GIS-Based Decision Support System for Safe and Sustainable Building Construction Site in a Mountainous Region

The site selection process for a building entails evaluating a variety of factors with varying degrees of importance or percentage influence. In order to ensure that critical site selection factors are not overlooked, a methodology for calculating a building’s safe site selection must be developed. The study identified three broad aspects widely considered in site selection, namely environmental, physical, and socioeconomic criteria. To assess the safest site selection of residential building construction for sustainable urban growth, we used GIS-based multi-criteria decision-making approach that combined Fuzzy-AHP and weighted linear combination (WLC) aggregation method used to calculate the SSPZ. The final safe site suitability map was generated by aggregating all aspects such as geophysical, socio-economic and Geo-environmental thematic layers and their associated Fuzzy-AHP weights using the weighted linear combination method. The sites potential index’s mean value of 0.513 with standard deviation of 0.340, minimum and maximum GeoPhySSSI are 0.0 and 0.91, respectively, SSS index is classified into zones by histogram profile using natural breaks (jenks)” Subsequently, safe sites identified and divided into six classes namely no construction, very low suitable site low suitable site, moderate suitable site, high suitable site, and very high suitable site.“ According to the statistical analysis, 3.64% and 32.12% of the total area were under very high and high SSSZ, while 26.40% and 6.22% accounted to the moderate and low suitable potential, respectively” Our findings suggest that integrating the fuzzy collection with AHP is highly desirable in terms of alternative and decision-making effectiveness. The study reveals that the areas of high and moderate suitability are located near existing habitant area, major roads, and educational and health services; they are not located in restricted/protected areas or are vulnerable to natural hazards. The findings indicate that unsuitable and less-suitable land uses such as vegetation, protected areas, and agriculture lands cover nearly one-third area of Abha-Khamis Mushyet regions, implying that using Fuzzy-AHP and GIS techniques will significantly aid in the conservation of the environment. This would significantly mitigate adverse effects on the ecosystem and climate.

Component-scaled signal reconstruction for enhanced noise filtration

This research introduces a procedure for signal denoising based on linear combinations of intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). The method, termed component-scaled signal reconstruction, employs the standard EMD algorithm, with no enhancements to decompose the signal into a set of IMFs. The problem of mode mixing is leveraged for noise removal by constructing an optimal linear combination of the potentially mixed IMFs. The optimal linear combination is determined using an optimization routine with an objective function that maximizes and minimizes the information and noise, respectively, in the denoised signal. The method is demonstrated by applying it to a computer-generated voice sample and the displacement response of a cantilever beam with local stiffness nonlinearity. In the first application, the noise is introduced into the sample manually by adding a Gaussian white-noise signal to the signal. In the second application, the response of the entire beam is filmed using two 1-megapixel cameras, and the three-dimensional displacement field is extracted using digital image correlation. The noise in this application arises entirely from the images captured. The proposed method is compared to existing EMD, ensemble EMD, and LMD based denoising approaches and is found to perform better.

Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation

This paper aims at ensuring an efficient recommendation. It proposes a new context-aware semantic-based probabilistic situations injection and adaptation using an ontology approach and Bayesian-classifier. The idea is to predict the relevant situations for recommending the right services. Indeed, situations are correlated with the user’s context. It can, therefore, be considered in designing a recommendation approach to enhance the relevancy by reducing the execution time. The proposed solution in which four probability-based-context rule situation items (user’s location and time, user’s role, their preferences and experiences) are chosen as inputs to predict user’s situations. Subsequently, the weighted linear combination is applied to calculate the similarity of rule items. The higher scores between the selected items are used to identify the relevant user’s situations. Three context parameters (CPU speed, sensor availability and RAM size) of the current devices are used to ensure adaptive service recommendation. Experimental results show that the proposed approach enhances accuracy rate with a high number of situations rules. A comparison with existing recommendation approaches shows that the proposed approach is more efficient and decreases the execution time.

