Hasil untuk "Computer software"

Anqi Jiang, Meng Huan, Dong-Oun Choi et al.

Abstract Achieving sustainable practices in the jewelry industry necessitates the adoption of optimized eco-design approaches. The optimization of eco-friendly jewelry design was investigated in this study through an integrated analysis of materials, digital manufacturing, and predictive modeling. Sustainable techniques were identified, and an artificial neural network (ANN) model was developed to predict environmental impacts based on material properties and design attributes. The applicability of the model was validated, and insights were generated to drive eco-innovation and facilitate the transition towards sustainable practices in the jewelry industry. Key findings demonstrated the superior sustainability of renewable biomaterials, specifically Biomaterials 2-5 derived from lingo-cellulosic sources, compared to conventional materials. Consistently, simpler design configurations outperformed intricate designs. The relationships were effectively captured by the ANN model, providing a reliable evidence-based approach. Quantitative linkages between design attributes and sustainability metrics were established by the study, offering valuable guidance for optimization strategies. Significant results demonstrate that Biomaterials 2-5 exhibit average carbon footprints of 1.1–1.2 kg and water usage of 9–16.5 L, compared to 2.1 kg and 24.5 L for precious metals. Simplified designs exhibit carbon footprints of 0.8 kg and water usage of 11.5 L, whereas intricate designs show footprints of 3.1 kg and water usage of 33 L. These predictions establish renewable biomaterials and streamlined configurations as preferable paradigms for sustainable jewelry. Based on the findings, recommendations include prioritizing the use of Biomaterials 2-5 and streamlined configurations through the implementation of incentives. Transitioning operations towards biomaterial-focused infrastructure and emerging technologies is suggested to further enhance sustainability. Additionally, international cooperation and the development of standards are proposed to address sustainability challenges holistically. The empirical and computational findings of this study establish optimization methodologies that can inspire transformative sustainability practices in the jewelry industry.

Ariel Soares Teles, Alaa Abd-alrazaq, Thomas F. Heston et al.

Jayavani Vankara , Muddada Murali Krishna , Sekharamahanti S. Nandhini et al.

Due to the lack of precise medical testing for autism, such as blood tests to detect the illness, diagnosing autism spectrum disorder (ASD) has proven to be challenging. The prevalence of restrictive and/or repetitive behaviors and difficulties and impairments in social communication are hallmarks of autism spectrum disorders. This behavioral condition has been identified. Doctors assess the child's developmental history and behavior to make a diagnosis. Research results. This research used a hybrid Multi Label-Graph Convolutional Network (ML-GCN) with label-attentive neighborhood convolution to categorize the autism spectrum disorder. It offers a clear and effective graph wrapper module in particular for collecting the local attribute data of a specific node to produce a logical representation of node functioning. Additionally, the homeopathic theory recommends developing a taxonomy for attention-related terms. Furthermore, developed an adaptive graph technique that allows the model to learn the kernel for each layer dynamically and uniquely, allowing the model to acquire more valuable and efficient features. On three frequently used reference datasets, including customized and non-specialized networks, comprehensive tests were conducted to validate the neural network-based approach to multi-label classification.

Saif AlDeen AlSharman, Osama Al-Khaleel, Mahmoud Al-Ayyoub

The growing use of the Internet of Things (IoT) across a vast number of sectors in our daily life noticeably exposes IoT internet-connected devices, which generate, share, and store sensitive data, to a wide range of cyber threats. Software Defined Networks (SDNs) can play a significant role in enhancing the security of IoT networks against any potential attacks. The goal of the SDN approach to network administration is to enhance network performance and monitoring. This is achieved by allowing more dynamic and programmatically efficient network configuration; hence, simplifying networks through centralized management and control. There are many difficulties for manufacturers to manage the risks associated with evolving technology as the technology itself introduces a variety of vulnerabilities and dangers. Therefore, Intrusion Detection Systems (IDSs) are an essential component for keeping tabs on suspicious behaviors. While IDSs can be implemented with more simplicity due to the centralized view of an SDN, the effectiveness of modern detection methods, which are mainly based on machine learning (ML) or deep learning (DL), is dependent on the quality of the data used in their modeling. Anomaly-based detection systems employed in SDNs have a hard time getting started due to the lack of publicly available data, especially on the data layer. The large majority of existing literature relies on data from conventional networks. This study aims to generate multiple types of Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks over the data plane (Southbound) portion of an SDN implementation. The cutting-edge virtualization technology is used to simulate a real-world environment of Docker Orchestration as a distributed system. The collected dataset contains examples of both benign and suspicious forms of attacks on the data plane of an SDN infrastructure. We also conduct an experimental evaluation of our collected dataset with well-known machine learning-based techniques and statistical measures to prove their usefulness. Both resources we build in this work (the dataset we create and the baseline models we train on it) can be useful for researchers and practitioners working on improving the security of IoT networks by using SDN technologies.

