Current Processing Methods Research Articles

Polymer-Based Extracting Materials in the Green Recycling of Rare Earth Elements: A Review.

Rare earth elements (REEs) are becoming increasingly important in the development of modern and green energy technologies with the demand for REEs predicted to grow in the foreseeable future. The importance of REEs lies in their unique physiochemical properties, which cannot be reproduced using other elements. REEs are sourced through mining, with global exploration of additional commercially viable mining sites still ongoing. However, there is a growing need for recycling of REEs due to the current supply of REEs not matching the growing demand, the environmental impact of REE mining and processing (the so-called "balance problem"), and the generation of large volumes of harmful electronic waste (e-waste). Industrial REE processing is mainly carried out by hydrometallurgy processes, particularly solvent extraction (SX) and ion exchange (IX) technologies. However, these methods have a significant environmental impact due to their intensive use of harmful and nonsustainable reagents. This Review highlights the development of approaches involving polymer-based extracting materials for REE manufacturing as more sustainable alternatives to current industrial REE processing methods. These materials include supported liquid membranes (SLMs), solvent impregnated resins (SIRs), macro and micro capsules, polymer inclusion membranes (PIMs), and micro polymer inclusion beads (μPIBs). Polymer-based extracting materials have the advantage of more economical regent usage while applying the same extractants used in commercial SX, enabling applications analogous to the current industrial process. These materials can be fabricated by a variety of methods in a diverse range of physical formats, with the advantages and disadvantages of each material type described and discussed in this Review along with their applications to REE processing, including e-waste recycling and mineral processing.

Organic Ionic Plastic Crystal/Polymer Composite Electrolyte Prepared By Solution Casting Method for Solid-State Lithium Batteries

Solid-state electrolytes have been considered as a promising candidate to address the safety issues for next-generation lithium batteries. Organic ionic plastic crystals (OIPCs) are attracting increasing interests as solid electrolyte materials due to their unique advantages, such as plasticity, nonflammability, good thermal and electrochemical stability. In this study, an OIPC-based composite electrolyte consisting of the OIPC 1-ethyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide (Pyr12TFSI), lithium bis(trifluoromethylsulfonyl)imide (LiTFSI) and the polymer polyvinylidene fluoride (PVDF) has been developed by a facile solution casting strategy. A free-standing and flexible OIPC/polymer composite membrane was fabricated by the solution casting method, which not only provides flexibility and better electrode/electrolyte contact, but also is more compatible with current battery processing methods. The composite membrane used in Li/Li symmetric cell was cycled stably over 900 h at a current density of 0.1 mA cm−2 at 50 °C, demonstrating that the OIPC/polymer composite electrolyte enabled the reversible and stable lithium plating and stripping behaviors. Further tests of the OIPC/polymer composite as solid electrolyte in LiFePO4/Li cell presented a high specific capacity of 149 mAh g−1 at 0.1 C and a long cycle life of over 440 cycles with capacity retention of 89% at 0.5 C at 50 °C, which showed improved rate capability and cycling stability in comparison with the composites with similar compositions but obtained by powder pressing method. This study demonstrated the potential of the OIPC/polymer composite solid electrolyte prepared by solution casting method and will promote the development of high-performance OIPC-based composite electrolytes for solid-state batteries.

Glacier dynamic study of the Terre de Lambert glacier in Greenland based on Landsat 8 and Sentinel-1 data

