bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts
Authors: Vanarse Anup , Osseiran Adam , Rassau Alexander , van der Made Peter
Published: January 2022

Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-rich sensor data. These advancements are complemented by the application of bioinspired algorithms and architectures based on findings from neurophysiological studies focusing on the biological olfactory pathway. The application of spiking neural networks (SNNs), and concepts from neuromorphic engineering in general, are one of the key factors that has led to the design and development of efficient bioinspired e-nose systems. However, only a limited number of studies have focused on deploying these models on a natively event-driven hardware platform that exploits the benefits of neuromorphic implementation, such as ultra-low-power consumption and real-time processing, for simplified integration in a portable e-nose system. In this paper, we extend our previously reported neuromorphic encoding and classification approach to a real-world dataset that consists of sensor responses from a commercial e-nose system when exposed to eight different types of malts. We show that the proposed SNN-based classifier was able to deliver 97% accurate classification results at a maximum latency of 0.4 ms per inference with a power consumption of less than 1 mW when deployed on neuromorphic hardware. One of the key advantages of the proposed neuromorphic architecture is that the entire functionality, including pre-processing, event encoding, and classification, can be mapped on the neuromorphic system-on-a-chip (NSoC) to develop power-efficient and highly-accurate real-time e-nose systems.
Wearable Sensors and Machine Learning for Hypovolemia Problems in Occupational, Military and Sports Medicine: Physiological Basis, Hardware and Algorithms
Authors: Kimball Jacob P. , Inan Omer T. , Convertino Victor A. , Cardin Sylvain , Sawka Michael N.
Published: January 2022

Hypovolemia is a physiological state of reduced blood volume that can exist as either (1) absolute hypovolemia because of a lower circulating blood (plasma) volume for a given vascular space (dehydration, hemorrhage) or (2) relative hypovolemia resulting from an expanded vascular space (vasodilation) for a given circulating blood volume (e.g., heat stress, hypoxia, sepsis). This paper examines the physiology of hypovolemia and its association with health and performance problems common to occupational, military and sports medicine. We discuss the maturation of individual-specific compensatory reserve or decompensation measures for future wearable sensor systems to effectively manage these hypovolemia problems. The paper then presents areas of future work to allow such technologies to translate from lab settings to use as decision aids for managing hypovolemia. We envision a future that incorporates elements of the compensatory reserve measure with advances in sensing technology and multiple modalities of cardiovascular sensing, additional contextual measures, and advanced noise reduction algorithms into a fully wearable system, creating a robust and physiologically sound approach to manage physical work, fatigue, safety and health issues associated with hypovolemia for workers, warfighters and athletes in austere conditions.
A Lab-on-a-Chip Based Automatic Platform for Continuous Nitrites Sensing in Aquaculture
Authors: Catini Alexandro , Capuano Rosamaria , Tancredi Giuseppe , Dionisi Giulio , Di Giuseppe Davide , Filippi Joanna , Martinelli Eugenio , Di Natale Corrado
Published: January 2022

In aquaculture, the density of fish stock, use of feeding, and surrounding environmental conditions can easily result in an excessive concentration of harmful compounds that require continuous monitoring. Chemical sensors are available for most of these compounds, however, operative conditions and continuous monitoring in water make the development of sensors suitable for long and unattended deployments difficult. A possible solution is the development of engineered automatic labs where the uptake of sample and the contact with water is reduced and the use of a minimal quantity of reagents enables the implementation of reliable chemical assays. In this paper, a platform for automatic chemical assays is presented. The concept is demonstrated with the detection of nitrites based on the well-known colorimetric Griess reaction. The platform is centered around a lab-on-a-chip where reagents and water samples are mixed. The color of the reaction product is measured with low-cost optoelectronic components. Results show the feasibility of the approach with a minimum detectable concentration of about 0.1 mg/L which is below the tolerance level for aquaculture farms.
Evaluation of Calibration Equations by Using Regression Analysis: An Example of Chemical Analysis
Authors: Chen Hsuan-Yu , Chen Chiachung
Published: January 2022

