bluuebunny/crossref_metadata_2025 huggingface dataset:
Clinical Outcomes of Third-Generation Cephalosporin Definitive Therapy for Bloodstream Infections Due to Enterobacterales with Potential AmpC Induction: A Single-Center Retrospective Study
Authors: Vossius Gilles , Meex Cécile , Moerman Filip , Thys Marie , Ernst Marie , Bourgeois Marie-Eve , Wagner Léa , Delahaye Thibaut , Darcis Gilles
Published: September 2023

The recommended therapy for severe infections caused by AmpC-inducible Enterobacterales (AmpC-E) typically involves cefepime or carbapenems. In an era of emerging resistance to these antimicrobials, we aim to assess the impact of third-generation cephalosporins (3GCs) vs. alternative antibiotics on clinical outcomes in bloodstream infections (BSIs) due to AmpC-E. We retrospectively included hospitalized adult patients with BSIs caused by 3GC-susceptible AmpC-E between 2012 and 2022, comparing the outcomes of 3GC and non-3GC definitive therapies. The primary outcome was overall treatment failure (OTF), encompassing 90-day all-cause mortality, 90-day reinfection, and 90-day readmission. Secondary outcomes comprised components of the OTF, in-hospital all-cause mortality, and length-of-stay. Within a total cohort of 353 patients, OTF occurred in 46.5% and 41.5% in the 3GC- and non-3GC-therapy groups, respectively (p = 0.36). The 3GC-therapy group exhibited a longer length-of-stay (38 vs. 21 days, p = 0.0003) and higher in-hospital mortality (23.3% vs. 13.4%, p = 0.019). However, the 90-day mortality, 90-day reinfection, and 90-day readmission were comparable between the therapy groups. Subgroup analyses involving high-risk AmpC-E and 3GC vs. standard-of-care yielded similar conclusions. Overall, our findings suggest that 3GC definitive therapy may not result in poorer clinical outcomes for the treatment of BSIs caused by AmpC-E.
Monitoring Monkeypox: Safeguarding Global Health through Rapid Response and Global Surveillance
Authors: Giovanetti Marta , Cella Eleonora , Moretti Sonia , Scarpa Fabio , Ciccozzi Alessandra , Slavov Svetoslav Nanev , Benedetti Francesca , Zella Davide , Ceccarelli Giancarlo , Ciccozzi Massimo , Borsetti Alessandra
Published: September 2023

Monkeypox, a viral zoonotic disease, has emerged as a significant global threat in recent years. This review focuses on the importance of global monitoring and rapid response to monkeypox outbreaks. The unpredictable nature of monkeypox transmissions, its potential for human-to-human spread, and its high morbidity rate underscore the necessity for proactive surveillance systems. By analyzing the existing literature, including recent outbreaks, this review highlights the critical role of global surveillance in detecting, containing, and preventing the further spread of monkeypox. It also emphasizes the need for enhanced international collaboration, data sharing, and real-time information exchange to effectively respond to monkeypox outbreaks as a global health concern. Furthermore, this review discusses the challenges and opportunities of implementing robust surveillance strategies, including the use of advanced diagnostic tools and technologies. Ultimately, these findings underscore the urgency of establishing a comprehensive global monitoring framework for monkeypox, enabling early detection, prompt response, and effective control measures to protect public health worldwide.
An Optimized Error Compensation Method for Phase Measurement Profilometry
Authors: Guo Benjun , Xu Yuanping , Zhang Chaolong , Tang Jianfeng , Tang Dan , Kong Chao , Jin Jin
Published: September 2023

Phase measurement profilometry (PMP) is primarily employed to analyze the morphology of a functional surface with precision. Historically, one of the most complex and persistent challenges in PMP has been reducing errors stemming from inconsistent indicators at the edges of a surface. In response to this challenge, we propose an optimized error compensation methodology specifically designed to handle edge artefacts. This methodology introduces the Hilbert transform and object surface albedo as tools to detect the edges of the artefact region that need to be compensated. Moreover, we analyze the characteristics of the sinusoidal fringe waveform propagation direction and investigate the reconstruction results of the fringe vertical to the current directions to compensate for edge artefacts. The experimental results for various objects show that the optimized approach can compensate for edge artefacts by projecting in two directions and reducing the projection by half. The compensated root mean square error (RMSE) for planar objects can be reduced by over 45%.
Growth, Physiology and Nutritional Quality of C4 Halophyte Portulaca oleracea L. Grown Aeroponically in Different Percentages of Artificial Seawater under Different Light-Emitting Diode Spectral Qualities
Authors: He Jie , Leng Su Yee , Qin Lin
Published: September 2023

