bluuebunny/crossref_metadata_2025 huggingface dataset:
Glutamate, Humic Acids and Their Combination Modulate the Phenolic Profile, Antioxidant Traits, and Enzyme-Inhibition Properties in Lettuce
Authors: De Gregorio Marco Armando , Zengin Gökhan , Alp-Turgut Fatma Nur , Elbasan Fevzi , Ozfidan-Konakci Ceyda , Arikan Busra , Yildiztugay Evren , Zhang Leilei , Lucini Luigi
Published: April 2023

Lettuce (Lactuca sativa L., Asteraceae) is a popular vegetable leafy crop playing a relevant role in human nutrition. Nowadays, novel strategies are required to sustainably support plant growth and elicit the biosynthesis of bioactive molecules with functional roles in crops including lettuce. In this work, the polyphenolic profile of lettuce treated with glutamic acid (GA), humic acid (HA), and their combination (GA + HA) was investigated using an untargeted metabolomics phenolic profiling approach based on high-resolution mass spectrometry. Both aerial and root organ parts were considered, and a broad and diverse phenolic profile could be highlighted. The phenolic profile included flavonoids (anthocyanins, flavones, flavanols, and flavonols), phenolic acids (both hydroxycinnamics and hydroxybenzoics), low molecular weight phenolics (tyrosol equivalents), lignans and stilbenes. Overall, GA and HA treatments significantly modulated the biosynthesis of flavanols, lignans, low molecular weight phenolics, phenolic acids, and stilbene. Thereafter, antioxidant capacity was evaluated in vitro with 2,2-diphenyln-1-picrylhydrazyl (DPPH), 2,2′-Azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS), ferric reducing antioxidant power (FRAP), and cupric ion reducing antioxidant capacity (CUPRAC) assays. In addition, this study examined the inhibitory properties of enzymes, including acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), tyrosinase, alpha-amylase, and alpha-glucosidase. Compared to individual treatments, the combination of GA + HA showed stronger antioxidant abilities in free radical scavenging and reducing power assays in root samples. Moreover, this combination positively influenced the inhibitory effects of root samples on AChE and BChE and the tyrosinase inhibitory effect of leaf samples. Concerning Pearson’s correlations, antioxidant and enzyme inhibition activities were related to phenolic compounds, and lignans in particular correlated with radical scavenging activities. Overall, the tested elicitors could offer promising insights for enhancing the functional properties of lettuce in agricultural treatments.
Embedded MPC Strategies for ESP-Lifted Oil Wells: Hardware-in-the-Loop Performance Analysis of Nonlinear and Robust Techniques
Authors: Santana Bruno A. , Matos Victor S. , Santana Daniel D. , Martins Márcio A. F.
Published: April 2023

This paper proposes embedded model predictive control strategies for oil-production processes equipped with electric submersible pump (ESP) installations. The novelty of this paper is the robustness and computational performance analysis of the Robust Infinite-Horizon Model Predictive Controller (RIHMPC) and Nonlinear Model Predictive Controller (NMPC) strategies, which have not yet been documented by the oil and gas exploration and production literature. The proposed method to embed the control laws is flexible with different hardware and is based on automatic code generation, which facilitates the project workflow. Hardware-in-the-loop simulation cases were used to compare the performance of both control strategies embedded in the Teensy 4.1 microcontroller, using key indices for real applications. The results showed that the RIHMPC strategy is a very promising alternative for real-time operation in ESP-lifted oil wells, with overall performance similar to the NMPC controller, even in noisy and plant–model mismatch scenarios, and using only linear models in its formulation.
The Impact of Air Source Heat Pump on the Production Performance of Broiler Chicks
Authors: Hu Chenming , Qiu Mohan , Yu Chunlin , Yang Li , Zhu Qubo , Liu Anfang , Du Longhuan , Yang Chaowu
Published: April 2023

Air source heat pump (ASHP) is a good new energy heating system. To explore the effect of ASHP on the production of yellow-feather broiler chicks, 31,500 one-day-old yellow broiler chicks were divided into three chicken houses with the same building structure but different heating methods (ASHP, CCF, CB). During the experiment, the parameters of heating time, temperature uniformity, gas concentration, weight gain, survival rate and production benefit were analyzed and evaluated. Results showed that the difference in NH3, CO2, and H2S concentrations was not significant in all test groups (p > 0.05). Only group II detected the CO gas. In winter and spring, the weight of the chickens in group II were weighed the least at 35 days of age, and were significantly different from the ASHP and CB system (p < 0.05). There was no significant difference in body weight between ASHP and CB (p > 0.05). Group II had the lowest evenness and survival, the slowest warming, the worst uniformity of temperature distribution, and the highest cost. It is concluded that the ASHP was very environmentally friendly and has the highest economy, which is worth promoting and using.
Neighborhood Selection Synchronization Mechanism-Based Moving Source Localization Using UAV Swarm
Authors: Zhou Yongkun , Gao Wei , Rao Bin , Ding Bowen , Wang Wei
Published: April 2023

