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

The article highlights the Panama-Pacific International Exposition which was held in San Francisco, California in 1915. The record-breaking attendance of more than 18 million visitors made for a revenue-generating enterprise that accelerated the recovery of San Francisco from fire and earthquake of 1906. There were three lantern slides from a collection of eighty-nine slides taken during the exposition. The images showed artists at work and crowds viewing the "Nations of the East." The decorative effects of the lighting scheme at the exposition were wonderful that a number of postcards were made showing the view of the whole fairground.

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 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]

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]

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 article announces the 2009 Annual Meeting for the "Journal of Finance," which will be held in San Francisco, California, from January 3, 2009 to January 5, 2009, the results of the 2008 election, which includes Jeremy Stein, Darrell Duffie, and John Cochrane, and that the AFA and the Department of Finance at Ohio State University have entered into a joint venture to maintain and enhance the finance faculty directory held on the OSU Web site.

Objective Workplace studies have linked hand/wrist tendinosis to forceful and repetitive hand exertions, but the associations are not consistent. We report findings from a prospective study of right wrist tendinosis among blue-collar workers. Methods Workers (N=413) at four industries were followed for 28 months with questionnaires and physical examinations every 4 months to identify incident cases of right wrist tendinosis. Exposure assessment of force and repetition were based on field measurements and video analysis to determine repetition rate and the percent time (% time) in heavy pinch (>1 kg-force) or power grip (>4 kg-force). All exposure variables were measured at the level of the individual and task. For workers responsible for >1 task, a time-weighted average exposure was calculated based on task hours per week. A proportional hazards model was used to assess the relationship between exposures and incidence of wrist tendinosis. Results During the 481 person-years of follow-up, there were 26 incident cases of right wrist tendinosis [incidence rate (IR) 5.40 cases per 100 person-years]. Adjusting for age, gender, and repetition, wrist tendinosis was associated with % time spent in heavy pinch [hazard ratio (HR) 5.01, 95% CI 1.27-19.79]. Composite exposure measure American Conference of Industrial Hygienists Threshold Limit Value (ACGIH-TLV) for hand activity level (HR 3.95, 95% CI 1.52-10.26) was also associated with the outcome for the medium-exposure group using video-based total repetition rate. Conclusions The workplace factors predicting wrist tendinosis were time-weighted average values of% time spent in heavy pinch and the ACGIH-TLV for Hand Activity Level. The % time spent in power grip was not a significant predictor, nor were any measures of repetition. An exposure-response relationship was observed for the % time spent in heavy pinch. These findings may improve programs for preventing occupational wrist tendinosis. [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 article elaborates on the study conducted by Charles Cobb, a neurosurgeon at the California Pacific Medical Center, in San Francisco, California. Aside from practicing his profession, he is also a virus hunter, Since 1998, he studied on whether viruses have an association with brain tumours. He and his co-authors discovered that the human cytomegalovirus (HCMV) needs a particular cellular receptor, normally involved in growth-factor signaling, to infect a type of cancer cell in the brain. He found out that this type of virus causes defects at birth and fatal diseases in immunocompromised people, especially patients with AIDS. People who are infected with the virus is associated with the occurrence of glioma, an extremely aggressive form of brain cancer that is difficult to treat.

The article analyzes the missing link between T lymphocytes and psoriasis. The observations of Immunologist Yan Zheng and his colleagues at the company Genentech in San Francisco, California are discussed in detail. An overview of the cause for the white cell infiltration into the skin and the role of IL-22 in causing the disease of psoriasis, through experimentation on mice, is presented. The article also presents the observations of Wenjun Ouyang on TH 17 and T22 to find the missing link.

The article presents abstracts on topics related to federal reserve banks in San Francisco, California, including the study of the Great Moderation sources, the calibration of exposure at default (EAD) for corporate credit lines, and the constructing price index with weights on the prices of different personal consumption expenditure goods.

Presents a program for the 87th Annual Meeting of the American Economic Association which will be held in San Francisco, California on December 27-30, 1974.

The article examines the effect of spatial under-sampling of a priori data on the retrieval of NO2 columns from satellite observations of solar radiation in San Francisco, California. High resolution moderate resolution imaging spectroradiometer (MODIS) surface reflectance and aerosol data are used. The relative uncertainties for individual measurements can be large given the spatial variations in NO2. The impact of aerosol on airmass factor (AMF) is less than 50 percent.

This special issue of the California Management Review contains a selection of the best papers presented at the Ninth Annual Meeting of the Strategic Management Society, which was held in San Francisco in on October 11-14, 1989. Guest editors Robert Harris and David Mowery provide an overview of the topic of "Strategies for Innovation" (the theme of the conference) and discuss its critical importance to management practice. They provide an introduction to the articles in this special issue and conclude with an exploration of other dimensions of strategy and innovation. [ABSTRACT FROM AUTHOR]

Background: Poor communication between referring clinicians and specialists may lead to inefficient use of specialist services. San Francisco General Hospital implemented an electronic referral system (eReferral) that facilitates iterative pre-visit communication between referring and specialty clinicians to improve the referral process.Objective: The purpose of the study was to determine the impact of eReferral (compared with paper-based referrals) on specialty referrals.Design: The study was based on a visit-based questionnaire appended to new patient charts at randomly selected specialist clinic sessions before and after the implementation of eReferral.Participants: Specialty clinicians.Main Measures: The questionnaire focused on the self-reported difficulty in identifying referral question, referral appropriateness, need for and avoidability of follow-up visits.Key Results: We collected 505 questionnaires from speciality clinicians. It was difficult to identify the reason for referral in 19.8% of medical and 38.0% of surgical visits using paper-based methods vs. 11.0% and 9.5% of those using eReferral (p-value 0.03 and <0.001). Of those using eReferral, 6.4% and 9.8% of medical and surgical referrals using paper methods vs. 2.6% and 2.1% were deemed not completely appropriate (p-value 0.21 and 0.03). Follow-up was requested for 82.4% and 76.2% of medical and surgical patients with paper-based referrals vs. 90.1% and 58.1% of eReferrals (p-value 0.06 and 0.01). Follow-up was considered avoidable for 32.4% and 44.7% of medical and surgical follow-ups with paper-based methods vs. 27.5% and 13.5% with eReferral (0.41 and <0.001).Conclusion: Use of technology to promote standardized referral processes and iterative communication between referring clinicians and specialists has the potential to improve communication between primary care providers and specialists and to increase the effectiveness of specialty referrals. [ABSTRACT FROM AUTHOR]

The ideas of the papers in this special section have originally been presented at the first edition of the workshop on Specification and Validation of Real Time and Embedded Systems (SVERTS) that was held as a satellite workshop of the UML 2003 Conference in San Francisco. The motivation for initiating this workshop was the fact that UML started to be used more and more also for modelling real-time and embedded software systems, whereas it was lacking features for appropriately modelling this kind of systems. Another motivation was the fact that UML was missing an accepted dynamic semantics, or at least a framework for defining profiles not just in terms of syntax, but also their semantics. The six papers in this section reflect all parts of this problem. [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]