Search

The primary focus of brand equity research has been on how brand knowledge creates value for firms through customer behavior in product markets. Using archival data and five experiments, this article tests a framework that outlines the unique role brands play in the labor market. The framework distinguishes between vertical and horizontal differentiation and shows that vertical brand differentiation is associated with lower pay, whereas horizontal brand differentiation is associated with higher pay. Employees are also vertically and horizontally differentiated, and firms high in horizontal brand differentiation pay more for employees who match their brands' differentiating characteristics (i.e., brand-relevant complementarities). Results show that these brand–pay relationships have important downstream effects on employee behavior and, consequently, on firm profits. Specifically, leveraging vertical brand differentiation to lower pay represents a false economy because profits are attenuated by negative effects on employee productivity and retention. In contrast, when managers at firms high on horizontal brand differentiation pay more, profits increase via the same mediating employee behaviors. Six firm strategies and investments that influence firm bargaining power in the employee–brand matching process are found to moderate the brand–pay relationship and downstream effects on profits. [ABSTRACT FROM AUTHOR]

We report on a laboratory experiment testing the predictions of the Diamond–Mortensen–Pissarides (DMP) search-and-matching model, which is a workhorse, decentralized model of unemployment and the labor market. We focus on the job vacancy posting problem that firms face in the DMP model. We explore the model's comparative statics predictions concerning variations in the separation rate, the vacancy posting cost, and the firm's surplus earned per employee. Across all treatments, we find strong evidence for an inverse relationship between vacancies and unemployment, consistent with the Beveridge curve. We also find that the results of our various comparative statics exercises are in-line with the predictions of the theory.

When well-known food resources are running out, animals extinguish their foraging behavior in that food patch and increasingly work for reward-related information to decrease outcome uncertainty. In the absence of such information, a potentially successful strategy consists of spending more time and effort searching for profitable locations—a phenomenon known from extinction learning experiments conducted in conventional conditioning chambers. Here, we tested this hypothesis by means of a semi-natural “foraging board” allowing pigeons to move and look for food items inside perforated holes. The holes could be covered with a slit plastic tape, hiding the food items they contained while making them accessible to pigeons. Our goal was to determine how pigeons forage on hidden food items in an area associated with uncertainty (one in three holes baited, on average) when visible or hidden food items were available in an adjacent area associated with certainty (each hole baited). The number of food items was equivalent in both areas. We expected longer time spent and more pecks given in the uncertain vs. certain area with the food items visible in the certain area, as well as longer time spent and more pecks given per visit in the uncertain vs. certain area with the food items either visible or hidden in the certain area. Our results confirm these predictions.

The study of approximate matching in the Massively Parallel Computations (MPC) model has recently seen a burst of breakthroughs. Despite this progress, we still have a limited understanding of maximal matching which is one of the central problems of parallel and distributed computing. All known MPC algorithms for maximalmatching either take polylogarithmic time which is considered inefficient, or require a strictly superlinear space of n1+Ω(1) per machine. In this work, we close this gap by providing a novel analysis of an extremely simple algorithm, which is a variant of an algorithm conjectured to work by Czumaj, Lacki, Madry, Mitrovic, Onak, and Sankowski [15]. The algorithm edge-samples the graph, randomly partitions the vertices, and finds a random greedy maximal matching within each partition.We show that this algorithm drastically reduces the vertex degrees. This, among other results, leads to an O(log log Δ) round algorithm for maximal matching with O(n) space (or even mildly sublinear in n using standard techniques). As an immediate corollary, we get a 2 approximate minimum vertex cover in essentially the same rounds and space, which is the optimal approximation factor under standard assumptions. We also get an improved O(log log Δ) round algorithm for 1 + ε approximate matching. All these results can also be implemented in the congested clique model in the same number of rounds. [ABSTRACT FROM AUTHOR]

Technology-augmented choice-making impacts many facets of business. The use of economic incentives under the ubiquitous mobile ecosystem for prosocial behavior has been shown to be particularly effective. He build on the previous work on this topic and study how mobile-based economic incentives and environments influence charitable giving behavior. In contrast to traditional fund-raising, we consider the use of mobile devices to generate giving in small denominations, which we term microgiving. In collaboration with a US-based mobile app provider, we incorporated a functionality that allowed users to contribute their in-app reward points to charity. To encourage donations, we used economic incentives in the form of monetary subsidies, i. e., rebates or matching grants, as well as digital nudges in the form of push notifications. ITe studied the effects of these factors on giving behavior across two large-scale field experiments. Focusing on the different aspects of smartphones that could differentially impact charitable giving behavior--namely the intensely private and personal nature of smartphones--we examined how the visibility of donation decisions affects giving behavior by toggling audience effects. Our results show that the effectiveness of incentives is contingent upon the magnitude of the incentive as well as the extent to which individual decisions are visible to others. To situate our results in relation to the traditional medium of charitable giving, we propose an analytical model that internalizes the subsidy rates and the audience effect. This study provides initial empirical evidence and an analytical model to advance technology-augmented charitable giving that can provide insights to organizations and senice providers. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC), as a computing model that provides services on the user side, can effectively solve the problems of high delay and resource shortage in traditional cloud computing when processing massive data. However, existing edge computing resources are still limited, and difficult to provide services to users in inaccessible remote areas. Considering that unmanned aerial vehicle (UAV) has the advantages of easy deployment, high flexibility and low cost, a UAV-assisted MEC hierarchical computation offloading framework is proposed. Firstly, contract theory is used to solve the information asymmetry problem between the platform and the UAV, and the UAV is encouraged to provide computing services. By analyzing the attributes and conditions of feasible contracts, the optimal contract is designed using the Lagrange multiplier method. Secondly, by constructing the preference set between UAV and mobile user (MU), a mobile user and unmanned aerial vehicle bilateral matching (MUBM) algorithm is proposed to establish the connection between user tasks and UAV computing resources. Finally, the feasibility and effectiveness of the contract were verified through experiments. The experimental results also prove the stability of the MUBM. [ABSTRACT FROM AUTHOR]

