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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]

• Proximity to extreme weather event plays role in type of policy response. • Little connection between city media engagement and its influence on policy response. • Boston and New York City overlapped in communicating current and projected climate risk. • Boston displayed proactive climate policy responses while New York City pursued reactive climate policies. The potential for extreme weather events to open windows for public policy changes or transitions has been studied from various theoretical and analytical perspectives. Following extreme weather events, the media (including popular press and journalism) contributes to critical moments, where a potential window opens to transform public understandings of issues and challenges in established extreme event discourses. This paper examines government engagement with the media to better understand conditions of the post-event policy window from the perspective of critical discourse moments. More specifically, the paper uses keyword analysis and critical discourse analysis to examine discursive constructions of the post-event policy window following Hurricane Sandy by two city governments– Boston and New York. We contrast the language of city government engagement with media about post-disaster policy responses and compare this with the language of proposed city policies on climate risk adaptation and mitigation strategies. The outcomes of this analysis illustrate differences between government engagement with media in the post-disaster window and their eventual climate policy responses. The results raise questions regarding the influence of the media on post-disaster policy responses and focus attention on the proximity of an extreme weather event as potentially affecting the type of policy response (either proactive or reactive) by a governing body. [ABSTRACT FROM AUTHOR]

Four experiments were conducted to examine the effects of a recombinant bacterial expansin-like protein (BsEXLX1) from Bacillus subtilis and a commercial exogenous fibrolytic enzyme (EFE) preparation for ruminants on hydrolysis of pure substrates (cellulose and xylan) and in vitro digestibility of bermudagrass haylage (BMH). Recombinant Escherichia coli BL21 strain was used to express BsEXLX1; the protein was purified using an affinity column. In experiment 1, carboxymethylcellulose, Whatman #1 filter paper (General Electric, Boston, MA) and oat-spelt xylan substrates were subjected to 4 treatments (1) sodium citrate buffer (control), (2) BsEXLX1 (162 µg/g of substrate), (3) EFE (2.3 mg/g of substrate), and (4) EFE + BsELX1 in 3 independent runs. Samples were incubated at optimal conditions for both additives (pH 5 and 50°C) or at ruminal (pH 6 and 39°C) or ambient (pH 6 and 25°C) conditions for 24 h and sugar release was measured. In experiment 2, digestibility in vitro of BMH was examined after treatment with the following: (1) control (buffer only), (2) BsEXLX1 (162 µg/g of dry matter), (3) EFE (2.2 mg/g of dry matter), and (4) EFE + BsEXLX1 in 3 independent runs at 39°C for 24 h. Experiment 3 examined effects of EFE and BsEXLX1 on simulated preingestive hydrolysis and profile of released sugars from BMH after samples were suspended in deionized water with sodium azide at 25°C for 24 h in 2 independent runs. In experiment 4, the sequence of the BsEXLX1 purified protein was compared with 447 ruminal bacterial genomes to identify similar proteins from the rumen. In experiment 1, compared with EFE alone, EFE and BsEXLX1 synergistically increased sugar release from carboxymethylcellulose and Whatman #1 filter paper under all simulated conditions; however, hydrolysis of xylan was not improved. In experiment 2, compared with EFE alone, treatment with EFE and BsEXLX1 increased neutral detergent fiber and acid detergent fiber digestibility of bermudagrass haylage (by 5.5 and 15%, respectively) and total volatile fatty acid concentrations, and decreased acetate-propionate ratio. In experiment 3, compared with EFE alone. The EFE and BsEXLX1 synergistically reduced concentrations of neutral detergent fiber and acid detergent fiber and increased release of sugars by 9.3%, particularly cellobiose (72.5%). In experiment 4, a similar sequence to that of BsEXLX1 was identified in Bacillus licheniformis , and similar hypothetical protein sequences were identified in Ruminococcus flavefaciens strains along with different protein structures in E. xylanophilum and Lachnospiraceae. This study showed that an expansin-like protein synergistically increased the hydrolysis of pure cellulose substrates and the hydrolysis and digestibility in vitro of BMH. [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]