Edible halophyte Portulaca oleracea L., known as purslane, was grown in two percentages of artificial seawater (ASW) under two combined red (R) and blue (B) LED spectra. High salinity (40% ASW) negatively affected shoot productivity and leaf growth of purslane compared to those grown in 10% ASW. Photosynthetic pigment and total reduced nitrogen concentrations were significantly higher in purslane grown in 10% ASW than in 40% ASW. However, LED spectral quality did not markedly influence these parameters. Grown in 10% ASW under R/B 2.2, purslane had the highest maximum nitrate reductase activity, while those in 40% ASW under R/B 2.2 had the highest activation state. Under both light qualities, purslane had a sevenfold increase in proline concentration in 40% ASW than in 10% ASW. Total phenolic compounds’ concentration was the highest in 10% ASW under R/B 0.9, while there were no significant differences in the accumulation of total soluble sugars and ascorbic acids among all plants. Antioxidant enzymes activities were lower in 40% ASW under R/B 2.2 compared to the other conditions. In conclusion, salinity affected the yield, physiology and nutritional quality of purslane. The impacts of LED spectral quality on purslane were only reflected by certain physiological and nutritional parameters.
Agronomic Evaluation and Molecular Cytogenetic Characterization of Triticum aestivum × Thinopyrum spp. Derivative Breeding Lines Presenting Perennial Growth Habits
Authors: Morgan Robin , Danilova Tatiana , Newell Matthew , Cai Xiwen , Jones Stephen
Published: September 2023

The transition from annual to perennial growth habits can contribute to increased sustainability and diversification of staple cropping systems like those based on annual wheat. Amphiploids between Triticum aestivum and Thinopyrum spp. can present a wheat-like morphology and post sexual cycle regrowth. The complex and unpredictable nature of the chromosomal rearrangements typical of inter-generic hybrids can hamper progress in the development of this new crop. By using fluorescence in situ hybridization, we described the genomic constitution of three perennial wheat breeding lines that regrew and completed a second year of production in field conditions in Washington state (USA). Two breeding lines presented stable, 56-chromosome partial amphiploids; however, their chromosome composition differed significantly. The third breeding line presented an unstable karyotype with a chromosome number ranging from 53 to 58 across eight individuals. The agronomic performance of the perennial breeding lines was evaluated for two growing seasons from 2020 to 2022. The grain yields of the perennial lines were lower than the grain production of the annual wheat control line in the first season. The perennial lines displayed vigorous regrowth after the initial harvest; however, worsening environmental conditions in the second season of growth hampered subsequent growth and grain yield. This information facilitates the breeding work necessary to improve key traits by grouping agronomically valuable individuals according to their genomic constitution.
Comparing Non-Thermal Plasma and Cold Stratification: Which Pre-Sowing Treatment Benefits Wild Plant Emergence?
Authors: Gudyniene Vilma , Juzenas Sigitas , Stukonis Vaclovas , Mildaziene Vida , Ivankov Anatolii , Norkeviciene Egle
Published: September 2023

Meadow restoration and creation projects have faced a lack of local seed diversity due to the limited availability of seed sources. Non-thermal plasma technologies are being developed for agriculture and do not cause damage to heat-sensitive biological systems. This technology has shown the potential to improve agronomic seed quality by enhancing germination and promoting plant growth. However, there is almost no information about the effect of non-thermal plasma pretreatment on the seedlings’ emergence of wild plant species. Therefore, this study aimed to evaluate the effect of non-thermal plasma on the emergence of 17 plant seeds originating from local meadows in Lithuania and compare it with the cold stratification pretreatment. The results obtained indicate that there were differences in emergence parameters among the species. However, NTP did not show statistically significant differences from the control. Non-thermal plasma improved the kinetic parameters of emergence for a few specific species’ seeds, such as Anthyllis vulneraria and Prunella grandiflora, while the cold stratification pretreatment enhanced emergence for a broader range of plants. Significant differences were observed between non-thermal plasma and stratification pretreatment, as well as between the control and stratification groups. Both methods also had a negative impact.
Effects of Common Biochar and Acid-Modified Biochar on Growth and Quality of Spinach in Coastal Saline Soils
Authors: Wang Juan , Shi Danyi , Huang Chengzhen , Zhai Biyu , Feng Shaoyuan
Published: September 2023

The rational development and efficient utilization of saline soils can alleviate the problem of insufficient arable land faced by agricultural production in China. A prominent problem is improving soil salt and water conditions for promoting land resources’ productivity in coastal areas. Biochar is widely used for soil improvement, as it has remarkable properties. A pot experiment was conducted to study the effects of two kinds of biochar (common biochar and acid-modified biochar) with three addition rates (2%, 4%, and 8%) on the growth, yield, photosynthetic characteristics, and quality of spinach. The results revealed that 2% and 4% common biochar increased the plant height, stem diameter, and leaf area index, effectively improving the yield of spinach and water productivity, while 8% common biochar was detrimental to the growth of spinach to some extent. Acid-modified biochar significantly benefited the growth and increased the water productivity of spinach, ensuring high yields, while also improved quality. Similarly, acid-modified biochar was less effective at high additions than at low-to-medium additions. The integrated biological response version 2 (IBRV2) values under acid-modified biochar treatments were all significantly higher than those under common biochar, but there is no significant difference among three treatments in the same biochar group, which suggested a pronounced amelioration in spinach growth within saline-alkali soil upon the incorporation of acid-modified biochar. Overall, applying acid-modified biochar at the rate of 4% exhibited enormous potential for increasing the yield and quality of spinach in saline soils.
2D Fluid-PIC Simulations of Hall Thrusters with Self-Consistent Resolution of the Space-Charge Regions
Authors: Lopez Ortega Alejandro , Mikellides Ioannis G.
Published: September 2023

