Recent findings in action mechanics showing torques result from rates of variation in impulsive action motivated this more fundamental approach to estimate maximum power from wind turbines. Newton’s third law of equality of action and reaction provides a strictly causal mechanism of wind power from the deflection of wind momentum by twice its angle (θ) of incidence on rotor blades. The lateral reaction needed to conserve wind momentum provides the turning moment for the turbine rotors. This direct approach challenges the current continuum mechanism for generating power from flows of kinetic energy in wind passing through the areas swept by rotating blades. Action mechanics integrates the rates of impulsive wind action on turbine blades as torques (∫mrvdθ/dt ≡ mv2) exerted on rotor surfaces at decreasing radii. Windward torque (Tw) is estimated from rotor dimensions, the angle of wind incidence and radial action of wind impulses on the blade surfaces (also ∫mrvdθ/dt ≡ mv2). A leeward torque (Tb) for back reaction of turbine blades on air mimics drag exerted parallel to the plane of rotation of the blade. Net torque is then converted to potential power (Tw - Tb)Ω by the angular velocity (Ω) of the turbine rotors, a function of tip speed ratio to wind speed. New contributions from action mechanics for managing wind power include larger estimates of its possible magnitude by including vortical energy, much larger than the kinetic energy. Better predictions of limits to wind power can be made, by including control of optimal wind angle and blade length. An analysis of the equivalence of deflected air momentum on turbine blades or air foils for aircraft reveals that even the lifting action on air foils can be explained by the normal reaction to the momentum in an air stream, also deflected by an angle twice that (2θ) of incidence, validating application of action mechanics to airflight. A mechanism for release of vortical field energy from laminar flow of air in anticyclones is predicted in turbulent downstream wakes, possibly assisting achievement of maximum power output by wind farms. Significant heat release by downwind turbulence from vortical energy requires care for their location. Diligence demands that use of windfarms as major sources of renewable energy should minimize any environmental impacts, such as drying of landscapes.

Advances in applied mechanics have facilitated a better understanding of the recycling of heat and work in the troposphere. This goal is important to meet practical needs for better management of climate science. Achieving this objective may require the application of quantum principles in action mechanics, recently employed to analyze the reversible thermodynamics of Carnot’s heat engine cycle. The testable proposals suggested here seek to solve several problems including (i) the phenomena of decreasing temperature and molecular entropy but increasing Gibbs energy with altitude in the troposphere; (ii) a reversible system storing thermal energy to drive vortical wind flow in anticyclones while frictionally warming the Earth’s surface by heat release from turbulence; (iii) vortical generation of electrical power from translational momentum in airflow in wind farms; and (iv) vortical energy in the destructive power of tropical cyclones. The scalar property of molecular action (@t ≡ ∫mvds, J-sec) is used to show how equilibrium temperatures are achieved from statistical equality of mechanical torques (mv2 or mr2ω2); these are exerted by Gibbs field quanta for each kind of gas phase molecule as rates of translational action (d@t/dt ≡ ∫mr2ωdϕ/dt ≡ mv2). These torques result from the impulsive density of resonant quantum or Gibbs fields with molecules, configuring the trajectories of gas molecules while balancing molecular pressure against the density of field energy (J/m3). Gibbs energy fields contain no resonant quanta at zero Kelvin, with this chemical potential diminishing in magnitude as the translational action of vapor molecules and quantum field energy content increases with temperature. These cases distinguish symmetrically between causal fields of impulsive quanta (Σhν) that energize the action of matter and the resultant kinetic torques of molecular mechanics (mv2). The quanta of these different fields display mean wavelengths from 10−4 m to 1012 m, with radial mechanical advantages many orders of magnitude greater than the corresponding translational actions, though with mean quantum frequencies (v) similar to those of radial Brownian movement for independent particles (ω). Widespread neglect of the Gibbs field energy component of natural systems may be preventing advances in tropospheric mechanics. A better understanding of these vortical Gibbs energy fields as thermodynamically reversible reservoirs for heat can help optimize work processes on Earth, delaying the achievement of maximum entropy production from short-wave solar radiation being converted to outgoing long-wave radiation to space. This understanding may improve strategies for management of global changes in climate.

