Search

Rapid construction has led to the use of various new methods in the construction process other than regular concreting. Use of ferrocement technology is one of the rapidly growing methods due to its various advantages like less construction time, cost effectiveness and high strength. In this review, experimental results on various parameters like shear, flexure and bending behavior of ferro cement beams from various journals have been discussed.

In this letter, a model-free co-design scheme of triggering-driven controller is proposed for probabilistic Boolean control networks (PBCNs) in order to achieve feedback stabilization with minimum controller efforts. Specifically, $Q$ -learning ( $Q\text{L}$ ) algorithm is exploited to devise a self-triggered strategy wherein the controller update time is computed in advance by using the current state information. A new self-triggered $Q\text{L}$ (ST $Q\text{L}$ ) algorithm is presented to achieve the co-design of feedback controller and self-triggered scheme rendering the closed-loop system stable at a given equilibrium point. Finally, some examples are presented to demonstrate the effectiveness of the proposed method.

In this letter, the output tracking control design of switched Boolean control networks (SBCNs) is investigated via state feedback and output feedback control. The algebraic state-space representation method which resorts to the semi-tensor product (STP) of matrices is utilized; necessary and sufficient conditions for the solvability of the output tracking control problem are presented. A constructive procedure is given to obtain all possible switching signal-dependent state feedback and output feedback controllers under arbitrary switching signals such that the output of SBCNs tracks a time-varying reference trajectory. Finally, a SBCN model of the lactose regulation in the Escherichia Coli bacteria is considered to illustrate the effectiveness of the proposed results.

In this paper, the set stabilization of switched Boolean control networks (SBCNs) under sampled-data feedback control is addressed. Here, the control input is switching signal-dependent, and SBCNs can switch only at the sampling instants. First, the sampled point control invariant subset (SPCIS) of SBCNs is defined, and an algorithm is provided to obtain the largest SPCIS under arbitrary switching signal. Based on the largest SPCIS, some necessary and sufficient conditions are presented for the set stabilization of SBCNs by switching signal-dependent sampled-data (SSDSD) state feedback control. Furthermore, a constructive procedure is given to design all possible SSDSD state feedback controllers. Finally, some examples are presented to illustrate the effectiveness of the obtained results.

In this work, strategies to devise an optimal feedback control of probabilistic Boolean control networks (PBCNs) are discussed. Reinforcement learning (RL) based control is explored in order to minimize model design efforts and regulate PBCNs with high complexities. A Q-learning random forest (QLRF) algorithm is proposed; by making use of the algorithm, state feedback controllers are designed to stabilize the PBCNs at a given equilibrium point. Further, by adopting QLRF stabilized closed-loop PBCNs, a Lyapunov function is defined, and a method to construct the same is presented. By utilizing such Lyapunov functions, a novel self-triggered control (STC) strategy is proposed, whereby the controller is recomputed according to a triggering schedule, resulting in an optimal control strategy while retaining the closed-loop PBCN stability. Finally, the results are verified using computer simulations.

The efficient handling of nitrogen has become a critical issue in modern agriculture, from a financial standpoint, as well as in regard to reducing the environmental impacts of using an excessive amount of nitrogen fertilizer. Manure compost is useful for maintaining or raising soil chemical levels without excessive NO3− accumulation; however, for the best grain yield, it should be combined with N fertilizer. Via this study, we aimed to develop an optimal decision support system that indicates when to initiate fertilization based on nitrogen-limited (N-limited) crop growth dynamics. An optimal nitrogen fertilizer (N-fertilizer) management system increases crop yield while maintaining a balance between fertilizer supply and crop demand. This study used the N-limited crop growth model (LINTUL3) to develop an optimal decision support system. In this work, we formulated and resolved two optimization challenges: (i) maximization of biomass growth; and (ii) maximization of growth with the least cost paid on N-fertilizer and its application. Furthermore, two case studies were developed based on the number of fields: (i) optimization for a single field, and (ii) optimization for multiple fields. In the case of multiple fields, it is hypothesized that a fertilizer treatment for one field can leak to other fields and affect the nitrogen dynamics of different fields. Finally, numerical simulations were carried out supporting the theory developed in the paper. The simulations showed that when the proposed work was employed to achieve the goal of optimal nitrogen management for a crop, a 28% to 53% increase in biomass growth under certain scenarios was attained.

