bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal Design Methodology of Tapered Waveguide Transducers for Thickness Monitoring
Authors: Jia Jiuhong , Ren Yue , Wang Weiming , Liao Zuoyu , Zhang Xiancheng , Tu Shan-Tung
Published: April 2020

For the purpose of providing transducers for long-term monitoring of wall thinning of critical pressure equipment in corrosion or high temperature environments, the optimal design methodology for tapered waveguide units was proposed in the present study. Firstly, the feasibility of the quasi-fundamental shear horizontal (SH0*) wave propagating in the tapered waveguide units was analyzed via numerical simulations, and the transmitting limitations of the non-dispersive SH0* wave were researched. Secondly, several tapered waveguide transducers with varying cross-sections to transmit pure SH0* wave were designed according to the numerical results. Experimental investigations were carried out, and the results were compared with waveguide transducers with a prismatic cross-section. It was found that the tapered waveguide units can transmit non-dispersive shear horizontal waves and suppress the wave attenuation at the same time. The experimental results agreed very well with the numerical simulations. Finally, high-temperature experiments were carried out, and the reliability of thickness measuring by the tapered waveguide transducers was validated. The errors between the measured and the true thicknesses were small. This work paves a solid foundation for the optimal design of tapered waveguide transducers for thickness monitoring of equipment in harsh environments.
Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors
Authors: Sigcha Luis , Costa Nélson , Pavón Ignacio , Costa Susana , Arezes Pedro , López Juan Manuel , De Arcas Guillermo
Published: April 2020

Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson’s disease (PD). The occurrence of FOG reduces the patients’ quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms’ evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients’ homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system).
Passive Visible Light Detection of Humans
Authors: Deprez Kenneth , Bastiaens Sander , Martens Luc , Joseph Wout , Plets David
Published: April 2020

This paper experimentally investigates passive human visible light sensing (VLS). A passive VLS system is tested consisting of one light emitting diode (LED) and one photodiode-based receiver, both ceiling-mounted. There is no line of sight between the LED and the receiver, so only reflected light can be considered. The influence of a human is investigated based on the received signal strength (RSS) values of the reflections of ambient light at the photodiode. Depending on the situation, this influence can reach up to ± 50 % . The experimental results show the influence of three various clothing colors, four different walking directions and four different layouts. Based on the obtained results, a human pass-by detection system is proposed and tested. The system achieves a detection rate of 100% in a controlled environment for 21 experiments. For a realistic corridor experiment, the system keeps its detection rate of 100% for 19 experiments.
Deep Learning-Based Upper Limb Functional Assessment Using a Single Kinect v2 Sensor
Authors: Ma Ye , Liu Dongwei , Cai Laisi
Published: April 2020

We develop a deep learning refined kinematic model for accurately assessing upper limb joint angles using a single Kinect v2 sensor. We train a long short-term memory recurrent neural network using a supervised machine learning architecture to compensate for the systematic error of the Kinect kinematic model, taking a marker-based three-dimensional motion capture system (3DMC) as the golden standard. A series of upper limb functional task experiments were conducted, namely hand to the contralateral shoulder, hand to mouth or drinking, combing hair, and hand to back pocket. Our deep learning-based model significantly improves the performance of a single Kinect v2 sensor for all investigated upper limb joint angles across all functional tasks. Using a single Kinect v2 sensor, our deep learning-based model could measure shoulder and elbow flexion/extension waveforms with mean CMCs >0.93 for all tasks, shoulder adduction/abduction, and internal/external rotation waveforms with mean CMCs >0.8 for most of the tasks. The mean deviations of angles at the point of target achieved and range of motion are under 5° for all investigated joint angles during all functional tasks. Compared with the 3DMC, our presented system is easier to operate and needs less laboratory space.
Anomaly Detection Using Electric Impedance Tomography Based on Real and Imaginary Images
Authors: Sapuan Imam , Yasin Moh , Ain Khusnul , Apsari Retna
Published: April 2020

This research offers a method for separating the components of tissue impedance, namely resistance and capacitive reactance. Two objects that have similar impedance or low contrast can be improved through separating the real and imaginary images. This method requires an Electrical Impedance Tomography (EIT) device. EIT can obtain potential data and the phase angle between the current and the potential measured. In the future, the device is very suitable for imaging organs in the thorax and abdomen that have the same impedance but different resistance and capacitive reactance. This device consists of programmable generators, Voltage Controlled Current Source (VCCS), mulptiplexer-demultiplexer potential meters, and phase meters. Data collecting was done by employing neighboring, while reconstruction was used the linear back-projection method from two different data frequencies, namely 10 kHz and 100 kHz. Phantom used in this experiment consists of distillated water and a carrot as an anomaly. Potential and phase data from the device is reconstructed to produce impedance, real, and imaginary images. Image analysis is performed by comparing the three images to the phantom. The experimental results show that the device is reliable.
Edge4TSC: Binary Distribution Tree-Enabled Time Series Classification in Edge Environment
Authors: Ma Chao , Shi Xiaochuan , Li Wei , Zhu Weiping
Published: April 2020

