bluuebunny/crossref_metadata_2025 huggingface dataset:
Prospects of Computer-Aided Molecular Design of Coumarins as Ecotoxicologically Safe Plant Protection Agents
Authors: Rastija Vesna , Vrandečić Karolina , Ćosić Jasenka , Kanižai Šarić Gabriella , Majić Ivana , Karnaš Maja
Published: May 2023

Coumarins are secondary plant metabolites widely distributed in higher plants, bacteria, fungi, and sponges. This great structural diversity of these natural compounds and their synthesized derivatives enables their wide range of pharmacological activities, such as antioxidant; antibacterial; antifungal; anti-human immunodeficiency infection; anti-tubercular; and anti-cancer activities, which were very well reviewed previously. There are also many reports about their effectiveness against plant pathogenic pests (phytopathogenic fungi, bacteria, nematodes, and insects). These secondary metabolites protect environmental enemies and competing plants. However, there is still limited literature on coumarins’ practical applications in agriculture, as well as their effects on beneficial populations of soil organisms. This review summarizes recent knowledge about the effects of natural and synthesized coumarins on phytopathogens and beneficial populations of soil organisms. A separate section is dedicated to poorly reviewed computer-aided molecular design (CAMD) methods of agrochemicals. It also reviewed CAMD techniques to develop low-toxicity and environmentally safe pesticides. Despite the many positive effects of coumarins related to plant protection, they do possess properties harmful to the environment and health. These properties are described in the last section. Prediction of coumarin hazardous properties using a quantitative structure-activity relationship (QSAR) approach was also reviewed.
A Study of Magnetic Mill Productivity
Authors: Całus Dariusz , Makarchuk Oleksandr , Domanowski Piotr , Bujnowski Sławomir
Published: May 2023

The paper explores the characteristic indicators of the operation of a magnetic mill. A magnetic mill is a device designed for grinding or mixing substances by interaction with ferromagnetic working elements moving in a rotating magnetic field. The study established the main factors influencing force interaction between the components of such a system. An analysis of the magnetic field inside the working zone of the mill was conducted and the method of calculating the quantitative indicators of this interaction was found. The method responds to changes in the size of these elements, their position in the mill working area and changes in the intensity of the magnetic field. A mathematical model was developed. The model is used for calculating the trajectories of movement of the ferromagnetic elements that are placed in a rotating magnetic field and are confined by space of the working zone of the mill. Indicators directly related to the productivity of the grinding/mixing process were determined following an analysis of the simulation results. Based on comparison of the results obtained by calculation and experimental methods, it was proven that the proposed method is suitable for evaluating the productivity of the grinding/mixing process in a real technological system containing a magnetic mill.
Lithium Battery SOH Estimation Based on Manifold Learning and LightGBM
Authors: Zhang Mei , Yin Jun , Feng Tao
Published: May 2023

In order to accurately identify the state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries, this paper proposes an SOH estimation algorithm for lithium-ion batteries based on stream learning and LightGBM. To address the problem of inconsistent data length, which makes it difficult to establish the state mapping relationship between degraded data and health state, the health factors in this paper are extracted from capacity degradation features, entropy features, and correlation coefficient features. Then, the landmark isometric mapping (L-ISOMAP) manifold learning algorithm is used to dimensionally reduce the input feature set and map the high-dimensional features to the low-dimensional space to solve the dimensional explosion problem. Finally, a LightGBM prediction model is developed to perform SOH prediction on different datasets, and the superiority of the multidimensional model is evaluated. The experimental results show that the goodness-of-fit is 0.98 and above, and the MSE values are below 4 × 10−4. Comparing several prediction models, the LightGBM model has the best performance and better results in several indexes, such as MSE and RMSE. Under different working conditions, the proposed model in this paper has a goodness-of-fit of more than 0.98 in dataset B, which proves that the proposed model has a strong generalization ability.
Wart-Treatment Efficacy Prediction Using a CMA-ES-Based Dendritic Neuron Model
Authors: Song Shuangbao , Zhang Botao , Chen Xingqian , Xu Qiang , Qu Jia
Published: May 2023

Warts are a prevalent condition worldwide, affecting approximately 10% of the global population. In this study, a machine learning method based on a dendritic neuron model is proposed for wart-treatment efficacy prediction. To prevent premature convergence and improve the interpretability of the model training process, an effective heuristic algorithm, i.e., the covariance matrix adaptation evolution strategy (CMA-ES), is incorporated as the training method of the dendritic neuron model. Two common datasets of wart-treatment efficacy, i.e., the cryotherapy dataset and the immunotherapy dataset, are used to verify the effectiveness of the proposed method. The proposed CMA-ES-based dendritic neuron model achieves promising results, with average classification accuracies of 0.9012 and 0.8654 on the two datasets, respectively. The experimental results indicate that the proposed method achieves better or more competitive prediction results than six common machine learning models. In addition, the trained dendritic neuron model can be simplified using a dendritic pruning mechanism. Finally, an effective wart-treatment efficacy prediction method based on a dendritic neuron model, which can provide decision support for physicians, is proposed in this paper.
Inverse Kinematics: An Alternative Solution Approach Applying Metaheuristics
Authors: López-Muñoz Raúl , Portilla-Flores Edgar A. , Corona-Ramírez Leonel G. , Vega-Alvarado Eduardo , Maya-Rodríguez Mario C.
Published: May 2023