To obtain the accurate time difference of arrival (TDOA) and frequency difference of arrival (FDOA) for passive localization in an unmanned aerial vehicle (UAV) swarm, UAV swarm network synchronization is necessary. However, most of the traditional distributed time synchronization protocols are based on iteration, which hinders efficiency improvement. High communication costs and long convergence times are often required in large-scale UAV swarm networks. This paper presents a neighborhood selection-all selection (NS-AS) synchronization mechanism-based moving source localization method for UAV swarms. First, the NS-AS synchronization mechanism is introduced, to model the UAV swarm network synchronization process. Specifically, the UAV neighbors are first grouped by sector, and the most representative neighbors are selected from each sector for the state update calculation. When the UAV swarm network reaches a fully connected state, the synchronization mechanism is switched to select all neighbors, to improve the convergence speed. Then, the TDOA-FDOA joint localization algorithm is employed to locate the moving radiation source. Through simulation, the effectiveness of the proposed method is verified by the system convergence and localization performance under different parameters. Experimental results show that the synchronization mechanism based on NS-AS effectively improves the convergence speed of the system while ensuring the accuracy of moving radiation source localization.
Accurate Retrieval of the Whole Flood Process from Occurrence to Recession Based on GPS Original CNR, Fitted CNR, and Seamless CNR Series
Authors: Tong Zhifeng , Su Mingkun , Zheng Fu , Shang Junna , Wu Juntao , Shen Xiaoliang , Chang Xin
Published: April 2023

The CNR (Carrier-to-Noise Ratio) of GPS (Global Positioning System) satellites is highly relevant to the multipath error. The multipath error is more serious in the flood environment since the reflection and diffraction coefficients of water are much higher compared to dry soil. Thus, the amplitude of CNR will decrease in the flood environment. In this study, the relationship between multipath error, flooding, and CNR is introduced in theory. Then, by using the characteristic of the orbital repetition period, the stability of CNR between 2 adjacent days in a static observation environment is demonstrated by 32 MGEX (Multi-GNSS Experiment) stations in different latitude and longitude regions of the world. The results show that the average RMS of different CNRs between two adjacent days is only about 0.62 dB-Hz. In addition, the correlation coefficient of CNRs between two adjacent days is analyzed. The correlation coefficient of the original signal CNR is 0.997. Moreover, after mitigating the influence of random noise and lower CNR, the correlation coefficients of the fitted CNRs larger than 40 dB-Hz can reach 0.999. Thus, based on the fluctuation in original CNR, fitted CNR, and seamless series characteristics of CNR, the whole flood process from occurrence to recession can be retrieved. A flood that occurred in Zhengzhou City, China, from DOY 200 to DOY 202, 2021 is used to demonstrate the process of retrieval. The experimental results indicate that the flood appeared at about 15:30 pm on DOY 200, reached a peak at approximately 8:30 am on DOY 202, and totally subsided at about 10:00 am on DOY 202. In conclusion, the CNR can be effectively used to retrieve the whole process of the flood, which lays a foundation for researching flood detection and warning based on GPS satellites.
Edge-Preserved Low-Rank Representation via Multi-Level Knowledge Incorporation for Remote Sensing Image Denoising
Authors: Feng Xiaolin , Tian Sirui , Abhadiomhen Stanley Ebhohimhen , Xu Zhiyong , Shen Xiangjun , Wang Jing , Zhang Xinming , Gao Wenyun , Zhang Hong , Wang Chao
Published: April 2023

The low-rank models have gained remarkable performance in the field of remote sensing image denoising. Nonetheless, the existing low-rank-based methods view residues as noise and simply discard them. This causes denoised results to lose many important details, especially the edges. In this paper, we propose a new denoising method named EPLRR-RSID, which focuses on edge preservation to improve the image quality of the details. Specifically, we considered the low-rank residues as a combination of useful edges and noisy components. In order to better learn the edge information from the low-rank representation (LRR), we designed multi-level knowledge to further distinguish the edge part and the noise part from the residues. Furthermore, a manifold learning framework was introduced in our proposed model to better obtain the edge information, as it can find the structural similarity of the edge part while suppressing the influence of the non-structural noise part. In this way, not only the low-rank part is better learned, but also the edge part is precisely preserved. Extensive experiments on synthetic and several real remote sensing datasets showed that EPLRR-RSID has superior advantages over the compared state-of-the-art (SOTA) approaches, with the mean edge protect index (MEPI) values reaching at least 0.9 and the best values in the no-reference index BRISQUE, which represents that our method improved the image quality by edge preserving.
First Open-Coast HF Radar Observations of a 2-Phase Volcanic Tsunami, Tonga 2022
Authors: Lipa Belinda , Barrick Donald , Whelan Chad , Losekoot Marcel , Parikh Hardik
Published: April 2023

