State Of Unit Research Articles

Estimating the influence of the rigidity of support assemblies on the resonance phenomena and the vibration state of a hydraulic unit

Many factors affect the vibration of the hydraulic unit, including the natural oscillation frequencies of the structural elements, which in some cases may contribute to an increase in vibration because of the occurrence of resonance phenomena. In this work, the case of design optimization of the support assemblies of the shaft pipeline of the hydrogenerator-motor was considered, installed at HSPP during its reconstruction in order to improve vibration parameters. The effect of increasing the stiffness of the upper crosspiece and reducing the weight of the rotating parts on the value of the first and second critical rotation frequencies was analyzed. Based on the results of computations, operability of the proposed new reinforced structure of the upper crosspiece of the hydrogenerator-motor with increased rigidity was confirmed. Using this design could increase the first critical rotation frequency from 9 Hz (540 rpm) to 17.6 Hz (1056 rpm). This would lead to the avoidance of resonance phenomena caused by the proximity of the first critical rotation frequency of the rotor to the rated rotation frequency (600 rpm). The computations were performed in a three-dimensional setting in two stages for each of the considered design cases. At the first stage, the supporting elements rigidity of the shaft line of the hydrogenerator-motor were studied by determining the structural element elastic deformations when it was loaded by a transverse force. At the second stage, these determined stiffnesses of the support elements were used as input data for calculating the critical rotation frequencies of the hydraulic unit rotor

A Digital Twin Based System for Diagnosing and Controlling the Power-generating Boiler Unit State

A Digital Twin Based System for Diagnosing and Controlling the Power-generating Boiler Unit State

Coordinative Self‐assembly of π‐Electron Magnetic Porphyrins

Abstractπ‐Electron magnetic compounds on surfaces have emerged as a powerful platform to interrogate spin interactions at the atomic scale, with great potential in spintronics and quantum technologies. A key challenge is organizing these compounds over large length scales, while elucidating their resulting magnetic properties. Herein, we offer a relevant contribution toward this objective, which consists of using on‐surface synthesis coupled with coordination chemistry to promote the self‐assembly of π‐electron magnetic porphyrin species. A porphyrin precursor equipped with carbonitrile moieties in a trans arrangement was prepared by solution synthesis and deposited on Au(111)/mica. Depending on the specific growth protocol, surface‐promoted reactions led to the transformation of the precursor into non‐magnetic Au‐CN coordinated porphyrin monomers, covalent porphyrin dimers, and one‐dimensional porphyrin polymers (based on porphyrin monomers or covalent porphyrin dimers), as revealed by scanning probe microscopy studies combined with theoretical calculations. Interestingly, the scanning tunneling microscopy tip could convert such closed‐shell porphyrin units into open‐shell species by the removal of some peripheral hydrogen atoms. The magnetic features (i.e., singlet or triplet ground state) of the porphyrin units comprising the polymers were investigated for polymers of different lengths. No magnetic exchange coupling between adjacent units was observed, suggesting protection of the magnetic entities.

Deep Learning Application of LSTM(P) to predict the risk factors of etiology cardiovascular disease

Cardiovascular vascular disease (CVD) is a leading cause of death in the world. By 2025, it is estimated that 23.6 million people will be attacked by CVD. Thus, the health care industry is established in order to collect a large number of CVD information of cardiovascular disease and to support doctors in finding and recognizing its potential risk factors of CVD through mining and analyzing the information. This structured and unstructured case information may be used to find out potential patterns of diseases and symptoms using deep learning algorithms. The risk factors associated with cardiovascular disease are known from epidemiology, and this is the first prospective investigation on the condition in the community free mobility population. Physicians can anticipate cardiovascular disease and take early action if it can be predicted. Clinical data analysis is therefore considered to be the prediction of cardiovascular disease. The main contents and contributions are as follows: We start off with trying to predict using the classic data mining and machine learning techniques, and the results are not at all ideal. Generally speaking, after analysis, it is mainly caused by imbalance of data sets. To solve the problem of CVD data imbalance, a SMOTE oversampling method is proposed aiming at the imbalance of cardiovascular data collected in the Framingham community. In order to ensure accurate data collection during operation, the relationship between LSTM(P) and unit state is then tried, and the prediction technique of cardiovascular disease utilising LSTM(P) is realised. Lastly, tests are conducted to confirm the 4434 individuals' initial medical information in the data set. The algorithm has an MCC score of 0.96 and an accuracy of about 94%.

