Fast Process Research Articles

A Rapid, Efficient Method for Anodic Aluminum Oxide Membrane Room-Temperature Multi-Detachment from Commercial 1050 Aluminum Alloy.

For commercial processes, through-hole AAO membranes are fabricated from high-purity aluminum by chemical etching. However, this method has the disadvantages of using heavy-metal solutions, creating large amounts of material waste, and leading to an irregular pore structure. Through-hole porous alumina membrane fabrication has been widely investigated due to applications in filters, nanomaterial synthesis, and surface-enhanced Raman scattering. There are several means to obtain freestanding through-hole AAO membranes, but a fast, low-cost, and repetitive process to create complete, high-quality membranes has not yet been established. Here, we propose a rapid and efficient method for the multi-detachment of an AAO membrane at room temperature by integrating the one-time potentiostatic (OTP) method and two-step electrochemical polishing. Economical commercial AA1050 was used instead of traditional high-cost high-purity aluminum for AAO membrane fabrication at 25 °C. The OTP method, which is a single-step process, was applied to achieve a high-quality membrane with unimodal pore distribution and diameters between 35 and 40 nm, maintaining a high consistency over five repetitions. To repeatedly detach the AAO membrane, two-step electrochemical polishing was developed to minimize damage on the AA1050 substrate caused by membrane separation. The mechanism for creating AAO membranes using the OTP method can be divided into three major components, including the Joule heating effect, the dissolution of the barrier layer, and stress effects. The stress is attributed to two factors: bubble formation and the difference in the coefficient of thermal expansion between the AAO membrane and the Al substrate. This highly efficient AAO membrane detachment method will facilitate the rapid production and applications of AAO films.

Reaction Dynamics of CO2 Hydrogenation on Iron Catalysts Using ReaxFF Molecular Dynamics Simulation.

The conversion of CO2 to hydrocarbons using catalysts is a promising route to utilize CO2 and produce more valuable chemicals in a sustainable manner. Recent studies have shown that iron-based catalysts perform well for the hydrogenation of CO2. While the hydrogenation reaction mechanism in the gas phase is straightforward, when catalyzed by iron it has been demonstrated to involve various chemical transformations, and the selectivity and conversion are strongly dependent on the particle size. To further investigate the dependence of the reactivity of iron catalysts on cluster size, we performed reactive molecular dynamics simulations using the ReaxFF force field (ReaxFF-MD) for iron nanoclusters of various sizes in a CO2 and H2-rich environment. We demonstrated that the homogeneous hydrogenation of CO2 was correctly described by this ReaxFF model. The dissociation mechanism of CO2 on the Fe4, Fe16 clusters, and the bcc(100) Fe slab agrees with previous DFT results. The ReaxFF-MD simulations suggest a strong dependence of reactivity on the cluster size, with the Fe4 cluster having the highest reactivity. We show that ReaxFF-MD provides a route to understand reaction mechanisms in these nonequilibrium reactive processes where fast processes and local minima are important.

A physically-informed machine learning model for freeform bending

AbstractThis work aims at a fast computational process model of the free-form bending process. It proposes a novel physically-informed machine learning model, which is trained with experimental data of bending constant radii and utilizes additional physical bending knowledge by integrating Timoshenko’s beam theory. The model is able to predict the resulting plastic deformation of the tube after exiting the die by computing an elastic representation of the tube’s deformation with beam theory at each time step. This elastic representation serves as input for a regression model similar to a partially connected neural network. This physically-informed machine learning model generalizes the constant training radii to complex bend geometries consisting of transitional sections and true spline geometries. It is compared to a benchmark finite element simulation and has an improved prediction quality for complex kinematics while reducing the computation time by four orders of magnitude.

Promotion of Fast and Efficient Singlet Fission Process of PDI Dimers by Selenium Substitution.

Singlet fission (SF) is a triplet generation mechanism capable of turning a singlet exciton into two triplet excitons. It has the potential to enhance the power conversion efficiency of single-junction solar cells. Perylene diimides (PDIs) are a class of dye molecules with photovoltaic properties and are beginning to receive more and more attention due to their potential for SF. Here, we report a selenium-substituted PDI dimer, Se-PDI-II, and we studied its SF mechanism by using steady-state, transient absorption, and time-resolved photoluminescence spectroscopy. Compared with the unsubstituted dimer PDI-II, we found that the introduction of selenium atoms can suppress excimer emission during the SF process, showing much higher SF efficiency and triplet yield.

