Success Ratio Research Articles

Accurate dynamic vision system calibration using circular control points

Dynamic vision system (DVS) is a high time resolution camera. Due to the lack of standard frame pipeline, DVS cannot directly detect traditional control points (such as chessboard corners and circle centers). Two methods based on blinking pattern and calibration panel motion are proposed in this paper to solve the problem. The detection accuracy of blinking circular control points on the Light-Emitting Diodes (LED) is greatly improved by the proposed event frame convertor and the root mean square (RMS) is less than 0.04. Besides, this paper presents an incomplete grid detection method to increase the detection success ratio (0.97) and an effective event frames selection strategy to decrease the RMS (0.15). The results show that the standard deviation of both methods is substantially reduced compared to the conventional methods.

Multigenerational impacts of EE2 on reproductive fitness and immune competence of marine medaka

Estrogenic endocrine disrupting chemicals (EEDC) have been suspected to impact offspring in a transgenerational manner via modifications of the germline epigenome in the directly exposed generations. A holistic assessment of the concentration/ exposure duration-response, threshold level, and critical exposure windows (parental gametogenesis and embryogenesis) for the transgenerational evaluation of reproduction and immune compromise concomitantly will inform the overall EEDC exposure risk. We conducted a multigenerational study using the environmental estrogen, 17α-ethinylestradiol (EE2), and the marine laboratory model fish Oryzias melastigma (adult, F0) and their offspring (F1-F4) to identify transgenerationally altered offspring generations and phenotype persistence. Three exposure scenarios were used: short parental exposure, long parental exposure, and a combined parental and embryonic exposure using two concentrations of EE2 (33ng/L, 113ng/L). The reproductive fitness of fish was evaluated by assessing fecundity, fertilization rate, hatching success, and sex ratio. Immune competence was assessed in adults via a host-resistance assay. EE2 exposure during both parental gametogenesis and embryogenesis was found to induce concentration/ exposure duration-dependent transgenerational reproductive effects in the unexposed F4 offspring. Furthermore, embryonic exposure to 113 ng/L EE2 induced feminization of the directly exposed F1 generation, followed by subsequent masculinization of the F2 and F3 generations. A sex difference was found in the transgenerationally impaired reproductive output with F4 females being sensitive to the lowest concentration of EE2 (33 ng/L) upon long-term ancestral parent exposure (21 days). Conversely, F4 males were affected by ancestral embryonic EE2 exposure. No definitive transgenerational impacts on immune competence were identified in male or female offspring. In combination, these results indicate that EEDCs can be transgenerational toxicants that may negatively impact the reproductive success and population sustainability of fish populations.

D2OP: A Fair Dual-Objective Weighted Scheduling Scheme in Internet of Everything

In times of the Internet of Everything (IoE), the power of the Internet is growing exponentially, followed by a surge in the number of network requests. The conflict between people’s high requirements for quality of experience (QoE) and limited computing resources are becoming increasingly prominent. Therefore, an appropriate offloading method is required to better ease this conflict. In this paper, a highly efficient scheduling architecture of information processing under the big data flow of the IoE is proposed to enhance the scheduling performance. First, we construct a dual-channel processing model to describe the entire data flow and node devices. Second, we carefully consider the choice of weighting method to better find a balance between dual objectives. Third, a dual-objective deep Q-network (DQN)-based offloading algorithm with principal component analysis weighting method (D2OP) is proposed to collaboratively minimize task response time and machine load in a more reasonable allocation. To verify the performance of the D2OP, a series of experiments are conducted from multiple angles. The experimental results demonstrate its better performance than the three comparison algorithms in reducing response time, load balance, and increasing task success ratio.

Factors Affecting Success of New Drug Clinical Trials.

Clinical trials are an essential process in the development of new drugs. In spite of time-consuming processes and high costs, the overall success rate of clinical trials is only 7.9%, which is a high risk for biopharmaceutical companies. However, despite these huge risks, research on finding factors affecting clinical trials to overcome and manage to risks has been insufficient. Considering these characteristics of the pharmaceutical industry, this study investigated the factors affecting the success of sponsor-initiated clinical trials. The success factors investigated were categorized into four factors: quality of clinical trials, speed of clinical trials, relationship type, and communication. Logistic regression was performed to measure each factor by analyzing 24,295 cases of Phase 1 to 4 trials from ClinicalTrials.gov. Because of the analysis, the factors affecting the success of the clinical trials were varied according to each clinical phase and the drug types: New Molecular Entity (NME)/Biologics, and the success ratio in the quality variable affected the overall clinical trial phases. Additionally, the experience, speed, relationship type, and communication variables were also found to be statistically significant for the success of each phase and drug type.

