bluuebunny/crossref_metadata_2025 huggingface dataset:
Using Machine Learning to Identify Feelings of Energy and Fatigue in Single-Task Walking Gait: An Exploratory Study
Authors: Kadry Ahmed M. , Torad Ahmed , Elwan Moustafa Ali , Kakar Rumit Singh , Bradley Dylan , Chaudhry Shafique , Boolani Ali
Published: March 2022

The objective of this study was to use machine learning to identify feelings of energy and fatigue using single-task walking gait. Participants (n = 126) were recruited from a university community and completed a single protocol where current feelings of energy and fatigue were measured using the Profile of Moods Survey–Short Form approximately 2 min prior to participants completing a two-minute walk around a 6 m track wearing APDM mobility monitors. Gait parameters for upper and lower extremity, neck, lumbar and trunk movement were collected. Gradient boosting classifiers were the most accurate classifiers for both feelings of energy (74.3%) and fatigue (74.2%) and Random Forest Regressors were the most accurate regressors for both energy (0.005) and fatigue (0.007). ANCOVA analyses of gait parameters comparing individuals who were high or low energy or fatigue suggest that individuals who are low energy have significantly greater errors in walking gait compared to those who are high energy. Individuals who are high fatigue have more symmetrical gait patterns and have trouble turning when compared to their low fatigue counterparts. Furthermore, these findings support the need to assess energy and fatigue as two distinct unipolar moods as the signals used by the algorithms were unique to each mood.
Posture Monitoring for Health Care of Bedridden Elderly Patients Using 3D Human Skeleton Analysis via Machine Learning Approach
Authors: Chiang Jui-Chiu , Lie Wen-Nung , Huang Hsiu-Chen , Chen Kuan-Ting , Liang Jhih-Yuan , Lo Yu-Chia , Huang Wei-Hao
Published: March 2022

For bedridden elderly people, pressure ulcer is the most common and serious complication and could be prevented by regular repositioning. However, due to a shortage of long-term care workers, repositioning might not be implemented as often as required. Posture monitoring by using modern health/medical caring technology can potentially solve this problem. We propose a RGB-D camera system to recognize the posture of the bedridden elderly patients based on the analysis of 3D human skeleton which consists of articulated joints. Since practically most bedridden patients were covered with a blanket, only four 3D joints were used in our system. After the recognition of the posture, a warning message will be sent to the caregiver for assistance if the patient stays in the same posture for more than a predetermined period (e.g., two hours). Experimental results indicate that our proposed method is capable of achieving a high accuracy in posture recognition (above 95%). To the best of our knowledge, this application of using human skeleton analysis for patient care is novel. The proposed scheme is promising for clinical applications and will undertake an intensive test in health care facilities in the near future after redesigning a proper RGB-D (Red-Green-Blue-Depth) camera system. In addition, a desktop computer can be used for multi-point monitoring to reduce cost, since real-time processing is not required in this application.
Delay Analysis and the Control of Electro-Hydrostatic Actuators
Authors: Zhang Jiaming , Li Lingwei , Zhang Xinglong , Zhang Tianhong , Yuan Yuan
Published: March 2022

The electro-hydrostatic actuator (EHA) has a high energy density and a compact structure so it is widely used in all/more electric aircraft. To gain energy-saving capability, the hydraulic lock can be applied in the EHA system, which can overcome the change of load after the position control is complete, reducing the motor’s need to work continuously. Due to the use of a hydraulic lock in the EHA system, there is a more significant position output delay compared with the EHA system. The delay would lead to a dynamic response error. Therefore, an electro-hydrostatic actuator with a bidirectional hydraulic lock (BHL-EHA) model is established that can reflect the position output delay and it can be used to analyze its influence factors. According to the analysis, the factors affecting the delay are pump speed, external load, hydraulic lock cracking pressure, friction load, pump displacement, fluid volume of the system, and effective bulk modulus; thus, an equation that can be used to calculate the delay is proposed. At the same time, the experimental system of the BHL-EHA system is established, and the parameters of the experimental facility are completely consistent with the simulation model. Finally, a delay compensation control method is proposed, and the control time parameter t0 is calculated based on the equation proposed above. The results of the simulation and the experiment show that the method can compensate for the delay caused by the hydraulic lock. Combined with a feedforward control, the method can dismiss the dynamic response error of 0.6 Hz sinusoidal tracking characteristics.
Design and Shear Analysis of an Angled Morphing Wing Skin Module
Authors: Yu Jinrui , Ma Jiayao
Published: March 2022

