Self-organizing Method Research Articles

Combination of multivariate data analysis and mixing modelling to assess tracer potential of contaminants of emerging concern in aquifers

Collected evidence has shown that contaminants of emerging concern (CECs) in conjunction with more conventional tracers (major ions, nutrients, isotopes etc.) can be used to trace pollution origin in aquatic systems. However, in highly mixed aquifer systems signals obtained from conventional tracers overlap diminishing their potential to be used as tracers. In this study, we present an approach that incorporates multivariate statistical analysis (principal component analysis (PCA) and Kohonen's Self-Organizing Map method (SOM)) and mixing modelling to identify the most suitable CECs to be employed as anthropogenic tracers. The study area is located in the Besòs River Delta (Barcelona, NE Spain) and represents the highly mixed aquifer system. A one-year monthly based monitoring campaign was performed to collect the information about the concentrations of 105 CECs as well as major and minor ions in the river and along the groundwater flow. The dimensionality of the obtained dataset was reduced to 25 CECs, based on their estimated health risk effects, for multivariate data analysis. The obtained results showed the overlap of conventional tracers' signals obtained from PCA. In case of CECs, PCA revealed differences in their distributions allowing the differentiation of the roles of natural attenuation processes, local and regional flows on their occurrence in different parts of the aquifer. This was not possible to do using solely CECs' distribution profiles. SOMs provided the lacking information about the modality of the distribution of each CECs, revealing their ability to represent factors controlling the groundwater hydrochemistry, which assist in defining their tracer potential. Based on the obtained results four identified persistent CECs, two with unimodal (lamotrigine and 5-Desamino-5-oxo-lamotrigine) and two with bimodal (carbamazepine and diazepam (higher modality was not revealed)) distributions, were selected to run a mixing model to compare their tracer performance.

UJI HOMOGENITAS DAN HETEROGENITAS CLUSTER DATA INTERKOMPARASI ANAK TIMBANGAN

Intercomparison of mass quantities is an activity to compare performance between laboratories ofmass quantities and reference laboratories which produces intercomparative data in the form ofconventional mass values and uncertainty values. Clustering of weights was carried out to obtain ananalysis of the laboratory’s participants management. The analysis carried out is very dependent on theaccuracy of the cluster. Clustering using self-organizing maps algorithm, and the results are tested for thelevel of homogeneity within the cluster and the level of heterogeneity between clusters using the icdrate value.The clustering of intercomparison’s data using the self-organizing maps method resulted in an icdratevalue of 0,0713 for an artifact of 1000 g and an icdrate of 0,2889 for an artifact of 200g

Introducing effective parameters for predicting job burnout using a self-organizing method based on group method of data handling neural network.

In addition to affecting people's bodily and mental health, the Covid-19 epidemic has also altered the emotional and mental well-being of many workers. Especially in the realm of institutions and privately held enterprises, which encountered a plethora of constraints due to the peculiar circumstances of the epidemic. It was thus anticipated that the present study would use a group method of data handling (GMDH) neural network for analyzing the relationship of demographic factors, Coronavirus, resilience, and the burnout in startups. The test methodology was quantitative. The research examined 384 startup directors and representatives, which is a sizable proportion of the limitless community. The BRCS, the MBI-GS, and custom-made assessments of stress due to the Coronavirus were all used to collect data. Cronbach's alpha confirmed the polls' dependability, and an expert panel confirmed the surveys' authenticity. The GMDH neural network's inherent potential for self-organization was used to choose the most useful properties automatically. The trained network has a three-layered topology with 4, 3, and 2 neurons in each of the hidden layers. The GMDH network has significantly reduced the computational load by using just 7 parameters of marital status, stress of covid-19, job experience, professional efficiency, gender, age, and resilience for burnout categorization. After comparing the neural network's output with the acquired data, it was determined that the constructed network accurately classified all of the information. Among the achievements of this research, high accuracy in predicting job burnout, checking the performance of neural network in determining job burnout and introducing effective characteristics in determination of this parameter can be mentioned.

