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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]

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]

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]

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]

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]

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]

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]

Red beds (RBs) are widely distributed in all continents of the world. With the rapid development of road and railway construction, the construction of roads and railways in RB areas is increasing. However, when RB is the filler, it will expose three aspects of serious deficiencies in compaction, mechanical bearing capacity, and water stability, which will cause fatal harm to the subgrade. Therefore, RB has to be abandoned and cannot be used as a subgrade filler. In this paper, different microwave irradiation temperatures (DMITs) from room temperature of 25°C to 700°C were used to reveal the enhancing mechanism of RB in three aspects based on the analysis results of microscopic composition and structure. The microscopic composition and structure show that RB can be divided into a low-temperature section (25°C–400°C) and a high-temperature section (500°C–700°C) under the microwave condition. The low-temperature section has some improvement in the engineering performance of RB, but the influence is limited. The engineering performance of RB is improved completely in the high-temperature section. Specifically, the coarse size of RB increases with the rise in microwave temperature, which is beneficial for improving fine size. The compaction property of RB is not largely affected by the water content and can still meet the most stringent subgrade filler requirements, even under saturated conditions or 0. The California bearing ratio (CBR) value can maintain more than 20% under the ultimate working condition, far higher than the code requirements. The increase of internal friction angle and cohesion can enhance the stability of the cut slope, which is conducive to construction in remote mountainous areas. The water resistance of RB is positively correlated with the microwave irradiation temperature, especially at 700°C; RB experiences secondary hardening after encountering water, the mechanical strength increases, and the softening coefficient reaches 107.44%. The research shows that after microwave high-temperature stabilization, the engineering characteristics of the RB are thoroughly strengthened, and it becomes a high-quality subgrade filler to eliminate the three diseases of RB subgrade completely. Compared with the traditional method of adding stabilizers, microwave in situ RB stabilization reduces the site construction's complexity, saves a lot of stabilizer use and transportation costs, and can effectively reduce carbon emissions. This study provides a positive perspective on the in situ application of the RB soft rock subgrade. It could be a starting point for understanding the microwave-based stabilization of soft rock soils subgrade filler in situ. [ABSTRACT FROM AUTHOR]

Vaccine mandates can take many forms, and different kinds of mandates can implicate an array of values in diverse ways. It follows that good ethics arguments about particular vaccine mandates will attend to the details of individual policies. Furthermore, attention to particular mandate policies—and to attributes of the communities they aim to govern—can also illuminate which ethics arguments may be more salient in particular contexts. If ethicists want their arguments to make a difference in policy, they should attend to these kinds of empirical considerations. This paper focuses on the most common and contentious vaccine mandate reform in the contemporary United States: the elimination of nonmedical exemptions (NMEs) to school and daycare vaccine mandates. It highlights, in particular, debates about California's Senate Bill 277 (SB277), which was the first successful recent effort to eliminate NMEs in that country. We use media, secondary sources, and original interviews with policymakers and activists to identify and evaluate three ethics arguments offered by critics of SB277: parental freedom, informed consent, and children's rights to care and education. We then turn to one ethics argument often offered by advocates of SB277: harm prevention. We note, however, that three arguments for mandates that are common in the immunization ethics literature—fairness/free‐riding, children's rights to vaccination, and utilitarianism—did not play a role in debates about SB277. [ABSTRACT FROM AUTHOR]

Premise: A comparison of methods using different materials to exclude light from stems to prevent stem CO2 exchange (i.e., photosynthesis), without affecting stem conductance to water vapor, surface temperature, and relative humidity, was conducted on stems of avocado trees in California. Methods and Results: The experiment featured three materials: aluminum foil, paper‐based wrap, and mineral‐based paint. We examined stem CO2 exchange with and without the light exclusion treatments. We also examined stem surface temperature, relative humidity, and photosynthetic active radiation (PAR) under the cover materials. All materials reduced PAR and stem CO2 exchange. However, aluminum foil reduced stem surface temperature and increased relative humidity. Conclusions: Methods used to study stem CO2 exchange through light exclusion have historically relied on methods that may induce experimental artifacts. Among the methods tested here, mineral‐based paint effectively reduced PAR without affecting stem surface temperature and relative humidity around the stem. [ABSTRACT FROM AUTHOR]

Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how water is used to produce food may help support the increase of Crop Water Productivity (CWP; kg/m3), the ratio of crop output per unit of water input (or crop per drop). Previous large-scale CWP studies have been useful for broad water use modeling at coarser resolutions. However, obtaining more precise CWP, especially for specific crop types in a particular area and growing season as outlined here are important for informing farm-scale water management decision making. Therefore, this study focused on California's Central Valley utilizing high-spatial resolution satellite imagery of 30 m (0.09 hectares per pixel) to generate more precise CWP for commonly grown and water-intensive irrigated crops. First, two products were modeled and mapped. 1. Landsat based Actual Evapotranspiration (ETa; mm/d) to determine Crop Water Use (CWU; m3/m2), and 2. Crop Productivity (CP; kg/m2) to estimate crop yield per growing season. Then, CWP was calculated by dividing CP by CWU and mapped. The amount of water that can be saved by increasing CWP of each crop was further calculated. For example, in the 434 million m2 study area, a 10% increase in CWP across the 9 crops analyzed had a potential water savings of 31.5 million m3 of water. An increase in CWP is widely considered the best approach for saving maximum quantities of water. This paper proposed, developed, and implemented a workflow of combined methods utilizing cloud computing based remote sensing data. The environmental implications of this work in assessing water savings for food and water security in the 21st century are expected to be significant. [ABSTRACT FROM AUTHOR]

