Feasibility Constraints Research Articles

Illicit Resilience: Revisiting Political Ecologies of Conservation Noncompliance in the Context of the Kunming-Montreal Global Biodiversity Framework

Over the past several decades, the spatial extent of national parks, forest reserves, and other protected areas has expanded rapidly, encompassing more than 16 percent of the Earth’s terrestrial and inland water area in 2024. This expansion of protected areas has often led to population displacements and heightened restrictions on access to land and natural resources for indigenous peoples and other local communities, at times precipitating various conflicts, contestations, and broader forms of noncompliance with conservation regulations. Despite ongoing challenges in resolving trade-offs between conservation and livelihood objectives, the recently established Kunming-Montreal Global Biodiversity Framework (GBF) seeks to achieve even more ambitious conservation targets. These include GBF Target 3—sometimes referred to as the “30 × 30” target—of encompassing 30 percent of all terrestrial and marine ecosystems in protected areas or other effective area-based conservation measures by 2030. The GBF’s vision for achieving Target 3 implicitly assumes, however, that widespread challenges related to noncompliance in conservation governance can largely be resolved in their present manifestation(s), and prevented from reemerging as the scale of the global conservation estate increases. Seeking to deepen our understanding of the emergence and recurrence of illicit or otherwise non-rule-complying behaviors in everyday practices of conservation governance, we draw on a case study of the Manas Biosphere Reserve, a UNESCO Man and Biosphere Reserve in the India-Bhutan borderlands region, northeastern India. Highlighting key findings from this case study, we foreground the concept of illicit resilience to encompass a suite of informal factors and dynamics that lead to the persistence of de facto land uses and management regimes in ways that often strikingly diverge from de jure prescriptions for resource conservation within protected areas. These dynamics include (1) long-term insurgency and political unrest; (2) collusive corruption, patronage, and local politics; and (3) elite-driven processes of informal resettlement and subsequent occupation of forestland. In short, we argue that a deeper understanding of such forms of illicit resilience and their underlying drivers will be crucial for more accurately conceptualizing both feasibility constraints on, and socioenvironmental justice implications of, efforts to rapidly upscale the spatial remit of conservation areas by 2030 and beyond.

Stability vs. no justified envy

Stability and “no justified envy” are used almost synonymously in the matching theory literature. However, they are conceptually different and have logically separate properties. We generalize the definition of justified envy to environments with arbitrary school preferences, feasibility constraints, and contracts, and show that stable allocations may admit justified envy. When choice functions are substitutable, the outcome of the deferred acceptance algorithm is both stable and admits no justified envy.

Contrastive domain-invariant generalization for remaining useful life prediction under diverse conditions and fault modes

As industrial equipment becomes increasingly complex, necessitating operation under varied conditions and often exhibiting diverse failure modes, traditional deep learning models built on data from the original environment become inapplicable. Moreover, in actual industrial scenarios, the generalization capability of Domain Adaptation and classic Domain Generalization methods is severely impacted when there is a lack of multiple source domain and target domain data, due to the cost or feasibility constraints associated with collecting extensive monitoring data. In this paper, a single domain Contrastive Domain-Invariant Generalization (CDIG) method for estimating the remaining useful life under different conditions and fault modes is proposed. This method first defines homologous signals as the foundational data. Subsequently, it learns domain-invariant features by encouraging two feature extraction processes to extract latent features of homologous signals as similarly as possible. Additionally, multiple condition-based attention, pooling, and a novel equalization loss function are utilized to regulate the generation of domain-invariant features. Ultimately, the RUL predictor is trained by source domain data, operational conditions, and temporal information to facilitate its applicability across diverse domains. Case studies demonstrate that CDIG achieves satisfactory predictive results under unseen conditions, highlighting the potential of the proposed method as an effective predictive tool.

