Sorting Method Research Articles

Deep learning approaches for classification of copper-containing metal scrap in recycling processes

Separating copper from iron scrap is a critical operation in metal recycling and achieving this with low cost sensoric equipment like RGB cameras instead of XRF/XRT is becoming increasingly attractive. In this article, the groundwork for creating an image classification model to separate copper from iron scrap has been performed. Twenty of the most common and most easily available CNN architectures were trained on 2200 metal scrap specimens and evaluated inline on a sensor-based sorting rig for their prediction accuracy and their inference latency to mimic real circumstances in an industrial setting. Out of these evaluated architectures, DenseNet-201 with 98% accuracy in inline tests is recommended if potent hardware is available. Otherwise AlexNet with 92% accuracy or MobileNet-V2 with 90% accuracy are recommended for further investigation and model creation if hardware restrictions apply. Based on the presented results in this article, the initial cumbersome surveyance of the most suitable network architecture can be substantially reduced and the creation of a sorting model can be streamlined. This article thus provides the basis for creating an inline applicable sorting method for scrap metal that uses low cost sensorics equipment and can provide reasonably high accuracy in its prediction.

The Potential for Hyperspectral Imaging and Machine Learning to Classify Internal Quality Defects in Macadamia Nuts

Tree nuts are rich in nutrients, and global production and consumption have doubled during the last decade. However, nuts have a range of quality defects that must be detected and removed during post-harvest processing. Tree nuts can develop hidden internal discoloration, and current sorting methods are prone to subjectivity and human error. Therefore, non-destructive, real-time methods to evaluate internal nut quality are needed. This study explored the potential for VNIR (400–1000 nm) hyperspectral imaging to classify brown center disorder in macadamias. This study compared the accuracy of classifiers developed using images of kernels imaged in face-up and face-down orientations. Classification accuracy was excellent using face-up (>97.9%) and face-down (>94%) images using ensemble and linear discriminate models before and after wavelength selection. Combining images to form a pooled dataset also provided high accuracy (>90%) using artificial neural network and support vector machine models. Overall, HSI has great potential for commercial application in nut processing to detect internal brown centers using images of the outside kernel surface in the VNIR range. This technology will allow rapid and non-destructive evaluation of intact nut products that can then be marketed as a high-quality, defect-free product, compared with traditional methods that rely heavily on representative sub-sampling.

Improved NSGA2 algorithm for recovery of interrupted departure flights

To solve the problem of airline flight disruption caused by emergencies, the disrupted departing flights are restored, a dual-objective optimization model is constructed to minimize the total delay cost of airlines and minimize the total delay time of passengers. An adaptive non-dominated sorting genetic algorithm based on dominance intensity is designed, and three improved operations are proposed: a fast dominance sorting method, a new congestion distance and an adaptive elite retention strategy. The proposed algorithm is verified by the operation data of an airline at Fuzhou Airport. The experimental results show that compared with the traditional plan-first-serve method, the solution obtained by the proposed algorithm is greatly optimized. Compared with the constraint method, the solution time is generally lower than the constraint method and the solution result is close to the optimal result obtained by the constraint method. Compared with multi-objective optimization algorithms such as NSGA2 and MOEAD, the proposed algorithm shows better performance and can solve the problem effectively and efficiently, providing a basis for airlines to achieve optimized solutions.

An in-depth study of sorting algorithms

Sorting algorithms are very popular fundamental algorithms in the field of computer science. Its job is sorting statistics so that they are arranged according to certain rules. In this paper, the kinds of sorting algorithms and their research method of them will be discussed. It will consist of discussing bubble sort, selection sorting, insertion sorting, hill sorting, and merge sorting. Those of them are that arrange a group of elements according to a certain number of ones. They are widely used in computer science and data processing in all walks of life. The most common sorting methods are numerical order and lexicographic order. Efficient sorting algorithms are significant in some of the algorithms, such as search and merge algorithms, so that they can arrive at the correct solution. Dealing with textual data and producing readable output are also important applications of sorting. The outcome is a permutation or recombination of the original input. So learning about the principles of those sorting algorithms by recognizing the code or reading pictures is significant. It is also necessary to be familiar with the applications of those algorithms which are beneficial to the daily life.

