Regional development represents a fundamental pillar of economic growth, territorial cohesion, and sustainable resource management. As Georgia continues to navigate economic and political transitions, spatial-territorial planning emerges as a strategic tool for fostering balanced regional development and minimizing socio-economic disparities. This study examines the role of functional spatial planning in regional economic sustainability by conducting a comparative analysis of planning methodologies implemented in four EU member states—Germany, France, the Netherlands, and Sweden. The research explores the theoretical underpinnings of spatial zoning, evaluates the effectiveness of various land-use planning strategies, and assesses economic incentives for regional development. Additionally, the study integrates qualitative methods, including expert interviews and stakeholder surveys, to provide policy recommendations tailored to Georgia’s specific development context. The findings suggest that adopting an integrated spatial planning framework, leveraging data-driven land-use modeling, and implementing sustainability-oriented economic incentives can significantly enhance Georgia’s regional economic resilience. This research was supported by Shota Rustaveli National Science Foundation of Georgia (SRNSFG) [grant number: YS-24-308]

Whether statins benefit patients with type 2 diabetes mellitus (T2DM) with low predicted 10-year cardiovascular risk is uncertain. To evaluate the effectiveness and safety of statin initiation for primary prevention among adults with T2DM stratified by predicted 10-year risk for cardiovascular disease (CVD). Cohort study using target trial emulation. U.K. primary care using the IQVIA Medical Research Data database. Persons aged 25 to 84 years with a diagnosis of T2DM between 2005 and 2016 and no history of coronary artery disease, myocardial infarction, stroke, heart failure, myopathy, liver disease, rheumatic heart disease, schizophrenia, or cancer. Statin initiation versus noninitiation, with estimation of the observational analogues of the intention-to-treat effect. Statin initiators were propensity score-matched to noninitiators in a 1:4 ratio within 4 QRISK3 strata of 10-year predicted cardiovascular risk: low (<10%), intermediate (10% to 19%), high (20% to 29%), and very high (≥30%). Absolute risk differences (RDs) and risk ratios (RRs) at 10 years of follow-up for all-cause mortality and major CVD, as well as myopathy and liver dysfunction. Statin initiation was associated with reductions in all-cause mortality and major CVD across QRISK3 strata. In the low-risk stratum, RDs and RRs were -0.53% (95% CI, -0.90% to -0.08%) and 0.80 (95% CI, 0.67 to 0.97), respectively, for all-cause mortality and -0.83% (95% CI, -1.28% to -0.34%) and 0.78 (95% CI, 0.66 to 0.91), respectively, for major CVD. A small increased risk for myopathy was observed in the moderate-risk stratum only, and there was no associated increased risk for liver dysfunction in any stratum. Unmeasured confounding and underascertainment of some hospitalization outcomes. Statin use in T2DM for primary prevention was associated with reductions in all-cause mortality and major CVD across the full spectrum of predicted cardiovascular risk. National Natural Science Foundation of China.

We propose an efficient algorithm for matching two correlated Erdős–Rényi graphs with n vertices whose edges are correlated through a latent vertex correspondence. When the edge density [Formula: see text] for a constant [Formula: see text], we show that our algorithm has polynomial running time and succeeds to recover the latent matching as long as the edge correlation is nonvanishing. This is closely related to our previous work on a polynomial-time algorithm that matches two Gaussian Wigner matrices with nonvanishing correlation, and it provides the first polynomial-time random graph matching algorithm (regardless of the regime of q) when the edge correlation is below the square root of the Otter constant (which is ≈0.338). Funding: This research was partially supported by the National Key Research and Development Program of China [Grant 2023YFA1010103], the National Natural Science Foundation of China [Key Program Project 12231002], and the New Cornerstone Science Foundation [XPLORER Prize]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2024.0487 .

