bluuebunny/crossref_metadata_2025 huggingface dataset:
Surface Metrology Based on Scanning Conoscopic Holography for In Situ and In-Process Monitoring of Microtexture in Paintings
Authors: Daffara Claudia , Mazzocato Sara
Published: September 2022

In the field of engineering, surface metrology is a valuable tool codified by international standards that enables the quantitative study of small-scale surface features. However, it is not recognized as a resource in the field of cultural heritage. Motivated by this fact, in this work, we demonstrate the use and the usefulness of surface metrology based on scanning conoscopic holography for monitoring treatments on the Venetian masterpiece by Tintoretto St. Martial in Glory with the Saints Peter and Paul. We carried out in situ and in-process monitoring of the painting microtexture during an experimental, innovative laser–chemical treatment, and we performed a statistical analysis based on ISO areal field parameters. A wide and in-band roughness analysis through the complementary use of amplitude, spatial, and hybrid parameters confirmed the noninvasive nature of the whole treatment on the painting surface topography, giving us the chance to review and critically discuss the use of these parameters in a real case in heritage science.
Development and Calibration of a Low-Cost, Piezoelectric Rainfall Sensor through Machine Learning
Authors: Antonini Andrea , Melani Samantha , Mazza Alessandro , Baldini Luca , Adirosi Elisa , Ortolani Alberto
Published: September 2022

In situ measurements of precipitation are typically obtained by tipping bucket or weighing rain gauges or by disdrometers using different measurement principles. One of the most critical aspects of their operational use is the calibration, which requires the characterization of instrument responses both in laboratory and in real conditions. Another important issue with in situ measurements is the coverage. Dense networks are desirable, but the installation and maintenance costs can be unaffordable with most of the commercial conventional devices. This work presents the development of a prototype of an impact rain gauge based on a very low-cost piezoelectric sensor. The sensor was developed by assembling off-the-shelf and reused components following an easy prototyping approach; the calibration of the relationship between the different properties of the voltage signal, as sampled by the rain drop impact, and rainfall intensity was established using machine-learning methods. The comparison with 1-minute rainfall obtained by a co-located commercial disdrometer highlights the fairly good performance of the low-cost sensor in monitoring and characterizing rainfall events.
Can We Quantify Aging-Associated Postural Changes Using Photogrammetry? A Systematic Review
Authors: Dilian Omer , Kimmel Ron , Tezmah-Shahar Roy , Agmon Maayan
Published: September 2022

Background: Aging is widely known to be associated with changes in standing posture. Recent advancements in the field of computerized image processing have allowed for improved analyses of several health conditions using photographs. However, photogrammetry’s potential for assessing aging-associated postural changes is yet unclear. Thus, the aim of this review is to evaluate the potential of photogrammetry in quantifying age-related postural changes. Materials and Methods: We searched the databases PubMed Central, Scopus, Embase, and SciELO from the beginning of records to March 2021. Inclusion criteria were: (a) participants were older adults aged ≥60; (b) standing posture was assessed by photogrammetric means. PRISMA guidelines were followed. We used the Newcastle–Ottawa Scale to assess methodological quality. Results: Of 946 articles reviewed, after screening and the removal of duplicates, 11 reports were found eligible for full-text assessment, of which 5 full studies met the inclusion criteria. Significant changes occurring with aging included deepening of thoracic kyphosis, flattening of lumbar lordosis, and increased sagittal inclination. Conclusions: These changes agree with commonly described aging-related postural changes. However, detailed quantification of these changes was not found; the photogrammetrical methods used were often unvalidated and did not adhere to known protocols. These methodological difficulties call for further studies using validated photogrammetrical methods and improved research methodologies.
Recent Advances in Nondestructive Method and Assessment of Corrosion Undercoating in Carbon–Steel Pipelines
Authors: May Zazilah , Alam Md Khorshed , Nayan Nazrul Anuar
Published: September 2022

Carbon–steel pipelines have mostly been utilized in the oil and gas (OG) industry owing to their strength and cost-effectiveness. However, the detection of corrosion under coating poses challenges for nondestructive (ND) pipeline monitoring techniques. One of the challenges is inaccessibility because of the pipeline structure, which leads to undetected corrosion, which possibly leads to catastrophic failure. The drawbacks of the existing ND methods for corrosion monitoring increase the need for novel frameworks in feature extraction, detection, and characterization of corrosion. This study begins with the explanations of the various types of corrosion in the carbon–steel pipeline in the OG industry and its prevention methods. A review of critical sensors integrated with various current ND corrosion monitoring systems is then presented. The importance of acoustic emission (AE) techniques over other ND methods is explained. AE data preprocessing methods are discussed. Several AE-based corrosion detection, prediction, and reliability assessment models for online pipeline condition monitoring are then highlighted. Finally, a discussion with future perspectives on corrosion monitoring followed by the significance and advantages of the emerging AE-based ND monitoring techniques is presented. The trends and identified issues are summarized with several recommendations for improvement in the OG industry.
Time-Series-Based Personalized Lane-Changing Decision-Making Model
Authors: Ye Ming , Pu Lei , Li Pan , Lu Xiangwei , Liu Yonggang
Published: September 2022

