Multiplexing regulated traffic streams: design and performance

The main network solutions for supporting QoS rely on traf- fic policing (conditioning, shaping). In particular, for IP networks the IETF has developed Intserv (individual flows regulated) and Diffserv (only ag- gregates regulated). The regulator proposed could be based on the (dual) leaky-bucket mechanism. This explains the interest in network element per- formance (loss, delay) for leaky-bucket regulated traffic. This paper describes a novel approach to the above problem. Explicitly using the correlation structure of the sources’ traffic, we derive approxi- mations for both small and large buffers. Importantly, for small (large) buffers the short-term (long-term) correlations are dominant. The large buffer result decomposes the traffic stream in a stream of constant rate and a periodic impulse stream, allowing direct application of the Brownian bridge approximation. Combining the small and large buffer results by a concave majorization, we propose a simple, fast and accurate technique to statistically multiplex homogeneous regulated sources. To address heterogeneous inputs, we present similarly efficient tech- niques to evaluate the performance of multiple classes of traffic, each with distinct characteristics and QoS requirements. These techniques, applica- ble under more general conditions, are based on optimal resource (band- width and buffer) partitioning. They can also be directly applied to set GPS (Generalized Processor Sharing) weights and buffer thresholds in a shared resource system

LineWalker: Line Search for Black Box Derivative-Free Optimization and Surrogate Model Construction

This paper describes a simple, but effective sampling method for optimizing and learning a discrete approximation (or surrogate) of a multi-dimensional function along a one-dimensional line segment of interest. The method does not rely on derivative information and the function to be learned can be a computationally-expensive ``black box'' function that must be queried via simulation or other means. It is assumed that the underlying function is noise-free and smooth, although the algorithm can still be effective when the underlying function is piecewise smooth. The method constructs a smooth surrogate on a set of equally-spaced grid points by evaluating the true function at a sparse set of judiciously chosen grid points. At each iteration, the surrogate's non-tabu local minima and maxima are identified as candidates for sampling. Tabu search constructs are also used to promote diversification. If no non-tabu extrema are identified, a simple exploration step is taken by sampling the midpoint of the largest unexplored interval. The algorithm continues until a user-defined function evaluation limit is reached. Numerous examples are shown to illustrate the algorithm's efficacy and superiority relative to state-of-the-art methods, including Bayesian optimization and NOMAD, on primarily nonconvex test functions.Comment: 58 pages, 7 main figures, 29 total figure

IN-VITRO RELEASE STUDY AND ANTIMICROBIAL PROPERTY EVALUATION OF OFLOXACIN LOADED POLY (2-HYDROXYETHYL METHACRYLATE) / POLY (CAPROLACTONE) / POLY (ETHYLENE GLYCOL) HYDROGEL SYSTEM FOR BURN WOUND MANAGEMENT

Monomer 2-hydroxy ethyl methacrylate containing small amounts of poly(caprolactone) and poly(ethylene glycol) incorporated with an antibiotic ofloxacin was polymerized by photopolymerization technique using 2,4,6 trimethyl benzoyl diphenyl phosphine oxide (TPO) as photoinitiator. Encapsulation efficiency and in vitro drug release was studied using UV-visible spectroscopy.  Swelling analysis was resorted to compare fluid uptake ability of hydrogel containing the drug with bare polymer. Zone of inhibition assay showed hydrogel containing 1% Ofloxacin to possess strong antimicrobial property Hemolysis assay demonstrated the hydrogel system to be non-hemolytic. Non-cytotoxic character of the hydrogel was confirmed using fibroblast cells. Cell adhesion studies showed non-attachment of fibroblasts to the polymer and improved cell proliferation simultaneously. Key words: Ofloxacin, Encapsulation efficiency, Hydrogel, AntimicrobialÂ

EFEKTIVITAS PENGARUH TERAPI OIL PULLING MENGGUNAKAN MINYAK BUNGA MATAHARI TERHADAP JUMLAH BAKTERI DALAM SALIVA: EFFECTIVITY OF OIL PULLING THERAPY USING SUNFLOWER OIL ON BACTERIA COUNT IN SALIVA

Oil pulling merupakan salah satu cara untuk menyingkirkan bakteri yang tersembunyi di rongga mulut. Terapi oil pulling adalah modifikasi berkumur dengan minyak yang berasal dari pengobatan Ayurveda ribuan tahun yang lalu. Terapi ini dilakukan dengan berkumur sejumlah minyak selama 5-8 menit dan dengan demikian minyak yang dikumur dapat menarik keluar bakteri-bakteri yang tersembunyi di celah gigi dan poket gingival. Tujuan penelitian ini adalah untuk mengetahui efektivitas terapi oil pulling dengan menggunakan minyak bunga matahari terhadap jumlah bakteri dalam saliva pada mahasiswa. Jenis penelitian yang digunakan adalah eksperimental klinis dengan rancangan pre-test dan post-test. Penelitian ini dilakukan pada 20 orang mahasiswa di Fakultas Kedokteran Gigi. Sampel secara random dibagi menjadi 2 kelompok yaitu kelompok perlakuan melakukan terapi oil pulling dan kontrol dengan berkumur akuades. Sebelum memulai penelitian sampel saliva dari kedua kelompok diperiksa kemudian kelompok perlakuan diberi sesendok makan minyak bunga matahari sedangkan kelompok kontrol diberi akuades. Kedua kelompok berkumur selama 5 menit dan kemudian sampel air kumur diambil. Sampel saliva bercampur air kumur kemudian dibawa ke laboratorium untuk diinkubasi dan dihitung jumlah bakteri. Data yang diperoleh dianalisis dengan uji Mann-Whitney dan uji Wilcoxon. Hasil penelitian menunjukkan ada perbedaan jumlah bakteri yang signifikan dalam saliva antara sebelum dan sesudah melakukan terapi oil pulling (p< 0,005), sedangkan pada kelompok kontrol menunjukkan tidak ada perbedaan jumlah bakteri yang signifikan dalam saliva sebelum dan sesudah berkumur akuades (p= 0,071). Sebagai kesimpulan, terapi oil pulling efektif dalam menarik bakteri dalam rongga mulut dan sekaligus menjaga kesehatan rongga mulut

