Cloud computing is a rapidly growing paradigm which has evolved from having a monolithic to microservices architecture. The importance of cloud data centers has expanded dramatically in the previous decade, and they are now regarded as the backbone of the modern economy. Cloud-based microservices architecture is incorporated by firms such as Netflix, Twitter, eBay, Amazon, Hailo, Groupon, and Zalando. Such cloud computing arrangements deal with the parallel deployment of data-intensive workloads in real time. Moreover, commonly utilized cloud services such as the web and email require continuous operation without interruption. For that purpose, cloud service providers must optimize resource management, efficient energy usage, and carbon footprint reduction. This study presents a conceptual framework to manage the high amount of microservice execution while reducing response time, energy consumption, and execution costs. The proposed framework suggests four key agent services: (1) intelligent partitioning: responsible for microservice classification; (2) dynamic allocation: used for pre-execution distribution of microservices among containers and then makes decisions for dynamic allocation of microservices at runtime; (3) resource optimization: in charge of shifting workloads and ensuring optimal resource use; (4) mutation actions: these are based on procedures that will mutate the microservices based on cloud data center workloads. The suggested framework was partially evaluated using a custom-built simulation environment, which demonstrated its efficiency and potential for implementation in a cloud computing context. The findings show that the engrossment of suggested services can lead to a reduced number of network calls, lower energy consumption, and relatively reduced carbon dioxide emissions.

Adulteration of meat products is a delicate issue for people around the globe. The mixing of lard in meat causes a significant problem for end users who are sensitive to halal meat consumption. Due to the highly similar lipid profiles of meat species, the identification of adulteration becomes more difficult. Therefore, a comprehensive spectral detailing of meat species is required, which can boost the adulteration detection process. The experiment was conducted by distributing samples labeled as “Pure (80 samples)” and “Adulterated (90 samples)”. Lard was mixed with the ratio of 10–50% v/v with beef, lamb, and chicken samples to obtain adulterated samples. Functional groups were discovered for pure pork, and two regions of difference (RoD) at wavenumbers 1700–1800 cm⁻¹ and 2800–3000 cm⁻¹ were identified using absorbance values from the FTIR spectrum for all samples. The principal component analysis (PCA) described the studied adulteration using three principal components with an explained variance of 97.31%. The multiclass support vector machine (M-SVM) was trained to identify the sample class values as pure and adulterated clusters. The acquired overall classification accuracy for a cluster of pure samples was 81.25%, whereas when the adulteration ratio was above 10%, 71.21% overall accuracy was achieved for a group of adulterated samples. Beef and lamb samples for both adulterated and pure classes had the highest classification accuracy value of 85%, whereas chicken had the lowest value of 78% for each category. This paper introduces a comprehensive spectrum analysis for pure and adulterated samples of beef, chicken, lamb, and lard. Moreover, we present a rapid M-SVM model for an accurate classification of lard adulteration in different samples despite its low-level presence.

In recent years, the field of nanophotonics has progressively developed. However, constant demand for the development of new light source still exists at the nanometric scale. Light emissions from graphene-based active materials can provide a leading platform for the development of two dimensional (2-D), flexible, thin, and robust light-emitting sources. The exceptional structure of Dirac’s electrons in graphene, massless fermions, and the linear dispersion relationship with ultra-wideband plasmon and tunable surface polarities allows numerous applications in optoelectronics and plasmonics. In this article, we present a comprehensive review of recent developments in graphene-based light-emitting devices. Light emissions from graphene-based devices have been evaluated with different aspects, such as thermal emission, electroluminescence, and plasmons assisted emission. Theoretical investigations, along with experimental demonstration in the development of graphene-based light-emitting devices, have also been reviewed and discussed. Moreover, the graphene-based light-emitting devices are also addressed from the perspective of future applications, such as optical modulators, optical interconnects, and optical sensing. Finally, this review provides a comprehensive discussion on current technological issues and challenges related to the potential applications of emerging graphene-based light-emitting devices.

