Local Components Research Articles

Quantitative shear stress imaging in high throughput in-line Si wafer inspection

Defect inspection methods are basic steps in electronic chip manufacturing and power device production processes in the semiconductor industry. Based on stress induced photoelastic anisotropy, in-line polarized stress imaging (PSI) and quantitative determination of the local shear stress component are possible in single crystalline silicon wafers. Hereafter, such polarization stress images are compared and validated quantitatively with results of finite element simulations based on different 3D elastic models. Uniquely, high resolution at high throughputs is achieved for 300 mm wafer size in the present quantitative stress imaging study.

SwinDPSR: Dual-Path Face Super-Resolution Network Integrating Swin Transformer

Whether to use face priors in the face super-resolution (FSR) methods is a symmetry problem.Various face priors are used to describe the overall and local face features, making the generation of super-resolution face images expensive and laborious. FSR methods that do not require any prior information tend to focus too much on the local features of the face, ignoring the modeling of global information. To solve this problem, we propose a dual-path facial image super-resolution network (SwinDPSR) fused with Swin Transformer. The network does not require additional face priors, and it learns global face shape and local face components through two independent branches. In addition, the channel attention ECA module is used to aggregate the global and local face information in the above dual-path sub-networks, which can generate corresponding high-quality face images. The results of face super-resolution reconstruction experiments on public face datasets and a real-scene face dataset show that SwinDPSR is superior to previous advanced methods both in terms of visual effects and objective indicators. The reconstruction results are evaluated with four evaluation metrics: peak signal-to-noise ratio (PSNR), structural similarity (SSIM), learned perceptual image patch similarity (LPIPS), and mean perceptual score (MPS).

Extreme weather shocks and state-level inflation of the United States

This study investigates the impact of a metric of extreme weather shocks on 32 state-level inflation rates of the United States (US) over the quarterly period of 1989:01 to 2017:04. In this regard, we first utilize a dynamic factor model with stochastic volatility (DFM-SV) to filter out the national factor from the local components of overall, non-tradable and tradable inflation rates, to ensure that the effect of regional climate risks is not underestimated, given the derived sizeable common component. Second, using impulse responses derived from linear and nonlinear local projections models, we find statistically significant increases in the state (and national) factor of overall inflation rates, with the aggregate effect being driven by the tradable sector relative to the non-tradable one, particularly across the agricultural states in comparison to the non (less)-agricultural ones. Our findings have important policy implications.

SAD-ARGRU: A Metro Passenger Flow Prediction Model for Graph Residual Gated Recurrent Networks

This paper proposes a graph residual gated recurrent network subway passenger flow prediction model considering the flat-peak characteristics, which firstly proposes the use of an adaptive density clustering method, which is capable of dynamically dividing the flat-peak time period of subway passenger flow. Secondly, this paper proposes graph residual gated recurrent network, which uses a graph convolutional network fused with a residual network and combined with a gated recurrent network, to simultaneously learn the temporal and spatial characteristics of passenger flow. Finally, this paper proposes to use the spatial attention mechanism to learn the spatial features around the subway stations, construct the spatial local feature components, and fully learn the spatial features around the stations to realize the local quantization of the spatial features around the subway stations. The experimental results show that the graph residual gated recurrent network considering the flat-peak characteristics can effectively improve the prediction performance of the model, and the method proposed in this paper has the highest prediction accuracy when compared with the traditional prediction model.

Еffectiveness of immunocorrective therapy for frequently ill children

Introduction. All acute respiratory infections are characterized by damage to the mucous membrane of the respiratory tract, a decrease in local immune defense and a weakening of nonspecific protective factors.Aim. To characterize the clinical effectiveness of the use of the immunocorrective drug in frequently ill children.Materials and methods. The article assessed the microbiological spectrum of the rhinopharyngeal landscape in 65 children: 33 children took an immunocorrective drug in addition to conventional therapy – the main group; 32 children received conventional therapy (the comparison group), and also investigated the possibility of regulating their polypathogenic microbiota using topical bacterial lysates.Results and discussion. The result of the therapy was a clinical improvement characterized by the relief of pharyngoscopic signs of chronic tonsillitis in children while taking topical bacterial lysates in form of lozenges. Analysis of the microbiota of the oropharyngeal mucosa in the studied patients of both groups before the start of therapy revealed the species diversity of opportunistic flora, among which Streptococcus pyogenes, Staphylococcus aureus, the family Enterobacteriaceae and Candida albicans were identified in all patients in diagnostic significant titers of 105–1010. After treatment in the main group, the ratio of the microbiota of the nasopharynx significantly changed, stimulation of the production of local immunity components was noted, which was confirmed by a decrease in the intensity of contamination of microbiota with polypathogenic functions to diagnostically insignificant titers. When comparing the species composition of the microbiota in the studied groups after treatment with topical bacterial lysates, a statistically significant positive effect was found without additional use of antibacterial drugs.Conclusions. It has been shown that the immunocorrective drug eliminates pharyngoscopic signs of rhinopharyngeal dysbiosis in children who are often and long-term ill.

