bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Testing of the Peanut Pod Cleaning Device
Authors: Zhu Chenhui , Chen Bo , Li Jiongqi , Liu Yuan , Yang Liquan , Wang Wanzhang , Zhang Hongmei
Published: January 2023

Due to the design of peanut harvesters and cleaners, peanut pods are often mixed with soil, gravel, peanut straw, and other impurities. To solve this problem, this study focused on designing a peanut pod cleaning device by integrating a negative pressure centrifugal fan, a hydrometric cleaning sieve, and a reversible long-mesh cleaning sieve. The relative motion of the peanut pod on the sieve was discussed, its stress analyzed, the design parameters of the sieve and fan determined, and the operation of the device was monitored by using sensors, which accurately recorded and adjusted the working parameters. Finally, the key parameters were tested, and the results showed that the design requirements were met at a vibration frequency of 5.5 Hz, a hydrometric cleaning sieve inclination of 15°, a reciprocating long mesh cleaning sieve inclination of 5°, and a fan speed of 1500 rev/min; the mean loss is 2.26%, and the mean impurity is 3.18%. The findings can be used to provide technical support and reference for the development of peanut pod cleaning devices.
Investigation of CO2 Splitting on Ceria-Based Redox Materials for Low-Temperature Solar Thermochemical Cycling with Oxygen Isotope Exchange Experiments
Authors: Uxa Daniel , Dörrer Lars , Schulz Michal , Knoblauch Nicole , Fielitz Peter , Roeb Martin , Schmücker Martin , Borchardt Günter
Published: January 2023

The surface exchange and bulk transport of oxygen are highly relevant to ceria-based redox materials, which are envisaged for the solar thermochemical splitting of carbon dioxide in the future. Experimental investigations of oxygen isotope exchange on CeO2-δ, Ce0.9M3+0.1O1.95-δ (with M3+ = Y, Sm) and Ce0.9M4+0.1O2-δ (with M4+ = Zr) samples were carried out for the first time utilizing oxygen-isotope-enriched C18O2 gas atmospheres as the tracer source, followed by Secondary Ion Mass Spectrometry (SIMS), at the temperature range 300 ≤ T ≤ 800 °C. The experimental K˜O and D˜O data reveal promising results in terms of CO2 splitting when trivalent (especially Sm)-doped ceria is employed. The reaction temperatures are lower than previously proposed/reported due to the weak temperature dependency of the parameters K˜O and D˜O. The majority of isotope exchange experiments show higher values of K˜O and D˜O for Sm-doped cerium dioxide in comparison to Y-doped and Zr-doped ceria, as well as nominally undoped ceria. The apparent activation energies for both K˜O and D˜O are lowest for Sm-doped ceria. Using Zr-doped cerium oxide exhibits various negative aspects. The Zr-doping of ceria enhances the reducibility, but the possible Zr-based surface alteration effects and dopant-induced migration barrier enhancement in Zr-doped ceria are detrimental to surface exchange and oxygen diffusion at lower temperatures of T ≤ 800 °C.
Adsorption of Phosphates onto Mg/Al-Oxide/Hydroxide/Sulfate-Impregnated Douglas Fir Biochar
Authors: Navarathna Chanaka M. , Pennisson Jaylen E. , Dewage Narada Bombuwala , Reid Claudia , Dotse Charles , Jazi Mehdi Erfani , Rodrigo Prashan M. , Zhang Xuefeng , Farmer Erin , Watson Colton , Craig Daniel O. , Ramirez Arissa , Walker Michael , Madduri Sunith , Mohan Dinesh , Mlsna Todd E.
Published: January 2023

Nitrates and phosphates, found in fertilizers, are the most common eutrophication-causing agents. Douglas fir biochar (BC), a syngas byproduct, was treated with different Al/Mg ratios of sulfate (5% w/w metal loading) followed by an NaOH treatment. The greatest phosphate uptake at 25 °C and pH 7 was attributed to the composite with a Mg/Al 2:1 ratio prepared at pH 13 (AMBC). Batch AMBC phosphate uptake was optimized for initial pH, equilibrium time, temperature, and initial phosphate concentration. Phosphate removal following pseudo-2nd-order kinetics and increases gradually before reaching a max at pH 11, with 95% phosphate uptake in 15 mins. The Sips isotherm model provided the best sorption data fit resulting in a 42.1 mg/g capacity at 25 °C and pH 11. Endothermic and spontaneous adsorption were determined using van ’t Hoff’s plots. BET, XRD, XPS, SEM, TEM, and EDS were used to characterize the biochar before and after phosphate sorption. Used AMBC has the potential to be exploited as a phosphate fertilizer as a key part of an environmentally friendly agricultural management plan.
Investigating Key Targets of Dajianzhong Decoction for Treating Crohn’s Disease Using Weighted Gene Co-Expression Network
Authors: Zhao Yi , Wang Shengpeng , Hu Yuanjia , Wang Yitao
Published: January 2023

