Efficient Sorting Research Articles

Operating policies for robotic cellular warehousing systems

Robotic Cellular Warehousing Systems provide an innovative robot-to-goods picking approach designed to improve robot transportation efficiency, where robots move to pick items and transport the picked items to workstations. In this study, we investigate the optimal operating policies for such a system by comparing two picking strategies (pick-while-sort and pick-then-sort) and three robot-to-workstation assignment rules (random, closest, and dedicated). Specifically, we develop dedicated closed queuing networks to model robot-to-goods picking and estimate warehouse throughput under different policies through single-class and multi-class models. The effectiveness of these analytical models is validated through numerical simulations, with an average gap of 5.53% between simulation and analytical results. Additionally, we conduct a series of numerical experiments to examine the impact of various factors on warehouse performance, including the numbers of robots and workstations, robot capacity, order size, and sorting efficiency. Based on the experimental findings, we provide managerial implications that offer insights into optimizing resource allocation and system configuration. These insights enable warehouse managers to improve operational efficiency and overall performance.

Fruit Sorting Based on Maturity Reduces Internal Disorders in Vapor Heat-Treated ‘B74’ Mango

Postharvest internal disorders (IDs) in mango fruit present a significant challenge to the industry, with their underlying causes still unclear. This study investigated the relationship between fruit maturity and the susceptibility of vapor heat-treated (VHT) ‘B74’ mangoes to IDs in three experiments. In the first experiment, fruit were categorized into three maturity groups based on dry matter content (DMC): <15%, 15–17%, and >17%, using a handheld near-infrared device. Half of the fruit in each group underwent VHT, while the remainder were untreated controls. Flesh cavity with white patches (FCWP) was the only disorder observed exclusively in VHT fruit. The incidence and severity of FCWP was significantly higher (p < 0.05) in fruit with <15% DMC, with 12.4% incidence and a severity score of 0.2 on a 0–3 scale (0: healthy and 3: severely affected), compared to more mature fruit. In the second experiment, the fruits were harvested at early and late maturity stages, with average DMC values of 14.5% and 17.4%, respectively. The fruit was subjected to no VHT, VHT, and VHT following a 12 h pre-conditioning period at 37 ± 1 °C. Consistent with the first experiment, FCWP was observed only in VHT fruit, with early-harvested fruit displaying a significantly higher (p < 0.05) FCWP incidence (26.9%) and severity (0.3) compared to late-harvested fruit (8.3% incidence and 0.1 severity). Pre-conditioning significantly reduced FCWP, particularly in early-harvested fruit. In the third experiment, fruit maturity sorted based on density was assessed, followed by VHT and simulated sea freight under controlled (CA) and ambient atmospheres. Fruit density did not effectively differentiate maturity considering DMC as a maturity indicator. Storage conditions significantly reduced (p < 0.05) flesh browning incidence from 71.1% under ambient conditions to 33.3% under CA. This study highlights fruit maturity as a key factor in the susceptibility of ‘B74’ mangoes to postharvest IDs following VHT. Therefore, sorting fruit based on DMC at harvest or at the packing facility prior to VHT serves as a valuable decision support for reducing IDs in VHT fruit. Further research will explore advanced technologies to enable rapid and efficient fruit sorting based on DMC.

Developing WasteSAM: A novel approach for accurate construction waste image segmentation to facilitate efficient recycling

The escalating volume of construction activities and resultant waste generation underscores the imperative for developing sophisticated segmentation models to facilitate efficient sorting and recycling processes. This study introduces WasteSAM, an enhanced iteration of the segment anything model (SAM), specifically tailored to address the intricate complexities inherent in construction waste imagery. Drawing upon a comprehensive dataset comprising over 15,000 masks representing five distinct categories of construction materials, WasteSAM exhibits notably superior segmentation capabilities. Quantitative analysis demonstrates significant performance improvements, with WasteSAM outperforming the original SAM model by an average of 23.9% in dice similarity coefficient and 30.0% in normalized surface distance metrics. The integration of stereo-image techniques in refining the training dataset has facilitated WasteSAM in more accurately discerning the three-dimensional structure of waste materials, thereby augmenting the precision of waste classification. Noteworthy is the model’s adeptness in handling intricate textures and patterns across diverse imaging modalities, including varying lighting conditions and complex object interactions. While showing promising results, this study also highlights the need for high-quality, diverse datasets that reflect real-world construction site complexities, rather than merely larger datasets.

