Continuous Motion Estimation of Elbow Joint Based on Multi-Modal Information Fusion / 医用生物力学

Objective Aiming at the problems of lacking initiative in upper limb rehabilitation training equipment, single training mode, and low active participation of patients, an upper limb continuous motion estimation algorithm model based on multi-modal information fusion was proposed, so to realize accurate estimation of elbow joint torque. Methods Firstly, the surface electromyography (sEMG) signal and posture signal of participants were collected at four angular velocities, and the time domain characteristics of the signal were extracted. The principal component analysis was adopted to multi-feature fusion. The back propagation neural network (BPNN) was optimized through the additional momentum and the adaptive learning rate method. The particle swarm optimization (PSO) algorithm was used to optimize the neural network and a continuous motion estimation model based on PSO-BPNN was constructed. Finally, the joint torque calculated by the second type of Lagrangian equation was used as the accurate value to train the model. The performance of the model was compared with the traditional BP neural network model. Results The root mean square error (RMSE) of the traditional BP neural network model was 558.9 N·m, and the R2 coefficient was 77.19%, Whereas the RMSE and the R2 coefficient of the optimized model were 113.6 mN·m and 99.12%, respectively.Thereby, the accuracy of torque estimation was improved apparently. Conclusions The method for continuous motion estimation of the elbow joint proposed in this study can estimate the motion intention accurately, and provide a practical scheme for the active control of upper exoskeleton rehabilitation robot.

Study on noninvasive blood glucose detection method using the near-infrared light based on particle swarm optimization and back propagation neural network / 生物医学工程学杂志

Most of the existing near-infrared noninvasive blood glucose detection models focus on the relationship between near-infrared absorbance and blood glucose concentration, but do not consider the impact of human physiological state on blood glucose concentration. In order to improve the performance of prediction model, particle swarm optimization (PSO) algorithm was used to train the structure paramters of back propagation (BP) neural network. Moreover, systolic blood pressure, pulse rate, body temperature and 1 550 nm absorbance were introduced as input variables of blood glucose concentration prediction model, and BP neural network was used as prediction model. In order to solve the problem that traditional BP neural network is easy to fall into local optimization, a hybrid model based on PSO-BP was introduced in this paper. The results showed that the prediction effect of PSO-BP model was better than that of traditional BP neural network. The prediction root mean square error and correlation coefficient of ten-fold cross-validation were 0.95 mmol/L and 0.74, respectively. The Clarke error grid analysis results showed that the proportion of model prediction results falling into region A was 84.39%, and the proportion falling into region B was 15.61%, which met the clinical requirements. The model can quickly measure the blood glucose concentration of the subject, and has relatively high accuracy.

Research on gait recognition and prediction based on optimized machine learning algorithm / 生物医学工程学杂志

Aiming at the problems of individual differences in the asynchrony process of human lower limbs and random changes in stride during walking, this paper proposes a method for gait recognition and prediction using motion posture signals. The research adopts an optimized gated recurrent unit (GRU) network algorithm based on immune particle swarm optimization (IPSO) to establish a network model that takes human body posture change data as the input, and the posture change data and accuracy of the next stage as the output, to realize the prediction of human body posture changes. This paper first clearly outlines the process of IPSO's optimization of the GRU algorithm. It collects human body posture change data of multiple subjects performing flat-land walking, squatting, and sitting leg flexion and extension movements. Then, through comparative analysis of IPSO optimized recurrent neural network (RNN), long short-term memory (LSTM) network, GRU network classification and prediction, the effectiveness of the built model is verified. The test results show that the optimized algorithm can better predict the changes in human posture. Among them, the root mean square error (RMSE) of flat-land walking and squatting can reach the accuracy of 10 -3, and the RMSE of sitting leg flexion and extension can reach the accuracy of 10 -2. The R 2 value of various actions can reach above 0.966. The above research results show that the optimized algorithm can be applied to realize human gait movement evaluation and gait trend prediction in rehabilitation treatment, as well as in the design of artificial limbs and lower limb rehabilitation equipment, which provide a reference for future research to improve patients' limb function, activity level, and life independence ability.

