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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]

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

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

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

In this paper, we present a simple yet effective rotation averaging pipeline, termed Incremental Rotation Averaging (IRA), which is inspired by the well-developed incremental Structure from Motion (SfM) techniques. Unlike the traditional rotation averaging methods which estimate all the absolute rotations simultaneously and focus on designing either robust loss function or outlier filtering strategy, here the absolute rotations are estimated in an incremental way. Similar to the incremental SfM, our IRA is robust to relative rotation outliers and could achieve accurate rotation averaging results. In addition, we propose several key techniques, such as initial triplet and Next-Best-View selection, Weighted Local/Global Optimization, and Re-Rotation Averaging, to push the rotation averaging results one step further. Ablation studies and comparison experiments on the 1DSfM, Campus, and San Francisco datasets demonstrate the effectiveness of our IRA and its advantages over the state-of-the-art rotation averaging methods in accuracy and robustness. [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]

Most criminal defendants cannot afford to hire an attorney. To provide constitutionally mandated legal services, states commonly use either private court-appointed attorneys or a public defender organization. This paper investigates the relative efficacy of these two modes of indigent defense by comparing outcomes of codefendants assigned to different types of attorneys within the same case. Using data from San Francisco, I show that in multiple defendant cases, public defender assignment is plausibly as good as random. I find that public defenders reduce the probability of any prison sentence by 22% and the length of prison sentences by 10%. [ABSTRACT FROM AUTHOR]

This paper shows the research developed in order to evaluate two resilience indicators, PHRI and Rsys, in the San Francisco de Cunuguachay pressurized irrigation network, specifically in the Yulchirón 2 branch. In this context, the irrigation branch was designed to operate on an on-demand basis and in shifts in order to evaluate the indicators in both operation modes, subjecting the network to unfavourable events. The resilience at the level of pressures and demands of the branch is estimated to remain operational in the different disruptive events, meeting the minimum conditions of the initial design. In this regard, with the implementation of resilience indicators in irrigation networks, it is possible to diagnose the response of the network to changes in its operation. Therefore, the use of indicators allows for obtaining a more reliable and adaptable network to changes in its operation. Consequently, the use of indicators allows for obtaining more reliable and adaptable networks to changes, since the engineer can make the right decisions in the project, improving the planning and management of irrigation networks. [ABSTRACT FROM AUTHOR]

Background: Symptom checkers have been widely used during the COVID-19 pandemic to alleviate strain on health systems and offer patients a 24-7 self-service triage option. Although studies suggest that users may positively perceive web-based symptom checkers, no studies have quantified user feedback after use of an electronic health record–integrated COVID-19 symptom checker with self-scheduling functionality. Objective: In this paper, we aimed to understand user experience, user satisfaction, and user-reported alternatives to the use of a COVID-19 symptom checker with self-triage and self-scheduling functionality. Methods: We launched a patient-portal–based self-triage and self-scheduling tool in March 2020 for patients with COVID-19 symptoms, exposures, or questions. We made an optional, anonymous Qualtrics survey available to patients immediately after they completed the symptom checker. Results: Between December 16, 2021, and March 28, 2022, there were 395 unique responses to the survey. Overall, the respondents reported high satisfaction across all demographics, with a median rating of 8 out of 10 and 288/395 (47.6%) of the respondents giving a rating of 9 or 10 out of 10. User satisfaction scores were not associated with any demographic factors. The most common user-reported alternatives had the web-based tool not been available were calling the COVID-19 telephone hotline and sending a patient-portal message to their physician for advice. The ability to schedule a test online was the most important symptom checker feature for the respondents. The most common categories of user feedback were regarding other COVID-19 services (eg, telephone hotline), policies, or procedures, and requesting additional features or functionality. Conclusions: This analysis suggests that COVID-19 symptom checkers with self-triage and self-scheduling functionality may have high overall user satisfaction, regardless of user demographics. By allowing users to self-triage and self-schedule tests and visits, tools such as this may prevent unnecessary calls and messages to clinicians. Individual feedback suggested that the user experience for this type of tool is highly dependent on the organization's operational workflows for COVID-19 testing and care. This study provides insight for the implementation and improvement of COVID-19 symptom checkers to ensure high user satisfaction. [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 this paper, a supervised manifold-learning method is proposed for PolSAR feature extraction and classification. Based on the tensor algebra, the proposed method characterizes each pixel with a local neighbourhood centered at it, thereby combining the spatial and polarimetric information within the image. The inherent spatial information is beneficial to alleviate the influence of speckle noise and improve the stability of the extracted features. In addition, the label information of training samples is utilized in feature extraction, therefore the discriminability of different classes can be well preserved. The tensor discriminative locality alignment (TDLA) method is applied to find the multilinear transformation from the original feature space to the low-dimensional feature space. Based on the extracted features in the low-dimensional space, the SVM classifier is applied to achieve the final classification result. A real PolSAR data set acquired over San Francisco is adopted for performance evaluation. The experimental results show that the proposed method can not only improve the classification accuracy, but also alleviate the influence of speckle noise. In addition, the spatial details can be well preserved, demonstrating the superior performance of the proposed method. [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]