Labeling and sorting of avian primordial germ cells utilizing Lycopersicon Esculentum lectin.

Avian species are essential resources for human society, with their preservation and utilization heavily dependent on primordial germ cells (PGCs). However, efficient methods for isolating live PGCs from embryos remain elusive in avian species beyond chickens, and even in chickens, existing techniques have shown limited efficiency. In this study, we present a rapid, simple, and cost-effective method for labeling and sorting circulating-stage PGCs across various avian species, including Carinatae and Ratitae, using Lycopersicon Esculentum (Tomato) lectin (LEL). Notably, this method demonstrates high sorting efficiency by identifying a wide range of PGC subtypes while preserving the proliferative and migratory potential of chicken PGCs. This approach is anticipated to significantly contribute to the conservation, research, and agricultural industries related to avian species globally.

Improving Spike Sorting Efficiency with Separability Index and Spectral Clustering

This study explores the effectiveness of spectral clustering for spike sorting and proposes a Separability Index to measure the difficulty of spike sorting for a signal. The accuracy of spectral clustering is evaluated using different feature sets, including raw samples, first and second derivatives, and principal components analysis (PCA), and compared to two previously published methods. The results obtained over a dataset including 16 signals show that raw samples, with an average accuracy of 73.84%, are effective for spectral clustering-based spike sorting. The analysis demonstrates that the proposed Separability Index can be utilized to classify signals beforehand, reducing the cost and processing time of large datasets. Furthermore, the proposed index can reveal spike sorting difficulty, making it a valuable tool for comparing the performance of various spike sorting methods in depth. The proposed method has higher accuracy (up to 23%) compared to two previously published methods, and its accuracy is aligned with the Separability Index (correlation coefficient = 0.71). Overall, this study contributes to the field of spike sorting and offers insights into leveraging spectral clustering for this task.

Corrigendum to: Current Status of Patent Research on Picking End Effector

An error appeared in the citation of Tables 2 and 3 in the manuscript titled “Current Status of Patent Research on Picking End Effector”, 2024; 18(6): e190523217074 [1]. <p> Original: <p> Li [69] developed a self-triggering apple picking and collecting machine, which consists of five parts: a selftriggering gripper, a telescopic adjustment device, a rotary turntable, a collecting device, and a floor car. The gripper part innovatively adopts a selftriggering gripper design. The invention solves the problem of low apple-picking efficiency and causes less damage to apples. <p> Wang et al. [78] invented an end effector suitable for gripping spherical fruits. The fingers of the bionic human form an effective gripping space through the upper gripping finger, the lower gripping finger, and the auxiliary gripping finger, providing reliable gripping performance. This leads to the avoidance of mechanical damage to the fruit. This device has been found to improve fruit sorting efficiency and reduce labor, but its applicability is not wide enough. <p> Corrected: <p> Li [69] developed a self-triggering apple picking and collecting machine, which consists of five parts: a selftriggering gripper, a telescopic adjustment device, a rotary turntable, a collecting device, and a floor car. The gripper part innovatively adopts a selftriggering gripper design. The invention solves the problem of low apple-picking efficiency and causes less damage to apples (Table 2). <p> Wang et al. [78] invented an end effector suitable for gripping spherical fruits. The fingers of the bionic human form an effective gripping space through the upper gripping finger, the lower gripping finger, and the auxiliary gripping finger, providing reliable gripping performance. This leads to the avoidance of mechanical damage to the fruit. This device has been found to improve fruit sorting efficiency and reduce labor, but its applicability is not wide enough (Table 3). <p> The original article can be found online at https://www.eurekaselect.com/article/131925

