Search Results (836)

This paper explores the complexities of measuring impact from placemaking in the context of public and community housing (sometimes known as social or subsidised housing) in Aotearoa New Zealand. Placemaking refers to a range of practices and interventions—including the provision or facilitation of access to community infrastructure—that seek to cultivate a positive sense of place through everyday experiences, spaces, relationships, and rituals. Drawing on interviews with four community housing providers (CHPs), analysis of their documentation, and tenant survey and interview data from two of those CHPs, this research examines providers’ change theories about placemaking in relation to tenants’ experiences of safety, belonging and connectedness, including access to local amenities, ease of getting around, and a sense of neighbourhood and community affiliation. Based on the importance of these variables to wellbeing outcomes, the study highlights the potential of placemaking to support tenant wellbeing, while also recognising that providers must navigate trade-offs and co-benefits, limited resources, and varying levels of tenant engagement. While placemaking can help to foster feelings of connection, belonging and safety, its impact depends on providers’ capacity to initiate and sustain such efforts amidst competing demands and constraints. The study offers indicative findings and recommendations for future research. Although the impacts of placemaking and community infrastructure provision are difficult to quantify, research findings are synthesised into a prototype framework to support housing providers in their decision-making and housing development processes. The framework, which should be adapted and evaluated in situ, potentially also informs other actors in the built environment—including architects, landscape architects, urban designers, planners, developers and government agencies. In Aotearoa New Zealand, where housing provision occurs within a colonial context, government agencies have obligations under Te Tiriti o Waitangi to actively protect Māori rights and to work in partnership with Māori in housing policy and delivery. This underscores the importance of placemaking practices and interventions that are culturally and contextually responsive. Full article

With the increasing integration of renewable energy, virtual power plants (VPPs) have emerged as key market participants by aggregating distributed energy resources. However, their involvement in electricity markets is increasingly challenged by two major uncertainties: price volatility and the intermittency of renewable generation. This study presents the first application of Information Gap Decision Theory (IGDT) within a two-stage cooperative scheduling framework for VPPs. A novel bidding strategy model is proposed, incorporating both robust and opportunistic optimization methods to explicitly account for decision-making behaviors under different risk preferences. In the day-ahead stage, a risk-responsive bidding mechanism is designed to address price uncertainty. In the real-time stage, the coordinated dispatch of micro gas turbines, energy storage systems, and flexible loads is employed to minimize adjustment costs arising from wind and solar forecast deviations. A case study using spot market data from Shandong Province, China, shows that the proposed model not only achieves an effective balance between risk and return but also significantly improves renewable energy integration and system flexibility. This work introduces a new modeling paradigm and a practical optimization tool for precision trading under uncertainty, offering both theoretical and methodological contributions to the coordinated operation of flexible resources and the design of electricity market mechanisms. Full article

This paper explores the integration of financial and operational flows in Supply Chain Finance (SCF) through the lens of Information Processing Theory (IPT). Despite increasing adoption of SCF solutions like reverse factoring and trade credit, existing literature lacks a unified theoretical framework that captures both financial and organizational complexities. Drawing from 47 peer-reviewed articles in leading supply chain journals, this study identifies key SCF dimensions—task characteristics, environment, and interdependence—as primary sources of uncertainty and information processing needs. It then examines how IT systems, coordination mechanisms, and organizational design enhance processing capacity, enabling firms to build SCF capabilities such as risk assessment, supplier onboarding, and financial process standardization. These capabilities facilitate financial supply chain integration through data connectivity, embedded flows, and collaborative planning. The study contributes a comprehensive conceptual model that connects SCF uncertainties, processing strategies, and performance outcomes, addressing theoretical and managerial gaps. It further provides a foundation for future empirical research and strategic design of SCF systems to enhance supply chain resilience and financial efficiency. Full article