• Solidarity Economy (SE) as a transformational political response to poverty. • SE practitioners express concerns about in-movement exclusionary and marginalising dynamics. • The commoning of space and resources expands solidarity economies capacity for collective action. This paper stages an encounter between Relational Poverty Theory (RPT) and the solidarity economy movement. RPT understands poverty as the dynamic product of economic exploitation, political exclusion and cultural marginalization. The solidarity economy movement can be seen as a transformative political response to these dynamics aiming to replace exploitation with cooperation, exclusion with participation and marginalisation with practices of inclusion. Globally, more than sixty solidarity economy movements are coordinating efforts, developing associative relations between cooperative economic institutions, social justice movements, and one another. While these developments are encouraging, many practitioners are concerned about the movement's future. Solidarity economy practitioners we encountered in our US-based research were concerned with the movement's vulnerability to co- optation and exploitation or (un)witting perpetuation of the very dynamics of exclusion and marginalisation it seeks to transcend. We take this as evidence of the enduring power of poverty-dynamics and testament to the incisive, critical insights of RPT. However, what remains unanswered is how the solidarity economy might succeed in its own terms? We deploy Gibson-Graham's theorization of postcapitalist politics to answer this question, charting the movement's possibilities, specifically how it works by creating and sharing spaces and monetary and non-monetary resources in pursuit of its objectives. Two organizations we encountered in our research—Stone Soup, a cooperative incubator in Worcester, Massachusetts and CERO, a commercial composting cooperative in Boston, — illustrate what Gibson-Graham name "a politics of commoning." Both of these organisations work by sharing spatial, financial, and political resources in ways that are cooperative, participatory and inclusionary. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is a measurement tool to measure the electrical activity of brain observed due to chemical variation in brain. The EEG analysis has important role in feature extraction and classification methods for detecting and predicting various brain diseases. Epilepsy is a major disease characterized by seizures and observed due to sudden abnormal electrical discharges in the brain. Mostly the research is carried out to classify the normal EEG signal from epileptic EEG signal. The objective of the paper is to identify pre-seizure state and seizure state of EEG signal using time and frequency features. Classifying these states of EEG signal using fuzzy classifier helps in predicting seizures. The methodology uses hypothetical testing to refine feature selection and pattern adapted wavelet transform to improve classification. The appropriate feature selection reduces computational complexity of the classifier. The artifacts are removed from the EEG signal using Independent Component Analysis (ICA). The CHB-MIT EEG scalp dataset from Children's Hospital, Boston is used for experimentation. The result shows classification accuracy of 96.48 %, True Positive Rate 96.52% and False Positive Rate 0.352 for seizure detection and 96.02% accuracy for Pre-seizure state detection with 13–110 s earlier than the onset seizures. [ABSTRACT FROM AUTHOR]

• The Grasselli team at SOHIO developed and optimized the SOHIO multicomponent catalyst for ammoxidation of propene and ammonia to acrylonitrile. • The Grasselli team deduced mechanistic oncepts from their experiments for guidance in optimization, but later accepted the Goddard mechanism. • This stimulated Goddard at Caltech to use Quantum Mechanics to provide a new mechanistic basis to explain atomistic details of mechanism. This paper is based on the talk given by Goddard at the symposium "New Vistas in Heterogeneous Catalysis: Symposium in honor of Robert Grasselli" sponsored by the CATL division of the ACS held on August 21, 2018 at the ACS national meeting in Boston MA. It tells the story about how Bob Grasselli had a dramatic influence on the research I have carried out on heterogeneous catalysis, particularly on ammoxidation. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals play an important role in the epilepsy detection. In the past decades, the automatic detection system of epilepsy has emerged and performed well. In this paper, a novel sparse representation-based epileptic seizure classification based on the dictionary learning with homotopy (DLWH) algorithm is proposed. The performance of the proposed method evaluates on two public EEG databases provided by the Bonn University and Childrens Hospital Boston-Massachusetts Institute of Technology (CHB-MIT), various classification cases that include health and seizure; non-seizure and seizure; inter ictal (seizure-free interval) and ictal (seizure). The results show that the DLWH only completes the test with 19.671 s compared with the traditional sparse representation methods with high degree of automation, which are better than those obtained using the well-known dictionary learning method. Besides, two publicly available benchmark databases recognition rates are as high as 100%, 99.5%, with average of 99.5% and 95.06%,% respectively, and the results show that the epileptic detection system based on the dictionary learning has a high application value. [ABSTRACT FROM AUTHOR]

The building sector of the U.S. currently consumes over 40% of the U.S. primary energy supply. Estimates suggest that between 5% and 30% of any building's annual energy consumption is unknowingly wasted due to pathologically malfunctioning lighting and comfort conditioning systems. This paper presents analytical methods embodied within useful software tools to quickly identify and evaluate selected faults in air handling units (AHU) that cause large building energy inefficiencies. Algorithm for faults like stuck dampers and leaking dampers have been developed and tested in this particular case study. These damper fault detection algorithms can be applied to outdoor air and return air dampers both. The technical contributions of this work include expert rules that adapt to the Heating, Ventilation and Air-Conditioning (HVAC) equipment scale and operation and methods for sorting fault signals according to user-defined interests such as annual cost of energy inefficiencies. The combination of expert-rule based fault detection combined with first principles thermodynamic modeling is a unique contribution of this work that leads to quicker fault detection with minimal non-intrusive measurements. These contributions are particularly unique in their treatment of models and the careful consideration of user-interests in fault evaluation. As a first step to developing this general framework for fault detection, first-order faults such as stuck dampers and imbalanced airflows within several large air-handling units were targeted. The algorithms focused on detecting faults with minimal data and non-intrusive measurements. An example is presented of the potential energy savings in a large academic building that has been monitored. Real data from an academic building in Boston was collected from the building energy management system (BEMS) for the purpose of this study. Savings of around $3400/month or 18% of the monthly cost when a stuck damper fault occurred over an entire month in an air handler. Another fault that was investigated was that of leaking dampers which lead to savings of approximately $500/month or around 2.5% of the monthly energy expense. The savings would accrue if the fault were corrected; otherwise the occurrence of the fault causes a waste of money and energy predicted. The algorithms developed can be applied to large commercial office buildings, academic buildings, hospitals and other modern buildings in various climates as long as they have data collecting capabilities. [ABSTRACT FROM AUTHOR]

