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The Atlantic Coast of the United States is subjected to damage from wind and flooding as a result of coastal storms. This paper seeks to model the joint probability of exceedance for wind speed and flood elevation, two common measures of storm severity, using the storm data provided by the North Atlantic Coast Comprehensive Study. A case study is performed for Boston, Massachusetts. A copula is used to model the dependence between storm severity measures. The Gaussian copula and the Archimedean copulas (i.e., Gumbel, Clayton, and Frank) are examined in this paper. The correlation matrix used to construct the Gaussian copula is estimated empirically, and the parameters of the Archimedean copulas are estimated by a semi-parametric estimation method. The Gumbel copula, which is found to describe the dependence between the severity measures better than other copulas, is used to develop a copula-based joint probability model. The methodology presented in this paper is used to estimate joint hazard curves for wind speed and flood elevation for different mean recurrence intervals. The results can be used to define hazard scenarios in a multihazard study, estimate building performance under coastal storms, and assess community resilience. [ABSTRACT FROM AUTHOR]

Recently, the City of Boston implemented a mobile, cloud-based app. Originally called "Citizens Connect," but now called "Bos:311," the app allows citizens to report issues related to the city's infrastructure, e.g., graffiti. Rich information in the form of pictures and descriptions provided from mobile devices, along with GPS location information, facilitates crowdsourcing monitoring of the city's infrastructure. This app provides "straight through" processing of citizen requests, reengineering the processes of monitoring and fixing city infrastructure. As a result, operations and management accounting information can be more accurate and used in real time. In addition, the cloud-based capture of information from multiple cities allows comparisons and benchmarking of critical information that likely would not be possible in other settings, while allowing economic system use by large numbers of users. This paper investigates a number of hypotheses about the use of the mobile app generated from both Hirschman's and Hammer's theories. [ABSTRACT FROM AUTHOR]

The purpose of this article is to add to the collective body of knowledge on student behavior and attitudes relative to the adoption of digital textbooks. The article summarizes an ongoing research project that examines past, current and evolving behavior in the classroom related to digital textbooks and school. It includes students, faculty and administrative attitudes behaviors and perceptions. This research was undertaken at the Sawyer Business School of Suffolk University in Boston, Massachusetts. Student attitudes and behavior toward their use of digital textbooks (eTextbooks) in higher education was examined in an ongoing longitudinal study over two years at Sawyer Business School, Suffolk University. Students in the class were divided into six teams. Five of the teams were assigned an eTextbook device and the sixth team was given a paper textbook for use through the semester. The digital technologies examined were: Amazon Kindle, Sony eReader Touch, Apple iPad, enTourage eDGe, and CourseSmart. Student attitudes and behaviors were examined pre and post class by survey each semester, and during the semesters through quizzes, journals and classroom discussion. Differential learning was measured between the six teams. Student attitudes and behaviors are becoming more receptive to and accepting of using digital textbooks each year. There was no significant difference in learning between the eTextbook devices teams or between them and the paper textbook team. [ABSTRACT FROM AUTHOR]

The early decades of the pastoral care movement were characterized by a remarkable collaboration with psychiatry. While historians of the religious aspects of this movement have noted the reliance of pastoral care on psychiatry and psychology, it has been less clear how and why mental health professionals elected to work with clergy. This paper uses the Michigan Society of Pastoral Care (MSPC), one of the early training programs for hospital chaplains on the model of the Boston‐based Institute for Pastoral Care, as a window to explore the interactions between psychiatry and religion at mid century. Raymond Waggoner, the nationally recognized and well‐connected chair of the psychiatry department at the University of Michigan, was instrumental in expanding the influential pastoral care program at his hospital and in his state as part of his bigger mission of emphasizing the fundamental role of psychiatry in American life. Waggoner played a key role within the MSPC, in conjunction with leaders within the medical departments of the major hospitals in the state. All of the members of the MSPC viewed psychiatry's insights as essential for pastoral care, with the caveat that chaplains should remain pupils, not practitioners of psychotherapy. [ABSTRACT FROM AUTHOR]