Let k be a positive integer. A graph G is k -matching-connected if G − V (M) is connected and nontrivial, for each matching M of G with | M | ≤ k − 1. For a k -matching-connected graph G , an edge e ∉ E (G) is said to be addible if G + e is still k -matching-connected. In this paper, we prove that every 2-matching-connected graph G has no addible edge if and only if G is a cycle of length at least 4. We also show that every 3-matching-connected graph G with δ (G) ≥ 5 has an addible edge, and some examples are given to show that δ (G) = 5 is the best possible lower bound. [ABSTRACT FROM AUTHOR]

Background: Methods for matching in longitudinal cohort studies, such as sequential stratification and time-varying propensity scores, facilitate causal inferences in the context of time-dependent treatments that are not randomized where patient eligibility or treatment status changes over time. The tradeoffs in available approaches have not been compared previously, so we compare two methods using simulations based on a retrospective cohort of patients eligible for weight loss surgery, some of whom received it. Methods: This study compares matching completeness, bias, coverage, and precision among three approaches to longitudinal matching: (1) time-varying propensity scores (tvPS), (2) sequential stratification that matches exactly on all covariates used in tvPS (SS-Full) and (3) sequential stratification that exact matches on a subset of covariates (SS-Selected). These comparisons are made in the context of a deep sampling frame (50:1) and a shallow sampling frame (5:1) of eligible comparators. A simulation study was employed to estimate the relative performance of these approaches. Results: In 1,000 simulations each, tvPS retained more than 99.9% of treated patients in both the deep and shallow sampling frames, while a smaller proportion of treated patients were retained for SS-Full (91.6%) and SS-Selected (98.2%) in the deep sampling frame. In the shallow sampling frame, sequential stratification retained many fewer treated patients (73.9% SS-Full, 92.0% SS-Selected) than tvPS yet coverage, precision and bias were comparable for tvPS, SS-Full and SS-Selected in the deep and shallow sampling frames. Conclusion: Time-varying propensity scores have comparable performance to sequential stratification in terms of coverage, bias, and precision, with superior match completeness. While performance was generally comparable across methods, greater match completeness makes tvPS an attractive option for longitudinal matching studies where external validity is highly valued. [ABSTRACT FROM AUTHOR]

Ride-sharing services have attracted significant interest due to overcrowding, limited energy resources, and environmental concerns. This study proposes a mathematical programming model that integrates user preferences into the ride-sharing problem with transfer. Passenger transfer in ride-sharing problems addresses the limitations of the matching process, especially in less-populated areas. It allows passengers to be dropped off at meeting points and continue with another driver. Moreover, considering user preferences in ride-sharing systems is crucial for enhancing efficiency and user satisfaction. Accordingly, we propose a Preference-Driven Matching Algorithm for the matching process. Our proposed algorithm captures user preferences and provides potential matches. In addition, we introduce an Iterative Enhance-and-Optimize Algorithm capable of producing high-quality solutions within short computational times. We evaluate the efficiency of these approaches across various instances, focusing on real-scale scenarios. Based on the results, the model with preferences demonstrates effective performance compared to other methods. Our findings underscore the importance of user preferences in optimizing ride-sharing problems and highlight the trade-off between efficiency and user satisfaction. By incorporating user preferences, our approach results in higher user satisfaction, a more responsive and efficient system with reduced response times, and increased demand and revenue by servicing more users. [ABSTRACT FROM AUTHOR]

From early in the coronavirus disease 2019 (COVID‐19) pandemic, there was interest in using machine learning methods to predict COVID‐19 infection status based on vocal audio signals, for example, cough recordings. However, early studies had limitations in terms of data collection and of how the performances of the proposed predictive models were assessed. This article describes how these limitations have been overcome in a study carried out by the Turing‐RSS Health Data Laboratory and the UK Health Security Agency. As part of the study, the UK Health Security Agency collected a dataset of acoustic recordings, SARS‐CoV‐2 infection status and extensive study participant meta‐data. This allowed us to rigorously assess state‐of‐the‐art machine learning techniques to predict SARS‐CoV‐2 infection status based on vocal audio signals. The lessons learned from this project should inform future studies on statistical evaluation methods to assess the performance of machine learning techniques for public health tasks. [ABSTRACT FROM AUTHOR]

As open-source communities and other peer production projects face the challenge of sustaining themselves over time, the role of offline gatherings in fostering community resilience becomes a vital question. This study centres on the potential of offline social interactions as a means to mitigate declines within such communities, using Wikipedia as a case example. Using a comprehensive dataset spanning informal meetups within the German-language Wikipedia community from 2001 to 2020 and combining it with large scale online activity data, this study investigates the relationship between offline gatherings and online contribution behaviour. Results show that attending meetups has a positive effect on contributing towards Wikipedia both in the short and long term. By shedding light on the significance of offline social interactions in bolstering online communities, this study offers valuable insights into potential strategies for sustaining collaborative online environments amidst broader declines and further shows how offline and online activities intertwine. [ABSTRACT FROM AUTHOR]

A classic trade-off that school districts face when deciding which matching algorithm to use is that it is not possible to always respect both priorities and preferences. The student-proposing deferred acceptance algorithm (DA) respects priorities but can lead to inefficient allocations. We identify a new condition on school choice markets under which DA is efficient. Our condition generalizes earlier conditions by placing restrictions on how preferences and priorities relate to one another only on the parts that are relevant for the assignment. Whenever there is a unique allocation that respects priorities, our condition captures all the environments for which DA is efficient. We show through stylized examples and simulations that our condition significantly expands the range of known environments for which DA is efficient. We also discuss how our condition sheds light on existing empirical findings. [ABSTRACT FROM AUTHOR]