Many hybrid simulations of Hall thrusters, where electrons and ions are solved using hydrodynamics and particle-in-cell methods, respectively, assume that the ionized gas is quasi-neutral everywhere in the computational domain and apply so-called thin-sheath approximations to account for space-charge effects near solid boundaries. These approximations do not hold along boundaries near the exit of the thruster or in the near plume regions, where the plasma conditions can lead to Debye lengths on the order of or higher than the local grid resolution. We present a numerical scheme that fully resolves the conditions of the ionized gas in space-charge regions of any thickness and that is coupled consistently to a global hybrid simulation of Hall thrusters. We verify the numerical results with the closed-form solution for a Langmuir sheath in a simplified one-dimensional example, and then again in simulations where the model is integrated in a 2D multifluid/PIC axial–radial code called Hall2De. The new capability to resolve numerically large sheaths around solid boundaries in Hall thrusters allows for significantly more accurate assessments of ion sputtering, thus improving thruster lifetime predictions.
pH Effects on the Conformations of Galacturonan in Solution: Conformational Transition and Loosening, Extension and Stiffness
Authors: Paoletti Sergio , Donati Ivan
Published: September 2023

Calorimetric (from both isothermal micro-calorimetry and DSC), chiro-optical, viscometric and rheological data on aqueous solutions of pectic acid and low-methoxyl pectin (LMP), published over decades from different laboratories, have been comparatively revisited. The aim was to arrive at a consistent and detailed description of the behavior of galacturonan as a function of pH, i.e., of the degree of charging (as degree of dissociation, α) of the polyanion. The previously hypothesized pH-induced transition from a 31 to a 21 helix was definitely confirmed, but it has been shown, for the first time, that the transition is always coupled with loosening/tightening effects brought about by an increase in charge. The latter property has a twofold effect: the former effect is a purely physical one (polyelectrolytic), which is always a loosening one. However, in the very low range of pH and before the beginning of the transition, an increase in charge tightens the 31 helix by strengthening an intramolecular—but inter-residue—hydrogen bond. The value of the enthalpy change of 31 → 21 transition—+0.59 kcal·mol−1—is bracketed by those provided by theoretical modeling, namely +0.3 and +0.8 kcal·mol−1; the corresponding entropy value is also positive: +1.84 cal·mole r.u.−1·K−1. The enthalpic and the entropic changes in chain loosening amount only to about 23% of the corresponding 31 → 21 changes, respectively. Much like poly(galacturonic acid), the 31 conformation of LMP also stiffens on passing from pH = 2.5 to 3.0, to then start loosening and transforming into the 21 one on passing to pH = 4.0. Lowering the pH of a salt-free aqueous solution of LMP down to 1.6 brings about a substantial chain–chain association, which is at the root of the interchain junctions stabilizing the acid pH gels, in full agreement with the rheological results. A comparison of the enthalpic data reveals that, at 85 °C, LMP in acidic pH conditions has lost its initial order by about 2.3 times more than pectic acid brought from low charging to full neutralization (at α = 1.0) at 25 °C. A proper combination of experiments (enthalpic measurements) and theory (counterion condensation polyelectrolyte theory) succeeded in demonstrating, for the first time ever, a lyotropic/Hofmeister effect of the anion perchlorate in stabilizing the more disordered form of the 21 helix of galacturonan. The viscometric results in water showed that the 31 helix is capable of forming longer rheologically cooperative units compared with the 21 helix. Extrapolation to infinite ionic strength confirmed that, once all electrostatic interactions are cancelled, the elongation of the two helical forms is practically the same. At the same time, however, they indicated that the flexibility of the two-fold helix is more than fifteen times larger than that of the three-fold one. The result is nicely corroborated by a critical revisiting of 23Na relaxation experiments.
A Review: Study on the Enhancement Mechanism of Heat and Moisture Transfer in Deformable Porous Media
Authors: Li Panpan , Ma Cui , Chen Zhenqian , Wang Haoqi , Wang Yu , Bai Hao
Published: September 2023

The heat and moisture transfer process in deformable porous media commonly exists in material drying, solid waste treatment, bioengineering, and so on. The transfer process is accompanied by deformation of the solid skeleton and pore interface structure, which limits the transfer rate and affects quality. Microwave and ultrasound are the main representatives of reinforcement technology. However, as the moisture decreases, the energy utilization efficiency of microwaves decreases significantly. Based on the experimental and theoretical methods, the enhancement mechanism of ultrasound on the process is studied, which provides guidance for the wide application of ultrasonic enhancement. With the increase in ultrasound power, the pore area and the moisture effective diffusion coefficient gradually increase. A macroscope mathematical model for ultrasonic-coupled thermal-hydro-mechanical modeling is developed, and the results show that ultrasound increases the temperature gradient within material, resulting in higher moisture transmission rates with an ordered direction, and the alternating expansion and compression process results in smaller macroscopic deformations. Subsequently, the drying kinetic characteristics of typical deformable porous media such as municipal sludge, porous fibers, and activated alumina particles are investigated. The process parameters of the ultrasonic assisted drying system are optimized using the response surface method and artificial neural network model.
Exergy Loss Assessment Method for CNC Milling System Considering the Energy Consumption of the Operator
Authors: Feng Zhaohui , Zhang Hua , Li Wei , Yu Yajian , Guan Youjia , Ding Xinru
Published: September 2023

