Decrease Time Consumption Research Articles

OSPE as an Assessment Tool for Medical Students

Background: Recently, in the year 2019, the National Medical Commission (NMC) made it mandatory to implement objectively structured practical examination (OSPE) for pre- and para-clinical subjects. The OSPE is an assessment tool used during practical examinations for undergraduate medical students studying in phase I and phase II. Objectives: To compare the performance of the students by using OPSE and conventional practical examination methods using the scores obtained and to explore the student’s perceptions and faculty perceptions of objectively structured practical examination as an assessment tool to be used during regular practical examinations. Materials and Methods: The present study included phase I MBBS students in batch 2023-24 ofthe National Institute of Medical Sciences and Research, Jaipur. A total of 212 phase I MBBS students participated in the study after providing informed consent. Students were assessed using both conventional practical examination and objectively structured practical examination. Conventional practical examination was performed for abnormal urine analysis. In OSPE, we had kept five stations, two of which were observer type and three of which were responsive type time allotted was three minutes at each station. At the observer station psychomotor skills were assessed and in responsive station the cognitive skills were assessed. Each student had to attend 4 stations one observer station and 3 responsive stations. Both assessments were performed using a standardized checklist and the scores were subsequently marked. The scores were recorded out of 20 points. The mean scores obtained by the students were compared using Student’s t test and a p value <0.05 was considered to indicate statistical significance. The student’s perceptions and faculty perceptions about both conventional practical examinations and OSPE were assessed using a validated questionnaire in the Google Form link sent to the students in the Watts app group. Results: The frequency of distribution of students as per the scores obtained out of twenty marks revealed that out of 212 students in CPE 67 (31.6%) students had scores less than 50% and 145 (68.39%) students had scores greater than 50%. In OSPE method 29 (13.67%) Students had scored less than 50% and 183 (86.3%) students had scores greater than 50%. The mean scores for CPE were10.655 ± 3.477 and those for OSPE were 12.091 ± 2.55, the mean scores obtained by students were significantly greater for OSPE than for CPE (p<0.001). The vast majority of the students enjoyed OSPE because of the lack of teacher bias, nonpartiality, uniformity, decreased time consumption and student-centred and student friendly nature. The majority of the faculty felt that OSPE was not time consuming, eliminated observer bias, and should be implemented on a regular basis during all types of assessments and that they should better assess practical skills; moreover, the majority of the faculty agreed that OPSE is a good learning method for students. Conclusion: The present study showed that, compared with those on the CPE, the majority of the students on the OPSE had a greater than 50%, and there were significant differences in the mean scores obtained on the OSPE compared with those obtained on the conventional practical examination. The OSPE is a better tool for assessing students during practical examinations; it should be used regularly in all types of assessments. and it can also be used for learning purposes. Student and faculty perceptions of OSPE as a better assessment tool and learning tool were highly positive.

Enhancement of the CAST Block Algorithm Based on Novel S-Box for Image Encryption.

due to the rapid growth of data communication and multimedia system applications, security becomes a critical issue in the communication and storage of images. This study aims to improve encryption and decryption for various types of images by decreasing time consumption and strengthening security. An algorithm is proposed for encrypting images based on the Carlisle Adams and Stafford Tavares CAST block cipher algorithm with 3D and 2D logistic maps. A chaotic function that increases the randomness in the encrypted data and images, thereby breaking the relation sequence through the encryption procedure, is introduced. The time is decreased by using three secure and private S-Boxes rather than using six S-Boxes, as in the traditional method. Moreover, the CAST encryption algorithm was modified to be used on the private keys and substitution stage (S-Boxes), with the keys and S-Boxes of the encryption algorithm being generated according to the 2D and 3D chaotic map functions. The proposed system passed all evaluation criteria, including (MSE, PSNR, EQ, MD, SC, NC, AD, SNR, SIM, MAE, Time, CC, Entropy, and histograms). Moreover, the results also illustrate that the created S-Boxes passed all evaluation criteria; compared with the results of the traditional method that was used in creating S-Box, the proposed method achieved better results than other methods used in the other works. The proposed solution improves the entropy which is between (7.991-7.999), reduces the processing time which is between (0.5-11 s/Images), and improves NCPR, which is between (0.991-1). The proposed solution focuses on reducing the total processing time for encryption and decryption and improving transmission security. Finally, this solution provides a fast security system for surgical telepresence with secure real-time communication. The complexity of this work needs to know the S-Box creation method used, the chaotic method, the values of the chaotic parameters, and which of these methods was used in the encryption process.

