Second-order Dependence Research Articles

Reference to Patients in Nurse Shift Handover Meetings: Exploring the Dynamics of Referring Expressions

ABSTRACT This paper investigates the dynamics of referring expressions in hospital nurse handover meetings when discussing patients. We apply the methods of the Variable Length Markov Chain (VLMC) and network analyses to model the use of referring expressions and evaluate relationships between them. The models reveal second-order dependencies emerging for metonymy and noun phrases. Specifically, metonymy shows a greater association with the beginning of a reference, particularly in the context of other metonymies. In contrast, noun phrases tend to be more strongly associated with later points in the reference. Further, we introduce the notion of referential typicality, which measures the conformity of sequences of referring expressions to anticipated patterns. We show, for example, that consecutive noun phrases fall outside the typical pattern, whereas metonymical sequences and sequences of pronouns are highly typical. The transitions from metonymy or nouns to pronouns also closely align with a highly typical pattern. Using a Generalized Additive Model (GAM), we then track the overall evolution of referential typicality throughout the duration of handover meetings, from their beginning to end. The study reveals a subtle increase in referential typicality towards the end of these sessions, indicating a trend towards more consistent referencing as the discourse unfolds.

Pseudospectral particle-in-cell formulation with arbitrary charge and current-density time dependencies for the modeling of relativistic plasmas.

This paper introduces a formulation of the particle-in-cell (PIC) method for the modeling of relativistic plasmas, that leverages the ability of the pseudospectral analytical time-domain solver (PSATD) to handle arbitrary time dependencies of the charge and current densities during one PIC cycle (applied to second-order polynomial dependencies here). The formulation is applied to a modified set of Maxwell's equationsthat was proposed earlier in the context of divergence cleaning, and to recently proposed extensions of the PSATD-PIC algorithm. Detailed analysis and testings revealed that, under some condition, the formulation can expand the range of numerical parameters under which PIC simulations are stable and accurate when modeling relativistic plasmas such as, e.g., plasma-based particle accelerators.

Unraveling the Role of Particle Size and Nanostructuring on the Oxygen Evolution Activity of Fe-Doped NiO.

Nickel-based oxides and oxyhydroxide catalysts exhibit state-of-the-art activity for the sluggish oxygen evolution reaction (OER) under alkaline conditions. A widely employed strategy to increase the gravimetric activity of the catalyst is to increase the active surface area via nanostructuring or decrease the particle size. However, the fundamental understanding about how tuning these parameters influences the density of oxidized species and their reaction kinetics remains unclear. Here, we use solution combustion synthesis, a low-cost and scalable approach, to synthesize a series of Fe0.1Ni0.9O samples from different precursor salts. Based on the precursor salt, the nanoparticle size can be changed significantly from ∼2.5 to ∼37 nm. The OER activity at pH 13 trends inversely with the particle size. Using operando time-resolved optical spectroscopy, we quantify the density of oxidized species as a function of potential and demonstrate that the OER kinetics exhibits a second-order dependence on the density of these species, suggesting that the OER mechanism relies on O-O coupling between neighboring oxidized species. With the decreasing particle size, the density of species accumulated is found to increase, and their intrinsic reactivity for the OER is found to decrease, attributed to the stronger binding of *O species (i.e., a cathodic shift of species energetics). This signifies that the high apparent OER activity per geometric area of the smaller nanoparticles is driven by their ability to accumulate a larger density of oxidized species. This study not only experimentally disentangles the influence of the density of oxidized species and intrinsic kinetics on the overall rate of the OER but also highlights the importance of tuning these parameters independently to develop more active OER catalysts.

Unsymmetric Co-Facial "Salixpyrrole" Hydrogen Evolution Catalysts: Two Metals are Better than One.

