This study had one objective, namely, to investigate psychological distress among school students in the time of lockdown period due to the outbreak of the COVID-19 pandemic. Because COVID-19 is a newly developing disease with limited data at the national level, the sample size was estimated using a single population proportion calculation by assuming a prevalence of 54%, 95% of the confidence level and 5% of the margin of error. We used the p-value from previous studies [12, 31] which were conducted during the same pandemic (54.0%). The calculated sample size of this study was 382 participants. By adding a tolerable non-response rate (10%), the total sample size was 420 participants.
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It endeavours to postulate a smart, economical power supply system for pacemakers based on the principle of power harnessing using piezoelectric effect, on the contrary to the existing battery powered system, resulting an enormous reduction in pacemaker cost. The proposed system will use heart vibrations to generate power from piezoelectric transducer and provide a steady DC output to the pacemaker system, reducing or omitting the need of pacemaker replacement. The proffered system also has a scope of providing a warning by pre-detecting the possible threat of future heart-disease. The general objective of the project is to replace the need of expensive battery by harnessing power from heart vibrations to drive the pacemaker system to measure the real-time Blood Pressure of the patient and to produce an alarm by pre-sensing the probability of possible heart-failure or heart-disease.
Knowing the performance of the student in online classes is very difficult. Staff are taking online classes and conducting objective type examinations. It is very difficult to know at which level student is performing better and which is not. Blooms taxonomy is a technique developed by Dr. Benjamin Bloom in the year of 1956. Understudy appraisal is a critical bit of educating and is done through the procedure of examinations and readiness of test question papers has reliably involved scheme. Bloom's taxonomy is a lot of three progressive models used to group instructive learning goals into dimensions of multifaceted nature and explicitness. The cognitive domain list has been the primary focus of most traditional education and is frequently used to structure curriculum learning objectives, assessments and activities. Bloom's taxonomy is a device that can help human services educators expand the profundity of their students' learning.[3] The test with the taxonomy is creating assessments that measure every one of the six levels. There are 19 sorts of psychological procedures that can be grouped into six noteworthy classifications: recall, comprehend, apply, break down, assess, and make. There are four noteworthy classifications of learning: verifiable, applied, procedural, and meta cognitive. Instances of computer based appraisals of critical thinking are given dependent on the assessment of the subjective outcomes of youngsters' cooperation in an after-school computer club. In this paper, an objective question paper have been given using six levels of Bloom's taxonomy and asked students to write the test using online and evaluates the results of student performance[1].
Citation: Ma C, Hao W, Pan F, Xiang W (2018) Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm. PLoS ONE 13(6): e0198931.
The distribution route optimization problem of hazardous materials is a typical multi-objective optimization problem. Designing suitable multi-objective algorithms is important. Niche Pareto genetic algorithm (GA) [27], non-dominated sorting GA [28], and strong Pareto evolutionary algorithm [29], are representative algorithms. These algorithms have improved solving efficiency for special problems. However, these algorithms cannot be applied directly for a specific problem. Hence, this paper designs a new multi-objective GA based on route optimization characteristics of hazardous materials transportation.
The rest of this paper is organized as follows: Section 2 studies the road screening algorithm of hazardous materials alternative route selection; Section 3 builds a transportation route multi-objective robust optimization model of hazardous materials; Section 4 designs a new multi-objective GA; Section 5 presents a case study; Section 6 provides the conclusion.
When the neural network model is used to predict the section, the norm of the sample forecasting value and expected value error matrices are outputted as the objective function to make the residuals of the prediction value and expected value as small as possible. Fitness assignment function is used to sort fitness value.
Establishing the multi-objective optimization model and algorithm, and compiling the corresponding calculation program to determine the specific transportation route are necessary after obtaining the alternative transportation sections of hazardous materials.
where the objective function (5) expresses the minimization of hazardous materials transportation risks. The objective function (6) expresses the minimization of hazardous materials vehicle travel time. Constraint (7) expresses that the total tasks of vehicle k is not more than vehicle capacity. Constraint (8) expresses that task i is completed by one vehicle. Constraint (9) expresses the relationship of two variables. Constraints (11) and (12) are the branch elimination constraints, and , Constraint (13) expresses that the transportation risk of each section must be less than or equal to threshold r set by decision makers. Constraint (14) expresses that the transportation risk of each route must be less than or equal to threshold R set by decision makers, whereas Constraints (15) and (16) express the decision variables constraint.
