The fifth generation (5G) technology provides high transmission and low latency communication to users by integrating heterogeneous devices and maximum radio resource utilisation. Prolonged longevity and effective communication are achieved through active resource routing. In addition, interference-less routing and lossless resource allocation are essential for ensuring successful communication. The transmitting and receiving users rely on shared channels to maximise resource utilisation where interference has a significant issue. In this article, a coalition-based routing and resource optimisation (CRRO) method is proposed to improve the performance of 5G communications. The proposed method relies on the cooperative agreement between resource providers and allocation channels to distinguish the sharing intervals. Maximum routing conditions for devices, service providers, and user routing are met with high longevity. In the coalition-based resource selection, synchronisation between the communicating is considered to maximum interference-less routing efficiency. A modified deep transfer learning is included in the interference level verification based on longevity and low interference to verify the synchronised behaviour of the routing process. The proposed method significantly improves the performance of 5G networks. By dynamically optimising resource allocation, minimising interference, and extending the longevity of the routing, it adapts resource utilisation to the varying conditions of the network and the demands of the users. The proposed CRRO improves resource utilisation and routing longevity by 10.02 %, and 14.41 %, respectively, and reduces latency by 10.47 % for the maximum interval.

This study introduces an intelligent method to monitor grid-connected solar power stations, focussing on detecting problems in their energy output through the use of artificial neural networks (ANN). The main goal is to improve energy efficiency and bolster the reliability of solar power plants by forecasting their performance through real-time data analysis and modelling essential operational variables. The research was carried out in a solar field in AOULEF-ADRAR (South of Algeria), which covers six hectares and consists of 20,460 solar panels with an efficiency of 15 % to 20 %. The cumulative installed capacity is 5 MW, and the system is connected to a 30 kV electrical grid. The experimental findings validated the efficacy of the suggested ANN-based fault detection method. Subsequent to a sandstorm, the system exceeded standard operational limits, culminating in a total power overshoot of 200 KW. This procedure facilitated the identification of system faults and the execution of corrective measures, including the cleaning of PV modules to restore efficiency. The research highlights the importance of artificial intelligence (AI)-based monitoring systems to reduce downtime and maintenance expenses and guarantee consistent operation of photovoltaic plants under various environmental conditions. Research advocates for the integration of artificial neural networks with other machine learning methodologies, such as support vector machines, to improve fault prediction precision. Augmenting the data set by integrating data from various PV stations in different regions may improve the adaptability of the model to different environmental conditions. This method improves the creation of intelligent self-diagnosing solar power systems, promoting increased reliability and efficiency in the integration of global renewable energy.

As coal mining deepens, ground fissures in mining areas pose significant risks to safety and the environment. Traditional geological exploration methods are inefficient and costly, making the precise detection of large-scale fissures difficult. Deep learning methods using unmanned aerial vehicle (UAV) images have become a popular approach for fissure detection, although challenges such as insufficient accuracy and poor adaptability in complex backgrounds remain. This study proposes an residual-depthwise separable convolution UNet (RDC-UNet) model to address these issues, building on U-Net by incorporating residual connections (RC), depthwise separable convolutions (DSC), and the convolutional block attention module (CBAM) attention mechanism. The model was trained on a dataset of 300 UAV images from a DJI Mavic 3e and outperformed the benchmark models, achieving 86.12 % mPrecision, 75.01 % mRecall, 70.72 % mIoU, and 0.7951 mF1. Ablation experiments show that removing any core module leads to a performance drop, with the DSC module reducing mF1 by 3.28 %, CBAM decreasing mIoU by 2.67 %, and RC lowering mRecall by 3.96 %. RDC-UNet is highly efficient, requiring only 7.8 million parameters, with an inference time of 75 milliseconds and a memory footprint of 180 MB, much lower than other models. This makes it well-suited for real-time UAV-based fissure monitoring. The RDC-UNet model offers high accuracy and efficiency, making it an ideal solution for cost-effective real-time monitoring of ground fissures in mining areas.

Multilevel inverters (MLIs) provide a solution for high-power applications due to the production of a high-quality output voltage with low harmonic distortion. This technology is gaining popularity in the power industry for applications such as renewable energy systems, motor drives, and electric vehicles. MLIs are categorised on the basis of the number of levels in their output waveform. This article includes a discussion of two, three, and higher-level inverters with respect to design aspects and their application in the power industry, contributing to the development of sustainable and efficient systems. In addition, a packed inverter unit (PIU) with 11 IGBT switches and three uneven DC voltages is used to generate 11 voltage steps. From the suggested axioms, the amplitude of the DC sources is chosen to generate larger voltage steps with the fewest possible circuit components. Additionally, an 11-level inverter is simulated for fixed and variable R/RL loads. A comparison analysis of the circuit components between the developed circuit and existing MLIs is carried out. Finally, 11-level inverters are evaluated in real time for constant, fluctuating R/RL loads, and various performance metrics are noticed.

