Quick Evaluation Research Articles

A Conceptual Review on Heuristic Systematic Model in Mass Communication Studies

This research reviews the Heuristic-Systematic Model (HSM). It investigates how communication signals might influence people's opinions Shelly Chaiken (1987) developed this model, which is a dual-processing model with two information processing modalities, the first of which is: Heuristic processing, which includes features that are part of The message's source, template, length, and case name are all instances of "Heuristic Cues" that are given in the message to allow for a quick evaluation of the message's validity. The second kind is in-depth processing (Systematic processing), which searches for the substance of the message. To arrive at an accurate assessment of the message. In social psychology, binary information-processing models give empirical evidence for distinguishing the many ways individuals think about politics. Over the last two decades, binary information-processing models have grown in prominence in social psychology. Individual and collective decision-making, preconceptions and biases, personal perception and social categorization, individual and group decision-making, and even fundamental memory mechanisms are all explained by these theories. Keywords: Heuristic Systematic Model; Mass Communication; Public Opinion

Predicting Economic Activities Trends for Saudi and Non- Saudi Establishments in Saudi Arabia by Using Time Series Forecasting and Deep Learning Models

Data related to economic and stock market information are widely used for representing non- stationary time series. The economy goes through different phases, from recession to growth. This project focused on models predicting total added establishments in each economic activity. It aimed to predict the number of establishments after the impact of covid- 19 and estimate the accuracy of the prediction models. Prediction models used for inexpensive, quick evaluation of the added number of existing establishments leading to business risk mitigation. The research was conducted on data extracted from ministry of human resource and social development database. Therefore, the carried- out analysis highlights four activities under examination to capture the significance changes. These activities are construction, health, accommodation and information technology. In this study, five time series models are selected and applied to the business data describing expansion and recession. After model performance evaluation, deep learning models with respect to sliding window approach to predict short term values are recommended and perform better than traditional models. LSTM outperforms the other models in health with 18.22 RMSE and 90.65 RMSE for the information technology. DNN with two hidden layers got the best RMSE for accommodation and construction activities which is equal to 183.98 and 1387.78 respectively. Such work indicates that predicting overall added establishments may assist investors and companies in making economic choices, such as when to invest, increase, or reduce production.

Robust ptychographic X-ray speckle tracking with multilayer Laue lenses.

In recent years, X-ray speckle tracking techniques have emerged as viable tools for wavefront metrology and sample imaging applications, and have been actively developed for use at synchrotron light sources. Speckle techniques can recover an image free of aberrations and can be used to measure wavefronts with a high angular sensitivity. Since they are compatible with low-coherence sources they can be also used with laboratory X-ray sources. A new implementation of the ptychographic X-ray speckle tracking method, suitable for the metrology of highly divergent wavefields, such as those created by multilayer Laue lenses, is presented here. This new program incorporates machine learning techniques such as Huber and non-parametric regression and enables robust and quick wavefield measurements and data evaluation even for low brilliance X-ray beams, and the imaging of low-contrast samples. To realize this, a software suite was written in Python 3, with a C back-end capable of concurrent calculations for high performance. It is accessible as a Python module and is available as source code under Version 3 or later of the GNU General Public License.

Closed-Form Sum-Rate Analysis of Interference Alignment with Limited Feedback Based on Scalar Quantization and Random Vector Quantization

