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We investigate two research questions using a recent statewide transition from paper to computer-based testing: first, the extent to which test mode effects found in prior studies can be eliminated; and second, the degree to which online and paper assessments offer different information about underlying student ability. We first find very small mode effects for a more recent transition in Massachusetts. Second, we investigate the predictive evidence of validity for paper and online tests for predictions of future test scores and grades. We generally find minimal differences for the extent to which scores on paper tests can differentially predict future online versus paper test scores. Finally, online and paper test scores are similarly predictive of future grade point average. We conclude that the online test penalty can vary substantially by test and that extreme care should be taken when administering online tests to some students and paper tests to others. [ABSTRACT FROM AUTHOR]

This article refers to: RETRACTED ARTICLE: Nathan John and David Deuel, Disability in Mission: The Church's Hidden Treasure. (Peabody, Publishers Massachusetts: John Hendrickson, 2019). 167 pp. Paper, $19.95. We, the Author, Editors and Publisher of Journal of Disability & Religion, have retracted the following book review: Sunil Kathramalla (2021) Nathan John and David Deuel, Disability in Mission: The Church's Hidden Treasure. (Peabody, Publishers Massachusetts: John Hendrickson, 2019). 167 pp. Paper, $19.95., Journal of Disability & Religion, DOI: 10.1080/23312521.2021.1901639 The book review was mistakenly published as a duplicate of the below book reviews by the same author, and the author has agreed to retract. Kathramalla, S. (2020), John, Nathan and Deuel, David, eds. Disability in Mission: The Church's Hidden Treasure. Peabody, Massachusetts: John Hendrickson, 2019. 167 pp. International Review of Mission, 109: 144–145. Kathramalla, S. (2019), Disability in Mission: The Church's Hidden Treasure. Asian American Theological Forum, 8. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted book review will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as "Retracted." [ABSTRACT FROM AUTHOR]

The aim of the paper is to derive minimization algorithms based on the Nesterov accelerated gradient flow [Y. Nesterov, Gradient methods for minimizing composite objective function. Core discussion paper, (2007). Available at ; Y. Nesterov, A method of solving a convex programming problem with convergence rate $ \mathcal {O}(1/k^2) $ O (1 / k 2). In Doklady Akademii Nauk, Vol. 269, Russian Academy of Sciences, 1983, pp. 543–547; Y. Nesterov, Introductory Lectures on Convex Optimization: A basic course, Kluwer Academic Publishers, Massachusetts, 2004.] to compute the ground state of nonlinear Schrödinger equations, which can potentially include a fractional laplacian term. A comparison is developed with standard gradient flow formulations showing that the Nesterov accelerated gradient flow has some interesting properties but at the same time finds also some limitations due to the nature of the problem. A few simulations are finally reported to understand the behaviour of the algorithms and open the path to further complicate questions that require more advanced studies concerning the application of the Nesterov accelerated gradient flow to nonlinear Schrödinger equations. [ABSTRACT FROM AUTHOR]

Background: Engagement is recognized as an important factor in aphasia treatment response and outcomes, yet gaps remain in our understanding of engagement and practices that promote engagement from the client perspective. Aims: The purpose of this phenomenological study was to explore how clients with aphasia experience engagement during their inpatient aphasia rehabilitation. Methods & Procedures: An interpretative phenomenological analysis approach guided the study design and analysis. Data were collected through in‐depth interviews with nine clients with aphasia, recruited through purposive sampling, during their inpatient rehabilitation admission. Analysis was completed using a variety of analytic techniques including coding, memoing, triangulation between coders and team discussion. Outcomes & Results: The analysis revealed that for clients with aphasia in the acute phrase of recovery, the rehabilitation process resembles travelling on a journey through a foreign land. Successful engagement in the journey was accomplished when one had a therapist who served as a trusted guide and was able to be a friend, invested, adaptable, a co‐creator, encouraging and dependable. Conclusions & Implications: Engagement is a dynamic, multifaceted and person‐centred process involving the client, provider and rehabilitation context. Findings from this work have implications for measuring engagement, training student clinicians to be skilled facilitators in engaging their clients and implementing person‐centred practices that promote engagement within clinical settings. WHAT THIS PAPER ADDS: What is already known on the subject: Engagement is recognized as an important factor in rehabilitation treatment response and outcomes. Prior literature suggests that the therapist plays a critical role in facilitating engagement within the client–provider relationship. Communication impairments associated with aphasia may negatively impact a client's ability to develop interpersonal connections and participate in the rehabilitation process. There is a dearth of research directly exploring the topic of engagement in aphasia rehabilitation, particularly from the perspective of clients with aphasia. Capturing the client perspective can provide novel insights regarding practices to foster and maintain engagement in aphasia rehabilitation. What this paper adds to existing knowledge: This interpretative phenomenological study revealed that for individuals with aphasia in the acute phase of recovery, the rehabilitation process resembles travelling on a sudden and foreign journey. Successful engagement in the journey was accomplished when one had a therapist who served as a 'trusted guide' and was able to be a friend, invested, adaptable, a co‐creator, encouraging and dependable. Through the client experience, engagement is seen as a dynamic, multifaceted and person‐centred process involving the client, provider and rehabilitation context. What are the potential or actual clinical implications of this work?: The current study highlights the complexity and nuance of engagement within the rehabilitation context, which has implications for measuring engagement, training student clinicians to be skilled in engaging their clients and implementing person‐centred practices that promote engagement within clinical settings. It is necessary to recognize that client and provider interactions (and thus engagement) are embedded in and influenced by the broader healthcare system. With this in mind, a patient‐centred approach to engagement in aphasia care delivery cannot be achieved through individual efforts only and may require prioritization and action at the systems level. Future work is needed to explore barriers and facilitators to enacting engagement practices, in order to develop and test strategies to support practice change. [ABSTRACT FROM AUTHOR]

The complex environmental impact makes it difficult to predict wind speed with high precision for multiple wind turbines. Most existing research methods model the temporal dependence of wind speeds, ignoring the spatial correlation between wind turbines. In this paper, we propose a multi-wind turbine wind speed prediction model based on Weighted Diffusion Graph Convolution and Gated Attention Network (WDGCGAN). To address the strong nonlinear correlation problem among multiple wind turbines, we use the maximal information coefficient (MIC) method to calculate the correlation weights between wind turbines and construct a weighted graph for multiple wind turbines. Next, by applying Diffusion Graph Convolution (DGC) transformation to the weight matrix of the weighted graph, we obtain the spatial graph diffusion matrix of the wind farm to aggregate the high-order neighborhood information of the graph nodes. Finally, by combining the DGC with the gated attention recurrent unit (GAU), we establish a spatio-temporal model for multi-turbine wind speed prediction. Experiments on the wind farm data in Massachusetts show that the proposed method can effectively aggregate the spatio-temporal information of wind turbine nodes and improve the prediction accuracy of multiple wind speeds. In the 1h prediction task, the average RMSE of the proposed model is 28% and 33.1% lower than that of the Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN), respectively. [ABSTRACT FROM AUTHOR]

