Localization Performance Research Articles

Localization of impact on CFRP structure based on fiber Bragg gratings and CNN-LSTM-Attention

Low-velocity impacts can cause microscopic and invisible damage to carbon fiber reinforced polymer (CFRP) structures, potentially compromising their integrity and leading to catastrophic failures. Therefore, obtaining precise information about the impact location is crucial for monitoring the health of CFRP structures. In this paper, an impact localization system for CFRP structures was developed by using fiber Bragg grating (FBG) sensors, and impact signals detected by FBG sensors are demodulated by edge-filtering at high speed. An impact localization method of CFRP structure based on CNN-LSTM-Attention is proposed. The time difference of arrival (TDOA) between signals from different FBG sensors are collected to characterize the impact location, and attention mechanism is introduced into the CNN-LSTM model to augment the significance of TDOA of impact signal detected by proximal FBG sensors. The model is trained using the training set, its parameters are optimized using the validation set and the localization performance of different models are compared by the test set. The proposed impact localization method based on CNN-LSTM-Attention model was verified on a CFRP plate with an experiment area of 400 mm*400 mm. Experimental results prove the effectiveness and satisfactory performance of the proposed method.

Impact Localization in Complex Cylindrical Shell Structures Based on the Time-Reversal Virtual Focusing Triangulation Method.

In addressing the challenging issue of impact source localization for large-scale anisotropic stiffened compartmental cylindrical shell structures, this paper presents a novel impact localization method. The method is based on a time-reversal virtual focusing triangulation approach and does not rely on prior knowledge of the structure or specific measurements of wave velocity. By employing energy power filtering to select key sensors, wavelet packet decomposition is utilized to extract narrowband Lamb wave signals, which are then synthesized. Further enhancement of signal recognition is achieved through time-reversal amplification techniques. Experimental results demonstrate that under non-motorized operating conditions, this method achieves an average error of 0.89 m. Under motorized operating conditions, the average error is 1.12 m. Although the presence of background noise leads to an increase in error, the overall localization performance is superior to traditional triangulation methods. Additionally, selecting the top three sensors in terms of energy power ranking can more accurately record impact response.

Data-free knowledge distillation via generator-free data generation for Non-IID federated learning

Data heterogeneity (Non-IID) on Federated Learning (FL) is currently a widely publicized problem, which leads to local model drift and performance degradation. Because of the advantage of knowledge distillation, it has been explored in some recent work to refine global models. However, these approaches rely on a proxy dataset or a data generator. First, in many FL scenarios, proxy dataset do not necessarily exist on the server. Second, the quality of data generated by the generator is unstable and the generator depends on the computing resources of the server. In this work, we propose a novel data-Free knowledge distillation approach via generator-Free Data Generation for Non-IID FL, dubbed as FedF2DG. Specifically, FedF2DG requires only local models to generate pseudo datasets for each client, and can generate hard samples by adding an additional regularization term that exploit disagreements between local model and global model. Meanwhile, FedF2DG enables flexible utilization of computational resources by generating pseudo dataset locally or on the server. And to address the label distribution shift in Non-IID FL, we propose a Data Generation Principle that can adaptively control the label distribution and number of pseudo dataset based on client current state, and this allows for the extraction of more client knowledge. Then knowledge distillation is performed to transfer the knowledge in local models to the global model. Extensive experiments demonstrate that our proposed method significantly outperforms the state-of-the-art FL methods and can serve as plugin for existing Federated Learning methds such as FedAvg, FedProx, etc, and improve their performance.

Adaptation of lot quality assurance sampling to monitor seasonal malaria chemoprevention delivery performance.

Malaria Consortium supports delivery of seasonal malaria chemoprevention (SMC) to children ages 3-59months using sulfadoxine-pyrimethamine plus amodiaquine. Lot quality assurance sampling (LQAS) was adapted as a cost-efficient method for end-of-cycle SMC monitoring surveys across supported countries and an implementation challenges reporting system was established in Nigeria. We present a case study of its application in Nasarawa State. LQAS facilitated timely local performance assessment across 16 indicators. Development of new reporting tools has played a key role in stimulating national-level discussions on improvements to SMC supervisory processes and implementer training and provided a framework for engagement with local stakeholders.

Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems

As the informatization of power systems advances, the secure operation of power systems faces various potential network attacks and threats. The false data injection attack (FDIA) is a common attack mode that can lead to abnormal system operations and serious economic losses by injecting abnormal data into terminal links or devices. The current research on FDIA primarily focuses on detecting its existence, but there is relatively little research on the localization of the attacks. To address this challenge, this study proposes a novel FDIA localization method (GA-CNN-LSTM) that combines convolutional neural networks (CNNs), long short-term memory (LSTM), and a genetic algorithm (GA) and can accurately locate the attacked bus or line. This method utilizes a CNN to extract local features and combines LSTM with time series information to extract global features. It integrates a CNN and LSTM to deeply explore complex patterns and dynamic changes in the data, effectively extract FDIA features in the data, and optimize the hyperparameters of the neural network using the GA to ensure an optimal performance of the model. Simulation experiments were conducted on the IEEE 14-bus and 118-bus test systems. The results indicate that the GA-CNN-LSTM method achieved F1 scores for location identification of 99.71% and 99.10%, respectively, demonstrating superior localization performance compared to other methods.

AIE-active large Stokes-shift BODIPY Functionalized with Carbazolyl for Lysosome-Targeted Imaging in Living Cells

A large number of studies have shown that lysosomal microcircumstances changes can affect many physiological and pathological processes at the cellular level. However, the visual detection of lysosomal microcircumstances is relatively difficult due to low pH (4.5–6.0) value in lysosomal that require the probe not only stable under acidic condition but also has a good localization effect to lysosomal. Obviously, novel fluorescent which possessed both acidic stability and lysosomal-target property together with lysosomal viscosity active is highly demanded. Herein, a novel BODIPY molecular CarBDP based on carbazole group was rationally designed and synthesized for the lysosomal imaging. CarBDP exhibited AIE feature with a large Stokes shift of up to 157 nm. More importantly, co-localization assay of the CarBDP-treated MCF-7 cells indicated that CarBDP has a good localization effect on lysosomal (Rr = 0.7109) due to the carbazole group while the normal BODIPY that without carbazole group (PhBDP) shows poor localization performance, this was the first time that a small molecule can locate lysosomes only based on carbazole group. CarBDP exhibits strong solid emission with long fluorescence decay lifetime (τ = 44.54 ns) and was stable under acid condition.The probe CarBDP assembled with carbazole group was successfully utilized for lysosomal localization and mapping lysosomal viscosity in live cells, which provides a novel candidate tool for the determination of lysosomal microcircumstances.

Activists’ Strategic Interactions, Collaborative Tactics, and Local Environmental Performance

Activists’ Strategic Interactions, Collaborative Tactics, and Local Environmental Performance

Precision and Robust Models on Healthcare Institution Federated Learning for Predicting HCC on Portal Venous CT Images.

Hepatocellular carcinoma (HCC), the most common type of liver cancer, poses significant challenges in detection and diagnosis. Medical imaging, especially computed tomography (CT), is pivotal in non-invasively identifying this disease, requiring substantial expertise for interpretation. This research introduces an innovative strategy that integrates two-dimensional (2D) and three-dimensional (3D) deep learning models within a federated learning (FL) framework for precise segmentation of liver and tumor regions in medical images. The study utilized 131 CT scans from the Liver Tumor Segmentation (LiTS) challenge and demonstrated the superior efficiency and accuracy of the proposed Hybrid-ResUNet model with a Dice score of 0.9433 and an AUC of 0.9965 compared to ResNet and EfficientNet models. This FL approach is beneficial for conducting large-scale clinical trials while safeguarding patient privacy across healthcare settings. It facilitates active engagement in problem-solving, data collection, model development, and refinement. The study also addresses data imbalances in the FL context, showing resilience and highlighting local models' robust performance. Future research will concentrate on refining federated learning algorithms and their incorporation into the continuous implementation and deployment (CI/CD) processes in AI system operations, emphasizing the dynamic involvement of clients. We recommend a collaborative human-AI endeavor to enhance feature extraction and knowledge transfer. These improvements are intended to boost equitable and efficient data collaboration across various sectors in practical scenarios, offering a crucial guide for forthcoming research in medical AI.