A Fast 6DOF Visual Selective Grasping System Using Point Clouds

Visual object grasping can be complex when dealing with different shapes, points of view, and environments since the robotic manipulator must estimate the most feasible place to grasp. This work proposes a new selective grasping system using only point clouds of objects. For the selection of the object of interest, a deep learning network for object classification is proposed, named Point Encoder Convolution (PEC). The network is trained with a dataset obtained in a realistic simulator and uses an autoencoder with 1D convolution. The developed grasping algorithm used in the system uses geometry primitives and lateral curvatures to estimate the best region to grasp without previously knowing the object’s point cloud. Experimental results show a success ratio of 94% for a dataset with five classes, and the proposed visual selective grasping system can be executed in around 0.004 s, suitable for tasks that require a low execution time or use low-cost hardware.

IoT based secured data monitoring system for renewable energy fed micro grid system

Recently, the Wireless Sensor Network (WSN) plays a prominent role in many applications for efficiently monitoring the performance of the system. As the conventional power grid faces multiple issues in the process of power transmission, a smart grid based Internet of Things (IoT), which uses integrated electrical and communication infrastructure is employed in this study. The micro grid using Renewable Energy (RE) resources like wind and solar power is preferred in this work. The major objective of this study is to have perfect monitoring and control of the overall power grid using WSN. By connecting a smart micro grid with IoT, the issue of voltage violation and grid instability caused by power insufficiency is resolved. The IoT-based network integrates grid with the distributed source through WSN, which senses the data and forwards the gathered data to the cloud while taking a backup for future monitoring. Before forwarding, the data is highly encrypted using a hybrid approach known as Advanced Encryption Standard (AES) and Rivest Cipher 4 (RC4).The hybrid encryption generates improved ratio of transaction success with respect to malicious ratio indicating the secured data transmission preventing the intrusion of hackers. Simultaneously, the secured data is transmitted at high speed by making use of Pulse Coupled Neural Network (PCNN) along with Remora Optimization algorithm. The inclusion of optimization for the effective selection of the hyperparameters of neural network ensures the generation of global optimal solution which in turn enables the efficient identification of shortest path. The hardware in this system uses Arduino controller and the proposed approach aids in efficient monitoring of smart grid parameters facilitating uninterrupted power availability.

Prospective Clinical Split-Mouth Study of Two-Wing–Retained Resin-Bonded Anterior Fixed Dental Prostheses with Metallic and Ceramic Frameworks: 5-year Results

To analyze the clinical performance of two-wing–retained resin-bonded fixed dental prostheses (RBFDPs) after 5 years of clinical use with respect to technical and biologic complications, as well as survival and success rates. RBFDPs were fabricated from 3Y-TZP zirconia layered by hand (Lava Frame veneered with Lava Ceram; 3M ESPE) or metal (Remanium Star, Dentaurum; layered with Reflex, Wieland). The primary endpoints were debonding and fracture. The secondary endpoints (marginal integrity, marginal discoloration, abrasion of antagonist dentition, patient satisfaction, Gingival Index, and side effects) were evaluated at baseline and after 5 years. Survival and success rates were calculated using the Kaplan-Meier method. Log-rank test was used to compare the survival and success rates of the different materials. The mean observation time was 6 years and 10 months. The estimated cumulative success rate after 5 years was 88.9% ± 10% for metal-supported and 33% ± 16% for all-ceramic two-wing RBFDPs. After conversion into one-wing RBFDPs, the survival rate was 100% in both groups. Debonding of one of the two wings was the major complication. One zirconia framework fracture occurred. Metal-based two-wing RBFDPs showed a significantly higher success rate, but lower esthetic evaluation. Due to a reduction in technical complication rate and less invasiveness, one-wing RBFDPs should be preferred over two-wing RBFDPs whenever possible.

