State Of Unit Research Articles

Scenario analysis loss of heat removal from the spent fuel pool on nuclear power plant

The spent nuclear fuel storage system is designed to store and cool spent fuel for several years taking into account scheduled reloads and unloading of the entire core accumulated after use in a nuclear reactor. It consists of special pools or containers where spent fuel is placed for temporary storage before final treatment or disposal. These systems provide safe and efficient storage of spent fuel to prevent radioactive material from leaking into the environment and minimize risks to human health and the natural environment. The events that occurred during the Fukushima nuclear disaster on March 11, 2011, underscored the importance of safe storage of spent fuel in the spent fuel storage pool. Storage safety has therefore become a key aspect in this area. This article describes the heat sink loss calculations for the analytical substantiation of the emergency response instructions for the shutdown state of the Armenian NPP power unit No.2 using the RELAP5/Mod3.2 computer code. The initiating event in the event of loss of heat removal from the spent fuel pool is considered. The analysis of nuclear safety in the course of the development of a beyond design basis accident with a long NPP blackout was carried out in relation to the spent fuel pool of a power unit with a reactor plant WWER-440 (project V-270). The radiation consequences are estimated. The article provides calculations of the following accidents to determine the necessary actions of the operator: loss of heat removal from the spent fuel pool without operator action and loss of heat removal from the spent fuel pool from the organizations of subsequent make-up of the spent fuel pool with a boron cleaning pump (2NBO-2). The calculations are based on boundary and initial conditions corresponding to the assumptions of the «better estimate».

Protein Photodamaging Activity and Photocytotoxic Effect of an Axial-Connecting Phosphorus(V)porphyrin Trimer.

An axial-connecting trimer of the porphyrin phosphorus(V) complex was synthesized to evaluate the relaxation process of the photoexcited state and the photosensitizer activity. The photoexcitation energy was localized on the central unit of the phosphorus(V)porphyrin trimer. The photoexcited state of the central unit was relaxed through a process similar to that of the monomer phosphorus(V)porphyrin. The excited state of this axially connected type of phosphorus(V)porphyrin trimer was not deactivated through intramolecular electron transfer. The singlet oxygen generation quantum yield of the trimer was almost the same as that of the monomer. The phosphorus(V)porphyrin, trimer, and monomer bound to human serum albumin and oxidized the tryptophan residue via singlet oxygen generation and electron transfer during visible light irradiation. The photocytotoxicity of these phosphorus(V)porphyrins on two cell lines was examined. The monomer induced photocytotoxicity; however, the trimer did not show cytotoxicity with or without photoirradiation. In summary, the photoexcited state of the trimer was almost the same as that of the monomer, and these phosphorus(V)porphyrins demonstrated a similar protein-photodamaging activity. The difference in association between the photosensitizer molecules and cells is the key factor of phototoxicity by these phosphorus(V)porphyrins.

Development of intelligent automatic water intake system for open-pit mine

In order to solve the problem that the automation level of water intake system in open-pit mine is relatively low and the phenomenon of fault shutdown occurs from time to time, this paper innovatively develops an intelligent automatic water intake system based on LabVIEW suitable for open-pit mine. On the basis of extensive investigation of field conditions and in-depth analysis of domestic and foreign open-pit mine water intake system, the overall design scheme of this system was put forward. The selection of electric gate valve, ball valve, various sensors and Siemens 1200 series PLC was completed. The system software which integrates system initialization subroutine, pump rotation start-stop subroutine, sensor polling subroutine, fault protection and alarm subroutine was compiled. The monitoring software of Upper machine was developed based on LabVIEW software platform. The field debugging and operation results show that the system realizes the real-time monitoring of the operation state of the water intake unit, the on-demand start and stop and the intelligent rotation control of the water pump, which greatly reduces the labor intensity of the workers, prolongs the service life of the equipment, greatly improves the intelligent operation and maintenance level of pumping units of open-pit mine, and lays a solid foundation for green mining.

Local Agents of International Justice? On the Role of Subnational Units in Refugee Protection

Refugee protection depends, minimally, on the identification of agents capable of discharging international obligations in this area of international law. Commonly discussed “agents of justice” include states, IOs, and NGOs. This article focuses on a different set of actors: subnational units (cities, states, and provinces in federal States) and the legal mechanisms they may use to discharge international obligations in the area of refugee protection. I advance three distinct theoretical models for understanding subnational units’ responsibilities vis-à-vis international law: (1) derived delegated responsibilities; (2) derived back-up responsibilities; and (3) assumed responsibilities. I conclude by sketching some ways in which subnational units could play an even more salient role in the promotion of international law.

