Approximate probabilistic optimization using exact-capacity-approximate-response-distribution (ECARD)

Probabilistic structural design deals with uncertainties in response (e.g. stresses) and capacity (e.g. failure stresses). The calculation of the structural response is typically expensive (e.g., finite element simulations), while the capacity is usually available from tests. Furthermore, the random variables that influence response and capacity are often disjoint. In previous work we have shown that this disjoint property can be used to reduce the cost of obtaining the probability of failure via Monte Carlo simulations. In this paper we propose to use this property for an approximate probabilistic optimization based on exact capacity and approximate response distributions (ECARD). In Approximate Probabilistic Optimization Using ECARD, the change in response distribution is approximated as the structure is re-designed while the capacity distribution is kept exact, thus significantly reducing the number of expensive response simulations. ECARD may be viewed as an extension of SORA (Sequential Optimization and Reliability Assessment), which proceeds with deterministic optimization iterations. In contrast, ECARD has probabilistic optimization iterations, but in each iteration, the response distribution is approximated so as not to require additional response calculations. The use of inexpensive probabilistic optimization allows easy incorporation of system reliability constraints and optimal allocation of risk between failure modes. The method is demonstrated using a beam problem and a ten-bar truss problem. The former allocates risk between two different failure modes, while the latter allocates risk between members. It is shown that ECARD provides most of the improvement from risk re-allocation that can be obtained from full probabilistic optimization.     

Magnetic and transport properties of 214 oxides: Dependence on doping

We consider how doping can be described in terms of the charge-transfer insulator concept. We discuss and compare a few models for the band structure for the doped charges. This has led us to the conclusion that the band structure stability problem is one of the main issues in any correspondence between results for thet-J model and, say, the three-band model for the slightly doped layered oxides. The stability criterion is formulated and its implications discussed. Provided a phenomenological conduction band is chosen to satisfy the criterion of stability, a detailed picture of how dopants influence the spin wave spectrum atT=0 is presented. The basic physics for the destruction of the antiferromagnetic (AF) long-range order is rather model-independent: the long-range order (atT=0) disappears due to the Cerenkov effect when the Fermi velocity first exceeds the spin wave velocity. We then discuss the overall spectrum of spin excitations and see that the spin wave attenuation for x<x _c,T= 0 due to Landau damping appears in the range of magnon momentak(x)=2m ^* s±x. We also argue that in the presence of superconductivity, the Cerenkov effect is eliminated due to the gap in the spectrum. This may restore the role of the AF fluctuations as the main source of dissipation at the lowest temperatures. A brief discussion of how interaction with magnons may affect the hole spectrum concludes the paper.     

Solubility and thermodynamic properties of Y2O3 in LiF-YF3 melts

A systematic investigation was undertaken to measure the solubility of Y₂O₃ in several LiF-YF₂ melts, with the YF₃ composition ranging from 20 to 50 mole pct, in the temperature range of 998 to 1273 K. Experimental results showed that the solubility of Y₂O₄ in the melts increased with increase in temperature and also with increased YF₃ content. The activity of Y₂O₃ was calculated using the free energy of fusion of Y₂O₃, . The was deduced from the values of enthalpy, heat of fusion, and melting point of Y₂O₃. From the known values of activity, the activity coefficients of Y₂O₃ as a function of temperature and melt composition were calculated. Considering the ionic nature of the melt, activity coefficients were also calculated using Temkin’s ideal mixing and electrically equivalent fraction methods. The thermodynamic data, such as integral molar enthalpy, entropy, and free energy of formation, were calculated as a function of composition and temperature. The calculated thermodynamic data showed that the melt exhibited a negative deviation from ideal conditions.     

A nano-plasmonic HMIM waveguide based concurrent dual-band BPF using circular ring resonator

This article analyzes the transmission line characteristics of plasmonic hybrid metal insulator metal (HMIM) waveguide, circular ring resonator (CRR) based dual-band band-pass filters with two transmission poles in both pass-bands in the optical regime using coupled line feed. The transmission line characteristics of an HMIM waveguide, such as characteristic impedance (ZPV), effective refractive index (neff) and propagation length (Lspp), have been obtained by using full wave simulation. Using basic HMIM slot waveguide, a CRR with periodic loading of double- and triple-ring CRR is numerically analyzed. Two input ports have been used for excitation, which are located at the separation of 180° positions along the CRR, and are coupled with the ring by parallel coupled lines, producing the dual pass-bands with the synchronous excitation of two transmission poles. The proposed double-ring dual-band band-pass filter (DR-DB-BPF) offers 35 dB extinction ratio (ER), 299.69 nm free spectral range (FSR) and narrow band full width half maximum (FWHM) of 78.057—112.43 nm. The triple-ring DB-BPF (TR-DB-BPF) has 22.5 dB ER, FSR of 292.18 nm and FWHM of 42.751—59.58 nm. The proposed filters are very useful in the development of dual-band filters for electronic photonic integrated circuits (EPICs), as the optical signals are filtered at two wavelengths simultaneously.     

