bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance Characterization of a Fully Transportable Mid-Infrared Laser Heterodyne Radiometer (LHR)
Authors: Shen Fengjiao , Hu Xueyou , Lu Jun , Xue Zhengyue , Li Jun , Tan Tu , Cao Zhensong , Gao Xiaoming , Chen Weidong
Published: January 2023

A fully transportable laser heterodyne radiometer (LHR), involving a flexible polycrystalline mid-infrared (PIR) fiber-coupling system and operating around 8 µm, was characterized and optimized with the help of a calibrated high temperature blackbody source to simulate solar radiation. Compared to a mid-IR free-space sunlight coupling system, usually used in a current LHR, such a fiber-coupling system configuration makes the mid-infrared (MIR) LHR fully transportable. The noise sources, heterodyne signal, and SNR of the MIR LHR were analyzed, and the optimum operating local oscillator (LO) photocurrent was experimentally obtained. The spectroscopic performance of the MIR LHR was finally evaluated. This work demonstrated that the developed fully transportable MIR LHR could be used for ground-based atmospheric sounding measurements of multiple trace gases in the atmospheric column. In addition, it also has high potential for applications on spacecraft or on an airborne platform.
Design Methodology for a Magnetic Levitation System Based on a New Multi-Objective Optimization Algorithm
Authors: Reznichenko Igor , Podržaj Primož
Published: January 2023

Multi-objective (MO) optimization is a developing technique for increasing closed-loop performance and robustness. However, its applications to control engineering mostly concern first or second order approximation models. This article proposes a novel MO algorithm, suitable for the design and control of mechanical systems, which does not require any order reduction techniques. The controller parameters are determined directly from a special type of rapid analysis of simulated transient responses. The case study presented in this article consists of a magnetic levitation system. Certain difficulties such as the nonlinearity identification of the magnetic force and duo magnetic field sensor scheme were addressed. To point out the advantages of using the developed approach, the simulations as well as the experiments performed with the help of the created algorithm were compared to those made with common MO algorithms.
A Codeword-Independent Localization Technique for Reconfigurable Intelligent Surface Enhanced Environments Using Adversarial Learning
Authors: Luo Xuanshu , Meratnia Nirvana
Published: January 2023

Reconfigurable Intelligent Surfaces (RISs) not only enable software-defined radio in modern wireless communication networks but also have the potential to be utilized for localization. Most previous works used channel matrices to calculate locations, requiring extensive field measurements, which leads to rapidly growing complexity. Although a few studies have designed fingerprint-based systems, they are only feasible under an unrealistic assumption that the RIS will be deployed only for localization purposes. Additionally, all these methods utilize RIS codewords for location inference, inducing considerable communication burdens. In this paper, we propose a new localization technique for RIS-enhanced environments that does not require RIS codewords for online location inference. Our proposed approach extracts codeword-independent representations of fingerprints using a domain adversarial neural network. We evaluated our solution using the DeepMIMO dataset. Due to the lack of results from other studies, for fair comparisons, we define oracle and baseline cases, which are the theoretical upper and lower bounds of our system, respectively. In all experiments, our proposed solution performed much more similarly to the oracle cases than the baseline cases, demonstrating the effectiveness and robustness of our method.
Wearable Sensing System for NonInvasive Monitoring of Intracranial BioFluid Shifts in Aerospace Applications
Authors: Griffith Jacob L. , Cluff Kim , Downes Grant M. , Eckerman Brandon , Bhandari Subash , Loflin Benjamin E. , Becker Ryan , Alruwaili Fayez , Mohammed Noor
Published: January 2023

The alteration of the hydrostatic pressure gradient in the human body has been associated with changes in human physiology, including abnormal blood flow, syncope, and visual impairment. The focus of this study was to evaluate changes in the resonant frequency of a wearable electromagnetic resonant skin patch sensor during simulated physiological changes observed in aerospace applications. Simulated microgravity was induced in eight healthy human participants (n = 8), and the implementation of lower body negative pressure (LBNP) countermeasures was induced in four healthy human participants (n = 4). The average shift in resonant frequency was −13.76 ± 6.49 MHz for simulated microgravity with a shift in intracranial pressure (ICP) of 9.53 ± 1.32 mmHg, and a shift of 8.80 ± 5.2097 MHz for LBNP with a shift in ICP of approximately −5.83 ± 2.76 mmHg. The constructed regression model to explain the variance in shifts in ICP using the shifts in resonant frequency (R2 = 0.97) resulted in a root mean square error of 1.24. This work demonstrates a strong correlation between sensor signal response and shifts in ICP. Furthermore, this study establishes a foundation for future work integrating wearable sensors with alert systems and countermeasure recommendations for pilots and astronauts.
Fault Monitoring Based on the VLSW-MADF Test and DLPPCA for Multimodal Processes
Authors: Wang Shu , Wang Yicheng , Tong Jiarong , Chang Yuqing
Published: January 2023