Background: Crohn’s disease (CD) is an inflammatory bowel disease, cases of which have substantially increased in recent years. The classical formula Dajianzhong decoction (DD, Japanese: Daikenchuto) is often used to treat CD, but few studies have evaluated related therapeutic mechanisms. In this study, we investigated the potential targets and mechanisms of DD used for treating CD at the molecular level through the weighted gene co-expression network. Methods: The main chemical components of the three DD herbs (Zanthoxylum bungeanum Maxim., Zingiber officinale (Willd.) Rosc., and Ginseng Radix et Rhizoma) were searched for using the HERB database. The targets for each component were identified using the SwissTargetPrediction and HERB databases, whereas the disease targets for CD were retrieved from the GeneCards and DisGeNET databases. The functional enrichment analysis was performed on the common targets of DD and CD. High-throughput sequencing data for CD patients were retrieved from the Gene Expression Omnibus database, and WGCNA was performed to identify the key targets. The association between the key targets and DD ingredients was verified using molecular docking. Results: By analyzing the interaction targets between DD and CD, 196 overlapping genes were identified. The enrichment results indicated that the PI3K-AKT, TNF, MAPK, and IL-17 signaling pathways influenced the mechanism of action of DD in counteracting CD. Combined with WGCNA, four differentially expressed genes (SLC6A4, NOS2, SHBG, and ABCB1) and their corresponding 24 compounds were closely related to the occurrence of CD. Conclusions: By integrating gene co-expression network analysis, this study preliminarily reveals the internal molecular mechanism of DD in treating CD from a systematic perspective, validated by molecular docking. However, these findings require further validation.
Validation of Two Theoretically Derived Equations for Predicting pH in CO2 Biomethanisation
Authors: Zhang Yue , Heaven Sonia , Banks Charles J.
Published: January 2023

CO2 biomethanisation is a rapidly emerging technology which can contribute to reducing greenhouse gas emissions through the more sustainable use of organic feedstocks. The major technical limitation for in situ systems is that the reaction causes CO2 depletion which drives up pH, potentially leading to instability and even digestion failure. The study aimed to test fundamentally derived predictive equations as tools to manage H2 addition to anaerobic digesters. The methodology used data from the literature and from experimental digesters operated with excess H2 to a point of failure and subsequent recovery. Two equations were tested: the first relating pH to CO2 partial pressure (pCO2), and the second extending this to include the influence of volatile fatty acids and ammonia. The first equation gave good agreement for data from studies covering a wide range of operating conditions and digester types. Where agreement was not good, this could usually be explained, and in some cases improved, using the second equation, which also showed excellent predictive performance in the experimental study. The results validated the derived equations and identified typical coefficient values for some organic feedstocks. Both equations could provide a basis for process control of CO2 biomethanisation using routine monitoring of pH or pCO2 with additional analysis for volatile fatty acids and total ammonia nitrogen when required.
Special Issue “Heavy Oils Conversion Processes”
Authors: Kayukova Galina P. , Khelkhal Mohammed Amine , Vakhin Alexey V.
Published: January 2023

It is common knowledge that the world’s economic growth is mainly based on hydrocarbon exploitation and processing, regardless of the political efforts towards developing renewable energy [...]
Special Issue on “Process Design and Sustainable Development”
Authors: Glavič Peter
Published: January 2023

Thirty years ago, at the United Nations’ (UN) Earth Summit in Rio de Janeiro, Brazil, 178 countries adopted Agenda 21, a global partnership for sustainable development to improve human lives and protect the environment [...]
Impact of Subsidy Policy on Remanufacturing Industry’s Donation Strategy
Authors: Chen Xintong , Li Zonghuo , Wang Junjin
Published: January 2023

Motivated by the donation subsidy policy, this paper studies a supply chain consisting of a manufacturer and a remanufacturer. The manufacturer sells new and remanufactured products and can also donate two products. The remanufacturer can only sell and donate remanufactured products. Using the Stackelberg game model, we investigate the optimal production and donation strategies of two competing firms and discuss how the subsidy policy affects these strategies. Our main results include the following: First, the donation strategies of the two firms are not only affected by the subsidies but could also be influenced by the competitor’s donation decision, especially when the subsidy is high. Second, the subsidized products for sale in the market will decline as the subsidy increases. Therefore, a high subsidy always causes insufficient market supply. Third, the first-mover advantage may not make the manufacturer avoid a dilemma; however, when the remanufacturer becomes the leader in the market, the first-mover advantage will help the remanufacturer prevent any competitor donation threats. Lastly, the scenario where the manufacturer donates nothing and the remanufacturer donates seems to be a Pareto improvement for two firms, but this scenario is not stable, and the last equilibrium is that both firms decide to donate remanufactured products.
Particle Scale Insights into a KG-Pharma RoTab Tablet Press Feed-Frame System Using the Discrete Element Method (DEM) Modeling
Authors: Li Zihao , Kumar Rohit , Guzman Hector Rafael , Chiarella Renato Andrés
Published: January 2023