With global temperatures on the rise, the trend of glacial melting is becoming increasingly evident. This study focuses on the northeastern region of Greenland as the research subject, utilizing remote sensing technology and geographic information technology to study glacial dynamics. Initially, the research processes Landsat 8 data using QGIS to classify the widespread snow and ice cover, facilitating the observation and analysis of its temporal variations. The study then narrows its focus to Terre de Lambert for further analysis. Subsequently, using satellite data from Sentinel-1 and employing SNAP, the research conducts an in-depth analysis of glacier flow speed in specific regions. Through intricate computational models and algorithms, precise glacier flow direction and speed maps are generated. By combining historical data, the study compares and analyzes the glacier flow speed between 2018 and from June 20th to July 14th, 2023. Results indicate that the glacier flow speed in 2023 during the same time frame was faster. Throughout the research process, limitations in current data acquisition and processing methods were identified. Thus, the paper suggests improvements to data acquisition and processing tools to enhance the efficiency of glacier flow speed studies. Theres a fervent hope for the emergence of more convenient, streamlined, and customizable temporal dimension tools for glacier flow speed data in the future. The research findings aim to draw global citizens attention to the issue of glacial melting and inspire collective actions to protect our planet.

Study on pre-oxidation of silver concentrate and leaching behaviour of Zn, Cu and In during oxygen-pressure leaching

Given the limitations of current processing methods and the potential of high recovery of silver as a flotation concentrate, this study proposes a pre-oxidation followed by oxygen pressure sulphuric acid leaching route for zinc, copper and indium and thoroughly examines the alteration in micro-morphology of the silver concentrate of 1.22% Pb, 3010 g/t Ag and 649 g/t In. Under the optimal conditions of a sulphuric acid pre-oxidation temperature of 60 °C, and an acid-to-ore mass ratio of 0.6, over 15 h, the PO and -OH polar groups of flotation reagents on the surface of silver concentrate particles are effectively neutralized through sulphuric acid oxidation. This action prevents the formation of a hydration film and mineral inclusions, thereby facilitating the efficient leaching of valuable elements. With a set reaction temperature of 155 °C, an initial acidity of 160 g/L, a reaction time of 180 min, an oxygen partial pressure of 0.8 MPa and a liquid-to-solid ratio of 7 mL/g, the leaching efficiency of Zn, Cu and In in the silver concentrate reach 94.8%, 98.4% and 91.1%, respectively while the lead and silver grades in the leach residue increase to 2.88% and 7754 g/t, respectively.

High-energy density ultra-thick drying-free Ni-rich cathode electrodes for application in Lithium-ion batteries

Lithium-ion batteries (LIBs) can be considered as one of the pivotal components required to succeed in the drive towards a greener future. However, the current processing methods for the cathode electrode make it harmful and costly due to the toxic solvent drying and recovery stages. Additionally, the reliance on LIBs particularly emphasizes the imperative for increased energy density in both volume and weight, which predominantly relies on the cathode. To surmount these challenges, this research analyses the true requirements of a novel process and introduces a novel solvent-assisted binder (SaB) dry electrode approach to satisfy them. This innovative method incorporates a low-boiling point and relatively non-toxic solvent (ethanol) at less than 3 wt.%, enhancing the processability of the electrode without compromising safety, cost reductions, or environmental impact associated with traditional dry processes. The SaB process allows the calendaring to cause less damage to the electrode thanks to the binder distribution and allows for better fibrillization of the binder, improving the electrode microstructure. Over 100 cycles at 1C, the SaB dry electrode achieves 77.70% capacity retention, surpassing the standard dry electrode's 72.39% at a loading of approximately 30 mg cm−2 and is also capable of performing at ultra-high loadings up to 60 mg cm−2.

A Review of Multi-source Heterogeneous Twin Data Processing Methods in Traffic Scenes

As the complexity of intelligent public transportation systems continues to increase and the degree of interconnection of equipment at all levels gradually deepens, building a digital twin system will generate a large amount of multi-source heterogeneous data. Effective processing and in-depth mining of data can provide real-time and intelligent decision-making optimization for the construction of digital twin systems, thereby efficiently handling complex emergencies. This paper conducts a systematic review on the processing methods and technologies of multi-source heterogeneous twin data in traffic dynamic scenarios. First, the content and classification of multi-source heterogeneous twin data in the operation process of intelligent bus systems are clarified; secondly, the multi-source The data processing methods and technologies applied in each stage of data collection, data storage, data fusion and data analysis in heterogeneous twin data processing are analyzed, and the advantages, disadvantages and applications of various methods and technologies are analyzed; finally, corresponding to the multiple This paper summarizes the methods and technologies for processing multi-source heterogeneous twin data, and points out the challenges and development trends faced by the current multi-source heterogeneous twin data processing methods and technologies.

