bluuebunny/crossref_metadata_2025 huggingface dataset:
Rheotaxially Grown and Vacuum Oxidized SnOx Nanolayers for NO2 Sensing Characteristics at ppb Level and Room Temperature
Authors: Lyson-Sypien Barbara , Kwoka Monika
Published: March 2020

This work presents, for the very first time, very promising nitrogen dioxide (NO2) sensing characteristics of SnOx nanolayers obtained by the innovative and unique rheotaxial growth and vacuum oxidation (RGVO) processing technique. The NO2 gas sensing experiments were performed using the novel surface photovoltage gas sensing device. The measured detection limit at room temperature (RT) is as low as 10 ppb NO2 in synthetic air, whereas the detection limit calculated on the basis of signal to noise ratio is around 6 ppb NO2. For the complementary study of surface chemistry of RGVO SnOx nanolayers, including nonstoichiometry, presence of carbon contamination and surface bondings, the X-ray photoelectron spectroscopy (XPS) method was applied. The SnOx RGVO samples reveal nonstoichiometry because the relative concentration [O]/[Sn] equals 0.94 for the as deposited sample and increases upon subsequent air exposure and NO2 sensing. Moreover, carbon contamination has been recognized after exposing the RGVO SnOx nanolayers to the air and during the NO2 detection.
Inkjet-Printing of Nanoparticle Gold and Silver Ink on Cyclic Olefin Copolymer for DNA-Sensing Applications
Authors: Trotter Martin , Juric Daniel , Bagherian Zahra , Borst Nadine , Gläser Kerstin , Meissner Thomas , von Stetten Felix , Zimmermann André
Published: March 2020

Inkjet technology as a maskless, direct-writing technology offers the potential for structured deposition of functional materials for the realization of electrodes for, e.g., sensing applications. In this work, electrodes were realized by inkjet-printing of commercial nanoparticle gold ink on planar substrates and, for the first time, onto the 2.5D surfaces of a 0.5 mm-deep microfluidic chamber produced in cyclic olefin copolymer (COC). The challenges of a poor wetting behavior and a low process temperature of the COC used were solved by a pretreatment with oxygen plasma and the combination of thermal (130 °C for 1 h) and photonic (955 mJ/cm²) steps for sintering. By performing the photonic curing, the resistance could be reduced by about 50% to 22.7 µΩ cm. The printed gold structures were mechanically stable (optimal cross-cut value) and porous (roughness factors between 8.6 and 24.4 for 3 and 9 inkjet-printed layers, respectively). Thiolated DNA probes were immobilized throughout the porous structure without the necessity of a surface activation step. Hybridization of labeled DNA probes resulted in specific signals comparable to signals on commercial screen-printed electrodes and could be reproduced after regeneration. The process described may facilitate the integration of electrodes in 2.5D lab-on-a-chip systems.
Irrigation Scheduling Based on Wireless Sensors Output and Soil-Water Characteristic Curve in Two Soils
Authors: Jabro J.D. , Stevens W.B. , Iversen W.M. , Allen B.L. , Sainju U.M.
Published: March 2020

Data-driven irrigation planning can optimize crop yield and reduce adverse impacts on surface and ground water quality. We evaluated an irrigation scheduling strategy based on soil matric potentials recorded by wireless Watermark (WM) sensors installed in sandy loam and clay loam soils and soil-water characteristic curve data. Five wireless WM nodes (IRROmesh) were installed at each location, where each node consisted of three WM sensors that were installed at 15, 30, and 60 cm depths in the crop rows. Soil moisture contents, at field capacity and permanent wilting points, were determined from soil-water characteristic curves and were approximately 23% and 11% for a sandy loam, and 35% and 17% for a clay loam, respectively. The field capacity level which occurs shortly after an irrigation event was considered the upper point of soil moisture content, and the lower point was the maximum soil water depletion level at 50% of plant available water capacity in the root zone, depending on crop type, root depth, growth stage and soil type. The lower thresholds of soil moisture content to trigger an irrigation event were 17% and 26% in the sandy loam and clay loam soils, respectively. The corresponding soil water potential readings from the WM sensors to initiate irrigation events were approximately 60 kPa and 105 kPa for sandy loam, and clay loam soils, respectively. Watermark sensors can be successfully used for irrigation scheduling by simply setting two levels of moisture content using soil-water characteristic curve data. Further, the wireless system can help farmers and irrigators monitor real-time moisture content in the soil root zone of their crops and determine irrigation scheduling remotely without time consuming, manual data logging and frequent visits to the field.
Data Security and Trading Framework for Smart Grids in Neighborhood Area Networks
Authors: Junior Jayme Milanezi , da Costa João Paulo C. L. da , Garcez Caio C. R. , de Oliveira Albuquerque Robson de Oliveira , Arancibia Arnaldo , Weichenberger Lothar , de Mendonça Fábio Lucio Lopes de , Galdo Giovanni del , de Sousa Jr. Rafael T. de Sousa
Published: March 2020

