Traditional Local Methods Research Articles

Intraocular Pressure and Safety of Vitrectomy with Vitreous Gas Replacement using Low-concentration Nitrous Oxide Gas Anesthesia.

This study aims to evaluate the efficacy and safety of vitrectomy with vitreous gas replacement using low-concentration nitrous oxide (N2O) gas anesthesia, focusing on intraocular pressure (IOP) changes, pain, anxiety, and safety outcomes. This retrospective study analyzed 133 patients undergoing fluid-air exchange without use of such as SF6 or C3F8, at Saneikai Tsukazaki Hospital, Japan, from April 2019 to March 2022. Participants were divided into two groups: those receiving low-concentration nitrous oxide gas anesthesia (N2O group) and those receiving local anesthesia with room air inhalation (Air group). IOP, pain, anxiety levels, and intraoperative complications were assessed. No significant differences were found in IOP changes postoperatively between the N2O and Air groups. The N2O group reported significantly lower pain scores and had lower intraoperative systolic blood pressure and heart rate changes compared with the Air group. No significant intraoperative or postoperative complications were observed in either group. Vitrectomy with fluid-air exchange using low-concentration nitrous oxide gas anesthesia is safe, does not increase IOP, and may offer benefits in reducing intraoperative pain and stabilizing vital signs compared with traditional local anesthesia methods. This approach could be considered a viable option for vitreous surgery requiring fluid-air exchange.

Improved Intrusion Detection Based on Hybrid Deep Learning Models and Federated Learning.

The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model's detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models.

An Exploration of Face Recognition Methods Based on LBP Algorithm and PCA Analysis

Local Binary Pattern is an operator that can be effectively used for facial description, and its application field is relatively wide, which has made great contribution to the research and technical support related to face recognition. The traditional local texture feature extraction method of LBP operator is relatively single and cannot fully synthesize the problem of neighborhood correlation. Therefore, this paper proposes a face recognition method based on the combination of LBP algorithm and PCA principal component analysis method for the above problems. Through the application of this method, it can effectively weaken the influence of noise on the results and show the basic characteristic situation of the sub-block region, and the experiments of this method have confirmed that, compared with the use of PCA, LBP and other methods alone, the method in this study has a significant improvement in the recognition rate.

Effective and Safe Alternative to Traditional Local Anesthetic Methods for Mohs Micrographic Surgery and Surgical Excision.

Effective and Safe Alternative to Traditional Local Anesthetic Methods for Mohs Micrographic Surgery and Surgical Excision.

Local stereo matching with side window

Although local stereo matching methods have attracted much attention for their promising performance in disparity estimation, the issue of edge-blurring remains a challenge for existing local methods. To address this issue, we propose a novel local stereo matching method based on side window filtering technique, namely LSMSW. In the proposed method, the multiple-window aggregation strategy is adopted, which can adaptively select the window type according to the different texture information. Compared to traditional local methods, our cost aggregation model shows the significant performance in terms of accuracy and computational efficiency, especially for images with complex edges. The experimental results on the Middlebury benchmark confirm our achievements.

Hierarchical Central-Local Inverter-Based Voltage Control in Distribution Networks Considering Stochastic PV Power Admissible Range

This paper proposes a hierarchical distribution network voltage control method considering active and reactive power coordination of PV units in both central and local control stages. In contrast to the traditional local control methods, the proposed method defines the admissible range (AR) of PV power and determines it via centralized optimization to realize active PV power curtailments in the local control stage. The affine decision rule (ADR) is adopted to control the reactive power with respect to active power within AR. A distributionally robust chance constraint is designed, based on statistical indices of active PV power, to assess the probability that active PV power generation would fall inside AR. A two-stage optimization problem that can simultaneously provide central and local control strategies is proposed, which is transformed into tractable formulas based on the Gauss Inequality as well as ADR and binary expansion techniques. The proposed method solutions are compared with those of the theoretically optimal method, a robust central-local control method, and a local control method to show the value of hierarchical inverter control with AR in reducing PV power curtailment and ensuring nodal voltages within limits.

A comparison of simulation and traditional local anesthesia teaching methods among dental hygiene students.

