bluuebunny/crossref_metadata_2025 huggingface dataset:
A Low-Profile Dual-Polarized Magneto-Electric Dipole Antenna for 5G Applications
Authors: Zhu Yulong , Tan Qingquan , Fan Kuikui
Published: January 2023

A low-profile dual-polarized magneto-electric dipole (MED) is presented in this communication. The low profile was achieved using meander slots on the vertical magneto dipole, reducing the antenna height to 0.15λ0, where λ0 is the wavelength of the center frequency. Relatively, the proposed MED structure is easier to process and more stable than the traditional low-profile MED structure. The broadband performance for 5G Applications was realized based on MED structure, and the dual-polarization structure has wider coverage area and lower multipath transmission losses. Moreover, the orthogonal feeding structure provides a satisfying isolation between two ports. To verify the simulation results, a prototype of the proposed antenna was fabricated and measured. The results show that the overlapped operating frequency bandwidth with |S11| ≤ −10 dB, |S12| ≤ −20 dB was 36.8% from 3.1 GHz to 4.5 GHz, the peak gain reached 10.2 dBi, and the average gain exceeded 8.5 dBi. The measured 3 dB beamwidth with more than 44 degrees beamwidth was realized in both E-plane and H-plane. In addition, cross-polarization levels below −22 dB that covered the above frequency band were achieved. Compared with other MED antennas, in addition to broadband and high gain, the proposed antenna has the advantages of a low profile, easy processing, and low cost, which make it a competitive candidate for 5G applications.
Mandarin Electro-Laryngeal Speech Enhancement Using Cycle-Consistent Generative Adversarial Networks
Authors: Qian Zhaopeng , Xiao Kejing , Yu Chongchong
Published: January 2023

Electro-laryngeal (EL) speech has poor intelligibility and naturalness, which hampers the popular use of the electro-larynx. Voice conversion (VC) can enhance EL speech. However, if the EL speech to be enhanced is with complicated tone variation rules in Mandarin, the enhancement will be less effective. This is because the source speech (Mandarin EL speech) and the target speech (normal speech) are not strictly parallel. We propose using cycle-consistent generative adversarial networks (CycleGAN, a parallel-free VC framework) to enhance continuous Mandarin EL speech, which can solve the above problem. In the proposed framework, the generator is designed based on the neural networks of a 2D-Conformer-1D-Transformer-2D-Conformer. Then, we used Mel-Spectrogram instead of traditional acoustic features (fundamental frequency, Mel-Cepstrum parameters and aperiodicity parameters). At last, we converted the enhanced Mel-Spectrogram into waveform signals using WaveNet. We undertook both subjective and objective tests to evaluate the proposed approach. Compared with traditional approaches to enhance continuous Mandarin EL speech with variable tone (the average tone accuracy being 71.59% and average word error rate being 10.85%), our framework increases the average tone accuracy by 12.12% and reduces the average errors of word perception by 9.15%. Compared with the approaches towards continuous Mandarin EL speech with fixed tone (the average tone accuracy being 29.89% and the average word error rate being 10.74%), our framework increases the average tone accuracy by 42.38% and reduces the average errors of word perception by 8.59%. Our proposed framework can effectively address the problem that the source and target speech are not strictly parallel. The intelligibility and naturalness of Mandarin EL speech have been further improved.
Fock-Space Schrieffer–Wolff Transformation: Classically-Assisted Rank-Reduced Quantum Phase Estimation Algorithm
Authors: Kowalski Karol , Bauman Nicholas P.
Published: January 2023

We present an extension of many-body downfolding methods to reduce the resources required in the quantum phase estimation (QPE) algorithm. In this paper, we focus on the Schrieffer–Wolff (SW) transformation of the electronic Hamiltonians for molecular systems that provides significant simplifications of quantum circuits for simulations of quantum dynamics. We demonstrate that by employing Fock-space variants of the SW transformation (or rank-reducing similarity transformations (RRST)) one can significantly increase the locality of the qubit-mapped similarity-transformed Hamiltonians. The practical utilization of the SW-RRST formalism is associated with a series of approximations discussed in the manuscript. In particular, amplitudes that define RRST can be evaluated using conventional computers and then encoded on quantum computers. The SW-RRST QPE quantum algorithms can also be viewed as an extension of the standard state-specific coupled-cluster downfolding methods to provide a robust alternative to the traditional QPE algorithms to identify the ground and excited states for systems with various numbers of electrons using the same Fock-space representations of the downfolded Hamiltonian. The RRST formalism serves as a design principle for developing new classes of approximate schemes that reduce the complexity of quantum circuits.
A Study on Crowd Evacuation Model Considering Squeezing Equilibrium in Crowded Areas
Authors: Yang Longcheng , Wei Juan , Tang Zhihai , Hu Jun , Hu Zhouyi
Published: January 2023