This paper provides a new and significant conceptual framework to enhance understanding of how digital capability and corporate social responsibility (CSR) complement each other in achieving the trade-off of ambidextrous innovation. Building on resource orchestration theory, we propose that opportunity recognition can serve as a mediating bridge to convey the positive impact of digital capability on ambidextrous innovation. Furthermore, these effects are likely to be especially pronounced among enterprises with a higher level of CSR implementation according to the reciprocity principle of social capital theory. We conducted a questionnaire-based survey among executives from 225 non-listed companies and a longitudinal panel study of 1897 listed companies from 2009 to 2022. The results support our hypotheses, showing that CSR implementation strengthens the active indirect effect of digital capability on ambidextrous innovation through accurate opportunity recognition. This paper enriches the research on the positive consequences of digital capabilities, introduces opportunity recognition into resource orchestration theory from the perspective of intangible assets, verifies the mediating role of opportunity recognition between digital capabilities and ambidextrous innovation, and sheds light on how an organization’s CSR strategy and digital capabilities are complementary. CSR can catalyze the positive impact of an enterprise’s digital capability on opportunity recognition and ambidextrous innovation. We advise enterprises on sustainable development, emphasizing the importance of fulfilling their CSR strategies while enhancing their digital capabilities. Full article

This paper addresses the synergy between environmental and economic benefits in the green power trading market by constructing a collaborative game model for environmental rights value and electricity energy value. Based on this, a model for maximizing the benefits of flexible resource operation is proposed. Through the combination of non-cooperative and cooperative games, the conflict and synergy mechanisms of multiple stakeholders are quantified, and the Shapley value allocation rule is designed to achieve Pareto optimality. Simultaneously, considering the spatiotemporal regulation capability of flexible resources, dynamic weight adjustment, cross-period environmental rights reserve, and risk diversification strategies are proposed. Simulation results show that under the scenario of a carbon price of 50 CNY/ton (≈7.25 USD/ton) and a peak–valley electricity price difference of 0.9 CNY/kWh (≈0.13 USD/kWh), when the environmental weight coefficient α = 0.5, the total revenue reaches 6.857 × 10⁷ CNY (≈9.94 × 10⁶ USD), with environmental benefits accounting for 90%, a 15.3% reduction in carbon emission intensity, and a 1.74-fold increase in energy storage cycle utilization rate. This research provides theoretical support for green power market mechanism design and resource optimization scheduling under “dual-carbon” goals. Full article

Frequent disruptions to global supply chains, driven by factors such as trade restrictions and geopolitical conflicts, brought supply chain resilience to the forefront of both academic research and industry practice. Concurrently, the rapid advancement of artificial intelligence (AI) technologies in supply chain management in recent years offers new perspectives for researching resilience. Based on the Organizational Information Processing Theory (OIPT), this study explores the direct and indirect mechanisms through which AI usage impacts supply chain resilience from an information processing perspective. Within the OIPT framework, we develop a theoretical model incorporating AI usage, supply chain resilience, supply chain efficiency, supply chain collaboration, and digital information technology capability. We empirically test the model using survey data collected from 231 Chinese manufacturing senior executives and supply chain managers, employing partial least squares structural equation modeling (PLS-SEM). The findings reveal that AI usage has a significant direct positive effect on supply chain resilience. Additionally, supply chain efficiency and collaboration act as mediators in this relationship. Furthermore, we examined the moderating role of a firm’s digital information technology capability and found that it positively moderates the impact of AI usage on supply chain resilience. Full article