The COVID-19 pandemic forced dental educators to quickly modify the teaching-learning platform without testing outcomes of alternative teaching methods prior to implementation. One critical course affected was the teaching of local anesthesia (LA) that moved from practicing injections using the traditional student-to-student method to the simulation model using manikins. This study compared two LA teaching methods (student-to-student versus simulation) in two consecutive cohorts before and during the pandemic to assess differences in students' skill level and self-confidence. This quasi-experimental study recruited a convenience sample of dental hygiene students at the University of Minnesota during 2020 and 2021. Consent was collected prior to student-to-student injection practice and survey distribution. Data collected were clinical and summative clinical assessments to determine LA skill level, and student surveys to assess self-confidence. Analyses included descriptive statistics, linear models within and between cohorts, and inductive analysis for qualitative survey responses. Fifty of 57 students (88%) completed the survey, and 54 (95%) consented access to their clinical assessments. Skill level was significantly higher for the simulation cohort compared to the student-to-student cohort on the LA summative clinical assessment (59.0vs. 55.6, p=0.004). No differences were found in self-confidence between cohorts. A positive association was found between students' self-confidence and skill levels for both cohorts, but only student-to-student cohort results were statistically significant (student-to-student: r=0.44, p=0.02; simulation: r=0.24, p=0.26). Results support incorporating simulation manikin teaching methods for LA with a student-to-student partner component.

Weighted Local Ratio-Difference Contrast Method for Detecting an Infrared Small Target against Ground–Sky Background

Fast and robust detection of infrared small targets in a single image has always been challenging. The background residue in complex ground–sky background images leads to high false alarm rates when traditional local contrast methods are used because of the complexity and variability of the ground–sky background imaging environment. A weighted local ratio-difference contrast (WLRDC) method is proposed in this paper to address this problem and detect infrared small targets in the ground–sky background. First, target candidate pixels are obtained using a simple facet kernel filter. Second, local contrast saliency maps and weighted mappings are calculated on the basis of the local ratio-difference contrast and the spatial dissimilarity of the target, respectively. Third, the final weighted mapping can be obtained through the multiplication fusion strategy. Finally, a simple threshold segmentation method is employed to extract the target. Experimental results on six real ground–sky infrared scenes showed that the proposed method outperforms existing state-of-the-art methods.

Play and Learn with Tradisional Local Wisdom Game in School

In Indonesia, the number of online game addicted in children was experienting a very dangerous development. One doctor predicted that the highers number of online game addicted was Asia. This is very ironic. Since the first, the Indonesian nation has had many traditional games that can be used in learning activities at school. This traditional game was very beneficial for the development of children's abilities, especially children's motoric skills. In the midst of the dangers of massive online games, the RA Nurul Ulum educational institution in Sumbersalak Village used traditional local wisdom game methods and media in every learning activity. This was done so that the students continue to love and preserve the educational games of traditional local wisdom. From this background, this study aims at describing the application of traditional games in improving the gross motoric skills of early childhood in RA. The research method used was a descriptive qualitative approach. The results of the research show that the traditional local wisdom games used are engklek and jumping rope games. The application of this game can improve gross motoric skills in early childhood, stimulated children in developing cooperation, make children more creative, active, adept at jumping, jumping and being responsible in a game. From the application of this traditional game, children's gross motoric skills developed more rapidly and children get to know traditional games and their types.

Contemporary Residential Areasas an Example of Tradition-led Urban Designin Islamic Cities. Case study of Jazan in Saudi Arabia

This article focuses on how modern housing environment is shaped via the specifics of construction in extremely challenging weather conditions in the cities of Saudi Arabia. With the overall goal that modern housing must be eco- and human-friendly in mind, the authors have attempted to identify the characteristic features of the Islamic cities and traditional local methods of construction. The article presents typical solutions of local architecture and urban plans, expecting that they might render significant guidelines for the spatial organisation of modern cities. A case study of Jazan city and its neighbourhood has been intended to analyse not only the spatial design of the city but also a recently worked out Masterplan. Both the architecture and the urban planning of Jazan city might serve as a good example of modern interpretation of heritage that underlies high quality housing environment.

B-spline based interval field decomposition method

In this paper, we contribute a new B-spline based interval field decomposition method as a non-probabilistic approach that takes into account local effects in interval field modelling. With B-spline basis functions, the interval field formulation is highly intuitive and easy to construct. The computational efficiency outperforms the traditional local interval field decomposition method. The explicit expression of the proposed method facilitates the use of optimisation methods in determining field bounds where deterministic local values are available. The proposed method can incorporate the use of truncated hierarchical B-spline basis functions and multi-patch stitching method that facilitate modelling of inhomogeneous interval fields, which effectively address the spatial variability of the parameters describing the interval field. A numerical case of a simply supported beam with non-deterministic material parameters subjected to external loads is performed to illustrate the applicability of the proposed method.

