Global Constraints Research Articles

Observationally Constrained Projections of Twenty-First-Century Regional Warming in the Extratropical Northern Hemisphere

Abstract Physically based observational constraint methods can effectively reduce uncertainty in global warming projections but have not been widely applied at regional scales. We first develop and apply multivariate linear regression models for constraining projections of surface air temperature averaged over subcontinental regions in the extratropical Northern Hemisphere, based on a set of potential constraints including climatological metrics derived from tropical and subtropical low-level cloud and global average past warming trend, as well as a set of regional climate metrics previously used in the literature. We evaluate the performance of the multivariate linear regression models based on cross-validated tests using output from phases 5 and 6 of the Coupled Model Intercomparison Projects (CMIP). We find that linear regression models using global-scale low-cloud metrics alone perform more robustly than linear regression models using the past global mean warming trend or regional climate metrics as constraints. These results, while favoring global constraints over the set of regional constraints considered, do not preclude the existence of even better regional constraints for particular regions. Through model-based cross-validation, the projections constrained using low-level cloud metrics exhibit more accurate best estimate projections, narrower uncertainty ranges, and more reliable uncertainty estimates in most Northern Hemisphere regions when compared with unconstrained projections. Application of the approach to climate projections based on both Shared Socioeconomic Pathway (SSP) 1-2.6 and SSP5-8.5 using observed low-cloud metrics results in considerably narrower 5%–95% uncertainty ranges of twenty-first-century warming over subcontinental Northern Hemisphere land regions compared to unconstrained projections.

Isogeometric topology optimization for infill designs of porous structures with stress minimization in additive manufacturing

AbstractPorous structures by additive manufacturing have fascinating and compelling performance compared with solid structures. The stress‐related porous infill designs which could greatly mitigate the effects of the intrinsic high residual stress in additive manufacturing process have gained increasing attention. In the current work, we propose a promising Isogeometric Topology Optimization (ITO) method for porous infill structures with stress minimization to avoid the occurrence of stress concentrations in additive manufacturing. The IsoGeometric Analysis (IGA) and induced p‐norm aggregation are utilized to develop a stress‐minimization topology description model for infill design, which can remove the mesh dependency and offer benefits for improving numerical accuracy and convergence stability. We also introduced global volume constraints to easily control the usage of material and eliminate the over‐fine structures affecting the printing accuracy. Several numerical examples are performed to demonstrate the effectiveness and advantages of the proposed ITO method on porous infill designs with stress minimization. The laser powder bed fusion (LPBF) technique is employed to fabricate several prototypes, and the performance are evaluated by experiments. The advancements of our work are demonstrated effectively, which is adapted for additive manufacturing and practical application.

On the comparison between pre- and post-surgery nasal anatomies via computational fluid dynamics

Nasal breathing difficulties (NBD) are widespread and difficult to diagnose; the failure rate of their surgical corrections is high. Computational fluid dynamics (CFD) enables diagnosis of NBD and surgery planning, by comparing a pre-operative (pre-op) situation with the outcome of virtual surgery (post-op). An equivalent comparison is involved when considering distinct anatomies in the search for the functionally normal nose. Currently, this comparison is carried out in more than one way, under the implicit assumption that results are unchanged, which reflects our limited understanding of the driver of the respiratory function. The study describes how to set up a meaningful comparison. A pre-op anatomy, derived via segmentation from a CT scan, is compared with a post-op anatomy obtained via virtual surgery. State-of-the-art numerical simulations for a steady inspiration carry out the comparison under three types of global constraints, derived from the field of turbulent flow control: a constant pressure drop (CPG) between external ambient and throat, a constant flow rate (CFR) through the airways and a constant power input (CPI) from the lungs can be enforced. A significant difference in the quantities of interest is observed depending on the type of comparison. Global quantities (flow rate, pressure drop and nasal resistance) as well as local ones are affected. The type of flow forcing affects the outcome of the comparison between pre-op and post-op anatomies. Among the three available options, we argue that CPG is the least adequate. Arguments favouring either CFR or CPI are presented.

