Internal Relationship Research Articles

Formation mechanism and control of a kind of pit-shaped defect in Cf53 rolled products

Aiming at the frequent issue of pit-shaped surface defects in S-bearing and Al-killed steel products and the related poor castability due to submerged entry nozzle (SEN) clogging, the melting and casting process of a popular auto steel Cf53 has been studied based on in-situ investigation and experimental analysis. To clarify the generation of the pit-shaped defects, the local surface chemistry, evolution of steel inclusions throughout the melting process, SEN clogging substances, and the internal relationship among them were systematically analyzed. The results show that this type of defect in the Cf53 rolled products is caused by the large-sized composite inclusions rich in CaS, which originate from the molten steel during the refining process, gradually accumulate on the inner wall of the SEN during casting, and finally lead to SEN clogging. The loose structure of clogging inclusions makes them prone to fall off into the mold and get trapped by solidifying steel shell. The formation mechanism of the inclusions was studied through FactSage software calculation. The results show that the increase in total sulfur and total calcium content, and the decrease in total oxygen content and temperature in steel will lead to CaS inclusion formation, which agrees reasonably well with experimental results. An optimized refining process to reduce calcium addition into molten steel is proposed accordingly, which achieves a remarkably alleviation in casting SEN clogging and an elimination of the pit-shaped defects on the final rolled products as well.

Integration of the mechanism of digital economic forensics into the enterprise management system

The article examines the issue of introducing economic forensics tools into the enterprise management system in the emergence of a digital economy. Economic forensics has a great potential for use to ensure the economic security of enterprises by detecting, preventing and eliminating the consequences of economic and financial violations, but its mass implementation is hindered by the large expenditure of time and resources required by traditional forensics. It is argued that the implementation of economic forensics in the format of an information system using modern information technologies, in particular artificial intelligence, blockchain and machine learning, will allow the integration of economic forensics tools into the practice of enterprise management. The mechanism of digital economic forensics has been formalized as a system that combines the internal relationship and the order of processes and procedures, as well as their methodical, organizational, informational, legal and resource support, the functioning of which is aimed at increasing the economic security of enterprises based on forensics tools and the use of digital technologies. The mechanism is implemented as a sequence of stages of information collection and analysis, detection of risks / suspicious actions, response within the enterprise (with subsequent monitoring of the implementation) or complaints to law enforcement agencies. At each stage, appropriate departments are involved and appropriate digital tools are used, in particular to identify suspicious actions and transactions by comparing them with a database of similar actions from past periods. The advantages of the proposed mechanism include ensuring a systematic collection and analysis of information on economic transactions that may pose risks for the economic security of the enterprise; automated detection of risks and provision of warnings to the management of the enterprise; the possibility of preventing losses or compensating them in the early stages; the ability to resolve a crisis situation without recourse to law enforcement agencies and bearing the corresponding costs and damage to reputation; constant improvement of the quality of risk detection and classification by means of using artificial intelligence tools. All this together may increase the economic security of enterprises.

GraphSensor: A Graph Attention Network for Time-Series Sensor

Our work focuses on the exploration of the internal relationships of signals in an individual sensor. In particular, we address the problem of not being able to evaluate such intrasensor relationships due to missing rich and explicit feature representation. To solve this problem, we propose GraphSensor, a graph attention network, with a shared-weight convolution feature encoder to generate the signal segments and learn the internal relationships between them. Furthermore, we enrich the representation of the features by utilizing a multi-head approach when creating the internal relationship graph. Compared with traditional multi-head approaches, we propose a more efficient convolution-based multi-head mechanism, which only requires 56% of model parameters compared with the best multi-head baseline as demonstrated in the experiments. Moreover, GraphSensor is capable of achieving state-of-the-art performance in the electroencephalography dataset and improving the accuracy by 13.8% compared to the best baseline in an inertial measurement unit (IMU) dataset.

