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The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.

The relationship between renewable energy consumption (REC), foreign direct investment (FDI) inflows, economic growth, and their resulting impact on CO2 emissions is widely discussed area in energy and environmental literature; however, there is an unseen literature on moderation and mediation effect of per capita income and FDI inflows with the renewable energy consumption on CO2 emissions in developing countries like Pakistan, which is being evaluated in this study by using a consistent time series data for a period of 1975–2016. The results show that economic growth and FDI inflows both increase CO2 emissions, while REC substantially decreases CO2 emissions during the study time period. The results do not support the inverted U-shaped Environmental Kuznets Curve (EKC) hypothesis for per capita income (and FDI inflows) and per capita CO2 emissions in a country. The results supported ‘pollution haven hypothesis’ where FDI inflows damage the natural flora of the country. By inclusion of moderation and mediation effect of per capita income and FDI inflows with the REC on CO2 emissions averted the positive impact of REC, and converted into negative externality, where environmental sustainability agenda is compromised by lower environmental regulations and unsustainable production techniques that increase country’s economic growth. The study concludes that by adding REC in existing energy portfolio may help to reduce CO2 emissions while strict environmental compliance may disregard the negative externality of unsustainable production and it will support to achieve green development programmes in a country.

The objective of the study is to analyze the dynamic linkages between technology factors and carbon emission in a panel of 26 selected European countries from 2000 to 2017. The results of the panel fixed-effect regression model show the monotonic increasing function between agriculture technology and carbon emissions. In contrast, panel quantile regression confirmed the inverted U-shaped ‘Agriculture Technology Kuznets curve (ATKC)’ of carbon emissions at 30th quantile distribution to 80th quantile distribution with the turning points of 12,60,000 tractors to 9,68,000 tractors, respectively. The results further exhibit the negative relationship between high-technology exports and carbon emissions, as high-technology exports have a positive impact on environmental quality in order to reduce carbon emissions across countries. The relationship between ICT goods exports and carbon emissions is complimentary, while R&D expenditures have a negative relationship with carbon emissions in a given period. The study substantiates the ‘pollution haven hypothesis (PHH)’ that is controlled by trade liberalization policies. The telephone and mobile penetrations have a differential impact on carbon emissions in both of the prescribed statistical techniques, which needs fair economic policies in order to delimit carbon emissions through green ICT infrastructure. The results further exhibit the ‘material footprint’ that is visible at the earlier stages of economic development while it is substantially decreasing at the later stages to verify ‘environmental Kuznets curve (EKC)’ hypothesis with a turning point of US$45,700. Finally, the study shows the positive relationship between industry value-added and carbon emissions that sabotaged the process of green development across countries. The study concludes that green ICT infrastructure is imperative for sustainable production and consumption, and climate change protection with cleaner production techniques and environmental regulations that reshape the international policies towards sustained growth.

Closed-form evaluation of key performance indicators (KPIs) of telecommunication networks help perform mathematical analysis under several network configurations. This paper deals with a recent mathematical approach of indefinite quadratic forms to propose simple albeit exact closed-form expressions of the expectation of two significant logarithmic functions. These functions formulate KPIs which include the ergodic capacity and leakage rate of multi-user multiple-input multiple-output (MU-MIMO) systems in Rayleigh fading channels. Our closed-form expressions are generic in nature and they characterize several network configurations under statistical channel state information availability. As a demonstrative example of the proposed characterization, the derived expressions are used in the statistical transmit beamformer design in a broadcast MU-MIMO system to portray promising diversity gains using standalone or joint maximization techniques of the ergodic capacity and leakage rate. The results presented are validated by Monte Carlo simulations.

The study examined the long-run and causal relationship between international tourism receipts (ITR), social distribution, FDI inflows, and carbon (CO2) emissions to verify the different alternative and plausible hypotheses, i.e., environmental Kuznets curve (EKC) hypothesis, “pollution haven” hypothesis (PHH), and “resource efficiency” (REF) hypothesis, in a panel of Group of Seven (G-7) countries for the period of 1995–2015. The study employed panel random effect (RE) regression and panel causality test for robust inferences. The results show that ITR and FDI inflows increase CO2 emissions to verify PHH while government education expenditures (GEE) decrease CO2 emissions to substantiate the REF hypothesis across countries. The results validate the inverted U-shaped EKC relationship between CO2 emissions and economic growth (EG) with the turning point of US$30,900. In addition, GEE increase ITR while healthcare expenditures (HEXP) decrease ITR, which partially supported the REF hypothesis in a panel of countries. The impact of income inequality (INEQ) on ITR is positive at current time period while at later stages INEQ declines ITR that supported an inverted U-shaped relationship between them. The causality estimates confirm the bidirectional relationship between ITR and EG, while there is unidirectional casualty running from (i) ITR, EG, FDI inflows, and GEE to CO2 emissions, (ii) FDI inflows to ITR, (iii) GEE to EG, (iv) EG to social expenditures, (v) income inequality to health expenditures, (vi) social expenditures (SEXP) to ITR, and (vii) INEQ to ITR. There is no causal relationship found between ITR and EG during the study time period. The findings endorse the need for efficient resource spending, sustainable tourism (STR), and rational income distribution to improve environmental sustainability agenda in a panel of G-7 countries.

