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Incentive-based public transport demand management (PTDM) can effectively mitigate overcrowding issues in crowded urban rail systems. Analyzing passengers’ behavioral responses to the incentive can guide the design, implementation, and update of PTDM strategies. Though several studies reported passengers’ responses to fare incentives, they focused on passengers’ short-term behavioral responses. Limited studies explore passengers’ longitudinal behavioral responses for different types of adopters, which is important for policy assessment and adjustment. This paper explores and models passengers’ longitudinal behavior response to a pre-peak fare discount incentive using 18 months of smartcard data in public transport in Hong Kong. We classified adopters into six types based on their temporal travel pattern changes before and after the promotion. The longitudinal analysis reveals that among all adopters, 19% of users change their departure times to take advantage of fare discounts but do not contribute to the goal of reducing peak-hour travel. However, these adopters are more likely to sustain their changed behavior in a long term which is not desired by the incentive program. The spatial analysis shows that the origin station distribution of late adopters is relatively more diverse than the early adopters with more trips starting from distant areas. The diffusion modeling shows that the majority adopters are innovators and the word-of-mouth diffusion effect (imitators) is marginal. The discrete choice model results highlight the heterogeneous impact of factors on different types of adopters and their values of time changes. The significant factors common to adopters are: departure time flexibility, the expected money savings, the required departure time changes, and work locations. The findings are useful for public transport planners and policymakers for informed incentive design and management.

Floating treatment islands (FTIs) offer effective solutions for stormwater management, providing flood attenuation and pollutant removal capabilities. However, there remains a knowledge gap concerning their performance, specifically in terms of pollutant removal and sediment deposition. To address this gap, the present study employs computational fluid dynamics (CFD) modeling to investigate the intricate interactions within FTI systems. Various FTI configurations are analyzed, considering mass removal through FTIs and sediment deposition, the first time where these two processes were considered together in a CFD environment. The findings demonstrate that FTIs have a significant influence on flow patterns and mass removal. Notably, FTIs enhance mass removal compared to the control case, with larger sediment particles exhibiting higher removal rates. The correlation between the short-circuit index and sedimentation in FTI ponds highlights the potential of FTIs as indicators of treatment efficiency. Furthermore, the study focuses on mass removal exclusively through the FTI root zones. The positioning of FTIs within the pond has a considerable impact, resulting in differences of up to 20% in mass removal. Moreover, the FTI configuration exerts a more pronounced influence on mass removal through FTIs than through sediment deposition alone. In cases where both processes occur simultaneously, the presence of FTIs lead to higher mass removal, primarily attributed to the FTIs themselves, particularly in the initial segment. Remarkably, certain FTI configurations enable mass removal exceeding 70% for large sediment particles, even with a pond length less than half of the original.

The monitoring of soil moisture and sea surface salinity over the Earth has been profoundly enhanced during the last thirteen years due to a new generation of satellite sensors. L-band radiometry is currently the only technology providing direct measurements of soil moisture, insensitive to surface roughness and distribution of elements in the soil, and the only technology the only technology for measuring that allows us to measure sea surface salinity from space. The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions resolve global and local variability with a spatial resolution of approximately 43 km, a swath width close to 1000 km, and a sampling time, for each mission, of at least twice every 3 days. These resolutions and samplings can be increased by either merging data from the two sensors, and with complementary information gathered from other passive or active sensors, or with in situ information at higher spatial resolution. Numerous scientific studies based on the use of this new type of measurement have led to a better understanding and constraint of the processes governing the variability of the water cycle, ocean circulation and the Earth's climate. The continuity of measurements, and the increased spatial and radiometric resolution is critical for fulfilling scientific needs. Future L-band radiometry missions currently being planned in Europe (the Copernicus Imaging Microwave Radiometer), and in China (the Ocean Salinity mission) should provide better constraints on auxiliary parameters by combining multiple frequencies, but they will not have improved spatial resolution beyond SMOS and SMAP. The temporal continuity with SMOS and SMAP will likely not be ensured. In parallel, new concepts are being developed to increase spatial resolution of both land and ocean parameters.

We propose a phase-field model of shear fractures using the deviatoric stress decomposition. This choice allows us to use general three-dimensional Mohr–Coulomb’s failure function for formulating the relations and evaluating peak and residual stresses. We apply the model to a few benchmark problems of shear fracture and strain localization and report remarkable performance. Our model is able to capture conjugate failure modes under biaxial compression test and for the slope stability problem, a challenging task for most models of geomechanics.

