Silicon Nanoparticles and Methyl Jasmonate Improve Physiological Response and Increase Expression of Stress-related Genes in Strawberry cv. Paros Under Salinity Stress

Silicon - Salinity is one of the most crucial abiotic stresses, which is the consequence of an increase in the concentration of NaCl ions, influencing the plant’s growth, development, and...     

Uncertainty reduction in residual stress measurements by an optimised inverse solution using nonconsecutive polynomials

Many destructive methods for measuring residual stresses such as the slitting method require an inverse analysis to solve the problem. The accuracy of the result as well as an uncertainty component (the model uncertainty) depends on the basis functions used in the inverse solution. The use of a series expansion as the basis functions for the inverse solution was analysed in a previous work for the particular case where functions orders grew consecutively. The present work presents a new estimation of the model uncertainty and a new improved methodology to select the final basis functions for the case where the basis is composed of polynomials. Including nonconsecutive polynomial orders in the basis generates a larger space of possible solutions to be evaluated and allows the possibility to include higher-order polynomials. The paper includes a comparison with two other inverse analyses methodologies applied to synthetically generated data. With the new methodology, the final error is reduced and the uncertainty estimation improved.     

Estimation and fault diagnosis for non-linear system with time-varying faults and measurement noises: Application on two CSTRs in series

A continuously stirred tank reactor (CSTR) is largely used in water treatment and in chemical and biological processes. It is characterized by a complex non-linear behaviour. Operating large reactors in industry can be expensive, so a common trick used to reduce costs is to operate multiple CSTRs in series. Consequently, the CSTR is usually exposed to faults and noises. This paper addresses the design of a robust observer for estimation and fault diagnosis strategy on two CSTRs in series. The considered system is affected simultaneously by time-varying actuator and sensor faults with measurement noises. The Takagi-Sugeno multimodel approach is proposed to transform the non-linear model into an interpolation of several linear sub-models with non-measurable premise variables. The purpose of this brief is to provide the state and the fault estimation for the considered system using a proportional multiple integral (PMI) unknown input observer. The exponential stability conditions are studied with the Lyapunov theory and L2 optimization and formulated in terms of linear matrix inequalities. In addition, a comparative study with a PMI observer is conducted. Finally, the proposed observer is used for time-varying fault detection and isolation.     

Chitosan-Based Smart Polymeric Hydrogels and Their Prospective Applications in Biomedicine

With increasing scientific research, knowledge, and socioeconomic awareness, research, and industrial communities are now much more interested in biopolymers-based composite materials with multifaceted functionalities. Biopolymers are easily obtainable, economical, non-hazardous, and abundant. Several natural and synthetic polymers have been used to prepare hydrogels, microbeads, and microfibers for controlled drug release. Due to marvelous properties, chitosan has been widely explored for medical and pharmaceutical applications. In recent years, considerable attention has been paid to the chitosan-based hydrogels for biomedical applications, especially for transplantation, and for improving interfacial interactions for the living tissues. Hydrogels can be synthesized by co-polymerization, condensation, and cross-linking. This review spotlights recent advances in physicochemical characteristics, modification strategies, and smart blended hydrogels for an array of biomedical and pharmaceutical applications. The whole discussion reveals that among all the biopolymers, chitosan is the finest carrier for biomedical applications.     

Energy-efficient selection of relay for UWSNs in the Internet of underwater things

Internet of underwater things (IoUT) for underwater monitoring is known worldwide for smart interlinked underwater things that exhibit the capacity to monitor the vast unexplored waters of the oceans. Concept of IoUT has been derived from Internet of Things (IoT) in order to acquire the exquisite benefits of networking in underwater environment. IOT standards and technologies do not work well in underwater environment, such as infrared, Wi-Fi, and radio frequency (RF) due to high channel errors and limited range up to few meters. Acoustic waves, however, can be used to communicate both in shallow and deep oceans due to their low frequency (kHZ) signal. In context of IoUT, communication based on acoustic links enables different applications such as underwater exploration, environmental monitoring, and disaster prevention even without availability of GPS facility like free space environment. In unpredictable and changing underwater environment, energy efficiency becomes a major challenge during data routing along multiple devices. Batteries of the sensor nodes, autonomous underwater vehicle (AUV), and remotely operated vehicle (ROV) cannot be removed with easiness and difficult to recharge, and the only way out is efficient sensor node selection for relaying to save massive amount of energy. Energy aware channel routing protocol (ECARP) does not consider the depth of the node while selecting the relay nodes to forward the data. Relay node selection in underwater Internet of things (IOUT) is a primary problem addressed in this research based on channel state information (CSI) for establishing best path to relay information among IOUT devices. Our major focus was to develop better technique for the relay node selection using a CSI and select relay node by looking at its depth from ocean surface and residual energy in the proposed ED-CARP. Simulation results validate that proposed ED-CARP can decrease the communication cost and increase the network lifetime.     

