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Background: Digital neuropsychological tests reliably capture real-time, process-based behavior that traditional paper/pencil tests cannot detect, enabling earlier detection of neurodegenerative illness. We assessed relations between informant-based subtle and mild functional decline and process-based features extracted from the digital Trail Making Test-Part B (dTMT-B). Methods: A total of 321 community-dwelling participants (56.0% female) were assessed with the Functional Activities Questionnaire (FAQ) and the dTMT-B. Three FAQ groups were constructed: FAQ = 0 (unimpaired); FAQ = 1-4 (subtle impairment); FAQ = 5-8 (mild impairment). Results: Compared to the FAQ-unimpaired group, other groups required longer pauses inside target circles (p < 0.050) and produced more total pen strokes to complete the test (p < 0.016). FAQ-subtle participants required more time to complete the entire test (p < 0.002) and drew individual lines connecting successive target circles slower (p < 0.001) than FAQ-unimpaired participants. Lines connecting successive circle targets were less straight among FAQ-mild, compared to FAQ-unimpaired participants (p < 0.044). Using stepwise nominal regression (reference group = FAQ-unimpaired), pauses inside target circles classified other participants into their respective groups (p < 0.015, respectively). Factor analysis using six dTMT-B variables (oblique rotation) yielded a two-factor solution related to impaired motor/cognitive operations (48.96% variance explained) and faster more efficient motor/cognitive operations (28.88% variance explained). Conclusion: Digital assessment technology elegantly quantifies occult, nuanced behavior not previously appreciated, operationally defines critical underlying neurocognitive constructs related to functional abilities, and yields selected process-based scores that outperform traditional paper/pencil test scores for participant classification. When brought to scale, the dTMT-B test could be a sensitive tool to detect subtle-to-mild functional deficits in emergent neurodegenerative illnesses. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between territory and urban space, discussing the joint development processes of urban and territorial morphologies. The paper argues that territorial structure is a precursor to urban design. It also discusses how landscape architecture can respond to the morphological needs of contemporary urban design as the boundaries between city and territory merge. The introduction and framework review section examines various approaches to studying the relationship between urban morphology and interstitial spaces or unbuilt geographies, which are often still considered empty spaces, physically incorporated but excluded from urban design. It also briefly discusses the role that green spaces and territorial morphologies have played, or not played, in defining urban form from antiquity to modernity. The paper then focuses on the role of hydromorphologies in shaping the urban form of Rome, Boston and Bari. These cities are analyzed as case studies to discuss 20th-century approaches to urban planning in relation to territorial layout. Finally, this study analyzes a marginal area of the metropolitan city of Bari in order to propose possible landscape morphologies of reconnection for the resulting interstitial areas. [ABSTRACT FROM AUTHOR]

This article explores transformations in the Black press during some of the most repressive years of United States and global anticommunism in the 1940s and 1950s. Centering on an examination of the editorial politics of the Boston Chronicle , a daily newspaper founded by Caribbean immigrants in the early twentieth century, it argues that Black leftist internationalism continued to be visible in print despite a repressive political climate shaping the experiences of Black journalists and activists. The Chronicle , a relatively understudied Black newspaper, offers a somewhat different perspective on the evolution of a vibrant, transnational print culture that linked Black freedom struggles in the United States with anticolonial movements in the British Caribbean, Africa, and Latin America. An examination of the Chronicle' s coverage and journalists in a period of deepening anticommunist repression reveals ongoing links between Black activists, anticolonial movements, and the organized left before the 1960s. [ABSTRACT FROM AUTHOR]

Science and technology and skills have increasingly become the driving force to lead the development of knowledge economy. With the changes in the demand for knowledge workers and the location of enterprises, a new type of innovation space—innovation districts—has emerged. Countries have begun to identify and nurture innovation districts. Therefore, how to accurately identify innovation districts in cities has become an important research topic. The existing research on identifying innovation districts is mainly based on a qualitative description method at the element level. However, whether there are other potential innovation districts in urban space can be identified by quantitative identification. Therefore, the purpose of this paper is to establish an innovation district identification framework and a case study of the framework. The identification framework includes spatial identification and factor identification. In spatial identification, the identification index system is constructed based on the spatial location, range limit, and the surrounding area of innovative assets. In factor identification, the identification index system is constructed based on innovative assets, physical assets, and network assets, and Kendall Square and Boston innovation districts are used as the reference basis to determine whether the identified districts meet the standards of the constructed innovation districts. In empirical case research, spatial identification identified the Gaoxin South District (GXSD) spatial range. In factor identification, it was found that GXSD does not fully meet the standard of innovation district identification. This paper argues that the framework is essential for urban managers, planners, and urban designers to identify and evaluate high-quality innovation districts. [ABSTRACT FROM AUTHOR]