In today’s world, protection against electromagnetic waves, including high-power microwave (HPM) pulses, is becoming increasingly important. Hence, the aim of this research was to select an absorbing admixture, which, when used in an appropriate quantity, improves the effectiveness of shielding against electromagnetic waves and, at the same time, satisfies the requirements that are important from a construction engineering point of view. Altogether, eighteen admixtures (including two types of fibers), added in different quantities, and three types of aggregate have been tested. The compressive and flexural strength test results show that the greatest improvement in the tested mechanical properties was achieved in the case of admixtures such as steel fibers, carbon black P803, ferrite, and fly ash. Regarding the effect of the admixtures on shielding effectiveness, the best results were obtained for graphite in the form of flakes, graphite in the form of powder, carbon black N990 and P803, polypropylene and steel fibers, and hybrid admixtures, i.e., ashporite aggregate with carbon black and ashporite aggregate with graphite flakes. Regarding both mechanical properties and HPM pulse shielding effectiveness, the best effects were achieved in the case of the specimens with a high steel fiber content.

Presidential Graduate Fellowship from MIT Office of Graduate Education, J. A. Curtis (1953) Fund from MIT School of Architecture + Planning, Climate-Neutral and Smart Cities Mission HORIZON-MISS-2021-CIT-02

Successful deployment of geological carbon storage (GCS) requires an extensive use of reservoir simulators for screening, ranking and optimization of storage sites. However, the time scales of GCS are such that no sufficient long-term data is available yet to validate the simulators against. As a consequence, there is currently no solid basis for assessing the quality with which the dynamics of large-scale GCS operations can be forecasted. To meet this knowledge gap, we have conducted a major GCS validation benchmark study. To achieve reasonable time scales, a laboratory-size geological storage formation was constructed (the “FluidFlower”), forming the basis for both the experimental and computational work. A validation experiment consisting of repeated GCS operations was conducted in the FluidFlower, providing what we define as the true physical dynamics for this system. Nine different research groups from around the world provided forecasts, both individually and collaboratively, based on a detailed physical and petrophysical characterization of the FluidFlower sands. The major contribution of this paper is a report and discussion of the results of the validation benchmark study, complemented by a description of the benchmarking process and the participating computational models. The forecasts from the participating groups are compared to each other and to the experimental data by means of various indicative qualitative and quantitative measures. By this, we provide a detailed assessment of the capabilities of reservoir simulators and their users to capture both the injection and post-injection dynamics of the GCS operations.

Purpose Several studies have evaluated GHG mitigation solutions for asphalt and concrete mixtures at the network level. However, proposed solutions usually focus on partial decarbonization; the simultaneous interactions and effects of these solutions have not been fully studied. In this study, the state-level embodied carbon attributed to the U.S. pavement network was calculated using a dynamic material flow analysis and life cycle assessment model. Methods A pavement management system model was developed to forecast the performance and characteristics of the national pavement network using data science approaches. The annual quantity of materials required for the pavement treatment actions in each state was estimated based on budget allocation and regional decision trees. Then, multiple solutions for achieving carbon-neutral mixtures by 2050 were introduced to the state-level material flows to assess the GHG savings and abatement cost associated with the solutions. Results and discussion The annual GHG emissions of the U.S. pavement network will increase by 19% if no change in decarbonization occurs (from 11.9 to 13.3 Mt CO2eq). Under a projected improvement scenario, concrete and asphalt pavements are anticipated to have an average 38 and 14% embodied impact reduction by 2050, respectively. Nevertheless, carbon-neutral mixtures can only be achieved if multiple solutions for reducing, avoiding, and neutralizing the embodied impact are applied simultaneously. The effectiveness of many of these solutions depends on a 100% renewable electricity supply. From a cost perspective, 41% of the GHG savings toward carbon neutrality can be achieved at a negative or almost net-zero cost. Conclusions Carbon-neutral asphalt and concrete mixtures can be achieved if multiple solutions for reducing, avoiding, and neutralizing the embodied impact are applied simultaneously and the electricity grid is decarbonized rapidly. For future research, the scope of analysis for paveme

Metal-coordination bond localization in proteins with high amounts of metal-coordination affects structural folding and tensile strength.