Manipulating grading strategy for the efficient harvesting of industrial poplar plantations

ABSTRACT New short rotation poplar plantations yield a mix of biomass and logs, whereby the latter are used for board production and represent the most valuable component. Therefore, there is an interest in maximizing log yield, which must be balanced against the incremental time consumption incurred for manufacturing any additional logs. This study explored the optimization potential of manipulating log specifications and grading instructions in order to decrease time consumption or increase log yield. A controlled experiment was set up to determine the merits of 1) renouncing the manufacturing of a second small log to expedite processing or 2) decreasing small end diameter to increase the proportion of viable logs in any given stem. The experiment indicated that strategy one did not pay, since the productivity increase was minimal and non-significant, while the log yield losses were meaningful and significant. In contrast, strategy two was efficient in increasing log yield, without any negative effects on harvesting productivity.

A comprehensive review and evaluation on text predictive and entertainment systems

One of the most important ways to experience communication and interact with the systems is by handling the prediction of the most likely words to happen after typing letters or words. It is helpful for people with disabilities due to disabling people who could type or enter texts at a limited slow speed. Also, it is beneficial for people with dyslexia and those people who are not well with spells of words. Though, an input technology, for instance, the next word suggestion facilitates the typing process in smartphones as an example. This means that when a user types a word, then the system suggests the next words to be chosen in which the necessary word by the user. Besides, it can be used in entertainment as a gam, for example, to determine a target word and reach it or tackle it within 10 attempts of prediction. Generally, the systems depend on a text corpus, which was provided in the system to conduct the prediction. Writing every single word is time-consuming, therefore, it is vitally important to decrease time consumption by reducing efforts to input texts in the systems by offering most probable words for the user to select, this could be done via next word prediction systems. There are several techniques can be found in literature, which is utilized to conduct a variety of next word prediction systems by using different approaches. In this paper, a survey of miscellaneous techniques towards the next word prediction systems will be addressed. Besides, the evaluation of the prediction systems will be discussed. Then, a modal technique will be determined to be utilized for the next word prediction system from the perspective of easiness of implementation and obtaining a good result.

Using a fuzzy expert system as a decision support system to decrease time consumption in the UAST development process: A case study

Always the decision of the government to the private sector is faced with the challenges and high level of importance, because these decisions should be taken at the meeting of standards and functional requirements to overcome investors' interests. These decisions include education, and due to the governance nature of education and the governments' strategies in this area, the private sectors who act in it, have been affected by the consequences of different decisions. \In this paper, our motivation is to propose a system to decide on how to develop applied academic educations at the Applied ScienceEducation Centers (ASEC's) which are supervised by the University of Applied Science and Technology (UAST) in Iran. The method used is theFuzzy Inference System (FIS) to reach this goal. The model performed in this study consists of a two-part FIS for two purposes straight ahead.The first concerns the quantitative development of higher applied education, including the number of Course Request (CR) for each ASEC with 4 inputs and 242 rules. The latter, is related to the qualitative development of higher applied education, including the assessment of the capability and competence of each ASEC for specific CR with the course and student admission background, specialty, committee's and council’s opinion, and frequency of course in city or province with 9 inputs, 350 rules, and an output. Each section has its inputs and rules that are determined and used by experts in this domain. \Our results show that using this method, provides some cost and time savings. This model analyzes in less than 2 minutes, while in practice it could be gain within 1650 person /hours work in different committees that all of them have their costs. Also, results are very close to reality with the advantage that there is no way to apply personal preferences, which largely meets the needs of research.

Subspace-based feature extraction on multi-physiological measurements of automobile drivers for distress recognition

The automotive industry has accelerated the utilization of Intelligent Transport Systems (ITS) in vehicles for increased driving safety. In this paper, a novel and well-done subspace feature extraction scheme on the physiological signals acquired by wearable sensors, for drivers’ distress level detection to be introduced as an ITS is proposed and verified on the publicly available MIT-BIH PhysioNet Multi-parameter Database. The proposed scheme includes two phases where time-domain statistical feature extraction is first realized on the electrocardiogram (ECG), hand galvanic skin response (hand GSR), foot galvanic skin response (foot GSR), electromyogram (EMG), and respiration (RESP) signals, and secondly subspace feature vector construction is appreciated by applying Discriminative Common Vector (DCV) decomposition on the statistical feature vectors. The distress levels of the drivers are determined as low, moderate, and high by utilizing both the statistical and the subspace feature vectors using Support Vector Machines (SVM) classifier by 2-fold cross-validation technique. A maximum of 88.89 % classification accuracy is achieved using statistical features in 7384 s while it is increased to 100 % in 3,421 s when subspace features are employed. The increased classification accuracy in decreased time consumption evidently shows the success of the proposed feature extraction scheme.

