Additional Constraints Research Articles

De Sitter vacua in O(d,d) invariant cosmology

We perform a thorough analysis of de Sitter solutions in O(d,d) invariant cosmologies. Starting with a homogeneous and isotropic framework we examine conditions for the existence of such solutions to the vacuum field equations, nonperturbative in α′ in both the string frame and the Einstein frame. We elucidate the nature of the instability in the string frame vacuum. For the Einstein frame, we demonstrate that the de Sitter solutions cannot be eternal. We then extend our analysis to include Bianchi I universes where the O(d,d) symmetry includes scale factor exchange as well as scale factor duality. We show how the theory can be extended to the anisotropic case so that it admits de Sitter solutions, noting the crucial role played by the O(d,d) symmetry in satisfying any additional constraints. Published by the American Physical Society 2024

Weak lensing combined with the kinetic Sunyaev–Zel’dovich effect: a study of baryonic feedback

ABSTRACT Extracting precise cosmology from weak lensing surveys requires modelling the non-linear matter power spectrum, which is suppressed at small scales due to baryonic feedback processes. However, hydrodynamical galaxy formation simulations make widely varying predictions for the amplitude and extent of this effect. We use measurements of Dark Energy Survey Year 3 weak lensing (WL) and Atacama Cosmology Telescope DR5 kinematic Sunyaev–Zel’dovich (kSZ) to jointly constrain cosmological and astrophysical baryonic feedback parameters using a flexible analytical model, ‘baryonification’. First, using WL only, we compare the $S_8$ constraints using baryonification to a simulation-calibrated halo model, a simulation-based emulator model, and the approach of discarding WL measurements on small angular scales. We find that model flexibility can shift the value of $S_8$ and degrade the uncertainty. The kSZ provides additional constraints on the astrophysical parameters, with the joint WL + kSZ analysis constraining $S_8=0.823^{+0.019}_{-0.020}$. We measure the suppression of the non-linear matter power spectrum using WL + kSZ and constrain a mean feedback scenario that is more extreme than the predictions from most hydrodynamical simulations. We constrain the baryon fractions and the gas mass fractions and find them to be generally lower than inferred from X-ray observations and simulation predictions. We conclude that the WL + kSZ measurements provide a new and complementary benchmark for building a coherent picture of the impact of gas around galaxies across observations.

A Needle in a Haystack: Landscape Survey and Archaeological Detection Experiments in Apalachee Bay

ABSTRACTThis paper presents the results of a pilot landscape‐scale seismic survey undertaken in Apalachee Bay, Florida, across a submerged landscape that contains dozens of Pre‐Contact sites. In addition to the goals of improving the geophysical and remote sensing ground model for this submerged landscape, the survey also sought to undertake the first independent scientific test of the contentious ‘HALD’ methodology, an acoustic resonance method that it is claimed to identify knapped lithic artefacts at and/or below the seabed through the identification of distinct ‘haystack’ responses. The results of this work indicate that the HALD method, as currently described, produces results that could not be scientifically replicated in this survey. We conclude that any HALD ‘haystack’ signal should therefore not be considered as an example of detection of human‐modified lithic material but rather as a geophysical anomaly that requires additional constraints before it can be used to reliably identify human‐modified lithic materials. Thus, although the authors note that laboratory studies have successfully produced an acoustic signal in human‐modified lithics, the field‐based methods remain yet to be reliably determined. In addition to these results, the landscape mapping survey also recorded valuable information on buried and previously unrecorded landscape features that have archaeological significance and that may guide future site prospection. We therefore conclude that despite the results of the HALD test, the well‐preserved submerged landscape of Apalachee Bay region provides a highly useful testing ground for methods that can be deployed elsewhere globally.

