bluuebunny/crossref_metadata_2025 huggingface dataset:
Revisiting the Dissolution of Praziquantel in Biorelevant Media and the Impact of Digestion of Milk on Drug Dissolution
Authors: Eason Thomas , Ramirez Gisela , Clulow Andrew J. , Salim Malinda , Boyd Ben J.
Published: October 2022

Praziquantel is a poorly water-soluble drug used to treat parasitic infections. Previous studies have suggested that its rate and extent of dissolution in milk and biorelevant media are slow and limited compared to dissolution in the pharmacopoeial-recommended medium, despite being reported as displaying a positive food effect upon administration. This study aimed to revisit the dissolution of praziquantel in biorelevant media and milk to better understand this apparent dichotomy. The context of digestion was introduced to better understand drug solubilisation under more relevant gastrointestinal conditions. The amount of praziquantel solubilised in the various media during digestion was quantified using high performance liquid chromatography (HPLC) and the kinetics of dissolution were confirmed by tracking the disappearance of solid crystalline drug using in situ small angle X-ray scattering (SAXS). For the dissolution media, where sodium lauryl sulfate (SLS) is typically included as a wetting agent, a prominent effect of SLS on drug dissolution was also apparent where >2.5 fold more drug was solubilised in SLS-containing dissolution medium compared to that without (0.1 M HCl only). In milk, significant dissolution of praziquantel was observed only during digestion and not during dispersion, hence suggesting that (1) milk can be potentially administered with praziquantel to improve oral bioavailability and (2) incorporating a digestion step into existing in vitro dissolution testing can better reflect the potential for a positive food effect when lipids are present.
Cationic Polyethyleneimine (PEI)–Gold Nanocomposites Modulate Macrophage Activation and Reprogram Mouse Breast Triple-Negative MET-1 Tumor Immunological Microenvironment
Authors: Mulens-Arias Vladimir , Nicolás-Boluda Alba , Carn Florent , Gazeau Florence
Published: October 2022

Nanomedicines based on inorganic nanoparticles have grown in the last decades due to the nanosystems’ versatility in the coating, tuneability, and physical and chemical properties. Nonetheless, concerns have been raised regarding the immunotropic profile of nanoparticles and how metallic nanoparticles affect the immune system. Cationic polymer nanoparticles are widely used for cell transfection and proved to exert an adjuvant immunomodulatory effect that improves the efficiency of conventional vaccines against infection or cancer. Likewise, gold nanoparticles (AuNPs) also exhibit diverse effects on immune response depending on size or coatings. Photothermal or photodynamic therapy, radiosensitization, and drug or gene delivery systems take advantage of the unique properties of AuNPs to deeply modify the tumoral ecosystem. However, the collective effects that AuNPs combined with cationic polymers might exert on their own in the tumor immunological microenvironment remain elusive. The purpose of this study was to analyze the triple-negative breast tumor immunological microenvironment upon intratumoral injection of polyethyleneimine (PEI)–AuNP nanocomposites (named AuPEI) and elucidate how it might affect future immunotherapeutic approaches based on this nanosystem. AuPEI nanocomposites were synthesized through a one-pot synthesis method with PEI as both a reducing and capping agent, resulting in fractal assemblies of about 10 nm AuNPs. AuPEI induced an inflammatory profile in vitro in the mouse macrophage-like cells RAW264.7 as determined by the secretion of TNF-α and CCL5 while the immunosuppressor IL-10 was not increased. However, in vivo in the mouse breast MET-1 tumor model, AuPEI nanocomposites shifted the immunological tumor microenvironment toward an M2 phenotype with an immunosuppressive profile as determined by the infiltration of PD-1-positive lymphocytes. This dichotomy in AuPEI nanocomposites in vitro and in vivo might be attributed to the highly complex tumor microenvironment and highlights the importance of testing the immunogenicity of nanomaterials in vitro and more importantly in vivo in relevant immunocompetent mouse tumor models to better elucidate any adverse or unexpected effect.
Extracellular Vesicles as Delivery Vehicles for Therapeutic Nucleic Acids in Cancer Gene Therapy: Progress and Challenges
Authors: Du Rong , Wang Chen , Zhu Ling , Yang Yanlian
Published: October 2022

Extracellular vesicles (EVs) are nanoscale vesicles secreted by most types of cells as natural vehicles to transfer molecular information between cells. Due to their low toxicity and high biocompatibility, EVs have attracted increasing attention as drug delivery systems. Many studies have demonstrated that EV-loaded nucleic acids, including RNA-based nucleic acid drugs and CRISPR/Cas gene-editing systems, can alter gene expressions and functions of recipient cells for cancer gene therapy. Here in this review, we discuss the advantages and challenges of EV-based nucleic acid delivery systems in cancer therapy. We summarize the techniques and methods to increase EV yield, enhance nucleic acid loading efficiency, extend circulation time, and improve targeted delivery, as well as their applications in gene therapy and combination with other cancer therapies. Finally, we discuss the current status, challenges, and prospects of EVs as a therapeutic tool for the clinical application of nucleic acid drugs.
Preparing Hydrophobic Cellulose Nanofibers-SiO2 Films and Coating by One-Step Mechanochemical Method
Authors: Chen Xi , Zhang Lijiaqi , Wu Min , Huang Yong
Published: October 2022