Asynchronous time dependent switched model predictive control for nonlinear systems considering feasibility constraints

AbstractThis paper presents a model predictive control for nonlinear asynchronous switched systems, aiming to achieve H∞ performance and addressing feasibility issues. The method proposed guarantees the recursive feasibility of each sub‐system after switching moments in the presence of exogenous disturbances and unknown matched and mismatched time intervals due to asynchronous switching. The boundaries of the feasibility region for each sub‐system are determined in order to impose constraints on decision parameters and switching signals. These constraints aim to maximize the mentioned region while ensuring stability. Additionally, by identifying the common region of feasibility among different sub‐systems, conditions are established for initializing the sub‐systems at the switching moments to ensure that the system states remain within this feasibility region even under the worst switching scenario. Therefore, by ensuring the initial feasibility at the switching moments and demonstrating recursive feasibility, the overall feasibility of the developed MPC is guaranteed at all times. The benefits of the proposed approach are thoroughly investigated through the simulation of a chemical system. The results demonstrate the effectiveness of the proposed method.

Fuzzy adaptive finite-time formation control of unmanned ground vehicles with performance and feasibility constraints

This paper investigates the problem of fuzzy adaptive finite-time formation tracking control for unmanned ground vehicles (UGVs) systems with two different constraints. By utilizing the distance tracking error between the actual trajectory and the desired trajectory of each UGV, as well as the constraints of bearing angle, to achieve the performance and feasibility constraints, respectively. Furthermore, the fuzzy logic systems (FLSs) are used to approximate unknown nonlinear functions. Due to the limitations of the field of view and communication distance of UGVs, the controller is designed by using finite time stability theory and universal barrier function. Under the proposed control scheme, the stability of the closed-loop system can be ensured, feasibility and performance constraints are also achieved. Finally, the effectiveness of the proposed control design is verified through simulation results.

Feasible deployment of carbon capture and storage and the requirements of climate targets

Climate change mitigation requires the large-scale deployment of carbon capture and storage (CCS). Recent plans indicate an eight-fold increase in CCS capacity by 2030, yet the feasibility of CCS expansion is debated. Using historical growth of CCS and other policy-driven technologies, we show that if plans double between 2023 and 2025 and their failure rates decrease by half, CCS could reach 0.37 GtCO2 yr−1 by 2030—lower than most 1.5 °C pathways but higher than most 2 °C pathways. Staying on-track to 2 °C would require that in 2030–2040 CCS accelerates at least as fast as wind power did in the 2000s, and that after 2040, it grows faster than nuclear power did in the 1970s to 1980s. Only 10% of mitigation pathways meet these feasibility constraints, and virtually all of them depict <600 GtCO2 captured and stored by 2100. Relaxing the constraints by assuming no failures of CCS plans and growth as fast as flue-gas desulfurization would approximately double this amount.

Feasibility Checking and Constraint Refinement for Shared Control in Assistive Robotics

Feasibility Checking and Constraint Refinement for Shared Control in Assistive Robotics

The role of photovoltaic technology in achieving net zero energy target in residential buildings

The construction sector plays a pivotal role in reducing carbon emissions, facilitating the transition to renewable energy and promoting sustainable development. In the European Union, where decarbonization strategies are currently proving most incisive, legislative measures such as the Energy Performance of Buildings Directive (EPBD) have catalyzed a marked increase in the prevalence of energy-efficient buildings. In terms of decarbonization, such efforts can be achieved by massively applying Renewable Energy Sources (RES) both in new buildings as well as in existing ones, which represent the majority of the EU building stock. For such reasons, Building-Integrated/Applied Photovoltaic (BIPV/BAPV) systems offer a promising avenue for providing clean energy to the building sector. Nonetheless, despite their potential, the practical implementation of these systems on buildings faces feasibility constraints, mainly due to the limitation of the available installation surface.In such a framework, this study conducts a comprehensive evaluation of BIPV/BAPV installations potential to fulfill the electricity needs of residential buildings (i.e. achieve the net zero energy target), with a particular focus on multi-family structures within the European contest. A reference high-performance building, already assessed by the authors and therefore very well documented, was used as a test bed through detailed dynamic energy simulations. According to obtained results, the net zero energy target can be achieved for energy-efficient buildings with up to 3 and 5 storeys, in Nordic and Mediterranean climatic conditions, respectively. Assuming instead reference maximum consumption thresholds (resulting in higher energy consumption), achievable results are more restrictive, since zero-energy buildings are limited to a maximum of less than 2 storeys in Nordic climates and 3 in Mediterranean climates.In this particular historical period, the outlined results take on considerable relevance, as the highlighted feasibility limitations could jeopardise the implementation of zero emission targets, but they can also be used to properly orient energy policies under development.