Enhancing Bucket Sort Efficiency: A Combinatorial Approach to Algorithm Optimization

Abstract: The efficiency of sorting algorithms is critical to numerous computational processes, including data management, search operations, and real-time applications. Among these algorithms, Bucket Sort is notable for its linear-time performance when data is uniformly distributed over a defined range. However, its efficiency suffers significantly with non-uniform data distributions, resulting in unbalanced buckets and increased sorting time. In our research, we explore how combinatorial algorithms can enhance Bucket Sort’s efficiency by optimizing bucket partitioning, dynamic allocation, and internal sorting strategies. Through rigorous mathematical proofs and analysis, we demonstrate that the application of combinatorial techniques yields substantial improvements in average-case performance, particularly in handling skewed data distributions. Practical case studies in large-scale data processing illustrate the adaptability and effectiveness of these optimizations, supporting their potential in real-world applications.

The Investigation of Hyperparameters Influence on Fruit and Vegetable Image Recognition Performance Using SVM

This study aims to improve the efficiency of automatic classification and quality control of fruits and vegetables through image recognition technology, to achieve efficient and accurate intelligent sorting in agricultural production, reduce labor costs and improve market competitiveness. A high-quality image dataset of 36 fruit and vegetable categories from Kaggle is used in this study. The images in the dataset have been preprocessed to ensure that the data is suitable for the classification task and sets the stage for efficient training and evaluation of the model. Logistic regression was first used as the baseline model in order to compare the performance with the Support Vector Machine (SVM) model. Subsequently, hyperparameter tuning is performed to optimize the model to achieve the best cross-validation accuracy. Next, the SVM model is trained with the selected hyperparameters, and the training time is recorded. The performance of the model was evaluated in detail by confusion matrix and classification reports, and the test set was used for final validation to ensure that the model would also perform well on unseen data. The SVM model achieves an accuracy of 96% on both validation and test sets, which is a very good performance. The hyperparameters optimized by GridSearchCV (C = 10, gamma = 0.1, kernel = rbf) effectively improve the performance of the model, verifying that reasonable hyperparameter selection is crucial to the SVM model. These results show that the model has good generalization ability and potential to be applied to real-time classification tasks.

Magnetic particles–integrated microfluidics: from physical mechanisms to biological applications

Abstract Magnetic nanoparticles (MNPs) have garnered significant attention in biomedical applications. Due to their large surface area and tunable properties, MNPs are used in microfluidic systems, which allow for the manipulation and control of fluids at micro- or nanoscale. Using microfluidic systems allows for a faster, less expensive, and more efficient approach to applications like bioanalysis. MNPs in microfluidics can precisely identify and detect bioanalytes on a single chip by controlling analytes in conjunction with magnetic particles (MPs) and separating various particles for analytical functions at the micro- and nanoscales. Numerous uses for these instruments, including cell-based research, proteomics, and diagnostics, have been reported. The successful reduction in the size of analytical assays and the creation of compact LOC platforms have been made possible with the assistance of microfluidics. Microfluidics is a highly effective method for manipulating fluids as a continuous flow or discrete droplets. Since the implementation of the LOC technology, various microfluidic methods have been developed to improve the efficiency and precision of sorting, separating, or isolating cells or microparticles from their original samples. These techniques aim to surpass traditional laboratory procedures. This review focuses on the recent progress in utilizing microfluidic systems that incorporate MNPs for biological applications.

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.