Today's extensive requirements for the storage, management, and analysis of complex, dynamic, evolving, distributed, and heterogeneous data from different sources and platforms, e.g., Big data, generate enormous challenges for IT, especially database applications. That is why the demand for data reduction is increasingly coming from the world of databases, intending to reduce the costs of storing, processing, and querying Big data. There is a large number of different techniques for Big data reduction that can cause confusion and complicate this process. Because of that, the authors proposed a Big data reduction framework to structure and present both data reduction techniques and necessary components essential for a better understanding of the process. The importance and the components of the proposed framework are explained in this paper.

A novel method for calculating power output from wind turbines using Newtonian mechanics is proposed. This contrasts with current methods based on interception rates by aerofoils of kinetic energy to estimate power output, governed by the Betz limit of propeller theory. Radial action [mrωrδφ =@, J.sec] generates torques from impulses from air molecules at differing radii on rotor surfaces, both windward and leeward. Dimensionally, torque is a rate of action [(mrωδφ)/δt, MLT, Nm]. Integration of the windward torque [Tw, Nm] is achieved numerically using inputs of rotor dimensions, the angle of incidence (θ) of elastic wind impulse [δMv] on the blade surface, chord and blade lengths and the tip-speed ratio with wind speed. The rate of leeward or back torque [Tb , Nm] in the plane of rotation is estimated from radial impulses from the blade’s rotation on material particles, with magnitude varying with the square of the blade radius and its angular velocity. The net torque (Tw Tb) from these rates of action and reaction is converted to power by its product with the angular velocity of the turbine rotors [P = (Tw Tb)Ω, Watts or J sec], considered as an ideal Carnot cycle for wind turbines; its design should assist optimisation of the aerodynamic elements of turbine operation. A matter of concern must be predictions for a significant rate of heat production by wind turbines, represented partly by the magnitude of the leeward reaction torque but also by a greater release of heat downwind caused by a turbulent cascade in the wake of air flow following its impacts with the blades. Given the widespread occurrence of wind farms as sources of renewable energy and a need to minimise environmental impacts this new method should promote improved theory and practice regarding wind energy.

Our intention is to provide easy methods for estimating entropy and chemical potentials for gas phase reactions. Clausius’ virial theorem set a basis for relating kinetic energy in a body of independent material particles to its potential energy, pointing to their complementary role with respect to the second law of maximum entropy. Based on this partitioning of thermal energy as sensible heat and also as a latent heat or field potential energy, in action mechanics we express the entropy of ideal gases as a capacity factor for enthalpy plus the configurational work to sustain the relative translational, rotational, and vibrational action. This yields algorithms for estimating chemical reaction rates and positions of equilibrium. All properties of state including entropy, work potential as Helmholtz and Gibbs energies, and activated transition state reaction rates can be estimated, using easily accessible molecular properties, such as atomic weights, bond lengths, moments of inertia, and vibrational frequencies. We conclude that the large molecular size of many enzymes may catalyze reaction rates because of their large radial inertia as colloidal particles, maximising action states by impulsive collisions. Understanding how Clausius’ virial theorem justifies partitioning between thermal and statistical properties of entropy, yielding a more complete view of the second law’s evolutionary nature and the principle of maximum entropy. The ease of performing these operations is illustrated with three important chemical gas phase reactions: the reversible dissociation of hydrogen molecules, lysis of water to hydrogen and oxygen, and the reversible formation of ammonia from nitrogen and hydrogen. Employing the ergal also introduced by Clausius to define the reversible internal work overcoming molecular interactions plus the configurational work of change in Gibbs energy, often neglected; this may provide a practical guide for managing industrial processes and risk in climate change at the global scale. The concepts developed should also have value as novel methods for the instruction of senior students.