The inverse kinematics problem (IKP) is fundamental in robotics, but it gets harder to solve as the complexity of the mechanisms increases. For that reason, several approaches have been applied to solve it, including metaheuristic algorithms. This work presents a proposal for solving the IKP of a doubly articulated kinematic chain by means of a modified differential evolution (DE) algorithm. The novelty of the proposal is both in the modeling of the problem and the modification to the DE for solving it. The modeling is inspired by a technique used in animation software to recreate movements by dividing the complete trajectory in a number of segments. Each segment represents a single optimization problem linked to the IKP as a sequence that is solved by the modified DE where the initial population for each single problem is biased by using the solution of the previous one. The approach produces solutions for positioning the end effector in a specific point within the work space while minimizing the angular displacement between the initial and final poses. The proposal was able to obtain solutions requiring a fewer total execution cycles compared to the usual approach of solving only one optimization problem related to the inverse kinematics. Different trajectories were used to test the solutions generated by the proposed approach, and the set of conditions that must be covered to apply it to solve the IKP of a particular mechanism are presented.
Multi-Point Displacement Synchronous Monitoring Method for Bridges Based on Computer Vision
Authors: Chu Xi , Zhou Zhixiang , Zhu Weizhu , Duan Xin
Published: May 2023

Bridge displacement is an important part of safety evaluations. Currently, bridge displacement monitoring uses only a few measurement points, making it difficult to evaluate safety. To address this problem, we propose a multi-point displacement synchronous monitoring method. The structural surface has abundant natural texture features, so we use the feature points of the structural surface as the displacement measurement points and propose a feature point displacement calculation method. Furthermore, we conduct experiments on a beam in the laboratory and obtain the beam’s multi-point displacement monitoring results. The monitoring results show that the displacement of some feature points is mismatched. We propose the use of the structural deflection curve to eliminate the feature point displacement mismatches. This method uses the maximum rotation angle of the deflection curve to eliminate displacement mismatches. The results indicate that it is effective to eliminate displacement mismatches in simple structures, such as simply supported beams. Finally, we obtain the test beam’s multi-point displacement synchronous monitoring results. Compared with the 3D laser scanning measurement method, the maximum error of the monitoring results is 8.70%. Research shows that the main reason for the monitoring error is image noise, and the noise interference problem due to its application in practical bridges requires further investigation. Compared with traditional displacement monitoring, this method has significant economic, efficiency, and data integrity advantages. The method has application prospects for multi-point displacement monitoring of simple structures, such as simply supported beams.
Study of a Layered Plate Girder Element of Composite Materials and Its Applications
Authors: Han Yanqun , Chen Li , Hou Wenqi
Published: May 2023

This study aims to provide an effective method to study the behavior of a steel–concrete composite deck. First, the structural characteristics of the composite deck and the challenges arising in the computational analysis of the structure using general software are described. Then, an LPGE element that combines the plate element and the girder element into one element to conveniently construct the model with high computation efficiency is proposed. Based on the principle of multivariate field function, the constraint matrix for the plate and girder and the stiffness matrix for the LPGE are derived. The LPGE method is used to study the behavior of the composite deck through the computation of a steel truss arch bridge. The computation results are compared with the results obtained in ANSYS and the test results to verify the correctness and effectiveness of the LPGE method. The results of the paper offer references for the analysis of steel–concrete composite decks.
Numerical Simulation of Catalytic Methane Combustion in Al2O3 Directional Nanotubes Modified by Pt and Pd Catalyst
Authors: Shen Bin , Zhou Tianshun , Liu Xinlei , Qin Xianli , Li Wei
Published: May 2023

“Blind holes” are the main reasons for the reduced performance of microgas sensor carriers. To improve the “blind hole” of catalytic combustion methane sensors and therefore, their thermal stability, this study presents a numerical simulation of the catalytic combustion in an Al2O3− oriented ceramic array involving porous microthermal plates. A three-visualization model of the sensor is established using the FLUENT software, and the simulation results are systematically analyzed based on the dynamics and thermodynamic mechanism of the microgas sensor. The results show that the regularity of the surface reaction presents a circular distribution, with the center line of the channel serving as the axis symmetry. The total reaction velocity in the array hole increases gradually from the inlet to the outlet. The flow velocity at the inlet should be controlled at more than 1 × 10−8 m/s, which is more accurate compared with the concept of “uniform velocity” in previous studies. The optimum pore size at the inlet should be 150 nm, and the inner pore size of the wall should be slightly higher than 300 nm, which is a more careful division compared with previous pore-size studies. The efficient reaction position is from the inlet to the quarter of the hole. The simulation results make up for the deficiencies in the analysis of the process parameters of the methane sensor carrier array hole and the internal reaction change process, as well as provide innovative comments on the sensor structure design. Through digital simulations, the limitations associated with the experiments can be avoided, the theoretical study can be improved, theoretical support can be provided for experiments related to the improvement of thermal stability, the predictability of experiments can be improved, and the feasibility of the research proposal can be verified. These steps are important for the improvement of the “blind hole” problem of catalytic combustion methane sensors.
Structural Analysis and Optimization of Urban Gas Pressure Regulator Based on Thermo-Hydro-Mechanical Coupling
Authors: Cui Yue , Lin Nan , Yuan Zhong , Lan Huiqing , Wang Junqiang , Wang Huigang
Published: May 2023

