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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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

First Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping early-career researchers promote themselves alongside their papers. Conor Herlihy is first author on 'Suv420 enrichment at the centromere limits Aurora B localization and function', published in JCS. Conor is a Postdoc in the lab of Dr Ting Wu at Harvard Medical School, Boston, MA, where he is interested in understanding how the genome organizes and regulates itself in order to enable normal development and prevent the onset of diseases such as cancer. [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]

Little is known of the relationship between exposure to the smallest particles of air pollution and socio-demographic characteristics. This paper explores linkages between ultrafine particle (UFP) concentrations and indicators of both race/ethnicity and socioeconomic status in Boston, Massachusetts, USA. We used estimates of UFP based on a highly-resolved land-use regression model of concentrations. In multivariate linear regression models census block groups with high proportions of Asians were associated with higher levels of UFP in comparison to block groups with majority White or other minority groups. Lower UFP concentrations were associated with higher homeownership (indicating higher SES) and with higher female head of household (indicating lower socioeconomic status). One explanation for the results include the proximity of specific groups to traffic corridors that are the main sources of UFP in Boston. Additional studies, especially at higher geographic resolution, are needed in Boston and other major cities to better characterize UFP concentrations by sociodemographic factors. [ABSTRACT FROM AUTHOR]

Corneal blindness is a leading cause of visual impairment worldwide. The most common treatment is to replace the diseased cornea with standard corneal transplantation. In eyes at high risk of graft failure, the Boston keratoprosthesis type 1 (KPro) can be used to restore vision and is currently the most frequently used artificial cornea in the world. However, glaucoma is a well‐known complication of KPro surgery and is the most important threat to vision in KPro‐implanted eyes. This chronic disease is influenced by elevated intraocular pressure (IOP) and damages the optic nerve, leading to progressive vision loss. In KPro patients, glaucoma is highly prevalent and extremely challenging to manage, yet its exact cause remains unknown. [ABSTRACT FROM AUTHOR]

Solar forecasting plays a key part in the renewable energy transition. Major challenges, related to load balancing and grid stability, emerge when a high percentage of energy is provided by renewables. These can be tackled by new energy management strategies guided by power forecasts. This paper presents a data-driven and contextual optimisation forecasting (DCF) algorithm for solar irradiance that was comprehensively validated using short- and long-term predictions, in three US cities: Denver, Boston, and Seattle. Moreover, step-by-step implementation guidelines to follow and reproduce the results were proposed. Initially, a comparative study of two machine learning (ML) algorithms, the support vector machine (SVM) and Facebook Prophet (FBP) for solar prediction was conducted. The short-term SVM outperformed the FBP model for the 1- and 2- hour prediction, achieving a coefficient of determination (R2) of 91.2% in Boston. However, FBP displayed sustained performance for increasing the forecast horizon and yielded better results for 3-hour and long-term forecasts. The algorithms were optimised by further contextual model adjustments which resulted in substantially improved performance. Thus, DCF utilised SVM for short-term and FBP for long-term predictions and optimised their performance using contextual information. DCF achieved consistent performance for the three cities and for long- and short-term predictions, with an average R2 of 85%. [ABSTRACT FROM AUTHOR]

This paper develops a tractable methodology for designing an optimal priority system for assigning agents to heterogeneous items while accounting for agents' choice behavior. The space of mechanisms being optimized includes deferred acceptance and top trading cycles as special cases. In contrast to previous literature, I treat the inputs to these mechanisms, namely the priority distribution of agents and quotas of items, as parameters to be optimized. The methodology is based on analyzing large market models of one-sided matching using techniques from revenue management and solving a certain assortment planning problem whose objective is social welfare. I apply the methodology to school choice and show that restricting choices may be beneficial to student welfare. Moreover, I compute optimized choice sets and priorities for elementary school choice in Boston. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. [ABSTRACT FROM AUTHOR]

In the 1990s, labs on both sides of the Atlantic performed the largest genetic mutagenesis screen at that time using an emerging model organism: the zebrafish. Led by Christiane Nü sslein-Volhard in Tü bingen, Germany, and Wolfgang Driever in Boston, USA, these colossal screens culminated in 1996 with the publication of 37 articles in a special issue of Development, which remains the journal's largest issue to this day. To celebrate the anniversary of the zebrafish issue and reflect on the 25 years since its publication, five zebrafish researchers share what the issue means to them, how it has contributed to their career and its impact on the zebrafish community. [ABSTRACT FROM AUTHOR]

