Efficient Sorting Research Articles

Rapid Vacuolar Sorting of the Borate Transporter BOR1 Requires the Adaptor Protein Complex AP-4 in Arabidopsis.

Plants maintain nutrient homeostasis by controlling the activities and abundance of nutrient transporters. In Arabidopsis thaliana, the borate (B) transporter BOR1 plays a role in the efficient translocation of B under low-B conditions. BOR1 undergoes polyubiquitination in the presence of sufficient B and is then transported to the vacuole via multivesicular bodies (MVBs) to prevent B accumulation in tissues at a toxic level. A previous study indicated that BOR1 physically interacts with µ subunits of adaptor protein complexes AP-3 and AP-4, both involved in vacuolar sorting pathways. In this study, we investigated the roles of AP-3 and AP-4 subunits in BOR1 trafficking in Arabidopsis. The lack of AP-3 subunits did not affect either vacuolar sorting or polar localization of BOR1-GFP, whereas the absence of AP-4 subunits resulted in a delay in high-B-induced vacuolar sorting without affecting polar localization. Super-resolution microscopy revealed a rapid sorting of BOR1-GFP into AP-4-positive spots in the trans-Golgi network (TGN) upon high-B supply. These results indicate that AP-4 is involved in sequestration of ubiquitinated BOR1 into a TGN-specific subdomain "vacuolar-trafficking zone," and is required for efficient sorting to MVB and vacuole. Our findings elucidate the rapid vacuolar sorting process facilitated by AP-4 in plant nutrient transporters.

Smart Ellis Island? Tracing techniques of automating border control

The buzzword “smart borders” captures the latest instantiation of media technologies constituting state bordering. This article traces historical techniques of knowledge-production and decision-making at the border, in the case of Ellis Island immigration station, New York City (1892–1954). State bordering has long been enabled by media technologies, engulfed with imaginaries of neutral, unambiguous, efficient sorting between desired and undesired migrants—promises central to today’s “smart border” projects. Specifically, the use of “proxies” for decision-making is traced historically, for example, biometric or biographic data, collected as seemingly authentic and neutral stand-ins for the migrant. Techniques of selecting, storing, and correlating proxies through media technologies demonstrate how public health anxieties, eugenics, and scientific technocracy of the Progressive Era formed the context of proxies being entrusted to enable decision-making. This pre-digital history of automation reveals how the logics and politics of proxification endure in contemporary border regimes and automated media at large.

Machine vision-based detection of forbidden elements in the high-speed automatic scrap sorting line

Highly efficient industrial sorting lines require fast and reliable classification methods. Various types of sensors are used to measure the features of an object to determine which output class it belongs to. One technique involves the use of an RGB camera and a machine learning classifier. The paper is focused on protecting the sorting process against prohibited and dangerous items potentially present in the sorted material that pose a threat to the sorting process or the subsequent metallurgical process. To achieve this, a convolutional neural network classifier was applied under real-life conditions to detect forbidden elements in copper-based metal scrap. A laboratory stand simulating the working conditions in a high-speed scrap sorting line was prepared. Using this custom stand, training and test sets for machine learning were gathered and labeled. An image preprocessing algorithm was designed to increase the robustness of the resulting forbidden element detector system. The performance of multiple neural network architectures and data set augmentations was analyzed. The highest accuracy of 98.03% and F1-score of 97.16% were achieved with a DenseNet-based classifier. The results of this paper show the feasibility of using the presented solution on a high-speed industrial line.

Automated Sorting Conveyor Using Pneumatic Actuators for Industrial Applications at Morowali Metal Industry Polytechnic

The use of automatic sorting systems in industry plays a crucial role in saving time and reducing manual labor through automation. The main challenges are the high error rate and slow manual sorting processes. This research aims to design and develop an automatic sorting conveyor system using Arduino technology and pneumatic actuators to enhance sorting efficiency and accuracy. The proposed solution is a system capable of detecting and sorting objects based on size with very low error rates, thereby increasing productivity and reducing human error. The design and development of this system involve creating a 3D model of components such as the conveyor frame, belt, motor, pneumatic actuators, and sensors, and integrating them into a complete system. Testing was conducted with seven differently sized test objects to evaluate the system's performance in detecting and sorting objects based on size and height. Input and output voltage measurements ensure performance in line with the expected specifications. The research results demonstrate that the developed automatic sorting conveyor system effectively sorts objects with high accuracy. The system can detect and sort objects based on size with a 0% error rate, reducing the time required for the sorting process compared to manual methods. The use of pneumatic actuators and appropriate sensors ensures consistent and reliable system performance. The test results indicate that the system can be integrated into various industrial applications to enhance production efficiency. The developed automatic sorting conveyor system successfully improves the efficiency and accuracy of the sorting process in industry. It shows significant potential for reducing human error and increasing productivity, making it a practical solution for various industrial applications.

