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.

The past few years have witnessed remarkable progress in solution-processed methylammonium lead halide (CH ₃ NH ₃ PbX ₃ , X = halide) perovskite solar cells (PSCs) with reported photoconversion efficiency ( η ) exceeding 20% in laboratory-scale devices and reaching up to 13% in their large area perovskite solar modules (PSMs). These devices mostly employ mesoporous TiO ₂ nanoparticles (NPs) as an electron transport layer (ETL) which provides a scaffold on which the perovskite semiconductor can grow. However, limitations exist which are due to trap-limited electron transport and non-complete pore filling. Herein, we have employed TiO ₂ nanorods (NRs), a material offering a two-fold higher electronic mobility and higher pore-filing compared to their particle analogues, as an ETL. A crucial issue in NRs’ patterning over substrates is resolved by using precise Nd:YVO ₄ laser ablation, and a champion device with η ∼ 8.1% is reported via a simple and low cost vacuum-vapor assisted sequential processing (V-VASP) of a CH ₃ NH ₃ PbI ₃ film. Our experiments showed a successful demonstration of NRs-based PSMs via the V-VASP technique which can be applied to fabricate large area modules with a pin-hole free, smooth and dense perovskite layer which is required to build high efficiency devices.

Marine controlled-source electromagnetic method (MCSEM) for seabed logging (SBL) uses a dipole source to generate low-frequency signals, and arrays of receivers to map the potential presence of hydrocarbon reservoirs. The characteristic depth of sign

Background/Objectives: Deep transfer learning, leveraging convolutional neural networks (CNNs), has become a pivotal tool for brain tumor detection. However, key challenges include optimizing hyperparameter selection and enhancing the generalization capabilities of models. This study introduces a novel CART-ANOVA (Cartesian-ANOVA) hyperparameter tuning framework, which differs from traditional optimization methods by systematically integrating statistical significance testing (ANOVA) with the Cartesian product of hyperparameter values. This approach ensures robust and precise parameter tuning by evaluating the interaction effects between hyperparameters, such as batch size and learning rate, rather than relying solely on grid or random search. Additionally, it implements seven distinct classification schemes for brain tumors, aimed at improving diagnostic accuracy and robustness. Methods: The proposed framework employs a ResNet18-based knowledge transfer learning (KTL) model trained on a primary dataset, with 20% allocated for testing. Hyperparameters were optimized using CART-ANOVA analysis, and statistical validation ensured robust parameter selection. The model’s generalization and robustness were evaluated on an independent second dataset. Performance metrics, including precision, accuracy, sensitivity, and F1 score, were compared against other pre-trained CNN models. Results: The framework achieved exceptional testing accuracy of 99.65% for four-class classification and 98.05% for seven-class classification on the source 1 dataset. It also maintained high generalization capabilities, achieving accuracies of 98.77% and 96.77% on the source 2 datasets for the same tasks. The incorporation of seven distinct classification schemes further enhanced variability and diagnostic capability, surpassing the performance of other pre-trained models. Conclusions: The CART-ANOVA hyperparameter tuning framework, combined with a ResNet18-based KTL approach, significantly improves brain tumor classification accuracy, robustness, and generalization. These advancements demonstrate strong potential for enhancing diagnostic precision and informing effective treatment strategies, contributing to advancements in medical imaging and AI-driven healthcare solutions.