In recent years, autonomous driving technology has been changing from “human adapting to vehicle” to “vehicle adapting to human”. To improve the adaptability of autonomous driving systems to human drivers, a time-series-based personalized lane change decision (LCD) model is proposed. Firstly, according to the characteristics of the subject vehicle (SV) with respect to speed, acceleration and headway, an unsupervised clustering algorithm, namely, a Gaussian mixture model (GMM), is used to identify its three different driving styles. Secondly, considering the interaction between the SV and the surrounding vehicles, the lane change (LC) gain value is produced by developing a gain function to characterize their interaction. On the basis of the recognition of the driving style, this gain value and LC feature parameters are employed as model inputs to develop a personalized LCD model on the basis of a long short-term memory (LSTM) recurrent neural network model (RNN). The proposed method is tested using the US Open Driving Dataset NGSIM. The results show that the accuracy, F1 score, and macro-average area under the curve (macro-AUC) value of the proposed method for LC behavior prediction are 0.965, 0.951 and 0.983, respectively, and the performance is significantly better than that of other mainstream models. At the same time, the method is able to capture the LCD behavior of different human drivers, enabling personalized driving.
Simulation-Based Analysis of “What-If” Scenarios with Connected and Automated Vehicles Navigating Roundabouts
Authors: Tumminello Maria Luisa , Macioszek Elżbieta , Granà Anna , Giuffrè Tullio
Published: September 2022

Despite the potential of connected and automated vehicles (CAVs), there are still many open questions on how road capacity can be influenced and what methods can be used to assess its expected benefits in the progressive transition towards fully cooperative driving. This paper contributes to a better understanding of the benefits of CAV technologies by investigating mobility-related issues of automated vehicles operating with a cooperative adaptive cruise control system on roundabout efficiency using microscopic traffic simulation. The availability of the adjustment factors for CAVs provided by the 2022 Highway Capacity Manual allowed to adjust the entry capacity equations to reflect the presence of CAVs on roundabouts. Two mechanisms of entry maneuver based on the entry lane type were examined to compare the capacity target values with the simulated capacities. The microscopic traffic simulator Aimsun Next has been of great help in building the “what-if” traffic scenarios that we analysed to endorse hypothesis on the model parameters which affect the CAVs’ capabilities to increase roundabouts’ throughput. The results highlighted that the increasing penetration rates of CAVs have greater impacts on the operational performances of roundabouts, and provided a synthetic insight to assess the potential benefits of CAVs from an efficiency perspective.
Visual Detection and Image Processing of Parking Space Based on Deep Learning
Authors: Huang Chen , Yang Shiyue , Luo Yugong , Wang Yongsheng , Liu Ze
Published: September 2022

The automatic parking system based on vision is greatly affected by uneven lighting, which is difficult to make an accurate judgment on parking spaces in the case of complex image information. To solve this problem, this paper proposes a parking space visual detection and image processing method based on deep learning. Firstly, a 360-degree panoramic system was designed to photograph the vehicle environment. The image has been processed to obtain a panoramic aerial view, which was input as the original image of the parking space detection system. Secondly, the Faster R-CNN (Region-Convolutional Neural Network) parking detection model was established based on deep learning. It was aimed to detect and extract the parking space from the input image. Thirdly, the problems of uneven illumination and complex background were solved effectively by removing the background light from the image. Finally, a parking space extraction method based on the connected region has been designed, which further simplified the parking space extraction and image processing. The experiment results show that the mAP (mean Average Precision) value of the Faster R-CNN model using 101-Floor ResNet as the feature extraction network is 89.30%, which is 2.28% higher than that of the Faster R-CNN model using 50-Floor ResNet as the feature extraction network. The model built in this paper can detect most parking spaces well. The position of the output target box is accurate. In some test scenarios, the confidence of parking space recognition can even reach 100%. In summary, the proposed method can realize the effective identification and accurate positioning of parking spaces.
Non-Intrusive Load Monitoring
Authors: Fortuna Luigi , Buscarino Arturo
Published: September 2022

Non-Intrusive load monitoring (NILM) represents an emerging strategy based on the application of sevaral multidisciplinary topics [...]
Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture
Authors: Abunadi Ibrahim , Rehman Amjad , Haseeb Khalid , Parra Lorena , Lloret Jaime
Published: September 2022

