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Developing wireless communication technologies is essential to satisfy the requirements of new applications and the increasing proliferation of interconnected devices. This research presents a resilient terahertz (THz)-based secure transmission framework for an active intelligent reflecting surface (IRS)-enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). The IRS facilitates secure transmission for the primary transmitter (PT) by intelligently adjusting the phase shifts of the signals from the PT, while simultaneously transmitting its own data to an Internet of Things (IoT) device. In light of the existence of numerous eves and unpredictable channels in real-world situations, we concurrently optimize the active beamforming of the PT and the phase shifts of the IRS to enhance the secrecy of IRS-assisted secure relay networks while adhering to quality-of-service standards and secure communication rates. To address this intricate non-convex stochastic optimization issue, we propose a secure beamforming technique named DDPG-SR, utilizing an effective deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) scheme to determine the optimal beamforming approach against Eves. This method seeks to establish an optimal beamforming strategy to counteract Eves under dynamic environmental circumstances. Comprehensive simulation experiments confirm the effectiveness of our proposed solution, showcasing enhanced performance relative to conventional IRS methods, IRS backscattering-based anti-evesdropping techniques, and other benchmark tactics for secrecy performance.

Vehicular Ad Hoc Networks (VANETs) face challenges due to high vehicle mobility, which leads to frequent changes in network topology and unstable links. To tackle this, we propose a novel routing strategy called MPFD, which enhances message delivery rates and reduces End-to-End (E2E) delay. MPFD operates in two phases. The first phase, the message management phase, prioritizes messages based on urgency and controls message propagation by considering hop count (H) and Time-To-Live (TTL), ensuring only critical messages are forwarded. The second phase, the relay node selection phase, uses free nodes to identify the best relay for forwarding messages. Candidate nodes are evaluated based on community association, movement direction, and proximity to the destination. Simulations show that the MPFD strategy significantly improves message delivery speed, reduces overhead and E2E delay, and enhances the overall message delivery ratio in VANETs.

Cache-enabled Device-to-Device (D2D) communications is an effective way to improve data sharing. User Equipment (UE)-level caching holds the potential to reduce the data traffic burden on the core network. Licensed spectrum is utilized for D2D communications, but due to spectrum scarcity, exploring unlicensed spectrum is essential to enhance network capacity. In this paper, we propose caching at the UE-level and exploit both licensed and unlicensed spectrum for optimizing throughput. First, we propose a reinforcement learning-based data caching scheme leveraging an actor–critic network to improve cache-enabled D2D communications. Besides, licensed and unlicensed spectrum are devised for D2D communications considering interference from existing cellular and Wi-Fi users. A duty cycle-based unlicensed spectrum access algorithm is employed, guaranteeing the Signal-to-Interference and Noise Ratio (SINR) required by the users. The unlicensed spectrum is prone to data packets collisions. Therefore, Request-to-Send/Clear-to-Send (RTS/CTS) mechanism is utilized in conjunction with Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) to alleviate both the interference and packets collision problems of the unlicensed spectrum. Extensive simulations are performed to analyze the performance gain of our proposed scheme compared to the benchmarks under different network scenarios. The obtained results demonstrate that our proposed scheme possesses the potential to optimize network performance.

The rapid development of next-generation wireless networks has intensified the need for robust security measures, particularly in environments susceptible to eavesdropping. Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) have emerged as a transformative technology, offering full-space coverage by manipulating electromagnetic wave propagation. However, the inherent flexibility of STAR-RIS introduces new vulnerabilities, making secure communication a significant challenge. To overcome these challenges, we propose a deep reinforcement learning (DRL) based secure and efficient beamforming optimization strategy, leveraging the deep deterministic policy gradient (DDPG) algorithm. By framing the problem as a Markov decision process (MDP), our approach enables the DDPG algorithm to learn optimal strategies for beamforming and transmission and reflection coefficients (TARCs) configurations. This method is specifically designed to optimize phase-shift coefficients within the STAR-RIS environment, effectively managing the coupled phase shifts and complex interactions that are critical for enhancing physical layer security (PLS). Through extensive simulations, we demonstrate that our DRL-based strategy not only outperforms traditional optimization techniques but also achieves real-time adaptive optimization, significantly improving both confidentiality and network efficiency. This research addresses key gaps in secure wireless communication and sets a new standard for future advancements in intelligent, adaptable network technologies.

The rise of Vehicular Edge Computing (VEC) has gained attention for its ability to alleviate backhaul network load and provide ultra-low latency. In meeting the escalating computational needs of cutting-edge vehicular applications such as augmented reality and autonomous driving, the abundant computational resources of vehicles can prove critical for task computation in a VEC environment. Nevertheless, the high mobility of vehicles has the potential to disrupt ongoing task computation due to varying communication network connectivity. This paper proposes a task offloading scheme that leverages multi-hop vehicle computation resources in Vehicle-to-Vehicle (V2V) communication, relying on mobility analysis. Vehicles capable of fulfilling the requisite communication and computation demands via multi-hop connectivity can assist in performing tasks offloaded by the client vehicle, along with the single-hop vehicles in the vicinity of the client vehicle. We formulate an NP-hard optimization problem for task offloading to minimize all tasks’ weighted sum of computation delay. For this, a proximal policy optimization-based Multi-hop Vehicular Task Offloading (MVTO-PPO) scheme in vehicular edge computing is designed for low complexity that provides the optimal solution. Our approach involved modeling the task offloading process as a Markov decision process. We then developed an offloading decision algorithm that utilizes deep reinforcement learning to choose the appropriate vehicle for task execution. This approach improves the quality of environmental perception by enabling reasonable task offloading, ultimately leading to significant long-term benefits. Furthermore, we explore the integration of fifth-generation new-radio vehicle-to-everything (5G NR V2X) communication, utilizing both cellular links and millimeter wave technology to enhance system performance. Simulation results demonstrate that the proposed algorithm significantly reduces task offloading delays, outperforming benchmark approaches in various scenarios.

In the distributed environment of the Social Internet of Things (SIoT), where nodes rely primarily on the “store-carry-forward” mechanism for message delivery, it has been determined that forming community structures is advantageous for message propagation. Nevertheless, due to the dynamic nature of nodal social relationships and inaccurate community division, routing algorithm performance is inferior. This paper proposes a novel SIoT routing algorithm, the Free Node-based Routing Algorithm (FNRA) to resolve these issues. The FNRA algorithm presents a comprehensive framework for message forwarding, differentiating between in-community and out-of-community scenarios. The algorithm compares the forwarding capabilities of relay and source nodes within the community. A graph-theoretic approach is used to classify source, relay, and destination nodes into four distinct structures outside the community. Considering node-node, node-community, community-node, and community-community interactions, the algorithm addresses situations where the source, destination, and relay nodes do not belong to the same community. FNRA dynamically selects appropriate forwarding strategies to expedite message delivery to the destination node by leveraging the community structure and the presence of free nodes. The FNRA algorithm exhibits superior performance through detailed experimental simulations by enhancing message delivery rate, decreasing network overhead, and reducing average latency compared to existing routing algorithms. This demonstrates the algorithm's effectiveness in optimizing overall network performance.

