Operation Mechanism Research Articles

Risk performance analysis model of escort operation in Arctic waters via an integrated FRAM and Bayesian network

Escort operation is an effective mean to ensure the safety of ship navigation in the Arctic ice area and expand the window period for ship navigation. At the same time, the operation mode between icebreaker and escorted ship may also causes collision accident. In order to scientifically reflect the complex coupling relationship in the escort operation system in Arctic waters and effectively manage the navigation risks. This study proposes to use the functional resonance analysis method (FRAM) to identify the risk factors of ship escort operation in Arctic waters, and uses the Bayesian network (BN) method to establish a risk assessment model for escort operation collision accident. The cloud model is used to process the uncertain data information. The proposed method is applied during the actual escort operation of a commercial ship on the Arctic Northeast Passage. According to the model simulation results, the risk performance of ship escort operation in Arctic waters is quantitatively analyzed, and the key risk causes are further analyzed. This study has positive significance for better understanding the risk evolution mechanism of ship escort operation in Arctic ice area and helping relevant management departments to take risk control measures.

Application of Advanced Characterization Techniques for Lithium Iron Phosphate: Paving the Way for an In-Depth Understanding of Operation and Fading Mechanisms

Application of Advanced Characterization Techniques for Lithium Iron Phosphate: Paving the Way for an In-Depth Understanding of Operation and Fading Mechanisms

Zero-Dimensional Lead-Free Perovskite Single Crystals for the Sensitive Detection of Visible Light and X-Rays.

The excellent performance and facile fabrication process of perovskite semiconductor materials make them highly promising candidates in the field of optoelectronic devices. Whether for visible light or X-rays, detectors with low detection limits can better identify weak light signals, significantly reducing the intensity of the received light source during device operation. In particular, for X-ray detectors, reducing X-ray dosage can greatly enhance the safety of X-ray imaging technology. This work develops detectors with low detection limit based on zero-dimensional (0D) MA3Bi2I9 single crystals. The anisotropy of MA3Bi2I9 and its influence on the detector's performance were investigated in detail. Detectors perpendicular to the (001) crystal plane have better weak light detection capability and require significantly less light intensity compared to devices parallel to the (001) crystal plane. X-ray detector with a limit of detection (LoD) of 0.68 nGyair s‒1 and sensitivity of 5072 μC Gyair‒1 cm‒2 was obtained. The present study offers comprehensive insights into the operational mechanism of layered perovskites, which can provide valuable guidelines for future designs and fabrications of highly sensitive photo and X-ray detectors.

Digital twin-driven cloud manufacturing system: an implementation framework, operating mechanism and key technologies

ABSTRACT Cloud manufacturing (CMfg), as a manufacturing mode to realize resource collaboration and service sharing, can help enterprises reduce costs, increase efficiency and enhance competitiveness. Existing CMfg relies on historical service evaluation results as the basis for task matching and thus lacks effective methods for the precise assessment of the adaptability to current tasks and the sustainability for future endeavors; throughout the service process, deficiencies occur including a lack of real-time assessment, prediction, and optimization capabilities for service execution outcomes. Motivated by the shortcomings of existing CMfg, this article focuses on how to enhance the dynamism and timeliness of the CMfg service process. A novel digital twin (DT)-driven CMfg service model is proposed. Specifically, a framework and an operation mechanism of DT-driven CMfg service are proposed; three key technologies related to DT in CMfg service are proposed, including model migration based matching modeling technology, multi-agent reinforcement learning based CMfg service composition optimization, and DT data-driven CMfg service performance prediction, which provide a theory and method for the DT and CMfg integration construction. The experiment demonstrates that the proposed methods have good performance for the production of cast aluminum engine fan bracket and provide a holistic understanding of DT in CMfg service.

Path Analysis of The Carbon Market's Impact on Corporate Carbon Emission Reduction

The focus of this study is to explore how carbon markets have been effective in motivating companies to reduce carbon emissions globally. Through the setting of emission quotas and a series of fair and strict trading rules, the carbon market not only stimulates the intrinsic motivation of enterprises to reduce greenhouse gas emissions but also pushes forward climate governance on a global scale. This paper elaborates on the theoretical basis and operational mechanism of the carbon market, especially its key role in facilitating enterprises to reduce emissions through economic incentives, improving their ability to comply with environmental regulations, creating more market access opportunities, promoting innovation in environmental technologies, and enhancing the green brand image of enterprises in the eyes of the public. This study aims to provide insights for business managers to help them optimize their carbon reduction strategies and references for policymakers to promote corporate emissions reduction more efficiently and accurately when formulating relevant policies to jointly address global climate change challenges.