Homogeneous Space Construction and Projection for Single-Cell Expression Prediction Based on Deep Learning.

Predicting cellular responses to perturbations is an unsolved problem in biology. Traditional approaches assume that different cell types respond similarly to perturbations. However, this assumption does not take into account the context of genome interactions in different cell types, which leads to compromised prediction quality. More recently, deep learning models used to discover gene-gene relationships can yield more accurate predictions of cellular responses. The huge difference in biological information between different cell types makes it difficult for deep learning models to encode data into a continuous low-dimensional feature space, which means that the features captured by the latent space may not be continuous. Therefore, the mapping relationship between the two conditional spaces learned by the model can only be applied where the real reference data resides, leading to the wrong mapping of the predicted target cells because they are not in the same domain as the reference data. In this paper, we propose an information-navigated variational autoencoder (INVAE), a deep neural network for cell perturbation response prediction. INVAE filters out information that is not conducive to predictive performance. For the remaining information, INVAE constructs a homogeneous space of control conditions, and finds the mapping relationship between the control condition space and the perturbation condition space. By embedding the target unit into the control space and then mapping it to the perturbation space, we can predict the perturbed state of the target unit. Comparing our proposed method with other three state-of-the-art methods on three real datasets, experimental results show that INVAE outperforms existing methods in cell state prediction after perturbation. Furthermore, we demonstrate that filtering out useless information not only improves prediction accuracy but also reveals similarities in how genes in different cell types are regulated following perturbation.

Probabilistic neural network-based diagnosis of blockage faults in air conditioning and refrigeration units

Abstract The refrigeration unit is a core component of the air conditioner, and its typical faults are pipe scaling and blockage, which can cause refrigerant leakage in serious cases, and these faults can affect the cooling effect of the air conditioner and even affect the safety of using the air conditioner in serious cases. Therefore, it is necessary to study the fault diagnosis system of the refrigeration unit. The traditional fault diagnosis system for refrigeration units, which generally uses wireless sensor networks to transmit data, is a challenge because of its slow diagnosis speed and the low accuracy rate of fault diagnosis for refrigeration units. This paper uses a probabilistic neural network (PNN) to debug a blockage fault diagnosis algorithm system for simulated fault training. The system successfully achieves four levels of pre-warning for blockage faults: “uncertainty, micro-blockage, blockage, and severe blockage”, with the fastest pre-warning time of “uncertainty, micro-blockage” being less than 2 seconds. The experimental results of this paper show that the PNN system can take timely measures against blockage faults, and the fault diagnosis accuracy of the model reaches 97.97%, which is 13.34% higher than the traditional fault identification algorithm. The PNN model can pinpoint the blockage faults of the refrigeration unit, which enables the working state of the refrigeration unit to be continuously optimized, and the air conditioner to maintain energy-efficient operation. The fault simulation of the air conditioning refrigeration unit in automatic control mode verifies the blockage fault diagnosis’s accuracy and the algorithm’s feasibility, providing reference and learning for the development of fault diagnosis algorithms.

Real-time economic and safe operation diagnosis of hydropower units

In order to ensure the safety and economy of the operation of hydropower units under the big data technology application platform and the automatic generation control system, and to tap the maximum power generation potential of the units, an effective method of economic operation evaluation of hydropower units combined with state diagnosis is proposed. Taking Unit 1 of Longtan Hydropower Station as an example, the economic evaluation and status diagnosis of the daily operating conditions are carried out, the real-time efficiency of the turbine is queried through the unit operating conditions data, and the economic operation of the unit is comprehensively evaluated using the standard water consumption index. In addition, the real-time operation condition of the unit shall be diagnosed according to the working condition zoning diagram, and the economic operation condition shall be combined with the unit state operation condition for joint assessment. It is strictly forbidden to operate the unit in the vibration zone and the vortex zone and it is determined that the unit can maximize the power generation efficiency after operating in the safe operation zone.

