Self-organizing Method Research Articles

In-situ self-organized anode interlayer enables organic solar cells with simultaneously simplified processing and greatly improved efficiency to 17.8%

Highly efficient organic solar cells (OSCs) are often obtained with a multilayer structure, in which active layer is sandwiched between anode and cathode interlayer. Here we present a simple strategy to simultaneously obtain anode interlayer and boost performances of OSCs by directly adding a tiny small molecule [2-(9H-carbazol-9-yl)ethyl] phosphonic acid (2PACz) into the active layer. It is found that 2PACz with high surface energy prefers to self-organize at the bottom of PM6:Y6-BO bulk heterojunction (BHJ) blend and in-situ form anode interlayer during the film formation. Encouragingly, green solvent o-xylene processed OSCs with substantial improvement of photovoltaic performances are observed in self-organized 2PACz device with best power conversion efficiency (PCE) reaching 17.8%, notably higher than that of control device based on independently spin-coated 2PACz with a PCE of 16.8%. In-depth studies indicate the in-situ self-organized 2PACz underneath BHJ is beneficial to enhance build-in potential, suppress surface trap-assisted recombination, and induce ordered molecule stacking. Moreover, this self-organization method is implanted in a large-area OSC with 1 cm2 device area and an outstanding PCE of 15.8% is achieved. These results above highlight the great potential of in-situ self-organization method in simplifying device processing as well as improving efficiency, which make it an ideal strategy for large-area OSCs manufacture.

Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior

Cooperative search-attack is an important application of unmanned aerial vehicle (UAV) swarm in military field. The coupling between path planning and task allocation, the heterogeneity of UAVs, and the dynamic nature of task environment greatly increase the complexity and difficulty of the UAV swarm cooperative search-attack mission planning problem. Inspired by the collaborative hunting behavior of wolf pack, a distributed self-organizing method for UAV swarm search-attack mission planning is proposed. First, to solve the multi-target search problem in unknown environments, a wolf scouting behavior-inspired cooperative search algorithm for UAV swarm is designed. Second, a distributed self-organizing task allocation algorithm for UAV swarm cooperative attacking of targets is proposed by analyzing the flexible labor division behavior of wolves. By abstracting the UAV as a simple artificial wolf agent, the flexible motion planning and group task coordinating for UAV swarm can be realized by self-organizing. The effectiveness of the proposed method is verified by a set of simulation experiments, the stability and scalability are evaluated, and the integrated solution for the coupled path planning and task allocation problems for the UAV swarm cooperative search-attack task can be well performed.

Evaluation and Analysis of Multimedia Collaborative Building Design Relying on Particle Swarm Optimization Algorithm

This article proposes improvements to the placement, organizational structure, and adaptability of the evaluation model and proposes a comprehensive evaluation model for the multiattribute scheme of the particle swarm optimization algorithm. The structure table of the multimedia building design plan evaluation index, the plan evaluation table, and the standardized model of the plan evaluation value of the common method was proposed. It also proposes the import of “with tutor” type data of particle swarm optimization algorithm as well as the self-organizing neural network evaluation model and calculation method of “no tutor” type resource import. According to the complexity of architectural design evaluation and the nonlinear relationship between related influencing factors, we have studied the principles and methods of applying system analysis, designed comprehensive evaluation models, and comprehensively evaluated architectural design schemes.

Welche Führung braucht die neue Arbeitswelt? Transformationale und authentische Führung, Autonomie und das Wohlbefinden von Geführten

What sort of leadership is needed in the new world of work? Transformational and authentic leadership, autonomy and the well-being of the led Objectives: We investigate whether transformational and authentic leaders have a positive impact on the well-being (work engagement, job satisfaction) of those they lead and whether this effect can be explained by increased autonomy. Transformational and authentic leadership could thus be suitable approaches for a changing world of work in which flexibility and dynamism are increasingly high on the agenda as a result, among other things, of the increase in self-organising methods of work. Method: A longitudinal investigation with three measurement times across 20 months was conducted with 1875 employees from 273 teams. Employees assessed the leadership styles of their respective leaders, their autonomy with a time lag of approximately 12 months as well as their work engagement and job satisfaction about eight months later. Data were analysed with multilevel structural equation models in order to control for the nested data structure. Autoregressors (measured at T1) were included for autonomy as the proposed mediator, as well as for the two dependent variables. Results: There were significant positive direct longitudinal relationships between transformational as well as authentic leadership with job satisfaction, yet not with work engagement about 20 months later. Both leadership styles were positively related to autonomy appr. 12 months later, and autonomy in turn was positively related to well-being indicators about eight months later. Overall the hypothesized indirect relationships between leadership and well-being mediated through an increase in autonomy were confirmed for authentic leadership, but not for transformational leadership. Conclusions: Both transformational and authentic leadership show positive correlations to well-being. But only authentic leadership can be shown to promote autonomy, which can be seen as central in dealing with the challenges of the modern world of work. Keywords: leadership – changing world of work – well-being - autonomy