Morphing wing skin can greatly improve the performance of aircraft by adjusting the shape of the wings according to different flight conditions. However, it is a challenge to maintain a smooth aerodynamic wing skin surface during the deformation process. Here, we propose an angled morphing wing skin module based on a silicon rubber matrix reinforced by carbon-fiber-reinforced polymer rods, which takes advantage of the tensile stress generated during shear to prevent it from wrinkling under large shear deformation. Experiments conducted on a series of wing skin modules with varying initial angles indicate that by starting from an angled configuration, the skin module can withstand a pure shear deformation of 92° without wrinkling, 53% larger than existing designs. A parametric analysis was also conducted to analyze the effects of geometric and material parameters on the wrinkle-free deformation range. Finally, a theoretical model based on the energy method was developed to unveil the underlying wrinkle prevention mechanism and to estimate the critical wrinkling angle of the skin. The proposed design can potentially greatly expand the shear morphing capability of aircraft wings, leading to larger variation in sweepback angle and therefore superior aerodynamic performance.
Comparisons of Dynamic Landslide Models on GIS Platforms
Authors: Wu Yuming , Tian Aohua , Lan Hengxing
Published: March 2022

Numerical simulation is one of the methods to assess landslide movement processes, which is beneficial for engineering design and urban planning. With the development of computer technology, GIS has gradually become the mainstream platform for landslide simulation due to data availability and algorithm integrability. However, the dynamic processes of landslides are complicated, which makes integration difficult on GIS platforms. Some assumptions are applied to simplify these dynamic processes and solve this problem. Generally, there are two main types of numerical models on GIS platforms: models based on the Eulerian description and models based on the Lagrangian description. Case studies show that Eulerian models are suitable for flow-like movement, and Lagrangian models are suitable for discrete rigid bodies movement. Different models face different problems: the Eulerian-based models show numerical diffusion and oscillation, and the Lagrangian-based model needs to consider complicated shear and collision processes. In addition, the 3-D model can describe more details in the z-direction, while the depth-averaged model can obtain a reasonable range of motion, depth, and speed quickly. From the view of numerical simulation, inappropriate models, assumptions, and numerical schemes will produce errors. The landslide type refers to several forms of mass wasting associated with a wide range of ground movements, which guides establishing dynamic models and numerical schemes on GIS platforms and helps us obtain results accurately.
Effects of an Adapted Sports Intervention on Elderly Women in Need of Long-Term Care: A Pilot Study
Authors: Kawano Takashi , Moriki Goro , Bono Shinya , Masumoto Junya , Kaji Nobuyuki , Jung Hungu , Yamasaki Masahiro
Published: March 2022

This study aimed to evaluate the effects of an adapted sports intervention on elderly women in need of long-term care (NLTC). Although participation in sports activities positively impacts subjective health status, few studies have evaluated the safety, comfort, and effectiveness of competitive sports in elderly women in NLTC. In this study, ten elderly women in NLTC (age: 80.6 ± 8.2 years) were asked to participate in boccia, a sport adapted to prevent falls. Participants completed the Profile of Mood States 2nd Edition Short-Form and the Medical Outcome Study 36-Item Short-Form Health Survey Version 2. The results showed an improvement in mood states (anger–hostility, tension–anxiety, and total mood disturbance) of elderly women in the NLTC group compared with the control group. Therefore, boccia, an adapted sport, can be considered a safe and competitive option for such women.
Bacterial Hosts and Genetic Characteristics of Antibiotic Resistance Genes in Wastewater Treatment Plants of Xinjiang (China) Revealed by Metagenomics
Authors: Liu Ziteng , Yao Junqin , Ma Huiying , Rukeya Abudukelimu , Liang Zenghui , Du Wenyan , Chen Yinguang
Published: March 2022

Antibiotic resistance genes (ARGs) pose a widespread concern for human health and wastewater treatment plants (WWTPs) are considered to be a major source of ARG transmission. In this paper, the potential hosts and genetic characteristics of ARGs in the influent, activated sludge and effluent of WWTPs in Xinjiang were studied by metagenomics. Bacitracin resistance gene (bacA), beta-lactamase gene (class A beta-lactamase), multidrug resistance genes (mexD, qacEdelta1), and sulfonamide resistance genes (sul1, and sul2) are persistent antibiotic resistance genes (PARGs). The potential hosts of ARGs were mainly pathogens, with Escherichia coli (12.9%), Acinetobacter johnsonii (8.94%), and Klebsiella pneumoniae (5.30%) accounting for the highest proportions. Chromosomal sequences and plasmid sequences accounted for 42.0% and 22.6% of ARG-carrying contigs (ACCs) in the influent, respectively. Meanwhile, the effluent contained 58.3% of ACCs in plasmids and 8.30% in chromosomes. Bacitracin resistance genes and multidrug resistance genes were mainly carried by chromosomes, while resistance genes for macrolide–lincosamide–streptogramin (MLS), vancomycin, sulfonamide, beta-lactam, tetracycline, chloramphenicol, and aminoglycoside were mainly carried by plasmids. ICEPae690-sul1-qacEdelta1 and ICEPmiChn3-sul2 were stable coexistence structures and heighten the transfer potential of ARGs in the environment. This study provided a clearer picture of host bacterial sources and genetic context of ARGs in the environment.
Photodynamic Therapy Using 5-Aminolevulinic Acid (Ala) for the Treatment of Chronic Periodontitis: A Prospective Case Series
Authors: Lauritano Dorina , Moreo Giulia , Palmieri Annalisa , Vella Fedora Della , Petruzzi Massimo , Botticelli Daniele , Carinci Francesco
Published: March 2022

