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Earthquake nowcasting has been proposed as a means of tracking the change in large earthquake potential in a seismically active area. The method was developed using observable seismic data, in which probabilities of future large earthquakes can be computed using Receiver Operating Characteristic methods. Furthermore, analysis of the Shannon information content of the earthquake catalogs has been used to show that there is information contained in the catalogs, and that it can vary in time. So an important question remains, where does the information originate? In this paper, we examine this question using stochastic simulations of earthquake catalogs. Our catalog simulations are computed using an Earthquake Rescaled Aftershock Seismicity ("ERAS") stochastic model. This model is similar in many ways to other stochastic seismicity simulations, but has the advantage that the model has only 2 free parameters to be set, one for the aftershock (Omori‐Utsu) time decay, and one for the aftershock spatial migration away from the epicenter. Generating a simulation catalog and fitting the two parameters to the observed catalog such as California takes only a few minutes of wall clock time. While clustering can arise from random, Poisson statistics, we show that significant information in the simulation catalogs arises from the "non‐Poisson" power‐law aftershock clustering, implying that the practice of de‐clustering observed catalogs may remove information that would otherwise be useful in forecasting and nowcasting. We also show that the nowcasting method provides similar results with the ERAS model as it does with observed seismicity. Plain Language Summary: Earthquake nowcasting was proposed as a means of tracking the change in the potential for large earthquakes in a seismically active area, using the record of small earthquakes. The method was developed using observed seismic data, in which probabilities of future large earthquakes can be computed using machine learning methods that were originally developed with the advent of radar in the 1940s. These methods are now being used in the development of machine learning and artificial intelligence models in a variety of applications. In recent times, methods to simulate earthquakes using the observed statistical laws of earthquake seismicity have been developed. One of the advantages of these stochastic models is that it can be used to analyze the various assumptions that are inherent in the analysis of seismic catalogs of earthquakes. In this paper, we analyze the importance of the space‐time clustering that is often observed in earthquake seismicity. We find that the clustering is the origin of information that makes the earthquake nowcasting methods possible. We also find that a common practice of "aftershock de‐clustering", often used in the analysis of these catalogs, removes information about future large earthquakes. Key Points: Earthquake nowcasting tracks the change in the potential for large earthquakes, using information contained in seismic catalogsWe analyze the information contained in the space‐time clustering that is observed in earthquake seismicityWe find that "aftershock de‐clustering" of catalogs removes information about future large earthquakes that the nowcasting method uses [ABSTRACT FROM AUTHOR]

The inability of labor markets to function effectively to satisfy the needs of employers and workers suggests that there is a growing need for policy interventions to promote workplace cultures of learning and innovation. Past research suggests that publicly funded incumbent worker training programs are a promising antidote against market failures. With only a handful of studies published in the last two decades, however, this is one of the least-researched types of business support programs. This paper examines the impact of a state program in California that uses a pay-for-performance approach to reimburse employers that train their employees: the California Employment Training Panel (ETP). Based on a mixed-methods study of ETP, the authors found that, overall, ETP had positive and significant impacts on company sales and firm size. The study suggests the need to abandon ideological debates and engage in more evidence-based policy discussions about incumbent worker training programs. [ABSTRACT FROM AUTHOR]

In order to realize the accurate and reliable prediction of the change trend of dissolved oxygen (DO) content in California perch aquaculture water, this paper proposes a second-order hybrid optimization support vector machine (SVR) model based on Differential Evolution (DE) and Gray Wolf Optimizer (GWO), shortened to DE-GWO-SVR, to predict the DO content with the characteristics of nonlinear and non-smooth water quality data. Experimentally, data for the water quality, including pH, water temperature, conductivity, salinity, total dissolved solids, and DO, were collected. Pearson's correlation coefficient (PPMCC) was applied to explore the correlation between each water quality parameter and DO content. The optimal DE-GWO-SVR model was established and compared with models based on SVR, back-propagation neural network (BPNN), and their optimization models. The results show that the DE-GWO-SVR model proposed in this paper can effectively realize the nonlinear prediction and global optimization performance. Its R2, MSE, MAE and RMSE can be up to 0.94, 0.108, 0.2629, and 0.3293, respectively, which is better than those of other models. This research provides guidance for the efficient prediction of DO in perch aquaculture water bodies for increasing the aquaculture effectiveness and reducing the aquaculture risk, providing a new exploratory path for water quality monitoring. [ABSTRACT FROM AUTHOR]

Prisons in the United States serve as a site and embodiment of gendered and racialized state violence. The US incarcerates more people than any other nation in both numbers and per capita rates. Individuals incarcerated in women's prisons are 10% of the total prison population, yet women's prisons remain understudied, and the violence that occurs in women's facilities is rampant, widespread, and operates in particular racialized and gendered ways. This paper centers the forced sterilizations that occurred in California state prisons over the last two decades. We consider how reproduction and the nuclear family have served as a primary site of racial capitalism and eugenic ideology. While eugenic policies were popularized and promoted across the US and globally in the 20th century, the violent ideas underlying eugenic ideology have been a constant presence throughout US history. The height of the eugenics era is marked by the forcible sterilization of institutionalized 'deviant' bodies. While discussions of eugenics often center these programs, the reach of eugenic policies extends far beyond surgical interventions. We utilize a reproductive justice lens to argue that the hierarchical, racialized social stratification necessary for the existence of prisons constructs and sustains the 'deviant' bodies and families that predicate eugenic logic, policies, and practices. In this conceptual paper, we draw from ongoing research to argue that prisons, as institutions and as a product of racial capitalism, perpetuate the ongoing violent legacy of eugenics and name abolition as a central component of the fight to end reproductive oppression. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Patients are being encouraged to complete forms electronically using patient portals rather than on paper, but willingness of older adults to make this transition is uncertain. METHODS: The authors analyzed data for 4105 Kaiser Permanente Northern California 2020 Member Health Survey respondents aged 65-85 years who answered a question about willingness to complete online forms and questionnaires using a patient portal. Data weighted to the Kaiser Permanente Northern California membership were used to estimate percentages of older adults willing to complete patient portal forms and questionnaires. Chi-square tests and log-Poisson regression models that included sociodemographic, internet use, and patient portal variables were used to identify factors predictive of willingness. RESULTS: Overall, 59.6% of older adults were willing to complete patient portal forms, 17.6% were not willing, and 22.8% were not sure. Adults aged 75-85 (49.5%) vs 65-74 years (64.8%) and Black (51.9%) and Latino (46.5%) vs White (62.8%) adults were less likely to indicate willingness. In addition to racial and ethnic differences and younger age, higher educational attainment, use of the internet alone (vs internet use with help or not at all), having an internet-enabled computer or tablet, and having sent at least 1 message through the patient portal increased likelihood of being willing. CONCLUSIONS: Health care teams should assess older adults' capabilities and comfort related to completion of patient portal-based forms and support those willing to make the digital transition. Paper forms and oral collection of information should remain available for those unable or unwilling to make this digital transition. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of a multirotor uncrewed aircraft and AirCore system (UAAS) for measuring vertical profiles of wind velocity (speed and direction) and the mole fractions of methane (CH 4) and carbon dioxide (CO 2) , and it presents a use case that combines UAAS measurements and dispersion modeling to quantify CH 4 emissions from a dairy farm. To evaluate the atmospheric sensing performance of the UAAS, four field deployments were performed at three locations in the San Joaquin Valley of California where CH 4 hotspots were observed downwind of dairy farms. A comparison of the observations collected on board the UAAS and an 11 m meteorological tower show that the UAAS can measure wind velocity trends with a root mean squared error varying between 0.4 and 1.1 m s -1 when the wind magnitude is less than 3.5 m s -1. Findings from UAAS flight deployments and a calibration experiment also show that the UAAS can reliably resolve temporal variations in the mole fractions of CH 4 and CO 2 occurring over periods of 10 s or longer. Results from the UAAS and dispersion modeling use case further demonstrate that UAASs have great potential as low-cost tools for detecting and quantifying CH 4 emissions in near real time. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs), responsible for extreme weather conditions, are mid-latitude systems that can cause significant damage to coastal areas. While forecasting ARs beyond two weeks remains a challenge, past research suggests potential benefits may come from properly accounting for the changes in sea surface temperature (SST) through air–sea interactions. In this paper, we investigate the impact of ARs on SST over the North Pacific by analyzing 25 years of ocean reanalysis data using an SST budget equation. We show that in the region of strong ocean modification, ocean dynamics can offset over 100% of the anomalous SST warming that would otherwise arise from atmospheric forcing. Among all ocean processes, ageostrophic advection and vertical mixing (diffusion and entrainment) are the most important factors in modifying the SST tendency response. The SST tendency response to ARs varies spatially. For example, in coastal California, the driver of enhanced SST warming is the reduction in ageostrophic advection due to anomalous southerly winds. Moreover, there is a large region where the SST shows a warming response to ARs due to the overall reduction in the total clouds and subsequent increase in total incoming shortwave radiation. Strong air-sea interactions during atmospheric rivers often lead to modest upper ocean heat changes. The authors show that interior ocean dynamics are compensating for these air-sea exchanges. These findings can help improve subseasonal forecasts. [ABSTRACT FROM AUTHOR]