Background: Uptake of HIV pre-exposure prophylaxis (PrEP) remains low among transgender people as compared to other subgroups, despite high rates of HIV acquisition. In California, Latinx people comprise 40% of the population and Latina transgender women experience some of the highest burden of HIV of any subgroup, indicating a critical need for appropriate services. With funding from the California HIV/AIDS Research Programs, this academic-community partnership developed, implemented, and evaluated a PrEP project that co-located HIV services with gender affirming care in a Federally Qualified Heath Center (FQHC). Trans and Latinx staff led intervention adaptation and activities. Methods: This paper engages qualitative methods to describe how a PrEP demonstration project- Triunfo- successfully engaged Spanish-speaking transgender Latinas in services. We conducted 13 in-depth interviews with project participants and five interviews with providers and clinic staff. Interviews were conducted in Spanish or English. We conducted six months of ethnographic observation of intervention activities and recorded field notes. We conducted thematic analysis. Results: Beneficial elements of the intervention centered around three intertwined themes: creating trusted space, providing comprehensive patient navigation, and offering social support "entre nosotras" ("between us women/girls"). The combination of these factors contributed to the intervention's success supporting participants to initiate and persist on PrEP, many of whom had previously never received healthcare. Participants shared past experiences with transphobia and concerns around discrimination in a healthcare setting. Developing trust proved foundational to making participants feel welcome and "en casa/ at home" in the healthcare setting, which began from the moment participants entered the clinic and continued throughout their interactions with staff and providers. A gender affirming, bilingual clinician and peer health educators (PHE) played a critical part in intervention development, participant recruitment, and patient navigation. Conclusions: Our research adds nuance to the existing literature on peer support services and navigation by profiling the multifaced roles that PHE served for participants. PHE proved instrumental to empowering participants to overcome structural and other barriers to healthcare, successfully engaging a group who previously avoided healthcare in clinical settings. [ABSTRACT FROM AUTHOR]

A decisive aspect of site evaluation for construction is the presence of anthropogenic materials occurring in the geological environment. The geotechnical properties of blast-furnace slag were investigated as a potential substitute for aggregates in the construction industry. The basic geotechnical parameters of the slag were determined, which are critical for evaluating its stability, environmental impact, and usability in geotechnical construction. The research focused on monitoring the physical and mechanical properties of the two samples, and also included mineralogical analysis. The obtained results demonstrated that the slag belongs to the category of poorly graded gravel, G2/GP, and gravel with an admixture of fine-grained soil, G3/G-F. In addition, other important parameters, such as the water disintegration of the slag aggregate, the minimum and maximum bulk densities, the California bearing ratio (CBR), the oedometric modulus (Eoed), and shear tests (the angle of internal friction φ and cohesion c), were determined. The results from this paper provide important information for the proper management of blast-furnace slag so to minimize its environmental impact and achieve sustainability in the mining industry. At the same time, it enables a better understanding of the use of slag as a substitute for aggregates in geotechnical tasks. Despite its local importance in relation to the investigated case, the presented study has significant educational and scientific value for the construction sector, where it is necessary to evaluate anthropogenic activities and materials. [ABSTRACT FROM AUTHOR]

Antibiograms are collections of antimicrobial susceptibility data for a particular bacterial organism and host species. Antibiograms are important tools for antimicrobial stewardship, as they may be used to guide empiric antimicrobial therapy and assess trends in antimicrobial resistance, maximizing treatment success and preserving the efficacy of currently available pharmaceuticals. Targeted use of antimicrobials is critically important to minimize the spread of antimicrobial resistance, which may be conveyed between animals and humans directly but may also be spread through the environment and ecological niches, such as soil, water, and wildlife reservoirs. To effectively utilize antibiograms as part of a comprehensive antimicrobial stewardship plan, veterinarians need to know data characteristics, including the source population, body site (when possible), and number of isolates included, in addition to the animal species and bacterial organisms for which each breakpoint was developed. Although widely used in human health systems, antibiograms are not often available in veterinary medicine. This paper describes antibiogram creation and use, discusses antibiogram development by US veterinary diagnostic laboratories, and shares California's process to create and promote livestock antibiograms . The companion Currents in One Health article by Burbick et al, AJVR, September 2023, addresses the benefits and challenges associated with developing veterinary antibiograms. [ABSTRACT FROM AUTHOR]

Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two‐parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain‐hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude‐longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way. Plain Language Summary: Earthquake nowcasting refers to the determination of hazard for major earthquakes at the present time, the recent past, and the near future. Nowcasting is an idea borrowed from economics, markets, and meteorology, where it has been frequently used. In this paper, we show that there is order hidden within chaotic earthquake seismicity using a very simple transformation of the data. Small earthquakes appear to transition from unstable stick‐slip events that produce seismic waves, to stable sliding where no seismic waves are produced. Our hypothesis is that this transition is due to a material phenomenon called strain‐hardening, that is frequently observed in laboratory rock mechanics experiments. The result is a state variable time series, computed over the last 51 years in California, that strongly resembles the long‐anticipated cycle of stress accumulation and release. Using supervised machine learning techniques, we can optimize the two‐parameter model. From that optimized model, we can rigorously calculate the probability of current hazard from major earthquakes. Extending these methods, we can also compute spatial hazard as well. The result is a new method for assessing earthquake hazard that may be useful for a variety of applications. Key Points: "Chaotic" seismicity contains hidden structure in the form of state variable time seriesStandard data science methods can be used to convert the time series to probabilitiesBoth temporal and spatial probabilities can be computed [ABSTRACT FROM AUTHOR]

Some of the deadliest wildfires in the U.S., such as California’s 2018 wildfires, have been ignited by power systems. In an effort to prevent and minimize the ignition of wildfires, or control them if ignited, energy companies have developed wildfire mitigation plans. This paper provides energy companies and power system operators, engineers, researchers, and suppliers an overview of the state-of-the-art studies that address key topics in these wildfire mitigation plans and compares the wildfire mitigation plans of several energy companies. The key topics include grid design and system hardening, asset management and inspection, situational awareness and forecasting, operational response, vegetation management, public safety power shutoff, and risk-spend efficiency. This paper also presents a comparison of several energy companies’ decision-making criteria for initiating a public safety power shutoff. Finally, we discuss opportunities for future research studies that could help energy companies prevent wildfire ignitions. [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]

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]

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 (CH4) and carbon dioxide (CO2), and presents a use case that combines UAAS measurements and dispersion modeling to quantify CH4 emissions from a dairy farm. To evaluate the atmospheric sensing performance of the UAAS, four field deployments were performed at three locations 15 in the San Joaquin Valley of California where CH4 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 CH4 and CO2 occurring over 10 second periods or longer. Results from the UAAS and 20 dispersion modeling use case further demonstrate that UAAS have great potential as a low-cost tool for detecting and quantifying CH4 emissions in near real-time. [ABSTRACT FROM AUTHOR]

This paper presents the analytical formulation and computational implementation of a conceptual probabilistic performance-based seismic design (PBSD) framework for ordinary standard bridge (OSB) structures. An updated probabilistic performance-based earthquake engineering (PBEE) assessment methodology is used for the parametric performance assessment of four distinct OSBs in California to investigate the effects of varying key structural design parameters over a primary design parameter space on risk-based structural performance measures. The dire need for a systematic PBSD framework for OSBs is illustrated given the significant variability in risk-based performance levels exhibited by these traditionally designed testbed OSBs. A PBSD methodology involving the design of the bridge piers is proposed wherein a risk-based feasible design domain in the primary design parameter space is identified which facilitates the risk-informed design/decision making process in the face of uncertainty. Sensitivities of risk-based feasible design domains compared with other (secondary) design variables and with the method employed for damage hazard assessment are investigated. [ABSTRACT FROM AUTHOR]

Roads constructed over poor ground conditions that carry high traffic volumes and axle loads may, over time, experience persisting occurrences of pavement distress. Road construction, be it a new or a rehabilitation project, is both costly and time-consuming. An appealing option that improves pavement durability, reduces construction cost and time, accelerates strength development over a short curing period, and decreases pavement thickness is to chemically stabilize local road materials. In contrast to other aggregate materials, there has been a gap in evaluating and quantifying the feasibility of chemical stabilization of sandstone aggregate that possesses marginal quality as a road construction material. Therefore, the aim of this paper is to provide an experimental investigation of relative strength development of sandstone aggregate subbase of marginal quality stabilized with combinations of Portland composite cement (PCC), ordinary Portland cement (OPC), and styrene–butadiene latex copolymer (or polymer). The samples were prepared with different gravel-to-sand (G:S) ratios of 2.1, 1.2, and 0.4 and subsequently stabilized with a polymer-cement (P/C) blend. Samples were compacted and cured under dry and wet conditions before measuring their unconfined compressive strength (UCS), indirect tensile strength (ITS), and California bearing ratio (CBR). At the same curing condition and P/C contents, it was found that the UCS and CBR of P/C-stabilized samples increased with increasing G:S ratios. At the same polymer and cement contents, the P/C-stabilized samples with the highest G:S ratio of 2.1 also demonstrated a significantly higher rate of UCS strength development compared with the samples with lower G:S ratio and unstabilized samples. Soil stabilization with a combination of 0.75% polymer and 5% cement contents yielded the highest UCS that was approximately seven times the UCS of unstabilized samples and an ITS of 0.73 MPa. Stabilization with a combination of 0.75% polymer and 3% cement contents yielded the highest CBR value that significantly exceeded 30% after only 4 days of soaking and was seven times that of unstabilized samples. This paper has thus demonstrated the potential improvement to the strength development of sandstone aggregate subbase stabilized with P/C blend under the optimal curing condition and the importance of identifying and using the optimal proportion of polymer and cement to achieve the target strength development and improvement. [ABSTRACT FROM AUTHOR]

