bluuebunny/crossref_metadata_2025 huggingface dataset:
Contamination by Trace Elements and Oxidative Stress in the Skeletal Muscle of Scyliorhinus canicula from the Central Tyrrhenian Sea
Authors: Filice Mariacristina , Reinero Francesca Romana , Cerra Maria Carmela , Faggio Caterina , Leonetti Francesco Luigi , Micarelli Primo , Giglio Gianni , Sperone Emilio , Barca Donatella , Imbrogno Sandra
Published: February 2023

Marine pollution, due to the regular discharge of contaminants by various anthropogenic sources, is a growing problem that imposes detrimental influences on natural species. Sharks, because of a diet based on smaller polluted animals, are exposed to the risk of water contamination and the subsequent bioaccumulation and biomagnification. Trace elements are very diffuse water pollutants and able to induce oxidative stress in a variety of marine organisms. However, to date, studies on sharks are rather scarce and often limited to mercury. In this context, the present study aimed to analyze the accumulation of trace elements and their putative correlation with the onset of an oxidative status in the muscle of the lesser spotted dogfish Scyliorhinus canicula, from the Central Mediterranean Sea. Ecotoxicological analysis detected the presence of Pb, As, Cd, Mn, Zn, Ni, Cu, and Fe; no significant differences were observed between sexes, while a negative correlation was found between Pb and animal length. Analysis of oxidative stress markers showed either positive or negative correlation with respect to the presence of trace elements. Lipid peroxidation (TBARS) positively correlated with Zn, Ni, and Fe; SOD enzyme activity negatively correlated with Cu and Ni; LDH was negatively correlated with Fe and positively correlated with Pb. Moreover, positive correlations between the leukocyte count and Mn and Zn, as well as with LDH activity, were also observed. The data suggested that, in sharks, trace elements accumulation may affect oxidant and antioxidant processes with important outcomes for their physiology and health.
Selenium Protects Mouse Hypothalamic Cells from Glucocorticoid-Induced Endoplasmic Reticulum Stress Vulnerability and Insulin Signaling Impairment
Authors: An Katlyn J. , Hanato Ashley N. , Hui Katherine W. , Pitts Matthew W. , Seale Lucia A. , Nicholson Jessica L. , Toh Pamela , Kim Jun Kyoung , Berry Marla J. , Torres Daniel J.
Published: February 2023

The use of glucocorticoid medications is known to cause metabolic side effects such as overeating, excess weight gain, and insulin resistance. The hypothalamus, a central regulator of feeding behavior and energy expenditure, is highly responsive to glucocorticoids, and it has been proposed that it plays a role in glucocorticoid-induced metabolic defects. Glucocorticoids can alter the expression and activity of antioxidant enzymes and promote the accumulation of reactive oxygen species. Recent evidence indicates that selenium can counter the effects of glucocorticoids, and selenium is critical for proper hypothalamic function. This study sought to determine whether selenium is capable of protecting hypothalamic cells from dysfunction caused by glucocorticoid exposure. We treated mHypoE-44 mouse hypothalamic cells with corticosterone to study the effects on cellular physiology and the involvement of selenium. We found that corticosterone administration rendered cells more vulnerable to endoplasmic reticulum stress and the subsequent impairment of insulin signaling. Supplementing the cell culture media with additional selenium alleviated endoplasmic reticulum stress and promoted insulin signaling. These findings implicate a protective role of selenium against chronic glucocorticoid-induced hypothalamic dysfunction.
Co-Targeting of BTK and TrxR as a Therapeutic Approach to the Treatment of Lymphoma
Authors: Wang Sicong , Clapper Erin , Tonissen Kathryn F. , Di Trapani Giovanna
Published: February 2023

Diffuse large B-cell lymphoma (DLBCL) is a haematological malignancy representing the most diagnosed non-Hodgkin’s lymphoma (NHL) subtype. Despite the approved chemotherapies available in clinics, some patients still suffer from side effects and relapsed disease. Recently, studies have reported the role of the Trx system and the BCR signalling pathway in cancer development and drug resistance. In this regard, we assessed a potential link between the two systems and evaluated the effects of [Au(d2pype)2]Cl (TrxR inhibitor) and ibrutinib (BTK inhibitor) alone and in combination on the cell growth of two DLBCL lymphoma cell lines, SUDHL2 and SUDHL4. In this study, we show higher expression levels of the Trx system and BCR signalling pathway in the DLBCL patient samples compared to the healthy samples. The knockdown of TrxR using siRNA reduced BTK mRNA and protein expression. A combination treatment with [Au(d2pype)2]Cl and ibrutinib had a synergistic effect on the inhibition of lymphoma cell proliferation, the activation of apoptosis, and, depending on lymphoma cell subtype, ferroptosis. Decreased BTK expression and the cytoplasmic accumulation of p65 were observed after the combination treatment in the DLBCL cells, indicating the inhibition of the NF-κB pathway. Thus, the co-targeting of BTK and TrxR may be an effective therapeutic strategy to consider for DLBCL treatment.
Aurora Kinase A Regulation by Cysteine Oxidative Modification
Authors: Lee In-Gyun , Lee Bong-Jin
Published: February 2023

