bluuebunny/crossref_metadata_2025 huggingface dataset:
High Purity of α-Lactalbumin from Binary Protein Mixture by Charged UF Membrane Far from the Isoelectric Point to Limit Fouling
Authors: Mazzei Rosalinda , Szymczak Anna Maria , Drioli Enrico , Al-Fageeh Mohamed , Aljohi Mohammed A. , Giorno Lidietta
Published: October 2021

Separation and high recovery factor of proteins similar in molecular mass is a challenging task, and heavily studied in the literature. In this work, a systematic study to separate a binary protein mixture by charged ultrafiltration membranes without affecting membrane performance was carried out. α-lactalbumin (ALA, 14.4 kDa) and β-lactoglobulin (BLG, 18.4 kDa) were used as a binary model system. These two proteins are the main proteins of whey, a very well-known byproduct from the dairy industry. Initially, a systematic characterization of individual proteins was carried out to determine parameters (protein size and aggregation, zeta potential) which could influence their passage through a charged membrane. Then, the influence of operating parameters (such as initial protein concentration, pH, and critical pressure) on the UF process was investigated, so as to identify conditions that limit membrane fouling whilst maximizing protein recovery factor and purity. The study permitted to identify process conditions able to fully separate ALA from BLG, with high purity (95%) and recovery factor (80%), in a single UF step. Compared to studies reported in literature, here, the main approach used was to carry out a charged UF process far from proteins isoelectric point (pI) to limit protein aggregation and membrane fouling.
Development of Environmentally Friendly Cellulose Derivative-Based Hydrogels for Contact Lenses Using a Radiation Crosslinking Technique
Authors: Hiroki Akihiro , Taguchi Mitsumasa
Published: October 2021

Although they have potential environmental pollution issues, soft contact lenses are value-added biodevices for medical use. Thus, it is important to reconsider starting materials and production methods to achieve sustainable development. In this study, hydrogels composed of hydroxypropyl cellulose (HPC) as an environmentally friendly material were produced by radiation and investigated for use in disposable soft contact lenses. HPC-based hydrogels with good mechanical properties and transparency were prepared by irradiation of electron beams on highly concentrated aqueous solutions containing HPC, polyethylene glycol #1000 dimethacrylate (23G), and 2-hydroxyethyl methacrylate (HEMA). The addition of 23G to HPC aqueous solutions resulted in an increase in the gel fraction as well as improved mechanical properties, such as tensile strength and elongation at break. The tensile strength and the elongation at break of HPC/HEMA/23G hydrogel obtained by the further addition of HEMA with HPC/23G aqueous solutions exhibited 0.2 MPa and 124%, which were approximately 2.0 and 1.8 times larger than these of the pure HPC hydrogel, respectively. The evaluation of the properties of the HPC/HEMA/23G hydrogel demonstrated its potential as a soft contact material with improved mechanical properties.
Practical Formation Control for Multiple Anonymous Robots System with Unknown Nonlinear Disturbances
Authors: Xu Peng , Tao Jin , Xu Minyi , Xie Guangming
Published: October 2021

This paper mainly investigates formation control problems for a group of anonymous mobile robots with unknown nonlinear disturbances on a plane, in which all robots can asymptotically converge to any formation patterns without collision, and maintain any required relative distance with neighboring robots. To solve this problem, all robots are modeled as kinematic points and can only acquire information from other robots and their targets. Furthermore, a flexible distributed control law is designed to solve the formation problem while no collisions between any robots can be guaranteed during the whole process. The outstanding feature of the proposed control method is that it can force all mobile robots to form not only uniform circle formations but also non-uniform and non-circular formations with moving target centers. At last, both theoretical analysis and numerical simulations show the feasibility of the proposed control law.
Integrated Assessment of Surface Water Quality in Danube River Chilia Branch
Authors: Teodorof Liliana , Ene Antoaneta , Burada Adrian , Despina Cristina , Seceleanu-Odor Daniela , Trifanov Cristian , Ibram Orhan , Bratfanof Edward , Tudor Mihaela-Iuliana , Tudor Marian , Cernisencu Irina , Georgescu Lucian Puiu , Iticescu Catalina
Published: October 2021

The Chilia branch is the north branch of the Danube River at the Romania-Ukraine border in the Danube Delta; it is a complex system with economic and ecological values. The surface water quality is a major concern and monitoring programs have been developed at the national and international level. The objective of this study was to evaluate the water quality of the Chilia branch in different sampling points from the mouth to the discharge in the Black Sea. The assessment of water quality was done at the individual level taking into account the nutrient concentrations and the standard limits for good ecological status and at integrative levels, using CCME WQI. The longitudinal distribution of Chilia branch water quality was done using GIS method. A total of 106 water samples were collected between 2013 and 2019 from five sampling points. At the individual level, the Chilia branch has a good ecological status except for its levels of total nitrogen, due to the organic nitrogen contribution in 2015. In 90% of nutrient concentrations, low values predominate and high values are considered extreme; only in 10% do high values predominate and low values are considered extreme concentrations. In equal percentages, 50% of the nutrient concentrations have a high degree of heterogeneity and the other 50% of concentration values are very close to the average values, with a high degree of homogeneity. CCME WQI method indicated that 39.93% of surface waters from the Chilia branch had an excellent quality, 45.45% a good quality, and only 14.62% a fair quality.
A Unified Framework of Deep Learning-Based Facial Expression Recognition System for Diversified Applications
Authors: Hossain Sanoar , Umer Saiyed , Asari Vijayan , Rout Ranjeet Kumar
Published: October 2021

