Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of a series of large �ares from a sunspot group eruption on VLF propagation

The sunspot group continues to evolve, causing �ares to erupt under the action of the atmospheric magnetic �eld. After the �are erupts, the ejected X-rays cause the electron concentration in the ionosphere to increase, and the equivalent re�ection height of the ionosphere decreases. We investigate the correlation between the series of �ares erupted by sunspot groups and the propagation of VLF signals. We used NOAA sunspot activity area data and solar �are data released by GOSE satellite, counted the series of �ares erupted by sunspot group in 2000, and analyzed the size of the �are. Among them, the sunspot group numbered AR9077 erupted continuously with 15 M-level and above �ares, X Level 3 �ares. Received data from the Alpha VLF navigation station in Haikou, and observed a series of large �ares erupted by AR9077. After the �are disk erupted, the phase anomaly of the VLF signal in the ground-ionosphere was caused. Analyze the �are according to the phase anomaly change, calculate the phase velocity when the �are occurs, the equivalent re�ection height change, and the X-ray �ux to predict the size of the �are. The calculations are consistent with the comparison of data published by the GOSE satellite. The observations show that the VLF signal correlates well with solar �ares and sunspots. The VLF signal can be used as a reliable solution for predicting sunspots and solar �ares, avoiding the impact of continuous �ares of sunspots.
On computational complexity, using randomized pooling for convolutional neural network

: Randomization is a technique used in algorithms as a strategy that uses a random source as part of its logic. It is used in traditional algorithms to reduce time or space complexity. Many efforts have been made to increase the precision of convolutional neural networks (CNN) in various application domains, but less has been done to minimize the computational complexity of this model. In this work, we introduce randomized pooling (RPool) for CNN. RPool has reduced the number of operations of the CNN. Consequently, the computation time of the algorithm is reduced and the accuracy is improved. The MNIST dataset is used to demonstrate the pooling layer (PL) of CNN and compare the results of standard CNN with our proposed RPool for CNN. The simulation results show that as the number of epochs increases, the training and testing time of our proposed RPool decreases while the accuracy increases. We achieved 96.95% accuracy at epoch 10 and 8.85% decrease in training time, which demonstrates the superiority of our proposed RPool for CNN over its competitor average pooling (AvgPool).
Tracing Of Identical Key Points For Recognition Of Copy-Move Forgery

Rapid development of digital image processing along with various image editing tools, it is quiet easy to tamper these images without having any traces of modification; hence determining the authenticity of digital image is considered as the social issue. Moreover, several agencies like news services, intelligence service and law firms rely upon the image for various purpose; as a result, variety of researches has been carried out to detect the forgery evidence. Copy Move image forgery (CMF) is most common and popular type of image tampering where regions are selected by attacker and other region are copied into it. Moreover, development of robust and automatic approach for copy move forgery are still considered as an open challenge; thus in this research work, we design and develop an automatic mechanism named TIK-RCMF (Tracing of Identical Key points for Recognition of Copy-Move Forgery). TIK-RCMF tends to obtain the optimal solution in all three steps i.e. extraction of features, similarity matching and tracing of identical points and localization of forgery. In first step, image level optimization is used; in second step optimal similarity checking is utilized and third step optimal localization is achieved. Moreover, performance evaluation is carried out considering the three dataset namely “dataset”, “GRIP” and “FAU” considering both image level and pixel level. Furthermore, evaluation is carried out through comparing with different existing mechanism of forgery detection and comparative analysis suggests that TIK-RCMF is efficient than the other models.
METHODS, IMPROVEMENT AND CHALLENGES ON CLOTH AND HAIR ANIMATION