As a core component in the gas transmission process, the internal wall surface of a gas pressure regulator is prone to failure due to long-term exposure to a high-pressure gas environment, resulting in poor reliability of the regulator. Thus, a thermo-hydro-mechanical coupling model for the FL gas pressure regulator is established in this paper, and the thermo-hydro-mechanical coupling results are verified by engineering data. The effect of valve opening on the parameters (temperature, deformation, and stress) of the gas pressure regulator is studied in detail through simulation. The results show that the stress is greater at the sleeve, valve bore, and outlet valve seat wall under the opening of 20% of the regulator. Finally, the response surface method is used to optimize the regulator to obtain a good fit and high predictive ability of the response surface equation. The optimal parameters for the gas pressure regulator are as follows: the wall thickness of the sleeve is 7.25 mm, the diameter of the valve bore is 25 mm, and the wall thickness of the outlet seat is 31.05 mm. The maximum equivalent stress with this combination of parameters is 135.62 MPa.
Estimating Liquefaction Susceptibility Using Machine Learning Algorithms with a Case of Metro Manila, Philippines
Authors: Galupino Joenel , Dungca Jonathan
Published: May 2023

Soil liquefaction is a phenomenon that can occur when soil loses strength and behaves like a liquid during an earthquake. A site investigation is essential for determining a site’s susceptibility to liquefaction, and these investigations frequently generate project-specific geotechnical reports. However, many of these reports are frequently stored unused after construction projects are completed. This study suggests that when these unused reports are consolidated and integrated, they can provide valuable information for identifying potential challenges, such as liquefaction. The study evaluates the susceptibility of liquefaction by considering several geotechnical factors modeled by machine learning algorithms. The study estimated site-specific characteristics, such as ground elevation, groundwater table elevation, SPT N-value, soil type, and fines content. Using a calibrated model represented by an equation, the investigation determined several soil properties, including the unit weight and peak ground acceleration (PGA). The study estimated PGA using a linear model, which revealed a significant positive correlation (R2 = 0.89) between PGA, earthquake magnitude, and distance from the seismic source. On the Marikina West Valley Fault, the study also assessed the liquefaction hazard for an anticipated 7.5 M and delineated a map that was validated by prior studies.
On Hijacking LED Walls
Authors: Roman Mathilde
Published: May 2023

In recent years, the LED walls originally used in outdoor spaces by advertising companies to extend the consumption of images in our daily life have been appropriated by artists and installed in gallery spaces. When viewed nearby or when walking around them, LED walls become in some way dysfunctional: The images fade, points and color distortions appear, and the spectacle of the machine interruputs our habitual viewing patterns. This article focuses on three recent works which disrupt immersive viewing regimes through what I call “hijacking” advanced LED technology. Lucy Raven (Tucson, AZ, USA), Demolition of a Wall (Album 2), 2022. Eija-Liisa Ahtila (Helsinki, Finland), Potentiality for Love, 2018. Marco Fusinato (Sidney, Australia), Desastres, Australian Pavilion, Venice Biennale, 2022. These three artists use the sculptural and spectacular effect of freestanding LED walls to call attention to our habitual capitalist relation to LED technology. Through performative or narrative pieces, these artists deploy poetic and artistic effects to explore the politics of technological immersion in capitalist societies.
Climate Potential for Apple Growing in Norway—Part 2: Assessment of Suitability of Heat Conditions under Future Climate Change
Authors: Vujadinović Mandić Mirjam , Vuković Vimić Ana , Fotirić Akšić Milica , Meland Mekjell
Published: May 2023

The commercial apple production in Norway is limited to the small regions along the fjords in the southwest part of the country and around lakes or near the sea in the southeast with favorable climate. Due to the rapid rate of climate change over the recent decades, it is expected that suitable heat conditions for apple growing will expand to the areas that were previously too cold. This study analyses the heat suitability of future climate (2021–2100) under the RCP8.5 scenario for 6 common apple varieties in Norway: Discovery, Gravenstein, Summerred, Aroma, Rubinstep and Elstar. Previously established heat requirement criteria (based on the temperature threshold for the full blooming and growing degree days sum between the full bloom and harvest) are applied to the temperature outputs of the regional climate models downscaled to 1 km resolution. The assessment indicates that as temperature rises, heat conditions suitable for cultivation of all 6 apple varieties will expand. According to the ensemble median value, areas with the favorable heat conditions for growing at least one of the considered apple varieties will increase 25 times in the period 2021–2040 and 60 times in the period 2041–2060, compared to the referent period 1971–2000. At the same time, areas suitable for all 6 apple varieties will increase 3 times in the first, and 3.8 times in the latter period. The favorable areas will advance from south and southeast northwards and inland in the eastern region, along the west and northwestern coastline towards higher latitudes, and along continental parts of fjords. The fastest expansion of heat suitable conditions is expected for Discovery and Gravenstein. The findings of this study are relevant for zoning apple production future potential and for strategical planning of climate change adaptation measures within the sector. Weather-related risks, such as risks from winter low temperatures, spring frost, drought and extreme precipitation were not considered.
Response Time of Vegetation to Drought in Weihe River Basin, China
Authors: Fan Jingjing , Wei Shibo , Liu Guanpeng , Zhou Xiong , Li Yunyun , Wu Chenyu , Xu Fanfan
Published: May 2023