In the pharmaceutical industry, tablets are a common dosage form. As part of the manufacturing process, powder in a tablet press passes through a feeding system called the feed-frame before reaching the die cavity. Under different manufacturing conditions (e.g., paddle speed, turret speed), issues such as segregation of powder blend components, over lubrication, and drug particle attrition often occur in the tablet press feed-frame, which affect the final drug product quality. Therefore, developing a particle-scale understanding of powder behavior in a feed-frame is essential. This paper used the discrete element method (DEM) to study powder flow in the feed-frame of a kg-pharma RoTab tablet press. Simulation results show that an increase in paddle speed has less effect on tablet mass than turret speed and that an increase in turret speed reduces tablet mass and increases variability in the tablet mass. The effect of paddle speed and turret speed on the API content in the tablet is small, but large paddle speeds exacerbate the segregation of powder blend components in the feed frame, increase particle traveled distance, particle shear work, and torque on the paddle wheel, thereby increasing particle over lubrication and attrition. However, the increase in paddle speed reduces the mean residence time of drug particles and prevents over lubrication at low turret speeds. Increasing the turret speed reduces tablet mass, particle shear work, paddle torque, and mean residence time. Although increasing turret speed can effectively prevent particle attrition and over lubrication, short residence times are not conducive to mixing excipients and API components, leading to higher tablet mass variability. This study not only gives us a particle-level insight into the process but can also be used to inform and optimize the design of experimental studies.
Rubber Tiles Made from Secondary Raw Materials with Immobilized Titanium Dioxide as Passive Air Protection
Authors: Benjak Paula , Radetić Lucija , Tomaš Marija , Brnardić Ivan , Radetić Benjamin , Špada Vedrana , Grčić Ivana
Published: January 2023

The immobilization of titanium dioxide, particularly commercial TiO2 P25, on the surface of recycled rubber tiles presents a solution for achieving passive air protection. A completely new purpose for tiles was obtained by addressing air pollution and related health issues. Modified rubber tiles were prepared using a sol–gel method with three different proportions of TiO2 (2, 4, and 10 g) in the solution. The nature of TiO2 nanoparticles and their respective binding on the tile surface was determined using scanning electron microscopy (SEM) equipped with electron dispersion X-ray spectrometry (EDS) and Fourier-transform infrared (FTIR) spectroscopy. The SEM-EDS results showed that the most successful immobilization was achieved with the lowest amount of TiO2 in the sol–gel solution. The FTIR results confirmed a band at 950 cm−1 that was attributed to the Ti-O-Si bond. The stability and environmental impact of the treated rubber substrates were investigated by a leaching test. Photocatalytic oxidation was confirmed by the oxidation of NH3 to N2. Based on the results obtained, rubber substrates with an addition of 2 g of TiO2 have demonstrated prospects for further tests of the photocatalytic degradation of airborne pollutants.
Dissolvable Film-Controlled Buoyancy Pumping and Aliquoting on a Lab-On-A-Disc
Authors: Kilcawley Niamh A. , Voebel Toni C. , Early Philip L. , McArdle Niamh A. , Renou Marine , Rio Jeanne , Saint-Martin Godefroi , Glynn Macdara T. , Zontar Daniel , Brecher Christian , Ducrée Jens , Kinahan David J.
Published: January 2023

Lab-on-a-Disc (LoaD) has great potential for applications in decentralised bioanalytical testing where speed and robustness are critical. Here, a disc-shaped microfluidic chip is rotated to pump liquid radially outwards; thus, all microfluidic structures must be fitted into the available radial length. To overcome this limitation, several centripetal pumping technologies have been developed. In this work, we combine buoyancy pumping, enabled by displacing aqueous samples and reagents centripetally inwards by a dense liquid (fluorocarbon FC-40), with dissolvable film (DF) to automate a multi-step assay. The DF dissolves in the presence of water but is not in contact with the FC-40. Therefore, the FC-40 can be stored behind the DF membranes and is autonomously released by contact with the arriving aqueous sample. Using this technology, tasks such as blood centrifugation can be located on the disc periphery where ‘disc real estate’ is less valuable and centrifugal forces are higher. To demonstrate this, we use the combination of the buoyancy-driven centripetal pumping with DF barriers to implement a fully automated multi-parameter diagnostic assay on the LoaD platform. The implemented steps include plasma extraction from a structure, automatically triggered metering/aliquoting, and the management of five onboard stored liquid reagents. Critically, we also demonstrate highly accurate aliquoting of reagents using centripetal pumping. We also provide a mathematical model to describe the pumping mechanism and apply lumped-element modelling and Monte Carlo simulation to estimate errors in the aliquoting volumes caused by manufacturing deviations.
Understanding Personal Stances on Religion: The Relevance of Organizational Behavior Variables
Authors: Soares Maria Eduarda , Teixeira Alfredo
Published: January 2023