The Influence of Local Government Size and Economic Growth on Local Government Financial Performance

Performance measurement is an important element in assessing the effectiveness of policy implementation, including financial regulations at the local government level. By measuring financial performance, we can obtain valuable feedback regarding the suitability and accuracy of implementing these regulations. The aim of this research is to analyze the influence of local government size and economic growth on local government financial performance. In this study, the sample used in this research was a purposive sampling sample, with the population of Regional Governments and Regency/City Regional People's Representative Councils (DPRD) in Indonesia in 2018.

Federated learning implementation based on DDS data distribution service

Federated learning allows multiple local private clients to collaborate on training the same model without sharing each client's private data. It can achieve collaborative training between users while protecting data privacy and security. Therefore, it is advocated for use in the fields of Internet of Things, Internet of Vehicles, etc. There are security issues in the transmission process of federated learning models. How to ensure data privacy during local data collection, local model parameter transmission, model aggregation and performance testing while maximizing the use of private data is a question that people have been exploring. This paper proposes a DDS-based FL framework (FL-DDS) to achieve secure transmission of model parameters. The client and server are used as nodes in the DDS security domain, and the DDS data distribution mechanism is used to transmit model parameters. Through the DDS topic-based publish-subscribe mechanism, the global model and local model are transmitted through the DDS topic. At the same time, the DDS authentication component, access control component and encryption component are used to achieve domain-level security and intra-domain security of model transmission. Experiments show that (FL-DDS) can protect the privacy of model parameters without affecting communication performance.

Discovering an inference recipe for weakly-supervised object localization

Most existing weakly-supervised object localization (WSOL) methods have improved training procedures for better localization performance. However, the inference procedure has been overlooked. We observe that the useful information for localization is missed by the current inference practice of WSOL. To address this limitation, we propose a new test-time ingredient for WSOL: binarizing the penultimate feature map and their corresponding weights of the last linear layer. With this simple remedy, the proposed method consistently improves the localization performance of the existing training methods for WSOL. Extensive evaluation including with three different backbone networks on three different WSOL benchmarks validates its effectiveness. In addition, we demonstrate our method is also able to improve weakly-supervised semantic segmentation performances on PASCAL VOC dataset. Lastly, since our method is only applied during the testing phase, our performance gain comes with negligible computational overheads.

Phononic crystals with incomplete line defects: applications in high-performance and broadband acoustic energy localization and harvesting

Using phononic crystals (PnCs) to enhance the electrical output performance of piezoelectric energy harvesting (PEH) devices and broaden the frequency range of harvesting energy is crucial to solving the self-energy of low-power devices such as wireless sensors. In this work, an ultra-wide full-band gap PnC was designed. The concept of a PnC with an incomplete line defect was proposed. The energy localization and harvesting of incomplete line defect PnCs and traditional point defect and line defect PnCs were studied by finite element analysis. The results show that compared with a point defect and a line defect, the output electric power of an incomplete line defect was increased by 31.88 times and 2.51 times, respectively, and the energy localization and harvesting frequency band were widened. By exploring the influence of the periodicity of the vertical incomplete line defect direction on the electrical output performance of the PnC-based PEH system, it is found that the electrical output performance of the 5 × 3 incomplete line defect PnC is the best, and the maximum output voltage and output electric power are 27.36 V and 17.29 mW, respectively. This work provides new insights and ideas for improving the energy localization and harvesting performance of PnC-based PEH systems.

Effect of Budget Participation, Transparency and Internal Control of Accountability and Local Government Financial Performance Bulukumba Regency

Objective: The study aimed to analyze several key factors affecting the accountability and financial performance of the Regional Government of Bulukumba Regency. Specifically, it sought to examine (1) the effect of budget participation on accountability and financial performance, (2) the impact of transparency on accountability and financial performance, and (3) the role of internal supervision on accountability and financial performance. Methods: This research utilized a quantitative approach, gathering primary data through questionnaires distributed to 201 respondents. The study was conducted from September 2022 to February 2023. Data analysis was performed using Structural Equation Modelling (SEM) with the assistance of Amos Version 25 and SPSS Version 25. Results: The findings of the study revealed several key insights: (1) Budget participation, transparency, and internal supervision were found to have a positive and significant effect on accountability. (2) Accountability, in turn, positively and significantly impacted the financial performance of the Regional Government of Bulukumba Regency. (3) Both budget participation and transparency were found to have a positive and significant effect on financial performance. (4) Internal supervision showed a positive but not significant effect on financial performance. (5) Overall, budget participation, transparency, and internal supervision positively and significantly influenced the financial performance of the Regional Government of Bulukumba Regency through the mediation of accountability. Conclusion: The study concluded that budget participation, transparency, and internal supervision play crucial roles in enhancing accountability and financial performance within the Regional Government of Bulukumba Regency. Accountability serves as a significant mediator in translating these factors into improved financial outcomes. The findings underscore the importance of fostering participatory budgeting, transparency, and robust internal supervision to drive better governance and financial results.