In recent decades, networked smart devices and cutting-edge technology have been exploited in many applications for the improvement of agriculture. The deployment of smart sensors and intelligent farming techniques supports real-time information gathering for the agriculture sector and decreases the burden on farmers. Many solutions have been presented to automate the agriculture system using IoT networks; however, the identification of redundant data traffic is one of the most significant research problems. Additionally, farmers do not obtain the information they need in time, such as data on water pressure and soil conditions. Thus, these solutions consequently reduce the production rates and increase costs for farmers. Moreover, controlling all agricultural operations in a controlled manner should also be considered in developing intelligent solutions. Therefore, this study proposes a framework for a system that combines fog computing with smart farming and effectively controls network traffic. Firstly, the proposed framework efficiently monitors redundant information and avoids the inefficient use of communication bandwidth. It also controls the number of re-transmissions in the case of malicious actions and efficiently utilizes the network’s resources. Second, a trustworthy chain is built between agricultural sensors by utilizing the fog nodes to address security issues and increase reliability by preventing malicious communication. Through extensive simulation-based experiments, the proposed framework revealed an improved performance for energy efficiency, security, and network connectivity in comparison to other related works.
Chip Pad Inspection Method Based on an Improved YOLOv5 Algorithm
Authors: Xu Jiangjie , Zou Yanli , Tan Yufei , Yu Zichun
Published: September 2022

Chip pad inspection is of great practical importance for chip alignment inspection and correction. It is one of the key technologies for automated chip inspection in semiconductor manufacturing. When applying deep learning methods for chip pad inspection, the main problem to be solved is how to ensure the accuracy of small target pad detection and, at the same time, achieve a lightweight inspection model. The attention mechanism is widely used to improve the accuracy of small target detection by finding the attention region of the network. However, conventional attention mechanisms capture feature information locally, which makes it difficult to effectively improve the detection efficiency of small targets from complex backgrounds in target detection tasks. In this paper, an OCAM (Object Convolution Attention Module) attention module is proposed to build long-range dependencies between channel features and position features by constructing feature contextual relationships to enhance the correlation between features. By adding the OCAM attention module to the feature extraction layer of the YOLOv5 network, the detection performance of chip pads is effectively improved. In addition, a design guideline for the attention layer is proposed in the paper. The attention layer is adjusted by network scaling to avoid network characterization bottlenecks, balance network parameters, and network detection performance, and reduce the hardware device requirements for the improved YOLOv5 network in practical scenarios. Extensive experiments on chip pad datasets, VOC datasets, and COCO datasets show that the approach in this paper is more general and superior to several state-of-the-art methods.
Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain
Authors: Phan Trung C. , Pranata Adrian , Farragher Joshua , Bryant Adam , Nguyen Hung T. , Chai Rifai
Published: September 2022

This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range of motion in the sagittal plane, Ward’s method, a combination of K-means and Ensemble clustering method for classification algorithm, and Bayesian neural network to validate the result of Ward’s method and the combination of K-means and Ensemble clustering method. The classification results and effect size show that Ward clustering is the optimal method where precision and recall percentages of all clusters are above 90, and the overall accuracy of the Bayesian Neural Network is 97.9%. The statistical analysis reported a significant difference in the range of motion of the knee, hip and trunk between each cluster, F (9, 1136) = 195.67, p < 0.0001. The results of this study suggest that there are four different lifting techniques in people with CLBP. Additionally, the results show that even though the clusters demonstrated similar pain levels, one of the clusters, which uses the least amount of trunk and the most knee movement, demonstrates the lowest pain self-efficacy.
Nonlinear Tactile Estimation Model Based on Perceptibility of Mechanoreceptors Improves Quantitative Tactile Sensing
Authors: Sagara Momoko , Nobuyama Lisako , Takemura Kenjiro
Published: September 2022

Tactile sensing has attracted significant attention as a tactile quantitative evaluation method because the tactile sensation is an important factor while evaluating consumer products. Although the human tactile perception mechanism has nonlinearity, previous studies have often developed linear regression models. In contrast, this study proposes a nonlinear tactile estimation model that can estimate sensory evaluation scores from physical measurements. We extracted features from the vibration data obtained by a tactile sensor based on the perceptibility of mechanoreceptors. In parallel, a sensory evaluation test was conducted using 10 evaluation words. Then, the relationship between the extracted features and the tactile evaluation results was modeled using linear/nonlinear regressions. The best model was concluded by comparing the mean squared error between the model predictions and the actual values. The results imply that there are multiple evaluation words suitable for adopting nonlinear regression models, and the average error was 43.8% smaller than that of building only linear regression models.
Investigating Stroke Effects on Respiratory Parameters Using a Wearable Device: A Pilot Study on Hemiplegic Patients
Authors: Di Tocco Joshua , Lo Presti Daniela , Zaltieri Martina , Bravi Marco , Morrone Michelangelo , Sterzi Silvia , Schena Emiliano , Massaroni Carlo
Published: September 2022

