Process For Various Applications Research Articles

Cochineal (Dactylopius coccus Costa) Pigment Extraction Assisted by Ultrasound and Microwave Techniques

Carminic acid is a natural pigment typically found in several insect taxa, including specific insects such as “grana cochinilla fina” in Mexico (Dactylopius coccus Costa). Commercially, it is also referred to as carmine, which is a more concentrated solution presenting as at least 50% carminic acid. To date, this dye has been used in the pharmaceutical, food and cosmetic industries. Unfortunately, one of the main limitations has to do with establishing the appropriate extraction and purification protocol. Currently, there is growing interest in developing eco-friendly and efficient pigment extraction processes for various applications. In this study, we compare the ultrasound- and microwave-assisted extraction versus with a conventional method to obtain carminic acid from cochineal. To do this, we considered three factors that influence the extraction process as independent variables: solvent volume, temperature and irradiation time. The optimization was carried out using the response surface methodology, employing a three-factor and three-level Box–Behnken experimental design. Carminic acid contents were quantified by UV–Vis spectroscopy, and extracts were evaluated by infrared spectroscopy to verify the integrity of the carminic acid molecule. The yield obtained by ultrasound-assisted extraction was 49.2 ± 3.25, with an efficiency of 31.3 mg/min, while microwave-assisted extraction showed a yield of 40.89 ± 2.96, with an efficiency of 27.3 mg/min. Both methods exceeded the extract yield (31.9 ± 3.4%) and efficiency (10.6 mg/min) obtained with the conventional method, demostrating that ultrasound- and microwave-assisted extraction are viable alternatives for obtaining carminic acid, with the potential to be scaled up to an industrial level.

Construction of Flexible Deterministic Sparse Measurement Matrix in Compressed Sensing Using Legendre Sequences.

Compressed sensing (CS) is an innovative signal acquisition and reconstruction technology that has broken through the limit of the Nyquist sampling theory. It is widely employed to optimize the measurement processes in various applications. One of the core challenges of CS is the construction of a measurement matrix. However, traditional random measurement matrices are often impractical. Additionally, many existing deterministic binary measurement matrices fail to provide the required flexibility for practical applications. In this study, inspired by the observation that pseudo-random sequences share similar properties with random sequences, we constructed a deterministic sparse measurement matrix with a flexible measurement number based on an pseudo-random sequence-the Legendre sequence. Empirical analysis of the phase transition and an assessment of the practical features of the proposed measurement matrix were conducted. We validated the effectiveness of the proposed measurement matrix on randomly synthesized signals and images. The results of our simulations reveal that our proposed measurement matrix performs better than several other measurement matrices, particularly in terms of accuracy and efficiency.

The effect of advection on the adsorption kinetics of ammonia onto a polyaniline-sensitive surface in channel flow

This numerical study investigates the effect of advection on NH3 adsorption kinetics onto a doped Polyaniline (PAni) sensitive surface in channel flow, under diffusion-limited conditions characterized by a high Damköhler number. The effects of various parameters, such as gas inlet velocity, NH3 concentration, and channel height, are tested and discussed. Results show that increasing channel height increases equilibrium time, while higher flow velocity reduces it. Local-scale analysis indicates that adsorption primarily occurs upstream and downstream of the sensitive surface initially, creating a depleted zone in between. An Adsorption Capacity Coefficient (ACC) is introduced to evaluate adsorption efficiency, revealing an inverse relationship between equilibrium time and efficiency. This study enhances the understanding of adsorption mechanisms by emphasizing the balance between diffusion and advection. These findings could be extended to other gases and sensitive materials, optimizing adsorption processes in various applications.

A review on microfiltration membranes: fabrication, physical morphology, and fouling characterization techniques

Microfiltration is a commonly used pressure-driven membrane separation process for various applications. Depending on the manufacturing method, either tortuous or capillary pore structures are obtained. The structure plays an important role in controlling flux, selectivity, but most importantly, the fouling tendency of the membrane. This review attempts to cover past and current developments in physical morphology and fouling characterization methods, along with the manufacturing methods for microfiltration membranes. The limitations and advantages of direct microscopic techniques and gas-liquid displacement as an indirect method are discussed for physical characterization. Additionally, the current state of the art and technical challenges for various in-situ and ex-situ fouling characterization techniques are also discussed. Finally, some directions for future research are outlined.

