Identification and Quantification of Key Volatile Flavor Compounds Employing Different Adjunct Starter Cultures in Reduced-fat Cheddar Cheeses by Using GC and GC-MS

Background and Objective: Reduced fat cheese often exhibits poor sensory quality due to the reduction of fat, which plays a critical role in flavor and texture. Therefore, there is the challenge to produce a reduced fat cheese with improved sensory attributes and texture that is also comparable to its full-fat counterpart. The main objective of this research was to investigate the effect of different adjunct starter cultures of Streptococcus thermophilus, Lactococcus helvetius and Lactococcus casei, alone or mixed, on the sensory prperties and volatile flavor compounds of reduced-fat cheddar cheeses (formulated with xanthan gum) and compare them with full-fat cheddar cheese during 75-day ripening.Materials and Methods: Eight treatments according to completely randomized design with a control cheddar cheese (no adjunct starter cultures) were designed. Extraction of the volatiles was carried out using headspace solid phase microextraction. Identification and quantification of volatile flavor compounds were done by gas chromatography-Mass spectrophotometry and gas chromatography, respectively. The sensory analyses were carried out by the 5-point hedonic scale, using trained panelists.Results and Conclusion: Among all the flavor compounds, more than 98% of the headspace volatile flavor compounds belonged to aldehydes, ketons, esters, alcohols, and acids. As compared to non-inoculated full-fat control, the use of Streptococcus thermophilus, Lactococcus casei and Lactococcus helveticus as adjunct starter cultures in the reduced fat cheddar cheese formulation increased the amount of volatile flavor compounds and enhanced the sensory attributes. The combination of this mixed culture with the reduced fat cheddar cheese containing xanthan gum as fat replacer is a viable alternative to improve the quality characteristics of reduced-fat cheddar cheese.Conflict of interest: The authors declare that there is no conflict of interest

June 20, 1990 Earthquake of Northern Iran: An Overview

On the day of June 20, 1990 at 21:00 GMT (June 21, 1990, at 00:30 local time) a destructive earthquake of magnitude 7.6 shook Northern provinces of Iran. It caused widespread geotechnical and structural damages covering an approximate area of 10,000 sq. km resulting in 37,000 life losses, 100,000 injuries, and 100,000 building failures leaving more than 400,000 people homeless. This paper summarizes the post-earthquake investigations and discusses the general engineering aspects of the June 20, 1990 seismological, geotechnical, and structural aspects of the event. Lessons learned are also discussed

Architecting the Future: A Model for Enterprise Integration in the Metaverse

Although it has a history that goes back about three decades, Metaverse has grown to be one of the most talked-about subjects today. Metaverse gradually increased its influence in the realm of business discourse after initially being restricted to discussions about entertainment. Before getting deep into the Metaverse, it should be noted that failure and deviating from the business path are highly likely for an enterprise that relies heavily on information technology (IT) because of improper use and thinking about IT. The idea of enterprise architecture (EA) emerged as a management strategy to address this issue. As the first school of thought of EA, it sought to transform IT from an unnecessary burden in an enterprise to a guiding and supporting force. Then an extended EA model is suggested as a result of the attempt made in this paper to use the idea of EA to steer virtual enterprises on Metaverse-based platforms. Finally, to evaluate the conceptual model and demonstrate that the Metaverse can support businesses, three case studies Decentraland, Battle Infinity, and Rooom were utilized.Comment: 10 pages, 6 figures, 3 table

Climate, Weather, Socio-economic and Electricity Usage Data for The Residential and Commercial Sectors in FL, U.S

This paper presents the data that is used in the article entitled “Climate sensitivity of end-use electricity consumption in the built environment: An application to the state of Florida, United States” (Mukhopadhyay and Nateghi, 2017) [1]. The data described in this paper pertains to the state of Florida (during the period of January 1990 to November 2015). It can be classified into four categories of (i) state-level electricity consumption data; (ii) climate data; (iii) weather data; and (iv) socio-economic data. While, electricity consumption data and climate data are obtained at monthly scale directly from the source, the weather data was initially obtained at daily-level, and then aggregated to monthly level for the purpose of analysis. The time scale of socio-economic data varies from monthly-level to yearly-level. This dataset can be used to analyze the influence of climate and weather on the electricity demand as described in Mukhopadhyay and Nateghi (2017

A multi-paradigm framework to assess the impacts of climate change on end-use energy demand

Projecting the long-term trends in energy demand is an increasingly complex endeavor due to the uncertain emerging changes in factors such as climate and policy. The existing energy-economy paradigms used to characterize the long-term trends in the energy sector do not adequately account for climate variability and change. In this paper, we propose a multi-paradigm framework for estimating the climate sensitivity of end-use energy demand that can easily be integrated with the existing energy-economy models. To illustrate the applicability of our proposed framework, we used the energy demand and climate data in the state of Indiana to train a Bayesian predictive model. We then leveraged the end-use demand trends as well as downscaled future climate scenarios to generate probabilistic estimates of the future end-use demand for space cooling, space heating and water heating, at the individual household and building level, in the residential and commercial sectors. Our results indicated that the residential load is much more sensitive to climate variability and change than the commercial load. Moreover, since the largest fraction of the residential energy demand in Indiana is attributed to heating, future warming scenarios could lead to reduced end-use demand due to lower space heating and water heating needs. In the commercial sector, the overall energy demand is expected to increase under the future warming scenarios. This is because the increased cooling load during hotter summer months will likely outpace the reduced heating load during the more temperate winter months

Predicting Urban Reservoir Levels Using Statistical Learning Techniques

Urban water supplies are critical to the growth of the city and the wellbeing of its citizens. However, these supplies can be vulnerable to hydrological extremes, such as droughts and floods, especially if they are the main source of water for the city. Maintaining these supplies and preparing for future conditions is a crucial task for water managers, but predicting hydrological extremes is a challenge. This study tested the abilities of eight statistical learning techniques to predict reservoir levels, given the current hydroclimatic conditions, and provide inferences on the key predictors of reservoir levels. The results showed that random forest, an ensemble, tree-based method, was the best algorithm for predicting reservoir levels. We initially developed the models using Lake Sidney Lanier (Atlanta, Georgia) as the test site; however, further analysis demonstrated that the model based on the random forest algorithm was transferable to other reservoirs, specifically Eagle Creek (Indianapolis, Indiana) and Lake Travis (Austin, Texas). Additionally, we found that although each reservoir was impacted differently, streamflow, city population, and El Niño/Southern Oscillation (ENSO) index were repeatedly among the most important predictors. These are critical variables which can be used by water managers to recognize the potential for reservoir level changes

Natural convection flow of air in an inclined open cavity

Consider natural convection flow in a two dimensional inclined open cavity for both transient and steady-state flow. The left hand vertical wall of cavity is heated and facing an opening. The top and bottom boundaries are insulated. Numerical solutions are obtained for the Navier-Stokes equations on a non-staggered grid using a finite volume scheme. Results are considered for a wide range of Rayleigh numbers varying from 10 5 to 10 10 with Prandtl number 0.7 and inclination angles from 10 ? to 90 ? . The flow is steady at the low Rayleigh number for all angles and becomes unsteady at the high Rayleigh number for all angles. The critical Rayleigh numbers for all angles are obtained and we show that the critical Rayleigh number decreases as the inclination angle increases