Aurora kinase A (AURKA), which is a member of serine/threonine kinase family, plays a critical role in regulating mitosis. AURKA has drawn much attention as its dysregulation is critically associated with various cancers, leading to the development of AURKA inhibitors, a new class of anticancer drugs. As the spatiotemporal activity of AURKA critically depends on diverse intra- and inter-molecular factors, including its interaction with various protein cofactors and post-translational modifications, each of these pathways should be exploited for the development of a novel class of AURKA inhibitors other than ATP-competitive inhibitors. Several lines of evidence have recently shown that redox-active molecules can modify the cysteine residues located on the kinase domain of AURKA, thereby regulating its activity. In this review, we present the current understanding of how oxidative modifications of cysteine residues of AURKA, induced by redox-active molecules, structurally and functionally regulate AURKA and discuss their implications in the discovery of novel AURKA inhibitors.
Lower Bound for Sculpture Garden Problem: Localization of IoT Devices
Authors: Eskandari Marzieh , Sadeghi Bigham Bahram , Zahedi-Seresht Mazyar
Published: February 2023

The purpose of the current study is to investigate a special case of art gallery problem, namely a sculpture garden problem. In this problem, for a given polygon P, the ultimate goal is to place the minimum number of guards (landmarks) to define the interior polygon P by applying a monotone Boolean formula composed of the guards. Using this problem, it can replace the operation-based method with time-consuming, pixel-based algorithms. So, the processing time of some problems in the fields of machine vision, image processing and gamification can be strongly reduced. The problem has also many applications in mobile device localization in the Internet of Things (IoT). An open problem in this regard is the proof of Eppstein’s conjecture, which has remained an open problem since 2007. According to his conjecture, in the worst case, n−2 vertex guards are required to describe any n-gon. In this paper, a lower bound is introduced for the special case of this problem (natural vertex guard), which shows that if a polygon can be defined with natural vertex guards, then n−2 is a lower bound.
Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
Authors: Bencak Primož , Hercog Darko , Lerher Tone
Published: February 2023

Robotic bin-picking performance has been gaining attention in recent years with the development of increasingly advanced camera and machine vision systems, collaborative and industrial robots, and sophisticated robotic grippers. In the random bin-picking process, the wide variety of objects in terms of shape, weight, and surface require complex solutions for the objects to be reliably picked. The challenging part of robotic bin-picking is to determine object pick-points correctly. This paper presents a simulation model based on ADAMS/MATLAB cosimulation for robotic pick-point evaluation for a 2-F robotic gripper. It consists of a mechanical model constructed in ADAMS/View, MATLAB/Simulink force controller, several support functions, and the graphical user interface developed in MATLAB/App Designer. Its functionality can serve three different applications, such as: (1) determining the optimal pick-points of the object due to object complexity, (2) selecting the most appropriate robotic gripper, and (3) improving the existing configuration of the robotic gripper (finger width, depth, shape, stroke width, etc.). Additionally, based on this analysis, new variants of robotic grippers can be proposed. The simulation model has been verified on a selected object on a sample 2-F parallel robotic gripper, showing promising results, where up to 75% of pick-points were correctly determined in the initial testing phase.
Effects of a Short Half-Time Re-Warm-Up Program on Matches Running Performance and Fitness Test Performance of Male Elite Youth Soccer Players
Authors: Christaras Michail , Michailidis Yiannis , Mandroukas Athanasios , Vardakis Lazaros , Christoulas Kosmas , Metaxas Thomas
Published: February 2023

The aim of the study was to investigate the effect of a half-time short re-warm-up (RW) strategy on the performance of young soccer players in tests of physical abilities and running performance during matches. Twenty-three players (under 17) participated in the study. Body temperature, 10 m sprint, 30 m sprint, the Illinois agility test, countermovement jump (CMJ), and squat jump (SJ) were measured, immediately post-warm-up. Then in one condition, the tests were repeated after a passive rest, and in the other condition, after a 12 min passive rest and 3 min RW. Furthermore, the RW was applied at half-time of two of the four matches where the running performance was measured in the first quarter of the two halves. The results showed that the two conditions differed significantly in the 10 m sprint and CMJ performances (p < 0.001 and p = 0.049, respectively). After the passive rest, a significant decrease in body temperature and performance in SJ was observed (p < 0.001 for both). No other differences were observed. In conclusion, the short RW program can limit the decrement in performance in power tests such as sprints and jumps.
A Survey on Consensus Protocols and Attacks on Blockchain Technology
Authors: Guru Abhishek , Mohanta Bhabendu Kumar , Mohapatra Hitesh , Al-Turjman Fadi , Altrjman Chadi , Yadav Arvind
Published: February 2023