This paper develops a framework for analyzing two personal stances on religion—Adherence to Religion and Autonomy from Religion. We propose that the two stances are independent constructs rather than opposite poles of the same continuum. Each stance is conceptualized as a higher-order construct, with different first-order measures of motivations, beliefs and perceptions. With these conceptualizations, we explore the relevance of Organizational Behavior research for informing Religious Studies. We test a nomological network of personal stances on religion with structural equations modelling and a sample of 3072 Catholic participants. The results provide support for the use of higher-order constructs. The first-order measures that possess the highest influence are Relatedness Motivation for Adherence, and Perception of Church Politics for Autonomy. The model explains 23% of Religious Commitment, and thus identifies relevant predictors for participation in rituals, a crucial issue for the maintenance and development of the relationship with the Catholic Church.
Innovative Remote Sensing Identification of Cyanobacterial Blooms Inspired from Pseudo Water Color
Authors: Cao Zhen , Jing Yuanyuan , Zhang Yuchao , Lai Lai , Liu Zhaomin , Yang Qiduo
Published: January 2023

The identification and monitoring of cyanobacterial blooms (CBs) is critical for ensuring water security. However, traditional methods are time-consuming and labor-intensive and are not ideal for large-scale monitoring. In operational monitoring, the existing remote sensing methods are also not ideal due to complex surface features, unstable models, and poor robustness thresholds. Here, a novel algorithm, the pseudo-Forel-Ule index (P-FUI), is developed and validated to identify cyanobacterial blooms based on Terra MODIS, Landsat-8 OLI, Sentinel-2 MSI, and Sentinel-3 OLCI sensors. First, three parameters of P-FUI, that is, brightness Y, saturation s, and hue angle α, were calculated based on remote sensing reflectance. Then, the robustness thresholds of the parameters were determined by statistical analysis for a frequency distribution histogram. We validated the accuracy of our approach using high-spatial-resolution satellite data with the aid of field investigations. Considerable results were obtained by using water color differences directly. The overall classification accuracy is more than 93.76%, and the user’s accuracy and producer’s accuracy are more than 94.60% and 94.00%, respectively, with a kappa coefficient of 0.91. The identified cyanobacterial blooms’ spatial distribution with high, medium, and low intensity produced consistent results compared to those based on satellite data. Impact factors were also discussed, and the algorithm was shown to be tolerant of perturbations by clouds and high turbidity. This new approach enables operational monitoring of cyanobacterial blooms in eutrophic lakes.
Moist Convection in the Giant Planet Atmospheres
Authors: Palotai Csaba , Brueshaber Shawn , Sankar Ramanakumar , Sayanagi Kunio
Published: January 2023

The outer planets of our Solar System display a myriad of interesting cloud features, of different colors and sizes. The differences between the types of observed clouds suggest a complex interplay between the dynamics and chemistry at play in these atmospheres. Particularly, the stark difference between the banded structures of Jupiter and Saturn vs. the sporadic clouds on the ice giants highlights the varieties in dynamic, chemical and thermal processes that shape these atmospheres. Since the early explorations of these planets by spacecrafts, such as Voyager and Voyager 2, there are many outstanding questions about the long-term stability of the observed features. One hypothesis is that the internal heat generated during the formation of these planets is transported to the upper atmosphere through latent heat release from convecting clouds (i.e., moist convection). In this review, we present evidence of moist convective activity in the gas giant atmospheres of our Solar System from remote sensing data, both from ground- and space-based observations. We detail the processes that drive moist convective activity, both in terms of the dynamics as well as the microphysical processes that shape the resulting clouds. Finally, we also discuss the effects of moist convection on shaping the large-scale dynamics (such as jet structures on these planets).
A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
Authors: Zhao Dawei , Wang Jun , Zuo Luo , Wang Jipeng
Published: January 2023

For the problem of traditional passive radar target detection, the cross-correlation method is a popular solution. By processing the reference signal and the target echo signal coherently, the method is able to extract the target parameters, such as distance, velocity, and azimuth. However, the estimation performance is limited by the low signal-to-noise ratio. Therefore, a new cross-correlation algorithm for passive radar target detection is proposed in this paper. The echo signal received by the echo antenna and the reference signal received by the reference antenna are processed by differential operations to transform the single signal into multiple signals. Then, the differential reference signal is used to eliminate the signal phase in the differential echo signal. With the two-dimensional Fourier transform, the coherent accumulation of multiple signals is realized based on matched filtering of the single signal. This makes the secondary accumulation of the target signal coherent, while the interference part can only obtain incoherent secondary accumulation. In this way, the signal-to-noise ratio for target detection can be greatly improved. The paper presents the theoretical derivation of the algorithm and the quantitative analysis of its performance. The results of both simulated and measured data show that the detection performance is greatly improved compared with the traditional method.
Estimation of Gully Growth Rate and Erosion Amount Using UAV and Worldview-3 Images in Yimeng Mountain Area, China
Authors: Zhang Guanghe , Zhao Weijun , Yan Tingting , Qin Wei , Miao Xiaojing
Published: January 2023