Multi-Particle sorting using signals from particles trapped by single optical fiber tweezers

We propose a multi-particle sorting method based on single optical fiber tweezer particle-trapped signals. When particles are trapped to the optical axis, the instantaneous trapped signal is collected by the photodetector. There are significant differences in the instantaneous trapped signal intensity for particles with different refractive indices. Specifically, the variation in instantaneous trapped signal intensity correlates well with changes in the refractive index of the particles in a linear relationship. We conducted 60 sets of experiments, which showed that the method accurately sorts yeast cells, silica (SiO2) microspheres, and polystyrene (PS) microspheres. Additionally, the method’s simple structure, high accuracy, ability to simultaneously sort multiple particles, and potential to handle large quantities of particles provide a new approach to particle identification and detection. Consequently, this method is widely used in chemistry, microbiology, and medical diagnostics.

Expression of TLR7/8 in canine sperm and evaluation of the effect of ligand R848 on the sorting of canine X/Y sperm

The aim of this study was to analyze the expression pattern of Toll receptor 7/8 (TLR7/8) in canine sperm, and explore the feasibility of using TLR7/8 ligand resiquimod(R848)to separate canine X and Y sperm. In this study, cellular immunofluorescence was used to analyze the expression of TLR7/8 in canine sperm, real-time fluorescence quantitative PCR was used to calculate the proportion of X sperm in the lower layer of the incubation solution with R848 to evaluate the sorting effect of R848 on canine X/Y sperm, and sperm quality detection system was used to analyze the effect of R848 on the motility of canine sperm. The mechanism of effect of R848 on canine sperm motility was analyzed by Western blot. The results showed that TLR8 was not expressed in all canine sperm, while TLR7 was expressed in all canine sperm and was localized in the head and tail of sperm. When 0.4 μM R848 was incubated with canine sperm for 1 h, the total motility, average path velocity (VAP), average straight-line velocity (VSL), and average curved-line velocity (VCL) of canine sperm were significantly decreased(P<0.05). There was no significant difference between the lower and upper layers of the R848 treatment group and the control group(P>0.05), and the proportion of X sperm was nearly half. The levels of NF-κB and GSK3α/β phosphorylation of sperm in R848 treatment group were significantly increased compared with control group(P<0.05). The above results showed that TLR7/8 was not differentially expressed in canine X and Y sperm. R848 could decrease the motility of canine spermatozoa and inhibit sperm motility by the GSK3α/ β-hexokinase pathway through the phosphorylation of NFκB and GSK3α/β, while could not separate X and Y spermatozoa. The method of sorting X/Y sperm based on TLR7/8 is not feasible for dogs.

Photonuclear partial reaction cross sections: Systematic uncertainties and reliability

The well-known significant systematic disagreements between cross sections of partial photoneutron reactions (γ,1n) and (γ, 2n) obtained in various experiments majority of which have been carried out at Livermore (USA) and Saclay (France) were analyzed using the objective physical criteria of data reliability. It was found that experimental data for more than 50 nuclei from 51V to 209Bi in general do not meet those criteria and therefore are more or less unreliable. The experimental-theoretical method based on the Combined PhotoNuclear Reaction Model (CPNRM) was used to evaluate partial reaction cross sections that meet the reliability criteria. It was shown using the analysis in detail of differences between evaluated and experimental cross sections that the major sources of large systematic uncertainties in obtained cross sections are certain shortcomings of experimental neutron multiplicity sorting method have been used for indirect separation of partial reactions. It was shown that the newly evaluated cross sections of partial photoneutron reactions differ significantly (at least noticeably) from the ones obtained using the method of neutron multiplicity sorting but agree with experimental data obtained by alternative methods used for reliable direct separation of partial reactions. The consolidated review of many problems with partial photoneutron reaction experimental data reliability and some ways to solve ones are presented.