Isotropic Growth on Cartesian Voxel Grids with von Neumann Neighborhoods for Rapid Generation of Synthetic Microstructures

Randomized instantiations of synthetic microstructures are needed to assess the statistical significance of microstructure variability. Algorithms that use Voronoi tessellations of random points produce synthetic polycrystalline microstructures with a less realistic appearance than random packing of spheroidal particles followed by growth-until-impingement. Additionally, Voronoi tessellations offer limited control of morphological parameters and are challenging to implement when the desired volume fraction is less than unity. However, unless additional physics-based constraints are applied, growth on a Cartesian voxel grid utilizing a von Neumann neighborhood element results in anisotropic growth that is generally not observable along the primary axes. The present work describes both analytical and empirically optimized corrections for directional growth rates and a framework for their inclusion in a synthetic microstructure generation algorithm. The presented algorithm can produce synthetic microstructures similar to those produced by random seed Monte Carlo techniques in a fraction of the computational time.

Application of a Robust Maximum Diversified Portfolio to a Small Economy’s Stock Market: An Application to Fiji’s South Pacific Stock Exchange

In this study, we apply a novel approach of portfolio diversification—the robust maximum diversified (RMD)—to a small and developing economy’s stock market. Using monthly returns data from August 2019 to May 2024 of 18/19 stocks listed on Fiji’s South Pacific Stock Exchange (SPX), we construct the RMD portfolio and simulate with additional constraints. To implement the RMD portfolio, we replace the covariance matrix with a matrix comprising unexplained variations. The RMD procedure diversifies weights, and not risks, hence we need to run a pairwise regression between two assets (stocks) and extract the R-square to create a P-matrix. We compute each asset’s beta using the market-weighted price index, and the CAPM to calculate market-adjusted returns. Next, together with other benchmark portfolios (1/N, minimum variance, market portfolio, semi-variance, maximum skewness, and the most diversified portfolio), we examine the expected returns against the risk-free (RF) rate. From the simulations, in terms of expected return, we note that eight portfolios perform up to the RF rate. Specifically, for returns between 4 and 5%, we find that max. RMD with positive Sharpe and Sortino (as constraints) and the most diversified portfolio offer comparable returns, although the latter has slightly lower standard deviation and downside volatility and contains 94% of all the stocks. Portfolios with returns between 5% and the RF rate are the minimum-variance, the semi-variance, and the max. RMD with positive Sharpe; the latter coincides with the RF rate and contains the most (94%) stocks compared to the other two. An investor with a diversification objective, some risk tolerance and return preference up to the RF rate can consider the max. RMD with positive Sharpe. However, depending on the level of risk-averseness, the minimum-variance or the semi-variance portfolio can be considered, with the latter having lower downside volatility. Two portfolios offer returns above the RF rate—the market portfolio (max. Sharpe) and the maximum Sortino. Although the latter has the highest return, this portfolio is the least diversified and has the largest standard deviation and downside volatility. To achieve diversification and returns above the RF rate, the market portfolio should be considered.

Reachable Set Computation with Terminal Velocity Constraints

This work establishes a general-purpose method for computing the exact boundary of the reachable set in a specified direction, with additional constraints imposed on the terminal velocities. Reachable sets with rest boundary conditions, that is, zero-velocity constraints imposed at initial and final times, are of interest for formation flying and spacecraft rendezvous and docking applications. The rest-to-rest reachability problem is extended to compute reachable neighboring circular orbits and a modified problem formulation is developed to exclusively compute the maximum and minimum reachable circular orbits. Reachable sets are computed by applying classical optimal control techniques to a maximum position optimization problem. A solution method for the optimization problem is outlined, which makes use of a Newton’s method solving schema. Additional constraints are added to the problem to compute maxima along specified directions, allowing for the characterization of the reachable set in as many directions as desired. Example reachable sets are computed to illustrate the use of this method on various systems, including an application to relative motion.

Framework of Physically Consistent Homogenization and Multiscale Modeling of Shell Structures**

A framework of physically consistent homogenization and multiscale modeling (PCHMM) for reduced-order analysis of plate/shell structures is developed in this paper. To address the inapplicability of conventional periodic boundary conditions and Hill’s condition involved in homogenization of shear-deformable shell structures, the paper proposes physically consistent boundary conditions and modified Hill’s condition for plate/shells. Unlike the PCHMM method for beams, considering the contradiction between high-order displacement fields induced by shear forces and low-order kinematic assumptions, additional constraints are applied to the plate/shell structure sectional strains during the solution of perturbation fields. The correctness and effectiveness of the proposed plate/shell PCHMM framework and method are verified by typical numerical examples. The proposed theory can also be conveniently embedded into commercial finite element software for homogenization and multiscale analysis of structures such as microscale metamaterials like lattice plates and large complex structures like aircraft fuselage sections.