Interactive CAD Layout of Reflection-Based Mirror Metrology Systems

Concentrating solar mirrors need to accurately focus sunlight onto a receiver. Since mirror surface normal determines reflected light direction, reflection-based metrology methods that measure surface normal have the advantage of directly measuring the mirror’s intended functional performance characteristic. Several methods have been reported that produce high-resolution measurements of mirror surface normal of concentrating solar mirrors [1-8]; these systems typically employ an optical target, a camera, and sometimes an external light source. These elements must be placed in a configuration that satisfies multiple feasibility constraints, including mechanical and optical visibility and reflection constraints. For complex problems or installations in cluttered environments, it can be difficult to analyze all of these constraints manually. We have created a computer automated design (CAD) tool that aids in visualization of reflection-based metrology systems, making it possible to determine layouts for complex metrology design problems. The tool is implemented SolidWorks, a commercial CAD system [9]. By exploiting the SoidWorks constraint satisfaction mechanism, the tool is able to present interactive visualization of reflection and field of view constraints in real time without any additional code or macros. The tool supports ease of use and multiple design configurations through an ordinary spreadsheet interface. The tool can be used to address a variety of reflection-based metrology design problems, and is available to the public as open source.

Regulating Misinformation: Political Irrationality as a Feasibility Constraint

AbstractThis paper argues that the well-established fact of political irrationality imposes substantial constraints on how governments may combat the threat of political misinformation. Though attempts at regulating misinformation are becoming increasingly popular, both among policymakers and theorists, I intend to show that, for a wide range of anti-misinformation interventions (collectively termed “debunking” and “source labeling”), these attempts ought to be abandoned. My argument relies primarily on the fact that most people process politically-relevant information in biased and motivated ways. Since debunking or factual correction of politically relevant misinformation (as well as source labeling) themselves consist of providing politically-relevant information, they are also very likely to be processed in irrational ways. This makes it extremely difficult to effectively correct people’s political beliefs and political information processing. Since governments should not pursue policies likely to be futile, they should refrain from mandating such interventions. My conclusion is of relevance to considerable literature in digital ethics of misinformation. It shows that many celebrated works in the field ignore political irrationality and fail to consider its implications.

Feasibility of peak temperature targets in light of institutional constraints

Despite faster-than-expected progress in clean energy technology deployment, global annual CO2 emissions have increased from 2020 to 2023. The feasibility of limiting warming to 1.5 °C is therefore questioned. Here we present a model intercomparison study that accounts for emissions trends until 2023 and compares cost-effective scenarios to alternative scenarios with institutional, geophysical and technological feasibility constraints and enablers informed by previous literature. Our results show that the most ambitious mitigation trajectories with updated climate information still manage to limit peak warming to below 1.6 °C (‘low overshoot’) with around 50% likelihood. However, feasibility constraints, especially in the institutional dimension, decrease this maximum likelihood considerably to 5–45%. Accelerated energy demand transformation can reduce costs for staying below 2 °C but have only a limited impact on further increasing the likelihood of limiting warming to 1.6 °C. Our study helps to establish a new benchmark of mitigation scenarios that goes beyond the dominant cost-effective scenario design.

Step-by-Step Description of Percutaneous Full-Endoscopic C2 Ganglionectomy: An Anatomic Feasibility Study in Human Cadavers.