Modeling and assessing the sustainability of machining systems has been considered to be a crucial approach to improving the environmental performance of machining processes. As the most common machining system, the computer numerical control (CNC) milling system is a typical man–machine cooperative system where the activities of the machine tool and operator generate material and energy consumption. However, the energy consumption of the operator in the CNC milling system has often been ignored in most existing research. Therefore, existing methods fail to provide a comprehensive understanding of the sustainability of the CNC milling system. To fill this gap, an exergy loss assessment method is proposed to investigate the sustainability of the CNC milling system, where the energy consumption of the operator, the energy consumption of the machine tool, and material consumption are taken into consideration. The key performance indexes of the energy consumption of the operator, the energy consumption of the machine tool, the exergy loss, and the specific exergy loss (SEL) are analyzed and modeled. To demonstrate the feasibility of the proposed method, a case study was carried out on a three-axis machining center (XH714D), in which the energy consumption of the operator, the energy consumption of the machine tool, the exergy loss of energy consumption, the exergy loss of chips, the exergy loss of compressed air, the exergy loss of cutting tool wear, the exergy loss of cooling liquid dissipation, and the SEL were found to be 169,750 J, 758,211 J, 603,131 J, 2,031,404 J, 22,023 J, 301,868 J, 2673 J, and 88.04 J/mm3, respectively. The proposed method is effective to assess the sustainability of the CNC milling system, and the established exergy loss models build a good basis for exergy efficiency optimization.
An Experimental Investigation on the Shear-Seepage Coupling Failure Behavior of Split Grouting-Reinforced Body
Authors: Zhang Yaolei , Sui Haitong , Yang Lei , Lin Rongfeng
Published: September 2023

A split grouting-reinforced body (SGRB) is the new surrounding rock that forms after split grouting reinforcement in tunnels and underground engineering, and its shear-seepage behavior is one of the critical factors affecting tunnel stability. The effects of seepage pressure, confining pressure, and the roughness of the soil–slurry interface on the shear-seepage characteristics of SGRB specimens were investigated using a modified triaxial shear-seepage coupling test system. The failure mechanism for the SGRB was analyzed taking into account its seepage behavior and mechanical characteristics. The results showed that the seepage process of the SGRB specimens could be divided into four stages according to the seepage velocity, including the waterless, rapid, decelerating, and steady seepage stages, and the corresponding water turbidity in the seepage stages was classified as turbid, mildly turbid, or clear, respectively. The peak shear stress of the soil–slurry interface of the SGRB specimens under seepage was lower than that in the waterless environment, and the peak shear stress decreased from 57.25 kPa (waterless) to 29.37 kPa (a seepage pressure of 0.08 MPa), marking a reduction of 50.74%. The seepage phenomenon of the specimens was related to the ‘seepage-to-confining ratio’, and its critical points in the waterless, seepage, and seepage surge stages were 0.2, 0.4, and 0.6, respectively.
Application of Three-Dimensional Printing Technology to the Manufacture of Petroleum Drill Bits
Authors: Liu Baxian , Wang Yifei , Jiang Junjie , Zhang Bihui , Zhou Jian , Huang Kuilin
Published: September 2023

Drill bits are the main rock-breaking tools in the petroleum and gas industry. Their performance directly affects the quality, efficiency, and cost of drilling. Drill bit manufacturing mainly employs traditional mold forming processes such as milling molding and press molding, which have low production efficiency and long processing cycles and are not conducive to rapid responses to field requirements. Inadequate production accuracy makes it difficult to produce drill bits with complex structures. Three-dimensional (3D) printing technology has fast molding speeds and high molding accuracy. In this paper, 3D printing was applied for the first time to the manufacture of molds for carcass polycrystalline diamond compact (PDC) drill bits and PDC–cone hybrid drill bits. In comparison with forging and milling molding, 3D printing improved production efficiency. The manufactured molds had higher machining accuracy. The ability of 3D printing to make molds with complex surfaces enables the development of drill bits with complex structures. A field experiment was conducted on a PDC drill bit produced by 3D printing, which had a higher rate of penetration and was more efficient in breaking rocks than bits manufactured by traditional processes. The ROP of the drill bit increased by 20.1–25.8%, and the drilling depth increased by 7.7–29.5%. It is therefore feasible to apply 3D printing to the manufacture of petroleum drill bits.
An Integrated Control Approach for Shifting Process of Single-Axis Parallel Hybrid Electric Vehicle with a Multi-Speed AMT Gearbox
Authors: Huang Cheng , Du Changqing
Published: September 2023