High-Performance Semiconducting Carbon Nanotube Transistors Using Naphthalene Diimide-Based Polymers with Biaxially Extended Conjugated Side Chains.

Polymer-wrapped single-walled carbon nanotubes (SWNTs) are a potential method for obtaining high-purity semiconducting (sc) SWNT solutions. Conjugated polymers (CPs) can selectively sort sc-SWNTs with different chiralities, and the structure of the polymer side chains influences this sorting capability. While extensive research has been conducted on modifying the physical, optical, and electrical properties of CPs through side-chain modifications, the impact of these modifications on the sorting efficiency of sc-SWNTs remains underexplored. This study investigates the introduction of various conjugated side chains into naphthalene diimide-based CPs to create a biaxially extended conjugation pattern. The CP with a branched conjugated side chain (P3) exhibits reduced aggregation, resulting in improved wrapping ability and the formation of larger bundles of high-purity sc-SWNTs. Grazing incidence X-ray diffraction analysis confirms that the potential interaction between sc-SWNTs and CPs occurs through π-π stacking. The field-effect transistor device fabricated with P3/sc-SWNTs demonstrates exceptional performance, with a significantly enhanced hole mobility of 4.72 cm2 V-1 s-1 and high endurance/bias stability. These findings suggest that biaxially extended side-chain modification is a promising strategy for improving the sorting efficiency and performance of sc-SWNTs by using CPs. This achievement can facilitate the development of more efficient and stable electronic devices.

Sediment sorting within a relatively wave-exposed and sandy subtidal seagrass (Zostera marina) meadow

Seagrasses impact on sedimentary intertidal and subtidal ecosystems by affecting local hydrodynamics, geomorphology and sediment properties. Their influence on hydrodynamics is to reduce flow velocities in their canopies, and this leads to increased net sedimentation rates and reduction of the grain size. Most investigations of the seagrass-hydrodynamics-sediment feedback system has been carried out over silt and fine-sand beds under tide-dominated conditions, mostly in the intertidal zone. Here, we use sedimentological data from a relatively wave-exposed and subtidal seagrass (Zostera marina) meadow in the Isles of Scilly with a fine-to-medium sand bed and show that the sand within the seagrass meadow is indeed finer than in adjacent, unvegetated regions. However, in contrast to previous studies, this is not due to increased mud/silt content within the seagrass meadow, but an almost 0.1-mm shift in the median sediment size across the sand fraction from 0.25 mm (fine to medium sand) to 0.35 mm (medium sand). The studied seagrass meadow is characterised by a distinct spatial structure comprising of vegetated ‘ridges’ and bare sand ‘valleys’. Even the bare sand valleys within the seagrass meadow are characterised by significantly coarser sand than the adjacent vegetated ridges, providing further confirmation of the efficiency of sediment sorting by wave processes that takes place within seagrass meadows. It is concluded that any numerical modelling involving sediment transport processes associated with seagrass environments must account for variability in the textural characteristics.

The Smart City Waste Classification Management System

In response to the growing demands of urbanization, our research presents a pioneering Smart City Waste Classification Management System utilizing advanced computer vision techniques for efficient and accurate waste sorting. This system integrates the innovative CT-Net algorithm, which synergizes the strengths of Convolutional Neural Networks (CNNs) and Transformer architectures to tackle the complex challenges posed by varied and unpredictable urban waste characteristics. Extensive evaluations on multiple datasets, including the proprietary Huawei Cloud waste dataset, demonstrate that our model significantly outperforms existing methodologies in terms of precision, robustness, and processing speed. By deploying this technology within urban waste management frameworks, cities can achieve remarkable improvements in sustainability and operational efficiency.