Rule-Based Energy Management System to Enhance PV Self-Consumption in a Building: A Real Case

The building sector has an important role in the decarbonization of energy. Buildings energy management systems can act on flexible loads to contribute to the integration of renewable energies. This article presents the design, implementation and evaluation of a rule-based energy management systems (RB-EMS) in the frame of a collective self-consumption (CSC) system based on photovoltaic (PV) energy in the Technology Park of Izarbel, in Bidart, France. The RB-EMS acts on the heat ventilation and air conditioning (HVAC) system of one of the buildings involved in the CSC in order to maximize the PV energy self-consumption rate (SCR). Internet of things (IoT) has been developed and implemented in order to retrieve consumption, production and temperature data, and to be able to act on the HVAC. A new 5-step methodology has been developed to design and adjust the RB-EMS. This methodology is mainly based on tests carried out to find the relationship between ON/OFF states of the internal units of the HVAC and the heat pumps’ consumption. Finally, the RB-EMS is tested in heating operating mode. The results show that the RB-EMS allows obtaining a SCR of 89%, and that the users comfort limits are respected.

Interfacial separation in CRTS Ⅱ type slab track structure subjected to continuous high temperatures: Thermal and moisture effects

To investigate the formation mechanism of cracking in track slab-CA mortar layers under continuous high-temperature conditions, a thermal and moisture coupling deformation theory was used to analyze interlayer damage in ballastless track structures. Firstly, the thermal and moisture coupling deformation and interlayer damage model of the CRTS Ⅱ type slab track structure was established by integrating the thermal and moisture coupling transfer model with the cohesive force damage model through the use of equivalent temperature differentials. The accuracy of the model was verified by setting up an indoor test bench. Secondly, based on the coupled heat and humidity transfer model of the slab-type ballastless track, the distribution characteristics of the temperature and capillary pressure fields were calculated and analyzed using COMSOL Multiphysics® software and the summer meteorological data in Hangzhou, China. The results indicate that the influence depth of drying rapidly developed to 20 mm on day 1, and expanded to 60 mm on day 6; the expansion rate gradually slowed until it reached 120 mm on the 25th day and finally 150 mm on the 40th day. Finally, based on this, the finite element software ABAQUS was used to comparatively analyze the thermal deformation and coupling deformation of both thermal and moisture effects, as well as interlayer damage in the CRTS Ⅱ type slab track structure in the unit state and longitudinal connection state. The results indicate that drying shrinkage can hinder upward arch displacement and damage to the track slab based on the temperature gradient, while significantly worsening upward warping displacement and damage to the track slab. The upward arch displacement decreased by 21.24 % in the unit state and 22.67 % in the longitudinally connected state on the 40th day, while the upward warping displacement increased by 62.17 % and 32.22 %, respectively. Therefore, it is crucial to investigate how moisture deformation impacts the cracking of the track slab-CA mortar layer, as a significant research factor.

