Operation Status Research Articles

Analysis of poor work postures during morning care operations of intensive care unit nurses: a field research

BackgroundPoor working postures in morning care operations contribute significantly to work-related muscle fatigue and low back pain among ICU (Intensive Care Unit, ICU) nurses. However,there are few field studies on the relationship between Poor working postures and work-related muscle fatigue, low back pain among ICU nurses.ObjectiveThis study aims to assess the frequency and degree of posture hazards associated with poor working postures during morning care operations of ICU nurses.MethodsTwo ICUs in a Grade-A tertiary hospital in Hunan Province were randomly selected for the field study in May–June 2021 research. The image data of the participating nurses’ morning care work postures were collected by observation method and image recording method, and the postural hazard level was assessed by the two-person interpretation method of the work posture analysis system. The mean and standard deviation, frequency and percentage were used for the basic statistical description, and the general linear model multivariate Hotelling’s T2 test was used to calculate the Hotelling’s T2 and F statistics to explore the pattern of change in the postural hazard score curves.ResultsA total of 45 nurses were observed during morning care operations, resulting in 47.8 h of video data and 34,428 effective static operation posture pictures. Among these pictures, 21,166 (61.5%) depicted poor working postures. Nurses spent up to 7 min and 30 s maintaining a challenging posture during oral care, with the highest postural hazard score (M = 2.2, SD = 0.3) observed during defecation care. Each patient requires a total of 25–30 min of oral care per day.The Hotelling’s T2 test indicated that the change curve of the postural hazard score in each step revealed the largest second-order F value, F = 52.931, P < 0.001.DiscussionICU nurses frequently adopt poor working postures during morning care operations. Further research should focus on optimizing safe working postures to prevent or minimise detrimental occupational hazards resulting from poor working postures.Research on occupational low back pain among ICU nurses can help them work more efficiently, and thereby improving their nursing level.

A hybrid prediction model of blast furnace permeability index combining least square support vector machine and artificial neural network

Blast furnace (BF) permeability index is a crucial parameter that can quickly, intuitively, and comprehensively reflect the furnace condition. Accurate prediction of this index is crucial for optimising production efficiency and ensuring the stable operation of the BF. In this study, a hybrid permeability index prediction model is constructed by combining a least squares support vector machine and an artificial neural network, using the mean shift clustering algorithm (MSCA) to classify the BF conditions is applied. The results show that the MSCA algorithm shows remarkable precision in classifying the stable and unstable operating states of BF, achieving an impressive accuracy rate of 93.98%. The hybrid prediction model could accurately predict the permeability index and has a mean absolute error of 0.6877, a mean square error of 0.4721 and an R2 of 0.9215, highlighting its robust predictive performance. These findings underscore the practical significance of our model in enhancing BF operational efficiency and reliability.

PROSPECTS AND SOLUTIONS FOR BUILDING SUSTAINABLE BANCASSURANCE IN VIETNAM

Bancassurance is a relatively young market in Vietnam, yet its contribution to the total revenue of the insurance sector has witnessed rapid growth and is expected to continue expanding in the future. This paper provides a theoretical foundation on bancassurance, analyzes its development prospects, and examines the current state of bancassurance operations in Vietnam. Additionally, it highlights certain limitations in the implementation of bancassurance in Vietnam. Based on these findings, the paper proposes solutions for building a sustainable bancassurance model in the country.