Towards classification of patients based on surface EMG data of temporomandibular joint muscles using self-organising maps

The study considers the need for an effective method of classification of patients with a temporomandibular joint disorder (TMD). The self-organising map method (SOM) was applied to group patients and used together with the cross-correlation approach to interpret the processed (rectified and smoothed by using root mean square (RMS) algorithm) surface electromyography signal (sEMG) obtained from testing the muscles (two temporal muscles and two masseters) of the temporomandibular joint (TMJ) during selected jaw movements. SOM’s Unified distance matrix (U-matrix) maps consist of formed clusters that correspond to similarities in input datasets. The results showed that SOM was able to encode muscular responses and create clusters. Information about the level of similarity between the activity of right, left, ipsilateral, and contralateral pairs of muscles was provided by intra cross-correlation coefficient (CC). A low intra CC value may indicate instability of the TMJ function. Information about the level of similarity between the sEMG signals of the same muscles tested in two different patients was provided by inter CC. SOM analysis can be used to interpret the activation of muscular systems, and by comparing the results of different individuals also to identify their TMJ health. Using the cross-correlation approach, one can find similarities in the sEMG data of different patients that can be used to provide clinically useful information. Such findings could be used to improve the clinical diagnosis of TMD and assess muscle activity during treatment.

Automated Construction of Neural Network Potential Energy Surface: The Enhanced Self-Organizing Incremental Neural Network Deep Potential Method.

In recent years, the use of deep learning (neural network) potential energy surface (NNPES) in molecular dynamics simulation has experienced explosive growth as it can be as accurate as quantum chemistry methods while being as efficient as classical mechanic methods. However, the development of NNPES is highly nontrivial. In particular, it has been troubling to construct a dataset that is as small as possible yet can cover the target chemical space. In this work, an ESOINN-DP method is developed, which has the enhanced self-organizing incremental neural network (ESOINN) and a newly proposed error indicator at its core. With ESOINN-DP, one can construct the NNPES with little human intervention, and this method ensures that the constructed reference dataset covers the target chemical space with minimum redundancy. The performance of the ESOINN-DP method has been well validated by developing neural network potential energy surfaces for water clusters, tripeptides, and by de-redundancy of a sub-dataset of the ANI-1 database. We believe that the ESOINN-DP method provides a novel idea for the construction of NNPES and, especially, the reference datasets, and it can be used for molecular dynamics (MD) simulations of various gas-phase and condensed-phase chemical systems.

Functional and Harmonious Selforganization of Large Intellectual Agent Ensembles with Smart Hybrid Competencies via Wireless and Mobile Networks

Artificial intelligence of large ensembles of intelligent agents in terms of computing power surpasses human intelligence. He is capable of artificial thinking and understanding. Giant ensembles of intellectual agents with artificial consciousness and intelligence are able, for the results set by the person necessary for him, to find solutions for their obtaining on the basis of professional competence and experience accumulation. The professional competence of artificial intelligence is the ability to use technologies, including computer vision, natural language processing, speech recognition and synthesis, intelligent decision support, as well as the use of synergistic methods, functional self-organizing methods and utility and preference criteria. For artificial intelligence, the functional organization of the system is important. The functionalism of artificial intelligence does not depend on its carrier, allows many methods of its functional implementation, as well as to form the completeness of its functions. Giant ensembles of intellectual agents with a full set of functions gradually and flexibly form events into solutions or rational behavior to obtain a given necessary result. Intelligent artificial intelligence has psychological, analytical, research, language, professional and behavioral hybrid competencies. Each competence is exercised by an intelligent agent with a competent functional professional manner. Intelligent agents form an ensemble with intelligent ethical artificial intelligence. The article is devoted to functional harmonious selforganization of ensembles of intellectual agents.Functional harmonious self-organization of the interaction of intellectual agents in different environments is carried out via wireless and mobile networks on the basis of data of a specific environment obtained by analytical competent intellectual agents. As result of research, the law of the golden section of the functional harmonious self-organization of ensembles of intellectual agents was revealed. Further research will focus on the effective use of wireless and mobile networks in the practical application of smart agent ensembles.

