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Monarch butterflies, Danaus plexippus L., in western North America are undergoing a seemingly volatile shift in seasonal population dynamics which includes novel winter-breeding on non-native milkweed in urban areas of northern California. The survival, viability and outcome of monarch pupae in the wild was evaluated for the first time by regular monitoring of a cohort of 104 pupae in a San Francisco managed urban landscape during the winter of 2021/22. Seventeen pupae were destroyed by landscaping activities. Almost half (49.4%) of the remaining pupae eclosed, with ∼ 70% likely infected with varying levels of the protozoan parasite Ophryocystis elektroscirrha (OE). A third of the pupae (32.2%) died of unknown causes, while the remainder (18.4%) disappeared, presumed predated. Two monarchs were killed while eclosing, by European paper wasps, Polistes dominula (Christ, 1791). Microscopic examination of pupal exuviae for spores of OE appeared to be a satisfactory proxy for microscopic examination of butterfly abdomens, in determining the presence/absence of infection. The developing importance of winter breeding in western monarch ecology necessitates modifications to conservation guidelines that enhance the survival of immature stages, acknowledging the role of non-native milkweeds while minimizing parasite infection. [ABSTRACT FROM AUTHOR]

Public sentiment can effectively evaluate the public's feelings of well-being in the urban environment and reflect the quality of the spatial environment to a certain extent. Previous studies on the relationship between public sentiment and urban built environmental factors have yielded meaningful results. However, few studies have focused on the effect of micro-built environment on public sentiment at the street level, which directly shapes people's perceptions. In addition, the nonlinear relationship and synergistic effect among urban built environmental factors have been commonly disregarded in previous studies, resulting in an incomplete understanding of the impact of urban built environment on public emotions. Therefore, this paper takes San Francisco as a study case to explore the complex relationship between urban built environmental factors and public emotions. Specifically, this paper measures the polarity of public emotions through sentiment analysis on Twitter data, establishes a comprehensive built environment index system from both macro- and micro- perspectives, and subsequently explores the complex relationship between the urban built environment and public sentiment through the OLS model and Shapley Additive Explanation algorithm. Results show that: (1) micro-built environmental factors have a significant influence on public emotion, although they have been frequently ignored. (2) Public sentiment tends to be more positive in areas with recreation facilities, mixed land use, rich street view visual environment, suitable thermal and acoustic environment, balanced income, and a suitable degree of high population density. (3) A nonlinear relationship and threshold effect exist between the built environmental variables and the semantic orientations of public emotion. Environment improvement strategies based on the synergic effect between variables can effectively promote the generation of positive emotions. Our empirical findings can offer valuable insights to promote feelings of well-being and foster an urban development approach through strategic interventions within the urban built environment. [ABSTRACT FROM AUTHOR]

The unique neighborhood style of San Francisco Chinatown is influenced by Chinese and American culture, history and social system. Through field investigation, Chinese and English literature collection, and spatial analysis, this paper interprets the motivation of its style evolution. In the process of research, the author tries to analyze the changes and driving forces of Chinatown in San Francisco from three dimensions: the city scale, the blocks scale, and the building scale. The results show that the formation process of Chinatown in San Francisco and the range of its blocks are deeply influenced by the world political pattern in the early 20th century, the Sino-American multi-party regulations and Sino-American relations. The architectural style of the block is influenced by the Eastern and Western cultures, which is a distinctive architectural feature. The traditional architecture is deeply influenced by the architectural style of southern Fujian, while the new architecture is mainly based on the modernist architectural style. Among the main factors affecting the evolution of Chinatown in San Francisco, gold rush, earthquake, traditional Chinese architectural style and immigration policy to China are the internal mechanisms of the formation of Chinatown's spatial characteristics. [ABSTRACT FROM AUTHOR]

Cities around the world are facing increased sensitivity to drought effects. Climate-change-induced drought affects not only the natural hydrology of the broad macroclimate but also those in the urban microclimates. The increasing frequency and duration of droughts are creating challenges for urban water utilities to convey water through distribution systems to customers reliably and consistently. This has led many urban areas like San Francisco, California, to search for unique alternative water supply projects to help bolster the drought resilience of the coupled human and natural water system. This paper focuses on applying the features of resilience (i.e., plan, adapt, absorb, and recover) through a drought resilience matrix to water supply alternatives to analyze how the addition of these projects would increase the overall water system's drought resilience. San Francisco, California, was used as the case study to test the use of this matrix. Three portfolios (modifying existing supply, recycling, and desalination, as well as local approaches) were created and tested in the matrix. Each portfolio is composed of various alternative water supply projects that the San Francisco Public Utilities Commission (SFPUC) is considering for implementation. Results concluded that the local approaches portfolio provided the most drought resilience, with the recycling and desalination portfolio providing the least resilience. The study approach and the presented findings will provide guidance to water utility professionals in supply planning to enhance drought resilience. [ABSTRACT FROM AUTHOR]

A key component of infrastructure seismic risk assessment is the execution of nonlinear time-history analyses with a suite of selected ground motions scaled to site-specific spectra. When interested in the evaluation of distributed systems residing near major active faults, such an approach is challenged by the scarcity of records adequately incorporating region and site-specific features known to impact the nonlinear response of distributed infrastructure substantially (pulses, spectral shape, spatial variability). This paper assesses the differences in the seismic demand posed to a suspension bridge as obtained from real ground motions selected based on state-of-the-art methods and arrays of site-specific simulated ground motions generated from physics-based wave-propagation models. The objective is to characterize the biases in structural response estimates introduced by the use of real ground motions in code-compliant approaches. A total of 15 realizations of an M7 Hayward fault earthquake and a detailed nonlinear simulation model representative of the West San Francisco–Oakland Bay Bridge are utilized as a case study. Damage limit states associated with structural drifts are defined for each bridge component as a function of the spread of plasticity. The results indicate that the median and variability of the demands posed by the simulated motions and real ground motions scaled to the same site-specific spectra are markedly different. Evidence from this study highlights the sensitivity of complex structures to multiple characteristics of the ground motions that might be biased by current scaling methods. Finally, fundamental characteristics of the scaled records leading to differences in the bridge response are discussed providing the basis to inform current ground motion selection and scaling procedures. [ABSTRACT FROM AUTHOR]

