Fast Process Research Articles

Ionic Liquid‐Filled Polyamide Microcapsules Obtained by Interfacial Polymerization

AbstractMicroencapsulated ionic liquids (ILs) have a wide range of exciting properties. They can be synthesized by a variety of methods, of which interfacial polymerization represents a very simple, fast, and reliable production process, but the number of available shell materials that can be obtained by this method is limited. However, recent advances in the field of interfacial polymerization have described the formation of a polyamide membrane at the IL/organic phase interface, which is extended in this work to the first microencapsulation of ILs with a polyamide shell, providing an additional and versatile shell material. The IL‐filled microcapsules are obtained using a surfactant‐free synthesis process and inexpensive, low‐toxicity monomers and solvents and feature an exceptionally high core‐to‐shell weight ratio enabling a broad range of possible applications.

Rapid Mg substitution to Ga-sites and slow defect recovery revealed by depth-resolved photoluminescence in Mg/N-ion-implanted GaN

Toward p-type GaN formation by Mg ion implantation (I/I) applicable to devices, depth-resolved photoluminescence (PL) revealed key behaviors during activation annealing for precise profile control, such as Mg substitution into Ga-sites (MgGa) and recovery of I/I defects. Depth profiles of the MgGa acceptor concentration were measured for Mg-I/I and Mg/N-I/I samples after ultra-high-pressure annealing at 1300 °C for 1–60 min. The cycle of low-damage dry etching and PL measurement was repeated over the I/I depth, and the MgGa concentration was estimated at each depth based on the calibration curve for the PL intensity ratio between acceptor-bound excitons (A0XA) and free excitons (FXA). In the region deeper than the I/I peak of 0.3 μm, almost all of the Mg atoms rapidly substituted into Ga-sites during the short annealing process. By contrast, the Mg substitution ratios in the shallower region were low when the annealing process was short but were improved by the sequential N-I/I. The low substitution ratio can be explained by MgGa bonding with nitrogen vacancy (VN)-related defects, while the implanted N-ions can compensate them. The PL intensity near the mean implantation depth of Mg/N-I/I was gradually improved as the annealing duration was increased to 60 min, indicating a slow reduction of nonradiative recombination centers. Simultaneously, the green luminescence associated with the VN-related defects decreased in intensity with increasing annealing time. Therefore, the main effect of prolonging annealing is the enhancement of slow defect recovery rather than enhancement of the Mg substitution as a fast process.

Further validating the robotic microsurgery platform through preclinical studies on rat femoral artery and vein.

Introduction This research aims to validate the proficiency and accuracy of the robotic microsurgery platform using rat femoral vessel model. Materials and Methods Total of 256 rat femoral vessels were performed, half using robotic and the other by manual microanastomosis by 8 microsurgeons with less than 5 years of experience given 8 trials (rats) each. Vessel demographics, proficiency (duration of suture and Structured Assessment of Robotic Microsurgical Skills (SARMS)) and accuracy (patency and Scanning Electron Microscopic (SEM)) was analyzed between the two groups. Results Using the robot, an average of 4 trials was needed to reach a plateau in total anastomosis time and patency. Significant more time was required for each vessel anastomosis (34.33 versus 21.63 minutes on the 8th trial, p<0.001) one factor being higher number of sutures compared to the hand-sewn group (artery: 7.86±0.51 versus 5.86±0.67, p=0.035, vein: 12.63±0.49 versus 9.57±0.99, p=0.055). The SARMS scores became nonsignificant between the two group on the 4th trial. The SEM showed higher tendency of unevenly spaced sutures, infolding, tears in the vessel wall for the hand-sewn group. Conclusion Using the robot, similar patency, accuracy, and proficiency can be reached through fast but steep learning process within 4 trials (anastomosis of 8 vessels) as the hand-sewn group. The robotic anastomosis may take longer time, but this is due to the increased number of sutures reflecting higher precision and accuracy. Further insight of precision and accuracy was found through the SEM demonstrating the possibility of the robot to prevent unexpected and unwanted complications.

