bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis and Properties of the Gallium-Containing Ruddlesden-Popper Oxides with High-Entropy B-Site Arrangement
Authors: Dąbrowa Juliusz , Adamczyk Jan , Stępień Anna , Zajusz Marek , Bar Karolina , Berent Katarzyna , Świerczek Konrad
Published: September 2022

For the first time, the possibility of obtaining B-site disordered, Ruddlesden–Popper type, high-entropy oxides has been proven, using as an example the LnSr(Co,Fe,Ga,Mn,Ni)O4 series (Ln = La, Pr, Nd, Sm, or Gd). The materials were synthesized using the Pechini method, followed by sintering at a temperature of 1200 °C. The XRD analysis indicated the single-phase, I4/mmm structure of the Pr-, Nd-, and Sm-based materials, with a minor content of secondary phase precipitates in La- and Gd-based materials. The SEM + EDX analysis confirms the homogeneity of the studied samples. Based on the oxygen non-stoichiometry measurements, the general formula of LnSr(Co,Fe,Ga,Mn,Ni)O4+δ, is established, with the content of oxygen interstitials being surprisingly similar across the series. The temperature dependence of the total conductivity is similar for all materials, with the highest conductivity value of 4.28 S/cm being reported for the Sm-based composition. The thermal expansion coefficient is, again, almost identical across the series, with the values varying between 14.6 and 15.2 × 10−6 K−1. The temperature stability of the selected materials is verified using the in situ high-temperature XRD. The results indicate a smaller impact of the lanthanide cation type on the properties than has typically been reported for conventional Ruddlesden–Popper type oxides, which may result from the high-entropy arrangement of the B-site cations.
On the Microstructure, Residual Stress and Fatigue Performance of Laser Metal Deposited TC17 Alloy Subjected to Laser Shock Peening
Authors: An Zhibin , He Weifeng , Zhou Xin , Zhou Liucheng , Nie Xiangfan
Published: September 2022

Laser shock peening (LSP) has been employed to improve the mechanical properties of repaired aerospace engine components via laser metal deposition (LMD). This study looked at cross-sectional residual stress, microstructure and high cyclic fatigue performance. The outcomes demonstrated that a compressive residual stress layer with a value of 240 MPa was formed at a depth of 200 μm in the laser melting deposited zone and the microhardness was improved by 13.1%. The findings of electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM) analysis revealed that misorientation increased and dislocation features were observed after LSP which is beneficial to the enhancement of fatigue performance. The high cycle fatigue data illustrated that the LMD+LSPned samples exhibited 61% improvement in comparison to the as-LMD samples. In the aerospace sector, LSP and LMD are therefore very effective and promising techniques for restoring high-value components.
Hydrogen-Induced Dislocation Nucleation and Plastic Deformation of ⟨001⟩ and ⟨11¯0⟩ Grain Boundaries in Nickel
Authors: Li Jiaqing , Wu Ziyue , Teng Lin , Deng Guanyu , Wang Rui , Lu Cheng , Li Weidong , Huang Xin , Liu Yu
Published: September 2022

The grain boundary (GB) plays a crucial role in dominating hydrogen-induced plastic deformation and intergranular failure in polycrystal metals. In the present study, molecular dynamics simulations were employed to study the effects of hydrogen segregation on dislocation plasticity of a series of symmetrical tilt grain boundaries (STGBs) with various hydrogen concentrations. Our study shows that hydrogen both enhances and reduces dislocation nucleation events from STGBs, depending on different GB structures. Specifically, for ⟨001⟩ STGBs, hydrogen does not affect the mode of heterogeneous dislocation nucleation (HDN), but facilitates nucleation events as a consequence of hydrogen disordering the GB structure. Conversely, hydrogen retards dislocation nucleation due to the fact that hydrogen segregation disrupts the transformation of boundary structure such as Σ9 (2 2 1¯) ⟨11¯0⟩ STGB. These results are helpful for deepening our understanding of GB-mediated hydrogen embrittlement (HE) mechanisms.
An Equilibrium Decision-Making Approach for Cutting Parameters of a Novel Five-Axis Hybrid Kinematic Machining Unit
Authors: Tang Tengfei , Luo Haiwei , Tang Weimin , Zhang Jun
Published: September 2022

To fully disclose the machining potential of a newly developed five-axis hybrid kinematic machining unit (HKMU), an equilibrium decision-making approach for cutting parameters is proposed. With this proposition, a response surface method-based surrogate model is developed to describe the mapping relationships between three design objectives and five cutting parameters. A multi-objective optimization model is further established to find feasible Pareto solutions to cutting parameters. Based on this, the technique for order preference by similarity to ideal solution (TOPSIS) and engineering decision preferences are adopted to make the final decision of cutting parameters. To illustrate the application of the proposed approach, a case study is carried out on face milling of an exemplary HKMU. The equilibrium decisions of three customized machining schemes lead to the machining duration, the cutting force, and the surface roughness reduction by 44%, 43%, and 9%, respectively. This result supports that the proposed equilibrium decision-making approach is able to find the best-compromised solutions for cutting parameters of the HKMU. It is expected that with minor modifications, the proposed approach can be applied to other multi-axis machining devices for finding accurate yet efficient cutting parameter solutions.
Deep Learning Model for Global Spatio-Temporal Image Prediction
Authors: Nikezić Dušan P. , Ramadani Uzahir R. , Radivojević Dušan S. , Lazović Ivan M. , Mirkov Nikola S.
Published: September 2022