Short-term prediction of offshore wind power based on CNN and LSTM

Abstract This paper presents an innovative short-term wind power forecasting model based on deep learning, aimed at improving the economic benefits and system stability of wind power projects. The model combines a CNN, known for efficient feature extraction, with an LSTM network, recognized for its exceptional time series processing ability. Additionally, an attention mechanism is applied to probabilistically assign weights, enhancing key feature influence and further improving wind direction prediction accuracy. First, the LSTM predicts the gas properties of the unit state, followed by a convolutional model with attention mechanisms to forecast wind power. Experimental results demonstrate that this model significantly reduces prediction errors and enhances accuracy. Specifically: 1) A comprehensive deep learning model is proposed, integrating CNN and LSTM advantages, with an attention mechanism to enhance performance. 2) Key issues in wind power forecasting are effectively addressed, improving accuracy and stability. 3) Both real and simulated data demonstrate the model’s exceptional performance. 4) Experimental results confirm that the model surpasses traditional methods with lower forecast errors and higher accuracy. Future work will focus on refining the model to handle complex environments and overcoming technical challenges in practical applications.

A closed-form continuous-depth neural-based hybrid difference features re-representation network for RUL prediction

Remaining Useful Life (RUL) prediction contributes to ensuring the reliability of mechanical systems and improving their maintenance plans. Recently, prediction methods based on deep learning have undergone rapid development. However, there are significant differences between multivariate monitoring sequences and utilizing a single model for feature extraction, which leads to reaching suboptimization. Additionally, focusing solely on a specific feature dimension usually imposes notable limitations on the model. This paper proposes a Closed-form Continuous-depth neural (CfC)-based hybrid difference Features Re-representation Network (CfC-F2RN) for RUL prediction. This method comprehensively utilizes hybrid difference features through two stages: initial feature representation and feature re-representation. In the initial feature representation stage, a Long Short-Term Memory (LSTM) is employed to capture the hidden state information of each time step in sequence data. Next, a novel attention mechanism is utilized to extract the hidden states and obtain a deep feature map. In the feature re-representation stage, the encoded features are re-represented using a decoder composed of the CfC model. Finally, the hidden state of the last LSTM unit in the encoder, serving as supplementary information, is combined with the decoded features to form a dual-latent feature representation of the mixed differential features. The prediction subnetwork is then adopted to accomplish RUL prediction. The superiority of CfC-F2RN is substantiated through benchmarking against established methods using the C-MAPSS dataset.

A GAN-based stepwise full-field mechanical prediction model for architected metamaterials

The exploration of mechanical metamaterials, characterized by unique unit cells with significant macroscopic mechanical properties, is of crucial importance. Traditional methods relying on bioinspired structures lack broad design space and controllability to some extent, especially when practical requirements have no biological counterparts. Hence, there is a pressing need to advance methodologies for the expansive design of unit cells, catering to diverse practical demands. Inspired by architected material, a latent design paradigm gains prominence for its extensive possibilities and unique distribution. However, wide design space faces challenges in accurately assessing the deformation history and stress states of diverse units, requiring validation through experiments or simulations, and imposing efficiency constraints on design. Using patterns generated by a mimetic corrosion algorithm and computational simulations, a GAN-based machine learning surrogate model is constructed to predict the history of deformation and stress fields of architected unit cells with accuracy and efficiency, in which a stepwise mapping strategy is proposed to augment comprehension of mechanical information in image processing. In distinct cases covering both explored domains and several unexplored domains, the model achieves effective predictions, demonstrating the robustness and adaptability of the model in extrapolating predictions of both time domain and design space. The proposed method demonstrates efficacy, stability, and generalizability, pioneering a solution for the mechanical history prediction of architected metamaterials in a sparse data setting, thereby overcoming the limitations of the training set.

Maternity care practices supportive of breastfeeding in U.S. advanced neonatal care units, United States, 2022.

To describe breastfeeding-supportive practices in U.S. maternity hospitals with advanced neonatal care units (ANCU). Using 2022 mPINC data, we calculated the percent of hospitals reporting (1) breastfeeding-supportive practices for "most" (≥80%) mother-baby dyads in ANCUs, by hospital demographic characteristics and (2) infant receipt of mother's own milk or donor milk at any time while in the ANCU, by ANCU level. More than 90% of hospitals reported that "most" mothers (≥80%) were advised on some breastfeeding-supportive practices. Fewer hospitals reported that "most" mothers expressed milk within one hour of birth (37%) or that kangaroo care was practiced for "most" eligible newborns (63%). Receipt of mother's own milk varied by unit level and state. Breastfeeding-supportive practices requiring the technical competency of healthcare providers (e.g., early milk expression, kangaroo care) are less likely to be implemented in ANCU settings compared to practices centered around providing advice or education.

