bluuebunny/crossref_metadata_2025 huggingface dataset:
Disease in a Dish: Using Stem Cells to Study Human Diseases
Authors: Sarel-Gallily Roni , Vershkov Dan
Published: April 2022

Have you ever wondered how scientists investigate human diseases and develop treatments to improve human health? For many years, the main way to do so was to use research animals. Animal studies have greatly improved our understanding of human diseases, but some disorders are unique to humans, which limits the usefulness of research animals. So scientists began using a special type of human cells, called embryonic stem cells. These cells are present in the body only during the early stages of development, and they can give rise to all types of cells and tissues. How can we produce stem cells from patients with certain diseases? How can we make these cells develop into the type of cell affected by the disease and use them to test new drugs in the lab? In this article, we explore how human stem cells can help us understand and treat human diseases.
Dual-Axis MEMS Resonant Scanner Using 128∘Y Lithium Niobate Thin-Film
Authors: Lu Yaoqing , Liu Kangfu , Wu Tao
Published: April 2022

The micro-electro-mechanical systems (MEMS) resonant scanners are in great demand for numerous light scanning applications. Recently, the development of LiDAR in micro-robotics and mobile devices has led to the requirement of ultra-small systems with low driving voltage, low power, compact size and high performance. We have first proposed the dual-axis MEMS scanner using the lithium niobate (LN) thin-film platform, which is expected to fulfill the requirement. This paper describes the actuation principle and scanner structure, meanwhile develops the analytical model for the scanner. The analytical model is later validated by the finite element analysis. The performance of the proposed scanner is improved with the optimization of the orientation of LN and layer thickness. The proposed scanner achieves the θopt·D·f up to 937.8∘·mm·kHz in simulation. The simulated optical angle in the x-axis and y-axis are 50∘ and 42∘ at 1 V, corresponding to resonant frequencies of 79.9 kHz and 558.2 kHz, respectively. With the superior performance of large deflection, high scanning frequency, high figure of merit and low voltage, the proposed MEMS scanner is a promising candidate for fast scanner applications (e.g., wavelength-selective switches and submicron biomedical system), especially the application of LiDAR in mobile devices or micro-robotics.
Efficient Modelling of Acoustic Metamaterials for the Performance Enhancement of an Automotive Silencer
Authors: Deery Daniel , Flanagan Lara , O’Brien Gordon , Rice Henry J. , Kennedy John
Published: April 2022

Significant potential for acoustic metamaterials to provide a breakthrough in sound attenuation has been unlocked in recent times due to advancements in additive manufacturing techniques. These materials allow the targeting of specific frequencies for sound attenuation. To date, acoustic metamaterials have not been demonstrated in a commercial automotive silencer for performance enhancement. A significant obstacle to the practical use of acoustic metamaterials is the need for low cost and efficient modelling strategies in the design phase. This study investigates the effect of acoustic metamaterials within a representative automotive silencer. The acoustic metamaterial design is achieved using a combination of analytical and finite element models, validated by experiment. The acoustic metamaterial is then compared with commonly used techniques in the silencer industry to gauge the effectiveness of the acoustic metamaterials. COMSOL simulations were used to validate the developed test rig and were compared to experimental results which were obtained using the two-load transmission loss test method. Through this testing method, the implementation of a labyrinthine metamaterial cylinder proved to be a significant improvement in transmission loss within the silencer, with an increase in transmission loss of 40 dB at 1500 Hz. The research has successfully shown that acoustic metamaterials can be used in practical settings, such as an automotive silencer, to improve the overall sound attenuating performance. The described analytical model demonstrates the potential for industrially relevant low cost design tools.
Optimal Escape from Sun-Earth and Earth-Moon L2 with Electric Propulsion
Authors: Mascolo Luigi , Casalino Lorenzo
Published: April 2022

Optimal low-thrust trajectories for the direct escape from the Earth’s sphere of influence, starting from Sun-Earth or Earth-Moon L2, are analyzed with an indirect optimization method. The dynamic model considers four-body gravitation and JPL ephemeris; solar radiation pressure is also considered. Specific techniques and improvements to the method are introduced to tackle the highly chaotic and nonlinear dynamics of motion close to Lagrangian points, which challenges the remarkable precision of the indirect method. The results show that escape trajectories have optimal performance when the solar perturbation acts favorably in both thrust and coast phases. The effects of the solar and Moon perturbations are more evident in the Earth-Moon L2 escapes compared with those from the Sun-Earth L2. EML2 escapes have single- or two-burn solutions depending on the trajectory deflection, which is needed to have a favorable solar perturbation. The SEL2 escapes, on the contrary, mainly have a single initial burn and a long coast arc, but need an additional final thrust arc if the required C3 is high. As applications of such Lagrangian Point trajectories, results include considerations about escape maneuvers from different SEL2 high-fidelity Lyapunov orbits and escape for interplanetary trajectories towards near-earth asteroids.
On the Fundamental Possibility of a Supersonic Civil Aircraft to Comply with ICAO Noise Requirements Using Existing Technologies
Authors: Kopiev Victor F. , Belyaev Ivan V. , Dunaevsky Andrey I. , Poukhov Andrey A. , Trofimovsky Igor L.
Published: April 2022

