bluuebunny/crossref_metadata_2025 huggingface dataset:
Genome-Wide Identification and Expression Analysis of eIF Family Genes from Brassica rapa in Response to TuMV Resistance
Authors: Huang Wenyue , Wang Shaoxing , Zhang Shifan , Li Fei , Zhang Hui , Sun Rifei , Zhang Shujiang , Li Guoliang
Published: August 2022

Brassica rapa is one of the most important leafy vegetables worldwide, and has a long history of cultivation. However, it has not been possible to completely control the damage of turnip mosaic virus (TuMV), a serious virus in B. rapa, to production. In this study, the genome-wide identification and expression detection of eIF family genes from B. rapa in response to TuMV resistance were analyzed, including the identification of eIF family genes, chromosomal distribution, three-dimensional (3D) structure and sequence logo analyses, and the expression characterization as well as differential metabolite analysis of eIF family genes in resistant/susceptible lines, which may further prove the whole-genome tripling (WGT) event in B. rapa evolution and provide evidence for the functional redundancy and functional loss of multicopy eIF genes in evolution. A qRT-PCR analysis revealed that the relative expressions of eIF genes in a susceptible line (80461) were higher than those in a resistant line (80124), which may prove that, when TuMV infects host plants, the eIF genes can combine with the virus mRNA 5′ end cap structure and promote the initiation of virus mRNA translation in the susceptible B. rapa line. In addition, the metabolite substances were detected, the differences in metabolites between disease-resistant and disease-susceptible plants were mainly manifested by altered compounds such as flavonoids, jasmonic acid, salicylic acid, ketones, esters, etc., which inferred that the different metabolite regulations of eIF family genes and reveal the resistance mechanisms of eIF genes against TuMV in brassica crops. This study may lay a new theoretical foundation for revealing eIF family gene resistance to TuMV in B. rapa, as well as advancing our understanding of virus–host interactions.
CRISPR-Cas9 Gene Editing of the Sal1 Gene Family in Wheat
Authors: Mohr Toni , Horstman James , Gu Yong Q. , Elarabi Nagwa I. , Abdallah Naglaa A. , Thilmony Roger
Published: August 2022

The highly conserved Sal1 encodes a bifunctional enzyme with inositol polyphosphate-1-phosphatase and 3′ (2′), 5′-bisphosphate nucleotidase activity and has been shown to alter abiotic stress tolerance in plants when disrupted. Precise gene editing techniques were used to generate Sal1 mutants in hexaploid bread wheat. The CRISPR (Clustered Regulatory Interspaced Short Palindromic Repeats) Cas9 system with three guide RNAs (gRNAs) was used to inactivate six Sal1 homologous genes within the Bobwhite wheat genome. The resulting mutant wheat plants with all their Sal1 genes disabled had slimmer stems, had a modest reduction in biomass and senesced more slowly in water limiting conditions, but did not exhibit improved yield under drought conditions. Our results show that multiplexed gRNAs enabled effective targeted gene editing of the Sal1 gene family in hexaploid wheat. These Sal1 mutant wheat plants will be a resource for further research studying the function of this gene family in wheat.
Silicon-Induced Tolerance against Arsenic Toxicity by Activating Physiological, Anatomical and Biochemical Regulation in Phoenix dactylifera (Date Palm)
Authors: Khan Taimoor , Bilal Saqib , Asaf Sajjad , Alamri Safiya Salim , Imran Muhammad , Khan Abdul Latif , Al-Rawahi Ahmed , Lee In-Jung , Al-Harrasi Ahmed
Published: August 2022

Arsenic is a toxic metal abundantly present in agricultural, industrial, and pesticide effluents. To overcome arsenic toxicity and ensure safety for plant growth, silicon (Si) can play a significant role in its mitigation. Here, we aim to investigate the influence of silicon on date palm under arsenic toxicity by screening antioxidants accumulation, hormonal modulation, and the expression profile of abiotic stress-related genes. The results showed that arsenic exposure (As: 1.0 mM) significantly retarded growth attributes (shoot length, root length, fresh weight), reduced photosynthetic pigments, and raised reactive species levels. Contrarily, exogenous application of Si (Na2SiO3) to date palm roots strongly influenced stress mitigation by limiting the translocation of arsenic into roots and shoots as compared with the arsenic sole application. Furthermore, an enhanced accumulation of polyphenols (48%) and increased antioxidant activities (POD: 50%, PPO: 75%, GSH: 26.1%, CAT: 51%) resulted in a significant decrease in superoxide anion (O2•−: 58%) and lipid peroxidation (MDA: 1.7-fold), in silicon-treated plants, compared with control and arsenic-treated plants. The Si application also reduced the endogenous abscisic acid (ABA: 38%) under normal conditions, and salicylic acid (SA: 52%) and jasmonic acid levels (JA: 62%) under stress conditions as compared with control and arsenic. Interestingly, the genes; zeaxanthin epoxidase (ZEP) and 9-cis-epoxycarotenoid dioxygenase (NCED-1) involved in ABA biosynthesis were upregulated by silicon under arsenic stress. Likewise, Si application also upregulated gene expression of plant plasma membrane ATPase (PMMA-4), aluminum-activated malate transporter (ALMT) responsible for maintaining cellular physiology, stomatal conductance, and short-chain dehydrogenases/reductases (SDR) involved in nutrients translocation. Hence, the study demonstrates the remarkable role of silicon in supporting growth and inducing arsenic tolerance by increasing antioxidant activities and endogenous hormones in date palm. The outcomes of our study can be employed in further studies to better understand arsenic tolerance and decode mechanism.
Highly Thermal Conductive and Electrically Insulating Epoxy Composites Based on Zinc-Oxide-Coated Silver Nanowires
Authors: Zhang Li , Zhu Wenfeng , Qi Guoquan , Li Houbu , Qi Dongtao , Qi Shuhua
Published: August 2022

