bluuebunny/crossref_metadata_2025 huggingface dataset:
Trajectory Planning and Simulation Study of Redundant Robotic Arm for Upper Limb Rehabilitation Based on Back Propagation Neural Network and Genetic Algorithm
Authors: Qie Xiaohan , Kang Cunfeng , Zong Guanchen , Chen Shujun
Published: May 2022

In this study, a Back Propagation (BP) neural network algorithm based on Genetic Algorithm (GA) optimization is proposed to plan and optimize the trajectory of a redundant robotic arm for the upper limb rehabilitation of patients. The feasibility of the trajectory was verified by numerical simulations. First, the collected dataset was used to train the BP neural network optimized by the GA. Subsequently, the critical points designated by the rehabilitation physician for the upper limb rehabilitation were used as interpolation points for cubic B−spline interpolation to plan the motion trajectory. The GA optimized the planned trajectory with the goal of time minimization, and the feasibility of the optimized trajectory was analyzed with MATLAB simulations. The planned trajectory was smooth and continuous. There was no abrupt change in location or speed. Finally, simulations revealed that the optimized trajectory reduced the motion time and increased the motion speed between two adjacent critical points which improved the rehabilitation effect and can be applied to patients with different needs, which has high application value.
Design and Evaluation of Low-Cost Vibration-Based Machine Monitoring System for Hay Rotary Tedder
Authors: Mystkowski Arkadiusz , Kociszewski Rafał , Kotowski Adam , Ciężkowski Maciej , Wojtkowski Wojciech , Ostaszewski Michał , Kulesza Zbigniew , Wolniakowski Adam , Kraszewski Grzegorz , Idzkowski Adam
Published: May 2022

Vibration monitoring provides a good-quality source of information about the health condition of machines, and it is often based on the use of accelerometers. This article focuses on the use of accelerometer sensors in fabricating a low-cost system for monitoring vibrations in agricultural machines, such as rotary tedders. The aim of the study is to provide useful data on equipment health for improving the durability of such machinery. The electronic prototype, based on the low-cost AVR microcontroller ATmega128 with 10-bit ADC performing a 12-bit measurement, is able to acquire data from an accelerometer weighing up to 10 g. Three sensors were exposed to low accelerations with the use of an exciter, and their static characteristics were presented. Standard experimental tests were used to evaluate the constructed machine monitoring system. The self-contained prototype system was calibrated in a laboratory test rig, and sinusoidal and multisinusoidal excitations were used. Measurements in time and frequency domains were carried out. The amplitude characteristic of the preformed system differed by no more than 15% within a frequency range of 10 Hz–10 kHz, compared to the AVM4000 commercial product. Finally, the system was experimentally tested to measure acceleration at three characteristic points in a rotational tedder, i.e., the solid grease gearbox, the drive shaft bearing and the main frame. The RMS amplitude values of the shaft vibrations on the bearing in relation to the change in the drive shaft speed of two tedders of the same type were evaluated and compared. Additionally, the parameters of kurtosis and crest factor were compared to ascertain the bearing condition.
MobilePrune: Neural Network Compression via ℓ0 Sparse Group Lasso on the Mobile System
Authors: Shao Yubo , Zhao Kaikai , Cao Zhiwen , Peng Zhehao , Peng Xingang , Li Pan , Wang Yijie , Ma Jianzhu
Published: May 2022

It is hard to directly deploy deep learning models on today’s smartphones due to the substantial computational costs introduced by millions of parameters. To compress the model, we develop an ℓ0-based sparse group lasso model called MobilePrune which can generate extremely compact neural network models for both desktop and mobile platforms. We adopt group lasso penalty to enforce sparsity at the group level to benefit General Matrix Multiply (GEMM) and develop the very first algorithm that can optimize the ℓ0 norm in an exact manner and achieve the global convergence guarantee in the deep learning context. MobilePrune also allows complicated group structures to be applied on the group penalty (i.e., trees and overlapping groups) to suit DNN models with more complex architectures. Empirically, we observe the substantial reduction of compression ratio and computational costs for various popular deep learning models on multiple benchmark datasets compared to the state-of-the-art methods. More importantly, the compression models are deployed on the android system to confirm that our approach is able to achieve less response delay and battery consumption on mobile phones.
Smart Polymer Composite Deck Monitoring Using Distributed High Definition and Bragg Grating Fiber Optic Sensing
Authors: Young Stephen , Penumadu Dayakar , Patchen Andrew D. , Laggis George , Michaud Joey , Bradley Abram , Davis Ryan , Unser John , Davis Matthew
Published: May 2022