Review on Automated Storage and Retrieval System for Warehouse

The swift expansion of e-commerce and supply chain operations has significantly enhanced the efficiency of warehouse management systems, establishing them as vital components in augmenting organizations' competitiveness. This paper delves into warehouse sorting systems to enhance the sorting process, reduce error rates, and simplify internal warehouse procedures. It aims to develop a scalable, adaptable, and efficient warehouse sorting system that can maximize sorting efficiency while effectively responding to changing market demands through the use of advanced automation technologies. The study provides an in-depth review of the literature that has explored the Automated Storage and Retrieval System (ASRS) within the context of warehouse operations. It offers a comprehensive introduction to the operational systems of warehouses, detailing each type of ASRS along with the technologies that can improve the efficiency and accuracy of these systems. Moreover, the paper thoroughly investigates and classifies the ASRS design decision problem and compares multiple types of ASRS. The analysis aims to delineate the distinctions among various ASRS configurations, assessing their scalability, adaptability, and their impact on operational efficacy in warehouse environments Through this comparative review, the paper emphasizes the potential enhancements in sorting processes that modern ASRS can provide, ensuring that warehouse operations can rapidly adapt to market changes and demands. The goal is to highlight best practices and technological innovations that can lead to more responsive and efficient warehouse management systems. This exploration contributes to a better understanding of how cutting-edge automation and adaptable system designs can significantly influence the efficiency of warehouse sorting processes.

Classification of Rice Grains by Image Processing

General Background: The assessment of the surface quality of pre-treated rice grains is crucial for determining their market acceptability, storage stability, processing quality, and overall consumer satisfaction. Traditional evaluation methods are often time-consuming and yield subjective classifications. Specific Background: The lack of a comprehensive tagged image dataset hinders the application of advanced convolutional neural networks (CNN) for detailed damage classification of healthy rice grains. Knowledge Gap: Existing datasets and methods limit the effective exploration of sophisticated CNN models for categorizing rice types, particularly in identifying subtle damage characteristics. Aims: The study aims to create a robust rice grain classification system using image processing techniques, primarily deep learning algorithms, to improve the classification of rice varieties. Results: Utilizing a dataset of 75,000 images across five widely cultivated rice varieties in Turkey, we achieved classification accuracies of 100% for CNN, 99.95% for deep neural networks (DNN), and 99.87% for artificial neural networks (ANN). Novelty: The proposed approach represents a significant advancement in rice classification technology, employing a combination of image acquisition, feature extraction, and machine learning to streamline the process, effectively addressing the challenges faced in traditional methods. Implications: The findings underscore the potential for improved sorting and grading efficiency in the rice industry, facilitating better market outcomes and consumer satisfaction through enhanced quality control Highlights: Innovative: Uses CNN for accurate rice variety classification. Data-Driven: Analyzes 75,000 images for enhanced quality evaluation. Impactful: Increases efficiency in sorting and grading processes. Keywords: Rice Classification, Image Processing, CNN, Machine Learning, Agricultural Technology

Efficient indexing and querying of annotations in a pangenome graph.

The current reference genome is the backbone of diverse and rich annotations. Simple text formats, like VCF or BED, have been widely adopted and helped the critical exchange of genomic information. There is a dire need for tools and formats enabling pangenomic annotation to facilitate such enrichment of pangenomic references. The Graph Alignment Format (GAF) is a text format, tab-delimited like BED/VCF files, which was proposed to represent alignments. GAF could also be used to store paths representing annotations in a pangenome graph, but there are no tools to index and query them efficiently. Here, we present extensions to vg and HTSlib that provide efficient sorting, indexing, and querying for GAF files. With this approach, annotations overlapping a subgraph can be extracted quickly. Paths are sorted based on the IDs of traversed nodes, compressed with BGZIP, and indexed with HTSlib/tabix via our extensions for the GAF format. Compared to the binary GAM format, GAF files are easier to edit or inspect because they are plain text, and we show that they are twice as fast to sort and half as large on disk. In addition, we updated vg annotate, which takes BED or GFF3 annotation files relative to linear sequences and projects them into the pangenome. It can now produce GAF files representing these annotations' paths through the pangenome. We showcase these new tools on several applications. We projected annotations for all Human Pangenome Reference Consortium Year 1 haplotypes, including genes, segmental duplications, tandem repeats and repeats annotations, into the Minigraph-Cactus pangenome (GRCh38-based v1.1). We also projected known variants from the GWAS Catalog and expression QTLs from the GTEx project into the pangenome. Finally, we reanalyzed ATAC-seq data from ENCODE to demonstrate what a coverage track could look like in a pangenome graph. These rich annotations can be quickly queried with vg and visualized using existing tools like the Sequence Tube Map or Bandage.