We describe results from coastal radar systems that observed anomalous current flows generated by the volcanic eruption in the Tongan archipelago on 15 January 2022 UTC, reporting the first radar detection of a volcanic tsunami. The eruption caused small tsunamis along the western U.S. Coast, generating some damage in a few harbors. The highest tsunami signal in U.S. tide gauge data from the California coast occurred at Arena Cove, with significant heights detected at Port San Luis and Crescent City. We analyze correlated wave orbital velocity detections by High Frequency (HF) radars along the coast between Gerstle Cove and Santa Barbara. Signals observed by the radars indicate that the event had two phases, each with its own distinct genesis: an initial weak surface disturbance, most likely generated by the wave of atmospheric pressure that moved outward from the blast source at just below the speed of sound, followed by a stronger disturbance that arrived approximately 3.5 h later, matching the arrival time for a wave moving entirely through the water from the volcano to the U.S. West Coast. We conclude that this phase consists of a conventional water wave tsunami and weaker waves generated by the pulse. We also report the detection of a small pulse-generated event off the west coast of Florida. Radar observations are compared with water level measurements at nearby tide gauges and a DART buoy, and with observations of barometric pressure. We point out that a Proudman near-resonance at the Tonga Trench is unlikely to explain the second phase observations. Comparison with tide gauge signals at San Francisco, generated by the Krakatoa eruption in 1883, support our conclusions.
AiTLAS: Artificial Intelligence Toolbox for Earth Observation
Authors: Dimitrovski Ivica , Kitanovski Ivan , Panov Panče , Kostovska Ana , Simidjievski Nikola , Kocev Dragi
Published: April 2023

We propose AiTLAS—an open-source, state-of-the-art toolbox for exploratory and predictive analysis of satellite imagery. It implements a range of deep-learning architectures and models tailored for the EO tasks illustrated in this case. The versatility and applicability of the toolbox are showcased in a variety of EO tasks, including image scene classification, semantic image segmentation, object detection, and crop type prediction. These use cases demonstrate the potential of the toolbox to support the complete data analysis pipeline starting from data preparation and understanding, through learning novel models or fine-tuning existing ones, using models for making predictions on unseen images, and up to analysis and understanding of the predictions and the predictive performance yielded by the models. AiTLAS brings the AI and EO communities together by facilitating the use of EO data in the AI community and accelerating the uptake of (advanced) machine-learning methods and approaches by EO experts. It achieves this by providing: (1) user-friendly, accessible, and interoperable resources for data analysis through easily configurable and readily usable pipelines; (2) standardized, verifiable, and reusable data handling, wrangling, and pre-processing approaches for constructing AI-ready data; (3) modular and configurable modeling approaches and (pre-trained) models; and (4) standardized and reproducible benchmark protocols including data and models.
Absorption/Attenuation Spectral Description of ESKAPEE Bacteria: Application to Seeder-Free Culture Monitoring, Mammalian T-Cell and Bacteria Mixture Analysis and Contamination Description
Authors: Wacogne Bruno , Belinger Podevin Marine , Vaccari Naïs , Koubevi Claudia , Codjiová Céline , Gutierrez Emilie , Bourgeois Pauline , Davoine Lucie , Robert-Nicoud Marjorie , Rouleau Alain , Frelet-Barrand Annie
Published: April 2023

Despite numerous innovations, measuring bacteria concentrations on a routine basis is still time consuming and ensuring accurate measurements requires careful handling. Furthermore, it often requires sampling small volumes of bacteria suspensions which might be poorly representative of the real bacteria concentration. In this paper, we propose a spectroscopy measurement method based on a description of the absorption/attenuation spectra of ESKAPEE bacteria. Concentrations were measured with accuracies less than 2%. In addition, mixing the mathematical description of the absorption/attenuation spectra of mammalian T-cells and bacteria allows for the simultaneous measurements of both species’ concentrations. This method allows real-time, sampling-free and seeder-free measurement and can be easily integrated into a closed-system environment.
Autonomous Self-Adaptive and Self-Aware Optical Wireless Communication Systems
Authors: Esmail Maged Abdullah
Published: April 2023

