bluuebunny/crossref_metadata_2025 huggingface dataset:
A Comparative Metabolomic Analysis Reveals the Nutritional and Therapeutic Potential of Grains of the Traditional Rice Variety Mappillai Samba
Authors: Rajagopalan Veera Ranjani , Manickam Sudha , Muthurajan Raveendran
Published: February 2022

Rice (Oryza sativa L.) is the staple food of the majority of the population, particularly in Asia and Africa. Enriching rice with nutritional and therapeutic contents can improve its benefits for patients with lifestyle disorders. This study aimed to profile the phytochemical contents of the therapeutically known traditional rice Mappillai Samba against white rice CBMAS 14065 using non-targeted gas chromatography–mass spectrometry (GC-MS/MS). An analysis of the data using a mass spectrometry–data independent analysis (MS-DIAL) and MetaboAnalyst identified 113 metabolites belonging to 21 different classes of metabolites. A partial least square-discriminant analysis (PLS-DA) revealed 43 variable importance in projection (VIP) metabolites. This study identified therapeutically important metabolites, including phenylpropanoids, phytosterols, flavonoids, and polyamines, in the grains of Mappillai Samba. Three significant metabolic pathways, viz., phenylpropanoid biosynthesis, ubiquinone and other terpenoid-quinone biosynthesis, and steroid biosynthesis, were responsible for the grain metabolome variation between CBMAS 14065 and Mappillai Samba. Overall, the results of this study unravelled the biochemical complexity of Mappillai Samba, paving the way for the genetic mapping of the therapeutic compound accumulation in rice and the development of similar therapeutic rice varieties through molecular breeding.
Genome-Wide Analyses of Aspartic Proteases on Potato Genome (Solanum tuberosum): Generating New Tools to Improve the Resistance of Plants to Abiotic Stress
Authors: Norero Natalia , Rey Burusco María , D’Ippólito Sebastián , Décima Oneto Cecilia , Massa Gabriela , Castellote Martín , Feingold Sergio , Guevara María
Published: February 2022

Aspartic proteases are proteolytic enzymes widely distributed in living organisms and viruses. Although they have been extensively studied in many plant species, they are poorly described in potatoes. The present study aimed to identify and characterize S. tuberosum aspartic proteases. Gene structure, chromosome and protein domain organization, phylogeny, and subcellular predicted localization were analyzed and integrated with RNAseq data from different tissues, organs, and conditions focused on abiotic stress. Sixty-two aspartic protease genes were retrieved from the potato genome, distributed in 12 chromosomes. A high number of intronless genes and segmental and tandem duplications were detected. Phylogenetic analysis revealed eight StAP groups, named from StAPI to StAPVIII, that were differentiated into typical (StAPI), nucellin-like (StAPIIIa), and atypical aspartic proteases (StAPII, StAPIIIb to StAPVIII). RNAseq data analyses showed that gene expression was consistent with the presence of cis-acting regulatory elements on StAP promoter regions related to water deficit. The study presents the first identification and characterization of 62 aspartic protease genes and proteins on the potato genome and provides the baseline material for functional gene determinations and potato breeding programs, including gene editing mediated by CRISPR.
Variation in the Primary and Secondary Metabolites, Antioxidant and Antibacterial Potentials of Tomatoes, Grown in Soil Blended with Different Concentration of Fly Ash
Authors: Dominic Sajid , Hussain Abdullah Ijaz , Saleem Muhammad Hamzah , Alshaya Huda , Jan Basit Latief , Ali Shafaqat , Wang Xiukang
Published: February 2022

The aim of the study was to investigate the variation in nutritional composition, antioxidant, and antibacterial activities of tomatoes grown in fly ash blended soils, i.e.,T1 (soil 100% + 0% fly ash), T2 (soil 75%+ 25% fly ash), and T3 (soil 50% + 50% fly ash) soils. The tomato samples were harvested and screened for their proximate analysis, mineral composition, vitamin C contents, phenolic profile, and antioxidant and antibacterial activities. Results showed that T3 soil crop has high proximate composition, vitamin C, and phenolic contents as compared with T2 and T1 soil crops. Furthermore, significant variation in the elements analysis was observed in the crops grown in T1, T2, and T3 soils. T3 soil crop exhibited better antioxidant activity in term of total phenolic contents (TPC), total flavonoids contents (TFC), DPPH radical scavenging capacity, and ferric reducing potential as compared with T2 and T1 soil crops. Similarly, maximum inhibition zones were observed for T3 soil crop when tested for Staphylococcus aureus isolate 1 and 3 and methicillin-resistant Staphylococcus aureus (MRSA). In conclusion, the crop grown in T3 blended soil had high antioxidant and antibacterial potentials and better nutritional composition.
The Fruit Proteome Response to the Ripening Stages in Three Tomato Genotypes
Authors: Choi Hyo-Gil , Park Dong-Young , Kang Nam-Jun
Published: February 2022

