bluuebunny/crossref_metadata_2025 huggingface dataset:
Dual Projection Fusion for Reference-Based Image Super-Resolution
Authors: Lin Ruirong , Xiao Nanfeng
Published: May 2022

Reference-based image super-resolution (RefSR) methods have achieved performance superior to that of single image super-resolution (SISR) methods by transferring texture details from an additional high-resolution (HR) reference image to the low-resolution (LR) image. However, existing RefSR methods simply add or concatenate the transferred texture feature with the LR features, which cannot effectively fuse the information of these two independently extracted features. Therefore, this paper proposes a dual projection fusion for reference-based image super-resolution (DPFSR), which enables the network to focus more on the different information between feature sources through inter-residual projection operations, ensuring effective filling of detailed information in the LR feature. Moreover, this paper also proposes a novel backbone called the deep channel attention connection network (DCACN), which is capable of extracting valuable high-frequency components from the LR space to further facilitate the effectiveness of image reconstruction. Experimental results show that we achieve the best peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) performance compared with the state-of-the-art (SOTA) SISR and RefSR methods. Visual results demonstrate that the proposed method in this paper recovers more natural and realistic texture details.
JSQE: Joint Surveillance Quality and Energy Conservation for Barrier Coverage in WSNs
Authors: Shao Xuemei , Chang Chih-Yung , Zhao Shenghui , Kuo Chin-Hwa , Roy Diptendu Sinha , Pi Xinzhe , Yang Shin-Jer
Published: May 2022

Barrier coverage is a fundamental issue in wireless sensor networks (WSNs). Most existing works have developed centralized algorithms and applied the Boolean Sensing Model (BSM). However, the critical characteristics of sensors and environmental conditions have been neglected, which leads to the problem that the developed mechanisms are not practical, and their performance shows a large difference in real applications. On the other hand, the centralized algorithms also lack scalability and flexibility when the topologies of WSNs are dynamically changed. Based on the Elfes Sensing Model (ESM), this paper proposes a distributed Joint Surveillance Quality and Energy Conservation mechanism (JSQE), which aims to satisfy the requirements of the desired surveillance quality and minimize the number of working sensors. The proposed JSQE first evaluates the sensing probability of each sensor and identifies the location of the weakest surveillance quality. Then, the JSQE further schedules the sensor with the maximum contribution to the bottleneck location to improve the overall surveillance quality. Extensive experiment results show that our proposed JSQE outperforms the existing studies in terms of surveillance quality, the number of working sensors, and the efficiency and fairness of surveillance quality. In particular, the JSQE improves the surveillance quality by 15% and reduces the number of awake sensors by 22% compared with the relevant TOBA.
Machine Learning Approach to Quadratic Programming-Based Microwave Imaging for Breast Cancer Detection
Authors: Costanzo Sandra , Flores Alexandra , Buonanno Giovanni
Published: May 2022

In this work, a novel technique is proposed that combines the Born iterative method, based on a quadratic programming approach, with convolutional neural networks to solve the ill-framed inverse problem coming from microwave imaging formulation in breast cancer detection. The aim is to accurately recover the permittivity of breast phantoms, these typically being strong dielectric scatterers, from the measured scattering data. Several tests were carried out, using a circular imaging configuration and breast models, to evaluate the performance of the proposed scheme, showing that the application of convolutional neural networks allows clinicians to considerably reduce the reconstruction time with an accuracy that exceeds 90% in all the performed validations.
Solving the Integral Differential Equations with Delayed Argument by Using the DTM Method
Authors: Hetmaniok Edyta , Pleszczyński Mariusz , Khan Yasir
Published: May 2022

Recently, a lot of attention has been paid to the field of research connected with the wireless sensor network and industrial internet of things. The solutions found by theorists are next used in practice in such area as smart industries, smart devices, smart home, smart transportation and the like. Therefore, there is a need to look for some new techniques for solving the problems described by means of the appropriate equations, including differential equations, integral equations and integro-differential equations. The object of interests of this paper is the method dedicated for solving some integro-differential equations with a retarded (delayed) argument. The proposed procedure is based on the Taylor differential transformation which enables to transform the given integro-differential equation into a respective system of algebraic (nonlinear, very often) equations. The described method is efficient and relatively simple to use, however a high degree of generality and complexity of problems, defined by means of the discussed equations, makes impossible to obtain a general form of their solution and enforces an individual approach to each equation, which, however, does not diminish the benefits associated with its use.
Supporting Smart Home Scenarios Using OWL and SWRL Rules
Authors: Reda Roberto , Carbonaro Antonella , de Boer Victor , Siebes Ronald , van der Weerdt Roderick , Nouwt Barry , Daniele Laura
Published: May 2022