Frequent droughts may have negative influences on the ecosystem (i.e., terrestrial vegetation) under a warming climate condition. In this study, the linear regression method was first used to analyze trends in vegetation change (normalized difference vegetation index (NDVI)) and drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)). The Pearson Correlation analysis was then used to quantify drought impacts on terrestrial vegetation in the Weihe River Basin (WRB); in particular, the response time of vegetation to multiple time scales of drought (RTVD) in the WRB was also investigated. The trend analysis results indicated that 89.77% of the area of the basin showed a significant increasing trend in NDVI from 2000 to 2019. There were also significant variations in NDVI during the year, with the highest rate in June (0.01) and the lowest rate in January (0.002). From 2000 to 2019, SPI and SPEI at different time scales in the WRB showed an overall increasing trend, which indicated that the drought was alleviated. The results of correlation analysis showed that the response time of vegetation to drought in the WRB from 2000 to 2019 was significantly spatially heterogeneous. For NDVI to SPEI, the response time of 12 months was widely distributed in the north; however, the response time of 24 months was mainly distributed in the middle basin. The response time of NDVI to SPI was short and was mainly concentrated at 3 and 6 months; in detail, the response time of 3 months was mainly distributed in the east, while a response time of 6 months was widely distributed in the west. In autumn and winter, the response time of NDVI to SPEI was longer (12 and 24 months), while the response time of NDVI to SPI was shorter (3 months). From the maximum correlation coefficient, the response of grassland to drought (SPEI and SPI) at different time scales (i.e., 6, 12, and 24 months) was higher than that of cultivated land, forestland, and artificial surface. The results may help improve our understanding of the impacts of climatic changes on vegetation cover.
Projected Future Changes in Extreme Climate Indices over Central Asia Using RegCM4.3.5
Authors: Ozturk Tugba
Published: May 2023

This work projected future extreme climate indices’ changes over Central Asia (The Coordinated Regional Climate Downscaling Experiment—CORDEX Region 8). Changes were calculated for 2071–2100 relative to 1971–2000. Climate simulations were obtained by downscaling the RegCM4.3.5 to 50 km resolution under RCP4.5 and 8.5 with HadGEM2-ES and MPI-ESM-MR. The results indicate that the Central Asian domain will experience warmer and more extreme temperatures with increasing radiative forcing. The annual lowest value of minimum daily temperature was simulated to increase remarkably, up to 8 degrees, especially in high latitudes, with a more than 12 degree increase projected over Siberia. A strong growth in the percentage of warm nights and an increase in the days of warm spells for the whole region, with a decrease in cold spell duration, are anticipated. Model results show an expected reduction of up to 30% in precipitation totals over the domain, except for the increased precipitation over Siberia, the Himalayas, and Tibetan Plateau. Extreme precipitation events are projected to have an increase of 20% over the whole domain, with an 80% increase over high topographical areas.
Quantifying the Spatio-Temporal Pattern Differences in Climate Change before and after the Turning Year in Southwest China over the Past 120 Years
Authors: Wang Meng , Wang Shouyan , An Zhengfeng
Published: May 2023

In conjunction with Earth’s ongoing global warming, the Southwest China (SWC) region has become a fascinating case study on the control of local climate change. Moreover, an entire period of climate change may partially mask the patterns in some stages. Therefore, in this research, we investigated the spatial patterns of the significant turning years of climatic factor change, and determined the heterogeneity of the spatial patterns of climate change before and after the significant turning years. We used the long time-series of the CRU datasets (CRU_TS4.02) from 1901 to 2017 with a piecewise linear regression model to explore the significant turning-year distribution characteristics of inter-annual and inter-seasonal climate factor changes, and further describe and quantize the differences in the spatio-temporal patterns of climate factors before and after the significant turning years on the grid scale in SWC. Overall, the trends in temperature and precipitation factors in SWC were segmented over the last 120 years, with significant turning years with different regional and stepwise characteristics. In terms of timing, temperature and precipitation factors changed significantly in 1954 and 1928, respectively, and overall temporal variability (0.04 °C/(10 a) (p < 0.05), −0.48 mm/(10 a)) masked the magnitude or direction of variability (0.13 °C/(10 a) and 0.16 °C/(10 a) both at the level of p < 0.05 before the turning year, 19.56 mm/(10 a) (p < 0.05) and 1.19 mm/(10 a) after the turning year) around the watershed years. Spatially, the significant turning years were concentrated in the periods 1940–1993 (temperature) and 1910–2008 (precipitation), and the distribution pattern of the turning years was patchy and concentrated. The turning years of temperature factors were gradually delayed from east to west, and the variability of climate factors before and after the turning years exhibited significant shifts in location (e.g., temperature decreased from southeast to northwest before the turning year and increased after the turning year). After the turning year, the warming variability of the temperature factor increased, while the increasing variability of the precipitation factor decreased. Further integrated analysis revealed that the increase in variability of the climate factor after the turning year was mainly due to the increase in winter and autumn variability (0.05 °C/(10 a), 7.30 mm/(10 a) in autumn; and 0.12 °C/(10 a), 1.97 mm/(10 a) in winter). To the extent that this study provides a necessary academic foundation for efficiently unveiling the spatio-temporal variability properties of climate factors against the background of modern global climate change, more attention should be paid to the location and phase of the study.
Study of Time-Frequency Domain Characteristics of the Total Column Ozone in China Based on Wavelet Analysis
Authors: Tang Chaoli , Zhu Fangzheng , Wei Yuanyuan , Tian Xiaomin , Yang Jie , Zhao Fengmei
Published: May 2023

