Optimization Of Processing Research Articles

SELECTION OF TOOLS AND MACHINING PARAMETERS FOR PIPE WALLS

This review paper examines the techniques and factors involved in pipe wall processing, with a focus on the essential technologies utilized in this field. The study revealed crucial elements, such as ASTM A350-LF2 Class 1 carbon steel, renowned for its exceptional mechanical qualities and ability to withstand low temperatures. An exhaustive analysis has been conducted on the chemical makeup of this steel to comprehend its capacity to adjust and endure in various operational environments. The study focused on examining several particle separation processes, including as turning, milling, drilling, and grinding, in order to determine the exact equipment and criteria needed for each technique. The cooling and lubricating process received particular emphasis, with thorough investigation of several agents, including CASTROL HYSOL T15, and their impact on surface quality and tool longevity. The utilization of these compounds has been discovered to substantially decrease tool temperature and enhance machining quality. Upon closer examination of the processing techniques specified in section A-A of the tube wall, it was determined that advanced procedures such as floating plug drawing and micro-sized spiral tube hydroforming were discovered. These techniques allow the attainment of superior surface quality and precise dimensions, which are crucial for guaranteeing the structural integrity of the pipe. Cutting tools, processing equipment, and coolants and lubricants are essential components in the manufacturing of pipe walls. Nanoparticulate cooling and lubrication systems, which are advanced technologies, have demonstrated their effectiveness in minimizing friction and heat during machining. As a result, these systems enhance productivity and prolong the lifespan of tools. According to the article, the essential components for attaining high-quality manufacture of pipe walls are cutting tools, processing equipment, and coolants and lubricants. Additional study is advised to enhance processing parameters, create eco-friendly coolants and lubricants, and utilize artificial intelligence for predictive modeling and optimization of processing. This establishes the groundwork for future research and innovation in the pipe wall production business, with the objective of attaining enhanced efficiency and sustainability in production processes.

Neural Network Methods in the Development of MEMS Sensors

As a kind of long-term favorable device, the microelectromechanical system (MEMS) sensor has become a powerful dominator in the detection applications of commercial and industrial areas. There have been a series of mature solutions to address the possible issues in device design, optimization, fabrication, and output processing. The recent involvement of neural networks (NNs) has provided a new paradigm for the development of MEMS sensors and greatly accelerated the research cycle of high-performance devices. In this paper, we present an overview of the progress, applications, and prospects of NN methods in the development of MEMS sensors. The superiority of leveraging NN methods in structural design, device fabrication, and output compensation/calibration is reviewed and discussed to illustrate how NNs have reformed the development of MEMS sensors. Relevant issues in the usage of NNs, such as available models, dataset construction, and parameter optimization, are presented. Many application scenarios have demonstrated that NN methods can enhance the speed of predicting device performance, rapidly generate device-on-demand solutions, and establish more accurate calibration and compensation models. Along with the improvement in research efficiency, there are also several critical challenges that need further exploration in this area.

Risk assessment of homologous variants of biotech trait proteins using a bridging approach

ABSTRACT A transgenic protein is frequently expressed as different homologous variants in genetically modified crops due to differential processing of targeting peptides or optimization of activity and specificity. The aim of this study was to develop a science-based approach for risk assessment of homologous protein variants using dicamba mono-oxygenase (DMO) as a case study. In this study, DMO expressed in the next-generation dicamba-tolerant maize, sugar beet and soybean crops exhibited up to 27 amino acid sequence differences in the N-terminus. Structure modeling using AlphaFold, ESMFold and OpenFold demonstrates that these small N-terminal extensions lack an ordered secondary structure and do not disrupt the DMO functional structure. Three DMO variants were demonstrated to have equivalent immunoreactivity and functional activity ranging from 214 to 331 nmol/min/mg. Repeated toxicity studies using each DMO variant found no test substance-related adverse effects. These results support that homologous protein variants, which have demonstrated physicochemical and functional equivalence, can leverage existing safety data from one variant without requiring additional de novo safety assessments.