In the current era, blockchain has approximately 30 consensus algorithms. This architecturally distributed database stores data in an encrypted form with multiple checks, including elliptical curve cryptography (ECC) and Merkle hash tree. Additionally, many researchers aim to implement a public key infrastructure (PKI) cryptography mechanism to boost the security of blockchain-based data management. However, the issue is that many of these are required for advanced cryptographic protocols. For all consensus protocols, security features are required to be discussed because these consensus algorithms have recently been attacked by address resolution protocols (ARP), distributed denial of service attacks (DDoS), and sharding attacks in a permission-less blockchain. The existence of a byzantine adversary is perilous, and is involved in these ongoing attacks. Considering the above issues, we conducted an informative survey based on the consensus protocol attack on blockchain through the latest published article from IEEE, Springer, Elsevier, ACM, Willy, Hindawi, and other publishers. We incorporate various methods involved in blockchain. Our main intention is to gain clarity from earlier published articles to elaborate numerous key methods in terms of a survey article.
Natural Compounds Isolated from African Mistletoes (Loranthaceae) Exert Anti-Inflammatory and Acetylcholinesterase Inhibitory Potentials: In Vitro and In Silico Studies
Authors: Mfotie Njoya Emmanuel , Maza Hermine L. D. , Swain Shasank S. , Chukwuma Chika. I. , Mkounga Pierre , Nguekeu Mba Yves M. , Mashele Samson S. , Makhafola Tshepiso J. , McGaw Lyndy J.
Published: February 2023

Despite the medicinal uses of Phragmanthera capitata, Globimetula dinklagei and Tapinanthus bangwensis against memory loss, convulsions and pain, their efficacy against brain-dysfunction diseases and inflammation models has not yet been studied. Therefore, this study aims to investigate the anti-inflammatory and acetylcholinesterase (AChE) inhibitory potentials of their crude extracts and isolated natural compounds by combining in vitro and in silico experiments. Crude extracts and isolated compounds were tested in vitro for their AChE inhibitory activity by using Ellman’s method. Additionally, their anti-inflammatory activity was determined by evaluating the nitric oxide (NO) production inhibitory activity in lipopolysaccharide-stimulated RAW 264.7 macrophage cells and the 15-lipoxygenase (15-LOX) inhibitory activity by using the ferrous oxidation xylenol orange assay. Furthermore, the in silico efficacy of natural compounds was investigated against ten putative target enzymes relevant in Alzheimer’s disease (AD) pathogenesis and inflammation. It was found that the crude extracts had weak to moderate inhibitory potential against AChE, with the extract of T. bangwensis being the most active (50% inhibitory concentration (IC50) = 48.97 µg/mL). Six natural compounds, namely, 3-O-β-D-glucopyranosyl-28-hydroxy-α-amyrin (2), apigenin- 8-C-β-D-glucopyranoside (3), globimetulin B (5), globimetulin C (6), bangwaursene B (8) and 3β-acetoxy-11,12-epoxytaraxerol (9), were identified for the first time as having anti-AChE potential, among which (9) had the highest efficacy with an IC50 of 13.89 µM. Among the anti-AChE compounds, (5) was also efficient against NO production and 15-LOX, and the data are in agreement with the docking score. In summary, compounds (5) and (9) are the most prominent lead compounds that should be further tested experimentally against molecular targets of AD and inflammation.
Cable Force Optimization of Cable-Stayed Bridge Based on Multiobjective Particle Swarm Optimization Algorithm with Mutation Operation and the Influence Matrix
Authors: Wang Lifeng , Xiao Ziwang , Li Min , Fu Ning
Published: February 2023

To compensate the incapability of traditional cable force adjustment methods to automatically optimize cable forces, this paper proposes Midas/Civil and MATLAB as a structure calculator and a cable force optimizer, and external memory as a data transfer. Initial solutions from conventional methods can be optimized by internalizing the influence matrix into the multiobjective particle swarm optimization algorithm with mutation operation and constructing the mathematical model of cable force optimization, and then, a series of Pareto frontier solution sets are obtained. For the first time, fuzzy set theory is introduced for selecting Pareto presolution set for the optimization of cable-stayed bridges, to solve the final reasonable dead load state of bridges. By using this method, the peak vertical displacement of a main girder of the optimized cable-stayed bridge decreased from −11 mm to −6 mm, with a reduction of 45%. Before and after optimization, the difference of peak negative bending moment at the top of the pier was 34.8%, indicating that the main beam was more evenly stressed and the alignment was more reasonable.
The Use of Machine Learning and Satellite Imagery to Detect Roman Fortified Sites: The Case Study of Blad Talh (Tunisia Section)
Authors: Bachagha Nabil , Elnashar Abdelrazek , Tababi Moussa , Souei Fatma , Xu Wenbin
Published: February 2023

