bluuebunny/crossref_metadata_2025 huggingface dataset:
Resveratrol Enhances Inhibition Effects of Cisplatin on Cell Migration and Invasion and Tumor Growth in Breast Cancer MDA-MB-231 Cell Models In Vivo and In Vitro
Authors: Yang Meng-Die , Sun Yang , Zhou Wen-Jun , Xie Xiao-Zheng , Zhou Qian-Mei , Lu Yi-Yu , Su Shi-Bing
Published: April 2021

Triple-negative breast cancer (TNBC) is a refractory type of breast cancer that does not yet have clinically effective drugs. The aim of this study is to investigate the synergistic effects and mechanisms of resveratrol combined with cisplatin on human breast cancer MDA-MB-231 (MDA231) cell viability, migration, and invasion in vivo and in vitro. In vitro, MTS assays showed that resveratrol combined with cisplatin inhibits cell viability as a concentration-dependent manner, and produced synergistic effects (CI < 1). Transwell assay showed that the combined treatment inhibits TGF-β1-induced cell migration and invasion. Immunofluorescence assays confirmed that resveratrol upregulated E-cadherin expression and downregulated vimentin expression. Western blot assay demonstrated that resveratrol combined with cisplatin significantly reduced the expression of fibronectin, vimentin, P-AKT, P-PI3K, P-JNK, P-ERK, Sma2, and Smad3 induced by TGF-β1 (p < 0.05), and increased the expression of E-cadherin (p < 0.05), respectively. In vivo, resveratrol enhanced tumor growth inhibition and reduced body weight loss and kidney function impairment by cisplatin in MDA231 xenografts, and significantly reduced the expressions of P-AKT, P-PI3K, Smad2, Smad3, P-JNK, P-ERK, and NF-κB in tumor tissues (p < 0.05). These results indicated that resveratrol combined with cisplatin inhibits the viability of breast cancer MDA231 cells synergistically, and inhibits MDA231 cells invasion and migration through Epithelial-mesenchymal transition (EMT) approach, and resveratrol enhanced anti-tumor effect and reduced side of cisplatin in MDA231 xenografts. The mechanism may be involved in the regulations of PI3K/AKT, JNK, ERK and NF-κB expressions.
Efficacy of Phytochemicals Derived from Avicennia officinalis for the Management of COVID-19: A Combined In Silico and Biochemical Study
Authors: Mahmud Shafi , Paul Gobindo Kumar , Afroze Mirola , Islam Shirmin , Gupt Swagota Briti Ray , Razu Mamudul Hasan , Biswas Suvro , Zaman Shahriar , Uddin Md. Salah , Khan Mala , Cacciola Nunzio Antonio , Emran Talha Bin , Saleh Md. Abu , Capasso Raffaele , Simal-Gandara Jesus
Published: April 2021

The recent coronavirus disease 2019 (COVID-19) pandemic is a global threat for healthcare management and the economic system, and effective treatments against the pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus responsible for this disease have not yet progressed beyond the developmental phases. As drug refinement and vaccine progression require enormously broad investments of time, alternative strategies are urgently needed. In this study, we examined phytochemicals extracted from Avicennia officinalis and evaluated their potential effects against the main protease of SARS-CoV-2. The antioxidant activities of A. officinalis leaf and fruit extracts at 150 µg/mL were 95.97% and 92.48%, respectively. Furthermore, both extracts displayed low cytotoxicity levels against Artemia salina. The gas chromatography–mass spectroscopy analysis confirmed the identifies of 75 phytochemicals from both extracts, and four potent compounds, triacontane, hexacosane, methyl linoleate, and methyl palminoleate, had binding free energy values of −6.75, −6.7, −6.3, and −6.3 Kcal/mol, respectively, in complexes with the SARS-CoV-2 main protease. The active residues Cys145, Met165, Glu166, Gln189, and Arg188 in the main protease formed non-bonded interactions with the screened compounds. The root-mean-square difference (RMSD), root-mean-square fluctuations (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen bond data from a molecular dynamics simulation study confirmed the docked complexes′ binding rigidity in the atomistic simulated environment. However, this study′s findings require in vitro and in vivo validation to ensure the possible inhibitory effects and pharmacological efficacy of the identified compounds.
Acanthopanax henryi: Review of Botany, Phytochemistry and Pharmacology
Authors: Li Xiao-Jun , Tang Si-Qi , Huang Hao , Luo Jiao , Zhang Xiao-Dan , Yook Chang-Soo , Whang Wan-Kyunn , Kim Youn-Chul , Liu Xiang-Qian
Published: April 2021