When a single-axis parallel hybrid electric vehicle (HEV) equipped with a multi-speed AMT gearbox is in its shifting process, the superposition of dynamic characteristics of multiple power sources and the intervention and withdrawal of AMT transmissions can easily cause significant vehicle longitudinal jerk. To achieve rapid and smooth output changes during the shifting process, this paper proposes an integrated multi-stage robust shifting control method for a single-axis parallel hybrid electric vehicles with a multi-speed AMT gearbox. First, models of key driveline components are constructed, and the shifting process is divided into five stages to provide a clear description of the control problem. Subsequently, we reproduce an integrated multistage robust control method to achieve favorable switching performance and control robustness under external disturbances. We propose a data-driven model predictive control strategy based on additional constraints in the torque unloading and recovery phases. Simultaneously, we present a joint control algorithm that integrates the optimal control and disturbance suppression in the speed synchronization phase. In addition, we develop a sliding mode auto-disturbance rejection control algorithm to achieve accurate position tracking of the shift actuator in the pickup and engage phases. Finally, simulations and bench tests are carried out to verify the effectiveness of the robust control method under different driving conditions. The results demonstrate that the proposed control method can not only coordinate the torque across different power sources and clutch while minimizing vehicle longitudinal jerk, shift time, and friction work, but also provides apparent robustness to model uncertainties and external disturbance. Therefore, the proposed method may offer a theoretical reference for the actual vehicle controller during shifting.
Research on Data Collection Methods for Assembly Performance of Array Antennas in Digital Twin Workshops
Authors: Guo Xuepeng , Liu Linyan , Wang Zhexin , Wang Huifen , Du Xiaodong , Shi Jiancheng , Wang Yue
Published: September 2023

Aimed at the characteristics of multi-source heterogeneity and the rapid generation of data in digital twin workshops, as well as the current situation where communication protocols between equipment within the workshop vary greatly and are difficult to interconnect, a data integration method based on OPC UA is designed. Firstly, combining the process flow and data source characteristics of array antenna assembly, a data collection and transmission scheme based on OPC UA was designed. Secondly, a process information model of array antenna assembly was established to realize data perception and transmission and solve the difficulties of complex data structure, high real-time requirements, and heterogeneous data in digital twin workshop. Finally, the proposed method and model were applied to the performance prediction platform for an array antenna assembly process based on digital twins, achieving perception of process data during the assembly process of array antennas, and achieving performance prediction and visualization for various stages of array antennas based on assembly process data.
Quantitative Analysis of Soil Cd Content Based on the Fusion of Vis-NIR and XRF Spectral Data in the Impacted Area of a Metallurgical Slag Site in Gejiu, Yunnan
Authors: Zhang Zhenlong , Wang Zhe , Luo Ying , Zhang Jiaqian , Feng Xiyang , Zeng Qiuping , Tian Duan , Li Chao , Zhang Yongde , Wang Yuping , Chen Shu , Chen Li
Published: September 2023

Vis-NIR and XRF spectroscopy are widely used in monitoring heavy metals in soil due to their advantages of being fast, non-destructive, cost-effective, and non-polluting. However, when used individually, XRF and vis-NIR may not meet the accuracy requirements for Cd determination. In this study, we focused on the impact area of a non-ferrous metal smelting slag site in Gejiu City, Yunnan Province, fused the pre-selected vis-NIR and XRF spectra using the Pearson correlation coefficient (PCC), and identified the characteristic spectra using the competitive adaptive reweighted sampling (CARS) method. Based on this, a quantitative model for soil Cd concentration was established using partial least squares regression (PLSR). The results showed that among the four fusion spectral quantitative models constructed, the model combining vis-NIR spectral second-order derivative transformation and XRF spectral first-order derivative transformation (D2(vis-NIR) + D1(XRF)) had the highest coefficient of determination (R2 = 0.9505) and the smallest root mean square error (RMSE = 0.1174). Compared to the estimation models built using vis-NIR and XRF spectra alone, the average computational time of the fusion models was reduced by 68.19% and 63.92%, respectively. This study provides important technical means for real-time and large-scale on-site rapid estimation of Cd content using multi-source spectral fusion.
Optimization Method for Hot Air Reflow Soldering Process Based on Robust Design
Authors: Ran Linjie , Chen Dong , Chen Cai , Gong Yubing
Published: September 2023

The process design of hot air reflow soldering is one of the key factors affecting the quality of PCBA (Printed Circuit Board Assembly) component products. In order to improve the product quality during the design process, this paper proposes a robust optimization-based finite element simulation analysis method including significant influencing factor screening, robustness evaluation, robust optimization, and reliability verification for the reflow soldering process. The simulation model of the reflow soldering process temperature field based on experiments is constructed and validated. Sensitivity analysis is used to select important influencing factors, such as the last five set temperature zones (T5 to T9) in the reflow oven and the thermal properties of materials such as PCBs (printed circuit boards), BGAs (ball grid arrays), and solder paste, as well as noise factors like the heating environment during the soldering process. Several surrogate models are used to construct the response surface, and the optimal fitting scheme is selected to effectively avoid poor fitting caused by inappropriate surrogate models. The 6σ robust optimization approach is introduced to evaluate and optimize the robustness of the process design parameter where the heating factor is chosen as the optimization target. The reliability analysis method is employed to validate the product quality. This paper establishes a comprehensive robustness analysis method for hot air reflow soldering, effectively reducing design costs and addressing the lack of robustness analysis in the current hot air reflow soldering process design.
A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework
Authors: Wang Yanjun , Lin Yunhao , Huang Huiqing , Wang Shuhan , Wen Shicheng , Cai Hengfan
Published: September 2023