Communication at terahertz (THz) frequencies has emerged as a potential answer to the challenge of providing massive capacity and ultra-high data speeds for 6G wireless networks. However, THz waves are vulnerable to obstruction because of their attenuation during propagation and poor diffraction. The development of reconfigurable intelligent surfaces (RIS) technology has shown promise in addressing these issues. The RIS enables the development of smart radio settings with enhanced spectrum coverage and efficiency by regulating the phase shifts of passively reflecting elements. Amidst the vast potential of RIS-based THz communication, a noticeable void exists in comprehensive research, calling for a systematic survey to shed light on its principles, use cases, performance evaluation, challenges, and future trajectories. This review investigates the exciting field of RIS-based THz communication in fifth-generation (B5G), sixth-generation (6G), and beyond. Our study provides a comprehensive overview of this emerging technology, covering a variety of use cases, including channel estimation (CE), coverage, security, sum rate, and energy efficiency. We also investigate resource allocation strategies for THz communication using RIS. Additionally, the performance of RIS-based THz systems with multiple-input and multiple-output (MIMO) and massive MIMO and unmanned aerial vehicle (UAV) technologies is analyzed. By analyzing the existing literature, we illuminate the various approaches, methodologies, and future developments in this field. We also emphasize open issues and research opportunities in THz communication based on RIS. This survey is a valuable resource, contributing to understanding the emerging paradigms and research frontiers of RIS-based THz communication.

Abstract The objective was to determine the postoperative hypersensitivity of two-step Total-etch as compared to one-step Universal adhesives followed by composite restorations on 100 patients by applying Total-etch on one tooth and Universal adhesive on another tooth. The bonds and teeth were randomly selected. Postoperative hypersensitivity was recorded by visual analog scale before, immediately after, and 24 h after the restoration using cold stimulus. The Mann–Whitney test was applied for statistical comparison of postoperative hypersensitivity between the two bonds as well as for any significant difference in genders with each bond. No significant difference was found between postoperative hypersensitivity of the two adhesives before (p-value = 0.57), immediately after (p-value = 0.604), and 24 h after (p-value = 0.728) the restoration. Males showed more hypersensitivity with Total-etch as compared to females before (p-value = 0.037), immediately after (p-value = 0.047), and 24 h after the restoration (p-value = 0.022). No significant difference was found between gender and Universal adhesive at all three stages (p-value > 0.05). The results suggest no significant difference in postoperative hypersensitivity between the two materials when good sample size and proper technique were observed along with the removal of bias like different patients having different pain perceptions and multiple operators having different operating skills. Males showed more hypersensitivity to Total-etch. Trial registration number: Australian New Zealand Clinical Trials. Registry number: ACTRN12622001213730. (Retrospectively registered: 09/09/2022).

The escalating demands of advanced automotive technologies exert considerable pressure on modern vehicles’ computational capabilities. This scenario underscores the critical need for vehicular edge computing (VEC) networks, which leverage 5G/6G communications to facilitate computational offloading. However, providing seamless access to these services while simultaneously adhering to stringent latency and security requirements presents a formidable challenge. The advent of reconfigurable intelligent surfaces (RIS) heralds a new era of possibilities, which includes enhancing connectivity, boosting data transmission rates, consequently reducing delay, and improving the physical layer security of communication channels. This paper delves into the utilization of zero-energy RIS (ze-RIS) in the context of vehicular computation offloading. Our primary goal is to ensure secure access while optimizing operational efficiency in compliance with various task-related and environmental requirements. The ze-RIS-assisted secure task efficient offloading (DRSTO) scheme is a novel deep reinforcement learning (DRL) framework that cleverly switches communication connections to optimize task offloading efficiency and security thereby resolving this issue. At its core, our assessment strategy revolves around the DRSTO model’s secrecy and efficiency factor which serve as both a performance measure and a reward function. Time efficiency and rate of confidentiality are used to evaluate this aspect which provides a thorough evaluation of the scheme’s success. Extensive testing and comparison have shown that the DRSTO scheme’s efficiency factor can be significantly increased, from 6.05 to 18.10. In addition, the rate of job success has increased dramatically, from 2.12% to 4.63%. When compared to other models that were evaluated, the DRSTO scheme consistently better on several metrics including reward, time frames per step (TFPS) ratio and DRL characteristics.

In this article, we present the mathematical foundations of generative machine intelligence and link them with mean-field-type game theory. The key interaction mechanism is self-attention, which exhibits aggregative properties similar to those found in mean-field-type game theory. It is not necessary to have an infinite number of neural units to handle mean-field-type terms. For instance, the variance reduction in error within generative machine intelligence is a mean-field-type problem and does not involve an infinite number of decision-makers. Based on this insight, we construct mean-field-type transformers that operate on data that are not necessarily identically distributed and evolve over several layers using mean-field-type transition kernels. We demonstrate that the outcomes of these mean-field-type transformers correspond exactly to the mean-field-type equilibria of a hierarchical mean-field-type game. Due to the non-convexity of the operators’ composition, gradient-based methods alone are insufficient. To distinguish a global minimum from other extrema—such as local minima, local maxima, global maxima, and saddle points—alternative methods that exploit hidden convexities of anti-derivatives of activation functions are required. We also discuss the integration of blockchain technologies into machine intelligence, facilitating an incentive design loop for all contributors and enabling blockchain token economics for each system participant. This feature is especially relevant to ensuring the integrity of factual data, legislative information, medical records, and scientifically published references that should remain immutable after the application of generative machine intelligence.

A specialized version of collaborative driving is convoy driving. It is referred to as the practice of driving more than one vehicle consecutively in the same lane with a small inter-vehicle distance, maintaining the same speed. Extensive research has been conducted on convoys of heavy-duty trucks on the highway; however, limited research has studied convoy driving in an urban environment. The complex dynamics of an urban environment require short-lived collaboration with varying numbers of vehicles rather than collaborating over hours. The motivation of this research is to investigate how convoy driving can be realized to address the challenges of an urban environment and achieve the benefits of autonomous driving such as reduced fuel consumption, travel time, improved safety, and ride comfort. In this work, the best-fitted coalitional game framework is utilized to formulate the convoy driving problem as a coalition formation game in an urban environment. A hypothesis is formulated that traveling in a coalition is more beneficial for a vehicle than traveling alone. In connection with this, a coalitional game and an all-comprehensive utility function are designed, modeled, and implemented to facilitate the formation of autonomous vehicle coalitions for convoy driving. Multiple solution concepts, such as the Shapley allocation, the Nucleolus, and the Core, are implemented to solve and analyze the proposed convoy driving game. Furthermore, several coalition formation strategies such as traveling mode selection, selecting optimal coalitions, and making decisions about coalition merging are developed to analyze the behavior of the vehicles. In addition to this, extensive numerical experiments with different settings are conducted to evaluate and validate the performance of the proposed study. The experimental results proved the hypothesis that traveling in a convoy is significantly more beneficial than traveling alone. We conclude that traveling in a convoy is beneficial for coalition sizes of two to four vehicles with an inter-vehicle spacing of less than 4 m considering the limitations of an urban environment. Traveling in a coalition allows vehicles to save on fuel, minimize travel time and enhance safety and comfort. Furthermore, the findings of this research state that achieving the enormous benefits of traveling in a coalition requires finding the right balance between inter-vehicle distance and coalition size. In the future, we plan to extend this work by studying the evolving dynamics of the coalitions and the environment.

The increasing complexity of modern automotive applications presents difficulties when running them on the on-board units (OBUs) of vehicles. While 5G/6G vehicular edge computing networks (VECNs) offer potential solutions through computation task offloading, ensuring prompt, energy-efficient access to these networks remains a significant challenge. To overcome these challenges, reconfigurable intelligent surfaces (RIS) can play an important role in 6G vehicular networks. With RIS, networks can provide better connectivity, increased data rate and energy efficient access, and communication channel security. In this paper, we utilize zero-energy RIS (ze-RIS) to aid vehicular computation offloading while maximizing the energy and time savings while meeting the task and environmental constraints. A joint power and offloading mechanism controlling DRL-driven RIS-assisted energy efficient task offloading (DREEO) scheme is proposed. DREEO utilizes a hybrid approach that combines binary and partial offloading mechanisms, complemented by an intelligent communication link switching mechanism. This strategy helps in saving both energy and time effectively. An efficiency factor, serving as both a performance indicator and a reward function, is introduced for the DRL agent, considering both saved energy and time. Through extensive evaluations, DREEO scheme shown an increase in task success rate from 2.13% to 7.36% and has improved the efficiency factor from 21.97 to 51.27. Furthermore, compared to other evaluated schemes, the DREEO scheme consistently outperforms them in terms of reward and the TFPS ratio, the DRL properties.

