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Wildfire occurrence and spread are affected by atmospheric and land-cover conditions, and therefore meteorological and land-cover parameters can be used in area burned prediction. We apply three forecast methods, a generalized linear model, regression trees, and neural networks (Levenberg–Marquardt backpropagation) to produce monthly wildfire predictions 1 year in advance. The models are trained using the Global Fire Emissions Database version 4 with small fires (GFEDv4s). Continuous 1-year monthly fire predictions from 2011 to 2015 are evaluated with GFEDs data for 10 major fire regions around the globe. The predictions by the neural network method are superior. The 1-year moving predictions have good prediction skills over these regions, especially over the tropics and the southern hemisphere. The temporal refined index of agreement (IOA) between predictions and GFEDv4s regional burned areas are 0.82, 0.82, 0.8, 0.75, and 0.56 for northern and southern Africa, South America, equatorial Asia and Australia, respectively. The spatial refined IOA for 5-year averaged monthly burned area range from 0.69 in low-fire months to 0.86 in high-fire months over South America, 0.3–0.93 over northern Africa, 0.69–0.93 over southern Africa, 0.47–0.85 over equatorial Asia, and 0.53–0.8 over Australia. For fire regions in the northern temperate and boreal regions, the temporal and spatial IOA between predictions and GFEDv4s data in fire seasons are 0.7–0.79 and 0.24–0.83, respectively. The predictions in high-fire months are better than low-fire months. This study illustrates the feasibility of global fire activity outlook forecasts using a neural network model and the method can be applied to quickly assess the potential effects of climate change on wildfires.

Carbonaceous aerosols significantly affect global radiative forcing and climate through absorption and the scattering of sunlight. Black carbon (BC) and brown carbon (BrC) are light-absorbing carbonaceous aerosols. The direct radiative effect (DRE) of BrC is uncertain. A recent study suggests that BrC absorption is comparable to BC in the upper troposphere over biomass burning regions and that the resulting radiative heating tends to stabilize the atmosphere. Yet current climate models do not include proper physical and chemical treatments of BrC. In this study, we derived a BrC global biomass burning emission inventory on the basis of the Global Fire Emissions Database version 4 (GFED4), developed a module to simulate the light absorption of BrC in the Community Atmosphere Model version 5 (CAM5) of the Community Earth System Model (CESM), and investigated the photobleaching effect and convective transport of BrC on the basis of Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) and Deep Convective Clouds and Chemistry Project (DC3) measurements. The model simulations of BC were also evaluated using HIAPER (High-Performance Instrumented Airborne Platform for Environmental Research) Pole-to-Pole Observations (HIPPO) measurements. We found that globally BrC is a significant absorber, the DRE of which is 0.10 W m−2, more than 25 % of BC DRE (+0.39 W m−2). Most significantly, model results indicated that BrC atmospheric heating in the tropical mid and upper troposphere is larger than that of BC. The source of tropical BrC is mainly from wildfires, which are more prevalent in the tropical regions than higher latitudes and release much more BrC relative to BC than industrial sources. While BC atmospheric heating is skewed towards the northern mid-latitude lower atmosphere, BrC heating is more centered in the tropical free troposphere. A possible mechanism for the enhanced convective transport of BrC is that hydrophobic high molecular weight BrC becomes a larger fraction of the BrC and less easily activated in a cloud as the aerosol ages. The contribution of BrC heating to the Hadley circulation and latitudinal expansion of the tropics is likely comparable to BC heating.

In this paper, we extend the adaptive partition-based approach for solving two-stage stochastic programs with fixed recourse matrix and fixed cost vector to the multistage stochastic programming setting where the stochastic process is assumed to be stage-wise independent. The proposed algorithms integrate the adaptive partition-based strategy with a popular approach for solving multistage stochastic programs, the stochastic dual dynamic programming (SDDP) algorithm, according to two main strategies. These two strategies are distinct from each other in the manner by which they refine the partitions during the solution process. In particular, we propose a refinement outside SDDP strategy whereby we iteratively solve a coarse scenario tree induced by the partitions, and refine the partitions in a separate step outside of SDDP, only when necessary. We also propose a refinement within SDDP strategy where the partitions are refined in conjunction with the machinery of the SDDP algorithm. We then use, within the two different refinement schemes, different tree-traversal strategies which allow us to have some control over the size of the partitions. We performed numerical experiments on a hydro-thermal power generation planning problem. Numerical results show the effectiveness of the proposed algorithms that use the refinement outside SDDP strategy in comparison to the standard SDDP algorithm and algorithms that use the refinement within SDDP strategy.

This paper presents a novel unsupervised domain adaptation (UDA) framework that integrates information-theoretic principles to mitigate distributional discrepancies between source and target domains. The proposed method incorporates two key components: (1) relative entropy regularization, which leverages Kullback–Leibler (KL) divergence to align the predicted label distribution of the target domain with a reference distribution derived from the source domain, thereby reducing prediction uncertainty; and (2) measure propagation, a technique that transfers probability mass from the source domain to generate pseudo-measures—estimated probabilistic representations—for the unlabeled target domain. This dual mechanism enhances both global feature alignment and semantic consistency across domains. Extensive experiments on benchmark datasets (OfficeHome and DomainNet) demonstrate that the proposed approach consistently outperforms State-of-the-Art methods, particularly in scenarios with significant domain shifts. These results confirm the robustness, scalability, and theoretical grounding of our framework, offering a new perspective on the fusion of information theory and domain adaptation.

