Climate change is causing a rise in global temperatures. High summertime temperatures can lead to overheating, where the temperature in homes becomes a risk to health and comfort. To reduce overheating in new residential buildings, a new part of the English Building Regulations, Part O: Overheating (‘Part O’) was introduced in 2022. This study evaluates the application of this new regulation to real-world projects. Semi-structured interviews were carried out with 30 experts across the construction and policy sectors. Experts identified that Part O has barriers to its application, including conflicts with broader parts of the building regulations, limitations in compliance modelling assumptions for passive and active technologies, a lack of futureproofing for rising temperatures, and inadequate assurance that homes designed to the regulations would be constructed to a high quality. To improve the use of Part O, this paper suggests recommendations for how the building regulations can minimise conflict, streamline processes, and support innovation to deliver heat resilient homes. This includes using academic evidence to better support technical modelling assumptions, and in the longer-term utilising upcoming modelling methodologies (Home Energy Model) to integrate Part O modelling with broader parts of building design and compliance requirements. To deliver a shift in high quality, heat resilient and net zero homes, this paper recommends a long-term shift in regulatory approaches to focus on absolute performance outputs, building audits and post occupancy evaluation. • Evaluation of Part O: overheating building regulations to support revisions. • Interview data gathered from 30 experts across construction and policy. • Barriers identified in application of Part O and broader building regulations. • Near-term recommendations to futureproof and improve assumptions. • Long-term recommendations to streamline regulations and audit homes.

Tracking patient preferences is vital to medical decision-making, but evidence suggests that the standard method for tracking the preferences of incapacitated or incompetent patients (ie, surrogates) is inaccurate. Recent proposals suggest that artificial intelligence preference predictors (AIPPs) can improve preference tracking for these patients, but have faced significant objections. While many of these objections depend on unsettled empirical or technical assumptions, one prominent objection-that AIPPs rely inappropriately on impersonal information-seems to be an in-principle challenge to AIPPs. In this paper, we show that even granting an implausibly strong version of this objection, AIPPs may provide value to clinical decision-making. To show this, we develop suggestions that AIPPs may support best interest decision-making (BIDM) by improving the accuracy, consistency and speed of BIDM, and show that the prevalence of BIDM in the intensive care unit (ICU) grants this application of AIPPs significant moral and practical consequence. This not only clears a path to improve BIDM but also establishes a safe harbour-a relatively uncontroversial yet impactful space-in which proponents may develop AIPPs sufficently to resolve empirical and technical questions about their potential. We conclude by highlighting key questions for the application of AIPPs to BI determinations, setting an agenda for the deeper examination of a largely overlooked application of these tools.

Neural network-assisted RNA velocity imputation for empowering transcript dynamics-based analyses.

Life Cycle Assessment (LCA) is widely used to evaluate the environmental impact of power generation systems and inform energy and climate policy decisions. In recent years, numerous LCA studies have examined the life-cycle implications of power plants utilizing renewable, nuclear, and fossil fuel technologies. Nevertheless, the resultant data is fragmented, exhibiting significant diversity among investigations attributable to disparities in system boundaries, technical assumptions, and methodological selections. This document offers a systematic overview of peer-reviewed LCA studies and Environmental Product Declarations (EPDs) evaluating the environmental implications of predominant power production technologies, such as solar photovoltaic, wind, hydropower, nuclear, and natural gas power plants. Various environmental effect categories are evaluated, with a specific focus on Global Warming Potential as the most frequently reported and policy-relevant metric. The review consolidates documented impact ranges, assesses the effects of plant size and technological design, and evaluates the contribution of several life cycle stages to overall environmental performance. The findings emphasize prevalent tendencies and significant variability among technologies and studies, illustrating the susceptibility of LCA results to modeling assumptions and data sources. Although current LCAs offer relevant insights into the environmental impact of electricity generation, the review highlights enduring methodological deficiencies, particularly the inadequate handling of uncertainty, the static portrayal of long-lasting infrastructures, and the lack of explicit attention to technological risk. This study consolidates and critically evaluates existing literature, providing a thorough reference on the life-cycle environmental consequences of power plants and facilitating a more educated interpretation of LCA results within energy system planning and policy analysis.