Objective: To determine the association of diet and dietary practices with dental caries among adults. Design: A case-control study. Setting: Operative Department, Rawal Institute of Health Sciences, Islamabad, Pakistan. Participants: 300 participants of both genders, aged 25–50 years. Interventions: A food frequency questionnaire and a patient proforma were used to determine the frequency and preferences of diet and dietary habits that may be associated with dental caries among adults, respectively. The diet and dietary habits of 150 adults with caries (cases) were compared with those of 150 adults without dental caries (control). An independent sample T-test was applied to determine the difference in mean age. Mann-Whitney and Chi-Square tests were applied to determine the significance of diet and dietary habits respectively. Multivariate logistic regression analysis determined the odd ratio change in significant variables. P-value ≤0.05 was considered significant. Results: Refined sugar (p-value = 0.69), fruit juices (p-value = 0.45), carbonated beverages (p-value = 0.91), duration of consumption of sugary food (p-value = 0.07), and frequency of brushing (p-value = 0.15) were not found to be significantly associated with dental caries in adults. The gender (p-value = 0.02), preferred time for eating sugary foods (p-value

Non-Orthogonal Multiple Access (NOMA) has emerged as a novel air interface technology for massive connectivity in Sixth-Generation (6G) era. The recent integration of NOMA in Backscatter Communication (BC) has triggered significant research interest due to its applications in low-powered Internet of Things (IoT) networks. However, the link security aspect of these networks has not been well investigated. This article provides a new optimization framework for improving the physical layer security of the NOMA ambient BC system. Our system model takes into account the simultaneous operation of NOMA IoT users and the Backscatter Node (BN) in the presence of multiple EavesDroppers (EDs). The EDs in the surrounding area can overhear the communication of Base Station (BS) and BN due to the wireless broadcast transmission. Thus, the chief aim is to enhance link security by optimizing the BN reflection coefficient and BS transmit power. To gauge the performance of the proposed scheme, we also present the suboptimal NOMA and conventional orthogonal multiple access as benchmark schemes. Monte Carlo simulation results demonstrate the superiority of the NOMA BC scheme over the pure NOMA scheme without the BC and conventional orthogonal multiple access schemes in terms of system secrecy rate.

The rapid evolution of communication systems towards the next generation has led to an increased deployment of Internet of Things (IoT) devices for various real-time applications. However, these devices often face limitations in terms of processing power and battery life, which can hinder overall system performance. Additionally, applications such as augmented reality and surveillance require intensive computations within tight timeframes. This research focuses on investigating a mobile edge computing (MEC) network empowered by unmanned aerial vehicle intelligent reflecting surfaces (UAV-IRS) to enhance the computational energy efficiency of the system through optimized resource allocation. The MEC infrastructure incorporates the energy transfer circuit (ETC) and edge server (ES), co-located with the intelligent access point (AP). To eliminate interference between energy transfer and data transmission, a time-division multiple access method is utilized. In the first phase, the ETC wirelessly transfers power to low-power IoT devices, which efficiently harvest and store the received energy in their batteries. In the second phase, IoT devices employ the stored energy for local computing or offloading tasks. Furthermore, the presence of tall buildings may obstruct communication routes, impacting system functionality. To address these challenges, we propose an optimization framework that simultaneously considers time, power, phase shift design, and local computational resources. This joint optimization problem is non-convex and non-linear, making it NP-hard. To tackle this complexity, we decompose the problem into subproblems and solve them iteratively using a convex optimization toolbox like CVX. Through simulations, we demonstrate that our proposed optimization framework significantly improves 40.7% system performance compared to alternative approaches.

Unmanned aerial vehicles (UAVs) have attracted much attention for civil and military uses because of their high mobility and adaptable deployment capabilities in open spaces. They facilitate agile communications and ubiquitous connectivity. UAVs benefit from dominant line-of-sight communication links but are more susceptible to adversary eavesdropping attacks. Since upper-layer cryptography methods may be insufficient, physical-layer security (PLS) is an attractive alternative. PLS capitalizes on the inherent randomness of wireless channels to improve information confidentiality, particularly in UAV systems. This article provides a comprehensive overview of PLS in the context of UAV systems, examining various communication channels, including ground-to-air (G2A), ground-to-ground (G2G), air-to-ground (A2G), and air-to-air (A2A). First, we give a general review of UAV communications, emphasizing its functions as user equipment, base station (BS), and mobile relay. Following that, we provide an overview of PLS and its forms. The survey organizes the most advanced PLS techniques against passive wireless eavesdropping regarding the channels they are designed to protect. These schemes are further categorized into optimization and secrecy performance analysis. We examine technical aspects within each sub-category and classify core contributions based on approaches and objectives to improve secrecy performance. Moreover, we added summary tables of the identified schemes considering optimization and secrecy performance analysis domains for each category, providing an in-depth understanding of their technical aspects. In addition, we discuss open research issues and identify future research directions to address evolving threats and requirements in UAV communications to improve information confidentiality.

Data caching is a promising technique to alleviate the data traffic burden from the backhaul and minimize data access delay. However, the cache capacity constraint poses a significant challenge to obtaining content through the cache resource that degrades the caching performance. In this paper, we propose a novel two-tier caching mechanism for data caching on mobile user equipment (UE) and the small base station (SBS) level in ultra-dense 6G heterogeneous networks for reducing data access failure via cache resources. The two-tier caching enables users to retrieve their desired content from cache resources through device-to-device (D2D) communications from neighboring users or the serving SBS. The cache-enabled UE exploits millimeter-wave (mmWave)-based D2D communications, utilizing line-of-sight (LoS) links for high-speed data transmission to content-demanding mobile UE within a limited connection time. In the event of D2D communication failures, a dual-mode hybrid system, combining mmWave and microwave μWave technologies, is utilized to ensure effective data transmission between the SBS and UE to fulfill users’ data demands. In the proposed framework. the data transmission speed is optimized through mmWave signals in line-of-sight (LoS) conditions. In non-LoS scenarios, the system switches to μWave mode for obstacle-penetrating signal transmission. Subsequently, we propose a reinforcement learning (RL) approach to optimize cache decisions through the approximation of the Q action-value function. The proposed technique undergoes iterative learning, adapting to dynamic network conditions to enhance the content placement policy and minimize delay. Extensive simulations demonstrate the efficiency of our proposed approach in significantly reducing network delay compared with benchmark schemes.

The cooperative, connected, and automated mobility (CCAM) infrastructure plays a key role in understanding and enhancing the environmental perception of autonomous vehicles (AVs) driving in complex urban settings. However, the deployment of CCAM infrastructure necessitates the efficient selection of the computational processing layer and deployment of machine learning (ML) and deep learning (DL) models to achieve greater performance of AVs in complex urban environments. In this paper, we propose a computational framework and analyze the effectiveness of a custom-trained DL model (YOLOv8) when deployed in diverse devices and settings at the vehicle-edge-cloud-layered architecture. Our main focus is to understand the interplay and relationship between the DL model’s accuracy and execution time during deployment at the layered framework. Therefore, we investigate the trade-offs between accuracy and time by the deployment process of the YOLOv8 model over each layer of the computational framework. We consider the CCAM infrastructures, i.e., sensory devices, computation, and communication at each layer. The findings reveal that the performance metrics results (e.g., 0.842 mAP@0.5) of deployed DL models remain consistent regardless of the device type across any layer of the framework. However, we observe that inference times for object detection tasks tend to decrease when the DL model is subjected to different environmental conditions. For instance, the Jetson AGX (non-GPU) outperforms the Raspberry Pi (non-GPU) by reducing inference time by 72%, whereas the Jetson AGX Xavier (GPU) outperforms the Jetson AGX ARMv8 (non-GPU) by reducing inference time by 90%. A complete average time comparison analysis for the transfer time, preprocess time, and total time of devices Apple M2 Max, Intel Xeon, Tesla T4, NVIDIA A100, Tesla V100, etc., is provided in the paper. Our findings direct the researchers and practitioners to select the most appropriate device type and environment for the deployment of DL models required for production.

