Feasible Conditions Research Articles

Feasible low bone density condition for assessing bioactivity in ex-in vivo and in vivo studies.

To choose a critical animal model for assessments of bone repair with implant installation by comparing senile rats (SENIL) to young ovariectomized rats (OXV). For the ex-in vivo study, the femurs were precursors for bone marrow mesenchymal stem cells. Cellular responses were performed, including cell viability, gene expression of osteoblastic markers, bone sialoprotein immunolocalization, alkaline phosphatase activity, and mineralized matrix formation. For the in vivo study, the animals received implants in the region of the bilateral tibial metaphysis for histometric, microtomography, reverse torque, and confocal microscopy. Cell viability showed that the SENIL group had lower growth than OVX. Gene expression showed more critical responses for the SENIL group (p<0.05). The alkaline phosphatase activity obtained a lower expression in the SENIL group, as for the mineralization nodules (p<0.05). The in vivo histological parameters and biomechanical analysis showed lower data for the SENIL group. The confocal microscopy indicated the presence of a fragile bone in the SENIL group. The microtomography was similar between the groups. The histometry of the SENIL group showed the lowest values (p<0.05). In experimental studies with assessments of bone repair using implant installation, the senile model promotes the most critical bone condition, allowing a better investigation of the properties of biomaterials and topographic changes.

Competitive Equilibriums of Multi-Agent Systems over an Infinite Horizon

In this paper, we investigate the properties of competitive equilibriums for dynamic multi-agent systems (MAS) over an infinite horizon. When there is no external resource supply, a group of dynamic agents with distributed resource allocations form a transactive market to share their resources at a specific price. Each agent makes decisions on the locally consumed resource and the traded resource. The system reaches a competitive equilibrium when each agent's payoff is maximized and the tradings are balanced. Firstly, we consider general utility functions and show that under feasibility assumptions, any competitive equilibrium maximizes the social welfare. Secondly, we prove that for sufficiently small initial conditions, the social welfare maximization solution constitutes a competitive equilibrium with zero price. We also prove for general feasible initial conditions, there exists a time instant after which the optimal price, corresponding to a competitive equilibrium, becomes zero. Finally, we specifically focus on quadratic MAS for which the system-level social welfare optimization becomes a classical constrained linear quadratic regulator (CLQR) problem. We construct explicitly a feasible set of initial conditions under which the price under a competitive equilibrium is zero for all time.

Alternative Ways of LNG Transportation Selection and Risk Management in Perspective of Bangladesh: A Case Study

One of the nations with the densest populations is Bangladesh. The nation's energy use is increasing progressively. One of the greenest forms of energy is LNG. Natural gas, a liquefied gas, uses ultra-low temperatures and purification. In this study, the assessment of LNG carrier vehicles was evaluated from the perspective of Bangladesh. A cryogenic liquid tanker truck, called an LNG carrier vehicle, was found in the survey to carry liquefied natural gas in an environment with extremely low temperatures. Even though LNG is flammable, explosive, cryogenic, and has other characteristics, the safety design of an LNG carrier vehicle is crucial for the security of road transportation. Time consumption analysis and economic section analysis were conducted, and the truck was found to be the ultimate solution. LNG cryogenic liquid storage tanks, trailer running gear, piping systems, a control box, and other parts are often found in LNG carrier vehicles. Utilizing statistical tools, the validity of the survey and its findings will be evaluated by the Statistical Package for the Social Sciences (SPSS). The safety design study for these two systems was the topic of this work. A report on the compatibility of the best method for transporting LNG within all feasible safety conditions will be made possible by the research.

LQG Control Over SWIPT-enabled Wireless Communication Network

In this paper, we consider using simultaneous wireless information and power transfer (SWIPT) to recharge the sensor in the LQG control, which provides a new approach to prolonging the network lifetime. We analyze the stability of the proposed system model and show that there exist two critical values for the power splitting ratio α. Then, we propose an optimization problem to derive the optimal value of α. This problem is non-convex but its numerical solution can be derived by our proposed algorithm efficiently. Moreover, we provide the feasible condition of the proposed optimization problem. Finally, simulation results are presented to verify and illustrate the main theoretical results.