Performance Optimization in Distributed SQL Environments : A Comprehensive Analysis of Presto Query Engine

This comprehensive article examines performance optimization techniques for Presto, a distributed SQL query engine widely adopted for large-scale data analytics. Through systematic analysis of both theoretical frameworks and empirical evidence, we present a multifaceted approach to enhancing query performance and resource utilization. The article encompasses critical aspects including memory management strategies, parallel processing optimization, and storage connector configurations across diverse deployment scenarios. Our investigation reveals that strategic implementation of query planning algorithms, coupled with fine-tuned JVM configurations, can yield performance improvements of up to 40% in complex analytical workloads. The article also introduces a novel framework for workload-specific optimization patterns, validated through extensive testing across various data scales and query complexities. Through detailed case studies of large-scale deployments, we demonstrate how combined optimization techniques can significantly reduce query latency while maintaining system stability. Furthermore, we present empirical evidence supporting the effectiveness of adaptive resource allocation strategies in mixed-workload environments. These findings contribute to the growing body of knowledge in distributed query processing optimization and provide practical guidelines for organizations seeking to enhance their Presto deployments.

Revealing hidden interactions in mean performance through distributional analyses: Evidence from Chinese lexical decision performance.

Important insights in visual word recognition have been provided by studies examining the combined influence of multiple factors on participants' mean response times to English words in the lexical decision task. However, to make progress towards a complete understanding of how meaning is activated by print, researchers need to conduct more detailed analyses of behavioral patterns beyond mean response latencies and accuracies, particularly how variables influence different components of response time distributions. Moreover, it is critical to extend patterns found in English to the diverse scripts encountered by readers across the world. The present study is the first to explore the theoretically important effects of stimulus quality and word frequency on lexical decisions involving two-character Mandarin Chinese and Cantonese Chinese words, using participants from Singapore and Hong Kong, respectively. Despite the profound differences between the English and Chinese writing systems, we observed remarkably similar trade-offs in the stimulus quality × word frequency interaction across different portions of the response time distribution for both orthographies, indicating that the optimization of lexical processing by leveraging available codes in response to task demands extends across multiple and highly diverse writing systems.

Recent Advances in Near‐β Titanium Alloys: Microstructure Control, Deformation Mechanisms, and Oxidation Behavior

Near‐β titanium alloys are used as promising structural materials for aerospace, biomedical, and other advanced applications due to their excellent combination of high specific strength and superior corrosion resistance. Precise control of the microstructure and mechanical properties through thermomechanical processing and heat treatment is paramount for exploiting the full potential of these alloys. This review article provides a comprehensive and critical assessment of the state‐of‐the‐art research on the microstructure evolution, deformation mechanisms, and oxidation behavior of near‐β titanium alloys. Furthermore, the challenges and emerging opportunities in the development of near‐β titanium alloys have also been identified, ranging from alloy design and processing optimization to multiscale characterization and integrated computational materials engineering. This review article provides a timely and comprehensive roadmap for the research and development of near‐β titanium alloys, paving the way for unlocking their full potential in critical industries.

Innovative blends with probiotic potential: processing optimization, bioactive compounds and sensory evaluation of yellow mombin (Spondias mombin) and Talinum triangulare beverages

Innovative blends with probiotic potential: processing optimization, bioactive compounds and sensory evaluation of yellow mombin (Spondias mombin) and Talinum triangulare beverages

Impact of morphology of hydrophilic and hydrophobic bentonites on improving the pour point in the recycling of waste cooking oils

Waste Cooking Oils (WCOs) are generated worldwide through industrial food processing and household use, posing environmental concerns upon disposal. Bentonites often showed to be effective in removing minor contaminants in vegetable oil refining. The present research focused on the processing of raw WCOs using four bentonites, two commercials and two obtained by ball milling of the latest. The different bentonites (hydrophobic and hydrophilic) were characterized before and after ball milling (BM) procedure, including an exhaustive analyses of crystal structure, morphology and surface area via x-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and N2-physisorption technique. Optimization of BM processing in terms of milling time was achieved within 60 min. The milled powders were then tested as adsorbents for recycling WCOs with different degrees of decomposition (expressed in terms of free fatty acids, FFAs, content). Employing a design of experiments approach, the impact of five parameters (FFAs content, temperature, specific surface area, stirring, BM time) on the resulting pour point (PP), taken as a quality benchmark for recycled oil, was assessed. Quantitative multivariate statistical analysis revealed temperature's negligible role and identified the significant impact of two characteristics of the bentonite (specific superficial area and ball milling time), as well as the relevant role of the stirring during the treatment. At the end, hydrophilic bentonite resulted able to improve the PP of waste oils with a low content of free fatty acids of about 10 °C.

