bluuebunny/crossref_metadata_2025 huggingface dataset:
Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in Cannabis Sativa
Authors: Tran Jonathan , Vassiliadis Simone , Elkins Aaron C. , Cogan Noel O. I. , Rochfort Simone J.
Published: February 2023

Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography–mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II–Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4–100% accuracy prediction), the handheld device also performed well (83.1–100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.
Joint Aperture and Power Allocation Strategy for a Radar Network Localization System Based on Low Probability of Interception Optimization
Authors: Xue Chenyan , Wang Ling , Zhu Daiyin
Published: February 2023

In the process of using the Distributed Radar Network Localization System (DRNLS) further to improve the survivability of a carrier platform, the random characteristics of the system’s Aperture Resource Allocation (ARA) and Radar Cross Section (RCS) are often not fully considered. However, the random characteristics of the system’s ARA and RCS will affect the power resource allocation of the DRNLS to a certain extent, and the allocation result is an essential factor determining the performance of the DRNLS’s Low Probability of Intercept (LPI). Therefore, a DRNLS still has some limitations in practical application. In order to solve this problem, a joint allocation scheme of aperture and power for the DRNLS based on LPI optimization (JA scheme) is proposed. In the JA scheme, the fuzzy random Chance Constrained Programmin model for radar antenna aperture resource management (RAARM-FRCCP model) can minimize the number of elements under the given pattern parameters. The random Chance Constrained Programmin model for minimizing Schleher Intercept Factor (MSIF-RCCP model) built on this basis can be used to achieve DRNLS optimal control of LPI performance on the premise of ensuring system tracking performance requirements. The results show that when RCS has some randomness, its corresponding uniform power distribution result is not necessarily the optimal scheme. Under the condition of meeting the same tracking performance, the required number of elements and power will be reduced to a certain extent compared with the number of elements in the whole array and the power corresponding to the uniform distribution. The lower the confidence level is, the more times the threshold is allowed to pass, and the lower the power is, so that the DRNLS can have better LPI performance.
An Interface ASIC Design of MEMS Gyroscope with Analog Closed Loop Driving
Authors: Zhang Huan , Chen Weiping , Yin Liang , Fu Qiang
Published: February 2023

This paper introduces a digital interface application-specific integrated circuit (ASIC) for a micro-electromechanical systems (MEMS) vibratory gyroscope. The driving circuit of the interface ASIC uses an automatic gain circuit (AGC) module instead of a phase-locked loop to realize a self-excited vibration, which gives the gyroscope system good robustness. In order to realize the co-simulation of the mechanically sensitive structure and interface circuit of the gyroscope, the equivalent electrical model analysis and modeling of the mechanically sensitive structure of the gyro are carried out by Verilog-A. According to the design scheme of the MEMS gyroscope interface circuit, a system-level simulation model including mechanically sensitive structure and measurement and control circuit is established by SIMULINK. A digital-to-analog converter (ADC) is designed for the digital processing and temperature compensation of the angular velocity in the MEMS gyroscope digital circuit system. Using the positive and negative diode temperature characteristics, the function of the on-chip temperature sensor is realized, and the temperature compensation and zero bias correction are carried out simultaneously. The MEMS interface ASIC is designed using a standard 0.18 μM CMOS BCD process. The experimental results show that the signal-to-noise ratio (SNR) of sigma-delta (ΣΔ) ADC is 111.56 dB. The nonlinearity of the MEMS gyroscope system is 0.03% over the full-scale range.
Study of Nonlinear Excitation Circuits for Fluxgate Magnetometer
Authors: Zhang Chenhao , Zhang Yiming , Wang Xuhong , Meng Hongyan
Published: February 2023

This paper presents the common methods and corresponding drawbacks concerning nonlinear analysis of fluxgate excitation circuits and emphasizes the importance of nonlinear analysis for these circuits. With regard to the nonlinearity of the excitation circuit, this paper proposes the use of the core-measured hysteresis curve for mathematical analysis and the use of a nonlinear model that considers the coupling effect of the core and winding and influence of the historical magnetic field on the core for simulation analysis. The feasibility of mathematical calculations and simulation for the nonlinear study of fluxgate excitation circuit is verified via experiments. The results demonstrate that, in this regard, the simulation is four times better than a mathematical calculation. The simulation and experimental results of the excitation current and voltage waveforms under different excitation circuit parameters and structures are essentially consistent, with a difference in current of no more than 1 mA, thereby verifying the effectiveness of the nonlinear excitation analysis method.
COVID-Net USPro: An Explainable Few-Shot Deep Prototypical Network for COVID-19 Screening Using Point-of-Care Ultrasound
Authors: Song Jessy , Ebadi Ashkan , Florea Adrian , Xi Pengcheng , Tremblay Stéphane , Wong Alexander
Published: February 2023

