Conversion Method Research Articles

Mapping Paleolacustrine Deposits with a UAV-borne Multispectral Camera: Implications for Future Drone Mapping on Mars

NASA’s Ingenuity Mars Helicopter has marked a new era in planetary exploration by employing unmanned aerial vehicles (UAVs) to enhance our understanding of planetary surfaces. This study evaluates the potential of UAVs for mapping Martian environments, with Lake Natron, Tanzania, serving as an analog for Martian paleolakes. During two field seasons (2023 January and July), we used a Phantom 4 Pro drone equipped with a MicaSense RedEdge-M multispectral camera, supplemented by in situ analysis using a TerraSpec Halo VNIR-SWIR spectrometer, to capture high-resolution imagery and spectral data. Almost all image processing and analysis, except for image mosaic and digital elevation model (DEM) generation, was performed using Python scripting. We benchmarked the onboard image processing capabilities using a Raspberry Pi 5 single-board computer. Processing steps include digital number (DN)-to-radiance conversion, assessment of the best radiance-to-reflectance conversion method, image mosaic creation, DEM generation, calculation of optimal band indices, and selection of the best classification technique. The research underscores Lake Natron’s diverse lithologies as a suitable analog site and demonstrates significant improvements in classification when normalized elevation data are incorporated with spectral index maps through unsupervised classification methods. The study also addresses challenges related to high-resolution image transmission and processing, advocating for advanced techniques such as image compression and low-power computational models. Additionally, it highlights computational and power limitations as key obstacles, suggesting that emerging technologies such as photonic computing and hybrid controllers could provide viable solutions. These findings emphasize the transformative potential of UAVs in planetary exploration while outlining key areas for future research and technological development.

Thermochemical processing for the sustainable disposal of spent filter waste

High-efficiency particulate air filters are widely used for indoor air purification. Spent filter waste (SFW), which can trap infectious and toxic substances, is primarily treated via incineration. This method causes environmental concerns, particularly regarding the generation of carbon dioxide (CO2) and other air pollutants. To cope with these issues, this work proposes the pyrolysis of SFW using CO2 as a sustainable alternative to conventional incineration. The introduction of CO2 enhanced reactivity during pyrolysis, potentially offering a more sustainable process. The SFW consisted of filtered particles and two distinct filter/support layers, and the presence of toxic chemicals and primary polymer constituents was characterized. While CO2 had a minimal impact on enhancing syngas production, owing to its slow reaction rate during SFW pyrolysis, adding a nickel-based catalyst significantly improved CO2 reactivity. This resulted in a 649.7% increase in carbon monoxide (CO) production compared to that in pyrolysis under N2. The potential of this pyrolysis system for reducing CO2 emissions was evaluated against that of conventional incineration. Overall, this study presents a promising method for the pyrolytic conversion of SFW into combustible gases, particularly CO, while leveraging CO2 utilisation to mitigate global warming.

Process Simulation, Thermodynamic and System Optimization for the Low-Carbon Alcohols Production via Gasification of Second-Generation Biomass

Biomass-to-liquid fuels technology offers a promising method for high-value biomass conversion, addressing environmental toxicity and fossil fuel non-renewability. However, challenges such as low system efficiency and identifying efficiency losses persist. In this study, a comprehensive process model of a low-carbon alcohols production system via biomass gasification was developed, based on the first demonstration project in China. The innovation of this study lies in its detailed experimental validation, as the model simulation was performed using laboratory data and verified with pilot-scale platform data ensuring high accuracy. Additionally, the study conducted a thorough sensitivity analysis of system parameters, energy, and exergy assessments to find the proper operating conditions, including equivalent ratio, biomass type, and reactor temperature and pressure. The simulation results demonstrated an energy efficiency of 34.67% and an exergy efficiency of 31.24%. Through operating parameters and heat recovery measures, these efficiencies increased by 11.66% and 8%, respectively. This research not only obtains improved operating parameters for the pilot-scale platform but also provides actionable insights for enhancing the yields of target products and upgrading low-grade energy utilization. These findings offer valuable guidance for the commercialization of bio-syngas alcohols production systems, highlighting significant advancements in efficiency and system performance.