Transformation power or development pressure: economic growth targets and urban carbon productivity

Maintaining moderate economic growth targets (EGTs) is the key for local governments to effectively implement the “carbon peak and carbon neutrality” goals under the refreshed development pattern. Utilizing panel data of 276 prefecture-level cities in mainland China from 2010 to 2020, and employing methods such as intermediary and threshold models, this study empirically analyzes the internal mechanism of EGT’s impact on urban carbon productivity (UCP). Our findings demonstrate that: ① The overall EGT during the analyzed period is not conducive to improving UCP. This conclusion remains valid after a series of robustness tests. This effect is more pronounced in the central region and resource-based cities than in the east-west region and non-resource-based cities. ② EGT not only directly suppresses UCP but also exerts indirect negative impacts on UCP from three aspects: delaying the digital economy (DE), constraining financial expansion (FE), and hindering green technology innovation (GTI). This negative indirect effect is similar to or even surpasses the direct effect, suggesting that the internal relationship between EGT and “dual-carbon” goals should be re-evaluated from a new compound perspective. ③ EGT not only has a simple linear impact on UCP but also significantly exhibits a dynamic evolution pattern in inverted “U” shape. That is, as EGT continuously upgrades, a nonlinear impact on UCP emerges in the form of “promoting first, suppressing later”. This indicates that surpassing the “degree” limit for EGT will be detrimental to the improvement of UCP. This study broadens the scope of carbon productivity analysis by introducing a new perspective centered on EGT. The insights gleaned from this research offer valuable guidance for local governments to effectively manage economic growth expectations and promote the synchronized achievement of dual-carbon objectives.

Non-linear effects of children's daily travel distance on their travel mode choice considering different destinations

The scientific layout of child-friendly facilities in the community is of great significance to children's daily activities and healthy growth, which is also a vital concern of the United Nations. However, there is little evidence to prove the intrinsic mechanism of children's daily travel behavior and daily travel destinations. Therefore, this study takes 118 communities in Shuangliu District, Chengdu, as an example and uses a eXtreme Gradient Boosting (XGBoost) model to explore the non-linear effects and threshold effects of children's daily travel distance on their travel mode choices by considering different destinations of children's daily travel in the communities. We found that children's daily travel distances and destinations are vital for their travel mode choice, especially the most significant effect of travel distance. For different children's daily travel destinations, travel distance has a more substantial threshold effect on children's non-walking travel mode choice. Therefore, to scientifically and rationally distribute child-friendly facilities in the community, which may help children's healthy development, a child-friendly city should be planned, constructed, or renewed through the internal logical relationship between children's daily travel destinations, travel distances, and travel mode choices should be considered comprehensively.

Grid dispatching model and benefit analysis of concentrating solar power plants considering flexibility

Abstract With the increasing penetration rate of renewable energy with volatility and uncertainty, flexibility has become an important factor to consider in the operation of the power system. To study the grid-connected dispatching and benefit analysis of CSP plants considering flexible supply-demand balance in the electricity market, the output level of CSP is modeled according to the internal steady state energy flow relationship. Secondly, we model the supply-demand characteristics of flexible resources. Finally, we establish an optimization dispatching model that considers the penalty of insufficient flexibility. The calculation example verifies the effectiveness of considering flexibility supply-demand balance and CSP plants in improving system flexibility.

A comprehensive DEMATEL-ISM model-based safety analysis of the Lianghekou earth-rock dam

Earth-rock dam failures account for the largest proportion of all dam failure accidents. There are many factors inducing accidents in hydroelectric projects, and the relationships between them are intricate and complex. Therefore, it is crucial to explore the relationship between the influencing factors and identify the key factors leading to accidents. Through an analysis of the factors influencing earth rock dam failures, an index system for failure influence factors was constructed in this paper. Considering complexity and integration in influence factors analysis, a DEMATEL-ISM model (Decision Making Trial and Evaluation Laboratory; Interpretive Structural Model) analysis method was employed to examine the internal relationship among various factors based on the influence degree between them, and a MICMAC model (Matrix Impacts Cross-reference Multiplication Applied to a Classification methodology) was introduced to analyze the hierarchical relationship between various factors. The results showed that The results show that the seismic capacity and flood discharge capacity of the dam body are the key influencing factors of dam safety during the operation of the earth-rock dams. The comprehensive method employed in this paper overcame the complexity of evaluation results and was capable of more directly presenting relationships of factors. As suggested by these results, the analysis model employed in this paper has great significance for preparing a flexible-efficient management scheme for earth-rock dams.

