Fixed points of generalized cyclic contractions without continuity and application to fractal generation

In this paper, we define a generalized cyclic contraction and prove a unique fixed point theorem for these contractions. An illustrative example is given, which shows that these contraction mappings may admit the discontinuities and also that an existing result in the literature is effectively generalized by the theorem. We apply the fixed point result for generation of fractal sets through construction of an iterated function system and the corresponding Hutchinsion–Barnsley operator. The above construction is illustrated by an example. The study here is in the context of metric spaces

SEAMLESS EXPENSE SHARING FOR REMOTE PURCHASES”

The present disclosure discloses payment processing that seamless expense sharing for remote purchases. When the user does not have plurality of physical card then the user may provide plurality of information associated with the card of the user. Based on the provided information QR code may be generated which may be also shared to family and friends via email or MMS. The generated QR code is provided to POS associated with the merchant. The merchant may send details to the third party for authorization based on which the details is sent to issuer for final authorization. Once the issuer approves, the link may be sent to user through which he/she may provide the approval for the payment. When the amount gets credited to the merchant, then the successful message may be sent to the user

Automatic Detection of Volcanic Unrest Using Blind Source Separation with a Minimum Spanning Tree Based Stability Analysis

Repeated synthetic aperture radar (SAR) acquisitions can be utilized to produce measurements of ground deformations and associated geohazards, such as it can be used to detect signs of volcanic unrest. Existing time series algorithms like permanent scatterer analysis and small baseline subset are computationally demanding and cannot be applied in near real time to detect subtle, transient, and precursory deformations. To overcome this problem, we have adapted a minimum spanning tree based spatial independent component analysis method to automatically detect sources related to volcanic unrest from a time series of differential interferograms. For a synthetic dataset, we first utilize the algorithm's capability to isolate signals of geophysical interest from atmospheric artifacts, topography, and other noise signals, before monitoring the evolution of these signals through time in order to detect the onset of a period of volcanic unrest, in near real time. In this article, we first demonstrate our approach on synthetic datasets having different signal strengths and temporal complexities. Second, we demonstrate our approach on a couple of real datasets, one acquired in 2017-2019 over the Colima volcano, Mexico, showing the occurrence of previously unrecognized short-term deformation events and the other over Mt. Thorbjorn in Iceland acquired over 2020. This shows the strength of the deep learning application to differential interferometric SAR measurements, and highlights that deformation events occurring without eruptions, which may have previously been undetected

Deep learning, remote sensing and visual analytics to support automatic flood detection

Floods can have devastating consequences on people, infrastructure, and the ecosystem. Satellite imagery has proven to be an efficient instrument in supporting disaster management authorities during flood events. In contrast to optical remote sensing technology, Synthetic Aperture Radar (SAR) can penetrate clouds, and authorities can use SAR images even during cloudy circumstances. A challenge with SAR is the accurate classification and segmentation of flooded areas from SAR imagery. Recent advancements in deep learning algorithms have demonstrated the potential of deep learning for image segmentation demonstrated. Our research adopted deep learning algorithms to classify and segment flooded areas in SAR imagery. We used UNet and Feature Pyramid Network (FPN), both based on EfficientNet-B7 implementation, to detect flooded areas in SAR imaginary of Nebraska, North Alabama, Bangladesh, Red River North, and Florence. We evaluated both deep learning methods' predictive accuracy and will present the evaluation results at the conference. In the next step of our research, we develop an XAI toolbox to support the interpretation of detected flooded areas and algorithmic decisions of the deep learning methods through interactive visualizations

