Efficient energy for one node and multi-nodes of wireless body area network

<span>Compression sensing approaches have been used extensively with the idea of overcoming the limitations of traditional sampling theory and applying the concept of pressure during the sensing procedure. Great efforts have been made to develop methods that would allow data to be sampled in compressed form using a much smaller number of samples. Wireless body area networks (WBANs) have been developed by researchers through the creation of the network and the use of miniature equipment. Small structural factors, low power consumption, scalable data rates from kilobits per second to megabits per second, low cost, simple hardware deployment, and low processing power are needed to hold the wireless sensor through lightweight, implantable, and sharing communication tools wireless body area network. Thus, the proposed system provides a brief idea of the use of WBAN using IEEE 802.15.4 with compression sensing technologies. To build a health system that helps people maintain their health without going to the hospital and get more efficient energy through compression sensing, more efficient energy is obtained and thus helps the sensor battery last longer, and finally, the proposed health system will be more efficient energy, less energy-consuming, less expensive and more throughput.</span>

Curvature and self-assembly of semi-conducting nanoplatelets

Semi-conducting nanoplatelets are two-dimensional nanoparticles whose thickness is in the nanometer range and controlled at the atomic level. They have come up as a new category of nanomaterial with promising optical properties due to the efficient confinement of the exciton in the thickness direction. In this perspective, we first describe the various conformations of these 2D nanoparticles which display a variety of bent and curved geometries and present experimental evidences linking their curvature to the ligand-induced surface stress. We then focus on the assembly of nanoplatelets into superlattices to harness the particularly efficient energy transfer between them, and discuss different approaches that allow for directional control and positioning in large scale assemblies. We emphasize on the fundamental aspects of the assembly at the colloidal scale in which ligand-induced forces and kinetic effects play a dominant role. Finally, we highlight the collective properties that can be studied when a fine control over the assembly of nanoplatelets is achieved.

Interfacial-confined coordination to single-atom nanotherapeutics

Pursuing and developing effective methodologies to construct highly active catalytic sites to maximize the atomic and energy efficiency by material engineering are attractive. Relative to the tremendous researches of carbon-based single atom systems, the construction of bio-applicable single atom materials is still in its infancy. Herein, we propose a facile and general interfacial-confined coordination strategy to construct high-quality single-atom nanotherapeutic agent with Fe single atoms being anchored on defective carbon dots confined in a biocompatible mesoporous silica nanoreactor. Furthermore, the efficient energy conversion capability of silica-based Fe single atoms system has been demonstrated on the basis of the exogenous physical photo irradiation and endogenous biochemical reactive oxygen species stimulus in the confined mesoporous network. More importantly, the highest photothermal conversion efficiency with the mechanism of increased electron density and narrow bandgap of this single atom structure in defective carbon was proposed by the theoretical DFT calculations. The present methodology provides a scientific paradigm to design and develop versatile single atom nanotherapeutics with adjustable metal components and tune the corresponding reactions for safe and efficient tumor therapeutic strategy.

A new smart approach of an efficient energy consumption management by using a machine-learning technique

Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is satisfied. It divides the measured data for actual power (A_p ) of the electrical model into two portions: the training portion is selected for different maximum actual powers, and the validation portion is determined based on the minimum output power consumption and then used for comparison with the actual required input power. Simulation results show the energy expenditure problem can be solved with good accuracy in energy consumption by reducing the maximum rate (A_p ) in a given time (24) hours for a single house, as well as electricity’s bill cost, is reduced.