Actual industrial processes often exhibit multimodal characteristics, and their data exhibit complex features, such as being dynamic, nonlinear, multimodal, and strongly coupled. Although many modeling approaches for process fault monitoring have been proposed in academia, due to the complexity of industrial data, challenges remain. Based on the concept of multimodal modeling, this paper proposes a multimodal process monitoring method based on the variable-length sliding window-mean augmented Dickey–Fuller (VLSW-MADF) test and dynamic locality-preserving principal component analysis (DLPPCA). In the offline stage, considering the fluctuation characteristics of data, the trend variables of data are extracted and input into VLSW-MADF for modal identification, and different modalities are modeled separately using DLPPCA. In the online monitoring phase, the previous moment’s historical modal information is fully utilized, and modal identification is performed only when necessary to reduce computational cost. Finally, the proposed method is validated to be accurate and effective for modal identification, modeling, and online monitoring of multimodal processes in TE simulation and actual plant data. The proposed method improves the fault detection rate of multimodal process fault monitoring by about 14% compared to the classical DPCA method.
Comparative Study of Fuzzy Rule-Based Classifiers for Medical Applications
Authors: Czmil Anna
Published: January 2023

The use of machine learning in medical decision support systems can improve diagnostic accuracy and objectivity for clinical experts. In this study, we conducted a comparison of 16 different fuzzy rule-based algorithms applied to 12 medical datasets and real-world data. The results of this comparison showed that the best performing algorithms in terms of average results of Matthews correlation coefficient (MCC), area under the curve (AUC), and accuracy (ACC) was a classifier based on fuzzy logic and gene expression programming (GPR), repeated incremental pruning to produce error reduction (Ripper), and ordered incremental genetic algorithm (OIGA), respectively. We also analyzed the number and size of the rules generated by each algorithm and provided examples to objectively evaluate the utility of each algorithm in clinical decision support. The shortest and most interpretable rules were generated by 1R, GPR, and C45Rules-C. Our research suggests that GPR is capable of generating concise and interpretable rules while maintaining good classification performance, and it may be a valuable algorithm for generating rules from medical data.
Elastomer-Embedded Multiplexed Optical Fiber Sensor System for Multiplane Shape Reconstruction
Authors: Leal-Junior Arnaldo , Macedo Leandro , Avellar Leticia , Frizera Anselmo
Published: January 2023

This paper presents the development and application of a multiplexed intensity variation-based sensor system for multiplane shape reconstruction. The sensor is based on a polymer optical fiber (POF) with sequential lateral sections coupled with a flexible light-emitting diode (LED) belt. The optical source modulation enables the development of 30 independent sensors using one photodetector, where the sensor system is embedded in polydimethylsiloxane (PDMS) resin in two configurations. Configuration 1 is a continuous PDMS layer applied in the interface between the flexible LED belt and the POF, whereas Configuration 2 comprises a 20 mm length PDMS layer only on each lateral section and LED region. The finite element method (FEM) is employed for the strain distribution evaluation in different conditions, including the strain distribution on the sensor system subjected to momentums in roll, pitch and yaw conditions. The experimental results of pressure application at 30 regions for each configuration indicated a higher sensitivity of Configuration 1 (83.58 a.u./kPa) when compared with Configuration 2 (40.06 a.u./kPa). However, Configuration 2 presented the smallest cross-sensitivity between sequential sensors (0.94 a.u./kPa against 45.5 a.u./kPa of Configuration 1). Then, the possibility of real-time loading condition monitoring and shape reconstruction is evaluated using Configuration 1 subjected to momentums in roll, pitch and yaw, as well as mechanical waves applied on the sensor structure. The strain distribution on the sensor presented the same pattern as the one obtained in the simulations, and the real-time response of each sensor was obtained for each case. In addition, the possibility of real-time loading condition estimation is analyzed using the k-means algorithm (an unsupervised machine learning approach) for the clusterization of data regarding the loading condition. The comparison between the predicted results and the real ones shows a 90.55% success rate. Thus, the proposed sensor device is a feasible alternative for integrated sensing in movement analysis, structural health monitoring submitted to dynamic loading and robotics for the assessment of the robot structure.
Comparison of Artificial Neural Network and Polynomial Approximation Models for Reflectance Spectra Reconstruction
Authors: Lazar Mihael , Hladnik Aleš
Published: January 2023

