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The bone inducing factor derived from BF osteosarcoma was purified in the following manner. Step 1. The sarcoma, grown in CBA mice, was excised and lyophilized. Step 2. The powder was washed with chilled acetone. Step 3. The acetone-treated powder was then homogenized with chilled distilled water. Step 4. Washing with 0.15M KCl. Step 5. The precipitate was incubated in in 0.2 N NH2OH, pH7.0, for 48 H at 25 degrees. After Step 5, the bone-forming activity showed a slight increase; however, the factor remained insoluble. The properties of the factor were as follows. The factor is relatively relatively heat stable; the osteogenic activity survived the treatment at 75 degrees for 15 min or at 55 degrees for 19 h. The activity was easily lost by mechanical shaking. Incubation with DNase, RNase, neuraminidase, chondroitinase ABC and beta-galactosidase left the osteogenic activity intact, but treatment with either pronase or collagnease destroyed this activity. The results suggest that the factor may be a protein. The activity was seen with the lyophilized BF osteosarcoma cells (without matrix), and it is probable that the factor was exclusively synthesized in the cells. The bone formation, observed across a millipore filter when living BF osteosarcoma enclosed in a millipore chamber was implanted in mice, suggests the synthesis and secretion of the factor from the cells.

Abstract Analytical solutions for superadiabatic filtration combustion of lean methane-air mixtures in a monolithic porous burner are sought. The one-dimensional, local volume-averaged equations of energy and species conservation that assume a non-thermal equilibrium (i.e., the two-medium treatment), are converted by a coordinate transformation using a combustion wave speed and solved to obtain close-form solutions. A parametric examination varying inlet gas velocity, fuel equivalence ratio, porosity, and thermal conductivity and diffusivity of the solid phase of the porous burner proves the validity of the analytical solutions which are in an excellent agreement with the numerical benchmark. The analytical solutions depict the key features of the filtration combustion such as non-thermal equilibrium between the solid and gas phases, superadiabatic flame temperature, and internal heat recirculation between solid and gas phases of the porous burner.

Abstract This study newly introduces a complementary cooperative algorithm considering generative adversarial network (GAN)-Conditional Latent Space (CLS) combined with bidirectional long short-term memory (BLSTM) for improved and efficient lithium-ion rechargeable battery state prediction. The GAN-CLS algorithm, which is an advanced method of GAN, can generate corresponding images from an input label description. Long short-term memory (LSTM) is a specific recurrent neural network (RNN) architecture that can predict sequences more accurately than conventional RNNs. In terms of battery state prediction, the combination of two methods (GAN-CLS and LSTM) surely provides more improved and efficient rechargeable battery state prediction in contrast to conventional state predictors. The procedure of this study is as follows. First, we propose methods to enhance the data from battery charge/discharge by converting prepared data to images; then, the GAN-CLS method is used to generate corresponding battery data from previous images. Subsequently, the generated data is used to train the BLSTM model. Finally, the trained model is used to predict the battery state. By various experiments and verification, it is concluded that the proposed study can be a good solution for rechargeable battery state prediction (reduction of the time cost 50 times in modeling and 20 times in train/test, provision of a more accurate prediction mean square error (MSE) smaller than 0.0025 and the average MSE less than 0.0013).

Efficiency of energy resource use is a key factor for a sustainable energy future. By matching the exergy levels of supply and demand, Energy Quality Management (EQM) of building energy supply systems may achieve more efficient use of energy resources. Beyond this, environmental impact of energy supply systems is another essential issue. This work addresses the influence of EQM on CO 2 emissions in the operation optimization of a Distributed Energy System (DES). A multi-objective linear programming problem is formulated to reduce energy costs and increase the overall exergy efficiency. Total CO 2 emissions are evaluated for the optimized operation strategies of the DES. The operators of the DES can choose the operation strategy from the Pareto front, based on their priorities and also aware of effects on CO 2 emissions. Results demonstrate more efficient use of energy resources and reduction in CO 2 emissions through EQM, as compared with conventional energy supply systems.