As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects of life and the global healthcare systems, the adoption of rapid and effective screening methods to prevent the further spread of the virus and lessen the burden on healthcare providers is a necessity. As a cheap and widely accessible medical image modality, point-of-care ultrasound (POCUS) imaging allows radiologists to identify symptoms and assess severity through visual inspection of the chest ultrasound images. Combined with the recent advancements in computer science, applications of deep learning techniques in medical image analysis have shown promising results, demonstrating that artificial intelligence-based solutions can accelerate the diagnosis of COVID-19 and lower the burden on healthcare professionals. However, the lack of large, well annotated datasets poses a challenge in developing effective deep neural networks, especially in the case of rare diseases and new pandemics. To address this issue, we present COVID-Net USPro, an explainable few-shot deep prototypical network that is designed to detect COVID-19 cases from very few ultrasound images. Through intensive quantitative and qualitative assessments, the network not only demonstrates high performance in identifying COVID-19 positive cases, using an explainability component, but it is also shown that the network makes decisions based on the actual representative patterns of the disease. Specifically, COVID-Net USPro achieves 99.55% overall accuracy, 99.93% recall, and 99.83% precision for COVID-19-positive cases when trained with only five shots. In addition to the quantitative performance assessment, our contributing clinician with extensive experience in POCUS interpretation verified the analytic pipeline and results, ensuring that the network’s decisions are based on clinically relevant image patterns integral to COVID-19 diagnosis. We believe that network explainability and clinical validation are integral components for the successful adoption of deep learning in the medical field. As part of the COVID-Net initiative, and to promote reproducibility and foster further innovation, the network is open-sourced and available to the public.
Dynamic Spectrum Sharing Based on Deep Reinforcement Learning in Mobile Communication Systems
Authors: Liu Sizhuang , Pan Changyong , Zhang Chao , Yang Fang , Song Jian
Published: February 2023

The rapid development of mobile communication services in recent years has resulted in a scarcity of spectrum resources. This paper addresses the problem of multi-dimensional resource allocation in cognitive radio systems. Deep reinforcement learning (DRL) combines deep learning and reinforcement learning to enable agents to solve complex problems. In this study, we propose a training approach based on DRL to design a strategy for secondary users in the communication system to share the spectrum and control their transmission power. The neural networks are constructed using the Deep Q-Network and Deep Recurrent Q-Network structures. The results of the conducted simulation experiments demonstrate that the proposed method can effectively improve the user’s reward and reduce collisions. In terms of reward, the proposed method outperforms opportunistic multichannel ALOHA by about 10% and about 30% for the single SU scenario and the multi-SU scenario, respectively. Furthermore, we explore the complexity of the algorithm and the influence of parameters in the DRL algorithm on the training.
3D Autonomous Surgeon’s Hand Movement Assessment Using a Cascaded Fuzzy Supervisor in Multi-Thread Video Processing
Authors: Rashidi Fathabadi Fatemeh , Grantner Janos L. , Shebrain Saad A. , Abdel-Qader Ikhlas
Published: February 2023

The purpose of the Fundamentals of Laparoscopic Surgery (FLS) training is to develop laparoscopic surgery skills by using simulation experiences. Several advanced training methods based on simulation have been created to enable training in a non-patient environment. Laparoscopic box trainers—cheap, portable devices—have been deployed for a while to offer training opportunities, competence evaluations, and performance reviews. However, the trainees must be under the supervision of medical experts who can evaluate their abilities, which is an expensive and time-consuming operation. Thus, a high level of surgical skill, determined by assessment, is necessary to prevent any intraoperative issues and malfunctions during a real laparoscopic procedure and during human intervention. To guarantee that the use of laparoscopic surgical training methods results in surgical skill improvement, it is necessary to measure and assess surgeons’ skills during tests. We used our intelligent box-trainer system (IBTS) as a platform for skill training. The main aim of this study was to monitor the surgeon’s hands’ movement within a predefined field of interest. To evaluate the surgeons’ hands’ movement in 3D space, an autonomous evaluation system using two cameras and multi-thread video processing is proposed. This method works by detecting laparoscopic instruments and using a cascaded fuzzy logic assessment system. It is composed of two fuzzy logic systems executing in parallel. The first level assesses the left and right-hand movements simultaneously. Its outputs are cascaded by the final fuzzy logic assessment at the second level. This algorithm is completely autonomous and removes the need for any human monitoring or intervention. The experimental work included nine physicians (surgeons and residents) from the surgery and obstetrics/gynecology (OB/GYN) residency programs at WMU Homer Stryker MD School of Medicine (WMed) with different levels of laparoscopic skills and experience. They were recruited to participate in the peg-transfer task. The participants’ performances were assessed, and the videos were recorded throughout the exercises. The results were delivered autonomously about 10 s after the experiments were concluded. In the future, we plan to increase the computing power of the IBTS to achieve real-time performance assessment.
Attention-Based Bi-Prediction Network for Versatile Video Coding (VVC) over 5G Network
Authors: Choi Young-Ju , Lee Young-Woon , Kim Jongho , Jeong Se Yoon , Choi Jin Soo , Kim Byung-Gyu
Published: February 2023