Characterisation of wood combustion and emission under varying moisture contents using multiple imaging techniques

In this study, the complex combustion process of natural wood, with different moisture contents, has been characterised using a pioneering approach that integrated Mid-Wavelength Infrared (MWIR) Hyperspectral Imaging (HSI), Schlieren imaging, Long-Wavelength Infrared (LWIR) thermal imaging and visual imaging. The experiment involved the combustion of oak wood samples with moisture contents varying from dry (defined as 0%) to 30%. This setup enabled a detailed investigation into the combustion process, examining aspects such as weight loss rates, thermal behaviours, spectral radiance of gas emissions and flow structures. Higher wood moisture levels were associated with decreased fuel consumption and sustainability of the combustion. The radiance ratios of CH4/CO2 and CO/CO2 were higher in samples with increased moisture during the initial combustion stages, indicating reduced efficiency in gas-phase combustion. The different combustion stages: flaming and smouldering, were characterised by the thermal profiles. Results demonstrated the moisture level significantly affected the initiation of both flaming and smouldering combustion, as moisture can suppress the thermal pyrolysis of flammable gases during ignition and the evaporation of the residual water can cool the surface locally after ignition. Additionally, the samples with high moisture content transitioned more quickly and directly to smouldering combustion. This transition is further evidenced by lower emissions of flammable gases and a significant decrease in the intensity gradient of flow structures in high moisture conditions. This comprehensive study highlights the potential of multi-wavelength techniques in combustion research. The findings have significant implications for optimising biomass combustion processes in various applications, contributing to the broader field of energy and combustion science.

Analysis of Carbon Dots Synthesized at Different Hydrothermal Carbonization Temperatures and Their Ferric Ion Detection Performances

In this article, carbon dots (CDs) are fabricated via hydrothermal carbonization (HTC) at different temperatures ranging from 120 to 210 °C. Regarding the temperature variations, they demonstrate a crucial role in specific identification in the ability to differentiate the chemical structures and optical properties of the resulting CD features. Both heteroatoms‐doped core structure, and the functionalized surface state display a changed that correlates with the temperature variations mentioned. The CDs which are synthesized at 180 °C contain the appropriate balance of doped heteroatoms and surface functional groups which further display the most promising performance of the quenching effect of up to 17% toward ferric ions. This work contributes to the growing understanding of CDs and provides valuable insights for the future development of applications that may favor certain interest in the creation or designing process in various applications.

CGgraph: An Ultra-Fast Graph Processing System on Modern Commodity CPU-GPU Co-processor

In recent years, many CPU-GPU heterogeneous graph processing systems have been developed in both academic and industrial to facilitate large-scale graph processing in various applications, e.g., social networks and biological networks. However, the performance of existing systems can be significantly improved by addressing two prevailing challenges: GPU memory over-subscription and efficient CPU-GPU cooperative processing.In this work, we propose CGgraph, an ultra-fast CPU-GPU graph processing system to address these challenges. In particular, CGgraph overcomes GPU-memory over-subscription by extracting a subgraph which only needs to be loaded into GPU memory once, but its vertices and edges can be used in multiple iterations during the graph processing procedure. To support efficient CPU-GPU co-processing, we design a CPU-GPU cooperative processing scheme, which balances the workloads between CPU and GPU by on-demand task allocation. To evaluate the efficiency of CG-graph, we conduct extensive experiments, comparing it with 7 state-of-the-art systems using 4 well-known graph algorithms on 6 real-world graphs. Our prototype system CGgraph outperforms all existing systems, delivering up to an order of magnitude improvement. Moreover, CGgraph on a modern commodity machine with a CPU-GPU co-processor yields superior (or at the very least, comparable) performance compared to existing systems on a high-end CPU-GPU server.

Experimental characterization of Spherical Bragg Resonators for electromagnetic emission engineering at microwave frequencies

This work reports experimental investigation and numerical validation of millimeter-sized Spherical Bragg Resonators (SBRs) fabricated using 3D printing technology. The frequency dependencies of the reflection and transmission coefficients were analyzed, and eigenfrequency values were calculated to examine the density of photonic states in air/PLA-polylactide SBRs, showing the appearance of an eigenmode and an increase in the local density of states in the core of a defect cavity. A decay rate enhancement of {sim 10}^{2} was obtained for a dipole placed in the core of the defect SBR. The study also investigated the influence of the source position on the resonator's electromagnetic wave energy. Scattering efficiencies up to order twelve of the multipole electric and magnetic contribution in a 10-layer SBR were calculated to validate the presence of the resonant modes observed in the scattering measurements performed for parallel and perpendicular polarizations. The results demonstrate that SBRs can act as omnidirectional cavities to enhance or inhibit spontaneous emission processes by modifying the density of electromagnetic states compared to free space. This finding highlights the potential of SBRs engineering spontaneous electromagnetic emission processes in various applications, including dielectric nanoantennas, optoelectronics devices, and quantum information across the entire electromagnetic spectrum.

