Typical Process Research Articles

Follow the money: funding acquisition processes of nascent ventures

ABSTRACT Answering the call for more process-oriented research into the inception and development of companies, this paper analyses the funding acquisition process of nascent ventures. Based on optimal matching techniques combined with multinomial logistic regression, we identify how the most typical funding acquisition processes of nascent ventures evolve and identify in which circumstances ventures pursue the respective processes. First, and in line with pecking-order theory (POT), we find a standard pattern of founder self-funding. Second, we theorize under which initial conditions, and how, ventures deviate from the path that is set out by POT. The degree of innovativeness and complexity of the venture’s offering determine which funding sequence is chosen. With this study we, first, show the importance of initial venture characteristics and strategy for the further resource acquisition and corresponding venture development process and, second, introduce the optimal matching technique to the realm of the entrepreneurship literature.

Hot Strip Mill Gearbox Monitoring and Diagnosis Based on Convolutional Neural Networks Using the Pseudo-Labeling Method

The steel industry is typical process manufacturing, and the quality and cost of the products can be improved by efficient operation of equipment. This paper proposes an efficient diagnosis and monitoring method for the gearbox, which is a key piece of mechanical equipment in steel manufacturing. In particular, an equipment maintenance plan for stable operation is essential. Therefore, equipment monitoring and diagnosis to prevent unplanned plant shutdowns are important to operate the equipment efficiently and economically. Most plant data collected on-site have no precise information about equipment malfunctions. Therefore, it is difficult to directly apply supervised learning algorithms to diagnose and monitor the equipment with the operational data collected. The purpose of this paper is to propose a pseudo-label method to enable supervised learning for equipment data without labels. Pseudo-normal (PN) and pseudo-abnormal (PA) vibration datasets are defined and labeled to apply classification analysis algorithms to unlabeled equipment data. To find an anomalous state in the equipment based on vibration data, the initial PN vibration dataset is compared with a PA vibration dataset collected over time, and the equipment is monitored for potential failure. Continuous wavelet transform (CWT) is applied to the vibration signals collected to obtain an image dataset, which is then entered into a convolutional neural network (an image classifier) to determine classification accuracy and detect equipment abnormalities. As a result of Steps 1 to 4, abnormal signals have already been detected in the dataset, and alarms and warnings have already been generated. The classification accuracy was over 0.95 at d=4, confirming quantitatively that the status of the equipment had changed significantly. In this way, a catastrophic failure can be avoided by performing a detailed equipment inspection in advance. Lastly, a catastrophic failure occurred in Step 9, and the classification accuracy ranged from 0.95 to 1.0. It was possible to prevent secondary equipment damage, such as motors connected to gearboxes, by identifying catastrophic failures promptly. This case study shows that the proposed procedure gives good results in detecting operation abnormalities of key unit equipment. In the conclusion, further promising topics are discussed.

Fabrication of gyroid-structured, hierarchically-porous hydroxyapatite scaffolds by a dual-templating method

Freeze-casting is a widely recognized method for producing porous materials. However, in the typical freeze-casting process, the pore morphology is single, and the pore size is mostly distributed at the micrometer scale due to the limitation of the nature of ice dendrites. In this study, ice template and the 3D-printed template we integrated to develop the dual-templating process to fabricate gyroid-structured, hierarchically-porous hydroxyapatite scaffolds. Microstructural characterizations by SEM and micro-CT demonstrated that the average diameter of macro-porosity, micro-porosity, and average porosity of the dual-scale gyroid scaffolds were 667.4 ± 95.8 μm, 20.8 ± 5.7 μm, and 66%, respectively. The fish scale-derived hydroxyapatite (HAp) powder was utilized as the raw material due to the advantages of hydroxyapatite, including eco-friendly, bio-compatibility, non-toxicity, and high ion removal ability. Assuredly, the dual-scale hydroxyapatite scaffolds have greatly improved the permeability (445.6 ± 171.3 × 10−13 m2) and Pb (II) ion adsorption efficiency (>98%) compared to traditionally freeze-casted, single-scale ones. In summary, the dual-scale, gyroid-structured hydroxyapatite scaffolds have great potential to be applied in the industrious wastewater treatment field.