In the past decade, time series data have been generated from various fields at a rapid speed, which offers a huge opportunity for mining valuable knowledge. As a typical task of time series mining, Time Series Classification (TSC) has attracted lots of attention from both researchers and domain experts due to its broad applications ranging from human activity recognition to smart city governance. Specifically, there is an increasing requirement for performing classification tasks on diverse types of time series data in a timely manner without costly hand-crafting feature engineering. Therefore, in this paper, we propose a framework named Edge4TSC that allows time series to be processed in the edge environment, so that the classification results can be instantly returned to the end-users. Meanwhile, to get rid of the costly hand-crafting feature engineering process, deep learning techniques are applied for automatic feature extraction, which shows competitive or even superior performance compared to state-of-the-art TSC solutions. However, because time series presents complex patterns, even deep learning models are not capable of achieving satisfactory classification accuracy, which motivated us to explore new time series representation methods to help classifiers further improve the classification accuracy. In the proposed framework Edge4TSC, by building the binary distribution tree, a new time series representation method was designed for addressing the classification accuracy concern in TSC tasks. By conducting comprehensive experiments on six challenging time series datasets in the edge environment, the potential of the proposed framework for its generalization ability and classification accuracy improvement is firmly validated with a number of helpful insights.
Innovative Technologies for Cultural Heritage. Tattoo Sensors and AI: The New Life of Cultural Assets
Authors: Talamo Maurizio , Valentini Federica , Dimitri Andrea , Allegrini Ivo
Published: April 2020

Conservation and restoration of cultural heritage is something more than a simple process of maintaining the existing. It is an integral part of the improvement of the cultural asset. The social context around the restoration shapes the specific actions. Today, preservation, restoration, enhancement of cultural heritage are increasingly a multidisciplinary science, meeting point of researchers coming from heterogeneous study areas. Data scientists and Information technology (IT) specialists are increasingly important. In this context, networks of a new generation of smart sensors integrated with data mining and artificial intelligence play a crucial role and aim to become the new skin of cultural assets.
Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks
Authors: Ortega Samuel , Halicek Martin , Fabelo Himar , Camacho Rafael , Plaza María de la Luz , Godtliebsen Fred , M. Callicó Gustavo , Fei Baowei
Published: April 2020

Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful information about the chemical composition of tissue and its morphological features in a single image modality. Deep learning (DL) techniques have demonstrated the ability of automatic feature extraction from data for a successful classification. In this study, we exploit HSI and DL for the automatic differentiation of glioblastoma (GB) and non-tumor tissue on hematoxylin and eosin (H&E) stained histological slides of human brain tissue. GB detection is a challenging application, showing high heterogeneity in the cellular morphology across different patients. We employed an HSI microscope, with a spectral range from 400 to 1000 nm, to collect 517 HS cubes from 13 GB patients using 20× magnification. Using a convolutional neural network (CNN), we were able to automatically detect GB within the pathological slides, achieving average sensitivity and specificity values of 88% and 77%, respectively, representing an improvement of 7% and 8% respectively, as compared to the results obtained using RGB (red, green, and blue) images. This study demonstrates that the combination of hyperspectral microscopic imaging and deep learning is a promising tool for future computational pathologies.
Efficient Two-Dimensional Direction Finding Algorithm for Rectilinear Sources Under Unknown Mutual Coupling
Authors: Xie Jian , Wang Qiuping , Wang Yuexian , Yang Xin
Published: April 2020

Digital communication signals in wireless systems may possess noncircularity, which can be used to enhance the degrees of freedom for direction-of-arrival (DOA) estimation in sensor array signal processing. On the other hand, the electromagnetic characteristics between sensors in uniform rectangular arrays (URAs), such as mutual coupling, may significantly deteriorate the estimation performance. To deal with this problem, a robust real-valued estimator for rectilinear sources was developed to alleviate unknown mutual coupling in URAs. An augmented covariance matrix was built up by extracting the real and imaginary parts of observations containing the circularity and noncircularity of signals. Then, the actual steering vector considering mutual coupling was reparameterized to make the rank reduction (RARE) property available. To reduce the computational complexity of two-dimensional (2D) spectral search, we individually estimated y-axis and x-axis direction-cosines in two stages following the principle of RARE. Finally, azimuth and elevation angle estimates were determined from the corresponding direction-cosines respectively. Compared with existing solutions, the proposed method is more computationally efficient, involving real-valued operations and decoupled 2D spectral searches into twice those of one-dimensional searches. Simulation results verified that the proposed method provides satisfactory estimation performance that is robust to unknown mutual coupling and close to the counterparts based on 2D spectral searches, but at the cost of much fewer calculations.
Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment
Authors: Carotenuto Federico , Brilli Lorenzo , Gioli Beniamino , Gualtieri Giovanni , Vagnoli Carolina , Mazzola Mauro , Viola Angelo Pietro , Vitale Vito , Severi Mirko , Traversi Rita , Zaldei Alessandro
Published: April 2020

