bluuebunny/crossref_metadata_2025 huggingface dataset:
Short-Term Demand Forecasting of Urban Online Car-Hailing Based on the K-Nearest Neighbor Model
Authors: Xiao Yun , Kong Wei , Liang Zijun
Published: December 2022

Accurately forecasting the demand of urban online car-hailing is of great significance to improving operation efficiency, reducing traffic congestion and energy consumption. This paper takes 265-day order data from the Hefei urban online car-hailing platform from 2019 to 2021 as an example, and divides each day into 48 time units (30 min per unit) to form a data set. Taking the minimum average absolute error as the optimization objective, the historical data sets are classified, and the values of the state vector T and the parameter K of the K-nearest neighbor model are optimized, which solves the problem of prediction error caused by fixed values of T or K in traditional model. The conclusion shows that the forecasting accuracy of the K-nearest neighbor model can reach 93.62%, which is much higher than the exponential smoothing model (81.65%), KNN1 model (84.02%) and is similar to LSTM model (91.04%), meaning that it can adapt to the urban online car-hailing system and be valuable in terms of its potential application.
Decision Support System for Emergencies in Microgrids
Authors: Fotopoulou Maria , Rakopoulos Dimitrios , Petridis Stefanos
Published: December 2022

The usual operation of a microgrid (MG) may often be challenged by emergencies related to extreme weather conditions and technical issues. As a result, the operator often needs to adapt the MG’s management by either: (i) excluding disconnected components, (ii) switching to islanded mode or (iii) performing a black start, which is required in case of a blackout, followed by either direct reconnection to the main grid or islanded operation. The purpose of this paper is to present an optimal Decision Support System (DSS) that assists the MG’s operator in all the main possible sorts of emergencies, thus providing an inclusive solution. The objective of the optimizer, developed in Pyomo, is to maximize the autonomy of the MG, prioritizing its renewable production. Therefore, the DSS is in line with the purpose of the ongoing energy transition. Furthermore, it is capable of taking into account multiple sorts of Distributed Energy Resources (DER), including Renewable Energy Sources (RES), Battery Energy Storage Systems (BESS)—which can only be charged with renewable energy—and local, fuel-based generators. The proposed DSS is applied in a number of emergencies considering grid-forming and grid-following mode, in order to highlight its effectiveness and is verified with the use of PowerFactory, DIgSILENT.
Mathematical Model and Synthetic Data Generation for Infra-Red Sensors
Authors: Leja Laura , Purlans Vitālijs , Novickis Rihards , Cvetkovs Andrejs , Ozols Kaspars
Published: December 2022

A key challenge in further improving infrared (IR) sensor capabilities is the development of efficient data pre-processing algorithms. This paper addresses this challenge by providing a mathematical model and synthetic data generation framework for an uncooled IR sensor. The developed model is capable of generating synthetic data for the design of data pre-processing algorithms of uncooled IR sensors. The mathematical model accounts for the physical characteristics of the focal plane array, bolometer readout, optics and the environment. The framework permits the sensor simulation with a range of sensor configurations, pixel defectiveness, non-uniformity and noise parameters.
Deep Reinforcement Learning Based Resource Allocation for D2D Communications Underlay Cellular Networks
Authors: Yu Seoyoung , Lee Jeong Woo
Published: December 2022

In this paper, a resource allocation (RA) scheme based on deep reinforcement learning (DRL) is designed for device-to-device (D2D) communications underlay cellular networks. The goal of RA is to determine the transmission power and spectrum channel of D2D links to maximize the sum of the average effective throughput of all cellular and D2D links in a cell accumulated over multiple time steps, where a cellular channel can be allocated to multiple D2D links. Allowing a cellular channel to be shared by multiple D2D links and considering performance over multiple time steps require a high level of system overhead and computational complexity so that optimal RA is practically infeasible in this scenario, especially when a large number of D2D links are involved. To mitigate the complexity, we propose a sub-optimal RA scheme based on a multi-agent DRL, which operates with shared information in participating devices, such as locations and allocated resources. Each agent corresponds to each D2D link and multiple agents perform learning in a staggered and cyclic manner. The proposed DRL-based RA scheme allocates resources to D2D devices promptly according to dynamically varying network set-ups, including device locations. The proposed sub-optimal RA scheme outperforms other schemes, where the performance gain becomes significant when the densities of devices in a cell are high.
Fixed-Time Synchronization of Coupled Oscillator Networks with a Pacemaker
Authors: Guo Xiufeng , Rao Pengchun , Wu Zhaoyan
Published: December 2022

This paper investigates the fixed-time synchronization problem of a Kuramoto–oscillator network in the presence of a pacemaker. Based on the framework of the cyber-physical system (CPS), fixed-time synchronization criteria of such network are presented respectively for identical and non-identical oscillators. In virtue of Lyapunov stability analyses, sufficient conditions are deduced for achieving phase agreement and frequency synchronization for arbitrary initial phases and/or frequencies under distributed control strategies. Theoretical analysis shows that synchronization can be achieved in a fixed time, which is unrelated to initial phases/frequencies. Furthermore, the upper bounds of synchronization time are also obtained. Finally, the numerical simulations also verify the effectiveness of the derived results.
Robust Financial Fraud Alerting System Based in the Cloud Environment
Authors: Stojanović Branka , Božić Josip
Published: December 2022