A new crowd evacuation model is established to solve the stagnation problem of traditional social force models in a complex and dense scene. In the proposed model the acting forces between pedestrians, and between pedestrians and obstacles in the traditional social force model, are improved to find out the relationship in the two cases which are within the influence range and are not intersected, and those which are intersected and not greater than the maximum degree of squeezing, and to solve it for parameter optimization. The simulation platform built is used to compare the performance of the traditional social force model and the improved model, and to deeply analyze the relationship between the evacuation time and the degree of squeezing. The results show that as the evacuation time increases, the crowd in the emergency exit area is getting denser, the optimized model is distributed more evenly, and the probability of squeezing is lower. The optimized model has better stability in terms of the ability to control the intersection without exceeding the maximum degree of squeezing. Due to less squeezing, the optimized model can reduce the time of passing through the exit to a large extent. Therefore, the way to resolve the disorderly evacuation of pedestrians caused by excessive crowd density in the evacuation process is to solve optimization parameters.
Detectability of Subsurface Defects in Polypropylene/Glass Fiber Composites Using Multiple Lock-In Frequency Modulated Algorithms
Authors: Chung Yoonjae , Lee Seungju , Shrestha Ranjit , Kim Wontae
Published: January 2023

Recently, thermographic testing has been applied as a non-destructive evaluation technology for composite materials due to its remarkable advantages, such as its non-contact, non-destructive, and high-speed properties, it is harmless to the human body and easy to use, as well as having both qualitative and quantitative inspection performance. In this paper, multiple lock-in frequency modulated thermography is applied to investigate defects on the subsurface of polypropylene/glass fiber composites, and experimental investigations were conducted on various defect conditions for qualitative and quantitative estimation. A thin rectangle-shaped PP-GF plate of dimension 100 × 180 mm and a depth of 3 mm with various 24 defects conditions was considered as a test sample. The sample was stimulated at multiple modulation frequencies, viz., 1 Hz, 0.2 Hz, 0.1 Hz, 0.05 Hz, 0.03 Hz, and 0.01 Hz. For qualitative and quantitative evaluation, two representatives, multiple lock-in frequency modulated algorithms of lock-in thermography, HA and FFT, were applied to the raw data. Moreover, their detectability performance was compared in terms of phase contrast and signal-to-noise ratio.
Experimental Analysis of the Behaviour of Piled Raft Foundations in Loose Sand
Authors: Bralović Nemanja , Despotović Iva , Kukaras Danijel
Published: January 2023

This paper presents the experimental analysis that was conducted on small-scale 1g physical models of piled raft foundation structures with a group of 2 × 2 piles in loose sand. The purpose of the piles was to reduce the settlement of the raft. The test program included twelve experiments, three of which were conducted on a raft alone and nine on piled rafts at pile distances of 3d, 4d, and 5d and pile lengths of 10d, 20d, and 40d, where d is pile diameter. The test results show that the current conventional approach to design of piled raft foundations, at a high safety load factor in piles that assume to take the whole external applied load, is very conservative. Instead, it is more economical to apply a low bearing capacity factor for piles as settlement reducers and maximize use of raft bearing capacity to carry part of the external load.
Automatic Hepatic Vessels Segmentation Using RORPO Vessel Enhancement Filter and 3D V-Net with Variant Dice Loss Function
Authors: Svobodova Petra , Sethia Khyati , Strakos Petr , Varysova Alice
Published: January 2023

The segmentation of hepatic vessels is crucial for liver surgical planning. It is also a challenging task because of its small diameter. Hepatic vessels are often captured in images of low contrast and resolution. Our research uses filter enhancement to improve their contrast, which helps with their detection and final segmentation. We have designed a specific fusion of the Ranking Orientation Responses of Path Operators (RORPO) enhancement filter with a raw image, and we have compared it with the fusion of different enhancement filters based on Hessian eigenvectors. Additionally, we have evaluated the 3D U-Net and 3D V-Net neural networks as segmentation architectures, and have selected 3D V-Net as a better segmentation architecture in combination with the vessel enhancement technique. Furthermore, to tackle the pixel imbalance between the liver (background) and vessels (foreground), we have examined several variants of the Dice Loss functions, and have selected the Weighted Dice Loss for its performance. We have used public 3D Image Reconstruction for Comparison of Algorithm Database (3D-IRCADb) dataset, in which we have manually improved upon the annotations of vessels, since the dataset has poor-quality annotations for certain patients. The experiments demonstrate that our method achieves a mean dice score of 76.2%, which outperforms other state-of-the-art techniques.
Enhanced Anti-Skin Aging Effects of Fermented Black Ginseng (Panax ginseng C.A. Meyer) by Aspergillus niger KHNT-1
Authors: Ramadhania Zelika Mega , Yang Dong Uk , Moektiwardojo Moelyono , Han Yaxi , Park Jin Kyu , Rupa Esrat Jahan , Yang Deok Chun , Lee Seung Jin , Kang Se Chan
Published: January 2023

Functional foods to prevent aging, particularly skin aging, have grown in popularity as society ages and science uncovers the pathological mechanisms of aging. In this study, we used processed ginseng, one of the famous functional foods, along with fermentation utilizing the novel Aspergillus niger strain KHNT-1 to enhance its value and efficacy. We successfully produced fermented black ginseng (FBG) and investigated its anti-skin aging properties, such as anti-melanogenic, anti-wrinkle, and antioxidant activities. The results showed that FBG at 100 μg/mL inhibited tyrosinase and melanin production in IBMX-stimulated B16F10 cells. FBG significantly reduced tyrosinase, TYRP-1, and MITF mRNA expression. Moreover, FBG stimulated anti-wrinkle effects in UVB-irradiated human dermal fibroblast (FBG) cells by inhibiting elastase activity, and decreasing MMP-1 and MMP-9 while increasing COL-1 mRNA expression. Furthermore, FBG showed antioxidant activity by reducing ROS levels in H2O2-induced HaCat cells. All tests showed that FBG had better anti-melanogenic, anti-wrinkle, and antioxidant activities than black ginseng (BG) and white ginseng (WG). Interestingly, FBG had lower toxicity in B16F10, HDF, and HaCat cells compared with BG. Thus, this study discovers the underlying mechanism of FBG as a functional material with anti-skin-aging properties.
A Novel Soft Robotic Exoskeleton System for Hand Rehabilitation and Assistance Purposes
Authors: Kladovasilakis Nikolaos , Kostavelis Ioannis , Sideridis Paschalis , Koltzi Eleni , Piliounis Konstantinos , Tzetzis Dimitrios , Tzovaras Dimitrios
Published: January 2023