This study investigates the impact of countries’ fundamental risk on the speed of adjustment (SOA) towards firms’ target capital structures. Using a dataset comprising 17,747 non-financial firms from 44 countries, this study finds that a reduction in country-specific fundamental risk significantly increases a firm’s rate of leverage adjustment. More specifically, we observe that a one standard deviation reduction in fundamental risk results in a substantial 12.79% increase in SOA for book leverage and a 4.81% increase for market leverage. The study also finds evidence of the influence of individual dimensions of fundamental risk on SOA. It implies that improved operational efficiency, high foreign accessibility, enhanced corporate transparency, and increased political stability expedite the pace of leverage adjustment within firms. Robustness checks using a machine learning random forest estimator predicted leverage targets to corroborate these findings. The results highlight the critical role of institutional quality in reducing financing frictions and promoting more efficient corporate capital adjustments. These insights have profound implications for policymakers, emphasising the need to strengthen institutional and regulatory frameworks to enhance capital market integrity and reduce friction, which could ultimately create value for the firm stakeholders. Full article

This study explores how FinTech and artificial intelligence (AI) adoption shape efficiency and financial stability in dual-banking systems. It focuses on 26 listed Islamic and conventional banks across 11 countries in the MENA and Southeast Asia regions between 2020 and 2024. To measure digital adoption, we create a seven-component FinTech Adoption Index. We use fixed-effects regressions to examine its impact on cost efficiency, profitability, solvency stability, and credit risk. This analysis also controls bank size, capitalization, and macroeconomic conditions. The results show a clear adoption gap. Conventional banks consistently score 0.5–0.8 points higher on the FinTech Index compared to Islamic banks. Each additional FinTech component raised operating costs by about 0.8%, but improved profitability slightly by only 0.03%. This suggests that technological integration creates upfront costs before any real efficiency gains are seen. However, the stability benefits are stronger. FinTech adoption increases the Z-score by 3.6 points and lowers the non-performing loan ratio by 0.1%. Islamic banks gain more stability benefits due to their risk-sharing contracts and asset-backed financing structures. Overall, an efficiency–stability trade-off emerges. Conventional banks focus more on profitability, while Islamic banks gain resilience, but face slower efficiency improvements. By combining the Resource-Based View and Financial Stability Theory, this study provides the first multi-country evidence of how governance structures shape digital transformation in dual-banking markets. The findings offer practical guidance for regulators and bank managers around balancing innovation, efficiency, and stability. Full article

This paper explores the application of transform-domain sparsification and compressed sensing (CS) techniques to improve the efficiency and quality of magnetic resonance imaging (MRI). We implement and evaluate three sparsifying methods—discrete wavelet transform (DWT), fast Fourier transform (FFT), and discrete cosine transform (DCT)—which are used to simulate subsampled reconstruction via inverse transforms. Additionally, one accurate CS reconstruction algorithm, basis pursuit (BP), using the L₁-MAGIC toolbox, is implemented as a benchmark based on convex optimization with L₁-norm minimization. Emphasis is placed on basis pursuit (BP), which satisfies the formal requirements of CS theory, including incoherent sampling and sparse recovery via nonlinear reconstruction. Each method is assessed in MATLAB R2024b using standardized DICOM images and varying sampling rates. The evaluation metrics include peak signal-to-noise ratio (PSNR), root mean square error (RMSE), structural similarity index measure (SSIM), execution time, memory usage, and compression efficiency. The results show that although discrete cosine transform (DCT) outperforms the others under simulation in terms of PSNR and SSIM, it is inconsistent with the physics of MRI acquisition. Conversely, basis pursuit (BP) offers a theoretically grounded reconstruction approach with acceptable accuracy and clinical relevance. Despite the limitations of a controlled experimental setup, this study establishes a reproducible benchmarking framework and highlights the trade-offs between the quality of transform-based reconstruction and computational complexity. Future work will extend this study by incorporating clinically validated CS algorithms with L₀ and nonconvex Lp (0 < p < 1) regularization to align with state-of-the-art MRI reconstruction practices. Full article