For evaluating the resistance performance of cement-stabilized soils in cold regions, the variation of the strength of the cemented sand–gravel (CSG) mixture concerning the hydration process should be explored. This paper aims to study the effect of freeze–thaw (F–T) cycles on the strength and microstructure of a CSG mixture with 10% cement that is subjected to 12 cycles of freezing at a temperature of −23°C for 24 h and then melted at room temperature of 24°C for the next 24 h. The uniaxial compressive strength (UCS), California bearing ratio (CBR), and weight volume loss of the samples were measured after individual F–T cycles. Furthermore, the change in the microstructure of the CSG mixture in various F–T cycles was explored. The results showed a considerable reduction in the UCS up to Cycle 3, then a slight increase for Cycles 3–6, and finally a gradual decrease for further cycles. However, the CBR and weight loss slightly fluctuated up to Cycle 6, and then gradually decreased for subsequent cycles. The majority of compounds of hydrated cement were damaged in the first three cycles. In the following cycles, between Cycles 3 and 6, the portlandite compound was dissolved and recrystallized within the microvoids. Depending on the environmental conditions, carbonation may be generated from the hydrated cement fraction, which fills the microvoids and improves the strength and structure of the mixture. During further cycles after the sixth cycle, the mechanical action of the ice lenses coupled with the disintegration of the hydrate compounds imposed many new microvoids and cracks with considerable length and width, which intensified the strength reduction of the moisture and weakened the adhesion between grains. Since cement is widely used in pavement and dam engineering for stabilizing soils, the durability of cemented soils is of prime concern. This study may help improve the durability and resistance of cemented soils in cold climates. The F–T action not only influences the macrostructure of cement-stabilized soils by imposing a wide crack and ice lens but also induces a considerable change in the complexes existing in the hydrated cement paste of the mixture. Three patterns govern the change of the mixture microstructure in various F–T cycles that correspond to the observed trend in strength. The mentioned trend for the microstructure change and, consequently, the strength variation of the CSG mixture are associated with many factors such as pH, cement content, CO2 content, moisture content within the mixture, and relative humidity within the environment. Accordingly, the pattern of microstructural changes in the CSG mixture after the middle F–T cycles may vary depending on environmental conditions. [ABSTRACT FROM AUTHOR]

Coordinating management of groundwater, surface water, and irrigated crops is fundamental economically for many arid and semi-arid regions. This paper examines conjunctive water management for agriculture using hydro-economic optimization modeling. The analysis is integrated across two timescales: a two-stage stochastic decadal model for managing annual and perennial crops spanning dry and wet years and a far-horizon dynamic program embedding the decadal model into a longer groundwater policy setting. The modeling loosely represents California's San Joaquin Valley and has insights for many irrigated arid and semi-arid regions relying on groundwater with variable annual hydrology. Results show how conjunctive water management can stabilize crop decisions and improve agricultural profitability across different water years by pumping more in dry years and increasing recharging groundwater in wetter years. Using groundwater as a buffer for droughts allows growing more higher-value perennial crops, which maximizes profit even with water-scarce conditions. Nevertheless, ending overdraft in basins with declining groundwater for profit-maximizing farming reduces annual crops to maintain more profitable perennial crops through droughts. Results are affected by economic discount rates and future climates. Operating and opportunity costs from forgone annual crops can reduce aquifer recharge early in regulatory periods. [ABSTRACT FROM AUTHOR]

Aim: As climate change increases the frequency and severity of droughts in many regions, conservation during drought is becoming a major challenge for ecologists. Droughts are multidimensional climate events whose impacts may be moderated by changes in temperature, water availability or food availability, or some combination of these. Simultaneously, other stressors such as extensive anthropogenic landscape modification may synergize with drought. Useful observational models for guiding conservation decision‐making during drought require multidimensional, dynamic representations to disentangle possible drought impacts, and consequently, they will require large, highly resolved data sets. In this paper, we develop a two‐stage predictive framework for assessing how drought impacts vary with species, habitats and climate pathways. Location: Central Valley, California, USA. Methods: We used a two‐stage counterfactual analysis combining predictive linear mixed models and N‐mixture models to characterize the multidimensional impacts of drought on 66 bird species. We analysed counts from the eBird participatory science data set between 2010 and 2019 and produced species‐ and habitat‐specific estimates of the impact of drought on relative abundance. Results: We found that while fewer than a quarter (16/66) of species experienced abundance declines during drought, nearly half of all species (27/66) changed their habitat associations during drought. Among species that shifted their habitat associations, the use of natural habitats declined during drought while use of developed habitat and perennial agricultural habitat increased. Main Conclusions: Our findings suggest that birds take advantage of agricultural and developed land with artificial irrigation and heat‐buffering microhabitat structure, such as in orchards or parks, to buffer drought impacts. A working lands approach that promotes biodiversity and mitigates stressors across a human‐induced water gradient will be critical for conserving birds during drought. [ABSTRACT FROM AUTHOR]

Background: This paper describes the protocols for a randomized controlled trial using a parallel-group trial design that includes an intervention designed to address social isolation and loneliness among people experiencing homelessness known as Miracle Friends and an intervention that combines Miracles Friends with an economic poverty-reduction intervention known as Miracle Money. Miracle Friends pairs an unhoused person with a volunteer "phone buddy." Miracle Money provides guaranteed basic income of $750 per month for 1 year to Miracle Friends participants. The study will examine whether either intervention reduces social isolation or homelessness compared to a waitlist control group. Methods: Unhoused individuals who expressed interest in the Miracle Friends program were randomized to either receive the intervention or be placed on a waitlist for Miracle Friends. Among those randomized to receive the Miracle Friends intervention, randomization also determined whether they would be offered Miracle Money. The possibility of receiving basic income was only disclosed to study participants if they were randomly selected and participated in the Miracle Friends program. All study participants, regardless of assignment, were surveyed every 3 months for 15 months. Results: Of 760 unhoused individuals enrolled in the study, 256 were randomized to receive Miracle Friends, 267 were randomized to receive Miracle Money, and 237 were randomized to the waitlist control group. In the two intervention groups, 360 of 523 unhoused individuals were initially matched to a phone buddy. Of the 191 study participants in the Miracle Money group who had been initially matched to a volunteer phone buddy, 103 were deemed to be participating in the program and began receiving monthly income. Discussion: This randomized controlled trial will determine whether innovative interventions involving volunteer phone support and basic income reduce social isolation and improve housing outcomes for people experiencing homelessness. Although we enrolled unhoused individuals who initially expressed interest in the Miracle Friends program, the study team could not reach approximately 30% of individuals referred to the study. This may reflect the general lack of stability in the lives of people who are unhoused or limitations in the appeal of such a program to some portion of the unhoused population. Trial registration: ClinicalTrials.gov NCT05408884 (first submitted on May 26, 2022). [ABSTRACT FROM AUTHOR]

Reduced defense against large herbivores has been suggested to be part of the "island syndrome" in plants. However, empirical evidence for this pattern is mixed. In this paper, we present two studies that compare putative physical and chemical defense traits from plants on the California Channel Islands and nearby mainland based on sampling of both field and common garden plants. In the first study, we focus on five pairs of woody shrubs from three island and three mainland locations and find evidence for increased leaf area, decreased marginal leaf spines, and decreased concentrations of cyanogenic glycosides in island plants. We observed similar increases in leaf area and decreases in defense traits when comparing island and mainland genotypes grown together in botanic gardens, suggesting that trait differences are not solely driven by abiotic differences between island and mainland sites. In the second study, we conducted a common garden experiment with a perennial herb—Stachys bullata (Lamiaceae)—collected from two island and four mainland locations. Compared to their mainland relatives, island genotypes show highly reduced glandular trichomes and a nearly 100-fold reduction in mono- and sesquiterpene compounds from leaf surfaces. Island genotypes also had significantly higher specific leaf area, somewhat lower rates of gas exchange, and greater aboveground biomass than mainland genotypes across two years of study, potentially reflecting a broader shift in growth habit. Together, our results provide evidence for reduced expression of putative defense traits in island plants, though these results may reflect adaptation to both biotic (i.e., the historical absence of large herbivores) and climatic conditions on islands. [ABSTRACT FROM AUTHOR]

