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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]

Probabilistic seismic hazard analysis (PSHA) has been recognized as a reasonable method for quantifying seismic threats. Traditionally, this method ignores the effect of the focal depth, in which the ground motion prediction equations (GMPEs) are applied to estimate the probability distribution associated with the possible motion levels induced by the site earthquakes, but it is limited by the unclear geological conditions, which makes it difficult to provide a uniform equation, and these equations cannot express the non-linear relationship under geological conditions. Hence, this paper proposed a method to consider the seismic focal depth for the PSHA with the example of California and used a back propagation neural network (BPNN) to predict the peak ground acceleration (PGA) instead of the GMPEs. Firstly, the measured PGA and unknown PGA seismic data applicable to this method were collected separately. Secondly, the unknown PGA data were supplemented by applying the BPNN based on the measured PGA data. Lastly, based on the full-probability equation, PSHA considering the focal depth was completed and compared with the current California seismic zoning results. The results showed that using the BPNN in the PSHA can ensure computational accuracy and universality, making it more suitable for regions with unclear geological structures and providing the possibility of adding other parameters to be considered for the influence of the PSHA. [ABSTRACT FROM AUTHOR]

By adjusting the content of geopolymer in geopolymer stabilized phosphogypsum (GSP) as roadbed filler, along with the mixing ratio, this paper mainly explores tendencies in the mechanical properties and water stability of GSP. This research is based on macro-mechanical properties such as unconfined compressive strength, resilience modulus, California bearing ratio and shear strength. It is also based on water stability tests, such as the water soaking test, dry and wet cycle test and expansion test, to explore changes in water stability. As for the durability of GSP, this paper is mainly based on the realization of a long time observation of mechanical properties and water stability. In the existing research, most of the stabilized phosphogypsum (PG) base material or roadbed filler consists of cement, lime, etc. In this paper, a new exploration is carried out on the composition of stabilized PG material, realized without the participation of cement. The 28 d compressive strength of GSP reaches 2.5 MPa, and over time this strength grows, which prevents the phenomenon of strength inversion that may occur in conventional cement-stabilized PG. In addition, a long-term soaking experiment was designed in this study based on the material after the strength was stabilized for up to 90 d. After the strength was steady, the GSP with the best water stability still had a softening coefficient of 80% after experiencing water immersion for 7 d. After determining the feasibility of the mechanical properties and water stability of GSP as roadbed filler, we further explored the strength formation mechanism of GSP by microscopic tests (XRD and SEM). This shows that geopolymer can stabilize PG in two main ways: one is the hydration reaction with PG to generate C-S-H gel and ettringite, and the other is to connect PG not involved in the chemical reaction to form a dense whole through generated hydration products. Geopolymer, stabilizing a high amount of PG, not only provides a new method for the consumption of PG, but also has more stable performance than cement, and has certain advantages in economy. In addition, the advantage of this study is that good performance can be achieved by simply sieving PG and adjusting the geopolymer ratio in practical engineering projects. [ABSTRACT FROM AUTHOR]

Single chip seals are used by many agencies to maintain or preserve their roadways. While the construction and performance of single chip seals can be easily found from literature, the construction of double chip seals with and without paving fabric or paving mats is still not common. This paper investigates four double chip seal strategies used in a pilot project constructed on US 395 in Inyo County, California, by Caltrans. Within the double chip seal project limits, eight Performance Evaluation Sections (PESs) using four treatment strategies were established for detailed performance monitoring and evaluation: 1—a 3/8-inch asphalt rubber chip seal followed by a 1/4-inch PME chip seal without pavement-reinforcing fabric (PRF) or a paving mat as a control section; 2—a 3/8-inch PME chip seal over PRF, followed by a 1/4-inch PME chip seal; 3—a 3/8-inch PME chip seal over a paving mat, followed by a 1/4-inch PME chip seal; and 4—an asphalt rubber 3/8-inch chip seal over a paving mat, followed by a 1/4-inch PME chip seal. This pilot project was monitored during construction and evaluated 1 year later to help identify any construction issues and was used to improve the specifications and performance of Caltrans' chip seals. This paper presents the initial findings following construction, and the one-year performance of the pilot project and lessons learned. The findings presented were accomplished by using these four treatment strategies on a highway with a very adverse high desert climate type and high traffic volumes. Project reviews are also planned for up to seven years to determine the long-term project performance. [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]

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]

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]

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]

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]

Some financial disruptions that started in California, U.S., in March 2023, resulting in the closure of several medium-size U.S. banks, shed new light on the role of liquidity in business cycle dynamics. In the normal path of the business cycle, liquidity and output mutually interact. Small shocks generally lead to mean reversion through market forces, as a low degree of liquidity dissipation does not significantly disrupt the economic dynamics. However, larger shocks and greater liquidity dissipation arising from runs on financial institutions and contagion effects can trigger tipping points, financial disruptions, and economic downturns. The latter poses severe challenges for Central Banks, which during normal times, usually maintain a hands-off approach with soft regulation and monitoring, allowing the market to operate. However, in severe times of liquidity dissipation, they must swiftly restore liquidity flows and rebuild trust in stability to avoid further disruptions and meltdowns. In this paper, we present a nonlinear model of the liquidity–macro interaction and econometrically explore those types of dynamic features with data from the U.S. economy. Guided by a theoretical model, we use nonlinear econometric methods of a Smooth Transition Regression type to study those features, which provide and suggest further regulation and monitoring guidelines and institutional enforcement of rules. [ABSTRACT FROM AUTHOR]

The present paper continues the series of publications by the authors devoted to solving the problem of recognition regions with potential high seismicity. It is aimed at the development of the mathematical apparatus and the algorithmic base of the FCAZ method, designed for effective recognition of earthquake-prone areas. A detailed description of both the mathematical algorithms included in the FCAZ in its original form and those developed in this paper is given. Using California as an example, it is shown that a significantly developed algorithmic FCAZ base makes it possible to increase the reliability and accuracy of FCAZ recognition. In particular, a number of small zones located at a fairly small distance from each other but having a close "internal" connection are being connected into single large, high-seismicity areas. [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]