The digitalisation of finance influenced the emergence of new technological concepts for existing user needs. Financial technology, or fintech, provides improved services for customers and new economic value for businesses. As such, fintech services require on-demand availability on a 24/7 basis. For this reason, they are often deployed in cloud environments that allow connectivity with ubiquitous devices. This allows customers to perform online transactions, which are overseen by the respective financial institutions. However, such cloud-based systems introduce new challenges for information security. On one hand, they represent attractive targets for cyberattacks. On the other, financial frauds can still go unnoticed by the financial institutions in charge. This paper contributes to both challenges by introducing the concept for a cloud-based system architecture for fraud detection and client profiling in the banking domain. Therefore, a systematic risk assessment was conducted in this context, and exploitation probabilities were inferred for multiple attack scenarios. In addition, formal verification was accomplished in order to determine the effects of successful vulnerability exploits. The consequences of such security violations are discussed, and considerations are given for improving the resilience of fintech systems.
Comparison of Modern Control Methods for Soft Robots
Authors: Grube Malte , Wieck Jan Christian , Seifried Robert
Published: December 2022

With the rise in new soft robotic applications, the control requirements increase. Therefore, precise control methods for soft robots are required. However, the dynamic control of soft robots, which is required for fast movements, is still an open topic and will be discussed here. In this contribution, one kinematic and two dynamic control methods for soft robots are examined. Thereby, an LQI controller with gain scheduling, which is new to soft robotic applications, and an MPC controller are presented. The controllers are compared in a simulation regarding their accuracy and robustness. Additionally, the required implementation effort and computational effort is examined. For this purpose, the trajectory tracking control of a simple soft robot is studied for different trajectories. The soft robot is beam-shaped and tendon-actuated. It is modeled using the piecewise constant curvature model, which is one of the most popular modeling techniques in soft robotics. In this paper, it is shown that all three controllers are able to follow the examined trajectories. However, the dynamic controllers show much higher accuracy and robustness than the kinematic controller. Nevertheless, it should be noted that the implementation and computational effort for the dynamic controllers is significantly higher. Therefore, kinematic controllers should be used if movements are slow and small oscillations can be accepted, while dynamic controllers should be used for faster movements with higher accuracy or robustness requirements.
Ultrasound Probe and Hand-Eye Calibrations for Robot-Assisted Needle Biopsy
Authors: Liu Jihao , Sun Wenyuan , Zhao Yuyun , Zheng Guoyan
Published: December 2022

In robot-assisted ultrasound-guided needle biopsy, it is essential to conduct calibration of the ultrasound probe and to perform hand-eye calibration of the robot in order to establish a link between intra-operatively acquired ultrasound images and robot-assisted needle insertion. Based on a high-precision optical tracking system, novel methods for ultrasound probe and robot hand-eye calibration are proposed. Specifically, we first fix optically trackable markers to the ultrasound probe and to the robot, respectively. We then design a five-wire phantom to calibrate the ultrasound probe. Finally, an effective method taking advantage of steady movement of the robot but without an additional calibration frame or the need to solve the AX=XB equation is proposed for hand-eye calibration. After calibrations, our system allows for in situ definition of target lesions and aiming trajectories from intra-operatively acquired ultrasound images in order to align the robot for precise needle biopsy. Comprehensive experiments were conducted to evaluate accuracy of different components of our system as well as the overall system accuracy. Experiment results demonstrated the efficacy of the proposed methods.
AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome
Authors: Konradi Jürgen , Zajber Milla , Betz Ulrich , Drees Philipp , Gerken Annika , Meine Hans
Published: December 2022

Disorders of swallowing often lead to pneumonia when material enters the airways (aspiration). Flexible Endoscopic Evaluation of Swallowing (FEES) plays a key role in the diagnostics of aspiration but is prone to human errors. An AI-based tool could facilitate this process. Recent non-endoscopic/non-radiologic attempts to detect aspiration using machine-learning approaches have led to unsatisfying accuracy and show black-box characteristics. Hence, for clinical users it is difficult to trust in these model decisions. Our aim is to introduce an explainable artificial intelligence (XAI) approach to detect aspiration in FEES. Our approach is to teach the AI about the relevant anatomical structures, such as the vocal cords and the glottis, based on 92 annotated FEES videos. Simultaneously, it is trained to detect boluses that pass the glottis and become aspirated. During testing, the AI successfully recognized the glottis and the vocal cords but could not yet achieve satisfying aspiration detection quality. While detection performance must be optimized, our architecture results in a final model that explains its assessment by locating meaningful frames with relevant aspiration events and by highlighting suspected boluses. In contrast to comparable AI tools, our framework is verifiable and interpretable and, therefore, accountable for clinical users.
Dynamic Smoothing, Filtering and Differentiation of Signals Defining the Path of the UAV
Authors: Antipov Aleksey S. , Kokunko Julia G. , Krasnova Svetlana A. , Utkin Victor A.
Published: December 2022