The tomato is a horticultural crop that appears in various colors as it ripens. Differences in the proteome expression abundance of a tomato depend on its genotype and ripening stage. Thus, this study aimed to confirm the differences in changes in the proteome according to four ripening stages (green, breaker, turning, and mature) of three tomato genotypes, i.e., yellow, black, and red tomatoes, using a gel-based proteomic technique. The number of protein spots shown as two-dimensional electrophoresis (2-DE) gels differed according to tomato genotype and ripening stage. A total of 286 variant proteins were determined using matrix-assisted laser desorption-time of flight (MALDI-TOF) mass spectrometry (MS) analysis, confirming 233 identified protein functions. In three tomato genotypes in each ripening stage, grouping according to the Munich Information Center for Protein Sequences (MIPS) functional categories confirmed the variant proteins involved in the following: energy processes (21%); metabolism (20%); protein fate (15%); protein synthesis (10%); a protein with a binding function or cofactor requirement (8%); cell rescue, defense, and virulence (8%); cellular transport, transport facilitation, and transport routes (6%); the biogenesis of cellular components (5%); cell cycle and DNA processing (2%); others (5%). Among the identified protein spots in the function category, two proteins related to metabolism, four related to energy, four related to protein synthesis, and two related to interaction with the cellular environment showed significantly different changes according to the fruit color by the ripening stage. This study reveals the physiological changes in different types of tomatoes according to their ripening stage and provides information on the proteome for further improvement.
Single-Phase Flow Model of a Screw Reactor for Decontamination of Radioactive Graphite Waste Using Surface Gasification
Authors: Yang In-Hwan
Published: February 2022

A screw reactor is a promising apparatus for decontaminating radioactive graphite waste by uniform gasification under ambient air. However, developing the design equation for a screw reactor is difficult due to the reactor’s fundamentally intricate gas and solid interactions. In this study, we performed three-dimensional computational fluid dynamics simulations to predict and characterize the graphite particles that flow through the screw reactor and are thermally gasified. This was done using the Eulerian single-fluid approach coupled with the experimentally established kinetic model for graphite gasification. The numerical results show that the counter-rotating flow, generated along the rotating screw of the reactor by the relative motion of the reactor wall to the rotating screw, mixes particles spatially and reduces their axial velocity. The diameter of the feed graphite particles can be reduced by as much as 28% depending on the screw rotating velocity and the temperature of the reactor shell, according to the conducted numerical calculations. These numerical simulations can be used to provide proper operating parameters for the laboratory-scale screw reactor by which to decontaminate radioactive graphite waste by gasifying the radiocarbons, together with a part of the graphite matrix, on the surface of the graphite particles.
HPW/PAM Catalyst for Oxidative Desulfurization-Synthesis, Characterization and Mechanism Study
Authors: Zheng Ziyang , Wu Qian , Zhong Shengyang
Published: February 2022

In this work, polyacrylamide (PAM) was first used in the loading of heteropoly acids, and then the HPM/PAM-n catalyst was synthesized by simple reaction. The FTIR and SEM measurements showed that the HPM/PAM-n (n = 10,000, 20,000, 30,000) was successfully synthesized. In addition, the HPM/PAM-n effect on desulfurization was measured, which showed the optimal desulfurization efficiency. The optimal process condition for HPM/PAM-10000 desulfurization was optimized by a single-factor experiment. The optimal condition was as follows: The temperature was 60 °C, the amount of the catalyst was 0.2 g, the oxygen to sulfur ratio was 16, and the reaction time is 100 min. The catalyst was suitable for recycled use, and the desulfurization efficiency was high after 10 times. In the end, the oxidative desulfurization mechanism was put forward.
Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins
Authors: Novák Petr , Vyskočil Jiří
Published: February 2022

Smart production systems conforming the Industry 4.0 vision are based on subsystems that are integrated in a way that supports high flexibility and re-configurability. Specific components and devices, such as industrial and mobile robots or transport systems, now pose full-blown systems, and the entire Industry 4.0 production system constitutes a system-of-systems. Testing, fine-tuning, and production planning are important tasks in the entire engineering production system life-cycle. All these steps can be significantly supported and improved by digital twins, which are digitalized replicas of physical systems that are synchronized with the real systems at runtime. However, the design and implementation of digital twins for such integrated, yet partly stand-alone, industrial sub-systems can represent challenging and significantly time-consuming engineering tasks. In this article, the problem of the digital twin design for discrete-event production systems is addressed. The article also proposes to utilize a formal description of production resources and related production operations that the resources can perform. An executable version of such formalization can be automatically derived into a form of a digital twin. Such a derived digital twin can be enhanced with operation duration times that are obtained with process mining methods, leading to more realistic simulations for the entire production system. The proposed solution was successfully tested and validated in the Industry 4.0 Testbed, equipped with four robots and a transport system, which is utilized as a use-case in this article.
Special Issue on “Industrial Chemistry Reactions: Kinetics, Mass Transfer and Industrial Reactor Design”
Authors: Santacesaria Elio , Tesser Riccardo , Russo Vincenzo
Published: February 2022

