Industries strive to prevent ecologically destructive actions in their supply chains. At the same time, the optimization of their resources is a major concern for industries to minimize carbon emissions, boost sustainable practices, and improve a country’s long-term economic development. Therefore, the objective of this study is to examine the impact of Green Supply Chain Management (GSCM) methods on operational performance with the mediation of technological innovation, in the context of Pakistani manufacturing firms. The partial least square-structural modeling (PLS-SEM) method is adopted in this paper. Data were gathered from 223 different manufacturing firms in Pakistan and then analyzed among these variables. The data show good validity and reliability, and structural model explains 61% of the variance in operational performance and 45.4% of the variance in technical innovation, demonstrating its predictive validity. The R-square criteria classify R-square entities of 0.67, 0.33, and 0.19 as considerable, moderate, and weak, respectively. It is demonstrated that all the f-square values are greater than 0.020 and 0.35, indicating a significant effect on the model’s validity. The findings of this study reveal that GSCM practices have a significantly positive effect on both technological innovation and operational performance. Technological innovation has a direct influence on operational performance and has a partial mediating effect on the relationship between GSCM practices and operational performance. Therefore, this research offers managers insight into the importance of technological innovation and GSCM practice adoption to achieve competitive advantages. It further provides the groundwork for managers, practitioners, and environmental management researchers to emphasize the value of GSCM practice in improving operational performance.

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.

We propose the design of a novel fractal antenna that is both unique and performance-driven. Two important antenna design features, miniaturization and wideband operation, are combined in this work. A ring-shaped antenna is designed using the well-known fractal geometry. This hybrid geometry is a fusion of meander and Koch curve shapes. The geometrical construction of the proposed antenna is compared to the standard Koch curve geometry. It is shown that combining the meander and Koch curve shapes increases the effective electrical length. The wider bandwidth is achieved by bringing the higher modes together. The overall dimensions of proposed meander Koch curve fractal ring antenna are 45×25×1.6mm³. The resonance frequency of the antenna is between 4.94 and 6.12GHz ( BW=21.83), which covers the entire 5GHz WLAN band. The prototype has been fabricated and experimentally verified.

The domain of underwater wireless communication (UWC) link is gaining much attention due to an increase in various underwater activities such as offshore hydrocarbon exploration, underwater unmanned vehicles (UUV), and military practices. Increased bandwidth and a reliable data link are mainly required for such activities. Both requirements of the domain are heavily affected by the highly conductive property of the seawater. This paper demonstrates the performance evaluation of radiofrequency-UWC, focusing on surface wave analysis, to propose a reliable solution for offshore activities. A constructive interference scheme can be useful due to the sharp difference in the properties of the two mediums (air and seawater). To that end, an experimental setup is created, and a corresponding finite element method (FEM) based simulation of the radio-based wireless link is run. This is because it has higher bandwidth and speed than acoustic and optical approaches. A conduction current mechanism transmits and receives data in a synthetic water tank containing a prepared conductive media (saltwater). The study of changing depths of transmitter-receiver nodes in saltwater shows that surface waves cause significant noise reception in shallow water (less than dipole length, below water level). During a series of experiments in the tank, the lowest bit error rate (BER) is observed only when the node’s submerged height was greater than dipole length. As a result, it is meant to provide a genuine data channel model. The discovery and analysis will aid in the development of a dependable underwater data link, with applications including short-range diver-to-diver communication, and UUV capability.

Perovskite inorganic-organic solar cells are fabricated as a sandwich structure of mesostructured TiO₂ as electron transport layer (ETL), CH₃NH₃PbI₃ as active material layer (AML), and Spiro-OMeTAD as hole transport layer (HTL). The crystallinity, structural morphology, and thickness of TiO₂ layer play a crucial role to improve the overall device performance. The randomly distributed one dimensional (1D) TiO₂ nanowires (TNWs) provide excellent light trapping with open voids for active filling of visible light absorber compared to bulk TiO₂. Solid-state photovoltaic devices based on randomly distributed TNWs and CH₃NH₃PbI₃ are fabricated with high open circuit voltage V_oc of 0.91 V, with conversion efficiency (CE) of 7.4%. Mott-Schottky analysis leads to very high built-in potential (V_bi) ranging from 0.89 to 0.96 V which indicate that there is no depletion layer voltage modulation in the perovskite solar cells fabricated with TNWs of different lengths. Moreover, finite-difference time-domain (FDTD) analysis revealed larger fraction of photo-generated charges due to light trapping and distribution due to field convergence via guided modes, and improved light trapping capability at the interface of TNWs/CH₃NH₃PbI₃ compared to bulk TiO₂.

Energy harvesting (EH) is an emerging technology for enabling sustainable wireless communication in sixth-generation (6G) networks, particularly for energy-constrained network nodes that are difficult to maintain or recharge. In this paper, we propose and analyze an adaptive energy-harvesting decode-and-forward (DF) relaying framework over generalized $\alpha$-$\mu$ fading channels. The proposed framework consists of a multi-antenna source, an energy-constrained multi-antenna relay, and a multi-antenna destination, with direct source-to-destination communication assumed infeasible due to severe channel impairments. To sustain relay operation, the relay harvests energy from received radio-frequency (RF) signals using time-switching (TSR) and power-splitting (PSR) protocols, then forwards the decoded information in half-duplex mode. The generalized $\alpha$-$\mu$ distribution provides a unified statistical characterization of wireless propagation across diverse 6G scenarios and encompasses several well-known fading models as special cases, such as Rayleigh, Nakagami-m, Weibull, one-sided Gaussian, and exponential fading. The study considers multiple transmission strategies, including optimal rate adaptation (ORA), optimal power and rate adaptation (OPRA), channel inversion with fixed rate (CIFR), and truncated channel inversion with fixed rate (TIFR). Exact-form expressions for the outage probability, Bit error rate (BER) using the moment-generating-function approach, and channel capacity are derived for the considered adaptive transmission strategies. The diversity order is also characterized to evaluate system performance under different numbers of antenna elements, transmit power levels, energy-harvesting ratios, and fading severities. Numerical results from Monte Carlo simulations demonstrate the performance of the proposed framework under different system configurations, including Amazon Kubernetes Service (AKS). The analytical results closely match the simulation results, validating the derived expressions. Overall, the proposed EH-enabled Decode-and-Forward (DF) relaying framework provides a flexible approach to improving reliability, reducing outage probability and BER, and enhancing channel capacity in energy-constrained 6G wireless networks.