Active compound, antioxidant, antiproliferative and effect on STZ induced zebrafish of various crude extracts from Boletus qriseipurpureus

Boletus qriseipurpureus (gelam mushroom) is a mushroom used by locals in Tok Bali, Kelantan, Malaysia to treat diabetes, cervical cancer and breast cancer. The active compounds in B. qriseipurpureus remain unidentified. Therefore, in this study, we investigated the potential medicinal properties of B. qriseipurpureus extracts (hot water, cold water and methanol extracts) by conducting preliminary phytochemical screening, in vitro antioxidant, in vitro antiproliferative and in vivo antidiabetic test. Biochemical assays were performed to detect the presence of alkaloids, anthraquinones, flavonoids, reducing sugars, saponins, steroids and tannins in B. qriseipurpureus extracts. 1,1-diphenyl-2-picrylhydrazyl (DPPH) was used to evaluate the antioxidant capacity of B. qriseipurpureus extracts, while MTT cell proliferation assay (3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide) was used to investigate the extracts antiproliferative activity. The effects of B. qriseipurpureus extracts on streptozotocin (STZ) induced zebrafish were examined at the concentration of 45 mg/mL, 90 mg/mL and 135 mg/mL. The effect of extracts were measured by the regenerative growth rate of the amputated caudal fin for fourteen days post transection. Screening of the mushroom extracts for active compounds revealed the presence of alkaloids, flavonoids, saponins and tannins in all test extracts. Reducing sugars and anthraquinones are only detected in hot water and cold water of B. qriseipurpureus extracts. The half maximal inhibitory concentration (IC50) of DPPH by hot water, cold water and methanol extract of B. qriseipurpureus are 1.79 mg/mL, 1.97 mg/mL and 3.98 mg/mL respectively. The MTT assay indicated that all extracts exhibited significant antiproliferative effects on MCF-7 cell line after 72 hours with IC50 of 7.7 mg/mL for hot water extract, 8.2 mg/ mL for cold water extract and 16.1 mg/mL for methanol extract but do not display any significant cytotoxic effect. The STZ induced zebrafish treated with 135 mg/mL of hot water B. qriseipurpureus extract for 14 days showed the highest regeneration growth rate of caudal fin (5.04 ± 0.43%) compared to fish treated with metformin (5.72 ± 0.64 %). In this study, we showed the potential of hot water B. qriseipurpureus extracts as a potent therapeutic agent for diabetes and as an alternative natural source of antioxidant

Operational Challenges in Conducting a Subnational TB Prevalence Survey in India: Lessons Learned for Resource-Limited, High-Burden Settings

Estimating the burden of TB at the subnational level is critical to planning and prioritizing resources for TB control activities according to the local epidemiological situation. We report the experiences and operational challenges of implementing a TB prevalence survey at the subnational level in India. Information was collected from research reports that gathered data from periodic meetings, informal discussions with study teams, letters of communication, and various site visit reports. During the implementation of the survey, several challenges were encountered, including frequent turnover in human resources, lack of survey participation and community engagement, breakdown of X-ray machines, laboratory issues that delayed sputum sample testing, delays in X-ray reading, and network and Internet connectivity issues that impeded data management. To help ensure the survey was implemented in a timely manner, we developed several solutions, including planning ahead to anticipate challenges, ensuring timely communication, having a high commitment from all stakeholders, having strong team motivation, providing repetitive hands-on training, and involving local leaders to increase community engagement. This experience may help future states and countries that plan to conduct TB prevalence surveys to address these anticipated challenges and develop alternative strategies well in advance

Programmatic implications of a sub-national TB prevalence survey in India

Subnational TB estimates are crucial for making informed decisions to tailor TB control activities to local TB epidemiolog

Calibration of VELC detectors on-board Aditya-L1 mission

Aditya-L1 is the first Indian space mission to explore the Sun and solar atmosphere with seven multi-wavelength payloads, with Visible Emission Line Coronagraph (VELC) being the prime payload. It is an internally occulted coronagraph with four channels to image the Sun at 5000 \AA~ in the field of view 1.05 - 3 \rsun, and to pursue spectroscopy at 5303 \AA, 7892 \AA~ and 10747 \AA~ channels in the FOV (1.05 - 1.5 \rsun). In addition, spectropolarimetry is planned at 10747 \AA~ channel. Therefore, VELC has three sCMOS detectors and one InGaAs detector. In this article, we aim to describe the technical details and specifications of the detectors achieved by way of thermo-vacuum calibration at the CREST campus of the Indian Institute of Astrophysics, Bangalore, India. Furthermore, we report the estimated conversion gain, full-well capacity, and readout noise at different temperatures. Based on the numbers, it is thus concluded that it is essential to operate the sCMOS detectors and InGaAs detectors at $-5^{\circ}$ and $-17^{\circ}$ C, respectively, at the spacecraft level.Comment: Accepted for publication in Experimental Astronomy; 13 Pages, 5 Figures and 8 Table

Cardiovascular diseases prediction by machine learning incorporation with deep learning

It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures