Local Approach Research Articles

A signal energy-based approach for acoustic source localization in composite laminates

Composite laminates are widely used in various fields, but their structural properties make them prone to impact damage. Thus, the real-time localization of the impact source in composite laminates is of great importance. The signal energy-based approach for acoustic source localization, as a new technique proposed in recent years, has advantages in that it can be applied in the case of low signal-to-noise ratio and does not need to have a priori knowledge of material properties. In this paper, an acoustic source localization technique for composite laminates based on an improved signal energy-based approach is proposed. The prior knowledge of the orientation of the symmetry axes of the composite laminate is not needed. The proposed approach is verified by numerical simulation and experiment. The results show that proposed approach has good stability and accuracy. The error analysis and suggestions for improving the feasibility of this technique are also given.

Active reinforcement learning based approach for localization of target ROI (region of interest) in cervical cell images

Active reinforcement learning based approach for localization of target ROI (region of interest) in cervical cell images

MobileNetV2 with Spatial Attention module for traffic congestion recognition in surveillance images

Traffic congestion recognition is essential for road traffic condition monitoring and improving transportation operation efficiency. Recent works have proposed using computer vision to develop cost-effective traffic congestion detection systems. This paper proposed a custom framework for recognizing traffic congestion using a trained advanced CNNs model on highway surveillance images. The proposed CNNs model uses an attention mechanism that significantly helps the network performing better. Specifically, a spatial attention module is proposed to aggregate model features and increase representational power. Exploring the optimal model choice for edge device deployment: MobileNetV2 is considered the best model choice for this task due to its lowest FLOPs and params. Furthermore, the accuracy of MobileNetV2 with attention module on the test set exceeds that of several advanced CNNs models such as VGG16, GoogLeNet, ResNet50, Efficientnetb0, DenseNet121. For a selected dataset of real-world traffic congestion images, test accuracy of 98.58% strongly supports the model’s efficiency in recognizing traffic congestion rendering image samples. In addition, proposed model also achieves 98.82% on the UCSD dataset, which as same as sort-of-the-art method. In summary, we have developed a congestion detection framework equipped with spatial attention module. Extensive experimental validation has demonstrated that using MobileNetV2 as the backbone network achieves state-of-the-art performance. The framework also uses the Grad-CAM approach for visual interpretability and localization of traffic congestion in surveillance images. Judging from the results of Grad-CAM, attention-integrated models can localize traffic congestion in surveillance images better. Our model is developed under the Pytorch framework using python3.8.

Impacts of urbanization on multiple dimensions of bird diversity in Atlantic forest landscapes

Urbanization is resulting in habitat loss and fragmentation around the world, but its effects on biodiversity remain poorly understood. We investigated how the increase of urban cover surrounding nine Brazilian Atlantic forest reserves (14 ha - 1058 ha) affects the taxonomic, functional and phylogenetic diversity of bird communities, considering forest and non-forest species separately. Using a multi-scale local landscape approach, we estimated the percentage of urban cover in circular buffers of varying radii (200–1000 m). We calculated 10 complementary metrics of bird diversity and modelled them against the percentage of urban cover using generalized additive models. Urbanization had a negative, non-linear impact on the taxonomic diversity of bird communities, with a sharp decrease of up to 50 % in the effective number of rare, typical and dominant species in landscapes with 20–40 % of urbanization. This decrease was not restricted to forest species (urban avoiders), but also affected non-forest ones (urban utilizers and dwellers). The impact of urbanization on functional and phylogenetic diversity were less evident, suggesting that the vulnerability to urbanization has a weak phylogenetic and phenotypic signal. We conclude that urbanization impacts a broad spectrum of bird clades and traits and drastically reduce the taxonomic diversity of bird communities.

Scalp acupuncture guidance for identifying the optimal site for transcranial electrical stimulation of the hand.

Transcranial electrical stimulation (tES) often targets the EEG-guided C3/C4 area that may not accurately represent M1 for hand muscles. This study aimed to determine if the neuroanatomy-based scalp acupuncture-guided site (AC) was a more effective spot than the C3 site for neuromodulation. Fifteen healthy subjects received one 20-minute session of high-definition transcranial alternating current stimulation (HD-tACS) intervention (20Hz at 2mA) at the AC or C3 sites randomly with a 1-week washout period. Subjects performed ball-squeezing exercises with the dominant hand during the HD-tACS intervention. The AC site was indiscernible from the finger flexor hotspot detected by TMS. At the baseline, the MEP amplitude from finger flexors was greater with less variability at the AC site than at the C3 site. HD-tACS intervention at the AC site significantly increased the MEP amplitude. However, no significant changes were observed after tACS was applied to the C3 site. Our results provide evidence that HD-tACS at the AC site produces better neuromodulation effects on the flexor digitorum superficialis (FDS) muscle compared to the C3 site. The AC localization approach can be used for future tES studies.