The future age of optical networks demands autonomous functions to optimize available resources. With autonomy, the communication network should be able to learn and adapt to the dynamic environment. Among the different autonomous tasks, this work considers building self-adaptive and self-awareness-free space optic (FSO) networks by exploiting advances in artificial intelligence. In this regard, we study the use of machine learning (ML) techniques to build self-adaptive and self-awareness FSO systems capable of classifying the modulation format/baud rate and predicting the number of channel impairments. The study considers four modulation formats and four baud rates applicable in current commercial FSO systems. Moreover, two main channel impairments are considered. The results show that the proposed ML algorithm is capable of achieving 100% classification accuracy for the considered modulation formats/baud rates even under harsh channel conditions. Moreover, the prediction accuracy of the channel impairments ranges between 71% and 100% depending on the predicted parameter type and channel conditions.
A Vicarious Technique for Understanding and Diagnosing Hyperspectral Spatial Misregistration
Authors: Conran David N. , Ientilucci Emmett J.
Published: April 2023

Pushbroom hyperspectral imaging (HSI) systems intrinsically measure our surroundings by leveraging 1D spatial imaging, where each pixel contains a unique spectrum of the observed materials. Spatial misregistration is an important property of HSI systems because it defines the spectral integrity of spatial pixels and requires characterization. The IEEE P4001 Standards Association committee has defined laboratory-based methods to test the ultimate limit of HSI systems but negates any impacts from mounting and flying the instruments on airborne platforms such as unmanned aerial vehicles (UAV’s) or drones. Our study was designed to demonstrate a novel vicarious technique using convex mirrors to bridge the gap between laboratory and field-based HSI performance testing with a focus on extracting hyperspectral spatial misregistration. A fast and simple extraction technique is proposed for estimating the sampled Point Spread Function’s width, along with keystone, as a function of wavelength for understanding the key contributors to hyperspectral spatial misregistration. With the ease of deploying convex mirrors, off-axis spatial misregistration is assessed and compared with on-axis behavior, where the best performance is often observed. In addition, convex mirrors provide an easy methodology to exploit ortho-rectification errors related to fixed pushbroom HSI systems, which we will show. The techniques discussed in this study are not limited to drone-based systems but can be easily applied to other airborne or satellite-based systems.
A Novel Method for Bearing Fault Diagnosis under Variable Speed Based on Envelope Spectrum Fault Characteristic Frequency Band Identification
Authors: Pei Di , Yue Jianhai , Jiao Jing
Published: April 2023

Rolling element bearing (REB) vibration signals under variable speed (VS) have non-stationary characteristics. Order tracking (OT) and time-frequency analysis (TFA) are two widely used methods for REB fault diagnosis under VS. However, the effect of OT methods is affected by resampling errors and close-order harmonic interference, while the accuracy of TFA methods is mainly limited by time-frequency resolution and ridge extraction algorithms. To address this issue, a novel method based on envelope spectrum fault characteristic frequency band identification (FCFBI) is proposed. Firstly, the characteristics of the bearing fault vibration signal’s envelope spectrum under VS are analyzed in detail and the fault characteristic frequency band (FCFB) is introduced as a new and effective representation of faults. Then, fault templates based on FCFB are constructed as reference for fault identification. Finally, based on the calculation of the correlation coefficients between the envelope spectrum and fault templates in the extended FCFB, the bearing fault can be diagnosed automatically according to the preset correlation coefficient criterion. Two bearing VS experiments indicate that the proposed method can achieve satisfactory diagnostic accuracy. The comparison of OT and TFA methods further demonstrates the comprehensive superiority of the proposed method in the overall consideration of accuracy, diagnostic time, tachometer dependency, and automatic degree.
The Degradation of Daguerreotypes and the Relationship with Their Multi-Material Structure: A Multimodal Investigation
Authors: Quintero Balbas Diego , Cattaneo Barbara , Cagnini Andrea , Belluzzo Paolo , Rossi Sandra , Fontana Raffaella , Striova Jana
Published: April 2023

Preserving and analytically examining daguerreotypes is particularly challenging because of their multi-material and multi-component structure. Various sensors have been exploited to examine mainly the image plates of the daguerreotypes even though the degradation goes beyond this component. Micro-analyses have been the preferred method due to the nanoscale structure of the image particles. In this work, we propose comprehensive multi-modal non-invasive sensing to investigate the corrosion products present in nine daguerreotypes from the Fondazione Alinari per la Fotografia (FAF, Florence, Italy). The methodology proposed includes chemical and morphological analyses: portable X-ray fluorescence spectrometry (pXRF), Raman microspectroscopy (μ-Raman), and micro-Fourier transform infrared spectroscopy in reflection mode (μ-rFTIR) for the chemical identification. For the first time, optical coherence tomography (OCT) was deployed to record the cross-sectional and morphological data of the relevant corrosion formations on daguerreotypes in a contactless way. The results allowed the characterization, in a non-invasive mode at a microscopic level, of a wide range of degradation products produced by the interaction of the different elements present in the structure of the daguerreotypes. The aim was to verify the performance of the proposed methodology and to link the chemical and physical complexity of the entire structure, disclosed by the state-of-art sensors, to the daguerreotype degradation. The results draw attention to the need to monitor not only the image condition but the whole object as a partially closed system in constant interaction internally and with the environment.
Application of Air-Coupled Ground Penetrating Radar Based on F-K Filtering and BP Migration in High-Speed Railway Tunnel Detection
Authors: Lei Yang , Jiang Bo , Su Guofeng , Zou Yong , Qi Falin , Li Baoqing , Jia Feiyu , Tian Tian , Qu Qiming
Published: April 2023

