Initial Assignment Research Articles

Non-vetoed matching with status quo

We consider a matching market where there is an initial matching a priori. Both sides of the market can veto the new matching whenever they would rather keep their initial assignment. We propose a stability notion, called “conditional stability”. Our first mechanism—“Non-Vetoed Deferred Acceptance” (NDA)—is non-vetoed, conditionally stable, and strategy-proof. These three properties are incompatible with (two-sided) conditional constrained efficiency; hence NDA is not conditionally constrained efficient. However, no mechanism that is non-vetoed, conditionally stable, and strategy-proof improves on NDA. Lastly, we propose a non-vetoed, conditionally stable, and conditionally constrained efficient mechanism.

The value of improving insurance quality: Evidence from long-run Medicaid attrition

The US government increasingly provides public health insurance coverage through private firms. We examine associated welfare implications for beneficiaries, using a ‘revealed preference’ framework based on beneficiaries’ program attrition rates. Focusing on the Medicaid program in New York State, we exploit quasi-random variation in the initial assignment at birth to public versus private Medicaid based on birth weight. We find that infants assigned to private Medicaid at birth are less likely to subsequently leave Medicaid. We provide suggestive evidence that reduced attrition reflects beneficiary responses to improved program quality, rather than alternative mechanisms such as private Medicaid plans reducing re-enrollment barriers.

Structure Revision of Formyl Phloroglucinol Meroterpenoids: A Unified Approach Using NMR Fingerprinting and DFT NMR and ECD Analyses

NMR fingerprints are valuable tools for analyzing complex natural product mixtures and identifying incorrectly assigned structures in the literature. Our diagnostic NMR fingerprints for formyl phloroglucinol meroterpenoids revealed discrepancies in the structures reported for eucalyprobusal C (1a) and eucalypcamal K (2a). NMR fingerprinting PCA analyses identified 1a as an oxepine-diformyl phloroglucinol and 2a as an oxepine 3-acyl-1-formyl phloroglucinol, contrary to their initial assignments as pyrano-diformyl and pyrano 3-acyl-1-formyl phloroglucinols, respectively. Extensive reinterpretation of their reported one- and two-dimensional NMR data, coupled with GIAO DFT-calculated 1H and 13C NMR chemical shift and DP4+ analyses, supported the unequivocal reassignment of eucalyprobusal C to 1b and eucalypcamal K to 2b. The absolute configurations of the revised oxepine-containing phloroglucinol meroterpenoids were confirmed via the reinterpretation of their reported ROESY and NOESY NMR data, along with comparative TDDFT-calculated and experimental ECD spectra.

Vibrational Study of the Inward Proton Pump Xenorhodopsin NsXeR: Switch Order Determines Vectoriality

Common proton pumps, e.g. HsBR and PR, transport protons out of the cell. Xenorhodopsins (XeR) were the first discovered microbial rhodopsins which come as natural inward proton pumps. In this work we combine steady-state (cryo-)FTIR and Raman spectroscopy with time-resolved IR and UV/Vis measurements to roadmap the inward proton transport of NsXeR and pinpoint the most important mechanistic features. Through the assignment of characteristic bands of the protein backbone, the retinal chromophore, the retinal Schiff base and D220, we could follow the switching processes for proton accessibility in accordance with the isomerization / switch / transfer model. The corresponding transient IR signatures suggest that the initial assignment of D220 as the proton acceptor needs to be questioned due to the temporal mismatch of the Schiff base and D220 protonation steps. The switching events in the K-L and MCP-MEC transitions are finely tuned by changes of the protein backbone and rearrangements of the Schiff base. This finely tuned mechanism is disrupted at cryogenic temperatures, being reflected in the replacement of the previously reported long-lived intermediate GS* by an actual redshifted (O-like) intermediate.