An attention-based weight adaptive multi-task learning framework for slab head shape prediction and optimization during the rough rolling process

During the hot rolling process, predicting and optimizing the slab head unstable deformation is crucial for improving product yield. However, the multivariate, strongly coupled, nonlinear characteristics of the hot rolling process pose a significant challenge to information extraction. In this paper, an attention-based weight adaptive multi-task learning framework (AWAMTL) is proposed for slab head unstable deformation prediction and optimization. Initially, the unstable deformation zone of the slab is represented using the defect width deviation, defect length, and defect area, and the process variables are converted into image data to construct a slab head shape dataset. Subsequently, a convolutional neural network (CNN) architecture that incorporates inter-block connections and attention mechanisms is designed for information extraction. A weight adaptive method multi-task learning method is introduced to balance the training process of three targets, in addition, the Huber loss function and branching network are incorporated into the model training. The experiment results demonstrate that the proposed data conversion method can improve information aggregation, and the AWAMTL framework enhances the model performance, which outperforms the other compared models. Furthermore, based on the proposed prediction model and optimization algorithm, the short stroke control process is optimized. For the defect width deviation, defect length and defect area, the mean improvement rate for all slabs are 10.30 %, 21.89 % and 23.50 %, respectively.

Catalytic synthesis of SiC coated graphite and its application in carbon containing castables

SiC coated graphites were prepared via a catalytic conversion method, employing graphite flakes and silicon powder as the starting materials, and Fe, Co and Ni as catalysts, respectively. The effects of firing temperature and various kinds of catalyst on the growth and morphologies of SiC coating on graphite surface were evaluated. Density functional theory (DFT) calculations revealed that the catalytic role of catalysts on the formation of SiC could be attributed to the occurrence of chemisorption between CO (g) molecule and catalysts owing to charge transfer effects. The graphites.coated with SiC displayed better oxidation resistance and water wettability than uncoated graphites. Also, mechanical strength, thermal shock resistance, slag resistance and oxidation resistance of a model of Al2O3-C castables containing coated graphites were better than its uncoated counterparts.

Hue-preserving lightness and saturation modification in RGB color space for protanopia and deuteranopia

Colors are not seen in the same way among all people. For example, to dichromats such as protanope and deuteranope, red and green appear similar ochre-like colors. Therefore, it is difficult for dichromats to distinguish such color combinations. The purpose of this study is to propose a color conversion method that produces images that are easy for dichromats to discriminate colors, without significantly changing the impression of the original image for both dichromats and trichromats. The proposed method modifies the lightness of pixels to take into account colors that are difficult for dichromats to discriminate, thus enabling color discrimination. Furthermore, by also modifying the saturation of the pixels, the image quality degradation caused by overemphasis, which occurs when only modifying the lightness, is suppressed. To demonstrate the effectiveness of the proposed method, experiments using various images are conducted. From the resultant images, it can be confirmed that the proposed method achieves the color conversion that serves its purpose. The main results of quantitative evaluation are as follows. (1) In the index evaluating the degree of contrast improvement for colors which are difficult for dichromats to discriminate, the average value of the proposed method is about 0.84, and indicates that the proposed method is comparable to the conventional methods. (2) In the index represented by the ratio of the number of pixels clipped with white or black, the average of the proposed method is 4.38×10−4 and indicates that there are almost no clipped white and black pixels.

Upaya Meningkatkan Kemampuan Berbahasa Anak Usia Dini (4-5 Tahun) Melalui Metode Cakap

The development of language skills in early childhood often encounters a range of obstacles. This occurs because early childhood education frequently employs conventional teaching methods that do not sufficiently engage the children’s needs in active language use. Thus, it is necessary to develop the methods adapting to the children's needs to improve language skills in early childhood, namely the conversation method. This research aims to explain the language skills of early childhood before and after the conversation method is applied. The research design is called Classroom Action Research (CAR). In this research, there we four stages include: 1) determining the population and sample, 2) data and sources of data, 3) data collection techniques, 4) data analysis techniques. The research results show that there is an improvement in children's language skills after implementing the conversation method. This can be seen from the results of observations of teacher activities in cycle I and cycle II which have increased, from an average value of 67.40% to 83.94%. The results of observations of children's activities in cycle I and cycle II also increased, from an average value of 56.62% to 78.45%.

Model Conversion and Application Framework for Rectangular Aqueduct Superstructures: Integrating Revit and Midas Civil

To improve the modeling efficiency of finite element analysis models for rectangular aqueducts based on the BIM information model, this study proposes a model conversion method that integrates Revit software and Midas Civil. By accessing the attribute information stored in the structural component objects of the BIM model, the finite element modeling parameters required for structural analysis are obtained. A secondary development program has been established using the C# programming language on the Visual Studio 2022 platform, resulting in the creation of a model interface program (Revit-Midas Civil) that facilitates the conversion of model information. This has saved designers time in constructing finite element models. The feasibility and reliability of the proposed model conversion method have been validated through a multi-girder rectangular aqueduct. The results indicate that the model conversion method successfully transformed structural analysis model information from Midas Civil into Revit model information, significantly enhancing the modeling efficiency and accuracy of rectangular aqueduct structures.

