bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Hybrid MPPT Technique to Maximize Power Harvesting from PV System under Partial and Complex Partial Shading
Authors: Sarwar Sajid , Javed Muhammad Yaqoob , Jaffery Mujtaba Hussain , Arshad Jehangir , Ur Rehman Ateeq , Shafiq Muhammad , Choi Jin-Ghoo
Published: January 2022

Photovoltaic (PV) system has been extensively used over the last few years because it is a noise-free, clean, and environmentally friendly source of energy. Maximum Power Point (MPP) from the PV energy systems is a challenging task under modules mismatching and partial shading. Up till now, various MPP tracking algorithms have been used for solar PV energy systems. Classical algorithms are simple, fast, and useful in quick tracing the MPP, but restricted to uniform weather conditions. Moreover, these algorithms do not search the Global Maxima (GM) and get stuck on Local Maxima (LM). However, bio-inspired algorithms help find the GM but their main drawback is that they take more time to track the GM. This paper addresses the issue by using the combination of conventional Incremental Conductance (InC) with variable step size and bio-inspired Dragonfly Optimization (DFO) algorithms leading to a hybrid (InC-DFO) technique under multiple weather conditions, for instance, Uniform Irradiance (UI), Partial Shading (PS), and Complex Partial Shading (CPS). To check the robustness of the proposed algorithm, a comparative analysis is done with six already implemented techniques. The results indicate that the proposed technique is simple, efficient with a quicker power tracking capability. Furthermore, it reduces undesired oscillation around the MPP especially, under PS and CPS conditions. The proposed algorithm has the highest efficiencies of 99.93%, 99.88%, 99.92%, and 99.98% for UI, PS1, PS2, and CPS accordingly among all techniques. It has also reduced the settling time of 0.75 s even in the case of the CPS condition. The performance of the suggested method is also verified using real-time data from the Beijing database.
Cold Atmospheric Plasma, Platelet-Rich Plasma, and Nitric Oxide Synthesis Inhibitor: Effects Investigation on an Experimental Model on Rats
Authors: Caba Bogdan , Gardikiotis Ioannis , Topala Ionut , Mihaila Ilarion , Mihai Cosmin Teodor , Luca Catalina , Pasca Sorin , Caba Ioana Cezara , Dimitriu Gabriel , Huzum Bogdan , Serban Ionela Lacramioara
Published: January 2022

The evolution of reconstructive methods for defects of the human body cannot yet replace the use of flap surgery. Research is still preoccupied with the ideal techniques for offering the best chances of survival of the flaps. In our study, we investigated the effects of cold atmospheric plasma (CAP), N-nitro-L-arginine methyl ester (L-NAME), and platelet-rich plasma (PRP) injectable solutions on flap survival using an in vivo model. Twenty-four Wistar rats (four groups) had the McFarlane flap raised and CAP, L-NAME, and PRP substances tested through a single dose subcutaneous injection. The control group had only a saline solution injected. To the best of our knowledge, this is the first study that evaluated a CAP activated solution through injection on flaps. The flap survival rate was determined by clinical examination (photography documented), hematology, thermography, and anatomopathological tests. The image digital analysis performed on the flaps showed that the necrosis area (control—49.64%) was significantly lower for the groups with the three investigated solutions: CAP (14.47%), L-NAME (18.2%), and PRP (23.85%). Thermography exploration revealed less ischemia than the control group on the CAP, L-NAME, and PRP groups as well. Anatomopathological data noted the best degree of angiogenesis on the CAP group, with similar findings on the L-NAME and PRP treated flaps. The blood work did not indicate infection or a strong inflammatory process in any of the subjects. Overall, the study shows that the CAP activated solution has a similar (better) impact on the necrosis rate (compared with other solutions with known effects) when injected on the modified dorsal rat skin flap, and on top of that it can be obtained fast, in unlimited quantities, non-invasively, and through a standardized process.
Comparative Study of AC Signal Analysis Methods for Impedance Spectroscopy Implementation in Embedded Systems
Authors: Kallel Ahmed Yahia , Hu Zheng , Kanoun Olfa
Published: January 2022

For embedded impedance spectroscopy, a suitable method for analyzing AC signals needs to be carefully chosen to overcome limited processing capability and memory availability. This paper compares various methods, including the fast Fourier transform (FFT), the FFT with barycenter correction, the FFT with windowing, the Goertzel filter, the discrete-time Fourier transform (DTFT), and sine fitting using linear or nonlinear least squares, and cross-correlation, for analyzing AC signals in terms of speed, memory requirements, amplitude measurement accuracy, and phase measurement accuracy. These methods are implemented in reference systems with and without hardware acceleration for validation. The investigation results show that the Goertzel algorithm has the best overall performance when hardware acceleration is excluded or in the case of memory constraints. In implementations with hardware acceleration, the FFT with barycentre correction stands out. The linear sine fitting method provides the most accurate amplitude and phase determinations at the expense of speed and memory requirements.
Multipointer Coattention Recommendation with Gated Neural Fusion between ID Embedding and Reviews
Authors: Shao Jianjie , Qin Jiwei , Zeng Wei , Zheng Jiong
Published: January 2022

