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   Plastics have become inevitable for human beings in their daily life. Million tons of plastic waste is entering in oceans, soil, freshwater, and sediments. Invasion of plastics in different ecosystems is causing severe problems to inhabitants. Wild animals such as seabirds, fishes, crustaceans, and other invertebrates are mostly effected by plastic entanglements and organic pollutants absorbed and carried by plastics/microplastics. Plastics can also be potentially harmful to human beings and other mammals. Keeping in view the possible harms of plastics, some mitigation strategies must be adopted which may include the use of bioplastics and some natural polymers such as squid-ring teeth protein. This review focuses on the possible sources of intrusion and fate of plastics in different ecosystems, their potential deleterious effects on wildlife, and the measures that can be taken to minimize and avoid the plastic use.

Text-to-image generation is the process of generating images from a given text description. It is the most challenging task to produce consistently realistic images according to our conditions. We have considered this problem in our study and proposed a neural network-based model that can generate good-quality images from text descriptions. In this research, we have used a Generative Adversarial Network (GAN) for the generation of images with Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). RNN is used for creating word embeddings from textual sentences and for extracting important features from images we have used CNN. The generator model is used for generating images from text and this generated image is used as input to the discriminator with further matched text, mismatched text, and real images from the dataset. These experiments are performed on the Oxford 102-flowers dataset. We also modified this existing dataset and created a new version of this dataset, oxford-102 flowers (beta) consisting of 15 text descriptions for each image. The model is trained on these two datasets for generating images of 64 x 64, 128 x 128, and 256 x 256 resolution. Generator and discriminator loss during training of mode are calculated. The inception Score and peak signal-to-noise ratio are performance metrics that we have used for model evaluation. Our model achieves an inception score of 4.15 on the oxford-102 flowers dataset of 64 x 64 resolution, 3.87 on 256 x 256 resolution, and 3.97 on 128 x 128 oxford-102 flowers (beta). PSNR values are 28.25 dB and 30.12dB on the original and annotated dataset. Experiments show the outstanding performance of our methodology as compared to the existing models in terms of inception score and PSNR value.

As the most popular technologies of the 21st century, artificial intelligence (AI) and the internet of things (IoT) are the most effective paradigms that have played a vital role in transforming the agricultural industry during the pandemic. The convergence of AI and IoT has sparked a recent wave of interest in artificial intelligence of things (AIoT). An IoT system provides data flow to AI techniques for data integration and interpretation as well as for the performance of automatic image analysis and data prediction. The adoption of AIoT technology significantly transforms the traditional agriculture scenario by addressing numerous challenges, including pest management and post-harvest management issues. Although AIoT is an essential driving force for smart agriculture, there are still some barriers that must be overcome. In this paper, a systematic literature review of AIoT is presented to highlight the current progress, its applications, and its advantages. The AIoT concept, from smart devices in IoT systems to the adoption of AI techniques, is discussed. The increasing trend in article publication regarding to AIoT topics is presented based on a database search process. Lastly, the challenges to the adoption of AIoT technology in modern agriculture are also discussed.

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field.

This paper presents a novel approach to reducing undesirable coupling in antenna arrays using custom-designed resonators and inverse surrogate modeling. To illustrate the concept, two standard patch antenna cells with 0.07λ edge-to-edge distance were designed and fabricated to operate at 2.45 GHz. A stepped-impedance resonator was applied between the antennas to suppress their mutual coupling. For the first time, the optimum values of the resonator geometry parameters were obtained using the proposed inverse artificial neural network (ANN) model, constructed from the sampled EM-simulation data of the system, and trained using the particle swarm optimization (PSO) algorithm. The inverse ANN surrogate directly yields the optimum resonator dimensions based on the target values of its S-parameters being the input parameters of the model. The involvement of surrogate modeling also contributes to the acceleration of the design process, as the array does not need to undergo direct EM-driven optimization. The obtained results indicate a remarkable cancellation of the surface currents between two antennas at their operating frequency, which translates into isolation as high as −46.2 dB at 2.45 GHz, corresponding to over 37 dB improvement as compared to the conventional setup.

Machine learning (ML) has emerged as a powerful tool in transformer condition assessment, enabling more accurate diagnostics by leveraging historical test data. However, imbalanced datasets, often characterized by limited samples in poor transformer conditions, pose significant challenges to model performance. This study investigates the application of oversampling techniques to enhance ML model accuracy in predicting the Health Index of transformers. A dataset comprising 3850 transformer tests collected from utilities across Indonesia was used. Key parameters, including oil quality, dissolved gas analysis, and paper condition factors, were employed as inputs for ML modeling. To address the class imbalance, various oversampling methods, such as the Synthetic Minority Oversampling Technique (SMOTE), Borderline-SMOTE, SMOTE-Tomek, and SMOTE-ENN, were implemented and compared. This study explores the impact of these techniques on model performance, focusing on classification accuracy, precision, recall, and F1-score. The results reveal that all SMOTE-based methods improved model performance, with SMOTE-ENN yielding the best outcomes. It significantly reduced classification errors, particularly for minority classes, ensuring better predictive reliability. These findings underscore the importance of advanced oversampling techniques in improving transformer diagnostics. By effectively addressing the challenges posed by imbalanced datasets, this research provides a robust framework for applying ML in transformer condition monitoring and other domains with similar data constraints.