This work proposes a facial expression recognition system for a diversified field of applications. The purpose of the proposed system is to predict the type of expressions in a human face region. The implementation of the proposed method is fragmented into three components. In the first component, from the given input image, a tree-structured part model has been applied that predicts some landmark points on the input image to detect facial regions. The detected face region was normalized to its fixed size and then down-sampled to its varying sizes such that the advantages, due to the effect of multi-resolution images, can be introduced. Then, some convolutional neural network (CNN) architectures were proposed in the second component to analyze the texture patterns in the facial regions. To enhance the proposed CNN model’s performance, some advanced techniques, such data augmentation, progressive image resizing, transfer-learning, and fine-tuning of the parameters, were employed in the third component to extract more distinctive and discriminant features for the proposed facial expression recognition system. The performance of the proposed system, due to different CNN models, is fused to achieve better performance than the existing state-of-the-art methods and for this reason, extensive experimentation has been carried out using the Karolinska-directed emotional faces (KDEF), GENKI-4k, Cohn-Kanade (CK+), and Static Facial Expressions in the Wild (SFEW) benchmark databases. The performance has been compared with some existing methods concerning these databases, which shows that the proposed facial expression recognition system outperforms other competing methods.
Hysteresis-Based Current Control Approach by Means of the Theory of Switched Systems
Authors: Thielmann Malte , Hans Florian
Published: October 2021

In this paper, a novel hysteresis-based current control approach is presented. The basis of the developed control approach is the theory of switched systems, in particular, the system class of switched systems with multiple equilibria. The proposed approach guarantees the convergence of the state trajectory into a region around a reference trajectory by selective switching between the individual subsystems. Here, the reference trajectory is allowed to be time varying, but lies within the state space spanned by the subsystem equilibria. Since already published approaches only show convergence to a common equilibrium of all subsystems, the extension to the mentioned state space is a significant novelty. Moreover, the approach is not limited to the number of state variables, nor to the number of subsystems. Thus, the applicability to a large number of systems is given. In the course of the paper, the theoretical basics of the approach are first explained by referring to a trivial example system. Then, it is shown how the theory can be applied to a practical application of a voltage source converter that is connected to a permanent-magnet synchronous motor. After deriving the limits of the presented control strategy, a simulation study confirms the applicability on the converter system. The paper closes with a detailed discussion about the given results.
An Integrative Experimental Approach to Design Optimization and Removal Strategies of Supporting Structures Used during L-PBF of SS316L Aortic Stents
Authors: Grad Marius , Nadammal Naresh , Schultheiss Ulrich , Lulla Philipp , Noster Ulf
Published: October 2021

One of the fundamental challenges in L-PBF of filigree geometries, such as aortic stents used in biomedical applications, is the requirement for a robust yet easily removable support structure that allows each component to be successfully fabricated without distortion. To solve this challenge, an integrative experimental approach was attempted in the present study by identifying an optimal support structure design and an optimized support removal strategy for this design. The specimens were manufactured using four different support structure designs based on the geometry exposed to the laser beam during the L-PBF. Support removal procedures included sand blasting (SB), glass bead blasting (GB), and electrochemical polishing (ECP). The two best-performing designs (line and cross) were chosen due to shorter lead times and lower material consumption. As an additional factor that indicates a stable design, the breaking load requirement to remove the support structures was determined. A modified line support with a 145° included angle was shown to be the best support structure design in terms of breaking load, material consumption, and manufacturing time. All three procedures were used to ensure residue-free support removal for this modified line support design, with ECP proving to be the most effective.
Integration of Functional Link Neural Networks into a Parameter Estimation Methodology
Authors: Le Tuan-Ho , Tang Mengyuan , Jang Jun Hyuk , Jang Hyeonae , Shin Sangmun
Published: October 2021