Nano-ZnO particles were deposited on the surface of silver nanowires (AgNWs) by the precipitation method, and the obtained AgNW@ZnO filler with core-shell structure was added to epoxy resin to prepare AgNW@ZnO/EP thermally conductive composites. The ZnO coating on the surface of AgNWs effectively improves the thermal conductivity of the composites. When 8 wt% AgNW@ZnO filler was added to the epoxy resin, the thermal conductivity of the AgNW@ZnO/EP composite increased to 0.77 W/(m·K). The enhancement of the thermal conductivity is attributed to the fact that ZnO effectively improves the interface bonding between AgNWs and the epoxy matrix, thus strengthening the contact between AgNWs. In addition, the electrical insulation of the AgNW@ZnO/EP composites was improved upon the introduction of the ZnO coating over AgNWs. For the filler content of 8 wt%, the volume resistivity of the AgNW@ZnO/EP composites was found to be higher than 1013 Ω·cm. AgNW@ZnO/EP composites have also exhibited low dielectric constant and good thermal stability.
In Situ Zymography Analysis of Matrix Metalloproteinases Activity Following Endodontic Irrigation Protocols and Correlation to Root Dentine Bond Strength
Authors: Baruwa Abayomi Omokeji , Mazzitelli Claudia , Maravic Tatjana , Martins Jorge N. R. , Mazzoni Annalisa , Ginjeira António
Published: August 2022

The objective was to evaluate the effect of different root canal irrigating solutions on the activity of matrix metalloproteinases (MMPs), and correlation to the push-out bond strength (PBS) and nanoleakage expression (NL) in the root dentin. Seventy-two single-rooted teeth were treated endodontically and distributed into four groups (n = 6 for in-situ zymography, n = 10 for PBS, and n = 2 for NL per group) according to the irrigating solutions used: (I) saline (S); (II) 5.25% sodium hypochlorite (SH); (III) 5.25% SH + 10% citric acid (CA); and (IV) 5.25% SH + 10% CA + 0.2% chlorhexidine (CHX). After root canal obturation, post space was prepared to receive the glass fiber post. Dual-cure resin was used for luting and light polymerization was performed. The root/fiber post assemblies were sectioned and subjected to in situ zymography, and PBS and NL expression analysis tests. The enzymatic activity was quantified and expressed as a percentage of the green fluorescence, while fractographic evaluation was performed after PBS with a stereomicroscope, and data were statistically analyzed at p < 0.05. The zymography analysis shows high expression of MMPs in the middle third of the root in all groups, while the most abundant activity of MMPs following the irrigating solutions is observed in groups I and III, where saline and citric acid are used, respectively. Inversely, group IV, where chlorhexidine is the final rinse, records the lowest MMP activity with the highest PBS, and the statistical analysis of the groups are ranked as: IV > II > III > I (p < 0.05). The combination of SH, CA, and CHX results in lower expression of MMPs and higher push-out bond strength of fiber posts to root dentin, with no difference seen in the nanoleakage expression (p > 0.05); hence, this irrigation regime with chlorhexidine as a final rinse is more favorable than other combinations in ensuring optimal adhesion to root dentine.
Silsesquioxane-Doped Electrospun Nanofibrillar Membranes for Separation Systems
Authors: Frydrych Miłosz , Sztorch Bogna , Brząkalski Dariusz , Kozera Rafał , Konieczna Roksana , Osiecki Tomasz , Przekop Robert E.
Published: August 2022

In this study, a series of cage siloxanes (CS), e.g., three polyhedral oligomeric silsesquioxanes (SSQs) and one spherosilicate (SS) derivative, were applied as functional additives for the preparation of poly(lactic acid)-based (PLA) nanofibrillar membranes with an electrospinning technique utilizing an efficient spinning wire electrode setup. The impact of the additives’ structure, chemistry, and electrospinning parameters on the obtained materials’ morphology (scanning electron microscopy) and physicochemical (thermogravimetry, differential scanning calorimetry, contact angle analysis, air flow analysis) properties is discussed. It is presented that applying organosilicon additives may extend the already tuneable properties of the membranes produced by electrospinning performed under different conditions and that they enable to obtain nanofibres of smaller diameter, which in turn increases the membrane porosity. Furthermore, the solvent-assisted electrospinning method allowed for unparalleled mixing of the PLA matrix with the CS additives, as no traces of free additives were visible on the membranes by scanning electron microscopy (SEM) imaging. The resulting membranes can be utilized as filter materials.
Understanding the Influence of Gypsum upon a Hybrid Flame Retardant Coating on Expanded Polystyrene Beads
Authors: Bhoite Sangram P. , Kim Jonghyuck , Jo Wan , Bhoite Pravin H. , Mali Sawanta S. , Park Kyu-Hwan , Hong Chang Kook
Published: August 2022

