bluuebunny/crossref_metadata_2025 huggingface dataset:
Microstructure and Mechanical Properties of Metal Foams Fabricated via Melt Foaming and Powder Metallurgy Technique: A Review
Authors: Parveez Bisma , Jamal Nur Ayuni , Anuar Hazleen , Ahmad Yusilawati , Aabid Abdul , Baig Muneer
Published: August 2022

Metal foams possess remarkable properties, such as lightweight, high compressive strength, lower specific weight, high stiffness, and high energy absorption. These properties make them highly desirable for many engineering applications, including lightweight materials, energy-absorption devices for aerospace and automotive industries, etc. For such potential applications, it is essential to understand the mechanical behaviour of these foams. Producing metal foams is a highly challenging task due to the coexistence of solid, liquid, and gaseous phases at different temperatures. Although numerous techniques are available for producing metal foams, fabricating foamed metal still suffers from imperfections and inconsistencies. Thus, a good understanding of various processing techniques and properties of the resulting foams is essential to improve the foam quality. This review discussed the types of metal foams available in the market and their properties, providing an overview of the production techniques involved and the contribution of metal foams to various applications. This review also discussed the challenges in foam fabrications and proposed several solutions to address these problems.
A New Method of Bearing Remaining Useful Life Based on Life Evolution and SE-ConvLSTM Neural Network
Authors: Yang Shuai , Liu Yongqiang , Liao Yingying , Su Kang
Published: August 2022

The degradation process of bearing performance in the whole life cycle is a complex and nonlinear process. However, the traditional neural network-based approaches usually consider the degradation process of bearing performance as linear, which does not accord with the actual situation of bearing degradation. To overcome this shortcoming, a rolling bearing’s remaining useful life prediction method based on a Squeeze-and-Excitation-Convolutional long short-term memory (SE-ConvLSTM) neural network was proposed based on the construction of a new health index in the process of bearing life evolution. The proposed method considered the change rule of the health indicator during the whole life cycle evolution of bearings, then constructed the health indicator by using the SE-ConvLSTM neural network, effectively improving the model prediction accuracy and training efficiency. Firstly, the original data are filtered and denoised by Ensemble Empirical Mode Decomposition. Combined with Principal Component Analysis (PCA) dimensionality reduction and the Local Outlier Factor (LOF) algorithm, the bearing’s life evolution interval is divided. Then, the health indicator is constructed based on the proposed SE-ConvLSTM model, and the remaining useful life of rolling bearings is predicted by a particle filter and double exponential model. The proposed method is compared with other related methods with the PHM2012 dataset, and the results show that the proposed method has higher accuracy in remaining useful life predictions. Compared with the traditional method, the health index construction based on the division of the lifespan evolution interval has higher practical significance.
Effects of Media Coverage on Global Stability Analysis and Optimal Control of an Age-Structured Epidemic Model with Multi-Staged Progression
Authors: Liu Chao , Chen Peng , Jia Qiyu , Cheung Lora
Published: August 2022

In this paper, a hybrid SEIAM model for infectious disease with a continuous age structure is established, where combined dynamic effects of media coverage and multi-staged infected progression on threshold dynamics are discussed. Sufficient conditions for uniform persistence of the solution are studied by using the basic reproduction number. By constructing appropriate Lyapunov functions, the global stability analysis of endemic equilibrium is investigated based on Lyapunov–LaSalle’s stability theorem. In order to minimize costs incurred due to applied controls and infectious disease burden, an optimal cost-effective control strategy associated with disease treatment and media coverage is discussed. Numerical simulations are carried out to show consistency with theoretical analysis.
Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments
Authors: Abdulhussain Sadiq H. , Mahmmod Basheera M. , AlGhadhban Amer , Flusser Jan
Published: August 2022

Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face image datasets, ORL and FEI. Different state-of-the-art face recognition methods were compared with the proposed method in order to evaluate its accuracy. We demonstrate that the proposed method achieves the highest recognition rate in different considered scenarios. Based on the obtained results, it can be seen that the proposed method is robust against noise and significantly outperforms previous approaches in terms of speed.
Spongenolactones A–C, Bioactive 5,5,6,6,5-Pentacyclic Spongian Diterpenes from the Red Sea Sponge Spongia sp.
Authors: Tai Chi-Jen , Ahmed Atallah F. , Chao Chih-Hua , Yen Chia-Hung , Hwang Tsong-Long , Chang Fang-Rong , Huang Yusheng M. , Sheu Jyh-Horng
Published: August 2022

Three new 5,5,6,6,5-pentacyclic spongian diterpenes, spongenolactones A–C (1–3), were isolated from a Red Sea sponge Spongia sp. The structures of the new metabolites were elucidated by extensive spectroscopic analysis and the absolute configurations of 1–3 were determined on the basis of comparison of the experimental circular dichroism (CD) and calculated electronic circular dichroism (ECD) spectra. Compounds 1–3 are the first 5,5,6,6,5-pentacyclic spongian diterpenes bearing an β-hydroxy group at C-1. These metabolites were assayed for their cytotoxic, antibacterial, and anti-inflammatory activities. All three compounds were found to exert inhibitory activity against superoxide anion generation in fMLF/CB-stimulated human neutrophils. Furthermore, 1 showed a higher activity against the growth of Staphylococcus aureus in comparison to 2.
Nanoporous Carbon Electrodes Derived from Coffee Side Streams for Supercapacitors in Aqueous Electrolytes
Authors: Selinger Julian , Stock Sebastian , Schlemmer Werner , Hobisch Mathias , Kostoglou Nikolaos , Abbas Qamar , Paris Oskar , Mitterer Christian , Hummel Michael , Spirk Stefan
Published: August 2022