The Arctic is an important natural laboratory that is extremely sensitive to climatic changes and its monitoring is, therefore, of great importance. Due to the environmental extremes it is often hard to deploy sensors and observations are limited to a few sparse observation points limiting the spatial and temporal coverage of the Arctic measurement. Given these constraints the possibility of deploying a rugged network of low-cost sensors remains an interesting and convenient option. The present work validates for the first time a low-cost sensor array (AIRQino) for monitoring basic meteorological parameters and atmospheric composition in the Arctic (air temperature, relative humidity, particulate matter, and CO2). AIRQino was deployed for one year in the Svalbard archipelago and its outputs compared with reference sensors. Results show good agreement with the reference meteorological parameters (air temperature (T) and relative humidity (RH)) with correlation coefficients above 0.8 and small absolute errors (≈1 °C for temperature and ≈6% for RH). Particulate matter (PM) low-cost sensors show a good linearity (r2 ≈ 0.8) and small absolute errors for both PM2.5 and PM10 (≈1 µg m−3 for PM2.5 and ≈3 µg m−3 for PM10), while overall accuracy is impacted both by the unknown composition of the local aerosol, and by high humidity conditions likely generating hygroscopic effects. CO2 exhibits a satisfying agreement with r2 around 0.70 and an absolute error of ≈23 mg m−3. Overall these results, coupled with an excellent data coverage and scarce need of maintenance make the AIRQino or similar devices integrations an interesting tool for future extended sensor networks also in the Arctic environment.
Wearable Hand Module and Real-Time Tracking Algorithms for Measuring Finger Joint Angles of Different Hand Sizes with High Accuracy Using FBG Strain Sensor
Authors: Kim Jun , Kim Byung , Jang Minsu , Kang Kyumin , Kim Dae , Ju Byeong-Kwon , Kim Jinseok
Published: April 2020

This paper presents a wearable hand module which was made of five fiber Bragg grating (FBG) strain sensor and algorithms to achieve high accuracy even when worn on different hand sizes of users. For real-time calculation with high accuracy, FBG strain sensors move continuously according to the size of the hand and the bending of the joint. Representatively, four algorithms were proposed; point strain (PTS), area summation (AREA), proportional summation (PS), and PS/interference (PS/I or PS/I_ α ). For more accurate and efficient assessments, 3D printed hand replica with different finger sizes was adopted and quantitative evaluations were performed for index~little fingers (77 to 117 mm) and thumb (68~78 mm). For index~little fingers, the optimized algorithms were PS and PS/I_ α . For thumb, the optimized algorithms were PS/I_ α and AREA. The average error angle of the wearable hand module was observed to be 0.47 ± 2.51° and mean absolute error (MAE) was achieved at 1.63 ± 1.97°. These results showed that more accurate hand modules than other glove modules applied to different hand sizes can be manufactured using FBG strain sensors which move continuously and algorithms for tracking this movable FBG sensors.
Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach
Authors: Liu Ivan , Ni Shiguang , Peng Kaiping
Published: April 2020

Heart rate variability (HRV) provides essential health information such as the risks of heart attacks and mental disorders. However, inconvenience related to the accurate detection of HRV limits its potential applications. The ubiquitous use of smartphones makes them an excellent choice for regular and portable health monitoring. Following this trend, smartphone photoplethysmography (PPG) has recently garnered prominence; however, the lack of robustness has prevented both researchers and practitioners from embracing this technology. This study aimed to bridge the gap in the literature by developing a novel smartphone PPG quality index (SPQI) that can filter corrupted data. A total of 226 participants joined the study, and results from 1343 samples were used to validate the proposed sinusoidal function-based model. In both the correlation coefficient and Bland–Altman analyses, the agreement between HRV measurements generated by both the smartphone PPG and the reference electrocardiogram improved when data were filtered through the SPQI. Our results support not only the proposed approach but also the general value of using smartphone PPG in HRV analysis.
Habit Representation Based on Activity Recognition
Authors: Lee Jaeryoung , Melo Nicholas
Published: April 2020