During the last decade, soft robotic systems, such as actuators and grippers, have been employed in various commercial applications. Due to the need to integrate robotic mechanisms into devices operating alongside humans, soft robotic systems concentrate increased scientific interest in tasks with intense human–robot interaction, especially for human-exoskeleton applications. Human exoskeletons are usually utilized for assistance and rehabilitation of patients with mobility disabilities and neurological disorders. Towards this direction, a fully functional soft robotic hand exoskeleton system was designed and developed, utilizing innovative air-pressurized soft actuators fabricated via additive manufacturing technologies. The CE-certified system consists of a control glove that copies the motion from the healthy hand and passes the fingers configuration to the exoskeleton applied on the affected hand, which consists of a soft exoskeleton glove (SEG) controlled with the assistance of one-axis flex sensors, micro-valves, and a proportional integral derivative (PID) controller. Each finger of the SEG moves independently due to the finger-dedicated motion control system. Furthermore, the real-time monitoring and control of the fabricated SEG are conducted via the developed software. In addition, the efficiency of the exoskeleton system was investigated through an experimental validation procedure with the involvement of healthy participants (control group) and patients, which evaluated the efficiency of the system, including safety, ergonomics, and comfort in its usage.
Study of the Synergistic Effect of Induction Heating Parameters on Heating Efficiency Using Taguchi Method and Response Surface Method
Authors: Li Jiajie , Zhang Peng , Hu Jinlan , Zhang Yanfei
Published: January 2023

This work designed an intercalation internal induction heating coil in a mold and drew a plate-type steel for the heating mold (size: 300 mm × 200 mm × 40 mm). First, to explore the influence rule of special-shaped coils on induction heating effects, the temperature rise curve on the mold surface was simulated at different heating depths, currents, and frequencies. Next, the extent to which these three factors affect the maximum mean temperature and temperature uniformity was discussed using the Taguchi method and the analysis of variance (ANOVA). Results show that heating depth and current are important factors influencing the target results, while frequency only has a small impact. The maximum mean temperature reaches its peak level when the heating depth, current, and frequency are at the respective values of 5 mm, 1200 A, and 40 KHz and the optimal temperature uniformity can be achieved when these values are 7 mm, 800 A, and 20 KHz, respectively. Finally, the synergistic effect of different factors on target results was analyzed using the response surface method (RSM).
Study on Stress–Strain Relationship of Coir Fiber-Reinforced Red Clay Based on Duncan–Chang Model
Authors: Jiang Xueliang , Guo Jiahui , Yang Hui , Bao Shufeng , Wen Changping , Chen Jiayu
Published: January 2023

Compared with other natural fibers, coir fiber has good strength characteristics and long-term anti-biodegradation ability. At present, most studies on randomly distributed coir fiber-reinforced soil have focused on cohesionless soil or granular soil. In this paper, the influence of randomly distributed coir fiber on the deviatoric stress and shear strength index of red clay with different fiber content was assessed by a consolidated undrained (CU) triaxial compression test. Since the hyperbolic variational character of the stress–strain relation of the samples conformed to the hyperbolic hypothesis of the Duncan–Chang model of nonlinear elastic model, the Duncan–Chang model was used to fit it, and the influences of fiber content and confining pressure on the parameters of the Duncan–Chang model were studied. The fiber content was determined by testing to be 0%, 0.2%, 0.25%, 0.3%, 0.35% and 0.4% of the dry soil mass. It has been found that coir fiber distributed in a random radial manner can significantly increase the deviatoric stress of red clay, and thus can be effectively used in the case of soil and fiber mixing. The cohesion of the red clay first increases and then decreases with the increase in fiber content, with an optimum content of 0.3%. The internal friction angle changes little with increasing fiber content.
Dynamic IoT Malware Detection in Android Systems Using Profile Hidden Markov Models
Authors: Abanmi Norah , Kurdi Heba , Alzamel Mai
Published: January 2023

The prevalence of malware attacks that target IoT systems has raised an alarm and highlighted the need for efficient mechanisms to detect and defeat them. However, detecting malware is challenging, especially malware with new or unknown behaviors. The main problem is that malware can hide, so it cannot be detected easily. Furthermore, information about malware families is limited which restricts the amount of “big data” that is available for analysis. The motivation of this paper is two-fold. First, to introduce a new Profile Hidden Markov Model (PHMM) that can be used for both app analysis and classification in Android systems. Second, to dynamically identify suspicious calls while reducing infection risks of executed codes. We focused on Android systems, as they are more vulnerable than other IoT systems due to their ubiquitousness and sideloading features. The experimental results showed that the proposed Dynamic IoT malware Detection in Android Systems using PHMM (DIP) achieved superior performance when benchmarked against eight rival malware detection frameworks, showing up to 96.3% accuracy at 5% False Positive Rate (FP rate), 3% False Negative Rate (FN rate) and 94.9% F-measure.
Adaptive Neural Trajectory Tracking Control for Synchronous Generators in Interconnected Power Systems
Authors: Tapia-Olvera Ruben , Beltran-Carbajal Francisco , Valderrabano-Gonzalez Antonio
Published: January 2023