The percutaneous full-endoscopic C2 ganglionectomy (PEC2G), an innovative procedure developed for the surgical treatment of intractable occipital neuralgia, was firstly reported by us in 2021. However, a universally accepted and well-articulated protocol modality remains elusive. The primary objective of this anatomic investigation was to meticulously elucidate the standard procedural steps of PEC2G and assess the anatomic features supporting the safe implementation of PEC2G. Eighteen fresh adult cadavers were incorporated into this study. From this sample, 3 cadavers were subjected to bilateral PEC2G. Each procedure was documented and assessed, leading to the formulation of standard procedure criteria for PEC2G. Subsequently, 10 sets of anatomic parameters pertinent to this procedure were identified, quantified, and analyzed in 15 cadavers after complete bilateral endoscopic exposure of the C2 ganglion. An assessment of the technical feasibility and potential constraints associated with PEC2G was conducted, providing invaluable insights into the procedure's anatomic considerations. All 3 cadavers successfully underwent the PEC2G without any observed complications, such as dura tears or vertebral artery injuries. The C2 inferior articular process emerged as the optimal bony target for puncture, with the C2 pedicle serving as the standard guiding landmark en route to the C2 ganglion. In the 15 cadavers subjected to the planned procedure, 10 sets of anatomic parameters were quantified, establishing a foundational understanding of the anatomy in the context of PEC2G procedure. The results demonstrated that the characteristic of anatomic data pertinent to surgical site supported the safe implementation of PEC2G. This study contributes the standard surgical steps and crucial anatomic parameters relevant to PEC2G. The characteristic of anatomic data bolsters the safety credentials of this technique, which offers a reliable approach to achieve C2 ganglionectomy. These insights undeniably establish a robust foundation for the ongoing refinement and broader adoption of PEC2G.

Can Conversational Receptiveness Build Trust in the Media?

Trust in nonpartisan news is essential to civil society—but is declining in the United States. However, language that demonstrates active engagement with opposing views may build trust. One way to demonstrate such active engagement is conversational receptiveness: the use of linguistic features such as agreement, acknowledgment, subjectivity, and positive emotion, among others. A review of prior work on conversational receptiveness suggests its usefulness in interpersonal conflict. This toolkit might effectively apply also to the challenge of restoring trust in nonpartisan media. A demonstration study illustrates proof of concept: In 600 opinion articles from prominent news sources, more receptive language was associated with reader trust. Pending programmatic research will address limitations, feasibility constraints, open questions, and future empirical directions—including causal tests in applied settings. At a minimum, extrapolating conversational receptivity from its role in interpersonal conflict suggests a role in building trust in nonpartisan media. Conversational receptiveness might present a cost-effective, scalable approach for media producers to bridge political divides and rebuild trust—without alienating existing audiences.

Supervised machine learning to predict smoking lapses from Ecological Momentary Assessments and sensor data: Implications for just-in-time adaptive intervention development.

Specific moments of lapse among smokers attempting to quit often lead to full relapse, which highlights a need for interventions that target lapses before they might occur, such as just-in-time adaptive interventions (JITAIs). To inform the decision points and tailoring variables of a lapse prevention JITAI, we trained and tested supervised machine learning algorithms that use Ecological Momentary Assessments (EMAs) and wearable sensor data of potential lapse triggers and lapse incidence. We aimed to identify a best-performing and feasible algorithm to take forwards in a JITAI. For 10 days, adult smokers attempting to quit were asked to complete 16 hourly EMAs/day assessing cravings, mood, activity, social context, physical context, and lapse incidence, and to wear a Fitbit Charge 4 during waking hours to passively collect data on steps and heart rate. A series of group-level supervised machine learning algorithms (e.g., Random Forest, XGBoost) were trained and tested, without and with the sensor data. Their ability to predict lapses for out-of-sample (i) observations and (ii) individuals were evaluated. Next, a series of individual-level and hybrid (i.e., group- and individual-level) algorithms were trained and tested. Participants (N = 38) responded to 6,124 EMAs (with 6.9% of responses reporting a lapse). Without sensor data, the best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.899 (95% CI = 0.871-0.928). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUCper person = 0.524-0.994; median AUC = 0.639). 15/38 participants had adequate data for individual-level algorithms to be constructed, with a median AUC of 0.855 (range: 0.451-1.000). Hybrid algorithms could be constructed for 25/38 participants, with a median AUC of 0.692 (range: 0.523 to 0.998). With sensor data, the best-performing group-level algorithm had an AUC of 0.952 (95% CI = 0.933-0.970). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUCper person = 0.494-0.979; median AUC = 0.745). 11/30 participants had adequate data for individual-level algorithms to be constructed, with a median AUC of 0.983 (range: 0.549-1.000). Hybrid algorithms could be constructed for 20/30 participants, with a median AUC of 0.772 (range: 0.444 to 0.968). In conclusion, high-performing group-level lapse prediction algorithms without and with sensor data had variable performance when applied to out-of-sample individuals. Individual-level and hybrid algorithms could be constructed for a limited number of individuals but had improved performance, particularly when incorporating sensor data for participants with sufficient wear time. Feasibility constraints and the need to balance multiple success criteria in the JITAI development and implementation process are discussed.