Abstract Dynamic modeling of HVAC system is extremely important for control analysis toward building energy saving. In order to provide researchers a simulation platform to analyze different control strategies, this paper introduces a simulation platform with customized Simulink block library based on dynamic HVAC component model. As an initiating effort, the current simulation platform is composed of basic modular HVAC components, including conduit, damper/valve, fan/pump, flow merge, flow split, heating coil, cooling coil, and zone. These modules are developed into Simulink customized blocks. The simulation platform is capable to calculate the flow rates of fresh air, exhaust air, and return air based on system characteristic and fan curve with customizable basic/advanced control strategies. A case study is proposed using single-zone, constant volume system by comparing the uncontrolled, fixed temperature and damper position controlled, and schedule based reset controlled cases. The simulation result proves that schedule based temperature and damper position reset has a significant impact on energy saving for both heating and cooling seasons. This simulation platform can be especially useful for analyzing the dynamic performance of different HVAC control strategies.

Abstract In this paper, we investigate distributed thermal-electrochemical modeling of a Lithium-Ion battery cell to include the effect of temperature distribution across the thickness of the cell as a first step to study the module level temperature distribution at high charging rates. Most recent works have focused on lumped thermal models for a Li-Ion cell which ignore any temperature differential across cell thickness. However, even a small temperature differential across cell thickness at the cell level can contribute to significant temperature differential in the thickness direction of stacked-up Li-Ion cells at the module level. Such temperature differential can potentially impact the battery charging control system, especially at high charging rates. Here, the thermal-electrochemical partial differential and algebraic equations for a Li-ion cell are solved via a spatial finite difference method. Simulation results show that the temperature differentials over the cell thickness at the cell level are not insignificant, particularly at high charging rates.

Abstract This paper describes the development of a meta-heuristic based algorithm to solve the optimal transmission switching (OTS) problem. The approach solves the mixed-integer non-linear problem considering simultaneous optimization of transmission topology and generation dispatch. The algorithm is the first ever application of particle swarm optimization (PSO) to model OTS and operates using combined real and binary (CRB) variables to solve a weighted sum of interdependent multiple objective functions. The unique stochastic generation principle of combinatorial variables is based on a blend of both uniform and biased Gaussian probability distribution functions. The required binary values of swarms are generated from the continuous values of the random Gaussian distribution with the application of the Heaviside function. All the transmission lines are considered as potential switch variables the operations of which are limited by different aspects. The algorithm can tackle the complex optimization problem with many constraints of varying difficulty. The randomness in CRB variables and unpredictability in switching may generate infeasible particle(s) resulting in island formation. The algorithm also proposes regeneration of these infeasible particle(s) by modifying them to a feasible one to avoid this islanding as well as to have stable switching possibilities. The improvement in the computational efficiency of the algorithm is proposed with the adoption of Micro-PSO for small load deviations. A wide range of unique solutions are obtained based on the preferences of the system operator. This OTS algorithm is tested using the IEEE 57 bus and the IEEE 118 bus system and encouraging results are obtained.

Abstract Global warming is a major challenge that we are facing today that involves the emission of harmful greenhouse gases like carbon dioxide (CO 2 ) into the atmosphere. Pressurized pipelines are considered to be the most efficient and reliable way to transport CO 2 due to the high density and low viscosity of CO 2 . Any accidental discharge from such high pressure pipelines may result in significant damage to the ambient atmosphere and a powerful threat to human health. The unusual phase transition behavior of CO 2 post leak can pose challenging risks for modeling the safe transportation of CO 2 , which is one of the most critical process design considerations in a carbon capture and storage (CCS) area. The current consequence model described in this work predicts the transient jet release rates and the concentration variations of pure CO 2 over a given period of time and distance in Fluent 16.2. This has been validated against experimental work carried out by BP’s DF1 project at the Spadeadam site. The work has been extended to study the effect of terrain on the final downwind concentration of CO 2 . This consequence model could successfully predict the minimum safe distances to populated areas and can be used as a risk assessment tool for planning emergency response in case of pipeline leakage during CO 2 transport.