PENERAPAN METODE PEMBELAJARAN SELF ORGANIZED LEARNING ENVIRONMENT DALAM MENGEMBANGKAN SOFT SKILL MAHASISWA

This research examines the application of the self-organized learning environment learning method in an effort to develop the soft skills of students in the Applied Managerial Accounting Undergraduate Study Program at the Bali State Polytechnic. This research method is descriptive qualitative using interview and questionnaire data collection techniques for 96 respondents. The result is a self-organized learning environment learning method that combines internet technology as a reference source for students with lecturers as a reference source that can develop students' soft skills such as having an initiative, independent, trustworthy and communication skills.

Design and Performance Analysis of 25 kW Class HTS Induction/Synchronous Motor With Self-Organizing Method for Transportation Applications

We proposed a non-empirical self-organizing design method to obtain a unique stator structure for a high-temperature superconducting induction/synchronous machine. The method starts from a line current model with an approximately ideal sinusoidal distribution of the air-gap magnetic field. It keeps increasing the cross-sectional area of the windings under appropriate constraints to achieve a model with realistic current density. In this work, we applied this method to design a 25 kW-class high-temperature superconducting induction/synchronous motor. In the model, each slot has three-phase windings with different areas representing different numbers of turns. The width and height of the two slots are different for each pole and phase. The results demonstrated that the self-organizing design method significantly suppressed the torque ripple of the motor and reduced the high-order harmonics. The self-organizing windings are equivalent to a short-pitching configuration, which produce a more sinusoidal air-gap flux density than windings in general design. Reducing torque ripple to the lowest possible level facilitates the practical use of superconducting machines for transportation applications.

Application of the Self-Organizing Map Method in February Temperature and Precipitation Pattern over China: Comparison between 2021 and 2022

In this study, we compared two anomalous wet February periods in 2021 and 2022 in China. The same anomalies appeared in the spatial distribution of precipitation, with anomalous precipitation centered over the southeast coast. However, temperature discrepancies appeared in most of China, with anomalously high temperatures in 2021 and lower temperatures in 2022. Both instances of increased precipitation were attributed to warm and moist advection from the south, with transport in 2021 being partly enhanced by the South China Sea cyclone, whereas transport in 2022 was mainly due to the subtropical western North Pacific anticyclone. Therefore, in this study, we aimed to compare and analyze temperature and precipitation anomalies in February 2021 and 2022 using the self-organizing map method. Warm events in East Asia and cold events in Siberia and the Tibetan Plateau types were obtained by mode 1, which contained 2021. Mode 6 exhibited opposite warm types in Siberia and cold types in southern Asia, including February temperature and precipitation anomalies in 2022. Based on the results of this study, we can conclude that precipitation anomalies in February 2021 and 2022 occurred under different temperature and circulation anomalies, and both were influenced by La Niña events. Autumn sea ice loss in the Barents Sea contributed significantly to warm and rainy events in February 2021. However, the cold and rainy events of February 2022 were closely related to the strengthening of the Siberian High.

Stochastic regional-based profit-maximizing hub location problem: A sustainable overview

Climate change is one of the most important concerns globally, and countries need to improve their transportation infrastructure to handle flows with the least air pollution. In specific cities and regions, carbon emissions may cause in future many issues for people and societies. Although transportation hubs are very helpful in the reduction of transportation costs, they may cause an increase in carbon emissions in some regions, leading to long-term environmental problems. In this study, a model is developed for the profit-maximizing hub location problem that incorporates three pillars of sustainability. The proposed model is not only designed to maximize profit but also to control carbon emissions using a carbon cap policy and to reduce differences in emissions between regions with consideration of population density. This scheme will lead to achieving a sustainable transportation network. A two-stage stochastic programming approach is employed to cope with demand uncertainty. An enhanced sampling based on the self-organizing map method was utilized to cluster scenarios that lead to dealing with small-sized problems. Furthermore, classical Benders decomposition, Pareto-optimal cut Benders decomposition, and L-shaped algorithms are employed to solve the proposed models more efficiently. The proposed models are analyzed using the well-known Turkish Network (TR) data set. Computational results demonstrate that the proposed models help to achieve sustainability. Factors of sustainability such as environmental and social can be achieved by slightly reducing the amount of profit in the economic factor. Furthermore, results show that the L-shaped algorithm with a multi-cut scheme outperforms the commercial solver, classical Benders decomposition, and Pareto-optimal cut Benders decomposition algorithms in large-size instances.

