Rapid Search Research Articles

Heuristic computing technique for numerical solutions of nonlinear fourth order Emden–Fowler equation

The present study presents numerical solution of the fourth order, singular, nonlinear Emden–Fowler equation integrated computational intelligence by exploiting the efficiency of feedforward artificial neural networks (ANNs), global optimization strength of genetic algorithms (GAs) and rapid local search of sequential quadratic programming (SQP). ANN is applied to discretize the nonlinear Emden–Fowler differential equation and consequentially construct a fitness/merit function on mean squared error sense. The optimization of the decision variables of ANN to solve the multi-singular, fourth order, nonlinear Emden–Fowler equation is performed by integrating capability GAs and SQP. The proficiency of the presented approach is certified on precision, convergence and stability indices, which are further enhanced through effectiveness, significance and reliability measures on statistical data for solving three different cases of the singular systems.

A fast PCB hole detection method based on geometric features

Computer vision methods have raised great interest in the manufacture of printed circuit boards (PCBs). The detection and localization of circular holes on PCBs are very important for positioning or defect detection. In this paper, a new method for such detection is presented. The proposed method first uses an adaptive edge detector to obtain the edge image. Then it utilizes the symmetry of the circle and transforms the PCB image into distance space where a Euclidean distance relationship is shown. Then a rapid circle center search (RCCS) algorithm is used to locate the center of each circle. This algorithm first scans the possible locations of each center and then filters out false selections by measuring how close the candidate center is to the contour with the Hausdorff distance. In addition, the proposed method is compared with the method based on the circular Hough transform for multiple test images in terms of speed and accuracy.

Stochastic numerical technique for solving HIV infection model of CD4+ T cells

The intension of the present work is to present the stochastic numerical approach for solving human immunodeficiency virus (HIV) infection model of cluster of differentiation 4 of T-cells, i.e., CD4+ T cells. A reliable integrated intelligent computing framework using layered structure of neural network with different neurons and their optimization with efficacy of global search by genetic algorithms supported with rapid local search methodology of active-set method, i.e., hybrid of GA-ASM, is used for solving the HIV infection model of CD4+ T cells. A comparison between the present results for different neurons-based models and the numerical values of the Runge–Kutta method reveals that the present intelligent computing techniques is trustworthy, convergent and robust. Statistics-based observation on different performance indices further demonstrates the applicability, effectiveness and convergence of the present schemes.

SuperCRUNCH: A bioinformatics toolkit for creating and manipulating supermatrices and other large phylogenetic datasets

Abstract Phylogenies with extensive taxon sampling have become indispensable for many types of ecological and evolutionary studies. Many large‐scale trees are based on a ‘supermatrix’ approach, which involves amalgamating thousands of published sequences for a group. Constructing up‐to‐date supermatrices can be challenging, especially as new sequences may become available almost constantly. Additionally, genomic datasets (composed of thousands of loci) are becoming common in phylogenetics and phylogeography, and present novel challenges for constructing such datasets. Here we present SuperCRUNCH, a Python toolkit for assembling large phylogenetic datasets. It can be applied to GenBank sequences, unpublished sequences or combinations of GenBank and unpublished data. SuperCRUNCH constructs local databases and uses them to conduct rapid searches for user‐specified sets of taxa and loci. Sequences are parsed into putative loci and passed through rigorous filtering steps. A post‐filtering step allows for selection of one sequence per taxon (i.e. species‐level supermatrix) or retention of all sequences per taxon (i.e. population‐level dataset). Importantly, SuperCRUNCH can generate ‘vouchered’ population‐level datasets, in which voucher information is used to generate multi‐locus phylogeographic datasets. SuperCRUNCH offers many options for taxonomy resolution, similarity filtering, sequence selection, alignment and file manipulation. We demonstrate the range of features available in SuperCRUNCH by generating a variety of phylogenetic datasets. Output datasets include traditional species‐level supermatrices, large‐scale phylogenomic matrices and phylogeographic datasets. Finally, we briefly compare the ability of SuperCRUNCH to construct species‐level supermatrices relative to alternative approaches. SuperCRUNCH generated a large‐scale supermatrix (1,400 taxa and 66 loci) from 16 GB of GenBank data in ~1.5 hr, and generated population‐level datasets (<350 samples, <10 loci) in <1 min. It outperformed alternative methods for supermatrix construction in terms of taxa, loci and sequences recovered. SuperCRUNCH is a modular bioinformatics toolkit that can be used to assemble datasets for any taxonomic group and scale (kingdoms to individuals). It allows rapid construction of supermatrices, greatly simplifying the process of updating large phylogenies with new data. It is also designed to produce population‐level datasets. SuperCRUNCH streamlines the major tasks required to process phylogenetic data, including filtering, alignment, trimming and formatting. SuperCRUNCH is open‐source, documented and available at https://github.com/dportik/SuperCRUNCH.