Polarimetric target decomposition algorithms have played an important role in extracting the scattering characteristics of buildings, crops, and other fields. However, there is limited research on the scattering characteristics of grasslands and a lack of volume scattering models established for grasslands. To improve the accuracy of the polarimetric target decomposition algorithm applicable to grassland environments, this paper proposes an adaptive polarimetric target decomposition algorithm (APD) based on the anisotropy degree (A). The adaptive volume scattering model is used in APD to model volume scattering in forest and grassland regions separately by adjusting the value of A. When A > 1, the particle shape becomes a disk, and the grassland canopy is approximated as a cloud layer composed of randomly oriented disk particles; when A < 1, the particle shape is a needle, simulating the scattering mechanism of forests. APD is applied to an L-band AirSAR dataset from San Francisco, a C-band AirSAR dataset from Hunshandak grassland in Inner Mongolia Autonomous Region, and an X-band COSMO-SkyMed dataset from Xiwuqi grassland in Inner Mongolia Autonomous Region to verify the effectiveness of this method. Comparison studies are carried out to test the performance of APD over several target decomposition algorithms. The experimental results show that APD outperforms the algorithms tested in terms of this study in decomposition accuracy for grasslands and forests on different bands of data. [ABSTRACT FROM AUTHOR]

This paper discusses a landmark ruling by the Chilean Supreme Court of August 9, 2023 dealing with the right to mental privacy, originated with an action for constitutional protection filed on behalf of Guido Girardi Lavin against Emotiv Inc., a North American company based in San Francisco, California that is commercializing the device "Insight." This wireless device functions as a headset with sensors that collect information about the brain's electrical activity (i.e., neurodata). The discussion revolves around whether neurodata can be considered personal data and whether they could be classified into a special category. The application of the present legislation on data (the most obsolete, such as the Chilean law, and the most recent EU law) does not seem adequate to protect neurodata. The use of neurodata raises ethical and legal concerns that are not fully addressed by current regulations on personal data protection. Despite not being necessarily considered personal data, neurodata represent the most intimate aspects of human personality and should be protected in light of potential new risks. The unique characteristics of neurodata, including their interpretive nature and potential for revealing thoughts and intentions, pose challenges for regulation. Current data protection laws do not differentiate between different types of data based on their informational content, which is relevant for protecting individual rights. The development of new technologies involving neurodata requires particular attention and careful consideration to prevent possible harm to human dignity. The regulation of neurodata must account for their specific characteristics and the potential risks they pose to privacy, confidentiality, and individual rights. The answer lies in the reconfiguration of human rights known as "neurorights" that goes beyond the protection of personal data. [ABSTRACT FROM AUTHOR]

This paper examines spatiotemporal patterns and socioeconomic influences on host participation in Airbnb's short-term rental (STR) marketplace in San Francisco during the years 2019–2022, a four-year period that spans the COVID-19 pandemic. This provides the motivation for the study to examine how San Francisco's demographic and socioeconomic fluctuations influenced Airbnb hosts to rent their properties on the platform. To do so, Airbnb property densities, indicators of host participation, are estimated at the census tract level and subsequently mapped in a GIS along with points of interest (POIs) located all over the city. Mapping unveils spatiotemporal patterns and changes in Airbnb property densities, which are also analyzed for spatial autocorrelation using Moran's I. Clusters and outliers of property densities are identified using K-means clustering and geostatistical methods such as local indicators of spatial association (LISA) analysis. Locationally, San Francisco's Airbnb hotspots are not located in the city's core, unlike other major Airbnb markets in metropolitan areas. Instead, such hotspots are in the city's northeastern neighborhoods around ethnic enclaves, in close proximity to POIs that are frequented by visitors, and have a higher proportion of hotel and lodging employment and lower median household income. A conceptual model posits associations of Airbnb property densities with sixteen demographic, socioeconomic factors, indicators of trust, social capital, and sustainability, along with proximity to points of interest. Ordinary least squares (OLS) regressions reveal that occupation in professional, scientific, and technical services, hotel and lodging employment, proximity to POIs, and proportion of Asian population are the dominant factors influencing host participation in San Francisco's shared accommodation economy. The occupational influences are novel findings for San Francisco. These influences vary somewhat for two main types of properties—entire home/apartment and private rooms. Implications of these findings are discussed in relation to supply side motivations of Airbnb hosts to participate in San Francisco's STR marketplace. [ABSTRACT FROM AUTHOR]

With climate change increasingly threatening people in uneven and disproportional ways, community-based solutions and interventions have become critical to ensure equitable, just, and inclusive climate action in cities. This paper examines community-based climate action in San Francisco (USA) through the lens of climate justice and intersectionality. Through a qualitative analysis of community-based organisations (CBOs) and their justice-oriented adaptation and mitigation efforts, our research examines the contributing factors and pathways by which CBO climate action leads to intersectional climate justice. Our analysis examines how CBOs 1) recognise and rectify historical and compounding vulnerabilities; 2) plan and act in ways that are people-centric and place-based; and 3) work collectively with organisations and government through alliances, coalitions, and participatory processes. We find that CBOs have the potential to work collectively to ensure processes are just and outcomes are equitable for those most at risk of climate change. [ABSTRACT FROM AUTHOR]

The movie Try Harder! features a group of students at Lowell High School in San Francisco, California, as they navigate their elite public institution and apply to top-tier universities. A critical analysis of this film allows us to understand new trends and emerging discourses in urban cities, showing the way racialized groups are pit against each other to legitimize white supremacy. I employ the lenses of critical studies of race to examine the following questions: (1) how does the model minority myth shape urban contexts? and (2) how are racial, political, and academic discourses constructing notions of merit via the film Try Harder? Few studies examine the model minority myth in relation to urban contexts, despite its rising influence in political debates and education policy. This paper aims to fill this void by addressing the changing landscape of urban education and academic discourse. [ABSTRACT FROM AUTHOR]