In the field of robust design, most estimation methods for output responses of input factors are based on the response surface methodology (RSM), which makes several assumptions regarding the input data. However, these assumptions may not consistently hold in real-world industrial problems. Recent studies using artificial neural networks (ANNs) indicate that input–output relationships can be effectively estimated without the assumptions mentioned above. The primary objective of this research is to generate a new, robust design dual-response estimation method based on ANNs. First, a second-order functional-link-NN-based robust design estimation approach has been proposed for the process mean and standard deviation (i.e., the dual-response model). Second, the optimal structure of the proposed network is defined based on the Bayesian information criterion. Finally, the estimated response functions of the proposed functional-link-NN-based estimation method are applied and compared with that obtained using the conventional least squares method (LSM)-based RSM. The numerical example results imply that the proposed functional-link-NN-based dual-response robust design estimation model can provide more effective optimal solutions than the LSM-based RSM, according to the expected quality loss criteria.
Comparison of the Effect of Enhancing Dry Fermented Sausages with Salvia hispanica and Nigella sativa Seed on Selected Physicochemical Properties Related to Food Safety during Processing
Authors: Borrajo Paula , Karwowska Małgorzata , Stasiak Dariusz M. , Lorenzo Jose M. , Żyśko Marlena , Solska Elżbieta
Published: October 2021

The aim of the study is to compare the effects of Salvia hispanica (Chia) seed and Nigella sativa (Black Cumin) seed in traditionally produced dry fermented sausages with reduced nitrites on the changes in physicochemical parameters (water content, pH, and water activity), thioarbituric acid reactive substances (TBARS), color parameters, the content of nitrosopigments, and microbial counts throughout the production process. At the end of the processing, the content of biogenic amines was also determined. Five samples were analyzed during the 30-day production process: the control sample, samples with 1% and 2% additions of chia seed, and samples with 1% and 2% additions of black cumin seed. It was indicated that the addition of chia or black cumin did not exert any effect on water content or water activity changes in fermented sausages. At the end of production, the samples were characterized by low water activity, in the range of 0.798–0.813. The sausages with chia seeds were characterized by the lowest pH due to the highest proliferation of lactic acid bacteria. TBARS values did not alter in both control and black cumin sausages throughout the experiment. Microbiological analysis showed that the addition of chia or black cumin seed enhances the proliferation of the lactic acid bacteria and caused a reduction in the number of Enterobacteriaceae in comparison to the control.
Methods for Studying Bacterial–Fungal Interactions in the Microenvironments of Soil
Authors: Mandolini Edoardo , Probst Maraike , Peintner Ursula
Published: October 2021

Due to their small size, microorganisms directly experience only a tiny portion of the environmental heterogeneity manifested in the soil. The microscale variations in soil properties constrain the distribution of fungi and bacteria, and the extent to which they can interact with each other, thereby directly influencing their behavior and ecological roles. Thus, to obtain a realistic understanding of bacterial–fungal interactions, the spatiotemporal complexity of their microenvironments must be accounted for. The objective of this review is to further raise awareness of this important aspect and to discuss an overview of possible methodologies, some of easier applicability than others, that can be implemented in the experimental design in this field of research. The experimental design can be rationalized in three different scales, namely reconstructing the physicochemical complexity of the soil matrix, identifying and locating fungi and bacteria to depict their physical interactions, and, lastly, analyzing their molecular environment to describe their activity. In the long term, only relevant experimental data at the cell-to-cell level can provide the base for any solid theory or model that may serve for accurate functional prediction at the ecosystem level. The way to this level of application is still long, but we should all start small.
Improvement of the Chimney Effect in Stack Ventilation
Authors: Antczak-Jarząbska Romana , Pawłowski Krzysztof , Niedostatkiewicz Maciej
Published: October 2021

The article is focused on the airflow in a ventilation system in a building. The work examines the methods which enhance the chimney effect. In this paper, three cases with different chimneys were analyzed for the full-scale experiment. These cases were characterized by different geometrical and material parameters, leading to differences in the intensity of the ventilation airflow. The common denominator of the cases was the room with the air inlet and outlet to the ventilation system. The differences between the experimental cases concerned the chimney canal itself, and more precisely its part protruding above the roof slope. The first experimental case concerned a ventilation canal made in a traditional way, from solid ceramic brick. The second experimental case concerned the part that led out above the roof slope with a transparent barrier, called a solar chimney. In the third experimental case, a rotary type of chimney cap was installed on the chimney to improve the efficiency of stack ventilation. All these cases were used to determine the performance of natural ventilation—Air Change per Hour (CH). Additionally, the paper presents a technical and economic comparison of the solutions used.
Novel Sodium Alginate/Polyvinylpyrrolidone/TiO2 Nanocomposite for Efficient Removal of Cationic Dye from Aqueous Solution
Authors: Elessawy Noha A. , Gouda Marwa H. , Elnouby Mohamed S. , Zahran Hoda F. , Hashim Aly , Abd El-Latif Mona M. , Santos Diogo M. F.
Published: October 2021