Deep learning has gained widespread interest in the task of building semantic segmentation modelling using remote sensing images; however, neural network models require a large number of training samples to achieve better classification performance, and the models are more sensitive to error patches in the training samples. The training samples obtained in semi-supervised classification methods need less reliable weakly labelled samples, but current semi-supervised classification research puts the generated weak samples directly into the model for applications, with less consideration of the impact of the accuracy and quality improvement of the weak samples on the subsequent model classification. Therefore, to address the problem of generating and optimising the quality of weak samples from training data in deep learning, this paper proposes a semi-supervised building classification framework. Firstly, based on the test results of the remote sensing image segmentation model and the unsupervised classification results of LiDAR point cloud data, this paper quickly generates weak image samples of buildings. Secondly, in order to improve the quality of the spots of the weak samples, an iterative optimisation strategy of the weak samples is proposed to compare and analyse the weak samples with the real samples and extract the accurate samples from the weak samples. Finally, the real samples, the weak samples, and the optimised weak samples are input into the semantic segmentation model of buildings for accuracy evaluation and analysis. The effectiveness of this paper’s approach was experimentally verified on two different building datasets, and the optimised weak samples improved by 1.9% and 0.6%, respectively, in the test accuracy mIoU compared to the initial weak samples. The results demonstrate that the semi-supervised classification framework proposed in this paper can be used to alleviate the model’s demand for a large number of real-labelled samples while improving the ability to utilise weak samples, and it can be used as an alternative to fully supervised classification methods in deep learning model applications that require a large number of training samples.
A Downscaling Method Based on MODIS Product for Hourly ERA5 Reanalysis of Land Surface Temperature
Authors: Wang Ning , Tian Jia , Su Shanshan , Tian Qingjiu
Published: September 2023

Land surface temperature (LST) is a critical parameter for the dynamic simulation of land surface processes and for analyzing variations on regional or global scales. Obtaining LST with high spatiotemporal resolution is a subject of intensive and ongoing research. This study proposes a pixel-wise temporal alignment iterative linear regression model for downscaling based on MODIS LST products. This approach allows us to address the problem of high temporal resolution but low spatial resolution of the ERA5 reanalysis LST product while remaining immune to the pixel loss caused by clouds. The hourly ERA5 LST of the study area for 2012–2021 was downscaled to a 1000 m resolution, and its accuracy was verified by comparison with measured data from meteorological stations. The downscaled LST offers intricate details and is faithful to the LST characteristics of distinct land-cover categories. In comparison with other downscaling techniques, the proposed technique is more stable and preserves the spatial distribution of the ERA5 LST with minimal missing pixels. The pixel-wise average R2 and mean absolute error for the MODIS view times are 0.87 and 2.7 K, respectively, for cloud-free conditions on a 1000 m scale. The accuracy verification using data from meteorological stations indicates that the overall error is lower during cloudless periods rather than during overcast periods, during the night rather than during the day, and at MODIS view times rather than at non-view times. The maximum and minimum mean errors are 0.13 K for cloud-free periods and −0.98 K for cloudy periods, indicating a slight underestimation and overestimation, respectively. Conversely, the maximum and minimum mean absolute errors are 2.01 K for the daytime and 0.85 K for the nighttime. Therefore, the model ensures higher accuracy during cloudy periods with only the clear-sky LST used as input data, making it suitable for long-term, all-weather ERA5 LST downscaling.
SCA-Net: Multiscale Contextual Information Network for Building Extraction Based on High-Resolution Remote Sensing Images
Authors: Wang Yuanzhi , Zhao Qingzhan , Wu Yuzhen , Tian Wenzhong , Zhang Guoshun
Published: September 2023

Accurately extracting buildings is essential for urbanization rate statistics, urban planning, resource allocation, etc. The high-resolution remote sensing images contain rich building information, which provides an important data source for building extraction. However, the extreme abundance of building types with large differences in size, as well as the extreme complexity of the background environment, result in the accurate extraction of spatial details of multi-scale buildings, which remains a difficult problem worth studying. To this end, this study selects the representative Xinjiang Tumxuk urban area as the study area. A building extraction network (SCA-Net) with feature highlighting, multi-scale sensing, and multi-level feature fusion is proposed, which includes Selective kernel spatial Feature Extraction (SFE), Contextual Information Aggregation (CIA), and Attentional Feature Fusion (AFF) modules. First, Selective kernel spatial Feature Extraction modules are used for cascading composition, highlighting information representation of features, and improving the feature extraction capability. Adding a Contextual Information Aggregation module enables the acquisition of multi-scale contextual information. The Attentional Feature Fusion module bridges the semantic gap between high-level and low-level features to achieve effective fusion between cross-level features. The classical U-Net, Segnet, Deeplab v3+, and HRNet v2 semantic segmentation models are compared on the self-built Tmsk and WHU building datasets. The experimental results show that the algorithm proposed in this paper can effectively extract multi-scale buildings in complex backgrounds with IoUs of 85.98% and 89.90% on the two datasets, respectively. SCA-Net is a suitable method for building extraction from high-resolution remote sensing images with good usability and generalization.
Cooperative Dynamic Motion Planning for Dual Manipulator Arms Based on RRT*Smart-AD Algorithm
Authors: Long Houyun , Li Guang , Zhou Fenglin , Chen Tengfei
Published: September 2023