Quantitatively assessing personal health status is gaining increasing attention due to the improvement of diagnostic technology and the increasing occurrence of chronic pathologies. Monitoring physiological parameters allows for retrieving a general overview of the personal health status. Respiratory activity can provide relevant information, especially when pathologies affect the muscles and organs involved in breathing. Among many technologies, wearables may represent a valid solution for continuous and remote monitoring of respiratory activity, thus reducing healthcare costs. The most popular wearables used in this arena are based on detecting the breathing-induced movement of the chest wall. Therefore, their use in patients with impaired chest wall motion and abnormal respiratory kinematics can be challenging, but literature is still in its infancy. This study investigates the performance of a custom wearable device for respiratory monitoring in post-stroke patients. We tested the device on six hemiplegic patients under different respiratory regimes. The estimated respiratory parameters (i.e., respiratory frequency and the timing of the respiratory phase) demonstrated good agreement with the ones provided by a gold standard device. The promising results of this pilot study encourage the exploitation of wearables on these patients that may strongly impact the treatment of chronic diseases, such as hemiplegia.
A Blockchain-Enabled Incentive Trust Management with Threshold Ring Signature Scheme for Traffic Event Validation in VANETs
Authors: Ahmed Waheeb , Di Wu , Mukathe Daniel
Published: September 2022

As a part of the intelligent transportation system, vehicular ad hoc networks (VANETs) provide timely information about road events and traffic to improve road safety and traffic efficiency. However, VANETs face many challenges, such as attacks from malicious vehicles, identity privacy leakage, and the absence of trust between vehicular nodes. In addition, vehicles nearby an event usually lack the motivation to participate in the traffic event validation whenever it occurs, which requires the cooperation of vehicles on the network. To solve these problems, a blockchain-enabled incentive trust model with a privacy-preserving threshold ring signature scheme for VANETs is proposed. Firstly, a threshold ring signature scheme is designed in order to allow participants in the non-trusted environment to anonymously witness the message’s authenticity and reliability while guaranteeing the vehicle’s privacy. Second, a blockchain-enabled incentive trust management model is presented to enable the roadside units (RSUs) to thwart various attacks and guarantee the trustworthiness of event messages transmitted in VANETs and also motivate the senders of the traffic information and their witnesses with incentives. Finally, to improve efficiency, a practical Byzantine fault-tolerant consensus mechanism is used. Our proposed system is demonstrated to be effective and secure for VANETs, according to both security analysis and performance evaluation.
Multi-Modal Learning-Based Equipment Fault Prediction in the Internet of Things
Authors: Nan Xin , Zhang Bo , Liu Changyou , Gui Zhenwen , Yin Xiaoyan
Published: September 2022

The timely detection of equipment failure can effectively avoid industrial safety accidents. The existing equipment fault diagnosis methods based on single-mode signal not only have low accuracy, but also have the inherent risk of being misled by signal noise. In this paper, we reveal the possibility of using multi-modal monitoring data to improve the accuracy of equipment fault prediction. The main challenge of multi-modal data fusion is how to effectively fuse multi-modal data to improve the accuracy of fault prediction. We propose a multi-modal learning framework for fusion of low-quality monitoring data and high-quality monitoring data. In essence, low-quality monitoring data are used as a compensation for high-quality monitoring data. Firstly, the low-quality monitoring data is optimized, and then the features are extracted. At the same time, the high-quality monitoring data is dealt with by a low complexity convolutional neural network. Moreover, the robustness of the multi-modal learning algorithm is guaranteed by adding noise to the high-quality monitoring data. Finally, different dimensional features are projected into a common space to obtain accurate fault sample classification. Experimental results and performance analysis confirm the superiority of the proposed algorithm. Compared with the traditional feature concatenation method, the prediction accuracy of the proposed multi-modal learning algorithm can be improved by up to 7.42%.
Low-Cost, Open-Source, Emoncms-Based SCADA System for a Large Grid-Connected PV System
Authors: Ahsan Luqman , Baig Mirza Jabbar Aziz , Iqbal Mohmmad Tariq
Published: September 2022

This article describes a low-cost Supervisory Control and Data Acquisition (SCADA) system for a PV plant with local data logging. Typically, SCADA systems that are available on the market are proprietary (commercial), which are expensive and individually configured for a particular site. The main objective of this paper is to design a low-cost and open-source monitoring solution (hardware and software) to meet the requirements. The hardware used for this SCADA consisted of Arduino, Raspberry Pi, sensors, serial communication cables, and an open-source web view platform. This open-source platform manipulates, logs, and visualizes PV and environmental data. Emoncms runs on the Debian operating system. Field instruments were connected to two remote terminal units (RTUs). A PV array provided data to the RTU1, while an inverter output provided data to the RTU2, and the Raspberry Pi received the collected data in JSON format. As these data arrived, Emoncms used Emonhub as its main module, which refines data and then displays it on Emoncms’s WebView. The Raspberry Pi also stores data locally. Data logging was tested for 6 h, but the final results showed that data logging can last much longer. From an hour to a year, the data trend can be viewed on a user-friendly dashboard.
Method of Fuel Injector Diagnosis Based on Analysis of Current Quantities
Authors: Więcławski Krzysztof , Figlus Tomasz , Mączak Jędrzej , Szczurowski Krzysztof
Published: September 2022