Interference alignment (IA) is a promising interference management technique to achieve the theoretical optimal degree of freedom (DoF) performance in multi-user cooperation scenarios. However, the effective achievable sum-rate performance of IA is largely affected by the feedback overhead and accuracy of channel state information (CSI) and decoding information (DI). Therefore, it is critical to establish the exact relationship between feedback overhead and the achievable sum-rate of IA to obtain the optimal effective performance. Most existing IA performance analysis approaches focus on the vector quantization (VQ)-based feedback strategy, but the implementation complexity of VQ will be excessive when more quantization bits are required to achieve the expected quantization accuracy for larger-sized matrices or higher signal-to-noise ratio (SNR) regimes. Moreover, the obtained achievable sum-rate formulas are too complicated for quick performance evaluation. In this paper, a new sum-rate performance analysis method for IA under different quantization and feedback strategies is proposed to achieve a trade-off between accuracy and complexity, and the closed-form achievable sum-rate expressions are derived. First, in the IA case with random vector quantization (RVQ)-based CSI feedback, the quantization error of RVQ is transformed into the equivalent VQ error of the Gaussian channel error, based on which the achievable sum-rate formula is obtained. Second, in the IA case with scalar quantization (SQ)-based CSI feedback, the relationship between the effective sum-rate and SQ bits is established. Third, in the IA case with SQ-based CSI feedback and RVQ-based DI feedback, the achievable sum-rate formula is derived by combining these two kinds of quantization errors. Finally, the simulation results confirm that the theoretical results are accurate enough, which can help to determine the optimal CSI feedback overhead in practical channel conditions. Moreover, the theoretical and simulation results demonstrate that RVQ may be more applicable to IA scenarios with fewer receiving antennas and low SNR regimes.

Comparing machine learning and interpolation methods for loop-level calculations

The need to approximate functions is ubiquitous in science, either due to empirical constraints or high computational cost of accessing the function. In high-energy physics, the precise computation of the scattering cross-section of a process requires the evaluation of computationally intensive integrals. A wide variety of methods in machine learning have been used to tackle this problem, but often the motivation of using one method over another is lacking. Comparing these methods is typically highly dependent on the problem at hand, so we specify to the case where we can evaluate the function a large number of times, after which quick and accurate evaluation can take place. We consider four interpolation and three machine learning techniques and compare their performance on three toy functions, the four-point scalar Passarino-Veltman D_0D0 function, and the two-loop self-energy master integral M. We find that in low dimensions (d = 3), traditional interpolation techniques like the Radial Basis Function perform very well, but in higher dimensions (d=5, 6, 9) we find that multi-layer perceptrons (a.k.a neural networks) do not suffer as much from the curse of dimensionality and provide the fastest and most accurate predictions.

Evaluation of surgical skill using machine learning with optimal wearable sensor locations.

Evaluation of surgical skills during minimally invasive surgeries is needed when recruiting new surgeons. Although surgeons’ differentiation by skill level is highly complex, performance in specific clinical tasks such as pegboard transfer and knot tying could be determined using wearable EMG and accelerometer sensors. A wireless wearable platform has made it feasible to collect movement and muscle activation signals for quick skill evaluation during surgical tasks. However, it is challenging since the placement of multiple wireless wearable sensors may interfere with their performance in the assessment. This study utilizes machine learning techniques to identify optimal muscles and features critical for accurate skill evaluation. This study enrolled a total of twenty-six surgeons of different skill levels: novice (n = 11), intermediaries (n = 12), and experts (n = 3). Twelve wireless wearable sensors consisting of surface EMGs and accelerometers were placed bilaterally on bicep brachii, tricep brachii, anterior deltoid, flexor carpi ulnaris (FCU), extensor carpi ulnaris (ECU), and thenar eminence (TE) muscles to assess muscle activations and movement variability profiles. We found features related to movement complexity such as approximate entropy, sample entropy, and multiscale entropy played a critical role in skill level identification. We found that skill level was classified with highest accuracy by i) ECU for Random Forest Classifier (RFC), ii) deltoid for Support Vector Machines (SVM) and iii) biceps for Naïve Bayes Classifier with classification accuracies 61%, 57% and 47%. We found RFC classifier performed best with highest classification accuracy when muscles are combined i) ECU and deltoid (58%), ii) ECU and biceps (53%), and iii) ECU, biceps and deltoid (52%). Our findings suggest that quick surgical skill evaluation is possible using wearables sensors, and features from ECU, deltoid, and biceps muscles contribute an important role in surgical skill evaluation.

23‐4: Quick Detection and Evaluation of Irregular Response‐Time Behavior for High Frame Rate Displays Through Noticeable Artifacts

Efficient detection of response time issues for high frame rate displays including 120Hz TVs and 360Hz gaming monitors is proposed. Unwanted features of response time are directly exposed and converted into noticeable artifacts by the proposed pattern sequences, enabling quick and easy evaluation or benchmarking for motion performance.

