bluuebunny/crossref_metadata_2025 huggingface dataset:
Artificial Eyes with Emotion and Light Responsive Pupils for Realistic Humanoid Robots
Authors: Strathearn Carl
Published: September 2021

This study employs a novel 3D engineered robotic eye system with dielectric elastomer actuator (DEA) pupils and a 3D sculpted and colourised gelatin iris membrane to replicate the appearance and materiality of the human eye. A camera system for facial expression analysis (FEA) was installed in the left eye, and a photo-resistor for measuring light frequencies in the right. Unlike previous prototypes, this configuration permits the robotic eyes to respond to both light and emotion proximal to a human eye. A series of experiments were undertaken using a pupil tracking headset to monitor test subjects when observing positive and negative video stimuli. A second test measured pupil dilation ranges to high and low light frequencies using a high-powered artificial light. This data was converted into a series of algorithms for servomotor triangulation to control the photosensitive and emotive pupil dilation sequences. The robotic eyes were evaluated against the pupillometric data and video feeds of the human eyes to determine operational accuracy. Finally, the dilating robotic eye system was installed in a realistic humanoid robot (RHR) and comparatively evaluated in a human-robot interaction (HRI) experiment. The results of this study show that the robotic eyes can emulate the average pupil reflex of the human eye under typical light conditions and to positive and negative emotive stimuli. However, the results of the HRI experiment indicate that replicating natural eye contact behaviour was more significant than emulating pupil dilation.
Towards Building the OP-Mapped WENO Schemes: A General Methodology
Authors: Li Ruo , Zhong Wei
Published: September 2021

A serious and ubiquitous issue in existing mapped WENO schemes is that most of them can hardly preserve high resolutions, but in the meantime prevent spurious oscillations in the solving of hyperbolic conservation laws with long output times. Our goal for this article was to address this widely known problem. In our previous work, the order-preserving (OP) criterion was originally introduced and carefully used to devise a new mapped WENO scheme that performs satisfactorily in long simulations, and hence it was indicated that the OP criterion plays a critical role in the maintenance of low-dissipation and robustness for mapped WENO schemes. Thus, in our present work, we firstly defined the family of mapped WENO schemes, whose mappings meet the OP criterion, as OP-Mapped WENO. Next, we attentively took a closer look at the mappings of various existing mapped WENO schemes and devised a general formula for them. That helped us to extend the OP criterion to the design of improved mappings. Then, we created a generalized implementation of obtaining a group of OP-Mapped WENO schemes, named MOP-WENO-X, as they are developed from the existing mapped WENO-X schemes, where the notation “X” is used to identify the version of the existing mapped WENO scheme. Finally, extensive numerical experiments and comparisons with competing schemes were conducted to demonstrate the enhanced performances of the MOP-WENO-X schemes.
Pre-Clinical Research Advancements Relating to Improving the Diagnosis and Treatment of Malignant Pleural Mesothelioma: A Review
Authors: Johnson Ben , Lee Kenneth , Cheng Yuen Yee
Published: September 2021

Malignant pleural mesothelioma (MPM) is a rare, aggressive cancer of the lung lining that is predominantly associated with occupational exposure to asbestos. MPM is responsible for thousands of deaths worldwide every year, with the median survival of MPM of 8–14 months. There are limited biomarkers available in the clinic to effectively diagnose MPM, an invasive biopsy procedure is usually required to provide a definitive diagnosis. Due to the long latency period associated with MPM disease presentation, the cancer is usually at an advanced stage at the time of diagnosis where treatment options are largely ineffective at controlling disease progression. Previous MPM-based pre-clinical studies have made significant strides in determining the exact molecular mechanisms associated with asbestos carcinogenesis. Exploring less invasive blood-based biomarkers and treatment strategies involving targeted therapy, immunotherapy, and virotherapy is particularly important. Research in these areas is of crucial importance in relation to improving the rate of novel diagnostic biomarkers and treatment strategies progressing through to clinical trials and ultimately into the clinical setting. This review comprehensively summarises both previous and current pre-clinical research developments that have specifically contributed to an improved understanding of MPM disease biology, and the development of novel diagnostic biomarkers and treatment strategies.
DNA Barcode Authentication of Devil’s Claw Herbal Dietary Supplements
Authors: Diaz-Silveira Genelle L. , Deutsch Joan , Little Damon P.
Published: September 2021

