The issue of the energy shortage is affecting the entire planet. This is occurring because of massive population and industry growth around the world. As a result, the entire world is attempting to implement green networking systems and manufacture the power/energy efficient products. This research work discusses the green networking system technologies. This work introduces a power‐efficient control unit (CU) design and implemented on the Zynq SoC (System on Chip) ultrascale field programmable gate array (FPGA). The VIVADO HLx Design Suite is used to simulate and analyze the CU model which is considered as one of the key components of central processing unit (CPU), used for data communication purposes. The CU is made suitable for the green communication by making it power‐efficient. Therefore, the power consumption of the CU is analyzed for the various set frequency value ranging between 100 MHz and 5 GHz, and it is discovered that as the clock frequency rises up, the total power consumption also tends to get increased. The total power of the proposed model is reduced by 77.42%, 21.29%, and 17.93% from three models, respectively, being compared in the present paper. Final results shows that the CU is better suited to run at low frequencies to optimize power consumption.

This study aims to identify the critical parameters for implementing a sustainable artificial intelligence (AI) cloud system in the information technology industry (IT). To achieve this, an AHP-ISM-MICMAC integrated hybrid multi-criteria decision-making (MCDM) model was developed and implemented. The analytic hierarchy process (AHP) was used to determine the importance of each parameter, while interpretive structural modeling (ISM) was used to establish the interrelationships between the parameters. The cross-impact matrix multiplication applied to classification (MICMAC) analysis was employed to identify the driving and dependent parameters. A total of fifteen important parameters categorized into five major groups have been considered for this analysis from previously published works. The results showed that technological, budget, and environmental issues were the most critical parameters in implementing a sustainable AI cloud system. More specifically, the digitalization of innovative technologies is found to be the most crucial among the group from all aspects, having the highest priority degree and strong driving power. ISM reveals that all the factors are interconnected with each other and act as linkage barriers. This study provides valuable insights for IT industries looking to adopt sustainable AI cloud systems and emphasizes the need to consider environmental and economic factors in decision-making processes.

A pentaband antenna is presented based on the conducting copper material printed on an FR4 substrate for the applications operating in the Gigahertz frequencies. The antenna has a substrate material with a dielectric constant of 4.4. The conducting copper is printed on the FR4 substrate acting as the radiating element and ground. The antenna radiating element has a defected circular structure with a cross stub. The proposed structure is operating at 2.64 GHz, 4.87 GHz, 7.86 GHz, 10.74 GHz, and 13.67 GHz. The antenna is simulated using CST software. The antenna is fabricated and validated with the measurement of return loss. The antenna simulated results like surface current distribution, gain, directivity, and radiation pattern prove that the proposed structure with its compact size is the right candidate for the GHz application.

For WLAN/WIMAX applications, a brand-new tree-shaped metamaterial-loaded microstrip antenna is suggested. The reduced ground plane size and 4.4 dielectric constant (r) and 0.02 loss tangent (δ) dielectric are used to manufacture the 15×16×1.6mm³ microstrip antenna. Two X-shaped slots are added to achieve the characteristics needed for WIMAX applications at 5.5GHz. Additionally, a split-ring resonator is added to the structure to increase its bandwidth. It runs for WLAN applications with a center frequency of 5.8GHz. The proposed structures measured impedance bandwidth is 45.39 with SRR and 53.48 without SRR, respectively. The proposed antenna is capable of satisfying the major requirements of modern wireless devices such as multiband operation, compact size, large bandwidth, and planar structure. The best outcomes are attained with the aid of parametric analysis of feed width, ground height, and slot width. All electromagnetic simulations were performed using CST Studio software. The measured results and the simulation agree. The waveguide extraction approach is used to demonstrate the SRRs permeability property. The suggested antenna had adequate impedance matching, was small, and had a wide bandwidth.

The building integrated semitransparent photovoltaic (BISTPV) system is an emerging technology which replaces the conventional building material envelopes and roof. The performance prediction of the BISTPV system places a vital role in the reduction of the energy consumption in the building. In this work, the artificial neural network (ANN) is used to predict the performance of this system by optimizing the important parameter of the feature selection. The Elman neural network (EN) algorithm, feed forward neural network (FN), and generalized regression neural network model (GRN) are investigated in this study. The performance metrics of the errors are analysed such as the root mean square error (RMSE), mean absolute percentage error (MAPE), and mean square root (MSE). According to the findings, the model behaves consistently at the specified time and place in the experiment. Forecasters utilizing neural network models will have better accuracy if they use techniques like EN, FFN, and GRN having the RMSE of 0.25, 0.37, and 0.45, respectively.