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The interaction and diversification of different behavior patterns of individual communication sources in cyberspace make the crisis evolution increasingly complex. Based on the activity and attitude of individual communication sources, a nonlinear dynamic system is obtained, and numerical simulation is carried out in combination with model threshold, and a co performance model of strong relationship and weak relationship social platform is constructed. The theoretical analysis is verified by numerical simulation on scale-free networks, and the propagation and evolution characteristics of strong and weak social platforms under different settings are discussed from the aspects of the heterogeneity of positive information dissemination sources' propagation ability, information dissemination sources' enthusiasm, information dissemination sources' heterogeneity and inter layer activity heterogeneity. [ABSTRACT FROM AUTHOR]

Background: High-definition maps can provide necessary prior data for autonomous driving, as well as the corresponding beyond-line-of-sight perception, verification and positioning, dynamic planning, and decision control. It is a necessary element to achieve L4/L5 unmanned driving at the current stage. However, currently, high-definition maps still have problems such as a large amount of data, a lot of data redundancy, and weak data correlation, which make autonomous driving fall into difficulties such as high data query difficulty and low timeliness. In order to optimize the data quality of high-definition maps, enhance the degree of data correlation, and ensure that they better assist vehicles in safe driving and efficient passage in the autonomous driving scenario, it is necessary to clarify the information system thinking of high-definition maps, propose a complete and accurate model, determine the content and functions of each level of the model, and continuously improve the information system model. Objective: The study aimed to put forward a complete and accurate high-definition map information system model and elaborate in detail the content and functions of each component in the data logic structure of the system model. Methods: Through research methods such as the modeling method and literature research method, we studied the high-definition map information system model in the autonomous driving scenario and explored the key technologies therein. Results: We put forward a four-layer integrated high-definition map information system model, elaborated in detail the content and functions of each component (map, road, vehicle, and user) in the data logic structure of the model, and also elaborated on the mechanism of the combined information of each level of the model to provide services in perception, positioning, decision making, and control for autonomous driving vehicles. This article also discussed two key technologies that can support autonomous driving vehicles to complete path planning, navigation decision making, and vehicle control in different autonomous driving scenarios. Conclusions: The four-layer integrated high-definition map information model proposed by this research institute has certain application feasibility and can provide references for the standardized production of high-definition maps, the unification of information interaction relationships, and the standardization of map data associations. [ABSTRACT FROM AUTHOR]

Purpose: When resources are extracted and used by society, they are not necessarily lost for future generations. Therefore, recent publications on impact assessment of abiotic resource use in life cycle assessment focus on a decreased accessibility of resources due to dissipation, rather than depletion. In a previous study, dissipation was defined as a function of the global change in accessible stock due to human actions, and the global amount of the accessible stock, assuming a very long-term time perspective (more than 500 years). In this paper, a short-term time perspective (25 years) is adopted. Methods: The same generic characterization model is used, but different choices are outlined to derive characterization factors for a short-term perspective (25 years). To illustrate how the short term might be approached, a preliminary set of characterization factors is developed, based on assumptions and estimates. Results: The problem of resource use is defined as follows: the decrease of accessibility on a global level of primary and/or secondary elements over the short term due to the net result of compromising actions (i.e., emissions, dissipation in the technosphere, occupation in use, and exploration for new stocks). Characterization factors are derived based on assumptions, like the following: the accessible stock is based on present estimates of accessible stocks in the environment and the technosphere; estimates of accessible stocks in the technosphere are based on past extractions and generic recycling rates; all flows that are presently not recycled are assumed to be inaccessible. Finally, weighting between elements and the functions they have for the present society is based on the added value of the economic sector that is affected due to the decreased accessibility. Discussion and conclusion: A preliminary set of characterization factors is proposed for 55 elements. They assess the impact of the present use of resources on the decreased accessibility in the short term due to emissions and dissipation in the technosphere. However, calculation of impact category scores is still hampered by a lack of appropriate data for dissipative flows in life cycle inventory databases. The presented calculations are based on several simplifications and proxies. A more detailed distinction of dissipative flows and estimates of stocks in the technosphere may be possible based on (dynamic) SFA modelling of elements in different applications. To derive a more mature set of characterization factors, it is recommended to use the presented model as a basis and further elaborate or replace the proxies. [ABSTRACT FROM AUTHOR]

