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Fluoride removal from aqueous solution by adsorption using fly ash cenospheres (FAC) modified with paper mill lime mud (LM) as composite adsorbent had been investigated. The characterization of FAC and composite adsorbent were analyzed by Scanning electron spectroscope (SEM), Energy dispersive spectrometer (EDS), Brunauer emmett teller (BET) and Fourier transform infrared (FTIR), which demonstrated that the porous structure of composite adsorbent was obtained after surface modification. Adsorption of fluoride on modified fly ash cenospheres was fitted with pseudo-second-order model and Langmuir model. Response surface methodology (RSM) was employed to investigate the effects of F− concentration, pH, adsorbent dosage and temperature on the removal efficiency. Analysis of variance (ANOVA) was used to test the adequacy of the mathematical models. The Nonelectrostatic model of modified fly ash cenospheres adsorbing fluoride was built through the Generalized composite method, indicating that two inner-spherical complexes, ≡SF and ≡SOHF−, were formed in the adsorption process by means of the ligand exchange and surface complexation. Optimization of the adsorption conditions enabled the realization of the practical needs for fluoride contaminated water. Image 1 • A low-cost fly ash cenospheres modified with paper mill lime mud adsorbent was prepared. • Response surface methodology was employed to research the effects on fluoride removal. • Nonelectrostatic model-Generalized composite approach had successfully described and predicted the adsorption process. • ≡SF and ≡SOHF− formed in the removal of fluoride via the ligand exchange and surface complexation. [ABSTRACT FROM AUTHOR]

The archetype of the self underlies the ego-complex at its root and source in the unconscious. None of these assigns to the self the role that I feel to be so essential in the syndrome. SP 50-52 sp In regard to the method of study of these processes of the archaic, unconscious psyche in psychosis, I find myself baffled as to how to set up anything like a scientific approach to the material. In this hypothesis the model is easy to visualize but difficult to understand in terms of experience; the self is seen in this case as the center of the total psychic organism, and the ego is subsumed within this totality as the center of the field of consciousness only, that is, of contents accessible to consciousness; between the two are the autonomous complexes, which are groupings of contents not accessible to ego-consciousness (Figure 1). Reconstitutive process in the psychopathology of the self1: The following paper by J.W. Perry is published with permission from the Annals of the New York Academy of the Sciences where it was first published in January 1962. The imagery I refer to is a whole class of symbolic representations of centrality and organization, of order and highest authority; I hope to demonstrate that in these we see representations of the self and processes that transform or reorganize the self in the unconscious psyche. [Extracted from the article]

The 2020 Technical Symposium in San Francisco will offer something for everyone—and many more dynamic papers and posters than we can cover in one sitting. We've compiled a few compelling research trends to keep in mind as you earmark your sessions; visit displayweek.org for a full schedule and more. [ABSTRACT FROM AUTHOR]

This Special Issue of the IEEE Journal of Solid-State Circuits is dedicated to a collection of the best papers selected from the 2020 IEEE International Solid-State Circuits Conference (ISSCC) that took place on February 16–20, 2020, in San Francisco, CA, USA. This issue covers papers from the Analog, Power Management, Data Converters, RF, and Wireless committees. [ABSTRACT FROM AUTHOR]

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]

This paper defines a smart home use case to automatically adjust home temperature and/or hot water. The main objective is to reduce the energy consumption of cooling, heating and hot water systems in smart homes. To this end, the residents set a temperature (i.e., X degree Celsius) for home and/or hot water. When the residents leave homes (e.g., for work), they turn off the cooling or heating devices. A few minutes before arriving at their residences, the cooling or heating devices start working automatically to adjust the home or water temperature according to the residents' preference (i.e., X degree Celsius). This can help reduce the energy consumption of these devices. To estimate the arrival time of the residents (i.e., drivers), this paper uses a machine learning-based street traffic prediction system. Unlike many related works that use machine learning for tracking and predicting residents' behaviors inside their homes, this paper focuses on predicting resident behavior outside their home (i.e., arrival time as a context) to reduce the energy consumption of smart homes. One main objective of this paper is to find the most appropriate machine learning and neural network-based (MLNN) algorithm that can be integrated into the street traffic prediction system. To evaluate the performance of several MLNN algorithms, we utilize an Uber's dataset for the city of San Francisco and complete the missing values by applying an imputation algorithm. The prediction system can also be used as a route recommender to offer the quickest route for drivers. [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]

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]

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]

