Macro Perspective Research Articles

MSSTGNN: Multi-Scaled Spatio-Temporal Graph Neural Networks for short- and long-term traffic prediction

Accurate traffic prediction plays a crucial role in ensuring traffic safety and minimizing property damage. The utilization of STGNNs in traffic prediction has gained significant attention from researchers aiming to capture the intricate time-varying relationships within traffic data. While existing STGNNs commonly rely on Euclidean distance to assess the similarity between nodes, which may fall short in reflecting POI or regional functions. The traffic network exhibits static from a macro perspective, whereas undergoes dynamic changes in the micro perspective. Previous researchers incorporating self-attention to capture time-varying features for constructing dynamic graphs have faced challenges in overlooking the connections between nodes due to the Softmax polarization effect, which tends to amplify extreme value differences, and fails to accurately represent the true relationships between nodes. To solve this problem, we introduce the Multi-Scaled Spatio-Temporal Graph Neural Networks (MSSTGNN), which aims to comprehensively capture characteristics within traffic from multiscale viewpoints to construct multi-perspective graphs. We employ a trainable matrix to enhance the predefined adjacency matrices and to construct an optimal dynamic graph based on both trend and period. Additionally, a graph aggregate technique is proposed to effectively merge trend and periodic dynamic graphs. TCN is developed to model nonstationary traffic data, and we leverage the skip and residual connections to increase the model depth. A two-stage learning approach and a novel MSELoss function is designed to enhance the model's performance. The experimental results demonstrate that the MSSTGNN model outperforms the existing methods, achieving state-of-the-art performances across multiple real-world datasets.

Optimization of bus stop layout considering multiple factors including passenger flow direction.

Bus stop layout typically requires consideration of urban population distribution, traffic conditions, and passenger flow demand to establish an efficient foundation for the bus system's operation. Based on the above key factors, this paper introduces a strategic method to optimize the bus stop layout from a macro perspective in order to save passengers' travel time and improve the attractiveness of the bus system. This approach accounts for the matching degree between the Origin-Destination (OD) direction of passengers and their walking direction heading to bus stops. Initially, we take into account factors such as the population and area of traffic districts, and urban road conditions. Utilizing the hypernetwork multidimensional data clustering method along with GIS technology, we construct an alternative set of bus stops based on the hypernetwork framework. This set serves as a reference for the positioning of newly built and moved bus stops. Subsequently, we develop a two-stage model for bus stop layout decision-making. The first stage focuses on determining the bus stop layout at the traffic district level, taking into account multi-factors including the passenger flow matching degree. The second stage is designed to mitigate the negative impact of bus stop optimization on the overall service level of the urban bus system. A case study conducted in XT city demonstrates the effectiveness of our approach. Post-optimization, there is a 15.83% increase in the alignment between passenger flow direction and bus stop layout. Additionally, the average travel time for passengers is reduced by 7.55 minutes.

The entanglement of learning spaces, pedagogies, and technologies with the global polycrisis

This concise paper emphasises and amplifies the themes of this year’s ASCILITE Conference by elaborating on their importance contributing to higher education’s vital agenda of advancing human rights, democracy, justice and sustainability. We address the conference themes —learning spaces, pedagogies, and technologies— from a macro perspective, considering a range of urgent global issues with which higher education is entangled. These issues shape the possibilities and risks of higher education everywhere, as well as technology enhanced learning in particular. In the paper, we consider a range of challenges posed by the current global polycrisis, present a framework for confronting these challenges within higher education, and offer a set of questions for collective consideration in order to strengthen the higher education community’s ability to cope with a threatening and unstable future.

Factors influencing healthcare workers' performance before and after the coronavirus disease 2019 pandemic: A bibliometric analysis with supplementary comparative analysis.