A performance evaluation framework of public health distribution network for essential medicines: lessons from the select Indian states

The present work has been carried out to develop a performance evaluation framework of distribution network design in public health with specific reference to essential medicines based on six different types of public health distribution networks (PHDNs) prevalent across six Indian states. We evaluated the performance of PHDNs on the criteria of cost and service and also on the sub-criteria of cost and service by following the analytic hierarchy process. We utilized the pairwise comparison method in determining the relative importance of criteria and sub-criteria by seeking responses from several experts and subsequently evaluated the PHDNs on all sub-criteria in a 7-point Likert scale by seeking views from the same experts. Finally, we developed the “cost–service matrix,” in which cost and service have been depicted in the horizontal and vertical axes, respectively. We demonstrated the performance of six PHDNs in this matrix in terms of cost and service by plotting scores obtained by them on cost and service, respectively. The findings of the study provide rich insights to the policy planners of public health.     

An Ophthalmic Evaluation of Central Serous Chorioretinopathy

Nowadays in the medical field, imaging techniques such as Optical Coherence Tomography (OCT) are mainly used to identify retinal diseases. In this paper, the Central Serous Chorio Retinopathy (CSCR) image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application methods. The first approach, which was focused on image quality, improves medical image accuracy. An enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter (BADWUMF). The classifier used here is to figure out whether the OCT image is a CSCR case or not. 150 images are checked for this research work (75 abnormal from Optical Coherence Tomography Image Retinal Database, in-house clinical database, and 75 normal images). This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be minimized. The total precision is 90 percent obtained from the Two Class Support Vector Machine (TCSVM) classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale (SNNWPB) classifier using the MATLAB 2019a program.     

Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System (LMS) is an application software that is used in automation, delivery, administration, tracking, and reporting of courses and programs in educational sector. The LMS which exploits machine learning (ML) has the ability of accessing user data and exploit it for improving the learning experience. The recently developed artificial intelligence (AI) and ML models helps to accomplish effective performance monitoring for LMS. Among the different processes involved in ML based LMS, feature selection and classification processes find beneficial. In this motivation, this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring (GSO-MFWELM) technique for LMS. The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS. The proposed GSO-MFWELM technique involves GSO-based feature selection technique to select the optimal features. Besides, Weighted Extreme Learning Machine (WELM) model is applied for classification process whereas the parameters involved in WELM model are optimally fine-tuned with the help of Mayfly Optimization (MFO) algorithm. The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance. The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects. The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.     

Optimal and Effective Resource Management in Edge Computing

Edge computing is a cloud computing extension where physical computers are installed closer to the device to minimize latency. The task of edge data centers is to include a growing abundance of applications with a small capability in comparison to conventional data centers. Under this framework, Federated Learning was suggested to offer distributed data training strategies by the coordination of many mobile devices for the training of a popular Artificial Intelligence (AI) model without actually revealing the underlying data, which is significantly enhanced in terms of privacy. Federated learning (FL) is a recently developed decentralized profound learning methodology, where customers train their localized neural network models independently using private data, and then combine a global model on the core server together. The models on the edge server use very little time since the edge server is highly calculated. But the amount of time it takes to download data from smartphone users on the edge server has a significant impact on the time it takes to complete a single cycle of FL operations. A machine learning strategic planning system that uses FL in conjunction to minimise model training time and total time utilisation, while recognising mobile appliance energy restrictions, is the focus of this study. To further speed up integration and reduce the amount of data, it implements an optimization agent for the establishment of optimal aggregation policy and asylum architecture with several employees’ shared learners. The main solutions and lessons learnt along with the prospects are discussed. Experiments show that our method is superior in terms of the effective and elastic use of resources.     

Event-triggered control for discrete-time multi-agent average consensus

We consider the average consensus problem for the multi-agent system in the discrete-time domain. Three triggering based control protocols are developed, which dictate the broadcast and control update instants of individual agents to alleviate communication and computational burden. Lyapunov-based design methods prescribe when agents should communicate and update their control so that the network converges to the average of agents' initial states. We start with a static version of the distributed event-triggering law and then generalize it so that it involves an internal auxiliary variable to regulate the threshold dynamically for each agent. The third protocol uses a self-triggering algorithm to avoid continuous listening wherein each agent estimates its next triggering time and broadcasts it to its neighbors at the current triggering time. Numerical simulations are shown to validate the efficacy of the proposed algorithms.