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Background: Uttarakhand, a hilly state in north India, reported the first coronavirus disease (COVID) case on 15 March 2020. Since then, the case numbers rose multiple folds. As Uttarakhand has been on a 'war-footing' amidst the recent second wave and is gearing up to fight against the third wave, the present study aims to uncover baseline clinical profile and in-hospital outcomes of COVID patients in Dehradun district (Uttarakhand) during the first wave. Methods: A record-based descriptive analysis was carried out for 671 COVID patients admitted to a private dedicated COVID hospital in Dehradun district between August 2020 and February 2021. Data was collected from medical records on a standardized abstraction form. Data was entered and analyzed using Statistical Package for Social Sciences (SPSS) version 20. Results: The present study showed most admitted COVID patients were males, aged 40 years and above, moderately ill, had co-morbidities with about one-fourth lately succumbed to death. The proportions of deaths, moderate-to-severe and severe category of illness were invariably high for those with co-morbidities irrespective of the gender. Females, age

Automated indoor environmental control is a research topic that is beginning to receive much attention in smart home automation. All machine learning models proposed to date for this purpose have relied on reinforcement learning using simple metrics of comfort as reward signals. Unfortunately, such indicators do not take into account individual preferences and other elements of human perception. This research explores an alternative (albeit closely related) paradigm called imitation learning. In the proposed architecture, machine learning models are trained with tabular data pertaining to environmental control activities of the real occupants of a residential unit. This eliminates the need for metrics that explicitly quantify human perception of comfort. Moreover, this article introduces the recently proposed deep attentive tabular neural network (TabNet) into smart home research by incorporating TabNet-based components within its overall framework. TabNet has consistently outperformed all other popular machine learning models in a variety of other application domains, including gradient boosting, which was previously considered ideal for learning from tabular data. The results obtained herein strongly suggest that TabNet is the best choice for smart home applications. Simulations conducted using the proposed architecture demonstrate its effectiveness in reproducing the activity patterns of the home unit’s actual occupants.

Liposomes have been used extensively as micro- and nanocarriers for hydrophobic or hydrophilic molecules. However, conventional liposomes are biodegradable and quickly eliminated, making it difficult to be used for delivery in specific routes, such as the oral and systemic routes. One way to overcome this problem is through complexation with polymers, which is referred to as a liposome complex. The use of polymers can increase the stability of liposome with regard to pH, chemicals, enzymes, and the immune system. In some cases, specific polymers can condition the properties of liposomes to be explicitly used in drug delivery, such as targeted delivery and controlled release. These properties are influenced by the type of polymer, crosslinker, interaction, and bond in the complexation process. Therefore, it is crucial to study and review these parameters for the development of more optimal forms and properties of the liposome complex. This article discusses the use of natural and synthetic polymers, ways of interaction between polymers and liposomes (on the surface, incorporation in lamellar chains, and within liposomes), types of bonds, evaluation standards, and their effects on the stability and pharmacokinetic profile of the liposome complex, drugs, and vaccines. This article concludes that both natural and synthetic polymers can be used in modifying the structure and physicochemical properties of liposomes to specify their use in targeted delivery, controlled release, and stabilizing drugs and vaccines.

Rapid advancements in technology, particularly in soil tools and agricultural machinery, have led to the proliferation of mechanized agriculture. The interaction between such tools/machines and soil is a complex, dynamic process. The modeling of this interactive process is essential for reducing energy requirements, excessive soil pulverization, and soil compaction, thereby leading to sustainable crop production. Traditional methods that rely on simplistic physics-based models are not often the best approach. Computational intelligence-based approaches are an attractive alternative to traditional methods. These methods are highly versatile, can handle various forms of data, and are adaptive in nature. Recent years have witnessed a surge in adapting such methods in all domains of engineering, including agriculture. These applications leverage not only classical computational intelligence methods, but also emergent ones, such as deep learning. Although classical methods have routinely been applied to the soil–machine interaction studies, the field is yet to harness the more recent developments in computational intelligence. The purpose of this review article is twofold. Firstly, it provides an in-depth description of classical computational intelligence methods, including their underlying theoretical basis, along with a survey of their use in soil–machine interaction research. Hence, it serves as a concise and systematic reference for practicing engineers as well as researchers in this field. Next, this article provides an outline of various emergent methods in computational intelligence, with the aim of introducing state-of-the-art methods to the interested reader and motivating their application in soil–machine interaction research.

The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article. It surveys the use of RL in various home energy management system (HEMS) applications. There is a focus on deep neural network (DNN) models in RL. The article provides an overview of reinforcement learning. This is followed with discussions on state-of-the-art methods for value, policy, and actor–critic methods in deep reinforcement learning (DRL). In order to make the published literature in reinforcement learning more accessible to the HEMS community, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using standard machine learning terminology. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. It suggests that research in this direction is still in its infancy. Lastly, the article proposes four performance metrics to evaluate RL methods.