Energy-efficient time and cost constraint scheduling algorithm using improved multi-objective differential evolution in fog computing

The recent surge in Internet of Things (IoT) applications and smart devices has led to a substantial rise in the data generation. One of the major issues involved is to meet strict quality of service (QoS) requirements for computing these applications in terms of execution time, cost and in an energy-efficient manner. To extract useful information, fast processing and analysis of data is needed. Consequently, moving all the data to centralized cloud data centers would lead to high processing times, increased cost and energy consumption and more bandwidth usage; thus, processing of applications with strict latency requirements becomes challenging. The addition of fog layer between cloud and IoT devices has provided promising solutions to such issues. However, efficient employment of computing resources in the hybrid infrastructure of fog and cloud nodes is of great significance and demands an optimal scheduling strategy. Toward this direction, a novel Pareto-based algorithm in fog computing, namely energy-efficient time and cost (ETC) constraint scheduling algorithm, is introduced in this paper for scheduling workflow applications. ETC attempts to optimize monetary cost along with time and energy objectives. Improved multi-objective differential evolution (I-MODE) meta-heuristic is introduced and incorporated with deadline-aware stepwise frequency scaling approach that is based on our previously proposed energy makespan multi-objective optimization (EM-MOO) algorithm. Synthetic and real-world application workflows are used to conduct evaluation of the proposed work with existing well-known algorithms from the literature. The experimental results for synthetic workflows reveal that the proposed algorithm lessens energy utilization by 14–21%, execution time by almost 25% and cost consumption by 22–27%, while for real-world application workflows, energy consumption is reduced by 12–24%, execution time by 14–16% and cost consumption by 23–29%.

Signal amplification platform based on 2D MOF-on-MOF architectures-derived Co-decorated carbon@nitrogen-doped porous carbon for enhanced electrochemical acetaminophen sensing

Two-dimensional (2D) carbon-carbon hybrids derived from metal-organic frameworks (MOFs) are regarded as an intriguing type of electrode material in electrochemical sensing. In this work, a Co-decorated carbon@nitrogen-doped porous carbon heterostructure (Co/C@NC) was prepared via the simple calcination of 2D ZIF-L(Co)@ZIF-8. In this MOF-on-MOF precursor, the outer ZIF-8 layer not only prevents the collapse of ZIF-L(Co) during calcination but also endows the outer carbon an extended surface area and porous structure for more accessible active sites and a fast mass transfer process. Meanwhile, the formed CoNPs could facilitate the generation of graphitic carbon layers, which enhances electrocatalytic activity and boosted conductivity. Owing to these merits, the Co/C@NC-based sensor displays high electrochemical activity for acetaminophen (APAP) detection with a wide linear range (4 × 10-7 - 2 × 10-4 M) and a lower detection limit (8.2 × 10-8 M). The constructed sensor has been utilized for the analysis of APAP in real samples, yielding acceptable recovery between 96.6% and 104.0%. This work presents an efficient and convenient method for designing MOF-on-MOF-derived 2D carbon-carbon hybrids, which hold a promising prospect in electrochemical analysis.

Conformational transformations of N-[arylsulfonylimino(methyl)methyl]-1,4-benzoquinonemonoimines

In solutions of N-[arylsulfonylimino(methyl)methyl]-1,4-benzoquinonemonoimines, several Z,E-isomerization processes occur simultaneously: a fast inversion isomerization process (topomerization) relative to the C=N1 quinoneimine bond; a slow inversion isomerization process relative to the C=N2 exocyclic bond; and a process of inhibited rotation around the =N1–С= bond that connects two imine fragments. In the 1H NMR spectra, isomerization with respect to the C=N2 exocyclic bond was only detected. The theoretical values of the isomerization barriers for the C=N1 and C=N2 bonds, as well as for the inhibited rotation around the =N1–C= bond were determined, along with the experimental Z,E-isomerization barriers for to the C=N2 bond. It was established that in the 1H NMR spectra for N-[arylsulfonylimino(methyl)methyl]-1,4-benzoquinonemonoimines, only Z, E-isomerization relative to the C=N2 bond can be observed.