Ozone is a very important trace gas in the atmosphere, it is like a “double-edged sword”. Because the ozone in the stratosphere can effectively help the earth’s organisms to avoid the sun’s ultraviolet radiation damage, the ozone near the ground causes pollution. Therefore, it is essential to explore the time-frequency domain variation characteristics of total column ozone and have a better understanding of its cyclic variation. In this paper, based on the monthly scale dataset of total column ozone (TCO) (September 2002 to February 2023) from Atmospheric Infrared Sounder (AIRS) carried by NASA’s Aqua satellite, linear regression, coefficient of variation, Mann-Kendall (M-K) mutation tests, wavelet analysis, and empirical orthogonal function decomposition (EOF) analysis were used to analyze the variation characteristics of the TCO in China from the perspectives of time domain, frequency domain, and spatial characteristics. Finally, this study predicted the future of TCO data based on the seasonal autoregressive integrated moving average (SARIMA) model in the time series algorithm. The results showed the following: (1) From 2003 to 2022, the TCO in China showed a slight downward trend, with an average annual change rate of −0.29 DU/a; the coefficient of variation analysis found that TCO had the smallest intra-year fluctuations in 2008 and the largest intra-year fluctuations in 2005. (2) Using the M-K mutation test, it was found that there was a mutation point in the total amount of column ozone in 2016. (3) Using wavelet analysis to analyze the frequency domain characteristics of the TCO, it was observed that TCO variation in China had a combination of 14-year, 6-year, and 4-year main cycles, where 14 years is the first main cycle with a 10-year cycle and 6 years is the second main cycle with a 4-year cycle. (4) The spatial distribution characteristics of the TCO in China were significantly different in each region, showing a distribution characteristic of being high in the northeast and low in the southwest. (5) Based on the EOF analysis of the TCO in China, it was found that the variance contribution rate of the first mode was as high as 52.85%, and its spatial distribution of eigenvectors showed a “-” distribution. Combined with the trend analysis of the time coefficient, this showed that the TCO in China has declined in the past 20 years. (6) The SARIMA model with the best parameters of (1, 1, 2) × (0, 1, 2, 12) based on the training on the TCO data was used for prediction, and the final model error rate was calculated as 1.34% using the mean absolute percentage error (MAPE) index, indicating a good model fit.
Synoptic Weather Patterns and Atmospheric Circulation Types of PM2.5 Pollution Periods in the Beijing-Tianjin-Hebei Region
Authors: Gu Shijie , Wu Shuai , Yang Luoqi , Hu Yincui , Tian Bing , Yu Yan , Ma Ning , Ji Pengsong , Zhang Bo
Published: May 2023

The variation of PM2.5 concentration in the atmosphere is closely related to the variation in weather patterns. The change in weather pattern is accompanied by the corresponding change in atmospheric circulation characteristics. It is necessary to explore the relationship between PM2.5 concentration changes and atmospheric circulation characteristics during pollution periods. In this paper, Lamb-Jenkinson objective classification method is applied to classify daily atmospheric circulation. The pollution periods are calculated and the atmospheric circulation variation rule is obtained. Combined with the physical parameter field (humidity, potential temperature, and potential height), a typical pollution period is analyzed. Additionally, the influence of atmospheric circulation type variation on PM2.5 concentration and transport channel during the pollution period was obtained. The results show that atmospheric circulation types in the study period are dominated by A-type (anticyclonic), N-type (north), and NE-type (northeast), indicating obvious seasonal differences, and the proportion of C-type (cyclonic) circulation was increased significantly in summer. During the pollution period analysis from 2 to 4 January 2019, atmospheric circulation type changed from N-type to NE-type (northeast), the wind direction changed from southeast wind, and the change of pressure gradient was consistent with the trend of the wind field. Moreover, the physical parameter field assisted in verifying the process of the pollution period from the conducive to the accumulation of PM2.5 to conducive to the deposition of pollutants and external transport. The research results would provide theoretical support for PM2.5 prediction during the pollution period and also supply a theoretical and technical basis for the establishment of ecological compensation standards for air pollution and atmospheric environmental control.
Bombieri-Type Inequalities and Their Applications in Semi-Hilbert Spaces
Authors: Altwaijry Najla , Dragomir Silvestru Sever , Feki Kais
Published: May 2023

