107,607 research outputs found
An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders
The data mining along with emerging computing techniques have astonishingly
influenced the healthcare industry. Researchers have used different Data Mining
and Internet of Things (IoT) for enrooting a programmed solution for diabetes
and heart patients. However, still, more advanced and united solution is needed
that can offer a therapeutic opinion to individual diabetic and cardio
patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced
healthcare system for proficient diabetes and cardiovascular diseases have been
proposed. The hybridization of data mining and IoT with other emerging
computing techniques is supposed to give an effective and economical solution
to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining,
Internet of Things, chatbots, contextual entity search (CES), bio-sensors,
semantic analysis and granular computing (GC). The bio-sensors of the proposed
system assist in getting the current and precise status of the concerned
patients so that in case of an emergency, the needful medical assistance can be
provided. The novelty lies in the hybrid framework and the adequate support of
chatbots, granular computing, context entity search and semantic analysis. The
practical implementation of this system is very challenging and costly.
However, it appears to be more operative and economical solution for diabetes
and cardio patients.Comment: 11 PAGE
Use of microwaves to improve nutritional value of soybeans for future space inhabitants
Whole soybeans from four different varieties at different moisture contents were microwaved for varying times to determine the conditions for maximum destruction of trypsin inhibitor and lipoxygenase activities, and optimal growth of chicks. Microwaving 150 gm samples of soybeans (at 14 to 28% moisture) for 1.5 min was found optimal for reduction of trypsin inhibitor and lipoxygenase activities. Microwaving 1 kgm samples of soybeans for 9 minutes destroyed 82% of the trypsin inhibitor activity and gave optimal chick growth. It should be pointed out that the microwaving time would vary according to the weight of the sample and the power of the microwave oven. The microwave oven used in the above experiments was rated at 650 watts 2450 MHz
Electrification in granular gases leads to constrained fractal growth
The empirical observation of aggregation of dielectric particles under the
influence of electrostatic forces lies at the origin of the theory of
electricity. The growth of clusters formed of small grains underpins a range of
phenomena from the early stages of planetesimal formation to aerosols. However,
the collective effects of Coulomb forces on the nonequilibrium dynamics and
aggregation process in a granular gas -- a model representative of the above
physical processes -- have so far evaded theoretical scrutiny. Here, we
establish a hydrodynamic description of aggregating granular gases that
exchange charges upon collisions and interact via the long-ranged Coulomb
forces. We analytically derive the governing equations for the evolution of
granular temperature, charge variance, and number density for homogeneous and
quasi-monodisperse aggregation. We find that, once the aggregates are formed,
the system obeys a physical constraint of nearly constant dimensionless ratio
of characteristic electrostatic to kinetic energy . This
constraint on the collective evolution of charged clusters is confirmed both by
the theory and the detailed molecular dynamics simulations. The inhomogeneous
aggregation of monomers and clusters in their mutual electrostatic field
proceeds in a fractal manner. Our theoretical framework is extendable to more
precise charge exchange mechanism, a current focus of extensive
experimentation. Furthermore, it illustrates the collective role of long-ranged
interactions in dissipative gases and can lead to novel designing principles in
particulate systems
Joint Resource Partitioning and Offloading in Heterogeneous Cellular Networks
In heterogeneous cellular networks (HCNs), it is desirable to offload mobile
users to small cells, which are typically significantly less congested than the
macrocells. To achieve sufficient load balancing, the offloaded users often
have much lower SINR than they would on the macrocell. This SINR degradation
can be partially alleviated through interference avoidance, for example time or
frequency resource partitioning, whereby the macrocell turns off in some
fraction of such resources. Naturally, the optimal offloading strategy is
tightly coupled with resource partitioning; the optimal amount of which in turn
depends on how many users have been offloaded. In this paper, we propose a
general and tractable framework for modeling and analyzing joint resource
partitioning and offloading in a two-tier cellular network. With it, we are
able to derive the downlink rate distribution over the entire network, and an
optimal strategy for joint resource partitioning and offloading. We show that
load balancing, by itself, is insufficient, and resource partitioning is
required in conjunction with offloading to improve the rate of cell edge users
in co-channel heterogeneous networks
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