With the increasing elderly population, attention has been drawn to the development of applications for habit assessment using activity data from smart environments that can be implemented in care facilities. In this paper, we introduce a novel habit assessment method based on information of human activities. First, a recognition system tracks the user’s activities of daily living by collecting data from multiple object sensors and ambient sensors that are distributed within the environment. Based on this information, the activities of daily living are expressed using Fourier series representation. The durations and sequence of the activities are represented by the phases and amplitudes of the harmonics. In this manner, each sequence is represented in a form that we refer to as a behavioral spectrum. After that, signals are clustered to find habits. We also calculate the variability, and by comparing the explained variance, the types of habits are found. For an evaluation, two datasets (young and elderly population) were used, and the results showed the potential habits of each group. The outcomes of this study can help improve and expand the applications of smart homes.
Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning
Authors: Szeląg Bartosz , Drewnowski Jakub , Łagód Grzegorz , Majerek Dariusz , Dacewicz Ewa , Fatone Francesco
Published: April 2020

The paper presented the methodology for the construction of a soft sensor used for activated sludge bulking identification. Devising such solutions fits within the current trends and development of a smart system and infrastructure within smart cities. In order to optimize the selection of the data-mining method depending on the data collected within a wastewater treatment plant (WWTP), a number of methods were considered, including: artificial neural networks, support vector machines, random forests, boosted trees, and logistic regression. The analysis conducted sought the combinations of independent variables for which the devised soft sensor is characterized with high accuracy and at a relatively low cost of determination. With the measurement results pertaining to the quantity and quality of wastewater as well as the temperature in the activated sludge chambers, a good fit can be achieved with the boosted trees method. In order to simplify the selection of an optimal method for the identification of activated sludge bulking depending on the model requirements and the data collected within the WWTP, an original system of weight estimation was proposed, enabling a reduction in the number of independent variables in a model—quantity and quality of wastewater, operational parameters, and the cost of conducting measurements.
A Cooperative Phase-Steering Technique with On-Off Power Control for Spectrum Sharing-Based Wireless Sensor Networks
Authors: Lee Sangku , Yoon Janghyuk , Jung Bang Chul
Published: April 2020

With the growth of the number of Internet of Things (IoT) devices, a wide range of wireless sensor networks (WSNs) will be deployed for various applications. In general, WSNs are constrained by limitations in spectrum and energy resources. In order to circumvent these technical challenges, we propose a novel cooperative phase-steering (CPS) technique with a simple on-off power control for generic spectrum sharing-based WSNs, which consists of a single secondary source (SS) node, multiple secondary relay (SR) nodes, a single secondary destination (SD) node, and multiple primary destination (PD) nodes. In the proposed technique, each SR node that succeeds in packet decoding from the SS and for which its interference power to the PD nodes is lower than a certain threshold is allowed to transmit the signal to the SD node. All SR nodes that are allowed to transmit signals to the SD node adjust the phase of their transmit signal such that the phase of received signals at the SD node from the SR nodes is aligned to a certain angle. Moreover, we mathematically analyze the outage probability of the proposed scheme. Our analytical and simulation results show that the proposed technique outperforms the conventional cooperative relaying schemes in terms of outage probability. Through extensive computer simulations, it is shown that the analytical results match well with the simulated outage probability as a lower bound.
Distributed Robust Filtering for Wireless Sensor Networks with Markov Switching Topologies and Deception Attacks
Authors: Zhu Fengzeng , Liu Xu , Wen Jiwei , Xie Linbo , Peng Li
Published: April 2020

This paper is concerned with the distributed full- and reduced-order l 2 - l ∞ state estimation issue for a class of discrete time-invariant systems subjected to both randomly occurring switching topologies and deception attacks over wireless sensor networks. Firstly, a switching topology model is proposed which uses homogeneous Markov chain to reflect the change of filtering networks communication modes. Then, the sector-bound deception attacks among the communication channels are taken into consideration, which could better characterize the filtering network communication security. Additionally, a random variable obeying the Bernoulli distribution is used to describe the phenomenon of the randomly occurring deception attacks. Furthermore, through an adjustable parameter E, we can obtain full- and reduced-order l 2 - l ∞ state estimator over sensor networks, respectively. Sufficient conditions are established for the solvability of the addressed switching topology-dependent distributed filtering design in terms of certain convex optimization problem. The purpose of solving the problem is to design a distributed full- and reduced-order filter such that, in the presence of deception attacks, stochastic external interference and switching topologies, the resulting filtering dynamic system is exponentially mean-square stable with prescribed l 2 - l ∞ performance index. Finally, a simulation example is provided to show the effectiveness and flexibility of the designed approach.
Predicting Spatial Visualization Problems’ Difficulty Level from Eye-Tracking Data
Authors: Li Xiang , Younes Rabih , Bairaktarova Diana , Guo Qi
Published: April 2020