Coffee, as one of the most traded resources, generates a vast amount of biogenic by-products. Coffee silver skins (CSS), a side stream from the roasting process, account for about 4 wt.%. Despite the abundancy of CSS, possible routes to generate added value for broad applications are limited. Herein, we present an approach to use CSS as a precursor material for supercapacitor electrodes. KOH activated carbon (AC) was produced from CSS. The resulting AC—CSS was characterized by X-ray diffraction, gas sorption analysis, scanning electron microscopy, and Raman spectroscopy. The highly porous AC—CSS exposes a specific surface area of more than 2500 m2 g−1. Electrodes formed with AC—CSS were electrochemically characterized by performing cyclic voltammetry and galvanostatic cycling. The electrodes were further assembled into a supercapacitor device and operated using 1 M sulfuric acid as electrolyte. In addition, various quinones were added to the electrolyte and their impact on the capacitance of AC—CSS electrodes was analyzed. In this work, we were able to show that CSS are a valuable source for supercapacitor applications and that coffee-waste-derived quinones can act as capacitance enhancers. Thus, the findings of this research show a valuable path towards sustainable and green energy storage solutions.
Predicting Sensitivity to Adverse Lifestyle Risk Factors for Cardiometabolic Morbidity and Mortality
Authors: Pomares-Millan Hugo , Poveda Alaitz , Atabaki-Pasdar Naemieh , Johansson Ingegerd , Björk Jonas , Ohlsson Mattias , Giordano Giuseppe N. , Franks Paul W.
Published: August 2022

People appear to vary in their susceptibility to lifestyle risk factors for cardiometabolic disease; determining a priori who is most sensitive may help optimize the timing, design, and delivery of preventative interventions. We aimed to ascertain a person’s degree of resilience or sensitivity to adverse lifestyle exposures and determine whether these classifications help predict cardiometabolic disease later in life; we pooled data from two population-based Swedish prospective cohort studies (n = 53,507), and we contrasted an individual’s cardiometabolic biomarker profile with the profile predicted for them given their lifestyle exposure characteristics using a quantile random forest approach. People who were classed as ‘sensitive’ to hypertension- and dyslipidemia-related lifestyle exposures were at higher risk of developing cardiovascular disease (CVD, hazards ratio 1.6 (95% CI: 1.3, 1.91)), compared with the general population. No differences were observed for type 2 diabetes (T2D) risk. Here, we report a novel approach to identify individuals who are especially sensitive to adverse lifestyle exposures and who are at higher risk of subsequent cardiovascular events. Early preventive interventions may be needed in this subgroup.
Evaluation of PepT1 (SLC15A1) Substrate Characteristics of Therapeutic Cyclic Peptides
Authors: Bajraktari-Sylejmani Gzona , von Linde Teresa , Burhenne Jürgen , Haefeli Walter Emil , Sauter Max , Weiss Johanna
Published: August 2022

The human peptide transporter hPepT1 (SLC15A1), physiologically transporting dipeptides and tripeptides generated during food digestion, also plays a role in the uptake of small bioactive peptides and peptide-like drugs. Moreover, it might be addressed in prodrug strategies of poorly absorbed drugs. We hypothesised that the cyclic drug peptides octreotide and pasireotide could be substrates of this transporter because their diameter can resemble the size of dipeptides or tripeptides due to their strong structural curvature and because they reach the systemic circulation in Beagle dogs. For investigating possible hPepT1 substrate characteristics, we generated and characterised a CHO-K1 cell line overexpressing SLC15A1 by transfection and selection via magnetic beads. Possible inhibition of the uptake of the prototypical substrate Gly-Sar by octreotide and pasireotide was screened, followed by quantifying the uptake of the cyclic peptides in cells overexpressing SLC15A1 compared with the parental cell line. Although inhibition of Gly-Sar uptake was observed, uptake of octreotide and pasireotide was not increased in SLC15A1 overexpressing cells, indicating a lack of transport by hPepT1. Our data clearly indicate that octreotide and pasireotide are nonsubstrate inhibitors of hPepT1 and that their oral bioavailability cannot be explained by absorption via hPepT1.
Bilayer Hydrogels for Wound Dressing and Tissue Engineering
Authors: Luneva Olga , Olekhnovich Roman , Uspenskaya Mayya
Published: August 2022