Hybrid Clustering Algorithm Based on Improved Density Peak Clustering

In the era of big data, unsupervised learning algorithms such as clustering are particularly prominent. In recent years, there have been significant advancements in clustering algorithm research. The Clustering by Density Peaks algorithm is known as Clustering by Fast Search and Find of Density Peaks (density peak clustering). This clustering algorithm, proposed in Science in 2014, automatically finds cluster centers. It is simple, efficient, does not require iterative computation, and is suitable for large-scale and high-dimensional data. However, DPC and most of its refinements have several drawbacks. The method primarily considers the overall structure of the data, often resulting in the oversight of many clusters. The choice of truncation distance affects the calculation of local density values, and varying dataset sizes may necessitate different computational methods, impacting the quality of clustering results. In addition, the initial assignment of labels can cause a ‘chain reaction’, i.e., if one data point is incorrectly labeled, it may lead to more subsequent data points being incorrectly labeled. In this paper, we propose an improved density peak clustering method, DPC-MS, which uses the mean-shift algorithm to find local density extremes, making the accuracy of the algorithm independent of the parameter dc. After finding the local density extreme points, the allocation strategy of the DPC algorithm is employed to assign the remaining points to appropriate local density extreme points, forming the final clusters. The robustness of this method in handling uncertain dataset sizes adds some application value, and several experiments were conducted on synthetic and real datasets to evaluate the performance of the proposed method. The results show that the proposed method outperforms some of the more recent methods in most cases.

2-SAT discrete Hopfield neural networks optimization via Crow search and fuzzy dynamical clustering approach

<abstract> <p>Within the swiftly evolving domain of neural networks, the discrete Hopfield-SAT model, endowed with logical rules and the ability to achieve global minima of SAT problems, has emerged as a novel prototype for SAT solvers, capturing significant scientific interest. However, this model shows substantial sensitivity to network size and logical complexity. As the number of neurons and logical complexity increase, the solution space rapidly contracts, leading to a marked decline in the model's problem-solving performance. This paper introduces a novel discrete Hopfield-SAT model, enhanced by Crow search-guided fuzzy clustering hybrid optimization, effectively addressing this challenge and significantly boosting solving speed. The proposed model unveils a significant insight: its uniquely designed cost function for initial assignments introduces a quantification mechanism that measures the degree of inconsistency within its logical rules. Utilizing this for clustering, the model utilizes a Crow search-guided fuzzy clustering hybrid optimization to filter potential solutions from initial assignments, substantially narrowing the search space and enhancing retrieval efficiency. Experiments were conducted with both simulated and real datasets for 2SAT problems. The results indicate that the proposed model significantly surpasses traditional discrete Hopfield-SAT models and those enhanced by genetic-guided fuzzy clustering optimization across key performance metrics: Global minima ratio, Hamming distance, CPU time, retrieval rate of stable state, and retrieval rate of global minima, particularly showing statistically significant improvements in solving speed. These advantages play a pivotal role in advancing the discrete Hopfield-SAT model towards becoming an exemplary SAT solver. Additionally, the model features exceptional parallel computing capabilities and possesses the potential to integrate with other logical rules. In the future, this optimized model holds promise as an effective tool for solving more complex SAT problems.</p> </abstract>

Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

With the unprecedented prevalence of IIoT and 5G technology, various applications supported by industrial communication systems have generated exponentially increased processing tasks, which makes task assignment inefficient due to insufficient workers. In this paper, an Intelligent and Trustworthy task assignment method based on Trust and Social relations (ITTS) is proposed for scenarios with many tasks and few workers. Specifically, ITTS first makes initial assignments based on trust and social influences, thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each worker. Then, an intelligent Q-decision mechanism based on workers' social relation is proposed, which adopts the first-exploration-then-utilization principle to allocate tasks. Only when a worker cannot cope with the assigned tasks, it initiates dynamic worker recruitment, thus effectively solving the worker shortage problem as well as the cold start issue. More importantly, we consider trust and security issues, and evaluate the trust and social circles of workers by accumulating task feedback, to provide the platform a reference for worker recruitment, thereby creating a high-quality worker pool. Finally, extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53% and profit by 42.34%-47.19%.

