Behavioral Analysis System Research Articles

A novel framework for integrating solar renewable source into smart cities through digital twin simulations

In this study, the challenges of incorporating PV systems and wind energy sources into smart city power grids are addressed. The fluctuating nature of renewable energy is considered, and microgrid energy management is investigated using probabilistic and deterministic assumptions. To enhance energy management, digital twin simulations are employed, providing a virtual representation of the power grid. This enables accurate analysis and prediction of system behavior in different scenarios. Digital twins help simulate the effects of load profiles on microgrids, facilitating better energy planning and decision-making. The proposed framework considers objective functions related to cost reduction, voltage profile improvement, and voltage stability enhancement. The whale optimization algorithm (WOA) is applied to solve the complex and nonlinear energy management problem, aiming to minimize costs and enhance voltage stability. Simulation results show that an optimally installed PV system and wind turbines reduce energy costs and improve overall system efficiency in smart cities. Furthermore, the objective function of WOA outperforms particle swarm optimization (PSO) and differential evolution algorithm (DEA) in achieving the desired outcomes.

Physical system behavior and energy flow analysis of a solar driven hydrogen production plant based on two-step thermochemical redox cycles

Two-step thermochemical water splitting using concentrated solar power offers promising potential for green hydrogen production as a part of a future sustainable energy economy. In this study, a system and energy flow analysis of a large scale 250 kW reactor prototype is presented by using a complex 3D-simulation model. Thus, the physical dynamic system behavior and potential efficiency improvements are discussed with respect to plant design and operational strategy. Regarding the plant design, different kind of heat losses as well as the influence of reactor design parameters and material modifications are analyzed. Regarding the operational strategy, crucial control parameters as cycle durations, temperatures, and mass flow rates are varied in a broad range and analyzed with respect to plant efficiency. By comparing commonly used operational strategies, the temperature swing operation turns out to be by far more efficient than any isothermal or near-isothermal operation for the current plant concept.

Disturbance Decoupling for a Single-Phase Pulse Width Modulation Rectifier Based on an Extended H-Infinity Filter

The growing utilization of single-phase pulse width modulation (PWM) rectifiers in various applications has spurred interest in detecting and monitoring faults in these devices. In particular, voltage and current sensors play a crucial role in the control loop of these rectifiers. However, sensor faults can significantly affect the converter’s performance and availability. This paper introduces a novel and efficient method for detecting and decoupling sensor faults in single-phase PWM rectifiers. The proposed method utilizes residual generation and incorporates an extended filter within the rectifier. Unlike conventional filters, the presented fault detection and isolation (FDI) method effectively eliminates the influence of disturbances on the residual signal. This feature helps prevent false alarms in the monitored system, ensuring reliable fault detection. To evaluate the effectiveness of the approach, hardware-in-the-loop and simulation tests were conducted. The results from these tests provide substantial evidence supporting the efficacy of the proposed method. The hardware-in-the-loop experiments involved real-world implementation, validating the practicality and reliability of the approach. Meanwhile, simulation tests allowed for a comprehensive analysis of system behavior and performance under various fault scenarios. The findings demonstrate the rapid and dependable nature of the proposed method for detecting and decoupling sensor faults in single-phase PWM rectifiers. By effectively mitigating the impact of disturbances on the residual signal, false alarms are minimized, ensuring accurate fault detection. The experimental validation highlights the practical applicability and effectiveness of the proposed approach, making it a valuable contribution to fault detection in single-phase PWM rectifiers.

Interface Behavior Analysis of Different Geomembrane Liner Systems Based on PIV Techniques

The interfacial friction performance of the liner system has an important influence on the overall stability of modern landfills, but the interface of the liner system using conventional geomembranes may have problems such as shear failure and slip along the liner system. Accordingly, the concept of a ribbed geomembrane was proposed. Based on the laboratory model tests, the variation laws of p–s curves and the additional stress of sand were studied under different values of shape, rib spacing, and rib height. A series of particle image velocimetry (PIV) analyses of sand particles were performed to provide insight into the reinforcement mechanism of the ribbed geomembrane liner system. The results indicated that the ribbed geomembrane could obviously improve the stability of the liner system compared with the smooth geomembrane. The ribbed geomembrane formed an indirect influence area with sand in a specific range. The ribbed geomembrane with optimal values of rib spacing and rib height was beneficial to reduce the settlement of the upper sand and the stress redistribution of the sand particles.