Prediction of Surface Roughness of Abrasive Belt Grinding of Superalloy Material Based on RLSOM-RBF.

It is difficult to accurately predict the surface roughness of belt grinding with superalloy materials due to the uneven material distribution and complex material processing. In this paper, a radial basis neural network is proposed to predict surface roughness. Firstly, the grinding system of the superalloy belt is introduced. The effects of the material removal process and grinding parameters on the surface roughness in belt grinding were analyzed. Secondly, an RBF neural network is trained by reinforcement learning of a self-organizing mapping method. Finally, the prediction accuracy and simulation results of the proposed method and the traditional prediction method are analyzed using the ten-fold cross method. The results show that the relative error of the improved RLSOM-RBF neural network prediction model is 1.72%, and the R-value of the RLSOM-RBF fitting result is 0.996.

Recovery of Natural Scenery Image by Content Using Wiener-Granger Causality: A Self-Organizing Methodology

One of the most important applications of data science and data mining is is organizing, classifying, and retrieving digital images on Internet. The current focus of the researchers is to develop methods for the content based exploration of natural scenery images. In this research paper, a self-organizing method of natural scenes images using Wiener-Granger Causality theory is proposed. It is achieved by carrying out Wiener-Granger causality for organizing the features in the time series form and introducing a characteristics extraction stage at random points within the image. Once the causal relationships are obtained, the k-means algorithm is applied to achieve the self-organizing of these attributes. Regarding classification, the k−NN distance classification algorithm is used to find the most similar images that share the causal relationships between the elements of the scenes. The proposed methodology is validated on three public image databases, obtaining 100% recovery results.

Emerging Consumer Healthy Lifestyles in Lithuania

Consumer lifestyle is considered one of the important predictors of sustainable consumption behavior at the individual, community and societal levels. In this paper, the healthy lifestyle of consumers is analyzed and defined as the lifestyle that explains how people live in terms of health. This study focuses on consumers’ healthy lifestyle clusters and offers an updated healthy lifestyle measurement tool that can be used to segment consumers into specific segments according to six healthy lifestyle domains: physical, mental, emotional, social, spiritual and intellectual health. An online survey with 645 respondents of different socio-demographic profiles was conducted in Lithuania. Based on data collected through questionnaires, specific segments were identified using self-organizing maps and cluster analysis methods. The findings suggest that four different segments could represent consumers concerning their healthy lifestyles. The results will be of use to companies initiating marketing campaigns to target different consumer groups with their brands and offering healthy lifestyle-related products and services to consumers in Lithuania. The findings are also valuable for public policymakers and opinion leaders who foster healthy lifestyles and seek to form a public opinion regarding sustainable consumption.

Комплексный анализ эффективности системы «Оператор - машина» на основе методов искусственного интеллекта

Methodological approaches to the analysis of the effectiveness of complex human-machine systems (SMS) based on the use of methods of heuristic self-organization and artificial neural networks are considered. Modeling of the system taking into account a large number of factors and several output variables that characterize the MFM is based on obtaining multilayer perceptrons with the exception of factors with low sensitivity

ЗАСТОСУВАННЯ НЕЙРОННИХ МЕРЕЖ МГУА ДЛЯ ВИЯВЛЕННЯ ЗМІНИ УМОВ РОБОТИ НАСОСНОЇ СТАЦІЇ ВОДОПОСТАЧАННЯ, ЗУМОВЛЕНИХ ВПЛИВОМ СЕЗОННИХ ЧИННИКІВ