A sustainable city relies on renewable energy, which promotes the development of electric vehicles. To support electric vehicles, the concept of charging vehicles while driving has been put forward. Under such circumstances, constructing solar panels on urban roads is an innovative option with great benefits, and the accurate calculation of road photovoltaic power generation is a prerequisite. In this paper, we propose a novel framework for predicting and calculating the solar radiation and electric energy that can be collected from the roads. Google Street View images are collected to measure the sky obstruction of roads which is integrated with the solar radiation model to estimate the irradiation receiving capability. In addition to sky obstruction, we also take the impact of traffic conditions and weather situations into consideration in the calculation. Radiation maps at different times in a year are produced from our work to analyze the roads photovoltaic distribution. In order to test the feasibility of our framework, we take Boston as a case study. Results show that roads in Boston can generate abundant electricity for all future electric vehicles in the city. What's more, main roads through Boston exhibit better power generation potential, and the effect of the traffic condition is limited. Our calculation framework confirms that utilizing solar panels as road surfaces is a great supplement of city power with the unique ability to charge moving cars. • A new framework is proposed to assess the photovoltaic capacity of city roads. • Roads' solar energy is estimated precisely by street views and solar radiation model. • Street canyon, weather, traffic, trees are considered in the radiation calculation. • Traffic condition in Boston shows limited impact on solar energy generation of roads. • Solar energy obtained from urban road network can support all Boston's cars. [ABSTRACT FROM AUTHOR]

In the paper, we first thoroughly examine and prove properties of the participation index of spatio-temporal sequential patterns. Then, we introduce notions of a closure of a spatio-temporal sequential pattern and a closed spatio-temporal sequential pattern, as well as investigate and prove their properties. In particular, we prove that the set of all participation index strong closed spatio-temporal sequential patterns constitues a lossless representation of all participation index strong spatio-temporal sequential patterns. We also propose an algorithm, called CST-SPMiner, for discovering all participation index strong closed spatio-temporal sequential patterns. CST-SPMiner is an adaptation of the STBFM algorithm, which was proposed recently for the discovery of spatio-temporal sequential patterns with high participation index. While STBFM uses the CSP-tree structure for time-efficient candidate patterns generation and evaluation, CST-SPMiner uses it also for fast identification of closed patterns. Efficiency and effectiveness of our algorithm were verified on real crime data for Boston. [ABSTRACT FROM AUTHOR]

Abstract In The Image of the City Lynch describes how individuals perceive and recall features in urban spaces. The most distinctive elements in the urban landscape - categorised in paths, nodes, edges, districts and landmarks - give shape to individuals' mental representation of the city. Lynch's approach has stimulated research into spatial cognition, urban design and artificial intelligence, and it still represents an essential pillar in the analysis of urban dynamics. Nevertheless, an explicit link between The Image of the City and GIScience has not been completely explored yet. In this paper, a computational approach to The Image of the City is proposed. Different perspectives in spatial cognition and GIS research are integrated to obtain a complete Image of the City, in which the most salient elements are shared by a large part of citizens. Nodes, paths and districts were identified through network science techniques. Methods drawn from the information approach to The Image of the City are used to detect landmarks, integrating the complexity of points of reference in their visual, structural and semantic components, as conceptualised by Lynch and successive research. The methods were applied to the central area of Boston and built using freely available spatial datasets. Results were compared to Lynch's maps to evaluate the methodology: besides a considerable discrepancy with regard to landmarks, a good correspondence for paths, nodes, edges and districts was found. Highlights • An approach to computationally extract Lynch's Image of the city is advanced. • For Boston, our computational Image matches well with Lynch's Image. • The most satisfactory outcomes were obtained for districts and paths. • Out of the five elements, landmarks were found most challenging to extract. [ABSTRACT FROM AUTHOR]