Building footprint information is crucial for comprehending global urban development processes. Deep learning algorithms have shown significant potential in building extraction from high spatial resolution imagery. However, the requirement for large-scale annotated data has been a limitation for applying deep learning methods to city-level or national-level building mapping. The dynamic change and distinct landscape variation of cities in different geographic locations further emphasizes the need for automatic building footprint extraction. In this paper, we propose a Self-supervised Knowledge Transfer (SKTrans) framework for extracting building footprints from unlabeled remote sensing images over large areas. To address building tone differences cross regions and time periods, a tone shift mechanism is introduced to reduce the contrast, brightness, and saturation differences between buildings. Furthermore, considering building style diversity across different cities, a knowledge integration module is proposed to develop a comprehensive depiction including tonal differences, multi-scale variance, structural attributes and semantic confusion between roads and buildings. The effectiveness of SKTrans is demonstrated on three public datasets of WHU, Massachusetts, and Inria. To further evaluate the feasibility of large-scale mapping for automatic building footprint, global generalization experiments cross regions and time periods, which is based on imagery from five countries of different continents were implemented. The comprehensive results demonstrate that SKTrans surpasses existing state-of-the-art methods, supporting the time-series city-level building mapping without labeled dataset. Additionally, the architectural landscape pattern changes can be quantitatively analyzed based on the generalization results to facilitate sustainable urban development. Overall, SKTrans provides a new insight to meet the needs of high-precision label-free building mapping and update from large-scale multi-style remote sensing images. [ABSTRACT FROM AUTHOR]

Background: Use of the National Healthcare Safety Network (NHSN) has been essential to the success of the Massachusetts Hemovigilance Program and has allowed for the timely identification of signals and trends over a defined population that correlate with national and international hemovigilance (HV) data. Here, we outline how the NHSN system is used for monitoring HV data in Massachusetts and encourage adoption of NHSN for nationwide HV surveillance. Study Design and Methods: A collaboration that grew over time between local HV stakeholders and the Massachusetts Department of Public Health (MDPH) resulted in the change from a paper‐based method of reporting adverse reactions and monthly transfusion activity for compliance with state requirements to replacement with statewide adoption of reporting via NHSN. Results: Over 1.5 million blood products were transfused in Massachusetts between 2017 and 2021, with 3000 adverse reactions among 10 defined types reported. Using NHSN, MDPH has been able to produce numerous reports, publications, and presentations that have made previously non‐obtainable HV and blood utilization data available. Discussion: Although limitations to these self‐reported data exist, such as lack of external validation, successful statewide implementation of NHSN for hospital blood bank reporting is possible and has benefits beyond those for regulatory oversight. It results in standardized, actionable data at both the hospital and state level, enabling inter‐facility comparisons, benchmarking, and opportunities for practice improvement. [ABSTRACT FROM AUTHOR]

Bisphenol A (BPA) is widely used as a color developer in thermal paper. Thermal paper is ubiquitous in daily life due to its use in cash register receipts, so opportunities for human contact abound. For this study, 10 blank cash register receipts were obtained from businesses in suburban Boston. BPA was extracted and analysis of concentration was performed using gas chromatograph/flame ionization detector. In some receipts, BPA was not detected but in others it was as high as 19 mg for a 12-inch long receipt, which is in line with concentrations indicated in patents. This study is intended to highlight the potential for human exposure to BPA as well as the ease with which exposure may be reduced through the use of BPA-free thermal paper. [ABSTRACT FROM AUTHOR]

In wind engineering, to accurately estimate the nonlinear dynamic response of structures while considering uncertainties of hurricanes, a suite of wind records representing the hurricane hazards of a given location is of great interest. Such a suite generally consists of a large number of hurricane wind records, which may lead to highly computational cost for structural analysis. To reduce the computational demand while still preserving the accuracy of the uncertainty quantification process, this paper proposes a machine learning approach to select a representative subset of all collected hurricane wind records for a location. First, hurricane wind records, which are expressed as time series with information that includes both wind speed and direction, are collected from a synthetic hurricane catalog. The high dimensional hurricane wind records are then compressed into a set of low dimensional latent feature vectors using an artificial neural network, designated as an autoencoder. The latent feature vectors represent the important patterns of wind records such as duration, magnitude, and the changing of wind speeds and directions over time. The wind records are then clustered by applying the k -means algorithm on the latent features, and a subset of records is selected from each cluster. The wind records selected from each cluster are those whose latent feature points are closest to the centroid of all latent feature points in that cluster. In order to do regional analysis while taking into account that the hurricane wind records are site-specific, this paper suggests that a region can be discretized into a set of grids, with the proposed hurricane selection approach applied to each grid. This procedure is demonstrated using Massachusetts as a testbed. [ABSTRACT FROM AUTHOR]

The article reports that immunologist Luk van Parijs was fired from the Massachusetts Institute of Technology (MIT) in Cambridge, Massachusetts. The school alleged that he had confessed to faking data in one published paper, several unpublished manuscripts and grant applications. Van Parijs's academic future may be shot to pieces. But his scientific past, so far is intact. He has contributed to roughly 40 papers stretching back to 1994, many of them in the blossoming field of RNA interference. None has been publicly labeled fraudulent or retracted. MIT has not said which paper it found to be problematic. Other investigations are continuing. One of the biggest problems in these fraud things says Kathleen Case, publisher at the American Association for Cancer Research in Philadelphia, Pennsylvania is that the investigations get finished. And the last thing people think of is the journals. Large-scale fraud cases are rare. But scientists whose work is challenged have often co-authored dozens or even hundreds of papers.

