Search Results (77)

Project-based learning (PBL) holds significant promise as a student-centered instructional approach in secondary mathematics, yet teachers’ capacity to leverage it for diverse learners is deeply shaped by their affective experiences during implementation. This narrative inquiry examines the affective journeys of three high school mathematics teachers as they adopted PBL in classrooms characterized by varied student readiness levels and engagement profiles. We document teachers’ affective journeys as they experience initial PBL awareness, participate in active experimentation, and work toward forming emerging commitments and facilitator identities. Analysis of focus groups and interviews reveals three affective tensions: comfort versus transformative reflection, epistemic ideals versus ontological school realities, and career risk versus identity change. The findings demonstrate how teachers can navigate iterative methods of trial, how teacher educators can better support future teachers’ use of student-centered pedagogies, and how administrators can be aware of the impact that teacher affect can have on performance and pedagogy. Implications address how this broader education community can support differentiated, inquiry-based mathematics instruction. This support can be advanced through scope-controlled projects, micro-evidence portfolios, and incremental adoption pathways. These approaches honor teachers’ emotional labor while supporting their sustained use of student-centered learning strategies intended to broaden opportunities for participation and learning in mathematics classrooms. Full article

Planning HVDC-connected renewable energy bases is constrained by the cost of full 8760 h multi-scenario replay and the risk that temporal reduction obscures coupled renewable scarcity, ramping stress, storage depletion, and delivery recovery. This study develops an auditable and extensible workflow in which a seven-dimensional coupled state generates continuous preparation–shock–recovery risk windows that feed replay-based metric diagnosis, surrogate-guided query ordering, and replay-authoritative decision release. Decision-LCB prioritizes a finite 600-vector pool using exact investment costs, predicted variable costs, and heuristic tree dispersion; an information firewall, a separate 120-vector calibration pool, and a replay-all fallback prevent unreplayed predictions from certifying feasibility. Decision-LCB recovered the finite-pool oracle in 60/60 original cases and 59/60 pre-label-frozen follow-up cases; it recorded 0/60 empirical false stops in each layer and achieved 60/60 within-tolerance outcomes and 60/60 gate passes in the follow-up. Ridge cost-greedy recovered 60/60 follow-up oracles with the same pass and false-stop counts, supporting cost-aware acquisition but not a unique LCB advantage. A passing gate requires 360 total vector replays, 40% fewer than full enumeration, whereas failure requires 720, 20% more. Simultaneous lower-bound coverage of the 360 sealed candidates was 0/60, and the measured local implementation increased online and end-to-end wall-clock times by 346.75% and 408.53%; therefore, neither simultaneous regret protection nor runtime improvement is claimed. The principal contribution is therefore architectural rather than algorithm-specific: alternative acquisition policies and higher-fidelity network-constrained or project-specific replay engines can be integrated while the information firewall, chronological replay authority, and replay-all safeguard remain fixed. This modularity provides a route to larger finite design spaces, richer technology portfolios, and broader scenario sets when the simulator cost dominates workflow overheads, while measured-data and deployment-specific validation remain necessary. Full article

Public responses during health crises are shaped by interacting risk perceptions, affect, trust, information needs, overload, misinformation, and protective behavior. Existing applications of the Risk Information Seeking and Processing (RISP) model are largely static and therefore cannot represent stochastic multichannel exposure, delayed correction, or policy feedback. We develop the Stochastic Probabilistic Dynamic RISP (SP-D-RISP) model, which recasts RISP as a bounded stochastic state-space system. Its symmetry structure is explicit: the channel-allocation mechanism is equivariant under simultaneous relabeling of channels and their parameter blocks, while the multi-agent dynamics are invariant to agent relabeling under exchangeable sampling and a label-independent policy. Channel-specific effects, heterogeneous traits, rumor shocks, and interventions generate symmetry breaking. The model combines softmax–multinomial channel competition, discounted Bayesian trust updating, and policy-coupled state transitions. Projection guarantees feasible states by construction, whereas stronger stochastic stability is conditional on a coefficient-level small-gain criterion. For the stationary bounded-memory specification, this criterion is sufficient for Wasserstein contraction, uniqueness of the invariant distribution, and geometric forgetting of initial conditions. The criterion is formulated at the coefficient level and is kept distinct from finite-horizon simulation diagnostics. For the fully disclosed semi-synthetic coefficient vector, the scenario-specific gain matrices have spectral radii between $0.852765$ and $0.857123$; the worst-case column-sum norm is $0.983948$. Thus, the fixed-policy kernels satisfy the stated contraction certificate. For deterministic time-varying paths, the calculation is used only as a common-path one-step certificate, and for the threshold-adaptive rule, it is used only mode by mode rather than as a stationary invariant-law claim. While concentration bounds and Monte Carlo inference quantify population and replication uncertainty, a semi-synthetic experiment with 2500 heterogeneous agents over 90 days examines trust and literacy heterogeneity, clarification delays, communication volume, and intervention portfolios. Within the calibrated SP-D-RISP scenarios, the simulations suggest that higher communication volume may reduce modeled protective behavior when overload effects dominate knowledge gains, delayed clarification may increase transient misinformation, and an integrated portfolio can yield a more favorable simulated outcome profile than the evaluated single-lever strategies. Full article