A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in P M 2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality ( O 3 and CO ) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O 3 and CO , respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly P M 2.5 and P M 10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for P M 2.5 and P M 10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily P M 2.5 and P M 10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978. [ABSTRACT FROM AUTHOR]

Secondary crashes (SCs) are typically defined as the crash that occurs within the spatiotemporal boundaries of the impact area of the primary crashes (PCs), which will intensify traffic congestion and induce a series of road safety issues. Predicting and analyzing the time and distance gaps between the SCs and PCs will help to prevent the occurrence of SCs. In this paper, a combined data-driven method of static and dynamic approaches is applied to identify SCs. Then, the random forests (RF) method is implemented to predict the two gaps using temporal, primary crash, roadway, and real-time traffic characteristics data collected from 2016 to 2019 at California interstate freeways. Subsequently, the SHapley Additive explanation (SHAP) approach is employed to interpret the RF outputs. The results show that the traffic volume, speed, lighting, and population are considered the most significant factors in both gaps. Furthermore, the main and interaction effects of factors are also quantified. High volume possibly promotes the time and distance gaps, while low volume inhibits them. And volume affects the distance gap inconsiderably when it falls between 300 and 400 veh/5 min. Traffic conditions with high speed and low volume are strongly associated with short-time and short-distance gaps. Darker surroundings probably accelerate the occurrence of SCs. Moreover, crashes involving the violation categories of improper turns or unsafe lane changes likely result in long time and distance gaps. These results have important implications for proposing traffic management and improving road safety. [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]

We propose a new machine learning‐based method for nowcasting earthquakes to image the time‐dependent earthquake cycle. The result is a timeseries that may correspond to the process of stress accumulation and release. The timeseries are constructed by using principal component analysis of regional seismicity. The patterns are found as eigenvectors of the cross‐correlation matrix of a collection of seismicity timeseries in a coarse grained regional spatial grid (pattern recognition via unsupervised machine learning). The eigenvalues of this matrix represent the relative importance of the various eigenpatterns. Using the eigenvectors and eigenvalues, we compute the weighted correlation timeseries of the regional seismicity. This timeseries has the property that the weighted correlation generally decreases prior to major earthquakes in the region, and increases suddenly just after a major earthquake occurs. As in a previous paper (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020ea001097), we find that this method produces a nowcasting timeseries that resembles the hypothesized regional stress accumulation and release process characterizing the earthquake cycle. We then address the problem of whether the timeseries contain information regarding future large earthquakes. For this, we compute a receiver operating characteristic and determine the decision thresholds for several future time periods of interest (optimization via supervised machine learning). We find that signals can be detected that can be used to characterize the information content of the timeseries. These signals may be useful in assessing present and near‐future seismic hazards. Plain Language Summary: Major earthquakes on fault systems in a tectonically active region are thought to occur in approximately repetitive cycles as a result of the buildup and release of tectonic forces (stress). Nowcasting is a technique adopted from weather, finance, and other fields that use readily observable proxy data to represent the unobservable stress accumulation process of interest. This paper presents a method that computes a timeseries representing the weighted correlation of small earthquake activity in the California region from 1950 to 2020. Prior to major magnitude M > 7 earthquakes, the timeseries trends toward lower values. Just after the earthquake occurs, the timeseries increases suddenly in association with the earthquake, before resuming its gradual trend toward lower values. Plotting the timeseries on an inverted scale, one sees a cyclic behavior that strongly resembles the hypothesized earthquake cycle. In principle, we can therefore use this timeseries for nowcasting, as a proxy for stress accumulation and release. Using methods of signal detection first developed for radar by the British in the 1940's, we find that the timeseries contain information about future large earthquakes that can be used for hazard assessment. Key Points: The current state of the earthquake cycle of tectonic stress accumulation and release is unobservable with existing methodsWe show that readily observable small earthquake correlations can be used to nowcast the current state of the earthquake cycleMachine learning techniques indicate that signals corresponding to future large earthquakes can be detected in a correlation time series [ABSTRACT FROM AUTHOR]