РОЗРОБЛЕННЯ МОВНО–ІНФОРМАЦІЙНОГО ІНСТРУМЕНТАРІЮ АВТОМАТИЗОВАНОГО ЕКСТРАГУВАННЯ З ТЕКСТУ СЕМАНТИЧНОЇ І КОГНІТИВНОЇ ІНФОРМАЦІЇ

The article deals with the role of semantic research for the creation of intelligent systems for automated processing of language information. An approach to the development of linguistic and informational tools for automated extraction of semantic and cognitive information from the text is outlined. The issue of increasing the efficiency of high-quality and fast processing of digital information, the creation of highly intelligent, fast-acting information systems was raised. The theoretical basis of the research is the lexicographic theory of symbolic semantic systems developed by academician V. A. Shyrokov and the theory of semantic states of natural language units built within it, which is based on the principles of lexical semantics and is oriented towards the use of computer technologies in semantic research. An integrated lexicographic system created in the Ukrainian Language and Information Fund of the National Academy of Sciences of Ukraine was selected as the source base of the study, the core component of which is the computer version of the «Dictionary of the Ukrainian Language» in 20 volumes, which is the most representative source of semantic information that provides a description of the lexical meaning of a word according to many parameters with the involvement of pragmatic, connotative, syntagmatic and linguistic contexts. In the study, the formalism of the theory of semantic states is used to build a linguistic and informational toolkit for automatic recognition of semantic and cognitive information in texts written in natural languages. The proposed semantic classifier provides an explicit presentation of the information extracted from the text, which enables its further software processing thanks to the unified interpretation of the operators of the semantic state formula and the results of the semantic indexing of the text. The expediency of using the developed language and information toolkit for solving such important tasks of computer linguistics as determining formal criteria for distinguishing lexical homonyms and LSV of a polysemous word, correct establishment of structural contrasts «meaning — shades», «lexeme — lexical-semantic variants» in interpreting the meaning of the latter, the task of unifying the description of the lexical meaning of words of one semantic field.

Hydropower Unit State Evaluation Model Based on AHP and Gaussian Threshold Improved Fuzzy Comprehensive Evaluation

Because a single monitoring index cannot fully reflect the overall operating status of the hydropower unit, a comprehensive state evaluation model for hydropower units based on the analytic hierarchy process (AHP) and the Gaussian threshold improved fuzzy evaluation is proposed. First, the unit equipment was divided into a hierarchical system, and a three-tier structure system (target layer-project layer-index layer) of the unit was constructed, and the weight of each component in the system was determined by the comprehensive weighting method. Secondly, according to the characteristics of the normal distribution of the historical health data of the unit, the upper and lower limits of the index were determined based on the Gaussian threshold principle, the real-time monitoring index degradation degree was calculated according to the index limit, and the degradation degree was applied to the fuzzy evaluation model to obtain the fuzzy judgment matrix. The result of assessment was divided into four sections: good, qualified, vigilant, and abnormal. Finally, combined with the unit hierarchical structure system, the weighted calculation of the fuzzy judgment matrix of each indicator, the overall fuzzy judgment matrix of the upper-level indicators of the unit was obtained, and the operating status of the unit was judged according to the matrix. Taking a real power plant unit as an example, the model was verified, and compared with other evaluation methods, the effectiveness and advantages of the proposed method were verified. In addition, the method proposed in this paper effectively solved the problems of index weighting and index limit determination in the existing model of unit condition evaluation.

Political and Legal Problems of the Soviet–Yugoslav Military Co-Operation During the World War II: The Case of the 1st Yugoslav Rifle Brigade

ABSTRACT The first, fundamental step of Soviet-Yugoslav military co-operation during the Second World War was the formation of the 1st Yugoslav Rifle Brigade. It was made on the own initiative of the Soviet leadership, without the participation of the any Yugoslav authorities. The political state of this military unit from the very beginning was unstable due to the obviously low loyalty of most of the personnel. The Soviet authorities chose a cautious, soft course for their political work in the unit. However, the leadership of the People’s Liberation Army of Yugoslavia (PLA), which incorporated the Yugoslav military unit created in the USSR, considered it necessary to take decisive measures to eradicate any disloyalty. Gradually, at the direction of the leadership of the PLA, the brigade underwent a radical ‘political reformatting’.

Multi-modal multi-step wind power forecasting based on stacking deep learning model

Wind power is becoming a clean and effective energy source for electric power generation. However, the abnormity, multi-modal, and uncertainty represented in wind power data are commonly undesired. Thus, accurate wind power forecasting is a significant method for keeping the power system operations steady. To solve these issues, a multi-modal multi-step wind power forecasting model is presented. To obtain this, the density-based spatial clustering of applications with noise (DBSCAN) is improved by the k-dimensional tree (kd-tree) for detecting abnormal data. Then, the low-rank matrix fusion method fuses the wind speed, wind direction, and air density modalities for obtaining a unified representation. To further increase model accuracy, we propose a stacking deep learning model (SDLM) for overcoming the uncertainty phenomenon, which contains the bidirectional gated recurrent unit (BGRU) and leaky echo state network (LESN). The final forecasting results are acquired by a meta-learning operator. To validate the accuracy and stability of the presented approach, the inland and offshore wind farm datasets are used for forecasting. The contrastive results demonstrate that the presented model outperforms satisfactory performance in multi-step wind power prediction.