Advancements in Edge Detection Techniques for Image Enhancement: A Comprehensive Review

Edge detection is a fundamental algorithm in image processing and computer vision, widely applied in various domains such as medical imaging and autonomous driving. This comprehensive literature review critically evaluates the latest edge detection methods, encompassing classical approaches (Sobel, Canny, and Prewitt) and advanced techniques based on deep learning, fuzzy logic, and optimization algorithms. The review summarises the significant contributions and advancements in the field by synthesizing insights from numerous research papers. It also examines the combination of edge detection with current image processing methods and discusses its impact on real-life applications. The review highlights the strengths and limitations of existing edge detection strategies and proposes future avenues for investigation. Various research shows that classical edge detection methods like Sobel, Canny, and Prewitt still play a significant role in the field. However, advanced methods utilizing deep learning, fuzzy logic, and optimization algorithms have shown promising results in enhancing edge detection accuracy. Combining edge detection with current image processing methods has demonstrated improved clarity and interpretation of images in real-life applications, including medical imaging and machine learning systems. Despite the progress made, there are still limitations and challenges in existing edge detection strategies that require further investigation. Future research should address these shortcomings and explore new edge detection algorithm development avenues. By understanding the current state of the art and its implications, researchers and practitioners can make informed decisions and contribute to advancing edge detection in image processing and analysis. Overall, this review serves as a valuable guide for researchers and practitioners working in the field, providing a thorough understanding of the state-of-the-art edge detection techniques, their implications for image processing, and their potential for further development.

Investigating Lexical and Syntactic Differences in Written and Spoken English Corpora

This paper presents an analysis of the differences between written text and the transcription of spoken text using current Natural Language Processing (NLP) methods. The purpose of the study is to investigate the long and rich history of attempts to differentiate spoken and written text in fields such as linguistics, communication, and rhetoric, which date back to the early 20th century. Given the availability of large quantities of machine-readable data and machine learning algorithms that can handle them, it is possible to use a large number of derived features. The research focuses on syntactic and lexical differences in written books and transcriptions of speeches by United States presidents. The analysis investigates morphological, lexical, syntactical, and text-level aspects. In this process, multiple features have been considered including lexical diversity, syllable count, frequency of parts of speech, and features relating to the parse tree, like the average length of noun phrases, and the use of interrogative sentences, among others. This study will enhance our understanding of the difference between written text and the transcription of spoken text in various disciplines including computer science, applied linguistics, communication, and similar fields.

A Review of Research on the Application and Development of Meteorological Monitoring in the Field of Agriculture

The application of meteorological monitoring technology in the agricultural field has become an indispensable and important component of agricultural production and management processes.In this paper, the application of meteorological monitoring in the agricultural field is summarized,including the types of current meteorological monitoring technologies,data acquisition and processing methods,as well as their practical application effects in different agricultural environments,including meteorological stations,satellite remote sensing,unmanned aerial vehicles(UAVs),and sensor networks [4].Then,aspects of meteorological data acquisition and processing methods,data collection,transmission,and storage are explored.Next,the application effects of meteorological monitoring technology in crop cultivation,yield estimation, plant diseases and insect pests,livestock husbandry,and greenhouse agriculture are discussed in detail for different types of agricultural environments.Finally,the development trends and future research directions of meteorological monitoring technology are discussed.