Fiber-reinforced polymer composites are an excellent choice for bridge decks due to high strength, lightweight, resistance to corrosion, and long-term durability with a 100-year design life. Structural health monitoring is useful for the long-term assessment of the condition of the bridge structure and obtaining a response to complex loads considering environmental conditions. Bridge structures have been studied primarily using distributed fiber optic sensing, such as Brillouin scattering; however, critical events, including damage detection, can be missed due to low spatial resolution. There is also a critical need to conduct a comprehensive study of static and dynamic loading simultaneously for fiber-reinforced composite bridge structures. In this study, a novel approach was implemented using two sensor technologies, optical frequency domain reflectometry and fiber Bragg grating-based sensors, embedded in a glass-fiber-reinforced composite bridge deck to simultaneously monitor the deformation response of the bridge structure. The optical frequency domain reflectometry sensor utilizing Rayleigh scattering provides high spatial strain resolution were positioned strategically based on expected stress distributions to measure strain in the longitudinal, transverse, and diagonal directions along the span of the composite bridge. Furthermore, fiber Bragg grating based sensors are used to monitor the response to dynamic vehicular loading and deformations from an automotive-crash-type event on the bridge structure. To monitor environmental variables such as temperature, a custom wireless configured sensor package was developed for the study and integrated with a composite bridge located in Morgan County, Tennessee. Additionally, a triaxial accelerometer was used to monitor the vehicular dynamic loading of the composite bridge deck in parallel with fiber Bragg grating sensors. When appropriate, mid-point displacements were compared with strain-distribution measurements from the fiber optic sensor-based data.
Wavelet-Based Identification for Spinning Projectile with Gasodynamic Control Aerodynamic Coefficients Determination
Authors: Lichota Piotr , Jacewicz Mariusz , Głębocki Robert , Miedziński Dariusz
Published: May 2022

Identification of a spinning projectile controlled with gasodynamic engines is shown in this paper. A missile model with a measurement inertial unit was developed from Newton’s law of motion and its aerodynamic coefficients were identified. This was achieved by applying the maximum likelihood principle in the wavelet domain. To assess the results, this was also performed in the time domain. The outcomes were obtained for two cases: when noise was not present and when it was included in the data. In all cases, the identification was performed in the passive mode, i.e., no special system identification experiments were designed. In the noise-free case, aerodynamic coefficients were estimated with high accuracy. When noise was included in the data, the wavelet-based estimates had a drop in their accuracy, but were still very accurate, whereas for the time domain approach the estimates were considered inaccurate.
Printed Potentiometric Nitrate Sensors for Use in Soil
Authors: Baumbauer Carol L. , Goodrich Payton J. , Payne Margaret E. , Anthony Tyler , Beckstoffer Claire , Toor Anju , Silver Whendee , Arias Ana Claudia
Published: May 2022

Plant-available nitrogen, often in the form of nitrate, is an essential nutrient for plant growth. However, excessive nitrate in the environment and watershed has harmful impacts on natural ecosystems and consequently human health. A distributed network of nitrate sensors could help to quantify and monitor nitrogen in agriculture and the environment. Here, we have developed fully printed potentiometric nitrate sensors and characterized their sensitivity and selectivity to nitrate. Each sensor comprises an ion-selective electrode and a reference electrode that are functionalized with polymeric membranes. The sensitivity of the printed ion-selective electrodes was characterized by measuring their potential with respect to a commercial silver/silver chloride reference electrode in varying concentrations of nitrate solutions. The sensitivity of the printed reference electrodes to nitrate was minimized with a membrane containing polyvinyl butyral (PVB), sodium chloride, and sodium nitrate. Selectivity studies with sulphate, chloride, phosphate, nitrite, ammonium, calcium, potassium, and magnesium showed that high concentrations of calcium can influence sensor behavior. The printed ion-selective and reference electrodes were combined to form a fully printed sensor with sensitivity of −48.0 ± 3.3 mV/dec between 0.62 and 6200 ppm nitrate in solution and −47 ± 4.1 mV/dec in peat soil.
FlexLip: A Controllable Text-to-Lip System
Authors: Oneață Dan , Lőrincz Beáta , Stan Adriana , Cucu Horia
Published: May 2022

The task of converting text input into video content is becoming an important topic for synthetic media generation. Several methods have been proposed with some of them reaching close-to-natural performances in constrained tasks. In this paper, we tackle a subissue of the text-to-video generation problem, by converting the text into lip landmarks. However, we do this using a modular, controllable system architecture and evaluate each of its individual components. Our system, entitled FlexLip, is split into two separate modules: text-to-speech and speech-to-lip, both having underlying controllable deep neural network architectures. This modularity enables the easy replacement of each of its components, while also ensuring the fast adaptation to new speaker identities by disentangling or projecting the input features. We show that by using as little as 20 min of data for the audio generation component, and as little as 5 min for the speech-to-lip component, the objective measures of the generated lip landmarks are comparable with those obtained when using a larger set of training samples. We also introduce a series of objective evaluation measures over the complete flow of our system by taking into consideration several aspects of the data and system configuration. These aspects pertain to the quality and amount of training data, the use of pretrained models, and the data contained therein, as well as the identity of the target speaker; with regard to the latter, we show that we can perform zero-shot lip adaptation to an unseen identity by simply updating the shape of the lips in our model.
Determination of Quality Parameters in Mangetout (Pisum sativum L. ssp. arvense) by Using Vis/Near-Infrared Reflectance Spectroscopy
Authors: García-García María del Carmen , Martín-Expósito Emilio , Font Isabel , Martínez-García Bárbara del Carmen , Fernández Juan A. , Valenzuela Juan Luis , Gómez Pedro , Río-Celestino Mercedes del
Published: May 2022

