Miracle-dz/UniFAR-FTUs huggingface dataset:
FMPR-Net: False Matching Point Removal Network for Very-High-Resolution Satellite Image Registration

Image registration is the most basic preprocessing method used to unify coordinates among multitemporal very-high-resolution (VHR) satellite images, thus allowing the acquisition of reliable data on the Earth’s surface. Although image registration requires multiple matching points (MPs), false MPs (FMPs) are included because of the similar spectral patterns and noise. However, removing FMPs from VHR satellite image pairs is challenging, especially when the images are directly affected by complex factors, such as shadow, relief displacement, and terrain shielding. Therefore, we propose an FMP removal network (FMPR-net) based on deep learning to eliminate effectively the FMPs to improve registration accuracy. The training dataset is produced by a semiautomatic method. It involves the generation of image patch pairs based on a matching process of scale-invariant feature transform (SIFT) and the assignment of labels referring to the characteristics of true MPs (TMPs) and FMPs. The FMPR-net is designed in a Siamese format consisting of two matching point deep feature extractors (MDFEs). The architecture of the MDFE consists of one main network and three branch networks to achieve robust extraction of meaningful deep features describing the characteristics of MPs. The FMPR-net removes the FMPs using a true matching probability calculated based on the similarity between deep features. Experiments conducted on four pairs of VHR satellite images have demonstrated that the FMPR-net can effectively remove the FMPs. Consequently, accurate VHR satellite image registration is possible by reducing uncertainty caused by the FMPs.
FL_GIoT: Federated Learning Enabled Edge-Based Green Internet of Things System: A Comprehensive Survey

In today’s world, the importance of the Green Internet of Things (GIoT) in the transformed sustainable smart cities cannot be overstated. For a variety of applications, the GIoT may make use of advanced machine learning (ML) methodologies. However, owing to high processing costs and privacy issues, centralized ML-based models are not a feasible option for the large data kept at a single cloud server and created by multiple devices. In such circumstances, edge-based computing may be used to increase the privacy of GIoT networks by bringing them closer to users and decentralizing them without requiring a central authority. Nonetheless, enormous amounts of data are stored in a distribution mechanism, and managing them for application purposes remains a difficulty. Hence, federated learning (FL) is one of the most promising solutions for bringing learning to end devices through edge computing without sharing private data with a central server. Therefore, the paper proposes a federated learning-enabled edge-based GIoT system, which seeks to improve the communication strategy while lowering liability in terms of energy management and data security for data transmission. The proposed model uses FL to produce feature values for data routing, which could aid in sensor training for identifying the best routes to edge servers. Furthermore, combining FL-enabled edge-based techniques simplifies security solutions while also allowing for a more efficient computing system. The experimental results show an improved performance against existing models in terms of network overhead, route interruption, energy consumption, and end-to-end delay, route interruption.
How Transferable are Herbarium-Field Features in Few-Shot Plant Identification with Triplet Loss?

Due to the limited tropical plant field photos but increasing digitized herbarium specimens, cross-domain plant identification has been employed to investigate the use of herbarium specimens on automated plant identification. However, the domain shift between herbarium and field images makes identifying plant species across these two domains challenging. One of the recent papers shows the superiority of triplet loss in preserving similarities between the crossed domains. Nevertheless, the impact of data deficiency on the performance of this triplet loss model has yet to be studied in depth in this field. Specifically, the transferability of the model features trained from limited cross-domain data to target images in the field domain. Therefore, this paper investigates the robustness of cross-domain plant features learned using this triplet loss metric learning approach compared to the supervised classification approach under general and few-shot experimental settings. Detailed experiments show that the triplet loss metric approach outperformed the supervised classification approach in the few-shot setting and achieved comparable results in the general experimental setting. In addition, the feature dictionary generation schemes composed of various herbarium field feature combinations we proposed boost our models’ performance significantly compared to a single feature type dictionary strategy.
The Local Phase in the Scale-Space Monogenic Signal: An Edge-Detection Filter