A large number of different skin diseases such as hits, acute, and chronic wounds dictate the search for alternative and effective treatment options. The wound healing process requires a complex approach, the key step of which is the choice of a dressing with controlled properties. Hydrogel-based scaffolds can serve as a unique class of wound dressings. Presented on the commercial market, hydrogel wound dressings are not found among proposals for specific cases and have a number of disadvantages—toxicity, allergenicity, and mechanical instability. Bilayer dressings are attracting great attention, which can be combined with multifunctional properties, high criteria for an ideal wound dressing (antimicrobial properties, adhesion and hemostasis, anti-inflammatory and antioxidant effects), drug delivery, self-healing, stimulus manifestation, and conductivity, depending on the preparation and purpose. In addition, advances in stem cell biology and biomaterials have enabled the design of hydrogel materials for skin tissue engineering. To improve the heterogeneity of the cell environment, it is possible to use two-layer functional gradient hydrogels. This review summarizes the methods and application advantages of bilayer dressings in wound treatment and skin tissue regeneration. Bilayered hydrogels based on natural as well as synthetic polymers are presented. The results of the in vitro and in vivo experiments and drug release are also discussed.
Cytotoxicity Enhancement in MCF-7 Breast Cancer Cells with Depolymerized Chitosan Delivery of α-Mangostin
Authors: Herdiana Yedi , Wathoni Nasrul , Shamsuddin Shaharum , Muchtaridi Muchtaridi
Published: August 2022

The application of α-mangostin (AMG) in breast cancer research has wide intentions. Chitosan-based nanoparticles (CSNPs) have attractive prospects for developing anticancer drugs, especially in their high flexibility for modification to enhance their anticancer action. This research aimed to study the impact of depolymerized chitosan (CS) on the cytotoxicity enhancement of AMG in MCF-7 breast cancer cells. CSNPs effectivity depends on size, shape, crystallinity degree, and charge surface. Modifying CS molecular weight (MW) is expected to influence CSNPs’ characteristics, impacting size, shape, crystallinity degree, and charge surface. CSNPs are developed using the method of ionic gelation with sodium tripolyphosphate (TPP) as a crosslinker and spray pyrolysis procedure. Nanoparticles’ (NPs) sizes vary from 205.3 ± 81 nm to 450.9 ± 235 nm, ZP charges range from +10.56 mV to +51.56 mV, and entrapment efficiency from 85.35% to 90.45%. The morphology of NPs are all the same spherical forms. In vitro release studies confirmed that AMG–Chitosan–High Molecular Weight (AMG–CS–HMW) and AMG–Chitosan–Low Molecular Weight (AMG–CS–LMW) had a sustained-release system profile. MW has a great influence on surface, drug release, and cytotoxicity enhancement of AMG in CSNPs to MCF-7 cancer cells. The preparations AMG–CS–HMW and AMG–CS–LMW NPs considerably enhanced the cytotoxicity of MCF-7 cells with IC50 values of 5.90 ± 0.08 µg/mL and 4.90 ± 0.16 µg/mL, respectively, as compared with the non-nano particle formulation with an IC50 of 8.47 ± 0.29 µg/mL. These findings suggest that CSNPs can enhance the physicochemical characteristics and cytotoxicity of AMG in breast cancer treatment.
Light-Emitting-Diode-Assisted, Fungal-Pigment-Mediated Biosynthesis of Silver Nanoparticles and Their Antibacterial Activity
Authors: Nuanaon Nobchulee , Bhatnagar Sharad , Motoike Tatsuya , Aoyagi Hideki
Published: August 2022

Nanoparticle synthesis, such as green synthesis of silver nanoparticles (AgNPs) using biogenic extracts, is affected by light, which changes the characteristics of particles. However, the effect of light-emitting diodes (LEDs) on AgNP biosynthesis using fungal pigment has not been examined. In this study, LEDs of different wavelengths were used in conjunction with Talaromyces purpurogenus extracellular pigment for AgNP biosynthesis. AgNPs were synthesized by mixing 10 mL of fungal pigment with AgNO3, followed by 24 h exposure to LEDs of different wavelengths, such as blue, green, orange, red, and infrared. All treatments increased the yield of AgNPs. The solutions exposed to blue, green, and infrared LEDs exhibited a significant increase in AgNP synthesis. All AgNPs were then synthesized to determine the optimum precursor (AgNO3) concentration and reaction rate. The results indicated 5 mM AgNO3 as the optimum precursor concentration; furthermore, AgNPs-blue LED had the highest reaction rate. Dynamic light scattering analysis, zeta potential measurement, transmission electron microscopy, and Fourier transform infrared spectroscopy were used to characterize the AgNPs. All LED-synthesized AgNPs exhibited an antimicrobial potential against Escherichia coli and Staphylococcus aureus. The combination of LED-synthesized AgNPs and the antibiotic streptomycin demonstrated a synergistic antimicrobial activity against both bacterial species.
Proteomic Discovery and Validation of Novel Fluid Biomarkers for Improved Patient Selection and Prediction of Clinical Outcomes in Alzheimer’s Disease Patient Cohorts
Authors: Awasthi Shivangi , Spellman Daniel S. , Hatcher Nathan G.
Published: August 2022