Despite the pervasiveness of IoT domotic devices in the home automation landscape, their potential is still quite under-exploited due to the high heterogeneity and the scarce expressivity of the most commonly adopted scenario programming paradigms. The aim of this study is to show that Semantic Web technologies constitute a viable solution to tackle not only the interoperability issues, but also the overall programming complexity of modern IoT home automation scenarios. For this purpose, we developed a knowledge-based home automation system in which scenarios are the result of logical inferences over the IoT sensors data combined with formalised knowledge. In particular, we describe how the SWRL language can be employed to overcome the limitations of the well-known trigger-action paradigm. Through various experiments in three distinct scenarios, we demonstrated the feasibility of the proposed approach and its applicability in a standardised and validated context such as SAREF
Flexible Planar Monopole Built-in GIS PD Sensor Based on Meandering Technology
Authors: Zhang Shuo , Zhang Guozhi , Lu Changyue , Tian Hanlv , Liu Jianben , Zhang Xiaoxing
Published: May 2022

To address the problem of low space utilization of existing rigid Ultra-High Frequency (UHF) sensors for partial discharge (PD) in Gas-Insulated Switchgears (GIS) and the problem of disrupting the electric field distribution inside the GIS. This paper draws on the idea of flexible wearable antennas and introduces planar monopole antennas commonly used in the communication field as GIS PD detection sensors and carried out research on flexible planar monopole sensing technology built into GIS PD. The VSWR of monopole antenna in the UHF low band is optimized by the meandering technique. The size of the designed flexible antenna is 142 mm × 195 mm × 0.28 mm. The simulation and physical test results show that the improved monopole antenna with meandering technology has a VSWR of ≤2 in the frequency bands 570 MHz–830 MHz, 1.38 GHz–1.8 GHz, and 2.2 GHz–2.76 GHz when the bending radius is 0 mm, 200 mm, and 400 mm, respectively. The VSWR in the frequency band 450 MHz–3 GHz is ≤5. A 220 kV GIS PD detection platform was built to test the performance of the designed antenna, and the results showed that the antenna could detect the PD signal after bending deformation with a high Signal Noise Ratio (SNR).
Caged-Sphere Optofluidic Sensors: Whispering Gallery Resonators in Wicking Microfluidics
Authors: Riesen Nicolas , Peterkovic Zane Q. , Guan Bin , François Alexandre , Lancaster David G. , Priest Craig
Published: May 2022

The rapid development of optofluidic technologies in recent years has seen the need for sensing platforms with ease-of-use, simple sample manipulation, and high performance and sensitivity. Herein, an integrated optofluidic sensor consisting of a pillar array-based open microfluidic chip and caged dye-doped whispering gallery mode microspheres is demonstrated and shown to have potential for simple real-time monitoring of liquids. The open microfluidic chip allows for the wicking of a thin film of liquid across an open surface with subsequent evaporation-driven flow enabling continuous passive flow for sampling. The active dye-doped whispering gallery mode microspheres placed between pillars, avoid the use of cumbersome fibre tapers to couple light to the resonators as is required for passive microspheres. The performance of this integrated sensor is demonstrated using glucose solutions (0.05–0.3 g/mL) and the sensor response is shown to be dynamic and reversible. The sensor achieves a refractive index sensitivity of ~40 nm/RIU, with Q-factors of ~5 × 103 indicating a detection limit of ~3 × 10−3 RIU (~20 mg/mL glucose). Further enhancement of the detection limit is expected by increasing the microsphere Q-factor using high-index materials for the resonators, or alternatively, inducing lasing. The integrated sensors are expected to have significant potential for a host of downstream applications, particularly relating to point-of-care diagnostics.
Newly Designed and Experimental Test of the Sediment Trap for Horizontal Transport Flux
Authors: Liu Tao , Fei Zihang , Guo Lei , Zhang Jiarui , Zhang Shaotong , Zhang Yan
Published: May 2022

The transport processes of marine suspended sediments are important to the material cycle and the shaping of seafloor topography. Existing sediment monitoring methods are limited in their use under high concentration conditions, and are not effective in monitoring and capturing sediment in 3D directions, and there is an inability to accurately explain sediment transport processes. To infer the transport process of suspended sediments, this study proposed a time-series vector in situ observation device. An accompanying time-series analytic method was developed for sediment transport fluxes. The correlation between the internal and external flow velocities of the capture tube was established through indoor tests, and then the applicability of the device was verified by the correlation between the theoretical capture quality and the actual capture quality, and the analytic formula of the flux was refined. The proposed observation technique can be used for in situ long-term observation and sampling of marine suspended sediments under conventional and even extreme sea conditions, achieving accurate time-series suspended sediment capture and high-resolution transport flux analysis. The technique thus provides a more effective means for scientific research into the dynamics of seafloor sedimentation, the mechanisms of ocean carbon sinks, and the processes of the carbon cycle.
Estimation Based on Chirp Modulation for Desired and Interference Power and Channel Occupancy Ratio in LoRa
Authors: Takyu Osamu , Kobayashi Gaku , Adachi Koichi , Ohta Mai , Fujii Takeo
Published: May 2022

