International students’ cultural experience: A study based on Chinese undergraduate students in the context of the UK

This case study research was designed to investigate the cultural experiences of Chinese international students in the UK. The study was carried out in two stages. The first stage used an online questionnaire to explore Chinese international students’ general cultural experience in the UK with participants across the whole UK (N = 108). The case was defined with the data gathered from this online questionnaire, and the research subjects were narrowed to undergraduate Chinese international students in a British university (N =12). The second stage of data collection took place over 12 months. Data were collected using two research methods: three one-to-one interviews, each lasting for about an hour, and monthly self-evaluation entries between interviews, combining qualitative and quantitative methods. The findings showed that Chinese international students might face great challenges in relation to language, academic and daily life, and that they encountered racial discrimination in the UK. However, during the cultural adjustment process, they lacked systematic, conscious and effective skills and awareness to overcome cultural challenges. The COVID-19 pandemic outbreak made life in the UK more difficult for Chinese international students as it enforced changes in all aspects of their lives. At the same time, it was found that these Chinese international students might suffer even greater racism and meet great challenges when returning home. The difficulties they faced exposed gaps in international student support. They highlighted the need for a better understanding of international students’ problems and a more well-rounded support system. Finally, the data suggested that current models and definitions might not adequately describe international students’ cultural adaptation. What is needed is a more recent, relevant and flexible theory for studying international students’ cultural experiences and measuring their cultural adaptation outcomes. The study has a number of implications for understanding international students’ needs and providing better support services. Firstly, it calls for academic support, which focuses specifically on international students’ issues and struggles in adjusting to the UK academic environment. Secondly, the findings clearly indicate that institutions should provide systematic cultural introduction sessions to cover a wider range of issues in greater depth and involve broader communities to make international students feel at ease in the host culture. Thirdly, the research calls for a support system better suited to international students’ needs

Deep Reinforcement Learning for Energy-Aware Intelligent Edge Computing

To address the challenges of massive data processing and low latency requirements, edge computing (EC) has emerged as a compelling solution that deploys computing power and data storage at the network edge, enabling faster data processing and improved user experiences. Energy-aware system management and optimization for EC is crucial for extending the operational lifetime of battery-powered edge devices and decreasing operational costs of edge infrastructures, ultimately facilitating sustainable and eco-friendly EC systems. However, the dynamic task arrival, heterogeneous computing tasks (varying data sizes, resource demands, and service performance requirements), fluctuating wireless network conditions, as well as uneven workload distribution and resource constraints at edge nodes pose considerable difficulties in the design of effective system management and optimization strategies. Traditional methods often rely on expert knowledge and cannot effectively adapt to highly dynamic EC systems. Driven by recent advancements in deep reinforcement learning (DRL), which excels at learning optimal decision-making policies directly from complex and high-dimensional environments, this research aims to leverage advanced DRL techniques to autonomously learn efficient management and optimization strategies for energy-aware intelligent EC. Firstly, this research develops a model-free DRL-based task offloading approach for collaborative EC to optimize the peer offloading among edge servers and computing resource scheduling, subject to the constraints of limited energy resources at edge devices and restricted computing capabilities of edge servers. Experimental results show that the developed approach can effectively adapt to the EC system changes, achieving a 16% higher system income than the Double Deep Q-Network (DDQN) baseline. Then, this research proposes a safe DRL-based joint charging scheduling and computation offloading scheme for electric vehicle (EV)-assisted EC to minimize the system energy consumption and its variance for enhanced power grid stability, while satisfying the performance requirements of computation tasks and charging demands of EVs. The feasibility of learning a charging scheduling strategy that can satisfy the charging demands of EVs is theoretically proven. Simulation results demonstrate that the proposed scheme can achieve near-optimal performances and realize up to 24% improvement over three state-of-the-art algorithms. Next, a new multi-agent demand response (DR) management approach is designed to optimize workload migration among edge nodes and computing resource scheduling, aiming at maximizing the system utility of EC from both providing computing services to users and reducing energy consumption in response to varying DR signals. A reward sharing mechanism is proposed to distribute local rewards among neighboring edge nodes, thereby facilitating collaborative policies that collectively maximize the overall system utility. Evaluation results on real-world datasets show the proposed approach outperforms two state-of-the-art algorithms by 14% and the NSD (No Service Degradation) baseline by 68% in system utility. Finally, this research exploits the intelligent charging station recommendation for EVs, in which charging stations equipped with computing power serve as edge nodes to process data and make charging recommendation decisions. A novel real-time distributed charging station recommendation algorithm based on federated meta-reinforcement learning (MRL) is introduced to minimize the charging duration experienced by EVs while accounting for dynamic EV charging requests and time-varying charging station availability. Simulation experiments using realistic datasets showcase that our algorithm reduces EV charging duration by 25% compared to the state-of-the-art multi-agent reinforcement learning method, while effectively balancing charging requests across stations