Mathematical models based on decision hypergraphs for designing a storage cabinet

We study the problem of designing a cabinet made up of a set of shelves that contain compartments whose contents slide forward on opening. Considering a set of items candidate to be stored in the cabinet over a given time horizon, the problem is to design a set of shelves and a set of compartments on each shelf, and select the items to insert into the compartments. The objective is to maximize the sum of the profits of the selected items. We call our problem the Storage Cabinet Physical Design (SCPD) problem. The SCPD problem combines a two-staged two-dimensional knapsack problem for designing the shelves and compartments with a set of temporal knapsack problems for selecting and assigning items to compartments. We formalize the SCPD problem and formulate it as a maximum cost flow problem in a decision hypergraph with additional linear constraints. To reduce the size of this model, we break symmetries, generalize graph compression techniques and exploit dominance rules for precomputing subproblem solutions. We also present a set of valid inequalities to improve the linear relaxation of the model. We empirically show that solving the arc-flow model with our enhancements outperforms solving a compact mixed integer linear programming formulation of the SCPD problem.

Gender issues and needs of people with disabilities

<p>身心障礙者在特殊性別需求上具有複雜的性別議題，尤其是女性身心障礙者在生殖健康、性別暴力和慢性病管理上所遭遇的諸多挑戰，也會因交通不便和經濟困難，難以獲得持續的慢性病照護與管理。男性身心障礙者則在心理健康和事故預防方面需求較高，社會期望和性別角色壓力使他們難以尋求心理支持。跨性別身心障礙者在性別確認治療上面臨複雜需求，且在醫療機構中經常遭遇歧視和不理解。醫療機構可加強對醫療工作者進行性別意識，以提升包容性和服務品質。因此，需要從各層面加強對身心障礙者特殊性別健康需求，提供性別敏感性的培訓課程，並建立有效的支持機制，包括性別暴力防治措施和心理支持服務，以確保身心障礙者特殊性別議題中，獲得平等權利和安全福祉。</p> <p> </p><p>Individuals with disabilities often face complex gender-related problems because of their special needs. In particular, women with disabilities often face many challenges pertaining to reproductive health, gender-based violence, and chronic disease management. In addition, transportation difficulties and financial constraints further hinder their access to continuous chronic disease care and management. Compared with women, men with disabilities have higher needs for mental health and accident prevention; however, social expectations and gender role pressure often deter such men from seeking psychological support. Transgender individuals with disabilities have complex requirements related to gender-affirming treatment and often face discrimination and misunderstanding in medical settings. To solve this problem, medical institutions should enhance gender awareness among their medical personnel to promote inclusivity and improve service quality. Therefore, it is necessary to strengthen the focus on the specific gender health needs of individuals with disabilities from various aspects. This includes providing gender-sensitive training courses, and establishing effective support mechanisms, such as measures to prevent gender-based violence prevention and offering psychological support services. These efforts are essential to ensure that individuals with disabilities receive equal rights and safety in addressing their unique gender issues.</p> <p> </p>

A hybrid constrained continuous optimization approach for optimal causal discovery from biological data.