Mathematical methods are the basis of most models that describe the natural phenomena around us. However, the well-known conventional mathematical models for atmospheric modeling have some limitations. Machine learning with Big Data is also based on mathematics but offers a new approach for modeling. There are two methodologies to develop deep learning models for spatio-temporal image prediction. On these bases, two models were built—ConvLSTM and CNN-LSTM—with two types of predictions, i.e., sequence-to-sequence and sequence-to-one, in order to forecast Aerosol Optical Thickness sequences. The input dataset for training was NASA satellite imagery MODAL2_E_AER_OD from Terra/MODIS satellites, which presents global Aerosol Optical Thickness with an 8 day temporal resolution from 2000 to the present. The obtained results show that the ConvLSTM sequence-to-one model had the lowest RMSE error and the highest Cosine Similarity value. The advantages of the developed DL models are that they can be executed in milliseconds on a PC, can be used for global-scale Earth observations, and can serve as tracers to study how the Earth’s atmosphere moves. The developed models can be used as transfer learning for similar image time-series forecasting models.
ECG Signal Features Classification for the Mental Fatigue Recognition
Authors: Butkevičiūtė Eglė , Michalkovič Aleksėjus , Bikulčienė Liepa
Published: September 2022

Mental fatigue is a major public health issue worldwide that is common among both healthy and sick people. In the literature, various modern technologies, together with artificial intelligence techniques, have been proposed. Most techniques consider complex biosignals, such as electroencephalogram, electro-oculogram or classification of basic heart rate variability parameters. Additionally, most studies focus on a particular area, such as driving, surgery, etc. In this paper, a novel approach is presented that combines electrocardiogram (ECG) signal feature extraction, principal component analysis (PCA), and classification using machine learning algorithms. With the aim of daily mental fatigue recognition, an experiment was designed wherein ECG signals were recorded twice a day: in the morning, i.e., a state without fatigue, and in the evening, i.e., a fatigued state. PCA analysis results show that ECG signal parameters, such as Q and R wave amplitude values, as well as QT and T intervals, presented with the largest differences between states compared to other ECG signal parameters. Furthermore, the random forest classifier achieved more than 94.5% accuracy. This work demonstrates the feasibility of ECG signal feature extraction for automatic mental fatigue detection.
YOLOv4-Driven Appearance Grading Filing Mechanism: Toward a High-Accuracy Tomato Grading Model through a Deep-Learning Framework
Authors: Cheng Yu-Huei , Tseng Cheng-Yen , Nguyen Duc-Man , Lin Yu-Da
Published: September 2022

In traditional agricultural quality control, agricultural products are screened manually and then packaged and transported. However, long-term fruit storage is challenging in tropical climates, especially in the case of cherry tomatoes. Cherry tomatoes that appear rotten must be immediately discarded while grading; otherwise, other neighboring cherry tomatoes could rot. An insufficient agricultural workforce is one of the reasons for an increasing number of rotten tomatoes. The development of smart-technology agriculture has become a primary trend. This study proposed a You Only Look Once version 4 (YOLOv4)-driven appearance grading filing mechanism to grade cherry tomatoes. Images of different cherry-tomato appearance grades and different light sources were used as training sets, and the cherry tomatoes were divided into four categories according to appearance (perfect (pedicled head), good (not pedicled head), defective, and discardable). The AI server ran the YOLOv4 deep-learning framework for deep image learning training. Each dataset group was calculated by considering 100 of the four categories as the difference, and the total numbers of images were 400, 800, 1200, 1600, and 2000. Each dataset group was split into an 80% training set, 10% verification set, and 10% test set to overcome the identification complexity of different appearances and light source intensities. The experimental results revealed that models using 400–2000 images were approximately 99.9% accurate. Thus, we propose a new mechanism for rapidly grading agricultural products.
Fault-Tolerant Integrated Guidance and Control Design for Hypersonic Vehicle Based on PPO
Authors: Song Jia , Luo Yuxie , Zhao Mingfei , Hu Yunlong , Zhang Yanxue
Published: September 2022

Aiming at the problem of the terminal guidance phase of hypersonic vehicles (HSV) under fault condition, and considering the existence of various uncertain parameters and actuator faults in the control system, a fault-tolerant integrated guidance and control design of a hypersonic vehicle based on the proximal policy optimization algorithm (PPO) is proposed. First, in view of the problem that the separate guidance and control loop design cannot make full use of the coupling relationship between the two, the relationship between the guidance loop and the control loop is considered and an integrated guidance and control system of HSV is established. Then, the integrated guidance and control problem is converted into a reinforcement learning model, the action space, state observation space, and reward function of the PPO agent are designed, and the network is initialized and designed. Simulations verify the feasibility of the proposed PPO-based IGC system.
On the Complexity of Stable and Biased Majority
Authors: Concha-Vega Pablo , Goles Eric , Montealegre Pedro , Ríos-Wilson Martín
Published: September 2022