In terms of low power consumption and long-range communication—low-power wide-area networks (LPWAN) are suitable for wireless sensor networks. Long-range (LoRa) wireless communication is one of the standards of LPWAN. LoRa shares common frequency spectrum bands with both multiple transmitters, which are the sensors in the LoRa system (and those in the other system). Therefore, co-channel interference (CCI) degrades the packet delivery rate. To avoid CCI, the CCI power and the occurrence probability of CCI in the target channel are estimated, then the sensor decides whether to use the channel and where the occurrence probability of CCI is defined as the channel occupancy ratio (COR). If a large signal power is obtained at the receiver, the received signal can be demodulated because of the capture effect. The desired signal power must also be estimated for the capture effect. In this study, we propose an estimation scheme based on chirp modulation of LoRa under spectrum sharing among other systems. The proposed scheme estimates the desired signal power, CCI power, and COR. From the computer simulation results, we clarify the advantages of the proposed scheme in terms of estimation accuracy and packet delivery rate.
Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors
Authors: Campanini Isabella , Merlo Andrea , Disselhorst-Klug Catherine , Mesin Luca , Muceli Silvia , Merletti Roberto
Published: May 2022

Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG.
Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
Authors: Li Yue , Yin Zhenyu , Ma Yue , Xu Fulong , Yu Haoyu , Han Guangjie , Bi Yuanguo
Published: May 2022

Over recent years, traditional manufacturing factories have been accelerating their transformation and upgrade toward smart factories, which are an important concept within Industry 4.0. As a key communication technology in the industrial internet architecture, time-sensitive networks (TSNs) can break through communication barriers between subsystems within smart factories and form a common network for various network flows. Traditional routing algorithms are not applicable for this novel type of network, as they cause unnecessary congestion and latency. Therefore, this study examined the classification of TSN flows in smart factories, converted the routing problem into two graphical problems, and proposed two heuristic optimization algorithms, namely GATTRP and AACO, to find the optimal solution. The experiments showed that the algorithms proposed in this paper could provide a more reasonable routing arrangement for various TSN flows with different time sensitivities. The algorithms could effectively reduce the overall delay by up to 74% and 41%, respectively, with promising operating performances.
Unsupervised Domain Adaptive 1D-CNN for Fault Diagnosis of Bearing
Authors: Shao Xiaorui , Kim Chang-Soo
Published: May 2022

Fault diagnosis (FD) plays a vital role in building a smart factory regarding system reliability improvement and cost reduction. Recent deep learning-based methods have been applied for FD and have obtained excellent performance. However, most of them require sufficient historical labeled data to train the model which is difficult and sometimes not available. Moreover, the big size model increases the difficulties for real-time FD. Therefore, this article proposed a domain adaptive and lightweight framework for FD based on a one-dimension convolutional neural network (1D-CNN). Particularly, 1D-CNN is designed with a structure of autoencoder to extract the rich, robust hidden features with less noise from source and target data. The extracted features are processed by correlation alignment (CORAL) to minimize domain shifts. Thus, the proposed method could learn robust and domain-invariance features from raw signals without any historical labeled target domain data for FD. We designed, trained, and tested the proposed method on CRWU bearing data sets. The sufficient comparative analysis confirmed its effectiveness for FD.
Application of Three-Repetition Tests Scheme to Improve Integrated Circuits Test Quality to Near-Zero Defect
Authors: Yeh Chung-Huang , Chen Jwu-E
Published: May 2022

In this research, the normal distribution is assumed to be the product characteristic, and the DITM (Digital Integrated Circuit Test Model) model is used to evaluate the integrated circuits (IC) test yield and test quality. Testing technology lags far behind manufacturing technology due to the different rates of development of the two technologies. As a result, quality control will pose significant challenges in pursuing high-quality near-zero defect products (automotive and biomedical electronics and avionics, etc.). In order to ensure product quality, we propose an effective repeated testing method (three-repetition tests scheme, TRTS), which utilizes the move test guardband (TGB) to improve the test yield and test quality. Based on the data in the International Roadmap for Devices and Systems table in 2021, the DITM model is used to estimate the future trend of semiconductor chip test yield, and the retest method (TRTS) is applied improve the test results. The method of repeated testing can increase the test yield and increase the shipment of semiconductor products. By estimating the test cost and profit, the method of repeated testing can obtain chips with near-zero defects with more corporate profits through increased product shipments.
Wide Residual Relation Network-Based Intelligent Fault Diagnosis of Rotating Machines with Small Samples
Authors: Chen Zuoyi , Wang Yuanhang , Wu Jun , Deng Chao , Jiang Weixiong
Published: May 2022