Hui LI, Xingping WEI, Cheng LIU et al.

In order to solve the uncertainty and complexity of risk factors and the subjectivity of risk assessment of water inrush disasters in karst tunnels, the risk of water inrush disaster has been scientifically assessed. According to the Zhongliangshan karst tunnel project on the middle route of Chengdu-Chongqing, the study constructed a risk assessment model of water inrush disasters in karst tunnels based on variable weight-cloud model. First of all, referring to the research methods of Wu Xin, Liu Dunwen and others, and consulting professors in the field of geological disasters and engineers from tunnel construction and inspection units, a total of 8 experts determined the grade and classification standard of each influencing factor on water inrush disasters of karst tunnels, and clarified the parameter value of each influencing factor of Zhongliangshan tunnel on the middle route of Chengdu-Chongqing. In this study, five influencing factors were selected to construct an index system of risk assessment of the water inrush in karst tunnels. These five factors include formation lithology (calcium carbonate content in strata and rock structure), geologic structures (water-conducting fault structure, water-blocking fault structure and fold structure), surface catchment conditions, tunnel spatial locations and alternating conditions of groundwater circulation. In addition, the grading standards of water inrush disasters were determined, and accordingly the disasters were divided into five risk levels, low, mild, moderate, high and highest.Firstly, the cloud model was used to determine digital characteristics of the risk level of each index. The diagram of membership cloud of each influencing factor was drawn by MATLAB. The single factor membership degree (μj(x)) of each influencing factor was calculated according to parameter values of water inrush disasters in karst tunnels. Secondly, the analytic hierarchy process (AHP) was used to determine the constant weight. In order to avoid the situation that the constant weight does not change with the state value of the index to be evaluated, the punitive variable weight method was used to determine the variable weight vector (W(x)) and the comprehensive membership degree (U). Finally, according to the principle of maximum membership degree, risk levels of water inrush disasters in karst tunnels were calculated, and water inrush disaster situations of 7 sections in Zhongliangshan Tunnel on the middle route of Chengdu-Chongqing were determined. The results show that water inrush disasters in Zhongliangshan tunnel are between level III and level VI, with a high risk. Among them, DK15 + 630-DK15 + 680 and DK16 + 750-DK16 + 78 are the sections with a moderate risk; DK16 + 020-DK16 + 460 are of high risk; DK14 + 720-DK15 + 630, DK15 + 680-DK16 + 020, DK16 + 460-DK16 + 750 and DK16 + 785-DK17 + 380 are the sections with a highest risk. Water inrush disasters of karst tunnels can be attributed to a variety of influencing factors. The parameter value of each influencing factor of the high-risk section is higher than that of the low-risk section, and the risk of water inrush disasters in a transition zone between a karst area and a non-karst area is the highest. With the large porosity, the developed karst, and active groundwater, the soluble rock stratum is a three-medium system of pores, fissures and pipelines, which provides conditions for the occurrence of water inrush disasters and thus increases disaster possibility.The assessment result is in consistency with the actual situation of water inrush and tunneling. The consistency indicates that the risk assessment index and its system are applicable to water inrush assessments in karst tunnel areas. The cloud model intuitively reflects a fuzzy membership of risk; the variable weight theory constructs an equilibrium function, and each index is weighted according to the specific situation. It is a good solution to the problem of mutual neutralization between the indexes in the risk assessment of water inrush in karst tunnels, which is conducive to observing the change range and relative importance of each index. The risk assessment method of water inrush disasters of karst tunnels constructed in this paper can realize the objectivity of risk classification of water inrush disasters in tunnels from a multiple decision-making perspective, which is applicable to the risk assessment of karst tunnels and provides reference for the tunnel quality control and life assessment in the future.