This paper proposes a modeling approach for characterizing the probability of wildfire ignition caused by faults on power distribution systems. The proposed model serves as a starting point in research literature to illustrate, from an analytical perspective, the many factors that influence wildfire ignitions in power distribution systems. This paper presents the series of events that leads to power-system-related wildfire ignitions, and characterizes the wildfire ignition probability as a combination of the probability that a fault occurs along a power distribution line segment, and the probability that the fault results in the sustained ignition of a vegetation fuel bed surrounding the line. The proposed model integrates a variety of data including environmental conditions, power system protection settings, and power system line flows. A case study is performed on a test 33-bus distribution system using observed historical weather data from a high-threat fire district in California. The California case study is utilized to investigate the effects of three primary factors (wind speed, line congestion, and protection settings) on wildfire ignition probability. [ABSTRACT FROM AUTHOR]

Simple Summary: Excessive use of groundwater resources in the Central Valley in California has led to major land sinking over the years. In this study, we rely on satellite imagery to monitor and assess the extent of this sinking. Specifically, we use images from a satellite that emits microwaves which allows us to directly obtain the deformation at a specific time. Then, we apply a machine learning algorithm to the resulting data in an attempt to effectively predict short-term future deformation. We find that the algorithm applied has a low error when compared to the actual data. This shows that machine learning algorithms could be incorporated into models that assess potential hazards associated with land sinking. Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers' integrity. [ABSTRACT FROM AUTHOR]

Unnatural disasters, such as floods and wildfires, are making many areas difficult to inhabit. For relocation to unfold in a safer and more equitable way, it must be done in a manner that (1) aligns with community values in each locality, (2) navigates legal barriers to managed retreat, and (3) creates blue-sky funding for adaptation, including managed retreat planning and implementation. This paper argues that developers continuing to build in climate vulnerable areas could and should help cover the risk of their actions. Part I lays out the legal importance of planning for retreat, as well as the need for initial funding for community-level planning and experienced personnel. Few scholars have explored options for municipalities to fund difficult conversations about and initial steps towards managing retreat. Thus, Part II explores how community benefit agreements between communities and developers in climate-vulnerable areas could play a role in bridging the gap between research and implementation. Part II also introduces the idea of a climate resilience development fee, which could provide the needed blue-sky funding to implement managed retreat. Part III analyzes the validity of a climate resilience impact fee in California and Florida, two states in which the consequences of climate change are severe enough for communities to begin to consider managed retreat. [ABSTRACT FROM AUTHOR]

The existing machine learning (ML) models for vibration-based damage assessments often use highly compressed features or are restricted to preprocessed signals with fixed durations and sampling rates. Additionally, the learning capacities of ML models and computational resources are limited, which restricts using raw signals as direct input. This paper studied Mel filter banks (MFBs) for seismic signal processing, inspired by speech recognition technology. It is argued that the same filter designs in audio engineering may not be appropriate for seismic records, and therefore, a customized filter bank formulation was developed. Hybrid deep learning models for rapid assessments (HyDRA) were introduced as multibranch neural network architectures that enable end-to-end training for different types of processed vibration data structures. Moreover, the performance-based earthquake engineering (PBEE) equation was adjusted to integrate ML model uncertainties for probabilistic assessments. The proposed concepts were validated in a case study based on a data set of 32,400 nonlinear time-history analyses of a highway bridge in California. Insights and guidelines are provided for optimum filter design based on 5,184 experiments. Several HyDRA architectures were compared with benchmark models. The optimized MFB feature type outperformed features obtained from continuous wavelet transform and a stacked vector of conventional earthquake engineering indexes. A Bayesian variant of HyDRA was investigated to showcase its integration in the modified PBEE equation. Adopting custom filter banks with the HyDRA architecture enables effective feature extraction from raw vibration records by diversifying feature space and preserving information in the time and frequency domains. [ABSTRACT FROM AUTHOR]

In this paper, we report on the scaffold-level assembled genome for the federally endangered, California endemic crustacean Lepidurus packardi (the Vernal Pool Tadpole Shrimp). L. packardi is a key food source for other conserved California species including the California Tiger Salamander Ambystoma californiense. It faces significant habitat loss and fragmentation as vernal pools are threatened by urbanization, agricultural conversion, and climate change. This resource represents the first scaffold-level genome of any Lepidurus species. The assembled genome spans 108.6 Mbps, with 6 chromosome-length scaffolds comprising 71% of total genomic length and 444 total contigs. The BUSCO score for this genome is 97.3%, suggesting a high level of completeness. We produced a predicted gene set for this species trained on the Daphnia magna set of genes and predicted 17,650 genes. These tools can aid researchers in understanding the evolution and adaptive potential of alternative reproductive modes within this species. [ABSTRACT FROM AUTHOR]

Traffic flow prediction has been essential for traffic management and road network planning. However, the complex urban road network and the strong spatialtemporal correlation of traffic flow data make this problem difficult. Existing prediction methods cannot fully utilize the spatial-temporal correlations in traffic flow data. Therefore, we propose a deep learning model called ResGAT-ABiGRU which combines Residual Network (ResNet), Graph Attention Network (GAT), Attention Mechanism, and the Bidirectional Gated Recurrent Unit (BiGRU). Firstly, GAT is used to capture the spatial correlations of traffic flow data, and then the time characteristics are extracted by Bidirectional GRU. Secondly, the ResNet module stacks multiple GAT layers and designs the attention mechanism to assign weights for different flow sequences to further capture spatial relations. Finally, we obtain the output through the fully connected layers. Validation traffic data from California, USA, is used for verification. The results show that the ResGAT-ABiGRU model proposed in this paper has higher prediction accuracy. Compared the model's performance with the Gated Recurrent Unit (GRU) baseline model, and the root means square error (RMSE) is reduced by 22.75%, and compared to the T-gcn model, the root mean square error is reduced by 3.29%. [ABSTRACT FROM AUTHOR]