The synchronous generator is one of the most important active components in current electric power systems. New control methods should be designed to guarantee an efficient dynamic performance of the synchronous generator in strongly interconnected nonlinear power systems over a wide range of variable operating conditions. In this context, active suppression capability for different uncertainties and external disturbances represents a current trend in the development of new control design methodologies. In this paper, a new adaptive neural control scheme based on differential flatness with a modified structure including B-spline Neural Networks for transient stabilization and tracking of power-angle reference profiles for synchronous generators in interconnected electric power systems is introduced. These features are attained due to the advantages extracted of these two approaches: (a) a control design stage based on a power system model by differential flatness and (b) an adaptive performance using a correct design of B-spline Neural Networks, minimizing parameter dependency. The effectiveness of the proposed algorithm is demonstrated by simulation results in two test systems: single machine infinite bus and an interconnected power system. Transient stability and robust power-angle reference profile tracking are both verified.
Binary Starling Murmuration Optimizer Algorithm to Select Effective Features from Medical Data
Authors: Nadimi-Shahraki Mohammad H. , Asghari Varzaneh Zahra , Zamani Hoda , Mirjalili Seyedali
Published: January 2023

Feature selection is an NP-hard problem to remove irrelevant and redundant features with no predictive information to increase the performance of machine learning algorithms. Many wrapper-based methods using metaheuristic algorithms have been proposed to select effective features. However, they achieve differently on medical data, and most of them cannot find those effective features that may fulfill the required accuracy in diagnosing important diseases such as Diabetes, Heart problems, Hepatitis, and Coronavirus, which are targeted datasets in this study. To tackle this drawback, an algorithm is needed that can strike a balance between local and global search strategies in selecting effective features from medical datasets. In this paper, a new binary optimizer algorithm named BSMO is proposed. It is based on the newly proposed starling murmuration optimizer (SMO) that has a high ability to solve different complex and engineering problems, and it is expected that BSMO can also effectively find an optimal subset of features. Two distinct approaches are utilized by the BSMO algorithm when searching medical datasets to find effective features. Each dimension in a continuous solution generated by SMO is simply mapped to 0 or 1 using a variable threshold in the second approach, whereas in the first, binary versions of BSMO are developed using several S-shaped and V-shaped transfer functions. The performance of the proposed BSMO was evaluated using four targeted medical datasets, and results were compared with well-known binary metaheuristic algorithms in terms of different metrics, including fitness, accuracy, sensitivity, specificity, precision, and error. Finally, the superiority of the proposed BSMO algorithm was statistically analyzed using Friedman non-parametric test. The statistical and experimental tests proved that the proposed BSMO attains better performance in comparison to the competitive algorithms such as ACO, BBA, bGWO, and BWOA for selecting effective features from the medical datasets targeted in this study.
Janus: Hierarchical Multi-Blockchain-Based Access Control (HMBAC) for Multi-Authority and Multi-Domain Environments
Authors: Malamas Vangelis , Palaiologos George , Kotzanikolaou Panayiotis , Burmester Mike , Glynos Dimitris
Published: January 2023

Although there are several access control systems in the literature for flexible policy management in multi-authority and multi-domain environments, achieving interoperability and scalability, without relying on strong trust assumptions, is still an open challenge. We present HMBAC, a distributed fine-grained access control model for shared and dynamic multi-authority and multi-domain environments, along with Janus, a practical system for HMBAC policy enforcement. The proposed HMBAC model supports: (a) dynamic trust management between different authorities; (b) flexible access control policy enforcement, defined at the domain and cross-domain level; (c) a global source of truth for all entities, supported by an immutable, audit-friendly mechanism. Janus implements the HMBAC model and relies on the effective fusion of two core components. First, a Hierarchical Multi-Blockchain architecture that acts as a single access point that cannot be bypassed by users or authorities. Second, a Multi-Authority Attribute-Based Encryption protocol that supports flexible shared multi-owner encryption, where attribute keys from different authorities are combined to decrypt data distributedly stored in different authorities. Our approach was implemented using Hyperledger Fabric as the underlying blockchain, with the system components placed in Kubernetes Docker container pods. We experimentally validated the effectiveness and efficiency of Janus, while fully reproducible artifacts of both our implementation and our measurements are provided.
Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
Authors: You Shaoze , Zhu Hua , Li Menggang , Li Yutan , Tang Chaoquan
Published: January 2023

