of such vegetation dynamics. In this study, spatial patterns in the response of the normalized difference vegetation index (NDVI) to climate change and its dynamic characteristics during the growing season were examined for the Tibetan Plateau, by using a pixel-scale-based geographically weighted regression (GWR) based on the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data, as well as data for temperature and moisture indices collected at meteorological stations, for the period 1982–2015. The results show the following. Spatial nonstationary relationships, primarily positive, were found between the NDVI and climatic factors in the Tibetan Plateau. However, warming adversely affected vegetation growth and cover in some arid and semiarid regions of the northeast and west Tibetan Plateau. Additionally, precipitation played a dominant role in the NDVI of the Tibetan Plateau in the largest area (accounting for 39.7% of total area). This suggests that increased moisture conditions considerably facilitated vegetation growth and cover in these regions during the study period. Temperature mainly played a dominant role in the NDVI in some parts of the plateau sub-cold zone and some southeastern regions of the Tibetan Plateau. In particular, the minimum temperature was the dominant driver of NDVI over a larger area than any of the other temperature indices. Furthermore, spatial regressions between NDVI dynamics and climatic variability revealed that a faster warming rate in the arid and semiarid regions impeded vegetation growth through mechanisms such as drought intensification. Moisture variability was found to act as a key factor regulating the extent of vegetation cover on the south Tibetan Plateau.
Wearable Technologies for Mental Workload, Stress, and Emotional State Assessment during Working-Like Tasks: A Comparison with Laboratory Technologies
Authors: Giorgi Andrea , Ronca Vincenzo , Vozzi Alessia , Sciaraffa Nicolina , di Florio Antonello , Tamborra Luca , Simonetti Ilaria , Aricò Pietro , Di Flumeri Gianluca , Rossi Dario , Borghini Gianluca
Published: March 2021

The capability of monitoring user’s performance represents a crucial aspect to improve safety and efficiency of several human-related activities. Human errors are indeed among the major causes of work-related accidents. Assessing human factors (HFs) could prevent these accidents through specific neurophysiological signals’ evaluation but laboratory sensors require highly-specialized operators and imply a certain grade of invasiveness which could negatively interfere with the worker’s activity. On the contrary, consumer wearables are characterized by their ease of use and their comfortability, other than being cheaper compared to laboratory technologies. Therefore, wearable sensors could represent an ideal substitute for laboratory technologies for a real-time assessment of human performances in ecological settings. The present study aimed at assessing the reliability and capability of consumer wearable devices (i.e., Empatica E4 and Muse 2) in discriminating specific mental states compared to laboratory equipment. The electrooculographic (EOG), electrodermal activity (EDA) and photoplethysmographic (PPG) signals were acquired from a group of 17 volunteers who took part to the experimental protocol in which different working scenarios were simulated to induce different levels of mental workload, stress, and emotional state. The results demonstrated that the parameters computed by the consumer wearable and laboratory sensors were positively and significantly correlated and exhibited the same evidences in terms of mental states discrimination.
Measurement of Physical Activity by Shoe-Based Accelerometers—Calibration and Free-Living Validation
Authors: Fridolfsson Jonatan , Arvidsson Daniel , Grau Stefan
Published: March 2021

There is conflicting evidence regarding the health implications of high occupational physical activity (PA). Shoe-based accelerometers could provide a feasible solution for PA measurement in workplace settings. This study aimed to develop calibration models for estimation of energy expenditure (EE) from shoe-based accelerometers, validate the performance in a workplace setting and compare it to the most commonly used accelerometer positions. Models for EE estimation were calibrated in a laboratory setting for the shoe, hip, thigh and wrist worn accelerometers. These models were validated in a free-living workplace setting. Furthermore, additional models were developed from free-living data. All sensor positions performed well in the laboratory setting. When the calibration models derived from laboratory data were validated in free living, the shoe, hip and thigh sensors displayed higher correlation, but lower agreement, with measured EE compared to the wrist sensor. Using free-living data for calibration improved the agreement of the shoe, hip and thigh sensors. This study suggests that the performance of a shoe-based accelerometer is similar to the most commonly used sensor positions with regard to PA measurement. Furthermore, it highlights limitations in using the relationship between accelerometer output and EE from a laboratory setting to estimate EE in a free-living setting.
Acoustic Optical Fiber Sensor Based on Graphene Oxide Membrane
Authors: Monteiro Catarina S. , Raposo Maria , Ribeiro Paulo A. , Silva Susana O. , Frazão Orlando
Published: March 2021