Knowledge of surface reflection of an object is essential in many technological fields, including graphics and cultural heritage. Compared to direct multi- or hyper-spectral capturing approaches, commercial RGB cameras allow for a high resolution and fast acquisition, so the idea of mapping this information into a reflectance spectrum (RS) is promising. This study compared two modelling approaches based on a training set of RGB-reflectance pairs, one implementing artificial neural networks (ANN) and the other one using multivariate polynomial approximation (PA). The effect of various parameters was investigated: the ANN learning algorithm—standard backpropagation (BP) or Levenberg-Marquardt (LM), the number of hidden layers (HLs) and neurons, the degree of multivariate polynomials in PA, the number of inputs, and the training set size on both models. In the two-layer ANN with significantly fewer inputs than outputs, a better MSE performance was found where the number of neurons in the first HL was smaller than in the second one. For ANNs with one and two HLs with the same number of neurons in the first layer, the RS reconstruction performance depends on the choice of BP or LM learning algorithm. RS reconstruction methods based on ANN and PA are comparable, but the ANN models’ better fine-tuning capabilities enable, under realistic constraints, finding ANNs that outperform PA models. A profiling approach was proposed to determine the initial number of neurons in HLs—the search centre of ANN models for different training set sizes.
Estimation of One-Repetition Maximum, Type, and Repetition of Resistance Band Exercise Using RGB Camera and Inertial Measurement Unit Sensors
Authors: Hwang Byunggon , Shim Gyuseok , Choi Woong , Kim Jaehyo
Published: January 2023

Resistance bands are widely used nowadays to enhance muscle strength due to their high portability, but the relationship between resistance band workouts and conventional dumbbell weight training is still unclear. Thus, this study suggests a convolutional neural network model that identifies the type of band workout and counts the number of repetitions and a regression model that deduces the band force that corresponds to the one-repetition maximum. Thirty subjects performed five different exercises using resistance bands and dumbbells. Joint movements during each exercise were collected using a camera and an inertial measurement unit. By using different types of input data, several models were created and compared. As a result, the accuracy of the convolutional neural network model using inertial measurement units and joint position is 98.83%. The mean absolute error of the repetition counting algorithm ranges from 0.88 (seated row) to 3.21 (overhead triceps extension). Lastly, the values of adjusted r-square for the 5 exercises are 0.8415 (chest press), 0.9202 (shoulder press), 0.8429 (seated row), 0.8778 (biceps curl), and 0.9232 (overhead triceps extension). In conclusion, the model using 10-channel inertial measurement unit data and joint position data has the best accuracy. However, the model needs to improve the inaccuracies resulting from non-linear movements and one-time performance.
Technological Transformation of Telco Operators towards Seamless IoT Edge-Cloud Continuum
Authors: Oztoprak Kasim , Tuncel Yusuf Kursat , Butun Ismail
Published: January 2023

This article investigates and discusses challenges in the telecommunication field from multiple perspectives, both academic and industry sides are catered for, surveying the main points of technological transformation toward edge-cloud continuum from the view of a telco operator to show the complete picture, including the evolution of cloud-native computing, Software-Defined Networking (SDN), and network automation platforms. The cultural shift in software development and management with DevOps enabled the development of significant technologies in the telecommunication world, including network equipment, application development, and system orchestration. The effect of the aforementioned cultural shift to the application area, especially from the IoT point of view, is investigated. The enormous change in service diversity and delivery capabilities to mass devices are also discussed. During the last two decades, desktop and server virtualization has played an active role in the Information Technology (IT) world. With the use of OpenFlow, SDN, and Network Functions Virtualization (NFV), the network revolution has got underway. The shift from monolithic application development and deployment to micro-services changed the whole picture. On the other hand, the data centers evolved in several generations where the control plane cannot cope with all the networks without an intelligent decision-making process, benefiting from the AI/ML techniques. AI also enables operators to forecast demand more accurately, anticipate network load, and adjust capacity and throughput automatically. Going one step further, zero-touch networking and service management (ZSM) is proposed to get high-level human intents to generate a low-level configuration for network elements with validated results, minimizing the ratio of faults caused by human intervention. Harmonizing all signs of progress in different communication technologies enabled the use of edge computing successfully. Low-powered (from both energy and processing perspectives) IoT networks have disrupted the customer and end-point demands within the sector, as such paved the path towards devising the edge computing concept, which finalized the whole picture of the edge-cloud continuum.
The Digital Twin Paradigm Applied to Soil Quality Assessment: A Systematic Literature Review
Authors: Silva Letícia , Rodríguez-Sedano Francisco , Baptista Paula , Coelho João Paulo
Published: January 2023

This article presents the results regarding a systematic literature review procedure on digital twins applied to precision agriculture. In particular, research and development activities aimed at the use of digital twins, in the context of predictive control, with the purpose of improving soil quality. This study was carried out through an exhaustive search of scientific literature on five different databases. A total of 158 articles were extracted as a result of this search. After a first screening process, only 11 articles were considered to be aligned with the current topic. Subsequently, these articles were categorised to extract all relevant information, using the preferred reporting items for systematic reviews and meta-analyses methods. Based on the obtained results, there are two main conclusions to draw: First, when compared with industrial processes, there is only a very slight rising trend regarding the use of digital twins in agriculture. Second, within the time frame in which this work was carried out, it was not possible to find any published paper on the use of digital twins for soil quality improvement within a model predictive control context.
LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)
Authors: Do Jae Seok , Kareem Akeem Bayo , Hur Jang-Wook
Published: January 2023