This paper examines the affective and temporal dimensions of hope's use in the non‐profit spaces of poverty governance. Drawing on 14 months of qualitative fieldwork at financial coaching programmes in Boston, Massachusetts, this paper explores how financial coaches understand the function of hope in their work with low‐ and moderate‐income clients. By tracing how financial coaches use hope to encourage clients to participate in and build allegiance to the contemporary economy, this paper argues that hope is foundational to how contemporary poverty alleviation programmes, of which financial coaching is indicative, depoliticise clients' lived experiences of impoverishment. Amid a resurgence of interest in the geographies of hope, the significance of this inquiry lies in its ability to offer an alternate reading of the seemingly benevolent, positive, or radical uses of hope by conceptualising hope as a placating force that cements existing economic arrangements and forecloses alternative political imaginaries. The inquiry undertaken in this paper, then, has implications not only for our understanding of the persistence of poverty, but also indicates the role of non‐profit organisations in the production and ongoing maintenance of broader systems of capitalist exploitation. This paper examines the affective and temporal dimensions of hope's use in the non‐profit spaces of poverty governance. Drawing on 14 months of qualitative fieldwork at financial coaching programmes in Boston, Massachusetts, this paper explores how financial coaches understand the function of hope in their work with low and moderate‐income clients. This paper argues that hope is foundational to how contemporary poverty alleviation programmes, of which financial coaching is indicative, depoliticise clients' lived experiences of impoverishment. [ABSTRACT FROM AUTHOR]

Port operators and liner shipping companies attempt to gain cost and competitive advantages by providing services in complementary maritime-related areas. In line with this purpose, companies undertake a task known as Global Terminal Operator (GTO). Substantially, there are more than 21 GTOs of various sizes involved in container terminal operations worldwide, handling 80% of the total containerized cargo traffic. In recent years, there have been drastic changes in the GTO market with numerous mergers, acquisitions, and concessions. However, academic and sectoral studies have fallen short of keeping up with these changes. To fill the gap in research, a dynamic portfolio analysis has been carried out within the scope of this novel study for the first time for the GTOs with the help of BCG Matrix analyses. Findings reveal that GTOs followed different development paths with respect to their distinctive characteristics. Major operators maintained their market dominance, unlike the minors who followed an unsteady path of development. This paper contributes to the academic literature by conducting a comprehensive market analysis for the GTO sector and revealing the dynamic development courses as well as strategic competitive positions of the operators from a managerial perspective toward shaping future strategic plans of the industry stakeholders. [ABSTRACT FROM AUTHOR]

Responding to the polarized view of artists either being displaced or co-opted in urban regeneration, this paper unveils a middle ground in which artists also act as partners or co-producers in these processes. We illustrate this intermediary role in property development processes with reference to a case study of the Seaport Innovation District in Boston by drawing upon a survey of resident artists and interviews with artists, planners, architects and economic development specialists. The paper makes two contributions to the literature. It offers a fresh look at the dialectical relationship between cultural and economic modes of production by more fully integrating consideration of artists’ sensibilities and practice with those of a host of built environment disciplinary practices including architects, planners and real estate developers. As a result, it extends extant analysis of urban regeneration-led gentrification from its urban planning, geography and sociology disciplinary roots into the disciplines of architecture and real estate. Our findings lead onto a research agenda centered on consideration of the intermediary roles played by creative and cultural sector actors and how creativity might be captured to positive effect, in the production of the built environment. [ABSTRACT FROM AUTHOR]