Opto-Acoustic Slopping Prediction System in Basic Oxygen Furnace Converters

Today, everyday objects are becoming more and more intelligent and some-times even have self-learning capabilities. These self-learning capacities in particular also act as catalysts for new developments in the steel industry.Technical developments that enhance the sustainability and productivity of steel production are very much in demand in the long-term. The methods of Industry 4.0 can support the steel production process in a way that enables steel to be produced in a more cost-effective and environmentally friendly manner. This thesis describes the development of an opto-acoustic system for the early detection of slag slopping in the BOF (Basic Oxygen Furnace) converter process. The prototype has been installed in Salzgitter Stahlwerks, a German steel plant for initial testing. It consists of an image monitoring camera at the converter mouth, a sound measurement system and an oscillation measurement device installed at the blowing lance. The camera signals are processed by a special image processing software. These signals are used to rate the amount of spilled slag and for a better interpretation of both the sound data and the oscillation data. A certain aspect of the opto-acoustic system for slopping detection is that all signals, i.e. optic, acoustic and vibratory, are affected by process-related parameters which are not always relevant for the slopping event. These uncertainties affect the prediction of the slopping phenomena and ultimately the reliability of the entire slopping system. Machine Learning algorithms have been been applied to predict the Slopping phenomenon based on the data from the sensors as well as the other process parameters.Idag blir vardagliga föremål mer och mer intelligenta och ibland har de självlärande möjligheter. Dessa självlärande förmågor fungerar också som katalysatorer för den nya utvecklingen inom stålindustrin. Teknisk utveckling som stärker hållbarheten och produktiviteten i stålproduktionen är mycket efterfrågad på lång sikt. Metoderna för Industry 4.0 kan stödja stålproduktionsprocessen på ett sätt som gör att stål kan produceras på ett mer kostnadseffektivt och miljövänligt sätt. Denna avhandling beskriver utvecklingen av ett opto-akustiskt system för tidig detektering av slaggsslipning i konverteringsprocessen BOF (Basic Oxygen Furnace). Prototypen har installerats i Salzgitter Stahlwerks, en tysk stålverk för första provning. Den består av en bildövervakningskamera på omvandlarens mun, ett ljudmätningssystem och en oscillationsmätningsenhet som installeras vid blåsans. Kamerans signaler behandlas av en speciell bildbehandlingsprogram. Dessa signaler används för att bestämma mängden spilld slagg och för bättre tolkning av både ljuddata och oscillationsdata. En viss aspekt av det optoakustiska systemet för släckningsdetektering är att alla signaler, dvs optiska, akustiska och vibrerande, påverkas av processrelaterade parametrar som inte alltid är relevanta för slöjningsevenemanget. Dessa osäkerheter påverkar förutsägelsen av slopfenomenerna och i slutändan tillförlitligheten för hela slöjningssystemet. Maskininlärningsalgoritmer har tillämpats för att förutsäga Slopping-fenomenet baserat på data från sensorerna liksom de andra processparametrarna

A clinicoepidemiological study of 50 cases of cutaneous tuberculosis in a tertiary care teaching hospital in Pokhara, Nepal

<b>Background:</b> Cutaneous tuberculosis (TB) is essentially an invasion of the skin by <i>Mycobacterium tuberculosis</i>, the same bacteria that causes pulmonary tuberculosis. <b>Aim:</b> This study was conducted to study the common types of cutaneous TB and to find the management pattern in a tertiary teaching hospital in Pokhara, Nepal. <b>Materials and Methods:</b> All the cases of cutaneous TB were biopsied and furthermore investigated by performing Mantoux test, sputum examination, fine needle aspiration cytology, chest X-ray and ELISA. <b>Results:</b> In this study, we found that tuberculosis verrucous cutis (48&#x0025;) had a higher incidence than other types of cutaneous TB. More males were affected than were females (1.2:1). Commonly affected sites were the limb and the buttock (48&#x0025;). The most commonly affected age group was 16-25 years (40&#x0025;). All cases (except two) were more than 15 mm in size in the Mantoux test. The histopathological picture was typical in all except three cases. All patients were treated with antitubercular treatment as per the national guidelines. <b>Conclusion:</b> The most common type of cutaneous TB was tuberculosis verrucous cutis and the most commonly affected sites were the limb and the buttock. As cutaneous TB sometimes reflects the presence of pulmonary tuberculosis, its incidence should not be ignored

Automatic flood detection from Sentinel-1 data using a nested UNet model and a NASA benchmark dataset

During flood events near real-time, synthetic aperture radar (SAR) satellite imagery has proven to be an efficient management tool for disaster management authorities. However, one of the challenges is accurate classification and segmentation of flooded water. A common method of SAR-based flood mapping is binary segmentation by thresholding, but this method is limited due to the effects of backscatter, geographical area, and surface characterstics. Recent advancements in deep learning algorithms for image segmentation have demonstrated excellent potential for improving flood detection. In this paper, we present a deep learning approach with a nested UNet architecture based on a backbone of EfficientNet-B7 by leveraging a publicly available Sentinel‑1 dataset provided jointly by NASA and the IEEE GRSS Committee. The performance of the nested UNet model was compared with several other UNet-based convolutional neural network architectures. The models were trained on flood events from Nebraska and North Alabama in the USA, Bangladesh, and Florence, Italy. Finally, the generalization capacity of the trained nested UNet model was compared to the other architectures by testing on Sentinel‑1 data from flood events of varied geographical regions such as Spain, India, and Vietnam. The impact of using different polarization band combinations of input data on the segmentation capabilities of the nested UNet and other models is also evaluated using Shapley scores. The results of these experiments show that the UNet model architectures perform comparably to the UNet++ with EfficientNet-B7 backbone for both the NASA dataset as well as the other test cases. Therefore, it can be inferred that these models can be trained on certain flood events provided in the dataset and used for flood detection in other geographical areas, thus proving the transferability of these models. However, the effect of polarization still varies across different test cases from around the world in terms of performance; the model trained with the combinations of individual bands, VV and VH, and polarization ratios gives the best results