The performance of healthcare workers directly impacts patient safety and treatment outcomes. This was particularly evident during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to analyze research trends on factors influencing work performance among healthcare workers through bibliometric analysis and conduct a comparative analysis from macro and micro perspectives before and after the COVID-19 pandemic to complement the existing research. This study involved a bibliometric analysis of 1408 articles related to work performance in the healthcare field published between 2010 and 2023, using the Web of Science, Scopus, and PubMed databases, and 37 articles were selected to determine the factors influencing work performance. By conducting a bibliometric analysis of the articles based on country, institution, journal, co-cited references, and keywords, this study identified a significant growth trend regarding the factors influencing work performance in the healthcare field, and research hotspots shifted from organizational factors like standard towards psychological factors such as burnout, anxiety, and depression following the outbreak of the COVID-19 pandemic. Subsequently, this study extracted 10 micro-level and 9 macro-level influencing factors from the selected articles for supplementary analysis. Furthermore, this study conducted a comparative analysis of the impact of these factors on work performance before and after the COVID-19 pandemic. This study addressed the limitations of previous studies regarding incomplete extraction of factors influencing work performance and unclear comparisons of parameters before and after the COVID-19 pandemic. The findings provide insights and guidance for improving the performance of healthcare workers.

Aligning common prosperity with sustainable development goals 3 and 7 through sustainable insurance

This paper develops a capped-call option model to evaluate sustainable insurance for achieving common prosperity. It integrates policyholder protection (Sustainable Development Goal 3 (SDG 3)) and the cap-and-trade mechanism (SDG 7) in modeling the Gini coefficient, thereby connecting SDGs 3 and 7 with common prosperity. The main findings are as follows. Life insurance policies that prioritize saving features enhance policyholder protection (SDG 3) but can detract from common prosperity. This impact is magnified when the regulatory cap within the cap-and-trade scheme for carbon-intensive manufacturers becomes stricter. High investment risks for carbon-intensive manufacturers reduce policyholder protection but support common prosperity. A stricter cap-and-trade scheme (SDG 7) also reduces policyholder protection and contradicts common prosperity. This paper avoids concluding any inherent inconsistency between the SDGs and common prosperity, as our analysis is based on the structure-conduct-performance paradigm rather than a macro perspective. Policymakers should carefully balance objectives between SDG 3 and common prosperity to ensure that policies promoting individual security do not compromise broader societal well-being and economic equality, as outlined by the SDGs.

A three-stage framework for optimal site selection of hybrid offshore wind-photovoltaic-wave-hydrogen energy system: A case study of China

The integration of offshore wind-photovoltaic-wave-hydrogen production is a sustainable energy development concept that could smooth the intermittency of renewable energy generation, reduce energy abandonment, and improve energy efficiency. However, the successful deployment of a hybrid offshore wind-photovoltaic-wave-hydrogen energy system is premised on the selection of an appropriate location, which necessitates a robust framework to support this decision. This study proposes a three-stage decision framework based on Geographic Information System and multi-criteria decision-making to determine the optimal location from macro and micro perspectives. To ensure the effectiveness of site selection, it develops a comprehensive criteria system, including exclusion criteria and evaluation criteria, encompassing geographical, resource, economy, risk, and sustainable potential aspects. Then, the spatial simulation of alternative sites is conducted according to exclusion criteria to rule out infeasible alternative sites. The multi-criteria decision-making in the third stage is performed in a rough-fuzzy environment to eliminate the uncertainty in decision-making. Finally, a case study is conducted on Hainan Island, China. The results show that the western sea area of Dongfang City is the optimal location to deploy the hybrid offshore wind-photovoltaic-wave-hydrogen energy system. The proposed framework offers a reference for practical offshore renewable energy project implementation.

The Research Advance in Ethical Issues of AI Application

The topic of AI has become more and more discussed ever since the release of ChatGPT, as its astonishing intelligence has made humans to challenge the boundary. This vagueness then contributes to the emergence of discussions on the ethics of AI. Aiming to clarify the many current academic discussions on the ethics of artificial intelligence and to recognize different ethical perspectives, this study conducts a literature review of the research and discussions among different scholars on ethical issues in the application of artificial intelligence technologies. It is found that the current relevant discussion extends from a micro perspective emphasizing the individual to a macro perspective emphasizing the human race. Research advances on ethical issues in AI applications can be categorized into three levels: personal, social, and global. Among which, the individual level focuses on the ethical issues in algorithmic logic and user data management. The social level focuses on the social employment and public accountability systems. While the global level discusses intellectual property disputes and the social identity of AI robots. Finally, the author gave an evaluation of the gaps and challenges of studying AI ethics and emphasize the importance of addressing ethical issues in AI development and deployment. The paper aims to serve as information for scholars and policymakers, fostering ethical awareness in the AI research community and helping AI technologies to be developed and used responsibly.