Web app for quick evaluation of subjective answers using natural language processing

In current digital climate, education sector is evolving as the computer technology advances. Education is being digitized: online classes, online examination methods are conducted, etc. During examination, students are assessed by their answers having given for the question set by a teacher. Today many tools are available to assess the performance of a student using multi choice questions tools which provide instant evaluation, but there are available very limited and operational tools where subjective type answer of students are evaluated. This paper presents a web-based application to address this challenge. It automates the process of subjective answers checking and generates results through using natural language processing methods, like keyword matching semantic, lexical analysis and cosine similarity. Experiments show that appreciated by the teacher result and the system estimation does not have much difference which signifies that the system evaluates answers with a 97 % accuracy. The presented system not only reduces manpower but also eliminates the traditional method of conducting exclusively subjective exams using paper documents. It also eliminates the delays in the paper checking, result generation process. The cases of information leak are being reduced and the objectivity of the assessment is being increased.

SIMPLIFIED FORMULAS FOR SOME BESSEL FUNCTIONS AND THEIR APPLICATIONS IN EXTENDED SURFACE HEAT TRANSFER

Bessel functions find many applications in Physics and Engineering fields. Some of these applications are in the analysis of extended surface heat transfer where the cross-sections vary. Tables of various kinds of Bessel functions are available in most handbooks of mathematics. However, the use of tables is not always convenient, particularly for applications where many values must be computed. In the applications of Bessel functions in extended surface heat transfer, graphs are also available to provide quick evaluations of the values needed. However, reading these graphs always needs interpolation; this will be cumbersome and time-consuming if there are many readings to be taken. Mathematical formulas for Bessel functions are available but they are usually complicated. Software to calculate values of Bessel functions is also available. Excel, Maple, and Mathematica can also be used to compute the values of Bessel functions. A user can write a program for an application that involves Bessel functions. However, the use of Bessel functions in Excel is limited while Maple and Mathematica are expensive commercial software. In this paper, formulas for Bessel functions of and are simplified with adequate accuracy that can be used to easily compute values needed in the extended surface heat transfer analysis. It is found that errors for and are relatively small (maximum errors are 0.004% and 0.003%, respectively) in the range of 0.05 to 3.75 while the maximum error for is 3.678% for the same range. However, the maximum error for is reduced to 0.166 if the range is from 0.25 to 3.75.

Work Zone Crash Occurrence Prediction Based on Planning Stage Work Zone Configurations Using an Artificial Neural Network

Work zones are essential to maintain and improve road infrastructure. However, work zones affect traffic safety, and crashes and fatalities associated with work zones in the U.S.A. have increased substantially. Most existing work zone crash studies are not able to support the improvement of work zone planning and configuration, despite providing insights about individual crash level attributes. This study proposes an artificial neural network-based approach to predict the crash occurrence in work zones using only work zone configurations and design parameters. The goal is to explore whether using simple work zone configuration features available at the planning stage as the input can achieve satisfactory work zone crash prediction. The performance of the proposed model is satisfactory and comparable with existing studies using more comprehensive features. The proposed approach, early in the work zone design and planning stage, can provide designers and decision-makers with quick work zone safety evaluation for design alternatives and suggest extra resources and attention needed.

An extensive appraisal of weight-sharing on the NAS-Bench-101 benchmark

Weight-sharing (WS) has recently emerged as a paradigm to accelerate the automated search for efficient neural architectures, a process dubbed Neural Architecture Search (NAS). By using and training the same set of weights for the whole search space, WS allows for the quick evaluation of millions of architectures, where classical NAS approaches require lengthy individual trainings. Although very appealing, WS is not without drawbacks and several works have started to question its capabilities on small hand-crafted benchmarks. In this paper, we take advantage of the NAS-Bench-101 dataset to challenge the efficiency of a uniform-sampling based WS variant on several representative search spaces. After reviewing previous studies on WS and highlighting several of their shortcomings, we introduce our own experimental setup, from which we extract several good practices that one should keep in mind when evaluating WS. With our experiments we first establish that, given the correct evaluation procedure, WS is able to produce accuracy scores decently correlated with standalone ones. We then provide evidence that on some search spaces, this WS variant is able to rapidly find better than random architectures, whilst it is equivalent or sometimes even worse than a baseline random search on others, as we find that given the same budget, the probability of superiority of an architecture found using WS over an architecture found through random search can vary between 7% and 78% depending on the search space. We present evidence that the search space itself has an intricate effect on the capabilities of WS and can bias weight-sharing towards certain architectural patterns with no clear accuracy advantage. We conclude that the impact of WS is heavily search-space dependent and difficult to anticipate for a given problem.

