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The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

This work aims to contribute to the existing literature by investigating at the impact of financial development on ecological footprint. To achieve this goal, we have employed Driscoll-Kraay panel regression model for a panel of 59 Belt and Road countries in the period from 1990 to 2016. The findings suggest that financial development increases ecological footprint. Moreover, economic growth, energy consumption, foreign direct investment (FDI), and urbanization pollute the environment by increasing ecological footprint. In addition, several diagnostic tests have been applied to confirm the reliability and validity of the results. From the outcome of the study, various policy implications have been proposed for Belt and Road countries to minimize the ecological footprint.

This study examines the impact of energy consumption, financial development, globalization, economic growth, and urbanization on carbon dioxide emissions in the presence of Environmental Kuznets Curve (EKC) model for BRICS economies, by using a family of econometric techniques robust to heterogeneity and cross-sectional dependence. Results from LM test, CIPS and CADF unit root test, Westerlund Cointegration test, the Dynamic seemingly unrelated regression (DSUR), and Dumitrescu-Hurlin Granger causality test show that (i) the data is cross sectionally dependent and heterogeneous; (ii) carbon dioxide emissions, energy consumption, financial development, globalization, economic growth, square of GDP and urbanization have integration of order one; (iii) the examined variables are co-integrated; (iv) energy consumption and financial development contribute to the carbon dioxide emissions whereas globalization and urbanization have negative but insignificant relationship with carbon dioxide emissions; (v) supports the EKC hypothesis in BRICS economies; (vi) bidirectional causality exists among energy consumption, financial development, economic growth and square of GDP with carbon dioxide emissions whereas globalization and urbanization have unidirectional relationship with carbon dioxide emissions. Since these panel techniques account for heterogeneity and cross-sectional dependence in their estimation procedure, the empirical results are robust and reliable for policy recommendations. Furthermore, this study also uses time series tests (ADF, P-P, and FMOLS) to find the empirical results for each of the country and finds mixed results. Empirical findings directed towards some important policy implications.

This study investigates the impact of Internet use, financial development, economic growth, and trade openness on carbon dioxide (CO 2 ) emissions in selected European Union (EU) countries. To this end, pooled mean group (PMG) estimator is utilized for panel data from 2001 to 2014. Empirical findings suggest that Internet use has long-run relationship with CO 2 emissions and lowering the environmental quality in EU countries. Also, the electricity consumption has a positive and significant effect on CO 2 emissions. Moreover, interestingly, economic growth and financial development have a diminishing negative impact on CO 2 emission. Heterogeneous panel Granger causality results suggest unidirectional causality running from Internet use to CO 2 emissions. The finding implies that the European Union countries did not achieve the level of green information and telecommunication (ICTs) consumption. Overall, the innovative findings indicate that Internet use is raising the threat to the sustainable development. Thus, to curb and mitigate CO 2 emissions from Internet use and electricity consumption is the need of time to maintain the sustainable development in EU countries.

Large language models (LLMs) are artificial intelligence (AI) based computational models designed to understand and generate human like text. With billions of training parameters, LLMs excel in identifying intricate language patterns, enabling remarkable performance across a variety of natural language processing (NLP) tasks. After the introduction of transformer architectures, they are impacting the industry with their text generation capabilities. LLMs play an innovative role across various industries by automating NLP tasks. In healthcare, they assist in diagnosing diseases, personalizing treatment plans, and managing patient data. LLMs provide predictive maintenance in automotive industry. LLMs provide recommendation systems, and consumer behavior analyzers. LLMs facilitates researchers and offer personalized learning experiences in education. In finance and banking, LLMs are used for fraud detection, customer service automation, and risk management. LLMs are driving significant advancements across the industries by automating tasks, improving accuracy, and providing deeper insights. Despite these advancements, LLMs face challenges such as ethical concerns, biases in training data, and significant computational resource requirements, which must be addressed to ensure impartial and sustainable deployment. This study provides a comprehensive analysis of LLMs, their evolution, and their diverse applications across industries, offering researchers valuable insights into their transformative potential and the accompanying limitations.

The linkage between financial development and energy consumption is widely investigated in the literature. However, the non-linear relationship between financial development and energy demand is still under debate. Therefore, this study aims to examine the non-linear relationship between financial development, economic growth, and energy consumption in OECD countries. The study uses the Driscoll–Kraay standard errors panel regression model for spanning from 1980 to 2016. The empirical findings indicate that an inverted U-shape relationship exists between financial development and energy consumption as well as between economic growth and energy consumption. Moreover, the feedback hypothesis is found between financial development and energy use. Additionally, income and energy use granger cause each other. The innovative findings contribute to extant literature, which is of special interest to the country’s policymakers regarding energy efficiency.

Hydrogen is a promising clean energy carrier, but its storage is challenging. In this study, we investigate the potential of NaMTH3 (MT=Sc, Ti, V) hydride perovskite as solid-state hydrogen storage material. Using density functional theory (DFT), we comprehensively analyze their structural, hydrogen storage, phonon, electronic, elastic, and thermodynamic properties. Mechanical stability is assessed through calculation of lattice parameters, bulk and shear moduli, Poisson’s ratio, and Young’s modulus based on elastic constants. All three hydrides were found to be stable mechanically. Furthermore, the anisotropy factor was also investigated. Results show that the investigated hydrides are brittle and metallic. Their metallic character is due to the significant interplay between phonons and electrons. We also investigated their enthalpy, entropy, free energy, Debye temperatures, and specific heat capacities to investigate thermal stability.

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.

Hourly traffic volumes, collected by automatic traffic recorders (ATRs), are of paramount importance since they are used to calculate average annual daily traffic (AADT) and design hourly volume (DHV). Hence, it is necessary to ensure the quality of the collected data. Unfortunately, ATRs malfunction occasionally, resulting in missing data, as well as unreliable counts. This naturally has an impact on the accuracy of the key parameters derived from the hourly counts. This study aims to solve this problem. ATR data from New South Wales, Australia was screened for irregularities and invalid entries. A total of 25% of the reliable data was randomly selected to test thirteen different imputation methods. Two scenarios for data omission, i.e., 25% and 100%, were analyzed. Results indicated that missForest outperformed other imputation methods; hence, it was used to impute the actual missing data to complete the dataset. AADT values were calculated from both original counts before imputation and completed counts after imputation. AADT values from imputed data were slightly higher. The average daily volumes when plotted validated the quality of imputed data, as the annual trends demonstrated a relatively better fit.

With the burgeoning population of the world, the successful germination of seeds to achieve maximum crop production is very important. Seed germination is a precise balance of phytohormones, light, and temperature that induces endosperm decay. Abscisic acid and gibberellins—mainly with auxins, ethylene, and jasmonic and salicylic acid through interdependent molecular pathways—lead to the rupture of the seed testa, after which the radicle protrudes out and the endosperm provides nutrients according to its growing energy demand. The incident light wavelength and low and supra-optimal temperature modulates phytohormone signaling pathways that induce the synthesis of ROS, which results in the maintenance of seed dormancy and germination. In this review, we have summarized in detail the biochemical and molecular processes occurring in the seed that lead to the germination of the seed. Moreover, an accurate explanation in chronological order of how phytohormones inside the seed act in accordance with the temperature and light signals from outside to degenerate the seed testa for the thriving seed’s germination has also been discussed.