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]

Copyright of Intersecciones en Antropología is the property of Universidad Nacional del Centro de la Provincia de Buenos Aires, Facultad de Ciencias Sociales 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.)

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 use of SF6 gas decomposition components for the fault diagnosis of gas-insulated equipment (GIE) has received widespread attention and been extensively adopted by the power industry. However, the present focus is mainly on the use of a component type or absolute content for generating qualitative judgments on the existence of a fault, and the practical diagnosis method for field diagnosis is extremely lacking. In this paper, ten-year fault data were collected and analyzed. Decomposition components were extracted and combined, based on the decomposition characteristics of SF6, and enhancement factors were given to three component characteristic quantities using fuzzy C-means clustering algorithm. The component characteristic quantities that characterized the high-energy discharge fault, partial discharge, and partial over-thermal fault of the device were extracted: % (SOF2+SO2), % (9SO2F2), and % (5CO2). With adoption of these characteristic quantities as the coordinate axes of the triangle coordinate system, a triangle fault diagnosis method for field diagnosis of SF6 GIE was constructed. The diagnosis method was validated by a large number of experimental data and field failure data, and 128 of the 150 experimental samples were correctly recognized. Moreover, the test conditions did not considerably affect the diagnostic results of the method. This technique can provide a simple and effective field diagnostics technology for the power industry. [ABSTRACT FROM AUTHOR]

To model the responses of electromagnetic surveys for geophysical subsurface sensing, a mixed total field/scattered field-based discontinuous Galerkin frequency-domain (TF/SF DGFD) method is proposed in this paper. The proposed TF/SF DGFD method is implemented at a subdomain level based on the domain decomposition technique. Different subdomains can employ either the TF DGFD framework or the SF DGFD framework, which are then coupled through the Riemann transmission condition. To balance the computation efficiency and accuracy for practical applications, the proposed method prefers to using the SF DGFD framework for subdomains with sources and using the TF DGFD framework for the remaining subdomains. At the interfaces between total field and scattered field subdomains, the Riemann transmission condition is slightly modified by incorporating the background fields due to the physically imposed sources in the background media. In this way, the proposed method only requires surface integrals of the background fields as extra overhead instead of elementwise integration of the scattering objects for the purely scattered field-based method, which can improve the computational efficiency. Also, it is more accurate than the purely TF DGFD method given the same mesh. Numerical examples are studied to examine the performance of the proposed method, which is proven to have better accuracy than the TF DGFD method. The TF/SF DGFD method will facilitate modeling of electromagnetic surveys under complicated geophysical environments for subsurface sensing. [ABSTRACT FROM AUTHOR]