The difficulty level of learning tasks is a concern that often needs to be considered in the teaching process. Teachers usually dynamically adjust the difficulty of exercises according to the prior knowledge and abilities of students to achieve better teaching results. In e-learning, because there is no teacher involvement, it often happens that the difficulty of the tasks is beyond the ability of the students. In attempts to solve this problem, several researchers investigated the problem-solving process by using eye-tracking data. However, although most e-learning exercises use the form of filling in blanks and choosing questions, in previous works, research focused on building cognitive models from eye-tracking data collected from flexible problem forms, which may lead to impractical results. In this paper, we build models to predict the difficulty level of spatial visualization problems from eye-tracking data collected from multiple-choice questions. We use eye-tracking and machine learning to investigate (1) the difference of eye movement among questions from different difficulty levels and (2) the possibility of predicting the difficulty level of problems from eye-tracking data. Our models resulted in an average accuracy of 87.60% on eye-tracking data of questions that the classifier has seen before and an average of 72.87% on questions that the classifier has not yet seen. The results confirmed that eye movement, especially fixation duration, contains essential information on the difficulty of the questions and it is sufficient to build machine-learning-based models to predict difficulty level.
Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics
Authors: Mejía-Salazar Jorge Ricardo , Rodrigues Cruz Kamilla , Materón Vásques Elsa María , Novais de Oliveira Jr. Osvaldo
Published: April 2020

Point-of-care (PoC) diagnostics is promising for early detection of a number of diseases, including cancer, diabetes, and cardiovascular diseases, in addition to serving for monitoring health conditions. To be efficient and cost-effective, portable PoC devices are made with microfluidic technologies, with which laboratory analysis can be made with small-volume samples. Recent years have witnessed considerable progress in this area with “epidermal electronics”, including miniaturized wearable diagnosis devices. These wearable devices allow for continuous real-time transmission of biological data to the Internet for further processing and transformation into clinical knowledge. Other approaches include bluetooth and WiFi technology for data transmission from portable (non-wearable) diagnosis devices to cellphones or computers, and then to the Internet for communication with centralized healthcare structures. There are, however, considerable challenges to be faced before PoC devices become routine in the clinical practice. For instance, the implementation of this technology requires integration of detection components with other fluid regulatory elements at the microscale, where fluid-flow properties become increasingly controlled by viscous forces rather than inertial forces. Another challenge is to develop new materials for environmentally friendly, cheap, and portable microfluidic devices. In this review paper, we first revisit the progress made in the last few years and discuss trends and strategies for the fabrication of microfluidic devices. Then, we discuss the challenges in lab-on-a-chip biosensing devices, including colorimetric sensors coupled to smartphones, plasmonic sensors, and electronic tongues. The latter ones use statistical and big data analysis for proper classification. The increasing use of big data and artificial intelligence methods is then commented upon in the context of wearable and handled biosensing platforms for the Internet of things and futuristic healthcare systems.
Parametric Study of Bolt Clamping Effect on Resonance Characteristics of Langevin Transducers with Lumped Circuit Models
Authors: Kim Jinhyuk , Lee Jungwoo
Published: April 2020

We recently proposed a numerical model using equivalent circuit models to analyze the resonance characteristics of Langevin transducers and design them in a systematic manner. However, no pre-load torque biased by a metal bolt was considered in the model. Here, a parametric study is, therefore, carried out to reveal how model parameters are adapted to incorporate the pre-compression effect into our existing model. Analytical results are compared with corresponding experimental data, particularly regarding the input electrical impedance and effective electromechanical coupling coefficient for the transducer at resonance modes. The frequency response of input impedance is presented as a function of torque, both theoretically and experimentally. For 10.0 N·m bias, for instance, both resonance and anti-resonance frequencies are calculated as 38.64 kHz and 39.78 kHz, while these are measured as 38.62 kHz and 39.77 kHz by the impedance analyzer. The impedance difference between these cases is 14 Ω at resonance and 9 kΩ at anti-resonance, while the coupling coefficients in both cases become 0.238 and 0.239, respectively. Hence, these test results are closely matched with their theoretical values. Consequently, this study provides a quantitative guideline that specifies the pre-loading condition of bolt clamps with proper parameter settings to predict the intended resonance characteristics of Langevin transducers.
Improved Multilayered (Bi,Sc)O3-(Pb,Ti)O3 Piezoelectric Energy Harvesters Based on Impedance Matching Technique
Authors: Kim Bo Su , Ji Jae-Hoon , Kim Hong-Tae , Kim Sung-Jin , Koh Jung-Hyuk
Published: April 2020

