bluuebunny/crossref_metadata_2025 huggingface dataset:
Possibility for Replicating Mechanoscopic Surface Marks in the Hybrid Vacuum-Pressure Casting Process
Authors: Frankiewicz Mariusz , Kobiela Karol , Kurzynowski Tomasz
Published: March 2021

Vacuum-pressure casting technology allows small batches of components to be manufactured from polymer materials, mainly from thermosetting plastics such as polyurethane and epoxy resins. Apart from being very simple, the process is also advantageous in that it offers a very accurately reproduced geometrical structure of the surfaces of master patterns used in mold manufacturing. This article presents the results of analyses performed for the process of replicating mechanoscopic marks with the use of three vacuum casting variants, including a hybrid vacuum-pressure casting process developed in particular for the replication purposes. The main research objective was to analyze and evaluate the influence of the parameters of the individual process variants on the quality of the obtained cast parts and on the replication accuracy without introducing additional artifacts on their surfaces. The article discusses the individual stages of the process and provides an analysis of their parameters. The replicas were evaluated for their porosity and reproduction quality with the use of CT methods and comparative photographs obtained from a light microscope.
Dynamic Impact Surface Damage Analysis of 3D Woven Para-Aramid Armour Panels Using NDI Technique
Authors: Abtew Mulat Alubel , Boussu Francois , Bruniaux Pascal , Hong Yan
Published: March 2021

The effects of the yarn composition system inside 3D woven high-performance textiles are not well investigated and understood against their final ballistic impact behaviour. The current study aims to examine the ballistic impact performances of armour panels made of different 3D woven fabric variants through postmortem observations. Four high-performance five-layer 3D woven fabric variants were engineered based on their different warp yarn compositions but similar area density. A 50 × 50 cm2 armour system of each variant, which comprises eight nonbonded but aligned panels, namely, 3D-40-8/0 (or 8/0), 3D-40-8/4 (or 8/4), 3D-40-8/8 (or 8/8) and 3D-40-4/8 (or 4/8), were prepared and moulded to resemble female frontal morphology. The armour systems were then tested with nonperforation ballistic impacts according to the National Institute of Justice (NIJ) 0101.06 standard Level-IIIA. Two high-speed cameras were used to capture the event throughout the test. Nondestructive investigation (NDI) using optical microscopic and stereoscopic 3D digital images were employed for the analysis. The armour panels made of the 8/0 and 4/8 fabric variants were perforated, whereas the armour made of the 8/8 and 8/4 fabric variants showed no perforation. Besides, the armour made of the 8/4 fabric variant revealed higher local and global surface displacements than the other armours. The current research findings are useful for further engineering of 3D woven fabric for seamless women’s impact protective clothing.
Development and In Vitro Evaluation of 2-Methoxyestradiol Loaded Polymeric Micelles for Enhancing Anticancer Activities in Prostate Cancer
Authors: Alhakamy Nabil A. , Ahmed Osama A. A. , Fahmy Usama A. , Md Shadab
Published: March 2021

The present study aimed to formulate and optimize 2ME-loaded PMs (2ME-PMs) for enhancing the anticancer activity of 2ME in prostate cancer (PC). The 2ME-PMs were formulated using PEG-PLGA (PL), Tween 80 (TW80), and alpha-lipoic acid (ALA). The optimization was carried out using a Box-Behnken design with the PL, TW80, and ALA as the independent variables and particle size (PS) as the response. The formulation was optimized for the lowest possible PS, and the software suggested optimum formula with 100.282 mg, 2%, and 40 mg for PL, TW80, and ALA, respectively. The optimized PMs had spherical morphology with PS of 65.36 ± 2.2 nm, polydispersity index (PDI) of 0.273 ± 0.03, and entrapment efficiency of 65.23 ± 3.5%. The in vitro drug release was 76.3 ± 3.2% after 24 h. The cell line studies using PC-3 cells showed IC50 values of 18.75 and 54.41 µmol for 2ME-PM and 2ME, respectively. The estimation of tumor biomarkers was also carried out. The tumor biomarkers caspase-9 (17.38 ± 1.42 ng/mL), tumor protein P53 (p53) (1050.0 ± 40.9 pg/mL), nitric oxide (NO) (0.693 ± 0.03 pg/mL), interleukin-1β (IL-1β) (25.84 ± 2.23 pg/mL), nuclear factor kappa B (NF-kB) (0.719 ± 0.07 pg/mL), interleukin-6 (IL-6) (2.53 ± 0.16 folds), and cyclooxygenase-2 (COX-2) (3.04 ± 0.5 folds) were determined for 2ME-PMs and the results showed that these values changed significantly compared to those of 2ME. Overall, the results showed that the formulation of 2ME to 2ME-PMs enhances the anticancer effect. The exploration of the combined advantages of PEG, PLGA, ALA, and PMs in cancer therapy and the delivery of 2ME is the major importance of this research work. PEG reduces the elimination of 2ME, PLGA enhances 2ME loading, ALA has an inherent apoptotic effect, and PMs can efficiently target tumor cells.
19% Efficient P3CT-Na Based MAPbI3 Solar Cells with a Simple Double-Filtering Process
Authors: Chiang Shou-En , Ke Qi-Bin , Chandel Anjali , Cheng Hsin-Ming , Yen Yung-Sheng , Shen Ji-Lin , Chang Sheng Hsiung
Published: March 2021

