bluuebunny/crossref_metadata_2025 huggingface dataset:
Selective Thermal Transformation of Automotive Shredder Residues into High-Value Nano Silicon Carbide
Authors: Hemati Sepideh , Hossain Rumana , Sahajwalla Veena
Published: October 2021

Automotive waste represents both a global waste challenge and the loss of valuable embedded resources. This study provides a sustainable solution to utilise the mixed plastics of automotive waste residue (ASR) as a resource that will curtail the landfilling of hazardous waste and its adverse consequences to the environment. In this research, the selective thermal transformation has been utilised to produce nano silicon carbide (SiC) using mixed plastics and glass from automotive waste as raw materials. The composition and formation mechanisms of SiC nanoparticles have been investigated by X-ray diffraction (XRD), X-ray-Photoelectron Spectroscopy (XPS) and Transmission Electron Microscopy (TEM). The as synthesised SiC nanoparticles at 1500 °C has uniform spherical shapes with the diameters of the fixed edges of about 50–100 nm with a porous structure. This facile way of synthesising SiC nanomaterials would lay the foundations for transforming complex wastes into value-added, high-performing materials, delivering significant economic and environmental benefits.
The Impact of the Coronavirus Disease (COVID-19) Pandemic on Nurses’ Turnover Intention: An Integrative Review
Authors: Falatah Rawaih
Published: October 2021

The COVID-19 pandemic has increased the demand and workload on nurses. In addition, the number of critical cases, the uncertainty about the disease, and the incidence rate of death from the disease impose a psychological stress on nurses. Considering the alarming issues of stress, burnout, and turnover among nurses even before the pandemic, the pandemic might have amplified such issues. Thus, the impact of the COVID-19 pandemic on nurses’ turnover and turnover intention warrants investigation. The aim of this review is to appraise and integrate the current pre- and post-coronavirus disease (COVID-19) literature on nurse turnover, published between 2016 and 2021. Forty-three studies on nurses’ turnover intention were appraised and synthesized. The reviewed literature suggested that nurses’ turnover intention increased significantly after the COVID-19 pandemic. Post-COVID-19-pandemic studies focused more on predicting nurses’ turnover intention through the pandemic’s negative impact on the nurses’ psychological wellbeing. The findings of this review should be considered by nurse managers and leaders in the development of policies and programs to reduce the negative impact of COVID-19 on nurse retention.
Surface Characterization of P-Type Point Contact Germanium Detectors
Authors: Edzards Frank , Hauertmann Lukas , Abt Iris , Gooch Chris , Lehnert Björn , Liu Xiang , Mertens Susanne , Radford David C. , Schulz Oliver , Willers Michael
Published: October 2021

P-type point contact (PPC) germanium detectors are used in rare event and low-background searches, including neutrinoless double beta (0νββ) decay, low-energy nuclear recoils, and coherent elastic neutrino-nucleus scattering. The detectors feature an excellent energy resolution, low detection thresholds down to the sub-keV range, and enhanced background rejection capabilities. However, due to their large passivated surface, separating the signal readout contact from the bias voltage electrode, PPC detectors are susceptible to surface effects such as charge build-up. A profound understanding of their response to surface events is essential. In this work, the response of a PPC detector to alpha and beta particles hitting the passivated surface was investigated in a multi-purpose scanning test stand. It is shown that the passivated surface can accumulate charges resulting in a radial-dependent degradation of the observed event energy. In addition, it is demonstrated that the pulse shapes of surface alpha events show characteristic features which can be used to discriminate against these events.
Genetic Evidence of the Black Death in the Abbey of San Leonardo (Apulia Region, Italy): Tracing the Cause of Death in Two Individuals Buried with Coins
Authors: Raele Donato Antonio , Panzarino Ginevra , Sarcinelli Giuseppe , Cafiero Maria Assunta , Maria Tunzi Anna , Dellù Elena
Published: October 2021

The Abbey of San Leonardo in Siponto (Apulia, Southern Italy) was an important religious and medical center during the Middle Ages. It was a crossroads for pilgrims heading along the Via Francigena to the Sanctuary of Monte Sant’Angelo and for merchants passing through the harbor of Manfredonia. A recent excavation of Soprintendenza Archeologica della Puglia investigated a portion of the related cemetery, confirming its chronology to be between the end of the 13th and beginning of the 14th century. Two single graves preserved individuals accompanied by numerous coins dating back to the 14th century, hidden in clothes and in a bag tied to the waist. The human remains of the individuals were analyzed in the Laboratorio di Antropologia Fisica of Soprintendenza ABAP della città metropolitana di Bari. Three teeth from each individual were collected and sent to the Istituto Zooprofilattico Sperimentale di Puglia e Basilicata to study infectious diseases such as malaria, plague, tuberculosis, epidemic typhus and Maltese fever (Brucellosis), potentially related to the lack of inspection of the bodies during burial procedures. DNA extracted from six collected teeth and two additional unrelated human teeth (negative controls) were analyzed using PCR to verify the presence of human DNA (β-globulin) and of pathogens such as Plasmodium spp., Yersinia pestis, Mycobacterium spp., Rickettsia spp. and Brucella spp. The nucleotide sequence of the amplicon was determined to confirm the results. Human DNA was successfully amplified from all eight dental extracts and two different genes of Y. pestis were amplified and sequenced in 4 out of the 6 teeth. Molecular analyses ascertained that the individuals buried in San Leonardo were victims of the Black Death (1347–1353) and the data confirmed the lack of inspection of the corpses despite the presence of numerous coins. This study represents molecular evidence, for the first time, of Southern Italy’s involvement in the second wave of the plague pandemic.
Hepatomegaly Associated with Non-Obstructive Sinusoidal Dilation in Experimental Visceral Leishmaniasis
Authors: Maeda Kota , Sadoughi Sonya , Morimoto Ayako , Uchida Kazuyuki , Chambers James , Sanjoba Chizu , Yamagishi Junya , Goto Yasuyuki
Published: October 2021