Intense thermal shock generators made of micron-thick SnO2 layers for the on-chip rapid thermal processing in air

Thermal shock-resistant heaters are essential in various electronic devices, including chemical sensors and pyroelectric electron emitters. The advent of ultrafast high-temperature sintering and shock synthesis of nanomaterials has increased the demand for air-operating high-rate joule heaters. Moreover, incorporating such heaters at the chip level would revolutionize current rapid thermal processing methods in semiconductor technology. Here, transparent micron-thick SnO2 resistors are grown by spray pyrolysis on alumina, silica, and oxidized silicon chips and used as thermal shock-resistant air-operating heaters. These heaters can generate repeated heating cycles at rates of 15,000 °C/s up to 900 °C/s. Tin is a group IV element and its impact on Si- and SiO2-based devices is minimal making the presented device silicon compatible. The superior performance of the presented heaters is attributed to the tin oxide’s unique combination of physicochemical properties including high-temperature chemical stability in oxidizing atmospheres, suitable intrinsic conductivity, and optimal negative temperature coefficient of resistance. The application of the SnO2 heaters for the rapid on-chip high-temperature oxidation and sintering of low-melting-point metal thin films, while preserving the metal layer’s nanotexture, is demonstrated.

Current data processing methods and reporting standards for untargeted analysis of volatile organic compounds using direct mass spectrometry: a systematic review

IntroductionUntargeted direct mass spectrometric analysis of volatile organic compounds has many potential applications across fields such as healthcare and food safety. However, robust data processing protocols must be employed to ensure that research is replicable and practical applications can be realised. User-friendly data processing and statistical tools are becoming increasingly available; however, the use of these tools have neither been analysed, nor are they necessarily suited for every data type.ObjectivesThis review aims to analyse data processing and analytic workflows currently in use and examine whether methodological reporting is sufficient to enable replication.MethodsStudies identified from Web of Science and Scopus databases were systematically examined against the inclusion criteria. The experimental, data processing, and data analysis workflows were reviewed for the relevant studies.ResultsFrom 459 studies identified from the databases, a total of 110 met the inclusion criteria. Very few papers provided enough detail to allow all aspects of the methodology to be replicated accurately, with only three meeting previous guidelines for reporting experimental methods. A wide range of data processing methods were used, with only eight papers (7.3%) employing a largely similar workflow where direct comparability was achievable.ConclusionsStandardised workflows and reporting systems need to be developed to ensure research in this area is replicable, comparable, and held to a high standard. Thus, allowing the wide-ranging potential applications to be realised.

Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes.

This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis.

Development of a deep learning model for the automated detection of green pixels indicative of gout on dual energy CT scan

BackgroundDual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Most software labels MSU as green and calcium as blue. There are limitations in the current image processing methods of segmenting green-encoded pixels. Additionally, identifying green foci is tedious, and automated detection would improve workflow. This study aimed to determine the optimal deep learning (DL) algorithm for segmenting green-encoded pixels of MSU crystals on DECTs. MethodsDECT images of positive and negative gout cases were retrospectively collected. The dataset was split into train (N = 28) and held-out test (N = 30) sets. To perform cross-validation, the train set was split into seven folds. The images were presented to two musculoskeletal radiologists, who independently identified green-encoded voxels. Two 3D Unet-based DL models, Segresnet and SwinUNETR, were trained, and the Dice similarity coefficient (DSC), sensitivity, and specificity were reported as the segmentation metrics. ResultsSegresnet showed superior performance, achieving a DSC of 0.9999 for the background pixels, 0.7868 for the green pixels, and an average DSC of 0.8934 for both types of pixels, respectively. According to the post-processed results, the Segresnet reached voxel-level sensitivity and specificity of 98.72 % and 99.98 %, respectively. ConclusionIn this study, we compared two DL-based segmentation approaches for detecting MSU deposits in a DECT dataset. The Segresnet resulted in superior performance metrics. The developed algorithm provides a potential fast, consistent, highly sensitive and specific computer-aided diagnosis tool. Ultimately, such an algorithm could be used by radiologists to streamline DECT workflow and improve accuracy in the detection of gout.