Nuclear-encoded mitochondrial ribosomal proteins (MRPs) are crucial for mitochondrial function and hence cellular energy production. A new paper in Development investigates the function of MRPs in early mouse development, thanks to a battery of knockout mutants. We caught up with first author (and recent graduate) Agnes Cheong and her supervisor Jesse Mager, Associate Professor at the Department of Veterinary and Animal Sciences at the University of Massachusetts, Amherst, to find out more. [ABSTRACT FROM AUTHOR]

This article offers for inspection the proposition that the adversarial evidence-based litigation process is unsuitable for resolving custody cases in general and relocation cases in particular. It analyzes the leading cases from New York, Massachusetts, California, England, Canada, and Australia. It reaches a conclusion that no jurisdiction has devised a legal standard or formula that enables a judge to predict the future best interest of a child if that child is allowed to relocate with one parent away from the other. For this reason, the court has a duty to offer as sophisticated and friendly a settlement process and atmosphere as possible. However, knowing that judges will still be required to resolve these difficult cases because they often seem impervious to settlement, the article offers thirty-six factors that a court should consider in all move-away cases. By relying on each of these factors that is relevant to the case, the parents will have an understanding of why the decision was made the way it was and it will also allow for effective appellate review. [ABSTRACT FROM AUTHOR]

Presents several abstracts of research on specimen mineralogy that were presented at the 30th Rochester Mineralogical Symposium. "Mineralogy of the Kimberlites From New York State," by M. Lupulescu; "Mineralogy of the Hiddenite and Emerald Vein Assemblage, Hiddenite, North Carolina," M. A. Wise, A. J. Anderson and R. L. Isaac; "Diopside From the Atwater Quarry Area, Westfield, Massachusetts," by W. Shelton, G. W. Robinson, D. Burgess and D. Levine.

Reviews the efforts of the Martin Luther King Jr., Papers Project to prepare a definitive, multivolume edition of King's papers as part of a long-term effort to preserve the historical legacy of the African-American freedom struggle. The initial two volumes of `The Papers of Martin Luther King, Jr.'; Historical and intellectual context in which King's Crozer and Boston University academic papers were created; Selective use of appropriated passages; Citation and attribution practices; Details.

Automatic extraction of building contours from high-resolution images is of great significance in the fields of urban planning, demographics, and disaster assessment. Network models based on convolutional neural network (CNN) and transformer technology have been widely used for semantic segmentation of buildings from high resolution remote sensing images (HRSI). However, the fixed geometric structure and the local receptive field of the convolutional kernel are not good at global feature extraction, and the transformer technique with self-attention mechanism introduces computational redundancies and extracts local feature details poorly in the process of modeling the global contextual information. In this paper, a dual-branch fused reconstructive transformer network, DFRTNet, is proposed for efficient and accurate building extraction. In the encoder, the traditional transformer is reconfigured by designing the local and global feature extraction module (LGFE); the branch of global feature extraction (GFE) performs dynamic range attention (DRA) based on the idea of top-k attention for extracting global features; furthermore, the branch of local feature extraction (LFE) is used to obtain fine-grained features. The multilayer perceptron (MLP) is employed to efficiently fuse the local and global features. In the decoder, a simple channel attention module (CAM) is used in the up-sampling part to enhance channel dimension features. Our network achieved the best segmentation accuracy on both the WHU and Massachusetts building datasets when compared to other mainstream and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Efficient road and building footprint extraction from satellite images are predominant in many remote sensing applications. However, precise segmentation map extraction is quite challenging due to the diverse building structures camouflaged by trees, similar spectral responses between the roads and buildings, and occlusions by heterogeneous traffic over the roads. Existing convolutional neural network (CNN)-based methods focus on either enriched spatial semantics learning for the building extraction or the fine-grained road topology extraction. The profound semantic information loss due to the traditional pooling mechanisms in CNN generates fragmented and disconnected road maps and poorly segmented boundaries for the densely spaced small buildings in complex surroundings. In this paper, we propose a novel attention-aware segmentation framework, Multi-Scale Supervised Dilated Multiple-Path Attention Network (MSSDMPA-Net), equipped with two new modules Dynamic Attention Map Guided Index Pooling (DAMIP) and Dynamic Attention Map Guided Spatial and Channel Attention (DAMSCA) to precisely extract the building footprints and road maps from remotely sensed images. DAMIP mines the salient features by employing a novel index pooling mechanism to retain important geometric information. On the other hand, DAMSCA simultaneously extracts the multi-scale spatial and spectral features. Besides, using dilated convolution and multi-scale deep supervision in optimizing MSSDMPA-Net helps achieve stellar performance. Experimental results over the seven benchmark building and road extraction datasets namely, Porto, Shanghai, Massachusetts Road, Massachusetts Building, Synthinel-1, WHU Satellite I and WHU aerial Imagery dataset, ensures MSSDMPA-Net as the state-of-the-art (SOTA) method for building and road extraction as our method beats the next best alternatives by 5.94%, 2.55%, 3.97%, 11.64%, 6.86%, 6.92%, 2.57% IOU and 3.98%, 1.90%, 2.43%, 7.17%, 3.98%, 4.99%, 1.37% F1 score, respectively, over the mentioned datasets. The code has been released in: https://github.com/shirshabose/MSSDMPA-Net. • We propose a novel attention-guided pooling mechanism to alleviate information loss. • Prior generated probability map guided novel attention for multi-scale geometry-aware feature aggregation. • Multi-scale optimized with deep supervision to generate prior probability maps. • Dilated convolutions used to extract features of objects of varied shapes. • Multi-scale feature aggregator network for building and road segmentation. [ABSTRACT FROM AUTHOR]

Individual talkers vary in their relative use of different cues to signal phonological contrast. Previous work provides limited and conflicting data on whether such variation is modulated by cue trading or individual differences in speech style. This paper examines differential cue weighting patterns in Mandarin sibilants as a test case for these hypotheses. Standardized Mandarin exhibits a three-way place contrast between retroflex, alveopalatal, and alveolar sibilants with individual differences in relative weighting of spectral center of gravity (COG) and the second formant of the following vowel (F2). In results from a speech production task, cue weights of COG and F2 are inversely correlated across speakers, demonstrating a trade-off relationship in cue use. These findings are consistent with a cue trading account of individual differences in contrast signaling. [ABSTRACT FROM AUTHOR]

Lists reports presented at the General Scientific Meetings of the Marine Biological Laboratory on August 14-16, 1995.