Non-homogeneous soil’s high gravel content (also known as the “soil-rock dual structure”) may render it more prone to erosion and the significant development of gullies. In order to reveal the morphological characteristics and erosion rate of gullies in “soil-rock dual structure” areas, this study focused on the Shagou Reservoir basin in the Yimeng mountain area as the study area. Based on a complete digital orthophoto map (DOM, 0.03 m) and a digital elevation model (DEM, 0.03 m) acquired by an unmanned aerial vehicle (UAV), the researchers calculated the length (L), top width (TW), depth (D), area (A) and volume (V) of 19 gullies and built and optimized the volume estimation model. The DOM and the DEM were used to modify the morphological parameters of 43 gullies extracted from high-resolution remote sensing (RS) stereopair images (Worldview, 0.5 m), and the development and evolution of gully erosion were evaluated in large scale. The results showed that: (1) after correction, the average relative errors of parameters L, TW, D and A computed from the UAV data and the high-resolution RS stereopair image data fell below 0.005%; (2) the mean of TW/D was 5.20, i.e., the lateral erosion development of gullies far outweighed the downcutting erosion. The retrogressive erosion, lateral erosion and downcutting erosion rates of gullies were 0.01~0.83 m/a (averaged at 0.23 m/a), 0.01~0.68 m/a (averaged at 0.25 m/a) and 0.01~0.19 m/a (averaged at 0.09 m/a), respectively, between 2014 and 2021; (3) the volume-area (V-A) model for gullies is the optimal one (p < 0.01, R2 = 0.944).A total of 90.7% of the gully volume was growing at an erosion rate of 0.42~399.39 m³/a and the total erosion rate of the gullies was 3181.56 m3/a from 2014 to 2021. These research findings can serve as a basis for the quantitative modeling of gully erosion in water-eroded locations with a large-dimension “soil-rock dual structure”.
Generating Paired Seismic Training Data with Cycle-Consistent Adversarial Networks
Authors: Zhang Zheng , Yan Zhe , Jing Jiankun , Gu Hanming , Li Haiying
Published: January 2023

Deep-learning-based seismic data interpretation has received extensive attention and focus in recent years. Research has shown that training data play a key role in the process of intelligent seismic interpretation. At present, the main methods used to obtain training data are synthesizing seismic data and manually labeling the real data. However, synthetic data have certain feature differences from real data, and the manual labeling of data is time-consuming and subjective. These factors limit the application of deep learning algorithms in seismic data interpretation. To obtain realistic seismic training data, we propose label-to-data networks based on cycle-consistent adversarial networks in this work. These networks take random labels and unlabeled real seismic data as input and generate synthetic seismic data that match the random labels and have similar features to the real seismic data. Quantitative analysis of the generated data demonstrate the effectiveness of the proposed methods. Meanwhile, test results on different data indicate that the generated data are reliable and can be applied for seismic fault detection.
Dynamic Changes, Spatiotemporal Differences, and Ecological Effects of Impervious Surfaces in the Yellow River Basin, 1986–2020
Authors: Zhang Jing , Du Jiaqiang , Fang Shifeng , Sheng Zhilu , Zhang Yangchengsi , Sun Bingqing , Mao Jialin , Li Lijuan
Published: January 2023

Impervious surfaces (IS) are one of the most important components of the earth’s surface, and understanding how IS have expanded is vital. However, few studies on IS or urbanization have focused on the cradle of the Chinese nation—the Yellow River Basin (YRB). In this study, the Random Forest and Temporal Consistency Check methods were employed to generate long-term maps of IS in the YRB based on Landsat imagery. To explore the dynamics and differences in IS, we developed a spatiotemporal analysis and put forward regional comparisons between different research units of the YRB. We documented the remote sensing-based ecological index (RSEI) in multiple circular zones to discuss the ecological effects of the expansion of IS. The IS extraction strategy achieved excellent performance, with an average overall accuracy of 90.93% and kappa coefficient of 0.79. The statistical results demonstrated that the spatial extent of IS areas in the YRB increased to 18,287.36 km2 in 2020 which was seven times more than that in 1986, at rates of 166 km2/a during 1986–2001, 365 km2/a during 2001–2010, and 1044 km2/a during 2011–2020. Our results indicated that the expansion and densification of IS was slow in core urban areas with high initial IS fraction (ISF), significant in the suburban or rural areas with low initial ISF, and obvious but not significant in the exurb rural or depopulated areas with an initial ISF close to 0. The multiyear RSEI indicated that environmental quality of the YRB had improved with fluctuations. The ecological effects of the impervious expansion slightly differed in urban core areas versus outside these areas. When controlling the urban boundary, more attention should be paid to the rational distribution of ecologically important land. These results provide comprehensive information about IS expansion and can provide references for delineating urban growth boundaries.
Multiscale Feature Fusion for the Multistage Denoising of Airborne Single Photon LiDAR
Authors: Si Shuming , Hu Han , Ding Yulin , Yuan Xuekun , Jiang Ying , Jin Yigao , Ge Xuming , Zhang Yeting , Chen Jie , Guo Xiaocui
Published: January 2023