In this paper, we consider variable selection for a class of semiparametric spatial autoregressive models based on exponential squared loss (ESL). Using the orthogonal projection technique, we propose a novel orthogonality-based variable selection procedure that enables simultaneous model selection and parameter estimation, and identifies the significance of spatial effects. Under appropriate conditions, we show that the proposed procedure is consistent and the resulting estimator has oracle properties. Furthermore, some simulation studies and an analysis of the Boston housing price data are also carried out to examine the finite-sample performance of the proposed method. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) has received increasingly more attention because of its high efficiency and ease of implementation. However, the existing ELM algorithms generally suffer from the drawbacks of noise sensitivity and poor robustness. Therefore, we combine the advantages of twin hyperplanes with the fast speed of ELM, and then introduce the characteristics of heteroscedastic Gaussian noise. In this paper, a new regressor is proposed, which is called twin extreme learning machine based on heteroskedastic Gaussian noise (TELM-HGN). In addition, the augmented Lagrange multiplier method is introduced to optimize and solve the presented model. Finally, a significant number of experiments were conducted on different data-sets including real wind-speed data, Boston housing price dataset and stock dataset. Experimental results show that the proposed algorithms not only inherits most of the merits of the original ELM, but also has more stable and reliable generalization performance and more accurate prediction results. These applications demonstrate the correctness and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Analysis of the unofficial runner-up in the 1961–62 design competition for Boston City Hall—the scheme by the Philadelphia-based team of Mitchell/Giurgola Architects (MGA) in association with David A. Crane and Thomas R. Vreeland Jr.—reveals not the creation of an isolated monument but a humanist restructuring of a city's urban spaces at the heart of a modernist-inspired post-war government centre. Unusually for the time, this scheme was developed through deep dialogue with an existing urban morphology, historic buildings, and the literal "ground" of the city. This paper highlights the scheme as the first in a series of significant urban design projects undertaken by MGA in the United States in the 1960s and 1970s. These projects sought to directly engage with the structure, spaces, and artefacts of the historic American city. Further, MGA's City Hall also crystallised compositional themes that would be pursued and developed by the practice in subsequent decades. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of a context-based fuzzy C-means (CFCM)-based multi-granular fuzzy model (MGFM) with hierarchical tree structures. For this purpose, we propose three types of hierarchical tree structures (incremental, aggregated, and cascaded types) in the design of MGFM. In general, the conventional fuzzy inference system (FIS) has problems, such as time consumption and an exponential increase in the number of if–then rules when processing large-scale multivariate data. Meanwhile, the existing granular fuzzy model (GFM) reduces the number of rules that increase exponentially. However, the GFM not only has overlapping rules as the cluster centers become closer but also has problems that are difficult to interpret due to many input variables. To solve these problems, the CFCM-based MGFM can be designed as a smaller tree of interconnected GFMs. Here, the inputs of the high-level GFMs are taken from the output to the low-level GFMs. The hierarchical tree structure is more computationally efficient and easier to understand than a single GFM. Furthermore, since the output of the CFCM-based MGFM is a triangular fuzzy number, it is evaluated based on a performance measurement method suitable for the GFM. The prediction performance is analyzed from the automobile fuel consumption and Boston housing database to present the validity of the proposed approach. The experimental results demonstrate that the proposed CFCM-based MGFM based on the hierarchical tree structure creates a small number of meaningful rules and solves prediction-related problems by making them explainable. [ABSTRACT FROM AUTHOR]

Aiming at the problem that fuzzy neural network (FNN) is difficult to be adjusted automatically its structure when there is no the threshold of loss function, as well as the problem that the neuron number of the regularization layer of FNN is adjusted by self-organizing algorithm when the structure of FNN is not stable yet, a structural design strategy of self-organizing recursive FNN based on the Boston matrix (SORFNN-BOSTON) is proposed. Compared with other self-organizing algorithms, the method used in this paper does not need to set the threshold of loss function. In addition to the indicators representing the importance of neurons in most self-organizing algorithms, the change rate is used to represent the change of the parameters of the neural network. The change rate is used to determine when the relevant parameters are stable, which further improves the reliability of the neuron adjustment process. Through the simulation of predicting Mackey-Glass time sequence, the final number of neurons in the hidden layer and the testing error are 6 and 0.110 respectively. Comparisons with other self-organizing algorithms show that the testing error decreased by 76.6% at most and 13.3% at least, which proves the practicability of the method. [ABSTRACT FROM AUTHOR]

This paper traces the centrality of the human face in the construction of modern individuality. It argues that the face of individuality no less than that of typology, is mired in and born of historical and political conditions that are subsequently disavowed in order that the individual (and the face she bears) is rendered a product of nature, an instantiation of the universal. Attempting to denaturalize and defamiliarize the authority invested in the face, this paper maps out three interrelated arguments: that the human face is historically produced; that its history is closely tethered to the production of modern subjectivity, and that its status as a purveyor of meaning relies upon the reiteration of preexisting norms through which it can be "read." And yet, while this paper turns to the nineteenth century to trace the novel privileging of the face as an extension of selfhood, interwoven through this history is the figure of the "effaced" Muslim woman and the Muslim terrorist type. [ABSTRACT FROM AUTHOR]