Devil’s claw is the vernacular name for a genus of medicinal plants that occur in the Kalahari Desert and Namibia Steppes. The genus comprises two distinct species: Harpagophytum procumbens and H. zeyheri. Although the European pharmacopeia considers the species interchangeable, recent studies have demonstrated that H. procumbens and H. zeyheri are chemically distinct and should not be treated as the same species. Further, the sale of H. zeyheri as an herbal supplement is not legal in the United States. Four markers were tested for their ability to distinguish H. procumbens from H. zeyheri: rbcL, matK, nrITS2, and psbA-trnH. Of these, only psbA-trnH was successful. A novel DNA mini-barcode assay that produces a 178-base amplicon in Harpagophytum (specificity = 1.00 [95% confidence interval = 0.80–1.00]; sensitivity = 1.00 [95% confidence interval = 0.75–1.00]) was used to estimate mislabeling frequency in a sample of 23 devil’s claw supplements purchased in the United States. PCR amplification failed in 13% of cases. Among the 20 fully-analyzable supplements: H. procumbens was not detected in 75%; 25% contained both H. procumbens and H. zeyheri; none contained only H. procumbens. We recommend this novel mini-barcode region as a standard method of quality control in the manufacture of devil’s claw supplements.
Recent Advances on Cellulose Nanocrystals and Their Derivatives
Authors: Peng Shuting , Luo Qiguan , Zhou Guofu , Xu Xuezhu
Published: September 2021

Nanocellulose, typically cellulose nanocrystals (CNCs), has excellent properties and is widely used. In particular, CNC has a small dimension, high chemical reactivity, and high sustainability, which makes it an excellent candidate as a starting material to be converted into nanocellulose derivatives. Chemical modification is essential for obtaining the desired products; the modifications create different functional attachment levels and generate novel microstructures. Recent advances on nanocellulose derivatives have not yet been reviewed and evaluated for the last five years. Nanocellulose derivative materials are being used in a wide variety of high-quality functional applications. To meet these requirements, it is essential for researchers to fully understand CNCs and derivative materials, precisely their characteristics, synthesis methods, and chemical modification approaches. This paper discusses CNC and its derivatives concerning the structural characteristics, performance, and synthesis methods, comparing the pros and cons of these chemical modification approaches reported in recent years. This review also discusses the critical physicochemical properties of CNC derivative products, including solubility, wetting performance, and associated impacts on properties. Lastly, this paper also comments on the bottlenecks of nanocellulose derivatives in various applications and briefly discusses their future research direction.
Rosa’s Theory of Resonance: Its Importance for (the Science of) Religion and Hope
Authors: Žalec Bojan
Published: September 2021

This article deals with the resonance theory of the German sociologist Hartmut Rosa, which has aroused a lot of interest among scholars in the humanities and social sciences, including researchers in the field of religion. The article focuses on its importance for religion, particularly the science of religion and hope. The author presents Rosa’s theory first from the anthropological and sociological aspect. He then turns to Rosa’s understanding of religion. On this basis, the author draws his conclusions, which are as follows: The main significance of the resonance theory for religion and the science of religion is in the rehabilitation of religion as an anthropological constant. It follows that Rosa’s theory of resonance is an important contribution to substantiating the importance of religion and supporting its cultivation. Secondly, Rosa’s theory is an important contribution and support to the flourishing of hope due to its scientific support for religion. Another contribution of Rosa’s theory to hope is that it helps us understand the connection between resonance, existential hope, and meaning, and thus contributes to our being more successful in developing existential hope and discovering the meaning of our lives and world. This is important for our quality of life.
Editorial of Special Issue “Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans”
Authors: Cuadrado Javier , Naya Miguel Á.
Published: September 2021

The combination of physical sensors and computational models to provide additional information about system states, inputs and/or parameters, in what is known as virtual sensing, is becoming more and more popular in many sectors, such as the automotive, aeronautics, aerospatial, railway, machinery, robotics and human biomechanics sectors [...]
A Proposal of Implementation of Sitting Posture Monitoring System for Wheelchair Utilizing Machine Learning Methods
Authors: Ahmad Jawad , Sidén Johan , Andersson Henrik
Published: September 2021