As a piezoelectric material, (Bi,Sc)O3-(Pb,Ti)O3 ceramics have been tested and analyzed for sensors and energy harvester applications owing to their relatively high Curie temperature and high piezoelectric coefficient. In this work, we prepared optimized (Bi,Sc)O3-(Pb,Ti)O3 piezoelectric materials through the conventional ceramic process. To increase the output energy, a multilayered structure was proposed and designed, and to obtain the maximum output energy, impedance matching techniques were considered and tested. By varying and measuring the energy harvesting system, we confirmed that the output energies were optimized by varying the load resistance. As the load resistance increased, the output voltage became saturated. Then, we calculated the optimized output power using the electric energy formula. Consequently, we identified the highest output energy of 5.93 µW/cm2 at 3 MΩ for the quadruple-layer harvester and load resistor using the impedance matching system. We characterized and improved the electrical properties of the piezoelectric energy harvesters by introducing impedance matching and performing the modeling of the energy harvesting component. Modeling was conducted for the piezoelectric generator component by introducing the mechanical force dependent voltage sources and load resistors and piezoelectric capacitor connected in parallel. Moreover, the generated output voltages were simulated by introducing an impedance matching technique. This work is designed to explain the modeling of piezoelectric energy harvesters. In this model, the relationship between applied mechanical force and output energy was discussed by employing experimental results and simulation.
A Collocation Method for Numerical Solution of Nonlinear Delay Integro-Differential Equations for Wireless Sensor Network and Internet of Things
Authors: Amin Rohul , Nazir Shah , García-Magariño Iván
Published: April 2020

Wireless sensor network and industrial internet of things have been a growing area of research which is exploited in various fields such as smart home, smart industries, smart transportation, and so on. There is a need of a mechanism which can easily tackle the problems of nonlinear delay integro-differential equations for large-scale applications of Internet of Things. In this paper, Haar wavelet collocation technique is developed for the solution of nonlinear delay integro-differential equations for wireless sensor network and industrial Internet of Things. The method is applied to nonlinear delay Volterra, delay Fredholm and delay Volterra–Fredholm integro-differential equations which are based on the use of Haar wavelets. Some examples are given to show the computational efficiency of the proposed technique. The approximate solutions are compared with the exact solution. The maximum absolute and mean square roots errors for distant number of collocation points are also calculated. The results show that Haar method is efficient for solving these equations for industrial Internet of Things. The results are compared with existing methods from the literature. The results exhibit that the method is simple, precise and efficient.
Applications of Nanotechnology in Sensor-Based Detection of Foodborne Pathogens
Authors: Kumar Harsh , Kuča Kamil , Bhatia Shashi Kant , Saini Kritika , Kaushal Ankur , Verma Rachna , Bhalla Tek Chand , Kumar Dinesh
Published: April 2020

The intake of microbial-contaminated food poses severe health issues due to the outbreaks of stern food-borne diseases. Therefore, there is a need for precise detection and identification of pathogenic microbes and toxins in food to prevent these concerns. Thus, understanding the concept of biosensing has enabled researchers to develop nanobiosensors with different nanomaterials and composites to improve the sensitivity as well as the specificity of pathogen detection. The application of nanomaterials has enabled researchers to use advanced technologies in biosensors for the transfer of signals to enhance their efficiency and sensitivity. Nanomaterials like carbon nanotubes, magnetic and gold, dendrimers, graphene nanomaterials and quantum dots are predominantly used for developing biosensors with improved specificity and sensitivity of detection due to their exclusive chemical, magnetic, mechanical, optical and physical properties. All nanoparticles and new composites used in biosensors need to be classified and categorized for their enhanced performance, quick detection, and unobtrusive and effective use in foodborne analysis. Hence, this review intends to summarize the different sensing methods used in foodborne pathogen detection, their design, working principle and advances in sensing systems.
Domination, Power, Supremacy: Confronting Anthropolitics with Ecological Realism
Authors: Ruuska Toni , Heikkurinen Pasi , Wilén Kristoffer
Published: April 2020

In this article, we study politics as domination. From our point of view, domination, especially in the Anthropocene, has had two vital components—power and supremacy. In order to dominate, one has to have power over others. In addition, the politics of domination, such as colonial oppression of Latin America, has required reasoning, justification, and legitimation, often connected to superiority (because of religion, society, or civilization) from the oppressor’s end. Past and present political ideologies and programs, such as colonialism, imperialism, but also welfare state capitalism, neoliberalism and increasingly popular Green New Deal are examples of what we call “anthropolitics”, an anthropocentric approach to politics based on domination, power, and supremacist exploitation. In contrast to the prevailing anthropolitics, this article discusses post-Anthropocene politics, characterized by localization and decentralization, as well as a steep reduction of matter–energy throughput by introducing a theoretical frame called ecological realism.
Assessing the Efficiency of Sustainable Cities Using an Empirical Approach
Authors: Calzada-Infante Laura , López-Narbona Ana María , Núñez-Elvira Alberto , Orozco-Messana Javier
Published: April 2020