Visceral leishmaniasis (VL) is the most severe form of leishmaniasis caused by protozoan parasites of the genus Leishmania. Hepatomegaly is one of the most frequent clinical manifestations of VL, whereas immunopathology of the symptom has not been well investigated. Using our chronic model of experimental VL, we examined the influence of Leishmania donovani infection on the liver by clinical, histological, and biochemical analyses. The infected mice showed increased liver weight 24 weeks post-infection. Although an increase in serum ALT and inflammatory cell accumulation were observed in the livers of infected mice, no apparent parenchymal necrosis or fibrosis was observed. Tissue water content analyses demonstrated that increased liver weight was predominantly due to an increase in water weight. Together with the finding of hepatic sinusoidal dilation, these results suggested that edema associated with sinusoidal dilation causes hepatomegaly in L. donovani infection. Immunostaining of platelets and erythrocytes showed no thrombus formation or damage to the sinusoidal endothelium in the liver of infected mice. Taken together, these results suggest that hepatomegaly during experimental VL is caused by non-obstructive sinusoidal dilation.
Alkaloid Biosynthesis in the Early Stages of the Germination of Argemone mexicana L. (Papaveraceae)
Authors: Xool-Tamayo Jorge , Tamayo-Ordoñez Yahaira , Monforte-González Miriam , Muñoz-Sánchez José Armando , Vázquez-Flota Felipe
Published: October 2021

The synthesis of the benzylisoquinoline alkaloids, sanguinarine and berberine, was monitored in Argemone mexicana L. (Papaveracea) throughout the early stages of its hypocotyl and seedling development. Sanguinarine was detected in the cotyledons right after hypocotyl emergence, and it increased continuously until the apical hook unbent, prior to the cotyledonary leaves unfolding, when it abruptly fell. In the cotyledonary leaves, it also remained at low levels. Throughout development, berberine accumulation required the formation of cotyledonary leaves, whereas it was quickly detected in the hypocotyl from the time it emerged. Interestingly, the alkaloids detected in the cotyledons could have been imported from hypocotyls, because no transcriptional activity was detected in there. However, after turning into cotyledonary leaves, important levels of gene expression were noted. Taken together, these results suggest that the patterns of alkaloid tissue distribution are established from very early development, and might require transport systems.
Genome-Wide Identification, Genomic Organization, and Characterization of Potassium Transport-Related Genes in Cajanus cajan and Their Role in Abiotic Stress
Authors: Siddique Muhammad Hussnain , Babar Naeem Iqbal , Zameer Roshan , Muzammil Saima , Nahid Nazia , Ijaz Usman , Masroor Ashir , Nadeem Majid , Rashid Muhammad Abdul Rehman , Hashem Abeer , Azeem Farrukh , Fathi Abd_Allah Elsayed
Published: October 2021

Potassium is the most important and abundant inorganic cation in plants and it can comprise up to 10% of a plant’s dry weight. Plants possess complex systems of transporters and channels for the transport of K+ from soil to numerous parts of plants. Cajanus cajan is cultivated in different regions of the world as an economical source of carbohydrates, fiber, proteins, and fodder for animals. In the current study, 39 K+ transport genes were identified in C. cajan, including 25 K+ transporters (17 carrier-like K+ transporters (KUP/HAK/KTs), 2 high-affinity potassium transporters (HKTs), and 6 K+ efflux transporters (KEAs) and 14 K+ channels (9 shakers and 5 tandem-pore K+ channels (TPKs). Chromosomal mapping indicated that these genes were randomly distributed among 10 chromosomes. A comparative phylogenetic analysis including protein sequences from Glycine max, Arabidopsis thaliana, Oryza sativa, Medicago truncatula Cicer arietinum, and C. cajan suggested vital conservation of K+ transport genes. Gene structure analysis showed that the intron/exon organization of K+ transporter and channel genes is highly conserved in a family-specific manner. In the promoter region, many cis-regulatory elements were identified related to abiotic stress, suggesting their role in abiotic stress response. Abiotic stresses (salt, heat, and drought) adversely affect chlorophyll, carotenoids contents, and total soluble proteins. Furthermore, the activities of catalase, superoxide, and peroxidase were altered in C. cajan leaves under applied stresses. Expression analysis (RNA-seq data and quantitative real-time PCR) revealed that several K+ transport genes were expressed in abiotic stress-responsive manners. The present study provides an in-depth understanding of K+ transport system genes in C. cajan and serves as a basis for further characterization of these genes.
A Proposal for the Evaluation of HTV Silicone Rubber Composite Insulators
Authors: Kokalis Christos-Christodoulos A. , Kontargyri Vassiliki T. , Gonos Ioannis F.
Published: October 2021

