A case of cerebral sinus venous thrombosis resulting in mortality in pregnant woman: late diagnosis

Cerebral venous sinus thrombosis (CVST) is a rare condition. The most frequent symptoms and signs are headache, focal seizures with or without secondary generalization, unilateral or bilateral paresis and papilledema. We report a case of CVST in a 30-yearold female that presented with headache, diminution of vision, swelling and pain in both eyes and bilateral restricted extraocular movements. She expired after 3 days of treatment as she was diagnosed late. Due to its diverse and varied clinical presentation, CVST should be considered as differential in almost any brain syndrome

ASXC2 approach: a service-X cost optimization strategy based on edge orchestration for IIoT.

Most computation-intensive industry applications and servers encounter service-reliability challenges due to the limited resource capability of the edge. Achieving quality data fusion and accurate service reliability with optimized service-x execution cost is challenging. While existing systems have taken into account factors such as device service execution, residual resource ratio, and channel condition; the service execution time, cost, and utility ratios of requested services from devices and servers also have a significant impact on service execution cost. To enhance service quality and reliability, we design a 2-step adaptive service-X cost consolidation (ASXC 2) approach. This approach is based on the node-centric Lyapunov method and distributed Markov mechanism, aiming to optimize the service execution error rate during offloading. The node-centric Lyapunov method incorporates cost and utility functions and node-centric features to estimate the service cost before offloading. Additionally, the Markov mechanism-inspired service latency prediction model design assists in mitigating the ratio of offload-service execution errors by establishing a mobility-correlation matrix between devices and servers. In addition, the non-linear programming multi-tenancy heuristic method design help to predict the service preferences for improving the resource utilisation ratio. The simulations show the effectiveness of our approach. The model performance is enhanced with 0.13% service offloading efficiency, 0.82% rate of service completion when transmitting data size is 400 kb, and 0.058% average service offloading efficiency with 40 CPU Megacycles when the vehicle moves 60 Km/h speed around the server communication range. Our model simulations indicate that our approach is highly effective and suitable for lightweight, complex environments

Efficient LiDAR-trajectory affinity model for autonomous vehicle orchestration.

Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target's object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms latency compared to other approaches. Our system trains a single pair frame in 169.71 ms with affinity estimation time of 12.19 ms, track association time of 0.19 ms and mATE of 0.245 compared to state-of-the-art approaches

SYNTHESIS AND ANTIOXIDANT ACTIVITY OF THE 2-METHYL BENZIMIDAZOLE

2-methyl benzimidazole is a heterocyclic organic compound having an important pharmacophoric group which is used in medicinal industry. o- Phenyldiamine was treated with acid in the presence of polyphosphoric acid and other solvents. The presence of specific group was determined by FTIR spectroscopy. The obtaining compound was screened by the antioxidant activity by using the DPPH method. Key words- 2- methyl benzimidazole, o- phenyldiamine, antioxidant activity, DPPH method

A DRL-based service offloading approach using DAG for edge computational orchestration.

Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high required computational social service requests (SRs) in a time slot based on directed acyclic graph (DAG) is an NP-complete problem. Most state-of-art methods concentrate on the energy preservation of networks but neglect the resource sharing cost and dynamic subservice execution time (SET) during the computation and resource sharing. This article proposes a two-step deep reinforcement learning (DRL)-based service offloading (DSO) approach to diminish edge server costs through a DRL influenced resource and SET analysis (RSA) model. In the first level, the service and edge server cost is considered during service offloading. In the second level, the R-retaliation method evaluates resource factors to optimize resource sharing and SET fluctuations. The simulation results show that the proposed DSO approach achieves low execution costs by streamlining dynamic service completion and transmission time, server cost, and deadline violation rate attributes. Compared to the state-of-art approaches, our proposed method has achieved high resource usage with low energy consumption

A Novel Algorithm for Global Optimization: Rat Swarm Optimizer

This paper presents a novel bio-inspired optimization algorithm called Rat Swarm Optimizer (RSO) for solving the challenging optimization problems. The main inspiration of this optimizer is the chasing and attacking behaviors of rats in nature. This paper mathematically models these behaviors and benchmarks on a set of 38 test problems to ensure its applicability on different regions of search space. The RSO algorithm is compared with eight well-known optimization algorithms to validate its performance. It is then employed on six real-life constrained engineering design problems. The convergence and computational analysis are also investigated to test exploration, exploitation, and local optima avoidance of proposed algorithm. The experimental results reveal that the proposed RSO algorithm is highly effective in solving real world optimization problems as compared to other well-known optimization algorithms. Note that the source codes of the proposed technique are available at: http://www.dhimangaurav.co

Air versus water temperature of aquatic habitats in Delhi: Implications for transmission dynamics of Aedes aegypti

The present study was planned to characterize the microclimate experienced by Aedes larvae in different breeding habitats by determining the temperature variations in water kept in containers during different months under natural conditions. The study was conducted in three municipal zones of Delhi. In each site, four types of container material (plastic, cement, iron and ceramic) were chosen for recording the water temperature in the containers. Daily air and water temperatures (mean, maximum and minimum values) recorded by HOBO and Tidbit data loggers, respectively, were compared using analysis of variance and Tukey’s honest significant difference (HSD) tests. Mean monthly temperature of water varied from 16.9 to 33.0 °C in tin containers, 17.3 to 35.6°C in plastic containers, 14.3 to 28.5°C in ceramic pots, 23.3 to 30.4°C in cemented underground tanks (UGT) and 15.8 to 35.1°C in cemented overhead tanks (OHTs). Corresponding values for the air temperature ranged from 17.7 to 36.1°C. The difference between temperature of water in the containers and air temperature was highest for ceramic pots. Daily mean, maximum and minimum temperatures recorded by different data loggers differed significantly (P<0.05). When Tukey HSD test was applied for data analysis, the daily mean air temperature differed significantly from the water temperature in tin and ceramic pots as well as cemented OHTs. The temperature of water in the different breeding habitats investigated was lower than the air temperature. Moreover, actual air temperature as recorded by HOBO was higher than the temperature recorded by local weather stations. Considering the ongoing climate change, cemented UGT and earthen pots may be more productive breeding habitats for the Aedes mosquito in the near future, while plastic and cemented OHTs might no longer be suitable for Aedes breeding

A spring search algorithm applied to engineering optimization problems

At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke&rsquo;s law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems. In the SSA, search agents are weights joined through springs, which, as Hooke&rsquo;s law states, possess a force that corresponds to its length. The mathematics behind the algorithm are presented in the text. In order to test its functionality, it is executed on 38 established benchmark test functions and weighed against eight other optimization algorithms: a genetic algorithm (GA), a gravitational search algorithm (GSA), a grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), teaching&ndash;learning-based optimization (TLBO), a grey wolf optimizer (GWO), a spotted hyena optimizer (SHO), as well as an emperor penguin optimizer (EPO). To test the SSA&rsquo;s usability, it is employed on five engineering optimization problems. The SSA delivered better fitting results than the other algorithms in unimodal objective function, multimodal objective functions, CEC 2015, in addition to the optimization problems in engineering