Acanthopanax henryi (Oliv.) Harms (Araliaceae), also known as Eleutherococcus henryi and Caoyewujia (Hengliwujia) in Chinese, is a widely used traditional Chinese herb with the effects of expelling wind and removing dampness, relaxing the muscles and stimulating the blood circulation, and regulating the flow of qi to alleviate pain in the theory of Traditional Chinese Medicine. Acanthopanax henryi (AH, thereafter) possesses ginseng-like activities and is known as ginseng-like herb. In the past decade, a great number of phytochemical and pharmacological studies on AH have been carried out. Several kinds of chemical compositions have been reported, including terpenoids (monoterpenoids, diterpenoids, and triterpenoid saponins), phenylpropanoids, caffeoyl quinic acid derivatives, flavonoids, lignans, sterols, fatty acids, etc., among which, triterpenoid saponins were considered to be the most active components. Considerable pharmacological experiments in vitro have demonstrated that AH possessed anti-neuroinflammatory, anti-adipogenic, anti-inflammatory, antibacterial, anti-cancer, anti-oxidation, anti-AChE, anti-BuChE, and antihyaluronidase activities. The present review is an up-to-date and comprehensive analysis of the botany, phytochemistry, and pharmacology of AH.
Pharmacokinetic Characterization of LW6, a Novel Hypoxia-Inducible Factor-1α (HIF-1α) Inhibitor in Mice
Authors: Lee Ji-Yoon , Lee Kiho , Lee Kyeong , Kang Jong Soon , Kim Min Ju , Yoo Dong Gu , Kim Jung Ah , Shin Eun Jin , Oh Soo Jin
Published: April 2021

LW6, an (aryloxyacetylamino)benzoic acid derivative, was recently identified to be an inhibitor of hypoxia-inducible factor-1α (HIF-1α), which is an attractive target for cancer therapeutics. Although LW6 is known to act by inhibiting the accumulation of HIF-1α, pharmacokinetics needs to be evaluated to assess its potential as an anti-tumor agent. Here, we investigated the plasma pharmacokinetics and metabolism of LW6 in mice. LW6 exhibited a small volume of distribution (0.5 ± 0.1 L/kg), and a short terminal half-life (0.6 ± 0.1 h). Following intravenous or oral administration, LW6 was rapidly converted to its active metabolite, (4-adamantan-1-yl-phenoxy)acetic acid (APA). Although LW6 was rapidly absorbed, its oral bioavailability, estimated using AUClast values, was low (1.7 ± 1.8%). It was slowly degraded in mouse liver microsomes (t1/2 > 1 h) and serum (t1/2 > 6 h). About 54% or 44.8% of LW6 was available systemically as APA in the mouse after a single intravenous or oral administration, respectively. Thus, our results indicated the need to simultaneously consider the active metabolite as well as the parent compound for successful evaluation during lead optimization.
FEM Simulation of Frequency-Selective Surface Based on Thermoelectric Bi-Sb Thin Films for THz Detection
Authors: Tukmakova Anastasiia , Tkhorzhevskiy Ivan , Sedinin Artyom , Asach Aleksei , Novotelnova Anna , Kablukova Natallya , Demchenko Petr , Zaitsev Anton , Zykov Dmitry , Khodzitsky Mikhail
Published: April 2021

Terahertz (THz) filters and detectors can find a wide application in such fields as: sensing, imaging, security systems, medicine, wireless connection, and detection of substances. Thermoelectric materials are promising basis for THz detectors’ development due to their sensitivity to the THz radiation, possibility to be heated under the THz radiation and produce voltage due to Seebeck effect. Thermoelectric thin films of Bi-Sb solid solutions are semimetals/semiconductors with the band gap comparable with THz energy and with high thermoelectric conversion efficiency at room temperature. Detecting film surface can be transformed into a periodic frequency selective surface (FSS) that can operate as a frequency filter and increases the absorption of THz radiation. We report for the first time about the simulation of THz detector based on thermoelectric Bi-Sb thin-filmed frequency-selective surface. We show that such structure can be both detector and frequency filter. Moreover, it was shown that FSS design increases not only a heating due to absorption but a temperature gradient in Bi-Sb film by two orders of magnitude in comparison with continuous films. Local temperature gradients can reach the values of the order of 100 K·mm−1. That opens new perspectives for thin-filmed thermoelectric detectors’ efficiency increase. Temperature difference formed due to THz radiation absorption can reach values on the order of 1 degree. Frequency-transient calculations show the power dependence of film temperature on time with characteristic saturation at times around several ms. That points to the perspective of reaching fast response times on such structures.
Is the Household Microwave Recommended to Obtain Antioxidant-Rich Extracts from Lycium barbarum Leaves?
Authors: Pollini Luna , Ianni Federica , Verducci Giuseppa , Blasi Francesca , Cossignani Lina
Published: April 2021

Nowadays, much interest is devoted to the extraction of plant materials, considering also their waste and by-products, to obtain antioxidant-rich products. The effect of household microwave-assisted extraction (MAE) on the phenolic content and antioxidant activity of Lycium barbarum leaf extracts was investigated. An experimental design approach was adopted considering solid/liquid ratio (1, 3, and 5 g of leaves in 150 mL water), irradiation time (1, 3, and 5 min), and microwave power (300, 400, and 500 W) as independent variables. These three factors and their interactions were studied to evaluate the effect of MAE conditions on the responses of total phenolic content, antioxidant activity, and chlorogenic acid content. The results showed that the analytical parameters were positively influenced by the solid/liquid ratio and time. On the contrary, microwave power was inversely correlated with the investigated responses. This research revealed that microwave extraction conditions should be carefully monitored to obtain bioactive-rich aqueous extracts with high antioxidant activity. A comparison with household traditional methods showed an unexpected lower phenolic content and antioxidant activity for MAE extract in respect to the decoction and infusion. In fact, it was found that L. barbarum leaf infusion had the best functional properties, regarding the investigated characteristics. The outcome of this study has implications for raising awareness that household preparation conditions strongly affect the health properties of herbal extracts.
Accurate Insulating Oil Breakdown Voltage Model Associated with Different Barrier Effects
Authors: Ghoneim Sherif S. M. , Dessouky Sobhy S. , Boubakeur Ahmed , Elfaraskoury Adel A. , Abou Sharaf Ahmed B. , Mahmoud Karar , Lehtonen Matti , Darwish Mohamed M. F.
Published: April 2021