The impressive developments in commercially available technologies, in terms of new equipment and faster computers, allow us to solve ever-more complicated chemical and technical issues within industrial chemistry and reaction engineering fields [...]
Research and Modelling the Ability of Waste from Water and Wastewater Treatment to Remove Phosphates from Water
Authors: Šarko Julita , Leonavičienė Teresė , Mažeikienė Aušra
Published: February 2022

This research investigated the ability of two materials, which are waste generated during water treatment and wastewater treatment, to remove phosphates from water. The selected materials were quartz sand used in drinking water treatment plants (OQS) and incinerated (600 °C) sewage sludge (ISS). The materials were chosen for their composition: both contain aluminium, iron, and calcium. The experiments were carried out in the laboratory (in batch and in columns stand). Modelling of the sorption processes was performed on the basis of results from experiments in batches. The maximum adsorption capacity of the OQS was 1.14 mg/g obtained using the linearized Langmuir model and the maximum adsorption capacity of the ISS was 0.86 mg/g for the linearized Langmuir model (in batch). A pseudo-first-order model obtained using a nonlinear fit can accurately explain phosphate adsorption kinetics using both adsorbents: OQS and ISS. During the column filtration experiment, a higher sorption capacity of the ISS filter media was achieved −2.1 mg of phosphate phosphorus per gram of filter media. The determined adsorption capacity of the investigated materials was average, but the reuse of this waste would help to solve the issues of the circular economy.
Robotic Mapping Approach under Illumination-Variant Environments at Planetary Construction Sites
Authors: Hong Sungchul , Shyam Pranjay , Bangunharcana Antyanta , Shin Hyuseoung
Published: February 2022

In planetary construction, the semiautonomous teleoperation of robots is expected to perform complex tasks for site preparation and infrastructure emplacement. A highly detailed 3D map is essential for construction planning and management. However, the planetary surface imposes mapping restrictions due to rugged and homogeneous terrains. Additionally, changes in illumination conditions cause the mapping result (or 3D point-cloud map) to have inconsistent color properties that hamper the understanding of the topographic properties of a worksite. Therefore, this paper proposes a robotic construction mapping approach robust to illumination-variant environments. The proposed approach leverages a deep learning-based low-light image enhancement (LLIE) method to improve the mapping capabilities of the visual simultaneous localization and mapping (SLAM)-based robotic mapping method. In the experiment, the robotic mapping system in the emulated planetary worksite collected terrain images during the daytime from noon to late afternoon. Two sets of point-cloud maps, which were created from original and enhanced terrain images, were examined for comparison purposes. The experiment results showed that the LLIE method in the robotic mapping method significantly enhanced the brightness, preserving the inherent colors of the original terrain images. The visibility and the overall accuracy of the point-cloud map were consequently increased.
A Quality Control Methodology for Heterogeneous Vehicular Data Streams
Authors: Remoundou Konstantina , Alexakis Theodoros , Peppes Nikolaos , Demestichas Konstantinos , Adamopoulou Evgenia
Published: February 2022

The rapid evolution of sensors and communication technologies has led to the production and transfer of mass data streams from vehicles either inside their electronic units or to the outside world using the internet infrastructure. The “outside world”, in most cases, consists of third-party applications, such as fleet or traffic management control centers, which utilize vehicular data for reporting and monitoring functionalities. Such applications, in most cases, in order to facilitate their needs, require the exchange and processing of vast amounts of data which can be handled by the so-called Big Data technologies. The purpose of this study is to present a hybrid platform suitable for data collection, storing and analysis enhanced with quality control actions. In particular, the collected data contain various formats originating from different vehicle sensors and are stored in the aforementioned platform in a continuous way. The stored data in this platform must be checked in order to determine and validate them in terms of quality. To do so, certain actions, such as missing values checks, format checks, range checks, etc., must be carried out. The results of the quality control functions are presented herein, and useful conclusions are drawn in order to avoid possible data quality problems which may occur in further analysis and use of the data, e.g., for training of artificial intelligence models.
Sharing Studies between 5G IoT Networks and Fixed Service in the 6425–7125 MHz Band with Monte Carlo Simulation Analysis
Authors: Pastukh Alexander , Tikhvinskiy Valery , Devyatkin Evgeny , Kulakayeva Aigul
Published: February 2022

This work presents sharing studies between 5G networks and point-to-point fixed service in the 6425–7125 MHz band. In this research, we provide simulations of interference from 5G downlink and uplink to fixed service in the frequency band 6425–7125 MHz. We evaluated several scenarios of interference, which include cross-border scenarios, as well as scenarios of interference within the borders of one administration. The obtained results of this work are presented as protection distance and frequency offsets that are required in order to achieve compatibility between 5G and FS in the 6425–7125 MHz band. The spectrum engineering techniques presented in this research can help different companies and regulatory administrations in their spectrum management and frequency regulation activities and seriously improve the efficiency of implementation for 5G technologies.
A Machine Vision—Based Pipe Leakage Detection System for Automated Power Plant Maintenance
Authors: Bao Nengsheng , Fan Yuchen , Ye Zihao , Simeone Alessandro
Published: February 2022