Role of Speckle Tracking Echocardiography in the Assessment of Subclinical Left Ventricular Dysfunction in Post COVID-19 Patients

Background: The utilization of two-dimensional speckle-tracking echocardiography (2-D STE) serves as a precise measurable technique for conducting an assessment of both global and localized cardiac performance, exhibiting remarkable sensitivity. Its utility is particularly noteworthy in the early identification of cardiac impairments that are not clinically evident. This study’s objective was to investigate the effectiveness of STE in identifying latent left ventricular dysfunction among individuals who have recuperated from COVID-19. Methods: This study was conducted at a single center; this analytical cross-sectional analysis involved 100 individuals over the age of 18. The study comprised two cohorts: one with 50 post COVID-19 patients, confirmed through a positive PCR test within a month of diagnosis, and a control group of 50 healthy subjects be compatible for age, sex, and risk factors. Subsequently, the post COVID-19 patients were stratified based on high-sensitivity troponin levels into two subgroups: Group I included those exhibiting signs of myocardial damage, while Group II consisted of patients showing no evidence of myocardial injury. Results: Elderly individuals, a greater prevalence of hypertension (HTN), raised levels of D-dimer and, serum C-reactive protein (CRP) along with increased Left ventricular end-diastolic volume (LVEDV) and diminished Left ventricular global longitudinal strain (LVGLS) over a one-month observation period, were all significantly linked to myocardial injury, as indicated by a p-value < 0.05. The LVGLS was a reliable indicator of cardiac injuries in COVID-19 survivors, with an AUC of 0.947 and a CI ranging from 0.886 to 1.00 (P < 0.001), particularly at a threshold of ≤ -20%, where it achieved 87.2% sensitivity and 100% specificity. Approximately 35 patients exhibited an LVGLS below this threshold. Furthermore, D-dimer and CRP levels post COVID-19 were notably elevated in those with cardiac injuries in comparison with those not, a difference that was statistically significant (P < 0.05). Furthermore, during the follow-up after one month period, post COVID-19 CRP levels, LVEDV, and LVGLS remained significantly higher in patients with myocardial injury in comparison with those didn’t have myocardial injury (p < 0.05). Conclusions: LV GLS is acknowledged as a noteworthy, autonomous predictor of outcome for myocardial injury in individuals diagnosed with COVID-19. This metric offers preliminary insights into the infection’s severity, potentially aiding in the early intervention and management strategies for affected patients.

Research on local government budget performance management issues

Research on local government budget performance management issues

Computational Fluid Dynamic Investigation of Local Flow-Field Conditions in Lab Polymer Electrolyte Membrane Fuel Cells to Identify Degradation Stressors and Performance Enhancers

The use of polymer electrolyte membrane (PEM) fuel cells as an alternative to internal combustion engines can significantly contribute to the decarbonization of the transport sector, especially for heavy-duty applications. However, degradation is still an issue for this type of component, affecting their durability and performance. In this scenario, a detailed analysis of the anodic and cathodic distributors’ flow-field geometry may help to identify some local stressors that trigger the degradation mechanism, such as local hot spots and reactants not having a uniform distribution. A computational fluid dynamic (CFD) methodology is able to provide a volumetric description of a PEM fuel cell so it can be a useful tool to better understand the physical phenomena that govern the component operations. In this work, the open-source simulation library openFuelCell2 is adopted for a detailed analysis of two different PEM fuel cells characterized by standard distributor geometries, namely a parallel channel geometry and a serpentine configuration. The library, based on the OpenFOAM code, has been extended with a novel implementation accounting for the catalytic activity reduction due to the platinum oxide (PtOx) formation occurring under certain particular conditions. The adopted methodology is firstly validated resorting to experimental data acquired for the two different fuel cell configurations. The analysis highlights that the PtOx formation leads to a reduction in the fuel cell performance reaching up to 60–80% when operating at high voltages. Then, the effect of the distributor geometries on the component performance is investigated by resorting to in-plane and through-plane physical quantity distribution, such as reactant concentration, pressure or velocity fields. While the parallel flow channel configuration shows some diffusion losses under the rib, the serpentine channel geometry configuration can achieve some local performance peaks thanks to the convective flow in the gas diffusion layer (GDL) driven by local pressure gradients. Furthermore, the local enhancement in terms of higher current density under the rib is associated with an effective heat removal due to the high thermal capacity of the bipolar plate, avoiding the generation of local hot spots.