This study focuses on an ad hoc machine-learning method for locating archaeological sites in arid environments. Pleiades (P1B) were uploaded to the cloud asset of the Google Earth Engine (GEE) environment because they are not yet available on the platform. The average of the SAR data was combined with the P1B image in the selected study area called Blad Talh at Gafsa, which is located in southern Tunisia. This pre-desert region has long been investigated as an important area of Roman civilization (106 BCE). The results show an accurate probability map with an overall accuracy and Kappa coefficient of 0.93 and 0.91, respectively, when validated with field survey data. The results of this research demonstrate, from the perspective of archaeologists, the capability of satellite data and machine learning to discover buried archaeological sites. This work shows that the area presents more archaeological sites, which has major implications for understanding the archaeological significance of the region. Remote sensing combined with machine learning algorithms provides an effective way to augment archaeological surveys and detect new cultural deposits.
Application of Machine Learning for Prediction and Process Optimization—Case Study of Blush Defect in Plastic Injection Molding
Authors: Mollaei Ardestani Alireza , Azamirad Ghasem , Shokrollahi Yasin , Calaon Matteo , Hattel Jesper Henri , Kulahci Murat , Soltani Roya , Tosello Guido
Published: February 2023

Injection molding is one of the most important processes for the mass production of plastic parts. In recent years, many researchers have focused on predicting the occurrence and intensity of defects in injected molded parts, as well as the optimization of process parameters to avoid such defects. One of the most frequent defects of manufactured parts is blush, which usually occurs around the gate location. In this study, to identify the effective parameters on blush formation, eight design parameters with effect probability on the influence of this defect have been investigated. Using a combination of design of experiments (DOE), finite element analysis (FEA), and ANOVA, the most significant parameters have been identified (runner diameter, holding pressure, flow rate, and melt temperature). Furthermore, to provide an efficient predictive model, machine learning methods such as basic artificial neural networks, their combination with genetic algorithms, and particle swarm optimization have been applied and their performance analyzed. It was found that the basic artificial neural network (ANN), with an average accuracy error of 1.3%, provides the closest predictions to the FEA results. Additionally, the process parameters were optimized using ANOVA and a genetic algorithm, which resulted in a significant reduction in the blush defect area.
Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
Authors: Park Jaesung , Lim Yujin
Published: February 2023

The energy consumption of a multi-access edge computing (MEC) system must be reduced to save operational costs. Determining a set of active MEC servers (MECSs) that can minimize the energy consumption of the MEC system while satisfying the service delay requirements of the tasks is an NP-complete problem. To solve this problem, we take a bio-inspired approach. We note that the sleep control problem of the MECS differentiates the operational mode among neighboring MECSs. Therefore, by mimicking the cell differentiation process in a biological system, we designed a distributed sleep control method. Each MECS periodically gathers the utilization and delta levels of the neighboring MECSs. Subsequently, by using the gathered information and the Delta–Notch inter-cell signaling model, a MECS autonomously decides whether to sleep. We evaluated the performance of our method through extensive simulations. Compared with a conventional method, the proposed method reduces energy consumption in a MEC system by more than 13% while providing a comparable service delay. In addition, our method reduces the variations in the service delay by more than 35%.
An Attention-Based Method for Remaining Useful Life Prediction of Rotating Machinery
Authors: Deng Yaohua , Guo Chengwang , Zhang Zilin , Zou Linfeng , Liu Xiali , Lin Shengyu
Published: February 2023

Data imbalance and large data probability distribution discrepancies are major factors that reduce the accuracy of remaining useful life (RUL) prediction of high-reliability rotating machinery. In feature extraction, most deep transfer learning models consider the overall features but rarely attend to the local target features that are useful for RUL prediction; insufficient attention paid to local features reduces the accuracy and reliability of prediction. By considering the contribution of input data to the modeling output, a deep learning model that incorporates the attention mechanism in feature selection and extraction is proposed in our work; an unsupervised clustering method for classification of rotating machinery performance state evolution is put forward, and a similarity function is used to calculate the expected attention of input data to build an input data extraction attention module; the module is then fused with a gated recurrent unit (GRU), a variant of a recurrent neural network, to construct an attention-GRU model that combines prediction calculation and weight calculation for RUL prediction. Tests on public datasets show that the attention-GRU model outperforms traditional GRU and LSTM in RUL prediction, achieves less prediction error, and improves the performance and stability of the model.
Semi-Supervised Adversarial Transfer Networks for Cross-Domain Intelligent Fault Diagnosis of Rolling Bearings
Authors: Pan Baisong , Wang Wuyan , Wen Juan , Li Yifan
Published: February 2023

In recent advances, deep learning-based methods have been broadly applied in fault diagnosis, while most existing studies assume that source domain and target domain data follow the same distribution. As differences in operating conditions lead to the deterioration of diagnosis performance, domain adaptation technology has been introduced to bridge the distribution gap. However, most existing approaches generally assume that source domain labels are available under all health conditions during training, which is incompatible with the actual industrial situation. To this end, this paper proposes a semi-supervised adversarial transfer networks for cross-domain intelligent fault diagnosis of rolling bearings. Firstly, the Gramian Angular Field method is introduced to convert time domain vibration signals into images. Secondly, a semi-supervised learning-based label generating module is designed to generate artificial labels for unlabeled images. Finally, the dynamic adversarial transfer network is proposed to extract the domain-invariant features of all signal images and provide reliable diagnosis results. Two case studies were conducted on public rolling bearing datasets to evaluate the diagnostic performance. An experiment under variable operating conditions and an experiment with different numbers of source domain labels were carried out to verify the generalization and robustness of the proposed approach, respectively. Experiment results demonstrate that the proposed method can achieve high diagnosis accuracy when dealing with cross-domain tasks with deficient source domain labels, which may be more feasible in engineering applications than conventional methodologies.
Zanthoxylum bungeanum Essential Oil: Extraction and Component Analysis for α-Glucosidase Inhibitory Activity and the Underlying Mechanism Based on Molecular Docking
Authors: Liang Shaoqi , Hu Wei , Cheng Wensi , Zhang Sheng , Zou Ruisi
Published: February 2023