Objectives: Dental fear and anxiety are known determinants of delaying or avoiding dental care and vary considerably based on factors such as age and gender. However, little is known about dental fear and anxiety in racial/ethnic minority populations, which bear a disproportionate burden of poor oral health outcomes. Structural and social pathways responsible for producing these disparities are also understudied. Experiences of racism over the lifecourse may contribute to poor oral health outcomes through a pathway of dental fear and anxiety. This paper aimed to evaluate perceived experiences with racism, dental fear and anxiety, and the utilization of dental services, in the Black Women's Health Study (BWHS), a United States‐based prospective cohort. Methods: Analysis of prospective data obtained from a geographic subset of participants in the BWHS was conducted. In 2014, BWHS participants residing in Massachusetts responded to a mailed oral health questionnaire that included the Index of Dental Anxiety and Fear (IDAF‐4C+) instrument (N = 484; 69% response rate). Previously collected demographic and health information, along with reported experiences of everyday and lifetime racism, obtained from national BWHS questionnaires between 1995 and 2009, were merged with the Massachusetts‐based sub‐sample. Associations between high dental anxiety (HDA) (mean IDAF‐4C+ score ≥2.5 on the dental fear and anxiety module) and oral health outcomes and perceived racism and HDA were explored via prevalence ratios (PR) calculated using log‐binomial regression models, including adjustment for potential confounders. Results: Reported exposures to everyday racism occurred weekly on average for the top 25% of the sample, while 13% of participants reported exposure to multiple (n = 3) experiences of unfair treatment due to their race over their lifetime. HDA was prevalent among 17.8% of the sample and was significantly associated with indicators of poor oral health status. High exposures to everyday and lifetime experiences of racism were positively associated with HDA (PR = 1.08; 95% CI: 0.90, 1.58 and PR = 1.72; 95% CI: 1.03, 2.88, respectively). Conclusions: Significant associations between racism and HDA, and between HDA and poor oral health and reduced utilization of dental care were observed. Dental anxiety may be a pathway through which perceived experiences with racism may impact oral health outcomes. [ABSTRACT FROM AUTHOR]

Accurately segmenting roads is challenging due to substantial intra-class variations, indistinct inter-class distinctions, and occlusions caused by shadows, trees, and buildings. To address these challenges, it is crucial to simultaneously pay attention to important texture details and perceive global geometric context information. Recent research has shown that CNN-Transformer hybrid structures outperform using CNN or Transformer alone. CNN excels at extracting local detail features and Transformer naturally has the ability to perceive global context information. In this paper, we propose a dual-branch network module called ConSwin, which combines the advantages of ResNet and Swin Transformer for better road information extraction. Taking ConSwin as the basic encoding module, we build an hourglass-shaped network with an encoder-decoder structure. In order to better transfer texture and structural detail information between the encoder and decoder, we also design two novel connection blocks. We conduct comparative experiments on the Massachusetts and CHN6-CUG datasets, and the proposed method outperforms state-of-the-art methods in terms of overall accuracy, IOU, and F1 metrics. Other experiments verify the effectiveness of our method, while visualization results demonstrate its ability to obtain better road representations. [ABSTRACT FROM AUTHOR]

Boston's "Big Dig" construction project resulted in the excavation of multiple archaeological sites dating from the seventeenth to nineteenth centuries, including the Great House/Three Cranes Tavern in Charlestown, Massachusetts (USA). An otherwise unremarkable pit below the tavern foundation contained bones originally identified as a cat skeleton, which has subsequently been reidentified as a dog. This paper discusses site context, osteological evidence for the dog's reclassification, and the shifts in cultural meaning this may indicate. Employing an osteobiographical approach, it draws together points of connection between the modern skeletal assessment, a series of 1980s excavations, and the motivations of eighteenth-century tavern inhabitants. [ABSTRACT FROM AUTHOR]

This paper seeks to use eye-tracking emulation software to understand and forecast the human responses to signage and directional cues in Massachusetts, USA. Using eye-tracking emulation software, this study measured the unconscious visual responses people are projected to have to designs and layouts in new built environments, focusing on what makes streets most walkable. The study found differences between the way the brain takes in conventional automobile-oriented residential developments versus new urbanist layouts, with the former lacking key fixation points. The study's discoveries explain why new urbanist layouts promote walking effortlessly and conventional automobile-oriented residential developments cannot. [ABSTRACT FROM AUTHOR]

Land conservation efforts throughout the United States sustain ecological benefits while generating wealth in the housing market through capitalization of amenities. This paper estimates the benefits of conservation that are capitalized into proximate home values and quantifies how those benefits are distributed across demographic groups. Using detailed property and household-level data from Massachusetts, we estimate that new land conservation led to $62 million in new housing wealth equity. However, houses owned by low-income or Black or Hispanic households are less likely to be located near protected areas, and hence, these populations are less likely to benefit financially. Direct study of the distribution of this new wealth from capitalized conservation is highly unequal, with the richest quartile of households receiving 43%, White households receiving 91%, and the richest White households receiving 40%, which is nearly 140% more than would be expected under equal distribution. We extend our analysis using census data for the entire United States and observe parallel patterns. We estimate that recent land conservation generated $9.8 billion in wealth through the housing market and that wealthier and White households benefited disproportionately. These findings suggest regressive and racially disparate incidence of the wealth benefits of land conservation policy. [ABSTRACT FROM AUTHOR]

Segmentation of a road portion from a satellite image is challenging due to its complex background, occlusion, shadows, clouds, and other optical artifacts. One must combine both local and global cues for an accurate and continuous/connected road network extraction. This paper proposes a model using fractional derivative-based weighted skip connections on a densely connected convolutional neural network for road segmentation. Weights corresponding to the skip connections are determined using Grunwald–Letnikov fractional derivative. Fractional derivatives being non-local in nature incorporates memory into the system and thereby combine both local and global features. Experiments have been performed on two open source widely used benchmark databases v i z. Massachusetts Road database (MRD) and Ottawa Road database (ORD). Both these datasets represent different road topography and network structure including varying road widths and complexities. Result reveals that the proposed system demonstrated better performance than the other state-of-the-art methods by achieving an F1-score of 0.748 and the mIoU of 0.787 at fractional order 0.4 on the MRD and a mIoU of 0.9062 at fractional order 0.5 on the ORD. [ABSTRACT FROM AUTHOR]

Objectives: In this study, we aimed to explore the oral and emotional health challenges experienced by a sample of refugees in Massachusetts across different stages of resettlement using a mixed methods approach. Methods: We collaborated with two Federally Qualified Health Centers to identify and recruit participants for either surveys (n = 69) or semi-structured interviews (n = 12). Data collection was conducted in 2018. We performed descriptive statistics using STATA 14, and analyzed the interviews using qualitative methods. Results: Overall, cost and lack of structure were the largest barriers identified for accessing dental care in participants' home and host countries. In the US, participants reported receiving state-provided public health insurance, but still experienced disrupted access to dental care due to coverage limitations. We identified several mental health risk factors that may affect participants' oral health, including trauma, depression, and sleeping problems. Despite these challenges, participants also identified areas of resilience and adaptability in both attitude and actions. Conclusions: The themes identified in our study suggest that refugees have attitudes, beliefs, and experiences that contribute to their perspectives on oral health care. While some of the reported barriers to access dental care were attitudinal, others were structural. Access to dental care in the US was reported to be structured and available, but with limited coverage issues. This paper underscores the oral and emotional health aspects of refugees for future considerations and planning of appropriate, affordable and cost-effective policies in the global health care systems. [ABSTRACT FROM AUTHOR]