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]

The study presented in this paper analyzed four long-term pavement performance (LTPP) test sections located in the states of New York (NY) and California (CA). Two of them are flexible pavement sections, whereas the other two are composite pavement sections. Two levels of analysis—in-state analysis and cross-state analysis—were performed for these pavement sections to determine the impacts of traffic and climate conditions. The performance of the pavement sections was evaluated in respect of thermal cracking and rutting resistance. The in-state analysis focused on comparing the pavement sections located in the same state. The two pavement sections located in CA exhibited insignificant variation in thermal cracking, although one of them had an additional 1.5" (38 mm) dense-graded asphaltic concrete (AC) layer. On the other hand, the additional 1.5" (38 mm) AC layer resulted in a significant reduction in the rutting depth in one pavement section. The in-state analysis of the two pavement sections located in NY revealed that the 0.8" (20.4 mm) chip seal layer had significantly low resistance to thermal cracking and rutting. The cross-state analysis examined pavement sections of comparable structural capacities—two with low structural capacity, and two with high structural capacity. The performance comparison of the two pavement sections with low structural capacity revealed that the chip seal layer exhibited a significantly high rutting depth, i.e., low rutting resistance under high traffic loads in a freezing climate. On the contrary, the two pavement sections with high structural capacity showed relatively high rutting resistance in both warmer and freezing climates. Furthermore, this paper presents the pavement deterioration models for rutting and thermal cracking in the LTPP test sections. These models were developed using multiple linear regression considering the pavement service life (age), traffic load (average annual daily truck traffic, AADTT), and climate impact (freezing index, FI). The deterioration models had coefficients of determination (r2) in the range of 0.82–0.99 and standard errors varying from 0.01 to 9.92, which indicate that the models are reliable. [ABSTRACT FROM AUTHOR]

The misuse and abuse of opioids has become a serious public health threat in the United States. The state of California has been hit particularly hard by the opioid epidemic, with a noticeable increase in opioid-related fatalities and hospitalizations. This brief report paper aims to contribute to the growing literature by conducting a geospatial analysis of opioid dispensing patterns in California in 2021. The primary objective was to identify areas characterized by high-risk opioid dispending patterns and explore possible contributing factors. This retrospective study analyzed data from over 7 million records of opioid and benzodiazepine prescriptions dispensed by outpatient pharmacies in California in 2021. A series of generalized linear regression models was employed to assess the impact of neighborhood characteristics on opioid recipients and high-risk opioid dispensing. The study defined high-risk opioid dispensing behavior as: (1) multiple provider episodes, (2) overlapping opioid prescriptions for seven or more days, (3) overlapping opioid and benzodiazepine prescriptions for seven or more days, and (4) a high standardized dosage of opioid prescriptions per month. The study identified variables associated with high-risk opioid dispensing behaviors, including age, population density, income, and housing-related variables, as well as marital status and family-related variables. The study uncovered that there are noticeable disparities in opioid dispensing among different racial and ethnic groups within California. The findings indicated a correlation of high-risk dispensing indicators with certain demographic and socioeconomic factors. There was a substantial regional variation in opioid dispensing practices, with certain rural areas having higher rates of opioid prescriptions than urban areas. [ABSTRACT FROM AUTHOR]

This paper examines the impacts of three different potential evapotranspiration (PET) models on drought severity and frequencies indicated by the standardized precipitation index (SPEI). The standardized precipitation-evapotranspiration index is a recent approach to operational monitoring and analysis of drought severity. The standardized precipitation-evapotranspiration index combines precipitation and temperature data, quantifying the severity of a drought as the difference in a timestep as the difference between precipitation and PET. The standardized precipitation-evapotranspiration index thus represents the hydrological processes that drive drought events more realistically than the standardized precipitation index at the expense of additional computational complexity and increased data demands. The additional computational complexity is principally due to the need to estimate PET within each time step. The standardized precipitation-evapotranspiration index was originally defined using the Thornthwaite PET model. However, numerous researchers have demonstrated the standardized precipitation-evapotranspiration index is sensitive to the PET model adopted. PET models requiring sparse meteorological inputs, such as the Thornthwaite model, have particular utility for drought monitoring in data scarce environments. The aridity index (AI) investigates the spatiotemporal changes in the hydroclimatic system. It is defined as the ratio between potential evapotranspiration and precipitation. It is used to characterize wet (humid) and dry (arid) regions. In this study, a sensitivity analysis for the standardized precipitation-evapotranspiration and aridity indexes was carried out using three different PET models; namely, the Penman–Monteith model, a temperature-based parametric model and the Thornthwaite model. The analysis was undertaken in six gauge stations in California region where long-term drought events have occurred. Having used the Penman–Monteith model as the PET model for estimating the standardized precipitation-evapotranspiration index, our findings highlight the presence of uncertainty in defining the severity of drought, especially for large timescales (12 months to 48 months), and that the PET parametric model is a preferable model to the Thornthwaite model for both the standardized precipitation-evapotranspiration index and the aridity indexes. The latter outcome is worth further consideration for when climatic studies are under development in data scarce areas where full required meteorological variables for Penman–Monteith assessment are not available. [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]

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]