E-WASTE MANAGEMENT THROUGH DEEP LEARNING: A SEQUENTIAL NEURAL NETWORK APPROACH

The goal of this research is to improve the management of electronic trash (e-waste) by using a Sequential Neural Network (SNN) with TensorFlow and Keras as part of an advanced deep learning technique. In order to address the growing problem of e-waste, the research collects a large amount of data from images of e-waste and then carefully preprocesses and augments those images. With precision, recall, and F1 scores of 87%, 86%, and 86%, respectively, the SNN architecture—which incorporates dropout, pooling, and convolutional layers—achieved an amazing 100% classification accuracy. These outstanding outcomes show how well the model can classify e-waste components, suggesting that it has the potential to be used in real-world scenarios. The results indicate that the SNN-based approach greatly improves the accuracy and efficiency of e-waste sorting, promoting environmental sustainability and resource conservation. By automating the sorting process, the suggested system decreases the need for manual labor, minimizes human error, and speeds up processing. The study emphasizes the model's suitability for integration into current e-waste management workflows, providing a scalable and dependable way to expedite the recycling process. Additionally, the model's real-time applicability highlights its potential to revolutionize current e-waste management practices, making a positive ecological impact. . Future research endeavors will center on broadening the dataset to include a wider range of e-waste image categories, investigating more advanced deep learning architectures, and incorporating the system with Internet of Things (IoT) devices to improve real-time monitoring and management.

Single-molecule analysis reveals the phosphorylation of FLS2 governs its spatiotemporal dynamics and immunity

The Arabidopsis thaliana FLAGELLIN-SENSITIVE2 (FLS2), a typical receptor kinase, recognizes the conserved 22 amino acid sequence in the N-terminal region of flagellin (flg22) to initiate plant defense pathways, which was intensively studied in the past decades. However, the dynamic regulation of FLS2 phosphorylation at the plasma membrane after flg22 recognition needs further elucidation. Through single-particle tracking, we demonstrated that upon flg22 treatment the phosphorylation of Ser-938 in FLS2 impacts its spatiotemporal dynamics and lifetime. Following Förster resonance energy transfer-fluorescence lifetime imaging microscopy and protein proximity indexes assays revealed that flg22 treatment increased the co-localization of GFP-tagged FLS2/FLS2S938D but not FLS2S938A with AtRem1.3-mCherry, a sterol-rich lipid marker, indicating that the phosphorylation of FLS2S938 affects FLS2 sorting efficiency to AtRem1.3-associated nanodomains. Importantly, we found that the phosphorylation of Ser-938 enhanced flg22-induced FLS2 internalization and immune responses, demonstrating that the phosphorylation may activate flg22-triggered immunity through partitioning FLS2 into functional AtRem1.3-associated nanodomains, which fills the gap between the FLS2S938 phosphorylation and FLS2-mediated immunity.

Single-molecule analysis reveals the phosphorylation of FLS2 governs its spatiotemporal dynamics and immunity.

The Arabidopsis thaliana FLAGELLIN-SENSITIVE2 (FLS2), a typical receptor kinase, recognizes the conserved 22 amino acid sequence in the N-terminal region of flagellin (flg22) to initiate plant defense pathways, which was intensively studied in the past decades. However, the dynamic regulation of FLS2 phosphorylation at the plasma membrane after flg22 recognition needs further elucidation. Through single-particle tracking, we demonstrated that upon flg22 treatment the phosphorylation of Ser-938 in FLS2 impacts its spatiotemporal dynamics and lifetime. Following Förster resonance energy transfer-fluorescence lifetime imaging microscopy and protein proximity indexes assays revealed that flg22 treatment increased the co-localization of GFP-tagged FLS2/FLS2S938D but not FLS2S938A with AtRem1.3-mCherry, a sterol-rich lipid marker, indicating that the phosphorylation of FLS2S938 affects FLS2 sorting efficiency to AtRem1.3-associated nanodomains. Importantly, we found that the phosphorylation of Ser-938 enhanced flg22-induced FLS2 internalization and immune responses, demonstrating that the phosphorylation may activate flg22-triggered immunity through partitioning FLS2 into functional AtRem1.3-associated nanodomains, which fills the gap between the FLS2S938 phosphorylation and FLS2-mediated immunity.