Automated Generation of Shuttling Sequences for a Linear Segmented Ion Trap Quantum Computer

A promising approach for scaling-up trapped-ion quantum computer platforms is by storing multiple trapped-ion qubit sets ('ion crystals') in segmented microchip traps and to interconnect these via physical movement of the ions ('shuttling'). Already for realizing quantum circuits with moderate complexity, the design of suitable qubit assignments and shuttling schedules require automation. Here, we describe and test algorithms which address exactly these tasks. We describe an algorithm for fully automated generation of shuttling schedules, complying to constraints imposed by a given trap structure. Furthermore, we introduce different methods for initial qubit assignment and compare these for random circuit (of up to 20 qubits) and quantum Fourier transform-like circuits, and generalized Toffoli gates of up to 40 qubits each. We find that for quantum circuits which contain a fixed structure, advanced assignment algorithms can serve to reduce the shuttling overhead.

The effectiveness of medical nurses in treating children with silver diamine fluoride in a school-based caries prevention program.

The sustainability of school-based oral health programs depends on the utilization of effective, efficient treatments and the availability of a trained clinical workforce. The objective of this study was to determine whether registered nurses are comparable to dental hygienists in the application and effectiveness of silver diamine fluoride (SDF) and fluoride varnish (FV) for the prevention of dental caries. CariedAway was a school-based study of SDF and FV versus dental sealants and atraumatic restorations. Within the SDF + FV arm, participants were treated by either a licensed dental hygienist or a registered nurse, both under the supervision of a paediatric dentist. Although initial treatment assignment in CariedAway was randomized, assignment to provider was not. The proportion of children who remained caries free after 2 years was assessed for non-inferiority using two-group proportion tests, adjusting for the clustering effect of schools. A total of 417 children with no untreated caries at baseline were analysed including 298 treated by hygienists and 119 by nurses. The proportion of children who remained caries free after 2 years was 0.81 and 0.80 for those treated by hygienists and nurses, respectively, for a difference of 0.01 (95% CI = -0.07, 0.098) and within the pre-determined non-inferiority margin. Nurses may be effective in treating children with silver diamine fluoride and other fluoride varnishes in school-based oral health programs.

Intention-to-treat versus as-treated versus per-protocol approaches to analysis.

Randomized controlled trials (RCTs) are considered the most rigorous study design for testing hypotheses and the gold standard for evaluating intervention effectiveness. However, RCTs are often conducted under the assumption of ideal conditions that may differ from real-world scenarios in which various issues, such as loss to follow-up, mistakes in participant enrollment or intervention, and low subject compliance or adherence, may occur. There are various group-defining strategies for analyzing RCT data, including the intention-to-treat (ITT), as-treated, and per-protocol (PP) approaches. The ITT principle involves analyzing all participants according to their initial group assignments, regardless of study completion and compliance or adherence to treatment protocols. This approach aims to replicate real-world clinical settings in which several anticipated or unexpected conditions may occur with regard to the study protocol. For the PP approach, only participants who meet the inclusion criteria, complete the interventions according to the study protocols, and have primary outcome data available are included. This approach aims to confirm treatment effects under optimal conditions. In general, the ITT principle is preferred for superiority and inequality trials, whereas the PP approach is preferred for equivalence and non-inferiority trials. However, both analytical approaches should be conducted and their results compared to determine whether significant differences exist. Overall, using both the ITT and PP approaches can provide a more complete picture of the treatment effects and ensure the reliability of the trial results.

On reachable assignments under dichotomous preferences

We consider the problem of determining whether a target item assignment can be reached from an initial item assignment by a sequence of pairwise exchanges of items between agents. In particular, we consider the situation where each agent has a dichotomous preference over the items, that is, each agent evaluates each item as acceptable or unacceptable. Furthermore, we assume that communication between agents is limited, and the relationship is represented by an undirected graph. Then, a pair of agents can exchange their items only if they are connected by an edge and the involved items are acceptable. We prove that this problem is PSPACE-complete even when the communication graph is complete (that is, every pair of agents can exchange their items), and this problem can be solved in polynomial time if an input graph is a tree.

Racial Differences in Triage for Emergency Department Patients with Subjective Chief Complaints.