SC⁃BPSO: A Filter Fused BPSO Feature Selection Method in Hepatocellular Carcinoma Classification / 中国生物化学与分子生物学报

Early diagnosis of cancer can significantly improve the survival rate of cancer patients, especially in patients with hepatocellular carcinoma (HCC). Machine learning is an effective tool in cancer classification. How to select high⁃classification accuracy feature subsets with low dimension in complex and high⁃dimensional cancer datasets is a difficult problem in cancer classification. In this paper, we propose a novel feature selection method, SC⁃BPSO: a two⁃stage feature selection method implemented by combining the Spearman correlation coefficient, chi⁃square independent test⁃based filter method, and binary particle swarm optimal (BPSO) based wrapper method. It has been applied to the cancer classification of high⁃dimensional data to classify normal samples and HCC samples. The dataset in this paper is obtained from 130 liver tissue microRNA sequence data (64 hepatocellular carcinoma, 66 normal liver tissue) from National Center for Bioinformatics (NCBI) and European Bioinformatics Institute (EBI). First, the liver tissue microRNA sequence data was preprocessed to extract the three types of features of microRNA expression, editing level and post⁃editing expression. Then, the parameters of the SC⁃BPSO algorithm in the liver cancer classification were adjusted to select a subset of key features. Finally, classifiers were used to establish classification models, predict the results, and compare the classification results with the feature subset selected by the information gain filter, the information gain ratio filter and the BPSO wrapper feature selection algorithm using the same classifier. Using the feature subset selected by the SC⁃BPSO algorithm, the classification accuracy is up to 98. 4%. The experimental results showed that compared with the other three feature selection algorithms, the SC⁃ BPSO algorithm can effectively find feature subsets with relatively small size and higher accuracy. This may have important implications for cancer classification with a small number of samples and high⁃ dimension features.

Application of an extreme learning machine network with particle swarm optimization in syndrome classification of primary liver cancer / 中西医结合学报

OBJECTIVE@#By optimizing the extreme learning machine network with particle swarm optimization, we established a syndrome classification and prediction model for primary liver cancer (PLC), classified and predicted the syndrome diagnosis of medical record data for PLC and compared and analyzed the prediction results with different algorithms and the clinical diagnosis results. This paper provides modern technical support for clinical diagnosis and treatment, and improves the objectivity, accuracy and rigor of the classification of traditional Chinese medicine (TCM) syndromes.@*METHODS@#From three top-level TCM hospitals in Nanchang, 10,602 electronic medical records from patients with PLC were collected, dating from January 2009 to May 2020. We removed the electronic medical records of 542 cases of syndromes and adopted the cross-validation method in the remaining 10,060 electronic medical records, which were randomly divided into a training set and a test set. Based on fuzzy mathematics theory, we quantified the syndrome-related factors of TCM symptoms and signs, and information from the TCM four diagnostic methods. Next, using an extreme learning machine network with particle swarm optimization, we constructed a neural network syndrome classification and prediction model that used "TCM symptoms + signs + tongue diagnosis information + pulse diagnosis information" as input, and PLC syndrome as output. This approach was used to mine the nonlinear relationship between clinical data in electronic medical records and different syndrome types. The accuracy rate of classification was used to compare this model to other machine learning classification models.@*RESULTS@#The classification accuracy rate of the model developed here was 86.26%. The classification accuracy rates of models using support vector machine and Bayesian networks were 82.79% and 85.84%, respectively. The classification accuracy rates of the models for all syndromes in this paper were between 82.15% and 93.82%.@*CONCLUSION@#Compared with the case of data processed using traditional binary inputs, the experiment shows that the medical record data processed by fuzzy mathematics was more accurate, and closer to clinical findings. In addition, the model developed here was more refined, more accurate, and quicker than other classification models. This model provides reliable diagnosis for clinical treatment of PLC and a method to study of the rules of syndrome differentiation and treatment in TCM.