Problem definition: This paper considers the operations management problems under a newly proposed choice model referred to as a focal multinomial logit (FMNL) model. It generalizes the famous multinomial logit (MNL) model and various well-studied consideration-set choice models and can effectively capture irrational choice behaviors such as the context effect, halo effect, and choice overload, as well as the effect of focality. Methodology/results: We focus on the threshold focal set and various focal parameter settings, including the constant, cardinality, and linear threshold FMNL models, as well as a broader model that satisfies certain regularity conditions and subsumes the above models. We analyze the computational complexity and propose polynomial-time exact or approximation algorithms for assortment optimization problems under different focal parameters. We then characterize the optimal strategy for the joint price and assortment optimization problem. Our investigation into the statistical properties of maximum-likelihood estimators addresses identifiability, consistency, and convergence rates, as well as their implications on operations decisions. We also present a convex mixed-integer nonlinear programming reformulation method that achieves a global optimal estimator for model calibration. Managerial implications: Through extensive numerical experiments on synthetic and real data sets, we demonstrate the efficiency of the proposed algorithms, highlight the issues of model misspecification, and reveal revenue improvement under the family of FMNL models. Our analyses suggest that retailers should consider the impact of focality to potentially improve demand estimation accuracy and operations performance. Funding: L. Guan acknowledges financial support from the Fundamental Research Funds for the Central Universities [Grant 2025CX13014]. K. Nip acknowledges financial support from the National Natural Science Foundation of China [Grant 72571183]. L. Zhang acknowledges financial support from the National Natural Science Foundation of China [Grant 72471156] and the Hong Kong Research Grant Council [Grant GRF 16209923]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0381 .

Dr. Pratim Sengupta is a professor of learning sciences, and a member of the Graduate Faculty of Computational Media Design at the University of Calgary, where he has also served as Research Chair of STEM Education. Prior to joining the University of Calgary, Dr. Sengupta was a professor at Vanderbilt University's Peabody College, where he co-founded and chaired the Learning Sciences PhD program. He is the recipient of a National Science Foundation CAREER Award (2012) for his research on developing agent-based programming languages and integrating computational modeling in K12 science and math classrooms, and a Paul D. Fleck Award from the Banff Center for Arts and Creativity for his work on public computing. He is a Fellow of the International Society for Design and Development in Education. Significant editorial roles include executive editor of Cognition and Instruction (2017 – 2025), and senior editor for Oxford Research Encyclopedia of Education (2018 – ongoing). Prior to completing his PhD in learning sciences at Northwestern University, Dr. Sengupta attended Presidency College, Kolkata (India), the Indian Institute of Technology, Kharagpur (India) and Northwestern University (USA), where he received his undergraduate and graduate degrees in Physics. I first encountered Pratim’s work about five years ago while researching scholarship on transdisciplinarity in STEM and STEM education. At that time, and still today, there was a challenge in finding work that was truly transdisciplinary. Pratim’s conceptualization of coding as a participatory and embodied process aligned closely with my own view of transdisciplinary STEM. This interview is a slightly modified transcript of our conversation about transdisciplinarity and the dangers and opportunities of STEM education.

We are recipients of a National Science Foundation (NSF) ADVANCE grant, which was awarded to support our work on faculty service. In early May 2025, we received an early termination notice for the grant. Uncertainty about the grant’s longevity began after the 2024 United States presidential election and was intensified when we received an initial email pausing the grant in late January 2025. To cope with this uncertainty, the first author drew upon her work in poetic inquiry to create found poetry from the email messages we received between January and May of 2025. We present these poems to document a partial history of the recent NSF grant defunding process, and in recognition of the importance and potential impact of political poetry.

Min-max optimization of a function f from [Formula: see text] to [Formula: see text] is an important framework for modeling robustness in adversarial settings with applications to optimization, economics, and deep learning. Oftentimes, f is nonconvex–nonconcave, and finding a global min-max point is computationally intractable. There is a long line of work that seeks computationally tractable algorithms for alternatives to the min-max optimization formulation. However, many of these alternative solution concepts guarantee the existence of solution points only under strong assumptions on f, such as convexity or monotonicity of its gradient. We propose a new solution concept, the [Formula: see text]-greedy adversarial equilibrium, and show that it can serve as a computationally tractable alternative to min-max optimization. We prove the existence of such a point for any smooth bounded function with Lipschitz Hessian and give an algorithm that converges to an [Formula: see text]-greedy adversarial equilibrium in a number of evaluations of f, [Formula: see text], and [Formula: see text] that is polynomial in d, [Formula: see text], and the bounds of f and its Lipschitz constant. Funding: This work was supported by the National Science Foundation [Grant CCF-1908347]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2023.0262 .