Purpose: This paper investigated the impact of COVID-19 stay-at-home regulations on the spatial distribution of commercial burglary in San Francisco. Method: Geocoded commercial burglary data for 2003–2021 from San Francisco were analyzed using the Andresen Spatial Point Pattern Test to examine potential spatial movement in commercial burglary. Results: Concentrations of commercial burglary spatially shifted following the emergence of COVID-19 and the subsequent stay-at-home orders. Original hot spots experienced a relative decrease in commercial burglary, and crime spread out throughout the city. These spatial changes in commercial burglary appear to be related to variations in guardianship and criminal opportunity. Conclusion: These findings suggest that the dramatic shifts in routine activities resulting from the COVID-19 policies could disrupt spatial burglary patterns, especially burglary clustering. Law enforcement should consider the spatial dynamics of commercial burglary, as well as land use heterogeneity, to effectively respond to crime during periods of social distancing and business shutdowns. [ABSTRACT FROM AUTHOR]

In the wake of seismic events, prompt and accurate building damage assessment is crucial to inform post-disaster interventions and recovery efforts. This paper advances a novel multi-period planning strategy for post-earthquake building inspections, conceptualizing the task as a multi-period orienteering problem (MPOP). In this framework, each building selected for inspection hosts a specific reward indicating its informative value for damage assessment. The objective is to design inspection routes that maximize damage information acquisition while adhering to time constraints. After data collection, we utilize a Gaussian process regression (GPR) model to estimate the damage in uninspected buildings. To validate our approach, we conduct an earthquake simulation with realistic building information from San Francisco. The experimental outcomes reveal that our multi-period damage assessment framework maintains robust performance across diverse scenarios and consistently surpasses conventional period-by-period inspection strategies, yielding enhanced damage information acquisition and greater precision in damage estimation. This outcome underscores the effectiveness of our proposed method in strengthening post-earthquake damage assessment and improving recovery planning. • Formulate the inspection routing problem as a multi-period orienteering problem. • A Gaussian process regression model is applied to make damage estimations. • Compare efficiency of multi-period routing strategy and period-by-period strategy. • Validate the framework on instances from San Francisco. [ABSTRACT FROM AUTHOR]

Background/Objectives: The purpose of this preliminary study was to evaluate the diagnostic performance of an AI-driven platform, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA), for assessing endodontic treatment outcomes using panoramic radiographs (PANs). Materials and Methods: The study included 55 PAN images of 55 patients (15 males and 40 females, aged 12–70) who underwent imaging at a private dental center. All images were acquired using a Hyperion X9 PRO digital cephalometer and were evaluated using Diagnocat, a cloud-based AI platform. The AI system assessed the following endodontic treatment features: filling probability, obturation adequacy, density, overfilling, voids in filling, and short filling. Two human observers independently evaluated the images, and their consensus served as the reference standard. The diagnostic accuracy metrics were calculated. Results: The AI system demonstrated high accuracy (90.72%) and a strong F1 score (95.12%) in detecting the probability of endodontic filling. However, the system showed variable performance in other categories, with lower accuracy metrics and unacceptable F1 scores for short filling and voids in filling assessments (8.33% and 14.29%, respectively). The accuracy for detecting adequate obturation and density was 55.81% and 62.79%, respectively. Conclusions: The AI-based system showed very high accuracy in identifying endodontically treated teeth but exhibited variable diagnostic accuracy for other qualitative features of endodontic treatment. [ABSTRACT FROM AUTHOR]

This paper assesses the equity impacts of for-hire autonomous vehicles (AVs) and investigates regulatory policies that promote spatial and social equity in future autonomous mobility ecosystems. To this end, we consider a multimodal transportation network, where a ride-hailing platform operates a fleet of AVs to offer mobility-on-demand services in competition with a public transit agency that offers transit services on a transportation network. A game-theoretic model is developed to characterize the intimate interactions between the ride-hailing platform, the transit agency, and multiclass passengers with distinct income levels. An algorithm is proposed to compute the Nash equilibrium of the game and conduct an ex-post evaluation of the performance of the obtained solution. Based on the proposed framework, we evaluate the spatial and social equity in mobility benefits using the Theil index, and find that although the proliferation of for-hire AVs in the ride-hailing network improves overall mobility, the benefits are not fairly distributed among distinct locations or population groups, implying that the deployment of AVs will enlarge the existing spatial and social inequity gaps in the transportation network if no regulatory intervention is in place. To address this concern, we investigate two regulatory policies that can improve transport equity: (a) a minimum service-level requirement on ride-hailing services, which improves the spatial equity in the transport network; (b) a subsidy on transit services by taxing ride-hailing services, which promotes the use of public transit and improves the spatial and social equity of the transport network. We show that the minimum service-level requirement entails a trade-off: as a higher minimum service level is imposed, the spatial inequity reduces, but the social inequity will be exacerbated. On the other hand, subsidies on transit services improve mobility for low-income households in underserved areas. In certain regimes, the subsidy increases public transit ridership and simultaneously bridges spatial and social inequity gaps. These results are validated through realistic numerical studies for San Francisco. [ABSTRACT FROM AUTHOR]