An ecosystem of interconnected technologies to increase efficiencies in blood establishments: The example of the Blood and Tissue Bank of Aragón, Spain.

Blood establishments face environmental, financial, demographic and societal challenges that may impair sustainable blood supply to patients. This study presents the technologies (devices and software) assembled in a global ecosystem implemented by the Blood and Tissue Bank of Aragón (BTBA), Spain, over the last decade to overcome these challenges. Descriptive yearly activity data (2013-2023) of BTBA were retrospectively collected to evaluate the impact of different technologies on blood processing efficiency, focusing on the production of blood components (red blood cell concentrates [RCCs], platelet concentrates [PCs]) and plasma. Operator satisfaction about the technologies introduced in daily routine work was also monitored. Between 2013 and 2023, the annual production decreased by 16.0% for RCCs and increased by 13.3% for PCs. From 2020, all PCs were treated with pathogen reduction technology, and no inventory stock-out was reported. The lowest PC expiry rate (0.2%) was observed after the implementation of the software for blood processing and PC stock management. The deployment of this software also improved plasma recovery: on average, an extra plasma volume of 9 mL was collected per donation in 2023 compared to 2015. A survey confirmed staff satisfaction. The progressive implementation of automated and software-based solutions was key to increasing efficiencies in BTBA. This enabled the optimization of blood processing by maximizing productivity, enhancing traceability, reducing overproduction and wastage and increasing the yield of recovered plasma, while ensuring blood product safety and staff satisfaction.

Navigating the depths of refuse-derived fuel in Canada: From heterogeneity to insightful analysis

ABSTRACT Refuse-derived fuel (RDF) presents inherent heterogeneity, encompassing diverse physical and chemical compositions. This study aims to comprehensively investigate the thermogravimetric characteristics of key RDF fractions and compare them with RDF compositions from Canada and other countries. A representative RDF sample was obtained using three ASTM standard procedures, including the quartering technique, manual sorting, and laboratory preparation. Five major RDF fractions – cardboard (46%), mixed papers (17%), mixed plastics (19%), other organics (3%), and fines (13%) – were manually separated and subjected to cryogenic grinding for characterization analysis. Thermogravimetric characterization at 20°C/min in a nitrogen atmosphere, along with proximate/ultimate analysis and heating value measurements, revealed significant variability in decomposition behavior. DTG analysis showed that LDPE exhibited the highest thermal stability, which peaks at 483.6°C, whereas cardboard and mixed paper underwent single-step decomposition, peaks at 361.4°C, and 359.3°C, respectively. In contrast, mixed plastics, other organics, fines, and raw RDF displayed complex, multi-step decomposition behaviors, underscoring the heterogeneous nature of RDF and informing thermal processing optimization. The Canadian RDF sample showed notably higher cardboard 45% and fines content 13% compared to other studies from different countries averaged 25% cardboard, and 8% fines. Additionally, sorting a 2 kg representative sample required 5 man-hours per kilogram, highlighting the labor-intensive nature of the process.

Bilateral Defect Cutting Strategy for Sawn Timber Based on Artificial Intelligence Defect Detection Model.

Solid wood is renowned as a superior material for construction and furniture applications. However, characteristics such as dead knots, live knots, piths, and cracks are easily formed during timber's growth and processing stages. These features and defects significantly undermine the mechanical characteristics of sawn timber, rendering it unsuitable for specific applications. This study introduces BDCS-YOLO (Bilateral Defect Cutting Strategy based on You Only Look Once), an artificial intelligence bilateral sawing strategy to advance the automation of timber processing. Grounded on a dual-sided image acquisition platform, BDCS-YOLO achieves a commendable mean average feature detection precision of 0.94 when evaluated on a meticulously curated dataset comprising 450 images. Furthermore, a dual-side processing optimization module is deployed to enhance the accuracy of defect detection bounding boxes and establish refined processing coordinates. This innovative approach yields a notable 12.3% increase in the volume yield of sawn timber compared to present production, signifying a substantial leap toward efficiently utilizing solid wood resources in the lumber processing industry.