A new correlation for calculating wellhead oil flow rate using artificial neural network

A separator and multiphase flow meters are considered the most accurate tools used to measure the surface oil flow rates. However, these tools are expensive and time consuming. Thus, this study aims to develop a correlation for accurate and quick evaluation of well surface flow rates and consequently the well inflow performance relationship. In order to achieve the abovementioned aim, this study uses artificial neural network (ANN) for flow rates prediction particularly in artificial lifted wells especially in the absence of wellhead pressure data. The ANN model is developed and validated by utilizing 350 data points collected from numerous fields located in Nile Delta and Western Desert of Egypt with inputs include; wellhead temperature, gas liquid ratio, water cut, surface and bottomhole temperatures, water cut, surface production rates, tubing cross section area, and well depth. The results of this study show that, the collected data are distributed as follows; 60% for training, 30% for testing and 10% for the validation processes with R2 of 0.96 and mean square error (MSE) of 0.02. A comparison study is implemented between the new ANN correlation and other published correlations (Gilbert, Ros and Achong correlations) to show the robustness of the developed correlation. The results show that the developed correlation able to predict oil flow rates accurately with the lowest mean square error.

A Machine Learning-Based Approach for Quick Evaluation of Live Simulations in Embodiment Design

AbstractSupporting product developers in early design phases with Live-Simulation can enhance the quality of early product designs. Live-Simulation can also facilitate a democratization of simulation and puts away pressure from simulation experts. In this paper, a machine learning based quick evaluation tool is proposed to support product developers in interpreting Live-Simulation results. The proposed tool enables a quick evaluation of the Live-Simulation results and enables product developers to further enhance their simulations. The tool is shown within a use case in bike rocker switch design.

Gridding Optimization for Hydraulic Fractured Well in Reservoir Simulation Using Well Test Analysis for Long Term Prediction

Analytic models, complex simulations, and simple models are being used to predict the production performance of hydraulically fractured shale. Analytical models such as decline curve analysis and rate transient analysis are used for a quick evaluation of reservoir performance. However, they have considerable limitations. For instance, decline curve analysis cannot honor the physical phenomena in shale wells that are related to hydraulic fracture, reservoir characteristics, and fluid flow. On the other hand, even though explicit hydraulic fracture modeling is the most comprehensive approach when compared with other traditional techniques, it cannot guarantee to model enough hydraulic fracture effects. Hence, calibration of the model, which commonly is referred to in the oil and gas industry as history matching, becomes a must. However, history matching of an explicit hydraulic fracture model with limited information is time-consuming and cumbersome. Especially history matching of a full field shale gas/oil model with many wells is a daunting task. In this study, we propose a workflow to integrate numerical reservoir simulation and well test analysis. In the workflow, information such as fracture half-length and enhanced effective permeability are obtained from pressure transient analysis and are used to calibrate grid properties in the vicinity of the plane covered by the fracture length and width. Finally, the simulation model is calibrated using pressure and flow rate data, and it is used for the long-term performance prediction of a hydraulic fractured well. The workflow was evaluated by using a synthetic reservoir model whose permeability mimicked that of a shale formation. As a result, the workflow thus enabled the use of coarse grid blocks, which, in turn, reduced the simulation time to just 1.5% of the simulation runtime consumed by a reference fine grid model.