Compliance with environmental protection regulations, in particular, community noise requirements, constitutes one of the major obstacles for designing future supersonic civil aircraft. Although there are several noise sources that contribute to the total noise level of supersonic aircraft, it is the turbulent jet that appears most problematic; jet noise is a dominant noise source for low-to moderate- bypass-ratio engines, and at present there are no effective methods of jet noise reduction other than decreasing jet speed by increasing bypass ratio, which, in turn, is constrained by aerodynamic requirements for supersonic flight. The present study considers a concept of supersonic civil aircraft under the assumption that its total noise is determined by turbulent jets; it is shown that compliance of the supersonic aircraft with the current regulations for subsonic aircraft noise (Chapter 14 Volume I Annex 16 ICAO) would require the decreased jet speed that corresponds to a prohibitively high bypass ratio of aircraft engines. To enable jet noise reduction without necessarily increasing bypass ratio, a novel configuration of supersonic aircraft is proposed that meets the requirements of Chapter 14, thereby demonstrating that the norms of Chapter 14 are achievable for future supersonic civil aircraft with the use of existing technologies.
A Real-Time Trajectory Optimization Method for Hypersonic Vehicles Based on a Deep Neural Network
Authors: Wang Jianying , Wu Yuanpei , Liu Ming , Yang Ming , Liang Haizhao
Published: April 2022

Considering the high-efficient trajectory planning requirements for hypersonic vehicles, this paper proposes a real-time trajectory optimization method based on a deep neural network. First, the trajectory optimization model of the hypersonic vehicle reentry phase is developed. The pseudo-spectral method is used to perform the trajectory optimization offline, and multiple optimal trajectory data are obtained. In addition, based on the inherent relationship between the state and control variables of a trajectory, a neural network is established to predict the current control outputs. The sample library of optimal trajectory data is used to train the parameters of the deep neural network to obtain an optimal neural network model. Finally, the simulation verification of the hypersonic vehicle reentry phase is performed. The simulation results show that under the condition of the initial value deviation and environmental interference, the proposed deep learning-based method can achieve a fast generation of hypersonic vehicle optimal trajectories, while achieving the advantages of high computational efficiency and reliability. Compared to traditional trajectory optimization algorithms, the proposed method has the generalization capability that satisfies the accuracy requirements and meets the demands of online real-time trajectory optimization.
Effect of Streamwise Perturbation Frequency on Formation Mechanism of Ligament and Droplet in Liquid Circular Jet
Authors: Zhou Chenglin , Zou Jianfeng , Zhang Yang
Published: April 2022

In order to study the influence of streamwise forcing on the formation mechanism of liquid ligaments and droplets in the primary breakup process of liquid circular jet, the VOF interface capturing method-based direct numerical simulation was adopted, and a range of sinusoidal velocity disturbances with different frequencies were considered. The selected disturbance frequency range is 0–3000 kHz. This work analyzes the evolution process of the jet surface waves at different disturbance frequencies, and the coupling effect of the jet tip and liquid core on the overall spray field from overall structure, liquid ligament, and droplet formation. The results show that different disturbance frequencies affect the droplet shape distribution and size distribution in spray field. Current work provides guidance for the control of the thermoacoustic instability of the engine and design of the nozzle.
Experimental Assessment of the Elastic Properties of Exocarp–Mesocarp and Beans of Coffea arabica L. var. Castillo Using Indentation Tests
Authors: Tinoco Hector A. , Buitrago-Osorio Jaime , Perdomo-Hurtado Luis , Lopez-Guzman Juliana , Ibarra Carlos A. , Rincon-Jimenez Alexander , Ocampo Olga , Berrio Lina V.
Published: April 2022

The development of selective coffee harvesting technologies requires detailed knowledge of the geometrical, physical, and mechanical properties of the subsystems of the coffee bush, including the elastic properties of the substructures of fruit and the coffee bean, which are directly related to the selectivity problem. The elastic properties of the mesocarp–exocarp and bean are not described in the literature due to the difficulty of characterizing these locally, since measuring each component of the coffee fruit structure (mesocarp–exocarp and bean) is not an easy task. However, determining the elastic properties (of the mesocarp–exocarp and bean) could help create realistic simulations as an initial estimation for selective coffee harvesting studies. The present work aims to bridge the gap in the mechanical characterization of the sub-structures of the coffee fruits by assessing the elastic properties of the mesocarp–exocarp and bean. Indentation tests were performed on eighty Coffee arabica L. var. Castillo fruits and beans, which were previously classified into four ripening stages using fruit color data in the CIELab color space. Young’s modulus and indentation hardness of the mesocarp–exocarp structure and beans were calculated, applying the Oliver and Pharr indentation model and Hertz contact theory.
Development and Validation of an HPLC Method for the Determination of Meloxicam and Pantoprazole in a Combined Formulation
Authors: Ahmad Raneem , Hailat Mohammad , Zakaraya Zainab , Al Meanazel Osaid , Abu Dayyih Wael
Published: April 2022