Fixing collapsed dry anaerobic digestion system of kitchen waste caused by severe VFAs accumulation

Severe inhibition by volatile fatty acids (VFAs) frequently occurs during the dry anaerobic digestion of kitchen waste at high organic load rate (OLR), leading to decreased biogas production and even the collapse of the anaerobic digestion system. In this study, VFAs accumulation was significantly reduced by adding powder-activated carbon (PAC) or alkaline additives (AA), thus restoring the collapsed system to a stable operating state. With the addition of PAC or AA, the maximal OLR reached 11.14 g VS/(L·d), which was 30 % higher than observed without any additives (control group), while the VFAs concentration was maintained below 3000 mg/L. At this OLR, the volumetric biogas production rate stabilized at 5.1 L/(L·d) in the presence of PAC and AA, while that of the control group gradually decreased to zero. The VFAs concentration with PAC addition was 86 % lower than that of the control group, possibly because PAC might stimulate the formation of direct interspecies electron transfer between syntrophic bacteria and methanogens (Methanosarcina), thereby promoting VFAs degradation. The addition of AA, which resulted in a 95 % decrease in ammonia-nitrogen concentration, can provide a good growth environment for the microorganisms involved in acidogenesis and hydrogenotrophic methanogenesis.

Research on Ship Data Analysis Platform Development and Its Application in Teaching

This paper delves into the significant importance of developing a ship data analysis platform, which aims to enhance the operational efficiency and safety of the shipping industry by integrating big data processing technologies. The development of the platform centers on crucial aspects such as data collection, preprocessing, analysis, and visualization, enabling real-time monitoring and intelligent prediction of ship operational status. This platform not only assists in optimizing route planning and reducing energy consumption costs but also provides robust support for navigational safety. In project-based teaching, this platform serves as a bridge between theory and practice, cultivating students' abilities in big data processing and analysis through case studies, practical exercises, and other forms, thereby promoting the cultivation of high-quality talents and technological innovation in the field of shipping.

Research on Multi-Sensor Fusion Fault Diagnosis Method Based on Spatiotemporal Attention Mechanism

As industrial systems become increasingly complex, real-time monitoring and intelligent management of industrial equipment have become imperative. However, the limitations in coverage and accuracy of single sensors make it challenging to comprehensively characterize the operational state of equipment, leading to reduced system reliability and increased pressures on data transmission and storage. To address these challenges, this study presents a novel fault diagnosis method based on multi-sensor fusion using a spatio-temporal attention mechanism. Initially, one-dimensional convolutional neural networks (1D-CNN) are employed to extract features from raw signals, effectively capturing local characteristics and ensuring the integrity and validity of fault signals. Subsequently, the spatiotemporal attention mechanism adjusts the feature weights based on the temporal and spatial correlations of different sensors, as well as their respective importance, thereby capturing the spatio-temporal dependencies across multiple sensors and enhancing the efficacy of information fusion. Finally, the proposed method is validated through experiments on a nickel flash smelting furnace system. The results demonstrate that the method achieves a fault diagnosis accuracy exceeding 97.78%, significantly enhancing fault detection and decision-making performance.

Polarization self-modulation in a coaxially end-pumped orthogonally polarized laser

A scheme of orthogonally polarized laser (OPL) with polarization self-modulation (PSM) in the coaxial end-pumping configuration is proposed in this work. The gain medium consisted of two Nd:YVO4 crystals with orthogonal optical axes, and a quarter-wave plate served as a polarization-switching device. By rotating the electric vector during each cavity round trip, frequency degeneration and correlated operation of two polarization components were achieved. The polarization eigenstates in two orthogonal directions were analyzed using the Jones matrix and eigenequation. The spectral distribution was also briefly discussed based on thermal effects. The results showed that two eigenmodes located at 45° and 135° to the crystal axis, respectively, with a frequency difference equaled to half the free spectral range of the resonator. In the experiments, it was found for the first time that the modulation of eigenstates can be realized by adjusting pump parameters (including pump power, beam size, focusing position, and pump wavelength) and cavity anisotropy, which enables the manipulation of degree of polarization (DOP) and monitoring the resonator properties. Overall, tuning the laser polarization eigenstate offers a flexible method to switch the operational states of the OPL, including the longitudinal mode frequency difference, DOP, polarization angle, etc. It is believed this scheme provides a valuable reference to realize compact, high-beam-quality, and robust OPLs for applications such as dual-frequency light source development, precision measurement and polarization control.