Emerging vehicular applications with strict latency and reliability requirements pose high computing requirements, and current vehicles’ computational resources are not adequate to meet these demands. In this scenario, vehicles can get help to process tasks from other resource-rich computing platforms, including nearby vehicles, fixed edge servers, and remote cloud servers. Nonetheless, different vehicular communication network (VCN) modes need to be utilized to access these computing resources, improving applications and networks’ performance and quality of service (QoS). In this paper, we present a comprehensive survey on the vehicular task offloading techniques under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). For the task/computation offloading, we present the classification of methods under the V2V, V2I, and V2X communication domains. Besides, the task/computation offloading categories are each sub-categorized according to their schemes’ objectives. Furthermore, the literature on vehicular task offloading is elaborated, compared, and analyzed from the perspectives of approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.

The inspection and maintenance of electrical power lines (PL) via unmanned aerial vehicles (UAV) require fast and accurate PL detection to ensure smooth and secure electrical operations. However, the detection of PLs from aerial images is a highly challenging task due to the thin nature of PLs and the inherent noisy image backgrounds. Traditional line and edge detection methods do not detect the PLs accurately due to the cluttered backgrounds while the more recent deep learning (DL) CNNs are also not feasible for efficient PL detection due to the coarse bounding boxes and the computationally expensive pixel-based segmentations. Hence, in this study we propose PLPose, a novel framework for detecting the PLs via key points-based pose estimation technique and adapt the MobileNetV3 CNN for this task (kMobileNetV3), by adding a simple key point detection head to predict the PL key points. We also introduce a novel data-centric architecture (kMobileNetV3 + UDP), by adding the unbiased data processing (UDP) module to our kMobileNetV3, for faster and more accurate key point-based PL detection along with novel methods for data annotations and performance evaluation. Evaluations of PLPose on three benchmark PL datasets (PLDM, PLDU and the Mendeley Powerline Dataset) reveal that our proposed framework outperforms the state-of-the-art top-down pose estimation networks (HRNet-w32, HRNet-w32 + UDP and Resnet-50 Simple Baselines) in processing speed (∼29 FPS) and model size (5.23 M) for PL detection. Thus, the comprehensive experimental results demonstrate the effectiveness of our proposed framework. Our code is available from Github (https://www.github.com/rubeea/pl_mmpose).

Automotive-Industry 5.0 will use Beyond Fifth-Generation (B5G) communications to provide robust, abundant computation resources and energy-efficient data sharing among various Intelligent Transportation System (ITS) entities. Based on the vehicle communication network, the Internet of Vehicles (IoV) is created, where vehicles’ resources, including processing, storage, sensing, and communication units, can be leveraged to construct Vehicular Cloudlet (VC) to realize resource sharing. As Connected and Autonomous Vehicles (CAV) onboard computing is becoming more potent, VC resources (comprising stationary and moving vehicles’ idle resources) seems a promising solution to tackle the incessant computing requirements of vehicles. Furthermore, such spare computing resources can significantly reduce task requests’ delay and transmission costs. In order to maximize the utility of task requests in the system under the maximum time constraint, this paper proposes a Secondary Resource Allocation (SRA) mechanism based on a dual time scale. The request service process is regarded as M/M/1 queuing model and considers each task request in the same time slot as an agent. A Partially Observable Markov Decision Process (POMDP) is constructed and combined with the Multi-Agent Reinforcement Learning (MARL) algorithm known as QMix, which exploits the overall vehicle state and queue state to reach effective computing resource allocation decisions. There are two main performance metrics: the system’s total utility and task completion rate. Simulation results reveal that the task completion rate is increased by 13%. Furthermore, compared with the deep deterministic policy optimization method, our proposed algorithm can improve the overall utility value by 70% and the task completion rate by 6%.

The rapid growth of Automotive-Industry 5.0 and its emergence with beyond fifth-generation (B5G) communications, is making vehicular edge computing networks (VECNs) increasingly complex. The latency constraints of modern automotive applications make it difficult to run complex applications on vehicle on-board units (OBUs). While multi-access edge computing (MEC) can facilitate task offloading to execute these applications, it is still a challenge to access them promptly and optimally. Traditional algorithms struggle to guarantee accuracy in such dynamic environment, but deep reinforcement learning (DRL) methods offer improved accuracy, robustness, and real-time decision-making capabilities. In this paper, we propose a DRL-based mobility, contact, and load aware cooperative task offloading (DCTO) scheme. DCTO is designed for both cellular and mmWave radio access technologies (RATs), and both binary and partial offloading mechanisms. DCTO targets delay minimization by opportunistically switching RATs and offloading mechanisms. We consider relative efficacy and neutrality factors as key performance indicators and use them to derive the DRL agent’s reward function. Extensive evaluations demonstrate that the DCTO scheme exhibits a substantial enhancement in task success rate, with an increase from 2.61% to 21.34%. It also improves the efficacy factor from 1.38 to 3.52 and reduces the neutrality factor from 4.99 to 0.76. Furthermore, the average task processing time is reduced by a range of 3.77% to 24.15%. Additionally, the DCTO scheme outperforms the other evaluated schemes in terms of reward and TFPS ratio.

Background: Acute gallstone pancreatitis is quite common throughout the globe. Objective: To assess the outcome of laparoscopic cholecystectomy in patients with acute pancreatitis secondary to gallstone. Methods: A prospective observational study was conducted at the Department of Surgery, Jinnah Postgraduate Medical Center, Karachi, Pakistan between July 2020 to June 2021. All patients who were diagnosed with gallstones pancreatitis were eligible to take part in the study. Other causes of acute pancreatitis like drugs, hyperlipidemia, endoscopic retrograde cholangiopancreatography (ERCP), postoperative and idiopathic were excluded from the study. Patients were offered laparoscopic cholecystectomy. Early laparoscopic cholecystectomy was considered for patients operated between the time of the attack to within 10 days. Operative and postoperative parameters like the mean operative time, the number of ports, mean hospital stay, placement of a drain, and postoperative complications were recorded and analyzed. Results: A total of 383 patients were evaluated with a median age of 47 (IQR=36-58) years. 41 (18.5%) patients had difficult dissection. The median operative time was 47 (IQR=38-54) minutes while the median hospital stay was 7 (IQR=4-7) days. Sixty patients had drain placement. Only minor complications were seen in 20 (9%) patients. Furthermore, the majority of the patients in the gallbladder group i.e. 96.53% had complete recovery while the mortality rate was also very low i.e. 0.46%. Conclusion: Laparoscopic cholecystectomy can be safely performed in selected cases of acute gallstones pancreatitis to prevent further attacks and other consequences of delayed treatment.