A bi-population cooperative scatter search algorithm for distributed hybrid flow shop scheduling with machine breakdown

The occurrence of machine breakdowns is a frequent and dynamic phenomenon in the production process. The implementation of effective preventive measures can mitigate such events and result in reduced production costs. This paper investigates the distributed hybrid flow shop scheduling problem with machine breakdown (DHFSSPB) considering short maintenance time. The bi-population cooperative scatter search (BCSS) algorithm is proposed to address the DHFSSPB, wherein the search for the optimal scheduling sequence is transformed into genetic evolution aiming to obtain a gene chain with both minimum lower bound and minimum cost attributes. Firstly, the DHFSSPB problem is modeled through a combination of predictive maintenance strategy and right-shift rescheduling rule. Subsequently, a diversification approach is developed to facilitate attribute inheritance, enhance the efficiency of job allocation, and establish a reference set. The reference set is partitioned into two subpopulations based on lower bound attributes and cost attributes, respectively. The corresponding hybrid search strategies are designed to enhance the efficiency of job sorting and machine selection for subpopulations with distinct attributes. The cooperative evolution between subpopulations occurs through the competitive interaction and fusion of individuals. An enhanced reinforcement learning approach is proposed to expedite the acceleration of individual attribute evolution by leveraging evolutionary knowledge acquired from populations, thereby effectively guiding the evolutionary trajectories of individuals. Additionally, a method for evaluating the population during the learning process is developed based on problem characteristics to enhance learning efficiency. Experimental results demonstrate that BCSS outperforms the comparative algorithm in solving the DHFSSPB.

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.

Intelligent detection of coal gangue in mining Operations using multispectral imaging and enhanced RT-DETR algorithm for efficient sorting

The coal gangue generated during coal mining causes serious environmental pollution. To efficiently screen coal gangue, we developed an efficient approach combining multi-spectral imaging with an improved Real-Time Detection Transformer (RT-DETR) for coal gangue detection. Firstly, spectral data from 25 bands were optimized into 3 bands and combined into a pseudo RGB image. Then, the backbone network was enhanced through lighter Faster-Block and Efficient Multi-scale Attention (EMA) mechanisms, with learnable position coding introduced in the Attention based Intra scale Feature Interaction (AIFI) layer and the Re-param Convolutional (RepC3) structure of the Cross scale Feature Fusion Module (CCFM) layer upgraded to Dilated Re-param Block (DRB). Additionally, we propose a Stable Diffusion model based on Low-Rank Adaptation (LoRA) fine-tuning for image generation and enhance the model’s robustness by combining it with data augmentation to simulate specific environments. Our results showed that the method achieves detection accuracy, recall, and mean average precision of 92.18%, 86.78%, and 91.95%, respectively. The lightweight model and high detection accuracy provide an effective solution for mine sorting.

Deep Recyclable Trash Sorting Using Integrated Parallel Attention.

Sorting recyclable trash is critical to reducing energy consumption and mitigating environmental pollution. Currently, trash sorting heavily relies on manpower. Computer vision technology enables automated trash sorting. However, existing trash image classification datasets contain a large number of images without backgrounds. Moreover, the models are vulnerable to background interference when categorizing images with complex backgrounds. In this work, we provide a recyclable trash dataset that supports model training and design a model specifically for trash sorting. Firstly, we introduce the TrashIVL dataset, an image dataset for recyclable trash sorting encompassing five classes (TrashIVL-5). All images are collected from public trash datasets, and the original images were captured by RGB imaging sensors, containing trash items with real-life backgrounds. To achieve refined recycling and improve sorting efficiency, the TrashIVL dataset can be further categorized into 12 classes (TrashIVL-12). Secondly, we propose the integrated parallel attention module (IPAM). Considering the susceptibility of sensor-based systems to background interference in real-world trash sorting scenarios, our IPAM is specifically designed to focus on the essential features of trash images from both channel and spatial perspectives. It can be inserted into convolutional neural networks (CNNs) as a plug-and-play module. We have constructed a recyclable trash sorting network building upon the IPAM, which produces an acuracy of 97.42% on TrashIVL-5 and 94.08% on TrashIVL-12. Our work is an effective attempt of computer vision in recyclable trash sorting. It makes a positive contribution to environmental protection and sustainable development.