The combination of adsorption and photodegradation processes is an effective technique for the removal of dye contaminants from water, which is motivating the development of novel adsorbent-photocatalyst materials for wastewater treatment. Herein, novel nanocomposite porous beads were developed using titanium dioxide (TiO2) nanotubes embedded in a sodium alginate (SA)/polyvinylpyrrolidone (PVP) matrix using calcium chloride solution as a crosslinker. The prepared nanocomposite beads’ performance was examined as an adsorbent-photocatalyst for the breakdown of methylene blue in aqueous solutions. Several operation factors influencing the dye decomposition process, including photocatalyst dosage, illumination time, light intensity, and stability were investigated. The findings demonstrated that the removal activity of the beads changed with the TiO2 weight ratio in the composite. It was found that SA/PVP/TiO2-3 nanocomposite beads presented the greatest deterioration efficiency for methylene blue dye (98.9%). The cycling ability and reusability of the prepared SA/PVP/TiO2 nanocomposite beads recommend their use as efficient, eco-friendly materials for the treatment of wastewaters contaminated with cationic dyes.
Multi-Center Healthcare Data Quality Measurement Model and Assessment Using OMOP CDM
Authors: Kim Ki-Hoon , Choi Wona , Ko Soo-Jeong , Chang Dong-Jin , Chung Yeon-Woog , Chang Se-Hyun , Kim Jae-Kwon , Kim Dai-Jin , Choi In-Young
Published: October 2021

Healthcare data has economic value and is evaluated as such. Therefore, it attracted global attention from observational and clinical studies alike. Recently, the importance of data quality research emerged in healthcare data research. Various studies are being conducted on this topic. In this study, we propose a DQ4HEALTH model that can be applied to healthcare when reviewing existing data quality literature. The model includes 5 dimensions and 415 validation rules. The four evaluation indicators include the net pass rate (NPR), weighted pass rate (WPR), net dimensional pass rate (NDPR), and weighted dimensional pass rate (WDPR). They were used to evaluate the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) at three medical institutions. These indicators identify differences in data quality between the institutions. The NPRs of the three institutions (A, B, and C) were 96.58%, 90.08%, and 90.87%, respectively, and the WPR was 98.52%, 94.26%, and 94.81%, respectively. In the quality evaluation of the dimensions, the consistency was 70.06% of the total error data. The WDPRs were 98.22%, 94.74%, and 95.05% for institutions A, B, and C, respectively. This study presented indices for comparing quality evaluation models and quality in the healthcare field. Using these indices, medical institutions can evaluate the quality of their data and suggest practical directions for decreasing errors.
Effect of Quarry Rock Dust as a Binder on the Properties of Fly Ash and Slag-Based Geopolymer Concrete Exposed to Ambient and Elevated Temperatures
Authors: Hussain Khadim , Butt Faheem , Alwetaishi Mamdooh , Waqas Rana Muhammad , Aslam Fahid , Ibraheem Muhammad , Xulong Zhu , Ahmad Naveed , Tufail Rana Faisal , Musarat Muhammad Ali , Hussein Enas E.
Published: October 2021

This study presents the performance of quarry rock dust (QRD) incorporated fly ash (FA) and slag (SG) based geopolymer concretes (QFS-GPC) exposed to ambient and elevated temperatures. A total of five QFS-GPC mix types were prepared. The quantity of FA (50%) was kept constant in all the mixes, and SG was replaced by 5%, 10%, 15%, and 20% of QRD. The fresh, hardened properties of the QFS-GPC mixes, viz., workability, compressive strength, splitting tensile strength, and flexural strengths, and XRD for identification of reaction phases were evaluated. The prepared mixes were also heated up to 800 °C to evaluate the residual compressive strength and weight loss. The workability of the QFS-GPC mixes was observed to be reduced by increasing the dosage (0 to 20%) of QRD. Superplasticizer (SP) was used to maintain the medium standard of workability. The compressive, tensile, and flexural strengths were increased by replacing SG with QRD up to 15%, whereas a further higher dosage (20%) of QRD reduced the mechanical strengths of the QFS-GPC mixes. The strength of the QFS-GPC specimens, heated to elevated temperatures up to 800 °C, was reduced persistently with the increased contents of QRD from 0 to 20%. It was concluded from the study that QFS-GPC can be used to achieve 30 MPa strength of concrete.
Driver Fatigue Detection Based on Residual Channel Attention Network and Head Pose Estimation
Authors: Ye Mu , Zhang Weiwei , Cao Pengcheng , Liu Kangan
Published: October 2021

Driver fatigue is the culprit of most traffic accidents. Visual technology can intuitively judge whether the driver is in the state of fatigue. A driver fatigue detection system based on the residual channel attention network (RCAN) and head pose estimation is proposed. In the proposed system, Retinaface is employed for face location and outputs five face landmarks. Then the RCAN is proposed to classify the state of eyes and the mouth. The RCAN includes a channel attention module, which can adaptively extract key feature vectors from the feature map, which significantly improves the classification accuracy of the RCAN. In the self-built dataset, the classification accuracy of the eye state of the RCAN reaches 98.962% and that of the mouth state reaches 98.561%, exceeding other classical convolutional neural networks. The percentage of eyelid closure over the pupil over time (PERCLOS) and the mouth opening degree (POM) are used for fatigue detection based on the state of eyes and the mouth. In addition, this article proposes to use a Perspective-n-Point (PnP) method to estimate the head pose as an essential supplement for driving fatigue detection and proposes over-angle to evaluate whether the head pose is excessively deflected. On the whole, the proposed driver fatigue system integrates 3D head pose estimation and fatigue detection based on deep learning. This system is evaluated by the four datasets and shows success of the proposed method with their high performance.
Transmission Delay-Based Uplink Multi-User Scheduling in IEEE 802.11ax Networks
Authors: Kim Yonggang , Kim Gyungmin , Oh Youngwoo , Choi Wooyeol
Published: October 2021

