Recent research on single-image super-resolution (SISR) using deep convolutional neural networks has made a breakthrough and achieved tremendous performance. Despite their significant progress, numerous convolutional neural networks (CNN) are limited in practical applications, owing to the requirement of the heavy computational cost of the model. This paper proposes a multi-path network for SISR, known as multi-path deep CNN with residual inception network for single image super-resolution. In detail, a residual/ResNet block with an Inception block supports the main framework of the entire network architecture. In addition, remove the batch normalization layer from the residual network (ResNet) block and max-pooling layer from the Inception block to further reduce the number of parameters to preventing the over-fitting problem during the training. Moreover, a conventional rectified linear unit (ReLU) is replaced with Leaky ReLU activation function to speed up the training process. Specifically, we propose a novel two upscale module, which adopts three paths to upscale the features by jointly using deconvolution and upsampling layers, instead of using single deconvolution layer or upsampling layer alone. The extensive experimental results on image super-resolution (SR) using five publicly available test datasets, which show that the proposed model not only attains the higher score of peak signal-to-noise ratio/structural similarity index matrix (PSNR/SSIM) but also enables faster and more efficient calculations against the existing image SR methods. For instance, we improved our method in terms of overall PSNR on the SET5 dataset with challenging upscale factor 8× as 1.88 dB over the baseline bicubic method and reduced computational cost in terms of number of parameters 62% by deeply-recursive convolutional neural network (DRCN) method.

Nitrogen doping with carbon material substantially enhances the electrochemical properties in lithium and sodium batteries. However, direct treating at high temperature fails to create high nitrogen content, thus limiting the morphological as well as electrochemical performances. Herein, a hydrophilic material xanthan and acid‐treated melamine are doped by a dual process of hydrothermal and carbonization, enabling high nitrogen content (28%) carbon spheres. The highly defected nanosphere structures (ID/IG = 1.14) enable the high specific surface area of 388 m2 g−1, which facilitates a large number of lithium/sodium ions and gives rise to remarkable electrochemical performances. When applied as an anode, the nitrogen‐doped porous sphere xanthan (NPS‐XAN) delivers a superior discharge capacity of 390 mAh g−1 after 1000 cycles at a current density of 1 A g−1 for lithium anode and maintains a discharge capacity of 262 mAh g−1 after 2800 cycles at 1 A g−1 for sodium anode. This work signifies superior capacity anodes for Li/Na‐ion batteries and contributes to the long‐life cycling energy application.

In the last two decades electricity shortage has hampered the economic growth of Pakistan. To overcome these crises, thermal power plants were commissioned to bridge the supply and demand gap. Deployment of thermal power generation resulted in an unsustainable energy mix with the higher cost of generation. In the last decade, policymakers have shown considerable interest in deploying renewable energy generally and wind energy particularly. Therefore, this paper evaluates some important drivers and barriers to wind power generation. SWOT-Delphi approach with Relative Importance Index (RII) analysis has been applied. The results show that the deployment of wind power can enhance energy security and environmental sustainability. Major barriers to wind energy are the presence of competitive energy resources, policy implications, and poor grid infrastructure. With this contrasting environment, the evaluation of drivers and barriers of wind power are insightful for formulating sustainable energy planning strategies for future generation mix.

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In this paper, we analyze the performance of a dual-hop cooperative decode-and-forward (DF) relaying system with beamforming under different adaptive transmission techniques over κ − μ shadowed fading channels. We consider multiple antennas at the source and destination, and communication takes place via a single antenna relay. The published work in the literature emphasized the performance analysis of dual-hop DF relaying systems, in conjunction with different adaptive transmission techniques for classical fading channels. However, in a real scenario, shadowing of the line-of-sight (LoS) signal is caused by complete or partially blockage of the LoS by environmental factors such as trees, buildings, mountains, etc., therefore, transmission links may suffer from fading as well as shadowing, either concurrently or separately. Hence, the κ − μ shadowed fading model was introduced to emulate such general channel conditions. The κ − μ shadowed fading model is a general fading model that can perfectly model the fading and shadowing effects of the wireless channel in a LoS propagation environment, and it includes some classical fading models as special cases, such as κ − μ , Rician, Rician-shadowed, Nakagami- m ^ , One-sided Gaussian, and Rayleigh fading. In this work, we derive the outage probability and average capacity expressions in an analytical form for different adaptive transmission techniques: (1) optimal power and rate adaptation (OPRA); (2) optimal rate adaptation and constant transmit power (ORA); (3) channel inversion with a fixed rate (CIFR); and (4) truncated channel inversion with a fixed rate (TIFR). We evaluate the system performance for different arrangements of antennas and for different fading and shadowing parameters. The obtained analytical expressions are verified through extensive Monte Carlo simulations.