Introduction: Academic libraries are facing two trends: the rising cost of journal subscriptions--the ongoing "serials crisis"--and the increasing availability of scholarly journal articles through free, open access channels. Methods: This study analyzed the bibliographies from 116 research projects created by students across disciplines at a small community college and classified citations to journal articles based on the simplest way students could have accessed them. Results: More than 60% of the scholarly journal articles that students used are available to them for free, and an additional 18% are available through state-sponsored database subscriptions available to any Massachusetts resident. Only 17% of the journal articles used were available to students solely through the library's database packages, and more than half of those articles were used by students in the associate degree of nursing (ADN) program. Discussion: These findings call into question the value of expensive subscription databases to community college libraries and suggest a pattern of diminishing return on investment and value. The trend toward cancellation of "big deal" journal packages at large institutions addresses similar concerns, although the dynamics are somewhat different at small institutions. Conclusion: The paper concludes by suggesting some steps that small academic libraries might take to adapt to this changing publishing environment, in the areas of collection development and information literacy instruction, in order to better serve students. [ABSTRACT FROM AUTHOR]

Recent advances in deep learning technology have triggered radical progress in the autonomy of ground vehicles. Marine coastal Autonomous Surface Vehicles (ASVs) that are regularly used for surveillance, monitoring, and other routine tasks can benefit from this autonomy. Long haul deep sea transportation activities are additional opportunities. These two use cases present very different terrains—the first being coastal waters—with many obstacles, structures, and human presence while the latter is mostly devoid of such obstacles. Variations in environmental conditions are common to both terrains. Robust labeled datasets mapping such terrains are crucial in improving the situational awareness that can drive autonomy. However, there are only limited such maritime datasets available and these primarily consist of optical images. Although, long wave infrared (LWIR) is a strong complement to the optical spectrum that helps in extreme light conditions, a labeled public dataset with LWIR images does not currently exist. In this paper, we fill this gap by presenting a labeled dataset of over 2900 LWIR segmented images captured in coastal maritime environment over a period of 2 years. The images are labeled using instance segmentation and classified into seven categories—sky, water, obstacle, living obstacle, bridge, self, and background. We also evaluate this dataset across three deep learning architectures (UNet, PSPNet, DeepLabv3) and provide detailed analysis of its efficacy. While the dataset focuses on the coastal terrain, it can equally help deep sea use cases. Such terrain would have less traffic, and the classifier trained on cluttered environment would be able to handle sparse scenes effectively. We share this dataset with the research community with the hope that it spurs new scene understanding capabilities in the maritime environment. [ABSTRACT FROM AUTHOR]

Road extraction from remote sensing images is significant for urban planning, intelligent transportation, and vehicle navigation. However, it is challenging to automatically extract roads from remote sensing images because the scale difference of roads in remote sensing images varies greatly, and slender roads are difficult to identify. Moreover, the road in the image is often blocked by the shadows of trees and buildings, which results in discontinuous and incomplete extraction results. To solve the above problems, this paper proposes a multiscale feature encoding and long-range context-aware network (MECA-Net) for road extraction. MECA-Net adopts an encoder–decoder structure and contains two core modules. One is the multiscale feature encoding module, which aggregates multiscale road features to improve the recognition ability of slender roads. The other is the long-range context-aware module, which consists of the channel attention module and the strip pooling module, and is used to obtain sufficient long-range context information from the channel dimension and spatial dimension to alleviate road occlusion. Experimental results on the open DeepGlobe road dataset and Massachusetts road dataset indicate that the proposed MECA-Net outperforms the other eight mainstream networks, which verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As third-generation solar cells, dye-sensitized solar cells (DSSCs) can show bifacial harvesting capabilities by utilizing transparent conducting oxides as a counter electrode. Herein, the electrolyte is considered a critical layer from the optical perspective. In this paper, an attempt to estimate the optical properties of Iodine-based electrolytes, typically used in dye-sensitized solar cells, is demonstrated. The refractive index for electrolyte as an effective medium is calculated to be 1.4535 ± 0.005 for an effective thin film of 33.4 ± 0.5 μm thickness, using the near-infrared Fabry–Perot resonances. The extinction (absorption and scattering) and dispersion spectra for the prepared electrolyte were fitted using Lorentz–Dude (LD) model. Finally, the utility of the extracted optical parameters was examined through a finite difference time domain solver, Massachusetts Institute for Technology Electromagnetic Equation Propagation. The simulated optical transmission spectrum perfectly agreed with the measured spectrum with less than 0.1% root–mean–square error. The demonstrated attempt to accurately estimate the refractive index of electrolyte used in DSSCs fabrication may impacted theoreticians who are interested with the optoelectronic modelling of such electrochemical cells, as well as those dealing with optoelectronic devices informatics. [ABSTRACT FROM AUTHOR]

Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such as dates of symptom onset and reporting for each reported case and a good source for modeling delays. Current methods for modeling reporting delays are not particularly appropriate for line list data, which typically has missing symptom onset dates that are non-ignorable for modeling reporting delays. In this paper, we develop a Bayesian approach that dynamically integrates imputation and estimation for line list data. Specifically, this Bayesian approach can accurately estimate the epidemic curve and instantaneous reproduction numbers, even with most symptom onset dates missing. The Bayesian approach is also robust to deviations from model assumptions, such as changes in the reporting delay distribution or incorrect specification of the maximum reporting delay. We apply the Bayesian approach to COVID-19 line list data in Massachusetts and find the reproduction number estimates correspond more closely to the control measures than the estimates based on the reported curve. Author summary: Interventions meant to control infectious diseases are often determined and judged using surveillance data on the number of new cases of disease. In many diseases, there are substantial delays between the time when an individual is infected or shows symptoms and when the case is actually reported to a public health authority, such as the CDC. This reported data often collects information on symptom onset dates for some individuals. In this paper, we describe a method that imputes missing onset dates for all individuals and recreates the history of the disease progression in a population according to symptom onset dates, which are the best observable proxy available for infection dates. Our method also estimates the instantaneous reproduction number and is robust to many deviations from the assumptions of the model. We show, using a COVID-19 dataset from Massachusetts that our method accurately follows the implementation of control measures in the state. [ABSTRACT FROM AUTHOR]