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by progressive cognitive decline. The two cardinal neuropathological hallmarks of AD include the buildup of cerebral β amyloid (Aβ) plaques and neurofibrillary tangles of hyperphosphorylated tau. The current disease-modifying treatments are still not effective enough to lower the rate of cognitive decline. There is an urgent need to identify early detection and disease progression biomarkers that can facilitate AD drug development. The current established readouts based on the expression levels of amyloid beta, tau, and phospho-tau have shown many discrepancies in patient samples when linked to disease progression. There is an urgent need to identify diagnostic and disease progression biomarkers from blood, cerebrospinal fluid (CSF), or other biofluids that can facilitate the early detection of the disease and provide pharmacodynamic readouts for new drugs being tested in clinical trials. Advances in proteomic approaches using state-of-the-art mass spectrometry are now being increasingly applied to study AD disease mechanisms and identify drug targets and novel disease biomarkers. In this report, we describe the application of quantitative proteomic approaches for understanding AD pathophysiology, summarize the current knowledge gained from proteomic investigations of AD, and discuss the development and validation of new predictive and diagnostic disease biomarkers.
Towards an Explainable Universal Feature Set for IoT Intrusion Detection
Authors: Alani Mohammed M. , Miri Ali
Published: August 2022

As IoT devices’ adoption grows rapidly, security plays an important role in our daily lives. As part of the effort to counter these security threats in recent years, many IoT intrusion detection datasets were presented, such as TON_IoT, BoT-IoT, and Aposemat IoT-23. These datasets were used to build many machine learning-based IoT intrusion detection models. In this research, we present an explainable and efficient method for selecting the most effective universal features from IoT intrusion detection datasets that can help in producing highly-accurate and efficient machine learning-based intrusion detection systems. The proposed method was applied to TON_IoT, Aposemat IoT-23, and IoT-ID datasets and resulted in the selection of six universal network-flow features. The proposed method was tested and produced a high accuracy of 99.62% with a prediction time reduced by up to 70%. To provide better insight into the operation of the classifier, a Shapley additive explanation was used to explain the selected features and to prove the alignment of the explanation with current attack techniques.
Coupling Effects Analysis and Suppression in a Highly Integrated Ka-Band Receiver Front-End MMIC for a Passive Millimeter-Wave Imager System
Authors: Chen Xi , Hu Anyong , Gong Jianhao , Altaf Amjad , Miao Jungang
Published: August 2022

This paper presents the coupling effects analysis and suppression of a highly integrated receiver front-end MMIC for a passive millimeter-wave imager system. The receiver MMIC consists of a low-noise amplifier, double-balanced image-reject mixer, frequency quadrupler, and analog phase shifter. In order to integrate these devices into a compact single chip without affecting the core performance, coupling problems need to be solved. We analyze the influence of coupling effects on the image rejection ratio, and propose corresponding solutions for three different coupling paths. (1) The coupling in the LO-RF path of the mixer is solved by designing a double-balanced mixer with high isolation characteristics. (2) The coupling between the LO chain and the LNA from space and dielectric is suppressed by optimizing the two main transmission lines spacing and adding isolation vias. (3) The coupling caused by the line crossing is restrained by designing a differential line crossover structure. The design and implementation of the MMIC are based on 0.15 µm GaAs pHEMT process. The receiver chip has 6.1~8.7 dB conversion gain in 32~36 GHz, less than 3.5 dB of noise figure, and more than 35 dB of image rejection ratio. The measurement results show that the receiver MMIC is especially suitable for high-sensitivity passive millimeter-wave imaging systems.
An Efficient IAKF Approach for Indoor Positioning Drift Correction
Authors: Lin Shang-Hsien , Chang Chien Hung-Hsien , Wang Wei-Wen , Lin Kuang-Hao , Li Guan-Jin
Published: August 2022

In this study, an indoor positioning shift correction architecture was developed with an improved adaptive Kalman filter (IAKF) algorithm for the people interference condition. Indoor positioning systems (IPSs) use ultra-wideband (UWB) communication technology. Triangulation positioning algorithms are generally employed for determining the position of a target. However, environmental communication factors and different network topologies produce localization drift errors in IPSs. Therefore, the drift error of real-time positioning points under various environmental factors and the correction of the localization drift error are discussed. For localization drift error, four algorithms were simulated and analyzed: movement average (MA), least square (LS), Kalman filter (KF), and IAKF. Finally, the IAKF algorithm was implemented and verified on the UWB indoor positioning system. The measurement results showed that the drift errors improved by 60% and 74.15% in environments with and without surrounding crowds, respectively. Thus, the coordinates of real-time positioning points are closer to those of actual targets.
OFDM System Design for Measured Ultrasonic Underwater Channels
Authors: Cobacho-Ruiz Pablo , Cañete Francisco Javier , Martos-Naya Eduardo , Fernández-Plazaola Unai
Published: August 2022

In this paper, we present the development of a multicarrier modulation system of low complexity for broadband underwater acoustic communications (UAC), whose frequency band is located in the ultrasonic range, specifically between 32 kHz and 128 kHz. Underwater acoustic channels are recognized among the most hostile communication channels due to their strong time and frequency selectivity and, hence, the design of high-performance systems is a challenge that is difficult to resolve at the present time with state-of-art technology. The aim of the proposed system is to reach a reasonable bit rate, between 40 and 50 Kbps, over these channels that allows, for instance, the transmission of video signals of limited quality. We describe an orthogonal frequency division multiplexing (OFDM) modem prototype with a parameter setting and design specifically adapted to the channel nature. For this purpose, actual measurements carried out at the Mediterranean sea, on shallow waters, have been used to evaluate the system performance and to optimize the design. A discussion on several modulations and OFDM configurations is presented that leads to the selection of differential and non-differential quadri-phase shift keying (QPSK) as good candidates depending on synchronization capabilities.
Fluorescence Assay for Detecting Four Organophosphorus Pesticides Using Fluorescently Labeled Aptamer
Authors: Li He , Huang Xue , Huang Jingcheng , Bai Mengyuan , Hu Mengjiao , Guo Yemin , Sun Xia
Published: August 2022