This paper presents a new direct maximum power point tracking (MPPT) with a reference voltage ( V r e f ) based on the metaheuristic earthquake algorithm (EA) where the optimization variable is the V r e f for hard-switching converters. The efficiency and performance of EA-MPPT- V r e f is compared with the perturb-and-observe (P&O) counterpart technique due to the fact that it is widely used for commercial products. Static and dynamic responses for both MPPT strategies are evaluated, which correspond to steady-state oscillations when they are near the maximum power point (MPP), and the tracking-speed, respectively. The efficiency was evaluated with the EN 50530 standard. The results show that the new MPPT proposed is a competitive method using the EA to obtain the optimal voltage reference. From static results, EA-MPPT VP presented a better efficiency of 5.13% and 3.23% for European and California energy commission (CEC) efficiency, respectively. Whereas, from dynamic results, MPPT- V r e f techniques presented an efficiency from 95.13% to 99.91%, and 99.01% to 99.91% of the total power of the PV system for P&O and EA strategies, respectively. [ABSTRACT FROM AUTHOR]

Despite lower‐than‐average total precipitation in the western states of the U.S., the 2021 "precipitation roller coaster" defined as large precipitation swings has pointed to a strong hydroclimatic intensity (HYINT). Here we examine the 2021 HYINT using an index—a product of the average precipitation intensity (INT) and dry spell length (DSL). HYINT exhibited an extremely high value in the western U.S. in 2021. INT and DSL contribute differently to the 2021 HYINT, with large spatial variability. Overall, the 2021 extreme HYINT in central California and Utah is tied more to large INT, than to DSL. Meanwhile, the historical trends in INT and DSL may have contributed to the extreme 2021 HYINT event. The fraction of attributable risk framework reveals that the 2021 extreme HYINT is more likely to occur with anthropogenic forcing (e.g., 7.3 times more likely for HYINT exceeding 1.3) than natural forcing alone. Plain Language Summary: The western U.S. is a hotspot for studying climate change impacts on the hydrological cycle. Despite lower‐than‐average total precipitation in 2021, the contrasting dryness and wetness in the western U.S. has been widely reported as a "precipitation roller coaster." In this paper we quantified the "precipitation roller coaster" using an index (hydroclimatic intensity [HYINT])—a product of average precipitation intensity during wet days and dry spell length (DSL). The study found that the 2021 extreme HYINT event was largely attributable to the combined impacts of precipitation intensity and DSL in California and Utah, with precipitation intensity playing a more important role. In contrast, the 2021 precipitation event in other western states exhibited divergent contributions from precipitation intensity and DSL. The southwestern U.S. has been identified as a hotspot for increasing HYINT, which is tied more to the increasing DSL than the precipitation intensity. The trends in DSL and precipitation intensity may have played a key role in driving the 2021 extreme HYINT event. Using climate model experiments with and without anthropogenic forcing, an extreme HYINT event in the western U.S. is more likely to occur with anthropogenic forcing. Key Points: Hydroclimatic intensity (HYINT) exhibited extremely high values in parts of the western U.S. in 2021, mainly caused by average precipitation intensityHYINT shows a significant rising trend in most of the southwestern U.S. mainly tied to a rising dry spell length trendThe extreme HYINT event is more likely to occur under anthropogenic forcing than natural forcing alone [ABSTRACT FROM AUTHOR]

This paper presents a methodological framework to evaluate the resilience, with the primary focus on performance degradation, of levee-protected electric power networks to flooding in a changing climate. To this end, a multi-disciplinary framework is established by integrating climate science, hydrology, and electric power network analysis. The framework quantifies the effect of climate change on flood hazard levels in a levee-protected area and the subsequent changes in the resilience of the electric power network. In the first step, the changes in the exposure of levee-protected regions to flooding hazard in a warming climate are calculated. Then, the probabilities of failure of power network components due to flooding under current and projected future climate are determined. Finally, the power system resilience index is used to assess the system resiliency for pre-flooding (baseline), historic flooding, and two projected future flooding scenarios. For demonstration, the proposed framework is applied to a levee-protected area in Northern California. The IEEE 118-bus standard test system is employed to represent the power network in the study area. Results reveal that climate change can considerably decrease the system resilience of the levee-protected electric power network. The findings of this study can contribute towards more resilient power network systems under a changing climate. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic required significant public health interventions from local governments. Although nonpharmaceutical interventions often were implemented as decision rules, few studies evaluated the robustness of those reopening plans under a wide range of uncertainties. This paper uses the Robust Decision Making approach to stress-test 78 alternative reopening strategies, using California as an example. This study uniquely considers a wide range of uncertainties and demonstrates that seemingly sensible reopening plans can lead to both unnecessary COVID-19 deaths and days of interventions. We find that plans using fixed COVID-19 case thresholds might be less effective than strategies with time-varying reopening thresholds. While we use California as an example, our results are particularly relevant for jurisdictions where vaccination roll-out has been slower. The approach used in this paper could also prove useful for other public health policy problems in which policymakers need to make robust decisions in the face of deep uncertainty. [ABSTRACT FROM AUTHOR]