Pisum sativum L. ssp. arvense, is colloquially called tirabeque or mangetout because it is eaten whole; its pods are recognized as a delicatessen in cooking due to its crunch on the palate and high sweetness. Furthermore, this legume is an important source of protein and antioxidant compounds. Quality control in this species requires the analysis of a large number of samples using costly and laborious conventional methods. For this reason, a non-chemical and rapid technique as near-infrared reflectance spectroscopy (NIRS) was explored to determine its physicochemical quality (color, firmness, total soluble solids, pH, total polyphenols, ascorbic acid and protein content). Pod samples from different cultivars and grown under different fertigation treatments were added to the NIRS analysis to increase spectral and chemical variability in the calibration set. Modified partial least squares regression was used for obtaining the calibration models of these parameters. The coefficients of determination in the external validation ranged from 0.50 to 0.88. The RPD (standard deviation to standard error of prediction ratio) and RER (standard deviation to range) were variable for quality parameters and showed values that were characteristic of equations suitable for quantitative prediction and screening purposes, except for the total soluble solid calibration model.
MIMO Radio Frequency Identification: A Brief Survey
Authors: Alotaibi Majid , Murad Mohsin , Alhuthali Shakir A. H. , Al-Osaimi Faisal R. , Aldosari Fahd
Published: May 2022

In this paper, we briefly look at the latest state-ot-the-art in the domain of multi-input multi-output (MIMO) radio frequency identification (RFID) systems while detailing the work done in the domain of anti-collision, range enhancements, bit error rate (BER) improvements and security. Various passive ultra-high frequency (UHF) RFID implementations are considered that employ multiple antennas at the reader and single or multiple antennas at each tag. We look at several recent works those explored MIMO for RFID receivers. When using MIMO at the backscatter channel, significant improvements can be achieved in the BER as well as range extension. With the extra reliability and increased throughput, such systems can be deployed in many important applications like large tag reading scenarios and accurate tracking. Increased throughput is directly dependent on estimation of tag quantity in a bulk reading environment and usually estimators designed for single antenna systems under-perform in such settings causing low signal to noise ratio (SNR) when employed in MIMO systems where tag signal overlapping can happen more often. One of the key challenges is to keep the design of the RFID tag simple, cutting cost and power requirement when employing anti-collision schemes. We provide a brief survey in some of the recent developments related to MIMO RFID systems, the protocols and algorithms used, and improvements achieved.
An Industrial Fault Diagnostic System Based on a Cubic Dynamic Uncertain Causality Graph
Authors: Bu Xusong , Nie Hao , Zhang Zhan , Zhang Qin
Published: May 2022

This study presents an industrial fault diagnosis system based on the cubic dynamic uncertain causality graph (cubic DUCG) used to model and diagnose industrial systems without sufficient data for model training. The system is developed based on cloud native technology. It contains two main parts, the diagnostic knowledge base and the inference method. The knowledge base was built by domain experts modularly based on professional knowledge. It represented the causality between events in the target industrial system in a visual and graphical form. During the inference, the cubic DUCG algorithm could dynamically generate the cubic causal graph according to the real-time data and perform the logic and probability calculations based on the generated cubic DUCG models, visually displaying the dynamic causal evolution of faults. To verify the system’s feasibility, we rebuild a fault-diagnosis model of the secondary circuit system of No. 1 at the Ningde nuclear power plant based on the new system. Twenty-four fault cases were used to test the diagnostic accuracy of the system, and all faults were correctly diagnosed. The results showed that it was feasible to use the cubic DUCG platform for fault diagnosis.
Between-Day Reliability of Commonly Used IMU Features during a Fatiguing Run and the Effect of Speed
Authors: Dimmick Hannah L. , van Rassel Cody R. , MacInnis Martin J. , Ferber Reed
Published: May 2022

The purpose of this study was to determine if fatigue-related changes in biomechanics derived from an inertial measurement unit (IMU) placed at the center of mass (CoM) are reliable day-to-day. Sixteen runners performed two runs at maximal lactate steady state (MLSS) on a treadmill, one run 5% above MLSS speed, and one run 5% below MLSS speed while wearing a CoM-mounted IMU. Trials were performed to volitional exhaustion or a specified termination time. IMU features were derived from each axis and the resultant. Feature means were calculated for each subject during non-fatigued and fatigued states. Comparisons were performed between the two trials at MLSS and between all four trials. The only significant fatigue state × trial interaction was the 25th percentile of the results when comparing all trials. There were no main effects for trial for either comparison method. There were main effects for fatigue state for most features in both comparison methods. Reliability, measured by an intraclass coefficient (ICC), was good-to-excellent for most features. These results suggest that fatigue-related changes in biomechanics derived from a CoM-mounted IMU are reliable day-to-day when participants ran at or around MLSS and are not significantly affected by slight deviations in speed.
Static Balance Digital Endpoints with Mon4t: Smartphone Sensors vs. Force Plate
Authors: Karlinsky Keren Tchelet , Netz Yael , Jacobs Jeremy M. , Ayalon Moshe , Yekutieli Ziv
Published: May 2022