Animation may create the illusion that something is moving by taking a succession of drawings and displaying them one after another. This phenomenon is known as the illusion of movement. The generation and control of movement is a significant obstacle in both hair and clothing animation. The purpose of this study is to investigate the techniques utilized in hair and clothing animation, as well as how these techniques have changed over time and what obstacles they currently face. For this study, a systematic literature review (SLR) method was used, and 47 manuscripts from various databases between the years of 1987 and 2022 were found. According to the study, continuum model and clump mode were used for hair animation while geometric model and physical model were used for clothing animation. As technology has advanced, improvements in seams, wrinkles, blonde hair, and shading have made it easier for animators to animate clothing and hair. The animated item or character also looks better and more realistic as a result of this improvement. The precision, estimation of human shapes and sizes utilizing various devices and various fabric materials are the final obstacles for clothing animation, although it is still challenging to produce realistic hair animation in interactive applications.
Post‐fire, rainfall intensity–peak discharge relations for three mountainous watersheds in the western USA

Wildfire alters the hydrologic response of watersheds, including the peak discharges resulting from subsequent rainfall. Improving predictions of the magnitude of flooding that follows wildfire is needed because of the increase in human population at risk in the wildland–urban interface. Because this wildland–urban interface is typically in mountainous terrain, we investigated rainfall‐runoff relations by measuring the maximum 30 min rainfall intensity and the unit‐area peak discharge (peak discharge divided by the area burned) in three mountainous watersheds (17–26·8 km2) after a wildfire.
A NOVEL HYBRID INTRUSION DETECTION MODEL FOR INTERNET OF THINGS USING MACHINE LEARNING

The identification of attacks in the infrastructure of the Internet of Things (IoT) is becoming a progressive problem in the field of IoTs. The proliferation of IoT infrastructures across all domains has coincided with an increase in the number of attacks and threats. To address this issue, a new hybrid intrusions detection system (IDS) model is developed in this paper for attacks detection and classification. The proposed model is a combination of a metaheuristic algorithm and a machine learning technique. The metaheuristic algorithm called Binary Enhanced-Whale Optimization Algorithm (BEWOA) is used for the selection of features and the machine learning algorithm called Random Nearest Neighbor (r-NN) is utilized for the classification. This BEWOA-rNN model is evaluated using the NSL-KDD and UNSW-NB-15 datasets. The workflow of the model includes the preprocessing, dataset splitting, feature selection and classification. Using the normalization technique, the preprocessing process is performed for data standardization and data cleaning. After preprocessing, the features from the datasets are selected using the BEWOA algorithm for improving the classification performance of the model. Based on the selected features the classification is performed and the performance is measured in terms of detection rate, accuracy, specificity, f-measure, and precision. The performance evaluation is measured individually for both the datasets using the BEWOA-rNN model. The model obtained 99.22% accuracy, 98.82% detection rate, 99.63% specificity, 99.64% precision, and 99.23% f-measure using the NSL-KDD dataset. The model obtained 99.06% accuracy, 98.90% detection rate, 99.22% specificity, 99.24% precision, and 99.07% f-measure using the UNSW-NB-15 dataset.
THE APPLICATION OF MACHINE LEARNING APPROACH TO ADDRESS THE GPV BIAS ON POS TRANSACTION

Each transaction always produces junk data or bias data either due to errors or intentions. The junk data volume is always increase day by day, mainly in the using of public and free to use applications. Junk data is a disruption in every decision making which can cause the material or immaterial losses. This kind of problems are also occurring in the Qasir.id application, a POS application developed by PT. Solusi Teknologi Niaga for MSME entrepreneurs in Indonesia. In the company case, the junk data of POS transaction causes a poor quality of GPV (Gross Payment Value) information. The article presents the results of study in the POS transaction junk data handling. The junk data handling is performed by to validate three machine learning techniques and to deploy the best model in the company's Business Intelligence (BI) system. Based on the result of qualitative and quantitative evaluations, it is shown that the proposed approach provide a significant contribution to the company's decision-making process. The evaluation applied to the operational data sample reveals the accuracy score in the handling of junk data is 0.96 in precision, 0.73 in recall value, and the f1 score is 0.831. Whereas the qualitative evaluation based on users feed back of two-month operation indicates that users were greatly assisted in decision-making regarding the GPV.
Broad Ischemic Stroke Cohort Endovascular Mechanical Clot Retrieval in a