Refaldi Ergian, Ali Tarmuji

<p>Saat ini proses proses manajemen bisnis di SAHA CELL dilakukan dengan menghitung secara manual menggunakan kalkulator dan harus menghapal harga setiap produk oleh karyawan. Karyawan juga akan menulis kode dan harga produk dalam buku catatan harian. Kondisi ini membuat transaksi menjadi tidak valid, karena karyawan salah menuliskan kode dan harga yang menyebabkan kerugian SAHA CELL. Penelitian ini menyelesaikan masalah di dalam proses bisnis SAHA CELL menggunakan framework Flutter berbasis BLOC (<em>Business Logic Component</em>). Sistem yang dibangun terdiri dari aplikasi mobile dan web berbasis <em>hybrid </em>sehingga memungkinkan proses dilakukan secara <em>online</em> dan <em>offline</em>. Hasil dari pengujian sistem menggunakan <em>Pengguna Acceptance Test</em> oleh pemilik toko menghasilkan keberhasilan 100% yang mengindikasi sistem dapat diterima baik.</p>

Anshu Dubey, Klaus Weide, Jared O’Neal et al.

Flash-X is a highly composable multiphysics software system that can be used to simulate physical phenomena in several scientific domains. It derives some of its solvers from FLASH, which was first released in 2000. Flash-X has a new framework that relies on abstractions and asynchronous communications for performance portability across a range of increasingly heterogeneous hardware platforms. Flash-X is meant primarily for solving Eulerian formulations of applications with compressible and/or incompressible reactive flows. It also has a built-in, versatile Lagrangian framework that can be used in many different ways, including implementing tracers, particle-in-cell simulations, and immersed boundary methods.

Rishikesh Magar, Yuyang Wang, Amir Barati Farimani

Abstract Machine learning (ML) models have been widely successful in the prediction of material properties. However, large labeled datasets required for training accurate ML models are elusive and computationally expensive to generate. Recent advances in Self-Supervised Learning (SSL) frameworks capable of training ML models on unlabeled data mitigate this problem and demonstrate superior performance in computer vision and natural language processing. Drawing inspiration from the developments in SSL, we introduce Crystal Twins (CT): a generic SSL method for crystalline materials property prediction that can leverage large unlabeled datasets. CT adapts a twin Graph Neural Network (GNN) and learns representations by forcing graph latent embeddings of augmented instances obtained from the same crystalline system to be similar. We implement Barlow Twins and SimSiam frameworks in CT. By sharing the pre-trained weights when fine-tuning the GNN for downstream tasks, we significantly improve the performance of GNN on 14 challenging material property prediction benchmarks.

XI Yingjie, LI Kewen, XU Yanhui, ZHU Jianbing

Fault is the main factor that controls the formation and distribution of oil and gas fields, so the detection and identification of fault plays an important role in the exploration oil and gas fields.Based on the Attention-UNet model, this paper proposes an improved SPD-UNet model for fault identification in earthquake images.SPD-UNet introduces dilated convolution, which can effectively enhance image feature extraction while expanding the receptive field and preventing resolution loss.At the same time, the dilated convolutions in the pyramid structure are stacked to form the SPD module, which avoids the local information loss of dialted convolutions, and improves the correlation between fault information and image identification accuracy.Experimental results show that SPD-UNet exhibits a higher identification accuracy than SegNet and ResUNet.The fault position and shape identified by SPD-UNet are closer to actual information.

WANG Jinhe, CHE Zhilong, ZHANG Nan, MENG Fanyun, SU Cuili, TAN Hao

The stereo matching method based on Convolutional Neural Network(CNN) does not make full use of the feature map information of each level in the image,resulting in the poor feature extraction performance in the ill posed region in the image.This paper proposes a stereo matching method based on multi-scale and multi-level features.A pooled pyramid layer is designed at the front end of the CNN model with double tower structure to extract the multi-scale low-level structural features of the image.In the back end of the network model,the high-level semantic features of the last three layers of network are fused to extract image features,and the disparity map is output after similarity measurement of image features.The experimental results on KITTI 2015 dataset show that compared with the LUO and Anita methods,the proposed method reduces the pixel error accuracy from 14.65% and 8.30% to 8.02%,and can obtain a disparity map with better detail information.