A Simple Normalized Analytical Model for Oil Production of SAGD Process and Its Applications in Athabasca Oil Sands

Summary Since the late 1980s, when the Alberta Oil Sands Technology and Research Authority Underground Test Facility project first demonstrated the feasibility of the steam-assisted gravity drainage (SAGD) technology, many commercial SAGD projects were brought online in Western Canada. Now, many of these projects have late-life SAGD wells approaching their ultimate SAGD recovery factors. Although these projects have demonstrated highly variable production performance, there is an opportunity to use the industry production data to find what they have in common and develop a normalized SAGD model. For this paper, we collected oil production history from several leading SAGD projects with late-life production in the Athabasca oil sands area and confirmed the three stages in an SAGD project lifespan: chamber rising, chamber spreading, and chamber falling stages. By normalizing the field data, all SAGD projects converged to one type curve, regardless of reservoir quality and operating conditions. Based on this observation, a new simple normalized model is derived to model the bitumen production in a typical SAGD process for Athabasca oil sands. The new model bridges the gap between the existing SAGD analytical model and conventional decline analysis and provides oil production forecasts based on the inputs for the five-component recovery factor method defined in the Canadian Oil and Gas Evaluation Handbook(Society of Petroleum Evaluation Engineers 2018). The model has been applied to one of the thermal projects to history match the field production. By running a Monte Carlo simulation, this model further demonstrates its capability to capture the uncertainty of the production forecast for the project at different stages of SAGD operation. In addition, by properly modifying the type curve of the analytical model, a similar workflow can be used to model cases with special reservoir quality or different operational limitations.

Transformative Implementation of Android-Based Point of Sale System at Shafira’s Buffet Stall

Shafira’s Buffet Stall is a small eatery located at 37 Gubeng Kertajaya V Raya Street. They offer a diverse range of delicacies, from fried snacks to vegetable dishes. The typical business process at the stall involves customers either dining in or requesting takeaways. After making their selections, the customers proceed to the cashier to have their orders tallied. Observing this process, the idea of developing an Android-based Point of Sale (POS) system struck the author. This system aims to streamline and record all transactions efficiently. The Point-of-Sale system is designed to facilitate seamless transactions and includes a cashier module to record sales. The decision to develop this POS application was driven by the need for accurate transaction records and seamless management. The Android platform was chosen due to its popularity as the most widely used mobile operating system in Indonesia, boasting an impressive 89% market share as of December 28, 2022. Additionally, in urban areas of East Java, around 68.75% of the population utilize mobile phones, further justifying the choice of the Android platform for the system. The application offers features such as login credentials for two types of accounts: cashier and administrator (faktur). It allows access to functions like managing transactions, adding new menu items, listing available menu items, and generating daily and weekly transaction reports. The implementation and testing of the application were performed through two iterations. After incorporating the necessary improvements based on black box testing, all features functioned smoothly. Usability testing resulted in a remarkable 5.73% increase in overall user satisfaction. This Android-based POS application proves to be an invaluable tool for the business owner, providing insights into popular menu items and their daily and weekly sales. It assists in making informed decisions regarding menu restocking and daily sales strategies.

Fabrication of blazed gratings by tilted reactive ion beam etching with the side mask for augmented reality applications

A novel manufacturing method using the side mask and tilted reactive ion beam etching (RIBE) is proposed for the fabrication of blazed gratings. Electron beam lithography was carried out to pattern a groove with a nanoscale line width, and then appropriate mask materials were filled into the SiO2 trench by atomic layer deposition. After being etched by RIBE at specific tilted angles, the required blazed gratings were achieved. In contrast to the typical fabrication process with a patterned mask on top of the surface to be etched, V-shaped nano structures filled into the trenches were used as the side mask. During etching, the surface material located in the shadow of the side mask along incident ion beams was not etched. The depth and blazed angle of blazed gratings are determined by the height of the side mask and the mounting angle of the sample, respectively. In addition, the fabricated blazed gratings can be used as imprinting moulds to duplicate blazed gratings for augmented reality applications. Due to more options for side mask materials, this method is able to provide high repeatability and accurate controllability for fabricating blazed gratings.