Despite the remarkable success of Carnot’s heat engine cycle in founding the discipline of thermodynamics two centuries ago, false viewpoints of his use of the caloric theory in the cycle linger, limiting his legacy. An action revision of the Carnot cycle can correct this, showing that the heat flow powering external mechanical work is compensated internally with configurational changes in the thermodynamic or Gibbs potential of the working fluid, differing in each stage of the cycle quantified by Carnot as caloric. Action (@) is a property of state having the same physical dimensions as angular momentum (mrv = mr2ω). However, this property is scalar rather than vectorial, including a dimensionless phase angle (@ = mr2ωδφ). We have recently confirmed with atmospheric gases that their entropy is a logarithmic function of the relative vibrational, rotational, and translational action ratios with Planck’s quantum of action ħ. The Carnot principle shows that the maximum rate of work (puissance motrice) possible from the reversible cycle is controlled by the difference in temperature of the hot source and the cold sink: the colder the better. This temperature difference between the source and the sink also controls the isothermal variations of the Gibbs potential of the working fluid, which Carnot identified as reversible temperature-dependent but unequal caloric exchanges. Importantly, the engine’s inertia ensures that heat from work performed adiabatically in the expansion phase is all restored to the working fluid during the adiabatic recompression, less the net work performed. This allows both the energy and the thermodynamic potential to return to the same values at the beginning of each cycle, which is a point strongly emphasized by Carnot. Our action revision equates Carnot’s calorique, or the non-sensible heat later described by Clausius as ‘work-heat’, exclusively to negative Gibbs energy (−G) or quantum field energy. This action field complements the sensible energy or vis-viva heat as molecular kinetic motion, and its recognition should have significance for designing more efficient heat engines or better understanding of the heat engine powering the Earth’s climates.

Entropy of Vostok ice core data together with our notion of Kalman Filter Harmonic Bank (KFHB) Climate Prediction Engine (CPE) are introduced in this paper. In particular we examine CO2 Cycle 1 data (the most recent data cycle), and analyze so called Spectral Entropy of CO2 harmonics obtained by standard Fast Fourier Transform (FFT) analysis. We also introduce treatment of Vostok Data as a sample from a corresponding non stationary stochastic process for which, instead of FFT, we can use Karhunen-Loeve Expansion (KLE) for a set of discrete data values and the corresponding autocorrelation matrix, defining Representation Entropy as a broader concept compared to Spectral Entropy for FFT. Initial results for Spectral Entropy are presented as a measure of amplitude and energy analysis informational effectiveness which determines a set of signal harmonics implemented in a form of KFHB whereas each harmonic is generated by a two state Kalman Filter. The total signal is then represented as a sum of a set of amplitude or energy significant harmonics (hence the name Kalman Filter Harmonic Bank). Spectral Entropy calculations point to a suitable number of FFT generated harmonics to be used for signal synthesis by harmonic truncation. We also analyze using amplitude vs. energy (amplitude squared) as a base for entropic calculations. Similarly in the case of KLE, Representation Entropy would play the same role. Ultimately we are working to implement this approach into an effective Machine Learning short and long term CPE. It is critical to perform very detailed time and frequency data analysis as a solid base for the CPE methodology for modelling variations in climate.

The Vostok ice core data cover 420,000 years indicating the natural regularity of Earth's surface temperature and climate. Here, we consider four major cycles of similar duration, ranging from 86,000 to 128,000 years, comprising 15% of periods for the warming interglacials compared to some 85% of cooling periods. Globally, we are near the peak of a rapid warming period. We perform a detailed frequency analysis of temperature and CO2 cycles, as a primary stage in building a logical Climate Prediction Engine (CPE), illustrated with specific harmonics. This analysis can be repeated for all harmonics and various cycle combinations. Our time correlation estimates the CO2 time lag for temperature at 400-2300 years, depending on the cycle, longer on average than previously concluded. We also perform Fast-Fourier transform analysis, identifying a full harmonic spectrum for each cycle, plus an energy analysis to identify each harmonic amplitude - to achieve further prediction analysis using a Kalman filter harmonic bank. For Vostok data we can use combinations of different cycles compared to the most recent for learning and then the current ongoing cycle for testing. Assuming causal time regularity, more cycles can be employed in training, hence reducing the prediction error for the next cycle. This results in prediction of climate data with both naturally occurring as well as human forced CO2 values. We perform this detailed time and frequency analysis as a basis for improving the quality of our climate prediction methodologies, with particular attention to testing alternative hypotheses of the possible causes of climate change. These include the effect on albedo of suspended dust and increasing water vapor with temperature in initiating interglacial warming, the effect of temperature and pH values of surface water on ambient level of CO2 in the atmosphere and finding a larger latent heat capacity in the atmosphere required to sustain its circulatory motions, leading to friction and turbulent release of heat in boundary layer. All these potentials can be examined in an effective CPE.