As the demands of various network-dependent services such as Internet of things (IoT) applications, autonomous driving, and augmented and virtual reality (AR/VR) increase, the fifthgeneration (5G) network is expected to become a key communication technology. The latest video coding standard, versatile video coding (VVC), can contribute to providing high-quality services by achieving superior compression performance. In video coding, inter bi-prediction serves to improve the coding efficiency significantly by producing a precise fused prediction block. Although block-wise methods, such as bi-prediction with CU-level weight (BCW), are applied in VVC, it is still difficult for the linear fusion-based strategy to represent diverse pixel variations inside a block. In addition, a pixel-wise method called bi-directional optical flow (BDOF) has been proposed to refine bi-prediction block. However, the non-linear optical flow equation in BDOF mode is applied under assumptions, so this method is still unable to accurately compensate various kinds of bi-prediction blocks. In this paper, we propose an attention-based bi-prediction network (ABPN) to substitute for the whole existing bi-prediction methods. The proposed ABPN is designed to learn efficient representations of the fused features by utilizing an attention mechanism. Furthermore, the knowledge distillation (KD)- based approach is employed to compress the size of the proposed network while keeping comparable output as the large model. The proposed ABPN is integrated into the VTM-11.0 NNVC-1.0 standard reference software. When compared with VTM anchor, it is verified that the BD-rate reduction of the lightweighted ABPN can be up to 5.89% and 4.91% on Y component under random access (RA) and low delay B (LDB), respectively.
Acoustic Velocity Measurement for Enhancing Laser UltraSound Imaging Based on Time Domain Synthetic Aperture Focusing Technique
Authors: Yoon Taeil , Kim Younggue , Awais Muhammad , Lee Byeongha
Published: February 2023

A method to enhance laser ultrasound (LUS) image reconstruction with the time-domain synthetic aperture focusing technique (T-SAFT) is presented, in which the acoustic velocity is extracted in situ with curve fitting. The operational principle is provided with the help of a numerical simulation, and the confirmation is provided experimentally. In these experiments, an all-optic LUS system was developed by using lasers for both excitation and detection of ultrasound. The acoustic velocity of a specimen was extracted in situ by fitting a hyperbolic curve to its B-scan image. The needle-like objects embedded within a polydimethylsiloxane (PDMS) block and a chicken breast have been successfully reconstructed using the extracted in situ acoustic velocity. Experimental results suggest that knowing the acoustic velocity in the T-SAFT process is important not only in finding the depth location of the target object but also for producing a high resolution image. This study is expected to pave the wave to the development and usage of all-optic LUS for bio-medical imaging.
Leveraging Metaheuristic Unequal Clustering for Hotspot Elimination in Energy-Aware Wireless Sensor Networks
Authors: Alsolai Hadeel , Maashi Mashael , Saeed Muhammad Kashif , Mohamed Abdullah , Assiri Mohammed , Abdelbagi Sitelbanat , Drar Suhanda , Abdelmageed Amgad Atta
Published: February 2023

Wireless sensor networks (WSNs) are becoming a significant technology for ubiquitous living and continue to be involved in active research because of their varied applications. Energy awareness will be a critical design problem in WSNs. Clustering is a widespread energy-efficient method and grants several benefits such as scalability, energy efficiency, less delay, and lifetime, but it results in hotspot issues. To solve this, unequal clustering (UC) has been presented. In UC, the size of the cluster differs with the distance to the base station (BS). This paper devises an improved tuna-swarm-algorithm-based unequal clustering for hotspot elimination (ITSA-UCHSE) technique in an energy-aware WSN. The ITSA-UCHSE technique intends to resolve the hotspot problem and uneven energy dissipation in the WSN. In this study, the ITSA is derived from the use of a tent chaotic map with the traditional TSA. In addition, the ITSA-UCHSE technique computes a fitness value based on energy and distance metrics. Moreover, the cluster size determination via the ITSA-UCHSE technique helps to address the hotspot issue. To demonstrate the enhanced performance of the ITSA-UCHSE approach, a series of simulation analyses were conducted. The simulation values stated that the ITSA-UCHSE algorithm has reached improved results over other models.
Development of the New Sensor Based on Functionalized Carbon Nanomaterials for Promethazine Hydrochloride Determination
Authors: Samardžić Mirela , Peršić Mateja , Széchenyi Aleksandar , Jozanović Marija , Pukleš Iva , Budetić Mateja
Published: February 2023