BIM Data Model Based on Multi-Scale Grids in Civil Engineering Buildings

The construction of digital twin cities is a current research hotspot; GIS technology and BIM technology are widely used in the field of digital twin cities. However, BIM is still subject to major limitations in its applications, mainly due to huge amounts of model data, low query efficiency and accuracy, non-uniform marking systems, etc. The reason is that the BIM model itself focuses more on the expression of visual effects and lacks spatial calculation ability and the utilization of spatial location information. Secondly, the current lightweight processing methods for BIM models are mostly based on geometric transformation and rendering optimization, focusing more on the data compression and visual quality of the model, which essentially does not change the data structure of the BIM model, and it is difficult to establish the mapping relationship between spatial location and spatial data, information, and resources. In addition, current coding methods proposed for BIM models are mostly based on the line classification method, which realizes the identification of components based on the classification of their attributes, and the location information is stored according to the attributes or natural language descriptions, which need to be parsed and translated when they are used, and this procedure ignores the importance of spatial location in daily management and emergency management. The importance of spatial location in daily management and emergency management is also ignored. Based on this kind of identification code, it is impossible to directly analyze and apply spatial location data. Therefore, this paper takes the combination of GIS technology and BIM technology as the starting point and proposes a BIM data modeling method based on the BeiDou grid code, based on the efficiency of its underlying data organization and the accuracy of its real geographic location expression on the one hand and the completeness of the information expression by BIM and fine three-dimensional visualization on the other hand. Finally, a series of experiments are carried out based on the method. Through visualization modeling and efficiency experiments, different feature models are meshed to verify the feasibility and efficiency of the model. Through coding and information query experiments, the model′s data organization capability, data dynamic carrying capability, and efficient spatial computation capability and practical application capability are verified.

Valorisation of Rice Husk and Bubble Wrap Plastic Waste through Co-Torrefaction to Optimise Biochar Production

Rice husk is one of the highest generated agricultural waste estimated to be 11.1-13.8 million tons in Indonesia in 2021. This quantity makes rice husk a potential biomass for conversion into energy to minimize the negative impacts of current processing methods, such as open-burning and mulching. Torrefaction was conducted at 300°C for 30 minutes in this study to enhance the characteristics of biomass fuel by producing biochar with improved combustion properties. The biochar produced from torrefaction of rice husk has a calorific value of 15.04 MJ/kg with a volatile matter content of 42.94% and fixed carbon content of 17.94%. Despite this improvement compared to raw rice husk, this biochar tends to have a relatively high mass loss, with a mass yield of only 54%. Therefore, the addition of LDPE plastic feedstock in the form of bubble wrap was carried out up to 50% of the feedstock mass, resulting in further optimization at the RH50 variation, which increased the calorific value to 19.98 MJ/kg and mass yield to 72%. These changes were statistically tested and found to be significant, indicating that torrefaction is a promising technology for rice husk processing, and bubble wrap in this study can significantly contribute to enhancing the characteristics of rice husk as a renewable fuel source.

Research Progress of Eye Movement Analyses and its Detection Algorithms in Alzheimer's Disease.

Alzheimer's disease (AD) has been considered one of the most challenging forms of dementia. The earlier the people are diagnosed with AD, the easier it is for doctors to find a treatment. Based on the previous literature summarizing the research results on the relationship between eye movement and AD before 2013, this paper reviewed 34 original eye movements research papers only closely related to AD published in the past ten years and pointed out that the prosaccade (4 papers) and antisaccade (5 papers) tasks, reading tasks (3 papers), visual search tasks (3 papers) are still the research objects of many researchers, Some researchers have looked at King-Devick tasks (2 papers), reading tasks (3 papers) and special tasks (8 papers), and began to use combinations of different saccade tasks to detect the relationship between eye movement and AD, which had not been done before. These reflect the diversity of eye movement tasks and the complexity and difficulty of the relationship between eye movement and AD. On this basis, the current processing and analysis methods of eye movement datasets are analyzed and discussed in detail, and we note that certain key data that may be especially important for the early diagnosis of AD by using eye movement studies cannot be miss-classified as noise and removed. Finally, we note that the development of methods that can accurately denoise and classify and quickly process massive eye movement data is quite significant for detecting eye movements in early diagnosis of AD.