RESEARCH ON VALUE-ADDED INTERNAL AUDITING IN ENTERPRISES BASED ON VALUE CHAIN THEORY

With the continuous development of the market economy, the business environment of enterprises has become increasingly complex. As a critical component of corporate governance, internal auditing plays a vital role in regulating business operations. However, traditional compliance-based internal auditing can no longer meet the development needs of modern enterprises. Researching value-added internal auditing to provide more effective value-added services for enterprises has become a significant topic in current internal auditing research and practice. Based on value chain theory, this paper introduces the concept of value-added internal auditing, explores the differences between traditional internal auditing and value-added internal auditing, analyzes existing problems in the implementation of value-added internal auditing in enterprises, and proposes a framework for constructing a value-added internal auditing system through organizational restructuring, institutional improvement, and the optimization of operational mechanisms. KEYWORDS: Value Chain, Internal Auditing, Value-Added Auditing

Next-Gen Test Automation in Life Insurance: Self-Healing Frameworks

The exponential complexity of contemporary Life Insurance applications - shaped by stringent regulatory compliance requirements, dynamic customer - centric innovation, and expansive product diversification - has magnified the challenges of maintaining traditional test automation frameworks. These frameworks often falter under the weight of frequent application updates, evolving user interfaces (UI), and fluctuating backend integrations, leading to brittle test scripts and heightened maintenance costs. Self-healing test automation frameworks offer a cutting-edge, AI-driven solution to this paradigm. By leveraging advanced machine learning (ML) algorithms and intelligent heuristics, these frameworks autonomously detect, diagnose, and remediate test failures caused by changes in application elements, workflows, or environments. Through real-time adaptation to UI modifications, API updates, and evolving test environments, self-healing frameworks ensure uninterrupted testing cycles with minimal manual intervention, significantly improving efficiency and scalability. This paper provides a comprehensive exploration of the architectural design and operational mechanisms of self- healing test automation frameworks within the context of Life Insurance applications. Core focus areas include their ability to dynamically recalibrate element locators and adapt test scripts during runtime, thereby enhancing test coverage, optimizing maintenance workflows, and reducing the overall defect detection lifecycle. Through an in-depth analysis, the paper highlights the integration of AI-powered locator optimization engines, the deployment of predictive analytics for early anomaly detection, and the orchestration of continuous self-healing pipelines in CI/CD ecosystems. Detailed case studies from the Life Insurance domain illustrate tangible benefits such as accelerated regression cycles, substantial cost reductions, and improved system resilience. Keywords: Artificial Intelligence, Machine Learning, Self – Healing, Life Insurance Applications, Quality Assurance, Test Maintenance Efficiency

Online monitoring and fault early warning prediction method for the operational status of steam turbine sliding pin systems

Abstract In modern power systems, ensuring the safe and reliable operation of the sliding pin system in large steam turbine generator sets is crucial. However, the measurable parameters in the current distributed control system are insufficient for fault early detection of the sliding pin system’s operational state. Additionally, there is a lack of relevant research in this area at present. This paper utilizes a typical 300 MW-class unit as a case study. By analyzing the operational mechanism and fault modes of the sliding pin system, a method for online monitoring of its operational status based on cylinder expansion measurement parameters is proposed. Based on this foundation, taking the advantage of long short-term memory (LSTM) network to effectively extract features from univariate time series, and integrating improved particle swarm optimization (IPSO) for automatic hyperparameter optimization, a multi-step prediction model and fault prediction method for the operational status of sliding pin systems based on IPSO-LSTM is designed. Test results based on various performance evaluation metrics indicate that the IPSO-LSTM algorithm significantly enhances the prediction model’s accuracy. Specifically, the TVIWAC-PSO model, which varies the inertia weight (TVIW) and acceleration coefficients simultaneously in the PSO algorithm, optimizes by enhancing global search in the early stages and emphasizing local search in the later stages of iteration. Furthermore, TVIWAC-PSO demonstrates superior performance in optimizing the hyperparameters of the LSTM algorithm. Finally, based on the gap standard between sliding pins and keyway in the actual operating procedures of the unit, combined with the low-pressure cylinder and rotor expansion difference operation standard, thresholds for anomaly detection and early fault prediction of the sliding pin system’s operational status are provided. This study holds significant engineering application value.

