bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection of Coronaviruses in Bats in Lebanon during 2020
Authors: Kandeil Ahmed , Abi-Said Mounir , Badra Rebecca , El-Shesheny Rabeh , Al-Karmalawy Ahmed A. , Alnajjar Radwan , Khalid Zumama , Kamel Mina Nabil , Abi Habib Walid , Abdallah Jad , Dhanasekaran Vijaykrishna , Webby Richard , Kayali Ghazi
Published: June 2023

Bats are considered the main reservoir of coronaviruses (CoVs), and research evidence suggests the essential role of bats in the emergence of Severe Acute Respiratory Syndrome Coronaviruses (SARS-CoV) and SARS-CoV-2. SARS-CoV-like viruses have been recently detected in bats in different countries. In 2020, we conducted surveillance for CoVs among six different bat species in Lebanon. Of 622 swab specimens taken, 77 tested positive. Alpha- and Beta- CoVs were identified in samples collected from different species. Our results show that SARS-like coronaviruses circulate in bats in this region, and we provide new data on their genetic diversity. The interaction between the spike of the detected SARS-CoV-like viruses and the human angiotensin-converting enzyme 2 (hACE2) receptor could be crucial in understanding the origin of the epidemic. The 3D protein structure analysis revealed that the receptor-binding domains of the SARS-like virus identified in Lebanon bind to the hACE2 protein more efficiently than to the spike of the SARS-CoV-2 strain. The spike of the detected SARS-CoV-like viruses does not contain the recognition site of furin at the cleavage site. Thus, our study highlights the variety of bat coronaviruses in Lebanon and suggests the zoonotic potential for other SARS-CoV-like viruses.
How to Create Indeterminately Identical Fictional Objects
Authors: Paganini Elisa
Published: June 2023

Suppose that fictional objects are abstract objects dependent for their existence and their identity on the creative intentions of their authors. Is an author who intends to create indeterminately identical fictional objects committed to incoherent created objects? My claim is that she is not so committed. I argue that indeterminate identity is an ambiguous notion, allowing for an incoherent interpretation and for at least three coherent ones; and I show that if an author of fiction applies coherent indeterminate identity when creating fictional objects, she succeeds in creating coherent objects, whereas she fails to create fictional objects when she tries to apply incoherent indeterminate identity in her creation. In so doing, I offer a reply to a challenge first raised by Everett against realist philosophers on fictional objects and more recently reproposed by Friedell, allowing for the creation of fictional objects along the lines proposed by Evnine.
An Investigation of High-Performance Pressure Sensor Employing a Polymer-Defect-Based 1D Annular Photonic Crystal
Authors: Ameen Ayman A. , Panda Abinash , Mehaney Ahmed , Almawgani Abdulkarem H. M. , Pradhan Dipika D. , Ali Ghassan Ahmed , Ali Yahya Ali Abdelrahman , Elsayed Hussein A.
Published: June 2023

This study aims to theoretically address the design and analysis of an efficient pressure sensor designed using a polymer-based defective 1D annular photonic crystal (APC). The 1D APC comprises an alternate arrangement of Si and SiO2 in a cylindrical fashion, incorporating a central defect layer. The investigation of the reflectance characteristics of the proposed structure is conducted by separately considering the polystyrene (PS) and the polymethyl methacrylate (PMMA) polymer materials as the defect layer. The pressure-sensitive refractive index of the polymers and the constituent materials of the APC play a vital role in envisaging the pressure-sensing application. The cornerstone of this study is represented by the shift analysis regarding the wavelength of the defect mode inside the band gap using different applied pressures, employing the modified transfer matrix method (MTMM). Various geometrical parameters like the defect polymer layer’s thickness and the APC period were carefully optimized to achieve an improved sensing performance. The proposed design demonstrated a remarkable pressure sensitivity and FoM of 51.29 nm/GPa and 301.7 GPa−1, respectively, which is considerably high in the current research scenario. It is believed that the proposed structure can be an apt candidate for an innovative high-performance pressure sensor, and could play a key role in photonic integrated circuits.
Soil Carbon Stocks and Greenhouse Gas Mitigation of Agriculture in the Brazilian Cerrado—A Review
Authors: de Carvalho Arminda Moreira , de Jesus Douglas Rodrigues , de Sousa Thais Rodrigues , Ramos Maria Lucrécia Gerosa , de Figueiredo Cícero Célio , de Oliveira Alexsandra Duarte , Marchão Robélio Leandro , Ribeiro Fabiana Piontekowski , Dantas Raíssa de Araujo , Borges Lurdineide de Araújo Barbosa
Published: June 2023