Improving Quality Control Methods to Test Strengthening Technologies: A Multilevel Model of Acoustic Pulse Flow

This article describes an approach that makes it possible to substantiate quality control criteria and methods to improve strengthening technologies. The approach was used to test the quality of products made using these technologies and analyze different strengthening methods applied to structural materials. In the experiment, samples of welded joints subjected to various types of strengthening were used that underwent acoustic emission (AE) testing. The results of quick evaluations produced by the proposed multilevel model of acoustic pulse flow were compared with the results of long-term cyclic tests to make a conclusion about the effectiveness of the approach being discussed. To improve strengthening quality control, a method is proposed that can be applied to complex and large-sized structures in the construction industry.

THE RELATION BETWEEN SERVICE QUALITY AND SATISFACTION OF CLASS 1 INPATIENTS AT GRANDMED LUBUK PAKAM HOSPITAL

Service quality is a measure of how well the level of service provided is able to meet customer (patient) expectations. Service quality consists of real, reliability, responsiveness, assurance, empathy. Patient satisfaction is a measure of the success of health service performance, this can be seen from the suitability of expectations and reality received by patients in service. This study aims to determine the relation between service quality and satisfaction of class 1 inpatients at Grandmed Lubuk Pakam Hospital. This study uses an analytical survey with a cross sectional approach. The sample in this study was inpatient class 1 at the Grandmed Hospital Lubuk Pakam as many as 32 respondents with the accidental sampling method using the chi square test with a 95% confidence level = (0.05). The results showed that there was a significant relationship between service quality and satisfaction of class 1 inpatients where tangible p (0.038) <α (0.05), reliability p (0.003) <α (0.05), responsiveness p (0.026) <α (0.05), assurance p (0.002) <α (0.05) and empathy p (0.005) <α (0.05). It is expected for health workers to improve the quality of services provided, especially in class I inpatient rooms, clean the room, apply smiles, greetings, speed, can provide excellent service and punctuality in the examination schedule for patients so as to create a relationship between patients and nurses. For hospital management, it is necessary to develop an information management system to facilitate service and quick evaluation of hospital needs.

Stress prediction of bridges using ANSYS soft and general regression neural network

Bridge managers are often eager to identify the carrying capacities and service performance characteristics of bridge structures. In view of the massive number of bridges managed and maintained in China, providing a quick evaluation of bridge performance bridges has become an urgent challenge for bridge engineering. Aiming at many adverse problems of bridge load tests, such as their high costs, long time consumption, low efficiency, impact on traffic, and damage to bridge structures, a new method for bridge static behavior prediction is proposed herein, based on dynamic load test data. The method adopts a generalized regression neural network algorithm with fast convergence and a nonlinear approximation, determines the sensitive parameters based on a full-bridge sensitivity analysis, forms training samples using a uniform design sampling method, and establishes intelligent agent model. The initial finite element analysis model of the bridge structure is updated using the proposed proxy model and dynamic load test data, and the static behavior of the existing bridge structure is accurately predicted based on the updated finite element model. To verify the correctness of the proposed method, a practical bridge is considered as the research object, and the static response of the bridge structure is predicted. The predicted results are consistent with the static load test results for the bridge, verifying the correctness of the proposed method. The results show that the maximum error between the theoretical calculation result and measured value is only 4.34% and the minimum error is only 3.52%. The strain prediction results are consistent with the measured results, and the modified theoretical analysis model provides high accuracy for strain prediction results under different working conditions. The application in practical engineering shows that the prediction results under different load conditions are adaptable and highly accurate.

Considerations for the Feasibility of Neutralizing Antibodies as a Surrogate Endpoint for COVID-19 Vaccines.

To effectively control and prevent the pandemic of coronavirus disease 2019 (COVID-19), suitable vaccines have been researched and developed rapidly. Currently, 31 COVID-19 vaccines have been approved for emergency use or authorized for conditional marketing, with more than 9.3 billion doses of vaccines being administered globally. However, the continuous emergence of variants with high transmissibility and an ability to escape the immune responses elicited by vaccines poses severe challenges to the effectiveness of approved vaccines. Hundreds of new COVID-19 vaccines based on different technology platforms are in need of a quick evaluation for their efficiencies. Selection and enrollment of a suitable sample of population for conducting these clinical trials is often challenging because the pandemic so widespread and also due to large scale vaccination. To overcome these hurdles, methods of evaluation of vaccine efficiency based on establishment of surrogate endpoints could expedite the further research and development of vaccines. In this review, we have summarized the studies on neutralizing antibody responses and effectiveness of the various COVID-19 vaccines. Using this data we have analyzed the feasibility of establishing surrogate endpoints for evaluating the efficacy of vaccines based on neutralizing antibody titers. The considerations discussed here open up new avenues for devising novel approaches and strategies for the research and develop as well as application of COVID-19 vaccines.