Industrial pipework maintenance inspection can be automated through machine vision-based effusion monitoring. However, colorless effusions such as water can be difficult to detect in a complex industrial environment due to weak illumination and poor visibility of the background. This paper deploys the reflective characteristics of effusion and its lower temperature compared to the environment in order to develop an automatic inspection system for power plant pipeworks’ maintenance. Such a system is aimed at detecting the colorless fluid effusion based on dual source images and a contour features algorithm. In this respect, a visible light source unit highlights the reflective features of the effusion edge. Meanwhile, high-definition images of the potential effusion are acquired under both visible and infrared lights. A customized image processing procedure extracts the potential effusion features from the infrared image to retrieve the region of interest for segmentation purposes and transfer such information to the visible light image to determine the effusion contour. Finally, a decision-making support tool based on the image contour closure is enabled for classification purposes. The implementation of the proposed system is tested on a real industrial environment. Experimental results show a classification accuracy up to 99%, demonstrating excellent suitability in meeting industrial requirements.
GAN-Based Image Colorization for Self-Supervised Visual Feature Learning
Authors: Treneska Sandra , Zdravevski Eftim , Pires Ivan Miguel , Lameski Petre , Gievska Sonja
Published: February 2022

Large-scale labeled datasets are generally necessary for successfully training a deep neural network in the computer vision domain. In order to avoid the costly and tedious work of manually annotating image datasets, self-supervised learning methods have been proposed to learn general visual features automatically. In this paper, we first focus on image colorization with generative adversarial networks (GANs) because of their ability to generate the most realistic colorization results. Then, via transfer learning, we use this as a proxy task for visual understanding. Particularly, we propose to use conditional GANs (cGANs) for image colorization and transfer the gained knowledge to two other downstream tasks, namely, multilabel image classification and semantic segmentation. This is the first time that GANs have been used for self-supervised feature learning through image colorization. Through extensive experiments with the COCO and Pascal datasets, we show an increase of 5% for the classification task and 2.5% for the segmentation task. This demonstrates that image colorization with conditional GANs can boost other downstream tasks’ performance without the need for manual annotation.
Discussion of the Influence of Multiscale PCA Denoising Methods with Three Different Features
Authors: Zhang Chizhou , Sun Tao
Published: February 2022

Bioinformation is information generated from biological movement. By using a variety of modern technologies, we can use this information to form a meaningful model for researchers to study. An electromyographic (EMG) signal is one type of bioinformation that is used in many areas to help people study human muscle movement. This information can help in both clinical areas and industrial areas. EMG is a very complicated signal, so processing it is vital. The processing of EMG signals is divided into collection, denoising, decomposition, feature extraction and classification steps. In this article, the wavelet denoising step and several decomposition processes are discussed to show the usage of this technique in the final classification step. At the end of the study, we find that after the wavelet denoising step, the classification accuracy, which uses the K-nearest neighbor of the independent component analysis features, improves, but the accuracy of the wavelet coefficient features and autoregression coefficient features decreases.
Using Acoustic Vibrations as a Method for Implant Insertion Assessment in Total Hip Arthroplasty
Authors: Wei Jonathan C. J. , Crezee Willem H. A. , Jongeneel Hilda , De Haas Tobias S. A. , Kool Wesley L. A. , Blaauw Bryan J. , Dankelman Jenny , Horeman Tim
Published: February 2022

The success of total hip arthroplasty depends on the experience of the surgeon, and one of the ways the surgeon currently determines the final implant insertion depth is to listen to the change in audible pitch of the hammering sound. We investigated the use of vibration emissions as a novel method for insertion quality assessment. A non-invasive contact microphone-based measurement system for insertion depth estimation, fixation and fracture detection was developed using a simplified in vitro bone/implant (n = 5). A total of 2583 audio recordings were analyzed in vitro to obtain energy spectral density functions. Out of the four main resonant peaks under in vitro conditions, broach insertion depth statistically correlates to increasing 3rd and 4th peak frequencies. Degree of fixation was also observed as higher goodness of fit (0.26–0.78 vs. 0.12–0.51 between two broach sizes, the latter undersized). Finally, however, the moment of fracture could not be predicted. A cadaveric in situ pilot study suggests comparable resonant frequencies in the same order of magnitudes with the bone model. Further understanding of the signal patterns are needed for an early warning system diagnostic system for imminent fractures, bone damage, improving accuracy and quality of future procedures.
On the Design of a Thermo-Magnetically Activated Piezoelectric Micro-Energy Generator: Working Principle
Authors: Rendon-Hernandez Adrian A. , Basrour Skandar
Published: February 2022