A high-efficiency inverted-type CH3NH3PbI3 (MAPbI3) solar cell was fabricated by using a ultrathin poly[3-(4-carboxybutyl)thiophene-2,5-diyl]-Na (P3CT-Na) film as the hole transport layer. The averaged power conversion efficiency (PCE) can be largely increased from 11.72 to 18.92% with a double-filtering process of the P3CT-Na solution mainly due to the increase in short-circuit current density (JSC) from 19.43 to 23.88 mA/cm2, which means that the molecular packing structure of P3CT-Na thin film can influence the formation of the MAPbI3 thin film and the contact quality at the MAPbI3/P3CT-Na interface. Zeta potentials, atomic-force microscopic images, absorbance spectra, photoluminescence spectra, X-ray diffraction patterns, and Raman scattering spectra are used to understand the improvement in the JSC. Besides, the light intensity-dependent and wavelength-dependent photovoltaic performance of the MAPbI3 solar cells shows that the P3CT-Na thin film is not only used as the hole transport layer but also plays an important role during the formation of a high-quality MAPbI3 thin film. It is noted that the PCE values of the best P3CT-Na based MAPbI3 solar cell are higher than 30% in the yellow-to-near infrared wavelength range under low light intensities. On the other hand, it is predicted that the double-filtering method can be readily used to increase the PCE of polymer based solar cells.
Facile Synthesis of Carboxymethyl Cellulose Coated Core/Shell SiO2@Cu Nanoparticles and Their Antifungal Activity against Phytophthora capsici
Authors: Hai Nguyen Thi Thanh , Cuong Nguyen Duc , Quyen Nguyen Tran , Hien Nguyen Quoc , Hien Tran Thi Dieu , Phung Nguyen Thi Thanh , Toan Dao Khac , Huong Nguyen Thi Thu , Phu Dang Van , Hoa Tran Thai
Published: March 2021

Cu nanoparticles are a potential material for creating novel alternative antimicrobial products due to their unique antibacterial/antifungal properties, stability, dispersion, low cost and abundance as well as being economical and ecofriendly. In this work, carboxymethyl cellulose coated core/shell SiO2@Cu nanoparticles (NPs) were synthesized by a simple and effective chemical reduction process. The initial SiO2 NPs, which were prepared from rice husk ash, were coated by a copper ultrathin film using hydrazine and carboxymethyl cellulose (CMC) as reducing agent and stable agent, respectively. The core/shell SiO2@Cu nanoparticles with an average size of ~19 nm were surrounded by CMC. The results indicated that the SiO2@Cu@CMC suspension was a homogenous morphology with a spherical shape, regular dispersion and good stability. Furthermore, the multicomponent SiO2@Cu@CMC NPs showed good antifungal activity against Phytophthora capsici (P. capsici). The novel Cu NPs-based multicomponent suspension is a key compound in the development of new fungicides for the control of the Phytophthora disease.
An In Vivo Proposal of Cell Computing Inspired by Membrane Computing
Authors: Arteta Albert Alberto , Díaz-Flores Ernesto , López Luis Fernando de Mingo , Gómez Blas Nuria
Published: March 2021

Intractable problems are challenging and not uncommon in Computer Science. The computing generation we are living in forces us to look for an alternative way of computing, as current computers are facing limitations when dealing with complex problems and bigger input data. Physics and Biology offer great alternatives to solve these problems that traditional computers cannot. Models like Quantum Computing and cell computing are emerging as possible solutions to the current problems the conventional computers are facing. This proposal describes an in vivo framework inspired by membrane computing and based on alternative computational frameworks that have been proven to be theoretically correct such as chemical reaction series. The abilities of a cell as a computational unit make this proposal a starting point in the creation of feasible potential frameworks to enhance the performance of applications in different disciplines such as Biology, BioMedicine, Computer networks, and Social Sciences, by accelerating drastically the way information is processed by conventional architectures and possibly achieving results that presently are not possible due to the limitations of the current computing paradigm. This paper introduces an in vivo solution that uses the principles of membrane computing and it can produce non-deterministic outputs.
Interaction between Antifungal Isoxazolo[3,4-b]Pyridin 3(1H)-One Derivatives and Human Serum Proteins Analyzed with Biomimetic Chromatography and QSAR Approach
Authors: Ciura Krzesimir , Fedorowicz Joanna , Kapica Hanna , Pastewska Monika , Sawicki Wiesław , Sączewski Jarosław
Published: March 2021