3D Centrifugation‐Enabled Priming of Synaptic Activation Promotes Primary T Cell Expansion

AbstractAutologous cell therapy depends on T lymphocyte expansion efficiency and is hindered by suboptimal interactions between T cell receptors (TCR) and peptide‐MHC molecules. Various artificial antigen presenting cell systems that enhance these interactions are often labor‐intensive, fabrication costly, highly variable, and potentially unscalable toward clinical setting. Here, 3D centrifugation‐enabled priming of T cell immune‐synapse junctions is performed to generate tight T cell–Dynabead aggregates at a rate 200‐fold faster than that of conventional 24‐h bulk shaking. Furthermore, by forming T cell–Dynabead aggregates in the starting culture, two‐ to sixfold greater T cell expansion is achieved over conventional T cell expansion for cancer patient‐derived primary T cells while limiting over‐activation. Creating 3D T cell–Dynabead aggregates as the “booster” material enables highly efficient polyclonal T cell expansion without the need for complex surface modification of artificial antigen‐presenting cells (APCs). This method can be modularly adapted to existing T cell expansion processes for various applications, including adoptive cell therapies (ACTs).

ICSF: An Improved Cloth Simulation Filtering Algorithm for Airborne LiDAR Data Based on Morphological Operations

Ground filtering is an essential step in airborne light detection and ranging (LiDAR) data processing in various applications. The cloth simulation filtering (CSF) algorithm has gained popularity because of its ease of use advantage. However, CSF has limitations in topographically and environmentally complex areas. Therefore, an improved CSF (ICSF) algorithm was developed in this study. ICSF uses morphological closing operations to initialize the cloth, and estimates the cloth rigidness for providing a more accurate reference terrain in various terrain characteristics. Moreover, terrain-adaptive height difference thresholds are developed for better filtering of airborne LiDAR point clouds. The performance of ICSF was assessed using International Society for Photogrammetry and Remote Sensing urban and rural samples and Open Topography forested samples. Results showed that ICSF can improve the filtering accuracy of CSF in the samples with various terrain and non-ground object characteristics, while maintaining the ease of use advantage of CSF. In urban and rural samples, ICSF obtained an average total error of 4.03% and outperformed another eight reference algorithms in terms of accuracy and robustness. In forested samples, ICSF produced more accuracy than the well-known filtering algorithms (including the maximum slope, progressive morphology, and cloth simulation filtering algorithms), and performed better with respect to the preservation of steep slopes and discontinuities and vegetation removal. Thus, the proposed algorithm can be used as an efficient tool for LiDAR data processing.

A spatial–spectral classification framework for multispectral LiDAR

ABSTRACT Precise classification of Light Detection and Ranging (LiDAR) point cloud is a fundamental process in various applications, such as land cover mapping, forestry management, and autonomous driving. Due to the lack of spectral information, the existing research on single wavelength LiDAR classification is limited. Spectral information from images could address this limitation, but data fusion suffers from varying illumination conditions and the registration problem. A novel multispectral LiDAR successfully obtains spatial and spectral information as a brand-new data type, namely, multispectral point cloud, thereby improving classification performance. However, spatial and spectral information of multispectral LiDAR has been processed separately in previous studies, thereby possibly limiting the classification performance of multispectral LiDAR. To explore the potential of this new data type, the current spatial–spectral classification framework for multispectral LiDAR that includes four steps: (1) neighborhood selection, (2) feature extraction and selection, (3) classification, and (4) label smoothing. Three novel highlights were proposed in this spatial – spectral classification framework. (1) We improved the popular eigen entropy-based neighborhood selection by spectral angle match to extract a more precise neighborhood. (2) We evaluated the importance of geometric and spectral features to compare their contributions and selected the most important features to reduce feature redundancy. (3) We conducted spatial label smoothing by a conditional random field, accounting for the spatial and spectral information of the neighborhood points. The proposed method demonstrated by a multispectral LiDAR with three channels: 466 nm (blue), 527 nm (green), and 628 nm (red). Experimental results demonstrate the effectiveness of the proposed spatial – spectral classification framework. Moreover, this research takes advantages of the complementation of spatial and spectral information, which could benefit more precise neighborhood selection, more effective features, and satisfactory refinement of classification result. Finally, this study could serve as an inspiration for future efficient spatial–spectral process for multispectral point cloud.