On the example of a control system for an unmanned aerial vehicle, we consider the problems of filtering, smoothing and restoring derivatives of reference action signals. These signals determine the desired spatial path of the plant at the first approximation. As a rule, researchers have considered these problems separately and have used different methods to solve each of them. The paper aims to develop a unified approach that provides a comprehensive solution to mentioned problems. We propose a dynamic admissible path generator. It is constructed as a copy of the canonical control plant model with smooth and bounded sigmoid corrective actions. For the deterministic case, a synthesis procedure has been developed, which ensures that the output variables of the generator track a non-smooth reference signal. Moreover, it considers the constraints on the velocity and acceleration of the plant. As a result, the generator variables produce a naturally smoothed spatial curve and its derivatives, which are realizable reference actions for the plant. The construction of the generator does not require exact knowledge of the plant parameters. Its dynamic order is less than that of the standard differentiators. We confirm the effectiveness of the approach by the results of numerical simulation.
Non-Contact Monitoring of ECG in the Home Environment—Selecting Optimal Electrode Configuration
Authors: Bujnowski Adam , Osiński Kamil , Przystup Piotr , Wtorek Jerzy
Published: December 2022

Capacitive electrocardiography (cECG) is most often used in wearable or embedded measurement systems. The latter is considered in the paper. An optimal electrocardiographic lead, as an individual feature, was determined based on model studies. It was defined as the possibly highest value of the R-wave amplitude measured on the back of the examined person. The lead configuration was also analyzed in terms of minimizing its susceptibility to creating motion artifacts. It was found that the direction of the optimal lead coincides with the electrical axis of the heart. Moreover, the electrodes should be placed in the areas preserving the greatest voltage and at the same time characterized by the lowest gradient of the potential. Experimental studies were conducted using the developed measurement system on a group of 14 people. The ratio of the R-wave amplitude (as measured on the back and chest, using optimal leads) was less than 1 while the SNR reached at least 20 dB. These parameters allowed for high-quality QRS complex detection with a PPV of 97%. For the “worst” configurations of the leads, the signals measured were practically uninterpretable.
A Tightly Integrated Navigation Method of SINS, DVL, and PS Based on RIMM in the Complex Underwater Environment
Authors: Yang Huibao , Gao Xiujing , Huang Hongwu , Li Bangshuai , Jiang Jiehong
Published: December 2022

Navigation and positioning of autonomous underwater vehicles (AUVs) in the complex and changeable marine environment are crucial and challenging. For the positioning of AUVs, the integrated navigation of the strap-down inertial navigation system (SINS), Doppler velocity log (DVL), and pressure sensor (PS) has a common application. Nevertheless, in the complex underwater environment, the DVL performance is affected by the current and complex terrain environments. The outliers in sensor observations also have a substantial adverse effect on the AUV positioning accuracy. To address these issues, in this paper, a novel tightly integrated navigation model of the SINS, DVL, and PS is established. In contrast to the traditional SINS, DVL, and PS tightly integrated navigation methods, the proposed method in this paper is based on the velocity variation of the DVL beam by applying the DVL bottom-track and water-track models. Furthermore, a new robust interacting multiple models (RIMM) information fusion algorithm is proposed. In this algorithm, DVL beam anomaly is detected, and the Markov transfer probability matrix is accordingly updated to enable quick model matching. By simulating the motion of the AUV in a complex underwater environment, we also compare the performance of the traditional loosely integrated navigation (TLIN) model, the tightly integrated navigation (TTIN) model, and the IMM algorithm. The simulation results show that because of the PS, the velocity and height in the up-change amplitude of the four algorithms are small. Compared with the TLIN algorithm in terms of maximum deviation of latitude and longitude, the RIMM algorithm also improves the accuracy by 39.1243 m and 26.4364 m, respectively. Furthermore, compared with the TTIN algorithm, the RIMM algorithm improves latitude and longitude accuracy by 1.8913 m and 11.8274 m, respectively. A comparison with IMM also shows that RIMM improves the accuracy of latitude and longitude by 1.1506 m and 7.2301 m, respectively. The results confirm that the proposed algorithm suppresses the observed noise and outliers of DVL and further achieves quick conversion between different DVL models while making full use of the effective information of the DVL beams. The proposed method also improves the navigation accuracy of AUVs in complex underwater environments.
An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals
Authors: Awan Amna Waheed , Usman Syed Muhammad , Khalid Shehzad , Anwar Aamir , Alroobaea Roobaea , Hussain Saddam , Almotiri Jasem , Ullah Syed Sajid , Akram Muhammad Usman
Published: December 2022

Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated autonomously from human central nervous system. Researchers have developed multiple methods for the classification of these signals for emotion detection. However, due to the non-linear nature of these signals and the inclusion of noise, while recording, accurate classification of physiological signals is a challenge for emotion charting. Valence and arousal are two important states for emotion detection; therefore, this paper presents a novel ensemble learning method based on deep learning for the classification of four different emotional states including high valence and high arousal (HVHA), low valence and low arousal (LVLA), high valence and low arousal (HVLA) and low valence high arousal (LVHA). In the proposed method, multimodal signals (EEG, ECG, and GSR) are preprocessed using bandpass filtering and independent components analysis (ICA) for noise removal in EEG signals followed by discrete wavelet transform for time domain to frequency domain conversion. Discrete wavelet transform results in spectrograms of the physiological signal and then features are extracted using stacked autoencoders from those spectrograms. A feature vector is obtained from the bottleneck layer of the autoencoder and is fed to three classifiers SVM (support vector machine), RF (random forest), and LSTM (long short-term memory) followed by majority voting as ensemble classification. The proposed system is trained and tested on the AMIGOS dataset with k-fold cross-validation. The proposed system obtained the highest accuracy of 94.5% and shows improved results of the proposed method compared with other state-of-the-art methods.
GC-MLP: Graph Convolution MLP for Point Cloud Analysis
Authors: Wang Yong , Geng Guohua , Zhou Pengbo , Zhang Qi , Li Zhan , Feng Ruihang
Published: December 2022

With the objective of addressing the problem of the fixed convolutional kernel of a standard convolution neural network and the isotropy of features making 3D point cloud data ineffective in feature learning, this paper proposes a point cloud processing method based on graph convolution multilayer perceptron, named GC-MLP. Unlike traditional local aggregation operations, the algorithm generates an adaptive kernel through the dynamic learning features of points, so that it can dynamically adapt to the structure of the object, i.e., the algorithm first adaptively assigns different weights to adjacent points according to the different relationships between the different points captured. Furthermore, local information interaction is then performed with the convolutional layers through a weight-sharing multilayer perceptron. Experimental results show that, under different task benchmark datasets (including ModelNet40 dataset, ShapeNet Part dataset, S3DIS dataset), our proposed algorithm achieves state-of-the-art for both point cloud classification and segmentation tasks.
The Simulation-Based Approach for Random Speckle Pattern Representation in Synthetically Generated Video Sequences of Dynamic Phenomena
Authors: Zdziebko Paweł , Dworakowski Ziemowit , Holak Krzysztof
Published: December 2022

Structural health monitoring systems that employ vision data are under constant development. Generating synthetic vision data is an actual issue. It allows, for example, for obtention of additional data for machine learning techniques or predicting the result of observations using a vision system with a reduced number of experiments. A random speckle pattern (RSP) fixed on the surface of the observed structure is usually used in measurements. The determination of displacements of its areas using digital image correlation (DIC) methods allows for extracting the structure’s deformation in both static and dynamic cases. An RSP modeling methodology for synthetic image generation is developed within this paper. The proposed approach combines the finite element modeling technique and simulation results with the Blender graphics environment to generate video sequences of the mechanical structure with deformable RSP attached to it. The comparative analysis showed high compliance of the displacement between the synthetic images processed with the DIC method and numerical data.
Fabrication of Vertical MEMS Actuator with Hollow Square Electrode for SPR Sensing Applications
Authors: Kim Kihyun , Lee Yeonsu , Llamas-Garro Ignacio , Kim Jung-Mu
Published: December 2022

In this study, an electrostatically driven vertical MEMS actuator was designed using a hollow square electrode. To attain vertical actuation, a hollow square-shaped electrode was designed on the glass substrate. The silicon proof mass, containing a step, was utilized to realize analogue actuation without pull-in. The vertical MEMS actuator was fabricated using the SiOG (Silicon on Glass) process and the total actuator size was 8.3 mm × 8.3 mm. The fabricated proof mass was freestanding due to eight serpentine springs with 30 μm width. The vertical movement of the MEMS actuator was successfully controlled electrostatically. The measured vertical movement was 5.6 µm for a voltage of 40 V, applied between the top silicon structure and the hollow square electrode. The results shown here confirm that the proposed MEMS actuator was able to control the vertical displacement using an applied voltage.
Accurate Ship Detection Using Electro-Optical Image-Based Satellite on Enhanced Feature and Land Awareness
Authors: Lee Sang-Heon , Park Hae-Gwang , Kwon Ki-Hoon , Kim Byeong-Hak , Kim Min Young , Jeong Seung-Hyun
Published: December 2022

This paper proposes an algorithm that improves ship detection accuracy using preprocessing and post-processing. To achieve this, high-resolution electro-optical satellite images with a wide range of shape and texture information were considered. The developed algorithms display the problem of unreliable detection of ships owing to clouds, large waves, weather influences, and shadows from large terrains. False detections in land areas with image information similar to that of ships are observed frequently. Therefore, this study involves three algorithms: global feature enhancement pre-processing (GFEP), multiclass ship detector (MSD), and false detected ship exclusion by sea land segmentation image (FDSESI). First, GFEP enhances the image contrast of high-resolution electro-optical satellite images. Second, the MSD extracts many primary ship candidates. Third, falsely detected ships in the land region are excluded using the mask image that divides the sea and land. A series of experiments was performed using the proposed method on a database of 1984 images. The database includes five ship classes. Therefore, a method focused on improving the accuracy of various ships is proposed. The results show a mean average precision (mAP) improvement from 50.55% to 63.39% compared with other deep learning-based detection algorithms.
Frequency Modulation Approach for High Power Density 100 Hz Piezoelectric Vibration Energy Harvester
Authors: Ju Dengfeng , Wang Lu , Li Chunlong , Huang Hui , Liu Hongjing , Liu Kewen , Wang Qian , Han Xiangguang , Zhao Libo , Maeda Ryutaro
Published: December 2022