Random wavenumber and nonlinear parametric effect of speckle field

Speckle field is a relatively common phenomenon. But the speckle has special application value in nonlinear optical domain because it can be used to suppress different nonlinear processes that are caused by high power laser. To enhance the suppression capability, it is necessary to reveal the basic mechanism of the speckle parameter nonlinear optical interaction process. In this work, the coupling wave equation under the wave number mismatch condition is used to analyze the parameter process of speckles field. The solving process of the coupling wave equation is introduced in detail. And the wave number or phase matching condition is fully discussed. Furthermore, the threshold of the nonlinear gain is analyzed when the wave number does not fully meet the matching condition. To describe the solution of the coupling wave equation more clearly, the undetermined coefficient of the exact analytical solution is discussed. Since the boundary field will affect the confirmation of the undetermined coefficient, the characteristic of boundary field should be analyzed first. The nonlinear process of the speckle field is a three-wave interaction process. The different boundary conditions will affect the three-wave interaction process. And it is found that if the complex amplitudes of the three waves at the boundary are not zero, the undetermined coefficient will be changed with the phrase parameters of the three waves. To achieve the maximum value, the boundary waves must meet the phase matching condition. The wave number of the speckle filed is not an invariant, because of its random distribution characteristic. Therefore, during the analysis of the three-wave interaction process, the segment handling method is used to ensure the effective solving of the first order coupling wave equation. On the other hand, the randomly fluctuation of the wave number destroys the phase matching condition of the boundary. It is just through the basic mechanism that the speckle field can be used to suppress the nonlinear gain of high-power optical field. Both the theoretical analyses and the numerical calculation results show that the speckle field has good suppression effect for some typical nonlinear parameter process, such as stimulated Brillouin scattering.

Probabilistički višekriterijumski robustni dizajn i njegova primena u sečenju metala

Introduction/purpose: Cutting is a typical material process. However, an appropriate solution for simultaneous optimization of material machinability and tool life in material cutting processes has not been obtained yet. In this article, probabilistic multi-objective robust design (PMORD) is expounded and the robust design problem in the simultaneous optimization of material machinability and tool life is analyzed by taking the machining of ferrite-bainite dual-phase steel as an example. Methods: According to PMORD, the arithmetic mean and its deviation of various performance and utility indexes of alternative schemes are evaluated as twin independent responses, which respectively contribute a part of the partial preferable probability to the performance indexes. In the evaluation, the arithmetic average of the utility index is taken as the representative of the utility attribute, and the evaluation is made in accordance with the function or preference of the utility index. However, the deviation term is generally used as an unbeneficial type of the index (that is, the smaller the better) to participate in the evaluation. The product of the two parts of partial preferable probability is the actual partial preferable probability of the corresponding performance index. The product of the partial preferable probability of all performance utility indexes gives the total preferable probability of the corresponding scheme, which is the only index for each scheme to participate in the competition in robust design. Results: The optimization result of this example is that the tool life is 1297.3333 s (standard deviation is 2.0817 s) and the surface roughness is 2.22mm (standard deviation is 0.2mm), while the corresponding working conditions are that the heat treatment temperature of the material is 790°C, the cutting speed is 150m/min, the feed speed is 0.15mm/rev, and the cutting depth is 0.2mm. Conclusion: The example of the parameter optimization of cutting of ferrite-bainite dual-phase steel by means of PMORD indicates the rationality of the appropriate solution.

Flipping the Online Classroom to Teach Statistical Data Analysis Software: A Quasi-Experimental Study

The flipped classroom (FC) is a pedagogical model with an active learning concept and a hybrid course design that reverses the typical lecture process. Given the widespread use of online delivery methods, there is a need to explore the FC in delivering statistics courses. This study employs a prospective quasi-experimental study design to evaluate the effectiveness of the flipped method and to compare it with the traditional method of teaching statistical data analysis software. Each of the 71 students enrolled in a medicine program was allocated to either a traditional classroom or a FC. The difference between the two teaching methods is evaluated using overall assessment performance as the main outcome measure. Our results show that the teaching method had a large and significant effect on assessment performance, with the FC method exhibiting a higher student overall performance than the traditional classroom. The data suggest integrating the FC in teaching statistical data analysis software is a helpful alternative to the traditional classroom. This study may serve as a guide and inspiration for educators in statistics courses to incorporate the concept of flipped learning, so as to engage students in a more active learning experience.