BACKGROUND AND PURPOSE: Our aim was to describe an expanded experience with endovascular mechanical embolectomy in a broad group of patients, including those not meeting entry criteria for the MERCI multicenter trials. METHODS: We performed an analysis of all patients with ischemic stroke treated with the Merci Clot Retrieval Device at a single academic center outside of the Mechanical Embolus Removal in Cerebral Ischemia (MERCI) trials. RESULTS: Twenty-four patients were treated with the device. Nine were MERCI trial ineligible: 4 received intravenous (IV) tissue plasminogen activator (tPA), 1 received IV tPA and was younger than 18 years of age, and 4 had time-to-treatment of longer than 8 hours. Mean age was 64 years (range, 14–89 years; 42% women). Median National Institutes of Health Stroke Scale (NIHSS) score was 21 (range, 11–30). Median symptoms-to-procedure-start time was 303 minutes (range, 85–2385 minutes). Recanalization (Thrombolysis in Myocardial Infarction, 2–3) was achieved in 15/24 (63%). In device-only patients, recanalization occurred in 10/16. In patients who failed IV tPA undergoing rescue embolectomy, recanalization was achieved in 4/5. Three patients unresponsive to device therapy received rescue intra-arterial tPA/abciximab; recanalization was achieved in 2/3. Recanalization was achieved in 3/4 patients in whom treatment was started longer than 8 hours after symptom onset. Asymptomatic hemorrhage occurred in 38%; symptomatic hemorrhage, in 8%. Three device fractures occurred; none worsened clinical outcome. In-hospital mortality was 17%; 90-day mortality, 29%. Good 90-day functional outcome (modified Rankin Scale, (cid:1) 2) was achieved by 25% (6/24). CONCLUSIONS: Endovascular mechanical embolectomy is an effective means of achieving revascularization in patients with acute ischemic stroke, including patients with late treatment start and intravenous tPA failure. Device-based therapy achieved recanalization in nearly two thirds of patients and good clinical outcomes in one fourth, with symptomatic hemorrhage in less than one tenth.
A NOVEL APPROACH FOR CONTENT EQUIVALENCE ANALYSIS IN COMPRESSED DOCUMENT IMAGES A SYSTEMATIC STUDY

Rapid growth of digital data with complex content has led to various challenges in processing. Exponential increase in the size of ‘Big Data’ due to videos, audios, images and textual content has created several problems which need to be addressed by the research community. Currently, huge amount of digital data is generated by various sources. The high quality data require more space and consume excessive bandwidth during transmission. To overcome these issues, digital data are stored in compressed form using different compression algorithms stated in literature. In order to analyze these data traditional schemes use decompression techniques which are a time consuming process and increases the computation overhead. To overcome these issues, currently compressed domain image processing techniques have been adopted where complete decompression may not be required. In this work, we adopt document image processing in compressed domain which contains printed text in the document images. Our main aim is to identify the similarity and find the equivalence between two or more compressed document images. In order to achieve this, first of all, we apply JPEG encoding which generates encoded data. This data further processed through the proposed line, word and character segmentation scheme. Further, we apply SIFT (Scale-Invariant Feature Transform) to extract the feature from compressed domain segmented data. Finally, feature matching scheme is applied which uses Brute force feature matcher and k-nearest neighbor. We have tested this approach on publically available PubLayNet, IIIT-AR-13K, and Tobacco-3482 datasets which contains large scale document images. The experimental analysis shows the robustness of proposed approach to identify the similarity between compressed documents images.
Rationale and design of the POLEM trial: avelumab plus fluoropyrimidine-based chemotherapy as adjuvant treatment for stage III mismatch repair deficient or POLE exonuclease domain mutant colon cancer: a phase III randomised study

