Search

Flexible/wearable gas sensor technology is gaining huge interest in the current era of the Internet of Things for its applications in personal environmental monitoring, healthcare, and safety. In this respect, the resistive gas sensor has received a great deal of attention in the flexible/wearable sensing field owing to its easy read-out mechanism, low power consumption, and simple fabrication with miniaturization. In the last few years, two-dimensional (2D) materials have sparked significant attention in the development of resistive gas sensors on flexible or wearable sensing platforms due to their excellent electrical, physiochemical, and mechanical properties. In this review, recent research advancements in the field of flexible or wearable gas sensors using emerging 2D materials including graphene, transition metal carbides, and nitrides (collectively known as MXenes), phosphorene, and transition metal dichalcogenides (TMDs) are discussed. The sensing performance of the flexible/wearable gas sensors are explained in detail with the relation between 2D sensing materials and various substrates (i.e. polymer, textile, and paper). In addition, gas sensing applications of 2D materials-based flexible/wearable resistive sensors are highlighted with current challenges to future perspectives. [ABSTRACT FROM AUTHOR]

In the petroleum industry, drilling optimizationinvolves the selection of operating conditions for achievingthe desired depth with the minimum expenditurewhile requirements of personal safety, environment protection,adequate information of penetrated formationsand productivity are fulfilled. Since drilling optimizationis highly dependent on the rate of penetration (ROP), estimationof this parameter is of great importance duringwell planning. In this research, a novel approach called‘optimized support vector regression’ is employed for makinga formulation between input variables and ROP. Algorithmsused for optimizing the support vector regressionare the genetic algorithm (GA) and the cuckoo search algorithm(CS). Optimization implementation improved thesupport vector regression performance by virtue of selectingproper values for its parameters. In order to evaluatethe ability of optimization algorithms in enhancing SVRperformance, their results were compared to the hybridof pattern search and grid search (HPG) which is conventionallyemployed for optimizing SVR. The results demonstratedthat the CS algorithm achieved further improvementon prediction accuracy of SVR compared to the GAand HPG as well. Moreover, the predictive model derivedfrom back propagation neural network (BPNN), which isthe traditional approach for estimating ROP, is selectedfor comparisons with CSSVR. The comparative results revealedthe superiority of CSSVR. This study inferred thatCSSVR is a viable option for precise estimation of ROP.

There is an increasing need for the development of low-cost and highly sensitive gas sensors for environmental, commercial, and industrial applications in various areas, such as hazardous gas monitoring, safety, and emission control in combustion processes. Considering this, resistive-based gas sensors using metal oxide semiconductors (MOSs) have gained special attention owing to their high sensing performance, high stability, and low cost of synthesis and fabrication. The relatively low final costs of these gas sensors allow their commercialization; consequently, they are widely used and available at low prices. This review focuses on the important MOSs with different morphologies, including quantum dots, nanowires, nanofibers, nanotubes, hierarchical nanostructures, and other structures for the fabrication of resistive gas sensors. [ABSTRACT FROM AUTHOR]

In this paper, the improvement of the sensitivity of a capacitive MEMS pressure sensor is investigated. The proposed spring for the sensor can increase the sensitivity. Silicon is used as the substrate and gold and aluminium nitrate are used as the diaphragm and the dielectric layer, respectively. The dimensions of the diaphragm are 150 µm × 150 µm, which is suspended by four springs. The air gap between the diaphragm and the top electrode is 1.5 µm. The proposed structure is an efficient sensor for the pressures in the range of 1–20 kPa. By using the proposed design, the sensitivity of the MEMS sensor in 18 kPa has improved to 663 (× 10−3 pF/kPa). [ABSTRACT FROM AUTHOR]

Deposition of the wax is one of the thorny issues in the petroleum industry, invoking costly problems during the transportation and production of crude oil. Owing to its devastating impacts on oil companies' economy, it is essential to develop a simple and robust strategy for the quantitative estimation of wax deposition. In this paper, support vector regression (SVR) is first proposed to estimate the amount of wax deposition. Subsequently, an artificial neural network (ANN) is developed for wax deposition prediction. Eventually, a sophisticated committee machine (CM) is constructed for combining the results of the SVR and ANN models. Optimal contribution of each model in final prediction of the wax deposit is determined through genetic algorithm in CM. Statistical error analysis shows that the CM model performs better than the individual models performing alone. [ABSTRACT FROM AUTHOR]