Law enforcement agencies are allegedly meant "to protect and serve" and yet there are numerous examples of state violence and brutality against citizens, especially because of racial profiling and racist stereotypes. One often ignored blight on American history is Executive Order 9066. Law enforcement agencies played an integral part in the round up of Japanese American families and the implementation of President Franklin Delano Roosevelt's infamous wartime executive order. This paper argues that the actions of law enforcement in the lead-up to the forced removal of Japanese Americans, in the operation of the prison camps, and in the aftermath of Japanese Internment demonstrate how deeply rooted nativism coupled with wartime hysteria resulted in racialized violence against Japanese immigrants and Japanese American citizens. Law enforcement did not protect and serve Japanese Americans and this paper examines how this state violence is part of a shameful legacy that must be part of discussions about policing and race in America. Moreover, this paper shines a light on the policing of everyday life for Japanese Americans during this historical period. This project arises out of my family history. My great-great-grandfather, a leader in the San Francisco Japanese community, fought for his civil rights all the way to the U.S. Supreme Court in a case against the San Francisco Sheriff in 1902. My grandmother was born after World War II, but her two older siblings, her parents, grandfather, and extended family were imprisoned at Topaz War Relocation Center in rural Utah. In this telling of my family's story, I offer a heretofore underexamined aspect of the criminalization of Japanese Americans' everyday life and the ways that government action and law enforcement controlled this community. I also subvert the dominant narrative of silence and shame about pre-war Japanese exclusion and Executive Order 9066 by turning this shame squarely onto the state to encourage accountability and aid future discussions of policing and race in America. [ABSTRACT FROM AUTHOR]

The development and renewal of gas sensor technology have enabled more and more low-cost gas sensors to form a carbon monitoring network to meet the requirements of the city. In the context of China's commitment to achieving the "double carbon" target by 2060, this paper reviews the principles of four standard gas sensors and the application of several low-cost sensors in urban carbon monitoring networks, with the aim of providing a practical reference for the future deployment of carbon monitoring networks in Chinese cities. Moreover, the types, prices, and deployment of the sensors used in each project are summarized. Based on this review, non-dispersive infrared sensors have the best performance among the sensors and are commonly used in many cities. Lots of urban climate networks in cities were summarized by many reviews in the literature, but only a few sensors were studied, and they did not consider carbon dioxide (CO2) sensors. This review focuses on the dense CO2 urban monitoring network, and some case studies are also discussed, such as Seoul and San Francisco. To address the issue of how to better ensure the balance between cost and accuracy in the deployment of sensor networks, this paper proposes a method of simultaneously deploying medium-precision and high-precision fixed sensors and mobile sensors to form an urban carbon monitoring network. Finally, the prospects and recommendations, such as different ways to mitigate CO2 and develop an entire carbon monitoring system for future urban carbon monitoring in China, are also presented. [ABSTRACT FROM AUTHOR]

The finite element method is commonly used to conduct the excavation project and to ensure the safety of the adjacent building. Hence, the analysis method should be rigorous, and the soil parameters used in the analysis should be carefully determined. In this paper, a small strain stiffness model was performed to assess the influence of small-strain soil behavior on ground movement induced by deep excavation in soft clays. The results revealed that when the stiff soil is far away from the excavation base, the hardening small strain model should be used to obtain a better prediction. On the other hand, the ground movement is restrained by the stiff soil when the location of stiff soil is near the excavation base. Under such conditions, the results from hardening soil and hardening small strain models are similar. In addition, this paper also provides a method to determine the small strain parameters (shear modulus and shear strain). Furthermore, the analysis method was verified through several case histories in Taipei and San Francisco. The results from case studies concluded that the small strain parameter should be determined by considering soil conditions and construction methods. [ABSTRACT FROM AUTHOR]

The field of Walkable Neighborhood Systems is about neighborhoods as systems of land use, mobility, and transportation pricing, and how they can be understood as dense areas delineated by attractive walking distances. To date, four papers have been published in this field: Neighborhood density and travel mode (Lewis in Int J Sustain Dev World Ecol, 2018. https://doi.org/10.1080/13504509.2017.1321052), Walkable Neighborhood Systems (Lewis and Adhikari), San Francisco's neighborhoods and auto dependency (Lewis and Grande in Cities, 2019. https://doi.org/10.1016/j.cities.2018.12.017), and the Mismeasurement of Mobility for Walkable Neighborhood Systems (Lewis et al., Mismeasurement of Mobility for Walkable Neighborhood Systems, Mineta Transportation Institute, 2020). This paper summarizes the concepts and definitions of the field and replicates for Boston the methodology used in the San Francisco case study. We found 54 Boston neighborhoods using maps in a GIS and guidelines to determine neighborhood boundaries based on walking distances and land use. We establish much higher accuracy than using census blocks. We analyze neighborhood density as our independent variable and three dependent variables: sustainable modes (mode split), food sources, and walk score. The data is presented in tables, correlations, and choropleth-based graphics. Density correlated strongly with the dependent variables. The paper concludes with ideas about how to improve the performance of Walkable Neighborhood Systems for affordability, sustainability, mobility, health and safety, design, and community. [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]