Using local sources (roof runoff, stormwater, graywater, and onsite wastewater) to meet non-potable water demands can minimize potable water use in buildings and increase supply reliability. In 2017, an Independent Advisory Panel developed a risk-based framework to identify pathogen log reduction targets (LRTs) for onsite non-potable water systems (ONWSs). Subsequently, California's legislature mandated the development and adoption of regulations--including risk-based LRTs--for use in multifamily residential, commercial, and mixed-use buildings. A California Expert Panel was convened in 2021 to (1) update the LRT requirements using new, quantitative pathogen data and (2) propose treatment trains capable of meeting the updated LRTs. This paper presents the updated risk-based LRTs for multiple pathogens (viruses, protozoa, and bacteria) and an expanded set of end-uses including toilet flushing, clothes washing, irrigation, dust and fire suppression, car washing, and decorative fountains. The updated 95th percentile LRTs required for each source water, pathogen, and end-use were typically within 1-log10 of the 2017 LRTs regardless of the approach used to estimate pathogen concentrations. LRT requirements decreased with influent pathogen concentrations from wastewater to graywater to stormwater to roof runoff. Cost and footprint estimates provide details on the capital, operations and maintenance, and siting requirements for ONWS implementation. [ABSTRACT FROM AUTHOR]

This study uses an ensemble Kalman method for near-surface seismic site characterization of 154 network earthquake monitoring stations in California to improve the resolution of S -wave velocity (VS) and P -wave velocity (VP) profiles—up to the resolution depth—coupled with better quantification of uncertainties compared to previous site characterization studies at this network. These stations were part of the Yong et al. site characterization project, with selected stations based on future recordings of ground motions that are expected to exceed 10 per cent peak ground acceleration in 50 yr. To estimate VS and VP from experimental dispersion data, Yong et al. investigated these stations using linearized (local search and iteration) routines, and Yong et al. later studied a subset of these stations using nonlinear (global search and optimization) routines. In both studies, the selection of model parameters—that is, discretization of the VS and VP profiles with only five fixed thickness layers—was mainly based on trial and error. In contrast, this paper uses an approximate Bayesian method to assimilate experimental dispersion data and sequentially update an ensemble of particle estimates that span the VS and VP parameter spaces. Doing so, we systematically determine the most probable profiles conditioned on the experimental dispersion data, the introduced noise levels, and a priori knowledge in the form of physical constraints. We consider two configurations to discretize the soil depth from the surface to half of the maximum discernible wavelength obtained from the experimental dispersion data, namely refined and coarse models, and two initial models for each configuration to study solution multiplicity. Our results suggest that using the refined model for the top surface layers improves the resolution of near-surface site characteristics and the model's success rate in capturing dispersion data at high frequencies. All models result in similar VS but distinct VP profiles, with increasing uncertainty at deeper layers, suggesting that the fundamental mode of Rayleigh wave dispersion data is not adequate to constrain the P -wave velocity profile and the S -wave velocity close to the resolution depth. [ABSTRACT FROM AUTHOR]

Native California bees and other wild pollinators, which are essential to many fruit and vegetable crops, are being threatened by climate change, pesticides and habitat degradation. Carbon farming, a set of practices that sequester carbon in the soil or woody biomass, can create habitat that supports these pollinators. This paper focuses on habitat management and farming practices that both increase carbon sequestration and benefit pollinator communities. By incentivizing and supporting conservation practices that incorporate carbon farming, we can protect wild pollinators and increase the resilience of California agriculture in the face of ongoing climate change. [ABSTRACT FROM AUTHOR]

• The California mandate for solar PV systems on residential rooftops will have a negative occupational safety impact. • Results indicate that 16.6 recordable non-fatal injuries are expected to occur between 2020 and 2029 as a result of this policy. • The impact could be reduced by 18% by eliminating work on roofs. Introduction: A 2018 change to the California building code mandates that new residential construction in the state include rooftop solar photovoltaic power systems beginning in 2020. As residential construction (especially work on rooftops) is among the more dangerous occupations in the United States, this paper seeks to quantify the increased risks to workers as a result of this mandate. Method: An analysis of the trends by occupation of nonfatal safety incident rates in the United States combined with a Monte Carlo simulation provide an estimate of the uncertain impact of this new mandate. Results: Recordable safety incidents are anticipated to increase by a total of 16.6 incidents (standard deviation = 1.0 incidents) over the 2020–2029 time period as a result of this policy change. However, lessons from Germany and other industries offer potential avenues to reduce the negative social impact of this mandate. Conclusions: While it is not possible to increase employment in any sector without increasing the expected number of occupational injuries to some degree, these results indicate that risks could be considerably reduced by making solar PV system design decisions that increase worker productivity and reduce roof exposure time. Practical Applications: Changes such as eliminating work on roofs could decrease the expected number of recordable injuries over the 10-year period by 0.30 incidents per year (a reduction of 18%). [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]