Real-Time Object Detection

Object detection is a pivot and prime process in various applications and procedures such as surveillance, classification, recognition and prediction including image retrieval, computer visioning, video streams and many more. Real-time object detection requires identification of different kinds of objects specified in images, videos or live feed streams. It is basic and important to maintain the level of accuracy along with quick inference. This paper proposes an algorithm to perform the real-time object detection typically leverage machine learning, deep learning to produce effective results. The goal is to power machines to identify defined objects in a live feed, videos or images and achieve desired outcomes. The algorithm used is YOLO version3 (YOLOv3). It presents a fast and accurate object detection method with higher performance. To create reliable applications for resolving practical problems, computer vision techniques like tracking and counting are combined. It is the improved proposal to many machine learning algorithms like CNN, RNN, YOLO v1 and v2. Some of the applications are traffic surveillance, vehicle detection, face detection and recognition, number-plate detection, overspeed tracking and more. The factors included are segmentation, accuracy, precision, fastness, performance and efficiency. It is useful to meet the needs of growing technology.

Comparative Study of the Magnetic Behavior of FINEMET Thin Magnetic Wires: Glass-Coated, Glass-Removed, and Cold-Drawn

In this paper, a comparative investigation of the magnetic behavior and its stress dependence in the case of FINEMET glass-coated, glass-removed, and cold-drawn microwires at low and high frequencies, respectively, is presented. The experimental results show major differences between their magnetic properties depending on the preparation method and microwire diameter. The evolution of the magnetic permeability, coercivity, and magnetoimpedance responses with the applied tensile force was investigated and analyzed in correlation with the stresses induced during preparation, their relief following annealing, and the annealing-induced structural transformations. The coercivity dependence on applied force was found to show the highest sensitivity in the glass-removed microwires, while the magnetic permeability and magnetoimpedance sensitivity to force were found to be higher in the cold-drawn samples. The results of this comparative study will enable an enhanced material selection process for various applications in miniaturized magnetic and stress sensors with increased sensitivity.

Face Shape Classification Using Swin Transformer Model

Face shape classification plays a crucial role in various applications, including facial recognition and personalized beauty rec- ommendations like hairstyles. In some cases, it is tricky to establish one's genuine facial shape, making it difficult to further process this information. This paper attempts to harness the advantages of the Swin Transformer to classify face shapes with higher accuracy. We also compared the test results with augmentation to evaluate the improvement. Our proposed method obtained 86.34% accuracy with augmentation, which is decent for further processing in various applications that require face shape recognition.

Novel Contiguous Cross Propagation Neural Network Built CAD for Lung Cancer

The present progress of visual-based detection of the diseased area of a malady plays an essential part in the medical field. In that case, the image processing is performed to improve the image data, wherein it inhibits unintended distortion of image features or it enhances further processing in various applications and fields. This helps to show better results especially for diagnosing diseases. Of late the early prediction of cancer is necessary to prevent disease-causing problems. This work is proposed to identify lung cancer using lung computed tomography (CT) scan images. It helps to identify cancer cells’ affected areas. In the present work, the original input image from Lung Image Database Consortium (LIDC) typically suffers from noise problems. To overcome this, the Gabor filter used for image processing is highly enhanced. In the next stage, the Spherical Iterative Refinement Clustering (SIRC) algorithm identifies cancer-suspected areas on the CT scan image. This approach can help radiologists and medical experts recognize cancer diseases and syndromes so that serious progress can be avoided in the early stages. These new methods help to remove unwanted portions of the CT image and better utilization the image. The subspace extraction of features approach is beneficial for evaluating lung cancer. This paper introduces a novel approach called Contiguous Cross Propagation Neural Network that tends to locate regions afflicted by lung cancer using CT scan pictures (CCPNN). By using the feature values from the fourth step of the procedure, the proposed CCPNN tends to categorize the lesion in the lung nodular site. The efficiency of the suggested CCPNN approach is evaluated using classification metrics such as recall (%), precision (%), F-measure (percent), and accuracy (%). Finally, the incorrect classification ratios are determined to compare the trained networks’ effectiveness, through these parameters of CCPNN, it obtains the outstanding performance of 98.06% and it has provided the lowest false ratio of 1.8%.