As the demands for uplink traffic increase, improving the uplink throughput has attracted research attention in IEEE 802.11 networks. To avoid excessive competition among stations and enhance the uplink throughput performance, the IEEE 802.11ax standard supports uplink multi-user transmission scenarios, in which AP triggers certain stations in a network to transmit uplink data simultaneously. The performance of uplink multi-user transmissions highly depends on the scheduler, and station scheduling is still an open research area in IEEE-802.11ax-based networks. In this paper, we propose a transmission delay-based uplink multi-user scheduling method. The proposed method consists of two steps. In the first step, the proposed method makcreateses station clusters so that stations in each cluster have similar expected transmission delays. The transmission delay-based station clustering increases the ues of uplink data channels during the uplink multi-user transmission scenario specified in IEEE 802.11ax. In the second step, the proposed method selects cluster for uplink multi-user transmissions. The cluster selection can be performed with a proportional fair-based approach. With the highly channel-efficient station cluster, the proposed scheduling method increases network throughput performance. Through the IEEE 802.11ax standard compliant simulations, we verify the network throughput performance of the proposed uplink scheduling method.
Transformers in Pedestrian Image Retrieval and Person Re-Identification in a Multi-Camera Surveillance System
Authors: Tahir Muhammad , Anwar Saeed
Published: October 2021

Person Re-Identification is an essential task in computer vision, particularly in surveillance applications. The aim is to identify a person based on an input image from surveillance photographs in various scenarios. Most Person re-ID techniques utilize Convolutional Neural Networks (CNNs); however, Vision Transformers are replacing pure CNNs for various computer vision tasks such as object recognition, classification, etc. The vision transformers contain information about local regions of the image. The current techniques take this advantage to improve the accuracy of the tasks underhand. We propose to use the vision transformers in conjunction with vanilla CNN models to investigate the true strength of transformers in person re-identification. We employ three backbones with different combinations of vision transformers on two benchmark datasets. The overall performance of the backbones increased, showing the importance of vision transformers. We provide ablation studies and show the importance of various components of the vision transformers in re-identification tasks.
A Spatial Compounding Method for Non-Delayed Sequential Beamforming
Authors: Liang Siyi , Wang Lidai
Published: October 2021

We present a new spatial compounding method to improve the contrast of ultrasonic images for non-delayed sequential beamforming (NDSB). Sequential beamforming adopts more than one beamformer to reconstruct B-mode images which has the advantage of simple front-end electronics and fast data transfer rate. Via field pattern analysis, we propose a compounding method where two more sub-images can be reconstructed along with the NDSB sub-image. These sub-images can be seen as being produced with different transmit origins; thus, their summation enhances image contrast. Image quality was analyzed in terms of spatial resolution, contrast ratio (CR), and contrast-to-noise ratio (CNR). The proposed compounding method improves the lateral resolution up to 41%. In vitro results confirm a 13.0-dB CR and 4.0-dB CNR improvement. In vivo results reveal 10.9-dB and 6.0-dB improvement in CR and CNR for cross-section jugular vein and 8.0-dB and 4.5-dB improvement in CR and CNR for the longitudinal-section carotid artery.
A Shallow–Deep Feature Fusion Method for Pedestrian Detection
Authors: Liu Daxue , Zang Kai , Shen Jifeng
Published: October 2021

In this paper, a shallow–deep feature fusion (SDFF) method is developed for pedestrian detection. Firstly, we propose a shallow feature-based method under the ACF framework of pedestrian detection. More precisely, improved Haar-like templates with Local FDA learning are used to filter the channel maps of ACF such that these Haar-like features are able to improve the discriminative power and therefore enhance the detection performance. The proposed shallow feature is also referred to as weighted subset-haar-like feature. It is efficient in pedestrian detection with a high recall rate and precise localization. Secondly, the proposed shallow feature-based detection method operates as a region proposal. A classifier equipped with ResNet is then used to refine the region proposals to judge whether each region contains a pedestrian or not. The extensive experiments evaluated on INRIA, Caltech, and TUD-Brussel datasets show that SDFF is an effective and efficient method for pedestrian detection.
Semantic Multigranularity Feature Learning for High-Resolution Remote Sensing Image Scene Classification
Authors: Ma Xinyi , Xiao Zhifeng , Yun Hong-sik , Lee Seung-Jun
Published: October 2021

