Search

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 article focuses on the use of airflow drying technique in the process of paper conservation. It states that the technique was suggested by R. Futernick in paper conservation in 1988 and was first introduced at the Western Regional Paper Conservation Laboratory in San Francisco, California. It discusses the physical basics of the drying process in paper conservation such as drying stack which was set up from corrugated board.

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]

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 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 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 article offers information on the Jewish communities in California where some are historical, holding a remnant of the Jewish population that resided there decades or a century ago. It discusses a paper titled "The Marine Heritage Project," written in the late 1800s including inspirational references to Judaism at that time in San Francisco and discusses the contribution of Levi Strauss to Judaism in San Francisco.

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]

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]

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]

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]

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 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]

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]

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]

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]

Women make up an extraordinary proportion of the individuals in murals worldwide, which can be viewed as a great base for communicating undivided, visual messages of female empowerment, mutual tolerance, and social justice in a world that is unjust, phallogocentric, (still) misogynistic, and strongly prejudicial against women. At the same time, it can be seen as politically opportune and fitting to stress even further the female perspective and send a clear political statement regarding women's rights and issues. In this respect, this paper focuses on the visualization of the politics of tolerance and liberation expressed in the Maestrapeace mural of the renowned education community center building called "Edificio de Mujeres" or the Women's Building in San Francisco, California, which pays tribute to a number of feminist icons from history and fiction, and the names of more than 600 women written in calligraphy, which is a vivid, undivided, and written presentation of the visual politics of female empowerment and tolerance, at the same time advocating for independence, self-determination, gender equality, and social justice. The building's spectacular mural, painted exclusively by seven women artists is a broad-based work of art that presents a culmination of a multicultural, multiethnic, and diverse female world that is built, or more precisely painted on the principles of inclusion, acceptance, and open-mindedness. [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]

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]

For a long time, Polarimetric Synthetic Aperture Radar (PolSAR) data was not available free of cost, so the applications were limited. With the recent increase in availability of PolSAR data due to missions like ALOS-2, UAVSAR and Sentinel, the data can be acquired through these sensors periodically. Since the volume of data is large, applying traditional classifiers and conventional machine learning algorithms becomes confounding, as in that case, manual feature extraction must be done by the researcher for training the model. To automate this feature extraction step and accelerate the process, many researchers have used various deep learning algorithms. However, most studies fail to tap into the potential of PolSAR data by not utilizing the complete range of complex data of float-32 bit-depth. The work in this paper suggests the development of a generalized deep learning model for nine elements of coherency matrix [ T 3 ] in the float-32 data-space from ALOS/PALSAR-2 L-Band sensor. Semantic segmentation is used to classify land-covers into various classes like water, settlement, forest, open land, wetlands etc. This is done through modification on the UNet backbone. The deep learning model is trained using data on San Francisco and New Delhi regions and tested on data from Mumbai region. It is observed that the classification accuracy for Mumbai region is " 93.82 % ". This kind of system can assist the decision makers like urban planners to take informed decisions. [ABSTRACT FROM AUTHOR]

The purpose of the present paper is to set forth a methodology proposal for assessing quality pathologies (aesthetic and superficial) in historic buildings located in the city center of Lima (Peru), in order to identify the pathologies having the strongest impact on the overall quality of the structure, as well as their main causes. As a study case, the methodology is applied to the "Basilica and Convent of San Francisco", which allowed concluding that fissures are the pathologies that most affect the structure and earthquakes, and external vibrations are the main causes thereof. [ABSTRACT FROM AUTHOR]

As a special dual-polarization technique, compact polarimetric (CP) synthetic aperture radar (SAR) has already been widely studied and installed on some spaceborne systems due to its superiority to quad-polarization; moreover, quad-pol information can be explored and reconstructed from the CP SAR data. In this paper, a refined model is proposed to estimate the quad-pol information for the CP mode. This model involves CP decomposition, wherein the polarization degree is introduced as the volume scattering model parameter. Moreover, a power-weighted model for the co-polarized coherence coefficient is proposed to avoid the iterative approach in pseudo-quad-pol information reconstruction. Experiments were implemented on the simulated Gaofen-3 and ALOS-2 data collected over San Francisco. Compared with typical reconstruction models, the proposed refined model shows its superiority in estimating the quad-pol information. Furthermore, terrain classification experiments using a complex-value convolutional neural network (CV-CNN) were performed on AIRSAR Flevoland data to validate the reconstruction effectiveness for classification applications. [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]