This paper discusses a method of diagnosing electromagnetic valves of injection systems in combustion engines. Based on multiple analyses of electrical quantities occurring in the course of the electromagnetic injector work and physical relationships between them, the quantities have been demonstrated on which the fluctuation of the electromagnetic force in the injector depends. Moreover, the results of its fluctuations have been mapped to the electric quantities controlling the fuel injector’s work. The research has shown that the current and voltage waveforms contain information on electrical properties of the injector coil and its mechanical properties determining the injector’s technical health as well as that of the fuel system.
In-Vehicle Visible Light Communications Data Transmission System Using Optical Fiber Distributed Light: Implementation and Experimental Evaluation
Authors: Beguni Cătălin , Căilean Alin-Mihai , Avătămăniței Sebastian-Andrei , Zadobrischi Eduard , Stoler Raul , Dimian Mihai , Popa Valentin , Béchadergue Bastien , Chassagne Luc
Published: September 2022

Visible light communications emerges as a promising wireless communication technology that has been found suitable for numerous indoor and outdoor applications. In this article, a new in-vehicle VLC system is designed, implemented, and experimentally evaluated. The purpose of this new system is to provide car passengers with optical wireless communications. The proposed system consists of a VLC emitter integrated into the vehicle’s ambient lighting system and a mobile VLC receiver. Unlike any previous works, this article proposes a VLC emitter in which the light from a 3 W LED is distributed on a 2 square meter surface using 500 optical fibers whose main purpose is a decorative one. The proposed prototype has been implemented on a car and evaluated in relevant working conditions. The experimental evaluation of the proposed system has demonstrated the viability of the proposed concept and showed a data rate of 250 kb/s while providing a BER lower than 10−7. As far as we know, the proposed concept is totally new in the VLC literature, opening a new area of utilization for VLC technology: using VLC with optical fiber distributed light.
A Secure and Traceable Vehicles and Parts System Based on Blockchain and Smart Contract
Authors: Chen Chin-Ling , Zhu Zhi-Peng , Zhou Ming , Tsaur Woei-Jiunn , Wu Chih-Ming , Sun Hongyu
Published: September 2022

As society advances, so does the total number of vehicles on the road, creating a massive consumer market for automobiles. According to statistics, a major portion of today’s traffic difficulties are caused by accidents caused by subpar cars and auto parts. As a result, each country has, over time, enacted equivalent rules and regulations to prevent such tragedies. However, in the face of profit, some people are desperate enough to employ illegal parts and illegally modified cars, and auto fraud is rampant. As a result, we employ the blockchain of the symmetrical Blockchain’s digital ledger and smart contract technology to build a decentralized supply chain system that can identify specific parts. In this study, we design and discuss the proposed system framework by user functions and the flow of parts based on blockchain, and we discuss communication protocols that use the symmetry and asymmetry cryptography, algorithms, properties, and security of the mechanism while providing related analysis and comparing the properties and costs of the system with other studies. Overall, the proposed method has the potential to successfully address the issue of automobile fraud.
Affective Recommender System for Pet Social Network
Authors: Cheng Wai Khuen , Leong Wai Chun , Tan Joi San , Hong Zeng-Wei , Chen Yen-Lin
Published: September 2022

In this new era, it is no longer impossible to create a smart home environment around the household. Moreover, users are not limited to humans but also include pets such as dogs. Dogs need long-term close companionship with their owners; however, owners may occasionally need to be away from home for extended periods of time and can only monitor their dogs’ behaviors through home security cameras. Some dogs are sensitive and may develop separation anxiety, which can lead to disruptive behavior. Therefore, a novel smart home solution with an affective recommendation module is proposed by developing: (1) an application to predict the behavior of dogs and, (2) a communication platform using smartphones to connect with dog friends from different households. To predict the dogs’ behaviors, the dog emotion recognition and dog barking recognition methods are performed. The ResNet model and the sequential model are implemented to recognize dog emotions and dog barks. The weighted average is proposed to combine the prediction value of dog emotion and dog bark to improve the prediction output. Subsequently, the prediction output is forwarded to a recommendation module to respond to the dogs’ conditions. On the other hand, the Real-Time Messaging Protocol (RTMP) server is implemented as a platform to contact a dog’s friends on a list to interact with each other. Various tests were carried out and the proposed weighted average led to an improvement in the prediction accuracy. Additionally, the proposed communication platform using basic smartphones has successfully established the connection between dog friends.
Collaborative Task Offloading and Service Caching Strategy for Mobile Edge Computing
Authors: Liu Xiang , Zhao Xu , Liu Guojin , Huang Fei , Huang Tiancong , Wu Yucheng
Published: September 2022