The objective of the present work is to study theoretically, the steady-state performance of finite hydrodynamic porous journal bearings under micropolar lubrication. The fluid pressure in porous matrix follows Darcy’s equation. In the film zone, the modified Reynolds equation is developed considering micropolar lubrication theory. Both Darcy’s and modified Reynolds equations are solved using finite difference technique with successive over-relaxation method to obtain the steady-state film pressure. The steady-state performance characteristics are evaluated in terms of non-dimensional load carrying ability, friction force and friction parameter of a finite journal bearing for different values of micropolarity viz, lm and N, and varying characteristics of porous bearing. The results show that the micropolar fluid is better than the Newtonian fluid when load carrying ability and friction parameters are concerned. The slip has a beneficial effect in terms of increase in load carrying ability and reduction in friction parameter approximately to 0.7, beyond which the advantage diminishes. lm exhibits an optimal value around 10.0. In case of industrial machinery and home appliances which uses the porous bearings and where the lubricant either gets contaminated with micro-elements or dust, mixing of additives will return better results.

Accurate prediction of an off-normal event in a nuclear reactor is dependent upon the availability of sensory data, reactor core physical condition, and understanding of the underlying phenomenon. This work presents a method to project the data from some discrete sensory locations to the overall reactor domain during conduction cooldown scenarios similar to High Temperature Gas-cooled Reactors (HTGRs). The existing models for conductive cooldown in a heterogeneous multi-body system, such as an assembly of prismatic blocks or pebble beds relies on knowledge of the thermal contact conductance, requiring significant knowledge of local thermal contacts and heat transport possibilities across those contacts. With a priori knowledge of bulk geometry features and some discrete sensors, a machine learning approach was devised. The presented work uses an experimental facility to mimic conduction cooldown with an assembly of 68 cylindrical rods initially heated to 1200 K. High-fidelity temperature data were collected using an infrared (IR) camera to provide training data to the model and validate the predicted temperature data. The machine learning approach used here first converts the macroscopic bulk geometry information into Graph-Laplacian, and then uses the eigenvectors of the Graph-Laplacian to develop Kernel functions. Support vector regression (SVR) was implemented on the obtained Kernels and used to predict the thermal response in a packed rod assembly during a conduction cooldown experiment. The usage of SVR modeling differs from most models today because of its representation of thermal coupling between rods in the core. When trained with thermographic data, the average normalized error is less than 2% over 400 s, during which temperatures of the assembly have dropped by more than 500 K. The rod temperature prediction performance was significantly better for rods in the interior of the assembly compared to those near the exterior, likely due to the model simplification of the surroundings.

Outages in an overhead power distribution system are caused by multiple environmental factors, such as weather, trees, and animal activity. Since they form a major portion of the outages, the ability to accurately estimate these outages is a significant step towards enhancing the reliability of power distribution systems. Earlier research with statistical models, neural networks, and committee machines to estimate weather-related and animal-related outages has reported some success. In this paper, a deep neural network ensemble model for outage estimation is proposed. The entire input space is partitioned with a distinct neural network in the ensemble performing outage estimate in each partition. A novel algorithm is proposed to train the neural networks in the ensemble, while simultaneously partitioning the input space in a suitable manner. The proposed approach has been compared with the earlier approaches for outage estimation for four U.S. cities. The results suggest that the proposed method significantly improves the estimates of outages caused by wind and lightning in power distribution systems. A comparative analysis with a previously published model for animal-related outages further establishes the overall effectiveness of the deep neural network ensemble.

This paper investigates the problem of proportionally fair double-sided energy auction involving buying and selling agents. The grid is assumed to be operating under islanded mode. A distributed auction algorithm that can be implemented by an aggregator, as well as a possible approach by which the agents may approximate price anticipation is considered. Equilibrium conditions arising due to price anticipation is analyzed. A modified auction to mitigate the resulting loss in efficiency due to such behavior is suggested. This modified auction allows the aggregate social welfare of the agents to be arbitrarily close to that attainable with price taking agents. Next, equilibrium conditions when the aggregator collects a surcharge price per unit of energy traded is examined. A bi-objective optimization problem is identified that takes into account both the agents' social welfare as well as the aggregator's revenue from the surcharge. The results of extensive simulations, which corroborate the theoretical analysis, are reported.

With advancement in the field of structural engineering, hunt for smarter materials has channelised the research towards the application of composite material. It is the high specific weight and specific stiffness of this material that have drawn the interest of different industrial sectors. Civil engineers also picked up composites to use it as a roofing material. Laminated composite shells, which can cover large column-free area and reduces dead weight of structure, show vulnerability under sudden impact due to their low transverse shear resistances. This study utilises finite element tool to investigate the dynamic response of a multilayered laminated composite hypar shells for fully clamped boundary condition. This class of shells is unique in a sense that the curvature has only the radius of cross curvature and these shells do not admit easy closed form solution particularly when the boundary conditions are complicated. Contact behavior of impactor and impacted mass has been modeled by modified Hertzian contact law and time-dependent equations are solved using Newmark’s time integration technique. Basic aim is to analyse the shell for symmetrically placed multilayered angle and cross ply lamination under different impact velocities.