Synergistic effects of defect engineering and functionalizing ionic liquids within UiO-66(Zr)-NH2 frameworks towards improving removal efficiency of phosphate and methyl orange

In this study, novel ionic liquid (IL)-entrapped UiO-66(Zr)-NH2 frameworks are proposed as efficient adsorbents to remove phosphate and methyl orange (MO). First, cetyltrimethylammonium bromide (CTAB) surfactant-defected UiO-66(Zr)-NH2 with tuned ligand deficiencies was produced using the continuous tubular reactor under microwave irradiation, making it a fast and scalable process. Upon changing the CTAB/ligand molar ratio from 0.5 to 1.5 and 2.5, the reaction yield deminised from ∼72–68 and 59 %, while the linker deficiency per Zr6 unit increased from ∼ 0.54–1.72 and 2.01, respectively. Defect-contained UiO-66(Zr)-NH2 was favourably entrapped with 1-butyl-3-methylimidazoliumbromide ([Bmim]+[Br]-) ionic liquid using a ship-in-bottle method to obtain host-guest [Bmim]+[Br]-@UiO-66(Zr)-NH2 composites, named Bmim@UN. Results revealed that IL-functionalized UiO-66(Zr)-NH2 comprised N-heteroaromatic rings and NH2 groups, creating a synergetic effect that enhanced the phosphate and MO adsorptions. At pH 4.5 and 27 °C, the Bmim@UN loaded 12 wt% IL had the equilibrium adsorbing capacity for phosphate and MO of ∼ 197 and ∼ 98 mg/g, respectively, and their corresponding adsorption rate constant of ∼ 0.0036 g mg−1min−1 and 0.0022 g mg−1min−1. Notably, the harvested Bmim@UN exhibited good simultaneous decontamination of phosphate and MO in their binary solution, where MO further accelerated the phosphate adsorption. The mechanism studies revealed that electrostatic interactions, hydrogen bonds, cation-π, and π-π interactions drove these adsorptions. The study demonstrates that combining defect engineering and IL-functionalizing of MOF has constructed efficient materials for wastewater remediation.

Faktor – Faktor yang Mempengaruhi Efektivitas Rekrutmen pada PT Gramedia Asri Media

In Indonesia, especially in the employment sector, a qualified workforce is needed so that companies can remain competitive and experience rapid growth. The demand for qualified labor continues to increase every year, which makes competition among job seekers increasingly fierce. In addition, many new graduates face difficulties in finding a job due to lack of access to information. Therefore, there are several factors that can facilitate job seekers in the application and job search process, such as recruitment sources, company branding, and a fast and efficient selection process. These factors help recruiters in finding candidates that match the needs of the company and the position required.

Lane Line and Object Detection Using Yolo v3

In the contemporary age, creating autonomous vehi- cles is a crucial starting point for the advancement of intelligent transportation systems that rely on sophisticated telecommu- nications network infrastructure, including the upcoming 6g networks. The paper discusses two significant issues, namely, lane detection and obstacle detection (such as road signs, traffic lights, vehicles ahead, etc.) using image processing algorithms. To address issues like low accuracy in traditional image processing methods and slow real-time performance of deep learning-based methods, barriers for smart traffic lane and object detection algorithms are proposed. We initially rectify the distorted image resulting from the camera and then employ a threshold algorithm for the lane detection algorithm. The image is obtained by extracting a specific region of interest and applying an inverse perspective transform to obtain a top-down view. Finally, we apply the sliding window technique to identify pixels that belong to each lane and modify it to fit a quadratic equation. The Yolo algorithm is well-suited for identifying various types of obstacles, making it a valuable tool for solving identification problems. Finally, we utilize real-time videos and a straightforward dataset to conduct simulations for the proposed algorithm. The simula- tion outcomes indicate that the accuracy of the proposed method for lane detection is 97.91% and the processing time is 0.0021 seconds. The proposal for detecting obstacles has an accuracy rate of 81.90% and takes only 0.022 seconds to process. Compared to the conventional image processing technique, the proposed method achieves an average accuracy of 89.90% and execution time of 0.024 seconds, demonstrating a robust capability against noise. The findings demonstrate that the suggested algorithm can be implemented in self-driving car systems, allowing for efficient and fast processing of the advanced network.