A majority automata is a two-state cellular automata, where each cell updates its state according to the most represented state in its neighborhood. A question that naturally arises in the study of these dynamical systems asks whether there exists an efficient algorithm that can be implemented in order to compute the state configuration reached by the system at a given time-step. This problem is called the prediction problem. In this work, we study the prediction problem for a more general setting in which the local functions can be different according to their behavior in tie cases. We define two types of local rules: the stable majority and biased majority. The first one remains invariant in tie cases, and the second one takes the value 1. We call this class the heterogeneous majority cellular automata (HMCA). For this latter class, we show that in one dimension, the prediction problem for HMCA is in NL as a consequence of the dynamics exhibiting a type of bounded change property, while in two or more dimensions, the problem is P-Complete as a consequence of the capability of the system of simulating Boolean circuits.
Optical Solitons of the Generalized Nonlinear Schrödinger Equation with Kerr Nonlinearity and Dispersion of Unrestricted Order
Authors: Kudryashov Nikolay A.
Published: September 2022

The family of the generalized Schrödinger equations with Kerr nonlinearity of unrestricted order is considered. The solutions of equations are looked for using traveling wave reductions. The Painlevé test is applied for finding arbitrary constants in the expansion of the general solution into the Laurent series. It is shown that the equation does not pass the Painlevé test but has two arbitrary constants in local expansion. This fact allows us to look for solitary wave solutions for equations of unrestricted order. The main result of this paper is the theorem of existence of optical solitons for equations of unrestricted order that is proved by direct calculation. The optical solitons for partial differential equations of the twelfth order are given in detail.
Imaging Characterization of Non-Rheumatoid Retro-Odontoid Pseudotumors: Comparison with Atlantoaxial Manifestation of Rheumatoid Arthritis
Authors: Song You-Seon , Lee In-Sook , Nam Kyoung-Hyup , Kim Dong-Hwan , Han In-Ho , Lee Hwangbo , Jeong Yeon-Joo , Yeom Jeong-A
Published: September 2022

Backgroundand Objectives: To date, imaging characterization of non-rheumatic retro-odontoid pseudotumors (NRROPs) has been lacking; therefore, NRROPs have been confused with atlantoaxial joint involvement of rheumatoid arthritis (RA). It is important to differentiate these two disease because the treatment strategies may differ. The purpose of this study is to characterize imaging findings of NRROPs and compare them with those of RA. Material and Methods: From January 2015 to December 2019, 27 patients (14 women and 13 men) with NRROPs and 19 patients (15 women and 4 men) with RA were enrolled in this study. We evaluated various imaging findings, including atlantoaxial instability (AAI), and measured the maximum diameter of preodontoid and retro-odontoid spaces with magnetic resonance imaging (MRI) and computed tomography (CT). Results: Statistical significance was considered for p < 0.05. AAI was detected in eight patients with NRROPs and in all patients with RA (p < 0.0001). Seventeen patients with NRROPs and six patients with RA showed spinal cord compression (p = 0.047). Compressive myelopathy was observed in 14 patients with NRROPs and in 4 patients with RA (p = 0.048). Subaxial degeneration was observed in 25 patients with NRROPs and in 9 patients with RA (p = 0.001). Moreover, C2-3 disc abnormalities were observed in 11 patients with NRROPs and in 2 patients with RA (p = 0.02). Axial and longitudinal diameter of retro-odontoid soft tissue and preodontoid and retro-odontoid spaces showed significant differences between NRROP and RA patients (p < 0.0001). Furthermore, CT AAI measurements were differed significantly between NRROP and RA patients (p < 0.05). Conclusions: NRROPs showed prominent retro-odontoid soft tissue thickening, causing compressive myelopathy and a high frequency of subaxial and C2-3 degeneration without AAI.
A Comparative Evaluation of Dentogingival Tissue Using Transgingival Probing and Cone-Beam Computed Tomography
Authors: Das Gotam , Ahmed Abdul Razzaq , Suleman Ghazala , Lal Abhishek , Rana Muhammad Haseeb , Ahmed Naseer , Arora Suraj
Published: September 2022

Background and Objective: Gingival biotype can be assessed using a variety of invasive and non-invasive procedures, such as direct probing, transgingival probing, ultrasound-guided approaches, and, for the more sophisticated, cone-beam computed tomography. The aim of this study was to evaluate gingival biotype in relation to transgingival probing and cone-beam computed tomography (CBCT). Materials and Methods: This study included a total of two hundred healthy individuals. Gingival thickness was assessed and measured from the right and left maxillary central incisor teeth using CBCT and transgingival probing of the attached gingiva. The measurements were analyzed with regard to tooth type (central incisor). Linear measurements for gingival biotype were measured using both methods. Correlations and differences between measurement methods were assessed. Results: The mean age of study participants was 32.49 ± 8.61 years. The radiographic measurements on CBCT were 1.34 ± 0.17 mm for the right central and 1.28 ± 0.21mm for the left central. The transgingival probing measurements were 1.31 ± 0.18 for the right central and 1.22 ± 0.21mm for the left central. Conclusion: As per the results of this study, there is a significant positive correlation between transgingival probing and CBCT measurements of gingival biotypes.
Experimental Proof of a Transformation Product Trap Effect with a Membrane Photocatalytic Process for VOC Removal
Authors: Gérardin Fabien , Simard Julien , Favre Éric
Published: September 2022