The Semantic Features of Noun-verb Metaphorical Compound verbs in Mandarin Chinese

As a closed category, noun-verb metaphorical compound verbs are not productive but still frequently used in Mandarin Chinese. The internal relationship between the morphemes is subordinate, often with the noun morphemes suggesting the vehicles of metaphor processes and the verb morphemes indicating the tenors. Even though these compounds are already formed through metaphor, their meanings can further develop through metaphor and thereby gain new metaphorical meanings. In addition, we examine the usages and semantic features of two metaphorical compound verbs can-shi and jing-tun through a corpus-based collocation study to tease out people’s internal understanding of their meanings.

The Semantic Features of Noun-verb Metaphorical Compound verbs in Mandarin Chinese

As a closed category, noun-verb metaphorical compound verbs are not productive but still frequently used in Mandarin Chinese. The internal relationship between the morphemes is subordinate, often with the noun morphemes suggesting the vehicles of metaphor processes and the verb morphemes indicating the tenors. Even though these compounds are already formed through metaphor, their meanings can further develop through metaphor and thereby gain new metaphorical meanings. In addition, we examine the usages and semantic features of two metaphorical compound verbs can-shi and jing-tun through a corpus-based collocation study to tease out people’s internal understanding of their meanings.

A Long-Term Power Supply Risk Evaluation Method for China Regional Power System Based on Probabilistic Production Simulation

To qualify the risk of extreme weather events for power supply security during the long-term power system transformation process, this paper proposes a risk probability evaluation method based on probabilistic production simulation. Firstly, the internal relationship of extreme weather intensity and duration is depicted using the copula function, and the influences of extreme weather on power security are described using the guaranteed power output ability coefficient, which can provide the extreme scenario basis for probabilistic production simulation. Then, a probabilistic production simulation method is proposed, which includes a typical-year scenario and extreme weather events. Meanwhile, an index system is proposed to qualify the power security level, which applies the loss of load expectation (LOLE) and time of loss of load expectation (TOLE) under different scenarios and other indices to reveal the long-term power security trend. Finally, the long-term power supply risks for the Yunnan provincial power system are analyzed using the proposed method, validating that the proposed method is capable of characterizing the influences of extreme weather on power security. The security level of different long-term power transformation schemes is evaluated.

Coupling intention and actions of vehicle–pedestrian interaction: A virtual reality experiment study

The interactions between vehicles and pedestrians are complex due to their interdependence and coupling. Understanding these interactions is crucial for the development of autonomous vehicles, as it enables accurate prediction of pedestrian crossing intentions, more reasonable decision-making, and human-like motion planning at unsignalized intersections. Previous studies have devoted considerable effort to analyzing vehicle and pedestrian behavior and developing models to forecast pedestrian crossing intentions. However, these studies have two limitations. First, they mainly focus on investigating variables that explain pedestrian crossing behavior rather than predicting pedestrian crossing intentions. Moreover, some factors such as age, sensation seeking and social value orientation, used to establish decision-making models in these studies are not easily accessible in real-world scenarios. In this paper, we explored the critical factors influencing the decision-making processes of human drivers and pedestrians respectively by using virtual reality technology. To do this, we considered available kinematic variables and analyzed the internal relationship between motion parameters and pedestrian behavior. The analysis results indicate that longitudinal distance and vehicle acceleration are the most influential factors in pedestrian decision-making, while pedestrian speed and longitudinal distance also play a crucial role in determining whether the vehicle yields or not. Furthermore, a mathematical relationship between a pedestrian’s intention and kinematic variables is established for the first time, which can help dynamically assess when pedestrians desire to cross. Finally, the results obtained in driver-yielding behavior analysis provide valuable insights for autonomous vehicle decision-making and motion planning.

