Elucidation of interactive effects of synthesis conditions on the characteristics of mesoporous silicas templated using polyoxide surfactant

A series of mesoporous silicas (MS-1-MS-9) were synthesized at different gel compositions using a triblock copolymer (TCP), poly(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide), as the surfactant. The interactive effects of acidity, the contents of tetraethyl orthosilicate (TEOS) and the surfactant, and the gelling temperature on the characteristics of the final material were simultaneously characterized. Increasing acidity favored mesopore formation. A material with a surface area of 760 m²/g, mostly in the mesoporous range, was obtained at 1.0(TEOS):0.017(TCP):7.3HCl:115.7H₂O. Mesopore formation was predominantly determined by the TEOS:TCP ratio and was promoted with its increase from 1.56:1 to 2.09:1. A further increase to 2.61:1 was detrimental. Whereas increasing the TCP content to 3.5% w/w improved micellization, a further increase to 4.6% should be avoided. Mesoporous silicas showed low crystallinity but a high degree of hexagonal mesoscopic organization. The weak surface acidity was attributed to surface silanols, the number of which was proportional to mesoporosity.     

MMU GASPFA: A COTS multimodal biometric database

This paper describes the baseline corpus of a new multimodal biometric database, the MMU GASPFA (Gait–Speech–Face) database. The corpus in GASPFA is acquired using commercial off the shelf (COTS) equipment including digital video cameras, digital voice recorder, digital camera, Kinect camera and accelerometer equipped smart phones. The corpus consists of frontal face images from the digital camera, speech utterances recorded using the digital voice recorder, gait videos with their associated data recorded using both the digital video cameras and Kinect camera simultaneously as well as accelerometer readings from the smart phones. A total of 82 participants had their biometric data recorded. MMU GASPFA is able to support both multimodal biometric authentication as well as gait action recognition. This paper describes the acquisition setup and protocols used in MMU GASPFA, as well as the content of the corpus. Baseline results from a subset of the participants are presented for validation purposes.     

Comparing the performance of multispectral vegetation indices and machine-learning algorithms for remote estimation of chlorophyll content: a case study in the Sundarbans mangrove forest

Optical vegetation indices (VIs) have been used to retrieve and assess biophysical variables from satellite reflectance data. These indices, however, also are sensitive to a number of confounding factors, such as canopy geometry, soil optical properties, and solar position. This suggests that VIs should be used cautiously for biophysical parameter estimation. Among biophysical variables, chlorophyll content is of particular importance as an indicator of photosynthetic activity. The goal of this study is to investigate the performance of multispectral optical VIs for chlorophyll content estimation in the world’s largest mangrove forest, the Sundarbans, and to compare these with machine-learning algorithms (MLAs). To this end, we have investigated the performance of 15 multispectral VIs and six state-of-the-art MLAs that are widely used for adaptive data fitting. The MLAs are Artificial Neural Networks (ANNs), Genetic Algorithm (GA), Gaussian Processes for Machine Learning (GPML), Kernel Ridge Regression (KRR), Locally Weighted Polynomials (LWP), and Multivariate Adaptive Regression Splines (MARS). We use an in situ data set of reflectance and chlorophyll measurements to develop and validate our models. Each MLA was evaluated 500 times with random partitions of training and validation data. Results showed that the weight optimization and term selection used within GA produce the most reliable chlorophyll content estimation. However, green normalized difference VI (GNDVI) is a simple and computationally efficient VI that produces results that are nearly as accurate as GA in terms of model fit and performance. Results also show that all methods except ANNs and MARS produce a quasi-linear relationship between spectral reflectance and chlorophyll content. Statistical transformations of GNDVI and chlorophyll content have the capability of further reducing model error.     

The Meta-Protocol framework

A communication protocol is a set of rules shared by two or more communicating parties on the sequence of operations and the format of messages to be exchanged. Standardization organizations define protocols in the form of recommendations (e.g., RFC) written in technical English, which requires a manual translation of the specification into the protocol implementation. This human translation is error-prone due in part to the ambiguities of natural language and in part due to the complexity of some protocols. To mitigate these problems, we divided the expression of a protocol specification into two parts. First, we designed an XML-based protocol specification language (XPSL) that allows for the high-level specification of a protocol—expressed as a Finite State Machine (FSM)—using Component-Based Software Engineering (CBSE) principles. Then, the components required by the protocol are specified in any suitable technical language (formal or informal). In addition, we developed the multi-layer Meta-Protocol framework, which allows for on-the-fly protocol discovery and negotiation, distribution of protocol specifications and components, and automatic protocol implementation in any programming language.     