GOVERNABILIDADE EMPRESARIAL PELA PERSPECTIVA FINANCEIRA ESTRATÉGICA

every company is made up of several sectors, all of unique importance. There is, however, one that stands out for reflecting a more specific nucleus, whose collection of information has an impact on the business decision-making process, that is, the financial one. The movement and administration of the measured capital are the most challenging tasks for the financial manager, so that it goes beyond mere mathematical calculations. It involves the organizational structure in its systemic view, the mapping of the market with its needs and expectations, the determination of objective criteria, regardless of the unpredictable risk factors. These are able to change the decision chain to the point of transforming business strategic planning and its corresponding way of managing. Then the administrator or the financial manager emerges as the protagonist of the corporation's leverage or setback, insofar as the manipulation of the information obtained, some of which are privileged, may assist in the exact allocation of the funds raised, the results of which may give the company notoriety. Corporate governance thus takes a macro perspective, so that financial strategies will be fundamental to mitigate eventual failures in the business endeavor.

Investigation on the influence of mesoscale geometric features on the surface white layer characteristics in titanium alloy milling using ball end mills

A white layer will be generated on the machined surface when milling titanium alloy. Its existence will make the surface of the workpiece more brittle, and it is easy to produce cracks during the machining process, which greatly harms machining. Aiming at this problem, this paper takes the ball-end milling cutter with mesoscopic geometric characteristics as the research object, establishes the theoretical model of the white layer thickness of the workpiece under the micro-texture and cutting edge factors, analyzes the single factor of micro-texture parameters and cutting edge parameters, and the interaction between them and cutting parameters on the milling force-thermal characteristics and the white layer of the workpiece. The influence law reveals its mechanism from the macro and micro perspectives. It uses the white layer thickness as the evaluation index to optimize the mesoscopic geometric characteristic parameters by using the simulated annealing algorithm. The results demonstrate that the mesoscopic geometric feature of the ball-end milling cutter effectively reduces the thickness of the white layer, achieving a 38% reduction compared to that observed with a non-textured milling cutter. The thickness of the white layer exhibits a positive correlation with the distance L from the blade and a negative correlation with the edge radius R. The interaction between the cutting edge radius R and the cutting depth a p influences the milling force and heat, alters the micro-texture of the workpiece surface, induces the α + β phase transformation, and affects the white layer thickness. Based on the predictive model for white layer thickness, the optimization results for comprehensive milling performance are as follows: R is 59.39 μm, L is 110.01 μm, D is 58.68 μm, L1 is 130.59 μm, a p is 0.30 mm, V is 147.39 mm/min, f is 0.08 mm/z.

Traffic advisory for ship encounter situation based on linear dynamic system

Enhancing Situation Awareness (SA) is crucial for maritime traffic safety. Various indicators have been developed to assess risks in encounter situations and support the SA of Vessel Traffic Service Operators (VTSOs) and Officers on Watch (OOW), including collision risk and traffic complexity. Despite the widespread use of these navigational aids, ship collision incidents have not been effectively reduced. This paper abstracts ship encounter situations as linear dynamic systems to enhance the understanding of traffic situations. A traffic advisory framework based on the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) is proposed by integrating complexity metrics with risk indicators. The proposed method is validated through simulations of head-on, overtaking, and crossing scenarios, demonstrating its ability to accurately assess encounter complexity and issue advisories for free navigation, complexity, and resolution. Finally, we discuss the practical application of the proposed method through real-world experiments conducted in the waters of Qiongzhou Strait. The results indicate that the proposed method effectively quantifies the complexity of ship encounter situations and identifies high-collision-risk vessels from a microscopic perspective while providing insights into maritime traffic surveillance from a macro perspective.