This paper describes in detail a step-by-step methodology for obtaining specimens from the housing material (high temperature vulcanized (HTV) silicone rubber with aluminum trihydrate (ATH) filler) of composite insulators (finished products), for five well known and commonly used tests. The aim of the paper is to render practical clarifications and additions to the instructions for five tests on composite insulators provided by international standards. Additionally, the ranges of the results of these tests are presented. More specifically, shore A hardness measurement, tensile strength and elongation at break test, tear strength test, density measurement and inclined plane test were conducted on the housing material of ten, new, unaged medium voltage composite insulators made by six different manufacturers. The results of these tests are presented as a contribution to the existing knowledge and a comparative study of corresponding results of previous investigations is performed. The presented procedures for specimens’ preparation, as well as the results (arithmetical ranges), could be used as guidelines for future testing on the housing material (HTV silicone rubber with ATH filler) of composite insulators, either by researchers and manufacturers, during laboratory testing and material development processes, or by customers (distribution and transmission networks owners), during batch acceptance tests.
Yoga as an Integrative Therapy for Mental Health Concerns: An Overview of Current Research Evidence
Authors: Park Crystal L. , Slattery Jeanne M.
Published: October 2021

Background: Because the prevalence of mental health concerns is high and access or full responsiveness to pharmacological or psychotherapeutic treatment for many individuals is low, there has been increased interest in yoga as a potential therapy for many mental health concerns. Approach: We synthesize and critique current research on the efficacy of yoga relative to pharmacological approaches for anxiety disorders, mood disorders, posttraumatic stress disorder, obsessive-compulsive disorder, and eating disorders. Results: Yoga has been tested mostly as a complementary treatment to standard psychiatric and psychotherapeutic approaches. Findings from efficacy trials largely support the notion that yoga can help reduce symptoms of many psychiatric conditions, including anxiety, depression, and PTSD symptoms, above and beyond the effects achieved by standard pharmacological treatments alone; however, most evidence is of poor to moderate quality. Plausible transdiagnostic bottom-up and top-down mechanisms of yoga’s therapeutic effects have been advanced but remain untested. Conclusions: While results should be considered preliminary until more rigorous evidence is available, yoga appears to have the potential to provide many people suffering with psychiatric symptoms additional relief at relatively little cost. Yoga may be a viable complementary therapy to psychiatric and psychotherapeutic approaches for people with mental health challenges.
Quantum Uncertainty and Energy Flux in Extended Electrodynamics
Authors: Minotti Fernando , Modanese Giovanni
Published: October 2021

In quantum theory, for a system with macroscopic wavefunction, the charge density and current density are represented by non-commuting operators. It follows that the anomaly I=∂tρ+∇·j, being essentially a linear combination of these two operators in the frequency-momentum domain, does not admit eigenstates and has a minimum uncertainty fixed by the Heisenberg relation ΔNΔϕ≃1, which involves the occupation number and the phase of the wavefunction. We give an estimate of the minimum uncertainty in the case of a tunnel Josephson junction made of Nb. Due to this violation of the local conservation of charge, for the evaluation of the e.m. field generated by the system it is necessary to use the extended Aharonov–Bohm electrodynamics. After recalling its field equations, we compute in general form the energy–momentum tensor and the radiation power flux generated by a localized oscillating source. The physical requirements that the total flux be positive, negative or zero yield some conditions on the dipole moment of the anomaly I.
Design of a Compact Microreactor/Heat-Exchanger for a Distributed Production of Liquid Hydrocarbons from Methanol
Authors: Hu Guannan , Cherkasov Nikolay , Rebrov Evgeny V.
Published: October 2021

The paper compares conceptual designs of a microstructured reactor/heat-exchanger for the small-scale production of C8+ range hydrocarbons from methanol over H-ZSM-5 catalytic coatings. In these designs, air was used as a cooling fluid in the adjacent cooling channels. The heat transfer characteristics of a single-zone reactor (with channels 500 μm in diameter) and a two-zone reactor (with an additional coolant inlet) have been compared. A single reaction zone was not able to reduce the temperature gradient below 15 K, while a two-zone configuration, with a counter-current fluid flow in the upstream section and co-current flow in the downstream section, demonstrated a near-isothermal behaviour, with a mean temperature of 653 K.
Beyond Translated vs. Indigenous: Turkish Protestant Christian Hymnody as Global and Local Identity
Authors: Perigo Jeremy
Published: October 2021