An improved relative integral capacity-K-means clustering method for capacity pre-sorting of decommissioned power batteries

Abstract Capacity sorting is the primary prerequisite for the stepwise utilization of decommissioned power batteries. This study proposes an improved relative integral capacity-K-means clustering (RIC-KMC) method for preliminary sorting of decommissioned power battery capacity. Firstly, according to the electrochemical aging characteristics of decommissioned power batteries, combined with the ampere-time integration algorithm, a new lossless extraction method of the capacity characteristics of Lithium-ion batteries based on the segment voltage is proposed. Second, to minimize the interference of environmental factors and sampling errors on the charging capacity, the relative amount of charging capacity is introduced. Finally, to complete the capacity sorting before the ladder utilization, an improved RIC-KMC method is proposed, which combines the electrochemical aging feature values of decommissioned power batteries extracted from the segment voltage charging capacity with the K-means clustering algorithm. Using three sets of battery charging and discharging data to validate the sorting algorithm, the capacity sorting errors are reduced by 0.926%, 12.381%, and 10.185%, respectively, which verification the validity of the proposed RIC-KMC method. This study provides a new solution for capacity pre-sorting of decommissioned power batteries for stepwise utilization.

APPLICATION OF MACHINE LEARNING METHODS TO ENHANCE THE PERFORMANCE OF BIG DATA SORTING ALGORITHMS

The use of machine learning methods to optimize big data sorting algorithms has become an urgent research topic due to the growing volume of information and the requirements for their rapid processing. Machine learning provides opportunities to automate and improve traditional sorting methods, allowing you to reduce the cost of computing resources and time. This is achieved by analyzing the characteristics of the data and preprocessing them using classification and regression. The main advantages of using machine learning in sorting big data include improving the accuracy and adaptability of algorithms to different types of data, which is especially important for areas with large amounts of information, such as finance, medicine and logistics. Progressive machine learning algorithms such as supporting vectors, decision trees, and gradient boosting demonstrate high efficiency and potential for further development and integration.

Industrial optical sorting for marine plastic litter management

The aim of this study is to evaluate the possibility of incorporating marine litter from the coast into the management system of the non-selectively collected fraction of Municipal Solid Waste (MSW). With this purpose, the technical feasibility of automatic separation of marine plastic litter is evaluated, which would allow for subsequent recycling. This solution requires mechanical–biological-treatment plants (MBT) to be able to sort marine plastic litter. Poly (ethylene terephthalate) (PET) bottles constitute one of the most commonly reported types of marine plastics. Considering the sorting methods employed in MBT plants, optical sorting tests were performed with marine PET litter and compared with those for post-consumer municipal PET waste, in order to assess whether the current technology implemented in these plants would allow their automatic sorting. Results show that the separation efficiency of PET degraded under marine conditions and outdoors is very high, and close to that of post-consumer municipal PET waste. The separation efficiency by transparent colour for different types of PET materials evaluated is also very effective and similar to that obtained for separation by chemical composition. Therefore, the chemical differences observed would not affect the efficiency of automatic sorting of marine origin PET in a waste treatment plant.

DEEP NEURAL NETWORKS AS A TOOL FOR ENHANCING THE EFFICIENCY OF PLASTIC WASTE SORTING

In the recycling industry, there is an urgent need for high-quality sorted material. The problems of sorting centers related to the difficulties of sorting and cleaning plastic leads to the accumulation of waste in landfills instead of recycling, emphasizing the need to develop effective automated sorting methods. This study proposes an intelligent plastic classification model developed on the basis of a convolutional neural network (CNN) using architectures such as MobileNet, ResNet and EfficientNet. The models were trained on a dataset of more than 4,000 images distributed across five categories of plastic. Among the tested architectures, proposed EfficientNet-SED demonstrated the highest classification accuracy – 99.1%, which corresponds to the results of previous research in this area. These findings highlight the potential of using advanced CNN architectures to improve the efficiency of plastic recycling processes.