AttGAN: attention gated generative adversarial network for spatio-temporal super-resolution of ocean phenomena

ABSTRACT This study proposes an innovative deep learning-aided approach based on generative adversarial networks named AttGAN, which is specialized for solving the spatio-temporal super-resolution problem of ocean datasets with complex waveforms and turbulent features. The proposed method can efficiently restore the desired fine high-frequency details while maintaining high efficiency. The key idea of the proposed approach is to incorporate an attention gate in the generator, which allows for emphasizing salient features for super-resolution tasks. In addition, residual convolutional blocks are used in the generator and discriminator to extract features. By establishing regression loss, physical constraints, and adversarial loss into a comprehensive loss function, a generator that exhibits high fidelity and strong generalization capabilities is trained. The proposed AttGAN is evaluated by experiments conducted on two datasets, and the experimental results validate its superiority over state-of-the-art physical constraint models in both qualitative and quantitative metrics. Moreover, the AttGAN has faster processing times than the corresponding ocean numerical models and better quality of results. Impact statement

Auto ignition assisted synthesis of magnetic CaFe2O4: Exploring role of fuel molecule and amplest dye dismissal

The present work explores the pioneering use of different fuels for the synthesis of CaFe2O4 (CF) nanopowder through auto-ignition synthesis (AIS) with potential application in the acid fuchsine (AF) dye removal. The results showed significant alternation in structure, morphology, and magnetic properties depending on fuel type. The morphology of the CaFe2O4 nanopowders changed from a rock-like appearance for dextrose to a porous, thick-walled network structure for sucrose. All synthesized particles showed superparamagnetic behavior, with a crystallite size of 9 nm to 16 nm. The different adsorption process parameters, including the solution pH, adsorbent amount, and contact time, were studied and optimized. The Langmuir isotherm well explains the adsorption of AF, and the maximum monolayer capacity of CF-dextrose, CF-xylitol, CF-sorbitol, and CF-sucrose was found to be 788, 1024, 1125, and 1297 mg/g, respectively. The pseudo-second-order (PSO) kinetic model describes the adsorption data and the value of rate constant (k2 = 2.36E-05 for CF-dextrose, 3.11E-05 for CF-xylitol, 2.70E-05 for CF-sorbitol, and 5.36E-05 for CF-sucrose) explain fast adsorption process. Furthermore, the CF nanopowder exhibits better regeneration and reusability capacity after multiple consecutive cycles, and magnetic separation aptitude makes it more convenient to isolate the adsorbent after the adsorption process.

Al-doped Li4SiO4 for cyclic solar energy storage and CO2 capture: An experimental and DFT study

The substantial consumption of fossil fuels gives rise to excessive carbon dioxide emissions, and mitigating them has emerged as a pressing global priority. The use of renewable energy and carbon capture, utilization, and storage are recognized as two major approaches for carbon neutrality. Li4SiO4 materials exhibit promising prospects in both thermochemical heat storage for solar energy power and cyclic high-temperature CO2 capture. The reactivity between Li4SiO4 and CO2 plays the vital role in its energy storage and CO2 capture performance. Herein, we prepare Al-doped Li4SiO4 materials through an impregnated suspension technique, simultaneously achieving high contact areas and lattice defects to enhance both fast chemisorption and ion diffusion processes. The results show that Al-doped Li4SiO4 exhibits high average energy density of 56677.08 kJ/kmol within 30 cycles. Additionally, the granulated Al-doped Li4SiO4-based pellets, produced via the freeze-drying technique, exhibit negligible energy density loss rate of less than 9 % and acceptable mechanical strength. Furthermore, the CO2 sorption performance also reaches a high value of 0.27 g/g. The reaction mechanism between Li4SiO4 and CO2 has been investigated at both macroscopic and microscopic levels through kinetic analysis and DFT calculations. The simulated Al-doped Li4SiO4/CO2 sorption system possesses increased adsorption energy level from −0.81 eV for the undoped one to −0.95 eV, thereby leading to the improved CO2 sorption. In general, Al-doped Li4SiO4 materials demonstrate great potential for applications in thermochemical heat storage and CO2 capture.

Enhancing Cloud Data Deduplication with Dynamic Chunking and Public Blockchain

The majority of cloud service providers (CSPs) store and remove customer data according to certain principles. The majority of them have designed their cloud platform to have very high levels of consistency, speed, availability, and durability. Their systems are built with these performance characteristics in mind, and the requirement to ensure precise and rapid data deletion must be carefully balanced. In the public blockchain, this paper suggests employing the rapid content-defined Chunking algorithm for data duplication. Acute data is frequently outsourced by individuals and organizations to distant cloud servers since doing so greatly reduces the headache of maintaining infrastructure and software. However, because user data is transmitted to cloud storage providers and stored on a remote cloud, ownership and control rights are nonetheless separated. Users thus have significant challenges when attempting to confirm the integrity of private information. According to the experiment results, the suggested dynamic chunking has a fast processing time that is on par with fixed-length chunking and significantly improves deduplication processing capability.