The synergy effect of multi-country policy actions announced in reaction to global risk: A network structure perspective.

The policy actions of countries reflect adaptive responses of local components within the system to the dynamic global risk landscape. These responses can generate interactions and synergy effects on alleviating the evolution of global risks. Adopting a network perspective, the study proposes a theoretical framework that connects three structural characteristics of policy synergy, namely, synergy scale, alignment intensity, and timing synchronization. Focusing on the Covid-19 pandemic as a typical global risk context, the study finds that policy synergy with a larger scale, stronger alignment intensity, and more synchronized timing has a positive impact on mitigating global risks. The effect of alignment intensity is particularly pronounced when polycentric governance involves 20 countries facing severe risks, whereas the effect of timing synchronization is more significant when the multicenter group comprises more countries. Building upon the concept of an efficient scale of polycentric governance in various dimensions, this study develops a policy synergy index model. Through multiple empirical analyses, this study validates the causal relationship between policy synergy and the future evolution of global pandemic risk. Policymakers can leverage the dynamic changes in the policy synergy to predict future risk situations and implement well-rounded and appropriate policy actions, thereby enhancing the efficacy of the synergy effect of multi-country policy actions for risk governance.

PT-symmetric PINN for integrable nonlocal equations: Forward and inverse problems.

Since the PT-symmetric nonlocal equations contain the physical information of the PT-symmetric, it is very appropriate to embed the physical information of the PT-symmetric into the loss function of PINN, named PTS-PINN. For general PT-symmetric nonlocal equations, especially those equations involving the derivation of nonlocal terms due to the existence of nonlocal terms, directly using the original PINN method to solve such nonlocal equations will face certain challenges. This problem can be solved by the PTS-PINN method, which can be illustrated in two aspects. First, we treat the nonlocal term of the equation as a new local component so that the equation is coupled at this time. In this way, we successfully avoid differentiating nonlocal terms in neural networks. On the other hand, in order to improve the accuracy, we make a second improvement, which is to embed the physical information of the PT-symmetric into the loss function. Through a series of independent numerical experiments, we evaluate the efficacy of PTS-PINN in tackling the forward and inverse problems for the nonlocal NLS equation, the nonlocal derivative NLS equation, the nonlocal (2+1)-dimensional NLS equation, and the nonlocal three-wave interaction systems. The numerical experiments demonstrate that PTS-PINN has good performance. In particular, PTS-PINN has also demonstrated an extraordinary ability to learn large space-time scale rogue waves for nonlocal equations.

A Framework for Structural Analysis of Icebreakers during Ramming of First-Year Ice Ridges

This paper presents a framework for structural analysis of icebreakers during ramming of first-year ice ridges. The framework links the ice-ridge load and the structural analysis based on the physical characteristics of ship–ice-ridge interactions. A ship–ice-ridge interaction study was conducted to demonstrate the feasibility of the proposed framework. A PC-2 icebreaker was chosen for the ship–ice interaction study, and the geometrical and physical properties of the ice ridge were determined based on empirical data. The ice ridge was modeled by solid elements equipped with the continuous surface cap model (CSCM). To validate the approach, the simulated ice resistance was computed using the Lindqvist solution and in situ tests of R/V Xuelong 2. First, the local ice-induced pressure on the hull shell was determined based on numerical simulations. Subsequently, the local ice pressure was applied to local deformable sub-structural models of the PC-2 icebreaker hull by means of triangular impulse loads. Finally, the structural response of sub-structural models with refined meshes was computed. This case study demonstrates that the proposed framework is suitable for structural analysis of ice-induced stresses in local hull components. The results show that the ice load and the structural response obtained based on the four first-year ice-ridge models show obvious differences. Furthermore, the ice load and corresponding structural response increases with the width of the ridge and with increasing ship speed.