National Park Service Internal Structures Toward Agency Resilience: A Mixed-Methods, Multi-Site, Mesoscale Investigation

Agencies focused on protected areas’ conservation and recreation require internal capacity-building to enhance their organizational resilience (the ability to adapt and persist). Yet, internal capacity-building is often underemphasized as agencies attend to external relationships. This omission can lessen an agency’s ability to adapt to emergent stressors and opportunities. Specifically, relationships within an agency’s groups (i.e., divisions)—the intra-organizational mesoscale between individuals and whole organizations—can increase an agency’s ability to efficiently build resilience-enhancing adaptive capacity. The U.S. National Park Service (NPS) adopted this mesoscale in their Urban Agenda goal of One NPS, or building resilience-enhancing, agency-wide internal relationships. We investigated intra-organizational relationships in this context, examining relationships within (i.e., bonds) and across (i.e., bridges) three NPS groups: parks (physical spaces), programs (community outreach), and offices (administrative functions). Pairing qualitative interviews with quantitative social network analysis in Detroit, Tucson, and Boston, we examined internal relationship prevalence, supports, and opportunities. The NPS has program and office presence in each of these three urban areas but different proximities to national park units, which are the typical face of the NPS. Across these urban areas, we found that the parks group exhibits more bonds (park-to-park relationships), the programs group exhibits more bridges (program-to-park or program-to-office), and the offices group exhibits a mixture of bonds and bridges. To further One NPS and organizational resilience, cultivating bridges among the three groups is key. This investigation may inform a more strategic focusing of an agency’s limited resources. We highlight five directions for protected area managers’ consideration toward this aim: build bridges locally among groups; consider group composition when identifying divisions (i.e., programs appear more heterogeneous or dissimilar from each other than do the park and office groups); reflect on parks’ centrality to NPS identity and relationships; seek organizational structures supportive of relationship development; and focus on the organizational mesoscale for resilience-enhancing adaptive capacity. These directions for strategic focusing of organizational resources are based in the longstanding work of the NPS but ultimately transcend this single agency, providing targeted guidance for protected area agencies in building internal capacity toward external, public-oriented goals.

Snow Penetration Depth Inversion in Tibetan Plateau Based on Coherence Scattering Model with X-band Data

Abstract. The snow penetration depth is a necessary input parameter to accurately evaluate the thickness and vertical structure of snow layer, which is conducive to understanding the freeze-thaw process of ice and snow and predicting the change of ice and snow mass balance. Interferometric Synthetic Aperture Radar (InSAR) exhibits great potential in estimating snow penetration depth due to the emission of microwave band and the ability of operating under all-day and all-weather conditions. In previous studies, the penetration depth of snow was obtained based on InSAR coherence, but the external factors such as temperature, humidity, topography, dielectric constant and so on also affect the penetration dept. In order to extract the penetration depth in detail, this paper this paper adopts a uniform volume model as the basis, introduces two parameters describing the physical properties of snow: the volume ambiguity height and the volumetric decorrelation. Then, the magnitude of volumetric decorrelation is used to establish the internal relationship between the magnitude and the volume ambiguity height to estimate the snow penetration depth. In order to verify the effectiveness and feasibility of this method, a TSX/TDX InSAR pair covering southeast Tibetan Plateau in 2012 is applied to estimate the snow penetration depth. And results indicate that X-band penetration depths range mainly between −2.753 meters and −4.589 meters with an average value of −3.671 meters m and standard deviation of 0.918 meters in snowy regions on the plateau.

Force/Acid-Activated and Radical-Regulated photochromic luminescence in a binuclear Zero-Dimensional complex for multi-level anti-counterfeiting

Traditional static optical anti-counterfeiting technology can only output a single emission signal, which is easy to replicate and difficult to meet the needs in current high-tech fields. Multi-level stimuli-responsive luminescent materials with internal logical relationship can improve the degree of anti-counterfeiting and resist against the flood of counterfeiting. In this work, we propose to use force and acid dual stimuli to restrict the dissipation path of photogenerated free radicals, which further lead to the radical-actuated photochromic luminescence (PCL) performance. Based on this strategy, a zero-dimensional (0D) dinuclear zinc complex Zn2(9-AC)4(IM)2 (1) with blue luminescent properties (450 nm) was designed and synthesized. The interaction between 9-AC and IM (as 9-AC/IM exciplex) can be cut off by force-induced rearrangement of structural units in the spatial dimensions or acid-induced protonation of 9-AC, achieving the luminescent transformation from 9-AC/IM exciplex (447 nm) to 9-AC/9-AC excimer (480 nm) or 9-HAC (485 nm). At the same time, the PCL performance was activated and the emission intensity quenching rate increased from 4.77 % to 93.86 % or 44.93 % under UV light irradiation, respectively. The solid-state ultraviolet–visible absorption spectra, electron paramagnetic resonance spectra and theoretical calculation confirmed that the activation of PCL performance is due to the fact that the 9-AC• radicals are more difficult to dissipate upon force- or acid-induced stimuli. Combining mechanochromic luminescence (MCL) and PCL, a dynamic anti-counterfeiting quick response (QR) code with multi-level and fast-responsive logic sequence have been designed, which are promising for the application in time-resolved information encryption. This work provides an effective strategy to construct multi-level stimuli-responsive luminescence as novel anti-counterfeiting materials by changing the photogenerated free radical dissipation pathway to activate PCL properties.