Mobile edge computing (MEC), which sinks the functions of cloud servers, has become an emerging paradigm to solve the contradiction between delay-sensitive tasks and resource-constrained terminals. Task offloading assisted by service caching in a collaborative manner can reduce delay and balance the edge load in MEC. Due to the limited storage resources of edge servers, it is a significant issue to develop a dynamical service caching strategy according to the actual variable user demands in task offloading. Therefore, this paper investigates the collaborative task offloading problem assisted by a dynamical caching strategy in MEC. Furthermore, a two-level computing strategy called joint task offloading and service caching (JTOSC) is proposed to solve the optimized problem. The outer layer in JTOSC iteratively updates the service caching decisions based on the Gibbs sampling. The inner layer in JTOSC adopts the fairness-aware allocation algorithm and the offloading revenue preference-based bilateral matching algorithm to get a great computing resource allocation and task offloading scheme. The simulation results indicate that the proposed strategy outperforms the other four comparison strategies in terms of maximum offloading delay, service cache hit rate, and edge load balance.
Correction of Radiometry Data for Temperature Effect on Dark Current, with Application to Radiometers on Profiling Floats
Authors: O’Brien Terence , Boss Emmanuel
Published: September 2022

Measurements of daytime radiometry in the ocean are necessary to constrain processes such as photosynthesis, photo-chemistry and radiative heating. Profiles of downwelling irradiance provide a means to compute the concentration of a variety of in-water constituents. However, radiometers record a non-negligible signal when no light is available, and this signal is temperature dependent (called the dark current). Here, we devise and evaluate two consistent methods for correction of BGC-Argo radiometry measurements for dark current: one based on measurements during the day, the other based on night measurements. A daytime data correction is needed because some floats never measure at night. The corrections are based on modeling the temperature of the radiometer and show an average bias in the measured value of nearly 0.01 W m−2 nm−1, 3 orders of magnitude larger than the reported uncertainty of 2.5×10−5 W m−2 nm−1 for the sensors deployed on BGC-Argo floats (SeaBird scientific OCR504 radiometers). The methods are designed to be simple and robust, requiring pressure, temperature and irradiance data. The correction based on nighttime profiles is recommended as the primary method as it captures dark measurements with the largest dynamic range of temperature. Surprisingly, more than 28% of daytime profiles (130,674 in total) were found to record significant downwelling irradiance at 240–250 dbar. The correction is shown to be small relative to near-surface radiance and thus most useful for studies investigating light fields in the twilight zone and the impacts of radiance on deep organisms. Based on these findings, we recommend that BGC-Argo floats profile occasionally at night and to depths greater than 250 dbar. We provide codes to perform the dark corrections.
Hierarchical Dynamic Bayesian Network-Based Fatigue Crack Propagation Modeling Considering Initial Defects
Authors: Xu Yang , Zhu Bin , Zhang Zheng , Chen Jiahui
Published: September 2022

Orthotropic steel decks (OSDs) are inevitably subjected to fatigue damage caused by cycled vehicle loads in long-span bridges. This study establishes a probabilistic analysis framework integrating the dynamic Bayesian network (DBN) and fracture mechanics to model the fatigue crack propagation considering mutual correlations among multiple fatigue details. Both the observations of fatigue crack length from field inspection and monitoring data of vehicle loads from the weight-in-motion (WIM) system are utilized. First, fracture mechanics-based uncertainty analysis is performed to determine the multiple uncertainty sources in the Paris crack propagation model, material property, and observation data of crack length. The uncertainty of monitoring data of vehicle loads is ignored because of its high accuracy; consequently, the vehicle-load-related uncertainty is spontaneously ignored, which is also demonstrated to be very small on the investigated actual bridges. Second, a hierarchical DBN model is introduced to construct mutual dependencies among various uncertainties and intra-correlations in the propagation process of multiple fatigue cracks at different components. Third, a stochastic traffic model is established based on the WIM monitoring data and multi-scale finite element analysis via substructure techniques to determine the probability distribution of vehicle-load-related parameters. After variable discretization, a refined exact inference algorithm in a forward–backward–forward manner is adopted to estimate the posterior distribution of equivalent initial crack length and update the established DBN model. Finally, the proposed method is demonstrated by a numerical case study and a typical application on an actual cable-stayed bridge with steel box girders using OSDs in China. The results show that the probability distribution of equivalent initial crack size can be spontaneously derived with a larger mean value than the results of conventional empirical analysis. Furthermore, the component-level fatigue reliability is tracked and predicted based on the established crack propagation model.
Design, Simulation, and Fabrication of Multilayer Al2O3 Ceramic Micro-Hotplates for High Temperature Gas Sensors
Authors: Tang Bolun , Shi Yunbo , Li Jianwei , Tang Jie , Feng Qiaohua
Published: September 2022