Land cover classification is one of the most important applications of POLSAR images. In this paper, a hybrid biogeography-based optimization support vector machine (HBBOSVM) has been introduced to classify POLSAR images of RADARSAT 2 in band C acquired from San Francisco, USA. The main purpose of this classification is to minimize the number of features and maximize classification accuracy. The proposed method consists of three main steps: preprocessing, feature selection and classification. As preprocessing, radiometric calibration, speckle reduction and feature extraction have been performed. In the proposed HBBO, the combination of onlooker bee of artificial bee colony (ABC) and migration operator of biogeography-based optimization has been applied in order to optimal feature selection. Then, SVM has been used to classify the pixels into specific labels of land-covers. The ground truth samples have been generated by google earth image, Pauli RGB image, high resolution image and national land cover database (NLCD 2006). The performance of HBBOSVM has been compared with BBOSVM, ABCSVM, particle swarm optimization support vector machine (PSOSVM) and the results of previous studies. In addition, the performance of HBBO is evaluated upon 20 well-known benchmark problems. According to the obtained results, the overall accuracy and average accuracy of HBBOSVM are 96.01% and 93.37% respectively which is the best result in comparison with other results. The HBBOSVM has better performance than other algorithms in terms of overall accuracy, kappa coefficient, average accuracy, convergence trend, and stability. In addition, the HBBO can be considered as a successful meta-heuristic for benchmark problems. This paper displays that the combined approach of optimization and machine learning methods provides powerful results. [ABSTRACT FROM AUTHOR]

In this paper I ask what is at stake when we move past static ontologies of the 'gayborhood' as a form of commercial and residential concentration in decline to theorise gay urban activism as a mode of queer social reproduction, through which queer caring labour 'redeems' the dislocations of the neoliberal city structured by oedipalised and capitalist social relations. Through well-documented formal and informal collective action, queers in the urban West have organised in response to health crises, exclusion and systemic threats of violence. Returning to socialist feminist imaginaries of care beyond the 'social', and to Guy Hocquenghem's often-overlooked theory of the sociality of the anus, this paper draws on excerpts from the film Milk, the poetry of Thom Gunn and a discussion of gay men's volunteering to examine San Francisco as a queer urban space constituted through a network of encounters, crossings, intimacies and labours enacted through the mundane caring practices of everyday life. I ask in what ways we can think of gay urban space as continuously made and remade through non-monogamous sex practices that perform the messy marrying of public and private, and erotic and platonic. [ABSTRACT FROM AUTHOR]

This paper examines the materiality of digitally mapping eviction and landlord geographies, focusing on struggles and contradictions that critical GIS and counter-mapping collectives encounter in attempting to produce data, maps, and tools useful for housing justice organizing. I look at how public restrictions of parcel ownership and eviction data, along with limited accessibility of free and open source mapping software, often instantiate increased reliance upon technocapitalist data and infrastructure. Many of these systems are precipitative of gentrification geographies, thereby at odds with the politics of antidisplacement mapping. Meanwhile, there are a growing number of cartographic labs and companies that produce geospatial data related to evictions and property ownership, but that prioritize data accumulation and scalability over grounded housing justice. By exploring paradoxes within this space, I theorize the conjuncture of datafied property and propertied data landscapes. Homing in on San Francisco landscapes of the Tech Boom 2.0., I draw upon my own experience working with the Anti-Eviction Mapping Project and partner groups. I conclude by looking to housing justice and land rematriation practices based upon grounded relationalities - models that offer emancipatory trajectories for digitally mapping property and dispossession. [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]

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]

The 2013 IEEE Pulsed Power and Plasma Science Conference (PPPS 2013) was held at the Hyatt Regency in San Francisco, CA, USA in June 2013. The PPPS 2013 combined the 19th IEEE Pulsed Power Conference (PPC) and the 40th IEEE International Conference on Plasma Science (ICOPS). This conference marks the third combined IEEE PPPS meeting, following the very successful PPPS 2001 in Las Vegas, NV, USA, and the PPPS 2007 in Albuquerque, NM, USA. The most recent ICOPS was held in 2012 in Edinburg, Scotland, and the most recent PPC was held in 2011 in Chicago, IL, USA. [ABSTRACT FROM PUBLISHER]