Zanthoxylum bungeanum essential oil was extracted using a low-eutectic solvent (choline chloride and 1,3-butanediol) and steam distillation. The results showed that the yield (9.36 and 10.00%) did not differ significantly between the two methods. The α-glucosidase inhibitory activity of essential oils from 20 species was screened by the PNPG (4-p-nitrophenyl-a-d-glucopyranosylase) colorimetric method, and the results showed that the inhibition rate of Zanthoxylum bungeanum essential oil reached 57.1%, far more than those of the other essential oils tested. The main components and contents of Zanthoxylum bungeanum essential oil were terpineol-4-ol (13.13%), (-)-β-pinene (11.17%), γ-terpinene (9.45%), terpinyl acetate (9.36%), and α-terpineol (5.40%), which were identified by GC-MS (gas chromatography–mass spectrometry). According to the literature, linalool and limonene are also high-content components. These seven components were docked with the α-glucosidase molecule. It was found that the binding energies between terpineol-4-ol, (-)-β-pinene, γ-terpinene, terpinyl acetate, α-terpineol, linalool, limonene, and α-glucosidase were −28.88, −25.54, −26.37, −28.46, −27.63, −25.95, and −25.53 (KJ/mol), respectively, similar to those of anthocyanins. From the research results, the following conclusions can be drawn: the extraction rate of Zanthoxylum bungeanum essential oil can be increased with steam distillation. Of the many plant essential oils, Zanthoxylum bungeanum essential oil shows excellent anti-α-glucosidase activity, suggesting that it has potential for application in hypoglycemic drugs. The strong binding ability of the monoterpene-based chemical components with the α-glucosidase molecule revealed the chemical mechanism of the inhibitory effect of Zanthoxylum bungeanum essential oil on α-glucosidase. The above conclusions provide a theoretical basis for the utilization of Zanthoxylum bungeanum essential oil in hypoglycemic drugs and further expand the applications of small-molecule chemical components.
Capacitor-Less Low-Power Neuron Circuit with Multi-Gate Feedback Field Effect Transistor
Authors: Lee Junhyeong , Cha Misun , Kwon Min-Woo
Published: February 2023

Recently, research on artificial neuron circuits imitating biological systems has been actively studied. The neuron circuit can implement an artificial neural network (ANN) capable of low-power parallel processing by configuring a biological neural network system in hardware. Conventional CMOS analog neuron circuits require many MOSFETs and membrane capacitors. Additionally, it has low energy efficiency in the first inverter stage connected to the capacitor. In this paper, we propose a low-power neuron circuit with a multi-gate feedback field effect transistor (FBFET) that can perform integration without a capacitor to solve the problem of an analog neuron circuit. The multi-gate FBFET has a low off-current due to its low operating voltage and excellent sub-threshold characteristics. We replace the n-channel MOSFET of the inverter with FBFET to suppress leakage current. FBFET devices and neuron circuits were analyzed using TACD and SPICE mixed-mode simulation. As a result, we found that the neuron circuit with multi-gate FBFET has a low subthreshold slope and can completely suppress energy consumption. We also verified the temporal and spatial integration of neuron circuits.
Identifying and Assessing Health and Safety Program Implementation Barriers in the Construction Industry: A Case of Saudi Arabia
Authors: Al-Otaibi Ali , Kineber Ahmed
Published: February 2023

A significant issue in the construction sector is poor safety performance. Construction sites are hazardous and prone to crashes, with several risks that can lead to accidents, injuries, or even the deaths of site workers or passing road users. Numerous studies have addressed this topic but there aren’t many studies in Saudi Arabia that are comparable. The purpose of this research is to determine which obstacles are standing in the way of the construction industry’s adoption of health and Safety Program. Starting with literature research, obstacles to the implementation of safety initiatives are noted. An analysis method known as the Partial Least Square Structural Equation Model (PLS-SEM) was utilized to look into additional barriers. It is recommended that a national governance structure be established in order to overcome these barriers and improve safety performance in the construction industry. Decision-makers would move decisively to establish and enhance safety practices in light of the proposed model’s outcomes. By identifying, assessing, and reducing the primary obstacles to safety program implementation, the study’s findings help Saudi Arabia improve safety practices for building projects.
Fuzzy Linear Active Disturbance Rejection Control Method for Permanent Magnet Electromagnetic Hybrid Suspension Platform
Authors: Qin Yao , Yang Jie , Guo Haoqi , Wang Yongzhuang
Published: February 2023