In gas sensors composed of semiconductor metal oxides and two-dimensional materials, the gas-sensitive material is deposited or coated on a metallic signal electrode and must be selective and responsive at a specific temperature. The microelectromechanical devices hosting this material must keep it at the correct operating temperature using a micro-hotplate robust to high temperatures. In this study, three hotplate designs were investigated: electrodes arranged on both sides of an AlN substrate, a micro-hotplate buried in an alumina ceramic substrate, and a beam structure formed using laser punching. The last two designs use magnetron-sputtered ultra-thin AlN films to separate the upper Au interdigital electrodes and lower Pt heating resistor in a sandwich-like structure. The temperature distribution is simulated by the Joule heat model, and the third design has better energy consumption performance. This design was fabricated, and the effect of the rough surface of the alumina ceramic on the preparation was addressed. The experimental results show that the micro-hotplate can operate at nearly 700 °C. The micro-hotplate heats to nearly 240 °C in 2.4 s using a power of ~340 mW. This design makes ceramic-based micro-hotplates a more practical alternative to silicon-based micro-hotplates in gas sensors.
Characterization of the Plastic Scintillator Detector System Exradin W2 in a High Dose Rate Flattening-Filter-Free Photon Beam
Authors: Thrower Sara , Prajapati Surendra , Holmes Shannon , Schüler Emil , Beddar Sam
Published: September 2022

(1) Background: The Exradin W2 is a commercially available scintillator detector designed for reference and relative dosimetry in small fields. In this work, we investigated the performance of the W2 scintillator in a 10 MV flattening-filter-free photon beam and compared it to the performance of ion chambers designed for small field measurements. (2) Methods: We measured beam profiles and percent depth dose curves with each detector and investigated the linearity of each system based on dose per pulse (DPP) and pulse repetition frequency. (3) Results: We found excellent agreement between the W2 scintillator and the ion chambers for beam profiles and percent depth dose curves. Our results also showed that the two-voltage method of calculating the ion recombination correction factor was sufficient to correct for the ion recombination effect of ion chambers, even at the highest DPP. (4) Conclusions: These findings show that the W2 scintillator shows excellent agreement with ion chambers in high DPP conditions.
Effects of Weak Bedding Plane, Fault, and Extreme Rainfall on the Landslide Event of a High Cut-Slope
Authors: Sun Yiqing , Li Deying , Miao Fasheng , She Xiangjie , Yang Shuo , Xie Xiaoxu
Published: September 2022

High cut-slopes are widespread in engineering constructions and often converted into landslides. Some extreme circumstances facilitate the landslide process, such as the weak bedding plane, rainfall, and faults. Therefore, this paper intends to offer insights into the influence of the weak bedding plane, extreme rainfall as well as faults on the landslide process of the high cut-slope. In this paper, the Anling landslide in Anhui Province, China, is selected as an example. Geological surveys, displacement monitoring, data analysis, as well as numerical simulation are carried out. The entire excavation construction and landslide deformation process are simulated to reveal the formation mechanism of the landslide using the finite difference code, FLAC3D. The effects of the fault on the landslide and the effectiveness of adjusting protection measures by adding piles are investigated on the basis of the finite difference analysis. According to monitoring data and numerical simulation, the weak bedding plane and extreme rainfall are considered the main factors leading to the Anling landslide. Field investigation and numerical experiments indicate that the fault shall facilitate and accelerate the landslide process. The construction of piles in a suitable position for the landslide is a reasonable and economical measure to stabilize the landslide.
A Radio Environment Map Updating Mechanism Based on an Attention Mechanism and Siamese Neural Networks
Authors: Zhen Pan , Zhang Bangning , Xie Chen , Guo Daoxing
Published: September 2022

A radio environment map (REM) is an effective spectrum management tool. With the increase in the number of mobile devices, the wireless environment changes more and more frequently, bringing new challenges to REM updates. Traditional update methods usually rely on the amount of data collected for updating without paying attention to whether the wireless environment has changed enough. In particular, a waste of computational resources results from the frequently updated REM when the wireless environment does not change much. When the wireless environment changes a lot, the REM is not updated promptly, resulting in a decrease in REM accuracy. To overcome the above problems, this work combines the Siamese neural network and an attention mechanism in computer vision and proposes an update mechanism based on the amount of wireless environmental change starting from image data. The method compares the newly collected crowdsourced data with the constructed REM in terms of similarity. It uses similarity to measure the necessity of the REM to be updated. The algorithm in this paper can achieve a controlled update by setting a similarity threshold with good controllability. In addition, the effectiveness of the algorithm in detecting changes of the wireless environment has been demonstrated by combing simulation data.
Representative Ring Signature Algorithm Based on Smart Contract
Authors: Li Qiude , Yi Wenlong , Zhao Xiaomin , Yin Hua , Gerasimov Igor
Published: September 2022

Traditional ring signature algorithms suffer from large signature data capacity and low speed of signature and verification during collective signing. In this work, we propose a representative ring signature algorithm based on smart contracts. By collecting the opinions of the signatory based on multiparty secure computation, the proposed technique protects the privacy of the signatory during the data interaction process in the consortium chain. Moreover, the proposed method uses smart contracts to organize the signature process and formulate a signature strategy of “one encryption per signature” to prevent signature forgery. It uses the Hyperledger Fabric framework as the signature test platform of the consortium chain to perform the experiments. We compare the results of the proposed method with the ECC ring signature scheme. The experimental results show that in the worst case, the signature volume of the proposed method decreases by more than two times, and the signature speed and verification speed increase by more than three times. Therefore, in the collective signature scenario of transaction verification in the consortium chain, the proposed method is verified to be innovative and practical.
LC-HRMS-Based Profiling: Antibacterial and Lipase Inhibitory Activities of Some Medicinal Plants for the Remedy of Obesity
Authors: Sapkota Basanta Kumar , Khadayat Karan , Aryal Babita , Bashyal Jyoti , Jaisi Shankar , Parajuli Niranjan
Published: September 2022