This paper deals with a new design of a thermo-magnetically activated piezoelectric generator. This proposed generator exploits the temperature-dependent magnetization of a ferromagnetic material, which is exposed to temporary change of temperature cycles. To promote a better understanding of the operation of this mechanism, a global coupled numerical model is presented, which is able to predict the static and dynamic behavior of the generator. It is shown that with some modifications to the physical design, the generator can be tuned for different activation temperatures. Energy densities of 280 and 67 µJcm−3 were achieved by the proposed model of the generator for its opening and closing commutation, respectively.
Magnitude and Shape of the Forces Applied on the Foot Rest and Paddle by Elite Kayakers
Authors: Bonito Pedro , Sousa Miguel , Ferreira Fernando José , Justo Jorge Fonseca , Gomes Beatriz Branquinho
Published: February 2022

The study aimed to investigate the magnitude and shape of the forces applied on the foot rest, foot strap, and paddle. Thirteen elite male kayakers participated in this study and performed a 2-min test simulating 500 m race pace in a kayak ergometer. Forces applied by the kayakers on the paddle, foot rest, and foot strap were measured with load cells and recorded by an electronic measuring system. The magnitude of the peak forces applied on the foot rest (left: 543.27 ± 85.93; right: 524.39 ± 88.36) approximately doubled the ones applied on the paddle (left: 236.37 ± 19.32; right: 243.92 ± 28.89). The forces on the foot strap were similar in magnitude to the paddle forces (left: 240.09 ± 74.92; right: 231.05 ± 52.01). A positive correlation was found between the peak forces applied on the foot rest and paddle on the same side (p < 0.001). When comparing the best and worst kayakers’ performance, the best showed greater forces magnitudes and synchronization of the peak forces. Analyses of the force–time curves, including not only the forces applied by the kayaker on the paddle but also the ones applied on the foot rest and strap, should be considered relevant in terms of technique analyses.
The Investigation of Adoption of Voice-User Interface (VUI) in Smart Home Systems among Chinese Older Adults
Authors: Song Yao , Yang Yanpu , Cheng Peiyao
Published: February 2022

Driven by advanced voice interaction technology, the voice-user interface (VUI) has gained popularity in recent years. VUI has been integrated into various devices in the context of the smart home system. In comparison with traditional interaction methods, VUI provides multiple benefits. VUI allows for hands-free and eyes-free interaction. It also enables users to perform multiple tasks while interacting. Moreover, as VUI is highly similar to a natural conversation in daily lives, it is intuitive to learn. The advantages provided by VUI are particularly beneficial to older adults, who suffer from decreases in physical and cognitive abilities, which hinder their interaction with electronic devices through traditional methods. However, the factors that influence older adults’ adoption of VUI remain unknown. This study addresses this research gap by proposing a conceptual model. On the basis of the technology adoption model (TAM) and the senior technology adoption model (STAM), this study considers the characteristic of VUI and the characteristic of older adults through incorporating the construct of trust and aging-related characteristics (i.e., perceived physical conditions, mobile self-efficacy, technology anxiety, self-actualization). A survey was designed and conducted. A total of 420 Chinese older adults participated in this survey, and they were current or potential users of VUI. Through structural equation modeling, data were analyzed. Results showed a good fit with the proposed conceptual model. Path analysis revealed that three factors determine Chinese older adults’ adoption of VUI: perceived usefulness, perceived ease of use, and trust. Aging-related characteristics also influence older adults’ adoption of VUI, but they are mediated by perceived usefulness, perceived ease of use, and trust. Specifically, mobile self-efficacy is demonstrated to positively influence trust and perceived ease of use but negatively influence perceived usefulness. Self-actualization exhibits positive influences on perceived usefulness and perceived ease of use. Technology anxiety only exerts influence on perceived ease of use in a marginal way. No significant influences of perceived physical conditions were found. This study extends the TAM and STAM by incorporating additional variables to explain Chinese older adults’ adoption of VUI. These results also provide valuable implications for developing suitable VUI for older adults as well as planning actionable communication strategies for promoting VUI among Chinese older adults.
Vegetable Size Measurement Based on Stereo Camera and Keypoints Detection
Authors: Zheng Bowen , Sun Guiling , Meng Zhaonan , Nan Ruili
Published: February 2022

This work focuses on the problem of non-contact measurement for vegetables in agricultural automation. The application of computer vision in assisted agricultural production significantly improves work efficiency due to the rapid development of information technology and artificial intelligence. Based on object detection and stereo cameras, this paper proposes an intelligent method for vegetable recognition and size estimation. The method obtains colorful images and depth maps with a binocular stereo camera. Then detection networks classify four kinds of common vegetables (cucumber, eggplant, tomato and pepper) and locate six points for each object. Finally, the size of vegetables is calculated using the pixel position and depth of keypoints. Experimental results show that the proposed method can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length. The work provides an innovative idea for solving the vegetable’s non-contact measurement problems and can promote the application of computer vision in agricultural automation.
An Architecture for Service Integration to Fully Support Novel Personalized Smart Tourism Offerings
Authors: Sabbioni Andrea , Villano Thomas , Corradi Antonio
Published: February 2022