In this work, we reported a rapid and sensitive fluorescence assay in homogenous solution for detecting organophosphorus pesticides by using tetramethylrhodamine (TAMRA)-labeled aptamer and its complementary DNA (cDNA) with extended guanine (G) bases. The hybridization of cDNA and aptamer drew TAMRA close to repeated G bases, then the fluorescence of TAMRA was quenched by G bases due to the photoinduced electron transfer (PET). Upon introducing the pesticide target, the aptamer bound to pesticide instead of cDNA because of the competition between pesticide and cDNA. Thus, the TAMRA departed from G bases, resulting in fluorescence recovery of TAMRA. Under optimal conditions, the limits of detection for phorate, profenofos, isocarbophos, and omethoate were 0.333, 0.167, 0.267, and 0.333 µg/L, respectively. The method was also used in the analysis of profenofos in vegetables. Our fluorescence design was simple, rapid, and highly sensitive, which provided a means for monitoring the safety of agricultural products.
Sea Cucumber Detection Algorithm Based on Deep Learning
Authors: Zhang Lan , Xing Bowen , Wang Wugui , Xu Jingxiang
Published: August 2022

The traditional single-shot multiBox detector (SSD) for the recognition process in sea cucumbers has problems, such as an insufficient expression of features, heavy computation, and difficulty in application to embedded platforms. To solve these problems, we proposed an improved algorithm for sea cucumber detection based on the traditional SSD algorithm. MobileNetv1 is selected as the backbone of the SSD algorithm. We increase the feature receptive field by receptive field block (RFB) to increase feature details and location information of small targets. Combined with the attention mechanism, features at different depths are strengthened and irrelevant features are suppressed. The experimental results show that the improved algorithm has better performance than the traditional SSD algorithm. The average precision of the improved algorithm is increased by 5.1%. The improved algorithm is also more robust. Compared with YOLOv4 and the Faster R-CNN algorithm, the performance of this algorithm on the P-R curve is better, indicating that the performance of this algorithm is better. Thus, the improved algorithm can stably detect sea cucumbers in real time and provide reliable feedback information.
Active Ultrasonic Structural Health Monitoring Enabled by Piezoelectric Direct-Write Transducers and Edge Computing Process
Authors: Wong Voon-Kean , Rabeek Sarbudeen Mohamed , Lai Szu Cheng , Philibert Marilyne , Lim David Boon Kiang , Chen Shuting , Raja Muthusamy Kumarasamy , Yao Kui
Published: August 2022

While the active ultrasonic method is an attractive structural health monitoring (SHM) technology, many practical issues such as weight of transducers and cables, energy consumption, reliability and cost of implementation are restraining its application. To overcome these challenges, an active ultrasonic SHM technology enabled by a direct-write transducer (DWT) array and edge computing process is proposed in this work. The operation feasibility of the monitoring function is demonstrated with Lamb wave excited and detected by a linear DWT array fabricated in situ from piezoelectric P(VDF-TrFE) polymer coating on an aluminum alloy plate with a simulated defect. The DWT array features lightweight, small profile, high conformability, and implementation scalability, whilst the edge-computing circuit dedicatedly designed for the active ultrasonic SHM is able to perform signal processing at the sensor nodes before wirelessly transmitting the data to a remote host device. The successful implementation of edge-computing processes is able to greatly decrease the amount of data to be transferred by 331 times and decrease the total energy consumption for the wireless module by 224 times. The results and analyses show that the combination of the piezoelectric DWT and edge-computing process provides a promising technical solution for realizing practical wireless active ultrasonic SHM system.
Making Group Decisions within the Framework of a Probabilistic Hesitant Fuzzy Linear Regression Model
Authors: Sultan Ayesha , Sałabun Wojciech , Faizi Shahzad , Ismail Muhammad , Shekhovtsov Andrii
Published: August 2022

A fuzzy set extension known as the hesitant fuzzy set (HFS) has increased in popularity for decision making in recent years, especially when experts have had trouble evaluating several alternatives by employing a single value for assessment when working in a fuzzy environment. However, it has a significant problem in its uses, i.e., considerable data loss. The probabilistic hesitant fuzzy set (PHFS) has been proposed to improve the HFS. It provides probability values to the HFS and has the ability to retain more information than the HFS. Previously, fuzzy regression models such as the fuzzy linear regression model (FLRM) and hesitant fuzzy linear regression model were used for decision making; however, these models do not provide information about the distribution. To address this issue, we proposed a probabilistic hesitant fuzzy linear regression model (PHFLRM) that incorporates distribution information to account for multi-criteria decision-making (MCDM) problems. The PHFLRM observes the input–output (IPOP) variables as probabilistic hesitant fuzzy elements (PHFEs) and uses a linear programming model (LPM) to estimate the parameters. A case study is used to illustrate the proposed methodology. Additionally, an MCDM technique called the technique for order preference by similarity to ideal solution (TOPSIS) is employed to compare the PHFLRM findings with those obtained using TOPSIS. Lastly, Spearman’s rank correlation test assesses the statistical significance of two rankings sets.
Effect of Behavioral Precaution on Braking Operation of Elderly Drivers under Cognitive Workloads
Authors: Kajiwara Yusuke , Murata Eri
Published: August 2022