At Turkey’s first national worship conferences in 2011, a passionate debate arose on whether Western music or indigenous Turkish music was most appropriate for worship. Some Turks felt that the Western missionaries were imposing indigenous musics on Turks as a type of “reverse colonization”. They felt that the current Western musical styles were best for worship. One Turk stated, “the saz is being forced down our throats”. Other Turks felt liberated to sing and play songs in traditional Turkish musical styles. The debate at the conference highlights the desire of missionaries and Turks to see renewal in congregational hymnody. Nevertheless, the Western vs. indigenous Turkish music debate reduces complex historical, musical, and liturgical issues into a divisive binarism. Using hymns sung in corporate worship in Turkey as a source, I will analyze here the quantity of musical localization in Turkish Protestant worship seeking to present musical localization as a lens to examine Turkish Christian liturgical identity.
Muscle Activation in Older Females after a Community-Based Resistance Training Program: A Pilot Study
Authors: Syed-Abdul Majid Mufaqam , Ball Stephen D.
Published: October 2021

Many randomized controlled trials utilizing resistance training have shown improvements in muscle activation in older adults. However, these programs lack applicability to community-dwelling older adults due to several challenges. Therefore, the purpose of this study was to test the effects of an eight-week community-based resistance training program on muscle activation in older adults. Eight females (age: 61 ± 4 years, body mass index (BMI): 31.7 ± 5.7 kg/m2) were enrolled in the study. The surface electromyography (sEMG) activity was evaluated before and after training for three muscles (biceps brachii, upper trapezius, and rectus femoris). Additional weight load tests were performed to measure muscle activation in response to the added resistance. After eight weeks of training, no changes were observed in muscle activation for biceps brachii and upper trapezius but was decreased for rectus femoris (p = 0.04). Furthermore, in response to weight loads, biceps brachii and upper trapezius improved muscle activation after training. In summary, eight weeks of community-based resistance training program non-significantly improved the activation of upper-body muscles in older adults.
Modern Trends in the Field of Solving Transboundary Problems in Groundwater Extraction
Authors: Golovina Ekaterina , Karennik Karina
Published: October 2021

The intensive exploitation of water resources negatively affects the ecological situation in many international water objects. The problem of a shortage of drinking water is aggravating, water bodies are subject to pollution by domestic and industrial wastewater, and the water quality in the basins of international water courses continues to deteriorate. This situation primarily requires legal regulation. Until recently, despite a significant number of existing international treaties on the use of transboundary waters, there was no universal international legal act to systematize the basic principles and norms of behavior of the states in this area. The most important direction for improving public administration in the field of use and protection of transboundary groundwater bodies is the development and expansion of international cooperation and the legal consolidation of the rights and obligations of states through the adoption of legislative acts on various levels. Modern international policy in the field of transboundary groundwater resources management is undergoing significant changes. The very concept of a transboundary territory in relation to groundwater extraction was adopted both in Russia and in Europe in the early 2000s. However, the main criteria for the definition of a transboundary territory have not yet been formulated anywhere. This gives rise to the need to define a transboundary aquifer in international legislation, as well as to limit the size of this aquifer. This paper is devoted to the analysis of approaches to the definition of “transboundary aquifer”, emerging problems in this area, the legal framework, and conflicts in the field of transboundary regulation of groundwater. The zone of responsibility in groundwater extraction at the transboundary territory of neighboring states is defined.
A Fault Diagnosis Method of Modular Analog Circuit Based on SVDD and D–S Evidence Theory
Authors: Sun Peng , Yang Zhiming , Jiang Yueming , Jia Shaohua , Peng Xiyuan
Published: October 2021

In the actual fault diagnosis process of an analog circuit, there is often a problem due to the lack of fault samples, leading to the low-accuracy of diagnostic models. Therefore, using positive samples that are easy to obtain to establish diagnostic models became a research hotspot in the field of analog circuit fault diagnosis. This paper proposes a method based on Support Vector Data Description (SVDD) and Dempster–Shafer evidence theory (D–S evidence theory) for fault diagnosis of modular analog circuit. Firstly, the principle of circuit module partition is proposed to divide the analog circuit under test, and the output port of each module is selected as test point. Secondly, the paper extracts the feature of the time-domain and frequency-domain output signals of the circuit module through Principal Component Analysis (PCA). Thirdly, four state detection models based on SVDD are established to judge the working state of each circuit module, including TSG, TSP, FSG, and FSP state detection model. Finally, the D–S theory is introduced to integrate the test results of each model for locating fault circuit module. To verify the effectiveness of the proposed method, the dual bandpass filter circuit is selected for simulation and hardware experiment. The results show that the proposed method can locate the analog fault effectively and has a higher diagnosis accuracy.
Operational Parameters for Sub-Nano Tesla Field Resolution of PHMR Sensors in Harsh Environments
Authors: Jeon Taehyeong , Das Proloy Taran , Kim Mijin , Jeon Changyeop , Lim Byeonghwa , Soldatov Ivan , Kim CheolGi
Published: October 2021