Green and sustainable cellulose-based hydrophobic coatings are increasingly the subject of scientific and industrial research. However, few researchers pay attention to preparing it by a one-step method. Therefore, a superhydrophobic coating composed of hydrophobic SiO2 and cellulose nanofiber modified by 3,4-dichlorophenyl isocyanate was manufactured through one-step ball milling. It was found that the ball milling can promote SiO2 dispersion and achieve the preparation of modified nanocellulose, which further disperse SiO2 nanoparticles to form film or coating. Compared with the ultrasonic dispersion method, the composite coating prepared by ball milling method can obtain higher water contact angle and more stable hydrophobic properties. The hydrophobic cellulose nanofiber can load 1.5 equivalents of SiO2 nanoparticles to form a uniform film with the water contact angle of 158.0° and low moisture absorption. When this nanocomposite is used as a coating material, it can impart super-hydrophobicity to paper surface with water contact angle of 155.8°. This work provides a facile way to prepare superhydrophobic nanocellulose/nanoparticles composite coatings and films, thereby broadening the ways of dispersing nanoparticles and constructing superhydrophobic coatings.
Evaluation and Characterization of Hard-Shell Capsules Formulated by Using Goatskin Gelatin
Authors: Zilhadia Zilhadia , Harahap Yahdiana , Jaswir Irwandi , Anwar Effionora
Published: October 2022

Gelatin is used as an additive in medicine, food, and cosmetics. Gelatin from goatskin is a new excipient that has not been explored by researchers, including for hard-shell capsules. The aim of this study was to evaluate and characterize the hard-shell capsules produced from goatskin gelatin. The goatskin gelatin was extracted by an acid hydrolysis method, and the functional properties were investigated. Hard-shell capsules were then produced from goatskin gelatin, evaluated, and characterized. The gelatin extracted from goatskin had 56.9% ± 0.95 clarity and a pH of 5.11 ± 0.09, 97.51% ± 1.1 protein content, 9.23% ± 0.08 water content, 0.18% ± 0.07 ash content, 2.08% ± 0.35 fat content, gel strength of 298 ± 2.64 gbloom, and viscosity of 27.33 ± 2.07 mPs. The gelatin has met the requirements to be made into hard-shell capsules. The average weight of the hard-shell capsules produced was 96.9 mg with 8.69 standard deviation. The average size of the body and cap length was 18.84 ± 0.64 mm and 10.98 ± 0.30 mm, respectively. The results of capsule evaluation and characterization were as follows: the pH was 4.82 ± 1,27, water content was 10.03 ± 0.21, disintegration time was 4.02 ± 2.09 min, and there was no microbial growth. Thus, the capsules made have met the requirements and can be produced in a large quantity.
Mechanochemical Treatment in High-Shear Thermokinetic Mixer as an Alternative for Tire Recycling
Authors: Bianchi Otávio , Pereira Patrícia Bereta , Ferreira Carlos Arthur
Published: October 2022

This publication highlights the use of a high-speed thermokinetic mixer as an alternative to recycling ground tire rubber (GTR) using mechanochemical treatment. The GTR initially had a gelled fraction of 80% and presented a reduction of up to 50% of gel fraction in the most intensive condition (5145 rpm, n2). The processing condition at the lowest speed (2564 rpm, n1) resulted in greater selectivity in chain scission (K~1). However, in the most intense processing condition (10 min to n2), more significant degradation was observed via random scission, reduction in the glass transition temperature, Tg (11 °C), increase in the soluble polymeric fraction, and a more significant reduction in the density of bonds occurs. The artificial neural network could describe and correlate the thermal degradation profile with the processing conditions and the physicochemical characteristics of the GTR. The n2 velocity resulted in the formation of particles with a smoother and more continuous surface, which is related to the increase in the amount of soluble phase. The approach presented here represents an alternative to the mechanochemical treatment since it can reduce the crosslink density with selectivity and in short times (1–3 min).
Dynamic Splitting Tensile Behaviour of Concrete Confined by Natural Flax and Glass FRP
Authors: Wang Wenjie , Mo Zonglai , Zhang Yunpeng , Chouw Nawawi
Published: October 2022