Due to the drastic increase of electricity prosumers, i.e., energy consumers that are also producers, smart grids have become a key solution for electricity infrastructure. In smart grids, one of the most crucial requirements is the privacy of the final users. The vast majority of the literature addresses the privacy issue by providing ways of hiding user’s electricity consumption. However, open issues in the literature related to the privacy of the electricity producers still remain. In this paper, we propose a framework that preserves the secrecy of prosumers’ identities and provides protection against the traffic analysis attack in a competitive market for energy trade in a Neighborhood Area Network (NAN). In addition, the amount of bidders and of successful bids are hidden from malicious attackers by our framework. Due to the need for small data throughput for the bidders, the communication links of our framework are based on a proprietary communication system. Still, in terms of data security, we adopt the Advanced Encryption Standard (AES) 128 bit with Exclusive-OR (XOR) keys due to their reduced computational complexity, allowing fast processing. Our framework outperforms the state-of-the-art solutions in terms of privacy protection and trading flexibility in a prosumer-to-prosumer design.
A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures
Authors: Huang Jing , Luo Xiong , Peng Xiaoyan
Published: March 2020

In this study, a novel classification method for a driver’s cognitive stress level was proposed, whereby the interbeat intervals extracted from an electrocardiogram (ECG) signal were transferred to pictures, and a convolution neural network (CNN) was used to train the pictures to classify a driver’s cognitive stress level. First, we defined three levels of tasks and collected the ECG signal of the driver at different cognitive stress levels by designing and performing a driving simulation experiment. We extracted the interbeat intervals and converted them to pictures according to the number of consecutive interbeat intervals in each picture. Second, the CNN model was used to train the data set to recognize the cognitive stress levels. Classification accuracies of 100%, 91.6% and 92.8% were obtained for the training set, validation set and test set, respectively, and were compared with those the BP neural network. Last, we discussed the influence of the number of interbeat intervals in each picture on the performance of the proposed classification method. The results showed that the performance initially improved with an increase in the number of interbeat intervals. A downward trend was observed when the number exceeded 40, and when the number was 40, the model performed best with the highest accuracy (98.79%) and a relatively low relative standard deviation (0.019).
Accuracy Verification of Spatio-Temporal and Kinematic Parameters for Gait Using Inertial Measurement Unit System
Authors: Yeo Sang Seok , Park Ga Young
Published: March 2020

Inertial measurement unit systems are wearable sensors that can measure the movement of a human in real-time with relatively little space and high portability. The purpose of this study was to investigate the accuracy of the inertial measurement unit (IMU) system for gait analysis by comparing it with measurements obtained using an optical motion capture (OMC) system. To compare the accuracies of these two different motion capture systems, the Spatio-temporal and kinematic parameters were measured in young adults during normal walking. Thirty healthy participants participated in the study. Data were collected while walking 5 strides on a 7 m walkway at a self-selected speed. Results of gait analysis showed that the Spatio-temporal (stride time, stride length, cadence, step length) and kinematic (knee joint peak to peak of movement) parameters were not significantly different in the participant. Spatio-temporal and kinematic parameters of the two systems were compared using the Bland–Altman method. The results obtained showed that the measurements of Spatio-temporal and kinematic parameters of gait by the two systems were similar, which suggested that IMU and OMC systems could be used interchangeably for gait measurements. Therefore, gait analysis performed using the wearable IMU system might efficiently provide gait measurements and enable accurate analysis.
Inland Lakes Mapping for Monitoring Water Quality Using a Detail/Smoothing-Balanced Conditional Random Field Based on Landsat-8/Levels Data
Authors: Wei Lifei , Zhang Yu , Huang Can , Wang Zhengxiang , Huang Qingbin , Yin Feng , Guo Yue , Cao Liqin
Published: March 2020

The sustainable development of water resources is always emphasized in China, and a set of perfect standards for the division of inland water environment quality have been established to monitor water quality. However, most of the 24 indicators that determine the water quality level in the standards are non-optically active parameters. The weak optical characteristics make it difficult to find significant correlations between the single parameters and the remote sensing imagery. In addition, traditional on-site testing methods have been unable to meet the increasingly extensive water-quality monitoring requirements. Based on the above questions, it’s meaningful that the supervised classification process of a detail-preserving smoothing classifier based on conditional random field (CRF) and Landsat-8 data was proposed in the two study areas around Wuhan and Huangshi in Hubei Province. The random forest classifier was selected to model the association potential of the CRF. The results (the first study area: OA = 89.50%, Kappa = 0.841; the second study area: OA = 90.35%, Kappa = 0.868) showed that the water-quality monitoring based on CRF model is feasible, and this approach can provide a reference for water-quality mapping of inland lakes. In the future, it may only require a small amount of on-site sampling to achieve the identification of the water quality levels of inland lakes across a large area of China.
Validation of Low-Cost Sensors in Measuring Real-Time PM10 Concentrations at Two Sites in Delhi National Capital Region
Authors: Sahu Ravi , Dixit Kuldeep Kumar , Mishra Suneeti , Kumar Purushottam , Shukla Ashutosh Kumar , Sutaria Ronak , Tiwari Shashi , Tripathi Sachchida Nand
Published: March 2020

