Reduce Processing Time Research Articles

Comparison Between Empirical Strategies for Predicting Endpoint Phosphorus Content in BOF Steelmaking Process

Dephosphorization is a reaction of important role in steelmaking process, and the correct adequacy of endpoint phosphorus content would improve the quality and productivity of steel in basic oxygen furnace (BOF) processing. Aiming to meet the required steel specifications and reduce process time, two different empirical strategies were established for predicting the endpoint phosphorus content in BOF steelmaking process: linear regression and neural network. Eight variables that affect the endpoint phosphorus content (selected as output) were determined as the input variables of the models. The performances of predictions were evaluated simultaneously with the sensitivity analysis of the model to variations in the values of its input variables. Sensitivity analysis is essential as it reveals the impact of input variables on results, although it is often neglected due to its complexity and the need for multiple simulations. Integrating sensitivity analysis with prediction techniques allows for identifying key variables and making decisions. Both empirical models are suitable and reliable for decision making in the process and can be used as tools for predicting the endpoint phosphorus content, where the neural network has higher accuracy. The sensitivity analysis showed that the two variables that most affect the response of the empirical models were the percentage of oxygen volume of oxygen blown until the sub-lance in relation to the estimated total volume, and the phosphorus concentration in the sub-lance.

Revolutionizing ELISA: A one-step blocking approach using lipid bilayer coatings

Enzyme-linked immunosorbent assay (ELISA) is an essential technique for biomolecule detection in diagnostics and research, but traditional protocols are time-consuming and variable. Conventional blocking agents like bovine serum albumin (BSA) pose ethical issues and potential assay interference. This study presents a one-step lipid-blocking approach to simplify the ELISA procedure. Compared to conventional blockers, the Lipid Universal Coating Assembly (LUCA) antifouling blocker showed comparable sensitivity while significantly reducing processing time and steps. Furthermore, it showed improved co-blocking efficacy and signal intensity in conventional protocols when combined with minimal BSA concentrations. Stability tests under accelerated aging confirmed the robustness of the LUCA antifouling blocker. Additionally, it effectively facilitated antibody detection for COVID-19 antigens in direct ELISA and human IL-6 antigen in Sandwich ELISA, suggesting its versatile applicability. Taken together, our findings reveal that one-step lipid blocking not only streamlines the ELISA process but also maintains high sensitivity and specificity, providing an efficient, user-friendly, and environmentally friendly alternative to traditional ELISA blockers and a robust platform for rapid diagnostics.

A Novel Low-Complexity and Parallel Algorithm for DCT IV Transform and Its GPU Implementation

This study proposes a novel factorization method for the DCT IV algorithm that allows for breaking it into four or eight sections that can be run in parallel. Moreover, the arithmetic complexity has been significantly reduced. Based on the proposed new algorithm for DCT IV, the speed performance has been improved substantially. The performance of this algorithm was verified using two different GPU systems produced by the NVIDIA company. The experimental results show that the novel proposed DCT algorithm achieves an impressive reduction in the total processing time. The proposed method is very efficient, improving the algorithm speed by more than 4-times—that was expected by segmenting the DCT algorithm into four sections running in parallel. The speed improvements are about five-times higher—at least 5.41 on Jetson AGX Xavier, and 10.11 on Jetson Orin Nano—if we compare with the classical implementation (based on a sequential approach) of DCT IV. Using a parallel formulation with eight sections running in parallel, the improvement in speed performance is even higher, at least 8.08-times on Jetson AGX Xavier and 11.81-times on Jetson Orin Nano.

Effect of freeze-thaw and PEF pretreatments on the kinetics and microstructure of convective and ultrasound-assisted drying of orange peel

The main waste generated by juice industry comprises orange peels, which have a great upcycling potential once stabilized. Drying is the most used method for this purpose, but the high energy consumption prompts interest in its intensification. This study assessed the influence of freeze-thaw and pulsed electric field (PEF) pretreatments in conventional and airborne ultrasound-assisted drying (50 °C) of orange peels. None of these pretreatments alone got to reduce processing times significantly, but combined with ultrasound-assisted drying produced a significant shortening of the process. This was particularly important in the lower intensity PEF pretreatment tested (0.33 kJ/kg), indicating the existence of optimum conditions to carry out the pretreatments. Microstructure analysis revealed that the application of ultrasound during drying led to better preservation of the sample structure. Thus, the integration of pretreatment techniques to ultrasound-assisted drying may not only shorten the process but also help to preserve the original structure.