Introduction: Black and Hispanic patients are frequently assigned lower acuity triage scores than White patients. This can lead to longer wait times, less aggressive care, and worse outcomes. In this study we aimed to determine whether these effects are more pronounced for patients with subjective complaints. Methods: We performed a retrospective analysis for all adult visits between 2016-2019 at an urban academic emergency department (ED) with acuity-based pods. We determined rates of initial high-acuity triage both across all patients and among the subset located in the high-acuity pod at time of disposition (either through initial assignment or subsequent up-triage). Analysis was performed for common chief complaints categorized as subjective (chest pain, dyspnea, any pain); observed (altered mental status); numeric (fever, hypotension); or protocolized (stroke, ST-elevation myocardial infarction). We constructed logistic regression models to control for age, race, gender, method of arrival, and final disposition. Results: We analyzed 297,355 adult ED visits. Black and Hispanic patients were less likely to be triaged to high-acuity beds (adjusted odds ratio [aOR] 0.76, 95% confidence interval [CI] 0.73-0.79 for Black, and aOR 0.87, 95% CI 0.84-0.90 for Hispanic patients). This effect was more pronounced for those with subjective chief complaints, including chest pain (aOR 0.76, 95% CI 0.73-0.79 for Black and 0.88, 95% CI 0.78-0.99 for Hispanic patients), dyspnea (aOR 0.79, 95% CI 0.68-0.92 and 0.8, 95% CI 0.72-0.99), and any pain (aOR 0.83, 95% CI 0.75-0.92 and 0.89, 95% CI 0.82-0.97, respectively). Among patients in the high-acuity pod at time of disposition, Black and Hispanic patients were disproportionately triaged to lower acuity pods on arrival (aOR 1.47, 95% CI 1.33-1.63 for Black and aOR 1.27, 95% CI 1.15-1.40 for Hispanic adults), with significant differences observed only for subjective chief complaints. No differences were observed for observed, objective, or protocolized complaints in either analysis. Conclusion: Black and Hispanic adults, including those who ultimately required high-acuity resources, were disproportionately triaged to lower acuity pods. This effect was more pronounced for patients with subjective chief complaints. Additional work is needed to identify and overcome potential bias in the assessment of patients with subjective chief complaints in ED triage.

ScAce: an adaptive embedding and clustering method for single-cell gene expression data.

Since the development of single-cell RNA sequencing (scRNA-seq) technologies, clustering analysis of single-cell gene expression data has been an essential tool for distinguishing cell types and identifying novel cell types. Even though many methods have been available for scRNA-seq clustering analysis, the majority of them are constrained by the requirement on predetermined cluster numbers or the dependence on selected initial cluster assignment. In this article, we propose an adaptive embedding and clustering method named scAce, which constructs a variational autoencoder to simultaneously learn cell embeddings and cluster assignments. In the scAce method, we develop an adaptive cluster merging approach which achieves improved clustering results without the need to estimate the number of clusters in advance. In addition, scAce provides an option to perform clustering enhancement, which can update and enhance cluster assignments based on previous clustering results from other methods. Based on computational analysis of both simulated and real datasets, we demonstrate that scAce outperforms state-of-the-art clustering methods for scRNA-seq data, and achieves better clustering accuracy and robustness. The scAce package is implemented in python 3.8 and is freely available from https://github.com/sldyns/scAce.

Effects of cashew nut shell extract supplementation on production, rumen fermentation, metabolism, and inflammatory biomarkers in transition dairy cows