Background 10%–15% of early-stage colon cancers harbour either deficient mismatch repair (dMMR), microsatellite instability high (MSI-H) or POLE exonuclease domain mutations, and are characterised by high tumour mutational burden and increased lymphocytic infiltrate. Metastatic dMMR colon cancers are highly sensitive to immune checkpoint inhibition, and recent data show POLE-mutant tumours are similarly responsive. We are conducting a phase III randomised trial to determine if the addition of the anti-PD-L1 antibody avelumab following adjuvant chemotherapy improves disease-free survival (DFS) in patients with stage III dMMR/MSI-H or POLE mutant colon cancer and is a cost-effective approach for the UK National Health Service (NHS). Methods We are recruiting patients with completely resected, stage III colon cancer confirmed to have dMMR/MSI-H, locally or POLE exonuclease domain mutation on central testing. Eligible patients are randomised in a 1:1 ratio to standard fluoropyrimidine-based chemotherapy (capecitabine, oxaliplatin for 12 weeks or capecitabine for 24 weeks) or chemotherapy, followed by avelumab (10 mg/kg, 2 weekly for 24 weeks). Stratification is by chemotherapy received and MMR/MSI-H status. The primary endpoint is DFS. Secondary endpoints include overall survival, toxicity, quality of life and health resource use. The 3-year DFS rate in the control arm is expected to be ~75%. Avelumab is expected to improve the 3-year DFS rate by 12% (ie, 87%). Target accrual is 402 patients, which provides 80% power to detect an HR of 0.48 for DFS at a two-sided alpha of 0.05. This national, multicentre phase III trial is sponsored by the Royal Marsden NHS Foundation Trust and it is anticipated that approximately 40 centres in the UK will participate. This study opened to recruitment in August 2018. Trial registration number NCT03827044
COTTON BASIS: REGIONAL AND SEASONAL DIFFERENCES

The monthly cotton basis is calculated and examined for seven regions for the period of August 2000 – July 2006. As the major market shifted from domestic mills to the world market, the cash price and basis is more affected by export demand. The results indicate the same seasonal basis pattern for all seven regions and some differences between the cotton bases across the regions. Compared with the seasonal and regional differences identified by earlier research covering the period, August 1988 – July 1998, both seasonal and regional basis patterns have changed as the export market for U.S. cotton has become dominate.
SVM TECHNOLOGY CLASSIFICATION FOR KNOWLEDGE MANAGEMENT AND CRS

The method of support vector machines, also called large margin machines (SVM), is a derivative of computer learning, a solid theoretical basis different from its ancestor of neural networks. The SVM method is known as a very interesting advance in its principle, its implementation and its extension to multiclass problems. Over the past decade, research has focused on adapting the method to particular problems such as novelty detection and clustering as well as improving its performance (optimization and parallelization) and its application. in different domains. such as imaging, sound, banks, biology, management and knowledge management. As a result, several applications have experienced real success with the use of SVMs. In this article, we have highlighted in depth the correlation of SVMs especially on knowledge management as well as the companies implemented Corporate Social Responsibility (CRS). The purpose of the classification is to apply the variables according to the proposed method based on SVM improves the classification accuracy of the classifiers compared to the simple term-based data classification method in order to simplify and facilitate the decision making .
Eﬀectiveness of Corticosteroid Therapy in COVID-19 Induced Olfactory Dysfunction: a systematic review and meta-analysis of Randomized Controlled Trials.