Water At School: Environmental Actions

Objective: Implement environmental education actions as an educational and integrative space for environmental education actions and activities covering social actors in the southern region of the state of Tocantins. Theoretical Framework: Part of the theoretical contributions on environmental education activities in schools, meaning that water education must be approached from a spatial and temporal dimension as a local approach based on river basins as a unit of study. Method: It was developed in stages: 1st leveling meetings between the executing team and the agents involved, 2nd stage: diagnosis/mapping and 3rd stage: Proposing and executing actions. Results and Discussion: It was observed that the subjects, when participating in the actions, transform gestures, ideas and emotions that allow an opening to the understanding of Environmental Education as care and promotion of life. Research Implications: The school environment is considered a favorable space for the study of issues related to rational water consumption, as it is an environment that forms the character of citizens, who, among other knowledge, must be aware of the importance of preserving the environment, as well as the relevance of sustainable use. Originality/Value: It differentiates itself by being multidisciplinary involving several areas of human knowledge, such as biology, chemistry, ecology, social sciences, among others, and involves various segments of society, namely: prefectures, schools, universities, federal institutes, basin committees hydrographic agencies and the state water resources secretariat and has a defined scope and resources.

A Hybrid Approach for Damage Detection and Localization on a Thin Metallic Plate

This paper illustrates a hybrid method for identifying structural damage that includes concepts from Physics-Based approaches within a Data-Driven framework. The lessons learned from the use of damage identification methods based on the analysis of modal curvature allows us to develop effective feature engineering in the development of Machine Learning (ML) algorithms. Moreover, the training of the algorithms exploits data provided from a population of damage cases generated by Finite Element (FE) analysis. The above data-analysis pipeline is applied to the identification of damage in a rectangular metallic plate for which measured modal data are available from a dedicated experimental campaign in CNR-INM laboratory using accelerometers. The damage is modelled as a stiffness reduction over a small area being also representative of material thinning due to corrosion. To have meaningful samples of the training database, the modal convergence of the FE model to the real structure is guaranteed by a structural optimization process. Experimentally identified noise, representative of real-life applications, is then added to the FE results before algorithm training. Damage existence and position are determined by a Novelty Detection approach and a Regression Neural Network, respectively. First, damage is identified on new test cases generated by the same FE procedure used for training. Secondly, the trained algorithms are applied to the experimental dataset. Sensitivity analysis on several parameters (number of samples for training, damage severity and noise levels) is numerically carried out to understand the applicability limits of the present methodology.

A Physics-Based Learning Approach for ROADM-Induced Anomaly Localization and Estimation

A Physics-Based Learning Approach for ROADM-Induced Anomaly Localization and Estimation

Localized delivery of metformin via 3D printed GelMA-Nanoclay hydrogel scaffold for enhanced treatment of diabetic bone defects

BackgroundDiabetic bone defects present significant challenges for individuals with diabetes. While metformin has been explored for bone regeneration via local delivery, its application in treating diabetic bone defects remains under-explored. In this study, we aim to leverage 3D printing technology to fabricate a GelMA-Nanoclay hydrogel scaffold loaded with metformin specifically for this purpose. The objective is to assess whether the in situ release of metformin can effectively enhance osteogenesis, angiogenesis, and immunomodulation in the context of diabetic bone defects. Materials and methodsUtilizing 3D printing technology, we constructed a GelMA-Nanoclay-Metformin hydrogel scaffold with optimal physical properties and biocompatibility. The osteogenic, angiogenic, and immunomodulatory characteristics of the hydrogel scaffold were thoroughly investigated through both in vitro and in vivo experiments. ResultsGelMA10%-Nanoclay8%-Metformin5mg/mL was selected as the bioink for 3D printing due to its favorable swelling rate, degradation rate, mechanical strength, and drug release rate. Through in vitro investigations, the hydrogel scaffold extract, enriched with metformin, demonstrated a substantial enhancement in the proliferation and migration of BMSCs within a high-glucose microenvironment. It effectively enhances osteogenesis, angiogenesis, and immunomodulation. In vivo experimental outcomes further underscored the efficacy of the metformin-loaded GelMA-Nanoclay hydrogel scaffold in promoting superior bone regeneration within diabetic bone defects. ConclusionsIn conclusion, while previous studies have explored local delivery of metformin for bone regeneration, our research is pioneering in its application to diabetic bone defects using a 3D printed GelMA-Nanoclay hydrogel scaffold. This localized delivery approach demonstrates significant potential for enhancing bone regeneration in diabetic patients, offering a novel approach for treating diabetic bone defects. The translational potential of this articleOur study demonstrates, for the first time, the successful loading of the systemic antidiabetic drug metformin onto a hydrogel scaffold for localized delivery. This approach exhibits significant efficacy in mending diabetic bone defects, presenting a promising new avenue for the treatment of such conditions.