A calibration curve is used to express the relationship between the response of the measuring technique and the standard concentration of the target analyst. The calibration equation verifies the response of a chemical instrument to the known properties of materials and is established using regression analysis. An adequate calibration equation ensures the performance of these instruments. Most studies use linear and polynomial equations. This study uses data sets from previous studies. Four types of calibration equations are proposed: linear, higher-order polynomial, exponential rise to maximum and power equations. A constant variance test was performed to assess the suitability of calibration equations for this dataset. Suspected outliers in the data sets are verified. The standard error of the estimate errors, s, was used as criteria to determine the fitting performance. The Prediction Sum of Squares (PRESS) statistic is used to compare the prediction ability. Residual plots are used as quantitative criteria. Suspected outliers in the data sets are checked. The results of this study show that linear and higher order polynomial equations do not allow accurate calibration equations for many data sets. Nonlinear equations are suited to most of the data sets. Different forms of calibration equations are proposed. The logarithmic transformation of the response is used to stabilize non-constant variance in the response data. When outliers are removed, this calibration equation’s fit and prediction ability is significantly increased. The adequate calibration equations with the data sets obtained with the same equipment and laboratory indicated that the adequate calibration equations differed. No universe calibration equation could be found for these data sets. The method for this study can be used for other chemical instruments to establish an adequate calibration equation and ensure the best performance.
Markerless Image Alignment Method for Pressure-Sensitive Paint Image
Authors: Suzuki Kyosuke , Inoue Tomoki , Nagata Takayuki , Kasai Miku , Nonomura Taku , Matsuda Yu
Published: January 2022

We propose a markerless image alignment method for pressure-sensitive paint measurement data replacing the time-consuming conventional alignment method in which the black markers are placed on the model and are detected manually. In the proposed method, feature points are detected by a boundary detection method, in which the PSP boundary is detected using the Moore-Neighbor tracing algorithm. The performance of the proposed method is compared with the conventional method based on black markers, the difference of Gaussian (DoG) detector, and the Hessian corner detector. The results by the proposed method and the DoG detector are equivalent to each other. On the other hand, the performances of the image alignment using the black marker and the Hessian corner detector are slightly worse compared with the DoG and the proposed method. The computational cost of the proposed method is half of that of the DoG method. The proposed method is a promising for the image alignment in the PSP application in the viewpoint of the alignment precision and computational cost.
Analysis of Dynamic Magnetoelastic Coupling in Mechanically Driven Magnetoelectric Antennas
Authors: Luong Kevin Q. T. , Wang Yuanxun (Ethan)
Published: January 2022

Mechanically driven magnetoelectric antennas are a promising new technology that enable a reduction in antenna size by many orders of magnitude, as compared to conventional antennas. The magnetoelastic coupling in these antennas, a phenomenon playing a direct role in determining performance, has been modeled using approaches that are severely lacking in both accuracy and tractability. In response to this problem, we take a physics-based approach to the analysis of magnetoelastic coupling. We find that certain directions of applied stress will maximize the coupling and we derive general expressions to quantify it. Our results are applied in comprehensive simulations that demonstrate the dynamic nature of the coupling as well as the impact of various operating conditions and material properties. Our work contributes analytical expressions and associated insight that can serve not only as guidelines for the design of mechanically driven magnetoelectric antennas, but also as stepping stones towards the development of more accurate models.
Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning
Authors: Bustamante-Bello Rogelio , García-Barba Alec , Arce-Saenz Luis A. , Curiel-Ramirez Luis A. , Izquierdo-Reyes Javier , Ramirez-Mendoza Ricardo A.
Published: January 2022

Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs. However, it is uncommon for cities to have real-time reactive systems that detect the different problems they have to fix on the pavement. This work proposes a solution to detect anomalies in streets through state analysis using sensors within the vehicles that travel daily and connecting them to a fog-computing architecture on a V2I network. The system detects and classifies the main road problems or abnormal conditions in streets and avenues using Machine Learning Algorithms (MLA), comparing roughness against a flat reference. An instrumented vehicle obtained the reference through accelerometry sensors and then sent the data through a mid-range communication system. With these data, the system compared an Artificial Neural Network (supervised MLA) and a K-Nearest Neighbor (Supervised MLA) to select the best option to handle the acquired data. This system makes it desirable to visualize the streets’ quality and map the areas with the most significant anomalies.
Multi-GNSS Combined Orbit and Clock Solutions at iGMAS
Authors: Zhou Wei , Cai Hongliang , Chen Guo , Jiao Wenhai , He Qianqian , Yang Yuguo
Published: January 2022