The Olympic Games are a major undertaking that promise both large costs and potentially large benefits to host cities. This paper lays out the potential economic benefits of hosting the Olympics and details how, in the vast majority of cases, these gains are unlikely to cover the costs of hosting the event. The ideas are then applied to the experience of Boston in its ultimately unsuccessful bid for the 2024 Summer Olympics. The ideas behind this paper were first presented at "Behind the Games: The Effect of the Olympics on Host Cities," UConn Law School, Hartford, CT, April 2019. [ABSTRACT FROM AUTHOR]

This paper serves as an introduction to the "Contested urban territories: decolonized perspectives" special issue. The idea for this issue emerged during our reflections on a socioterritorial perspective, preeminent in the current Latin American analysis of contemporary urban struggles (Schwarz and Streule, 2016). It aims to contribute to these ongoing debates about a specific understanding of urban territories from a postcolonial and decolonized perspective by combining contributions from two paper sessions we organized at the 2017 meeting of the American Association of Geographers in Boston with additional papers by scholars who could not participate in the conference. All seven contributions tackle the question of what a relational and dynamic conceptualization of territory may contribute to current debates in the urban studies field. Put more precisely, to which extent are socioterritorial approaches of value for a further decentering and pluralizing of urban theory? What is their significance to research on urban social movements? And, finally, how does such a socioterritorial perspective nurture and complement an analysis of the social production of space? The present special issue invites the reader to get familiar with new concepts and engage in a critical reflection on the conditions of knowledge production in urban geography and beyond. Kurzfassung: Dieser Artikel ist der Auftakt zum Themenheft "Contested urban territories: decolonized perspectives". Die Idee zu diesem Heft entstand in Auseinandersetzung mit einer sozioterritorialen Perspektive, welche zeitgenössische lateinamerikanische Analysen aktueller urbaner Kämpfe prägt (Schwarz und Streule, 2016). Mit dem Ziel, diese laufenden Debatten rund um ein spezifisches Verständnis von urbanen Territorien aus einer post- bzw. dekolonialen Perspektive weiterzuführen, versammeln wir im Themenheft Beiträge aus zwei Veranstaltungen auf der Jahrestagung der American Association of Geographers 2017 in Boston, und ergänzen diese mit weiteren Artikeln von Autorinnen und Autoren, die nicht an dieser Konferenz teilnehmen konnten. Die sieben Beiträge befassen sich mit der Frage, was eine relationale und dynamische Konzeptualisierung von Territorium zu aktuellen Debatten in der Stadtforschung beitragen kann. Genauer: Inwieweit sind sozioterritoriale Ansätze für die Dezentrierung und Pluralisierung von Stadttheorie von Nutzen? Welche Relevanz haben sie für eine Analyse städtischer sozialer Bewegungen? Wie nährt und ergänzt eine sozioterritoriale Perspektive nicht zuletzt eine Analyse der sozialen Produktion von Raum? Das Heft lädt somit ein, neue Begrifflichkeiten kennenzulernen und kritisch über Wissensproduktion nachzudenken – für die Stadtgeographie und darüber hinaus. [ABSTRACT FROM AUTHOR]