As the number and length of high-speed railway tunnels increase in China, implicit defects such as insufficient lining thicknesses, voids, and poor compaction have become increasingly common, posing a serious threat to train operation safety. It is, therefore, imperative to conduct a comprehensive census of the defects within the tunnel linings. In response to this problem, this study proposes a high-speed railway tunnel detection method based on vehicle-mounted air-coupled GPR. Building on a forward simulation of air-coupled GPR, the study proposes the F-K filtering and BP migration algorithms based on the practical considerations of random noise and imaging interference from the inherent equipment. Through multi-dimensional quantitative comparisons, these algorithms are shown to improve the spectrum entropy values and instantaneous amplitude ratios by 4.6% and 11.6%; and 120% and 180%, respectively, over the mean and bandpass filtering algorithms, demonstrating their ability to suppress clutter and enhance the internal signal prominence of the lining. The experimental results are consistent with the forward simulation trends, and the verification using the ground-coupled GPR detection confirms that air-coupled GPR can meet the requirements of high-speed railway tunnel lining inspections. A comprehensive GPR detection model is proposed to lay the foundation for a subsequent defect census of high-speed railway tunnels.
Smart Task Assistance in Mixed Reality for Astronauts
Authors: Sun Qingwei , Chen Wei , Chao Jiangang , Lin Wanhong , Xu Zhenying , Cao Ruizhi
Published: April 2023

Mixed reality (MR) registers virtual information and real objects and is an effective way to supplement astronaut training. Spatial anchors are generally used to perform virtual–real fusion in static scenes but cannot handle movable objects. To address this issue, we propose a smart task assistance method based on object detection and point cloud alignment. Specifically, both fixed and movable objects are detected automatically. In parallel, poses are estimated with no dependence on preset spatial position information. Firstly, YOLOv5s is used to detect the object and segment the point cloud of the corresponding structure, called the partial point cloud. Then, an iterative closest point (ICP) algorithm between the partial point cloud and the template point cloud is used to calculate the object’s pose and execute the virtual–real fusion. The results demonstrate that the proposed method achieves automatic pose estimation for both fixed and movable objects without background information and preset spatial anchors. Most volunteers reported that our approach was practical, and it thus expands the application of astronaut training.
Evaluation of Spatial Gas Temperature and Water Vapor Inhomogeneities in TDLAS in Circular Multipass Absorption Cells Used for the Analysis of Dynamic Tube Flows
Authors: Witt Felix , Bohlius Henning , Ebert Volker
Published: April 2023

The use of optical circular multipass absorption cells (CMPAC) in an open-path configuration enables the sampling free analysis of cylindrical gas flows with high temporal resolution and only minimal disturbances to the sample gas in the pipe. Combined with their robust unibody design, CMPACs are a good option for many applications in atmospheric research and industrial process monitoring. When deployed in an open-path configuration, the effects of inhomogeneities in the gas temperature and composition have to be evaluated to ensure that the resulting measurement error is acceptable for a given application. Such an evaluation needs to consider the deviations caused by spectroscopic effects, e.g., nonlinear effects of temperature variations on the intensity of the spectral line, as well as the interaction of the temperature and concentration field with the characteristic laser beam pattern of the CMPAC. In this work we demonstrate this novel combined evaluation approach for the CMPAC used as part of the tunable diode laser absorption spectroscopy (TDLAS) reference hygrometer in PTB’s dynH2O setup for the characterization of the dynamic response behavior of hygrometers. For this, we measured spatially resolved, 2D temperature and H2O concentration distributions, and combined them with spatially resolved simulated spectra to evaluate the inhomogeneity effects on the line area of the used H2O spectral line at 7299.43 cm−1. Our results indicate that for dynH2O, the deviations caused by the interaction between large concentration heterogeneities and the characteristic sampling of the beam pattern of the CMPAC are three orders of magnitude larger than deviations caused by small temperature heterogeneity induced spectroscopic effects. We also deduce that the assumption that the “path-integrated” H2O concentration derived with the open-path CMPAC setup represents an accurate H2O area average in the flow section covered by the CMPAC in fact shows significant differences of up to 16% and hence does not hold true when large H2O concentration gradients are present.
Evaluation of Federated Learning in Phishing Email Detection
Authors: Thapa Chandra , Tang Jun Wen , Abuadbba Alsharif , Gao Yansong , Camtepe Seyit , Nepal Surya , Almashor Mahathir , Zheng Yifeng
Published: April 2023