Designing ligand architectures that can mimic enzyme active sites is a promising approach for developing efficient small molecule activation catalysts for sustainable energy applications. Some key design features include chemically distinct binding pockets for multiple metal centers and a three-dimensional structure that controls the positioning of catalytic sites. With these principles in mind, mono- and bimetallic unsymmetric cofacial palladium complexes, 2 and 3, respectively, bearing ligands with calixpyrrole and salen coordination sites, or "salixpyrrole" ligands, are reported. These species were accessed in a straightforward Schiff-base reaction with appreciable yields. In addition, both 2 and 3 were found to be active hydrogen evolution electrocatalysts using para-toluenesulfonic acid monohydrate as the proton source. The two salixpyrrole species displayed different mechanisms of action, with 2 showing a second-order dependence on acid concentration, whereas 3 exhibited a first-order dependence. Moreover, the bimetallic catalyst was significantly more efficient, with higher turnover frequencies, 4640 s-1 vs 1680 s-1 for 2, and lower overpotentials, 0.39 V vs 0.69 V for 2. The results reported herein provide proof-of-concept that bimetallic catalysts with chemically distinct binding sites demonstrate enhanced catalytic properties in comparison to monometallic or symmetric analogues.

IN-STORAGE WEIGHT LOSS BY TABLE BEETROOTS DEPENDING ON THE ROOT PULP STRUCTURE

Purpose - to determine the in-storage weight loss by table beetroots depending on the root pulp structure. Methods. 1. General scientific: hypotheses – drawing up designs of experiments; experiments - conducting laboratory tests; analysis and synthesis – summarizing and concluding. 2. Computantional and analytical. Results. Beetroots naturally lose their weight during storage and this process is uneven. At the beginning, it is quite rapid, then it gradually slows down, accelerating in spring. Of the studied beet cultivars, the greatest losses of weight during storage were registered for cvs. 'Renova' and 'Tsylindra'. The weight loss by beetroots was correlated with a decrease in the dry matter content, which was spent on the respiration of beetroots to support their vital activity. In beet cvs. 'Renova' and 'Opolskyi', the decrease in the dry matter during storage was greater, amounting to 5.6 and 4.5%, respectively. There was a relationship between cell area and in-storage water evaporation. It was revealed that in cells with the largest area of 140.04·103 μm, the evaporation-caused losses amounted to 77.7% (cv. 'Karilon'), while in cells with the smallest area of 62.89·103 μm, this parameter was only 10.5% (cv. 'Opolskyi'). Conclusions. There was a direct strong correlation between water evaporation and cell size (r = 0.791). There was a significant second-order curvilinear dependence of beetroot weight loss on cultivars' features (R2 = 0.6041–0.9910).

Transient kinetic analysis of the standard SCR reduction half cycle on Cu-SSZ-13 catalysts: Roles of temperature, hydrothermal aging and H2O feed content

Transient Response Methods (TRM) are applied to investigate the reduction half-cycle (RHC) of the Standard SCR reaction over a state-of-the-art Cu-SSZ-13 catalyst at temperatures ranging from 135 to 350°C. We assess the effects of hydrothermal aging (HTA), water feed content and temperature on the reaction redox chemistry. By testing three samples with different aging levels, we reveal the CuII reduction rate to be unaffected by HTA in the whole T-range under investigation. Kinetic analysis assesses the inhibiting role of water on the RHC, consistent with literature. As long as enough NH3 is stored on the catalyst surface, reduction transients are accurately predicted assuming a second-order dependence of the reduction rate on the copper(II) fraction, also at temperatures exceeding 200°C. Remarkably, under wet conditions the low-T CuII reduction mechanism remains the same at least up to 300°C.