PyMouseTracks: flexible computer vision and RFID based system for multiple mouse tracking and behavioral assessment.

PyMouseTracks (PMT) is a scalable and customizable computer vision and RFID-based system for multiple rodent tracking and behavior assessment that can be set up within minutes in any user-defined arena at minimal cost. PMT is composed of the online Raspberry Pi-based video and RFID acquisition with subsequent offline analysis tools. The system is capable of tracking up to 6 mice in experiments ranging from minutes to days. PMT maintained a minimum of 88% detections tracked with an overall accuracy >85% when compared to manual validation of videos containing 1-4 mice in a modified home-cage. As expected, chronic recording in home-cage revealed diurnal activity patterns. In open-field, it was observed that novel non-cagemate mouse pairs exhibit more similarity in travel trajectory patterns than cagemate pairs over a 10-minute period. Therefore, shared features within travel trajectories between animals may be a measure of sociability that has not been previously reported. Moreover, PMT can interface with open-source packages such as Deeplabcut and Traja for pose estimation and travel trajectory analysis, respectively. In combination with Traja, PMT resolved motor deficits exhibited in stroke animals. Overall, we present an affordable, open-sourced, and customizable/scalable mouse behavior recording and analysis system.Statement of SignificanceAn affordable, customizable, and easy-to-use open-source rodent tracking system is described. Most current tools, commercial or otherwise, can only be fully automated to track multiple animals in a single defined environment and are not easily setup within custom arenas or cages. Moreover, many tools require users to have extensive hardware and software knowledge. In contrast, PMT is easy to install and can be adapted to track rodents of any coat color in any user-defined environment with few restrictions allowing quantification of behavior in multiple animals simultaneously.

Change of attachment representation after Cognitive Behavioral Analysis System of Psychotherapy

Change of attachment representation after Cognitive Behavioral Analysis System of Psychotherapy

Addressing the Catch-22 of Using Mixed Methods in Behavior Disorders Intervention Research

Limited emotional and behavioral disorders (EBD)–focused intervention mixed methods research (MMR) has been published, particularly in top-tier EBD journals (i.e., Behavior Disorders and Journal of Emotional and Behavioral Disorders). The lack of published MMR creates what could be perceived as a Catch-22 situation wherein those who conduct EBD intervention research are not encouraged to conduct MMR because they do not see published examples in the journals that they read. The purpose of this article is to demonstrate how the routine use of MMR should be considered and suggest why doing so is a worthwhile endeavor. An overview of potential barriers to conducting intervention MMR in the field of EBD is provided. Examples of the use of MMR with Functional Behavior Analysis and Multi-tiered Systems of Support for students with Tier 2 needs are discussed. Details regarding the use of a mixed methods profile analysis to understand treatment successes and challenges are provided. Our hope is that this article will inspire EBD researchers to consider MMR as they develop interventions for children with EBD.

Analysis of Physical Education Classroom Teaching after Implementation of the Chinese Health Physical Education Curriculum Model: A Video-Based Assessment.

This study assessed the Chinese health physical education curriculum model recently suggested to meet the recommended physical education curriculum reforms addressing the declining physical and mental health of students in China. We used video analyses of 41 physical education classroom teaching cases with a physical education classroom teaching behavior analysis system to provide quantitative and qualitative behavioral data. We established reference ranges for classroom teaching behavior indicators, summarized classroom teaching patterns, and assessed classroom discourse and the emotional climate. Notable findings included teachers in elementary schools using closed-ended questions, predictable responses, and general feedback significantly more often than teachers in senior high school, and ball sports instructors using demonstration and competition significantly more frequently than instructors in athletics. Overall, three teaching patterns were most commonly used-lecture, practice, and dialogue-with practice being dominant. Analysis of the top 50 most commonly spoken words by teachers identified five types of discourse-motivational, directive, specialized, transitional, and regulatory-with motivational words being most frequent. The classroom atmosphere was mainly positive. These findings provide evidence that the use of this curriculum model may bring positive changes to physical education classroom teaching methods in China and will inform subsequent innovative physical education classroom teaching practices.