Aim: The objective of this study was to compare the efficacy of supportive periodontal therapy (i.e., scaling and root planning, SRP) alone versus ALADENT medical device used in association with SRP in the treatment of chronic periodontitis in adult patients. Materials and Methods: A total of 20 patients with a diagnosis of chronic periodontitis (40 localized chronic periodontitis sites) aged between 35 and 55 were selected. None of these patients previously received any surgical or non-surgical periodontal therapy, and they presented radiographic evidence of moderate bone loss. Two non-adjacent sites in different quadrants were identified and observed in each patient, analyzing treatment effectiveness (split-mouth design). Clinical pocket depth, clinical attachment loss, and bleeding on probing were evaluated at time 0 and after 6 months, while microbial analysis (MA) was conducted at baseline and after 15 days. Significant differences were calculated using SPSS program and paired simple statistic t-test. Results: Total bacteria loadings had a statistically significant reduction before and after treatment with SRP (left site) (total average decrease of 27%). The sites treated with SRP plus ALADENT (right) showed a significantly reduced total bacterial loading compared to the untreated sites (right) (total average decrease of 75%). Mean values of CAL/PD and percentages data of BOP, recorded after SRP + ALADENT therapy, showed a higher reduction (CAL = 2.42, PD = 2.87 mm, 90% of sites with no bleeding) than those obtained after SRP treatment (CAL = 4.08 mm, PD = 4.73 mm, 70% of sites with no bleeding). Conclusion: The treatment of moderate and severe chronic periodontitis should include, beside SRP, the use of ALADENT medical device, which has been proved to be a useful adjuvant therapy.
A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph
Authors: da Silva Rocha Élisson , Endo Patricia Takako
Published: March 2022

Introduction: Dental segmentation in panoramic radiograph has become very relevant in dentistry, since it allows health professionals to carry out their assessments more clearly and helps them to define the best possible treatment plan for their patients. Objectives: In this work, a comparative study is carried out with four segmentation algorithms (U-Net, DCU-Net, DoubleU-Net and Nano-Net) that are prominent in the medical literature on segmentation and we evaluate their results with the current state of the art of dental segmentation in panoramic radiograph. Methods: These algorithms were tested with a dataset consisting of 1500 images, considering experiment scenarios with and without augmentation data. Results: DoubleU-Net was the model that presented the best results among the analyzed models, reaching 96.591% accuracy and 92.886% Dice using the dataset with data augmentation. Another model that stood out was Nano-Net using the dataset without data augmentation; this model achieved results close to that of the literature with only 235 thousand trainable parameters, while the literature model (TSASNet) contains 78 million. Conclusions: The results obtained in this work are satisfactory and present paths for a better and more effective dental segmentation process.
Characteristics of EHF Wave Propagation in Hypersonic Plasma Sheaths Magnetized by Dipole Magnetic Fields
Authors: Bai Bo , Zhao Ziyang , Yuan Kai , Tang Rongxin , Deng Xiaohua , Mao Mingyang , Xiong Jiawei , Wang Kaili
Published: March 2022

Communication blackout is always a serious threat to the flight tasks of modern hypersonic vehicles moving in near space. EHF communication is considered as a potential solution to the blackout problem. Nevertheless, EHF waves suffer from severe attenuation in hypersonic plasma sheaths. An external magnetic field could mitigate EHF wave attenuation in hypersonic plasma sheaths. Dipole magnetic fields, generated by coils, are feasible in realistic scenarios. In the present study, a model for EHF wave propagation in hypersonic plasma sheaths magnetized by dipole magnetic fields that are generated with coils is developed. The dissipation caused by the inhomogeneity of dipole magnetic fields and the magnetic field component of electromagnetic waves are compared with the dissipation yielded by the collision between electrons and neutral particles. The results show that collision is still the main dissipation mechanic for EHF waves. The study also found that, in the blunt-coned plasma sheath, the mitigation effect of a dipole magnetic field is weaker than that of a uniform magnetic field. The mechanics which yield the difference is analyzed. In addition, the relation between the characteristics of EHF waves and the coil parameters is investigated. Suggestions for the coil parameters and the operation frequencies of the EHF communication systems are made based on the investigation.
Is the Fovea Ulnaris Truly Isometric during Forearm Rotation?—An In Vivo Retrospective Analysis Using Superimpositions of Three-Dimensional Reconstructions
Authors: Shin Seung-Han , Park Joonyoung , Kwak Dai-Soon , Chung Yang-Guk
Published: March 2022