The permanent magnetic suspension (PMS) train has distinct advantages in reducing energy consumption and increasing the suspension air gap. Aiming at the problems of uncontrollable magnetic field and underdamping of the PMS system, the electromagnetic damping structure is introduced, and a fuzzy linear active disturbance rejection control (Fuzzy-LADRC) method with an air gap deviation integrator is proposed. Firstly, the permanent magnet electromagnetic hybrid suspension (PEMS) system is constructed, and the model analysis is carried out to prove that the system is controllable and observable, and the hardware experimental platform is built. Secondly, a LADRC method is designed based on the model of the platform suspension control unit, and fuzzy control is introduced into the LADRC state error feedback control theory for parameter self-tuning to improve the adaptability and robustness of the platform. At the same time, an air gap deviation integrator is introduced to further improve the control accuracy. Finally, the comparison experiments with LADRC and PID control methods are carried out in terms of stability, anti-interference, robustness and adaptability. The experimental results show that the proposed Fuzzy-LADRC method has the best control effect. It provides practical reference for improving the stability of the PMS train.
Integration of Mahalanobis-Taguchi System and Time-Driven Activity-Based Costing in a Production Environment
Authors: Mohd Zaini Sri Nur Areena , Mohd Safeiee Filzah Lina , Abdul Ghani Ahmad Shahrizan , Jaafar Nur Najmiyah , Abu Mohd Yazid
Published: February 2023

System integration is the act of combining numerous distinct subsystems into one bigger system that allows the subsystems to work together. The integrated system removes necessity of repeating operations. The purpose of this work was to investigate the best system integration in the production environment. A few methods were tested such as conventional, Mahalanobis-Taguchi System (MTS), Activity-Based Costing (ABC) and Time-Driven Activity-Based Costing (TDABC). As a result, critical activities may now be completed more effectively while reducing expenses. The organization should define the relation between cost and quality through system integration. As a consequence of system integration, four forms of integration are described, namely, integration A (conventional-ABC), integration B (conventional-TDABC), integration C (MTS-ABC), and integration D (MTS-TDABC). Integration D is the best in the production environment when compared to others because MTS recognizes the degree of contribution for each parameter that impacts the increase or decline in the final cost. Moreover, TDABC determines capacity cost rate from the costs associated with capacity provided, and time equations with versatility to dissipate the product’s complex nature. As a result of the integration of MTS and TDABC, various degrees of parameter contributions impact the time equations and capacity cost rate to generate a lower cost of product in the production environment.
State Observer Based Robust Backstepping Fault-Tolerant Control of the Free-Floating Flexible-Joint Space Manipulator
Authors: Xie Limin , Yu Xiaoyan
Published: February 2023

Actuator failure and joint flexibility will dramatically impact space robot system control. In this paper, free-floating flexible-joint space-manipulator dynamic-modeling is studied and a state-observer-based robust backstepping fault-tolerant control is proposed for the system joint actuator failure. Based on the flexible-joint simplified model, the system’s rigid-flexible coupled-dynamic equations are established according to momentum conservation, angular momentum conservation, and the Lagrange equation. Then the system is decoupled based on the singular perturbation method. For the slow subsystem, a robust backstepping fault-tolerant controller base on a state observer is designed to eliminate the angle error, compensate for the uncertain parameter and the external disturbance, and achieve the joint-trajectory asymptotic-tracking. The use of a speed filter makes it inappropriate to measure and provide feedback about the system’s velocity signals, so the controller is simpler and more precise. For the fast subsystem, a velocity differential-feedback control is adopted to suppress the system vibration caused by the flexible joint, to ensure the stability of the system. Finally, the feasibility and effectiveness of the model and control method are proved by some simulations. The simulation results indicate that the proposed fault-tolerant control method can make the free-floating flexible-joint space manipulator system track the desired trajectory accurately and steadily, regardless of whether the actuator fails or not.
Experimental Analysis and Simulation of a Porous Absorbing Layer for Noise Barriers
Authors: Sousa Laura , Pereira Luís , Montes-González David , Ramos Denilson , Amado-Mendes Paulo , Barrigón-Morillas Juan Miguel , Godinho Luís
Published: February 2023

Noise barriers are common noise mitigation measures usually implemented near roads or railways, with proven efficiency. This work presents a study of a porous concrete material incorporating expanded clay as aggregate, to be used on the sound-absorption layer of noise barriers. A theoretical material model is calibrated using experimental data and then used to estimate the diffuse field sound absorption from the normal incidence sound absorption estimation/measurement. Validation of such estimation is performed by comparing to reverberant room measurements. Numerical simulations are carried out using the boundary element method (BEM) and the CNOSSOS-EU calculation method to assess the performance of different types of barriers incorporating this material. L-shaped and vertical barriers are tested, as well as low-height and conventional (taller) barriers, employed in the context of a railway noise scenario. Different results are obtained by the two methods, mainly due to the different underlying physical principles. Good insertion loss values may be obtained using both conventional and low-height noise barriers together with the porous concrete material if a careful choice of its location within the barrier is made.
Assessment of the NO2 Spatio-Temporal Variability over Thessaloniki, Greece, Using MAX-DOAS Measurements and Comparison with S5P/TROPOMI Observations
Authors: Karagkiozidis Dimitris , Koukouli Maria-Elissavet , Bais Alkiviadis , Balis Dimitris , Tzoumaka Paraskevi
Published: February 2023