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]

This paper introduces a series of high resolution (100-meter) population grids for eight different sociodemographic variables across the state of California using data from the 2020 census. These layers constitute the 'CA-POP' dataset, and were produced using dasymetric mapping methods to downscale census block populations using fine-scale residential tax parcel boundaries and Microsoft's remotely-sensed building footprint layer as ancillary datasets. In comparison to a number of existing gridded population products, CA-POP shows good concordance and offers a number of benefits, including more recent data vintage, higher resolution, more accurate building footprint data, and in some cases more sophisticated but parsimonious and transparent dasymetric mapping methodologies. A general accuracy assessment of the CA-POP dasymetric mapping methodology was conducted by producing a population grid that was constrained by population observations within block groups instead of blocks, enabling a comparison of this grid's population apportionment to block-level census values, yielding a median absolute relative error of approximately 30% for block group-to-block apportionment. However, the final CA-POP grids are constrained by higher-resolution census block-level observations, likely making them even more accurate than these block group-constrained grids over a given region, but for which error assessments of population disaggregation is not possible due to the absence of observational data at the sub-block scale. The CA-POP grids are freely available as GeoTIFF rasters online at github.com/njdepsky/CA-POP, for total population, Hispanic/Latinx population of any race, and non-Hispanic populations for the following groups: American Indian/Alaska Native, Asian, Black/African-American, Native Hawaiian and other Pacific Islander, White, other race or multiracial (two or more races) and residents under 18 years old (i.e. minors). [ABSTRACT FROM AUTHOR]

Wildfires are natural or man‐made disasters that continuously threaten portions of the transmission and distribution grid, and thus the stability of the electric grid. This paper presents a two‐stage framework for assessing power system‐wildfire risk using a data‐driven wildfire prediction model. The first stage of the framework estimates the spatio‐temporal probability of potential wildfire ignition and propagation using a deep neural network in combination with the wildfire physical spread model. Analysis reveals similar spatial and temporal patterns between the model‐predicted wildfire ignition potential and actual wildfire ignition. Motivated by these observations, the second stage assesses the wildfire risk in the power grid operation in terms of potential loss of load by de‐energisation, through combining geospatial information system data of the power grid topology and the stochastic spatio‐temporal wildfire model developed in the first stage. The electric power utility applications introduced by the proposed framework are twofold: 1) a spatio‐temporal risk model for proactive de‐energisation against potential power system failure‐induced wildfire, and 2) a spatio‐temporal spreading model for optimal grid operations against exogenous wildfire. The proposed model, based on real‐world dataset, is demonstrated on the IEEE 24‐bus test system mapped to a study area in Northern California, while the results illustrate the proposed model can achieve the best performance in potential wildfire ignition detection (AUC of 0.995) compared to other baselines, as well as demonstrates the risk‐aware operation of the power system enabled by the proposed framework. [ABSTRACT FROM AUTHOR]

Anderson, R.B.; Carter, O.T.; Pearce, K.G., and Capdevila, L.A., 2022. User perceptions of the Pleasure Point seawall in Santa Cruz county, California, U.S.A. Journal of Coastal Research, 38(4), 828–843. Coconut Creek (Florida), ISSN 0749-0208. Communities up and down the California coast face the looming threat of rising seas, coastal erosion, and shoreline retreat. In this paper, the researchers use anthropological methods and a user survey to explore the social dimensions of the Pleasure Point seawall to assess some of the longer-term implications of coastal armoring in California and the U.S. more broadly. The users surveyed had an overall positive to neutral opinion of a coastal retention structure (in this case a seawall). About 35% had positive opinions of the seawall, 30% had neutral opinions, and 18% had negative opinions. While these results trend positive to neutral, they also reveal divided community opinions about and experiences with the seawall. The open-ended portions of the survey, combined with participant observation and follow-up interviews, add another qualitative dimension to this data. The participants in the survey and research expressed concerns about engineering and ecological issues, whether questions about how long the structure will last or observations/opinions about its impacts on local surf conditions, backwash, and loss of beach sand. Participants also expressed concerns about the social impacts of the seawall, including access issues, crowds, growing risks, problems with increasing numbers of inexperienced users, tourism growth, and fears about gentrification and local displacement. Combined, these insights illustrate how seawalls and other coastal armoring structures can and should be understood as engineered structures with critical social impacts that intersect with their physical, material, and environmental impacts. [ABSTRACT FROM AUTHOR]