A little less than half of the students of higher Ed institutions in the US graduate in four years, and only around 60% finish in six years. Retention rates are also less than ideal. Colleges have been experimenting with a variety of programs and policies to address this issue, especially less selective institutions whose rates are significantly lower. In this paper, we evaluate a student success and retention program called Early Alert that was implemented at a public state university in the US with a medium-to-large student body. Our dataset contains several years' worth of information on students' socio-demographic characteristics, class standing and average grades (GPAs), as well as their midterm and final grades in undergraduate courses. We employ several causal inference techniques developed for observational studies and elicit negative average treatment effects on the treated (ATT). Since it is conceivable that unobserved confounders are the real drivers of our empirical results, not the treatment, we carry out two different types of sensitivity analyses. Together with our treatment effect estimations, they lead us to the main conclusion that Early Alert does not improve student performance, at least not in the short run (as measured by course performance), and likely has a negligible impact. [ABSTRACT FROM AUTHOR]

In matching models, pairwise-stable outcomes do not generally exist without substantial restrictions on both preferences and the topology of the network of contracts. We address the foundations of matching markets by developing a matching model with a continuum of agents that allows for arbitrary preferences and network structures. We show that pairwise-stable outcomes are guaranteed to exist. When agents can interact with multiple other counterparties, pairwise stability is too weak of a solution concept, and we argue that a refinement of it called tree stability is the most appropriate solution concept in this setting. Our main results show that tree-stable outcomes exist for arbitrary preferences and network topologies. [ABSTRACT FROM AUTHOR]

It is common to conduct causal inference in matched observational studies by proceeding as though treatment assignments within matched sets are assigned uniformly at random and using this distribution as the basis for inference. This approach ignores observed discrepancies in matched sets that may be consequential for the distribution of treatment, which are succinctly captured by within-set differences in the propensity score. We address this problem via covariate-adaptive randomization inference, which modifies the permutation probabilities to vary with estimated propensity score discrepancies and avoids requirements to exclude matched pairs or model an outcome variable. We show that the test achieves type I error control arbitrarily close to the nominal level when large samples are available for propensity score estimation. We characterize the large-sample behaviour of the new randomization test for a difference-in-means estimator of a constant additive effect. We also show that existing methods of sensitivity analysis generalize effectively to covariate-adaptive randomization inference. Finally, we evaluate the empirical value of combining matching and covariate-adaptive randomization procedures using simulations and analyses of genetic damage among welders and right-heart catheterization in surgical patients. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to investigate and highlight the most important factors influencing the candidate's selection for residency matching by program directors of surgical and non-surgical specialties in Saudi Arabia. Method: This cross-sectional study was conducted in the Central, Eastern, Western, Northern, and Southern regions of Saudi Arabia from February 1, 2023, to August 31, 2023. A total of sixty-one program directors completed a questionnaire, which was adapted and validated from a previous study with permission and tailored to meet the objectives of this research. Results: The mean age of the study population was 40.2 years, with surgical specialty program directors being older at 41.7 years and nonsurgical specialty program directors being younger at 39.6 years. In both cases, the highest importance was given to 'Applicant's attitude and professionalism', followed in surgical specialties by 'Doing clinical rotations in the PD institution' and 'Performance in clinical rotation during internship' in non-surgical specialties. with a mean score of 4.13 and 4.95, respectively. The importance of 'The impression/opinion of current residents' has had a certain importance in both specialties. While the number of research projects was prioritized more by surgical PDs (38.6%) compared to non-surgical PDs (29.4%). Conclusion: This study highlights the critical role of professionalism and a positive attitude in clinical rotations and interviews for program acceptance in Saudi Arabia. The significance of letters of recommendation and research focus varies between surgical and non-surgical program directors. Program directors should adopt effective recruitment strategies that incorporate practical implications during internships. Key messages: This research focuses on the evolving area of residency applicant selection criteria in Saudi Arabia. In 2024, over 6400 individuals were nominated for residency programs across the country, with approximately 5100 successfully matched following interviews. These statistics underscore the complexity and competitiveness of the matching system in Saudi Arabia, underscoring the urgency for similar research. Such studies are essential to provide guidance to future applicants, helping them navigate the selection process effectively and avoid common pitfalls. [ABSTRACT FROM AUTHOR]

Firms seeking business partners and individuals seeking life partners face several challenges in addition to finding available candidates. One of these challenges is uncertainty about a candidate's compatibility with the match-seeker on various subjective criteria. Another challenge is authentication of objective quality features, particularly in online settings where credentials can be easily misrepresented. Online matching platforms can address these challenges through counseling services that help assess mutual compatibility of match-seekers and authentication services to validate match-seekers' credentials. Using a stylized model, we show that the platform's decisions to offer counseling and authentication services are influenced by match-seeker behavior in response to factors such as the platform's counseling capability, the composition of the matching market, and the interactions between the two services. This leads to optimal strategies for the platform that are not obvious and can even be counterintuitive. For instance, offering better counseling does not necessarily result in higher revenues for the platform. And it may be optimal for the platform to reduce its access fee when it decides to offer an authentication service. We also show that when the platform's counseling capability is modest, an increase in this capability increases the value of authentication (i.e., counseling complements authentication); however, when the platform's counseling capability is high, counseling and authentication become substitutes, because narrowing the search to only high-value candidates based on authentication can exclude some compatible candidates. Our analysis and results provide valuable insights for platform operators on the relative value of counseling and authentication, and for researchers studying matching platforms for vertically and horizontally differentiated markets. This paper was accepted by Hemant Bhargava, information systems. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03807. [ABSTRACT FROM AUTHOR]