Model for investigating cathode dual-population microfluidic microbial fuel cells

Microfluidic microbial fuel cells (MMFC) are one of the most promising power sources. However, due to the lack of clarity in the internal operating mechanism, the output performance is suboptimal. Thus, a comprehensive two-dimensional cathode dual-population model is developed to gain deeper insights into internal workings. Based on verifying the accuracy of the model, the influence of temperature, ionic strength, and spacing of electrode on the performance and microbial growth of MMFC are explored. The finding reveal a nonlinear trend in the performance of MMFC at temperatures of 293.15 K and 313.15 K. Furthermore, the impact of electrode spacing and ionic strength on the performance of MMFC is examined, thereby emphasizing the applicability in experimental research and numerical simulation. This study provided insights into the operating mechanism of dual-population microbial microfluidic fuel cells.

Enhanced wind energy harvesting through omnidirectional airflow metamaterial concentrators

Wind energy generation is constrained by low density and intermittency. To address these challenges, this study proposes an omnidirectional airflow metamaterial concentrator lens that utilizes a novel complex gradient structure inspired by metamaterial principles. The study's framework includes the design, simulation, and performance evaluation of the concentration lens under varying wind conditions. The lens amplifies the local flow energy density by up to 360% compared to the impinging influx. Numerical simulation analysis elucidates the underlying operational mechanism, demonstrating a significant reduction in the startup wind speed and increased energy capture efficiency. Results indicate that concentrator lens can enhance wind energy exploitation by improving the efficiency of energy capture and providing a viable solution to mitigate the limitations of current wind energy technologies.

Time-frequency attention mechanism-based model for enhancing wind speed prediction accuracy

Wind energy constitutes a substantial renewable energy source, and its efficient exploitation depends significantly on precise wind speed forecasting. However, achieving accurate predictions in this area is challenging. Taking into account the consistent physical information contained within the time and frequency domain of a time signal, a novel time–frequency attention block and model is proposed. The pivotal component, denoted as the time–frequency attention (TFA) block, normalizes the time series of wind speed, utilizes the discrete cosine transform to obtain frequency domain information, and subsequently maps the time–frequency data to a high-dimensional space, employing an attention mechanism for adaptive fusion. After residual connections and multi-layer stacking, the predictive values are obtained by connecting with fully connected layers. Experimental results demonstrate that, compared to state-of-the-art models such as the Transformer and TimesNet, the TFA model exhibits favorable predictive performance. While hyperparameters do exert some influence on the predictive performance of the TFA model, its overall performance remains stable and consistently optimal. Regarding the operational mechanism of the model, visualization of the intermediate outputs confirms the TFA model’s exceptional ability to capture temporal and frequency domain fluctuations, as well as mean features

Modeling and application research of absolute gravimeter driven by a V-shaped linear ultrasonic motor.

A V-shaped linear ultrasonic motor (LUSM), used as a driving element for the absolute gravimeter, generates structural vibrations when the motor starts and stops, which interfere with and influence the accurate measurement of the absolute gravimeter. In order to address this problem and provide better stability and measurement accuracy, this paper designs a fuzzy proportional integral differential (PID) controller and realizes the drive control of the motor on a field programmable gate array (FPGA) using the Verilog hardware description language (HDL). The advantages of the proposed controller are verified by comparing the simulation results with those of the traditional PID controller. First, the technical index of the LUSM as the driving element is studied through theoretical analysis based on the operation mechanism of the absolute gravimeter. Then, the relationship between the output performance and the input parameters (excitation voltage, preload, and driving frequency) of the motor in a vacuum/natural environment is experimentally investigated. Finally, an experimental system is constructed, and experiments are conducted to measure the acceleration of gravity and compare with the test results of the habitual FG-5 absolute gravimeter. The experimental results show that the output performance of the V-shaped LUSM meets the requirements of the driving element of the absolute gravimeter. The combination of the two control methods can realize the repeatable and stable operation of the system. Moreover, the motor with a mass of only 92g can output force directly without transmission components. The complexity and the weight of the system are significantly reduced, which has certain advantages.