Decision Tree Based Salp Swarm Optimization for Multi Medical Data Classification with Feature Reduction Technique

Abstract The ambitious task in the domain of medical informatics is medical data classification. From medical datasets, intention to ameliorate human burden with the medical data classification entails to taking in classification designs. The medical data classification is the major focus of this paper, where a Decision Tree based Salp Swarm Optimization (DT-SWO) algorithm is proposed. After pre-processingthe hybrid feature selection method selects the medical data features. The high dimensional features are reduced by Discriminant Independent Component Analysis (DICA) and DT-SWO is to classify the most relevant class of medical data. The details of four datasets namely Leukemia, Diffuse Larger B-cell Lymphomas (DLBCL), Lung cancer and Colon relating to four diseases for heart, liver, cancer and lungs are collected from the UCI machine learning repository. Ultimately, the experimental outcomes demonstrated that the proposed DT-SWO algorithm is suitable for medical data classification than other algorithms.

Feature fusion of electrocardiogram and surface electromyography for estimating the fatigue states during lower limb rehabilitation / 生物医学工程学杂志

In the process of lower limb rehabilitation training, fatigue estimation is of great significance to improve the accuracy of intention recognition and avoid secondary injury. However, most of the existing methods only consider surface electromyography (sEMG) features but ignore electrocardiogram (ECG) features when performing in fatigue estimation, which leads to the low and unstable recognition efficiency. Aiming at this problem, a method that uses the fusion features of ECG and sEMG signal to estimate the fatigue during lower limb rehabilitation was proposed, and an improved particle swarm optimization-support vector machine classifier (improved PSO-SVM) was proposed and used to identify the fusion feature vector. Finally, the accurate recognition of the three states of relax, transition and fatigue was achieved, and the recognition rates were 98.5%, 93.5%, and 95.5%, respectively. Comparative experiments showed that the average recognition rate of this method was 4.50% higher than that of sEMG features alone, and 13.66% higher than that of the combined features of ECG and sEMG without feature fusion. It is proved that the feature fusion of ECG and sEMG signals in the process of lower limb rehabilitation training can be used for recognizing fatigue more accurately.

Spatial epidemiological characteristics and prediction models of bacterial dysentery in Chongqing from 2009 to 2016 based on meteorological elements / 上海交通大学学报（医学版）

Objective: To analyze the spatial epidemiological characteristics of bacillary dysentery and its correlation with meteorological elements in Chongqing, and to construct its incidence prediction model, thus providing scientific basis for the prevention and control of bacterial dysentery. Methods: The data of bacterial dysentery cases and meteorological factors from 2009 to 2016 in Chongqing was collected in this study. Descriptive methods were employed to investigate the epidemiological distribution of bacillary dysentery. Spatiotemporal scanning statistics was used to analyze spatiotemporal characteristics of bacillary dysentery. DCCA coefficient method was used to quantify the correlation between the incidence of bacillary dysentery and meteorological elements. Both Boruta algorithm and particle swarm optimization algorithm (PSO) combined with support vector machine for regression model (SVR) were used to establish the prediction model for the incidence of bacterial dysentery. Results: ①The mean annual reported incidence of bacillary dysentery in Chongqing from 2009 to 2016 was 29.394/100 000. Children <5 years old had the highest incidence (295.892/100 000) among all age categories and scattered children had the highest proportion (50.335%) among all occupation categories. The seasonal incidence peak was from May to October. Bacterial dysentery showed a significant spatial-temporal aggregation that the most likely clusters for disease was found mainly in the main urban areas and main gathering time was from June to October. ②The most important meteorological elements associated with the incidence of bacterial dysentery were monthly mean atmospheric pressure (ρDCCA=-0.918), monthly mean maximum temperature (ρDCCA=0.875) and monthly mean temperature (ρDCCA=0.870). ③The mean squared error (MSE), mean absolute percentage error (MAPE) and square correlation coefficient (R2) of PSO_SVR model constructed based on meteorological elements were 0.055, 0.101 and 0.909, respectively. Conclusion: The main urban areas of Chongqing and the northeast of Chongqing should be regarded as the key areas for the prevention and control of bacillary dysentery. At the same time, according to the characteristics of bacillary dysentery, relevant health departments should take targeted measures to control the spread and prevalence of bacillary dysentery among children <5 years old, scattered children and farmers. The PSO_SVR model constructed based on meteorological elements has good predictive performance and can provide scientific theoretical support for the prevention and control of bacterial dysentery.