Purpose. Development of a mechanism identification of changes in the actual operation conditions of the water supply facility depending on the season. Methodology. Methods of intellectual analysis of profiles of mode indicators were used to identify regularities in the formation of the operation mode of the facility. The mathematical apparatus of pattern recognition algorithm with training was used to classify the profiles of mode indicators. Methods of self-organization of models of complex systems were used for structural and parametric identification of the classifier model. Results. The necessity of the analysis of the characteristics of operation mode of the pumping station of water supply, obtained from the monitoring system, to identify regularities in the formation of water supply was substantiated. The daily graph of water consumption from the water supply network was used as a mode indicator of the water supply process. Indicators of water supply volumes and graph unevenness were used to describe the water supply graph. The expediency of application of self-organization methods for solving the problem of classification and construction of the classifier model was substantiated. Structural and parametric identification of the classifier model for daily water consumption graphs was performed using GMDH Neural Networks. The search for the optimal model was performed in three classes of neural networks. Better neural network structure was chosen on the basis of criterion of regularity. The K-block cross-validation strategy was used to test the models. The results of the verification of the classifier model showed the high quality of the classification. Originality. A method for identifying changes in the operation conditions of the water supply facility due to the influence of seasonal factors, based on the usage of the classifier of profiles of daily water consumption graphs from the water supply network, was proposed. Practical value. The constructed model of the classifier allows defining of belonging of a profile of the daily water consumption graph, received from monitoring system of the water supply mode, to one of typical classes. The fact of class change indicates a change in the actual operation conditions of the water supply facility. Conclusions. Analysis of daily graphs of water consumption using the pattern recognition algorithm makes it possible to establish the change in the actual operation conditions of the water supply facility caused by the influence of seasonal factors. The usage of neural networks of GMDH makes it possible to perform automatically structural and parametric identification of the classifier model. Application of the offered principles is a basis of effective planning of operation modes of pumping station of water supply. References 16, tables 3.

Flocking-Based Self-Organized Aggregation Behavior Method for Swarm Robotics

Flocking-Based Self-Organized Aggregation Behavior Method for Swarm Robotics

Development of a model for evaluating the effectiveness of innovative startups based on information cycles and using neural networks

An integrated approach to the creation and development of innovative startup projects in the field of information technology is considered. To conduct research, the authors proposed a model of information cycles of startup projects based on the creation of an information model of such projects. At the same time there are dynamic processes of changes in the parameters of the model, which are turbulent in nature and require the use of tools and methods of artificial intelligence for research. The key areas of knowledge of such influence are defined. The mathematical model of processes of management of development of IT startups on the basis of creation and development of a difficult IT product, taking into account influences of environments of the project is constructed, the basic characteristics are allocated and parameters are defined. To do this, the construction of predictive models is proposed to be carried out by modified Demarc trends, the method of self-organization and the neural network. The modeling of the main objective functions of the mathematical model of these processes is performed. The analysis of the received results is carried out and the conclusions are made.

An LRFM Model to Analyze Outpatient Loyalty From a Medical Center in Taiwan

This research is intended to study the behaviors of outpatients in a medical center and constructs a set of data exploration procedures such that hospital management can deal with patient relationship management more effectively. This study adopts LRFM (length, recency, frequency, and monetary) model and cluster analysis, including self-organizing maps and K-means method, to categorize 321,908 outpatients of the medical center into 12 groups and then uses the multidimensional customer clustering philosophy to classify the outpatients. Outpatients can be categorized into five different types of groups, namely, core customer groups, potential customer groups, new customer groups, lost customer groups, and resource-consuming customer groups. In addition, seven types of outpatients based on five types of categories are identified. The similarities and differences of each group based on the patients’ characteristics are analyzed to give differentiation strategy advices for hospital management. Hospital management thus can design the optimal service strategies, provide the best care services, enhance hospital’s performance, and reduce the overall cost to establish quality relationships with outpatients.

Self-organized and Unsupervised Information Integration Method by Combining Capacity and PCA

Self-organized and Unsupervised Information Integration Method by Combining Capacity and PCA

Fuzzy logic in reliability assessment of short-term forecast models for mining equipment

Many industries are yet being faced with the critical problems connected with prediction and prevention of process equipment accidents and hazardous events, including coal mines and transportation systems. Specific interest lies in prediction of the remaining life of mining equipment in the period of its operation. The authors try to attempt an integration of the self-organization and fuzzy logic methods to enhance reliability of forecasts for mining equipment status given the monitoring data uncertainty. The algorithm is proposed for the adaptive introduction of excessive information using a few forecast models. As a result, complexing of forecasts by two–three analogs of the variation criterion in selection of efficient forecast of multi-dimensional nonstationary events is explained. Considering the importance of haulage processes in the mining industry, the integrated forecast models can be extended for motor road and rail transport. In motor road and rail transportation, the traffic timing is strict; however, accidents yet occur and can lead to injuries and even death. In the aggregate, the managerial solutions on safety can be better substantiated.

