Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization

This paper presents a new approach for solving short-term hydrothermal scheduling (HTS) using an integrated algorithm based on teaching learning based optimization (TLBO) and oppositional based learning (OBL). The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydro reservoirs, water transport delay and scheduling time linkage that make the problem of optimization difficult using standard optimization methods. To overcome these problems, the proposed quasi-oppositional teaching learning based optimization (QOTLBO) is employed. To show its efficiency and robustness, the proposed QOTLBO algorithm is applied on two test systems. Numerical results of QOTLBO are compared with those obtained by two phase neural network, augmented Lagrange method, particle swarm optimization (PSO), improved self-adaptive PSO (ISAPSO), improved PSO (IPSO), differential evolution (DE), modified DE (MDE), fuzzy based evolutionary programming (Fuzzy EP), clonal selection algorithm (CSA) and TLBO approaches. The simulation results reveal that the proposed algorithm appears to be the best in terms of convergence speed, solution time and minimum cost when compared with other established methods. This method is considered to be a promising alternative approach for solving the short-term HTS problems in practical power system.     

Electrical resistivity of the hole doped La0.8Sr0.2MnO3 manganites: Role of electron–electron/phonon/magnon interactions

In this work, a quantitative analysis of reported metallic and insulating behaviour of resistivity in perovskite manganites La_0.8Sr_0.2MnO₃ is established. An effective inter-ionic interaction potential (EIoIP) with the long-range Coulomb, van der Waals (vdW) interaction and short-range repulsive interaction up to second-neighbour ions within the Hafemeister and Flygare approach was employed to estimate the Debye and Einstein temperature and was found to be consistent with the available experimental data. The electrical resistivity data in low temperature regime (T < T_MI) were theoretically analyzed within the framework of the classical electron–phonon model of resistivity, for example, the Bloch–Gruneisen (BG) model. The Bloch–Gruneisen (BG) model and terms T², T^4.5 simplify the electron–phonon, electron–electron and electron–magnon scattering processes. On the other hand, in high temperature regime (T > T_MI) the insulating nature is discussed with Mott's variable range hopping (VRH) model and small polaron conduction (SPC) model. For T > T_MI SPC model is more appropriate than the VRH model. The SPC model consistently retraces the higher temperature resistivity behaviour (T > θ_D/2). The metallic and semiconducting resistivity behaviours of La_0.8Sr_0.2MnO₃ manganites are analyzed, to the knowledge, for the first time highlighting the importance of electron–phonon, electron–electron, electron–magnon interactions and small polaron conduction.     

Electron Injection via Interfacial Atomic Au Clusters Substantially Enhance the Visible-Light-Driven Photocatalytic H2 Production of the PF3T Enclosed TiO2 Nanocomposite

A hybrid composite of organic–inorganic semiconductor nanomaterials with atomic Au clusters at the interface decoration (denoted as PF3T@Au-TiO₂) is developed for visible–light-driven H₂ production via direct water splitting. With a strong electron coupling between the terthiophene groups, Au atoms and the oxygen atoms at the heterogeneous interface, significant electron injection from the PF3T to TiO₂ occurs leading to a quantum leap in the H₂ production yield (18 578 µmol g⁻¹ h⁻¹) by ≈39% as compared to that of the composite without Au decoration (PF3T@TiO₂, 11 321 µmol g⁻¹ h⁻¹). Compared to the pure PF3T, such a result is 43-fold improved and is the best performance among all the existing hybrid materials in similar configurations. With robust process control via industrially applicable methods, it is anticipated that the findings and proposed methodologies can accelerate the development of high-performance eco-friendly photocatalytic hydrogen production technologies.     

Development of Algorithm for Person Re-Identification Using Extended Openface Method

Deep learning has risen in popularity as a face recognition technology in recent years. Facenet, a deep convolutional neural network (DCNN) developed by Google, recognizes faces with 128 bytes per face. It also claims to have achieved 99.96% on the reputed Labelled Faces in the Wild (LFW) dataset. However, the accuracy and validation rate of Facenet drops down eventually, there is a gradual decrease in the resolution of the images. This research paper aims at developing a new facial recognition system that can produce a higher accuracy rate and validation rate on low-resolution face images. The proposed system Extended Openface performs facial recognition by using three different features i) facial landmark ii) head pose iii) eye gaze. It extracts facial landmark detection using Scattered Gated Expert Network Constrained Local Model (SGEN-CLM). It also detects the head pose and eye gaze using Enhanced Constrained Local Neural field (ECLNF). Extended openface employs a simple Support Vector Machine (SVM) for training and testing the face images. The system’s performance is assessed on low-resolution datasets like LFW, Indian Movie Face Database (IMFDB). The results demonstrated that Extended Openface has a better accuracy rate (12%) and validation rate (22%) than Facenet on low-resolution images.     