Surface charging of spacers is one of the main factors restricting the industrialization of DC gas insulated lines (GILs). The effects of ion mobility, gas pressure and temperature on surface charge behavior are important issues to be clarified. In this paper, a selection method of ion mobility is reviewed and reconsidered. A multiphysics-coupled model considering ion mobility, species of ions, temperature, and gas pressure is investigated. A mathematical model of ion mobility under low and high electric fields is presented and the critical electric field strengths are calculated, based on the current density vs electric field strength. The results show that the two critical electric field strength have different characteristics with gas pressure and temperature. The ion mobility in SF 6 gas has a completely different calculation model in high electric field and low electric field, and these two ion mobilities are equal only at a specific temperature and gas pressure. This paper can provide a reference for the selection of ion mobility in DC GILs under conditions of temperature and pressure. [ABSTRACT FROM AUTHOR]

Crime detection is one of the most important research applications in machine learning. Identifying and reducing crime rates is crucial to developing a healthy society. Big Data techniques are applied to collect and analyse data: determine the required features and prime attributes that cause the emergence of crime hotspots. The traditional crime detection and machine learning-based algorithms lack the ability to generate key prime attributes from the crime dataset, hence most often fail to predict crime patterns successfully. This paper is aimed at extracting the prime attributes such as time zones, crime probability, and crime hotspots and performing vulnerability analysis to increase the accuracy of the subject machine learning algorithm. We implemented our proposed methodology using two standard datasets. Results show that the proposed feature generation method increased the performance of machine learning models. The highest accuracy of 97.5% was obtained when the proposed methodology was applied to the Naïve Bayes algorithm while analysing the San Francisco dataset. [ABSTRACT FROM AUTHOR]

This work surveys the performance of several empirical models, all recalibrated to a common data set, that were developed over the past 25 years to relate freshwater flow and salinity in the San Francisco Estuary (estuary). The estuary's salinity regime--broadly regulated to meet urban, agricultural, and ecosystem beneficial uses--is managed in spring and certain fall months to meet ecosystem objectives by controlling the 2 parts per thousand bottom salinity isohaline position (referred to as X2). We tested five empirical models for accuracy, mean, and transient behavior. We included a sixth model, employing a machine learning framework and variables other than outflow, in this survey to compare fitting skill, but did not subject it to the full suite of tests applied to the other five empirical models. Model performance was observed to vary with hydrology, year, and season, and in some cases exhibited unique limitations as a result of mathematical formulation. However, no single model formulation was found to be consistently superior across a wide range of tests and applications. One test revealed that the models performed equally well when recalibrated to a uniformly perturbed input time-series. Thus, while the models may be used to identify anomalies or seasonal biases (the latter being the subject of a companion paper), their use as inverse models to infer freshwater outflow to the estuary from salinity observations is not expected to improve upon the absolute accuracy of existing outflow estimates. This survey suggests that, for analyses that span a long hydrologic record, an ensemble approach--rather than the use of any individual model on its own--may be preferable to exploit the strengths of individual models. [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 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]

In 1984, San Francisco in effect banned bathhouses in a fear-based response to the raging AIDS epidemic. Despite San Francisco being a progressive leader in the fight against HIV, these venues remained closed for over 36 years, even as our scientific understanding of virus prevention of evolved. The persistence of these regulations, despite advances in contemporary scientific understandings of HIV transmission, exemplify how long fear-based regulations can last and serves as a reminder that it usually requires a concerted effort from community activists to see changes made when science and policy do not align. In 1984, San Francisco effectively shut down gay bathhouses in a desperate attempt to curb HIV transmission, assuming that these venues create what is presently referred to as "super spreader events." Despite changes in the global understanding of HIV and scientific advances in medication, these cultural centers remained effectively banned for over 36 years.1 These closures illustrate a fear-based reaction that resulted from a limited knowledge of HIV. Given PrEP and a deeper understanding of the mechanics of transmission, society can now take proactive steps to arrest the spread of the virus. In 2020, this new understanding of HIV transmission--aided by pointed advocacy and community engagement--finally opened the door for bathhouses to return when Supervisor Raphael Mandelman introduced a measure to lift the ban that the city's Board of Supervisors ultimately approved. This paper will tell the story of advocacy that led to a better alignment of city regulations with contemporary scientific and community practices, and argue that, when properly operated, bathhouses assist, rather than impede, efforts to control the transmission of HIV, as well as other STIs. Following this introduction, I will detail the history of bathhouses within San Francisco and the broader gay community, then examine the arguments that were initially made in favor of the closures. I will then examine the steps taken in the past several years to finally counter these arguments and reverse the ban in city regulation. As I hope to illuminate, these decisions were emotional and driven by fear; however, given current knowledge of the science around transmission, city officials were able to revisit this issue--setting a precedent that any policies that regulate LGBTQI+ sexuality based on fear of HIV should be revisited through the lens of current practices. [ABSTRACT FROM AUTHOR]