Acoustic signal analysis for detecting defects inside an arc magnet using a combination of variational mode decomposition and beetle antennae search

An accurate, rapid signal analysis is crucial in the acoustic-based detection for internal defects in arc magnets. Benefiting from the adaptive decomposition without the mode mixing, variational mode decomposition (VMD), has emerged as a promising technology for processing and analyzing acoustic signals. However, improper parameter settings are the root cause of inaccurate VMD results, while existing optimization methods for VMD parameters are only applicable to a single signal with exclusive signal characteristics, rather than different signals with similar features. Therefore, we developed a new acoustic signal analysis method combining VMD, beetle antennae search (BAS), and naive Bayes classification (NBC), and then applied it for detecting internal defects of arc magnets. In this method, multiple optimizations for different signals are simplified to a one-time optimization for the whole signal group by a specially designed parameter-related fitness function. Since the coordinates of the function maximum value in a parameter space correspond to the unified parameter setting generating the overall optimal processing effect for all signals, BAS is introduced to achieve a rapid search of coordinates. With the obtained unified parameter setting, each acoustic signal of arc magnets can be consistently processed by VMD. Next, two modes stemmed from VMD are screened out by an energy threshold, and their specific frequency information is extracted as features representing the internal defects. NBC is carried out to learn and identify the extracted features. The experimental validation of the proposed method was conducted by detecting various arc magnets. Experimental results indicate that the identification accuracy reaches 100% and the detection speed per a single arc magnet approximately ranges between 1.7 and 4.5 s. This work provides not only a new strategy for the parameter optimization of VMD, but also a practical solution for the internal defect detection of arc magnets.

Impact of the Spatial Configuration of Streets Networks on Urban Growth: A Case Study of Abha City, Saudi Arabia

The rapid economic growth of Saudi cities after the discovery of oil in 1960 had some disadvantages, the most significant of which was the rapid search for alternatives to urban development and the acceleration of urban growth in its major cities and villages. That contributed to the implementation of certain planning policies based on the heavy use of large-scale vehicles. These policies have been instrumental in increasing horizontal expansion, which has been linked to the increased need to rely on cars as a means of movement and not to adopt public transport such as metro and railways. In this research, one of the main cities in the Asir region, which is the administrative capital of Abha, which has been evaluated and analyzed. Space syntax is an informative theory that stands on the structure of urban graphs. Utilizing syntactic analysis, the impact of highways and arterial roads on the integration and connectivity orientation in the urban structure of the city is analyzed, and the constraints and opportunities for development in the building use map are identified. Resulting in isolated parts of districts with no pedestrian routes to connect them, this methodology allows us to determine the main points in the structure of the city where pedestrian accessibility can be added in order to provide the remedy to overcome the shortage in the city’s network system (i.e., crossovers, tunnels, etc.). In addition, the impact on the human dimension of the living community, the diversity of land use, and real estate financial classifications were discussed, while the principles of sustainable planning were used to enhance the integration of walkability in the built environment, which was the goal of this study.

Abstract P1-02-05: Breast cancer development at the fat-gland interface (FGI): Importance of coronal imaging and ultrasound tomography