We have presented a new edge-detection filter to enhance potential-field data by using the local phase in the Poisson scale-space monogenic signal. The local phase in the monogenic signal is a phase-based filter that uses the firstorder Riesz transform and the data. The Poisson scalespace representation of the data is equivalent to perform an upward continuation of the data. To enhance lowand highwavenumber features, a band-pass filter is created by using two Poisson scale-space representations of the data. The main advantages of this local phase filter are: 1) the simplicity in implementation, 2) the low computational load, 3) the weak sensitive to noise and 4) the ability in enhancing linear responses that are not clearly seen in the original data. Tests on synthetic magnetic data showed that the proposed filter has the advantage of enhancing not only strong magnetic anomalies from near-surface sources but also weak anomalies from deep-seated sources. The application of the proposed filter to aeromagnetic data from the northwest portion of the Para-Maranhao Basin, Brazil, enhanced east-west and northeast-southwest lineaments not clearly seen in the data. The east-west lineaments were interpreted as the magnetic expression of the Saint Paul Fracture Zone that cuts across the continental margin controlling basement structures whereas the northeastsouthwest lineaments were interpreted as related to a late amalgamation of different terrains that composed the Sao Luis Craton.
Velocity and abundance of silicon ions in the solar wind

Using data from the ISEE-3 ion composition instrument (ICI), velocities and abundances of silicon ions in the solar wind have been determined. The period of investigation covers the maximum of solar cycle 21, beginning with launch of ISEE-3 in August 1978 and ending at the removal of the spacecraft from the Lagrangian Point L1 in June 1982. The results generally confirm previous ICI observations of iron, the other heavy element with a low first ionization potential measured with the ICI. Silicon ions (and other ions in the same M/Q range) tend to stream at the bulk velocity of /sup 4/He/sup + +/ in low-speed solar wind. At high-speed solar wind. Si lags by up to or about 20 km/s after /sup 4/He/sup + +/. By means of a minimum variance estimation technique, fluxes (and densities) of silicon in the solar wind have been obtained free of bias. An average Si/O flux ratio of 0.19 +- 0.04 is derived. This value is larger by a factor of 3 or 4 than the Si/O abundance ratio at the solar surface. copyright American Geophysical Union 1989
RETINAL VESSELS EXTRACTION BASED ON IMPROVING LINE DETECTION

In the medical field, medical images have supported doctors in detecting some dangerous problems related to health. These problems are caused by abnormalities in retinal blood vessels. Therefore, analysis of retinal vascular features is a good way to detect these abnormalities that help doctors make suitable treatment plans in the early stage. One of the retinal analysis tasks is retinal blood vessel extraction which plays an important role because it needs to be done before any measurement can be created. This paper proposed an approach for retinal blood vessel extraction based on multi-scale line detection. In the proposed method, the input image is applied by a multi-scale line detection technique to detect retinal blood vessels. As a result, our method can work effectively to extract more vessels. The performance of the proposed method evaluates both quantitatively and qualitatively on publicly available DRIVE datasets with an accuracy average reaching 96%. The result of the proposed method is better than the other methods.
Behavioural interventions for tobacco cessation in India: A systematic review and meta-analysis

Tobacco consumption is an area of public health concern in India. One of the unmet needs of many low-resource countries is to provide cost-effective tobacco cessation interventions for reducing tobacco-related mortality. This article reviews studies on non-pharmacological interventions for tobacco cessation in India. A systematic review by PICO (population, intervention, comparison, outcome) of behavioural intervention-based tobacco cessation studies that met the inclusion criteria, with a minimum 1-month follow-up, reporting outcomes in terms of frequencies or percentages published between 2010 and 2020 was performed. Following the review stages, 16 studies comprising 9,613 participants were included in the review. A pooled estimate was derived using both fixed-effects and random-effects models. The intervention showed good overall efficacy for any tobacco user (relative risk [RR] = 1.73 [95% confidence interval [CI]: 1.58–1.90) (fixed-effect model)] and (RR = 2.02 [95% CI: 1.64–2.48] [random-effects model]). Behavioural intervention studies targeted towards only smokers (RR of 1.81 [95% CI: 1.55–2.11] and 1.96 [95% CI: 1.52–2.53]) and combined smoking and smokeless tobacco users (RR of 1.69 [95% CI: 1.50–1.90] and 2.12 [95% CI: 1.49–3.01]) were equally efficacious. The review provides the effectiveness of behavioural interventions in quitting tobacco among users of both smoking and smokeless forms of tobacco. The review findings are of particular significance to inform health policy decisions on the integration of cost-effective brief behavioural intervention into existing health care services in resource-constrained countries.
Few Shot Learning Guided by Emotion Distance for Cross-corpus Speech Emotion Recognition