Simple Summary: Enteroliths are stone-like concretions of salts that form in the large colon of some horses. The disease associated with their presence is known as enterolithiasis and may manifest itself as chronic or acute abdominal pain. Enterolithiasis particularly occurs in some geographic areas such as California and Florida, although it has also been occasionally reported elsewhere. Gastric ulceration, defined as lesions of the mucosal lining of the stomach, is a well-documented pathological condition that affects horses of all ages and breeds throughout the world. Many risk factors for the development of gastric ulcerations have been identified, and some others have been suspected but never proven. This paper documents the prevalence of gastric ulceration in horses affected by enterolithiasis and were surgically treated and compares it with the prevalence of gastric ulceration in horses affected by similar intestinal diseases that were also surgically treated. Enterolithiasis is a well-documented cause of colic in horses, especially in some geographic areas such as California and Florida. This retrospective case-control study aims at comparing the prevalence of gastric ulcers in horses affected by enterolithiasis to that in horses affected by other types of large intestinal obstruction. Two hundred and ninety-six horses were included in the study sample. Horses that had surgery for the removal of one or more enteroliths were included in the study as cases. Patients that had surgery for large intestinal simple obstructions other than enterolithiasis (large colon displacement, non-strangulating large colon torsion, and large and small colon impactions) were selected to match case horses for age, sex, and breed and included as controls. A total of 101/148 horses with enteroliths (68%) had gastric ulcers diagnosed during hospitalization, compared with 46/148 of matched controls (31%). There was a significant association between enterolithiasis and gastric ulceration (odds ratio 4.76, p < 0.0001), and a greater prevalence in Thoroughbreds as compared with other breeds (odds ratio 22.6, p < 0.0001). We concluded that enterolithiasis is significantly associated with gastric ulceration (p < 0.0001). The association is stronger in Thoroughbreds. [ABSTRACT FROM AUTHOR]

Building energy simulation plays a significant role in buildings, with applications such as building performance evaluation, retrofit decisions and the optimization of building operations. However, the wide range of model inputs has limited much research into empirically customized case studies due to the insufficient availability of data inputs or the lack of systematic feature selection of key inputs. To address this gap, this study proposes the concept of minimum variable sets (MVSs) for building energy-prediction models to improve the general applicability of building energy prediction using forward simulation. An MVS, in this paper, refers to a variable set that contains the most indispensable energy-related variables/features for annual building energy prediction. This study developed MVSs for office buildings by applying feature engineering algorithms to a Building Performance Database (BPD), which was established by integrating the design of experiments (DoE) method with high-dimensional data-space metrics, as well as parallel simulation. Supervised feature dimension reduction methods and multiple statistical criteria were adopted to choose different numbers of indispensable variables from the primary 16 building variables. The hierarchical MVSs that consist of the selected variables are characterized by the most influential features for building energy prediction, with certain requirements for prediction accuracy. To further improve the feasibility of MVSs, this study utilized two separate office buildings located in Shanghai and California as validation cases and provided comparable prediction accuracies across different sizes of MVS. The results showed that the MVS that has 12 variables has higher prediction accuracy than that which has 9 variables, followed by that which has 7 variables. Finally, the quantitatively hierarchical correlations between different sizes of MVS with different prediction accuracies for annual building energy could provide potential support for reasonable decision-making regarding building energy model variables, especially when comprehensive consideration is needed of the limited cost and data availability, and the acceptable accuracy of building energy. [ABSTRACT FROM AUTHOR]

Environmental problems and production losses associated with irrigated agriculture, such as salinity, degradation of receiving waters, such as rivers, and deep percolation of saline water to aquifers, highlight water-quality concerns that require a paradigm shift in resource-management policy. New tools are needed to assist environmental managers in developing sustainable solutions to these problems, given the nonpoint source nature of salt loads to surface water and groundwater from irrigated agriculture. Equity issues arise in distributing responsibility and costs to the generators of this source of pollution. This paper describes an alternative approach to salt regulation and control using the concept of "Real-Time Water Quality management". The approach relies on a continually updateable WARMF (Watershed Analysis Risk Management Framework) forecasting model to provide daily estimates of salt load assimilative capacity in the San Joaquin River and assessments of compliance with salinity concentration objectives at key monitoring sites on the river. The results of the study showed that the policy combination of well-crafted river salinity objectives by the regulator and the application of an easy-to use and maintain decision support tool by stakeholders have succeeded in minimizing water quality (salinity) exceedances over a 20-year study period. [ABSTRACT FROM AUTHOR]

The increasing frequency of destructive wildfire incidents in the United States, particularly in the West, is well-documented, and the key causal variables are increasingly well understood. Among stakeholders with heightened concerns about risks from destructive wildfire are insurance companies and the homeowners they insure. The cancellation and nonrenewal of insurance due to wildfire risk has received media attention in the wake of major wildfire seasons, particularly in California. However, less attention has been directed to wildfire-related risks borne by lower-income policy holders, specifically. For example, the probability of maintaining or replacing an at-risk policy increases when a homeowner invests in fire protection measures. However, these investments are comparatively costly for lower-income homeowners. The present research aims to identify regions in the lower 48 states where moderate and high wildfire risk, lower income, and insurability are coterminous risks. The concentration of at-risk homes in counties with comparatively high wildfire hazard potential and comparatively higher poverty rates are considered. This paper also considers how the concentrated market share of insurance underwriting may pose a risk to lower income homeowners, considering the overlap between highly concentrated insurance markets and states with high wildfire risk and higher poverty rates. [ABSTRACT FROM AUTHOR]

Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) represents crop water use and is a key indicator of crop health. Accurate estimation of ET is critical for agricultural irrigation and water resource management. ET retrieval using energy balance methods with remotely sensed thermal infrared data as the key input has been widely applied for irrigation scheduling, yield prediction, drought monitoring and so on. However, limitations on the spatial and temporal resolution of available thermal satellite data combined with the effects of cloud contamination constrain the amount of detail that a single satellite can provide. Fusing satellite data from different satellites with varying spatial and temporal resolutions can provide a more continuous estimation of daily ET at field scale. In this study, we applied an ET fusion modeling system, which uses a surface energy balance model to retrieve ET using both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and then fuses the Landsat and MODIS ET retrieval timeseries using the Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM). In this paper, we compared different STARFM ET fusion implementation strategies over various crop lands in the central California. In particular, the use of single versus two Landsat-MODIS pair images to constrain the fusion is explored in cases of rapidly changing crop conditions, as in frequently harvested alfalfa fields, as well as an improved dual-pair method. The daily 30 m ET retrievals are evaluated with flux tower observations and analyzed based on land cover type. This study demonstrates improvement using the new dual-pair STARFM method compared with the standard one-pair STARFM method in estimating daily field scale ET for all the major crop types in the study area. [ABSTRACT FROM AUTHOR]