The decomposition of volatile organic compounds by photocatalytic oxidation (PCO) has been widely studied. However, the technological development of this oxidative technique has to address how to handle the formation of transformation products. The work presented here investigates the original combination of a dense membrane separation process and PCO to intensify the trapping and reduction of PCO transformation products. Specific monitoring of toluene PCO transformation products, such as benzene and formaldehyde, in the outflow of both permeate and retentate compartments was proposed. The influence of operating parameters on the process, i.e., light intensity, pressure, membrane type, and catalyst mass, was also studied. The results reveal that membrane separation-PCO hybridization is particularly effective for reducing the presence of benzene and formaldehyde in the effluent treated. The benzene concentration in the outflow of the hybrid module can be reduced by a factor of 120 compared to that observed during the PCO of toluene alone.
Carbon-Based Transducers for Solid-Contact Calcium Ion-Selective Electrodes: Mesopore and Nitrogen-Doping Effects
Authors: Zhang Yirong , Tang Yitian , Liang Rongfeng , Zhong Lijie , Xu Jiexian , Lu Huici , Xu Xiaofeng , Han Tingting , Bao Yu , Ma Yingming , Gan Shiyu , Niu Li
Published: September 2022

Solid-contact ion-selective electrodes (SC-ISEs) exhibit great potential in the detection of routine and portable ions which rely on solid-contact (SC) materials for the transduction of ions to electron signals. Carbon-based materials are state-of-the-art SC transducers due to their high electrical double-layer (EDL) capacitance and hydrophobicity. However, researchers have long searched for ways to enhance the interfacial capacitance in order to improve the potential stability. Herein, three representative carbon-based SC materials including nitrogen-doped mesoporous carbon (NMC), reduced graphene oxide (RGO), and carbon nanotubes (CNT) were compared. The results disclose that the NMC has the highest EDL capacitance owing to its mesopore structure and N-doping while maintaining high hydrophobicity so that no obvious water-layer effect was observed. The Ca2+-SC-ISEs based on the SC of NMC exhibited high potential stability compared with RGO and CNT. This work offers a guideline for the development of carbon-material-based SC-ISEs through mesoporous and N-doping engineering to improve the interfacial capacitance. The developed NMC-based solid-contact Ca2+-SC-ISE exhibited a Nernstian slope of 26.3 ± 3.1 mV dec−1 ranging from 10 μM to 0.1 M with a detection limit of 3.2 μM. Finally, a practical application using NMC-based SC-ISEs was demonstrated through Ca2+ ion analysis in mineral water and soil leaching solutions.
The Use of Selective Laser Melting in Mandibular Retrognathia Correction
Authors: Čretnik Andrej , Fekonja Anita
Published: September 2022

Digitalization and additive manufacturing offer new possibilities in the manufacturing of individualized medical and dental products. In the paper we present the results of the first 30 consecutive growing patients (15 males and 15 females), with a mean age of 13.69 years (SD = 1.26), who were treated for mandibular retrognathia (skeletal Class II malocclusion), using fixed sagittal guidance (FSG) appliance, individually manufactured by selective laser melting (SLM). Lateral cephalometric radiographs were taken before (T0) and after (T1) treatment and a detailed cephalometric analysis was performed. with a special focus on a time period for malocclusion correction. The analyzed data were compared with the control group (CG; treated with intermaxillary Class II elastics) that was matched for pretreatment age and pretreatment cephalometric measurements. Both methods were effective in the correction of Class II malocclusion, but the time period of correction was significantly shorter (16.03 ± 1.09 months vs. 20.65 ± 4.12 months) with the FSG appliance. After treatment visual skeletal and dentoalveolar effects were achieved, with statistically significant differences measured in mandibular incisors inclination (0.45° in FSG and 2.84° in CG) and distance (−0.61 mm in FSG and 0.13 mm in CG), in mandibular first molar inclination (−1.07° in FSG and 1.18° in CG) and overbite (−3.82 mm in FSG and −2.46 mm in CG), all in favor of FSG appliance. After the final mean treatment time of 16.03 ± 1.09 months, visual skeletal and dentoalveolar effects were achieved, with significant differences in sagittal (SNB angle, SNPg angle, mandibular length (CoGn) and consequently decrease in ANB angle) as well as in vertical (lower anterior facial height (LAFH) and gonial angle) measurements noted, with no reported complications. As the time needed for malocclusion correction was comparable with the reports in the traditional use of the functional appliance and as all the cosmetical and functional changes in all the treated patients remained stable after a 2-year observational period, growing patients with Class II malocclusion could benefit with this type of treatment. As all the cosmetical and functional changes in all the treated patients remained stable after a 2-year observational period, growing patients with Class II malocclusion could benefit from the treatment with FSG appliance.
The Use of Gut Microbial Modulation Strategies as Interventional Strategies for Ageing
Authors: Siddiqui Ruqaiyyah , Mungroo Mohammad Ridwane , Alharbi Ahmad M. , Alfahemi Hasan , Khan Naveed Ahmed
Published: September 2022

Gut microbial composition codevelops with the host from birth and is influenced by several factors, including drug use, radiation, psychological stress, dietary changes and physical stress. Importantly, gut microbial dysbiosis has been clearly associated with several diseases, including cancer, rheumatoid arthritis and Clostridium difficile-associated diarrhoea, and is known to affect human health and performance. Herein, we discuss that a shift in the gut microbiota with age and reversal of age-related modulation of the gut microbiota could be a major contributor to the incidence of numerous age-related diseases or overall human performance. In addition, it is suggested that the gut microbiome of long-lived animals such as reptiles should be investigated for their unique properties and contribution to the potent defense system of these species could be extrapolated for the benefit of human health. A range of techniques can be used to modulate the gut microbiota to have higher abundance of “beneficial” microbes that have been linked with health and longevity.
Acoustic Dispersion in Low Permeability Unconventional Reservoir Rocks and Shales at In Situ Stress Conditions
Authors: Omovie Sheyore John , Myers Mike , Castagna John P.
Published: September 2022

