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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]

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]

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.

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

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

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]

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

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]

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

In 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]

Provides information on the annual meeting of pediatric radiology society to be held at San Francisco Fairmont Hotel on May 5-10, 2005. Format changes for the presentation of papers and workshop; Theme of the conference; List of schedule of activities.

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

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]

This paper evaluates the metabolism-based performance of a number of centralised and decentralised water reuse strategies and their impact on integrated urban water systems (UWS) based on the nexus of water-energy-pollution. The performance assessment is based on a comprehensive and quantitative framework of urban water metabolism developed for integrated UWS over a long-term planning horizon. UWS performance is quantified based on the tracking down of mass balance flows/fluxes of water, energy, materials, costs, pollutants, and other environmental impacts using the WaterMet2 tool. The assessment framework is defined as a set of key performance indicators (KPIs) within the context of the water-energy-pollution nexus. The strategies comprise six decentralised water reuse configurations (greywater or domestic wastewater) and three centralised ones, all within three proportions of adoption by domestic users (i.e. 20, 50, and 100%). This methodology was demonstrated in the real-world case study of San Francisco del Rincon and Purisima del Rincon cities in Mexico. The results indicate that decentralised water reuse strategies using domestic wastewater can provide the best performance in the UWS with respect to water conservation, green house gas (GHG) emissions, and eutrophication indicators, while energy saving is almost negligible. On the other hand, centralised strategies can achieve the best performance for energy saving among the water reuse strategies. The results also show metabolism performance assessment in a complex system such as integrated UWS can reveal the magnitude of the interactions between the nexus elements (i.e. water, energy, and pollution). In addition, it can also reveal any unexpected influences of these elements that might exist between the UWS components and overall system. [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]

Provides information on meetings of The Minerals, Metals & Materials Society (TMS) in 2005 in the U.S. The 2005 TMS Annual Meeting from February 13 to 17, 2005 in San Francisco, California; 2005 Electronic Material Conference from June 22 to 24, 2005 in Santa Barbara, California; MS&T 2005 from September 25 to 28, 2005 in Pittsburgh, Pennsylvania.

In this paper, a multi-step etching method based on Bosch process was investigated to fabricate a slightly tapered via. The diameter of vias was scaled from 40 to 100 µm. Isotropic etching step was added into Bosch process to control the angle of the tapered vias. The slope angle could be adjusted by changing the time settings of isotropic etching step. The influence of the platen temperature was also studied. The passivation and etching steps are extremely sensitive to temperature. Silicon grass could be formed at low temperature. The two different processes of isotropic SF6 etching and Cl2/HBr etching were also compared. The wrinkles and cracks were observed on the surface after treatment with isotropic SF6 etching. The Cl2/HBr etching method is much better for removing the scallops. [ABSTRACT FROM AUTHOR]

Multi-focus image fusion is a process of combining a set of images that have been captured from the same scene but with different focuses in order to construct an additional sharper image. This process plays an important role in the image processing and machine vision fields. Various algorithms have been developed for this task. The key challenge in the design of multi-focus image fusion algorithms is how to evaluate the local content information of each image from the source images. Simple, but effective, block-based techniques at pixel level are widely used for multi-focus image fusion. However, a fixed block size may not be applicable to every application. A block size that is too small or too large is also not desirable. Hence, optimization of the block size is necessary in order to obtain a fused image that comprises the sharper parts of the source images. Recently, a number of techniques based on evolutionary computation have been applied to block-based multi-focus image fusion. The artificial bee colony (ABC) algorithm is one of the more popular evolutionary computational approaches used to find an optimal solution. In this paper, an efficient and robust block-based multi-focus image fusion method based on the optimal selection of sharper image blocks from source images using best-so-far ABC strategies is proposed. Experiment results show that the proposed method is able to provide good results and outperforms other conventional methods, both visually and quantitatively. [ABSTRACT FROM AUTHOR]