Industry 5.0, also known as the “smart factory”, is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system’s operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system.
Design and Validation of a Low-Level Controller for Hierarchically Controlled Exoskeletons
Authors: Herron Connor W. , Fuge Zachary J. , Kogelis Madeline , Tremaroli Nicholas J. , Kalita Bhaben , Leonessa Alexander
Published: January 2023

In this work, a generalized low-level controller is presented for sensor collection, motor input, and networking with a high-level controller. In hierarchically controlled exoskeletal systems, which utilize series elastic actuators (SEAs), the hardware for sensor collection and motor command is separated from the computationally expensive high-level controller algorithm. The low-level controller is a hardware device that must collect sensor feedback, condition and filter the measurements, send actuator inputs, and network with the high-level controller at a real-time rate. This research outlines the hardware of two printed circuit board (PCB) designs for collecting and conditioning sensor feedback from two SEA subsystems and an inertial measurement unit (IMU). The SEAs have a joint and motor encoder, motor current, and force sensor feedback that can be measured using the proposed generalized low-level controller presented in this work. In addition, the high and low-level networking approach is discussed in detail, with a full breakdown of the data storage within a communication frame during the run-time operation. The challenges of device synchronization and updates rates of high and low-level controllers are also discussed. Further, the low-level controller was validated using a pendulum test bed, complete with full sensor feedback, including IMU results for two open-loop scenarios. Moreover, this work can be extended to other hierarchically controlled robotic systems that utilize SEA subsystems, such as humanoid robots, assistive rehabilitation robots, training simulators, and robotic-assisted surgical devices. The hardware and software designs presented in this work are available open source to enable researchers with a direct solution for data acquisition and the control of low-level devices in a robotic system.
Diffusion of White-Hat Botnet Using Lifespan with Controllable Ripple Effect for Malware Removal in IoT Networks
Authors: Bin Ahmadon Mohd Anuaruddin , Yamaguchi Shingo
Published: January 2023

Self-propagating malware has been infecting thousands of IoT devices and causing security breaches worldwide. Mitigating and cleaning self-propagating malware is important but challenging because they propagate unpredictably. White-hat botnets have been used to combat self-propagating malware with the concept of fight fire-with-fire. However, white-hat botnets can also overpopulate and consume the resource of IoT devices. Later, lifespan was introduced as a self-destruct measure to restrain white-hat botnets’ overpopulation, but unable to change based on real-time situations. This paper proposes a method for diffusing white-hat botnets by controlling lifespan. The main contribution of this paper is that the method uses a dynamic lifespan that increases and decreases based on the congregation’s situation of the self-propagating malware and white-hat botnets. The method tackles the problem of overpopulation of white-hat botnets since they can self-propagate by controlling the ripple effect that widens the white-hat botnet’s diffusion area but suppresses the number of white-hat botnets to achieve a ’zero-botnet’ situation. The effectiveness in reducing the overpopulation rate was confirmed. The experiment result showed that the ripple effect could reduce the number of white-hat botnets in the network by around 80%, depending on different control parameters.
Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
Authors: Duan Ruifeng , Chen Ziyu , Zhang Haiyan , Wang Xu , Meng Wei , Sun Guodong
Published: January 2023

Aiming to address the problems of the high bit error rate (BER) of demodulation or low classification accuracy of modulation signals with a low signal-to-noise ratio (SNR), we propose a double-residual denoising autoencoder method with a channel attention mechanism, referred to as DRdA-CA, to improve the SNR of modulation signals. The proposed DRdA-CA consists of an encoding module and a decoding module. A squeeze-and-excitation (SE) ResNet module containing one residual connection is modified and then introduced into the autoencoder as the channel attention mechanism, to better extract the characteristics of the modulation signals and reduce the computational complexity of the model. Moreover, the other residual connection is further added inside the encoding and decoding modules to optimize the network degradation problem, which is beneficial for fully exploiting the multi-level features of modulation signals and improving the reconstruction quality of the signal. The ablation experiments prove that both the improved SE module and dual residual connections in the proposed method play an important role in improving the denoising performance. The subsequent experimental results show that the proposed DRdA-CA significantly improves the SNR values of eight modulation types in the range of −12 dB to 8 dB. Especially for 16QAM and 64QAM, the SNR is improved by 8.38 dB and 8.27 dB on average, respectively. Compared to the DnCNN denoising method, the proposed DRdA-CA makes the average classification accuracy increase by 67.59∼74.94% over the entire SNR range. When it comes to the demodulation, compared with the RLS and the DnCNN denoising algorithms, the proposed denoising method reduces the BER of 16QAM by an average of 63.5% and 40.5%, and reduces the BER of 64QAM by an average of 46.7% and 18.6%. The above results show that the proposed DRdA-CA achieves the optimal noise reduction effect.
Automatic Detection of Cognitive Impairment with Virtual Reality
Authors: Mannan Farzana A. , Porffy Lilla A. , Joyce Dan W. , Shergill Sukhwinder S. , Celiktutan Oya
Published: January 2023