Optimal integration of PV generators and D-STATCOMs into the electrical distribution system to reduce the annual investment and operational cost: A multiverse optimization algorithm and matrix power flow approach

In this work, we address the problem of optimally integrating photovoltaic distributed generators (PV) and distributed static compensators (D-STATCOMs) in distributed electrical systems (DES). Previous methods have struggled with the complexities of non-linear mathematical models and the integration of operating and investment costs while meeting the operational constraints of these devices. We propose a solution using a master-slave methodology that combines a discrete-continuous version of the multiverse optimization algorithm (MVO), with a matrix power flow method based on successive approximation (MAX). This approach aims to reduce annual costs by efficiently managing the grid’s operating costs and the investment and operational costs of PV and D-STATCOMs. Various researchers have suggested methodologies for solving the problem of optimal D-STATCOM integration in DES, focusing on minimizing investment and operating costs as the objective function. However, these studies often lack comparative methodologies and statistical analysis to validate the performance of their proposed approaches. Additionally, they do not consider the impact of variable power demand. We compared our methodology with other master-slave approaches that utilize different optimization algorithms, including the vortex search algorithm (VSA), crow search algorithm (CSA), a continuous genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). These comparisons were made using two test systems with 33 and 69 nodes, which incorporate variations in power demand and PV production from a region in Colombia. Our statistical analysis included evaluations of the best and average solutions, standard deviations, differences between the best and average solutions, and a dispersion analysis using box-and-whisker plots. The results demonstrate that MVO outperforms other methods, providing the best results with reduced processing times in both test systems.

Implementation of Agent Systems in Big Data Management: Integrating Artificial Intelligence for Data Mining Optimization

Background: The rapid growth of data generated across various domains necessitates advanced methodologies for effective data management and extraction of meaningful insights. Traditional data processing techniques often struggle with the volume, variety, and velocity of big data. The integration of Agent Systems and Artificial Intelligence (AI) presents a promising approach to address these challenges by enhancing the efficiency and effectiveness of data mining processes. Objective: This study aims to explore the implementation of Agent Systems in big data management, focusing on how the integration of AI can optimize data mining operations. By leveraging the capabilities of intelligent agents, we seek to improve the accuracy, speed, and scalability of data analysis. Methods: A hybrid research methodology was employed, combining a systematic literature review with an empirical case study. The literature review analyzed previous research on Agent Systems, AI, and big data management to identify key trends and challenges. The empirical case study involved deploying an AI-integrated Agent System within a large-scale data environment to evaluate its performance. Key performance indicators (KPIs) such as processing time, accuracy, and scalability were measured and analyzed. Results: The findings indicate that the integration of AI within Agent Systems significantly enhances the data mining process. The system demonstrated a reduction in processing time by 40%, an increase in data analysis accuracy by 25%, and improved scalability, handling larger datasets more efficiently compared to traditional methods. These improvements were attributed to the autonomous and adaptive nature of agent systems, which enabled dynamic data processing and real-time decision-making. Conclusion: The study concludes that the implementation of AI-integrated Agent Systems in big data management offers substantial benefits, including optimized data mining performance. This integration facilitates more efficient and effective data analysis, which is crucial for organizations dealing with large volumes of data. Future research should focus on further refining these systems and exploring their application across different sectors.

