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Effective yet farmer accepted water allocation policies are paramount for sustainability, but most water allocation models often fail in practice. Predicated on assumptions of perfect rationality and single‐level optimization, these models neglect farmers' psychological decision‐making processes (e.g., loss aversion), leading to low acceptance and resulting implementation challenges. To bridge this critical gap, this paper introduces a novel framework that enhances farmers' acceptance of water allocation schemes. This framework integrates Prospect Theory (PT) to model farmers' behavioral responses to gains and losses, combined with Bi‐level Multi‐objective Programming (BLMOP) to capture hierarchical interactions between water managers and farmers. By embedding PT‐derived reference points into BLMOP objective functions, we translate psychological thresholds into actionable allocation rules. Application in China's Heihe River Basin demonstrates how the framework aligns with farmers' psychological reference points to achieve behavioral efficacy, enhances water‐use efficiency through optimized allocation, and boosts stakeholder satisfaction via bi‐level coordination. Results indicated: (a) PT quantification reveals key psychological thresholds (e.g., 1.98 × 105 CNY/ha prospect return for economic crops), identifying scenarios that best align with farmers' expectations; (b) Incorporating PT insights under variable flows reduced total allocation by 1.80 × 105 m3, demonstrating efficiency improvements driven by behavioral insights, while increasing composite planting satisfaction by 15.52%; and (c) The PT‐BLMOP framework outperformed benchmarks, achieving the highest comprehensive satisfaction index (0.67) and increasing total crop prospect returns by 289 million CNY. The findings support the transferability of this framework for developing high‐adoption, behaviorally informed water policies aimed at water‐scarce agricultural systems.

Incorporating water footprints and virtual water into crop redistribution provides a new approach for efficient water resources utilization and synergistic development of water surplus and scarce regions. In this work, the absolute and comparative advantage of the production‐based blue and gray water footprint (PWFblue and PWFgray), the calorie‐based blue water footprint (CWFblue) and the net benefit‐based blue water footprint (NBWFblue) were used as coefficients to establish a bi‐level crop redistribution model. The mode considers upper‐level decision makers interested in maximizing food security and ecological security and lower‐level decision makers interested in water use efficiency, water use benefits and net benefits. The model was applied in the Hetao Irrigation District (HID), China. The results showed that after optimization, the PWFblue, CWFblue, NBWFblue, and gray water footprint (GWF) of the HID were reduced by 23.32%, 5.60%, 17.40%, and 6.67%, respectively. National benefits were improved, especially when considering synergistic optimization, although the net benefits of HID was affected. The calorie supply increased by 9.6 × 109 kcal, the GWF decreased by 8.29 × 106 m3, and water use efficiency and benefits were improved in China. In contrast, the calorie supply and the net benefits of the HID decreased, while the GWF increased. Moreover, multiple stakeholders were involved in crop redistribution and required national synergies. The bi‐level model proved more suitable than the multi‐objective model. The model proposed in this work considers synergies outside the region in crop redistribution within the region, and can provide new insight for water and soil resources management in arid and semi‐arid regions. Key Points Virtual water flow embedded in optimization model reflecting comparative advantage Absolute advantage and comparative advantage synergize interregional interests Bi‐level optimization model trade‐offs regional authority and sub‐regions

Abstract Central sleep apnea is not a single disorder; it can present as an isolated disorder or as a part of other clinical syndromes. In some conditions, such as heart failure, central apneic events are due to transient inhibition of ventilatory motor output during sleep, owing to the overlapping influences of sleep and hypocapnia. Specifically, the sleep state is associated with removal of wakefulness drive to breathe; thus, rendering ventilatory motor output dependent on the metabolic ventilatory control system, principally PaCO2. Accordingly, central apnea occurs when PaCO2 is reduced below the “apneic threshold”. Our understanding of the pathophysiology of central sleep apnea has evolved appreciably over the past decade; accordingly, in disorders such as heart failure, central apnea is viewed as a form of breathing instability, manifesting as recurrent cycles of apnea/hypopnea, alternating with hyperpnea. In other words, ventilatory control operates as a negative—feedback closed-loop system to maintain homeostasis of blood gas tensions within a relatively narrow physiologic range, principally PaCO2. Therefore, many authors have adopted the engineering concept of “loop gain” (LG) as a measure of ventilatory instability and susceptibility to central apnea. Increased LG promotes breathing instabilities in a number of medical disorders. In some other conditions, such as with use of opioids, central apnea occurs due to inhibition of rhythm generation within the brainstem. This review will address the pathogenesis, pathophysiologic classification, and the multitude of clinical conditions that are associated with central apnea, and highlight areas of uncertainty.