P001 Audit on giant cell arteritis fast track clinic in Tayside

Abstract Background/Aims Giant cell arteritis (GCA) is a systemic inflammatory disease of medium to large sized arteries. Diagnosis of GCA is a medical emergency due to the consequence of permanent blindness if not treated early with glucocorticoids. The objective of this audit was to assess the effectiveness of the GCA fast track clinic (FTC) in NHS Tayside. The current practice was compared with British Society of Rheumatology (BSR) standards. Methods This was a retrospective audit from March 2019 to May 2021. All patients referred with suspicion of GCA were included. Main outcomes measured were referral time, investigations and management. Data were collected through electronic records. Results 120 patients were included (median age 71 years; F71.7%). 35% of patients were diagnosed with GCA. Most common symptoms were headache (94.2%), jaw claudication (26.7%), visual symptoms (36.7%), PMR symptoms (21.7%), systemic symptoms (50%). As seen in Table 1, 50% with visual symptoms were seen by ophthalmology within 24 hours. 70.8% were seen by rheumatology within 24 hours. 89% were given steroids within 24 hours from suspicion. 80.8% were investigated with ultrasound or biopsy. Comparing with expected standards, provision of written information, advice on recurrence of symptoms, calculation of CRP, documentation of bone protection was achieved at 100%. Immediate follow-up time was 6 weeks. One patient lost vision while on steroids. Conclusion Early recognition and prompt management of GCA is crucial for a good prognosis. NHS Tayside’s FTC has allowed for quick evaluation for most patients within 24 hours and has improved prognosis in patients. Disclosure D. Subramanian: None. S. Bhat: None.

A quick evaluation of ecological restoration based on arthropod communities and trophic guilds in an urban ecological preserve in Mexico City

BackgroundRestoration practices usually emphasize on the structural part of the biodiversity; also, most studies have focused on plants and very few have been conducted on arthropods and its function after restoration. The Pedregal de San Angel Ecological Reserve (PSAER) is a protected area immersed in Mexico City and it has been drastically affected by different anthropogenic disturbances. The aim of this study was to compare the relative diversity, richness, and abundance of species level identification, but also the composition through an analysis of ordination of taxonomic (species, family, and order level) and functional (trophic guild) traits of arthropods in three sites subjected to ecological restoration within the PSAER. Restored sites were also compared to conserved and disturbed sites, to evaluate whether restoration efforts are effective at the reserve.MethodsArthropods were sampled using pan traps during September 2013 in 11 sites (three restored, four conserved and four disturbed) inside the PSAER. All sampled species were taxonomically identified at species of morphospecies (inside a family) and assigned to a trophic guild. Differences in diversity, richness and abundance were evaluated through effective number of species, comparisons of Chao’s1 estimated richness and a non-parametric Kruskal–Wallis test, respectively. Both taxonomic and trophic guild composition were evaluated using a multivariate analysis and a post hoc test.ResultsWe found some differences in richness, abundance, and diversity between sites, but not a clear pattern of differentiation between restored to disturbed sites. The NMDS showed differences at species and order level, and with trophic guilds, among site types. Families were not useful to differentiate types of sites. Regarding guilds, predators were more abundant in conserved sites, while phytophagous insects were more abundant in disturbed sites.ConclusionsSpecies and order level were useful to identify differences in communities of arthropods in sites with different management. The trophic guild approach provides information about the functional state of the restored sites. Nevertheless, our quick evaluation shows that restoration efforts at PSAER have not been successful in differentiate restored to disturbed sites yet.