Promethazine hydrochloride (PM) is a widely used drug so its determination is important. Solid-contact potentiometric sensors could be an appropriate solution for that purpose due to their analytical properties. The aim of this research was to develop solid-contact sensor for potentiometric determination of PM. It had a liquid membrane containing hybrid sensing material based on functionalized carbon nanomaterials and PM ions. The membrane composition for the new PM sensor was optimized by varying different membrane plasticizers and the content of the sensing material. The plasticizer was selected based on calculations of Hansen solubility parameters (HSP) and experimental data. The best analytical performances were obtained using a sensor with 2-nitrophenyl phenyl ether (NPPE) as the plasticizer and 4% of the sensing material. It had a Nernstian slope (59.4 mV/decade of activity), a wide working range (6.2 × 10−7 M–5.0 × 10−3 M), a low limit of detection (1.5 × 10−7 M), fast response time (6 s), low signal drift (−1.2 mV/h), and good selectivity. The working pH range of the sensor was between 2 and 7. The new PM sensor was successfully used for accurate PM determination in a pure aqueous PM solution and pharmaceutical products. For that purpose, the Gran method and potentiometric titration were used.
Land Cover Changes Utilising Landsat Satellite Imageries for the Kumasi Metropolis and Its Adjoining Municipalities in Ghana (1986–2022)
Authors: Frimpong Bernard Fosu , Koranteng Addo , Atta-Darkwa Thomas , Junior Opoku Fosu , Zawiła-Niedźwiecki Tomasz
Published: February 2023

Forest loss, unbridled urbanisation, and the loss of arable lands have become contentious issues for the sustainable management of land. Landsat satellite images for 1986, 2003, 2013, and 2022, covering the Kumasi Metropolitan Assembly and its adjoining municipalities, were used to analyse the Land Use Land Cover (LULC) changes. The machine learning algorithm, Support Vector Machine (SVM), was used for the satellite image classification that led to the generation of the LULC maps. The Normalised Difference Vegetation Index (NDVI) and Normalised Difference Built-up Index (NDBI) were analysed to assess the correlations between the indices. The image overlays of the forest and urban extents and the calculation of the annual deforestation rates were evaluated. The study revealed decreasing trends in forestlands, increased urban/built-up areas (similar to the image overlays), and a decline in agricultural lands. However, there was a negative relationship between the NDVI and NDBI. The results corroborate the pressing need for the assessment of LULC utilising satellite sensors. This paper contributes to the existing outlines for evolving land design for the promotion of sustainable land use.
Emissivity Correction and Thermal Pattern Reconstruction in Eddy Current Pulsed Thermography
Authors: Li Kongjing , Tian Gui Yun , Ahmed Junaid
Published: February 2023

Emissivity variations are one of the most critical challenges in thermography technologies; this is due to the temperature calculation strongly depending on emissivity settings for infrared signal extraction and evaluation. This paper describes an emissivity correction and thermal pattern reconstruction technique based on physical process modelling and thermal feature extraction, for eddy current pulsed thermography. An emissivity correction algorithm is proposed to address the pattern observation issues of thermography in both spatial and time domains. The main novelty of this method is that the thermal pattern can be corrected based on the averaged normalization of thermal features. In practice, the proposed method brings benefits in enhancing the detectability of the faults and characterization of the materials without the interference of the emissivity variation problem at the object’s surfaces. The proposed technique is verified in several experimental studies, such as the case-depth evaluation of heat-treatment steels, failures, and fatigues of gears made of the heat-treated steels that are used for rolling stock applications. The proposed technique can improve the detectability of the thermography-based inspection methods and would improve the inspection efficiency for high-speed NDT&E applications, such as rolling stock applications.
Separation of Leather, Synthetic Leather and Polymers Using Handheld Laser-Induced Breakdown Spectroscopy
Authors: Gilon Nicole , Soyer Margaux , Redon Mathilde , Fauvet Patrice
Published: February 2023

Genuine leather is produced from animal skin by chemical tanning using chemical or vegetable agents, while synthetic leather is a combination of fabric and polymer. The replacement of natural leather by synthetic leather is becoming more difficult to identify. In this work, Laser Induced Breakdown Spectroscopy (LIBS) is evaluated to separate between very similar materials: leather, synthetic leather, and polymers. LIBS is now widely employed to provide a specific fingerprint from the different materials. Animal leathers processed with vegetable, chromium, or titanium tanning were analyzed together with polymers and synthetic leather from different origins. The spectra exhibited typical signatures from the tanning agents (Cr, Ti, Al) and the dyes and pigments, but also from polymer characteristic bands. The principal factor analysis allowed to discriminate between four main groups of samples representing the tanning processes and the polymer or synthetic leather character.
The Edge Application of Machine Learning Techniques for Fault Diagnosis in Electrical Machines
Authors: de las Morenas Javier , Moya-Fernández Francisco , López-Gómez Julio Alberto
Published: February 2023