Non-invasive estimation of the powder size distribution from a single speckle image

Non-invasive characterization of powders may take one of two approaches: imaging and counting individual particles; or relying on scattered light to estimate the particle size distribution (PSD) of the ensemble. The former approach runs into practical difficulties, as the system must conform to the working distance and other restrictions of the imaging optics. The latter approach requires an inverse map from the speckle autocorrelation to the particle sizes. The principle relies on the pupil function determining the basic sidelobe shape, whereas the particle size spread modulates the sidelobe intensity. We recently showed that it is feasible to invert the speckle autocorrelation and obtain the PSD using a neural network, trained efficiently through a physics-informed semi-generative approach. In this work, we eliminate one of the most time-consuming steps of our previous method by engineering the pupil function. By judiciously blocking portions of the pupil, we sacrifice some photons but in return we achieve much enhanced sidelobes and, hence, higher sensitivity to the change of the size distribution. The result is a 60 × reduction in total acquisition and processing time, or 0.25 seconds per frame in our implementation. Almost real-time operation in our system is not only more appealing toward rapid industrial adoption, it also paves the way for quantitative characterization of complex spatial or temporal dynamics in drying, blending, and other chemical and pharmaceutical manufacturing processes.

Adaptive random-grid and progressive color secret sharing with CNN super-resolution on a many-core system

This study introduces an innovative method combining Convolutional Neural Networks (CNN) with random grid generation for enhanced visual secret sharing. Our approach supports color images, reduces processing time, and offers non-pixel expansion and flexible share combinations. Utilizing GPU computation, it significantly improves practicality and efficiency by operating in a feed-forward manner, avoiding complex optimization. Experimental results demonstrate superior metrics: maximum PSNR of 32 dB, 99% NCC correlation with benchmarks, 2.9% NAE, and SSIM of 98%. We achieve substantial speedups– 493 × for 256 × 256 and 3145 × for 1024 × 1024 images–compared to sequential models. The scheme exhibits robustness against attacks, evidenced by CMY component and share histogram similarities, and scalability shown in combinatorial explosion visualization. These findings underscore the efficacy and efficiency of our approach, advancing secure image sharing applications significantly.

Integrated multi-objective optimization of rough and finish cutting parameters in plane milling for sustainable machining considering efficiency, energy, and quality

Plane milling is one of the most prevalent methods in metalworking, recognized for its potential to uncover energy-saving opportunities by optimizing cutting parameters. However, existing research on cutting parameters optimization primarily adopts a phased independent optimization approach, with some studies focusing on rough milling parameters and others on finish milling parameters under predetermined allowances. These research methods lack integrated optimization for both rough and finish milling parameters, leading to suboptimal outcomes. To address this issue, this paper proposes an integrated multi-objective optimization method for both rough and finish cutting parameters in plane milling. The aim is to achieve sustainable machining by considering efficiency, energy consumption, and quality. Specifically, the research accomplishes three key objectives: (1) developing detailed models for the efficiency, energy, and quality of plane milling; (2) presenting an integrated multi-objective optimization model and approach for determining rough and finish cutting parameters in plane milling; (3) conducting a specific case study to validate the effectiveness and feasibility of the proposed model and approach. Experimental findings demonstrated that optimizing cutting parameters in the plane milling process through an integrated approach can reduce processing time by 19.37%, decrease energy consumption by 16.88%, and significantly improve the surface roughness of the workpiece by 27.07% compared to traditional empirical methods. Furthermore, compared to independent optimization schemes, processing time is reduced by 2.99%, energy consumption is lowered by 4.72%, and surface roughness is improved by 13.16%.

Experimental performance analysis of methanol adsorption in granular activated carbon packed bed through design of a double pipe heat exchanger with longitudinal fins

Activated carbon (AC) is essential for its exceptional adsorption properties and high surface area in various applications. Adsorption/desorption (A/D) processes with AC can be exothermic or endothermic, necessitating improved heat exchanger (HE) designs due to AC's low thermal conductivity. This study presents an A/D HE design with longitudinal fins in the sorption bed to enhance heat transfer during methanol A/D in AC. Bed heating/cooling uses water flowing through pipes inside the HE. Experimental investigations focus on thermal characteristics and A/D efficiency under diverse conditions, comparing results to a base non-finned HE design. Various experiments measured HE shell temperatures at different water flow rates while varying the sorption bed condition (vacuumed, filled with air, or methanol). The finned HE design showed a 1.8 °C increase in maximum bed surface temperature and a 3-h, 40-min reduction in process time compared to the non-finned HE. Subsequent tests with the finned HE at different water flow rates (5, 10, and 15 l/min) demonstrated superior heat transfer at 15 l/min due to turbulent flow enhancement. Additionally, adsorption ceased at a bed temperature of 54 °C. These findings highlight the efficacy of finned HE for optimizing methanol A/D processes, offering insights into improving heat transfer in AC-based systems.