Research on Low-Voltage Arc Fault Based on CNN–Transformer Parallel Neural Network with Threshold-Moving Optimization

Low-voltage arc fault detection can effectively prevent fires, electric shocks, and other accidents, reducing potential risks to human life and property. The research on arc fault circuit interrupters (AFCIs) is of great significance for both safety in production scenarios and daily living disaster prevention. Considering the diverse characteristics of loads between the normal operational state and the arc fault condition, a parallel neural network structure is proposed for arc fault recognition, which is based on a convolutional neural network (CNN) and a Transformer. The network uses convolutional layers and Transformer encoders to process the low-frequency current and high-frequency components, respectively. Then, it uses Softmax classification to perform supervised learning on the concatenated features. The method combines the advantages of both networks and effectively reduces the required depth and computational complexity. The experimental results show that the accuracy of this method can reach 99.74%, and with the threshold-moving method, the erroneous judgment rate can be lower. These results indicate that the parallel neural network can definitely detect arc faults and also improve recognition efficiency due to its lean structure.

Distributed Secondary Control of DC Microgrid with Power Management Based on Time-of-Use Pricing and Internal Price Rate

This paper presents a novel approach to manage distributed DC microgrids (DCMG) by integrating a time-of-use (ToU) electricity pricing scheme and an internal price rate calculation mechanism. The proposed power-management system is designed to effectively handle uncertainties such as utility grid (UG) availability, fluctuating electricity prices, battery state of charge (SOC) levels, and frequent plug-ins and plug-outs of electric vehicles (EVs). Uncertainties in DCMG systems often lead to inefficiencies, power imbalances, and inexact voltage regulation issues within DCMGs. In addition, to maintain the power balance and constant voltage regulation under various operational states, the proposed scheme also incorporates secondary control into the DCMG power-management system. Unlike the existing approaches that often fail to adapt dynamically to changing conditions, the proposed method is the first approach to consider the concept of internal price rate in designing the DCMG power management. To address this challenge, this approach proposes a more resilient power-management strategy to enhance the efficiency and adaptability of DCMG systems. Extensive simulations and experimental validations demonstrate the practicality and adaptability of the proposed control strategy under diverse test conditions, including operation transitions between grid-connected mode (GCM) and islanded mode (IM), low battery SOC condition, operation transition from the current control mode (CCM) to distributed secondary control mode (DSCM), and EV plug-in scenarios. The test results confirm that the proposed method enhances the reliability, efficiency, and economic viability of DCMG systems, making it a promising solution for future smart grid and renewable energy integrations.

Higher-rank sectors in the hexagon formalism and marginal deformations

Abstract The hexagon approach provides an integrability framework for the computation of structure constants in N = 4 super Yang-Mills theory in four dimensions. Three-point functions are cut into two hexagonal patches, on which the excitations of the long-range Bethe ansatz of the spectrum problem scatter. To this end, the Bethe states representing the operators also need to be cut into two parts. In rank-one sectors such entangled states are fairly straightforward to construct so that most applications of the method have so far been restricted to this simplest case. In this article we construct entangled states for operators in psu(1, 1|2) sectors, importing a minimum of information from the nested Bethe ansatz. The idea is successfully tested against free field theory for a sample set of correlators with up to three higher-rank operators.&amp;#xD;Further, we take a look at the same correlators in the presence of marginal deformations of the theory. While a systematic modification of the hexagon procedure remains out of reach for now, in practical applications the undeformed amplitudes are surprisingly efficient, especially for a certain one-parameter deformation.