This paper presents a posture recognition system aimed at detecting sitting postures of a wheelchair user. The main goals of the proposed system are to identify and inform irregular and improper posture to prevent sitting-related health issues such as pressure ulcers, with the potential that it could also be used for individuals without mobility issues. In the proposed monitoring system, an array of 16 screen printed pressure sensor units was employed to obtain pressure data, which are sampled and processed in real-time using read-out electronics. The posture recognition was performed for four sitting positions: right-, left-, forward- and backward leaning based on k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), decision tree (DT) and LightGBM machine learning algorithms. As a result, a posture classification accuracy of up to 99.03 percent can be achieved. Experimental studies illustrate that the system can provide real-time pressure distribution value in the form of a pressure map on a standard PC and also on a raspberry pi system equipped with a touchscreen monitor. The stored pressure distribution data can later be shared with healthcare professionals so that abnormalities in sitting patterns can be identified by employing a post-processing unit. The proposed system could be used for risk assessments related to pressure ulcers. It may be served as a benchmark by recording and identifying individuals’ sitting patterns and the possibility of being realized as a lightweight portable health monitoring device.
Novel Technique to Measure Pulse Wave Velocity in Brain Vessels Using a Fast Simultaneous Multi-Slice Excitation Magnetic Resonance Sequence
Authors: Jung Ju-Yeon , Lee Yeong-Bae , Kang Chang-Ki
Published: September 2021

In this study, we proposed a novel pulse wave velocity (PWV) technique to determine cerebrovascular stiffness using a 3-tesla magnetic resonance imaging (MRI) to overcome the various shortcomings of existing PWV techniques for cerebral-artery PWV, such as long scan times and complicated procedures. The technique was developed by combining a simultaneous multi-slice (SMS) excitation pulse sequence with keyhole acquisition and reconstruction (SMS-K). The SMS-K technique for cerebral-artery PWV was evaluated using phantom and human experiments. In the results, common and internal carotid arteries (CCA and ICA) were acquired simultaneously in an image with a high temporal resolution-of 48 ms for one measurement. Vascular signals at 500 time points acquired within 30 s could generate pulse waveforms of CCA and ICA with 26 heartbeats, allowing for the detection of PWV changes over time. The results demonstrated that the SMS-K technique could provide more PWV information with a simple procedure within a short period of time. The procedural convenience and advantages of PWV measurements will make it more appropriate for clinical applications.
Colorimetric Detection of Chromium(VI) Ions in Water Using Unfolded-Fullerene Carbon Nanoparticles
Authors: Babazadeh Saeedeh , Bisauriya Ramanand , Carbone Marilena , Roselli Ludovica , Cecchetti Daniele , Bauer Elvira Maria , Sennato Simona , Prosposito Paolo , Pizzoferrato Roberto
Published: September 2021

Water pollution caused by hexavalent chromium (Cr(VI)) ions represents a serious hazard for human health due to the high systemic toxicity and carcinogenic nature of this metal species. The optical sensing of Cr(VI) through specifically engineered nanomaterials has recently emerged as a versatile strategy for the application to easy-to-use and cheap monitoring devices. In this study, a one-pot oxidative method was developed for the cage opening of C60 fullerene and the synthesis of stable suspensions of N-doped carbon dots in water–THF solutions (N-CDs-W-THF). The N-CDs-W-THF selectively showed variations of optical absorbance in the presence of Cr(VI) ions in water through the arising of a distinct absorption band peaking at 550 nm, i.e., in the transparency region of pristine material. Absorbance increased linearly, with the ion concentration in the range 1–100 µM, thus enabling visual and ratiometric determination with a limit of detection (LOD) of 300 nM. Selectivity and possible interference effects were tested over the 11 other most common heavy metal ions. The sensing process occurred without the need for any other reactant or treatment at neutral pH and within 1 min after the addition of chromium ions, both in deionized and in real water samples.
Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies
Authors: Aznan Aimi , Gonzalez Viejo Claudia , Pang Alexis , Fuentes Sigfredo
Published: September 2021

Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.
Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
Authors: Wu Yang , Hu Guyu , Jin Fenglin , Tang Siqi
Published: September 2021

The satellite network plays an increasingly important role in the global communication. With the development of communication technology, quality of service requirements have become more and more complex and diverse and the quality of service routing strategy of software-defined satellite network has become a more and more hot and difficult issue. In this paper, an interval-type-2 fuzzy set routing algorithm is proposed. Firstly, the multi- quality of service routing problem in software-defined satellite network is modeled. Then, the interval-type-2 fuzzy set routing algorithm is proposed to make fuzzy decisions. A series of experiments conducted in Network Simulator (Version 2.35) have proved that the proposed interval type-2 fuzzy set routing algorithm can reduce average delay, increase total throughput and reduce packet drop rate.
Monitoring Lower Back Activity in Daily Life Using Small Unintrusive Sensors and Wearable Electronics in the Context of Rheumatic and Musculoskeletal Diseases
Authors: Baijot Mathieu , Puers Robert , Kraft Michael
Published: September 2021