Cognitive impairment features in neuropsychiatric conditions and when undiagnosed can have a severe impact on the affected individual’s safety and ability to perform daily tasks. Virtual Reality (VR) systems are increasingly being explored for the recognition, diagnosis and treatment of cognitive impairment. In this paper, we describe novel VR-derived measures of cognitive performance and show their correspondence with clinically-validated cognitive performance measures. We use an immersive VR environment called VStore where participants complete a simulated supermarket shopping task. People with psychosis (k=26) and non-patient controls (k=128) participated in the study, spanning ages 20–79 years. The individuals were split into two cohorts, a homogeneous non-patient cohort (k=99 non-patient participants) and a heterogeneous cohort (k=26 patients, k=29 non-patient participants). Participants’ spatio-temporal behaviour in VStore is used to extract four features, namely, route optimality score, proportional distance score, execution error score, and hesitation score using the Traveling Salesman Problem and explore-exploit decision mathematics. These extracted features are mapped to seven validated cognitive performance scores, via linear regression models. The most statistically important feature is found to be the hesitation score. When combined with the remaining extracted features, the multiple linear regression model resulted in statistically significant results with R2 = 0.369, F-Stat = 7.158, p(F-Stat) = 0.000128.
Real Depth-Correction in Ground Penetrating RADAR Data Analysis for Bridge Deck Evaluation
Authors: Pashoutani Sepehr , Zhu Jinying
Published: January 2023

When ground penetrating radar (GPR) is used for the non-destructive evaluation of concrete bridge decks, the rebar reflection amplitudes should be corrected for rebar depths to account for the geometric spreading and material attenuation of the electromagnetic wave in concrete. Most current depth-correction methods assume a constant EM wave velocity in the entire bridge deck and correct GPR amplitudes based on the two-way travel time (TWTT) instead of the actual rebar depth. In this paper, we proposed a depth-correction algorithm based on the real rebar depths. To compare different depth-correction methods, we used gprMax software to simulate GPR signals in four models with various dielectric constants and conductivity. The comparison shows that the TWTT-based depth-correction method tends to over-correct GPR amplitudes so that underestimates the deterioration level of concrete decks at certain locations. Two depth-based correction methods are proposed that use migrated amplitudes and further normalize the corrected amplitude by rebar depth (attenuation rate). These methods are then applied to GPR data collected on two bridges, and the results were validated by other NDE methods and chloride concentration test.
Design of a CMOS Image Sensor with Bi-Directional Gamma-Corrected Digital-Correlated Double Sampling
Authors: Cho Jaehee , Choo Hyunseon , Lee Suhyeon , Yoon Seungju , Kam Gyuwon , Kim Sooyoun
Published: January 2023

We present a 640 × 480 CMOS image sensor (CIS) with in-circuit bi-directional gamma correction with a proposed digital-correlated double sampling (CDS) structure. To operate the gamma correction in the CIS, the transfer function of the analog-to-digital converter can be changed by controlling the clock frequency of the counter using analog CDS. However, the analog CDS is vulnerable to capacitor mismatch, clock feedthrough, etc. Therefore, we propose a digital-CDS method with a hold-and-go counter structure to operate the bi-directional gamma correction in the CIS. The proposed CIS achieves a 10-bit resolution using a global log-exponential counter and configurable column reset counter with a resolution of 8/9 bits. The sensor was fabricated in a 0.11 μm CIS process, and the full chip area was 5.9 mm × 5.24 mm. The measurement results showed a maximum SNR improvement of 10.41% with the proposed bi-directional gamma-corrected digital-CDS with the hold-and-go counter. The total power consumption was 6.3 mW at a rate of 16.6 frames per second with analog, pixel, and digital supply voltages of 3.3 V, 3.3 V, and 1.5 V, respectively.
Effect of a Passive Exosuit on Sit-to-Stand Performance in Geriatric Patients Measured by Body-Worn Sensors—A Pilot Study
Authors: Lindemann Ulrich , Krespach Jana , Daub Urban , Schneider Marc , Sczuka Kim S. , Klenk Jochen
Published: January 2023