Compared with the existing modes of LiDAR, single-photon LiDAR (SPL) can acquire terrain data more efficiently. However, influenced by the photon-sensitive detectors, the collected point cloud data contain a large number of noisy points. Most of the existing denoising techniques are based on the sparsity assumption of point cloud noise, which does not hold for SPL point clouds, so the existing denoising methods cannot effectively remove the noisy points from SPL point clouds. To solve the above problems, we proposed a novel multistage denoising strategy with fused multiscale features. The multiscale features were fused to enrich contextual information of the point cloud at different scales. In addition, we utilized multistage denoising to solve the problem that a single-round denoising could not effectively remove enough noise points in some areas. Interestingly, the multiscale features also prevent an increase in false-alarm ratio during multistage denoising. The experimental results indicate that the proposed denoising approach achieved 97.58%, 99.59%, 95.70%, and 77.92% F1-scores in the urban, suburban, mountain, and water areas, respectively, and it outperformed the existing denoising methods such as Statistical Outlier Removal. The proposed approach significantly improved the denoising precision of airborne point clouds from single-photon LiDAR, especially in water areas and dense urban areas.
Self-Associated 1,8-Naphthalimide as a Selective Fluorescent Chemosensor for Detection of High pH in Aqueous Solutions and Their Hg2+ Contamination
Authors: Said Awad I. , Staneva Desislava , Angelova Silvia , Grabchev Ivo
Published: January 2023

A novel diamino triazine based 1,8-naphthalimide (NI-DAT) has been designed and synthesized. Its photophysical properties have been investigated in different solvents and its sensory capability evaluated. The fluorescence emission of NI-DAT is significantly impacted by the solvent polarity due to its inherent intramolecular charge transfer character. Moreover, the fluorescence emission quenched at higher pH as a result of photo-induced electron transfer (PET) from triazine moiety to 1,8-naphthalimide after cleaving hydrogen bonds in the self-associated dimers. Furthermore, the new chemosensor exhibited a good selectivity and sensitivity towards Hg2+ among all the used various cations and anions in the aqueous solution of ethanol (5:1, v/v, pH = 7.2, Tampon buffer). NI-DAT emission at 540 nm was quenched remarkably only by Hg2+, even in the presence of other cations or anions as interfering analytes. Job’s plot revealed a 2:1 stoichiometric ratio for NI-DAT/Hg2+ complex, respectively.
Automated Bowel Sound and Motility Analysis with CNN Using a Smartphone
Authors: Kutsumi Yuka , Kanegawa Norimasa , Zeida Mitsuhiro , Matsubara Hitoshi , Murayama Norihito
Published: January 2023

Bowel sound (BS) is receiving more attention as an indicator of gut health since it can be acquired non-invasively. Current gut health diagnostic tests require special devices that are limited to hospital settings. This study aimed to develop a prototype smartphone application that can record BS using built-in microphones and automatically analyze the sounds. Using smartphones, we collected BSs from 100 participants (age 37.6 ± 9.7). During screening and annotation, we obtained 5929 BS segments. Based on the annotated recordings, we developed and compared two BS recognition models: CNN and LSTM. Our CNN model could detect BSs with an accuracy of 88.9% andan F measure of 72.3% using cross evaluation, thus displaying better performance than the LSTM model (82.4% accuracy and 65.8% F measure using cross validation). Furthermore, the BS to sound interval, which indicates a bowel motility, predicted by the CNN model correlated to over 98% with manual labels. Using built-in smartphone microphones, we constructed a CNN model that can recognize BSs with moderate accuracy, thus providing a putative non-invasive tool for conveniently determining gut health and demonstrating the potential of automated BS research.
Estimation of Airflow Parameters for Tail-Sitter UAV through a 5-Hole Probe Based on an ANN
Authors: Li Xiaoda , Wu Yongliang , Shan Xiaowen , Zhang Haofan , Chen Yang
Published: January 2023

Fixed-wing vertical take-off and landing (VTOL) UAVs have received more and more attention in recent years, because they have the advantages of both fixed-wing UAVs and rotary-wing UAVs. To meet its large flight envelope, the VTOL UAV needs accurate measurement of airflow parameters, including angle of attack, sideslip angle and speed of incoming flow, in a larger range of angle of attack. However, the traditional devices for the measurement of airflow parameters are unsuitable for large-angle measurement. In addition, their performance is unsatisfactory when the UAV is at low speed. Therefore, for tail-sitter VTOL UAVs, we used a 5-hole pressure probe to measure the pressure of these holes and transformed the pressure data into the airflow parameters required in the flight process using an artificial neural network (ANN) method. Through a series of comparative experiments, we achieved a high-performance neural network. Through the processing and analysis of wind-tunnel-experiment data, we verified the feasibility of the method proposed in this paper, which can make more accurate estimates of airflow parameters within a certain range.
Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals
Authors: Kapoor Bhaskar , Nagpal Bharti , Jain Praphula Kumar , Abraham Ajith , Gabralla Lubna Abdelkareim
Published: January 2023

