Regularity of Weak Solutions to Elliptic Problem with Irregular Data

This paper is concerned with the study of the nonlinear elliptic equations in a bounded subset Ω ⊂ RN Au = f, where A is an operator of Leray-Lions type acted from the space W1,p(·)0(Ω) into its dual. when the second term f belongs to Lm(·), with m(·) > 1 being small. we prove existence and regularity of weak solutions for this class of problems p(x)-growth conditions. The functional framework involves Sobolev spaces with variable exponents as well as Lebesgue spaces with variable exponents.

Intrusion Detection System Using Deep Learning

With recent advancements in computer network technologies, there has been a growth in the number of security issues in networks. Intrusions like denial of service, exploitation from inside a network, etc. are the most common threat to a network's credibility. The need of the hour is to detect attacks in real time, reduce the impact of the threat, and secure the network. Recent developments in deep learning approaches can be of great assistance in dealing with network interference problems. Deep learning approaches can automatically differentiate between usual and irregular data with high precision and can alert network managers to problems. Deep neural network (DNN) architectures are used with differing numbers of hidden units to solve the limitations of traditional ML models. They also seek to increase predictive accuracy, reduce the rate of false positives, and allow for dynamic changes to the model as new research data is encountered. A thorough comparison of the proposed solution with current models is conducted using different evaluation metrics.

INVESTIGATION OF POINTNET FOR SEMANTIC SEGMENTATION OF LARGE-SCALE OUTDOOR POINT CLOUDS

Semantic segmentation of point clouds is indispensable for 3D scene understanding. Point clouds have credibility for capturing geometry of objects including shape, size, and orientation. Deep learning (DL) has been recognized as the most successful approach for image semantic segmentation. Applied to point clouds, performance of the many DL algorithms degrades, because point clouds are often sparse and have irregular data format. As a result, point clouds are regularly first transformed into voxel grids or image collections. PointNet was the first promising algorithm that feeds point clouds directly into the DL architecture. Although PointNet achieved remarkable performance on indoor point clouds, its performance has not been extensively studied in large-scale outdoor point clouds. So far, we know, no study on large-scale aerial point clouds investigates the sensitivity of the hyper-parameters used in the PointNet. This paper evaluates PointNet’s performance for semantic segmentation through three large-scale Airborne Laser Scanning (ALS) point clouds of urban environments. Reported results show that PointNet has potential in large-scale outdoor scene semantic segmentation. A remarkable limitation of PointNet is that it does not consider local structure induced by the metric space made by its local neighbors. Experiments exhibit PointNet is expressively sensitive to the hyper-parameters like batch-size, block partition and the number of points in a block. For an ALS dataset, we get significant difference between overall accuracies of 67.5% and 72.8%, for the block sizes of 5m × 5m and 10m × 10m, respectively. Results also discover that the performance of PointNet depends on the selection of input vectors.

Filling the gaps in Leishmania naiffi and Leishmania guyanensis genome plasticity

Insufficient and irregular data reports on Leishmaniasis, issuing from the developing world, have left much to be desired in terms of understanding the molecular signatures producing distinct infectious phenotypes of the disease. Herein, we report on the complete genome sequencing of Leishmania naiffi and Leishmania guyanensis, sampled from patients in regions of Colombia and Venezuela. In this study, the isolates of cutaneous lesions from both species presented limited structural variation at the chromosomal level, low gene copy number variation, and high genetic heterogeneity. We compared these sequences to the reference genomes hitherto related from Brazil and French Guyana. Although of the same species, we note a consequential genomic disparity between the Venezuelan and French Guyanese isolates of L. guyanensis. Although less significant on the global schema of cutaneous and mucosal disease, such genomic studies of L. naiffi and L. guyanensis substantiate the gaps in understanding of the molecular architecture and multivariate clinical pictures of Leishmaniasis, on an international scale.

