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).
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A new networking paradigm, Vehicular Edge Computing (VEC), has been introduced in recent years to the vehicular network to augment its computing capacity. The ultimate challenge to fulfill the ...requirements of both communication and computation is increasingly prominent, with the advent of ever-growing modern vehicular applications. With the breakthrough of VEC, service providers directly host services in close proximity to smart vehicles for reducing latency and improving quality of service (QoS). This paper illustrates the VEC architecture, coupled with the concept of the smart vehicle, its services, communication, and applications. Moreover, we categorized all the technical issues in the VEC architecture and reviewed all the relevant and latest solutions. We also shed some light and pinpoint future research challenges. This article not only enables naive readers to get a better understanding of this latest research field but also gives new directions in the field of VEC to the other researchers.
Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to ...cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.
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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.
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.
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The envisioned smart city domains are expected to rely heavily on artificial intelligence and machine learning (ML) approaches for their operations, where the basic ingredient is data. Privacy of the ...data and training time have been major roadblocks to achieving the specific goals of each application domain. Policy makers, the research community, and the industrial sector have been putting their efforts into addressing these issues. Federated learning, with its distributed and local training approach, stands out as a potential solution to these challenges. In this article, we discuss the potential interplay of different technologies and AI for achieving the required features of future smart city services. Having discussed a few use-cases for future eHealth, we list design goals and technical requirements of the enabling technologies. The paper confines its focus on federated learning. After providing the tutorial on federated learning, we analyze the Federated Learning research literature. We also highlight the challenges. A solution sketch and high-level research directions may be instrumental in addressing the challenges.
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Blockchain technology is fast becoming the most transformative technology of recent times and has created hype and optimism, gaining much attention from the public and private sectors. It has been ...widely deployed in decentralized crypto currencies such as Bitcoin and Ethereum. Bitcoin is the success story of a public blockchain application that propelled intense research and development into blockchain technology. However, scalability remains a crucial challenge. Both Bitcoin and Ethereum are encountering low-efficiency issues with low throughput, high transaction latency, and huge energy consumption. The scalability issue in public Blockchains is hindering the provision of optimal solutions to businesses and industries. This paper presents a systematic literature review (SLR) on the public blockchain scalability issue and challenges. The scope of this SLR includes an in-depth investigation into the scalability problem of public blockchain, associated fundamental factors, and state-of-art solutions. This project managed to extract 121 primary papers from major scientific databases such as Scopus, IEEE explores, Science Direct, and Web of Science. The synthesis of these 121 articles revealed that scalability in public blockchain is not a singular term. A variety of factors are allied to it, with transaction throughput being the most discussed factor. In addition, other interdependent vita factors include storages, block size, number of nodes, energy consumption, latency, and cost. Generally, each term is somehow directly or indirectly reliant on the consensus model embraced by the blockchain nodes. It is also noticed that the contemporary available consensus models are not efficient in scalability and thus often fail to provide good QoS (throughput and latency) for practical industrial applications. Our findings exemplify that the Internet of Things (IoT) would be the leading application of blockchain in industries such as energy, finance, resource management, healthcare, education, and agriculture. These applications are, however, yet to achieve much-desired outcomes due to scalability issues. Moreover, Onchain and offchain are the two major categories of scalability solutions. Sagwit, block size expansion, sharding, and consensus mechanisms are examples of onchain solutions. Offchain, on the other hand, is a lighting network.
The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep ...learning-based approaches for PL segmentation, these models are still vulnerable to the class imbalance present in the data. The PLs occupy only a minimal portion (1-5%) of the aerial images as compared to the background region (95-99%). Generally, this class imbalance problem is addressed via the use of PL-specific detectors in conjunction with the popular class balanced cross entropy (BBCE) loss function. However, these PL-specific detectors do not work outside their application areas and a BBCE loss requires hyperparameter tuning for class-wise weights, which is not trivial. Moreover, the BBCE loss results in low dice scores and precision values and thus, fails to achieve an optimal trade-off between dice scores, model accuracy, and precision-recall values. In this work, we propose a generalized focal loss function based on the Matthews correlation coefficient (MCC) or the Phi coefficient to address the class imbalance problem in PL segmentation while utilizing a generic deep segmentation architecture. We evaluate our loss function by improving the vanilla U-Net model with an additional convolutional auxiliary classifier head (ACU-Net) for better learning and faster model convergence. The evaluation of two PL datasets, namely the Mendeley Power Line Dataset and the Power Line Dataset of Urban Scenes (PLDU), where PLs occupy around 1% and 2% of the aerial images area, respectively, reveal that our proposed loss function outperforms the popular BBCE loss by 16% in PL dice scores on both the datasets, 19% in precision and false detection rate (FDR) values for the Mendeley PL dataset and 15% in precision and FDR values for the PLDU with a minor degradation in the accuracy and recall values. Moreover, our proposed ACU-Net outperforms the baseline vanilla U-Net for the characteristic evaluation parameters in the range of 1-10% for both the PL datasets. Thus, our proposed loss function with ACU-Net achieves an optimal trade-off for the characteristic evaluation parameters without any bells and whistles. Our code is available at Github.
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A facial cutaneous sinus of dental origin isa relatively uncommon condition; in theabsence of the specific dental symptoms it ismostly misdiagnosed. Tidwell et al in 1997reported a case which took ...over 15 years torecognize a dental origin1. Such patients seekstreatment from a general physician or surgeoninstead of a dental surgeon and are subjected tomultiple remedies like surgical excisions,cauterization, multiple antibiotic regimens andeven radiotherapy with ultimate recurrencebecause the primary dental cause is frequentlyoverlooked. Misdiagnosis leads to thedestructive treatment of local cutaneous lesion,this may produce unsightly scar, whichproduces aesthetic problems and is verystressful for the patient2.
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To determine the association of diet and dietary practices with dental caries among adults.
A case-control study.
Operative Department, Rawal Institute of Health Sciences, Islamabad, Pakistan.
300 ...participants of both genders, aged 25–50 years.
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.
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 <0.001), smoking (p-value <0.001), and tea consumption (p-value = 0.02) were found to be significantly associated with dental caries.
Adults who regularly consumed sugar as a snack other than regular mealtimes were more likely to be associated with dental caries. Men, smokers, and adults who frequently took shots of sugar with their tea were more likely to be associated with dental caries.
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