Abstract PURPOSE. Recent analyses of tumor location by breast magnetic resonance (MR) imaging have shown that breast cancers were significantly more likely to be located, at or near the fat-gland interface (FGI). However, these analyses did not include benign masses or coronal evaluations. Ultrasound tomography (UST) is a novel multi-sequence tomographic form of sonography with anatomic and physiologic information that can improve the sensitivity of breast cancer detection, particularly in women with dense breasts. The purpose of this study was to use the native coronal imaging plane of UST SoftVue (Delphinus Medical Technologies, Inc.) to map the locations of cancers, fibroadenomas and cysts, relative to the FGI, in order to facilitate the detection and characterization of breast masses in future UST screening. METHODS AND MATERIALS. Mass location in coronal UST images was determined by a MQSA certified radiologist with extensive UST imaging experience. The relationship of the lesion to the FGI was noted for a total of 427 breast masses (117 cancers, 135 fibroadenomas, 135 cysts and 40 other benign findings), confirmed by biopsy or by hand-held ultrasound for masses that fulfilled all criteria for a simple cyst. The FGI was defined as the circumferential interface between the peripheral fat and parenchyma as determined by examining sound speed (SS) images, which reliably differentiate the much lower SS of fat compared to that of dense tissue. Mass location was placed into three groups: (i) completely surrounded by dense fibroglandular tissue, (ii) completely surrounded by fat or (iii) partially surrounded by both (i.e., the FGI). Lesion locations relative to FGI were compared using the chi-squared test. RESULTS. Cancers were located at the FGI in 93.2% (109/117) of the cases and were surrounded by fat in 6.0% (7/117) or parenchyma in 0.9% (1/117) (p<0.001) of the cases, respectively (Table 1). For fibroadenomas, 63.0% (85/135) were found at the FGI while 29.9% of cysts were found at the FGI. Moreover, 68.9% of cysts and 31.1% of fibroadenomas were fully surrounded by dense tissue which was dramatically more than cancers (0.9%). DISCUSSION. Coronal imaging by UST provides reliable circumferential evaluation of the FGI, which can be augmented by axial/sagittal reconstructions as needed for additional confirmation of anterior/posterior aspects of the FGI. Our results confirm the previously reported MRI findings that cancers have a very strong tendency to be located at the FGI and extend these findings by demonstrating that benign masses have a weaker tendency in that regard. With the introduction of UST, our finding that breast cancers occur more frequently at the fat-gland interface may facilitate the conspicuity, rapid search and localization of cancers during screening of women with dense breasts. Perhaps equally important for future specificity of screening women with dense breasts, fibroglandular tissue surrounded only 0.9% of cancers and was much more commonly associated with benign masses. CONCLUSIONS. The native coronal imaging plane by SoftVue UST facilitated detection and localization of cancers, which were much more likely to be found at the FGI (p<0.001), compared to masses surrounded by fibroglandular tissue were much more likely to be benign. UST is a novel new technology that has the potential for improved breast cancer detection. This study supports its possible integration into clinical practice. Table 1Type of massAt FGIIn Dense tissueIn FatCancer109 (93%)1 (1%)7 (6%)Fibroadenoma85 (63%)42 (31%)8 (6%)Cyst39 (29%)93 (69%)3 (2%) Citation Format: Peter Littrup, Mark Sak, Neb Duric, Rachel Brem. Breast cancer development at the fat-gland interface (FGI): Importance of coronal imaging and ultrasound tomography [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-02-05.

Two-dimensional launch window method to search for launch opportunities of interplanetary missions

Accurate and highly efficient approaches to obtain mission opportunities are still the goals of mission planners of interplanetary explorations. The search for launch opportunities not only determines the specified launch window of the mission but also presents the performance requirements for the interplanetary probe and its launch vehicle. An effective method, namely the two-dimensional launch window method, is developed from a completely new perspective to determine all the launch opportunities of the mission in this research. For a fixed launch time, the method to determine all the time windows in the dimension of Time-of-Flight (TOF) is firstly proposed and these time windows represent all the launch opportunities for the given launch time. And then, the two-dimensional launch window method is proposed, which computes the time windows in both the launch time and TOF dimensions to achieve all launch opportunities of the mission. Numerical examples are provided to demonstrate the accuracy and high efficiency of the method. Compared with the widely-used pock-chop plot method, the proposed method reduces the computational time by two orders of magnitude for the same search precision, and thus is especially suitable for the cases involving rapid, high-precision, and/or large-scale searches for mission opportunities.

Scoping review of mode of anaesthesia in emergency surgery.

Emergency surgery encompasses more than 50 per cent of the surgical workload; however, research efforts are disproportionally low. The mode of anaesthesia used during emergency surgery may affect outcomes, but the extent of research and the impact of the different modes of anaesthesia used are unclear. MEDLINE and Embase were searched using scoping review methodology with a rapid systematic search strategy, identifying any study comparing locoregional (local, nerve block, subarachnoid, epidural) anaesthesia with general anaesthesia. All studies describing outcomes of emergency surgery with differing modes of anaesthesia were identified. Excluded were: studies published before 2003, studies enrolling patients aged less than 18 years and studies using sedation only. Forty-two studies were identified, describing 11 surgical procedures. Most publications were retrospective cohort studies (32). A very broad range of clinical and patient-reported outcomes were described, with wide variation in the outcomes reported in different studies. Reporting of mode of anaesthesia is inconsistent across different procedures and is often absent. There is a need for directed research efforts to improve the reporting standards of anaesthesia interventions, to understand the role of different modes of anaesthesia in specific emergency surgical procedures, and to standardize outcome reporting using core outcome sets.