PSO+: nonlinear fitting fluorescence data based on particle swarm optimizing combine with other iteration algorithm / 生物医学工程学杂志

The convective polymerase chain reaction (CCPCR) uses the principle of thermal convection to allow the reagent to flow in the test tube and achieve the purpose of amplification by the temperature difference between the upper and lower portions of the test tube. In order to detect the amplification effect in real time, we added a fluorophore to the reagent system to reflect the amplification in real time through the intensity of fluorescence. The experimental results show that the fluorescence curve conforms to the S-type trend of the amplification curve, but there is a certain jitter condition due to the instability of the thermal convection, which is not conducive to the calculation of the cycle threshold (CT value). In order to solve this problem, this paper uses the dynamic method, using the double S-type function model to fit the curve, so that the fluorescence curve is smooth and the initial concentration of the nucleic acid can be deduced better to achieve the quantitative purpose based on the curve. At the same time, the PSO+ algorithm is used to solve the double s-type function parameters, that is, particle swarm optimization (PSO) algorithm combined with Levenberg-Marquardt, Newton-CG and other algorithms for curve fitting. The proposed method effectively overcoms PSO randomness and the shortcoming of traditional algorithms such as Levenberg-Marquardt and Newton-CG which are easy to fall into the local optimal solution. The of the data fitting result can reach 0.999 8. This study is of guiding significance for the future quantitative detection of real-time fluorescent heat convection amplification.

Survey of social wasps (Hymenoptera, Vespidae, Polistinae) in Amazon rainforest fragments in Acre, Brazil / Levantamento de vespas sociais (Hymenoptera, Vespidae, Polistinae) em fragmentos de floresta Amazônica no Acre, Brasil

ABSTRACT The State of Acre, in the southwestern Brazilian Amazon, harbors high biodiversity and a high degree of endemisms. Nevertheless, there are few studies on the diversity of social wasps occurring in this region. This study presents a list of social wasps (Hymenoptera, Vespidae, Polistinae) collected actively with attractive bait in three rainforest fragments in Acre. A total of 758 wasps belonging to 11 genera and 36 species were collected. Nineteen species were new distribution records for Acre and three others were new records for Brazil. Based on our results, further investigations should lead to a significant increase in Polistinae diversity in this region, producing information for biogeographic studies and management of natural areas.

RESUMO O estado do Acre é parte da Amazônia Ocidental brasileira, uma área que abriga uma grande biodiversidade e alto grau de endemismos. Contudo, poucos estudos investigaram a diversidade de vespas sociais nesta região. Este trabalho apresenta os resultados de uma amostragem de vespas sociais (Hymenoptera,Vespidae, Polistinae) em três fragmentos de floresta primária no estado do Acre. As vespas foram coletadas ativamente com o uso de solução atrativa. Um total de 758 vespas, pertencentes a 11 gêneros e 36 espécies, foi amostrado, sendo 19 dessas espécies novos registros de distribuição para o Acre e três novos registros para o Brasil. Nossos resultados indicam que novas investigações devem levar a um aumento significativo da diversidade de espécies de vespas Polistinae conhecida para a região. Nosso estudo produziu subsídios de grande utilidade para estudos biogeográficos e fornece informações importantes para a tomada de decisões sobre o manejo de áreas naturais.