Study on the Spatiotemporal Evolution and Driving Factors of Ecological Security in Stages Based on the DPSIRM-SBM Model: A Case Study of the Yangtze River Economic Belt.

Scientific assessment of urban ecological security (ES) is an important prerequisite to realize regional sustainable development. Previous studies lack the consideration of quality and poor systematic correlation, which could not reflect the internal dynamic relationship. On the basis of considering the time lag, this study divided the research process into the natural operation stage and the management feedback stage based on the driving forces, pressures, state, impacts, responses, management (DPSIRM) framework model and DEA theory, so as to effectively overcome the above shortcomings. Finally, we analyzed the spatio-temporal characteristics and influencing factors of the ES level of 108 cities in the Yangtze River Economic Belt (YREB) during 2005-2019. The results showed that: (a) both two stages showed a slow and fluctuating upward trend in time series, and the level of urban ES in the management feedback stage was significantly higher than that in the natural operation stage; (b) with the passage of time, the spatial distribution of ES in the natural operation stage gradually developed towards the middle and downstream of the YREB, while the management feedback stage mainly evolved from the midstream to the edge area; (c) the level of urban ES presented a different degree of spatial agglomeration phenomenon, and showed an increasing trend over time; and (d) the key influencing factors gradually changed from pressure to response during 2005-2019. This research aims to provide an innovative perspective for the measurement of urban ES, and provide scientific reference for improving urban ecological sustainable development.

Where is my attention? An explainable AI exploration in water detection from SAR imagery

Attention mechanisms have found extensive application in Deep Neural Networks (DNNs), with numerous experiments over time showcasing their efficacy in improving the overall performance of DNNs. Considering the black-box nature of DNNs, it is unclear how attention mechanisms affect model decision-making processes. Therefore, it is of great significance to explore the internal relationship between the focused area of attention mechanisms and the prediction results. For the first time, an explainable framework for the attention mechanism of Synthetic Aperture Radar (SAR) image analytics is proposed, integrating DNN and eXplainable Artificial Intelligence (XAI), which realizes the effective explanation for attention mechanisms across different locations. The framework consists of three parts: the water extraction network which contains the attention mechanism at different locations; the proposed Attention- Gradient Class Activation Mapping (A-GCAM) method for attribution analysis of attention mechanisms; and we invent Category-specific channel Score of Confidence Mapping (CSCM) to perform geo-visualization for the output features of attention mechanisms. Experiments are conducted with the widely used Sentinel-1 system for water detection, in which three attention mechanisms with different characteristics are added to the encoder and decoder in DNNs. The results show that the framework can make the decision-making process of attention mechanisms transparent, thus improving their comprehensiveness and trustworthiness in various tasks and providing a reliable approach to selecting suitable attention mechanisms for the given task.

Diagnosis of Open-Circuit Faults in DC-Field Excited Variable Flux Reluctance Machine Utilizing Armature and Field Currents

More electric aircraft has high reliability requirements, and the DC-field excited modular variable flux reluctance machine (MVFRM) is its key component. The MVFRM has both armature and field windings, in which AC (alternating currents) and DC (direct currents) are supplied. Hence, traditional fault diagnosis is not applicable for MVFRM. In this paper, the diagnostic methodology for open-circuit faults in both windings is proposed by utilizing armature currents and field currents. Based on the internal relationship between the armature currents with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">αβ</i> -axis currents and the field currents, the characteristics and diagnosis mechanisms of armature winding and field winding open-circuit faults are analyzed. The per-unit threshold setting method is adopted to improve the rapidity of the algorithm without reducing robustness. The rapidity and effectiveness of the fault diagnosis algorithm are verified by different types of open-circuit fault diagnosis experiments. Furthermore, the robustness of the fault diagnosis algorithm is verified by the variable speed and different loads of open-circuit fault diagnosis experiments.