Standing up from a seated position is a prerequisite for any kind of physical mobility but many older persons have problems with the sit-to-stand (STS) transfer. There are several exosuits available for industrial work, which might be adapted to the needs of older persons to support STS transfers. However, objective measures to quantify and evaluate such systems are needed. The aim of this study was to quantify the possible support of an exosuit during the STS transfer of geriatric patients. Twenty-one geriatric patients with a median age of 82 years (1.–3.Q. 79–84 years) stood up at a normal pace (1) from a chair without using armrests, (2) with using armrests and (3) from a bed with pushing off, each condition with and without wearing an exosuit. Peak angular velocity of the thighs was measured by body-worn sensors. It was higher when standing up with exosuit support from a bed (92.6 (1.–3.Q. 84.3–116.2)°/s versus 79.7 (1.–3.Q. 74.6–98.2)°/s; p = 0.014) and from a chair with armrests (92.9 (1.–3.Q. 78.3–113.0)°/s versus 77.8 (1.–3.Q. 59.3–100.7)°/s; p = 0.089) compared to no support. There was no effect of the exosuit when standing up from a chair without using armrests. In general, it was possible to quantify the support of the exosuit using sensor-measured peak angular velocity. These results suggest that depending on the STS condition, an exosuit can support older persons during the STS transfer.
A Contention-Free Cooperative MAC Protocol for Eliminating Heterogenous Collisions in Vehicular Ad Hoc Networks
Authors: Linn Nyi Nyi , Liu Kai , Gao Qiang
Published: January 2023

In vehicular ad hoc networks (VANETs), efficient data dissemination to a specified number of vehicles with minimum collisions and limited access delay is critical for accident prevention in road safety. However, packet collisions have a significant impact on access delay, and they may lead to unanticipated link failure when a range of diversified collisions are combined due to complex traffic conditions and rapid changes in network topology. In this paper, we propose a distributed contention-free cooperative medium access control (CFC-MAC) protocol to reduce heterogenous collisions and unintended access delay in stochastic traffic scenarios. Firstly, we develop a cooperative communication system model and cooperative forwarding mechanism to explore the optimum road path between the source and destination by identifying the potential cooperative vehicles. Secondly, we propose a vectorized trajectory estimation mechanism to suppress merging collisions by identifying the relative velocity of vehicles with different speeds in a specific time interval. Based on the case study, typical heterogeneous collisions and aggregated heterogeneous collisions at dissociated positions and associated positions are investigated. In both cases, we propose the corresponding collision-resolving mechanisms by methodically recapturing the colliding time slot or acquiring the available free time slots after identifying the access vehicles and comparing the received signal strengths. Performance analysis for collision probability and access delay is conducted. Finally, the simulation results show that the proposed protocol can achieve deterministic access delay and a minimal collision rate, substantially outperforming the existing solutions.
User-Driven Relay Beamforming for mmWave Massive Analog-Relay MIMO
Authors: Iwabuchi Masashi , Ramamoorthi Yoghitha , Sakaguchi Kei
Published: January 2023

Sixth-generation mobile communication (6G) aims to further improve capacity and reliability by controlling the radio propagation environment. Millimeter wave (mmWave) high-frequency band communication offers large bandwidth at the cost of high attenuation, even for smaller distances. Due to this, fewer multiple input multiple outputs (MIMO) multiplexing is possible at the base station (BS). Distributed analog relay nodes with beamforming capability improve the received power and MIMO multiplexing of mmWave communication. Due to limited signal processing, the analog relay node cannot perform beam search and tracking using these mmWave reference signals. The beam search and tracking are possible at BS or user equipment at the cost of increased control overhead. To reduce this overhead and provide relay-based 6G communication, we propose user-driven relay beamforming methods which can obtain the benefits of a massive analog relay MIMO. Assuming vehicular-to-everything (V2X) as a 6G application, we considered a relay-beam control method that uses the user information (location, velocity, acceleration, and direction of the terminal) contained in intelligent transport systems (ITS) messages called Cooperative Awareness Message (CAM). Simulation results show that the proposed method significantly reduces the overhead and the obtains benefits of the massive analog-relay MIMO. Furthermore, the accuracy of CAM’s location information, the control period, and the effects of UE mobility are evaluated and presented. The results also show that the proposed method can work effectively in future V2X applications.
Advanced Sensing and Safety Control for Connected and Automated Vehicles
Authors: Huang Chao , Wang Yafei , Hang Peng , Zuo Zhiqiang , Leng Bo
Published: January 2023

The connected and automated vehicle (CAV) is a promising technology, anticipated to enhance the safety and effectiveness of mobility [...]
Date Palm Seed Extract for Mild Steel Corrosion Prevention in HCl Medium
Authors: Mohammed Naba Jasim , Othman Norinsan Kamil , Al-Gburi Ahmed Jamal Abdullah , Yusop Rahimi M.
Published: January 2023