Utilização de modelos geométricos simplificados de uma córnea para fins de otimização / Use of simplified geometrical models of a cornea for optimization purposes

Resumo Otimização por Enxame de Partículas (PSO) é uma técnica de inteligência artificial (AI), que pode ser usada para encontrar soluções aproximadas para problemas numéricos de maximização e minimização extremamente difíceis. Neste trabalho, utilizou-se um algoritmo PSO para comparar os deslocamentos sofridos por uma amostra de córnea humana submetida à uma pressão interna de 45 mmHg com resultados de simulações numéricas e identificar valores otimizados para propriedades hiperelásticas da córnea (µ e α). Por meio dos resultados das simulações via análise inversa pelo Método dos Elementos Finitos (MEF), em conjunto com o algoritmo PSO, foram encontrados valores otimizados de µ = 0,047 e α = 106,7. Quando comparado com resultados otimizados por meio de um software comercial, foram encontrados erros de aproximadamente 0,15%. Por meio dos resultados obtidos, verificou-se ainda que, variando os valores dos coeficientes de inércia da partícula no algoritmo PSO, os resultados podem sofrer ligeira melhoria, o que demonstra potencial uso do PSO em conjunto com análise inversa do MEF para caracterização de materiais hiperelásticos, utilizando modelos geométricos simplificados

Abstract Particle Swarm Optimization (PSO) is an artificial intelligence technique (AI) that can be used to find approximate solutions to numerical problems of maximization and minimization. In this study, it was used a PSO algorithm to compare displacements from human cornea sample subjected to internal pressure of 45 mmHg with Results of numerical simulations were provided which identified optimized values for hyperelastic properties of the cornea (µ and α). By means of the results from numerical simulations via inverse analysis by the Finite Element Method (FEM), in conjunction with the PSO algorithm, optimized values of µ = 0.047 and α = 106.7 were found. When compared with optimized results from commercial software, errors around 0.15% were found. Results showed that, varying the values of particle inertia coefficients in the PSO algorithm, simulated displacements have improved when compared to experimental data. This demonstrates the potential use of PSO algorithm in conjunction with the FEM inverse analysis for hyperelastic materials characterization, using simplified geometrical models

Diurnal flight periodicity of a Neotropical ant assemblage (Hymenoptera, Formicidae) in the Atlantic Forest

ABSTRACT In this study we document for the first time flight patterns along a 24 h time range for an ant assemblage in one of the most diverse ecosystems on Earth, the Brazilian Atlantic Forest. Malaise traps were used to analyze the diurnal nuptial flights of a Neotropical ant assemblage during five days. Traps captured 802 individuals, revealing a remarkably high diversity (42 ant species), with samples strongly male biased (1:22). Contrariwise to similar studies, we found only a small proportion of species engaged in nocturnal nuptial flights, with diurnal flights accounting for an impressive 95% of all ant flight activity recorded. For the 18 most common species, three ant groups could be identified regarding flight period: sunrise, sunset, and continuous flight activity. Similarity analyses, however, suggest that closely related time ranges of flight activity may actually not be continuous. Further, three species showed pulsed flight activity, at varied hours of the day. Two species of Hypoponera showed flight activity at different periods of the day, suggesting congeneric staggered nuptial flights. Our results match long-term studies of ant assemblages showing high diversity of flight phenologies in hyperdiverse tropical ant assemblages and provide the first data on the reproductive phenology for several Neotropical ant species.

Energy Efficient Clustering in Multi-hop Wireless Sensor Networks Using Differential Evolutionary MOPSO

ABSTRACT The primary challenge in organizing sensor networks is energy efficacy. This requisite for energy efficacy is because sensor nodes capacities are limited and replacing them is not viable. This restriction further decreases network lifetime. Node lifetime varies depending on the requisites expected of its battery. Hence, primary element in constructing sensor networks is resilience to deal with decreasing lifetime of all sensor nodes. Various network infrastructures as well as their routing protocols for reduction of power utilization as well as to prolong network lifetime are studied. After analysis, it is observed that network constructions that depend on clustering are the most effective methods in terms of power utilization. Clustering divides networks into inter-related clusters such that every cluster has several sensor nodes with a Cluster Head (CH) at its head. Sensor gathered information is transmitted to data processing centers through CH hierarchy in clustered environments. The current study utilizes Multi-Objective Particle Swarm Optimization (MOPSO)-Differential Evolution (DE) (MOPSO-DE) technique for optimizing clustering.