Cobalt catalyzed practical hydroboration of terminal alkynes with time-dependent stereoselectivity

Stereodefined vinylboron compounds are important organic synthons. The synthesis of E−1-vinylboron compounds typically involves the addition of a B-H bond to terminal alkynes. The selective generation of the thermodynamically unfavorable Z-isomers remains challenging, necessitating improved methods. Here, such a proficient and cost-effective catalytic system is introduced, comprising a cobalt salt and a readily accessible air-stable CNC pincer ligand. This system enables the transformation of terminal alkynes, even in the presence of bulky substituents, with excellent Z-selectivity. High turnover numbers (>1,600) and turnover frequencies (>132,000 h−1) are achieved at room temperature, and the reaction can be scaled up to 30 mmol smoothly. Kinetic studies reveal a formal second-order dependence on cobalt concentration. Mechanistic investigations indicate that the alkynes exhibit a higher affinity for the catalyst than the alkene products, resulting in exceptional Z-selective performance. Furthermore, a rare time-dependent stereoselectivity is observed, allowing for quantitative conversion of Z-vinylboronate esters to the E-isomers.

Invariant scaling laws for plane Couette flow with wall-transpiration

The effect of the wall-transpiration Reynolds number ReV0 on the large-scale rolls of the plane Couette flow with wall-transpiration is investigated using resolvent analysis. We observe streamwise-elongated large-scale rolls, moving closer to the suction wall and becoming narrower in the wall-normal and spanwise direction as well as shortened in the streamwise direction for growing ReV0 due to the decreasing characteristic length of the sheared region. Invariant scaling laws describing the growth of the amplification gain over various parameters and determining the optimal parameters yielding most amplification are found enhancing our understanding of the influence on the coherent structures, which can be used to predict their exact size. In a potential second step, this allows to manipulate and efficiently modify these flow characteristics increasing the functionality for various engineering applications as for mass, momentum and heat transfer, which is dominated by turbulent superstructures. For large enough wall-transpiration, the most amplified structures are oblique at optimal streamwise wavenumbers αmax≠0 growing linearly with ReV0. The optimal spanwise wavenumber βmax yielding most amplified structures shows a polynomial dependence of second order on ReV0. For a constant ratio of the wall-transpiration and streamwise Reynolds numbers γ=ReV0Re, the most amplified structures occur at optimal streamwise Reynolds numbers represented by Remax·γa=C. The amplification gain changes its trend from growing with Re2 to decreasing with Re−1.368 after Remax is reached. The resulting streamwise structures are identical for each set of Remax and γ within this invariant scaling law.

Enhancing Demand Prediction: A Multi-Task Learning Approach for Taxis and TNCs

Taxis and Transportation Network Companies (TNCs) are important components of the urban transportation system. An accurate short-term forecast of passenger demand can help operators better allocate taxi or TNC services to achieve supply–demand balance in real time. As a result, drivers can improve the efficiency of passenger pick-ups, thereby reducing traffic congestion and contributing to the overall sustainability of the program. Previous research has proposed sophisticated machine learning and neural-network-based models to predict the short-term demand for taxi or TNC services. However, few of them jointly consider both modes, even though the short-term demand for taxis and TNCs is closely related. By enabling information sharing between the two modes, it is possible to reduce the prediction errors for both. To improve the prediction accuracy for both modes, this study proposes a multi-task learning (MTL) model that jointly predicts the short-term demand for taxis and TNCs. The model adopts a gating mechanism that selectively shares information between the two modes to avoid negative transfer. Additionally, the model captures the second-order spatial dependency of demand by applying a graph convolutional network. To test the effectiveness of the technique, this study uses taxi and TNC demand data from Manhattan, New York, as a case study. The prediction accuracy of single-task learning and multi-task learning models are compared, and the results show that the multi-task learning approach outperforms single-task learning and benchmark models.

Deep Learning Model for Quality Assessment of Urinary Bladder Ultrasound Images using Multi-scale and Higher-order Processing.