A Fabry–Pérot acoustic sensor based on a graphene oxide membrane was developed with the aim to achieve a faster and simpler fabrication procedure when compared to similar graphene-based acoustic sensors. In addition, the proposed sensor was fabricated using methods that reduce chemical hazards and environmental impacts. The developed sensor, with an optical cavity of around 246 µm, showed a constant reflected signal amplitude of 6.8 ± 0.1 dB for 100 nm wavelength range. The sensor attained a wideband operation range between 20 and 100 kHz, with a maximum signal-to-noise ratio (SNR) of 32.7 dB at 25 kHz. The stability and sensitivity to temperatures up to 90 °C was also studied. Moreover, the proposed sensor offers the possibility to be applied as a wideband microphone or to be applied in more complex systems for structural analysis or imaging.
Can EEG Be Adopted as a Neuroscience Reference for Assessing Software Programmers’ Cognitive Load?
Authors: Medeiros Júlio , Couceiro Ricardo , Duarte Gonçalo , Durães João , Castelhano João , Duarte Catarina , Castelo-Branco Miguel , Madeira Henrique , de Carvalho Paulo , Teixeira César
Published: March 2021

An emergent research area in software engineering and software reliability is the use of wearable biosensors to monitor the cognitive state of software developers during software development tasks. The goal is to gather physiologic manifestations that can be linked to error-prone scenarios related to programmers’ cognitive states. In this paper we investigate whether electroencephalography (EEG) can be applied to accurately identify programmers’ cognitive load associated with the comprehension of code with different complexity levels. Therefore, a controlled experiment involving 26 programmers was carried. We found that features related to Theta, Alpha, and Beta brain waves have the highest discriminative power, allowing the identification of code lines and demanding higher mental effort. The EEG results reveal evidence of mental effort saturation as code complexity increases. Conversely, the classic software complexity metrics do not accurately represent the mental effort involved in code comprehension. Finally, EEG is proposed as a reference, in particular, the combination of EEG with eye tracking information allows for an accurate identification of code lines that correspond to peaks of cognitive load, providing a reference to help in the future evaluation of the space and time accuracy of programmers’ cognitive state monitored using wearable devices compatible with software development activities.
Portable Pulsed Coherent Lidar for Noncooperation Targets at the Few-Photon Level
Authors: Pang Chengkai , Zhang Qiongqiong , Li Zhaohui , Wu Guang
Published: March 2021

The decoherence in coherent lidar becomes serious with the increase in distance. A small laser spot can suppress the decoherence of the echo light from noncooperation targets. However, it is very difficult to keep a small light spot over a long distance. In this paper, a pulsed coherent lidar with high sensitivity at the few-photon level was demonstrated. A phase plate was used to modulate the wavefront of the laser to achieve 100 m focusing which reduced the decoherence effect. Based on coherent detection and time-of-flight (TOF) measurements, long-distance laser ranging and imaging on all days was realized. A signal classification and superposition method was used to extract the echo signal submerged in noise. The system was experimentally demonstrated by ranging different noncooperation targets within 105.0 m. The measurement rate was 10 k/s, and the measurement uncertainty was 1.48 cm. In addition, laser imaging was realized at ~50.0 m. The system was simple and portable as well as eye safe, and it may offer new application possibilities in automated vehicle lidar.
Gaussian Approach for the Synthesis of Periodic and Aperiodic Antenna Arrays: Method Review and Design Guidelines
Authors: Buttazzoni Giulia , Babich Fulvio , Pastore Stefano , Vatta Francesca , Comisso Massimiliano
Published: March 2021

