RCLNet: an effective anomaly-based intrusion detection for securing the IoMT system.

The Internet of Medical Things (IoMT) has revolutionized healthcare with remote patient monitoring and real-time diagnosis, but securing patient data remains a critical challenge due to sophisticated cyber threats and the sensitivity of medical information. Traditional machine learning methods struggle to capture the complex patterns in IoMT data, and conventional intrusion detection systems often fail to identify unknown attacks, leading to high false positive rates and compromised patient data security. To address these issues, we propose RCLNet, an effective Anomaly-based Intrusion Detection System (A-IDS) for IoMT.

ERIRMS Evaluation of the Reliability of IoT-Aided Remote Monitoring Systems of Low-Voltage Overhead Transmission Lines.

Researchers have studied instances of power line technical failures, the significant rise in the energy loss index in the line connecting the distribution transformer and consumer meters, and the inability to control unauthorized line connections. New, innovative, and scientific approaches are required to address these issues while enhancing the reliability and efficiency of electricity supply. This study evaluates the reliability of Internet of Things (IoT)-aided remote monitoring systems specifically designed for a low-voltage overhead transmission line.

Unlocking growth: Investigating asymmetry in the financial inclusion-growth nexus in financially inclusive middle east economies.

Financial inclusion is a crucial element of financial development that transmits cheap financial services to provide advantages to entire segments of society and stimulates economic growth. Our investigation evaluates the asymmetric financial inclusion-economic growth nexus in the top 10 financially inclusive Middle East nations (Israel, Oman, Iran, Qatar, Turkey, Saudi Arabia, Bahrain, Egypt, United Arab Emirates, and Kuwait). Earlier studies adopted panel data tools, which yielded typical outcomes on the association between financial inclusion and economic growth despite few economies did not indicate such a link individually.

Dataset of numerical study of the impact of two-step injection (direct and port) of hydrogen-enriched and natural gas mixtures on engine efficiency and exhaust emissions.

A study was conducted to examine the effects of two-step fuel injection on a modified four-cylinder engine that was converted from port to direct injection. The primary fuel source utilized was hydrogen-enriched compressed natural gas (HCNG), which replaced the conventional gasoline. In the initial phase of the procedure, compressed natural gas (CNG) was introduced into the intake manifold at a concentration of 10 % by mass, relative to the total fuel mixture.

Gossypol derivate as a green anti-corrosion agent in the aqueous phase of crude oil.

Keeping recruitment of green and cost-effective solutions for environmental challenges in view, the current work was designed to solve the problems related to metal corrosion in the aqueous phases of crude oil in chemical industries. Green materials can play an important role in protecting metals from this corrosion. Hence, the green anti-corrosion material based upon gossypol derivate is suggested to solve the above problems.

Correction: Yuldashev et al. Parking Lot Occupancy Detection with Improved MobileNetV3. 2023, , 7642.

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Early () Leaf-Based Disease Detection through Computer Vision, YOLOv8, and Contrast Stretching Technique.

() trees play a vital role in various industries and in environmental sustainability. They are widely used for paper production, timber, and as windbreaks, in addition to their significant contributions to carbon sequestration. Given their economic and ecological importance, effective disease management is essential.

Calculating the fatigue strength of load-bearing structures of special self-propelled rolling stock.

This research primarily focuses on the strength indicators of the bearing structures of ADM-1 special self-propelled rolling stock. The special self-propelled rolling stock used by Uzbek railroads reaching the end of their functional life is a pertinent problem as Uzbekistan's railway system is growing rapidly, but there is a lack of enough funds to buy new special self-propelled rolling stock. Hence, it is vital to fix the issues with ADM-1 special self-propelled rolling stock by overhauling them.

Mask region-based convolutional neural network and VGG-16 inspired brain tumor segmentation.

The process of brain tumour segmentation entails locating the tumour precisely in images. Magnetic Resonance Imaging (MRI) is typically used by doctors to find any brain tumours or tissue abnormalities. With the use of region-based Convolutional Neural Network (R-CNN) masks, Grad-CAM and transfer learning, this work offers an effective method for the detection of brain tumours.

Artificial intelligence probabilities scheme for disease prevention data set construction in intelligent smart healthcare scenario.

In the face of an aging population, smart healthcare services are now within reach, thanks to the proliferation of high-speed internet and other forms of digital technology. Data problems in smart healthcare, unfortunately, put artificial intelligence in this area to serious limitations. There are several issues, including a lack of standard samples, noisy data interference, and actual data that is missing.

Decomposing scale, technique and composition effects of foreign direct investment on environmental quality.