Background: Vaccine hesitancy, delaying or refusing to vaccinate despite the availability of vaccines, impedes the progress of achieving optimal HPV vaccine coverage. Little is known about the sources of human papillomavirus (HPV) vaccine hesitancy among racially/ethnically and geographically diverse communities. The purpose of this paper is to explore HPV vaccine hesitancy among rural, Slavic, and Latino communities that reside in counties with low HPV vaccine uptake rates. Methods: Key informant interviews and focus groups were conducted with rural, Slavic, and Latino communities that reside within counties in California that have low HPV vaccine up to date rates (16–25%). Qualitative data were transcribed verbatim and analyzed using inductive and deductive thematic analysis. Results: A total of seven focus groups and 14 key informant interviews were conducted with 39 individuals from seven California counties. Salient themes that contributed to HPV vaccine hesitancy included the following: social media and the anti-vaccination movement; a strong belief in acquiring immunity naturally; prior vaccine experiences; and vaccine timing concerns. Participants suggested the provision of culturally appropriate, in-language, in-person easy to understand HPV vaccine education to mitigate HPV vaccine hesitancy. Conclusions: Our findings can inform future interventions to increase HPV vaccine uptake among hesitant communities. [ABSTRACT FROM AUTHOR]

The increase in both the frequency and magnitude of natural disasters, coupled with recent advancements in artificial intelligence, has introduced prospects for investigating the potential of new technologies to facilitate disaster response processes. Preliminary Damage Assessment (PDA), a labor-intensive procedure necessitating manual examination of residential structures to ascertain post-disaster damage severity, stands to significantly benefit from the integration of computer vision-based classification algorithms, promising efficiency gains and heightened accuracy. Our paper proposes a Vision Transformer (ViT)-based model for classifying damage severity, achieving an accuracy rate of 95%. Notably, our model, trained on a repository of over 18,000 ground-level images of homes with damage severity annotated by damage assessment professionals during the 2020–2022 California wildfires, represents a novel application of ViT technology within this domain. Furthermore, we have open sourced this dataset—the first of its kind and scale—to be used by the research community. Additionally, we have developed a publicly accessible web application prototype built on this classification algorithm, which we have demonstrated to disaster management practitioners and received feedback on. Hence, our contribution to the literature encompasses the provision of a novel imagery dataset, an applied framework from field professionals, and a damage severity classification model with high accuracy. [ABSTRACT FROM AUTHOR]

Morro manzanita Arctostaphyos morroensis (Ericaceae) is a long-lived, shrub endemic to San Luis Obispo County, southern California, USA. It was listed as threatened under the U.S. Endangered Species Act in 1994, with identified threats being residential and urban development, including lack of protection on private land and lack of management on public lands, competition with invasive non-native plants, and risks of extinction associated with small and isolated populations. Our goal in this paper is to summarize and supplement the current knowledge of Morro manzanita. We review the literature on the species' description, reproductive ecology, germination cues, short-term response to fire. and distribution. We conducted field surveys to report on long-term response to fire, resampling the previously studied prescribed burn site 25 yr post-fire. Finally, we summarize the current land management of sites that support Morro manzanita and threats faced by this species. We conclude with specific recommendations for management and future study towards supporting conservation of this species and its maritime chaparral community. [ABSTRACT FROM AUTHOR]

Traffic accidents are a nationwide public health concern, but autonomous vehicles (AVs) have the potential to significantly reduce accident severity and frequency by eliminating their most common cause, human error. By analyzing the data published by California's Department of Motor Vehicles, researchers have identified the factors that influence AV crash severity, however, none do so through a literature review. This paper's aims are multi-faceted: to understand AVs' operation on public roadways by identifying and classifying the factors contributing to accident severity, to develop a list of strategies that address the public's safety concerns, and to acknowledge the ethics of unavoidable collisions. To fulfill these objectives, a comprehensive literature review was conducted based on a keyword search. Following a multi-step screening and exclusion process, detailed review was performed of 107 relevant publications, and the factors contributing to increased crash severity were classified into 14 categories. The literature revealed that AVs are not at fault in most accidents, although they have a higher propensity than conventional cars to be involved in rear-end collisions, and they are specifically designed to minimize the number of accidents, but may face unavoidable ones. For the benefit of policymakers and manufacturers, 11 strategies that address the moral dilemma of these accidents and 7 strategies that address concerns about AV safety to improve public perception were identified. [ABSTRACT FROM AUTHOR]

This case study examines overheard speech in a third-generation heritage Spanish Mexican family. It presents Spanish use longitudinally and describes overheard Spanish word use in interaction. Transcribed on CLAN to create a plurilingual corpus, ethnographic video data consisted of 24 h across three sampling periods, yielding nearly 30,000 Spanish, English, and language mixed utterances. Quantitative analyses indicate strong Spanish use in the first sample, before dropping. Qualitative descriptions show the third-generation target-child's attunement to overheard Spanish, and her agency to use Spanish. Overheard input helps her use Spanish words, influencing her social encounters. This paper examines what we coded as overheard input in heritage language acquisition and socialization research. The language practices of one multigenerational Mexican family in California are explored, accounting for how their language practices in multiparty interaction co-create meaning, and how they help a third-generation child use Spanish words grounded in daily experiences. The findings contribute to the discussion of bilingualism in general and definitions of heritage bilingualism in particular. The results underscore the understudied role of overhead speech produced by a diversity of multigenerational family members and word learning. Participation frameworks are dynamically constructed by all participants as permeable, inclusive, and engage the children's use of inherited bilingual and bicultural practices, suggesting that heritage bilingualism is not just about abstract grammar. [ABSTRACT FROM AUTHOR]

Water system management models represent different purposes, such as water supply, flood control, recreation, and hydropower. When building large-scale system models to represent these diverse objectives, their most appropriate time steps for each purpose often do not coincide. A monthly time step is usually sufficient for water supply modeling, but it can be too coarse for flood control, hydropower, and energy operations, where hourly time steps are preferred. Large-scale water management and planning models mostly employ monthly time steps, but using monthly average energy prices underestimates hydropower revenue and overestimates pumping energy cost because these plants tend to operate during times with above- or below-average energy prices within any month. The approach developed here uses hourly varying prices depending on the percent of monthly operating hours. This paper examines an approach that approximately incorporates hourly energy price variations for hydropower and pumping into large-scale monthly time-step water system model operations without affecting water delivery results. Results from including hourly varying energy prices in a large-scale monthly water supply model of California (CALVIN) are presented. CALVIN is a hydroeconomic linear programming optimization model that allocates water to agricultural and urban users with an objective to minimize total scarcity costs, operating costs, and hydropower revenue loss. Thirteen hydropower plants are modeled with hourly varying prices, and their revenue increased by 25 to 58% compared to revenue calculated with monthly average constant energy prices. Hydropower revenue improvements are greater in critically dry years. For pumping plants modeled with hourly varying prices, the energy use cost decreased by 10 to 59%. This study improves system representation and results for large-scale modeling. [ABSTRACT FROM AUTHOR]

