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Firms have traditionally responded to environmental violations by increasing information disclosure and/or communication to manage stakeholder perceptions. As such, these approaches may be symbolic in nature, with no genuine intention to improve the environment. We draw from restorative justice grounded in stakeholder theory and explore a relatively new approach in the form of supplemental environmental projects (SEPs) aimed at restoring the environment, and empirically examine the role of corporate governance (board structure) in firms' decisions to undertake reparative actions. Using environmental violations and SEPs data from the US Environmental Protection Agency between 2002 and 2015, we find that firms with smaller boards are more likely to undertake SEPs. We also find that firms with higher board independence and CEO duality undertake SEPs more frequently; however, board gender diversity and the existence of a sustainability committee appear to have no impacts. These results are robust to propensity score matching and an alternative data source. We extend the scope of stakeholder theory by emphasizing a new approach—restorative justice—by which corporations can repair damaged relationships and also improve the environment. We also contribute to corporate governance and environmentalism literature by identifying governance structures that promote environmental restorative justice. Thus, our study will inform different stakeholders, including regulators, shareholders, and boards of directors, and will open new avenues for business ethics scholarship.

The concept of energy security has become an increasingly challenging issue in Africa, forcing energy-deficient countries to forge mutual partnerships with energy sufficient countries to access it for their domestic consumption. This study formulates a composite index of energy security in Africa as well as evaluates its impacts and trends using a sample of 28 countries on the continent, during the 2000–2018 period by using a principal composite factor analysis (PCA), with the series of 13 variables. Further interpretation was carried out using these tests: Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett’s test of sphericity, Pearson correlation test, and Cronbach’s alpha test. The key results show a trend of energy insecurity among the countries studied, as energy imports loads high in most countries as well as per capita emission, together with fossil fuel source consumption correlating high. These results validate the stark reality on the African continent. The inference from the results of the anaylsis conclude that the principal component analysis (PCA) results of the energy index were considered fit and reliable for the analysis, with the most important Cronbach’s alpha test coefficient of 0.8797, far above the standard 0.6 model reliability level. Based on this study, the paper proffers there should be increased intra-regional trading of energy among the various power pools on the continent and increased regional renewable energy investments as well as investment in energy infrastructure, measures to reduce electricity system losses, environmental sustainability, and the adoption of energy in efficiency on the continent.

In this work, the heat transfer features and stagnation point flow of Magnetohydrodynamics (MHD) hybrid second-grade nanofluid through a convectively heated permeable shrinking/stretching sheet is reported. The purpose of the present investigation is to consider hybrid nanofluids comprising of Alumina Al 2 O 3 and Copper Cu nanoparticles within the Sodium Alginate (SA) as a host fluid for boosting the heat transfer rate. Also, the effects of free convection, viscous dissipation, heat source/sink, and nonlinear thermal radiation are considered. The converted nonlinear coupled fuzzy differential equations (FDEs) with the help of triangular fuzzy numbers (TFNs) are solved using the numerical scheme bvp4c. The numerical results are acquired for various engineering parameters to study the Nusselt number, skin friction coefficient, velocity, and temperature distribution through figures and tables. For the validation, the current numerical results were found to be good as compared to existing results in limiting cases. It is also inspected by this work that with the enhancement of the volume fraction of nanoparticles, the heat transfer rate also increases. So, it may be taken as a fuzzy parameter for a better understanding of fuzzy variables. For the comparison, the volume fraction of nanofluids and hybrid nanofluid are said to be TFN [0, 0.1, 0.2]. In the end, we can see that fuzzy triangular membership functions (MFs) have not only helped to overcome the computational cost but also given better accuracy than the existent results. Finding from fuzzy MFs, the performance of hybrid nanofluids is better than nanofluids.