Nonsteroidal anti-inflammatory drugs are the most commonly prescribed anti-inflammatory drugs worldwide. The most common side effects are gastrointestinal. Pantoprazole, a proton pump inhibitor (PPI), can be used to prevent these events from occurring. In this study, we attempt to develop and validate a novel method for determining and validating the fixed-dose combination of meloxicam and pantoprazole. A new method has been developed and validated to estimate pantoprazole and meloxicam in a fixed-dose combination using RP-HPLC. In order to separate the drugs, a mobile phase phosphate buffer/acetate was used (30:70, v/v), with a pH of 3.4 and a flow rate of 1.0 mL/min at 25 °C. The detection wavelength for the drugs was at a wavelength of 310 nm. The retention times for meloxicam and pantoprazole were 6 and 9 min, respectively. In concentrations ranging from 0.1 to 200 mg/L, the linearity of the detector was established. The r was 0.9998 for both drugs. Recovery rates ranged from 98 to 102% on average. According to the guidelines of the International Council on Harmonization, the results were satisfactory. Using the method presented herein, the pharmaceutical formulation of the combined meloxicam and pantoprazole can be routinely tested.
Host Defence Peptides: A Potent Alternative to Combat Antimicrobial Resistance in the Era of the COVID-19 Pandemic
Authors: Ali Waqas , Elsahn Ahmad , Ting Darren S. J. , Dua Harminder S. , Mohammed Imran
Published: April 2022

One of the greatest challenges facing the medical community today is the ever-increasing trajectory of antimicrobial resistance (AMR), which is being compounded by the decrease in our antimicrobial armamentarium. From their initial discovery to the current day, antibiotics have seen an exponential increase in their usage, from medical to agricultural use. Benefits aside, this has led to an exponential increase in AMR, with the fear that over 10 million lives are predicted to be lost by 2050, according to the World Health Organisation (WHO). As such, medical researchers are turning their focus to discovering novel alternatives to antimicrobials, one being Host Defence Peptides (HDPs). These small cationic peptides have shown great efficacy in being used as an antimicrobial therapy for currently resistant microbial variants. With the sudden emergence of the SARS-CoV-2 variant and the subsequent global pandemic, the great versatility and potential use of HDPs as an alternative to conventional antibiotics in treating as well as preventing the spread of COVID-19 has been reviewed. Thus, to allow the reader to have a full understanding of the multifaceted therapeutic use of HDPs, this literature review shall cover the association between COVID-19 and AMR whilst discussing and evaluating the use of HDPs as an answer to antimicrobial resistance (AMR).
Plant Response to Cold Stress: Cold Stress Changes Antioxidant Metabolism in Heading Type Kimchi Cabbage (Brassica rapa L. ssp. Pekinensis)
Authors: Eom Seung Hee , Ahn Min-A , Kim Eunhui , Lee Hee Ju , Lee Jin Hyoung , Wi Seung Hwan , Kim Sung Kyeom , Lim Heung Bin , Hyun Tae Kyung
Published: April 2022

Cold stress is known as the important yield-limiting factor of heading type Kimchi cabbage (HtKc, Brassica rapa L. ssp. pekinensis), which is an economically important crop worldwide. However, the biochemical and molecular responses to cold stress in HtKc are largely unknown. In this study, we conducted transcriptome analyses on HtKc grown under normal versus cold conditions to investigate the molecular mechanism underlying HtKc responses to cold stress. A total of 2131 genes (936 up-regulated and 1195 down-regulated) were identified as differentially expressed genes and were significantly annotated in the category of “response to stimulus”. In addition, cold stress caused the accumulation of polyphenolic compounds, including p-coumaric, ferulic, and sinapic acids, in HtKc by inducing the phenylpropanoid pathway. The results of the chemical-based antioxidant assay indicated that the cold-induced polyphenolic compounds improved the free-radical scavenging activity and antioxidant capacity, suggesting that the phenylpropanoid pathway induced by cold stress contributes to resistance to cold-induced reactive oxygen species in HtKc. Taken together, our results will serve as an important base to improve the cold tolerance in plants via enhancing the antioxidant machinery.
Detachable Soft Actuators with Tunable Stiffness Based on Wire Jamming
Authors: Bai Long , Yan Hao , Li Jiafeng , Shan Jiefeng , Hou Penghao
Published: April 2022