The deviation of specific electrical conductance (EC) from conservative mixing behavior is well-established in the scientific literature. This principle is based on the observation that, as salt concentration in a water sample increases, the mobility of individual ions in the sample decreases, and thus their ability to conduct electricity decreases. Despite this fact, some commonly used models for salinity transport in the San Francisco Estuary (estuary) utilize EC as a primary simulation constituent, treating it as a conservative quantity. Such a modeling approach has likely been followed to exploit the wide availability of EC data for model calibration and validation, and to obviate the need to translate between EC and salinity in a domain characterized by multiple source waters with varying ionic make-ups. Arguably, this approach provides a reasonable tradeoff between data translation error and model simulation error. In this paper, we critically evaluate this approach, employing an extensive salinity data set that includes measurements of EC and major ion concentrations in the estuary. We demonstrate and quantify EC deviation from steady-state, conservative mixing behavior; review the conservative mixing behavior of three bulk salinity measures (practical salinity, ionic strength, and limiting equivalent conductance); and evaluate their source-dependent correlations with EC in the estuary. We find limiting equivalent conductance--a value that assumes uninhibited mobility among individual ions in a water sample--to be an attractive alternative for salinity transport in the estuary. In addition to being a conservative quantity, it is consistently correlated with EC in the estuary's dominant source waters, and thus addresses concerns related to data-translation error. We conclude this paper discussing pros and cons of adopting various salinity-transport model constituents. [ABSTRACT FROM AUTHOR]

Water quality extremes, which water quality models often struggle to predict, are a grave concern to water supply facilities. Most existing water quality models use mean error functions to maximize the predictability of water quality mean value. This paper describes a composite quantile regression neural network (CQRNN) model, which simultaneously estimates non-crossing regression quantiles by minimizing the composite quantile regression error function. This method can improve the prediction of extremes. This paper evaluates the performance of CQRNN for predicting extreme values of turbidity and total organic carbon (TOC) and compares with quantile regression (QR), linear regression (LR), and k-nearest neighbors (KNN) in an application to the Hetch Hetchy Regional Water System, which is the primary water supply for San Francisco, CA. CQRNN is superior to QR, LR, and KNN for predicting the mean trend and extremes of turbidity and TOC, especially for the non-Gaussian turbidity data. The performance of CQRNN is the most stable relative to other methods over different training sample sizes. [ABSTRACT FROM AUTHOR]

Removing redundant features and improving classifier performance necessitates the use of meta-heuristic and deep learning (DL) algorithms in feature selection and classification problems. With the maturity of DL tools, many data-driven polarimetric synthetic aperture radar (POLSAR) representation models have been suggested, most of which are based on deep convolutional neural networks (DCNNs). In this paper, we propose a hybrid approach of a new multi-objective binary chimp optimization algorithm (MOBChOA) and DCNN for optimal feature selection. We implemented the proposed method to classify POLSAR images from San Francisco, USA. To do so, we first performed the necessary preprocessing, including speckle reduction, radiometric calibration, and feature extraction. After that, we implemented the proposed MOBChOA for optimal feature selection. Finally, we trained the fully connected DCNN to classify the pixels into specific land-cover labels. We evaluated the performance of the proposed MOBChOA-DCNN in comparison with nine competitive methods. Our experimental results with the POLSAR image datasets show that the proposed architecture had a great performance for different important optimization parameters. The proposed MOBChOA-DCNN provided fewer features (27) and the highest overall accuracy. The overall accuracy values of MOBChOA-DCNN on the training and validation datasets were 96.89% and 96.13%, respectively, which were the best results. The overall accuracy of SVM was 89.30%, which was the worst result. The results of the proposed MOBChOA on two real-world benchmark problems were also better than the results with the other methods. Furthermore, it was shown that the MOBChOA-DCNN performed better than methods from previous studies. [ABSTRACT FROM AUTHOR]

Generative urban design is an emerging field which seeks to apply a procedural algorithm to grow urban forms parametrically. These tools are mainly used for cityscapes in the entertainment industry. Currently, there is active research to repurpose them as semantically meaningful tools for urban design. This paper reviews the two primary approaches to generative urban design: CityEngine and Urban Induction (UI). After consideration UI was chosen as the preferred approach. This paper is a commentary on and assumes a background knowledge of UI. A novel synthesis of UI and Form-Based Codes is proposed as a lightweight method of generation. This method is then tested on a 3,040 acre site in San Francisco. Results indicate while the method works conceptually in simple cases it should be expanded into a comprehensive toolset before it can be considered an open-source replacement for CityEngine. [ABSTRACT FROM AUTHOR]