A low-cost and effective flame retarding expanded polystyrene (EPS) foam was prepared herein by using a hybrid flame retardant (HFR) system, and the influence of gypsum was studied. The surface morphology and flame retardant properties of the synthesized flame retardant EPS were characterized using scanning electron microscopy (SEM) and cone calorimetry testing (CCT). The SEM micrographs revealed the uniform coating of the gypsum-based HFR on the EPS microspheres. The CCT and thermal conductivity study demonstrated that the incorporation of gypsum greatly decreases the peak heat release rate (PHRR) and total heat release (THR) of the flame retarding EPS samples with acceptable thermal insulation performance. The EPS/HFR with a uniform coating and the optimum amount of gypsum provides excellent flame retardant performance, with a THR of 8 MJ/m2, a PHRR of 53.1 kW/m2, and a fire growth rate (FIGRA) of 1682.95 W/m2s. However, an excessive amount of gypsum weakens the flame retardant performance. The CCT results demonstrate that a moderate gypsum content in the EPS/HFR sample provides appropriate flame retarding properties to meet the fire safety standards.
Effect of CeO2–GO Nanocomposite on the Anticorrosion Properties of Epoxy Coating in Simulated Acid Rain Solution
Authors: Liu Ruidan , Liu Xiaoyan , Yang Heng , Jie Handuo , Li Tianyu , Lyu Kai , Shah Surendra P.
Published: August 2022

The lamellar structure of graphene oxide and the filling effect of nano-cerium oxide particles together provide a good barrier and stability to coating. In this paper, cerium oxide-graphene oxide (4:1) nanocomposite was prepared by the hydrothermal synthesis method. The effect of cerium oxide–graphene oxide (4:1) nanocomposite on the anticorrosion properties of epoxy coating in simulated acid rain solution was studied by open circuit potential (OCP), electrochemical impedance spectroscopy (EIS), Mott–Schottky curve, Tafel curve, and micromorphological characterization, in order to compare it with pure epoxy coating, graphene oxide epoxy coating, and cerium oxide epoxy coating. The obtained results showed that cerium oxide–graphene oxide (4:1) epoxy coating’s protection efficiency was as high as 98.62%. These results indicated that cerium oxide–graphene oxide modified anticorrosive coating had an excellent application prospect in an acid rain environment. Meanwhile, owing to the poor protection ability of epoxy resin and unstably hydrolysis product of CeO2 to the acidic medium, the resistance of CeO2–GO (4:1)/EP coating to acidic corrosive medium was relatively poorer than that of neutral and saline-alkali corrosive medium.
Oral Microbiome in Orthodontic Acrylic Retainer
Authors: Kasibut Punnisa , Kuvatanasuchati Jintakorn , Thaweboon Boonyanit , Sirisoontorn Irin
Published: August 2022

The oral microbiome can be shifted if the patients wear the acrylic retainers for a lengthy period. It is essential to understand the components of the plaque in order to forestall the development of dental caries and gingivitis. The aim of this study is to report the bacterial communities that adhere to the acrylic retainers by full-length nanopore 16S sequencing. Six healthy participants were allocated into 2 groups (chemical tablet and brushing groups). Plaque samples were collected from the acrylic retainer surfaces before and after cleaning. The bacterial communities were reported using full-length nanopore 16S sequencing. The results showed that 7 distinct phyla were identified by sequencing. The most prevalent of these was the Firmicutes. We found a total of 72 genera. The most common microorganism across all samples was Streptococcus, followed by Neisseria, Rothia, and Gemella. The beta diversity showed a significant difference between before and after cleaning (p < 0.05). This study revealed the novel finding that a combination of chemical and mechanical cleaning methods was the most effective method of eliminating retainer biofilms. Moreover, retainer cleaning tablets did not alter the homeostatic balance of the bacterial communities adhering to the acrylic retainers.
Religious Populisms in the Asia Pacific
Authors: Yilmaz Ihsan , Morieson Nicholas
Published: August 2022