This study investigates the liquidity dynamics in non-traditional financial markets by simulating trading environments for fractional ownership of illiquid alternative investments, grounded in empirical tick data from a Swiss FinTech platform covering December 2022 to June 2024. The research translates an operational digital secondary market into a heterogeneous agent-based simulation model within the theoretical framework of market microstructure and complex systems theory. The main objective is to assess whether a simple agent-based model (ABM) can replicate empirical liquidity patterns and to evaluate how market rules and parameter changes influence simulated liquidity distributions. The findings show that (i) the simulated liquidity closely matches empirical distributions not only in mean and variance but also in higher-order moments; (ii) the ABM reproduces key stylized facts observed in the data; and (iii) seemingly simple interventions in market rules can have unintended consequences on liquidity due to the complex interplay between agent behavior and trading mechanics. These insights have practical implications for digital platform designers, investors, and regulators, highlighting the importance of accounting for agent heterogeneity and endogenous market dynamics when shaping secondary market structures. Full article

Optimizing thermoelectric (TE) performance in two-dimensional materials has emerged as a pivotal strategy for sustainable energy conversion. This study systematically investigates the regulatory mechanisms of uniaxial strain (−2% to +2%), temperature (300–800 K), and out-of-plane electric fields (0–1.20 eV/Å) on the thermoelectric properties of monolayer MoS₂ via first-principles calculations combined with Boltzmann transport theory. Key findings reveal that uniaxial strain modulates the bandgap (1.56–1.86 eV) and carrier transport, balancing the trade-off between the Seebeck coefficient and electrical conductivity. Temperature elevation enhances carrier thermal excitation, boosting the power factor to 28 × 10¹⁰ W·m⁻¹·K⁻²·s⁻¹ for p-type behavior and 27 × 10¹⁰ W·m⁻¹·K⁻²·s⁻¹ for n-type behavior at 800 K. The breakthrough lies in the exceptional suppression of lattice thermal conductivity (κ₁) by out-of-plane electric fields—at 1.13 eV/Å, κ₁ is reduced to single-digit values (W·m⁻¹·K⁻¹), driving ZT to ~4 for n-type MoS₂ at 300 K. This work demonstrates that synergistic engineering of strain, temperature, and electric fields effectively decouples the traditional trade-off among the Seebeck coefficient, conductivity, and thermal conductivity, providing a core optimization pathway for 2D thermoelectric materials via electric field-mediated κ₁ regulation. Full article

This study examines how air transport infrastructure and trade flows influence economic growth across ASEAN countries, with a focus on identifying the threshold levels at which these factors begin to enhance growth. Despite increasing investment in regional logistics and connectivity, policymakers often lack evidence-based thresholds to guide infrastructure and trade policy for long-term development. Addressing this gap, this study applies a Dynamic Panel Threshold Model to uncover the tipping points at which improvements in air cargo volume (lnCargo) and air transport infrastructure quality (lnQAir) translate into stronger economic growth. By employing System-GMM and First-Difference GMM estimations, the analysis captures the threshold effects of air cargo volume (lnCargo) and air transport infrastructure quality (lnQAir) on economic growth over varying regimes. The results reveal significant single-threshold effects for both lnCargo and lnQAir, indicating that their contributions to economic growth become substantial after surpassing specific critical levels. When air cargo volume exceeds approximately 267,067 tons per year (lnCargo > 5.5875), its positive effect on economic growth strengthens, particularly when accompanied by high-quality infrastructure. Similarly, air transport infrastructure quality exhibits a significantly stronger impact on economic growth once it exceeds the critical threshold of lnQAir = 1.5476 (≈4.7001 index points). These findings emphasize the complementarity between trade flows and infrastructure, aligning with endogenous growth theory, which suggests that infrastructure investments yield increasing returns when integrated with trade expansion. Policy implications suggest that ASEAN economies should adopt demand-driven infrastructure development aligned with trade dynamics, prioritizing regional connectivity, logistics efficiency, and investment attraction to sustain long-term economic growth. Full article