We consider a dynamic system with multiple types of customers and servers. Each type of waiting customer or server joins a separate queue, forming a bipartite graph with customer-side queues and server-side queues. The platform can match the servers and customers and then the matched pairs leave the system. The platform will charge a customer a price according to their type when they arrive and will pay a server a price according to their type. The arrival rate of each queue is determined by the price according to some unknown demand or supply functions. Our goal is to design pricing and matching algorithms to maximize the profit of the platform with unknown demand and supply functions while keeping queue lengths of both customers and servers below a predetermined threshold. The difficulties of the problem include simultaneous learning and decision making and the tradeoff between maximizing profit and minimizing queue length. We use a longest-queue-first matching algorithm and propose a learning-based pricing algorithm, which combines gradient-free stochastic projected gradient ascent with bisection search. We prove that our proposed algorithm yields a sublinear regret [Formula: see text] and queue-length bound [Formula: see text], where T is the time horizon. We further establish a tradeoff between the regret bound and the queue-length bound. Funding: This research was supported in part by the National Science Foundation [Grants 2112471, 2207548, 2228974, and 2240981]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsy.2024.0073 .

Problem definition: Influencer marketing has become a prevalent strategy to promote products through social media. This paper examines the value of influencer marketing when followers not only learn from the influencer’s signal but can also engage in social learning by observing peers’ purchase behaviors and reviews. Methodology/results: We adopt an information design framework to analyze how a firm should value an influencer based on two key dimensions: the accuracy of the influencer’s past recommendations (informativeness) and the extent to which followers rely exclusively on the influencer versus learning from peers (charisma). Managerial implications: Our model uncovers insights about the interaction between information design and social learning. First, the naive intuition that the influencer is less valuable with social learning does not always hold. The influencer holds greater value under the social learning context when customers have a moderate intention to buy as her endorsement reinforces customer convictions, making them resilient against later negative feedback from other followers. Second, when the firm can strategically select an influencer, the optimal information structure is biased toward the positive signals: always endorse good products (true-positive rate of one) but sometimes endorse bad products (nonzero false-positive rate). Third, the optimal influencer when social learning exists has a lower false-positive rate than the one without social learning, meaning that when there exists subsequent social learning, it becomes even more important to have an influencer whose positive endorsement is trustworthy. In other words, the optimal influencer should be able to reveal more information with social learning than without social learning. Funding: This work was supported by the National Science Foundation [Grant 2208189]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2025.0348 .

Over the past few years, the United States has implemented a second round of data management policies, exemplified by the 2023 NIH Data Management and Sharing Policy and 2022 “Nelson Memo.” Effectively supporting public access to data and a data sharing culture at an academic research institution requires collaboration across various research support staff and central offices as well as knowledge of the current practices of researchers. Two research support groups at Duke University, the University Libraries (DUL) and the Office of Scientific Integrity (DOSI), have forged a strong working relationship for supporting data management and sharing practices, including an active Teams channel for communication, developing tools collaboratively, delivering trainings, and providing co-consults for data management. To more effectively understand “the state of play” at our institution, DUL and DOSI analyzed data management and sharing plans (DMSPs) submitted to the National Science Foundation (NSF) in 2021. The project team used a modified version of the DART rubric (https://osf.io/qh6ad/) to score DMSPs against required elements in key areas, including types of data; standards for data and metadata; access, sharing, and preservation; limitations on access, distribution, and reuse; and roles and responsibilities. In this paper we will present the key findings from the DMSP assessment project and discuss how, as data management specialists, we can use this information to plan for ongoing education, training, and resource development using a cross-campus collaboration model.

Maternal and neonatal outcomes after infection with monkeypox virus clade I during pregnancy in DR Congo: a pooled, prospective cohort study.

The even-swap method builds the dominance relations between alternatives based on trade-offs between criteria. This dominance relation is affected by the order (i.e., the decision path) in which decision makers put alternatives and criteria into trade-offs. Such a phenomenon is referred to as path dependence. Although some psychological factors contributing to path dependence have been identified, studies on the causes and conditions that lead to path dependence are still lacking, making it difficult to quantify the influence of decision path on dominance relations. This paper reveals that the trade-off process in the even-swap method resembles the matching process used to generate indifference curves. Inspired by this finding, indifference curves are used to analyze the trade-off process and obtain trade-off curves. Based on these trade-off curves, a mathematical definition of path dependence is proposed, which serves as the occurrence condition of path dependence in the even-swap method. A sensitivity analysis is conducted to examine the effects of risk attitude, loss attitude, and inconsistent reference points on path dependence. The results reveal two interdependent necessary conditions for path dependence: (1) the presence of kinks in the utility function and (2) the use of inconsistent reference points across trade-offs. Furthermore, path dependence is found to be more likely when both evaluation distances are less than 1 and evaluation gap ratios approach 1. Finally, two practical insights are provided to help avoid path dependence. Funding: This research was supported by the Sichuan Science and Technology Program [Grants 2025NSFSC1961, 2025NSFJQ0072], the National Natural Science Foundation of China [Grants 72171158, 72371173], and the Team Development Program at Sichuan University.