Fuel consumption is an important factor to be considered in the process of weather routing. How to choose an appropriate route according to the requirements is particularly important. This paper proposes a method to optimize the ship weather routing. Based on the original genetic algorithm, the trigonometric function selection operator is introduced, the mutation operator is improved to increase the search range in the initial stage of the algorithm, and gradually narrow the search range in the middle and late stages of the algorithm, thus the convergence is sped up and the running time of the algorithm is reduced. Aiming at the incomplete ship speed fuel consumption comparison table, this paper uses SPSS (Statistical Product and Service Solutions) software to perform curve fitting and a curve with the best fitting degree is found. Then the curve equation is used to calculate the total fuel consumption of the case ship (S175 container ship) sailing through between the two ports Yokohama (35° N, 141° E) and San Francisco (37° N, 123° W), to verify the performance of the improved algorithm. Aiming at the minimum fuel consumption, the fuel consumption of the optimized route is reduced by 7.84% compared with that of the Rhumb Line. [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]

This paper proposes and analyzes a genetic algorithm based permutation control logic applied to the aggregator of an offshore multi-source park. The energy losses at the common coupling point are accounted for in the feedback. This paper focuses on offshore distributed energy resources, such as floating photovoltaic (PV), wind, and wave power. The main contributions of this research are the development of a control system that is capable of tracking the set-point imposed by the demand curve for each source individually, the introduction of a capacity factor for combined offshore floating PV/wind/wave power farms, and the unveiling of pure offshore renewable sources as potential storage-less flexibility service providers. The results of a case study for a site near San Francisco showed that energy losses and capacity factors are positively influenced by implementing the proposed approach. [ABSTRACT FROM AUTHOR]

This paper aims to emphasize the fundamental role of unconscious processes in our adaptation. We will point out how we are able to unconsciously perform higher mental functions such as setting goals and planning how to pursue them, dealing with complex data, and making choices and judgments. In the first part of this paper, we will describe the main features of conscious and unconscious processes as pointed out by recent empirical research studies, and we will see how safety is essential in pursuing our fundamental goals, and how unconscious mental processes are strongly oriented towards preserving our safety and pursuing these goals. Finally, we will discuss control-mastery theory (CMT), an integrative, relational, cognitive-dynamic theory of mental functioning, psychopathology, and psychotherapy processes developed by Joseph Weiss and empirically validated by Weiss, Harold Sampson, and the San Francisco Psychotherapy Research Group over the last 50 years. This conceptualizes unconscious processes starting from this "higher unconscious mental functioning" paradigm and, in accordance with research data, stresses that our main goal is to adapt to reality and pursue adaptive developmental goals while preserving our safety. Three clinical vignettes will help show how the concepts proposed by CMT have important implications for therapeutic process. [ABSTRACT FROM AUTHOR]

Late in 1914, Universal released The Master Key, a motion picture serial in 15 installments. Robert Z. Leonard directed and starred, and the project was marketed as the 'most expensive serial yet'. The weekly film release was paralleled by a syndicated newspaper feuilleton, illustrated by stills from the motion pictures. Stills were also incorporated into an illustrated reissue of Fleming Wilson's story, as well as in promotional materials, like illustrated ads and a 20-page poster stamp album published by the Wentz Printing Company. By way of a cross-media investigation of the (1914) serial The Master Key, this study contributes a case in point of how film stills circulated outside the movie theater. The film material has been reported lost, but some reels have been preserved at the Library of Congress. One of the preserved reels contains the episode set in San Francisco's Chinatown. For this paper, various materials from The Master Key's-cross media release have been acquired through online fan forums and collectible auctions. Following recent historical studies on film serials and the "seriality" of stock characters, Chinatown scenes and the picturesque and efforts to write spatial film history, this paper argues that the proliferation of film images across media formats conceptualized The Master Key's imagery of San Francisco Chinatown as a "serialized" space, standardizing cinematic iconography in the public eye. [ABSTRACT FROM AUTHOR]

Purpose: An economic impact study conducted in 2010 predicted that hosting the 34th America's Cup in 2013 would result in $1.37 billion in total economic benefits to the San Francisco Bay Area. The goal of this paper is to examine the ex post effects of this competition on real taxable sales in the Bay Area. Design/methodology/approach: A panel data set of quarterly observations on taxable sales transactions for all counties in the state of California is employed. These data are explored using two estimation methodologies: difference-in-differences and synthetic control. Findings: Results from a difference-in-differences analysis and a synthetic control analysis produce similar findings. Namely, the 34th America's Cup competition appears to have had a minimal, short-lived impact on San Francisco and no measurable impact on two nearby counties. Practical implications: The empirical results in this paper underscore the findings of previous research showing that ex ante economic impact studies tend to overstate the net economic benefits of hosting mega-events. Social implications: The results of this paper may serve as a warning to policy makers considering using tax dollars to host a mega-event that such events often do not generate the economic gains reported in typical economic impact studies. Originality/value: This is the first paper to econometrically explore the impact of hosting the America's Cup on taxable sales transactions in a region. This paper also employs the relatively new empirical methodology called synthetic control. [ABSTRACT FROM AUTHOR]