Understanding causal effects is a fundamental goal of science and underpins our ability to make accurate predictions in unseen settings and conditions. While direct experimentation is the gold standard for measuring and validating causal effects, the field of causal graph theory offers a tantalizing alternative: extracting causal insights from observational data. Theoretical analysis has shown that this is indeed possible, given a large dataset and if certain conditions are met. However, biological datasets, frequently, do not meet such requirements but evaluation of causal discovery algorithms is typically performed on synthetic datasets, which they meet all requirements. Thus, real-life datasets are needed, in which the causal truth is reasonably known. In this work we first construct such a large-scale real-life dataset and then we perform on it a comprehensive benchmarking of various causal discovery methods. We find that the PC algorithm is particularly accurate at estimating causal structure, including the causal direction which is critical for biological applicability. However, PC does only produces cause-effect directionality, but not estimates of causal effects. We propose PC-NOTEARS (PCnt), a hybrid solution, which includes the PC output as an additional constraint inside the NOTEARS optimization. This approach combines PC algorithm's strengths in graph structure prediction with the NOTEARS continuous optimization to estimate causal effects accurately. PCnt achieved best aggregate performance across all structural and effect size metrics. https://github.com/zhu-yh1/PC-NOTEARS.

Random forests regression for soft interval data

Analyzing soft interval data for uncertainty quantification has attracted much attention recently. Within this context, regression methods for interval data have been extensively studied. As most existing works focus on linear models, it is important to note that many problems in practice are nonlinear in nature and the development of nonlinear regression tools for interval data is crucial. This paper proposes an interval-valued random forests model that defines the splitting criterion of variance reduction based on an L 2 type metric in the space of compact intervals. The model simultaneously considers the centers and ranges of the interval data as well as their possible interactions. Unlike most linear models that require additional constraints to ensure mathematical coherences, the proposed random forests model estimates the regression function in a nonparametric way, and so the predicted interval length is naturally nonnegative without any constraints. Simulation studies show that the new method outperforms typical existing regression methods for various linear, semi-linear, and nonlinear data archetypes and under different error measures. To demonstrate the applicability, a real data example is presented where the price range data of the Dow Jones Industrial Average index and its component stocks are analyzed.

A Three-Dimensional Reconstruction Method Based on Telecentric Epipolar Constraints

When calibrating a microscopic fringe projection profile system with a telecentric camera, the orthogonality of the camera causes an ambiguity in the positive and negative signs of its external parameters. A common solution is to introduce additional constraints, which often increase the level of complexity and the calibration cost. Another solution is to abandon the internal/external parameter models derived from the physical imaging process and obtain a numerically optimal projection matrix through the least squares solution. This paper proposes a novel calibration method, which derives a telecentric epipolar constraint model from the conventional epipolar constraint relationship and uses this constraint relationship to complete the stereo calibration of the system. On the one hand, since only the camera’s intrinsic parameters are needed, there is no need to introduce additional constraints. On the other hand, the solution is optimized based on the full consideration of the imaging model to make the parameters confirm to the physical model. Our experiments proved the feasibility and accuracy of the method.

Unlocking the potential of CYGNSS for pan-tropical inland water mapping through multi-source data and transformer

Cyclone Global Navigation Satellite System (CyGNSS) data are widely recognized for their sensitivity to inland water bodies. However, the detection of water bodies using single CyGNSS data is subject to uncertainties, presenting challenges for large-scale and accurate water system detection. In this study, we employ CyGNSS data for regression estimation to map inland water bodies. In comparison to previous studies, we incorporate additional constraints, including topographic factors, vegetation information, soil moisture, and latitude and longitude data. Leveraging the U-shaped structure, Swin Transformer, and ContextModule, we effectively extract water body distribution information, referred to as CFRT. Through rigorous performance comparison with prevalent deep learning models, our method demonstrates remarkable accuracy. The generated water percent exhibits notable consistency with the reference data, achieving a root mean square error (RMSE) of 7.15% and a mean intersection over union of 0.778 within the reachable area of the CyGNSS data. Our approach emphasizes the significance of utilizing multi-source data to substantially enhance the accuracy of CyGNSS water system detection.