Let β > 0 . Motivated by the notion of jumbled graphs introduced by Thomason, the expander mixing lemma and Haemers's vertex separation inequality, we say that a graph G with n vertices is a weakly (n , β) -graph if | X | | Y | (n - | X |) (n - | Y |) ≤ β 2 holds for every pair of disjoint proper subsets X, Y of V(G) with no edge between X and Y. It is an (n , β) -graph if in addition X and Y are not necessarily disjoint. Using graph eigenvalues, we show that every graph can be an (n , β) -graph and/or a weakly (n , β) -graph for some specific value β . For instances, the expander mixing lemma implies that a d-regular graph on n vertices with the second largest absolute eigenvalue at most λ is an (n , λ / d) -graph, and Haemers's vertex separation inequality implies that every graph is a weakly (n , β) -graph with β ≥ μ n - μ 2 μ n + μ 2 , where μ i denotes the i-th smallest Laplacian eigenvalue. This motivates us to study (n , β) -graph and weakly (n , β) -graph in general. Our main results include the following. (i) For any weakly (n , β) -graph G, the matching number α ′ (G) ≥ min 1 - β 1 + β , 1 2 · (n - 1) . If in addition G is a (U, W)-bipartite graph with | W | ≥ t | U | where t ≥ 1 , then α ′ (G) ≥ min { t (1 - 2 β 2) , 1 } · | U | . (ii) For any (n , β) -graph G, α ′ (G) ≥ min 2 - β 2 (1 + β) , 1 2 · (n - 1). If in addition G is a (U, W)-bipartite graph with | W | ≥ | U | and no isolated vertices, then α ′ (G) ≥ min { 1 / β 2 , 1 } · | U | . (iii) If G is a weakly (n , β) -graph for 0 < β ≤ 1 / 3 or an (n , β) -graph for 0 < β ≤ 1 / 2 , then G has a fractional perfect matching. In addition, G has a perfect matching when n is even and G is factor-critical when n is odd. (iv) For any connected (n , β) -graph G, the toughness t (G) ≥ 1 - β β . For any connected weakly (n , β) -graph G, t (G) > 5 (1 - β) 11 β and if n is large enough, then t (G) > 1 2 - ε 1 - β β for any ε > 0 . The results imply many old and new results in spectral graph theory, including several new lower bounds on matching number, fractional matching number and toughness from eigenvalues. In particular, we obtain a new lower bound on toughness via normalized Laplacian eigenvalues that extends a theorem originally conjectured by Brouwer from regular graphs to general graphs. [ABSTRACT FROM AUTHOR]

The development of green products in manufacturing enterprises is challenged by limited independent innovation capabilities and insufficient green technology. Based on knowledge-based theory, this study investigates the influence of boundary-spanning green technology search (BGTS) and technology integration capability (TIC) on green product development performance (GPDP). Using polynomial regression analysis with response surface methodology (PRA with RSM) on data from 341 companies, the following findings were obtained: (1) Higher levels of BGTS and TIC congruence are associated with higher GPDP compared to lower levels of congruence. (2) The degree of discrepancy between the BGTS and TIC has an inverted U-shaped relationship with GPDP. (3) GPDP is higher in the "low BGTS–high TIC" combination than in the "high BGTS–low TIC" combination. (4) Green technology innovation capability mediates the relationship between BGTS-TIC congruence and GPDP. (5) Strategic foresight positively moderates the relationship between the BGTS-TIC congruence and green technology innovation capability. These findings enrich the research content of organizational search theory, deepen the research of green technology innovation, expand research on the development of green products, and provide a decision-making reference for manufacturing enterprises' green transition practices. [ABSTRACT FROM AUTHOR]

The 2-Decomposition Conjecture, equivalent to the 3-Decomposition Conjecture stated in 2011 by Hoffmann-Ostenhof, claims that every connected graph G with vertices of degree 2 and 3, for which G \ E (C) is disconnected for every cycle C, admits a decomposition into a spanning tree and a matching. In this work we present two main results focused on developing a strategy to prove the 2-Decomposition Conjecture. One of them is a list of structural properties of a minimum counterexample for this conjecture. Among those properties, we prove that a minimum counterexample has girth at least 5 and its vertices of degree 2 are at distance at least 3. Motivated by the class of smallest counterexamples, we show that the 2-Decomposition Conjecture holds for graphs whose vertices of degree 3 induce a collection of cacti in which each vertex belongs to a cycle. The core of the proof of this result may possibly be used in an inductive proof of the 2-Decomposition Conjecture based on a parameter that relates the number of vertices of degree 2 and 3 in a minimum counterexample. [ABSTRACT FROM AUTHOR]

In matching markets, objects are allocated to agents without monetary transfers, based on agents' preferences. However, agents may not have enough information to determine their preferences over the objects precisely. How should a benevolent planner optimally reveal information to maximize social welfare in this context? I show that when agents are symmetric and there are just two options, letting each agent know his rank in the realized distribution of preferences – but not his actual preferences – always improves social welfare over providing no information. When there are more objects, this rank-based information policy generalizes to the Object Recommendation (OR) Signal, which consists of simply recommending each agent to pick his socially-optimal choice. Under a mild regularity condition, I show that, when agents' a priori relative preferences over the objects are "not too strong", the OR Signal, used together with any standard ordinal mechanism, not only maximizes welfare, but achieves the unconstrained social optimum — formalizing the intuition that when people do not have strong opinions over several options, it is easy to sway them. • In this paper we tackle the information design problem of a benevolent planner in a school choice problem, where students do not fully know their own preferences over schools. • When students are symmetric and there are just two schools, letting each student know his rank in the realized distribution of preferences – but not his actual preferences – always improves social welfare over providing no information. • When there are more schools, this rank-based information policy generalizes to the Object Recommendation (OR) Signal, consisting of simply recommending each student to pick his socially-optimal choice — which achieves first best aggregate welfare, as long as students' a priori preferences over schools are not "too strong". [ABSTRACT FROM AUTHOR]