Due to a sedentary lifestyle, the amount of people suffering from musculoskeletal back diseases has increased over the last few decades. To monitor and cure these disabilities, sensors able to monitor the patient for long-term measurement during daily life and able to provide real-time feedback are required. There are only a few wearable systems that are capable to acquire muscle activity (sEMG) and posture at the same time. Moreover, previously reported systems do not target back sensor and typically comprise bulky uncomfortable solutions. In this paper, we present a new wearable sensor network that is designed to measure muscle activity and posture specialized for back measurement. Special care was taken to propose a discrete and comfortable solution. The prototype only measures 3.1 mm in thickness on the spine, making this sensor system the thinnest and lightest one in the literature to our best knowledge. After testing, it was shown that the sensor system is able to acquire two surface electromyography signals concurrently, to gather acceleration and rotation speed from the patient’s lower back, and to transmit data to a computer or a smartphone via serial communication or Bluetooth low energy for a few hours for later processing and analysis.
Selection of Essential Neural Activity Timesteps for Intracortical Brain–Computer Interface Based on Recurrent Neural Network
Authors: Yang Shih-Hung , Huang Jyun-We , Huang Chun-Jui , Chiu Po-Hsiung , Lai Hsin-Yi , Chen You-Yin
Published: September 2021

Intracortical brain–computer interfaces (iBCIs) translate neural activity into control commands, thereby allowing paralyzed persons to control devices via their brain signals. Recurrent neural networks (RNNs) are widely used as neural decoders because they can learn neural response dynamics from continuous neural activity. Nevertheless, excessively long or short input neural activity for an RNN may decrease its decoding performance. Based on the temporal attention module exploiting relations in features over time, we propose a temporal attention-aware timestep selection (TTS) method that improves the interpretability of the salience of each timestep in an input neural activity. Furthermore, TTS determines the appropriate input neural activity length for accurate neural decoding. Experimental results show that the proposed TTS efficiently selects 28 essential timesteps for RNN-based neural decoders, outperforming state-of-the-art neural decoders on two nonhuman primate datasets (R2=0.76±0.05 for monkey Indy and CC=0.91±0.01 for monkey N). In addition, it reduces the computation time for offline training (reducing 5–12%) and online prediction (reducing 16–18%). When visualizing the attention mechanism in TTS, the preparatory neural activity is consecutively highlighted during arm movement, and the most recent neural activity is highlighted during the resting state in nonhuman primates. Selecting only a few essential timesteps for an RNN-based neural decoder provides sufficient decoding performance and requires only a short computation time.
Automating a Dehazing System by Self-Calibrating on Haze Conditions
Authors: Ngo Dat , Lee Seungmin , Lee Gi-Dong , Kang Bongsoon
Published: September 2021

Existing image dehazing algorithms typically rely on a two-stage procedure. The medium transmittance and lightness are estimated in the first stage, and the scene radiance is recovered in the second by applying the simplified Koschmieder model. However, this type of unconstrained dehazing is only applicable to hazy images, and leads to untoward artifacts in haze-free images. Moreover, no algorithm that can automatically detect the haze density and perform dehazing on an arbitrary image has been reported in the literature to date. Therefore, this paper presents an automated dehazing system capable of producing satisfactory results regardless of the presence of haze. In the proposed system, the input image simultaneously undergoes multiscale fusion-based dehazing and haze-density-estimating processes. A subsequent image blending step then judiciously combines the dehazed result with the original input based on the estimated haze density. Finally, tone remapping post-processes the blended result to satisfactorily restore the scene radiance quality. The self-calibration capability on haze conditions lies in using haze density estimate to jointly guide image blending and tone remapping processes. We performed extensive experiments to demonstrate the superiority of the proposed system over state-of-the-art benchmark methods.
Mechanosensory Hairs and Hair-like Structures in the Animal Kingdom: Specializations and Shared Functions Serve to Inspire Technology Applications
Authors: Boublil Brittney L. , Diebold Clarice Anna , Moss Cynthia F.
Published: September 2021

Biological mechanosensation has been a source of inspiration for advancements in artificial sensory systems. Animals rely on sensory feedback to guide and adapt their behaviors and are equipped with a wide variety of sensors that carry stimulus information from the environment. Hair and hair-like sensors have evolved to support survival behaviors in different ecological niches. Here, we review the diversity of biological hair and hair-like sensors across the animal kingdom and their roles in behaviors, such as locomotion, exploration, navigation, and feeding, which point to shared functional properties of hair and hair-like structures among invertebrates and vertebrates. By reviewing research on the role of biological hair and hair-like sensors in diverse species, we aim to highlight biological sensors that could inspire the engineering community and contribute to the advancement of mechanosensing in artificial systems, such as robotics.
Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach
Authors: Deif Mohanad A. , Solyman Ahmed A. A. , Alsharif Mohammed H. , Uthansakul Peerapong
Published: September 2021