This paper examines the context, precedents, contents, distribution, and socio-economic ramifications of deed restrictions in early-nineteenth century Boston, reviewing examples of restrictions' use and a comprehensive survey of the 1800–1839 deeds of five grantors. It shows how Boston's government and then private land developers began using deed restrictions in regards to building use, materials, height, and bulk in select geographies during this period. They did so to guarantee prestigious and stable home and work environments to wealthy consumers in the fast-changing urban context, in turn bringing into existence a prized and exclusive stratum of urban residential and commercial property. [ABSTRACT FROM AUTHOR]

In this paper, we prove the compressible Euler limit from the Boltzmann equation with hard sphere collisional kernel and complete diffusive boundary condition in half-space by employing the Hilbert expansion which includes interior and Knudsen layers. This rigorously justifies the corresponding formal analysis in Sone's book [ Molecular gas dynamics , Birkhäuser Boston, Inc., Boston, MA, 2007] in the context of short time smooth solutions, and also generalizes the classic Caflisch's result [Comm. Pure Appl. Math. 33 (1980), pp. 651–666] to initial-boundary problem case. [ABSTRACT FROM AUTHOR]

By revisiting three empirical qualitative studies, the paper elaborates on conceptual and methodological issues pertaining to clarification of the use of the concept of politicization and "ordinary relationships to politics." The first study was conducted at the end of the 1990s on voluntary associations of young people of foreign descent in the French suburbs; the second was devoted to ordinary relationships to politics among young people in working-class neighborhoods in France; the third involves ongoing fieldwork examining non-profit organizations and their relationship to the state, focusing notably on evangelical non-profits in the Boston area of the USA. Although the research questions were different, they dealt with ordinary relationships to politics (ORP). This notion encompasses two dimensions. On the one hand, the idea that what determines one's relationship to politics is not only political, but also social: that we need to "embed" the study of relationships to politics into social dimensions. This is related to the study of the determination of relationships to politics. On the other hand, the idea that a relationship to politics is not only a relationship to the institutionalized political field, but that we need to adopt a broader definition of what politicization is, in order to grasp its ordinary forms, especially—but not only—when we deal with the working class. In this case, what is at stake is the definition of politicization. Nowadays, there is relative consensus in the literature regarding the determination of relationships to politics, but the definition of what politicization is remains much more controversial. In this paper, I present the terms of this controversy and the value in and limits of the various perspectives and, more specifically, I elaborate on how the changes in the type of fieldwork I conducted, in the national contexts, and the theoretical questions I asked impacted on the way I chose to define politicization. I suggest combining three definitions of politicization: (i) the legitimist or realist conception based on the relationship to the institutional political sphere; (ii) the conception of politicization as the identification of shared problems calling for collective solutions; and finally, (iii) approaching politicization as the readiness to be moved, to consider points of view other than those initially adopted. [ABSTRACT FROM AUTHOR]

Many semiparametric spatial autoregressive (SSAR) models have been used to analyze spatial data in a variety of applications; however, it is a common phenomenon that heteroscedasticity often occurs in spatial data analysis. Therefore, when considering SSAR models in this paper, it is allowed that the variance parameters of the models can depend on the explanatory variable, and these are called heterogeneous semiparametric spatial autoregressive models. In order to estimate the model parameters, a Bayesian estimation method is proposed for heterogeneous SSAR models based on B-spline approximations of the nonparametric function. Then, we develop an efficient Markov chain Monte Carlo sampling algorithm on the basis of the Gibbs sampler and Metropolis–Hastings algorithm that can be used to generate posterior samples from posterior distributions and perform posterior inference. Finally, some simulation studies and real data analysis of Boston housing data have demonstrated the excellent performance of the proposed Bayesian method. [ABSTRACT FROM AUTHOR]

In this paper, it is aim to propose an instrumental variable rank estimation method for varying coefficient spatial autoregressive models. The newly proposed method provides a highly efficient and robust alternative to the existing quasi-maximum likelihood estimation or GMM estimation, and can be implemented using the existing R software package conveniently. Under mild conditions, the consistency and asymptotic normality of the resulting estimators are established. The finite sample properties of the proposed method are investigated through Monte Carlo simulation studies. Finally, the Boston house price data and crime data of Tokyo are analyzed to illustrate the usefulness of the proposed estimation method. [ABSTRACT FROM AUTHOR]