Fuel break effectiveness in wildland-urban interface (WUI) is not well understood during downslope wind-driven fires even though various fuel treatments are conducted across the western United States. The aim of this paper is to examine the efficacy of WUI fuel breaks under the influence of strong winds and dry fuels, using the 2018 Camp Fire as a case study. The operational fire growth model Prometheus was used to show: (1) downstream impacts of 200 m and 400 m wide WUI fuel breaks on fire behavior and evacuation time gain; (2) how the downstream fire behavior was affected by the width and fuel conditions of the WUI fuel breaks; and (3) the impacts of background wind speeds on the efficacy of WUI fuel breaks. Our results indicate that WUI fuel breaks may slow wildfire spread rates by dispersing the primary advancing fire front into multiple fronts of lower intensity on the downstream edge of the fuel break. However, fuel break width mattered. We found that the lateral fire spread and burned area were reduced downstream of the 400 m wide WUI fuel break more effectively than the 200 m fuel break. Further sensitivity tests showed that wind speed at the time of ignition influenced fire behavior and efficacy of management interventions. [ABSTRACT FROM AUTHOR]

The length–weight relationships (LWR) of the common seadragon Phyllopteryx taeniolatus (Lacepède, 1804) and the leafy seadragon Phycodurus eques (Günther, 1865), Syngnathidae, are presented in this paper, based on specimens raised in the Birch Aquarium at Scripps, La Jolla, California. Furthermore, we used the length at known age of 40 specimens of common and 15 leafy seadragons to estimate the parameter of the von Bertalanffy growth function (VBGF) for these species. Some of the pros and cons of the newly proposed length type used, 'scalene length' are presented. The parameters of the LWR and the VBGF parameters are discussed, with an emphasis on the rearing conditions, the peculiar anatomy of seadragons, especially of P. eques, and on the Gill-Oxygen Limitation Theory (GOLT). [ABSTRACT FROM AUTHOR]

An accurate and reliable forecast for traffic flow is regarded as one of the foundational functions in an intelligent transportation system. In this paper, a new model for traffic flow forecasting, named EnGS-DGR, is designed based on ensemble learning of graph convolutional network (GCN), sequence-to-sequence (Seq2Seq) learning model, and dynamic graph reconfiguration (DGR) algorithm. At the first stage, instead of employing entire nodes in the traffic network, the DGR algorithm is proposed to reconstruct the traffic graph topology consisting of traffic nodes with tight correlation under a specific forecasting interval, where the degree of correlation among the traffic nodes is quantized from the perspective of multi-view clustering. At the second stage, GCN-Seq2Seq integration strategy is introduced to extract the data of the spatio-temporal dependence and forecast traffic flow. We applied the proposed EnGS-DGR to two different datasets from the highways of Los Angeles County and of California's Bay Area; the simulation results show that the proposed EnGS-DGR is superior to other eight popular models for traffic flow forecasting in terms of three common performance metrics. [ABSTRACT FROM AUTHOR]

The tribe Loteae of the family Leguminosae has major centers of diversity in the Mediterranean Region and California. It is used here to illustrate different traditions of taxonomic interpretation of infraspecific variation among researchers in different continents. Recent accounts of European and Mediterranean taxa commonly make an emphasis on recognizing subspecies. In contrast, accounts of North American Loteae recognize all infraspecific taxa as varieties. Harmonization of taxonomic concepts across continents is essential for a more accurate representation of data in global biodiversity databases. It is reasonable to use the rank of subspecies for geographically defined infraspecific units with localized zones of intergradation. Such subspecies may be at least partially compatible across continents and taxonomic groups. In the present paper, we discuss taxa traditionally included in or associated with Acmispon glaber (Vogel) Brouillet (=Syrmatium glabrum Vogel) and demonstrate a need for recognizing three species, of which one has three geographically defined subspecies. Required nomenclatural combinations are proposed. It is proposed that subspecies rather than varieties should be recognized in a number of other species of North American Loteae (and other plant groups). Inland subspecies of Acmispon glaber and A. maritimus offer an opportunity for comparative analysis in an eco-evo-devo framework. [ABSTRACT FROM AUTHOR]

An innovative payload containing a sensitive mid-wave infrared camera was flown on an uncrewed aircraft system (UAS) to acquire thermal imagery along a reach of the Sacramento River, California, USA. The imagery was used as input for an ensemble particle image velocimetry (PIV) algorithm to produce near-continuous maps of surface flow velocity along a reach approximately 1 km in length. To assess the accuracy of PIV velocity estimates, in situ measurements of flow velocity were obtained with an acoustic Doppler current profiler (ADCP). ADCP measurements were collected along pre-planned cross-section lines within the area covered by the imagery. The PIV velocities showed good agreement with the depth-averaged velocity measured by the ADCP, with R 2 values ranging from 0.59–0.97 across eight transects. Velocity maps derived from the thermal image sequences acquired on consecutive days during a period of steady flow were compared. These maps showed consistent spatial patterns of velocity vector magnitude and orientation, indicating that the technique is repeatable and robust. PIV of thermal imagery can yield velocity estimates in situations where natural water-surface textures or tracers are either insufficient or absent in visible imagery. Future work could be directed toward defining optimal environmental conditions, as well as limitations for mapping flow velocities based on thermal images acquired via UAS. [ABSTRACT FROM AUTHOR]