The sudden increase in patients with severe COVID-19 has obliged doctors to make admissions to intensive care units (ICUs) in health care practices where capacity is exceeded by the demand. To help with difficult triage decisions, we proposed an integration system Xtreme Gradient Boosting (XGBoost) classifier and Analytic Hierarchy Process (AHP) to assist health authorities in identifying patients’ priorities to be admitted into ICUs according to the findings of the biological laboratory investigation for patients with COVID-19. The Xtreme Gradient Boosting (XGBoost) classifier was used to decide whether or not they should admit patients into ICUs, before applying them to an AHP for admissions’ priority ranking for ICUs. The 38 commonly used clinical variables were considered and their contributions were determined by the Shapley’s Additive explanations (SHAP) approach. In this research, five types of classifier algorithms were compared: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), Random Forest (RF), and Artificial Neural Network (ANN), to evaluate the XGBoost performance, while the AHP system compared its results with a committee formed from experienced clinicians. The proposed (XGBoost) classifier achieved a high prediction accuracy as it could discriminate between patients with COVID-19 who need ICU admission and those who do not with accuracy, sensitivity, and specificity rates of 97%, 96%, and 96% respectively, while the AHP system results were close to experienced clinicians’ decisions for determining the priority of patients that need to be admitted to the ICU. Eventually, medical sectors can use the suggested framework to classify patients with COVID-19 who require ICU admission and prioritize them based on integrated AHP methodologies.
Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector
Authors: Ooi Alexander Ze Hwan , Embong Zunaina , Abd Hamid Aini Ismafairus , Zainon Rafidah , Wang Shir Li , Ng Theam Foo , Hamzah Rostam Affendi , Teoh Soo Siang , Ibrahim Haidi
Published: September 2021

Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation.
Beta-Lactam Antibiotic Discrimination Using a Macromolecular Sensor in Water at Neutral pH
Authors: Xu Yifei , Bonizzoni Marco
Published: September 2021

Penicillins and cephalosporins belong to the β-lactam antibiotic family, which accounts for more than half of the world market for antibiotics. Misuse of antibiotics harms human health and the environment. Here, we describe an easy, fast, and sensitive optical method for the sensing and discrimination of two penicillin and five cephalosporin antibiotics in buffered water at pH 7.4, using fifth-generation poly (amidoamine) (PAMAM) dendrimers and calcein, a commercially available macromolecular polyelectrolyte and a fluorescent dye, respectively. In aqueous solution at pH 7.4, the dendrimer and dye self-assemble to form a sensor that interacts with carboxylate-containing antibiotics through electrostatic interaction, monitored through changes in the dye’s spectroscopic properties. This response was captured through absorbance, fluorescence emission, and fluorescence anisotropy. The resulting data set was processed through linear discriminant analysis (LDA), a common pattern-base recognition method, for the differentiation of cephalosporins and penicillins. By pre-hydrolysis of the β-lactam rings under basic conditions, we were able to increase the charge density of the analytes, allowing us to discriminate the seven analytes at a concentration of 5 mM, with a limit of discrimination of 1 mM.
Magnetoelectric Response of Laminated Cantilevers Comprising a Magnetoactive Elastomer and a Piezoelectric Polymer, in Pulsed Uniform Magnetic Fields
Authors: Glavan Gašper , Belyaeva Inna A. , Ruwisch Kevin , Wollschläger Joachim , Shamonin Mikhail
Published: September 2021

The voltage response to pulsed uniform magnetic fields and the accompanying bending deformations of laminated cantilever structures are investigated experimentally in detail. The structures comprise a magnetoactive elastomer (MAE) slab and a commercially available piezoelectric polymer multilayer. The magnetic field is applied vertically and the laminated structures are customarily fixed in the horizontal plane or, alternatively, slightly tilted upwards or downwards. Six different MAE compositions incorporating three concentrations of carbonyl iron particles (70 wt%, 75 wt% and 80 wt%) and two elastomer matrices of different stiffness are used. The dependences of the generated voltage and the cantilever’s deflection on the composition of the MAE layer and its thickness are obtained. The appearance of the voltage between the electrodes of a piezoelectric material upon application of a magnetic field is considered as a manifestation of the direct magnetoelectric (ME) effect in a composite laminated structure. The ME voltage response increases with the increasing total quantity of the soft-magnetic filler in the MAE layer. The relationship between the generated voltage and the cantilever’s deflection is established. The highest observed peak voltage around 5.5 V is about 8.5-fold higher than previously reported values. The quasi-static ME voltage coefficient for this type of ME heterostructures is about 50 V/A in the magnetic field of ≈100 kA/m, obtained for the first time. The results could be useful for the development of magnetic field sensors and energy harvesting devices relying on these novel polymer composites.
How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing
Authors: Girolami Michele , Belli Dimitri , Chessa Stefano , Foschini Luca
Published: September 2021