The Car and Beam prototype is a teaching piece of equipment, inspired by Ball and Beam systems. It consists of a beam supported in its center by means of a rotating axis installed in two rolling bearings, allowing the beam to rotate through the actuation of a servo motor. A car is coupled to this beam, and its displacement is measured using a linear encoder. This paper focuses on two key aspects: firstly, it offers a mathematical model of the Car and Beam system, and secondly, it outlines the development of a state-feedback tracking controller through pole placement to this system. To validate the modeling and control approach, we present simulation and experimental results using three different reference profiles: step, square wave, and sine wave. Our findings demonstrate the effectiveness of the control strategy in tracking predefined trajectories, both in simulation and with the physical prototype. In conclusion, this study highlights the efficacy of the methodology employed for mathematical modeling and the controller's design in the context of this specific application. The results indicate promising potential for further exploration in this domain.

Agriculture is the primary source of income for the majority of the Indian farming community. Plantation crops play a significant part in improving the farmers' economic condition. The proposed work aims to develop a system model for prognostic reasoning by analyzing the impact of fertilizer and irrigation on areca nut crop yield, as well as to predict diseases that may affect areca nut palms using data analytics and a formal statistical approach. The dataset is constructed by interacting with the farmers in the Mangaluru region of Karnataka, India. To find the optimal features, the formal statistical test chi-square is applied. The performance of various classifiers, such as Logistic Regression, Nave Bayes, Support Vector Machine, Decision Tree, and Random Forest, is examined during prognostic reasoning. For disease prediction and crop yield, the decision tree outperformed other classifiers with an accuracy of 96% and 95.86%, respectively. The most significant irrigation type and fertilizer for increasing areca nut crop yield are also identified. [ABSTRACT FROM AUTHOR]

A fundamentally new possibility of obtaining additional information about the state of objects and processes of the subject area is considered. It is shown that wave processes can be investigated and analyzed through additional information obtained from the parameters of the functioning of the hardware elements of a computer information system. A method of revealing wave processes in a computer system is described. Experiments confirm the possibility of obtaining and using additional information from a secondary source for the information system to perform its regular functions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Elderly care becomes more and more important as elderly populations increase. Many solutions are proposed for monitoring elderly activities of daily living (ADL) or healthcare related data. There are two aspects to be tackled to enable monitoring for elderly care. On the one hand, the up-to-date ADL sensors and biosensors greatly facilitate collection of various ADL and healthcare related data. On the other hand, data captured by sensors need to be gathered from residences of elderly people to local community center for monitoring and analysis. Therefore, gathering sensor data plays a significant role for building elderly care systems or architectures. Most current elderly care systems or architectures are developed based on reliable network infrastructures. However, such presumption is less practical in network deficient environments such as geographically depopulated rural and remote areas. In this paper, we propose a data gathering system model for elderly care based on autonomous device-to-device (D2D) communications, which is self-contained and independent from other network infrastructures. The proposed system model doesn’t need central control; thus, it can be simply deployed in network deficient environments or for users without access to existing networks. The idea behind is to use ad hoc D2D links by introducing moving devices. Procedures of data gathering using D2D are proposed and configured by modifying the two-way untargeted discovery protocols of IEEE Std 802.15.8. To examine the feasibility of the proposed system model, a prototype D2D system is developed using off-the-shelf radio modules and ADL sensors. We examine the efficacy of the prototype system through field trials in a mountainous village. Our obtained results confirm the usefulness of the proposed D2D solution for gathering ADL sensor data for elderly care in network deficient environments. Examples of using the gathered ADL data to detect anomalous events are presented.

The management of the operation of unmanned aircraft systems and their integration into the common airspace faces various challenges, among which those associated with determining the airspace capacity can also be included. This paper focuses on this challenge and proposes an approach to determine the airspace capacity through the utilization of mathematical modelling, algorithmization, and statistical methods. The constructed model accurately determines airspace capacity, considering factors such as aircraft size, control methods, and potential conflicts resolved at the strategic level of traffic management. Results from simulations demonstrate the model’s validity and effectiveness, highlighting its potential application in planning and evaluating services that are provided to unmanned aircraft.