The fovea ulnaris is considered to be the center of rotation on the ulnar head during forearm rotation. The purpose of this study was to investigate whether the fovea ulnaris is truly isometric during forearm rotation in vivo. The three-dimensional reconstruction models of 21 wrist computed tomography images taken in supination and pronation were investigated. The models were superimposed so that the two ulnar heads were in the same position. Numerous points were set on the surface of the ulnar head with a mean distance of 0.2 mm between the nearest two points. Then, the models were superimposed with respect to the radius, and the distance between the same points on the ulnar head in pronation and supination (DFR) was measured. The rotation center was defined as the point with the shortest DFR. The isometric point was defined as a rotation center with a DFR of less than 0.2 mm. An isometric point was found in three cases and not in 18 cases. The distance the rotation center moved during forearm rotation (DFR of the rotation center) ranged from 0.1 mm to 2.4 mm. The position of the rotation center in the radioulnar direction was significantly correlated with the translation of the ulnar head and the amount of forearm rotation. The rotation center was located more ulnarly when the translation of the ulnar head or the amount of forearm rotation was greater. The isometricity of the foveal insertion of the TFCC during forearm rotation may not be consistent in vivo. The center of rotation on the ulnar head during forearm rotation appears to shift ulnarly with increasing translation of the ulnar head or forearm rotation.
Optimization of Soybean Protein Extraction Using By-Products from NaCl Electrolysis as an Application of the Industrial Symbiosis Concept
Authors: Ovando Emilio , Rodríguez-Sifuentes Lucio , Martínez Luz María , Chuck-Hernández Cristina
Published: March 2022

Defatted soybean flour is generated during the oil extraction process of soybean, and it has a protein content of ~50%. On the other hand, an alkaline solution of NaOH is produced during the electrolysis process of NaCl in a novel method used to make a potent disinfectant/antiseptic (HOCl). In the present work, we suggest using these two products to produce soy protein isolate (SPI), aiming to create an industrial symbiosis. A Box–Behnken experimental design was executed, and a surface response analysis was performed to optimize temperature, alkaline solution, and time used for SPI extraction. The SPI produced at optimal conditions was then characterized. The experimental results fit well with a second-order polynomial equation that could predict 93.15% of the variability under a combination of 70 °C, alkaline solution 3 (pH 12.68), and 44.7 min of the process. The model predicts a 49.79% extraction yield, and when tested, we obtained 48.30% within the confidence interval (46.66–52.93%). The obtained SPI was comparable in content and structure with a commercial SPI by molecular weight and molecular spectroscopy characterization. Finally, the urease activity (UA) test was negative, indicating no activity for trypsin inhibitor. Based on the functional properties, the SPI is suitable for food applications.
Feature Enhanced Anchor-Free Network for School Detection in High Spatial Resolution Remote Sensing Images
Authors: Fu Han , Fan Xiangtao , Yan Zhenzhen , Du Xiaoping , Jian Hongdeng , Xu Chen
Published: March 2022

Object detection in remote sensing images (RSIs) is currently one of the most important topics, which can promote the understanding of the earth and better serve for the construction of digital earth. In addition to single objects, there are many composite objects in RSIs, especially primary and secondary schools (PSSs), which are composed of some parts and surrounded by complex background. Existing deep learning methods have difficulty detecting composite objects effectively. In this article, we propose a feature enhanced Network (FENet) based on an anchor-free method for PSSs detection. FENet can not only realize more accurate pixel-level detection based on enhanced features but also simplify the training process by avoiding hyper-parameters. First, an enhanced feature module (EFM) is designed to improve the representation ability of complex features. Second, a context-aware strategy is used for alleviating the interference of background information. In addition, complete intersection over union (CIoU) loss is employed for bounding box regression, which can obtain better convergence speed and accuracy. At the same time, we build a PSSs dataset for composite object detection. This dataset contains 1685 images of PSSs in Beijing–Tianjin–Hebei region. Experimental results demonstrate that FENet outperforms several object detectors and achieves 78.7% average precision. The study demonstrates the advantage of our proposed method on PSSs detection.
Improvement of the Mechanical Properties by Surface Modification of ZnCl2 and Polydopamine in Aramid Fiber Composites
Authors: Xu Guangxian , Jin Yinghai , Song Junhee
Published: March 2022

Although aramid fiber has the advantage of excellent chemical and mechanical properties, the performance of the composites may be reduced due to the low interfacial adhesion between the fabric and the matrix. The surface of the aramid fiber was modified to improve the interfacial properties. The surface of the aramid fibers was treated with ZnCl2 and polydopamine. After the pretreated fabrics were made into a composite material by the vacuum-assisted resin transfer molding (VARTM) process, their mechanical performance was investigated. The highest impact energy was shown in the concentration of 6 wt% ZnCl2 and 1.5 g/L polydopamine, which is 20% better than that of the untreated material. In the bending strength, the condition of 1.5 g/L polydopamine resulted in the highest value and increased by 13% compared to the untreated material. The hybrid surface treatment of ZnCl2 and polydopamine did not significantly affect the tensile strength.
Periosteal Needling to the Cervical Articular Pillars as an Adjunct Intervention for Treatment of Chronic Neck Pain and Headache: A Case Report
Authors: Perreault Thomas , Cummings Mike , Dommerholt Jan , Hayes Deanna , Hobbs Jonathan
Published: March 2022