In modern power systems, power transformers are considered vital components that can ensure the grid’s continuous operation. In this regard, studying the breakdown in the transformer becomes necessary, especially its insulating system. Hence, in this study, Box–Behnken design (BBD) was used to introduce a prediction model of the breakdown voltage (VBD) for the transformer insulating oil in the presence of different barrier effects for point/plane gap arrangement with alternating current (AC) voltage. Interestingly, the BBD reduces the required number of experiments and their costs to examine the barrier parameter effect on the existing insulating oil VBD. The investigated variables were the barrier location in the gap space (a/d)%, the relative permittivity of the barrier materials (εr), the hole radius in the barrier (hr), the barrier thickness (th), and the barrier inclined angle (θ). Then, only 46 experiment runs are required to build the BBD model for the five barrier variables. The BBD prediction model was verified based on the statistical study and some other experiment runs. Results explained the influence of the inclined angle of the barrier and its thickness on the VBD. The obtained results indicated that the designed BBD model provides less than a 5% residual percentage between the measured and predicted VBD. The findings illustrated the high accuracy and robustness of the proposed insulating oil breakdown voltage predictive model linked with diverse barrier effects.
Development and Commissioning of a Small-Scale, Modular and Integrated Plant for the Quasi-Continuous Production of Crystalline Particles
Authors: Dobler Timo , Buchheiser Simon , Gleiß Marco , Nirschl Hermann
Published: April 2021

Increasing global competition, volatile markets and the demand for individual products challenge companies in almost all business sectors and require innovative solutions. In the chemical and pharmaceutical industries, these include modular design, the integration of several unit operations in one apparatus and the development of small-scale, versatile multipurpose plants. An example for such a modular, integrated and small-scale system is the belt crystallizer. This device combines the process steps cooling crystallization, solid-liquid separation and contact drying in a single plant. The basis of the apparatus is a belt filter in which the vacuum trays below the filter medium are replaced by temperature control and filtration units. Due to identical dimensions, it is possible to arrange the individual functional units in any order, which in turn allows a high degree of flexibility and rapid adaptation to customer requirements. Within the scope of the publication, the commissioning of the belt crystallizer takes place. First of all, the general functionality of the plant concept is demonstrated using sucrose as model system. Further experiments show that the particle size and the distribution width of the manufactured crystals can be specifically influenced by the selected process parameters, e.g., temperature profile during cooling and residence time.
Enhanced Variable Neighborhood Search-Based Recovery Supplier Selection for Post-Disruption Supply Networks
Authors: Chen Yuting , Lou Ping , Jiang Wen
Published: April 2021

With the increasing reliance on global sourcing and the growth in the likelihood of disruptive incidents, today’s supply networks are more prone to unexpected natural and man-made disruptive events. In order to alleviate the losses caused by these disruptive events, when a large-scale event disrupts multiple suppliers simultaneously, a single or several critical suppliers should be selected from the disrupted ones to assist them to recover their production as soon as possible. The selection of these recovery suppliers is of great importance in the recovery process of the entire supply network. Thus, this paper proposes a recovery supplier selection method from the view of the supply network structure. Firstly, a tripartite graph-based supply model is proposed to depict a two-stage supply network, which consists of multiple manufacturers and suppliers as well as the diverse product supply-demand interdependence connecting them. To measure the impacts caused by supplier disruptions and to evaluate the effectiveness of recovery supplier decisions, two supply network performance metrics reflecting product supply availability are also given. Then, the recovery supplier selection problem is described as a combinatorial optimization problem. To solve this problem, a heuristic algorithm, with enhanced variable neighborhood search (EVNS) is designed based on the general framework of a variable neighborhood search. Finally, experiments based on a real-world supply network are conducted. The experimental results indicate that the proposed method is applicable and effective.
Mineral Deposits Safeguarding and Land Use Planning—The Importance of Creating Shared Value
Authors: Wårell Linda
Published: April 2021