Background: Pregnancy with raised renal parameters was considered high risk but the degree of this risk is unclear. Hence the present study was done to study the effect of raised renal parameters among women with preeclampsiaon adverse maternal and fetal outcomes. Methodology: A Case -Control study was conducted among 100 patients of Pre-eclampsia,50 patients of Preeclampsia with raised renal parameters were included in the study group as cases and 50 matched preeclamptic patients with normal renal parameters were chosen as controls for the study. Matching was done with respect to certain variables like age group, gestational age, gravida and associated co-morbid condition (Pre-Eclampsia) to avoid bias, for a period of one year to study the effect of altered renal function test sonmaterna land fetal outcome in preeclampsia. Results: Compared to women in the control group, women in the study group had 3.7 times increased odds of preterm delivery (OR, 3.7; 95% CI, 1.0-15.2) and 2.4 times increased odds of delivery via caesarean section (OR, 2.4; 95% CI, 1.09-5.4). Women in the study group was also associated with two-fold increased odds of low birth weight (OR, 2.1; 95% CI, 0.5-9.6). Raised renal parameters among pre-eclamptic women was not associated with increased risk of maternal death in the present study. Conclusion: Raised / abnormal renal parameters in pregnancy is independently associated with adverse maternal and fetal outcomes when other variables and a comorbid condition (Pre-eclampsia)was controlled by matching. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept of Conditional Deployable Biometrics (CDB), designed to deliver consistent performance across various biometric matching scenarios, including intra-modal, multimodal, and cross-modal applications. The CDB framework provides a versatile and deployable biometric authentication system that ensures reliable matching regardless of the biometric modality being used. To realize this framework, we have developed CDB-Net, a specialized deep neural network tailored for handling both periocular and face biometric modalities. CDB-Net is engineered to handle the unique challenges associated with these different modalities while maintaining high accuracy and robustness. Our extensive experimentation with CDB-Net across five diverse and challenging in-the-wild datasets illustrates its effectiveness in adhering to the CDB paradigm. These datasets encompass a wide range of real-world conditions, further validating the model's capability to manage variations and complexities inherent in biometric data. The results confirm that CDB-Net not only meets but exceeds expectations in terms of performance, demonstrating its potential for practical deployment in various biometric authentication scenarios. [ABSTRACT FROM AUTHOR]

This article presents a matching algorithm for bipartite graphs containing repetitive structures and represented by intension as Set-Based Graphs. Under certain conditions on the structure of the graphs, the computational cost of this novel algorithm is not affected by the cardinality of the sets of vertices and edges. The main application of the algorithm is that of matching large Equation-Based Models where provided that most equations are defined using for loop statements that iterate over vectors of unknown variables, the computational cost becomes independent of the growth of the vectors involved. Besides introducing the algorithm, the article describes its implementation in a Modelica compiler and studies its performance over different test models. [ABSTRACT FROM AUTHOR]

Effective preoperative matching of organ transplantation is essential for the success of organ transplantation. The current methods of preoperative matching for xenotransplantation are derived from human allogeneic organ transplantation. However, these methods are simple and cannot accurately predict whether rejection will occur after transplantation. A noteworthy feature of xenotransplantation lies in the stability of the donor source and the clarity of its genotype, which facilitate convenient sampling and ensure robust repeatability. The multi-link and multi-dimensional preoperative matching is conducive to accurately screening suitable donors and reducing the occurrence of postoperative rejection after xenotransplantation, including making full use of the advantages of donor control in xenotransplantation, expanding tissue level matching methods on the basis of improving traditional cytological matching, paying attention to the role of endothelial cells in matching, and developing organic-level matching methods. Therefore, this article reviews the methods of allogeneic organ transplantation matching, current methods, existing problems and possible breakthroughs of xenotransplantation matching, in order to provide reference for further research on xenotransplantation matching. [ABSTRACT FROM AUTHOR]

Previous studies reported mixed findings on autistic individuals' pitch perception relative to neurotypical (NT) individuals. We investigated whether this may be partly due to individual differences in cognitive abilities by comparing their performance on various pitch perception tasks on a large sample (n = 164) of autistic and NT children and adults. Our findings revealed that: (i) autistic individuals either showed similar or worse performance than NT individuals on the pitch tasks; (ii) cognitive abilities were associated with some pitch task performance; and (iii) cognitive abilities modulated the relationship between autism diagnosis and pitch perception on some tasks. Our findings highlight the importance of taking an individual differences approach to understand the strengths and weaknesses of pitch processing in autism. [ABSTRACT FROM AUTHOR]

Life course epidemiology is hampered by the absence of large studies with exposures and outcomes measured at different life stages in the same individuals. We describe when the effect of an exposure (A) on an outcome (Y) in a target population is identifiable in a combined ("synthetic") cohort created by pooling an early-life cohort including measures of A with a late-life cohort including measures of Y. We enumerate causal assumptions needed for unbiased effect estimation in the synthetic cohort and illustrate by simulating target populations under four causal models. From each target population, we randomly sampled early- and late-life cohorts and created a synthetic cohort by matching individuals from the two cohorts based on mediators and confounders. We estimated the effect of A on Y in the synthetic cohort, varying matching variables, the match ratio, and the strength of association between matching variables and A. Finally, we compared bias in the synthetic cohort estimates when matching variables did not d-separate A and Y to the bias expected in the original cohort. When the set of matching variables includes all variables d-connecting exposure and outcome (i.e., variables blocking all backdoor and front-door pathways), the synthetic cohort yields unbiased effect estimates. Even when matching variables did not fully account for confounders, the synthetic cohort estimate was sometimes less biased than comparable estimates in the original cohort. Methods based on merging cohorts may hasten the evaluation of early- and mid-life determinants of late-life health but rely on available measures of both confounders and mediators. [ABSTRACT FROM AUTHOR]

Summary: Most recent empirical applications of matching with transferable utility have imposed a natural restriction: that the joint surplus be separable in the sources of unobserved heterogeneity. We propose here two simple methods to estimate models in this class. The first method is a minimum distance estimator that relies on the generalized entropy of matching. The second applies to the more special but popular Choo and Siow model, which reformulates as a generalized linear model with two‐way fixed effects. Neither method requires solving for the stable matching. Both methods are easy to apply and perform very well. [ABSTRACT FROM AUTHOR]

During COVID-19, the Norwegian Government provided compensation to amend firms' decreased revenues, yet it should be neutral and tailored to losses only. However, firm resources or institutional factors may have influenced the compensation, which we study here. Survey data showed a high probability of compensation among large firms, although they were not hit particularly hard by COVID-19. Also, compensation was prevalent for firms in the hospitality, tourism, and culture industry, and a likely explanation is that they were hit hard. However, their probability of compensation was prevalent compared to firms in other industries, regardless of revenue losses. We assume that large firms' compensation was due to their resources to apply for funding successfully, while institutional factors explained the compensation for firms in the hospitality, tourism, and culture industry. [ABSTRACT FROM AUTHOR]