Low-Complexity Multistep Model Predictive Current Control for Linear Induction Machines

In this article, the predictive horizon of model predictive current control (MPCC) is extended for achieving better operational performance. However, the computational burden of MPCC exponentially increases with its predictive steps, and thus, the predictive step is usually chosen as one. In order to explore the potential advantage of multistep MPCC (M-MPCC), one simplified search method is proposed so that the M-MPCC can be executed in practice with greatly reduced complexity. In the proposed search method, a large number of possible voltage vector sequences can be excluded in advance with the guide of the defined reference voltage vector sequence. Then, one simplified M-MPCC is applied to two 3 kW arc induction machines with a large radius, which can make a full demonstration on the drive performance of linear induction machine in practice. The comprehensive simulation and experiments have clearly indicated the relationship between the predictive step and the performance of the proposed M-MPCC. It has persuasively verified the feasibility of low complexity and decreasing time consumption based on the proposed search method in this work.

A New Nodes-Based Model for Optimization of Heat Exchanger Network Synthesis

Searching the global optimal value in heat exchanger network synthesis (HENS) becomes more difficult along with the increasing scale of cases. In structural optimization model, such as stage-wise superstructure, solution domain can be enlarged by adding the stages of network, which would involve multiple integer and continuous variables in optimization and result in a decreasing computational efficiency. Thus, a new nodes-based model with stream splits is proposed, which can promote the quality of result and decrease time consumption at the same time. The characteristic of this model is that it changes the matching mode of heat exchangers by setting the number of nodes on streams to quantify the positions of heat exchangers. The proposed nodes-based model has more flexibility in generating and eliminating the heat exchangers and strong ductility in exploring the solution domain. The random walk with compulsive evolution is modified for enhancing its searching ability and applying modified random walk algorithm with compulsive evolution (RWCE) into the new proposed model. The obtained results of Case 1, Case 2 and Case 3 are superior to those reported in the literature, which decreases 106 USD·a−1, 1253 USD·a−1, 23 444 USD·a−1, respectively, verifying the robust effectiveness of this new model in solving HENS problem.

Post-processing technique of two-grid algorithm for wave propagation with Debye polarization in nonlinear dielectric materials

In this paper, we develop the superconvergence analysis of the implicit second-order two-grid discrete scheme with the lowest Nédélec element for wave propagation with Debye Polarization in nonlinear Dielectric materials. Our main contribution will have two parts. On one hand, in order to overcome the difficulty of misconvergence of classical two-grid algorithm by the lowest Nédélec elements, we employ the Newton-type Taylor expansion at the superconvergent solutions for the nonlinear terms on coarse mesh, which is different from the classical numerical solution on the coarse mesh. On the other hand, we push the two-grid solution to high accuracy by the interpolation post-processing technique. Such a design can both improve the computational accuracy in spatial and decrease time consumption simultaneously. Based on this design, we can obtain the convergent rate O(τ2+h2+H3), and the spatial convergence can be obtained by choosing the mesh size h=O(H32). At last, one numerical experiment is illustrated to verify our theoretical results.

Superconvergence analysis of two-grid FEM for Maxwell’s equations with a thermal effect

Based on two-grid algorithm, we develop the superconvergence analysis of fully-discrete scheme with the lowest-order Nédélec element and Crank–Nicolson scheme for the magneto-heat coupling model, which is also considered as Maxwell’s equations with a thermal effect. Then, main process of numerical analysis has two parts: On one hand, we utilize Newton-type Taylor expansion on superconvergent solutions for those nonlinear terms on coarse mesh, differing from the numerical solution, which makes our given two-grid method successful. On the other hand, we prove the solutions on the fine mesh to be higher accuracy by the postprocessing interpolation technique. Such a design is conducive to improving the computational accuracy in space and decreasing time consumption simultaneously. By employing the skill, we can obtain the convergent rate of O(Δt2+h2+H3), which means that the space mesh size should satisfy h=O(H32). Finally, the numerical example is presented to verify the theoretical results.

One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study.

The segmentation of buildings in remote-sensing (RS) images plays an important role in monitoring landscape changes. Quantification of these changes can be used to balance economic and environmental benefits and most importantly, to support the sustainable urban development. Deep learning has been upgrading the techniques for RS image analysis. However, it requires a large-scale data set for hyper-parameter optimization. To address this issue, the concept of “one view per city” is proposed and it explores the use of one RS image for parameter settings with the purpose of handling the rest images of the same city by the trained model. The proposal of this concept comes from the observation that buildings of a same city in single-source RS images demonstrate similar intensity distributions. To verify the feasibility, a proof-of-concept study is conducted and five fully convolutional networks are evaluated on five cities in the Inria Aerial Image Labeling database. Experimental results suggest that the concept can be explored to decrease the number of images for model training and it enables us to achieve competitive performance in buildings segmentation with decreased time consumption. Based on model optimization and universal image representation, it is full of potential to improve the segmentation performance, to enhance the generalization capacity, and to extend the application of the concept in RS image analysis.