Comparative Study of Silicon and Silicon Carbide Semiconductors

This research paper provides an in - depth comparison of intrinsic and extrinsic semiconductors, focusing on their electrical properties, mechanisms of operation, and implications for technological applications. Through a detailed examination of band theory, charge carrier dynamics, and the impact of doping, this study elucidates the fundamental differences between these two types of semiconductors. Utilizing silicon as the primary material, the paper explores how these differences influence the efficiency and functionality of semiconductor devices, addressing potential uncertainties in experimental methodologies and highlighting recent breakthroughs in the field.

Artificial Visual Network with Fully Modeled Retinal Direction-Selective Neural Pathway for Motion Direction Detection in Grayscale Scenes

The complexity and functional evolution of mammalian visual systems have always been a focal point in neuroscience and biological science research. The primary neurons that output motion direction signals have been a focal point of research in visual neuroscience for nearly 130 years. These neurons are widely present in the cortex and retina of mammals. Although the relevant pathways have been discovered and studied for almost 60 years due to experimental accessibility, research still remains at the cellular level. The specific functions and overall operational mechanisms of the component neurons in the motion direction-selective pathways are yet to be clearly elucidated. In this study, we modeled existing relevant neuroscience conclusions based on the symmetry and asymmetry of whole cells in the retina-to-cortex pathway and proposed a quantitative mechanism for motion direction selectivity pathways, called the Artificial Visual System (AVS). By tests based on 1 million instances of 2D, eight-direction grayscale moving objects, including 10 randomly shaped objects of various sizes, we confirm AVS’s high effectiveness on motion direction detecting. Furthermore, by comparing the AVS with two well-known convolutional neural networks, namely LeNet-5 and EfficientNetB0, we verify its efficiency, generalization, and noise resistance. Moreover, the analysis indicates that the AVS exhibits evident biomimetic characteristics and application advantages concerning hardware implementation, biological plausibility, interpretability, parameter count, and learning difficulty.

Theoretical Construction and Operational Mechanisms of Intrapreneurial Capabilities: An Exploratory Single-Case Study of Chinese Manufacturing Enterprise

Theoretical Construction and Operational Mechanisms of Intrapreneurial Capabilities: An Exploratory Single-Case Study of Chinese Manufacturing Enterprise

Fault diagnosis of HVCB via the subtraction average based optimizer algorithm optimized multi channel CNN-SABO-SVM network

The mechanical fault diagnosis of HVCB is important to ensure the stability of electric power systems. Aiming at the problem of poor diagnostic performance of deep learning methods under limited samples, this paper proposes an HVCB operating mechanism fault diagnosis model (multi-channel CNN-SABO-SVM, MCCSS) based on multimodal data fusion features and Subtraction-Average-Based Optimizer (SABO). This model extracts and fuses features from the input two-dimensional data using a multi-channel CNN network and then uses the multimodal data fusion features to diagnose HVCB faults. Additionally, the SVM is used instead of the Softmax classifier to classify the fused features of vibration and sound, compensating for the poor diagnostic performance and generalization ability of the CNN network in small sample data scenarios. To further enhance the fault diagnosis performance of the SVM, the SABO is introduced for hyperparameter optimization of the SVM classifier. An HVCB fault test platform was established to train and test the model with limited data. The experimental results show that, compared with the multi-channel CNN-SVM and the CNN model based on unimodal signals, the proposed multi-channel CNN-SABO-SVM model improves the accuracy by 2.66% and 10.66%, respectively, and effectively addresses the challenge of circuit breaker fault diagnosis with limited samples.

Effect of households’ members disability and serious illness on public health insurance subscription among urban refugees during the COVID-19 pandemic in Kenya