Sensitivity of model-based leakage localisation in water distribution networks to water demand sampling rates and spatio-temporal data gaps

ABSTRACT Model-based leakage localisation in water distribution networks requires accurate estimates of nodal demands to correctly simulate hydraulic conditions. While digital water meters installed at household premises can be used to provide high-resolution information on water demands, questions arise regarding the necessary temporal resolution of water demand data for effective leak localisation. In addition, how do temporal and spatial data gaps affect leak localisation performance? To address these research gaps, a real-world water distribution network is first extended with the stochastic water end-use model PySIMDEUM. Then, more than 700 scenarios for leak localisation assessment characterised by different water demand sampling resolutions, data gap rates, leak size, time of day for analysis, and data imputation methods are investigated. Numerical results indicate that during periods with high/peak demand, a fine temporal resolution (e.g., 15 min or lower) is required for the successful localisation of leakages. However, regardless of the sampling frequency, leak localisation with a sensitivity analysis achieves a good performance during periods with low water demand (localisation success is on average 95%). Moreover, improvements in leakage localisation might occur depending on the data imputation method selected for data gap management, as they can mitigate random/sudden temporal and spatial fluctuations of water demands.

A Distributed Task Allocation Method for Multi-UAV Systems in Communication-Constrained Environments

This paper addresses task allocation to multi-UAV systems in time- and communication-constrained environments by presenting an extension to the novel heuristic performance impact (PI) algorithm. The presented algorithm, termed local reassignment performance impact (LR-PI), consists of an improved task inclusion phase, a novel communication and conflict resolution phase, and a systematic method of reassignment for unallocated tasks. Considering the cooperation in accomplishing tasks that may require multiple UAVs or an individual UAV, the task inclusion phase can build the ordered task list on each UAV with a greedy approach, and the significance value of tasks can be further decreased and conflict-free assignments can be reached eventually. Furthermore, the local reassignment for unallocated tasks focuses on maximizing the number of allocated tasks without conflicts. In particular, the non-ideal communication factors, such as bit error, time delay, and package loss, are integrated with task allocation in the conflict resolution phase, which inevitably exist and can degrade task allocation performance in realistic communication environments. Finally, we show the performance of the proposed algorithm under different communication parameters and verify the superiority in comparison with the PI-MaxAsses and the baseline PI algorithm.

Comparison of kink-band structures and specificities of cell wall polysaccharides in modern and ancient flax fibres

Flax (Linum usitatissimum L.) is a plant of industrial importance, its fibres being presently used for high-value textile applications, composite reinforcements as well as natural actuators. Human interest in this fibre-rich plant dates back several millennia, including to Ancient Egypt where flax was used extensively in various quotidian items. While the recent technical developments of flax fibres continue to diversify through scientific research, the historical use of flax also has rich lessons for today. Through careful examination of ancient Egyptian and modern flax fibres, this study aims to conduct a multi-scale characterization from the yarn to the fibre cell wall scale, linking differences in structure and polysaccharide content to the mechanical performance and durability of flax. Here, a multi-scale biochemical study is enriched by scanning electron microscopy and nanomechanical investigations. A key finding is the similarity of cellulose features, crystallinity index and local mechanical performances between ancient and modern fibres. Biochemically speaking, monosaccharides analysis, deep-UV and NMR investigations demonstrate that ancient fibres exhibit less pectins but a similar hemicellulosic content, especially through uronic acids and galactose, suggesting the sensitivity of these non-crystalline components.

Local Separation Performance for Distillation Processes in Rotating Packed Beds: Transferability and Applicability

Local Separation Performance for Distillation Processes in Rotating Packed Beds: Transferability and Applicability