The (in)effectiveness of financial consumer protection: Quasi-experimental evidence from consumer finance in China

The booming consumer finance sector has grappled with internal and external challenges, which call forth strong regulatory responses to financial consumer protection (FCP). Existing studies, however, have explored the economic consequences of FCP from a macro or meso perspective and failed to evaluate the overall effectiveness of the FCP framework. To remedy this, we collected a novel dataset from an online complaint platform in China, which included over 12,000 real-world complaints from March 2019 to September 2022. Built on the dataset, we employ a regression discontinuity in time approach to examine the effect of a representative FCP policy (i.e., Implementation Measures of the People's Bank of China for Protecting Financial Consumers' Rights and Interests) on the average rating of the consumers' perception of dispute resolution. We confirm the effectiveness of the FCP policy in improving dispute resolution, and the results remain robust for a series of tests. Moreover, the effectiveness of the FCP policy varies in different aspects of perceptions and complaint topics. The heterogeneous analysis demonstrates that first-mover and foreign-shareholding consumer finance companies are more responsive to the Measure. Finally, regulatory punishment and media attention are two channels in which FCP policy benefits consumers' perception of dispute resolution. We further discuss some important policy and managerial implications.

Research on scenario extrapolation and emergency decision-making for fire and explosion accidents at university laboratories based on BN-CBR

To solve the problems of suddenness, uncertainty and untimely emergency decision-making related to fire and explosion accidents in university laboratories, a combined method of BN and CBR is introduced to analyze laboratory accidents. By summarizing the characteristics of 72 accident cases worldwide, four scenario elements with key roles are extracted by combining the public safety triangle theoretical model; a BN is established from the macro perspective, which is based on the construction of dynamic scenarios; the evolution path is analyzed via BN theory; and the probability of occurrence of accidents is quantified from the microscopic perspective, with a focus on the analysis of the accidental evolution process. A case similarity calculation is carried out via CBR, and the construction of a BN-CBR-assisted decision-making model is completed, verified and corrected in an case study. The results show that the BN-CBR model can quickly determine the accident evolution path and the most similar historical cases, and its quantitative probability calculation enables one to comprehensively grasp the real-time state of the whole accident and the emergency response in a timely manner, which provides a new way to approach emergency decision-making of accidents.

Study on the Relationship between Groundwater and Land Subsidence in Bangladesh Combining GRACE and InSAR

Due to a heavy reliance on groundwater, Bangladesh is experiencing a severe decline in groundwater storage, with some areas even facing land subsidence. This study aims to investigate the relationship between groundwater storage changes and land subsidence in Bangladesh, utilizing a combination of GRACE and InSAR technologies. To clarify this relationship from a macro perspective, the study employs GRACE data merged with GLDAS to analyze changes in groundwater storage and SBAS-InSAR technology to assess land subsidence. The Dynamic Time Warping (DTW) method calculates the similarity between groundwater storage and land subsidence time series, incorporating precipitation and land cover types into the data analysis. The findings reveal the following: (1) Groundwater storage in Bangladesh is declining at an average rate of −5.55 mm/year, with the most significant declines occurring in Rangpur, Mymensingh, and Rajshahi. Notably, subsidence areas closely match regions with deeper groundwater levels; (2) The similarity coefficient between the time series of groundwater storage and land subsidence changes exceeds 0.85. Additionally, land subsidence in different regions shows an average lagged response of 2 to 6 months to changes in groundwater storage. This study confirms a connection between groundwater dynamics and land subsidence in Bangladesh, providing essential knowledge and theoretical support for further research.

Inclusive leadership toward reshaping corporate purpose for sustainable development

This paper delves into the complex relationship between business leadership, sustainability, and inclusivity, representing a step toward developing a more inclusive leadership approach to sustainable development that fosters shared power relations between business leaders and marginalized members of society. With environmental and social conditions worsening, it is urgent for corporations to move away from the neoliberal profit-maximization models advocated by Milton Friedman and instead prioritize humanity and the environment. This shift requires a fundamental restructuring of businesses to move beyond profit maximization and address societal power imbalances by including all stakeholders. Our inclusive leadership for sustainable development framework, rooted in symbolic interactionism, offers a holistic lens for including marginalized groups. At the microlevel, it focuses on business leaders’ personas, characterized by pro-demographic diversity and biodiversity, cognitive complexity for sustainable development, and social empathy, which can potentially create a macro-level impact. These characteristics, accompanied by macro perspectives toward repurposing corporations away from neoliberalism, would be a step forward in cultivating shared power dynamics between business leaders and marginalized communities for the betterment of society.