Influence of control parameters on the main reliability indicators of machine parts

The mechanisms of influence of tolerances on the formation of indicators of reliability of machine parts are determined. It is established that in order to control the residual life, it is necessary to diagnose the state of a specific machine unit in order to obtain estimated reliability parameters in order to determine the possibility of its further operation. Keywords: reliability, control, failure, life, diagnostics, machine. ykataev@mail.ru

RESEARCH OF PROBLEMS AND PROSPECTS OF OPERATION OF ROD PUMPS

This article shows the current state of downhole rod pumping units. The main ways of reducing the costs of maintenance, selection and purchase of oil-producing deep pumps are substantiated. One of the most effective methods of increasing the service life of pumps is to increase their wear resistance. It is established that the main direction for increasing the pump life is the modeling of its design solutions.

Operation characteristics based on a novel performance model based on capacity utilization rate of a variable refrigerant flow air conditioning system

In the context of a large market share, enhancing the actual performance of variable refrigerant flow (VRF) air-conditioning systems is of considerable importance. The operating states of indoor units (IUs) significantly influence the energy efficiency of a VRF system with multiple indoor terminals. Therefore, it is challenging to address accurate and concise representation of the operational characteristics of IUs for analyzing their impact on system performance. This study explored a novel performance index called capacity utilization rate (CUR) to illustrate the effect of IUs on system performance and developed a performance model based on this index. Simulation and experimental results demonstrated that energy efficiency and evaporating temperature decrease with a reduction in CUR under specific ambient temperatures and part-load rates. The proposed performance model was applied to real buildings in field tests to examine the operational characteristics of the VRF system. This research offers valuable insights for system design, performance simulation, and performance evaluation of VRF systems.

Distributed event‐triggered algorithms for the management of networked battery systems

Distributed algorithms have recently been developed for a networked battery system composed of multiple battery units, each of which serves as an energy storage unit. These emerging distributed algorithms rely on continuous communication among battery units and/or continuous update of power allocation for each battery unit. To reduce the communication and computational burden, we propose distributed event-triggered algorithms for the management of networked battery systems with unknown battery unit parameters. In particular, two event-triggered distributed estimators are designed for each battery unit to respectively estimate the average battery unit state and the average desired power with any specified level of accuracy. Two dynamic event-triggering mechanisms are proposed for the distributed estimators that determine when each battery unit communicates with its neighbors. Based on these two event-triggered distributed estimators, an event-triggered distributed adaptive power allocating law along with a static event-triggering mechanism is designed for each battery unit to update the power allocation in a discrete-time manner. It is shown that the proposed power allocating laws achieve state-of-charge balancing among all battery units while delivering the desired total power in either the charging or discharging mode without requiring either continuous communication or continuous update of power allocation. It is also shown that the Zeno behavior is excluded for all the three event-triggering mechanisms. A simulation example is provided to validate the effectiveness of the proposed design.

Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm

To improve the identification accuracy of the vibration states of hydraulic units, an improved artificial rabbits optimization algorithm (IARO) adopting an adaptive weight adjustment strategy is developed for optimizing the support vector machine (SVM) to obtain an identification model, and the vibration signals with different states are classified and identified. The variational mode decomposition (VMD) method is used to decompose the vibration signals, and the multi-dimensional time-domain feature vectors of the signals are extracted. The IARO algorithm is used to optimize the parameters of the SVM multi-classifier. The multi-dimensional time-domain feature vectors are input into the IARO-SVM model to realize the classification and identification of vibration signal states, and the results are compared with those of the ARO-SVM model, ASO-SVM model, PSO-SVM model and WOA-SVM model. The comparative results show that the average identification accuracy of the IARO-SVM model is higher at 97.78% than its competitors, which is 3.34% higher than the closest ARO-SVM model. Therefore, the IARO-SVM model has higher identification accuracy and better stability, and can accurately identify the vibration states of hydraulic units. The research can provide a theoretical basis for the vibration identification of hydraulic units.