Convoy driving, a specialized form of collaborative autonomous driving, offers a promising solution to the multifaceted challenges that transportation systems face, including traffic congestion, pollutant emissions, and the coexistence of connected autonomous vehicles (CAVs) and human-driven vehicles on the road, resulting in mixed traffic flow. While extensive research has focused on the collective societal benefits of convoy driving, such as safety and comfort, one critical aspect that has been overlooked is the willingness of individual vehicles to participate in convoy formations. While the collective benefits are evident, individual vehicles may not readily embrace this paradigm shift without explicit tangible benefits and incentives to motivate them. Moreover, the objective of convoy driving is not solely to deliver societal benefits but also to provide incentives and reduce costs at the individual level. Therefore, this research bridges this gap by designing and modeling the societal benefits, including traffic flow optimization and pollutant emissions, and individual-level incentives necessary to promote convoy driving. We model a fundamental diagram of mixed traffic flow, considering various factors such as CAV penetration rates, coalition intensity, and coalition sizes to investigate their relationships and their impact on traffic flow. Furthermore, we model the collaborative convoy driving problem using the coalitional game framework and propose a novel utility function encompassing incentives like car insurance discounts, traffic fine reductions, and toll discounts to encourage vehicle participation in convoys. Our experimental findings emphasize the need to strike a balance between CAV penetration rate, coalition intensity, size, and speed to realize the benefits of convoy driving at both collective and individual levels. This research aims to align the interests of road authorities seeking sustainable transportation systems and individual vehicle owners desiring tangible benefits, envisioning a future where convoy driving becomes a mutually beneficial solution.

COVID-19 related leukoencephalopathy can be multifactorial given the systemic effects of the viral disease. We present couple of cases with typical clinico-imaging stigmata of COVID-19 resulting in severe respiratory insufficiency. MR brain imaging revealed confluent diffuse supratentorial white matter T2 hyperintensity with restricted diffusion during the sub-acute course of the disease. The MR imaging pattern of leukoencephalopathy was non-specific but more comparable to delayed post-hypoxic leukoencephalopathy (DPHL) as also previously reported in COVID-19. Interestingly, T2 imaging showed unusual but peculiar finding of “accentuated medullary veins” in the superficial zones. No dural venous sinus thrombosis or micro-hemorrhages were present to explain “dots and stripes” due to dilated medullary veins. The patho-mechanism of this findings is not clear but may possibly be related to demyelination as DPHL has shown to be a demyelinating process. We present a review of COVID-related leukoencephalopathy with discussion on hypoxia-induced demyelinating process with accentuated medullary veins as possible associated marker.

A facial cutaneous sinus of dental origin is a relatively uncommon condition; in the absence of the specific dental symptoms it is mostly misdiagnosed. Tidwell et al in 1997 reported a case which took over 15 years to recognize a dental origin1. Such patients seeks treatment from a general physician or surgeon instead of a dental surgeon and are subjected to multiple remedies like surgical excisions, cauterization, multiple antibiotic regimens and even radiotherapy with ultimate recurrence because the primary dental cause is frequently overlooked. Misdiagnosis leads to the destructive treatment of local cutaneous lesion, this may produce unsightly scar, which produces aesthetic problems and is very stressful for the patient2.

Unequal data distribution among different classes usually cause a class imbalance problem. Due to the class imbalance, the classification models become biased toward the majority class and misclassify the minority class. Class imbalance issue becomes more complex when it occurs in multi-class data. The most common method to handle the class imbalance is data resampling that involves either over-sampling minority class instances or under-sampling majority class instances. In the case of under-sampling, there is a chance of losing some crucial information, whereas over-sampling can cause an overfitting problem. Therefore, we propose a novel Cluster-based Hybrid Sampling for Imbalance Data (CBHSID) approach to address these issues. The CBHSID calculates the mean of the data observations based on the number of classes. It uses the calculated mean as a threshold value to segregate majority and minority classes. CBHSID applies affinity propagation cluster analysis to each class to create sub-clusters and calculates the distance of each data item of sub-cluster using centroid mean. CBHSID removes data observations that are away from the center of sub-cluster during under-sampling. On the other hand, during the over-sampling, it generates synthetic samples using data observations near to the center of sub-cluster. We compared CBHSID with a few state-of-the-art data balancing methods on 12 binary and 4 multi-class benchmark datasets. Based on Geometric-Mean (G-Mean), Recall, and F1-score, our method outperformed the other compared methods on 14 datasets out of 16. Results also revealed that CBHSID is suitable for addressing class imbalance issues in both binary and multi-class classifications. In the current state, we have only validated CBHSID on stationary data streams. Consequently, CBHSID can further be tested on non-stationary data streams in online learning environments.

BackgroundDespite the significant progress in the treatment of multiple myeloma (MM), the disease remains untreatable and its cure is still an unmet clinical need. Neoplastic transformation in MM is initiated in the germinal centers (GCs) of secondary lymphoid tissue (SLT) where B cells experience extensive somatic hypermutation induced by follicular dendritic cells (FDCs) and T-cell signals.ObjectiveWe reason that secreted protein acidic and rich in cysteine (SPARC), a common stromal motif expressed by FDCs at the origin (SLTs) and the destination (BM) of MM, plays a role in the pathogenesis of MM, and, here, we sought to investigate this role.MethodsThere were 107 BM biopsies from 57 MM patients (taken at different time points) together with 13 control specimens assessed for SPARC gene and protein expression and compared with tonsillar tissues. In addition, regulation of myeloma-promoting genes by SPARC-secreting FDCs was assessed in in vitro GC reactions (GCRs).ResultsSPARC gene expression was confirmed in both human primary (BM) and secondary (tonsils) lymphoid tissues, and the expression was significantly higher in the BM. Sparc was detectable in the BM and tonsillar lysates, co-localized with the FDC markers in both tissues, and stimulation of FDCs in vitro induced significantly higher levels of SPARC expression than unstimulated controls. In addition, SPARC inversely correlated with BM PC infiltration, ISS staging, and ECOG performance of the MM patients, and in vitro addition of FDCs to lymphocytes inhibited the expression of several oncogenes associated with malignant transformation of PCs.ConclusionFDC-SPARC inhibits several myelomagenic gene expression and inversely correlates with PC infiltration and MM progression. Therapeutic induction of SPARC expression through combinations of the current MM drugs, repositioning of non-MM drugs, or novel drug discovery could pave the way to better control MM in clinically severe and drug-resistant patients.

Mooring systems are incorporated from mooring (thin) lines that are constituted of fiber ropes, steel wires, and chains. Mooring systems are used for the stationary keeping of floating units during the drilling process of oil and gas from offshore deep water, and the unloading of productions to the shuttle storage tanker. However, it is crucial to monitor mooring systems for early-stage failure detection in mooring lines during offshore mooring operations to avoid any unexpected losses, including human injuries, and catastrophic failure. This paper addresses the challenges of mooring line detection, and proposes a deep learning-based approach for the detection of mooring lines from marine images using the bounding box. A convolutional neural network, Inception v3, is used for the detection and classification of thin-line objects from marine images, and it is a pre-trained model with 1000 classes. Furthermore, various testing samples have been evaluated for assessing the performance of the pre-trained proposed model. According to the results, it has been observed that the proposed model obtained the highest accuracy (87.33%) in classifying the mooring line objects from images, but failed to accurately detect mooring lines. Furthermore, in a few highlighted cases, the performance of the model decreased in terms of accuracy due to the misclassification and wrong detection of mooring line objects. Despite this, the proposed study furnishes a potential solution for the detection of failure in mooring lines from marine images.

Environment perception plays a crucial role in enabling collaborative driving automation, which is considered to be the ground-breaking solution to tackling the safety, mobility, and sustainability challenges of contemporary transportation systems. Despite the fact that computer vision for object perception is undergoing an extraordinary evolution, single-vehicle systems’ constrained receptive fields and inherent physical occlusion make it difficult for state-of-the-art perception techniques to cope with complex real-world traffic settings. Collaborative perception (CP) based on various geographically separated perception nodes was developed to break the perception bottleneck for driving automation. CP leverages vehicle-to-vehicle and vehicle-to-infrastructure communication to enable vehicles and infrastructure to combine and share information to comprehend the surrounding environment beyond the line of sight and field of view to enhance perception accuracy, lower latency, and remove perception blind spots. In this article, we highlight the need for an evolved version of the collaborative perception that should address the challenges hindering the realization of level 5 AD use cases by comprehensively studying the transition from classical perception to collaborative perception. In particular, we discuss and review perception creation at two different levels: vehicle and infrastructure. Furthermore, we also study the communication technologies and three different collaborative perception message-sharing models, their comparison analyzing the trade-off between the accuracy of the transmitted data and the communication bandwidth used for data transmission, and the challenges therein. Finally, we discuss a range of crucial challenges and future directions of collaborative perception that need to be addressed before a higher level of autonomy hits the roads.