Recently, the interaction information from reviews has been modeled to acquire representations between users and items and improve the sparsity problem in recommendation systems. Reviews are more responsive to information about users’ preferences for the different aspects and attributes of items. However, how to better construct the representation of users (items) still needs further research. Inspired by the interaction information from reviews, auxiliary ID embedding information is used to further enrich the word-level representation in the proposed model named MPCAR. In this paper, first, a multipointer learning scheme is adopted to extract the most informative reviews from user and item reviews and represent users (items) in a word-by-word manner. Then, users and items are embedded to extract the ID embedding that can reveal the identity of users (items). Finally, the review features and ID embedding are input to the gated neural network for effective fusion to obtain richer representations of users and items. We randomly select ten subcategory datasets from the Amazon dataset to evaluate our algorithm. The experimental results show that our algorithm can achieve the best results compared to other recommendation approaches.
Battery Energy Storage Participation in Automatic Generation Control of Island Systems, Coordinated with State of Charge Regulation
Authors: Papakonstantinou Apostolos G. , Papathanassiou Stavros A.
Published: January 2022

Efficient storage participation in the secondary frequency regulation of island systems is a prerequisite towards their complete decarbonization. However, energy reserve limitations of storage resources pose challenges to their integration in centralized automatic generation control (AGC). This paper presents a frequency control method, in which battery energy storage systems (BESSs) participate in automatic frequency restoration reserve (aFRR) provision, through their integration in the AGC of an island system. A local state of charge (SOC) controller ensures safe operation of the BESS in case of disturbances, without jeopardizing system security when available energy reserves are diminishing. The aFRR participation factors of regulating units are altered when the storage systems approach their SOC limits, re-allocating their reserves to other load-following units. Restoration of BESS energy reserves is achieved by integrating SOC regulation in the real-time economic dispatch of the system, formulated as a mixed-integer linear programming problem and solved every few minutes to determine the base points of the AGC units. A small autonomous power system, comprising conventional units, renewable energy sources and a BESS, is used as a study case to evaluate the performance of the proposed method, which is compared with alternative approaches to secondary regulation with BESS participation.
A Study on a New Independent Metering Valve for Hydraulic Boom Excavator
Authors: Nguyen Thanh-Ha , Do Tri-Cuong , Ahn Kyoung-Kwan
Published: January 2022

Nowadays, hydraulic excavators are an indispensable part of the construction industry; however, conventional hydraulic excavators consume a great deal of fossil fuel and release a large amount of pollution emissions into the environment. This causes many unwanted costs, therefore, effective solutions are required to solve the above-mentioned problems. In this paper, a new independent metering system is proposed to improve energy-saving and reduce costs of a conventional system. In detail, a directional valve is used to control movement and three electro-hydraulic poppet valves are integrated to adjust the flow rate at the inlet and outlet ports of the boom cylinder. In addition, a control strategy based on the coordination between the speed of the pump and the opening area of the spool valve is designed to improve the performance of the system. Specifically, the valves are controlled based on the strategy that the meter-in valve is opened fully to reduce throttling losses and that the meter-out valve is controlled to reduce leakage. The speed of the pump is adjusted according to the feedback position signal. To demonstrate the effectiveness of the new configuration, a real test bench of the boom system was built under laboratory conditions. From the experimental results, the new independent metering valve system not only works with a high tracking precision, but it also reduces energy consumption. Compared with a conventional independent metering system, the fuel economy of the proposed structure can achieve a reduction of approximately 6.5%.
Research into the Safe Distance for Opening a Car Door in Order to Avoid Approaching Motorcycles from the Rear
Authors: Huang Cheng-Yong
Published: January 2022

Car drivers may misjudge the speed and distance of oncoming vehicles when getting out of their vehicles, leading to door crash accidents. The author invented a patented side-view mirror with distance markers to prevent such door crash accidents. Through the means of behavioural observation experiments, the main objective of this research was to observe the minimum safe distance from the rear of an approaching vehicle when opening the door of the car. The experimental results determined that the use of the Dutch Reach method yielded the shortest safe distance. Compared to the other three judgment solutions, the use of the patented side-view mirror with a distance marker to judge the minimum average safety distance had the longest distance and was the safest, meaning that it is able to help female drivers to judge the distance of approaching motorcycles from the rear and to maintain a sufficient safe distance when opening the door.
Grid-Based Hybrid Genetic Approach to Relaxed Flexible Flow Shop with Sequence-Dependent Setup Times
Authors: Juárez-Pérez Fredy , Cruz-Chávez Marco Antonio , Rivera-López Rafael , Ávila-Melgar Erika Yesenia , Eraña-Díaz Marta Lilia , Cruz-Rosales Martín H.
Published: January 2022

In this paper, a hybrid genetic algorithm implemented in a grid environment to solve hard instances of the flexible flow shop scheduling problem with sequence-dependent setup times is introduced. The genetic algorithm takes advantage of the distributed computing power on the grid to apply a hybrid local search to each individual in the population and reach a near optimal solution in a reduced number of generations. Ant colony systems and simulated annealing are used to apply a combination of iterative and cooperative local searches, respectively. This algorithm is implemented using a master–slave scheme, where the master process distributes the population on the slave process and coordinates the communication on the computational grid elements. The experimental results point out that the proposed scheme obtains the upper bound in a broad set of test instances. Also, an efficiency analysis of the proposed algorithm indicates its competitive use of the computational resources of the grid.
The Influence of Composition and Recipe Dosage on the Strength Characteristics of New Geopolymer Concrete with the Use of Stone Flour
Authors: Beskopylny Alexey N. , Shcherban’ Evgenii M. , Stel’makh Sergey A. , Mailyan Levon R. , Meskhi Besarion , El’shaeva Diana
Published: January 2022

