State Of Unit Research Articles

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.

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.

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.

Zintl Phase versus Covalent Metal: Chemical Bonding in Silicon Dumbbells of Ca5Si3 and CaSi3.

Silicon dumbbells constitute identifiable anionic molecular species in Zintl phases and so-called covalent metals holding units with homopolar bonding inside a metallic framework. Based on electron-precise Ca5Si3 and metallic CaSi3, the chemical bonding in Si2 units is investigated by computational quantum chemical methods considering the dual nature of the wave function. This concerted wave-vector and real space study substantiates that the Si2 dumbbells in Ca5Si3 can be referred to as molecular building units Si26- with additional metallic and ionic contributions in the solid. In the covalent metal CaSi3, however, the bonding within the dumbbells falls short of fulfilling the octet rule. As a result, antibonding states of the Si2 building units are depopulated and attend metallic interactions, simultaneously giving rise to stronger covalent Si-Si bonds.

Tackling rolling security-constrained unit commitment under uncertainty with Monte Carlo value search and empirical knowledge

Recently, the growing uncertainty in the power system has created an increasing need for security-constrained unit commitment (SCUC) solved on a rolling perspective (i.e., RSCUC). This puts a high requirement on the decision efficiency of the solution algorithm. To overcome the challenge, this paper proposes a POML-MCVS algorithm, which integrates a probability-oriented machine learning (POML) technique with Monte-Carlo value search (MCVS) algorithm to improve overall performance. First, POML is employed to identify the decision probabilities of units through leveraging empirical knowledge, and determine the on/off states of units with high decision probabilities. This facilitates improving the decision efficiency while maintaining the decision accuracy as much as possible. Then, MCVS is employed to optimize the rest variables and make the rolling decisions. As a modified version of Monte Carlo tree search, MCVS is able to detect the promising decisions, and evaluate their values efficiently through constructing the search tree asymmetrically, which further improves the decision efficiency. Numerical simulations conducted on a modified IEEE 39-bus system and a real 2778-bus system demonstrate the effectiveness of the proposed algorithm.

Video-Based Sign Language Recognition via ResNet and LSTM Network.

Sign language recognition technology can help people with hearing impairments to communicate with non-hearing-impaired people. At present, with the rapid development of society, deep learning also provides certain technical support for sign language recognition work. In sign language recognition tasks, traditional convolutional neural networks used to extract spatio-temporal features from sign language videos suffer from insufficient feature extraction, resulting in low recognition rates. Nevertheless, a large number of video-based sign language datasets require a significant amount of computing resources for training while ensuring the generalization of the network, which poses a challenge for recognition. In this paper, we present a video-based sign language recognition method based on Residual Network (ResNet) and Long Short-Term Memory (LSTM). As the number of network layers increases, the ResNet network can effectively solve the granularity explosion problem and obtain better time series features. We use the ResNet convolutional network as the backbone model. LSTM utilizes the concept of gates to control unit states and update the output feature values of sequences. ResNet extracts the sign language features. Then, the learned feature space is used as the input of the LSTM network to obtain long sequence features. It can effectively extract the spatio-temporal features in sign language videos and improve the recognition rate of sign language actions. An extensive experimental evaluation demonstrates the effectiveness and superior performance of the proposed method, with an accuracy of 85.26%, F1-score of 84.98%, and precision of 87.77% on Argentine Sign Language (LSA64).

Palliative Care in the Intensive Care Unit: The Standard of Care

Intensive care unit–based palliative care has evolved over the past 30 years due to the efforts of clinicians, researchers, and advocates for patient-centered care. Although all critically ill patients inherently have palliative care needs, the path was not linear but rather filled with the challenges of blending the intensive care unit goals of aggressive treatment and cure with the palliative care goals of symptom management and quality of life. Today, palliative care is considered an essential component of high-quality critical care and a core competency of all critical care nurses, advanced practice nurses, and other intensive care unit clinicians. This article provides an overview of the current state of intensive care unit–based palliative care, examines how the barriers to such care have shifted, reviews primary and specialist palliative care, addresses the impact of COVID-19, and presents resources to help nurses and intensive care unit teams achieve optimal outcomes.

