Template Pattern Research Articles

Spontaneous assembly of condensate networks during the demixing of structured fluids

Liquid-liquid phase separation, whereby two liquids spontaneously demix, is ubiquitous in industrial, environmental, and biological processes. While isotropic fluids are known to condense into spherical droplets in the binodal region, these dynamics are poorly understood for structured fluids. Here, we report the unique observation of condensate networks, which spontaneously assemble during the demixing of a mesogen from a solvent. Condensing mesogens form rapidly elongating filaments, rather than spheres, to relieve distortion of an internal smectic mesophase. As filaments densify, they collapse into bulged discs, lowering the elastic free energy. Additional distortion is relieved by retraction of filaments into the discs, which are straightened under tension to form a ramified network. Understanding and controlling these dynamics may provide different avenues to direct pattern formation or template materials.

The Deep-Match Framework: R-Peak Detection in Ear-ECG.

The Ear-ECG provides a continuous Lead I like electrocardiogram (ECG) by measuring the potential difference related to heart activity by electrodes which are embedded within earphones. However, the significant increase in wearability and comfort enabled by Ear-ECG is often accompanied by a degradation in signal quality - an obstacle that is shared by the majority of wearable technologies. We aim to resolve this issue by introducing a Deep Matched Filter (Deep-MF) for the highly accurate detection of R-peaks in wearable ECG, thus enhancing the utility of Ear-ECG in real-world scenarios. The Deep-MF consists of an encoder stage, partially initialised with an ECG template, and an R-peak classifier stage. Through its operation as a Matched Filter, the encoder searches for matches with an ECG template in the input signal, prior to filtering these matches with the subsequent convolutional layers and selecting peaks corresponding to the ground-truth ECG. The latent representation of R-peak information is then fed into a R-peak classifier, of which the output provides precise R-peak locations. The proposed Deep Matched Filter is evaluated using leave-one-subject-out cross-validation over 36 subjects with an age range of 18-75, with the Deep-MF outperforming existing algorithms for R-peak detection in noisy ECG. The Deep-MF achieves a median R-peak recall of 94.9% and a median precision of 91.2% across subjects when evaluated with leave-one-subject-out cross validation. Overall, this Deep-Match framework serves as a valuable step forward for the real-world functionality of Ear-ECG and, through its interpretable operation, the acceptance of deep learning models in e-Health.

Template-assisted growth of Ga-based nanoparticle clusters on Si: effect of post-annealing process on the Ga ion beam exposed 2D arrays fabricated by focused ion beam nanolithography

We demonstrate template-assisted growth of gallium-based nanoparticle clusters on silicon substrate using a focused ion beam (FIB) nanolithography technique. The nanolithography counterpart of the technique steers a focussed 30 kV accelerated gallium ion beam on the surface of Si to create template patterns of two-dimensional dot arrays. Growth of the nanoparticles is governed by two vital steps namely implantation of gallium into the substrate via gallium beam exposure and formation of the stable nanoparticles on the surface of the substrate by subsequent annealing at elevated temperature in ammonia atmosphere. The growth primarily depends on the dose of implanted gallium which is in the order of 107 atoms per spot and it is also critically influenced by the temperature and duration of the post-annealing treatment. By controlling the growth parameters, it is possible to obtain one particle per spot and particle densities as high as 109 particles per square centimetre could be achieved in this case. The demonstrated growth process, utilizing the advantages of FIB nanolithography, is categorized under the guided organization approach as it combines both the classical top-down and bottom-up approaches. Patterned growth of the particles could be utilized as templates or nucleation sites for the growth of an organized array of nanostructures or quantum dot structures.

An introduction to acoustic location Template-based positioning model

Indoor location information, which is an indispensable label for linking the vast amount of data circulating in the world of IoT, 5G, and AI, is essential. This paper proposes a novel template pattern matching-based acoustic positioning method, Location Template-based Positioning Model, for indoor acoustic source localisation. This innovative technology, similar to the passive SONAR systems on submarines, uses audible acoustic waves to locate acoustic sources in complex indoor environments. The proposed LTPM is a statistical model, and it calculates the coordinates of acoustic sources by matching input acoustic signals with template acoustic signals that are pre-collected from preset locations. A comprehensive overview of the principles and implementations of LTPM is provided, and its feasibility is verified through preliminary positioning tests in this paper. It is hoped to provide research references for the advancement of pattern matching-based acoustic indoor positioning technologies.

