An experimental study on micro-structural and geotechnical characteristics of expansive clay mixed with EPS granules

© 2020 Pavement structures constructed on the expansive soil subgrade experience a higher upward pressure compared to any other subgrade material. The upward pressure is caused due to high swelling and shrinkage characteristics of expansive clay soil. The present study has investigated and identified the mechanisms by which a remolded expansive soil can be modified to reduce the upward pressure and swelling (heave). To achieve this, a lightweight, environmentally friendly, and high pressure resistive expanded polystyrene (EPS) granules have been used with expansive soil s from three different locations of Madhya Pradesh state, India. The study has been performed to understand the swelling and strength characteristics of soil with and without the use of EPS (density = 21.6 kg/m3) as per ASTM specifications. The chemical and microstructural components of the expansive soil were investigated using autotuned total reflectance Fourier transform infrared (ATR-FTIR), X-ray diffraction (XRD), and scanning electron microscope (SEM). Several laboratory experiments, including optimum moisture content, maximum dry unit weight, grain-size distribution, liquid limit, plastic limit, shrinkage limit, free swell index, unconfined compressive strength, and pressure swelling tests were carried out on the statically compacted expansive clay soil specimen with and without EPS (0.25%, 0.50%, 1.00%). The maximum addition of EPS was considered as 1% as the very high expansion was observed, and beyond this, further addition of EPS was not feasible. The results show that the swelling pressure, expansion percentage, and time rate of swell decrease, whereas the unconfined compressive strength (UCS) increases with the addition of EPS. The inclusion of EPS in expansive clay soil exponentially reduced the heave and the upward pressure, whereas the maximum UCS was observed at 0.5%

Comparative Simulation Analysis of Process Parameter Variations in 20 nm Triangular FinFET

Technology scaling below 22 nm has brought several detrimental effects such as increased short channel effects (SCEs) and leakage currents. In deep submicron technology further scaling in gate length and oxide thickness can be achieved by changing the device structure of MOSFET. For 10–30 nm channel length multigate MOSFETs have been considered as most promising devices and FinFETs are the leading multigate MOSFET devices. Process parameters can be varied to obtain the desired performance of the FinFET device. In this paper, evaluation of on-off current ratio (Ion/Ioff), subthreshold swing (SS) and Drain Induced Barrier Lowering (DIBL) for different process parameters, that is, doping concentration (1015/cm3 to 1018/cm3), oxide thickness (0.5 nm and 1 nm), and fin height (10 nm to 40 nm), has been presented for 20 nm triangular FinFET device. Density gradient model used in design simulation incorporates the considerable quantum effects and provides more practical environment for device simulation. Simulation result shows that fin shape has great impact on FinFET performance and triangular fin shape leads to reduction in leakage current and SCEs. Comparative analysis of simulation results has been investigated to observe the impact of process parameters on the performance of designed FinFET

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

ENHANCEMENT OF BIOGAS PRODUCTION USING OCIMUM SANCTUM EXTRACT MEDIATED GREEN SYNTHESIZED SUPERPARAMAGNETIC IRON OXIDE NANO-PARTICLES

Managing landfill waste, sewage sludge, and such other bio-degradable wastes are likely to be challenging task at present. Such biodegradable waste can be processed scientifically to obtain useful products. By utilizing these wastes in an anaerobic digester, biogas rich with methane gas is generated & the residue slurry is used as organic fertilizer. Bio digestion involves slow chemical reactions including high retention period for substrate. A novel concept of dosing engineered super paramagnetic iron oxide nanoparticles prepared with the aids of Ocimum sanctum leaf extract is used to improve biogas production in anaerobic digestion processes. Additional iron content in the digestion process increases the production rate as it helps in reducing CO2 to form CH4. Generally nanoparticles catalyze better than its bulk analogs and reduce the problem of high retention period. Iron oxide nanoparticles are unstable, they can be designed to provide ions in controlled manner, and the highest ever reported improvement of biogas production is obtained

11β Hydroxysteroid dehydrogenase – 1 activity in type 2 diabetes mellitus: a comparative study

Abstract Background A comparative study of 11 β HSD 1 activity in type 2 diabetes mellitus subjects with respect to fasting blood glucose and other metabolic parameters was conducted. Methods A case control experimental study was performed enrolling thirty type 2 diabetes mellitus patients and thirty age, gender and BMI matched controls using cortisone acetate test. Results The rise of serum cortisol after oral 25 mg cortisone acetate from baseline (dexamethasone suppressed level) is higher in subjects with type 2 diabetes and is associated with exercise, BMI, SGOT but not daily calorie intake, lipid parameters and thyroid status. Fasting blood glucose after overnight 1 mg oral dexamethasone is a strong predictor of 11HSD1 activity, irrespective of presence of type 2 diabetes. Conclusion 11β HSD 1 activity is higher in type 2 diabetes mellitus subjects, especially those who are lean. Future 11 β HSD 1 inhibitors targeting metabolic syndrome, will be most useful in those with increased fasting blood glucose. The role of DHEAS and vitamin D status needs to be explored

Porous gold nanospheres by controlled transmetalation reaction: a novel material for application in cell imaging

Hollow shell nanostructures have numerous potential applications due to their interesting optical and electronic properties, which can be tuned by varying their shape, size, and shell thickness. In this paper we describe a simple galvanic replacement reaction (transmetalation reaction) involving sacrificial silver nanoparticles and Au(III) ions using a dialysis membrane. The dialysis membrane acts as a partial barrier that provides excellent control over the kinetics of reaction. This process results in the formation of porous gold nanospheres that improve the fluorescence in cell staining by offering an enhanced surface area for binding of the fluorescent dye, propidium iodide (PI). Such porous nanostructures could be ideal candidates for applications such as catalysis, enzyme immobilization, and drug delivery

Studies on comparison of nano-urea and prilled urea for enhancing maize (Zea mays) growth and productivity

An experiment was conducted during the rainy (kharif) season of 2022–23 at the ICAR-Indian Agricultural Research Institute, Hazaribagh, Jharkhand to assess how well nano- urea performs in maize under different field conditions and investigate its compatibility with other N sources. Results showed that root weight (fresh and dry), partial factor productivity (N, P2O5 and K2O), N concentration (grain and stover), grain yield, biological yield and harvest index of maize were all significantly affected by the application of various N rates and nano-fertilizers. The grain yield of maize was comparable to the yield obtained under RDF and 75% of the recommended N + two nano-urea sprays. These findings indicate that the concurrent use of these nano-fertilizers has the potential to reduce N fertilization by as much as 25%. Furthermore, the results highlight the prospect of augmenting biological yield of maize by incorporating 2 nano-urea sprays alongside the prescribed N quantity from prilled urea, as well as full applications of P2O5 and K2O