Global navigation services from the quad-constellation of GPS, GLONASS, BDS, and Galileo are now available. The international GNSS monitoring and assessment system (iGMAS) aims to evaluate the navigation performance of the current quad systems under a unified framework. In order to assess impact of orbit and clock errors on the positioning accuracy, the user range error (URE) is always taken as a metric by comparison with the precise products. Compared with the solutions from a single analysis center, the combined solutions derived from multiple analysis centers are characterized with robustness and reliability and preferred to be used as references to assess the performance of broadcast ephemerides. In this paper, the combination method of iGMAS orbit and clock products is described, and the performance of the combined solutions is evaluated by various means. There are different internal precisions of the combined orbit and clock for different constellations, which indicates that consistent weights should be assigned for individual constellations and analysis centers included in the combination. For BDS-3, Galileo, and GLONASS combined orbits of iGMAS, the root-mean-square error (RMSE) of 5 cm is achieved by satellite laser ranging (SLR) observations. Meanwhile, the SLR residuals are characterized with a linear pattern with respect to the position of the sun, which indicates that the solar radiation pressure (SRP) model adopted in precise orbit determination needs further improvement. The consistency between combined orbit and clock of quad-constellation is validated by precise point positioning (PPP), and the accuracies of simulated kinematic tests are 1.4, 1.2, and 2.9 cm for east, north, and up components, respectively.
Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems
Authors: El Mrabet Zakaria , Sugunaraj Niroop , Ranganathan Prakash , Abhyankar Shrirang
Published: January 2022

Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.
Spectral Fingerprint Investigation in the near Infra-Red to Distinguish Harmful Ethylene Glycol from Isopropanol in a Microchannel
Authors: Bodo Elisabetta , Merlo Sabina , Bello Valentina
Published: January 2022

Ethylene glycol (EG) and isopropanol (ISO) are among the major toxic alcohols that pose a risk to human health. However, it is important to distinguish them, since EG is more prone to cause renal failure, and can thus be more dangerous when ingested than ISO. Analysis of alcohols such as isopropanol and ethylene glycol generally can be performed with a complex chromatographic method. Here, we present an optical method based on absorption spectroscopy, performed remotely on EG-ISO mixtures filling a microchannel. Mixtures of ethylene glycol in isopropanol at different volume concentrations were analyzed in a contactless manner in a rectangular-section glass micro-capillary provided with integrated reflectors. Fiber-coupled broadband light in the wavelength range 1.3–1.7 µm crossed the microchannel multiple times before being directed towards an optical spectrum analyzer. The induced zig-zag path increased the fluid–light interaction length and enhanced the effect of optical absorption. A sophisticated theoretical model was developed and the results of our simulations were in very good agreement with the results of the experimental spectral measurements. Moreover, from the acquired data, we retrieved a responsivity parameter, defined as power ratio at two wavelengths, that is linearly related to the EG concentration in the alcoholic mixtures.
Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs
Authors: Nepal Upesh , Eslamiat Hossein
Published: January 2022

In-flight system failure is one of the major safety concerns in the operation of unmanned aerial vehicles (UAVs) in urban environments. To address this concern, a safety framework consisting of following three main tasks can be utilized: (1) Monitoring health of the UAV and detecting failures, (2) Finding potential safe landing spots in case a critical failure is detected in step 1, and (3) Steering the UAV to a safe landing spot found in step 2. In this paper, we specifically look at the second task, where we investigate the feasibility of utilizing object detection methods to spot safe landing spots in case the UAV suffers an in-flight failure. Particularly, we investigate different versions of the YOLO objection detection method and compare their performances for the specific application of detecting a safe landing location for a UAV that has suffered an in-flight failure. We compare the performance of YOLOv3, YOLOv4, and YOLOv5l while training them by a large aerial image dataset called DOTA in a Personal Computer (PC) and also a Companion Computer (CC). We plan to use the chosen algorithm on a CC that can be attached to a UAV, and the PC is used to verify the trends that we see between the algorithms on the CC. We confirm the feasibility of utilizing these algorithms for effective emergency landing spot detection and report their accuracy and speed for that specific application. Our investigation also shows that the YOLOv5l algorithm outperforms YOLOv4 and YOLOv3 in terms of accuracy of detection while maintaining a slightly slower inference speed.
Dynamic Scheduling of Contextually Categorised Internet of Things Services in Fog Computing Environment
Authors: Krivic Petar , Kusek Mario , Cavrak Igor , Skocir Pavle
Published: January 2022

Fog computing emerged as a concept that responds to the requirements of upcoming solutions requiring optimizations primarily in the context of the following QoS parameters: latency, throughput, reliability, security, and network traffic reduction. The rapid development of local computing devices and container-based virtualization enabled the application of fog computing within the IoT environment. However, it is necessary to utilize algorithm-based service scheduling that considers the targeted QoS parameters to optimize the service performance and reach the potential of the fog computing concept. In this paper, we first describe our categorization of IoT services that affects the execution of our scheduling algorithm. Secondly, we propose our scheduling algorithm that considers the context of processing devices, user context, and service context to determine the optimal schedule for the execution of service components across the distributed fog-to-cloud environment. The conducted simulations confirmed the performance of the proposed algorithm and showcased its major contribution—dynamic scheduling, i.e., the responsiveness to the volatile QoS parameters due to changeable network conditions. Thus, we successfully demonstrated that our dynamic scheduling algorithm enhances the efficiency of service performance based on the targeted QoS criteria of the specific service scenario.
Pose Determination of the Disc Cutter Holder of Shield Machine Based on Monocular Vision
Authors: Peng Dandan , Zhu Guoli , Zhang Dailin , Xie Zhe , Liu Rui , Hu Jinlong , Liu Yang
Published: January 2022

The visual measurement system plays a vital role in the disc cutter changing robot of the shield machine, and its accuracy directly determines the success rate of the disc cutter grasping. However, the actual industrial environment with strong noise brings a great challenge to the pose measurement methods. The existing methods are difficult to meet the required accuracy of pose measurement based on machine vision under the disc cutter changing conditions. To solve this problem, we propose a monocular visual pose measurement method consisting of the high precision optimal solution to the PnP problem (OPnP) method and the highly robust distance matching (DM) method. First, the OPnP method is used to calculate the rough pose of the shield machine’s cutter holder, and then the DM method is used to measure its pose accurately. Simulation results show that the proposed monocular measurement method has better accuracy and robustness than the several mainstream PnP methods. The experimental results also show that the maximum error of the proposed method is 0.28° in the direction of rotation and 0.32 mm in the direction of translation, which can meet the measurement accuracy requirement of the vision system of the disc cutter changing robot in practical engineering application.
Effect of Photodynamic Therapy on Halitosis: A Systematic Review of Randomized Controlled Trials
Authors: Motta Pamella de Barros , Motta Lara Jansiski , Campos Thalita Molinos , Gonçalves Marcela Leticia Leal , Santos Elaine Marcílio , Martimbianco Ana Luiza Cabrera , de Andrade David José Casimiro , Mesquita-Ferrari Raquel Agnelli , Fernandes Kristianne Porta Santos , Horliana Anna Carolina Ratto Tempestini , Bussadori Sandra Kalil
Published: January 2022

Background: This systematic review aimed to assess the effectiveness and safety of aPDT for the treatment of halitosis. Methods: Search strategies were conducted in October 2021 without language or data restrictions, on the following databases: MEDLINE, EMBASE, CENTRAL, LILACS and BBO, as well as a manual search. Randomized clinical trials (RCTs) with parallel design were considered for inclusion, assessing individuals (adolescents and adults) with a clinical diagnosis of halitosis treated with photodynamic therapy (aPDT). Primary outcomes assessed were halitosis measurements, adverse events and quality of life. The risk of bias for each included study was evaluated with the Cochrane Risk of Bias tool and the certainty of the body of the evidence was assessed with the GRADe approach. Results: Six RCTs (total of 225 participants) were included and due to clinical diversities it was not possible to group the outcome data in meta-analyses. Based on very low-certainty evidence (GRADE) the results showed that, when compared to tongue scraper, aPDT seems to promote a little to no difference in reducing halitosis and in the microbiological analysis. No adverse events were reported. Considering aPDT combined with tongue scraper, better outcome results were observed when compared to tongue scraper alone. Conclusions: Based on very low-certainty evidence, the findings of this review are uncertain about the effects of aPDT for halitosis control. Further RCTs with higher number of participants and long term assessments need to be conducted to support the use of this intervention. The protocol was registered in the PROSPERO database (number: CRD42020215319) on 19 November 2020—retrospectively registered.
Neural Fourier Energy Disaggregation
Authors: Nalmpantis Christoforos , Virtsionis Gkalinikis Nikolaos , Vrakas Dimitris
Published: January 2022

Deploying energy disaggregation models in the real-world is a challenging task. These models are usually deep neural networks and can be costly when running on a server or prohibitive when the target device has limited resources. Deep learning models are usually computationally expensive and they have large storage requirements. Reducing the computational cost and the size of a neural network, without trading off any performance is not a trivial task. This paper suggests a novel neural architecture that has less learning parameters, smaller size and fast inference time without trading off performance. The proposed architecture performs on par with two popular strong baseline models. The key characteristic is the Fourier transformation which has no learning parameters and it can be computed efficiently.
High-Precision Low-Cost Gimballing Platform for Long-Range Railway Obstacle Detection
Authors: Assaf Elio Hajj , von Einem Cornelius , Cadena Cesar , Siegwart Roland , Tschopp Florian
Published: January 2022