Study on the co-phasing simulation and experimental of segmented mirror based on broadband light

At present, the co-phasing detection algorithms for segmented mirrors struggle to balance the measurement range and accuracy. This paper proposes a coarse co-phasing measurement method using the incoherent properties of a broadband (400–800 nm) light source combined with the incoherence of two semicircular diffraction patterns of visible light as a template to detect the piston error, and a narrowband algorithm for fine co-phasing measurement. The diversity factors between template patterns and actual diffraction patterns can be calculated by the cross-correlation algorithm. This proposed method addresses long measurement time caused by low target flow rate and is characterized by wide measurement range, high precision and high energy utilization. The method is theoretically researched and simulated and an experimental system which was constructed to verify the effectiveness of this method achieves 15 nm detection accuracy, and the measurement range using the proposed method can match the measurement range of the actuator.

Predicting Chinese tourists’ B&B preferences through a method of online reviews causality analytic

With the enormous numbers of online reviews posted daily, automatically acquired and analyzed the information that rich of causal logics is of great significance for Chinese B&B enterprises to make accurate business decisions. This study proposed a method based on Chinese NLP technologies to reveal and describe the formation process of tourists' behavioral preferences better than the previous entity relationship extraction and analysis methods. Firstly, Cause-effect syntactic and template pattern had been applied to extraction event casual relations in the online reviews texts. Then, Doc2vec-Kmeans clustering algorithm and key-phrases sorting algorithm were used to generalize data. Finally, causality graph was constructed for visual analysis. To evaluate the performance of the proposed approach, we collected 549, 732 Chinese B&B reviews from Ctrip.com and Qunaer.com. The survey identified 7 common preferences that travelers' concern about nowadays and reveals the reasons behind each of them, as well as concluded that the ‘services’ and ‘conveniences’ of Chinese B&Bs are the trends of travelers’ preferences in the future selections. The results indicated that the proposed approach outperforms the other entity relation extraction method in event forecasting, and most of the subjects believed that the proposed approach can provide more comprehensive and logical traveler's potential demand information.

High-fidelity synthesis of microhole templates with low-surface-energy-enabled self-releasing photolithography.

Material patterning through templates has provided an efficient way to meet the critical requirement for surface function in various fields. Here, we develop a self-releasing photolithographic process to make large-area freestanding templates with precise patterns. The low surface energy of substrates by hydrophobic treatment with proper silane modification ensures the template self-releasing. This method eliminates the need of mechanical separation or any sacrificial layers. Major steps including UV exposure and baking are optimized to realize high-quality structures and the final release of templates. The negative photoresists of SU-8 and polyimide are chosen to confirm the feasibility of this process. Wafer-scale freestanding templates with uniform microhole arrays are obtained with high structural fidelity, smooth surfaces and excellent flexibility. The hole size ranges from several to several tens of micrometers with an extremely low variation (<1%). These advantages could promote the application of precisely structured templates for surface patterning in material and surface science.

MRI Features of Pelvic Nerve Involvement in Endometriosis.

Endometriosis is a common condition that mostly affects people assigned as female at birth. The most common clinical symptom of endometriosis is pain. Although the mechanism for this pain is poorly understood, in some cases, the nerves are directly involved in endometriosis. Endometriosis is a multifocal disease, and the pelvis is the most common location involved. Nerves in the pelvis can become entrapped and involved in endometriosis. Pelvic nerves are visible at pelvic MRI, especially when imaging planes and sequences are tailored for neural evaluation. In particular, high-spatial-resolution anatomic imaging including three-dimensional isotropic imaging and contrast-enhanced three-dimensional short inversion time inversion-recovery (STIR) fast spin-echo sequences are useful for nerve imaging. The most commonly involved nerves are the sciatic, obturator, femoral, pudendal, and inferior hypogastric nerves and the inferior hypogastric and lumbosacral plexuses. Although it is thought to be rare, the true incidence of nerve involvement in endometriosis is not known. Symptoms of neural involvement include pain, weakness, numbness, incontinence, and paraplegia and may be constant or cyclic (catamenial). Early diagnosis of neural involvement in endometriosis is important to prevent irreversible nerve damage and chronic sensorimotor neuropathy. Evidence of irreversible damage can also be seen at MRI, and radiologists should evaluate pelvic nerves that are commonly involved in endometriosis in their search pattern and report template to ensure that this information is incorporated into treatment planning.