The advent of digitization has brought about new technologies that enable advanced condition monitoring and fault diagnosis under the Industry 4.0 paradigm. While vibration signal analysis is a commonly used method for fault detection in literature, it often involves the use of expensive equipment in difficult-to-reach locations. This paper presents a solution for fault diagnosis of electrical machines by utilizing machine learning techniques on the edge, classifying information coming from motor current signature analysis (MCSA) for broken rotor bar detection. The paper covers the process of feature extraction, classification, and model training and testing for three different machine learning methods using a public dataset to then export the results to diagnose a different machine. An edge computing approach is adopted for the data acquisition, signal processing and model implementation on an affordable platform, the Arduino. This makes it accessible for small and medium-sized companies, albeit with the limitations of a resource-constrained platform. The proposed solution has been tested on electrical machines in the Mining and Industrial Engineering School of Almadén (UCLM) with positive results.
Measuring the Effectiveness of a Multicomponent Program to Manage Academic Stress through a Resilience to Stress Index
Authors: Figueroa Carlos , Ayala Andrés , Trejo Luis A. , Ramos Bertha , Briz Clara L. , Noriega Isabella , Chávez Alejandro
Published: February 2023

In this work, we evaluate the effectiveness of a multicomponent program that includes psychoeducation in academic stress, mindfulness training, and biofeedback-assisted mindfulness, while enhancing the Resilience to Stress Index (RSI) of students through the control of autonomic recovery from psychological stress. Participants are university students enrolled in a program of excellence and are granted an academic scholarship. The dataset consists of an intentional sample of 38 undergraduate students with high academic performance, 71% (27) women, 29% (11) men, and 0% (0) non-binary, with an average age of 20 years. The group belongs to the “Leaders of Tomorrow” scholarship program from Tecnológico de Monterrey University, in Mexico. The program is structured in 16 individual sessions during an eight-week period, divided into three phases: pre-test evaluation, training program, and post-test evaluation. During the evaluation test, an assessment of the psychophysiological stress profile is performed while the participants undergo a stress test; it includes simultaneous recording of skin conductance, breathing rate, blood volume pulse, heart rate, and heart rate variability. Based on the pre-test and post-test psychophysiological variables, an RSI is computed under the assumption that changes in physiological signals due to stress can be compared against a calibration stage. The results show that approximately 66% of the participants improved their academic stress management after the multicomponent intervention program. A Welch’s t-test showed a difference in mean RSI scores (t = −2.30, p = 0.025) between the pre-test and post-test phases. Our findings show that the multicomponent program promoted positive changes in the RSI and in the management of the psychophysiological responses to academic stress.
Determination of the Accuracy of the Straight Bevel Gear Profiles by a Novel Optical Coaxial Multi-Ring Measurement Method
Authors: Li Junheng , Zhang Dehai , Li Yanqin , Ma Xuanxiong , Wang Tao , Wu Chao
Published: February 2023

Straight bevel gears are widely used in mining equipment, ships, heavy industrial equipment, and other fields due to their high capacity and robust transmission. Accurate measurements are essential in order to determine the quality of bevel gears. We propose a method for measuring the accuracy of the top surface profile of the straight bevel gear teeth based on binocular visual technology, computer graphics, error theory, and statistical calculations. In our method, multiple measurement circles are established at equal intervals from the small end of the top surface of the gear tooth to the large end, and the coordinates of the intersection points of these circles with the tooth top edge lines of the gear teeth are extracted. The coordinates of these intersections are fitted to the top surface of the tooth based on NURBS surface theory. The surface profile error between the fitted top surface of the tooth and the designed surface is measured and determined based on the product use requirements, and if this is less than a given threshold, the product is acceptable. With a module of 5 and an eight-level precision, such as the straight bevel gear, the minimum surface profile error measured was −0.0026 mm. These results demonstrate that our method can be used to measure surface profile errors in the straight bevel gears, which will broaden the field of in-depth measurements for the straight bevel gears.
A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization
Authors: Avola Danilo , Cinque Luigi , Foresti Gian Luca , Lanzino Romeo , Marini Marco Raoul , Mecca Alessio , Scarcello Francesco
Published: February 2023

During flight, unmanned aerial vehicles (UAVs) need several sensors to follow a predefined path and reach a specific destination. To this aim, they generally exploit an inertial measurement unit (IMU) for pose estimation. Usually, in the UAV context, an IMU entails a three-axis accelerometer and a three-axis gyroscope. However, as happens for many physical devices, they can present some misalignment between the real value and the registered one. These systematic or occasional errors can derive from different sources and could be related to the sensor itself or to external noise due to the place where it is located. Hardware calibration requires special equipment, which is not always available. In any case, even if possible, it can be used to solve the physical problem and sometimes requires removing the sensor from its location, which is not always feasible. At the same time, solving the problem of external noise usually requires software procedures. Moreover, as reported in the literature, even two IMUs from the same brand and the same production chain could produce different measurements under identical conditions. This paper proposes a soft calibration procedure to reduce the misalignment created by systematic errors and noise based on the grayscale or RGB camera built-in on the drone. Based on the transformer neural network architecture trained in a supervised learning fashion on pairs of short videos shot by the UAV’s camera and the correspondent UAV measurements, the strategy does not require any special equipment. It is easily reproducible and could be used to increase the trajectory accuracy of the UAV during the flight.
Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns
Authors: Liu Yi , Jiang Yuxin , Gao Zengliang , Liu Kaixin , Yao Yuan
Published: February 2023