The integration of variable stiffness materials and structures into soft robots is a popular trend, allowing soft robots to switch between soft and rigid states in different situations. This concept combines the advantages of rigid mechanisms and soft robots, resulting in not only excellent flexibility but also tunable stiffness for high load capacity and fast and precise operation. Here, a stiffness-tunable soft actuator based on wire/fiber jamming structure is proposed, where the fiber-reinforced soft actuator is responsible for the bending motion, and the jamming structure acts as a stiffness-tunable layer controlled by vacuum pressure. The primary design objective of this study is to fabricate a jamming structure with wide-range stiffness, universal adaptability and high dexterity. Thus, the behaviors of wire/fiber jamming structures with different layouts, materials and wire arrangements are analyzed, and a theoretical model is developed to predict the effect of geometric parameters. Experimental characterizations show that the stiffness can be significantly enhanced in the bending direction, while the stiffness is smaller in the torsion direction. Additionally, by integrating Velcro strips into the design, a quick and detachable scheme for the stiffness-tunable soft actuator is achieved. Application examples exhibit high load capacity and good shape adaptability.
Aerial Robotics for Inspection and Maintenance: Special Issue Editorial
Authors: Suarez Alejandro , Cacace Jonathan , Orsag Matko
Published: April 2022

The significant advances in last decade in the research and technology of multi-rotor design, modeling and control, supported by the increasing variety of commercially available platforms, components and manufacturers, have facilitated a rise in the novel applications of aerial robots, capable of not only perceiving, but also interacting with the environment, allowing the realization of diverse operations and tasks in areas and workspaces that are difficult to access by human operators or ground vehicles [...]
Hardware-Implemented Security Processing Unit for Program Execution Monitoring and Instruction Fault Self-Repairing on Embedded Systems
Authors: Zhang Zhun , Wang Xiang , Hao Qiang , Xu Dongdong , Wang Jiqing , Liu Jiakang , Ma Jinhui , Zhang Jinlei
Published: April 2022

Embedded systems are increasingly applied in numerous security-sensitive applications, such as industrial controls, railway transports, intelligent vehicles, avionics and aerospace. However, embedded systems are compromised in the execution of untrusted programs, where the instructions could be maliciously tampered with to cause unintended behaviors or program execution failures. Particularly for remote-controlled embedded systems, program execution monitoring and instruction fault self-repair are important to avoid unintended behaviors and execution interruptions. Therefore, this paper presents a hardware-enhanced embedded system with the integration of a Security Processing Unit (SPU) in which integrity signature checking and checkpoint-rollback mechanisms are coupled to achieve real-time program execution monitoring and instruction fault self-repairing. This System-on-Chip (SoC) design was implemented and validated on the Xilinx Virtex-5 FPGA development platform. Based on the evaluation of the SPU in terms of the performance overhead, security capability, and resource consumption, the experimental results show that, while the CPU executes different benchmarks, the average performance overhead of the SPU lowers to 1.92% at typical 8-KB I/D caches, and it provides both program monitoring and fault self-repairing capabilities. Unlike conventional hardware detection technologies that require manual handling to recovery program executions, the CPU–SPU collaborative SoC is a resilient architecture equipped with instruction tampering detection and a post-detection strategy of instruction fault self-repairing. Moreover, the embedded system satisfies a good balance between high security and resource consumption.
Seismic Risk Assessment of Urban Areas by a Hybrid Empirical-Analytical Procedure Based on Peak Ground Acceleration
Authors: Nikolić Željana , Benvenuti Elena , Runjić Luka
Published: April 2022

The seismic risk assessment of existing urban areas provides important information for the process of seismic risk reduction in different phases of planning and emergency management. Between different large-scale assessment approaches, a vulnerability index method is often used for the first screening of the buildings and vulnerability classification. However, this method cannot fully predict the effects of a specific seismic action on buildings. This paper fully extends the scale of the settlement and properly upgrades a methodology previously proposed by authors to predict seismic damage and the risk to a restricted number of masonry buildings in the Croatian settlement Kaštel Kambelovac located along the Adriatic coast. The proposed approach is based on a hybrid empirical-analytical procedure that combines seismic vulnerability indices with critical peak ground accelerations for different limit states computed through a non-linear pushover analysis. The procedure’s outcomes are the computation of a relationship linking vulnerability indices to peak ground acceleration for a series of states, corresponding to damage limitation, significant damage, and near collapse. The described methodology is used to estimate seismic risk in terms of damage and the index of seismic risk for selected return periods. The general methodology has allowed a full seismic vulnerability assessment of the whole Croatian settlement of Kaštel Kambelovac.
Recent Trends in Microbial Approaches for Soil Desalination
Authors: Mokrani Slimane , Nabti El-hafid , Cruz Cristina
Published: April 2022

Soil salinization has become a major problem for agriculture worldwide, especially because this phenomenon is continuously expanding in different regions of the world. Salinity is a complex mechanism, and in the soil ecosystem, it affects both microorganisms and plants, some of which have developed efficient strategies to alleviate salt stress conditions. Currently, various methods can be used to reduce the negative effects of this problem. However, the use of biological methods, such as plant-growth-promoting bacteria (PGPB), phytoremediation, and amendment, seems to be very advantageous and promising as a remedy for sustainable and ecological agriculture. Other approaches aim to combine different techniques, as well as the utilization of genetic engineering methods. These techniques alone or combined can effectively contribute to the development of sustainable and eco-friendly agriculture.
Texture and Materials Image Classification Based on Wavelet Pooling Layer in CNN
Authors: Fortuna-Cervantes Juan Manuel , Ramírez-Torres Marco Tulio , Mejía-Carlos Marcela , Murguía José Salomé , Martinez-Carranza José , Soubervielle-Montalvo Carlos , Guerra-García César Arturo
Published: April 2022