Static balance tests are conducted in various clinics for diagnosis and treatment adjustment. As a result of population aging, the accessibility of these tests should be increased, in the clinic, and for remote patient examination. A number of publications have already conducted static balance evaluations using the sensors embedded in a smartphone. This study focuses on the applicability of using smartphone-based balance assessment on a large scale while considering ease of use, safety, and reliability. The Mon4t® app was used to acquire the postural motion using different smartphone devices, different smartphone locations, and various standing postures. The signals derived from the app were compared to the center of pressure displacement derived from a force plate. The results showed moderate to high agreement between the two methods, particularly at the tandem stance (0.69 ≤ r ≤ 0.91). Preliminary data collection was conducted on three healthy participants, followed by 50 additional healthy volunteers, aged 65+. The results demonstrated that the Mon4t app can serve as an accessible and inexpensive static balance assessment tool, both in clinical settings and for remote patient monitoring, which is key for enabling telehealth.
Technical Consideration towards Robust 3D Reconstruction with Multi-View Active Stereo Sensors
Authors: Jang Mingyu , Lee Seongmin , Kang Jiwoo , Lee Sanghoon
Published: May 2022

It is possible to construct cost-efficient three-dimensional (3D) or four-dimensional (4D) scanning systems using multiple affordable off-the-shelf RGB-D sensors to produce high-quality reconstructions of 3D objects. However, the quality of these systems’ reconstructions is sensitive to a number of factors in reconstruction pipelines, such as multi-view calibration, depth estimation, 3D reconstruction, and color mapping accuracy, because the successive pipelines to reconstruct 3D meshes from multiple active stereo sensors are strongly correlated with each other. This paper categorizes the pipelines into sub-procedures and analyze various factors that can significantly affect reconstruction quality. Thus, this paper provides analytical and practical guidelines for high-quality 3D reconstructions with off-the-shelf sensors. For each sub-procedure, this paper shows comparisons and evaluations of several methods using data captured by 18 RGB-D sensors and provide analyses and discussions towards robust 3D reconstruction. Through various experiments, it has been demonstrated that significantly more accurate 3D scans can be obtained with the considerations along the pipelines. We believe our analyses, benchmarks, and guidelines will help anyone build their own studio and their further research for 3D reconstruction.
Radio Frequency Energy Harvesting Technologies: A Comprehensive Review on Designing, Methodologies, and Potential Applications
Authors: Ibrahim Husam Hamid , Singh Mandeep Jit , Al-Bawri Samir Salem , Ibrahim Sura Khalil , Islam Mohammad Tariqul , Alzamil Ahmed , Islam Md Shabiul
Published: May 2022

Radio frequency energy harvesting (RF-EH) is a potential technology via the generation of electromagnetic waves. This advanced technology offers the supply of wireless power that is applicable for battery-free devices, which makes it a prospective alternative energy source for future applications. In addition to the dynamic energy recharging of wireless devices and a wide range of environmentally friendly energy source options, the emergence of the RF-EH technology is advantageous in facilitating various applications that require quality of service. This review highlights the abundant source of RF-EH from the surroundings sources, including nearby mobile phones, Wi-Fi, wireless local area network, broadcast television signal or DTS, and FM/AM radio signals. In contrast, the energy is captured by a receiving antenna and rectified into a working direct current voltage. This review also summarizes the power of RF-EH technology, which would provide a guideline for developing RF-EH units. The energy harvesting circuits depend on cutting-edge electrical technology to achieve significant efficiency, given that they are built to perform with considerably small current and voltage. Hence, the review includes a thorough analysis and discussion of various RF designs and their pros and cons. Finally, the latest applications of RF-EH are presented.
Smart and Future Applications of Internet of Multimedia Things (IoMT) Using Big Data Analytics
Authors: Sharma Rohit , Gavalas Damianos , Peng Sheng-Lung
Published: May 2022

This Special Issue is focused on breakthrough developments in the field of Internet of Multimedia Things (IoMT), particularly on smart and future applications of IoMT using big data analytics [...]
Contactless Inductive Flow Tomography for Real-Time Control of Electromagnetic Actuators in Metal Casting
Authors: Glavinić Ivan , Galindo Vladimir , Stefani Frank , Eckert Sven , Wondrak Thomas
Published: May 2022