Most of the literature on religion’s relationship with populism is Eurocentric and has so far focused on European populist party discourses and, to a degree, on the United States, in particular, on the Christian identity populism of the Tea Party and the Trump movement within the Republican Party. However, across the Asia-Pacific region, religion has become an important component of populist discourses. It has been instrumentalised by populists in many nations in the region, including some of the most populous countries in the world, India, Indonesia, and Pakistan. Moreover, the relationship between religions other than Christianity and populism has all too rarely been studied, except for Turkey. This paper therefore surveys the Asia-Pacific region to comprehend how populists in the region incorporate religion into their discourses and the impact religious populism has on Asia-Pacific societies. It asks two questions: “What role does religion play in populist discourses?” and “How has religion’s incorporation into populist discourse impacted society?” To answer these questions, the paper examines four nations which have recently been ruled by governments espousing, to different degrees and in different ways, religious populism: India, Pakistan, Malaysia, and Sri Lanka. By choosing these nations, we can examine the relationship between populism and Islam, Hinduism, and Buddhism, and between religion and populism within a variety of religious, ethnic, and political contexts. The paper argues that religion is instrumentalised in populist discourses across the Asia-Pacific region in a variety of ways. First, religion is used to construct ingroups and outgroups, which serve a populist narrative in which the religion of the ingroup is superior yet threatened by the religion(s) of the outgroup(s). Second, religion is used to empower religious authorities, which support populist parties and movements. Third, religion is instrumentalised by populists in order to frame themselves, and in particular their leader, as a sacred or holy figure. The paper also argues that religion’s incorporation into populist discourse has impacted society by legitimising authoritarianism, increasing religious divisions, and justifying the oppression of religious minorities. The paper concludes by noting some differences between populists in Europe and the Asia-Pacific region.
Spectral Indices as a Tool to Assess the Moisture Status of Forest Habitats
Authors: Młynarczyk Adam , Konatowska Monika , Królewicz Sławomir , Rutkowski Paweł , Piekarczyk Jan , Kowalewski Wojciech
Published: August 2022

Measurement of water content in forest habitats is considered essential in ecological research on forests, climate change, or forest management. In the traditional forest habitat classification, two systems of habitat conditions analysis are found: single factor and multifactor methods. Both are laborious and therefore costly. Remote sensing methods provide a low-cost alternative. The aim of the presented study was to find the relationship between the spectral indices obtained from satellite images and the forest habitats moisture indices used traditionally in the Polish forest habitats classification. The scientific hypothesis of the research is as follows: it is possible to assess the variation in the humidity of forest habitats on the basis of spectral indices. Using advanced geographic information system (GIS) technology, 923 research plots were tested, where habitat studies performed with the traditional methods were compared with the analysis of 191 spectral indices calculated for Sentinel-2 image data. The normalized difference vegetation index (NDVI) has proved to be the most useful to the assessing of moisture of forest habitats. The ranking of the most correlated indices was calculated as Eintg—the product of the absolute value of the slope and the mean square error complement, and for the top five indices was as follows: NDVI = 0.248619, EXG = 0.242112, OSAVI = 0.239412, DSWI-4 = 0.238784, and RDVI = 0.236995. The results also highlight the impact of water reservoirs on the humidity and trophicity of forest habitats, showing a decrease in the fertility of habitats with an increase in distance from the water reservoir. The results of the study can be used to preparing maps of the diversity of forest types, especially in hard-to-reach places, as well as to assess changes in the moisture status of habitats, which may be useful, for example, in the assessment of the fire risk of forest habitats. We have proved that NDVI can be used in applications for which it was not originally designed.
The Effect of Error Non-Orthogonality on Triple Collocation Analyses
Authors: Vogelzang Jur , Stoffelen Ad , Verhoef Anton
Published: August 2022

Triple collocation analysis is an established technique for calculating the relative linear intercalibration coefficients and observation error variances for physical quantities measured simultaneously in space and time by three different observation systems. A simple parameterized error model is used. It relies on a few assumptions, one of which is that the observation errors are independent of the magnitude of the observed quantities. This is referred to as error orthogonality. Using an ocean surface vector winds data set of 44,948 collocations, this study shows that the violation of error orthogonality does affect the calibration coefficients but has only a small second-order effect on the observation error variances of the calibrated data.
Monitoring Land Use/Cover Changes by Using Multi-Temporal Remote Sensing for Urban Hydrological Assessment: A Case Study in Beijing, China
Authors: Kabeja Crispin , Li Rui , Rwabuhungu Rwatangabo Digne Edmond , Duan Jiawei
Published: August 2022

Understanding the change in hydrological response due to urban dynamics is important for better flood preparedness and future sustainable urban planning. This study investigated the influence of urban land cover change on spatiotemporal changes in flood peak discharge and flood volume within a rapidly urbanizing catchment located in Beijing, China. We used Landsat satellite data ranging from 1986 to 2017 to monitor and quantify urban growth. Moreover, the Hydrological Modeling System (HEC-HMS) coupled with meteorological data was utilized to examine the impact of urban growth on hydrological responses. The results revealed that major changes in land use/cover (LULC) were detected in the urban landscape, which increased from 25.22% to 65.48% of the total catchment area, while agricultural land decreased from 64.85% to 25.28% during 1986–2017. The flood peak discharge and flood volume average of the three rainstorms events reached 7.02% and 11.93%, respectively. Furthermore, the changes in flood peak discharge and flood volume were more obvious at the sub-catchment scale. These findings indicate that urban growth enhanced the possible flooding risk in the study catchment. This study improves the understanding of the isolated impacts of urbanization on flooding and provides essential information for sustainable urban planning.
SGSNet: A Lightweight Depth Completion Network Based on Secondary Guidance and Spatial Fusion
Authors: Chen Baifan , Lv Xiaotian , Liu Chongliang , Jiao Hao
Published: August 2022