Immediately after an earthquake a healthcare system within a city, comprising several hospitals, endures an extraordinary demand. This paper proposes a new methodology to estimate whether the hospital network has enough capacity to withstand the emergency caused by an earthquake. The ability of healthcare facilities and to provide a broad spectrum of emergency services immediately after a seismic event is assessed through a metamodel that assumes waiting time as main response parameter to assess the hospital network performance. The First Aid network of San Francisco subjected to a 7.2 Mw magnitude earthquake has been used as case study. The total number of injuries and their distributions among the six major San Francisco's Emergency Departments have been assessed and compared with their capacity that has been determined using a survey conducted by the medical staff of the hospitals. The numerical results have shown that three of the six considered San Francisco's hospitals cannot provide emergency services to the estimated injured. Two alternatives have been proposed to improve the performance of the network. The first one redistributes existing resources while the second one considers additional resources by designing a new Emergency Department. [ABSTRACT FROM AUTHOR]

A Correction to this paper has been published: https://doi.org/10.1007/s12237-021-00937-y [ABSTRACT FROM AUTHOR]

Using data collected during a 4 year ethnography, this paper examines how the 1996 Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) has impacted homeless women in San Francisco who are also victims of domestic violence. Specifically this paper looks at how the behavior of abusive men prevents homeless women from successfully navigating the new welfare-to-work requirements and maintaining stable employment. Findings indicate that despite the discourse touting the success of welfare reform, the 1996 PRWORA has further disenfranchised an already devastated population systematically forcing them further onto the margins of society. [ABSTRACT FROM AUTHOR]

The Bubonic Plague outbreak that wormed its way through San Francisco's Chinatown in 1900 tells a story of prejudice guiding health policy, resulting in enormous suffering for much of its Chinese population. This article seeks to discuss the potential for hidden "prejudice" should Artificial Intelligence (AI) gain a dominant foothold in healthcare systems. Using a toy model, this piece explores potential future outcomes, should AI continue to develop without bound. Where potential dangers may lurk will be discussed, so that the full benefits AI has to offer can be reaped whilst avoiding the pitfalls. The model is produced using the computer programming language MATLAB and offers visual representations of potential outcomes. Interwoven with these potential outcomes are numerous historical models for problems caused by prejudice and recent issues in AI systems, from police prediction and facial recognition software to recruitment tools. Therefore, this research's novel angle, of using historical precedents to model and discuss potential futures, offers a unique contribution. [ABSTRACT FROM AUTHOR]

This paper focuses on the contemporary phenomenon of artists who, because of institutional changes in the social organization of the visual arts, become their own gatekeepers by running their own galleries, managing their own exposure and re-establishing direct contact with their audiences and communities. The author contends that the ways in which artists have pursued and organized their own galleries shed light on both the functioning of the established social organization of art and the circumstances that bring about its change. The paper explores the organization, operations and viability of visual artist-managed galleries in the San Francisco and Santa Cruz areas of California as an alternative to the established organization of the art market. [ABSTRACT FROM AUTHOR]

Drug overdose is now the leading cause of unintentional death nationwide, driven by increased prescription opioid overdoses. To better understand urban opioid overdose deaths, this paper examines geographic, demographic, and clinical differences between heroin-related decedents and prescription opioid decedents in San Francisco from 2010 to 2012. During this time period, 331 individuals died from accidental overdose caused by opioids (310 involving prescription opioids and 31 involving heroin). Deaths most commonly involved methadone (45.9 %), morphine (26.9 %), and oxycodone (21.8 %). Most deaths also involved other substances (74.9 %), most commonly cocaine (35.3 %), benzodiazepines (27.5 %), antidepressants (22.7 %), and alcohol (19.6 %). Deaths were concentrated in a small, high-poverty, central area of San Francisco and disproportionately affected African-American individuals. Decedents in high-poverty areas were significantly more likely to die from methadone and cocaine, whereas individuals from more affluent areas were more likely die from oxycodone and benzodiazepines. Heroin decedents were more likely to be within a younger age demographic, die in public spaces, and have illicit substances rather than other prescription opioids. Overall, heroin overdose death, previously common in San Francisco, is now rare. Prescription opioid overdose has emerged as a significant concern, particularly among individuals in high-poverty areas. Deaths in poor and affluent regions involve different causative opioids and co-occurring substances. [ABSTRACT FROM AUTHOR]

Proof School is a private secondary school in San Francisco that focuses on providing a comprehensive math education for students who have a passion for the subject. The school offers a wide range of math courses, including Algebra, Calculus, and Measure Theory, and has a faculty composed of highly qualified teachers. Proof School is committed to creating a diverse and inclusive community, and it offers need-blind admissions and financial aid to ensure affordability for all families. The school also emphasizes extracurricular activities, such as math competitions, game design, and artistic endeavors, to foster a well-rounded educational experience. [Extracted from the article]