Incorporating the complex rheological behavior of blood in microvascular network simulations: Two-phase modeling and a model reduction approach

Vessel flow resistance in microvascular network simulations is often computed using the single-phase approximation, where the no-slip boundary condition is imposed on the microvessel wall, and the "in vitro viscosity law" or "the relative hematocrit" relationships proposed by Pries et al. (1992) are utilized. However, these models have limitations due to their reliance on experimental data, which inherently contain measurement errors, thereby affecting their consistency and accuracy. To moderate these limitations, biphasic flow models can be employed, allowing for adaptable rheological descriptions of blood. Nevertheless, this approach presents challenges in terms of automation and computational cost. In order to address these challenges, we propose a monophasic steady-state model that consistently incorporates a wall-slip condition in place of the cell-free layer contribution. Model reduction is achieved by establishing an explicit relationship for the apparent slip length, which is calibrated through the regression of biphasic model results. Subsequently, simulations are conducted on microvessel network structures. The predictions of wall shear stress by the monophasic model exhibit deviations of less than 2 % from the expected values, within the range of vessel radii (10–75 μm) and discharge hematocrits (0.2–0.7). Furthermore, the computational time required is reduced by almost an order of magnitude compared to the biphasic model, rendering it suitable for multiscale biochemical simulations. The developed model closely approximates the flow resistance predictions of the biphasic model, while its modular nature allows for easy implementation of future modifications. Both models successfully reproduce experimental data by Lipowsky et al. (1978). Finally, we derive semi-analytical expressions for the apparent slip length and discuss why it is more appropriate to impose only the "relative hematocrit" relationship as a global constraint for calculating the hemodynamics in microvessels, rather than relying on the "in vitro viscosity law", which provides unreliable predictions at low values of the discharge hematocrit.

3D Point Cloud Instance Segmentation Considering Global Shape Contour Constraints

Aiming to solve the problem that spatially distributed similar instances cannot be distinguished in 3D point cloud instance segmentation, a 3D point cloud instance segmentation network, considering the global shape contour, was proposed. This research used the global-to-local design idea and added the global shape constraint to solve this problem. A Transformer module (Global Shape Attention, GSA) that can capture the shape contour information of the instance in the scene was designed. This module encoded the shape contour information into the Transformer structure as a Key-Value and extracted the instance fused with the global shape contour features, for instance, segmentation. At the same time, the network directly predicted the instance mask in an end-to-end mode, avoiding heavy post-processing algorithms. Many experiments have been conducted on the S3DIS, ScanNet, and STPL3D datasets, and our experimental results showed that our proposed network can efficiently capture the shape contour information of scene instances and can help to alleviate the problem of the difficulty distinguishing between spatially distributed similar instances in a scene, improving the efficiency and stability of instance segmentation.

Thermo-hydraulic efficiency of lung-inspired compact plate heat exchangers made using additive manufacturing techniques with steel, aluminum and titanium powders

The selection of material for compact plate heat exchangers (CPHEs) is of increasing importance due to global economic and supply constraints. Additionally, the influence of material selection on the thermo-hydraulic characteristics of CPHEs is an area of ongoing research. This study aims to address these issues by analyzing the thermo-hydraulic performance of CPHEs made from steel, aluminum, and titanium materials with small, complex channels. Using an additive manufacturing method (specifically Direct Metal Laser Sintering), lung-inspired CPHEs of identical geometry and roughness were manufactured from steel, aluminum and titanium powders. The thermo-hydraulic characteristics of CPHEs as well as that of a traditional one with Chevron-type, were investigated using both experimental and numerical techniques under specific operating conditions to determine the optimum between maximum heat transfer and minimum pressure drop. The findings of this study reveal that as the temperature difference between the inlet on the hot and cold sides, as well as the flow rate, were increased, there was a corresponding increase in both amount of heat transferred and loss of pressure across all investigated CPHEs. Compared to the chevron type brazed plate heat exchanger, the CPHE made from aluminum showed a 75.2 % and 11.2 % increase in heat transfer and a 31.8 % and 10.9 % reduction in pressure drop at 3 and 6 L/min, respectively, for a temperature difference of 90–40 °C. This study suggests that the use of materials with different thermal conductivities in CPHEs may offer a promising solution to achieve elevated heat transfer rates while minimizing pressure drop.