Currently, considering global trends and challenges, as well as the UN sustainable development goals and the ESG plan, the development of geopolymer binders for the production of geopolymer concrete has become an urgent area of construction science. This study aimed to reveal the influence of the component composition and recipe dosage on the characteristics of fine-grained geopolymer concrete with the use of stone flour. Eleven compositions of geopolymer fine-grained concrete were made from which samples of the mixture were obtained for testing at the beginning and end of setting and models in the form of beams and cubes for testing the compressive strength tensile strength in bending. It was found that the considered types of stone flour can be successfully used as an additive in the manufacture of geopolymer concrete. An analysis of the setting time measurements showed that stone flour could accelerate the hardening of the geopolymer composite. It was found that the addition of stone waste significantly improves the compressive strength of geopolymers in comparison with a geopolymer composite containing only quartz sand. The maximum compressive strength of 52.2 MPa and the tensile strength in bending of 6.7 MPa provide the introduction of potassium feldspar in an amount of 15% of the binder mass. Microstructural analysis of the geopolymer composite was carried out, confirming the effectiveness of the recipe techniques implemented in this study.
The Application of Electrochemical Oscillation Methods for Identification of Traditional Chinese Medicine Materials
Authors: Shi Xin , Zheng Yuhong , Fu Li
Published: January 2022

Electrochemical oscillation reflects the overall characteristics of the system under test in terms of redox activity. It has proven to be advantageous in analyzing and processing complex components of herbal systems, such as polysaccharides and proteins. Therefore, it is widely used in the quantitative or qualitative tests of traditional Chinese medicines (TCMs) for identification and quality control. Electrochemical oscillation has several advantages such as high sensitivity, stability and micro sample requirement. Compared with other traditional methods, the interaction of multi-component in the TCMs was taken into account, which provides new ideas for the search of TCMs. Here, we presented a brief introduction on the progress on the topic, which promoted the development of electrochemical oscillation and the standardization of TCMs in the last twenty years. Electrochemical oscillation method is cheap, sensitive, fast, stable and convenient for the identification and quality control of TCMs. Reaction systems and the visualization of the fingerprints can be improved in the future.
Responses of Laterally Loaded Single Piles Subjected to Various Loading Rates in a Poroelastic Soil
Authors: Gui Meen-Wah , Alebachew Alex A.
Published: January 2022

Groundwater table has an important role in soil–structure interaction problems. However, analysis of laterally loaded single piles has often been conducted by solely considering the mechanics of the soil skeleton or decoupling the interactive mechanics of the soil skeleton and the fluid flux; in other words, most analyses were performed without taking into consideration the coupling effect between the soil skeleton and the fluid flux. To improve our understanding of the hydromechanical coupling effect on laterally loaded single piles, a series of finite element study on laterally loaded single piles in saturated porous media was conducted. The effect of pile cap geometries, cap widths, cap embedment depths, and pile lengths, on the response of laterally loaded single piles was also studied. The loading condition of the pile was found to have a significant effect on the generation of excess pore-water pressure. The lateral displacement and bending moment computed at the maximum excess pore water pressure, which in turn, is equivalent to an undrained analysis, produced the minimum responses among all the other loading conditions. The effect of pile cap geometries was found to be much less significant than anticipated.
Soft-Output Detector Using Multi-Layer Perceptron for Bit-Patterned Media Recording
Authors: Jeong Seongkwon , Lee Jaejin
Published: January 2022

As conventional data storage systems are faced with critical problems such as the superparamagnetic limit, bit-patterned media recording (BPMR) has received significant attention as a promising next-generation magnetic data storage system. However, the reduced spacing between islands at increased areal density causes severe intersymbol and intertrack interference, which degrade BPMR system performance. In this study, we introduce a soft-output detector using multi-layer perceptron to predict reliable information. A received signal is equalized and detected by the MLP detector. The MLP detector provides a well-estimated value by using the binary-cross entropy function as a loss function and the identity function as an activation function for the output layer of the MLP detector. This study investigates the received probability distributions out of the detectors and compares the performance of various versions against a conventional detector. Compared with the conventional detection, the proposed MLP detectors provide a small variance and better BER performance than the conventional detection. Simulations of MLP designs show an advantage over conventional detection. Moreover, the proposed MLP detectors with the demodulator exhibit better BER performance than the conventional detector with the demodulator.
Numerical Simulation on the Response of Adjacent Underground Pipelines to Super Shallow Buried Large Span Double-Arch Tunnel Excavation
Authors: Wang Jianxiu , Cao Ansheng , Wu Zhao , Sun Zhipeng , Lin Xiao , Sun Lei , Liu Xiaotian , Li Huboqiang , Sun Yuanwei
Published: January 2022