Flax fibre has been used to reinforce concrete composite, but its dynamic properties have not been thoroughly studied. This study investigates the dynamic splitting tensile properties of plain concrete (PC) confined by flax-fibre-reinforced polymer (FFRP) and glass-fibre-reinforced polymer (GFRP). The dynamic splitting tensile tests were carried out on PC, FFRP-PC, and GFRP-PC cylinder specimens by the high-speed servo-hydraulic machine, with the impact-induced strain rates ranging from 0.1 to 58 s−1. The effect of the FRP confinement, FRP thickness and strain rate on the dynamic splitting tensile behaviour were assessed. The results indicated that similar confinement effectiveness of FFRP and GFRP is observed. The dynamic tensile strength of 1- and 2-layer FFRP-PC increased by 29% and 67%, and the one- and two-layer GFRP-PC increased by 32% and 84%, respectively. FFRP-PC and GFRP-PC cylinders showed less sensitivity to the strain rate compared with PC. The empirical relationship between the tensile DIF and strain rate for PC, FFRP-PC and GFRP-PC was proposed based on experimental data. The proposed model was developed to predict the dynamic splitting tensile strength. The results suggested the potential of FFRP composites applied into concrete structures under extreme dynamic loadings.
Influence of Power Supply Ripple on Injection Locking of Magnetron with Frequency Pushing Effect
Authors: Zhang Zihao , Zhou Yongjie , Lai Shimiao , Wang Ge , Zhu Huacheng , Yang Yang
Published: October 2022

The influence of anode voltage ripple on injection locking of a magnetron with frequency pushing effect has been studied systematically. Theoretical analysis shows that, when power supply ripple and injection ratio are constant, frequency pushing effect will increase the magnetron’s locking bandwidth. Meanwhile, the locking bandwidth decreases with the increase of power supply ripple. Thus, to achieve injection locking, both power supply ripple and frequency pushing effect must be considered. The experiment results show that at injection ratio μ of 0.003, frequency pushing effect at 0.5, and power supply ripple increases from 0% to 1% and 2.5%, the locking bandwidth of magnetron decreases by 0.32 MHz, 2.12 MHz. With the amplitude of ripple increasing, the spectrum after injection locking deteriorates, and output amplitude reduces, which verifies the theoretical analysis. Considering anode voltage ripple and frequency pushing effect, the research results contribute to the realization of high-quality output of injection locked magnetrons.
Effects of Polyethylene Microplastics and Phenanthrene on Soil Properties, Enzyme Activities and Bacterial Communities
Authors: Liu Shasha , Huang Kaibo , Yuan Guodong , Yang Chengfang
Published: October 2022

Microplastics (MPs) or polycyclic aromatic hydrocarbons (PAHs) pollution has received increasing concern due to their ubiquitous distribution and potential risks in soils. However, nothing is known about the influences of PAHs-MPs combined pollution on soil ecosystems. To address the knowledge gap, a 1-year soil microcosm experiment was conducted to systematically investigate the single and combined effect of polyethylene (PE) /phenanthrene (PHE) on soil chemical properties, enzymatic activities and bacterial communities (i.e., diversity, composition and function). Results showed that PE and PHE-PE significantly decreased soil pH. The available phosphorus (AP) and neutral phosphatase activity were not considerably changed by PHE, PE and PHE-PE. Significant enhancement of dehydrogenase activity in a PHE-PE amended system might be due to the degradation of PHE by indigenous bacteria (i.e., Sphingomonas, Sphingobium), and PE could enhance this stimulative effect. PHE and PHE-PE led to a slight increase in soil organic matter (SOM) and fluorescein diacetate hydrolase (FDAse) activity but a decrease in available nitrogen (AN) and urease activity. PE significantly enhanced the functions of nitrogen cycle and metabolism, reducing SOM/AN contents but increasing urease/FDAse activities. There were insignificant impacts on overall community diversity and composition in treated samples, although some bacterial genera were significantly stimulated or attenuated with treatments. In conclusion, the addition of PHE and PE influenced the soil chemical properties, enzymatic activities and bacterial community diversity/composition to some extent. The significantly positive effect of PE on the nitrogen cycle and on metabolic function might lead to the conspicuous alterations in SOM/AN contents and urease/FDAse activities. This study may provide new basic information for understanding the ecological risk of PAHs-MPs combined pollution in soils.
A Shift Schedule to Optimize Pure Electric Vehicles Based on RL Using Q-Learning and Opt LHD
Authors: Yu Xin , Zhao Ling , Zhang Kun , Guo Hongqiang
Published: October 2022

Range anxiety is a problem that restricts the development of pure electric vehicles. For this reason, much research starts from a shift schedule and strives to improve mileage. However, the proposed shift schedules have poor adaptive ability and are not suitable for dynamic conditions. In this paper, a shift schedule based on reinforcement learning (RL) is proposed, which uses Q-learning for optimization. However, the massive state variables and huge Q table in the state space put forward higher requirements on the computing power and storage space of the controller. Traditionally, the application of RL algorithms needs to rely on expensive GPU devices. To reduce high costs, we use an innovative treatment method, the optimal Latin hypercube design (Opt LHD), which is used for sampling, and state reduction is performed on the state space. Based on the above, the mileage is effectively improved by applying the shift schedule based on RL.
Green Composites Based on PLA and Cotton Fabric Waste: Preparation and Characterization
Authors: O-Charoen Narongchai , Kampeerapappun Piyaporn , Charoenlarp Khanittha , Petchwattana Nawadon , Jansri Ektinai
Published: October 2022