Abstract Under some technical assumptions of a global nature, we establish the weight part of Serre’s conjecture for mod p Galois representations for CM fields that are tamely ramified and sufficiently generic at p .

Abstract The spatial Kepler problem with a perturbation satisfying the rotational symmetry w.r.t. the z -axis and the reflection symmetry w.r.t. the ( x , y )-plane, can be reduced to an Hamiltonian system with 2 degrees of freedom after fixing the angular momentum. For small enough perturbations, we show that for certain choices of energy and angular momentum, the corresponding energy surface is compact and diffeomorphic to S 3 , and on each compact energy surface there is a unique z -symmetric brake orbit, which forms a Hopf link with a planar relative periodic orbit. Moreover under some additional technical assumptions, by applying recent results from symplectic dynamics (Cristofaro-Gardiner et al 2023 Geom. Topol. 27 3801–31) and Franks’ theorem, we prove there are infinitely many relative periodic orbits on each compact energy surface. These results can be applied to the motion of a satellite around a uniformly mass-distributed ellipsoid and the n -pyramidal problem, where one point mass moves along the z -axis and n other equal point masses form a regular n -gon perpendicular to the z -axis.

Recycled aggregates materials are often relegated to downcycled applications such as backfilling. Different barriers limit their reintegration into higher-value construction use. This paper develops a probabilistic model of material flows using a Markov chain framework to simulate transitions between four states: Input, Use/Waste, Recycling, and Disposal. The model draws on Eurostat data covering non-metallic minerals in 27 EU Member States (2014–2023) and incorporates barrier-adjusted transition probabilities reflecting economic, technological, institutional, and social constraints. Scenario simulations reveal that improvements in recycling probabilities can yield nonlinear gains in material retention. However, once structural barriers are introduced, system performance declines sharply—even under favourable technical assumptions. The results suggest that modest policy interventions may have outsized effects if targeted toward key transition points.

Abstract Statistical bias adjustment has become a common practice to increase the relevance of climate model outputs for impact studies and other societal applications. However, the application of bias adjustment raises fundamental issues identified in the literature, calling into question the credibility of the adjusted climate information. In the attempt to address the usability gap of climate model output despite these unresolved issues, different approaches to bias adjustment have emerged—from applying a single consistent method across studies, selecting the most suitable method for a given use case, to employing an ensemble of bias adjustment methods. This paper examines how these approaches rest on both methodological assumptions and implicit value judgments about what constitutes usable climate information and for whom it is produced. Building on recent literature in the philosophy of science, we propose a framework for evaluating the usability of climate projections in the context of bias adjustment and apply this framework to evaluate the different approaches to bias adjustment. To evaluate the credibility of the adjusted climate information, the paper provides a detailed discussion of two key methodological assumptions underlying different approaches, the interpretation of performance differences of bias adjustment methods and changes to the climate model trend and ensemble through bias adjustment. Through this perspective, we aim to situate bias adjustment in the discussion around usable climate information and the production of climate services, while offering a practical discussion of assumptions for climate impact researchers and climate service practitioners working with bias adjustment methods. Significance Statement Statistical bias adjustment of climate model output has become common practice but raises fundamental issues unresolved in the literature. Informed by the development of the software package ibicus for the comparison and evaluation of bias adjustment methods, this perspective provides both a technical discussion of methodological assumptions of prevalent approaches to bias adjustment and a philosophical reflection on the associated interpretations of usable climate information. Both of these aspects inform the approach to bias adjustment chosen in practice. We argue that the discussion of both technical assumptions and implicit value judgments conducted here is important to guide future method development and can serve as a practical guide to users of bias adjustment and organizations who aim to provide actionable climate services.