Introduction: Covid-19 has been strongly associated with the loss of smell also known as anosmia. It is one of the ﬁrst symptoms experienced by patients who encounter covid-19. The condition has been reported to have a detrimental impact on psychological and emotional well-being, physical health, and relationships thus compromising the quality of life. Various reasons have been proposed to justify the cause of anosmia, with apoptosis of olfactory cells and inﬂammation of the mucosa amongst a few. The following study aims to shed light on the eﬃcacy of intranasal corticosteroids in the treatment of these conditions. Methods: From their inception to 20 May 2023, a complete search of electronic databases including PubMed, MEDLINE, Scopus, and Cochrane was conducted to look for studies consisting of data regarding short-term or long-term anosmia in COVID-19 patient populations who were treated using intra-nasal corticosteroids.After a rigorous process of study selection, 7 studies were shortlisted and utilized to conduct a meta-analysis. Results: The improvement in the olfactory score at 2 and 4 weeks was statistically higher in the treatment group (Standard Mean Diﬀerence [SMD]= 1.01 [0.07, 1.94], p-value= 0.03) as compared to the control group (SMD = 1.25 [0.78, 1.73], P = 0.00004). However, the results in the 4 weeks group were not signiﬁcant. Similarly, the eﬀect of corticosteroids in patients in whom anosmia lasted for more than 4 weeks also turned out to be insigniﬁcant (SMD = 0.51 [-0.12, 1.14], a p-value of 0.11, I2 =78%). According to the subgroup analysis, there were no signiﬁcant diﬀerences in the eﬃcacy of corticosteroids in short and long-term anosmia (RR = 1.26 [0.83, 1.92] and 1.57 [0.04, 70.02], p-value = 0.13). Conclusion: As per the ﬁndings of the meta-analysis, corticosteroids do play a role in the improvement of the olfactory dysfunction caused by COVID-19. However, there was no diﬀerence in their eﬃcacy for the treatment of short-term versus long-term anosmia patients.
An ALMA survey of submillimetre galaxies in the Extended Chandra Deep Field‐South: detection of [C ii] at z = 4.4

We present Atacama Large Millimeter Array (ALMA) 870-mu m (345-GHz) observations of two submillimetre galaxies (SMGs) drawn from an ALMA study of the 126 submillimetre sources from the LABOCA Extended Chandra Deep Field-South Survey (LESS). The ALMA data identify the counterparts to these previously unidentified submillimetre sources and serendipitously detect bright emission lines in their spectra which we show are most likely to be [CII] 157.74 mu m emission yielding redshifts of z = 4.42 and 4.44. This blind detection rate within the 7.5-GHz bandpass of ALMA is consistent with the previously derived photometric redshift distribution of SMGs and suggests a modest, but not dominant (less than or similar to 25 per cent), tail of 870-mu m selected SMGs at z greater than or similar to 4. We find that the ratio of L-[CII]/L-FIR in these SMGs is much higher than seen for similarly far-infrared-luminous galaxies at z similar to 0, which is attributed to the more extended gas reservoirs in these high-redshift ultraluminous infrared galaxies (ULIRGs). Indeed, in one system we show that the [C II] emission shows hints of extended emission on greater than or similar to 3 kpc scales. Finally, we use the volume probed by our ALMA survey to show that the bright end of the [CII] luminosity function evolves strongly between z = 0 and similar to 4.4, reflecting the increased interstellar medium cooling in galaxies as a result of their higher star formation rates. These observations demonstrate that even with short integrations, ALMA is able to detect the dominant fine-structure cooling lines from high-redshift ULIRGs, measure their energetics and spatially resolved properties and trace their evolution with redshift.
AN EFFICIENT APPROACH FOR DETECTING TINY OBJECTS IN MASSIVE BACKGROUND BASED ON SPLIT-ATTENTION NETWORK

With the fast development of deep learning, object detection based on vision has achieved great progress in recent years. Though considerable progress has been made, there still exist challenges for objects with tiny size. One of the main reason is that feature representatives of tiny objects become sparse and weak due to their tiny size in an enormous background. This makes tiny objects difficult to be detected with state-of-the-art object detectors. This paper proposes an efficient method for detecting tiny objects in massive background. First, ResNest architecture is adopted as the backbone to extract features from input images. ResNest captures cross-channel feature correlations, while preserving independent representation in the meta structure. As a result, ResNest architecture achieves better speed-accuracy trade-offs than state-of-the-art deep CNN-based models without incurring excessive computational costs. Next, feature maps generated by the backbone are used to build feature pyramid following FPN network. Finally, this paper proposes an attention network in the detection part to solve problems of occlusion, noise, and blurring and effectively enhance the representations of tiny objects in complex backgrounds based on multi-dimensional attention network and inception module. Experiment results on the AI-TOD dataset show that the proposed method is very efficient in terms of the detection ability of very tiny and tiny objects.