Visual analysis of an electroencephalogram (EEG) by medical professionals is highly time-consuming and the information is difficult to process. To overcome these limitations, several automated seizure detection strategies have been introduced by combining signal processing and machine learning. This paper proposes a hybrid optimization-controlled ensemble classifier comprising the AdaBoost classifier, random forest (RF) classifier, and the decision tree (DT) classifier for the automatic analysis of an EEG signal dataset to predict an epileptic seizure. The EEG signal is pre-processed initially to make it suitable for feature selection. The feature selection process receives the alpha, beta, delta, theta, and gamma wave data from the EEG, where the significant features, such as statistical features, wavelet features, and entropy-based features, are extracted by the proposed hybrid seek optimization algorithm. These extracted features are fed forward to the proposed ensemble classifier that produces the predicted output. By the combination of corvid and gregarious search agent characteristics, the proposed hybrid seek optimization technique has been developed, and is used to evaluate the fusion parameters of the ensemble classifier. The suggested technique’s accuracy, sensitivity, and specificity are determined to be 96.6120%, 94.6736%, and 91.3684%, respectively, for the CHB-MIT database. This demonstrates the effectiveness of the suggested technique for early seizure prediction. The accuracy, sensitivity, and specificity of the proposed technique are 95.3090%, 93.1766%, and 90.0654%, respectively, for the Siena Scalp database, again demonstrating its efficacy in the early seizure prediction process.
An Electrochemical Sensor Based on Electropolymerization of β-Cyclodextrin on Glassy Carbon Electrode for the Determination of Fenitrothion
Authors: Wang Rong , Wang Shulong , Qin Caihong , Nie Qiyang , Luo Yougang , Qin Qi-Pin , Wang Ruijuan , Liu Baiquan , Luo Dongxiang
Published: January 2023

An electrochemical sensor enabled by electropolymerization (EP) of β-cyclodextrin on glassy carbon electrode (β-CDP/GCE) is built for the determination of fenitrothion (FNT). The effects of the EP cycles, pH value, and enrichment time on the electrochemical response of FNT were studied. With the optimum conditions, good linear relationships between the current of the reduction peak of the nitroso derivative of FNT and the concentration are obtained in the range of 10–150 and 150–4000 ng/mL, with a detection limit of 6 ng/mL (S/N = 3). β-CDP/GCE also exhibits a satisfactory applicability in cabbage and tap water, with recovery values between 98.43% and 112%. These outstanding results suggest that β-CDP/GCE could be a new effective alternative for the determination of FNT in real samples.
Improved Drill State Recognition during Milling Process Using Artificial Intelligence
Authors: Kurek Jarosław , Krupa Artur , Antoniuk Izabella , Akhmet Arlan , Abdiomar Ulan , Bukowski Michał , Szymanowski Karol
Published: January 2023

In this article, an automated method for tool condition monitoring is presented. When producing items in large quantities, pointing out the exact time when the element needs to be exchanged is crucial. If performed too early, the operator gets rid of a good drill, also resulting in production downtime increase if this operation is repeated too often. On the other hand, continuing production with a worn tool might result in a poor-quality product and financial loss for the manufacturer. In the presented approach, drill wear is classified using three states representing decreasing quality: green, yellow and red. A series of signals were collected as training data for the classification algorithms. Measurements were saved in separate data sets with corresponding time windows. A total of ten methods were evaluated in terms of overall accuracy and the number of misclassification errors. Three solutions obtained an acceptable accuracy rate above 85%. Algorithms were able to assign states without the most undesirable red-green and green-red errors. The best results were achieved by the Extreme Gradient Boosting algorithm. This approach achieved an overall accuracy of 93.33%, and the only misclassification was the yellow sample assigned as green. The presented solution achieves good results and can be applied in industry applications related to tool condition monitoring.
Towards Context-Aware Facial Emotion Reaction Database for Dyadic Interaction Settings
Authors: Sham Abdallah Hussein , Khan Amna , Lamas David , Tikka Pia , Anbarjafari Gholamreza
Published: January 2023

Emotion recognition is a significant issue in many sectors that use human emotion reactions as communication for marketing, technological equipment, or human–robot interaction. The realistic facial behavior of social robots and artificial agents is still a challenge, limiting their emotional credibility in dyadic face-to-face situations with humans. One obstacle is the lack of appropriate training data on how humans typically interact in such settings. This article focused on collecting the facial behavior of 60 participants to create a new type of dyadic emotion reaction database. For this purpose, we propose a methodology that automatically captures the facial expressions of participants via webcam while they are engaged with other people (facial videos) in emotionally primed contexts. The data were then analyzed using three different Facial Expression Analysis (FEA) tools: iMotions, the Mini-Xception model, and the Py-Feat FEA toolkit. Although the emotion reactions were reported as genuine, the comparative analysis between the aforementioned models could not agree with a single emotion reaction prediction. Based on this result, a more-robust and -effective model for emotion reaction prediction is needed. The relevance of this work for human–computer interaction studies lies in its novel approach to developing adaptive behaviors for synthetic human-like beings (virtual or robotic), allowing them to simulate human facial interaction behavior in contextually varying dyadic situations with humans. This article should be useful for researchers using human emotion analysis while deciding on a suitable methodology to collect facial expression reactions in a dyadic setting.
Comparative Study of Machine Learning Models for Bee Colony Acoustic Pattern Classification on Low Computational Resources
Authors: Robles-Guerrero Antonio , Saucedo-Anaya Tonatiuh , Guerrero-Mendez Carlos A. , Gómez-Jiménez Salvador , Navarro-Solís David J.
Published: January 2023