We study contextual dynamic pricing, where a decision maker posts personalized prices based on observable contexts and receives binary purchase feedback indicating whether the customer’s valuation exceeds the price. Each valuation is modeled as an unknown latent function of the context, corrupted by independent and identically distributed market noise from an unknown distribution. Relying only on Lipschitz continuity of the noise distribution and bounded valuations, we propose a minimax-optimal algorithm. To accommodate the unknown distribution, our method discretizes the relevant noise range to form a finite set of candidate prices, then applies layered data partitioning to obtain confidence bounds substantially tighter than those derived via the elliptical potential lemma. A key advantage is that estimation bias in the valuation function cancels when comparing upper confidence bounds, eliminating the need to know the Lipschitz constant. The framework extends beyond linear models to general function classes through offline regression oracles. Our regret analysis depends solely on the oracle’s estimation error, typically governed by the statistical complexity of the class. These techniques yield a regret upper bound matching the minimax lower bound up to logarithmic factors. Furthermore, we refine these guarantees under additional structures—for example, linear valuation models, second-order smoothness, sparsity, and known noise distribution or observable valuations—and compare our bounds and assumptions with prior dynamic-pricing methods. Finally, numerical experiments corroborate the theory and show clear improvements over benchmark methods. Funding: X. Gong’s research is generously supported by the National Natural Science Foundation of China [Grant 72501238]. W. You’s research is generously supported by the Hong Kong Research Grants Council [Grant GRF 16212823]. J. Zhang’s research is generously supported by the Hong Kong Research Grants Council [Theme-based Research Project T32-615/24-R]. Supplemental Material: All supplemental materials, including the code, data, and files required to reproduce the results are available at https://doi.org/10.1287/opre.2025.1779 .

ABSTRACT This paper deals with Kant's elaboration of a metaphysical foundation of the principle of inertia in the Metaphysical Foundations of Natural Science. Many of Kant's contemporaries treat inertia not as an issue of mathematical physics but rather as a general feature of material objects that is addressed by metaphysics and, to some extent, by theology as well. In turn, inertia is often seen as the reason why matter is fundamentally passive, thus providing an argument against materialism. In particular, Abraham Gotthelf Kästner and Johann Samuel Traugott Gehler are considered on this score. They agree with Kant in that the principle of inertia follows from the general causal principle. Contrary to Kant, Kästner and Gehler treat inertia as a phenomenon of experience, whereas it seems a unique feature of Kant's approach to conceive of inertia as expressing the lifelessness of matter.

We study the role of correlation in matching markets, where multiple decision makers simultaneously face selection problems from the same pool of candidates. We propose a model in which a candidate’s priority scores across different decision makers exhibit varying levels of correlation dependent on the candidate’s sociodemographic group. Such differential correlation can arise in school choice because of the varying prevalence of selection criteria, in college admissions because of test-optional policies, or because of algorithmic monoculture, that is, when decision makers rely on the same algorithms and data sets to evaluate candidates. We show that higher correlation for one of the groups generally improves the outcome for all groups, leading to higher efficiency. However, students from a given group are more likely to remain unmatched as their own correlation level increases. This implies that it is advantageous to belong to a low-correlation group. Finally, we extend the tie-breaking literature to multiple priority classes and intermediate levels of correlation. Overall, our results point to differential correlation as a previously overlooked systemic source of group inequalities in school, university, and job admissions. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Funding: This work was partially supported by MIAI @ Grenoble Alpes [Grants ANR-19-P3IA-0003 and ANR-23-PEIA-0003], by the French National Research Agency (ANR) [Grants ANR-19-CE48-0018 and ANR-20-CE23-0007], and by the National Science Foundation [Grant DMS-1928930] and by the Alfred P. Sloan Foundation [Grant G-2021-16778], which funded B. Pradelski’s residency at the Simons Laufer Mathematical Sciences Institute (formerly MSRI) in Berkeley, CA, during the Fall 2023 semester. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06067 .