Use of intelligence based approach for modeling of crude oil saturation pressure is viable alternative since this parameter plays influential role in the reservoir calculation. The objective of current study is to develop a smart model based on fusing of support vector regression model and optimization technique for learn the relation between the saturation pressure and compositional data viz. temperature, hydrocarbon and non-hydrocarbon compositions of crudes, and heptane-plus specifications. The optimization methods improve performance of the support vector regression (SVR) model through finding the proper value of their free parameters. The optimization methods which embedded in the SVR formulation in this study are genetic algorithm (GA), imperialist competitive algorithm (ICA), particle swarm optimization algorithm (PSO), cuckoo search algorithm (CS), and bat-inspired algorithm (BA). The optimized models were applied to experimental data given in open source literatures and the performance of optimization algorithm was assessed by virtue of statistical criteria. This evaluation resulted clearly show the superiority of BA when integrated with support vector regression for determining the optimal value of its parameters. In addition, the results of aforementioned optimized models were compared with currently available predictive approaches. The comparative results revealed that hybrid of BA and SVR yield robust model which outperform other models in term of higher correlation coefficient and lower mean square error. [ABSTRACT FROM AUTHOR]

Abstract: Over the past decade, several new approaches have emerged to renewable energy education that seek to address the needs of the 21st century for sustainable energy supply systems. In addition, unlike conventional architectural education which does not cover renewable energy issues in its curriculum, these days, renewable energy education has an identity of its own within the discipline of architecture. This paper will clarify academic obstacles that are impeding the development of sustainable architectural education. These obstacles include: ambiguous definitions of sustainable architecture, confusion over the meaning of sustainability, and lack of experts in this field. In the last part of this paper we compare academic architectural education in the field of renewable energy in two countries, Iran as a developing country in Asia and Australia as a developed country in Oceania. Lessons can be drawn from these case studies in regard to the obstacles and progress in the development of sustainable architecture education. [Copyright &y& Elsevier]

Sustainability is a crucial issue for our times and architecture has an important role to play in sustainable development. Buildings are responsible for approximately 40% of the total world annual energy consumption. In addition, over the past decade, several new approaches have emerged for the incorporation of sustainability and renewable energy into architectural education. One way of reducing building energy consumption is to educate architects to design buildings that are powered by renewable forms of energy for heating, lighting, cooling, ventilation and hot water supply. A comparison of sustainable architectural education in economically developed and developing countries provides some insights about how to modernise the architectural curriculum to facilitate the process of sustainable development. The key findings of this research are to: (1) include principles of sustainable development in the core content of all architecture courses, including the role of renewable energy, energy efficiency and embodied energy; (2) offer design studio courses in architectural education that involve the practical use of renewable energy technology; (3) offer short courses and flexible courses, which are particularly attractive to busy professional architects who want to keep their knowledge base up to date; and (4) offer online courses to attract foreign students and that are also suitable for disabled students. [ABSTRACT FROM AUTHOR]

Shear wave velocity (VS) afford petroleum engineers to favorable information for implementing of geomechanical, geophysical, and reservoir characterization studies. Hence, it is all-important to present a scheme for estimation of this parameter. The primary objective of this study is introducing a novel strategy for determining shear wave velocity from seismic data. For achieving the aforementioned objective, four-steps procedure are followed in this study: (1) Suitable seismic attributes (SAs) are selected as independent variables to estimate shear wave velocity by using step-wise regression method; (2) Input variables which selected in the first step are transformed into higher correlated data space through alternation conditional expectation (ACE); (3) Quantitative formulation between shear wave velocity and ACE transformed of input variables is made through three improved models namely optimized neural network (ONN), optimized support vector regression (OSVR), and optimized fuzzy inference system (OFIS). Optimization implementation of intelligence models are achieved through cuckoo search method (CS); (4) Committee machine (CM) using cuckoo search method is employed for integrating optimized models so reaps those benefits. The efficiency of proposed models is assessed founded on statistical parameters. The obtained results corroborate the superb performance of committee machine in preference to its elements. This paper infers the proposed strategy is suitable for modeling of shear wave velocity as a function of seismic data. • In current study, seismic attributes were formulated to shear wave velocity. • Optimized SVR, ANN and FIS and were employed for modeling aforementioned formulation. • ACE was used for simplifying the problem for intelligence models. • The optimization techniques which used in this study was CS. • Committee approach of aforementioned models enhanced accuracy of the prediction compared with individual methods. [ABSTRACT FROM AUTHOR]