In precision beekeeping, the automatic recognition of colony states to assess the health status of bee colonies with dedicated hardware is an important challenge for researchers, and the use of machine learning (ML) models to predict acoustic patterns has increased attention. In this work, five classification ML algorithms were compared to find a model with the best performance and the lowest computational cost for identifying colony states by analyzing acoustic patterns. Several metrics were computed to evaluate the performance of the models, and the code execution time was measured (in the training and testing process) as a CPU usage measure. Furthermore, a simple and efficient methodology for dataset prepossessing is presented; this allows the possibility to train and test the models in very short times on limited resources hardware, such as the Raspberry Pi computer, moreover, achieving a high classification performance (above 95%) in all the ML models. The aim is to reduce power consumption and improves the battery life on a monitor system for automatic recognition of bee colony states.
Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries
Authors: Lin Qizhe , Li Xiaoqi , Tu Bicheng , Cao Junwei , Zhang Ming , Xiang Jiawei
Published: January 2023

The state of charge (SOC) for a lithium-ion battery is a key index closely related to battery performance and safety with respect to the power supply system of electric vehicles. The Kalman filter (KF) or extended KF (EKF) is normally employed to estimate SOC in association with the relatively simple and fast second-order resistor-capacitor (RC) equivalent circuit model for SOC estimations. To improve the stability of SOC estimation, a two-stage method is developed by combining the second-order RC equivalent circuit model and the eXogenous Kalman filter (XKF) to estimate the SOC of a lithium-ion battery. First, approximate SOC estimation values are observed with relatively poor accuracy by a stable observer without considering parameter uncertainty. Second, the poor accuracy SOC results are further fed into XKF to obtain relative stable and accurate SOC estimation values. Experiments demonstrate that the SOC estimation results of the present method are superior to those of the commonly used EKF method. It is expected that the present two-stage XKF method will be useful for the stable and accurate estimation of SOC in the power supply system of electric vehicles.
LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases
Authors: Podder Prajoy , Das Sanchita Rani , Mondal M. Rubaiyat Hossain , Bharati Subrato , Maliha Azra , Hasan Md Junayed , Piltan Farzin
Published: January 2023

This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet.
Siam Deep Feature KCF Method and Experimental Study for Pedestrian Tracking
Authors: Tang Di , Jin Weijie , Liu Dawei , Che Jingqi , Yang Yin
Published: January 2023

The tracking of a particular pedestrian is an important issue in computer vision to guarantee societal safety. Due to the limited computing performances of unmanned aerial vehicle (UAV) systems, the Correlation Filter (CF) algorithm has been widely used to perform the task of tracking. However, it has a fixed template size and cannot effectively solve the occlusion problem. Thus, a tracking-by-detection framework was designed in the current research. A lightweight YOLOv3-based (You Only Look Once version 3) mode which had Efficient Channel Attention (ECA) was integrated into the CF algorithm to provide deep features. In addition, a lightweight Siamese CNN with Cross Stage Partial (CSP) provided the representations of features learned from massive face images, allowing the target similarity in data association to be guaranteed. As a result, a Deep Feature Kernelized Correlation Filters method coupled with Siamese-CSP(Siam-DFKCF) was established to increase the tracking robustness. From the experimental results, it can be concluded that the anti-occlusion and re-tracking performance of the proposed method was increased. The tracking accuracy Distance Precision (DP) and Overlap Precision (OP) had been increased to 0.934 and 0.909 respectively in our test data.
Application of Machine Learning Methods for an Analysis of E-Nose Multidimensional Signals in Wastewater Treatment
Authors: Piłat-Rożek Magdalena , Łazuka Ewa , Majerek Dariusz , Szeląg Bartosz , Duda-Saternus Sylwia , Łagód Grzegorz
Published: January 2023

The work represents a successful attempt to combine a gas sensors array with instrumentation (hardware), and machine learning methods as the basis for creating numerical codes (software), together constituting an electronic nose, to correct the classification of the various stages of the wastewater treatment process. To evaluate the multidimensional measurement derived from the gas sensors array, dimensionality reduction was performed using the t-SNE method, which (unlike the commonly used PCA method) preserves the local structure of the data by minimizing the Kullback-Leibler divergence between the two distributions with respect to the location of points on the map. The k-median method was used to evaluate the discretization potential of the collected multidimensional data. It showed that observations from different stages of the wastewater treatment process have varying chemical fingerprints. In the final stage of data analysis, a supervised machine learning method, in the form of a random forest, was used to classify observations based on the measurements from the sensors array. The quality of the resulting model was assessed based on several measures commonly used in classification tasks. All the measures used confirmed that the classification model perfectly assigned classes to the observations from the test set, which also confirmed the absence of model overfitting.