A system model of a proton exchange membrane green hydrogen generation system is developed with the intent of demonstrating the dynamic interactions of the system with varying inputs such as electrolyzer current density and hydrogen pressure. The model includes physics-based modules that are validated with available experimental data for the power electronics, electrolyzer stack, pressure swing adsorption dryer, hydrogen compressor, and cooling circuit components of the system. A single electrolyzer system and multi-electrolyzer system are evaluated with varying operating conditions and multi-stack system operating strategies. Increasing electrolyzer cathode pressure results in an overall increase in specific energy consumption but may allow the use of low-pressure storage without the need for a hydrogen compressor. For a theoretical case study of excess renewable energy generation in Texas, the system achieves an overall efficiency of 46.5 % defined as the efficiency of converting electricity into an equivalent energy content of the produced hydrogen. [Display omitted] • The system rapidly loses efficiency below 40 % of the rated electrolyzer power. • Hydrogen loss is the primary reason for low efficiency at low electrolyzer power. • Increasing hydrogen pressure reduces system efficiency and operating range. • Large-scale systems can increase efficiency with power management strategies. • System modeling can be used to find the optimal installed electrolyzer capacity. [ABSTRACT FROM AUTHOR]

Methanol is a crucial commodity in the chemical industry and is employed as precursor for many products. It can be used to store fluctuating renewable energy, specifically benefiting from its liquid state at ambient temperatures. As the demand for green, renewable methanol is projected to soar in the next decades, environmentally friendly and sustainable pathways for its production have to be provided. Through the combination of proton-conducting high temperature electrolysis for the provision of dry H2 with a heterogeneously catalyzed hydrogenation of CO2, efficient and simple power-to-methanol production processes can be established. Here, a novel power-to-methanol system model capable of real-time transient simulation is presented and viable operating windows are determined for different key operating parameters of the respective main process stages. A techno-economic assessment is carried out to determine the specific production costs of renewable methanol. Specific methanol production costs of 2419 € t−1MeOH for small-scale applications (1.12 MW) were retrieved, which corresponds to a more than fourfold increase over the current market price of conventionally produced methanol. Increases in system scale are found to decrease the methanol production costs due to economy-of-scale effects. The sensitivity of the process economics is assessed with regards to crucial operational and capital characteristics.

Modern management processes must solve the complex problem of optimal decision-making and timely and effective forecasting of their results, to select successful combinations of solutions in a particular production area of the new information and innovation society that has replaced the industrial era. Considering the above issues, the modelling process as an effective mechanism for decision-making and risk management deserves special attention. The purpose of the study was to highlight modelling as an effective method of decision-making in green production in a post-industrial society, and to justify the feasibility of its use at different levels of the production process. During the study, theoretical methods were applied, in particular, the study of scientific materials and research results, description and comparison. The results were also generalized and analysed. The article considers main approaches to the strategy of the introduction of modelling of decision-making in the field of green production in the post-industrial society, and provides a detailed definition of its structural elements and priority principles. It is clarified that modelling as a management tool is used in the development of rather complex production and management decisions and is the construction of a model or system of models of the process or phenomenon under study for its study, analysis, and formation of an appropriate fan of alternatives to the result. The main types of modelling according to different classifications are analysed, and the possibilities of applying some of them in the decision-making system in the production plane are investigated. Conclusions on the efficiency and effectiveness of the use of the modern innovation and information economic space in production processes are provided. The expediency of using different approaches to decision-making modelling, depending on some influencing factors, is substantiated. The possibility of solving the industrial multitasking of decision-making by creating models is proved. These research results are of practical importance for improving management processes in the field of green production in a post-industrial society, regardless of ownership, production volumes and scope of activity