친환경 화장품 브랜드의 경험디자인 구성요소 사례 연구

The value of eco-friendliness which has been continuously increasing since the Industrial Revolution and rapid industrial development has reached a major turning point after the COVID-19 pandemic. In particular cosmetics which are products closely related to the body are not simply products applied to the body but can secure a competitive advantage when they satisfy consumers' eco-friendly values and experience elements. Therefore, this study is a study that analyzes eco-friendly cosmetics brands as components of experience design in line with changing consumer needs and the eco-friendly cosmetics brand market. Through prior research we looked into the research trends of eco-friendly cosmetics and experience design and derived experience design components for analyzing eco-friendly cosmetics brand cases. For balanced research the cosmetics brands subject to the case analysis were selected as top brands according to brand reputation big data analysis results and five brands were selected by company size and domestic/overseas. For the analysis a workshop was held with five service experience design experts. The study was conducted by adopting a closed card sorting method that classifies and analyzes the information provided in the five categories of experience design components (Sensory Perception experience, Feeling &amp; Emotions experience, Intellectual Cognition experience, Behavior experience, and Social Relationship experience). Through case analysis it was found that eco-friendly cosmetic brands with high brand reputation and preference have all five components of experience design and meet the needs of consumers. Therefore in order to secure a differentiated competitive advantage for eco-friendly cosmetics brands it is important to establish a strategy that balances the components of experience design and the study is significant in that it can be used as a reference paper when planning eco-friendly cosmetics brands.

Construction and comparative analysis of an early screening prediction model for fatty liver in elderly patients based on machine learning

Objective: To construct a prediction model for fatty liver disease (FLD) among elderly residents in community using machine learning (ML) algorithms and evaluate its effectiveness. Methods: The physical examination data of 4989 elderly people (aged over 60 years) in a street of Shanghai from 2019 to 2023 were collected. The subjects were divided into a training set and a testing set in a 7:3 ratio. Using feature selection and importance sorting methods, eight indicators were selected, including high-density lipoprotein cholesterol, body mass index, uric acid, triglycerides, albumin, red blood cell, white blood cell, and alanine aminotransferase. Six ML models, including Categorical Features Gradient Boosting, eXtreme Gradient Boosting, Light Gradient Boosting Machine, Random Forest, Decision Tree, and Logistic Regression, were constricted, and their predictive performances were compared via accuracy, precision, recall, F1 score, and Area Under Receiver Operating Characteristic Curve. Results: Among the six ML models, the Categorical Features Gradient Boosting model demonstrated the highest prediction accuracy of 0.74 for FLD in elderly community population, along with a precision of 0.70, a recall of 0.73, a F1 score of 0.71, and an area under the curve of 0.74. Conclusions: In the context of rapid development of artificial intelligence, a community-based elderly FLD prediction model constructed using ML algorithms aid family general practitioners in the early diagnosis, early treatment, and health management of local FLD patients.

Isolation and Whole-Cell Patch-Clamp Recording of Hippocampal Microglia from Adult Mice.

Microglia are resident immune cells in the brain that interact with neurons to maintain the homeostasis of the central nervous system (CNS). Studies show that the microglial surface expresses potassium channels that regulate microglial activation, while abnormalities in these potassium channels can lead to neural diseases. Currently, whole-cell patch-clamp recordings of microglia are mostly performed on cultured primary microglia from fetal or newborn mice due to difficulties in conducting electrophysiological evaluations on acutely isolated microglia. This study introduces an easy-to-follow protocol for isolating hippocampal microglia from adult mice and performing whole-cell patch-clamp recordings on the isolated cells. Briefly, the brain was removed from a mouse after decapitation, the hippocampus was dissected bilaterally, and microglia were isolated using an adult mouse brain dissociation kit. The microglia were then purified using a magnetic-activated cell sorting (MACS) method and seeded onto coverslips. Successful microglial isolation was confirmed by immunofluorescent staining with anti-CD11 and anti-Iba1 antibodies. A cover slip was placed in a recording chamber, and the whole-cell potassium currents of the acutely isolated microglia were recorded under voltage-clamp conditions.