Bryophytes constitute a heterogeneous group of plants which includes three clades: approximately 14,000 species of mosses (Bryophyta), 6000 species of liverworts (Marchantiophyta), and 300 species of hornworts (Anthocerotophyta). They are common in almost all ecosystems, where they play important roles. Bryophytes lack developed physical barriers, yet they are rarely attacked by herbivores or pathogens. Instead, they have acquired the ability to produce a wide range of secondary metabolites with diverse functions, such as phytotoxic, antibacterial, antifungal, insect antifeedant, and molluscicidal activities. Secondary metabolites in bryophytes can also be involved in stress tolerance, i.e., in UV-absorptive and drought- and freezing-tolerant activities. Due to these properties, for centuries bryophytes have been used to combat health problems in many cultures on different continents. Currently, scientists are discovering new, unique compounds in bryophytes with potential for practical use, which, in the age of drug resistance, may be of considerable importance. The aim of this review is to present bryophytes as a potential source of compounds with miscellaneous possible uses, with a focus on volatile compounds and antibacterial, antifungal, and cytotoxic potential, and as sources of materials for further promising research. The paper also briefly refers to the methods of compound extraction and acquisition. Formulas of compounds were drawn by the authors using ChemDraw software (PerkinElmer, Boston, MA, USA) with reference to data published in various papers, the ACD/Labs dictionary database, PubChem, and Scopus. The data were gathered in February 2022. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning has become more and more popular in recent years. The according planning of the standpoint network is a crucial issue influencing the overhead and the resulting point cloud. Fully static approaches are both cost and time extensive, whereas fully kinematic approaches cannot produce the same data quality. Stop-and-go scanning, which combines the strengths of both strategies, represents a good alternative solution. In the scanning process, the standpoint planning is by now mostly a manual process based on expert knowledge and relying on the surveyor's experience. This paper provides a method based on Mixed Integer Linear Programming (MILP) ensuring an optimal placement of scanner standpoints considering all scanner-related constraints (e.g. incidence angle), a full coverage of the scenery, a sufficient overlap for the subsequent registration and an optimal route planning solving a Traveling Salesperson Problem (TSP). This enables the fully automatic application of autonomous systems for providing a complete model while performing a stop-and-go laser scanning, e.g. with the Spot robot from Boston Dynamics. Our pre-computed solution, i.e. standpoints and trajectory, has been evaluated surveying a real-world environment using a 360° panoramic laser scanner and successfully compared with a precise LoD2 building model of the underlying scene. The performed ICP-based registration issued from our fully automatic pipeline turns out to be a very good and safe alternative of the otherwise laborious target-based registration. [ABSTRACT FROM AUTHOR]

Epilepsy is a chronic and recurrent brain dysfunction disease. An acute epileptic attack will interfere with a patient's normal behavior and consciousness, having a great impact on their life. The purpose of this study was to design a seizure prediction model to improve the quality of patients' lives and assist doctors in making diagnostic decisions. This paper presents a transformer-based seizure prediction model. Firstly, the time-frequency characteristics of electroencephalogram (EEG) signals were extracted by short-time Fourier transform (STFT). Secondly, a three transformer tower model was used to fuse and classify the features of the EEG signals. Finally, when combined with the attention mechanism of transformer networks, the EEG signal was processed as a whole, which solves the problem of length limitations in deep learning models. Experiments were conducted with a Children's Hospital Boston and the Massachusetts Institute of Technology database to evaluate the performance of the model. The experimental results show that, compared with previous EEG classification models, our model can enhance the ability to use time, frequency, and channel information from EEG signals to improve the accuracy of seizure prediction. [ABSTRACT FROM AUTHOR]

This paper explores a novel high-dimensional sparse multiplicative model, which deal with data with positive responses, particularly in economical and biomedical researches. The proposed regularized method is conducted on the least product relative error (LPRE), and can be applied on various penalties including adaptive Lasso, SCAD, and MCP. An adjusted ADMM algorithm is adopted to obtain the estimators based on LPRE loss. Additionally, we prove the consistency and compute the convergence rates of the estimator. To validate the effectiveness of the proposed method, we conduct extensive numerical studies and real data analysis, yielding valuable insights and practical applications, utilizing well-known datasets of the Boston housing data and gold price data. [ABSTRACT FROM AUTHOR]

The urban radio‐frequency (RF) noise generated by our cities continues to change with time. Although models exist to describe the RF noise as functions of frequency and urban land use types, very few models describe the spatial character or structure of the noise on the scales of city blocks (50–150 m). The goal of this work is to investigate the connection between urban morphology and the higher‐order spatial statistics of the noise field. To achieve this goal, a large measurement campaign was conducted in Boston, Massachusetts. Many spatial measurements allowed for calculation of spatial correlation functions of noise power in three different neighborhoods, which were used to quantify the spatial structure of the fields. A statistical point source model is then developed, with adjustable parameters relating to urban morphology. Good agreement between the model and the experimental correlation functions suggests the 25 MHz urban noise field is well described by a random network of fixed point sources, radiating with a 1/r power law behavior. Plain Language Summary: Our modern cities are filled with electronic devices. Each device can emit radiation and contribute to what is called the urban radio‐frequency noise field. The noise field is the combined effect from all these devices. If the noise field is strong enough it can negatively impact wireless communication, and the use of other electric devices. It is important to better understand the nature of the noise field in order to mitigate and plan for its negative effects. This paper describes in detail how the noise field is distributed in space, or its spatial structure. A theoretical model or tool is developed to help predict how the noise field looks spatially. Key Points: Radio‐frequency noise spatial correlation functions were calculated for three different neighborhoods in Boston, MassachusettsGood theoretical agreement suggests the noise field is well described by a random network of point sources radiating with a 1/r behavior [ABSTRACT FROM AUTHOR]