Autonomous Ultrasound Image Quality Assessment (US-IQA) is a promising tool to aid the interpretation by practicing sonographers and to enable the future robotization of ultrasound procedures. However, autonomous US-IQA has several challenges. Ultrasound images contain many spurious artifacts, such as noise due to handheld probe positioning, errors in the selection of probe parameters and patient respiration during the procedure. Further, these images are highly variable in appearance with respect to the individual patient's physiology. We propose to use a deep Convolutional Neural Network (CNN), USQNet, which utilizes a Multi-scale and Local-to-Global Second-order Pooling (MS-L2GSoP) classifier to conduct the sonographer-like assessment of image quality. This classifier first extracts features at multiple scales to encode the inter-patient anatomical variations, similar to a sonographer's understanding of anatomy. Then, it uses second-order pooling in the intermediate layers (local) and at the end of the network (global) to exploit the second-order statistical dependency of multi-scale structural and multi-region textural features. The L2GSoP will capture the higher-order relationships between different spatial locations and provide the seed for correlating local patches, much like a sonographer prioritizes regions across the image. We experimentally validated the USQNet for a new dataset of the human urinary bladder ultrasound images. The validation involved first with the subjective assessment by experienced radiologists' annotation, and then with state-of-the-art CNN networks for US-IQA and its ablated counterparts. The results demonstrate that USQNet achieves a remarkable accuracy of 92.4% and outperforms the SOTA models by 3 - 14% while requiring comparable computation time.

Catalytic role of the enol ether intermediate in the intramolecular Stetter reaction: a computational perspective.

The intramolecular Stetter reaction catalyzed by a carbene is investigated by density functional theory (DFT) calculations and kinetic simulations. Catalyst 1 first reacts with aldehyde 2 to give the primary adduct (PA). The PA undergoes the intramolecular oxa-Michael reaction to irreversibly generate enol ether intermediate 9. The conversion of the enol ether to the Breslow intermediate (BI) requires the assistance of a base such as the PA. The next step involves formation of a carbon-carbon bond through the Michael addition, and expulsion of the catalyst generates the Stetter product 7. Calculations show that the catalytic cycle is composed of two irreversible processes: the first one involves the exergonic formation of the enol ether intermediate, while the second one is the conversion of the enol ether to the final product. Kinetic simulations using initial concentrations of [1]0 = 0.005 M and [2]0 = 0.025 M demonstrate that under a steady-state condition, 35% of the catalyst rests on the state of the enol ether (0.0018 M). The catalyst resting state therefore consists of the unbound form (the free catalyst) and its bound form (the enol ether species). According to variable time normalization analysis, the reaction exhibits a second-order dependence (first order in catalyst and first order in substrate), which agrees with experiments. The oxa-Michael reaction to form the enol ether is identified to be turnover limiting in the intramolecular Stetter reaction, which rationalizes the observed electronic effect of the Michael acceptor on the reactivity, as well as the measured isotope effect with respect to the aldehydic proton/deuteron. The base that participates in the BI formation has a significant effect on the build-up of the resting state 9 and the active catalyst concentration. In addition, the thermodynamic stability of the enol ether is found to depend on the tether length between the aromatic aldehyde and the Michael acceptor, as well as the chemical nature of the carbene catalyst. The favorability for the oxa-Michael reaction is therefore suggested to govern the reactivity of the intramolecular Stetter transformation.

N-Nitrosation Based Fluorescence Turn-On Nitric Oxide Probe: Kinetic and Cell Imaging Studies.

Nitric oxide (NO) is a ubiquitous messenger molecule playing a key role in various physiological and pathological processes. However, producing a selective turn-on fluorescence response to NO is a challenging task due to (a) the very short half-life of NO (typically in the range of 0.1-10 s) in the biological milieu and (b) false positive responses to reactive carbonyl species (RCS) (e.g., dehydroascorbic acid and methylglyoxal etc.) and some other reactive oxygen/nitrogen species (ROS/RNS), especially with o-phenylenediamine (OPD) based fluorosensors. To avoid these limitations, NO sensors should be designed in such a way that they react spontaneously with NO to give turn-on response within the time frame of t1/2 (typically in the range of 0.1-10 s) of NO and λem in the visible wavelength along with good cell permeability to achieve biocompatibility. With these views in mind, a N-nitrosation based fluorescent sensor, NDAQ, has been developed that is highly selective to NO with ∼27-fold fluorescence enhancement at λem = 542 nm with high sensitivity (LOD = 7 ± 0.4 nM) and shorter response time, eliminating the interference of other reactive species (RCS/ROS/RNS). Furthermore, all the photophysical studies with NDAQ have been performed in 98% aqueous medium at physiological pH, indicating its good stability under physiological conditions. The kinetic assay illustrates the second-order dependency with respect to NO concentration and first-order dependency with respect to NDAQ concentration. The biological studies reveal the successful application of the probe to track both endogenous and exogenous NO in living organisms.