In the present study, we assessed for the first time the performance of our custom-designed low-cost Particulate Matter (PM) monitoring devices (Atmos) in measuring PM10 concentrations. We examined the ambient PM10 levels during an intense measurement campaign at two sites in the Delhi National Capital Region (NCR), India. In this study, we validated the un-calibrated Atmos for measuring ambient PM10 concentrations at highly polluted monitoring sites. PM10 concentration from Atmos, containing laser scattering-based Plantower PM sensor, was comparable with that measured from research-grade scanning mobility particle sizers (SMPS) in combination with optical particle sizers (OPS) and aerodynamic particle sizers (APS). The un-calibrated sensors often provided accurate PM10 measurements, particularly in capturing real-time hourly concentrations variations. Quantile–Quantile plots (QQ-plots) for data collected during the selected deployment period showed positively skewed PM10 datasets. Strong Spearman’s rank-order correlations (rs = 0.64–0.83) between the studied instruments indicated the utility of low-cost Plantower PM sensors in measuring PM10 in the real-world context. Additionally, the heat map for weekly datasets demonstrated high R2 values, establishing the efficacy of PM sensor in PM10 measurement in highly polluted environmental conditions.
Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence
Authors: Naghdi Sharareh , O’Keefe Kyle
Published: March 2020

One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies.
Incremental Composition Process for the Construction of Component-Based Management Systems
Authors: Rana Tauseef , Bangash Yawar Abbas , Baz Abdullah , Rana Toqir Ahmad , Imran Muhammad Ali
Published: March 2020

Cyber-physical systems (CPS) are composed of software and hardware components. Many such systems (e.g., IoT based systems) are created by composing existing systems together. Some of these systems are of critical nature, e.g., emergency or disaster management systems. In general, component-based development (CBD) is a useful approach for constructing systems by composing pre-built and tested components. However, for critical systems, a development method must provide ways to verify the partial system at different stages of the construction process. In this paper, for system architectures, we propose two styles: rigid architecture and flexible architecture. A system architecture composed of independent components by coordinating exogenous connectors is in flexible architecture style category. For CBD of critical systems, we select EX-MAN from flexible architecture style category. Moreover, we define incremental composition mechanism for this model to construct critical systems from a set of system requirements. Incremental composition is defined to offer preservation of system behaviour and correctness of partial architecture at each incremental step. To evaluate our proposed approach, a case study of weather monitoring system (part of a disaster management) system was built using our EX-MAN tool.
A Formal and Quantifiable Log Analysis Framework for Test Driving of Autonomous Vehicles
Authors: Sung Kyungbok , Min Kyoung-Wook , Choi Jeongdan , Kim Byung-Cheol
Published: March 2020

We propose a log analysis framework for test driving of autonomous vehicles. The log of a vehicle is a fundamental source to detect and analyze events during driving. A set of dumped logs are, however, usually mixed and fragmented since they are generated concurrently by a number of modules such as sensors, actuators and programs. This makes it hard to analyze them to discover latent errors that could occur due to complex chain reactions among those modules. Our framework provides a logging architecture based on formal specifications, which hierarchically organizes them to find out a priori relationships between them. Then, algorithmic or implementation errors can be detected by examining a posteriori relationships. However, a test in a situation of certain parameters, so called an oracle test, does not necessarily trigger latent violations of the relationships. In our framework, this is remedied by adopting metamorphic testing to quantitatively verify the formal specification. As a working proof, we define three metamorphic relations critical for testing autonomous vehicles and verify them in a quantitative manner based on our logging system.
MuPeG—The Multiple Person Gait Framework
Authors: Delgado-Escaño Rubén , Castro Francisco M. , R. Cózar Julián , Marín-Jiménez Manuel J. , Guil Nicolás
Published: March 2020