Simple Summary: Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. This study aims to develop and evaluate an advanced algorithmic approach that integrates thermal imaging, statistical texture analysis, and t-distributed stochastic neighbor embedding (t-SNE) to accurately detect subclinical mastitis in dairy sheep. This approach focuses on improving the accuracy and reliability of non-invasive subclinical mastitis detection by using more sophisticated algorithmic procedures than traditional temperature differential methods, thereby enhancing livestock management and animal health monitoring. Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. Thermal imaging presents a promising avenue for non-invasive detection, but existing methodologies often rely on simplistic temperature differentials, potentially leading to inaccurate assessments. This study proposes an advanced algorithmic approach integrating thermal imaging processing with statistical texture analysis and t-distributed stochastic neighbor embedding (t-SNE). Our method achieves a high classification accuracy of 84% using the support vector machines (SVM) algorithm. Furthermore, we introduce another commonly employed evaluation metric, correlating thermal images with commercial California mastitis test (CMT) results after establishing threshold conditions on statistical features, yielding a sensitivity (the true positive rate) of 80% and a specificity (the true negative rate) of 92.5%. The evaluation metrics underscore the efficacy of our approach in detecting subclinical mastitis in dairy sheep, offering a robust tool for improved management practices. [ABSTRACT FROM AUTHOR]

As a result of the advocacy of Indigenous communities and increasing evidence of the ecological importance of fire, California has invested in the restoration of intentional burning (the practice of deliberately lighting low-severity fires) in an effort to reduce the occurrence and severity of wildfires. Recognizing the growing need to monitor the impacts of these smaller, low-severity fires, we leveraged Sentinel-2 imagery to reveal important inter- and intra-annual variation in grasslands before and after fires. Specifically, we explored three methodological approaches: (1) the complete time series of the normalized burn ratio (NBR), (2) annual summary metrics (mean, fifth percentile, and amplitude of NBR), and (3) maps depicting spatial patterns in these annual NBR metrics before and after fire. We also used a classification of pre-fire vegetation to stratify these analyses by three dominant vegetation cover types (grasses, shrubs, and trees). We applied these methods to a unique study area in which three adjacent grasslands had diverging fire histories and showed how grassland recovery from a low-severity intentional burn and a high-severity wildfire differed both from each other and from a reference site with no recent fire. On the low-severity intentional burn site, our results showed that the annual NBR metrics recovered to pre-fire values within one year, and that regular intentional burning on the site was promoting greater annual growth of both grass and shrub species, even in the third growing season following a burn. In the case of the high-severity wildfire, our metrics indicated that this grassland had not returned to its pre-fire phenological signals in at least three years after the fire, indicating that it may be undergoing a longer recovery or an ecological shift. These proposed methods address a growing need to study the effects of small, intentional burns in low-biomass ecosystems such as grasslands, which are an essential part of mitigating wildfires. [ABSTRACT FROM AUTHOR]

The freeze-drying of biopolymers presents a fresh option with greater potential for application in soil subgrade stabilization. A freeze-dried combination of β-glucan (BG) and γ-poly-glutamic acid (GPA) biopolymers was used to treat low compressible clay (CL) and low compressible silt (ML) soils in dosages of 0.5%, 1%, 1.5%, and 2%. The California bearing ratio (CBR) test for the treated specimens was performed under three curing conditions: (i) thermal curing at 60 °C, (ii) air-curing for seven days followed by submergence for 4 days, and (iii) no curing, i.e., tested immediately after mixing. To investigate the influence of shear strength on the freeze-dried biopolymer-stabilized soil specimens and their variations with aging, unconfined compressive strength (UCS) tests were conducted after thermal curing at 60 °C for 3 days, 7 days, and 7 days of thermal curing followed by 21 days of air curing. The maximum CBR of 125.3% was observed for thermally cured CL and a minimum CBR of 6.1% was observed under soaked curing conditions for ML soils. Scanning electron microscopy (SEM), infrared spectroscopy, average particle size, permeability, and adsorption tests revealed the pore filling, biopolymer adsorption and coating on the soil surface, and agglomeration of the soil along with the presence of hydrogen bonds, covalent amide bonds, and Van der Waals forces that contributed to the stiffening of the stabilized soil. Using three-dimensional (3D) finite element analysis (FEA) and layered elastic analysis (LEA), a mechanistic–empirical pavement design was carried out for the stabilized soil and a design thickness catalog was prepared for the maximum CBR. The cost reductions for a 1 km section of the pavement were expected to be 12.5%. [ABSTRACT FROM AUTHOR]

This paper evaluates the energy performance of shallow ground source heat pumps using the state-of-art whole building energy simulation tool. In particular, the paper presents a systematic and easy to implement approach to model the energy performance of shallow and helical ground heat exchangers and assess their energy efficiency benefits to heat and cool buildings. The modeling approach is based on the implementation of G-functions, generated using a validated numerical model, in a state-of-art whole building energy simulation tool. Both the numerical model and the simulation tool are applied to assess the energy performance of various shallow geothermal systems designed to meet heating and cooling needs for detached single-family homes in California. Specifically, a series of sensitivity analyses is conducted to determine the energy performance of the shallow geothermal systems in 16 locations representing all California climate zones. It is found that the suitability and the efficiency of the shallow geothermal systems vary widely and depend on several factors including their design specifications as well as the climate conditions. Compared with conventional air-to-air heat pumps, the shallow ground source heat pumps can be more energy efficient in most climate zones in California except those locations with extreme weather conditions resulting in either heating or cooling only operation. Moreover, configurations of shallow ground source heat pumps with 16 boreholes with 6.7 m (22 ft) depth are found to be cost-effective in several California climate zones. [ABSTRACT FROM AUTHOR]