Probabilistic safety analysis of the large primary circuit leakages accident scenarios in the VVER-reactor

Probabilistic safety analysis of the loss of coolant accidents in the VVER-type reactor plant has been performed taking into account the internal initiating events of the reactor primary circuit large leakages. Swedish program code RiskSpectrum PSA was used for probabilistic safety analysis. Logical probabilistic models of the accident scenarios of the large primary circuit leakages in the VVER-type reactor were developed taking into account different operation modes of the power plant unit. Critical paths and probabilities of the accident scenarios occurrence of the large leakages were identified. The critical path of development of the reactor primary circuit large leakages accident with large leakages of 140–346 mm diameters is the accident sequence with a leak in the pipeline to the reactor pressurizer vessel. It has been established that the greatest contribution to the failure of safety functions during this initiating event is made by the failures due to common causes of the supporting systems (cooling and ventilation systems), critical consumers cooling circuit, emergency injection system. The critical path of the accident with large leakages of 346–850 mm diameters is the accident sequence with the rupture of any of the four primary circuit loops. The greatest contribution to the failure of safety functions during this initiating event is made by the failures due to common causes of the emergency injection system elements. Based on the accident analysis, recommendations for increase of performance reliability of safety functions during the large leakages accidents under all operation states of the nuclear power plant unit were given. In order to increase reliability of safety systems, it is necessary to eliminate failures due to common causes of equipment, increase the reliability of operation of supporting systems, change the maintenance and checking equipment procedures.

Establishing a tailings storage facility database in Gauteng

As an initial step of zero harm, an understanding of the scale and magnitude of Tailings Storage Facilities (TSFs) is necessary for risk assessments. There is no publicly available TSF database in each South African province with details on TSF distribution of risks and characteristics. TSF-related cases such as the Jagersfontein Tailings Dam failure and the crime challenges by illegal miners at the North Sands Dump are significant motivations to establish a publicly accessible TSF database, especially in Gauteng. The objectives of the study were to establish a TSF database comprising the spatial distribution of TSFs and to classify each TSF by its operational activity status. The Department of Mineral Resources and Energy publishes data on TSFs, and it was used to create the database. Due to coordinate data limitations, only 94 TSFs could be presented on ArcGIS Pro. The study finds that there is almost an equal split of active and inactive TSFs in Gauteng. Most active TSFs are situated in the East Rand of Gauteng. Furthermore, companies that own some of the TSFs in Gauteng include Durban Roodepoort Deep Gold, which owns 70 % of TSFs, followed by AfriSam (6 %) and Blyvoor Gold (4 %). Companies need to disclose all their TSF data to strengthen the database.

Ship emission variations during the COVID-19 from global and continental perspectives

The COVID-19 pandemic and the International Maritime Organization's (IMO) 2020 fuel-switching policy have profoundly impacted global maritime activities, leading to unprecedented changes in shipping emissions. This study aimed to examine the effects from different scales and investigate the underlying drivers. The big data model Ship Emission Inventory Model (SEIM) was updated and applied to analyze the spatiotemporal pattern of global ship emissions as well as the main contributors in 2019 and 2020. Overall, ships emitted NOx, CO, HC, CO2, and N2O declined by 7.4 %–13.8 %, while SO2, PM2.5, and BC declined by 40.9 %–81.9 % in 2020 compared with 2019. The decline in CO2 emissions indicated a comparable reduction across vessel tonnages. Ship emissions occurring at cruising status accounted for over 90 % of the ship's CO2 emission reduction. Container ships, chemical tankers, and Ro-Ro vessels were the primary contributors to the emission reductions, with container ships alone responsible for 39.4 % of the CO2 decrease. The ship's CO2 emissions variations revealed the decline-rebound patterns in response to the pandemic. Asian-related routes saw emissions drop in February 2020, followed by a rebound in May, while European and American routes experienced declines starting in May, with a recovery in August. Further analysis of CO2 emission in Exclusive Economic Zones (EEZs) showed high temporal consistency between vessel CO2 emissions, sailing speeds, and international trade volumes across continents, and exhibited heterogeneity in main contributing ship type of emission reduction on continental scale. Our study reveals the short-term fluctuation characteristics of global ship emissions during the pandemic, particularly focusing on their spatiotemporal evolution and the inherent disparities. The results highlight the correlation between global ship emissions and trade, as well as the operational status of ships, and their rigidity.