The quality of the urban environment is crucial for societal well-being. Yet, measuring and tracking the quality of urban environment, their evolution, and spatial disparities is difficult due to the amount of on-the-ground data needed to capture these patterns. The growing availability of street view images presents new prospects in identifying urban features. However, the reliability and consistency of these methods across different locations and time remains largely unexplored. We aim to develop a comprehensive index of urban quality and change at the street segment level using Google Street View (GSV) imagery. We focus on eight object classes that indicate urban decay or contribute to an unsightly urban space, such as potholes, graffiti, garbage, tents, barred or broken windows, discolored or dilapidated façades, weeds, and utility markings. We train an object detection model on a dataset of images from different cities and assess the performance of these urban indices. We evaluate the effectiveness of this method in various urban contexts over time and discuss its potential for urban planning and public policy. We demonstrate the use of these indices in three applications: the Tenderloin in San Francisco, the Doctores and Historic Center neighborhoods in Mexico City, and South Bend, Indiana. [ABSTRACT FROM AUTHOR]

With its emphasis on empowerment, individual and community capacity building, and translating research findings into action, community-based participatory research (CBPR) may be particularly advantageous in work with urban immigrant populations. This paper highlights eight ways in which CBPR has been shown to add value to work with urban underserved communities. It then describes the background, context, and methods of an ecological CBPR project, the Chinatown Restaurant Worker Health and Safety Study, conducted in San Francisco, California, and draws on study processes and outcomes to illustrate each of the eight areas identified. Challenges of using CBPR, particularly with urban immigrant populations, briefly are described, drawing again on the Chinatown study to provide illustrative examples. We discuss lessons learned, through this and other studies, for the effective use of CBPR with urban immigrant populations. We conclude that despite its challenges, this transdisciplinary, community-partnered and action-oriented approach to inquiry can make substantial contributions to both the processes and the outcomes of the research. [ABSTRACT FROM AUTHOR]

This paper investigates the ability of two different adaptive neuro-fuzzy inference systems (ANFIS) including grid partitioning (GP) and subtractive clustering (SC), in modeling daily pan evaporation (E). The daily climatic variables, air temperature, wind speed, solar radiation and relative humidity of two automated weather stations, San Francisco and San Diego, in California State are used for pan evaporation estimation. The results of ANFIS-GP and ANFIS-SC models are compared with multivariate non-linear regression (MNLR), artificial neural network (ANN), Stephens-Stewart (SS) and Penman models. Determination coefficient (R), root mean square error (RMSE) and mean absolute relative error (MARE) are used to evaluate the performance of the applied models. Comparison of results indicates that both ANFIS-GP and ANFIS-SC are superior to the MNLR, ANN, SS and Penman in modeling E. The results also show that the difference between the performances of ANFIS-GP and ANFIS-SC is not significant in evaporation estimation. It is found that two different ANFIS models could be employed successfully in modeling evaporation from available climatic data. [ABSTRACT FROM AUTHOR]

Information about various topics discussed at the annual meeting held by orthopaedic group Orthopaedic Research Society in San Francisco, California on February 4-7, 2012 is presented. Topics include mesenchymal stem cells (MSCs), progressive ankylosis protein (ANK) and bone repair inflammation. The association of bone aging and osteoporosis is also tackled.

Few existing studies have examined health and oral health needs and treatment-seeking behavior among the homeless and injection drug users (IDUs). This paper describes the prevalence and correlates of health and oral health care needs and treatment-seeking behaviors in homeless IDUs recruited in San Francisco, California, from 2003 to 2005 ( N = 340). We examined sociodemographic characteristics, drug use patterns, HIV status via oral fluid testing, physical health using the Short Form 12 Physical Component Score, self-reported needs for physical and oral health care, and the self-reported frequency of seeking medical and oral health care. The sample had a lower health status as compared to the general population and reported a frequent need for physical and oral health care. In bivariate analysis, being in methadone treatment was associated with care-seeking behavior. In addition, being enrolled in Medi-Cal, California's state Medicaid program, was associated with greater odds of seeking physical and oral health care. Methamphetamine use was not associated with higher odds of needing oral health care as compared to people who reported using other illicit drugs. Homeless IDUs in San Francisco have a large burden of unmet health and oral health needs. Recent cuts in Medi-Cal's adult dental coverage may result in a greater burden of oral health care which will need to be provided by emergency departments and neighborhood dental clinics. [ABSTRACT FROM AUTHOR]