Abstract This research was done with the aim of providing a systematic model of employee training using artificial intelligence. The research method is a combination of qualitative and quantitative methods. The statistical community in the qualitative section includes an unlimited number of experts familiar with the subject, and the statistical sample in this section is 20 people selected by the snowball method. The statistical population in the quantitative section includes all the specialists and experts related to the research subject in an unlimited number, of which 384 people were selected as a sample by a simple random method. Researcher-made questionnaires with confirmed validity and reliability were used to collect the data of this research. Data analysis in this research was done at two levels of descriptive statistics and inferential statistics. To complete the Delphi process in this research, Kendall's coefficient was used with the help of SPSS software. In order to test the research model, structural equation technique was used through Smart-PLS statistical software, and the results show the appropriate fit of the model. The findings of the research show that the inputs of the model include 1- educational data, 2- personal information, 3- educational needs, 4- user feedback, and 5- data of the work environment. The model process also includes 1- determining the needs and goals, 2- collecting data, 3- pre-processing the data, 4- training the artificial intelligence model, 5- evaluating and improving the model, 6- implementing and deploying, and 7- monitoring and updating. Finally, the outputs of the model include 1- individual feedback, 2- educational suggestions, 3- monitoring and follow-up, and 4- support and guidance. Extended abstract Introduction In 1950, Alan Turing, an English mathematician, wrote an article entitled "Computing Machines and Intelligence" and posed the question: "Can machines think?" This led to further exploration of the use of machines to support human decision-making, with the formation of a workshop in 1956. The workshop was organized by John McCarthy, an American mathematician, who focused on the "study of artificial intelligence" (Frehywot, 2023). As science and technology advance, since 2013, when Frye and Osborne estimated that nearly half of US jobs are at risk of high automation, AI has been at the top of policymakers' agendas, and now the consensus is that AI creates fundamental changes in the labor market. With the use of artificial intelligence, many skills that were important in the past become automated; many jobs are also obsolete or transformed; and artificial intelligence is increasingly used (Tuomi, 2018). Artificial intelligence includes various related technologies, often supported by machine learning algorithms, whereby it achieves set goals through supervision (human-guided) or unsupervised (autonomous machine) (Walsh et al., 2019). Today, most experts believe that the implementation of smart technology will dynamically transform work environments. There are applications of artificial intelligence in all industries and professions, and human resource management is no exception to this rule. Therefore, for the organization to remain relevant and maintain its competitive advantage, it is necessary to adopt new technological developments (Kaushal et al., 2023). Today, the surprising speed of innovation in business processes and technology requires that the employees of organizations continuously acquire new skills and be able to adapt to changing practices. Thus, educational needs become more personalized (Ford et al., 2017). Employee skill development was once done entirely by on-the-job supervisors; but now with the increasing demand for new skills, technological advances have enabled training and development on mobile platforms, such as smartphones and laptops (Maity, 2019). Artificial intelligence should be applied to organizational learning to help companies solve their training challenges. When recruiting and hiring new employees, the main challenge is to quickly and effectively make them fully aware of and understand the organization's internal policies and procedures. Machine learning features have been incorporated into various HR software systems to facilitate greater efficiency (Iqbal, 2018). Even more comprehensively, the use of various artificial intelligence technologies can help companies develop a learning organizational culture and avoid the common training design model based on traditional competency model analysis (Chen, 2023). According to the above, considering the development of digital technology, especially artificial intelligence, and the increasing demand for personalized training, the past training methods are no longer able to meet personal needs, and the adoption of artificial intelligence-based training can effectively fill the shortage of personalized training. Therefore, the main question that the researcher seeks to answer in this research is: "What is the system model of employee training using artificial intelligence?" Theoretical foundations Artificial intelligence is often