The resolution of planar-Hall magnetoresistive (PHMR) sensors was investigated in the frequency range from 0.5 Hz to 200 Hz in terms of its sensitivity, average noise level, and detectivity. Analysis of the sensor sensitivity and voltage noise response was performed by varying operational parameters such as sensor geometrical architectures, sensor configurations, sensing currents, and temperature. All the measurements of PHMR sensors were carried out under both constant current (CC) and constant voltage (CV) modes. In the present study, Barkhausen noise was revealed in 1/f noise component and found less significant in the PHMR sensor configuration. Under measured noise spectral density at optimized conditions, the best magnetic field detectivity was achieved better than 550 pT/√Hz at 100 Hz and close to 1.1 nT/√Hz at 10 Hz for a tri-layer multi-ring PHMR sensor in an unshielded environment. Furthermore, the promising feasibility and possible routes for further improvement of the sensor resolution are discussed.
A Machine Learning Framework for Balancing Training Sets of Sensor Sequential Data Streams
Authors: Setiawan Budi Darma , Serdült Uwe , Kryssanov Victor
Published: October 2021

The recent explosive growth in the number of smart technologies relying on data collected from sensors and processed with machine learning classifiers made the training data imbalance problem more visible than ever before. Class-imbalanced sets used to train models of various events of interest are among the main reasons for a smart technology to work incorrectly or even to completely fail. This paper presents an attempt to resolve the imbalance problem in sensor sequential (time-series) data through training data augmentation. An Unrolled Generative Adversarial Networks (Unrolled GAN)-powered framework is developed and successfully used to balance the training data of smartphone accelerometer and gyroscope sensors in different contexts of road surface monitoring. Experiments with other sensor data from an open data collection are also conducted. It is demonstrated that the proposed approach allows for improving the classification performance in the case of heavily imbalanced data (the F1 score increased from 0.69 to 0.72, p<0.01, in the presented case study). However, the effect is negligible in the case of slightly imbalanced or inadequate training sets. The latter determines the limitations of this study that would be resolved in future work aimed at incorporating mechanisms for assessing the training data quality into the proposed framework and improving its computational efficiency.
Temperature Prediction Using Multivariate Time Series Deep Learning in the Lining of an Electric Arc Furnace for Ferronickel Production
Authors: Leon-Medina Jersson X. , Camacho Jaiber , Gutierrez-Osorio Camilo , Salomón Julián Esteban , Rueda Bernardo , Vargas Whilmar , Sofrony Jorge , Restrepo-Calle Felipe , Pedraza Cesar , Tibaduiza Diego
Published: October 2021

The analysis of data from sensors in structures subjected to extreme conditions such as the ones used in smelting processes is a great decision tool that allows knowing the behavior of the structure under different operational conditions. In this industry, the furnaces and the different elements are fully instrumented, including sensors to measure variables such as temperature, pressure, level, flow, power, electrode positions, among others. From the point of view of engineering and data analytics, this quantity of data presents an opportunity to understand the operation of the system under normal conditions or to explore new ways of operation by using information from models provided by using deep learning approaches. Although some approaches have been developed with application to this industry, it is still an open research area. As a contribution, this paper presents an applied deep learning temperature prediction model for a 75 MW electric arc furnace, which is used for ferronickel production. In general, the methodology proposed considers two steps: first, a data cleaning process to increase the quality of the data, eliminating both redundant information as well as atypical and unusual data, and second, a multivariate time series deep learning model to predict the temperatures in the furnace lining. The developed deep learning model is a sequential one based on GRU (gated recurrent unit) layer plus a dense layer. The GRU + Dense model achieved an average root mean square error (RMSE) of 1.19 °C in the test set of 16 different thermocouples radially distributed on the furnace.
The Impact of Cybersecurity Practices on Cyberattack Damage: The Perspective of Small Enterprises in Saudi Arabia
Authors: Alharbi Fawaz , Alsulami Majid , AL-Solami Abdullatif , Al-Otaibi Yazeed , Al-Osimi Meshaal , Al-Qanor Fahad , Al-Otaibi Khalid
Published: October 2021

Small and medium-sized enterprises represent the majority of enterprises globally and yet have some difficulties in understanding the impact that cybersecurity threats could have on their businesses and the damage they could do to their assets. This study aims to measure the effectiveness of security practices at small-sized enterprises in Saudi Arabia in the event of a cybersecurity attack. Our paper is among the first research papers to measure the effectiveness of cybersecurity practices and the threat posed by cybersecurity breaches among small enterprises in the event of cybersecurity attacks. A total of 282 respondents participated, all of them representing small-sized enterprises in Saudi Arabia. The study applies multiple regression tests to analyze the effectiveness of 12 cybersecurity practices in three aspects: financial damage, loss of sensitive data, and restoration time, at small enterprises. The findings indicate that having an inspection team and a recovery plan may limit the financial damage caused by cybersecurity attacks on small enterprises. The results also show that cybersecurity awareness, knowledge of cybersecurity damage, and professionals’ salaries were related to the loss of sensitive data. Furthermore, the results indicate that contact with cybersecurity authorities and having an inspection team have statistically significant effects on restoration time.
Novel Prediction of Diagnosis Effectiveness for Adaptation of the Spectral Kurtosis Technology to Varying Operating Conditions
Authors: Kolbe Stuart , Gelman Len , Ball Andrew
Published: October 2021