Emotion processing in concrete and abstract words: evidence from eye fixations during reading

ABSTRACT We replicated and extended the findings of Yao et al. [(2018). Differential emotional processing in concrete and abstract words. Journal of Experimental Psychology: Learning, Memory, and Cognition, 44(7), 1064–1074] regarding the interaction of emotionality, concreteness, and imageability in word processing by measuring eye fixation times on target words during normal reading. A 3 (Emotion: negative, neutral, positive) × 2 (Concreteness: abstract, concrete) design was used with 22 items per condition, with each set of six target words matched across conditions in terms of word length and frequency. (e.g. shocking, reserved, fabulous) and concrete (e.g. massacre, calendar, treasure) target words appeared (separately) within contextually neutral, plausible sentences. Sixty-three participants each read all 132 experimental sentences while their eye movements were recorded. Analyses using Gamma generalised linear mixed models revealed significant effects of both Emotion and Concreteness on all fixation measures, indicating faster processing for emotional and concrete words. Additionally, there was a significant Emotion × Concreteness interaction which, critically, was modulated by Imageability in early fixation time measures. Emotion effects were significantly larger in higher-imageability abstract words than in lower-imageability ones, but remained unaffected by imageability in concrete words. These findings support the multimodal induction hypothesis and highlight the intricate interplay of these factors in the immediate stages of word processing during fluent reading.

Adsorption kinetics of organic phosphates on goethite and aluminium oxide: The equation used to describe the reaction

AbstractAdsorption kinetics of three organic phosphate compounds (OPs) with varying molecular sizes and structures and inorganic phosphate (Pi) were investigated on α‐Al2O3 and poorly crystalline goethite. The organic phosphates were inositol hexaphosphate (IHP), glycerol phosphate (GlyP) and glucose‐6‐phosphate (G6P), and the inorganic phosphate was KH2PO4. Batch adsorption experiments were performed at 25°C. We tested the sorption kinetic data using various non‐linear models/equations and on their transformed linear forms by applying appropriate statistics. Besides, we also used a modified non‐linear equation having four parameters to this effect. Data were found to fit best with the modified equation and described the whole sorption process satisfactorily. For sorption of compounds on to the surface of these minerals, the equation with four parameters may be used in contrast with many standard equations applied for kinetic studies in soils. Sorption was described to take place in two processes: a fast one that takes place in less than 45 min and a slow one that takes place in several hours or more. The rate of the slow process did not depend directly on the concentration of phosphate compounds in solution, but depended linearly on the amount of phosphate that was adsorbed during the fast process. These initially adsorbed ions carrying some amount of negative charge likely hindered the movement of subsequent adsorbate ions to the solid surface due to decreased surface potential. This caused the variation in fast and slow sorption rate constants. Sorption densities increased in the order, Pi >Gly P >G6P >IHP, which revealed that the sorption density and initial sorption rate of OPs decreased with increasing molecular weights of OPs.

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SpiReco: Fast and Efficient Recognition of High-Speed Moving Objects With Spike Camera

Benefited from the high temporal resolution and high dynamic range, spike cameras have shown great potential in recognizing high-speed moving objects. However, the computer vision community has not explored this task due to the lack of spike data and annotations of high-speed moving objects. This paper contributes a novel dataset, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SpiReco</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Spi</i> king datasets for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reco</i> gnition), by recording high-speed moving objects using a spike camera. To annotate the dataset, image labels from established datasets such as MNIST, CIFAR10, and CALTECH101 are utilized. Based on this new dataset, this paper proposes the first spike-based object recognition framework. The proposed framework includes a denoise module, which is designed to suppress spike noise by learning spatio-temporal correlation from neighbouring pixels. Additionally, a motion enhancement module is introduced to address high-speed and random motions. Afterward, binarized neural networks are adopted to save computation costs. These efforts result in a fast and efficient processing framework for spiking data. Experimental results demonstrate the effectiveness of the proposed methods. For example, the proposed spike-based recognition framework achieves 80.2% accuracy in recognizing 101 classes of high-speed moving objects using only 2.2ms of spike streams. The SpiReco is available at https://github.com/Evin-X/SpiReco.