defined as a computer system with the ability to perform tasks normally associated with intelligent beings. The first explicit definition of artificial intelligence came in a funding proposal to the Rockefeller Foundation in 1955. This definition states that "any aspect of learning or any other characteristic of intelligence can originally be described so sufficiently precise that a machine can be built to simulate it." This initial definition quickly led to deep discussions. In practice, the early developers of artificial intelligence interpreted intelligence and thinking as the mechanical processing of logical statements (Tuomi, 2018). In another definition, artificial intelligence is defined as "making a machine behave in a way that would be called intelligent if a human behaved." Although artificial intelligence was defined in 1955, it has recently gained worldwide recognition due to the technological revolution. Artificial intelligence is discussed as non-human intelligence designed to perform specific activities and tasks (Kaushal et al., 2023). McCarthy describes artificial intelligence as the science and engineering of building intelligent machines through algorithms or sets of rules, that the machine follows to mimic human cognitive functions, such as learning and problem-solving (Frehywot, 2023). This definition provides a combination of AI capabilities and what it is. Human resource management has undergone an early revolution with the help of artificial intelligence, which has gradually affected human resource operations. These functions, which were already performed entirely by humans, are being recreated with the help of a computer assistant. HR functions such as performance appraisal, learning and development, and talent acquisition are some of the areas where artificial intelligence has been introduced (Kaushal et al., 2023). By means of a variety of artificial intelligence technologies, it can be more comprehensive to help companies to form a learning organization culture that uses the traditional instructional design model based on traditional gap analysis. A customized curriculum can comprehensively test and locate staff levels through technical tools, and intelligently promote customized courses (Jia et al., 2018). In the process of education, AI technology can help learners automatically record learning data. Employees can simply enter learning objectives, archives and learn key points, and the course is automatically completed by the AI teacher (Jia et al., 2018). Methodology The current research is applicable in terms of its purpose. In terms of the method of data collection, it is descriptive-correlation in nature. The statistical population of this research in the qualitative part is made up of experts related to management issues (human resource management and system design) with a master's degree or higher in Sistan and Baluchistan province. In the quantitative part, all the employees of the departments of Sistan and Baluchistan province who have a bachelor's degree or higher, formed the statistical population of this research. The statistical population is considered unlimited in both qualitative and quantitative sections. Therefore, the sample size in the qualitative section is considered to be 20 people, selected by snowball method. In the quantitative part, Cochran's formula was used for unlimited communities with an error level of five percent, and the sample size of 384 people was determined and selected by simple random method. The tool of data collection in this research was researcher-made questionnaires. SPSS and Smart-PLS statistical software were used to analyze the data of this research. Findings The value of Cronbach's alpha for all variables of the research questionnaire is more than 0.7. Therefore, the research questionnaire has acceptable reliability. Also, the composite reliability value of all variables is higher than 0.7, which indicates the internal consistency of the reflective measurement model. All values related to convergent validity are also above 0.5, which indicates the similarity or internal validity of the reflective measurement model. Also, the reliability of the data collection tool and the quality of the reflective measurement model have also been confirmed. Conclusion Considering the importance and necessity of organizations to use artificial intelligence in the process of human resources management, a model for training employees using artificial intelligence was presented in this research, which has three parts according to the system mode: input, process and output. Model inputs include 1- educational data, 2- personal information, 3- educational needs, 4- user feedback, and 5- workplace data. The process of the model includes 1- determining the needs and goals, 2- collecting data, 3- pre-processing the data, 4- training the artificial intelligence model, 5- evaluating and improving the model, 6- implementing and deploying, and 7- monitoring and updating. The outputs of the model include 1- individual feedback, 2- educational suggestions, 3- monitoring and follow-up, and 4- support and guidance.