Background: Artificial intelligence (AI) is increasingly entering hospital practice through diagnostic, predictive, workflow, operational, and generative applications. Hospitals often govern these systems as procurement or information-technology projects rather than as clinical–organizational interventions requiring indication, evaluation, monitoring, accountability, and withdrawal. Objective: To refine the concept of an “AI Hospital Formulary” as an operational, proportional, and accountable framework for the safe, equitable, and sustainable adoption of hospital AI. Design and Methods: This is a perspective article using a structured, non-systematic narrative synthesis and conceptual framework development. Targeted literature and policy sources were identified through purposive searches and citation chaining through 20 July 2026. The synthesis compares the formulary with existing oversight approaches, maps its lifecycle gates to regulatory and risk management duties, and applies the framework to a worked example based on published evaluations of the Epic Sepsis Model. The EQUATOR reporting-guideline selection tool was consulted, and SANRA was used to strengthen the narrative synthesis component. Framework: The revised framework combines a hospital-wide AI register, a standardized formulary monograph, six lifecycle gates, proportional review pathways, governance-of-governance safeguards, cloud and data-sovereignty controls, continuous monitoring of technical and behavioral feedback loops, and explicit renewal or deprescribing criteria. The worked example shows how version-specific evidence can lead to local validation, controlled implementation, restriction, suspension, or renewal rather than automatic adoption. Conclusions: Hospitals should not merely purchase, install, and update AI systems. They should prescribe, monitor, audit, renew, restrict, and, when necessary, deprescribe them. The AI Hospital Formulary is proposed as a complementary institutional layer that converts external standards and existing governance approaches into documented portfolio decisions at the hospital level. Full article

Payroll-anchored retail borrowers—individuals whose monthly remuneration is routed into an account at the lending institution through a salary-project arrangement—constitute the volume backbone of unsecured consumer lending in Kazakhstan, generating the largest origination flow, the lowest realized default rate, and the majority of the systemic regulatory and capital sensitivities of second-tier banks. Payroll anchoring also changes the lender’s information set, which motivates a study of how that advantage translates into model performance and borrower outcomes. We design and internally validate an explainable hybrid artificial-intelligence framework stratified by client tenure into two production models: a Weight-of-Evidence (WOE) logistic-regression scorecard for new salary-project applicants, and a hybrid scorecard for repeat applicants, in which a stacked ensemble of LightGBM, CatBoost and a multi-head self-attention neural network contributes a single WOE-encoded predictor to a second-stage L2-regularized logistic regression. The hybrid recovers a substantial share of the ensemble’s discriminatory lift while preserving an auditable, monotone scorecard at the point of decision, and isotonic recalibration restores the predicted probabilities of default to the empirical bad-rate scale required for IFRS 9 expected-credit-loss accrual and risk-based pricing. We report discrimination, calibration and stability evidence under a strict anti-leakage protocol and set out the structural preconditions under which the architecture transfers to other emerging-market payroll-anchored portfolios. We are explicit about scope: a true out-of-time validation and a full group-conditional fairness audit are identified as required next steps rather than claimed here. The contribution is a reproducible, interpretable scoring design that exploits payroll visibility while retaining full coefficient interpretability inside the production decision engine. Full article