In mobile edge computing, local edge servers can host cloud-based services, which reduces network overhead and latency but requires service migrations as users move to new locations. It is challenging to make migration decisions optimally because of the uncertainty in such a dynamic cloud environment. In this paper, we formulate the service migration problem as a Markov decision process (MDP). Our formulation captures general cost models and provides a mathematical framework to design optimal service migration policies. In order to overcome the complexity associated with computing the optimal policy, we approximate the underlying state space by the distance between the user and service locations. We show that the resulting MDP is exact for the uniform 1-D user mobility, while it provides a close approximation for uniform 2-D mobility with a constant additive error. We also propose a new algorithm and a numerical technique for computing the optimal solution, which is significantly faster than traditional methods based on the standard value or policy iteration. We illustrate the application of our solution in practical scenarios where many theoretical assumptions are relaxed. Our evaluations based on real-world mobility traces of San Francisco taxis show the superior performance of the proposed solution compared to baseline solutions. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to estimate major ion concentrations in estuaries from a known specific conductance. Assuming two-source conservative mixing at steady state, we propose an extension of the Practical Salinity Scale 1978 to estimate concentrations of major ions and total dissolved solids as functions of the conductivity ratio, which is the ratio of sample conductivity and seawater conductivity. We employ an extensive salinity data set that includes measurements of specific conductance and concentrations of major ions and total dissolved solids to demonstrate its validity and limitations for the San Francisco estuary. We show the proposed extension to be valid in waters influenced by seawater intrusion at specific conductance values greater than or equal to 0.25 mS/cm and propose adjusted model constants to account for ionic composition under less saline conditions. We observed evidence of seasonal bias in model residuals and hypothesize that this bias is related to the simplified assumption of fixed upstream end member concentrations. This finding of seasonal bias suggests that upstream end member concentrations are more reflective of high alkalinity freshwater contributions from December through June, compared to other months. [ABSTRACT FROM AUTHOR]

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This paper examines the production of settler ecologies through nineteenth century swamp reclamation projects in California. It focuses on the transformation of inland swamps into agricultural land and San Francisco salt marshes and tidelands into urban real estate. I argue that swamp reclamation was both an economic and a racial project. Swamp reclamation sought to transform perceived wastelands into productive property. Swamp reclamation was also a racial project, in at least three ways. First, it aimed to transform colonial environments for the health of the white settler body. Second, draining swamps and making solid land depended on a racialized labor force. Third, swamp reclamation was accelerated through government subsidies that largely benefitted white settlers at the same time the state of California disenfranchised Black, Chinese, and Indigenous residents and supported racial immigration policies. These formations of race, nature, and property were established by law and political economy, and undergirded by settler epistemologies of space and nature. By studying the discourses and practices of swamp reclamation in nineteenth century California, this paper contributes to scholarship on the production of settler ecologies under conditions of racial capitalism. [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]

In this paper five members of the "Community Psychoanalytic Track and Consortium" (CPT&C) in San Francisco, California, each holding different positionalities and functioning in different roles, come together in dialog with the shared aim to bring themselves and their readers inside the CPT&C. This writing project recapitulates principles of the CPT&C's vision itself: to form polyvocal groups with the shared task of supporting each other in our various roles, and members of community mental health organizations in their work and lives. From that effort new forms of psychoanalytic learning and work are generated; in this instance the process translates into a new writing form. [ABSTRACT FROM AUTHOR]

The Millennium Tower is a 58-story reinforced concrete building that was constructed in San Francisco, California, between 2005 and 2009. The Tower is founded on an embedded pile-supported mat with pile tips bearing in dense marine deposits that overlie an overconsolidated marine clay layer known locally as Old Bay clay. This clay layer experienced stress increases from Tower self-weight and from multiple episodes of dewatering between 2006 and 2018 at the Tower site and neighboring sites, including one that was sustained for 6 years. Settlements of the Tower foundation have been measured since 2006, and lateral deflections of the Tower have been inferred and measured since 2009. The data show that during multiple episodes of "loading" (from stress increase or dewatering), settlements initially accelerated and then gradually slowed over time, as expected from consolidation principles. Similarly, lateral deflections (from foundation tilt) (1) accelerated following foundation construction activities at adjacent sites (dewatering and excavation); and (2) oriented toward adjacent excavations, which at various times occurred to the project south, north, east, and west of the Tower site. An objective of this paper is to describe this case history, including the geotechnical site conditions and results of a monitoring program that tracked foundation settlements, Tower tilt, groundwater levels at the Tower site, and ground inclinations over time. We also evaluate soil deformation mechanisms that likely produced the movements. We find that settlement amounts and time variations are well captured by one-dimensional and three-dimensional analyses of volume change in Old Bay clay and other foundation soils from primary consolidation and secondary compression, provided that time variations of stress increase and groundwater level are accounted for. Three-dimensional analyses also capture time variations of lateral deflections, which were caused by volume change and shear deformations in foundation soils, the latter having been affected by unloading from adjacent excavations. [ABSTRACT FROM AUTHOR]