Background: Pediatric asthma is currently the most prevalent chronic disease in the United States, with children in lower income families disproportionately affected. This increased health burden is partly due to lower-quality and insufficient maintenance of affordable housing. A movement towards 'green' retrofits that improve energy efficiency and increase ventilation in existing affordable housing offers an opportunity to provide cost-effective interventions that can address these health disparities.Methods: We combine indoor air quality modeling with a previously developed discrete event model for pediatric asthma exacerbation to simulate the effects of different types of energy retrofits implemented at an affordable housing site in Boston, MA.Results: Simulation results show that retrofits lead to overall better health outcomes and healthcare cost savings if reduced air exchange due to energy-saving air tightening is compensated by mechanical ventilation. Especially when exposed to indoor tobacco smoke and intensive gas-stove cooking such retrofit would lead to an average annual cost saving of over USD 200, while without mechanical ventilation the same children would have experienced an increase of almost USD 200/year in health care utilization cost.Conclusion: The combination of indoor air quality modeling and discrete event modeling applied in this paper can allow for the inclusion of health impacts in cost-benefit analyses of proposed affordable housing energy retrofits. [ABSTRACT FROM AUTHOR]

Let G be a real linear reductive group and K be a maximal compact subgroup. Let P be a minimal parabolic subgroup of G with complexified Lie algebra p, and n be its nilradical. In this paper we show that: for any admissible finitely generated moderate growth smooth Fréchet representation V of G, the inclusion VK ⊂ V induces isomorphisms Hi(n,VK) ≅ Hi(n,V) (i ≥ 0), where VK denotes the (g,K) module of K finite vectors in V. This is called Casselman's comparison theorem (see Henryk Hecht and Joseph L. Taylor [ A remark on Casselman's comparison theorem , Birkhäuser Boston, Boston, Ma, 1998, pp. 139–146]). As a consequence, we show that: for any k ≥ 1, nkV is a closed subspace of V and the inclusion VK ⊂ V induces an isomorphism VK/nkVK = V/nkV. This strengthens Casselman's automatic continuity theorem (see W. Casselman [Canad. J. Math. 41 (1989), pp. 385–438] and Nolan R. Wallach [ Real reductive groups , Academic Press, Boston, MA, 1992]). [ABSTRACT FROM AUTHOR]

As part of being named one of ten emerging leaders in engineering education, Arizona State University (ASU) was invited to participate in the MIT-Olin Colloquium on the Global State of the Art in Engineering Education held in Boston in April 2019. As a group we shared innovations at our respective institutions and identified opportunities to collaborate to continue to advance the state of engineering education. In response to the guidelines for this special issue, this paper provides details on a few representative ASU achievements to date, and identifies opportunities for the continued evolution of the Fulton Schools of Engineering. [ABSTRACT FROM AUTHOR]

Tatiana Erukhimova, an Instructional Professor at Texas A&M University, has been awarded the 2024 David Halliday and Robert Resnick Award for Excellence in Undergraduate Physics Teaching. Erukhimova is known for her approach to teaching, which focuses on creating a strong sense of community and personal connection with her students. She uses innovative and research-backed pedagogy, including interactive lectures, problem-solving, peer instruction, and experimental demonstrations. Erukhimova has received numerous awards for her teaching and has also mentored graduate students pursuing instructional faculty positions. Additionally, she has created outreach programs that provide experiential learning opportunities for students beyond the classroom. Chandralekha Singh, a Distinguished Professor at the University of Pittsburgh, has been awarded the 2024 John David Jackson Excellence in Graduate Physics Education Award. Singh is recognized for her research in physics education and her contributions to graduate physics education at the national and international level. She has published high-quality papers and served on various committees within the American Association of Physics Teachers (AAPT). Singh's research focuses on quantum mechanics, cognitive issues in learning physics, and improving equity and inclusion in physics learning environments. She has also conducted workshops and conferences on these topics. [Extracted from the article]