The number of accidents by elderly drivers caused by the erroneous tread of a brake pedal or accelerator pedal has increased. A recent study reported that the number of accidents could be reduced by preparing for braking mistakes due to driving behavior by using a simulator. However, related studies have pointed out that driving behavior in simulators does not always reflect driving behavior in the real world. This paper focuses on the posture of the left foot as a behavioral precaution and provides insights into braking mistakes by comparing behavioral precautions taken on simulators and on public roads. In the experimental results, cognitive and action errors increased with age, but elderly drivers are less likely to have an accident when they are exposed to the risk of collision in situations with a mental workload by making space for the right foot to step on the brake pedal. Elderly drivers with coping skills had their left foot perpendicular to the ground and their body was unstable. This result was different from the driving behavior in the simulator, but it was not possible to identify that this difference was the cause of the collision accidents. Coping skills were predicted with 70% accuracy from the left foot posture of an elderly driver near the intersection. We expanded the system’s range of use and enhanced its usefulness by predicting coping skills derived from natural driving behavior in the real world. The contributions of this study are as follows. We clarify the effect of behavioral precautions on the braking operation of elderly drivers when under a cognitive workload. We provide new insights into the use of behavioral precautions in older drivers’ braking operations in the real world. We predicted coping skills from natural driving behavior near intersections in the real world.
Long Short-Term Memory Neural Network with Transfer Learning and Ensemble Learning for Remaining Useful Life Prediction
Authors: Wang Lixiong , Liu Hanjie , Pan Zhen , Fan Dian , Zhou Ciming , Wang Zhigang
Published: August 2022

Prediction of remaining useful life (RUL) is greatly significant for improving the safety and reliability of manufacturing equipment. However, in real industry, it is difficult for RUL prediction models trained on a small sample of faults to obtain satisfactory accuracy. To overcome this drawback, this paper presents a long short-term memory (LSTM) neural network with transfer learning and ensemble learning and combines it with an unsupervised health indicator (HI) construction method for remaining-useful-life prediction. This study consists of the following parts: (1) utilizing the characteristics of deep belief networks and self-organizing map networks to translate raw sensor data to a synthetic HI that can effectively reflect system health; and (2) introducing transfer learning and ensemble learning to provide the required degradation mechanism for the RUL prediction model based on LSTM to improve the performance of the model. The performance of the proposed method is verified by two bearing datasets collected from experimental data, and the results show that the proposed method obtains better performance than comparable methods.
REDA: A New Methodology to Validate Sensor Systems for Person Detection under Variable Environmental Conditions
Authors: Meltebrink Christian , Komesker Magnus , Kelsch Carolina , König Daniel , Jenz Mario , Strotdresch Marvin , Wegmann Benjamin , Weltzien Cornelia , Ruckelshausen Arno
Published: August 2022

Perception of the environment by sensor systems in variable environmental conditions is very complex due to the interference influences. In the field of autonomous machines or autonomous vehicles, environmental conditions play a decisive role in safe person detection. A uniform test and validation method can support the manufacturers of sensor systems during development and simultaneously provide proof of functionality. The authors have developed a concept of a novel test method, “REDA”, for this purpose. In this article, the concept is applied and measurement data are presented. The results show the versatile potential of this test method, through the manifold interpretation options of the measurement data. Using this method, the strengths and weaknesses of sensor systems have been identified with an unprecedented level of detail, flexibility, and variance to test and compare the detection capability of sensor systems. The comparison was possible regardless of the measuring principle of the sensor system used. Sensor systems have been tested and compared with each other with regard to the influence of environmental conditions themselves. The first results presented highlight the potential of the new test method. For future applications, the test method offers possibilities to test and compare manifold sensing principles, sensor system parameters, or evaluation algorithms, including, e.g., artificial intelligence.
The Association between Pre-season Running Loads and Injury during the Subsequent Season in Elite Gaelic Football
Authors: Fisher Paul , Faulkner Maria , McCann Michael , Doherty Rónán
Published: August 2022