Background: Barriers exist for the provision of surgery for permanent contraception in the postpartum period. Prenatal counseling has been associated with increased rates of fulfillment of desired postpartum contraception in general, although it is unclear if there is impact on permanent contraception specifically. Thus, we aimed to investigate the association between initial timing for prenatal documentation of a contraceptive plan for permanent contraception and fulfillment of postpartum contraception for those receiving counseling. Methods: This is a planned secondary analysis of a multi-site cohort study of patients with documented desire for permanent contraception at the time of delivery at four hospitals located in Alabama, California, Illinois, and Ohio over a two-year study period. Our primary exposure was initial timing of documented plan for contraception (first, second, or third trimester, or during delivery hospitalization). We used univariate and multivariable logistic regression to analyze fulfillment of permanent contraception before hospital discharge, within 42 days of delivery, and within 365 days of delivery between patients with a documented plan for permanent contraception in the first or second trimester compared to the third trimester. Covariates included insurance status, age, parity, gestational age, mode of delivery, adequacy of prenatal care, race, ethnicity, marital status, and body mass index. Results: Of the 3103 patients with a documented expressed desire for permanent contraception at the time of delivery, 2083 (69.1%) had a documented plan for postpartum permanent contraception prenatally. After adjusting for covariates, patients with initial documented plan for permanent contraception in the first or second trimester had a higher odds of fulfillment by discharge (aOR 1.57, 95% C.I 1.24–2.00), 42 days (aOR 1.51, 95% C.I 1.20–1.91), and 365 days (aOR 1.40, 95% C.I 1.11–1.75), compared to patients who had their first documented plan in the third trimester. Conclusions: Patients who had a documented prenatal plan for permanent contraception in trimester one and two experienced higher likelihood of permanent contraception fulfillment compared to those with documentation in trimester three. Given the barriers to accessing permanent contraception, it is imperative that comprehensive, patient-centered counseling and documentation regarding future reproductive goals begin early prenatally. Plain language summary: Permanent contraception is a highly desired form of postpartum contraception in the United States, however there are several barriers to accessing it. In this paper, we investigate whether the timing of when a patient has a documented plan for postpartum contraception has an impact on if they achieve postpartum contraception. This is a cohort study from four hospitals in Illinois, Ohio, California, and Alabama for patients with a desire for postpartum permanent contraception documented in their medical record. We specifically investigated the trimester (first, second, or third) where a patient had a plan for permanent contraception first documented. We then used univariate and multivariate models to determine the relationship between the timing of a plan for permanent contraception and if a patient achieved the procedure at three time-points: hospital discharge, 42-days, and 365-days. Our findings showed that of the 3103 patients in our cohort, only 69.1% of them had a documented plan for postpartum contraception at any point before going to the hospital for their delivery admission. We additionally found that patients who had a documented plan for permanent contraception in the first or second trimester had a higher odds of receiving their postpartum contraception procedure compared to people who had their first documented plan in the third trimester. This showed us the importance of earlier counseling regarding contraception for pregnant patients. There are many barriers to accessing postpartum contraception, so having patient focused counseling about future goals around reproductive health early on in pregnancy is critical. [ABSTRACT FROM AUTHOR]

Current literature emphasizes the necessary and increasing role of the emergency department (ED) psychologist. This perspective paper will illustrate that the recent focus on an ED psychologist is necessary, but insufficient. Equally important, is an understanding of when a patient in a potential crisis does not require an ED admission, but rather an assessment that is made prior to the patient going to the ED. The essential role of an outpatient crisis team is vital in differentiating when an ED admission is indicated for a psychiatric crisis (true positive) and when an ED admission is not indicated for a psychiatric crisis (false positive). Evaluating crises prior to ED admissions accomplishes two critical healthcare objectives in a parallel process: 1) accurately assessing the proper level of care needed when a patient reports they are experiencing acute psychiatric symptoms (which may or may not necessitate emergency department level of care, and 2) reducing burden on an already over-extended ED when emergency care is not indicated. Our findings are uniquely drawn from a highly diverse youth patient population in Northern California, United States. [ABSTRACT FROM AUTHOR]

Navigating the healthcare conundrum in the Blue Zone of Loma Linda, California, requires understanding the unique factors that make this region stand out in terms of health and longevity. But more important is understanding the healthcare system sustaining the Blue Zone in Loma Linda, California. In an era marked by soaring healthcare costs and diminishing reimbursement rates, hospitals and physicians face an unprecedented challenge: providing excellent patient care while maintaining financial sustainability. This leadership perspective publication paper delves into the multifaceted struggles encountered by healthcare and hospital leaders, exploring the root causes, implications, and potential solutions for this complex issue. As we examine the evolving healthcare landscape, we aim to shed light on the critical need for innovative approaches to sustain the future of healthcare excellence in one of the five original Blue Zones. [ABSTRACT FROM AUTHOR]

Photogrammetry is a valuable tool for 3D documentation, mapping, and monitoring of underwater environments. However, the ground control surveys necessary for georeferencing and validation of the reconstructed bathymetry are difficult and time consuming to perform underwater, and thus impractical to scale to larger areas. Underwater direct georeferencing, using a differential GNSS receiver synchronized with an underwater camera system, offers an attractive alternative to surveying underwater ground control points in conditions when the seafloor is clearly visible from the surface. In this paper, the design of an underwater direct georeferencing system using mostly commercial off the shelf components is presented. The accuracy of the system is evaluated against geodetic survey based on trilateration and leveling, as well as by RTK (real time kinematic) positioning using a tilt-compensated GNSS receiver mounted on an extended pole to allow measurements of points in up to 7 m in water depth. Tests were conducted in a controlled outdoor pool setting with depths from 1–3 m, as well as in a 10 m × 10 m test plot established on the seafloor in a near-shore environment by Catalina Island, California at depths from 4–10 m. Comparing the geometry of the photogrammetric reconstruction with the geodetic survey yielded sub centimeter consistency, and 1 mm accuracy in length measurement was achieved when compared with calibrated 0.5 m scale bars. Through repeated surveys of the same area, repeatability of georeferencing is demonstrated within expectations for differential GNSS positioning, with horizontal errors at sub centimeter level, and vertical errors of up to 3 cm in the worst cases. These tests demonstrate the benefits of the underwater direct georeferencing approach in shallow waters, which can be scaled up much more easily than measuring underwater ground control points with traditional approaches, making this an ideal option for collecting accurate bathymetry of the seafloor over large coastal areas with clear waters. [ABSTRACT FROM AUTHOR]

This work uses social media postings as a proxy for missing legal underage consumption data to quantify e-cigarette tax effects. E-cigarette use, or vaping, is a public health concern, with particular worries about underage use. The U.S. government is expected to introduce federal taxation on vaping. The potential deterrent effect of such a tax on underage users is understudied because of the absence of underage use data. To cut through this data fog, we use Instagram images from 2016 to 2018 to estimate whether a tax change in California affected vaping-related posting by underage users. This posting behavior is a potential proxy for consumption behavior. We use image analysis (residual neural networks) and latent factor models for causal inference of tax effects. Compared with counterfactuals, we find a relative decline in the incidence of underage users (in effect a slower increase in postings) in vaping-related images in California for about six months, with no effects after. There is gender and race heterogeneity in these effects. Our proposed approach of using near real-time social media images can be helpful to regulators and managers, especially in the rapidly changing landscape expected in the coming months. History: Olivier Toubia served as the senior editor for this article. This paper was accepted through the Marketing Science: Frontiers review process. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2022.0241. [ABSTRACT FROM AUTHOR]

INTRODUCTION Brick-and-mortar vape shops specialize in the sale of e-cigarettes and remain a primary source for purchasing emerging e-cigarette products. New regulatory policies have been implemented at local-, state- and federal-level; the retail environment at vape shops and product preferences among vape shop customers shifted accordingly. METHODS From 2019 to 2023, we collected anonymous interview data from vape shop customers (n=572) from 83 vape shops in Southern California. We aggregated the data by month and treated each month as the unit of analysis to document changes in recruitment efforts among the vape shops in relation to major policy implementations over 4 years. We also examined the systematic fluctuations and trends in customers' e-cigarette product preferences and nicotine content in these products. RESULTS The monthly average shop-level consent rate was 52.9% (SD=8.7), with an overall decreasing trend over time. It was necessary for our data collection team to approach a greater number of vape shops to obtain consent with implementation of various state and federal tobacco regulations and following COVID-19. We observed an increase in the purchase of disposable products and nicotine concentrations in the products, while the average use frequency remained the same. CONCLUSIONS Our findings demonstrated that user preferences, product characteristics and challenges in research involving vape shops are closely associated to changes in regulations. We documented a dramatic increase in nicotine concentration in products. Future policies restricting the amount of nicotine in tobacco products at the federal level are necessary to protect consumers from further nicotine addiction. This study provides documentation over time of the drastic increases in nicotine concentration among e-cigarette users as a result of the fluctuations in the product market. Regulating nicotine content in tobacco products could safeguard against further unsafe modifications in e-cigarettes and other types of tobacco products. [ABSTRACT FROM AUTHOR]