Expected Surge in Maternal Mortality and Severe Morbidity among African-Americans in the Era of COVID-19 Pandemic.

Prior to the COVID-19 pandemic, African-American mothers were three times as likely to die from pregnancy-related causes compared to white mothers. The impact of the pandemic among African-Americans could further worsen the racial disparities in maternal mortality (MM) and severe maternal morbidity (SMM). This study aimed to create a theoretical framework delineating the contributors to an expected rise in maternal mortality (MM) and severe maternal morbidity (SMM) among African-Americans in the era of the COVID-19 pandemic due to preliminary studies suggesting heightened vulnerability of African-Americans to the virus as well as its adverse health effects. Rapid searches were conducted in PubMed and Google to identify published articles on the health determinants of MM and SMM that have been or likely to be disproportionately affected by the pandemic in African-Americans. We identified socioeconomic and health trends determinants that may contribute to future adverse maternal health outcomes. There is a need to intensify advocacy, implement culturally acceptable programs, and formulate policies to address social determinants of health.

Optimal Control for Connected and Autonomous Vehicles at Signal-Free Intersections

Abstract The development of connected and autonomous vehicles (CAVs) is one of the central aspects in the pathway towards future intelligent mobility systems. This paper addresses the problem of coordinating CAVs crossing an uncontrolled intersection so as to maintain safe and efficient traffic flow. The proposed control strategy is based on an optimal control framework that is formulated to minimize a weighted sum of total energy consumption and travel time of all CAVs by finding the optimal velocity trajectory of each vehicle. The design procedure starts with a proper formulation of the autonomous intersection crossing problem for CAVs, with various cases of energy recovery capability by the CAVs considered, to also investigate the influence of powertrain electrification on the intersection crossing problem. This yields an optimal control problem (OCP) with nonlinear and nonconvex dynamics and constraints. In order to ensure a rapid solution search and a unique global optimum, the OCP is reformulated via convex modeling techniques. Numerical results validate the effectiveness of the proposed approaches, while the trade-off between energy consumption and travel time is illustrated by Pareto optimal solutions.

XML security protection scheme based on Kerberos authentication and polynomials authorization.

With XML becoming a promising standard for data storage, describing, transferring and exchanging information on the Internet, data security and privacy protection of XML become the focus of research in recent years. In order to achieve the authorization of legitimate user and ensure the secure access to sensitive information, in this paper, in the context of cloud storage, with the purpose of sharing sensitive XML information, a polynomial authorization scheme with Kerberos authentication was proposed, which was based on the users' access purpose and privacy policy. In this scheme, first, Kerberos authentication was used to identify the user, and then the polynomial whose coefficients were from the leaf node address was used to complete the authorization of user. For the legitimate user, under the interaction of authorization polynomials and the global structure view, authorization matrix is generated dynamically, its temporary and dynamic characteristics greatly improves the security of the system. Finally, with the help of authorization matrix and auxiliary information tables, security queries were successfully completed. The experimental results show that the scheme not only effectively protects XML sensitive data, but also reduces the server's storage pressure, at the same time it is beneficial to the rapid search and information positioning.

Identifying the composition and atomic distribution of Pt-Au bimetallic nanoparticle with machine learning and genetic algorithm

Bimetallic nanoparticles (AmBn) usually exhibit rich catalytic chemistry and have drawn tremendous attention in heterogeneous catalysis. However, challenged by the huge configuration space, the understanding toward their composition and distribution of A/B element is known little at the atomic level, which hinders the rational synthesis. Herein, we develop an on-the-fly training strategy combing the machine learning model (SchNet) with the genetic algorithm (GA) search technique, which achieve the fast and accurate energy prediction of complex bimetallic clusters at the DFT level. Taking the 38-atom PtmAu38-m nanoparticle as example, the element distribution identification problem and the stability trend as a function of Pt/Au composition is quantitatively resolved. Specifically, results show that on the Pt-rich cluster Au atoms prefer to occupy the low-coordinated surface corner sites and form patch-like surface segregation patterns, while for the Au-rich ones Pt atoms tend to site in the core region and form the core-shell (Pt@Au) configuration. The thermodynamically most stable PtmAu38-m cluster is Pt6Au32, with all the core-region sites occupied by Pt, rationalized by the stronger Pt-Pt bond in comparison with Pt-Au and Au-Au bonds. This work exemplifies the potent application of rapid global search enabled by machine learning in exploring the high-dimensional configuration space of bimetallic nanocatalysts.