Deteksi Kesegaran Telur Ayam pada Citra Cangkang Menggunakan Metode Convolutional Neural Network (CNN)

This research developed an egg freshness detection system using the Convolutional Neural Network (CNN) method to analyze egg shell images. A dataset consisting of 1,898 images of fresh and stale egg shells was used in this study. The pre-processing stage included resizing and labeling data, followed by training the CNN model using the SqueezeNet and AlexNet architectures. The results showed an accuracy of 90% in classifying egg freshness. The use of CNN proved to be more effective than conventional methods such as thresholding and K-Nearest Neighbor (KNN). This research makes a significant contribution to improving the efficiency and accuracy of egg sorting in Indonesia, reducing reliance on manual methods, and speeding up the sorting process.

Active microparticle propulsion pervasively powered by asymmetric AC field electrophoresis

HypothesisSymmetry breaking in an electric field-driven active particle system can be induced by applying a spatially uniform, but temporally non-uniform, alternating current (AC) signal. Regardless of the type of particles exposed to sawtooth AC signals, the unevenly induced polarization of the ionic charge layer leads to a major electrohydrodynamic effect of active propulsion, termed Asymmetric Field Electrophoresis (AFEP). ExperimentsSuspensions containing latex microspheres of three sizes, as well as Janus and metal-coated particles were subjected to sawtooth AC signals of varying voltages, frequencies, and time asymmetries. Particle tracking via microscopy was used to analyze their motility as a function of the key parameters. FindingsThe particles exhibit field-colinear active propulsion, and the temporal reversal of the AC signal results in a reversal of their direction of motion. The experimental velocity data as a function of field strength, frequency, and signal asymmetry are supported by models of asymmetric ionic concentration-polarization. The direction of particle migration exhibits a size-dependent crossover in the low frequency domain. This enables new approaches for simple and efficient on-chip sorting. Combining AFEP with other AC motility mechanisms, such as induced-charge electrophoresis, allows multiaxial control of particle motion and could enable development of novel AC field-driven active microsystems.

Design and application of coal gangue sorting system based on deep learning

With the advancement of science and technology, coal-washing plants are transitioning to intelligent, information-based, and professional sorting systems. This shift accelerates the construction a modern economic system characterized by green and low-carbon development, thereby promoting the high-quality advancement of the coal industry. Traditional manual gangue picking and multi-axis robotic arm gangue selection currently suffer from low recognition accuracy, slow sorting efficiency, and high worker labor intensity. This paper proposes a deep learning-based, non-contact gangue recognition and pneumatic intelligent sorting system. The system constructs a dynamic database containing key feature information such as the target gangue's contour, quality, and center of mass. The system elucidates the relationships between ejection speed, mass, volume, angle of incidence, and the impact energy matching mechanism. Demonstration experiments using the system prototype for coal gangue sorting reveal that, compared to existing robotic arm sorting methods in coal washing plants, this system achieves a gangue identification accuracy exceeding 97%, a sorting rate above 91%, and a separation time of less than 3 s from identification to separation, thereby effectively enhancing raw coal purity.

Sustainability considerations in bio-hydrogen from bio-algae with the aid of bio-algae cultivation and harvesting: Critical review

Abstract Microalgae present an enticing alternative to conventional fossil fuel-dependent technologies for producing hydrogen, offering an intriguing and sustainable energy source. Numerous strains of microalgae are under investigation for their capacity to generate hydrogen, alongside various techniques and breakthroughs being developed to optimize the process. However, significant hurdles must be addressed for commercial viability, including the high manufacturing costs and the necessity for efficient harvesting and sorting methods. This paper delves into several aspects concerning hydrogen synthesis in algae, encompassing microalgae anatomy and physiology, hydrogen synthesis via photosynthesis and dark fermentation, and the integration of microalgal hydrogen synthesis with other renewable energy sources. The potential for microalgal hydrogen generation is considered pivotal in transitioning toward a future reliant on more renewable and sustainable energy sources. This review aims to serve as a valuable resource for researchers, decision-makers, and anyone interested in the advancement of environmentally conscious energy technology. The primary objective of this research paper is to scrutinize the challenges, opportunities, and potential outcomes associated with eco-friendly bio-hydrogen production through algae. It evaluates the current technological hurdles facing bio-hydrogen synthesis from algae. Graphical abstract Highlights Interest in developing renewable fuels, such as hydrogen from biomass, has surged due to escalating energy demands and the imperative to curtail greenhouse gas emissions. Overview of bio-hydrogen production pathway, reactor designs, and configurations for bio-hydrogen production from bio-algae were explored. Environmental, social sustainability and economic feasibility have been reviewed. Discussion Will bio-hydrogen from bio-algae be a future renewable energy? Which is the best pathway to produce bio-hydrogen from bio-algae? Regarding greenhouse gas emissions, how does the generation of bio-hydrogen from bio-algae compare to conventional hydrogen production techniques? What difficulties lie in increasing the amount of bio-hydrogen produced by bio-algae to satisfy major energy demands?