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Do gender diversity quotas enhance firm value? This broad question has sparked significant debate because, whereas diversity is generally thought to improve board functioning, prior research in international settings suggests mandating diversity may have adverse consequences if it moves firms away from market equilibrium. Exploiting the political events leading up to the passage of California Senate Bill (SB) 826 as an exogenous shock to assess the market implications of board gender composition, we document either significantly positive or insignificant two-day abnormal returns for California firms across a variety of model specifications. Importantly, this finding contradicts the strong claims of contemporary studies of SB 826 that the California mandate was value-destructive. We reconcile these findings by providing critical evidence that contemporaneous results lack robustness to alternative model specifications and provide an incomplete picture by failing to comprehensively examine a broader set of event dates or account for mixed messaging on the final event date. We also provide some evidence of significant negative abnormal returns to litigation milestones that threaten to overturn SB 826 with the magnitude increasing in the size of board gender deficits. Finally, we provide evidence that there is no observable decline in newly appointed female director quality following the implementation of SB 826 relative to either preregulation or male director appointments. Collectively, our results suggest nonnegative (if not positive) consequences to California firms arising from SB 826 and directly counter assertions that mandated gender diversity is detrimental to firm value. This paper was accepted by Brian Bushee, accounting. Funding: The authors gratefully acknowledge research funding from Brigham Young University and the University of Toronto. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4825. [ABSTRACT FROM AUTHOR]

Quality disclosures regarding medical outcomes such as patient mortality are common healthcare policy instruments. Mixed evidence exists on whether quality disclosures improve outcomes for disclosed ailments. Moreover, disclosure policies can generate spillovers and impact ailments not targeted by the policy. We examine the effects of quality disclosure regulation on mortality improvements in disclosed and nondisclosed ailments. We use patient records for California hospitals for 1995–2014 and construct three groups of ailments: those that were the target of disclosure regulation, complementary ailments that were not the target of disclosure regulation but are medically related to the disclosed ailments, and medically unrelated ailments. Using a difference-in-differences design, we find that quality disclosure regulation is associated with declines in mortality risk of disclosed ailments ranging from 11.5% to 23%. Quality disclosure regulation is also associated with improvements for complementary ailments that are not the target of the disclosure policy. Such positive spillover effects yield an estimated 15.2% decline in mortality risk. Consistent with demand-side pressures driving improvements, market shares become sensitive to disclosed quality measures after disclosure regulation. Our findings of direct and spillover effects have implications for disclosure regulation. This paper was accepted by David Simchi-Levi, accounting. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4817. [ABSTRACT FROM AUTHOR]

California Utility Firm Implements Innovative Model, Reducing Costs by 4% A California utility firm has successfully implemented a pioneering model to balance electricity demand and supply while minimizing costs. By utilizing direct load control contracts (DLCCs), the firm can reduce energy consumption during peak hours. Researchers developed an integer stochastic dynamic optimization problem that considers monthly and annual constraints, allowing for effective execution of DLCCs. Incorporating a "reduce-to-threshold" policy to flatten energy-consumption curves during high demand, the model was verified using real data from the California Independent System Operator. When implemented, the utility firm achieved an impressive cost reduction of approximately 4%. Sensitivity analysis was conducted to enhance customer experience and improve DLCC contract features. The success of this innovative model highlights the potential of DLCCs and advanced optimization techniques in the energy sector, offering a blueprint for other utility companies seeking to optimize grid stability and reduce costs. Balancing electricity demand and supply is one of the most critical tasks that utility firms perform to maintain grid stability and reduce system cost. Demand-response programs are among the strategies that utilities use to reduce electricity consumption during peak hours and flatten the energy-consumption curve. Direct load control contracts (DLCCs) are a class of incentive-based demand-response programs that allow utilities to assign "calls" to customer groups to reduce their energy usage by a prespecified amount for a given length of time. Given the rapid expansion of such contracts, in this paper, we develop an integer stochastic dynamic optimization problem for executing DLCCs that minimizes total system cost subject to monthly and annual constraints on the number of times and hours customers can be called. We develop a hierarchical approximation approach, which consists of an annual problem and monthly problems, to solve the DLCC implementation problem effectively and in a reasonable amount of time. Motivated by the practice in a large utility firm in California, we incorporate a reduce-to-threshold policy that attempts to flatten energy-consumption curves whenever demand exceeds a given threshold. We verified the quality of our proposed approach on real data from the California Independent System Operator, which is the umbrella organization of the utility firms in California, and measured the quality of our solution against a lower bound. A large utility firm in California implemented our model and informed us that the additional reduction in cost was approximately 4%. Our sensitivity analysis reports the impact of managerial concerns on some policies to enhance customer experience and provides insights for improving the features of DLCC contracts. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2021.0638. [ABSTRACT FROM AUTHOR]

Stream-terrace morphostratigraphy and optically stimulated luminescence (OSL) geochronology indicate that storm-driven sedimentation has caused down-system decoupling of the uppermost reaches of McMillan Creek (southern California, USA) from the lower reaches of McMillan Creek since 1960 ± 190 yr B.P. This is significant because source-to-sink studies report high degrees of sediment transport connectivity over millennial time scales during periods of high fluvial discharge in sediment routing systems. The most recent relatively large-magnitude episode of sedimentation emplaced a sediment slug in the ephemeral channel of McMillan Creek. The sediment slug is correlated to the "California Storm of January 1862" via OSL dating. In this paper, a conceptual model of sediment slug dynamics in an ephemeral stream over 16 decades is developed based on fluvial sedimentation events that in most instances included reworking slug-derived sediment. Due to the episodic nature of streamflow in ephemeral streams and the dearth of sediment transport between streamflow events, sediment slug coherency is sustained over longer periods of time in ephemeral streams than in perennial streams having steady or variable flow regimes. The longevity of sediment-slug coherency in ephemeral streams leads to more prolonged down-system decoupling in sediment routing systems than down-system decoupling caused by ordinary fluvial sedimentation. In McMillan Creek, it is possible that up-system decoupling driven by sedimentation has been contemporaneous with down-system decoupling, but factors other than sedimentation may have a more significant role in up-system decoupling. Source-to-sink studies completed in areas having a Mediterranean climate cannot assume that sediment flux out of upland source areas includes the total amount of sediment available for transport. [ABSTRACT FROM AUTHOR]

The increasing need for mental health support and a shortage of therapists have led to the development of the eXtended-reality Artificial Intelligence Assistant (XAIA). This platform combines spatial computing, virtual reality (VR), and artificial intelligence (AI) to provide immersive mental health support. Utilizing GPT-4 for AI-driven therapy, XAIA engaged participants with mild-to-moderate anxiety or depression in biophilic VR environments. Speaking with an AI therapy avatar in VR was considered acceptable, helpful, and safe, with participants observed to engage genuinely with the program. However, some still favored human interaction and identified shortcomings with using a digital VR therapist. The study provides initial evidence of the acceptability and safety of AI psychotherapy via spatial computing, warranting further research on technical enhancements and clinical impact. [ABSTRACT FROM AUTHOR]

Data centers are characterized by high energy consumption, with operating costs being extremely sensitive to electricity prices. Therefore, modern data centers are often equipped with microgrids for power supply, which adjust their operational strategies based on the given electricity prices to minimize costs. However, existing research has overlooked the flexible pricing potential of electricity retailers, prompting this study to propose a non-cooperative game theory- based optimization method for data center electricity procurement negotiation. A two-layer optimization model is established for data center electricity pricing. The upper layer focuses on electricity price optimization, modeling price negotiation as a Stackelberg game and adopting a weighted average cost approach to describe the electricity procurement prices. The lower layer addresses data center operational optimization, formulated as a nonlinear programming problem. To enable rapid solution convergence, a hybrid problemsolving method combining a genetic algorithm and branch-and-cut algorithm is proposed. Finally, a simulation is conducted using a data center located in California, United States, to validate the proposed method. The results demonstrate that the proposed method can reduce data center operational costs by 3% and increase the revenue for electricity retailers by 17%, achieving a win-win outcome. [ABSTRACT FROM AUTHOR]

English walnuts are the walnut species of choice for nut production. In the United States, edible English walnuts are almost exclusively produced in California, using soil and water resources on more than 370,000 acres. Scion cultivars of English walnuts are grafted onto rootstocks. Traditional rootstocks are seedling populations of so-called Paradox hybrids generated from crosses of black walnut with English walnut. These rootstocks are susceptible to soil-borne diseases, including crown gall, Phytophthora root and crown rot, and plant-parasitic nematodes. Strategies to respond to these diseases include the use of newly developed clonal walnut rootstocks with genetic resistance. In a survey conducted during 2020 through 2021, walnut growers revealed their willingness to pay higher prices for clonal walnut rootstocks with some disease resistance. The survey showed that they were most concerned with crown gall and nematodes, and were willing to pay significant price premiums for rootstocks that are resistant to these pathogens. [ABSTRACT FROM AUTHOR]

• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

The nearshore waters of the Northern California Current support an important seasonal foraging ground for Pacific Coast Feeding Group (PCFG) gray whales. We examine gray whale distribution, habitat use, and abundance over 31 years (1992-2022) using standardized nearshore (< 5 km from shore) surveys spanning a large swath of the PCFG foraging range. Specifically, we generated density surface models, which incorporate detection probability into generalized additive models to assess environmental correlates of gray whale distribution and predict abundance over time. We illustrate the importance of coastal upwelling dynamics, whereby increased upwelling only yields higher gray whale density if interspersed with relaxation events, likely because this combination optimizes influx and retention of nutrients to support recruitment and aggregation of gray whale prey. Several habitat features influence gray whale distribution, including substrate, shelf width, prominent capes, and river estuaries. However, the influence of these features differs between regions, revealing heterogeneity in habitat preferences throughout the PCFG foraging range. Predicted gray whale abundance fluctuated throughout our study period, but without clear directional trends, unlike previous abundance estimates based on mark-recapture models. This study highlights the value of long-term monitoring, shedding light on the impacts of variable environmental conditions on an iconic nearshore marine predator., (© 2024. The Author(s).)