This paper presents new results related to Bombieri’s generalization of Bessel’s inequality in a semi-inner product space induced by a positive semidefinite operator A. Specifically, we establish new inequalities that generalize the classical Bessel inequality and extend previous results in this area. Furthermore, our findings have applications to the study of operators on positive semidefinite inner product spaces, also known as semi-Hilbert spaces, and contribute to a deeper understanding of their properties and applications. Our work has implications for various fields, including functional analysis and operator theory.
Well-Posedness Scheme for Coupled Fixed-Point Problems Using Generalized Contractions
Authors: Sagheer Dur-e-Shehwar , Urooj Isma , Batul Samina , Aloqaily Ahmad , Mlaiki Nabil
Published: May 2023

In this study, we present a more general class of rational-type contractions in the domain of Hilbert spaces, along with a novel coupled implicit relation. We develop several intriguing results and consequences for the existence of unique coupled fixed points. Further, we investigate a necessary condition that guarantees the well-posedness of a coupled fixed-point problem of self-mappings in Hilbert spaces. Some new observations proposed in this research broaden and extend previously published results in the literature.
Application of Ant Colony Optimization Algorithm Based on Triangle Inequality Principle and Partition Method Strategy in Robot Path Planning
Authors: Wu Shuai , Li Qingxia , Wei Wenhong
Published: May 2023

Path planning is an important area of mobile robot research, and the ant colony optimization algorithm is essential for analyzing path planning. However, the current ant colony optimization algorithm applied to the path planning of mobile robots still has some limitations, including early blind search, slow convergence speed, and more turns. To overcome these problems, an improved ant colony optimization algorithm is proposed in this paper. In the improved algorithm, we introduce the idea of triangle inequality and a pseudo-random state transfer strategy to enhance the guidance of target points and improve the search efficiency and quality of the algorithm. In addition, we propose a pheromone update strategy based on the partition method with upper and lower limits on the pheromone concentration. This can not only improve the global search capability and convergence speed of the algorithm but also avoid the premature and stagnation phenomenon of the algorithm during the search. To prevent the ants from getting into a deadlock state, we introduce a backtracking mechanism to enable the ants to explore the solution space better. Finally, to verify the effectiveness of the proposed algorithm, the algorithm is compared with 11 existing methods for solving the robot path planning problem, including several ACO variants and two commonly used algorithms (A* algorithm and Dijkstra algorithm), and the experimental results show that the improved ACO algorithm can plan paths with faster convergence, shorter path lengths, and higher smoothness. Specifically, the algorithm produces the shortest path length with a standard deviation of zero while ensuring the most rapid convergence and the highest smoothness in the case of the shortest path in four different grid environments. These experimental results demonstrate the effectiveness of the proposed algorithm in path planning.
Li3BO3-Li3PO4 Composites for Efficient Buffer Layer of Sulphide-Based All-Solid-State Batteries
Authors: Ji Yong Jun , Noh Sungwoo , Seong Ju Yeong , Lee Sangheon , Park Yong Joon
Published: May 2023

All-solid-state batteries (ASSBs) based on sulphide electrolytes are promising next-generation energy storage systems because they are expected to have improved safety, increased volumetric energy density, and a wide operating temperature range. However, side reactions at the cathode/electrolyte interface deteriorate the electrochemical performance and limit the commercialization of ASSBs. Surface coating of the cathode is an efficient approach for overcoming this issue. In this study, new Li3BO3 (LBO)-Li3PO4 (LPO) composites were applied as coating materials for high-Ni cathodes (NCM). PO4-based materials (such as LPO) have been used as coating layers because of their good chemical stability in sulphide electrolytes. However, the ionic conductivity of LPO is slightly insufficient compared to those of generally used ternary oxides. The addition of LBO could compensate for the low ionic conductivity of LPO and may provide better protection against sulphide electrolytes owing to the effect of LBO, which has been used as a good coating material. As expected, the LBO-LPO composites (LBPO) NCM exhibited superior discharge capacity, rate capability, and cyclic performance compared to the pristine and LPO-coated NCMs. X-ray photoelectron spectroscopy (XPS) and energy-dispersive X-ray spectroscopy (EDS) analyses confirmed that the LBPO coating on the cathodes successfully suppressed the byproduct formation and an undesirable interfacial layer, which are attributed to interfacial side reactions. This result clearly shows the potential of the LBPO coating as an excellent buffer layer to stabilise the oxide cathode/sulphide electrolyte interface.
An Inverse Method to Determine Mechanical Parameters of Porcine Vitreous Bodies Based on the Indentation Test
Authors: Zu Haicheng , Zhang Kunya , Zhang Haixia , Qian Xiuqing
Published: May 2023