The depth completion task aims to generate a dense depth map from a sparse depth map and the corresponding RGB image. As a data preprocessing task, obtaining denser depth maps without affecting the real-time performance of downstream tasks is the challenge. In this paper, we propose a lightweight depth completion network based on secondary guidance and spatial fusion named SGSNet. We design the image feature extraction module to better extract features from different scales between and within layers in parallel and to generate guidance features. Then, SGSNet uses the secondary guidance to complete the depth completion. The first guidance uses the lightweight guidance module to quickly guide LiDAR feature extraction with the texture features of RGB images. The second guidance uses the depth information completion module for sparse depth map feature completion and inputs it into the DA-CSPN++ module to complete the dense depth map re-guidance. By using a lightweight bootstrap module, the overall network runs ten times faster than the baseline. The overall network is relatively lightweight, up to thirty frames, which is sufficient to meet the speed needs of large SLAM and three-dimensional reconstruction for sensor data extraction. At the time of submission, the accuracy of the algorithm in SGSNet ranked first in the KITTI ranking of lightweight depth completion methods. It was 37.5% faster than the top published algorithms in the rank and was second in the full ranking.
Automatic Defect Description of Railway Track Line Image Based on Dense Captioning
Authors: Wei Dehua , Wei Xiukun , Jia Limin
Published: August 2022

The state monitoring of the railway track line is one of the important tasks to ensure the safety of the railway transportation system. While the defect recognition result, that is, the inspection report, is the main basis for the maintenance decision. Most previous attempts have proposed intelligent detection methods to achieve rapid and accurate inspection of the safety state of the railway track line. However, there are few investigations on the automatic generation of inspection reports. Fortunately, inspired by the recent advances and successes in dense captioning, such technologies can be investigated and used to generate textual information on the type, position, status, and interrelationship of the key components from the field images. To this end, based on the work of DenseCap, a railway track line image captioning model (RTLCap for short) is proposed, which replaces VGG16 with ResNet-50-FPN as the backbone of the model to extract more powerful image features. In addition, towards the problems of object occlusion and category imbalance in the field images, Soft-NMS and Focal Loss are applied in RTLCap to promote defect description performance. After that, to improve the image processing speed of RTLCap and reduce the complexity of the model, a reconstructed RTLCap model named Faster RTLCap is presented with the help of YOLOv3. In the encoder part, a multi-level regional feature localization, mapping, and fusion module (MFLMF) are proposed to extract regional features, and an SPP (Spatial Pyramid Pooling) layer is employed after MFLMF to reduce model parameters. As for the decoder part, a stacked LSTM is adopted as the language model for better language representation learning. Both quantitative and qualitative experimental results demonstrate the effectiveness of the proposed methods.
A Smart Wristband Integrated with an IoT-Based Alarming System for Real-Time Sweat Alcohol Monitoring
Authors: Khemtonglang Kodchakorn , Chaiyaphet Nataphiya , Kumsaen Tinnakorn , Chaiyachati Chanyamon , Chuchuen Oranat
Published: August 2022

Breathalyzer is a common approach to measuring blood alcohol concentration (BAC) levels of individuals suspected of drunk driving. Nevertheless, this device is relatively high-cost, inconvenient for people with limited breathing capacity, and risky for COVID-19 exposure. Here, we designed and developed a smart wristband integrating a real-time noninvasive sweat alcohol metal oxide (MOX) gas sensor with a Drunk Mate, an Internet of Thing (IoT)-based alarming system. A MOX sensor acquired transdermal alcohol concentration (TAC) which was converted to BAC and sent via the IoT network to the Blynk application platform on a smartphone, triggering alarming messages on LINE Notify. A user would receive an immediate alarming message when his BAC level reached an illegal alcohol concentration limit (BAC 50 mg%; TAC 0.70 mg/mL). The sensor readings showed a high linear correlation with TAC (R2 = 0.9815; limit of detection = 0.045 mg/mL) in the range of 0.10–1.05 mg/mL alcohol concentration in artificial sweat, achieving an accuracy of 94.66%. The sensor readings of ethanol in water were not statistically significantly different (p > 0.05) from the measurements in artificial sweat and other sweat-related solutions, suggesting that the device responded specifically to ethanol and was not affected by other electrolytes in the artificial sweat. Moreover, the device could continuously monitor TAC levels simulated in real-time in an artificial sweat testing system. With the integration of an IoT-based alarming system, the smart wristband developed from a commercial gas sensor presented here offers a promising low-cost MOX gas sensor monitoring technology for noninvasive and real-time sweat alcohol measurement and monitoring.
UAV and IoT-Based Systems for the Monitoring of Industrial Facilities Using Digital Twins: Methodology, Reliability Models, and Application
Authors: Sun Yun , Fesenko Herman , Kharchenko Vyacheslav , Zhong Luo , Kliushnikov Ihor , Illiashenko Oleg , Morozova Olga , Sachenko Anatoliy
Published: August 2022