Self-organized cascade collaborative optimization method for associated unit processes

The process manufacturing industry includes multiple continuous unit processes, which cooperate with each other to complete a specific production task. Owing to the coupling between processes, it is often difficult to optimize a unit process to achieve global optimization. Considering the influence of the energy-mass coupling between the associated unit processes and the self-organized motion in the system on the process optimization operation, this study proposes a self-organized cascade collaborative optimization method for associated unit processes. By gradually producing a description of the operating status and coupling relationship of the system, a global optimization model can be constructed. First, reaction efficiency is proposed to evaluate the operating status of the system, and an online estimation model is constructed. Second, self-organized criticality is introduced to describe the evolution of the operating status in the collaborative production unit, and the mechanism knowledge and production data characteristics are combined to construct a coupling relationship model describing coupling and collaboration in the cascade system. Finally, a global optimization method for the system is constructed according to the coupling relationship between the associated unit processes and the cascading characteristic of the cascade system and within the unit process. The experimental results show that the method can realize global optimization of the cascade system, which improves the economic benefits compared with the existing production conditions and ensures stability of the system.

Long-Term Changes, Synoptic Behaviors, and Future Projections of Large-Scale Anomalous Precipitation Events in China Detected by a Deep Learning Autoencoder

Abstract The frequency of large-scale anomalous precipitation events over China has increased during 1961–2018. However, it remains challenging to understand the mechanisms associated with these anomalous events showing different spatial patterns. Here, we applied an autoencoder technique to identify large-scale anomalous precipitation events for both observations and model simulations, which were then classified into several patterns through a self-organizing map method. The synoptic behavior and atmospheric circulation background of different anomalous patterns were also analyzed using simultaneous composite analyses. Results show that occurrences of different anomalous precipitation patterns have increased significantly, except those centered in North China, Northeast China, and the Yangtze River basin. The anomalous precipitation patterns manifest various intraseasonal distributions, which are linked to zonal oscillations of the western North Pacific subtropical high (WNPSH) and meridional displacements of East Asia westerly jet (EAJ). Accompanied by the westward movement of WNPSH, anticyclonic systems transport warm moist air from the Indian Ocean and the South China Sea to converge with the cold air caused by anomalous cyclones over the northwest flank of the WNPSH, leading to large-scale anomalous precipitation in these regions. Besides WNPSH, the northward and southward displacements of EAJ also favor the occurrence of anomalous precipitation events in northern and southern China, respectively. Our study also illustrates that the occurrence frequency of anomalous precipitation events is projected to increase remarkably under the Shared Socioeconomic Pathway 5–8.5 (SSP585) scenario by rates of twofold to fourfold.

SUNDER: Self-organized grouping and entrapping method for swarms in multitarget environments

For swarm robots moving in a harsh or uncharted outdoor environment without GPS guidance and global communication, algorithms that rely on global-based information are infeasible. Typically, traditional gene regulatory networks (GRNs) that achieve superior performance in forming trapping pattern towards targets require accurate global positional information to guide swarm robots. This article presents a gene regulatory network with Self-organized grouping and entrapping method for swarms (SUNDER-GRN) to achieve adequate trapping performance with a large-scale swarm in a confined multitarget environment with access to only local information. A hierarchical self-organized grouping method (HSG) is proposed to structure subswarms in a distributed way. In addition, a modified distributed controller, with a relative coordinate system that is established to relieve the need for global information, is leveraged to facilitate subswarms entrapment toward different targets, thus improving the global multi-target entrapping performance. The results demonstrate the superiority of SUNDER-GRN in the performance of structuring subswarms and entrapping 10 targets with 200 robots in an environment confined by obstacles and with only local information accessible.

Fractional gradient descent algorithm for switching models using self-organizing maps: One set data or all the collected data

This paper proposes two fractional gradient descent algorithms for switching models. Each submodel is assigned a weight which can determine the identity of the submodel in each sampling instant. By using the fractional gradient descent algorithms, the parameters of each submodel can be obtained, and then the weights of all the submodels can be estimated based on the self-organizing maps method. These two algorithms can deal with different kinds of switching models on a case by case basis. In addition, compared with the traditional identification algorithms, the proposed methods have two advantages: (1) has faster convergence rates; (2) has less computational efforts. Simulation example demonstrates the effectiveness of the proposed methods.