This paper presents a complete overview of the recently developed Gaussian approach for the synthesis of both periodic and aperiodic linear antenna arrays in conjunction with an exhaustive numerical investigation of the achievable performance. The position and excitation synthesis problems are jointly modeled to enable a direct mutual comparison between the two strategies. To this aim, different parameter settings are selected to analyze the results in terms of achieved beamwidth and maximum sidelobe levels as a function of the array aperture and of the number of radiating elements. The insights inferred from this numerical investigation are exploited to derive a novel first-step procedure with the purpose of enabling an antenna engineer to quickly identify the most suitable design approach, thus reducing the time required for antenna system development.
Context-Aware Emotion Recognition in the Wild Using Spatio-Temporal and Temporal-Pyramid Models
Authors: Do Nhu-Tai , Kim Soo-Hyung , Yang Hyung-Jeong , Lee Guee-Sang , Yeom Soonja
Published: March 2021

Emotion recognition plays an important role in human–computer interactions. Recent studies have focused on video emotion recognition in the wild and have run into difficulties related to occlusion, illumination, complex behavior over time, and auditory cues. State-of-the-art methods use multiple modalities, such as frame-level, spatiotemporal, and audio approaches. However, such methods have difficulties in exploiting long-term dependencies in temporal information, capturing contextual information, and integrating multi-modal information. In this paper, we introduce a multi-modal flexible system for video-based emotion recognition in the wild. Our system tracks and votes on significant faces corresponding to persons of interest in a video to classify seven basic emotions. The key contribution of this study is that it proposes the use of face feature extraction with context-aware and statistical information for emotion recognition. We also build two model architectures to effectively exploit long-term dependencies in temporal information with a temporal-pyramid model and a spatiotemporal model with “Conv2D+LSTM+3DCNN+Classify” architecture. Finally, we propose the best selection ensemble to improve the accuracy of multi-modal fusion. The best selection ensemble selects the best combination from spatiotemporal and temporal-pyramid models to achieve the best accuracy for classifying the seven basic emotions. In our experiment, we take benchmark measurement on the AFEW dataset with high accuracy.
Studies of Indium Tin Oxide-Based Sensing Electrodes in Potentiometric Zirconia Solid Electrolyte Gas Sensors
Authors: Dietrich Stefan , Kusnezoff Mihails , Michaelis Alexander
Published: March 2021

A zirconia-based potentiometric solid electrolyte gas sensor with internal solid state reference was used to study the response behavior of platinum cermet and indium tin oxide sensing electrodes. Target gases included both oxygen and carbon monoxide in nitrogen-based sample gas mixtures. It was found that with the indium tin oxide sensing electrode, the low-temperature behavior is mainly a result of incomplete equilibration due to contaminations of the electrode surface. On the other hand, some of these contaminant species have been identified as being pivotal for the higher carbon monoxide sensitivity of the indium tin oxide sensing electrode as compared to platinum cermet electrodes.
The Importance of Real-World Validation of Machine Learning Systems in Wearable Exercise Biofeedback Platforms: A Case Study
Authors: Argent Rob , Bevilacqua Antonio , Keogh Alison , Daly Ailish , Caulfield Brian
Published: March 2021