In chemical processes, packed columns are frequently employed in various unit operations. However, the flow rates of gas and liquid in these columns are often constrained by the risk of flooding. To ensure the safe and efficient operation of packed columns, it is crucial to detect flooding in real time. Conventional flooding monitoring methods rely heavily on manual visual inspections or indirect information from process variables, which limit the real-time accuracy of results. To address this challenge, we proposed a convolutional neural network (CNN)-based machine vision approach for non-destructive detection of flooding in packed columns. Real-time images of the packed column were captured using a digital camera and analyzed with a CNN model, which was been trained on a dataset of recorded images to identify flooding. The proposed approach was compared with deep belief networks and an integrated approach of principal component analysis and support vector machines. The feasibility and advantages of the proposed method were demonstrated through experiments on a real packed column. The results showed that the proposed method provides a real-time pre-alarm approach for detecting flooding, enabling process engineers to quickly respond to potential flooding events.
TouchRoller: A Rolling Optical Tactile Sensor for Rapid Assessment of Textures for Large Surface Areas
Authors: Cao Guanqun , Jiang Jiaqi , Lu Chen , Gomes Daniel Fernandes , Luo Shan
Published: February 2023

Tactile sensing is important for robots to perceive the world as it captures the physical surface properties of the object with which it is in contact and is robust to illumination and colour variances. However, due to the limited sensing area and the resistance of their fixed surface when they are applied with relative motions to the object, current tactile sensors have to tap the tactile sensor on the target object a great number of times when assessing a large surface, i.e., pressing, lifting up, and shifting to another region. This process is ineffective and time-consuming. It is also undesirable to drag such sensors as this often damages the sensitive membrane of the sensor or the object. To address these problems, we propose a roller-based optical tactile sensor named TouchRoller, which can roll around its centre axis. It maintains being in contact with the assessed surface throughout the entire motion, allowing for efficient and continuous measurement. Extensive experiments showed that the TouchRoller sensor can cover a textured surface of 8 cm × 11 cm in a short time of 10 s, much more effectively than a flat optical tactile sensor (in 196 s). The reconstructed map of the texture from the collected tactile images has a high Structural Similarity Index (SSIM) of 0.31 on average when compared with the visual texture. In addition, the contacts on the sensor can be localised with a low localisation error, 2.63 mm in the centre regions and 7.66 mm on average. The proposed sensor will enable the fast assessment of large surfaces with high-resolution tactile sensing and the effective collection of tactile images.
A Self-Adaptive Gallery Construction Method for Open-World Person Re-Identification
Authors: Casao Sara , Azagra Pablo , Murillo Ana C. , Montijano Eduardo
Published: February 2023

Person re-identification, or simply re-id, is the task of identifying again a person who has been seen in the past by a perception system. Multiple robotic applications, such as tracking or navigate-and-seek, use re-identification systems to perform their tasks. To solve the re-id problem, a common practice consists in using a gallery with relevant information about the people already observed. The construction of this gallery is a costly process, typically performed offline and only once because of the problems associated with labeling and storing new data as they arrive in the system. The resulting galleries from this process are static and do not acquire new knowledge from the scene, which is a limitation of the current re-id systems to work for open-world applications. Different from previous work, we overcome this limitation by presenting an unsupervised approach to automatically identify new people and incrementally build a gallery for open-world re-id that adapts prior knowledge with new information on a continuous basis. Our approach performs a comparison between the current person models and new unlabeled data to dynamically expand the gallery with new identities. We process the incoming information to maintain a small representative model of each person by exploiting concepts of information theory. The uncertainty and diversity of the new samples are analyzed to define which ones should be incorporated into the gallery. Experimental evaluation in challenging benchmarks includes an ablation study of the proposed framework, the assessment of different data selection algorithms that demonstrate the benefits of our approach, and a comparative analysis of the obtained results with other unsupervised and semi-supervised re-id methods.
Detection of Management-Frames-Based Denial-of-Service Attack in Wireless LAN Network Using Artificial Neural Network
Authors: Abdallah Abdallah Elhigazi , Hamdan Mosab , Gismalla Mohammed S. M. , Ibrahim Ashraf Osman , Aljurayban Nouf Saleh , Nagmeldin Wamda , Khairi Mutaz H. H.
Published: February 2023