Self-Organizing Interval Type-2 Fuzzy Neural Network Compensation Control Based on Real-Time Data Information Entropy and Its Application in n-DOF Manipulator.

In order to solve the high-precision motion control problem of the n-degree-of-freedom (n-DOF) manipulator driven by large amount of real-time data, a motion control algorithm based on self-organizing interval type-2 fuzzy neural network error compensation (SOT2-FNNEC) is proposed. The proposed control framework can effectively suppress various types of interference such as base jitter, signal interference, time delay, etc., during the movement of the manipulator. The fuzzy neural network structure and self-organization method are used to realize the online self-organization of fuzzy rules based on control data. The stability of the closed-loop control systems are proved by Lyapunov stability theory. Simulations show that the algorithm is superior to a self-organizing fuzzy error compensation network and conventional sliding mode variable structure control methods in control performance.

Distributed Self-Organizing Control of CAVs Between Multiple Adjacent-Ramps

Traffic self-organizing is controlled autonomously by rules that rely on adaptation to local variations in traffic state and enable effective coordination of the vehicular traffic at a network level. Combined self-organizing network with intelligent transportation, we proposed a distributed self-organizing method for Connected and Autonomous Vehicles (CAVs), which aimed to improve the efficiency and safety between Multiple Adjacent-Ramps (multi-ARs). To make the mainline formation more stable, the speed of ramp vehicles was adjusted to ensure suitable speed and headway of the mainline formation. In the test, the multi-ARs in the East Ring Interchange on the Inner Ring Express in Chongqing was selected to collect the initial data sample by the drones and fixed-point cameras. Under the respective scenarios of conventional driving and intelligent networks, the Python, SUMO, and TraCI were adopted to run simulations and validate the proposed model. Results showed that our model could keep Time to Conflict (TTC) above 1.4s, reduce the average delay by 34.22%, reduce the lane-changing times by 28.07%, reduce single lane occupancy to 8% and improve average speed by 3.68% of multi-ARs. To verify the applicability of the proposed model, experiments were carried out under different traffic volumes, demonstrating the relevance of the proposed method for medium-to-high-density traffic flows. It can provide a basis for traffic engineers and policymakers to maintain the stable development of the urban expressways and ensure the overall operation quality of the multi-ARs.

Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data.

Controlled ovarian stimulation is tailored to the patient based on clinical parameters but estimating the number of retrieved metaphase II (MII) oocytes is a challenge. Here, we have developed a model that takes advantage of the patient's genetic and clinical characteristics simultaneously for predicting the stimulation outcome. Sequence variants in reproduction-related genes identified by next-generation sequencing were matched to groups of various MII oocyte counts using ranking, correspondence analysis, and self-organizing map methods. The gradient boosting machine technique was used to train models on a clinical dataset of 8,574 or a clinical-genetic dataset of 516 ovarian stimulations. The clinical-genetic model predicted the number of MII oocytes better than that based on clinical data. Anti-Müllerian hormone level and antral follicle count were the two most important predictors while a genetic feature consisting of sequence variants in the GDF9, LHCGR, FSHB, ESR1, and ESR2 genes was the third. The combined contribution of genetic features important for the prediction was over one-third of that revealed for anti-Müllerian hormone. Predictions of our clinical-genetic model accurately matched individuals' actual outcomes preventing over- or underestimation. The genetic data upgrades the personalized prediction of ovarian stimulation outcomes, thus improving the in vitro fertilization procedure.

Reconstructing subsurface temperature profiles with sea surface data worldwide through deep evidential regression methods

In this paper, the combination of the deep evidential regression network (DERN) and empirical orthogonal function (EOF) methods, namely, the DERN-EOF method, was proposed to reconstruct the subsurface temperature profiles. Firstly, the DERN method was compared with the previous machine learning methods used in the subsurface temperature field reconstruction, for example the Self-organizing map method. The new network was shown to have certain advantages of higher accuracy and the capability to provide uncertainty estimation, which enables the reconstruction of the subsurface field with higher belief. Analysis of the method performance suggests that the DERN could improve reconstruction accuracy by almost 20% and that the uncertainty statistics agrees well with the data distribution. Then the DERN method combined with the EOF method was used to reconstruct subsurface temperature profiles at a global scale. Results suggest that the reconstruction error in highly dynamic regions, like the Kuroshio Extension and Gulf Stream regions, is significantly higher than that in other regions, and the uncertainties increase with the reconstruction error, suggesting that the proposed method can provide a relatively believable uncertainty estimation.