Flow control of liquid metals based on the actual flow condition is important in many metallurgical applications. For instance, the liquid steel flow in the mould of a continuous caster strongly influences the product quality. The flow can be modified by an electromagnetic brake (EMBr). However, due to the lack of appropriate flow measurement techniques, the control of those actuators is usually not based on the actual flow condition. This article describes the recent developments of the Contactless Inductive Flow Tomography (CIFT) towards a real-time monitoring system, which can be used as an input to the control loop for an EMBr. CIFT relies on measuring the flow-induced perturbation of an applied magnetic field and the solution of an underlying linear inverse problem. In order to implement the CIFT reconstructions in combination with EMBr, two issues have to be solved: (i) compensation of the effects of the change in EMBr strength on the CIFT measurement system and (ii) a real-time solution of the inverse problem. We present solutions of both problems for a model of a continuous caster with a ruler-type EMBr. The EMBr introduces offsets of the measured magnetic field that are several orders of magnitude larger than the very flow-induced perturbations. The offset stems from the ferromagnetic hysteresis exhibited by the ferrous parts of the EMBr in the proximity of the measurement coils. Compensation of the offset was successfully achieved by implementing a numerical model of hysteresis to predict the offset. Real-time reconstruction was achieved by precalculating the computationally heavy matrix inverses for a predefined set of regularization parameters and choosing the optimal one in every measurement frame. Finally, we show that this approach does not hinder the reconstruction quality.
Photogrammetric Method to Determine Physical Aperture and Roughness of a Rock Fracture
Authors: Torkan Masoud , Janiszewski Mateusz , Uotinen Lauri , Baghbanan Alireza , Rinne Mikael
Published: May 2022

Rock discontinuities play an important role in the behavior of rock masses and have a high impact on their mechanical and hydrological properties, such as strength and permeability. The surfaces roughness and physical aperture of rock joints are vital characteristics in joint shear strength and fluid flow properties. This study presents a method to digitally measure the physical aperture of a rock fracture digitized using photogrammetry. A 50 cm × 50 cm rock sample of Kuru grey granite with a thoroughgoing fracture was digitized. The data was collected using a high-resolution digital camera and four low-cost cameras. The aperture and surface roughness were measured, and the influence of the camera type and 3D model rasterization on the measurement results was quantified. The results showed that low-cost cameras and smartphones can be used for generating 3D models for accurate measurement of physical aperture and roughness of rock fractures. However, the selection of appropriate rasterization grid interval plays a key role in accurate estimations. For measuring the physical aperture from the photogrammetric 3D models, reducing rasterization grid interval results in less scattered measurement results and a small rasterization grid interval of 0.1 mm is recommended. For roughness measurements, increasing the grid interval results in smaller measurement errors, and therefore a larger rasterization grid interval of 0.5 mm is recommended for high-resolution smartphones and 1 mm for other low-cost cameras.
Research and Implementation of Autonomous Navigation for Mobile Robots Based on SLAM Algorithm under ROS
Authors: Zhao Jianwei , Liu Shengyi , Li Jinyu
Published: May 2022

Aiming at the problems of low mapping accuracy, slow path planning efficiency, and high radar frequency requirements in the process of mobile robot mapping and navigation in an indoor environment, this paper proposes a four-wheel drive adaptive robot positioning and navigation system based on ROS. By comparing and analyzing the mapping effects of various 2D-SLAM algorithms (Gmapping, Karto SLAM, and Hector SLAM), the Karto SLAM algorithm is used for map building. By comparing the Dijkstra algorithm with the A* algorithm, the A* algorithm is used for heuristic searches, which improves the efficiency of path planning. The DWA algorithm is used for local path planning, and real-time path planning is carried out by combining sensor data, which have a good obstacle avoidance performance. The mathematical model of four-wheel adaptive robot sliding steering was established, and the URDF model of the mobile robot was established under a ROS system. The map environment was built in Gazebo, and the simulation experiment was carried out by integrating lidar and odometer data, so as to realize the functions of mobile robot scanning mapping and autonomous obstacle avoidance navigation. The communication between the ROS system and STM32 is realized, the packaging of the ROS chassis node is completed, and the ROS chassis node has the function of receiving speed commands and feeding back odometer data and TF transformation, and the slip rate of the four-wheel robot in situ steering is successfully measured, making the chassis pose more accurate. Simulation tests and experimental verification show that the system has a high precision in environment map building and can achieve accurate navigation tasks.
The Effect of Preconditioning Strategies on the Adsorption of Model Proteins onto Screen-Printed Carbon Electrodes
Authors: Romih Tea , Konjević Ivan , Žibret Lea , Fazarinc Ika , Beltram Ajda , Majer David , Finšgar Matjaž , Hočevar Samo B.
Published: May 2022