Accurately evaluating the response capability of electric vehicles (EVs) is very important for an EV aggregator (EVA) to formulate reasonable bidding strategies in ancillary service markets. Most of the methods proposed in the existing literature only evaluate EVs' temporal response capability while ignoring their spatial distribution. In addition, the evaluation results provided by the existing methods are typically deterministic, which fails to characterize the uncertainty of EV trip. The above two issues pose high risk of economic loss for the EVA. To this end, a probabilistic evaluation method of spatial‐temporal response capability for EVA is proposed in this paper. The gravity model is adopted to calculate a spatial transfer probability matrix describing EV owners' trip characteristics between different areas at each time in a region which is divided into several different areas according to the evaluation requirements. Then, the trip chains of EVs are modelled based on the spatial transfer probability matrix, and the states of charge (SOCs) of EVs are tracked in the process. The spatial‐temporal response capability of EVA is evaluated based on charging–discharging states and states of charge of EVs, and the probabilistic evaluation results of response capability are obtained by multiple Monte Carlo simulations. The effectiveness of the proposed method is verified on a real dataset from San Francisco, CA, USA. [ABSTRACT FROM AUTHOR]

The future e-mobility ecosystem will be a complex structure with different stakeholders seeking to optimize their operation and benefits. In this paper, a day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling framework is proposed including electric vehicles (EVs), charging stations (CSs), and retailers. To facilitate V2G services and to avoid congestion at CSs, two types of trips, i.e., mandatory and optional trips, are defined and formulated. Also, EV drivers’ preferences are added to the model as cost/revenue threshold and extra driving distance to enhance the practical aspects of the scheduling framework. An iterative process is proposed to solve the non-cooperative Stackelberg game by determining the optimal routes and CS for each EV, optimal operation of each CS and retailers, and optimal V2G and G2V prices. Extensive simulation studies are carried out for two different e-mobility ecosystems of multiple retailers and CSs as well as numerous EVs based on real data from San Francisco, the USA. The simulation results show that the optional trips not only reduces the cost of EVs and PV curtailment by 8.8-24.2% and 26.4-28.5% on average, respectively, in different scenarios, but also mitigates congestion during specific hours while respecting EV drivers’ preferences. Moreover, the simulation results revealed the significant impact of EV drivers' preferences on the optimal solutions and cost/revenue of the stakeholders. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes the use of a floating solar photovoltaic (FSPV) power plant as an alternative renewable energy resource for the San Francisco River Integration Project (SFIP), which aims to deliver water to 12 million people in the Brazilian semiarid. The SFIP requires considerable amounts of energy in a region that has had increasing electricity costs and consumption of water in the past decade. By simulating an FSPV power plant at the System Advisor Model (SAM) using techniques and parameters of real FSPV projects, the results demonstrated the techno‐economic feasibility of this technology linked to the SFIP. The economic outcomes are positive net present value (NPV), $2.8 million, and a payback varying from 10.5 to 11.7 years. The levelized cost of electricity (LCOE) of $32.17/MWh is smaller than the current energy rates paid by the SFIP administrator, and the water costs to final consumers could be reduced by 40%. In addition, the FSPV's capacity factor was 21.1%, and the system could minimize water evaporation from one of the SFIP's reservoirs by 16.7%. The system can also create revenues for the San Francisco and Parnaiba Valleys Development Company (CODEVASF) by trading the excess of electricity with the grid. This paper also analyses the FSPV's environmental impacts and its relevance under the water–energy nexus in the Brazilian Northeast. The FSPV could minimize the SFIP's operational costs, avoid environmental impacts, and improve the efficiency of water and energy management. Such components are crucial when analyzing the water–energy nexus in such a region, marked by strong competition for water access and long periods of drought. [ABSTRACT FROM AUTHOR]

Accessory dwelling units (ADUs) have been championed as a low-cost solution—both monetarily and politically—for America's housing affordability crisis. However, do these micro-units provide a city's lower-income community members with much needed affordable housing? And which income groups benefit from the theoretical supplemental income created by ADUs? Using socioeconomic data, building permit information, and geospatial analysis, this paper explores these questions by conducting a case study of the archetype of America's housing affordability crisis: San Francisco, California. Finding that, within the city and sample frame, few ADU permits have been filed in the most price-vulnerable communities, this paper challenges the theoretical benefits of ADUs espoused by politicians and academics. If the city continues to lean on ADU legislation, local legislators must strengthen current enforcement mechanisms and more narrowly tailor ADU-related rental restrictions. By offering model legislation and code language, this paper illustrates how San Francisco and other communities across the country can better ensure ADUs live up to their promise of affordability and access. [ABSTRACT FROM AUTHOR]