Background: Asians and Asian Americans have the lowest rate of mental health service utilization (25%) in the US compared to other racial/ethnic groups (39 − 52%), despite high rates of depression, anxiety, and suicidal ideation. The lack of culturally-responsive mental health trainings hinders access to mental health services for these populations. We assessed the mental health priorities of Asian communities in Greater Boston and evaluated cultural responsiveness of the Mental Health First Aid (MHFA), a first-responder training teaching participants skills to recognize signs of mental health and substance use challenges, and how to appropriately respond. Methods: This is community-based participatory research with the Boston Chinatown Neighborhood Center (BCNC), Asian Women For Health (AWFH), and the Addressing Disparities in Asian Populations through Translational Research (ADAPT) Coalition. We conducted focus groups with community-based organization staff and community members to assess mental health priorities of Asian populations in Boston, MA. We then evaluated the utility and cultural-responsiveness of the English-language MHFA for Asian populations through pre- and post-training questionnaires and focus groups with community participants. Paired t-tests were used to evaluate questionnaire responses. Thematic analysis was used to analyze interviews. Results: In total, ten staff and eight community members participated in focus groups, and 24 community members completed the MHFA and pre- and post-training questionnaires. Common mental health challenges in the Asian communities reported by participants were loneliness, high stigma around mental illnesses, academic pressure, and acculturation stress. Compared to pre-training, MHFA participants demonstrated lower personal mental health stigma (p < 0.001) and higher mental health literacy (p = 0.04) post-training. Participants also noted the lack of data statistics and case studies relevant to Asian populations in the training, and desired the training be offered in languages spoken by Asian ethnic subgroups (e.g., Chinese, Vietnamese). Conclusion: Cultural-responsiveness of the MHFA for Asian populations could be improved with the inclusion of data and case studies that capture common mental health challenges in the Asian communities and with translation of the MHFA to non-English languages predominant in Asian communities. Increasing the cultural relevance and language accessibility of the MHFA could facilitate wider adoption of these trainings across communities and help to reduce mental health stigma and gaps in literacy and service utilization. [ABSTRACT FROM AUTHOR]

To inform clinical trial design and real-world precision pediatric oncology practice, we classified diagnoses, assessed the landscape of mutations, and identified genomic variants matching trials in a large unselected institutional cohort of solid tumors patients sequenced at Dana-Farber / Boston Children's Cancer and Blood Disorders Center. Tumors were sequenced with OncoPanel, a targeted next-generation DNA sequencing panel. Diagnoses were classified according to the International Classification of Diseases for Oncology (ICD-O-3.2). Over 6.5 years, 888 pediatric cancer patients with 95 distinct diagnoses had successful tumor sequencing. Overall, 33% (n = 289/888) of patients had at least 1 variant matching a precision oncology trial protocol, and 14% (41/289) were treated with molecularly targeted therapy. This study highlights opportunities to use genomic data from hospital-based sequencing performed either for research or clinical care to inform ongoing and future precision oncology clinical trials. Furthermore, the study results emphasize the importance of data sharing to define the genomic landscape and targeted treatment opportunities for the large group of rare pediatric cancers we encounter in clinical practice. Mutation profiling of pediatric cancers can help determine treatment options, however, large-scale datasets are rare. Here, the authors describe an institutional application of targeted sequencing to pediatric solid tumours, and identify potential therapeutic implications for identified mutations. [ABSTRACT FROM AUTHOR]

Background: Social needs inhibit receipt of timely medical care. Social needs screening is a vital part of comprehensive cancer care, and patient navigators are well-positioned to screen for and address social needs. This mixed methods project describes social needs screening implementation in a prospective pragmatic patient navigation intervention trial for minoritized women newly diagnosed with breast cancer. Methods: Translating Research Into Practice (TRIP) was conducted at five cancer care sites in Boston, MA from 2018 to 2022. The patient navigation intervention protocol included completion of a social needs screening survey covering 9 domains (e.g., food, transportation) within 90 days of intake. We estimated the proportion of patients who received a social needs screening within 90 days of navigation intake. A multivariable log binomial regression model estimated the adjusted rate ratios (aRR) and 95% confidence intervals (CI) of patient socio-demographic characteristics and screening delivery. Key informant interviews with navigators (n = 8) and patients (n = 21) assessed screening acceptability and factors that facilitate and impede implementation. Using a convergent, parallel mixed methods approach, findings from each data source were integrated to interpret study results. Results: Patients' (n = 588) mean age was 59 (SD = 13); 45% were non-Hispanic Black and 27% were Hispanic. Sixty-nine percent of patients in the navigators' caseloads received social needs screening. Patients of non-Hispanic Black race/ethnicity (aRR = 1.25; 95% CI = 1.06–1.48) and those with Medicare insurance (aRR = 1.13; 95% CI = 1.04–1.23) were more likely to be screened. Screening was universally acceptable to navigators and generally acceptable to patients. Systems-based supports for improving implementation were identified. Conclusions: Social needs screening was acceptable, yet with modest implementation. Continued systems-based efforts to integrate social needs screening in medical care are needed. [ABSTRACT FROM AUTHOR]