The Framingham Heart Study is the longest-running cardiovascular epidemiological study, starting in 1948. This paper gives an overview of the various cohorts, collected data, and most important research findings to date. In brief, the Framingham Heart Study, funded by the National Institutes of Health and managed by Boston University, spans 3 generations of well phenotyped White persons and 2 cohorts comprised of racial and ethnic minority groups. These cohorts are densely phenotyped, with extensive longitudinal follow-up, and they continue to provide us with important information on human cardiovascular and noncardiovascular physiology over the lifespan, as well as to identify major risk factors for cardiovascular disease. This paper also summarizes some of the more recent progress in molecular epidemiology and discusses the future of the study. [ABSTRACT FROM AUTHOR]

The Markforged Metal X (MfMX) printing machine (Markforged Inc., Massachusetts, USA) is one of the latest introduced additive manufacturing (AM) devices. It is getting popular because of its safety, simplicity, and ability to utilize various types of powders/filaments for printing. Despite this, only a few papers have so far reported the various properties and performances of the components fabricated by the MfMX printer. In this study, the microstructure and mechanical properties of MfMX-fabricated 17-4 stainless steel (ss) in the as-printed and heat-treated conditions were investigated. XRD and microscopy analyses revealed a dominant martensitic microstructure with some retained austenite phase. The microstructure is generally characterized by patterned voids that were unfilled due to a lack of fusion between the adjacent filaments. Disregarding these defects (voids), the porosity of the dense region was less than 4%. Depending on the heat treatment conditions, the hardness and tensile strength were enhanced by 17–28% and 21–27%, respectively. However, the tensile strength analyzed in this work was low compared with some previous reports for L-PBF-fabricated 17-4 ss. In contrast, the hardness of the as-printed (331 ± 28 HV) and heat-treated samples under the H900 condition (417 ± 29 HV) were comparable with (and even better than) some reports in the literature, despite the low material density. The results generally indicated that the Markforged printer is a promising technology when the printing processes are fully developed and optimized. [ABSTRACT FROM AUTHOR]

Background: The HEALing (Helping to End Addiction Long-termSM) Communities Study (HCS) is a multi-site parallel group cluster randomized wait-list comparison trial designed to evaluate the effect of the Communities That Heal (CTH) intervention compared to usual care on opioid overdose deaths. Covariate-constrained randomization (CCR) was applied to balance the community-level baseline covariates in the HCS. The purpose of this paper is to evaluate the performance of model-based tests and permutation tests in the HCS setting. We conducted a simulation study to evaluate type I error rates and power for model-based and permutation tests for the multi-site HCS as well as for a subgroup analysis of a single state (Massachusetts). We also investigated whether the maximum degree of imbalance in the CCR design has an impact on the performance of the tests.Methods: The primary outcome, the number of opioid overdose deaths, is count data assessed at the community level that will be analyzed using a negative binomial regression model. We conducted a simulation study to evaluate the type I error rates and power for 3 tests: (1) Wald-type t-test with small-sample corrected empirical standard error estimates, (2) Wald-type z-test with model-based standard error estimates, and (3) permutation test with test statistics calculated by the difference in average residuals for the two groups.Results: Our simulation results demonstrated that Wald-type t-tests with small-sample corrected empirical standard error estimates from the negative binomial regression model maintained proper type I error. Wald-type z-tests with model-based standard error estimates were anti-conservative. Permutation tests preserved type I error rates if the constrained space was not too small. For all tests, the power was high to detect the hypothesized 40% reduction in opioid overdose deaths for the intervention vs. comparison group both for the overall HCS and the subgroup analysis of Massachusetts (MA).Conclusions: Based on the results of our simulation study, the Wald-type t-test with small-sample corrected empirical standard error estimates from a negative binomial regression model is a valid and appropriate approach for analyzing cluster-level count data from the HEALing Communities Study.Trial Registration: ClinicalTrials.gov http://www.Clinicaltrials: gov ; Identifier: NCT04111939. [ABSTRACT FROM AUTHOR]

The article presents a study on the withdrawal resistance of J-lags from several species of hardwood trees. The researchers installed 19 J-lags in red oaks growing at the Urban Horticulture Center (UHC) in Blacksburg, Virginia, in July 2005. They found that lags offer significant withdrawal resistance in species with moderately dense wood in the absence of decay. They also suggested that decay associated with J-lag installation is minimal after four years for sugar maple and paper birch growing in Massachusetts.

The article introduces an article in the series about the treatment resistance and patient authority at the Austen Riggs Center in Stockbridge, Massachusetts. It notes that the paper explores and illustrate the work of treatment teams as an adjunctive therapeutic psychoanalysis modality as an approach for treatment resistant patients at the center.

The article presents the winner for the "The Journal of Experimental Biology" Outstanding Paper Prize, 2005. Ms. Havalee T. Henry from Richard Marsh's lab at Northeastern University in Boston bagged the first prize with her publication "Performance of guinea fowl Numida meleagris during dumping requires storage and release of elastic energy". Henry is an undergrad at the University of Connecticut, but has specialized in Molecular and Cell Biology, as well as having a great interest in muscle physiology.

Transgender patients are at elevated risk of intimate partner violence (IPV), but national guidelines do not recommend routine screening for this population. This paper explores the feasibility and effectiveness of routine IPV screening of transgender patients in a primary care setting by describing an existing screening program and identifying factors associated with referral and engagement in IPV-related care for transgender patients. An IPV "referral cascade" was created for 1,947 transgender primary care patients at an urban community health center who were screened for IPV between January 1, 2014 to May 31, 2016: (a) Of those screening positive, how many were referred? (b) Of those referred, how many engaged in IPV-specific care within 3 months? Logistic regression identified demographic correlates of referral and engagement. Of the 1,947 transgender patients screened for IPV, 227 screened positive. 110/227 (48.5%) were referred to either internal or external IPV-related services. Of those referred to on-site services, 65/103 (63.1%) had an IPV-related appointment within 3 months of a positive screen. IPV referral was associated with being assigned male at birth (AMAB) versus assigned female at birth (AFAB) (AOR = 2.69, 95% CI 1.52, 4.75) and with nonbinary, rather than binary, gender identity (AOR = 2.07, 95%CI 1.09, 3.73). Engagement in IPV-related services was not associated with any measured demographic characteristics. Similar to published rates for cisgender women, half of transgender patients with positive IPV screens received referrals and two-thirds of those referred engaged in IPV-specific care. These findings support routine IPV screening and referral for transgender patients in primary care settings. Provider training should focus on how to ensure referrals are made for all transgender patients who screen positive for IPV, regardless of gender identity, to ensure the benefits of screening accrue equally for all patients. [ABSTRACT FROM AUTHOR]