Micromorphology evolution and properties of strontium-zinc-phosphate chemical conversion coatings on Ti:Effect of Zn2+ and Ca2+ in the reaction solution

Bioactive strontium zinc phosphate (SrZn2(PO4)2, SZP) coatings have attracted much attention in the surface modification of medical metal materials. And the micro-morphologies of that coating have an important influence on cellular function and osseointegration. This study prepared a variety of micro-morphology SrZn2(PO4)2 coatings on titanium (Ti) using the phosphate chemical conversion (PCC) method. The effect of Zn2+ concentration in the reaction solution on the micro-morphology and properties of the SZP coatings was studied. The results showed that the SZP crystal size increased with increasing concentration of Zn2+ in the reaction solution, which also led to a more regular morphology of the coatings on Ti. A corresponding increase in the thickness, hydrophilicity, and roughness of the SZP coatings was also observed because of the increase in Zn2+ concentration. The bond strength of the coatings showed an initial increase followed by a slight decline with rising Zn2+ concentration. Moreover, the addition of Ca2+ in the reaction solution also significantly affected the micro-morphology of the SZP coatings, changing the crystal morphology from rectangular blocky to shuttle-shaped lamellar. The wettability, bonding strength, porosity, and ion release rate of the coatings were also increased because of the addition of Ca2+. This study elucidated the mechanism of Zn2+ and Ca2+ ions in the reaction solution on the morphology and properties of SZP coatings. The findings provided a novel approach to controlling and optimizing the micro-morphology of phosphate coatings to enhance their osteogenic activity.

A Hexagon Sensor and A Layer-Based Conversion Method for Hexagon Clusters

A Hexagon Sensor and A Layer-Based Conversion Method for Hexagon Clusters

Text Classification based on Spiking Neural Network with LSTM Conversion

In recent years, spiking neural network (SNN) have attracted much attention due to their low energy consumption and have achieved remarkable results in the fields of vision and information processing. However, the application of SNNs in the field of natural language processing (NLP) is still relatively small. Given that current popular large-scale language models rely on huge arithmetic and energy consumption, it is of great practical importance to explore SNN-based approaches to implement NLP tasks in a more energy-efficient way. This paper investigates the conversion method from LSTM network to SNN and compares the performance of the original LSTM network with the converted SNN in a text classification task. The paper experimentally compares the accuracy of different LSTM-based models using different methods on the same dataset. The experimental results show that the converted SNN is able to achieve similar performance to the original LSTM network with significantly lower power consumption in text classification tasks on multiple datasets.

Rapid Prototyping of Multiscale Flexible Printed Circuit Boards With NeXus, a Robotic Additive Manufacturing System

Abstract This paper demonstrates the NeXus system, a multiscale robotic additive manufacturing platform developed at the Louisville Automation and Robotics Research Institute, as a rapid prototyping tool through additively manufacturing a multilayer flexible printed circuit board (FPC) with a printed strain sensor and soldered surface mounted devices (SMD). Manufacturing of the demonstrator requires the application and curing of multiple materials with specialized properties, tools for automated assembly, and software advances to streamline the process enabling the use of industry-standardized programs to command the NeXus system. Additive manufacturing processes supported by the NeXus include aerosol jet printing (AJP) for fine feature silver conducting lines, direct write ink-jet printing for insulating materials, and intense pulsed light (IPL) for curing materials between depositions. The NeXus system transports and manipulates parts using a six-degree-of-freedom (DOF) high-precision positioner. Solder paste deposition and pick-and-place (PnP) procedures are performed by a 4DOF Selective Compliance Articulated Robot Arm (SCARA). Conversion methods between traditional printed circuit board (PCB) design software and production-ready command scripts were developed to translate basic drawings into command scripts. Multilayer structures with AJP 50-μm wide lines, an insulating bridge with a thickness of around 100 μm, and SMDs soldered to silver AJP pads were integrated within the demonstrator. An operational amplifier and other SMDs reduce the complexity of the accompanying control circuit and amplify the sensor's response by 1830 times. The successful fabrication of the demonstrator FPC highlights the rapid prototyping ability of the NeXus system.