(1) Background: Periosteal dry needling (PDN) involves clinicians using a solid filiform needle to stimulate bone for analgesic purposes. This case report presents the use of PDN to the cervical articular pillars (CAPs) in an 85-year-old female with chronic neck pain and headache. (2) Case description: PDN was applied to the right C2–C3 articular pillars, following trigger point dry needling (TrPDN) and manual therapy, in order to provide a direct sensory stimulus to the corresponding sclerotomes. PDN added over two treatments led to improved cervical range of motion and eliminated the patient’s neck pain and headache at 1 week follow-up. (3) Outcomes: At discharge, clinically relevant improvements were demonstrated on the numeric pain rating scale (NPRS), which improved from an 8/10 on intake to a 0/10 at rest and with all movements. In addition, the patient exceeded the risk adjusted predicted four-point score improvement and the minimal clinically important improvement (MCII) value of four points on the Focus on Therapeutic Outcomes (FOTO) Neck Functional Status (Neck FS). At one month post-discharge, the patient remained symptom-free. (4) Discussion: In the context of an evidence-informed approach for neck pain and headache, PDN led to marked improvements in pain and function. Patient outcomes exceeded predictive analytic expectations for functional gains and efficient utilization of visits and time in days. Combined with other interventions, PDN to the CAPs could be a viable technique to treat chronic neck pain with headache.
Multicriteria Route Planning for In-Operation Mass Transit under Urban Data
Authors: Lin Fandel , Hsieh Hsun-Ping
Published: March 2022

Multicriteria route planning is a crucial transportation planning issue under the field of GIS-based multicriteria decision analysis (GIS-MCDA) with broad applications. A searching algorithm is proposed to solve the multicriteria route planning problem with spatial urban information and constraints such an existing transit network in operation, certain vertices to be visited in the path, total number of vertices been visited, and length or range for the path. Evaluation of two in-operation mass-transit systems from Chicago and Tainan show that our method can retrieve solutions in a Pareto-optimal sense over comparative methods between profit under queried constraints (the expected passenger flow to be maximized, referring to the social welfare for the public) and cost for construction as well as maintenance (the cost of route to be minimized, referring to the sustainability for the government) with reasonable runtime over comparative methods.
Adaptive Sliding Mode Control Integrating with RBFNN for Proton Exchange Membrane Fuel Cell Power Conditioning
Authors: Xiao Xuelian , Lv Jianguo , Chang Yuhua , Chen Jinzhou , He Hongwen
Published: March 2022

Proton exchange membrane fuel cells (PEMFC) are considered a promising solution for renewable energy application. To meet industrial requirements, the power source consisting of PEMFC is required to be power regulator to generate a stable and desired current and/or voltage under various working conditions. In this article, the adaptive sliding mode control integrating with the radial basis function neural network (RBFNN) approach for DC/DC buck converter-based PEMFC is presented to address perturbations from inner parameters as well as external disturbances in terms of power conditioning. Sliding mode control (SMC) and backstepping schemes are integrated to tackle the nonlinear and coupled outputs resulting in large control errors and slow response caused by PEMFC characteristics. To accurately estimate the parametric uncertainties and disturbance injections, such as buck converter parameter varying and PEMFC operation point changing, the RBFNN adaptive law is developed according to the defined Lyapunov and Gaussian functions overcoming the limitations of non-/linear parameter estimating. Simulations and experiments on the PEMFC power supply prototype governed by the DS1104 board are carried out. The comparative results indicate that the proposed RBFNN estimation associated with the backstepping SMC can reduce up to 7.5% overshoot and smooth PEMFC voltage and inductor current when disturbance changes in a voltage regulation experiment. Thus, the proposed method can regulate the current or voltage of a PEMFC power supply with robustness, adaptivity, and no chattering phenomenon.
Research on the Coupled Modulation Transfer Function of the Discrete Sampling System with Hexagonal Fiber-Optic Imaging Bundles
Authors: Li Wenxian , Han Chengshan , Wu Congjun , Huang Yawei , Zhang Hang
Published: March 2022

In this study, we developed a numerical model of the coupled modulation transfer function (coupled-MTF) based on the discrete sampling system from the perspective of optical system imaging quality evaluation for coupled two-dimensional discrete sampling characteristics of the hexagonally aligned fiber-optic imaging bundles and CCD image elements. The results show that when the spatial frequency of the input target signal deviates from the Nyquist frequency by 1%, an increase in the number of fibers leads to a faster convergence of the oscillation of the coupled-MTF, and the coupled-MTF converges to a stable value when the number of fibers reaches 1000 × 1000. The deviation of the spatial frequency of the input target signal from the Nyquist frequency is within 1%, and the oscillatory convergence of the coupled-MTF accelerates with increasing deviation. The coupled-MTF oscillates with the deviation period of the wave peak of the input target signal from the initial position of the fiber center, and the theoretical oscillation spatial period is twice the fiber diameter. This study produces important guidelines for the selection of the number of fibers, input spatial frequency, and initial position deviation of the hexagonally arranged fiber imaging bundles.
An Accurate State Visualization of Multiplexed and PWM Fed Peripherals in the Virtual Simulators of Embedded Systems
Authors: Radecki Andrzej , Rybicki Tomasz
Published: March 2022