A heuristic algorithm to solve the one‐warehouse multiretailer problem with an emission constraint

AbstractIn this paper, we consider the one‐warehouse multiretailer problem with a global carbon emission cap constraint (OWMR‐EC). This constraint aims at limiting the carbon emissions related to the production, setup, and inventory‐holding operations. We develop a penalized relaxation (PR) method to heuristically solve the considered problem, both with and without the possibility of having initial inventory. This heuristic uses in itself another heuristic that we propose to solve the standard one‐warehouse multiretailer problem (OWMR). Our PR method is tested on numerous instances adapted from the literature. Our results indicate that the penalized method is able to find between 87.4% and 89.8% of feasible solutions for this NP‐hard problem, with an average optimality gap of 2.1% and 2.2% depending on the algorithms we use to solve the different subproblems involved in the method. The results show that our method is highly effective in terms of run‐time and solution quality, when a feasible solution is found. Furthermore, the results indicate that the heuristic for the standard OWMR is also very effective. We further perform a sensitivity analysis on the optimal solutions of the OWMR‐EC to better understand the implications of the carbon emission cap constraint. The sensitivity analysis indicates that the marginal cost of reducing carbon emissions increases as the emission cap decreases. The analysis also shows that the correlation between the cost and emission parameters has an important impact on the potential to further lower the emissions, compared to the emission of the minimum cost solution.

Financing the hydrogen industry: exploring demand and supply chain dynamics.

The hydrogen industry has garnered substantial attention as a pivotal solution in addressing the intricate challenges of energy transition and achieving decarbonization across diverse sectors. The efficacy of deploying hydrogen technologies hinges upon the availability of robust financing mechanisms that can adequately support the dynamic demands and intricate supply chain intricacies inherent in the hydrogen sector. This comprehensive study is underpinned by a rigorous and systematic review of prior research on the hydrogen economy, leveraging authoritative databases including Web of Science, Scopus, and a range of consultancy-based reports. The study meticulously assesses the escalating interest in hydrogen as a paramount clean energy alternative, emphasizing its significance in propelling the multifaceted development and expansion of hydrogen supply chain dynamics. Furthermore, this research critically scrutinizes the intricate financial facets of the hydrogen sector, with a specific focus on delineating the drivers of demand and unraveling the complexities interwoven within the supply chain. Building upon this analysis, the study offers a forward-looking perspective on hydrogen financing, which considers emerging technologies, evolving policy landscapes, and dynamic market trends. In the face of existing global constraints within the hydrogen supply chain, innovative financing mechanisms such as green bonds, project financing underwritten by risk guarantees through public-private partnership paradigms, venture capital-equity models, and carbon pricing mechanisms emerge as indispensable tools poised to address these challenges effectively.

Morphogenetic metasurfaces: unlocking the potential of Turing patterns

The reaction-diffusion principle imagined by Alan Turing in an attempt to explain the structuring of living organisms is leveraged in this work for the procedural synthesis of radiating metasurfaces. The adaptation of this morphogenesis technique ensures the growth of anisotropic cellular patterns automatically arranged to satisfy local electromagnetic constraints, facilitating the radiation of waves controlled in frequency, space, and polarization. Experimental validations of this method are presented, designing morphogenetic metasurfaces radiating far-field circularly polarized beams and generating a polarization-multiplexed hologram in the radiative near-field zone. The exploitation of morphogenesis-inspired models proves particularly well suited for solving generative design problems, converting global physical constraints into local interactions of simulated chemical reactants ensuring the emergence of self-organizing meta-atoms.