Application of least squares support vector machines and partial least squares in quantitation on purification of Gardenia jasminoides intermediate using NIR spectroscopy / 中草药

Objective: To establish the quantitative models for analyzing the content of critical quality indicators in the purification process of Gardenia jasminoides intermediate in Reduning Injection using near-infrared (NIR) spectroscopy. Methods: The contents of shanzhiside, geniposidic acid, deacetyl asperulosidic acid methyl ester, genipin-1-β-D-gentiobioside, geniposide, chlorogenic acid, and total acid were determined by the reference method and NIR spectra were acquired. After removing the outliers, selecting the optimal spectral preprocessing method and selecting the best spectral wavelength, partial least squares (PLS) and the least squares support vector machines (LS-SVM) were used to build the models for predicting the contents of the above quality indicators in 18 unknown samples. Results: For shanzhiside, geniposidic acid, deacetyl asperulosidic acid methyl ester, genipin-1-β-D-gentiobioside, geniposide, chlorogenic acid, and total acid, the relative standard errors of prediction (RSEP) was lower than 3% for PLS models and LS-SVM models, indicating both methods could exhibit the satisfactory fitting results and predictive abilities. However, the LS-SVM models of shanzhiside and total acid showed lower predictive errors than PLS models. For geniposidic acid, deacetyl asperulosidic acid methyl ester, genipin-1-β-D-gentiobioside, geniposide, and chlorogenic acid, both models have the closer predictive errors. Conclusion: S-SVM shows better predictive performance than PLS. The established NIR quantitative models can be used for rapidly measuring the content of critical quality indicators in the purification process of G. jasminoides intermediate in Reduning Injection.

Qualidade de investimentos no uso de água pluvial: Particles Swarm Optimization para a maximização do valor presente líquido / Quality of investments in rainwater harvesting systems: Particles Swarm Optimization for maximization of net present value

A análise da qualidade dos investimentos deve fazer parte da tomada de decisão pela implantação dos sistemas de aproveitamento de água pluvial, de modo a avaliar se os investimentos na redução dos impactos ambientais são atrativos também sob o ponto de vista dos investidores. Considerando-se os métodos baseados no fluxo de caixa descontado, o valor presente líquido é o melhor indicador para tal avaliação, pois permite decidir qual é o melhor entre dois projetos excludentes, além de apresentar uma rotina de cálculo simples e de fácil entendimento pelos usuários. Este trabalho teve como objetivo propor uma ferramenta para a análise da qualidade dos investimentos em um sistema de aproveitamento de água pluvial, baseada na otimização do valor presente líquido, por meio da técnica de Particles Swarm Optimization. Para demonstrar a utilização da ferramenta proposta, desenvolveu-se um estudo de caso para uma edificação pública existente. Foram considerados três cenários para a tarifa água: sem reajuste e com valores limite e máximo do reajuste histórico das tarifas da concessionária local: 5,69 e 19,63% ao ano. Em todos os cenários foram encontradas duas alternativas: existência de cobrança da tarifa de esgoto correspondente ao volume de água pluvial utilizado e inexistência dela. Os resultados obtidos com o uso da ferramenta proposta podem auxiliar na tomada de decisão para uso dos sistemas de aproveitamento da água pluvial.

Analysis of the quality of investments should be part of decision-making for implementing rainwater-harvesting systems in order to evaluate whether investments in environmental impacts mitigation are also attractive from the point of view of investors. Considering discounted cash flow methods, net present value is the best indicator for this analysis, because it allows deciding which is the best between two mutually exclusive projects, and it also presents a simple calculation routine and is easily understood by users. This work aimed at presenting a decision-making analysis tool for rainwater-harvesting systems, which is based on the maximization of net present value, using the Particles Swarm Optimization technique. The decision tool was used to evaluate the quality of the investments for implementing a rainwater-harvesting system, considering a building of the public tax category. Three scenarios of the water bill were considered: current rate structure and increases of 100%, 150% and 200%. All scenarios contemplate two alternatives: with sewage rate corresponding to the rainwater use and no fee. Results illustrate opportunities for investments in rainwater-harvesting systems.