In this article, we investigate the spatio-temporal variability of tropospheric NO2 Vertical Column Densities (VCDs) and surface concentrations that were retrieved using Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements over Thessaloniki, Greece, for the period June 2020 to November 2022. The MAX-DOAS system captures the NO2 seasonal variability very well, reporting low concentrations during summer and higher concentrations in the winter, as well as the diurnal cycle with higher NO2 amounts in the morning followed by a reduction towards noon. The “weekend effect” is evident, with approximately 30% lower NO2 concentrations on the weekends compared to the working days. An excellent agreement is found with in situ data from a nearby air quality monitoring station with Pearson’s correlation coefficients ranging between R = 0.90 and R = 0.99. The spatial variability is assessed by comparing the NO2 concentrations at four azimuth viewing directions of the MAX-DOAS system. Despite the large variability due to short- and long-term temporal variations, higher NO2 concentrations of up to 25% (statistically significant at the 95% confidence level) are reported for the azimuth that crosses the entire city center and an urban area compared to those pointing towards the sea. The MAX-DOAS tropospheric NO2 columns are then compared to those measured by the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5P satellite. Despite the generally good correlation (R = 0.72 and R = 0.89 for the daily and monthly data, respectively), a clear underestimation of TROPOMI is found (approximately 55% in winter, 21% in spring and 40% during autumn and summer), mainly due to the much larger satellite footprint that provides a smoother perception of the NO2 concentration, while the MAX-DOAS measurements are more affected by local emissions.
Identifying Weak Adhesion in Single-Lap Joints Using Lamb Wave Data and Artificial Intelligence Algorithms
Authors: Ramalho Gabriel M. F. , M. Lopes António , Carbas Ricardo J. C. , Da Silva Lucas F. M.
Published: February 2023

In the last few years, the application of adhesive joints has grown significantly. Adhesive joints are often affected by a specific type of defect known as weak adhesion, which can only be effectively detected through destructive tests. In this paper, we propose nondestructive testing techniques to detect weak adhesion. These are based on Lamb wave (LW) data and artificial intelligence algorithms. A dataset consisting of simulated LW time series extracted from single-lap joints (SLJs) subjected to multiple levels of weak adhesion was generated. The raw time series were pre-processed to avoid numerical saturation and to remove outliers. The processed data were then used as the input to different artificial intelligence algorithms, namely feedforward neural networks (FNNs), long short-term memory (LSTM) networks, gated recurrent unit (GRU) networks, and convolutional neural networks (CNNs), for their training and testing. The results showed that all algorithms were capable of detecting up to 20 different levels of weak adhesion in SLJs, with an overall accuracy between 97% and 99%. Regarding the training time, the FNN emerged as the most-appropriate. On the other hand, the GRU showed overall faster learning, being able to converge in less than 50 epochs. Therefore, the FNN and GRU presented the best accuracy and had relatively acceptable convergence times, making them the most-suitable choices. The proposed approach constitutes a new framework allowing the creation of standardized data and optimal algorithm selection for further work on nondestructive damage detection and localization in adhesive joints.
Fire Safety of Plane Steel Truss according to System Reliability Analysis Combined with FORM Method: The Probabilistic Model and SYSREL Computation
Authors: Kubicka Katarzyna , Sokol Milan
Published: February 2023

This article focuses on the reliability analysis of the plane steel truss under fire conditions. The safety of the structure was estimated by system reliability analysis combined with First Order Reliability Method (FORM). The authors created the C++ code, which enables us to prepare the advanced probabilistic model for bearing capacity in the selected time of fire duration. Searching cut-sets for system analysis was performed in the C++ code, where stiffness matrix spectral analysis was employed. It was found that a probabilistic model has significant influence on the reliability indices. The research showed that depending on the probabilistic model, the sensitivity of the reliability index to individual variables is different.
Study on the Localization of Defects in Typical Steel Butt Welds Considering the Effect of Residual Stress
Authors: Chen Yue , Pan Xuehao , Deng Lingfang
Published: February 2023

When using magnetic memory detection technology to locate weld cracks and porous defects, the traditional zero-point polarity theory leads to misjudgments in defect location and difficulty in distinguishing between the residual stress and the magnetic signals generated by defects due to the influence of external noise and residual stress. Therefore, this paper considers the different mechanisms of magnetic signal generation in areas where crack- and porosity-type defects and residual stresses are located and discusses research focused on the detection of weld defect localization considering the influence of residual stresses. Using the mechanism of magnetic signal generation as a starting point, the three-dimensional magnetic modulus gradient polarity determination method is proposed to distinguish residual stress and defects’ magnetic signals. Through the COMSOL simulation of a welding defect’s finite element magnetic signal, the resulting magnetic signal is converted into a characteristic determination formula for characterization. To verify the accuracy of the simulated characterization, the 3D magnetic signal is extracted and verified manually. Finally, a double orthogonal wavelet transform is introduced to eliminate the random noise in the gradient of the three-dimensional magnetic modulus. The results show that the theoretical analysis, numerical simulation, and experimental results agree with each other. The three-dimensional magnetic modulus gradient values of cracks and pores are much higher than that of the defect-free residual stress area. The three-dimensional magnetic gradient modulus can locate defects and characterize the lengths of defects. The dual orthogonal wavelet eliminates noise interference while improving the accuracy of locating three-dimensional magnetic modulus gradient defects.
Efficient Machine Learning Model for Predicting the Stiffness of Circular Footings on Clay Overlying Sand
Authors: Qi Chongchong , Zheng Jiashuai , Meng Chuiqian , Wu Mengting
Published: February 2023