The excavation of a shallow buried tunnel may cause stress redistribution in surrounding rock, and cause deformation, damage, and even destruction of adjacent underground pipelines. The land part of the Haicang undersea tunnel in Xiamen of China was a super shallow buried large span double-arch tunnel. Its construction was restricted by both underground excavation safe and adjacent pipeline protection. Multiple groups of working conditions were designed considering the relative position of pipe and tunnel, pipeline and tunnel construction parameters. Numerical simulation was used to study the influence of pipeline horizontal distance, buried depth, pipeline diameter, pipeline wall thickness, pipeline shape, pipeline material and excavation method on the response of adjacent underground pipelines. The results show that the relative position of pipe and tunnel, and the construction method of the double-arch tunnel have a great influence on pipeline deformation. Pipeline material, pipeline diameter and excavation method have a great influence on pipeline stress. The construction method was the key factor affecting the stress and deformation of the pipeline. The three-step reserved core soil method can effectively control the stress and deformation of underground pipelines. The research results can provide a reference for similar projects.
A Proposed Uncertainty Reduction Criterion of Renewable Energy Sources for Optimal Operation of Distribution Systems
Authors: Ali Eman , El-Sehiemy Ragab , Abou El-Ela Adel , Tostado-Véliz Marcos , Kamel Salah
Published: January 2022

Power system operation and planning studies face many challenges with increasing of renewable energy sources (RESs) penetration. These challenges revolve around the RESs uncertainty and its applications on probabilistic forecasting, power system operation optimization and power system planning. This paper proposes a novel and effective criterion for uncertainties modeling of the RESs as well as system loads. Four sorting stages are applied for the proposed uncertainty cases reduction. Added to that, it proposes three different uncertainty reduction strategies for obtaining different accuracy and speed options. The proposed reduction strategies are tested on medium and large scale distribution systems; IEEE 69-bus and 118-bus systems. The obtained results verify the effectiveness of the proposed criterion in uncertainties modeling in distribution systems with acceptable level of accuracy.
A Fatigue Reliability Assessment for Rail Tension Clamps Based on Field Measurement Data
Authors: Kim Ji-Hyeon , Park Yeun-Chul , Kim Mancheol , Sim Hyoung-Bo
Published: January 2022

Tension clamps play an important role in maintaining the track gauge by fixing the rails to the sleepers. Damage to the tension clamps was observed on an urban railway. The cause of the fracturing of the tension clamps was identified and reliability analyses on the fatigue failure of the tension clamps were performed. The stress ranges were estimated by measuring the strain at the locations where most of the fractures occurred during train operation. Afterward, a statistical model of the stress ranges was developed using the measured data. The statistical parameters of the stress ranges for the reliability analysis were estimated based on the field measurement data. The reliability indexes were calculated for the inner and outer rails and for the inside and outside track gauges of each rail. The variations of the reliability index for the years in service and the number of cycles were investigated. The results of the reliability analyses showed a consistency with the field observations.
Fast and Accurate Solution of Integral Formulations of Large MQS Problems Based on Hybrid OpenMP–MPI Parallelization
Authors: Ventre Salvatore , Cau Francesca , Chiariello Andrea , Giovinco Gaspare , Maffucci Antonio , Villone Fabio
Published: January 2022

This paper proposes an optimal strategy to parallelize the solution of large 3D magneto-quasi-static (MQS) problems, by combining the MPI and OpenMP approaches. The studied numerical problem comes from a weak-form integral formulation of a MQS problem and is finally cast in terms of a large linear system to be solved by means of a direct method. For this purpose, two main tasks are identified: the assembly and the inversion of the matrices. The paper focuses on the optimization of the resources required for assembling the matrices, by exploiting the feature of a hybrid OpenMP–MPI approach. Specifically, the job is shared between clusters of nodes in parallel by adopting an OpenMP paradigm at the node level and a MPI one at the process level between nodes. Compared with other solutions, such as pure MPI, this hybrid parallelization optimizes the available resources, with respect to the speed, allocated memory, and the communication between nodes. These advantages are clearly observed in the case studies analyzed in this paper, coming from the study of large plasma fusion machines, such as the fusion reactor ITER. Indeed, the MQS problems associated with such applications are characterized by a huge computational cost that requires parallel computing approaches.
Advances on Smart Cities and Smart Buildings
Authors: Roccotelli Michele , Mangini Agostino Marcello
Published: January 2022

Modern cities are facing the challenge of combining competitiveness on a global city scale and sustainable urban development to become smart cities [...]
Early Risk Prediction of Diabetes Based on GA-Stacking
Authors: Tan Yaqi , Chen He , Zhang Jianjun , Tang Ruichun , Liu Peishun
Published: January 2022