A method of precise and real-like embedded systems peripherals visualization in virtual simulations is presented in the article. For high frequency peripheral inputs, blended graphics of real-look representations of peripherals are used, allowing for an accurate visualization with consideration of their input signal nature based on PWM and multiplexing schemes. An accurate simulator of a didactic embedded system called MKD-51 is introduced as a plug in for Keil μVision IDE. A comparison with a real microprocessor system using multiplexing seven-segment LED modules as well as LED bar fed by PWM is shown. The achieved quantity results are included to illustrate compliance with real devices and improvements in visualization accuracy due to applying additional blending mapping function. Brightness estimation differences of LED visualization compared to a real device are at a single percentage level for seven-segment multiplexed modules.
Fully Convolutional Neural Network Prediction Method for Aerostatic Performance of Bluff Bodies Based on Consistent Shape Description
Authors: Li Ke , Li Hai , Li Shaopeng , Chen Zengshun
Published: March 2022

The shape of a bluff body section is of high importance to its aerostatic performance. Obtaining the aerostatic performance of a specific shape based on wind tunnel tests and CFD simulations takes a lot of time, which affects evaluation efficiency. This paper proposes a novel fully convolutional neural network model that enables rapid prediction from shape to aerostatic performance. Its main innovations are: (1) The proposal of a new shape description method in which the shape is described by the combination of the wall distance field and the space coordinate field, which can efficiently express the influencing factors of the shape on the aerostatic performance. (2) A step-by-step strategy in which the pressure field is used as the model output and then the calculation of the aerostatic coefficient is proposed. Compared with the simple direct prediction of the aerostatic coefficient, the logical connection between input and output can be enhanced and the prediction accuracy can be improved. It is found that the model proposed in this paper has good prediction accuracy, and its average relative error is 9.42% compared with the CFD calculation results. Compared with the direct use of the shape as the model input, the accuracy is improved by 13.25%; compared with the direct use of the drag coefficient as the model output, the accuracy is improved by 10%. Compared with traditional CFD calculations and wind tunnel experiments, this method can be used as a fast auxiliary screening method for the optimization of the aerodynamic shapes of bluff body sections.
129Xe: A Wide-Ranging NMR Probe for Multiscale Structures
Authors: Boventi Matteo , Mauri Michele , Simonutti Roberto
Published: March 2022

Porous materials are ubiquitous systems with a large variety of applications from catalysis to polymer science, from soil to life science, from separation to building materials. Many relevant systems of biological or synthetic origin exhibit a hierarchy, defined as spatial organization over several length scales. Their characterization is often elusive, since many techniques can only be employed to probe a single length scale, like the nanometric or the micrometric levels. Moreover, some multiscale systems lack tridimensional order, further reducing the possibilities of investigation. 129Xe nuclear magnetic resonance (NMR) provides a unique and comprehensive description of multiscale porous materials by exploiting the adsorption and diffusion of xenon atoms. NMR parameters like chemical shift, relaxation times, and diffusion coefficient allow the probing of structures from a few angstroms to microns at the same time. Xenon can evaluate the size and shape of a variety of accessible volumes such as pores, layers, and tunnels, and the chemical nature of their surface. The dynamic nature of the probe provides a simultaneous exploration of different scales, informing on complex features such as the relative accessibility of different populations of pores. In this review, the basic principles of this technique will be presented along with some selected applications, focusing on its ability to characterize multiscale materials.
A Design of CGK-Based Granular Model Using Hierarchical Structure
Authors: Yeom Chan-Uk , Kwak Keun-Chang
Published: March 2022

In this paper, we propose context-based GK clustering and design a CGK-based granular model and a hierarchical CGK-based granular model. Existing fuzzy clustering generates clusters using Euclidean distances. However, there is a problem in that performance decreases when a cluster is created from data with strong nonlinearity. To improve this problem, GK clustering is used. GK clustering creates clusters using Mahalanobis distance. In this paper, we propose context-based GK (CGK) clustering, which adds a method that considers the output space in the existing GK clustering, to create a cluster that considers not only the input space but also the output space. there is. Based on the proposed CGK clustering, a CGK-based granular model and a hierarchical CGK-based granular model were designed. Since the output of the CGK-based granular model is in the form of a context, it has the advantage of verbally expressing the prediction result, and the CGK-based granular model with a hierarchical structure can generate high-dimensional information granules, so meaningful information with high abstraction value granules can be created. In order to verify the validity of the method proposed in this paper, as a result of conducting an experiment using the concrete compressive strength database, it was confirmed that the proposed methods showed superior performance than the existing granular models.
Kaempferol Regresses Carcinogenesis through a Molecular Cross Talk Involved in Proliferation, Apoptosis and Inflammation on Human Cervical Cancer Cells, HeLa
Authors: Afroze Nazia , Pramodh Sreepoorna , Almutary Abdulmajeed G. , Rizvi Tahir A. , Rais Naushad , Raina Ritu , Faiyazuddin Md. , Alnuqaydan Abdullah M. , Hussain Arif
Published: March 2022