Utilizing laboratory measurements on dry and fully brine-saturated well-lithified shale reservoir rocks and comparing them to log data and theoretical modeling, we find no statistically significant intrinsic dispersion from seismic to sonic and laboratory measurement frequencies due to fluid effects. Under in situ stress conditions, the Gassmann zero-frequency P-wave velocity prediction for a Permian basin sample was within 0.3% of the measured velocity on the brine-saturated sample at an ultrasonic frequency. At deviatoric stresses ranging from 1.7 to 70 MPa on the same sample, the percent error in the Gassmann P-wave velocity prediction ranges from 0.3 to 2.2%. These results are consistent with the lack of significant dispersion in the reported velocities and theoretical predictions in the Cotton Valley shale. Based on the Biot–Gassmann model, the characteristic frequency in both formations occurs at ~1010 Hz. Applying a squirt flow model also predicts transition to the high-frequency regime occurring at ~109 Hz for both formations. The comparison of the sonic log measurements for four different shale/tight rock formations to ultrasonic measurements taken from core samples under in situ stress conditions confirms these findings. To our knowledge, this is the first extensive comparison of the sonic log to ultrasonic core data measurements. For these rocks, there is no clearly observable or predicted significant dispersion due to fluid effects at in situ stress conditions within the frequency range for which measurements are made.
Single and Multiplex Immunohistochemistry to Detect Platelets and Neutrophils in Rat and Porcine Tissues
Authors: Arnold Stephanie , Watts Sarah , Kirkman Emrys , Page Clive P. , Pitchford Simon C.
Published: September 2022

Platelet–neutrophil complexes (PNCs) occur during the inflammatory response to trauma and infections, and their interactions enable cell activation that can lead to tissue destruction. The ability to identify the accumulation and tissue localisation of PNCs is necessary to further understand their role in the organs associated with blast-induced shock wave trauma. Relevant experimental lung injury models often utilise pigs and rats, species for which immunohistochemistry protocols to detect platelets and neutrophils have yet to be established. Therefore, monoplex and multiplex immunohistochemistry protocols were established to evaluate the application of 22 commercially available antibodies to detect platelet (nine rat and five pig) and/or neutrophil (four rat and six pig) antigens identified as having potential selectivity for porcine or rat tissue, using lung and liver sections taken from models of polytrauma, including blast lung injury. Of the antibodies evaluated, one antibody was able to detect rat neutrophil elastase (on frozen and formalin-fixed paraffin embedded (FFPE) sections), and one antibody was successful in detecting rat CD61 (frozen sections only); whilst one antibody was able to detect porcine MPO (frozen and FFPE sections) and antibodies, targeting CD42b or CD49b antigens, were able to detect porcine platelets (frozen and FFPE and frozen, respectively). Staining procedures for platelet and neutrophil antigens were also successful in detecting the presence of PNCs in both rat and porcine tissue. We have, therefore, established protocols to allow for the detection of PNCs in lung and liver sections from porcine and rat models of trauma, which we anticipate should be of value to others interested in investigating these cell types in these species.
Ultra-Fast Construction of Novel S-Scheme CuBi2O4/CuO Heterojunction for Selectively Photocatalytic CO2 Conversion to CO
Authors: Shi Weina , Qiao Xiu , Wang Jichao , Zhao Miao , Ge Hongling , Ma Jingjing , Liu Shanqin , Zhang Wanqing
Published: September 2022

Herein, step-scheme (S-scheme) CuBi2O4/CuO (CBO/CuO) composite films were successfully synthesized on glass substrates by the ultra-fast spraying-calcination method. The photocatalytic activities of the obtained materials for CO2 reduction in the presence of H2O vapor were evaluated under visible light irradiation (λ > 400 nm). Benefiting from the construction of S-scheme heterojunction, the CO, CH4 and O2 yields of the optimal CBO/CuO composite reached 1599.1, 5.1 and 682.2 μmol/m2 after irradiation for 9 h, and the selectivity of the CO product was notably enhanced from below 18.5% to above 98.5% compared with those of the bare samples. In the sixth cycling experiment, the yields of main products decreased by less than 15%, and a high CO selectivity was still kept. The enhanced photocatalytic performance of CO2 reduction was attributed to the efficient separation of photogenerated charge carriers. Based on the photocatalytic activity, band structure and in situ-XPS results, the S-scheme charge transfer mechanism was conformed. The study provides an insight into the design of S-scheme photocatalysts for selective CO2 conversion.
Genome-Wide Analysis of UGT Genes in Petunia and Identification of PhUGT51 Involved in the Regulation of Salt Resistance
Authors: Dong Lili , Tang Ziyan , Yang Tianyin , Hao Fuling , Deng Xinyi
Published: September 2022