The development of effective, nontoxic antifungal agents is one of the most important challenges for medicinal chemistry. A series of isoxazolo [3,4-b]pyridine-3(1H)-one derivatives previously synthesized in our laboratory demonstrated promising antifungal properties. The main goal of this study was to investigate their retention behavior in a human serum proteins-high-performance liquid chromatography (HSA-HPLC) system and explore the molecular mechanism of HSA-isoxazolone interactions using a quantitative structure–retention relationship (QSRR) approach. In order to realize this goal, multiple linear regression (MLR) modeling has been performed. The proposed QSRR models presented correlation between experimentally determined lipophilicity and computational theoretical molecular descriptors derived from Dragon 7.0 (Talete, Milan, Italy) software on the affinity of isoxazolones to HSA. The calculated plasma protein binding (PreADMET software) as well as chromatographic lipophilicity (logkw) and phospholipophilicity (CHIIAM) parameters were statistically evaluated in relation to the determined experimental HAS affinities (logkHSA). The proposed model met the Tropsha et al. criteria R2 > 0.6 and Q2 > 0.5 These results indicate that the obtained model can be useful in the prediction of an affinity to HSA for isoxazolone derivatives and they can be considered as an attractive alternative to HSA-HPLC experiments.
Research Regarding the Energy Recovery from Municipal Solid Waste in Maramures County Using Incineration
Authors: Ungureanu Miorita , Jozsef Juhasz , Brezoczki Valeria Mirela , Monka Peter , Ungureanu Nicolae Stelian
Published: March 2021

This paper presents a part of the study referring to exploring Energy Recovery from Municipal Solid Waste in Maramures County. In order to analyze the possibility of energetic recovery of municipal solid waste (MSW), data referring to the management system of MSW from Maramures county were cumulated and processed in a first stage in order to estimate the quantity of municipal solid waste and its composition, which might be recovered energetically. In the next stage, samples of municipal solid waste were collected from landfills, which were submitted to specific processing and analyses. The experimental data were processed and in the end the energy potential of municipal solid waste from Maramures county was found. This study will help stakeholders and those involved in waste management to assess the possibility of energy recovery. The analysis of the study concluded that municipal solid waste in Maramures County is a potential source of renewable energy.
Dynamic Modelling and Simulation of a Multistage Flash Desalination System
Authors: Huang Qiu-Yun , Jiang Ai-Peng , Zhang Han-Yu , Wang Jian , Xia Yu-Dong , He Lu
Published: March 2021

As the leading thermal desalination method, multistage flash (MSF) desalination plays an important role in obtaining freshwater. Its dynamic modeling and dynamic performance prediction are quite important for the optimal control, real-time optimal operation, maintenance, and fault diagnosis of MSF plants. In this study, a detailed mathematical model of the MSF system, based on the first principle and its treatment strategy, was established to obtain transient performance change quickly. Firstly, the whole MSF system was divided into four parts, which are brine heat exchanger, flashing stage room, mixed and split modulate, and physical parameter modulate. Secondly, based on mass, energy, and momentum conservation laws, the dynamic correlation equations were formulated and then put together for a simultaneous solution. Next, with the established model, the performance of a brine-recirculation (BR)-MSF plant with 16-stage flash chambers was simulated and compared for validation. Finally, with the validated model and the simultaneous solution method, dynamic simulation and analysis were carried out to respond to the dynamic change of feed seawater temperature, feed seawater concentration, recycle stream mass flow rate, and steam temperature. The dynamic response curves of TBT (top brine temperature), BBT (bottom brine temperature), the temperature of flashing brine at previous stages, and distillate mass flow rate at previous stages were obtained, which specifically reflect the dynamic characteristics of the system. The presented dynamic model and its treatment can provide better analysis for the real-time optimal operation and control of the MSF system to achieve lower operational cost and more stable freshwater quality.
A Graph-Based Dynamic Modeling for Palm Oil Refining Process
Authors: Ashaari Azmirul , Ahmad Tahir , Awang Siti Rahmah , Shukor Noorsufia Abd
Published: March 2021

Graph theory is a well-established mathematical concept that is widely used in numerous applications such as in biology, chemistry and network analysis. The advancement in the theory of graph has led to the development of a concept called autocatalytic set. In this paper, a mathematical modeling technique namely graph-based dynamic modeling of palm oil refining process is introduced. The system parameters are identified in detail in the beginning of the paper. The parameters involved are the chemical compounds used or produced during the refining process. These identified parameters are then modeled as the vertices and edges of the graph. The dynamicity of the system is then simulated and analyzed. The system is simulated using MATLAB software programing. The two final products produced by the refining process agreed with results obtained from other published methods. Hence, the effectiveness and simplicity of the model are established.
Corynebacterium diphtheriae Proteome Adaptation to Cell Culture Medium and Serum
Authors: Möller Jens , Nosratabadi Fatemeh , Musella Luca , Hofmann Jörg , Burkovski Andreas
Published: March 2021