The continuous evolution of IT (information technology) technologies is radically transforming many technical areas and social aspects, also reshaping the way we behave and looking for entertainment and leisure services. In that context, tourism experiences request to enhance the level of user involvement and integration and to create an ever more personalized and connected experience, by leveraging on the differentiated tourist services and information locally present in the territory, by pushing active participation of customers, and by taking advantage of the ever-increasing presence of sensors and IoT (Internet of Things) devices deployed in many realities. However, the deep fragmentation of services and technologies adopted in tourism context characterizes the whole information provided also by customer sensing and IoTs (Internet of Things) heterogeneity and deep clashes with an effective organization of smart tourism. This article presents APERTO5.0 (an Architecture for Personalization and Elaboration of services and data to Reshape Tourism Offers 5.0), an innovative architecture aiming at a whole integration and deep facilitation of tourism service and information organization and blending, to enable the re-provisioning of novel services as advanced aggregates or re-elaborated ones. The proposed solution will demonstrate its effectiveness in the context of Smart Tourism by choosing the real use case of the “Francigena way” (a pilgrim historical path), the Italian part.
Microfluidic Point-of-Care (POC) Devices in Early Diagnosis: A Review of Opportunities and Challenges
Authors: Yang Shih-Mo , Lv Shuangsong , Zhang Wenjun , Cui Yubao
Published: February 2022

The early diagnosis of infectious diseases is critical because it can greatly increase recovery rates and prevent the spread of diseases such as COVID-19; however, in many areas with insufficient medical facilities, the timely detection of diseases is challenging. Conventional medical testing methods require specialized laboratory equipment and well-trained operators, limiting the applicability of these tests. Microfluidic point-of-care (POC) equipment can rapidly detect diseases at low cost. This technology could be used to detect diseases in underdeveloped areas to reduce the effects of disease and improve quality of life in these areas. This review details microfluidic POC equipment and its applications. First, the concept of microfluidic POC devices is discussed. We then describe applications of microfluidic POC devices for infectious diseases, cardiovascular diseases, tumors (cancer), and chronic diseases, and discuss the future incorporation of microfluidic POC devices into applications such as wearable devices and telemedicine. Finally, the review concludes by analyzing the present state of the microfluidic field, and suggestions are made. This review is intended to call attention to the status of disease treatment in underdeveloped areas and to encourage the researchers of microfluidics to develop standards for these devices.
Hyperspectral Image Labeling and Classification Using an Ensemble Semi-Supervised Machine Learning Approach
Authors: Manian Vidya , Alfaro-Mejía Estefanía , Tokars Roger P.
Published: February 2022

Hyperspectral remote sensing has tremendous potential for monitoring land cover and water bodies from the rich spatial and spectral information contained in the images. It is a time and resource consuming task to obtain groundtruth data for these images by field sampling. A semi-supervised method for labeling and classification of hyperspectral images is presented. The unsupervised stage consists of image enhancement by feature extraction, followed by clustering for labeling and generating the groundtruth image. The supervised stage for classification consists of a preprocessing stage involving normalization, computation of principal components, and feature extraction. An ensemble of machine learning models takes the extracted features and groundtruth data from the unsupervised stage as input and a decision block then combines the output of the machines to label the image based on majority voting. The ensemble of machine learning methods includes support vector machines, gradient boosting, Gaussian classifier, and linear perceptron. Overall, the gradient boosting method gives the best performance for supervised classification of hyperspectral images. The presented ensemble method is useful for generating labeled data for hyperspectral images that do not have groundtruth information. It gives an overall accuracy of 93.74% for the Jasper hyperspectral image, 100% accuracy for the HSI2 Lake Erie images, and 99.92% for the classification of cyanobacteria or harmful algal blooms and surface scum. The method distinguishes well between blue green algae and surface scum. The full pipeline ensemble method for classifying Lake Erie images in a cloud server runs 24 times faster than a workstation.
Deep Reinforcement Learning Based Optical and Acoustic Dual Channel Multiple Access in Heterogeneous Underwater Sensor Networks
Authors: Liu Enhong , He Rongxi , Chen Xiaojing , Yu Cunqian
Published: February 2022