Homotropic Cooperativity in Iron-Catalyzed Alkyne Cyclotrimerizations.

Enhancing catalytic activity through synergic effects is a current challenge in homogeneous catalysis. In addition to the well-established metal-metal and metal-ligand cooperation, we showcase here an example of self-activation by the substrate in controlling the catalytic activity of the two-coordinate iron complex [Fe(2,6-Xyl2C6H3)2] (1, Xyl = 2,6-Me2C6H3). This behavior was observed for aryl acetylenes in their regioselective cyclotrimerization to 1,2,4-(aryl)-benzenes. Two kinetically distinct regimes are observed dependent upon the substrate-to-catalyst ratio ([RC≡CH]0/[1]0), referred to as the low ([RC≡CH]0/[1]0 < 40) and high ([RC≡CH]0/[1]0 > 40) regimes. Both showed sigmoidal kinetic response, with positive Hill indices of 1.85 and 3.62, respectively, and nonlinear Lineweaver-Burk replots with an upward curvature, which supports positive substrate cooperativity. Moreover, two alkyne molecules participate in the low regime, whereas up to four are involved in the high regime. The second-order rate dependence on 1 indicates that binuclear complexes are the catalytically competent species in both regimes, with that in the high one being 6 times faster than that involved in the low one. Moreover, Eyring plot analyses revealed two different catalytic cycles, with a rate-determining step more endergonic in the low regime than in the high one, but with a more ordered transition state in the high regime than in the low one.

Thermal decomposition of vegetative fuels and the impact of measured variations on simulations of wildfire spread

This manuscript presents new measurement data from milligram-scale thermal decomposition experiments – thermogravimetric analysis (TGA) and microscale combustion calorimetry (MCC) – conducted on stems and leaves (i.e., needles) of six plant species commonly found across the United States. For each fuel, measurement data from TGA experiments was analyzed to determine effective thermal decomposition mechanisms and the associated kinetics of their constituent reactions. Results of this model calibration were compared to standard (i.e., ‘generalized’) approaches to obtain kinetic parameters available in the literature. MCC experiments were repeated under identical experimental conditions to determine the heats of complete combustion of all gaseous volatiles produced by these vegetative fuels and to validate the decomposition mechanisms and species char yields determined from TGA data. Through a coupled analysis of TGA and MCC measurement data, an estimate of the heats of combustion of the gaseous volatiles produced by individual reaction steps in the fuel’s decomposition was also made. Between different fuels, distinct differences were measured in the onset temperature of decomposition, the temperature range of decomposition, the number of apparent reactions, and the peak measured mass loss and heat release rates (as well as the temperatures at which they occur). To analyze the impact of these variations on predictions of wildfire behavior, a modeling study was then conducted in which simulations of wildland fire experiments were repeated using the thermal decomposition mechanisms and heats of combustion determined for each of the fuel species tested in this work. Model-predicted fire spread rate in these simulations varied between 0.34 m s−1 and 0.80 m s−1 demonstrating a notable dependence (up to a factor of 2x) on measured variations in thermal decomposition mechanism and a second-order dependence on the use of either reaction-step-specific or single (global) heats of combustion.