This paper suggests a methodology (conception and principles) for building two-mode monitoring systems (SMs) for industrial facilities and their adjacent territories based on the application of unmanned aerial vehicle (UAV), Internet of Things (IoT), and digital twin (DT) technologies, and a set of SM reliability models considering the parameters of the channels and components. The concept of building a reliable and resilient SM is proposed. For this purpose, the von Neumann paradigm for the synthesis of reliable systems from unreliable components is developed. For complex SMs of industrial facilities, the concept covers the application of various types of redundancy (structural, version, time, and space) for basic components—sensors, means of communication, processing, and presentation—in the form of DTs for decision support systems. The research results include: the methodology for the building and general structures of UAV-, IoT-, and DT-based SMs in industrial facilities as multi-level systems; reliability models for SMs considering the applied technologies and operation modes (normal and emergency); and industrial cases of SMs for manufacture and nuclear power plants. The results obtained are the basis for further development of the theory and for practical applications of SMs in industrial facilities within the framework of the implementation and improvement of Industry 4.0 principles.
Enhanced Communications on Satellite-Based IoT Systems to Support Maritime Transportation Services
Authors: Monzon Baeza Victor , Ortiz Flor , Herrero Garcia Samuel , Lagunas Eva
Published: August 2022

Maritime transport has become important due to its ability to internationally unite all continents. In turn, during the last two years, we have observed that the increase of consumer goods has resulted in global shipping deadlocks. In addition, the future goes through the role of ports and efficiency in maritime transport to decarbonize its impact on the environment. In order to improve the economy and people’s lives, in this work, we propose to enhance services offered in maritime logistics. To do this, a communications system is designed on the deck of ships to transmit data through a constellation of satellites using interconnected smart devices based on IoT. Among the services, we highlight the monitoring and tracking of refrigerated containers, the transmission of geolocation data from Global Positioning System (GPS), and security through the Automatic Identification System (AIS). This information will be used for a fleet of ships to make better decisions and help guarantee the status of the cargo and maritime safety on the routes. The system design, network dimensioning, and a communications protocol for decision-making will be presented.
An Intelligent Epileptic Prediction System Based on Synchrosqueezed Wavelet Transform and Multi-Level Feature CNN for Smart Healthcare IoT
Authors: Song Kunpeng , Fang Jiajia , Zhang Lei , Chen Fangni , Wan Jian , Xiong Neal
Published: August 2022

Epilepsy is a common neurological disease worldwide, characterized by recurrent seizures. There is currently no cure for epilepsy. However, seizures can be controlled by drugs and surgeries in about 70% of epileptic patients. A timely and accurate prediction of seizures can prevent injuries during seizures and improve the patients’ quality of life. In this paper, we proposed an intelligent epileptic prediction system based on Synchrosqueezed Wavelet Transform (SWT) and Multi-Level Feature Convolutional Neural Network (MLF-CNN) for smart healthcare IoT network. In this system, we used SWT to map EEG signals to the frequency domain, which was able to measure the energy changes in EEG signals caused by seizures within a well-defined Time-Frequency (TF) plane. MLF-CNN was then applied to extract multi-level features from the processed EEG signals and classify the different seizure segments. The performance of our proposed system was evaluated with the publicly available CHB-MIT dataset and our private ZJU4H dataset. The system achieved an accuracy of 96.99% and 94.25%, a sensitivity of 96.48% and 97.76%, a specificity of 97.46% and 94.07% and a false prediction rate (FPR/h) of 0.031 and 0.049 FPR/h on the CHB-MIT dataset and the ZJU4H dataset, respectively.
Containerized Architecture Performance Analysis for IoT Framework Based on Enhanced Fire Prevention Case Study: Rwanda
Authors: Hitimana Eric , Bajpai Gaurav , Musabe Richard , Sibomana Louis , Jayavel Kayavizhi
Published: August 2022

Nowadays, building infrastructures are pushed to become smarter in response to desires for the environmental comforts of living. Enhanced safety upgrades have begun taking advantage of new, evolving technologies. Normally, buildings are configured to respond to the safety concerns of the occupants. However, advanced Internet of Things (IoT) techniques, in combination with edge computing with lightweight virtualization technology, is being used to improve users’ comfort in their homes. It improves resource management and service isolation without affecting the deployment of heterogeneous hardware. In this research, a containerized architectural framework for support of multiple concurrent deployed IoT applications for smart buildings was proposed. The prototype developed used sensor networks as well as containerized microservices, centrally featuring the DevOps paradigm. The research proposed an occupant counting algorithm used to check occupants in and out. The proposed framework was tested in different academic buildings for data acquisition over three months. Different deployment architectures were tested to ensure the best cases based on efficiency and resource utilization. The acquired data was used for prediction purposes to aid occupant prediction for safety measures as considered by policymakers.
Programming of Industrial Robots Using a Laser Tracker
Authors: Szybicki Dariusz , Obal Paweł , Kurc Krzysztof , Gierlak Piotr
Published: August 2022

This paper presents the use of a laser tracker for programming paths of industrial robots. The idea of operation, application, and the offered accuracy of the laser trackers are discussed. Then, the developed method of using a tracker for determining the path points of an industrial robot is presented. The requirements related to the developed method are indicated. The parameters of the industrial robot related to the repeatability and accuracy of the robots and the Absolute Accuracy option—increasing the accuracy—are characterized. The developed method is based on the accuracy of robots and the conversion of the position and orientation of individual coordinate systems. In the developed method, the robot is not used during programming and the programmer indicates the path points with the help of a retroreflector. The algorithm, which is the most important part of the developed method, has been implemented in the Rapid programming language to automate the programming process. This article provides an example of the use of the developed method to program the robot path during the grinding process. The advantages of the developed method are characterized and compared to existing solutions. In addition, the limitations and achieved accuracies are indicated.
A Classification Method for Electronic Components Based on Siamese Network
Authors: Cheng Yahui , Wang Aimin , Wu Long
Published: August 2022