New agricultural practices and land-use intensification in the Cerrado biome have affected the soil carbon stocks. A major part of the native vegetation of the Brazilian Cerrado, a tropical savanna-like ecoregion, has been replaced by crops, which has caused changes in the soil carbon (C) stocks. To ensure the sustainability of this intensified agricultural production, actions have been taken to increase soil C stocks and mitigate greenhouse gas emissions. In the last two decades, new agricultural practices have been adopted in the Cerrado region, and their impact on C stocks needs to be better understood. This subject has been addressed in a systematic review of the existing data in the literature, consisting of 63 articles from the Scopus database. Our review showed that the replacement of Cerrado vegetation by crop species decreased the original soil C stocks (depth 0–30 cm) by 73%, with a peak loss of 61.14 Mg ha−1. However, when analyzing the 0–100 cm layer, 52.4% of the C stock data were higher under cultivated areas than in native Cerrado soils, with a peak gain of 93.6 Mg ha−1. The agricultural practices implemented in the Brazilian Cerrado make low-carbon agriculture in this biome possible.
Study on Casing Safety Evaluation in High-Temperature Wells with Annular Pressure Buildup
Authors: Wang Hao , Li Mu , Zhao Qing , Hao Weiwei , Zhang Hui , Li Yafei , Huang Pengpeng , Zou Yi
Published: June 2023

In high-temperature wells, annular pressure buildup (APB) caused by temperature increase is a widespread phenomenon in production, especially in offshore thermal recovery wells. It increases the load on the tubing and casing and consequently threatens the wellbore integrity. Hence, research on casing safety evaluation and APB management has great significance for field production. In this paper, the tubing and casing safety evaluation and APB limit determination methods are presented considering the effect of thermal stress and APB. Based on the case study of an offshore thermal recovery well, an APB-management chart and the recommended optimal range of APB are provided. Finally, an analysis of three commonly used mitigation methods is presented. The effect and the recommended parameters of these mitigation methods are further discussed. The research results show that the thermal stress and APB phenomena affect the stress distribution of the casing and may bring great danger to the wellbore integrity. Maintaining the APB in the safety range is necessary for field production. It is recommended that the annular pressure be kept below the critical value given in this paper. Injecting nitrogen in annulus A and installing rupture disks are both effective methods to improve casing safety. In the case study, the APB decrease percentage is more than 75% when nitrogen is injected in annulus A. However, the nitrogen pressure, the rupture pressure and the installation depth of the rupture disk need to be determined via casing safety evaluation. The effect of optimizing the steel grade and thickness of the tubing and casing is not significant. They can be used as assistance methods when other mitigation methods are adopted.
Influence of Central Air on Flow and Combustion Characteristics and Low-Load Stabilization Performance of a Babcock Burner
Authors: Huang Chunchao , Li Zhengqi , Wang Yufei , Lu Yue , Liu Huacai , Chen Zhichao
Published: June 2023

On a cold single-phase test stand, the effect of central air on the exit flow field of Babcock, Germany, burner was investigated. Industrial measurements were taken for a 700 MW wall-fired pulverized-coal utility boiler with above burners. Gas temperature, gas composition and concentration in the burner area were measured at 444 MW, 522 MW and 645 MW loads, respectively. Only when the central air mass flow was zero did a center reflux zone exist in the burner outlet area. The steady combustion of faulty coal was aided by early mixing of primary and secondary air, which was made possible by the decreased central air mass flow. At all different loads, the pulverized coal in center region took a long distance to ignite. The temperature in center steadily dropped as central air mass flow decreased, while the temperature in secondary air region gradually rose. Within 1.5 m from the primary air duct outlet, the highest CO concentration was 25 ppm and the highest O2 concentration was close to 21% under all loads. The gas concentration near sidewall was more influenced by load. With all valves opening of burner center air at 30%, the boiler was able to operate safely and stably without oil at a load of 262 MW. The furnace chamber temperature in burner area reached 1056.1 °C.
Droplet Based Estimation of Viscosity of Water–PVP Solutions Using Convolutional Neural Networks
Authors: Mrad Mohamed Azouz , Csorba Kristof , Galata Dorián László , Nagy Zsombor Kristóf , Charaf Hassan
Published: June 2023

The viscosity of a liquid is the property that measures the liquid’s internal resistance to flow. Monitoring viscosity is a vital component of quality control in several industrial fields, including chemical, pharmaceutical, food, and energy-related industries. In many industries, the most commonly used instrument for measuring viscosity is capillary viscometers, but their cost and complexity pose challenges for these industries where accurate and real-time viscosity information is vital. In this work, we prepared fourteen solutions with different water and PVP (Polyvinylpyrrolidone) ratios, measured their different viscosity values, and produced videos of their droplets. We extracted the images of the fully developed droplets from the videos and we used the images to train a convolutional neural network model to estimate the viscosity values of the water–PVP solutions. The proposed model was able to accurately estimate the viscosity values of samples of unseen chemical formulations with the same composition with a low MSE score of 0.0243 and R2 score of 0.9576. The proposed method has potential applications in scenarios where real-time monitoring of liquid viscosity is required.
Observer-Based Approximate Affine Nonlinear Model Predictive Controller for Hydraulic Robotic Excavators with Constraints
Authors: Wang Jian , Zhang Hao , Hao Peng , Deng Hua
Published: June 2023