Abstract: The paper considers makespan minimization on a single machine subject to release dates in the relocation problem, originated from a resource-constrained redevelopment project in Boston. Any job consumes a certain amount of resource from a common pool at the start of its processing and returns to the pool another amount of resource at its completion. In this sense, the type of our resource constraints extends the well-known constraints on resumable resources, where the above two amounts of resource are equal for each job. In this paper, we undertake the first complexity analysis of this problem in the case of arbitrary release dates. We develop an algorithm, based on a multi-parametric dynamic programming technique (when the number of parameters that undergo enumeration of their values in the DP-procedure can be arbitrarily large). It is shown that the algorithm runs in pseudo-polynomial time when the number  of distinct release dates is bounded by a constant. This result is shown to be tight: (1) it cannot be extended to the case when is part of the input, since in this case the problem becomes strongly NP-hard, and (2) it cannot be strengthened up to designing a polynomial time algorithm for any constant , since the problem remains NP-hard for . A polynomial-time algorithm is designed for the special case where the overall contribution of each job to the resource pool is nonnegative. As a counterpart of this result, the case where the contributions of all jobs are negative is shown to be strongly NP-hard. [Copyright &y& Elsevier]

Abstract: There is a lack of consensus as to whether the relationship between the built environment and travel is causal and, if it is, the extent of this causality. This problem is largely caused by inappropriate research designs adopted in many studies. This paper proposes a new method (based on path choice) to investigate the causal effect of the pedestrian environment on the utility of walking. Specifically, the paper examines how the pedestrian environment affects subway commuters’ egress path choice from a station to their workplaces in downtown Boston. The path-based measure is sensitive enough to capture minor differences in the environment experienced by pedestrians. More importantly, path choice is less likely to correlate with job and housing location choices, and therefore largely avoids the self-selection problem. The results suggest that the pedestrian environment can significantly affect a person’s walking experience and the utility of walking along a path. [Copyright &y& Elsevier]

Due to the rapid development of artificial intelligence, seizure detection has achieved great success in terms of accuracy and speed. However, low-power seizure detection algorithms remain a challenge. In this paper, a seizure detection method is presented based on spiking neural network (SNN) and support vector machine (SVM) for neuromorphic implementation. To be specific, the power threshold is applied in advance before SNN to avoid unnecessary computing on electroencephalogram (EEG) that is unlikely to be a seizure. A small-scale three-layer fully-connected SNN model is used to discriminate seizure efficiently. An ultra-low dimensional SVM is adopted to further improve accuracy at low computational cost. The computation complexity is reduced to 6 K add operations with power threshold, and no multiply operation is used. Experiments on the Children's Hospital Boston-Massachusetts Institute of Technology dataset show that the accuracy and sensitivity can achieve 95.07% and 88.44%, respectively. The energy consumption of the proposed method is smaller than other previous solutions, which is suitable to be integrated as near sensor intelligence for wearable long-term seizure detection. [ABSTRACT FROM AUTHOR]

Let G be a finite simple group. In this paper we consider the existence of small subsets A of G with the property that, if y ∈ G is chosen uniformly at random, then with high probability y invariably generates G together with some element of A. We prove various results in this direction, both positive and negative. As a corollary, we prove that two randomly chosen elements of a finite simple group of Lie type of bounded rank invariably generate with probability bounded away from zero. Our method is based on the positive solution of the Boston–Shalev conjecture by Fulman and Guralnick, as well as on certain connections between the properties of invariable generation of a group of Lie type and the structure of its Weyl group. [ABSTRACT FROM AUTHOR]

The experience of walking through a city is influenced by amenities and the visual qualities of its built environment. This paper uses thousands of pedestrian trajectories obtained from GPS signals to construct a desirability index for streets in Boston. We create the index by comparing the actual paths taken by pedestrians with the shortest path between any origin-destination pairs. The index captures pedestrians' willingness to deviate from their shortest path and provides a measure of the scenic and experience value provided by different parts of the city. We then use computer vision techniques combined with georeferenced data to measure the built environment of streets. We show that desirable streets have better access to public amenities such as parks, sidewalks, and urban furniture. They are also sinuous, visually enclosed, have less complex facades, and have more diverse business establishments. These results further our understanding of the value that the built environment brings to pedestrians, enhancing our capacity to design more lively and functional environments. • The paper proposes a desirability index capturing streets' experience value using pedestrian's deviations from the shortest path. • The empirical analysis uses computer vision techniques to uncover the built environment characteristics that desirable streets have in common. • The results suggest desirable streets are visually enclosed with less complex facades and have better access to parks, sidewalks, and businesses. [ABSTRACT FROM AUTHOR]

Built environments contribute significantly to mitigating climate change. However, existing buildings, which form most urban infrastructures, do not typically meet contemporary stringent energy efficiency standards. They are naturally continuously deteriorating, making them a continuous negative contributor to their surrounding environments, which keeps getting worse. Therefore, there is a need to develop performance diagnosis frameworks and approaches for accurate Building Energy Model (BEM) simulations to develop impactful retrofitting design solutions that could make existing buildings perform closer to current efficiency measures. This paper reports on research that focuses explicitly on calibrating envelopes of existing BEMs using drones equipped with thermography sensors. The study specifically focuses on the automation of on-the-fly envelope U-value estimations and verification of calibrated envelope BEMs. The paper examines a renovated campus building in Boston, MA., representing material degradation, thermal bridging, and insulation failures using thermal imaging. A BEM is then calibrated, and post-renovation metered and modeled wintertime heating energy are compared. Goodness of fit measures showcase BEM performance improvement from 21.8%to 0.9%, which demonstrates the utility of the proposed framework. Further research is recommended to expand the focus on anomalies in the envelope and increase the scope from the building scale to the neighborhood scale. [ABSTRACT FROM AUTHOR]