Martin Holmes, Janelle Jenstad, Kathryn Tomasek

Tobias Stål, Anya M. Reading

Researchers use 2D and 3D spatial models of multivariate data of differing resolutions and formats. It can be challenging to work with multiple datasets, and it is time consuming to set up a robust, performant grid to handle such spatial models. We share ‘agrid’, a Python module which provides a framework for containing multidimensional data and functionality to work with those data. The module provides methods for defining the grid, data import, visualisation, processing capability and export. To facilitate reproducibility, the grid can point to original data sources and provides support for structured metadata. The module is written in an intelligible high level programming language, and uses well documented libraries as numpy, xarray, dask and rasterio.   Funding statement: This research was supported under Australian Research Council’s Special Research Initiative for Antarctic Gateway Partnership (Project ID SR140300001).

Kashif Ayyub, Saqib Iqbal, Ehsan Ullah Munir et al.

In the era of web 2.0, online forums, blogs and Twitter are becoming primary sources for sharing views, opinions and comments about different topics. Classifying these views, opinions and comments is known as sentiment analysis which is an active research area. Sentiment analysis has vast applications in different fields of life, such as marketing, e-commerce and business. Under the umbrella of sentiment analysis, sentiment quantification that deals with estimating relative frequency of class of interest is being investigated by researchers nowadays. In sentiment quantification, exploring effect of new features and comparison of diverse types of classifiers to assess their effectiveness needs further investigation. In this paper, we explore diverse feature sets and classifiers for sentiment quantification. In addition, empirical performance analysis of conventional machine learning techniques, ensemble-based methods and state-of-the-art deep learning algorithms on basis of features set, is performed. The computed results show that the diverse features sets affect the performance of classifiers in sentiment quantification. The results also confirm that the deep learning techniques perform better than the conventional machine learning algorithms.

Xu Lingxiang, Chen Jiawei, Ding Guohui et al.

Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perspectivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorized according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future.