Nanoimprint lithography guiding templates for advanced magnetic media fabrication

Nanoimprint lithography presents unique opportunities for advanced magnetic storage media with ordered bit arrangements such as bit patterned media or heated dot magnetic recording. Providing sub-10 nm resolution and full disk imprinting capability, UV-nanoimprint lithography based on rigid quartz templates bears the entitlement for patterned recording media manufacturing with high throughput at low cost. However, a key challenge is the fabrication of the high-resolution template that can transfer the desired pattern onto the disk with high fidelity and low line edge roughness. In this article, we present fabrication routes and overcome challenges to the fabrication of quartz templates suitable for self-alignment and guiding purposes to be used for template replication toward full disk imprints. Guiding patterns down to 40 nm pitch are prepared using a rotary electron beam lithography tool. We compare three different process approaches to fabricate an etching mask for patterning the quartz. Two methods target chromium patterning, one with traditional lift-off and another by dry etching, both using an e-beam resist mask. The third approach is based on the development of a carbon-based Tri-layer hard mask. The template pattern profile is optimized for imprint-suitable sidewall angles using dry etching in a CF4/O2 chemistry. The templates were characterized using scanning electron microscopy and atomic force microscopy to evaluate the quality of the transferred pattern as well as line edge roughness. Our results show that the Tri-layer process using carbon resulted in the lowest line edge roughness of ≈0.65 nm at the imprinted disk level. In addition, we show that Tri-layer masking allowed for the use of conventional ZEP e-beam resist and fast writing speeds, while gaining high selectivity during quartz patterning.

A Study of Digitization Strategies and Audience Interaction in New Media Art Exhibitions in Museums

Abstract With the rapid development of digital technology, the digital system based on the fuzzy kano model provides a digital strategy for new media art exhibition museum information dissemination, digital display, and other fields. In this paper, the fuzzy Kano model is used to design the digital museum, and the system design starts from the five elements of user experience. The digital system is designed from the levels of the strategy layer, scope layer, structural layer, framework layer, and performance layer, respectively. Clustering the sensors first is used to obtain the functional area in the classification of audience behavior. The optimal clustering results can be achieved through spectral clustering of sensor graphs. Clustering is proposed using the typical movement pattern extraction algorithm. Optimization indexes are set to achieve optimal typical movement patterns. The auxiliary sensors capture the interactive movements of the audience with the artwork and collect logical information. Behavioral pattern templates for various types of viewers are constructed using viewer attribute labels. In the user classification method based on behavioral patterns, edges in the graph are used as features, and an optimization problem is constructed to solve the importance of each feature for audience classification. The implementation of interactive features greatly enhances the interactive experience of the audience. It makes the audience’s novelty evaluation score of the system reach 8.671. The classification algorithm based on the behavioral model performs well in all evaluation indexes, which indicates that the system proposed in this paper meets the audience’s digital and interactive needs for new media art exhibitions.

Biosynthesis of Ag-doped ZnO nanorods using template Bacillus sp. and polyethylene glycol via sol-gel-hydrothermal methods for antifungal application