Antioxidants, phenols, caffeine content and volatile compounds in coffee beverages obtained by different methods

The objective of the research was to evaluate the antioxidant activity, phenols and volatile compounds of different types coffee infusions. We worked with the Catimor coffee variety and used five methods to obtain the infusion (espresso, V60, siphon, French press and a traditional local method). For each infusion, the antioxidant capacity was determined with the 2,2-Diphenyl-1-Picrylhydrazyl and 2,2′-azinobis-3-ethylbenzothiazoline-6-sulfonic acid techniques, the phenolic content was determined with the Folin-Ciocalteu technique, and aromatic volatile compounds were determined with gas chromatography coupled with mass spectrometry. The extraction method that yielded the coffee infusions with the most antioxidant activity, phenolic compounds and caffeine content was espresso; however, this coffee had the fewest aromatic volatile compounds. Although they had lower antioxidant activity, the infusions obtained with the French press had the highest content of volatile aromatic compounds and produced a cup that was free of pyridine, an undesirable compound in coffee due to its rotten smell.

Assessment of pain and intraoperative anxiety by computerized and traditional local anesthetic methods in periodontal therapies: A comparative study

Assessment of pain and intraoperative anxiety by computerized and traditional local anesthetic methods in periodontal therapies: A comparative study

3D inversion of large-scale frequency-domain airborne electromagnetic data using unstructured local mesh

Airborne electromagnetic (AEM) methods have been more and more widely used in mineral exploration, environmental and engineering studies, and ground water investigation. However, compared with ground-based electromagnetic (EM) methods, such as magnetotelluric or controlled-source EM, AEM methods generally produce large amounts of data, which leads to very costly 3D EM inversions. We have developed a new 3D AEM inversion scheme based on the finite-element method and unstructured tetrahedral local meshes. This is different from the traditional local mesh method in that the traditional method uses regular cuboids for 3D AEM inversions, whereas our scheme uses irregular tetrahedral meshes that can easily accommodate the topography and complex underground structure. Moreover, because we create our local mesh by extracting from part of the global model mesh, the relationship between the local and global meshes is straightforward, so we can easily create a projection of the Jacobian matrix between global and local meshes and rapidly construct the global Jacobian matrix for 3D EM inversions. After formulating the boundary value problem based on the finite-element method, we verify the accuracy of our modeling algorithm by checking against the semianalytical solution for a homogeneous half-space model, and we test our inversion algorithm by running inversions on synthetic and survey data collected over Vesterålen, Norway. The numerical experiments demonstrate that our method can model the AEM responses at high accuracy and recover the subsurface main resistivity structures from synthetic and field data.

Principles and algorithms for forecasting groups of time series: Locality and globality

Global methods that fit a single forecasting method to all time series in a set have recently shown surprising accuracy, even when forecasting large groups of heterogeneous time series. We provide the following contributions that help understand the potential and applicability of global methods and how they relate to traditional local methods that fit a separate forecasting method to each series: •Global and local methods can produce the same forecasts without any assumptions about similarity of the series in the set.•The complexity of local methods grows with the size of the set while it remains constant for global methods. This result supports the recent evidence and provides principles for the design of new algorithms.•In an extensive empirical study, we show that purposely naïve algorithms derived from these principles show outstanding accuracy. In particular, global linear models provide competitive accuracy with far fewer parameters than the simplest of local methods.

Local Neighbourhood Edge Responsive Image Descriptor for Texture Classification Using Gaussian Mutated JAYA Optimization Algorithm

Local feature descriptor plays a significant role in texture classification. However, in the traditional local binary pattern method, image pixels are converted into a binary pattern based on the relationship between center and neighborhood pixels. This paper introduces a novel feature extraction method named local neighbourhood edge responsive binary pattern to extract and categorize reliable texture features from images for achieving an automated classification of different types of ocean bottom sediments. Initially, the basic magnitude variance pixel values are derived depending on an odd and even pixel value of a 3 × 3 image patch. Further, the edge information is extracted using the local directional pattern method from all images. The edge response of the image is obtained using a kirsch mask in all eight directions. The encoding condition is then applied to both the local intensity and the edge information to create a unique descriptor value. Finally, a new learning algorithm called GMJAYA-ELM combines the Gaussian mutated JAYA (GMJAYA) with an extreme learning machine (ELM) for texture classification. The GMJAYA is used to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks. Experimental findings indicate that the suggested method yields better accuracy and sensitivity efficiency among various groups. The proposed algorithm is tested by contrasting outcomes with standard learning schemes such as PSO-ELM, GA-ELM, ABC-ELM, Birdswarm-ELM, and JAYA-ELM, suggesting the dominance of GMJAYA-ELM.