Analyzing sedimentation patterns in the Naumure Multipurpose Project (NMP) reservoir using 1D HEC-RAS modeling

Naumure Multipurpose Project (NMP) featuring a 169 m high Concrete Face Rock Filled Dam (CFRD) is the proposed reservoir project in the West Rapti River with an installed capacity of 218.34 MW. Most of the rivers in Nepal carry significant sediment loads that will consequently catalyze reservoir sedimentation. This phenomenon prevails as the primary factor in reducing reservoirs useful life, making ineffective for both flood control and hydroelectricity generation. Ultimately, such process of sedimentation has adverse impacts on projects economic feasibility and long-term sustainability. Therefore, the objective of this research was to examine the expected sediment deposition pattern in the NMP reservoir throughout its operational period by employing 1D HEC-RAS model to simulate the sedimentation process. The model was simulated for 10, 20, 30, 40 and 50 years. Yang’s equation as a sediment transport function, Active layer as a bed sorting method and Toffaleti as a fall velocity method were best suited for the river reach. The delta deposition was formed between 11 km and 22 km upstream of the dam region in the Jhimruk river, with the sediment deposition depth reaching peaks of about 23 m, 38 m, 39 m, 41 m and 49 m in 10, 20, 30, 40 and 50 years, respectively. Similarly, the delta deposition was formed between 13 km and 33 km upstream of dam region in the Madi river, with the sediment deposition depth reaching peaks of about 47 m, 62 m, 60 m, 68 m and 75 m in 10, 20, 30, 40 and 50 years respectively. Headcutting of delta deposition occurred between 20 and 30 year due to high flood during low stage of reservoir. Furthermore, the study revealed that about 6.22%, 11.61%, 15.94%, 22.96% and 25.65% of the storage capacity of NMP reservoir will be depleted in 10, 20, 30, 40 and 50 years respectively.

ANALYSIS OF THE TRANSLATION OF COLORS FOUND IN W. SHAKESPEARE’S SONNETS 1-126

This article presents the analysis on the colors used by W. Shakespeare in Sonnets 1-126 dedicated to a young man, and their translation in Samuil Marshak’s and Khamit Yergaliev’s variants. The most frequently used colors in the Sonnets 1-126 of the English writer are revealed in this work. Excerpts from the Sonnets with colors using are given and compared with their Russian and Kazakh translations. The purpose of this study is a detailed analysis in three variants of the colors appeared in W. Shakespeare’s sonnets 1-126. In the process of scientific work, the differences and similarities of the translated variants, as well as the features of each writer, are determined. The idea of the article is to compare the translation of colors in these Sonnets, to identify the colors most frequently used by the author and by the translators, to allocate the colors into thematic groups of sonnets proposed by A. Anikst. The scientific significance of this work lies in the insufficiency of research on the interpretation of colors in translated variants of W. Shakespeare’s sonnets. The practical significance of the study is determined by the usefulness of the presented data and results in the classes on the theory and practice of translation, comparative lexicology and grammar. In the article, the methods of analysis and synthesis, the sorting method, the comparative method were widely used and led to corresponding results. This research work will be able to give direction for further research on the analysis of the translated variants of the Sonnets, the translation of English colors into Russian and Kazakh languages. Keywords: translation of the Sonnets, indirect translation, colors translation, complex colors interpretation, the thematic groups of the Sonnets, Shakespeare’s color palette, translation transformations, comparative analysis.

Only strong short-term contrarian effect exists in Chinese stock market: The role of the T+1 trading mechanism

We use an alternative sorting method which groups stocks into return intervals rather than percentiles to test the relationship between the T+1 trading mechanism and the short-term contrarian effect in the China's A-share market. Our sample period is from January 2010 to December 2020. China's stock market exhibits a significant contrarian effect in daily, weekly, and monthly frequencies, while the momentum effect is not significant. This paper argues that the special T+1 trading mechanism of China's stock market is the reason for this typical anomaly. The essence of the T+1 trading mechanism is a marketability put option, and this paper takes the volatility as the core of the analysis. This study shows that stock market volatility has a significant impact on the overnight return, with higher volatility leading to a higher discount. Through different daily, weekly, and monthly frequency groupings, the study finds that the winner's portfolio would reverse significantly in the next period. Under the T+1 mechanism, the high return stock portfolio is also characterized by high volatility and high turnover. The contrarian effect of China's stock market is unique in that both the winner's portfolio and the loser's portfolio will fall, but the winner's portfolio will fall more. The reversal of the winner's portfolio dominates in the entire market. The paper provides a new explanation for the contrarian effect and adds a unique explanation to the study of worldwide contrarian effect.