This paper examines the relationship between Federal Open Market Committee (FOMC) communication surprises, global risk sentiment, and net portfolio debt inflows to twelve major emerging market economies over the period 2000–2024. Exploiting a high-frequency U.S. Monetary Policy Event-Study Database, we estimate panel fixed-effects regressions and local projections at quarterly frequency. We find that global risk sentiment, proxied by the VIX, is a robust and persistent driver of emerging market capital flows, while Fed communication surprises are statistically insignificant in normal times and in the 2022–2024 tightening cycle. A striking exception is the 2013 taper tantrum—the episode of severe capital outflow pressure triggered by Chairman Bernanke’s May 2013 congressional testimony signalling a possible tapering of asset purchases. Regime interaction tests reveal a large, highly significant negative effect of communication surprises on flows during this episode alone, with no comparable effect in 2022. Local projections confirm that the taper tantrum generated a sharp initial outflow followed by partial reversal, while VIX effects are contemporaneous but not persistent. We empirically test for market learning, finding that reduced sensitivity to Fed communication reflects a discrete recalibration after the 2013 shock rather than a gradual learning process. Regarding capital flows, the taper tantrum is clearly the exception, not the rule. Full article

Construction contract durations are fixed during procurement, yet delivery often changes after risks materialize and formal extensions of time are approved. Although delays, extension-of-time claims, change orders, and risk-based duration estimation are well studied, less is known about how contract-amendment records can be converted into duration multipliers for planning. This paper develops a quantitative, document-based Risk-Calibrated Duration Multiplier framework linking initially contracted duration, approved extensions, and documented risk causes. The framework was applied to 197 signed works contracts from 60 projects within a broader portfolio of 63 EU-funded water and wastewater infrastructure projects, predominantly administered under FIDIC Red and Yellow Book conditions. The analysis combined duration multipliers, impact-weighted attribution of multi-risk amendments, risk-time coefficients, bootstrap uncertainty assessment, concentration indicators, benchmark regression models, and reconstruction validation. For completed contracts, the mean multiplier was 1.372, with P50, P80, and P90 values of 1.233, 1.635, and 1.886. Public-law procedural and design risk categories accounted for 60.9% of the total extension premium. The results show that contract-amendment records can be transformed into statistically interpretable planning parameters and used as a portfolio learning and contract-governance tool for more realistic infrastructure contract planning. Full article

Decarbonising the residential building sector requires not only technical solutions, but also financially viable delivery models. This paper examines the economic performance of Energy Plus Service (EPS) schemes applied to deep renovation projects under uncertainty, with particular attention to the role of project scale and market conditions. The analysis is based on a portfolio of 21 residential buildings in Northern Italy and combines a Discounted Cash Flow (DCF) model with Monte Carlo simulation. Key sources of uncertainty include renovation costs, post-retrofit energy performance, rental values, and electricity prices, allowing for the estimation of probabilistic Net Present Value (NPV) outcomes. The results show a clear impact of residential asset spatial scale on financial outcomes. Small projects are generally unprofitable, while medium-sized assets are highly sensitive to uncertainty. Larger projects, instead, display a much higher likelihood of positive financial outcomes. Sensitivity analysis indicates that financial performance is driven mainly by investment costs and rental income, while energy-related variables play a more limited role. The findings suggest that the viability of EPS models depends as much on market conditions as on technical performance, pointing to a potential misalignment between energy policy objectives and private investment incentives. Results suggest that projects approaching 160 m² are more likely to achieve a 50% probability of a positive NPV, indicating a potential scale threshold beyond which EPS schemes become significantly more bankable and below which aggregation or additional de-risking measures are likely to be required. Full article

Urbanization and climate change are increasing pluvial flooding risks, thereby intensifying the need for more adaptive stormwater governance in Chinese Sponge City projects. Although public participation is widely recognized as important, current research frequently conceptualizes it as a simplified or static attribute and seldom provides explicit criteria for identifying representative projects in large urban portfolios. This study develops a life cycle-sensitive framework for evaluating public participation in Sponge City projects by conducting a cross-city comparison in China. The study integrates project inventory construction, evidence-based representative project selection, and a multidimensional participation measurement tool covering breadth, depth, identity, and potential across planning, design, construction, and maintenance using five national pilot cities: Jinan, Shanghai, Xiamen, Shenzhen, and Wuhan. The results show that the five representative projects display distinct life cycle participation profiles, rather than a single participation pattern, influenced by project type and governance arrangement. Maintenance emerges as the strongest documented stage, whereas design is the weakest, suggesting stronger documented governance continuity after project delivery than in front-end co-design. Recurrent weaknesses remain in substantive inclusion and feedback-adoption closure. Overall, the study frames participation as a structured governance capability, providing an auditable comparative framework for identifying participation strengths and weaknesses in Sponge City governance. Full article