In recent years, discriminative correlation filters (DCF) based trackers have been widely used in mobile robots due to their efficiency. However, underground coal mines are typically a low illumination environment, and tracking in this environment is a challenging problem that has not been adequately addressed in the literature. Thus, this paper proposes a Low-illumination Long-term Correlation Tracker (LLCT) and designs a visual tracking system for coal mine drilling robots. A low-illumination tracking framework combining image enhancement strategies and long-time tracking is proposed. A long-term memory correlation filter tracker with an interval update strategy is utilized. In addition, a local area illumination detection method is proposed to prevent the failure of the enhancement algorithm due to local over-exposure. A convenient image enhancement method is proposed to boost efficiency. Extensive experiments on popular object tracking benchmark datasets demonstrate that the proposed tracker significantly outperforms the baseline trackers, achieving high real-time performance. The tracker’s performance is verified on an underground drilling robot in a coal mine. The results of the field experiment demonstrate that the performance of the novel tracking framework is better than that of state-of-the-art trackers in low-illumination environments.
Analyzing Noise Robustness of Cochleogram and Mel Spectrogram Features in Deep Learning Based Speaker Recognition
Authors: Lambamo Wondimu , Srinivasagan Ramasamy , Jifara Worku
Published: January 2023

The performance of speaker recognition systems is very well on the datasets without noise and mismatch. However, the performance gets degraded with the environmental noises, channel variation, physical and behavioral changes in speaker. The types of Speaker related feature play crucial role in improving the performance of speaker recognition systems. Gammatone Frequency Cepstral Coefficient (GFCC) features has been widely used to develop robust speaker recognition systems with the conventional machine learning, it achieved better performance compared to Mel Frequency Cepstral Coefficient (MFCC) features in the noisy condition. Recently, deep learning models showed better performance in the speaker recognition compared to conventional machine learning. Most of the previous deep learning-based speaker recognition models has used Mel Spectrogram and similar inputs rather than a handcrafted features like MFCC and GFCC features. However, the performance of the Mel Spectrogram features gets degraded in the high noise ratio and mismatch in the utterances. Similar to Mel Spectrogram, Cochleogram is another important feature for deep learning speaker recognition models. Like GFCC features, Cochleogram represents utterances in Equal Rectangular Band (ERB) scale which is important in noisy condition. However, none of the studies have conducted analysis for noise robustness of Cochleogram and Mel Spectrogram in speaker recognition. In addition, only limited studies have used Cochleogram to develop speech-based models in noisy and mismatch condition using deep learning. In this study, analysis of noise robustness of Cochleogram and Mel Spectrogram features in speaker recognition using deep learning model is conducted at the Signal to Noise Ratio (SNR) level from −5 dB to 20 dB. Experiments are conducted on the VoxCeleb1 and Noise added VoxCeleb1 dataset by using basic 2DCNN, ResNet-50, VGG-16, ECAPA-TDNN and TitaNet Models architectures. The Speaker identification and verification performance of both Cochleogram and Mel Spectrogram is evaluated. The results show that Cochleogram have better performance than Mel Spectrogram in both speaker identification and verification at the noisy and mismatch condition.
Grid-Connected Solar Photovoltaic System for Nile Tilapia Farms in Southern Mexico: Techno-Economic and Environmental Evaluation
Authors: Delfín-Portela Elizabeth , Sandoval-Herazo Luis Carlos , Reyes-González David , Mata-Alejandro Humberto , López-Méndez María Cristina , Fernández-Lambert Gregorio , Betanzo-Torres Erick Arturo
Published: January 2023

Tilapia farming is the predominant aquaculture activity, with 4623 aquaculture farms in Mexico alone. It is relevant to apply technological alternatives to mitigate production costs, mainly those associated with supporting energy savings for aeration and water pumping in aquaculture farms. There is limited information confirming the feasibility of implementing photovoltaic systems connected to the grid (On grid-PV) in aquaculture farms. The working hypothesis proposed for the development of the work was that On Grid PV systems in Tilapia aquaculture farms in Mexico are technically feasible, economically viable and environmentally acceptable. Therefore, the objective of this research is to design a grid-connected photovoltaic system for rural Tilapia aquaculture farms in Mexico and analyze it with a feasibility assessment through technical, economic and environmental variables, as part of the link between academia and the productive sector. Methodologically, the On Grid-PV design was carried out in an aquaculture farm in Veracruz, Mexico, as a case study. It was developed in two stages: the field phase (1), where a non-participant observation guide and a survey with open questions were applied to perform the energy diagnosis, and the cabinet phase (2) where the calculation of the economic and environmental variables was carried out with the clean energy management software Retscreen expert, the engineering design was based on the Mexican Official Standard for electrical installations, and Sunny Design 5.22.5 was used to calculate and analyze the electrical parameters of the On Grid PV. The results revealed an investment cost of USD 30,062.61, the cost per KWp was of 1.36 USD/Watt, and the economic indicators were the net present value (USD 41,517.44), internal rate of return (38.2%) and cost–benefit ratio (5.6). Thus, the capital investment is recovered in 4.7 years thanks to the savings obtained by generating 2429 kW/h per month. As for the environment, it is estimated that 11,221 kg of CO2 equivalent would be released into the atmosphere without the On Grid-PV. In conclusion, the hypothesis is accepted and it is confirmed that On Grid-PV installations for Tilapia farms are technically feasible, economically viable and environmentally acceptable; their implementation would imply the possibility for aquaculture farms to produce Tilapia at a lower production cost and minimized environmental impact in terms of energy. It is recommended that aquaculture farmers in Mexico and the world implement this eco-technology that supports the sustainable development of aquaculture.
Detection of Primary User Emulation Attack Using the Differential Evolution Algorithm in Cognitive Radio Networks
Authors: Batool Rehna , Bibi Nargis , Muhammad Nazeer , Alhazmi Samah
Published: January 2023