Input‐to‐state practical stability analysis and controller design of switched affine systems: An observer‐based approach

AbstractThis paper addresses the problems of the input‐to‐state practical stability (ISPS) analysis and output feedback controller design for switched affine systems (SASs) subject to external disturbances. First, a switched affine observer is developed to estimate unmeasurable states. Then by combining the sampled‐data control approach, a less conservative mode‐dependent dynamic event‐triggered mechanism (ETM) is established. The proposed dynamic ETM cannot only avoid Zeno behavior but also reduce the network transmission burden effectively. Further, based on time‐dependent Lyapunov‐Krasovskii functional and state‐dependent switching laws, a set of feasible ISPS conditions are presented in the LMI forms by means of singular value decomposition. The designed switching law depends upon the sampled‐data information of the estimated state and gets rid of the chattering phenomenon. Finally, an application example of the DC‐DC flyback converter is given to verify the efficacy of the proposed algorithm.

Cost-Effective Control of Molecular Weight in Ultrasound-Assisted Emulsion Polymerization of Styrene.

This paper focuses on the determination of economically most feasible conditions to obtain polystyrene with various target molecular weights through ultrasound-assisted emulsion polymerization. Briefly, batch polymerizations of styrene have been performed by ultrasound-assisted emulsion polymerization process using different reaction feed compositions. Polymerization rates were calculated using the monomer conversions at various reaction times. Also, molecular weights of the synthesized polymers, as well as the Mark-Houwink constants, were determined by intrinsic viscosity and gel permeation chromatography measurements. It was found that the polydispersity index of the polymers is ranging from 1.2 to 1.5, and the viscosity average molecular weights are in between 100000-1500000 g/mol depending on the reaction conditions. Finally, model equations were also developed for response variables, and the most economical ways of reaching various target molecular weights were interpreted by response surface methodology based multi objective optimization.

Machine learning models representing catalytic activity for direct catalytic CO2 hydrogenation to methanol

Direct catalytic hydrogenation of CO2 to methanol is one of the most attractive ways to meet increasing fuel demand and reduce anthropogenic CO2 emission. An efficient catalyst is required for this highly complex structure-based reaction that simultaneously activates the CO2 molecule along with increased selectivity at feasible operating conditions. Application of machine learning models in catalysis research enables researchers to estimate and develop insights on the catalyst performances.This work focuses on development of machine learning (ML) models that include Multi Linear Regression, Least Absolute Shrinkage Selection Operator, Ridge Regression, Support Vector Regression, Gaussian Process Regression (GPR), Random Forest Regression, Gradient Boost Random Forest Regression (GBRT) and Artificial Neural Network (ANN) using published experimental data (698 datapoints) generated in a fixed bed reactor during the years 2010–2020. CO2 conversion and methanol selectivity were considered as catalytic activity performance indicators. Compared to other ML models, GBRT and ANN model predictions outperformed with R2 ∼ 0.95 and R2 ∼ 0.94 for CO2 conversion and with R2 ∼ 0.95 and R2 ∼ 0.95 for methanol selectivity respectively. Further, the input contributions using ANN models reveal that catalyst composition and calcination temperature are the significant inputs for CO2 conversion and methanol selectivity.

On the dynamics and control of a squirrel locking its head/eyes toward a fixed spot for safe landing while its body is tumbling in air.

An arboreal mammal such as a squirrel can amazingly lock its head (and thus eyes) toward a fixed spot for safe landing while its body is tumbling in air after unexpectedly being thrown into air. Such an impressive ability of body motion control of squirrels has been shown in a recent YouTube video, which has amazed public with over 100 million views. In the video, a squirrel attracted to food crawled onto an ejection device and was unknowingly ejected into air by the device. During the resulting projectile flight, the squirrel managed to quickly turn its head (eyes) toward and then keeps staring at the landing spot until it safely landed on feet. Understanding the underline dynamics and how the squirrel does this behavior can inspire robotics researchers to develop bio-inspired control strategies for challenging robotic operations such as hopping/jumping robots operating in an unstructured environment. To study this problem, we implemented a 2D multibody dynamics model, which simulated the dynamic motion behavior of the main body segments of a squirrel in a vertical motion plane. The inevitable physical contact between the body segments is also modeled and simulated. Then, we introduced two motion control methods aiming at locking the body representing the head of the squirrel toward a globally fixed spot while the other body segments of the squirrel were undergoing a general 2D rotation and translation. One of the control methods is a conventional proportional-derivative (PD) controller, and the other is a reinforcement learning (RL)-based controller. Our simulation-based experiment shows that both controllers can achieve the intended control goal, quickly turning and then locking the head toward a globally fixed spot under any feasible initial motion conditions. In comparison, the RL-based method is more robust against random noise in sensor data and also more robust under unexpected initial conditions.