Soheila Zarinjoi alvar

Abstract Social entrepreneurship is one of the issues in scientific circles that have been considered along with organizational and economic entrepreneurship. The development of social entrepreneurship is considered as one of the ways to solve social problems and harms. The category of social entrepreneurship can help accelerate the progress of the country. The purpose of this study was, therefore, to examine the mediating role of the antecedents of entrepreneurial intent concerning previous experiences of social problems and entrepreneurial intention. The present study is a descriptive survey research. Data were collected using a 19-item standard questionnaire by Hookerts (2017). The statistical population of the study was 120 female managers and activists of social entrepreneurship and the sample size was calculated to be 92 people using the Morgan table. To select the sample, a stratified random sampling method was used. Data analysis was performed by structural equation modeling and LISREL software. The results showed that at the 95% confidence level of the structural model of the research, it has a good fit. Previous social problems affect the moral commitment and self-efficacy of social entrepreneurship, but the effect of previous experience of social problems on entrepreneurial intent was rejected and finally, the positive role of two mediating variables was confirmed. Thus, moral commitment, social entrepreneurship self-efficacy affects the intention of social entrepreneurship. Key words Social Entrepreneurship, Social Self-Efficacy, Moral Commitment   Introduction One of the most desirable and controversial types of entrepreneurship is Social entrepreneurship. Today, the development of entrepreneurship, and in particular social entrepreneurship as a social and humanitarian mission, and one of the solutions to some social problems has attracted attention (Marjani et al., 2014, 277). The advancement of science and technology as well as the presence of rapid changes in the process closes the way to the continuity of repetitive methods and requires innovative and creative solutions. Based on old procedures, society can no longer overcome problems so it thinks of new ideas in line with its goals, and this is what highlights the need for entrepreneurship and social entrepreneurship (Dehghan, 2016, 2). In their activities, organizations use social entrepreneurship, making a great leap for economic and social well-being (Lakapa-, 2018, 2054)  In today's society, women have an important and expanding role in business. In recent years, the share of women in private business has risen as much as that of government. For this reason, the talent and role of women in the development of countries must be paid attention to (Zarrin Joy Alvar, 2012). So the main question is what effect does the previous experience of social problems have on women's social entrepreneurial intentions and their precedents?   Method The present study, which is a case study, is practical in terms of purpose and descriptive in terms of nature and method and is a case study. A standard questionnaire of 19 questions by Hokrets (2017) with an alpha coefficient of 0.94 is the data collection method. The opinion of professors and experts helped to ensure the accurate assessment of the test content, from the perspective of face-to-face validity. In this regard, the same number of questionnaires were collected from the sample population using a stratified random sampling method. The statistical population includes managers and active women experts in social entrepreneurship (managers, employees, volunteers, and social businesses supporters in for-profit and non-profit sections) is 120 people. Therefore, according to the Morgan table, a sample of 92 people was selected. Findings Hypotheses one and two explored the effect of previous experience of social problems on ethical commitment and entrepreneurial self-efficacy. Considering that the coefficient t for these hypotheses is equal to (06.06) and (4.26) which are not in the meaningless range (+1.96 and -1.96), it is clear that these hypotheses are significant (with 95% certainty can be argued that previous experience of social problems has a significant impact on moral commitment and self-efficacy). On the other hand, given that the standard coefficient of the path between these two variables is equal to (0.43) (0.33) and positive, it indicates the positive effect of previous experience of social problems on moral commitment and self-efficacy, and these hypotheses are accepted. The third hypothesis examines the impact of previous experience of social problems on social entrepreneurship intentions. Considering that the t coefficient for this hypothesis is equal to (1.23) which is in the meaningless range (+1.96 and -1.96), it is clear that this hypothesis is not significant (it can be claimed with 95% confidence That the previous experience of social problems did not have a significant effect on the intention of social entrepreneurship) and this hypothesis is rejected. The fourth hypothesis examines the effect of social entrepreneurship self-efficacy on social entrepreneurship intentions. Provided that the t coefficient is equal to (2.10) which is not in the meaningless range (+1.96 and -1.96), it is clear that this hypothesis is significant (it can be claimed with 95% confidence That social entrepreneurship self-efficacy has a significant impact on social entrepreneurship intentions). On the other hand, considering that the standard path coefficient between these two variables is equal to (0.14) and positive, it indicates the positive effect of social entrepreneurship self-efficacy on the intention of social entrepreneurship, and this hypothesis is accepted.     Conclusion The first and second hypotheses affirm that previous experience of social problems affects moral commitment, social entrepreneurship self-efficacy, and entrepreneurial intentions. Efficiency is social entrepreneurship, but it does not influence the intention of entrepreneurship, so all other hypotheses are accepted except for the third hypothesis. The findings also indicate a positive and significant effect of moral commitment on the intention of social entrepreneurship, therefore since this result is consistent with the researcher’s argument, this hypothesis is accepted. The ode event can be traced back to ethics. The results indicate a positive and significant effect of social entrepreneurship self-efficacy on social entrepreneurship intentions, so since this result is consistent with the researcher's argument, this hypothesis is accepted. From mental states to behaviors and attitudes, self-efficacy affects almost everything. People who believe that they can achieve the desired results through their actions feel more competent in facing problems. According to the results, it is clear that social challenges have a positive and significant impact on social entrepreneurship intentions through moral commitment and social entrepreneurship self-efficacy, and these two hypotheses are confirmed.     References Aboudi, M., and Hindijanifard, M. (2017). Entrepreneurial Intent and Social Entrepreneurial Intention in Students Case Study: Gonabad Higher Education Complex, 2nd International Conference on Management and Accounting, Tehran[Text in Persian]. Ajzen, I. (1991). The theory of planned behavior. 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Esneyder Alfonso Rey, Jonattan Steve Pico, Lely Adriana Luengas

A través de la medición de presión plantar se realizan diagnósticos e investigaciones clínicas con el fin de observar el comportamiento de la presión en las diferentes zonas de la planta del pie y así poder emitir conceptos de patologías, prescribir ayudas para reducir la presión, entre otros. Existen dispositivos que permiten estas mediciones, por lo general basados en sensores de presión de tipo resistivo y capacitivo; unos pocos trabajan el procesamiento de imágenes a partir de imágenes impresas, lo cual lleva a errores en el momento de la captura de la imagen, además no entregan valores de distribución de presión plantar. Se presenta el desarrollo de PIPLAB, una plataforma baropodométrica que permite adquirir una imagen de la huella plantar y visualizar mediante un software de computador la presión que soporta la planta de los pies, dando a conocer los puntos máximos de presión bajo cada pie y la repartición de cargas entre el antepié y retropié de la persona a evaluar, así mismo dar las mediciones cuantitativas para brindar al profesional de la salud una herramienta de valoración y seguimiento de patologías a nivel plantar.