Machine learning models are being utilized to provide wearable sensor-based exercise biofeedback to patients undertaking physical therapy. However, most systems are validated at a technical level using lab-based cross validation approaches. These results do not necessarily reflect the performance levels that patients and clinicians can expect in the real-world environment. This study aimed to conduct a thorough evaluation of an example wearable exercise biofeedback system from laboratory testing through to clinical validation in the target setting, illustrating the importance of context when validating such systems. Each of the various components of the system were evaluated independently, and then in combination as the system is designed to be deployed. The results show a reduction in overall system accuracy between lab-based cross validation (>94%), testing on healthy participants (n = 10) in the target setting (>75%), through to test data collected from the clinical cohort (n = 11) (>59%). This study illustrates that the reliance on lab-based validation approaches may be misleading key stakeholders in the inertial sensor-based exercise biofeedback sector, makes recommendations for clinicians, developers and researchers, and discusses factors that may influence system performance at each stage of evaluation.
Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes
Authors: Wang Yanyan , Wang Lin , Zheng Ruijuan , Zhao Xuhui , Liu Muhua
Published: March 2021

In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) to get the offloading decision and the number of tasks that can be offloaded. Specifically, we first construct a system with multiple local smart device task queues and multiple edge processor task queues. Then, we formulate an offloading strategy to minimize the queue length of tasks in each time slot by minimizing the Lyapunov drift optimization problem, so as to realize the stability of queues and improve the offloading performance. In addition, we give a theoretical analysis on the stability of the BMDCO algorithm by deducing the upper bound of all queues in this system. The simulation results show the stability of the proposed algorithm, and demonstrate that the BMDCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the computation delay.
A Deep-Learning Framework for the Detection of Oil Spills from SAR Data
Authors: Shaban Mohamed , Salim Reem , Abu Khalifeh Hadil , Khelifi Adel , Shalaby Ahmed , El-Mashad Shady , Mahmoud Ali , Ghazal Mohammed , El-Baz Ayman
Published: March 2021

Oil leaks onto water surfaces from big tankers, ships, and pipeline cracks cause considerable damage and harm to the marine environment. Synthetic Aperture Radar (SAR) images provide an approximate representation for target scenes, including sea and land surfaces, ships, oil spills, and look-alikes. Detection and segmentation of oil spills from SAR images are crucial to aid in leak cleanups and protecting the environment. This paper introduces a two-stage deep-learning framework for the identification of oil spill occurrences based on a highly unbalanced dataset. The first stage classifies patches based on the percentage of oil spill pixels using a novel 23-layer Convolutional Neural Network. In contrast, the second stage performs semantic segmentation using a five-stage U-Net structure. The generalized Dice loss is minimized to account for the reduced oil spill representation in the patches. The results of this study are very promising and provide a comparable improved precision and Dice score compared to related work.
Current Input Pixel-Level ADC with High SNR and Wide Dynamic Range for a Microbolometer
Authors: Lee Jeongho , Nam Ilku , Woo DooHyung
Published: March 2021

A readout circuit incorporating a pixel-level analog-to-digital converter (ADC) is studied for two-dimensional medium wavelength infrared microbolometer arrays. The signal-to-noise ratio (SNR) and charge handling capacity of the unit cell circuit are improved by using the current input pixel-level ADC. The charge handling capacity of the integrator is appropriately extended to maximize the integration time regardless of the magnitude of the input current and low power supply voltage. The readout circuit was fabricated using a 0.35-μm 2-poly 4-metal CMOS process for a 640 × 512 array with a pixel size of 40 μm × 40 μm. The peak SNR and dynamic range are 77.1 and 80.1 dB, respectively, with a power consumption of 0.62 μW per pixel.
Dual Piezoelectric Energy Investing and Harvesting Interface for High-Voltage Input
Authors: Khan Muhammad Bilawal , Saif Hassan , Lee Kyoungho , Lee Yoonmyung
Published: March 2021