Cognitive Radio Network (CRN) is an emerging technology used to solve spectrum shortage problems in wireless communications. In CRN, unlicensed secondary users (SUs) and licensed primary users (PUs) use spectrum resources at the same time by avoiding any interference from SUs. However, the spectrum sensing process in CRN is often disturbed by a security issue known as the Primary User Emulation Attack (PUEA). PUEA is one of the main security issues that disrupt the whole activity of CRN. The attacker transmits false information to interrupt the spectrum sensing process of CRN, which leads to poor usage of the spectrum. The proposed study uses a proficient Time Difference of Arrival (TDOA) based localization method using the Differential Evolution (DE) algorithm to identify the PUEA in CRNs. The DE algorithm is used to solve the objective function of TDOA values. The proposed methodology constructs a CRN and identifies PUEA. The proposed method aims to sense and localize PUEA efficiently. Mean Square Error (MSE) is the performance evaluation parameter that is used to measure the accuracy of the proposed technique. The results are compared with the previously proposed Firefly optimization algorithm (FA). It is clear from the results that DE converges faster than FA.
Loquat Leaf Extract Inhibits Oxidative Stress-Induced DNA Damage and Apoptosis via AMPK and Nrf2/HO-1 Signaling Pathways in C2C12 Cells
Authors: Kwon Young Hoon , Jang Jung Yoon , Lee Jun Ho , Choi Young Whan , Choi Yung Hyun , Kim Nam Deuk
Published: January 2023

Loquat (Eriobotrya japonica) leaf extract exhibits bioactive properties against a variety of diseases. However, it remains unclear whether loquat leaf extract can protect myoblasts from oxidative damage. To investigate the protective effect of loquat leaf ethanol extract (LE) against hydrogen peroxide (H2O2)-induced oxidative stress in C2C12 murine myoblasts and the effect of LE on cellular differentiation in C2C12 cells. LE inhibited H2O2-induced cytotoxicity and reduced both the expression level of γ-H2AX and reactive oxygen species formation. LE also inhibited H2O2-induced apoptosis, which resulted in the upregulation of B-cell lymphoma 2 and pro-caspase-3 and inhibition of poly(ADP-ribose) polymerase cleavage, and the dysfunction of mitochondria under H2O2-induced oxidative stress, which inhibited the release of cytochrome c from mitochondria to the cytoplasm. Moreover, LE upregulated p-AMP-activated protein kinase (AMPK), p-nuclear factor erythroid 2-related factor 2 (Nrf2), and heme oxygenase-1 (HO-1) and downregulated Kelch-like ECH-associated protein 1 in H2O2-treated C2C12 cells. In addition, LE promoted the differentiation of C2C12 cells into myotubes and increased the expression levels of myogenic proteins, myogenic differentiation 1 (MyoD) and myogenin. These findings suggest that LE may be a promising therapeutic candidate for treating oxidative stress-mediated myoblast injury and enhancing cellular differentiation of C2C12 murine myoblasts into myotubes.
The Development of Symbolic Expressions for the Detection of Hepatitis C Patients and the Disease Progression from Blood Parameters Using Genetic Programming-Symbolic Classification Algorithm
Authors: Anđelić Nikola , Lorencin Ivan , Baressi Šegota Sandi , Car Zlatan
Published: January 2023

Hepatitis C is an infectious disease which is caused by the Hepatitis C virus (HCV) and the virus primarily affects the liver. Based on the publicly available dataset used in this paper the idea is to develop a mathematical equation that could be used to detect HCV patients with high accuracy based on the enzymes, proteins, and biomarker values contained in a patient’s blood sample using genetic programming symbolic classification (GPSC) algorithm. Not only that, but the idea was also to obtain a mathematical equation that could detect the progress of the disease i.e., Hepatitis C, Fibrosis, and Cirrhosis using the GPSC algorithm. Since the original dataset was imbalanced (a large number of healthy patients versus a small number of Hepatitis C/Fibrosis/Cirrhosis patients) the dataset was balanced using random oversampling, SMOTE, ADSYN, and Borderline SMOTE methods. The symbolic expressions (mathematical equations) were obtained using the GPSC algorithm using a rigorous process of 5-fold cross-validation with a random hyperparameter search method which had to be developed for this problem. To evaluate each symbolic expression generated with GPSC the mean and standard deviation values of accuracy (ACC), the area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score were obtained. In a simple binary case (healthy vs. Hepatitis C patients) the best case was achieved with a dataset balanced with the Borderline SMOTE method. The results are ACC¯±SD(ACC), AUC¯±SD(AUC), Precision¯±SD(Precision), Recall¯±SD(Recall), and F1−score¯±SD(F1−score) equal to 0.99±5.8×10−3, 0.99±5.4×10−3, 0.998±1.3×10−3, 0.98±1.19×10−3, and 0.99±5.39×10−3, respectively. For the multiclass problem, OneVsRestClassifer was used in combination with GPSC 5-fold cross-validation and random hyperparameter search, and the best case was achieved with a dataset balanced with the Borderline SMOTE method. To evaluate symbolic expressions obtained in this case previous evaluation metric methods were used however for AUC, Precision, Recall, and F1−score the macro values were computed since this method calculates metrics for each label, and find their unweighted mean value. In multiclass case the ACC¯±SD(ACC), AUC¯macro±SD(AUC), Precision¯macro±SD(Precision), Recall¯macro±SD(Recall), and F1−score¯macro±SD(F1−score) are equal to 0.934±9×10−3, 0.987±1.8×10−3, 0.942±6.9×10−3, 0.934±7.84×10−3 and 0.932±8.4×10−3, respectively. For the best binary and multi-class cases, the symbolic expressions are shown and evaluated on the original dataset.
Radiomics Approach to the Detection of Prostate Cancer Using Multiparametric MRI: A Validation Study Using Prostate-Cancer-Tissue-Mimicking Phantoms
Authors: Alshomrani Faisal , Alsaedi Basim , Wei Cheng , Szewczyk-Bieda Magdalena , Gandy Stephen , Wilson Jennifer , Huang Zhihong , Nabi Ghulam
Published: January 2023