Finding the conditions that ensure the survival of species has occupied ecologists for decades. Theoretically, for mechanistic models such as MacArthur’s consumer-resource model, most of the efforts have concentrated on proving the stability of an equilibrium assuming that it is feasible, but overlooking the conditions that ensure its feasibility. Here, we address this gap by finding the range of conditions that lead to a feasible equilibrium of MacArthur’s consumer-resource model, where species competition is mediated by their consumption of similar resources, and study how changes in the system’s structural and parametric properties affect those ranges for communities of any size. We characterize the relationship between the loss of feasibility and the increase in complexity (measured by the system’s richness and connectance) by a power law that can be extended to random competition matrices. Focusing on the pool of consumers, we find that while the feasibility of the entire system decreases with the size of the pool, the expected fraction of feasible consumers increases—safety in consumer numbers. Focusing on the pool of resources, we find that if resources grow linearly, the larger the pool of resources, the lower the feasibility of the system and the expected fraction of feasible consumers—danger in resource numbers. However, if resources grow logistically, this pattern is reversed with a sublinear increase in feasibility, as it has been previously reported in experimental work. This work provides testable predictions for consumer-resource systems and is a gateway to exploring feasibility in other mechanistic models.

Background Based on the volume of tissue removed, conservative surgery (BCS) cannot always guarantee satisfactory cosmetic results, unless resorting to more complex oncoplastic approaches. Investigating an alternative to optimize aesthetic outcomes minimizing surgical complexity, was the purpose of this study. We assessed an innovative surgical procedure based on the use of a biomimetic polyurethane-based scaffold intended for regenerating soft-tissue resembling fat, in patients undergoing BCS for non-malignant breast lesions. Safety and performance of the scaffold, and safety and feasibility of the entire implant procedure were evaluated. Methods A volunteer sample of 15 female patients underwent lumpectomy with immediate device positioning, performing seven study visits with six-month follow-up. We evaluated incidence of adverse events (AEs), changes in breast appearance (using photographs and anthropomorphic measurements), interference with ultrasound and MRI (assessed by two independent investigators), investigator’s satisfaction (through a VAS scale), patient’s pain (through a VAS scale) and quality of life (QoL) (using the BREAST-Q© questionnaire). Data reported are the results of the interim analysis on the first 5 patients. Results No AEs were device related nor serious. Breast appearance was unaltered and the device did not interference with imaging. High investigator’s satisfaction, minimal post-operative pain and positive impact on QoL were also detected. Conclusions Albeit on a limited number of patients, data showed positive outcomes both in terms of safety and performance, paving the way to an innovative breast reconstructive approach with a potential remarkable impact on clinical application of tissue engineering. Trial registration ClinicalTrials.gov (NCT04131972, October 18, 2019).

The rapid advancements of Internet of Things (IoT) and Artificial Intelligence (AI) have catalyzed the development of adaptive traffic control systems (ATCS) for smart cities. In particular, deep reinforcement learning (DRL) models produce state-of-the-art performance and have great potential for practical applications. In the existing DRL-based ATCS, the controlled signals collect traffic state information from nearby vehicles, and then optimal actions (e.g., switching phases) can be determined based on the collected information. The DRL models fully ?trust? that vehicles are sending the true information to the traffic signals, making the ATCS vulnerable to adversarial attacks with falsified information. In view of this, this paper first time formulates a novel task in which a group of vehicles can cooperatively send falsified information to ?cheat? DRL-based ATCS in order to save their total travel time. To solve the proposed task, we develop CollusionVeh, a generic and effective vehicle-colluding framework composed of a road situation encoder, a vehicle interpreter, and a communication mechanism. We employ our framework to attack established DRL-based ATCS and demonstrate that the total travel time for the colluding vehicles can be significantly reduced with a reasonable number of learning episodes, and the colluding effect will decrease if the number of colluding vehicles increases. Additionally, insights and suggestions for the real-world deployment of DRL-based ATCS are provided. The research outcomes could help improve the reliability and robustness of the ATCS and better protect the smart mobility systems.

Globally, the spread and severity of COVID-19 have been distinctly non-uniform. Seasonality was suggested as a contributor to regional variability, but the relationship between weather and COVID-19 remains unclear and the focus of attention has been on outdoor conditions. Because humans spend most of their time indoors and because most transmission occurs indoors, we here, instead, investigate the hypothesis that indoor climate—particularly indoor relative humidity (RH)—may be the more relevant modulator of outbreaks. To study this association, we combined population-based COVID-19 statistics and meteorological measurements from 121 countries. We rigorously processed epidemiological data to reduce bias, then developed and experimentally validated a computational workflow to estimate indoor conditions based on outdoor weather data and standard indoor comfort conditions. Our comprehensive analysis shows robust and systematic relationships between regional outbreaks and indoor RH. In particular, we found intermediate RH (40–60%) to be robustly associated with better COVID-19 outbreak outcomes (versus RH < 40% or >60%). Together, these results suggest that indoor conditions, particularly indoor RH, modulate the spread and severity of COVID-19 outbreaks.