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There are many and complex big data in digital power grid regulation, which leads to the difficulty of big data asset management. Therefore, a model of big data asset management system in digital power grid regulation is constructed. The model consists of three parts: data acquisition, data safe storage and data index. The data acquisition architecture is designed, and the data acquisition results are filled with missing values and corrected with grey prediction method. Using AR-Tree index organization to realize the digital power grid regulation big data index, and achieve the goal of high-quality management of digital power grid regulation big data assets. Store the filled and corrected data in the blockchain to ensure data security. The experimental results show that the average recall and precision of this method are 96.9% and 97.9%, and the data acquisition quality is high. After the application of this method, there is almost no unsafe data, and the proportion of safe data is higher, which shows that this method can ensure the security of big data storage. The response time of digital power grid regulation big data index is below 0.21s, and the index efficiency is higher. [ABSTRACT FROM AUTHOR]

Background: High-definition maps can provide necessary prior data for autonomous driving, as well as the corresponding beyond-line-of-sight perception, verification and positioning, dynamic planning, and decision control. It is a necessary element to achieve L4/L5 unmanned driving at the current stage. However, currently, high-definition maps still have problems such as a large amount of data, a lot of data redundancy, and weak data correlation, which make autonomous driving fall into difficulties such as high data query difficulty and low timeliness. In order to optimize the data quality of high-definition maps, enhance the degree of data correlation, and ensure that they better assist vehicles in safe driving and efficient passage in the autonomous driving scenario, it is necessary to clarify the information system thinking of high-definition maps, propose a complete and accurate model, determine the content and functions of each level of the model, and continuously improve the information system model. Objective: The study aimed to put forward a complete and accurate high-definition map information system model and elaborate in detail the content and functions of each component in the data logic structure of the system model. Methods: Through research methods such as the modeling method and literature research method, we studied the high-definition map information system model in the autonomous driving scenario and explored the key technologies therein. Results: We put forward a four-layer integrated high-definition map information system model, elaborated in detail the content and functions of each component (map, road, vehicle, and user) in the data logic structure of the model, and also elaborated on the mechanism of the combined information of each level of the model to provide services in perception, positioning, decision making, and control for autonomous driving vehicles. This article also discussed two key technologies that can support autonomous driving vehicles to complete path planning, navigation decision making, and vehicle control in different autonomous driving scenarios. Conclusions: The four-layer integrated high-definition map information model proposed by this research institute has certain application feasibility and can provide references for the standardized production of high-definition maps, the unification of information interaction relationships, and the standardization of map data associations.

This research is devoted to one of the urgent problems in the field of security provision, implemented in various areas of human activity related to information systems. It is associated with a typical situation of discrepancy between the costs of improving security methods and the level of security achieved in this case. It is shown that one of the most promising methodological approaches aimed at finding a solution to this problem is related to the study of the prospects for adapting existing solutions with integration into the computing environment that implement the new technology. In accordance with this concept, the equivalent transition between information technologies should be implemented while maintaining the level of overall information security. The main research goal was determined – it concerns the development of an analytical model for controlling the equivalence of information technologies in information security systems. The current state in the field of information security was analyzed. It was revealed that the tools and mechanisms existing today and presented on the relevant market that prevent risks and threats to the functioning of information systems associated with data theft and distortion are “narrow”, that is, adapted to solving local problems facing attackers.

Copyright of Natural Gas & Oil is the property of Editorial Department of Natural Gas & Oil and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, most multi-target data association methods require the assumption that the target motion model is known, but this assumption is clearly not valid in a real environment. In the case of an unknown system model, the influence of environmental clutter and sensor detection errors on the association results should be considered, as well as the occurrence of strong target maneuvers and the sudden appearance of new targets during the association process. To address these problems, this paper designs a target tracking and data association algorithm based on reinforcement learning. First, this algorithm combines the dynamic exploration capability of reinforcement learning and the long-time memory function of LSTM network to design a policy network that predicts the probability of associating a point with its various possible source targets. Then, the Bayesian network and the multi-order least squares curve fitting method are combined to predict the location of target, and the results are fed into the Bayesian recursive function to obtain the reward. Simultaneously, some corresponding mechanisms are proposed for possible problems that interfere with the association process. Finally, the simulation experimental results show that this algorithm associates the results with higher accuracy compared to other algorithms when faced with the above problem. [ABSTRACT FROM AUTHOR]

A fractional-order dynamic model could more accurately model many real scenarios than an integer-order model and provide a more accurate description of numerous genuine dynamical processes. A seventh-order nonlinear proton exchange membrane fuel cell (PEMFC) model is linearised in this research, taking into account correct initial conditions and equilibrium points.We consider the fluctuating load current as a disturbance parameter to affect the system. The goal is to find a control law for the MIMO system using a fractional PID controller based on a genetic algorithm. The controller is a critical part of the fuel cell which controls its functioning and efficiency. The goal is accomplished by designing a fractional controller to adjust the natural response of the fuel cell reactor and maintain the desired Power output in the face of uncertainties and disturbances. The validation results demonstrate that the fractional PIλDµ(FOPID) control method has a smaller overshoot and higher stability than the PID control method. Moreover, it is also observed that the operation efficiency of the PEMFC has risen by 2% with a response timing of less than 0.1 s using the developed fractional PIλDµ(FOPID) control technique. [ABSTRACT FROM AUTHOR]