• There is an increase in the construction of supertall and megatall buildings. • Practical application of traditional surveying methods is limited by tower height. • Elevation changes may be monitored using strain measurements and prediction models. • Strain-based monitoring results agreed with conventional methods. • The strain-based method increased efficiency and decreased cost. Anticipated complexities associated with monitoring elevation changes during construction of the 326 m tall Salesforce Tower in San Francisco prompted the implementation of a strain-based system. This system utilizes strain gages embedded into the tower's reinforced concrete walls and the resulting measurements are inputs into one of three shortening estimation models of varying complexity and applicability. This methodology was successfully utilized to monitor time dependent shortening due to axial, shrinkage, and creep strains in the tower. Strain-gage based systems assist with slender tower construction in urban areas because the tower's slenderness coupled with adjacent obstacles stretch the limits of traditional or alternative instrument capabilities and they provide redundancy of measurement to ensure accuracy. This paper is a case study of how a strain-based elevation monitoring system helped this project to be successful. The most relevant findings are: 1) the shortening estimation models were in agreement with conventional survey measurements (±4.6 mm) within their recommended tower height and temperature range capabilities; 2) implementation of this system resulted in a net monitoring cost reduction on the order of 15 %, 400 less labor hours, and reduced potential for project delays; and 3) improvements are needed for supplying power to the sensors as there were interruptions, cut cables, and lack of sources that led to a 44 % sensor success rate. Given the need for structural health monitoring, the successful outcomes documented in this paper, and the potential for continued improvement by way of, for example, wireless and battery free sensing technologies, the work documented herein is believed to be an incremental advancement to the state-of-the-art of tall buildings. [ABSTRACT FROM AUTHOR]

This Special Topic section of Biomicrofluidics is dedicated to original papers from the 2006 Annual Meeting of the American Electrophoresis Society (AES: http://www.aesociety.org). This five-day meeting held in San Francisco, California, included five sessions on BioMEMS and Microfluidics and four sessions on Advances in Electrokinetics and Electrophoresis. AES and its corresponding symposia provide the most focused and well-organized meeting forum for diverse biological and engineering researchers working on electrokinetics. The work featured in this Special Topic section is no exception; it ranges from nanochannel electrophoresis to bioparticle sorting. [ABSTRACT FROM AUTHOR]

When facing multiple, competing, objectives such as those encountered in resilience-based design of buildings (e.g., cost reduction versus functionality preservation), there is no single design that can be declared objectively better than the rest. Multiobjective optimization (MOO) methods can instead be used to identify a group of design alternatives that represent the optimal trade-offs between competing objectives. In doing so, however, MOO relies on performance evaluation for a large number of design alternatives, representing a problem for performance-based building seismic design, which typically involves computationally intensive and time-consuming nonlinear analyses of building models subjected to suites of ground motion records. Furthermore, only minimal guidance exists for the post-MOO selection of a final design from the optimal set, further impeding its adoption in design of buildings. This paper addresses these issues through use of simplified structural models and methods applied within a MOO methodology, coupled with a newly adapted post-Pareto pruning approach to narrow down the optimal design set to a manageable-sized group of final design alternatives. The methodology was applied to optimize the design of a 7-story reinforced concrete moment frame office building in San Francisco. [ABSTRACT FROM AUTHOR]

In 2017, San Francisco (SF) implemented Law Enforcement Assisted Diversion (LEAD), a program Beckett described as harm reduction policing. Through a process and outcome evaluation of LEAD SF, this paper demonstrates the positive impacts of harm reduction policing, on those who use drugs and/or engage in sex work. When law enforcement officers used their discretion to divert individuals into LEAD rather than arrest, those individuals had significantly fewer felony and misdemeanor arrests and felony cases, in comparison to a propensity score matched group. The focus group and interview data describe that the collaboration, the warm handoff, and LEAD's harm reduction principles were mechanisms of success. However, obtaining officer buy-in was a key challenge. Despite that obstacle, LEAD SF's harm reduction policing reduces offending, improves the wellbeing of people who use drugs and engage in sex work, and allows the police to carry out their mandate to protect and serve. [ABSTRACT FROM AUTHOR]