In this paper, two novel consistency vectors are proposed, which when combined with appropriate machine learning algorithms, can be used to adapt the Spectral Kurtosis technology for optimum gearbox damage diagnosis in varying operating conditions. Much of the existing research in the field is limited to test apparatus run in constant and carefully controlled operating conditions, and the authors have previously publicised that the Spectral Kurtosis technology requires adaptation to achieve the highest possible probabilities of correct diagnosis when a gearbox is run in non-stationary conditions of speed and load. However, the authors’ previous adaptation has been computationally heavy using a brute-force approach unsuited to online use, and therefore, created the requirement to develop these two newly proposed vectors and allow computationally lighter techniques more suited to online condition monitoring. The new vectors are demonstrated and experimentally validated on vibration data collected from a gearbox run in multiple combinations of operating conditions; for the first time, the two consistency vectors are used to predict diagnosis effectiveness, with the comparison and proof of relative gains between the traditional and novel techniques discussed. Consistency calculations are computationally light and thus, many combinations of Spectral Kurtosis technology parameters can be evaluated on a dataset in a very short time. This study shows that machine learning can predict the total probability of correct diagnosis from the consistency values and this can quickly provide pre-adaptation/prediction of optimum Spectral Kurtosis technology parameters for a dataset. The full adaptation and damage evaluation process, which is computationally heavier, can then be undertaken on a much lower number of combinations of Spectral Kurtosis resolution and threshold.
The Experimental Verification of Direct-Write Silver Conductive Grid and ARIMA Time Series Analysis for Crack Propagation
Authors: Kurnyta Artur , Baran Marta , Kurnyta-Mazurek Paulina , Kowalczyk Kamil , Dziendzikowski Michał , Dragan Krzysztof
Published: October 2021

The paper presents experimental verification of customized resistive crack propagation sensors as an alternative method for other common structural health monitoring (SHM) techniques. Most of these are sensitive to changes in the sensor network configuration and a baseline dataset must be collected for the analysis of the structure condition. Sensors investigated within the paper are manufactured by the direct-write process with electrically conductive, silver-microparticle-filled paint to prepare a tailored measuring grid on an epoxy or polyurethane coating as a driving/insulating layer. This method is designed to enhance the functionality and usability compared to commercially available crack gauges. By using paint with conductive metal particles, the shape of the sensor measuring grid can be more easily adapted to the structure, while, in the previous approach, only a few grid-fixed sensors are available. A fatigue test on the compact tension (CT) specimen is presented and discussed to evaluate the ability of the developed sensors to detect and monitor fatigue cracks. Additionally, the ARIMA time series algorithm is developed both for monitoring and predicting crack growth, based on the acquired data. The proposed sensors’ verification reveal their good performance to detect and monitor fatigue fractures with a relatively low measurement error and ARIMA estimated crack length compared with the crack opening displacement (COD) gauge.
Characterization of Temperature Gradients According to Height in a Baroque Church by Means of Wireless Sensors
Authors: Ramírez Sandra , Zarzo Manuel , Perles Angel , García-Diego Fernando-Juan
Published: October 2021

The baroque church of Saint Thomas and Saint Philip Neri (Valencia, Spain), which was built between 1727 and 1736, contains valuable paintings by renowned Spanish artists. Due to the considerable height of the central nave, the church can experience vertical temperature gradients. In order to investigate this issue, temperatures were recorded between August 2017 and February 2018 from a wireless monitoring system composed of 21 sensor nodes, which were located at different heights in the church from 2 to 13 m from the floor level. For characterizing the temperature at high, medium and low altitude heights, a novel methodology is proposed based on sparse Partial Least Squares regression (sPLS), Linear Discriminant Analysis (LDA), and the Holt-Winters method, among others, which were applied to a time series of temperature. This approach is helpful to discriminate temperature profiles according to sensor height. Once the vertical thermal gradients for each month were characterized, it was found that temperature reached the maximum correlation with sensor height in the period between August 10th and September 9th. Furthermore, the most important features from the time series that explain this correlation are the mean temperature and the mean of moving range. In the period mentioned, the vertical thermal gradient was estimated to be about 0.043 ∘C/m, which implies a difference of 0.47 ∘C on average between sensor nodes at 2 m from the floor with respect to the upper ones located at 13 m from the floor level. The gradient was estimated as the slope from a linear regression model using height and hourly mean temperature as the predictor and response, respectively. This gradient is consistent with similar reported studies. The fact that such gradient was only found in one month suggests that the mechanisms of dust deposition on walls involved in vertical thermal gradients are not important in this case regarding the preventive conservation of artworks. Furthermore, the methodology proposed here was useful to discriminate the time series at high, medium and low altitude levels. This approach can be useful when a set of sensors is installed for microclimate monitoring in churches, cathedrals, and other historical buildings, at different levels and positions.
Scenario-Mining for Level 4 Automated Vehicle Safety Assessment from Real Accident Situations in Urban Areas Using a Natural Language Process
Authors: Park Sangmin , Park Sungho , Jeong Harim , Yun Ilsoo , So Jaehyun (Jason)
Published: October 2021

