The majority of video displays show images with an 8-bit depth, whether these are desktop monitors, laptop screens or mobile device screens. There are far more expensive and rare 10-bit monitors, but few of them are available to most eyes. Today, most 10-bit color in the TV market are when looking at 4K UHD TVs, while 1080p HDTV is 8-bits.
If you're interested in backing up or digtizing your Blu-ray collection and don't want to held back by copy protection restrictions and slow processing, EaseFab Blu-ray Ripper will be your best pick. It's available for Windows 11, 10, 8.1, 8, 7, etc. And it has a Mac version compatible with macOS Monterey and lower. With it, you can rip a full Blu-ray to MP4 (H.264/HEVC) in 10-15 minutes with best balance in quality & size. Below are the specific features that add a ton of value:
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Insert the Blu-ray disc to the BD drive and then click the Load Disc button to have Blu-ray movies uploaded to EaseFab Blu-ray Ripper. A few seconds later, the full title list of Blu-ray with the auto-checked main title are presented. Besides Blu-ray disc, BDMV Folder, ISO image and VIDEO_TS folder can also be taken as source for decoding.
A compelete Blu-ray is anywhere from 25-50GB and the rip size depends on how nice you want the quality. Simply put, Blu-ray = low compression and mp4 = high compression. Therefore, you can compress down to whatever size you want. The smaller size, the lower quality. Generally, for full length movies (1.5 - 2hrs) to h.264 codec, 1.2-1.8 gigs a movie will give good quality.
Middle molecular weight (MMW) pharmaceuticals (MW 400 4000) are attracting attention for their possible use in new medications. Sharp absorption peaks were observed in MMW pharmaceuticals at low temperatures by measuring with a high-resolution terahertz (THz) spectrometer. As examples, high-resolution THz spectra for amoxicillin trihydrate, atorvastatin calcium trihydrate, probucol, and α,β,γ,δ-tetrakis(1-methylpyridinium-4-yl)porphyrin p-toluenesulfonate (TMPyP) were obtained at 10 K. Typically observed as peaks with full width at half-height (FWHM) values as low as 5.639 GHz at 0.96492 THz in amoxicillin trihydrate and 8.857 GHz at 1.07974 THz for probucol, many sharp peaks of MMW pharmaceuticals could be observed. Such narrow absorption peaks enable evaluation of the crystal quality of MMW pharmaceuticals and afford sensitive detection of impurities.
The NASA/FAA aircraft safety reseach programs for general aviation aircraft are discussed. Energy absorption of aircraft subflooring and redesign of interior flooring are being studied. The testing of energy absorbing configurations is described. The three NASA advanced concepts performed at neary the maximum possible amount of energy absorption, and one of two minimum modifications concepts performed well. Planned full scale tests are described. Airplane seat concepts are being considered.
Supplying peak energy demand in a cost effective, reliable manner is a critical focus for utilities internationally. Successfully addressing peak energy concerns requires understanding of all the factors that affect electricity demand especially at peak times. This paper is based on past attempts of proposing models designed to aid our understanding of the influences on residential peak energy demand in a systematic and comprehensive way. Our model has been developed through a group model building process as a systems framework of the problem situation to model the complexity within and between systems and indicate how changes in one element might flow on to others. It is comprised of themes (social, technical and change management options) networked together in a way that captures their influence and association with each other and also their influence, association and impact on appliance usage and residential peak energy demand. The real value of the model is in creating awareness, understanding and insight into the complexity of residential peak energy demand and in working with this complexity to identify and integrate the social, technical and change management option themes and their impact on appliance usage and residential energy demand at peak times. PMID:25807384
Supplying peak energy demand in a cost effective, reliable manner is a critical focus for utilities internationally. Successfully addressing peak energy concerns requires understanding of all the factors that affect electricity demand especially at peak times. This paper is based on past attempts of proposing models designed to aid our understanding of the influences on residential peak energy demand in a systematic and comprehensive way. Our model has been developed through a group model building process as a systems framework of the problem situation to model the complexity within and between systems and indicate how changes in one element might flow on to others. It is comprised of themes (social, technical and change management options) networked together in a way that captures their influence and association with each other and also their influence, association and impact on appliance usage and residential peak energy demand. The real value of the model is in creating awareness, understanding and insight into the complexity of residential peak energy demand and in working with this complexity to identify and integrate the social, technical and change management option themes and their impact on appliance usage and residential energy demand at peak times.
In this letter, we present a brief comment regarding the recently published paper by Ivković et al., J Quant Spectrosc Radiat Transf 2015;154:1-8. Reference is made to previous experimental results to indicate that self absorption must have occurred; however, when carefully considering error propagation, both widths and peak-separation predict electron densities within the error margins. Yet the diagnosis method and the presented details on the use of the hydrogen beta peak separation are viewed as a welcomed contribution in studies of laser-induced plasma.
Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q -learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q -learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand.
Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q-learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q-learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand. PMID:29206159 2ff7e9595c
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