The use of artificial intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which has opened it up to a myriad of privacy, trust, and legal issues. Moreover, organizations have been loath to share emails, given the risk of leaking commercially sensitive information. Consequently, it has been difficult to obtain sufficient emails to train a global AI model efficiently. Accordingly, privacy-preserving distributed and collaborative machine learning, particularly federated learning (FL), is a desideratum. As it is already prevalent in the healthcare sector, questions remain regarding the effectiveness and efficacy of FL-based phishing detection within the context of multi-organization collaborations. To the best of our knowledge, the work herein was the first to investigate the use of FL in phishing email detection. This study focused on building upon a deep neural network model, particularly recurrent convolutional neural network (RNN) and bidirectional encoder representations from transformers (BERT), for phishing email detection. We analyzed the FL-entangled learning performance in various settings, including (i) a balanced and asymmetrical data distribution among organizations and (ii) scalability. Our results corroborated the comparable performance statistics of FL in phishing email detection to centralized learning for balanced datasets and low organizational counts. Moreover, we observed a variation in performance when increasing the organizational counts. For a fixed total email dataset, the global RNN-based model had a 1.8% accuracy decrease when the organizational counts were increased from 2 to 10. In contrast, BERT accuracy increased by 0.6% when increasing organizational counts from 2 to 5. However, if we increased the overall email dataset by introducing new organizations in the FL framework, the organizational level performance improved by achieving a faster convergence speed. In addition, FL suffered in its overall global model performance due to highly unstable outputs if the email dataset distribution was highly asymmetric.
NiCoP/g-C3N4 Nanocomposites-Based Electrochemical Immunosensor for Sensitive Detection of Procalcitonin
Authors: Chen Furong , Bao Layue , Zhang Ying , Wang Ruili , Liu Jinghai , Hai Wenfeng , Liu Yushuang
Published: April 2023

Herein, an ultra-sensitive and facile electrochemical biosensor for procalcitonin (PCT) detection was developed based on NiCoP/g-C3N4 nanocomposites. Firstly, NiCoP/g-C3N4 nanocomposites were synthesized using hydrothermal methods and then functionalized on the electrode surface by π-π stacking. Afterward, the monoclonal antibody that can specifically capture the PCT was successfully linked onto the surface of the nanocomposites with a 1-(3-Dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC) and N-Hydroxysuccinimide (NHS) condensation reaction. Finally, the modified sensor was employed for the electrochemical analysis of PCT using differential Pulse Voltammetry(DPV). Notably, the larger surface area of g-C3N4 and the higher electron transfer capacity of NiCoP/g-C3N4 endow this sensor with a wider detection range (1 ag/mL to 10 ng/mL) and an ultra-low limit of detection (0.6 ag/mL, S/N = 3). In addition, this strategy was also successfully applied to the detection of PCT in the diluted human serum sample, demonstrating that the developed immunosensors have the potential for application in clinical testing.
A Systematic Review of Mobile Phone Data in Crime Applications: A Coherent Taxonomy Based on Data Types and Analysis Perspectives, Challenges, and Future Research Directions
Authors: Okmi Mohammed , Por Lip Yee , Ang Tan Fong , Al-Hussein Ward , Ku Chin Soon
Published: April 2023

Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial–temporal patterns of crime, and ambient population measures have a significant impact on crime rates.
Q-Learning-Based Pending Zone Adjustment for Proximity Classification
Authors: Kwon Jung-Hyok , Lee Sol-Bee , Kim Eui-Jik
Published: April 2023

This paper presents a Q-learning-based pending zone adjustment for received signal strength indicator (RSSI)-based proximity classification (QPZA). QPZA aims to improve the accuracy of RSSI-based proximity classification by adaptively adjusting the size of the pending zone, taking into account changes in the surrounding environment. The pending zone refers to an area in which the previous result of proximity classification is maintained and is expressed as a near boundary and a far boundary. QPZA uses Q-learning to expand the size of the pending zone when the noise level increases and reduce it otherwise. Specifically, it calculates the noise level using the estimation error of a device deployed at a specific location. Then, QPZA adjusts the near boundary and far boundary separately by inputting the noise level into the near and far boundary adjusters, consisting of the Q-learning agent and reward calculator. The Q-learning agent determines the next boundary using the Q-table, and the reward calculator calculates the reward using the noise level. QPZA updates the Q-table of the Q-learning agent using the reward. To evaluate the performance of QPZA, we conducted an experimental implementation and compared the accuracy of QPZA with that of the existing approach. The results showed that QPZA achieves 11.69% higher accuracy compared to the existing approach, on average.
Microwave Devices for Wearable Sensors and IoT
Authors: Costanzo Alessandra , Augello Elisa , Battistini Giulia , Benassi Francesca , Masotti Diego , Paolini Giacomo
Published: April 2023