Distributed fixed-time control for high-order multi-agent systems with FTESO and feasibility constraints

In this study, a fixed-time distributed anti-disturbance control strategy is designed for a class of higher-order multi-agent systems in an undirected static topology. Inspired by the existing literature, the strategy introduces a unique way of defining neighborhood errors during the design process to deal with the connectivity maintenance and collision avoidance problems among the neighbor agents in the system. The control strategy not only solves the full state constraint and transient steady state performance constraint control problems simultaneously, but also considers the effects of internal uncertainties and external disturbances of the system, which enhances the difficulty of controller design. To address these challenges, a fixed-time extended state observer is introduced to obtain the estimation of uncertainty and disturbance . Subsequently, an integrated backstepping control scheme is designed to feed-forward compensate for uncertainties and disturbances, while constraining the transient steady-state performance of the system to remain within a predefined performance boundaries. This is achieved by combining the fixed-time prescribed performance and the barrier Lyapunov function. Finally, a simulation case is presented to verify the effectiveness of the designed control strategy.

Research on motion planning system for wall-climbing mobile manipulator for large steel structures welding operation

Purpose The purpose of this study is to address the welding demands within large steel structures by presenting a global spatial motion planning algorithm for a mobile manipulator. This algorithm is based on an independently developed wall-climbing robot, which comprises a four-wheeled climbing mobile platform and a six-degree-of-freedom robotic manipulator, ensuring high mobility and operational flexibility. Design/methodology/approach A convex hull feasible domain constraint is developed for motion planning in the mobile manipulator. For extensive spatial movements, connected sequences of convex polyhedra are established between the composite robot’s initial and target states. The composite robot’s path and obstacle avoidance optimization problem are solved by constraining the control points on B-spline curves. A dynamic spatial constraint rapidlye-xploring random trees-connect (RRTC) motion planning algorithm is proposed for the manipulator, which quickly generates reference paths using spherical spatial constraints at the manipulator’s end, eliminating the need for complex nonconvex constraint modeling. Findings Experimental results show that the proposed motion planning algorithm achieves optimal paths that meet task constraints, significantly reducing computation times in task conditions and shortening operation times in non-task conditions. Originality/value The algorithm proposed in this paper holds certain application value for the realization of automated welding operations within large steel structures using mobile manipulator.

A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints

Controllable Pareto front learning (CPFL) approximates the Pareto optimal solution set and then locates a non-dominated point with respect to a given reference vector. However, decision-maker objectives were limited to a constraint region in practice, so instead of training on the entire decision space, we only trained on the constraint region. Controllable Pareto front learning with Split Feasibility Constraints (SFC) is a way to find the best Pareto solutions to a split multi-objective optimization problem that meets certain constraints. In the previous study, CPFL used a Hypernetwork model comprising multi-layer perceptron (Hyper-MLP) blocks. Transformer can be more effective than previous architectures on numerous modern deep learning tasks in certain situations due to their distinctive advantages. Therefore, we have developed a hyper-transformer (Hyper-Trans) model for CPFL with SFC. We use the theory of universal approximation for the sequence-to-sequence function to show that the Hyper-Trans model makes MED errors smaller in computational experiments than the Hyper-MLP model.