Wireless Local Area Networks (WLANs) have become an increasingly popular mode of communication and networking, with a wide range of applications in various fields. However, the increasing popularity of WLANs has also led to an increase in security threats, including denial of service (DoS) attacks. In this study, management-frames-based DoS attacks, in which the attacker floods the network with management frames, are particularly concerning as they can cause widespread disruptions in the network. Attacks known as denial of service (DoS) can target wireless LANs. None of the wireless security mechanisms in use today contemplate defence against them. At the MAC layer, there are multiple vulnerabilities that can be exploited to launch DoS attacks. This paper focuses on designing and developing an artificial neural network (NN) scheme for detecting management-frames-based DoS attacks. The proposed scheme aims to effectively detect fake de-authentication/disassociation frames and improve network performance by avoiding communication interruption caused by such attacks. The proposed NN scheme leverages machine learning techniques to analyse patterns and features in the management frames exchanged between wireless devices. By training the NN, the system can learn to accurately detect potential DoS attacks. This approach offers a more sophisticated and effective solution to the problem of DoS attacks in wireless LANs and has the potential to significantly enhance the security and reliability of these networks. According to the experimental results, the proposed technique exhibits higher effectiveness in detection compared to existing methods, as evidenced by a significantly increased true positive rate and a decreased false positive rate.
Surface Crack Monitoring by Rayleigh Waves with a Piezoelectric-Polymer-Film Ultrasonic Transducer Array
Authors: Li Xiaotian , Wong Voon-Kean , Yousry Yasmin Mohamed , Lim David Boon Kiang , Christopher Subhodayam Percis Teena , Yao Kui , Feng Liuyang , Qian Xudong , Fan Zheng
Published: February 2023

This paper presents a method for measuring surface cracks based on the analysis of Rayleigh waves in the frequency domain. The Rayleigh waves were detected by a Rayleigh wave receiver array made of a piezoelectric polyvinylidene fluoride (PVDF) film and enhanced by a delay-and-sum algorithm. This method employs the determined reflection factors of Rayleigh waves scattered at a surface fatigue crack to calculate the crack depth. In the frequency domain, the inverse scattering problem is solved by comparing the reflection factor of the Rayleigh waves between the measured and the theoretical curves. The experimental measurement results quantitatively matched the simulated surface crack depths. The advantages of using the low-profile Rayleigh wave receiver array made of a PVDF film for detecting the incident and reflected Rayleigh waves were analyzed in contrast with those of a Rayleigh wave receiver using a laser vibrometer and a conventional lead zirconate titanate (PZT) array. It was found that the Rayleigh waves propagating across the Rayleigh wave receiver array made of the PVDF film had a lower attenuation rate of 0.15 dB/mm compared to that of 0.30 dB/mm of the PZT array. Multiple Rayleigh wave receiver arrays made of the PVDF film were applied for monitoring surface fatigue crack initiation and propagation at welded joints under cyclic mechanical loading. Cracks with a depth range of 0.36–0.94 mm were successfully monitored.
Evaluation of Training on Good Agronomic Practices for Seed Yam Production in Nigeria
Authors: Mignouna Djana Babatima , Aighewi Beatrice Anim , Maroya Norbert , Akinribido Bolanle , Balogun Morufat , Akintayo Oluyemi T. , Amah Delphine , Awotide Bola , Dontsop Nguezet Paul Martin , Abdoulaye Tahirou , Asiedu Robert , Manyong Victor
Published: February 2023

The second phase of the Yam Improvement for Income and Food Security in West Africa (YIIFSWA II) project was implemented through a strong collaboration between research and private sector. Private seed companies were provided with a wide variety of training and development activities on good agronomic practices (GAPs). However, the effects of the training activities were yet to be evaluated. This paper evaluated the contribution of the training on high-quality seed yam production through paying particular attention to how it was tailored to the needs of various farmers, and how the knowledge gained was transferred to the field. The study employed an applied Kirkpatrick model and descriptive statistics to measure the indicators of training effectiveness. Results from a statistical population of 172 farmers using 179 demonstration fields showed that the farmers’ training on GAPs was effective, based on four criteria of the Kirkpatrick training evaluation process. The satisfactory results at the beginning of the hierarchical model have implications on the satisfactory results at the end. Improving the effectiveness of a training depends on defining factors such as (i) Training based on satisfaction in meeting the needs of your trainees and the seed production business; (ii) Training based on learning objectives in improving the trainees’ skills and knowledge; and (iii) Training based on learner attitude change in applying new skills and knowledge in the fields. Conclusions drawn from this study indicated that the training was a reasonable value-added mechanism of improved agricultural knowledge and practices for enhancing high-quality seed yam production in Nigeria.
Agricultural Wastes as Renewable Biomass to Remediate Water Pollution
Authors: Noor Awal , Khan Sher Ali
Published: February 2023