Sooting tendencies of diesel fuel component mixtures follow a linear mixing rule

With the growing importance of climate change, soot emissions from engines have been receiving increasing attention since black carbon is the second largest source of global warming. A sooting tendency can be used to quantify the extent of soot formation in a combustion device for a given fuel molecule, and therefore to quantify the soot reduction benefits of alternative fuels. However real fuels are complex mixtures of multiple components. In this work, we have used experimental methods to investigate how the sooting tendency of a blended fuel mixture is related to the sooting tendencies of the individual components. A test matrix was formulated that includes sixteen mixtures of six components that are representative of the main categories of hydrocarbons in diesel (eicosane (ECO) for alkanes, isocetane (ICE) for isoalkanes, butylcyclohexane (BCH) for cycloalkanes, 1-methylnaphthalene (1MN) for aromatics, tetralin for naphthoaromatics, and methyl-decanoate (MDC) for oxygenates). Most of the mixtures contain three to five components. The sooting tendency of each mixture was characterized by yield sooting index (YSI), which is based on the soot yield when a methane/air nonpremixed flame is doped with 1000 ppm of the test fuel. The YSIs were measured experimentally. The results show that the blending behavior is linear, i.e., the YSI of the mixtures is the mole-fraction-weighted average of the component YSIs. Experimental results have shown that the sooting tendency of a fuel mixture can be accurately estimated as the linear combination of the individual components. In addition, mass density of the mixtures is also measured, and a linear blending rule is applied to test whether mixing rules exist for mass density of diesel mixtures in this study. Results also have shown that the mixing rule tested in this study is valid and mass density of a mixture can be accurately estimated from the linear combination of the individual components.

Does Board Collective Suitability Affect Performance and Risk? Evidence from European Banks

This paper analyzes the features of the boards of large listed European banks and their degree of “collective suitability” as formalized by the Capital Requirements Directives (CRD4) and evaluates whether closer proximity to the collective suitability regulatory paradigm affects banks’ performance, risk and risk-adjusted performance. We leverage Self-Organizing Maps (SOMs) to analyze board features and suitability (i.e. competence, diversity, independence and time commitment) jointly as a multifaceted, non-linear combination of all board variables, rather than evaluating the single variables individually as in the mainstream literature. Using a hand-collected dataset based on numerous features of boards of directors, we find that European banks’ boards can be classified in four different board archetypes characterized by different degrees of collective suitability. Our findings also suggest positive relationships between the degree of collective suitability and performance, risk-adjusted performance, and risk, confirming that the regulatory provisions on governance are going in the right direction, enhancing effective and prudent management.

Axion-like particle oscillations

String theory compactifications may generate many light axion-like particles (ALPs) with weak couplings to electromagnetism. In general, a large number of ALPs may exist, with a linear combination having a potentially observable coupling to electromagnetism. The basis in which only one ALP couples to electromagnetism is in general misaligned with the mass basis. This leads to mixing between the `electromagnetic' ALP and a number of `hidden' ALPs that do not interact directly with the photon. The process is analagous to neutrino oscillations. I will discuss the phenomenological consequences of this mixing for astrophysical ALP signals, in particular showing that it may significantly reduce the predicted signal in experiments such as the CERN Axion Solar Telescope.

Importance of linear combination modeling for quantification of Glutathione and GABA levels using Hadamard-edited MRS

Purpose: Two main approaches are used for spectral analysis of edited data: simple peak fitting and linear combination modeling (LCM) with a simulated basis set. Recent consensus recommended LCM as the method of choice for the spectral analysis of edited data. The aim of this study is to compare the performance of simple peak fitting and LCM in a test-retest dataset, hypothesizing that the more sophisticated LCM approach will improve quantification of HERMES data compared with simple peak fitting. Methods: A test-retest dataset was re-analyzed using Gannet (simple peak fitting) and Osprey (LCM). These data were obtained from the dorsal anterior cingulate cortex of twelve healthy volunteers, with TE 80 ms for HERMES and TE 120 ms for MEGA-PRESS of glutathione (GSH). Within-subject coefficients of variance (CVs) were calculated to quantify between-scan reproducibility of each metabolite estimate. Results: The reproducibility of HERMES GSH estimates was substantially improved using LCM compared to simple peak fitting, from a CV of 19.0% to 9.9%. For MEGA-PRESS data, the GSH reproducibility was similar using LCM and simple peak fitting, with CVs of 7.3% and 8.8% respectively. Conclusion: Linear combination modeling with simulated basis functions substantially improves the reproducibility of GSH quantification for HERMES data.

A Composite Order Generalization of Modular Moonshine

We introduce a generalization of Brauer character to allow arbitrary finite length modules over discrete valuation rings. We show that the generalized super Brauer character of Tate cohomology is a linear combination of trace functions. Using this result, we find a counterexample to a conjecture of Borcherds about vanishing of Tate cohomology for Fricke elements of the Monster.