During the last few decades many European countries have developed policies directed towards mineral deposit safeguarding. However, as other land uses often are in conflict with mineral deposit safeguarding, the implementation of these policies is many times more difficult in practice. The aim of this paper is to investigate the link between land use planning and mineral resources, when using a shared value perspective. The analysis is focused on the mineral-rich Nordic countries—Sweden, Norway and Finland—and a number of mining projects are analyzed. The analysis rests in Porter and Kramer’s arguments for the importance of creating shared values. The results indicate that a shared value perspective has been present in the analyzed case studies, as many of the key ways for creating shared value are identified in the projects. This illustrates the importance of linking social value to economic value in mining projects, even if this is not clearly stated in the relevant legislation. As it is often the unpredictability of the regulatory framework that hinders mineral extraction, it is suggested that Social Impact Assessments (or similar) are formalized in the regulatory framework to ensure that social value is linked more clearly to the land use process related to access to minerals.
Design and Experiments of a Portable Seabed Integrated Detection Sonar
Authors: Ma Jingxin , Li Haisen , Zhu Jianjun , Du Weidong , Xu Chao , Wang Xinyang
Published: April 2021

The integrated observation of seabed topography, sediment geomorphology and sub-bottom profile information is very important for seabed remote sensing and mapping. To improve the efficiency of seabed detection and meet the needs of portable development of detection equipment, we developed a portable seabed feature integrated detection sonar (PSIDS) with whcih a single sonar device can simultaneously detect the above three types of seabed information. The underwater transducer is mainly composed of the following three components: a parametric emission array as the sound source, a high frequency receiving linear array for multibeam echo signal collection, and a two-dimensional vector hydrophone for receiving the low-frequency sediment echo signal. Field experiments were conducted to validate the performance of the PSIDS on 11–17 January 2018 in Jiaozhou Bay, China. (1) PSIDS could perform the functions of both multibeam sonar and sub-bottom profiler; (2) The synchronously and integrated measurement of various seabed information was achieved by alternately emitting multibeam echo-sounding and sub-bottom profiling signal using parametric source. The detection results proved the feasibility and practicability of PSIDS to achieve multiple seafloor characteristics. PSIDS provides a new idea for developing integrated seabed detection sonar. In terms of convenience and data fusion, it is a good option to use this equipment for integrated seabed detection.
Evolving Paradigm of Prothrombin Time Diagnostics with Its Growing Clinical Relevance towards Cardio-Compromised and COVID-19 Affected Population
Authors: Saha Anubhuti , Bajpai Ashutosh , Krishna Vinay , Bhattacharya Shantanu
Published: April 2021

Prothrombin time (PT) is a significant coagulation (hemostasis) biomarker used to diagnose several thromboembolic and hemorrhagic complications based on its direct correlation with the physiological blood clotting time. Among the entire set of PT dependents, candidates with cardiovascular ailments are the major set of the population requiring lifelong anticoagulation therapy and supervised PT administration. Additionally, the increasing incidence of COVID affected by complications in coagulation dynamics has been strikingly evident. Prolonged PT along with sepsis-induced coagulopathy (SIC score > 3) has been found to be very common in critical COVID or CAC-affected cases. Considering the growing significance of an efficient point-of-care PT assaying platform to counter the increasing fatalities associated with cardio-compromised and coagulation aberrations propping up from CAC cases, the following review discusses the evolution of lab-based PT to point of care (PoC) PT assays. Recent advances in the field of PoC PT devices utilizing optics, acoustics, and mechanical and electrochemical methods in microsensors to detect blood coagulation are further elaborated. Thus, the following review holistically aims to motivate the future PT assay designers/researchers by detailing the relevance of PT and associated protocols for cardio compromised and COVID affected along with the intricacies of previously engineered PoC PT diagnostics.
A Heterogeneous Hardware Accelerator for Image Classification in Embedded Systems
Authors: Pérez Ignacio , Figueroa Miguel
Published: April 2021

Convolutional neural networks (CNN) have been extensively employed for image classification due to their high accuracy. However, inference is a computationally-intensive process that often requires hardware acceleration to operate in real time. For mobile devices, the power consumption of graphics processors (GPUs) is frequently prohibitive, and field-programmable gate arrays (FPGA) become a solution to perform inference at high speed. Although previous works have implemented CNN inference on FPGAs, their high utilization of on-chip memory and arithmetic resources complicate their application on resource-constrained edge devices. In this paper, we present a scalable, low power, low resource-utilization accelerator architecture for inference on the MobileNet V2 CNN. The architecture uses a heterogeneous system with an embedded processor as the main controller, external memory to store network data, and dedicated hardware implemented on reconfigurable logic with a scalable number of processing elements (PE). Implemented on a XCZU7EV FPGA running at 200 MHz and using four PEs, the accelerator infers with 87% top-5 accuracy and processes an image of 224×224 pixels in 220 ms. It consumes 7.35 W of power and uses less than 30% of the logic and arithmetic resources used by other MobileNet FPGA accelerators.
Highly Sensitive and Cost-Effective Portable Sensor for Early Gastric Carcinoma Diagnosis
Authors: Oo Saw-Lin , Venkatesh Shishir , Karthikeyan Vaithinathan , Arava Clement Manohar , Pathikonda Spoorthy , Yu Peter K. N. , Lau Terrence C. K. , Chen Xianfeng , Roy Vellaisamy A. L.
Published: April 2021