Higher-level autonomous driving necessitates the best possible execution of important moves under all conditions. Most of the accidents in recent years caused by the AVs launched by leading automobile manufacturers are due to inadequate decision-making, which is a result of their poor perceivance of environmental information. In today’s technology-bound scenarios, versatile sensors are used by AVs to collect environmental information. Due to various technical and natural calamities, the environmental information acquired by the sensors may not be complete and clear, due to which the AVs may misinterpret the information in a different context, leading to inadequate decision-making, which may then lead to fatal accidents. To overcome this drawback, effective preprocessing of raw sensory data is a mandatory task. Pre-processing the sensory data involves two vital tasks, namely data cleaning and data fusion. Since the raw sensory data are complex and exhibit multimodal characteristics, more emphasis is given to data preprocessing. Since more innovative models have been proposed for data cleaning, this study focused on data fusion. In particular, this study proposed a generic data fusion engine, which classifies different formats of sensory data and fuses them accordingly to improve accuracy. This study proposed a generic framework to fuse the text, image, and audio data. In the first stage of this research, an innovative hybrid model was proposed to fuse multispectral image and video data. Simple and efficient models to extract the salient image features were also proposed. The hybrid image fusion model that was proposed did not yield satisfactory outcomes when combining 3D point cloud data, and its performance declined when evaluating large datasets. To address this issue, the study expanded by introducing an advanced generative adversarial network (GAN) to transform the hybrid image fusion model into a machine learning model capable of handling substantial datasets. Additionally, customized kernel functions were suggested to fuse 3D point cloud data effectively. The performance of the proposed models was assessed using standard metrics and datasets, comparing them with existing popular models. The results revealed that the proposed image fusion model outperformed the other models.

Objective: To compare the result of the combination of hyperbaric bupivacaine plus fentanyl with hyperbaric bupivacaine alone in patients undergoing caesarean section in spinal anaesthesia. Study Design: Comparative, cross-sectional study. Place and Duration of Study: Department of Anesthesia, Fauji Foundation Hospital, Rawalpindi Pakistan, from Dec 2017 to Jun 2018. Methodology: After consulting the institutional ethical review committees a total of 60 females between ages 18-40 years were enrolled for caesarean section delivery. They were divided into two groups. The study group (n=30) received a subarachnoid injection of 0.5% hyperbaric bupivacaine (10mg) 2ml with 25ug of fentanyl 0.5ml and control group (n=30) was injected 0.5% hyperbaric bupivacaine 12.5mg (2.5 ml) only. Pain experienced during the procedure was assessed by using 10-point visual analogue scoring method. The mean duration of analgesia, mean arterial blood pressure and heart rate after surgery were compared between two groups. Results: The mean duration of analgesia was 206.5/min ± 6.4 in the study group and it was 163.6min ± 7.2 in the control group (p=0.001). Mean arterial BP after surgery was 92.3mmHg ± 3.8 in the study group and 88.7mmHg ± 4.1 in the control group (p=0.001). The mean heart rate recorded after surgery was 75.2/min ± 5.2 in the study group and it was 70.4/min ± 6.1 in the control group (p=0.001). Conclusions: The mean duration of analgesia was significantly longer in the study group when compared with the control group with better mean arterial blood pressure and heart rate response after Caesarean section.

Abstract Background The aim of the present study was to compare the presence of psychiatric disorders in people with hyperthyroidism and euthyroid patients attending the Endocrinology Outpatient Department at the Shri Maharaja Hari Singh Hospital in Kashmir, India. Seventy-five patients with hyperthyroidism and an equal number of euthyroid patients participated in the study. Participants were selected using stratified random sampling. All patients were female. There was no significant difference between the two groups in terms of demographic features. Hormonal screening was performed by immunoassay and haemagglutination method. For the mental health assessment, the Mini-International Neuropsychiatric Interview (MINI), Hamilton Depression Rating Scale [HAM-D], and Hamilton Anxiety Rating Scale [HAM-A] were used. Results There was a higher prevalence of psychiatric disorders among the hyperthyroidism group (60% versus 34.7%). In particular, there was a higher prevalence of major depressive disorder, suicidality, generalised anxiety disorder, panic attacks, and agoraphobia. In some cases, the prevalence of a psychiatric disorder diminished after endocrinological treatment. Conclusions Screening patients with hyperthyroid disorders for psychiatric symptoms and disorders, and providing timely care when necessary, can go a long way in improving the quality of life of this population. It is imperative to establish routine screening and timely care of mental health symptoms and disorders in patients with hyperthyroidism.

This study aims to estimate genetic parameters and genetic trends for early growth and reproductive traits of Doyogena sheep. Data used in the study were collected over 6 years (2013–2018). Studied traits were birth weight (BWT), weaning weight (WWT), 6-month weight (SMWT), average daily gains from birth to weaning (ADG0-3), average daily gains from weaning to 6-month age (ADG3-6), average daily gain from birth to 6-month age (ADG0-6), litter size (LS), lambing interval (LI), age at first lambing (AFL), and annual reproductive rate (ARR). (Co) variance components and genetic parameters were estimated using restricted maximum likelihood (REML). The analyses were carried out using WOMBAT program. Univariate analysis was applied to estimate genetic parameters. Six different animal models were fitted by including or excluding maternal effects. The direct heritability estimates for BWT, WWT, SMWT, ADG0-3, ADG3-6 and ADG0-6 were 0.33 ± 0.06, 0.31 ± 0.06, 0.14 ± 0.06, 0.13 ± 0.04, 0.11 ± 0.07, and 0.02 ± 0.05 respectively. Direct heritability for LS, LI, and AFL were 0.28 ± 0.12, 0.20 ± 0.5, and 0.001 ± 0.3, respectively. The maternal heritability estimates for BWT, WWT, and LS were 0.24 ± 0.12, 0.60 ± 0.07, and 0.24 ± 0.08, respectively. The genetic correlation between BWT with WWT and BWT with SMWT were 0.21 ± 0.07 and 0.21 ± 0.09, respectively. Genetic progress for most of the studied traits has shown promising improvements. Thus, continuation of selection, therefore, suggested for more improvements in the performance of Doyogena sheep. Direct heritability estimates decrease as lamb age increases and selection based on earlier body weight will be more efficient.

Objective: To describe the resultant Otologic morbidity and report on the early outcomes following blasts occurring in twin cities of Quetta and Peshawar. Study Design: Case series. Place and Duration of Study: ENT department Tertiary Care Centre Quetta and Tertiary Care Hospital Peshawar. Study period was, from Jan 2013 to Dec 2013. Methodology: All bomb blast patients brought to the hospitals were included in study. Participants completed Symptom Assessment Forms followed by detailed ENT examination and Pure Tone Audiograms on arrival and after 6 weeks. Results: A total of 504 patients were included initially of which 80% of the patients were male. About 57.8% of the patients complained of ear injury, 21.6% of the total patients had tympanic membrane perforation on initial presentation. Chances of spontaneous closure of perforation were 20.9% in our study. Chances of hearing improvement were 17.9% in our study at the end of the study period. Conclusion: Blast related otologic injuries constituted a major source of morbidity The most common type of hearing loss following a blast trauma was mild to moderate conductive type. Chances of recovery of hearing following blast do exist (17.9%). Suspected patients should be regularly assessed and followed up. Much work needs to be done to study the impact of blast trauma on hearing in our country.