Predictors of alignment between aid donors’ and recipients’ interests in evaluating the effectiveness of the Shelter.Ukraine initiative: Contribution analysis

This article evaluates the effectiveness of the Shelter.Ukraine Initiative, a civic response to the humanitarian crisis resulting from Russia’s full-scale invasion of Ukraine. The Initiative aims to address pressing needs through a local umbrella funding approach. Utilizing an evaluative framework that combines multiple approaches, including causal attribution techniques, contribution analysis, and regression analysis, we assess the effectiveness of the Shelter.Ukraine Initiative. We uncover influencing factors that contribute to the Initiative success. These include organizational ambidexterity, learning-by-doing, inter-organizational structure, and advanced technology for financial resource distribution. Our findings underscore the importance of reconfiguring the humanitarian aid architecture to enhance effectiveness. In addition, we suggest that the insights gained from our study can inform future evaluations of humanitarian aid initiatives. By incorporating similar evaluative frameworks and methodologies, researchers can assess the effectiveness of other initiatives and identify key factors contributing to success.

Exploring the Practicality of Differentially Private Federated Learning: A Local Iteration Tuning Approach

Exploring the Practicality of Differentially Private Federated Learning: A Local Iteration Tuning Approach

Fiscal policy and public debt: Government investment is most effective to promote sustainability

This paper aims to quantify the effects of government expenditure and its components, i.e. government consumption and investment, on output and public debt sustainability. The Local Projections approach is applied to a dataset of 14 OECD countries considered for the 1981–2017 period. Fiscal policy shocks have been identified using the Blanchard and Perotti strategy and the narrative approach based on fiscal consolidation episodes. Multipliers of total government spending are above the unit and government investment multipliers are higher than consumption ones. Although all fiscal policy shocks reduce the public debt-to-GDP ratio, government investment is the most effective tool for promoting public debt sustainability.

Oral co-polymeric raft-forming nano gels for targeted empagliflozin delivery against stomach cancer (SGC7901)

Empagliflozin (EMP) is known for its poor safety and efficacy profile due to its fast body distribution and poor solubility. Accordingly, an oral long-acting and floating/raft-forming nano gel was optimized to release coated EMP nanoparticles, and the released EMP nanoparticles showed enhanced dissolution compared to raw EMP particles. To repurpose EMP for cancer treatment, EMP shows anti-cancer and anti-inflammatory effects against cancer cells. EMP nanoparticles were characterized using FT-IR, PXRD, SEM, EMP encapsulation assay, and release studies. The raft-forming gel encapsulating the EMP was optimized and characterized. The EMP co-polymeric nanoparticles were studied to investigate EMP anti-cancer and anti-inflammatory activities against stomach cancer cells. The solubility of EMP nanoparticles was enhanced in 0.1 N HCl and pH 6.8 by 5 and 12 folds, respectively, compared to raw EMP powder. The particle size and zeta-potential values of improved EMP nanoparticles were 135.40 ± 18.60 nm, and −19.30 ± 0.80 mV, respectively. FT-IR, PXRD, SEM and TEM characterizations revealed polymeric coating of EMP particles. The study suggested that this optimized controlled-release raft-forming gel is a promising local oral approach against stomach cancer. The repurposing of EMP co-polymeric nanoparticles for stomach cancer and associated gastritis treatment was justified.