Smart city governance increasingly relies on AI-enabled planning systems, digital twins, vulnerability scoring tools, and capital investment platforms to allocate climate-resilient housing and infrastructure investments. Yet existing smart-urbanism and adaptation frameworks do not adequately specify how such systems should encode well-being, equity, and climate uncertainty when translating urban data into ranked projects and funded portfolios. This paper develops the Caring Urban Governance Framework for AI-enabled urban prioritization through a structured scoping review and conceptual framework analysis integrating climate-risk decision-making under deep uncertainty, built-environment pathways affecting psychosocial well-being, and public-sector algorithmic accountability. The framework proposes a five-layer architecture linking urban form and infrastructure, climate exposure and environmental resources, psychosocial mediators of well-being, algorithmic design choices, and institutional governance, with explicit feedback loops. Its main outputs are an auditable decision architecture, eight mechanism-based propositions for empirical testing, an operational specification matrix for objective functions, equity constraints, robust logic, and documentation, and an analytical validation of construct clarity, coherence, literature congruence, and operationalizability. The analysis argues that aligning AI-enabled urban prioritization with SDG 11 requires treating well-being-supportive living conditions as a decision objective, constraining optimization with equity conditions, and institutionalizing auditability and contestability to reduce distributive and psychosocial harm in public investment planning. Full article

Energy efficiency retrofits are widely promoted for public buildings, yet evidence from large-scale real-world projects remains limited compared with simulation-based assessments. This study leverages measured pre- and post-retrofit operational data from 530 public building retrofit projects across 11 provinces/municipalities in China to quantify realized energy-saving performance and screening-level cost-effectiveness across building types and climate zones. Wilcoxon and Kruskal–Wallis tests were employed to ensure statistical rigor. Retrofit measures were grouped into seven categories (e.g., HVAC, lighting, envelope, monitoring/management), and a median-based four-quadrant framework was employed to characterize investment–savings profiles by climate zone and building function. Across the full sample, mean energy use intensity decreased by 19.1%, with 99.2% of projects achieving positive savings. Savings varied markedly by building type: commercial and hotels achieved the highest savings intensities (26.5–28.0 kWh/(m²·a)), while education and cultural buildings generally showed lower gains, with some projects having < 10 kWh/(m²·a). Technology performance exhibited distinct climate and building suitability. Envelope retrofits were most effective in the Cold and Hot Summer–Cold Winter zones (13.30–22.06 kWh/(m²·a)) but yielded limited benefits in the Hot Summer–Warm Winter zone (~1.73 kWh/(m²·a)). HVAC and lighting upgrades delivered comparatively stable savings across climates and building types and dominated retrofit portfolios. Based on these findings, we propose a tiered strategy: prioritizing HVAC and envelope upgrades for high-load sectors while focusing on low-cost optimizations for educational facilities to mitigate investment risks. The findings provide large-scale empirical evidence to support climate- and building-specific retrofit prioritization and investment decision-making under real-world operating conditions. Full article

Bond ETF rebalancing is difficult to describe with return and risk objectives alone, because a portfolio that looks attractive on paper may still be impractical if it requires large and unstable trades. This paper proposes a topology-aware multi-objective particle swarm optimization framework for bond ETF allocation under credit-risk-related constraints. The method jointly considers annualized return, CVaR, and diversification, while enforcing long-only, exposure, and hard maximum-step turnover constraints. The central idea is to treat the swarm as a communication graph: particles exchange information through an explicit topology, and this topology affects how feasible regions are explored and how leaders are selected. When a candidate portfolio update violates the turnover budget, it is repaired toward the feasible set before evaluation, so that the search remains tied to tradable rebalancing decisions. We test the framework in a walk-forward out-of-sample backtest on U.S. bond ETFs from 2008 to 2024. The empirical analysis compares stronger classical and evolutionary baselines, four communication topologies, hard-versus-soft turnover control, stress-period behavior, and a synthetic scalability proxy. The results suggest that hard turnover repair is effective in truncating extreme rebalancing events, while communication topology changes the return–risk–turnover profile. In our experiments, the ring topology gives the most stable default behavior. Overall, the evidence suggests that topology is not just an implementation detail in swarm-based portfolio search, but a design choice that affects constrained multi-objective allocation. Full article

