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The study empirically examines the environmental Kuznets curve (EKC) hypotheses by investigating the relationship between ecological footprint, economic growth, energy consumption, and population growth. The study uses ecological footprint as a measurement of environmental degradation which is a more comprehensive indicator and considers all factors responsible for environmental degradation. Keeping in view the problem of cross-sectional dependence, a more efficient estimation tools like pooled mean group and augmented mean group have been used to estimate the long-run parameters for 22 European countries from 1995 through 2015. Results of the study found a quadratic relationship between income growth and ecological footprint and support validity of EKC. Energy consumption positively contributes to ecological footprint, while population growth plays no significant role in determining environmental quality. The long-run estimates of the study are validated through robustness analysis by employing dynamic ordinary least square (DOLS) and fully modified ordinary least square (FMOLS) techniques. Dumitrescu and Hurlin (2012) panel non-causality test indicated that there is a unidirectional causality running from GDP to ecological footprint while bidirectional causality running between energy consumption and ecological footprint. The study identified that population growth in European region is not a severe issue as compared to intensive energy consumption. Policies which restrict emission, deforestation, and water pollution should be adopted for sustainability of environment.

A low-cost, low-power, and low data-rate solution is proposed to fulfill the requirements of information monitoring for actual large-scale agricultural farms. A small-scale farm can be easily managed. By contrast, a large farm will require automating equipment that contributes to crop production. Sensor based soil properties measurement plays an integral role in designing a fully automated agricultural farm, also provides more satisfactory results than any manual method. The existing information monitoring solutions are inefficient in terms of higher deployment cost and limited communication range to adapt the need of large-scale agriculture farms. A serial based low-power, long-range, and low-cost communication module is proposed to confront the challenges of monitoring information over long distances. In the proposed system, a tree-based communication mechanism is deployed to extend the communication range by adding intermediate nodes. Each sensor node consists of a solar panel, a rechargeable cell, a microcontroller, a moisture sensor, and a communication unit. Each node is capable to work as a sensor node and router node for network traffic. Minimized data logs from the central node are sent daily to the cloud for future analytics purpose. After conducting a detailed experiment in open sight, the communication distance measured 250 m between two points and increased to 750 m by adding two intermediate nodes. The minimum working current of each node was 2 mA, and the packet loss rate was approximately 2–5% on different packet sizes of the entire network. Results show that the proposed approach can be used as a reference model to meet the requirements for soil measurement, transmission, and storage in a large-scale agricultural farm.

Distributed Denial of Service (DDoS) attacks, advanced persistent threats, and malware actively compromise the availability and security of Internet services. Thus, this paper proposes an intelligent agent system for detecting DDoS attacks using automatic feature extraction and selection. We used dataset CICDDoS2019, a custom-generated dataset, in our experiment, and the system achieved a 99.7% improvement over state-of-the-art machine learning-based DDoS attack detection techniques. We also designed an agent-based mechanism that combines machine learning techniques and sequential feature selection in this system. The system learning phase selected the best features and reconstructed the DDoS detector agent when the system dynamically detected DDoS attack traffic. By utilizing the most recent CICDDoS2019 custom-generated dataset and automatic feature extraction and selection, our proposed method meets the current, most advanced detection accuracy while delivering faster processing than the current standard.

The colloidal suspension of nanometer-sized particles of Fe 3 O 4 in traditional base fluids is referred to as Ferro-nanofluids. These fluids have many technological applications such as cell separation, drug delivery, magnetic resonance imaging, heat dissipation, damping, and dynamic sealing. Due to the massive applications of Ferro-nanofluids, the main objective of this study is to consider the MHD flow of water-based Ferro-nanofluid in the presence of thermal radiation, heat generation, and nanoparticle shape effect. The Caputo-Fabrizio time-fractional Brinkman type fluid model is utilized to demonstrate the proposed flow phenomenon with oscillating and ramped heating boundary conditions. The Laplace transform method is used to solve the model for both ramped and isothermal heating for exact solutions. The ramped and isothermal solutions are simultaneously plotted in the various figures to study the influence of pertinent flow parameters. The results revealed that the fractional parameter has a great impact on both temperature and velocity fields. In the case of ramped heating, both temperature and velocity fields decreasing with increasing fractional parameter. However, in the isothermal case, this trend reverses near the plate and gradually, ramped, and isothermal heating became alike away from the plate for the fractional parameter. Finally, the solutions for temperature and velocity fields are reduced to classical form and validated with already published results.

This article deals with the generalization of natural convection flow of Cu−Al2O3−H2O\\(Cu - Al_{2}O_{3} - H_{2}O\\) hybrid nanofluid in two infinite vertical parallel plates. To demonstrate the flow phenomena in two parallel plates of hybrid nanofluids, the Brinkman type fluid model together with the energy equation is considered. The Caputo–Fabrizio fractional derivative and the Laplace transform technique are used to developed exact analytical solutions for velocity and temperature profiles. The general solutions for velocity and temperature profiles are brought into light through numerical computation and graphical representation. The obtained results show that the velocity and temperature profiles show dual behaviors for 0<α<1\\(0 < \\alpha < 1\\) and 0<β<1\\(0 < \\beta < 1\\) where α and β are the fractional parameters. It is noticed that, for a shorter time, the velocity and temperature distributions decrease with increasing values of the fractional parameters, whereas the trend reverses for a longer time. Moreover, it is found that the velocity and temperature profiles oppositely behave for the volume fraction of hybrid nanofluids.