Multi-state traction control of regional CCHP system

The existing optimization scheduling of regional CCHP system does not take into account the different control objectives and response characteristics of each unit under multiple operating states in the whole operation domain, so it is difficult to achieve the unified traction control of multiple operation stages of multiple units. To this end, this paper proposes a unified traction control strategy for multi-state operation of regional CCHP system. Based on the different control objectives and response characteristics CCHP unit under multiple states, a two-layer traction control strategy of the regional CCHP system and unit was established. The upper level takes into account the different control objectives and response characteristics of each unit under different operating states, and optimizes the target operating state of each traction unit. The lower level realizes the efficient traction control of each unit under the target operating state based on the optimization decision results. Finally, a simulation example is designed to verify that this paper can effectively consider the different control objectives and response characteristics of CCHP unit under different operating states to achieve the optimal traction control.

Employee Professional Improvement Model Civil State in Resource Staff Work Units State Police Human Power Republic of Indonesia

The purpose of this study was to analyze the factors or variables that most dominantly influenced the increase in civil servant professionalism and to apply the development of a strategic model to increase the professionalism of civil servants in the National Police's SSDM satker by using the concept of Strategic Human Resource Management theory from Noe, et al. The method used is the mixed method methodology. Data collection was carried out through questionnaires and interviews as well as document review. The results showed (1) the factors that most dominantly influenced the increase in the professionalism of civil servants in the National Police's SSDM satker were aspects of job design, selection, training and development, compensation, performance management and employee relations which had not been carried out optimally.

Improving Technology Use, Digital Competence, and Access to Community Resources Among Older Participants in the University of Rhode Island Engaging Generations Cyber‐Seniors digiAGE Pilot Study

AbstractThis pilot study aimed to bridge the digital divide between older and younger adults. The goal was for older people in the state to become digitally literate by engaging them in a program that provides digital devices (i.e., Apple iPads), internet connectivity (i.e., through HotSpots), and training from supervised university student mentors. This project, funded as a key policy initiative through the state's unit on aging, specifically promoted social and economic equity by targeting participants from lower‐income communities and areas hit hardest by the COVID‐19 pandemic. Our university partnered with senior/community centers to recruit and support English‐ and Spanish‐speaking adults 50 years of age and older (age range: 55‐100, M=72.3, SD=8.5). For this paper, we examined changes in technology use and digital competence from the pre‐ to the post‐survey (collected over the phone) from older participants (N=145), and we examined how the program contributed to new ways for participants to connect to community resources. Based on statistical analyses, participants improved in digital competence (pre=2.06, post=2.74), technology use (pre=1.99, post=2.70), tablet use (pre=1.53, post=4.08), and the number of purposes in which participants used technology (pre=4.09, post=5.55; p's<.01). Themes that arose from the qualitative data included feeling more capable and confident in searching out new information, now knowing where to find activities and resources, and meeting with doctors and booking health appointments. This program addressed a significant community need during the pandemic and had success working with community partners. Policies for state grants that support broadband equity, digital literacy and digital equity initiatives should utilize this research to inform their efforts to address digital inclusion needs for older adults.

Analysis of the Functional Capabilities of Technical tools for Measuring Aircraft Mechanical Parameters

The purpose of research is to analyze the functionality of classical and advanced sensors for controlling the mechanical parameters of aircraft using the example of strain and vibration sensors to identify their current areas of development.Methods. Research methods are based on the concepts of the theory of sensory systems, the theory of diagnostics and forecasting of the technical condition of aircraft. Methods of multi-criteria analysis, parametric and structural synthesis are used. The principles of operation, as well as the functionality of the main classical sensors used in aircraft to control the parameters of deformation and vibration, are analyzed. A critical assessment of the possibilities of using the analyzed sensors for implementation in various tasks of aviation diagnostics of mechanical parameters has been made.Results. It has been established that the impact of flight loads on the airframe and critical components of aircraft is accompanied by the appearance of hidden deformations in the form of mechanical stresses, which are divided into two components: normal and tangential. Analytical dependencies are obtained for calculating the above quantities using fiber-optic sensors with distributed Bragg cells that convert the change in their own linear dimensions into a change in the reflected wavelength. A necessary condition for obtaining correct measurement results is the temperature compensation of the cells, which makes it possible to localize the places of deformations with an accuracy up to the location of a particular cell. The practical results of using alternative sensors for detecting hidden deformations (cracks) based on radio frequency identification methods in various frequency ranges are presented.Conclusion. The development of the method for diagnosing stress-strain states of aircraft complex units is the use of frequency-Doppler fiber-optic sensors with a high signal-to-noise ratio and a spherical radiation pattern, which will allow developing technical means for monitoring the dynamics of internal deformations of controlled units in real time. As promising areas of research in the field of creating new sensors with new physical properties, fiber-optic Bragg sensors with an inclined grating should be considered.