Objective: Recognizing a need for our EHR to be highly interoperable, our team at Duke Health enabled our Epic-based electronic health record to be compatible with the Boston Children's project called Substitutable Medical Apps and Reusable Technologies (SMART), which employed Health Level Seven International's (HL7) Fast Healthcare Interoperability Resources (FHIR), commonly known as SMART on FHIR.Methods: We created a custom SMART on FHIR-compatible server infrastructure written in Node.js that served two primary functions. First, it handled API management activities such rate-limiting, authorization, auditing, logging, and analytics. Second, it retrieved the EHR data and made it available in a FHIR-compatible format. Finally, we made required changes to the EHR user interface to allow us to integrate several compatible apps into the provider- and patient-facing EHR workflows.Results: After integrating SMART on FHIR into our Epic-based EHR, we demonstrated several types of apps running on the infrastructure. This included both provider- and patient-facing apps as well as apps that are closed source, open source and internally-developed. We integrated the apps into the testing environment of our desktop EHR as well as our patient portal. We also demonstrated the integration of a native iOS app.Conclusion: In this paper, we demonstrate the successful implementation of the SMART and FHIR technologies on our Epic-based EHR and subsequent integration of several compatible provider- and patient-facing apps. [ABSTRACT FROM AUTHOR]

The work investigates the behaviour of a deep excavation that forms part of a wide basement located in Boston, Massachusetts, USA. The glacial marine clay deposit is modelled with a kinematic hardening model for structured soils, its reduced bubble model version and the well-known Modified Cam Clay model. The aim of the work is to assess the effects of the added features of the advanced model, such as small-strain stiffness, structure degradation and anisotropy, on the numerical prediction of the overall behaviour of the excavation. The first part of the paper provides a brief description of the site conditions, ground profile and construction sequences for the case study under consideration. Then, the soil model calibration process using experimental undrained triaxial, self-boring pressuremeter and constant rate of strain consolidation tests is discussed. Finally, the comparison of the predicted wall deflection and ground settlements profiles with observed movements is presented. [ABSTRACT FROM AUTHOR]

Although one-way carsharing is suitable for more trip purposes than round-trip carsharing, many companies in the world operate only in the round-trip market. In this paper, we develop a method that optimizes the design of a one-way carsharing service between selected origin–destination pairs of an existing round-trip carsharing system. The goal is to supplement the established round-trip services with new one-way services and increase profitability. We develop an integer programming model to select the set of new one-way services and apply it to the case study of Boston, USA, considering only trips with one endpoint at a station in the round-trip Zipcar service network and the other endpoint at Logan Airport. The airport was chosen as a necessary endpoint for a one-way service because it is a very significant trip generator for which the round-trip carsharing is not suitable. Results show that these supplemental one-way services could be profitable. Enabling relocation operations between the existing round-trip stations and the Airport greatly improves the demand effectively satisfied, leads to an acceptable airport station size (in terms of the number of parking spots required), and is profitable; however, these benefits come with the need to manage relocation operations. [ABSTRACT FROM AUTHOR]

Among a number of variables shown to affect pedestrian route choice, path length and turns have stood out as the most consequential. Turns have been considered the superior variable by some architectural scholars of urban street networks, while transportation planners and geographers believe distance to be paramount. The longstanding debate between these two approaches has been reinvigorated with the emergence of big data and advanced computational methods. In this paper, we provide much-needed clarity to this debate by demonstrating how the relative effect of turns depends on the spatial properties of street networks. We postulate that certain properties of street networks make it possible to reduce the number of turns without substantially increasing route distance, and suggest that data from such environments are likely to show a large effect of turns on route choice. Conversely, networks where a reduction of in turns is necessarily accompanied by an increase in distance are expected to reduce the effect of turns. We test these hypotheses by examining the effects of both distance and turns on pedestrian route choice using a path size logit model calibrated on over 10,000 anonymized GPS traces of pedestrians each in San Francisco, CA and Boston, MA. We find that the effect of distance is consistently larger in magnitude, while the effect of turns depends on network geometry. Only in specific street networks can turns alone explain route choice behavior as well as distance. Our findings suggest that turns, as well as other environmental qualities of a route, should be considered in addition to, not in lieu of, distance. [ABSTRACT FROM AUTHOR]