Early risk prediction of diabetes could help doctors and patients to pay attention to the disease and intervene as soon as possible, which can effectively reduce the risk of complications. In this paper, a GA-stacking ensemble learning model is proposed to improve the accuracy of diabetes risk prediction. Firstly, genetic algorithms (GA) based on Decision Tree (DT) is used to select individuals with high adaptability, that is, a subset of attributes suitable for diabetes risk prediction. Secondly, the optimized convolutional neural network (CNN) and support vector machine (SVM) are used as the primary learners of stacking to learn attribute subsets, respectively. Then, the output of CNN and SVM is used as the input of the mate learner, the fully connected layer, for classification. Qingdao desensitization physical examination data from 1 January 2017 to 31 December 2019 is used, which includes body temperature, BMI, waist circumference, and other indicators that may be related to early diabetes. We compared the performance of GA-stacking with K-nearest neighbor (KNN), SVM, logistic regression (LR), Naive Bayes (NB), and CNN before and after adding GA through the average prediction time, accuracy, precision, sensitivity, specificity, and F1-score. Results show that prediction efficiency can be improved by adding GA. GA-stacking has higher prediction accuracy. Moreover, the strong generalization ability and high prediction efficiency of GA-stacking have also been verified on the early-stage diabetes risk prediction dataset published by UCI.
The Fusion of MRI and CT in the Planning of Brachytherapy for Cancer of the Uterine Cervix
Authors: Merten Roland , Fischer Mirko , Christiansen Hans , Ringe Kristina I. , Klapdor Rüdiger , Wichmann Jörn
Published: January 2022

Introduction: tumors of the uterine cervix are among the most common carcinomas in women. Intracervical brachytherapy is an indispensable part of curative treatment. Although the tumor is significantly more recognizable in MRI than in CT, the practical application of MRI in brachytherapy planning is still difficult. The present study examines the technical possibilities of merging CT and MRI. Materials and Methods: the treatment files and imaging of all 53 patients who had been irradiated by image-guided adaptive brachytherapy (IGABT) between January 2019 and August 2021 at the Department of Radiotherapy of the Hannover Medical School were evaluated, retrospectively. Patients were treated first with an external beam radiotherapy (EBRT) combined with simultaneous chemotherapy. After an average of 4.2 weeks, the preparation for IGABT began. The clinical target volume (CTV) for brachytherapy was contoured first in an MRI acquired before starting EBRT (MRI 1) and once more in a second MRI just before starting IGABT (MRI 2). Then, after inserting the intravaginal applicator, a CT-scan was acquired, and the CTV was contoured in the CT. Finally, the recordings of MRI 1, MRI 2, and the CT were merged, and the congruence of CTVs was quantitatively evaluated. Results: the CTV delineated in MRI 2 was, on average, 28% smaller than that in MRI 1 after an average applied radiation dose of 42 Gy. The CTV delineated in the CT covered an average of no more than 80.8% of the CTV delineated in MRI 2. The congruence of CTVs was not superior in patients with a smit sleeve in the cervical channel, with a 3D-volumetric MRI or with a contrast-enhanced sequence for MRI. Conclusion: the anatomical shape and position of the uterus is significantly changed by introducing a vaginal applicator. Despite the superior delimitability of the tumor in MRI, brachytherapy cannot be reliably planned by the image fusion of an MRI without a vaginal applicator.
Dielectric Slabs-Based Lens for Millimeter-Wave Beamforming
Authors: Alqahtani Ali H. , Aladadi Yosef T. , Alresheedi Mohammed T.
Published: January 2022

This paper proposes a dielectric slabs-based lens for millimeter-wave beamforming systems. The proposed lens is based on the graded steps of the effective refractive index of the semi-spherical lens. It consists of multiple dielectric slabs that match the selected gradient effective refractive index. These slabs have the same thicknesses and different radii. The slab thickness in this lens should not exceed a quarter of the operating wavelength to keep on a similar effective refractive index of the original semi-spherical lens. A horn antenna is used to examine the performance of the designed lens at 28 GHz frequency in terms of the maximum gain, sidelobe level, and 3 dB beamwidth. Sixteen switchable horn antennas are used to demonstrate lens capability for millimeter-wave beamforming. Every single antenna element is selected individually, thus the dielectric lens steers and enhances the corresponding radiation of the selected element in the desired direction.
Implementation of a Noise-Shaped Signaling System through Software-Defined Radio
Authors: Choi Junsung , Park Dongryul , Kim Suil , Ahn Seungyoung
Published: January 2022

Along with the development of electromagnetic weapons, Electronic Warfare (EW) has been rising as the future form of war. Especially in the area of wireless communications, high security defense systems such as Low Probability of Detection (LPD), Low Probability of Interception (LPI), and Low Probability of Exploitation (LPE) communication algorithms are being studied to prevent military force loss. One LPD, LPI, and LPE communication algorithm, physical-layer security, has been discussed and studied. We propose a noise signaling system, a type of physical-layer security, which modifies conventionally modulated I/Q data into a noise-like shape. To suggest the possibility of realistic implementation, we use Software-Defined Radio (SDR). Since there are certain hardware limitations, we present the limitations, requirements, and preferences of practical implementation of the noise signaling system. The proposed system uses ring-shaped signaling, and we present a ring-shaped signaling system algorithm, SDR implementation methodology, and performance evaluations of the system using the metrics of Bit Error Rate (BER) and Probability of Modulation Identification (PMI), which we obtain by using a Convolutional Neural Network (CNN) algorithm. We conclude that the ring-shaped signaling system can perform high LPI/LPE communication functioning because an eavesdropper cannot obtain the correct modulation scheme information. However, the performance can vary with the configurations of the I/Q data-modifying factors.
Research on Sparse Representation Method of Acoustic Microimaging Signals
Authors: Wang Kun , Leng Tao , Mao Jie , Lian Guoxuan , Zhou Changzhi
Published: January 2022