High-resolution remote sensing image scene classification is a challenging visual task due to the large intravariance and small intervariance between the categories. To accurately recognize the scene categories, it is essential to learn discriminative features from both global and local critical regions. Recent efforts focus on how to encourage the network to learn multigranularity features with the destruction of the spatial information on the input image at different scales, which leads to meaningless edges that are harmful to training. In this study, we propose a novel method named Semantic Multigranularity Feature Learning Network (SMGFL-Net) for remote sensing image scene classification. The core idea is to learn both global and multigranularity local features from rearranged intermediate feature maps, thus, eliminating the meaningless edges. These features are then fused for the final prediction. Our proposed framework is compared with a collection of state-of-the-art (SOTA) methods on two fine-grained remote sensing image scene datasets, including the NWPU-RESISC45 and Aerial Image Datasets (AID). We justify several design choices, including the branch granularities, fusion strategies, pooling operations, and necessity of feature map rearrangement through a comparative study. Moreover, the overall performance results show that SMGFL-Net consistently outperforms other peer methods in classification accuracy, and the superiority is more apparent with less training data, demonstrating the efficacy of feature learning of our approach.
Leaders’ Perspectives on IoT Security Risk Management Strategies in Surveyed Organizations Relative to IoTSRM2
Authors: Popescu Traian Mihai , Popescu Alina Madalina , Prostean Gabriela
Published: October 2021

In the COVID-19 and post-pandemic business environment, leveraging robust Internet of Things (IoT) security risk management strategies becomes of particular importance to IoT adopters. Thus, given that no research study was found to exclusively focus on the current state of IoT security risk management strategies in organizations, this article aims to support IoT security practitioners to peer benchmark and enhance their IoT security risk management strategies. In a nutshell, this study relies on a mixed methods research methodology, and its main contribution is the determination of the current state of the IoT security risk management strategies in the surveyed organizations relative to our IoT Security Risk Management Strategy Reference Model (IoTSRM2). Hence, this study entails designing and conducting a survey, analyzing survey responses, and reporting survey results based on our IoTSRM2 and proposed three-phased survey methodology. Furthermore, before discussing the related work, this article provides our survey results for the surveyed large and small-medium organizations, the surveyed large organizations, and the surveyed large Technology, Media, and Telecom (TMT) organizations. For instance, our results reveal that while most surveyed organizations perform IoT risk assessments and focus on IoT infrastructure resilience, they fail in strategizing IoT governance and risk management, among others.
Hybrid Harmony Search Algorithm Applied to the Optimal Coordination of Overcurrent Relays in Distribution Networks with Distributed Generation
Authors: Saldarriaga-Zuluaga Sergio D. , López-Lezama Jesús M. , Muñoz-Galeano Nicolás
Published: October 2021

In recent years, distributed generation (DG) has become more common in modern distribution networks (DNs). The presence of these small-scale generation units within a DN brings new challenges to protection engineers, since short-circuit currents tend to increase; additionally, as with microgrids, modern DNs may feature several operational modes depending on their topology and the availability of DG. This paper presents a methodology for the optimal coordination of overcurrent relays (OCRs) in modern DNs with a high presence of DG. Given the fact that protection coordination is a non-linear and non-convex optimization problem, a hybrid harmony search and simulated annealing (HS-SA) approach was implemented for its solution and compared against other techniques, such as conventional HS, genetic algorithm (GA), particle swarm optimization (PSO) and hybrid PSO-HS. Several tests were performed on a DN, considering different operative scenarios as a function of the DG available within the network. A comparison with other works reported in the specialized literature was carried out, evidencing the applicability and effectiveness of the HS-SA technique in solving the optimal OCR coordination problem in modern DNs.
Survey on Data Hiding Based on Block Truncation Coding
Authors: Kim Cheonshik , Yang Ching-Nung , Baek Jinsuk , Leng Lu
Published: October 2021

Data hiding technology has achieved many technological developments through continuous research over the past 20 years along with the development of Internet technology and is one of the research fields that are still receiving attention. In the beginning, there were an intensive amount of studies on digital copyright issues, and since then, interest in the field of secret communications has been increasing. In addition, research on various security issues using this technology is being actively conducted. Research on data hiding is mainly based on images and videos, and there are many studies using JPEG and BMP in particular. This may be due to the use of redundant bits that are characteristic of data hiding techniques. On the other hand, block truncation coding-based images are relatively lacking in redundant bits useful for data hiding. For this reason, researchers began to pay more attention to data hiding based on block-cutting coding. As a result, many related papers have been published in recent years. Therefore, in this paper, the existing research on data hiding technology of images compressed by block-cut coding among compressed images is summarized to introduce the contents of research so far in this field. We simulate a representative methodology among existing studies to find out which methods are effective through experiments and present opinions on future research directions. In the future, it is expected that various data hiding techniques and practical applications based on modified forms of absolute moment block truncation coding will continue to develop.
Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
Authors: Do Nguyet Quang , Selamat Ali , Krejcar Ondrej , Yokoi Takeru , Fujita Hamido
Published: October 2021