Research of Multi-band Image Recognition Applied to Ships

Aiming at the shortcomings of the traditional local feature recognition method of multi-band ship image target, the recognition speed is slow, and a local feature recognition method of multi-band ship image target is proposed. First, by detecting key points and feature matching, the local features of the multi-band ship image target are extracted; then, the multi-classifier combination is used to train and identify the local features through the linear classifier and the non-linear classifier, so that the design of local feature recognition method of multi-band ship image target is completed. Through comparison experiments, compared with the traditional local feature recognition method of multi-band ship image target, the experimental results show that the proposed local feature recognition method of multi-band ship image target has faster recognition speed.

A fast human action recognition network based on spatio-temporal features

Artificial intelligence models are widely used in the field of human activity recognition, and human action recognition is an important aspect of human activity recognition. The core of human action recognition is to understand the temporal relationship between video frames. Almost all state-of-the-art methods of human action recognition in videos use optical flow. However, traditional local optical flow estimation methods areexpensive and not trained end-to-end. In this paper, we propose a fast network for human action recognition. Our purpose is to improve the efficiency of optical flow feature extraction and explore the fusion method of spatio-temporal features. For spatio-temporal features, our method combines spatial features and temporal features into fusion features. In addition, we propose CNN with OFF instead of the VGG16 network, which is used to process optical flow features to obtain abundant features. Our model only needs RGB inputs to get the state-of-the-art accuracy of 91.5% on UCF-101, 67.9% on HMDB51, 83.3% on MSR Daily Activity3D, and 91.25% on Florence 3D action, respectively. Compared with most state-of-the-art video action recognition models, our proposed model can effectively improve the accuracy of human action recognition.

Seed Sourcing Strategies Considering Climate Change Forecasts: A Practical Test in Scots Pine

Research Highlights: We experimentally tested different seed sourcing strategies (local, predictive, climate-predictive, climate-adjusted, composite and admixture) under a climate change high emissions scenario using a Scots pine multi-site provenance test. Background and Objectives: There is an urgent need to conserve genetic resources and to support resilience of conifer species facing expected changes and threats. Seed sourcing strategies have been proposed to maximize the future adaptation and resilience of our forests. However, these proposals are yet to be tested, especially in long-lived organisms as forest trees, due to methodological constraints. In addition, some methods rely on the transfer of material from populations matching the future conditions of the sites. However, at the rear edge of the species, some specific problems (high fragmentation, high genetic differentiation, role of genetic drift) challenge the theoretical expectations of some of these methods. Materials and Methods: We used a Scots pine multi-site provenance test, consisting of seventeen provenances covering the distribution range of the species in Spain tested in five representative sites. We measured height, diameter and survival at 5, 10 and 15 years after planting. We simulated populations of 50 trees by bootstrapping material of the provenance test after removing the intra-site environmental effects, simulating different seed sourcing strategies. Results: We found that local and predictive methods behaved better than methods based on the selection of future climate-matching strategies (predictive-climate and climate-adjusted) and those combining several seed sources (composite and admixture seed sourcing strategies). Conclusions: Despite the theoretical expectations, for Scots pine, a forest tree species at its rear edge of its distribution, seed-sourcing methods based on climate matching or a combination of seed sources do not perform better than traditional local or predictive methods or they are not feasible because of the lack of future climate-matching populations.

The Sensitivity Analysis of the Core Lower Head Molten Pool Model Based on Variance Decomposition

Abstract The lower head molten pool model is an important model for the in-vessel retention (IVR) effectiveness evaluation. It has the characteristics of complex relationship, many input parameters, and large uncertainty. The traditional local sensitivity analysis method has the disadvantages of large amount of calculation and low efficiency when performing sensitivity analysis of complex models. Based on variance decomposition of the global sensitivity analysis method, the sensitivity analysis library (SALib) and IVR analysis code developed by Nuclear Power Institute of China (NPIC) are adopted to analyze the sensitivity of the input parameters for four key parameters, such as the lower head wall heat flux ratio. In this paper, the sensitivity coefficient and influence trend of input parameters on key results are obtained, which provides a reference for the optimization of the lower head molten pool model and severe accident mitigation strategies.