Soft adhesive contacts are ubiquitous in nature and are increasingly used in synthetic systems, such as flexible electronics and soft robots, due to their advantages over traditional joining techniques. While methods to study the failure of adhesives typically apply tensile loads to the adhesive joint, less is known about the performance of soft adhesives under shear and torsion, which may become important in engineering applications. A major challenge that has hindered the characterization of shear/torsion-induced delamination is imposed by the fact that, even after delamination, contact with the substrate is maintained, thus allowing for frictional sliding and re-adhesion. In this work, we address this gap by studying the controlled delamination of soft cylinders under combined compression and torsion. Our experimental observations expose the nucleation of delamination at an imperfection and its propagation along the circumference of the cylinder. The observed sequence of ‘stick-slip’ events and the sensitivity of the delamination process to material parameters are explained by a theoretical model that captures axisymmetric delamination patterns, along with the subsequent frictional sliding and re-adhesion. By opening up an avenue for improved characterization of adhesive failure, our experimental approach and theoretical framework can guide the design of adhesives in future applications.

Abstract. Aerosol indirect radiative forcing (IRF), which characterizes how aerosols alter cloud formation and properties, is very sensitive to the preindustrial (PI) aerosol burden. Dimethyl sulfide (DMS), emitted from the ocean, is a dominant natural precursor of non-sea-salt sulfate in the PI and pristine present-day (PD) atmospheres. Here we revisit the atmospheric oxidation chemistry of DMS, particularly under pristine conditions, and its impact on aerosol IRF. Based on previous laboratory studies, we expand the simplified DMS oxidation scheme used in the Community Atmospheric Model version 6 with chemistry (CAM6-chem) to capture the OH-addition pathway and the H-abstraction pathway and the associated isomerization branch. These additional oxidation channels of DMS produce several stable intermediate compounds, e.g., methanesulfonic acid (MSA) and hydroperoxymethyl thioformate (HPMTF), delay the formation of sulfate, and, hence, alter the spatial distribution of sulfate aerosol and radiative impacts. The expanded scheme improves the agreement between modeled and observed concentrations of DMS, MSA, HPMTF, and sulfate over most marine regions, based on the NASA Atmospheric Tomography (ATom), the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA), and the Variability of the American Monsoon Systems (VAMOS) Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) measurements. We find that the global HPMTF burden and the burden of sulfate produced from DMS oxidation are relatively insensitive to the assumed isomerization rate, but the burden of HPMTF is very sensitive to a potential additional cloud loss. We find that global sulfate burden under PI and PD emissions increase to 412 Gg S (+29 %) and 582 Gg S (+8.8 %), respectively, compared to the standard simplified DMS oxidation scheme. The resulting annual mean global PD direct radiative effect of DMS-derived sulfate alone is −0.11 W m−2. The enhanced PI sulfate produced via t

Significance Phosphonates are a class of phosphorus metabolites characterized by a highly stable C-P bond. Phosphonates accumulate to high concentrations in seawater, fuel a large fraction of marine methane production, and serve as a source of phosphorus to microbes inhabiting nutrient-limited regions of the oligotrophic ocean. Here, we show that 15% of all bacterioplankton in the surface ocean have genes phosphonate synthesis and that most belong to the abundant groups Prochlorococcus and SAR11. Genomic and chemical evidence suggests that phosphonates are incorporated into cell-surface phosphonoglycoproteins that may act to mitigate cell mortality by grazing and viral lysis. These results underscore the large global biogeochemical impact of relatively rare but highly expressed traits in numerically abundant groups of marine bacteria.

© 2019 Elsevier Inc. We study optimal contracting between a firm selling a divisible good that exhibits positive externality and a group of agents in a social network. The extent of externality that each agent receives from the consumption of neighboring agents is privately held and is unknown to the firm. By explicitly characterizing the optimal multilateral contract, we demonstrate how inefficiency in an agent's trade propagates through the network and creates unequal and network-dependent downward distortion in other agents' trades. Furthermore, we describe bilateral contracts (non-linear pricing schemes) and characterize their explicit dependence on the network structure. We show that the firm will benefit from uncertainty in an agent's valuation of other agents' externality. We describe the profit gap between multilateral and bilateral contracts and analyze the consequences of the explicit dependence of the contracts on network structure. When the network is balanced in terms of homogeneity of agents' influence, network structure has no impact on the firm's profit for bilateral contracts. On the other hand, when the influences are heterogeneous with high dispersion (as in core-periphery networks) the restriction to bilateral contracts can result in profit losses that grow unbounded with the size of networks.