Textile waste, from both consumption and production, has dramatically increased due to a lack of diversification in its use. Increasing the number of textile alternatives can help to solve these problems. Producing a green composite product is an interesting alternative method. The objectives of this work were to study the preparation and characterization of green composites created from PLA and cotton fabric waste (CFW) and to consider the effect of the CFW content on the composites. The procedure of the research began with CFW pellets preparation; this was subsequently compounded with PLA pellets using a melt-mixing technique with a twin-screw extruder at ratios of 90:10, 80:20, and 70:30 wt% between the PLA and cotton fabric waste, respectively. Then, the testing specimens were produced by compression molding. The experiments demonstrated that an increase in the CFW caused an increase in the viscosity, stiffness, Tg, Tm, and water absorption of the composites. The decomposition temperature of the composites showed a range of 302.41 to 361.22 °C; this decreased when the CFW increased. An increase in the CFW also produced greater and clearer phase separation and roughness on the fracture surface area.
Editorial to Special Issue “Remote Sensing Image Denoising, Restoration and Reconstruction”
Authors: Egiazarian Karen , Pižurica Aleksandra , Lukin Vladimir
Published: October 2022

The motivations behind this Special Issue, announced in 18 August 2020, were the following [...]
LiDAR Odometry and Mapping Based on Neighborhood Information Constraints for Rugged Terrain
Authors: Wang Gang , Gao Xinyu , Zhang Tongzhou , Xu Qian , Zhou Wei
Published: October 2022

The simultaneous localization and mapping (SLAM) method estimates vehicles’ pose and builds maps established on the collection of environmental information primarily through sensors such as LiDAR and cameras. Compared to the camera-based SLAM, the LiDAR-based SLAM is more geared to complicated environments and is not susceptible to weather and illumination, which has increasingly become a hot topic in autonomous driving. However, there has been relatively little research on the LiDAR-based SLAM algorithm in rugged scenes. The following two issues remain unsolved: on the one hand, the small overlap area of two adjacent point clouds results in insufficient valuable features that can be extracted; on the other hand, the conventional feature matching method does not take point cloud pitching into account, which frequently results in matching failure. Hence, a LiDAR SLAM algorithm based on neighborhood information constraints (LoNiC) for rugged terrain is proposed in this study. Firstly, we obtain the feature points with surface information using the distribution of the normal vector angles in the neighborhood and extract features with discrimination through the local surface information of the point cloud, to improve the describing ability of feature points in rugged scenes. Secondly, we provide a multi-scale constraint description based on point cloud curvature, normal vector angle, and Euclidean distance to enhance the algorithm’s discrimination of the differences between feature points and prevent mis-registration. Subsequently, in order to lessen the impact of the initial pose value on the precision of point cloud registration, we introduce the dynamic iteration factor to the registration process and modify the corresponding relationship of the matching point pairs by adjusting the distance and angle thresholds. Finally, the verification based on the KITTI and JLU campus datasets verifies that the proposed algorithm significantly improves the accuracy of mapping. Specifically in rugged scenes, the mean relative translation error is 0.0173%, and the mean relative rotation error is 2.8744°/m, reaching the current level of the state of the art (SOTA) method.
Adaptive Robust Radar Target Detector Based on Gradient Test
Authors: Wang Zeyu , Liu Jun , Chen Hongmeng , Yang Wei
Published: October 2022

The exact knowledge of the signal steering vector is not always known, which may result in detection performance degradation when a signal mismatch occurs. In this paper, we discuss the problem of designing a robust radar target detector in the background of Gaussian noise whose covariance matrix is unknown. To improve robustness to mismatched signals, a random perturbation that follows the complex normal distribution is added under the alternative hypothesis. Since traditional detectors that divide complex parameters into real parts and imaginary parts are sometimes difficult to obtain, a new robust, complex parameter gradient test is derived directly from the complex data. Moreover, the CFAR property of the new detector is proven. The performance assessment indicates that the gradient detector exhibits suitable robustness to the mismatched signals.
Encouraging and Detecting Preferential Incipient Slip for Use in Slip Prevention in Robot-Assisted Surgery
Authors: Waters Ian , Jones Dominic , Alazmani Ali , Culmer Peter
Published: October 2022