Effect of external magnetic field on the fretting wear mechanism of the ferromagnetic materials

With the widespread implementation of maglev technology in fields such as rail transit and military, it is imperative to investigate the effects of magnetic fields on fretting damage in metallic materials. This study conducted multi-parameter fretting wear tests on typical ferromagnetic counterparts in the presence of an external permanent magnetic field. Subsequently, the wear behavior and damage mechanism of the fretting interface were elucidated through multi-scale characterization analysis. The results indicated that the operation state of the fretting interface was shifted towards the partial slip regime under the magnetic field. Meanwhile, the external magnetic field transformed the dominant wear mechanism from abrasive wear to adhesive wear. The influence of the external magnetic field on fretting wear has been demonstrated to manifest in dual aspects: Firstly, the Hertzian contact stress at the interface is enhanced by the magnetic field induction force, resulting in a remarkable reduction of about 79.83 % in accumulated dissipation energy; Secondly, the effectiveness of debris being expelled from the worn interface decreased, and the wear progression was further impeded by the accumulated debris, so that the abrasion loss is significantly reduced by about 60.66 %. These findings provide valuable theoretical data and practical reference for the protection of fretting damage in ferromagnetic materials under the action of a magnetic field.

Concept of the digital state: concept, nature and ontology

The article is devoted to the study of the phenomenon and concept of the digital state. The general concept and meaning of digitization in public administration is revealed. The problems of relevant interpretation and the limits of applicability of this concept to public administration are explained. The author’s concept and definition are presented, explaining the concept, nature and ontology of the digital state. The author suggests to consider the digital state in 4 interpretive projections: 1) as an ecosystem of operational, multi-service and proactive state digital super-services; 2) as an integrated computer-software meta-platform (platform system); 3) as an organizational and technological approach and corresponding paradigm; 4) as the digital ontology of state-building and functioning, public administration and public policy. According to the author’s interpretation, the concept of the “digital state” structurally includes the concepts: 1) the concept of the “digital government”; 2) the “digital democracy” concept; 3) the concept of the “digital justice”; 4) the concept of the “digital electoral technologies”; 5) the concept of the “digital public control over public administration”. The article details the author’s concept of “digital government”. The article is based on application of research methods of analysis and synthesis, induction, deduction and abduction, classification and formalization; through application of research methods, the essential descriptive characteristics of the phenomenon and the concept of the digital state were revealed.

Spatial and Temporal Variations in Offshore Crude Oil Transportation: Insights From China's Coastal Ports

ABSTRACTAnalyzing the spatial and temporal variations of offshore crude oil transportation at China's coastal ports is critical for the country's energy security. The automatic identification system provides high‐resolution real‐time data on China's offshore crude oil transportation, offering more precise spatial and temporal insights than existing statistical studies. In this study, we propose a novel method for calculating offshore crude oil transportation volumes by considering the operational status of crude oil tankers as “empty” or “full.” This approach enhances reliability and provides detailed port‐level transport data. Our findings reveal notable similarities between the centrality measures of maritime oil transportation and oil tanker networks, but significant differences in community divisions. Specifically, in the tanker networks, Chinese ports are categorized into three regions, with one region being dominant and the other two regions containing only four ports. In contrast, the oil flow networks show China divided into two major regions and two minor ones. This study offers valuable contributions to strengthening energy security in China and supports informed policy development and implementation.

Understanding human responses to air source heat pump noise: examining tonal changes and operating cycles

Air Source Heat Pumps are pivotal in the Government of the United Kingdom's target of achieving net zero carbon emissions by 2050. However, their widespread adoption faces challenges, with noise being among the main barriers to entry and a restricting factor at the planning stages. This study addresses concerns regarding noise propagation from outdoor units to indoor environments through a two-part experiment, aiming to capture human responses to Air Source Heat Pump noise based on the changes in the characteristics of noise emissions at various operating conditions, background noise levels, and source distances. The first part evaluates peoples' responses under static operating conditions to 20-second recordings, using the Self-Assessment Manikin scale and an annoyance scale. Following each recording, they used the Self-Assessment Manikin pictorial scale and an annoyance scale to report their response. In the second part, responses to transient Air Source Heat Pump operation cycles are assessed using 60-second recordings. Participants evaluated each recording at two points: after a transition in the operating cycle and at the end of the recording. The findings of the study provide insights into the human response to heat pump noise, essential for developing strategies to facilitate wider Air Source Heat Pump adoption.