School closings in response to COVID-19 reduced the opportunity for students to engage in all learning including career and technical education (CTE). As a result of the COVID-19 disruption student opportunities for work-based learning and completion of hours required for professional certification was severely reduced. The absence of these experiences had the potential to create disruptions in the skill and experience accumulation for youth and their transition to the labor market. Prior research demonstrated that CTE training and certifications in high school improve earnings and employment outcomes for students, particularly those from marginalized communities. This paper uses administrative data from Massachusetts to investigate whether COVID-19 disruptions differentially influenced outcomes for CTE students relative to non-CTE students. We find graduation rates fell for all students, but students in specialized CTE high schools experienced smaller declines. These smaller differences were apparent for CTE students from lower-income families and those with disabilities. In contrast, students in large cities where the influence of COVID-19 was elevated fared less well, even among CTE participants. In contrast to educational attainment, graduating seniors continued earning industry credentials comparably to pre-COVID-19 rates. This work illuminates how the pandemic disrupted youth transitions to adulthood, particularly in marginalized communities. [ABSTRACT FROM AUTHOR]

Neuropsychological assessment using the Boston Process Approach (BPA) suggests that an analysis of the strategy or the process by which tasks and neuropsychological tests are completed, and the errors made during test completion convey much information regarding underlying brain and cognition and are as important as overall summary scores. Research over the last several decades employing an analysis of process and errors has been able to dissociate between dementia patients diagnosed with Alzheimer's disease, vascular dementia associated with MRI-determined white matter alterations, and Parkinson's disease; and between mild cognitive impairment subtypes. Nonetheless, BPA methods can be labor intensive to deploy. However, the recent availability of digital platforms for neuropsychological test administration and scoring now enables reliable, rapid, and objective data collection. Further, digital technology can quantify highly nuanced data previously unobtainable to define neurocognitive constructs with high accuracy. In this paper, a brief review of the BPA is provided. Studies that demonstrate how digital technology translates BPA into specific neurocognitive constructs using the Clock Drawing Test, Backward Digit Span Test, and a Digital Pointing Span Test are described. Implications for using data driven artificial intelligence-supported analytic approaches enabling the creation of more sensitive and specific detection/diagnostic algorithms for putative neurodegenerative illness are also discussed. [ABSTRACT FROM AUTHOR]

Road segmentation is one of the essential tasks in remote sensing. Large-scale high-resolution remote sensing images originally have larger pixel sizes than natural images, while the existing models based on Transformer have the high computational cost of square complexity, leading to more extended model training and inference time. Inspired by the long text Transformer model, this paper proposes a novel hybrid attention mechanism to improve the inference speed of the model. By calculating several diagonals and random blocks of the attention matrix, hybrid attention achieves linear time complexity in the token sequence. Using the superposition of adjacent and random attention, hybrid attention introduces the inductive bias similar to convolutional neural networks (CNNs) and retains the ability to acquire long-distance dependence. In addition, the dense road segmentation result of remote sensing image still has the problem of insufficient continuity. However, multiscale feature representation is an effective means in the network based on CNNs. Inspired by this, we propose a multi-scale patch embedding module, which divides images by patches with different scales to obtain coarse-to-fine feature representations. Experiments on the Massachusetts dataset show that the proposed HA-RoadFormer could effectively preserve the integrity of the road segmentation results, achieving a higher Intersection over Union (IoU) 67.36% of road segmentation compared to other state-of-the-art (SOTA) methods. At the same time, the inference speed has also been greatly improved compared with other Transformer based models. [ABSTRACT FROM AUTHOR]

Aim: This study aims to present a general bibliometric overview of the development status of global nursing research from 2000 to 2019. Design: A longitudinal bibliometric analysis of nursing research was conducted. Methods: Nursing research publications (N = 88,665) were obtained from Web of Science. Bibliometric method was used to map the output and citation impact trends of countries/regions, institutions, disciplines, and journals and analyse the research collaboration among countries/regions and institutions. Results: The global paper output in nursing research increased steadily over the past two decades and it varied in different countries/regions with the USA being far ahead of the others. The paper output and cross‐border collaboration are mainly distributed in several developed countries like the USA, the UK, Australia, and Canada. The University of Pennsylvania, Harvard University, University of Toronto, and University of North Carolina at Chapel Hill have high academic influence in the field of nursing. Increasing attention from academic fields has been paid to research on nursing. Journal of Advanced Nursing is the most prolific and most cited journal in nursing field. Conclusion: Nursing research has developed steadily over the last two decades. Both the scientific output and research collaboration are disproportionally distributed between high‐income countries/regions and low‐ and middle‐income countries/regions. Most research and collaboration have taken place in a few developed countries across North America, Europe, and Oceania. Impact: The study highlighted the need for policy makers and funding agencies, especially those from low‐ and middle‐income countries/regions, to allocate research funding that supports the nursing higher education and international cooperation so as to promote the development of high‐quality nursing research in those countries/regions. At the same time, researchers from non‐English‐speaking countries/regions should attach more importance to publishing papers in English, strengthening the academic exchanges with international nursing colleagues and better integrating into the international academic community. [ABSTRACT FROM AUTHOR]

Many scholars have explored the intersection of 21st-century technologies with Fourth Amendment jurisprudence. Some have approached this as digital-age versions of papers, effects, and the curtilage, while others have addressed the third-party Miller doctrine. One theory gaining support, based partly on the concurring Supreme Court opinions of United States v. Jones, is the Mosaic Theory, which argues that data collection that is constitutional in isolation can aggregate to create an unconstitutional intrusion. One underexplored area is its intersection with automated license plate readers (ALPRs). Multiple authors have argued for the Mosaic Theory's application to limit or ban ALPRs, and in 2020, Massachusetts' highest court held that the Fourth Amendment could be violated by less than a year's use of ALPRs. This Note criticizes that ruling and fills the gap in the literature by examining how the Mosaic Theory, despite sounding promising on paper, would be unworkable in practice regarding ALPRs and unlikely to be approved by the Supreme Court. This Note instead proposes alternatives to limit the growing reach of ALPRs. [ABSTRACT FROM AUTHOR]

The article provides information on the treatment resistance or treatment refractory patients at the Austen Riggs Center in Stockbridge, Massachusetts. Donna Elmendorf and Margaret Parish are the patients who offer their crucial opportunity in the therapeutic community program. The author further discusses that the opportunity could deepen the learning beyond the individual psychotherapy.