The article presents information on the psychology of newspapers along with an exhaustive study of the treatment, which Boston newspapers accorded to revision of the Neutrality Act that gripped the attention of the U.S. in the fall of 1939. This investigation is based upon a complete sample of weekday and Sunday editions of English-language newspapers published in Boston, Massachusetts. The extent to which this simplification of the story took place in the Boston papers was estimated as carefully as possible. The evidence indicates that editors and newswriters attempt to give as comprehensive and adequate a representation of events as they dare; while the readers insist upon selecting, sharpening, and pointing the issue still further to suit their desire for simplification and definiteness. Newspapers must dramatize and select in order to produce in their readers the emotional integration required for a good fight. A newspaper's pattern of influence is built around its editorial policy. Most papers do to a certain extent select news items favoring the editorial policy of the paper, and reject those that are opposed. In summary, the evidence reported in this study is interpreted as supporting five generalizations which are offered here as tentative laws in the new field of the psychology of newspapers: (1) issues are skeletonized; (2) any given newspaper's field of influence is well-patterned; (3) readers are more emotional than editors; (4)public interest as reflected in newspapers is variable in time; (5) public interest rapidly fatigues and presses for an early closure.

In this paper, we present a new explainability formalism designed to shed light on how each input variable of a test set impacts the predictions of machine learning models. Hence, we propose a group explainability formalism for trained machine learning decision rules, based on their response to the variability of the input variables distribution. In order to emphasize the impact of each input variable, this formalism uses an information theory framework that quantifies the influence of all input–output observations based on entropic projections. This is thus the first unified and model agnostic formalism enabling data scientists to interpret the dependence between the input variables, their impact on the prediction errors and their influence on the output predictions. Convergence rates of the entropic projections are provided in the large sample case. Most importantly, we prove that computing an explanation in our framework has a low algorithmic complexity, making it scalable to real-life large datasets. We illustrate our strategy by explaining complex decision rules learned using XGBoost, Random Forest or Deep Neural Network classifiers on various datasets such as Adult Income , MNIST , CelebA , Boston Housing , Iris , as well as synthetic ones. We finally make clear its differences with the explainability strategies LIME and SHAP , which are based on single observations. Results can be reproduced using the freely distributed Python toolbox https://gems-ai.aniti.fr/. [ABSTRACT FROM AUTHOR]

Environmental data with a high spatio-temporal resolution is vital in informing actions toward tackling urban sustainability challenges. Yet, access to hyperlocal environmental data sources is limited due to the lack of monitoring infrastructure, consistent data quality, and data availability to the public. This paper reports environmental data (PM, NO2, temperature, and relative humidity) collected from 2020 to 2022 and calibrated in four deployments in three global cities. Each data collection campaign targeted a specific urban environmental problem related to air quality, such as tree diversity, community exposure disparities, and excess fossil fuel usage. Firstly, we introduce the mobile platform design and its deployment in Boston (US), NYC (US), and Beirut (Lebanon). Secondly, we present the data cleaning and validation process, for the air quality data. Lastly, we explain the data format and how hyperlocal environmental datasets can be used standalone and with other data to assist evidence-based decision-making. Our mobile environmental sensing datasets include cities of varying scales, aiming to address data scarcity in developing regions and support evidence-based environmental policymaking. Measurement(s) Particulate matter, gaseous pollutants Technology Type(s) Low-cost environmental sensing platform (City Scanner) Sample Characteristic - Environment City Sample Characteristic - Location Global [ABSTRACT FROM AUTHOR]

This paper solves a question raised in the paper of Huong, Yao, and Yen [Optimal processes in a parametric optimal economic growth model. Taiwanese J Math. 2020. Available from: ] about optimal economic growth problems with production functions and utility functions being all in the form of AK functions. By using a solution existence theorem from the paper of Huong [Solution existence theorems for finite horizon optimal economic growth problems. Preprint arXiv:2001.03298v2. Submitted] and a maximum principle from the book of Vinter [Optimal control. Boston (MA): Birkhäuser; 2000], we prove that the problem in question has a unique solution and give a comprehensive synthesis of the optimal processes. Our results show that if the value of total factor productivity is enough high and the planning time is short, then expanding the production facility does not lead to a higher total consumption satisfaction of the society. Meanwhile, if the value of total factor productivity is enough high and the planning time is relatively long, then the highest total consumption satisfaction of the society is attained only if the largest expansion of the production facility is made until a special time. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of optimizing objective functions in engineering problems influenced by multiple environmental factors, such as temperature and humidity. Traditional modeling approaches often struggle to capture the complexities of non-ideal situations. In this research, we propose a novel approach called the Environment Parameter Fixed Algorithm (EPFA) for optimizing the objective function of a deep neural network (DNN) trained in a specific environment. By fixing the environmental parameters in the DNN defined objective function, we transform the original optimization problem into a control parameter optimization problem. We integrate EPFA-CLS (Controllable local-Optimal Solution) with Gradient Descent and algorithms such as Adagrad to obtain the optimal solution. To demonstrate the concept, we apply our approach to an optimal course model and validate it using optimal course and Boston house price datasets. The results demonstrate the effectiveness of our approach in handling optimization problems in complex environments, offering promising outcomes for practical engineering applications. [ABSTRACT FROM AUTHOR]