Facile and efficient early detection of cancer is a major challenge in healthcare. Herein we developed a novel sensor made from a polycarbonate (PC) membrane with nanopores, followed by sequence-specific Oligo RNA modification for early gastric carcinoma diagnosis. In this design, the gastric cancer antigen CA72-4 is specifically conjugated to the Oligo RNA, thereby inhibiting the electrical current through the PC membrane in a concentration-dependent manner. The device can determine the concentration of cancer antigen CA72-4 in the range from 4 to 14 U/mL, possessing a sensitivity of 7.029 µAU−1mLcm−2 with a linear regression (R2) of 0.965 and a lower detection limit of 4 U/mL. This device has integrated advantages including high specificity and sensitivity and being simple, portable, and cost effective, which collectively enables a giant leap for cancer screening technologies towards clinical use. This is the first report to use RNA aptamers to detect CA72-4 for gastric carcinoma diagnosis.
A Semi-Supervised Transfer Learning with Grid Segmentation for Outdoor Localization over LoRaWans
Authors: Chen Yuh-Shyan , Hsu Chih-Shun , Huang Chan-Yin
Published: April 2021

During the training phase of the supervised learning, it is not feasible to collect all the datasets of labelled data in an outdoor environment for the localization problem. The semi-supervised transfer learning is consequently used to pre-train a small number of labelled data from the source domain to generate a kernel knowledge for the target domain. The kernel knowledge is transferred to a target domain to transfer some unlabelled data into the virtual labelled data. In this paper, we have proposed a new outdoor localization scheme using a semi-supervised transfer learning for LoRaWANs. In the proposed localization algorithm, a grid segmentation concept is proposed so as to generate a number of virtual labelled data through learning by constructing the relationship of labelled and unlabelled data. The labelled-unlabelled data relationship is repeatedly fine-tuned by correctly adding some more virtual labelled data. Basically, the more the virtual labelled data are added, the higher the location accuracy will be obtained. In the real implementation, three types of signal features, RSSI, SNR, and timestamps, are used for training to improve the location accuracy. The experimental results illustrate that the proposed scheme can improve the location accuracy and reduce the localization error as opposed to the existing outdoor localization schemes.
Single-Line Bidirectional Optical Add/Drop Multiplexer for Ring Topology Optical Fiber Networks
Authors: Li Chung-Yi , Chang Ching-Hung , Lin Zih-Guei
Published: April 2021

A new type of passive single-line bidirectional optical add/drop multiplexer (SBOADM) is proposed and experimentally demonstrated. When the proposed SBOADM is placed as a node of a ring topology optical fiber network, the special routing function of the SBOADM can always drop down the desired downstream signals whether the signals are injected into the SBOADM in either the clockwise (CW) or counterclockwise (CCW) direction and can upload and send back the upstream signals via the reversed optical pathway of the downstream signals. Once fiber link failure occurs in the optical network, the blocked network connections can be recovered immediately by sending out the downstream signals in both the CW and CCW directions of the fiber ring. As in all passive devices, the SBOADM needs no power supply or complicated network management to achieve the bidirectional function. Thus, the proposed device is an optimal solution to enhance the stability and reliability of rapidly developed optical fiber networks.
Development of a Numerical Model to Predict the Dielectric Properties of Heterogeneous Asphalt Concrete
Authors: Cao Qingqing , Al-Qadi Imad L.
Published: April 2021

Ground-penetrating radar (GPR) has been used for asphalt concrete (AC) pavement density prediction for the past two decades. Recently, it has been considered as a method for pavement quality control and quality assurance. A numerical method to estimate asphalt pavement specific gravity from its dielectric properties was developed and validated. A three-phase numerical model considering aggregate, binder, and air void components was developed using an AC mixture generation algorithm. A take-and-add algorithm was used to generate the uneven air-void distribution in the three-phase model. The proposed three-phase model is capable of correlating pavement density and bulk and component dielectric properties. The model was validated using field data. Two methods were used to calculate the dielectric constant of the AC mixture, including reflection amplitude and two-way travel time methods. These were simulated and compared when vertical and longitudinal heterogeneity existed within the AC pavement layers. Results indicate that the reflection amplitude method is more sensitive to surface thin layers than the two-way travel time methods. Effect of air-void content, asphalt content, aggregate gradation, and aggregate dielectric constants on the GPR measurements were studied using the numerical model.
Who Is to Blame? The Appearance of Virtual Agents and the Attribution of Perceived Responsibility
Authors: Matsui Tetsuya , Koike Atsushi
Published: April 2021

Virtual agents have been widely used in human-agent collaboration work. One important problem with human-agent collaboration is the attribution of responsibility as perceived by users. We focused on the relationship between the appearance of a virtual agent and the attribution of perceived responsibility. We conducted an experiment with five agents: an agent without an appearance, a human-like agent, a robot-like agent, a dog-like agent, and an angel-like agent. We measured the perceived agency and experience for each agent, and we conducted an experiment involving a sound-guessing game. In the game, participants listened to a sound and guessed what the sound was with an agent. At the end of the game, the game finished with failure, and the participants did not know who made the mistake, the participant or the agent. After the game, we asked the participants how they perceived the agents’ trustworthiness and to whom they attributed responsibility. As a result, participants attributed less responsibility to themselves when interacting with a robot-like agent than interacting with an angel-like robot. Furthermore, participants perceived the least trustworthiness toward the robot-like agent among all conditions. In addition, the agents’ perceived experience had a correlation with the attribution of perceived responsibility. Furthermore, the agents that made the participants feel their attribution of responsibility to be less were not trusted. These results suggest the relationship between agents’ appearance and perceived attribution of responsibility and new methods for designs in the creation of virtual agents for collaboration work.
Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network
Authors: Aamir Muhammad , Ali Tariq , Irfan Muhammad , Shaf Ahmad , Azam Muhammad Zeeshan , Glowacz Adam , Brumercik Frantisek , Glowacz Witold , Alqhtani Samar , Rahman Saifur
Published: April 2021