Phishing detection with high-performance accuracy and low computational complexity has always been a topic of great interest. New technologies have been developed to improve the phishing detection rate and reduce computational constraints in recent years. However, one solution is insufficient to address all problems caused by attackers in cyberspace. Therefore, the primary objective of this paper is to analyze the performance of various deep learning algorithms in detecting phishing activities. This analysis will help organizations or individuals select and adopt the proper solution according to their technological needs and specific applications’ requirements to fight against phishing attacks. In this regard, an empirical study was conducted using four different deep learning algorithms, including deep neural network (DNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM), and gated recurrent unit (GRU). To analyze the behaviors of these deep learning architectures, extensive experiments were carried out to examine the impact of parameter tuning on the performance accuracy of the deep learning models. In addition, various performance metrics were measured to evaluate the effectiveness and feasibility of DL models in detecting phishing activities. The results obtained from the experiments showed that no single DL algorithm achieved the best measures across all performance metrics. The empirical findings from this paper also manifest several issues and suggest future research directions related to deep learning in the phishing detection domain.
Sensitivity Analysis of PROMETHEE II for the Evaluation of Environmental Websites
Authors: Kabassi Katerina , Martinis Aristotelis
Published: October 2021

The quality of content and the attractiveness of an environmental website can create an environmentally friendly attitude before one visits a secured area. However, a website should be evaluated to ensure that its goal is met. For this reason, the websites of environmental content have been evaluated using a combination of AHP and PROMETHEE II. More specifically, the websites of environmental content that have been selected to be evaluated are the websites of the national parks of Italy. The main contribution of the particular paper is on comparing PROMETHEE II with three other common MCDM models (SAW, WPM, TOPSIS) and performing a sensitivity analysis to make the comparison more thorough. As a result, the conclusions drawn by this experiment involve the appropriateness of PROMETHEE II for the ranking of environmental websites as well as the robustness of the different MCDM models. The experiment revealed that the PROMETHEE II model was found to be very effective in ranking environmental websites and is the most robust model compared to the other ones. Furthermore, the evaluation of the websites of national parks in Italy revealed that the electronic presence of national parks is at an early stage.
Effects of Fences and Green Zones on the Air Flow and PM2.5 Concentration around a School in a Building-Congested District
Authors: Park Soo-Jin , Kang Geon , Choi Wonsik , Kim Do-Yong , Kim Jinsoo , Kim Jae-Jin
Published: October 2021

We investigated the effects of wall- and tree-type fences on the airflow and fine particular matter (PM2.5) concentration around a school using a computational fluid dynamics (CFD) model. First, we validated the simulated wind speeds and PM2.5 concentrations against measured values, and the results satisfied the recommended criteria of the statistical validation indices used. Then, we evaluated the fence effects for 16 inflow directions by conducting numerical simulations with different fence types and heights. With east–southeasterly inflow, relatively high PM2.5 from the road was transported to the school. However, the wall-type fence prevented the PM2.5 from the road from entering the school, and the PM2.5 concentration decreased significantly downwind of the fence. With east–northeasterly inflow, the horizontal wind speed decreased due to the drag caused by the tree-type fence, resulting in a shift in the flow convergence region. The PM2.5 concentration decreased in the region of strengthened upward flow. This occurred because the number of pollutants transported from the background decreased. A comparison of the two fence types revealed that the effect of the tree-type fence on inbound pollutants was more significant, due to increased upward flows, than the effect of the wall-type fence.
Description of the Guelder Rose Fruit in Terms of Chemical Composition, Antioxidant Capacity and Phenolic Compounds
Authors: Mazur Monika , Szperlik Jakub , Salejda Anna Marietta , Krasnowska Grażyna , Kolniak-Ostek Joanna , Bąbelewski Przemysław
Published: October 2021

The Viburnum opulus (guelder rose) fruit is a Polish native fruit that grows across almost the entire territory of the country, except for the Tatra Mountains. The fruits are rich in nutrients and biologically active compounds, however, they are rarely used for food production or as a dietary ingredient. This study assessed this fruit for dry matter (17.3% ± 0.7), extract (13% ± 0.53), titratable acidity (1.78 g/100 g ± 0.48), pectin (1.10% ± 0.97), vitamin C (47.79 mg/100 g ± 1.02), the amount of sugar identified as fructose (6.85 g/100 g ± 0.67), antioxidant activity, ABTS (14.64 ± 0.61 μM Trolox/100 g), FRAP (211.14 ± 1.04 µM Trolox/100 g) and total polyphenolic content TPC (1695.88 ± 0.86 mg GAE/100 g). The analysis of phenolic identification was performed using the ACQUITY Ultra Performance LC (UPLC) system with the binary solvent manager and PDA detector.
Effect of Pyrolysis Temperature on Copper Aqueous Removal Capability of Biochar Derived from the Kelp Macrocystis pyrifera
Authors: Araya Matías , Rivas Jorge , Sepúlveda Graciela , Espinoza-González Camila , Lira Sebastián , Meynard Andrés , Blanco Elodie , Escalona Nestor , Ginocchio Rosanna , Garrido-Ramírez Elizabeth , Contreras-Porcia Loretto
Published: October 2021