Given the highly nonlinear and strongly constrained nature of the electro-hydraulic system, we proposed an observer-based approximate nonlinear model predictive controller (ANMPC) for the trajectory tracking control of robotic excavators. A nonlinear non-affine state space equation with identified parameters is employed to describe the dynamics of the electro-hydraulic system. Then, to mitigate the plant-model mismatch caused by the first-order linearization, an approximate affine nonlinear state space model is utilized to represent the explicit relationship between the output and input and an ANMPC is designed based on the approximate nonlinear model. Meanwhile, the Extended Kalman Filter was introduced for state observation to deal with the unmeasurable velocity information and heavy measurement noises. Comparative experiments are conducted on a 1.7-ton hydraulic robotic excavator, where ANMPC and linear model predictive control are used to track a typical excavation trajectory. The experimental results provide evidence of convincing trajectory tracking performance.
Advances in Deep Eutectic Solvents: New Green Solvents
Authors: Ren Shuhang , Mu Tiancheng , Wu Weize
Published: June 2023

This Special Issue, entitled “Advances in deep eutectic solvents: New green solvents”, was organized to collect original research articles on the latest developments in the new green solvents, deep eutectic solvents (DESs), and their applications [...]
A Refractive Index- and Density-Matched Liquid–Liquid System Developed Using a Novel Design of Experiments
Authors: Tang Jianxin , Wang Chenfeng , Liu Fei , Yang Xiaoxia , Wang Rijie
Published: June 2023

Refractive index and density matching are essential for optical measurements of neutrally buoyant liquid–liquid flows. In this study, we proposed a design of experiments (DoE) to develop refractive index and density matching systems, including objective setting, candidates screening, sampling and fitting, and a detailed matching process. Candidates screening criteria based on the density and refractive index ranges of the aqueous and organic phases were used. Using the DoE, we proposed a system with a ternary aqueous phase potassium thiocyanate (KSCN)/ammonium thiocyanate (NH4SCN) solution and m-dichlorobenzene/tripropionin solution as the organic phase to achieve the tuning of the RI and density simultaneously. Empirical correlations of the refractive index and density with respect to the concentration and temperature for the three mixtures were obtained by combining Latin hypercube sampling with binary polynomial fitting. Correlations were validated with existing data in the literature and were found to align with deviations as low as 4×10−4 for the refractive index and 2×10−3 g⋅cm−3 for the density. Using the correlations, the refractive indices for the ternary aqueous phase, the binary organic phase, and the device materials were matched to be equal. Density matching was performed for the liquid–liquid phases as well. Refractive index- and density-matched recipes could be obtained for a wide range of temperatures (15–65 °C) and device materials (PMMA, borosilicate glass, quartz, and silica gel). These recipes provide options for the optical measurement of a liquid–liquid system required to neutralize buoyancy.
Fabricating Porous Carbon Materials by One-Step Hydrothermal Carbonization of Glucose
Authors: Yao Ziyun , Zhang Wenqi , Yu Xinying
Published: June 2023

The present study concerned the production of glucose-based porous carbon materials by a one-step acid-catalyzed HTC. The samples were characterized by elemental analysis (EA), scanning electron microscope (SEM), Brunauer–Emmett–Teller (BET), Fourier transform infrared (FTIR) and point of zero charge (pzc). Experimental results showed that the addition of sulfuric acid (SA) with different dosages in the HTC system could improve the yield of products and reduce chemical oxygen demand (COD) of the process water. When the glucose and acid was at a mass ratio of 1:4 (glucose: SA = 1:4), the hydrochar obtained (H-G9) had a larger specific surface area (SBET = 296.71 m2/g) and higher abundance of functional groups on the surface than that of other samples, such as sulfur-containing functional groups and carboxylic groups, belonged to the mesoporous material with highly negatively surface charged. H-G9 exhibited the optimum adsorption for methylene blue (MB). H-G9 adsorbed MB with an initial concentration of 10 mg/L at pH 6 and 25 °C. The adsorption isotherm of MB on H-G9 demonstrated that Freundlich isotherm could be better applied. Regeneration efficiency of 88% was achieved by HTC process for saturated H-G9. This study prepared a porous carbon material by the simple one-step hydrothermal carbonization of glucose in the presence of SA. The maximum monolayer adsorption capacity as high as 332.46 mg/g for MB, which was well beyond that of commercial activated carbon (259.37 mg/g). This indicates that H-G9 has great potential for the removal of MB from wastewater.
Effect of Organic Powders on Surface Quality in Abrasive Blasting Process
Authors: Anaç Nergizhan , Doğan Zekeriya
Published: June 2023