The preconditioning and modification of the supporting electrode surface is an essential step in every biosensor architecture. In particular, when using screen-printed carbon electrodes (SPEs) as inexpensive and convenient disposable sensor substrates, their somewhat lower electrochemical (surface) reproducibility might represent a complex hurdle. Herein, we investigated the effect of selected preconditioning strategies, such as cyclic voltammetric pretreatment, in H2SO4 and H2O2 and plasma pretreatment with a positive and negative glow discharge, which all improved the electrochemical stability of the unmodified SPEs. Furthermore, we studied the influence of preconditioning strategies on the adsorption kinetics of the two most commonly used building blocks for biosensor preparation, i.e., bovine serum albumin (BSA) and protein A. We observed an advantageous effect of all the examined preconditioning strategies for the modification of SPEs with protein A, being the most effective the negative glow discharge. On the other hand, BSA exhibited a more complex adsorption behavior, with the negative glow discharge as the only generally beneficial preconditioning strategy providing the highest electrochemical stability. Protein A revealed a more substantial impact on the electrochemical signal attenuation than BSA considering their same concentrations in the modification solutions. For both BSA and protein A, we showed that the concentrations of 5 and 10 μg mL−1 already suffice for an electrochemically satisfactorily stable electrode surface after 60 min of incubation time, except for BSA at the positive-plasma-treated electrode.
Fall Detection with the Spatial-Temporal Correlation Encoded by a Sequence-to-Sequence Denoised GAN
Authors: Hsu Wei-Wen , Guo Jing-Ming , Chen Chien-Yu , Chang Yao-Chung
Published: May 2022

Falling is a major cause of personal injury and accidental death worldwide, in particular for the elderly. For aged care, a falling alarm system is highly demanded so that medical aid can be obtained immediately when the fall accidents happen. Previous studies on fall detection lacked practical considerations to deal with real-world situations, including the camera’s mounting angle, lighting differences between day and night, and the privacy protection for users. In our experiments, IR-depth images and thermal images were used as the input source for fall detection; as a result, detailed facial information is not captured by the system for privacy reasons, and it is invariant to the lighting conditions. Due to the different occurrence rates between fall accidents and other normal activities, supervised learning approaches may suffer from the problem of data imbalance in the training phase. Accordingly, in this study, anomaly detection is performed using unsupervised learning approaches so that the models were trained only with the normal cases while the fall accident was defined as an anomaly event. The proposed system takes sequential frames as the inputs to predict future frames based on a GAN structure, and it provides (1) multi-subject detection, (2) real-time fall detection triggered by motion, (3) a solution to the situation that subjects were occluded after falling, and (4) a denoising scheme for depth images. The experimental results show that the proposed system achieves the state-of-the-art performance and copes with the real-world cases successfully.
Optimized Methods for Analytical and Functional Comparison of Biosimilar mAb Drugs: A Case Study for Avastin, Mvasi, and Zirabev
Authors: Gürel Büşra , Çapkın Eda , Parlar Ayhan , Özkan Aylin , Çorbacıoğlu Meltem , Dağlikoca Duygu Emine , Yüce Meral
Published: May 2022

Bevacizumab is a humanized therapeutic monoclonal antibody used to reduce angiogenesis, a hallmark of cancer, by binding to VEGF-A. Many pharmaceutical companies have developed biosimilars of Bevacizumab in the last decade. The official reports provided by the FDA and EMA summarize the analytical performance of biosimilars as compared to the originators without giving detailed analytical procedures. In the current study, several key methods were optimized and reported for analytical and functional comparison of bevacizumab originators (Avastin, Altuzan) and approved commercial biosimilars (Zirabev and Mvasi). This case study presents a comparative analysis of a set of biosimilars under optimized analytical conditions for the first time in the literature. The chemical structure of all products was analyzed at intact protein and peptide levels by high-resolution mass spectrometry; the major glycoforms and posttranslational modifications, including oxidation, deamidation, N-terminal PyroGlu addition, and C-terminal Lys clipping, were compared. The SPR technique was used to reveal antigen and some receptor binding kinetics of all products, and the ELISA technique was used for C1q binding affinity analysis. Finally, the inhibition performance of the samples was evaluated by an MTS-based proliferation assay in vitro. Major glycoforms were similar, with minor differences among the samples. Posttranslational modifications, except C-terminal Lys, were determined similarly, while unclipped Lys percentage was higher in Zirabev. The binding kinetics for VEGF, FcRn, FcγRIa, and C1q were similar or in the value range of originators. The anti-proliferative effect of Zirabev was slightly higher than the originators and Mvasi. The analysis of biosimilars under the same conditions could provide a new aspect to the literature in terms of the applied analytical techniques. Further studies in this field would be helpful to better understand the inter-comparability of the biosimilars.
A Brief Review of Chromatography in Croatia
Authors: Ukić Šime , Ašperger Danijela , Bolanča Tomislav
Published: May 2022

Although the Republic of Croatia is a relatively small country geographically, it can boast numerous scientists who have left indelible marks in various scientific fields. However, this paper is exclusively about chromatography as one of the most important analytical techniques of our time. The development of chromatography in the Republic of Croatia and the role that three institutions have played in it—the Faculty of Chemical Engineering and Technology of the University of Zagreb, the Croatian Society of Chemical Engineers, and the Central European Group for Separation Sciences—will be briefly discussed.
Optimization of Steam Distillation Process and Chemical Constituents of Volatile Oil from Angelicaesinensis Radix
Authors: Wan Na , Lan Jing , Wu Zhenfeng , Chen Xinying , Zheng Qin , Gong Xingchu
Published: May 2022