Disparidades sociodemográficas no câncer colorretal no Brasil, 1990-2019

ABSTRACT In the world, colorectal cancer presents high rates of incidence and mortality, with differences according to the level of sociodemographic development. The objective of this study was to analyze the sociodemographic disparities of colorectal cancer in the Brazilian population aged 30 and older. This is a time series study of incidence, mortality, disability-adjusted life years (DALY), and prevalence, by sex, in Brazil and its Federal Units (FU) states from 1990 to 2019. The trend was estimated using Joinpoint regression analysis, and the Socio-Demographic Index (SDI) was used in the correlation analysis. The data analyzed were estimated by the Global Burden of Diseases Study 19. In Brazil and its states, the highest rates of these indicators were observed in males, with an increasing trend in both sexes. There was a positive association between SDI and all the analyzed indicators except for DALY in men. The differences in rates and trends between the states reflect the country’s development processes, such as urbanization and industrialization. More developed states have high rates with stable trends, while less developed states exhibit the opposite behavior, suggesting improved access to healthcare services and diagnosis.

Synthesis of Self-Checking Circuits for Train Route Traffic Control at Intermediate Stations with Control of Calculations Based on Weight-Based Sum Codes

When synthesizing systems for railway interlocking, it is recommended to use automated models to implement the logic of railway automation and remote control units. Finite-state machines (FSMs) can be implemented on any hardware component. When using relay technology, the functional safety of electrical interlocking is achieved by using uncontrolled (safety) relays with a high coefficient of asymmetry of failures in types 1 → 0 and 0 → 1. When using programmable components, the use of backup and diverse protection methods is required. This paper presents a flexible approach to synthesizing FSMs for railway automation and remote control units that offer both individual and route-based control. Unlike existing solutions, this proposal considers the pre-failure states of railway automation and remote control units during the finite-state machine synthesis stage. This enables the implementation of self-checking and self-diagnostic modules to manage automation units. By increasing the number of states for individual devices and considering the states of interconnected objects, the transition graphs can be expanded. This expansion allows for the synthesis of the transition graph of the control subsystem and other systems. The authors used a field-programmable gate array (FPGA) to implement a finite-state machine. In this case, the proposal is to encode the states of a finite-state machine using weight-based sum codes in the residue class ring based on a given modulus. The best coverage of errors occurring at the outputs of the logic converter in the structure of the FSM can be ensured by selecting the weighting coefficients and the value of the module. This paper presents an example of synthesizing an FPGA-based FSM using state encoding through modular weight-based sum codes. The operation of the synthesized device was modeled. It was found to operate according to the same algorithm as the real devices. When synthesizing self-checking and self-controlled train control devices, it is recommended to consider the solutions proposed in this paper.

FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology.

Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION (Functional Unit State IdentificatiON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics.

Trend Prediction and Operation Alarm Model Based on PCA-Based MTL and AM for the Operating Parameters of a Water Pumping Station.

In order to effectively predict the changing trend of operating parameters in the pump unit and carry out fault diagnosis and alarm processes, a trend prediction model is proposed in this paper based on PCA-based multi-task learning (MTL) and an attention mechanism (AM). The multi-task learning method based on PCA was used to process the operating data of the pump unit to make full use of the historical data to extract the key common features reflecting the operating state of the pump unit. The attention mechanism (AM) is introduced to dynamically allocate the weight coefficient of common feature mapping for highlighting the key common features and improving the prediction accuracy of the model when predicting the trend of data change for new working conditions. The model is tested with the actual operating data of a pumping station unit, and the calculation results of different models are compared and analyzed. The results show that the introduction of multi-task learning and attention mechanisms can improve the stability and accuracy of the trend prediction model compared with traditional single-task learning and static common feature mapping weights. According to the threshold analysis of the monitoring statistical parameters of the model, a multi-stage alarm model of pump unit operation condition monitoring can be established, which provides a theoretical basis for optimizing operation and maintenance management strategy in the process of pump station management.