The Asian summer monsoon (ASM) affects more than one-third of the world’s population due to its close connection with floods, droughts thus water resources in densely populated Asian countries. The effects of aerosols emitted in remote regions on the ASM, in contrast to local emissions, remain largely unclear. Here we demonstrate through a hierarchy of climate models that aerosol emissions from the central African wildfires could strengthen the circulation of the ASM (South Asian Monsoon in particular), increase precipitation over South Asia and reduce precipitation immediately north and south of it. The enhanced latent heating over South Asia provides a critical positive feedback to the initial strengthening of monsoon westerlies associated with wildfire-driven anomalous Rossby wave source. The atmospheric dynamical bridge discovered here effectively connects African biomass burning with hydroclimate variability over East Asia in boreal summer and offers a new source of monsoon predictability across a range of timescales.

The public reporting of hospitals’ quality of care is providing additional motivation for hospitals to deliver high-quality patient care. Hospital Compare, a consumer-oriented website by the Centers for Medicare and Medicaid Services (CMS), provides patients with detailed quality of care data on most US hospitals. Given that many quality metrics are the aggregate result of physicians’ individual clinical decisions, the question arises if and how hospitals could influence their physicians so that their decisions positively contribute to hospitals’ quality goals. In this paper, we develop a decision-theoretic model to explore how three different hospital interventions—incentivization, training, and nudging—may affect physicians’ decisions. We focus our analysis on Outpatient Measure 14 (OP-14), which is an imaging quality metric that reports the percentage of outpatients with a brain computed tomography (CT) scan, who also received a same-day sinus CT scan. In most cases, same-day brain and sinus CT scans are considered unnecessary, and high utilizing hospitals aim to reduce their OP-14 metric. Our model captures the physicians’ imaging decision process accounting for medical and behavioral factors, in particular the uncertainty in clinical assessment and a physician’s diagnostic ability. Our analysis shows how hospital interventions of incentivization, training, and nudging affect physician decisions and consequently OP-14. This decision-theoretic model provides a foundation to develop insights for policy makers on the multi-level effects of their policy decisions.

Total knee arthroplasty (TKA) is a widely-used treatment for end-stage knee osteoarthritis. However, it is common for patients to experience issues with knee joint function and abnormal gait following the surgery. Previous studies have primarily focused on concentric contraction of the quadriceps during TKA, neglecting the potential benefits of eccentric isokinetic training for the hamstrings. This protocol outlines a randomized, single-blind, controlled trial aimed at assessing the impact of eccentric isokinetic training for the hamstring muscles on pain, function, and gait in patients after TKA. Fifty participants between the ages of 50 and 80 with knee osteoarthritis undergo unilateral total knee arthroplasty (TKA) for the first time. They will be transferred to the rehabilitation department 10-14 days after the operation. The participants are randomly divided into two groups, with 25 participants in each group: the control group and the Hamstring training group. The Control group will receive routine rehabilitation treatment, while the Hamstring training group will receive a combination of routine rehabilitation treatment and isokinetic eccentric training of the hamstring. The intervention will last four consecutive weeks. Both groups will be assessed at three different times: before the intervention, after 4 weeks of intervention, and 4 weeks after the interventions (follow-up). The primary outcome will be functional capacity (TUGT) and Hospital for Special knee Score (HSS). Secondary outcomes will be knee-related health status (isokinetic knee position perception, Peak torque of hamstring strength), pain intensity (Visual analog scale, VAS) and 3D gait analysis. The study aims to provide relevant evidence on the effectiveness of eccentric hamstring muscle contraction training in improving knee joint function and walking function after TKA. https://www.chictr.org.cn/showproj.html?proj=195544, Identifier ChiCTR2300073497.

Capacitor voltage transformer (hereinafter referred to as CVT) with the growth of the capacitance of the operation period of aging, the phenomenon of breakdown, resulting in measurement, automation, protection and other equipment abnormalities. Under the influence of preventive test procedures, lack of accuracy, insufficient capacity, low test voltage, the preventive test project can not detect the initial failure of the capacitor and the breakdown of a few capacitor elements. The operating voltage as reference voltage, estimate feasibility analysis of CVT capacitor element of the state by the state of the secondary voltage, and through the field, find out more abnormal CVT can achieve CVT capacitor element of online monitoring function is proposed.

In the aftermath of a hurricane, humanitarian logistics plays a critical role in delivering relief items to the affected areas in a timely fashion. This paper proposes a novel stochastic look-ahead framework that implements a two-stage stochastic programming model in a rolling horizon approach to address the evolving uncertain logistics system state during the post-hurricane humanitarian logistics operations. The two-stage stochastic programming model that executes in this rolling horizon approach is formulated as a mixed-integer programming problem. The model aims to minimize the total cost incurred in the logistics operations, which consist of transportation cost and social cost. The social cost is measured as a function of deprivation for unsatisfied demand. Our extensive numerical results and sensitivity analysis demonstrate the effectiveness of the proposed approach in reducing the total cost incurred during the post-hurricane relief logistics operations compared to the two-stage stochastic programming model implemented in a static fashion.

In this paper, we propose partition-based decomposition algorithms for solving two-stage stochastic integer program with continuous recourse. The partition-based decomposition method enhance the classical decomposition methods (such as Benders decomposition) by utilizing the inexact cuts (coarse cuts) induced by a scenario partition. Coarse cut generation can be much less expensive than the standard Benders cuts, when the partition size is relatively small compared to the total number of scenarios. We conduct an extensive computational study to illustrate the advantage of the proposed partition-based decomposition algorithms compared with the state-of-the-art approaches.