Diverse local calibration approaches for chemometric predictive analysis of large near-infrared spectroscopy (NIRS) multi-product datasets

This study aimed to assess the predictive accuracy of Near-Infrared Spectroscopy (NIRS) across a large multi-product library, employing novel local calibration methodologies. Three local strategies were examined: LOCAL Algorithm, Locally Weighted Regression predicted on k-nearest neighbor selection (kNN-LWPLSR), along with a newly proposed algorithm within this study called Hybrid Local. These strategies were applied to an extensive multi-product dataset. When compared with Global PLS models, the results exhibited significant reductions in RMSEP values for all local strategies. Particularly, the kNN-LWPLSR demonstrated proficient prediction for the constituents of ADF and DM. The newly proposed method [Hybrid Local] exhibits comparable performance to the LOCAL Algorithm; however, it notably reduces the prediction time by half compared to the latter, representing a significant advancement for the practical implementation of NIRS technology within industrial processing scenarios.

AN METHOD FOR CREATING GEOINFORMATION 3D-MODEL OF AN AERODROME USING A DIGITAL TERRAIN MODEL

The creation of a three-dimensional geoinformation model of aerodrome is considered. An approach is proposed, consisting in supplementing a two-dimensional vector map with the values of the third coordinate, which are calculated using data from a digital terrain model. To implement this approach, the problem of interpolation of a function of two variables with a large number of nodal points is solved. The number of nodes where elevation values are set in a digital terrain model can be several thousand for each dimension. In this case, the use of global interpolation methods, for example, splines, in which function values at all nodal points are used to determine the values of a function at a given point falling into a grid cell, is computationally inefficient, since it requires a lot of machine time. It seems more promising to use a local approach to the interpolation problem. In this approach, the value of the function at an arbitrary point is determined by the values of the function at the nodal points of the grid closest to the specified point. Finite formulas are obtained that do not require solving equations, and allow interpolating functions for cases of linear and smooth approximation. An algorithm and a calculation program have been developed for a regular two-dimensional grid of nodes of a digital terrain model using the publicly available SRTM database. The results of numerical calculations of test cases with a comparison of linear and smooth approximations are presented. Using the proposed approach, a three-dimensional model of the Sochi aerodrome was created to simulate the movement of aircraft on its surface. The proposed approach and the interpolation method can be used to calculate the elevation of given points and to create three-dimensional cartographic models of the earth's surface.

HeMiRCA: Fine-Grained Root Cause Analysis for Microservices with Heterogeneous Data Sources

Microservices architecture improves software scalability, resilience, and agility but also poses significant challenges to system reliability due to their complexity and dynamic nature. Identifying and resolving anomalies promptly is crucial because they can quickly propagate to other microservices and cause severe damage to the system. Existing root-cause metric localization approaches rely on metrics or metrics-anomalies correlations but overlook other monitoring data sources ( e.g. , traces). We are the first to identify and leverage the anomaly-aware monotonic correlation between heterogeneous monitoring data, motivated by which we propose a novel framework, HeMiRCA , for hierarchical root cause analysis using Spearman correlation. HeMiRCA is based on the key observation that the microservice responsible for a particular type of fault exhibits a monotonic correlation between the trends of its associated metrics and the trace-based anomaly score of the system. HeMiRCA first calculates time-series anomaly scores using traces and then exploits the correlations between multivariate metrics and the scores to rank the suspicious metrics and microservices. HeMiRCA has been evaluated on two datasets collected from widely used microservice systems. The results show that HeMiRCA outperforms the state-of-the-art approaches by a large margin in identifying root causes at both service level and metric level, achieving a top-1 hit ratio of 82.7% and 74% on average, respectively.

From L-mode to the L–H transition, experiments on ASDEX upgrade and related gyrokinetic simulations

This work combines experimental observations from the ASDEX Upgrade tokamak with related gyrokinetic simulations of the turbulence moving from L-mode toward, and beyond, the L–H transition. Dedicated experiments have been performed with slow steps of increasing electron cyclotron heating power. Gyrokinetic simulations of the edge turbulence of these plasmas highlight the key roles of the non-linear electromagnetic effects and the external flow shear (E × B shear), both related to the evolution of the plasma pressure profile with increasing heating power. The increase in the plasma βe destabilizes turbulence at low toroidal mode numbers, that, in turn, is strongly suppressed by the external flow shear. This allows the plasma pressure profiles to evolve without a sharp rise in the turbulent fluxes. When all the experimentally measured plasma parameters are consistently included as inputs of the local gyrokinetic simulations, both the experimental electron and ion heat fluxes are quantitatively reproduced on the whole L-mode phase of the selected discharge. Simulations carried out with edge parameters of an ELM-free H-mode phase still show the importance of the mechanisms discussed earlier while also indicating possible limitations of the local approach.