State of art on evaluation of three- to six-dimensional novel additive manufacturing technology for biomedical applications

Additive manufacturing has evolved over the last few decades. Three-dimensional printing is a digital manufacturing technology that provides nearly endless options for the creation of an accessible instrument for all parts of various medical practices, including tissue engineering, through meticulous optimization of material, processing, and geometry for every point in an object. Three-dimensional printing has opened up a new, faster, and safer manufacturing process, despite its incapability to fabricate complex structures and objects. Recently, novel four-dimensional printing techniques have been developed for the transformation of typical stable three-dimensional printed parts into smart objects. The limitations of three-dimensional printing could be remedied with four-dimensional printing, by applying time as the fourth dimension. Self-repairing and speedy printing are two additional benefits of this technology's by using smart materials. By adapting this technology, numerous medical domains could be profited. Four-dimensional printing does not have the ability to produce curved complicated forms. However, five-dimensional printing overcomes the flaws seems in four-dimensional printing. Five-dimensional additive manufacturing relies on the rotation of both the print bed and the extruder head. Five-dimensional printing outlasts in terms of durability than three- and four-dimensional printing. Currently, a combination of the principles of four- and five-dimensional printing into a single process is called six-dimensional printing. In six-dimensional printing, the form changes over time due to the reaction of environmental factors, which is primarily used in biomedical applications. This paper summarizes extensive research on biomaterials in the field of biomedical science and discusses the present implications of three-, four-, five-, and six-dimensional printing techniques.

Complex Scene Understanding and Object Detection Algorithm Assisted by Artificial Intelligence

The purpose of this paper is to study the algorithm of object detection and scene understanding in complex scenes assisted by AI(Artificial Intelligence), so as to improve the machine's perception of the surrounding environment. Aiming at occlusion and dense scenes, this paper proposes a series of innovative algorithms by introducing technologies such as multi-scale detection, occlusion processing and real-time optimization. Experimental results show that the proposed algorithm has achieved excellent performance on several public data sets. Compared with the current mainstream object detection algorithm -YOLO, the method proposed in this paper has a significant improvement in accuracy. Especially in multi-scale object detection, occlusion and dense scene processing and real-time optimization, the proposed algorithm shows obvious advantages. The research results provide an effective solution for object detection and scene understanding in complex scenes, and promote the application of AI system in related fields.

Improving mechanical and electrical properties of Cu-Ni-Si alloy via machine learning assisted optimization of two-stage aging processing

Recent studies have shown that synergistic precipitation of continuous precipitates (CPs) and discontinuous precipitates (DPs) is a promising method to simultaneously improve the strength and electrical conductivity of Cu-Ni-Si alloy. However, the complex relationship between precipitates and two-stage aging process presents a significant challenge for the optimization of process parameters. In this study, machine learning models were established based on orthogonal experiment to mine the relationship between two-stage aging parameters and properties of Cu-5.3Ni-1.3Si-0.12Nb alloy with preferred formation of DPs. Two-stage aging parameters of 400 °C/75 min + 400 °C/30 min were then obtained by multi-objective optimization combined with an experimental iteration strategy, resulting in a tensile strength of 875 MPa and a conductivity of 41.43 %IACS, respectively. Such an excellent comprehensive performance of the alloy is attributed to the combined precipitation of DPs and CPs (with a total volume fraction of 5.4% and a volume ratio of CPs to DPs of 6.7). This study could provide a new approach and insight for improving the comprehensive properties of the Cu-Ni-Si alloys.

