Heat transfer prediction for radiant floor heating/cooling systems using artificial neural network (ANN)

Radiant floor cooling and heating systems (RHC) are gaining popularity as compared with conventional space conditioning systems. An understanding of the heat transfer capacity of the radiant system is desirable to design a space conditioning system using RHC technology. In the present work, a simplified heat flux model for RHC is developed for both cooling and heating modes of operation. The Artificial Neural Network (ANN) technique is used for the development of the simplified model. Experimental data from literature covering a wide operating range of the RHC is considered for model development and validation. Operating parameters such as mass flow rate (m_f), heat resistance (R_s), mean temperature of water flowing through the pipe (T_m), and operative temperature (T_op) are considered independent variables influencing the heat flux (q_t). The neural network consists of four input layers, one output layer, and one hidden layer with a feed-forward-back-propagation algorithm. A study on the selection of the optimum number of neurons in the range of 1–9 for the hidden layer is also performed. On the basis of the performance parameters, namely, average-absolute-relative-deviation (AARD = 0.11283) percentage, mean-square-error (MSE = 0.00055), and the coefficient of determination (R² = 0.9984), a hidden layer is modeled with five neurons.     

A machine learning assisted optical multistage interconnection network: Performance analysis and hardware demonstration

Integration of the machine learning (ML) technique in all-optical networks can enhance the effectiveness of resource utilization, quality of service assurances, and scalability in optical networks. All-optical multistage interconnection networks (MINs) are implicitly designed to withstand the increasing high-volume traffic demands at data centers. However, the contention resolution mechanism in MINs becomes a bottleneck in handling such data traffic. In this paper, a select list of ML algorithms replaces the traditional electronic signal processing methods used to resolve contention in MIN. The suitability of these algorithms in improving the performance of the entire network is assessed in terms of injection rate, average latency, and latency distribution. Our findings showed that the ML module is recommended for improving the performance of the network. The improved performance and traffic grooming capabilities of the module are also validated by using a hardware testbed.     

Assessment of nutritional properties and phenolic characterization of freshly harvested Dendrocalamus hamiltoni shoots and processed bamboo candy

Food Science and Biotechnology - The free and bound phenolic constituents in Dendrocalamus hamiltonii shoots were evaluated and compared to processed bamboo candy. Preliminary proximate analysis...     

Cu(II) ions removal from wastewater using starch nanoparticles (SNPs): An eco-sustainable approach

The complex structured starch particles were reduced to the nanoscale size range through hydrolysis utilizing low concentration acid assisted by ultrasound irradiation. The synthesized starch nanoparticles (SNPs) were characterized by transmission electron microscopy (TEM), Fourier-transform infrared (FTIR), and X-ray diffraction (XRD) techniques. The synthesized SNPs possessed surface activated entities, as many cationic functional groups were confirmed through the FTIR spectrum. Also, these SNPs were effectively utilized to separate heavy Cu metal ions from the synthetic ion solution. The SNPs were characterized using field emission scanning electron microscope (FESEM), X-ray photoelectron spectroscopy (XPS), and Brunauer–Emmett–Teller (BET) analysis for the surface modification after the adsorption process. The weak electrostatic interaction between the SNP surface and Cu ion was confirmed by the XPS spectrum and energy-dispersive X-ray spectroscopy. The maximum efficiency of Cu ions removal was about 93% at an optimal pH 5 and 25 mg/ml dosage. The adsorption equilibrium was obtained in 60 min. The nitrogen isotherm BET analysis of SNPs after adsorption shows a higher specific surface area of 18.552 m²/g, attributed to the interaction and presence of Cu ions on the SNP surface. The process feasibility was validated by the Langmuir isotherm model. The process exhibits pseudo-second-order adsorption kinetics and follows the Langmuir isotherm. The R_L predicted by the Langmuir isotherm mechanism is 0.017, implying favourable adsorption. The process is reproducible and allows for the separation of heavy metal ions from the wastewater through biosorption effectively.     

A High-Rate Li–CO2 Battery Enabled by 2D Medium-Entropy Catalyst

Lithium-air batteries based on CO₂ reactant (Li–CO₂) have recently been of interest because it has been found that reversible Li/CO₂ electrochemistry is feasible. In this study, a new medium-entropy cathode catalyst, (NbTa)_0.5BiS₃, that enables the reversible electrochemistry to operate at high rates is presented. This medium entropy cathode catalyst is combined with an ionic liquid-based electrolyte blend to give a Li–CO₂ battery that operates at high current density of 5000 mA g⁻¹ and capacity of 5000 mAh g⁻¹ for up to 125 cycles, far exceeding reported values in the literature for this type of battery. The higher rate performance is believed to be due to the greater stability of the multi-element (NbTa)_0.5BiS₃ catalyst because of its higher entropy compared to previously used catalysts with a smaller number of elements with lower entropies. Evidence for this comes from computational studies giving very low surface energies (high surface stability) for (NbTa)_0.5BiS₃ and transmission electron microscopystudies showing the structure being retained after cycling. In addition, the calculations indicate that Nb-terminated surface promotes Li–CO₂ electrochemistry resulting in Li₂CO₃ and carbon formation, consistent with the products found in the cell. These results open new direction to design and develop high-performance Li–CO₂ batteries.     

Quantification of Deviation of Size Dependent Field Enhancement Factor of Silicon Nanowires Array through Theoretical Modeling

Silicon - In the present paper, we report the observations of field emission (FE) from silicon nanowires array (Si-NWsA) synthesized on p-type Si (100) using wet chemical etching (WCE) approach,...     

Synthesis and Characterization of ZnO/CuO Nanocomposites as an Effective Photocatalyst and Gas Sensor for Environmental Remediation

Journal of Inorganic and Organometallic Polymers and Materials - Current study delineates the synthesis and environmental applications of ZnO/CuO nanocomposite in photocatalysis and gas sensing....     

A SiGe-Source Doping-Less Double-Gate Tunnel FET: Design and Analysis Based on Charge Plasma Technique with Enhanced Performance

Silicon - In this article, a distinctive charge plasma (CP) technique is employed to design two doping-less dual gate tunnel field effect transistors (DL-DG-TFETs) with Si0.5Ge0.5 and Si as source...