Generative Modeling to Predict Multiple Suitable Conditions for Chemical Reactions.

In synthesis planning, it is important to determine suitable reaction conditions such that a chemical reaction proceeds as intended. Recent research attempts based on machine learning have proven to be effective in recommending reaction elements for specific categories regarding critical chemical context and operating conditions. However, existing methods can only make a single prediction per reaction and do not directly provide a complete specification of the reaction elements as the prediction. Therefore, their achievable performance is limited. In this study, we propose a generative modeling approach to predict multiple different reaction conditions for a chemical reaction, each of which fully specifies critical reaction elements such that these elements can be directly used as a feasible reaction condition. We formulate the problem of predicting reaction conditions as sampling from a generative distribution. We model the distribution by introducing a variational autoencoder augmented with a graph neural network and learn it from a reaction dataset. For a query reaction, multiple predictions can be obtained by repeated sampling from the distribution. Through experimental investigation on the reaction datasets of four major types of cross-coupling reactions, we demonstrate that the proposed method significantly outperforms existing methods in retrieving ground-truth reaction conditions.

Distributed adaptive practical formation tracking for multi‐agent systems with actuator faults

AbstractThis paper mainly studies the practical fault tolerant formation tracking for multi‐agent systems under directed topology. The objective is to achieve the desired formation in the spatial positions and track the reference signal generated by one and multiple leaders. Considering that the real state information is unavailable, the distributed observer is proposed using the relative information of neighborhood agents, where the observer gain calculation is not constrained by the number of agents. With the adaptive updating mechanism, two effective distributed fault‐tolerant formation tracking protocols are proposed and the feasible condition of time‐varying formation is introduced. Furthermore, based on the proposed control protocol, the formation tracking errors and other signals converge to bounded region around the origin. Towards the end, three simulations are considered to illustrate the effectiveness of the proposed techniques.

Revealing the Phenotypic and Genomic Background for PHA Production from Rapeseed-Biodiesel Crude Glycerol Using Photobacterium ganghwense C2.2.

Polyhydroxyalkanoates (PHA) are promising biodegradable and biocompatible bioplastics, and extensive knowledge of the employed bacterial strain's metabolic capabilities is necessary in choosing economically feasible production conditions. This study aimed to create an in-depth view of the utilization of Photobacterium ganghwense C2.2 for PHA production by linking a wide array of characterization methods: metabolic pathway annotation from the strain's complete genome, high-throughput phenotypic tests, and biomass analyses through plate-based assays and flask and bioreactor cultivations. We confirmed, in PHA production conditions, urea catabolization, fatty acid degradation and synthesis, and high pH variation and osmotic stress tolerance. With urea as a nitrogen source, pure and rapeseed-biodiesel crude glycerol were analyzed comparatively as carbon sources for fermentation at 20 °C. Flask cultivations yielded 2.2 g/L and 2 g/L PHA at 120 h, respectively, with molecular weights of 428,629 g/mol and 81,515 g/mol. Bioreactor batch cultivation doubled biomass accumulation (10 g/L and 13.2 g/L) in 48 h, with a PHA productivity of 0.133 g/(L·h) and 0.05 g/(L·h). Thus, phenotypic and genomic analyses determined the successful use of Photobacterium ganghwense C2.2 for PHA production using urea and crude glycerol and 20 g/L NaCl, without pH adjustment, providing the basis for a viable fermentation process.

Artificial neural network-based model predictive control for optimal operating conditions in proton exchange membrane fuel cells

In the large-scale commercialization of proton exchange membrane fuel cells (PEMFC), efficient control of the dynamic operation requires the consideration of complex nonlinearities in current, temperature, pressure, and membrane hydration. In this study, an artificial neural network (ANN) model was developed and integrated with model predictive control (MPC) to provide optimal conditions for operating the PEMFC system under different current load changes. For this purpose, after modeling a 1.2 kW PEMFC in a MATLAB/Simulink environment, the accuracy of the model was validated within a maximum relative error of 2.30% compared to the experimental data. Next, the power production, temperature, and internal relative humidity of the model were extracted under all feasible operating conditions. The extracted data were used to train the nonlinear autoregressive network with exogenous inputs (NARX), and hyperparameters of neural networks were selected through Bayesian optimization. The NARX model trained with optimal parameters had a mean square error (MSE) of 2.14 × 10−4 and was integrated into the MPC to obtain neural network model predictive control (NNMPC). The developed NNMPC provides the optimal operating temperature and pressure for the PEMFC model at 10-s intervals through iterative simulation of the NARX under Bayesian optimization. To verify the performance of the control system, the developed NNMPC and fixed-setpoint scenario were compared. Each control scenario consisted of three current load profiles: low, medium, and high current. The NNMPC yielded improved system power by maintaining the optimal stack temperature, cathode pressure and membrane hydration for current changes at a low computational cost. The optimized power of the PEMFC system showed an average increase of 10.9% compared to fixed-setpoint condition.