Shared Patterns of Cognitive Control Behavior and Electrophysiological Markers in Adolescence

Abstract Behavioral parameters obtained from cognitive control tasks have been linked to electrophysiological markers. Yet, most previous research has investigated only a few specific behavioral parameters at a time. An integrated approach with simultaneous consideration of multiple aspects of behavior may better elucidate the development and function of cognitive control. Here, we aimed to identify shared patterns between cognitive control behavior and electrophysiological markers using stop-signal task data and EEG recordings from an adolescent sample (n = 193, aged 11–25 years). We extracted behavioral variables covering various aspects of RT, accuracy, inhibition, and decision-making processes, as well as amplitude and latency of the ERPs N1, N2, and P3. To identify shared patterns between the two sets of variables, we employed a principal component analysis and a canonical correlation analysis. First, we replicated previously reported associations between various cognitive control behavioral parameters. Next, results from the canonical correlation analysis showed that overall good task performance was associated with fast and strong neural processing. Furthermore, the canonical correlation was affected by age, indicating that the association varies depending on age. The present study suggests that although distributional and computational methods can be applied to extract specific behavioral parameters, they might not capture specific patterns of cognitive control or electrophysiological brain activity in adolescents.

Flash evaporation in a superheated liquid pool using water as medium

This paper proposes a fast cooling technology called flash evaporation or flashing to overcome cooling problems. Flashing is a pressure-driven phase change phenomenon widely studied because of its complex physics and numerous applications. An experimental system is fabricated to study the efficiency of the proposed process by considering water as a working fluid. A transparent acrylic column is used as the flash chamber, enabled with a high-speed camera to visualize and capture this fast and complex process. Experiments have been carried out with various initial temperatures from 65 °C to 80 °C, initial vacuum tank pressure ranging from 11.32 kPa to 41.32 kPa, and initial water heights of 80 mm and 140 mm. The flashing phenomenon is divided into two stages based on its xeffectiveness. From the present study, it becomes apparent that the drop in the temperature increases with the initial temperature and decreases with increasing vacuum tank pressure. The cooling rate is improved by 49.1% by increasing the initial pool temperature by 23%. The present study demonstrates an effective application of the flashing process in areas requiring high cooling rates and rapid vapour generation, such as refrigeration and drying.

Femtosecond laser-induced ultrafast electron dynamics and band gap renormalization in InSb

Studying ultrafast dynamical processes is essential for understanding light-matter coupling, which is crucial for the optoelectronic and photonic applications of semiconductor materials. Herein, we investigate the evolution of electron population near the conduction band minimum (CBM) and band gap in InSb (100) crystal under different laser fluences using time- and angle-resolved photoelectron spectra (TrARPES). In contrast to the pump-fluence-independent fast growth process (FP) of the electron population, the slow process (SP) is fluence sensitive, which is similar to the temperature-dependent behavior observed in InSb (110). In addition, we observe a band gap enlargement with increasing the photoexcitation flux. Comprehensive analysis reveals that these observed diverse results are attributed to the laser-tunable dielectric function on the surface of InSb. Our findings not only enhance the understanding of laser-semiconductor interactions but also broaden the potential applications in the field of optoelectronic and photonic devices.