A novel harvesting interface for multiple piezoelectric transducers (PZTs) is proposed for high-voltage energy harvesting. Pre-biasing a PZT prior to its mechanical deformation increases its damping force, resulting in higher energy extraction. Unlike the conventional harvesters where a PZT-generated output is assumed to be continuous sinusoidal and output polarity is assumed to be alternating every cycle, PZT-generated output from human motion is expected to be random. Therefore, in the proposed approach, energy is invested to the PZT only when PZT deformation is detected. Upon the motion detection, energy stored at a storage capacitor (CSTOR) from earlier energy harvesting cycle is invested to pre-bias PZT, enhancing energy extraction. The harvested energy is transferred to back CSTOR for energy investment on the next cycle and then excess energy is transferred to the battery. In addition, partial electric charge extraction (PECE) is adapted to extract a partial amount of charges from the PZT every time its voltage approaches the process limit of 40 V. Simulations with 0.35 µm BCD process show 7.61× (with PECE only) and 8.38× (with PECE and energy investment) improvement compared to the conventional rectifier-based harvesting scheme Proposed harvesting interface successfully harvests energy from excitations with open-circuit voltages up to 100 V.
The Prediction of Body Mass Index from Negative Affectivity through Machine Learning: A Confirmatory Study
Authors: Delnevo Giovanni , Mancini Giacomo , Roccetti Marco , Salomoni Paola , Trombini Elena , Andrei Federica
Published: March 2021

This study investigates on the relationship between affect-related psychological variables and Body Mass Index (BMI). We have utilized a novel method based on machine learning (ML) algorithms that forecast unobserved BMI values based on psychological variables, like depression, as predictors. We have employed various machine learning algorithms, including gradient boosting and random forest, with psychological variables relative to 221 subjects to predict both the BMI values and the BMI status (normal, overweight, and obese) of those subjects. We have found that the psychological variables in use allow one to predict both the BMI values (with a mean absolute error of 5.27–5.50) and the BMI status with an accuracy of over 80% (metric: F1-score). Further, our study has also confirmed the particular efficacy of psychological variables of negative type, such as depression for example, compared to positive ones, to achieve excellent predictive BMI values.
Synthesis of ZnAl2O4 and Evaluation of the Response in Propane Atmospheres of Pellets and Thick Films Manufactured with Powders of the Oxide
Authors: Huízar-Padilla Emilio , Guillén-Bonilla Héctor , Guillén-Bonilla Alex , Rodríguez-Betancourtt Verónica-María , Sánchez-Martínez A. , Guillen-Bonilla José Trinidad , Gildo-Ortiz Lorenzo , Reyes-Gómez Juan
Published: March 2021

ZnAl2O4 nanoparticles were synthesized employing a colloidal method. The oxide powders were obtained at 300 °C, and their crystalline phase was corroborated by X-ray diffraction. The composition and chemical structure of the ZnAl2O4 was carried out by X-ray and photoelectron spectroscopy (XPS). The optical properties were studied by UV-vis spectroscopy, confirming that the ZnAl2O4 nanoparticles had a direct transition with bandgap energy of 3.2 eV. The oxide’s microstructures were microbars of ~18.2 nm in size (on average), as analyzed by scanning (SEM) and transmission (TEM) electron microscopies. Dynamic and stationary gas detection tests were performed in controlled propane atmospheres, obtaining variations concerning the concentration of the test gas and the operating temperature. The optimum temperatures for detecting propane concentrations were 200 and 300 °C. In the static test results, the ZnAl2O4 showed increases in propane response since changes in the material’s electrical conductance were recorded (conductance = 1/electrical resistance, Ω). The increases were ~2.8 at 200 °C and ~7.8 at 300 °C. The yield shown by the ZnAl2O4 nanoparticles for detecting propane concentrations was optimal compared to other similar oxides categorized as potential gas sensors.
Accelerating On-Device Learning with Layer-Wise Processor Selection Method on Unified Memory
Authors: Ha Donghee , Kim Mooseop , Moon KyeongDeok , Jeong Chi Yoon
Published: March 2021