© 2015 IEEE. We explore the problem of inferring the graph Laplacian of a weighted, undirected network from snapshots of a single or multiple discrete-time consensus dynamics, subject to parameter uncertainty, taking place on the network. Specifically, we consider three problems in which we assume different levels of knowledge about the diffusion rates, observation times, and the input signal power of the dynamics. To solve these underdetermined problems, we propose a set of algorithms that leverage the spectral properties of the observed data and tools from convex optimization. Furthermore, we provide theoretical performance guarantees associated with these algorithms. We complement our theoretical work with numerical experiments, that demonstrate how our proposed methods outperform current state-of-the-art algorithms and showcase their effectiveness in recovering both synthetic and real-world networks.

BACKGROUND: Water arsenic (As) sources beyond a rural household's primary well may be a significant source for certain individuals, including schoolchildren and men working elsewhere. OBJECTIVE: To improve exposure assessment by estimating the fraction of drinking water that comes from wells other than the household's primary well in a densely populated area. METHODS: We use well water and urinary As data collected in 2000-2001 within a 25 km2 area of Araihazar upazila, Bangladesh, for 11,197 participants in the Health Effects of Arsenic Longitudinal Study (HEALS). We estimate the fraction of water that participants drink from different wells by imposing a long-term mass-balance constraint for both As and water. RESULTS: The mass-balance model suggest that, on average, HEALS participants obtain 60-75% of their drinking water from their primary household wells and 25-40% from other wells, in addition to water from food and cellular respiration. Because of this newly quantified contribution from other wells, As in drinking water rather than rice was identified as the largest source of As exposure at baseline for HEALS participants with a primary household well containing ≤50 µg/L As. SIGNIFICANCE: Dose-response relationships for As based on water As should take into account other wells. The mass-balance approach could be applied to study other toxicants.

Abstract When organic peat soils are sufficiently dry, they become flammable. In Southeast Asian peatlands, widespread deforestation and associated drainage create dry conditions that, when coupled with El Niño-driven drought, result in catastrophic fire events that release large amounts of carbon and deadly smoke to the atmosphere. While the effects of anthropogenic degradation on peat moisture and fire risk have been extensively demonstrated, climate change impacts to peat flammability are poorly understood. These impacts are likely to be mediated primarily through changes in soil moisture. Here, we used neural networks (trained on data from the NASA Soil Moisture Active Passive satellite) to model soil moisture as a function of climate, degradation, and location. The neural networks were forced with regional climate model projections for 1985–2005 and 2040–2060 climate under RCP8.5 forcing to predict changes in soil moisture. We find that reduced precipitation and increased evaporative demand will lead to median soil moisture decreases about half as strong as those observed during recent El Niño droughts in 2015 and 2019. Based on previous studies, such reductions may be expected to accelerate peat carbon emissions. Our results also suggest that soil moisture in degraded areas with less tree cover may be more sensitive to climate change than in other land use types, motivating urgent peatland restoration. Climate change may play an important role in future soil moisture regimes and by extension, future peat fire in Southeast Asian peatlands.

Ancestral genome reconstruction predicts public good cheaters in natural polysaccharide-degrading bacterioplankton communities.

AbstractDynamic fracture is an important area of materials analysis, assessing the atomic-level mechanisms by which materials fail over time. Here, we focus on brittle materials failure and show that an atomistically derived progressive transformer diffusion machine learning model can effectively describe the dynamics of fracture, capturing important aspects such as crack dynamics, instabilities, and initiation mechanisms. Trained on a small dataset of atomistic simulations, the model generalizes well and offers a rapid assessment of dynamic fracture mechanisms for complex geometries, expanding well beyond the original set of atomistic simulation results. Various validation cases, progressively more distinct from the data used for training, are presented and analyzed. The validation cases feature distinct geometric details, including microstructures generated by a generative neural network used here to identify novel bio-inspired material designs for mechanical performance. For all cases, the model performs well and captures key aspects of material failure.