In this paper, we investigate how to efficiently utilize channel bandwidth in heterogeneous hybrid optical and acoustic underwater sensor networks, where sensor nodes adopt different Media Access Control (MAC) protocols to transmit data packets to a common relay node on optical or acoustic channels. We propose a new MAC protocol based on deep reinforcement learning (DRL), referred to as optical and acoustic dual-channel deep-reinforcement learning multiple access (OA-DLMA), in which the sensor nodes utilizing the OA-DLMA protocol are called agents, and the remainder are non-agents. The agents can learn the transmission patterns of coexisting non-agents and find an optimal channel access strategy without any prior information. Moreover, in order to further enhance network performance, we develop a differentiated reward policy that rewards specific actions over optical and acoustic channels differently, with priority compensation being given to the optical channel to achieve greater data transmission. Furthermore, we have derived the optimal short-term sum throughput and channel utilization analytically and conducted extensive simulations to evaluate the OA-DLMA protocol. Simulation results show that our protocol performs with near-optimal performance and significantly outperforms other existing protocols in terms of short-term sum throughput and channel utilization.
Experimental Evaluation of IEEE 802.15.4z UWB Ranging Performance under Interference
Authors: Tiemann Janis , Friedrich Johannes , Wietfeld Christian
Published: February 2022

The rise of precise wireless localization for industrial and consumer use is continuing to challenge a significant amount of research. Recently the new ultra-wideband standard IEEE 802.15.4z was released to increase both the robustness and security of the underlying message exchanges. Due to the lack of accessible transceivers, most of the current research on this is of theoretical nature though. This work provides the first experimental evaluation of the ranging performance in realistic environments and also assesses the robustness to different sources of interference. To evaluate the individual aspects, a set of three different experiments are conducted. One experiment with realistic movement and two additional with targeted interference. It could be shown that the cryptographic additions of the new standard can provide sufficient information to improve the reliability of the ranging results under multi-user interference significantly.
Automatic Emotion Recognition in Children with Autism: A Systematic Literature Review
Authors: Landowska Agnieszka , Karpus Aleksandra , Zawadzka Teresa , Robins Ben , Erol Barkana Duygun , Kose Hatice , Zorcec Tatjana , Cummins Nicholas
Published: February 2022

The automatic emotion recognition domain brings new methods and technologies that might be used to enhance therapy of children with autism. The paper aims at the exploration of methods and tools used to recognize emotions in children. It presents a literature review study that was performed using a systematic approach and PRISMA methodology for reporting quantitative and qualitative results. Diverse observation channels and modalities are used in the analyzed studies, including facial expressions, prosody of speech, and physiological signals. Regarding representation models, the basic emotions are the most frequently recognized, especially happiness, fear, and sadness. Both single-channel and multichannel approaches are applied, with a preference for the first one. For multimodal recognition, early fusion was the most frequently applied. SVM and neural networks were the most popular for building classifiers. Qualitative analysis revealed important clues on participant group construction and the most common combinations of modalities and methods. All channels are reported to be prone to some disturbance, and as a result, information on a specific symptoms of emotions might be temporarily or permanently unavailable. The challenges of proper stimuli, labelling methods, and the creation of open datasets were also identified.
Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle
Authors: Lee Jae-In , Kim Nammon , Min Sawon , Kim Jeongwoo , Jeong Dae-Kyo , Seo Dong-Wook
Published: February 2022

Classifying space targets from debris is critical for radar resource management as well as rapid response during the mid-course phase of space target flight. Due to advances in deep learning techniques, various approaches have been studied to classify space targets by using micro-Doppler signatures. Previous studies have only used micro-Doppler signatures such as spectrogram and cadence velocity diagram (CVD), but in this paper, we propose a method to generate micro-Doppler signatures taking into account the relative incident angle that a radar can obtain during the target tracking process. The AlexNet and ResNet-18 networks, which are representative convolutional neural network architectures, are transfer-learned using two types of datasets constructed using the proposed and conventional signatures to classify six classes of space targets and a debris–cone, rounded cone, cone with empennages, cylinder, curved plate, and square plate. Among the proposed signatures, the spectrogram had lower classification accuracy than the conventional spectrogram, but the classification accuracy increased from 88.97% to 92.11% for CVD. Furthermore, when recalculated not with six classes but simply with only two classes of precessing space targets and tumbling debris, the proposed spectrogram and CVD show the classification accuracy of over 99.82% for both AlexNet and ResNet-18. Specially, for two classes, CVD provided results with higher accuracy than the spectrogram.
Photogrammetric Co-Processing of Thermal Infrared Images and RGB Images
Authors: Dlesk Adam , Vach Karel , Pavelka Karel
Published: February 2022

In some applications of thermography, spatial orientation of the thermal infrared information can be desirable. By the photogrammetric processing of thermal infrared (TIR) images, it is possible to create 2D and 3D results augmented by thermal infrared information. On the augmented 2D and 3D results, it is possible to locate thermal occurrences in the coordinate system and to determine their scale, length, area or volume. However, photogrammetric processing of TIR images is difficult due to negative factors which are caused by the natural character of TIR images. Among the negative factors are the lower resolution of TIR images compared to RGB images and lack of visible features on the TIR images. To eliminate these negative factors, two methods of photogrammetric co-processing of TIR and RGB images were designed. Both methods require a fixed system of TIR and RGB cameras and for each TIR image a corresponding RGB image must be captured. One of the methods was termed sharpening and the result of this method is mainly an augmented orthophoto, and an augmented texture of the 3D model. The second method was termed reprojection and the result of this method is a point cloud augmented by thermal infrared information. The details of the designed methods, as well as the experiments related to the methods, are presented in this article.
Special Issue: Analysis of the Main Classes of Lipid (Fat and Oil) Components in Food and Blood by Using HPLC and Gas Chromatographic Techniques
Authors: Naviglio Daniele
Published: February 2022