Design and Performance Analysis of a Tea Impurity Eliminator

The present tea production technology is extensive and unsanitary as tea processing has the issues of easily mixing in hair, animal hair, and other debris problems. The production enterprises can only use a large number of workers to manually sort the tea, requiring large labor for low efficient and unideal sorting results. Hence, tea production enterprises urgently need to solve this problem to meet the product to meet the quality requirements of health food. In this paper, the design and development of the vibration motor of the key components in the tea impurity removal unit were studied. The specific work is as follows. The dynamic analysis of the movement of the material on the vibrating working surface is performed, the motion state of the tea declutter vibration machine is determined as throwing motion, and the structure scheme of the elastic link eccentric type vibration machine is designed. The structure scheme of the elastic link eccentric vibration motor was modeled by Inventor 3D design software, and the suitable parameters were selected for trial production of a prototype.

Identifikasi Kematangan Buah Pisang Berdasarkan Variasi Jarak Menggunakan Metode K-Nearest Neighbor

This research aims to identify the level of ripeness of kepok bananas based on the color of their skin using the K-Nearest Neighbor (K-NN) method. Bananas are an important commodity in Indonesia, and various ripeness levels need to be identified. The current process of identifying banana ripeness is still done manually, which requires a lot of labor and tends to be subjective. The K-NN method is used to classify bananas based on their skin color. This research involves the collection of banana images with three ripeness levels (raw, ripe, and overripe) and the extraction of RGB color features from these images. Three distance methods, namely Euclidean, Minkowski, and Manhattan, are also employed to compare accuracy results. The evaluation results of this research show that the accuracy value for the Euclidean distance method is 84%, the Minkowski distance method is 82%, and the Manhattan distance method is 80%. Thus, the findings indicate that the K-NN method and the Euclidean distance method provide good results in identifying the ripeness level of bananas. By implementing the K-NN algorithm, this research attempts to address the weaknesses of the time-consuming and subjective manual identification process, with the hope of providing a more accurate and efficient solution for the banana industry. The results of this research can be used to automate the identification process of banana ripeness levels and improve efficiency in banana sorting. It is expected that this research can provide practical benefits to the community and serve as a basis for further research in this field.

Prediction of sperm motion behavior in microfluidic channel using sperm swimming model

Several investigations have recently been conducted using microfluidic channels to sort highly motile sperm and thereby increase the probability of fertilization. To further enhance the efficiency of sperm sorting, predicting sperm movement in microfluidic channels through simulation techniques could be beneficial. In this study, we constructed a sperm swimming model based on the concept of an agent-based model. This model allows analysis at the same spatio–temporal scale similar to microfluidic channels. Sperm movement was simplistically modeled as a random walk, utilizing the distribution of sperm velocity and deflection angle obtained from experimental data. We have developed a thigmotaxis model to describe the phenomenon where sperm near the wall exhibit a reduced tendency to move away from it. Additionally, we created a rheotaxis model, in which sperm reorient in the direction opposite to the flow depending on the shear rate. Using these models, we investigated sperm behaviors within a microchannel featuring a tapered area. The results reveal that sperm accumulate within the tapered area, leading to a significant increase in sperm concentration for specific flow velocity ranges in the microchannel. This model provides valuable information for predicting the effects of sperm sorting in various microfluidic channels.