Piezoelectric vibration energy harvester (PVEH) is a promising device for sustainable power supply of wireless sensor nodes (WSNs). PVEH is resonant and generates power under constant frequency vibration excitation of mechanical equipment. However, it cannot output high power through off-resonance if it has frequency offset in manufacturing, assembly and use. To address this issue, this paper designs and optimizes a PVEH to harvest power specifically from grid transformer vibration at 100 Hz with high power density of 5.28 μWmm−3g−2. Some resonant frequency modulation methods of PVEH are discussed by theoretical analysis and experiment, such as load impedance, additional mass, glue filling, axial and transverse magnetic force frequency modulation. Finally, efficient energy harvesting of 6.1 V output in 0.0226 g acceleration is tested in grid transformer reactor field application. This research has practical value for the design and optimization process of tunable PVEH for a specific vibration source.
In-Line Wood Defect Detection Using Simple Scalar Network Analyzer
Authors: Radwan Mohamed , Becker Noah , Thiel David V. , Espinosa Hugo G.
Published: December 2022

Timber is widely used in new structures. The leading causes of structural failure are sited at bolt connections, cavities, and knots. This paper introduces a simple method to detect bolts in wood using a UHF Scalar Network Analyzer (SNA). The electronics placed inside an aluminum box with a slot aperture transmit a microwave signal through the slot, and the near-field signal determines the reflection coefficient (S11). Major changes from baseline are an accurate method to detect cavities and bolts inside the wood. Experiments were conducted on pinewood beams with cross-section dimensions of (70 mm × 70 mm). The scalar network analyzer circuit can detect bolts and cavities within a 30 mm range from the wood surface. The technique can be used for timber beam preparation in an automated sawmill at speed.
Trial Analysis of the Relationship between Taste and Biological Information Obtained While Eating Strawberries for Sensory Evaluation
Authors: Maeda Keisuke , Togo Ren , Ogawa Takahiro , Adachi Shin-ichi , Yoshizawa Fumiaki , Haseyama Miki
Published: December 2022

This paper presents a trial analysis of the relationship between taste and biological information obtained while eating strawberries (for a sensory evaluation). This study used the visual analog scale (VAS); we collected questionnaires used in previous studies and human brain activity obtained while eating strawberries. In our analysis, we assumed that brain activity is highly correlated with taste. Then, the relationships between brain activity and other data, such as VAS and questionnaires, could be analyzed through a canonical correlation analysis, which is a multivariate analysis. Through an analysis of brain activity, the potential relationship with "taste" (that is not revealed by the initial simple correlation analysis) can be discovered. This is the main contribution of this study. In the experiments, we discovered the potential relationship between cultural factors (in the questionnaires) and taste. We also found a strong relationship between taste and individual information. In particular, the analysis of cross-loading between brain activity and individual information suggests that acidity and the sugar-to-acid ratio are related to taste.
Investigation of the Hue–Wavelength Response of a CMOS RGB-Based Image Sensor
Authors: Park Hyeon-Woo , Choi Ji-Won , Choi Ji-Young , Joo Kyung-Kwang , Kim Na-Ri
Published: December 2022

In this study, a non-linear hue–wavelength (H-W) curve was investigated from 400 to 650 nm. To date, no study has reported on H-W relationship measurements, especially down to the 400 nm region. A digital camera mounted with complementary metal oxide semiconductor (CMOS) image sensors was used. The obtained digital images of the sample were based on an RGB-based imaging analysis rather than multispectral imaging or hyperspectral imaging. In this study, we focused on the raw image to reconstruct the H-W curve. In addition, several factors affecting the digital image, such as exposure time or international organization for standardization (ISO), were investigated. In addition, cross check of the H-W response using laser was performed. We expect that our method will be useful as an auxiliary method in the future for obtaining the fluor emission wavelength information.
Novel Diagnosis Technologies for a Lack of Oil Lubrication in Gearmotor Systems, Based on Motor Current Signature Analysis
Authors: Farhat Mohamed Habib , Gelman Len , Conaghan Gerard , Kluis Winston , Ball Andrew
Published: December 2022