Kaempferol, a flavonoid, contains a plethora of therapeutic properties and has demonstrated its efficacy against cancer. This study aims to unravel the molecular targets that are being modulated by kaempferol on HeLa cells. Various assays were performed, namely: MTT assay, flow cytometry to analyze DNA content and quantitate apoptosis. Quantitative PCR and protein profiling were performed to evaluate the modulated manifestation of different genes involved in apoptosis, cell growth and inflammation. Kaempferol exhibited reduction in cell viability of HeLa cells (IC50 = 50 µM 48 h), whereas it did not show any significant effect on viability of the AC-16 cell line. Kaempferol-impacted apoptosis was definitive, as it induced DNA fragmentation, caused disruption of membrane potential, accumulation of cells in the G2-M phase and augmented early apoptosis. Consistently, kaempferol induced apoptosis in HeLa cells by modulating the expression of various genes at both transcript and protein levels. It upregulated the expression of pro-apoptotic genes, including APAF1, BAX, BAD, Caspases 3, and 9, etc., at the transcript level and Bad, Bax, p27, p53, p21, Caspases 3 and 8 etc. at the protein level, while it downregulated the expression of pro-survival gene BCL-2, BIRC8, MCL-1, XIAP, and NAIP at the transcript level and Bcl-2, XIAP, Livin, clap-2 at the protein level. Kaempferol attenuated oxidative stress by upregulating GSH activity and anti-inflammatory response by suppressing NF-kB pathways. Moreover, kaempferol averted rampant cell division and induced apoptosis by modulating AKT/MTOR and MAP kinase pathways. Hence, kaempferol can be considered as a natural therapeutic agent with a differential profile.
A New Vibration-Absorbing Wheel Structure with Time-Delay Feedback Control for Reducing Vehicle Vibration
Authors: Ma Kehui , Ren Chuanbo , Zhang Yongguo , Chen Yuanchang , Chen Yajie , Zhou Pengcheng
Published: March 2022

To address the contradiction between ride comfort and handling stability, a new vibration-absorbing wheel structure with time-delay feedback control was proposed by applying a time-delay feedback-controlled dynamic vibration absorber to the wheel structure. Using H2 optimization and the particle swarm optimization algorithm (PSO), the design parameters of the time-delay vibration-absorbing wheel structure were obtained with sprung mass acceleration (SMA), suspension deflection (SD) and dynamic tire deflection (DTD) as the optimization objective functions. The stability interval of the system was analyzed using the Routh–Hurwitz criterion and the Sturm criterion. The vehicle vibration reduction performance of the new vibration-absorbing wheel structure was simulated and verified in the time domain under harmonic excitation and random excitation. The simulation results show this new structure can effectively solve the contradiction between the ride comfort and handling stability. It provides a new technical idea for reducing vehicle vibration.
Investigating Shape Variation Using Generalized Procrustes Analysis and Machine Learning
Authors: Wöber Wilfried , Mehnen Lars , Curto Manuel , Tibihika Papius Dias , Tesfaye Genanaw , Meimberg Harald
Published: March 2022

The biological investigation of a population’s shape diversity using digital images is typically reliant on geometrical morphometrics, which is an approach based on user-defined landmarks. In contrast to this traditional approach, the progress in deep learning has led to numerous applications ranging from specimen identification to object detection. Typically, these models tend to become black boxes, which limits the usage of recent deep learning models for biological applications. However, the progress in explainable artificial intelligence tries to overcome this limitation. This study compares the explanatory power of unsupervised machine learning models to traditional landmark-based approaches for population structure investigation. We apply convolutional autoencoders as well as Gaussian process latent variable models to two Nile tilapia datasets to investigate the latent structure using consensus clustering. The explanatory factors of the machine learning models were extracted and compared to generalized Procrustes analysis. Hypotheses based on the Bayes factor are formulated to test the unambiguity of population diversity unveiled by the machine learning models. The findings show that it is possible to obtain biologically meaningful results relying on unsupervised machine learning. Furthermore we show that the machine learning models unveil latent structures close to the true population clusters. We found that 80% of the true population clusters relying on the convolutional autoencoder are significantly different to the remaining clusters. Similarly, 60% of the true population clusters relying on the Gaussian process latent variable model are significantly different. We conclude that the machine learning models outperform generalized Procrustes analysis, where 16% of the population cluster was found to be significantly different. However, the applied machine learning models still have limited biological explainability. We recommend further in-depth investigations to unveil the explanatory factors in the used model.
An Efficient Algorithm for Mapping Deep Learning Applications on the NoC Architecture
Authors: Khan Zeeshan Ali , Abbasi Ubaid , Kim Sung Won
Published: March 2022