The growth of IoT healthcare is aimed at providing efficient services to patients by utilizing data from local hospitals. However, privacy concerns can impede data sharing among third parties. Federated learning offers a solution by enabling the training of neural networks while maintaining the privacy of the data. To integrate federated learning into IoT healthcare, hospitals must be part of the network to jointly train a global central model on the server. Local hospitals can train the global model using their patient datasets and send the trained localized models to the server. These localized models are then aggregated to enhance the global model training process. The aggregation of local models dramatically influences the performance of global training, mainly due to the heterogeneous nature of patient data. Existing solutions to address this issue are iterative, slow, and susceptible to convergence. We propose two novel approaches that form groups efficiently and assign the aggregation weightage considering essential parameters vital for global training. Specifically, our method utilizes an autoencoder to extract features and learn the divergence between the latent representations of patient data to form groups, facilitating more efficient handling of heterogeneity. Additionally, we propose another novel aggregation process that utilizes several factors, including extracted features of patient data, to maximize performance further. Our proposed approaches for group formation and aggregation weighting outperform existing conventional methods. Notably, significant results are obtained, one of which shows that our proposed method achieves 20.8% higher accuracy and 7% lower loss reduction compared to the conventional methods.

Explainable Artificial Intelligence (XAI) is a well-established and dynamic field defined by an active research community that has developed numerous effective methods for explaining and interpreting the predictions of advanced machine learning models, including deep neural networks. Clustered Federated Learning (CFL) mitigates the difficulties posed by heterogeneous clients in traditional federated learning by categorizing related clients according to data characteristics, facilitating more tailored model updates, and improving overall learning efficiency. This paper introduces Explainable Clustered Federated Learning (XCFL), which adds explainability to clustered federated learning. Our method improves performance and explainability by selecting features, clustering clients, training local clients, and analyzing contributions using SHAP values. By incorporating feature-level contributions into cluster and global aggregation, XCFL ensures a more transparent and data-driven model update process. Weighted aggregation by feature contributions improves consumer diversity and decision transparency. Our results show that XCFL outperforms FedAvg and other clustering methods. Our feature-based explainability strategy improves model performance and explains how features affect clustering and model adjustments. XCFL’s improved accuracy and explainability make it a promising solution for heterogeneous and distributed learning environments.

Digital health is a field that aims to improve patient care through the use of technology, such as telemedicine, mobile health, electronic health records, and artificial intelligence. The aim of this review is to examine the challenges and potential solutions for the implementation and evaluation of digital health technologies. Digital tools are used across the world in different settings. In Australia, the Digital Health Translation and Implementation Program (DHTI) emphasizes the importance of involving stakeholders and addressing infrastructure and training issues for healthcare workers. The WHO's Global Task Force on Digital Health for TB aims to address tuberculosis through digital health innovations. Digital tools are also used in mental health care, but their effectiveness must be evaluated during development. Oncology supportive care uses digital tools for cancer patient intervention and surveillance, but evaluating their effectiveness can be challenging. In the COVID and post-COVID era, digital health solutions must be evaluated based on their technological maturity and size of deployment, as well as the quality of data they provide. To safely and effectively use digital healthcare technology, it is essential to prioritize evaluation using complex systems and evidence-based medical frameworks. To address the challenges of digital health implementation, it is important to prioritize ethical research addressing issues of user consent and addressing socioeconomic disparities in access and effectiveness. It is also important to consider the impact of digital health on health outcomes and the cost-effectiveness of service delivery.

The thermodynamic performance of a tapered roller bearing is governed by the irreversible dissipation within its lubricating film. Minimizing the total entropy generation rate, which arises from fluid friction and heat transfer, is paramount for enhancing mechanical efficiency and operational longevity. This study conducts a numerical investigation into the entropy optimization of a nanoparticle-enhanced Sutterby lubricant within a rough-walled, tapered bearing geometry. The flow is modeled using the continuity, momentum, and energy equations, coupled with an entropy transport equation. The non-Newtonian lubricant behavior is characterized by the Sutterby fluid model, with its shear-thinning intensity governed by the Weissenberg number ( ). The analysis focuses on the influence of key dimensionless parameters: the Reynolds number, the Hartmann number for magneto-hydrodynamic effects, and a defined surface roughness parameter. The analysis demonstrates that the total entropy generation, comprising frictional and thermal contributions is highly sensitive to the converging-diverging geometry. It is found that increasing the Weissenberg number significantly reduces the Bejan number, indicating a shift from thermal to viscous flow irreversibility dominance. Furthermore, the nanoparticle volume fraction directly enhances thermal transport, reducing the temperature gradient contribution to entropy generation. Optimal conditions for minimizing entropy are identified for specific combinations of Reynolds number, Weissenberg number and surface roughness. The results provide a rigorous thermodynamic framework for optimizing tapered bearing performance. By quantifying the interplay between rheology, inertia, and surface texture on the entropy generation map, this work establishes design criteria for minimizing irreversibility. This enables the development of high-efficiency lubrication systems utilizing advanced non-Newtonian nano lubricants.

Lumpy skin disease (LSD) is a critical problem for cattle populations, affecting both individual cows and the entire herd. Given cattle’s critical role in meeting human needs, effective management of this disease is essential to prevent significant losses. The study proposes a deep learning approach using the MobileNetV2 model and the RMSprop optimizer to address this challenge. Tests on a dataset of healthy and lumpy cattle images show an impressive accuracy of 95%, outperforming existing benchmarks by 4–10%. These results underline the potential of the proposed methodology to revolutionize the diagnosis and management of skin diseases in cattle farming. Researchers and graduate students are the audience for our paper.