Street attributes are thought to play an important role in influencing pedestrian route choices. Faced with alternatives, pedestrians have been observed to choose faster, safer, more comfortable, more interesting, or more beautiful routes. Literature on pedestrian route choice has provided methods for assessing the likelihood of such options using discrete choice models. However, route choice estimation, which is data intensive and computationally challenging, remains infrequently deployed in planning mobility analysis practice. Even when coefficients from previous studies are available, operationalizing them in foot-traffic predictions has been rare due to uncertainty involved in the transferability of behavioral effects from one context to another, as well as computational challenges of predicting route choice with custom attributes. This paper explores a simpler method of route choice prediction, implemented in the Urban Network Analysis toolbox, which assigns probabilities to available route options based on distance alone. We compare the accuracy of distance-weighted approaches to the more detailed utility-weighted approach using a large dataset of observed GPS pedestrian traces that include numerous trips between same intersections pairs in downtown San Francisco as a benchmark. Even though a utility-weighted model matches observed pedestrian flows most accurately, a distance-weighted model is only marginally inferior, on average. However, shortest-distance and highest-utility route predictions are both significantly inferior to the utility-weighted and distance-weighted sample-enumeration methods. Our findings suggest that simplified assumptions can be used to predict pedestrian flow in practice with existing software, opening pedestrian flow predictions to a wider range of planning and transportation applications. [ABSTRACT FROM AUTHOR]

Classical epidemiological models assume mass action. However, this assumption is violated when interactions are not random. With the recent COVID-19 pandemic, and resulting shelter in place social distancing directives, mass action models must be modified to account for limited social interactions. In this paper we apply a pairwise network model with moment closure to study the early transmission of COVID-19 in New York and San Francisco and to investigate the factors determining the severity and duration of outbreak in these two cities. In particular, we consider the role of population density, transmission rates and social distancing on the disease dynamics and outcomes. Sensitivity analysis shows that there is a strongly negative correlation between the clustering coefficient in the pairwise model and the basic reproduction number and the effective reproduction number. The shelter in place policy makes the clustering coefficient increase thereby reducing the basic reproduction number and the effective reproduction number. By switching population densities in New York and San Francisco we demonstrate how the outbreak would progress if New York had the same density as San Francisco and vice-versa. The results underscore the crucial role that population density has in the epidemic outcomes. We also show that under the assumption of no further changes in policy or transmission dynamics not lifting the shelter in place policy would have little effect on final outbreak size in New York, but would reduce the final size in San Francisco by 97%. [ABSTRACT FROM AUTHOR]

Recent debates in social and cultural geography on the inclusionary/exclusionary nature of space have brought our attention to the 'everywhere different' nature of racism across cities. Among these debates have been calls to interrogate the socio-spatial dimensions of new forms of racism, like Islamophobia as they evolve. This paper draws on the findings of an online survey conducted from September 2016 to April 2017 with young Muslim American residents of the Bay Area, California. It provides empirical material on the way young Muslims map 'the geography of Islamophobia' across this region to uncover how the racialization of Muslims has translated into perceptions of racism across city spaces. The findings indicate that Islamophobia occurs in various public spheres, particularly on public transport and in airports. There is a spatial concentration of Islamophobic spaces in the Bay Area, focussed in the North and Outer-East Bay regions – relatively rural parts of the region with a less significant Muslim population. Conversely, areas with larger Muslim populations were associated with lower levels of perceived Islamophobia. This paper highlights the need for more localised, socio-spatial engagements in racism that capture the evolving nature of the American racisms, and how they are spatialised across cities. [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]

• 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]

This paper explores the material and representational politics that are catalyzed by Anti-Mass , an installation made by Cornelia Parker from the remains of a burned Black church located in the deYoung Museum in San Francisco, California. Applying an attention to religious materiality (the interpretive circuit between words, bodies, and things), semiotic ideology (an approach that explores competing and ascendant meanings), and an ethnographic sensibility, I argue that three hermeneutic fields coalesced around Anti-Mass in the museum. The first interpretation is a Killmonger hermeneutic that read Anti-Mass as a profaning of Black sacred matter. The second is an Anti- hermeneutic , which sought to preserve representational openness prized by abstract expressionism. The third is a resurrection hermeneutic , which reflected popular notions of US multicultural transcendence and a more melancholic meditation on anti-Black religious violence expressed by adjacent artwork and embodied by the ethnographer herself via a lump corpothetics. [ABSTRACT FROM AUTHOR]