Addressing coordination problems in residential buyouts: Experimental evidence for managed retreat in the face of climate change-related threats

Voluntary home buyouts can be an effective way to protect homeowners from severe and repeated flood risk while also benefiting the local community by creating flood control areas and reducing local infrastructure costs. Successful implementation of residential buyout programs is complicated by homeowners’ (unobservable) attachment to place and community. In the presence of such preferences, standard posted price offers at fair market value are generally too low to induce a majority of homeowners to move, resulting in spatially fragmented (checkerboarded) buyout patterns, which can be troublesome from both a community and environmental perspective. To help resolve the resulting coordination problem, we experimentally test two acquisition mechanisms from the land conservation literature in the residential flood buyout context, posted price offers and reverse auctions, and pair them with two additional program features, agglomeration bonuses and target constraints (also known as provision points—a threshold required for the buyout to be implemented). Using an induced-value approach, we show that both reverse auctions and agglomeration bonuses are effective at increasing contiguity in this setting by inducing more homeowners to accept buyout offers and to move away from the neighborhood. However, reverse auctions have the advantage of being flexible enough to accommodate heterogeneity in place and community attachment. If external benefits are non-linearly increasing in the number of contiguous residential lots acquired, adding a target constraint to a reverse auction is a potential avenue for effective future buyouts.

Hybrid deep learning for design of nanophotonic quantum emitter lenses

Inverse design of nanophotonic structures has allowed unprecedented control over light. These design processes however are accompanied with challenges, such as their high sensitivity to initial conditions, computational expense, and complexity in integrating multiple design constraints. Machine learning approaches, however, show complementary strengths, allowing huge sample sets to be generated nearly instantaneously, and with transfer learning, allowing modifications in design parameters to be integrated with limited retraining. Herein we investigate a hybrid deep learning approach, leveraging the accuracy and performance of adjoint-based topology optimization to produce a high-quality training set for a convolutional generative network. We specifically explore this in the context of 3D nanophotonic lenses, used for focusing light between plane-waves and single-point, single-wavelength sources such as quantum emitters. We demonstrate that this combined approach allows higher performance than adjoint optimization alone when additional design constraints are applied; can generate large datasets (which further allows faster iterative training to be performed); and can utilize transfer learning to be retrained on new design parameters with very few new training samples. This process can be used for general nanophotonic design, and is particularly beneficial when a range of design parameters and constraints would need to be applied.

Enzymatic CO2 Capture in intensified packed-bed column bioreactors: A techno-economic assessment

To reduce the impact of industrial carbon dioxide (CO2) emissions on climate change, energy-efficient and cost-effective carbon capture technologies are needed. With this in mind, our research group has proposed an intensified enzyme-mediated CO2 capture process in packed-bed column bioreactors with carbonic anhydrase (CA) immobilized on the packing surface and on magnetic nanoparticles (MNPs). This study, in continuation of our efforts to amend these processes, aims to showcase a techno-economic assessment (TEA) of this promising technology in an industrial-scale context and to emphasize the inherent benefits of intensification. The TEA results suggest that this enzyme-mediated technology has the potential to reduce capture costs, chiefly due to the possibility of utilizing an alternative solvent that requires less energy to regenerate, while also being non-corrosive and non-toxic. The capture process consumes 39.8% less energy than the benchmark process utilizing a MEA-based solution. Immobilizing the enzyme on MNPs has tangible benefits, such as enhancing mass transfer and reducing the equipment footprint, which improves flexibility and contributes to the viability of the industrial process. The intensified enzyme-mediated CO2 capture process achieves a 28.6% reduction in capture cost compared to the benchmark technology. Sensitivity analyses were conducted to highlight the impact of key parameters on the overall cost-effectiveness of the technology, providing beneficial insights for further improvement. The results indicate that it is crucial to maximize enzyme lifespan and minimize energy consumption. The liquid flow rate and buffer concentration are additional constraints that should be thoughtfully considered to ensure the feasibility of this process.

