An inset fed-microstrip patch antenna (MPA) with a partial ground structure is constructed and evaluated in this paper. This article covers how to evaluate the performance of the designed antenna by using a combination of simulation, measurement, creation of the RLC equivalent circuit model, and the implementation of machine learning approaches. The MPA’s measured frequency range is 7.9–14.6 GHz, while its simulated frequency range is 8.35–14.25 GHz in CST microwave studio (CST MWS) 2018. The measured and simulated bandwidths are 6.7 GHz and 5.9 GHz, respectively. The antenna substrate is composed of FR-4 Epoxy, which has a dielectric constant of 4.4 and a loss tangent of 0.02. The equivalent model of the proposed MPA is developed by using an advanced design system (ADS) to compare the resonance frequencies obtained by using CST. In addition, the measured return loss of the prototype is compared with the simulated return loss observed by using CST and ADS. At the end, 86 data samples are gathered through the simulation by using CST MWS, and seven machine learning (ML) approaches, such as convolutional neural network (CNN), linear regression (LR), random forest regression (RFR), decision tree regression (DTR), lasso regression, ridge regression, and extreme gradient boosting (XGB) regression, are applied to estimate the resonant frequency of the patch antenna. The performance of the seven ML models is evaluated based on mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and variance score. Among the seven ML models, the prediction result of DTR (MSE = 0.71%, MAE = 5.63%, RMSE = 8.42%, and var score = 99.68%) is superior to other ML models. In conclusion, the proposed antenna is a strong contender for operating at the entire X-band and lower portion of the Ku-band frequencies, as evidenced by the simulation results through CST and ADS, it measured and predicted results using machine learning approaches.

The utilization of synthetic, natural, and waste fibers in asphalt mixtures is constantly increasing due to the capability of fibers to improve the mechanical performance of asphalt mixes. The combination of fibers in asphalt mixes contributes to ecological sustainability and cost benefits. The objective of this paper is to introduce a citation-based review on the incorporation of synthetic, natural, and waste fibers in bitumen, dense-graded asphalt mix, stone mastic asphalt, and porous asphalt mix. Additionally, this article aims to identify research gaps and provide recommendations for further work. The outputs of this article demonstrated that there has recently been a growing interest in the use of natural and waste fibers in asphalt mixtures. However, more future studies are needed to investigate the performance of fiber-modified stone mastic asphalt and porous asphalt mix in terms of resistance to aging and low-temperature cracking. Furthermore, the period of natural fibers’ biodegradability in asphalt mixtures should be investigated.

Emotions are vital for identifying an individual’s attitude and mental condition. Detecting and classifying emotions in Natural Language Processing applications can improve Human–Computer Interaction systems, leading to effective decision making in organizations. Several studies on emotion classification have employed word embedding as a feature extraction method, but they do not consider the sentiment polarity of words. Moreover, relying exclusively on deep learning models to extract linguistic features may result in misclassifications due to the small training dataset. In this paper, we present a hybrid feature extraction model using human-engineered features combined with deep learning based features for emotion classification in English text. The proposed model uses data augmentation, captures contextual information, integrates knowledge from lexical resources, and employs deep learning models, including Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Encoder Representation and Transformer (BERT), to address the issues mentioned above. The proposed model with hybrid features attained the highest Jaccard accuracy on two of the benchmark datasets, with 68.40% on SemEval-2018 and 53.45% on the GoEmotions dataset. The results show the significance of the proposed technique, and we can conclude that the incorporation of the hybrid features improves the performance of the baseline models.

High-gain DC–DC converters are being drastically utilized in renewable energy generation systems, such as photovoltaic (PV) and fuel cells (FC). Renewable energy sources (RES) persist with low-level output voltage; therefore, high-gain DC–DC converters are essentially integrated with RES for satisfactory performance. This paper proposes a non-isolated high-gain non-inverting interleaved DC–DC boost converter. The proposed DC–DC converter topology is comprised of two inductors and these are charging and discharging in series and parallel circuit configurations. The voltage multiplier technique is being utilized to produce high gain. The proposed topology is designed to operate in three modes of operation. Three switches are operated utilizing two distinct duty ratios to avoid the extreme duty ratio while having high voltage gain. Owing to its intelligent design, the voltage stress on the switches is also significantly reduced where the maximum stress is only 50% of the output voltage. The proposed converter’s steady-state analysis with two distinct duty ratios is thoroughly explored. Furthermore, a 160 W 20/400 V prototype is developed for performance analysis and validation. The converter topology can generate output voltage with a very high voltage gain of 20, which is verified by the prototype. Moreover, a high efficiency of 93.2% is attained by the proposed converter’s hardware prototype.

In this study, a novel reconfigurable triple-band monopole antenna for LoRa IoT applications is fabricated on an FR-4 substrate. The proposed antenna is designed to function at three distinct LoRa frequency bands: 433 MHz, 868 MHz, and 915 MHz covering the LoRa bands in Europe, America, and Asia. The antenna is reconfigurable by using a PIN diode switching mechanism, which allows for the selection of the desired operating frequency band based on the state of the diodes. The antenna is designed using CST MWS^® software 2019 and optimized for maximum gain, good radiation pattern and efficiency. The antenna with a total dimension of 80 mm × 50 mm × 0.6 mm (0.12${\lambda }_0\times 0.07{\lambda }_0$ × 0.001${\lambda }_0$ at 433 MHz) has a gain of 2 dBi, 1.9 dBi, and 1.9 dBi at 433 MHz, 868 MHz, and 915 MHz, respectively, with an omnidirectional H-plane radiation pattern and a radiation efficiency above 90% across the three frequency bands. The fabrication and measurement of the antenna have been carried out, and the results of simulation and measurements are compared. The agreement among the simulation and measurement results confirms the design’s accuracy and the antenna’s suitability for LoRa IoT applications, particularly in providing a compact, flexible, and energy efficient communication solution for different LoRa frequency bands.

Big data is quickly becoming a new area where administrative work can be improved. Even so, it is still in the early stages of being used in hospitals in countries with less technology. Therefore, there is an inadequate grasp of the evaluation of big data adoption preparedness in the healthcare sector as data-point-determined insights become crucially useful in healthcare institutions in underdeveloped nations. This process, called “digital transformation,” has a lot of benefits; for example, it helps healthcare organizations to create more efficient processes, offer different services, give better care, make more money, and cut costs. This paper aims to suggest and assess a conceptual framework that focuses on technological factors and can assist in determining the readiness of healthcare institutions in developing nations to utilize big data. Although the study can offer valuable perspectives on the advantages that can arise from adopting big data in the healthcare sector, it is important to highlight that leveraging big data analytics in healthcare has the potential to enhance the efficiency and effectiveness of healthcare services. This, in turn, can indirectly contribute to sustainability objectives by optimizing the allocation of resources, minimizing waste, and improving patient outcomes. A total of 328 healthcare workers from Malaysia were subjected to experimental testing of the model. The collected data were evaluated using the Smart PLS 3 program and the structural equation model (SEM). The study’s findings supported our hypotheses. The results showed that technological factors affected the participants’ perception of their readiness for big data, which ultimately influenced their interest in utilizing it. By concentrating on big data preparedness in the healthcare industry and ambition to utilize big data, this research provides an important theoretical contribution. Employees who are “big data ready” would benefit from the study’s results, as, through their recognition, said employees are more likely to increase the desire to use big data in Malaysia’s healthcare sectors.