Attempts to clear the misinterpretation on the study done by Kirshen and Fennessey (KF) devoted to Climatic Change and Water Resources Planning Criteria. Water supply system of the Massachusetts Water Resources Authority; Total system demand of the KF study; Description on the KF scenario.

OBJECTIVES: Emergency department (ED) crowding poses a severe public health threat, and identifying acceptable means of treating medical conditions in alternative sites of care is imperative. We compared patients' experiences with in-home urgent care via mobile integrated health (MIH) vs urgent care provided in EDs. STUDY DESIGN: Survey, completed on paper, online, or by telephone. We surveyed all patients who received MIH care for an urgent health problem (n = 443) and consecutive patients who visited EDs for urgent care (n = 1436). METHODS: Study participants were members of a managed care plan who were dually eligible for Medicare and Medicaid, 21 years or older, and treated either by MIH or in an ED for nonemergent conditions around Boston, Massachusetts, between February 2017 and June 2018. The survey assessed patients' perceptions of their urgent care experiences. RESULTS: A total of 206 patients treated by community paramedics and 718 patients treated in EDs completed surveys (estimated 66% and 62% response rates, respectively). Patients treated by MIH perceived higher- quality care, more frequently reporting "excellent" (54.7%) or "very good" (32.4%) care compared with ED patients (40.7% and 24.3%, respectively; P < .0001), and were significantly more likely to report that decisions made about their care were "definitely right" compared with patients treated in the ED (66.1% vs 55.6%; P = .02). CONCLUSIONS: Patients appear satisfied with receiving paramedic-delivered urgent care in their homes rather than EDs, perceiving higher-quality care. This suggests that in-home urgent care via MIH may be acceptable for patients with nonemergent conditions. [ABSTRACT FROM AUTHOR]

Focuses on the Harvard University School of Law's compulsory retirement policy for its faculty members. Retirement age; Conflicts in approaches to the problem of aging; Issues related to the school's retirement policy raised in the student paper 'Harvard Law Record.'

Background: Scientific activity for 3D bioprinting has increased over the past years focusing mainly on fully functional biological constructs to overcome issues related to organ transplants. This research performs a scientometric analysis on bioprinting based on a competitive technology intelligence (CTI) cycle, which assesses scientific documents to establish the publication rate of science and technology in terms of institutions, patents or journals. Although analyses of publications can be observed in the literature, the identification of the most influential authors and affiliations has not been addressed. This study involves the analysis of authors and affiliations, and their interactions in a global framework. We use network collaboration maps and Betweenness Centrality (BC) to identify of the most prominent actors in bioprinting, enhancing the CTI analysis. Results: 2088 documents were retrieved from Scopus database from 2007 to 2017, disclosing an exponential growth with an average publication increase of 17.5% per year. A threshold of five articles with ten or more cites was established for authors, while the same number of articles but cited five or more times was set for affiliations. The author with more publications was Atala A. (36 papers and a BC = 370.9), followed by Khademhosseini A. (30 documents and a BC = 2104.7), and Mironov (30 documents and BC = 2754.9). In addition, a small correlation was observed between the number of collaborations and the number of publications. Furthermore, 1760 institutions with a median of 10 publications were found, but only 20 within the established threshold. 30% of the 20 institutions had an external collaboration, and institutions located in and close to the life science cluster in Massachusetts showed a strong cooperation. The institution with more publications was the Harvard Medical School, 61 publications, followed by the Brigham and Women's hospital, 46 papers, and the Massachusetts Institute of Technology with 37 documents. Conclusions: Network map analysis and BC allowed the identification of the most influential authors working on bioprinting and the collaboration between institutions was found limited. This analysis of authors and affiliations and their collaborations offer valuable information for the identification of potential associations for bioprinting researches and stakeholders. [ABSTRACT FROM AUTHOR]

New England represents a critical arena for imagining the multiple scales and geographies of energy transition. To the south, the densely-populated states of Massachusetts, Connecticut, and Rhode Island power the regional political economy and increasingly aspire to expand their reliance on low-carbon energy sources. To the north, Hydro-Quebec, Canada's largest hydropower producer, stands poised to increase its hydroelectric production and electricity exports to the northeastern United States as it envisions its future as North America's "leading provider of clean energy." Citizens of northern New England, however, have argued that this vision for regional energy transition leaves them the role of "extension cord." This paper analyzes grassroots opposition to the Northern Pass project, a proposed high-capacity transmission line that would have increased imports of Quebecois hydroelectricity to southern New England. Drawing on interviews and from discourse and material analysis, this paper argues that opposition to Northern Pass was motivated by complex notions of stewardship, solidarity, resistance, and contestation that, rather than simply standing in the way of energy development, work to imagine more just futures for this transnational energy system at multiple scales obscured by accusations of NIMBYism, or the Not-In-My-Backyard movement. [ABSTRACT FROM AUTHOR]

This paper reflects upon ethnographic work carried out in a suburban Massachusetts town that has experienced particularly high rates of opioid mis/use. In open-ended conversations, residents attributed the local opioid crisis to institutional betrayals of various sorts. Building upon the idea that social capital tends to be health-enhancing, this paper traces the decline in the infrastructure of social capital and the associated decline in cultural capital or scripts for making sense out of life's challenges. [ABSTRACT FROM AUTHOR]

To assess whether a measure of leadership support for worker safety, health, and well-being predicts staff turnover in nursing homes after controlling for other factors. This paper uses administrative payroll data to measure facility-level turnover and uses a survey measure of nursing home leadership commitment to workers. In addition, we use data from Medicare to measure various nursing home characteristics. Nursing homes with at least 30 beds serving adults in California, Ohio, and Massachusetts were invited to participate in the survey. The analysis sample included 495 nursing homes. We used a multivariable ordinary least squares model with turnover rate as the dependent variable. We used an indicator for nursing homes who scored above the median on the measure of leadership that supports worker safety, health, and well-being. Control variables include bed count (deciles), ownership (corporate/noncorporate × for-profit/not-for-profit), percent of residents on Medicaid, state, being in a nonmetropolitan county, and total nurse staffing per patient day in the 2 quarters before the survey. The unadjusted turnover rate was lower for those nursing homes that scored higher on leadership commitment to worker safety, health, and well-being. After controlling for additional variables, greater leadership commitment was still associated with lower turnover but with some attenuation. We find that nursing homes with leadership that communicated and demonstrated commitment to worker safety, health, and well-being had relatively fewer nurses leave during the study period, with turnover rates approximately 10% lower than homes without. These findings suggest that leadership may be a valuable tool for reducing staff turnover. [ABSTRACT FROM AUTHOR]