(Communicated by Xinwei Liu) The alternating direction method of multipliers (ADMM) is one of the effective methods for solving two-block separable optimization problems with linear constraints, and has been widely applied in image processing, power systems, sparse learning, and other fields. Its essence is the application of the Douglas-Rachford splitting method to the dual problem. Classical ADMM updates the Lagrange multipliers only once per iteration, while symmetric ADMM achieves dual updates of the Lagrange multipliers in each iteration by introducing an additional multiplier update step, thereby significantly improving the convergence performance and numerical stability of the algorithm. In this paper, we propose a novel symmetric ADMM algorithmic framework for two-block separable nonconvex optimization problems with linear constraints, which introduces two distinct relaxation factors to enhance the flexibility and convergence efficiency of the algorithm. This method has been comprehensively studied for convex problems. However, for nonconvex problems, in the convergence analysis of symmetric alternating direction method of multipliers with two different relaxation factors without introducing Bregman distances or regularization terms, proving the monotonicity of the Lagrangian function remains a challenging problem. In terms of theoretical analysis, we establish the convergence theory of the proposed algorithm in this paper. Notably, our convergence proof does not rely on common technical assumptions such as Bregman distances or regularization terms. Specifically, by constructing a novel auxiliary function and under the mild condition that the Kurdyka–Łojasiewicz (KŁ) inequality is satisfied, we prove that the iterative sequence generated by the algorithm converges to a stationary point of the problem. The main contributions of this paper can be summarized as follows: First, we design a symmetric ADMM scheme with dual relaxation factors to solve two-block separable nonconvex and nonsmooth optimization problems with linear constraints, and the proposed method allows for a wider range of parameters, which can better adapt to the structural characteristics of different problems through flexible adjustment of relaxation parameters. Second, we establish a concise convergence analysis framework that does not depend on Bregman distances and regularization terms, reducing the complexity of theoretical analysis; moreover, it can degenerate into the classical ADMM. Finally, we validate the practical application effectiveness of the proposed algorithm through numerical experiments, and the experimental results demonstrate that the algorithm outperforms traditional ADMM and its variants in terms of convergence speed and solution accuracy.

This paper studies the classic price-based network revenue management (NRM) problem with demand learning. The retailer dynamically decides prices of n products over a finite selling season (of length T) subject to m resource constraints, with the purpose of maximizing the cumulative revenue. In this paper, we focus on a nonparametric demand model with some mild technical assumptions which are satisfied by most of the commonly used demand functions. We propose a robust ellipsoid method adapted to the NRM setting in a nontrivial manner. This is the first result which achieves the regret of the form [Formula: see text] (where [Formula: see text] is a polynomial function of [Formula: see text]) in the current literature on the nonparametric NRM problem. Funding: S. Miao gratefully acknowledges financial support provided by the Ruegg Family Scholar and the Leeds School of Business. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2022.0086 .

Abstract We reconsider the foundations of expected utility without assuming the linearity of the independence axiom. We consider a decision-maker who cancels out common outcomes when comparing a pair of lotteries with the same probability tree. We show that if the decision-maker is consistent with first-order stochastic dominance or topological continuity in weak convergence, then the decision-maker is an expected utility maximizer. First, this offers a simple method to differentiate behavior between prospect theory, canceling out common outcomes, and cumulative prospect theory, satisfying first-order stochastic dominance. Second, this offers a novel method to test technical continuity assumptions based on their behavioral content.

The Robbins-Monro algorithm is a recursive, simulation-based stochastic procedure to approximate the zeros of a function that can be written as an expectation. It is known that under some technical assumptions, Gaussian limit theorems approximate the stochastic performance of the algorithm. Here, we are interested in strong approximations for Robbins-Monro procedures. The main tool for getting them are local limit theorems, that is, studying the convergence of the density of the algorithm. The analysis relies on a version of parametrix techniques for Markov chains converging to diffusions. The main difficulty that arises here is the fact that the drift is unbounded.