Forging a collective entrepreneurial identity within existing organizations through corporate venturing

Purpose: This study unpacks how organizational members construct a collective entrepreneurial identity within an organization and attempt to instill entrepreneurial features in the organization's existing identity. Design/methodology/approach: The study draws on the cases of two venturing units, perceived as entrepreneurial groups within their respective parent companies. Semi-structured interviews and secondary data were collected and analyzed inductively and abductively. Findings: The data revealed that organizational members co-constructed a “corporate entrepreneur” role identity to form a collective shared belief and communities of practice around what it meant to act as an entrepreneurial group within their local corporate context and how it differentiated them from others. Members also clustered around the emergent collective entrepreneurial identity through sensegiving efforts to instill entrepreneurial features in the organization's identity, despite the tensions this caused. Originality/value: Previous studies in corporate entrepreneurship have theorized on the top-down dynamics instilling entrepreneurial features in an organization's identity, but have neglected the role of bottom-up dynamics. This study reveals two bottom-up dynamics that involve organizational members' agentic role in co-constructing and clustering around a collective entrepreneurial identity. This study contributes to the middle-management literature, uncovering champions' identity work in constructing a “corporate entrepreneur” role identity, with implications for followers' engagement in constructing a collective entrepreneurial identity. This study also contributes to the organizational identity literature, showing how tensions around the entrepreneurial group's distinctiveness may hinder the process of instilling entrepreneurial features in an organization's identity.</p

Dealing with uncertainty in cybersecurity decision support

The mathematical modeling of cybersecurity decision-making heavily relies on cybersecurity metrics. However, achieving precision in these metrics is notoriously challenging, and their inaccuracies can significantly influence model outcomes. This paper explores resilience to uncertainties in the effectiveness of security controls. We employ probabilistic attack graphs to model threats and introduce two resilient models: minmax regret and min-product of risks, comparing their performance. Building on previous Stackelberg game models for cybersecurity, our approach leverages totally unimodular matrices and linear programming (LP) duality to provide efficient solutions. While minmax regret is a well-known approach in robust optimization, our extensive simulations indicate that, in this context, the lesser-known min-product of risks offers superior resilience. To demonstrate the practical utility and robustness of our framework, we include a multi-dimensional decision support case study focused on home IoT cybersecurity investments, highlighting specific insights and outcomes. This study illustrates the framework’s effectiveness in real-world settings

Ocean heat uptake in eddying and non-eddying ocean circulation models in a warming climate

Ocean heat uptake is explored with non-eddying (28), eddy-permitting (0.258), and eddy-resolving (0.1258) ocean circulation models in a domain representing the Atlantic basin connected to a southern circumpolar channel with a flat bottom. The model is forced with a wind stress and a restoring condition for surface buoyancy that is linearly dependent on temperature, both being constant in time in the control climate. When the restore temperature is instantly enhanced regionally, two distinct processes are found relevant for the ensuing heat uptake: heat uptake into the ventilated thermocline forced by Ekman pumping and heat absorption in the deep ocean through meridional overturning circulation (MOC). Temperature increases in the thermocline occur on the decadal time scale whereas, over most of the abyss, it is the millennial time scale that is relevant, and the strength of MOC in the channel matters for the intensity of heat uptake. Under global, uniform warming, the rate of increase of total heat content increases with both diapycnal diffusivity and strengthening Southern Ocean westerlies. In models with different resolutions, ocean responses to uniform warming share similar patterns with important differences. The transfer by mesoscale eddies is insufficiently resolved in the eddy-permitting model, resulting in steep isopycnals in the channel and weak lower MOC, and this in turn leads to weaker heat uptake in the abyssal ocean. Also, the reduction of the Northern Hemisphere meridional heat flux that occurs in a warmer world because of a weakening MOC increases with resolution. Consequently, the cooling tendency near the polar edge of the subtropical gyre is most significant in the eddyresolving model. © 2013 American Meteorological Society