Synthesis of Ag-ZnO nanorods (Ag-ZnO NR) utilizing biological materials, specifically the extracellular enzymes of Bacillus sp. Combined with polyethylene glycol (PEG) as a nanorod pattern template has been proposed. Polyethylene glycol (PEG) of varying relative molecular mass (Mr): 6000, 8000, and 10000 was used. Characterization of Ag-ZnO NR with ultraviolet spectroscopy (UV–vis) revealed absorption at (λmax) = 300 - 330 nm, a blue shift region that was identical to the growth of the Ag-ZnO crystal nucleus. Infrared spectroscopy (FT-IR) profile of Bacillus sp. revealed high intensity at wave number 1600 -1650 cm−1, indicating the amine NH strain in the extracellular enzyme Bacillus sp.; 1430 – 1387 cm−1, indicating -CH3 strain, and 1050 cm−1 indicating CO in PEG. X-ray diffraction (XRD) showed high intensity at 2θ: 31.75°; 34.41°; 36.25° indicates hexagonal wurtzite ZnO (ICDS-ZnO 2017). In contrast, Ag-doped ZnO exhibited high intensity at 2θ: 38. 07°; 44.28°; 66.35° (ICDS-Ag) with crystallite size of 21.85 nm. Scanning electron microscope (SEM) revealed nanorod and spherical particles of 198.96 nm of Ag-ZnO NR PEG-10000. The antifungal effectiveness that was measured based on the number of free radicals •O2 and •OH in the photocatalytic reaction using a p-benzoquinone scavenger reached 85±0.7 %. The highest antifungal activity was detected with an inhibition zone of 3.65-3.04 mm. This finding is very promising for the green synthesis of ZnO and its application as future antifungal application in textile industries.

Machine vision–based automatic planning for microdroplet coating trajectory on irregularly shaped regions of metal surfaces

Metal surface coating technology plays a crucial role in various aspects of metal engineering such as enhancing metal oxidation resistance, corrosion prevention, insulation, decoration, and surface performance. This paper presents a machine vision–based automatic planning algorithm for microdroplet coating trajectory on irregularly shaped regions of metal surfaces, aiming to achieve uniform coating on different surface shapes and sizes. First, the process of microdroplet shape changes on metal surfaces is analyzed, the wetting circle diameter of a fully spread microdroplet is estimated, and the distance between two neighboring microdroplets is confirmed. Then, color pattern image processing (region contour extraction, feature point extraction, deburring, and equidistant offset algorithms) and template matching technology are developed to extract coating trajectory contours and accurately locate the workpieces on the workbench. To ensure a smooth and continuous coating trajectory, rational cubic Bézier curves are employed to interpolate the contour feature points, forming C1 continuous curves. Finally, taking into consideration the distance between two neighboring microdroplets, the continuous curves are equidistant offset and equichord discretized to determine the microdroplet locations. The experimental results demonstrate that the proposed algorithm successfully achieves arbitrary workpiece positioning, automatic planning of coating trajectory and uniform microdroplet coating on different surface shapes and sizes.

Research on the identification and integration of folk dance creation elements based on big data technology

Abstract In order to identify the elements of folk dance creation, two methods of continuous folk dance movement recognition are proposed in this paper. By considering the dance movement patterns as a whole and the templates as incomplete patterns, OE-DTW is applied to match them to segment and recognize each movement pattern one by one. To enhance the performance, a global restriction K-Repetition and endpoint detection condition are proposed to match with OE-DTW, and a penalty-based layer matching algorithm is proposed by using the layer structure feature of SegSVD so that the endpoint of the input movement pattern can be determined by the top layer matching of the latter when matching with the template pattern. Meanwhile, the similarity between two patterns based on this algorithm can also be calculated by the local results obtained from the weighted cumulative layer matching. Experiments show that the average recognition rate of OE-DTW is 0.891, SegSVD is 0.86, and CDP is 0.828. Both methods can effectively deal with the continuous action recognition problem, and they have better recognition results compared with CDP.

Peluang Penerapan Produksi Bersih di Industri “Konveksi Kaos &amp; Kemeja” Kota Pontianak

Various kinds of industrial activities besides having a positive impact also have a negative impact including non-product output which can be a polluter that harms the environment if it is not managed properly. Industrial waste is one of the serious problems in the era of industrialization. One of the industries that produce waste is the convection industry, which can have an impact on the environment if left untreated. Therefore, it must be handled from the beginning during the production process, one of the treatments is by applying clean technology. This research was conducted to identify the generation of waste generated in the clothing production process and to develop alternative clean production opportunities that can be applied along with an overview of economic and environmental benefits. Opportunities for the application of clean production that can be done is to recycle the fabric remnants from the production of T-shirt &amp; Shirt Convection into a variety of accessories that are worth selling with 4 different types of sewing patterns, namely abstraction, template, overlapping, and geometric patterns.