Background: In carpal tunnel syndrome (CTS), conservative management did not show satisfactory results, and some patients required revision surgery due to persistent or recurrent symptoms. Ultrasound (USG)-guided median nerve (MN) hydrodissection with a 5% dextrose injection is an advanced approach for treating symptoms of CTS. We prospectively investigated the patients to demonstrate the safety and effectiveness of this approach. Aims and Objectives: This study aims to evaluate the effect of USG-guided nerve hydrodissection using single injection of 5% dextrose on pain, MN conduction parameters, and functional status in patients with CTS of mild–to-moderate grade. The study also predicts about the safety of the procedure. Materials and Methods: A study was performed in the pain clinic of the tertiary care teaching institute of India for one year. The study included 15 patients diagnosed with mild-to-moderate CTS. The diagnosis was done on the basis of history, physical examination, and a nerve conduction velocity (NCV) study. Patients were given analgesics and NSAIDS and enrolled in the procedure. Parameters used for statistical analysis were Visual Analog Scale (VAS) scores, NCV study data (sensory conduction velocity [SNCV] and distal motor latency [DML]), and Boston carpal tunnel questionnaire (BCTQ) scores. Pre-injection parameters were compared with parameters 3 months after the injection to show the usefulness of this procedure. Results: A statistically significant reduction in VAS score was found in 74% of the cases (P<0.05). The nerve conduction study parameters have shown significantly higher SNCV and lower DML latency in 60% of cases (P=0.001 and P=0.001, respectively). Improved BCTQ scores were found in 80% of cases (for the symptom severity scale, P=0.001 and for the functional status scale, P<0.01). No complications such as allergic reactions, sensory loss, or paresis were recorded post-injection. However, transient injection site pain was present in all patients which did not last for more than 24 h. Conclusion: MN hydrodissection using a single injection of 5% dextrose under USG is a safe and effective approach offering pain relief, better MN conduction, and improved functional status in patients with CTS of mild-to-moderate grade. [ABSTRACT FROM AUTHOR]

People experiencing homelessness are more likely to utilize emergency departments than their non-homeless counterparts. However, obtaining a bed in a homeless shelter for patients can be complex. To better understand the challenges of finding a safe discharge plan for homeless patients in the emergency department, our team conducted interviews with emergency department social workers and homeless shelter case managers in the Boston area. We identified and mapped the stages in the processes performed by both parties, identifying challenges with successful placement into a shelter. Furthermore, we assembled a data dictionary of key factors considered when assessing a patient's fit for a homeless shelter. By identifying bottlenecks and areas of opportunity, this study serves as a first step in enabling homeless individuals to receive the post-discharge assistance they require. [ABSTRACT FROM AUTHOR]