Many existing fault diagnosis methods based on deep learning (DL) require numerous fault samples to train the diagnosis model. However, in industrial applications, rotating machines (RMs) operate in normal states for most of their service life with fault events being rare and thus failure samples are very limited. To solve the problem above, a novel wide residual relation network (WRRN) is proposed for intelligent fault diagnosis of the RMs. Specifically, the WRRN is trained by performing a series of learning tasks in RMs with sufficient samples to obtain knowledge about how to diagnose, and then it is directly transferred to realize fault task of the RM with small samples. In this method, a wide residual network-based feature extraction module is used to generate representative fault features from input samples, and a relation module is designed to calculate the relation score between the sample pairs so as to determine their categories. Extensive experiments are conducted on two RMs to validate the WRRN method. The results demonstrate that the WRRN can accurately identify the fault types of the RMs with only small samples or even one sample. The WRRN significantly outperforms the existing popular methods in diagnostic performance.
Characterization of Distant and Moderate Earthquakes with Inexpensive MEMS Sensors: Application to the Mw 6.3, 29th December 2020, Petrinja Event
Authors: Cascone Valeria , Boaga Jacopo
Published: May 2022

In this work, we evaluate the suitability of a new MEMS sensor prototype, called ASX1000 (ADEL s.r.l., Modena, Italy), for the monitoring of distant and moderate seismic events. This device is an inexpensive capacitive accelerometer with a relatively low level of instrumental noise; it can record both local and far seismic events. An experimental network built with ASX1000 MEMS, located in northern Italy, was able to record the Mw 6.3 Petrinja earthquake that occurred in December 2020; it had an epicentral distance of more than 350 km. We retrieved the strong motion parameters (PGA, pseudo-absolute velocity, and pseudo-absolute spectral acceleration) from the acceleration time histories recorded by the MEMS sensors. The obtained parameters were compared with the ones obtained by the closer high-quality seismometers, belonging to the INGV National Seismic Network. The comparison to the highest-quality sensors confirms a reasonable agreement of the inferred parameters. This work suggests that—in the near future—MEMS sensors could be adopted to integrate the existing seismic network. A denser coverage of sensors can sample more accurately the seismic wavefield, taking into account the large spatial variability of local geology and the relative differences in seismic response.
A Novel Forward-Propagation Workflow Assessment Method for Malicious Packet Detection
Authors: Balamurugan Nagaiah Mohanan , Kannadasan Raju , Alsharif Mohammed H. , Uthansakul Peerapong
Published: May 2022

In recent times, there has been a huge upsurge in malicious attacks despite sophisticated technologies in digital network data transmission. This research proposes an innovative method that utilizes the forward-propagation workflow of the convolutional neural network (CNN) algorithm to detect malicious information effectively. The performance comparison of this approach was accomplished using accuracy, precision, false-positive and false-negative rates with k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. To detect malicious packets in the original dataset, an experiment was carried out using CNN’s forward-propagation workflow method (N = 11) as well as the KNN and the SVM machine learning algorithms with a significant value of 0.005. The accuracy, precision, false-positive and false-negative rates were evaluated to detect malicious packets present in normal data packets. The mean performance measures of the proposed forward-propagation method of the CNN algorithm were evaluated using the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy (98.84%) and mean precision (99.08%) of the proposed forward propagation of the CNN algorithm appeared to be higher than the mean accuracy (95.55%) and mean precision (95.97%) of the KNN algorithm, as well as the mean accuracy (94.43%) and mean precision (94.58%) of the SVM algorithm. Moreover, the false-positive rate (1.93%) and false-negative rate (3.49%) of the proposed method appeared to be significantly higher than the KNN algorithm’s false-positive (4.04%) and false-negative (6.24%) as well as the SVM algorithm’s false-positive (5.03%) and false-negative rate (7.21%). Hence, it can be concluded that the forward-propagation method of the CNN algorithm is better than the KNN and SVM algorithms at detecting malicious information.
Single Image Dehazing Using Global Illumination Compensation
Authors: Zheng Junbao , Xu Chenke , Zhang Wei , Yang Xu
Published: May 2022