Seaweed biochar is an efficient alternative bioadsorbent for Cu2+ removal due to its low cost and heavy metal removal capacity. Using the slow pyrolysis process, we produced biochars from Macrocystis pyrifera at 300 (BC300), 450 (BC450), and 600 °C (BC600). The physicochemical and structural properties of the biochar samples improved with increasing pyrolysis temperature from 300 to 450 °C, whereas no significant differences were observed with further increases in temperature to 600 °C. The yield ranged between 49% and 62% and had a high ash content (57.5–71.1%). BC450 and BC600 presented the highest surface areas and higher porosities. The FTIR spectra indicated that an increase of temperature decreased the acidic functional groups due to depolymerization and the dehydration processes, increasing the aromatic structures and the presence of calcium carbonate. The fittings of the kinetic models were different for the BCs: for the BC450 and BC600 samples, the Cu2+ adsorption was well-represented by a pseudo-first-order model; for BC300, a better fit was obtained with the pseudo-second-order model. The rate-limiting step of Cu2+ adsorption on BCs was represented by both models, liquid film diffusion and intraparticle diffusion, with surface diffusion being more important in BC300 and BC600, and intraparticle diffusion in BC450, in agreement with the pore size of the biochar samples. The adsorption isotherms of all BCs showed Langmuir behavior, representative of a chemisorption process, which was corroborated by the energy adsorption values determined by the D–R model. The maximum monolayer Cu2+ adsorption capacities were 93.55 and 58.0 mg g−1 for BC600 and BC450, respectively, whereas BC450 presented the highest affinity. Other mechanisms involved in controlling heavy metal removal from aqueous suspensions using these seaweed biochars remain to be explored. We conclude that BC450 and BC600 from M. pyrifera are the most efficient adsorbents for Cu2+ aqueous removal and are thus an appropriate alternative for bioremediation.
Bioactive Fraction of Aronia melanocarpa Fruit Inhibits Adipogenic Differentiation of Cultured 3T3-L1 Cells
Authors: Lee Hwa-Young , Suh Kwang Sik , Kim Young Il , Jang Bong-Keun , Kim Bo-Hyung , Yim Sung-Vin
Published: October 2021

Obesity is caused by excessive fat cells and the overgrowth of adipocytes and is a major risk factor for several chronic illnesses. Aronia melanocarpa fruit is rich in anthocyanins and polyphenols and has protective effects against various diseases. In this study, we examined the effect of Aronia extract (Aronia bioactive fraction, ABF®) on the biomarkers of the adipogenic pathway during adipocyte differentiation of 3T3-L1 cells. Lipid accumulation was verified by Oil Red O staining. mRNA and protein expression of lipoprotein lipase (LPL), CCAAT/enhancer-binding protein α (C/EBPα), peroxisome proliferator-activated receptor γ (PPARγ), fatty acid-binding protein 2 (FABP2), and fatty acid synthase (FAS) were assayed by RT-qPCR and Western blot analyses. Adiponectin and leptin secretion were measured using enzyme-linked immunosorbent assays. ABF® treatment downregulated lipid accumulation based on Oil Red O staining. ABF®-treated cells exhibited decreased mRNA and protein expression of LPL, C/EBPα, PPARγ, FABP2, and FAS. Moreover, ABF® treatment significantly increased adiponectin secretion and decreased leptin secretion. In conclusion, ABF® has anti-adipogenic effects on the differentiation of 3T3-L1 cells and may be used as an anti-obesity nutraceutical.
A Randomized Bag-of-Birds Approach to Study Robustness of Automated Audio Based Bird Species Classification
Authors: Ghani Burooj , Hallerberg Sarah
Published: October 2021

The automatic classification of bird sounds is an ongoing research topic, and several results have been reported for the classification of selected bird species. In this contribution, we use an artificial neural network fed with pre-computed sound features to study the robustness of bird sound classification. We investigate, in detail, if and how the classification results are dependent on the number of species and the selection of species in the subsets presented to the classifier. In more detail, a bag-of-birds approach is employed to randomly create balanced subsets of sounds from different species for repeated classification runs. The number of species present in each subset is varied between 10 and 300 by randomly drawing sounds of species from a dataset of 659 bird species taken from the Xeno-Canto database. We observed that the shallow artificial neural network trained on pre-computed sound features was able to classify the bird sounds. The quality of classifications were at least comparable to some previously reported results when the number of species allowed for a direct comparison. The classification performance is evaluated using several common measures, such as the precision, recall, accuracy, mean average precision, and area under the receiver operator characteristics curve. All of these measures indicate a decrease in classification success as the number of species present in the subsets is increased. We analyze this dependence in detail and compare the computed results to an analytic explanation assuming dependencies for an idealized perfect classifier. Moreover, we observe that the classification performance depended on the individual composition of the subset and varied across 20 randomly drawn subsets.