In the field of electronics manufacturing, electronic component classification facilitates the management and recycling of the functional and valuable electronic components in electronic waste. Current electronic component classification methods are mainly based on deep learning, which requires a large number of samples to train the model. Owing to the wide variety of electronic components, collecting datasets is a time-consuming and laborious process. This study proposed a Siamese network-based classification method to solve the electronic component classification problem for a few samples. First, an improved visual geometry group 16 (VGG-16) model was proposed as the feature extraction part of the Siamese neural network to improve the recognition performance of the model under small samples. Then, a novel channel correlation loss function that allows the model to learn the correlation between different channels in the feature map was designed to further improve the generalization performance of the model. Finally, the nearest neighbor algorithm was used to complete the classification work. The experimental results show that the proposed method can achieve high classification accuracy under small sample conditions and is robust for electronic components with similar appearances. This improves the classification quality of electronic components and reduces the training sample collection cost.
An Early Warning System for Earthquake Prediction from Seismic Data Using Batch Normalized Graph Convolutional Neural Network with Attention Mechanism (BNGCNNATT)
Authors: Bilal Muhammad Atif , Ji Yanju , Wang Yongzhi , Akhter Muhammad Pervez , Yaqub Muhammad
Published: August 2022

Earthquakes threaten people, homes, and infrastructure. Early warning systems provide prior warning of oncoming significant shaking to decrease seismic risk by providing location, magnitude, and depth information of the event. Their usefulness depends on how soon a strong shake begins after the warning. In this article, the authors implement a deep learning model for predicting earthquakes. This model is based on a graph convolutional neural network with batch normalization and attention mechanism techniques that can successfully predict the depth and magnitude of an earthquake event at any number of seismic stations in any number of locations. After preprocessing the waveform data, CNN extracts the feature map. Attention mechanism is used to focus on important features. The batch normalization technique takes place in batches for stable and faster training of the model by adding an extra layer. GNN with extracted features and event location information predicts the event information accurately. We test the proposed model on two datasets from Japan and Alaska, which have different seismic dynamics. The proposed model achieves 2.8 and 4.0 RMSE values in Alaska and Japan for magnitude prediction, and 2.87 and 2.66 RMSE values for depth prediction. Low RMSE values show that the proposed model significantly outperforms the three baseline models on both datasets to provide an accurate estimation of the depth and magnitude of small, medium, and large-magnitude events.
PassTCN-PPLL: A Password Guessing Model Based on Probability Label Learning and Temporal Convolutional Neural Network
Authors: Ye Junbin , Jin Min , Gong Guoliang , Shen Rongxuan , Lu Huaxiang
Published: August 2022

The frequent incidents of password leakage have increased people’s attention and research on password security. Password guessing is an essential part of password cracking and password security research. The progression of deep learning technology provides a promising way to improve the efficiency of password guessing. However, the mainstream models proposed for password guessing, such as RNN (or other variants, such as LSTM, GRU), GAN and VAE still face some problems, such as the low efficiency and high repetition rate of the generated passwords. In this paper, we propose a password-guessing model based on the temporal convolutional neural network (PassTCN). To further improve the performance of the generated passwords, we propose a novel password probability label-learning method, which reconstructs labels based on the password probability distribution of the training set and deduplicates the training set when training. Experiments on the RockYou dataset showed that, when generating 108 passwords, the coverage rate of PassTCN with password probability label learning (PassTCN-PPLL) reached 12.6%, which is 87.2%, 72.6% and 42.9% higher than PassGAN (a password-guessing model based on GAN), VAEPass (a password-guessing model based on VAE) and FLA (a password-guessing model based on LSTM), respectively. The repetition rate of our model is 25.9%, which is 45.1%, 31.7% and 17.4% lower than that of PassGAN, VAEPass and FLA, respectively. The results confirm that our approach not only improves the coverage rate but also reduces the repetition rate.
The Role of a Polymer-Based E-Nose in the Detection of Head and Neck Cancer from Exhaled Breath
Authors: Anzivino Roberta , Sciancalepore Pasqua Irene , Dragonieri Silvano , Quaranta Vitaliano Nicola , Petrone Paolo , Petrone Domenico , Quaranta Nicola , Carpagnano Giovanna Elisiana
Published: August 2022