Review of Individual Placement and Support Employment Intervention for Persons with Substance Use Disorder

Objectives: Work is a critical part of recovery for many people with chronic health conditions, including Substance Use Disorders (SUD). Individual Placement and Support (IPS) is an evidence-based practice designed initially for adults with mental illness. Although the evidence for using IPS with individuals with severe mental illness is well documented, less is known about the efficacy of using IPS with consumers with substance use disorders, and the results have yet to be systematically evaluated and organized. Key components of zero exclusion, rapid competitive job search, and incorporation with treatment services as well as benefits are components that make IPS a strong practice to incorporate into substance abuse treatment. This study aims to evaluate and organize the evidence base of using IPS with adults with substance use disorders. Results: A systematic review was conducted of meta-analyses, reviews, and individual studies from 2000 through 2019, measuring the efficacy of IPS with individuals with SUD or comorbid SUD. Databases searched were Scopus, PubMed, and PsychInfo. Experimental and quasi-experimental studies are reviewed and critiqued for their application to a SUD population. Then, barriers and facilitators of IPS implementation with this population are discussed. Conclusions/Importance: There is a high evidence to support to application of IPS for persons with SUD, both singly and when combined with a mental health disorder. Barriers to IPS implementation including episodic treatment, risk of relapse, and housing or criminal justice instability make the IPS program a useful best practice to consider for this population.

CRISPRitz: rapid, high-throughput and variant-aware in silico off-target site identification for CRISPR genome editing.

Clustered regularly interspaced short palindromic repeats (CRISPR) technologies allow for facile genomic modification in a site-specific manner. A key step in this process is the in silico design of single guide RNAs to efficiently and specifically target a site of interest. To this end, it is necessary to enumerate all potential off-target sites within a given genome that could be inadvertently altered by nuclease-mediated cleavage. Currently available software for this task is limited by computational efficiency, variant support or annotation, and assessment of the functional impact of potential off-target effects. To overcome these limitations, we have developed CRISPRitz, a suite of software tools to support the design and analysis of CRISPR/CRISPR-associated (Cas) experiments. Using efficient data structures combined with parallel computation, we offer a rapid, reliable, and exhaustive search mechanism to enumerate a comprehensive list of putative off-target sites. As proof-of-principle, we performed a head-to-head comparison with other available tools on several datasets. This analysis highlighted the unique features and superior computational performance of CRISPRitz including support for genomic searching with DNA/RNA bulges and mismatches of arbitrary size as specified by the user as well as consideration of genetic variants (variant-aware). In addition, graphical reports are offered for coding and non-coding regions that annotate the potential impact of putative off-target sites that lie within regions of functional genomic annotation (e.g. insulator and chromatin accessible sites from the ENCyclopedia Of DNA Elements [ENCODE] project). The software is freely available at: https://github.com/pinellolab/CRISPRitzhttps://github.com/InfOmics/CRISPRitz. Supplementary data are available at Bioinformatics online.

Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming

In this paper, a novel application of biologically inspired computing paradigm is presented for solving initial value problem (IVP) of electric circuits based on nonlinear RL model by exploiting the competency of accurate modeling with feed forward artificial neural network (FF-ANN), global search efficacy of genetic algorithms (GA) and rapid local search with sequential quadratic programming (SQP). The fitness function for IVP of associated nonlinear RL circuit is developed by exploiting the approximation theory in mean squared error sense using an approximate FF-ANN model. Training of the networks is conducted by integrated computational heuristic based on GA-aided with SQP, i.e., GA-SQP. The designed methodology is evaluated to variants of nonlinear RL systems based on both AC and DC excitations for number of scenarios with different voltages, resistances and inductance parameters. The comparative studies of the proposed results with Adam’s numerical solutions in terms of various performance measures verify the accuracy of the scheme. Results of statistics based on Monte-Carlo simulations validate the accuracy, convergence, stability and robustness of the designed scheme for solving problem in nonlinear circuit theory.