Evaluating fine tuned deep learning models for real-time earthquake damage assessment with drone-based images

Earthquakes pose a significant threat to life and property worldwide. Rapid and accurate assessment of earthquake damage is crucial for effective disaster response efforts. This study investigates the feasibility of employing deep learning models for damage detection using drone imagery. We explore the adaptation of models like VGG16 for object detection through transfer learning and compare their performance to established object detection architectures like YOLOv8 (You Only Look Once) and Detectron2. Our evaluation, based on various metrics including mAP, mAP50, and recall, demonstrates the superior performance of YOLOv8 in detecting damaged buildings within drone imagery, particularly for cases with moderate bounding box overlap. This finding suggests its potential suitability for real-world applications due to the balance between accuracy and efficiency. Furthermore, to enhance real-world feasibility, we explore two strategies for enabling the simultaneous operation of multiple deep learning models for video processing: frame splitting and threading. In addition, we optimize model size and computational complexity to facilitate real-time processing on resource-constrained platforms, such as drones. This work contributes to the field of earthquake damage detection by (1) demonstrating the effectiveness of deep learning models, including adapted architectures, for damage detection from drone imagery, (2) highlighting the importance of evaluation metrics like mAP50 for tasks with moderate bounding box overlap requirements, and (3) proposing methods for ensemble model processing and model optimization to enhance real-world feasibility. The potential for real-time damage assessment using drone-based deep learning models offers significant advantages for disaster response by enabling rapid information gathering to support resource allocation, rescue efforts, and recovery operations in the aftermath of earthquakes.

Raman Spectroscopic Analysis of Steviol Glycosides: Spectral Database and Quality Control Algorithms

Besides all sharing an extraordinary high (i.e., up to ~450 times) sweetening power as compared to sucrose and while presenting strong similarities in their molecular structures, molecules belonging to the family of diterpene glycosides (i.e., the secondary metabolites of Stevia rebaudiana) differ in specific structural details that strongly impact on their levels of sweetness and bitter aftertaste. Given the nutritional and pharmacological benefits of steviol secondary metabolites as natural dietetic and anti-diabetic remedies, extraction and purification of steviol glycosides from plant material are nowadays widely spread among many countries. However, an unpleasant bitter aftertaste, which is linked to a genetic variation in human bitter taste receptors, hampers the full exploitation of such benefits and calls for a prompt improvement in organoleptic property control of stevia products. A deeper understanding of the molecular structure of different steviol glycosides and the consequent development of promptly measurable criteria for the organoleptic performance of their mixtures will support processing optimization and control of taste profiles within desired yields. The present research aimed at establishing Raman spectroscopic algorithms for quantitative characterizations of raw stevia-based sweetener products. First, a series of twelve high-purity diterpene glycosides were analyzed by high spectrally resolved Raman spectroscopy and their spectra analyzed in order to establish a complete Raman library of molecular structures. Then, quantitative spectroscopic parameters were built up and applied to characterize the organoleptic property of five different commercially available samples including the recently developed Rebaudioside M isoform. Raman spectroscopy was confirmed as a versatile analytical technique that could be used for quantitative quality control tasks on the production line and for prompt in situ characterizations of purchased products.

Artificial intelligence and machine learning applications for cultured meat.

Cultured meat has the potential to provide a complementary meat industry with reduced environmental, ethical, and health impacts. However, major technological challenges remain which require time-and resource-intensive research and development efforts. Machine learning has the potential to accelerate cultured meat technology by streamlining experiments, predicting optimal results, and reducing experimentation time and resources. However, the use of machine learning in cultured meat is in its infancy. This review covers the work available to date on the use of machine learning in cultured meat and explores future possibilities. We address four major areas of cultured meat research and development: establishing cell lines, cell culture media design, microscopy and image analysis, and bioprocessing and food processing optimization. In addition, we have included a survey of datasets relevant to CM research. This review aims to provide the foundation necessary for both cultured meat and machine learning scientists to identify research opportunities at the intersection between cultured meat and machine learning.

Postoperative rehabilitation after knee arthroplasty

Postoperative rehabilitation after knee arthroplasty plays adecisive role in restoring the function and mobility of the affected joint. However, there is still disagreement regarding the setting, structure and content of rehabilitation after knee arthroplasty, and the evidence on the individual measures is largely unclear. The aim of this article is to provide an evidence-based overview of the current status of rehabilitation after knee arthroplasty and to critically discuss the points that are still unclear. In view of the increasing prevalence of knee osteoarthritis and the rising number of knee endoprosthesis implantations, the optimization and scientific processing of postoperative rehabilitation is more important than ever in order to be able to offer scientifically sound, practice-oriented and cost-effective rehabilitation measures in the future. This review is based on asystematic literature search in Medline, Cochrane Library and Web of Science databases on the topic of postoperative rehabilitation after knee arthroplasty. Regarding specific treatment components, duration and frequency after knee arthroplasty, the evidence is unclear. Passive therapies should only be used supportive to active interventions. Educational programmes before and after knee arthroplasty can play acrucial role in outcome and patient satisfaction. Regular strength training should always be combined with centrally oriented components, such as motor imagery, to achieve better movement visualization and central anchoring. There is still afrequent lack of scientific evidence regarding individual therapeutic measures, their intensity, frequency, duration, exercise selection and their specific implementation in rehabilitation after knee arthroplasty. In the future, digital diagnostic and training tools will become established in both inpatient and outpatient therapy, supporting the urgently needed data collection for the scientific analysis of individual therapeutic measures.