Disruption management during the airline scheduling process can be compartmentalized into proactive and reactive processes depending upon the time of schedule execution. The state of the art for decision-making in airline disruption management involves a heuristic human-centric approach that does not categorically study uncertainty in proactive and reactive processes for managing airline schedule disruptions. Hence, this paper introduces an uncertainty transfer function model (UTFM) framework that characterizes uncertainty for proactive airline disruption management before schedule execution, reactive airline disruption management during schedule execution, and proactive airline disruption management after schedule execution to enable the construction of quantitative tools that can allow an intelligent agent to rationalize complex interactions and procedures for robust airline disruption management. Specifically, we use historical scheduling and operations data from a major U.S. airline to facilitate the development and assessment of the UTFM, defined by hidden Markov models (a special class of probabilistic graphical models) that can efficiently perform pattern learning and inference on portions of large data sets. We employ the UTFM to assess two independent and separately disrupted flight legs from the airline route network. Assessment of a flight leg from Dallas to Houston, disrupted by air traffic control hold for bad weather at Dallas, revealed that proactive disruption management for turnaround in Dallas before schedule execution is impractical because of zero transition probability between turnaround and taxi-out. Assessment of another flight leg from Chicago to Boston, disrupted by air traffic control hold for bad weather at Boston, showed that proactive disruption management before schedule execution is possible because of non-zero state transition probabilities at all phases of flight operation. [ABSTRACT FROM AUTHOR]

While designing sustainable and resilient urban built environment is increasingly promoted around the world, significant data gaps have made research on pressing sustainability issues challenging to carry out. Pavements are known to have strong economic and environmental impacts; however, most cities lack a spatial catalog of their surfaces due to the cost-prohibitive and time-consuming nature of data collection. Recent advancements in computer vision, together with the availability of street-level images, provide new opportunities for cities to extract large-scale built environment data with lower implementation costs and higher accuracy. In this paper, we propose CitySurfaces, an active learning-based framework that leverages computer vision techniques for classifying sidewalk materials using widely available street-level images. We trained the framework on images from New York City and Boston and the evaluation results show a 90.5% mIoU score. Furthermore, we evaluated the framework using images from six different cities, demonstrating that it can be applied to regions with distinct urban fabrics, even outside the domain of the training data. CitySurfaces can provide researchers and city agencies with a low-cost, accurate, and extensible method to collect sidewalk material data which plays a critical role in addressing major sustainability issues, including climate change and surface water management. • The first study using semantic segmentation for large-scale classification of sidewalk materials. • Active learning approach to reduce annotation costs. • Increased accuracy, without relying on pre-trained models. • Strong performance in extracting surface information from cities outside of the training domain. • The model weights and generated datasets are made publicly available. [ABSTRACT FROM AUTHOR]

[Display omitted] Dynamic modeling of building performance is essential for understanding how current buildings will respond to projected changes in environmental conditions, and how these responses will in turn affect building occupants. Additionally, expected shifts in background energy systems will change the carbon intensity of energy delivered to buildings, while shifts in atmospheric composition will alter the global warming potential of each greenhouse gas. This paper models these three dynamic systems simultaneously to determine which projected shifts will have the largest influence on global warming impacts and occupant satisfaction metrics throughout the use phase of an office building prototype model in different climate zones of the United States. The median of RCP8.5 and RCP4.5 climate scenarios were used to morph local weather files to simulate the electricity and natural gas needed to meet heating and cooling loads in each location, as well as thermal comfort and daylighting metrics. These RCPs were also used to estimate shifts in the global warming potential factors for CO 2 , CH 4 , and N 2 O. Projected shifts in the electricity generation mix and upgrades to transmissions and distribution infrastructure in each region were used to estimate future carbon intensity factors for building site energy use. Compared to the baseline results, decarbonization of the electricity grid results in the largest shift in building operational GHG emissions, far outweighing emissions increases due to higher cooling loads. Shifts in ambient temperatures and direct and diffuse radiation had heterogeneous effects on occupant comfort, particularly in Boston across different seasons and orientations. Understanding how building performance is expected to respond to complex future trends will assist in designing adaptive buildings that take advantage of projected changes. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we explore how the approach of optimal navigation (Cajueiro (2009) ) can be used to evaluate the centrality of a node and to characterize its role in a network. Using the subway network of Boston and the London rapid transit rail as proxies for complex networks, we show that the centrality measures inherited from the approach of optimal navigation may be considered if one desires to evaluate the centrality of the nodes using other pieces of information beyond the geometric properties of the network. Furthermore, evaluating the correlations between these inherited measures and classical measures of centralities such as the degree of a node and the characteristic path length of a node, we have found two classes of results. While for the London rapid transit rail, these inherited measures can be easily explained by these classical measures of centrality, for the Boston underground transportation system we have found nontrivial results. [Copyright &y& Elsevier]