Gait recognition is being employed as an effective approach to identify people without requiring subject collaboration. Nowadays, developed techniques for this task are obtaining high performance on current datasets (usually more than 90 % of accuracy). However, those datasets are simple as they only contain one subject in the scene at the same time. This fact limits the extrapolation of the results to real world conditions where, usually, multiple subjects are simultaneously present at the scene, generating different types of occlusions and requiring better tracking methods and models trained to deal with those situations. Thus, with the aim of evaluating more realistic and challenging situations appearing in scenarios with multiple subjects, we release a new framework (MuPeG) that generates augmented datasets with multiple subjects using existing datasets as input. By this way, it is not necessary to record and label new videos, since it is automatically done by our framework. In addition, based on the use of datasets generated by our framework, we propose an experimental methodology that describes how to use datasets with multiple subjects and the recommended experiments that are necessary to perform. Moreover, we release the first experimental results using datasets with multiple subjects. In our case, we use an augmented version of TUM-GAID and CASIA-B datasets obtained with our framework. In these augmented datasets the obtained accuracies are 54.8 % and 42.3 % whereas in the original datasets (single subject), the same model achieved 99.7 % and 98.0 % for TUM-GAID and CASIA-B, respectively. The performance drop shows clearly that the difficulty of datasets with multiple subjects in the scene is much higher than the ones reported in the literature for a single subject. Thus, our proposed framework is able to generate useful datasets with multiple subjects which are more similar to real life situations.
Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices
Authors: Lim Hyun-Kyo , Kim Ju-Bong , Heo Joo-Seong , Han Youn-Hee
Published: March 2020

Reinforcement learning has recently been studied in various fields and also used to optimally control IoT devices supporting the expansion of Internet connection beyond the usual standard devices. In this paper, we try to allow multiple reinforcement learning agents to learn optimal control policy on their own IoT devices of the same type but with slightly different dynamics. For such multiple IoT devices, there is no guarantee that an agent who interacts only with one IoT device and learns the optimal control policy will also control another IoT device well. Therefore, we may need to apply independent reinforcement learning to each IoT device individually, which requires a costly or time-consuming effort. To solve this problem, we propose a new federated reinforcement learning architecture where each agent working on its independent IoT device shares their learning experience (i.e., the gradient of loss function) with each other, and transfers a mature policy model parameters into other agents. They accelerate its learning process by using mature parameters. We incorporate the actor–critic proximal policy optimization (Actor–Critic PPO) algorithm into each agent in the proposed collaborative architecture and propose an efficient procedure for the gradient sharing and the model transfer. Using multiple rotary inverted pendulum devices interconnected via a network switch, we demonstrate that the proposed federated reinforcement learning scheme can effectively facilitate the learning process for multiple IoT devices and that the learning speed can be faster if more agents are involved.
Sleep Apnea Detection with Polysomnography and Depth Sensors
Authors: Schätz Martin , Procházka Aleš , Kuchyňka Jiří , Vyšata Oldřich
Published: March 2020

This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have the same sensitivity to breathing changes as in PSG records. The breathing signal from depth sensors can be used for classification of sleep apnea events with the same success rate as with PSG data. The recent development of computational technologies has led to a big leap in the usability of range imaging sensors. New depth sensors are smaller, have a higher sampling rate, with better resolution, and have bigger precision. They are widely used for computer vision in robotics, but they can be used as non-contact and non-invasive systems for monitoring breathing and its features. The breathing rate can be easily represented as the frequency of a recorded signal. All tested depth sensors (MS Kinect v2, RealSense SR300, R200, D415 and D435) are capable of recording depth data with enough precision in depth sensing and sampling frequency in time (20–35 frames per second (FPS)) to capture breathing rate. The spectral analysis shows a breathing rate between 0.2 Hz and 0.33 Hz, which corresponds to the breathing rate of an adult person during sleep. To test the quality of breathing signal processed by the proposed workflow, a neural network classifier (simple competitive NN) was trained on a set of 57 whole night polysomnographic records with a classification of sleep apneas by a sleep specialist. The resulting classifier can mark all apnea events with 100% accuracy when compared to the classification of a sleep specialist, which is useful to estimate the number of events per hour. When compared to the classification of polysomnographic breathing signal segments by a sleep specialist, which is used for calculating length of the event, the classifier has an F 1 score of 92.2% Accuracy of 96.8% (sensitivity 89.1% and specificity 98.8%). The classifier also proves successful when tested on breathing signals from MS Kinect v2 and RealSense R200 with simulated sleep apnea events. The whole process can be fully automatic after implementation of automatic chest area segmentation of depth data.
Study of Rotation and Bending Effects on a Flexible Hybrid Implanted Power Transfer and Wireless Antenna System
Authors: Shadid Reem , Haerinia Mohammad , Noghanian Sima
Published: March 2020