Designing Claim Systems in Health Insurance Companies with Microservices and Event-Driven Architecture Approach

Through digital transformation, insurance companies, especially in the health sector, are increasingly adopting modern technologies to enhance efficiency and service quality. Health insurance allows individuals or families to mitigate the financial risks associated with high and unexpected medical expenses. One crucial area is insurance claim, where a fast and accurate process is key to customer satisfaction. This study proposes the design and architecture of an insurance claim system using a microservices and event-driven approach. This approach enables insurance companies to break down applications into separate components, facilitating scalability, flexibility, and easier maintenance. Additionally, with an event-driven approach, the system can quickly respond to changes and events in the business environment. A comprehensive analysis shows that implementing microservices and event-driven architecture in the insurance claim system can enhance overall system performance, scalability, and resilience. For insurance companies, adopting microservices and event-driven architecture can lead to increased operational efficiency, reduced time to market for new products, and improved customer experiences through faster claim processing. Policyholders will benefit from quicker claim resolutions and a more transparent and responsive claim process. This study provides valuable insights for health insurance companies looking to upgrade their IT infrastructure to meet future challenges. The findings from this research will be documented to support the development of insurance business technology, specifically for health insurance claims in Indonesia.

Kinh tế số nhìn từ thành công của Đan Mạch và bài học kinh nghiệm cho Việt Nam

Digital economic development is an inevitable trend of modern society based on the application of information and communications technology to socio-economic development. With large data scale and fast processing speed, the digital economy has given birth to new business models and highly digitized goods and services. Based on the synthesis of general theoretical issues related to the digital economy, the article focuses on researching Denmark's experience in developing the digital economy and drawing some practical lessons to suggest applications for Vietnam in the process of promoting the implementation of the Digital Economy and Digital Society Development Strategy to 2025, with a vision to 2030. At the same time, to accomplish the goal of the article, the author used the research method of synthesizing and analyzing secondary data from documents and scientific works published domestically and internationally related to the topic research problem and the scope of the study in terms of time is from the period 2016- 2024 and the Danish government's orientation to 2025.

Neurophysiological Pathways of Unconscious Emotion Processing in Depression: Insights From a Simultaneous Electroencephalography–Functional Magnetic Resonance Imaging Measurement

BackgroundMajor depressive disorder (MDD) is characterized by strong emotional dysregulation. Mechanisms driving the negative affect in depression may be fast processes existing on an unconscious level. MethodsA priming task was conducted using simultaneous electroencephalography–functional magnetic resonance imaging measurement involving presentation of facial expressions (happy, sad, and neutral) to examine the neurophysiological pathway of biased unconscious emotion processing in MDD. Priming prior to a target emotion created unconscious (16.7-ms primer) and conscious (150-ms primer) trials. A large sample (N = 126) was recruited, containing healthy control participants (n = 66; 37 women) and participants with MDD (n = 60; 31 women). ResultsThe healthy control group showed a shorter reaction time in happy but not in sad or neutral trials compared with the MDD group. N170 amplitudes were lower in trials with unconscious than conscious primer presentation. N170 amplitudes correlated with cortical (right fusiform gyrus, right middle temporal gyrus, right inferior temporal gyrus, left supplementary motor area, right middle frontal gyrus) and subcortical brain regions (right amygdala). The strength of N170 and brain activity correlation increased when the stimulus was consciously presented. Presented emotions did not affect the correlation of N170 values and brain activity. ConclusionsOur findings show that MDD may exhibit biased emotion regulation abilities at a behavioral and neurophysiological level. Face-sensitive event-related potentials demonstrate a correlation with heightened brain activity in regions associated with both face recognition (fusiform gyrus) and emotion processing (amygdala). These findings are evident in both MDD and healthy control groups, with lower effect sizes in the MDD group indicating reduced emotion recognition and processing abilities.

Comparative analysis of the performance of selected database management system

Currently, in the era of large amounts of generated data, a significant challenge is fast data processing. The aim of the article is to conduct a comparative analysis of the performance of relational and non-relational databases using MS SQL Server, MongoDB, and CouchDB as examples. The comparison was carried out based on developed research scenarios designed to examine the execution time of CRUD operations. A performance ranking of databases was established based on the obtained results, with SQL Server taking the first place, MongoDB the second, and CouchDB the third. For the operations of deletion, downloading, and updating of data, MS SQL Server proved to be the most efficient database. Only in the operation of inserting data did MongoDB turn out to be the most efficient database.

Fast Processing without Processors: the Conductance Matrices

Fast Processing without Processors: the Conductance Matrices