Feasible Conditions Research Articles

Direct amidation of ferrocenyl/ phenyl β-chlorocinnamaldehyde assisted by chalcogenide metal carbonyl cluster

Herein, we report a direct amidation reaction of ferrocenyl/phenyl substituted β-chlorocinnamaldehyde in presence of a robust and chalcogen stabilized iron selenide carbonyl cluster Fe3Se2(CO)9. The amidation of vinylic Csp2-Cl and amides was established and Csp2-N bond formation was obtained at strongly feasible reaction conditions. The wide substrate scope of the reaction has been investigated for functionally different amides and β-chloro derivatives. The present method was found to be superior in terms of the time duration, substrate scope, and use of inexpensive metal. Moreover, 2 mol % of cluster Fe3Se2(CO)9 was found to be highly efficient to couple various substituted primary amides (aliphatic, hetero, aromatics) with β-chlorocinnamaldehyde in just 4 h under aerobic conditions. To the best of our minds, it is the first report where the α, β-unsaturated derivatives of ferrocene have been explored for direct amidation reaction.

The Role of a Child Tax Policy: Population, Human Capital, and Economic Growth

This study analyzes the impact of a child tax policy on the population, human capital, and economic growth in an overlapping generations model with endogenous fertility and human capital. The model shows that the child tax policy inhibits population growth and promotes human capital accumulation, generating a trade-off between the quantity and quality of children. The reduced population and increased human capital both contribute to per capita income. The balance between the negative impact of the child tax policy on the population and the positive impact on per capita income ultimately determines the impact on aggregate growth. The results show that the effect on aggregate growth rate is positive when parents' preference for children is large enough, and negative otherwise. In addition, with the pay-as-you-go pension system, I find that, under feasible conditions, the effect of the child tax policy on pensions is the same as that on total growth.

Finite-Time Stabilization of Markov Switching Singularly Perturbed Models

This brief is concerned with the issue of finite-time stabilization of discrete-time stochastic singularly perturbed models, in which the stochastic process is regulated by a Markov chain with partially unknown transition probabilities (TPs). The slow-state and fast-state variable are considered in the modeling, and the corresponding Markov switching model with a singularly perturbed parameter is obtained in a unified framework. Ill-conditioned problems caused by a small singular perturbation parameter are prevented by developing a finite-time stability criterion for the resultant system. Furthermore, feasible conditions are derived for the desired finite-time state feedback controller by using matrix inequalities that are independent of the singularly perturbed parameter. Finally, a gear-driven DC motor model is applied to illustrate the effectiveness of the described control strategy.

RBF Neural Network Sliding Mode Control for Passification of Nonlinear Time-Varying Delay Systems with Application to Offshore Cranes.

This paper is devoted to studying the passivity-based sliding mode control for nonlinear systems and its application to dock cranes through an adaptive neural network approach, where the system suffers from time-varying delay, external disturbance and unknown nonlinearity. First, relying on the generalized Lagrange formula, the mathematical model for the crane system is established. Second, by virtue of an integral-type sliding surface function and the equivalent control theory, a sliding mode dynamic system can be obtained with a satisfactory dynamic property. Third, based on the RBF neural network approach, an adaptive control law is designed to ensure the finite-time existence of sliding motion in the face of unknown nonlinearity. Fourth, feasible easy-checking linear matrix inequality conditions are developed to analyze passification performance of the resulting sliding motion. Finally, a simulation study is provided to confirm the validity of the proposed method.

Numerical study of heat transport mechanism in hybrid nanofluid [(Cu-Al2O3)/water] over a stretching/shrinking porous wedge

The numerical exploration of the dynamics of hybrid type nanofluid (Cu-Al2O3)/water over a fascinating domain of stretching/shrinking porous wedge is performed. The problem is designed and studied numerically by fluctuating the flow parameters. From the results, dominant behavior of stretching/shrining and suction/injection parameters observed on the velocity field. These are prominent at the wedge surface due to significant influence of suction/injection and stretchable characteristics of wedge. Further study revealed that (Cu-Al2O3)/water has better conductive characteristics than normal Cu/water due to optimum dominant thermophysical values of hybrid nanoparticles. The temperature drops prominently for [Formula: see text] with stretching of wedge. Further, injecting fluid from the wedge surface favors the temperature variations. Finally, the results aligned with published data under certain feasible conditions and found that the conducted study is valid.