Synergistic Task and Motion Planning With Reinforcement Learning-Based Non-Prehensile Actions

Robotic manipulation in cluttered environments requires synergistic planning among prehensile and non-prehensile actions. Previous works on sampling-based Task and Motion Planning (TAMP) algorithms, e.g. PDDLStream, provide a fast and generalizable solution for multi-modal manipulation. However, they are likely to fail in cluttered scenarios where no collision-free grasping approaches can be sampled without preliminary manipulations. To extend the ability of sampling-based algorithms, we integrate a vision-based Reinforcement Learning (RL) non-prehensile procedure, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pusher</i> . The pushing actions generated by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pusher</i> can eliminate interlocked situations and make the grasping problem solvable. Also, the sampling-based algorithm evaluates the pushing actions by providing rewards in the training process, thus the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pusher</i> can learn to avoid situations leading to irreversible failures. The proposed hybrid planning method is validated on a cluttered bin-picking problem and implemented in both simulation and real world. Results show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pusher</i> can effectively improve the success ratio of the previous sampling-based algorithm, while the sampling-based algorithm can help the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pusher</i> learn pushing skills.

Supervised Machine Learning and Multiple Regression Approach to Predict Successfulness of Matrix Acidizing in Hydraulic Fractured Sandstone Formation

The success rate of matrix acidizing in hydraulic fractured sandstone formation is less than 55%, much lower compared to the more than 91% success rate in carbonate formation. The need for alternative approaches to help the success ratio in matrix acidizing is crucial. This paper demonstrates a modeling technique to improve the success ratio of matrix acidizing in a hydraulic fractured sandstone formation. Supervised machine learning with 4 models of a neural network, logistic regression, tree, and random forest was selected to predict the successfulness of matrix acidizing in hydraulic fracturing. In parallel, multivariate analysis of principal component regression and partial least square regression approach were utilized to predict the oil gain of the job. For qualitative prediction, the results showed that the random forest was the best model to predict the successfulness of the job with the area under the curve (AUC) of 0.68 and precision of 0.73 in the training model with 70% of the data. Subsequently, the validation test with the rest of the data (30% data) gave 0.51 AUC and 61% precision. For quantitative prediction, the net oil gain was evaluated by using principal component regression (PCR) and partial least square regression (PLS-R). The PCR and PLS-R model gave a coefficient of determination (Rsquare) of 0.22 and 0.35, respectively. The p-value of PLS-R was 0.047 (95% confidence interval) which indicates that the model is significant. The results of this work demonstrate the potential application of supervised machine learning, principal component regression, and partial least square regression to improve candidate selection of oil wells for matrix acidizing especially in hydraulic fractured wells with limited design data.

An Experience of a Single Comprehensive Stroke Center after Implementation of Routine Tenecteplase Administration for Acute Ischemic Stroke Treatment. (P7-5.023)

<h3>Objective:</h3> The objectives of this study were to determine if there is a significant difference between Alteplase and Tenecteplase in improvement of NIHSS, door to needle time (DTNT), disability at discharge determined by modified Rankin scale (mRS), and rate of intracranial hemorrhage (ICH). <h3>Background:</h3> There is growing interest in using Tenecteplase for acute ischemic stroke (AIS) treatment. Currently there is no clear consensus on its indication and dosage. We transitioned to Tenecteplase administration for AIS treatment in March 2021, at a dosage of 0.25 mg/kg. <h3>Design/Methods:</h3> A retrospective comparison analysis on the outcomes of AIS patients who were treated with Alteplase between April 2020 – March 2021 or Tenecteplase between April 2021 – March 2022. <h3>Results:</h3> Of 216 patients, 45.8% (n = 99) were given Alteplase and 54.1% (n = 117) were given Tenecteplase. There was no significant difference in baseline characteristics except hyperlipidemia, substance use, and history of smoking. No mean difference in mRS was measured at admission or discharge between Alteplase (1.42+/− 1.77) and Tenecteplase (1.32 +/− 1.56, p-value 0.703) and delta NIHSS between Alteplase and Tenecteplase (p-value 0.926). There was no difference in DTNT between both groups (p-value 0.172). Successful recanalization ratio was not significantly different. Symptomatic ICH was of no significant difference between the two groups: occurring in 5.05% patients receiving Alteplase and 5.13% patients receiving Tenecteplase (p-value 0.682). Rate of mortality was 13.13% (n = 13) in Alteplase and 11.11% (n = 13) in Tenecteplase, also of no significant difference (p-value 0.683). <h3>Conclusions:</h3> The results suggest that Tenecteplase has a similar efficacy and safety profile to Alteplase in real world practice. These findings are similar to results of previous randomized trials. At the dose of 0.25 mg/kg, Tenecteplase appears to be a reasonable alternative to Alteplase for use with acute ischemic strokes within 4.5 hours of onset. <b>Disclosure:</b> Dr. Cawley has nothing to disclose. Dr. Simon has nothing to disclose. Dr. Radomski has nothing to disclose. Dr. Kuns has nothing to disclose. Dr. Bejleri has nothing to disclose. Dr. Seol has nothing to disclose. Dr. Razak has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Janssen Pharmaceuticals.

