Deep Reinforcement Learning-Based Mapless Crowd Navigation with Perceived Risk of the Moving Crowd for Mobile Robots

Current state-of-the-art crowd navigation approaches are mainly deep reinforcement learning (DRL)-based. However, DRL-based methods suffer from the issues of generalization and scalability. To overcome these challenges, we propose a method that includes a Collision Probability (CP) in the observation space to give the robot a sense of the level of danger of the moving crowd to help the robot navigate safely through crowds with unseen behaviors. We studied the effects of changing the number of moving obstacles to pay attention during navigation. During training, we generated local waypoints to increase the reward density and improve the learning efficiency of the system. Our approach was developed using deep reinforcement learning (DRL) and trained using the Gazebo simulator in a non-cooperative crowd environment with obstacles moving at randomized speeds and directions. We then evaluated our model on four different crowd-behavior scenarios. The results show that our method achieved a 100% success rate in all test settings. We compared our approach with a current state-of-the-art DRL-based approach, and our approach has performed significantly better, especially in terms of social safety. Importantly, our method can navigate in different crowd behaviors and requires no fine-tuning after being trained once. We further demonstrated the crowd navigation capability of our model in real-world tests.Comment: 6 pages, 7 figure

DSCOT: An NFT-Based Blockchain Architecture for the Authentication of IoT-Enabled Smart Devices in Smart Cities

Smart city architecture brings all the underlying architectures, i.e., Internet of Things (IoT), Cyber-Physical Systems (CPSs), Internet of Cyber-Physical Things (IoCPT), and Internet of Everything (IoE), together to work as a system under its umbrella. The goal of smart city architecture is to come up with a solution that may integrate all the real-time response applications. However, the cyber-physical space poses threats that can jeopardize the working of a smart city where all the data belonging to people, systems, and processes will be at risk. Various architectures based on centralized and distributed mechanisms support smart cities; however, the security concerns regarding traceability, scalability, security services, platform assistance, and resource management persist. In this paper, private blockchain-based architecture Decentralized Smart City of Things (DSCoT) is proposed. It actively utilizes fog computing for all the users and smart devices connected to a fog node in a particular management system in a smart city, i.e., a smart house or hospital, etc. Non-fungible tokens (NFTs) have been utilized for representation to define smart device attributes. NFTs in the proposed DSCoT architecture provide devices and user authentication (IoT) functionality. DSCoT has been designed to provide a smart city solution that ensures robust security features such as Confidentiality, Integrity, Availability (CIA), and authorization by defining new attributes and functions for Owner, User, Fog, and IoT devices authentication. The evaluation of the proposed functions and components in terms of Gas consumption and time complexity has shown promising results. Comparatively, the Gas consumption for minting DSCoT NFT showed approximately 27%, and a DSCoT approve() was approximately 11% more efficient than the PUF-based NFT solution.Comment: 18 pages, 15 figures, 5 tables, journa

Evaluation of the effects of highly saline and warm seawaters on corrosivity of marine assets

In marine environment, the corrosion rate of metallic structures vary remarkably with the change in climatic conditions and seawater composition across geographical locations. The corrosion in brackish and polluted seawaters is even more complicated due to the presence of different chemical species and untreated effluents. The complex correlation between the above average temperature and salinity with the high nutrient content in polluted seawater tends to accelerate the rate of biological activities and microbiological induced corrosion (MIC). This research paper has investigated the short-term corrosion of cupronickel (Cu-Ni) 90/10 alloy, and mild steel in the highly saline and warm seawaters. Field experiments for general corrosion under fully immersed condition were conducted at two site locations, represented as site 1 for pollutantrich seawaters and site 2 for natural seawaters in the North Indian Ocean. The experiments were conducted for a period of up to two months and coupons for each metal alloy were recovered from both sites after an exposure period of 15, 30, 45, and 60 days, respectively. In both environmental conditions, significantly high mass loss and corrosion rates were recorded for each metal alloys. Despite the same temperature of seawater and immersion depth at both sites, average corrosion losses at site 1 were found to be 5 and 3 times higher than that of site 2 for Cu-Ni alloy 90/10, and mild steel coupons, respectively

Profiling Obese Subgroups in National Health and Nutritional Status Survey Data using Machine Learning Techniques: A Case Study from Brunei Darussalam