Acoustic microimaging (AMI), a technology for high-resolution imaging of materials using a scanning acoustic microscope, has been widely used for non-destructive testing and evaluation of electronic packages. Recently, the internal features and defects of electronic packages have reached the resolution limits of conventional time domain or frequency domain AMI methods with the miniaturization of electronic packages. Various time-frequency domain AMI methods have been developed to achieve super-resolution. In this paper, the sparse representation of AMI signals is studied, and a constraint dictionary-based sparse representation (CD-SR) method is proposed. First, the time-frequency parameters of the atom dictionary are constrained according to the AMI signal to constitute a constraint dictionary. Then, the AMI signal is sparsely decomposed using the matching pursuit algorithm, and echoes selection and echoes reconstruction are performed. The performance of CD-SR was quantitatively evaluated by simulated and experimental ultrasonic A-scan signals. The results demonstrated that CD-SR has superior longitudinal resolution and robustness.
Investigating Planting Concrete Suitability by Evaluating the Physiological Indexes of Three Ground Cover Plants
Authors: Li Di , Tao Yuwei , Mou Sijia , Lyu Bingyang , Lin Wei , Zeng Chengcheng , Li Nian , Zhang Xiaoxia , Guo Li , Chen Qibing
Published: January 2022

Concrete is one of the most widely used construction materials for basic infrastructure worldwide, especially in developing areas undergoing rapid urbanization. However, concrete inhibits energy exchange between soil and other ecosystem components. To enhance the fluxion of information between ecosystems, surface vegetation, and basement soil, this paper aims to explore the tolerance of plants growing on PC. Therefore, we investigated two different PC sample groups with aggregate particle diameters of 5–10 and 15–20 mm. After curing, the samples were used to plant three ground cover plants (Cynodon dactylon(L.) Pers, Agrostis stolonifera, and Sasa argenteostriatus, e.g., Camus), and the results were compared with those from normal soil without PC underneath as a reference. During an observational period of 12 weeks, the growth and height of the plants were documented and analysed. The physiological indexes of free proline (Pro), malondialdehyde (MDA), chlorophyll (Chl), relative electrical conductivity (REC), and soluble protein (SP) were investigated. The correlations and significant differences between these indexes based on the treatments were analysed. Then, principal component analysis (PCA) was used to determine the main variables affecting plant growth. The results showed that there were significant differences between the PC groups and the natural growth group. The growth and height of the three plant species under near-natural (nonconcrete) conditions were better than those of the plants in the PC treatments. The plants in the large-particle concrete (LC) treatment group showed better adaptability than those in the small-particle concrete (SC) treatment group in terms of growth, although both PC treatments resulted in various degrees of damage. PCA showed that SP, REC, and MDA were the most influential factors on plant growth in this study.
PB-NCC: A Popularity-Based Caching Strategy with Number-of-Copies Control in Information-Centric Networks
Authors: Li Yuanhang , Wang Jinlin , Han Rui
Published: January 2022

The Information-Centric Network (ICN), designed for efficient content acquisition and distribution, is a promising candidate architecture for the future Internet. In-network caching in ICN makes it possible to reuse contents and the Name Resolution System (NRS) makes cached contents better serve users. In this paper, we focused on the ICN caching scenario equipped with an NRS, which records the positions of contents cached in ICN. We propose a Popularity-based caching strategy with Number-of-Copies Control (PB-NCC) in this paper. PB-NCC is proposed to solve the problems of unreasonable content distribution and frequent cache replacement in traditional caching strategies in ICN. We examine PB-NCC with a large number of experiments in different topologies and workloads. The simulation results reveal that PB-NCC can improve the cache hit ratio by at least 8.85% and reduce the server load by at least 11.34% compared with other on-path caching strategies, meanwhile maintaining a low network latency.
Classification of Adulterated Particle Images in Coconut Oil Using Deep Learning Approaches
Authors: Palananda Attapon , Kimpan Warangkhana
Published: January 2022

In the production of coconut oil for consumption, cleanliness and safety are the first priorities for meeting the standard in Thailand. The presence of color, sediment, or impurities is an important element that affects consumers’ or buyers’ decision to buy coconut oil. Coconut oil contains impurities that are revealed during the process of compressing the coconut pulp to extract the oil. Therefore, the oil must be filtered by centrifugation and passed through a fine filter. When the oil filtration process is finished, staff inspect the turbidity of coconut oil by examining the color with the naked eye and should detect only the color of the coconut oil. However, this method cannot detect small impurities, suspended particles that take time to settle and become sediment. Studies have shown that the turbidity of coconut oil can be measured by passing light through the oil and applying image processing techniques. This method makes it possible to detect impurities using a microscopic camera that photographs the coconut oil. This study proposes a method for detecting impurities that cause the turbidity in coconut oil using a deep learning approach called a convolutional neural network (CNN) to solve the problem of impurity identification and image analysis. In the experiments, this paper used two coconut oil impurity datasets, PiCO_V1 and PiCO_V2, containing 1000 and 6861 images, respectively. A total of 10 CNN architectures were tested on these two datasets to determine the accuracy of the best architecture. The experimental results indicated that the MobileNetV2 architecture had the best performance, with the highest training accuracy rate, 94.05%, and testing accuracy rate, 80.20%.
Outpatient Appointment Optimization: A Case Study of a Chemotherapy Service
Authors: Tran Quoc Nhat Han , Nguyen  Nhan Quy , Chehade Hicham , Amodeo Lionel , Yalaoui Farouk
Published: January 2022