Abstract Modular product family architecture (PFA), in coordination with the supply chain, assists manufacturers in achieving lower costs and higher efficiency by sharing a common platform. Despite its advantages, however, the prevailing practice of PFA emphasises architectural aspects that do not focus on the interface requirements for an efficient supply chain. In particular, the individual modules and components are assumed to have equal and/or fixed connection values, thereby overlooking the impact of modularity on the supply chain architecture (SCA). Explicit considerations of alternative modular configurations can invoke changes in granularity to reduce supply chain costs. Furthermore, the general approach of SCA is predominately focused on cost without emphasising commonality, reducing the benefit of modularity. The major challenge is, therefore, to determine the optimal granularity of modules under a coherent framework of product family modularity and supply chain modularity, which are often widely different. To resolve the problem, a bi-level programming (BLP) model is proposed in which the integrated effects of commonality and cost of supply chain modularity are investigated with the architectural and interface modularity (AIM) of product design. The proposed leader–follower decision structure interactively and hierarchically optimises commonality and cost to ensure product design and supply chain coherence and integrity. The experimental results for refrigerator PFA and its supply chain reveal that the proposed integrated modularisation model saves 11.54% and 5.95% SCA cost compared with cost-based and commonality-based models. In algorithmic comparisons, the proposed nested bi-level particle swarm optimisation (NBL-PSO) shows an enhanced performance for various problem instances with lower standard deviation (design cost: 3.3% and SCA cost: 6.4%) compared with the genetic algorithm.

This paper presents a bi-level adaptive computed-current impedance controller for electrically driven robots. This study aims to reduce calculation complexities by utilizing the electrical equations of actuators, instead of the entire model of the electromechanical system. Moreover, taking the dynamical effects of mechanical parts into account through the current’s feedback, external disturbances are compensated. In order to handle uncertainties, a bi-level optimization problem is formulated to obtain guaranteed stability besides the estimation convergence. An adaptation rule and its optimal tuning gain are achieved. The proposed method is applied to control of a rehabilitation robot to evaluate its performance.

Abstract Background Continuous positive airway pressure (CPAP) and pressure support ventilation (PSV) can improve respiratory mechanics and gas exchange, but different airway pressures have not been compared in foals. Hypothesis/Objectives Assess the effect of different airway pressures during CPAP and PSV have on respiratory function in healthy foals with pharmacologically induced respiratory insufficiency. We hypothesized that increased airway pressures would improve respiratory mechanics and increased positive end-expiratory pressure (PEEP) would be associated with hypercapnia. Animals Six healthy foals from a university teaching herd. Methods A prospective, 2-phase, 2-treatment, randomized cross-over study design was used to evaluate sequential interventions in sedated foals using 2 protocols (CPAP and PSV). Outcome measures included arterial blood gases, spirometry, volumetric capnography, lung volume and aeration assessed using computed tomography (CT). Results Sedation and dorsal recumbency were associated with significant reductions in arterial oxygen pressure (PaO2), respiratory rate, and tidal volume. Continuous positive airway pressure was associated with improved PaO2, without concurrent hypercapnia. Volumetric capnography identified improved ventilation:perfusion (V/Q) matching and increased carbon dioxide elimination during ventilation, and spirometry identified decreased respiratory rate and increased tidal volume. Peak inspiratory pressure was moderately associated with PaO2 and lung volume. Improved pulmonary aeration was evident in CT images, and lung volume was increased, particularly during CPAP. Conclusions and Clinical Importance Both CPAP and PSV improved lung mechanics and gas exchange in healthy foals with induced respiratory insufficiency.