UDP-glycosyltransferase (UGT) plays an essential role in regulating the synthesis of hormones and secondary metabolites in plants. In this study, 129 members of the Petunia UGT family were identified and classified into 16 groups (A–P) based on phylogenetic analysis. The same subgroups have conserved motif compositions and intron/exon arrangement. In the promoters of the Petunia UGT genes, several cis-elements associated with plant hormones, growth and development, and abiotic stress have been discovered. Their expression profiles in five tissues were revealed by tissue expression based on RNA-seq data. Subcellular localization analysis showed that PhUGT51 was located in the nucleus and cell membrane. Salt stress caused an increase in the expression level of PhUGT51, but the expression level remained stable with the growth over time. In addition, the overexpression of PhUGT51 caused a significant increase in salt resistance. Our study systematically analyses the UGT gene family in Petunia for the first time and provides some valuable clues for the further functional studies of UGT genes.
Selected Indices to Identify Water-Stress-Tolerant Tropical Forage Grasses
Authors: Zuffo Alan Mario , Steiner Fábio , Aguilera Jorge González , Ratke Rafael Felippe , Barrozo Leandra Matos , Mezzomo Ricardo , Santos Adaniel Sousa dos , Gonzales Hebert Hernán Soto , Cubillas Pedro Arias , Ancca Sheda Méndez
Published: September 2022

Periods of soil water stress have been recurrent in the Cerrado region and have become a growing concern for Brazilian tropical pasture areas. Thus, the search for forage grasses more tolerant to water stress has intensified recently in order to promote more sustainable livestock. In a greenhouse experiment, the degree of water stress tolerance of nine tropical forage grass cultivars was studied under different soil water regimes. The investigation followed a 9 × 3 factorial design in four randomized blocks. Nine cultivars from five species of perennial forage grasses were tested: Urochloa brizantha (‘BRS Piatã’, ‘Marandu’, and ‘Xaraés’), Panicum maximum (‘Aruana’, ‘Mombaça’, and ‘Tanzânia’), Pennisetum glaucum (‘ADR 300’), Urochloa ruziziensis (‘Comum’), and Paspalum atratum (‘Pojuca’). These cultivars were grown in pots under three soil water regimes (high soil water regime—HSW (non-stressful condition), middle soil water regime—MSW (moderate water stress), and low soil water regime—LSW (severe water stress)). Plants were exposed to soil water stress for 25 days during the tillering and stalk elongation phases. Twelve tolerance indices, including tolerance index (TOL), mean production (MP), yield stability index (YSI), drought resistance index (DI), stress tolerance index (STI), geometric mean production (GMP), yield index (YI), modified stress tolerance (k1STI and k2STI), stress susceptibility percentage index (SSPI), abiotic tolerance index (ATI), and harmonic mean (HM), were calculated based on shoot biomass production under non-stressful (YP) and stressful (YS) conditions. Soil water stress decreased leaf area, plant height, tillering capacity, root volume, and shoot and root dry matter production in most cultivars, with varying degrees of reduction among tropical forage grasses. Based on shoot biomass production under controlled greenhouse conditions, the most water-stress-tolerant cultivars were P. maximum cv. Mombaça and cv. Tanzânia under the MSW regime and P. maximum cv. Aruana and cv. Mombaça under the LSW regime. P. maximum cv. Mombaça has greater adaptability and stability of shoot biomass production when grown under greenhouse conditions and subjected to soil water stress. Therefore, this forage grass should be tested under field conditions to confirm its forage production potential for cultivation in tropical regions with the occurrence of water stress. The MP, DI, STI, GMP, YI, k2STI, and HM tolerance indices were the most suitable for identifying forage grass cultivars with greater water stress tolerance and a high potential for shoot biomass production under LSW regime.
A Solar-Rechargeable Radiation Dosimeter Design for Radiation Hazard Zone Located with LoRa Network
Authors: Guo Cheng-Yan , Lin Tzu-Lu , Hsieh Tung-Li
Published: September 2022

Since the nuclear energy leakage that occurred at the Fukushima nuclear power plant in Japan, people have paid more attention to the danger of environmental radiation. Environmental radiation is monitored using Geiger counters, which are not easy to obtain in some areas. Therefore, this research proposes an open-source and low-cost handheld Geiger counter that uses solar energy to charge lithium-ion batteries. Our design can provide a low-cost environmental radiation monitoring platform and effectively enhance the public’s scientific education awareness of radiation hazards. The measured dose rate can be output through the serial port, allowing a LoRa wireless network to transmit data to a database. When the sensing network deployed by the radiometer detects that the radiation value of the area is abnormally increased, it can issue an alarm to the government for the first time. Moreover, the low-power radiometer design can reduce energy consumption, reduce the burden on the ecological environment caused by the deployment of the sensing network, and provide sustainability for the environment.
Deep Learning with LPC and Wavelet Algorithms for Driving Fault Diagnosis
Authors: Gong Cihun-Siyong Alex , Su Chih-Hui Simon , Liu Yuan-En , Guu De-Yu , Chen Yu-Hua
Published: September 2022