The Enlightenment Significance and Value Function of Aesthetic Psychological Structure Theory to Contemporary Aesthetic Education

In today's quality education, aesthetic education has become an important subject of modern education. Combined with the practice of aesthetic education, this paper discusses the nature, function, process and method of the construction of aesthetic psychology comprehensively. Using the "intermediary" of aesthetic psychological structure, cultivate and improve students 'aesthetic imagination, as the key point and breakthrough of aesthetic education teaching, and improve students' aesthetic psychological structure, so as to create a new situation of "hard teaching" into "happy teaching" and "hard learning" into "happy learning". The paper discusses the internal relationship between aesthetic education and the construction of aesthetic psychology. Only when the aesthetic psychological structure of the learning subject is constantly improved, the power of aesthetics will gradually emerge. In the construction of aesthetic psychology, the learning subject can be self-shaping, self-perfection until self-realization. Through aesthetic education to change people's way of thinking, create the comprehensive and harmonious development of human beings, teach people how to have the ability to appreciate and create beauty, and at the same time is closely related to everyone's aesthetic psychological structure. Therefore, aesthetic education is actually the educational process of how to construct people's aesthetic psychological structure at a higher level. Therefore, the aesthetic psychological structure is a very important concept, which not only affects the improvement of individual aesthetic ability, but also has enlightening significance on how to conduct aesthetic education. It is the most fundamental task and purpose to promote students to construct their own aesthetic psychological structure and improve the national quality.

Development and Implementation of a Multilayer Deep Learning-Based Bank Credit Risk Forecasting System

The complexity of the financial environment and the international community makes the capital flow face various challenges, and it is difficult to obtain accurate credit prediction results in the actual application environment. Considering the complex non-linear characteristics of customer information, the Analytic Hierarchy Process is studied to meet the needs of bank credit risk assessment. On this basis, a depth neural network with different complexities was selected for the three indicators built to classify the features. The composition of the neural network module and the number of neurons were determined by experiment, and Dropout was used to prevent overfitting of the test dataset. Stability and ablation experiments showed that the model can control the error between datasets to 0.021. The ablation experiment showed that the numbers of hidden layers and neurons were the best. Simulation tests showed that the sensitivity and accuracy of this method were 85.25% and 92.55%, respectively, which were superior to other classification methods. The real data of banks in the past four years were tested. The results could accurately classify the risks of enterprises and individual customers, and the results of stress test showed that the model is stable. It is found that traditional credit risk assessment models rely on statistical means and rule decisions, and these methods may not fully reveal the complex non-linear relationship and the internal relationship of financial indicators in high-dimensional data. The combination of deep learning technology and hierarchical analysis can better deal with and explain the complex non-linear problems in bank risk assessment.

Influence of cladding tube structure on transmission characteristics of negative curvature terahertz fiber

A novel type of polarization negative curvature terahertz fiber has been designed, which has a cladding region composed of two long semi-circular cladding tubes in the horizontal direction and four long semi-elliptical cladding tubes in the oblique direction. The optimal fiber dimensions are determined to investigate the internal relationship between polarization characteristics and fiber cladding structure. The transmission performances of the negative curvature fiber are investigated by varying the thickness of fiber cladding tubes and adjusting the structural characteristics of cladding tubes in different directions. Relatively good transmission performances can be obtained with a cladding tube thickness of 80 μm. The birefringence can be stably maintained at the order of 10−4, and the confinement loss of the x-polarized mode at 2.55 THz is 0.036 dB cm−1. Moreover, the dispersion coefficient of the two polarized modes is stable between ±0.6 ps (THz·cm)−1 in the 2.3–2.6 THz band. The bent performance of the designed fiber at 2.52 THz is also discussed. The results show that the fiber has low bending sensitivity, and can still maintain excellent birefringence and low confinement loss in the bending state. For the y polarization, the birefringence can be basically stabilized at the order of 10−4 within a bending radius of 10–60 cm.