Host-pathogen interactions are often studied in vitro using primary or immortal cell lines. This set-up avoids ethical problems of animal testing and has the additional advantage of lower costs. However, the influence of cell culture media on bacterial growth and metabolism is not considered or investigated in most cases. To address this question growth and proteome adaptation of Corynebacterium diphtheriae strain ISS3319 were investigated in this study. Bacteria were cultured in standard growth medium, cell culture medium, and fetal calf serum. Mass spectrometric analyses and label-free protein quantification hint at an increased bacterial pathogenicity when grown in cell culture medium as well as an influence of the growth medium on the cell envelope.
Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World
Authors: Pranckutė Raminta
Published: March 2021

Nowadays, the importance of bibliographic databases (DBs) has increased enormously, as they are the main providers of publication metadata and bibliometric indicators universally used both for research assessment practices and for performing daily tasks. Because the reliability of these tasks firstly depends on the data source, all users of the DBs should be able to choose the most suitable one. Web of Science (WoS) and Scopus are the two main bibliographic DBs. The comprehensive evaluation of the DBs’ coverage is practically impossible without extensive bibliometric analyses or literature reviews, but most DBs users do not have bibliometric competence and/or are not willing to invest additional time for such evaluations. Apart from that, the convenience of the DB’s interface, performance, provided impact indicators and additional tools may also influence the users’ choice. The main goal of this work is to provide all of the potential users with an all-inclusive description of the two main bibliographic DBs by gathering the findings that are presented in the most recent literature and information provided by the owners of the DBs at one place. This overview should aid all stakeholders employing publication and citation data in selecting the most suitable DB.
A Way Forward for Discernment in Congregations: LGBTQ+ Inclusion Discernment
Authors: Harris Helen , Yancey Gaynor , Dawson Kimberly , Gregory Jess
Published: March 2021

Christian congregations and denominations are increasingly struggling with questions of LGBTQ+ inclusion. The questions of hiring ministers who are LGBTQ+ persons, including LGBTQ+ persons in leadership, and conducting LGBTQ+ marriages are just a few of the discernment discussions that congregations are considering. This article reports on the findings of 97 interviews in 21 congregations reporting on their own discernment conversations. Researchers identified commonalities and differences in groups of congregations with processes that participants saw as instructive. This article is the third in a series of three: common models in the literature, lessons learned from the 97 interviews, and potential processes for congregations considering discernment.
Biosensor for Detecting Fetal Growth Restriction in a Low-Resource Setting
Authors: Jacobsen Anders , Schmiegelow Christentze , Sørensen Bjarke , Msemo Omari A. , Nielsen Karsten , Nielsen Birgitte Bruun , Møller Sofie Lykke , Lusingu John P. A. , Minja Daniel T. R. , Hedegaard Morten , Riknagel Diana
Published: March 2021

One strategy for improving detection of fetal growth restriction (FGR) is developing biosensors identifying placental dysfunction as a leading pathogenesis for FGR. The aim of this pilot study was to investigate the performance of a biosensor specified to detect placental dysfunction by means of maternal arterial turbulence acoustics in a low-resource setting. A cohort of 147 singleton pregnant women were prospectively followed with double-blinded biosensor tests, sonographic estimation of fetal weight (EFW) and Doppler flow at 26–28, 32–34 and 37–39 weeks of pregnancy. Full term live births with recorded birth weights (BWs) and without major congenital malformations were included. Outcomes were defined as (A) a solitary biometric measure (BW < 3rd centile) and as (B) a biometric measure and contributory functional measure (BW < 10th centile and antenatally detected umbilical artery pulsatility index > 95th centile). Data from 118 women and 262 antenatal examinations were included. Mean length of pregnancy was 40 weeks (SD ± 8 days), mean BW was 3008 g (SD ± 410 g). Outcome (A) was identified in seven (6%) pregnancies, whereas outcome (B) was identified in one (0.8%) pregnancy. The biosensor tested positive in five (4%) pregnancies. The predictive performance for outcome (A) was sensitivity = 0.29, specificity = 0.97, p = 0.02, positive predictive value (PPV) was 0.40 and negative predictive value (NPV) was 0.96. The predictive performance was higher for outcome (B) with sensitivity = 1.00, specificity = 0.97, p = 0.04, PPV = 0.20 and NPV = 1.00. Conclusively, these pilot-study results show future potential for biosensors as screening modality for FGR in a low-resource setting.
Worry about Climate Change and Urban Flooding Risk Preparedness in Southern Italy: A Survey in the Simeto River Valley (Sicily, Italy)
Authors: Nanni Paola , Peres David J. , Musumeci Rosaria E. , Cancelliere Antonino
Published: March 2021