Many previous studies have shown that open-source technologies help democratize information and foster collaborations to enable addressing global physical and societal challenges. The outbreak of the novel coronavirus has imposed unprecedented challenges to human society. It affects every aspect of livelihood, including health, environment, transportation, and economy. Open-source technologies provide a new ray of hope to collaboratively tackle the pandemic. The role of open source is not limited to sharing a source code. Rather open-source projects can be adopted as a software development approach to encourage collaboration among researchers. Open collaboration creates a positive impact in society and helps combat the pandemic effectively. Open-source technology integrated with geospatial information allows decision-makers to make strategic and informed decisions. It also assists them in determining the type of intervention needed based on geospatial information. The novelty of this paper is to standardize the open-source workflow for spatiotemporal research. The highlights of the open-source workflow include sharing data, analytical tools, spatiotemporal applications, and results and formalizing open-source software development. The workflow includes (i) developing open-source spatiotemporal applications, (ii) opening and sharing the spatiotemporal resources, and (iii) replicating the research in a plug and play fashion. Open data, open analytical tools and source code, and publicly accessible results form the foundation for this workflow. This paper also presents a case study with the open-source spatiotemporal application development for air quality analysis in California, USA. In addition to the application development, we shared the spatiotemporal data, source code, and research findings through the GitHub repository. [ABSTRACT FROM AUTHOR]

This paper aims to study the feasibility of low cement content foamed concrete using waste lime mud (LM) and fly ash (FA) as mineral additives. The LM/FA ratio was first optimized based on the compressive strength. Isothermal calorimetry test, ESEM, and XRD were used to investigate the role of LM during hydration. Afterward, the optimized LM/FA ratio (1/5) was used to design foamed concrete with various wet densities (600, 700, 800 and 900 kg/m3) and LM–FA dosages (0%, 50%, 60%, 70% and 80%). Flowability measurements and mechanical measurements including compressive strength, flexural strength, splitting strength, elastic modulus, and California bearing ratio were conducted. The results show that the foamed concretes have excellent workability and stability with flowability within 170 and 190 mm. The high alkalinity of LM accelerated the hydration of FA, thereby increasing the early strength. The significant power functions were fitted for the relationships between flexural/splitting and compressive strength with all correlation coefficients (R2) larger with 0.95. The mechanical properties of the foamed concrete increased with the density increasing or LM–FA dosage decreasing. The compressive strength, tensile strength, CBR of all prepared foamed concretes were higher than the minimum requirements of 0.8 and 0.15 MPa and 8%, respectively in the standard. [ABSTRACT FROM AUTHOR]

In 2020, wildfires scarred over 4,000,000 hectares in the western United States, devastating urban populations and ecosystems alike. The significant impact that wildfires have on plants, animals, and human environments makes wildfire adaptation, management, and mitigation strategies a critical task. This study uses satellite imagery from Landsat to calculate burn severity and map the fire progression for the Dolan Fire of central Coastal California which occurred in August 2020. Several environmental factors, such as temperature, humidity, fuel type, topography, surface conditions, and wind velocity, are known to affect wildfire spread and burn severity. The aim of this study is the investigation of the relationship between these environmental factors, estimates of burn severity, and fire spread patterns. Burn severity is calculated and classified using the Difference in Normalized Burn Ratio (dNBR) before being displayed as a time series of maps. The Dolan Fire had a moderate severity burn with an average dNBR of 0.292. The ignition site location, when paired with the patterns of fire spread, is consistent with wind speed and direction data, suggesting fire movement to the southeast of the fire ignition site. Patterns of increased burn severity are compared with both topography (slope and aspect) and fuel type. Locations that were found to be more susceptible to high burn severity featured Long Needle Timber Litter and Mature Timber fuels, intermediate slope angles between 15 and 35°, and north- and east-facing slopes. This study has implications for the future predictive modeling of wildfires that may serve to develop wildfire mitigation strategies, manage climate change impacts, and protect human lives. [ABSTRACT FROM AUTHOR]

Large-particle-size graded crushed stone mixtures (LPS-GCSMs) can improve the shortcomings of conventional graded crushed stone, such as low strength, high deformation, and a low modulus of resilience. At present, there is no systematic research on the gradation design and field evaluation of the LPS-GCSMs. In this study, compaction and California bearing ratio (CBR) tests and field construction conditions were combined to design six kinds of gradation of LPS-GCSM, and the optimum gradation was revealed. In order to improve the mechanical properties of LPS-GCSM, 2.5% cement was added to the mixture to prepare a low-content cement-modified LPS-GCSM (LCC-LPS-GCSM) based on the suggested gradation. The mechanical properties of the LCC-LPS-GCSM were investigated through unconfined compression strength (UCS) and compression rebound modulus (CRM) tests. Moreover, the compaction and deflection properties of LPS-GCSM and LCC-LPS-GCSM were examined through the test battery. The results showed that the optimum gradation of LPS-GCSM can be achieved with a combination of aggregate sizes of 20–40 mm, 10–20 mm, 5–10 mm, and 0–5 mm at a ratio of 44:20:10:26. The passing rates of 19 mm and 4.75 mm should be approximately at the median value of the gradation in view of field construction uniformity and a coarse aggregate interlocking effect. The UCS and CRM values of LCC-LPS-GCSM increased rapidly from 0 day to 28 days while they slowed after 28 days, which was similar to those of cement-stabilized materials. The field detection suggested that LPS-GCSM exhibited favorable compaction and that the addition of cement improved the stability of the field compaction of the mixture. Adding a subbase course of LPS-GCSM between the old pavement and the LCC-LPS-GCSM base can lead to more uniform stress on the base. The results of this study provide a reference for the gradation design of LPS-GCSM and optimization of the design indicators. [ABSTRACT FROM AUTHOR]