Highly accurate supervised deep learning-based classifiers for polarimetric synthetic aperture radar (PolSAR) images require large amounts of data with manual annotations. Unfortunately, the complex echo imaging mechanism results in a high labeling cost for PolSAR images. Extracting and transferring knowledge to utilize the existing labeled data to the fullest extent is a viable approach in such circumstances. To this end, we are introducing unsupervised deep adversarial domain adaptation (ADA) into PolSAR image classification for the first time. In contrast to the standard learning paradigm, in this study, the deep learning model is trained on labeled data from a source domain and unlabeled data from a related but distinct target domain. The purpose of this is to extract domain-invariant features and generalize them to the target domain. Although the feature transferability of ADA methods can be ensured through adversarial training to align the feature distributions of source and target domains, improving feature discriminability remains a crucial issue. In this paper, we propose a novel polarimetric scattering characteristics-guided adversarial network (PSCAN) for unsupervised PolSAR image classification. Compared with classical ADA methods, we designed an auxiliary task for PSCAN based on the polarimetric scattering characteristics-guided pseudo-label construction. This approach utilizes the rich information contained in the PolSAR data itself, without the need for expensive manual annotations or complex automatic labeling mechanisms. During the training of PSCAN, the auxiliary task receives category semantic information from pseudo-labels and helps promote the discriminability of the learned domain-invariant features, thereby enabling the model to have a better target prediction function. The effectiveness of the proposed method was demonstrated using data captured with different PolSAR systems in the San Francisco and Qingdao areas. Experimental results show that the proposed method can obtain satisfactory unsupervised classification results. [ABSTRACT FROM AUTHOR]

This paper presents a new method called the functional distributional clustering algorithm (FDCA) that seeks to identify spatially contiguous clusters and incorporate changes in temporal patterns across overcrowded networks. This method is motivated by a graph-based network composed of sensors arranged over space where recorded observations for each sensor represent a multi-modal distribution. The proposed method is fully non-parametric and generates clusters within an agglomerative hierarchical clustering approach based on a measure of distance that defines a cumulative distribution function over temporal changes for different locations in space. Traditional hierarchical clustering algorithms that are spatially adapted do not typically accommodate the temporal characteristics of the underlying data. The effectiveness of the FDCA is illustrated using an application to both empirical and simulated data from about 400 sensors in a 2.5 square miles network area in downtown San Francisco, California. The results demonstrate the superior ability of the the FDCA in identifying true clusters compared to functional only and distributional only algorithms and similar performance to a model-based clustering algorithm. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to describe how resilience unfolded in a project-based organization with the support of organizational values through changing leadership styles. The rapidly announced restrictions on businesses during the coronavirus disease 2019 (COVID-19) provided an opportunity to observe and study resilience unfold. Design/methodology/approach: The process-perspective case study approach of a structural and civil engineering design firm in San Francisco, California, USA, integrates interviews, observations, document analysis and information tracking via email and Microsoft Teams. The researchers adopted a leadership perspective, where the units of analysis are the internal management and the employees' behaviors. Findings: In the case examined, the capability represented in the organizational values influenced the choice of situation-appropriate leadership styles to support employees. The values of relationship, passion and trust influenced the dominant choice of a transformational style, where stability and excellence facilitate a transactional style – all equally important for the balance and resilience of the project-based organization. Originality/value: This study demonstrated that when organizational values support leaders in cultivating a learning environment, those values provide stability for leaders to promote resilience. To the best of the researchers' knowledge, no previous work described how situational-, transformational- and transactional-leadership styles evolve in response to a crisis and together facilitate organizational resilience. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a virtual training system by applying simulation techniques such as: Full Simulation and Hardware-in-the-Loop (HIL). This virtual reality system is intended to be a teaching and learning tool focused on the area of industrial robotics. For this purpose, mathematical models (kinematic and dynamic) have been considered. These models determine the characteristics and restrictions of the movements of a Scara SR-800 robot. The robot is then virtualized to simulate position and trajectory tasks within virtual environments. The Unity 3D graphic engine (Unity Software Inc., San Francisco, CA, USA), allows the design and development of the training system which is composed of a laboratory environment and an industrial environment. The same that contribute to the visualization and evaluation of the movements of the robot through the proposed control algorithm using the mathematical software (MatLab, manufactured by MathWorks, USA), through shared memories. This software in turn can be linked to an electronic board (Raspberry Pi) for data acquisition through a wireless connection. Finally, the stability and robustness of the implemented controllers are analyzed, as well as the correct operation of the virtual training system. [ABSTRACT FROM AUTHOR]