Sustainability is a multidisciplinary discipline posing a difficult problem as a result of its integrated assessment. From a broad perspective, it considers the impact of human activities (using different resources) and natural conditions on local environments. Urban development has been identified as one of the most important reasons for environmental and social degradation. To address the complexity of sustainability and its impact, policymakers need to be equipped with the right toolkit to foresee the integrated effect of projects and plans on urban sustainability more effectively in their policy design. In this paper, we propose a tool to assess the sustainable performance of urban areas through a common framework of indicators which provides an integrated measurement based on the relative efficiency of key input variables on desirable and undesirable outputs. Using Data Envelopment Analysis (DEA), we propose a procedure for determining the relative efficiency of relevant urban areas, proposing this method as a candidate for integrated sustainability measurement. The selection of variables is based on dimensions which can be addressed from a political perspective for achieving more desirable outputs, or reducing the undesirable ones, controlling for key resources as much as possible. Our analysis takes a comprehensive scope including an environmental and socioeconomic perspective. This will be useful to identify weaknesses and strengths to improve the integrated performance of cities. Our array of indicators, based on standardized key performance indicators (KPIs) will enable policymakers to gather an insightful impact of their proposals in urban sustainability carrying out a global sustainability impact assessment through DEA. The main goal is to gather the urban experience of transforming cities into smarter cities and putting technological progress at the service of their societies.
Location of Cows and Pigs in Suburban Areas of Polish Metropolitan Centers
Authors: Pepliński Benedykt
Published: April 2020

The main aim of this paper was to assess the impact of socioeconomic and agricultural factors on cow and pig density in suburban areas located up to 50 km from the 11 biggest cities in Poland. This study relied on data retrieved from all municipalities located within 50 km from central cities. The impact of the previously mentioned socioeconomic and agricultural factors was assessed using the Pearson correlation coefficient, analysis of variance (ANOVA), and regression analysis. This study shows that the lowest cow and pig density was recorded in the two inner rings. From 1996 to 2010, the gap between them and the levels recorded in other rings became much more pronounced. The analysis only corroborated the high correlation between livestock (cow and pig) density and the share of corresponding livestock farms in the total number of farms as well as the average herd size. Regarding other agricultural and socioeconomic factors, the correlation was weak or low except for a moderate correlation between the entrepreneurship indicator and pig density. The regression analysis demonstrated that the share of cow and pig farms in the total number of farms, average herd size, residential housing density, and unemployment rate had a significant impact on the density of animal groups considered.
Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling
Authors: Nguyen Phong Tung , Ha Duong Hai , Nguyen Huu Duy , Van Phong Tran , Trinh Phan Trong , Al-Ansari Nadhir , Le Hiep Van , Pham Binh Thai , Ho Lanh Si , Prakash Indra
Published: April 2020

Groundwater is one of the most important sources of fresh water all over the world, especially in those countries where rainfall is erratic, such as Vietnam. Nowadays, machine learning (ML) models are being used for the assessment of groundwater potential of the region. Credal decision trees (CDT) is one of the ML models which has been used in such studies. In the present study, the performance of the CDT has been improved using various ensemble frameworks such as Bagging, Dagging, Decorate, Multiboost, and Random SubSpace. Based on these methods, five hybrid models, namely BCDT, Dagging-CDT, Decorate-CDT, MBCDT, and RSSCDT, were developed and applied for groundwater potential mapping of DakLak province of Vietnam. Data of 227 groundwater wells of the study area were utilized for the construction and validation of the models. Twelve groundwater potential conditioning factors, namely rainfall, slope, elevation, river density, Sediment Transport Index (STI), curvature, flow direction, aspect, soil, land use, Topographic Wetness Index (TWI), and geology, were considered for the model studies. Various statistical measures, including area under receiver operating characteristic (AUC) curve, were applied to validate and compare the performance of the models. The results show that performance of the hybrid CDT ensemble models MBCDT (AUC = 0.770), BCDT (AUC = 0.731), Dagging-CDT (AUC = 0.763), Decorate-CDT (AUC = 0.750), and RSSCDT (AUC = 0.766) improved significantly in comparison to the single CDT (AUC = 0.722) model. Therefore, these developed hybrid models can be applied for better ground water potential mapping and groundwater resources management of the study area as well as other regions of the world.
Resource Recovery of Waste Nd–Fe–B Scrap: Effective Separation of Fe as High-Purity Hematite Nanoparticles
Authors: Zhu Suiyi , Su Ting , Chen Yu , Qu Zhan , Lin Xue , Lu Ying , Huo Mingxin
Published: April 2020

