Research on Evacuation Path Planning Based on Improved Sparrow Search Algorithm

Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale Algorithm,Gray Wolf Algorithm,Improved Gray Wolf Algorithm,Sparrow Search Algorithm,and MSSA Algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential.

Constructing Test Suites for k-n Softw are System

In software testing,many troublesome faults are caused by interaction of input parameters. If automatic efficient test-case generator( AETG) ,in parameter order( IPO) or orthogonal Latin square is used in the software system under test,the whole test set cannot be run completely due to time or budget constraints. In this paper,according to the number of parameter k and their values n,a software system under test can be divided into four types. As for k-2 system,an algorithm was proposed to construct test cases,based on the longitudinal binary string set and method of controlling variables. As for k -n valued parameters whose n was a prime or power of prime,a method of covering array of test sets was designed to construct test sets by taking orthogonal array and derive arrays from orthogonal array and two useful conditions. As for k parameters whose n-value was not all equal,an experience algorithm was presented in this paper. The experimental results show that the size of test sets from the proposed methods is better than that from AETG,IPO,and orthogonal Latin square.

Deciding Quasi-Reducibility Using Witnessed Test Sets

A new approach to deciding quasi-reducibility is proposed by intro- ducing witnesses. Furthermore, an algorithm for constructing witnessed test sets of lelh-linear rewrite systems h. been designed. Compared with the standard test set approach presented by Kapur, Narendran and Zhang, the method proposed generates test sets of smaller size and therefore has more efficient applications .

Predicting Amazon customer reviews with deep confidence using deep learning and conformal prediction

In this investigation,we have shown that the combination of deep learning,including natural language processing,and conformal prediction results in highly predictive and efficient temporal test set sentiment estimates for 12 categories of Amazon product reviews using either in-category predictions,i.e.the model and the test set are from the same review category or cross-category predictions,i.e.using a model of another review category for predicting the test set.The similar results from in-and cross-category predictions indicate high degree of generalizability across product review categories.The investigation also shows that the combination of deep learning and conformal prediction gracefully handles class imbalances without explicit class balancing measures.

The Pharmacophore Hypothesis of Novel Inhibitors for Aurora A Kinase

A three-dimensional pharmacophore model was developed from a series of inhibitors of Aurora A kinase to discover new potent anti-cancer agents using the HypoGen module in the Catalyst software. The pharmacophore model was developed based on the structure of 20 currently available inhibitors, which were carefully selected from the literature. The best hypothesis （Hypo 1） was defined by four features： one hydrogen-bond donor and three hy- drophobic points, with the best correlation coefficient of 0.909, the lowest rms deviation of 1.563, and the highest cost difference of 99.075. The Hypo 1 was then validated by a test set consisting of 24 compounds and by a cross-validation of 95% confidence level through randomizing the data using the CatScramble program, which suggested that a predictive pharmacophore model had been successfully obtained.

Functional-Level Fault Simulation with Concurrent and Parallel Mechanisms Using Object-Oriented VLSI Model

The functional-level test has been proposed as an alternative to reduce the complexity of test when VLSI gets larger and more complicated. It has been successful for circuits such as memories, PLAs and microprocessors. However, the functional-level test for general functional models has seldom been studied. This paper presents an object-oriented VLSI model and a functional-level fault simulation methodology for general functional model. Based on the proposed VLSI model, FFS (Functional-level Fault Simulator) with friendly visual interface has been implemented on Microsoft Windows platform by use of C++. It is an integral part of FMVS (Functional test Modeling and Verification System)-an extended subsystem of TeDS (Test Development System). The goal of FFS is to determine the fault coverage, generate fault dictionary and compact original test set at the function-level. In order to be efficient, FFS uses the concurrent and parallel mechanisms by taking advantage of the object-oriented VLSI model. The object-oriented VLSI model based fault simulation has been validated in the functional-level test by simulation results and the satisfying performance of FFS.