Growth and Digestion Level of Baronang Fish (Siganus sp.) Fed with the Addition of Alstonia acuminata Plants

One of the main challenges in cultivation is the availability of feed made from local raw materials. Artificial feed with good nutritional value is known to increase fish growth and feed digestibility. Therefore, we must look for cheaper raw materials so that we can reduce feed prices. One approach that can be used is to use natural ingredients, both vegetable and animal, based on local raw materials, as energy providers for fish growth. The use of the Alstonia acuminata plant as a local raw material component for feed is aimed at increasing growth, feed digestibility, and resistance to disease. This research aims to determine the addition of A. acuminata plants at different doses in feed to increase the growth, survival, and feed efficiency of rabbitfish fry. This research used a completely randomized design consisting of four treatments and three replications. A. acuminata plants at doses of 0, 1, 2, and 3% were mixed into isoprotein feed. The test fish used were rabbitfish fry with an initial weight of around 10–20 g, kept in an aquarium measuring 50 cm x 40 cm x 40 cm with a stocking density of 10 fish per aquarium. Fish were given test feed twice a day at 07.00 and 17.00 at satiation. Maintenance was carried out for 42 days. Growth measurements were carried out at the beginning and end of the experiment. The results showed that treatment C provided absolute growth, the highest specific growth rate, a 100% survival rate, as well as high feed efficiency.

Asymptotics for short maturity Asian options in jump-diffusion models with local volatility

We present a study of the short maturity asymptotics for Asian options in a jump-diffusion model with a local volatility component, where the jumps are modeled as a compound Poisson process. The analysis for out-of-the-money Asian options is extended to models with Lévy jumps, including the exponential Lévy model as a special case. Both fixed and floating strike Asian options are considered. Explicit results are obtained for the first-order asymptotics of the Asian options prices for a few popular models in the literature: the Merton jump-diffusion model, the double-exponential jump model, and the Variance Gamma model. We propose an analytical approximation for Asian option prices which satisfies the constraints from the short-maturity asymptotics, and test it against Monte Carlo simulations. The asymptotic results are in good agreement with numerical simulations for sufficiently small maturity.

Angle-Resolved Optical Characterization of a Plasmonic Triangular Array of Elliptical Holes in a Gold Layer

Plasmonic arrays are grating-like structures able to couple an incoming electromagnetic field into either localized or propagating surface plasmonic modes. A triangular array of elliptical holes in a gold layer were realized resorting to displacement Talbot lithography. Scanning electron microscopy was used to evaluate the geometrical features and finite time domain simulations were performed to verify the consistency of the design. The optical response was characterized by angle-resolved reflectance and transmittance measurements. The results demonstrate the good quality and uniformity of the array. Furthermore, the study on the dependence of the optical response on both the hexagonal lattice and the elliptical hole-defined symmetry properties was conducted allowing the distinction of their effects on both the localized and propagating plasmonic modes. The results indicate that the localized component of the plasmonic modes is mainly affected by the elliptical shape, while the propagating part is influenced by the hexagonal lattice symmetry.

Microstructure driven charge carrier separation in superior thin g-C3N4 nanosheets towards enhanced photocatalytic activities

The performance of graphic carbon nitride (g-C3N4) depended strongly on solar energy harvesting and photogenerated charge carrier separation and transfer. Defect engineering (local component and crystallinity adjustment) helps g-C3N4 to improve photocatalytic activity. In this paper, superior thin g-C3N4 nanosheets with typical microstructures were created using a two-step thermal polymerization at high temperature by adjusting precursors. g-C3N4 nanosheets obtained using melamine revealed fine crystallinity compared with that created using dicyandiamide even though related flat microstructure observed for two samples. In contrast, a wrinkled microstructure with self-assembled amorphous/crystalline junctions was created by urea. Interestingly, the wrinkled one revealed the fastest degradation for RhB which was degraded over within 5 min and a high H2 generation rate of 3473 μmol·g−1·h−1 compared with other samples. This performance is ascribed that wrinkle ultra-thin g-C3N4 nanosheets with amorphous-crystalline junctions significantly improved light harvesting ability and photogenerated carrier separation and transport efficiencies. Furthermore, almost no H2 was generated using molten-salt (NaCl and KCl) assisted porous g-C3N4 nanosheets. These results suggest that increased active sites generated by defect engineering were crucial for the photocatalytic performance of g-C3N4. The detailed discussion on microstructure formation provides an important platform for the construction of highly efficient g-C3N4 based photocatalysts.