This paper presents the first systematic study on power control strategies for Modular-Gravity Energy Storage (M-GES), a novel, high-performance, large-scale energy storage technology with significant research and application potential. Addressing the current research gap in M-GES power control technology, we propose two corresponding compensation modes and several power control strategies for Hybrid M-GES and M-GES. The effectiveness of these strategies is validated through simulations on the MATLAB/Simulink platform, including tests using sinusoidal power and feasibility verification based on a natural California load curve. Additionally, we analyze the normal and abnormal operating conditions of M-GES. In conclusion, the characteristics of the proposed control strategies are summarized, providing a guideline for further research. [Display omitted] • Modular-gravity energy storage (M-GES) power control system studied. • Two compensation modes and four control strategies are systematically studied and validated. • The center switching method with hysteresis control (CSM-H) excels in continuous compensation mode. • The center switching method with hysteresis control and dead zone (CSM-HD) excels in overlap compensation mode. • Based on the power control system proposed in this paper, the M-GES plant performs highly in natural power systems. [ABSTRACT FROM AUTHOR]

The evaluation of communities' vulnerability to prolonged power outages offers valuable insights for prioritizing improvements in infrastructure resilience, thereby alleviating societal consequences. This study proposes a data-driven approach aiming at developing a Social Vulnerability Index (SoVI) to prolonged power outages leveraging three county-level datasets in California including (1) demographic features, (2) power outage factors, and (3) backup power factors. Furthermore, the study conducts a sensitivity analysis on three distinct datasets under two scenarios (Scenario 1: the current SoVI in 2022, Scenario 2: the prediction of SoVI in the year 2030). The results of Scenario 1 indicate that the counties with more affected customers, the number of power outages, and less education attainment tend to be more vulnerable to power outages in 2022. Scenario 1 reveals that the number of affected customers and power outages are the primary features influencing around 29% and 18% of counties, while educational attainment, public Electric Vehicles (EVs) chargers, and homes with rooftop photovoltaic (PV) substantially impact approximately 32%, 11%, and 8% of counties, respectively. However, in Scenario 2, crucial factors affecting the anticipated SoVI in 2030 include public EV chargers, houses with rooftop PV, power outages, and adults living alone. In contrast to Scenario 1, the prevalence of adults living alone has emerged as a notable factor impacting SoVI in 2030, while both scenarios underscore the pivotal role of EV chargers in influencing SoVI concerning power outages. The proposed SoVI facilitates informed policy decisions and infrastructure improvements in energy resilience, resource allocation, and disaster preparedness, contributing valuable insights for targeted interventions in these domains. • A data-driven approach was proposed to quantify the social vulnerability index (SoVI) to power outages. • This paper applies Principal Component Analysis (PCA) to identify the most correlated features from county-level datasets. • The paper conducts a sensitivity analysis to predict SoVI in2030 and evaluate how counties' dynamics change over time. • This paper provides insights into how policymakers can reduce the social vulnerability to power outages in California. [ABSTRACT FROM AUTHOR]

Introduction. An estimated one million people currently inhabit vehicles as their primary method of housing in publicly accessible areas across the United States. Few studies have investigated this untraditionally housed population, as separate from traditional homelessness, and no other studies have been found that currently explore vehicle resident's information behaviours. This paper offers preliminary findings from an ethnographic study that explores vehicle residents' information practices. Method. This work is based on four months of ethnographic research involving participant observation in Santa Cruz, California, which aimed to understand vehicle residents' information needs and access and the ways that various factors shaped their information practices. Analysis. Ethnographic field notes were analysed using the constant comparative method. Induction was used to identify and categorize recurring conceptualizations and phenomena. Results. Preliminary findings build upon previous research on information grounds. Initial results indicate that marginalizing policies, and in turn, social exclusion, created vibrant environments for information grounds to develop. Conclusion. Geographic locations and movements influence vehicle residents' information practices. Gaining a deeper understanding of these practices inform both theoretical implications for the information behaviour field and practical implications for local and federal governments to deliver support rather than create marginalizing policies for vehicle residents. [ABSTRACT FROM AUTHOR]

The volumetric instability of steel slag hinders its use in pavement layers and embankments. Accordingly, evaluating the swelling potential of steel slag and designing volumetrically stable steel slag mixtures is required to promote its sustainable utilization. This paper focuses on the comparison of the swelling response of basic-oxygen-furnace steel slag (BOFS), electric-arc-furnace steel slag (EAFS), BOFS-fly ash and BOFS-glass powder mixtures based on the results of three different types of tests: (i) long-term California bearing ratio (CBR) swelling, (ii) heated water bath swelling and (iii) autoclave tests. BOFS mixtures were prepared using glass powder or class F fly ash with 5%, 10%, and 20% replacement ratios. The compaction characteristics, permeability, and California bearing ratio (CBR) of steel slag and steel slag mixtures were also assessed within the framework of a detailed discussion of their swelling response. At the end of swelling tests, the measured maximum 1D strains of EAFS were all at negligible levels. Based on the test results, BOFS and BOFS-glass powder mixtures had ∼5.0–5.5% swelling strains after 20.5 months without signs of stabilization. 20% class F fly ash replacement stabilized the swelling strains of the mixture at about 0.75% after ∼3 months of monitoring in long-term tests. 1D swelling rates measured for all BOFS-class F fly ash mixtures decreased with increasing fly ash content. CBR and swelling response of BOFS-class F fly ash mixtures were more favorable compared to those of BOFS. The heated water bath tests provided higher estimates of the swelling strains compared to those obtained from autoclave tests for the steel slag mixtures that contain 15% or more fines. The results of this study showed that glass powder replacement is not effective in alleviating the swelling of BOFS. The volumetrically stable BOFS-class F fly ash mixtures indicated favorable mechanical properties for their use in pavement layers. [Display omitted] • Glass powder replacement is not effective in alleviating the swelling of BOF slag. • 20% class F fly ash replacement can suppress the 1D strains of BOF slag. • Autoclave may provide relatively low estimates of strain for steel slag with 15% or more fines. • Separate handling of EAF and ladle steel slag in the plants can help their utilization. • BOF steel slag-class F fly ash mixtures show potential to be used in bound pavement layers. [ABSTRACT FROM AUTHOR]

• Deriving/estimating plain strain fracture toughness for ASTM A322 material. • Developing flaw tolerance limit plots for ASTM A322 and ASTM A722 material. • Estimating remaining service life of existing post-tension grouted anchors. Brittle failure of grouted post-tensioned anchors used in trunnion girders at dams has given rise to concerns and questions surrounding their continued use and remaining service life. This paper reviews the brittle failure of five grouted post-tensioned anchor rods due to stress corrosion cracking at dams in California. To provide an early warning system to dam owners and expand on available resources for practicing engineers, this study derives plain strain fracture toughness from the failed grouted post-tension anchors using cross sectional photos. Using derived and available fracture toughness values, flaw tolerance limit plots, which can quickly illuminate the severity of a defect for various anchor diameters, were developed for semicircular and straight fronted flaw types using linear elastic fracture mechanics and stress intensity factors for both ASTM A322 and ASTM A722 materials. To help demonstrate the use of flaw tolerance limit plots and estimate the remaining service life of an in-situ anchor, an example problem was developed. [ABSTRACT FROM AUTHOR]