National Health and Nutritional Status Survey (NHANSS) is conducted annually by the Ministry of Health in Negara Brunei Darussalam to assess the population health and nutritional patterns and characteristics. The main aim of this study was to discover meaningful patterns (groups) from the obese sample of NHANSS data by applying data reduction and interpretation techniques. The mixed nature of the variables (qualitative and quantitative) in the data set added novelty to the study. Accordingly, the Categorical Principal Component (CATPCA) technique was chosen to interpret the meaningful results. The relationships between obesity and the lifestyle factors like demography, socioeconomic status, physical activity, dietary behavior, history of blood pressure, diabetes, etc., were determined based on the principal components generated by CATPCA. The results were validated with the help of the split method technique to counter verify the authenticity of the generated groups. Based on the analysis and results, two subgroups were found in the data set, and the salient features of these subgroups have been reported. These results can be proposed for the betterment of the healthcare industry.Comment: A Case study of Obese Subgroups from Brunei Darussalam: 15 Pages, 4 figures, journa

Incidental metastatic mediastinal atypical carcinoid in a patient with parathyroid adenoma: a case report.

Background: Atypical carcinoid arising from the mediastinal tissue is a rare neuroendocrine tumor and an association with parathyroid adenoma is very unusual. We report an unusual case of atypical carcinoid of mediastinum with metastasis in a patient presenting with parathyroid adenoma, which is the first case to be reported from Pakistan. Case presentation: A 51-year-old Pakistani man was seen in postoperative intensive care after right parathyroidectomy and mediastinal mass resection for the management of postoperative hypocalcaemia. He had a background history of dyspnea. Examination was unremarkable. Preoperative laboratory evaluation revealed a calcium level of 12.7 mg/dl, phosphate of 1.9 mg/dl, serum albumin of 4.8 g/dl, alkaline phosphate of 94 U/L, and serum intact parathyroid hormone level 413.8 pg/ml. A technetium-99m sestamibi parathyroid scan showed right parathyroid increased tracer uptake. Further workup revealed a large mediastinal mass which was diagnosed as atypical carcinoid after Tru-Cut biopsy. He underwent right-sided parathyroidectomy and resection of the mediastinal mass. The histopathology confirmed it to be a parathyroid adenoma and atypical carcinoid tumor of his mediastinum with metastasis in his lymph node and parathyroid gland. Somatostatin receptor scintigraphy revealed a well-defined focus in his left hypochondriac region consistent with a somatostatin receptor scintigraphy-avid tumor. He was started on everolimus and planned for octreotide therapy. Conclusions: We describe an incidental finding of atypical carcinoid of the mediastinum in a patient diagnosed as having parathyroid adenoma. Clinical manifestations of neuroendocrine syndromes are challenging. Some tumors cluster in a non-classic description with other common neoplasms. They rarely present in isolation, remain clinically silent, and need aggressive workup with the aid of imaging and histopathology

Numerical optimization of (FTO/ZnO/CdS/CH₃NH₃SnI₃/GaAs/Au) perovskite solar cell using solar capacitance simulator with efficiency above 23% predicted

The presented study deals with the investigations of the methyl ammonium tin halide (CH3NH3SnI3) based perovskite solar cells for optimized device performance using solar capacitance simulations software. Several necessary parameters such as metal work functions, thickness of structural layers, charge carrier’s mobility and defect density have been explored to evaluate the device performance. Calculations reveal that for the best efficiency of device the maximum thickness of the perovskite (CH3NH3SnI3) absorber layer must be 4.2 μm. The thickness values of 0.01 μm for ZnO electron transport layer (ETL), 0.871 μm for GaAs hole transport layer and 0.001 μm for CdS buffer layer have been found which proved to be optimum for maximum power conversion efficiency (PCE) of 23.80% for the device. The variation of open circuit voltage (Voc), Short circuit current (Jsc), Fill Factor (FF %), quantum efficiency (QE) against thickness of all layers and interface defect densities in FTO/ZnO/CdS/CH3NH3SnI3/GaAs/Au composition have been critically explored and their crucial role for the device performance has been reported. Heterojunctions between ZnO-ETL and CdS buffer layers have shown improved device performance and PCE. Current investigations may prove to be useful for designing and fabrication of climate friendly, non-toxic and highly efficient solar cells

FCD-AttResU-Net: An improved forest change detection in Sentinel-2 satellite images using attention residual U-Net

