Purpose The rank aggregation problem, which has many real-world applications, refers to combining multiple input rankings into a single aggregated ranking. In dynamic settings, where new rankings arrive over time, efficiently updating the aggregated ranking is essential. This paper aims to develop fast, theoretically grounded and practically efficient algorithms for dynamic rank aggregation. Design/methodology/approach The authors first develop left right (LR) aggregation, built on the LR tree data structure. The LR tree is inspired by the LR distance, a novel but equivalent formulation of the classical Spearman’s footrule distance, designed to support efficient incremental updates. They then analyze the classical Pick-A-Perm algorithm under Spearman’s footrule distance and show how it can also be maintained efficiently in the dynamic setting. Finally, they combine LR aggregation and Pick-A-Perm into a unified dynamic rank aggregation framework that returns the better of the two candidate aggregations at each step. Findings Experimental evaluations show that LR aggregation produces solutions close to optimal in practice. They prove that Pick-A-Perm yields an expected 2-approximation under Spearman’s footrule distance and they show that both LR aggregation and Pick-A-Perm (as well as their combination) can be implemented with O (n log n) update time and O(n2) space, independent of the number of rankings received. Originality/value To the best of the authors’ knowledge, this work provides the first near-linear-time dynamic rank aggregation framework that offers both a provable approximation guarantee and strong empirical performance in practice.

Modular indistinguishability: The aggregation problem

A framework for classifier-neutral ensemble multi-label feature selection via swarm optimization

A new genetic programming approach to dynamic multi-point dynamic aggregation problem

To prepare a modified asphalt with excellent road performance, thermoplastic polyurethane/graphene oxide (TPU/GO) incorporating dynamic disulfide bonds was developed as an additive and the synergistic effect of TPU and GO on asphalt was evaluated. Modified asphalts with different TPU/GO contents (2%, 4%, 6%, 8%) were prepared and TPU-modified asphalts were also prepared as control groups. The compatibility between TPU/GO and asphalt was evaluated by fluorescence microscopy (FM) and the dispersion of GO in TPU and asphalt was observed by emission scanning electron microscope (SEM). The road performance of modified asphalts was also assessed in this study. The FM results show that TPU/GO has good compatibility with asphalt, and the SEM results reveal that GO can be uniformly dispersed in TPU matrix, so that GO can also be evenly dispersed in asphalt and avoid the problem of GO aggregation in asphalt. The results also demonstrate that TPU/GO-modified asphalt comprehensively utilizes the respective advantages of TPU and GO. TPU/GO-modified asphalt has excellent low-temperature performance compared with base asphalt. The 5 °C ductility of 8%TPU/GO-modified asphalt is 440% higher than that of base asphalt and the BBR test also showed that the stress relaxation capacity of TPU/GO-modified asphalt is also significantly stronger than that of base asphalt. Moreover, the introduction of GO in asphalt can improve the creep recovery rate and complex modulus compared with TPU-modified asphalt, indicating better high-temperature rutting resistance. Comprehensive performance evaluation indicates that 8% TPU/GO-modified asphalt is the optimal dosage for engineering applications, balancing high-temperature rutting resistance, storage stability, anti-aging performance, and low-temperature behavior.

Abstract Metal equipment is prone to corrosion due to sudden changes in temperature and abundant precipitation in Northeast China. The anti-corrosion ability of the coating can be improved by doping graphene oxide (GO) into waterborne acrylic resin (AP); however, GO has problems of poor dispersion and easy aggregation. In this study, Hexamethylene diisocyanate (Tri-HDI) was used to improve the dispersibility of GO. The layer spacing of GO was expanded through the grafting and modification of its functional groups with p-phenylenediamine and polyvinylpyrrolidone (PVP), and its strength was improved by introducing the -Si group, thereby obtaining reinforced-dispersion graphene oxide (Reinforced Dispersion Graphene Oxide, RDGO). Finally, RDGO and AP were combined by the wet transfer method to prepare a new type of water-based anti-corrosion coating.

To address the problem of flow aggregation caused by gas phase aggregation in a helical axial gas-liquid pump under high gas void fraction (IGVF≥30%), this study, based on the Euler multiphase flow model and SST k-ω investigates the quantitative correlation between clearance dimensions and performance characteristics as well as internal flow patterns in multiphase pumps operating under varying inlet gas volume fractions. The findings reveal that gas phase aggregation, induced by radial pressure gradients, stemming from the density difference between gas and liquid phases, is the dominant mechanism governing gas accumulation within the flow passages. Implementing the slotted configuration with an optimal gap width coefficient ξ=21.4% resulted in a 3.38% enhancement in multiphase pump efficiency compared to the baseline model, with only a marginal head reduction, achieving significant overall performance optimization. Mechanistic analysis demonstrates that the slotted configuration establishes a fluid dynamic coupling between the pressure and suction surfaces, allows high-momentum flux to transfer from the pressure-side boundary layer, replenishing energy to the low-velocity region on the suction side, thereby effectively suppressing the axial adverse pressure gradient effect.