Due to the wide use of gearmotor systems in industry, many diagnostic techniques have been developed/employed to prevent their failures. An insufficient lubrication of gearboxes of these machines could shorten their life and lead to catastrophic failures and losses, making it important to ensure a required lubrication level. For the first time in worldwide terms, this paper proposed to diagnose a lack of gearbox oil lubrication using motor current signature analysis (MCSA). This study proposed, investigated, and experimentally validated two new technologies to diagnose a lack of lubrication of gear motor systems based on MCSA. Two new diagnostic features were extracted from the current signals of a three-phase induction motor. The effectiveness of the proposed technologies was evaluated for different gear lubrication levels and was compared for three phases of motor current signals and for a case of averaging the proposed diagnostic features over three phases. The results confirmed a high effectiveness of the proposed technologies for diagnosing a lack of oil lubrication in gearmotor systems. Other contributions were as follows: (i) it was shown for the first time in worldwide terms, that the motor current nonlinearity level increases with the reduction of the sgearbox oil level; (ii) novel experimental validations of the proposed two diagnostic technologies via comprehensive experimental trials (iii) novel experimental comparisons of the diagnosis effectiveness of the proposed two diagnostic technologies.
A Cost-Effective, Integrated Haptic Device for an Exoskeletal System
Authors: Rećko Maciej , Dzierżek Kazimierz , Grądzki Rafał , Živčák Jozef
Published: December 2022

The paper presents an innovative integrated sensor-effector designed for use in exoskeletal haptic devices. The research efforts aimed to achieve high cost-effectiveness for a design assuring proper monitoring of joint rotations and providing passive force feedback. A review of market products revealed that there is space for new designs of haptic devices with such features. To determine the feasibility of the proposed solution, a series of simulations and experiments were conducted to verify the adopted design concept. The focus was set on an investigation of the force of attraction between one and two magnets interacting with a steel plate. Further, a physical model of an integrated joint was fabricated, and its performance was evaluated and compared to a similar commercially available device. The proposed solution is cost-effective due to the use of standard parts and inexpensive components. However, it is light and assures a 19 Nm braking torque adequate for the intended use as a haptic device for upper limbs.
Resource Management for Massive Internet of Things in IEEE 802.11ah WLAN: Potentials, Current Solutions, and Open Challenges
Authors: Farhad Arshad , Pyun Jae-Young
Published: December 2022

IEEE 802.11ah, known as Wi-Fi HaLow, is envisioned for long-range and low-power communication. It is sub-1 GHz technology designed for massive Internet of Things (IoT) and machine-to-machine devices. It aims to overcome the IoT challenges, such as providing connectivity to massive power-constrained devices distributed over a large geographical area. To accomplish this objective, IEEE 802.11ah introduces several unique physical and medium access control layer (MAC) features. In recent years, the MAC features of IEEE 802.11ah, including restricted access window, authentication (e.g., centralized and distributed) and association, relay and sectorization, target wake-up time, and traffic indication map, have been intensively investigated from various aspects to improve resource allocation and enhance the network performance in terms of device association time, throughput, delay, and energy consumption. This survey paper presents an in-depth assessment and analysis of these MAC features along with current solutions, their potentials, and key challenges, exposing how to use these novel features to meet the rigorous IoT standards.
Dynamic Failure Risk Assessment of Wastewater Treatment and Reclamation Plant: An Industrial Case Study
Authors: Analouei Razieh , Taheriyoun Masoud , Amin Md Tanjin
Published: December 2022

Due to the growing scarcity of water resources, wastewater reuse has become one of the most effective solutions for industrial consumption. However, various factors can detrimentally affect the performance of a wastewater treatment plant (WWTP), which is considered a risk of not fulfilling the effluent requirements. Thus, to ensure the quality of treated wastewater, it is essential to analyze system failure causes and their potential outcomes and mitigation measures through a systematic dynamic risk assessment approach. This work shows how a dynamic Bayesian network (DBN) can be effectively used in this context. Like the conventional Bayesian network (BN), the DBN can capture complex interactions between failure contributory factors. Additionally, it can forecast the upcoming failure likelihood using a prediction inference. This proposed methodology was applied to a WWTP of the Moorchekhort Industrial Complex (MIC), located in the center of Iran. A total of 15 years’ time frame (2016–2030) has been considered in this work. The first six years’ data have been used to develop the DBN model and to identify the crucial risk factors that are further used to reduce the risk in the remaining nine years. The risk increased from 21% to 42% in 2016–2021. Applying the proposed risk mitigation measures can decrease the failure risk from 33% to 9% in 2022–2030. The proposed model showed the capability of the DBN in risk management of a WWTP system which can help WWTPs’ managers and operators achieve better performance for higher reclaimed water quality.
Effect of Electrofiltration on the Dewatering Kinetics of Arthrospira platensis and Biocompound Recovery
Authors: Aoude Christa , Grimi Nabil , El Zakhem Henri , Vorobiev Eugène
Published: December 2022