Recycling rare-earth elements from Nd magnet scrap (Nd–Fe–B scrap) is a highly economical process; however, its efficiency is low due to large portions of Fe impurity. In this study, the effective separation of Fe impurity from scrap was performed through an integrated nitric acid dissolution and hydrothermal route with the addition of fructose. Results showed that more than 99% of the scrap was dissolved in nitric acid, and after three dilutions that the Nd, Pr, Dy and Fe concentrations in the diluted acid were 9.01, 2.11, 0.37 and 10.53 g/L, respectively. After the acid was hydrothermally treated in the absence of fructose, only 81.8% Fe was removed as irregular hematite aggregates, whilst more than 98% rare-earth elements were retained. By adding fructose at an Mfructose/Mnitrate ratio of 0.2, 99.94% Fe was precipitated as hematite nanoparticles, and the loss of rare-earth elements was <2%. In the treated acid, the residual Fe was 6.3 mg/L, whilst Nd, Pr and Dy were 8.84, 2.07 and 0.36 g/L, respectively. Such composition was conducive for further recycling of high-purity rare-earth products with low Fe impurity. The generated hematite nanoparticles contained 67.92% Fe with a rare-earth element content of <1%. This value meets the general standard for commercial hematite active pharmaceutical ingredients. In this manner, a green process was developed for separating Fe from Nd–Fe–B scrap without producing secondary waste.
Research on Agricultural Carbon Emissions and Regional Carbon Emissions Reduction Strategies in China
Authors: Wang Guofeng , Liao Maolin , Jiang Jie
Published: April 2020

Carbon emissions and strategies for reducing them have become hot topics in recent years. This study firstly measured the total amount and the intensity of agricultural carbon emissions (i.e., agricultural carbon emission per capital) in China. The results show that China’s total carbon emission in 2016 was 272.022 million tons, which is 26.67% more than that in 2000, with an average annual increase of 1.67%. It then compared the regional differences of agricultural carbon emissions in China using the method of coefficient of variation and the Theil index. Following this, this paper finally provides scientific and technological support for the reduction of agricultural carbon emissions in China based on a matrix of carbon emission reduction strategies.
Towards Sustainable Textile and Apparel Industry: Exploring the Role of Business Intelligence Systems in the Era of Industry 4.0
Authors: Ahmad Sumera , Miskon Suraya , Alabdan Rana , Tlili Iskander
Published: April 2020

Industry 4.0 is a new era of industrial revolution in which textile and apparel (T&A) companies are adopting and integrating advanced technologies to achieve sustainability and a competitive edge. Previous studies have just focused on the perspective of big data utilization in Industry 4.0 and neglected the role of business intelligence systems (BIS), especially in the T&A industry. The current study is one of the first to investigate the determinants of BIS adoption with an eye towards understanding how BIS can resolve sustainability issues in T&A companies with Industry 4.0 technologies. Methodology: A qualitative research approach is applied with 14 semi-structured in-depth interviews from 12 of the world’s high-end T&A companies. The snowball and purposeful sampling strategy is used to select the participants. The qualitative content analysis technique is used to analyze the interview data. Results: The findings revealed various themes, such as sustainability issues in T&A companies, improved value creation processes with leading BI solutions, and perceived difficulties in the adoption of BIS. Major improvements are perceived in the apparel retail business because apparel companies are more prone to adopt the Industry 4.0 technologies with advanced business intelligence (BI) solutions. The results prove the pivotal role of economic sustainability in the adoption of BIS and Industry 4.0 technologies in T&A companies.
Urban Sprawl Risk Delimitation: The Concept for Spatial Planning Policy in Poland
Authors: Lityński Piotr , Hołuj Artur
Published: April 2020

Urban sprawl is a process shaping the space of contemporary urban areas. The costs generated by this phenomenon force central and local authorities to adopt and implement a spatial policy limiting those costs. However, there is no method in Polish spatial policy that determines the extent of this phenomenon around cities, and thus identifies the area of intervention. Therefore, the purpose of the article is to propose a method of delimitation of urban areas at risk of urban sprawl. The proposed method of delimitation honors the characteristics of urban sprawl relating to spatial structure, socio-economic processes and efficiency of spatial policy. The method can be useful for conducting spatial policy aimed at reducing costs due to urban sprawl. It particularly pertains to the policy implemented at the central and regional level. Research results indicate that, in most Polish urban areas, delimitations used thus far designate too little of the area around core cities. Although the goals of reducing the negative consequences of urban sprawl are formulated at the level of national spatial policy, the methods of delimitation used thus far do not take into account the specificity of this phenomenon. Underestimating the extent of urban sprawl results in a lack of effectiveness of spatial policy due to the omission of specific areas in public intervention. This particularity is related to the fact that these are usually external areas—the most distant from the core city. These areas have the highest costs for urban sprawl. At the same time, these are areas in the early stages of spatial growth, in which a consistent spatial structure can still be kept while implementing proper spatial policy.