Analysis of Economic Development Mechanism Based on Stochastic Dynamic Optimization Algorithm

With the continuous improvement of China’s economic level, the research of economic data management has been paid more and more attention. As the high-tech industry plays an increasingly important role in the world economy, it has become a strategic highland for all countries in the world. How to design an economic data management model that integrates data mining techniques to achieve deep data mining and efficient utilization has become a research focus. In order to study the technical selection model of economic data, a new economic data management model based on a data mining algorithm is proposed. In the study of economic management data, the optimization model of data management is designed based on computer technology. In the optimization of data classification, a data mining algorithm is adopted to fully realize the reasonable management of data. According to the principle of the Random Dynamic Optimization Algorithm, the algorithm is improved, and its convergence speed is accelerated. The improved algorithm is applied to solve the one-dimensional cutting problem. Compared with other algorithms, the conversion method greatly improves the utilization rate of raw materials. Then, the efficiency and adaptability of the algorithm are tested by determining the algorithm and applying it to the knapsack problem.

DeepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction.

Coarse-grained models have provided researchers with greatly improved computational efficiency in modeling structures and dynamics of biomacromolecules, but, to be practically useful, they need fast and accurate conversion methods back to the all-atom representation. Reconstruction of atomic details may also be required in the case of some experimental methods, like electron microscopy, which may provide Cα-only structures. In this contribution, we present a new method for recovery of all backbone atom positions from just the Cα coordinates. Our approach, called deepBBQ, uses a deep convolutional neural network to predict a single internal coordinate per peptide plate, based on Cα trace geometric features, and then proceeds to recalculate the cartesian coordinates based on the assumption that the peptide plate atoms lie in the same plane. Extensive comparison with similar programs shows that our solution is accurate and cost-efficient. The deepBBQ program is available as part of the open-source bioinformatics toolkit Bioshell and is free for download and the documentation is available online.

High-Na-Content Birnessite via P'3-Stacking with Tunable Active Facets for Advanced Aqueous Sodium-Ion Batteries.

Layered Na-birnessites are promising cathode materials for aqueous sodium-ion batteries due to their high theoretical capacity, low cost, and environmental benignity. However, the general O'3 Na-birnessites possess low Na content and dominant inactive {001} exposed facets, which compromise their Na storage capability and cycling stability. Herein, we develop a high-Na-content P'3-Na0.71MnO2·0.15H2O with highly enriched {010} active facets by a hydrothermal conversion method. In comparison with the O'3 Na-birnessite, the P'3 Na-birnessite with a high ratio of {010}/{001} exposed facets provides greatly increased open channels for Na+ diffusion, while the P'3 stacking affords a lower Na+ diffusion barrier, resulting in improved electrode kinetics with a large specific capacity of 176 mAh g-1 at 0.2 A g-1. More importantly, the P'3 Na-birnessite manifests solo Na+ intercalation/deintercalation with extraordinary cycling stability in an aqueous electrolyte, achieving 90.5% capacity retention after 60,000 cycles. When coupled with the NaTi2(PO4)3 anode, the P'3 Na-birnessite-based full cell delivers both high energy density and long cycle life, demonstrating the potential application in aqueous sodium-ion batteries. This study demonstrates an efficient method to prepare high-Na-content P'3 birnessite with tunable exposed facets and provides important insights into developing highly stable layered cathodes for sustainable aqueous sodium-ion batteries.

A novel intelligent bearing fault diagnosis method based on image enhancement and improved convolutional neural network

Bearing fault diagnostic is crucial for ensuring the normal operation of equipment. However, the working environment of mechanical equipment is often very harsh, the collection of fault data is limited in the actual industrial process. The problem of insufficient fault samples occurs, which makes the feature extraction ability of the deep learning models drop dramatically. To solve this terrible problem, an intelligent diagnosis framework based on image enhancement and an improved convolutional neural network (IEICNN) is outlined in this article. First, a signal conversion method based on feature fusion is employed to convert multi-channel one-dimensional vibration signals into red − green − blue (RGB) images to obtain more comprehensive fault data representation. In addition, a new image enhancement method is proposed, which uses the improved Squeeze and Excitation-Residual Network (SE-ResNet) to learn and extract the advanced feature map, and uses deconvolution neural networks to reconstruct RGB image. Finally, the Softmax classifier is applied to identify the health conditions of the bearing. The superiority and robustness of IEICNN are validated using two bearing datasets. By comparing with several progressive comparison approaches, it is proven that the proposed approach has better robustness and effectiveness in the case of limited data scarcity.