Analysis of the competitiveness of Brazilian poultry-meat exporting states (1999-2022)

This study analyzed the performance of Brazilian poultry-meat exporting federative units (states), using the Revealed Comparative Advantage (RCA) and the Relative Position (RPI) indices. Furthermore, the performance matrix based on the linear trend of the calculated indices was elaborated, covering the period from 1999 to 2022 with data obtained from the ComexStat database. The results reveal that the most efficient federative units in the sector are Distrito Federal, Paraná, Santa Catarina, Rio Grande do Sul and Mato Grosso do Sul. On the other hand, the other states examined presented comparative disadvantages. It is important to highlight that all analyzed states had surpluses in poultry meat exports, which contribute positively to their trade balance. Furthermore, throughout the period, most federative units presented stable indicators, which suggest a consistent and favorable scenario for the sector.

Nonlinear observer-based state-of-charge balancing of networked battery energy storage systems

In this paper, we propose an observer-based algorithm for balancing the state-of-charge (SoC) among battery units in a battery energy storage system (BESS). The dynamical behaviour of a battery unit is approximated by an equivalent circuit model, based on which a nonlinear SoC observer can be constructed. Power distribution laws are designed for the battery units according to the states of the battery units, the average battery state, and the average power demand. Distributed estimation algorithms for the average battery state and the average power demand, as well as SoC observers, are constructed to implement them. The BESS is shown to achieve SoC balancing among all its battery units while satisfying the power demand, as long as mild conditions on the underlying communication network and on the power demand are met. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

A novel decision optimization for thermal power unit based on condition-based predictive maintenance and equilibrium optimizer

A novel maintenance decision method for thermal power unit is proposed in paper. The proposed method tackles the issues of inflexibility and high cost in current thermal power unit maintenance strategies. It takes into account both intelligent fault prediction results and the current unit condition to make predictive maintenance decisions. It constructs a unit state model and a power generation prediction model based on DCS data from the thermal power unit. Constraints such as unit constraints, unit failure constraints, and unit power generation constraints are utilized in the construction of the objective function. The objective function incorporates start-stop cost, maintenance cost and downtime loss, and the Equilibrium Optimizer is used to determine the optimal maintenance strategy. Operational data from a power plant is used to verify the superiority of the method in enhancing unit power generation efficiency and the economic benefits of the power plant. The results show that the proposed method can reduce the cost by 34.9 % in case of minor failures.

Differences and Causal Mechanisms in the Lithospheric Thermal Structures in the Cratons in East China: Implications for Their Geothermal Resource Potential

The thermal structure of the lithosphere is key to understanding its thickness, properties, evolution, and geothermal resources. Cratons are known for their low heat flow and deep lithospheric roots. However, present-day cratons in East China have geothermal characteristics that are highly complex, with variable heat flow values, diverging from the typical thermal state of cratons. In this study, we conducted a detailed analysis of the geothermal geological background of the cratons in East China, summarizing the thermal state and tectono-thermal processes of different tectonic units, calculating the temperature at various depths, and discussing differences in temperature and thermal reservoirs at different depths. The observed lithospheric thermal thickness within the North Jiangsu Basin and the Bohai Bay Basin is notably reduced in comparison to that of the Jianghan Basin and the Southern North China Basin. The phenomenon of craton destruction during the Late Mesozoic emerges as a pivotal determinant, enhancing the geothermal resource prospects of both the Bohai Bay Basin and the North Jiangsu Basin. Our findings contribute significantly to the augmentation of theoretical frameworks concerning the origins of heat sources in global cratons. Furthermore, they offer invaluable insights for the methodical exploration, evaluation, advancement, and exploitation of geothermal resources.

On the Structural and Vibrational Properties of Solid Endohedral Metallofullerene Li@C60

The endohedral lithium fulleride, Li+@C60•−, is a potential precursor for new families of molecular superconducting and electronic materials beyond those accessible to date from C60 itself. Solid Li@C60 comprises (Li@C60)2 dimers, isostructural and isoelectronic with the (C59N)2 units found in solid azafullerene. Here, we investigate the structural and vibrational properties of Li@C60 samples synthesized by electrolytic reduction routes. The resulting materials are of high quality, with crystallinity far superior to that of their antecedents isolated by chemical reduction. They permit facile, unambiguous identification of both the reduced state of the fulleride units and the interball C-C bonds responsible for dimerization. However, severe orientational disorder conceals any crystal symmetry lowering due to the presence of dimers. Diffraction reveals the adoption of a hexagonal crystal structure (space group P63/mmc) at both low temperatures and high pressures, typically associated with close-packing of spherical monomer units. Such a situation is reminiscent of the structural behavior of the high-pressure Phase I of solid dihydrogen, H2.