The article discusses websites, DVDs, books, conferences, and products which provide technological resources for teachers. TeacherTube, http://www.teachertube.com, an online video library is reviewed. A book called "Global Information Technologies: Concepts, Methodologies, Tools and Applications," edited by Felix B. Tan is discussed. Websites which illustrate the lack of privacy people face are reviewed, including Zabasearch, http://www.zabasearch.com/, Real Time Satellite Tracking, http://www.n2yo.com, and Street View (Google Maps), http://maps.google.com. The symposium titled "Technology, Reading & Learning Diversity" in San Francisco, California, in January 2009 is announced.

The article discusses the research being conducted by molecular biologists Robert Place and Long-Cheng Li in San Francisco, California on microRNA (miRNA), a type of genetic regulator that dampens gene expression. According to the author, both scientists believe that their research has found miRNAs that could boost gene expression in cells. The author says that their work could generate a heated response in the field of RNA interference, which is the study of how short pieces of RNA regulate the expression of genes.

It is our great pleasure to host the special issue for the joint International Conference on Information Storage and Processing Systems and Micromechatronics for Information and Precision Equipment (ISPS/MIPE 2018). Both ISPS and IIP merged their own annual conferences into MIPE so that participants from both conferences get a chance to meet together. As a tradition of ASME ISPS conference, this year we granted five ISPS Division graduate student fellowships, featured by a monetary award, and four ISPS Division graduate student scholarships, featured by a conference fee waiver. [Extracted from the article]

This paper briefly reviews the sociological literature on the ‘New’ Chinatown phenomenon stressing its structural location vis-à-vis the ‘Old’ Chinatown and the homeland. It defines the New Chinatown as a panethnopolis, that is a global neighborhood with a majority population of Chinese immigrants and of other ethnic groups of mostly Asian descent. It analyzes more particularly the formation, development, and integration of San Francisco's Richmond District's New Chinatown into both the city where it is located and the network of transglobal sites to which it belongs. It provides an interpretation of the New Chinatown as a cultural enclave within the context of globalization theory. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic inspired calls for rapid decarceration of prisons and jails to slow the spread of disease in a high-risk congregate setting. Due to the rarity of intentionally-decarcerative policies, little is known about the effects of rapid decarceration on individuals with serious mental illness (SMI) substance use disorder (SUD), a population who receive many services via the criminal legal system (CLS). We conducted interviews with 13 key informants involved in CLS in San Francisco, CA to better understand the implication of the decarcerative policies put into practice in early 2020. Participants described a tension between the desire to have fewer people incarcerated and the challenges of accessing services and support – especially during the lockdown period of the pandemic – outside of the CLS given the number of services that are only accessible to those who have been arrested, incarcerated, or sentenced. These findings emphasize the need for investing in community social services rather than further expanding the CLS to achieve the goal of supporting individuals with SMI and SUD shrinking the US system of mass incarceration. [ABSTRACT FROM AUTHOR]

Managing urban growth patterns is essential for an economic and demographic development process which maximizes public and private investments in the urban environment. This paper presents the results of a modelling design effort to link a hybrid regional input-output model to demographic, labor force and energy models for use in planning in the San Francisco Bay Region. Extensive use of system feedback is a major component of this research effort. Summary results of the model outputs are presented in this paper. [ABSTRACT FROM AUTHOR]

Estuaries across the globe have been subject to extensive abiotic and biotic changes and are often monitored to track trends in species abundance. The San Francisco Estuary has been deeply altered by anthropogenic factors, which is reflected in substantial declines in some native and introduced fishes. To track trends in fish abundance, a multitude of monitoring programs have conducted regular fish surveys, some dating back to the late 1950s. While these surveys are all designed to track population-scale changes in fish abundance, they are methodologically distinct, with different target species, varying spatial coverage and sampling frequency, and different gear types. To compensate for individual survey limitations, we modeled pelagic fish distributions with integrated data from many sampling programs. We fit binomial generalized linear mixed models with spatial and spatiotemporal random effects to map annual trends in the spatially explicit detection probabilities of striped bass, Delta smelt, longfin smelt, threadfin shad, and American shad for the years 1980 to 2017. Overall, detection probability has declined by approximately 50% for striped bass and is now near zero for the two smelt species, while threadfin shad and American shad have both experienced fluctuations with only slightly reduced detection probabilities by 2017. Detection probabilities decreased dramatically for these fishes in the Central and South Delta, especially after the year 2000. In contrast, Suisun Marsh and the North Delta acted as refuge habitats with reduced levels of decline or even increased detection probabilities for some species. Our modeling approach, using disparate datasets, demonstrates the simultaneous spatially driven decline of pelagic fish species in a highly altered estuary. [ABSTRACT FROM AUTHOR]