Weber number and the outcome of binary collisions between quantum droplets

A theoretical analysis of binary collisions of quantum droplets under feasible experimental conditions is reported. Droplets formed from degenerate dilute Bose gases made up from binary mixtures of ultracold atoms are considered. Reliable expressions for the surface tension of the droplets are introduced based on a study of low energy excitations of their ground state within the random phase approximation. Their relevance is evaluated considering an estimation of the expected excitation energy having in mind the Thouless variational theorem. The surface tension expressions allow calculating the Weber number of the droplets involved in the collisions. Several regimes on the outcomes of the binary frontal collisions that range from the coalescence of the quantum droplets to their disintegration into smaller droplets are identified. Atoms losses of the droplets derived from self-evaporation and three-body scattering are quantified for both homo- and hetero-nuclear mixtures. Their control is mandatory for the observation of some interesting effects arising from droplets collisions.

Modelación de las condiciones de modificación de almidones para utilizarse como materiales de pared en la nano-encapsulación mediante secado por aspersión

In the present work, time-variable modeling of high-energy mechanical grinding (0, 20, 30, 40, 50, 60, 70, and 80&nbsp;min), was carried out to obtain the best grinding conditions process for obtaining modified starches with applications as wall materials with favorable viscosity and particle size characteristics for the nano-encapsulation of bioactive compounds by means of nano-spray drying. The pasting profile was affected by the different mechanical grinding times, since the maximum viscosity (95 ºC) decreased as the grinding time increased, but this was not the case in the cooling stage (30 ºC) since at times of 30 and 40 min the viscosity was more significant than the maximum viscosity. The modified starches showed larger particle sizes compared to their native counterpart, indicating the formation&nbsp;of agglomerations. The model presents an adequate fit with respect to the experimental data and the feasible and infeasible conditions of this process are represented.

Exponential stability of neural networks with a time-varying delay via a cubic function negative-determination lemma

The problem of global exponential stability of neural networks with a time-varying delay is studied in this article. Firstly, to fully utilize the cross-term relationships among state variables, an improved augmented delay-product-type Lyapunov-Krasovskii functional, including an extra double integral state, is established for the stability analysis. Accordingly, this augmented LKF derivative is a higher-order function of the time-varying delay. Then, three state vectors are considered to reduce the order of the function to cubic. So, to obtain the feasible negative-definiteness condition of this LKF derivative of non-convexity, a negative-determination lemma for cubic functions is employed to handle this problem. As a result, a novel stability criterion is obtained. Two well-known numerical examples illustrate the effectiveness of the criterion.

Static Output Feedback Control for T–S Fuzzy Systems via a Successive Convex Optimization Algorithm

This article focuses on developing a static output feedback (SOF) control scheme for Takagi–Sugeno fuzzy systems. The proposed SOF controller does not share the same premise membership functions with the model, which permits enhancing the flexibility in controller design and implementation. By contrast with the state feedback case, the SOF control generally results in nonconvex design conditions. To circumvent this problem, we develop a successive convex optimization algorithm, which is based on solving a sequence of more tractable convex optimization problems obtained by approximating the nonconvex constraints with some convex ones. As a heuristic algorithm, the validity of the developed successive convex optimization algorithm is highly affected by initial conditions, and, recognizing this, we put forward an iterative procedure for determining the feasible initial condition. Finally, two illustrative examples are presented to validate the efficiency of the proposed algorithms.