Cashew nut shell extract (CNSE) is a byproduct of the cashew nut industry, containing bioactive compounds that alter rumen fermentation patterns. Therefore, study objectives were to evaluate the effects of CNSE (59% anacardic acid and 18% cardol) on production, rumen fermentation variables, metabolism, and inflammation in transition dairy cows. A total of 51 multiparous Holstein cows were used in a randomized design and assigned to treatment based on their previous 305-d mature equivalent milk and parity. Cows were assigned to 1 of 2 treatments 21 d before expected calving: (1) CON (control diet; n = 17) or (2) CNSE-5.0 (control diet and 5.0 g/d CNSE granule [containing 50% CNSE]; n = 34). Following parturition, 17 cows (preselected at initial treatment assignment) from the CNSE-5.0 treatment were reallocated into a third treatment group: CNSE-2.5 (control diet and 2.5 g/d CNSE granule; n = 17), resulting in 3 total treatments postpartum: (1) CON, (2) CNSE-2.5, and (3) CNSE-5.0. Prepartum rumen pH was unaltered by treatment; however, postpartum rumen pH was increased (0.31 units) in CNSE cows relative to CON. Prepartum rumen ammonia N concentration tended to be decreased (34%) in CNSE-5.0 cows compared with CON, and there tended to be a quadratic effect on postpartum ammonia N, as it was decreased in CNSE-2.5 compared with CON and CNSE-5.0. Prepartum dry matter intake (DMI) was unaffected by treatment; however, postpartum DMI was increased (8%) in CNSE cows relative to CON. No treatment differences were observed in pre- or postpartum digestibility measurements. Milk and protein yields from cows fed CNSE tended to be increased (6% and 7%, respectively) relative to CON. No treatment differences were detected for energy-corrected milk, feed efficiency, body weight, body condition score, energy balance, milk composition, milk urea nitrogen, or somatic cell count. Prepartum fecal pH decreased (0.12 units) in CNSE-5.0 cows relative to CON cows but was similar between treatments postpartum. Supplementing CNSE did not affect prepartum glucose, nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHB), or insulin. However, prepartum circulating blood urea nitrogen tended to be decreased and glucagon was decreased in CNSE-5.0 cows compared with CON (9 and 20%, respectively). Additionally, CNSE supplementation decreased glucose and insulin concentrations postpartum relative to CON cows (6% and 20%, respectively). Quadratic effects were detected for postpartum circulating NEFA and BHB such that their levels were increased in CNSE-2.5 cows relative to CON and CNSE-5.0. Pre- and postpartum circulating serum amyloid A, lipopolysaccharide-binding protein, and haptoglobin were unaffected by treatment. Overall, CNSE influenced some key rumen fermentation variables, altered postabsorptive metabolism, and increased production parameters in transition dairy cows.

Research on a Coal Seam Gas Content Prediction Method Based on an Improved Extreme Learning Machine

With the rapid advancement of artificial neural network (ANN) algorithms, many researchers have applied these methods to mine gas prediction and achieved numerous research achievements. It is of great significance to study methods that can accurately predict the gas content for the prevention of gas disasters in mining areas. In order to enhance the accuracy, stability, and generalization capability of the gas content prediction model, the GASA-KELM prediction model was established using the GASA algorithm to improve the KELM initial parameter assignment method, and the prediction model based on BPNN and SVM was established under the same conditions. The experimental results show that the GASA-BPNN model failed to achieve the desired outcome within 800 iterations. On the other hand, the GASA-SVM and GASA-KELM models accomplished the goal in significantly fewer iterations, taking only 673 and 487 iterations, respectively. Moreover, the overall average relative errors of the cross-validated gas content predictions were 15.74%, 13.85%, and 9.87% for the three models, respectively. Furthermore, the total average variance of the test set was 3.99, 2.76, and 2.05 for the GASA-BPNN, GASA-SVM, and GASA-KELM models, respectively. As a result, compared with other ANN models, the GASA-KELM model demonstrates higher accuracy, stronger prediction stability, and generalization ability in the practical application. This novel model provides a basis for accurately predicting gas content and proposing effective regional gas management measures.

An aircraft final assembly line balancing problem considering resource constraints and parallel task scheduling

In this study, the balancing problem of an aircraft final assembly line (AFAL) is investigated. The decision is to assign all the assembly tasks to a given number of workstations, with the objectives to balance the workstation cycle time and to minimize the total resource investment cost. An integer linear programming model is developed to formulate the problem. A construction and improvement based two-stage heuristic approach is proposed to solve this problem. In the first stage, an initial task assignment is constructed based on rules that are evolved by a multi-dimensional archive of phenotypic elites algorithm. In the second stage, an improvement heuristic, consisting of several customized neighborhood operators, is developed to improve the initial task assignment. The proposed approach is compared with the existing methods and its variants. Computational experiments and a real case study reveal the superiority of our proposed approach.