This paper investigates the joint planning and operation of a ride-hailing network with a mixture of level-4 autonomous vehicles (AVs) and for-hire human drivers. Distinct from most existing works that focus on fully autonomous vehicles, we envision a more foreseeable future where ride-hailing platform utilizes a fleet of level-4 AVs to provide mobility-on-demand services in selected areas of the city, complemented by a group of for-hire drivers who can pick up or drop off passengers outside of the AV's service area. The platform not only makes planning decisions regarding the selection of service area of AVs, but also makes operational decisions regarding the ride fares, driver compensations, fleet size, and vehicle relocation strategies, etc., while interacting with passengers and for-hire human drivers on the transportation network. The overall profit-maximization problem of the platform is formulated as a mixed-integer nonlinear program, which is highly non-convex and difficult to address. To tackle this challenge, we developed a decomposition-based algorithm that can compute a near-optimal solution for the optimization problem, while offering a theoretical upper bound on the gap between the derived solution and the unknown globally optimal solution. The proposed method is validated in a numerical study for San Francisco. We show that activating level-4 AVs in selected areas (instead of over the entire network) can improve the platform profit by up to 20%. We also observe that as the cost of AV infrastructures reduces, the spatial diffusion of AV services will follow an interesting pattern, where the platform will first activate all high-demand zones for AV services simultaneously, and then progressively expand AV services to other areas one zone after another as a result of the complex trade-off between demand levels, activation cost, and network connectivity. We further evaluate the impacts of AV diffusion on human drivers and passengers and demonstrate that (a) human drivers will first be incentivized to relocate to lower-demand areas and then be forced to leave the TNC market if the cost of AV infrastructure continues to decline; (b) passengers in remote areas will surprisingly experience higher trip costs even though the cost of AV decreases, particularly after AV is activated in urban areas but before it is activated in remote areas. This finding reveals the unequal distribution of benefits in the autonomous ride-hailing network, underscoring the need for additional regulatory involvement. • An equilibrium model for ride-hailing services with level-4 autonomous vehicles. • A hierarchical decomposition method for non-convex mixed integer nonlinear program. • Investigated the trade-off between demand, activation cost, and network connectivity. • Examined the impact of level-4 AVs on passengers over the transportation network. [ABSTRACT FROM AUTHOR]

The timely transportation of goods to customers is an essential component of economic activities. However, heavy-duty diesel trucks used for goods delivery significantly contribute to greenhouse gas (GHG) emissions within many large metropolitan areas, including Los Angeles, New York, and San Francisco. To reduce GHG emissions by facilitating freight electrification, this paper proposes Joint Routing and Charging (JRC) scheduling for electric trucks. The objective of the associated optimization problem is to minimize the cost of transportation, charging, and tardiness. A large number of possible combinations of road segments as well as a large number of combinations of charging decisions and charging durations leads to a combinatorial explosion in the possible decisions electric trucks can make. The resulting mixed-integer linear programming (MILP) problem is thus extremely challenging because of the combinatorial complexity even in the deterministic case. Therefore, a Surrogate Level-Based Lagrangian Relaxation (SLBLR) method is employed to decompose the overall problem into significantly less complex truck subproblems. In the coordination aspect, each truck subproblem is solved independently of other subproblems based on the values of Lagrangian multipliers. In addition to serving as a means of guiding and coordinating trucks, multipliers can also serve as a basis for transparent and explanatory decision-making by trucks. Testing results demonstrate that even small instances cannot be solved using the off-the-shelf solver CPLEX after several days of solving. The SLBLR method, on the other hand, can obtain near-optimal solutions within a few minutes for small cases, and within 30 min for large ones. Furthermore, it has been demonstrated that as battery capacity increases, the total cost decreases significantly; moreover, as the charging power increases, the number of trucks required decreases as well. • Present a formulation of a joint heavy-duty vehicle fleet routing & charging problem. • Use surrogate level-based Lagrangian relaxation approach to solve the problem. • The proposed method obtains near-optimal solutions for realistic testing cases. [ABSTRACT FROM AUTHOR]

Background: Illicitly manufactured fentanyls and stimulants are implicated in the escalating US mortality from drug overdose. San Francisco, California (SF) has seen declining fentanyl injection while smoking has increased. Beliefs and behaviors surrounding this development are not well understood. Methods: The study used rapid ethnography to explore fentanyl and methamphetamine use in SF. The team conducted semi-structured interviews (n = 34) with participants recruited from syringe service programs. Video-recorded smoking sequences (n = 12), photography and daily field notes supplemented interview data. Results: Difficulty injecting and fear of overdose motivated transitions from injecting to smoking. Fentanyl was extremely cheap—$10/gram—with variability in quality. Foil was the most commonly used smoking material but glass bubbles, bongs and dabbing devices were also popular. No reliable visible methods for determining fentanyl quality existed, however, participants could gauge potency upon inhalation, and developed techniques to regulate dosage. Several participants reported at least hourly use, some reporting one or more grams of daily fentanyl consumption. Smoking was also very social, with people sharing equipment, drugs and information. Participants raised concerns about hygiene and overdose risk to others arising from shared equipment. Reportedly potent fentanyl 'residue' accumulated on smoking materials and was commonly shared/traded/stolen or consumed accidentally with diverse preferences for its use. Conclusion: Our data highlight fentanyl residue as a new overdose risk with potential mismatch between the potency of the residual drug and the recipient's tolerance. Further, large doses of fentanyl are being consumed (estimated at approximately 50 mg of pure fentanyl/day). Smoking fentanyl has potential health benefits over injecting and may be protective against overdose, but substantial uncertainty exists. However, SF overdose mortality hit a record high in 2023. Recommendations to reduce fentanyl smoking overdose risks through pacing, greater awareness of dosages consumed and checking tolerance of residue recipients are potentially viable interventions deserving further exploration. [ABSTRACT FROM AUTHOR]