Nonlinear Dynamic Granger Causality Analysis Framework for Root-Cause Diagnosis of Quality-Related Faults in Manufacturing Processes

Root-cause diagnosis of quality-related faults plays a crucial role in ensuring stable product quality and high-efficient production for modern manufacturing processes. However, there exists complex nonlinear and dynamic sequential characteristics for process and quality data before and after faults, which contain important fault information. How to fully explore and use this information to locate the root-cause and identify the propagation path is a hot topic. Thus, a new nonlinear dynamic Granger causality analysis method is developed for root-cause diagnosis, which will provide timely and useful reference information to take reasonable measures for field engineers. First, an optimal variable division based on minimal redundancy maximal relevance algorithm is presented to get the quality-related variables. Then, a new attention-based random disturbance gated recurrent unit is designed for nonlinear dynamic Granger causality analysis, aiming at locating the root-cause and identifying the propagation path of quality-related faults. A typical manufacturing process, the hot strip mill process is used for verification. The results show the practicability of the proposed method, and its strong robustness to noise. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Reasonable and feasible root-cause diagnosis technology will provide timely reference information for field operators to take maintenance measures. However, the nonlinear and dynamic characteristics of time-series data before and after quality-related faults have brought great challenges to the traditional root-cause diagnosis techniques. To address this issue, a practical nonlinear dynamic Granger causality analysis method is proposed, which is purely data driven without knowing complex mechanism knowledge. It will provide feasible solutions for safety monitoring of hot strip mill process as the representative of manufacturing processes.

Exploring the carbon-chain structure-activity relationship of CO2 and ketone-based absorbents

With regard to the physical absorbents for CO2 removal, the alteration of carbon-chain structure may benefit the CO2 absorption performance. However, the relationship between the carbon-chain structure of ketones absorbents and their CO2 absorption capacity has rarely been studied, and the related mechanism is unclear. In this study, theoretical calculations and thermodynamic experiments were carried out to explore the effects of the carbon-chain length and isomerism of ketones on their CO2 absorption performance. The density functional theory (DFT) simulations show that the F-∥ configuration is more stable than the others for the CO2+ketone complex in most cases. The followings are obtained according to the experimental results, which are pretty consistent with DFT calculations. The dissolution of CO2 in the ketone is a typical physical process. As the number of carbon atom increases, the CO2 absorption capacity of straight-chain ketones increases. Straight-chain ketones exhibit higher CO2 absorption capacity than branched-chain ones. The effect of increasing carbon-chain length and isomerism on CO2 absorption capacity are both stronger for the ketones with fewer carbon atoms; the influence mechanism can be attributed to different degrees of weak hydrogen-bond interaction between CO2 and ketone molecules, suggested by the DFT simulation results. The above conclusions are beneficial for the development of potential CO2 capture absorbents. In addition, the industrial application feasibility of surveyed ketone absorbents is also discussed.

Analysing Narration of Museum Exhibition Information for Young Visitors

Current museums lack innovation in their exhibition design resulting in a decline of young visitors' interest. There is a need to enhance visitors' experience in museum exhibition design to attract young visitors' interest in traditional Chinese culture. The purpose of this study is to analyze potential advanced narration of museum exhibition information for young visitors. This study conducted a systematic literature review synthesis process to evaluate the trend, identify the gaps, and recommend future approaches in designing alternative exhibition narration. Results will cover museum exhibition design, exhibition information, thematic narrative exhibition, exhibition design process, and high technology display. The results of the study include recommendations for a typical exhibition layout, a typical process of exhibiting artifacts in museums how best to narrate an exhibition, and the critical components in artifact narration. The significance of the study is supporting to increase the interest of young visitors in the museum exhibition by enhancing experience and further well-studied the traditional Chinese culture. Future study recommends to enhance cultural confidence through better inheritance and innovation through interactive exhibition design and gamified museum storytelling.

An integrated climate and water resource climate service prototype for long term water allocation in the Upper Yellow River region of China

Water Resourcing in China has historically been a complex issue requiring the ability to deal with regular floods, droughts and diverse water needs. Climate change represents another challenge to this sector, albeit one that is not traditionally considered by water managers. In this sector in China water management is predominantly based on historic, seasonal and annual forecast data while multi-annual and (multi-)decadal data are seldom used. In this paper, we present the co-development of a climate service prototype designed to provide water managers with insights into the impacts of climate change on the Upper Yellow River region for the next century. The paper is an outcome from our project that encouraged water resource planners and water resource managers to utilise long-term climate information to understand the uncertainties and the challenges our changing climate is likely to have in the region. Using an interdisciplinary team and adopting a user-centred, co-production approach, a prototype web-based data visualisation tool was developed. The development of the prototype was based on a design specification constructed from the findings of detailed interviews that allowed it to be developed and tested under SARS-CoV-2 pandemic restrictions that prevented the typical development process to be undertaken. The developed prototype presents climate information and communicates uncertainties regarding climate change in the remainder of the century through data sets that are typically used by the water sector in China in a simple, easy to understand style. Models that estimate river levels under different extraction scenarios and results about estimated river level and flow, and flood risk are also presented. The prototype was shown to be successful, as key messages relating to the impact of climate change and the challenges for water resource management could be effectively communicated through the tool interface. Practical implicationsUnderstanding the impacts of climate change on water resourcing is complicated and multifaceted. There is a need for better data about what water there is and how it is moving around between and within catchments. Estimates of past, present and future climate variables along with historical measurements of river flow can be used to help visualise some of the uncertainties and changes that may happen in the next 50 years. In addition, there is a need to understand changing water demands and water resource management practices. Current water resource management practices are based on historical conditions and assumptions that are less likely to hold true in a more variable and warmer climate. Communicating how future changes will impact future water resourcing is critical to water resources in a changing climate (Belcher et al. 2018).This research outlines the construction of a tool to visualise the impacts of climate change on water availability in part of China that is typically water scarce, using models developed using the Soil Water Assessment Toolkit (SWAT). A model of the Upper Yellow River (UYR) was developed to demonstrate the impact of climate change on river levels in the catchment based on climate variables. The rainfall-runoff model was based on climate predictions from the CMIP5 assessment HadGEM3-GC3.05 climate model and incorporated information about water resource allocations for different administrative regions of the catchment The general climate trend for the region is that it is expected to become significantly warmer. The total amount of precipitation is likely to be about the same, and yet it is expected that overall, the catchment will become significantly drier over time as winter shortens and summer lengthens. The outputs from the model reflect the changes in climate variables. The uncertainties were communicated via a Web based tool. Water resource managers in China helped to coproduce the tool by participating in workshops and providing feedback on prototypes. The workshops helped scientists and water resource managers to communicate about climate change impacts on water resources and water resource management.

DNA Polymerase-Steered Self-Propelled and Self-Enhanced DNA Walker for Rapid and Distinctly Amplified Electrochemical Sensing.

The development of a simple, rapid, easy-to-operate, and ultrasensitive DNA walker-based sensing system is challenging but would be very intriguing for the enormous applications in biological analysis and disease monitoring. Herein, a new self-propelled and self-enhanced DNA walking strategy was developed on the basis of a simple DNA polymerase-steered conversion from a typical alternate DNA assembly process. The sensing platform was fabricated easily by immobilizing only one hairpin probe (H1) and the sensing process was based on a simple one-step mixing with another hairpin-like DNA probe (H2) and DNA polymerase. The DNA polymerization could achieve target recycling and successive DNA walking steps. Interestingly, along with each DNA walking step, the new DNA walker sequence could be autonomously accumulated for a self-enhanced DNA walking effect. This provided a multilevel signal amplification ability for the ultrasensitive detection of the target with a low detection limit of 0.18 fM. Moreover, it could greatly reduce the reaction time with the sensing process finished within 1 h. The detection selectivity and the applicative potential in a complicated biological matrix were also demonstrated. Furthermore, the flexible control of sensing modes (self-enhanced DNA walking or the alternate DNA assembly) by using DNA polymerase or not offered a powerful means for sensing performance modulation. It thus opens a new avenue toward the development of a DNA walker-based sensing platform with both rapid and ultrasensitive features and might hold a huge potential for point-of-care diagnostic applications.

Annealing Effects of ZnO Thin Film on Photocatalytic Performances of Graphene Composites

The hybrid structure of Graphene and ZnO (Graphene/ZnO) is emerging as a novel material used to achieve the high performance of photocatalysis. In this study, we examined the ZnO characteristics that affect the photocatalytic activity of graphene/ZnO using a lamellar structure of graphene and ZnO thin films. Graphene samples were synthesized via chemical vapor deposition, and a typical wet process was applied to transfer them on sputter-deposited ZnO thin films with and without annealing. We confirmed that graphene-deposited ZnO demonstrated more efficient photocatalytic behavior toward the decomposition of methylene blue (as a model organic compound) with ordinary sputtered ZnO thin films. Again, ZnO thin films annealed at 1000 °C in an N2 gas atmosphere with graphene performed better than unannealed films. XRD analysis confirmed that pre-thermal treatment of a ZnO thin film promoted re-crystallization, which had less impact on the photocatalytic improvement. The attachment of graphene to the film is considered to contribute to the enhancement. Raman analysis revealed that the graphene coverage areas on the post-annealed ZnO increased by two times compared to that of an unannealed film where the unannealed film had a higher graphene layer. The results presented in this study demonstrate that an annealed ZnO thin film forms a better attachment with graphene, resulting in a larger graphene coverage area with fewer multilayers, which effectively improves the photocatalytic activity in composite structures.

Maceration of soybean oil with jabuticaba peel and rosemary leaves: An affordable and effective approach to enhance frying oil's stability

This research explored the antioxidant potential of rosemary and jabuticaba peel in enhancing the stability of soybean oil during frying. The oils were infused with a 5% concentration of these ingredients and then allowed to rest for 4 h before being used in a marathon of 16 potato frying cycles. The study measured and observed compelling improvements in oil's stability throughout these cycles, indicated by decreased acidity indices, peroxide levels, and polar compounds. Remarkably, rosemary and jabuticaba peel decreased acid indices by 39% and 31%, reduced peroxides by 58% and 42%, and curtailed polar compounds by 25% and 21%, respectively, after the completion of the frying sequences. Utilizing an exhaustive Check-all-that-apply (CATA) and consumer study for evaluation, significant sensory benefits were observed when using jabuticaba peel in the frying oil. The fried potatoes maintained a consistent and appealing taste, aroma, and golden appearance. Importantly, jabuticaba peel outperformed rosemary in preserving the key attribute of crispiness, effectively sustaining this desirable characteristic over sixteen consecutive frying cycles. What sets this study apart is its innovative focus on the previously unexplored antioxidant capabilities of jabuticaba peel, showing how these can transform the typical frying process. The results highlight the potential of natural antioxidants as a robust approach to increase the frying cycles of soybean oil, leading to more health-conscious and sustainable culinary practices.

Generalized Conditional Feedback System with Model Uncertainty

Model uncertainty creates a largely open challenge for industrial process control, which causes a trade-off between robustness and performance optimality. In such a case, we propose a generalized conditional feedback (GCF) system to largely eliminate conflicts between robustness and performance optimality. This approach leverages a nominal model to design an optimal control in the virtual domain and defines an ancillary feedback controller to drive the physical process to track the trajectory of the virtual domain. The effectiveness of the proposed GCF scheme is demonstrated in a simulation for six typical industrial processes and three model-based control methods, and in a half-quadrotor system control test. Furthermore, the GCF scheme is open to existing optimal control and robust control theories.