Solving the area-length systems in discrete gravity using homotopy continuation

Abstract Area variables are intrinsic to connection formulations of general relativity, in contrast to the fundamental length variables prevalent in metric formulations. Within 4D discrete gravity, particularly based on triangulations, the area-length system establishes a relationship between area variables associated with triangles and the edge length variables. This system is comprised of polynomial equations derived from Heron’s formula, which relates the area of a triangle to its edge lengths. Using tools from numerical algebraic geometry, we study the area-length systems. In particular, we show that given the ten triangular areas of a single 4-simplex, there could be up to 64 compatible sets of edge lengths. Moreover, we show that these 64 solutions do not, in general, admit formulae in terms of the areas by analyzing the Galois group, or monodromy group, of the problem. We show that by introducing additional symmetry constraints, it is possible to obtain such formulae for the edge lengths. We take the first steps toward applying our results within discrete quantum gravity, specifically for effective spin foam models.

Cooperative, Close and Remote Steric Effects on the Structural and Optical Properties of Copper(I) Bis‐Phenanthroline Complexes

AbstractThe synthesis as well as the structural, optical and computational characterization of seven new highly hindered homoleptic copper(I) phenanthroline complexes (namely C1–C7) is reported along with the comparison with the benchmark derivatives. Despite limited steric hindrance in direct proximity of the copper(I) coordination site, X‐ray structures show that the higher hindered derivatives of the series display a more optimal tetrahedral geometry with minimal D2 symmetry distortion. A novel remote control of the geometry, with steric hindrance away from the coordination site, leads to a favorable arrangement as demonstrated by structural and computational data. In addition, electrochemical and steady‐state and time‐resolved photophysical studies are presented which further support the beneficial effects on the ground‐state redox and excited‐state properties when both remote and close steric effects are exploited. Indeed, both an increase of the photoluminescence quantum yield and a prolongation of the excited state lifetime is observed for the highly‐substituted derivative C6 compared to benchmark counterparts on account of the reduced excited‐state flattening distortions imparted by the additional steric constraints.

Measuring differential particle correlations in relativistic nuclear collisions

This study explores the transverse momentum (pT) dependencies of symmetric and asymmetric correlations (SC and ASC) with one and two particles of interest in Au+Au collisions at 200 GeV. Leveraging a multiphase transport (AMPT) model, the investigation delves into the sensitivity of these correlations to the final state effects, providing valuable insights into their potential for constraining the final state effects' pT dependencies. The HIJING model is employed as a benchmark for nonflow correlations, shedding light on their impact on interpreting SC and ASC data. Moreover, the study points out that differential SC and ASC with one and two particles of interest (POIs) typically incorporate contributions from event-plane angle fluctuations. Consequently, this work highlights the significance of SC and ASC with one and two POIs as valuable tools for investigating the pT nature of the final state effects and advocates for comprehensive experimental measurements across various beam energies and system sizes to enhance understanding and provide additional constraints for theoretical models. Published by the American Physical Society 2024

Lassa Fever: Critical Review and Prospects for Control.

Lassa Fever is a deadly viral haemorrhagic disease, causing annually several hundreds of deaths in West Africa. This zoonotic disease is primarily transmitted to humans by rodents of the genus Mastomys, even though other rodents reportedly carry the Lassa virus, while secondary interhuman transmission accounts for approximately 20% of cases. Although this disease has been endemic in rural zones of Nigeria, Sierra Leone, Liberfia, and Guinea for hundreds of years, it is also characterised by epidemic outbreaks in the dry season, responsible for heavy death tolls. No licensed vaccine or satisfying treatment is currently available. Disease management is hindered by the incomplete knowledge of the epidemiology and distribution of the disease, resulting from an inadequate health and surveillance system. Additional scientific constraints such as the genetic diversity of the virus and the lack of understanding of the mechanisms of immune protection complexify the development of a vaccine. The intricate socio-economic context in the affected regions, and the lack of monetary incentive for drug development, allow the disease to persist in some of West Africa's poorest communities. The increase in the number of reported cases and in the fatality rate, the expansion of the endemic area, as well as the threat Lassa Fever represents internationally should urge the global community to work on the disease control and prevention. The disease control requires collaborative research for medical countermeasures and tailored public health policies. Lassa Fever, created by the interconnection between animals, humans, and ecosystems, and embedded in an intricate social context, should be addressed with a 'One Health' approach. This article provides an overview of Lassa Fever, focusing on Nigeria, and discusses the perspectives for the control of disease.