Study on the Microscopic Mechanism and Performance of TPU/SBR Composite-Modified Asphalt

To enhance the service life of traditional asphalt pavement and mitigate issues such as high-temperature rutting and low-temperature cracking, this study investigates the composite modification of matrix asphalt using thermoplastic polyurethane (TPU) and styrene-butadiene rubber (SBR). Initially, the study examines the conventional properties of the composite-modified asphalt from a macro perspective, analyzing the performance variations of asphalt before and after TPU and SBR modification. Subsequently, microscopic analysis is conducted to explore the microstructure, phase structure, and modification mechanisms of the composite-modified asphalt, with a focus on understanding the underlying reasons for performance changes. The influence of TPU and SBR on asphalt performance is evaluated comprehensively. It is found that TPU-modified asphalt demonstrates superior high-temperature performance, storage stability, and elastic recovery. Conversely, SBR-modified asphalt excels in ductility at low temperatures, though its storage stability decreases with increasing dosage. Based on a thorough analysis of the conventional properties of the two types of modified asphalt, the optimal dosages of TPU and SBR are determined to be 15% and 3.5%, respectively. In the composite-modified asphalt, TPU facilitates the even distribution of chemical components, creating a more stable cross-linked network structure. The compatibility of TPU, SBR, and asphalt contributes to the good storage stability of the composite-modified asphalt. While SBR effects physical modification, TPU induces chemical modification of asphalt. Consequently, the composite modification system benefits from both physical and chemical enhancements, resulting in excellent overall performance.

컨설턴트 역량에 관한 체계적 문헌고찰

The purpose of this study is to analyze previous research on consultant competencies, which has developed from various perspectives, and to suggest directions and implications for future research. Using a systematic literature review method, 42 domestic academic journal articles were selected for analysis, focusing on the publication period, journal type, keywords, and competencies required in different consulting fields. The findings are as follows: First, research on consultant competencies was minimal before 2010 but has increased significantly since. Second, journals show that consultant competencies are needed in diverse fields, with digital technology, talent development, and convergent thinking being key. University-affiliated institutions have contributed to strengthening the theoretical foundation through various methodologies. Third, keyword analysis highlighted relationship-building skills, service quality, and knowledge as essential competencies. Fourth, the analysis identified the need for a systematic competency model, emphasizing problem-solving, communication, customer orientation, change management, and adaptability. Based on these findings, this study highlights the need for systematic research on competencies within the consulting process from a macro perspective, while also emphasizing the importance of expanding research on consultants' intrinsic competencies from a micro perspective. Furthermore, it suggests that the application of methodologies combining various academic perspectives in a convergent approach should be expanded. This study is expected to serve as a foundational resource for future research on consultant competencies.

AN ANALYSIS OF BEHAVIOURAL FINANCE AND ITS INFLUENCE ON INVESTMENT DECISION

This research delves into the fascinating realm of Behavioural Finance, exploring its significant influence on investment decision-making among residents in India's National Capital Region (NCR). By drawing on insights from relevant studies, this work seeks to uncover the intricate link between human behaviour and financial decision-making within this demographic. The study begins by examining the unique financial behaviours of investors in the region, aiming to identify the key investor profiles that shape the investment environment. As a multidisciplinary field combining elements of psychology, sociology, and finance, Behavioural Finance provides the primary framework for this exploration. The research distinguishes between its micro and macro perspectives: the detailed analysis of individual behavioural traits of investors and the broader identification of inefficiencies in the market. Recognizing the pervasive impact of behavioural biases on investment behaviours and decisions, this study offers an in-depth review of diverse research within the Behavioural Finance domain. It sheds light on how human psychology and social dynamics interact with financial decision-making, highlighting the crucial role of Behavioural Finance in contemporary financial practices. In an era where financial markets are not merely governed by rationality but are also shaped by complex human behaviours, this research offers valuable insights into the dynamics of Behavioural Finance. By illuminating the distinct investment choices, the study aims to deepen our understanding of the factors influencing these decisions, ultimately equipping investors and financial professionals with more informed decision-making strategies.