Spatial regression models have recently received a lot of attention in a variety of fields to address the spatial autocorrelation effect. One important class of spatial models is the Conditional Autoregressive (CA). Theses models have been widely used to analyze spatial data in various areas, as geography, epidemiology, disease surveillance, civilian planning, mapping of poorness signals and others. In this article, we propose the Liu-type pretest, shrinkage and positive shrinkages estimators for the large-scale effect parameter vector of the CA regression model. The set of the proposed estimators are evaluated analytically via their asymptotic bias, quadratic bias, the asymptotic quadratic risks, and numerically via their relative mean squared errors. Our results demonstrate that the proposed estimators are more efficient than Liu-type estimator. To conclude this paper, we apply the proposed estimators to the Boston housing prices data, and applied a bootstrapping technique to evaluate the estimators based on their mean squared prediction error. [ABSTRACT FROM AUTHOR]

Ventriculo-ureteral (VU) shunting is a little-known method of managing hydrocephalus. This paper reviews contemporary uses of this shunting technique and describes its historical significance to the field of organ transplantation. The ureter may serve as a possible backup, or alternative, distal drainage site compared to the more common peritoneum, atrium, and pleural space. Sporadic contemporary uses of the VU shunt have been reported in unique situations, demonstrating a possible utility in modern neurosurgery. Interestingly, the VU shunt played an important role in the development of kidney transplantation. In the late 1940s and early 1950s, David Hume, a general surgery resident, and colleagues at the PBBH undertook a series of human kidney transplantations. Concurrently, Donald Matson, a pediatric neurosurgeon at Peter Bent Brigham, was utilizing the VU shunt in hydrocephalic patients. Dr. Matson's VU shunt technique involved total nephrectomy, and some of the kidneys harvested from Dr. Matson's were used by his general surgery colleagues in their transplantation trials. Although none of the transplanted kidneys from this series were successful, the transplant team in Boston, minus David Hume, went on to perform the world's first kidney transplant a few years later. This relatively unfamiliar procedure may be applicable to specific situations, and it is of historical importance to the field of transplantation. [ABSTRACT FROM AUTHOR]

This paper aims to contribute to research and policy that supports Latino children in early education regarding their transition into the school system and their bilingual development. It presents the results of a one-year longitudinal ethnographic study of four Latino children starting school at a transitional bilingual kindergarten in the Boston area. The analysis identifies dynamics and trends at the micro and meso levels that show that Spanish use and development in Latino children cannot be fully understood and supported if we consider it an individual ability instead of an organic and intrinsic component of broader dynamic socialization, emotional, and academic processes (such as the transition to school) of which English is also a part and in which parents and teachers participate. Recommendations for designing and implementing comprehensive programs to engage Latino families and educators to support transitions, bilingual development, and academic performance in young Latino children are presented. [ABSTRACT FROM AUTHOR]

The first three children's museums in the world opened in Brooklyn, New York (1899), Boston, Massachusetts (1913), and Detroit, Michigan (1917). This paper examines the contributions of children's museum professionals to museum education through presentations at the American Association of Museums given by the curators of the first three children's museums – Anna Billings Gallup's (Brooklyn), Delia I. Griffin (Boston), and Gertrude A. Gillmore (Detroit). A review of the papers they delivered to their colleagues illustrates how their pioneering educational approaches – such as encouraging visitors to interact with objects and creating opportunities for children to become empowered and invested museum visitors – continue to shape the field. It also highlights the value of including a seat at the table for children's museum professionals in conversations about museum education. [ABSTRACT FROM AUTHOR]