Increasing demand for rail transportation results in denser and more high-speed usage of the existing railway network, making new and more advanced vehicle safety systems necessary. Furthermore, high traveling speeds and the large weights of trains lead to long braking distances—all of which necessitates a Long-Range Obstacle Detection (LROD) system, capable of detecting humans and other objects more than 1000 m in advance. According to current research, only a few sensor modalities are capable of reaching this far and recording sufficiently accurate data to distinguish individual objects. The limitation of these sensors, such as a 1D-Light Detection and Ranging (LiDAR), is however a very narrow Field of View (FoV), making it necessary to use high-precision means of orienting to target them at possible areas of interest. To close this research gap, this paper presents a high-precision pointing mechanism, for the use in a future novel railway obstacle detection system, capable of targeting a 1D-LiDAR at humans or objects at the required distance. This approach addresses the challenges of a low target price, restricted access to high-precision machinery and equipment as well as unique requirements of our target application. By combining established elements from 3D printers and Computer Numerical Control (CNC) machines with a double-hinged lever system, simple and low-cost components are capable of precisely orienting an arbitrary sensor platform. The system’s actual pointing accuracy has been evaluated using a controlled, in-door, long-range experiment. The device was able to demonstrate a precision of 6.179 mdeg, which is at the limit of the measurable precision of the designed experiment.
Magnetic Nanoparticles Enhanced Surface Plasmon Resonance Biosensor for Rapid Detection of Salmonella Typhimurium in Romaine Lettuce
Authors: Bhandari Devendra , Chen Fur-Chi , Bridgman Roger C.
Published: January 2022

Salmonella is one of the major foodborne pathogens responsible for many cases of illnesses, hospitalizations and deaths worldwide. Although different methods are available to timely detect Salmonella in foods, surface plasmon resonance (SPR) has the benefit of real-time detection with a high sensitivity and specificity. The purpose of this study was to develop an SPR method in conjunction with magnetic nanoparticles (MNPs) for the rapid detection of Salmonella Typhimurium. The assay utilizes a pair of well-characterized, flagellin-specific monoclonal antibodies; one is immobilized on the sensor surface and the other is coupled to the MNPs. Samples of romaine lettuce contaminated with Salmonella Typhimurium were washed with deionized water, and bacterial cells were captured on a filter membrane by vacuum filtration. SPR assays were compared in three different formats—direct assay, sequential two-step sandwich assay, and preincubation one-step sandwich assay. The interaction of flagellin and MNPs with the antibody-immobilized sensor surface were analyzed. SPR signals from a sequential two-step sandwich assay and preincubation one-step sandwich assay were 7.5 times and 14.0 times higher than the direct assay. The detection limits of the assay were 4.7 log cfu/mL in the buffer and 5.2 log cfu/g in romaine lettuce samples.
Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence
Authors: Manimurugan S. , Almutairi Saad , Aborokbah Majed Mohammed , Narmatha C. , Ganesan Subramaniam , Chilamkurti Naveen , Alzaheb Riyadh A. , Almoamari Hani
Published: January 2022

Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient’s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%.
An Electrophysiological Model for Assessing Cognitive Load in Tacit Coordination Games
Authors: Laufer Ilan , Mizrahi Dor , Zuckerman Inon
Published: January 2022

Previously, it was shown that some people are better coordinators than others; however, the relative weight of intuitive (system 1) versus deliberate (system 2) modes of thinking in tacit coordination tasks is still not resolved. To address this question, we have extracted an electrophysiological index, the theta-beta ratio (TBR), from the Electroencephalography (EEG) recorded from participants while they were engaged in a semantic coordination task. Results have shown that individual coordination ability, game difficulty and response time are each positively correlated with cognitive load. These results suggest that better coordinators rely more on complex thought process and on more deliberate thinking while coordinating. The model we have presented may be used for the assessment of the depth of reasoning individuals engage in when facing different tasks requiring different degrees of allocation of resources. The findings as well as future research directions are discussed.
Game Theory-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
Authors: Yan Xiao , Huang Cheng , Gan Jianyuan , Wu Xiaobei
Published: January 2022

Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). WSNs collect and transmit data through sensor nodes. However, the energy carried by the sensor nodes is limited. The sensor nodes need to save energy as much as possible to prolong the network lifetime. This paper proposes a game theory-based energy-efficient clustering algorithm (GEC) for wireless sensor networks, where each sensor node is regarded as a player in the game. According to the length of idle listening time in the active state, the sensor node can adopt favorable strategies for itself, and then decide whether to sleep or not. In order to avoid the selfish behavior of sensor nodes, a penalty mechanism is introduced to force the sensor nodes to adopt cooperative strategies in future operations. The simulation results show that the use of game theory can effectively save the energy consumption of the sensor network and increase the amount of network data transmission, so as to achieve the purpose of prolonging the network lifetime.
Magnetic Flux Density Measurement Platform for an Inductive Wireless Power Transmitter Coil Design
Authors: Prosen Nataša , Milanovič Miro , Domajnko Jure
Published: January 2022