System In the volume's first contribution (in German), Neef focuses on comma placement in the contemporary German writing system. Programmatic considerations for an incorporation of punctuation into GFL teaching conclude the chapter; hope is they will resonate with the relevant communities and practitioners (also beyond the Germanophone world) as many of them can be generalized to teaching punctuation both to L2 and - arguably - L1 writers. Ström Herold & Levin analyze (in English) German, English, and Swedish non-fiction books from the 2000's - both originals and translations - in striving to rectify that punctuation (or the actual practices of using it) is usually but a footnote in translation studies. The main question, which is preliminarily answered through the analysis of comma usage in five term papers written by German L1 writers, is "whether prefields trigger non-standard commas at their right edge just because they are prefields" (p. 99). [Extracted from the article]

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]

This article introduces the special issue, "Community Archaeology of the African Diaspora." This collection of papers grew out of a session at the 2020 Society for Historical Archaeology conference in Boston, Massachusetts, with additional authors invited to add further geographical and methodological diversity. The papers in this issue address a single question—how are archaeologists currently involved with community archaeology projects related to the African Diaspora?—and reflects the wide array of approaches currently being implemented across the discipline. [ABSTRACT FROM AUTHOR]

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]

With the widespread application of spatial data in fields like econometrics and geographic information science, the methods to enhance the robustness of spatial econometric model estimation and variable selection have become a central focus of research. In the context of the spatial error model (SEM), this paper introduces a variable selection method based on exponential square loss and the adaptive lasso penalty. Due to the non-convex and non-differentiable nature of this proposed method, convex programming is not applicable for its solution. We develop a block coordinate descent algorithm, decompose the exponential square component into the difference of two convex functions, and utilize the CCCP algorithm in combination with parabolic interpolation for optimizing problem-solving. Numerical simulations demonstrate that neglecting the spatial effects of error terms can lead to reduced accuracy in selecting zero coefficients in SEM. The proposed method demonstrates robustness even when noise is present in the observed values and when the spatial weights matrix is inaccurate. Finally, we apply the model to the Boston housing dataset. [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]

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]

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]

Copyright of Architectural Journal / Jian Zhu Xue Bao is the property of Architectural Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 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]

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]

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]

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]

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]

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]

Copyright of New Architecture is the property of New Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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]

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]

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]

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]

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]

Over the past 40 years, college graduates in the USA have become increasingly concentrated in a small number of cities. This paper uses qualitative interviews to explore the processes bringing recent graduates of elite universities to one such city, metropolitan Boston, after graduation. Most respondents reported that their move to Boston was not driven by a clear preference for living there. Rather, they saw themselves as simultaneously choosing a job and a location in one bundled decision, with the job generally determining where they ended up. To reduce the cognitive complexity of the joint job-and-location search, graduates eliminated most options with minimal consideration. The options that remained were disproportionately in cities where the graduates or their universities had preexisting connections—even when the graduates themselves would have preferred to live elsewhere. The social nature of the post-college job search thus served to geographically concentrate these graduates beyond what either their own preferences or the geography of job opportunities would require. [ABSTRACT FROM AUTHOR]

Children experience and grapple with the ongoing effects of climate change in their daily lives. While they did not cause climate change nor should they have to solve it, children deserve educational opportunities to understand why and how it occurs as they prepare to address it. In this Methods and Strategies article, we share how children engaged in place-based art, science, and literacy activities designed to support them in further cultivating relationships with their ecological community and to communicate their findings with other children engaging in the same process across the three coastal communities of Boston, Toronto, and San Diego. Specifically, we highlight how crafting representations of their noticings and wonderings from family walks in their ecological communities supported children to analyze the interactions within ecosystems and express concern and care for the natural world as they engaged in the science practice of asking questions. We share how elementary teachers can support children in cultivating relationships with their local community as they make observations and ask questions to understand how climate change impacts their ecological communities. [ABSTRACT FROM AUTHOR]