The aim of this study was to determine if the quantity of running load performed in pre-season affects the incidence of injury in elite Gaelic footballers. It was hypothesized that a greater quantity of running loads completed will reduce the incidence rate of injury. A cohort of elite male Gaelic football players (n = 25) was prospectively monitored throughout one full season. This longitudinal study involved the collection of GPS data and injury data. Participants were retrospectively divided into two groups and assigned to a high (HTL, completed >50% of pre-season sessions, n = 13) or low (LTL, completed <50% of pre-season sessions, n = 12) training load group based on the percentage of pre-season sessions completed. Data for total distance, PlayerLoad™, meters covered at running speeds (4.0–4.84 m/s), meters covered at high running speeds (4.85–6.39 m/s), meters covered at sprint speeds (>6.4 m/s) and high-intensity running meters (high-speed running meters and sprint meters combined) were collected during all sessions. A one-way analysis of variance (ANOVA) was completed to understand the variation of external training load data across the different phases of the season. A series of repeated measures of ANOVA were subsequently completed to understand the variation of external training load data across seasonal phases within the training groups. Although the LTL group had a higher incidence rate of non-contact injuries (large effect size) per 1000 h of exposure in each phase of the season, statistical analysis revealed that there was no significant difference (F = 4.32, p = 0.173, partial η2 = 0.684, large) between the HTL (14.9 ± 4.17/1000 h) and the LTL (24.5 ± 7.36/1000 h) groups. Additionally, the HTL group was able to sustain greater running loads in the competitive phases of the season compared to the LTL group, total distance (F = 8.10, p < 0.001, partial η2 = 0.299, large), high-speed running distance (F = 8.74, p < 0.001, partial η2 = 0.304, large) and high-intensity running distance (F = 7.63, p < 0.001, partial η2 = 0.276, large). Furthermore, players who complete a greater proportion of running loads in pre-season may alter their body composition levels to more optimal levels, which in turn may reduce the risk of injury while also helping increase or maintain performance-related fitness markers such as aerobic fitness.
A Survey of Low Rate DDoS Detection Techniques Based on Machine Learning in Software-Defined Networks
Authors: Alashhab Abdussalam Ahmed , Zahid Mohd Soperi Mohd , Azim Mohamed A. , Daha Muhammad Yunis , Isyaku Babangida , Ali Shimhaz
Published: August 2022

Software-defined networking (SDN) is a new networking paradigm that provides centralized control, programmability, and a global view of topology in the controller. SDN is becoming more popular due to its high audibility, which also raises security and privacy concerns. SDN must be outfitted with the best security scheme to counter the evolving security attacks. A Distributed Denial-of-Service (DDoS) attack is a network attack that floods network links with illegitimate data using high-rate packet transmission. Illegitimate data traffic can overload network links, causing legitimate data to be dropped and network services to be unavailable. Low-rate Distributed Denial-of-Service (LDDoS) is a recent evolution of DDoS attack that has been emerged as one of the most serious vulnerabilities for the Internet, cloud computing platforms, the Internet of Things (IoT), and large data centers. Moreover, LDDoS attacks are more challenging to detect because this attack sends a large amount of illegitimate data that are disguised as legitimate traffic. Thus, traditional security mechanisms such as symmetric/asymmetric detection schemes that have been proposed to protect SDN from DDoS attacks may not be suitable or inefficient for detecting LDDoS attacks. Therefore, more research studies are needed in this domain. There are several survey papers addressing the detection mechanisms of DDoS attacks in SDN, but these studies have focused mainly on high-rate DDoS attacks. Alternatively, in this paper, we present an extensive survey of different detection mechanisms proposed to protect the SDN from LDDoS attacks using machine learning approaches. Our survey describes vulnerability issues in all layers of the SDN architecture that LDDoS attacks can exploit. Current challenges and future directions are also discussed. The survey can be used by researchers to explore and develop innovative and efficient techniques to enhance SDN’s protection against LDDoS attacks.
Wave-Type Interaction within a Robotic Swarm System for Decentralized Estimation of Global Geometric States
Authors: Origane Yuki , Kurabayashi Daisuke
Published: August 2022

For a robotic swarm system composed of autonomous mobile robots, controlling and using asymmetric global geometric states promotes the task performance of the swarm. This paper presents a systematic method for estimating asymmetric global geometric states over a swarm system. To overcome the limitations of local observation or communication ability, we propose a wave-type interaction among neighboring robots. We assume that each robot has a scalar state variable called a phase, which is manipulated through interactions. Through the analysis of eigenvalues of a graph Laplacian matrix corresponding to a local communication network of robots, we show that a robot can estimate global states, such as the size of an entire swarm, by frequency analysis of its phase. We also analyzed the stability of the wave-type interaction based on von-Neumann stability. We verified the proposed method by computer simulations, in which robots in a swarm detected the deformation in the shape of the swarm when the swarm was passing through a narrow area. The result will contribute to building a control system for swarms that can manipulate their shape or characteristics to adapt themselves based on tasks or environmental requirements.
A New Approach to the Embedding of Delamination in the Layerwise Theory of Laminated Composite Plates
Authors: Rakočević Marina , Žugić Ljiljana
Published: August 2022