Multi-stage warm started optimal motion planning for over-actuated mobile platforms

This work presents a computationally lightweight motion planner for over-actuated platforms. For this purpose, a general state-space model for mobile platforms with several kinematic chains is defined, which considers dynamics, nonlinearities and constraints. The proposed motion planner is based on a sequential multi-stage approach that takes advantage of the warm start on each step. Firstly, a globally optimal and smooth 2D/3D trajectory is generated using the Fast Marching Method. This trajectory is fed as a warm start to a sequential linear quadratic regulator that is able to generate an optimal motion plan without constraints for all the platform actuators. Finally, a feasible motion plan is generated considering the constraints defined in the model. In this respect, the sequential linear quadratic regulator is employed again, taking the previously generated unconstrained motion plan as a warm start. The motion planner has been deployed into the Exomars Testing Rover of the European Space Agency. This rover is an Ackermann-capable planetary exploration testbed that is equipped with a robotic arm. Several experiments were carried out demonstrating that the proposed approach speeds up the computation time and increases the success ratio for a martian sample retrieval mission, which can be considered as a representative use case of goal-constrained trajectory generation for an over-actuated mobile platform.

Clinical Outcomes of CAD/CAM (Lithium disilicate and Zirconia) Based and Conventional Full Crowns and Fixed Partial Dentures: A Systematic Review and Meta-Analysis.

Although CAD/CAM (computer-aided design/computer-aided manufacturing) restorations act as a favorable alternative to conventional metal-ceramic restorations for fixed dental prostheses, little is known about their intermediate and persistent clinical performance. This systematic review and meta-analysis aimed to assess the clinical performance in terms of biological, technical, and esthetic aspects and the survival and success ratios for single full crowns (SFCs) and fixed partial dentures (FPDs) fabricated by CAD/CAM and conventional techniques and according to the materials used (zirconia {ZC} and lithium disilicate {LD}). The population, intervention, control, outcome, and study design (PICOS) strategy was used to electronically search key terms in the PubMed, Cochrane Library, Embase, and Wiley Online databases for randomized control trials (RCTs) and cohort studies. The bias risks for RCTs and cohort studies were assessed using the Cochrane collaboration tool and the Newcastle-Ottawa Scale (NOS). Meta-analysis was performed using Rev5 from Cochrane. A total of 13 studies reporting on 1598 restorations in 1161 patients with a mean observation period of 3.6 years (minimum-maximum: 1-9.3 years) met the inclusion criteria. Meta-analysis of the included studies indicated that CAD/CAM manufacturing resulted in 1.17, 1.14, and 16.88 (95% CI: 0.64-2.17, 0.86-1.52, 7.59-37.56) higher biological, technical, and esthetic complications than conventional manufacturing of restorations. However, the difference was significant for esthetic complications only (p<0.00001). A significant difference was observed for all biological, technical, and aesthetic aspects between SFCs and FPDs (odds ratio {OR} = 2.61 vs. 1.78, 95% CI: 1.92-3.56 vs. 1.33-2.38; p<0.00001). The survival ratio of SFCs was 2.69 (95% CI: 1.98-3.65), significantly higher compared to the 1.76 (95% CI: 1.31-2.36) of FPDs (p<0.00001). The success ratio of FPDs at 1.18 (95% CI: 0.83-1.69) was significantly lower compared to SFCs at 2.36 (95% CI: 1.68-3.33). The clinical performance of LD with 2.42 (CI: 1.16-5.03) was significantly higher compared to ZC with 2.22 (CI: 1.78-2.77) (p<0.00001). The biological, technical, and aesthetic behaviors showed similar clinical outcomes between the CAD/CAM and conventional groups. LD could be a good alternative to zirconia, but its intermediate or persistent clinical performance needs to be evaluated. Overall, zirconia and CAD/CAM techniques must evolve further to outclass the conventional techniques used in the fabrication of SFCs and FPDs.