Loudness and preference judgments for noises of a heat pump

An increasing number of heat pumps have recently been installed to replace conventional heating systems to provide more environmentally friendly and energy-efficient indoor heating. Most commonly, air-to-water heat pumps are chosen. However, the sounds of their fan and compressor can lead to noise complaints and can reach the limit of noise emission regulations. To achieve a more pleasant environment, research is conducted to reduce the perceived noise of heat pumps. As part of an interdisciplinary research project, this study aims to investigate the annoyance ratings of a heat pump and characterize them in terms of equivalent loudness and preference penalties. The study uses a two-alternative forced-choice (2AFC) method with a 1-up-1-down rule to measure the point of subjective equality for six stimuli representing different operation states of the investigated device. This experiment aims to identify differences between loudness and preference, and combined with an evaluation of psychoacoustic parameters, its results are used to improve the acoustics of heat pumps and the overall user experience.

Bridge modal identification method based on adaptive VMD-HT algorithm

ABSTRACT Bridge engineering structures play a crucial role in transportation. Traditional methods of road and bridge closures for inspection and evaluation, while effective, increase bridge inspection costs and disrupt normal traffic flow. Therefore, a rapid detection and evaluation method for bridge structural modal parameters in the operational state is more suitable for practical engineering applications. However, under real operating conditions, bridge vibration signals typically contain a large amount of noise and interference, and modal identification must overcome issues of non-stationarity and non-linearity in the vibration signals caused by vehicle loads and environmental changes to ensure the accuracy of the identification. This study replaces the traditional Empirical Mode Decomposition algorithm in the Hilbert-Huang Transform with the Variational Mode Decomposition algorithm. It proposes a new method that combines the VMD algorithm with the Hilbert Transform, referred to as the Variational Mode Decomposition-Hilbert Transform method. Compared to the EMD algorithm, it offers significant improvements in suppressing mode mixing and endpoint effects. A vehicle-bridge coupling model was constructed, and the bridge’s natural modal information was successfully extracted from the vibration response signals. The research results indicate that the improved algorithm offers higher accuracy and faster computational speed compared to traditional identification algorithms.

Stochastic two-stage multi-objective unit commitment of distributed resource energy systems considering uncertainties and unit failures

Compared to centralized generation technology, distributed energy resource systems are susceptible to energy risks caused by boundary uncertainties and unit failures. This study introduces a stochastic two-stage multi-objective optimization method to address reliability-based unit commitment issues. In the day-ahead stage, operational state and reserve capacity are determined to minimize prescheduled operation costs based on forecasted parameters. In the real-time stage, a decision-dependent stochastic reliability method is proposed to simulate outage scenarios. Reserve resources within available units are allocated to mitigate forecasting errors and unit failures. Additionally, the grid interaction ratio and penalty cost are added to restrict the depth and frequency access to the grid. Four comparative cases analyze the effects of the proposed methodology. This method innovatively achieves the simulation of stochastic multi-unit outages and delete faulty units in the operation scheme. The optimal results show that the risks of electricity and cooling supply are underestimated, while the risks of heating are overestimated, compared to N-1 reliability. Furthermore, Pareto analysis of the multi-objective problem enhances independent operational capacity through utilization of reserve resources. Grid dispatch pressure is reduced since purchased power can be used as day-ahead planning. Thus, the methodology achieves collaborative optimization of reliability with a reduction of operation costs, offering effective guidance for engineering applications.