Optimization Algorithms for Real-Time Image Processing in IoT Networks

This study explores the application of optimization algorithms, with a focus on Particle Swarm Optimization (PSO), to enhance real-time image processing in Internet of Things (IoT) networks. Given the constraints of computational power and energy resources in IoT devices, efficient processing of image data is a critical challenge. The proposed PSO-based framework aims to minimize processing time and energy consumption while maintaining high accuracy in image analysis tasks. Experimental results demonstrate that PSO can reduce processing time by 10% and energy consumption by 15% on average, with a slight improvement in accuracy. Comparisons with traditional and other evolutionary optimization techniques reveal that PSO provides a superior balance between efficiency and performance, making it particularly well-suited for real-time IoT applications. The findings suggest that the integration of advanced optimization algorithms like PSO into IoT systems can significantly enhance their operational efficiency, scalability, and effectiveness, particularly in resource-constrained environments.

AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography.

Our research evaluates advanced artificial (AI) methodologies to enhance diagnostic accuracy in pulmonary radiography. Utilizing DenseNet121 and ResNet50, we analyzed 108,948 chest X-ray images from 32,717 patients and DenseNet121 achieved an area under the curve (AUC) of 94% in identifying the conditions of pneumothorax and oedema. The model's performance surpassed that of expert radiologists, though further improvements are necessary for diagnosing complex conditions such as emphysema, effusion, and hernia. Clinical validation integrating Latent Dirichlet Allocation (LDA) and Named Entity Recognition (NER) demonstrated the potential of natural language processing (NLP) in clinical workflows. The NER system achieved a precision of 92% and a recall of 88%. Sentiment analysis using DistilBERT provided a nuanced understanding of clinical notes, which is essential for refining diagnostic decisions. XGBoost and SHapley Additive exPlanations (SHAP) enhanced feature extraction and model interpretability. Local Interpretable Model-agnostic Explanations (LIME) and occlusion sensitivity analysis further enriched transparency, enabling healthcare providers to trust AI predictions. These AI techniques reduced processing times by 60% and annotation errors by 75%, setting a new benchmark for efficiency in thoracic diagnostics. The research explored the transformative potential of AI in medical imaging, advancing traditional diagnostics and accelerating medical evaluations in clinical settings.

High Throughput Manufacturing of Highly Ordered Mesoporous Thin Film Oxides for Hybrid-Polymer Nanocomposite Dielectrics

Highly ordered mesoporous metal oxide thin films have potential to become integrated into widely impactful dielectric materials due to the tunable properties of the oxide and its polymer filling; however, current manufacturing techniques to form the mesoporous oxide greatly limit scalable fabrication. Sol-gel mesoporous metal oxides generated via traditional processes require multi-day aging and high temperature, prolonged curing processes to yield controlled porosity at the expense of manufacturability. Moreover, high cost and unstable alkoxide precursors require precision over the solution precursor pH and subsequent thin film aging humidity after deposition.Solution combustion synthesis (SCS) is a next-generation method of generating metal oxides at reduced process temperatures (often <300 ºC) and using green precursors compared to the sol-gel approach. The metal oxide is typically produced from a metal nitrate, which provides both the metal cation and oxidant and an organic chelator fuel. The fuel acts to stabilize the cation against hydrolysis and to participate in the exothermic redox reaction that results in the metal oxide.Here, we present porogen integrated rapid oxidation (PIRO) which utilizes the principles of SCS to fabricate large-area mesoporous thin films of AlOx in less than 1 hour and at a temperature of 230 ºC. The PIRO method uses low-cost precursors to generate micelles with a controllable open-cell porosity ranging from ~30 – 60% and with varying degrees of closed-packed cubic ordering at a nearly 95% reduction in overall processing time. We show the precursor chemistry can be deposited via ultrasonic spray or blade coating at up to 6 m/min to manufacture large area mesoporous oxides and demonstrate filling of the porous AlOx with TOPAS® polymer to generate a hybrid nanocomposite with a dielectric constant of 6.26.