A motion control framework for autonomous water sampling and swing‐free transportation of a multirotor UAV with a cable‐suspended mechanism

AbstractIn this work, we present an end‐to‐end solution for autonomous water sampling by utilizing an unmanned aerial vehicle (UAV) with a cable‐suspended mechanism. Towards this direction, a sampling mechanism is initially designed in such a manner that the water sampling success ratio is maximized. However, the disturbances, acting on the submerged mechanism due to the water flow during the sampling procedure, impede the stabilization of the vehicle above the desired sampling position. Consequently, to achieve the precise hovering of the UAV, the vehicle's sensor suite is further augmented with a load cell, a depth sensor, an ultrasonic sensor, and a camera. The respective measurements are appropriately fused by employing an extended Kalman filter (EKF). Hence, an estimate of the disturbances is available in real‐time and is incorporated into a Model Predictive Control scheme which compensates for the aforementioned disturbances and stabilizes the vehicle above the sampling location. Finally, a complete water sampling mission entails the safe and swing‐free transportation of the mechanism towards the sampling location and, then, to a position where the collected samples are postprocessed by human operators. Consequently, a model predictive controller is employed which ensures the navigation of the vehicle to the desired waypoints while minimizing the swinging motion of the mechanism. The state of the mechanism is obtained by fusing measurements provided by the load cell and the camera with an EKF. The performance of the proposed framework, which aims to address all the aspects of a water sampling mission, is demonstrated through real experiments with an octorotor.

Implicit Overhearing Node-Based Multi-Hop Communication Scheme in IoT LoRa Networks.

Long range (LoRa) is a low-power wide-area technology because it is eminent for robust long-distance, low-bitrate, and low-power communications in the unlicensed sub-GHz spectrum used for the Internet of things (IoT) networks. Recently, several multi-hop LoRa networks have proposed schemes with explicit relay nodes to partially mitigate the path loss and longer transmission time bottlenecks of the conventional single-hop LoRa by focusing more on coverage expansion. However, they do not consider improving the packet delivery success ratio (PDSR) and the packet reduction ratio (PRR) by using the overhearing technique. Thus, this paper proposes an implicit overhearing node-based multi-hop communication (IOMC) scheme in IoT LoRa networks, which exploits implicit relay nodes for performing the overhearing to promote relay operation while satisfying the duty cycle regulation. In IOMC, implicit relay nodes are selected as overhearing nodes (OHs) among end devices with a low spreading factor (SF) in order to improve PDSR and PRR for distant end devices (EDs). A theoretical framework for designing and determining the OH nodes to execute the relay operations was developed with consideration of the LoRaWAN MAC protocol. Simulation results verify that IOMC significantly increases the probability of successful transmission, performs best in high node density, and is more resilient to poor RSSI than the existing schemes.

Mobility and Security Aware Real-Time Task Scheduling in Fog-Cloud Computing for IoT Devices: A Fuzzy-Logic Approach