Background: Endovascular treatment of lower-extremity peripheral disease (PAD) is associated with higher complication rates and suboptimal outcomes in women. Atherectomy has shown favourable outcomes in calcified lesions, minimising the incidence of stent placement caused by recoil or flow-limiting dissection. To date, there are no published mid-term outcomes evaluating the performance of atherectomy differentiated by sex. This study aims to evaluate sex-specific outcomes and prognostic factors affecting the results of atherectomy-assisted endovascular treatment in severe PAD. Methods: A retrospective analysis was conducted at a single centre in Germany, initiated by physicians and not sponsored by industry, on patients presenting with Rutherford categories ranging from III to V and featuring de novo occlusive or stenotic lesions of the superficial femoral (SFA) and/or popliteal arteries. The intervention involved rotational atherectomy-assisted angioplasty utilising the Jetstream (Boston, US®) device. The point of interest of this study was postinterventional clinical improvement as well as mid-term outcomes, including primary patency, over a targeted 2-year follow-up period. Statistical analysis utilised Cox regression (survival analysis) to calculate hazard ratios according to sex category. Comparative survival analysis was performed using the log-rank test and visually represented through Kaplan–Meier curves. Risk factors associated with absence of clinical improvement were examined across both sex groups utilising the chi-square or Fisher exact test, as appropriate. Results: A total of 98 patients (103 limbs) were initially included, with >75% having moderate-to-severe lesion calcification (>50%). A total of 84 patients (97 limbs, 62 male and 35 female) proceeded to a 2-year follow-up (mean 16.4 months for males and 16.1 for females) after a successful index procedure. Age distribution, Rutherford class, diabetes, chronic kidney disease (CKD), target vessel, lesion type, and length were balanced among both groups. Similar primary patency rates, of 89% among female and 91% among male limbs, were observed (p = 0.471). Female patients exhibited a lower rate of clinical improvement based on the Rutherford scale in comparison to males (80.6% vs. 94.5%, p = 0.048). CDK was the only significant prognostic factor across pooled data (odds ratio for CKD: 15.15, p < 0.001). Conclusions: Rotational atherectomy showed comparably high rates of mid-term primary patency, with low rates of bailout stent placement. These findings highlight the beneficial use of atherectomy in female patients who are per se at risk for higher rates of complications during and after endovascular interventions. [ABSTRACT FROM AUTHOR]

• Electricity, the most direct and effective use of RES, is in center of energy transition. • An interdisciplinary procedure suitable for the planning of RES is presented. • Key points and procedures for the assessment of large-scale RES installation is presented. • Simulation on Eygiption case shows the technical and economical viability. Due to the environmental impacts brought by current energy schemes, the energy transition, a new paradigm shift from fossil fuels to renewable energy, has been widely accepted and is being realized through collective international, regional, and local efforts. Electricity, as the most direct and effective use of renewable energy sources (RES), plays a key role in the energy transition. In this paper, we first discuss a viable pathway to energy transition through the electricity triangle, highlighting the role of RES in electricity generation. Further, we propose methodologies for the planning of wind and solar PV, as well as how to address their uncertainty in generation expansion problems. Finally, by using a web-based tool, "RES-PLAT" 1 1 https://res-plat.est.polito.it. , we demonstrate the scheme in a case study in Egypt, which evaluates the impacts and benefits of a large-scale RES expansion. 2 2 The short version of the paper was presented at Applied Energy Symposium: MIT A+ B, August 13–14, Boston. This paper is a substantial extension of the short version of the conference paper. [ABSTRACT FROM AUTHOR]

Community–university partnerships that purport to promote the public good are often fraught with institutional and cultural challenges that can contribute to the injustices they seek to address. This paper describes how one partnership has been navigating these tensions through a critical approach to power. The Co-Education/Co-Research (CORE) partnership has been built over the last decade between Tufts University and Dudley Street Neighborhood Initiative, a community organizing and planning group in Boston. We have been co-producing knowledge and action to further community control over development, and we have found that institutional shifts, such as co-governance and the equitable sharing of funding, are leading to longer term impacts for the community partner and breaking down the boundaries between university and community. However, using a relational view of power, we have also found that some of our everyday practices can subtly maintain and reinforce inequities, such as valuing academic knowledge over that of community residents and practitioners. Addressing these cultural and ideological challenges requires critical and reflexive practice. It is messy relational work that requires a lot of communication and trust and, most of all, time and long-term commitment. [ABSTRACT FROM AUTHOR]

According to a recent national survey, about ten percent of American adults, estimated 26 million, are likely to have food allergies (Gupta, et al. 2019). Food allergies have many psychosocial outcomes for adults, including social isolation, anxiety, and loneliness. Framing restaurants as a site of social engagement, this article seeks to describe the restaurant landscape for adults with food allergies in Boston. Using online user reviews from TripAdvisor and AllergyEats to identify locations of restaurants with mostly positive allergy-related reviews, this paper uses GIS to assess the geographic accessibility of allergy-friendly restaurants for locations across Boston. Reported allergy-friendly restaurants tend to in areas with many other restaurants, so allergy-friendly restaurants are as geographically accessible as other restaurants from many parts of the city. In areas with fewer restaurants, a single allergy-friendly restaurant may have a large impact on overall accessibility to allergy-friendly spaces. Limitations of the data suggest that further research is needed to understand the actual social and dining landscapes of food allergic adults. [ABSTRACT FROM AUTHOR]