The acceptance rate of young wasps by alien colonies depends on colony developmental stages in the swarm-founding wasp, Polybia paulista von ihering (Hymenoptera: Vespidae)

In social insects, newly emerged individuals learn the colony-specific chemical label from their natal comb shortly after their emergence. These labels help to identify each individual's colony of origin and are used as a recognition template against which individuals can discriminate nestmates from non-nestmates. Our previous studies with Polybia paulista von Ihering support this general pattern, and the acceptance rate of young female and male wasps decreased as a function of their age. Our study also showed in P. paulista that more than 90 percent of newly emerged female wasps might be accepted by conspecific unrelated colonies. However, it has not been investigated whether the acceptance rate of newly emerged female wasps depends on colony developmental stage of recipient colonies. We introduced newly emerged female wasps of P. paulista into different colony developmental stags of recipient colonies, i.e., worker-producing and male-producing colonies. We found that the acceptance rate of newly emerged female wasps by alien colonies was pretty lower by male-producing colonies than worker-producing colonies. This is the first study to show that the acceptance rate of young female wasps depends on stages of recipient colonies.

Diversity of social wasps (Hymenoptera: Vespidae) in Cerrado fragments of Uberlândia, Minas Gerais State, Brazil

Sudies of species survey are important to know the available natural resources and to get useful information about the ecological characteristics of a specific area. There are not studies, on this issue, related to social wasps, in the Triângulo Mineiro region. The present study describes the diversity of species found in fragments of the cerrado region in Uberlândia, MG, and their temporal distribution. The field study was conducted from October 2003 to September 2004, and 43 samplings were carried out using active searching (24) and point sampling (19) methodologies. Twenty-nine species were found in 10 genera, Polybia and Polistes corresponding to 51.5 percent of the total listing. Mischocyttarus cerberus styx Richards represented 26.5 percent of the total individuals recorded by active searching and Agelaia pallipes (Olivier) corresponded to 57.6 percent by point sampling. Two species had their first record for the state of Minas Gerais: Polybia striata (Fabricius) and M. cerberus styx. The studied fragments presented a high level of diversity (H’ = 0.66 to 1.16), a large number of rare species and a few common species. To collect the more common species the point sampling method was satisfactory while active searching was better to collect the rarer species. The active searching methodology was more efficient than point sampling, however some species could only be collected by point sampling. These factors showed that in order to carry out a wasp survey, a combination of different collection methodologies seems to be more appropriate.

Estudos de levantamento de espécies são importantes para o conhecimento dos recursos naturais além de contribuírem com informações de características ecológicas de uma determinada região. Não há estudos dessa natureza abordando vespas sociais no Triângulo Mineiro. O presente estudo investiga a diversidade das espécies encontradas em fragmentos de cerrado em Uberlândia, MG, e sua distribuição temporal. O trabalho de campo foi conduzido de outubro de 2003 a setembro de 2004, realizando-se 43 amostragens utilizando as metodologias de busca ativa e amostragem pontual. Foram encontradas 29 espécies em 10 gêneros, destacando-se Polybia e Polistes com 51,7 por cento dos espécimes coletados. Mischocyttarus cerberus styx Richards representou 26,5 por cento do total de indivíduos registrados para busca ativa e Agelaia pallipes (Olivier) correspondeu a 57,6 por cento para amostragem pontual. Duas espécies apresentaram primeiro registro para Minas Gerais: Polybia striata (Fabricius) e M. cerberus styx. Os fragmentos estudados apresentaram elevado índice de diversidade (H’ = 0,66 a 1,16), grande quantidade de espécies raras e poucas espécies comuns. O método de amostragem pontual foi satisfatório para coletar as espécies mais comuns enquanto a busca ativa foi importante para a coleta de espécies raras. A metodologia de busca ativa foi mais eficiente, em termos de abundância, que a amostragem pontual, embora algumas espécies só tenham sido coletadas pelo método de amostragem pontual. Esses fatores revelaram que para efetuar um levantamento de vespas uma combinação de diferentes metodologias de coleta parece ser o mais indicado.