Towards a Taylor-Carleman bilinearization approach for the design of nonlinear state-feedback controllers

The Carleman bilinearization is an approach that performs an exact conversion of a finite-dimensional nonlinear system into an infinite-dimensional bilinear system. A finite-dimensional system is later obtained through a truncation for analysis and control purposes. This paper investigates the linear matrix inequality (LMI)-based design of a switched state-feedback control law for the model obtained via Carleman bilinearization of a first-order nonlinear system. It is shown that in order to obtain feasible design conditions, the performance requirements must be relaxed in a neighborhood of the zero equilibrium point, so that problems arising from the uncontrollability of the linear part of the model can be avoided. The effectiveness of the proposed approach is shown using a numerical example and experimental results using a multi-input tank system.

Conditions for Feasibility of a Multicomponent Intervention to Reduce Social Isolation and Loneliness in Noninstitutionalized Older Adults.

Aims: To identify the factors conditioning the feasibility of an intervention to reduce social isolation and loneliness in noninstitutionalized older adults from the perspective of the intervention agents. Design: A Dimensional Grounded Theory study conducted from December 2019 to January 2020. Methods: Twelve participants were recruited from an experimental study developed in a health district of a southern Spanish city. Data were collected through focus group meetings, individual interviews, biograms, anecdote notebooks, and the field diaries of two participants not included in the other techniques. Transcripts were analyzed using thematic analysis. Findings: Findings were divided into three themes: (a) the elderly between the walls of loneliness, economic difficulties, losses, and the past; (b) intervention agents/volunteers between the walls of inexperience in the management of psychological/emotional processes, lack of moral authority, and difficulty in planning results adapted to the (elderly) person; and (c) intervention between the walls of (interest in) company and assistance at home, lack of involvement (“waiting for you to save them”), and withdrawal/“abandonment”. Conclusion: A profile of the specialized intervention agent, professionalized (or at least a mentored agent), with both technical and relational competencies; a clear understanding of the purposes of the intervention (empowerment, as opposed to having company or being helped with household chores) and the commitment to active participation by the elder; or adequate management of the completion of the intervention (flexibility, attachment management) are some of the main factors contributing to the feasibility of these approaches. Impact: The findings have potential implications in the field of primary healthcare because primary and community healthcare services can implement corrections to the proposed intervention and ensure its effectiveness under feasible conditions. The nurse is shown as the most appropriate profile to conduct this intervention, although more research is needed to analyze the feasibility of this type of intervention in the daily practice of community nurses.

Optimization of Photovoltaic and Wind Generation Systems for Autonomous Microgrids With PEV-Parking Lots

Lately, the integration of renewable energy sources (e.g., photovoltaic and wind generation systems) has been raised into microgrids interconnected with plug-in electric vehicles (PEVs). Such intermittent generation and charging/discharging PEV profiles are challenging to ensure the secure and optimal operation of microgrids. In this article, an optimization approach is proposed to determine the optimal locations and sizes of photovoltaic and wind generation systems in microgrids with PEV-parking lots. The developed approach addresses 1) the uncertainty of generation profiles of photovoltaic and wind generation systems and loads; 2) the DSTATCOM feature of photovoltaic and wind generation systems; and 3) microgrid constraints. The feasible PEV conditions are also considered, i.e., initial and preset conditions of their state of charge, arriving and departing times, and various controlled/uncontrolled charging schemes. To solve the planning model, we have developed a bilevel metaheuristic-based approach. The upper-level and lower-level optimization tasks are to optimize the decision variables of renewable energy sources and PEVs, respectively. Both energy losses through the lines and energy from the main grid are considered as subobjectives to be minimized. Various simulations and study cases are performed to assess the effectiveness of the proposed approach. The outcomes show the efficacy of the proposed approach to simultaneously plan renewable energy sources and manage PEVs to form an autonomous microgrid.