Design of Farm Livestock Manure Resource Management Platform Based on Intelligent Semantic Analysis of Real-Time Monitoring Images

With the rapid development of big data and 5G communication, the management of agricultural and livestock manure resources in rural areas has also embarked on a high-tech monitoring and platform design route. First, starting from the impact of pig manure on the environment, it analyzes how to use real-time monitoring images to semantically analyze the management and resource utilization of pig manure. According to the needs of building an intelligent behavior analysis system for agricultural storage ponds, first of all, establish a system that can not only express the purpose of use by farmers, but It can also express the direction of the construction of modern rural areas. Finally, based on the target positioning, identification and tracking in the dynamic scene, the deep processing and comprehensive development and utilization of feces are carried out, and the environmental management and resource development are combined. The results show that: from the current situation, the resource utilization of chicken manure mainly has two aspects, one is as feed, the other is as fertilizer; the resource utilization of pig manure and cow manure is suitable for chemical fertilizer utilization.

Inpatient psychotherapy of depressive disorders: options and challenges

The psychotherapy of depressive disorders has become established as acentral component of inpatient treatment in psychiatric and psychosomatic hospitals and furthermore constitutes an important component of the residency training in Germany; however, the number of studies examining the effectiveness and efficacy is limited. This narrative review summarizes the current state of research on inpatient psychotherapy for depressive disorders. The results of meta-analyses as well as practice-based observational studies from routine treatment in Germany, disorder-specific special programs, and side effects of inpatient psychotherapy are summarized. The number of studies on the efficacy of inpatient psychotherapy of depressive disorders is overall low. The main finding of the largest recent meta-analysis indicates that psychotherapy in clinics and other facilities has asignificant effect on depressive symptoms, with small to moderate effect sizes in randomized controlled studies. The effects are mostly maintained even after 9-15months follow-up. An observational study from routine treatment with avery large sample size reported large pre-post and pre-follow-up effect sizes. It additionally revealed factors that appear to be difficult to change during inpatient psychotherapy, negatively affect treatment success and could be specifically addressed in future trials. Special programs, such as inpatient Interpersonal Psychotherapy (IPT) and the inpatient Cognitive Behavioral Analysis System of Psychotherapy (CBASP) indicate acceptance and efficacy/effectiveness in an initial randomized controlled (IPT) study and in observational (CBASP) studies. Side effects of inpatient psychotherapy were reported by 60-94% of patients with depressive disorders, whereby a perceived dependence on the therapist or the therapeutic setting was identified as afrequent side effect. Overall, the results of the narrative review reveal that inpatient psychotherapy appears to be meaningful and effective for many patients with depressive disorders. Specific side effects, cost-effectiveness, and the question of differential indications (what works for whom?) should be further investigated.

Analysis of local system behavior in the foreign exchange-market using neural networks and Monte-Carlo method for predictıon and risk assessment

In this study, we attempt to show the reason behind the poor estimation of the future values of foreign exchange-rate (FXR) signals under difference-equation modeling, using the neural network approach for evaluating the local system coefficients. To do this, we have splitted EUR/USD and AUD/CAD signals into many small-segments and modeled each segment as the signal representation of a linear time-invariant (LTI) system using the simple linear difference equation (LDE) formulation. After a precise segmentation of each FXR signal such that an LTI system based modeling is highly accurate in each segment, it is observed that the coefficient values of the corresponding LDEs are highly volatile, which indicates that a reliable estimation would be very difficult using LDE modeling. Although the LDE coefficients are usually observed to take values within a small range over a given FXR signal segment (sample-set), frequent sudden-jumps in coefficient values do occur, which subjects/forces the dynamics of FXR signals to undertake the dynamics of these sudden-jumps rather than the dynamics of any other deterministic or stochastic process. To support this observation, the range of variation of the LDE coefficients over each segment is analyzed to quantify the volatility of the foreign-exchange market for risk assessment.