Vehicle fault detection and diagnosis (VFDD) along with predictive maintenance (PdM) are indispensable for early diagnosis in order to prevent severe accidents due to mechanical malfunction in urban environments. This paper proposes an early voiceprint driving fault identification system using machine learning algorithms for classification. Previous studies have examined driving fault identification, but less attention has focused on using voiceprint features to locate corresponding faults. This research uses 43 different common vehicle mechanical malfunction condition voiceprint signals to construct the dataset. These datasets were filtered by linear predictive coefficient (LPC) and wavelet transform(WT). After the original voiceprint fault sounds were filtered and obtained the main fault characteristics, the deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) architectures are used for identification. The experimental results show that the accuracy of the CNN algorithm is the best for the LPC dataset. In addition, for the wavelet dataset, DNN has the best performance in terms of identification performance and training time. After cross-comparison of experimental results, the wavelet algorithm combined with DNN can improve the identification accuracy by up to 16.57% compared with other deep learning algorithms and reduce the model training time by up to 21.5% compared with other algorithms. Realizing the cross-comparison of recognition results through various machine learning methods, it is possible for the vehicle to proactively remind the driver of the real-time potential hazard of vehicle machinery failure.
Enhanced Convolutional Neural Network for In Situ AUV Thruster Health Monitoring Using Acoustic Signals
Authors: Yeo Sang-Jae , Choi Woen-Sug , Hong Suk-Yoon , Song Jee-Hun
Published: September 2022

As the demand for ocean exploration increases, studies are being actively conducted on autonomous underwater vehicles (AUVs) that can efficiently perform various missions. To successfully perform long-term, wide-ranging missions, it is necessary to apply fault diagnosis technology to AUVs. In this study, a system that can monitor the health of in situ AUV thrusters using a convolutional neural network (CNN) was developed. As input data, an acoustic signal that comprehensively contains the mechanical and hydrodynamic information of the AUV thruster was adopted. The acoustic signal was pre-processed into two-dimensional data through continuous wavelet transform. The neural network was trained with three different pre-processing methods and the accuracy was compared. The decibel scale was more effective than the linear scale, and the normalized decibel scale was more effective than the decibel scale. Through tests on off-training conditions that deviate from the neural network learning condition, the developed system properly recognized the distribution characteristics of noise sources even when the operating speed and the thruster rotation speed changed, and correctly diagnosed the state of the thruster. These results showed that the acoustic signal-based CNN can be effectively used for monitoring the health of the AUV’s thrusters.
Automatic Meter Reading from UAV Inspection Photos in the Substation by Combining YOLOv5s and DeeplabV3+
Authors: Deng Guanghong , Huang Tongbin , Lin Baihao , Liu Hongkai , Yang Rui , Jing Wenlong
Published: September 2022

The combination of unmanned aerial vehicles (UAVs) and artificial intelligence is significant and is a key topic in recent substation inspection applications; and meter reading is one of the challenging tasks. This paper proposes a method based on the combination of YOLOv5s object detection and Deeplabv3+ image segmentation to obtain meter readings through the post-processing of segmented images. Firstly, YOLOv5s was introduced to detect the meter dial area and the meter was classified. Following this, the detected and classified images were passed to the image segmentation algorithm. The backbone network of the Deeplabv3+ algorithm was improved by using the MobileNetv2 network, and the model size was reduced on the premise that the effective extraction of tick marks and pointers was ensured. To account for the inaccurate reading of the meter, the divided pointer and scale area were corroded first, and then the concentric circle sampling method was used to flatten the circular dial area into a rectangular area. Several analog meter readings were calculated by flattening the area scale distance. The experimental results show that the mean average precision of 50 (mAP50) of the YOLOv5s model with this method in this data set reached 99.58%, that the single detection speed reached 22.2 ms, and that the mean intersection over union (mIoU) of the image segmentation model reached 78.92%, 76.15%, 79.12%, 81.17%, and 75.73%, respectively. The single segmentation speed reached 35.1 ms. At the same time, the effects of various commonly used detection and segmentation algorithms on the recognition of meter readings were compared. The results show that the method in this paper significantly improved the accuracy and practicability of substation meter reading detection in complex situations.
Correlation Study between the Organic Compounds and Ripening Stages of Oil Palm Fruitlets Based on the Raman Spectra
Authors: Azmi Muhammad Haziq Imran Md , Hashim Fazida Hanim , Huddin Aqilah Baseri , Sajab Mohd Shaiful
Published: September 2022

The degree of maturity of oil palm fresh fruit bunches (FFB) at the time of harvest heavily affects oil production, which is expressed in the oil extraction rate (OER). Oil palm harvests must be harvested at their optimum maturity to maximize oil yield if a rapid, non-intrusive, and accurate method is available to determine their level of maturity. This study demonstrates the potential of implementing Raman spectroscopy for determining the maturity of oil palm fruitlets. A ripeness classification algorithm has been developed utilizing machine learning by classifying the components of organic compounds such as β-carotene, amino acid, etc. as parameters to distinguish the ripeness of fruits. In this study, 47 oil palm fruitlets spectra from three different ripeness levels—under ripe, ripe, and over ripe—were examined. To classify the oil palm fruitlets into three maturity categories, the extracted features were put to the test using 31 machine learning models. It was discovered that the Medium, Weighted KNN, and Trilayered Neural Network classifier has a maximum overall accuracy of 90.9% by using four significant features extracted from the peaks as the predictors. To conclude, the Raman spectroscopy method may offer a precise and efficient means to evaluate the maturity level of oil palm fruitlets.
Geoinformation modeling of rational and efficient use of territories with high investment attractiveness
Authors:   State University of Land Management, Moscow, Golubenko V. A.
Published: September 2022

The proposals on geoinformation modeling, ensuring the rational use of the territories adjacent to Moscow with high investment attractiveness for the placement of multistorey housing construction, are presented.
Justifi cation of economic effi ciency of state investments in capital construction facilities
Authors:   Research analytical center (GAU NIAC), Moscow, Karakozova I. V. ,   National Research Moscow State University of civil engineering (NRU MGSU), Moscow, Prokhorova Yu. S. ,   National Research Moscow State University of civil engineering (NRU MGSU), Moscow
Published: September 2022