Collective-action problems affect the structure of stakeholder networks differently in policy settings (Berardo and Scholz 2010). However, interactions in policy settings do not usually occur in an institutional vacuum; instead, they are guided and constrained by agreed-on rules. Therefore, to better understand behavior in these settings, it is important to understand the parameters that guide and constrain it. Combining arguments from game theory and social network analysis, this paper focuses on how the nature of collective-action problems affect the design of formal institutional arrangements. The cases are two institutional arrangements for the provision of high-quality drinking water, in New York City and in Boston. The design of these arrangements is measured through Networks of Prescribed Interactions (NPIs), capturing patterns of interactions mandated by formal rules. NPI structures in each case are then compared analyzing their structural measures and applying exponential random graph models (ERGMs). By comparing these NPIs, the paper assesses the effects of collective-action problems on the design of formal institutional arrangements. Results show that cooperation problems are associated with designs prescribing redundant interactions that create a balanced distribution of responsibilities among the key actors to the agreement. [ABSTRACT FROM AUTHOR]

Presents a set of papers featured in the December 1999 issue of "Culture, Medicine and Psychiatry," which analyzed narratives of caregivers of elders with dementia in Boston, Massachusetts. Methods shared across the papers; Multidimensional framework for ethnicity; Directions for future research.

A couple of years ago, I worked in the writing center with a student on a paper about her identity development. She received high marks for content but lost points for writing. As I read her paper, two things struck me: First, she had grown up in Boston, but her parents were from the Democratic Republic of the Congo. At home, she spoke Lingala, which she hid from her friends after she was mocked. Second, many of her sentences were indecipherable. She wrote, for example, “I didn’t have an indistinguishable surface hair from different females in my class and they wouldn’t converse with me or simply give me disposition since I didn’t seem as though them.” (Savini) [ABSTRACT FROM AUTHOR]

This research paper presents the results of two studies investigating human mobility patterns in the 15 largest Metropolitan Statistical Areas (MSAs) in the United States. It studied 14 daily mobility parameters aggregated at the MSA level, derived from four primary mobility parameters: Number of Visited Locations (N_LOC), Number of Unique Visited Locations (N_ULOC), Radius of Gyration (R_GYR), and Distance Traveled (D_TRAV) over a 30-day period. The first study was conducted on data from two large MSAs, one coastal and one inland (Boston and Atlanta, respectively). The aim was to examine associations between daily values of mobility parameters aggregated at the MSA level and identify those carrying similar or identical information. Results of factor analysis showed that these could be adequately described by two independent factors, pointing to one or two of the mobility parameters as sufficient to represent the whole set in analyses based on associations. These could either be D_TRAV, as it had high loadings on both factors, or N_LOC and R_GYR due to their high loadings on the two extracted factors. The second study was conducted on daily mobility datasets from the 15 MSAs. The aim was to compare daily mobility patterns of these MSAs and group them based on their mobility pattern similarities. Factor analysis of the aggregated mean daily distances (D_TRAV) across different MSAs over the studied period classified them into two distinct groups: one predominantly composed of inland MSAs and the other primarily of coastal MSAs. Strong weekly cycle trends emerged in these groups. Specifically, individuals from the inland MSA group tended to travel the furthest on Fridays and the least on Sundays, whereas those from the coastal MSA group traveled the most on Saturdays and the least on Mondays. This weekly pattern was robust, with 7-day lag autocorrelations of mean daily parameter values ranging between 0.81 to 0.99, excluding the mean daily N_LOC. These findings offer a foundational understanding of MSA mobility patterns, paving the way for more detailed studies on the nuances of these patterns. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new class of heterogeneous spatial autoregressive models (heterogeneous SAR models) where the variance parameters are modeled in terms of covariates. In order to estimate the model parameters, as well as their corresponding standard error estimates, we proposed a computational efficient MCMC method which combines the Gibbs sampler with Metropolis-Hastings algorithm. The proposed estimate method is illustrated through numerous simulations and is applied to the Boston housing data. [ABSTRACT FROM AUTHOR]

It is well known that the increase in coastal flood hazard is expected to continue due primarily to climate change. While, previous studies have advanced probabilistic approaches for estimating future coastal flood hazard, two seminal issues have received little attention: (1) sea level trend projections that arise as a consequence of climate change not being incorporated in nonstationary coastal flood frequency analysis and (2) traditional stationary approaches that estimate return periods are not suited for analysis of nonstationary water levels. In this paper, a regression approach for sea level rise estimation is proposed to detrend water levels. The peak-over-threshold approach is used to analyze the probabilistic behavior of detrended water levels via a generalized Pareto distribution. Two interpretations of return period—the expected waiting time until an exceedance event occurs and the time associated with the expected number of exceedance events equal to one—are elaborated in a nonstationary context. By incorporating the existing sea level trend projections and using two interpretations of return period, flood hazard curves with uncertainty (i.e., water levels vs. return periods) are developed to describe the probabilistic behavior of future coastal flood hazards. Two case studies are conducted: one for Boston, Massachusetts, and one for New York City, New York. [ABSTRACT FROM AUTHOR]