Reliability Assessment of Damage Localization Techniques for Guided Wave-based Structural Health Monitoring Systems in Composite Stiffened Structures

In Guided Wave-based Structural Health Monitoring (GWSHM) systems, the reliability assessment is of utmost importance. The primary focus is the smallest damage that can be detected with 90 percent probability and 95 percent confidence. However, the probability of correct damage localization is equally crucial as the probability of detection. In this view, defining the smallest damage that can be localized accurately in a statistically meaningful way is quite challenging. Several localization techniques have been developed in the literature to localize damage resorting to a GW sensor network, like SAFT and RAPID. This article investigates the reliability of such SHM systems with these localization techniques using an experimental data set from the Open Guided Waves (OGW) platform and compares it with predictions obtained via a simulation model using the EFIT method. A carbon fibre-reinforced plate-like structure reinforced by an omega stringer and a GWSHM system employing twelve piezoceramic transducers bonded over the surface to excite and receive guided waves are used. Pseudo damage of thirteen different sizes are attached to three different locations using a damping tacky tape. Moreover, the EFIT modelling tool is used to simulate the experiment with an extensive number of damage scenarios at the defined three damage locations with a high degree of consistency with the experimental data, facilitating a more robust analysis. The Outcome concerning the influence and correlation between damage size on localization accuracy, considering various localization techniques, were explored and discussed. In addition, the reliability assessment allowed to establish the performance of the localization approaches in a rigorous way.

Damage localization in composite structures based on Lamb wave and modular artificial neural network

Lamb wave-based technology for damage diagnosis in composite structures has emerged as a promising tool for structural health monitoring (SHM). However, traditional SHM faces challenges in multi-sensor localization, including complex data processing, high costs, and cumbersome maintenance management. The current trend is to employ data-driven approaches, but achieving precise damage localization across the entire structure with fewer sensors still presents difficulties. To address this issue, a damage localization method based on Lamb wave envelope characteristics and modular artificial neural network (M-ANN) is developed. In this approach, the envelope characteristics are employed to characterize the damage information and reduce the data dimension. Subsequently, the M-ANN model is innovatively designed with dropout layers in each hidden layer, significantly enhancing performance on random datasets. In the composite laminate experiment using only four sensors, the proposed method achieves an average relative error of 2.96 % for random damage samples, with 95 % falling within 6.17 %, outperforming other advanced damage localization approaches. Additionally, the method is utilized to investigate the requirements for the relative density of data acquisition under different damage localization needs in composite structures.

A Classic and Effective Approach for Managing Totally Implantable Venous-Access Port Site Dehiscence in Patients with Cancer: The Limberg Flap

Background: Totally implantable venous-access ports (TIVAPs) are essential for long-term intravenous treatment in cancer patients, but TIVAP site dehiscence remains a significant issue. This study aims to investigate how Limberg flap transposition compares to primary repair and other local flap techniques in managing TIVAP site dehiscence.Methods: A retrospective study was conducted on patients who underwent repair operations, without TIVAP removal, for right subclavicular area TIVAP site dehiscence between May 2016 and March 2023. Outcomes of primary repair, Limberg flap transposition and other local flap surgeries were analyzed, focusing on incisional integrity and dehiscence recurrence rate.Results: Nine patients and 3,204 catheter days were included in the study, with a total of 15 surgical repair procedures performed for dehiscence, including cases of recurrence. Among them, Limberg flap transposition demonstrated the longest period of incisional integrity, with a mean of 231.4 days, surpassing primary repair (74.25 days) and other local flap techniques (29.5 days). Additionally, the Limberg flap exhibited the lowest recurrence rate, with 0.86 events per 1,000 catheter days, compared to primary repair (10.10 events per 1,000 catheter days) and other local flap approaches (16.95 events per 1,000 catheter days).Conclusion: Limberg flap transposition is considered as an effective solution for TIVAP site dehiscence, showing reduced complications and recurrence rates compared to primary repair and other local flap techniques. This study suggests Limberg flap transposition should be considered the preferred option for managing TIVAP dehiscence in cancer patients, thereby improving patient care and outcomes.