In this study, the steam distillation process of volatile oil from Angelicaesinensis Radix was optimized according to the concept of quality-by-design. A homemade glass volatile oil extractor was used to achieve better cooling of the volatile oil. First, the soaking time, distillation time, and liquid–material ratio were identified as potential critical process parameters by consulting the literature. Then, the three parameters were investigated by single factor experiments. The volatile oil yield increased with the extension in the distillation time, and first increased and then decreased with the increase in soaking time and liquid–material ratio. The results confirmed that soaking time, distillation time, and liquid–material ratio were all critical process parameters. The kinetics models of volatile oil distillation from Angelicaesinensis Radix were established. The diffusion model of spherical particle was found to be the best model and indicated that the major resistance of mass transfer was the diffusion of volatile oil from the inside to the surface of the medicinal herb. Furthermore, the Box–Behnken experimental design was used to study the relationship between the three parameters and volatile oil yield. A second-order polynomial model was established, with R2 exceeding 0.99. The design space of the volatile oil yield was calculated by a probability-based method. In the verification experiments, the average volatile oil yield reached 0.711%. The results showed that the model was accurate and the design space was reliable. In this study, 21 chemical constituents of volatile oil from Angelicaesinensis Radix were identified by gas chromatograph-mass spectrometer(GC-MS), accounting for 99.4% of the total volatile oil. It was found that the content of Z-ligustilide was the highest, accounting for 85.4%.
Phytochemical Characterization and Heavy Metal and Thermal Analyses of Saussurea hypoleuca Root and Evaluation of Its Anthelmintic and Antioxidant Activity In Vitro and In Silico
Authors: Elhady Sameh S. , Arshad Numera , Ishtiaq Saiqa , Bayram Roula , Amin Adnan , Bogari Hanin A. , Abdelhameed Reda F. A. , Youssef Fadia S. , Ashour Mohamed L.
Published: May 2022

Phytochemical characterization of the ethyl acetate fraction of Saussurea hypoleuca root extract resulted in the isolation of oleic acid (1) and luteolin (2), which were isolated for the first time from Saussurea hypoleuca root. A heavy metal analysis of the root powder performed using atomic absorption spectroscopy showed that the contents of iron, cadmium, lead, zinc, nickel, and copper were within the certified limits according to the WHO guidelines. Differential scanning calorimetry (DSC) revealed its crystalline and amorphous nature; meanwhile, standardization of the root with UHPLC revealed the presence of 14.79 ± 0.015 µg/mL of luteolin. Both the total methanol extract and the ethyl acetate fraction of the plant root held significant anthelmintic activity. Oleic acid and luteolin exhibited potent antioxidant activity, evidenced by their IC50 values, which were equal to 47.0 and 119.8 µg/mL, respectively, in a 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging assay. In silico studies showed that luteolin exerted the highest fitting within the binding sites of NADPH oxidase (Nox). For myeloperoxidase (MP), oleic acid revealed the best fitting in its active sites. The results of ADMET (absorption, distribution, metabolism, excretion, and toxicity) and TOPKAT (toxicity prediction) protocols revealed acceptable pharmacodynamic and pharmacokinetic characteristics, in addition to reasonable toxicity characteristics for both compounds. Thus, they can be incorporated into pharmaceutical dosage forms to combat oxidative stress.
Field and Laboratory Assessment of Different Concrete Paving Materials Thermal Behavior
Authors: Barišić Ivana , Netinger Grubeša Ivanka , Krstić Hrvoje , Kubica Dalibor
Published: May 2022

Impervious pavement surfaces within urban areas present serious environmental problems due to waterlogging, flooding and in particular, the urban heat island (UHI) phenomenon. Another issue that has recently been highlighted is user comfort in pedestrian and cycling areas. Materials that have potential for overcoming these issues include pervious concrete (PC), a new type of construction material with improved drainage properties and thermal properties. In this study, the thermal properties and behavior of commonly used concrete paving materials in urban areas (dense concrete (DC) and concrete pavers (P)) and pervious concrete (PC) paving flags were investigated and compared in terms of their thermal properties. Material behavior under different temperature conditions was investigated within laboratory research measuring thermal conductivity (λ) and the capacity for heating and cooling using infrared lamp. Complementary to laboratory tests, field research was conducted analyzing the surrounding conditions on pavement wearing course behavior under real weather conditions. Dense concrete paving material had the highest thermal conductivity coefficient and heat absorption capacity, and slowest heating and cooling speed, compared with the other paving materials. The results also highlighted the similar thermal properties of PC and P but with potentially improved user comfort for PC due to its draining properties. The base layer and surrounding characteristics had a significant influence on the thermal behavior of pavements, and future research should consider these parameters when addressing the UHI effect for different paving materials.
The Potential of GIS Tools for Diagnosing the SFS of Multi-Family Housing towards Friendly Cities—A Case Study of the EU Member State of Poland
Authors: Dawidowicz Agnieszka , Dudzińska Małgorzata
Published: May 2022