Abrasive blasting, sometimes known as sandblasting, is a method used to change the surface condition of materials, clean surfaces, and prepare surfaces for applications such as paint, bonding, coating, etc. The abrasive materials used in abrasive blasting are applied to the surface with compressed air or water and vary according to the purpose of application. The abrasive materials used have negative effects on the environment and human health. So far, organic materials have been used in limited applications in abrasive blasting. However, these materials have a high potential of usage since they are environmentally friendly, safe for human health, and have non-toxic and sustainable properties. In this study, the usability of three different organic wastes (walnut shell, olive pomace and mussel shell) recovered by recycling in abrasive blasting was investigated. In addition, the effect of blasting distance (5, 10 and 15 mm), blasting time (10, 20 and 30 s), powder type (mussel shell, olive pomace and walnut shell) and grain size (38, 45 and 63 µm) on surface roughness have been investigated using the Taguchi L9 experimental design. Regression models were built using ANOVA (Analysis of Variance). Moreover, the surface condition after abrasive blasting was examined using an Al2O3 abrasive and compared with other samples. As a result, 5 mm, 30 s, mussel shell and 45 µm test sets were recommended for “larger is better” and it was determined that the blasting time had the greatest effect on the surface roughness by 50.19%. On the other hand, 10 mm, 20 s, walnut shell and 63 µm test sets were recommended for “smaller is better”, and it was determined that blasting time had the greatest effect on the surface roughness by 39.02%. While there was an increase compared to the surface roughness values before abrasive blasting in the first set of experiments, it was determined that the organic material had a polishing rather than an abrasion effect in the second set of experiments.
The Decomposition of Dilute 1-Butene in Tubular Multilayer Dielectric Barrier Discharge Reactor: Performance, By-Products and Reaction Mechanism
Authors: Li Chao , Zhu Xiao , Wang Shiqiang , Guo Yafeng , Du Yu , Guan Yinxia , Tang Shiya
Published: June 2023

Butene is a typical component of exhaust gas in the petrochemical industry, the emission of which into the atmosphere would lead to air pollution. In this study, a tubular multilayer dielectric barrier discharge (TM-DBD) reactor was developed to decompose 1-butene at ambient pressure. The experimental results show that a decomposition efficiency of more than 99% and COx selectivity of at least 43% could be obtained at a specific energy density of 100 J/L with an inlet concentration of 1-butene ranging from 100 to 400 ppm. Increasing the volume ratio of O2/N2 from 0 to 20% and the specific energy density from 33 to 132 J/L were beneficial for 1-butene destruction and mineralization. Based on organic byproduct analysis, it was inferred that the nitrogenous organic compounds were the main products in N2 atmosphere, while alcohol, aldehyde, ketone, acid and oxirane were detected in the presence of O2. In addition, the contents of formaldehyde, acetaldehyde, ethyl alcohol, acetic acid and propionic acid increased with an increase in specific energy density, but the contents of propionaldehyde, ethyl oxirane, butyraldehyde and formic acid decreased. Three main pathways of 1-butene destruction were proposed involving Criegee intermediates and ozonolysis of the olefins, and the following degradation could be the dominant pathways rather than epoxidation. Overall, the developed TM-DBD system paved the way for scaling up the applications of plasma technology for gaseous pollutant decomposition.
Finite-Element Analysis on Energy Dissipation and Sealability of Premium Connections under Dynamic Loads
Authors: Yu Yang , Cao Yinping , Qu Zhan , Dou Yihua , Wang Zidi
Published: June 2023

In the process of high flow rate fracture and high gas production, the sealing performance of the premium connection decreases due to the dynamic load and vibration of downhole tubing strings, which may cause accidents. Existing static analysis methods cannot effectively explain this phenomenon. The main objective of this paper is to propose a novel analytical method for evaluating the sealing performance of a premium connection. In this paper, a dynamic model of sealing surfaces of the premium connection is established based on the vibration equation of elastic rod, and the hysteresis characteristics and energy dissipation mechanism of sealing surfaces are analyzed. Considering the influence of spherical radius, internal pressure, axial cyclic load amplitude, and modal vibration, a spherical-conical premium connection finite element model is established to analyze the influence laws of the connection’s energy dissipation and sealing performance. The results show that the sealing performance of the premium connection under dynamic load can be effectively analyzed by using energy dissipation theory compared with traditional static contact analysis. Compared with the vibration of the tubing string, the dynamic loads caused by the change of fluid pressure and flow rate in the tubing string have a significant influence on the connection’s sealing performance. When the internal pressure and axial cyclic loads are 80 MPa, 400 kN, or 60 MPa and 500 kN respectively, serious plastic deformation occurs in the thread and sealing surfaces, and the energy dissipation of the sealing surfaces increases significantly, which could lead to sealing failure.
Fault Diagnosis of Rotating Machinery Bearings Based on Improved DCNN and WOA-DELM
Authors: Wang Lijun , Ping Dongzhi , Wang Chengguang , Jiang Shitong , Shen Jie , Zhang Jianyong
Published: June 2023