The Internet of Things (IoT) paradigm is currently highly demanded in multiple scenarios and in particular plays an important role in solving medical-related challenges. RF and microwave technologies, coupled with wireless energy transfer, are interesting candidates because of their inherent contactless spectrometric capabilities and for the wireless transmission of sensing data. This article reviews some recent achievements in the field of wearable sensors, highlighting the benefits that these solutions introduce in operative contexts, such as indoor localization and microwave sensing. Wireless power transfer is an essential requirement to be fulfilled to allow these sensors to be not only wearable but also compact and lightweight while avoiding bulky batteries. Flexible materials and 3D printing polymers, as well as daily garments, are widely exploited within the presented solutions, allowing comfort and wearability without renouncing the robustness and reliability of the built-in wearable sensor.
An Adaptive TTT Handover (ATH) Mechanism for Dual Connectivity (5G mmWave—LTE Advanced) during Unpredictable Wireless Channel Behavior
Authors: Gannapathy Vigneswara Rao , Nordin Rosdiadee , Abu-Samah Asma , Abdullah Nor Fadzilah , Ismail Mahamod
Published: April 2023

Fifth Generation (5G) signals using the millimeter wave (mmWave) spectrums are highly vulnerable to blockage due to rapid variations in channel link quality. This can cause the devices or User Equipment (UE) to suffer from connection failure. In a dual connectivity (DC) network, the channel’s intermittency issues were partially solved by maintaining the UE’s connectivity to primary (LTE advanced stations) and secondary (5G mmWave stations) simultaneously. Even though the dual-connected network performs excellently in maintaining connectivity, its performance drops significantly due to the inefficient handover from one 5G mmWave station to another. The situation worsens when UE travels a long distance in a highly dense obstacle environment, which requires multiple ineffective handovers that eventually lead to performance degradation. This research aimed to propose an Adaptive TTT Handover (ATH) mechanism that deals with unpredictable 5G mmWave wireless channel behaviors that are highly intermittent. An adaptive algorithm was developed to automatically adjust the handover control parameters, such as Time-to-Trigger (TTT), based on the current state of channel condition measured by the Signal-to-Interference-Noise Ratio (SINR). The developed algorithm was tested under a 5G mmWave statistical channel model to represent a time-varying channel matrix that includes fading and the Doppler effect. The performance of the proposed handover mechanism was analyzed and evaluated in terms of handover probability, latency, and throughput by using the Network Simulator 3 tool. The comparative simulation result shows that the proposed adaptive handover mechanism performs excellently compared to conventional handovers and other enhancement techniques.
Distributed Refractive Index Sensing Based on Etched Ge-Doped SMF in Optical Frequency Domain Reflectometry
Authors: Fu Cailing , Sui Ronglong , Peng Zhenwei , Meng Yanjie , Zhong Huajian , Li Mingquan , Yin Xiaoyu , Wang Yiping
Published: April 2023

A distributed optical fiber refractive index sensor based on etched Ge-doped SMF in optical frequency domain reflection (OFDR) was proposed and demonstrated. The etched Ge-doped SMF was obtained by only using wet-etching, i.e., hydrofluoric acid solution. The distributed refractive index sensing is achieved by measuring the spectral shift of the local RBS spectra using OFDR. The sensing length of 10 cm and the spatial resolution of 5.25 mm are achieved in the experiment. The refractive index sensing range is as wide as 1.33–1.44 refractive index units (RIU), where the average sensitivity was about 757 GHz/RIU. Moreover, the maximum sensitivity of 2396.9 GHZ/RIU is obtained between 1.43 and 1.44 RIU.
Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach
Authors: Nallathambi Indumathi , Savaram Padmaja , Sengan Sudhakar , Alharbi Meshal , Alshathri Samah , Bajaj Mohit , Aly Moustafa H. , El-Shafai Walid
Published: April 2023