Intensive urbanization and related increase of impervious surfaces, causes negative impacts on the hydrological cycle, amplifying the risk of urban floods. These impacts can get even worse due to potential climate change impacts. The urban areas of the Simeto River Valley (SRV), the largest river valley in Sicily (Italy), have been repeatedly hit by intense rainfall events in the last decades that lead to urban flooding, causing several damages and, in some instances, threats to population. In this paper, we present the results of a 10-question survey on climate change and risk perception in 11 municipalities of the SRV carried out within the activities of the LIFE project SimetoRES, which allowed to collect 1143 feedbacks from the residents. The survey investigated: (a) the level of worry about climate change in relation to extreme storms, (b) elements of urban flooding risk preparedness: the direct experience of the residents during heavy rain events, their trust in a civil protection regional alert system, and their knowledge of the correct behavior in case of flood, and (c) the willingness of citizens to implement sustainable drainage actions for climate change adaptation in their own municipality and real estates. The results show that more than 52% of citizens has inadequate knowledge of the correct behavior during flooding events and only 30% of them feel responsible for mitigation of flooding risk. There is a modest willingness by the population to support the construction of sustainable urban drainage infrastructures. A statistical cross-analysis of the answers to the different questions, based on contingency matrices and conditional frequencies, has shown that a greater worry about climate change has no significant impact either on the behavior of people in dangerous situations occurring during flooding events or on the willingness to support financially sustainable solutions. These results suggest that to build a higher worry about climate change and related urban flooding risk is not sufficient to have better preparedness, and that more direct educative actions are necessary in the area.
Self-Secondaries Formed by Cold Spot Craters on the Moon
Authors: Chang Yiren , Xiao Zhiyong , Liu Yang , Cui Jun
Published: March 2021

Self-secondaries are a population of background secondaries, and they have been observed on top of impact melt and ballistically emplaced ejecta deposits on various planetary bodies. Self-secondaries are formed by impacts of sub-vertically launched ejecta, but the launch mechanism is not confirmed. The potential threat of self-secondaries to the theoretical and applicable reliability of crater chronology has been noted, but not constrained. Hitherto discovered self-secondaries were located around complex impact craters, but their potential existence around simple craters has not been discovered. Here we report the first discovery of self-secondaries around lunar cold spot craters, which are an extremely young population of simple craters formed within the past ~1 million years on the Moon. Self-secondaries are widespread on layers of cascading flow-like ejecta deposits around cold spot craters. The spatial density of self-secondaries dwarfs that of potential primary craters. The spatial distribution of self-secondaries is highly heterogeneous across the ejecta deposits. With respect to the impactor trajectory that formed cold spot craters, self-secondaries formed at the downrange of the ejecta deposits have the largest spatial density, while those at the uprange have the smallest density. This density pattern holds for all cold spot craters that were formed by non-vertical impacts, but self-secondaries do not exhibit other systematic density variations at different radial distances or at other azimuths with respect to the impactor trajectory. Among known mechanics of ejecting materials to the exterior of impact craters, impact spallation is the most likely scenario to account for the required large ejection velocities and angles to form self-secondaries. The production population of self-secondaries is estimated based on the highly diverse crater size-frequency distributions across the ejecta deposits of cold spot craters. For a better understanding of the impact history on the Moon, a systematic investigation for the effect of self-secondaries on lunar crater chronology is required.
Historical Changes in Land Use and Suitability for Future Agriculture Expansion in Western Bahia, Brazil
Authors: Pimenta Fernando Martins , Speroto Allan Turini , Costa Marcos Heil , Dionizio Emily Ane
Published: March 2021

Western Bahia is a critical region in Brazil’s recent expansion of agricultural output. Its outstanding increase in production is associated with strong growth in cropland area and irrigation. Here we present analyses of Western Bahian historical changes in land use, including irrigated area, and suitability for future agricultural expansion that respects permanent protection areas and the limits established by the Brazilian Forest Code in the Cerrado biome. For this purpose, we developed a land use and land cover classification database using a random forest classifier and Landsat images. A spatial multicriteria decision analysis to evaluate land suitability was performed by combining this database with precipitation and slope data. We demonstrate that between 1990 and 2020, the region’s total agricultural area increased by 3.17 Mha and the irrigated area increased by 193,480 ha. Throughout the region, the transition between the different classes of land use and land cover followed different pathways and was strongly influenced by land suitability and also appears to be influenced by Brazil’s new Forest Code of 2012. We conclude that even if conservation restrictions are considered, agricultural area could nearly double in the region, with expansion possible mostly in areas we classify as moderately suitable for agriculture, which are subject to climate hazards when used for rainfed crops but are otherwise fine for pastures and irrigated croplands.
Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation
Authors: Pérez-Sanz Fernando , Riquelme-Pérez Miriam , Martínez-Barba Enrique , de la Peña-Moral Jesús , Salazar Nicolás Alejandro , Carpes-Ruiz Marina , Esteban-Gil Angel , Legaz-García María Del Carmen , Parreño-González María Antonia , Ramírez Pablo , Martínez Carlos M.
Published: March 2021

Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.
RETRACTED: Li et al. W-GPCR Routing Method for Vehicular Ad Hoc Networks. Sensors 2020, 20, 3406
Authors: Li Min , Gu Zhiru , Long Yonghong , Shu Xiaohua , Rong Qing , Ma Ziji , Shao Xun
Published: March 2021

The journal retracts the article [...]
Use of Vibrational Optical Coherence Tomography to Analyze the Mechanical Properties of Composite Materials
Authors: Silver Frederick H. , Kelkar Nikita , Deshmukh Tanmay
Published: March 2021

Energy storage and dissipation by composite materials are important design parameters for sensors and other devices. While polymeric materials can reversibly store energy by decreased chain randomness (entropic loss) they fail to be able to dissipate energy effectively and ultimately fail due to fatigue and molecular chain breakage. In contrast, composite tissues, such as muscle and tendon complexes, store and dissipate energy through entropic changes in collagen (energy storage) and viscous losses (energy dissipation) by muscle fibers or through fluid flow of the interfibrillar matrix. In this paper we review the molecular basis for energy storage and dissipation by natural composite materials in an effort to aid in the development of improved substrates for sensors, implants and other commercial devices. In addition, we introduce vibrational optical coherence tomography, a new technique that can be used to follow energy storage and dissipation by composite materials without physically touching them.
A Low-Cost Active Reflector for Interferometric Monitoring Based on Sentinel-1 SAR Images
Authors: Luzi Guido , Espín-López Pedro F. , Mira Pérez Fermín , Monserrat Oriol , Crosetto Michele
Published: March 2021

The effectiveness of radar interferometric techniques in non-urban areas can often be compromised due to the lack of stable natural targets. This drawback can be partially compensated through the installation of reference targets, characterized by a bright and stable radar response. The installation of passive corner reflectors (PCR) often represents a valid aid, but these objects are usually cumbersome, and suffer from severe weather conditions; furthermore, the installation of a PCR can be difficult and costly, especially in places with hard accessibility. Active reflectors (AR) represent a less cumbersome alternative to PCRs, while still providing a stable phase response. This paper describes the design, implementation, and test of an AR prototype, designed to operate with the Sentinel-1 synthetic aperture radar (SAR), aimed at providing a fair performance/cost benefit. These characteristics, obtained through a tradeoff between the use of off-the-shelf components and a simple architecture, can make the setup of a dense network (i.e., tens of devices) in the monitored areas feasible. The paper reports the design, implementation, and the analysis of different tests carried out in a laboratory, and in a real condition in the field, to illustrate AR reliability and estimate its phase stability.
Wideband Channel Characterization for 6G Networks in Industrial Environments
Authors: Al-Saman Ahmed , Mohamed Marshed , Cheffena Michael , Moldsvor Arild
Published: March 2021

Wireless data traffic has increased significantly due to the rapid growth of smart terminals and evolving real-time technologies. With the dramatic growth of data traffic, the existing cellular networks including Fifth-Generation (5G) networks cannot fully meet the increasingly rising data rate requirements. The Sixth-Generation (6G) mobile network is expected to achieve the high data rate requirements of new transmission technologies and spectrum. This paper presents the radio channel measurements to study the channel characteristics of 6G networks in the 107–109 GHz band in three different industrial environments. The path loss, K-factor, and time dispersion parameters are investigated. Two popular path loss models for indoor environments, the close-in free space reference distance (CI) and floating intercept (FI), are used to examine the path loss. The mean excess delay (MED) and root mean squared delay spread (RMSDS) are used to investigate the time dispersion of the channel. The path loss results show that the CI and FI models fit the measured data well in all industrial settings with a path loss exponent (PLE) of 1.6–2. The results of the K-factor show that the high value in industrial environments at the sub-6 GHz band still holds well in our measured environments at a high frequency band above 100 GHz. For the time dispersion parameters, it is found that most of the received signal energy falls in the early delay bins. This work represents a first step to establish the feasibility of using 6G networks operating above 100 GHz for industrial applications.
Efficient and Privacy-Preserving Energy Trading on Blockchain Using Dual Binary Encoding for Inner Product Encryption
Authors: Gaybullaev Turabek , Kwon Hee-Yong , Kim Taesic , Lee Mun-Kyu
Published: March 2021