Abstract This paper aims to improve the overall task processing time of mobile real-time Internet of Things (IoT) applications in fog-cloud computing, considering the various resource and security requirements along with the time constraints of the task. Fog computing extends the cloud resources to serve the IoT devices at the network edge. In such a scenario, deciding whether the tasks should be processed at the fog layer or submitted to the cloud is critical. Moreover, for real-time applications, the mobility of IoT devices and the limited bandwidth available at the edge devices endanger the low processing time of the task. Besides, the security demands of some IoT applications (i.e. healthcare) require processing the tasks by specific fog or cloud servers to assure confidentiality of information, which may also delay the task processing time. Therefore, we first address the mobility issue by proposing three different algorithms that work on allocating the mobile IoT device to the appropriate edge device (i.e. fog gateway), considering the distance and bandwidth load factors. Then, we offer a novel task scheduling algorithm that uses fuzzy logic to optimize the distribution of tasks between the fog and cloud layers, considering the task security requirements. The algorithm selects the proper processing unit to execute the task in the fog layer by exploiting the task demands (i.e. computation, storage, bandwidth, security) and deadline. Results demonstrate that considering the factors of distance and available bandwidth load while allocating the IoT device to the fog gateway improves the task processing time better than adopting one aspect. Results also show that our proposed scheduling algorithm outperforms other existing algorithms regarding makespan, turnaround time, success ratio and processing time metrics.

Efficient distributed broadcasting algorithms for cognitive radio networks-enabled smart agriculture

Smart agriculture has a broad prospect with the fast development of information technologies, such as Internet of Things, big data, edge computing and artificial intelligence. As more intelligent devices are deployed for agricultural scenarios, such as quality monitoring and information aggregation, the communication channels among these devices will be crowded and the performance can be greatly affected. Cognitive Radio Networks (CRNs) are promising in promoting better spectrum utilization, where unlicensed users can opportunistically use the vacant spectrum assigned to licensed users. On the basis of CRNs, many agricultural scenarios can be empowered with efficient communication capabilities. However, the broadcasting problem, which handles the information dissemination, has not been thoroughly studied in CRNs. Existing works are either centralized solutions performing broadcast on single/multiple channels, or distributed algorithms without performance guarantee for general networks. In this paper, we propose efficient distributed algorithms with short broadcast delay and high success ratio. The difficulties lie in the non-uniform channel availability, which entails broadcasting on multiple channels even for single-hop neighbors, and the distributed behaviors, where the users are only aware of the local information. Our contributions are threefold. First, we propose an efficient distributed rendezvous algorithm which spurs the neighbors to find the common channels in a short time. Second, we handle the single-hop broadcast by presenting two distributed algorithms and they guarantee a successful broadcast in O(Δ2loglogN) and Δ time slots respectively, where N is the number of channels and Δ is the maximum number of neighbors. Finally, we extend these algorithms to multi-hop broadcast and the broadcast delay is only up to D times the delay of single-hop, where D is the network diameter. We also conduct extensive simulations and the results corroborate our theoretical analyses.

The simultaneous measurement of quaternary mixture in over-the-counter cold medications using sequential spectrophotometric resolution approach enhanced with in-lab sample enrichment

A sequential spectrophotometric resolution technique (SSRT) was developed in this study without the use of systematic separation procedures to determine drug of a quaternary combination; caffeine (CAF), pseudoephedrine (PSE), doxylamine succinate (DOX), and paracetamol (PAR). Their presence in a tablet with a gap ratio of 3:3:1:150, respectively, and their overlapping spectra with low absorptivities make their resolution and determination impossible without prior separation. successive ratio subtraction technique (SRST) and constant multiplication method were used to solve these problems. Furthermore, an in-lab sample enrichment technique was applied to increase minor components concentration and consequently their absorbanses (CAF, PSE, and DOX). The D0 absorption spectra were generated by successive ratios followed by subtraction and multiplication of the constants. The maximum absorbances of the drugs tested, namely (CAF, PSE, DOX and PAR) were measured at wavelengths of 272.0, 257.0, 260.0, and 248.0 nm, respectively. The limits of detection (LOD) and limits of quantification (LOQ) were 0.021, 0.124, 0.186, 0.137 and 0.070, 0.414, 0.621, 0.456 (µg/mL), respectively. The linearitiy ranges (µg/mL) were 1.0–22.0, 1.0–24.0, 10.0–90.0 and 1.0–15.0 for CAF, PSE, DOX, and PAR, respectively. The International Conference on Harmonization (ICH) guidelines were applied for method validation, and the results obtained were within the limited parameters. The finding results were compared to official and/or published analytical methods to determine the procedure's reliability. It was noted that there was no actual difference in accuracy and precision between both meyhods. The proposed technique is sensitive, selective and economic;so it can be applied to the simultaneous analysis of these drugs in their commercial tablets and/or in quality-control laboratories.