Cyber security is one of the major concerns of today’s connected world. For all the platforms of today’s communication technology such as wired, wireless, local and remote access, the hackers are present to corrupt the system functionalities, circumvent the security measures and steal sensitive information. Amongst many techniques of hackers, port scanning and Distributed Denial of Service (DDoS) attacks are very common. In this paper, the benefits of machine learning are taken into consideration for classification of port scanning and DDoS attacks in a mix of normal and attack traffic. Different machine learning algorithms are trained and tested on a recently published benchmark dataset (CICIDS2017) to identify the best performing algorithms on the data which contains more recent vectors of port scanning and DDoS attacks. The classification results show that all the variants of discriminant analysis and Support Vector Machine (SVM) provide good testing accuracy i.e. more than 90%. According to a subjective rating criterion mentioned in this paper, 9 algorithms from a set of machine learning experiments receive the highest rating (good) as they provide more than 85% classification (testing) accuracy out of 22 total algorithms. This comparative analysis is further extended to observe training performance of machine learning models through k-fold cross validation, Area Under Curve (AUC) analysis of the Receiver Operating Characteristic (ROC) curves, and dimensionality reduction using the Principal Component Analysis (PCA). To the best of our knowledge, a comprehensive comparison of various machine learning algorithms on CICIDS2017 dataset is found to be deficient for port scanning and DDoS attacks while considering such recent features of attack.

Abstract In this investigation, we demonstrate the quantum version of Montgomery identity for the functions of two variables. Then we use the result to derive some new Ostrowski-type inequalities for the functions of two variables via quantum integrals. We also consider the particular cases of the key results and offer some new integral inequalities.

Higher education requires access to Information and communication technologies (ICT’s). This exposure and access to ICT, coupled with the excessive usage of social media, has augmented the problem of cyberbullying among university students. Previous studies have investigated cyberbullying among school students while overlooking university students, who are actually more engaged in cyberbullying perpetration. In view of the gravity of the situation and its impact on the wellbeing of the university students, this study aims to understand the role of personal and psychological factors dragging Malaysian undergraduate students of public and private universities towards cyberbullying behaviour. In order to develop the framework, the study has utilized the ‘Theory of planned behavior’ and ‘Social Cognitive Theory’. The study is based on a quantitative research approach and employs a self-administered survey to collect data. The data has been analyzed through the Structured Equation Modeling (SEM) technique using SmartPLS. The results reveal that individual factors including cyberbullying awareness and personality traits are not associated with Malaysian undergraduate students’ cyberbullying behaviour. However, psychological factors, including self-esteem, internalizing behavior, and anti-social behavior, play an instrumental role in developing Malaysian undergraduate students’ cyberbullying attitude. The study also confirms that subjective norms assert a powerful positive impact on cyberbullying attitude of Malaysian undergraduates. Lastly, the study aims to contribute to the research on cyberbullying behavior by offering a conceptual validated model that predicts Malaysian university students’ cyberbullying behavior. This study also found that social media usage plays moderating role between cyberbullying intention and cyberbullying behavior. Parents, universities, and governments will benefit from this study by understanding factors to be considered when making a policy to reduce cyberbullying among university students.

Automated driving is expected to enormously evolve the transportation industry and ecosystems. Advancement in communications and sensor technologies have further accelerated the realization process of the autonomous driving goals. There are a number of autonomous driving initiatives around the world with varying objectives and scope, e.g. vehicle perception in a controlled environment or highway settings. Autonomous driving in a more complex environment with mixed traffic poses major challenges. The solutions for such environments is the focus of this paper. We start with a quick overview of current autonomous driving development activities worldwide. We then discuss the solution concept for autonomous driving in urban environments and its enabling components, e.g. road digitization and flexible communication infrastructure, to realize an urban autonomous driving testbed. We highlight the major challenges hindering the realization use-cases of Level 5 autonomous driving. Solution sketches to address these or similar changes are briefly discussed. We also implement some elements of the solution approaches on the real test-road. We demonstrate an artificial intelligence based approach for the analysis of real traffic data measured on the testbed. We implement approaches for predicting the network resource demands and allocation, which are crucial for realizing the use-cases of autonomous driving in complex environments. For the experiments, real data from the test-road is used. Results show that traffic patterns and resource demands are predicted accurately. These experiments are expected to instrumental for realizing other use-cases of autonomous driving.

Objective: To analyze the outcome of open surgical decortication of tuberculous empyema patients at their late presentation. Study Design: Cross sectional study. Place and Duration of Study: Patients (n = 128) with tuberculous empyema were subjected to decortication for one and half year starting from July 2017 to December 2018. Pre, intra, and post-operative characteristics of all patients were studied thoroughly with a 6-month follow up evaluation of the surgical outcomes. Results: Preoperative conditions were as follows: 6.25% patient had Broncho pleural fistula, 90.9% patients were diagnosed with cough, 34.7 with night sweating, 64.2% chest pain and 79.6% chest tightness. Decortication was performed in all cases, with debridement of necrotic tissue and repair of air leaks in some patients. 70 (54.7%) patients went through right side decortication, whereas 56 (43.7%) patients went through left sided decortication and 2 (1.56%) patients had bilateral. Mortality was 2 (1.56%), 1 patient died of septicemia while 1 of pulmonary embolism. Whereas morbidity was 9 (7%), 3 patients had wound infection, 4 had incomplete lung expansion, 1 patient each had prolonged air leak and post-operative bleeding. Conclusions: Delayed referral for surgery causes irreversible changes in the lung, and prolonged disease condition, which causes increased thickness of the pleura and injury to the underlying lung with a delayed recovery. Open decortication gives satisfactory results.

Fog Radio Access Networks (F-RANs) have been considered a groundbreaking technique to support the services of Internet of Things by leveraging edge caching and edge computing. However, the current contributions in computation offloading and resource allocation are inefficient; moreover, they merely consider the static communication mode, and the increasing demand for low latency services and high throughput poses tremendous challenges in F-RANs. A joint problem of mode selection, resource allocation, and power allocation is formulated to minimize latency under various constraints. We propose a Deep Reinforcement Learning (DRL) based joint computation offloading and resource allocation scheme that achieves a suboptimal solution in F-RANs. The core idea of the proposal is that the DRL controller intelligently decides whether to process the generated computation task locally at the device level or offload the task to a fog access point or cloud server and allocates an optimal amount of computation and power resources on the basis of the serving tier. Simulation results show that the proposed approach significantly minimizes latency and increases throughput in the system.

Coronavirus disease 2019 (COVID-19) is a viral disease, also known as severe acute respiratory syndrome coronavirus 2, was first reported in Wuhan, China, December 2019. Respiratory manifestations from the induced acute lung injury were the most common reported findings. Few cases showed extrapulmonary manifestations. COVID-19-associated neurological manifestations have not been widely reported. In this report, we describe a case of encephalopathy in a patient with COVID-19 infection.

The Software Development (SD) process requires collaboration and sharing of information in order to achieve the best results required and Social Media (SM) has helped this process immensely. SM is frequently utilized by programmers to strengthen their SD tools. In particular, communication is a key component in collaborative development, when designing large-scale software. An engineer can ask his colleagues around the world for help in solving problems or contact users for feedback. However, there is still a need to do research about the benefits as well as limitation of usage of SM in SD process. This study examines research conducted from 2009 until 2019 to help the researchers as well as businesses to learn more about the relation between SM and SD. Inclusion and exclusion criteria is imposed to sort out the research done during the mentioned time period. Type of data extracted from the selected papers includes authors’ name(s), title of the paper, year of publication, type of publication, country of origin, method used, data analysis, study setting and data collection approach. Furthermore, this paper answers to the questions posed in the study, such as what are the potential limitations of using SM for SD, and what reasons are given by the organizations for utilization of SM for SD. This paper provides a systematic literature review of 31 studies on SM and SD.