Convolutional Neural Networks (CNNs) have recently been proposed as a solution in texture and material classification in computer vision. However, inside CNNs, the internal layers of pooling often cause a loss of information and, therefore, is detrimental to learning the architecture. Moreover, when considering images with repetitive and essential patterns, the loss of this information affects the performance of subsequent stages, such as feature extraction and analysis. In this paper, to solve this problem, we propose a classification system with a new pooling method called Discrete Wavelet Transform Pooling (DWTP). This method is based on the image decomposition into sub-bands, in which the first level sub-band is considered as its output. The objective is to obtain approximation and detail information. As a result, this information can be concatenated in different combinations. In addition, wavelet pooling uses wavelets to reduce the size of the feature map. Combining these methods provides acceptable classification performance for three databases (CIFAR-10, DTD, and FMD). We argue that this helps eliminate overfitting and that the learning graphs reflect that the datasets show learning generalization. Therefore, our results indicate that our method based on wavelet analysis is feasible for texture and material classification. Moreover, in some cases, it outperforms traditional methods.
Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks
Authors: Kitada Shunsuke , Iyatomi Hitoshi , Seki Yoshifumi
Published: April 2022

Discontinuing ad creatives at an appropriate time is one of the most important ad operations that can have a significant impact on sales. Such operational support for ineffective ads has been less explored than that for effective ads. After pre-analyzing 1,000,000 real-world ad creatives, we found that there are two types of discontinuation: short-term (i.e., cut-out) and long-term (i.e., wear-out). In this paper, we propose a practical prediction framework for the discontinuation of ad creatives with a hazard function-based loss function inspired by survival analysis. Our framework predicts the discontinuations with a multi-modal deep neural network that takes as input the ad creative (e.g., text, categorical, image, numerical features). To improve the prediction performance for the two different types of discontinuations and for the ad creatives that contribute to sales, we introduce two new techniques: (1) a two-term estimation technique with multi-task learning and (2) a click-through rate-weighting technique for the loss function. We evaluated our framework using the large-scale ad creative dataset, including 10 billion scale impressions. In terms of the concordance index (short: 0.896, long: 0.939, and overall: 0.792), our framework achieved significantly better performance than the conventional method (0.531). Additionally, we confirmed that our framework (i) demonstrated the same degree of discontinuation effect as manual operations for short-term cases, and (ii) accurately predicted the ad discontinuation order, which is important for long-running ad creatives for long-term cases.
Silver Thread-Based Microfluidic Platform for Detection of Essential Oils Using Impedance Spectroscopy
Authors: Kojic Tijana , Kovacevic Bozica , Sinha Ankita , Simić Mitar , Stojanović Goran M.
Published: April 2022

Essential oils (EOs) have a long tradition of use in the medical and cosmetic fields based on their versatile properties, including fungicidal, antiparasitic, and bactericidal effects. Nowadays, with the development of industry and electronics, EOs are increasingly being used in the agricultural and food industries; health industries, including pharmacy and dental medicine; and as cosmetic enhancements. The purpose of this study is to develop a compact and portable platform for the detection of EO type and the concentration levels using knitted silver threads. The method is based on measuring the variation in values of the electrical parameters of the silver threads using electrochemical impedance spectroscopy (EIS). The impedance of the solutions applied on the testing platform was measured in the frequency range from 1 Hz to 200 kHz. The platform was tested using three types of essential oils: tea tree; clary sage; and cinnamon bark oil. Increasing the concentration of essential oils resulted in increasing the electrical resistance of the platform, decreasing the capacitance, and consequently increasing the impedance. The proposed cost-effective platform can be used for the fast determination of the type and quality of essential oils.
An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction
Authors: Ni Jianjun , Chen Yan , Gu Yu , Fang Xiaolong , Shi Pengfei
Published: April 2022

With the improvement of the living standards of the residents, it is a very important and challenging task to continuously improve the accuracy of PM2.5 (particulate matter less than 2.5 μm in diameter) prediction. Deep learning-based networks, such as LSTM and CNN, have achieved good performance in recent years. However, these methods require sufficient data to train the model. The performance of these methods is limited for the sites where the data is lacking, such as the newly constructed monitoring sites. To deal with this problem, an improved deep learning model based on the hybrid transfer learning strategy is proposed for predicting PM2.5 concentration in this paper. In the proposed model, the maximum mean discrepancy (MMD) is used to select which station in the source domain is most suitable for migration to the target domain. An improved dual-stage two-phase (DSTP) model is used to extract the spatial–temporal features of the source domain and the target domain. Then the domain adversarial neural network (DANN) is used to find the domain invariant features between the source and target domains by domain adaptation. Thus, the model trained by source domain site data can be used to assist the prediction of the target site without degradation of the prediction performance due to domain drift. At last, some experiments are conducted. The experimental results show that the proposed model can effectively improve the accuracy of the PM2.5 prediction at the sites lacking data, and the proposed model outperforms most of the latest models.
Optimal Unmanned Ground Vehicle—Unmanned Aerial Vehicle Formation-Maintenance Control for Air-Ground Cooperation
Authors: Zhang Jingmin , Yue Xiaokui , Zhang Haofei , Xiao Tiantian
Published: April 2022