As the research and development activities of automated vehicles have been active in recent years, developing test scenarios and methods has become necessary to evaluate and ensure their safety. Based on the current context, this study developed an automated vehicle test scenario derivation methodology using traffic accident data and a natural language processing technique. The natural language processing technique-based test scenario mining methodology generated 16 functional test scenarios for urban arterials and 38 scenarios for intersections in urban areas. The proposed methodology was validated by determining the number of traffic accident records that can be explained by the resulting test scenarios. That is, the resulting test scenarios are valid and represent a matching rate between the test scenarios and the increased number of traffic accident records. The resulting functional scenarios generated by the proposed methodology account for 43.69% and 27.63% of the actual traffic accidents for urban arterial and intersection scenarios, respectively.
Using Multi-Antenna Trajectory Constraint to Analyze BeiDou Carrier-Phase Observation Error of Dynamic Receivers
Authors: Xiong Chenyao , Li Qingsong , Wang Dingjie , Wu Jie
Published: October 2021

Appropriate cycle-slip and measurement-error models are essential for BeiDou carrier-phase-based integrity risk calculation. To establish the receiver’s measurement-error model, an accurate position reference of the GNSS antenna is fundamental for calculating the measurement error. However, it is still a challenge to acquire position references for dynamic BeiDou receivers, resulting in improper GNSS measurement-error models and unreliable integrity monitoring. This paper proposes an improved precise relative positioning scheme by adopting multi-antenna trajectory constraints for dynamic BeiDou receivers. The dynamic experiments show an obvious decline of 78.7%, at most, in the positioning failure rate of the proposed method, as compared with the traditional method. The position solutions obtained from the proposed approach are used as the reference to analyze the cycle-slip and measurement-error characteristics of the dynamic receiver. The field test results indicate that the cycle-slip rate decreases with the increase of signal-to-noise ratio (SNR), and cycle slipping obeys a positively skewed distribution that could be fitted by the Gaussian mixture model (GMM). On the other hand, the standard deviation of the carrier-phase measurement error is inversely proportional to SNR, and its distribution is characteristically fat-tailed, which could be fitted by the bi-normal model.
Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning
Authors: De Landro Martina , Felli Eric , Collins Toby , Nkusi Richard , Baiocchini Andrea , Barberio Manuel , Orrico Annalisa , Pizzicannella Margherita , Hostettler Alexandre , Diana Michele , Saccomandi Paola
Published: October 2021

Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although temperature imaging techniques provide an indirect measurement of the thermal damage, a degree of uncertainty remains about the treatment effect. Optical techniques are currently emerging as tools to directly assess tissue thermal damage. Among them, hyperspectral imaging (HSI) has shown promising results in image-guided surgery and in the thermal ablation field. The highly informative data provided by HSI, associated with deep learning, enable the implementation of non-invasive prediction models to be used intraoperatively. Here we show a novel paradigm “peak temperature prediction model” (PTPM), convolutional neural network (CNN)-based, trained with HSI and infrared imaging to predict LA-induced damage in the liver. The PTPM demonstrated an optimal agreement with tissue damage classification providing a consistent threshold (50.6 ± 1.5 °C) for the damage margins with high accuracy (~0.90). The high correlation with the histology score (r = 0.9085) and the comparison with the measured peak temperature confirmed that PTPM preserves temperature information accordingly with the histopathological assessment.
Humidity-Sensitive PMMA Fiber Bragg Grating Sensor Probe for Soil Temperature and Moisture Measurement Based on Its Intrinsic Water Affinity
Authors: Wang Heng , Gao Shixin , Yue Xiaoyu , Cheng Xin , Liu Qi , Min Rui , Qu Hang , Hu Xuehao
Published: October 2021