Survey on the Most Current Image Processing Methods in Huntington's Disease Diagnostics and Progression Assessment

Huntington's disease (HD) is a rare, incurable neurodegenerative disorder where fast and non-invasive diagnosis targeting patients' condition plays a crucial role. In modern medicine, various scientific areas are being combined, such as computing, medicine and biomedical engineering. This survey is focused on the most recent image processing methods applied not only for the purpose of diagnosing HD but also for the assessment of its progression severity, in order to contribute to the effort to prolong life of and to improve its quality.

Effect of Ultrasonic Cleaning after Laser Texturizing of Surface of AISI 316L Steel on the Degree of Wetting and Corrosion Resistance

One of the most common current processing methods in various scientific studies is the modification of surfaces of various structural materials via laser radiation (laser ablation technique). The laser texturizing of metal surfaces is one of the promising applications for the creation of hydrophobic surfaces with a high water contact angle, increased corrosion resistance, and other properties. This paper reports the results of experimental studies to determine the effect of ultrasonic surface cleaning after laser texturizing on the degree of wetting and corrosion resistance of AISI 316L steel. The results show that ultrasonic cleaning leads to the removal of micro-/nano-sized particles formed on the surface following the laser texturizing of roughness. This effect, in turn, helps us to obtain higher values for the water contact angle and to increase the corrosion resistance.

A Study on Underwater Image Processing Techniques

Abstract: In recent years, a potential interest has been developed in exploring and enhancing the quality of underwater images. The captured underwater images often experience scattering and high levels of noise due to the properties of light transit within the water and the biotic movement that occurs inside the ocean bottom. Underwater cameras or devices were fitted inside the unmanned underwater vehicles (UUVs), autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), ocean detector networks, etc to see the ocean bottom. Despite being a crucial detector for keeping an eye on underwater landscapes, modern underwater camera sensors or detectors contain a number of problems. Numerous solutions to the issues with conventional underwater photography have been put out during the last five years. With the help of the number of contributions and difficulties reported by more than 40 researchers in their research article, this study focused to mention the current underwater image processing methods. This article represents an overview of numerous underwater image-processing techniques utilized, including underwater picture de-scattering, underwater image colour refurbishment, and underwater image aspect evaluations. Also, the latest trends, problems, and challenges for creating and processing underwater image sensors were discussed.

Integrating IoT and Novel Approaches to Enhance Electromagnetic Image Quality using Modern Anisotropic Diffusion and Speckle Noise Reduction Techniques

Electromagnetic imaging is becoming more important in many sectors, and this requires high-quality pictures for reliable analysis. This study makes use of the complementary relationship between IoT and current image processing methods to improve the quality of electromagnetic images. The research presents a new framework for connecting Internet of Things sensors to imaging equipment, allowing for instantaneous input and adjustment. At the same time, the suggested system makes use of sophisticated anisotropic diffusion algorithms to bring out key details and hide noise in electromagnetic pictures. In addition, a cutting-edge technique for reducing speckle noise is used to combat this persistent issue in electromagnetic imaging. The effectiveness of the suggested system was determined via a comparison to standard imaging techniques. There was a noticeable improvement in visual sharpness, contrast, and overall clarity without any loss of information, as shown by the results. Incorporating IoT sensors also facilitated faster calibration and real-time modifications, which opened up new possibilities for use in contexts with a high degree of variation. In fields where electromagnetic imaging plays a crucial role, such as medicine, remote sensing, and aerospace, the ramifications of this study are far-reaching. Our research demonstrates how the Internet of Things (IoT) and cutting-edge image processing have the potential to dramatically improve the functionality and versatility of electromagnetic imaging systems.