Several efforts have been made to develop effective and robust vision-based solutions for human aerial action recognition. Generally, the existing methods rely on the extraction of either spatial features (patch-based methods) or skeletal key points (pose-based methods) that are fed to a classifier. The pose-based methods are generally regarded to be more robust to background changes and computationally efficient. Moreover, at the classification stage, the use of deep networks has generated significant interest within the community; however, the need remains to develop accurate and computationally effective deep learning-based solutions. To this end, this paper proposes a lightweight Transformer network-based method for human action recognition in aerial videos using the skeletal keypoints extracted with YOLOv8. The effectiveness of the proposed method is shown on a well-known public dataset containing …

The utilisation of passive cooling techniques involving Phase change materials (PCMs) represents a promising approach in the realm of battery thermal management systems (BTMS). Specifically, this study delves into the examination of a single cylindrical Panasonic 18,650 battery cell, employing a circumferential Latent Heat (LH) jacket, under various real-world automotive drive cycles. The challenge addressed in this research revolves around understanding the impact of haphazard behaviour in the battery's performance and thermal stability in the presence of proposed passive cooling. While drive cycle data, encompassing aggressive to casual driving scenarios, has been collected, there remains a need to evaluate how these driving behaviours affect the battery's performance and longevity. To address this issue, this study uses conjugated thermo-chemical and electrical models of the battery. These models …

The objectives of this study were to compare dengue virus (DENV) cases, deaths, case-fatality ratio [CFR], and meteorological parameters between the first and the recent decades of this century (2000–2010 vs. 2011–2022) and to describe the trends, seasonality, and impact of change of temperature and rainfall patterns on transmission dynamics of dengue in Bangladesh. For the period 2000–2022, dengue cases and death data from Bangladesh’s Ministry of Health and Family Welfare’s website, and meteorological data from the Bangladesh Meteorological Department were analyzed. A Poisson regression model was performed to identify the impact of meteorological parameters on the monthly dengue cases. A forecast of dengue cases was performed using an autoregressive integrated moving average model. Over the past 23 yr, a total of 244,246 dengue cases were reported including 849 deaths (CFR …

Several efforts have been made to develop effective and robust vision-based solutions for human action recognition in aerial videos. Generally, the existing methods rely on the extraction of either spatial features (patch-based methods) or skeletal key points (pose-based methods) that are fed to a classifier. Unlike the patch-based methods, the pose-based methods are generally regarded to be more robust to background changes and computationally efficient. Moreover, at the classification stage, the use of deep networks has generated significant interest within the community; however, the need remains to develop accurate and computationally effective deep learning-based solutions. To this end, this paper proposes a lightweight Transformer network-based method for human action recognition in aerial videos using the skeletal keypoints extracted using YOLOv8. The effectiveness of the proposed method is …

In recent years, the integration of the Social Internet of Things (SIoT) into the Internet of Things (IoT) has gained considerable attention, leading to a vast network of interconnected everyday objects. These objects generate a wealth of data that can provide valuable insights into our daily needs. Context-aware systems have emerged as intelligent systems that assist users in making service consumption choices based on their contextual information and preferences. However, the development of a context-aware trust management framework in SIoT faces a significant challenge due to the limited availability of dynamic and reliable context information from the objects users interact within their environments. To overcome this challenge, it is proposed to use contextual information obtained from IoT objects and other sources to construct a framework that establishes connections between the core modules of a SIoT …

A possible shift in climate zones in Southeast Asia (SEA) for different shared socioeconomic pathways (SSPs) is evaluated in this study. The ability of 19 Coupled Model Intercomparison Project (CMIP6) global climate models (GCMs) in reconstructing the Köppen-Geiger climate zones in SEA, estimated using reanalysis data (ERA5) for the period 1979-2014, was analysed using five categorical evaluation metrics. The best-performing models were selected to prepare an ensemble to project possible shifts in climate zones for different SSP scenarios in the future. Besides, future projections in climate variables were evaluated to assess the driving factor of climate shifts in the future. The results showed that three CMIP6 GCMs, EC-Earth3-Veg-LR, CMCC-ESM2 and CanESM5, had a higher skill in classifying the observed climate of SEA. Selected GCMs showed climate shifting in 3.4 to 12.6% of the total area of SEA for …

Background Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study’s feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. Results Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. Conclusion In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre …

Current developments in object tracking and detection techniques have directed remarkable improvements in distinguishing attacks and adversaries. Nevertheless, adversarial attacks, intrusions, and manipulation of images/ videos threaten video surveillance systems and other object-tracking applications. Generative adversarial neural networks (GANNs) are widely used image processing and object detection techniques because of their flexibility in processing large datasets in real-time. GANN training ensures a tamper-proof system, but the plausibility of attacks persists. Therefore, reviewing object tracking and detection techniques under GANN threats is necessary to reveal the challenges and benefits of efficient defence methods against these attacks. This paper aims to systematically review object tracking and detection techniques under threats to GANN-based applications. The selected studies were based …

This numerical study evaluates the cell level performance management of an Electric Vehicle (EV) battery with Latent Heat (LH) jacket (as passive cooling). In this regard, a battery cell is conjugated with Phase Change Material (PCM) is assessed under continuous cycles of discharging and re-charging. This study is validated with numerical and experimental data with less than 1% deviation captured from literature for a Panasonic 18650 PF Lithium-ion (Li-ion cell). The thermal and electrical performance of key parameters is assessed with and without the existence of a PCM under various climatic conditions including extreme winter −20 °C, winter 0 °C, ambient 25 °C, hot summer 40 °C, and extreme hot/desert 55 °C temperatures. In addition, the choice of PCMs and circumferential jacket thicknesses around the battery (1 mm, 3 mm, 5 mm, and 7 mm) is evaluated in terms of thermal performance for multiple …

A possible shift in climate zones in Southeast Asia (SEA) for different shared socioeconomic pathways (SSPs) is evaluated in this study. The ability of 19 Coupled Model Intercomparison Project (CMIP6) global climate models (GCMs) in reconstructing the Köppen-Geiger climate zones in SEA, estimated using reanalysis data (ERA5) for the period 1979-2014, was analysed using five categorical evaluation metrics. The best-performing models were selected to prepare an ensemble to project possible shifts in climate zones for different SSP scenarios in the future. Besides, future projections in climate variables were evaluated to assess the driving factor of climate shifts in the future. The results showed that three CMIP6 GCMs, EC-Earth3-Veg-LR, CMCC-ESM2 and CanESM5, had a higher skill in classifying the observed climate of SEA. Selected GCMs showed climate shifting in 3.4 to 12.6% of the total area of SEA for …