Arthrospira platensis (A. platensis) is a microalga with a wide range of commercial uses. One of the main concerns that needs to be addressed in microalgae biorefineries is the costs associated with the harvesting and concentration steps. Filtration has been shown to be an effective technique for concentrating microalgae and recent studies have attempted to enhance membrane filtration by applying an external electric field to the filtration cell. This study consisted of assessing the use of electrically assisted filtration (electrofiltration) at 60 A/m2 and 1 bar for the dewatering of A. platensis, as well as the effect of pretreating the microalgae with ultrasounds (US) on the filtration process. Untreated A. platensis exhibited better filtration kinetics than US-treated A. platensis, and electrofiltration was found to increase the cake dryness. More protein and pigments were present in the US-treated microalgae solution compared to the untreated microalgae, which led to the presence of higher concentrations of protein and pigments in the filtrate streams after pressure filtration at 1 bar without the application of an external electric field. Electrofiltration was found to consume less energy compared to traditional drying techniques used for A. platensis. However, electrofiltration degrades the biocompounds present in the filtrate and cake due to pH changes and other electrophoresis phenomena, which shows the need to optimize the process in future work.
Apoptotic Cell Death via Activation of DNA Degradation, Caspase-3 Activity, and Suppression of Bcl-2 Activity: An Evidence-Based Citrullus colocynthis Cytotoxicity Mechanism toward MCF-7 and A549 Cancer Cell Lines
Authors: Shaikh Ibrahim Ahmed , Alshabi Ali Mohamed , Alkahtani Saad Ahmed , Orabi Mohamed A. A. , Abdel-Wahab Basel A. , Walbi Ismail A. , Habeeb Mohammed Shafiuddin , Khateeb Masood Medleri , Shettar Arun K. , Hoskeri Joy H.
Published: December 2022

The objectives of this study are to investigate the cytotoxic effect of different Citrullus colocynthis extracts on breast and lung cancer cell lines using flow cytometry to gain mechanistic insights. C. colocynthis was extracted sequentially using the Soxhlet method. We first tested the plant extracts’ cytotoxicity on non-malignant L929 cells and cancerous breast (MCF-7) and lung (A549) cell lines. We observed that the IC50 of the methanol extract on the viability of MCF-7 and A549 cell lines was 81.08 µg/mL and 17.84 µg/mL, respectively, using the MTT assay. The aqueous and methanol extracts were less toxic when tested against the non-cancerous L929 cell line, with IC50 values of 235.48 µg/mL and 222.29 µg/mL, respectively. Then, using flow cytometry, we investigated the underlying molecular pathways with Annexin-V, Anti-Bcl-2, Caspase-3, and DNA fragmentation (TUNEL) assays. Flow cytometric and molecular marker analyses revealed that the methanol extract activated caspase-3 and inhibited Bcl-2 protein, causing early and late apoptosis, as well as cell death via DNA damage in breast and lung cancer cells. These findings indicate that the methanol extract of C. colocynthis is cytotoxic to breast and lung cancer cell lines. The total phenolic and flavonoid content analysis results showed the methanolic extract of C. colocynthis has a concentration of 326.25 μg GAE/g dwt and 274.61 μg QE/g dwt, respectively. GC-MS analysis of the methanol extract revealed phytochemicals relevant to its cytotoxicity.
The Social Representations of Zoo Goers toward Crocodiles and Turtles: Structural Analysis and Implications for Conservation
Authors: Neves João , Giger Jean-Christophe , Alves Vasco , Almeida Joana
Published: December 2022

Zoos have changed dramatically over the last century and today attract millions of people worldwide, being places where visitors can closely watch wildlife and learn about the species on display. Although present at most zoos, reptiles are challenging animals in terms of visitor interest and engagement, as some species do not fit aesthetic standards from the human standpoint, have culturally negative perceptions or generate aversive emotions. By studying zoo visitors’ social representations of crocodiles and turtles, we aimed to detail their structures, as well as identifying their prototypical elements that help to understand their emotional and cognitive framing. The findings show the crocodile’s prototypical image as a big, fearsome predator with teeth as its main physical attribute. Male visitors showed a more emotional perception of this animal. The turtle’s prototypical image is a slow, hard-shelled ancestral sea animal with a neutral-to-positive set of traits, with no particular differences between genders. Our results shed a more detailed light on some of the social constructs that make up the mental images of these animals, which can help the zoological community direct communication toward a more fluent conversation between stakeholders toward conservation.
Evaluation of Soil Organic Carbon Storage of Atillo in the Ecuadorian Andean Wetlands
Authors: Beltrán-Dávalos Andrés A. , Ayala Izurieta Johanna Elizabeth , Echeverria Guadalupe Magdy Mileni , Van Wittenberghe Shari , Delegido Jesús , Otero Pérez Xosé Luis , Merino Agustín
Published: December 2022

Identifying the SOC levels and revealing the potential of SOC storage of ecosystems difficult to sample and study are necessary contributions to the understanding of the global reserves of SOC. Wetlands store large amounts of SOC within their soils. They have an important role in water regulation and have great biological and floristic diversity. Therefore, this study aimed to assess the SOC stock in Atillo micro-watershed in the Ecuadorian Andean wetlands at two soil depths (0–30 cm and 30–60 cm below ground) and to assess the importance of the ecosystem and its conservation in favor of reducing emissions due to degradation processes. For that, we sampled the study zone with 101 composite samples of soil to obtain the SOC storage for each sample point in Mg/ha. A SOC estimation to evaluate its spatial distribution was performed using the geostatistical method Kriging. The results show a high storage capacity of the study zone with SOC values of 126 to 454 Mg/ha in the 0–30 cm soil profile and 148 to 350 Mg/ha in the 30–60 cm soil profile. The preservation and protection mechanisms of high SOC reserves should be taken into account to prevent the emission of CO2.
Catalytic Direct Decomposition of NOx Using Non-Noble Metal Catalysts
Authors: Shukla M. K. , Chauhan Balendra V. S. , Verma Sneha , Dhar Atul
Published: December 2022