We present a competitive matching model in which indivisible assets are reallocated among many traders. The model has three features: (i) traders are heterogeneous in their prospects as buyers, sellers, and also in their stand-alone values with endowed assets, (ii) buyers do not know true values of assets sold, (iii) sellers can disclose values of their assets by paying fees. Despite its complexity, the model admits closed-form solutions. Two main results emerge. First, if full Disclosure is facilitated by a monopolist, it captures a large fraction of the welfare gains. Second, adding the option of minimum disclosure, when combined with a cap regulation on price-dependent fees for full disclosure, significantly weakens the monopolist's power. [ABSTRACT FROM AUTHOR]

The multiplicative first Zagreb index is the product of the square of the degree of vertices in a graph G. The multiplicative reformulated first Zagreb index is defined as Π1, e (G) = Πx12x2∈E(G) (dG(x1) +dG(x1) - 2)²,where E(G) is the edge set of a graph G and dG(x1) is the degree of a vertex x1 in a graph G. In this paper, we characterize the minimum and maximum trees and unicyclic graphs with respect to matching and perfect matching using this graph invariant Π1,e(G) among the collection of all n-vertex graphs. [ABSTRACT FROM AUTHOR]

Purpose: The objective was to compare pain and related psychological factors during the preoperative and acute postoperative period between male and female patients, who underwent non mesh primary unilateral inguinal hernia repair. Methods: After ethics approval, informed consent was obtained, and data were collected. Male and female participants were compared by manually matching one-to-one on 10 variables. Descriptive statistics (mean ± standard deviation and frequency) as well as numerical rating scales from 0 to 10 were used. Comparison tests were performed using Chi-square or Fisher's Exact test for categorical data and independent samples t-test or non-parametric equivalent tests for numerical scores. p < 0.05 is reported as statistically significant. To control type I error, Bonferroni correction was used. Results: 72 participants with 36 matched pairs were included. Sex differences were found for operation length (p =.006), side of operation (p =.002), and hernia type (p =.013). Significant differences between the sexes were not found at the preoperative or postoperative time for resilience, pain interference or pain severity related measures, postoperative hernia pain incidence, pain catastrophizing, depression and anxiety symptoms, or return to normal activities. Conclusion: When controlling for known confounders and using a conservative Type I error rate, pain and related factors between the sexes did not differ significantly. [ABSTRACT FROM AUTHOR]

Abstract Background Methods for matching in longitudinal cohort studies, such as sequential stratification and time-varying propensity scores, facilitate causal inferences in the context of time-dependent treatments that are not randomized where patient eligibility or treatment status changes over time. The tradeoffs in available approaches have not been compared previously, so we compare two methods using simulations based on a retrospective cohort of patients eligible for weight loss surgery, some of whom received it. Methods This study compares matching completeness, bias, coverage, and precision among three approaches to longitudinal matching: (1) time-varying propensity scores (tvPS), (2) sequential stratification that matches exactly on all covariates used in tvPS (SS-Full) and (3) sequential stratification that exact matches on a subset of covariates (SS-Selected). These comparisons are made in the context of a deep sampling frame (50:1) and a shallow sampling frame (5:1) of eligible comparators. A simulation study was employed to estimate the relative performance of these approaches. Results In 1,000 simulations each, tvPS retained more than 99.9% of treated patients in both the deep and shallow sampling frames, while a smaller proportion of treated patients were retained for SS-Full (91.6%) and SS-Selected (98.2%) in the deep sampling frame. In the shallow sampling frame, sequential stratification retained many fewer treated patients (73.9% SS-Full, 92.0% SS-Selected) than tvPS yet coverage, precision and bias were comparable for tvPS, SS-Full and SS-Selected in the deep and shallow sampling frames. Conclusion Time-varying propensity scores have comparable performance to sequential stratification in terms of coverage, bias, and precision, with superior match completeness. While performance was generally comparable across methods, greater match completeness makes tvPS an attractive option for longitudinal matching studies where external validity is highly valued.

Abstract Ride-sharing services have attracted significant interest due to overcrowding, limited energy resources, and environmental concerns. This study proposes a mathematical programming model that integrates user preferences into the ride-sharing problem with transfer. Passenger transfer in ride-sharing problems addresses the limitations of the matching process, especially in less-populated areas. It allows passengers to be dropped off at meeting points and continue with another driver. Moreover, considering user preferences in ride-sharing systems is crucial for enhancing efficiency and user satisfaction. Accordingly, we propose a Preference-Driven Matching Algorithm for the matching process. Our proposed algorithm captures user preferences and provides potential matches. In addition, we introduce an Iterative Enhance-and-Optimize Algorithm capable of producing high-quality solutions within short computational times. We evaluate the efficiency of these approaches across various instances, focusing on real-scale scenarios. Based on the results, the model with preferences demonstrates effective performance compared to other methods. Our findings underscore the importance of user preferences in optimizing ride-sharing problems and highlight the trade-off between efficiency and user satisfaction. By incorporating user preferences, our approach results in higher user satisfaction, a more responsive and efficient system with reduced response times, and increased demand and revenue by servicing more users.

Abstract Purpose The aim of this study is to investigate and highlight the most important factors influencing the candidate’s selection for residency matching by program directors of surgical and non-surgical specialties in Saudi Arabia. Method This cross-sectional study was conducted in the Central, Eastern, Western, Northern, and Southern regions of Saudi Arabia from February 1, 2023, to August 31, 2023. A total of sixty-one program directors completed a questionnaire, which was adapted and validated from a previous study with permission and tailored to meet the objectives of this research. Results The mean age of the study population was 40.2 years, with surgical specialty program directors being older at 41.7 years and nonsurgical specialty program directors being younger at 39.6 years. In both cases, the highest importance was given to ‘Applicant’s attitude and professionalism’, followed in surgical specialties by ‘Doing clinical rotations in the PD institution’ and ‘Performance in clinical rotation during internship’ in non-surgical specialties. with a mean score of 4.13 and 4.95, respectively. The importance of ‘The impression/opinion of current residents’ has had a certain importance in both specialties. While the number of research projects was prioritized more by surgical PDs (38.6%) compared to non-surgical PDs (29.4%). Conclusion This study highlights the critical role of professionalism and a positive attitude in clinical rotations and interviews for program acceptance in Saudi Arabia. The significance of letters of recommendation and research focus varies between surgical and non-surgical program directors. Program directors should adopt effective recruitment strategies that incorporate practical implications during internships.