Increases in agricultural waste, population, and industrialization are leading to serious environmental problems, in particular drinking water contamination. Continuous efforts have been made to remediate water pollution through different approaches, either by decreasing the interring of pollutants or treatment of already contaminated water. The development of an efficient, cheaper, and renewable adsorbent is the focus of the current research. Agricultural wastes are cheap materials for this purpose and have attracted much attention of researchers. These agricultural wastes are either field residues such as stems, stalks, and leaves, or process residues such as husks, roots, and bagasse, as they have the same chemical composition (cellulose, hemicelluloses, and lignocelluloses). These wastes are processed using different methods to yield an efficient adsorbent. Chemical modification is used to prepare novel efficient adsorbents using agricultural wastes, rather than incineration of these materials. This review summarizes the research outcomes in terms of chemical modification and application of agricultural wastes used for the eradication of organic and inorganic pollutants from water.
The Transmission Effect Test of China’s Rotation Mechanism on the Cotton Reserve Market
Authors: Li Xiaoxiao , Wang Bo , Sun Lingyan , Zhu Honghui , Lv Ning , Zhang Jiaqi
Published: February 2023

As an important strategic material in China, cotton policy has positive significance in stabilizing the safety of domestic cotton, ensuring cotton income, and stabilizing cotton output. At the same time, affected by the future market price, the cotton industry supply chain cost management is particularly important. Based on the data of China’s cotton price, consumption, yield, and wheeling-in and wheeling-out from 1978 to 2016, the simultaneous equation model is used to empirically analyze the transmission effect of the rotation mechanism of reserve cotton (RMRC) on cotton supply and demand. Studies have shown that China’s cotton reserve policy and the RMRC play a positive role in balancing the supply and demand of the domestic cotton market and preventing excessive fluctuations in cotton prices. The transmission effect of the RMRC on domestic cotton yield and consumption is significant and there are certain differences. The transmission effect of domestic cotton yield is stronger than the transmission effect on domestic cotton consumption. The difference is owing to the transfer path of the RMRC being different. We should improve the regulation and control effect of the RMRC on cotton demand, realize the normalization of the RMRC, and clarify the policy objectives of the RMRC.
Impact of Chinese Carbon Emissions Trading Policy on Chongqing’s Carbon Emissions and Economic Development
Authors: Ju Xiaoyu , Wan Jie , Zhang Ziwei , Ma Chunai , Zhang Liangwei , Zhao Xiaodong
Published: February 2023

Balancing sustainable economic growth and environmental protection in developing countries is an important way to mitigate climate change. Based on panel data from nine provinces along the Silk Road Economic Belt between 2004 and 2021, this paper takes Chongqing, a pilot city for carbon trading rights, as the experimental group and the remaining eight provinces as the control group; we mainly analyze the impact of the carbon emissions trading policy on Chongqing’s carbon emissions and economic development after its implementation in 2013 using the difference-in-differences method (DID) and propensity score matching difference-in-differences method (PSM-DID). Then we use the synthetic control method (SCM) to test its robustness. The results showed that while maintaining the economic development speed, carbon trading helps to reduce carbon emissions. In addition, we also found that the economic activity effect and the energy intensity effect are the mediating effects of the carbon emissions reduction. Finally, taking the policy effect of the carbon emissions trading in Chongqing as a reference, this article confirmed the importance of aligning the carbon trading pathway with targeted green policies from the government. The government should drive the establishment of a regional carbon market in the nine provinces along the Silk Road Economic Belt, which could help to achieve sustainable development.
Performance Improvement of Solar Desalination System Based on CeO2-MWCNT Hybrid Nanofluid
Authors: Kaviti Ajay Kumar , Akkala Siva Ram , Ali Mohd Affan , Anusha Pulagam , Sikarwar Vineet Singh
Published: February 2023

There is a scarcity of freshwater resources and their quality is deteriorating. As a result, meeting human needs is getting more and more challenging. Additionally, significant health problems are brought on by a shortage of freshwater. Therefore, finding a sustainable alternative technique for producing clean water is necessary. Solar distillation is one of the methods that can be implemented to enhance the overall production of pure water. In this work, a hybrid nanofluid was prepared using a two-step method with cerium oxide (CeO2) nanoparticles and multi-walled carbon nanotubes (MWCNTs) in a ratio of 80:20. The concentrations of hybrid nanofluids investigated were 0.02%, 0.04%, and 0.06%. The surfactant cetyltrimethylammonium bromide (CTAB) was used to keep the hybrid nanofluid stable. The studies were carried out over three days in both conventional and modified stills at a constant depth of 1 cm of hybrid nanofluid. The modified still (MS) achieved a maximum production of 1430 mL compared to the conventional still’s (CS) maximum output of 920 mL. The CPL (Cost per liter) for MS was USD 0.039, and for CS, it was USD 0.045. The levels of TDS in the MS and CS were 96.38% and 92.55% lower than those in saline water. The fluoride ion level of saline water was 0.635 mg/L, whereas the distilled water of MS and CS are 0.339 mg/L and 0.414 mg/L, respectively.