Dynamic analysis of the e-SITR model for remote wireless sensor networks with noise and Sokol-Howell functional response

Wireless Sensor network (WSN) is used for a wide range of uses, including defense, healthcare, environmental tracking etc. In this work, we have formulated a Susceptible-infected-terminally infected-recovered (SITR) model to investigate the offensive worm dynamics in remote sensor networks utilizing Sokol-Howell functional response. The positive invariance and boundedness of the model have been investigated in the proposed work. In addition to that, the existence of possible equilibrium points with feasible conditions has been evaluated. The local stability of all equilibrium points and the global stability of the interior steady state of nodes are measured with the Rougth-Hurwitz criteria, Lasalle’s invariance principle, and Bendixson-Dulac criteria. The impact of noise on the proposed model is being investigated. The numerical imitations are carried out, which strengthens the diagnostic discoveries. Our numerical simulation reveals that growth rate, internode interference coefficients, and crashing rates are critical to the model's dynamics.

Optimal Energy-Centric Resource Allocation and Offloading Scheme for Green Internet of Things Using Machine Learning

Resource allocation and offloading in green Internet of Things (IoT) relies on the multi-level heterogeneous platforms. The energy expenses of the platform determine the reliability of green IoT based services and applications. This manuscript introduces a decisive energy management scheme for optimal resource allocation and offloading along with energy constraints. This scheme handles both the allocation and energy-cost in a balanced manner through deterministic task offloading. In particular, resource allocation solution for non-delay tolerant green IoT applications is focused by confining the failures of discrete tasks through neural learning. The dropout process augmented with the learning process improves the feasible conditions for resource handling and task offloading among the active IoT service providers. Through extensive simulations the performance of the proposed scheme is analyzed and energy consumption, failure rate, processing, and completion time metrics are used for a comparative study. Further, the optimal utilization and on-demand dissipation of such stored resources help to improve the sustainability of green power and communication technologies in the smart city environment.

ZnC3-2D a new material for hydrogen reversible storage predicted by first-principles calculations

In order to improve the transport, electronics and storage properties of graphene layers, we have constructed compact structures with graphene sheets. With the Use of the density functional theory (DFT) and ab-initio molecular dynamics (AIMD) calculations including van der Waals interactions, the structural stability and hydrogen storage properties of the neat ZnC3 monolayer have been systematically investigated, the adsorption of H2 molecules on ZnC3 shows good results. A high binding energy up to 0.3169 eV with 3.30 Å as equilibrium distance. A low activation energy allows the molecule to migrate on the surface so easily up to 0.022 eV. The investigation revealed a high gravimetric (GC) and volumetric (Vc) capacities up to 7.73 wt% and 98.44 g/l as a new 2D material for hydrogen storage. The hydrogenation/dehydrogenation (desorption) temperature is expected to be 215.55 K. The desorption temperature (TD) proved to be significantly greater as compared to the critical point of hydrogen (33 K). Furthermore, the TD calculations were confirmed with AIMD simulations of the adsorption of 17 H2 molecule on ZnC3. Thus, the monolayer ZnC3 could function as a reversible hydrogen storage substrate under feasible conditions.

Some sufficient conditions for global asymptotic stability of a hepatitis C virus mathematical model considering host immune system

For a hepatitis C virus (HCV) mathematical model considering host immune system which is studied by Blé et al. (2018), uniform persistence and global asymptotic stability of the endemic equilibrium of the model are considered, and a set of feasible sufficient conditions are obtained by constructing appropriate Lyapunov functions. Our results largely improved those in related works.

A Fit-for-Purpose Method to Measure Circulating Levels of the mRNA Component of a Liposomal-Formulated Individualized Neoantigen-Specific Therapy for Cancer

Autogene cevumeran is an individualized neoantigen-specific therapy (iNeST) under development for the treatment of various solid tumors. It consists of an RNA-Lipoplex (RNA-LPX) in which the encapsulated mRNA molecule encodes up to ten neoepitopes identified from each individual patient. In association with major histocompatibility complex (MHC) class I and MHC class II, these neoantigens can potentially stimulate and expand neoantigen-specific CD4+ and CD8+ T cells, leading to antitumor responses. As part of the pharmacokinetic (PK) property assessment of Autogene cevumeran in patients, both the lipid and mRNA content in circulation are measured. This work focused on our efforts to establish a sensitive and robust method for the measurement of mRNA levels of RNA-LPX in plasma. Due to the chemical characteristics of mRNA, extra precautions are required in order to effectively preserve mRNA integrity in human plasma during sample collection, handling and storage. To this end, a number of sample collection tubes and storage conditions were evaluated in order to inform the most optimal and operationally feasible conditions by which to preserve mRNA integrity during sample collection and upon freeze-thaw. PAXgene Blood ccfDNA tubes successfully prevented mRNA degradation and were subsequently selected for patient sample collection in the clinical trial. A branched DNA (bDNA)-based mRNA PK assay was developed to achieve the desired assay performance. Here, we discuss the evaluation of various sample collection and processing conditions as well as the optimization of the work flow during bDNA PK method development.

