Multiple material domain‐based computational tools for ease assessment of modal parameter in photonic crystal fibers

We report here the development of two computational tools PCFPS (Photonic Crystal Fiber Parameter Study) and PCFPA (Photonic Crystal Fiber Parameter Analysis), equipped with graphical user interface (GUI) for modeling of photonic crystal fiber. The tools are based on different structural parameters, and they provide characteristic analysis of the modal parameters from the structural parameters. The main feature of PCFPS is that it enables the user to find out the values of each defining modal parameter that has an immense contribution towards the manufacture of photonic crystal fiber. Additionally, PCFPA allows the user to observe the variation in the modal parameters with respect to the changes in structural parameters (such as d, Λ, d/Λ, and λ/>Λ). Besides their ease of use, these two schemes have high computational precision and adaptability, giving a novel platform to optical engineers to modulate the microstructured fibers according to their requirement.     

4D Printing of Shape Memory Materials for Textiles: Mechanism,Mathematical Modeling,and Challenges

Shape memory materials (SMMs) in 3D printing (3DP) technology garnered much attention due to their ability to respond to external stimuli, which direct this technology toward an emerging area of research, “4D printing (4DP) technology.” In contrast to classical 3D printed objects, the fourth dimension, time, allows printed objects to undergo significant changes in shape, size, or color when subjected to external stimuli. Highly precise and calibrated 4D materials, which can perform together to achieve robust 4D objects, are in great demand in various fields such as military applications, space suits, robotic systems, apparel, healthcare, sports, etc. This review, for the first time, to the best of the authors’ knowledge, focuses on recent advances in SMMs (e.g., polymers, metals, etc.) based wearable smart textiles and fashion goods. This review integrates the basic overview of 3DP technology, fabrication methods, the transition of 3DP to 4DP, the chemistry behind the fundamental working principles of 4D printed objects, materials selection for smart textiles and fashion goods. The central part summarizes the effect of major external stimuli on 4D textile materials followed by the major applications. Lastly, prospects and challenges are discussed, so that future researchers can continue the progress of this technology.     

Eco-efficiency analysis for remote area power supply selection in Western Australia

Clean Technologies and Environmental Policy - Remote area power supply (RAPS) systems in Western Australia account for more than 56% of total off-grid electricity supply in Australia and utilise...     

Multicriteria decision-making using Archimedean aggregation operators in Pythagorean hesitant fuzzy environment

In multicriteria decision-making (MCDM), the existing aggregation operators are mostly based on algebraic t-conorm and t-norm. But, Archimedean t-conorms and t-norms are the generalized forms of t-conorms and t-norms which include algebraic, Einstein, Hamacher, Frank, and other types of t-conorms and t-norms. From that view point, in this paper the concepts of Archimedean t-conorm and t-norm are introduced to aggregate Pythagorean hesitant fuzzy information. Some new operational laws for Pythagorean hesitant fuzzy numbers based on Archimedean t-conorm and t-norm have been proposed. Using those operational laws, Archimedean t-conorm and t-norm-based Pythagorean hesitant fuzzy weighted averaging operator and weighted geometric operator are developed. Some of their desirable properties have also been investigated. Afterwards, these operators are applied to solve MCDM problems in Pythagorean hesitant fuzzy environment. The developed Archimedean aggregation operators are also applicable in Pythagorean fuzzy contexts also. To demonstrate the validity, practicality, and effectiveness of the proposed method, a practical problem is considered, solved, and compared with other existing method.     

Lighting preferences in office spaces concerning the indoor thermal environment

The accurate prediction of the visual comfort zone in an indoor environment is difficult as it depends on many parameters. This is especially the case for large compact urban areas in which the density and shadow from neighboring buildings can limit the accessible daylighting in indoor spaces. This paper investigates the satisfaction range for illuminance regarding indoor air temperature in office buildings and the significant parameters affecting this range in six office buildings in Tehran, Iran. Lighting comfort has been evaluated by a subjective survey (509 total responses) and field measurement. The questionnaires were filled out in 146 and 109 rooms in summer and winter, respectively. The results show that the illuminance should not be less than 550 lx, while illuminance between 600 and 650 lx provides the highest satisfaction level. The satisfaction with lighting level is affected by individual parameters such as age, type of activity, and environmental parameters such as window orientation, external obscurations, and season. A relationship was observed between lighting level satisfaction and thermal condition acceptance, and the overall comfort depends more on thermal conditions than the lighting level.     

Design and fabrication of thermoelectric waste heat reutilization system—possible industrial application

The current article discussed the detail design and development of an experimental test rig to derive usable energy by utilizing the waste heat energy through a heat exchanger made of Bi₂Te₃ material. The accuracy including the efficiency of the fabricated device is demonstrated further by verifying the associated parameter through a simulation model (commercial finite element package, ANSYS 15.0). To imitate the waste hot air from the industry is achieved via a heat gun and fed to the test rig for the generation of thermoelectric power. The simulation model accuracy has been demonstrated by juxtaposing the associated experimental data and computational readings. Subsequently, the feasibility and optimum range of design parameters are established by comparing the experimental and the simulation data (triggered temperature difference, voltage output, and heat flux) generated at the interface of the thermoelectric power generators. In addition, the coefficient of determination (R²) value has been evaluated statistically and verified with the current experimental results for the demonstration of the relevancy. The statistical study shows the existence of the correlation between the current experimental and the simulation model. Also, the experimental result indicates the possible implementation of the newly developed system for the recovery from the waste heat either the automobile exhaust or any other kind of dissipated heat from the industries.     

An automated patient monitoring using discrete‐time wireless sensor networks

In recent years, Internet of Things (IoT) devices are used for remote health monitoring. For remotely monitoring a patient, only the health information at different time points are not sufficient; predicted values of biomarkers (for some future time points) are also important. In this article, we propose a powerful statistical model for an efficient dynamic patient monitoring using wireless sensor nodes through Bayesian Learning (BL). We consider the setting where a set of correlated biomarkers are measured from a patient through wireless sensors, but the sensors only report the ordinal outcomes (say, good, fair, high, or very high) to the sink based on some prefixed thresholds. The challenge is to use the ordinal outcomes for monitoring and predicting the health status of the patient under consideration. We propose a linear mixed model where interbiomarker correlations and intrabiomarker dependence are modeled simultaneously. The estimated and the predicted values of the biomarkers are transferred over the internet so that health care providers and the family members of the patient can remotely monitor the patient. Extensive simulation studies are performed to assess practical usefulness of our proposed joint model, and the performance of the proposed joint model is compared to that of some other traditional models used in the literature.     

Fast removal of Pb(II) and Cu(II) from contaminated water by groundwater treatment waste: impact of sorbent composition

ABSTRACT

A non-hazardous groundwater treatment waste (GWTW) was examined as a low-cost sorbent for Pb(II) and Cu(II) ions. The content of the dominant elements in GWTW was as follows: 78% Fe₂O₃, 7.4% P₂O₅, 7.4% CaO and 5.2% SiO₂. The removal of Pb(II) and Cu(II) was fast, and more than 67–95% of ions were accumulated by GWTW during the first 3 min. The sorption capacity of GWTW depends on solution pH, concentration and temperature. Equilibrium data fitted well with Langmuir–Freundlich and Langmuir-partition models. The inherently formed nano-adsorbent could be utilized for the treatment of water contaminated with Pb(II) and Cu(II) ions.