Substantial increases in non-native aquatic vegetation have occurred in the upper San Francisco Estuary over the last 2 decades, largely from the explosive growth of a few submerged and floating aquatic plant species. Some of these species act as ecosystem engineers by creating conditions that favor their further growth and expansion as well as by modifying habitat for other organisms. Over the last decade, numerous studies have investigated patterns of expansion and turn-over of aquatic vegetation species; effects of vegetation on ecosystem health, water quality, and habitat; and effects of particular species or communities on physical processes such as carbon and sediment dynamics. Taking a synthetic approach to evaluate what has been learned over the last few years has shed light on just how significant aquatic plant species and communities are to ecosystems in the Sacramento-San Joaquin Delta. Aquatic vegetation affects every aspect of the physical and biotic environment, acting as ecosystem engineers on the landscape. Furthermore, their effects are constantly changing across space and time, leaving many unanswered questions about the full effects of aquatic vegetation on Delta ecosystems and what future effects may result, as species shift in distribution and new species are introduced. Remaining knowledge gaps underlie our understanding of aquatic macrophyte effects on Delta ecosystems, including their roles and relationships with respect to nutrients and nutrient cycling, evapotranspiration and water budgets, carbon and sediment, and emerging effects on fish species and their habitats. This paper explores our current understanding of submerged and floating aquatic vegetation (SAV and FAV) ecology with respect to major aquatic plant communities, observed patterns of change, interactions between aquatic vegetation and the physical environment, and how these factors affect ecosystem services and disservices within the upper San Francisco Estuary. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for prioritising bridge retrofits within a regional road network subject to uncertain seismic hazard, using a technique that accounts for network performance while avoiding the combinatoric computational costs of exhaustive searches. Using global variance-based sensitivity analysis, a probabilistic ranking of bridges is determined according to how much their retrofit statuses influence the expected cost of the road network disruption. Bridges' total-order sensitivity (Sobol') indices are estimated with respect to the expected cost using the hybrid-point Monte Carlo approximation method. A bridge's total-order Sobol' index measures how much its retrofit status influences the variance of the expected cost of the road network performance and accounts for the effect of its interactions with other bridges' retrofit states. For 71 highway bridges in San Francisco, a retrofit strategy based on bridges' total-order Sobol' indices outperforms other heuristic strategies. The proposed method remains computationally tractable while accounting for the probabilistic nature of the seismic hazard, the uniqueness of individual bridges, network effects, and decision-makers' priorities. Because this method leverages existing risk assessment tools and models without imposing further assumptions, it should be extensible to other types of networks under different types of hazards and to other decision variables. [ABSTRACT FROM AUTHOR]

Similarity, correlation and association measures play an important role in statistics, information retrieval, data mining and data science, classification and machine learning, recommender systems and decision-making. They have numerous applications in ecology, social and behavioral sciences, biology and bioinformatics, social network and time series analysis, image and natural language processing. Often the measures with the same name introduced on different domains have different properties, and the measures with the same properties have different names. To unify analysis of measures defined on different domains, this paper considers these measures as functions defined on universal domain and satisfying some sets of properties. The general properties of similarity functions (SF) and dissimilarity functions (DF) under the joint name of resemblance functions (RF) studied on universal domain and illustrated by examples on specific domains. The known and the new methods of construction of similarity measures are considered. This paper discusses the following aspects of RF: relationship with fuzzy (valued) relations, T-transitivity and triangle inequality, Minkowski distance and data transformation, cosine SF, RF on domains with involution (negation), aggregation and transformations of RF, visualization of RF. The paper considers also the lattice of RF, composition and min-transitive transformations of SF (fuzzy proximity relations), applications to hierarchical clustering and non-probabilistic entropy of RF. In addition, the paper proposes the method of construction of correlation functions (association measures) using SF. Pearson correlation and Yule's Q association coefficients obtained as particular cases of the general method. One can use the paper as a survey of works on similarity and dissimilarity measures on specific domains, as a guide for constructing new similarity and correlation measures, as a base for the study of mathematical properties of resemblance functions on universal and specific domains, and also as a part of the course on Data Science. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the trailers for a telefiction series originally produced in English and simultaneously distributed in Spanish in Latin America. Looking (aired between 2014 and 2016 by HBO) was a contemporary dramedy series, a hybrid genre typical of the quality TV promoted by HBO, that told the story of three gay friends living in San Francisco. The aesthetics of the series reveals the auteur cinematic work of Andrew Haigh, a film director who applied his visual narrative repertoire to Looking. Using the multimodal analysis model proposed by Kaindl (2020) and the structure of communicative modes proposed by Chaume (2004) and Stöckl (2004), this paper analyzes the translation and Latin American adaptation of two trailers of the series to understand whether the semiotic integration of the paratexts represents or intensifies the narrative aspects of the hybrid genre series. [ABSTRACT FROM AUTHOR]

Copyright of Urban Studies (Sage Publications, Ltd.) is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recent studies on international networks have investigated the role of global cities as catalysts of knowledge flows. In this paper, we examine global cities in transnational networks of innovators. To assess the likelihood of scientists and inventors from different cities to take part in international teams, we propose a measure based on 3-hyperedges in the collaboration network constructed from scientific publications and patents. This measure is used to identify the most competitive global cities in the international network of innovators. To this end, we construct a null model based on the hypergeometric ensembles of random graphs. We find that five US cities play a leading role in transnational networks of innovators. Among them, San Francisco stands out as the most globalized city. [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]