In this paper, we study a complex outpatient planning problem in the chemotherapy department. The planning concerns sequences of patients’ treatment sessions subject to exact in-between resting periods (i.e., exact time-lags). The planning is constrained by the hospital infrastructure and the availability of medical staff (i.e., multiple time-varying resources’ availability). In order to maximize the patients’ service quality, the objective of the function considered is to minimize the total wait times, which is equivalent to the criteria for minimizing the total completion time. Our main contribution is a thorough analysis of this problem, using the Hybrid Flow Shop problem as a theoretical framework to study the problem. A novel Mixed Integer Linear Programming (MILP) is introduced. Concerning the resolution methods, priority-based heuristics and an adapted genetic algorithm (GA) are presented. Numerical experiments are conducted on historical data to compare the performances of the approximate resolution methods against the MILP solved by CPLEX. Numerical results confirm the performances of the proposed methods.
Characterization of Lacticaseibacillus rhamnosus, Levilactobacillus brevis and Lactiplantibacillus plantarum Metabolites and Evaluation of Their Antimicrobial Activity against Food Pathogens
Authors: Vougiouklaki Despina , Tsironi Theofania , Papaparaskevas Joseph , Halvatsiotis Panagiotis , Houhoula Dimitra
Published: January 2022

Lactic acid bacteria (LAB) play an important role as natural food preservatives. However, the characterization of the variety of their metabolites is limited. The objective of this study was to determine the production of specific metabolites of Lacticaseibacillus rhamnosus, Levilactobacillus brevis and Lactiplantibacillus plantarum by an optimized liquid chromatography with an ultraviolet/diode detection (HPLC-UV/DAD) method and to investigate their potential antimicrobial activity against specific food pathogens. Based on the results of this study, the main metabolites detected in Levilactobacillus brevis were 103.4 μg mL−1 DL-p-Hydroxyphenyllactic acid (OH-PLA) and 2.59 μg mL−1 vanillic acid, while 216.2 μg mL−1 OH-PLA, 19.0 μg mL−1 salicylic acid, 3.7 μg mL−1 vanillic acid, 6.9 μg mL−1 ferulic acid, 4.2 μg mL−1 benzoic acid and 1.4 μg mL−1 4-Hydrocinnamic acid were identified in the Lactiplantibacillus plantarum strain and 147.6 μg mL−1 OH-PLA and 4.9 μg mL−1 ferulic acid were identified in Lacticaseibacillus rhamnosus. This study provides alternative approaches for the molecules involved in the antimicrobial activity of food microorganism fermentation. These molecules may be used as antimicrobial ingredients in the food industry instead of conventional chemical preservatives.
Assessment of COVID-19 Lockdown Impact on the Air Quality in Eastern Spain: PM and BTX in Urban, Suburban and Rural Sites Exposed to Different Emissions
Authors: Ródenas Milagros , Soler Rubén , Borrás Esther , Vera Teresa , Diéguez José Jaime , Muñoz Amalia
Published: January 2022

In early 2020, the COVID-19 pandemic spread globally, and severe measures to control it were implemented. This study investigates the impact of the lockdown on the air quality of three provinces in the Valencia region, eastern Spain, in the years 2015–2020, focusing on particulate matter (PM). A thorough statistical analysis using different approaches is conducted. Hourly patterns are also assessed. In addition, the role of meteorological parameters on PM is explored. The results indicate an overall PM10 reduction of 16.5% when comparing the lockdown in 2020 and the 2015–2019 period, while PM2.5 increased by 3.1%. As expected, urban zones experienced higher reductions than suburban zones, which experienced a PM concentration increase. The impact of the drastic drops of benzene, toluene and xylene (77.4%, 58.0% and 61.8%, respectively) on the PM values observed in urban sites is discussed. Our study provides insights on the effect of activity changes over a wide region covering a variety of air quality stations, urban, suburban and rural, and different emission types. The results of this work are a valuable reference and suggest the need for considering different factors when establishing scientific air pollution control strategies.
Semigroup Structures and Commutative Ideals of BCK-Algebras Based on Crossing Cubic Set Structures
Authors: Öztürk Mehmet Ali , Yılmaz Damla , Jun Young Bae
Published: January 2022

First, semigroup structure is constructed by providing binary operations for the crossing cubic set structure. The concept of commutative crossing cubic ideal is introduced by applying crossing cubic set structure to commutative ideal in BCK-algebra, and several properties are investigated. The relationship between crossing cubic ideal and commutative crossing cubic ideal is discussed. An example to show that crossing cubic ideal is not commutative crossing cubic ideal is given, and then the conditions in which crossing cubic ideal can be commutative crossing cubic ideal are explored. Characterizations of commutative crossing cubic ideal are discussed, and the relationship between commutative crossing cubic ideal and crossing cubic level set is considered. An extension property of commutative crossing cubic ideal is established, and the translation of commutative crossing cubic ideal is studied. Conditions for the translation of crossing cubic set structure to be commutative crossing cubic ideal are provided, and its characterization is processed.
(ζ−m, ζm)-Type Algebraic Minimal Surfaces in Three-Dimensional Euclidean Space
Authors: Güler Erhan , Kişi Ömer
Published: January 2022