Over the last few years, a number of studies have quantified the role of radiomics, dynamic contrast enhancement and standard MRI (T2WI + DWI) in detecting prostate cancer; however, the aim of this paper was to assess the advantage of combining radiomics with other multiparametric magnetic resonance imaging (mpMRI) (T2-DWI-DCE) in improving the detection of prostate cancer. This study used 10 prostate-cancer-tissue-mimicking phantoms to obtain preclinical data. We then focused on 46 patients who underwent mpMRI and Transrectal Ultrasound (TRUS) guided biopsy between September 2016 and December 2017. The texture analysis parameters combined with the mpMRI and compared with the histopathology of TRUS biopsy have been assessed statistically by principal component analysis (PCA) and discriminant component analysis (DCA). The prediction model and goodness-of-fit were examined with the Akaike information criterion (AIC) and McFadden pseudo-R-squared. In the PCA, there was a higher separation between cancerous and noncancerous tissue in the preclinical compared with the clinical data. Both AIC and R2 showed an improvement in the model in cancer prediction by adding the radiomics to mpMRI. The discriminant analysis showed an accuracy of cancer prediction of 81% compared with 100% in the pre-clinical phantom data. Combining radiomics with mpMRI showed an improvement in prostate cancer prediction. The ex vivo experiments validated the findings of this study.
Reliability Techniques in Industrial Design
Authors: García-Sanz-Calcedo Justo , Sánchez-Barroso Gonzalo , González-Domínguez Jaime
Published: January 2023

Reliability engineering focuses on the ability of physical equipment to function without failure [...]
Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
Authors: Tang Xiaoyu , Xu Sijia , Ye Hui
Published: January 2023

In network edge computing scenarios, close monitoring of network data and anomaly detection is critical for Internet services. Although a variety of anomaly detectors have been proposed by many scholars, few of these take into account the anomalies of the data in business logic. Expert labeling of business logic exceptions is also very important for detection. Most exception detection algorithms focus on problems, such as numerical exceptions, missed exceptions and false exceptions, but they ignore the existence of business logic exceptions, which brings a whole new challenge to exception detection. Moreover, anomaly detection in the context of big data is limited to the need to manually adjust detector parameters and thresholds, which is constrained by the physiological limits of operators. In this paper, a neural network algorithm based on the combination of Labeling Neural Network and Relevant Long Short-Term Memory Neural Network is proposed. This is a semi-supervised exception detection algorithm that can be readily extended with business logic exception types. The self-learning performance of this multi-network is better adapted to the big data anomaly detection scenario, which further improves the efficiency and accuracy of network data anomaly detection and considers business scenario-based anomaly data detection. The results show that the algorithm achieves 96% detection accuracy and 97% recall rate, which are consistent with the business logic anomaly fragments marked by experts. Both theoretical analysis and simulation experiments verify its effectiveness.
Phytochemical Profiling of the Ethanolic Extract of Zaleya pentandra L. Jaffery and Its Biological Activities by In-Vitro Assays and In-Silico Molecular Docking
Authors: Shahid Afia , Khan Kashif ur Rehman , Rao Huma , Aati Hanan Y. , Sherif Asmaa E. , Khan Duraiz Ahmed , Basit Abdul , Umair Muhammad , Mueed Abdul , Esatbeyoglu Tuba , Korma Sameh A.
Published: January 2023