The dust-to-gas mass ratio of luminous galaxies as a function of their metallicity at cosmic noon

Aims. We aim to quantify the relation between the dust-to-gas mass ratio (DTG) and gas-phase metallicity of z = 2.1 − 2.5 luminous galaxies and contrast this high-redshift relation against analogous constraints at z = 0. Methods. We present a sample of ten star-forming main-sequence galaxies in the redshift range 2.1 &lt; z &lt; 2.5 with rest-optical emission-line information available from the MOSDEF survey and with ALMA 1.2 millimetre and CO J = 3 − 2 follow-up observations. The galaxies have stellar masses ranging from 1010.3 to 1010.6 M⊙ and cover a range in star-formation rate from 35 to 145 M⊙ yr−1. We calculated the gas-phase oxygen abundance of these galaxies from rest-optical nebular emission lines (8.4 &lt; 12 + log(O/H) &lt; 8.8, corresponding to 0.5−1.25 Z⊙). We estimated the dust and H2 masses of the galaxies (using a metallicity-dependent CO-to-H2 conversion factor) from the 1.2 mm and CO J = 3 − 2 observations, respectively, from which we estimated a DTG. Results. We find that the galaxies in this sample follow the trends already observed between CO line luminosity and dust-continuum luminosity from z = 0 to z = 3, extending such trends to fainter galaxies at 2.1 &lt; z &lt; 2.5 than observed to date. We find no second-order metallicity dependence in the CO – dust-continuum luminosity relation for the galaxies presented in this work. The DTGs of main-sequence galaxies at 2.1 &lt; z &lt; 2.5 are consistent with an increase in the DTG with gas-phase metallicity. The metallicity dependence of the DTG is driven by the metallicity dependence of the CO-to-H2 conversion factor. Galaxies at z = 2.1 − 2.5 are furthermore consistent with the DTG-metallicity relation found at z = 0 (i.e. with no significant evolution), providing relevant constraints for galaxy formation models. These results furthermore imply that the metallicity of galaxies should be taken into account when estimating cold-gas masses from dust-continuum emission, which is especially relevant when studying metal-poor low-mass or high-redshift galaxies.

Nanoscale Printing of Indium-Tin-Oxide by Femtosecond Laser Pulses.

For constructing optical and electrical micro-devices, the deposition/printing of materials with sub-1 μm precision and size (cross-section) is required. Crystalline c-ITO (indium tin oxide) nanostructures were patterned on glass with sufficient precision to form 20-50 nm gaps between individual disks or lines of ∼250 nm diameter or width. The absorbed energy density [J/cm3] followed a second-order dependence on pulse energy. This facilitated high-resolution and precise nanoscale laser-writing at a laser wavelength of 515 nm. Patterns for optical elements such as circular gratings and micro-disks were laser-printed using ITO as a resist. Unexposed amorphous a-ITO was chemically removed in aqueous 1% vol. HF solution. This use of a-ITO as a solid resist holds promise for metamaterial and micro-optical applications.

Nonlinear Mixed Convection in a Reactive Third-Grade Fluid Flow with Convective Wall Cooling and Variable Properties

Energy management and heat control whenever a reactive viscous fluid is the working medium has been one of the greatest challenges encountered by many in the field of chemical and industrial engineering. A mathematical approach to thedetermination of critical points beyond which the working environment becomes hazardous is presented in the present investigation together with the entropy generation analysis that guarantees the efficient management of expensive energy resources. In this regard, the nonlinear mixed convective flow behavior of a combustible third-grade fluid through a vertical channel with wall cooling by convection is investigated. The mathematical formulation captures the nonlinearities arising from second-order Boussinesq approximation and exponential dependence of internal heat generation, viscosity, and thermal conductivity on temperature. The resulting nonlinear boundary value problems were solved based on the spectral Chebyshev collocation method (SCCM) and validated with the shooting-Runge–Kutta method (RK4). The nonlinear effects on the flow velocity, temperature distribution, entropy generation, and Bejan heat irreversibility ratio are significant. Further analyses include the thermal stability of the fluid. Findings from the study revealed that flow, temperature, and entropy generation are enhanced byincreasing values of the Grashof number, the quadratic component of buoyancy, and the Frank-Kameneskii parameter, but are reducedbyincreasing the third-grade material parameter. Moreover, it was shown that increasing values of the third-grade parameter encourages the thermal stability of the flow, while increasing values of the linear and nonlinear buoyancy parameter destabilizes the flow. The present result is applicable to thick combustible polymers with increased molecular weight.