Abstract Consider a non-relativistic quantum particle with wave function inside a region $$\Omega \subset \mathbb {R}^3$$ Ω ⊂ R 3 , and suppose that detectors are placed along the boundary $$\partial \Omega $$ ∂ Ω . The question how to compute the probability distribution of the time at which the detector surface registers the particle boils down to finding a reasonable mathematical definition of an ideal detecting surface; a particularly convincing definition, called the absorbing boundary rule, involves a time evolution for the particle’s wave function $$\psi $$ ψ expressed by a Schrödinger equation in $$\Omega $$ Ω together with an “absorbing” boundary condition on $$\partial \Omega $$ ∂ Ω first considered by Werner in 1987, viz., $$\partial \psi /\partial n=i\kappa \psi $$ ∂ ψ / ∂ n = i κ ψ with $$\kappa >0$$ κ > 0 and $$\partial /\partial n$$ ∂ / ∂ n the normal derivative. We provide here a discussion of the rigorous mathematical foundation of this rule. First, for the viability of the rule it plays a crucial role that these two equations together uniquely define the time evolution of $$\psi $$ ψ ; we point out here how, under some technical assumptions on the regularity (i.e., smoothness) of the detecting surface, the Lumer-Phillips theorem implies that the time evolution is well defined and given by a contraction semigroup. Second, we show that the collapse required for the N-particle version of the problem is well defined. We also prove that the joint distribution of the detection times and places, according to the absorbing boundary rule, is governed by a positive-operator-valued measure.

Abstract In the setting of product systems over group-embeddable monoids, we consider nuclearity of the associated Toeplitz C*-algebra in relation to nuclearity of the coefficient algebra. Our work goes beyond the known cases of single correspondences and compactly aligned product systems over right least common multiple (LCM) monoids. Specifically, given a product system over a submonoid of a group, we show, under technical assumptions, that the fixed-point algebra of the gauge action is nuclear if and only if the coefficient algebra is nuclear; when the group is amenable, we conclude that this happens if and only if the Toeplitz algebra itself is nuclear. Our main results imply that nuclearity of the Toeplitz algebra is equivalent to nuclearity of the coefficient algebra for every full product system of Hilbert bimodules over abelian monoids, over $ax+b$ -monoids of integral domains and over Baumslag–Solitar monoids $BS^+(m,n)$ that admit an amenable embedding, which we provide for m and n relatively prime.

Motivated by the human-machine interaction such as recommending videos for improving customer engagement, we study human-guided human-machine interaction for decision making with private information. We model this interaction as a two-player turn-based game, where one player (Bob, a human) guides the other player (Alice, a machine) toward a common goal. Specifically, we focus on offline reinforcement learning (RL) in this game, where the goal is to find a policy pair for Alice and Bob that maximizes their expected total rewards based on an offline data set collected a priori. The offline setting presents two challenges: (i) We cannot collect Bob’s private information, leading to a confounding bias when using standard RL methods, and (ii) there is a distributional mismatch between the behavior policy used to collect data and the desired optimal policy we aim to learn. To tackle the confounding bias, we treat Bob’s previous action as an instrumental variable for Alice’s current decision making to adjust for the unmeasured confounding. We establish a novel identification result and propose a new off-policy evaluation (OPE) method for evaluating policy pairs in this two-player turn-based game. To tackle the distributional mismatch, we leverage the idea of pessimism and use our OPE method to develop an off-policy policy learning algorithm for finding a desirable policy pair for both Alice and Bob. Moreover, we prove that under some technical assumptions, the policy pair obtained through our method converges to the optimal one at a satisfactory rate. Finally, we conduct a simulation study to demonstrate the performance of the proposed method. This paper was accepted by Nicolas Stier for the Special Issue on the Human-Algorithm Connection. Funding: L. Wang’s research is partially supported by the National Science Foundation [Grant FRGMS-1952373]. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.04112 .