We compare responses from an online survey among 700 customers of transportation network companies (TNC) in Boston and Philadelphia to investigate TNC's impact on vehicle ownership, trip making, and mode choice. We first use a qualitative comparative analysis to examine changes in respondents' travel behavior and vehicle ownership after adopting TNC. We then use a random parameter logit regression analysis to investigate customers' preferences between transit and TNC based on a choice experiment. We find that in both cities, TNC allows customers, including those who currently do not own a car, to either delay purchasing a car or forgo a car altogether. TNC enables customers across income levels to take trips that they otherwise would not have taken. Meanwhile, TNC substitutes for more than complementing transit. The random parameter logit analysis indicates that when choosing between TNC and transit, individuals in both cities consider waiting time and overall travel time for transit to be more burdensome than those for TNC. Bostonians perceive the time spent walking to and from transit to be less burdensome, and the time spent traveling in vehicle to be more burdensome than do Philadelphians. Differences in built environment, mode share within transit systems, and income likely contribute to respondents' different values of time between the two cities. Our paper is the first to compare individual trade-off between transit and TNC in two cities with different urban settings and transit services. The findings have implications on transit service planning, station area improvements, parking regulations, and traffic management. [ABSTRACT FROM AUTHOR]

The article discusses the eighteenth century transactions at The Boston Bookstore of bookseller Benjamin Guild in Massachusetts based on the manuscript daybook maintained by Guild. Topics explored include the sale of books on politics and religion to early American readers, observations on the pricing structure of the bookstore, and the non-book items sold by Guild which range from quills to ink powder.

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]

Issues concerning spatial dependence among cross-sectional units in econometrics have received more and more attention. Motivated by a Boston housing price data analysis, this paper studies the sparse inference of varying coefficient partially linear spatial autoregressive model, which is quite valuable in econometrics with high-dimensional data. A novel, efficient and convenient one-step variable selection procedure is proposed by using a twofold penalty for simultaneous estimation and variable selection of the parametric components and varying coefficient functions, in which the varying coefficient functions are approximated by the B-spline basis. Under some regularity conditions, asymptotic properties of the resulting estimators are established, including consistency, asymptotic normality and the oracle property. Besides, the optimal choices of the tuning parameters are discussed and a practical iterative algorithm based on the locally quadratic approximation approach is presented for implementation. Finally, extensive numerical simulations and a Boston housing price data analysis are conducted to confirm the finite sample performance and theoretical findings of the new method. [ABSTRACT FROM AUTHOR]

Heteroscedasticity is often encountered in spatial-data analysis, so a new class of heterogeneous spatial autoregressive models is introduced in this paper, where the variance parameters are allowed to depend on some explanatory variables. Here, we are interested in the problem of parameter estimation and the variable selection for both the mean and variance models. Then, a unified procedure via double-penalized quasi-maximum likelihood is proposed, to simultaneously select important variables. Under certain regular conditions, the consistency and oracle property of the resulting estimators are established. Finally, both simulation studies and a real data analysis of the Boston housing data are carried to illustrate the developed methodology. [ABSTRACT FROM AUTHOR]

High-dimensional feature selection is of particular interest to researchers. In some domains, such as microarray data, it is quite common for a group of highly correlated explanatory variables to be of equal importance for inclusion in the predictive model. This paper proposes a new hybrid feature selection approach that integrates feature screening based on Kendall's tau and Elastic Net regularized regression (K -EN). K -EN as an approach that embeds the Elastic Net, has the advantage of the grouping effect, which automatically includes all the highly correlated variables in the group. The K -EN approach offers insightful solutions to high-dimensional regression problems and improves Elastic Net performance since the screening phase is preceded by a step that further reduces the number of explanatory variables by removing those that disagree with the target based on Kendall's tau. The use of Kendall's tau further enhances Elastic Net performance, as it is robust enough to handle heavy-tailed distributions, non-parametric models, outliers, and non-normal data with greater ease. K -EN is therefore a time-saving approach. The proposed algorithm is evaluated on four simulation scenarios and four publicly available datasets, including riboflavin, eyedata, Longley, and Boston Housing, and achieves 0.2528, 0.0098, 0.1007, and 0.4121 respectively as the Mean Squared Error (MSE). K -EN's MSEs are the best compared to those achieved by the state-of-the-art approaches reviewed in this paper. In addition, K -EN selects up to 100% of relevant features when run on simulated data. [ABSTRACT FROM AUTHOR]