In this study, we consider the patient, provider, and public health repercussions of San Francisco's (SF) COVID-related response to homelessness using tourist hotels to house people experiencing homelessness (PEH). We describe the demographics, medical comorbidities, and healthcare utilization patterns of a subset of PEH who accessed the shelter-in-place (SIP) hotel sites during the 2020–2021 pandemic. We focus on how SIP hotels impacted connection to outpatient care and higher-cost emergency utilization. Our mixed methods study integrates qualitative and quantitative data to consider the impact of this temporary housing initiative among a medically complex cohort in a time of increased morbidity and mortality related to substance use. We found that temporary SIP housing increased outpatient care and reduced higher-cost hospital utilization. Our results can inform the future design and implementation of integrated supportive housing models to reduce mortality and promote wellness for PEH. [ABSTRACT FROM AUTHOR]

The planet earth has been facing COVID-19 epidemic as a challenge in recent time. It is predictable that the world will be fighting the pandemic by taking precautions steps before an operative vaccine is found. The IoT produces huge data volumes, whether private or public, through the invention of IoT devices in the form of smart devices with an improved rate of IoT data generation. A lot of devices interact with each other in the IoT ecosystem through the cloud or servers. Various techniques have been presented in recent time, using data mining approach have proven help detect possible cases of coronaviruses. Therefore, this study uses machine learning technique (ABC and SVM) to predict COVID-19 for IoT data system. The system used two machine learning techniques which are Artificial Bee Colony algorithm with Support Vector Machine classifier on a San Francisco COVID-19 dataset. The system was evaluated using confusion matrix and had a 95% accuracy, 95% sensitivity, 95% specificity, 97% precision, 96% F1 score, 89% Matthews correlation coefficient for ABC-L-SVM and 97% accuracy, 95% sensitivity, 100% specificity, 100% precision, 97% F1 score, 93.1% Matthews correlation coefficient for ABC-Q-SVM. In conclusion, the system shows that the process of dimensionality reduction utilizing ABC feature extraction techniques can boost the classification production for SVM. It was observed that fetching relevant information from IoT systems before classification is relatively beneficial. [ABSTRACT FROM AUTHOR]

Proposals by the San Francisco Police Department (SFPD) to use bomb disposal robots for deadly force against humans have met with widespread condemnation. Media coverage of the furore has tended, incorrectly, to conflate these robots with autonomous weapon systems (AWS), the AI-based weapons used in armed conflict. These two types of systems should be treated as distinct since they have different sets of social, ethical, and legal implications. However, the conflation does raise a pressing question: what if the SFPD had proposed using AWS for law enforcement purposes? This article argues that current debate on AWS takes place within a 'killing paradigm' that leaves us ill-placed to understand the implications of using autonomous force outside of armed conflict. This is because 'lethality' is taken as the paradigmatic form of harm, meaning that other harms are understood in a way that is derivative of lethal harm. The article calls for more research on how other sorts of goods, such as freedom from domination, are imperilled by the use of autonomous force. The article also suggests that bringing research on AWS into dialogue with the sorts of concerns that have typically motivated political theory—e.g. power, coercion, the state—provides a fruitful starting point for addressing these issues. [ABSTRACT FROM AUTHOR]