High-resolution, small-form-factor image sensors enable the integration of mobile device cameras, which are increasingly being used for photographic documentation in many fields, including medicine. With the interface and handheld dermatoscopy, the smartphone camera forms an alternative tool to professional dermatoscopic systems for performing teledermatology and teledermoscopy. For the accurate diagnosis of skin diseases, image quality is essential, with sharpness and resolution being essential criteria. This paper focusses on measuring the sharpness and resolution of cameras used for image acquisition in dermatology using the spatial frequency response (SFR) method, which is based on standardised test charts featuring characteristic slanted contrast edges, known as edge SFR (eSFR) charts. The images were captured with mirrorless and DSLR cameras, smartphones, and a professional dermatoscopy video camera under typical dermatological conditions with digital cameras, mobile phones, and professional video dermatoscopes. Captured images were analysed, and the modulation transfer function (MTF) was defined to evaluate the performance of different camera optical systems applied for dermatological imaging. The results provide insight into the strengths and limitations of the various imaging devices and highlight their effectiveness in meeting the requirements of dermatological practice.

Many researchers are trying to make our lives easier with developments in the Internet of Things, industry 4.0, and artificial intelligence. However, when the security of the data, which is at the centre of all these developments, is not ensured, the processes that try to make the lives of human beings more comfortable turn into nightmares. The problem that is tried to be addressed in this study is to share the details of an approach that can be used as an encryption key in hardware encrypted data storage units that can be used to address security concerns that may arise during the transmission, processing, and storage of sensitive data. The proposed method has contributed to the hybrid random number generators, both by optimising the deterministic generators and the chaotic selection algorithm. The results of the successful analysis of the proposed architecture have confirmed that it will have potential in many practical applications in the future. It is thought that with projections for future studies, it will contribute to the field of global encryption software.

This study has conducted a forecast analysis of the energy demand and carbon dioxide (CO2) emissions of Turkey, a developing country. Considering Turkey’s rapidly increasing energy demand, various economic and social parameters have been used for the years 1990-2024. Both machine learning and deep learning methods have been applied, and artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), and linear regression (LR) algorithms have been used for two models. The performance of these models has been assessed using various error metrics. The ANN has demonstrated the highest accuracy in modelling energy demand, achieving a coefficient of determination of 98.89 %, while the RNN has shown the best performance in modelling CO2 emissions, with a coefficient of determination of 96.80 %. The findings have shown that the growth rates in energy demand and CO2 emissions are high in the early years but slowed in the following years. However, it has been determined that the general trend continued to increase. The study emphasises the need for Turkey to diversify its energy sources and increase the use of renewable energy to meet its increasing energy demand. It also has concluded that accelerating efforts to achieve net zero emission targets are critical to long-term energy security and environmental sustainability.

Small faults developed in coal seams are one of the major causes of coal mine accidents. Accurately predicting small faults in coal fields is an urgent requirement for efficient and safe production in coal mines. This article proposes a new small fault identification method that combines the empirical mode decomposition method and the seismic texture attribute extraction method to address the problem of large errors caused by noise in the results of small fault prediction. Firstly, the basic principles of the empirical mode method and the texture attribute method were studied, and then the fault recognition ability of this method was tested and analysed based on a small fault seismic forward modelling. Meanwhile, empirical mode decomposition is performed on actual seismic data to identify small faults by using texture attributes and by adding noise to the seismic record; this article compared the seismic record of texture properties in the presence and absence of noise. The results indicate that the texture attribute method can predict small faults well, but this method is easily disturbed by noise. The empirical mode decomposition method used in this paper can remove noise interference and highlight characteristics of the texture attribute. Therefore, the small fault prediction method that combines empirical mode decomposition with texture attributes can effectively identify small faults and play an important geological guarantee role in ensuring safe and efficient production in coal mines.

In this paper, we employ the analogy between the representation of the floating-point (FP) format and the representation level distribution of the piecewise uniform quantizer (PWUQ) to assess the performance of FP-based solutions more thoroughly. We present theoretical derivations to assess the performance of the FP format and the PWUQ determined by this format for input data from the Laplacian source. We also provide a performance comparison of two selected 8-bit FP-based PWUQs. Beyond the typical evaluation of the applied FP format, through the accuracy degradation caused by the application of both FP8 solutions in neural network compression, we also use objective quantization measures. This approach offers insights into the robustness of these 8-bit FP-based solutions with respect to changes in input variance, which can be important when the input variance changes. The results demonstrate that the allocation of bits to encode the exponent and mantissa in the FP8 format is important, as it can significantly impact overall performance.

The development and use of mine resources have had many adverse impacts on the environment of mining areas. Among them, ground fissures are the most serious. They not only threaten the ecological protection of mining areas but also hinder the sustainable exploitation of energy. To mitigate the damage to the ecological environment caused by mining areas and ensure sustainable long-term resource exploitation, it is of particular importance to identify ground fissures in mining areas efficiently. Therefore, this paper proposes a ground fissure identification model for UAV images in mining areas named DN-CAMSCBNet. This method integrates the channel attention mechanism and the dropout mechanism on the basis of the traditional U-Net. Meanwhile, it introduces the multiscale convolution block and Nesterov-accelerated adaptive moment estimation. These are used to enhance its ability to capture complex image features, expand the receptive field of the original model, reduce the number of parameters, and reduce computational complexity. To verify the segmentation performance of the model, it is compared with U-Net, D-CAMNet, and D-MSCBNet models. The experimental results show that the accuracy and precision of the DN-CAMSCBNet model can reach 99.47 % and 92.25 %, respectively, and the F1 score is 0.7699. All these are superior to comparison models and can provide strong support for the identification of ground fissures in mining areas.