Drones are increasingly capturing the world’s attention, transcending mere hobbies to revolutionize areas such as engineering, disaster aid, logistics, and airport protection, among myriad other fascinating applications. However, there is growing concern about the risks that they pose to physical infrastructure, particularly at airports, due to potential misuse. In recent times, numerous incidents involving unauthorized drones at airports disrupting flights have been reported. To solve this issue, this article introduces an innovative deep learning method proposed to effectively distinguish between drones and birds. Evaluating the suggested approach with a carefully assembled image dataset demonstrates exceptional performance, surpassing established detection systems previously proposed in the literature. Since drones can appear extremely small compared to other aerial objects, we developed a robust image-tiling technique with overlaps, which showed improved performance in the presence of very small drones. Moreover, drones are frequently mistaken for birds due to their resemblances in appearance and movement patterns. Among the various models tested, including SqueezeNet, MobileNetV2, ResNet18, and ResNet50, the SqueezeNet model exhibited superior performance for medium area ratios, achieving higher average precision (AP) of 0.770. In addition, SqueezeNet’s superior AP scores, faster detection times, and more stable precision-recall dynamics make it more suitable for real-time, accurate drone detection than the other existing CNN methods. The proposed approach has the ability to not only detect the presence or absence of drones in a particular area but also to accurately identify and differentiate between drones and birds. The dataset utilized in this research was obtained from a real-world dataset made available by a group of universities and research institutions as part of the 2020 Drone vs. Bird Detection Challenge. We have also tested the performance of the proposed model on an unseen dataset, further validating its better performance.

The importance of sustainable tourism is largely discussed in environmental literature under two different main streams: first, an ample amount of literature is available on the role of international tourism in economic development; second, the existing literature mainly focused on estimating tourism carbon footprints across countries. Limited work has been done on identifying the cost of carbon emissions on the tourism industry, which is evaluated in this study to fill the existing literature gap by using a large panel of 132 countries between 1995 and 2018. The results show that carbon emissions damage, methane (CH4), nitrous oxide (N2O) emissions, and population density substantially decrease inbound tourism and international tourism receipts that result in an impact on the increase in international tourism expenditures across countries. The ex-ante analysis shows that inbound tourism will likely decrease from 19.546% to 16.854% due to an increase in carbon emissions damage of 0.357% to 1.349% for the period 2020–2028. Subsequently, international tourism expenditures will decrease from 19.758% to 12.384% by increasing carbon emissions damage from0.832% to 1.025%. Finally, international tourism revenues will subsequently decline from23.362% to 18.197% due to lowering carbon emissions damage from 0.397% to −0.113% over a time horizon.

Climate change and increased greenhouse gas emissions boost the global average temperature to less than 2°C, which is the estimated breakeven point. The globe is moving into blue pollution economies as the environmental sustainability objective becomes more distorted. The study looked at three United Nations Sustainable Development Goals, namely (i) affordable and clean energy; (ii) industry, innovation, and infrastructure; and (iii) climate change, to see how far the Chinese economy has progressed toward green and clean development strategy. In the context of China, the “pollution damage function” was intended to refer to carbon damages related to carbon pricing, technological variables, sustained economic growth, incoming foreign investment, and green energy. The data was collected between 1975 and 2019 and analyzed using various statistical approaches. The results of the autoregressive distributed lag model suggest that carbon taxes on industrial emissions reduce carbon damages in the short and long run. Furthermore, a rise in inbound foreign investment and renewable energy demand reduces carbon damages in the short term, proving the “pollution halo” and “green energy” hypotheses; nonetheless, the results are insufficient to explain the stated results in the long run. In the long run, technology transfers and continued economic growth are beneficial in reducing carbon damages and confirming the potential of cleaner solutions in pollution mitigation. The causal inferences show the one-way relationship running from carbon pricing and technology transfer to carbon damages, and green energy to high-technology exports in a country. The impulse response estimates suggested that carbon tax, inbound foreign investment, and technology transfers likely decrease carbon damages for the next 10 years. On the other hand, continued economic growth and inadequate green energy sources are likely to increase carbon pollution in a country. The variance decomposition analysis suggested that carbon pricing and information and communication technology exports would likely significantly influence carbon damages over time. To keep the earth’s temperature within the set threshold, the true motivation to shift from a blue to a green economy required strict environmental legislation, the use of green energy sources, and the export of cleaner technologies. Graphical abstract Source: Authors’ self-extract

The ability of the underwater vehicle to determine its precise position is vital to completing a mission successfully. Multi-sensor fusion methods for underwater vehicle positioning are commonly based on Kalman filtering, which requires the knowledge of process and measurement noise covariance. As the underwater conditions are continuously changing, incorrect process and measurement noise covariance affect the accuracy of position estimation and sometimes cause divergence. Furthermore, the underwater multi-path effect and nonlinearity cause outliers that have a significant impact on positional accuracy. These non-Gaussian outliers are difficult to handle with conventional Kalman-based methods and their fuzzy variants. To address these issues, this paper presents a new and improved adaptive multi-sensor fusion method by using information-theoretic, learning-based fuzzy rules for Kalman filter covariance adaptation in the presence of outliers. Two novel metrics are proposed by utilizing correntropy Gaussian and Versoria kernels for matching theoretical and actual covariance. Using correntropy-based metrics and fuzzy logic together makes the algorithm robust against outliers in nonlinear dynamic underwater conditions. The performance of the proposed sensor fusion technique is compared and evaluated using Monte-Carlo simulations, and substantial improvements in underwater position estimation are obtained.