Group Equality in Adaptive Submodular Maximization

In this paper, we study the classic submodular maximization problem subject to a group equality constraint under both nonadaptive and adaptive settings. It is shown that the utility function of many machine learning applications, including data summarization, influence maximization in social networks, and personalized recommendation, satisfies the property of submodularity. Hence, maximizing a submodular function subject to various constraints can be found at the heart of many of those applications. On a high level, submodular maximization aims to select a group of most representative items (e.g., data points). However, the design of most existing algorithms does not incorporate the fairness constraint, leading to underrepresentation or overrepresentation of some particular groups. This motivates us to study the submodular maximization problem with group equality, in which we aim to select a group of items to maximize a (possibly nonmonotone) submodular utility function subject to a group equality constraint. To this end, we develop the first constant-factor approximation algorithm for this problem. The design of our algorithm is robust enough to be extended to solving the submodular maximization problem under a more complicated adaptive setting. Moreover, we further extend our study to incorporating a global cardinality constraint and other fairness notations. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Supplemental Material: The online supplement is available at https://doi.org/10.1287/ijoc.2022.0384 .

Устойчивое социально-экономическое развитие регионов Южного федерального округа в условиях внешних санкций

In recent years Russia has been going through drastic changes in the structure of socio-economic processes due to the external sanctions introduction. Under modern conditions is it important to find out whether the imposed sanctions have affected the development of the country’s regions and what development prospects should be expected in the future. The changes in the nature of the socio-economic development of the regions of the Southern Federal District, namely the Krasnodar Territory, Volgograd and Rostov regions, caused by sanctions, are investigated; measures to promote regional development in the conditions of external sanctions are proposed. In the context of global economic changes and political constraints that have had an impact on the country᾿s macroeconomic situation, regional development is of particular importance. The regions of the Southern Federal District are facing a number of complex problems, including a decrease in the external investment flow, limited access to international markets, an imbalance in the economic structure, as well as the deterioration of social indicators, which together create challenges for achieving sustainable socio-economic development. A statistical analysis of the socio-economic situation of the Krasnodar Territory, Volgograd, Rostov regions in dynamics is carried out. Possible consequences of sanctions are considered. The main socio-economic indicators of the three regions of the Southern Federal District are analyzed, as a result of which it is possible to assess their condition and further development prospects in the conditions of sanctions. It is concluded that in order to overcome the consequences of sanctions, it is necessary to introduce integrated approaches. Important strategic steps are economic diversification, stimulating innovation and research, supporting small and medium-sized businesses, as well as strengthening interregional partnerships. It is recommended to focus on human capital development providing access to education, health care and high-quality employment.

Online Distributed Learning for Aggregative Games With Feedback Delays

This paper proposes a new aggregative game (AG) model with feedback delays. The strategies of players are selected from given strategy sets and subject to global nonlinear inequality constraints. Both cost functions and constrained functions of players are time-varying, which reflects the changing nature of environments. At each time, each player only has access to its strategy set information, and the information of its current cost function and current constrained function is unknown. Due to feedback delays, the feedback information of corresponding cost functions and constrained functions is not revealed to players immediately after the selection of strategies. It would take a period of time for players to observe their feedback information. To address such an AG problem, a distributed learning algorithm is proposed with the local information from their neighbors and the delayed feedback information from environments, and it is applicable to time-varying weighted digraphs. We find that the two metrics of the algorithm grow sub-linearly with respect to the learning time. A simulation example is given to illustrate the performance of the proposed algorithm.

Global Interconnect Optimization

We propose a new comprehensive solution to global interconnect optimization. Traditional buffering algorithms mostly insert repeaters on a net-by-net basis based on slacks and possibly guided by global wires. We show how to integrate routing congestion, placement congestion, global timing constraints, power consumption, and additional constraints into a single resource sharing formulation. The core of our algorithm is a new buffered routing subroutine. Given a net and Lagrangean resource prices for routing, timing, placement, and power, it computes a buffered Steiner tree. The resource sharing framework provides a special multiplicative price update for fast convergence. Our algorithm is fast enough for practical instances. We demonstrate experimentally on 7nm microprocessor units that it significantly improves timing while reducing netlength and power consumption in an industrial design flow. Our implementation scales well under parallelization with up to 128 threads.