Intelligent manufacturing requires robots to adapt to increasingly complex tasks, and dual-arm cooperative operation can provide a more flexible and effective solution. Motion planning serves as a crucial foundation for dual-arm cooperative operation. The rapidly exploring random tree (RRT) algorithm based on random sampling has been widely used in high-dimensional manipulator path planning due to its probability completeness, handling of high-dimensional problems, scalability, and faster exploration speed compared with other planning methods. As a variant of RRT, the RRT*Smart algorithm introduces asymptotic optimality, improved sampling techniques, and better path optimization. However, existing research does not adequately address the cooperative motion planning requirements for dual manipulator arms in terms of sampling methods, path optimization, and dynamic adaptability. It also cannot handle dual-manipulator collaborative motion planning in dynamic scenarios. Therefore, in this paper, a novel motion planner named RRT*Smart-AD is proposed to ensure that the dual-arm robot satisfies obstacle avoidance constraints and dynamic characteristics in dynamic environments. This planner is capable of generating smooth motion trajectories that comply with differential constraints and physical collision constraints for a dual-arm robot. The proposed method includes several key components. First, a dynamic A* cost function sampling method, combined with an intelligent beacon sampling method, is introduced for sampling. A path-pruning strategy is employed to improve the computational efficiency. Strategies for dynamic region path repair and regrowth are also proposed to enhance adaptability in dynamic scenarios. Additionally, practical constraints such as maximum velocity, maximum acceleration, and collision constraints in robotic arm applications are analyzed. Particle swarm optimization (PSO) is utilized to optimize the motion trajectories by optimizing the parameters of quintic non-uniform rational B-splines (NURBSs). Static and dynamic simulation experiments verified that the RRT*Smart-AD algorithm for cooperative dynamic path planning of dual robotic arms outperformed biased RRT* and RRT*Smart. This method not only holds significant practical engineering significance for obstacle avoidance in dual-arm manipulators in intelligent factories but also provides a theoretical reference value for the path planning of other types of robots.
Polarization Properties of Coherently Superposed Rayleigh Backscattered Light in Single-Mode Fibers
Authors: Dong Hui , Zhang Hailiang , Hu Dora Juan Juan
Published: September 2023

The properties of the state of polarization (SOP) and the degree of polarization (DOP) of Rayleigh backscattered light (RBL) in single-mode fibers (SMF) are investigated theoretically and experimentally when the incident probe is a perfectly coherent continuous-wave (CW) light. It is concluded that the instantaneous DOP of the coherently superposed RBL is always 100%, and the instantaneous SOP is determined by the distributions of the birefringence and the optical phase along the SMF. Therefore, the instantaneous SOP of the coherently superposed RBL does not have a constant relationship with the SOP of the incident CW probe. Furthermore, the instantaneous SOP varies randomly with time because the optical phase is very sensitive to ambient temperature and vibration even in the lab environment. Further theoretical derivation and experimental verification demonstrate, for the first time, that the temporally averaged SOP of the coherently superposed RBL has a simple constant relationship with the SOP of the incident CW probe, and the temporally averaged DOP is 1/3 in an SMF with low and randomly distributed birefringence. The derived formulas and obtained findings can be used to enhance the modelling and improve the performances of phase-sensitive optical time-domain reflectometry and other Rayleigh backscattering based fiber-optic sensors.
Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax
Authors: Ivanenko Mikhail , Smolik Waldemar T. , Wanta Damian , Midura Mateusz , Wróblewski Przemysław , Hou Xiaohan , Yan Xiaoheng
Published: September 2023