Multiple objectives of multi-objective optimization may be in conflict with each other, which is different from single-objective optimization. The improvement of a sub-goal will lead to a decrease in another sub-target, that is, multiple sub-goals achieving optimum are impossible. Therefore, multi-objective optimization obtains a non-inferior solution set, the elements of which are called Pareto optimal or non-inferior optimal solutions. The Pareto optimal solution can also be interpreted as no solution exists better than at least one of the goals and not worse than other goals. The elements of the Pareto optimal solution set are not comparable to each other in terms of all objectives. Using the obtained Pareto set, decision makers selected one or many solutions from the Pareto optimal solutions as the optimal solution of multi-objective optimization problem according to other information or personal preference. Therefore, the main task of solving multi-objective optimization problem is to obtain widely distributed Pareto optimal solutions. In this paper, a multi-objective GA is designed to solve this model according to the multi-objective robust optimization model characteristics. The algorithm uses an improved selection strategy to complete the operation, applies partial matching cross transposition and single ortho swap methods to complete the operation of crossover and mutation, and employs the selected method to construct the Pareto optimal solution set.
The strength Pareto genetic algorithm (SPEA) is used to test the efficiency of the improved multi-objective genetic algorithm. The algorithm parameter and Pareto optimal solution set selection strategy are the same as those of the improved multi-objective genetic algorithm designed in this paper. The results are shown in Table 12. Compared with SPEA and under different values, the mean values of two objective functions obtained from the improved multi-objective genetic algorithm designed in this paper are better, and the operation time is reduced. The results show that the improved multi-objective genetic algorithm designed in this paper can not only obtain a more satisfactory solution, but also has faster convergence speed compared with the traditional genetic algorithm.
Optimization of distribution route is an important link to ensure safe transportation of hazardous materials. Scientific and reasonable distribution route design of hazardous materials can make hazardous materials reach the customer demand point safely, quickly, and economically. However, the optimized scheme may incur serious security risks if road screening is not carried out before route optimization. This paper extensively studies the problem of road screening for hazardous materials transportation, and builds road screening algorithm based on GA-LM-NN and the multi-objective robust optimization model of transportation route with adjustable robustness based on Bertsimas. The improved elitist selection strategy is used to complete choice operation, partial matching cross shift method, and single ortho swap method is used to complete crossover and mutation operation. The Pareto optimal solution set is constructed based on the exclusive method. The study shows that the proposed GA-LM-NN road screening algorithm can determine quickly the suitable transportation section sets of hazardous materials. Furthermore, transportation path multi-objective robust optimization model and algorithm can determine rapidly the Pareto solution set of different robustness transportation route. Finally, decision makers can choose suitable transportation routes from better robust Pareto solutions based on actual situation or preferences through a case study.
Full-waveform inversion (FWI) is a method used to determine properties of the Earth from information on the surface. We use the squared Wasserstein distance (squared $W_2$ distance) as an objective function to invert for the velocity as a function of position in the Earth, and we discuss its convexity with respect to the velocity parameter. In one dimension, we consider constant, piecewise increasing, and linearly increasing velocity models as a function of position, and we show the convexity of the squared $W_2$ distance with respect to the velocity parameter on the interval from zero to the true value of the velocity parameter when the source function is a probability measure. Furthermore, we consider a two-dimensional model where velocity is linearly increasing as a function of depth and prove the convexity of the squared $W_2$ distance in the velocity parameter on large regions containing the true value. We discuss the convexity of the squared $W_2$ distance compared with the convexity of the squared $L^2$ norm, and we discuss the relationship between frequency and convexity of these respective distances. We also discuss multiple approaches to optimal transport for non-probability measures by first converting the wave data into probability measures. 2ff7e9595c
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