Multivariate optimization of the electrochemical degradation for COD and TN removal from wastewater: An inverse computation machine learning approach

An inverse computation machine learning (ML) framework that couples genetic algorithm (GA) to feedforward neural network was developed to optimize the operating parameters for maximizing electrochemical removal of chemical oxygen demand (COD) and total nitrogen (TN) from wastewater. Conventional neural networks (NN) are generally unable to implement inverse computation from a desired abatement to feasible input conditions, and NN trained from small-scale datasets tends to be unreliable for multivariate simulation. To address this issue, we employed GA for tuning the weights and biases of the network to enhance the stability and generalization performance of NN, the optimization of multiple operating conditions was performed by the GA strategy of iterative evolution and global search.In this work, we investigated and analyzed the performance of multivariate optimization approaches such as orthogonal design, response surface methodology (RSM), traditional NN, and the developed neuro-genetic model (GANN). Significance and error analysis demonstrate that GANN exhibits superior stability and generalization ability with the highest R2 of 0.946 (COD), 0.874 (TN), and the lowest RMSE of 0.022 (COD), 0.028 (TN). The introduction of GA is significant for improving the prediction accuracy of NN derived from small-scale datasets. The average error of the GANN trained by 25 data points is lower than that of the RSM model derived from 54 data points. According to the validation outcomes, the scheme suggested by GANN achieves the best COD (93.6%) and TN (62.8%) degradation efficiencies, which are 6.1% and 9.9% higher than the optimal values in the original dataset, respectively. The proposed multivariate global optimization strategy can be extended to other cases, and the computational framework of GANN also contributes to the progress of more reliable ML models.

Online Condition Monitoring of Rotating Machines by Self-Powered Piezoelectric Transducer from Real-Time Experimental Investigations

This paper investigates self-powering online condition monitoring for rotating machines by the piezoelectric transducer as an energy harvester and sensor. The method is devised for real-time working motors and relies on self-powered wireless data transfer where the data comes from the piezoelectric transducer’s output. Energy harvesting by Piezoceramic is studied under real-time motor excitations, followed by power optimization schemes. The maximum power and root mean square power generation from the motor excitation are 13.43 mW/g2 and 5.9 mW/g2, which can be enough for providing autonomous wireless data transfer. The piezoelectric transducer sensitivity to the fault is experimentally investigated, showing the considerable fault sensitivity of piezoelectric transducer output to the fault. For instance, the piezoelectric transducer output under a shaft-misalignment fault is more than 200% higher than the healthy working conditions. This outcome indicates that the monitoring of rotating machines can be achieved by using a self-powered system of the piezoelectric harvesters. Finally, a discussion on the feasible self-powered online condition monitoring is presented.

Two-dimensional GeC3: a reversible, high-capacity hydrogen molecule storage material predicted by first-principles calculations.

In order to improve the transport, electronics and storage of graphene properties layers, we have constructed compact structures with graphene sheets, adsorption of H2 molecules on GeC3 shows good results. A high binding energy up to 0.3628 eV with 3.3024 Å as equilibrium distance. A low activation energy allows the molecule to migrate on the surface so easily up to 0.0042 eV. The investigation revealed a high gravimetric (GC) and volumetric (Vc) capacities up to 7.25 wt% and 98.41 g/dm3 as a new 2D material for hydrogen storage. The hydrogenation/dehydrogenation (desorption) temperature is expected to be 224.25 K. The desorption temperatures of H2 molecules indicate that the monolayer GeC3 could function as a reversible hydrogen storage substrate. Therefore, the results reveal that the GeC3 monolayer is a prospective, efficient, reversible hydrogen storage substrate with high gravimetric capacity under feasible conditions.