Mobile bicycle transects were conducted in Golden Gate Park and surrounding residential neighborhoods of San Francisco, CA, during the period July–October 2012. Measurements included air temperature at six heights from 0.15 to 2.5 m, as well as relative humidity, solar radiation, and surface radiometric temperature. Park temperatures were lower than surrounding neighborhoods at all measurement levels, and mean Park Cool Island (PCI) values ranged from 0.9 °C for grass playing fields to 1.9 °C for tree groves during typical cool (~ 16 °C) summer conditions. These increased to 2.6 °C and 3.7 °C, respectively, during periodic warm events (~ 30 °C). Grass fields produced strong temperature lapse profiles, similar to asphalt surfaces, while dense trees produced a near-isothermal profile. Shade enhanced cooling by 1–8 °C throughout the 2.5-m layer, peaking near the surface, and frequently producing inversions below 0.5 m. Complexity in the lower surface layer temperature profile suggests measurements of air temperature at one height may be insufficient to characterize the PCI. The park also produced a consistent humidity enhancement, with differences in vapor pressure between urban and park surfaces of up to 10 hPa. The humidity difference was positively correlated with the PCI and also doubled between cool and warm periods. This, along with assessment of the role of shade, and the daytime peak, suggests evaporative cooling and shade cooling are the main drivers of the PCI in Golden Gate Park. The advection of cool air from the park into leeward neighborhoods was limited below 2.5 m, and was only observable within the first few hundred meters. Implications for urban planning suggest urban green spaces that provide abundant vegetation, including dense tree canopies, and are widely accessible to inhabitants can be most effective for in situ thermal comfort, particularly temperature relief during heat events. [ABSTRACT FROM AUTHOR]

There is much debate about the extent to which water management in the upper San Francisco Estuary, California, affects the habitat and status of delta smelt (Hypomesus transpacificus), an endemic fish protected under the US and California Endangered Species Acts. For example, current management reflects the hypothesis that salinity, and in some cases the location of the tidally averaged salinity of 2 parts per thousand (X2), is a reliable indication of habitat quality. We evaluated hypotheses about environmental drivers of the quality of delta smelt habitat (probability of occupancy) during autumn that were developed by experts on the species and estuary. We fit Bayesian occupancy models, which account for imperfect detection, and identified those that best predicted the presence of delta smelt in catch data from 1980 to 2015. The most strongly supported model indicated that occupancy was associated with salinity (measured as specific conductance) and temperature, and detection was associated with body size, sample volume, water clarity, and tidal stage. The second most strongly supported model indicated that occupancy was associated with the abundance of a hypothesized competitor, threadfin shad (Dorosoma petenense), an expert-elicited index of predation intensity, and water clarity, and detection was associated with body size, sample volume, water clarity, and time of day. Our results suggested that clarity did not affect occupancy, but affected finer-resolution processes of local presence and detection at sampling stations. Spatial patterns in occupancy were consistent in wet and dry years, suggesting that management on the basis of salinity oversimplifies estimation of habitat quantity and quality. [ABSTRACT FROM AUTHOR]

Variation in freshwater flow into estuaries can profoundly alter abundance of estuarine organisms through a variety of mechanisms. In the San Francisco Estuary, California, an annual abundance index of juvenile longfin smelt Spirinchus thaleichthys has varied by ~ 100-fold over the range of flow, and over the last five decades the index has declined by over 100-fold. The unknown mechanisms for variation with flow may include removal of larvae by freshwater diversions during low-flow periods. Using data from larval trawl surveys during January–March 2009–2020, we estimated larval population size, its response to freshwater flow, and losses of larvae to freshwater diversions. Population size was estimated by a Bayesian hierarchical model linking a process model, with salinity and water clarity as covariates, to an observation model representing catch by a negative binomial distribution. Population size averaged across surveys within years—an index of the number of larvae produced—decreased over the study period from ~ 109 to 108 larvae. Population size was unrelated to freshwater flow in the year of hatching but positively related to the subsequent juvenile abundance index. Thus, the mechanisms underlying the strong variability in the annual abundance index of longfin smelt with freshwater flow are constrained to occur after March. Estimated proportional losses to water diversions accumulated over the period of vulnerability averaged 1.5% of the population, too low to measurably influence population dynamics. [ABSTRACT FROM AUTHOR]