Efficient phase unwrapping algorithm based on cubature information particle filter applied to unwrap noisy continuous phase maps.

This paper presents a new phase unwrapping algorithm for wrapped phase fringes through combining a cubature information particle filter with an efficient local phase gradient estimator and an efficient quality-guided strategy based on heap-sort. The cubature information particle filter that not only is independent from noise statistics but also is not constrained by the nonlinearity of the model constructed is applied to retrieve unambiguous phase from modulus 2π wrapped fringe patterns through constructing a recursive cubature information particle filtering phase unwrapping procedure to perform simultaneously phase unwrapping and noise filtering for the first time to our knowledge, which can be expected to obtain more robust solutions from wrapped phase fringes. Phase gradient estimate is one of the key steps in almost all phase unwrapping algorithms and is directly related to the precision and the efficiency of phase unwrapping procedure. Accordingly, an efficient local phase gradient estimator that is more efficient than ones published previously is deduced to obtain phase gradient information required by the proposed algorithm, which can drastically decrease time consumption of unwrapping procedure and drastically improve the efficiency of the algorithm. The efficient quality-guided strategy based on heap-sort guarantees that the proposed algorithm efficiently unwraps wrapped pixels along the path from the high-reliance regions to the low-reliance regions of wrapped phase images. In addition, the accelerated version of the proposed algorithm is further developed through combing with reversible modulo wavelet operators to solve phase unwrapping problem of wrapped phase images in wavelet transform domain, which can reduce the amount of wrapped pixels that need to be unwrapped, and can further decrease time consumption of unwrapping procedure performing on wrapped phase images. This algorithm and its accelerated version under the frame of wavelet transform are demonstrated with various types of wrapped phase images, showing acceptable solutions.

Moving target ranging method for a photon-counting system.

The photon-counting technique is finding increasing applications in laser ranging systems owing to its high detection sensitivity and time resolution; however, it is still challenging to extract moving tracks accurately from an intensive noise background for non-cooperative targets using the photon-counting technique. To resolve this issue, a ranging method based on the Hough transform is proposed according to the distribution characteristics of a moving target in a point cloud figure. The proposed method divides the ranging process into the initial stage and the tracking stage. A large amount of point cloud data is used to acquire an accurate measurement of the initial distance and velocity of the target in the initial stage, whereas a smaller amount of point cloud data is used to rapidly update the distance and velocity for real-time processing in the tracking stage. The experimental results demonstrated that compared with the local distance statistics method and the density-based filtering method, the proposed method could extract target tracks effectively with decreased time consumption and lower ranging errors in different detection conditions.

객체 및 속성정보 제어를 이용한 BIM기반 파라메트릭 인테리어 패널 디자인 접근방법

This paper aims to describe a case study of parametric interior design based on BIM (Building Information Modeling). As the practical use of BIM-based design grows, its influence expands into the field of interior architecture design. BIM makes possible to check various design plan, reach decision making in an effective way, and change design plan in an efficient method. Therefore, BIM is also promising field in interior architecture design. However, compared to other fields in architecture, engineering, and construction (AEC) industry, there have been less research and projects on BIM in the field of interior architecture. For increasing the feasibility of adapting BIM in interior architecture design, this paper describes a case study-projecting images for designing interior panels using parametric modeling. This process needs elaborate, delicate, and precise steps for harmonious output. For continuous use of building, users look forward to the design which variable and changeable according to user's preference and environment. Therefore, demand for parametric design in the interior design part such as panel pattern design for various decoration is growing. Treated process in this paper deals with an advanced phase which much effective in decreasing time consumption and useless part of process. Finally, this paper suggests the possibility of using BIM in the interior design process and field where BIM can be applied.