Applying Machine Learning to Elucidate Ultrafast Demagnetization Dynamics in Ni and Ni80Fe20

Understanding the correlation between fast and ultrafast demagnetization (UFD) processes is crucial for elucidating the microscopic mechanisms underlying UFD, which is pivotal for various applications in spintronics. Initial theoretical models attempt to establish this correlation but face challenges due to the complex interplay of physical phenomena. To address this, a variety of machine learning (ML) methods are employed, including supervised learning regression algorithms and symbolic regression (SR), to analyze limited experimental data and derive meaningful mathematical expressions between demagnetization time (τM) and the Gilbert damping factor (α). The results reveal that polynomial regression and K‐nearest neighbors algorithms perform best in predicting τM. Additionally, variable‐selection sure‐independence‐screening‐and‐sparsifying‐operator (VS‐SISSO) as a SR method suggests a direct correlation between τM and α for Ni and Ni80Fe20, indicating spin‐flip scattering predominantly influences the UFD mechanism. The developed models demonstrate promising predictive capabilities, validated against independent experimental data. Comparative analysis between different materials underscores the significant impact of material properties on UFD behavior. This study underscores the potential of ML in unraveling complex physical phenomena and offers valuable insights for future research in ultrafast magnetism.

Artificial Intelligence Supported Analysis of Children's Pictures: Preschool Period Applications

The aim of this study is to investigate the role of artificial intelligence tools in analyzing preschool children's drawings and to determine whether they are effective in analyzing children's drawings. The study group of the research consists of 4 preschool students in the age group of 5 within the scope of Melik Ahmet Gazi Primary School in Tokat province. The research is based on a qualitative research method called multiple case study and documents and semi-structured interview forms were used to collect data. The study group, which was determined by simple random sampling method, was administered the people, family, tree and house drawing tests and each child drew 4 pictures from each test. The data were analyzed and compared in detail by content analysis method using MaxQDA analysis program. The results show that both methods have strengths and weaknesses. While researchers evaluate in detail the first impression, developmental appropriateness and positioning of the drawings, AI has the advantages of speed and objectivity. AI is effective in identifying specific patterns and structures and excels in fast and accurate processing of objective data. Researchers are more successful with contextual and qualitative data.

Multiplexed Methylated DNA Immunoprecipitation Sequencing (Mx-MeDIP-Seq) to Study DNA Methylation Using Low Amounts of DNA

Background/Objectives: DNA methylation is a key epigenetic mark involved in regulating gene expression. Aberrant DNA methylation contributes to various human diseases, including cancer, autoimmune disorders, atherosclerosis, and cardiovascular diseases. While whole-genome bisulfite sequencing and methylated DNA immunoprecipitation (MeDIP) are standard techniques for studying DNA methylation, they are typically limited to a few samples per run, making them expensive and low-throughput. Therefore, an automation-friendly method is needed to increase throughput and reduce costs without compromising data quality. Methods and Results: We developed a novel method called Multiplexed Methylated DNA Immunoprecipitation Sequencing (Mx-MeDIP-Seq), which can be used to analyze many DNA samples in parallel, requiring only small amounts of input DNA. In this method, 10 different DNA samples were fragmented, purified, barcoded, and pooled prior to immunoprecipitation. In a head-to-head comparison, we observed a 99% correlation between MeDIP-Seq performed individually or combined as Mx-MeDIP-Seq. Moreover, multiplexed MeDIP led to more than 95% normalized percent recovery and a 25-fold enrichment ratio by qPCR, like the enrichment of the conventional method. This technique was successfully performed with as little as 25 ng of DNA, equivalent to 3400 to 6200 cells. Up to 10 different samples were processed simultaneously in a single run. Overall, the Mx-MeDIP-Seq method is cost-effective with faster processing to analyze DNA methylome, making this technique more suitable for high-throughput DNA methylome analysis. Conclusions: Mx-MeDIP-Seq is a cost-effective and efficient method for high-throughput DNA methylation analysis, offering faster processing and reduced sample requirements. This technique makes DNA methylome analysis more accessible for large-scale studies.