Graphene and its derivatives have emerged as peerless electrode materials for energy storage applications due to their exclusive electroactive properties such as high chemical stability, wettability, high electrical conductivity, and high specific surface area. However, electrodes from graphene-based composites are still facing some substantial challenges to meet current energy demands. Here, we applied one-pot facile solvothermal synthesis to produce nitrogen-doped reduced graphene oxide (N-rGO) nanoparticles using an organic solvent, ethylene glycol (EG), and introduced its application in supercapacitors. Electrochemical analysis was conducted to assess the performance using a multi-channel electrochemical workstation. The N-rGO-based electrode demonstrates the highest specific capacitance of 420 F g⁻¹ at 1 A g⁻¹ current density in 3 M KOH electrolyte with the value of energy (28.60 Whkg⁻¹) and power (460 Wkg⁻¹) densities. Furthermore, a high capacitance retention of 98.5% after 3000 charge/discharge cycles was recorded at 10 A g⁻¹. This one-pot facile solvothermal synthetic process is expected to be an efficient technique to design electrodes rationally for next-generation supercapacitors.

Plasmonic antennas are attractive optical components of the optoelectronic devices, operating in the far-infrared regime for sensing and imaging applications. However, low optical absorption hinders its potential applications, and their performance is limited due to fixed resonance frequency. In this article, a novel gate tunable graphene-metal hybrid plasmonic antenna with stacking configuration is proposed and investigated to achieve tunable performance over a broad range of frequencies with enhanced absorption characteristics. The hybrid graphene-metal antenna geometry is built up with a hexagon radiator that is supported by the Al₂O₃ insulator layer and graphene reflector. This stacked structure is deposited in the high resistive Si wafer substrate, and the hexagon radiator itself is a sandwich structure, which is composed of gold hexagon structure and two multilayer graphene stacks. The proposed antenna characteristics i.e., tunability of frequency, the efficiency corresponding to characteristics modes, and the tuning of absorption spectra, are evaluated by full-wave numerical simulations. Besides, the unity absorption peak that was realized through the proposed geometry is sensitive to the incident angle of TM-polarized incidence waves, which can flexibly shift the maxima of the absorption peak from 30 THz to 34 THz. Finally, an equivalent resonant circuit model for the investigated antenna based on the simulations results is designed to validate the antenna performance. Parametric analysis of the proposed antenna is carried out through altering the geometric parameters and graphene parameters in the Computer Simulation Technology (CST) studio. This clearly shows that the proposed antenna has a resonance frequency at 33 THz when the graphene sheet Fermi energy is increased to 0.3 eV by applying electrostatic gate voltage. The good agreement of the simulation and equivalent circuit model results makes the graphene-metal antenna suitable for the realization of far-infrared sensing and imaging device containing graphene antenna with enhanced performance.

The idea of saving human lives using machine learning algorithms has pervasive impression. This study has targeted the most significant cause of death and human health deterioration. In this paper we have collected data from different smokers having different smoking status. Several studies have tried to collect the factors but none of them have identified the factors that can lead to quitting this habit. We present a novel approach where machine learning algorithms are used for identification of the factors for smoking cessation. Due to their high classification rate, the used algorithms are Logistic Regression (LR), Decision Tree (J48) and Artificial Neural Network (ANN). Data set is generated by combining different type of sources, in order to identify factors that can classify the status of the smoker as smoker, former smoker and non-smoker. 33 Attributes were used with 10000 instances, class problem was divided in three classes Smoker, Non-Smoker and Former Smoker. The significance of an attribute obtained using the odds ratio, and factors were identified using classification with 10 fold cross validation combined with 1-1 and 1-against all method. The classification rate was highest for Logistic Regression-95.77%, for predicting the class value FSMKR, odds ratio for attribute L_STATUS and B_P_&_HYP have the highest value in predicting the class. Smokers who are living with their parents and not having blood pressure and Hypertension tend to show the cessation behavior. Attribute SMK_MORNING exhibits the pattern of smokers desire to smoke in morning which has the highest value for all three algorithms, while ANN shows only correctly classified instances. When ANN is combined with evaluator and search method results were obtained at 95.10% correctly classified instances.