A Review on the Out-of-Autoclave Process for Composite Manufacturing

Composite materials have gained increased usage due to their unique characteristic of a high-stiffness-to-weight ratio. High-performing composite materials are produced in the autoclave by applying elevated pressure and temperature. However, the process is characterized by numerous disadvantages, such as long cycle time, massive investment, costly tooling, and excessive energy consumption. As a result, composite manufacturers seek a cheap alternative to reduce cost and increase productivity. The out-of-autoclave (OoA) process manufactures composites by applying vacuum, pressure, and heat outside of the autoclave. This review discusses the common out-of-autoclave processes for various applications. The theoretical and practical merits and demerits are presented, and areas for future research are discussed.

Hybrid Composite Photocatalysts Membrane System For Industrial Water Treatment And Renewable Energy Applications

A combination between the composite photocatalyst and membrane system towards the development of hybrid process for various applications especially in water treatment and renewable energy synthesis. These hybrid processes overcome many of the difficulties that are associated with conventional methods and promote process intensification. The hybrid photocatalytic membrane process could reduce the downstream process and also degrade the toxic compound in the waste water. Effluent waste water generated from industrial and domestic process, it consists of complex organic compounds such as lignin, phenolic compounds, pectin, proteins and minerals etc. The effluent containing organic dyes and phenolic compounds releases toxic materials into aquo system poses environmental concern. The traditional effluent treatment very challenge process due to consist mixture of organic and inorganic pollutants such as lignin, resins, tannins, chlorinated phenol, waxes and heavy metals etc. Recently, many industries planning to reuse treated water for economic reasons and environmental protection.

SGLBP: Subgraph‐based Local Binary Patterns for Feature Extraction on Point Clouds

AbstractExtraction for points that can outline the shape of a point cloud is an important task for point cloud processing in various applications. The topology information of the neighbourhood of a point usually contains sufficient information for detecting features, which is fully considered in this study. Therefore, a novel method for extracting feature points based on the topology information is proposed. First, an improved ‐shape technique is introduced, generating two graphs for potential feature detection and neighbourhood description, respectively. Local binary pattern (LBP) is then applied to the subgraphs, thus subgraph‐based local binary patterns (SGLBPs) are generated for encoding the topology of the neighbourhoods of points, which helps to remove non‐feature points from potential feature points. The proposed method can directly process raw point clouds and needs no prior surface reconstruction or geometric invariants computation; furthermore, the proposed method detects feature points by analysing the topologies of the neighbourhoods of points, consequently promoting the effectiveness for tiny features and the robustness to noises and non‐uniformly sampling patterns. The experimental results demonstrate that the proposed method is robust and achieves state‐of‐the‐art performance.

Green chemistry method prepared effective copper nanoparticles by lemon flower (citrus) extract and its anti- microbial activity

Green chemistry method is a toxic less Development of metal nanoparticle towards ecofriendly biosynthesis process for various applications. In the present investigation, we fabricated the Copper Nanoparticles using a lemon plant and its family name RUTACEAE). Copper sulphate (CuSO4) was used as precursor for the formation of copper Nanoparticles by using flower extract of lemon. The physico chemical characterization of copper nanoparticle were analysed by XRD, SEM and DRS study. The SEM results show that the copper Nanoparticles are aggregated cross rectangle/spherical shape morphology. The optical characterization was carried out using UV – Vis analysis. The results are showed that the optimum concentration of flower extract is important for the synthesis of copper nanoparticles. The as prepared Copper nanoparticle have the efficient ability to inhibit the growth of various pathogenic microorganisms.

A Strategy for Wafer‐Scale Crystalline MoS2 Thin Films with Controlled Morphology Using Pulsed Metal–Organic Chemical Vapor Deposition at Low Temperature

Abstract2D semiconductor materials with layered crystal structures have attracted great interest as promising candidates for electronic, optoelectronic, and sensor applications due to their unique and superior characteristics. However, a large‐area synthesis process for various applications and practical mass production is still lacking. In particular, there is a limitation in that a high process temperature and a very long process time are required to deposit a crystallized 2D material on a large area. Herein, pulsed metal–organic chemical vapor deposition (p‐MOCVD) is proposed for the growth of wafer‐scale crystalline MoS2 thin films to overcome the existing limitations. In the p‐MOCVD process, precursors are repeatedly injected at regular intervals to enhance the migration of precursors on the surface. As a result, crystalline MoS2 is successfully synthesized at the lowest temperature (350 °C) reported so far in a very short process time of 550 s. In addition, it is found that the horizontal and vertical growth modes of MoS2 can be effectively controlled by adjusting key process parameters. Finally, various applications are presented by demonstrating the photodetector (detectivity = 18.1 × 106 at light power of 1 mW) and chemical sensor (response = 38% at 100 ppm of NO2 gas) devices.