The maintenance, retrofitting and upgrading of structural members, is one of the most critical issues in structural health maintaining. A unique challenge in strengthening concrete structures is selection of the strengthening method that will improve the strength and serviceability of the structure concerning the limitations of the constructability, building operations and budget. This study is focused on the influences of the various materials, techniques used; on shear, bending and bond strengths of various elements and joints of retrofitted structures. The influence of varying percentages of Nano silica and temperature effects are explained. Use of steel sheets is simple, cheap and has a high mechanical performance but possesses definite disadvantages. Retrofitting with FRP may provide a more economical and technically remarkable alternative to the traditional techniques in many situations. The Nano silica can be replaced upto 30% of Ordinary Portland Cement to enhance the strength of retrofitted structures. Bond strength is not only dependent on resin, but also on fibre thickness.

Concentration solar power (CSP) systems convert solar radiation to heat and use heat engines to convert the heat to electricity. The solar receiver over which the solar radiation is concentrated and converted to heat is the most important part of the CSP. To attain maximum efficiency, the receiver in the CSP systems needs to be coated with an efficient selective solar absorber coating. In recent years, a lot of research has been focused on solar selective coatings. This has resulted in the synthesis of novel coatings that have high thermal and chemical stability, long term durability, and excellent solar selectivity making them suitable for solar thermal applications. This report reviews various solar selective coatings based on transition metals and their compounds. Various failure mechanisms are discussed in detail along with suggested prevention methods. Several thermal stability and durability tests are reported with their benefits and limitations. The effect of long-term durability on the levelized cost of coating is also discussed. Finally, we list some excellent systems and explore different ways of improving the thermal stability for SSCs, thus providing a reference for the design and optimization of new SSCs.

Concentrating solar power is the most challenging and expensive yet highly efficient source of thermal energy from solar power. This is mainly due to the intermittency of the sun rays and expensive materials used to harness its energy. One of the main components adding to the cost is the solar selective absorber materials which are simply put spectrally selective coatings on a receiver system to capture maximum heat from the sun. These materials add to a large extent to the efficiency of converting the sun’s energy to thermal energy and in turn electricity. An ideal solar selective absorber possesses the property of absorbing maximum radiations in the solar spectrum and emit minimum in the thermal energy spectrum. In the current study, an inexpensive, simple and environment-friendly solar selective absorber is fabricated by a galvanic displacement reaction of copper nanoparticles on galvanised metal substrates. These copper nanoparticles have high absorptivity (0.8–0.9) by virtue of plasmon resonance property. The emissivity is low due to the highly reflective metal substrate. By varying size of the copper nanoparticles from 100 nm to 2 μm emissivity and absorptivity can be varied. However, achieving low emissivity and high absorptivity requires some optimising. The size depends on the concentration of precursor solution and immersion time of substrate. One of the remedies for controlling the deposition rate to tune the nanoparticle size and microstructure of deposited copper nanoparticle is by addition of a deposition inhibitor (e.g. Polyethylene glycol).

The disturbance decoupling problem (DDP) whereby the system outputs become insensitive to exogenous signals or disturbances plays a vital role in systems engineering and biological systems. Notably, many biological signalling systems with multiple outputs are usually susceptible to external environmental changes. The authors investigate the DDP for Boolean control networks (BCNs) and present a novel technique based on Boolean algebra to solve the DDP. In particular, the results on the DDP solvability are derived by transforming the system dynamics to a simplified form called output-friendly form. Furthermore, a constructive procedure based on the Karnaugh map to design all possible feedback controllers such that the states affecting the outputs are free from disturbances is proposed. Moreover, the presented results are extended to switched BCNs, and design all possible mode-independent feedback controllers. Finally, some examples including a Boolean model of Escherichia coli are provided to validate the main findings.

In this letter, we study the control of probabilistic Boolean control networks (PBCNs) by leveraging a model-free reinforcement learning (RL) technique. In particular, we propose a Q-learning (QL) based approach to address the feedback stabilization problem of PBCNs, and we design optimal state feedback controllers such that the PBCN is stabilized at a given equilibrium point. The optimal controllers are designed for both finite-time stability and asymptotic stability of PBCNs. In order to verify the convergence of the proposed QL algorithm, the obtained optimal policy is compared with the optimal solutions of model-based techniques, namely value iteration (VI) and semi-tensor product (STP) methods. Finally, some PBCN models of gene regulatory networks (GRNs) are considered to verify the obtained results.