In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI. Clarifying the relationship between HFs can help in identifying the correlation between unsafe behaviors and influential factors in hazardous chemical warehouse accidents. This paper aims to investigate the impact of HFs that contribute to HE, which has caused FI disasters, explosions, and incidents in the past. This paper investigates why and how HEs contribute to the most severe accidents that occur while storing and using hazardous chemicals. The impact of fireworks and match industry disasters has motivated the planning of mitigation in this proposal. This analysis used machine learning (ML) and recommends an expert system (ES). There were many significant correlations between individual behaviors and the chance of HE to occur. This paper proposes an ML-based prediction model for fireworks and match work industries in Sivakasi, Tamil Nadu. For this study analysis, the questionnaire responses are reviewed for accuracy and coded from 500 participants from the fireworks and match industries in Tamil Nadu who were chosen to fill out a questionnaire. The Chief Inspectorate of Factories in Chennai and the Training Centre for Industrial Safety and Health in Sivakasi, Tamil Nadu, India, significantly contributed to the collection of accident datasets for the FI in Tamil Nadu, India. The data are analyzed and presented in the following categories based on this study’s objectives: the effect of physical, psychological, and organizational factors. The output implemented by comparing ML models, support vector machine (SVM), random forest (RF), and Naïve Bayes (NB) accuracy is 86.45%, 91.6%, and 92.1%, respectively. Extreme Gradient Boosting (XGBoost) has the optimal classification accuracy of 94.41% of ML models. This research aims to create a new ES to mitigate HE risks in the fireworks and match work industries. The proposed ES reduces HE risk and improves workplace safety in unsafe, uncertain workplaces. Proper safety management systems (SMS) can prevent deaths and injuries such as fires and explosions.
Review of Artificial Intelligence Applications for Virtual Sensing of Underground Utilities
Authors: Oguntoye Kunle S. , Laflamme Simon , Sturgill Roy , Eisenmann David J.
Published: April 2023

Accurately identifying the location and depth of buried utility assets became a considerable challenge in the construction industry, for which accidental strikes can cause important economic losses and safety concerns. While the collection of as-built utility locations is becoming more accurate, there still exists an important need to be capable of accurately detecting buried utilities in order to eliminate risks associated with digging. Current practices typically involve the use of trained agents to survey and detect underground utilities at locations of interest, which is a costly and time-consuming process. With advances in artificial intelligence (AI), an opportunity arose in conducting virtual sensing of buried utilities by combining robotics (e.g., drones), knowledge, and logic. This paper reviewed methods that are based on AI in mapping underground infrastructure. In particular, the use of AI in aerial and terrestrial mapping of utility assets was reviewed, followed by a summary of AI techniques used in fusing multi-source data in creating underground infrastructure maps. Key observations from the consolidated literature were that (1) when leveraging computer vision methods, automatic mapping techniques vastly focus on manholes localized from aerial imagery; (2) when applied to non-intrusive sensing, AI methods vastly focus on empowering ground-penetrating radar (GPR)-produced data; and (3) data fusion techniques to produce utility maps should be extended to any utility assets/types. Based on these observations, a universal utility mapping model was proposed, one that could enable mapping of underground utilities using limited information available in the form of different sources of data and knowledge.
A Deep Learning Framework for Processing and Classification of Hyperspectral Rice Seed Images Grown under High Day and Night Temperatures
Authors: Díaz-Martínez Víctor , Orozco-Sandoval Jairo , Manian Vidya , Dhatt Balpreet K. , Walia Harkamal
Published: April 2023

A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube for classifying the seed images from high day and high night temperatures, both including a control group, is developed. A pixel-based seed classification approach is implemented using a deep neural network (DNN). The seed and pixel-based deep learning architectures are validated and tested using hyperspectral images from five different rice seed treatments with six different high temperature exposure durations during day, night, and both day and night. A stand-alone application with Graphical User Interfaces (GUI) for calibrating, preprocessing, and classification of hyperspectral rice seed images is presented. The software application can be used for training two deep learning architectures for the classification of any type of hyperspectral seed images. The average overall classification accuracy of 91.33% and 89.50% is obtained for seed-based classification using 3D-CNN for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The DNN gives an average accuracy of 94.83% and 91% for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The accuracies obtained are higher than those presented in the literature for hyperspectral rice seed image classification. The HSI analysis presented here is on the Kitaake cultivar, which can be extended to study the temperature tolerance of other rice cultivars.
Adaptive Optical Closed-Loop Control Based on the Single-Dimensional Perturbation Descent Algorithm
Authors: Chen Bo , Zhou Yilin , Li Zhaoyi , Jia Jingjing , Zhang Yirui
Published: April 2023

Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics. In this study, two different algorithms, the single-dimensional perturbation descent algorithm (SDPD) and the second-order stochastic parallel gradient descent algorithm (2SPGD), are proposed for wavefront sensorless adaptive optics, and a theoretical analysis of the algorithms’ convergence rates is presented. The results demonstrate that the single-dimensional perturbation descent algorithm outperforms the stochastic parallel gradient descent (SPGD) and 2SPGD algorithms in terms of convergence speed. Then, a 32-unit deformable mirror is constructed as the wavefront corrector, and the SPGD, single-dimensional perturbation descent, and 2SPSA algorithms are used in an adaptive optics numerical simulation model of the wavefront controller. Similarly, a 39-unit deformable mirror is constructed as the wavefront controller, and the SPGD and single-dimensional perturbation descent algorithms are used in an adaptive optics experimental verification device of the wavefront controller. The outcomes demonstrate that the convergence speed of the algorithm developed in this paper is more than twice as fast as that of the SPGD and 2SPGD algorithms, and the convergence accuracy of the algorithm is 4% better than that of the SPGD algorithm.