Human activity-based anomaly detection and recognition by surveillance video using kernel local component analysis with classification by deep learning techniques

Human activity-based anomaly detection and recognition by surveillance video using kernel local component analysis with classification by deep learning techniques

DSDCLNet: Dual-stream encoder and dual-level contrastive learning network for supervised multivariate time series classification

In recent years, deep learning approaches have shown remarkable advancements in Multivariate Time Series Classification (MTSC) tasks. However, the existing approaches primarily focus on capturing the long-term correlations of time series or identifying local key sequence fragments, inevitably neglecting the synergistic properties between global and local components. Additionally, most representation learning methods for MTSC rely on self-supervised learning, which limits their ability to fully exploit label information. Hence, this paper proposes a novel approach termed Dual-Stream Encoder and Dual-Level Contrastive Learning Network (DSDCLNet), which integrates a Dual-Stream Encoder (DSE) and Dual-level Contrastive Learning (DCL). First, to extract multiscale local-global features from multivariate time series data, we employ a DSE architecture comprising an Attention-Gated Recurrent Unit (AGRU) and a Dual-layer Multiscale Convolutional Neural Network (DMSCNN). Specifically, DMSCNN consists of a series of multi-scale convolutional layers and a max pooling layer. Second, to maximize the utilization of label information, a new loss function is designed, which combines classification loss, instance-level contrastive loss, and temporal-level contrastive loss. Finally, experiments are conducted on the UEA datasets and the results demonstrate that DSDCLNet achieves the highest average accuracy of 0.77, outperforming traditional approaches, deep learning approaches, and self-supervised approaches on 30, 23, and 27 datasets, respectively.

Data fusion of Raman spectra in MSPC for fault detection and diagnosis in pharmaceutical manufacturing

This study investigates the use of Raman spectroscopy fused with other types of data (e.g., pH, temperature and turbidity) for multivariate statistical process control of two pharmaceutical case studies: one simulated industrial-scale fed-batch process for the production of penicillin and one real lab-scale crystallization process. The monitoring schemes are built on local principal component analysis models and hyper-parameters are tuned with regards to highest accuracy in fault detection. Accuracies above 90% are obtained for all types of data and level of DF. Furthermore, for the first case study the model built solely on spectra achieves higher fault detection rates, when only considering faults that also result in off-specification quality. This is supported by the fact that the fault is not necessarily detected when it occurs, but rather when it starts to affect quality variables as measured by the spectra.

Ancient mitogenomes suggest complex maternal history of one of the oldest settlements of western India

The ancient township of Vadnagar tells a story of a long chain of cultural diversity and exchange. Vadnagar has been continuously habituated and shows a presence of rich cultural amalgamation and continuous momentary sequences between the 2th century BCE and present-day. Seven cultural periods developed a complex and enriched settlement at Vadnagar in spatio-temporality. Although archaeological studies done on this oldest settlement suggested a rich cultural heritage, the genetic studies to pinpoint the genetic ancestry was lacking till date. In our current study we have for the first time reconstructed the complete mitogenomes of medieval individuals of the Vadnagar archaeological site in Gujarat. The study aimed to investigate the cosmopolitan nature of the present population as well as the migratory pattern and the inflow of different groups through trade, cultural and religious practices. Our analysis suggests heterogeneous nature of the medieval population of Vadnagar with presence of deeply rooted local ancestral components as well as central Asian genetic ancestry. This Central Asian component associated with mitochondrial haplotype U2e was not shared with any individual from India, but rather with individuals from the Bronze Age of Tajikistan and with an earlier age of coalescence. In summary, we propose that the medieval site of Vadnagar in western India was rich in cultural and genetic aspects, with both local and western Eurasian components.

Reduced Biquaternion Windowed Linear Canonical Transform: Properties and Applications

The quaternion windowed linear canonical transform is a tool for processing multidimensional data and enhancing the quality and efficiency of signal and image processing; however, it has disadvantages due to the noncommutativity of quaternion multiplication. In contrast, reduced biquaternions, as a special case of four-dimensional algebra, possess unique advantages in computation because they satisfy the multiplicative exchange rule. This paper proposes the reduced biquaternion windowed linear canonical transform (RBWLCT) by combining the reduced biquaternion signal and the windowed linear canonical transform that has computational efficiency thanks to the commutative property. Firstly, we introduce the concept of a RBWLCT, which can extract the time local features of an image and has the advantages of both time-frequency analysis and feature extraction; moreover, we also provide some fundamental properties. Secondly, we propose convolution and correlation operations for RBWLCT along with their corresponding generalized convolution, correlation, and product theorems. Thirdly, we present a fast algorithm for RBWLCT and analyze its computational complexity based on two dimensional Fourier transform (2D FTs). Finally, simulations and examples are provided to demonstrate that the proposed transform effectively captures the local RBWLCT-frequency components with enhanced degrees of freedom and exhibits significant concentrations.