We present rotational misalignment and bending effects on a hybrid system to transfer power and data wirelessly for an implantable device. The proposed system consists of a high-frequency coil (13.56 MHz) to transfer power and an ultra-high frequency antenna (905 MHz) for data communication. The system performance and the transmitted power were studied under two misalignment conditions: (1) receiver rotation around itself with reference to the transmitter, and (2) bending of the implanted receiver under three different radii. Implanted receiver was printed on a flexible Kapton substrate and placed inside a layered body tissue model at a 30 mm depth. It is shown that the inductive link is stable under rotational misalignment and three bending conditions, whereas the communication data link is suitable to be used if the rotation angle is less than 75° or larger than 150°. The results show that the resonance frequency varies by 1.6%, 11.05%, and 6.62% for the bending radii of 120 mm, 80 mm, and 40 mm, respectively. Moreover, transmission efficiency varies by 4.3% for the bending radius of 120 mm. Decreasing the bending radius has more effects on antenna transmission efficiency that may cause severe losses in the communication link.
Distillation of an End-to-End Oracle for Face Verification and Recognition Sensors
Authors: Guzzi Francesco , De Bortoli Luca , Molina Romina Soledad , Marsi Stefano , Carrato Sergio , Ramponi Giovanni
Published: March 2020

Face recognition functions are today exploited through biometric sensors in many applications, from extended security systems to inclusion devices; deep neural network methods are reaching in this field stunning performances. The main limitation of the deep learning approach is an inconvenient relation between the accuracy of the results and the needed computing power. When a personal device is employed, in particular, many algorithms require a cloud computing approach to achieve the expected performances; other algorithms adopt models that are simple by design. A third viable option consists of model (oracle) distillation. This is the most intriguing among the compression techniques since it permits to devise of the minimal structure that will enforce the same I/O relation as the original model. In this paper, a distillation technique is applied to a complex model, enabling the introduction of fast state-of-the-art recognition capabilities on a low-end hardware face recognition sensor module. Two distilled models are presented in this contribution: the former can be directly used in place of the original oracle, while the latter incarnates better the end-to-end approach, removing the need for a separate alignment procedure. The presented biometric systems are examined on the two problems of face verification and face recognition in an open set by using well-agreed training/testing methodologies and datasets.
A Graphene-PEDOT:PSS Modified Paper-Based Aptasensor for Electrochemical Impedance Spectroscopy Detection of Tumor Marker
Authors: Yen Yi-Kuang , Chao Chen-Hsiang , Yeh Ya-Shin
Published: March 2020

A graphene and poly (3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) modified conductive paper-based electrochemical impedance spectroscopy (EIS) aptasensor has been successfully fabricated by a simple and continuous coating process. A graphene/PEDOT:PSS modified paper electrode forms the nanocomposite providing a conductive and sensitive substrate for further aptamer functionalization of the biosensor. This low-cost paper-based aptasensor exhibits its sensitivity to carcinoembryonic antigens (CEA) in standard buffer solutions and human serum samples in a linear range of 0.77–14 ng·mL−1. The limit of detection (LOD) is found to be 0.45 ng·mL−1 and 1.06 ng·mL−1 for CEA in both samples, separately. This aptamer-based sensing device was also evaluated and received a good correlation with the immunoassay detection method. The proposed paper-based aptasensor has demonstrated its potential as a rapid simple point-of-care analytical platform for early cancer diagnosis in less developed areas where manufacturing facilities, analytical instruments, and trained specialists are limited.
Special Issue “Advanced Nanomaterials Based Gas Sensors”
Authors: Vilanova Xavier
Published: March 2020

During the last several years, according to the works published in research journals, many nanostructured materials have been tested as sensing materials for gas-sensing applications. This trend has been observed for both metal oxides as well as carbon-based nanomaterials. More recently, it has also been extended to other materials based on chalcogenides. The field of applications for these sensors is very wide, including air quality, industrial safety and medical diagnosis, using different transducing mechanisms. Therefore, in this Special Issue, we have put together recent advances in this area.
Matched Field Processing Based on Bayesian Estimation
Authors: Zhu Guolei , Wang Yingmin , Wang Qi
Published: March 2020

In order to improve the robustness and positioning accuracy of the matched field processing (MFP) in underwater acoustic systems, we propose a conditional probability constraint matched field processing (MFP-CPC) algorithm in this paper, which protects the main-lobe and suppresses the side-lobe to the AMFP by the constraint parameters, such as the posterior probability density of source locations obtained by Bayesian criterion under the assumption of white Gaussian noise. Under such constraint, the proposed MFP-CPC algorithm not only has the same merit of a high resolution as AMFP but also improves the robustness. To evaluate the algorithm, the simulated and experimental data in an uncertain shallow ocean environment is used. From the results, MFP-CPC is robust to the moored source, as well as the moving source. In addition, the localization and tracking performances of using the proposed algorithm are consistent with the trajectory of the moving source.
W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
Authors: Rathore Rajkumar Singh , Sangwan Suman , Mazumdar Sukriti , Kaiwartya Omprakash , Adhikari Kabita , Kharel Rupak , Song Houbing
Published: March 2020