The existing dehazing algorithms hardly consider background interference in the process of estimating the atmospheric illumination value and transmittance, resulting in an unsatisfactory dehazing effect. In order to solve the problem, this paper proposes a novel global illumination compensation-based image-dehazing algorithm (GIC). The GIC method compensates for the intensity of light scattered when light passes through atmospheric particles such as fog. Firstly, the illumination compensation was accomplished in the CIELab color space using the shading partition enhancement mechanism. Secondly, the atmospheric illumination values and transmittance parameters of these enhanced images were computed to improve the performance of atmospheric-scattering models, in order to reduce the interference of background signals. Eventually, the dehazing result maps with reduced background interference were obtained with the computed atmospheric-scattering model. The dehazing experiments were carried out on the public data set, and the dehazing results of the foggy image were compared with cutting-edge dehazing algorithms. The experimental results illustrate that the proposed GIC algorithm shows enhanced consistency with the real-imaging situation in estimating atmospheric illumination and transmittance. Compared with established image-dehazing methods, the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) metrics of the proposed GIC method increased by 3.25 and 0.084, respectively.
Depressed Mood Prediction of Elderly People with a Wearable Band
Authors: Choi Jinyoung , Lee Soomin , Kim Seonyoung , Kim Dongil , Kim Hyungshin
Published: May 2022

Depression in the elderly is an important social issue considering the population aging of the world. In particular, elderly living alone who has narrowed social relationship due to bereavement and retirement are more prone to be depressed. Long-term depressed mood can be a precursor to eventual depression as a disease. Our goal is how to predict the depressed mood of single household elderly from unobtrusive monitoring of their daily life. We have selected a wearable band with multiple sensors for monitoring elderly people. Depression questionnaire has been surveyed periodically to be used as the labels. Instead of working with depression patients, we recruited 14 single household elderly people from a nearby community. The wearable band provided daily activity and biometric data for 71 days. From the data, we generate a depressed mood prediction model. Multiple features from the collected sensor data are exploited for model generation. One general model is generated to be used as the baseline for the initial model deployment. Personal models are also generated for model refinement. The general model has a high recall of 80% in an MLP model. Individual models achieved an average recall of 82.7%. In this study, we have demonstrated that we can generate depressed mood prediction models with data collected from real daily living. Our work has shown the feasibility of using a wearable band as an unobtrusive depression monitoring sensor even for elderly people.
Field-Effect Transistor-Based Biosensors for Environmental and Agricultural Monitoring
Authors: Elli Giulia , Hamed Saleh , Petrelli Mattia , Ibba Pietro , Ciocca Manuela , Lugli Paolo , Petti Luisa
Published: May 2022

The precise monitoring of environmental contaminants and agricultural plant stress factors, respectively responsible for damages to our ecosystems and crop losses, has nowadays become a topic of uttermost importance. This is also highlighted by the recent introduction of the so-called “Sustainable Development Goals” of the United Nations, which aim at reducing pollutants while implementing more sustainable food production practices, leading to a reduced impact on all ecosystems. In this context, the standard methods currently used in these fields represent a sub-optimal solution, being expensive, laboratory-based techniques, and typically requiring trained personnel with high expertise. Recent advances in both biotechnology and material science have led to the emergence of new sensing (and biosensing) technologies, enabling low-cost, precise, and real-time detection. An especially interesting category of biosensors is represented by field-effect transistor-based biosensors (bio-FETs), which enable the possibility of performing in situ, continuous, selective, and sensitive measurements of a wide palette of different parameters of interest. Furthermore, bio-FETs offer the possibility of being fabricated using innovative and sustainable materials, employing various device configurations, each customized for a specific application. In the specific field of environmental and agricultural monitoring, the exploitation of these devices is particularly attractive as it paves the way to early detection and intervention strategies useful to limit, or even completely avoid negative outcomes (such as diseases to animals or ecosystems losses). This review focuses exactly on bio-FETs for environmental and agricultural monitoring, highlighting the recent and most relevant studies. First, bio-FET technology is introduced, followed by a detailed description of the the most commonly employed configurations, the available device fabrication techniques, as well as the specific materials and recognition elements. Then, examples of studies employing bio-FETs for environmental and agricultural monitoring are presented, highlighting in detail advantages and disadvantages of available examples. Finally, in the discussion, the major challenges to be overcome (e.g., short device lifetime, small sensitivity and selectivity in complex media) are critically presented. Despite the current limitations and challenges, this review clearly shows that bio-FETs are extremely promising for new and disruptive innovations in these areas and others.
Analysis of Depth Cameras for Proximal Sensing of Grapes
Authors: Parr Baden , Legg Mathew , Alam Fakhrul
Published: May 2022