This paper investigates the air–ground cooperative time-varying formation-tracking control problem of a heterogeneous cluster system composed of an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). Initially, the structure of the UAV–UGV formation-control system is analyzed from the perspective of a cooperative combat system. Next, based on the motion relationship between the UAV–UGV in a relative coordinate system, the relative motion model between them is established, which can clearly reveal the physical meaning of the relative motion process in the UAV–UGV system. Then, under the premise that the control system of the UAG is closed-loop stable, the motion state of the UGV is modeled as an input perturbation. Finally, using a linear quadratic optimal control theory, a UAV–UGV formation-maintenance controller is designed to track the reference trajectory of the UGV based on the UAV–UGV relative motion model. The simulation results demonstrate that the proposed controller can overcome input perturbations, model-constant perturbations, and linearization biases. Moreover, it can achieve fast and stable adjustment and maintenance control of the desired UAV–UGV formation proposed by the cooperative combat mission planning system.
The Effects of Periodontal Laser Therapy on Pain in Adult Patients with Orthodontic Treatment: A Randomized Clinical Trial
Authors: Lazăr Ana-Petra , Dakó Timea , Bud Anamaria , Vlasa Alexandru , Ormenișan Alina , Mârțu Maria-Alexandra , Păcurar Mariana , Lazăr Luminița
Published: April 2022

(1) Background: Pain is the most common complaint of patients with fixed orthodontic treatment, especially when applying and activating the orthodontic device. The effectiveness of low-level laser therapy (LLLT) in reducing pain caused by the orthodontic treatment has been frequently studied over the last few years. In this study, we aim to evaluate the influence of laser therapy on pain caused by orthodontic forces acting on periodontal tissues during fixed orthodontic treatment. (2) Methods: A total of 36 patients who met the inclusion criteria were submitted to laser therapy after beginning a fixed orthodontic treatment. For each patient, one half-arch was treated with laser (HL), the opposite one being the control half-arch (HC). The evaluation of pain was conducted by asking the patients to rate their pain on a VAS scale from 0 to 10. (3) Results: Analyzing the VAS score values reported after applying the orthodontic device, we noticed that the pain increased in the first few hours, it was the highest 24 h after application, and it decreased towards the 7th day. The pain reported by most patients was significantly reduced in the HL compared to the HC, in all three moments that were evaluated (p < 0.0001). (4) Conclusions: Repeated LLLT in patients with orthodontic treatment leads to a significant decrease in pain levels, as the comparison between the laser half-arch and the control half-arch showed.
Port-Related Shipping Gas Emissions—A Systematic Review of Research
Authors: Bojić Filip , Gudelj Anita , Bošnjak Rino
Published: April 2022

The global increase in shipping activity has contributed to the degradation of air quality, which particularly affects traffic-dense port areas. Due to the environmental and public health impacts of air quality in port cities, a number of inventories using varying methodologies have been conducted over the past two decades to manage gas emissions in specific areas. The objective of this work is to determine one relevant methodology for estimating ship emissions in ports through a systematic review of the relevant literature. In this research, PRISMA guidelines were followed through a multi-layer bottom-up analysis approach to ensure the validity of the proposed methodology. The aforementioned methodology, as the end result of this research, is intended to provide an empirically structured basis for further development of a novel indexing model of ship gas emissions in port areas.
Control of Flexible Robot by Harmonic Functions
Authors: Neusser Zdeněk , Nečas Martin , Valášek Michael
Published: April 2022

This work deals with the control of flexible structures as underactuated systems. The invariant control method performs the control of a flexible robot as a representative of an underactuated system with zero dynamics. The control input is separated into two parts. The arbitrary part of the control input is designed to control the directly actuated part of the dynamic system. The invariant part of the control is selected to steer the system zero dynamics in the desired way. The harmonic functions create the base for the invariant part of the control function. The residual vibration cancellation is the target of the presented invariant control strategy. The harmonic function frequencies are overtaken from the so-called natural motion, amplitudes are the results of the optimization process. The main target of this paper is to show the invariant control approach and its application to the system with flexible elements.
Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
Authors: Chen Guangwei , Tang Waiching , Chen Shuo , Wang Shanyong , Cui Hongzhi
Published: April 2022