Enhancing vehicle localization by matching HD map with road marking detection

Accurate and robust vehicle localization, a fundamental task for autonomous driving, is still a challenging problem especially in GPS-denied scenarios. Recently, high definition (HD) map suggests a promising solution. However, the matching between online sensed data and HD map is difficult and time-consuming. In our work, road markings are selected as landmarks due to salient appearance features. Based on the detection results of road markings from the vehicle-borne images, the points of edge lines are employed to fit straight lines with RANSAC for outlier removal. The distances between the ego-vehicle and the fitted edge lines can be computed with camera-vehicle calibration in advance. Subsequently, the point-to-line distances from the sensed data are mapped into global linear constraints on the vehicle’s positions with the support of a lane-level HD map, which provides centimeter-level coordinates of road markings. Finally, the distances and the localization from the integrated navigation system (INS) are fused with the proposed linear Kalman filter based on the second-order Markov model (KF-MM2). The proposed method has been verified in different daily driving scenarios. Experimental results demonstrate that our method can achieve good performance with an average localization error of 0.53 m and a standard deviation of 0.17 m.

‘Made in Kyrgyzstan is gold!’ the rise of the informal Kyrgyzstani apparel industry

The Kyrgyzstani apparel industry has seen spectacular growth in recent years, despite informal structures, a predatory business environment, and intersecting crises. Contrary to other apparel producers in the Global South, ‘Made in Kyrgyzstan’ (MiK) has emerged from below and independently of multinational corporations or state-funded development initiatives. This article takes an ethnographic approach to examine how apparel producers navigate the challenging national and geopolitical environment in which their businesses are embedded. It does so based on long-term fieldwork, conducted before (2019), during (2020, online), and after (2021) the pandemic at the Dordoi Bazaar in Bishkek, the main distribution platform for locally produced apparel. Based on the ethnographic material, this article questions the meaningfulness of an informality framework in the corrupt context of the Kyrgyzstani state, and instead shifts the focus on attempts to build durable businesses that are capable of dealing with local and global constraints. Doing so, it makes two related points. Firstly, it traces the unique nature of post-Soviet economic transformation in Kyrgyzstan through the peculiar growth of the informal apparel industry. Secondly, it emphasises the relevance of endemic state corruption when studying informal economic practices on the ground.

An inertial neurodynamic algorithm for collaborative time-varying energy management for energy internet containing distributed energy resources

This paper investigates the energy management of an Energy Internet (EI) that integrates multiple energy networks. Based on the structure of EI and the fluctuation of load, an optimal energy management model considering part of the load as time-varying factor is proposed. Under the influence of load, the actual energy management problem (EMP) is formulated as a time-varying optimization problem subject to a set of global and local constraints. Its optimal solution varies continuously with time, which leads to difficulties in obtaining optimal solutions by conventional algorithms. Consequently, to obtain better tracking performance, this paper proposes a distributed inertial projection neurodynamics algorithm (DIPNA) based on the Nesterov accelerated gradient descent method. This algorithm tracks the optimal solution of the EMP with a fast convergence rate O1/t2, providing a referenceable active power for each energy unit in real time. Finally, the performance evaluation results demonstrated the effectiveness and fast convergence of the proposed algorithm.

Collision-Free Formation Control With Global Network Integrity Maintenance via Preserving Induced-Subgraph Connectivity

Network connectivity is a necessary prerequisite to realizing distributed formation control. However, it is challenging to maintain the global network connectivity of a formation with local information because the communications between topological agents may be disconnected as formation maneuvers due to the range-limited sensing capabilities of agents. In this article, we propose a novel connectivity-preserving formation strategy to address the challenge. The core idea is to decompose global connectivity constraint into the induced-subgraph-preserving problems of each agent. Different from the state-of-the-art methods, our strategy can acquire the local algebraic connectivity directly at the level of maximal cliques, instead of estimating the global algebraic connectivity by hundreds of iterations. Consequently, it yields more flexibility in formation maneuvers and requires lower computing costs. Moreover, we treat the entire formation as a single rigid body and employ a variant of the optimal reciprocal collision avoidance algorithm to optimize the overall movement of the formation so that agents can maintain the desired shape while avoiding obstacles. Here, the position and orientation of the desired formation are negotiated by the agents using only local peer-to-peer information. Simulation results verify that the proposed strategy can drive a group of agents to implement connectivity-preserving formations in obstacle-rich spaces.