Theoretical principles of sustainable development of territorial communities: public administrative aspect

The article establishes contradictions between the normatively fixed order of program management of the territory and the realization of the possibility of territorial communities to independently manage the administrative-territorial unit, implementing the provisions of the concept of sustainable development. Differences in the goals, tasks, indicators of the development of territorial communities during the implementation of socio-economic development programs and the implementation of the provisions of the state regulatory acts regarding sustainable development have been proven. It is noted that indicators of socio-economic development are not related to real management processes of the authority, although a significant part of them concerns the components of sustainable development: economic, social, environmental. The article proves that the introduction of reporting of executive bodies of local self-government to deputies and the population based on indicators of sustainable development can be carried out only with a paradigm shift from programmatic socio-economic management to a real assessment of the activities of local authorities. It was concluded that communities cannot carry out this change independently, it must be carried out systematically, centrally with methodical support from the state, as the introduction of new democratic forms of governance, unlike the traditions of the Soviet era. The analysis showed that in order to introduce indicators of sustainable development into the reporting of local self-government bodies on their activities, it is necessary to make changes to normative legal acts of different levels and of different legal weight. It is noted that the implementation of a complex combination of strategic planning taking into account the needs of the community, the peculiarities of the state of administrative-territorial units with the goals and indicators of sustainable development are the primary tasks of changing the development paradigm of our state and its territorial communities in the post-war period. The article emphasizes that the issue of territorial community management based on the concept of sustainable development will become a primary need after the end of the war, since for settlements affected by military actions, the issue of combining economic revival, social protection of the population, and environmental restoration in order to preserve resources for future generations will be the content of their existence.

ОБЩИЙ ПОДХОД К РЕШЕНИЮ ЗАДАЧИ ОБЕСПЕЧЕНИЯ РАБОТО-СПОСОБНОСТИ ТРАКТОРОВ В АПК

Agricultural production is characterized by the seasonality of mechanized work in the cultivation of crops. In addition, the amount of wear on aggregates and equipment systems is determined, on the one hand, by natural and climatic conditions and, on the other, by the nomenclature of operations performed in the planned period, organizational, production and technical conditions in a particular farm. Tractor units and systems are interconnect with each other and interdependent on each other, i.e. the amount of wear of some parts affects the wear of others. In this regard, the purpose of the research is to develop a concept for ensuring the operability of tractors in the agro-industrial complex by optimizing the resource consumption of aggregates and systems, taking into account the conditions of their operation. The most promising direction for improving the efficiency of tractor operation is the development of measures to optimize the resource consumption of their units and systems, taking into account the operating conditions of machinery in the agro-industrial complex. To predict changes in the physical parameter of a part, unit, and tractor system for the planned period of a certain amount of work, a concept is introduced - an indicator of resource consumption, which determines the amount of change in the diagnostic parameter of the unit state per unit of work performed. The assessment of the tractor condition is carry out by the level of equipment resource consumption, which makes it possible to promptly respond to the state of technical operation of equipment, the conditions of their operation and develop measures to optimize resource consumption. The resource consumption level (Urr) of the tractor, theoretically, can vary from 0 to 1. In this case, Urr=0 it is assumed that the tractor is written off from the balance sheet of the farm or is missing; with Urr=1, it is assumed that the tractor is in good condition and all tractor parameters have nominal values. With known patterns of changes in the level of resource consumption for various types of agricultural operations, it becomes possible to plan the range of work for each tractor for the planned period, taking into account the optimal resource consumption of their units and systems.