In this paper additive models with p-order autoregressive conditional symmetric errors based on penalized regression splines are proposed for modeling trend and seasonality in time series. The aim with this kind of approach is try to model the autocorrelation and seasonality properly to assess the existence of a significant trend. A backfitting iterative process jointly with a quasi-Newton algorithm are developed for estimating the additive components, the dispersion parameter and the autocorrelation coefficients. The effective degrees of freedom concerning the fitting are derived from an appropriate smoother. Inferential results and selection model procedures are proposed as well as some diagnostic methods, such as residual analysis based on the conditional quantile residual and sensitivity studies based on the local influence approach. Simulations studies are performed to assess the large sample behavior of the maximum penalized likelihood estimators. Finally, the methodology is applied for modeling the daily average temperature of San Francisco city from January 1995 to April 2020. [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]

This paper studies the optimal spatial pricing for a ride-sourcing platform subject to a congestion charge. The platform determines the ride prices over the transportation network to maximize its profit, while the regulatory agency imposes the congestion charge to reduce traffic congestion in the urban core. A network economic equilibrium model is proposed to capture the intimate interactions among passenger demand, driver supply, passenger and driver waiting times, platform pricing, vehicle repositioning and flow balance over the transportation network. The overall optimal pricing problem is cast as a non-convex program. An algorithm is proposed to approximately compute its optimal solution, and a tight upper bound is established to evaluate its performance loss with respect to the globally optimal solution. Using the proposed model, we compare the impacts of three forms of congestion charge: (a) a one-directional cordon charge on ride-sourcing vehicles that enter the congestion area; (b) a bi-directional cordon charge on ride-sourcing vehicles that enter or exit the congestion area; (c) a trip-based congestion charge on all ride-sourcing trips. We show that the one-directional congestion charge not only reduces the ride-sourcing traffic in the congestion area, but also reduces the travel cost outside the congestion zone and benefits passengers in these underserved areas. We establish that in all congestion charge schemes the largest share of the tax burden is carried by the platforms, as opposed to passengers and drivers. We further show that compared to other congestion charges, the one-directional cordon charge is more effective in congestion mitigation: to achieve the same congestion-mitigation target, it imposes a smaller cost on passengers, drivers, and the platform. On the other hand, compared with the other charges, the trip-based congestion charge is more effective in revenue-raising: to raise the same tax revenue, it leads to a smaller loss to passengers, drivers, and the platform. We validate these results through realistic numerical studies for San Francisco. • The optimal spatial pricing strategies are derived for the ride-sourcing platforms. • Empty vehicle relocation and interceptions during relocation are considered. • One-directional cordon price limits congestion in the urban core and reduces travel costs in the suburb. • One-directional cordon price is more effective in congestion mitigation. • Trip-based congestion charge is more effective in revenue-raising. [ABSTRACT FROM AUTHOR]

Current vehicles are equipped with a number of environmental sensors to improve safety and quality of life for passengers. Many researchers have shown that these sensors can also be exploited for opportunistic crowd-sensing. Useful new services can be developed on top of these data, like urban surveillance of Smart Cities. The spatio-temporal sensing coverage achievable with Vehicular Crowd-Sensing (VCS), however, is an open issue, since vehicles are not uniformly distributed over the road network, undermining the quality of potential services based on VCS data. In this paper, we present an evolution of the standard A ∗ routing algorithm, meant to increase VCS coverage by selecting a route in a random way among all those satisfying a parametric constraint on the total cost of the path. The proposed solution is based on an edge-computing paradigm, not requiring a central coordination but rather leveraging the computational resources available on-board, significantly reducing the back-end infrastructure costs. The proposed solution has been empirically evaluated on two public datasets of 450,000 real taxi trajectories from two cities, San Francisco and Porto, characterized by a very different road network topology. Results show sensible improvements in terms of achievable spatio-temporal sensing coverage of probe vehicles. [ABSTRACT FROM AUTHOR]