This paper presents a platform developed for automated magnetic flux density measurement. The platform was designed to be used to measure the magnetic flux density of the transmitter/receiver coil of an inductive wireless power transfer system. The magnetic flux density of a transmitter was measured using a small, 3-axis search coil. The search coil was positioned in the 3D space above the transmitter coil using a 3D positioning mechanism and used to measure the magnetic flux density at a specific point. The data was then sent to a computer application to visualize the magnetic flux density. The measured magnetic field could be used in combination with electromagnetic field solvers to design and optimize transmitter coils for inductive wireless power transfer systems.
The Sea Route Planning for Survey Vessel Intelligently Navigating to the Survey Lines
Authors: Yang Jiachen , Ni Tianlei , Liu Lin , Wen Jiabao , He Jingyi , Li Zhengjian
Published: January 2022

Marine surveying is an important part of marine environment monitoring systems. In order to improve the accuracy of marine surveying and reduce investment in artificial stations, it is necessary to use high-precision GNSS for shipborne navigation measurements. The basic measurement is based on the survey lines that are already planned by surveyors. In response to the needs of survey vessels sailing to the survey line, a method framework for the shortest route planning is proposed. Then an intelligent navigation system for survey vessels is established, which can be applied to online navigation of survey vessels. The essence of the framework is that the vessel can travel along the shortest route to the designated survey line under the limitation of its own minimum turning radius. Comparison and analysis of experiments show that the framework achieves better optimization. The experimental results show that our proposed method can enable the vessel to sail along a shorter path and reach the starting point of the survey line at the specified angle.
A Comparative Study of the Electrical and Electromechanical Responses of Carbon Nanotube/Polypropylene Composites in Alternating and Direct Current
Authors: Balam Abraham , Pech-Pisté Raúl , Valdez-Nava Zarel , Gamboa Fidel , Castillo-Atoche Alejandro , Avilés Francis
Published: January 2022

The electrical and electromechanical responses of ~200 µm thick extruded nanocomposite films comprising of 4 wt.% and 5 wt.% multiwall carbon nanotubes mixed with polypropylene are investigated under an alternating current (AC) and compared to their direct current (DC) response. The AC electrical response to frequency (f) and strain (piezoimpedance) is characterized using two configurations, namely one that promotes resistive dominance (resistive configuration) and the other that promotes the permittivity/capacitive contribution (dielectric configuration). For the resistive configuration, the frequency response indicated a resistive–capacitive (RC) behavior (negative phase angle, θ), with a significant contribution of capacitance for frequencies of 104 Hz and above, depending on the nanotube content. The piezoimpedance characterization in the resistive configuration yielded an increasing impedance modulus (|Z|) and an increasing (negative) value of θ as the strain increased. The piezoimpedance sensitivity at f = 10 kHz was ~30% higher than the corresponding DC piezoresistive sensitivity, yielding a sensitivity factor of 9.9 for |Z| and a higher sensitivity factor (~12.7) for θ. The dielectric configuration enhanced the permittivity contribution to impedance, but it was the least sensitive to strain.
Experimental Validation of a Model-Free High-Order Sliding Mode Controller with Finite-Time Convergence for Trajectory Tracking of Autonomous Underwater Vehicles
Authors: González-García Josué , Gómez-Espinosa Alfonso , García-Valdovinos Luis Govinda , Salgado-Jiménez Tomás , Cuan-Urquizo Enrique , Escobedo Cabello Jesús Arturo
Published: January 2022

Several control strategies have been proposed for the trajectory tracking problem of Autonomous Underwater Vehicles (AUV). Most of them are model-based, hence, detailed knowledge of the parameters of the robot is needed. Few works consider a finite-time convergence in their controllers, which offers strong robustness and fast convergence compared with asymptotic or exponential solutions. Those finite-time controllers do not permit the users to predefine the convergence time, which can be useful for a more efficient use of the robot’s energy. This paper presents the experimental validation of a model-free high-order Sliding Mode Controller (SMC) with finite-time convergence in a predefined time. The convergence time is introduced by the simple change of a time-base parameter. The aim is to validate the controller so it can be implemented for cooperative missions where the communication is limited or null. Results showed that the proposed controller can drive the robot to the desired depth and heading trajectories in the predefined time for all the cases, reducing the error by up to 75% and 41% when compared with a PID and the same SMC with asymptotic convergence. The energy consumption was reduced 35% and 50% when compared with those same controllers.
Less Is More: Audience Cognition of Comic Simplification in the Characters of Peking Opera
Authors: Wu Jiede , Sun Yikang , Lin Rung-Tai
Published: January 2022