The substantial threat of concurrent air pollutants to public health is increasingly severe under climate change. To identify the common drivers and extent of spatiotemporal similarity of PM 2.5 and ozone (O 3), this paper proposed a log Gaussian–Gumbel Bayesian hierarchical model allowing for sharing a stochastic partial differential equation and autoregressive model of order one (SPDE-AR(1)) spatiotemporal interaction structure. The proposed model, implemented by the approach of integrated nested Laplace approximation (INLA), outperforms in terms of estimation accuracy and prediction capacity for its increased parsimony and reduced uncertainty, especially for the shared O 3 sub-model. Besides the consistently significant influence of temperature (positive), extreme drought (positive), fire burnt area (positive), gross domestic product (GDP) per capita (positive), and wind speed (negative) on both PM 2.5 and O 3 , surface pressure and precipitation demonstrate positive associations with PM 2.5 and O 3 , respectively. While population density relates to neither. In addition, our results demonstrate similar spatiotemporal interactions between PM 2.5 and O 3 , indicating that the spatial and temporal variations of these pollutants show relatively considerable consistency in California. Finally, with the aid of the excursion function, we see that the areas around the intersection of San Luis Obispo and Santa Barbara counties are likely to exceed the unhealthy O 3 level for USG simultaneously with other areas throughout the year. Our findings provide new insights for regional and seasonal strategies in the co-control of PM 2.5 and O 3. Our methodology is expected to be utilized when interest lies in multiple interrelated processes in the fields of environment and epidemiology. [Display omitted] • PM 2.5 and O 3 pollutants are jointly analysed in California through a log Gaussian–Gumbel Bayesian hierarchical joint model. • Distinct spatiotemporal interactions are revealed with a shared SPDE-AR structure; common drivers of PM 2.5 and O 3 are identified. • Hotspots at risk of simultaneously exceeding unhealthy O 3 levels are identified. [ABSTRACT FROM AUTHOR]

Ecocultural restoration involves the reciprocal repair of ecosystems and revitalization of cultural practices to enhance their mutual resilience to natural and anthropogenic disturbances and climate change stressors. Resilient ecocultural systems are adapted to retain structure and function in the face of disturbances that remain within historical ranges of severity. To assist in ecocultural restoration and management, understanding how a system has historically responded to different types of disturbances is therefore invaluable in understanding how social-ecological resilience can be maintained in the face of future stressors and disturbances. However, records of disturbances and ecocultural responses can be limited for certain landscapes and human communities. In this methods paper, we demonstrate a mixed-method process for integrating oral history, field-based knowledge, archival information, and historical and contemporary aerial images to gain insight into the changes on the Klamath River in Northern California from the 1940s through 2020. We georegistered historical imagery, quantified changes between land cover classes, and contextualized these classifications with qualitative assessments of changes in larger surrounding areas. By synthesizing these data sources with field measurements, mining and other land survey maps, timber management plans, fire and flood histories, and interviews with members of the Karuk Tribe, we were able to reconstruct the land use and land cover change histories at five sites. We noted that recovery of canopy cover from fire and logging practices was faster than for flood, which was faster than recovery from mining, consistent with the relative severity of likely soil disturbance. By combining different sources of information with complementary strengths, we were able to provide managers with site-specific information on recovery from different types of disturbance. Though this approach was labor-intensive, with emerging tools for supervised classification of high-resolution imagery, mixed-method analytical historical ecology could be applied more broadly, supporting ecocultural restoration on a larger scale. [Display omitted] • Reconstructing landscape histories helps understand ecosystem response to disturbance. • Combining qualitative and quantitative analysis helps reconstruct land-use. • Synthesizing many historical data sources improves ecocultural restoration planning. • Manual classification could improve with machine learning 'human-in-the-loop' tools. [ABSTRACT FROM AUTHOR]

The quality of traffic services depends on the accuracy and completeness of the collected traffic data. However,the existing traffic data imputation methods usually only rely on the predefined road network structure to capture the spatio-temporal features and only consider the imputation effect from a single perspective, which are very limited for imputation of different missing patterns of road traffic data. In this paper, we propose a novel deep learning framework called Hierarchical Spatio-temporal Graph Convolutional Neural Networks(HSTGCN) to impute traffic data,through the macro layer and the road layer. The model constructs macro graph of the road network based on the data temporal correlation clustering, which can mine the temporal dependencies of road traffic data from a hierarchical perspective. Besides, a temporal attention mechanism and adaptive adjacency matrix are introduced in the road layer to better extract the spatio-temporal information of the road traffic data. Finally, we use graph convolution neural networks to learn the spatio-temporal feature representations of the road layer and macro layer, which are then fused to achieve data imputation. To illustrate the efficient performance of the model, experiments are conducted on traffic data collected from California and Seattle. The proposed model performs better than the comparison model for traffic data imputation. • A full dynamic graph is built based on the urban road structure. • Extracting spatio-temporal features from multiple dimensions using hierarchical thinking. • Gate-GCN perform effective feature selection. [ABSTRACT FROM AUTHOR]

Forest fires cause every year damages to biodiversity, atmosphere, and economy activities. Forest fire simulation have improved significantly, but input data describing fire scenarios are subject to high levels of uncertainty. In this work the two-stage prediction scheme is used to adjust unknown parameters. This scheme relies on an input data calibration phase, which is carried over following a genetic algorithm strategy. The calibrated inputs are then pipelined into the actual prediction phase. This two-stage prediction scheme is leveraged by the cloud computing paradigm, which enables high level of parallelism on demand, elasticity, scalability and low-cost. In this paper, all the models designed to properly allocate cloud resources to the two-stage scheme in a performance-efficient and cost-effective way are described. This Cloud-based Urgent Computing (CuCo) architecture has been tested using, as study case, an extreme wildland fire that took place in California in 2018 (Camp Fire). • Data-driven calibration to deal with uncertainty in forest fire spread prediction. • Cloud-based urgent computing implementation of a two-stage prediction model. • Use of utility function to deal with the cost-performance trade-off. • Validation against a deadly and destructive wildfire with promising results. [ABSTRACT FROM AUTHOR]

Disasters have been a long-standing concern to societies at large. With growing attention being paid to resilient communities, such concern has been brought to the forefront of resilience studies. However, there is a wide variety of definitions with respect to resilience, and a precise definition has yet to emerge. Moreover, much work to date has often focused only on the immediate response to an event, thus investigating the resilience of an area over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel framework utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires. Taking the Mendocino Complex and Camp wildfires - the largest and most deadly wildfires in California to date, respectively - as case studies, we capture the robustness and vulnerability of communities based on human mobility data from 2018 to 2019. The results show that demographic and socioeconomic characteristics alone only partially capture community resilience, however, by leveraging human mobility data and network analysis techniques, we can enhance our understanding of resilience over space and time, providing a new lens to study disasters and their long-term impacts on society. • Quantifying community resilience is an open research challenge • This paper develops a novel framework to quantify resilience after a disaster using network analysis • Using human mobility data associated with two wildfires, we measure robustness and vulnerability of communities • Results show community resilience closely tied to socio-economic and built environmental traits of the affected areas • Our approach paves a way to study disasters and their long-term impacts on society [ABSTRACT FROM AUTHOR]

Solar farms have PV arrays in arid and semi-arid regions where ensuring the system's reliability is paramount and face uncertain events like dust storms. The deposition of random dust patterns over panel arrays is called uneven soiling, which diminishes the power generation of such farms. This paper finds the most suitable hybrid algorithm model, the wavelet transform-based support vector regression variants (WT-SVR) algorithm, and the empirical model decomposition-based support vector regression variants (EMD-SVR) to predict the extent of soiling levels and uncertain events on PV arrays. The soiling dataset is taken from NREL's Soiling Station Number 3 in Imperial County, Calipatria, California, from December 30, 2014, to December 31, 2015. This research tested four SVR variants on soiling data, viz., ε SVR, LSSVR, TSVR, and ε TSVR, then compared with the benchmark random forest. The hyperparameters for each model are meticulously tuned to enhance the robustness of the trained algorithms. Results reveal that the WT-TSVR model outperforms the WT-SVR model in terms of wavelet transform decomposition by a margin of 91.6%. Similarly, the EMD-TSVR model showcases an 85.7% enhancement in performance over the EMD-SVR model based on empirical mode decomposition. All SVR variants outperform the benchmark model (RF). Furthermore, EMD models exhibit enhanced efficiency in forecasting random events compared to WT, which is attributed to their reduced computational time. This model applies to multi-cleaning agent robots, aligning with recommendations from the state-of-the-art literature. [ABSTRACT FROM AUTHOR]