This paper explores the topographical and socio-cultural developments during the Golden Age in New York, Los Angeles and San Francisco, three Beat Generation epicenters, which determined the deconstruction of traditional norms. Modifications at both city and society levels were represented by the emergence of countercultures, such as the Beat. The visibility received by urban problems, due to the increase in social demonstrations and activism, fostered the formation of a unified front that demanded equality and encouraged social and political movements, such as the Civil Rights and the Second Wave Feminism. The socio-political challenges which the American society was confronted with from the 1950s to the 1970s in these three cities, also reveal a few problems regarding the status of the Beats as well as of minorities in metropolises. [ABSTRACT FROM AUTHOR]

This paper investigates time-series correlations between macroscopic travel speed and parking garage occupancy in downtown area, using the real-time parking occupancy data via SFPark.org and travel speed data from HERE Maps for San Francisco downtown areas as a data-driven case study. This study significantly expands recent work on instantaneous correlations by incorporating variables as time series. The equivalency between the nonlinear regression with logistic curves and the single-node single hidden layer neural network is established. By testing time delay neural network models, this study investigates the time delay effects between macroscopic travel speed and parking garage occupancy. The performance of single-layer multi-nodes nonlinear autoregressive with exogenous inputs neural network is evaluated, which suggests such types of time series neural networks can effectively forecast macroscopic travel speed by using travel speed and parking occupancy information with various forecasting delay tabs. [ABSTRACT FROM AUTHOR]

This paper deals with the preparation and documentation activities of the Japanese Ministry of Foreign Affairs (JMOFA) for the San Francisco Peace Treaty of 1951. Between 1946 and 1949, JMOFA produced seven volumes of pamphlets on territorial issues in the preparation process leading up to the peace treaty. Among them, a series of four pamphlets under the title of Minor Islands Adjacent to Japan Proper was published. These pamphlets were distributed to the Allied Powers to good effect. Focus was placed on the Southern Kuriles, Ryukyus, and Liancourt Rocks (Dokdo), which Japan felt should be recognized as Japanese territory by the Allies. After the San Francisco Peace Treaty, Japan restored peaceful relations with the United States and its major allies, but hostile relations between Japan and its neighbors in East Asia persisted. It was natural that the Soviet Union (Russia), China, and Korea, excluded from the Peace Treaty, later came to engage in territorial disputes with Japan. [ABSTRACT FROM AUTHOR]

This paper describes the impact on transportation network companies (TNCs) of the imposition of a congestion charge and a driver minimum wage. The impact is assessed using a market equilibrium model to calculate the changes in the number of passenger trips and trip fare, number of drivers employed, the TNC platform profit, the number of TNC vehicles, and city revenue. Two types of charges are considered: (a) a charge per TNC trip similar to an excise tax, and (b) a charge per vehicle operating hour (whether or not it has a passenger) similar to a road tax. Both charges reduce the number of TNC trips, but this reduction is limited by the wage floor, and the number of TNC vehicles reduced is not significant. The time-based charge is preferable to the trip-based charge since, by penalizing idle vehicle time, the former increases vehicle occupancy. In a case study for San Francisco, the time-based charge is found to be Pareto superior to the trip-based charge as it yields higher passenger surplus, higher platform profits, and higher tax revenue for the city. [ABSTRACT FROM AUTHOR]

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]

John Weir Perry's influence on the understanding of the psychotic process through his research in San Francisco between 1950 and 1981 was groundbreaking, because it both verified and expanded upon C.G. Jung's research at the Burghölzli Hospital in Switzerland in the early 1900's. The author explores both the brilliance of Perry's contribution as a psychiatrist and Jungian analyst and also shows the flawed human, who, with his rare sensitivity to the psychotic process, devoted his life work to the schizophrenic population and their often ill-fated search for meaning. She tells how his creative engagement with the analytic processes of Self discovery eventually led to analytic boundary violations, which ultimately resulted in his indefinite suspension from membership in his local Jungian community. Further, this paper describes her reflections on the innovative work that influenced both the treatment of this population, as well as educating candidates in analytical training to be receptive to and cognizant of psychotic affects and imagery. The archetypal field of the psychotic process, its influence on the development of analytical psychology relative to the psychotic process, and one man's impact on the analytic community are considered. [ABSTRACT FROM AUTHOR]