• Narrative bicycle crash reports miss much of the information in standardized forms. • This paper examines if longer police narratives include more useful information. • Officers wrote more when onsite, during the day, and when a cyclist was injured. • Longer reports only marginally reduce missingness compared to a standardized form. • Police departments should adopt a standardized bicycle crash form, such as PBCAT. Bicycles gained significant popularity among Americans in 2020. Greater investment in adequate bicycle safety facilities will be needed. Crash data from police will undoubtedly play a role in decision-making. This research evaluated the data quality of text narratives in police reports on bicycle crashes. The aims were to identify situations in which police officers wrote more detail in the narrative text, investigate if longer reports translate to more in-depth crash descriptions, examine the extent to which narrative texts cover details useful for those charged with bicycle safety. This is a 4-year retrospective cohort study of vehicle-vs-bicycle crashes that occurred between January 1, 2009, and December 31, 2012, in Boston, Massachusetts (USA). Police reports were matched with the Pedestrian and Bicycle Crash Analysis Tool (PBCAT) to measure how much information was captured and when reports were more likely to capture more information. Police reports only captured most information in one area of the standardized form (Crash Typing), with average total missingness of over 75%. Longer reports did reduce the amount of missingness, and officers were more likely to write longer reports when they were on the crash site, when there was an injury, when the crash involved an extended car door, and during the day. A 100% increase in the report's words was associated with a four-percentage point reduction in PBCAT missingness. While longer reports result in less missingness when measured against the standardized crash form, the average report still misses most of the information that the form would capture. We recommend that police departments adopt a standardized form to facilitate information capture at the scene of bicycle-vehicle crashes. [ABSTRACT FROM AUTHOR]

This paper explores the interconnections between ride-hailing platforms and public transit systems in large US cities. Drawing upon qualitative interviews with expert key informants representing city government agencies, industry, community groups, and others, we find that ride-hailing platforms have catalyzed a downward spiral in many public transportation systems: as more people use ride-hailing instead of transit, transit systems receive less revenue and must reduce services to compensate, which makes transit seem even less desirable to would-be riders, leading more people to explore other transit options. Prior to the COVID-19 pandemic, preexisting transit deficiencies, shifting customer expectations, and stigmatization of transit systems and riders each contributed to ride-hailing platforms' successful encroachment upon public transit. The pandemic has fundamentally destabilized both transit systems and ride-hailing alternatives, but it portends an even greater decline in transit as people gravitate to privately owned vehicles and eschew sharing rides with others. Ride-hailing and transit partnerships, such as a Boston-based pilot project to provide paratransit services for people with disabilities, point to possibilities for complementary arrangements moving forward, but they remain constrained by their industry-focused market models. The current downward spiral is particularly concerning because it negatively impacts the most vulnerable and disempowered in society. • This study explores threats to public transportation by ride-hailing platforms and the COVID-19 pandemic in large US cities. • Interviews were conducted with expert key informants representing city government agencies, industry, and community groups. • Many public transportation systems are encountering a downward spiral with reduced ridership and loss of fare-based revenue. • This downward spiral negatively impacts the most vulnerable and disempowered in society, raising social justice concerns. • Corrective ride-hailing and transit partnerships are constrained by industry-focused market models. [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most common brain diseases, and seizures usually occur randomly. Accurately predicting seizures enable doctors and patients to carry out medical prevention timely. In seizure prediction studies, single-domain information input (time domain or frequency domain, Etc.) neglects some parts' information from signals. In this paper, we propose a novel deep learning framework named channel attention dual-input convolutional neural network (CADCNN) to obtain the signal's useful information fully. The spatial–temporal features extracted by short-time Fourier transform (STFT) are fed to the CADCNN, and the raw EEG signals are fed for further feature extraction. With the fusion of two inputs from different domains and the combination of channel attention, CADCNN can learn faithful and distinguishable representations of EEG signals and boost the temporal, spectrum, and spatial information utilization capability. We evaluate the proposed method using the Boston Children's Hospital-MIT scalp EEG public datasets. Compared with other state-of-the-art methods, the sensitivity, false prediction rate, specificity, and AUC of our proposed method reach 97.1%, 0.029h, 95.6%, and 0.917, respectively, presenting better performance and higher prediction accuracy. • We develop channel attention dual-input convolutional neural network to analyze epileptic EEG signals. • Our method enhances the model's ability to capture Spatio-temporal information. • Our method achieves high sensitivity and low false prediction rate. [ABSTRACT FROM AUTHOR]

The development and deployment of autonomous vehicles is inseparable from the questions about the future development of cities. Urban governance is vital in ensuring the viability, effectiveness and safety of the next generation urban mobility services, and various roles taken on by cities are pivotal for the creation and maintenance of markets for autonomous vehicles. In this paper, we examine how urban governance approaches for autonomous vehicles development and deployment in the United States are evolving within multi-scalar regulatory contexts. We conceptualize the various types of engagements by cities through four different roles: city as regulator, mediator, data catalyst, and promoter. These categories are not mutually exclusive, but complementary, and are intended to support the development of appropriate models as cities continue to evaluate options for their urban mobility futures. We present two illustrative examples, Pittsburgh, Pennsylvania, and Boston, Massachusetts, to analyze how cities vary in their approaches to the new urban mobility sector. These are two of the leading municipalities in the United States on autonomous vehicle testing and deployment, and their experiences are helpful to elucidating the variety of regulatory contexts, approaches to governance, and potential outcomes. [ABSTRACT FROM AUTHOR]