Cross-corpus speech emotion recognition (SER) is important for building robust and practical SER systems that can adapt to various real-world scenarios and acoustic conditions. Cross-corpus SER faces the problem of low accuracy partly due to the scarcity and inconsistency of labeled data in the target corpus, in which situation prior knowledge about the relationship between emotion categories may be helpful. Previous studies have suggested that discrete emotion categories and continuous emotion space can complement each other in describing emotions. In this paper, we propose to use the distance between emotion categories derived from their distribution in the continuous emotion space as prior knowledge for cross-corpus speech emotion recognition. We hypothesize that this prior knowledge can help the SER system to learn more meaningful and generalizable representations of emotions that are consistent across domains. Experiment results show that the proposed few-shot learning method based on metric learning leveraging the prior knowledge of emotion distance achieves good performance.
A lightweight high-resolution algorithm based on deep learning for layer-wise defect detection in laser powder bed fusion

The quality of the powder bed is critical in the laser powder bed fusion (LPBF) process, and defects in the powder bed likely affect the quality of the final part. With the development of artificial intelligence technology, machine learning methods have been widely applied in powder-bed defect detection. However, to achieve high-precision defect detection, it is often necessary to construct complex network models and use high-resolution powder bed image data. To address these issues, this study used an off-axis industrial camera to capture layer-wise powder bed image data and proposed a defect detection model based on YOLOv7x and channel pruning to achieve defect identification and localization of powder bed patch images. Furthermore, an end-to-end defect detection pipeline based on image processing methods was proposed to detect defects in layer-wise powder bed images. Finally, the gradient-based class activation map technique (Grad CAM++) was used to analyze the interpretability of the detection results of the model. The results indicated that the proposed model was more lightweight than other models (YOLOv7x, Faster R-CNN, and SSD), with a model size of only 12.4MB. The average time for detecting powder bed image patches was significantly reduced to only 3.4 ms, and the average detection accuracy was as high as 97.4%. This demonstrates that the proposed detection method has the advantages of faster detection speed, higher detection accuracy, and simpler models, providing a reference for the real-time online detection of powder bed defects.
Review of Data-Driven Techniques for On-Line Static and Dynamic Security Assessment of Modern Power Systems

The secure operation of the transmission grid is of primary importance for any power system operator. However, the introduction of new technologies, market deregulation, and increasing levels of interconnectivity have made the analysis and assessment of power system security both more challenging and essential than ever. In this context, data-driven-based methodologies are being increasingly employed to classify and anticipate insecure future states, and make inferences on potential triggers of undesired operational conditions. This paper provides a comprehensive and systematic review of this fast-moving research area and covers data-driven-based methodologies deployed in both static and dynamic security assessment. Particular attention is paid to recent trends, such as the use of spatiotemporal feature selection algorithms and the increasing research activity in short-term voltage stability and frequency stability, which are not yet widely assessed as a collective in the existing literature.
Step Size Control of Shared-error Normalized Least Mean Square Algorithm for Acoustic Echo and Noise Canceller

We propose a step size control method for an acoustic echo and noise canceller (AENC) based on a shared-error normalized least mean square (SENLMS) algorithm. The conventional AENC based on the SENLMS algorithm can optimize both the acoustic echo canceller (AEC) and the adaptive noise canceller (ANC). However, it is difficult to set the step size parameters to prevent the divergence of the conventional AENC because of the optimization of both AEC and ANC. To solve this problem, we investigate the constraint for the step size parameters of the AENC with the SENLMS algorithm and propose a step size control method. The constraint for the step size parameters is derived from the mean square value of the a posteriori error. Then, the step size parameter of the AEC is determined by the nonparametric variable step size (VSS) NLMS algorithm. Finally, the step size parameter of the ANC is automatically determined by the constraint and the variable step size parameter of the AEC. Simulation results show that the proposed AENC with the VSS-SENLMS algorithm can simultaneously prevent the divergence and maintain its the capability to reduce background noise and acoustic echo.
Impact of velocity model calibration on microseismic locations

Accurate locations of the microseismic events form the basis for interpretation of hydraulically stimulated regions. The velocity model, which in general is not well known, has a significant impact on the accuracy of located events. Correct knowledge of the anisotropy parameters and lithological heterogeneity, accompanied with a precise model calibration will reduce errors in microseismic locations. In this paper, we investigate the velocity model calibration process for multi-stage frac monitoring, in particular the use of single calibration shot. We generate synthetic microseismic events using elastic finite-difference modeling, assuming an initial isotropic velocity model based on the Barnett shale. We calibrate the initial model to best-fit arrival times from the shot(s) at known location(s). The predicted arrival times are based on a minimum traveltime tree algorithm. A similar approach is repeated for the initial model containing shale layer with vertical transverse isotropy. We then evaluate the behavior of errors in microseismic locations obtained with each calibrated velocity model using grid-search algorithm. As expected, the use of individual calibration shots results in significant microseismic location errors. Calibrating model with multiple known shots together improves the accuracy of microseismic locations. For the anisotropic case, the resulting errors are larger than in the isotropic case. It is therefore, important to calibrate velocity model with maximum number of available perforation shots, in particular for areas with strong anisotropy and lateral heterogeneity.
Determination of Sex from the Tibia in the Punjab Zone