Recent studies have applied the superior performance of deep learning to mobile devices, and these studies have enabled the running of the deep learning model on a mobile device with limited computing power. However, there is performance degradation of the deep learning model when it is deployed in mobile devices, due to the different sensors of each device. To solve this issue, it is necessary to train a network model specific to each mobile device. Therefore, herein, we propose an acceleration method for on-device learning to mitigate the device heterogeneity. The proposed method efficiently utilizes unified memory for reducing the latency of data transfer during network model training. In addition, we propose the layer-wise processor selection method to consider the latency generated by the difference in the processor performing the forward propagation step and the backpropagation step in the same layer. The experiments were performed on an ODROID-XU4 with the ResNet-18 model, and the experimental results indicate that the proposed method reduces the latency by at most 28.4% compared to the central processing unit (CPU) and at most 21.8% compared to the graphics processing unit (GPU). Through experiments using various batch sizes to measure the average power consumption, we confirmed that device heterogeneity is alleviated by performing on-device learning using the proposed method.
A Novel Stochastic Approach for Static Damage Identification of Beam Structures Using Homotopy Analysis Algorithm
Authors: Wu Zhifeng , Huang Bin , Tee Kong Fah , Zhang Weidong
Published: March 2021

This paper proposes a new damage identification approach for beam structures with stochastic parameters based on uncertain static measurement data. This new approach considers not only the static measurement errors, but also the modelling error of the initial beam structures as random quantities, and can also address static damage identification problems with relatively large uncertainties. First, the stochastic damage identification equations with respect to the damage indexes were established. On this basis, a new homotopy analysis algorithm was used to solve the stochastic damage identification equations. During the process of solution, a static condensation technique and a L1 regularization method were employed to address the limited measurement data and ill-posed problems, respectively. Furthermore, the definition of damage probability index is presented to evaluate the possibility of existing damages. The results of two numerical examples show that the accuracy and efficiency of the proposed damage identification approach are good. In comparison to the first-order perturbation method, the proposed method can ensure better accuracy in damage identification with relatively large measurement errors and modelling error. Finally, according to the static tests of a simply supported concrete beam, the proposed method successfully identified the damage of the beam.
Miniaturized Hybrid Frequency Reader for Contactless Measurement Scenarios Using Resonant Surface Acoustic Wave Sensors
Authors: Scheiner Benedict , Probst Florian , Michler Fabian , Weigel Robert , Koelpin Alexander , Lurz Fabian
Published: March 2021

Due to higher automation and predictive maintenance, it becomes more and more important to acquire as many data as possible during industrial processes. However, many scenarios require remote sensing since either moving parts would result in wear and tear of cables or harsh environments prevent a wired connection. In the last few years, resonant surface acoustic wave (SAW) sensors have promised the possibility to be interrogable wirelessly which showed very good results in first studies. Therefore, the sensor’s resonance frequency shifts due to a changed measurand and thus has to be determined. However, up to now frequency reader systems showed several drawbacks like high costs or insufficient accuracy that blocked the way for a widespread usage of this approach in the mass market. Hence, this article presents a miniaturized and low cost six-port based frequency reader for SAW resonators in the 2.45 GHz ISM band that does not require an external calculation unit. It is shown that it can be either used to evaluate the scenario or measure the frequency directly with an amplitude or phase measurement, respectively. The performance of the system, including the hardware and embedded software, is finally shown by wired and contactless torque measurements.
Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images
Authors: Lee Haemin , Jung Chang-Sik , Kim Ki-Wan
Published: March 2021

Autofocus is an essential technique for airborne synthetic aperture radar (SAR) imaging to correct phase errors mainly due to unexpected motion error. There are several well-known conventional autofocus methods such as phase gradient autofocus (PGA) and minimum entropy (ME). Although these methods are still widely used for various SAR applications, each method has drawbacks such as limited bandwidth of estimation, low convergence rate, huge computation burden, etc. In this paper, feature preserving autofocus (FPA) algorithm is newly proposed. The algorithm is based on the minimization of the cost function containing a regularization term. The algorithm is designed for postprocessing purpose, which is different from the existing regularization-based algorithms such as sparsity-driven autofocus (SDA). This difference makes the proposed method far more straightforward and efficient than those existing algorithms. The experimental results show that the proposed algorithm achieves better performance, convergence, and robustness than the existing postprocessing autofocus algorithms.
Seepage Time Soft Sensor Model of Nonwoven Fabric Based on the Extreme Learning Machine Integrating Monte Carlo
Authors: Zhang Jing , Fan Yiqiang , Zhang Lulu , Xu Chi , Dong Xiaobin , Liu Luyao , Zhang Zhongping , Qiu Xianbo
Published: March 2021