Network-on-chip (NoC) is replacing the existing on-chip communication mechanism in the latest, very-large-scale integration (VLSI) systems because of their fault tolerant design. However, in addition to the design challenges, NoC systems require a mechanism for proper application mapping in order to produce maximum benefits in terms of application-level latency, platform energy consumption, and system throughput. Similarly, the neural-network (NN)-based artificial intelligence (AI) techniques for deep learning are gaining particular interest. These applications can be executed on a cloud-based system, but some of these applications have to be executed on private cloud to integrate the data privacy. Furthermore, the public cloud systems can also be made from these NoC platforms to have better application performance. Therefore, there is a need to optimally map these applications on existing NoC-based architectures. If the application is not properly mapped, then it can create a performance hazard that may lead to delay in calculations, increase in energy consumption, and decrease in the platform lifetime. Hence, the real-time applications requiring AI services can implement these algorithms in NoC-based architectures with better real-time performance. In this article, we propose a multilevel mapping of deep learning AI applications on the NoC architectures and show its results for the energy consumption, task distribution profile, latency, and throughput. The simulation is conducted using the OCTAVE, and the simulation results show that the performance of the proposed mapping technique is better than the direct mapping techniques.
Engineering Method and Tool for the Complete Virtual Commissioning of Robotic Cells
Authors: Raffaeli Roberto , Bilancia Pietro , Neri Federico , Peruzzini Margherita , Pellicciari Marcello
Published: March 2022

Intelligent robotic manufacturing cells must adapt to ever-varying operating conditions, developing autonomously optimal manufacturing strategies to achieve the best quality and overall productivity. Intelligent and cognitive behaviors are realized by using distributed controllers, in which complex control logics must interact and process a wide variety of input/output signals. In particular, programmable logic controllers (PLCs) and robot controllers must be coordinated and integrated. Then, there is the need to simulate the robotic cells’ behavior for performance verification and optimization by evaluating the effects of both PLC and robot control codes. In this context, this work proposes a method, and its implementation into an integrated tool, to exploit the potential of ABB RobotStudio software as a virtual prototyping platform for robotic cells, in which real robots control codes are executed on a virtual controller and integrated with Beckhoff PLC environment. For this purpose, a PLC Smart Component was conceived as an extension of RobotStudio functionalities to exchange signals with a TwinCAT instance. The new module allows the virtual commissioning of a complete robotic cell to be performed, assessing the control logics effects on the overall productivity. The solution is demonstrated on a robotic assembly cell, showing its feasibility and effectiveness in optimizing the final performance.
Power Modeling and Experiment Study of Large Flapping-Wing Flying Robot during Forward Flight
Authors: Zhong Siping , Xu Wenfu
Published: March 2022

A power estimation approach for calculating the power of a flapping-wing air vehicle (FWAV) in forward flight is proposed in this paper. One of the challenges and essential points of FWAVs is endurance. In order to optimize FWAVs, it is necessary to analyze power required for flight in addition to kinematic and aerodynamic analyses of the prototype. Previously, calculating the power of birds was limited to calculating their average power, which assumed the lift was usually the same as the gravitational force. However, the lift varies with the flapping angle during flight. As a result, the power required for forward flight of FWAVs is determined in this work by using a kinematic model of the drive element and wing flapping, along with the aerodynamic model, which varies with the flapping angle during the flapping cycle. Experiments were performed with two prototypes with wingspans of 1.6 and 1.8 m, utilizing a wind tunnel platform. The correlations between power and angle of attack, flapping frequency, and incoming flow velocity were discovered, and recommendations for FWAVs and flying mode design were provided. However, several challenges are highlighted in the application of the model to practical design efforts.
Spreading of Dangerous Skin-Lightening Products as a Result of Colourism: A Review
Authors: Juliano Claudia C. A.
Published: March 2022

The use of bleaching products can have a medical or cosmetic purpose; in the latter case, skin whitening is most widespread in countries where darker skin tones prevail and can be driven by psychosocial, cultural and economic reasons. Skin-whitening products containing highly toxic active ingredients (in particular mercury derivatives, hydroquinone and corticosteroids) are easily found on the market; the use of these depigmenting agents can be followed by a variety of adverse effects, with very serious and sometimes fatal complications, and is currently an emerging health concern in many countries. This article concisely discusses the reasons for the current prevalence of skin lightening products and provides an overview of the skin lightening agents that pose a threat to human health. The review also reports market surveillance data on the circulation of banned skin lighteners in Europe, obtained through the Safety Gate system.
Model-Free Data Mining of Families of Rotating Machinery
Authors: Hofer Elizabeth , v. Mohrenschildt Martin
Published: March 2022

Machines designed to perform the same tasks using different technologies can be organized into families based on their similarities or differences. We are interested in identifying common properties and differences of such machines from raw sensor data for analysis and fault diagnostics. The usual first step is a feature extraction process that requires an understanding of the machine’s harmonics, bearing frequencies, etc. In this paper, we present a model-free path from the raw sensor data to statistically meaningful feature vectors. This is accomplished by defining a transform independent of the operating frequency and performing statistical reductions to identify the components with the largest variances, resulting in a low dimensional statistically meaningful feature space. To obtain an insight into the family relationships we perform a clustering. As the data set has some labeled characteristics we define an entropy-based measure to evaluate a clustering using the a priori-known labels, resulting in a symmetric measurement uniquely defining the clustering goal. Applying this hierarchically we obtain the family tree. The methods are presented can be applied in general situations. As a case study we apply them to a real data set of vibrating screens.