Due to growing concerns of global warming, reducing carbon emissions has become one of the major tasks for developing countries to meet the national demand for energy policies. The objective of this study is to measure the energy consumption, carbon emission and economic-environmental efficiency in terms of the environmental performance of the top 20 industrial countries by employing a data envelopment analysis (DEA) model from 2013 to 2017. This study used the trilemma of energy efficiency, CO₂ emission efficiency, and environmental efficiency, and also the contribution included the quantitative analysis of 20 industrial countries The results show that the energy efficiency of Australia, China, Japan, Saudi Arabia, and Poland are the best performing countries, whereas Mexico, Indonesia, Russia, and Brazil are identified as least efficient among all 20 countries. Furthermore, Russia’s energy intensity has a maximum score while Poland has a minimum score. Additionally, in the case of CO₂ emission efficiency, Brazil, France, and Saudi Arabia are considered as efficient while nine country’s scores were less than 0.5. The results show that most countries exhibit higher performance in economic efficiency than environmental efficiency. The study provides valuable information for energy policy-makers.

Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales.

The escalating incidence of thyroid cancer over the past decade underscores the imperative for effective classification and early detection of thyroid nodules. An automated system for this purpose could significantly aid physicians, particularly in expediting diagnostic processes. However, attaining this objective has proven challenging, primarily attributed to the constrained dataset size for medical images and the laborious process of feature extraction. This research addresses these challenges by thoroughly exploring the importance of extracting meaningful features for tumor detection and introducing a quantum-based convolutional neural network. The proposed approach employs a quantum filter transformation for intricate feature extraction, coupled with a classical neural network for the classification of thyroid nodules in ultrasound images. The classification process involves two distinct categorizations: distinguishing between nodules that are benign or malignant, and identifying the specific suspicious class to which the nodule is attributed. The amalgamation of both classifiers yields a comprehensive characterization of thyroid nodules, showcasing notable accuracy. For tumor classification, the model achieves an accuracy of 97.63%, precision of 97.72%, and recall of 97.64% on a test dataset containing 127 images. Similarly, for suspicious level classification, the model attains an accuracy of 93.87%, precision of 94.58%, and recall of 93.88% on a test dataset containing 98 images. These results surpass the performance of existing models, marking a significant advancement in the field of thyroid nodule classification. The proposed model represents a promising and innovative methodology that could offer valuable support mechanisms in clinical settings, facilitating the rapid classification and diagnosis of thyroid cancer.

Telepresence robots have become popular during the COVID-19 era due to the quarantine measures and the requirement to interact less with other humans. Telepresence robots are helpful in different scenarios, such as healthcare, academia, or the exploration of certain unreachable territories. IoT provides a sensor-based environment wherein robots acquire more precise information about their surroundings. Remote telepresence robots are enabled with more efficient data from IoT sensors, which helps them to compute the data effectively. While navigating in a distant IoT-enabled healthcare environment, there is a possibility of delayed control signals from a teleoperator. We propose a human cooperative telecontrol robotics system in an IoT-sensed healthcare environment. The deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) offered improved control of the telepresence robot to provide assistance to the teleoperator during the delayed communication control signals. The proposed approach can stabilize the system in aid of the teleoperator by taking the delayed signal term out of the main controlling framework, along with the sensed IOT infrastructure. In a dynamic IoT-enabled healthcare context, our suggested approach to operating the telepresence robot can effectively manage the 30 s delayed signal. Simulations and physical experiments in a real-time healthcare environment with human teleoperators demonstrate the implementation of the proposed method.