A novel bio-aerogel based on agroforestry waste chestnut shell for enhanced oil-water separation performance

Bio-based aerogels have attracted much attention due to their biodegradability and excellent oil-water separation properties. However, the preparation of inexpensive green aerogels with high separation efficiency is still a great challenge. In this paper, low-density, high-adsorption capacity and superhydrophobic chestnut shell-based bio-aerogels were prepared by chemical cross-linking and chemical vapor deposition using agricultural and forestry waste chestnut shells as the precursor of aerogels to achieve high efficiency of oil-water separation and to achieve the goal of “treating waste with waste”. The covalent cross-linking of chestnut shell cellulose and cross-linking agent to form silicone-containing functional groups improved the pore structure, hydrophobicity and mechanical properties of chestnut shell cellulose, and under the action of capillary force, the oil was retained in the pores of the bio-aerogels, which achieved the selective adsorption of oil and organic solvents in the oil-water mixture, with a separation efficiency of more than 98 %. After 10 cycles, the bio-aerogel still has good reusability. This eco-friendly and low-cost aerogel provides a practical method for resource conversion and recycling, as well as a green and innovative way for the treatment of oily wastewater.

Pulsed polarized vortex beam enabled by metafiber lasers

Pulsed polarized vortex beams, a special form of structured light, are generated by tailoring the light beam spatiotemporally and witness the growing application demands in nonlinear optics such as ultrafast laser processing and surface plasma excitation. However, existing techniques for generating polarized vortex beams suffer from either low compactness due to the use of bulky components or limited controlment of pulse performance. Here, an all-fiber technique combining plasmonic metafibers with mode conversion method is harnessed to generate high-performance pulsed polarized vortex beams. Plasmonic metafibers are utilized as saturable absorbers to produce Q-switched pulses with micro-second duration, while the offset splicing method is employed to partially convert the fundamental transverse mode (LP01\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{01}$$\\end{document}) to higher-order mode (LP11\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{11}$$\\end{document}). Eventually, a polarized vortex beams laser is achieved at the telecom band with a repetition frequency of 116.0 kHz. The impact of geometrical parameters including period of metafibers and offset of splicing on the spatiotemporal properties of pulsed polarized vortex beams is systematically investigated. Our findings could pave the way for design, control and generation of all-fiber pulsed polarized vortex beams, and also offer insights into the development of other types of structured laser sources.

Emergency conversion from Robotic Thoracoscopy to Thoracotomy: A safe, reproducible, and effective technique

Robotic thoracoscopic surgical operations are increasingly performed worldwide. Current lung resection trends indicate that the robotic approach has now surpassed conventional VATS (video assisted thoracoscopy surgery) in the United States. Emergency conversion to an open thoracotomy is an uncommon occurrence; however, preparedness for this infrequent but potentially catastrophic event is paramount. There is a paucity of step-by-step instruction available on how to convert from robotic thoracoscopy safely and reliably to thoracotomy. Herein, we present our conversion method which has been refined in an iterative fashion based on real-life experience and testing in a simulated environment. This method ensures pressure control of bleeding either by the surgeon or bedside assist while also maintaining vision of the operative field throughout the conversion process. At no point is bleeding control maintained by an unmanned robotic instrument. This technical report aims to provide thoracic surgery teams a safe and reliable option for emergency conversion during robotic thoracoscopy.

Experimental study of dam-break-like tsunami loads on the vertical structure with overhanging horizontal slab: Flat slab

Tsunamis present a significant risk to coastal infrastructure. This study conducts a comprehensive experimental investigation into the effects of tsunami impacts on a vertical structure equipped with an overhanging horizontal slab. Dam-break waves were generated in the laboratory to simulate tsunami bore. The uplift pressure of the tsunami bore on the horizontal suspended slab and the horizontal pressure on the vertical wall were analyzed by combining experimental data with water flow patterns. The results revealed that the impact process could be categorized into four stages: initial impact, run-up, quasi-steady, and recession. Two characteristic pressures were identified: a maximum pressure during the initial impact stage and a quasi-steady pressure with a longer duration. The maximum uplift pressure was found to increase with the relative position and tsunami bore height. However, this trend was influenced by the slab height and gravity, particularly on the side of the slab closest to the wall. As the slab height increased, the water flow reflection area expanded, diminishing the focusing effect caused by the slab and decreasing the quasi-steady uplift pressure. The uplift coefficient was observed to decrease with an increase in the relative slab height, and a new uplift coefficient envelope was proposed based on experimental data and published articles. Equations for the uplift pressure distribution and a novel conversion method between uplift pressure and horizontal pressure were introduced. Furthermore, based on the measured data and existing theories, equations for estimating the maximum and quasi-steady uplift pressures are presented and validated using published data. These findings provide valuable insights into understanding and estimating the impact of tsunami on structures.