Cumin (<em>Cuminium cyminium</em> L.): A Seed Spice Crop with Adopted Production Technology in Cumin Cultivated Regions

Cumin is a seed spice which finds its place in variety of global cuisines, especially in Indian context. India leads in the world in production of cumin with 70% of world’s production and consumes 90% of this produce. It is a high potential crop with great demand around the world due to changing food consumption behavior, and increasing demand for value-added products such as oil and powder. Cumin has a distinct flavor and aroma owing to presence of essential oils. Cumin has different biological and biomedical properties and finds use in various ayurvedic preparations in different forms. Cumin has been found in three types of colours: amber, white, and black. Among this amber is widely accepted and black also have unique flavor. Cumin is a crop of tropical and subtropical regions and suitable for cultivation on wide variety of soils. Cumin production can be easily done with very few hindrances such as frost injury, wilt and powdery mildew. There is a lot of scope and prospectus regarding its cultivation which can be exploited in other cumin suitable regions of the world through various agronomical innervations, crop improvement programs and biotechnological tools

Scaling up indigenous rainwater harvesting: a preliminary assessment in Rajasthan, India

Rainwater harvesting (RWH) has the potential to enhance the sustainability of ground and surface water to meet increasing water demands and constrained supplies, even under a changing climate. Since arid and semi-arid regions frequently experience highly variable spatiotemporal rainfall patterns, rural communities have developed indigenous RWH techniques to capture and store rainwater for multiple uses. However, selecting appropriate sites for RWH, especially across large regions, remains challenging since the data required to evaluate suitability using critical criteria are often lacking. This study aimed to identify the essential criteria and develop a methodology to select potential RWH sites in Rajasthan (India). We combined GIS modeling (multicriteria decision analysis) with applied remote sensing techniques as it has the potential to assess land suitability for RWH. As assessment criteria, spatial datasets relating to land use/cover, rainfall, slope, soil texture, NDVI, and drainage density were considered. Later, weights were assigned to each criterion based on their relative importance to the RWH system, evidence from published literature, local expert advice, and field visits. GIS analyses were used to create RWH suitability maps (high, moderate, and unsuited maps). The sensitivity analysis was also carried out for identified weights to check the inadequacy and inconsistency among preferences. It was estimated that 3.6%, 8.2%, and 27.3% of the study area were highly, moderately, and unsuitable, respectively, for Chauka implementation. Further, sensitivity analysis results show that LULC is highly sensitive and NDVI is the least sensitive parameter in the selected study region, which suggests that changing the weight of these parameters is more likely to decide the outcome. Overall, this study shows the applicability of the GIS-based MCDA approach for up-scaling the traditional RWH systems and its suitability in other regions with similar field conditions, where RWH offers the potential to increase water resource availability and reliability to support rural communities and livelihoods

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

Review Paper on Controlling the Speed of DC Motor with High Accuracy and Demerits of Other Speed Controlling Techniques

Abstract: The presented paper is concerned with the speed controlling of dc motor by using embedded system, which is easy to use and provide us very high accuracy. Here we have compared the different type of techniques which are used to control the speed of DC motor. We have used CMOS 8-bit microcontroller with 8K bytes in system programmable flash AT895S2. The most important advantage of a DC motor is that we can vary the relationship of speed-torque as per our requirement &amp; for that purpose we have use a simple technique which is known as Pulse Width Modulation, which is used to produce low and high pulses. The pulses produce is the cause of changing speed of motor. Therefore to achieve this we have use a microcontroller (AT89S52), which can be programmable to set the speed by changing the time period of duty cycle in the code

Graphene Nanoplatelet-Reinforced Poly(vinylidene fluoride)/High Density Polyethylene Blend-Based Nanocomposites with Enhanced Thermal and Electrical Properties

In this study, a graphene nanoplatelet (GNP) was used as a reinforcing filler to prepare poly(vinylidene fluoride) (PVDF)/high density polyethylene (HDPE) blend-based nanocomposites through a melt mixing method. Scanning electron microscopy confirmed that the GNP was mainly distributed within the PVDF matrix phase. X-ray diffraction analysis showed that PVDF and HDPE retained their crystal structure in the blend and composites. Thermogravimetric analysis showed that the addition of GNP enhanced the thermal stability of the blend, which was more evident in a nitrogen environment than in an air environment. Differential scanning calorimetry results showed that GNP facilitated the nucleation of PVDF and HDPE in the composites upon crystallization. The activation energy for non-isothermal crystallization of PVDF increased with increasing GNP loading in the composites. The Avrami n values ranged from 1.9&ndash;3.8 for isothermal crystallization of PVDF in different samples. The Young&rsquo;s and flexural moduli of the blend improved by more than 20% at 2 phr GNP loading in the composites. The measured rheological properties confirmed the formation of a pseudo-network structure of GNP-PVDF in the composites. The electrical resistivity of the blend reduced by three orders at a 3-phr GNP loading. The PVDF/HDPE blend and composites showed interesting application prospects for electromechanical devices and capacitors

Cellulose Nanocrystal Reinforced Chitosan Based UV Barrier Composite Films for Sustainable Packaging

In this study, green composite films based on cellulose nanocrystal/chitosan (CNC/CS) were fabricated by solution casting. FTIR, XRD, SEM, and TEM characterizations were conducted to determine the structure and morphology of the prepared films. The addition of only 4 wt.% CNC in the CS film improved the tensile strength and Young&rsquo;s modulus by up to 39% and 78%, respectively. Depending on CNC content, the moisture absorption decreased by 34.1&ndash;24.2% and the water solubility decreased by 35.7&ndash;26.5% for the composite films compared with neat CS film. The water vapor permeation decreased from 3.83 &times; 10&minus;11 to 2.41 &times; 10&minus;11 gm&minus;1 s&minus;1Pa&minus;1 in the CS-based films loaded with (0&ndash;8 wt.%) CNC. The water and UV barrier properties of the composite films showed better performance than those of neat CS film. Results suggested that CNC/CS nanocomposite films can be used as a sustainable packaging material in the food industry