Predictive Modeling of Light–Matter Interaction in One Dimension: A Dynamic Deep Learning Approach

The mathematical modeling and the associated numerical simulation of the light–matter interaction (LMI) process are well-known to be quite complicated, particularly for media where several electronic transitions take place under electromagnetic excitation. As a result, numerical simulations of typical LMI processes usually require a high computational cost due to the involvement of a large number of coupled differential equations modeling electron and photon behavior. In this paper, we model the general LMI process involving an electromagnetic interaction medium and optical (light) excitation in one dimension (1D) via the use of a dynamic deep learning algorithm where the neural network coefficients can precisely adapt themselves based on the past values of the coefficients of adjacent layers even under the availability of very limited data. Due to the high computational cost of LMI simulations, simulation data are usually only available for short durations. Our aim here is to implement an adaptive deep learning-based model of the LMI process in 1D based on available temporal data so that the electromagnetic features of LMI simulations can be quickly decrypted by the evolving network coefficients, facilitating self-learning. This enables accurate prediction and acceleration of LMI simulations that can run for much longer durations via the reduction in the cost of computation through the elimination of the requirement for the simultaneous computation and discretization of a large set of coupled differential equations at each simulation step. Our analyses show that the LMI process can be efficiently decrypted using dynamic deep learning with less than 1% relative error (RE), enabling the extension of LMI simulations using simple artificial neural networks.

Water storage capacity estimation for a large complex lake system incorporating the water levels during flooding season

Study regionDongting Lake, ChinaStudy focusWater storage capacity (WSC) of a large lake system plays a crucial role in flood control decision-making during the flood season. Dongting Lake, located downstream of Three Gorges Dam, has a significant influence over flood control in the middle and lower reaches of the Yangtze River (MLYR). To have a better WSC estimation of Dongting Lake, a multiple linear regression model was developed based on hydrodynamic simulation data of the typical flood processes in the past 3 decades.New hydrological insights for the regionThe multiple linear regression (MLR) model is developed by integrating of hydrodynamic simulation. Also, the effect of the water level (WL) at Chenglingji station and the WL difference in the lake (Yangliutan-Chenglingji) were investigated. The model can accurately estimate the WSC of Dongting Lake with an R2 of 0.984. The observation indicates that the WL difference within the lake has a great influence on the WSC and the flood regulation capacity ability with the same WL at Chenglingji station. The results of the study can provide a real-time estimation of the WSC of Dongting Lake which can provide useful information for flood control management.

(Invited) Plasma-Enhanced Atomic Layer Etching for Metals and Dielectric Materials

The critical dimensions of semiconductor devices are continuously shrinking with 3D device structure and are approaching to nanometer scale. The demand for dimension control in angstrom level is drastically increasing also in etching processes. Atomic layer etching (ALE) processes are being actively studied and developed for various semiconductor, dielectric materials as well as metals. In this talk, various plasma-enhanced ALE (PEALE) processes will be discussed for isotropic and anisotropic patterning of metals and dielectric materials such as molibdenum, ruthenium, cobalt, titanium nitride, tantalum nitride, halfnium oxide, zirconium oxides.[1-7] Typical ALE processes consist of surface modification step and removal step. For the surface modification, various fluorination, chlorination and oxidation schemes are applied through fluorocarbon deposition, halogenation, oxidation with radicals generated plasmas. For the removal or etching step, various schemes were applied including ion-bomardment, heating, ligand volatilization, ligand exchange, and halogenation. The surface characteristics and requirements of plasma-enhanced ALE will be also discussed.1) K. Koh, Y. Kim, C.-K. Kim, H. Chae, J. Vac. Sci. Technol. A, 36(1), 10B106 (2017)2) Y. Cho, Y. Kim, S. Kim, H. Chae, J. Vac. Sci. Technol. A, 38(2), 022604 (2020)3) Y. Kim, S. Lee, Y. Cho, S. Kim, H. Chae, J. Vac. Sci. Technol. A, 38(2), 022606 (2020)4) D. Shim, J. Kim, Y. Kim, H. Chae, J. Vac. Sci. Technol. B., 40(2) 022208 (2022)5) Y. Lee, Y. Kim, J. Son, H. Chae, J. Vac. Sci. Technol. A., 40(2) 022602 (2022)6) J. Kim, D. Shim, Y. Kim, H. Chae, J. Vac. Sci. Technol. A., 40(3) 032603 (2022)7) Y. Kim, S. Chae, H. Ha, H. Lee, S. Lee, H. Chae, Appl. Surf. Sci. 619, 156751 (2023)