Optofluidic sorting of microparticles using Airy beams

Microparticle sorting is a crucial technique with diverse applications spanning biomedical research, microfluidics, and beyond. Conventional methods often face limitations in throughput, precision, and sample integrity. Here we present an optofluidic approach for size-based directional sorting of microparticles using optical manipulation with Airy beams. By harnessing the non-diffracting properties and transverse gradient force of Airy beams, we achieve a non-invasive, high-precision sorting method capable of sorting and selectively clearing microparticles based on their sizes. We also demonstrate more complex operations involving wavefront modulation and microfluidic devices. The proposed method offers effective and versatile microparticle sorting, providing a powerful optofluidic tool for applications requiring precise and efficient sorting of microparticles while maintaining sample integrity.

Collaborative sorting: integrating pneumatic separation and manipulator techniques for efficient coal gangue sorting

ABSTRACT Coal is one of the important sources to meet the global energy demand. The intelligent treatment of coal gangue is of great significance to environmental protection and resource recycling. In the treatment of gangue, the use of manipulator sorting alone faces the challenge of being difficult to adapt to the increasing sorting intensity and high cost of use. In this study, we propose a hybrid sorting system that combines pneumatic separation and manipulator sorting to address the limitations of pneumatic separation sorting and manipulator. We design a task assignment algorithm based on genetic algorithms to realize their collaborative work for optimal overall sorting efficiency in a sequence of gangue. Experimental results demonstrate that the collaborative sorting system, incorporating both manipulator and high-pressure pneumatic separation, outperforms the single pneumatic separation sorting system with an average increase in sorting efficiency of 20.66%. Furthermore, the collaborative system increased sorting efficiency by up to 76.09% compared to single manipulator sorting. These findings validate the effectiveness of our proposed approach in achieving efficient sorting of coal gangue.

Identification of the recyclable content of polyethylene films in the Upper Austrian waste streams

ABSTRACT The European Union (EU) has set ambitious recycling targets for plastic packaging waste of 50% and 55% by the end of 2025 and 2030, respectively. Austria’s recycling rate is currently below 30%, necessitating significant improvements in collection, sorting, and recycling efficiencies. To enhance the sorting efficiency, understanding the composition and condition of waste is crucial. Our study focuses on the identification of the recyclable content of polyethylene (PE) films in Upper Austrian waste streams for plastics recycling. This involved the manual sorting of three waste streams through various steps. As a result, the occurrences of monolayer PE films in the post-consumer waste streams were obtained. Based on the apprehended data, a circularity indicator (CI), which is a rough estimation of material circularity that takes into account the losses in both quantity and quality when reprocessing materials, together with a sensitivity analysis was calculated for Austria, which revealed that reducing demand and increasing the amount of recycled materials is more beneficial to the CI than improving energy efficiency. This research fills knowledge gaps on the availability of PE film waste in the various waste streams and can be used as a basis for enhancing the recycling rate in PE film recycling.

Efficient Sorting of Semiconducting Single-Walled Carbon Nanotubes in Bio-Renewable Solvents Through Main-Chain Engineering of Conjugated Polymers.