Given a graph G , Min-Max-Acy-Matching is the problem of finding a maximal matching M in G of minimum cardinality such that the set of M -saturated vertices induces an acyclic subgraph in G. The decision version of Min-Max-Acy-Matching is known to be NP -complete. In this paper, we strengthen this result by proving that the decision version of Min-Max-Acy-Matching is NP -complete even for dually chordal graphs. Also, we give the first positive algorithmic result for Min-Max-Acy-Matching by proposing a linear-time algorithm for computing a minimum cardinality maximal acyclic matching in proper interval graphs, a subclass of dually chordal graphs. [ABSTRACT FROM AUTHOR]

A bisection of a graph is a bipartition of its vertex set in which the two parts differ in size by at most 1, and its size is the number of edges which across the two parts. Let G be a graph with n vertices, m edges and degree sequence d 1 , d 2 , ... , d n . Motivated by a few classical results on Max-Cut of graphs, Lin and Zeng proved that if G is { C 4 , C 6 } -free and has a perfect matching, then G has a bisection of size at least m / 2 + Ω (∑ i = 1 n d i ) , and conjectured the same bound holds for C 4 -free graphs with perfect matchings. In this paper, we confirm the conjecture under the additional condition that G is C 5 -free. [ABSTRACT FROM AUTHOR]

This article builds on the Empirical Monte Carlo simulation approach to study the estimation of Timing-of-Events (ToE) models. We exploit rich Swedish data of unemployed job seekers with information on participation in a training program to simulate placebo treatment durations. We first use these simulations to examine which covariates are key confounders to be included in dynamic selection models for training participation. The joint inclusion of specific short-term employment history indicators (notably, the share of time spent in employment), together with baseline socio-economic characteristics, regional and inflow timing information, is important to deal with selection bias. Next, we omit subsets of explanatory variables and estimate ToE models with discrete distributions for the ensuing systematic unobserved heterogeneity. In many cases, the ToE approach provides accurate effect estimates, especially if time-varying variation in the unemployment rate of the local labor market is taken into account. However, assuming too many or too few support points for unobserved heterogeneity may lead to large biases. Information criteria, in particular those penalizing parameter abundance, are useful to select the number of support points. A comparison with other duration models shows that a Stratified Cox model performs well with abundant multiple spells but less well when multiple spells are uncommon. The standard Cox regression model performs poorly in all configurations as it is unable to account for unobserved heterogeneity. [ABSTRACT FROM AUTHOR]

Matching is one of the most widely used study designs for adjusting for measured confounders in observational studies. However, unmeasured confounding may exist and cannot be removed by matching. Therefore, a sensitivity analysis is typically needed to assess a causal conclusion's sensitivity to unmeasured confounding. Sensitivity analysis frameworks for binary exposures have been well established for various matching designs and are commonly used in various studies. However, unlike the binary exposure case, there still lacks valid and general sensitivity analysis methods for continuous exposures, except in some special cases such as pair matching. To fill this gap in the binary outcome case, we develop a sensitivity analysis framework for general matching designs with continuous exposures and binary outcomes. First, we use probabilistic lattice theory to show that our sensitivity analysis approach is finite population exact under Fisher's sharp null. Second, we prove a novel design sensitivity formula as a powerful tool for asymptotically evaluating the performance of our sensitivity analysis approach. Third, to allow effect heterogeneity with binary outcomes, we introduce a framework for conducting asymptotically exact inference and sensitivity analysis on generalized attributable effects with binary outcomes via mixed-integer programming. Fourth, for the continuous outcome case, we show that conducting an asymptotically exact sensitivity analysis in matched observational studies when both the exposures and outcomes are continuous is generally NP-hard, except in some special cases such as pair matching. As a real data application, we apply our new methods to study the effect of early-life lead exposure on juvenile delinquency. An implementation of the methods in this work is available in the R package doseSens. [ABSTRACT FROM AUTHOR]

Objective: To compare expenditures between surgical and matched nonsurgical patients in a retrospective cohort study. Background: Bariatric surgery leads to substantial improvements in weight and weight-related conditions, but prior literature on postsurgical health expenditures is equivocal. Methods: In a retrospective study, total outpatient, inpatient, and medication expenditures 3 years before and 5.5 years after surgery were compared between 22,698 bariatric surgery [n = 7127 Rouxen-Y gastric bypass (RYGB), 15,571 sleeve gastrectomy (SG)] patients from 2012 to 2019 and 66,769 matched nonsurgical patients, using generalized estimating equations. We also compared expenditures between patients receiving the 2 leading surgical procedures in weighted analyses. Results: Surgical and nonsurgical cohorts were well matched, 80% to 81% females, with mean body mass index of 44 and mean age of 47 (RYGB) and 44 (SG) years. Estimated total expenditures were similar between surgical and nonsurgical groups 3 years before surgery ($27 difference, 95% CI: -42, 102), increased 6 months before surgery for surgical patients, and decreased below preperiod levels for both groups after 3 to 5.5 years to become similar (difference at 5.5 years = -$61, 95% CI: -166, 52). Long-term outpatient expenditures were similar between groups. Surgical patients' lower long-term medication expenditures ($314 lower at 5.5 years, 95% CI: -419, -208) were offset by a higher risk of hospitalization. Total expenditures were similar between patients undergoing RYGB and SG 3.5 to 5.5 years after surgery. Conclusions: Bariatric surgery translated into lower medication expenditures than matched controls, but not lower overall long-term expenditures. Expenditure trends appear similar for the two leading bariatric operations. [ABSTRACT FROM AUTHOR]

During COVID-19, the Norwegian Government provided compensation to amend firms’ decreased revenues, yet it should be neutral and tailored to losses only. However, firm resources or institutional factors may have influenced the compensation, which we study here. Survey data showed a high probability of compensation among large firms, although they were not hit particularly hard by COVID-19. Also, compensation was prevalent for firms in the hospitality, tourism, and culture industry, and a likely explanation is that they were hit hard. However, their probability of compensation was prevalent compared to firms in other industries, regardless of revenue losses. We assume that large firms’ compensation was due to their resources to apply for funding successfully, while institutional factors explained the compensation for firms in the hospitality, tourism, and culture industry.