FUTURE TRENDS IN SQL DATABASES AND BIG DATA ANALYTICS: IMPACT OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

This study systematically reviews the integration of machine learning (ML) and artificial intelligence (AI) into SQL databases and big data analytics, highlighting significant advancements and emerging trends. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive review of 60 selected articles published between 2010 and 2023 was conducted. The findings reveal substantial improvements in query optimization through ML algorithms, which adapt dynamically to changing data patterns, reducing processing times and enhancing performance. Additionally, embedding ML models within SQL databases facilitates real-time predictive analytics, streamlining workflows, and improving the accuracy and speed of predictions. AI-driven security systems provide proactive and real-time threat detection, significantly enhancing data protection. The development of hybrid systems that combine relational and non-relational databases offers versatile and efficient data management solutions, addressing the limitations of traditional systems. This study confirms the evolving role of AI and ML in transforming data management practices and aligns with and extends previous research findings.

Nanoscale single-vesicle analysis: High-throughput approaches through AI-enhanced super-resolution image analysis

The analysis of membrane vesicles at the nanoscale level is crucial for advancing the understanding of intercellular communication and its implications for health and disease. Despite their significance, the nanoscale analysis of vesicles at the single particle level faces challenges owing to their small size and the complexity of biological fluids. This new vesicle analysis tool leverages the single-molecule sensitivity of super-resolution microscopy (SRM) and the high-throughput analysis capability of deep-learning algorithms. By comparing classical clustering methods (k-means, DBSCAN, and SR-Tesseler) with deep-learning-based approaches (YOLO, DETR, Deformable DETR, and Faster R–CNN) for the analysis of super-resolution fluorescence images of exosomes, we identified the deep-learning algorithm, Deformable DETR, as the most effective. It showed superior accuracy and a reduced processing time for detecting individual vesicles from SRM images. Our findings demonstrate that image-based deep-learning-enhanced methods from SRM images significantly outperform traditional coordinate-based clustering techniques in identifying individual vesicles and resolving the challenges related to misidentification and computational demands. Moreover, the application of the combined Deformable DETR and ConvNeXt-S algorithms to differently labeled exosomes revealed its capability to differentiate between them, indicating its potential to dissect the heterogeneity of vesicle populations. This breakthrough in vesicle analysis suggests a paradigm shift towards the integration of AI into super-resolution imaging, which is promising for unlocking new frontiers in vesicle biology, disease diagnostics, and the development of vesicle-based therapeutics.

Automatic detection of infeasible paths in large-scale program based on program summaries

The existence of infeasible paths in a program reduces the coverage of test cases and causes a waste of valuable testing resources. Detecting infeasible paths allows for focusing testing resources on feasible paths. This paper introduces a method for detecting infeasible paths based on program summaries. Our proposed method partitions the program into sequential statements, conditional statements and loop statements, and automatically generates statement summaries and function summaries. It analyzes the summaries to extract the path constraints and determines the feasibility of paths. We implemented a detection tool named DTSIP based on this method, and conducted experiments using a set of benchmark programs and open source projects. The results confirm the effectiveness of our method in detecting infeasible paths. It can detect both intraprocedural and interprocedural infeasible paths, demonstrating its broad applicability. Our method overcomes challenges associated with analyzing complex paths, achieving efficient feasibility determination while reducing processing time.

Integrated processes (HPSE+scCO2) to prepare sterilized alginate-gelatine-based aerogel

Biopolymers application in biomedical areas has been limited due to the physicochemical degradation that occurs using conventional processing/sterilization methods (e.g., steam heat, γ-radiation, ethylene oxide). Aiming to avoid/minimize degradation and preserve their properties, supercritical carbon dioxide (scCO2) has been proposed as an alternative sterilization method for such materials. ScCO2 can simultaneously be used as a drying method to produce aerogels (i) and sterilize them (ii). However, a solvent exchange is required to prepare the alcogel from hydrogel, achievable through high-pressure solvent exchange (HPSE) (iii). This study integrated three processes: HPSE, scCO2 drying, and sterilization to prepare alginate-gelatine sterilized aerogels. Two scCO2 sterilization methods were tested. Results showed that sterilization did not compromise the aerogels’ chemical, thermal and swelling properties. Conversely, Young’s Modulus increased, and BET surface area decreased, due to the structural changes caused by the fast pressurization/depressurization rates applied during sterilization. Regarding the sterilization efficiency, results showed a reduction in contamination throughout the process, achieving a SAL of 10-4. The sterilized aerogels were non-cytotoxic in vitro and showed improved wound-healing properties. The innovative integrated process produced decontaminated/sterile and ready-to-use aerogels reducing process time by 75 %, from 2 days up to 12 h without compromising the aerogel’s properties.