Cold gas mass measurements for the era of large optical spectroscopic surveys

ABSTRACT Gas plays an important role in many processes in galaxy formation and evolution, but quantifying the importance of gas has been hindered by the challenge to measure gas masses for large samples of galaxies. Data sets of direct atomic and molecular gas measurements are sufficient to establish simple scaling relations, but often not large enough to quantify three-parameter relations, or second-order dependences. As an alternative approach, we derive here indirect cold gas measurements from optical emission lines using photoionization models for galaxies in the Sloan Digital Sky Survey (SDSS) main galaxy sample and the PHANGS-MUSE survey. We calibrate the gas surface density measurements using xCOLD GASS and PHANGS-ALMA molecular gas measurements to ensure that our measurements are reliable. We demonstrate the importance of taking into account the scale dependence of the relation between optical depth (τV) and gas surface density (Σgas) and provide a general prescription to estimate Σgas from τV, metallicity, and the dust-to-metal ratio, at any arbitrary physical resolution. To demonstrate that the indirect cold gas masses are accurate enough to quantify the role of gas in galaxy evolution, we study the mass–metallicity relation of SDSS galaxies and show that as a third parameter gas mass is better than star formation rate at reducing the scatter of the relation, as predicted by models and simulations.

Dynamic Learning in Large Matching Markets

We study a sequential matching problem faced by large centralized platforms where "jobs" must be matched to "workers" subject to uncertainty about worker skill proficiencies. Jobs arrive at discrete times (possibly in batches of stochastic size and composition) with "job-types" observable upon arrival. To capture the "choice overload" phenomenon, we posit an unlimited supply of workers where each worker is characterized by a vector of attributes (aka "worker-types") sampled from an underlying population-level distribution. The distribution as well as mean payoffs for possible workerjob type-pairs are unobservables and the platform's goal is to sequentially match incoming jobs to workers in a way that maximizes its cumulative payoffs over the planning horizon. We establish lower bounds on the regret of any matching algorithm in this setting and propose a novel rate-optimal learning algorithm that adapts to aforementioned primitives online. Our learning guarantees highlight a distinctive characteristic of the problem: achievable performance only has a second-order dependence on worker-type distributions; we believe this finding may be of interest more broadly.

Stochastic Analysis of the Marginal and Dependence Structure of Streamflows: From Fine-Scale Records to Multi-Centennial Paleoclimatic Reconstructions

The identification of the second-order dependence structure of streamflow has been one of the oldest challenges in hydrological sciences, dating back to the pioneering work of H.E Hurst on the Nile River. Since then, several large-scale studies have investigated the temporal structure of streamflow spanning from the hourly to the climatic scale, covering multiple orders of magni-tude. In this study, we expanded this range to almost eight orders of magnitude by analysing small-scale streamflow time series (in the order of minutes) from ground stations and large-scale streamflow time series (in the order of hundreds of years) acquired from paleocli-matic reconstructions. We aimed to determine the fractal behaviour and the long-range de-pendence behaviour of the streamflow. Additionally, we assessed the behaviour of the first four marginal moments of each time series to test whether they follow similar behaviours as sug-gested in other studies in the literature. The results provide evidence in identifying a common stochastic structure for the streamflow process, based on the Pareto–Burr–Feller marginal dis-tribution and a generalized Hurst–Kolmogorov (HK) dependence structure.