A bearing is a critical component in the transmission of rotating machinery. However, due to prolonged exposure to heavy loads and high-speed environments, rolling bearings are highly susceptible to faults, Hence, it is crucial to enhance bearing fault diagnosis to ensure safe and reliable operation of rotating machinery. In order to achieve this, a rotating machinery fault diagnosis method based on a deep convolutional neural network (DCNN) and Whale Optimization Algorithm (WOA) optimized Deep Extreme Learning Machine (DELM) is proposed in this paper. DCNN is a combination of the Efficient Channel Attention Net (ECA-Net) and Bi-directional Long Short-Term Memory (BiLSTM). In this method, firstly, a DCNN classification network is constructed. The ECA-Net and BiLSTM are brought into the deep convolutional neural network to extract critical features. Next, the WOA is used to optimize the weight of the initial input layer of DELM to build the WOA-DELM classifier model. Finally, the features extracted by the Improved DCNN (IDCNN) are sent to the WOA-DELM model for bearing fault diagnosis. The diagnostic capability of the proposed IDCNN-WOA-DELM method was evaluated through multiple-condition fault diagnosis experiments using the CWRU-bearing dataset with various settings, and comparative tests against other methods were conducted as well. The results indicate that the proposed method demonstrates good diagnostic performance.
Development of In-Line Measurement Techniques for Monitoring Powder Characteristics in a Multi-Stage Spray Drying Process
Authors: Frank Jennifer , Raiber Tobias V. , Grotenhoff Laura , Kohlus Reinhard
Published: June 2023

The integration of spray drying and agglomeration offers significant advantages, such us continuous production with lower energy consumption. However, it is a knife-edge process with a narrow operating window and limited degrees of freedom that decide between successful agglomeration and fluidized bed blockage due to excessive moisture. In this contribution, factors influencing the spray-through agglomeration process of skim milk powder as a model system were investigated via a design of experiments. Three in-line monitoring methods were applied and tested to observe the most important parameters in the agglomeration process—the product moisture and particle size distribution. Regarding the moisture content, a capacitive moisture sensor was calibrated with linear regression and a near-infrared sensor with partial least squares regression. Near-infrared spectroscopy was found to be the suitable method for determining the moisture content, while the capacitive moisture sensor mainly provides information on the bulk density, filling level, or fluidization state in the fluidized bed. Additionally, particle size distribution data were extracted from the spectral data using in-line data from a spatial filter velocimetry probe in the fluidized bed. This opens the potential to monitor both parameters in real time with a single non-invasive sensor.
Multiscale Volatility Analysis for Noisy High-Frequency Prices
Authors: Leung Tim , Zhao Theodore
Published: June 2023

We present a multiscale analysis of the volatility of intraday prices from high-frequency data. Our multiscale framework includes a fractional Brownian motion and microstructure noise as the building blocks. The proposed noisy fractional Brownian motion model is shown to possess a variety of volatility behaviors suitable for intraday price processes. Algorithms for numerical estimation from time series observations are then introduced, with a new Hurst exponent estimator proposed for the noisy fractional Brownian motion model. Using real-world high-frequency price data for a collection of U.S. stocks and ETFs, we estimate the parameters in the noisy fractional Brownian motion and illustrate how the volatility varies over different timescales. The Hurst exponent and noise level also exhibit an intraday pattern whereby the the noise ratio tends to be higher near market close.
Fiber Optic Acoustic Sensing to Understand and Affect the Rhythm of the Cities: Proof-of-Concept to Create Data-Driven Urban Mobility Models
Authors: García Luz , Mota Sonia , Titos Manuel , Martínez Carlos , Segura Jose Carlos , Benítez Carmen
Published: June 2023

In the framework of massive sensing and smart sustainable cities, this work presents an urban distributed acoustic sensing testbed in the vicinity of the School of Technology and Telecommunication Engineering of the University of Granada, Spain. After positioning the sensing technology and the state of the art of similar existing approaches, the results of the monitoring experiment are described. Details of the sensing scenario, basic types of events automatically distinguishable, initial noise removal actions and frequency and signal complexity analysis are provided. The experiment, used as a proof-of-concept, shows the enormous potential of the sensing technology to generate data-driven urban mobility models. In order to support this fact, examples of preliminary density of traffic analysis and average speed calculation for buses, cars and pedestrians in the testbed’s neighborhood are exposed, together with the accidental presence of a local earthquake. Challenges, benefits and future research directions of this sensing technology are pointed out.
Automatic Pear Extraction from High-Resolution Images by a Visual Attention Mechanism Network
Authors: Wang Jinjie , Ding Jianli , Ran Si , Qin Shaofeng , Liu Bohua , Li Xiang
Published: June 2023

At present, forest and fruit resource surveys are mainly based on ground surveys, and the information technology of the characteristic forest and fruit industries is evidently lagging. The automatic extraction of fruit tree information from massive remote sensing data is critical for the healthy development of the forest and fruit industries. However, the complex spatial information and weak spectral information contained in high-resolution images make it difficult to classify fruit trees. In recent years, fully convolutional neural networks (FCNs) have been shown to perform well in the semantic segmentation of remote sensing images because of their end-to-end network structures. In this paper, an end-to-end network model, Multi-Unet, was constructed. As an improved version of the U-Net network structure, this structure adopted multiscale convolution kernels to learn spatial semantic information under different receptive fields. In addition, the “spatial-channel” attention guidance module was introduced to fuse low-level and high-level features to reduce unnecessary semantic features and refine the classification results. The proposed model was tested in a characteristic high-resolution pear tree dataset constructed through field annotation work. The results show that Multi-Unet was the best performer among all models, with classification accuracy, recall, F1, and kappa coefficient of 88.95%, 89.57%, 89.26%, and 88.74%, respectively. This study provides important practical significance for the sustainable development of the characteristic forest fruit industry.
Combining Multi-Source Data and Feature Optimization for Plastic-Covered Greenhouse Extraction and Mapping Using the Google Earth Engine: A Case in Central Yunnan Province, China
Authors: Li Jie , Wang Hui , Wang Jinliang , Zhang Jianpeng , Lan Yongcui , Deng Yuncheng
Published: June 2023