Forest Change Detection (FCD) is a critical component of natural resource monitoring and conservation strategies, enabling informed decision-making. Various methods utilizing the power of artificial intelligence (AI) have been developed for detecting and categorizing changes in forest cover using remote sensing (RS) data. One prominent AI-powered approach is the U-Net, a deep learning (DL) architecture famous for its segmentation proficiency. However, the standard U-Net architecture fails to effectively capture intricate spatial dependencies and long-range contextual information present in remote sensing imagery. To address this research gap, we introduce an attention-residual-based novel DL model which leverages the U-Net architecture and Sentinel-2 satellite images to map alterations in forest vegetation cover in the tropical region. Our novel model enhances the U-Net architecture by seamlessly integrating the strengths of the U-Net, harnessing attention mechanisms strategically to amplify crucial features, and leveraging cutting-edge residual connections to facilitate the smooth flow of information and gradient propagation. These meticulous design choices enabled the precise feature extraction, resulting in improved computational performance of the proposed method compared to the Standard U-Net, Deeplabv3+, Deep Res-U-Net, and Attention U-Net. The classification results demonstrate the enhanced efficiency of our model, achieving a Mean Intersection over Union (MIoU) of 0.9330 on our test dataset. This performance surpasses the Attention U-Net (0.9146), Standard U-Net (0.9029), Deeplabv3+ (0.9247), and Deep Res-U-Net (0.9282). The comparative analysis of ground truth reproductions unveiled the superior detection capabilities of our model in accurately identifying forest and non-forest polygons, surpassing both the standard U-Net, and the U-Net augmented with attention mechanism, along with other state-of-the-art techniques, thereby highlighting its enhanced efficacy. The model’s broad applicability can support forest managers and ecologists in rapidly evaluating the long-term ramifications of infrastructure initiatives, such as roads, on tropical forests, including those in Brunei

A Comparative Analysis on Blockchain versus Centralized Authentication Architectures for IoT-Enabled Smart Devices in Smart Cities: A Comprehensive Review, Recent Advances, and Future Research Directions

Smart devices have become an essential part of the architectures such as the Internet of Things (IoT), Cyber-Physical Systems (CPSs), and Internet of Everything (IoE). In contrast, these architectures constitute a system to realize the concept of smart cities and, ultimately, a smart planet. The adoption of these smart devices expands to different cyber-physical systems in smart city architecture, i.e., smart houses, smart healthcare, smart transportation, smart grid, smart agriculture, etc. The edge of the network connects these smart devices (sensors, aggregators, and actuators) that can operate in the physical environment and collects the data, which is further used to make an informed decision through actuation. Here, the security of these devices is immensely important, specifically from an authentication standpoint, as in the case of unauthenticated/malicious assets, the whole infrastructure would be at stake. We provide an updated review of authentication mechanisms by categorizing centralized and distributed architectures. We discuss the security issues regarding the authentication of these IoT-enabled smart devices. We evaluate and analyze the study of the proposed literature schemes that pose authentication challenges in terms of computational costs, communication overheads, and models applied to attain robustness. Hence, lightweight solutions in managing, maintaining, processing, and storing authentication data of IoT-enabled assets are an urgent need. From an integration perspective, cloud computing has provided strong support. In contrast, decentralized ledger technology, i.e., blockchain, light-weight cryptosystems, and Artificial Intelligence (AI)-based solutions, are the areas with much more to explore. Finally, we discuss the future research challenges, which will eventually help address the ambiguities for improvement

Dynamic Regressor/Ensemble Selection for a Multi-Frequency and Multi-Environment Path Loss Prediction

Wireless network parameters such as transmitting power, antenna height, and cell radius are determined based on predicted path loss. The prediction is carried out using empirical or deterministic models. Deterministic models provide accurate predictions but are slow due to their computational complexity, and they require detailed environmental descriptions. While empirical models are less accurate, Machine Learning (ML) models provide fast predictions with accuracies comparable to that of deterministic models. Most Empirical models are versatile as they are valid for various values of frequencies, antenna heights, and sometimes environments, whereas most ML models are not. Therefore, developing a versatile ML model that will surpass empirical model accuracy entails collecting data from various scenarios with different environments and network parameters and using the data to develop the model. Combining datasets of different sizes could lead to lopsidedness in accuracy such that the model accuracy for a particular scenario is low due to data imbalance. This is because model accuracy varies at certain regions of the dataset and such variations are more intense when the dataset is generated from a fusion of datasets of different sizes. A Dynamic Regressor/Ensemble selection technique is proposed to address this problem. In the proposed method, a regressor/ensemble is selected to predict a sample point based on the sample’s proximity to a cluster assigned to the regressor/ensemble. K Means Clustering was used to form the clusters and the regressors considered are K Nearest Neighbor (KNN), Extreme Learning Trees (ET), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost). The ensembles are any combinations of two, three or four of the regressors. The sample points belonging to each cluster were selected from a validation set based on the regressor that made prediction with lowest absolute error per individual sample point. Implementation of the proposed technique resulted in accuracy improvements in a scenario described by a few sample points in the training data. Improvements in accuracy were also observed on datasets in other works compared to the accuracy reported in the works. The study also shows that using features extracted from satellite images to describe the environment was more appropriate than using a categorical clutter height value