The inhibition effects of the date palm seed extract corrosion of mild steel in 0.5 M HCl at different concentrations are investigated by potentiodynamic polarisation (PDP), electrochemical impedance spectroscopy (EIS) and weight loss tests. Additionally, this study provides a fundamental understanding of aromatic adsorption on iron (Fe) surfaces. Furthermore, the surface morphology and the extracts are performed using scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). The maximum inhibition efficiency of 95, 96, and 91% were realised at 1400 mg/L for PDP, EIS, and weight loss, respectively. The inhibitive action of the DPS extract against mild steel corrosion in an acid solution has been supported by SEM analysis. The FTIR showed that the extract contained hydroxyl (−OH) and methoxyl (−OCH3) functional groups. The DFT depicted the adsorption sites at the oxygen (O) and carbon (C) atoms as deduced from the Fukui functions, Mulliken atomic charge, and the highest occupied molecular orbital-lowest occupied molecular orbital (HOMO-LUMO) analysis. The DPS preferred to form chemical bonds by donating electrons to the Fe surface. The coordinate bonds between the O and C atoms and the metal surface resulted in a high inhibition efficiency value. In conclusion, date palm seed extract is an effective inhibitor to protect mild steel from corrosion in an acidic medium.
Highly Sensitive In-Capillary Derivatization and Field Amplified Sample Stacking to Analyze Narcotic Drugs in Human Serum by Capillary Zone Electrophoresis
Authors: Malak Monika , Ebrahim Hager , Sonbol Heba , Ali Ahmed , Aboulella Yasmine , Hadad Ghada , Emara Samy
Published: January 2023

An in-capillary derivatization (in-CAP-D) integrated with field amplified sample stacking (FASS) has been developed for the determination of morphine (MOR) and its metabolite, morphine-6-glucuronide (MOR-6-G) in human serum using capillary zone electrophoresis (CZE) and fluorescence detection (in-Cap-D-FASS-CZE). Acetonitrile was employed in removing proteins and extracting MOR and MOR-6-G into the clear supernatant containing codeine (COD) as an internal standard (IS). The derivatization was achieved in an in-capillary mode by introducing the acetonitrile-treated samples into a running electrolyte containing an oxidizing agent of potassium ferricyanide, whereas MOR, MOR-6-G and COD were oxidized into dimer derivatives with highly fluorescent intensity. The effectiveness and sensitivity of the in-Cap-D-FASS-CZE method were affected by many parameters, and the following conditions were found to be optimal: 70 m Mdisodium tetraboratedecahydrate (pH, 10.5), 0.30 mM ferrricyanide and a separation voltage of 10 kV. In order to perform the FASS, samples were electrokinetically injected for 20 s at 20 kV into the capillary that was pre-field with a 4 s water plug. Analysis was performed at ambient temperature (22 ± 1 °C). The method’s validation revealed good linearity with respect to peak area ratios of MOR and MOR-6-G with the IS and the corresponding concentrations over the ranges of 1–2000 and 1.2 to 2000 ng/mL, respectively. Following one oral dose of controlled-release MOR sulphate tablet, the validated in-Cap-D-FASS-CZE method successfully enabled the determination of MOR and MOR-6-G in clinical serum samples.
Sailing Uncharted Waters with Old Boats? COVID-19 and the Digitalization and Professionalization of Presidential Campaigns in Portugal
Authors: Santana-Pereira José , Ferrinho Lopes Hugo , Nina Susana Rogeiro
Published: January 2023

This article investigates the extent to which the COVID-19 pandemic fostered significant shifts in election campaigning. The argument is that COVID-19 might have had an impact on both digitalization and professionalization, which might have been regarded as necessary strategies to curb the difficulties brought about by the pandemic. We apply a most similar systems design with a threefold comparative scheme in order to capture and isolate such effects in the campaigns preceding the 2021 Portuguese presidential elections, using data from campaign spending, campaign activities, and social media activity and impact. Results show that the pandemic crisis has not, generally speaking, brought about a higher level of digitalization of electoral campaigns, in spite of online events having become more common. On the contrary, while there were signs of feebler patterns of normalization of online competition in 2021 vis-à-vis 2016, namely in terms of engagement, normalization was stronger after the lockdown than before. Lastly, relative investment in professionalization was similar in 2016 and 2021, and the difference between the budgeted and the actual investment in 2021 cannot be attributed to the worsening of the pandemic situation or to the lockdown. In sum, we depict a scenario of remarkable stability of the electoral campaigns put forward by presidential candidates in terms of digitalization and professionalization. Its possible causes and consequences are discussed.
Fintech Innovation in Social Service Provision: A Bibliometric Review
Authors: Munodei Alan , Sibindi Athenia Bongani
Published: January 2023