Wildfires present a significant risk to societies globally due to population growth, concentrated activities, climate change, and extreme environmental conditions. To establish effective fire suppression and management policies, it is crucial to determine whether the distance between ignition points and urban areas is increasing or decreasing. This study analyzes 101,597 fires in Spain and California between 2007 and 2015, where ignition points and all built-up areas were precisely geolocated in 2014. The study employs the Mann–Kendall test to determine trends and analyze the relative distance of wildfires to human buildings over time. The results indicate that wildfires are becoming statistically significantly closer in several areas. It is also observed that the majority of wildfires are moving away from buildings, although this is not a significant finding. These results suggest the possibility of further research into the reasons behind these observations and possible future developments and their consequences. [ABSTRACT FROM AUTHOR]

The Central Valley of California is one of the most prolific agricultural regions in the world. Agriculture is reliant on the conjunctive use of surface-water and groundwater. The lack of available surface-water and land-use changes have led to pumping-induced groundwater-level and storage declines, land subsidence, changes to streamflow and the environment, and the degradation of water quality. As a result, in part, the Sustainable Groundwater Management Act (SGMA) was developed. An examination of the components of SGMA and contextualizing regional model applications within the SGMA framework was undertaken to better understand and quantify many of the components of SGMA. Specifically, the U.S. Geological Survey (USGS) updated the Central Valley Hydrologic Model (CVHM) to assess hydrologic system responses to climatic variation, surface-water availability, land-use changes, and groundwater pumping. MODFLOW-OWHM has been enhanced to simulate the timing of land subsidence and attribute its inelastic and elastic portions. In addition to extending CVHM through 2019, the new version, CVHM2, includes several enhancements as follows: managed aquifer recharge (MAR), pumping with multi-aquifer wells, inflows from ungauged watersheds, and more detailed water-balance subregions, streamflow network, diversions, tile drains, land use, aquifer properties, and groundwater level and land subsidence observations. Combined with historical approximations, CVHM2 estimates approximately 158 km3 of storage loss in the Central Valley from pre-development to 2019. About 15% of the total storage loss is permanent loss of storage from subsidence that has caused damage to infrastructure. Climate extremes will likely complicate the efforts of water managers to store more water in the ground. CVHM2 can provide data in the form of aggregated input datasets, simulate climatic variations and changes, land-use changes or water management scenarios, and resulting changes in groundwater levels, storage, and land subsidence to assist decision-makers in the conjunctive management of water supplies. [ABSTRACT FROM AUTHOR]

Concerns over both water quality and quantity continue to increase globally. As the need for useable and potable water becomes more of a widespread issue, there is an opportunity to review and consider alternatives to how water is used, consumed, and sustained for future use by the world's population. A review of data across cities within North America shows improvement opportunities in water infrastructure systems. Using water audit and loss control data from the American Water Works Association (AWWA) and the Water Research Foundation (WRF), an analysis is provided to define opportunities for mitigating water losses among select North American Water Infrastructure systems in the U.S. states of Georgia and California. The research methodology used includes statistical analysis data while grouping utility sizes to identify utility cost opportunities. Variables on water loss and customer cost that have a positive impact on overall and long-term water system sustainability are identified. The analysis shows California, while having firm water guidance and higher rates compared to Georgia, also demonstrates less overall water loss. The results of the analysis are presented, showing comparison characteristics and opportunities for additional change to improve utility funding. [ABSTRACT FROM AUTHOR]

This study introduces a neural network-based approach to predict dust emissions, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), this research predicted PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. Preprocessing steps like outlier removal and normalization were employed to refine the dataset for training. The network's architecture was designed with two hidden layers and optimized using tanh activation and MSE loss functions through the Adam algorithm, striking a balance between model complexity and predictive accuracy. The model was trained on extensive field data from an almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The results demonstrate a notable predictive accuracy of the FNN model, with a mean squared error (MSE) of 0.02 and a mean absolute error (MAE) of 0.01, indicating high precision in forecasting PM2.5 levels. By integrating machine learning with agricultural practices, this research provides a significant tool for environmental management in almond production, offering a method to reduce harmful emissions while maintaining operational efficiency. This model presents a solution for the almond industry and sets a precedent for applying predictive analytics in sustainable agriculture. [ABSTRACT FROM AUTHOR]

We consider a constructive definition of the multivariate Pareto that factorizes the random vector into a radial component and an independent angular component. The former follows a univariate Pareto distribution, and the latter is defined on the surface of the positive orthant of the infinity norm unit hypercube. We propose a method for inferring the distribution of the angular component by identifying its support as the limit of the positive orthant of the unit p-norm spheres and introduce a projected gamma family of distributions defined through the normalization of a vector of independent random gammas to the space. This serves to construct a flexible family of distributions obtained as a Dirichlet process mixture of projected gammas. For model assessment, we discuss scoring methods appropriate to distributions on the unit hypercube. In particular, working with the energy score criterion, we develop a kernel metric that produces a proper scoring rule and presents a simulation study to compare different modeling choices using the proposed metric. Using our approach, we describe the dependence structure of extreme values in the integrated vapor transport (IVT), data describing the flow of atmospheric moisture along the coast of California. We find clear but heterogeneous geographical dependence. [ABSTRACT FROM AUTHOR]