The vitreous body keeps the lens and retina in place and protects these tissues from physical insults. Existing studies have reported that the mechanical properties of vitreous body varied after liquefaction, suggesting mechanical properties could be effective parameters to identify vitreous liquefaction process. Thus, in this work, we aimed to propose a method to determine the mechanical properties of vitreous bodies. Fresh porcine eyes were divided into three groups, including the untreated group, the 24 h liquefaction group and the 48 h liquefaction group, which was injected collagenase and then kept for 24 h or 48 h. The indentation tests were carried out on the vitreous body in its natural location while the posterior segment of the eye was fixed in the container. A finite element model of a specimen undertaking indentation was constructed to simulate the indentation test with surface tension of vitreous body considered. Using the inverse method, the mechanical parameters of the vitreous body and the surface tension coefficient were determined. For the same parameter, values were highest in the untreated group, followed by the 24 h liquefaction group and the lowest in the 48 h liquefaction group. For C10 in the neo-Hookean model, the significant differences were found between the untreated group and liquefaction groups. This work quantified vitreous body mechanical properties successfully using inverse method, which provides a new method for identifying vitreous liquefactions related studies.
Imagined Speech Classification Using EEG and Deep Learning
Authors: Abdulghani Mokhles M. , Walters Wilbur L. , Abed Khalid H.
Published: May 2023

In this paper, we propose an imagined speech-based brain wave pattern recognition using deep learning. Multiple features were extracted concurrently from eight-channel electroencephalography (EEG) signals. To obtain classifiable EEG data with fewer sensors, we placed the EEG sensors on carefully selected spots on the scalp. To decrease the dimensions and complexity of the EEG dataset and to avoid overfitting during the deep learning algorithm, we utilized the wavelet scattering transformation. A low-cost 8-channel EEG headset was used with MATLAB 2023a to acquire the EEG data. The long-short term memory recurrent neural network (LSTM-RNN) was used to decode the identified EEG signals into four audio commands: up, down, left, and right. Wavelet scattering transformation was applied to extract the most stable features by passing the EEG dataset through a series of filtration processes. Filtration was implemented for each individual command in the EEG datasets. The proposed imagined speech-based brain wave pattern recognition approach achieved a 92.50% overall classification accuracy. This accuracy is promising for designing a trustworthy imagined speech-based brain–computer interface (BCI) future real-time systems. For better evaluation of the classification performance, other metrics were considered, and we obtained 92.74%, 92.50%, and 92.62% for precision, recall, and F1-score, respectively.
Inhibition of SARS-CoV-2 Spike Protein Pseudotyped Virus Infection Using ACE2-Tethered Micro/Nanoparticles
Authors: Alkhaldi Soha Y. , Peng Ian , Peng Ching-An
Published: May 2023

Coronavirus disease 2019 (COVID-19) has caused a global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The viral infection is reliant upon the binding between angiotensin-converting enzyme 2 receptor (ACE2) and spike protein (S). Therefore, ACE2 is a key receptor for SARS-CoV-2 to infect the host. Nonetheless, as SARS-CoV-2 is constantly mutating into new variants that cause high infection rates, the development of prophylactic and therapeutic approaches remains a necessity to continue fighting mutated SARS-CoV-2 variants. In this study, ACE2-streptavidin fusion proteins expressed by recombinant DNA technology were anchored on biotinylated fluorescent polystyrene particles of various sizes ranging from 0.15 to 5 µm. The ACE2-tethered micro/nanoparticles were shown to prevent spike protein pseudotyped lentivirus entry into ACE2-expressing HEK293T cells. Compared to ACE2 in soluble form, micro-sized particles (2 and 5 µm) immobilized with ACE2 interfered more efficiently with viral attachment, entry, and the ensuing infection. Our results showed that particles functionalized with ACE2 could be used as efficient decoys to block the infection of SARS-CoV-2 strains.
Monocular 3D Human Pose Markerless Systems for Gait Assessment
Authors: Zhu Xuqi , Boukhennoufa Issam , Liew Bernard , Gao Cong , Yu Wangyang , McDonald-Maier Klaus D. , Zhai Xiaojun
Published: May 2023

Gait analysis plays an important role in the fields of healthcare and sports sciences. Conventional gait analysis relies on costly equipment such as optical motion capture cameras and wearable sensors, some of which require trained assessors for data collection and processing. With the recent developments in computer vision and deep neural networks, using monocular RGB cameras for 3D human pose estimation has shown tremendous promise as a cost-effective and efficient solution for clinical gait analysis. In this paper, a markerless human pose technique is developed using motion captured by a consumer monocular camera (800 × 600 pixels and 30 FPS) for clinical gait analysis. The experimental results have shown that the proposed post-processing algorithm significantly improved the original human pose detection model (BlazePose)’s prediction performance compared to the gold-standard gait signals by 10.7% using the MoVi dataset. In addition, the predicted T2 score has an excellent correlation with ground truth (r = 0.99 and y = 0.94x + 0.01 regression line), which supports that our approach can be a potential alternative to the conventional marker-based solution to assist the clinical gait assessment.
Surgical Phase Recognition in Inguinal Hernia Repair—AI-Based Confirmatory Baseline and Exploration of Competitive Models
Authors: Zang Chengbo , Turkcan Mehmet Kerem , Narasimhan Sanjeev , Cao Yuqing , Yarali Kaan , Xiang Zixuan , Szot Skyler , Ahmad Feroz , Choksi Sarah , Bitner Daniel P. , Filicori Filippo , Kostic Zoran
Published: May 2023