Multimodal Medical Image Registration Based on Gradient Vector Flow and Particle Swarm Optimization / 航天医学与医学工程

Objective To study the method based on gradient vector flow (GVF) and particle swarm optimization (PSO) for realizing multimodal medical image registration and improving its accuracy. Methods In view of three major components of image registration, i.e. the feature space, the similarity metric and the search strategy, a novel method was proposed with three improvements. Firstly, the GVF field was employed as the feature space. Then three similarity metrics were proposed based on GVF field. Finally, an improved PSO combined with crossover mechanism of genetic algorithm was utilized to search for the optimal transformation of two images. Results With 54 times of experiments on both simulated and real medical images, it was demonstrated that this method accurately registered the multimodal medical images to be superior to the method based on PSO of pixels, and the Walsh transform method. Conclusion The method based on GVF and PSO is effective for multimodal medical image registration.

NEURAL NETWORK TRAINING WITH PARALLEL PARTICLE SWARM OPTIMIZER / 药物分析学报

Objective To reduce the execution time of neural network training. Methods Parallel particle swarm optimization algorithm based on master-slave model is proposed to train radial basis function neural networks, which is implemented on a cluster using MPI libraries for inter-process communication. Results High speed-up factor is achieved and execution time is reduced greatly. On the other hand, the resulting neural network has good classification accuracy not only on training sets but also on test sets. Conclusion Since the fitness evaluation is intensive, parallel particle swarm optimization shows great advantages to speed up neural network training.

Substituição natural de rainhas fisogástricas e distância de vôo dos machos em Tiuba (Melipona compressipes fasciculata Smith, 1854) e Uruçu (Melipona scutellaris Latreille, 1811) (Apidae, Meliponini) / Natural substitutions of queens and flight distance of males in tiuba (Melipona compressipes fasciculata Smith, 1854) and uruçu (Melipona scutellaris Latreille, 1811) (Apidae, Meliponini)

Este trabalho relata detalhes da vida reprodutiva de duas espécies de abelhas sem ferrão. Rainhas velhas de Melipona compressipes fasciculata, no Maranhão, morrem e são substituídas com sucesso em todos os meses. Rainhas de Melipona scutellaris, trazidas de Lençóis (BA - nordeste do Brasil) para Uberlândia (MG, centro-sudeste do Brasil) morreram durante todos os meses e não mostraram a existência de trimestre preferencial para as novas rainhas iniciarem postura. Quarenta machos de M. scutellaris, após serem marcados no tórax e libertados em grupos de 10 a 100, 400, 800 e 1000 metros do meliponário, tiveram seus retornos observados. Todos os machos libertados a 100 e 400 metros regressaram ao meliponário, 7 de 10 machos e 2 de 10 machos retornaram de 800 e 1000 metros, respectivamente. Os machos esperam constantemente pela saída de uma rainha virgem, próximos às colônias órfãs, o que indica que a maioria das rainhas é inseminada próximo aos seus ninhos, portanto, a dispersão dos genes depende do vôo dos machos e da distância de enxameagem para ocupação de uma cavidade para o novo ninho.

This work reports on the reproductive life of the stingless bees (Meliponini). Old queens of Melipona compressipes fasciculata in Maranhão State, Brazil die and are successfully substituted by new ones in all months. Queens of Melipona scutellaris from Lençois (BA - northeastern Brazil) brought to Uberlândia (Central South eastern Brazil) died during all months, and there are no months where new queens are more able to begin egg laying. Forty males of M. scutellaris were marked in the thorax and released in groups of 10 at 100, 400, 800 and 1000 meters from the meliponary and their return to it was observed. All males released at 100 to 400 meters returned, 7 out of 10 and 2 out of 10 males returned respectively from 800 and 1000 meters. Males are constantly waiting for a virgin queen next to orphan colonies, the majority of the queens is inseminated in near the hive, therefore, gene dispersion depends more upon the males and of the occupation of a new cavity for nest.