Aims and Objectives: Sex determination is an important aspect of forensic science and it is 100% possible when all the skeletal bones are available. Material and Methods: In the present study, 96 tibia of known sex (62 males and 34 females) were considered. Results and Conclusion: The weight, length, midshaft circum­ ference and the widths at the upper and the lower end of the tibia were noted. The demarking points for the identification of sex from the various measurements of the tibia were calculated. None of these measurements can identify the sex of the tibia with 100% certainty. However, weight is the best discriminant factor for the identification of the male tibia and the width at the upper end is the best discriminant factor for the identifying the female tibia.
eCommons@AKU eCommons@AKU

Background: Various anticoagulant therapies are prescribed to patients under physicians ’ discretion and recently Direct Oral Anticoagulants(DOAC) have been under trials to evaluate their safety and efficacy. In addition to this, the regimen of DOACs and Aspirin is of keen interest as researchers continue to find an optimal regimen to treat blood clots in patients. This study is a systematic review and meta -analysis of randomized controlled trials and observational studies that asses the safety and efficacy of DOAC with and without Aspirin. Methods: We queried MEDLINE and Cochrane CENTRAL from their inception to April 2021, for published and randomized controlled trials and observational studies in any language that compared dual (DOAC + ASA) therapy or mono (DOAC alone) therapy in patients with AF. The results from the studies were presented as risk ratios (RRs) with 95% confidence intervals (CIs) and were pooled using a random-effects model. Endpoints of interest included major bleeding, myocardial infarction (MI), major adverse cardiovascular events (MACEs), hospitalizations, all-cause mortality, and stroke. Results: The risk of major bleeding was significantly lower in the DOAC alone group compared with DOAC plus aspirin group. Non-significant results were obtained (P value greater than 0.05) for other outcomes establishing that DOAC monotherapy was not superior to the combined regimen in reducing the risk of MACE, Stroke, Hospitalization, Death. Conclusion: Among patients with NVAF (Non valvular Atrial Fibrillation) and VTE (Venous thromboembolism) receiving anticoagulation prophylaxis, in terms of safety profile our comparisons showed a statistically significant reduction in Major Bleeding in DOAC Alone group compared with DOAC Plus Aspirin.
Dynamic compression of quartz

Measurements of the stress-density states achieved during shock compression of X-, Y-, and Z-cut crystalline quartz are presented. For the elastic shocks comparison is made with predictions based on acoustic measurements of the second- and third-order elastic coefficients, and observed differences, which are substantial, are used to calculate two of the fourth-order coefficients. The values of c1111 and c3333 are found to be 159.3 ± 20% × 1012 and 184.9 ± 20% × 1012 dynes/cm², respectively. This procedure, employing the difference between acoustic measurements and shock wave measurements to evaluate higher-order elastic coefficients, should be generally applicable to solids that sustain large-amplitude elastic waves.
Electrophoretic Deposition of Multi-Walled Carbon Nanotube Coatings on CoCrMo Alloy for Biomedical Applications

Carbon nanotubes are a promising material for use in innovative biomedical solutions due to their unique chemical, mechanical, electrical, and magnetic properties. This work provides a method for the development of ultrasonically assisted electrophoretic deposition of multi-walled carbon nanotubes on a CoCrMo dental alloy. Functionalization of multi-walled carbon nanotubes was carried out by chemical oxidation in a mixture of nitric and sulfuric acids. The modified and unmodified multi-walled carbon nanotubes were anaphoretically deposited on the CoCrMo alloy in an aqueous solution. Chemical composition was studied by Fourier transform infrared spectroscopy. Surface morphology was examined by scanning electron microscopy. The mechanism and kinetics of the electrochemical corrosion of the obtained coatings in artificial saliva at 37 °C were determined using the open-circuit potential method, electrochemical impedance spectroscopy, and anodic polarization curves. The capacitive behavior and high corrosion resistance of the tested electrodes were revealed. It was found that the kinetics of electrochemical corrosion of the CoCrMo electrode significantly decreased in the presence of the functionalized multi-walled carbon nanotube coating. Electrophoretic deposition was shown to be an effective, low-cost, and fast method of producing nanotubes with controlled thickness, homogeneity, and packing density.