Abstract This study assesses the impact of a voucher project that targeted vulnerable and poor pregnant women in Uganda. Highly subsidised vouchers gave access to a package of safe delivery services consisting of four antenatal visits, safe delivery, one postnatal visit, the treatment and management of selected pregnancy-related medical conditions and complications, and emergency transport. Vouchers were sold during the project’s operational period from 2016 to 2019. This study covers 8 out of 25 project-benefiting districts in Uganda and a total of 1,881 pregnancies, including both beneficiary and non-beneficiary mothers. Using a matching design, the results show a positive effect on the survival of new-born babies. The difference in the survival rate between the control group and the treatment group is 5.4% points, indicating that the voucher project reduced infant mortality by more than 65 per cent.

This paper presents the results of a numerical study of a planar antenna with a complex form factor. The antenna is based on a combination of two resonators, a disc resonator and a ring resonator. The feeding of the ring resonator is performed using a coplanar structure: pointwise by galvanic contact between the central conductor of the coplanar line and the ring resonator and by distributed electromagnetic coupling of the ring resonator and the aperture of the outer conductor of the coplanar line. The antenna was placed over a metal plane whose geometric dimensions were significantly larger than those of the antenna to exclude the influence of edge diffraction effects. In numerical simulation a complex approach including the method of semi-open resonator and the finite element method (FEM) implemented within the commercial package HFFS was used. The dependences of spectral, energy and polarization characteristics on material constants and frequency parameter have been investigated. It was found that within the framework of single-parameter optimization it is impossible to simultaneously achieve a high level of all important parameters. The values of frequencies of spectral lines in the spectral characteristics of the antenna are found with a relative error not worse than 1200 Hz. Frequency ranges within which there is no degeneration of oscillation types are established. The distributions of surface currents on the metal elements of the antenna, allowing to determine the position of phase centers of excitation, are presented. It is shown that the proposed antenna can provide an acceptable level of matching both at fixed frequencies and in sufficiently wide local frequency bands, reaching 11% with respect to the center frequency of the sub-band. The boundary values of gain coefficients in frequency bands are established. The simulation results allow to predict effective radiation with formation of practically single-lobe radiation pattern and presence of elliptical polarization.

To enhance coaching experiences in career development, diverse approaches must be developed and tested. This study's objective was to determine the effectiveness of a purpose-based career coaching framework on career decision-making difficulties. In this study, randomized experimental conditions and interviews were combined. A theoretical sampling procedure was used for a period of two years to generate samples for the study. A total of 187 youths were recruited, but 78 did not meet the study criteria, while 29 declined to take part. A sample of 80 youths met the study criteria and completed all phases of the study. Twelve one-to-one sessions, aligning with the Pb-CC framework, covered core techniques such as reimagining, spiritualizing, self-discovery, career exploration, identifying, and matching. Qualitative data was collected using a researcher-developed interview protocol, consisting of three open-ended questions such as: "How would you describe the meaning or purpose of your life?; What is the category of career that relates to your purpose?; and What do you believe about purpose and career?" In addition, Career Decision-Making Scale (CDDS) was used to collect data. Quantitative data were analyzed using repeated measures analysis of variance, while qualitative data were analyzed using thematic analysis, which includes the organization of data, the identification and development of themes, the saturation of themes, and the presentation of themes. At the end of the intervention, participants successfully identified their purpose and skillfully matched it with suitable careers. The effect of purpose-based career coaching has been found to be significant on career decision making. These findings bear practical significance for career counselling, policy implementation, industrialization, research and career development. [ABSTRACT FROM AUTHOR]

A spanning subgraph of G whose components are k disjoint paths is a spanning k-path of G. When applying inductive methods to construct a Hamiltonian cycle in a hypercube, the spanning k-path plays a crucial role. In this paper, we obtainned the following result. Let u,v,x,y be pairwise distinct vertices in Q4 with p(u)=p(v)≠p(x)=p(y). If M was a matching in Q4- {u,v,x,y}, then there exists a spanning 2-path Pu,x + Pv,y of Q4 passing through M. [ABSTRACT FROM AUTHOR]

Observational studies are frequently used in clinical research to estimate the effects of treatments or exposures on outcomes. To reduce the effects of confounding when estimating treatment effects, covariate balancing methods are frequently implemented. This study evaluated, using extensive Monte Carlo simulation, several methods of covariate balancing, and two methods for propensity score estimation, for estimating the average treatment effect on the treated using a hazard ratio from a Cox proportional hazards model. With respect to minimizing bias and maximizing accuracy (as measured by the mean square error) of the treatment effect, the average treatment effect on the treated weighting, fine stratification, and optimal full matching with a conventional logistic regression model for the propensity score performed best across all simulated conditions. Other methods performed well in specific circumstances, such as pair matching when sample sizes were large (n = 5000) and the proportion treated was < 0.25. Statistical power was generally higher for weighting methods than matching methods, and Type I error rates were at or below the nominal level for balancing methods with unbiased treatment effect estimates. There was also a decreasing effective sample size with an increasing number of strata, therefore for stratification-based weighting methods, it may be important to consider fewer strata. Generally, we recommend methods that performed well in our simulations, although the identification of methods that performed well is necessarily limited by the specific features of our simulation. The methods are illustrated using a real-world example comparing beta blockers and angiotensin-converting enzyme inhibitors among hypertensive patients at risk for incident stroke. [ABSTRACT FROM AUTHOR]