Globally, obesity is a serious health concern that causes numerous diseases, including type 2 diabetes, hypertension, cardiovascular diseases, etc. Medicinal plants have been used to aid in weight loss since ancient times. Thus, this research is focused on the exploration of pancreatic lipase inhibitory activity and secondary metabolite profiling of Bergenia ciliata, Mimosa pudica, and Phyllanthus emblica, selected based on an ethnobotanical survey. The lipase inhibition was investigated using 4-nitrophenyl butyrate (p-NPB) as a substrate. To uncover further therapeutic potentials of these medicinal plants, antimicrobial activity and minimum inhibitory concentration (MIC) of the extracts were also determined. The ethyl acetate plant extracts showed higher antimicrobial activity against Staphylococcus aureus, Escherichia coli, Salmonella typhi, and Shigella sonnei. The MIC of ethyl acetate extracts of medicinal plants considered in this study ranges from 1.56 to 6.25 mg/mL. The hexane fraction of Mimosa pudica and Phyllanthus emblica showed a higher lipase inhibitory activity as compared to others, with IC50 values of 0.49 ± 0.02 and 2.45 ± 0.003 mg/mL, respectively. In the case of Bergenia ciliata, the methanolic extract inhibited lipase more effectively than others, with an IC50 value of 1.55 ± 0.02 mg/mL (IC50 value of orlistat was 179.70 ± 3.60 µg/mL). A mass spectrometry analysis of various solvent/solvent partition fractions (extracts) revealed 29 major secondary metabolites. The research offers a multitude of evidence for using medicinal plants as antiobesity and antimicrobial agents.
Development of an Enzyme-Based Thin-Layer Chromatographic Assay for the Detection of Cyclooxygenase-2 Inhibitors
Authors: Urbain Aurélie , Trabelssi Nidhal , Bardot Valérie
Published: September 2022

The search for new anti-inflammatory drugs with less side effects requires simple, fast and reliable screening methods. In this context, we have developed a sensitive thin-layer chromatographic (TLC) assay on silica gel plates to detect cyclooxygenase-2 (COX-2) inhibition. COX-2 catalyzes two sequential enzymatic reactions: a first oxygenation step that converts arachidonic acid into prostaglandin G2, and a subsequent reduction of prostaglandin G2 into prostaglandin H2. Our test is based on the co-oxidation during this peroxidation step of a co-substrate, N,N,N′,N′-tetramethyl-p-phenylenediamine (TMPD), leading to a blue-grey product. As a consequence, COX-2 inhibitors appear on the TLC plate after revelation as clear spots against the colored background. Parameters such as concentrations of enzyme, substrate, and chromogenic reagent have been optimized. The limit of detection was found to be below the microgram for standard COX-2 inhibitors such as celecoxib or ibuprofen. The developed TLC assay was also conclusive when applied to 60 various natural pure compounds and some complex natural extracts. Results demonstrated a COX-2 inhibitory activity mostly for triterpene and sterol derivatives. This COX-2 TLC assay appears as a suitable low-cost and reliable strategy for the screening of natural extracts to discover new anti-inflammatory compounds.
Determination of Three Alkyl Camphorsulfonates as Potential Genotoxic Impurities Using GC-FID and GC-MS by Analytical QbD
Authors: Lee Kyoungmin , Yoo Wokchul , Jeong Jin Hyun
Published: September 2022

Camphorsulfonic acid salts are commonly used in the manufacturing production of active pharmaceutical ingredients (APIs) and have the potential to form alkyl camphorsulfonates, which can be considered as potential genotoxic impurities (PGIs). Alkyl camphorsulfonates should be controlled using the Threshold of Toxicological Concern (TTC) when detected in APIs due to their genotoxicity. An in silico study utilizing the ICH M7 guideline was performed in order to classify the alkyl camphorsulfonates that can be produced from the reaction of camphorsulfonic acid salts with methanol, ethanol, and isopropyl alcohol, which are commonly used solvents in API manufacturing processes. Two sensitive, reproducible, and accurate analytical methods using GC-FID and GC-MS were developed using the analytical Quality By Design (QbD) approaches for the quantitation of three alkyl camphorsulfonates in APIs satisfying the control limit of PGIs according to the TTC. The detection limits of the GC-FID method were found to be between 1.5 to 1.9 ppm, and the detection limits of the GC-MS method were found to be between 0.055 to 0.102 ppm. The method was validated in terms of accuracy, linearity, precision, detection limit, quantitation limit, specificity and robustness.