Background: Mortality related to opioid overdose in the U.S. has risen sharply in the past decade. In California, opioid overdose death rates more than tripled from 2018 to 2021, and deaths from synthetic opioids such as fentanyl increased more than seven times in those three years alone. Heightened attention to this crisis has attracted funding and programming opportunities for prevention and harm reduction interventions. Drug checking services offer people who use drugs the opportunity to test the chemical content of their own supply, but are not widely used in North America. We report on qualitative data from providers and clients of harm reduction and drug checking services, to explore how these services are used, experienced, and considered. Methods: We conducted in-depth semi-structured key informant interviews across two samples of drug checking stakeholders: "clients" (individuals who use drugs and receive harm reduction services) and "providers" (subject matter experts and those providing clinical and harm reduction services to people who use drugs). Provider interviews were conducted via Zoom from June-November, 2022. Client interviews were conducted in person in San Francisco over a one-week period in November 2022. Data were analyzed following the tenets of thematic analysis. Results: We found that the value of drug checking includes but extends well beyond overdose prevention. Participants discussed ways that drug checking can fill a regulatory vacuum, serve as a tool of informal market regulation at the community level, and empower public health surveillance systems and clinical response. We present our findings within three key themes: (1) the role of drug checking in overdose prevention; (2) benefits to the overall agency, health, and wellbeing of people who use drugs; and (3) impacts of drug checking services at the community and systems levels. Conclusion: This study contributes to growing evidence of the effectiveness of drug checking services in mitigating risks associated with substance use, including overdose, through enabling people who use and sell drugs to test their own supply. It further contributes to discussions around the utility of drug checking and harm reduction, in order to inform legislation and funding allocation. [ABSTRACT FROM AUTHOR]

Environmental monitoring systems have gained prominence across diverse applications, necessitating the integration of cutting-edge technologies. This article comprehensively explores such a system, emphasizing the integration of a Raspberry Pi cluster with the BME680 environmental sensor within a Kubernetes framework. This study encompasses the technical aspects of hardware configuration and places a significant focus on security benchmarks and robustness validation. The environmental monitoring infrastructure discussed in this article delves into the intricacies of the Raspberry Pi cluster's hardware setup, including considerations for scalability and redundancy. This research addresses critical security gaps in contemporary environmental monitoring systems, particularly vulnerabilities linked to IoT deployments. Amidst increasing threats, this study introduces a robust framework that integrates advanced security tools—HashiCorp (San Francisco, CA, USA) Vault v1.16 for dynamic secret management and OpenID Connect for authentication processes—to enhance applications and system integrity and resilience within the Kubernetes environment. The approach involves a multi-layered security architecture that fortifies the storage and management of credentials and ensures authenticated and authorized interactions within IoT networks. Furthermore, our research incorporates a series of security benchmark tests, including vulnerability scanning, penetration testing, and access control assessments. Additionally, this article addresses crucial aspects related to data management and analysis, detailing the methodologies employed for storing, processing, and deriving insights from the collected environmental data. It further explores the integration of the monitoring system with existing infrastructure and systems, facilitating seamless data sharing and interoperability and offering valuable insights into the system's ability to withstand potential threats and vulnerabilities. The integration of Raspberry Pi clusters with BME680 environmental sensors within a Kubernetes-managed framework significantly enhances the scalability and security of IoT systems. This study quantifies the improvements, demonstrating at least a 30% enhancement in system responsiveness and a minimum 40% reduction in vulnerability exposures, as verified by extensive security benchmarks, including penetration testing. These advancements facilitate robust, scalable IoT deployments, with potential applications extending beyond environmental monitoring to include industrial and urban settings. The incorporation of dynamic secret management with HashiCorp Vault and secure authentication with OpenID Connect provides a blueprint for developing resilient IoT architectures capable of supporting high-security and high-availability applications. In conclusion, this article contributes to the expanding body of knowledge in IoT and environmental monitoring and establishes a strong foundation for future work. These outcomes suggest promising directions for further research in secure IoT applications and present practical implications for the deployment of secure and scalable IoT solutions in critical infrastructures. [ABSTRACT FROM AUTHOR]

Interdisciplinary teaching approaches have gained significant importance in today's educational landscape. Among these approaches, decision-making competence plays a pivotal role by nurturing critical thinking and problem-solving skills. Focusing on the decision-making process regarding the sensibility of using sugar-substitutes, this study addresses three key questions. (1) Do chemistry and sports and health students differ in evaluation-competence? (2) What criteria do they use in evaluating the decision-making problem? (3) How do they use ChatGPT (OpenAI, San Francisco, CA, USA), an AI tool, in decision-making? Through surveys, the study revealed two evaluation-competence factors, identified decision-making criteria, and analyzed querying behavior using ChatGPT. The results revealed nuanced differences between the two student groups in the factors of evaluation-competence. While both groups showed improvements in recognizing moral dimensions and considering alternatives, chemistry students displayed enhanced abilities in establishing evaluation criteria and empathetic thinking. Furthermore, differences emerged in the criteria selected for decision-making and querying behavior, indicating varying engagement with ChatGPT. This study offers insights into enhancing interdisciplinary education and underscores the need for tailored interventions to address diverse student needs. [ABSTRACT FROM AUTHOR]

Background: Buprenorphine is an effective treatment for opioid use disorder (OUD); however, buprenorphine initiation can be complicated by withdrawal symptoms including precipitated withdrawal. There has been increasing interest in using low dose initiation (LDI) strategies to reduce this withdrawal risk. As there are limited data on withdrawal symptoms during LDI, we characterize withdrawal symptoms in people with daily fentanyl use who underwent initiation using these strategies as outpatients. Methods: We conducted a retrospective chart review of patients with OUD using daily fentanyl who were prescribed 7-day or 4-day LDI at 2 substance use disorder treatment clinics in San Francisco. Two addiction medicine experts assessed extracted chart documentation for withdrawal severity and precipitated withdrawal, defined as acute worsening of withdrawal symptoms immediately after taking buprenorphine. A third expert adjudicated disagreements. Data were analyzed using descriptive statistics. Results: There were 175 initiations in 126 patients. The mean age was 37 (SD 10 years). 71% were men, 26% women, and 2% non-binary. 21% identified as Black, 16% Latine, and 52% white. 60% were unstably housed and 75% had Medicaid insurance. Substance co-use included 74% who used amphetamines, 29% cocaine, 22% benzodiazepines, and 19% alcohol. Follow up was available for 118 (67%) initiations. There was deviation from protocol instructions in 22% of these initiations with follow up. 31% had any withdrawal, including 21% with mild symptoms, 8% moderate and 2% severe. Precipitated withdrawal occurred in 10 cases, or 8% of initiations with follow up. Of these, 7 had deviation from protocol instructions; thus, there were 3 cases with follow up (3%) in which precipitated withdrawal occurred without protocol deviation. Conclusions: Withdrawal was relatively common in our cohort but was mostly mild, and precipitated withdrawal was rare. Deviation from instructions, structural barriers, and varying fentanyl use characteristics may contribute to withdrawal. Clinicians should counsel patients who use fentanyl that mild withdrawal symptoms are likely during LDI, and there is still a low risk for precipitated withdrawal. Future studies should compare withdrawal across initiation types, seek ways to support patients in initiating buprenorphine, and qualitatively elicit patients' withdrawal experiences. [ABSTRACT FROM AUTHOR]