Underwater sensor networks (UWSNs) have witnessed significant R&D attention in both academia and industry due to their growing application domains, such as border security, freight via sea or river, natural petroleum production and the fishing industry. Considering the deep underwater-oriented access constraints, energy-centric communication for the lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network relying on geolocation and the quality of service-centric underwater relay node selection, without paying much attention to the dynamic underwater network environments. To this end, this paper presents an adapted whale and wolf optimization-based energy and delay-centric green underwater networking framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale-centric optimization in relay node selection. Firstly, an underwater relay node optimization model is mathematically derived, focusing on underwater whale dynamics for incorporating realistic underwater characteristics in networking. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm for selecting optimal and stable relay nodes for centric underwater communication paths. Thirdly, a complete workflow of the W-GUN framework is presented with an optimization flowchart. The comparative performance evaluation attests to the benefits of the proposed framework and is compared to state-of-the-art techniques considering various metrics related to underwater network environments.
Sleep in the Natural Environment: A Pilot Study
Authors: Chaudhry Fayzan F. , Danieletto Matteo , Golden Eddye , Scelza Jerome , Botwin Greg , Shervey Mark , De Freitas Jessica K. , Paranjpe Ishan , Nadkarni Girish N. , Miotto Riccardo , Glowe Patricia , Stock Greg , Percha Bethany , Zimmerman Noah , Dudley Joel T. , Glicksberg Benjamin S.
Published: March 2020

Sleep quality has been directly linked to cognitive function, quality of life, and a variety of serious diseases across many clinical domains. Standard methods for assessing sleep involve overnight studies in hospital settings, which are uncomfortable, expensive, not representative of real sleep, and difficult to conduct on a large scale. Recently, numerous commercial digital devices have been developed that record physiological data, such as movement, heart rate, and respiratory rate, which can act as a proxy for sleep quality in lieu of standard electroencephalogram recording equipment. The sleep-related output metrics from these devices include sleep staging and total sleep duration and are derived via proprietary algorithms that utilize a variety of these physiological recordings. Each device company makes different claims of accuracy and measures different features of sleep quality, and it is still unknown how well these devices correlate with one another and perform in a research setting. In this pilot study of 21 participants, we investigated whether sleep metric outputs from self-reported sleep metrics (SRSMs) and four sensors, specifically Fitbit Surge (a smart watch), Withings Aura (a sensor pad that is placed under a mattress), Hexoskin (a smart shirt), and Oura Ring (a smart ring), were related to known cognitive and psychological metrics, including the n-back test and Pittsburgh Sleep Quality Index (PSQI). We analyzed correlation between multiple device-related sleep metrics. Furthermore, we investigated relationships between these sleep metrics and cognitive scores across different timepoints and SRSM through univariate linear regressions. We found that correlations for sleep metrics between the devices across the sleep cycle were almost uniformly low, but still significant (p < 0.05). For cognitive scores, we found the Withings latency was statistically significant for afternoon and evening timepoints at p = 0.016 and p = 0.013. We did not find any significant associations between SRSMs and PSQI or cognitive scores. Additionally, Oura Ring’s total sleep duration and efficiency in relation to the PSQI measure was statistically significant at p = 0.004 and p = 0.033, respectively. These findings can hopefully be used to guide future sensor-based sleep research.
WiFi-Based Driver’s Activity Monitoring with Efficient Computation of Radio-Image Features
Authors: Akhtar Zain Ul Abiden , Wang Hongyu
Published: March 2020

Driver distraction and fatigue are among the leading contributing factors in various fatal accidents. Driver activity monitoring can effectively reduce the number of roadway accidents. Besides the traditional methods that rely on camera or wearable devices, wireless technology for driver’s activity monitoring has emerged with remarkable attention. With substantial progress in WiFi-based device-free localization and activity recognition, radio-image features have achieved better recognition performance using the proficiency of image descriptors. The major drawback of image features is computational complexity, which increases exponentially, with the growth of irrelevant information in an image. It is still unresolved how to choose appropriate radio-image features to alleviate the expensive computational burden. This paper explores a computational efficient wireless technique that could recognize the attentive and inattentive status of a driver leveraging Channel State Information (CSI) of WiFi signals. In this novel research work, we demonstrate an efficient scheme to extract the representative features from the discriminant components of radio-images to reduce the computational cost with significant improvement in recognition accuracy. Specifically, we addressed the problem of the computational burden by efficacious use of Gabor filters with gray level statistical features. The presented low-cost solution requires neither sophisticated camera support to capture images nor any special hardware to carry with the user. This novel framework is evaluated in terms of activity recognition accuracy. To ensure the reliability of the suggested scheme, we analyzed the results by adopting different evaluation metrics. Experimental results show that the presented prototype outperforms the traditional methods with an average recognition accuracy of 93.1 % in promising application scenarios. This ubiquitous model leads to improve the system performance significantly for the diverse scale of applications. In the realm of intelligent vehicles and assisted driving systems, the proposed wireless solution can effectively characterize the driving maneuvers, primary tasks, driver distraction, and fatigue by exploiting radio-image descriptors.
Design and Calibration of an Instrumented Seat Post to Measure Sitting Loads While Cycling
Authors: Sien Dieltiens , Jordi D’hondt , Juwet Marc , Shariatmadar Keivan , Versteyhe Mark
Published: March 2020