Robotic surgical platforms have helped to improve minimally invasive surgery; however, limitations in their force feedback and force control can result in undesirable tissue trauma or tissue slip events. In this paper, we investigate a sensing method for the early detection of slip events when grasping soft tissues, which would allow surgical robots to take mitigating action to prevent tissue slip and maintain stable grasp control while minimising the applied gripping force, reducing the probability of trauma. The developed sensing concept utilises a curved grasper face to create areas of high and low normal, and thus frictional, force. In the areas of low normal force, there is a higher probability that the grasper face will slip against the tissue. If the grasper face is separated into a series of independent movable islands, then by tracking their displacement it will be possible to identify when the areas of low normal force first start to slip while the remainder of the tissue is still held securely. The system was evaluated through the simulated grasping and retraction of tissue under conditions representative of surgical practice using silicone tissue simulants and porcine liver samples. It was able to successfully detect slip before gross slip occurred with a 100% and 77% success rate for the tissue simulant and porcine liver samples, respectively. This research demonstrates the efficacy of this sensing method and the associated sensor system for detecting the occurrence of tissue slip events during surgical grasping and retraction.
Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction
Authors: Chen Biao , Chen Chaoyang , Hu Jie , Sayeed Zain , Qi Jin , Darwiche Hussein F. , Little Bryan E. , Lou Shenna , Darwish Muhammad , Foote Christopher , Palacio-Lascano Carlos
Published: October 2022

Background: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. Methods: We used the Kinect® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. Results: The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. Conclusions: This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment.
Event-Based Angular Speed Measurement and Movement Monitoring
Authors: Azevedo George Oliveira de Araújo , Fernandes Bruno José Torres , Silva Leandro Honorato de Souza , Freire Agostinho , de Araújo Rogério Pontes , Cruz Francisco
Published: October 2022

Computer vision techniques can monitor the rotational speed of rotating equipment or machines to understand their working conditions and prevent failures. Such techniques are highly precise, contactless, and potentially suitable for applications without massive setup changes. However, traditional vision sensors collect a significant amount of data to process and measure the rotation of high-speed systems, and they are susceptible to motion blur. This work proposes a new method for measuring rotational speed processing event-based data applied to high-speed systems using a neuromorphic sensor. This sensor produces event-based data and is designed to work with high temporal resolution and high dynamic range. The main advantages of the Event-based Angular Speed Measurement (EB-ASM) method are the high dynamic range, the absence of motion blurring, and the possibility of measuring multiple rotations simultaneously with a single device. The proposed method uses the time difference between spikes in a Kernel or Window selected in the sensor frame range. It is evaluated in two experimental scenarios by measuring a fan rotational speed and a Router Computer Numerical Control (CNC) spindle. The results compare measurements with a calibrated digital photo-tachometer. Based on the performed tests, the EB-ASM can measure the rotational speed with a mean absolute error of less than 0.2% for both scenarios.
Classification between Normal and Cancerous Human Urothelial Cells by Using Micro-Dimensional Electrochemical Impedance Spectroscopy Combined with Machine Learning
Authors: Jeong Ho-Jung , Kim Kihyun , Kim Hyeon Woo , Park Yangkyu
Published: October 2022

Although the high incidence and recurrence rates of urothelial cancer of the bladder (UCB) are heavy burdens, a noninvasive tool for effectively detecting UCB as an alternative to voided urine cytology, which has low sensitivity, is yet to be reported. Herein, we propose an intelligent discrimination method between normal (SV-HUC-1) and cancerous (TCCSUP) urothelial cells by using a combination of micro-dimensional electrochemical impedance spectroscopy (µEIS) with machine learning (ML) for a noninvasive and high-accuracy UCB diagnostic tool. We developed a unique valved flow cytometry, equipped with a pneumatic valve to increase sensitivity without cell clogging. Since contact between a cell and electrodes is tight with a high volume fraction, the electric field can be effectively confined to the cell. This enables the proposed sensor to highly discriminate different cell types at frequencies of 10, 50, 100, 500 kHz, and 1 MHz. A total of 236 impedance spectra were applied to six ML models, and systematic comparisons of the ML models were carried out. The hyperparameters were estimated by conducting a grid search or Bayesian optimization. Among the ML models, random forest strongly discriminated between SV-HUC-1 and TCCSUP, with an accuracy of 91.7%, sensitivity of 92.9%, precision of 92.9%, specificity of 90%, and F1-score of 93.8%.
Quad-Port Circularly Polarized MIMO Antenna with Wide Axial Ratio
Authors: Kollipara Vamshi , Peddakrishna Samineni
Published: October 2022