DETECTING WORMHOLE ATTACKS IN WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSN), consisting of wireless sensors and control devices and methods of self-organization using intelligent algorithms, demonstrate broad prospects. WSNs are subject to numerous types of threats and attacks. One of which is a wormhole attack. This article provides an overview of wormhole attack detection methods in WSN, as well as a new optimized method for detecting this attack. The detection method is based on the removal of the edges of the wormhole and causes significant changes in the length of the shortest paths between network nodes.The accuracy of the proposed algorithm is not affected by the number of wormholes

An incentive mechanism-based framework to assure the quality of self-organizing peer review in preprint

PurposePreprint has become an important vehicle for academic communications and discussions. However, in preprint, there is a lack of a sufficient quality control mechanism such as peer review, which is a proven quality assurance practice that is used in traditional academic publishing services. To address the problem leveraging on the power of this practice, the authors introduce into preprint a self-organizing peer review method by applying the concept of token economy and the blockchain technology.Design/methodology/approachSpecifically, this paper proposes an idea that applies the token economy concept to the design of the incentive and penalty mechanisms for peer reviewers in preprint to assure the qualities of its publications. Steemit has been studied to demonstrate the characteristics of the mechanisms.FindingsA token economy-enhanced framework for self-organizing peer review in preprint is also proposed. The resulting preprint system is an academic community-oriented, self-organizing and blockchain-based content publishing system that is designed to run on both permissioned and permissionless blockchains.Research limitations/implicationsFirst, since peer review is on a voluntary basis and not profits oriented, the “monetary” incentive and penalty mechanisms borrowed from Steemit may conflict with academic ethics. Second, the authors proposed to deploy the authors’ token economy on blockchain, but the current mainstream decentralized blockchain services are too few to warrant a foreseeable successful future for the authors’ application. In fact, as the flagship of blockchain 2.0, the Ethereum blockchain suffers from the problem of scalability, which leads to its applications' lower performances, longer response times and eventually more negative user experiences as time goes by. Finally, the authors’ proposed version of preprint has not been implemented, and hence, its practical effectiveness and acceptance by academia are yet to be evaluated.Practical implicationsIn this paper, the authors proposed a token economy-based framework for self-organizing peer review in preprint leveraging on blockchain technology. This framework encourages positive interactions between authors and reviewers, which helps to establish a healthy academic ecology that produces more contents with better qualities. Application of a solution based on the authors’ framework should impact the current academic communities by offering a new academic peer reviewing tool that has a built-in mechanism for self-behavior correction and quality assurance.Social implicationsThrough adaption, the framework can be applied to other domains as well. In such domains, a large amount of feedbacks from partakers are needed and there exists a tremendous amount of work to filter noises in feedbacks so as to ensure that as many the quality ones as possible are delivered for a variety of purposes. The authors’ framework essentially impacts almost all domains where there exists a need to collect and filter large amount of feedbacks, and using the authors’ framework-based solution is cost-saving, which can be seen as a major potential contribution of the research.Originality/valueThe incentive and penalty mechanisms encourage positive interactions between authors and reviewers, and it helps to establish a healthy academic ecology that produces high-volume contents with good qualities.

Directed Induction of Retinal Organoids from Human Pluripotent Stem Cells.

Retinal cell transplantation is a promising therapeutic approach, which could restore the retinal architecture and stabilize or even improve the visual capabilities to the degenerated retina. Nevertheless, progress in cell replacement therapy presently faces the challenges of requiring an off-the-shelf source of high quality and standardized human retinas. Therefore, an easy and stable protocol is needed for the experiments. Here, we develop an optimized protocol, based on a self-organizing method with the use of exogenous molecules and reagent A as well as manual excision to generate the three-dimensional human retina organoids (RO). The human Pluripotent Stem Cells (PSCs)-derived RO expresses specific markers for photoreceptors. With the addition of COCO, a multifunctional antagonist, the differentiation efficiency of photoreceptor precursors and cones is significantly increased. The efficient use of this system, which has the benefits of cell lines and primary cells, and without the sourcing issues associated with the latter, could produce confluent retinal cells, especially photoreceptors. Thus, the differentiation of PSCs to RO provides an optimal and biorelevant platform for disease modelling, drug screening and cell transplantation.