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.
Over 1000 nm Near-Infrared Multispectral Imaging System for Laparoscopic In Vivo Imaging
Authors: Takamatsu Toshihiro , Kitagawa Yuichi , Akimoto Kohei , Iwanami Ren , Endo Yuto , Takashima Kenji , Okubo Kyohei , Umezawa Masakazu , Kuwata Takeshi , Sato Daiki , Kadota Tomohiro , Mitsui Tomohiro , Ikematsu Hiroaki , Yokota Hideo , Soga Kohei , Takemura Hiroshi
Published: April 2021

In this study, a laparoscopic imaging device and a light source able to select wavelengths by bandpass filters were developed to perform multispectral imaging (MSI) using over 1000 nm near-infrared (OTN-NIR) on regions under a laparoscope. Subsequently, MSI (wavelengths: 1000–1400 nm) was performed using the built device on nine live mice before and after tumor implantation. The normal and tumor pixels captured within the mice were used as teaching data sets, and the tumor-implanted mice data were classified using a neural network applied following a leave-one-out cross-validation procedure. The system provided a specificity of 89.5%, a sensitivity of 53.5%, and an accuracy of 87.8% for subcutaneous tumor discrimination. Aggregated true-positive (TP) pixels were confirmed in all tumor-implanted mice, which indicated that the laparoscopic OTN-NIR MSI could potentially be applied in vivo for classifying target lesions such as cancer in deep tissues.
An Open Source Low-Cost Device Coupled with an Adaptative Time-Lag Time-Series Linear Forecasting Modeling for Apple Trentino (Italy) Precision Irrigation
Authors: Figorilli Simone , Pallottino Federico , Colle Giacomo , Spada Daniele , Beni Claudio , Tocci Francesco , Vasta Simone , Antonucci Francesca , Pagano Mauro , Fedrizzi Marco , Costa Corrado
Published: April 2021

Precision irrigation represents those strategies aiming to feed the plant needs following the soil’s spatial and temporal characteristics. Such a differential irrigation requires a different approach and equipment with regard to conventional irrigation to reduce the environmental impact and the resources use while maximizing the production and thus profitability. This study described the development of an open source soil moisture LoRa (long-range) device and analysis of the data collected and updated directly in the field (i.e., weather station and ground sensor). The work produced adaptive supervised predictive models to optimize the management of agricultural precision irrigation practices and for an effective calibration of other agronomic interventions. These approaches are defined as adaptive because they self-learn with the acquisition of new data, updating the on-the-go model over time. The location chosen for the experimental setup is a cultivated area in the municipality of Tenna (Trentino, Alto Adige region, Italy), and the experiment was conducted on two different apple varieties during summer 2019. The adaptative partial least squares time-lag time-series modeling, in operative field conditions, was a posteriori applied in the consortium for 78 days during the dry season, producing total savings of 255 mm of irrigated water and 44,000 kW of electricity, equal to 10.82%.
Korean Grammatical Error Correction Based on Transformer with Copying Mechanisms and Grammatical Noise Implantation Methods
Authors: Lee Myunghoon , Shin Hyeonho , Lee Dabin , Choi Sung-Pil
Published: April 2021

Grammatical Error Correction (GEC) is the task of detecting and correcting various grammatical errors in texts. Many previous approaches to the GEC have used various mechanisms including rules, statistics, and their combinations. Recently, the performance of the GEC in English has been drastically enhanced due to the vigorous applications of deep neural networks and pretrained language models. Following the promising results of the English GEC tasks, we apply the Transformer with Copying Mechanism into the Korean GEC task by introducing novel and effective noising methods for constructing Korean GEC datasets. Our comparative experiments showed that the proposed system outperforms two commercial grammar check and other NMT-based models.
Automated IoT Device Identification Based on Full Packet Information Using Real-Time Network Traffic
Authors: Yousefnezhad Narges , Malhi Avleen , Främling Kary
Published: April 2021