Synthesis of Silver Nanoparticles from Vitex negundo Plant by Green Method and their Bactericidal Effects

Emerging nanotechnology has a significant impact on diverse fields such as medical, information technology, energy, advanced materials, textile, and many new upcoming areas. Out of all methods to produce nanoparticles, the green synthesis is the best one as it is cost-effective, non-toxic in nature, simple at feasible conditions. In our study, we are fabricating silver nanoparticles by a single-step green synthesis method using the water extract of the leaves of the Vitex negundo plant. The plant is well known for its rich supply of biologically active compounds. The plant consists of flavonoids, polyphenols, terpenoids, and many more metabolite compounds that are used to prevent many diseases. These help as a capping agent and stabilizing agent in the process of forming silver nanoparticles from silver nitrate solution. Thus, fabricated silver nanoparticles are subjected to characterization techniques such as UV-Visible spectroscopy, FTIR, DLS, SEM with EDX, and XRD to determine silver nanoparticles' size, structure, and shape. Further, the antimicrobial activity of these silver nanoparticles is evaluated against four bacterial strains. Two of them are gram-positive, and the other two are gram-negative. The gram-positive bacteria, namely M. luteus and B. subtilis have shown good activity compared to gram-negative bacteria, namely E. coli and Salmonella. typhi.

Event-based formation control of heterogeneous multiagent systems with leader agent of nonzero input

In this paper, the fully distributed event-triggered time-varying formation control of heterogeneous linear multiagent systems is studied. To make the reference trajectory controllable and flexible, the bounded input is introduced to leader agent. The intention of this paper is to decrease the unnecessary information transmission of agents via communication topology by designing the intermittent transmission control protocol. Firstly, the adaptive state compensator is designed to evaluate the state information of leader agent based on the estimation value of leader state and two kinds of event-triggered mechanisms. Then, two types of output-feedback time-varying formation controllers are presented based on different formation feasible conditions. By theoretical analysis, the estimation error of event-triggered state compensator is proved to be uniformly ultimately bounded and the event-triggered mechanisms prevent Zeno behavior, and the formation tracking errors converge to the small region of zero. Finally, some simulation experiments are provided to support the theoretical results.

A Low Power‐consumption and Transient Nonvolatile Memory Based on Highly Dense All‐Inorganic Perovskite Films

AbstractInorganic perovskite‐based memory devices have attracted tremendous attention due to their higher level of stability, more feasible synthesis conditions and better performance, as compared to organic–inorganic hybrid perovskite‐based devices. However, better film morphology is an essential issue for the performance of these devices. Here, the all‐inorganic halide perovskite cesium lead bromide (CsPbBr3) films are successfully fabricated via a unique solution processable deposition method at ambient conditions. These films possess satisfactory surface morphology with high crystallinity. These are utilized for reproducible resistive switching layers in the gold/CsPbBr3/indium tin oxide/Glass memory applications. A series of resistive switching layers (i.e., CsPbBr3) with varied thicknesses in the range of 200–500 nm are precisely tailored. The resistive switching responses and retention properties of CsPbBr3 based‐memory devices exhibit long‐term retention (exceeding 10000 s), high on/off ratio (up to 10000), low power consumption (set voltage at 0.25V), and good reproducibility. A model for the formation of filaments in the CsPbBr3 layer is proposed to describe the resistive switching mechanism. Furthermore, the devices exhibit excellent transient behavior, which is beneficial for the security of data storage. These characteristics reveal that cesium lead halide based memristor is promising to be utilized as the next‐generation smart memory device.

Fully distributed event-triggered bipartite formation tracking for multi-agent systems with multiple leaders and matched uncertainties

This paper investigates the bipartite time-varying output formation tracking issue for general linear multi-agent systems with multiple leaders and matched uncertainties. The outputs of followers are required to form two antagonistic prescribed time-varying subformations, one of which tracks the convex combination of the outputs of multiple leaders and the other tracks the symmetric convex combination. An independent asynchronous fully distributed event-triggered control protocol is formulated by using relative information between neighboring agents. It is shown that, with the formulated protocol, the bipartite time-varying output formation tracking can be achieved if the feasible formation condition is satisfied. The Zeno behavior is proved to be excluded. Then, we further construct a novel self-triggered control protocol to avoid continuous monitoring of estimate error. Since the protocol incorporates both event-triggered control and adaptive control, it efficiently avoids continuous communication among agents and can be implemented in a fully distributed manner. Moreover, it is noteworthy that in the case where relative information is available while absolute information is not, the protocol is applicable. Finally, the feasibility of the constructed protocols is verified by a numerical example.