Performance evaluation of a geothermal based integrated system for power,hydrogen and heat generation

In the present study, an integrated system is proposed and thermodynamically analyzed to reduce greenhouse gas (GHG) emissions while improving overall system performance. The integrated system is comprised of a supercritical carbon dioxide (CO₂) Rankine cycle cascaded by an Organic (R600) Rankine cycle, an electrolyzer, and a heat recovery system. It is designed to utilize a medium-to-high temperature geothermal energy source for power and hydrogen production, and thermal energy utilization for space heating. Therefore, parametric studies for the supercritical CO₂ cycle, the Organic (R600) cycle, and the overall system are conducted. In addition, the effect of various operational conditions, such as geothermal source, ambient and cooling water temperatures on the performance of each cycle and the integrated system, is illustrated. It is found that increasing geothermal source temperature results in slight increases of the exergetic efficiency of the overall system. The energy efficiencies of the CO₂ and Organic Rankine cycles do not considerably vary with source temperature changes. The decay of the cooling water temperature leads to a decrease in the overall system exergetic efficiency. The system configuration, which is introduced, is capable of producing about 180 kg/h for the geothermal source of mass flow rate of 40 kg/s and a temperature of 473 K.     

A new relaxed flexible bay structure representation and particle swarm optimization for the unequal area facility layout problem

The facility layout problem (FLP) with unequal area departments is a very hard problem to be optimally solved. In this article, a hybrid particle swarm optimization (PSO) and local search approach is proposed to solve the FLP with unequal area departments. The flexible bay structure (FBS), which is a very common layout in manufacturing and retail facilities, is used. Furthermore, the FBS is relaxed by allowing empty spaces in bays, which results in more flexibility while assigning departments in bays. The proposed PSO approach is used to solve the FLP instances from the literature with varying sizes. The comparative results show that the PSO approach is very promising and able to find the previously known-optimal solutions in very short CPU times. In addition, new best solutions have been found for some test problems. Improvements have been achieved by allowing partially filled bays.     

Effect of expandable graphite and ammonium polyphosphate on the thermal degradation and weathering of intumescent fire-retardant coating

This research aims to study the effect of ammonium polyphosphate and expandable graphite on the intumescent coating formulations (ICF). The coating presented in this research article is based on carbon source expandable graphite (EG), blowing agent melamine, acid source ammonium polyphosphate (APP), epoxy resin as a binder with polyamide amine. The stability of the developed coating was verified at 950°C for 1-hour fire test. The results showed that the coating is stable and well bond with the steel substrate. The char was characterized by using FESEM, XRD, FTIR, DTA, TGA, XPS, Py-GCMS and Weathering Test. The morphology of the char was studied by SEM of the coating after furnace fire test. XRD and FTIR show the presence of graphite, borophosphate; boron oxide and sassolite in the residual char. TGA and DTG disclosed that EG improved the residual mass of coating. XPS analysis showed the char residue of IF5-APP-EG contains carbon and oxygen contents 47.50 and 40.70, respectively. Py-GCMS analysis described that the IF5-APP-EG released less gaseous compounds. The weathering test illustrated that's the char expansion of coatings samples was decreased due to the presence of a humid environment and UV light. The IF5-APP-EG showed the maximum char expansion, lower substrate temperature and high residual weight among the studied formulations.     

Multiattribute group decision-making based on Pythagorean fuzzy Einstein prioritized aggregation operators

Pythagorean fuzzy set (PFS) is a powerful tool to deal with the imprecision and vagueness. Many aggregation operators have been proposed by many researchers based on PFSs. But the existing methods are under the hypothesis that the decision-makers (DMs) and the attributes are at the same priority level. However, in real group decision-making problems, the attribute and DMs may have different priority level. Therefore, in this paper, we introduce multiattribute group decision-making (MAGDM) based on PFSs where there exists a prioritization relationship over the attributes and DMs. First we develop Pythagorean fuzzy Einstein prioritized weighted average operator and Pythagorean fuzzy Einstein prioritized weighted geometric operator. We study some of its desirable properties such as idempotency, boundary, and monotonicity in detail. Moreover we propose a MAGDM approach based on the developed operators under Pythagorean fuzzy environment. Finally, an illustrative example is provided to illustrate the practicality of the proposed approach.     

Leakage mitigation in NW FET using negative Schottky junction drain and its process variation analysis

Journal of Computational Electronics - In this work, a Schottky junction on the drain side employing low workfunction (WF) metal is proposed as a method to suppress the OFF-state leakage in...