In an Internet of Things (IoT) environment, a large volume of potentially confidential data might be leaked from sensors installed everywhere. To ensure the authenticity of such sensitive data, it is important to initially verify the source of data and its identity. Practically, IoT device identification is the primary step toward a secure IoT system. An appropriate device identification approach can counteract malicious activities such as sending false data that trigger irreparable security issues in vital or emergency situations. Recent research indicates that primary identity metrics such as Internet Protocol (IP) or Media Access Control (MAC) addresses are insufficient due to their instability or easy accessibility. Thus, to identify an IoT device, analysis of the header information of packets by the sensors is of imperative consideration. This paper proposes a combination of sensor measurement and statistical feature sets in addition to a header feature set using a classification-based device identification framework. Various machine Learning algorithms have been adopted to identify different combinations of these feature sets to provide enhanced security in IoT devices. The proposed method has been evaluated through normal and under-attack circumstances by collecting real-time data from IoT devices connected in a lab setting to show the system robustness.
Gait Parameters Measured from Wearable Sensors Reliably Detect Freezing of Gait in a Stepping in Place Task
Authors: Diep Cameron , O’Day Johanna , Kehnemouyi Yasmine , Burnett Gary , Bronte-Stewart Helen
Published: April 2021

Freezing of gait (FOG), a debilitating symptom of Parkinson’s disease (PD), can be safely studied using the stepping in place (SIP) task. However, clinical, visual identification of FOG during SIP is subjective and time consuming, and automatic FOG detection during SIP currently requires measuring the center of pressure on dual force plates. This study examines whether FOG elicited during SIP in 10 individuals with PD could be reliably detected using kinematic data measured from wearable inertial measurement unit sensors (IMUs). A general, logistic regression model (area under the curve = 0.81) determined that three gait parameters together were overall the most robust predictors of FOG during SIP: arrhythmicity, swing time coefficient of variation, and swing angular range. Participant-specific models revealed varying sets of gait parameters that best predicted FOG for each participant, highlighting variable FOG behaviors, and demonstrated equal or better performance for 6 out of the 10 participants, suggesting the opportunity for model personalization. The results of this study demonstrated that gait parameters measured from wearable IMUs reliably detected FOG during SIP, and the general and participant-specific gait parameters allude to variable FOG behaviors that could inform more personalized approaches for treatment of FOG and gait impairment in PD.
A Tri-Band Cooled Receiver for Geodetic VLBI
Authors: López-Pérez José A. , Tercero-Martínez Félix , Serna-Puente José M. , Vaquero-Jiménez Beatriz , Patino-Esteban María , García-Carreño Pablo , González-García Javier , García-Pérez Óscar , Beltrán-Martínez Francisco J. , Albo-Castaño Carlos , Gallego-Puyol Juan D. , López-Fernández Isaac , Díez-González Carmen , Malo-Gómez Inmaculada , Barbas-Calvo Laura , de Vicente-Abad Pablo , López-Fernández José A.
Published: April 2021

This paper shows a simultaneous tri-band (S: 2.2–2.7 GHz, X: 7.5–9 GHz and Ka: 28–33 GHz) low-noise cryogenic receiver for geodetic Very Long Baseline Interferometry (geo-VLBI) which has been developed at Yebes Observatory laboratories in Spain. A special feature is that the whole receiver front-end is fully coolable down to cryogenic temperatures to minimize receiver noise. It was installed in the first radio telescope of the Red Atlántica de Estaciones Geodinámicas y Espaciales (RAEGE) project, which is located in Yebes Observatory, in the frame of the VLBI Global Observing System (VGOS). After this, the receiver was borrowed by the Norwegian Mapping Autorithy (NMA) for the commissioning of two VGOS radiotelescopes in Svalbard (Norway). A second identical receiver was built for the Ishioka VGOS station of the Geospatial Information Authority (GSI) of Japan, and a third one for the second RAEGE VGOS station, located in Santa María (Açores Archipelago, Portugal). The average receiver noise temperatures are 21, 23, and 25 Kelvin and the measured antenna efficiencies are 70%, 75%, and 60% in S-band, X-band, and Ka-band, respectively.
Autonomous Marine Robot Based on AI Recognition for Permanent Surveillance in Marine Protected Areas
Authors: Molina-Molina J. Carlos , Salhaoui Marouane , Guerrero-González Antonio , Arioua Mounir
Published: April 2021

The world’s oceans are one of the most valuable sources of biodiversity and resources on the planet, although there are areas where the marine ecosystem is threatened by human activities. Marine protected areas (MPAs) are distinctive spaces protected by law due to their unique characteristics, such as being the habitat of endangered marine species. Even with this protection, there are still illegal activities such as poaching or anchoring that threaten the survival of different marine species. In this context, we propose an autonomous surface vehicle (ASV) model system for the surveillance of marine areas by detecting and recognizing vessels through artificial intelligence (AI)-based image recognition services, in search of those carrying out illegal activities. Cloud and edge AI computing technologies were used for computer vision. These technologies have proven to be accurate and reliable in detecting shapes and objects for which they have been trained. Azure edge and cloud vision services offer the best option in terms of accuracy for this task. Due to the lack of 4G and 5G coverage in offshore marine environments, it is necessary to use radio links with a coastal base station to ensure communications, which may result in a high response time due to the high latency involved. The analysis of on-board images may not be sufficiently accurate; therefore, we proposed a smart algorithm for autonomy optimization by selecting the proper AI technology according to the current scenario (SAAO) capable of selecting the best AI source for the current scenario in real time, according to the required recognition accuracy or low latency. The SAAO optimizes the execution, efficiency, risk reduction, and results of each stage of the surveillance mission, taking appropriate decisions by selecting either cloud or edge vision models without human intervention.
A Systematic Review of Commercial Smart Gloves: Current Status and Applications
Authors: Caeiro-Rodríguez Manuel , Otero-González Iván , Mikic-Fonte Fernando A. , Llamas-Nistal Martín
Published: April 2021