Web-based Information System Design of Karang Taruna with Design Thinking Method Approach

Abstrak Karang Taruna Haur Galur berperan aktif dalam kesejahteraan sosial masyarakat di wilayah Kelurahan Sukagalih. Mulai dari menggerakkan Pemuda Unit hingga pemberdayaan UMKM Kelurahan Sukagalih melalui Program Kerja “Koperasi Serba Usaha”. Karang Taruna Haur Galur memiliki 14 Buku wajib, diantaranya 12 buku isian dan 2 buku pedoman. Banyak data yang dimiliki oleh Karang Taruna Haur Galur belum terstruktur dan tidak memiliki basis data yang terintegrasi. Hal ini mengakibatkan ketidakteraturan pada penyimpanan data, juga rawan terjadi redundansi data. Generasi yang belum tergabung dengan Karang Taruna merasa bahwa informasi tentang Karang Taruna Haur Galur belum merata. Tujuan dari penelitian ini adalah untuk memperbarui sistem perekaman data, media penyambung informasi antara Karang Taruna Haur Galur dan Masyarakat Kelurahan Sukagalih, serta menarik minat pemuda untuk bergabung dengan Karang Taruna Haur Galur dengan merancang sistem informasi berbasis web. Pada perancangan ini peneliti menggunakan metode design thinking. Penelitian ini menggunakan observasi dan wawancara langsung kepada pengguna, yang membantu memperoleh informasi mengenai kebutuhan pengguna akan komponen pembangun yang dibutuhkan. Pada akhirnya Karang Taruna Haur Galur dapat mengorganisir data yang dimiliki dan di sisi lain mendapatkan wawasan serta ketertarikan yang lebih luas. Abstract Karang Taruna Haur Galur plays an active role in the social welfare of Sukagalih Urban Village. Starting from mobilizing the Youth Unit also empowering UMKMs in Sukagalih Urban Village through the Work Program "Koperasi Serba Usaha". Karang Taruna Haur Galur owns 14 compulsory books, including 12 filling books and 2 manuals. The data organized by Karang Taruna Haur Galur is unstructured and yet to have an integrated database. This resulted in an irregular data storage and is also prone to data redundancies. Generations who have not joined Karang Taruna stated that information about Karang Taruna Haur Galur has not been well distributed. The purpose of this research is to improve the data recording system, media of information that connects Karang Taruna Haur Galur and Sukagalih Urban Village Society, also attracting the youth to join Karang Taruna Haur Galur by designing a web-based information system. This web-based information system uses the design thinking method. The research is conducted using direct observations and interviews to the users, as it will help to acquire information on the user's needs for the required building components. Eventually, Karang Taruna Haur Galur is able organize their data as well as gaining broader insights and engagement.

Time Series Analysis of Land Cover Change in Dry Mountains: Insights from the Tajik Pamirs

Greening and browning trends in vegetation have been observed in many regions of the world in recent decades. However, few studies focused on dry mountains. Here, we analyze trends of land cover change in the Western Pamirs, Tajikistan. We aim to gain a deeper understanding of these changes and thus improve remote sensing studies in dry mountainous areas. The study area is characterized by a complex set of attributes, making it a prime example for this purpose. We used generalized additive mixed models for the trend estimation of a 32-year Landsat time series (1988–2020) of the modified soil adjusted vegetation index, vegetation data, and environmental and socio-demographic data. With this approach, we were able to cope with the typical challenges that occur in the remote sensing analysis of dry and mountainous areas, including background noise and irregular data. We found that greening and browning trends coexist and that they vary according to the land cover class, topography, and geographical distribution. Greening was detected predominantly in agricultural and forestry areas, indicating direct anthropogenic drivers of change. At other sites, greening corresponds well with increasing temperature. Browning was frequently linked to disastrous events, which are promoted by increasing temperatures.

Towards Robust Calculation of Interannual CO2 Growth Signal from TCCON (Total Carbon Column Observing Network)