Motives: The need for sustainable urban development, including an improvement in residents’ quality of life, requires ongoing urban diagnostics. Assessments of multi-family housing estates play a very important role in this process. Population growth influences the expansion of housing estates in limited urban space. The extent to which spatial and functional structures (SFS) in housing developments meet the residents’ current needs should be evaluated. These needs undergo dynamic change and are influenced by economic, socio-cultural, sanitary, and ecological factors. Aim: The main objective of this study was to develop a methodology for assessing SFS solutions in open spaces in multi-family residential estates (MFREs) based on a complete list of SFS indicators, and to determine the potential of GIS tools and selected open data sources for automating this process. GIS was used to represent data. The intermediate goal was to determine differences in the SFS solutions of two MFREs that were built with different technologies and urban layouts in the last 70 years in the city of Olsztyn (Poland). Methods: An empirical study was conducted with the use of qualitative and quantitative methods based on a review of the literature, the results of a resident survey, and an analysis of spatial data in ArcGIS and QGIS software. Results: The residents’ needs for SFS in MFREs were identified. A list of 26 SFS indicators and their values (on a 3-point scale) was developed to assess multi-family housing. The applicability of GIS software and spatial data from the national spatial data infrastructure (NSDI) and other sources was assessed in the process. The research method was tested to reveal differences in SFS solutions in the compared MFREs.
The Role of Sustainability Reporting in Reducing Information Asymmetry: The Case of Family- and Non-Family-Controlled Firms
Authors: Al Natour Abdul Rahman , Meqbel Rasmi , Kayed Salah , Zaidan Hala
Published: May 2022

This study aims to examine the link between sustainability reporting and information asymmetry in family- and non-family-controlled firms for a sample of 641 UK firms listed in the FTSE all-share index during the period 2010–2017. The findings show a negative and significant relationship between sustainability reporting and IA. The results also show that the sustainability reporting–information asymmetry nexus is weaker in family-controlled firms. The findings of this study should improve our understanding of sustainability reporting motivations, particularly in companies that are controlled by families. Moreover, an explanation of the role of family-controlled firms in mitigating or exacerbating this relationship will surely help the British regulators improve corporate governance rules related to various ownership structures. For policy makers, it is important to confirm that sustainability reporting is representative of actual corporate activities and is not only used to mislead stakeholders.
Repositioning and Optimal Re-Allocation of Empty Containers: A Review of Methods, Models, and Applications
Authors: Abdelshafie Alaa , Salah May , Kramberger Tomaž , Dragan Dejan
Published: May 2022

Managing empty-container movements is one of the most challenging logistics problems in the shipping field. With the growth of global trade imbalance, the repositioning process has become necessary, immediately after emptying a container. The main contribution of this research paper is to enrich the most frequently used methods, models, and applications in the literature, for relaxing the empty-container-repositioning problem. The article presents practices that vary between organizational policies, technical solutions, and modelling applications. A review of optimization models has been used for comparisons, based on specified criteria, such as the time frame, inputs, outputs, scale of the project, and value. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was applied through the online database Web of Science (WOS). It gives a comprehensive description of all the relevant published documents. On the basis of conducting a brief systematic review, future research opportunities have been determined, considering the emerging phenomena in container transport chains.
Characterization and Prediction of Water Stress Using Time Series and Artificial Intelligence Models
Authors: Gorlapalli Amuktamalyada , Kallakuri Supriya , Sreekanth Pagadala Damodaram , Patil Rahul , Bandumula Nirmala , Ondrasek Gabrijel , Admala Meena , Gireesh Channappa , Anantha Madhyavenkatapura Siddaiah , Parmar Brajendra , Yadav Brahamdeo Kumar , Sundaram Raman Meenakshi , Rathod Santosha
Published: May 2022

In agroecosystems, drought is a critical climatic phenomenon that affects evapotranspiration and induces water stress in plants. The objective in this study was to characterize and forecast water stress in the Hyderabad region of India using artificial intelligence models. The monthly precipitation data for the period 1982–2021 was characterized by the standardized precipitation index (SPI) and modeled using the classical autoregressive integrated moving average (ARIMA) model and artificial intelligence (AI), i.e., artificial neural network (ANN) and support vector regression (SVR) model. The results show that on the short-term SPI3 time scale the studied region experienced extreme water deficit in 1983, 1992, 1993, 2007, 2015, and 2018, while on the mid-term SPI6 time scale, 1983, 1991, 2011, and 2016 were extremely dry. In addition, the prediction of drought at both SPI3 and SPI6 time scales by AI models outperformed the classical ARIMA models in both, training and validation data sets. Among applied models, the SVR model performed better than other models in modeling and predicting drought (confirmed by root mean square error—RMSE), while the Diebold–Mariano test confirmed that SVR output was significantly superior. A reduction in the prediction error of SVR by 48% and 32% (vs. ARIMA), and by 21% and 26% (vs. ANN) was observed in the test data sets for both SPI3 and SPI6 time scales. These results may be due to the ability of the SVR model to account for the nonlinear and complex patterns in the input data sets against the classical linear ARIMA model. These results may contribute to more sustainable and efficient management of water resources/stress in cropping systems.