This paper presents a new analytical approach to the embedding of delamination in the layerwise theory, which can be applied to determine the stress–strain state in the cross-section of laminated plates with internal delamination. The new approach is based on the layerwise theory, which transfers considerations from the level of the laminated plate to the level of the lamina. The paper presents a mathematical model and defines a calculation procedure for determining the state of the stress and strain in a cross-section with an internal delamination that occurred during plate production. The convergence and stability of the computational procedure, based on a new approach to the embedding of delamination in layerwise theory, are proven. It is also proven that the existence of internal delamination on the bond between layers of laminated plates significantly changes the stress-strain state of the cross-section, in relation to a cross-section without delamination. In numerical examples, the value of delamination in the plane (x, y) is determined and considered. The initial state after a static load or “zero state” of stress and strain in a cross-section with delamination represent the input for further and future nonlinear analyses that involve the growth, development, and propagation of delamination.
Research on the Coupling Coordination Relationship between Urban Rail Transit System and Sustainable Urban Development
Authors: Jiao Liudan , Wu Fengyan , Zhu Yinghan , Luo Qiudie , Luo Fenglian , Zhang Yu
Published: August 2022

With the accelerating urbanization and steady economic development in China, the urban built-up area is expanding and the population in the core area is proliferating. The pressure of insufficient urban infrastructure, especially public transportation capacity, is becoming increasingly evident, and urban rail transit (URT) systems are crucial to the sustainable development of cities. This paper collects data related to URT and sustainable urban development (SUD) in 42 cities in China in 2020, constructs a comprehensive evaluation index system, and quantitatively analyzes the coupling coordination degree of the two systems using the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method and coupling coordination degree model. Then, the influencing factors of the coupling coordination degree of URT and SUD are analyzed by combining the grey correlation analysis method. The results of this study show that: (1) There are significant differences between URT system development and SUD in 42 cities in China. (2) The average coupling coordination between URT development and SUD is 0.4406. More than half of the cities are in the slightly unbalanced category. (3) Factors, such as resident population, income level and urban built-up area, significantly influence the coupling and coordination level of URT and SUD. It is hoped that the research in this paper will advance the in-depth research on the level of coordination between URT and SUD coupling, provide a solid basis for future URT planning and construction in China and even other countries in the world, and make the planning and construction of URT in China more scientific and reasonable, to promote the sustainable development of cities.
B0 Correction for 3T Amide Proton Transfer (APT) MRI Using a Simplified Two-Pool Lorentzian Model of Symmetric Water and Asymmetric Solutes
Authors: Chen Yibing , Dang Xujian , Zhao Benqi , Zheng Zhuozhao , He Xiaowei , Song Xiaolei
Published: August 2022

Amide proton transfer (APT)-weighted MRI is a promising molecular imaging technique that has been employed in clinic for detection and grading of brain tumors. MTRasym, the quantification method of APT, is easily influenced by B0 inhomogeneity and causes artifacts. Current model-free interpolation methods have enabled moderate B0 correction for middle offsets, but have performed poorly at limbic offsets. To address this shortcoming, we proposed a practical B0 correction approach that is suitable under time-limited sparse acquisition scenarios and for B1 ≥ 1 μT under 3T. In this study, this approach employed a simplified Lorentzian model containing only two pools of symmetric water and asymmetric solutes, to describe the Z-spectral shape with wide and ‘invisible’ CEST peaks. The B0 correction was then performed on the basis of the fitted two-pool Lorentzian lines, instead of using conventional model-free interpolation. The approach was firstly evaluated on densely sampled Z-spectra data by using the spline interpolation of all acquired 16 offsets as the gold standard. When only six offsets were available for B0 correction, our method outperformed conventional methods. In particular, the errors at limbic offsets were significantly reduced (n = 8, p < 0.01). Secondly, our method was assessed on the six-offset APT data of nine brain tumor patients. Our MTRasym (3.5 ppm), using the two-pool model, displayed a similar contrast to the vendor-provided B0-orrected MTRasym (3.5 ppm). While the vendor failed in correcting B0 at 4.3 and 2.7 ppm for a large portion of voxels, our method enabled well differentiation of B0 artifacts from tumors. In conclusion, the proposed approach could alleviate analysis errors caused by B0 inhomogeneity, which is useful for facilitating the comprehensive metabolic analysis of brain tumors.
Acetylcholinesterase (AChE) Reversible Inhibitors: The Role of Oxamyl in the Production of Poisoned Baits
Authors: Biancardi Alberto , Aimo Cristina , Piazza Pierluigi , Lo Chiano Federica , Rubini Silva , Baldini Erika , Vertuani Silvia , Manfredini Stefano
Published: August 2022

Oxamyl is a highly toxic carbamate molecule with toxicological risk from contamination, used as an insecticide, nematicide, and acaricide on many field crops, vegetables, fruits, and ornamentals. Suspected poisoned animals and baits were collected between January 2018 and August 2021 from Lombardy and Emilia-Romagna regions and analyzed at the chemical toxicology laboratory of the Experimental Zooprophylactic Institute of Lombardy and Emilia-Romagna, located in Brescia. The analyses were carried out by an ion trap GC-MS system in 2467 suspected samples and showed the presence of oxamyl in 67 of these. In this study, we analyzed 47 (out of 67) positive baits: the provinces in which more cases have been recorded are Mantua, Ferrara, and Cremona, which overall had 72% of positivity. The nature of the analyzed samples was mostly corn (55.3%), followed by bird carcasses (19.1%), apples (14.8%), meatballs (2.1%), bread (2.1%), and other (8.5%). The use of oxamyl to produce poisoned baits is constantly increasing, proving that it must be considered as a public health risk for the possible consequences on target and non-target organisms, including humans.