This study suggests a new approach for solving telegraph equation, commonly called damped wave equation, arising in electromagnetic waves and propagation of electrical signals. In this paper, He–Laplace method, formulated by He’s variational iteration method and Laplace transformation, is used to find the exact solution or a closed approximate solution of differential equations. The most distinct aspect of this method is that there is no need to calculate integration for next iterations in recurrence relations and convolution theorem is kept away to calculate the Lagrange multipliers in Laplace transformation. Moreover, He’s polynomials via homotopy perturbation method is introduced to bring down the computational work in nonlinear terms as Laplace transform has some limitation to nonlinear terms. The results obtained by proposed method indicate that this approach is easy to implement and converges rapidly to exact solution. Several problems are illustrated to demonstrate the accuracy and stability of this method.

While lithium-ion batteries (LIBs) have pushed the progression of electric vehicles (EVs) as a viable commercial option, they introduce their own set of issues regarding sustainable development. This paper investigates how using end-of-life LIBs in stationary applications can bring us closer to meeting the sustainable development goals (SDGs) highlighted by the United Nations. We focus on how this practice can support three of these goals, namely Goal 7: Affordable and Clean Energy, Goal 12: Responsible Consumption and Production, and Goal 13: Climate Action. We present a literature review that details the aging mechanisms of LIBs, namely battery degradation, state of charge, state of health, depth of discharge, remaining useful life, and battery management systems. Then, we thoroughly examine the environmental and economic benefits of using second-life EV batteries in stationary applications and how they align with the SDGs. Our review of the literature summarizes the most relevant research in battery aging, giving a foundation for further research and allowing effective legislation to be written around EVs. Additionally, our examination of the benefits of using second-life batteries motivates initiatives for sustainable practices, helping both corporations and legislators orient their ideals towards the SDGs.

PurposeSoil microbial biomass (SMB), as the source and sink of soil nutrients, and its stoichiometry play a key role in soil organic carbon (SOC) and nitrogen (N) mineralization. The objective of this study was to investigate the responses of SOC and N mineralization to changes in microbial biomass and SOC, N, and phosphorus (P) stoichiometry resulted from long-term fertilization regimes.Materials and methodsSoil was sampled from a rice-wheat rotation system subjected to 37 years of nine fertilization treatments with different nutrient input amounts: control (CK), N alone, N combined with mineral phosphorus (NP), NP plus potassium (NPK), manure alone (M), and M combined with N (MN), NP (MNP), NPK (MNPK), and a higher rate of M with NPK (hMNPK). The sampled soil was incubated for the determination of SOC and N mineralization, C, N, and P stoichiometry of soil and SMB, and associated soil enzymes related to C and N cycling.Results and discussionRelative to the CK and treatments with mineral fertilizers, treatments with manure (M, MN, MNP, MNPK, and hMNPK) significantly increased SOC and N mineralization by 48–78% and 54–97%, respectively. Microbial metabolic quotient (qCO2) decreased by 32–55% in treatments with manure compared to the N and NP treatments, but showed no effect on the qCO2 when compared to the NPK treatment. The leucine amino peptidase (LAP) enzyme showed significant positive correlation with SOC and N mineralization, and negatively related to the qCO2. Significantly negative correlations were also observed between SOC and N mineralization and soil C:P and N:P ratio, as well as microbial biomass SMBC:SMBP and SMBN:SMBP stoichiometry, respectively. However, the availability of N and P had limited effects on the qCO2 after reaching a certain value (0.69–0.72 mg CO2-C g−1 MBC h−1).ConclusionsLower soil elemental (C:P and N:P) and microbial biomass stoichiometry (SMBC:SMBP and SMBN:SMBP) and increase of LAP resulted from combined application of manure and mineral fertilizers, accelerated SOC, and N mineralization. Mineral nutrient input with manure amendments could be an optimal strategy to meet the microbial stoichiometric demands and enhance nutrient availability for crops in agricultural ecosystems.

Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment while preserving patient data privacy and security. Traditional centralized approaches often encounter obstacles in data sharing due to privacy regulations and security concerns, hindering the development of advanced AI-based medical imaging applications. To overcome these challenges, this study proposes the utilization of federated learning. The proposed framework enables collaborative learning by training the segmentation model on distributed data from multiple medical institutions without sharing raw data. Leveraging the U-Net-based model architecture, renowned for its exceptional performance in semantic segmentation tasks, this study emphasizes the scalability of the proposed approach for large-scale deployment in medical imaging applications. The experimental results showcase the remarkable effectiveness of federated learning, significantly improving specificity to 0.96 and the dice coefficient to 0.89 with the increase in clients from 50 to 100. Furthermore, the proposed approach outperforms existing convolutional neural network (CNN)- and recurrent neural network (RNN)-based methods, achieving higher accuracy, enhanced performance, and increased efficiency. The findings of this research contribute to advancing the field of medical image segmentation while upholding data privacy and security.

This study is based on an analytical approach for nonlinear vibration of nonlinear evaluation equations which arises in shallow water waves. This approach is, coupled with variational iteration method and Laplace transformation, known as He–Laplace method. In order to show the capability of the proposed method, He’s polynomials are employed to handle nonlinear terms in illustrated problems due to specific limit of Laplace transformation. Significant results and their graphical representations demonstrated that He–Laplace method is most suitable for nonlinear problems, and it is enormously active for nonlinear vibrations and nonlinear waves.

The real-time forecasting of flood dynamics is a long-standing challenge traditionally addressed through numerical solutions of the Shallow Water Equations (SWEs). Numerical solutions of realistic flow problems using numerical schemes are often hindered by high computational costs, particularly due to the need for fine spatial and temporal discretization, complex boundary conditions, and the resolution of non-linearities inherent in the governing equations. In this study, we investigate the use of Physics-Informed Neural Networks (PINNs) to solve 1D and 2D SWEs in dam-break scenarios. The proposed PINN framework incorporates the governing partial differential equations along with the initial and boundary conditions directly within the training process of the network, ensuring physically consistent solutions. We conduct a systematic comparison of the solutions of SWE using the classical numerical scheme (Lax-Wendroff) with estimates of physics informed neural networks. For 1D SWE, a neural network is trained and validated on a dam-break problem, revealing that physics-informed models produce smoother but still acceptable estimates of wave propagation compared to standard numerical results. For 2D SWE, we consider various configurations of dam geometries along with varying initial profiles for water heights. Across all scenarios, reproduce the numerical baselines, albeit with limited accuracy, while avoiding spurious oscillations and numerical artifacts. Further tuning, achieved by incorporating numerical solutions into the PINN training, improved accuracy. This proof of concept demonstrates the potential of hybridized PINNs as a mesh-free, scalable, and generalizable framework for approximating solutions to nonlinear hyperbolic systems. Our results indicate that pre-trained, physics-informed models could serve as a viable alternative for real-time flood forecasting in complex domains.

Technological advancements have significantly improved cattle farming, particularly in sensor-based activity monitoring for health management, estrus detection, and overall herd supervision. However, such a sensor-based monitoring framework often illustrates several issues, such as high cost, animal discomfort, and susceptibility to false measurement. This study introduces a vision-based cattle activity monitoring approach deployed in a commercial Nestlé dairy farm, specifically one that is estrus-focused, where overhead cameras capture unconstrained herd behavior under variable lighting, occlusions, and crowding. A custom dataset of 2956 Images are collected and then annotated into four fine-grained behaviors—standing, lying, grazing, and estrus—enabling detailed analysis beyond coarse activity categories commonly used in prior livestock monitoring studies. Furthermore, computer vision-based deep learning algorithms are deployed on this dataset to classify the aforementioned classes. A comparative analysis of YOLOv8 and YOLOv9 is provided, which clearly illustrates that YOLOv8-L achieved a mAP of 91.11%, whereas YOLOv9-E achieved a mAP of 90.23%.