CREATION OF COMIC STRIPS USING CANVA IN TEACHING WRITING TO STUDENTS

This study simulates the making of comic strips using Canva in teaching writing skills. This research utilizes descriptive qualitative. The analysis shows that creating comic strips using Canva is easy and simple. Open Canva at www.canva.com/ on mobile/ desktop then sign in or register using email, Facebook, or Google. Search for "comics" to start designs. Browse templates, start with a blank page, or choose Canva's ready-made templates. Experiment with features comic designs then customize comics with color schemes, lawyers, font styles, and combinations of stickers, icons, and illustrations. Rearrange speech bubbles and frames, upload images, save/publish and share. Teachers can use Canva to create/design comic strips for writing English sentences/conversations. We can use the available comic strip template patterns or customize them as needed. For example, we create/design a comic strip about "My Daily Activity", we ask students to write sentences/essays of descriptive text according to the picture instructions in the comic strips from the activities of waking up to going to school using the present tense. We can ask students to write sentences/essays about "All About Me". We can ask students to write down what is depicted in the comic strips, for example, personal information including name, birthday, address, family, favorite, hobby, or ambition. Several ways need to be considered in creating comic strips such as the concept/story idea, storyline, and attractive image. For making interesting and educative learning comics, we can include an element of humor, insert material in character conversations, use casual language, and create attractive visuals.

Template Design for Complex Block Copolymer Patterns Using a Machine Learning Method.

This study represents the first attempt to address the inverse design problem of the guiding template for directed self-assembly (DSA) patterns using solely machine learning methods. By formulating the problem as a multi-label classification task, the study shows that it is possible to predict templates without requiring any forward simulations. A series of neural network (NN) models, ranging from the basic two-layer convolutional neural network (CNN) to the large NN models (32-layer CNN with 8 residual blocks), have been trained using simulated pattern samples generated by thousands of self-consistent field theory (SCFT) calculations; a number of augmentation techniques, especially suitable for predicting morphologies, have been also proposed to enhance the performance of the NN model. The exact match accuracy of the model in predicting the template of simulated patterns was significantly improved from 59.8% for the baseline model to 97.1% for the best model of this study. The best model also demonstrates an excellent generalization ability in predicting the template for human-designed DSA patterns, while the simplest baseline model is ineffective in this task.

Multimodal Biometrics Authentication in Healthcare Using Improved Convolution Deep Learning Model

In healthcare applications, biometric authentication is crucial in managing patient credential details. The limited usage of biometric traits causes personal details theft, treatment hacking, and payment hijacking. Multimodal biometrics should incorporate into the healthcare system to maintain security and privacy in healthcare applications. Previous methods ensure authentication and security consume high computation complexity and fail to maintain system reliability and scalability. Therefore, in this work, an improved convolution deep learning model (ICDLM) is applied to manage the patient traits in the healthcare center. Initially, Experimentation is conducted with SWAN DATASET, which consists of face, voice, and periocular biometric features. The biometric characteristics are investigated according to the super point convolution neural model. The model analyzes the trait descriptors and interest points to identify the patterns. The extracted patterns are utilized to match the patient while accessing the information in healthcare applications. This process uses template pattern matching (TPM), which minimizes the absolute difference while authenticating the users. The system’s efficiency implemented using Python and excellency is compared with traditional approaches. The system attains 98.31% and 98.67% accuracy on the number of users and attempts with a minimum false identification rate of 0.0125 and 0.0128 for several users and attempts.

423 Trends in Utilization of Standardized Letters of Recommendation in the 2021-2022 Neurosurgery Application Process

INTRODUCTION: Standardized LORs (SLORs) were introduced during the 2020-2021 neurosurgery residency application cycle, but suffered from poor inter-rater reliability and significant grade inflation. Changes were made to the SLOR template and utilization patterns in response to these shortcomings. METHODS: This cross-sectional study analyzed all SLORs submitted to a single neurosurgery residency program over the 2021-2022 cycle. Data from seven competency domains and the overall rating were recorded and stratified by academic category of letter writer. Inter-rater reliability was evaluated using Krippendorff’s Alpha. Frequency of letters written was evaluated using Kruskal-Wallis H Test. RESULTS: 90% of SLORs rated applicants among the Top 25%, but there was a significant decrease in the usage of the “Top 1%” and “Top 2-5%” ratings from the prior application cycle. Inter-rater reliability was poor across all competencies. Writers who completed one SLOR rated applicants higher and had a narrower range than those who completed multiple SLORs. CONCLUSIONS: Changes in the format and subsequent utilization patterns of SLORs have slightly decreased grade inflation; however, inter-rater reliability remains poor. The most wide-ranging evaluators submitted the highest number of SLORs, suggesting that future evaluation and usage of SLORs should emphasize letter-writer characteristics and numbers of SLORs written. Overall, SLORs have been well and broadly accepted with subtle improvements in the second year of utilization.