The article highlights Helen H. Kang's reflections on her career as an environmental lawyer, emphasizing the importance of bearing witness to environmental injustices, inspired by a story of resilience amidst conflict, and delves into the systemic issues of pollution burdens in marginalized communities, particularly in San Francisco's Bayview. It also discusses the historical and demographic factors contributing to these injustices, including the deliberate creation of segregated neighborhoods.

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In the early days of the Web, the International Museum of Women emerged with the mission to highlight the achievements of women throughout history. Headquartered in San Francisco, the museum experimented with the rise of new digital technologies and evolved into a virtual museum with global outreach. Through innovative online approaches, they actively engaged communities around the world and gathered perspectives from a wide spectrum of visitors, whose voices were included in the exhibition narratives. In order to safeguard the museum's legacy and its groundbreaking digital exhibitions, The Feminist Institute in New York launched a digital preservation initiative, working closely with the Global Fund for Women, a non-profit foundation that merged with the International Museum of Women in 2014. On July 31, 2023, we conducted an interview with Marie Williams Chant, Director of Archives and Special Projects, to delve into the museum's historical significance. We also discussed their archival strategies and procedures, along with its potential impact on the museum's legacies. [ABSTRACT FROM AUTHOR]

For the financial health of lenders and institutions, one important risk assessment called credit risk is about correctly deciding whether or not a borrower will fail to repay a loan. It not only helps in the approval or denial of loan applications but also aids in managing the non-performing loan (NPL) trend. In this study, a dataset provided by the LendingClub company based in San Francisco, CA, USA, from 2007 to 2020 consisting of 2,925,492 records and 141 attributes was experimented with. The loan status was categorized as "Good" or "Risk". To yield highly effective results of credit risk prediction, experiments on credit risk prediction were performed using three widely adopted supervised machine learning techniques: logistic regression, random forest, and gradient boosting. In addition, to solve the imbalanced data problem, three sampling algorithms, including under-sampling, over-sampling, and combined sampling, were employed. The results show that the gradient boosting technique achieves nearly perfect A c c u r a c y , P r e c i s i o n , R e c a l l , and F 1 s c o r e values, which are better than 99.92%, but its M C C values are greater than 99.77%. Three imbalanced data handling approaches can enhance the model performance of models trained by three algorithms. Moreover, the experiment of reducing the number of features based on mutual information calculation revealed slightly decreasing performance for 50 data features with A c c u r a c y values greater than 99.86%. For 25 data features, which is the smallest size, the random forest supervised model yielded 99.15% A c c u r a c y . Both sampling strategies and feature selection help to improve the supervised model for accurately predicting credit risk, which may be beneficial in the lending business. [ABSTRACT FROM AUTHOR]

Persistently, urban regions grapple with the ongoing challenge of vehicular traffic, a predicament fueled by the incessant expansion of the population and the rise in the number of vehicles on the roads. The recurring challenge of vehicular congestion casts a negative influence on urban mobility, thereby diminishing the overall quality of life of residents. It is hypothesized that a dynamic clustering method of vehicle trajectory data can provide an accurate and up-to-date representation of real-time traffic behavior. To evaluate this hypothesis, data were collected from three different cities: San Francisco, Rome, and Guayaquil. A dynamic clustering algorithm was applied to identify traffic congestion patterns, and an indicator was applied to identify and evaluate the congestion conditions of the areas. The findings indicate a heightened level of precision and recall in congestion classification when contrasted with an approach relying on static cells. [ABSTRACT FROM AUTHOR]

The San Francisco Estuary (estuary) and the Sacramento-San Joaquin Delta (the Delta) in California face significant challenges in managing water resources during extended droughts. Zooplankton are a vital trophic link between phytoplankton producers and higherlevel consumers such as predatory zooplankton and fish. However, there is still much to be learned about what drives zooplankton abundance and how they respond to drastic changes in environmental conditions, such as droughts. We found that during drought years zooplankton abundance and distribution changes varied for examined taxa. Significant declines in the abundance of Daphnia spp. and the copepod Pseudodiaptomus forbesi occurred in the Suisun Marsh and Suisun Bay regions. In contrast, abundance of the non-native copepod Limnoithona tetraspina increased in Suisun Marsh and the South-Central Delta during those same drought conditions. Salinity is a strong determinant of the presence and abundance of the studied taxa, and we showed that changes in the distribution of salinity as a result of low outflow conditions were an important factor in the regional abundance of zooplankton. Because of the expected increase in the frequency and severity of regional droughts, understanding how these conditions affect zooplankton in the estuary will benefit scientists and resource managers who aim to improve conditions for native fishes. [ABSTRACT FROM AUTHOR]