Abstract. The High Atlas, culminating at more than 4000 m, is the water tower of Morocco. While plains receive less than 400 mm of precipitation in an average year, the mountains can get twice as much, often in the form of snow between November and March. Snowmelt thus accounts for a large fraction of the river discharge in the region, particularly during spring. In parallel, future climate change projections point towards a significant decline in precipitation and enhanced warming of temperature for the area. Here, we build on previous research results on snow and climate modelling in the High Atlas to make detailed projections of snowpack and river flow response to climate change in this region. We develop end-of-century snowpack projections using a distributed energy balance snow model based on SNOW-17 and high-resolution climate simulations over Morocco with the MIT Regional Climate Model (MRCM) under a mitigation (RCP4.5) and a business-as-usual (RCP8.5) scenario. Snowpack water content is projected to decline by up to 60 % under RCP4.5 and 80 % under RCP8.5 as a consequence of strong warming and drying in the region. We also implement a panel regression framework to relate runoff ratios to regional meteorological conditions in seven small sub-catchments in the High Atlas. Relative humidity and the fraction of solid-to-total precipitation are found to explain about 30 % of the inter-annual variability in runoff ratios. Due to projected future atmospheric drying and the associated decline in snow-to-precipitation ratio, a 5 %–30 % decrease in runoff ratios and 10 %–60 % decrease in precipitation are expected to lead to severe (20 %–70 %) declines in river discharge. Our results have important implications for water resources planning and sustainability of agriculture in this already water-stressed region.

Collagen is the most abundant structural protein in humans, with dozens of sequence variants accounting for over 30% of the protein in an animal body. The fibrillar and hierarchical arrangements of collagen are critical in providing mechanical properties with high strength and toughness. Due to this ubiquitous role in human tissues, collagen-based biomaterials are commonly used for tissue repairs and regeneration, requiring chemical and thermal stability over a range of temperatures during materials preparation ex vivo and subsequent utility in vivo. Collagen unfolds from a triple helix to a random coil structure during a temperature interval in which the midpoint or Tm is used as a measure to evaluate the thermal stability of the molecules. However, finding a robust framework to facilitate the design of a specific collagen sequence to yield a specific Tm remains a challenge, including using conventional molecular dynamics modeling. Here we propose a de novo framework to provide a model that outputs the Tm values of input collagen sequences by incorporating deep learning trained on a large data set of collagen sequences and corresponding Tm values. By using this framework, we are able to quickly evaluate how mutations and order in the primary sequence affect the stability of collagen triple helices. Specifically, we confirm that mutations to glycines, mutations in the middle of a sequence, and short sequence lengths cause the greatest drop in Tm values.

IEEE Emerging applications of control, estimation, and machine learning pose resource constraints that limit which of the available sensors, actuators, or data can be simultaneously used. Therefore, researchers have proposed solutions within discrete optimization frameworks where the optimization is performed over finite sets. In this paper, we consider such problems but in adversarial environments, where in every step a number of the chosen elements in the optimization is removed due to failures/attacks. Specifically, we consider for the first time a sequential version of the problem that allows us to observe the failures and adapt, while the attacker also adapts to our response. We call the novel problem Robust Sequential submodular Maximization (RSM). Generally, the problem is computationally hard and no scalable algorithm is known for its solution. In this paper, we propose Robust and Adaptive Maximization (RAM), the first scalable algorithm. RAM adapts in every step to the history of failures. Also, it guarantees a near-optimal performance. Finally, we demonstrate RAM's near-optimality in simulations across various application scenarios, along with its robustness against several failure types, from worst-case to random.

Peatlands account for 15 to 30% of the world’s soil carbon (C) stock and are important controls over global nitrogen (N) cycles. However, C and N concentrations are known to vary among peatlands contributing to the uncertainty of global C inventories, but there are few global studies that relate peatland classification to peat chemistry. We analyzed 436 peat cores sampled in 24 countries across six continents and measured C, N, and organic matter (OM) content at three depths down to 70 cm. Sites were distinguished between northern (387) and tropical (49) peatlands and assigned to one of six distinct broadly recognized peatland categories that vary primarily along a pH gradient. Peat C and N concentrations, OM content, and C:N ratios differed significantly among peatland categories, but few differences in chemistry with depth were found within each category. Across all peatlands C and N concentrations in the 10–20 cm layer, were 440 ± 85.1 g kg-1 and 13.9 ± 7.4 g kg-1, with an average C:N ratio of 30.1 ± 20.8. Among peatland categories, median C concentrations were highest in bogs, poor fens and tropical swamps (446–532 g kg-1) and lowest in intermediate and extremely rich fens (375–414 g kg-1). The C:OM ratio in peat was similar across most peatland categories, except in deeper samples from ombrotrophic tropical peat swamps that were higher than other peatlands categories. Peat N concentrations and C:N ratios varied approximately two-fold among peatland categories and N concentrations tended to be higher (and C:N lower) in intermediate fens compared with other peatland types. This study reports on a unique data set and demonstrates that differences in peat C and OM concentrations among broadly classified peatland categories are predictable, which can aid future studies that use land cover assessments to refine global peatland C and N stocks.