Dynamic Calculation Method for Zonal Carbon Emissions in Power Systems Based on the Theory of Production Simulation and Carbon Emission Flow Theory

Power systems are the main source of carbon emissions. Currently, coordinated operation strategies of the source–grid–load–storage model considering carbon emissions is primarily expanded from the generation side. For practical power systems, where multiple types of generating units coexist at a single node, it is difficult to develop unit combination strategies that simultaneously consider carbon emission factors and power flow constraints. Therefore, a new power flow calculation method based on connectivity matrix theory was proposed, aiming to address the issues of existing approaches that are too coarse and unable to accurately represent the operating states of multiple units under each node. Furthermore, a new method for dynamic calculation of regional carbon emission based on connectivity matrix and carbon emissions flow was introduced to improve the accuracy of carbon emission measurements. Firstly, a simulation model for a coordinated optimization operation based on the minimum system cost for the source–grid–load was established and an optimal flow calculation method using a connectivity matrix was introduced. Second, a dynamic carbon emission calculation method, considering electricity sources, was developed by combining the results of the optimal power flow calculation with carbon emission flow theory. Finally, the effectiveness of the approach in this article was verified by the IEEE 14-bus system example and a provincial power grid, ensuring strict adherence to the conservation principle of carbon emissions between the supply and demand sides and satisfying power flow constraints.

Дослідження режимів розгону автомобіля Skoda Octavia на роликовому стенді

As you know, the main characteristics that determine the state of the power unit are the power level and torque. These parameters affect the dynamic qualities of the car and can also play a role in fuel consumption and emission levels. To check these characteristics, traction roller stands of the inertial type are used. These stands allow you to simulate real traffic conditions and loads. Usually, on such stands, the traction force on the driving wheels is measured, which makes it easy to calculate the power in certain driving modes. However, creating a full-fledged load on the driving wheels of the car being diagnosed requires the use of powerful loading and driving devices. This significantly increases the cost of the stand and its metal content. An alternative to this method is the compressed acceleration method. In this method, the load is created by a relatively small inertial mass together with a loading and driving device of moderate power. The diagnostic parameter in this method can be traction force on the wheels, acceleration, time or path of acceleration of the wheels in the selected speed range. For measurement accuracy, it is better to use acceleration time. However, all the advantages of the method can be manifested only with the correct choice of test modes. During testing, results depend on the accuracy and stability of maintaining the test mode. Acceleration time will be measured more accurately, the slower the wheels and rollers of the stand accelerate. During the tests, it is necessary to choose the high-speed measurement mode to ensure a sufficiently stable torque value. The article analyzes the acceleration modes of a car on a roller stand and their influence on the accuracy of diagnostics. The smallest error is achieved when measuring acceleration time from 50 to 70 km/h in IV gear with additional load or in V gear without load.

Calculation of the Thermal State of a Power-Supply Unit in the COMSOL Multiphysics Environment

Calculation of the Thermal State of a Power-Supply Unit in the COMSOL Multiphysics Environment

Diagnosis, Prevention, and Management of Fetal Growth Restriction (FGR).

Fetal growth restriction (FGR), or intrauterine growth restriction (IUGR), is still the second most common cause of perinatal mortality. The factors that contribute to fetal growth restriction can be categorized into three distinct groups: placental, fetal, and maternal. The prenatal application of various diagnostic methods can, in many cases, detect the deterioration of the fetal condition in time because the nature of the above disorder is thoroughly investigated by applying a combination of biophysical and biochemical methods, which determine the state of the embryo-placenta unit and assess the possible increased risk of perinatal failure outcome and potential for many later health problems. When considering the potential for therapeutic intervention, the key question is whether it can be utilized during pregnancy. Currently, there are no known treatment interventions that effectively enhance placental function and promote fetal weight development. Nevertheless, in cases with fetuses diagnosed with fetal growth restriction, immediate termination of pregnancy may have advantages not only in terms of minimizing perinatal mortality but primarily in terms of reducing long-term morbidity during childhood and maturity.