Abstract Vehicular edge computing (VEC) is a promising paradigm to offload resource-intensive tasks at the network edge. Owing to time-sensitive and computation-intensive vehicular applications and high mobility scenarios, cost-efficient task offloading in the vehicular environment is still a challenging problem. In this paper, we study the partial task offloading problem in vehicular edge computing in an urban scenario. Where the vehicle computes some part of a task locally, and offload the remaining task to a nearby vehicle and to VEC server subject to the maximum tolerable delay and vehicle’s stay time. To make it cost-efficient, including the cost of the required communication and computing resources, we consider to fully exploit the vehicular available resources. We estimate the transmission rates for the vehicle to vehicle and vehicle to infrastructure communication based on practical assumptions. Moreover, we present a mobility-aware partial task offloading algorithm, taking into account the task allocation ratio among the three parts given by the communication environment conditions. Simulation results validate the efficient performance of the proposed scheme that not only enhances the exploitation of vehicular computation resources but also minimizes the overall system cost in comparison to baseline schemes.

Autonomous driving of higher automation levels asks for optimal execution of critical maneuvers in all environments. A crucial prerequisite for such optimal decision-making instances is accurate situation awareness of automated and connected vehicles. For this, vehicles rely on the sensory data captured from onboard sensors and information collected through V2X communication. The classical onboard sensors exhibit different capabilities and hence a heterogeneous set of sensors is required to create better situation awareness. Fusion of the sensory data from such a set of heterogeneous sensors poses critical challenges when it comes to creating an accurate environment context for effective decision-making in AVs. Hence this exclusive survey analyses the influence of mandatory factors like data pre-processing preferably data fusion along with situation awareness toward effective decision-making in the AVs. A wide range of recent and related articles are analyzed from various perceptive, to pick the major hiccups, which can be further addressed to focus on the goals of higher automation levels. A section of the solution sketch is provided that directs the readers to the potential research directions for achieving accurate contextual awareness. To the best of our knowledge, this survey is uniquely positioned for its scope, taxonomy, and future directions.

In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant to the complex urban environment, specifically, highlighting the challenges that are unaddressed and are within the scope of collaborative autonomous driving. To this end, we model the dynamic urban environment leveraging a probabilistic graphical model (PGM). To develop the proposed solution, a realistic simulation environment is required. There are a number of simulators—CARLA (Car Learning to Act), one of the prominent ones, provides rich features and environment; however, it still fails on a few fronts, for example, it cannot fully capture the complexity of an urban environment. Moreover, the classical CARLA mainly relies on manual code and multiple conditional statements, and it provides no pre-defined way to do things automatically based on the dynamic simulation environment. Hence, there is an urgent need to extend the off-the-shelf CARLA with more sophisticated settings that can model the required dynamics. In this regard, we comprehensively design, develop, and implement an extension of a classical CARLA referred to as CARLA+ for the complex environment by integrating the PGM framework. It provides a unified framework to automate the behavior of different actors leveraging PGMs. Instead of manually catering to each condition, CARLA+ enables the user to automate the modeling of different dynamics of the environment. Therefore, to validate the proposed CARLA+, experiments with different settings are designed and conducted. The experimental results demonstrate that CARLA+ is flexible enough to allow users to model various scenarios, ranging from simple controlled models to complex models learned directly from real-world data. In the future, we plan to extend CARLA+ by allowing for more configurable parameters and more flexibility on the type of probabilistic networks and models one can choose. The open-source code of CARLA+ is made publicly available for researchers.

Autonomous vehicles and smart roads are not new concepts and the undergoing development to empower the vehicles for higher levels of automation has achieved initial milestones. However, the transportation industry and relevant research communities still require making considerable efforts to create smart and intelligent roads for autonomous driving. To achieve the results of such efforts, the CCAM infrastructure is a game changer and plays a key role in achieving higher levels of autonomous driving. In this paper, we present a smart infrastructure and autonomous driving capabilities enhanced by CCAM infrastructure. Meaning thereby, we lay down the technical requirements of the CCAM infrastructure: identify the right set of the sensory infrastructure, their interfacing, integration platform, and necessary communication interfaces to be interconnected with upstream and downstream solution components. Then, we parameterize the road and network infrastructures (and automated vehicles) to be advanced and evaluated during the research work, under the very distinct scenarios and conditions. For validation, we demonstrate the machine learning algorithms in mobility applications such as traffic flow and mobile communication demands. Consequently, we train multiple linear regression models and achieve accuracy of over 94% for predicting aforementioned demands on a daily basis. This research therefore equips the readers with relevant technical information required for enhancing CCAM infrastructure. It also encourages and guides the relevant research communities to implement the CCAM infrastructure towards creating smart and intelligent roads for autonomous driving.

Cellular vehicle-to-everything (C-V2X) is one of the enabling vehicular communication technologies gaining momentum from the standardization bodies, industry, and researchers aiming to realize fully autonomous driving and intelligent transportation systems. The 3rd Generation Partnership Project (3GPP) standardization body has actively been developing the standards evolving from 4G-V2X to 5G-V2X providing ultra-reliable low-latency communications and higher throughput to deliver the solutions for advanced C-V2X services. In this survey, we analyze the 3GPP standard documents relevant to V2X communication to present the complete vision of 3GPP-enabled C-V2X. To better equip the readers with knowledge of the topic, we describe the underlying concepts and an overview of the evolution of 3GPP C-V2X standardization. Furthermore, we provide the details of the enabling concepts for V2X support by 3GPP. In this connection, we carry out an exhaustive study of the 3GPP standard documents and provide a logical taxonomy of C-V2X related 3GPP standard documents divided into three categories: 4G, 4G & 5G, and 5G based V2X services. We provide a detailed analysis of these categories discussing the system architecture, network support, key issues, and potential solution approaches supported by the 3GPP. We also highlight the gap and the need for intelligence in the execution of different operations to enable the use-case scenarios of Level-5 autonomous driving. We believe, the paper will equip readers to comprehend the technological standards for the delivery of different ITS services of the higher level of autonomous driving.

Distributed video coding (DVC) is based on distributed source coding (DSC) concepts in which video statistics are used partially or completely at the decoder rather than the encoder. The rate-distortion (RD) performance of distributed video codecs substantially lags the conventional predictive video coding. Several techniques and methods are employed in DVC to overcome this performance gap and achieve high coding efficiency while maintaining low encoder computational complexity. However, it is still challenging to achieve coding efficiency and limit the computational complexity of the encoding and decoding process. The deployment of distributed residual video coding (DRVC) improves coding efficiency, but significant enhancements are still required to reduce these gaps. This paper proposes the QUAntized Transform ResIdual Decision (QUATRID) scheme that improves the coding efficiency by deploying the Quantized Transform Decision Mode (QUAM) at the encoder. The proposed QUATRID scheme’s main contribution is a design and integration of a novel QUAM method into DRVC that effectively skips the zero quantized transform (QT) blocks, thus limiting the number of input bit planes to be channel encoded and consequently reducing both the channel encoding and decoding computational complexity. Moreover, an online correlation noise model (CNM) is specifically designed for the QUATRID scheme and implemented at its decoder. This online CNM improves the channel decoding process and contributes to the bit rate reduction. Finally, a methodology for the reconstruction of the residual frame (R^) is developed that utilizes the decision mode information passed by the encoder, decoded quantized bin, and transformed estimated residual frame. The Bjøntegaard delta analysis of experimental results shows that the QUATRID achieves better performance over the DISCOVER by attaining the PSNR between 0.06 dB and 0.32 dB and coding efficiency, which varies from 5.4 to 10.48 percent. In addition to this, results determine that for all types of motion videos, the proposed QUATRID scheme outperforms the DISCOVER in terms of reducing the number of input bit-planes to be channel encoded and the entire encoder’s computational complexity. The number of bit plane reduction exceeds 97%, while the entire Wyner-Ziv encoder and channel coding computational complexity reduce more than nine-fold and 34-fold, respectively.