A simplified approach was used to determine if “Less is More” is still a trend in comics’ Peking opera characters. There were 225 website volunteers who took part in the study. Via a questionnaire survey and analysis, this study explored the feasibility of “simplification” in comedy. The results indicate that the proposed “simplifying” approach can be applied to the creation of dramatic characters, but the scale of simplification must be adjusted flexibly to suit different subjects. For audiences, there is not much recognition and sympathy for the works that are simplified in the extreme and the current symbolization. The simplification used in this study is merely the first step in testing the usefulness of simplification as an approach. It is used as a means of understanding the cognition of the audience to accept the simple features of Peking opera characters. In subsequent studies, the proposed “simplified” approach is necessary to adapt and improve with a view to practical application. It also requires an in-depth analysis of the cognitive differences of the different participants according to the cognitive and communication theories of artistic creation.
A New Practice to Monitor the Fabrication Process of Fab-Targeting Ligands from Bevacizumab by LC-MS: Preparation and Analytical Characterization
Authors: Marquet Franck , D’Atri Valentina , Guillarme Davy , Borchard Gerrit
Published: January 2022

The objective of this study was to qualitatively evaluate a Fab-targeting ligand preparation containing free thiol groups in the hinge region by using bevacizumab as a model. The evaluation focused on the purification of fragments through a nonaffinity-based process using a centrifugal ultrafiltration technique and mild reduction conditions for the intact production of F(ab’) fragments with specific inter-heavy-chain disulfide bonds cleavage. Under these conditions, F(ab’) fragments with a defined chemical composition were successfully obtained via proteolytic digestion followed by a controlled reduction reaction process maintaining the integrity of the binding sites. The ultrafiltration purification technique appears to be suitable for the removal of the digestive enzyme but inefficient for the removal of Fc fragments, thus requiring additional processing. A suitable analytical strategy was developed, allowing us to demonstrate the reformation of disulfide bridges between the two reduced cysteines within F(ab’) fragments.
Operation of the Egyptian Power Grid with Maximum Penetration Level of Renewable Energies Using Corona Virus Optimization Algorithm
Authors: Fayek Hady H. , Abdalla Omar H.
Published: January 2022

Countries around the world are looking forward to fully sustainable energy by the middle of the century to meet Paris climate agreement goals. This paper presents a novel algorithm to optimally operate the Egyptian grid with maximum renewable power generation, minimum voltage deviation and minimum power losses. The optimal operation is performed using Corona Virus Algorithm (CVO). The proposed CVO is compared to the Teaching and Learning-Based Optimization (TLBO) algorithm in terms of voltage deviation, power losses and share of renewable energies. The real demand, solar irradiance and wind speed in typical winter and summer days are considered. The 2020 Egyptian grid model is developed, simulated, and optimized using DIgSILENT software application. The results have proved the effectiveness of the proposed CVO, compared to the TLBO, to operate the grid with the highest share possible of renewables. The paper is a step forward to achieve Egyptian government targets to reach 20% and 42% penetration level of renewable energies by 2022 and 2035, respectively.
Understanding the Burden of Legal Financial Obligations on Indigent Washingtonians
Authors: Rafael Maria Katarina E. , Mai Chris
Published: January 2022

In criminal courts across the country, judges assess a variety of fines, fees and other legal financial obligations (LFOs) that many defendants struggle to pay. This paper examines the disproportionate burden that fine and fee assessment and collection practices impose on low-income, system-involved individuals, using administrative court data for criminal cases filed in Washington’s courts of limited jurisdiction between 2015 and 2020. The authors find that the majority of defendants do not or only partially pay their LFOs, but that these observations are more pronounced for indigent defendants. The authors also find that, of defendants who fully pay off their fines and fees, individuals with a public defender satisfy their debt after a greater number of days, as compared to individuals with private counsel. This is all in spite of public defender defendants generally being assessed smaller amounts in fines and fees at the outset. Additionally, the authors uncover that when defendants do pay off all of their fines and fees, they tend to do so on the day of assessment, with the likelihood of satisfying full payment generally decreasing as time goes on. These findings suggest that many people struggle with criminal justice debt, but that this problem disproportionately impacts indigent Washingtonians, subjecting them to a greater possibility of harm through the various methods of collections enforcement.
Know Your Safe Drinking Skills: Adaptation Strategies for the College Effect
Authors: Lin Carolyn A. , Christensen John L. , Basaran Anne Borsai
Published: January 2022