Introgression of Stay Green Quantitative Trait Locus (QTLS) into Elite Sorghum Variety by MABC

Sorghum is a major staple food crop for the people in semi-arid areas of Asia. Post-flowering drought is a global constraint of sorghum production. The study aimed to improve stay-green characteristics of GS-23 sorghum variety by transferring stg3A and stg3B QTL`s respectively from donor genotypes K260 and K359w of ICRISAT by using marker assisted backcrossing. The experimental material comprised of six basic generations P1, P2, F1, F2, BC1 and BC2 developed from crossing GS-23 × K260 and GS-23 × K359 were genotyped using a set of 133 SNPs and 79 SSR markers. Whereas, 53 polymorphic SNPs among parents and backcross F1s for stay green trait at maturity were used to track introgression of stay green trait. Similarly,10 SSR markers were found to be polymorphic were used to track introgression of stay green trait i.e., stay green trait QTL`s stg3A and stg3B from donor parent K260 and K359w respectively in GS-23 background across backcross population and to identify plants that were homozygous for the desired allele. In which 02 SNPs SnpSB0039 and SnpSB0093 were identified as polymorphic for both K260 and K359w. Whereas 17 SNPs identified polymorphic for K260 and 34 SNPs for K359w. These SNPs were validated in both F1 and BC1F1 populations of both the crosses. Similarly, out of 10 SSR markers utilized, 02 SSRs were identified polymorphic to K260 and 02 SSRs were identified for K359w. Whereas 06 SSRs were polymorphic to both the parents K260 and K359w. Prominent 02 SSRs viz., Xtxp 141_Fam and Xgap84_Vic found to be more reliable and polymorphic to both the parents. These SSRs were validated in both F1 and BC1F1 populations of both the crosses. The genotypic analysis revealed the presence of favorable alleles in homozygous conditions at markers loci associated with stg3A and stg3B QTL`s in BC populations, suggesting successful introgression of stay green QTLs from the donor parents to the recurrent parent. Therefore, our study demonstrated the utility of marker-assisted backcrossing for drought tolerance improvement of locally adapted sorghum variety.

Constraints on the spectral signatures of superconducting cosmic strings

ABSTRACT If they exist, networks of superconducting cosmic strings are capable of injecting copious amounts of electromagnetic energy into the background over a broad range of frequencies. We study this injection both analytically, as well as numerically using the thermalization code CosmoTherm. With our refined analytic formalism, we update constraints from CMB spectral distortions by following the injection of entropy, as well as energy, on the amplitude of the μ-distortion, leading to a significant improvement in those limits. Furthermore, we utilize the full shape of the distorted spectrum from CosmoTherm to include constraints from non-μ, non-y type distortions. Additionally, we use the outputs for the ionization history and global 21-cm signal to derive and update constraints on string model parameters using measurements from other data sets. Analysis of CMB anisotropies provides the most stringent constraints, though with a slightly modified shape and strength when compared to previous results. Modifications of the reionization history provide new bounds in the high current domain, and we also find that the observations of the low-frequency radio background probe a small region of parameter space not explored by other data sets. We also analyse global 21-cm constraints, and find that the inclusion of soft photon heating plays a crucial role, essentially removing any constraints in the considered parameter domain. Spectral distortion measurements from COBE/FIRAS are covered by other constraints, but our conservative forecast shows that a PIXIE-type satellite would probe important unexplored regions of parameter space.

Improved bit-based filtering algorithm for regular constraint

In this paper, we propose an improved filtering algorithm for regular constraint, which demand that the sequence of all assignments for variables can be accepted by a Deterministic Finite Automaton (DFA) or a regular language. Regular constraint is a type of global constraint in Constraint Satisfaction Problems (CSPs) and a natural expression of deterministic patterns in planning and decision making, and it utilizes the properties of DFA to pre-specify the optional paths for multiple variables. Thus, it is sensible to improve the filtering algorithm that enforces Generalized Arc Consistency (GAC) for regular constraint in order to minimize memory and time consumption. With the goal in mind, we encode DFA using bit vectors and then devise a bit operations algorithm to maintain the domain consistency for regular constraint. Lastly, we carry out an experiment to compare to the state-of-the-art filtering algorithm. The experimental results demonstrates that the proposed algorithm significantly improves both memory and time.