The hereditary vogtei was a natural consequence of foundation under German law. In most cases it was assumed by the locator and was associated with powers of a lord, including judicial powers. A hereditary vogt was essentially to be the representative of the town's owner -- thus, in the case of royal towns, the monarch (the vogt as a brachium regale). However, already by the end of Casimir the Great's reign we can observe a move away from the idea of hereditary vogtei. Probably one of the reasons for this was the reduction in the effectiveness of the hereditary vogt's actions due to the division of the vogtei through inheritance. However, during the reign of Władysław Jagiełło the hereditary vogtei was used for personal politics -- as another type of property which could be used to reward someone's services. There is a noticeable trend of accumulating property in the hands of noble families. Thus, an authority that undermined urban autonomy was being consolidated in the landscape of the urban system. The term denotes the extent of the burghers' independence from outside entities in the management of the town, including with regard to the election of councillors. The following correlations have been noted between the extent of autonomy and the nature of hereditary vogtei: rich vogtei in the hands of a nobleman effectively limited the autonomy of a town included in the so-called II or III tax category (Biecz, Bochnia, Myślenice and Pilzno); poor vogtei, even one held by a starosta/tenutary, was an insufficient basis for effectively limiting urban autonomy (Będzin, Ciężkowice, Częstochowa); the absence of vogtei (Proszowice) or vogtei bought out by the town (Połaniec, Sandomierz, Wieliczka) are circumstances that generally strengthen urban autonomy; the lack of vogtei (Chęciny) or vogtei bought out by a town (Nowy Sącz) may not have translated into significant autonomy, if the castle starosta successfully opposed it and the economic potential of the vogtei was small. [ABSTRACT FROM AUTHOR]

In order to improve the effect of agricultural data collection and transmission, based on the technology adopted by the mainstream agricultural environmental information monitoring system on the market, this paper uses game theory to innovatively improve the algorithm mechanism of wireless sensor nodes, optimise the clustering structure and routing method of wireless sensor networks, design a remote monitoring platform for field farm information and finally build a complete agricultural information monitoring system. Moreover, taking the temperature and humidity sensor as an example, this paper improves the parameters on the basis of testing the various performance of the sensor, and develops the software of the main chip on the basis of the improved node, and completes the driver design of the sensor. In addition, this paper constructs a wireless sensor network data transmission model based on game theory, and combines simulation experiments to conduct experimental research on the system model constructed in this paper. From the test results, it can be seen that the model constructed in this paper has a better effect.

Proton exchange membrane fuel cell (PEMFC) has drawn extensive attention as alternative energy with high power generation efficiency and low pollution. The dynamic performances of the PEMFC air system in a city bus, including oxygen excess ratio (OER) tracking and compressor movement, are essential for its stability and economy. In this paper, the study is concentrated on the control of OER under current disturbance and parameter uncertainty. To this end, a Lyapunov-based model reference adaptive controller (Lyapunov-MRAC) with PD controller is employed. Lyapunov-MRAC adjusts the controller in time to ensure the output of the plant following the desired reference model, and the PD controller further improves the dynamic performance of the controller. The proposed strategy, traditional Lyapunov-MRAC, and proportional–integral (PI) controller are implemented on a widely accepted nonlinear model describing the dynamics of the air supply system. The simulation results indicate that the proposed strategy outperforms traditional methods in OER tracking and overshoot restriction under current disturbance, and improves the robustness under the perturbation of compressor rotational inertia. In addition, the proposed strategy can reduce the control effort of the compressor so as to prolong the lifetime of the compressor.