Assessing the stiffness of circular foundations is the key to evaluating their deformation; thus, it is important for foundation design. The current determination methods for the stiffness coefficient are either time-consuming or inaccurate. In this paper, a novel stiffness prediction model has been proposed, using the decision tree (DT) algorithm optimized by particle size optimization (PSO). The condition of the embedded foundation, the embedded depth (ZD/2R), the thickness of the clay layer beneath the foundation base (T/2R), and the ratio of shear stiffness between clay and sand (Gsand/Gclay) were used as input variables, while the elastic stiffness coefficients (Kc, Kh, Km, and Kv) were used as output variables. The optimum DT model has undergone comprehensive validation, and independent model verification using extra simulations. The results illustrate that PSO could promote further increases in the capability of DT modeling in predicting stiffness coefficients. The optimum DT model achieved a good level of performance on stiffness coefficient modeling. (The R for the training set was greater than 0.98 for all of the stiffness coefficients.) The variable importance analysis showed that the T/2R was the most significant variable for all stiffness coefficients, followed by Gsand/Gclay. The optimum DT model achieved good predictive performance upon independent verification, with the R being 0.97, 0.99, 0.99, and 0.95 for Kv, Kh, Km, and Kc, respectively. The proposed reliable and efficient DT-PSO model for stiffness coefficients in layered soil could further promote the safe and efficient utilization of circular foundations.
Time Efficiency Improvement in Quadruped Walking with Supervised Training Joint Model
Authors: Yeoh Chin Ean , Ahn Min Sung , Choi Soomin , Yi Hak
Published: February 2023

To generate stable walking of a quadruped, the complexity of the configuration of the robot involves a significant amount of optimization that decreases to its time efficiency. To address this issue, a machine learning method was used to build a simplified control policy using joint models for the supervised training of quadruped robots. This study considered 12 joints for a four-legged robot, and each joint value was determined based on the conventional method of walking simulation and prepossessed, equaling 2508 sets of data. For data training, the multilayer perceptron model was used, and the optimized number of epochs used to train the model was 5000. The trained models were implemented in robot walking simulations, and they improved performance with an average distance error of 0.0719 m and a computational time as low as 91.98 s.
An Expandable Yield Prediction Framework Using Explainable Artificial Intelligence for Semiconductor Manufacturing
Authors: Lee Youjin , Roh Yonghan
Published: February 2023

Enormous amounts of data are generated and analyzed in the latest semiconductor industry. Established yield prediction studies have dealt with one type of data or a dataset from one procedure. However, semiconductor device fabrication comprises hundreds of processes, and various factors affect device yields. This challenge is addressed in this study by using an expandable input data-based framework to include divergent factors in the prediction and by adapting explainable artificial intelligence (XAI), which utilizes model interpretation to modify fabrication conditions. After preprocessing the data, the procedure of optimizing and comparing several machine learning models is followed to select the best performing model for the dataset, which is a random forest (RF) regression with a root mean square error (RMSE) value of 0.648. The prediction results enhance production management, and the explanations of the model deepen the understanding of yield-related factors with Shapley additive explanation (SHAP) values. This work provides evidence with an empirical case study of device production data. The framework improves prediction accuracy, and the relationships between yield and features are illustrated with the SHAP value. The proposed approach can potentially analyze expandable fields of fabrication conditions to interpret multifaceted semiconductor manufacturing.
Shelf-life Assessment on European Cucumber Based on Accelerated Temperature–Humidity Stresses
Authors: Rodriguez Borbon Manuel Ivan , Sohn Hansuk , Delgado Efren , Fuqua Donovan O. , Rodríguez Medina Manuel Arnoldo , Tlapa Diego , Baez-Lopez Yolanda
Published: February 2023

The supply chain has been significantly impacted recently, and as a result the handling of products at the delivery and supply stage is of great importance. Currently, the agronomic industry is one of the most studied, but the reliability of harvests is not usually evaluated. This study uses a novel reliability analysis with an accelerated life test to expose the European cucumber (Cucumis sativus L.) to accelerated temperature and humidity conditions. The objective is to observe the effect of both factors on the deterioration of the product. The analysis includes a degradation analysis to determine the significant factors causing degradation, followed by an accelerated life test (ALT) to determine the product’s shelf life. Finally, through the development of a reliability model, storage times for cucumbers under normal storage conditions are predicted.