Aquatic invasive species have drastically changed how the San Francisco Estuary functions. During the past 2 decades, the effects of invasive species in the estuary may have increased in response to frequent and severe drought conditions. The invasive overbite clam (Potamocorbula amurensis), and the Asian Clam (Corbicula fluminea) have well documented consequences on the estuarine food web, but their responses to drought are not well understood. Another invasive species, the jellyfish Maeotias marginata, can further affect the food web, but these effects have not been studied. We investigated the population responses of these invasive species to dry years and their potential effects on the pelagic food web using data from the Interagency Ecological Program's monitoring surveys. We found M. marginata rapidly moves upstream with changing salinities during dry years, though it sees its highest abundance during high-outflow years in Suisun Bay and Suisun Marsh. Grazing rates of M. marginata in the estuary have not been quantified but are potentially high during localized blooms. The two invasive clams overlap in distribution, but have opposite population responses to drought conditions, with increases in P. amurensis densities and decreases in C. fluminea densities in dry years. With increasing P. amurensis densities, the clams' combined annual filtration rates increase during drier years in the confluence and Suisun Marsh. Like M. marginata, P. amurensis also shifts upstream during droughts, but because adults cannot move immediately with a change in salinity, the population center of distribution shifts upstream the year after a dry year as a result of juvenile recruitment. If multiple dry years occur in a row, and both P. amurensis and M. marginata move upstream together, their effects on the food web could be compounded, and phytoplankton and zooplankton biomass could steeply decline in the confluence, affecting higher trophic levels in the estuary. [ABSTRACT FROM AUTHOR]

Droughts have major effects on estuaries because freshwater entry is one of the defining features of an estuary, and freshwater flow is an important variable that determines the interannual change in the environment. In the upper San Francisco Estuary (the Sacramento-San Joaquin Delta, Suisun Bay, and Suisun Marsh), the Mediterranean climate includes frequent multi-year droughts. To assess ecosystem responses to droughts in the upper estuary, the Interagency Ecological Program Drought Synthesis Team assembled a set of flow, water quality, chlorophyll, zooplankton, and fish data from 1975 to 2021 to test for differences between multi-year droughts and multi-year wet periods and tested for linear relationships between each variable and the Sacramento Valley Hydrologic Index (see definitions and relationships as outlined in Appendix A). Our models showed droughts decreased Delta outflow, project exports, zooplankton in Suisun Bay, and some fish species. We also found that droughts increased water clarity, salinity, nutrients, chlorophyll in the South Delta, zooplankton in the South Delta, and water residence time. Although our analyses only tested correlations, we hypothesized that most of the food-web responses could be traced to increased residence time, decreased transport rates, or both. However, some responses may have been caused by secondary effects, including shifts in salinity gradients, regional changes in water quality, or differences in top-down effects of increased predation and grazing rates. With increased frequency of droughts in the future, this increasingly low-outflow, warming, clearing estuary--which is invaded by non-native species and has low pelagic fish production--is rapidly becoming the new "normal.". [ABSTRACT FROM AUTHOR]

Past work has shown that interannual California coastal sea level variability is mostly of equatorial origin, and decades of satellite sea surface height (SSH) and in situ dynamic height observations indicate that this interannual signal propagates westward from the California coast as nondispersive Rossby waves (RWs). These observations agree with standard linear vertical mode theory except that even when mean flow and bottom topography are considered, the fastest baroclinic vertical mode RW in each case is always much slower (1.6–2.3 cm s−1) than the observed 4.2 cm s−1. This order-1 disagreement is only resolved if the standard bottom boundary condition that the vertical velocity w′ = 0 is replaced by perturbation pressure p′ = 0. Zero p′ is an appropriate bottom boundary condition because south of San Francisco the northeastern Pacific Ocean boundary acts approximately like an impermeable vertical wall to the interannual equatorial wave signal, and therefore equatorial quasigeostrophic p′ is horizontally constant along the boundary. Thus, if equatorial p′ = 0 at the bottom, then this condition also applies off California. The large-scale equatorial ocean boundary signal is due to wind-forced eastward group velocity equatorial Kelvin waves, which at interannual and lower frequencies propagate at such a shallow angle to the horizontal that none of the baroclinic equatorial Kelvin wave signal reaches the ocean floor before striking the eastern Pacific boundary. Off California this signal can thus be approximated by a first baroclinic mode with p′ = 0 at the bottom, and hence the long RW speed there agrees with that observed (both approximately 4.2 cm s−1). Significance Statement: The California Current System is one of the most biologically rich and best-documented coastal regions in the world. In this region coastal sea level propagates westward from the coast at about 110 km month−1, slow enough to enable us to make large-scale ocean climate forecasts of the California Current ecosystem using coastal sea level. Although the westward speed seems slow, theoretically it is about double what we would expect. Offered here is an explanation of why this speed is "too fast" by linking the California wave signal to the equator, El Niño, and the shallow equatorial ocean response to the wind. [ABSTRACT FROM AUTHOR]

• We apply Latent Dirichlet Allocation algorithm to discover activity patterns from floating vehicle trajectory data. • We incorporate activity patterns into traffic safety analysis to supplement traditional traffic exposure measures. • We use Graph Convolutional Network to explore multiple factors influencing spatial patterns of traffic crashes. • We estimate non-linear relationships between variables and traffic crashes using interpretive methodologies. In recent years, increased attention has been given to understanding the spatial pattern of crashes in urban areas. Accurately capturing the spatial relationship between crash counts and variables requires extracting hidden information from multiple data sources. In this study, we propose a machine learning model to explore the spatial impact of activity patterns on spatially aggregated crash counts. Our paper introduces a two-step framework: (a) the Latent Dirichlet Allocation (LDA) model, an unsupervised method for mining hidden activity patterns from floating vehicle trajectory data, and (b) the Graph Convolutional Network (GCN) model, which builds the spatial relationship between multi-source data. The data and hidden activity patterns were aggregated into 175 Traffic Analysis Zones (TAZs) in San Francisco using spatial partitioning. The GCN model provided higher prediction accuracy than commonly used machine learning algorithms that did not consider combined spatial relationships and those that only considered traditional vehicle counts data. Furthermore, we used attribution algorithms to obtain the respective weight scores of each factor. Our results reveal that daily vehicle kilometers traveled, road density, population density, commercial activity during weekends, and residential activity during morning peak hours on weekdays are factors associated with crashes. [ABSTRACT FROM AUTHOR]