Neighborhood disadvantage and adolescent sleep health: a longitudinal population-based study

Aggregating diverse human opinions in the digital era is essential in harnessing collective wisdom to unravel intricate management dilemmas. The aggregation process has significant hurdles to overcome due to the heterogeneity in the quality of individual opinions. This study introduces a CrowdRank machine learning architecture to provide an innovative solution to the central problem of opinion aggregation by learning to rank individual opinions. CrowdRank operates through two phases. (1) It leverages a BNN, pretrained on historical data, to conduct pairwise opinion comparisons. This network, designed to capture meaningful interactions between opinion features, adheres to key axiomatic principles to ensure a principled evaluation of opinion quality. (2) CrowdRank employs expectation propagation to synthesize these microassessments into a coherent global ranking of individual opinions. We validated the efficacy of our approach through a large-scale empirical investigation using real-world financial analyst forecasts. The validation results demonstrated the superiority of our approach over existing methods in accurately predicting both pairwise and aggregate opinion rankings. Importantly, CrowdRank significantly improves the objectivity and precision of collective financial analyst forecasts. This study contributes a theoretically robust and practically validated innovation to opinion aggregation and charts a new path in the application of machine learning to enhance the synthesis of collective wisdom. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partly supported by the National Natural Science Foundation of China [Grants 92370204 and 71974031]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0835 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0835 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

The Board of Educational Affairs is pleased to bestow the Award for Distinguished Contributions of Applications of Psychology to Education and Training. This award acknowledges psychologists who contribute to new teaching methods or solutions to learning problems through the use of research findings or evidence-based practices. The 2025 recipient of the APA Award for Distinguished Contributions of Applications of Psychology to Education and Training is Clarissa A. Thompson. Thompson's long-time passion for mathematics education and the translational applications of best practice principles and educational theory to support teacher training, parental support, and student understanding of mental representations of numbers makes her a fitting recipient of this award. Her generative and far-reaching research agenda spans multiple social sciences disciplines-cognitive, developmental, and educational psychology-whilst also prioritizing collaboration with scholars in special education, health psychology, and sociology to maximize impact across fields. Through consistent success in securing extramural funding from government entities such as the U.S. Department of Education, Institute of Education Sciences and the National Science Foundation, Thompson develops workshops and educational intervention projects that prioritize the needs of children and adult learners in the classroom, addressing their attitudes, anxieties, and confidence in mathematics learning, numerical cognition, and problem-solving. Beyond exemplary research, Thompson's influence extends through her leadership in the scientific community through journal editorship, and mentorship commitment to early-career and diverse scholars in educational and child psychology. She strives to create meaningful change in educational practice research whilst also nurturing the next generation of researchers. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The effect of long-acting cabotegravir and rilpivirine treatment on drug resistance in South Africa: a modelling study.

This article presents the results of phytosanitary monitoring conducted in citrus plantations across the Adjara-Guria region during the 2023-2024 growing seasons, within the framework of the Shota Rustaveli National Science Foundation of Georgia project (FR-22-2178). The survey revealed the presence of several citrus diseases caused by fungal, viral, and bacterial pathogens. This study focuses on the identification and description of major fungal diseases detected during the monitoring, including Citrus Scab (causal agent - Elsinoe fawcettii Bitanc. &amp; Jenkins), Anthracnose (causal agent - Colletotrichum gloeosporioides Penz. &amp; Sacc), Alternariosis (causal agent - Alternaria citri Ellis &amp; Pierce), Melanose (causal agent - Phomopsis citri H.S. Fawc), Fusarium wilt/rot (causal agent - Nectria haematococca and Fusarium spp.), and Sooty Mold (causal agent - Capnodium citri). These diseases exhibit high pathogenicity and pose a serious threat to the sustainable development of citrus cultivation in Georgia. The article provides a detailed analysis of disease symptomatology, along with the morphological and cultural characteristics of the associated fungal pathogens.. KEYWORDS :Citrus, Disease, Fungi, Monitoring, Pathogen.