This study investigates the impact of transitioning from flood to drip irrigation on sugarcane cultivation in Upper Egypt. It evaluates how planting methods—cane stalks and plantlets—affect sugar quality under both systems. A selected set of crop samples was analyzed for sucrose content in the official laboratory of the Ministry of Irrigation. The results offer insights into the economic benefits of drip irrigation, highlighting its role in enhancing sugar quality and returns. Drip irrigation, particularly with plantlets, achieves a maximum sucrose content of 14.3%, a 21.2% improvement over the 11.8% under flood irrigation. This sugar quality enhancement is accompanied by a substantial yield increase: up to 9,895.6 kg/acre with drip irrigation vs. 5,351.9 kg/acre with flood irrigation—an 84.9% increase. Drip systems also show higher water-use efficiency, generating 1.45 kg of sugar per cubic meter of water, compared to 0.46 kg/m³ for flood. Application efficiencies range from 85% to 90% for drip, versus 45%–50% for flood. The study highlights the potential of drip irrigation in arid regions like Upper Egypt, where water scarcity is a major concern. Integrating modern irrigation with local conditions enhances both production and sustainability. These findings emphasize the dual benefits of higher yield and water savings, maximizing returns and reinforcing agricultural resilience under climate and water stress.

The high uncertainty of distributed renewable energy, coupled with the complex statistical correlations among photovoltaic (PV) output power profiles across different geographical locations, significantly increases the difficulty of power system operation and planning. Efficient extraction of representative PV power generation scenarios is essential for reducing the computational burden of optimization models and improving decision-making efficiency. To address the challenge, a novel graph clustering model based on shared weight and adaptive fusion is proposed, which effectively captures the correlation among multiple PV power stations and extracts representative scenarios. An alternating optimization algorithm based on the Lagrange multiplier method and eigenvalue decomposition is proposed to obtain the global optimal solution with fast convergence, thereby improving computational efficiency. The highlight of this work is the dual validation through systematic theoretical proofs and multiple dimensional simulation experiments. In terms of theoretical proof, the low sensitivity of the model parameters ensures ease of use in real-world settings, while the proven convergence of the algorithm guarantees computational reliability. In terms of simulation experiments, the proposed clustering model is verified to have collaborative optimization capability, feature identification capability, high cohesion, low coupling, noise resistance, and parameter sensitivity, as well as the convergence of the solution algorithm using actual PV data from Australia. The effectiveness of this work in extracting representative scenarios of the PV output is verified through the probabilistic power flow analysis using the IEEE 69-bus network, significantly enhancing the efficiency and credibility of power system planning studies with high renewable penetration.

In recent years, the advance of convolutional neural networks (CNNs) helped image super-resolution (SR) research to achieve remarkable improvement. However, the majority of the SR methods are non-blind, assuming the image degradation is defined (e.g., bicubic). So, these methods struggle in case of unknown degradation. Recently, a blind SR task was developed to deal with this problem using degradation estimation. Although many models have been developed for blind SR, blind SR is still a challenging problem and needs to be improved further. Therefore, this paper proposes a Fourier-based Degradation-aware Transformer-style Network (FDATSRN) for a blind image SR. The idea of the FDATSRN is based on exploring the spatial context of the input image in the Fourier space and a large receptive field for restoring the SR image. This is achieved by designing a Fourier-based degradation-aware Transformer block (FDATB) to be the backbone of the FDATSRN model. The FDATB is designed to be a lightweight version of the SR-transformer block based on using the degradation-aware convolution, convolutional modulation, and Fourier unit. Extensive experiments are performed to show the efficiency of the proposed FDATSRN in handling a large receptive field.

Recently, the Transformer-based models have shown strong performance in many natural language processing (NLP) and vision tasks. However, these transformer models have high computation costs, limiting their practical applications. Therefore, a lightweight model called a mixer focal modulation network (MFMN) is proposed in this paper for image super-resolution (SR). The concept of the MFMN model is based on integrating both the focal modulation and convolution mixer by designing a mixer focal modulation module (MFMM). The MFMM is built similarly to the transformer block but without the multi-head self-attention (MHSA) module, which reduces the computation overhead of the MHSA module. The design of the focal modulation has three elements. (i) Hierarchical contextualization, designed based on utilizing a stack of depth-wise convolutional layers for encoding visual contexts from short to long ranges. (ii) It has gated aggregation for selectively gathering contexts for each query token based on its content. (iii) Element-wise modulation or affine transformation for fusing the aggregated context into the query. Also, MFMM allows the MFMN to make spatial and channel mixing, which improves the SR performance. Experimental results in multiple benchmarks are made to show the superior performance of our model in speed against the state-of-the-art methods. Finally, our model achieved around 10x faster run time compared to the lightweight Swin Transformer image restoration (LWSwinIR) at the scale of x2.

In image super-resolution (SR), the main aim is to improve the quantitative results, such as PSNR and perceptual quality of the image. Some models can improve one or both of them such as the Transformer model and diffusion models. In this paper, we take the merit of both the Transformer and diffusion models to improve the real-world image SR model. So, we designed a fusion strategy for the outcome images of both the Transformer and diffusion. This fusion represents the integration of multiple sources of information or features, creating a more cohesive and comprehensive representation. A new model called the double diffusion image super-resolution (DDiffSR) model is proposed based on fusing both the Transformer and the diffusion model. The transformer model is used to extract a compact image SR prior. Meanwhile, the diffusion model is based on the residual denoise diffusion model to generate high-resolution images. The new DDiffSR model achieved state-of-the-art traditional and real-world image SR results. Also, the model achieved more appealing visual results. For example, our model enhanced the PSNR by 1.85 dB for the Set14 dataset.

In the past few years, significant advancements in convolutional neural networks (CNNs) have significantly propelled the field of image super-resolution (SR) research. Nonetheless, many current SR techniques are limited in effectively addressing real-world data degradation, particularly in blind scenarios characterized by multi-modal, spatially variant, and unknown distributions. Based on this issue, we propose a degradation-aware Swin Transformer with sparse attention for blind SR. In this model, we proposed a degradation-aware residual Swin Transformer sparse attention block that is based on the Swin transformer layer, the non-local sparse attention (NLSA), and the degradation-aware convolutional (DA Cov). The Swin Transformer solves CNN’s problems because it has the ability to process images of large size and extract long-range dependency, which works as a local attention mechanism. Moreover, the NLSA is utilized to solve problems combined with non-local attention, which works as a global attention mechanism. Also, it prevents the model from attending to noisy and less informative locations by partitioning the deep feature pixels into different groups. The DA Cov is used to integrate the degraded kernel with extracted features. Moreover, our model shows superior visual quality and reconstruction accuracy with an efficient number of parameters and Mult-Adds. For example, on the Set5 dataset with a kernel size of 0.06 and a scaling factor of x4, our model achieved a 0.1 dB improvement in PSNR compared to DRAN.

Optimizing lithium-ion battery (LIB) packs for electric vehicles requires balancing the need to increase volumetric energy density with the necessity of effective thermal management to ensure performance and safety. Recently, the prismatic cell form-factor has enabled the cell-to-pack approach which increases the battery pack energy density. Additionally, dielectric fluid immersion cooling (DFIC) has emerged as a promising battery thermal management (BTM) technology. This article investigates the effectiveness of DFIC's in managing the thermal performance of modules composed of prismatic lithium-ion cells. Specifically, the influence of intercell spacing on cells' temperature, pressure drop across a module, and the volumetric energy density of the module was investigated. The electrochemical-thermal performance of cells at different mass flow rates of the coolant, coolant types, rates of discharge, and the resting …