Engineered cementitious composite (ECC) is a unique material, which can significantly contribute to self-healing based on ongoing hydration. However, it is difficult to model and predict the self-healing performance of ECC. Although different machine learning (ML) algorithms have been utilized to predict several properties of concrete, the application of ML on self-healing prediction is considerably rare. This paper aims to provide a comparative analysis on the performance of various machine learning models in predicting the self-healing capability of ECC. These models include four individual methods, linear regression (LR), back-propagation neural network (BPNN), classification and regression tree (CART), and support vector regression (SVR). To improve prediction accuracy, three ensemble methods, namely bagging, AdaBoost, and stacking, were also studied. A series of experimental works on the self-healing performance of ECC samples was conducted, and the results were used to develop and compare the accuracy among the ML models. The comparison results showed that the Stack_LR model had the best predictive performance, showing the highest coefficient of determination (R2), the lowest root-mean-squared error (RMSE), and the smallest prediction error (MAE). Among all individual models studies, the BPNN model performed the best in terms of the RMSE and R2, while SVR performed the best in terms of the MAE. Furthermore, SVR had the smallest prediction error (MAE) for crack widths less than 60 μm or greater than 100 μm, while CART had the smallest prediction error (MAE) for crack widths between 60 μm and 100 μm. The study concluded that the individual and ensemble methods can be used to predict the self-healing of ECC. Ensemble models were able to improve the accuracy of prediction compared to the individual model used as their base learner, i.e., a 2.3% to 4.9% reduction in MAE. However, selecting an appropriate individual and ensemble method is critical. To improve the performance accuracy, researchers should employ different ensemble methods to compare their effectiveness with different ML models.
Effects of Different Heat Treatment Methods on Organic Pollutants and Heavy Metal Content in Oil Sludge Waste and Ecotoxicological Evaluation
Authors: Sun Xuan , Yu Tao , Huang Yi , Xue Ming , Qu Chengtun , Yang Penghui , Zhang Xiaofei , Yang Bo , Li Jinling
Published: April 2022

The discharge of large amounts of oily sludge heat treatment residues constitutes a severe threat to the environment. However, little is known about the toxicity of these heat-treated residues. Current research has mainly focused on the toxic effects of single heavy metals or single hydrocarbons on plants, whereas the phytotoxic effects of hydrocarbon–metal mixtures have remained largely unexplored. In this study, pot experiments were conducted to evaluate the effects of different proportions of heat treatment residues (pyrolysis, heat-washing, and high-temperature oxidation residues) from three kinds of oily sludge on the physiological and biochemical parameters of mung bean plants. Higher proportions of residues decreased the germination rates and enzyme activity of mung beans compared to uncontaminated soil. When pyrolysis residue, hot-washing residue, and high-temperature thermal oxidation residue are used in green planting soil, their content must be lower than 30%, 90%, and 70%, respectively. Additionally, our findings indicated that the accumulation level of pollutants in oily sludge heat treatment residues was not high. However, the three kinds of residues exhibited different degrees of plant toxicity. The pyrolysis residue still exhibited strong ecotoxicity, even at low concentrations. In contrast, the toxicity of the hot-washing residue was much lower than that of the pyrolysis residue and the high-temperature thermal oxidation residue. Our findings indicated that mung bean is highly tolerant of contaminated soil and is therefore well suited for phytoremediation applications.
Accumulators and Bookmaker’s Capital with Perturbed Stochastic Processes
Authors: Cortis Dominic , Tamturk Muhsin
Published: April 2022

The sports betting industry has been growing at a phenomenal rate and has many similarities to the financial market in that a payout is made contingent on an outcome of an event. Despite this, there has been little to no mathematical focus on the potential ruin of bookmakers. In this paper, the expected profit of a bookmaker and probability of multiple soccer matches are observed via Dirac notations and Feynman’s path calculations. Furthermore, we take the unforeseen circumstances into account by subjecting the betting process to more uncertainty. A perturbed betting process, set by modifying the conventional stochastic process, is handled to scale and manage this uncertainty.
Statistically Convergent Sequences in Intuitionistic Fuzzy Metric Spaces
Authors: Pazar Varol Banu
Published: April 2022

In this paper, we introduce the concepts of statistical convergence and statistical Cauchy sequences with respect to the intuitionistic fuzzy metric spaces inspired by the idea of statistical convergence in fuzzy metric spaces. Then, we give useful characterizations for statistically convergent sequences and statistically Cauchy sequences.
On the Bias in Confirmatory Factor Analysis When Treating Discrete Variables as Ordinal Instead of Continuous
Authors: Robitzsch Alexander
Published: April 2022