Modern investment and construction activities in the Russian Federation in the current paradigm of providing a comfortable environment for human life should provide continuous creation (improvement) of the infrastructure of the territory. Every year, at the state level, investment programs are created, refl ecting the priority areas of construction and financed from the state budget. But the problems existing because of worsening of the quality of the construction production, not always high level of qualification of executors and necessary construction and investment control on all stages of life cycle of the object of capital construction have led to necessity to create the system that stipulates setting limits of budget obligations to cover existing risks and fulfill investment project in due time. To solve the stated problem, the subject area of the study is defined. The subject of the research: the process of formation of the cost of the object of capital construction. The aim of the research: creation of the mechanism for determining the economic effi ciency of public investments, depending on the emerging risks in the process of implementation of the object of capital construction throughout the life cycle. Research objectives: to develop a methodical approach to justify the basic principles and concepts associated with the system of formation of budget limit and reserves of funds for contingencies and costs in determining the cost of construction; to develop proposals for improving Russian legislation. Research methods: analysis and synthesis, graphic modeling, comparative estimates. Research results: a mechanism for determining the economic effi ciency of public investments depending on the coeffi cient of budgetary obligation limits, taking into account the size of the reserve funds for contingency work and costs at the stages of the life cycle of the object of capital construction when implementing the investment project. Scope of application of the results: the introduction of the mechanism for determining the economic efficiency of public investment in the activities of the Ministry of Construction, Housing and Communal Services of the Russian Federation, at the territorial level — in the activities of the executive authorities to plan and provide investment programs.
¿Existe un enfoque "latinoamericano" para la gobernanza migratoria?
Authors: Margheritis Ana , Pedroza Luicy
Published: September 2022

En las dos últimas décadas, se ha producido en América Latina una rápida evolución de los mecanismos de gestión de la migración, con planteamientos innovadores. Más recientemente, han surgido nuevas políticas para abordar tanto los problemas antiguos como otros inesperados y apremiantes, como los repentinos y enormes flujos procedentes de Venezuela y Centroamérica. Centrándonos en dos grandes subregiones —Centroamérica y Sudamérica— y en cinco áreas principales de implicación estatal (inmigración irregular, control de las fronteras, relaciones con la diáspora, gestión multilateral de la movilidad interregional y migración forzada), revisaremos la tesis de que, al inicio del siglo, surgió un enfoque propio latinoamericano. Documentamos la creciente divergencia entre países y subregiones que está llevando a la región a plegarse a las tendencias globales.
Revisión bibliográfica del modelo pedagógico y de evaluación en Escuela Nueva en Colombia
Authors: Cardona Echeverry Catalina
Published: September 2022

Este artículo corresponde a los resultados de la revisión bibliográfica acerca del Modelo Pedagógico y Evaluación en Escuela Nueva en Colombia. Apreciando el interés investigativo por su génesis para el desarrollo de la ruralidad colombiana. Se ubicaron 467 fuentes y se sintetiza la búsqueda en 31 productos investigativos en su mayoría con enfoques cualitativos con diversos tipos, métodos, diseños y técnicas de investigación educativa. Como resultado en coherencia con la sociedad del conocimiento las fuentes de datos reflejan un maestro que participa de una completa innovación educativa, ofreciendo una gama ilimitada de escenarios, donde todos los proyectos pedagógicos son posibles y el docente se reconfigura en un aula multigrado como acompañante para el aprendizaje y el centro de atención en el estudiante. El interés investigativo sobre evaluación se refleja en los últimos años en temas investigativos en prácticas evaluativas, criterios e instrumentos en el modelo pedagógico de Escuela Nueva.
Efeitos da telerreabilitação em paciente com Parkinson: estudo de caso
Authors: Rafaela de Novais Poleto Ânela , Da Silva Oliveira Evelyn , Brandão Pacito Marília , De Souza Fortaleza Marques Ana Claudia , Ferreira Aline Duarte
Published: September 2022

Objetivo. Analisar os efeitos físicos e funcionais da telerreabilitação fisioterapêutica em paciente com doença de Parkinson. Método. Foram convidados a participar do estudo 19 indivíduos com doença de Parkinson. Dos pacientes selecionados, apenas um paciente participou de todas as etapas da pesquisa. As avaliações foram: teste de coordenação (COO), teste de força e endurance de membros superiores (RESISFOR), Pegboard and Ring Test (PBRT) e Mini Best Test. As avaliações foram realizadas antes e após a telerreabilitação por meio de chamada de vídeo, sendo realizadas 12 sessões de 50 minutos, três vezes por semana, com exercícios ativos livres e resistidos para membros superiores e inferiores, equilíbrio estático e dinâmico, passo e passada com pista visual. Resultados. Paciente do sexo masculino, 78 anos, casado, com obesidade grau dois, Hoehn & Yahr grau 3. Os exercícios terapêuticos realizados durante a telerreabilitação levaram a efeitos positivos no domínio força, equilíbrio e capacidade funcional de membros superiores. Conclusão. Podemos concluir que atendimentos através da telerreabilitação, proporcionou melhora na força, coordenação, equilíbrio e capacidade funcional, melhorando assim a qualidade de vida do paciente com Parkinson.