Conjugated polymer sorting is recognized as an efficient and scalable method for the selective extraction of semiconducting single-walled carbon nanotubes (s-SWCNTs). However, this process typically requires the use of nonpolar and aromatic solvents as the dispersion medium, which are petroleum-based and carry significant production hazards. Moreover, there is still potential for improving the efficiency of batch purification. Here, this study presents fluorene-based conjugated polymer that integrates diamines containing ethylene glycol chains (ODA) as linkers within the main chain, to effectively extract s-SWCNTs in bio-renewable solvents. The introduction of ODA segments enhances the solubility in bio-renewable solvents, facilitating effective wrapping of s-SWCNTs in polar environments. Additionally, the ODA within the main chain enhances affinity to s-SWCNTs, thereby contributing to increased yields and purity. The polymer achieves a high sorting yield of 55% and a purity of 99.6% in dispersion of s-SWCNTs in 2-Methyltetrahydrofuran. Thin-film transistor arrays fabricated with sorted s-SWCNTs solution through slot-die coating exhibit average charge carrier mobilities of 20-23 cm2V⁻¹ s⁻¹ and high on/off current ratios exceeding 105 together with high spatial uniformity. This study highlights the viability of bio-renewable solvents in the sorting process, paving the way for the eco-friendly approach to the purification of SWCNTs.

Fractal Dimension Analysis of Pore Throat Structure in Tight Sandstone Reservoirs of Huagang Formation: Jiaxing Area of East China Sea Basin

The reservoir quality of tight sandstone is usually affected by pore throat structures, and understanding pore throat structures and their fractal characteristics is crucial for the exploration and development of tight sandstone gas. In this study, fractal dimensions of pore throat structures and the effect of diagenesis on the fractal dimension of tight sandstone sweet spot in Huagang Formation, Jiaxing area, East China Sea Basin were studied by means of thin sections, scanning electron microscopes, X-ray diffraction analysis, scanning electron microscope quantitative mineral evaluation, and high pressure mercury injection experiments. The results show that the total fractal dimension ranges of type I, type II, and type III sweet spots were 2.62–2.87, 2.22–2.56, and 2.71–2.77, respectively. The negative correlation between total fractal dimensions, porosity, and permeability of type I sweet spots was different from those of type II and type III sweet spots. The negative correlation between total fractal dimensions of type II and type III sweet spots and maximum mercury saturation, average pore throat radius, and skewness were significant, whereas the correlation between total fractal dimensions of type I sweet spots, and maximum mercury saturation, average pore throat radius and skewness were not significant. The positive correlation between the total fractal dimensions of type II and type III sweet spots and the relative sorting coefficient, displacement pressure, and efficiency of mercury withdrawal were significant, whereas the correlation between the total fractal dimension of type I sweet spots and relative sorting coefficients, displacement pressures and efficiency of mercury withdrawal were not significant. The effect of diagenesis on fractal dimensions was investigated. Compaction reduced the pore space of tight sandstone and increased fractal dimensions. Quartz cementation and calcite cementation blocked pores and throats, reduced pore space, and increased fractal dimensions. Chlorite coat can inhibit compaction, protect pore throat structures, and maintain fractal dimensions. Most clay minerals filled primary pores and secondary pores and increased fractal dimensions. Dissolution increased the pore space of tight sandstone and decreased the fractal dimensions of the pore throat structures. The pore throat structures of type I sweet spots were mainly composed of macropores, mesopores, transitional pores, and micropores, and the fractal dimension of type I sweet spots was chiefly controlled by chlorite coat formation, dissolution, and a small amount of compaction. This study provides a reference for pore throat structure and fractal dimension analysis of tight sandstone sweet spots.

Label-Free Continuous Cell Sorting Using Optofluidic Chip.

In the field of biomedicine, efficiently and non-invasively isolating target cells has always been one of the core challenges. Optical fiber tweezers offer precise and non-invasive manipulation of cells within a medium and can be easily integrated with microfluidic systems. Therefore, this paper investigated the mechanism of cell manipulation using scattering force with optical fiber tweezers. We employed flat-ended single-mode fiber to drive and sort cells and derived the corresponding scattering force formula based on the T-matrix model. A single-mode optical tweezers system for cell sorting was developed, and an optofluidic experimental platform was constructed that effectively integrates the optical system with microfluidic chips. The chip, featuring an expanded cross-channel design, successfully achieved continuous separation of yeast cells (8~10 µm in diameter) and polystyrene microspheres (15~20 µm in diameter), with a sorting efficiency of up to 86% and maintaining viability in approximately 90% of the yeast cells. Compared to other sorting systems, this system does not require labeling and can achieve continuous sorting with cell viability at a lower cost of instrumentation.