Objective: The current study investigates the effects of an alcohol-prevention program delivered to college students in a formal classroom setting. Participants: The sample comprised 231 first-year college students who enrolled in a multisection “First Year Experience” course at a large northeastern university in the United States. Method: A naturalistic experiment was conducted, with a baseline evaluation at the beginning of the semester and a post-experiment evaluation near the end of the semester. Results: Social drinking attitudes, proximal drinking norm and the college effect are significant predictors of pre- and post-intervention episodic drinking frequency. The intervention reduced episodic drinking frequency as well as perceived distal and proximal drinking norms. It also increased drinking attitudes and did not change perceived efficacy or drinking-outcome expectancies. Conclusions: Practitioners could consider implementing a similar intervention to allow students to learn and practice safe drinking skills in the first year of their college life.
Effect of Stoichiometry on Shape Memory Properties of Ti-Ni-Hf-Cu-Nb Shape Memory Alloys Manufactured by Suspended Droplet Alloying
Authors: Li Sheng , Wang Minshi , Essa Khamis , Gan Chunlei , Liu Chunyan , Attallah Moataz
Published: January 2022

A novel Ti-Ni-Hf-Cu-Nb shape memory alloy has been developed by a new combinatorial alloy synthesis method, the Suspended Droplet Alloying. The influence of alloying elements on the transformation temperature, the microstructure and the shape memory effect of this alloy have also been studied. It was found that Cu has a greater negative influence on the transformation temperature of Ti-Ni-Hf-CuX alloys (about −5 K/at.%) than on the Ti-Ni-CuX alloys (−0.67 K/at.%). In addition, the negative effect intensifies with increasing Hf content. The transformation temperature rapidly decreases with increasing Nb composition in the Ti-Ni-Cu-Nb and Ti-Ni-Hf-Cu-Nb alloys, with the solid solution of Nb in the matrix being 1 at.%. A Ti-Ni-Cu-Hf-Nb alloy with high thermal cycle stability has been developed, where the alloying elements affect the transformation behaviour via altering the slipping energy and forming different types of precipitations.
Otolith Analyses Highlight Morpho-Functional Differences of Three Species of Mullet (Mugilidae) from Transitional Water
Authors: D’Iglio Claudio , Natale Sabrina , Albano Marco , Savoca Serena , Famulari Sergio , Gervasi Claudio , Lanteri Giovanni , Panarello Giuseppe , Spanò Nunziacarla , Capillo Gioele
Published: January 2022

Otoliths are used in taxonomy and ichthyology as they can provide a wide range of information about specimens. They are an essential tool to monitor the most sensitive species for a sustainable exploitation level. Despite the increasing use of sagittae in research, their inter- and intra-specific variability and eco-functionality are still poorly explored. This paper aims to investigate the inter- and intra-specific variability of Mugilidae sagittae using morphological and morphometrical analysis, as well as scanning electron microscopy and shape analysis. The sagittae of 74 specimens belonging to three different Mugilidae species, collected from a coastal lagoon, were analyzed to give an accurate description of their morphology, morphometry, shape and crystalline habits. The results highlighted the intra- and inter-specific variability of sagittae, showing morphometrical differences among species and slight differences between left and right sagittae in C. labrosus individuals. Moreover, SEM images showed a peculiar crystal organization, with several different crystal habits and polymorphs. This study provides an accurate description of sagittae in the studied species, deepening the knowledge on inter- and intra-specific variations and crystal habits and providing data which will be useful for future studies on otoliths. With this data, it will be possible to improve conservation and exploitation sustainability in sensitive habitats.
The Role of Perceived Justice on Satisfaction with the Coach: Gender Differences in a Longitudinal Study
Authors: López-Gajardo Miguel Ángel , Ponce-Bordón José Carlos , Rubio-Morales Ana , Llanos-Muñoz Rubén , Díaz-García Jesús
Published: January 2022

The aim of this study was to determine the interaction between the factors of perceived justice with regard to players’ satisfaction with the coach’s behavior, and also to determine the evolution of these interactions across a season in elite male and female soccer. A longitudinal design was carried out, with three measurement points (i.e., at the beginning, in the middle, and at the end of a season). Participants were 439 professional soccer players (males = 227, females = 212), aged between 18 and 33 years (M = 23.81, SD = 4.53). Results showed gender differences in the factors that predict satisfaction with the coach. Women grant more importance to relational and motivational aspects. It was also confirmed that there are important variations across the season in both genders. These results can help to better understand which behaviors are more appropriate for coaches depending on gender and time of the season.