Application of Artificial Neural Networks in Predicting the Thermal Performance of Heat Pipes

The loss of energy by heat is a common problem in almost all areas of industry, and heat pipes are essential to increase efficiency and reduce energy waste. However, in many cases, they have complex theoretical equations with high percentages of error, limiting their development and causing dependence on empirical methods that generate a waste of time and material, resulting in significant expenses and reducing the viability of their use. Thus, Artificial Neural Networks (ANNs) can be an excellent option to facilitate the construction and development of heat pipes without knowledge of the complex theory behind the problem. This investigation uses experimental data from previous studies to evaluate the ability of three different ANNs to predict the thermal performance of heat pipes with different capillary structures, each of them in various configurations of the slope, filling ratio, and heat load. The goal is to investigate results in as many different scenarios as possible to clearly understand the networks’ capacity for modeling heat pipes and their operating parameters. We chose two classic ANNs (the most used, Multilayer Perceptron (MLP) network, and the Radial Basis Function (RBF) network) and the Extreme Learning Machine (ELM), which has not yet been applied to heat pipes studies. The ELM is an Unorganized Machine with a fast training process and a simple codification. The ANN results were very close to the experimental ones, showing that ANNs can successfully simulate the thermal performance of heat pipes. Based on the RMSE (error metric being reduced during the training step), the ELM presented the best results (RMSE = 0.384), followed by MLP (RMSE = 0.409), proving their capacity to generalize the problem. These results show the importance of applying different ANNs to evaluate the system deeply. Using ANNs in developing heat pipes is an excellent option for accelerating and improving the project phase, reducing material loss, time, and other resources.

A Fast and Cost-Effective Electronic Nose Model for Methanol Detection Using Ensemble Learning

Methanol, commonly used to cut costs in the production of counterfeit alcohol, is extremely harmful to human health, potentially leading to severe outcomes, including death. In this study, an electronic nose system was designed using 11 inexpensive gas sensors to detect the proportion of methanol in an alcohol mixture. A total of 168 odor samples were taken and analyzed from eight types of ethanol–methanol mixtures prepared at different concentrations. Only 4 features out of 264 were selected using the feature selection method based on feature importance. These four features were extracted from the data of MQ-3, MQ-4, and MQ-137 sensors, and the classification process was carried out using the data of these sensors. A Voting Classifier, an ensemble model, was used with Linear Discriminant Analysis, Support Vector Machines, and Extra Trees algorithms. The Voting Classifier achieved 85.88% classification accuracy before and 81.85% after feature selection. With its cost effectiveness, fast processing time, and practicality, the recommended system shows great potential for detecting methanol, which threatens human health in counterfeit drink production.

How does a change in climate variability impact the Greenland ice sheet surface mass balance?

Abstract. Given the long response time of ice sheets, simulations of the Greenland ice sheet typically exceed the availability of input climate data to reliably simulate the fast processes underlying surface mass balance. Strong feedback processes are known to make the mass balance sensitive to inter- and intra-annual variability. Even simulations with climate models do not always cover the full period of interest, motivating bridging these gaps using relatively coarsely resolved climate reconstructions or temporal interpolation methods. However, both of these approaches usually only provide information about the climatological average but not variability. We investigate how this simplification impacts the surface mass balance using the BErgen Snow SImulator. The model was run for up to 500 years using the same atmospheric climatology but different synthetic variabilities. While changing inter-annual variations has an impact of less than 5 % on the surface mass balance of the Greenland ice sheet, neglecting intra-annual variability by using a daily climatology causes a 40 % change in mass balance. Decomposing the total effect into contributions from different input variables, the biggest contributor is precipitation followed by temperature. Using a daily climatology, a small amount of snowfall every day overestimates the albedo and thus surface mass balance (SMB). We propose a correction that re-captures the effect of intermittent precipitation, reducing the SMB overestimation to 15 %–25 %. We conclude that simulations of the Greenland surface mass and energy balance should be forced with a transient climate, in particular for models that are calibrated with transient data.