Video-recorded robotic-assisted surgeries allow the use of automated computer vision and artificial intelligence/deep learning methods for quality assessment and workflow analysis in surgical phase recognition. We considered a dataset of 209 videos of robotic-assisted laparoscopic inguinal hernia repair (RALIHR) collected from 8 surgeons, defined rigorous ground-truth annotation rules, then pre-processed and annotated the videos. We deployed seven deep learning models to establish the baseline accuracy for surgical phase recognition and explored four advanced architectures. For rapid execution of the studies, we initially engaged three dozen MS-level engineering students in a competitive classroom setting, followed by focused research. We unified the data processing pipeline in a confirmatory study, and explored a number of scenarios which differ in how the DL networks were trained and evaluated. For the scenario with 21 validation videos of all surgeons, the Video Swin Transformer model achieved ~0.85 validation accuracy, and the Perceiver IO model achieved ~0.84. Our studies affirm the necessity of close collaborative research between medical experts and engineers for developing automated surgical phase recognition models deployable in clinical settings.
Insulin-Degrading Enzyme Interacts with Mitochondrial Ribosomes and Respiratory Chain Proteins
Authors: Yilmaz Ayse , Guerrera Chiara , Waeckel-Énée Emmanuelle , Lipecka Joanna , Bertocci Barbara , van Endert Peter
Published: May 2023

Insulin-degrading enzyme (IDE) is a highly conserved metalloprotease that is mainly localized in the cytosol. Although IDE can degrade insulin and some other low molecular weight substrates efficiently, its ubiquitous expression suggests additional functions supported by experimental findings, such as a role in stress responses and cellular protein homeostasis. The translation of a long full-length IDE transcript has been reported to result in targeting to mitochondria, but the role of IDE in this compartment is unknown. To obtain initial leads on the function of IDE in mitochondria, we used a proximity biotinylation approach to identify proteins interacting with wild-type and protease-dead IDE targeted to the mitochondrial matrix. We find that IDE interacts with multiple mitochondrial ribosomal proteins as well as with proteins involved in the synthesis and assembly of mitochondrial complex I and IV. The mitochondrial interactomes of wild type and mutant IDE are highly similar and do not reveal any likely proteolytic IDE substrates. We speculate that IDE could adopt similar additional non-proteolytic functions in mitochondria as in the cytosol, acting as a chaperone and contributing to protein homeostasis and stress responses.
CC-Chemokine Receptor-2 Expression in Osteoblasts Contributes to Cartilage and Bone Damage during Post-Traumatic Osteoarthritis
Authors: Willcockson Helen , Ozkan Huseyin , Valdés-Fernández José , Arbeeva Liubov , Mucahit Esra , Musawwir Layla , Hooper Lola B. , Granero-Moltó Froilán , Prósper Felipe , Longobardi Lara
Published: May 2023

In osteoarthritis (OA), bone changes are radiological hallmarks and are considered important for disease progression. The C-C chemokine receptor-2 (CCR2) has been shown to play an important role in bone physiology. In this study, we investigated whether Ccr2 osteoblast-specific inactivation at different times during post-traumatic OA (PTOA) progression improves joint structures, bone parameters, and pain. We used a tamoxifen-inducible Ccr2 inactivation in Collagen1α-expressing cells to obtain osteoblasts lacking Ccr2 (CCR2-Col1αKO). We stimulated PTOA changes in CCR2-Col1αKO and CCR2+/+ mice using the destabilization of the meniscus model (DMM), inducing recombination before or after DMM (early- vs. late-inactivation). Joint damage was evaluated at two, four, eight, and twelve weeks post-DMM using multiple scores: articular-cartilage structure (ACS), Safranin-O, histomorphometry, osteophyte size/maturity, subchondral bone thickness and synovial hyperplasia. Spontaneous and evoked pain were assessed for up to 20 weeks. We found that early osteoblast-Ccr2 inactivation delayed articular cartilage damage and matrix degeneration compared to CCR2+/+, as well as DMM-induced bone thickness. Osteophyte formation and maturation were only minimally affected. Late Collagen1α-Ccr2 deletion led to less evident improvements. Osteoblast-Ccr2 deletion also improved static measures of pain, while evoked pain did not change. Our study demonstrates that Ccr2 expression in osteoblasts contributes to PTOA disease progression and pain by affecting both cartilage and bone tissues.
Targeting Neutrophil β2-Integrins: A Review of Relevant Resources, Tools, and Methods
Authors: Conley Haleigh E. , Sheats M. Katie
Published: May 2023

Neutrophils are important innate immune cells that respond during inflammation and infection. These migratory cells utilize β2-integrin cell surface receptors to move out of the vasculature into inflamed tissues and to perform various anti-inflammatory responses. Although critical for fighting off infection, neutrophil responses can also become dysregulated and contribute to disease pathophysiology. In order to limit neutrophil-mediated damage, investigators have focused on β2-integrins as potential therapeutic targets, but so far these strategies have failed in clinical trials. As the field continues to move forward, a better understanding of β2-integrin function and signaling will aid the design of future therapeutics. Here, we provide a detailed review of resources, tools, experimental methods, and in vivo models that have been and will continue to be utilized to investigate the vitally important cell surface receptors, neutrophil β2-integrins.