Objective: The unprecedented events of 2020 required a pivot in scientific training to better prepare the biomedical research workforce to address global pandemics, structural racism, and social inequities that devastate human health individually and erode it collectively. Furthermore, this pivot had to be accomplished in the virtual environment given the nation-wide lockdown. Methods: These needs and context led to leveraging of the San Francisco Building Infrastructure Leading to Diversity (SF BUILD) theories of change to innovate a Virtual BUILD Research Collaboratory (VBRC). The purpose of VBRC was to train Black, Indigenous, and people of color (BIPOC) students to apply their unique perspectives to biomedical research. These training activities were evaluated using a pre-post survey design that included both validated and new psychosocial scales. A new scale was piloted to measure culturally relevant pedagogy. Results: VBRC scholars increased science identity on two items: thinking of myself as a scientist (+1point, p = 0.006) and belonging to a community of scientists (+1point, p = 0.069). Overall, scholars perceived stress also decreased over VBRC (-2.35 points, p = 0.02). Post VBRC, scholars had high agency scores (μ = 11.02, Md = 12, range = 6–12, σ = 1.62) and cultural humility scores (μ = 22.11, Md = 23, range = 12–24, σ = 2.71). No notable race/ethnic differences were found in any measures. Conclusions: Taken together, our innovative approach to data science training for BIPOC in unprecedented times shows promise for better preparing the workforce critically needed to address the fundamental gaps in knowledge at the intersection of public health, structural racism, and biomedical sciences. [ABSTRACT FROM AUTHOR]

Low-cost particulate matter (PM) sensors continue to grow in popularity, but issues such as aerosol-size-dependent sensitivity drive the need for effective calibration schemes. Here we devise a time-evolving calibration method for the Plantower PMS5003 PM 2.5 mass concentration measurements. We use 2 years of measurements from the Berkeley Environmental Air-quality and CO 2 Network sensors deployed in San Francisco and Los Angeles in our analysis. The calibration uses a hygroscopic growth correction factor derived from κ -Köhler theory, where the calibration parameters are determined empirically using US Environmental Protection Agency Air Quality System (EPA AQS) reference data at co-location sites during the period from 2021–2022. The parameters are found to vary cyclically through the seasons, and the seasonal cycles match changes in sulfate and elemental carbon PM composition fractions throughout the year. In both regions, the seasonal RH dependence calibration performs better than the uncalibrated data and data calibrated with the EPA's national Plantower calibration algorithm. In the San Francisco Bay Area, the seasonal RH dependence calibration reduces the root mean square error (RMSE) by ∼40 % from the uncalibrated data and maintains a mean bias much smaller than the EPA national calibration scheme (-0.90 vs -2.73 µ g m -3). We also find that calibration parameters forecasted beyond those fit with the EPA reference data continue to outperform the uncalibrated data and EPA calibration data, enabling real-time application of the calibration scheme even in the absence of reference data. While the correction greatly improves the data accuracy, non-Gaussian distribution of the residuals suggests that other processes besides hygroscopic growth can be parameterized for future improvement of this calibration. [ABSTRACT FROM AUTHOR]

This article explores the relationship between film and cities, focusing on how cities have influenced cinema and how cinema has shaped our understanding of urban spaces. It reviews four books that examine different aspects of this relationship, including censorship debates in early twentieth-century urban America, Black filmmaking in Los Angeles, location shooting in San Francisco, and the global circulation of urban imagery in film. The books highlight the intersections of regulation, race, and capital in the construction of cinematic representations of cities. The article provides a detailed analysis of each book's contributions to the field of film and urban history. It also discusses the representation of cities in Hollywood films, contrasting the depictions of San Francisco and Los Angeles in films from the late 1960s and early 1970s. The emergence of the L.A. Rebellion filmmakers, a group of Black independent filmmakers who focused on South Central Los Angeles, is also explored. The article mentions the book "Noir Urbanisms," which offers a global perspective on cinematic urban history by examining the portrayal of cities in film noir. Overall, the article emphasizes the importance of considering the political and economic factors that shape the representation of cities in film. [Extracted from the article]

Introduction: Inflammatory bowel disease (IBD) is a relative contraindication to external beam radiation therapy (EBRT) for prostate cancer patients due to fear of increased risk of gastrointestinal (GI) toxicity. High-dose-rate (HDR) brachytherapy, capable of minimizing radiation dose to surrounding tissues, is a feasible alternative. Given limited data, this study examined the safety profile of HDR brachytherapy in this setting. Material and methods: We conducted a retrospective review of patients with localized prostate cancer and IBD treated with HDR brachytherapy at the University of California San Francisco (UCSF), between 2010 and 2022. Eligibility criteria included biopsy-proven prostate cancer, no distant metastases, absence of prior pelvic radiotherapy, IBD diagnosis, and at least one follow-up visit post-treatment. Results: Eleven patients were included, with a median follow-up of 28.7 months. The median dose administered was 2700 cGy (range, 1500-3150 cGy) over 2 fractions (range, 1-3 fractions). Two patients also received EBRT. Rectal spacers (SpaceOAR) were applied in seven patients. All patients experienced acute genitourinary (GU) toxicity, ten of which were grade 1 and one was grade 2. Eight patients experienced late grade 1 GU toxicity, and three patients had late grade 2 GU toxicity. GI toxicities were similarly low-grade, with six grade 1 acute toxicity, no grade 2 or higher acute toxicity, six grade 1 late toxicity, and one late grade 2 GI toxicity. No grade 3 or higher acute or late GI or GU toxicities were reported. Conclusions: HDR brachytherapy appears to be a safe and tolerable treatment modality for patients with prostate cancer and IBD, with minimal acute and late GI and GU toxicity. These findings warrant multi-institutional validation due to small sample size. [ABSTRACT FROM AUTHOR]