Abstract The Arabian Peninsula exhibits extreme hot summers and has one of the world’s largest population growths. We use satellite observations and reanalysis as well as climate model projections to analyze morning and evening land surface temperatures (LSTs), to refer to processes at the surface, and wet bulb temperatures (WBTs) to measure human heat stress. We focus on three regions: the Persian Gulf and Gulf of Oman, the inland capital of Saudi Arabia, Riyadh and the irrigated agricultural region in Al-Jouf, Saudi Arabia. This study shows that the time of day is important when studying LST and WBT, with current and future WBT higher in the early summer evenings. It also shows that the effect of humidity brought from waterbodies or through irrigation can significantly increase heat stress. Over the coasts of the Peninsula, humidity decreases LST but increases heat stress via WBT values higher than 25 °C in the evening. Riyadh, located in the heart of the Peninsula has lower WBT of 15 °C–17.5 °C and LST reaching 42.5 °C. Irrigation in the Al-Jouf province decreases LST by up to 10° with respect to its surroundings, while it increases WBT by up to 2.5°. Climate projections over the Arabian Peninsula suggest that global efforts will determine the survivability in this region. The projected increase in LST and WBT are +6 °C and +4 °C, respectively, in the Persian Gulf and Riyadh by the end of the century, posing significant risks on human survivability in the Peninsula unless strict climate mitigation takes place.

Calibration is an essential process in making an agent-based simulator operational. In particular, the calibration for freight demand is challenging because of the model complexity and the shortage of available freight demand data compared with passenger data. This paper proposes a novel calibration method that relies solely on screenline counts, named screenline-based two-step calibration (SLTC). SLTC consists of two parts: (1) tour-based demand adjustment; and (2) model parameter updates. The former generates screenline-based tours by cloning/removing instances of the simulated goods vehicle tours, aiming to minimize the gaps between the observed and the simulated screenline counts. The latter updates the parameters of the commodity flow model which generates inputs to simulate goods vehicle tours. To demonstrate the practicality of the proposed method, we apply it to an agent-based urban freight simulator, SimMobility Freight. The result shows that SLTC allows the simulator to replicate the observed screenline counts with reasonable computational cost for calibration.

This paper introduces an integrated transportation and energy activity-based model (iTEAM), a tool for the evaluation of “green policies” aimed at enhancing sustainability and well-being. The model will simulate individual/household and organization/firm agents at a micro level. The aggregate simulation results will help forecast the impacts of policies on transport system efficiency and land-use dynamics in the simulated areas. This process is complemented by Material Flow Accounting (MFA) techniques, which account for a range of factors including wellbeing, waste production, and carbon emissions to calculate the urban metabolism.

Biomass burning organic aerosol (BBOA) in the atmosphere contains many compounds that absorb solar radiation, called brown carbon (BrC). While BBOA is in the atmosphere, BrC can undergo reactions with oxidants such as ozone which decrease absorbance, or whiten. The effect of temperature and relative humidity (RH) on whitening has not been well constrained, leading to uncertainties when predicting the direct radiative effect of BrC on climate. Using an aerosol flow-tube reactor, we show that the whitening of BBOA by oxidation with ozone is strongly dependent on RH and temperature. Using a poke-flow technique, we show that the viscosity of BBOA also depends strongly on these conditions. The measured whitening rate of BrC is described well with the viscosity data, assuming that the whitening is due to oxidation occurring in the bulk of the BBOA, within a thin shell beneath the surface. Using our combined datasets, we developed a kinetic model of this whitening process, and we show that the lifetime of BrC is 1 d or less below ∼1 km in altitude in the atmosphere but is often much longer than 1 d above this altitude. Including this altitude dependence of the whitening rate in a chemical transport model causes a large change in the predicted warming effect of BBOA on climate. Overall, the results illustrate that RH and temperature need to be considered to understand the role of BBOA in the atmosphere.

Collagen is the most abundant structural protein in humans, providing crucial mechanical properties, including high strength and toughness, in tissues. Collagen-based biomaterials are, therefore, used for tissue repair and regeneration. Utilizing collagen effectively during materials processing ex vivo and subsequent function in vivo requires stability over wide temperature ranges to avoid denaturation and loss of structure, measured as melting temperature (T m ). Although significant research has been conducted on understanding how collagen primary amino acid sequences correspond to T m values, a robust framework to facilitate the design of collagen sequences with specific T m remains a challenge. Here, we develop a general model using a genetic algorithm within a deep learning framework to design collagen sequences with specific T m values. We report 1,000 de novo collagen sequences, and we show that we can efficiently use this model to generate collagen sequences and verify their T m values using both experimental and computational methods. We find that the model accurately predicts T m values within a few degrees centigrade. Further, using this model, we conduct a high-throughput study to identify the most frequently occurring collagen triplets that can be directly incorporated into collagen. We further discovered that the number of hydrogen bonds within collagen calculated with molecular dynamics (MD) is directly correlated to the experimental measurement of triple-helical quality. Ultimately, we see this work as a critical step to helping researchers develop collagen sequences with specific T m values for intended materials manufacturing methods and biomedical applications, realizing a mechanistic materials by design paradigm.

AbstractSuccessful epidemic modeling requires understanding the implicit feedback control strategies used by populations to modulate the spread of contagion. While such strategies can be replicated with intricate modeling assumptions, here we propose a simple model where infection dynamics are described by a three parameter feedback policy. Rather than model individuals as directly controlling the contact rate which governs the spread of disease, we model them as controlling the contact rate’s derivative, resulting in a dynamic rather than kinematic model. The feedback policy used by populations across the United States which best fits observations is proportional-derivative control, where learned parameters strongly correlate with observed interventions (e.g., vaccination rates and mobility restrictions). However, this results in a non-zero “steady-state” of case counts, implying current mitigation strategies cannot eradicate COVID-19. Hence, we suggest making implicit policies a function of cumulative cases, resulting in proportional-integral-derivative control with higher potential to eliminate COVID-19.

We investigate the spatial and temporal memory effects of traffic density and velocity in the Nagel-Schreckenberg cellular automaton model. We show that the two-point correlation function of vehicle occupancy provides access to spatial memory effects, such as headway, and the velocity autocovariance function to temporal memory effects such as traffic relaxation time and traffic compressibility. We develop stochasticity-density plots that permit determination of traffic density and stochasticity from the isotherms of first- and second-order velocity statistics of a randomly selected vehicle. Specifically, provided ergodicity and stationarity, these stochasticity-density plots permit a direct determination of traffic properties from crowdsourced measurements of velocities of vehicles. We illustrate the predictive prowess of the approach for crowdsourced vehicle speed data collected by anonymous smartphone measurements for the state of Massachusetts, USA, as a powerful alternative to classical traffic property estimates from spatially distributed user counts.

Sunlight exposure can naturally mitigate microplastics pollution in the surface ocean, however it results in emissions of dissolved organic carbon (DOC) whose characteristics and fate remain largely unknown. In this work, we investigated the effects of solar radiation on polyether (TPU_Ether) and polyester (TPU_Ester) thermoplastic polyurethane, and on a thermoset polyurethane (PU_Hardened). The microplastics were irradiated with simulated solar light with a UV dose of 350 MJ m−2 , which corresponds to roughly 15 months outdoor exposure at 31° N latitude. The particles were characterized using ATRFTIR and elemental analysis. The DOC released to the aqueous phase was quantified by total organic carbon analysis and characterized by nontarget liquid chromatography coupled to high-resolution mass spectrometry. Polyurethane microplastics were degraded following mechanisms reconcilable with UV photo-oxidation. The carbon mass fraction released to the aqueous phase was 8.5 ± 0.5%, 3.7 ± 0.2%, and 2.8 ± 0.2% for TPU_Ether, TPU_Ester, and PU_Hardened, respectively. The corresponding DOC release rates, expressed as mg carbon per UV dose were 0.023, 0.013, and 0.010 mg MJ−1 for TPU_Ether, TPU_Ester and PU_Hardened, respectively. Roughly three thousand unique by-products were released from photo-weathered TPUs, whereas 540 were detected in the DOC of PU_Hardened. This carbon pool was highly complex and dynamic in terms of physicochemical properties and susceptibility to further photodegradation after dissolution from the particles. Our results show that plastics photodegradation in the ocean requires chemical assessment of the DOC emissions in addition to the analysis of aged microplastics and that polymer chemistry influences the chain scission products.

The COVID-19 pandemic has been inflicted upon humanity by the SARS-CoV-2 virus, the latest insidious incarnation of the coronaviruses group. While in its wake intense scientific research has produced breakthrough vaccines and cures, there still exists an immediate need to further understand the origin, mechanobiology and biochemistry, and destiny of this virus so that future pandemics arising from similar coronaviruses may be contained more effectively. In this Perspective, we discuss the various evidential findings of virus propagation and connect them to respective underpinning cellular biomechanical states leading to corresponding manifestations of the viral activity. We further propose avenues to tackle the virus, including from a "musical" vantage point, and contain its relentless strides that are currently afflicting the global populace.