Soojin Park, Sungyong Park, Young B. Park

With the emergence of various forms of smart devices and new paradigms such as the Internet of Things (IoT) concept, the IT (Information Technology) service areas are expanding explosively compared to the provision of services by single systems. A new system operation concept that has emerged in accordance with such technical trends is the IT ecosystem. The IT ecosystem can be considered a special type of system of systems in which multiple systems with various degrees of autonomy achieve common goals while adapting to the given environment. The single systems that participate in the IT ecosystem adapt autonomously to the current situation based on collected data from sensors. Furthermore, to maintain the services supported by the whole IT ecosystem sustainably, the configuration of single systems that participate in the IT ecosystem also changes appropriately in accordance with the changed situation. In order to support the IT ecosystem, this paper proposes an architecture framework that supports dynamic configuration changes to achieve the goal of the whole IT ecosystem, while ensuring the autonomy of single systems through the collection of data from sensors so as to recognize the situational context of individual participating systems. For the feasibility evaluation of the proposed framework, a simulated example of an IT ecosystem for unmanned forest management was constructed, and the quantitative evaluation results are discussed in terms of the extent to which the proposed architecture framework can continuously provide sustainable services in response to diverse environmental context changes.

Mohammad javad Jafari, najaf noorizadeh, Soheila khodakarim

Background and aims: The application of computer codes and numerical modeling in studies and industry is growing. When using computer codes, the accuracy of these encodings and modelings are big questions. Comparing the results of the software with experimental results is the best method of evaluating these programs. The objective of this study was to experimentally validate a software program developed for local exhaust ventilation design.  Methods: In this experimental study, seven different local exhaust ventilation systems were designed and constructed. Velocity pressure, static pressure, total pressure and air velocity were measured at key points of each system. All measured values were compared with similar calculated values by computer software. One-sample T test and Bland Altman plot were used to compare the similar measured and computed parameters. SPSS version 19 was applied for statistical analysis. Results: The percentage difference of measured and calculated air velocities at all channels and hoods were ignorable with maximum amount of 0.5%. Difference between the measured and the calculated values of velocity pressure, static pressure and total pressure in the entire systems were less than 5 percent. Bland-Altman plot and one sample T test showed that the differences were in the range of 2SD and confirmed matching. The results revealed that the air velocities measured with Pitot tube are much more accurate than those measured by thermal anemometer with maximum difference of 14.7%.  Conclusion: This study showed that the tested software has an acceptable accuracy with less than ± 5% of error. It also revealed that the application of Pitot tube is much more accurate that thermal anemometer.

Mehdi Neshat, Ghodrat Sepidname

In this article, the objective was to present effective and optimal strategies aimed at improving the Swallow Swarm Optimization (SSO) method. The SSO is one of the best optimization methods based on swarm intelligence which is inspired by the intelligent behaviors of swallows. It has been able to offer a relatively strong method for solving optimization problems. However, despite its many advantages, the SSO suffers from two shortcomings. Firstly, particles movement speed is not controlled satisfactorily during the search due to the lack of an inertia weight. Secondly, the variables of the acceleration coefficient are not able to strike a balance between the local and the global searches because they are not sufficiently flexible in complex environments. Therefore, the SSO algorithm does not provide adequate results when it searches in functions such as the Step or Quadric function. Hence, the fuzzy adaptive Swallow Swarm Optimization (FASSO) method was introduced to deal with these problems. Meanwhile, results enjoy high accuracy which are obtained by using an adaptive inertia weight and through combining two fuzzy logic systems to accurately calculate the acceleration coefficients. High speed of convergence, avoidance from falling into local extremum, and high level of error tolerance are the advantages of proposed method. The FASSO was compared with eleven of the best PSO methods and SSO in 18 benchmark functions. Finally, significant results were obtained.

C. Chapelle