Squatting in the “era of permanent crisis”

The phenomenon of squatting, which plays an important role in the life of the city in the “era of permanent crisis”, is a kind of response to the crisis and/or an attempt to implement utopia, introduction of a special community model or reflection of the ideas of living ideals, as well as a form and method of creative self-expression and self-organization. It is a dual phenomenon, which is also a space of danger, risk and opportunities for self-development / self-realization.

Particles and film preparation of ester-free type poly(trimethylene carbonate) derivatives bearing aromatic groups initiated with hydrophilic initiators

As our previous reports of facile synthetic ester-free type trimethylene carbonate-aromatic derivatives, we were herein prepared a series of particles and thin films of poly(trimethylene carbonate) (PTMC) bearing diphenyl-, bis-phenyl, and trityl pendants. The hydrophilic groups were featured at chain end while the side chain was introduced several hydrophobic aromatic groups as well as typical ester-free PTMC. Approaching the particle formation, oligo ethylene glycol was selected as hydrophilic initiator due to the high mobility, and biocompatibility. The synthetic PTMC particles were fabricated via self-organized precipitation method, its structural properties, morphologies, and surface behaviors were systematically explored. To examine the impact of structure, we investigated the degradation study of PTMC-trityl film coating which was enabled to convey a deceleration in enzymatic system.

Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil

Southeastern Brazil comprises an important geoeconomic and populous region in South America. Consequently, it is essential to analyze and understand the precipitation profiles in this region. Among different data sources and techniques available to perform such study, the use of clustering algorithms and information from the Global Precipitation Measurement (GPM) project rises as a convenient yet few exploited alternative. Precisely, this study employs the K-Means, the Hierarchical Ward, and the Self-Organizing Maps methods to cluster the annual and seasonal precipitation data from GPM project recorded from 2001 to 2019. The adopted methods are compared in terms of quantitative measures and the number of clusters defined through a well-established rule. The results demonstrate that the annual and seasonal periods are organized according to different number of clusters. Moreover, the results allow: identify the presence of a spatially heterogeneous distribution in the study area; to conclude that the K-Means algorithm is a suitable clustering method in the context of this investigation when compared to Ward’s Hierarchical and Self-Organizing Maps methods in terms of the Calinski-Harabasz and Davies-Bouldin measures; and that the spatial precipitation distribution over Southeastern Brazil is represented by 10 clusters in annual and summer periods, 11 clusters in autumn and spring and 9 clusters in winter period.

Self-organized multi-target trapping of swarm robots with density-based interaction

AbstractThe task of multi-target trapping in swarm robots can often be solved by global shape planning and target assignment, but it still remains a challenge to achieve fully self-organized multi-target trapping behavior based on local information. In this paper, inspired by the concept of spatial density in physics and biology, we proposed a novel density-based method to enable the swarm robots to entrap multiple targets with either single-ring, multi-ring or multi-subgroup formation in a distributed and self-organized way while neither communication among robots nor encirclement function is required. Each robot’s local spatial density is considered as the main clue for the individual’s motion decision-making and the enclosed configurations emerge from such individual-level interactions rather than being explicitly designed. Numerical simulations and real robotic experiments are conducted to validate the effectiveness of the proposed method. The results show that the proposed self-organized trapping method allows a swarm of robots to entrap multiple moving targets in a stable, flexible, noise-tolerate and size-scalable fashion.

Self-Organized Neural Network Method to Identify Crash Hotspots

Crash hotspot identification (HSID) is an essential component of traffic management authorities’ efforts to improve safety and allocate limited resources. This paper presents a method for identifying hotspots using self-organizing maps (SOM). The SOM method was used to identify high-risk areas based on five commonly used HSID methods: crash frequency, equivalent property damage only, crash rate, empirical Bayes, and the societal risk-based method. Crashes on a major road in Iran were examined using the proposed method. Based on these criteria, high-risk locations were grouped into six clusters, which provided appropriate criteria for each location depending on the importance of the cluster. The findings show that the identification of crash hotspots tends to focus on areas with more crashes and deaths, demonstrating that the research methodology was appropriate.