How the COVID-19 shutdown revealed the effectiveness of a northern Nigerian educational media program

ABSTRACT A team of researchers were investigating the impact of a Nigerian adaptation of Akili and Me when the COVID−19 pandemic struck. Schools shut down, interrupting the study’s quasi-experimental intervention design. Post-school reopening, researchers recontacted 363 children (mean age = 5.1, SD = 1.1 years) who had provided data at baseline and had completed the intervention. The analyses revealed that during the shutdown, participating children watched Akili and Me, beyond the exposure experienced through the study intervention. Across viewing groups and including the control group, researchers found the children knew the program’s characters using a program receptivity score. The researchers found no differences associated with study’s initial group assignments. Those children who could name more Akili and Me characters performed significantly better on the outcomes of literacy, numeracy, shape, socio-emotional development, controlling for sex, age, baseline score, and group assignment. This study offers promising evidence that locally-produced educational media interventions can impact early learning skills, especially during a crisis when children rely on educational media for home learning.

Estimating the Effect of a Treatment When There Is Nonadherence in a Trial.

Randomized trials offer a powerful strategy for estimating the effect of a treatment on an outcome. However, interpretation of trial results can be complicated when study subjects do not take the treatment to which they were assigned; this is referred to as nonadherence. Prior authors have described instrumental variable approaches to analyze trial data with nonadherence; under their approaches, the initial assignment to treatment is used as an instrument. However, their approaches require the assumption that initial assignment to treatment has no direct effect on the outcome except via the actual treatment received (i.e., the exclusion restriction), which may be implausible. We propose an approach to identification of a causal effect of treatment in a trial with 1-sided nonadherence without assuming exclusion restriction. The proposed approach leverages the study subjects initially assigned to control status as an unexposed reference population; we then employ a bespoke instrumental variable analysis, where the key assumption is "partial exchangeability" of the association between a covariate and an outcome in the treatment and control arms. We provide a formal description of the conditions for identification of causal effects, illustrate the method using simulations, and provide an empirical application.

Dynamic Optimization of Role Concepts for Role-Based Access Control Using Evolutionary Algorithms

To ensure the security of current information systems, role-based access control (RBAC) is a widely used concept. For this purpose, based on an initial assignment of permissions to users, permissions are grouped to roles, which are then assigned to users. The corresponding (NP-complete) optimization problem, the so-called role mining problem (RMP), aims at finding a minimal set of roles and a corresponding assignment of those roles to users. Previously, the RMP has been considered as a static optimization problem. However, the application of RBAC in real business use cases requires the inclusion of dynamically occurring events that reflect changes in the business environment of companies, as well as events that result from direct user interaction with the role mining process. Therefore, in this paper, we provide a comprehensive overview and classification of the most relevant events for role mining and present methods for integrating them into the framework of an evolutionary role mining algorithm. The functional performance of these methods as well as the overall performance gain of dynamic role mining compared to the static approach is then examined in a series of experiments.

DNA Barcodes of Mansonia (Mansonia) Blanchard, 1901 (Diptera, Culicidae).

Females of the genus Mansonia feed on the blood of humans, livestock, and other vertebrates to develop their eggs. The females' biting behavior may cause severe disturbance to blood hosts, with a negative impact on public health and economics. Certain species have been identified as potential or effective disease vectors. The accurate species identification of field-collected specimens is of paramount importance for the success of monitoring and control strategies. Mansonia (Mansonia) morphological species boundaries are blurred by patterns of intraspecific heteromorphism and interspecific isomorphism. DNA barcodes can help to solve taxonomic controversies, especially if combined with other molecular tools. We used cytochrome c oxidase subunit I (COI) gene 5' end (DNA barcode) sequences to identify 327 field-collected specimens of Mansonia (Mansonia) spp. The sampling encompassed males and females collected from three Brazilian regions and previously assigned to species based on their morphological characteristics. Eleven GenBank and BOLD sequences were added to the DNA barcode analyses. Initial morphospecies assignments were mostly corroborated by the results of five clustering methods based on Kimura two-parameter distance and maximum likelihood phylogeny. Five to eight molecular operational taxonomic units may represent taxonomically unknown species. The first DNA barcode records for Mansonia fonsecai, Mansonia iguassuensis, and Mansonia pseudotitillans are presented.