In many cases in science, many discoveries are made by pure chance, as happened for example to Alexander Fleming (Darvel (Scotland) 6 August 1881–London (England) 11 March 1955) who, while observing slides under a microscope in 1922, a few weeks after putting his nasal mucus on a Petri dish, noticed that cultures of microbes had developed all over the plate, except for his secretion [...]
Women Construction Workers in Bangladesh: Health, Wellbeing, and Domestic Abuse during the COVID-19 Pandemic
Authors: Clisby Suzanne Marie , Choudhury Tanzina
Published: February 2022

This article draws on in-depth research conducted during the first two years of the COVID-19 pandemic with a group of 35 women who work as construction labourers in Sylhet, northern Bangladesh. We particularly focus on these women’s narratives of economic crisis, domestic abuse, coercive control and intimate relations during the pandemic. Here, we consider the ways in which the COVID-19 pandemic between 2020 and 2021 particularly affected this group of women participants as they employed survival strategies to support their families through a time of extreme economic and social crisis. A key issue they raised was the negative impact the pandemic has had on their health and wellbeing, particularly exacerbated by an increase in experiences of intimate partner violence (IPV), more commonly termed domestic abuse or domestic violence in the local context. The violence they faced was not necessarily a new experience for many of these women, but it was intensified by pressures brought to bear on interpersonal relations within their household as a result of lack of access to incomes, rising levels of poverty, and the stresses placed on families trying to survive in a time of extreme socio-economic and health insecurity.
Temperature-Dependent Raman Studies of FAPbBr3 and MAPbBr3 Perovskites: Effect of Phase Transitions on Molecular Dynamics and Lattice Distortion
Authors: Mączka Mirosław , Ptak Maciej
Published: February 2022

Three-dimensional hybrid organic–inorganic lead halide perovskites are promising photovoltaic and light-emitting materials. A key phenomenon relevant for their optoelectronic applications is electron–phonon coupling. Since it can be strongly modified by structural deformation and changes in the dynamics of molecular cations, it is of great importance to study the temperature dependence of phonon properties of hybrid perovskites. In this work, temperature-dependent Raman scattering studies of FAPbBr3 and MAPbBr3 single crystals are reported in the 1800–22 cm−1 and 300–90 K ranges. The Raman data of MAPbBr3 showed clear anomalies near 236, 155 and 148 K, which were attributed to Pm3¯m→I4/mcm→P4/mmm (or Imma)→Pnma phase transitions. They also provided strong evidence that crystal structure of the phase stable in the 155–148 K range is very similar to structure of the I4/mcm phase, not structure of the lowest-temperature Pmna phase, as suggested in some reports. Therefore, the symmetry of this phase seems to be more likely P4/mmm rather than Imma. An analysis of the temperature evolution of MAPbBr3 Raman modes revealed that the phase transitions near 236 and 155 K are associated with weak distortion of the inorganic subnetwork and changes in the dynamics of MA+ ions. Very pronounced changes in the lattice modes region and a narrowing of bands below 148 K indicated that the phase transition to the Pnma phase is triggered by a freezing of MA+ motions, which in turn leads to strong distortion of the inorganic subnetwork. Raman studies of FAPbBr3 showed that this material behaves in a very different way than MAPbBr3. First of all, the molecular dynamics of FA+ cations are not frozen even in the lowest-temperature Pnma phase. Moreover, the distortion of the inorganic subnetwork is small in the Pnma phase. The observation of weak anomalies in the lattice modes region confirmed, however, that the two crystallographically resolved phase transitions (Pm3¯m→P4/mbm near 260 K and P4/mbm→Pnma near 150 K) lead to weak distortion of the inorganic subnetwork. On the other hand, an analysis of FA+ internal modes indicated that these transitions, as well as two other crystallographically unresolved transitions near 120 and 180 K, are triggered by a change of FA+ dynamics.
Bootstrap Prediction Intervals of Temporal Disaggregation
Authors: Lee Bu Hyoung
Published: February 2022

In this article, we propose an interval estimation method to trace an unknown disaggregate series within certain bandwidths. First, we consider two model-based disaggregation methods called the GLS disaggregation and the ARIMA disaggregation. Then, we develop iterative steps to construct AR-sieve bootstrap prediction intervals for model-based temporal disaggregation. As an illustration, we analyze the quarterly total balances of U.S. international trade in goods and services between the first quarter of 1992 and the fourth quarter of 2020.