Nonwoven fiber materials are materials with multifunctional purposes, and are widely used to make masks for preventing the new Coronavirus Disease 2019. Because of the complexity and particularity of their structure, it becomes difficult to model the penetration and flow characteristics of liquid in nonwoven fiber materials. In this paper, a novel seepage time soft sensor model of nonwoven fabric, based on Monte Carlo (MC), integrating extreme learning machine (ELM) (MCELM) is proposed. The Monte Carlo method is used to expand data samples. Then, an ELM method is used to establish the prediction model of the dyeing time of the nonwoven fiber material overlaps with the porous medium, as well as the insertion degree and height of the different quantity of hides. Compared with the back propagation (BP) neural network and radial basis function (RBF) neural network, the results show that the prediction model based on the MCELM method has significant power in terms of accuracy and prediction speed, which is conducive to the precise and rapid manufacture of nonwoven fiber materials in practical applications between liquid seepage characteristics and structural characteristics of porous media. Furthermore, the relationship between the proposed models has certain value for predicting the behavior and use of nonwoven fiber materials with different structural characteristics and related research processes.
The Fragility-Grievances-Conflict Triangle in the Middle East and North Africa (MENA): An Exploration of the Correlative Associations
Authors: Kivimäki Timo
Published: March 2021

The intention of this special issue of Social Sciences is to study state fragility and its relationship with conflict and grievances in the post-Cold War Middle East and North Africa (MENA). This article will lay the foundation for such a study by offering a conceptual foundation, data and the identification of the correlative associations that are specific to the MENA region. This article suggests that the relationship between political legitimacy, factionalism of the state, and conflict needs special, MENA-specific emphasis, as this relationship seems more prominently different in the MENA region, compared to the rest of the world. While in the rest of the world, different aspects of state fragility all relate to grievances and conflict dynamics, in the MENA region political factionalism has a disproportionate role in the explanation of conflict grievances and violence. Moreover, the role of oil dependence, and the impact of external intervention requires attention of specialists of the region.
Energy-Based Combined Nonlinear Observer and Voltage Controller for a PMSG Using Fuzzy Supervisor High Order Sliding Mode in a Marine Current Power System
Authors: Belkhier Youcef , Achour Abdelyazid , Shaw Rabindra Nath , Ullah Nasim , Chowdhury Md. Shahariar , Techato Kuaanan
Published: March 2021

A permanent magnet synchronous generator (PMSG) in s grid-connected tidal energy conversion system presents numerous advantages such as high-power density and ease of maintenance. However, the nonlinear properties of the generator and parametric uncertainties make the controller design more than a simple challenge. Within this paper we present a new combined passivity-based voltage control (PBVC) with a nonlinear observer. The PBVC is used to design the desired dynamics of the system, while the nonlinear observer serves to reconstruct the measured signals. A high order sliding-mode based fuzzy supervisory approach is selected to design the desired dynamics. This paper addresses the following two main parts: controlling the PMSG to guarantee the maximum tidal power extraction and integrate into to the grid-side converter (GSC), for this the new controller is proposed. The second task is to regulate the generated reactive power and the DC-link voltage to their references under any disturbances related to the machine-side converter (MSC). Furthermore, the robustness of the controller against parameter changes was taken into consideration. The developed controller is tested under parameter variations and compared to benchmark nonlinear control methods. Numerical simulations are performed in MATLAB/Simulink which clearly demonstrates the robustness of the proposed technique over the compared control methods. Moreover, the proposed controller is also validated using a processor in the loop (PIL) experiment using Texas Instruments (TI) Launchpad.