Engineering multilayer networks that efficiently connect sets of points in space is a crucial task in all practical applications that concern the transport of people or the delivery of goods. Unfortunately, our current theoretical understanding of the shape of such optimal transport networks is quite limited. Not much is known about how the topology of the optimal network changes as a function of its size, the relative efficiency of its layers, and the cost of switching between layers. Here, we show that optimal networks undergo sharp transitions from symmetric to asymmetric shapes, indicating that it is sometimes better to avoid serving a whole area to save on switching costs. Also, we analyze the real transportation networks of the cities of Atlanta, Boston, and Toronto using our theoretical framework and find that they are farther away from their optimal shapes as traffic congestion increases. Finding an optimal shape for transport networks, represented as multilayer structures, is a challenging problem. The authors propose analytical and computational frameworks to analyze sharp transitions from symmetric to asymmetric shapes in optimal networks, that can be applied for planning and development of improved multimodal transportation systems within a city. [ABSTRACT FROM AUTHOR]

In real estate valuation using the Hedonic Price Model (HPM) estimated via Ordinary Least Squares (OLS) regression, subjectivity and measurement errors in the independent variables violate the Gauss–Markov theorem assumption of a non-random coefficient matrix, leading to biased parameter estimates and incorrect precision assessments. In this contribution, the Errors-in-Variables model equipped with Total Least Squares (TLS) estimation is proposed to address these issues. It fully considers random errors in both dependent and independent variables. An iterative algorithm is provided, and posterior accuracy estimates are provided to validate its effectiveness. Monte Carlo simulations demonstrate that TLS provides more accurate solutions than OLS, significantly improving the root mean square error by over 70%. Empirical experiments on datasets from Boston and Wuhan further confirm the superior performance of TLS, which consistently yields a higher coefficient of determination and a lower posterior variance factor, which shows its more substantial explanatory power for the data. Moreover, TLS shows comparable or slightly superior performance in terms of prediction accuracy. These results make it a compelling and practical method to enhance the HPM. [ABSTRACT FROM AUTHOR]

The article discusses the symposium titled "Information Privacy Law at the Crossroads" that was held at Boston University School of Law in Boston, U.S. on November 3, 2023. Topics discussed include the impact of privacy loss on minoritized communities, the use of privacy as an antisubordination legal tool and the role of privacy in advancing values such as autonomy and equality. The attendees included Woody Hartzog, Organizer of the symposium and Editor-in-Chief, Keenan Hunt-Stone.

Background: Different split regimens of polyethylene glycol are routinely used and no guidelines are available to select an optimal protocol of ingestion. This study aims to compare the efficacy and side effect profile of two different regimens of polyethylene glycol bowel preparation solution: PEG (3 + 1) vs. PEG (2 + 2). Methods: 240 patients above the age of 18 years were included in the study between June 1st and November 31st, 2023. Patients were randomly assigned either to Group A, consisting of 115 patients receiving a 3 L of PEG the night before the colonoscopy, and 1 L the same morning of the procedure. Or to group B, where 125 patients ingested 2 L the night before the procedure, and the remaining 2 L the same morning. The cleansing efficacy was evaluated by the attending endoscopist using the Boston Bowel Preparation Scale, through a score assigned for each segment of the colon (0–3). Side effects, tolerability, and willingness to retake the same preparation were listed by an independent investigator using a questionnaire administered before the procedure. Results: A higher percentage of patients had gastric fullness with the 3 + 1 vs. 2 + 2 preparation (58.3% vs. 31.2%; p <.001). A higher Boston bowel preparation score was seen in patients who took the 2 + 2 vs. 3 + 1 preparation (7.87 vs. 7.23). Using the 2 + 2 preparation was significantly associated with higher Boston bowel preparation scores vs. the 3 + 1 preparation (OR = 1.37, p =.001, 95% CI 1.14, 1.64). After adjustment over other variables (age, gender, comorbidities, previous abdominal surgeries, presence of adenoma, and time between last dose and colonoscopy), results remained the same (aOR = 1.34, p =.003, 95% CI 1.10, 1.62). Conclusion: While both (2 + 2) and (3 + 1) regimens of polyethylene glycol are a good choice for a successful colonoscopy, we recommend the use of (2 + 2) regimen for its superior efficacy in bowel cleansing. [ABSTRACT FROM AUTHOR]