This work investigates the performance of five depth cameras in relation to their potential for grape yield estimation. The technologies used by these cameras include structured light (Kinect V1), active infrared stereoscopy (RealSense D415), time of flight (Kinect V2 and Kinect Azure), and LiDAR (Intel L515). To evaluate their suitability for grape yield estimation, a range of factors were investigated including their performance in and out of direct sunlight, their ability to accurately measure the shape of the grapes, and their potential to facilitate counting and sizing of individual berries. The depth cameras’ performance was benchmarked using high-resolution photogrammetry scans. All the cameras except the Kinect V1 were able to operate in direct sunlight. Indoors, the RealSense D415 camera provided the most accurate depth scans of grape bunches, with a 2 mm average depth error relative to photogrammetric scans. However, its performance was reduced in direct sunlight. The time of flight and LiDAR cameras provided depth scans of grapes that had about an 8 mm depth bias. Furthermore, the individual berries manifested in the scans as pointed shape distortions. This led to an underestimation of berry sizes when applying the RANSAC sphere fitting but may help with the detection of individual berries with more advanced algorithms. Applying an opaque coating to the surface of the grapes reduced the observed distance bias and shape distortion. This indicated that these are likely caused by the cameras’ transmitted light experiencing diffused scattering within the grapes. More work is needed to investigate if this distortion can be used for enhanced measurement of grape properties such as ripeness and berry size.
The Accuracy Assessment of the PREM and AK135-F Radial Density Models
Authors: Tenzer Robert , Ji Yuting , Chen Wenjin
Published: May 2022

The Earth’s synthetic density and gravitational models can be used to validate numerical methods for global (or large-scale) gravimetric forward and inverse modelling formulated either in the spatial or spectral domains. The Preliminary Reference Earth Model (PREM) density parameters can be adopted as a 1-D reference density model and further refined using more detailed 2-D or 3-D crust and mantle density models. Alternatively, the AK135-F density parameters can be used for this purpose. In this study, we investigate options for a refinement of the Earth’s synthetic density model by assessing the accuracy of available 1-D density models, specifically the PREM and AK135-F radial density parameters. First, we use density parameters from both models to estimate the Earth’s total mass and compare these estimates with published results. We then estimate the Earth’s gravity field parameters, particularly the geoidal geopotential number W0 and the mean gravitational attraction and compare them with published values. According to our results, the Earth’s total mass from the two models (the PREM and the AK135-F) differ less than 0.02% and 0.01%, respectively, when compared with the value adopted by the International Astronomical Union (IAU). The geoidal geopotential values of the two models differ from the value adopted by the IAU by less than 0.1% and 0.04%, respectively. The values of the mean gravitational attraction of the two models differ less than 0.02% and 0.08%, respectively, when compared with the value obtained from the geocentric gravitational constant and the Earth’s mean radius. These numerical findings ascertain that the PREM and AK135-F density parameters are suitable for defining a 1-D reference density model.
Blockchain for Future Wireless Networks: A Decade Survey
Authors: Rathod Tejal , Jadav Nilesh Kumar , Alshehri Mohammad Dahman , Tanwar Sudeep , Sharma Ravi , Felseghi Raluca-Andreea , Raboaca Maria Simona
Published: May 2022

The emerging need for high data rate, low latency, and high network capacity encourages wireless networks (WNs) to build intelligent and dynamic services, such as intelligent transportation systems, smart homes, smart cities, industrial automation, etc. However, the WN is impeded by several security threats, such as data manipulation, denial-of-service, injection, man-in-the-middle, session hijacking attacks, etc., that deteriorate the security performance of the aforementioned WN-based intelligent services. Toward this goal, various security solutions, such as cryptography, artificial intelligence (AI), access control, authentication, etc., are proposed by the scientific community around the world; however, they do not have full potential in tackling the aforementioned security issues. Therefore, it necessitates a technology, i.e., a blockchain, that offers decentralization, immutability, transparency, and security to protect the WN from security threats. Motivated by these facts, this paper presents a WNs survey in the context of security and privacy issues with blockchain-based solutions. First, we analyzed the existing research works and highlighted security requirements, security issues in a different generation of WN (4G, 5G, and 6G), and a comparative analysis of existing security solutions. Then, we showcased the influence of blockchain technology and prepared an exhaustive taxonomy for blockchain-enabled security solutions in WN. Further, we also proposed a blockchain and a 6G-based WN architecture to highlight the importance of blockchain technology in WN. Moreover, the proposed architecture is evaluated against different performance metrics, such as scalability, packet loss ratio, and latency. Finally, we discuss various open issues and research challenges for blockchain-based WNs solutions.
Simulation of Non-Carious Cervical Lesions by Computational Toothbrush Model: A Novel Three-Dimensional Discrete Element Method
Authors: Nam Jinsu , Nguyen Duong Hong , Lee Seungjun , Heo Seok-Mo , Park Junyoung
Published: May 2022