The rapidly increasing expansion of distributed energy resources (DER), such as renewable energy systems and energy storage systems into the electric power system and the integration of advanced information and communication technologies enable DER owners to participate in the electricity market for grid services. For more efficient and reliable power system operation, the concept of peer-to-peer (P2P) energy trading has recently been proposed. The adoption of blockchain technology in P2P energy trading has been considered to be the most promising solution enabling secure smart contracts between prosumers and users. However, privacy concerns arise because the sensitive data and transaction records of the participants, i.e., the prosumers and the distribution system operator (DSO), become available to the blockchain nodes. Many efforts have been made to resolve this issue. A recent breakthrough in a P2P energy trading system on an Ethereum blockchain is that all bid values are encrypted using functional encryption and peer matching for trading is performed securely on these encrypted bids. Their protocol is based on a method that encodes integers to vectors and an algorithm that securely compares the ciphertexts of these vectors. However, the comparison method is not very efficient in terms of the range of possible bid values because the amount of computation grows linearly according to the size of this range. This paper addresses this challenge by proposing a new bid encoding algorithm called dual binary encoding, which dramatically reduces the amount of computation as it is only proportional to the square of the logarithm of the size of the encoding range. Moreover, we propose a practical mechanism for rebidding the remaining amount caused when the amounts from the two matching peers are not equal. Finally, the feasibility of the proposed method is evaluated by using a virtual energy trade testbed and a private Ethereum blockchain platform.
Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review
Authors: Ciecholewski Marcin , Kassjański Michał
Published: March 2021

The segmentation of liver blood vessels is of major importance as it is essential for formulating diagnoses, planning and delivering treatments, as well as evaluating the results of clinical procedures. Different imaging techniques are available for application in clinical practice, so the segmentation methods should take into account the characteristics of the imaging technique. Based on the literature, this review paper presents the most advanced and effective methods of liver vessel segmentation, as well as their performance according to the metrics used. This paper includes results available for four imaging methods, namely: computed tomography (CT), computed tomography angiography (CTA), magnetic resonance (MR), and ultrasonography (USG). The publicly available datasets used in research are also presented. This paper may help researchers gain better insight into the available materials and methods, making it easier to develop new, more effective solutions, as well as to improve existing approaches. This article analyzes in detail various segmentation methods, which can be divided into three groups: active contours, tracking-based, and machine learning techniques. For each group of methods, their theoretical and practical characteristics are discussed, and the pros and cons are highlighted. The most advanced and promising approaches are also suggested. However, we conclude that liver vasculature segmentation is still an open problem, because of the various deficiencies and constraints researchers need to address and try to eliminate from the solutions used.
Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3–24 Months
Authors: Miller Alexander , Huvanandana Jacqueline , Jones Peter , Jeffery Heather , Carberry Angela , Slater Christine , McEwan Alistair
Published: March 2021

Undernutrition in infants and young children is a major problem leading to millions of deaths every year. The objective of this study was to provide a new model for body composition assessment using near-infrared reflectance (NIR) to help correctly identify low body fat in infants and young children. Eligibility included infants and young children from 3–24 months of age. Fat mass values were collected from dual-energy x-ray absorptiometry (DXA), deuterium dilution (DD) and skin fold thickness (SFT) measurements, which were then compared to NIR predicted values. Anthropometric measures were also obtained. We developed a model using NIR to predict fat mass and validated it against a multi compartment model. One hundred and sixty-four infants and young children were included. The evaluation of the NIR model against the multi compartment reference method achieved an r value of 0.885, 0.904, and 0.818 for age groups 3–24 months (all subjects), 0–6 months, and 7–24 months, respectively. Compared with conventional methods such as SFT, body mass index and anthropometry, performance was best with NIR. NIR offers an affordable and portable way to measure fat mass in South African infants for growth monitoring in low-middle income settings.
Detecting Anomalous Transactions via an IoT Based Application: A Machine Learning Approach for Horse Racing Betting
Authors: Min Moohong , Lee Jemin Justin , Park Hyunbeom , Lee Kyungho
Published: March 2021

During the past decade, the technological advancement have allowed the gambling industry worldwide to deploy various platforms such as the web and mobile applications. Government agencies and local authorities have placed strict regulations regarding the location and amount allowed for gambling. These efforts are made to prevent gambling addictions and monitor fraudulent activities. The revenue earned from gambling provides a considerable amount of tax revenue. The inception of internet gambling have allowed professional gamblers to par take in unlawful acts. However, the lack of studies on the technical inspections and systems to prohibit unlawful internet gambling has caused incidents such as the Walkerhill Hotel incident in 2016, where fraudsters placed bets abnormally by modifying an Internet of Things (IoT)-based application called “MyCard”. This paper investigates the logic used by smartphone IoT applications to validate the location of users and then confirm continuous threats. Hence, our research analyzed transactions made on applications that operated using location authentication through IoT devices. Drawing on gambling transaction data from the Korea Racing Authority, this research used time series machine learning algorithms to identify anomalous activities and transactions. In our research, we propose a method to detect and prevent these anomalies by conducting a comparative analysis of the results of existing anomaly detection techniques and novel techniques.
A Simulation Framework for the Integration of Artificial Olfaction into Multi-Sensor Mobile Robots
Authors: Ojeda Pepe , Monroy Javier , Gonzalez-Jimenez Javier
Published: March 2021

