An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms). Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods

Efficacy of topical curcuma longa in the healing of extraction sockets: A split-mouth clinical trial

Background: The healing process after dental extraction is influenced by various factors, and finding effective strategies for promoting wound healing and reducing postoperative discomfort remains a challenge. This study aimed to evaluate the effectiveness of topical Curcuma longa gel in reducing pain and promoting wound healing after dental extraction, with the secondary objective of assessing the occurrence of dry sockets. The study was a split-mouth randomized controlled trial conducted at the oral and maxillofacial surgery department over 3 months. Materials and Methods: This split-mouth randomized controlled trial consisted of a total of 21 patients undergoing bilateral extractions. One extraction socket was randomly assigned to the test group, where Curcuma. longa gel was applied, while the contralateral socket served as the control group, receiving a placebo. Pain and wound healing were evaluated using standardized scales on the 3rd and 7th days postextraction. Descriptive statistics, paired t-tests, and unpaired t-tests were performed using the SPSS software version 19. The statistical significance was fixed at P ≤ 0.05. Results: The test group showed significantly higher mean healing scores on the 3rd and 7th days compared to the control group. On the 7th day, the test group had significantly lower mean pain scores than the control group. No cases of dry sockets were observed in either group. Conclusion: Topical Curcuma longa gel demonstrated positive effects in promoting wound healing and reducing pain after dental extraction. Clinicians should consider the use of Curcuma longa gel as a post-extraction medicament, particularly in cases involving multiple or traumatic extractions

In Vitro Efficacy of Ebselen and BAY 11-7082 Against Naegleria fowleri

Primary amebic meningoencephalitis (PAM) is a fatal infection caused by the free-living ameba Naegleria fowleri, popularly known as the “brain-eating ameba.” The drugs of choice in treating PAM are the antifungal amphotericin B and an antileishmanial miltefosine, but these are not FDA-approved for this indication and use of amphotericin B is associated with severe adverse effects. Moreover, very few patients treated with the combination therapy have survived PAM. Therefore, development of efficient drugs is a critical unmet need to avert future deaths of children. Since N. fowleri causes extensive inflammation in the brain it is important to select compounds that can enter brain to kill ameba. In this study, we identified two central nervous system (CNS) active compounds, ebselen and BAY 11-7082 as amebicidal with EC50 of 6.2 and 1.6 μM, respectively. The closely related BAY 11-7085 was also found active against N. fowleri with EC50 similar to BAY 11-7082. We synthesized a soluble ebselen analog, which had amebicidal activity similar to ebselen. Transmission electron microscopy of N. fowleri trophozoites incubated for 48 h with EC50 concentration of ebselen showed alteration in the cytoplasmic membrane, loss of the nuclear membrane, and appearance of electron-dense granules. Incubation of N. fowleri trophozoites with EC50 concentrations of BAY 11-7082 and BAY 11-7085 for 48 h showed the presence of large lipid droplets in the cytoplasm, disruption of cytoplasmic and nuclear membranes and appearance of several vesicles and chromatin residues. Blood-brain barrier permeable amebicidal compounds have potential as new drug leads for Naegleria infection