We introduce the real minimal surfaces family by using the Weierstrass data (ζ−m,ζm) for ζ∈C, m∈Z≥2, then compute the irreducible algebraic surfaces of the surfaces family in three-dimensional Euclidean space E3. In addition, we propose that family has a degree number (resp., class number) 2m(m+1) in the cartesian coordinates x,y,z (resp., in the inhomogeneous tangential coordinates a,b,c).
Multi-Layered Blockchain Governance Game
Authors: Kim Song-Kyoo (Amang)
Published: January 2022

The research designs a new integrated system for the security enhancement of a decentralized network by preventing damages from attackers, particularly for the 51 percent attack. The concept of multiple layered design based on Blockchain Governance Games frameworks could handle multiple number of networks analytically. The Multi-Layered Blockchain Governance Game is an innovative analytical model to find the best strategies for executing a safety operation to protect whole multiple layered network systems from attackers. This research fully analyzes a complex network with the compact mathematical forms and theoretically tractable results for predicting the moment of a safety operation execution are fully obtained. Additionally, simulation results are demonstrated to obtain the optimal values of configuring parameters of a blockchain-based security network. The Matlab codes for the simulations are publicly available to help those whom are constructing an enhanced decentralized security network architecture through this proposed integrated theoretical framework.
Presence of a Mitovirus Is Associated with Alteration of the Mitochondrial Proteome, as Revealed by Protein–Protein Interaction (PPI) and Co-Expression Network Models in Chenopodium quinoa Plants
Authors: Di Silvestre Dario , Passignani Giulia , Rossi Rossana , Ciuffo Marina , Turina Massimo , Vigani Gianpiero , Mauri Pier Luigi
Published: January 2022

Plant mitoviruses belong to Mitoviridae family and consist of positive single-stranded RNA genomes replicating exclusively in host mitochondria. We previously reported the biological characterization of a replicating plant mitovirus, designated Chenopodium quinoa mitovirus 1 (CqMV1), in some Chenopodium quinoa accessions. In this study, we analyzed the mitochondrial proteome from leaves of quinoa, infected and not infected by CqMV1. Furthermore, by protein–protein interaction and co-expression network models, we provided a system perspective of how CqMV1 affects mitochondrial functionality. We found that CqMV1 is associated with changes in mitochondrial protein expression in a mild but well-defined way. In quinoa-infected plants, we observed up-regulation of functional modules involved in amino acid catabolism, mitochondrial respiratory chain, proteolysis, folding/stress response and redox homeostasis. In this context, some proteins, including BCE2 (lipoamide acyltransferase component of branched-chain alpha-keto acid dehydrogenase complex), DELTA-OAT (ornithine aminotransferase) and GR-RBP2 (glycine-rich RNA-binding protein 2) were interesting because all up-regulated and network hubs in infected plants; together with other hubs, including CAT (catalase) and APX3 (L-ascorbate peroxidase 3), they play a role in stress response and redox homeostasis. These proteins could be related to the higher tolerance degree to drought we observed in CqMV1-infected plants. Although a specific causative link could not be established by our experimental approach at this stage, the results suggest a new mechanistic hypothesis that demands further in-depth functional studies.
DNA Methylation Levels of the TBX5 Gene Promoter Are Associated with Congenital Septal Defects in Mexican Paediatric Patients
Authors: García-Flores Esbeidy , Rodríguez-Pérez José Manuel , Borgonio-Cuadra Verónica Marusa , Vargas-Alarcón Gilberto , Calderón-Colmenero Juan , Sandoval Juan Pablo , García-Montes José Antonio , Espinoza-Gutiérrez Víctor Manuel , Reyes-García Juan Gerardo , Cazarín-Santos Benny Giovanni , Miranda-Duarte Antonio , Gamboa-Domínguez Armando , Pérez-Hernández Nonanzit
Published: January 2022

The TBX5 gene regulates morphological changes during heart development, and it has been associated with epigenetic abnormalities observed in congenital heart defects (CHD). The aim of this research was to evaluate the association between DNA methylation levels of the TBX5 gene promoter and congenital septal defects. DNA methylation levels of six CpG sites in the TBX5 gene promoter were evaluated using pyrosequencing analysis in 35 patients with congenital septal defects and 48 controls. Average methylation levels were higher in individuals with congenital septal defects than in the controls (p < 0.004). In five CpG sites, we also found higher methylation levels in patients than in the controls (p < 0.05). High methylation levels were associated with congenital septal defects (OR = 3.91; 95% CI = 1.02–14.8; p = 0.045). The analysis of Receiver Operating Characteristic (ROC) showed that the methylation levels of the TBX5 gene could be used as a risk marker for congenital septal defects (AUC = 0.68, 95% CI = 0.56–0.80; p = 0.004). Finally, an analysis of environmental factors indicated that maternal infections increased the risk (OR = 2.90; 95% CI = 1.01–8.33; p = 0.048) of congenital septal defects. Our data suggest that a high DNA methylation of the TBX5 gene could be associated with congenital septal defects.