The aim of our study was to assess whether a polymer-based e-nose can distinguish head and neck cancer subjects from healthy controls, as well as from patients with allergic rhinitis. A total number of 45 subjects participated in this study. The first group was composed of 15 patients with histology confirmed diagnosis of head and neck cancer. The second group was made up of 15 patients with diagnoses of allergic rhinitis. The control group consisted of 15 subjects with a negative history of upper airways and/or chest symptoms. Exhaled breath was collected from all participants and sampled by a polymer-based e-nose (Cyranose 320, Sensigent, Pasadena, CA, USA). In the Principal Component Analysis plot, patients with head and neck cancer clustered distinctly from the controls as well as from patients with allergic rhinitis. Using canonical discriminant analysis, the three groups were discriminated, with a cross validated accuracy% of 75.1, p < 0.01. The area under the curve of the receiver operating characteristic curve for the discrimination between head and neck cancer patients and the other groups was 0.87. To conclude, e-nose technology has the potential for application in the diagnosis of head and neck cancer, being an easy, quick, non-invasive and cost-effective tool.
CNN–Aided Optical Fiber Distributed Acoustic Sensing for Early Detection of Red Palm Weevil: A Field Experiment
Authors: Ashry Islam , Wang Biwei , Mao Yuan , Sait Mohammed , Guo Yujian , Al-Fehaid Yousef , Al-Shawaf Abdulmoneim , Ng Tien Khee , Ooi Boon S.
Published: August 2022

Red palm weevil (RPW) is a harmful pest that destroys many date, coconut, and oil palm plantations worldwide. It is not difficult to apply curative methods to trees infested with RPW; however, the early detection of RPW remains a major challenge, especially on large farms. In a controlled environment and an outdoor farm, we report on the integration of optical fiber distributed acoustic sensing (DAS) and machine learning (ML) for the early detection of true weevil larvae less than three weeks old. Specifically, temporal and spectral data recorded with the DAS system and processed by applying a 100–800 Hz filter are used to train convolutional neural network (CNN) models, which distinguish between “infested” and “healthy” signals with a classification accuracy of ∼97%. In addition, a strict ML-based classification approach is introduced to improve the false alarm performance metric of the system by ∼20%. In a controlled environment experiment, we find that the highest infestation alarm count of infested and healthy trees to be 1131 and 22, respectively, highlighting our system’s ability to distinguish between the infested and healthy trees. On an outdoor farm, in contrast, the acoustic noise produced by wind is a major source of false alarm generation in our system. The best performance of our sensor is obtained when wind speeds are less than 9 mph. In a representative experiment, when wind speeds are less than 9 mph outdoor, the highest infestation alarm count of infested and healthy trees are recorded to be 1622 and 94, respectively.
On the Design and Implementation of the External Data Integrity Tracking and Verification System for Stream Computing System in IoT
Authors: Wang Hongyuan , Zu Baokai , Zhu Wanting , Li Yafang , Wu Jingbang
Published: August 2022

Data integrity is a prerequisite for ensuring data availability of IoT data and has received extensive attention in the field of IoT big data security. Stream computing systems are widely used in the field of IoT for real-time data acquisition and computing. However, the real-time, volatility, suddenness, and disorder of stream data make data integrity verification difficult. According to the survey, there is no mature and universal solution. To solve this issue, we constructed a data integrity verification algorithm scheme of the stream computing system (S-DIV) by utilizing homomorphic message authentication code and pseudo-random function security assumption. Furthermore, based on S-DIV, an external data integrity tracking and verification system is constructed to track and analyze the message data stream in real time. By verifying the data integrity of message during the whole life cycle, the problem of data corruption or data loss can be found in time, and error alarm and message recovery can be actively implemented. Then, we conduct the formal security analysis under the standard model and, finally, implement the S-DIV scheme in simulation environment. Experimental results show that the scheme can guarantee data integrity in an acceptable time without affecting the efficiency of the original system.
Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model
Authors: Hammad Mohamed , Chelloug Samia Allaoua , Alkanhel Reem , Prakash Allam Jaya , Muthanna Ammar , Elgendy Ibrahim A. , Pławiak Paweł
Published: August 2022

An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various heart-related conditions in patients. An automated diagnostic tool is required to detect significant episodes in long-term ECG records. It is a very challenging task for cardiologists to analyze long-term ECG records in a short time. Therefore, a computer-based diagnosis tool is required to identify crucial episodes. Myocardial infarction (MI) and conduction disorders (CDs), sometimes known as heart blocks, are medical diseases that occur when a coronary artery becomes fully or suddenly stopped or when blood flow in these arteries slows dramatically. As a result, several researchers have utilized deep learning methods for MI and CD detection. However, there are one or more of the following challenges when using deep learning algorithms: (i) struggles with real-life data, (ii) the time after the training phase also requires high processing power, (iii) they are very computationally expensive, requiring large amounts of memory and computational resources, and it is not easy to transfer them to other problems, (iv) they are hard to describe and are not completely understood (black box), and (v) most of the literature is based on the MIT-BIH or PTB databases, which do not cover most of the crucial arrhythmias. This paper proposes a new deep learning approach based on machine learning for detecting MI and CDs using large PTB-XL ECG data. First, all challenging issues of these heart signals have been considered, as the signal data are from different datasets and the data are filtered. After that, the MI and CD signals are fed to the deep learning model to extract the deep features. In addition, a new custom activation function is proposed, which has fast convergence to the regular activation functions. Later, these features are fed to an external classifier, such as a support vector machine (SVM), for detection. The efficiency of the proposed method is demonstrated by the experimental findings, which show that it improves satisfactorily with an overall accuracy of 99.20% when using a CNN for extracting the features with an SVM classifier.