Zaleya pentandra L. jaffery is the only species of the genus Zaleya that grows in the Cholistan desert, Pakistan. It is a Xero-halophyte plant with high phenolic and flavonoid content. The present research was designed to investigate the phytochemical composition, biological activities, and in silico molecular docking of the ethanolic extract of Z. pentandra. The phytochemical evaluation was done through preliminary phytochemical testing, estimation of total bioactive content, and gas chromatography–mass spectrometry (GC–MS) analysis for the identification of volatile compounds. For the evaluation of biological activities, antioxidants, and enzyme inhibition (α-glucosidase, cholinesterase, and tyrosinase), antibacterial and antiviral assays were performed. GC–MS analysis revealed the presence of 29 tentative volatile compounds. The ethanolic extract of Z. pentandra contains high phenolic content (119.6 ± 0.12 mg GAE/g extract) and flavonoid content (45.5 ± 0.19 mg QE/g extract), which correlates with the strong DPPH, FRAP, and enzyme inhibition results. The ethanolic extract of Z. pentandra also showed dose-dependent antibacterial activity. Micrococcus luteus and Pseudomonas aeruginosa were found to be most susceptible, with 16 mm and 17 mm zone of inhibitions at a maximum dose of 20 mg/mL. Antiviral results showed that the ethanol extract has excellent activity against H9, IBV, and NDV viral strains. Additionally, in silico molecular docking was performed in order to determine the interaction and binding affinity between the enzymes and compounds identified by GC–MS. α-glucosidase, cholinesterase, and tyrosinase showed the highest binding affinity toward 1,2-benzenedicarboxylic acid, 2-hydroxy-n-(2-phenylethyl) benzamide, γ-sitosterol, and lactose. These findings can serve as a benchmark for anti-diabetic-, neuro-, and skin-protective uses of this plant and can be used for the isolation of pure bioactive compounds in the future.
Error Dynamics Based Dual Heuristic Dynamic Programming for Self-Learning Flight Control
Authors: Huang Xu , Zhang Yuan , Liu Jiarun , Zhong Honghao , Wang Zhaolei , Peng Yue
Published: January 2023

A data-driven nonlinear control approach, called error dynamics-based dual heuristic dynamic programming (ED-DHP), is proposed for air vehicle attitude control. To solve the optimal tracking control problem, the augmented system is defined by the derived error dynamics and reference trajectory so that the actor neural network can learn the feedforward and feedback control terms at the same time. During the online self-learning process, the actor neural network learns the control policy by minimizing the augmented system’s value function. The input dynamics identified by the recursive least square (RLS) and output of the critic neural network are used to update the actor neural network. In addition, the total uncertainty term of the error dynamics is also identified by RLS, which can compensate for the uncertainty caused by inaccurate modeling, parameter perturbation, and so on. The outputs of ED-DHP include the rough trim surface, feedforward and feedback terms from the actor neural network, and the compensation. Based on this control scheme, the complete knowledge of system dynamics and the reference trajectory dynamics are not needed, and offline learning is unnecessary. To verify the self-learning ability of ED-DHP, two numerical experiments are carried out based on the established morphing air vehicle model. One is sinusoidal signal tracking at a fixed operating point, and the other is guidance command tracking with a morphing process at variable operating points. The simulation results demonstrate the good performance of ED-DHP for online self-learning attitude control and validate the robustness of the proposed scheme
The Obesity Paradox: Associations between the Body Mass Index and Self-Perceived Health, Depression Status, and Pain Level in Older People
Authors: Denche-Zamorano Ángel , Salas-Gómez Diana , Carlos-Vivas Jorge , Barrios-Fernandez Sabina , Merellano-Navarro Eugenio , Collado-Mateo Daniel , Olivares Pedro R. , Adsuar José Carmelo
Published: January 2023

Population growth and physical inactivity have led to health and social consequences derived from chronic diseases and decreased quality of life in the elderly. Some research suggests that overweight in older people may not be associated with the negative effects on some health indicators. This study analysed the associations between Self-Perceived Health (SPH), Depression status, Pain Level, and Body Mass Index (BMI) in people over 70 years who are residents in Spain, with a final sample composed of 13,895 participants. A cross-sectional study using data from the European Health Surveys in Spain (EHIS2014 and 2020) and the National Health Survey (ENSE2017) was conducted. Dependency associations were observed between SPH, Depression Status, and Pain Level with BMI in the outcomes from the three surveys analysed (p < 0.001). Negative SPH, Depression Status, and Severe/Extreme Pain Degree prevalence were higher in the Underweight groups, being the lowest in Normal-weight and Overweight groups (p < 0.05). High levels of negative SPH, Depression, and Severe/Extreme Pain risks were found in the Underweight compared to the Normal-weight group, but not in Overweight ones. Overweight was not linked with an increased risk of the conditions analysed compared to the Normal-weight groups in older residents in Spain. The Underweight group presented the highest negative SPH prevalence, Depression, and Severe/Extreme Pain. Moreover, Obesity increased the negative SPH, Depression, and Pain Degree risks compared to the Normal-weight and Overweight groups in this population.
Two-Stage Tour Route Recommendation Approach by Integrating Crowd Dynamics Derived from Mobile Tracking Data
Authors: Hu Yue , Fang Zhixiang , Zou Xinyan , Zhong Haoyu , Wang Lubin
Published: January 2023

Tourism activities essentially represent the interaction between crowds and attractions. Thus, crowd dynamics are critical to the quality of the tourism experience in personalized tour recommendations. In order to generate dynamic, personalized tour routes, this paper develops a tourist trip design problem with crowd dynamics (TTDP-CD), which is quantified with the crowd dynamics indicators derived from mobile tracking data in terms of crowd flow, crowd interaction, and crowd structure. TTDP-CD attempts to minimize the perceived crowding and maximize the assessed value of destinations while minimizing the total distance and proposes a two-stage route strategy of “global optimization first, local update later” to deal with the sudden increase in crowding in realistic scenarios. An evolutionary algorithm is extended with container-index coding, mixed mutation operators, and a global archive to create a personalized day tour route at the urban scale. To corroborate the performance of this approach, a case study was carried out in Dalian, China. The results demonstrate that the suggested method outperforms previous approaches, such as NSGA-II, MOPSO, MOACO, and WSM, in terms of performance and solution quality and decreases real-time crowding by an average of 7%.