Development of Magnetocaloric Microstructures from Equiatomic Iron–Rhodium Nanoparticles through Laser Sintering

Pronounced magnetocaloric effects are typically observed in materials that often contain expensive and rare elements and are therefore costly to mass produce. However, they can rather be exploited on a small scale for miniaturized devices such as magnetic micro coolers, thermal sensors, and magnetic micropumps. Herein, a method is developed to generate magnetocaloric microstructures from an equiatomic iron–rhodium (FeRh) bulk target through a stepwise process. First, paramagnetic near-to-equiatomic solid-solution FeRh nanoparticles (NPs) are generated through picosecond (ps)-pulsed laser ablation in ethanol, which are then transformed into a printable ink and patterned using a continuous wave laser. Laser patterning not only leads to sintering of the NP ink but also triggers the phase transformation of the initial γ- to B2-FeRh. At a laser fluence of 246 J cm⁻², a partial (52%) phase transformation from γ- to B2-FeRh is obtained, resulting in a magnetization increase of 35 Am² kg⁻¹ across the antiferromagnetic to ferromagnetic phase transition. This represents a ca. sixfold enhancement compared to previous furnace-annealed FeRh ink. Finally, herein, the ability is demonstrated to create FeRh 2D structures with different geometries using laser sintering of magnetocaloric inks, which offers advantages such as micrometric spatial resolution, in situ annealing, and structure design flexibility.     

Solar Radiation Prediction Using Satin Bowerbird Optimization with Modified Deep Learning

Solar energy will be a great alternative to fossil fuels since it is clean and renewable. The photovoltaic (PV) mechanism produces sunbeams’ green energy without noise or pollution. The PV mechanism seems simple, seldom malfunctioning, and easy to install. PV energy productivity significantly contributes to smart grids through many small PV mechanisms. Precise solar radiation (SR) prediction could substantially reduce the impact and cost relating to the advancement of solar energy. In recent times, several SR predictive mechanism was formulated, namely artificial neural network (ANN), autoregressive moving average, and support vector machine (SVM). Therefore, this article develops an optimal Modified Bidirectional Gated Recurrent Unit Driven Solar Radiation Prediction (OMBGRU-SRP) for energy management. The presented OMBGRU-SRP technique mainly aims to accomplish an accurate and time SR prediction process. To accomplish this, the presented OMBGRU-SRP technique performs data preprocessing to normalize the solar data. Next, the MBGRU model is derived using BGRU with an attention mechanism and skip connections. At last, the hyperparameter tuning of the MBGRU model is carried out using the satin bowerbird optimization (SBO) algorithm to attain maximum prediction with minimum error values. The SBO algorithm is an intelligent optimization algorithm that simulates the breeding behavior of an adult male Satin Bowerbird in the wild. Many experiments were conducted to demonstrate the enhanced SR prediction performance. The experimental values highlighted the supremacy of the OMBGRU-SRP algorithm over other existing models.     

Hierarchical NiCo@NiOOH@CoMoO4 Core–Shell Heterostructure on Carbon Cloth for High-Performance Asymmetric Supercapacitors

The fabrication of low-cost, effective, and highly integrated nanostructured materials through simple and reproducible methods for high-energy-density supercapacitors is highly desirable. Herein, an activated carbon cloth (ACC) is designed as the functional scaffold for supercapacitors and treated hydrothermally to deposit NiCo nanoneedles working as internal core, followed by a dip-dry coating of NiOOH nanoflakes core–shell and uniform hydrothermal deposition of CoMoO₄ nanosheets serving as an external shell. The structured core–shell heterostructure ACC@NiCo@NiOOH@CoMoO₄ electrode resulted in exceptional specific areal capacitance of 2920 mF cm⁻² and exceptional cycling stability for 10 000 cycles. Moreover, the fabricated electrode is developed into an asymmetric supercapacitor which demonstrates excellent areal capacitance, energy density, and power density within the broad potential window of 1.7 V with a cycling life of 92.4% after 10 000 charge–discharge cycles, which reflects excellent cycle life. The distinctive core–shell structure, highly conductive substrate, and synergetic effect of coated material results in more electrochemical active sites and flanges for effective electrons and ion transportation. This unique technique provides a new perspective for cost-efficient supercapacitor applications.     

Vertical Pod Autoscaling in Kubernetes for Elastic Container CollaborativeFramework

Kubernetes is an open-source container management tool which automates container deployment, container load balancing and container(de)scaling, including Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA). HPA enables flawless operation, interactively scaling the number of resource units, or pods, without downtime. Default Resource Metrics, such as CPU and memory use of host machines and pods, are monitored by Kubernetes. Cloud Computing has emerged as a platform for individuals beside the corporate sector. It provides cost-effective infrastructure, platform and software services in a shared environment. On the other hand, the emergence of industry 4.0 brought new challenges for the adaptability and infusion of cloud computing. As the global work environment is adapting constituents of industry 4.0 in terms of robotics, artificial intelligence and IoT devices, it is becoming eminent that one emerging challenge is collaborative schematics. Provision of such autonomous mechanism that can develop, manage and operationalize digital resources like CoBots to perform tasks in a distributed and collaborative cloud environment for optimized utilization of resources, ensuring schedule completion. Collaborative schematics are also linked with Bigdata management produced by large scale industry 4.0 setups. Different use cases and simulation results showed a significant improvement in Pod CPU utilization, latency, and throughput over Kubernetes environment.