This article studies a quad-port multi-input-multi-output (MIMO) circularly polarized antenna with good isolation properties. Using characteristic mode analysis (CMA), the first six distinct modes of the asymmetric square slot with an inverted L-strip are analyzed. In this study, modal parameter extraction is carried out for circular polarization (CP) radiation. A simple annular ring microstrip feed is excited to obtain broadband CP based on CMA. The single-unit feeding structure is replicated orthogonally four times to achieve a CP MIMO antenna. This antenna provides port isolation of more than 21 dB without the use of an additional decoupling element. The quad-port CP-MIMO antenna is simulated with a total dimension of 50 × 50 mm2. The antenna attains impedance matching (S11 < −10 dB) from 5.37 GHz to beyond 11 GHz with an axial ratio bandwidth (ARBW) of 4.65 GHz (5.61 GHz to 10.26 GHz). The peak realized gain of the MIMO antenna is measured at 5.69 dBi at 8.4 GHz. Additionally, the diversity performance parameters of the MIMO structure are computed. The advantages of the proposed structure have been evaluated by comparing it to previously reported MIMO structures. A prototype of the MIMO structure measurements was found to match the simulation results.
Research on Waste Plastics Classification Method Based on Multi-Scale Feature Fusion
Authors: Cai Zhenxing , Yang Jianhong , Fang Huaiying , Ji Tianchen , Hu Yangyang , Wang Xin
Published: October 2022

Microplastic particles produced by non-degradable waste plastic bottles have a critical impact on the environment. Reasonable recycling is a premise that protects the environment and improves economic benefits. In this paper, a multi-scale feature fusion method for RGB and hyperspectral images based on Segmenting Objects by Locations (RHFF-SOLOv1) is proposed, which uses multi-sensor fusion technology to improve the accuracy of identifying transparent polyethylene terephthalate (PET) bottles, blue PET bottles, and transparent polypropylene (PP) bottles on a black conveyor belt. A line-scan camera and near-infrared (NIR) hyperspectral camera covering the spectral range from 935.9 nm to 1722.5 nm are used to obtain RGB and hyperspectral images synchronously. Moreover, we propose a hyperspectral feature band selection method that effectively reduces the dimensionality and selects the bands from 1087.6 nm to 1285.1 nm as the features of the hyperspectral image. The results show that the proposed fusion method improves the accuracy of plastic bottle classification compared with the SOLOv1 method, and the overall accuracy is 95.55%. Finally, compared with other space-spectral fusion methods, RHFF-SOLOv1 is superior to most of them and achieves the best (97.5%) accuracy in blue bottle classification.
Contactless AC/DC Wide-Bandwidth Current Sensor Based on Composite Measurement Principle
Authors: Tan Xiangyu , Li Wenyun , Xu Xiaowei , Ao Gang , Zhou Fangrong , Zhao Jingjing , Tan Qinghua , Zhang Wenbin
Published: October 2022

With the accelerated construction of the smart grid, new energy sources such as photovoltaic and wind power are connected to the grid. In addition to power frequency, the current signal of power grid also includes several DC signals, as well as medium-high and high-frequency transient signals. Traditional current sensors for power grids are bulky, have a narrow measurement range, and cannot measure both AC and DC at the same time. Therefore, this paper designs a non-intrusive, AC-DC wide-bandwidth current sensor based on the composite measurement principle. The proposed composite current detection scheme combines two different isolation detection technologies, namely tunneling reluctance and the Rogowski coil. These two current sensing techniques are complementary (tunneling magnetoresistive sensors have good low-frequency characteristics and Rogowski coils have good high-frequency characteristics, allowing for a wide detection bandwidth). Through theoretical and simulation analysis, the feasibility of the composite measurement scheme was verified. The prototype of composite current sensor was developed. The DC and AC transmission characteristics of the sensor prototype were measured, and the sensitivity and linearity were 11.96 mV/A, 1.14%, respectively. Finally, the sweep current method and pulse current method experiments prove that the designed composite current sensor can realize the current measurement from DC to 17 MHz.
Low Compaction Level Detection of Newly Constructed Asphalt Pavement Based on Regional Index
Authors: Tang Jiaming , Huang Zhiyong , Li Weixiong , Yu Huayang
Published: October 2022

In order to improve the prediction accuracy regarding low compaction level of asphalt pavement, this paper carries out indoor tests to detect the voids and dielectric constants of AC-13, AC-16 and AC-25 asphalt mixtures, obtaining their relationship equations via linear fitting and determining the dielectric constant judgment threshold of low compaction level segregation risk points ε1. Based on the common mid-point method, three-dimensional ground-penetrating radar is used to obtain the dielectric constant of the physical engineering test section. The researcher can draw the distribution map of the low compaction level segregation risk area according to the judgment threshold ε1 of the rough segregation risk points; divide the connected risk areas; determine the regional convex hull; and calculate the regional indicators such as the regional area, the ratio of the convex risk points and the mean value of the regional dielectric constant. The response surface analysis method is used to acquire the model of risk area index and core void ratio. The model is employed to predict and verify the core void ratio in the risk area of the road section and verify the accuracy of the model. The results show that the error range between the predicted voids and the measured voids is −0.4%~+0.4%, and the mean absolute value of the error is 0.25%. Compared with the mean measured voids of 6.63%, the relative error is 3.77%, indicating that the model can accurately predict the regional low compaction level segregation degree.
sEMG-Based Hand Posture Recognition and Visual Feedback Training for the Forearm Amputee
Authors: Kim Jongman , Yang Sumin , Koo Bummo , Lee Seunghee , Park Sehoon , Kim Seunggi , Cho Kang Hee , Kim Youngho
Published: October 2022