Load scheduling is an effective way of utilizing loads of smart residential buildings according to the preferences of the inhabitants or grid demands, while reducing the cost of energy. This work proposes objective functions for load scheduling to confine the cost of energy within the consumers' preference range while keeping the load consumption closer to the load demand as possible, to minimize system loss during normal and pandemic condition such as COVID-19 periods, fulfilling the unique features of a net-zero energy building. The proposed objective function is implemented by considering the realistic grid power cost, levelized cost of renewable sources, battery, and incentives offered by the utility system existing in California, USA. In addition to three different types of days such as normal working days, weekends and pandemic situations, brown out power outages are considered as operating conditions. Particle swarm optimization (PSO) is utilized in all considered operating conditions. Two terms that account for the total energy cost savings and the total delayed/scheduled load over a fixed time horizon are formulated as performance indices to illustrate the effectiveness of the proposed objective functions for load scheduling. All of the cases are optimized by the Paticle Swarm Optimization (PSO) and non-optimized systems are simulated in the MATLAB environment. It is evident from the simulation results that the proposed objective function is very efficient in tackling the energy resources, loads and grid power to maximize cost savings and minimize shifting of loads for later hours for normal and pandemic situations in net-zero energy buildings. Moreover, it is equally effective in responding to any emergency situations such as brown out energy crisis situations, which are not considered in the literature so far. In all cases, the performance index also validates the effectiveness of the proposed objective function-based scheduling system for net-zero energy buildings. [ABSTRACT FROM AUTHOR]

Lithium is the principal component of high-energy-density batteries and is a critical material necessary for the economy and security of the United States. Brines from geothermal power production have been identified as a potential domestic source of lithium; however, lithium-rich geothermal brines are characterized by complex chemistry, high salinity, and high temperatures, which pose unique challenges for economic lithium extraction. The purpose of this paper is to examine and analyze direct lithium extraction technology in the context of developing sustainable lithium production from geothermal brines. In this paper, we are focused on the challenges of applying direct lithium extraction technology to geothermal brines; however, applications to other brines (such as coproduced brines from oil wells) are considered. The most technologically advanced approach for direct lithium extraction from geothermal brines is adsorption of lithium using inorganic sorbents. Other separation processes include extraction using solvents, sorption on organic resin and polymer materials, chemical precipitation, and membrane-dependent processes. The Salton Sea geothermal field in California has been identified as the most significant lithium brine resource in the US and past and present efforts to extract lithium and other minerals from Salton Sea brines were evaluated. Extraction of lithium with inorganic molecular sieve ion-exchange sorbents appears to offer the most immediate pathway for the development of economic lithium extraction and recovery from Salton Sea brines. Other promising technologies are still in early development, but may one day offer a second generation of methods for direct, selective lithium extraction. Initial studies have demonstrated that lithium extraction and recovery from geothermal brines are technically feasible, but challenges still remain in developing an economically and environmentally sustainable process at scale. [ABSTRACT FROM AUTHOR]

Unpaved roads are essential as transportation infrastructure. These roads are traditionally built utilizing local soils and aggregates. The application of different materials to improve the strength and the overall performance of these road solutions has been investigated. This study focuses on the assessment of the suitability of reinforcing a fine soil using natural fibres from the forest value chain—specifically, pine needles—for unpaved forest roads. The resilient response of both unreinforced and reinforced soils was assessed with RL-CBR tests (California Bearing tests with repeated load) for different percentages of natural fibres incorporated into the soil. The force–penetration response, CBR value, recoverable and permanent displacements, and equivalent modulus were estimated. The effect of different load stages on the elastic response of both unreinforced and reinforced soils was also evaluated. The CBR values improved with the addition of pine needles; the best response was observed for specimens reinforced with 1% (mass) of these natural fibres (the CBR value increased by 45%). The equivalent modulus also was improved, showing that the stiffness of the soil increased with the inclusion of reinforcement. The findings imply that RL-CBR studies offer a promising method for obtaining important design parameters, especially for unpaved forest roads where investments in soil characterization are frequently limited. [ABSTRACT FROM AUTHOR]

Determining the amount of heat units required to kill weed seeds is a crucial aspect for the success of weed control through soil solarization. Lab experiments were designed to determine the duration of exposure for weed seeds that is required to suppress germination at temperatures (40, 45, 50, 55, and 60 °C) in the range of those typically achieved during soil solarization in California. The species tested were annual sowthistle (Sonchus oleraceus L.), bristly oxtongue (Picris echioides L.), nettleleaf goosefoot (Chenopodium murale L.), redroot pigweed (Amaranthus retroflexus L.), common purslane (Portulaca oleracea L.), little mallow (Malva parviflora L.), and redstem filaree (Erodium cicutarium L.). Germination tests were performed to assess the germinability of the weed seeds. The germination suppression by the lab-simulated solarization temperatures differed among the species based on their seasonality. The cool-season annuals S. oleraceus and P. echioides were more susceptible to the heat treatments than the warm-season annuals P. oleracea, A. retroflexus, and C. murale. The hard-seeded weed species M. parviflora and E. cicutarium were the least susceptible to the heat treatments. The germination rates of S. oleraceus, P. echioides, and C. murale were reduced at all of the temperatures that were tested. The germination rates for A. retroflexus and M. parviflora were not affected by temperatures below 40 °C. The germination rates for P. oleracea were not affected by temperature below 45 °C and the germination of E. cicutarium was not affected by any of the temperatures that were tested. The duration (hours) of exposure and percent of germination suppression of the weed seeds were used to create thermal-time hazard models for weed seeds using logistic regression. [ABSTRACT FROM AUTHOR]

California and most other coastlines around the world are being impacted by both long-term sea-level rise and short-term extreme events. Due to California's long and intensively developed coastline, it is an important area for evaluating responses to these challenges. The predominant historic approach to coastal erosion in California and globally has been the construction of hard coastal armoring such as seawalls and rock revetments. The concept of living shorelines—defined as using natural elements like plants, sand, or rocks to stabilize the coastline—has been widely proposed as a soft or green response to coastal erosion and flooding. However, these approaches have very limited application in high-energy environments such as California's 1100-mile-long outer coast and are not realistic solutions for protection from wave attack at high tides or long-term sea-level rise. Each of the state's coastal communities need to identify their most vulnerable areas, develop adaptation plans, and plan eventual relocation strategies in response to an accelerating sea-level rise. [ABSTRACT FROM AUTHOR]