Non-carious cervical lesions (NCCLs) are saucer-shaped abrasions of a tooth. NCCLs can form due to various etiologies, including toothbrushing wear, acid erosion, and mechanical stress. Owing to this complex interplay, the mechanism of NCCLs in tooth abrasion has not been established. This study aims to develop a numerical method using a computational toothbrush to simulate NCCLs. The forces acting on the teeth and the amount of abrasion generated were evaluated. The discrete element method using in-house code, connected particle model, and Archard wear model were applied for brushing. In the toothbrush model, 42 acrylic tufts were fixed into a toothbrush head. The teeth models with enamel properties comprised four flat plates and two grooves to simulate the anterior teeth and NCCLs. The brushing speed and depth for one cycle were established as simulation parameters. The force applied within the ununiform plane was concentrated on several bristles as the toothbrush passed through the interproximal space. The brushing force (depth) had a greater effect on tooth abrasion than the brushing speed. Toothbrushing abrasion was mainly concentrated in the interproximal space. Therefore, forceful tooth brushing can cause NCCLs from the interproximal space to the cervical area of the tooth.
Context-Aware Pending Interest Table Management Scheme for NDN-Based VANETs
Authors: Zafar Waseeq Ul Islam , Rehman Muhammad Atif Ur , Jabeen Farhana , Ghouzali Sanaa , Rehman Zobia , Abdul Wadood
Published: May 2022

In terms of delivery effectiveness, Vehicular Adhoc NETworks (VANETs) applications have multiple, possibly conflicting, and disparate needs (e.g., latency, reliability, and delivery priorities). Named Data Networking (NDN) has attracted the attention of the research community for effective content retrieval and dissemination in mobile environments such as VANETs. A vehicle in a VANET application is heavily reliant on information about the content, network, and application, which can be obtained from a variety of sources. The information gathered can be used as context to make better decisions. While it is difficult to obtain the necessary context information at the IP network layer, the emergence of NDN is changing the tide. The Pending Information Table (PIT) is an important player in NDN data retrieval. PIT size is the bottleneck due to the limited opportunities provided by current memory technologies. PIT overflow results in service disruptions as new Interest messages cannot be added to PIT. Adaptive, context-aware PIT entry management solutions must be introduced to NDN-based VANETs for effective content dissemination. In this context, our main contribution is a decentralised, context-aware PIT entry management (CPITEM) protocol. The simulation results show that the proposed CPITEM protocol achieves lower Interest Satisfaction Delay and effective PIT utilization based on context when compared to existing PIT entry replacement protocols.
A Novel Method to Inspect 3D Ball Joint Socket Products Using 2D Convolutional Neural Network with Spatial and Channel Attention
Authors: Mustafaev Bekhzod , Tursunov Anvarjon , Kim Sungwon , Kim Eungsoo
Published: May 2022

Product defect inspections are extremely important for industrial manufacturing processes. It is necessary to develop a special inspection system for each industrial product due to their complexity and diversity. Even though high-precision 3D cameras are usually used to acquire data to inspect 3D objects, it is hard to use them in real-time defect inspection systems due to their high price and long processing time. To address these problems, we propose a product inspection system that uses five 2D cameras to capture all inspection parts of the product and a deep learning-based 2D convolutional neural network (CNN) with spatial and channel attention (SCA) mechanisms to efficiently inspect 3D ball joint socket products. Channel attention (CA) in our model detects the most relevant feature maps while spatial attention (SA) finds the most important regions in the extracted feature map of the target. To build the final SCA feature vector, we concatenated the learned feature vectors of CA and SA because they complement each other. Thus, our proposed CNN with SCA provides high inspection accuracy as well as it having the potential to detect small defects of the product. Our proposed model achieved 98% classification accuracy in the experiments and proved its efficiency on product inspection in real-time.
Validation of an Analytical Method for the Determination of Thiabendazole in Various Food Matrices
Authors: Choi Sun-Il , Han Xionggao , Lee Se-Jeong , Men Xiao , Oh Geon , Lee Doo-Sik , Lee Ok-Hwan
Published: May 2022

In many countries, thiabendazole is used as a fungicide to prevent the decay of food and to lengthen storage periods. However, in Korea, thiabendazole is unauthorised and does not have standards or specifications for use as a food additive. In this study, a simple analytical method was developed and validated using the HPLC–PDA method to detect thiabendazole in foods frequently consumed in South Korea. The calibration curve was obtained using samples of solid and liquid foods containing banana and citrus fruits containing concentrations in the range of 0.31–20.00 μg/mL with a satisfactory coefficient of determination (R2) of 0.999. The limit of detection (LOD) values for the solid and liquid food samples were 0.009 and 0.017 μg/mL, respectively, and the limit of quantification (LOQ) values were 0.028 and 0.052 μg/mL. The intra-day and inter-day precision values were less than 1.33% (relative standard deviation), and the recoveries of thiabendazole from spiked solid and liquid food samples ranged from 93.61 to 98.08% at concentration levels of 2.5, 5, and 10 μg/mL. In addition, the expanded uncertainties of the measu-rements ranged from 0.57 to 3.12%. These results showed that the developed method was appropriate for the quantitative analysis of thiabendazole in solid and liquid foods containing banana and citrus fruits.
A Review of the Removal of Dyestuffs from Effluents onto Biochar
Authors: Parthasarathy Prakash , Sajjad Samra , Saleem Junaid , Alherbawi Mohammad , Mckay Gordon
Published: May 2022