A sustainable development pattern integrating data centers and pasture-based agrivoltaic systems for ecologically fragile areas

Advances in information technology facilitate the construction of data centers (DCs), bringing huge social and economic benefits. However, the rapid development of DCs poses a heavy burden on the environment. To promote economic growth while mitigating environmental impacts, this paper proposes a sustainable development pattern that incorporates DCs and pasture-based photovoltaic (PPV) power stations in cities with rich solar energy but fragile ecological environments. An integrated method framework is applied to show the feasibility and potential of the DC-PPV model. A power generation model based on the DC electricity demand and local climate is established to calculate the installation capacity and pasture area of PPV stations, and a cost-benefit analysis is conducted to find the feasible conditions of the DC-PPV model from financial, technical and environmental perspectives. Then, the environmental potential of the DC-PPV is reflected by the GHG emission reduction from three paths, and the socioeconomic contribution is assessed using input-output (IO) analysis. The results show that the model can facilitate the local economy and effectively reduce carbon emissions. Under the planning DC scale in the study area, the model reduces GHG emissions by more than 3 million tons per year. Moreover, it converts more than 1140 hectares of Gobi land to grassland, which increases the carbon stock by 150,262 tCO2e and saves 39 million yuan of desert management.

A Method for Pyrrole Synthesis through Intramolecular Cyclization of γ-Alkynyl Oximes Promoted by SmI2

AbstractA new strategy for the synthesis of pyrrole through intramolecular cyclization of γ-alkynyl oximes promoted by SmI2 is reported for the first time. In contrast to the prior methods, the selection of oxime instead of oxime ether or ester as substrate makes the synthetic method without accompaniment of organic waste (ArCO2H, AcOH, or ROH) because the hydroxyl group in oxime acts as the leaving group and is finally removed in the form of water molecule in the reaction. From a synthetic point of view, a series of pyrroles were obtained in this one-pot reaction with a broad scope of substrates and feasible reaction conditions.

Optimization design of a bionic horse’s leg system driven by a cam-linkage mechanism

For equine-assisted therapy, a bionic horse configuration is presented. A bionic horse’s single leg system has two degrees of freedom (DOFs), which is driven by a cam-linkage mechanism and can adjust the leg’s endpoint trajectory. Based on analyzing the motion law of the bionic horse and the foot trajectory’s requirements of the leg system, a multi-objective optimization model is established based on the foot trajectory performance index. To determine the objective function, the errors between the ideal foot trajectory and the mechanism motion model’s foot trajectory, and the errors between the actual adjustable values and the target values for the motion range are taken as the optimization objective. In terms of the constraint conditions, the structural constraints, kinematic feasible region conditions, pressure angle requirements, et al., are considered. The ideal mechanism parameters are obtained by the intelligent optimization method. The results are verified by simulation modeling and experimental prototype, and the relevant ideas can provide a reference for the research of similar problems.

Cardiac efficiency and Starling's Law of the Heart.

The formulation by Starling of The Law of the Heart states that 'the [mechanical] energy of contraction, however measured, is a function of the length of the muscle fibre'. Starling later also stated that 'the oxygen consumption of the isolated heart … is determined by its diastolic volume, and therefore by the initial length of its muscular fibres'. This phrasing has motivated us to extend Starling's Law of the Heart to include consideration of the efficiency of contraction. In this study, we assessed both mechanical efficiency and crossbridge efficiency by studying the heat output of isolated rat ventricular trabeculae performing force-length work-loops over ranges of preload and afterload. The combination of preload and afterload allowed us, using our modelling frameworks for the end-systolic zone and the heat-force zone, to simulate cases by recreating physiologically feasible loading conditions. We found that across all cases examined, both work output and change of enthalpy increased with initial muscle length; hence it can only be that the former increases more than the latter to yield increased mechanical efficiency. In contrast, crossbridge efficiency increased with initial muscle length in cases where the extent of muscle shortening varied greatly with preload. We conclude that the efficiency of cardiac contraction increases with increasing initial muscle length and preload. An implication of our conclusion is that the length-dependent activation mechanism underlying the cellular basis of Starling's Law of the Heart is an energetically favourable process that increases the efficiency of cardiac contraction. KEY POINTS: Ernest Starling in 1914 formulated the Law of the Heart to describe the mechanical property of cardiac muscle whereby force of contraction increases with muscle length. He subsequently, in 1927, showed that the oxygen consumption of the heart is also a function of the length of the muscle fibre, but left the field unclear as to whether cardiac efficiency follows the same dependence. A century later, the field has gained an improved understanding of the factors, including the distinct effects of preload and afterload, that affect cardiac efficiency. This understanding presents an opportunity for us to investigate the elusive length-dependence of cardiac efficiency. We found that, by simulating physiologically feasible loading conditions using a mechano-energetics framework, cardiac efficiency increased with initial muscle length. A broader physiological importance of our findings is that the underlying cellular basis of Starling's Law of the Heart is an energetically favourable process that yields increased efficiency.