Objective Structured Clinical Examination as an assessment method for undergraduate medical students

Background: One of the most important objectives for University of Dammam is to send competent graduated physicians into the society, which cannot be accomplished without assessments of the clinical skills of the students. The Objective Structured Clinical Examination (OSCE) was recently implemented to improve the quality, reliability, validity, university standards, and international rankings, and to decrease time consumption by the exams. Objective: To study OSCE as an assessment method for undergraduate medical students. Materials and Methods: A pilot study was conducted over one semester. A total of 92 examinees took the OSCE and written exams in three groups. The OSCE comprised 20 clinical stations, which included noting histories, physical examinations, communication skills, and data interpretation. The written exam contained 80 multiple-choice questions. Results: Cronbach a’s by group were 0.62, 0.79, and 0.85. Correlations for all stations ranged from 0.6 to 0.8, which indicated good stability and internal consistency. The reliability of the written exam was found to be 0.85. The validity of the OSCE was assessed using Pearson correlations, which was found to be 0.6. Conclusion: The OSCE is the gold standard for student assessment and is more reliable and valid than the traditional style of exam.

New method for automatic body length measurement of the collembolan, Folsomia candida Willem 1902 (Insecta: Collembola).

The collembolan, Folsomia candida, is widely used in soil ecotoxicology. In recent years, growth rate of collembolans has become as frequently used endpoint as reproduction rate in ecotoxicological studies. However, measuring collembolan body sizes to estimate growth rate is a complicated and time-consuming task. Here we present a new image analysis method, which facilitates and accelerates the body length measurement of the collembolan Folsomia candida. The new software package, called CollScope, consists of three elements: 1) an imaging device; 2) photographing software; 3) an ImageJ macro for image processing, measurement and data analysis. We give a complete description of the operation of the software, the image analyzing process and describe its accuracy and reliability. The software with a detailed usage manual is attached as Supplementary Material. We report a case study to demonstrate that the automated measurement of collembolan body sizes is highly correlated with the traditional manual measurements (estimated measuring accuracy 0.05 mm). Furthermore, we performed a dose-response ecotoxicity test using cadmium-sulfate by using CollScope as well as classical methods for size measurement. Size data measured by CollScope or manually did not differ significantly. Furthermore the new software package decreased time consumption of the measurements to 42% when tested on 35 animals. Consequently, methodological investigations performed in this study should be regarded as a recommendation for any other routine dose-response study where body growth is an endpoint.

Intelligent Image Watermarking Using New Anti Arnold Transform

In this paper the new anti-Arnold transform pre-processing is used for decreasing time consumption in extracting the watermarks instead of using conventional Arnold transform and by exploiting watermarking algorithm which is used for having an optimized embedding. In this case the weighted mean in blending two images is used for being optimized by using genetic algorithm.

Sequential similarity detection algorithm based on image edge feature

This paper proposes a new sequential similarity detection algorithm (SSDA), which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorithms based on image feature. The algorithm adopts Sobel operator to deal with subgraph and template image, and regards the region which has maximum relevance as final result. In order to solve time-consuming problem existing in original algorithm, a coarse-to-fine matching method is put forward. Besides, the location correlation keeps updating and remains the minimum value in the whole scanning process, which can significantly decrease time consumption. Experiments show that the algorithm proposed in this article can not only overcome gray distortion, but also ensure accuracy. Time consumption is at least one time orders of magnitude shorter than that of primal algorithm.

An analytical approach to photonic reservoir computing – a network of SOA's – for noisy speech recognition

This paper seeks to investigate an approach of photonic reservoir computing for optical speech recognition on an examination isolated digit recognition task. An analytical approach in photonic reservoir computing is further drawn on to decrease time consumption, compared to numerical methods; which is very important in processing large signals such as speech recognition. It is also observed that adjusting reservoir parameters along with a good nonlinear mapping of the input signal into the reservoir, analytical approach, would boost recognition accuracy performance. Perfect recognition accuracy (i.e. 100%) can be achieved for noiseless speech signals. For noisy signals with 0–10db of signal to noise ratios, however, the accuracy ranges observed varied between 92% and 98%. In fact, photonic reservoir application demonstrated 9–18% improvement compared to classical reservoir networks with hyperbolic tangent nodes.

Advanced 3D indoor propagation model: calibration and implementation

A software tool, implementing a semi-deterministic model which provides swift predictions, accounts for all significant physical phenomena while utilising a clear-cut description of material properties, is presented in this article. The software can predict signal coverage, delay profiles and angle of arrivals for receivers located anywhere in a scenario using three probabilistic parameters to describe each of the materials. Probabilistic parameters can be efficiently optimised based on measured data by a genetic algorithm optimiser contained in the software tool enabling real material properties (constants) to be avoided. The semi-deterministic model is briefly described whereas its implementation into the tool is explained in greater detail. Measured narrowband, as well as wideband, data are presented, and the basic principles of the computer optimisation utilised in the tool are shown. Optimised results are compared with the measured ones and deviation is determined. The principle of a simple multi-threading algorithm which improves the tool performance while decreasing time consumption is presented along with computational times where a different number of threads are compared.