Smart gloves have been under development during the last 40 years to support human-computer interaction based on hand and finger movement. Despite the many devoted efforts and the multiple advances in related areas, these devices have not become mainstream yet. Nevertheless, during recent years, new devices with improved features have appeared, being used for research purposes too. This paper provides a review of current commercial smart gloves focusing on three main capabilities: (i) hand and finger pose estimation and motion tracking, (ii) kinesthetic feedback, and (iii) tactile feedback. For the first capability, a detailed reference model of the hand and finger basic movements (known as degrees of freedom) is proposed. Based on the PRISMA guidelines for systematic reviews for the period 2015–2021, 24 commercial smart gloves have been identified, while many others have been discarded because they did not meet the inclusion criteria: currently active commercial and fully portable smart gloves providing some of the three main capabilities for the whole hand. The paper reviews the technologies involved, main applications and it discusses about the current state of development. Reference models to support end users and researchers comparing and selecting the most appropriate devices are identified as a key need.
Electrochemical DNA Biosensor That Detects Early Celiac Disease Autoantibodies
Authors: Nguyen Anna B. N. , Maldonado Marcos , Poch Dylan , Sodia Tyler , Smith Andrew , Rowland Teisha J. , Bonham Andrew J.
Published: April 2021

Although it is estimated that more than one million Americans have celiac disease (CD), it remains challenging to diagnose. CD, an autoimmune and inflammatory response following the ingestion of gluten-containing foods, has symptoms overlapping with other diseases and requires invasive diagnostics. The gold standard for CD diagnosis involves serologic blood tests followed by invasive confirmatory biopsies. Here, we propose a less invasive method using an electrochemical DNA (E-DNA) biosensor for CD-specific autoantibodies (AABs) circulating in blood. In our approach, CD-specific AABs bind a synthetic neoepitope, causing a conformational change in the biosensor, as well as a change in the environment of an attached redox reporter, producing a measurable current reduction. We assessed the biosensor’s ability to detect CD-specific patient-derived AABs in physiological buffer as well as buffer supplemented with bovine serum. Our biosensor was able to detect AABs in a dose-dependent manner; increased signal change correlated with increased AAB concentration with an apparent dissociation constant of 0.09 ± 0.03 units/mL of AABs. Furthermore, we found our biosensor to be target-specific, with minimal off-target binding of multiple unrelated biomarkers. Future efforts aimed at increasing sensitivity in complex media may build upon the biosensor design presented here to further improve CD AAB detection and CD diagnostic tools.
Optimization-Based Online Initialization and Calibration of Monocular Visual-Inertial Odometry Considering Spatial-Temporal Constraints
Authors: Huang Weibo , Wan Weiwei , Liu Hong
Published: April 2021

The online system state initialization and simultaneous spatial-temporal calibration are critical for monocular Visual-Inertial Odometry (VIO) since these parameters are either not well provided or even unknown. Although impressive performance has been achieved, most of the existing methods are designed for filter-based VIOs. For the optimization-based VIOs, there is not much online spatial-temporal calibration method in the literature. In this paper, we propose an optimization-based online initialization and spatial-temporal calibration method for VIO. The method does not need any prior knowledge about spatial and temporal configurations. It estimates the initial states of metric-scale, velocity, gravity, Inertial Measurement Unit (IMU) biases, and calibrates the coordinate transformation and time offsets between the camera and IMU sensors. The work routine of the method is as follows. First, it uses a time offset model and two short-term motion interpolation algorithms to align and interpolate the camera and IMU measurement data. Then, the aligned and interpolated results are sent to an incremental estimator to estimate the initial states and the spatial–temporal parameters. After that, a bundle adjustment is additionally included to improve the accuracy of the estimated results. Experiments using both synthetic and public datasets are performed to examine the performance of the proposed method. The results show that both the initial states and the spatial-temporal parameters can be well estimated. The method outperforms other contemporary methods used for comparison.
Design of a Microwave Power Detection System in the 5G-Communication Frequency Band
Authors: Ren Qingying , Zuo Wen , Xu Jie , Jin Leisheng , Li Wei , Wang Debo
Published: April 2021

At present, the proposed microwave power detection systems cannot provide a high dynamic detection range and measurement sensitivity at the same time. Additionally, the frequency band of these detection systems cannot cover the 5G-communication frequency band. In this work, a novel microwave power detection system is proposed to measure the power of the 5G-communication frequency band. The detection system is composed of a signal receiving module, a power detection module and a data processing module. Experiments show that the detection frequency band of this system ranges from 1.4 GHz to 5.3 GHz, the dynamic measurement range is 70 dB, the minimum detection power is −68 dBm, and the sensitivity is 22.3 mV/dBm. Compared with other detection systems, the performance of this detection system in the 5G-communication frequency band is significantly improved. Therefore, this microwave power detection system has certain reference significance and application value in the microwave signal detection of 5G communication systems.