The employment of fintech as a product and service distribution mechanism in various sectors has been widely adopted for the provision of seamless services. The adoption of fintech by both individuals and organisations avails more convenience in product and service provision. The combination of fintech and social protection service provision has been receiving increased attention, especially during the COVID-19 pandemic era. This paper sought to investigate and show the importance of fintech in social protection research. The study aims to show the evolutionary idea of fintech in social protection to elucidate how much research has been done and elucidate the emerging areas surrounding fintech in social protection. A review of the literature that links social protection provision and fintech was conducted to establish whether more research is needed in integrating fintech with social protection provision. This study employed a bibliometric review to explore linkages that exist between financial technology (fintech) and social protection provision to establish whether the area needs further research. Data were sourced from the SCOPUS database using the Boolean search approach with the use of keywords and filters. Search results were processed and analysed in both SCOPUS and VOSviewer for visual and network positioning. The findings of the study show that fintech and social protection have received increased attention, as shown by the number of publications since 2018. Details of the most influential authors, documents, countries, and sources were documented. The results indicate the following emerging research themes: (1) the adoption of fintech in social protection service provision; (2) blockchain technology research on social protection, (3) fintech in health care service provision combined with health insurance; and (4) fintech as a cushion against the impacts of climate change.
Acknowledgment to the Reviewers of Solids in 2022
Authors:   Solids Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
The Concept of Optimal Dynamic Pedalling Rate and Its Application to Power Output and Fatigue in Track Cycling Sprinters—A Case Study
Authors: Dunst Anna Katharina , Hesse Clemens , Ueberschär Olaf
Published: January 2023

Sprint races in track cycling are characterised by maximal power requirements and high-power output over 15 to 75 s. As competition rules limit the athlete to a single gear, the choice of gear ratio has considerable impact on performance. Traditionally, a gear favouring short start times and rapid acceleration, i.e., lower transmission ratios, was chosen. In recent years, track cyclists tended to choose higher gear ratios instead. Based on a review of the relevant literature, we aimed to provide an explanation for that increase in the gear ratio chosen and apply this to a 1000 m time trial. Race data with continuous measurements of crank force and velocity of an elite track cyclist were analysed retrospectively regarding the influence of the selected gear on power, cadence and resulting speed. For this purpose, time-dependent maximal force-velocity (F/v) profiles were used to describe changes in performance with increasing fatigue. By applying these profiles to a physical model of track cycling, theoretical power output, cadence and resulting speed were calculated for different scenarios. Based on previous research results, we assume a systematic and predictable decline in optimal cadence with increasing fatigue. The choice of higher gear ratios seems to be explained physiologically by the successive reduction in optimal cadence as fatigue sets in. Our approach indicates that average power output can be significantly increased by selecting a gear ratio that minimises the difference between the realised cadence and the time-dependent dynamic optimum. In view of the additional effects of the gear selection on acceleration and speed, gear selection should optimally meet the various requirements of the respective sprint event.
Acknowledgment to the Reviewers of Standards in 2022
Authors:   Standards Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Asking about Risk in a Company: A New Approach to Learning ISO 45001 in Engineering Programs
Authors: Rodríguez-Martín Manuel , Rodríguez-Gonzálvez Pablo , Domingo Rosario
Published: January 2023

This paper proposes a workflow so that engineering scholars can learn to ask effective questions to establish systems of consultation to workers following the indications of the international standard ISO 45001 “Occupational health and safety management systems” (Chapter 5.1) in the context of a medium-size developer and construction company. The methodology has been specifically designed to assess whether it is possible for students to acquire skills in ISO 45001 by developing an innovative method for their own employee questionnaires, and this new standard requires professionals who know how to apply the fundamentals in an industrial context. An experiment with 31 mechanical engineering students was carried out. The new methodology was applied based on three learning levels: large group, working group, and individual student. Different research instruments have been applied to evaluate how they perceive the activity with respect to the usefulness for learning and adaptation to reality. It has also tried to evaluate the subjective sensation during the development of the activity: feelings of happiness, self-confidence, and satisfaction of the scholars. The results show that students considered the activity as interesting, useful for learning, and adapted to reality, posing an important challenge that may have served to improve intrinsic motivation in the learning of management systems.
Fetal Programming of Brain and Behavior through Ionizing Radiation
Authors: Lalonde Christine , Boreham Douglas , Tai T. C.
Published: January 2023

For decades, the Barker hypothesis and thrifty phenotype hypothesis have driven researchers to explore the development of metabolic syndrome through fetal programming. In this short review, we provide peer-reviewed support for the fetal programming of neural genetic activity and behavior in multiple neural regions: the prefrontal cortex, the cerebral cortex, the hippocampus, the cerebellum, and the hypothalamic–pituitary–adrenal axis. We also introduce ionizing radiation as a purported indirect driver of phenotypical changes. The predisposition of brain and behavioral phenotypes after gestational exposure to stressors can lead to aversive and harmful outcomes, rather than protective adaptations.