Detection of Cyberattacks and Anomalies in Cyber-Physical Systems: Approaches, Data Sources, Evaluation

Cyberattacks on cyber-physical systems (CPS) can lead to severe consequences, and therefore it is extremely important to detect them at early stages. However, there are several challenges to be solved in this area; they include an ability of the security system to detect previously unknown attacks. This problem could be solved with the system behaviour analysis methods and unsupervised or semi-supervised machine learning techniques. The efficiency of the attack detection system strongly depends on the datasets used to train the machine learning models. As real-world data from CPS systems are mostly not available due to the security requirements of cyber-physical objects, there are several attempts to create such datasets; however, their completeness and validity are questionable. This paper reviews existing approaches to attack and anomaly detection in CPS, with a particular focus on datasets and evaluation metrics used to assess the efficiency of the proposed solutions. The analysis revealed that only two of the three selected datasets are suitable for solving intrusion detection tasks as soon as they are generated using real test beds; in addition, only one of the selected datasets contains both network and sensor data, making it preferable for intrusion detection. Moreover, there are different approaches to evaluate the efficiency of the machine learning techniques, that require more analysis and research. Thus, in future research, the authors aim to develop an approach to anomaly detection for CPS using the selected datasets and to conduct experiments to select the performance metrics.

Data Analytics and Visualization of Adaptive Collaboration Simulations

Role-based collaboration (RBC) is an adaptive computational methodology that uses roles as underlying mechanisms to facilitate and analyze system behavior for entities that collaborate and coordinate their activities with or within these systems. In dynamic environments, including those that occur in large-scale simulations, visualization provides insights into complex systems behaviors. This article presents a visual analytics (VA) approach to studying dynamics involved in adaptive collaboration (AC) for large, multiagent simulation model using new open-source tools. The results show that time-varying systems can be steered for optimal performance and assessing adaptations using VA dashboards.

Exploring the Learning Process and Effectiveness of STEM Education via Learning Behavior Analysis and the Interactive-Constructive- Active-Passive Framework

Given the inadequacy of assessed outcomes (e.g., final exam) and the importance of evaluating the learning process in STEM education, we use deep learning to develop the STEM learning behavior analysis system (SLBAS) to assess the behavior of learners in STEM education. We map learner behavior to the ICAP (interactive, constructive, active, passive) framework, helping instructors to better understand the learning process of learners. The results show that SLBAS exhibits high accuracy. Moreover, Cohen’s kappa coefficient between expert coding and SLBAS is high enough to support replacing expert coding in the observation method with SLBAS to recognize the learning process of learners during STEM activities. Finally, statistical analysis establishes a correlation between the learning process and learning effectiveness. The results of this study are in line with most previous studies, demonstrating that STEM education differs from traditional teacher-centered courses in that it helps learners to improve the process of knowledge construction with practice and hands-on opportunities rather than simply receiving knowledge passively.

Behavior Analysis of Feeding Unit of a Paper Industry in Fuzzy Environment

The feeding unit is an important functional part in the paper industry that requires the development of effective maintenance plan based on its reliability analysis. This study analyzes reliability of the considered unit employing trapezoidal fuzzy numbers in fuzzy [Formula: see text]–[Formula: see text] methodology with Petri net modeling. In this analysis, the collected crisp input data of each subsystem has been fuzzified using trapezoidal fuzzy numbers. Then fuzzy values of several reliability factors of feeding unit, namely, failure rate, repair time, reliability, availability, etc. at ±15%, ±25% and ±40% spread levels have been calculated. Further, the obtained fuzzy values of reliability factors have been defuzzified to analyze system behavior of feeding unit. The analysis is useful for plant maintenance and operation management to understand the behavior of feeding unit in a more realistic manner.

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Prognostics and health management (PHM) is an engineering approach dealing with the diagnosis, prognosis, and management of the health state of engineering systems. Methods that can analyze system behavior, fault conditions, and degradation are crucial for PHM applications, as they create the basis for determining, predicting, and monitoring the health of engineering systems. Data-driven methods have been proven to be suitable for automated diagnosis or prognosis due to their pattern recognition and anomaly detection abilities. Moreover, they do not require knowledge of the underlying degradation process. However, training data-driven methods usually requires a large amount of data, whose collection, cleansing, organization, and preparation are generally very time-consuming and costly. There are usually little or no run-to-failure data available at market launch, especially for new systems such as new machine generations. Nevertheless, related systems, hereinafter referred to as similar systems, often already exist, differing only in some technical characteristics. In this paper, the similar system problem is defined, and explanations of the difficulties that arise when using data from similar systems are presented. Furthermore, it is discussed why the usage of these data offers great potential for condition diagnosis and prognosis of engineering systems. An overview of data-driven methods that can be used to utilize data from similar systems is provided, and the methods that such systems already consider are highlighted. Two related research areas are identified, namely, fleet learning and transfer learning. In the paper, it is shown that similar system approaches will become an important branch of research in PHM. However, some difficulties must be overcome.