Rotating Polarization Wave (RPW) is a novel Low Power Wide Area Networks (LPWAN) technology for robust connectivity and extended coverage area as compared to other LPWAN technologies such as LoRa and Sigfox when no error detection and correction is employed. Since, IoT and Machine-to-Machine (M2M) communication demand high reliability, RPW with error correction can significantly enhance the communication reliability for critical IoT and M2M applications. Therefore, this study investigates the performance of RPW with single bit error detection and correction using Hamming codes to avoid substantial overhead. Hamming (7,4) coded RPW shows a remarkable improvement of more than 40% in error performance compared to uncoded RPW thereby making it a suitable candidate for IoT and M2M applications. Error performance of coded RPW outperforms coded Chirp Spread Spectrum (CSS) modulation used in LoRa under multipath conditions by 51%, demonstrating superior adaptability and robustness under dynamic channel conditions. These findings provide valuable insights into the ongoing developments in wireless communication systems whilst reporting Q-RPW model as a new and effective method to address the needs of developing LPWAN and IoT ecosystems.

The development of the industrial Internet of Things (IIoT) promotes the integration of the cross-platform systems in fog computing, which enable users to obtain access to multiple application located in different geographical locations. Fog users at the network’s edge communicate with many fog servers in different fogs and newly joined servers that they had never contacted before. This communication complexity brings enormous security challenges and potential vulnerability to malicious threats. The attacker may replace the edge device with a fake one and authenticate it as a legitimate device. Therefore, to prevent unauthorized users from accessing fog servers, we propose a new secure and lightweight multi-factor authentication scheme for cross-platform IoT systems (SELAMAT). The proposed scheme extends the Kerberos workflow and utilizes the AES-ECC algorithm for efficient encryption keys management and secure communication between the edge nodes and fog node servers to establish secure mutual authentication. The scheme was tested for its security analysis using the formal security verification under the widely accepted AVISPA tool. We proved our scheme using Burrows Abdi Needham’s logic (BAN logic) to prove secure mutual authentication. The results show that the SELAMAT scheme provides better security, functionality, communication, and computation cost than the existing schemes.

Global concerns regarding environmental preservation and energy sustainability have emerged due to the various impacts of constantly increasing energy demands and climate changes. With advancements in smart grid, edge computing, and Metaverse-based technologies, it has become apparent that conventional private power networks are insufficient to meet the demanding requirements of industrial applications. The unique capabilities of 5G, such as numerous connections, high reliability, low latency, and large bandwidth, make it an excellent choice for smart grid services. The 5G network industry will heavily rely on the Internet of Things (IoT) to progress, which will act as a catalyst for the development of the future smart grid. This comprehensive platform will not only include communication infrastructure for smart grid edge computing, but also Metaverse platforms. Therefore, optimizing the IoT is crucial to achieve a sustainable edge computing network. This paper presents the design, fabrication, and evaluation of a super-efficient GSM triplexer for 5G-enabled IoT in sustainable smart grid edge computing and the Metaverse. This component is intended to operate at 0.815/1.58/2.65 GHz for 5G applications. The physical layout of our triplexer is new, and it is presented for the first time in this work. The overall size of our triplexer is only 0.007 λg2, which is the smallest compared to the previous works. The proposed triplexer has very low insertion losses of 0.12 dB, 0.09 dB, and 0.42 dB at the first, second, and third channels, respectively. We achieved the minimum insertion losses compared to previous triplexers. Additionally, the common port return losses (RLs) were better than 26 dB at all channels.

Soil contamination poses a severe threat in terms of deteriorated environmental quality and badly influences human health. Agricultural soils, mainly in the suburban areas, need detailed investigations for their heavy metal contents to avoid food chain contamination. This study was done to examine the heavy metals (Cu, Ni, Pb, Cr, Zn, and Mn) enrichment and accounts for contamination in arable soils of the Islamabad Capital Territory of Pakistan. Forty soil samples (soil depth 15–25 cm) were collected from cultivated areas of 41.88 km2 using a predefined GIS-based grid. Soil samples were analyzed for physico-chemical properties and total heavy metal content. Different types of environmental factors to assess the ecological risk were calculated to examine the contamination level in soils of the surveyed area. Results indicated that soil pH, in the surveyed area ranged from 7.27 to 7.98 with the mean value (7.54 ± 0.19). The Average Cu, Ni, Pb, Cr, Zn, and Mn contents in the soils were 11.7, 31, 33, 42, 86, and 1407 mg kg−1, respectively. The spatial dependence ranged from moderate to strong, provided an opportunity to prepare contour maps. The pollution load index of the surveyed area indicated a need for a more detailed study to monitor the enrichment of heavy metals. The whole area was classified into various zones based on differential heavy metal content for regional-scale information.