SWAT Model Performance Using Spatially Distributed Saturated Hydraulic Conductivity (Ksat) and Varying-Resolution DEMs

Saturated hydraulic conductivity (Ksat) is a hydrologic flux parameter commonly used to determine water movement through the saturated soil zone. Understanding the influences of land-use-specific Ksat on the model estimation error of water balance components is necessary to advance model predictive certainties and land management practices. An exploratory modeling approach was developed in the physically based Soil and Water Assessment Tool (SWAT) framework to investigate the effects of spatially distributed observed Ksat on local water balance components using three digital elevation model (DEM) resolution scenarios (30 m, 10 m, and 1 m). All three DEM scenarios showed satisfactory model performance during calibration (R2 > 0.74, NSE > 0.72, and PBIAS ≤ ±13%) and validation (R2 > 0.71, NSE > 0.70, and PBIAS ≤ ±6%). Results showed that the 1 m DEM scenario provided more realistic streamflow results (0.315 m3/s) relative to the observed streamflow (0.292 m3/s). Uncertainty analysis indicated that observed Ksat forcings and DEM resolution significantly influence predictions of lateral flow, groundwater flow, and percolation flow. Specifically, the observed Ksat has a more significant impact on model predictive confidence than DEM resolution. Results emphasize the potential uncertainty of using observed Ksat for hydrological modeling and demonstrate the importance of finer-resolution spatial data (i.e., 1 m DEM) applied in smaller watersheds.

A low-resistance local component design method based on biomimicry and a random forest model: A pipe elbow case study

The energy consumption of building transmission and distribution systems accounts for more than 30% of building energy consumption, and the prevalence of localized components in building transmission and distribution systems leads to severe energy losses and resistance effects during system operation. To solve this problem, our research team has developed a series of resistance reduction components. This paper proposes a low-resistance local component shape optimization method based on biomimicry and machine learning, taking pipe elbows as an example, and the normalized shape optimization parameters of elbows with different pipe diameters are given. Through numerical simulation, full-size experiments, energy dissipation analysis and other methods, the optimized elbow resistance reduction effect and resistance reduction mechanism are verified and analyzed. The results showed that the resistance reduction rate of the elbow was 19%–26% for different pipe diameters and Reynolds numbers, and the optimized elbow significantly reduced energy dissipation during the flow process. Previously, the shape optimization problem of local components focused mainly on rectangular ducts that can be simplified in two dimensions; this study extends the optimization objective to circular pipes, which provides an effective method for guiding the optimal design of low-resistance local components in building transmission and distribution systems.

Structural insights into peptidoglycan glycosidase EtgA binding to the inner rod protein EscI of the type III secretion system via a designed EscI-EtgA fusion protein.

Bacteria express lytic enzymes such as glycosidases, which have potentially self-destructive peptidoglycan (PG)-degrading activity and, therefore, require careful regulation in bacteria. The PG glycosidase EtgA is regulated by localization to the assembling type III secretion system (T3SS), generating a hole in the PG layer for the T3SS to reach the outer membrane. The EtgA localization was found to be mediated via EtgA interacting with the T3SS inner rod protein EscI. To gain structural insights into the EtgA recognition of EscI, we determined the 2.01 Å resolution structure of an EscI (51-87)-linker-EtgA fusion protein designed based on AlphaFold2 predictions. The structure revealed EscI residues 72-87 forming an α-helix interacting with the backside of EtgA, distant from the active site. EscI residues 56-71 also were found to interact with EtgA, with these residues stretching across the EtgA surface. The ability of the EscI to interact with EtgA was also probed using an EscI peptide. The EscI peptide comprising residues 66-87, slightly larger than the observed EscI α-helix, was shown to bind to EtgA using microscale thermophoresis and thermal shift differential scanning fluorimetry. The EscI peptide also had a two-fold activity-enhancing effect on EtgA, whereas the EscI-EtgA fusion protein enhanced activity over four-fold compared to EtgA. Our studies suggest that EtgA regulation by EscI could be trifold involving protein localization, protein activation, and protein stabilization components. Analysis of the sequence conservation of the EscI EtgA interface residues suggested a possible conservation of such regulation for related proteins from different bacteria.