The possibility of understanding the dynamics of human mobility and sociality creates the opportunity to re-design the way data are collected by exploiting the crowd. We survey the last decade of experimentation and research in the field of mobile CrowdSensing, a paradigm centred on users’ devices as the primary source for collecting data from urban areas. To this purpose, we report the methodologies aimed at building information about users’ mobility and sociality in the form of ties among users and communities of users. We present two methodologies to identify communities: spatial and co-location-based. We also discuss some perspectives about the future of mobile CrowdSensing and its impact on four investigation areas: contact tracing, edge-based MCS architectures, digitalization in Industry 5.0 and community detection algorithms.
Study on an Integral Algorithm of Load Identification Based on Displacement Response
Authors: Xu Xun , Zhu Yashan , Tian Kejing , Lin Tingcan , Li Yunyu
Published: September 2021

Load identification is a very important and challenging indirect load measurement method because load identification is an inverse problem solution with ill-conditioned characteristics. A new method of load identification is proposed here, in which a virtual function was introduced to establish integral structure equations of motion, and partial integration was applied to reduce the response types in the equations. The effects of loading duration, the type of basis function, and the number of basis function expansion items on the calculation efficiency and the accuracy of load identification were comprehensively taken into account. Numerical simulation and experimental results showed that our algorithm could not only effectively identify periodic and random loads, but there was also a trade-off between the calculation efficiency and identification accuracy. Additionally, our algorithm can improve the ill-conditionedness of the solution of load identification equations, has better robustness to noise, and has high computational efficiency.
Blockchain Technologies: Probability of Double-Spend Attack on a Proof-of-Stake Consensus
Authors: Karpinski Mikolaj , Kovalchuk Lyudmila , Kochan Roman , Oliynykov Roman , Rodinko Mariia , Wieclaw Lukasz
Published: September 2021

Two double-spend attack strategies on a proof-of-stake consensus are considered. For each strategy, the probability of its success is obtained, which depends on the network parameters and the number of confirmation blocks. These results can be used to define how many confirmation blocks a vendor should wait after a correspondent transaction before sending goods or services.
Static Attitude Determination Using Convolutional Neural Networks
Authors: dos Santos Guilherme Henrique , Seman Laio Oriel , Bezerra Eduardo Augusto , Leithardt Valderi Reis Quietinho , Mendes André Sales , Stefenon Stéfano Frizzo
Published: September 2021

The need to estimate the orientation between frames of reference is crucial in spacecraft navigation. Robust algorithms for this type of problem have been built by following algebraic approaches, but data-driven solutions are becoming more appealing due to their stochastic nature. Hence, an approach based on convolutional neural networks in order to deal with measurement uncertainty in static attitude determination problems is proposed in this paper. PointNet models were trained with different datasets containing different numbers of observation vectors that were used to build attitude profile matrices, which were the inputs of the system. The uncertainty of measurements in the test scenarios was taken into consideration when choosing the best model. The proposed model, which used convolutional neural networks, proved to be less sensitive to higher noise than traditional algorithms, such as singular value decomposition (SVD), the q-method, the quaternion estimator (QUEST), and the second estimator of the optimal quaternion (ESOQ2).
Robust Static Output-Feedback Control for MJLS with Non-Homogeneous Markov Chains: A Comparative Study Considering a Wireless Sensor Network with Time-Varying PER
Authors: Serafini Arthur R. C. , Delforno Leonardo , Palma Jonathan M. , Behrens Frank H. , Morais Cecília F.
Published: September 2021