A Comprehensive Method for Example-Based Color Transfer with Holistic–Local Balancing and Unit-Wise Riemannian Information Gradient Acceleration

Color transfer, an essential technique in image editing, has recently received significant attention. However, achieving a balance between holistic color style transfer and local detail refinement remains a challenging task. This paper proposes an innovative color transfer method, named BHL, which stands for Balanced consideration of both Holistic transformation and Local refinement. The BHL method employs a statistical framework to address the challenge of achieving a balance between holistic color transfer and the preservation of fine details during the color transfer process. Holistic color transformation is achieved using optimal transport theory within the generalized Gaussian modeling framework. The local refinement module adjusts color and texture details on a per-pixel basis using a Gaussian Mixture Model (GMM). To address the high computational complexity inherent in complex statistical modeling, a parameter estimation method called the unit-wise Riemannian information gradient (uRIG) method is introduced. The uRIG method significantly reduces the computational burden through the second-order acceleration effect of the Fisher information metric. Comprehensive experiments demonstrate that the BHL method outperforms state-of-the-art techniques in both visual quality and objective evaluation criteria, even under stringent time constraints. Remarkably, the BHL method processes high-resolution images in an average of 4.874 s, achieving the fastest processing time compared to the baselines. The BHL method represents a significant advancement in the field of color transfer, offering a balanced approach that combines holistic transformation and local refinement while maintaining efficiency and high visual quality.

RANCANG BANGUN SISTEM DIGITALISASI PENGARSIPAN (E-SIP) BERBASIS WEBSITE

An organization can never be separated from administrative activities. Archivesare an activity that cannot be separated from administrative activities. Archives playan important role for organizations because archives are used to store and maintainimportant records or documents related to the organization's activities. Apart fromthat, archives also help organizations to avoid the risk of losing information ordocuments that could be considered valuable. By having well-organized archives,organizations will find it easier to access the information they need. In the past,archives were managed manually, such as notebooks and documents which werestill in the form of paper, which required storage space and required time in the search process. Therefore, nowadays many organizations choose to switch to digitalarchiving, which is an easier and faster process than manual archiving. Archivesmanagement at BRIDA East Java Province has not been carried out using a digitalsystem. To minimize risks in archival storage, it is better to change the manualrecords management system to a digital archive. The preparation of this finalassignment used qualitative research methods. Qualitative research usually collectsdata by conducting observations, interviews, and documenting research activities.Because archiving in BRIDA East Java Province is still done manually, in thisresearch a website-based design idea called Electronic Archives (E-SIP) wasdesigned to simplify the digital archiving process. With the creation of this design,it is hoped that it will facilitate the management of archives at BRIDA East JavaProvince and that archiving can be carried out digital.

Analisis Sistem Akuntansi Pemberian Kredit Pemilikan Rumah (KPR) pada PT Bank Central Asia Tbk Cabang Pettarani Makassar

This study aims to understand the home ownership credit accounting system at PT Bank Central Asia Tbk Pettarani Makassar Branch, with a focus on simple requirements and a fast credit application process. Based on research conducted at PT Bank Central Asia Tbk Pettarani Makassar Branch, the use of the MyCore application in providing home ownership credit can facilitate the input of prospective debtor files in an effective and efficient manner, thereby speeding up the process. The author applies a qualitative research method, which produces descriptive data in the form of written notes and oral information from individuals observed during data collection in the field. The results of the study show that the process of applying for a home ownership loan at PT Bank Central Asia Tbk Pettarani Makassar Branch has become easier and faster because the requirements applied are general requirements that are already owned by customers. This allows the collection of files to be done more quickly.

Response to “Comment on ‘Fast data processing method for multispectral radiation thermometry based on Euclidean distance optimization’”

Response to “Comment on ‘Fast data processing method for multispectral radiation thermometry based on Euclidean distance optimization’”

A posteriori error estimation and adaptivity for temporal multiscale problems

AbstractIn science and engineering, problems over multiple scales in time often arise. Two examples are material damage in oscillating structures or plaque growth in pulsating blood vessels. Here the long term effects are of interest but they depend on the coupled fast‐changing physical processes which must be taken into account. One method to efficiently simulate these problems is to split them into an averaged slow scale process and a localized fast scale process. We apply the dual weighted residual method on a generalized formulation for this type of problem to obtain an error estimator which can be used for adaptive refinement. Special care must be taken to separate the slow scale and fast scale influences on the estimator.