Impact of artificial intelligence technology applications on corporate energy consumption intensity

Artificial intelligence (AI), as a new technology, not only revolutionizes economic development, but also provides an opportunity for environment governance. Extant studies primarily explore the environmental performance of AI from a macro perspective, while evidence on how AI technology applications affect firms’ energy-saving behavior is scarce. Employing Python technology to recognize AI-related keywords in the annual reports of listed enterprises and adopting data on corporate energy consumption from 2011 to 2020, we explore the impact of AI on corporate energy consumption intensity (CECI) and its mechanisms. We observe that AI technology applications reduce CECI. After a range of robustness tests, the conclusions are still solid. The mechanism analysis reveals that AI cuts CECI through spurring firm green innovation, stimulating firms to introduce new equipment, and reducing firms’ internal management costs. Heterogeneity analysis reveals that this negative impact is more prominent for SOEs and private enterprises’ energy intensity; we also find that this effect is more pronounced for high-tech industry enterprises and high-polluting enterprises. Our findings provide micro evidence for policymakers to reduce corporate energy intensity and realize energy conservation and emission abatement targets.

An isogeometric approach to dynamic structures for integrating topology optimization and optimal control at macro and micro scales

In the context of active vibration suppression, an integrated topology optimization framework is proposed for the optimal control of dynamic structures. This article formulates a new composite objective function by considering the external harmonic excitations with different excitation frequencies and the complex-valued displacement field. Within the proposed optimal control performance function, the symmetric weighting matrix, the magnitudes of which are related to the importance of the control forces, is integrated into dynamic compliance to serve the state displacement. Utilizing the non-uniform rational B-splines (NURBS) basis functions, the material interpolation model defined at control points is described by the NURBS surface, which is incorporated into the optimization formulation for optimal structural and vibration control design. The sensitivity results are deduced in terms of the pseudo-densities and the corresponding weights at control points from both macro and micro perspectives of structural dynamics. To demonstrate the effectiveness of the integrated topology optimization of dynamic structures at macro and micro scales, several numerical examples, including the topology optimization of solid beams in plane stress and Mindlin plates with 3D microstructures, are provided and discussed in terms of structural shape and topology, frequency response curve, and modal displacement.

Oil storage and debrining process in insoluble sediment voids for underground salt cavern energy storage: An experimental study

Large salt cavern oil storage is an effective underground oil storage method, which has been widely used in the United States, Germany, and France. However, the insoluble impurity sediment particles at the bottom of the salt cavern will occupy plenty of oil storage space. To enhance the oil storage capacity, the sediment void oil storage (SVOS) method was proposed. The debrining process from the sediment void prerequisite of the SVOS. It will affect the feasibility of oil injection into the sediment void and the operation efficiency of SVOS. Thus, a self-made debrining device of SVOS was built, and the dynamic debrining process in the sediment void was investigated to explore the debrining process from the sediment void. Six debrining parameters (oil-brine interface change, oil injection rate, brine discharge rate, oil viscosity, the second oil injection quantity, and ground temperature) were proposed and considered. Two kinds of oils (diesel and petrolatum) were conducted in the SVOS debrining experiments. The experimental results showed that the debrining rate remained stable, and it was relevant to the oil injection rate and the interaction of oil and sediments. The average debrining rate of the initial diesel debrining, second diesel debrining, and petrolatum debrining were 2.5 g/s, 2.55 g/s and 2.40 g/s, respectively. The 50 °C of ground temperature sped the debrining rate of high-viscosity oil storage in the sediment void. The oil-brine interface kept balance from the macro perspective, and the oil did not penetrate the inner brine. The oil-brine interface drops ratio of the initial diesel debrining, second diesel debrining, and petrolatum debrining process were 0.586 mm/s, 0.629 mm/s and 0.592 mm/s, respectively. The oil in the sediment voids generated movable and immobile bubbles. The petrolatum flow in the sediment void was easier than the diesel due to the low viscosity at 50 °C. The research has a certain reference value for the development of underground salt cavern oil storage.