The study provides a review of various applications of biomass-derived biochars, waste-derived biochars, and modified biochars as adsorbent materials for removing dyestuff from process effluents. Processing significant amounts of dye effluent discharges into receiving waters can supply major benefits to countries which are affected by the water crisis and anticipated future stress in many areas in the world. When compared to most conventional adsorbents, biochars can provide an economically attractive solution. In comparison to many other textile effluent treatment processes, adsorption technology provides an economic, easily managed, and highly effective treatment option. Several tabulated data values are provided that summarize the main characteristics of various biochar adsorbents according to their ability to remove dyestuffs from wastewaters.
Discourse, Difference, and Divergence: Exploring Media Representations and Online Public Sentiments toward Marginalized Urban Communities in Jamaica during the COVID-19 Pandemic
Authors: Mahabir Aleem , Anderson Romario , Kinlocke Robert , Smith Rose-Ann , Doughorty Kristinia , Madho Chandradath
Published: May 2022

Despite lacking the resources to adequately adhere to public health protocols, urban residents belonging to impoverished communities in Kingston, Jamaica were prominently featured in local news reports that highlighted their breaches of social distancing. These reports generated an overwhelmingly prejudiced online social discourse, characterized by derogatory and dehumanizing remarks. This research aims to explore the ways media representations of calls for social distancing have unearthed latent social cleavages and contributed to the othering of Kingston’s poor. A thematic analysis was used to understand the nature of the public response to these news reports. The comments were manually coded, and emergent themes were classified based on the sentiments expressed. The comments generated evoked class, place, race, and political tensions, potentially perpetuating the public perception of vulnerable groups as the ‘threatening other’. These comments possibly represent deeper problems associated with the social divergence of Jamaican society. The lack of sensitivity exhibited shows a general disregard for the stark social realities commonly experienced by subaltern groups. These findings suggest media coverage promoting public safety, while important, may inadvertently lead to heightened social tensions and perpetuate social stigmas against marginalized groups, potentially stirring social divergence and countering efforts toward inclusiveness and integration.
Sports Event Image, Satisfaction, Motivation, Stadium Atmosphere, Environment, and Perception: A Study on the Biggest Multi-Sport Event in Indonesia during the Pandemic
Authors: Kogoya Kenius , Guntoro Tri Setyo , Putra Miftah Fariz Prima
Published: May 2022

The National Sports Week (known in Bahasa Indonesia as Pekan Olahraga Nasional or PON) consumes a large budget as Indonesia’s largest multi-sports event. This raises the question of whether it is only a sporting event or has an impact on society. Studies related to multi-sports events, specifically in the form of local or small-scale, such as PON, are still limited in Asia. The aim of this study was to investigate six important domains (constructs) of the 20th PON (PON XX) held in Papua in 2021: sports event image, motivation, satisfaction, stadium atmosphere, environment, and perception of the impacts. In addition, this study assessed the correlations between those six constructs and associations between the explanatory variables (gender, distance of residence, and involvement in the sporting event) and those six constructs. A pre-tested questionnaire was used to assess those six constructs and the explanatory variables. We included 675 respondents aged between 17–57 years, with a mean age of 22.87 years in the study. The results showed that the implementation of PON XX was positively received by the community and their highest motivation to watch this event was for entertainment. The involvement of the participant in the event was significantly associated with sports event image, satisfaction, motivation, stadium atmosphere, environment, and the perception of PON impact. The distance of the residence from the venues was only significantly associated with the perceived impact of PON XX on the community while gender had no association with all six constructs. There was a strong correlation between the other five investigated constructs and the perceived impact of PON XX.
Institutions, Labor Market Insecurity, and Well-Being in Europe
Authors: Inanc Hande , Kalleberg Arne L.
Published: May 2022

We examined the differences among seventeen European countries regarding the extent to which several key institutional and labor market characteristics affect the degrees of insecurity that people feel about their jobs and employment prospects, as well as their well-being (economic security and subjective well-being). We estimate how macrostructural factors affect well-being by fitting random-intercept multi-level models and decomposing the variance across national and individual levels, using data from the 2004 and 2010 European Social Surveys. We find that there is substantial country-level variation in labor market insecurity, economic security and subjective well-being. Active labor market policies, the generosity of unemployment benefits, and collective bargaining coverage explain a considerable portion of the identified differences among countries in labor market insecurity and well-being. The effects of employment protection legislation vary depending on whether the worker has a permanent or temporary contract. We did not find substantial differences between 2004 and 2010, suggesting that the effects of institutions and worker power on labor market insecurity and well-being reflect longer-term structural changes associated with the transformation of employment relations.