We estimate a medium-scale DSGE model for the euro area from the introduction of the euro to mid-2023, testing for indeterminacy in monetary policy. While the indeterminacy model occasionally fits the data better, this result is fragile and sensitive to technical assumptions, particularly the correlation between sunspot and fundamental shocks. Additionally, inflation responses under indeterminacy contradict standard economic theory and empirical evidence. In contrast, the determinacy model aligns with both theory and empirical data, particularly in response to monetary policy shocks. Our findings support the view that monetary policy in the euro area has been sufficiently active to stabilize inflation and prevent self-fulfilling inflationary expectations.

Public transit subsidization often suffers from a double moral hazard problem, wherein both regulators and operators may reduce their efforts due to information asymmetry, thereby compromising service quality despite significant public investment. This paper develops a continuous-time principal-agent model to investigate optimal subsidy contract design under such conditions, where both parties exert costly, unobservable efforts that jointly determine stochastic service outcomes. Using stochastic dynamic programming and exponential utility functions, we derive closed-form solutions for the optimal contracts.Our analysis yields three key findings. First, under standard technical assumptions, the optimal subsidy contract takes a simple linear form based on final service quality, facilitating practical implementation. Second, the contract’s incentive intensity decreases with environmental uncertainty, highlighting a fundamental trade-off between risk-sharing and effort inducement. Third, a unique and mutually agreeable contract emerges as the parties’ risk preferences and productivity levels converge.This study extends the classic principal-agent framework by incorporating bilateral moral hazard in a dynamic setting, offering new theoretical insights into public-sector contract design. For policymakers, the results suggest that performance-based subsidies should be calibrated to account for operational uncertainty, and that regulators play an active role beyond mere funding. The proposed framework provides actionable guidance for designing effective, incentive-compatible subsidies to enhance public transit service delivery.

The coal-to-nuclear strategy offers a promising pathway for decarbonizing Colombia’s electricity sector while improving system reliability. This study evaluates the potential CO₂-equivalent (CO₂eq) emission reductions resulting from the replacement of coal-fired power plants with small modular reactors (SMRs) over the period 2035 to 2052. Two methodological approaches were used: a deterministic model based on projected installed capacities, decommissioning schedules, and fixed emission factors; and a stochastic Monte Carlo simulation incorporating uncertainty in emission rates and plant performance. The deterministic model estimates a total of 82.62 MtCO₂eq of avoided emissions, while the probabilistic approach yields a median value of 76.04 MtCO₂eq with a standard deviation of 6.58 MtCO₂eq. These consistent results across both methods demonstrate the robustness of the strategy under different technical assumptions. The findings support the viability of coal-to-nuclear replacement as a key contributor to Colombia’s climate goals. In addition to mitigating greenhouse gas emissions, the integration of SMRs could enhance grid resilience by reducing reliance on hydroelectric generation, which is vulnerable to climate variability, and by lowering local air pollution from coal combustion. The analysis underscores the importance of regulatory support and technical planning to enable the deployment of nuclear technologies as part of Colombia’s long-term energy transition.

Fan charts 2.0: Flexible forecast distributions with expert judgement

ABSTRACTThis work studies the distributionally robust evaluation of expected values over temporal data. A set of alternative measures is characterized by the causal optimal transport. We prove the strong duality and recast the causality constraint as minimization over an infinite‐dimensional test function space. We approximate test functions by neural networks and prove the sample complexity with Rademacher complexity. An example is given to validate the feasibility of technical assumptions. Moreover, when structural information is available to further restrict the ambiguity set, we prove the dual formulation and provide efficient optimization methods. Our framework outperforms the classic counterparts in the distributionally robust portfolio selection problem. The connection with the naive strategy is also investigated numerically.