Artificial Intelligence Function Management in Supporting the Process of Government Implementation and Public Services in Indonesia

This research investigates the impact of Artificial Intelligence (AI) on government administration and public service delivery in Indonesia. The study employed qualitative research methods, including semi-structured interviews and detailed case studies of selected AI projects. The research results indicate that AI has significantly improved administrative efficiency and public service delivery. The “Smart Administration System” has automated routine tasks, resulting in a 60% reduction in manual processing times and enhancing overall productivity. The “Policy Insight Tool” has supported better policy formulation through advanced predictive analytics and scenario modeling. Despite these successes, the study identified challenges such as resistance to change, technical difficulties, and data integration issues. Feedback from users also highlighted limitations in AI language processing and accessibility concerns.

Real-Time Personal Protective Equipment Non-Compliance Recognition on AI Edge Cameras

Despite advancements in technology, safety equipment, and training within the construction industry over recent decades, the prevalence of fatal and nonfatal injuries and accidents remains a significant concern among construction workers. Hard hats and safety vests are crucial safety gear known to mitigate severe head trauma and other injuries. However, adherence to safety protocols, including the use of such gear, is often inadequate, posing potential risks to workers. Moreover, current manual safety monitoring systems are laborious and time-consuming. To address these challenges and enhance workplace safety, there is a pressing need to automate safety monitoring processes economically, with reduced processing times. This research proposes a deep learning-based pipeline for real-time identification of non-compliance with wearing hard hats and safety vests, enabling safety officers to preempt hazards and mitigate risks at construction sites. We evaluate various neural networks for edge deployment and find that the Single Shot Multibox Detector (SSD) MobileNet V2 model excels in computational efficiency, making it particularly suitable for this application-oriented task. The experiments and comparative analyses demonstrate the pipeline’s effectiveness in accurately identifying instances of non-compliance across different scenarios, underscoring its potential for improving safety outcomes.

Adaptation of the protein misfolding cyclic amplification (PMCA) technique for the screening of anti-prion compounds.

Prion diseases result from the misfolding of the physiological prion protein (PrPC) to a pathogenic conformation (PrPSc). Compelling evidence indicates that prevention and/or reduction of PrPSc replication are promising therapeutic strategies against prion diseases. However, the existence of different PrPSc conformations (or strains) associated with disease represents a major problem when identifying anti-prion compounds. Efforts to identify strain-specific anti-prion molecules are limited by the lack of biologically relevant high-throughput screening platforms to interrogate compound libraries. Here, we describe adaptations to the protein misfolding cyclic amplification (PMCA) technology (able to faithfully replicate PrPSc strains) that increase its throughput to facilitate the screening of anti-prion molecules. The optimized PMCA platform includes a reduction in sample and reagents, as well as incubation/sonication cycles required to efficiently replicate and detect rodent-adapted and cervid PrPSc strains. The visualization of PMCA products was performed via dot blots, a method that contributed to reduced processing times. These technical changes allowed us to evaluate small molecules with previously reported anti-prion activity. This proof-of-principle screening was evaluated for six rodent-adapted prion strains. Our data show that these compounds targeted either none, all or some PrPSc strains at variable concentrations, demonstrating that this PMCA system is suitable to test compound libraries for putative anti-prion molecules targeting specific PrPSc strains. Further analyses of a small compound library against deer prions demonstrate the potential of this new PMCA format to identify strain-specific anti-prion molecules. The data presented here demonstrate the use of the PMCA technique in the selection of prion strain-specific anti-prion compounds.