• 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]

The uneven distribution of travel demand is incredibly commonplace in cities, but insufficient attention has been paid to this problem. In this paper, we explore the impact of the uneven distribution of travel demand on an accessible network. A model with a sparse and dense mixed grid transit network based on an uneven distribution of travel demand is proposed to provide a high-performance bus service. The transit network was composed of two parts: a dense grid network in the downtown area and a sparse grid network in the periphery. The objective function of the model included agency cost and passenger cost, where the decision variables were the downtown-to-city ratio, the downtown headway, stop spacing (line spacing), and ratio of the periphery headway to the downtown headway. This study validated the proposed model using the demands of San Francisco. The concentrated spatial demand resulted in a lower total cost, whereas the varying travel demand must be controlled within an appropriate range to maintain the bus performance. The stable bus lines and stops with a variable timetable of the proposed model are profitable for fast-growing cities. [ABSTRACT FROM AUTHOR]

Crime predictors have been sought after by governments and citizens alike for preventing or avoiding crimes. In this paper, we attempt to thoroughly analyze crime predictors from three Web open data sources: Google Street View (GSV), Twitter, and Foursquare, which provides visual, textual, and human behavioral data respectively. In contrast to existing works that attempt crime prediction at zip-code level or coarser granularity, we focus on street-level crime prediction. We transform data assigned to street-segments, and extract and determine strong predictors correlated with crime. Particularly, we are the first to discover visual clues on street outlooks that are predictive for crime. We focus on the city of San Francisco, and our extensive experiments show the effectiveness of predictors in a range of tests. We show that by analyzing and selecting strong predictors in Web open data, one could achieve significantly better crime prediction accuracy, comparing to traditional demographic data-based prediction. [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]

Copyright of Revista de Estudios Andaluces is the property of Revista de Estudios Andaluces 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.)

Railroads were instrumental in opening the western U.S. in the 19th century. The main incentive provided for the railroads to connect San Francisco, CA and Omaha, NB were land grants out of the public domain. In a period of 21 years (1850-1871), 174 million acres were patented (deeded) primarily to what would become four major railroads. Many acres were returned to the government, some were sold and many others were retained or disposed of through holding companies. This paper emphasizes an oft-overlooked reality: that in many cases the railroads leveraged these land grants in support of the growth and prosperity of the U.S. and that privatizing these lands to the railroads was the most productive use of those resources at that point in history. Further, those grants continue to bear fruit in the present day. We contend that the counterfactual, one in which those lands remained in the public domain and under control of the federal government, would not have yielded anywhere near the development and wealth that they created under the railroads’ control. [ABSTRACT FROM AUTHOR]

In this paper, we consider a scenario where a robot needs to establish connectivity with a remote operator or another robot, as it moves along a path. We are interested in answering the following question: what is the distance traveled by the robot along the path before it finds a connected spot? More specifically, we are interested in characterizing the statistics of the distance traveled along the path before it gets connected, in realistic channel environments experiencing path loss, shadowing and multipath effects. We develop an exact mathematical analysis of these statistics for straight-line paths and also mathematically characterize a more general space of loop-free paths (beyond straight paths) for which the analysis holds, based on the properties of the path such as its curvature. Finally, we confirm our theoretical analysis using extensive numerical results with real channel parameters from downtown San Francisco. [ABSTRACT FROM AUTHOR]