Background: Carpal tunnel syndrome is a frequently observed condition affecting the wrists and fingers, characterized by numbness, tingling, and sometimes pain. Objective: To evaluate and compare the efficiency of magnetic field therapy and ultrasound therapy in rehabilitating women with carpal tunnel syndrome (CTS). Patients and Methods: Forty women with mild to moderate CTS were included in the study, and an electromyography (EMG) and examination confirmed the diagnosis. The participants were assigned into two groups. Group A received magnetic field therapy, and Group B received ultrasound therapy. Patients for each group received a six-session clinical evaluation. The outcomes included the Boston Carpal Tunnel Syndrome Questionnaire (BCTSQ), handgrip strength, wrist joint movements (flexion and extension), as well as the Visual Analogue Scale (VAS). Results: The paired t-test analyses demonstrate a significant improvement in the Boston questionnaire and VAS in both intervention groups at a P-value ≤0.05. The independent sample t-test analysis for post-intervention for both groups showed superiority for magnetic field therapy; no significant differences were observed in the improvement of wrist movement (Flexion = 0.66, Extension = 0.71); the Visual Analog Scale (VAS) has been improved in both treatment groups with dominance for ultrasound therapy. Conclusion: Both magnetic field and ultrasound therapy effectively treat women with carpal tunnel syndrome. Magnetic field therapy was statistically superior to ultrasound. [ABSTRACT FROM AUTHOR]

Today, robotics technologies are revolutionizing surveying and construction in AEC fields, enhancing precision, safety, and efficiency and also in the realm of Cultural Heritage knowledge and protection, the integration of cutting-edge technologies is reshaping consolidated surveying methods for documentation, especially in risk scenarios. One such innovation making waves is the use of mobile mapping systems, where automation and expediency are the determining factors in technological development, both in the direction of in indoor positioning (visual/LiDAR SLAM, UWB, etc.) and 3D mapping of known and unknown spaces. The BLK ARC by Leica, equipping the Agile Mobile Robot Spot® by Boston Dynamics, as a dynamic sensing platform, is here presented and discussed. BLK ARC is part of a diverse landscape of mobile mapping systems, each offering unique features and specifications tailored to different surveying needs, from handheld devices and wearable systems, to vehicle-mounted systems, the options vary in dimension, weight, price, and technical capabilities and the recent companies and research are largely focusing on them. A preliminary evaluation takes into consideration the dual dynamic-static 3D data-types, considering local and global accuracy of the 3D data delivered from the first experimental tests in a case study. Different metrics have been considered including acquisition time, precision, resolution, density, accuracy, roughness, and completeness of the acquired data. [ABSTRACT FROM AUTHOR]

Determining the predictor variables that have a non-linear effect as well as those that have a linear effect on the response variable is crucial in additive semi-parametric models. This issue has been extensively investigated by many researchers in the area of semi-parametric linear additive models, and various separation methods are proposed by the authors. A popular issue that might affect both estimation and separation results is the existence of outliers among the observations. In order to address this lack of sensitivity towards extreme observations, robust estimating approaches are frequently applied. We propose a robust method for simultaneously identifying the linear and nonlinear components of a semi-parametric linear additive model, even in the presence of outliers in the observations. Additionally, this model is sparse in that it may be used to determine which explanatory variables are ineffective by giving accurate zero estimates for their coefficients. To assess the effectiveness of the proposed method, a comprehensive Monte Carlo simulation study is conducted along with an application to investigate the dataset, which includes Boston property prices dataset. [ABSTRACT FROM AUTHOR]

Langlands [ On the classification of irreducible representations of real algebraic groups , Math. Surveys Monogr., vol. 31, Amer. Math. Soc., Providence, RI, 1989, pp. 101–170] defined L-packets for real reductive groups. In order to refine the local Langlands correspondence, Adams-Barbasch-Vogan [ The Langlands classification and irreducible characters for real reductive groups , Progress in Mathematics, vol. 104, Birkhäuser Boston, Inc., Boston, MA, 1992] combined L-packets over all real forms belonging to an inner class. In the tempered setting, using different methods, Kaletha [Ann. of Math. (2) 184 (2016), pp. 559–632] also defines such combined L-packets with a refinement to the local Langlands correspondence. We prove that the tempered L-packets of Adams-Barbasch-Vogan and Kaletha are the same and are parameterized identically. [ABSTRACT FROM AUTHOR]