Climate change disproportionately threatens low- and middle-income countries, yet integrated assessments combining socio-economic fragility with physical hazards remain limited. This study quantifies multi-dimensional climate vulnerability and derives optimized adaptation policies for six representative nations (Bangladesh, Colombia, Kenya, Morocco, Pakistan, Vietnam) by fusing socio-economic indicators with climate risk data (2000–2024). A computational framework integrating unsupervised learning, dimensionality reduction, and predictive modeling was employed. Principal Component Analysis synthesized eight indicators into a Compound Vulnerability Score (CVS), while K-Means and DBSCAN identified distinct vulnerability regimes. XGBoost quantified driver importance, and Graph Neural Networks captured systemic interconnections. XGBoost identified projected drought risk (31.2%), precipitation change (18.1%), and poverty headcount (14.3%) as primary drivers. Graph networks demonstrated significant risk amplification in African nations (Morocco SRS: 0.728–0.874; Kenya SRS: 0.504–0.641) versus damping in Asian countries. A Reinforcement Learning (RL) agent was trained using Deep Q-Networks with experience replay to optimize intervention portfolios under budget constraints. The RL policy achieved a 23% reduction in systemic risk compared to uniform allocation baselines, generating context-specific priorities: drought management for Morocco (score 50) and Pakistan (40); poverty alleviation for Kenya (40); coastal protection for Bangladesh (40); agricultural resilience for Vietnam (35); and institutional capacity building for Colombia (50). In conclusion, socio-economic fragility non-linearly amplifies climate hazards, with poverty and drought risk constituting critical vulnerability multipliers. The AI-driven framework demonstrates that targeted interventions in high-sensitivity systems maximize systemic risk reduction. This integrated approach provides a replicable, evidence-based foundation for strategic adaptation finance allocation in an increasingly uncertain climate future. Full article

The management of large and heterogeneous building stocks requires decision-support tools capable of prioritising interventions under limited technical and financial resources. In this framework, the role of structural deterioration is rarely integrated within a unified prioritisation framework. This study proposes a rapid deterioration-based assessment for prioritising maintenance within heterogenous portfolios. The assessment is articulated into two levels. A Project Level (PL) is based on visual inspections and component-level condition ratings, while a Network Level (NL) introduces contextual and functional modifiers related to the relevance of each structural unit within the building stock. A seismic assessment procedure is integrated in proposed decision-making system for optimising intervention planning. The two assessments are integrated through a decision-tree logic providing an overall classification of buildings within portfolios. The proposed framework is applied to an industrial-oriented building stock located in Italy, comprising 79 structural units characterised by significant typological heterogeneity, including masonry, reinforced concrete, precast reinforced concrete, and steel buildings. The application illustrates the internal consistency of the proposed framework and its ability to support a transparent and articulated prioritisation process for maintenance and risk mitigation within heterogeneous building portfolios. Further applications to different building stocks are required to explore the general applicability of the methodology. Full article

The article examines the persistent stability of the weighted average cost of capital (WACC) disclosed by German DAX40 companies despite substantial declines in risk-free interest rates between 2004 and 2021. While theory suggests that WACC should reflect lower risk-free interest rates and decline as well with falling government bond yields, empirical evidence reveals minimal adjustment in reported WACC figures. Disclosed WACC of DAX40 companies remains between 7% and 8% as the yield of the ten-year German government bond fell from 4.1% to −0.2%. This study employs quantitative analyses to investigate whether systematic increases in risk exposure can explain this phenomenon. Using capital market data spanning from 2000 to 2023, we analyze five risk dimensions: systematic risk (beta factors), overall market volatility, risk aversion (lambda factors), earnings risk, and financial structure risk. Bootstrap analyses reveal a 41.5% reduction in beta factor variance, while volatility analyses demonstrate declining market risk exposure. The market price of risk analysis does not reveal definite findings. Earnings risk measures indicate improved financial stability, and debt ratios show modest declines. These findings suggest that observable risk parameters cannot explain persistent WACC levels, indicating a disconnect between theoretical WACC calculations and practitioner applications in investment project decision-making following value-based management principles. Full article