This paper deals with the problem of control through a semi-reliable communication channel, such as wireless sensor networks (WSN). Particularly, the case investigated is the one where the packet loss rate of the network is time-varying due to, for instance, variation in the distance between the nodes. Considering this practical motivation, the control system is modeled using a formulation based on discrete-time Markov jump linear systems (MJLS) with non-homogeneous Markov chains (time-varying transition probabilities). New control design conditions based on parameter-dependent linear matrix inequalities are proposed in order to solve this problem. The purpose is to demonstrate that this strategy is suitable to handle the networked control problem by comparing the temporal behavior of the closed-loop system with the Markovian controller and a standard proportional-integral-derivative (PID) controller. The case study presented in the paper considers the problem of the remote control of a Vertical Take-Off and Landing (VTOL) vehicle through a wireless communication channel. The network packet loss model employed in the case study is based on data collected on a wireless network workbench, which was previously developed and validated by the authors.
Spin-Orbital Conversion of a Strongly Focused Light Wave with High-Order Cylindrical–Circular Polarization
Authors: Kotlyar Victor V. , Stafeev Sergey S. , Kozlova Elena S. , Nalimov Anton G.
Published: September 2021

We discuss interesting effects that occur when strongly focusing light with mth-order cylindrical–circular polarization. This type of hybrid polarization combines properties of the mth-order cylindrical polarization and circular polarization. Reluing on the Richards-Wolf formalism, we deduce analytical expressions that describe E- and H-vector components, intensity patterns, and projections of the Poynting vector and spin angular momentum (SAM) vector at the strong focus. The intensity of light in the strong focus is theoretically and numerically shown to have an even number of local maxima located along a closed contour centered at an on-axis point of zero intensity. We show that light generates 4m vortices of a transverse energy flow, with their centers located between the local intensity maxima. The transverse energy flow is also shown to change its handedness an even number of times proportional to the order of the optical vortex via a full circle around the optical axis. It is interesting that the longitudinal SAM projection changes its sign at the focus 4m times. The longitudinal SAM component is found to be positive, and the polarization vector is shown to rotate anticlockwise in the focal spot regions where the transverse energy flow rotates anticlockwise, and vice versa—the longitudinal SAM component is negative and the polarization vector rotates clockwise in the focal spot regions where the transverse energy flow rotates clockwise. This spatial separation at the focus of left and right circularly polarized light is a manifestation of the optical spin Hall effect. The results obtained in terms of controlling the intensity maxima allow the transverse mode analysis of laser beams in sensorial applications. For a demonstration of the proposed application, the metalens is calculated, which can be a prototype for an optical microsensor based on sharp focusing for measuring roughness.
IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses
Authors: Albulayhi Khalid , Smadi Abdallah A. , Sheldon Frederick T. , Abercrombie Robert K.
Published: September 2021

This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets).
Color-Ratio Maps Enhanced Optical Filter Design and Its Application in Green Pepper Segmentation
Authors: Yu Jun , Kurihara Toru , Zhan Shu
Published: September 2021

There is a growing demand for developing image sensor systems to aid fruit and vegetable harvesting, and crop growth prediction in precision agriculture. In this paper, we present an end-to-end optimization approach for the simultaneous design of optical filters and green pepper segmentation neural networks. Our optimization method modeled the optical filter as one learnable neural network layer and attached it to the subsequent camera spectral response (CSR) layer and segmentation neural network for green pepper segmentation. We used not only the standard red–green–blue output from the CSR layer but also the color-ratio maps as additional cues in the visible wavelength and to augment the feature maps as the input for segmentation. We evaluated how well our proposed color-ratio maps enhanced optical filter design methods in our collected dataset. We find that our proposed method can yield a better performance than both an optical filter RGB system without color-ratio maps and a raw RGB camera (without an optical filter) system. The proposed learning-based framework can potentially build better image sensor systems for green pepper segmentation.
Observability Study on Passive Target Localization by Conic–Angle Measurements
Authors: Suh Taeil , Kim Woochan
Published: September 2021

Information from a passive linear array sensor is related to the conic angle formed by a target and the sensor in three-dimensional (3D) space so that the target localization system using the sensor should be also designed in 3D space. This paper presents an observability study of a passive target localization system created using conic angle information. The study includes the analysis of the sensor maneuver requirement needed to achieve system observability and simulations to demonstrate the results of the analytic scheme. The proposed sensor maneuver requirements satisfy the system observability conditions by using the local linearization approach of the Fisher information matrix. It is also shown that this requirement can be mitigated for special cases in which the depth difference between the sensor and the target is given. Using the simulation, it is shown that sensors following the proposed scheme are able to obtain meaningful information that can be used to estimate 3D target states.
Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study
Authors: Das Swagata , Sakoda Wataru , Ramasamy Priyanka , Tadayon Ramin , Ramirez Antonio Vega , Kurita Yuichi
Published: September 2021