Endobronchial Intubation With the King Vision® and McGrath® Laryngoscopes in Simulated Easy and Difficult Airways by Novices (eKingMath).

Objective. The competency of using video laryngoscopes (VL) for double-lumen tube (DLT) endobronchial intubations can be improved with constant training as assessed by measuring the learning curves. We hypothesized that the time to DLT intubation would be reduced over the intubation attempts. Design. A crossover manikin study. Settings. University-affiliated hospital. Participants. Forty-two novice medical students unfamiliar with DLT intubation. Interventions. Participants were randomly allocated to two sequences, including DLT intubation, using King Vision and McGrath VLs. Each participant completed 100 DLT intubation attempts on both simulated easy and difficult airways on two different mannikins using the study devices (25 attempts for each). Measurements and Main Results. The primary outcome was the time to DLT intubation. The secondary outcomes included the best glottic view, optimizing maneuvers, and intubation first-pass success. The use of King Vision VL was associated with a significantly shorter time to DLT intubation (P < 0.044 and P < 0.05, respectively) and a higher percentage of glottic opening (POGO) compared to the McGrath VL (P < 0.011 and P < 0.002, respectively) in the simulated "easy" and "difficult" over most of the intubation attempts. In the simulated "easy" airway, the first-pass success ratio was higher when using the King Vision VL (median [Minimum-Maximum] 100% [100%-100%] and 100% [88%-100%], P = 0.012). Conclusion. Novice medical students developed skills over intubation attempts, meaning achievement of a faster DLT intubation, better laryngeal exposure, and higher success rate on simulated "easy" and "difficult" airways. A median of 9 DLT intubations was required to achieve a 92% or greater DLT intubation success rate.

Long‐Term Prediction of Sudden Stratospheric Warmings With Geomagnetic and Solar Activity

AbstractThe polar vortex is a strong jet of westerly wind which forms each winter around the polar stratosphere. Sometimes, roughly every other winter, the polar vortex in the Northern Hemisphere experiences a dramatic breakdown and associated warming of the polar stratosphere. Such events are called sudden stratospheric warmings (SSWs) and they are known to have a significant influence on ground weather in Northern Eurasia and large parts of North America. Typically, these events are thought to occur due to planetary waves propagating to the stratosphere where they may disrupt the vortex. Here, we show that the SSW probability depends significantly on a favorable combination of geomagnetic and solar activity and the phase of the Quasi‐Biennial Oscillation (QBO). Using logistic regression models, we find that more SSWs occur when early‐winter geomagnetic activity (aa index) is low and QBO winds are easterly and when solar activity (F10.7 index) is high and QBO winds are westerly. We then examine the possibility of using these results to predict the occurrence probability of SSWs with several months lead time and evaluate the optimal lead times for all variables using cross‐validation methods. As a result, we find that the SSW probability can be predicted rather well and we can issue a probabilistic SSW prediction for the coming winter season with a success ratio of about 86% already in the preceding August. The results presented here are an important step toward improving the seasonal predictability of wintertime weather using information about solar and geomagnetic activity.

Allocating edge service resources to the up-offloaded vehicle tasks in ICV environment

Inspired by the rapid proliferation of intelligent transportation and smart city, ICV (Intelligent and Connected Vehicle) has drawn much attention, which not only brings great opportunities but also poses new challenges to the dynamic vehicle task serving mode. In this paper, we focus on the edge resource allocation process of the overloaded vehicle tasks that offloaded to edge servers in ICV environment. First of all, we introduce MEC and SDN technology into the traditional IoV (Internet of Vehicle) architecture to construct an SDN-assisted ICV network model. Next, a BiGRU+Attention edge service resource demands predicting model is designed, and based on the above model, we are able to further assess the future edge service resources availability and perceive the future vehicle mobility. Then, we build a task serving delay minimization problem for the edge service resource allocation process, where the impact of resource allocation decision-makings on the global network performance is also considered in the form of soft constraints. Furthermore, we put forward a timeslot-based edge service resource allocation algorithm with three phases. Finally, we simulate our scheme and another three schemes on NS3 platform, the results show that our algorithm outperforms in terms of average task serving delay, success ratio and load distribution.