Traditional instrumented seat posts determine context-induced seat loads to analyze damping properties. This paper presents an enhanced instrumented seat post able to measure all six load components to resolve user-induced seat loads. User-induced cycling loads consist of all loads the user applies to the bicycle during cycling and is measured at the steer stem, the seat post, and the pedals. Seat loads are essentially uncharted territory, as most studies only address pedal loading to study cycling technique. In this paper, a conventional seat post is redesigned by equipping it with a u-shaped component and strain gauges. The instrumented seat post is straightforward thanks to (i) the simple design, (ii) the gravitational calibration method, and (iii) the permitted clearance on the strain gauge alignment. Analyzing mean seat loading in function of the pedal cycle can provide extra insights into cycling technique and the related injuries. It is an interesting addition to the universally adopted method of utilizing singular pedal loads.
Impedimetric Detection of Ammonia and Low Molecular Weight Amines in the Gas Phase with Covalent Organic Frameworks
Authors: Rodríguez Andrés , Rico Elio , Sierra Cesar , Rodríguez Oscar
Published: March 2020

Two Covalent Organic Frameworks (COF), named TFP-BZ and TFP-DMBZ, were synthesized using the imine condensation between 1,3,5-triformylphloroglucinol (TFP) with benzidine (BZ) or 3,3-dimethylbenzidine (DMBZ). These materials were deposited, such as films over interdigitated electrodes (IDE), by chemical bath deposition, giving rise to TFP-BZ-IDE and TFP-DMBZ-IDE systems. The synthesized COFs powders were characterized by Powder X-Ray Diffraction (PXRD), Fourier Transform Infrared spectroscopy (FT-IR), solid-state Nuclear Magnetic Resonance (ssNMR), nitrogen adsorption isotherms, Scanning Electron Microscopy (SEM), and Raman spectroscopy, while the films were characterized by SEM and Raman. Ammonia and low molecular weight amine sensing were developed with the COF film systems using the impedance electrochemical spectroscopy (EIS). Results showed that the systems TFP-BZ-IDE and TFP-DMBZ-IDE detect low molecular weight amines selectively by impedimetric analysis. Remarkably, with no significant interference by other atmospheric gas compounds such as nitrogen, carbon dioxide, and methane. Additionally, both COF films presented a range of sensitivity at low amine concentrations below two ppm at room temperature.
Proof of Concept of Scalable Integration of Internet of Things and Blockchain in Healthcare
Authors: Satamraju Krishna Prasad , B Malarkodi
Published: March 2020

The advent of Internet of Things (IoT) brought innovation along with unprecedented benefits of convenience and efficacy in many operations that were otherwise very cumbersome. This innovation explosion has surfaced a new dimension of vulnerability and physical threat to the data integrity of IoT networks. Implementing conventional cryptographic algorithms on IoT devices is not future-proof as these devices are constrained in terms of computational power, performance, and memory. In this paper, we are proposing a novel framework, a unique model that integrates IoT networks with a blockchain to address potential privacy and security threats for data integrity. Smart contracts are instrumental in this integration process and they are used to handle device authentication, authorization and access-control, and data management. We further share a new design model for interfaces to integrate both platforms while highlighting its performance results over the existing models. With the incorporation of off-chain data storage into the framework, overall scalability of the system can be increased. Finally, our research concludes how the proposed framework can be fused virtually into any existing IoT applications with minimal modifications.
Sustainable Development Goals: A Powerful Framework for Embedding Ethics, CSR, and Sustainability in Management Education
Authors: Setó-Pamies Dolors , Papaoikonomou Eleni
Published: March 2020

In this editorial we are delighted to present the four papers included in this special issue. Each of them tackles different issues with important academic and managerial implications. Then, we will discuss the Sustainable Development Goals (SDGs) and the potential they represent for higher education institutions and management education, in particular. One of the most important challenges in this field will be how to introduce SDGs in management education, an area of interest for practitioners and academics.
Recovery of Gallium from Simulated GaAs Waste Etching Solutions by Solvent Extraction
Authors: Chen Wei-Sheng , Tien Ko-Wei , Wang Li-Pang , Lee Cheng-Han , Chung Yi-Fan
Published: March 2020