The CO2 growth rate is one of the key geophysical quantities reflecting the dynamics of climate change as atmospheric CO2 growth is the primary driver of global warming. As recent studies have shown that TCCON (Total Carbon Column Observing Network) measurement footprints embrace quasi-global coverage, we examined the sensitivity of TCCON to the global CO2 growth. To this end, we used the aggregated TCCON observations (2006-2019) to retrieve Annual Growth Rate of CO2 (AGR) at global scales. The global AGR estimates from TCCON (AGRTCCON) are robust and independent, from (a) the station-wise seasonality, from (b) the differences in time series across the TCCON stations, and from (c) the type of TCCON stations used in the calculation (“background” or “contaminated” by neighboring CO2 sources). The AGRTCCON potential error, due to the irregular data sampling is relatively low (2.4–17.9%). In 2006–2019, global AGRTCCON ranged from the minimum of 1.59 ± 2.27 ppm (2009) to the maximum of 3.27 ± 0.82 ppm (2016), whereas the uncertainties express sub-annual variability and the data gap effects. The global AGRTCCON magnitude is similar to the reference AGR from satellite data (AGRSAT = 1.57–2.94 ppm) and the surface-based estimates of Global Carbon Budget (AGRGCB = 1.57–2.85). The highest global CO2 growth rate (2015/2016), caused by the record El Niño, was nearly perfectly reproduced by the TCCON (AGRTCCON = 3.27 ± 0.82 ppm vs. AGRSAT = 3.23 ± 0.50 ppm). The overall agreement between global AGRTCCON with the AGR references was yet weakened (r = 0.37 for TCCON vs. SAT; r = 0.50 for TCCON vs. GCB) due to two years (2008, 2015). We identified the drivers of this disagreement; in 2008, when only few stations were available worldwide, the AGRTCCON uncertainties were excessively high (AGRTCCON = 2.64 ppm with 3.92 ppm or 148% uncertainty). Moreover, in 2008 and 2015, the ENSO-driven bias between global AGRTCCON and the AGR references were detected. TCCON-to-reference agreement is dramatically increased if the years with ENSO-related biases (2008, 2015) are forfeited (r = 0.67 for TCCON vs. SAT, r = 0.82 for TCCON vs. GCB). To conclude, this is the first study that showed promising ability of aggregated TCCON signal to capture global CO2 growth. As the TCCON coverage is expanding, and new versions of TCCON data are being published, multiple data sampling strategies, dynamically changing TCCON global measurement footprint, and the irregular sensitivity of AGRTCCON to strong ENSO events; all should be analyzed to transform the current efforts into a first operational algorithm for retrieving global CO2 growth from TCCON data.

Modelling bluetongue risk in Kazakhstan

Background Bluetongue is a serious disease of ruminants caused by the bluetongue virus (BTV). BTV is transmitted by biting midges (Culicoides spp.). Serological evidence from livestock and the presence of at least one competent vector species of Culicoides suggests that transmission of BTV is possible and may have occurred in Kazakhstan. Methods We estimated the risk of transmission using a mathematical model of the reproduction number R0 for bluetongue. This model depends on livestock density and climatic factors which affect vector density. Data on climate and livestock numbers from the 2466 local communities were used. This, together with previously published model parameters, was used to estimate R0 for each month of the year. We plotted the results on isopleth maps of Kazakhstan using interpolation to smooth the irregular data. We also mapped the estimated proportion of the population requiring vaccination to prevent outbreaks of bluetongue. Results The results suggest that transmission of bluetongue in Kazakhstan is not possible in the winter from October to March. Assuming there are vector-competent species of Culicoides endemic in Kazakhstan, then low levels of risk first appear in the south of Kazakhstan in April before spreading north and intensifying, reaching maximum levels in northern Kazakhstan in July. The risk declined in September and had disappeared by October. Conclusion These results should aid in surveillance efforts for the detection and control of bluetongue in Kazakhstan by indicating where and when outbreaks of bluetongue are most likely to occur. The results also indicate where vaccination efforts should be focussed to prevent outbreaks of disease. Graphical abstract

Quantifying the extent of visit irregularity in longitudinal data

The timings of visits in observational longitudinal data may depend on the study outcome, and this can result in bias if ignored. Assessing the extent of visit irregularity is important because it can help determine whether visits can be treated as repeated measures or as irregular data. We propose plotting the mean proportions of individuals with 0 visits per bin against the mean proportions of individuals with >1 visit per bin as bin width is varied and using the area under the curve (AUC) to assess the extent of irregularity. The AUC is a single score which can be used to quantify the extent of irregularity and assess how closely visits resemble repeated measures. Simulation results confirm that the AUC increases with increasing irregularity while being invariant to sample size and the number of scheduled measurement occasions. A demonstration of the AUC was performed on the TARGet Kids! study which enrolls healthy children aged 0–5 years with the aim of investigating the relationship between early life exposures and later health problems. The quality of statistical analyses can be improved by using the AUC as a guide to select the appropriate analytic outcome approach and minimize the potential for biased results.