CONTENTS AND SYSTEM OF EXPERT ACTIVITY IN THE FIELD OF COMPUTER TECHNOLOGIES: DEFINITION PROBLEMS

The article is devoted to the definition of the content and the system of expert activity in the field of computer technologies, which is based on the results of determining the general system of expert activity. Expert activity should be understood as the implementation by authorized agents on the basis of special knowledge in the field of science, technology, art, crafts, etc. Studies of objects, phenomena and processes in order to provide scientifically substantiated conclusions on the diverse issues that arise in the process of life of society. Such a definition of expert activity includes both judicial and non-judicial expert examination. At present, more than 500 laws are adopted in Ukraine, which in one way or another concern the conduct of expert assessments (most of them are valid at 2019). But no any among that laws directly devoted to the expert work in the field of computer technology. So the system and content of the expert work in the field of computer technology should be established, based on knowledge of the object of expertise – computer technology. It is considered as synonymous with the concept of “information technology” or “information and communication technologies”. Information technology – it is a purposeful organized set of information processes using computer facilities, which provide high speed data processing, rapid information search, dispersal of data, access to information sources regardless of places of their location. The system of expert activity in the field of computer technologies includes examinations belonging to a group of judicial (engineering, commodity, forensic, etc.) and non-judicial (scientific and scientific and technical expertise; examination of issues of quality and conformity of goods (products) to certain requirements; examination of issues of information security; examination of issues of environmental impact and the environment of human life, etc.), as well as presented by different kindsand species examinations that have different goals focused on the study of computer technology in their various aspects and provides solutions to diverse issues.

Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network

BackgroundMining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining in many research work. However, this method has two disadvantages: low learning efficiency and easy to fall into local optimum. Genetic algorithm has the excellence of rapid global search and avoiding falling into local optimum. It is scalable and easy to integrate with other algorithms. This work proposes an epistasis mining approach based on genetic tabu algorithm and Bayesian network (Epi-GTBN). It uses genetic algorithm into the heuristic search strategy of Bayesian network. The individual structure can be evolved through the genetic operations of selection, crossover and mutation. It can help to find the optimal network structure, and then further to mine the epistasis loci effectively. In order to enhance the diversity of the population and obtain a more effective global optimal solution, we use the tabu search strategy into the operations of crossover and mutation in genetic algorithm. It can help to accelerate the convergence of the algorithm.ResultsWe compared Epi-GTBN with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method has much better epistasis detection accuracy in the case of not affecting the efficiency for different datasets.ConclusionsThe presented methodology (Epi-GTBN) is an effective method for epistasis detection, and it can be seen as an interesting addition to the arsenal used in complex traits analyses.

A Multiobjective Particle Swarm Optimization Algorithm Based on Multipopulation Coevolution for Weapon-Target Assignment

Weapon-target assignment (WTA) is critical to command and decision making in modern battlefields and is a typical nondeterministic polynomial complete problem. To solve WTA problems with multiple optimization objectives, a multipopulation coevolution-based multiobjective particle swarm optimization (MOPSO) algorithm is proposed to realize the rapid search for the globally optimal solution. The algorithm constructs a master-slave population coevolution model. Each slave population corresponds to an objective function and is used to search for noninferior solutions. The master population receives all the noninferior solutions from the slave populations, repairs the gaps between the noninferior solutions, and generates a relatively optimal Pareto optimal solution set. In addition, to accelerate the slave populations searching for noninferior solutions and master population repairing the gaps between noninferior solutions, the particle velocity update method is improved. The simulation results show that the proposed algorithm has higher computational efficiency and achieves better solutions than existing algorithms capable of providing a good solution. The method is suitable for rapidly solving multiobjective WTA (MOWTA) problems.

Detecting, locating, and characterizing voids in disaster rubble for search and rescue

After natural and man-made disasters such as earthquakes, hurricanes, and explosions, victims may survive in voids that are formed naturally in collapsed structures. First responders need to identify and locate these critical voids for rapid search and rescue operations. Due to the complex and unstructured occlusions in disaster areas, visual and manual search is time-consuming and error-prone. In this study, we proposed a novel method to automatically detect, locate, and characterize voids buried in disaster rubble using ground penetrating radar (GPR). After preprocessing the collected radargrams, the boundaries of potential voids are segmented based on radar signal patterns, and the 95% confidence intervals are constructed around the segmented boundaries to account for uncertainties. To improve the detection accuracy, the geometric relations of the detected boundaries and their signal characteristics are examined to confirm the void existence. Then, the void location and dimension are estimated based on calibrated velocity of radar wave and its travel time. The effectiveness and efficiency of the proposed method were manifested by its performance in laboratory and field experiments. The contribution of this study is twofold. First, the feasibility of using GPR to detect, locate, and characterize voids in collapsed structures is experimentally tested, innovatively extending the application of GPR to search and rescue operations. Second, algorithms are developed to process non-intuitive radargrams to provide first responders actionable information.