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Flood risk communication is imperative to aiding people’s decision making in flood situations. These warnings can be communicated through navigation applications on mobile devices. The current study investigated how flood-depth information affected drivers’ actions given flood warnings from a mobile navigation application in a driving simulator. This study manipulated the type of flood warning presented to the participants in the driving scenarios and measured their actions given a potentially flooded roadway. Participants experienced six drives with different flood warning conditions. Results indicated that providing flood depth information helped drivers accurately estimate the depth of the flood and their perceived risks; including more detailed information was helpful for drivers to make informed decisions regarding a flooded roadway. We suggest that designers include flood depth information to help drivers accurately perceive the depth and risk regarding a flooded roadway.

 

The increasing threat of inland flooding due to precipitation changes and floodplain development necessitates efficient real-time flood detection and communication methods. While automated floodwarning systems facilitate such communication, they are susceptible to errors like false alarms and misses, which could undermine drivers’ trust during flood events. This study examined how system accuracy and error type impact perceived system reliability, as well as drivers’ trust and behaviors. Our results showed that both false alarms and misses lowered drivers’ perceived system reliability, and drivers were more inclined to follow recommendations from a system with higher reliability compared to one with low reliability. Misses and false alarms influenced drivers’ reliance and compliance behaviors differently. These findings help predict how system reliability level and error type shape drivers’ responses to automated flood-warning systems, potentially contributing to their design and calibration.

 

The Kondo lattice is one of the classic examples of strongly correlated electronic systems. We conduct a controlled study of the Kondo lattice in one dimension, highlighting the role of excitations created by the composite fermion operator. Using time-dependent matrix product state methods, we compute various correlation functions and contrast them with both large-N mean-field theory and the strong-coupling expansion. We show that the composite fermion operator creates long-lived, charge-e and spin-1/2 excitations, which cover the low-lying single-particle excitation spectrum of the system. Furthermore, spin excitations can be thought to be composed of such fractionalized quasiparticles with a residual interaction which tend to disappear at weak Kondo coupling.

Published by the American Physical Society2024 

Monitoring and forecasting hospitalization rates are of essential significance to public health systems in understanding and managing overall healthcare deliveries and strategizing long-term sustainability. Early-stage prediction of hospitalization rates is crucial to meet the medical needs of numerous patients during emerging epidemic diseases such as COVID-19. Nevertheless, this is a challenging task due to insufficient data and experience. In addition, relevant existing work neglects or fails to exploit the extensive contribution of external factors such as news, policies, and geolocations. In this paper, we demonstrate the significant relationship between hospitalization rates and COVID-19 infection cases. We then adapt a transfer learning architecture with dynamic location-aware sentiment and semantic analysis (TLSS) to a new application scenario: hospitalization rate prediction during COVID-19. This architecture learns and transfers general transmission patterns of existing epidemic diseases to predict hospitalization rates during COVID-19. We combine the learned knowledge with time series features and news sentiment and semantic features in a dynamic propagation process. We conduct extensive experiments to compare the proposed approach with several state-of-the-art machine learning methods with different lead times of ground truth. Our results show that TLSS exhibits outstanding predictive performance for hospitalization rates. Thus, it provides advanced artificial intelligence (AI) techniques for supporting decision-making in healthcare sustainability. 

1-Decanol has great value in the pharmaceutical and fragrance industries and plays an important role in the chemical industry. In this study, we engineered Escherichia coli to selectively synthesize 1-decanol by using enzymes of the core reverse β-oxidation (rBOX) pathway and termination module with overlapping chain-length specificity. Through screening for acyl-CoA reductase termination enzymes and proper regulation of rBOX pathway expression, a 1-decanol titer of 1.4 g/L was achieved. Further improvements were realized by engineering pyruvate dissimilation to ensure the generation of NADH through pyruvate dehydrogenase (PDH) and reducing byproduct synthesis via a tailored YigI thioesterase knockout, increasing 1-decanol titer to 1.9 g/L. The engineered strain produced about 4.4 g/L 1-decanol with a yield of 0.21 g/g in 36 h in a bi-phasic fermentation that used a dodecane overlay to increase 1-decanol transport and reduce its toxicity. Adjustment of pathway expression (varying inducer concentration) and cell growth (oxygen availability) enabled 1-decanol production at 6.1 g/L (0.26 g/g yield) and 10.05 g/L (0.2 g/g yield) using rich medium in shake flasks and bioreactor, respectively. Remarkably, the use of minimal medium resulted in 1-decanol production with 100% specificity at 2.8 g/L (0.14 g/g yield) and a per cell mass yield higher than rich medium. These 1-decanol titers, yields and purity are at least 10-fold higher than others reported to date and the engineered strain shows great potential for industrial production. Taken together, our findings suggest that using rBOX pathway and termination enzymes of proper chain-length specificity in combination with optimal chassis engineering should be an effective approach for the selective production of alcohols. 