A Cognitive Radio based Wireless Network (CRWN) is an emerging trend in wireless networks. Cognitive Radio (CR) has opened up many new opportunities to utilize the frequency spectrum to its fullest utilization. Many researchers and engineers are coming up with new protocols for CRWN. In this paper, existing MAC protocols for CRWN are studied and found that all the protocols are designed for optimized node performance but not the network performance. Moreover, individual node’s optimized performance may harm network’s performance. So, there is a gap of performance to be filled at an individual node and network-level. In this paper, the frame work of existing MAC protocol is modified to achieve the global optimized performance of CRWN under saturated and unsaturated traffic conditions. Also, majority of the existing work proposed their solutions assuming full information-based game theoretical models, whereas in this paper more realistic approach based on incomplete information is presented. A rigorous simulation study is carried out to compute various parameters such as channel capacity, throughput and delay to understand the limitations of the non-game theoretic approach. Later, a game theoretical framework to MAC protocol is applied for CRWN to improve the network performance in terms of these parameters. It is shown that the application of game theory to the MAC layer avoids collision and reduces the delay by 54% and energy consumption leading to enhancement of the overall throughput of a network by 57% respectively.

This article discusses four fundamental topics for future Distributed Computing Continuum Systems: their representation, model, lifelong learning, and business model. Further, it presents techniques and concepts that can be useful to define these four topics specifically for Distributed Computing Continuum Systems. Finally, this article presents a broad view of the synergies among the presented technique that can enable the development of future Distributed Computing Continuum Systems. [ABSTRACT FROM AUTHOR]

After undergoing an unprecedented energy reform, Mexico is poised to significantly grow its renewable energy share, and evolve from 80 MW installed solar photovoltaics (PV) capacity in 2015, to more than 6 GW by 2022. The impact on the overall electrical grid is therefore a crucial element to determine where the investments should be allocated -both in generation, and transmission-, to be optimally deployed. In this contribution, we model the generation, transmission and distribution assets in Mexico and optimize the investment in capacity to meet policy goals under three different plausible scenarios.

This paper outlines a new simulation approach to study the effect of factors on the state of charge (SOC) consistency of Li‐ion battery packs. The proposed method is based on a system model and the Monte Carlo method. The system model includes 100 serial connected equivalent circuit models and their respective parameters. This paper divides the internal and external factors into two categories: independent factors such as internal resistance, capacity, Coulombic efficiency, leakage current, and comprehensive factors related to temperature. The Monte Carlo method simulates the differences and distribution of independent factors among cells. Temperature‐dependent factors are modeled with empirical formulas. Simulation results show that the main factors affecting SOC consistency are differences in battery temperature, leakage current, and Coulombic efficiency. Meanwhile, slight differences in capacity and internal resistance have little effect on SOC inconsistency. Furthermore, this paper demonstrates the effectiveness of the system model. [ABSTRACT FROM AUTHOR]

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To handle increasing complexity in product development, model-based systems engineering (MBSE) approaches are well suited, in which the technical system is represented in a system model. To efficiently test requirements, domain models are integrated into the system model. For each purpose (e.g., battery lifetime calculation), there are typically several models at several fidelity levels. Since the model signatures (i.e., necessary inputs for the models and their outputs) differ depending on the fidelity level, not all models can be used in any development phase. In addition, due to the different model signatures, not all models can be combined arbitrarily to model networks. Currently, valid model networks in system models must be determined in a time-consuming, manual process. Therefore, this paper presents an approach that automates this task via the implementation of an algorithm that analyzes a system model and the model signatures and automatically returns all valid model networks. When input parameters, models or their signatures change, the algorithm updates automatically, and the user receives the valid model network without any manual effort. The approach is demonstrated with the running example of battery system development. [ABSTRACT FROM AUTHOR]

After undergoing an unprecedented energy reform, Mexico is poised to significantly grow its renewable energy share, and evolve from 80 MW installed solar photovoltaics (PV) capacity in 2015, to more than 6 GW by 2022. The impact on the overall electrical grid is therefore a crucial element to determine where the investments should be allocated -both in generation, and transmission-, to be optimally deployed. In this contribution, we model the generation, transmission and distribution assets in Mexico and optimize the investment in capacity to meet policy goals under three different plausible scenarios.