Gallium arsenide is used in semiconductor industries worldwide. Numerous waste etching solutions are produced during the processes of GaAs wafer production. Therefore, a complete and eco-friendly technology should be established to recover gallium as a gallium chloride solution and remove arsenic ion from waste GaAs etching solution. In this study, the gallium trichloride and arsenic trisulfide powders were dissolved in ammonia solutions to prepare the simulated solutions, and the pH value was adjusted to pH 2 by nitric acid. In the extraction step, the GaAs etching solutions were extracted using 0.5 M Cyanex 272 solutions in kerosene at pH 2 and 0.1 O/A ratio for 5 min. The extraction efficiency attained 77.4%, which had an optimal ratio of concentration, and the four steps extraction efficiency attained 99.5%. After extraction, iron sulfate heptahydrates were added into the raffinate, and the arsenic ions were precipitated. The removed rate attained 99.9% when the Fe/As ratio was 10. In the stripping step, the organic phase was stripped with 0.5 M hydrochloric acid at 1 O/A ratio for 3 min, and 97.5% gallium was stripped. Finally, the purity of gallium chloride solution was 99.95% and the gallium was seven times the concentration of the etching solutions.
Traditional and Local Knowledge in Chile: Review of Experiences and Insights for Management and Sustainability
Authors: Guerrero-Gatica Matías , Mujica María Isabel , Barceló Matías , Vio-Garay María Fernanda , Gelcich Stefan , Armesto Juan J.
Published: March 2020

Scientific interest in traditional and local knowledge (TLK) has grown in recent decades, because of the potential of TLK for improving management and conservation practices. Here, we synthesize and evaluate TLK studies in Chile, discuss how this progress compares to the international scientific literature in the field, and contextualize our results according to the multiple evidence base approach. We found 77 publications on the subject, a steady increase since 1980, and a peak production in the 1990s and the 2010s decades. Publications most often provide basic information on species names and lists of resource uses in terrestrial rather than marine ecosystems. Papers had an emphasis on natural, rather than social sciences. Work was concentrated on the extreme northern and southern regions of Chile where more indigenous populations are found. Indigenous ethnic groups received greater attention than non-indigenous people. Future work in Chile must broaden its attention to local and urban communities and focus on how TLK can contribute to management and sustainability, rather than only acquiring the basic knowledge contained in local and traditional communities. To better comprehend TLK’s contribution to policy measures, an interdisciplinary approach must be present to address these knowledge gaps.
Investigation and Application of a New Low-Carbon Material (Preplaced Aggregate Concrete) in Concrete-Filled Steel Tube Stub Columns
Authors: Lv Jing , Zhou Tianhua , Li Kunlun
Published: March 2020

As a new low-carbon material, development of preplaced aggregate concrete (PAC) will achieve huge economic and social benefits. However, few existing research is focused on applying PAC in structural elements. This paper is attempt to apply PAC in concrete-filled steel tube (CFST) stub columns and the bearing behaviors of PAC-filled steel tube (PACFST) stub columns under axial compression are also experimentally investigated. The results indicate that the failure modes of PACFST stub columns are all drum-like failure mode which are analogous to that of CFST stub columns. The axial load-axial strain curves of PACFST stub columns can be roughly divided into elastic stage, elastic-plastic stage and plastic stage. Under the similar ultimate load, the ultimate strains are a bit smaller than that of CFST stub columns. Comparison of the results of ultimate load of PACFST stub columns calculated using the existing relevant standards for the bearing capacity calculation methods of CFST stub columns, GB 50936 and JGJ 138 are much more suitable to assess the bearing capacity of PACFST stub columns. Approximately 15%~20% saving in cement consumption will be accomplished with popularization and utilization of PACFST stub columns as compared with CFST stub columns.
Influencing Factors for the Promotion of International Vocational Qualification and Certification: Evidences from International Project Manager Professionals in China
Authors: Bai Libiao , Bao Tana , Zhang Kaimin , Shi Huijing , Wang Zhiguo , Bai Sijun
Published: March 2020

Globalization has driven the promotion of international vocational qualification and certification (IVQC) to unify certification systems and standards. We explore IVQC promotion paths through the introduction and development of China’s International Project Manager Professional (IPMP) certification and identify and analyse IVQC processes’ influencing factors. Four factors (economic level, education level, employment level, sex ratio) are proposed; their impacts are hypothesized. Geographically weighted regression (GWR) is employed to identify factor impact relationships and validate assumptions. The results show that the four factors are positive for the promotion of IVQC. Economic level, employment level, and sex ratio contribute to the promotion of IVQC; employment level contributes most. Education level has relatively small impact. Therefore, IVQC is more likely to enter areas with developed economies, high employment rates, and more males. The promotion of IVQC can be facilitated by continuous social progress and international development. However, areas where salient factor levels are too low still present challenges.