Innovation-driven labor markets play a pivotal role in economic development, yet significant disparities exist in how efficiently countries transform innovation inputs into labor market outcomes. This study addresses the critical gap in benchmarking multi-stage innovation efficiency by developing an integrated framework combining Data Envelopment Analysis (DEA) Super Slack-Based Measure (Super-SBM) for static efficiency evaluation and the Malmquist Productivity Index (MPI) for dynamic productivity decomposition, enhanced with cooperative game theory for robustness testing. Focusing on the top 20 innovative economies over a 5-year period, we analyze key inputs (Innovation Index, GDP, trade openness) and outputs (labor force, unemployment rates), revealing stark efficiency contrasts: China, Luxembourg, and the U.S. demonstrate optimal performance (mean scores > 1.9), while Singapore and the Netherlands show significant underutilization (scores < 0.4). Our results identify a critical productivity shift period (average MPI = 1.325) driven primarily by technological advancements. This study contributes a replicable, data-driven model for cross-domain efficiency assessment and provides empirical evidence for policymakers to optimize innovation-labor market conversion. The methodological framework offers scalable applications for future research in computational economics and productivity analysis. Full article

Traditional educational assessment systems suffer from inefficient question selection strategies that fail to optimally probe student knowledge while requiring extensive testing time. We present a novel hierarchical probabilistic neural framework that integrates Bayesian inference with symmetric deep neural architectures to enable adaptive, efficient knowledge assessment. Our method models student knowledge as latent representations within a graph-structured concept dependency network, where probabilistic mastery states, updated through variational inference, are encoded by symmetric graph properties and symmetric concept representations that preserve structural equivalences across similar knowledge configurations. The system employs a symmetric dual-network architecture: a concept embedding network that learns scale-invariant hierarchical knowledge representations from assessment data and a question selection network that optimizes symmetric information gain through deep reinforcement learning with symmetric reward structures. We introduce a novel uncertainty-aware objective function that leverages symmetric uncertainty measures to balance exploration of uncertain knowledge regions with exploitation of informative question patterns. The hierarchical structure captures both fine-grained concept mastery and broader domain understanding through multi-scale graph convolutions that preserve local graph symmetries and global structural invariances. Our symmetric information-theoretic method ensures balanced assessment strategies that maintain diagnostic equivalence across isomorphic concept subgraphs. Experimental validation on large-scale educational datasets demonstrates that our method achieves 76.3% diagnostic accuracy while reducing the question count by 35.1% compared to traditional assessments. The learned concept embeddings reveal interpretable knowledge structures with symmetric dependency patterns that align with pedagogical theory. Our work generalizes across domains and student populations through symmetric transfer learning mechanisms, providing a principled framework for intelligent tutoring systems and adaptive testing platforms. The integration of probabilistic reasoning with symmetric neural pattern recognition offers a robust solution to the fundamental trade-off between assessment efficiency and diagnostic precision in educational technology. Full article

This study investigates the integration of LTspice simulations and structured reflective practices within a project-based learning (PBL) framework in a Microelectronic Circuits course. The course was designed to improve students’ conceptual understanding, problem-solving abilities, and engagement by embedding simulation-based assignments and guided reflections within a final design project. A qualitative case study was conducted with 49 third-year undergraduate electrical engineering students. The data sources included structured reflection submissions, researcher observations, and evaluations of project presentations. Thematic analysis identified five recurring themes: linking theory to practice, iterative problem-solving strategies, metacognitive awareness, peer engagement, and reflections on integration challenges and benefits. The results indicate that the LTspice simulations enabled the students to visualize circuit behavior, experiment with design parameters, and observe the effects of design trade-offs. The integration of structured reflection prompted deeper learning by helping the students recognize misconceptions, articulate troubleshooting strategies, and build confidence in circuit analysis. Although some students initially struggled with the complexity of the simulation software, the iterative and collaborative nature of the PBL process increased their motivation and promoted meaningful engagement. This study contributes to the growing body of research on active learning in engineering education and offers practical recommendations for implementing simulation-based learning environments that promote critical thinking, metacognition, and technical competence. Full article