Rapidly increasing numbers of the plastic-covered greenhouse (PCG) worldwide ensure food security but threaten environmental security; thus, accurate monitoring of the spatiotemporal pattern in plastic-covered greenhouses (PCGs) is necessary for modern agricultural management and environmental protection. However, many urgent issues still exist in PCG mapping, such as multi-source data combination, classification accuracy improvement, spatiotemporal scale expansion, and dynamic trend quantification. To address these problems, this study proposed a new framework that progressed layer by layer from multi-feature scenario construction, classifier and feature scenario preliminary screening, feature optimization, and spatiotemporal mapping, to rapidly identify large-scale PCGs by integrating multi-source data using Google Earth Engine (GEE), and the framework was first applied to Central Yunnan Province (CYP), where PCGs are concentrated but no relevant research exists. The results suggested that: (1) combining the random forest (RF) classifier and spectrum (S) + backscatter (B) + index (I) + texture (T) + terrain (Tr) feature scenario produced the highest F-score (95.60%) and overall accuracy (88.04%). (2) The feature optimization for the S + I + T + B + Tr scenario positively impacted PCG recognition, increasing the average F-score by 1.03% (96.63% vs. 95.60%). (3) The 6-year average F-score of the PCGs extracted by the combined RF algorithm and the optimal feature subset exceeded 95.00%, and its spatiotemporal mapping results indicated that PCGs were prominently agglomerated in the central CYP and continuously expanded by an average of 65.45 km2/yr from 2016 to 2021. The research reveals that based on the GEE platform, multi-source data can be integrated through a feature optimization algorithm to more efficiently map PCG spatiotemporal information in complex regions.
Considering Image Information and Self-Similarity: A Compositional Denoising Network
Authors: Zhang Jiahong , Zhu Yonggui , Yu Wenshu , Ma Jingning
Published: June 2023

Recently, convolutional neural networks (CNNs) have been widely used in image denoising, and their performance has been enhanced through residual learning. However, previous research mostly focused on optimizing the network architecture of CNNs, ignoring the limitations of the commonly used residual learning. This paper identifies two of its limitations, which are the neglect of image information and the lack of effective consideration of image self-similarity. To solve these limitations, this paper proposes a compositional denoising network (CDN), which contains two sub-paths, the image information path (IIP) and the noise estimation path (NEP), respectively. IIP is trained via an image-to-image method to extract image information. For NEP, it utilizes image self-similarity from the perspective of training. This similarity-based training method constrains NEP to output similar estimated noise distributions for different image patches with a specific kind of noise. Finally, image information and noise distribution information are comprehensively considered for image denoising. Experimental results indicate that CDN outperforms other CNN-based methods in both synthetic and real-world image denoising, achieving state-of-the-art performance.
Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots
Authors: Duque Andres F. , Patino Diego , Colorado Julian D. , Petro Eliel , Rebolledo Maria C. , Mondragon Ivan F. , Espinosa Natalia , Amezquita Nelson , Puentes Oscar D. , Mendez Diego , Jaramillo-Botero Andres
Published: June 2023

The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs.
An Improved UWB/IMU Tightly Coupled Positioning Algorithm Study
Authors: Zou Airu , Hu Wenwu , Luo Yahui , Jiang Ping
Published: June 2023

The combination of ultra-wide band (UWB) and inertial measurement unit (IMU) positioning is subject to random errors and non-line-of-sight errors, and in this paper, an improved positioning strategy is proposed to address this problem. The Kalman filter (KF) is used to pre-process the original UWB measurements, suppressing the effect of range mutation values of UWB on combined positioning, and the extended Kalman filter (EKF) is used to fuse the UWB measurements with the IMU measurements, with the difference between the two measurements used as the measurement information. The non-line-of-sight (NLOS) measurement information is also used. The optimal estimate is obtained by adjusting the system measurement noise covariance matrix in real time, according to the judgment result, and suppressing the interference of non-line-of-sight factors. The optimal estimate of the current state is fed back to the UWB range value in the next state, and the range value is dynamically adjusted after one-dimensional filtering pre-processing. Compared with conventional tightly coupled positioning, the positioning accuracy of the method in this paper is improved by 46.15% in the field experimental positioning results.
Advances in Silicon-Based Integrated Lidar
Authors: Hu Mingxuan , Pang Yajun , Gao Long
Published: June 2023