In the Steiner Path Aggregation Problem , our goal is to aggregate paths in a directed network into a single arborescence without significantly disrupting the paths. In particular, we are given a directed multigraph with colored arcs, a root, and k terminals, each of which has a monochromatic path to the root. Our goal is to find an arborescence in which every terminal has a path to the root, and its path does not switch colors too many times. We give an efficient algorithm that finds such a solution with at most 2 log 4 3 ⁡ k color switches. Up to constant factors this is the best possible universal bound, as there are graphs requiring at least log 2 ⁡ k color switches. • The guarantees of the classic heavy path decomposition are extended to general graphs, within a constant factor. • Dipaths from terminals to a root are aggregated into a single arborescence. • With monochromatic dipaths, the arborescence paths switch colors at most log times. • The algorithm iteratively extends paths of active terminals without allowing cycles.

Coupling of iron (II) phthalocyanine and layered double hydroxide via carbon nanotubes media with pyrolysis-free for high-stability rechargeable zinc-air batteries.

Robust removal of thallium(I) from water with nano-MnO2 implanted zwitterionic porous hydrogel.

Bacterial infections are a serious threat to life and health safety due to their high morbidity and mortality. Currently, the commonly used antimicrobial method is the use of antibiotics, but it is prone to drug resistance. Here, we designed and constructed a nanoplatform F-CLs@HA capable of fluorescent imaging localization at the site of bacterial infection as well as synergizing the three antimicrobial therapeutic methods of photodynamic therapy/gas therapy/chemodynamic therapy (PDT/GT/CDT). It is made by combining carbon dots (CDs) with l-arginine (CL-s) and encapsulated by hyaluronic acid (HA) together with Fe3O4. HA is able to target the CD44 receptor, which is overexpressed on inflammatory macrophages, and the higher level of hyaluronidase at the location of bacterial infection is able to hydrolyze HA, releasing antibacterial drugs. CDs are made by the high-temperature reaction of the near-infrared (NIR) dye cyanine 7 (Cy-7), which not only has a PDT effect but also improves the problem of aggregation quenching of small-molecule fluorescent dyes and realizes stable NIR fluorescence imaging. Moreover, Fe3O4 is able to release hydroxyl radicals (•OH) as a commonly used drug in CDT. In addition, nitric oxide (NO) released from l-arginine is highly reactive with reactive oxygen species (ROS), generating more toxic reactive nitrogen species (RNS) that induce bacterial death. Both in vivo and in vitro evaluations show that our nanoplatform has favorable imaging and therapeutic effects. In summary, F-CLs@HA has great potential for application in the treatment of bacterial infections.

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

Spectral reflectance measured from herbarium specimens represents a potentially vast source of information relevant to plant taxon identification and functional traits, which has inspired many laboratories world-wide to initiate next-generation spectral digitization from specimens. Combining these datasets into a coordinated global database would generate new capacity to model plant traits globally, enabling connection with remote sensing and ecological and biosphere models, as well as reconstruction of trait evolution. However, coordination is needed to avoid downstream problems in data aggregation due to variation in data standards and technical specifications of the instruments, optical setups, or measurement protocols. The International Herbarium Spectral Digitization (IHerbSpec) working group has initiated a globally collaborative program, outlining the central issues to address in establishing protocols, standards, and best practices, and proposing next steps. This collaborative effort will allow generation of replicable spectral reflectance data from plant specimens housed in herbaria around the world within ongoing digitization programs following community-defined standards and Findable, Accessible, Interoperable, and Reusable (FAIR) principles.

In the manufacture of single-crystal blades for aero-engines, the problem of eutectic aggregation on the upper surface of the blades has long been restricting the casting performance improvement. To investigate this phenomenon, this paper employs a simplified blade-like shape casting and focuses a 3rd generation nickel-based single-crystal superalloy as the research material. A systematic analysis is conducted to elucidate the distribution of γ/γ’ eutectic during solidification. Experimental results show distinct spatial variations in γ/γ’ eutectic distribution. Pronounced eutectic aggregation is observed on the upper surface of the blade but with sparse eutectic dispersion‌ on the lower regions of the casting. Relatively uniform eutectic distribution‌ dominates the mid-section of the specimen. To unravel the underlying mechanisms, this paper utilized a ‌multiphase volume-averaged solidification model‌, developed in prior work, to numerically simulate the γ/γ’ eutectic evolution during directional solidification. This computational framework enabled a comprehensive ‌quantitative analysis‌ of spatial and temporal variations in the eutectic volume fraction along the solidification direction. The integration of experimental and modeling approaches provides critical insights into the interplay between thermal gradients, alloy composition, and microstructural heterogeneity.