Optimization of High-Pressure Processing for Microbial Inactivation in Pigmented Rice Grass Juice and Quality Impact Assessment during Refrigerated Storage.

Pigmented rice grass juice (RGJ) is a good source of bioactive compounds, but fresh juice has a relatively short shelf life of only 7 days at 4 °C. The objectives of this study were to determine the optimal growth stage of pigmented rice grass, investigate the optimal condition of high-pressure processing (HPP) for bacterial inactivation in inoculated RGJ using response surface methodology (RSM), and evaluate quality changes in uninoculated HPP-treated juice during storage at 4 °C compared with heat-treated (85 °C/10 min) and untreated samples. Results revealed that the optimal growth stage of rice grass was 9 days with the highest total anthocyanin content of 158.92 mg/L. The optimal condition of HPP was determined to be 612 MPa, 11 min, and 36 °C, and inactivated Escherichia coli K12 and Listeria innocua with 6.43 and 5.02 log reductions, respectively, meeting FDA regulations. The lethality of bacteria after HPP treatment can be explained by damage to the cell membrane and the leakage of intracellular constituents such as protein and nucleic acid. During 12 weeks of storage at 4 °C, total plate counts and yeast and mold counts in uninoculated HPP-treated juice were not detected. Moreover, HPP did not significantly change phytochemical properties (p < 0.05), caused a minor impact on physicochemical properties of RGJ, and maintained the durability of juice samples during storage. Analysis of the phytochemical profile revealed that HPP treatment could preserve most of the phenolic compounds in RGJ and especially increase the contents of protocatechuic acid, 4-hydroxybenzoic acid, syringic acid, transcinnamic acid, isorhamnetin-3-o-glucoside, quercetin, and cyanidin-3-glucoside (p < 0.05). Overall, HPP is a potential pasteurization technique for microbial inactivation and nutritional preservation for rice grass juice.

A review of the botany, metabolites, pharmacology, toxicity, industrial applications, and processing of Polygalae Radix: the "key medicine for nourishing life".

Polygalae radix (PR) is the dried root of Polygala tenuifolia Willd. and Polygala sibirica L. and enjoys the reputation as the "key medicine for nourishing life." In this study, information about "Polygala tenuifolia Willd.," "Polygala sibirica L.," and "Yuanzhi" was retrieved from scientific databases, including Google Scholar, Baidu Scholar, Web of Science, PubMed, CNKI, and Wan Fang Data. Information from Chinese herbal medicine classics, Yaozhi Data, and the Gaide Chemical Network was also collected. Information related to botany, phytochemistry, pharmacology, toxicity, industrial applications, and processing is summarized in this paper to tap its potentialities and promote its further development and clinical application. More than 320 metabolites have been isolated from PR; saponins, xanthones, and oligosaccharide esters are the main functional metabolites. Pharmacological research shows that its pharmacological action mainly focuses on resisting nervous system diseases, and it also has the functions of anti-oxidation, anti-inflammation, anti-tumor, anti-pathogenic microorganisms and others. The gastrointestinal irritation of its saponins impeded its application, but this irritation can be reduced by controlling the dosage, compatibility with other herbs, or processing. The future progress of PR faces opportunities and challenges. More attention should be paid to the traditional application and processing methods of PR recorded in ancient books. The lack of safety and clinical studies has limited its application and transformation of achievements. Moreover, it is one-sided to take the content of only a few metabolites as the index of processing optimization and quality control, which cannot reflect the full pharmacological and toxicological activities of PR.