Thermo4PFM: Facilitating Phase-field simulations of alloys with thermodynamic driving forces

Phase-field modeling is a popular front-tracking approach used to model solidification. Its time-evolution equations are often coupled to alloy composition and/or thermal diffusion in high-resolution multiphysics approaches. Materials thermodynamic properties tabulated in CALPHAD databases can be used for phase-field modeling to parameterize bulk energies of alloys. In addition, they can be naturally integrated into models such as the Kim-Kim-Suzuki (KKS) model where driving forces depend on the differences between chemical potentials of co-existing phases. In that case, a small system of coupled nonlinear equations needs to be solved at every point in space where the phase-field order parameter is to be updated and evolved in time. We present Thermo4PFM, a solver for the KKS equations for binary and ternary alloys, with two or three phases, and parameterized with CALPHAD models. Thermo4PFM is open source, written in C++, and can take advantage of Graphics Processing Units (GPU) accelerators. Using OpenMP offload capabilities for C++ classes, an excellent performance is demonstrated on GPU using the LLVM compiler. CALPHAD data is read from simple JSON files using an open source parser from the boost library. Program summaryProgram Title: Thermo4PFMCPC Library link to program files:https://doi.org/10.17632/8j3ntp5c7k.1Developer's repository link:https://github.com/ORNL/Thermo4PFMLicensing provisions: BSD 3-clauseProgramming language: C++/OpenMPNature of problem: Accurate modeling of solidification in metallic alloys requires thermodynamic data associated with possible material phases. That data can be used in phase-field modeling (PFM) to evaluate the driving force responsible for phase changes and solidification front motion. Integrating that data into PFM requires the solution of a small system of coupled nonlinear differential equations — the Kim-Kim-Suzuki equations — that needs to be solved at every point of a discretization mesh.Solution method: Thermo4PFM implements a Newton-based solver for the Kim-Kim-Suzuki equations for a few special cases (binary and ternary alloys with two or three phases) for CALPHAD-based models of the bulk energy of the phases. The software is written in C++ and uses the Curiously Recurring Template Pattern and OpenMP offload capabilities to take advantage of GPU accelerators, when available, on modern High Performance Computing resources. It also uses the boost Property Tree library to parse input CALPHAD data.

Multi-Objective Optimization for Safety-Related Available E/E Architectures Scoping Highly Automated Driving Vehicles

Megatrends such as Highly Automated Driving (HAD) (SAE ≥ Level 3), electrification, and connectivity are reshaping the automotive industry. Together with the new technologies, the business models will also evolve, opening up new possibilities and new fields of competition. To cope with the ongoing advances, new Electric/Electronic (E/E) architecture patterns are emerging in the sector, distributing the vehicle functions across several processing devices and enhancing the connectivity between them via Ethernet-based networks. Upcoming systems will demand Safety-Related Availability (SaRA) requirements in mixed-critical E/E architectures that challenge the concept of freedom from interference defined in ISO 26262. This work explores the concepts of SaRA system development according to ISO 26262, building a framework based on model-based systems engineering to evaluate feasible next-generation automotive E/E architecture designs with a multi-objective analysis. Additionally, we propose a pattern template for SaRA systems to automate the architecture synthesis. To illustrate the framework created, we evaluate a set of automotive E/E architectures synthesized to support mixed-critical vehicle features, including SaRA SAE Level-3 functions, considering the communication networks’ performance as well as hardware and safety-related development costs. This work presents a methodology for original equipment manufacturers and Tier-1 suppliers that enables them to make the trade-offs arising in the design of E/E architectures based on quantified information.