Environmental DNA (eDNA) detection methods can complement traditional biomonitoring to yield new ecological insights in aquatic systems. However, the conceptual and methodological frameworks for aquatic eDNA detection and interpretation were developed primarily in freshwater environments and have not been well established for estuaries and marine environments that are by nature dynamic, turbid, and hydrologically complex. Environmental context and species life history are critical for successful application of eDNA methods, and the challenges associated with eDNA detection in estuaries were the subject of a symposium held at the University of California Davis on January 29, 2020 (https://marinescience.ucdavis.edu/engagement/past-events/edna). Here, we elaborate upon topics addressed in the symposium to evaluate eDNA methods in the context of monitoring and biodiversity studies in estuaries. We first provide a concise overview of eDNA science and methods, and then examine the San Francisco Estuary (SFE) as a case study to illustrate how eDNA detection can complement traditional monitoring programs and provide regional guidance on future potential eDNA applications. Additionally, we offer recommendations for enhancing communication between eDNA scientists and natural resource managers, which is essential for integrating eDNA methods into existing monitoring programs. Our intent is to create a resource that is accessible to those outside the field of eDNA, especially managers, without oversimplifying the challenges or advantages of these methods. [ABSTRACT FROM AUTHOR]

Estuaries are ecologically and economically important ecosystems but are threatened by non-native invasive species, including many species of submersed aquatic vegetation (SAV). Herbicides are the primary tool used to control SAV, but most field evaluations of herbicides have been conducted in lentic systems. Therefore, managers working in estuaries must base their SAV control programs largely on findings from systems fundamentally different from their own. We conducted a study in the Sacramento-San Joaquin Delta to determine efficacy of the widely used herbicide fluridone in an estuarine ecosystem. The primary goal of SAV removal was restoration of open water habitat for endangered Hypomesus transpacificus (Delta Smelt). Over 18 months and multiple sets of multi-week fluridone applications, we monitored concentrations of fluridone and responses by SAV across pairs of treated and reference sites. Fluridone concentrations in the water were generally below the 2–5 parts per billion required for SAV control. Monitoring demonstrated that these low water concentrations were likely due to dissipation by tides, despite use of pelleted fluridone formulations marketed for flowing water environments. Fluridone did, however, accumulate in sediment at concentrations hundreds of times higher than those measured in the water. Nonetheless, we did not observe lasting reductions in SAV abundance or changes in SAV community composition. By demonstrating lack of efficacy of one of the few herbicides permitted for use in this estuary, this study highlights the need for development of SAV management tools tailored to the challenges of hydrologically complex environments like estuaries. [ABSTRACT FROM AUTHOR]

Sensing SF6 decomposed species is an effective approach to evaluate the operation status of insulation devices. In this paper, we reported a novel nanomaterial, Rh-doped HfSe2 (Rh–HfSe2) monolayer, as a promising media to realize the selective and sensitive detection of SO2 based on the first-principles theory. Our calculations indicated that Rh–HfSe2 monolayer shows a good performance for SO2 adsorption, while it behaves inactive for SOF2 or SO2F2 adsorption. This allows its further exploration as a specific SO2 sensor in SF6 insulation devices. The electron deformation density (EDD) and density of state (DOS) are also adopted to analyze the electronic behavior of Rh–HfSe2 monolayer during gas adsorption. The frontier molecular orbital theory is carried out to understand the sensing mechanism of our proposed material towards SO2 molecule. So, this work steps forward to carry out our final aim of assembling a sensor array and fulfilling the selective detection of different SF6 decomposed species, which can provide significant guidance for the future trail research. [ABSTRACT FROM AUTHOR]

Preserving the genetic diversity of endangered species is fundamental to their conservation and requires an understanding of genetic structure. In turn, identification of landscape features that impede gene flow can facilitate management to mitigate such obstacles and help with identifying isolated populations. We conducted a landscape genetic study of the endangered salt marsh harvest mouse (Reithrodontomys raviventris), a species endemic to the coastal marshes of the San Francisco Estuary of California. We collected and genotyped > 500 samples from across the marshes of Suisun Bay which contain the largest remaining tracts of habitat for the species. Cluster analyses and a population tree identified three geographically discrete populations. Next, we conducted landscape genetic analyses at two scales (the entire study area and across the Northern Marshes) where we tested 65 univariate models of landscape features and used the best supported to test multivariable analyses. Our analysis of the entire study area indicated that open water and elevation (> 2 m) constrained gene flow. Analysis of the Northern Marshes, where low elevation marsh habitat is more continuous, indicated that geographic distance was the only significant predictor of genetic distance at this scale. The identification of a large, connected population across Northern Marshes achieves a number of recovery targets for this stronghold of the species. The identification of landscape features that act as barriers to dispersal enables the identification of isolated and vulnerable populations more broadly across the species range, thus aiding conservation prioritization. [ABSTRACT FROM AUTHOR]