Grouping structures arise naturally in many statistical modeling problems. Several methods have been proposed for variable selection that respect grouping structure in variables. Examples include the group LASSO and several concave group selection methods. In this article, we give a selective review of group selection concerning methodological developments, theoretical properties and computational algorithms. We pay particular attention to group selection methods involving concave penalties. We address both group selection and bi-level selection methods. We describe several applications of these methods in nonparametric additive models, semiparametric regression, seemingly unrelated regressions, genomic data analysis and genome wide association studies. We also highlight some issues that require further study.

Meta-learning aims to learn general knowledge with diverse training tasks conducted from limited data, and then transfer it to new tasks. It is commonly believed that increasing task diversity will enhance the generalization ability of meta-learning models. However, this paper challenges this view through empirical and theoretical analysis. We obtain three conclusions: (i) there is no universal task sampling strategy that can guarantee the optimal performance of meta-learning models; (ii) over-constraining task diversity may incur the risk of under-fitting or over-fitting during training; and (iii) the generalization performance of meta-learning models are affected by task diversity, task entropy, and task difficulty. Based on this insight, we design a novel task sampler, called Adaptive Sampler (ASr). ASr is a plug-and-play module that can be integrated into any meta-learning framework. It dynamically adjusts task weights according to task diversity, task entropy, and task difficulty, thereby obtaining the optimal probability distribution for meta-training tasks. Finally, we conduct experiments on a series of benchmark datasets across various scenarios, and the results demonstrate that ASr has clear advantages. The code is publicly available at https://github.com/WangJingyao07/Adaptive-Sampler.

As a key component of an integrated energy system (IES), energy storage can effectively alleviate the problem of the times between energy production and consumption. Exploiting the benefits of energy storage can improve the competitiveness of multi-energy systems. This paper proposes a method for day-ahead operation optimization of a building-level integrated energy system (BIES) considering additional potential benefits of energy storage. Based on the characteristics of peak-shaving and valley-filling of energy storage, and further consideration of the changes in the system’s load and real-time electricity price, a model of additional potential benefits of energy storage is developed. Aiming at the lowest total operating cost, a bi-level optimal operational model for day-ahead operation of BIES is developed. A case analysis of different dispatch strategies verifies that the addition of the proposed battery scheduling strategy improves economic operation. The results demonstrate that the model can exploit energy storage’s potential, further optimize the power output of BIES and reduce the economic cost.

With the increasing integration of distributed renewable energy, traditional power users are evolving into prosumers capable of both generation and consumption. However, their decentralized nature poses challenges in resource coordination. This study proposes a bi‐level optimization framework for distribution networks integrating peer‐to‐peer (P2P) energy trading and shared energy storage. The upper‐level model minimizes distribution system operator (DSO) operational costs, including network losses and storage management, while ensuring voltage stability. The lower‐level model enables prosumers to maximize P2P market profits through adaptive load adjustments and shared storage utilization. To address the nonlinear, high‐dimensional optimization challenges, an improved Convex‐Soft Actor‐Critic (C‐SAC) algorithm is developed, combining deep reinforcement learning with convex optimization to achieve privacy‐preserving distributed coordination. Case studies on an IEEE 33‐node system demonstrate that the framework increases prosumer profits by 56.9%, reduces DSO costs by 23.6%, and lowers network losses by 21.5% compared to non‐cooperative scenarios. The shared storage system reduces capacity and power requirements by 20% and 14.1%, respectively. The C‐SAC algorithm outperforms traditional methods (DDPG, SAC) in convergence speed and economic metrics, showing scalability across larger systems (IEEE 69/118 nodes). This work provides a model‐free solution for renewable‐rich distribution networks, balancing efficiency and operational security. This study proposes a bi‐level optimization framework for distribution networks integrating P2P energy trading and shared storage. The upper level minimizes DSO costs (network losses, storage management) with voltage stability constraints, while the lower level optimizes prosumer profits via adaptive load/storage adjustments. An improved Convex‐SAC algorithm combining deep reinforcement learning and convex optimization addresses nonlinear, high‐dimensional challenges. Tests on an IEEE 33‐node system show a 56.9% profit rise for prosumers, 23.6% cost reduction for DSOs, and 21.5% lower network losses. Shared storage cuts capacity/power needs by 20% and 14.1%. The framework demonstrates scalability in larger systems (IEEE 69/118 nodes), offering a model‐free solution for renewable‐rich grids that balances efficiency and security.