BackgroundThe adverse selection theory speculates a high level of demand for health insurance by people with vulnerable health conditions. However, the COVID-19 pandemic changed the prevailing narratives and pattern of healthcare utilization in many African countries. This study estimated the effects of household member’s disability and presence of serious illness on the probability of National Hospital Insurance Fund (NHIF) subscription with the average treatment effect (ATE) and average treatment effect on the treated (ATET).MethodsThe data were collected telephonically in 2020 using the sampling frame of the United Nations High Commission on Refugees (UNHCR). The respondents were refugees with active phone numbers who were registered by the UNHCR in Nairobi, Mombasa and Nakuru cities. A total of 2,438 completed the surveys. The data were analysed with Treatment Effects Probit regression model using the regression adjustment estimator.ResultsThe results showed that 24.89% of the respondents had health insurance. Also, 3.28%, 1.39% and 2.46%, respectively suffered from physical, cognitive and sensory disability, while 8.28% had some form of serious illness. The Probit regression results showed that probability of being health insured significantly increased (p < 0.05) with membership of community-based organizations (CBO), asset index, possession of bank savings account, residence in Nairobi and household size, while residence in Nakuru reduced it. The ATE for physical and cognitive disabilities were significant (p < 0.05) with 0.1100 and 0.1816, respectively, while that for serious illness was 0.1046 (p < 0.01). The ATET for physical disability and serious illness were also significant (p < 0.05) with 0.1251 and 0.0996, respectively.ConclusionIt was concluded that efforts to facilitate NHIF subscriptions among the refugees should be channelled among people with disability and serious illness. In addition, there is the need to promote refugees’ welfare through employment that can induce formal savings and promote less reliance on informal borrowing. The operational mechanisms and differences in healthcare service distribution between the three cities should be considered along some salient interventions for health insurance subscription that are channelled through some CBOs.

Optimization Strategies for the Construction and Application of Networked Intelligent Language Laboratory Practice Bases under the Perspective of Collaborative Education

Under the background of collaborative education, this paper discusses the construction and optimization strategies for the use of networked intelligent language laboratory practice bases. By analyzing its importance, including meeting the needs of educational informatization, improving the quality of language teaching, and cultivating students' innovation and practical abilities, measures such as strengthening school-enterprise cooperation, optimizing the practical teaching system, building a collaborative operation mechanism for laboratories, and optimization strategies for use are proposed. Case studies of Zhejiang International Studies University and the School of Foreign Languages of Xiamen University are conducted to emphasize the significance and development trend of the construction and optimization of this practice base.

Development and Application of a Rope Core Hydraulic Closed-Core Mechanism for Loose and Broken Strata

When employing the wire-line core drilling technology in loose strata, the core recovery rate is often low due to fragmented rock masses and the erosive effect of flushing fluids, severely affecting core quality and drilling efficiency. To address this, the paper designs a hydraulic closed-core cutting mechanism and water-sealed protective core bit compatible with traditional S75 wire-line core drilling tools. Comprehensive analysis and optimization of the design mechanism’s force, flow field patterns, and operational mechanisms have been conducted through numerical simulations, laboratory tests, and field applications. Studies indicate (1) the hydraulic cutting mechanism can smoothly close under flushing fluid action, with stress and strain within safe limits and the sealing area of the core barrel exceeding 75%; (2) post-optimization, the vortex flow within the flushing fluid flow control valve is significantly reduced, with the flow rate at the core after passing through the water-sealed protective core bit being less than 0.6 m/s, effectively protecting the core from being eroded by flushing fluids; (3) the hydraulic cutting mechanism has proven feasible through laboratory testing, with a recommended standard core claw with a 95% protective rate. In actual loose strata, the average core recovery rate reached 97.83%, an 88.13% increase compared to traditional drilling tools, demonstrating high engineering promotion value.

Effects of self-limiting oxide layer on optical and electronic properties of WTe2 for optoelectronic applications

The study focuses on the optical and electrical properties of Tungsten Ditelluride (WTe2), a type II Weyl semimetal, as well as the influence of its self-limiting oxide (SLO) layer that forms during natural oxidation. WTe2 exhibits promising applications in photodetection and energy harvesting due to its unique gapless linear dispersion and Berry-field-enhanced nonlinear optical effects. However, surface oxidation poses a challenge as it degrades the performance of WTe2. By employing spectroscopic ellipsometry and Raman spectroscopy, the progression of the oxide layer’s thickness and its impact on the optical constants of WTe2 were examined. The results revealed a rapid increase in oxide thickness within the first 24 h, and it reached saturation at ∼10 nm after 45 h of atmospheric exposure. Electrical properties were explored using Kelvin Probe Force Microscopy, uncovering a modification in surface potential and Fermi level following oxidation. Additionally, a SLO/WTe2 heterojunction device exhibited a wide region of positive and negative coexisting photocurrent, highlighting the potential for logical operations in ambient air. Finally, the mechanism of operation of the device is discussed. The electrical and optical properties of the pristine, partially oxidized and fully oxidized WTe2 are analyzed using density functional theory calculations. It shows that the SLO layer has a significant effect on the optical and electronic properties, which is instructive for future wavelength-modulated device optoelectronic logic operations.