Soil moisture measurement is very important for soil system monitoring. Compared to the traditional thermo-gravimetric technique, which is time-consuming and can be only performed in labs, the optic-fiber technique has unique advantages, such as small size, remote application in fields, fast response time and immunity to electromagnetic fields. In this paper, the soil moisture is measured by using a polymer optical fiber Bragg grating (POFBG) probe with a packaged dimension of 40 mm × 15 mm × 8 mm. Due to the intrinsic water-absorbing property of poly (methyl methacrylate) (PMMA), optical fiber Bragg gratings based on PMMA have been widely investigated for humidity measurement. Taking advantage of this, a sensor based on the POFBG is investigated to verify the soil condition. The POFBG is protectively integrated inside a stainless-steel package. A window is opened with a thin polypropylene mat as a filter, which allows the air to go through but prevents the soil from going inside to pollute the POFBG. The sensor probe is embedded in soils with different gravimetric soil moisture contents (SMCs) ranging from 0% to 40% and, then, insulated by polyethylene films to minimize the impact from the external environment, showing an average temperature cross sensitivity of −0.080 nm/°C. For a constant temperature, an exponential relationship between the Bragg wavelength and the SMC is obtained. For the SMCs between 8% and 24%, linear relationships are presented showing a temperature-corresponded sensitivity between 0.011 nm/% and 0.018 nm/%. The maximal sensitivity is calculated to be 0.018 nm/% at 20 °C, which is 28 times as high as that in the previous work. For the SMC over 24%, the sensor becomes insensitive because of humidity saturation in the cavity of the sensor probe. Though temperature cross sensitivity is problematic for SMC measurement, the influence could be eliminated by integrating another humidity-insensitive temperature sensor, such as a silica FBG temperature sensor.
Examining the Performance of Fog-Aided, Cloud-Centered IoT in a Real-World Environment
Authors: Aleisa Mohammed A. , Abuhussein Abdullah , Alsubaei Faisal S. , Sheldon Frederick T.
Published: October 2021

The fog layer provides substantial benefits in cloud-based IoT applications because it can serve as an aggregation layer and it moves the computation resources nearer to the IoT devices; however, it is important to ensure adequate performance is achieved in such applications, as the devices usually communicate frequently and authenticate with the cloud. This can cause performance and availability issues, which can be dangerous in critical applications such as in the healthcare sector. In this paper, we analyze the efficacy of the fog layer in different architectures in a real-world environment by examining performance metrics for the cloud and fog layers using different numbers of IoT devices. We also implement the fog layer using two methods to determine whether different fog implementation frameworks can affect the performance. The results show that including a fog layer with semi-heavyweight computation capability results in higher capital costs, although the in the long run resources, time, and money are saved. This study can serve as a reference for fundamental fog computing concepts. It can also be used to walk practitioners through different implementation frameworks of fog-aided IoT and to show tradeoffs in order to inform when to use each implementation framework based on one’s objectives.
Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition
Authors: Wang Aiguo , Zhao Shenghui , Keh Huan-Chao , Chen Guilin , Roy Diptendu Sinha
Published: October 2021

Human activity recognition plays a prominent role in numerous applications like smart homes, elderly healthcare and ambient intelligence. The complexity of human behavior leads to the difficulty of developing an accurate activity recognizer, especially in situations where different activities have similar sensor readings. Accordingly, how to measure the relationships among activities and construct an activity recognizer for better distinguishing the confusing activities remains critical. To this end, we in this study propose a clustering guided hierarchical framework to discriminate on-going human activities. Specifically, we first introduce a clustering-based activity confusion index and exploit it to automatically and quantitatively measure the confusion between activities in a data-driven way instead of relying on the prior domain knowledge. Afterwards, we design a hierarchical activity recognition framework under the guidance of the confusion relationships to reduce the recognition errors between similar activities. Finally, the simulations on the benchmark datasets are evaluated and results show the superiority of the proposed model over its competitors. In addition, we experimentally evaluate the key components of the framework comprehensively, which indicates its flexibility and stability.
An Efficient Surface Map Creation and Tracking Using Smartphone Sensors and Crowdsourcing
Authors: Ali Sarker Md. Rabiul , Hassanuzzaman Md , Biswas Purnendu , Dadon Saikot Hossain , Imam Tasmina , Rahman Tanzilur
Published: October 2021

Like Smart Home and Smart Devices, Smart Navigation has become necessary to travel through the congestion of the structure of either building or in the wild. The advancement in smartphone technology and incorporation of many different precise sensors have made the smartphone a unique choice for developing practical navigation applications. Many have taken the initiative to address this by developing mobile-based solutions. Here, a cloud-based intelligent traveler assistant is proposed that exploits user-generated position and elevation data collected from ubiquitous smartphone devices equipped with Accelerometer, Gyroscope, Magnetometer, and GPS (Global Positioning System) sensors. The data can be collected by the pedestrians and the drivers, and are then automatically put into topological information. The platform and associated innovative application allow travelers to create a map of a route or an infrastructure with ease and to share the information for others to follow. The cloud-based solution that does not cost travelers anything allows them to create, access, and follow any maps online and offline. The proposed solution consumes little battery power and can be used with lowly configured resources. The ability to create unknown, unreached, or unrecognized rural/urban road maps, building structures, and the wild map with the help of volunteer traveler-generated data and to share these data with the greater community makes the presented solution unique and valuable. The proposed crowdsourcing method of knowing the unknown would be an excellent support for travelers.