Nitrogen oxides (NOx) gases, such as nitrous oxide (N2O), nitrogen oxide (NO), and nitrogen dioxide (NO2), are considered the most hazardous exhausts exhaled by industries and stationary and non-stationary application engines. Investigation of catalytic decomposition of NO has been carried out on copper ion exchanged with different bases, such as COK12, Nb2O5, Y-zeolite, and ZSM5. The catalytic decomposition of NO is widely accepted as an excellent method for the abatement of NO. However, the catalyst that achieves the highest reactivity in terms of NO decomposition is still a matter of research. The present paper aims to extend the research on the reactivity of non-noble metal-based catalysts using the direct decomposition method to remove NO from diesel engine exhaust. The reactivity of catalysts was observed in a quartz fixed bed reactor of 10 mm diameter placed in a furnace maintained at a temperature of 200 °C to 600 °C. The flow of NO was controlled by a mass flow controller, and the gas chromatography technique was used to observe the reactivity of the catalysts. Analysis showed that adding Cu to COK12, Nb2O5, Y-zeolite, and ZSM5 supports resulted in a rise in NO decomposition compared to stand-alone supports. Further experimental trials on the performance of Cu-ZSM5 at varying flow rates of NO showed that the NO decomposition activity of the catalyst was higher at lower flow rates of NO.
Association of Co-Living and Age on the Type of Sports Practiced by Older People
Authors: Parra-Rizo María Antonia , Díaz-Toro Felipe , Hadrya Fatine , Pavón-León Patricia , Cigarroa Igor
Published: December 2022

Introdution: The environment and the type of co-living of older people are crucial to understanding how the nature of their context influences a healthy lifestyle. However, no studies have investigated to what extent their type of co-living may be associated with the sports and the profile practice according to their age. Objective: This study aimed to assess the association between the types of co-living and the age of the physically active elderly and the sports they practice. Hypothesis: It is thought that the age, situation, and satisfaction with the way of living in physically active older people differ according to the type of sport they practice. Methods: Cross-sectional study. We included 358 individuals aged between 61 and 93 years old (M = 69.66, SD = 4.74). Type of co-living was classified as living alone or living with others. The sports activities evaluated were: gym, dance, water activities, and yoga/pilates. Differences in the type of co-living and sports practiced were evaluated by ANOVA or Chi2. Results: Among the elderly who practice gymnastics, most of them live alone and in a tight core (p < 0.001) (Phi = 0.244). Furthermore, those who practice aquatic activities are more frequently the youngest (p < 0.001) (Phi = 0.198). Conclusion: Older people who do gymnastics have smaller living groups, those under 69 opt for gymnastics and aquatic activities, while those aged 70 and over prefer dance, yoga, and pilates.
Global Scientific Research and Trends Regarding Microbial Induced Calcite Precipitation: A Bibliometric Network Analysis
Authors: Jarwar Mazhar Ali , Dumontet Stefano , Nastro Rosa Anna , Sanz-Montero M. Esther , Pasquale Vincenzo
Published: December 2022

Microbial induced calcite precipitation (MICP) offers a host of interesting features, from both theoretical and practical standpoints. This process was firstly investigated as a geo-biological mechanism involved in carbonate mineral formation in both rocks and soil. The interest in its practical use has significantly increased in recent years, as MICP has been used in different fields, such as oil recovery, the improvement of soil geotechnical characteristics, and concrete healing. To the best of our knowledge, this work is the first attempt to carry out a bibliometric descriptive study of publications concerning MICP. We analyzed data from the Web of Science Core Collection (WoSCC), which provides comprehensive information for bibliometric analysis, including the Science Citation Index Expanded (SCI-E) and the Social Sciences Citation Index (SSCI). The bibliometric analysis was carried out on 1580 publications, from 2000 to August 2022, and included publication output; author; institution; country; collaborations between authors, institutes, and countries; and citation frequency. We created visualization maps, including research collaborations, using the VOSviewer program. MICP, carbonate precipitation, cementation, and soil improvement in terms of geotechnical properties are frequently used keywords. Although in the year 2000, only two papers were published on MICP, the number of publications has increased rapidly since 2014. In 2021, 333 papers were published. China leads the pack as the most productive country, followed by the USA and Australia. According to our results, the number of research papers has dramatically increased in the last 5 years. MICP use for concrete healing/cementation and soil geotechnical improvement, as well as the low environmental impact of such a technique, are becoming very popular topics among researchers. With the aging of concrete buildings, as well as with the worsening of environmental pollution and soil alterations, the research regarding MICP will play an ever increasing and crucial role in civil engineering and geotechnical fields, as well as in soil science. MICP also address Sustainable Development Goal 11, “building sustainable cities and communities.” Nevertheless, our study pointed out a concentration of the MICP studies in just a few countries. Russia and Brazil, for instance, seem to poorly contribute to MICP research. Greater cooperation among countries, along with the extension of the research network on this topic, would foster more rapid progress in MICP studies, from both practical and speculative standpoints.