sEMG-based gesture recognition is useful for human–computer interactions, especially for technology supporting rehabilitation training and the control of electric prostheses. However, high variability in the sEMG signals of untrained users degrades the performance of gesture recognition algorithms. In this study, the hand posture recognition algorithm and radar plot-based visual feedback training were developed using multichannel sEMG sensors. Ten healthy adults and one bilateral forearm amputee participated by repeating twelve hand postures ten times. The visual feedback training was performed for two days and five days in healthy adults and a forearm amputee, respectively. Artificial neural network classifiers were trained with two types of feature vectors: a single feature vector and a combination of feature vectors. The classification accuracy of the forearm amputee increased significantly after three days of hand posture training. These results indicate that the visual feedback training efficiently improved the performance of sEMG-based hand posture recognition by reducing variability in the sEMG signal. Furthermore, a bilateral forearm amputee was able to participate in the rehabilitation training by using a radar plot, and the radar plot-based visual feedback training would help the amputees to control various electric prostheses.
Multi-Arm Global Cooperative Coal Gangue Sorting Method Based on Improved Hungarian Algorithm
Authors: Ma Hongwei , Wei Xiaorong , Wang Peng , Zhang Ye , Cao Xiangang , Zhou Wenjian
Published: October 2022

The existing multi-manipulator sorting method for gangue that utilizes a multi-task allocation strategy is not satisfactory. The single manipulator working space is fixed, lowering the cooperation degree between the manipulators and leading to a low sorting rate. Therefore, this paper proposes a multi-manipulator cooperative sorting method that can work globally. First, a benefit function based on the sorting time and quality of the gangue is constructed by combining the gangue flow information and the manipulator state. The time parameter is obtained via the manipulator’s dynamic target tracking trajectory planning algorithm based on PID control. Secondly, the benefits matrix is standardized and updated many times to improve the Hungarian algorithm to achieve task allocation, and the initial solution with priority is obtained. Finally, the solutions are analyzed and processed cooperatively in order of priority. The conflicts between multiple robotic arms are eliminated through task cooperation and trajectory cooperation until the sorting task that the robot arm can execute is obtained from the allocation results. Experiments involving different sorting methods were completed on a multi-arm coal and gangue sorting experimental robot platform. The experimental results show that the sorting efficiency of the proposed method is about 10% and 20% higher than that of the fixed space dynamic and designated space fixed points methods, respectively, under different belt speeds. This method can guarantee system benefits, effectively implements cooperative control of multi-manipulator operations in the whole area, and improves the efficiency of coal gangue sorting.
Stateful Rotor for Continuity of Quaternion and Fast Sensor Fusion Algorithm Using 9-Axis Sensors
Authors: Kusaka Takashi , Tanaka Takayuki
Published: October 2022

Advances in micro-electro-mechanical systems technology have led to the emergence of compact attitude measurement sensor products that integrate acceleration, magnetometer, and gyroscope sensors on a single chip, making them important devices in the field of three-dimensional (3D) attitude measurement for unmanned aerial vehicles, smartphones, and other devices. Sensor fusion algorithms for posture measurement have become an indispensable technology in cutting-edge research, such as human posture measurement using wearable sensors, and stabilization problems in robot position and posture measurement. We have also developed wearable sensors and powered suits in our previous research. We needed a technology for the real-time measurement of a 3D human body motion. It is known that quaternions can be used to algebraically handle 3D rotations; however, sensor fusion algorithms for three sensors are presently complex. This is because these algorithms deal with the post-rotation attitude (pure quaternions) rather than rotation information (the rotor) to avoid a double covering problem involving the rotor. If we are dealing with rotation, it may be possible to make the algorithm simpler and faster by dealing directly with the rotor. In this study, to solve the double covering problem involving the rotor, we propose a stateful rotor and develop a technique for uniquely determining the time-varying states of the rotor. The proposed stateful rotor guarantees the continuity of the rotor parameters with respect to angular changes, and this paper confirms its effectiveness by simulating two rotations around an arbitrary axis. In addition, we verify experimentally that a fast sensor fusion method using stateful rotor can be used for attitude calculation. Experiments also confirm that the calculated results converge to the desired rotation angle for two spatial rotations around an arbitrary axis. Since the proposed stateful rotor extends and stabilizes the definition of the rotor, it is applicable to any algorithm that deals with time-varying quaternionic rotors. In this research, an algorithm based on a multiply–add operation is designed to reduce computational complexity as a high-speed calculation for embedded systems. This method is theoretically equivalent to other methods, while contributing to power saving and the cost reduction of products.
Design and Implementation of an Interoperable Architecture for Integrating Building Legacy Systems into Scalable Energy Management Systems
Authors: Ntafalias Aristotelis , Tsakanikas Sotiris , Skarvelis-Kazakos Spyros , Papadopoulos Panagiotis , Skarmeta-Gómez Antonio F. , González-Vidal Aurora , Tomat Valentina , Ramallo-González Alfonso P. , Marin-Perez Rafael , Vlachou Maria C.
Published: October 2022