Single Cell Droplet-Based Efficacy and Transcriptomic Analysis of a Novel Anti-KLRG1 Antibody for Elimination of Autoreactive T Cells.

Progress in developing improvements in the treatment of autoimmune disease has been gradual, due to challenges presented by the nature of these conditions. Namely, the need to suppress a patient's immune response while maintaining the essential activity of the immune system in controlling disease. Targeted treatments to eliminate the autoreactive immune cells driving disease symptoms present a promising new option for major improvements in treatment efficacy and side effect management. Monoclonal antibody therapies can be applied to target autoreactive immune cells if the cells possess unique surface marker expression patterns. Killer cell lectin like receptor G1 (KLRG1) expression on autoreactive T cells presents an optimal target for this type of cell depleting antibody therapy. In this study, we apply a variety of in vitro screening methods to determine the efficacy of a novel anti-KLRG1 antibody at mediating specific natural killer (NK) cell mediated antibody-dependent cellular cytotoxicity (ADCC). The methods include single-cell droplet microfluidic techniques, allowing timelapse imaging and sorting based on cellular interactions. Included in this study is the development of a novel method of sorting cells using a droplet-sorting platform and a fluorescent calcium dye to separate cells based on CD16 recognition of cell-bound antibody. We applied this novel sorting method to visualize transcriptomic variation between NK cells that are or are not activated by binding the anti-KLRG1 antibody using RNA sequencing. The data in this study reveals a reliable and target-specific cytotoxicity of the cell depleting anti-KLRG1 antibody, and supports our droplet-sorting calcium assay as a novel method of sorting cells based on receptor activation.

Evolutions of a ring Airy vortex beam and a ring Pearcey vortex beam in turbulent atmosphere and a comparative analysis of their channel efficiencies and OAM spectra

An optical vortex beam propagating through turbulent atmosphere encounters distortions in the wavefront that result in modal scattering. Abruptly autofocusing (AAF) beams with orbital angular momentum have gained significant attention due to their non-diffracting and self-healing nature. These warrant understanding of the behavior of these beams through turbulent atmosphere absolutely necessary. With this intuition, in the present work we investigate the behavior of two AAF beams, namely the ring Airy vortex beam (RAVB) and ring Pearcey vortex beam (RPVB) through the turbulent atmosphere in two cases—multiplexed and non-multiplexed. We propagate multiplexed as well as non-multiplexed RAVB and RPVB in different levels of turbulent atmosphere. In the non-multiplexed case, channel efficiency declines for both beams with increase in mode numbers. In the multiplexed case, increasing the gap between the mode sets results in a decrease in channel efficiency. We also report that in weak atmospheric turbulence RAVB outperforms RPVB in terms of channel efficiency. We use the optical transformation sorting (log-polar) method to demultiplex the optical beams at the output. Furthermore, we investigate and compare the orbital angular momentum (OAM) spectra of both beams in different levels of atmospheric turbulence and at different propagation distances. The comparison reveals that the spectra of RPVB are more dispersive as compared to those of RAVB.

Detection of coal gangue based on MSRCR algorithm and improved lightweight YOLOv8n

ABSTRACT Traditional methods for coal gangue sorting exhibit low efficiency, significant safety hazards, and limited applicability. Existing machine vision-based coal gangue image recognition methods struggle to balance model recognition speed and accuracy. In response to these challenges, this paper first utilizes the improved MSRCR algorithm to process images, enhancing the dark areas of coal gangue images while ensuring uniform brightness enhancement and image clarity. Furthermore, a novel lightweight coal gangue recognition method is proposed based on YOLOv8n, aiming to reduce data redundancy and improve recognition accuracy. Experimental results demonstrate that the improved lightweight model has a computational load of 7.1 GFLOPs, representing only 86.6% of the original model. The model detection rate is 73 fps, a 17 fps improvement over the original model. The accuracy, recall rate, and average precision reach 98.1%, 97.6%, and 98.9%, respectively, with improvements of 1.1, 0.9, and 0.5% points over the original model. The missing detection phenomenon is avoided effectively, and the accuracy and portability of the model are improved.