Device Simulation of Nanopillar-based n-CdS/p-CdTe Solar Cell with Enhanced and Efficient Carrier Collection

Silicon - With an objective of enhancing the device performance of a Nanopillar-based n-CdS/p-CdTe solar cell, an n-ZnO window and p-CdZnTe (CZT) electron reflecting layer has been proposed...     

Tetraphenylethene Derivatives Modulate the RNA Hairpin-G-Quadruplex Conformational Equilibria in Proto-oncogenes**

RNA G-quadruplex (GQs) sequences in 5′-UTRs of certain proto-oncogenes co-localize with hairpin (Hp) forming sequences resulting in intramolecular Hp-GQ conformational equilibria, which is suggested to regulate cancer development and progression. Thus, regulation of Hp-GQ equilibria with small molecules is an attractive but less explored therapeutic approach. Herein, two tetraphenylethene (TPE) derivatives, TPE−Py and TPE-MePy, were synthesized and their effect on Hp-GQ equilibrium was explored. FRET, CD and molecular docking experiments suggest that cationic TPE-MePy shifts the Hp-GQ equilibrium significantly towards the GQ conformer mainly through π-π stacking and van der Waals interactions. In the presence of TPE-MePy, the observed rate constant values for first and second folding steps were increased up to 14.6 and 2.6-fold, respectively. The FRET melting assay showed a strong stabilizing ability of TPE-MePy (ΔTm=4.36 °C). Notably, the unmethylated derivative TPE−Py did not alter the Hp-GQ equilibrium. Subsequently, luciferase assay analysis demonstrated that the TPE-MePy derivatives suppressed the translation efficiency by ∼5.7-fold by shifting the Hp-GQ equilibrium toward GQ conformers in the 5’-UTR of TRF2. Our data suggests that HpGQ equilibria could be selectively targeted with small molecules to modulate translation for therapy.     

Optimization of Gate all-around Junctionless Transistor Using Response Surface Methodology

Silicon - In this paper, a response surface methodology (RSM) –custom design based multiobjective optimization approach is proposed to optimize the electrical behaviour of n-type and p-type...     

An Integrated Process for D-Sorbitol Production over NiO/TiO2 Supported Ru Nanocatalyst: A Greener Approach

The catalytic transformation of D-glucose towards D-sorbitol is a well-established process in the biorefinery industry. Normally, this batch-wise hydrogenation over metallic catalysts suffers from several troubles such as low yielding and facile catalyst deactivation. To address these challenges, we, hereby fabricated 5NiO/TiO₂-supported Ru nanocatalysts (Ru-5NiO/TiO₂, 1.0 and 5.0 wt% Ru) and examined their efficacy in the continuous-flow reduction of D-glucose aqueous solution (20 wt%) to produce D-sorbitol for the first time. In this study, D-glucose conversion and D-sorbitol yield were represented as a function of residence time, where the feeding rate of D-glucose was systematically controlled to maximize the outcome of D-sorbitol. Remarkably, D-glucose was fully converted to 99.0% selectivity of D-sorbitol under optimized reaction conditions (100 °C, 8 MPa H₂ with a flow rate of 100 L h^?1). The experimental results showed that the mean activity and specific rate of 1.0 wt% Ru-5NiO/TiO₂ were achieved as 731.3 mmol h^??1 g^??1 and 1030.7 mmol h^?1 g^?1, respectively, which surpassed those of monometallic Ru-based catalysts (1.0 wt% Ru/TiO₂, 1.0 wt% Ru/SiO_2, and 1.6 wt% Ru/C). Moreover, the stability of 1.0 wt% Ru-5NiO/TiO₂ nanocatalyst was sustained for long-term operation (>?360 h) and no leaching of ruthenium was highly acknowledged.

Graphical Abstract