Recently, solar-induced chlorophyll fluorescence (SIF) is a promising tool to estimate gross primary production (GPP). Photosynthesis gradually saturates with the increasing light, but fluorescence tends to keep increasing, leading to a nonlinear SIF-GPP relationship. This nonlinearity occurs for sunlit leaves but not for shaded leaves for which photosynthesis is light-limited. However, the separation of sunlit and shaded SIF has not been systematically investigated when estimating GPP from SIF. Therefore, it is promising to develop a model for GPP estimation considering such differences. This study proposed an approach to separate the total canopy SIF emission (SIFtotal) from TROPOspheric Monitoring Instrument (TROPOMI) SIF into their sunlit and shaded components (SIFsun and SIFshade). The nonlinearity and linearity in SIF-GPP relationships for sunlit and shaded leaves were incorporated into a two-leaf hybrid model, which was fitted using flux tower data and then evaluated using leave-one-site-out crossing validation. We also elucidated the distinct SIF-GPP relationships between sunlit and shaded leaves using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model simulation. Compared to previously used linear (R2 = 0.68, RMSE = 2.13 gC⋅m^-2*d^-1) or hyperbolic (R2 = 0.72, RMSE = 2.01 gC⋅m^-2⋅d^-1) model based on the big-leaf assumption, our proposed two-leaf hybrid model has the best performance on GPP estimation (R2 = 0.77, RMSE = 1.79 gC⋅m^-2⋅d^-1). We also applied this two-leaf hybrid model to estimate the global GPP during the main growing season in Northern Hemisphere, which were highly correlated with several existing GPP products, with R2 ranging from 0.79 to 0.88. These results will improve our understanding of the relationship between SIF and GPP for sunlit and shaded leaves and will advance application of satellite SIF data to GPP estimation. 

Flood warnings can be communicated through mobile devices and should convey enough information to keep the user safe during a flood situation. However, the amount of detail included in the warning, such as the depth of the flood, may vary. The purpose of this study was to investigate how to best inform drivers of floods to keep them protected. Participants were tasked to drive to a restaurant in a driving simulator after receiving instructions and a type of flood information warning during each scenario (flood, no flood, flood of 6 inches, flood of 6 inches maximum). We found that participants accepted the alternate route more when in a scenario with a flood present compared to the no-flood scenario. These results deepened the understanding of human decisionmaking and can guide future flood warning designs to keep drivers protected from flooded roadways

 

Objective We investigated secondary–task–based countermeasures to the vigilance decrement during a simulated partially automated driving (PAD) task, with the goal of understanding the underlying mechanism of the vigilance decrement and maintaining driver vigilance in PAD. Background Partial driving automation requires a human driver to monitor the roadway, but humans are notoriously bad at monitoring tasks over long periods of time, demonstrating the vigilance decrement in such tasks. The overload explanations of the vigilance decrement predict the decrement to be worse with added secondary tasks due to increased task demands and depleted attentional resources, whereas the underload explanations predict the vigilance decrement to be alleviated with secondary tasks due to increased task engagement. Method Participants watched a driving video simulating PAD and were required to identify hazardous vehicles throughout the 45-min drive. A total of 117 participants were assigned to three different vigilance-intervention conditions including a driving-related secondary task (DR) condition, a non-driving-related secondary task (NDR) condition, and a control condition with no secondary tasks. Results Overall, the vigilance decrement was shown over time, reflected in increased response times, reduced hazard detection rates, reduced response sensitivity, shifted response criterion, and subjective reports on task-induced stress. Compared to the DR and the control conditions, the NDR displayed a mitigated vigilance decrement. Conclusion This study provided convergent evidence for both resource depletion and disengagement as sources of the vigilance decrement. Application The practical implication is that infrequent and intermittent breaks using a non-driving related task may help alleviate the vigilance decrement in PAD systems. 

Secondary organic aerosol (SOA) is ubiquitous in the atmosphere and plays a pivotal role in climate, air quality, and health. The production of low-volatility dimeric compounds through accretion reactions is a key aspect of SOA formation. However, despite extensive study, the structures and thus the formation mechanisms of dimers in SOA remain largely uncharacterized. In this work, we elucidate the structures of several major dimer esters in SOA from ozonolysis of α-pinene and β-pinene—substantial global SOA sources—through independent synthesis of authentic standards. We show that these dimer esters are formed in the particle phase and propose a mechanism of nucleophilic addition of alcohols to a cyclic acylperoxyhemiacetal. This chemistry likely represents a general pathway to dimeric compounds in ambient SOA. 

Mitochondrial DNA (mtDNA) is known to play a critical role in cellular functions. However, the fluorescent probe enantio-selectively targeting live-cell mtDNA is rare. We recently found that the well-known DNA ‘light-switch’ [Ru(phen)2dppz]Cl2 can image nuclear DNA in live-cells with chlorophenolic counter-anions via forming lipophilic ion-pairing complex. Interestingly, after washing with fresh-medium, [Ru(phen)2dppz]Cl2 was found to re-localize from nucleus to mitochondria via ABC transporter proteins. Intriguingly, the two enantiomers of [Ru(phen)2dppz]Cl2 were found to bind enantio-selectively with mtDNA in live-cells not only by super-resolution optical microscopy techniques (SIM, STED), but also by biochemical methods (mitochondrial membrane staining with Tomo20-dronpa). Using [Ru(phen)2dppz]Cl2 as the new mtDNA probe, we further found that each mitochondrion containing 1–8 mtDNA molecules are distributed throughout the entire mitochondrial matrix, and there are more nucleoids near nucleus. More interestingly, we found enantio-selective apoptotic cell death was induced by the two enantiomers by prolonged visible light irradiation, and in-situ self-monitoring apoptosis process can be achieved by using the unique ‘photo-triggered nuclear translocation’ property of the Ru complex. This is the first report on enantio-selective targeting and super-resolution imaging of live-cell mtDNA by a chiral Ru complex via formation and dissociation of ion-pairing complex with suitable counter-anions.