GCAM–GLORY v1.0: representing global reservoir water storage in a multi-sector human–Earth system model

Abstract. Reservoirs play a significant role in modifying the spatiotemporal availability of surface water to meet multi-sector human demands, despite representing a relatively small fraction of the global water budget. Yet the integrated modeling frameworks that explore the interactions among climate, land, energy, water, and socioeconomic systems at a global scale often contain limited representations of water storage dynamics that incorporate feedbacks from other systems. In this study, we implement a representation of water storage in the Global Change Analysis Model (GCAM) to enable the exploration of the future role (e.g., expansion) of reservoir water storage globally in meeting demands for, and evolving in response to interactions with, the climate, land, and energy systems. GCAM represents 235 global water basins, operates at 5-year time steps, and uses supply curves to capture economic competition among renewable water (now including reservoirs), non-renewable groundwater, and desalination. Our approach consists of developing the GLObal Reservoir Yield (GLORY) model, which uses a linear programming (LP)-based optimization algorithm and dynamically linking GLORY with GCAM. The new coupled GCAM–GLORY approach improves the representation of reservoir water storage in GCAM in several ways. First, the GLORY model identifies the cost of supplying increasing levels of water supply from reservoir storage by considering regional physical and economic factors, such as evolving monthly reservoir inflows and demands, and the leveled cost of constructing additional reservoir storage capacity. Second, by passing those costs to GCAM, GLORY enables the exploration of future regional reservoir expansion pathways and their response to climate and socioeconomic drivers. To guide the model toward reasonable reservoir expansion pathways, GLORY applies a diverse array of feasibility constraints related to protected land, population, water sources, and cropland. Finally, the GLORY–GCAM feedback loop allows evolving water demands from GCAM to inform GLORY, resulting in an updated supply curve at each time step, thus enabling GCAM to establish a more meaningful economic value of water. This study improves our understanding of the sensitivity of reservoir water supply to multiple physical and economic dimensions, such as sub-annual variations in climate conditions and human water demands, especially for basins experiencing socioeconomic droughts.

Equality‐embedded augmented Lagrangian neural network for DC optimal power flow

AbstractDirect current optimal power flow (DC‐OPF) problems need to be solved more frequently to maintain safety and economic power system operation. Traditional solvers take too much time to get optimal results. To overcome it, a new self‐supervised augmented Lagrangian neural network (ALNN) is proposed to solve DC‐OPF problem. The proposed ALNN consists of two neural networks: the control net and the penalty net. The control net predicts active power of generators; the penalty net updates the Lagrangian multipliers. The equality constraints are embedded into the control net to guarantee no equality violations. The generalized reduced gradient method is used to reduce theviolations of inequality constraint. The effectiveness of the proposed model is demonstrated on IEEE 118‐bus. The results show that with the help of equality embedding, the equality constraints are always satisfied, which in turn improves the feasibility of ALNN. Compared to the state‐of‐art models, the proposed model has higher feasibility and less constraint violations without comprising optimality. What is more, most of the inactive constraints can be found during the training process and then they are used to speed up the post‐processing part.

Ukraine and the opportunity costs of military aid

Abstract Western states have provided billions of dollars of military aid to Ukraine to help it in its war against Russia. Yet, despite numerous discussions of the ethical issues raised by the war, there has been little discussion of the opportunity costs of this military assistance. Is it right that the West has prioritized military assistance to Ukraine, when the conflict in Ukraine is just one of numerous challenges facing the world, from forced displacement to global poverty, preventable disease to climate change? This article offers the first detailed engagement with this issue, taking seriously the nonideal feasibility constraints that states face. It firsts set out the prima facie case against assisting Ukraine militarily due to the opportunity costs. It then explores—and largely rejects—two responses: that military assistance is required to protect Ukrainian self-determination and that it is needed to stop the march of authoritarianism. It argues that the opportunity costs objection has serious force and counts against much of the provision of military assistance to Ukraine (although not all). This argument has broader implications for the extent to which states should prioritize military aid over other means of addressing global challenges.