HEMC: A Dynamic Behavior Analysis System for Malware based on Hardware Virtualization

HEMC: A Dynamic Behavior Analysis System for Malware based on Hardware Virtualization

Analysis of the load-bearing system of the loader according to the results of dynamic tests in the environment of Ansys Motion

Problem. The T-156B hinge frame of the loader, which is an upgraded frame of an agricultural tractor, during work operations creates loads for which it was not designed. Such loads lead to breakdowns and further shorten the life of the entire machine. The frame is the basic element around which the machine is built. The loader that had just been removed from the conveyor did not have built-in durability and reliability. While designing a road construction machine, an account of the features, nature, and conditions of its operation should be taken. Goal. Develop a hinge frame design technique that takes into account the loads experienced by the frame with a virtual simulation of real-world situations during the loader process. Methodology. The adopted approach is based on the use of a computer program Solidworks with three-dimensional modeling and a program Ansys motion for calculating the dynamics of multicomponent bodies taking into account the hinge and contact interaction. Results. In the work were considered three cases of technological operations of the loader. 1. Lifting a full bucket. 2. Collision with an obstacle that is difficult to overcome 3. Movement over an uneven surface with the raised loader arm. The results of the calculations provided forces and torques in the hinge of the half-frames, as well as the effort of the frame. Based on the results obtained, the endurance of the frame in the Ansys motion software package was calculated. Originality. The analysis of load-bearing systems with a virtual simulation of the machine’s actual behavior with the help of Ansys motion software allows to obtain more accurate results, significantly reducing the time for the design and cost of field testing. Practical value. This approach to the analysis of the carrier system behavior can be recommended for designing and determining the technological capabilities of wheeled, tracked, and special machines.

Global Behavior Analysis of Nonlinear Energy Sink Systems Using the Parallelized Multi-Degrees-of-Freedom Cell Mapping Method

Abstract Nonlinear energy sink (NES) systems, when applied to a physical system with multiple interference sources, exhibit abundant nonlinear dynamic behaviors. However, current research in this respect is limited within the theoretical scope of deterministic systems. According to the theory of cell mapping, this paper introduces a parallel restructured algorithm to improve the performance of cell mapping and cell processing, and a parallelized multidegrees-of-freedom (DOF) cell mapping (PMDCM) method is given. With the method, the global behavior of NES systems is analyzed so that the dynamical behavior of multiple stable attractors within typical parameter intervals can be captured. The research results show that for NES systems, there is the phenomenon of multiple stable attractors coexisting in multiple typical parameter intervals, which occurs between periodic and periodic attractor, periodic and quasi-periodic, periodic and chaotic attractor. While revealing the corresponding relationship between different types of attractors and their basin of attraction, these findings verify that the new cell mapping method has high computational efficiency and accuracy and can provide a theoretical basis for the study of high-dimensional nonlinear systems' global behavior and optimal control.

Security Control Process Modeling During the Covid-19 Pandemic: Example Of Esenboğa Airport

Airport facilities are places where constantly updated measures, regulations and improvements are implemented. Simulation modeling plays an important role in ensuring the high efficiency and user friendliness of such arrangements. Especially in recent years, simulation has become an indispensable need for the aviation industry. Contrary to traditional theories, the simulation approach reveals a more realistic system behavior analysis. One of the most important operations carried out at airports is undoubtedly the security checkpoints. Simulation of airport security checkpoints is the basis for more efficient use of these service points, improving the service level, identifying bottlenecks in the system and producing solutions. In this study, it is aimed to calculate the delay and queue data created by the precautions and regulations applied in the Esenboğa Airport terminal building after the Covid-19 pandemic, at the security checkpoint after the visa checkpoint. In the study, it is aimed to determine the problems that can be caused by the precautions and regulations taken in airport security by modeling the security control process after the Covid-19 pandemic by using the simulation method. The simulation model was created by taking expert opinions in accordance with the operation carried out at the airport. Finally, the results of the simulation model were evaluated, and suggestions were made to the airport stakeholders.