This study aimed to investigate how the decomposing scale effect, technique effect and composition effect of foreign direct investment (FDI) impact on carbon dioxide (CO) emissions for 115 nations spanning 1999 to 2019 by employing Generalised Method of Moments (GMM) model. The results indicated that FDI, real GDP per capita, capital-labor ratio, institutional quality and urbanization increase CO emissions while the square of real GDP per capita and trade openness contributed to reducing CO emissions. Also, our findings fail to support Environmental Kuznets Curve (EKC) theory.

Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments.

Medical imaging and deep learning models are essential to the early identification and diagnosis of brain cancers, facilitating timely intervention and improving patient outcomes. This research paper investigates the integration of YOLOv5, a state-of-the-art object detection framework, with non-local neural networks (NLNNs) to improve brain tumor detection's robustness and accuracy. This study begins by curating a comprehensive dataset comprising brain MRI scans from various sources.

Linking clean energy consumption, globalization, and financial development to the ecological footprint in a developing country: Insights from the novel dynamic ARDL simulation techniques.

Developing countries have been facing economic difficulties for over three and a half decades due to numerous factors, including fossil fuel consumption and dwindling biocapacity. It is necessary to pinpoint the factors that may be culpable for poor environmental quality leading to a rising ecological footprint (EFP). This study explores the effect of clean energy, financial development (FDV), and globalization on the EFP in a developing country using the novel dynamic ARDL simulation techniques and the bootstrap causality test.

Exploring the impact of social capital, institutional quality and political stability on environmental sustainability: New insights from NARDL-PMG.

The social aspect of sustainable development is often considered the least strong component, particularly in terms of its analytical and theoretical foundations. Although there has been a recent increase in focus on social sustainability, the relationship between the environmental aspect and social capital is still not well understood. This research seeks to explore initial concepts on frameworks for analyzing the interface between environmental and social capital.

Ethnic and religious identities in Russian penal institutions: A case study of Uzbek Transnational Muslim prisoners.

Russia has become one of the main migration hubs worldwide following the collapse of the Soviet Union. The vast majority of migrant workers travel to Russia from three Central Asian countries. However, Russian immigration laws and policies are ambiguous and highly punitive.

Can clean energy and technology address environmental sustainability in G7 under the pre-set of human development?

Climate change presents challenges for both industrialized and developing nations, primarily due to insufficient pollution control. Increased fossil fuel usage escalates pollution levels, emphasizing the need to integrate more renewable energy into the energy mix, particularly to reduce carbon emissions. Consequently, public investment in renewable energy becomes pivotal to enhance the necessary technology for green energy production.

Can undergoing renewable energy transition assist the BRICS countries in achieving environmental sustainability?

The BRICS countries ratified the 2030 Sustainable Development Goals agenda whereby ensuring environmental sustainability is of paramount importance for these emerging market economies. Although the BRICS nations have recorded noteworthy economic growth trajectories over the last couple of decades, these nations have not fared well in terms of improving their environmental indicators, especially due to gradually becoming more fossil fuel dependent over time. Hence, this study aims to explore whether undergoing the renewable energy transition can directly and indirectly establish environmental sustainability in the BRICS countries by containing their annual growth rates of carbon dioxide emissions.

An empirical examination of the environmental sustainability-influencing mechanisms of renewable energy: contextual evidence from Next Eleven countries.

Recognizing the environmental development-related commitments made by the Next Eleven countries at 26th Conference of Parties (COP26), this study scrutinizes the repercussions accompanying good democratic governance, renewable energy transition, economic growth, and the ratification of the Kyoto Protocol on carbon emission figures of these emerging nations. In this regard, the period of analysis considered spans from 1990 to 2018 while the econometric analyses involve application of both parametric and non-parametric panel data estimators. Among the key findings, firstly, the outcomes from the parametric estimation methods verify that establishing better democratic governance and undergoing renewable energy transition, both independently and jointly, curb carbon emission levels, while higher economic growth and the signing of the Kyoto Protocol are responsible for boosting emissions the Next Eleven countries.

Does higher energy efficiency growth homogeneously affect carbon emission growth rate across developing Sub-Saharan African nations? The importance of utilizing clean energy.

Establishing a sustainable environment and acquiring a carbon-neutral status require Sub-Saharan African nations to reduce their year-on-year growth rates of carbon emission levels. Thus, this study considers a sample of 38 countries from this region and selects the time period from 2000 to 2020 for analyzing the annual carbon emission growth rate influencing impacts of energy efficiency, clean energy, institutional quality, international trade, and net receipts of foreign direct investment. Overall, for the full sample of Sub-Saharan African nations, the results verify that the enhancing the growth rate of energy efficiency improvement reduces both total and per capita annual carbon emission growth rates.

An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images.

Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting forest fire smoke encounter persistent issues, including a slow identification rate, suboptimal accuracy in detection, and challenges in distinguishing smoke originating from small sources.