Ride-hailing services such as Lyft, Uber, and Cabify operate through smartphone apps and are a popular and growing mobility option in cities around the world. These companies can adjust their fares in real time using dynamic algorithms to balance the needs of drivers and riders, but it is still scarcely known how prices evolve at any given time. This research analyzes ride-hailing fares before and during the COVID-19 pandemic, focusing on applications of time series forecasting and machine learning models that may be useful for transport policy purposes. The Lyft Application Programming Interface was used to collect data on Lyft ride supply in Atlanta and Boston over 2 years (2019 and 2020). The Facebook Prophet model was used for long-term prediction to analyze the trends and global evolution of Lyft fares, while the Random Forest model was used for short-term prediction of ride-hailing fares. The results indicate that ride-hailing fares are affected during the COVID-19 pandemic, with values in the year 2020 being lower than those predicted by the models. The effects of fare peaks, uncontrollable events, and the impact of COVID-19 cases are also investigated. This study comes up with crucial policy recommendations for the ride-hailing market to better understand, regulate and integrate these services. [ABSTRACT FROM AUTHOR]

The article presents information on activities of the World Association for Public Opinion Research (WAPOR) including the decision of WAPOR and the American Association for Public Opinion Research (AAPOR) to issue the Janet A. Harkness Student Paper Award, and the WAPOR 66th Annual Conference in Boston, Massachusetts from May 14-16, 2013.

The article presents objectives of the symposium on tautomers and biology, which is going to be held in Boston, Massachusetts in August 2010. Tautomerism modifies the structure activity relationship of enzyme and substrate and affects enzyme mechanism. Various aspects of tautomerism including calculations of tautomeric equilibrium constant and its use in molecular docking would be discussed at the symposia.

Religion and food culture are two closely related topics in the Christian discourse and have been the subject of extensive anthropological research. This paper takes the Boston Chinese Catholics as a case study, and it adopts an ethnographic research methodology to explore the ways in which the sense of belonging develops in the Church community, based on the analysis of food culture in this context. Chinese Catholics in Boston are mainly Fujian and Hong Kong immigrants, and the class, status, and economic differences between these two communities are well apparent. The Boston Chinese Catholic Church divides food into sacred and secular. During religious rituals, all Catholics share the sacred food, the Holy Body and the Holy Blood, which symbolize the unity of the Catholic Church. In everyday life, Fujian Catholic and Hong Kong Catholic community members follow the dietary habits of their community to maintain a division between each community's traditions. Over the years, the Boston Chinese Catholic Church has developed a flexible strategy—externally consistent and internally differentiated—to maintain the unity of the Church while embracing the cultural differences of its members. This flexible strategy has also influenced the way in which the Boston Chinese Catholic Church operates. This study indicates that we need to place more emphasis on the diversity and complexity of the members of the overseas Chinese Catholic Church and the specificities of the Catholic religion's discourse so that we can better understand the overall practices and methods of overseas Chinese Catholicism. This study is a catalyst for the study of overseas Chinese Catholicism, as well as the study of food culture, religion, community, and identity in that context. [ABSTRACT FROM AUTHOR]

Spatial units typically vary over many of their characteristics, introducing potential unobserved heterogeneity which invalidates commonly used homoskedasticity conditions. In the presence of unobserved heteroskedasticity, methods based on the quasi-likelihood function generally produce inconsistent estimates of both the spatial parameter and the coefficients of the exogenous regressors. A robust generalized method of moments estimator as well as a modified likelihood method have been proposed in the literature to address this issue. The present paper constructs an alternative indirect inference (II) approach which relies on a simple ordinary least squares procedure as its starting point. Heteroskedasticity is accommodated by utilizing a new version of continuous updating that is applied within the II procedure to take account of the parameterization of the variance–covariance matrix of the disturbances. Finite-sample performance of the new estimator is assessed in a Monte Carlo study. The approach is implemented in an empirical application to house price data in the Boston area, where it is found that spatial effects in house price determination are much more significant under robustification to heterogeneity in the equation errors. [ABSTRACT FROM AUTHOR]