Early and self-identification of locomotive degradation facilitates us with awareness and motivation to prevent further deterioration. We propose the usage of nine squat and four one-leg standing exercise features as input parameters to Machine Learning (ML) classifiers in order to perform lower limb skill assessment. The significance of this approach is that it does not demand manpower and infrastructure, unlike traditional methods. We base the output layer of the classifiers on the Short Test Battery Locomotive Syndrome (STBLS) test used to detect Locomotive Syndrome (LS) approved by the Japanese Orthopedic Association (JOA). We obtained three assessment scores by using this test, namely sit-stand, 2-stride, and Geriatric Locomotive Function Scale (GLFS-25). We tested two ML methods, namely an Artificial Neural Network (ANN) comprised of two hidden layers with six nodes per layer configured with Rectified-Linear-Unit (ReLU) activation function and a Random Forest (RF) regressor with number of estimators varied from 5 to 100. We could predict the stand-up and 2-stride scores of the STBLS test with correlation of 0.59 and 0.76 between the real and predicted data, respectively, by using the ANN. The best accuracies (R-squared values) obtained through the RF regressor were 0.86, 0.79, and 0.73 for stand-up, 2-stride, and GLFS-25 scores, respectively.
Determination of Genotoxic Azide Impurity in Cilostazol API by Ion Chromatography with Matrix Elimination
Authors: Páll Boglárka , Gyenge Zsuzsa , Kormány Róbert , Horváth Krisztián
Published: September 2021

Cilostazol is a commonly used active pharmaceutical ingredient (API) to treat and reduce the symptoms of intermittent claudication in peripheral vascular disease. Recently, it was found to be a potential medicine in the effective treatment of COVID-19. In addition to the positive effects of this API, genotoxic sodium azide is used in the synthesis of cilostazol that can appear in the API. In this work, a method was developed for the determination of sodium azide (as azide anion) in cilostazol API at 7.5 ppm limit level by using ion chromatography (IC) and liquid–liquid extraction (LLE) sample preparation. The liquid–liquid extraction allows the application of high sample concentrations. Because of the low limit concentration (7.5 ppm), 500 mg sample was dissolved in 5 mL solvent. By using LLE for sample preparation, the huge amount of cilostazol was omitted and column overload was avoided. The developed method was validated in accordance with the relevant guidelines. Specificity, accuracy, precision, limit of detection and limit of quantification parameters were evaluated. The calculated limit of detection was 0.52 ppm (S/N:3) and the limit of quantification was 1.73 ppm (S/N:10) for sodium azide. The recovery of the sodium azide was 102.4% and the prepared solutions were stable in the sample holder for 24 h.
Spatiotemporal Differentiation of Coupling and Coordination Relationship of the Tea Industry–Tourism–Ecological Environment System in Fujian Province, China
Authors: Cheng Qian , Luo Zhongheng , Xiang Ling
Published: September 2021

Correctly understanding and handling the relationships in the tea industry–tourism–ecological environment system is a prerequisite and foundation for the high-quality and sustainable development of the tea tourism industry. In this study, an evaluation index system of the tea industry–tourism–ecological environment system is established, the weight coefficient of each indicator is determined by the entropy weight method, and the coupling coordination degrees of Fujian Province and nine cities therein are evaluated by constructing a coupling coordination model from 2011 to 2019. The results indicate that the comprehensive development index of the tea industry–tourism–ecological environment system had an overall upward trend from 2011 to 2019, and the coupling coordination degree of the three systems changed from moderate maladjustment to high-quality coordination. There was spatial heterogeneity in the comprehensive development indices and coupling coordination degrees of the three systems, when considering the nine cities of Fujian Province. This study offers both theoretical and practical implications for further improving the development level and overall coordination between the tea industry, tourism, and the ecological environment in Fujian province.
Twenty Years of Common Agricultural Policy in Europe: A Bibliometric Analysis
Authors: Fusco Giulio
Published: September 2021

The last few years have been marked by the increasing attention paid by policymakers to agricultural policies. Within this scenario, the Common Agricultural Policy represents one of the main initiatives developed by the European Commission to enhance the agricultural sector. Academics have actively contributed to the debate through empirical studies in order to evaluate the main strengths and weakness related to the public investments made by the European Commission. However, despite the relevance of the topic, the scientific debate is characterized by a high degree of fragmentation caused by the involvement of academics with different scientific backgrounds. Building on this evidence, this paper aims to contribute to the scientific debate on Common Agricultural Policy through a bibliometric analysis. The findings reveal the existence of three independent and complementary research clusters.