Electrical impedance tomography (EIT) is a non-invasive technique for visualizing the internal structure of a human body. Capacitively coupled electrical impedance tomography (CCEIT) is a new contactless EIT technique that can potentially be used as a wearable device. Recent studies have shown that a machine learning-based approach is very promising for EIT image reconstruction. Most of the studies concern models containing up to 22 electrodes and focus on using different artificial neural network models, from simple shallow networks to complex convolutional networks. However, the use of convolutional networks in image reconstruction with a higher number of electrodes requires further investigation. In this work, two different architectures of artificial networks were used for CCEIT image reconstruction: a fully connected deep neural network and a conditional generative adversarial network (cGAN). The training dataset was generated by the numerical simulation of a thorax phantom with healthy and illness-affected lungs. Three kinds of illnesses, pneumothorax, pleural effusion, and hydropneumothorax, were modeled using the electrical properties of the tissues. The thorax phantom included the heart, aorta, spine, and lungs. The sensor with 32 area electrodes was used in the numerical model. The ECTsim custom-designed toolbox for Matlab was used to solve the forward problem and measurement simulation. Two artificial neural networks were trained with supervision for image reconstruction. Reconstruction quality was compared between those networks and one-step algebraic reconstruction methods such as linear back projection and pseudoinverse with Tikhonov regularization. This evaluation was based on pixel-to-pixel metrics such as root-mean-square error, structural similarity index, 2D correlation coefficient, and peak signal-to-noise ratio. Additionally, the diagnostic value measured by the ROC AUC metric was used to assess the image quality. The results showed that obtaining information about regional lung function (regions affected by pneumothorax or pleural effusion) is possible using image reconstruction based on supervised learning and deep neural networks in EIT. The results obtained using cGAN are strongly better than those obtained using a fully connected network, especially in the case of noisy measurement data. However, diagnostic value estimation showed that even algebraic methods allow us to obtain satisfactory results.
Seeking a Sufficient Data Volume for Railway Infrastructure Component Detection with Computer Vision Models
Authors: Gosiewska Alicja , Baran Zuzanna , Baran Monika , Rutkowski Tomasz
Published: September 2023

Railway infrastructure monitoring is crucial for transportation reliability and travelers’ safety. However, it requires plenty of human resources that generate high costs and is limited to the efficiency of the human eye. Integrating machine learning into the railway monitoring process can overcome these problems. Since advanced algorithms perform equally to humans in many tasks, they can provide a faster, cost-effective, and reproducible evaluation of the infrastructure. The main issue with this approach is that training machine learning models involves acquiring a large amount of labeled data, which is unavailable for rail infrastructure. We trained YOLOv5 and MobileNet architectures to meet this challenge in low-data-volume scenarios. We established that 120 observations are enough to train an accurate model for the object-detection task for railway infrastructure. Moreover, we proposed a novel method for extracting background images from railway images. To test our method, we compared the performance of YOLOv5 and MobileNet on small datasets with and without background extraction. The results of the experiments show that background extraction reduces the sufficient data volume to 90 observations.
Longitudinal Spin Seebeck Effect Thermopiles Based on Flexible Co-Rich Amorphous Ribbons/Pt Thin-Film Heterostructures
Authors: Correa Marcio A. , Svalov Andrey V. , Ferreira Armando , Gamino Matheus , Silva Edimilson F. da , Bohn Felipe , Vaz Filipe , de Oliveira Danniel F. , Kurlyandskaya Galina V.
Published: September 2023

Thermoelectric phenomena, such as the Anomalous Nernst and Longitudinal Spin Seebeck Effects, are promising for sensor applications in the area of renewable energy. In the case of flexible electronic materials, the request is even larger because they can be integrated into devices having complex shape surfaces. Here, we reveal that Pt promotes an enhancement of the thermoelectric response in Co-rich ribbon/Pt heterostructures due to the spin-to-charge conversion. Moreover, we demonstrated that the employment of the thermopiles configuration in this system increases the induced thermoelectric current, a fact related to the considerable decrease in the electric resistance of the system. By comparing present findings with the literature, we were able to design a flexible thermopile based on LSSE without the lithography process. Additionally, the thermoelectric voltage found in the studied flexible heterostructures is comparable to the ones verified for rigid systems.
Advancing Stepped-Waveform Radar Jamming Techniques for Robust False-Target Generation against LFM-CFAR Systems
Authors: Wang Yanqi , Wang Chao , Shi Qingzhan , Huang Jingjian , Yuan Naichang
Published: September 2023

This study investigates the utilization of a stepped wave frequency modulation jamming technique in radar systems. The objective is to enhance the effectiveness and robustness of false target jamming in the presence of linear frequency modulation (LFM) radars employing constant false alarm rate (CFAR) detection. The proposed method combines stepped frequency modulation with full pulse delay/sum repeat jamming to enhance resilience against uncertainties in target parameters. Theoretical analysis and simulation experiments are conducted to establish relationships between key jammer parameters, such as frequency slope and power compensation, and performance metrics, like false target distribution and CFAR masking. The results demonstrate that the proposed technique effectively maintains a dense distribution of false targets surrounding the protected target, even in the presence of uncertainties in position and signal-to-noise ratio. In comparison to existing methods, the utilization of stepped-waveform modulation enables improved control over target distribution and CFAR masking. Adaptive power allocation compensates for parameter errors, thereby enhancing robustness. Simulation results reveal that the proposed approach significantly reduces the probability of detecting the true target by over 95% under uncertain conditions, while previous methods experienced degradation. The integration of stepped waveforms optimizes false target jamming, thereby advancing electronic warfare capabilities in countering advanced radar threats. This study establishes design principles for resilient jamming architectures and supports enhanced survivability against radars employing pulse compression and CFAR detection. Moreover, the concepts proposed in this study have the potential for extension to emerging radar waveforms.