The building sector is responsible for a significant amount of energy consumption and greenhouse gas (GHG) emissions. Thus, the monitoring, control and optimization of energy consumption in buildings will play a critical role in the coming years in improving energy efficiency in the building sector and in reducing greenhouse gas emissions. However, while there are a significant number of studies on how to make buildings smarter and manage energy through smart devices, there is a need for more research on integrating buildings with legacy equipment and systems. It is therefore vital to define mechanisms to improve the use of energy efficiency in existing buildings. This study proposes a new architecture (PHOENIX architecture) for integrating legacy building systems into scalable energy management systems with focus also on user comfort in the concept of interoperability layers. This interoperable and intelligent architecture relies on Artificial Intelligence/Machine Learning (AI/ML) and Internet of Things (IoT) technologies to increase building efficiency, grid flexibility and occupant well-being. To validate the architecture and demonstrate the impact and replication potential of the proposed solution, five demonstration pilots have been utilized across Europe. As a result, by implementing the proposed architecture in the pilot sites, 30 apartments and four commercial buildings with more than 400 devices have been integrated into the architecture and have been communicating successfully. In addition, six Trials were performed in a commercial building and five key performance indicators (KPIs) were measured in order to evaluate the robust operation of the architecture. Work is still ongoing for the trials and the KPIs’ analysis after the implementation of PHOENIX architecture at the rest of the pilot sites.
A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach
Authors: Kelly Adam L. , Williams Craig A. , Cook Rob , Sáiz Sergio Lorenzo Jiménez , Wilson Mark R.
Published: October 2022

The talent development processes in youth football are both complex and multidimensional. The purpose of this two-fold study was to apply a multidisciplinary, machine learning approach to examine: (a) the developmental characteristics of under-9 to under-16 academy players (n = 98; Study 1), and (b) the characteristics of selected and deselected under-18 academy players (n = 18; Study 2). A combined total of 53 factors cumulated from eight data collection methods across two seasons were analysed. A cross-validated Lasso regression was implemented, using the glmnet package in R, to analyse the factors that contributed to: (a) player review ratings (Study 1), and (b) achieving a professional contract (Study 2). Results showed non-zero coefficients for improvement in subjective performance in 15 out of the 53 analysed features, with key findings revealing advanced percentage of predicted adult height (0.196), greater lob pass (0.160) and average dribble completion percentage (0.124), more total match-play hours (0.145), and an older relative age (BQ1 vs. BQ2: −0.133; BQ1 vs. BQ4: −0.060) were the most important features that contributed towards player review ratings. Moreover, PCDEQ Factor 3 and an ability to organise and engage in quality practice (PCDEQ Factor 4) were important contributing factors towards achieving a professional contract. Overall, it appears the key factors associated with positive developmental outcomes are not always technical and tactical in nature, where coaches often have their expertise. Indeed, the relative importance of these factors is likely to change over time, and with age, although psychological attributes appear to be key to reaching potential across the academy journey. The methodological techniques used here also serve as an impetus for researchers to adopt a machine learning approach when analysing multidimensional databases.
An Ensemble Machine Learning Technique for Detection of Abnormalities in Knee Movement Sustainability
Authors: Bansal Hunish , Chinagundi Basavraj , Rana Prashant Singh , Kumar Neeraj
Published: October 2022

The purpose of this study was to determine electromyographically if there are significant differences in the movement associated with the knee muscle, gait, leg extension from a sitting position and flexion of the leg upwards for regular and abnormal sEMG data. Surface electromyography (sEMG) data were obtained from the lower limbs of 22 people during three different exercises: sitting, standing, and walking (11 with and 11 without knee abnormality). Participants with a knee deformity took longer to finish the task than the healthy subjects. The sEMG signal duration of patients with abnormalities was longer than that of healthy patients, resulting in an imbalance in the obtained sEMG signal data. As a result of the data’s bias towards the majority class, developing a classification model for automated analysis of such sEMG signals is arduous. The sEMG collected data were denoised and filtered, followed by the extraction of time-domain characteristics. Machine learning methods were then used for predicting the three distinct movements (sitting, standing, and walking) associated with electrical impulses for normal and abnormal sets. Different anomaly detection techniques were also used for detecting occurrences in the sEMG signals that differed considerably from the majority of data and were hence used for enhancing the performance of our model. The iforest anomaly detection technique presented in this work can achieve 98.5% accuracy on the light gradient boosting machine algorithm, surpassing the previous results which claimed a maximum accuracy of 92.5% and 91%, improving accuracy by 6–7% for classification of knee abnormality using machine learning.