Silicon-based Lidar is an ideal way to reduce the volume of the Lidar and realize monolithic integration. It removes the moving parts in the conventional device and realizes solid-state beam steering. The advantages of low cost, small size, and high beam steering speed have attracted the attention of many researchers. In order to facilitate researchers to quickly understand the research progress and direction, this paper mainly describes the research progress of silicon-based integrated Lidar, including silicon-based optical phased array Lidar, silicon-based optical switch array Lidar, and continuous frequency-modulated wave Lidar. In addition, we also introduced the scanning modes and working principles of other kinds of Lidar, such as the Micro-Electro-Mechanical System, mechanical Lidar, etc., and analyzed the characteristics of the Lidars above. Finally, we summarized this paper and put forward the future expectations of silicon-based integrated Lidar.
DG-GAN: A High Quality Defect Image Generation Method for Defect Detection
Authors: He Xiangjie , Luo Zhongqiang , Li Quanyang , Chen Hongbo , Li Feng
Published: June 2023

The surface defect detection of industrial products has become a crucial link in industrial manufacturing. It has a series of chain effects on the control of product quality, the safety of the subsequent use of products, the reputation of products, and production efficiency. However, in actual production, it is often difficult to collect defect image samples. Without a sufficient number of defect image samples, training defect detection models is difficult to achieve. In this paper, a defect image generation method DG-GAN is proposed for defect detection. Based on the idea of the progressive generative adversarial, D2 adversarial loss function, cyclic consistency loss function, a data augmentation module, and a self-attention mechanism are introduced to improve the training stability and generative ability of the network. The DG-GAN method can generate high-quality and high-diversity surface defect images. The surface defect image generated by the model can be used to train the defect detection model and improve the convergence stability and detection accuracy of the defect detection model. Validation was performed on two data sets. Compared to the previous methods, the FID score of the generated defect images was significantly reduced (mean reductions of 16.17 and 20.06, respectively). The YOLOX detection accuracy was significantly improved with the increase in generated defect images (the highest increases were 6.1% and 20.4%, respectively). Experimental results showed that the DG-GAN model is effective in surface defect detection tasks.
Quantifying Hand Strength and Isometric Pinch Individuation Using a Flexible Pressure Sensor Grid
Authors: Conway Brian J. , Taquet Léon , Boerger Timothy F. , Young Sarah C. , Krucoff Kate B. , Schmit Brian D. , Krucoff Max O.
Published: June 2023

Modulating force between the thumb and another digit, or isometric pinch individuation, is critical for daily tasks and can be impaired due to central or peripheral nervous system injury. Because surgical and rehabilitative efforts often focus on regaining this dexterous ability, we need to be able to consistently quantify pinch individuation across time and facilities. Currently, a standardized metric for such an assessment does not exist. Therefore, we tested whether we could use a commercially available flexible pressure sensor grid (Tekscan F-Socket [Tekscan Inc., Norwood, MA, USA]) to repeatedly measure isometric pinch individuation and maximum voluntary contraction (MVC) in twenty right-handed healthy volunteers at two visits. We developed a novel equation informed by the prior literature to calculate isometric individuation scores that quantified percentage of force on the grid generated by the indicated digit. MVC intra-class correlation coefficients (ICCs) for the left and right hands were 0.86 (p < 0.0001) and 0.88 (p < 0.0001), respectively, suggesting MVC measurements were consistent over time. However, individuation score ICCs, were poorer (left index ICC 0.41, p = 0.28; right index ICC −0.02, p = 0.51), indicating that this protocol did not provide a sufficiently repeatable individuation assessment. These data support the need to develop novel platforms specifically for repeatable and objective isometric hand dexterity assessments.
Spatial Multiplexing of Whispering Gallery Mode Sensors
Authors: Holler Stephen , Speck Matthew
Published: June 2023

Whispering gallery mode resonators have proven to be robust and sensitive platforms for the trace detection of chemical and/or biological analytes. Conventional approaches using serially addressed resonators face challenges in simultaneous multi-channel (i.e., multi-species) detection. We present an alternative monitoring scheme that allows for the spatial multiplexing of whispering gallery mode resonators with the simultaneous observation of the resonance spectra from each of them. By imaging arrays of microspheres and monitoring the glare spot intensities through image processing routines, resonance spectra from multiple resonators may be simultaneously recorded without interference or confounding effects of serial excitation/detection. We demonstrate our multiplexed imaging approach with bulk refractive index variations and virus–antibody binding.
Measurement of Magnetic and Magnetostrictive Characteristics of Transformer Core Based on Triaxial Strain Gauge and B-H Vector Sensor
Authors: Wang Zhen , Fan Zheming , Li Xiang , Xu Kai , Yu Runjie
Published: June 2023