Mapping Volumetric Urban Space: A Critical Development Analysis of Multi- level Morphology of High-dense Cities

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A Use of Form-Based Code for Volumetric Morphology of High-density Cities

© 2018 IEEE. This paper presents the experimental use of Form-Based Code (FBC), an alternative approach to urban planning and regulation, for volumetric morphology towards more flexible and predictable development in high-density cities. Quantitative analysis of the Tsim Sha Tsui (TST) area of Hong Kong results in a workflow for using FBC in high-density contexts; combining transect matrix redefining, the integration of multiple variables, and parametric modelling. Findings indicate that a newly-defined transect matrix with multifarious types and subtypes enables the extension of the FBC study to encompass high-density conditions. Secondly, that FBC has the capacity for the assembly of multi-level regulations more suitable for volumetric urban forms. Thirdly, that the parametric regulation method of FBC can visualize and enumerate scenarios that conventional paper-based regulation cannot. The findings also suggest increasing the awareness of urban forms, rather than rigid land-use concerns, can be a critical influence when designers are pursuing sustainable communities within crowded contexts

Comment on Lee et al.: Incidence of second hip fracture and compliant use of bisphosphonate

Comment on Lee et al.: Incidence of second hip fracture and compliant use of bisphosphonat

Influence of impervious surface area and fractional vegetation cover on seasonal urban surface heating/cooling rates

The urban thermal environment is impacted by changes in urban landscape patterns resulting from urban expansion and seasonal variation. In order to cope effectively with urban heat island (UHI) effects and improve the urban living environment and microclimate, an analysis of the heating effect of impervious surface areas (ISA) and the cooling effects of vegetation is needed. In this study, Landsat 8 data in four seasons were used to derive the percent ISA and fractional vegetation cover (FVC) by spectral unmixing and to retrieve the land surface temperature (LST) from the radiative transfer equation (RTE). The percent ISA and FVC were divided into four different categories based on ranges 0-25%, 25-50%, 50-75%, and 75-100%. The LST with percent ISA and FVC were used to calculate the surface heating rate (SHR) and surface cooling rate (SCR). Finally, in order to analyze the heating effect of ISA and the cooling effect of vegetation, the variations of LST with SHR and SCR were compared between different percent ISA and FVC categories in the four seasons. The results showed the following: (1) In summer, SHR decreases as percent ISA increases and SCR increases as FVC increases in the study area. (2) Unlike the dependence of LST on percent ISA and FVC, the trends of SHR/SCR as a function of percent ISA/FVC are more complex for different value ranges, especially in spring and autumn. (3) The SHR (heating capacity) decreases with increasing percent ISA in autumn. However, the SCR (cooling capacity) decreases with increasing FVC, except in summer. This study shows that our methodology to analyze the variation and change trends of SHR, SCR, and LST based on different ISA and FVC categories in different seasons can be used to interpret urban ISA and vegetation cover, as well as their heating and cooling effects on the urban thermal environment. This analytical method provides an important insight into analyzing the urban landscape patterns and thermal environment. It is also helpful for urban planning and mitigating UHI

A Hybrid Framework for Lung Cancer Classification

Cancer is the second leading cause of death worldwide, and the death rate of lung cancer is much higher than other types of cancers. In recent years, numerous novel computer-aided diagnostic techniques with deep learning have been designed to detect lung cancer in early stages. However, deep learning models are easy to overfit, and the overfitting problem always causes lower performance. To solve this problem of lung cancer classification tasks, we proposed a hybrid framework called LCGANT. Specifically, our framework contains two main parts. The first part is a lung cancer deep convolutional GAN (LCGAN) to generate synthetic lung cancer images. The second part is a regularization enhanced transfer learning model called VGG-DF to classify lung cancer images into three classes. Our framework achieves a result of 99.84% ± 0.156% (accuracy), 99.84% ± 0.153% (precision), 99.84% ± 0.156% (sensitivity), and 99.84% ± 0.156% (F1-score). The result reaches the highest performance of the dataset for the lung cancer classification task. The proposed framework resolves the overfitting problem for lung cancer classification tasks, and it achieves better performance than other state-of-the-art methods