Artificial agents increasingly make decisions with far-reaching consequences. It is therefore imperative to ensure that their actions are not only functionally effective but also normatively appropriate. Two major paradigms address this challenge: machine ethics and value alignment. Machine ethics typically engages in \textit{moral} aggregation, especially through value and (descriptive) uncertainty aggregation. Value alignment approaches tend to rely on \textit{social} aggregation to manage value pluralism and moral uncertainty, often implicitly or indirectly. This paper disentangles these forms of aggregation and analyzes their roles across three stages of machine moral reasoning: moral evaluation, moral assessment, and moral decision. Rather than favoring one paradigm, we expose their mutual dependencies and respective blind spots, particularly under conditions of persistent moral disagreement. We argue that social aggregation cannot bypass deep normative commitments. Alignment by social aggregation cannot replace moral aggregation but merely relocates it---often opaquely.

Physical stability of an active pharmaceutical ingredient (API) is a key consideration in the development of a pharmaceutical drug. Solution conditions such as pH, excipient concentrations, and storage temperatures can impact the physical stability of a therapeutic peptide in formulation. Optimizing these conditions is a critical activity in achieving a higher stability of a therapeutic peptide product. A Thioflavin T (ThioT) fluorescent reporter assay is widely used to measure the aggregation of peptide products. ThioT kinetic assays are used to predict the propensity of fibril formation by using ThioT curves for a peptide stored in a solution. However, there is no analytical relationship that can be used to relate the physical stability for different formulation conditions, resulting in execution of large-scale stability assays that require significant resources for pharmaceutical companies. Therefore, there is a need to develop new artificial intelligence (AI) methods to predict future ThioT curves in a fast and cost-effective manner. Here, we combined an experimental measure of time-varying conformational states from ThioT assays with AI models to predict peptide aggregation in different formulation conditions during drug development. We formulated the peptide aggregation problem as "language translation" in natural language processing, wherein the sequence of aggregation states at earlier time points was used to predict (or "translate") the aggregation states for future time points. We developed a new sequence-to-sequence long short-term memory (LSTM)-based recurrent neural network (RNN) model to predict entire ThioT curves at future time points (6 and 12 months) using data sets from initial and 1 month ThioT curves for different conditions. We achieved an excellent average mean absolute error (MAE) of 2.04 for the model, which was used to predict and experimentally validate ThioT curves for a 6 month time point. In contrast to the LSTM, the multilayer perceptron (MLP) baseline model showed a higher MAE of 5.17. However, at the 12 month time point, with limited training data, both models achieved comparable results with average MAEs of 4.25 and 4.45 for LSTM and MLP, respectively. Therefore, we conclude that LSTM models can be used to predict future ThioT curves only using the initial and 1 month ThioT curves as input. We believe that the use of recurrent neural network models will benefit the pharmaceutical industry to predict and explore the formulation landscape for future physical stability measurements of APIs based on short-term stability data.

ABSTRACT In this paper, a novel temperature‐sensitive dual self‐healing magnetic polyurethane has been developed based on the Diels–Alder (DA) reaction between acrylamide and furfural and the Schiff base reaction between furfural and polyether amino. Unlike other self‐healing polymers that require external force, this polymer relies only on magnetic force to close the cross‐section, allowing remote control of the polymer without touching it. The polyurethane has a stable structural composition and excellent mechanical properties, exhibiting different properties over a narrow temperature range. By grafting functional groups on the surface of NdFeB to improve the dispersion of NdFeB, the problem of non‐uniform dispersion due to NdFeB deposition and aggregation during the polyurethane curing process has been partially solved. The thermal stability of the polymers was determined using a differential scanning calorimeter. The results show that magnetic actuation can make the cross‐section tighter than external force. The surface modification of NdFeB effectively improves the aggregation problem of NdFeB and improves the mechanical properties of polyurethane to a certain extent, which has a wide range of applications. Several simple modular magnetic drive models are constructed, which provide examples for more complex magnetic drive models in the future.

Self-healing waterborne polyurethane nanocomposites with high strength and toughness based on ureidopyrimidinone-modified cellulose nanocrystals.

The concept of the aggregate production function, together with it supposedly being a test of the neoclassical marginal product theory of distribution, is now central to much of neoclassical macroeconomic theory. Severe criticisms of this concept that include the Cambridge Capital Theory Controversies and the aggregation problem are now simply ignored. The reason is that estimates of the Cobb-Douglas and the Constant Elasticity of Substitution (CES) production functions, inter alia, give good statistical fits with the estimated output elasticities being often close to their factor shares. However, the reason for this is that in both cases all that is being estimated is merely the mathematical transformation of an accounting identity. As such, it has no economic implications and, in particular, says nothing about “the laws of production”. This analysis considers this criticism of the aggregate production function. In particular, it revisits and extends the criticisms made of Solow’s (1957) model of technical change and the use of the aggregate production function. It is shown that Solow’s (1974; 1987) defence of the aggregate production function is not convincing. The CES production function is also shown to be simply a transformation of the accounting identity and, consequently, also has no economic implications.

A novel approach designed to directly estimate microcanonical quantities from energy histograms is proposed, which enables the immediate systematic identification and classification of phase transitions in physical systems of any size by means of the recently introduced generalized microcanonical inflection-point analysis method. The application to the aggregation problem of GNNQQNY heptapeptides, for which the entire transition sequence is revealed, shows the power of this promising method.