The simulation of how a gas disperses in a environment is a necessary asset for the development of olfaction-based autonomous agents. A variety of simulators already exist for this purpose, but none of them allows for a sufficiently convenient integration with other types of sensing (such as vision), which hinders the development of advanced, multi-sensor olfactory robotics applications. In this work, we present a framework for the simulation of gas dispersal and sensing alongside vision by integrating GADEN, a state-of-the-art Gas Dispersion Simulator, with the Unity 3D, a video game development engine that is used in many different areas of research and helps with the creation of visually realistic, complex environments. We discuss the motivation for the development of this tool, describe its characteristics, and present some potential use cases that are based on cutting-edge research in the field of olfactory robotics.
Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank
Authors: Williamson James R. , Telfer Brian , Mullany Riley , Friedl Karl E.
Published: March 2021

Parkinson’s disease (PD) is a chronic movement disorder that produces a variety of characteristic movement abnormalities. The ubiquity of wrist-worn accelerometry suggests a possible sensor modality for early detection of PD symptoms and subsequent tracking of PD symptom severity. As an initial proof of concept for this technological approach, we analyzed the U.K. Biobank data set, consisting of one week of wrist-worn accelerometry from a population with a PD primary diagnosis and an age-matched healthy control population. Measures of movement dispersion were extracted from automatically segmented gait data, and measures of movement dimensionality were extracted from automatically segmented low-movement data. Using machine learning classifiers applied to one week of data, PD was detected with an area under the curve (AUC) of 0.69 on gait data, AUC = 0.84 on low-movement data, and AUC = 0.85 on a fusion of both activities. It was also found that classification accuracy steadily improved across the one-week data collection, suggesting that higher accuracy could be achievable from a longer data collection. These results suggest the viability of using a low-cost and easy-to-use activity sensor for detecting movement abnormalities due to PD and motivate further research on early PD detection and tracking of PD symptom severity.
Hierarchical Multipath Blockchain Based IoT Information Management Techniques for Efficient Distributed Processing of Intelligent IoT Information
Authors: Jeong Yoon-Su , Sim Sung-Ho
Published: March 2021

As cloud technology advances, devices such as IoT (Internet of Things) are being utilized in various areas ranging from transportation, manufacturing, energy, automation, space, defense, and healthcare. As the number of IoT devices increases, the safety of IoT information, which is vulnerable to cyber attacks, is emerging as an important area of interest in distributed cloud environments. However, integrity techniques are not guaranteed to easily identify the integrity threats and attacks on IoT information operating in the distributed cloud associated with IoT systems and CPS (Cyber-Physical System). In this paper, we propose a blockchain-based integrity verification technique in which large amounts of IoT information processed in distributed cloud environments can be guaranteed integrity in security threats related to IoT systems and CPS. The proposed technique aims to ensure the integrity of IoT information by linking information from IoT devices belonging to subgroups in distributed cloud environments to information from specific non-adjacent IoT devices and blockchain. This is because existing techniques rely on third-party organizations that the data owner can trust to verify the integrity of the data. The proposed technique identifies IoT information by connecting the paths of IoT pre- and subsequent blocks into block chains so that synchronization can be achieved between subgroups in distributed cloud environments. Furthermore, the proposed technique uses probabilistic similarity information between IoT information blocks to react flexibly to subgroups that constitute distributed clouds so that IoT information blocks are not exploited maliciously by third parties. As a result of performance evaluation, the proposed technique averaged 12.3% improvement in integrity processing time over existing techniques depending on blockchain size. Furthermore, the proposed technique has to hash the IoT information that constitutes a subgroup with probability-linked information, validating the integrity of large-capacity IoT information, resulting in an average of 8.8% lower overhead than existing techniques. In addition, the proposed technique has an average improvement of 14.3% in blockchain-based integrity verification accuracy over existing techniques, depending on the hash chain length.
Multilinear Mathematical Separation in Chromatography
Authors: Chen Yi , Zou Cong Ming , Bin Jun , Yang Min , Kang Chao
Published: March 2021

Chromatography is a powerful and generally applicable method for the analytical separation and quantification of the chemical constituents in complex mixtures because chromatographic separation can provide high selectivity by isolating all analytes from interferences. Multiway analysis based on the multilinear model is an increasingly widely used method for interference-free and fast determination of the chemical constituents also in complex mixtures because multilinear mathematical separation can provide high selectivity by extracting the pure signal of the analyte from the mixed signal of a real sample. By combining chromatographic separation with mathematical separation, multiway calibration method, multiway standard additions method, and multiway internal standard method can be established. Chromatography assisted by multiway analysis can reduce the requirements for complete chromatographic separation, save elution time, and decrease the consumption of the mobile phase, particularly when the peak coelution problem is difficult to solve. This review presents the fundamentals and analytical applications of multilinear mathematical separation in chromatography.