An integrated smart suit sensor and positioning system electronic Internet of Things (IoT) prototype has been developed to address the growing need for personal welfare monitoring of first-line responders, defenders, and workers exposed to industrial or other hazards, as well as other commercial and defense, and new applications in cloud-based IoT. The system provides a global positioning system (GPS) position map with coordinate data, current Greenwich mean time (GMT) readout, subject's heart rate, body temperature, and a long-wave thermal video camera that provides a forward-looking thermal image. Physiologic data and thermal imaging of the subject may be viewed by monitoring personnel using Internet browser connected to the system's static Internet protocol (IP) address. The system is Wi-Fi connected to a local network, which can be extended to enable secure connection to the Internet with incorporation of additional firmware. Details regarding hardware and software configuration are presented along with an appendix containing additional data. Source code for the software modules currently running on the prototype system is also available for interested parties or potential users and customers.

This study introduces Uncertainty Balance Principle (UBP) as a new concept/method for incorporating additional soft data into probabilistic credit risk assessment models. It shows that soft banking data, used for credit risk assessment, can be expressed and decomposed using UBP and thus enabling more uncertainty to be handled with a precise mathematical methodology. The results show that this approach has relevance to credit risk assessment models in the sense that it proved its usefulness for the purpose of soft-hard data fusion, it modified Probability of Default with soft data modeled using possibilistic (fuzzy) distributions and fused with hard probabilistic data via UBP and it obtained better classification prediction results on the overall sample. This was demonstrated on a simple example of one soft variable, two experts and a small sample and thus this is an approach/method that requires further research, enhancements and rigorous statistical testing for the application to a complete scoring and/or rating system

A hypothesis that the increasing application of both surface and ground water for irrigation of crops is a significant source of anthropogenic global warming is tested. In climate models, water is already assigned a major secondary amplifying role in warming, solely as a positive feedback from an atmosphere previously warmed by other GHGs. However, this conclusion ignores the direct anthropogenic forcing from increasing use of water in dry regions to grow crops for the human population. The area irrigated worldwide increased by around 1.5% annually between 1960 and 2000, almost trebling in magnitude. Importantly, though only a small proportion of the Earth’s surface, this additional water vapour is dynamically focussed on dry land, intensifying its potential to elevate the troposphere and reduce the regional OLR. Our modelling analysis suggests that the increase in atmospheric water vapour from irrigation could be significantly more than 1% by 2050 compared to 1950, imposing a global forcing exceeding 1.0 W/m2. Fortunately, this hypothesis can be tested, for example, using the satellite data on OLR acquired since the 1970s, relating this to local trends of increasing irrigation or major floods in arid regions. If found consistent with the data, current proposals to mitigate climate change by limiting combustion of fossil fuels may prove less effective. This prediction regarding the warming effect of increasing irrigation is tested using NCAR reanalysis data made possible by the natural experiments of the periodic flooding of Lake Eyre in Australia's semi-arid interior. It is recommended that this hypothesis be tested using data from local studies in irrigated regions such as changes in outgoing longwave radiation and in increased absorption of incoming shortwave radiation in air.

The periodicity of Vostok ice core climate temperature and gas concentration data indicate inherent long term past regularity of Earth’s climate, with a period of around 100,000 years, warming around 15,000 and cooling of around 85,000 years. At this point we are at the top of one of the warming periods. Vostok data cover around 430,000 years, ie 4 climate cycles (warming-cooling), of similar but not quite the same duration. In this paper we perform a detailed time and frequency analysis of these data for each of the cycles as well as their various combinations, including a full tested period of 430,000 years. Time correlation analysis allows for more accurate time lag estimate in each cycle already noted between temperature change and carbon dioxide content. We estimate these lags to lie between 1000-2500 years, longer than previously concluded. On the frequency side we perform Fast Fourier Analysis and identify full spectrum of harmonics for various cycles, and then perform energy analysis to identify which of the harmonics contributes the most. The idea is to reduce the computational load for further modeling and analysis using Kalman Filter based prediction method. Once the prediction model is defined (a follow up paper) data will be split into two segments, Learning and Testing, in preparation of a Machine Learning fine tunning methodology. We can use last three or last two or even just last cycle to learn on, and then the current on going cycle to test on. This will result in real time prediction of relevant climate data. Assuming causal time regularity, more of these cycles are employed in training, more the prediction error for the next cycle should be reduced. Hence it is critical to perform very detailed time and frequency analysis of Vostok data as a solid data base for the prediction model to follow.