As is well known, the magnetostrictive phenomenon of electrical steel sheet is the main source of electricity in equipment such as transformers. The magnetostrictive characteristic of the actual transformer core is more complicated than that of single-sheet steel. The magnetostriction phenomenon of the transformer core cannot be fully understood by studying only a single piece of electrical steel, so it is necessary to study the local magnetic characteristics of the transformer directly. In this paper, two-limb, one-phase transformer core with a multi-step-lap construction was assembled, a laminated magnetostrictive testing system based on triaxial strain gauges was built, and the local magnetic characteristics were studied using a self-developed B-H vector sensor. The magnetostrictive and magnetic properties in different local regions were measured and analyzed under several magnetization patterns, and the influence of DC bias on the magnetostrictive property of the corner, yoke, and limb of the core was investigated. The influence of the position of the clamp on the magnetostriction of the transformer core was also studied. The magnetostrictive strain of the single sheet and laminated core was compared and discussed. The results showed that the strain caused by the interaction between laminations in this area can be effectively reduced when clamping in the middle of the yoke.
Effect of Music Based Therapy Rhythmic Auditory Stimulation (RAS) Using Wearable Device in Rehabilitation of Neurological Patients: A Systematic Review
Authors: Scataglini Sofia , Van Dyck Zala , Declercq Véronique , Van Cleemput Gitte , Struyf Nele , Truijen Steven
Published: June 2023

(1) Background: Even though music therapy is acknowledged to have positive benefits in neurology, there is still a lack of knowledge in the literature about the applicability of music treatments in clinical practice with a neurological population using wearable devices. (2) Methods: a systematic review was conducted following PRISMA 2020 guidelines on the 29 October 2022, searching in five databases: PubMed, PEDro, Medline, Web of Science, and Science Direct. (3) Results: A total of 2964 articles were found, including 413 from PubMed, 248 from Web of Science, 2110 from Science Direct, 163 from Medline, and none from PEDro. Duplicate entries, of which there were 1262, were eliminated. In the first screening phase, 1702 papers were screened for title and abstract. Subsequently, 1667 papers were removed, based on population, duplicate, outcome, and poor study design. Only 15 studies were considered after 35 papers had their full texts verified. Results showed significant values of spatiotemporal gait parameters in music-based therapy rhythmic auditory stimulation (RAS), including speed, stride length, cadence, and ROM. (4) Conclusions: The current findings confirm the value of music-based therapy RAS as a favorable and effective tool to implement in the health care system for the rehabilitation of patients with movement disorders.
Progressive Model-Driven Approach for 3D Modeling of Indoor Spaces
Authors: Abdollahi Ali , Arefi Hossein , Malihi Shirin , Maboudi Mehdi
Published: June 2023

This paper focuses on the 3D modeling of the interior spaces of buildings. Three-dimensional point clouds from laser scanners can be considered the most widely used data for 3D indoor modeling. Therefore, the walls, ceiling and floor are extracted as the main structural fabric and reconstructed. In this paper, a method is presented to tackle the problems related to the data including obstruction, clutter and noise. This method reconstructs indoor space in a model-driven approach using watertight predefined models. Employing the two-step implementation of this process, the algorithm is able to model non-rectangular spaces with an even number of sides. Afterwards, an “improvement” process increases the level of details by modeling the intrusion and protrusion of the model. The 3D model is formed by extrusion from 2D to 3D. The proposed model-driven algorithm is evaluated with four benchmark real-world datasets. The efficacy of the proposed method is proved by the range of [77%, 95%], [85%, 97%] and [1.7 cm, 2.4 cm] values of completeness, correctness and geometric accuracy, respectively.
Determining the Nutrient Content of Hydroponically-Cultivated Microgreens with Immersible Silicon Photonic Sensors: A Preliminary Feasibility Study
Authors: Christofi Aristi , Margariti Georgia , Salapatas Alexandros , Papageorgiou George , Zervas Panagiotis , Karampiperis Pythagoras , Koukourikos Antonis , Tarantilis Petros A. , Kaparakou Eleftheria H. , Misiakos Konstantinos , Makarona Eleni
Published: June 2023

Microgreens have gained attention for their exceptional culinary characteristics and high nutritional value. The present study focused on a novel approach for investigating the easy extraction of plant samples and the utilization of immersible silicon photonic sensors to determine, on the spot, the nutrient content of microgreens and their optimum time of harvest. For the first time, it was examined how these novel sensors can capture time-shifting spectra caused by the molecules’ dynamic adhesion onto the sensor surface. The experiment involved four types of microgreens (three types of basil and broccoli) grown in a do-it-yourself hydroponic installation. The sensors successfully distinguished between different plant types, showcasing their discriminative capabilities. To determine the optimum harvest time, this study compared the sensor data with results obtained through standard analytical methods. Specifically, the total phenolic content and antioxidant activity of two basil varieties were juxtaposed with the sensor data, and this study concluded that the ideal harvest time for basil microgreens was 14 days after planting. This finding highlights the potential of the immersible silicon photonic sensors for potentially replacing time-consuming analytical techniques. By concentrating on obtaining plant extracts, capturing time-shifting spectra, and assessing sensor reusability, this research paves the way for future advancements in urban farming.