Aim: Variation in spatial predictions of species' ranges made by various models has been recognized as a significant source of uncertainty for modelling species distributions. Consensus approaches that combine the results of multiple models have been employed to reduce the uncertainty introduced by different algorithms. We evaluate how estimates of habitat suitability, projected using species distribution models (SDMs), varied among different consensus methods relative to the variation introduced by different global climate models (GCMs) and representative concentration pathways (RCPs) used for projection. Location: California Floristic Province (California, US portion). Methods: We modelled the current and future potential distributions of 82 terrestrial plant species, developing model predictions under different combinations of GCMs, RCPs, time periods, dispersal assumptions and SDM consensus methods commonly used to combine different species distribution modelling algorithms. We assessed how each of these factors contributed to the variability in future predictions of species habitat suitability change and aggregate measures of proportional change in species richness. We also related variability in species‐level habitat change to species' attributes. Results: Assuming full dispersal capacity, the variability between habitat predictions made by different consensus methods was higher than the variability introduced by different RCPs and GCMs. The relationships between species' attributes and variability in future habitat predictions depended on the source of uncertainty and dispersal assumptions. However, species with small ranges or low prevalence tended to be associated with high variability in range change forecasts. Main Conclusions: Our results support exploring multiple consensus approaches when considering changes in habitat suitability outside of species' current distributions, especially when projecting species with low prevalence and small range sizes, as these species tend to be of the greatest conservation concern yet produce highly variable model outputs. Differences in vulnerability between diverging greenhouse gas concentration scenarios are most readily observed for end‐of‐century time periods and within species' currently occupied habitats (no dispersal). [ABSTRACT FROM AUTHOR]

We exploit a city-level panel comprised of individual house price estimates to estimate the impact of COVID-19 on both small and big real-estate markets in California USA. Descriptive analysis of spot house price estimates, including contemporaneous price uncertainty and 30-day price change for individual properties listed on the online real-estate platform Zillow.com, together facilitate quantifying both the excess valuation and valuation confidence attributable to this global socio-economic shock. Our quasi-experimental pre-/post-COVID-19 design spans several years around 2020 and leverages contemporaneous price estimates of rental properties – i.e., off-market real estate entering the habitation market, just not for purchase and hence free of speculation – as an appropriate counterfactual to properties listed for sale, which are subject to on-market speculation. Combining unit-level matching and multivariate difference-in-difference regression approaches, we obtain consistent estimates regarding the sign and magnitude of excess price growth observed after the pandemic onset. Specifically, our results indicate that properties listed for sale appreciated an additional 1% per month above what would be expected in the absence of the pandemic. This corresponds to an excess annual price growth of roughly 12.7 percentage points, which accounts for more than half of the actual annual price growth in 2021 observed across the studied regions. Simultaneously, uncertainty in price estimates decreased, signaling the irrational confidence characteristic of prior asset bubbles. We explore how these two trends are related to market size, local market supply and borrowing costs, which altogether lend support for the counterintuitive roles of uncertainty and interruptions in decision-making. [ABSTRACT FROM AUTHOR]

Background: There is limited information on the extent and patterns of disparities in COVID-19 mortality throughout the pandemic. We aimed to examine trends in disparities by demographics over variants in the pre- and post-vaccine availability period among Californian workers using a social determinants of health lens. Methods: Using death certificates, we identified all COVID-19 deaths that occurred between January 2020 and May 2022 among workers aged 18–64 years in California (CA). We derived estimates for at-risk worker populations using the Current Population Survey. The waves of COVID-19 mortality in the pre-vaccine availability period were March 2020-June 2020 (wave 1), and July 2020-November 2020 (wave 2), and in the post-vaccine availability period: December 2020-May 2021 (wave 3), June 2021-January 2022 (wave 4), and February 2022-May 2022 (wave 5). Poisson regression models with robust standard errors were used to determine wave-specific mortality rate ratios (MRRs). We examined the change in MRR across waves by including an interaction term between each demographic characteristic and wave period in different models. The role of potential misclassification of Race/ethnicity on death certificates was examined using probabilistic quantitative bias analysis as sensitivity analysis. Results: Among the 24.1 million working age CA population included in the study, there were 26,068 COVID-19 deaths in the period between January 2020 and May 2022. Compared with their respective reference groups, workers who were 50–64 years old, male, Native Hawaiian, Latino, or African American, foreign-born; individuals who had lower education; and unmarried were disproportionately affected by COVID-19 mortality. While disparities by sex, race/ethnicity and foreign-born status narrowed in later waves (post-vaccine availability), disparities by age, education level and marital status did not change substantially across waves. Conclusion: Demographic disparities in COVID-19 mortality narrowed in the post-vaccine availability waves. However, the existence of disparities across all waves of the pandemic, even in an era of widespread vaccine coverage, could indicate remaining gaps in prevention and differential vulnerability. Addressing the underlying social, structural, and occupational factors that contribute to these disparities is critical for achieving health equity. [ABSTRACT FROM AUTHOR]

The CRISPR-Cas13 system has been proposed as an alternative treatment of viral infections. However, for this approach to be adopted as an antiviral, it must be optimized until levels of efficacy rival or exceed the performance of conventional approaches. To take steps toward this goal, we evaluated the influenza viral RNA degradation patterns resulting from the binding and enzymatic activity of mRNA-encoded LbuCas13a and two crRNAs from a prior study, targeting PB2 genomic and messenger RNA. We found that the genome targeting guide has the potential for significantly higher potency than originally detected, because degradation of the genomic RNA is not uniform across the PB2 segment, but it is augmented in proximity to the Cas13 binding site. The PB2 genome targeting guide exhibited high levels (>1 log) of RNA degradation when delivered 24 hours post-infection in vitro and maintained that level of degradation over time, with increasing multiplicity of infection (MOI), and across modern influenza H1N1 and H3N2 strains. Chemical modifications to guides with potent LbuCas13a function, resulted in nebulizer delivered efficacy (>1–2 log reduction in viral titer) in a hamster model of influenza (Influenza A/H1N1/California/04/09) infection given prophylactically or as a treatment (post-infection). Maximum efficacy was achieved with two doses, when administered both pre- and post-infection. This work provides evidence that mRNA-encoded Cas13a can effectively mitigate Influenza A infections opening the door to the development of a programmable approach to treating multiple respiratory infections. Author summary: For the CRISPR-Cas13 system to be adopted as an alternative treatment of viral infections, it must reach levels of efficacy that rival or exceed the performance of conventional approaches. We evaluated the degradation patterns resulting from the binding and enzymatic activity of mRNA-encoded LbuCas13a and two previously identified crRNAs, targeting conserved PB2 genomic and messenger RNA sequences. The genome targeting guide displayed higher potency than originally detected, because RNA degradation was augmented near the guide binding site, leading to 1-log of RNA degradation in vitro, which persisted over time, with increasing multiplicity of infection (MOI), and across modern influenza H1N1 and H3N2 strains. The addition of chemical modifications to the guide resulted in 1-2-log reduction in viral titer upon nebulizer delivery prophylactically or as a treatment in a hamster model of influenza. Maximum efficacy was achieved with two doses administered pre- and post-infection. [ABSTRACT FROM AUTHOR]

The Coastal California Gnatcatcher (Polioptila californica californica), a federally threatened species, is a flagship species for regional conservation planning in southern California (USA). An inhabitant of coastal sage scrub vegetation, the gnatcatcher has declined in response to habitat loss and fragmentation, exacerbated by catastrophic wildfires. We documented the status of gnatcatchers throughout their California range and examined post-fire recovery of gnatcatchers and their habitat. We used GIS to develop a habitat suitability model for Coastal California Gnatcatchers using climate and topography covariates and selected over 700 sampling points in a spatially balanced manner. Bird and vegetation data were collected at each point between March and May in 2015 and 2016. Presence/absence of gnatcatchers was determined during three visits to points, using area searches within 150 x 150 m plots. We used an occupancy framework to generate Percent Area Occupied (PAO) by gnatcatchers, and analyzed PAO as a function of time since fire. At the regional scale in 2016, 23% of the points surveyed were occupied by gnatcatchers, reflecting the effect of massive wildfires in the last 15 years. Similarly, PAO in the post-fire subset of points was 24%, with the highest occupancy in unburned (last fire <2002) habitat. Positive predictors of occupancy included percent cover of California sagebrush (Artemisia californica), California buckwheat (Eriogonom fasciculatum), and sunflowers (Encelia spp., Bahiopsis laciniata), while negative predictors included laurel sumac (Malosma laurina) and total herbaceous cover; in particular, non-native grasses. Our findings indicate that recovery from wildfire may take decades, and provide information to speed up recovery through habitat restoration. [ABSTRACT FROM AUTHOR]