Confirmatory factor analysis is some of the most widely used statistical techniques in the social sciences. Frequently, variables (i.e., items) stemming from questionnaires are analyzed. Two competing approaches for estimating confirmatory factor analysis can be distinguished. First, ordinal variables could be treated as in the case of continuous variables using Pearson correlations, and maximum likelihood estimation method would be applied. Second, an ordinal factor analysis based on polychoric correlations can be fitted. In the majority of the psychometric literature, there is a preference for the ordinal factor analysis based on polychoric correlations because the continuous treatment of variables results in biased factor loadings and biased factor correlations. This article argues that it is not legitimate to speak about bias when comparing the two competing factor analytic approaches because it depends on how true model parameters are defined. This decision can be made individually by a researcher. It is shown in simulation studies and analytical derivations that treating variables ordinally using polychoric correlations instead of continuous using Pearson correlations can also lead to biased estimates of factor loadings and factor correlations. Consequently, it should only be stated that different model parameters are defined in a continuous and an ordinal treatment, and one approach should not generally be preferred over the other.
Illusion of Truth: Analysing and Classifying COVID-19 Fake News in Brazilian Portuguese Language
Authors: Endo Patricia Takako , Santos Guto Leoni , de Lima Xavier Maria Eduarda , Nascimento Campos Gleyson Rhuan , de Lima Luciana Conceição , Silva Ivanovitch , Egli Antonia , Lynn Theo
Published: April 2022

Public health interventions to counter the COVID-19 pandemic have accelerated and increased digital adoption and use of the Internet for sourcing health information. Unfortunately, there is evidence to suggest that it has also accelerated and increased the spread of false information relating to COVID-19. The consequences of misinformation, disinformation and misinterpretation of health information can interfere with attempts to curb the virus, delay or result in failure to seek or continue legitimate medical treatment and adherence to vaccination, as well as interfere with sound public health policy and attempts to disseminate public health messages. While there is a significant body of literature, datasets and tools to support countermeasures against the spread of false information online in resource-rich languages such as English and Chinese, there are few such resources to support Portuguese, and Brazilian Portuguese specifically. In this study, we explore the use of machine learning and deep learning techniques to identify fake news in online communications in the Brazilian Portuguese language relating to the COVID-19 pandemic. We build a dataset of 11,382 items comprising data from January 2020 to February 2021. Exploratory data analysis suggests that fake news about the COVID-19 vaccine was prevalent in Brazil, much of it related to government communications. To mitigate the adverse impact of fake news, we analyse the impact of machine learning to detect fake news based on stop words in communications. The results suggest that stop words improve the performance of the models when keeping them within the message. Random Forest was the machine learning model with the best results, achieving 97.91% of precision, while Bi-GRU was the best deep learning model with an F1 score of 94.03%.
Decellularization of Full Heart—Optimizing the Classical Sodium-Dodecyl-Sulfate-Based Decellularization Protocol
Authors: Al-Hejailan Reem , Weigel Tobias , Schürlein Sebastian , Berger Constantin , Al-Mohanna Futwan , Hansmann Jan
Published: April 2022

Compared to cell therapy, where cells are injected into a defect region, the treatment of heart infarction with cells seeded in a vascularized scaffold bears advantages, such as an immediate nutrient supply or a controllable and persistent localization of cells. For this purpose, decellularized native tissues are a preferable choice as they provide an in vivo-like microenvironment. However, the quality of such scaffolds strongly depends on the decellularization process. Therefore, two protocols based on sodium dodecyl sulfate or sodium deoxycholate were tailored and optimized for the decellularization of a porcine heart. The obtained scaffolds were tested for their applicability to generate vascularized cardiac patches. Decellularization with sodium dodecyl sulfate was found to be more suitable and resulted in scaffolds with a low amount of DNA, a highly preserved extracellular matrix composition, and structure shown by GAG quantification and immunohistochemistry. After seeding human endothelial cells into the vasculature, a coagulation assay demonstrated the functionality of the endothelial cells to minimize the clotting of blood. Human-induced pluripotent-stem-cell-derived cardiomyocytes in co-culture with fibroblasts and mesenchymal stem cells transferred the scaffold into a vascularized cardiac patch spontaneously contracting with a frequency of 25.61 ± 5.99 beats/min for over 16 weeks. The customized decellularization protocol based on sodium dodecyl sulfate renders a step towards a preclinical evaluation of the scaffolds.
Number of Teeth Is Related to Craniofacial Morphology in Humans
Authors: Oeschger Elias S. , Kanavakis Georgios , Cocos Alina , Halazonetis Demetrios J. , Gkantidis Nikolaos
Published: April 2022

One of the most common dental anomalies in humans is the congenital absence of teeth, referred to as tooth agenesis. The association of tooth agenesis to craniofacial morphology has been previously investigated but remains unclear. We investigated this association by applying geometric morphometric methods in a large sample of modern humans. In line with previous studies, we report here that a reduced teeth number is linked to a less convex profile, as well as to a shorter face. The effects were similar for males and females; they increased as the severity of the tooth agenesis increased and remained unaltered by the inclusion of third molars and of allometry in the analysis. Furthermore, in cases with tooth agenesis only in the maxilla, there was no detectable effect in mandibular shape, whereas maxillary shape was affected independently of the location of missing teeth. The robustness of the present sample along with the shape analysis and the statistical approach applied, allowed for thorough testing of various contributing factors regarding the presence but also the magnitude of effects. The present findings suggest a relationship between number of teeth and overall craniofacial development and have evolutionary implications.