Abstract: High-technology/knowledge-intensive industries have become of increasing importance as sources of job growth and revenue to communities seeking to develop their economies. Communities want these industries so that they can be as economically vigorous as possible. However, although high-tech industries such as biotechnology are coveted as drivers of economic development, the local development impact of these clusters of regional innovation is not entirely positive. This is especially true with regard to the impact upon the low and semi-skilled populations. In some regions, the new growth generated by high-tech clusters has converted relatively inexpensive open space into haphazard commercial and industrial use that has contributed to sprawl, transportation congestion, lack of affordable housing, and gentrification. These problems are particularly evident in the Boston and San Diego metropolitan areas, which rank as the second and third largest U.S. biotechnology clusters respectively. This paper seeks to gauge the local economic development impact—especially with regard to the labor and real estate markets—of the biotechnology clusters in the San Diego and Boston metropolitan areas. [Copyright &y& Elsevier]

Adherence to anti retroviral therapy promotes viral suppression and extends the lives of individuals with HIV, yet illegal drug users are underrepresented among eligible persons receiving HIV treatment. One explanation for this is the assumption that drug users are less capable than others of adhering to complicated medication regimens. This paper investigates this assumption by making explicit a number of its underlying propositions and examining them in light of data collected from 52 active drug users living in and around Boston, USA, who were taking highly active antiretroviral therapy for HIV (HAART). The propositions are: (1) drug users lead "chaotic" lives; (2) active drug users are always using drugs; (3) being under the influence of drugs precludes taking medications as prescribed; and (4) drug users are intrinsically different from non-users in the lives they lead and the problems they face. Data collection consisted of a series of qualitative interviews with each participant. An analytic approach informed by grounded theory was used to construct thematic content categories from the data. Results revealed stability and control as well as "chaos" in the lives of study participants. Frequency of drug use varied considerably. Using did interfere with adherence, but not in every circumstance or all cases. Not "carrying" medications, competing priorities, and re-defining regimens were the most salient of a number of non-drug-use-related obstacles observed. Documentation of patterns of variation in corresponding data highlights the stereotypical quality of the propositions. Stereotyping risks overemphasis on drug use as a barrier to adherence for active users, and underemphasis on non-drug-use-related obstacles. Adherence capabilities of users, in contrast to inadequacies, are also obscured through stereotyping. As a medium for stigmatization, stereotyping may contribute to unequal treatment for drug users and other populations living with HIV. [ABSTRACT FROM AUTHOR]

The mathematical study of the small-world concept has fostered quite some interest, showing that small-world features can be identified for some abstract classes of networks. However, passing to real complex systems, as for instance transportation networks, shows a number of new problems that make current analysis impossible. In this paper we show how a more refined kind of analysis, relying on transportation efficiency, can in fact be used to overcome such problems, and to give precious insights on the general characteristics of real transportation networks, eventually providing a picture where the small-world comes back as underlying construction principle. [Copyright &y& Elsevier]

Understanding the market demand for decentralized water technologies and the impacts on the urban water systems (UWS) is critical to sustainable promotion in urban communities. In this paper, we developed a spatial agent-based model (ABM) that simulates the adoption of home-based rainwater harvesting (RWH) and greywater recycling (GWR) by single-family households in Boston, United States. We applied system dynamics modeling (SDM) to evaluate the impacts of decentralized technologies on the UWS. The change in carbon emissions of the UWS was modeled as the environmental benefit for household choice decisions in ABM. We validated our integration by comparing the simulated reservoir elevations with the reference-year values and comparing where early adoptions emerge with the reported installations. In the results, we first examined the sensitivity of the adoption and diffusion to the market promotion, neighbor's influence, environmental benefits, and economic attractiveness. Our simulation highlights the diffusion from central downtown to the suburban. RWH has higher adoptions than GWR, and RWH starts diffusing much earlier. Reservoir water availability is improved through the adoption of RWH and GWR. As the tradeoffs, reservoirs discharge more water for storage security, and hydropower generation becomes less as the need of water transfer decreases. The UWS do not reduce carbon emissions due to the high electricity consumption of GWR. Utilizing the improved water resources and reducing the impact of decentralized technologies are required for sustainable promotion of water decentralization. Our integrated modeling can be improved by including more critical socio-technical interactions for spatial planning of decentralized water technologies. • A framework for planning water at the nexus of human-infrastructure-environment. • Agent-based model to simulate spatial adoption of decentralized water technologies. • System dynamics model to quantify the impact of adoption on water system. • Rainwater harvesting has a higher potential among Boston single-family communities. • Suggestions are identified for sustainable promotion of decentralization. [ABSTRACT FROM AUTHOR]

The built environment is an important determinant of travel demand and mode choice. Establishing the relationship between the built environment and transit use using direct models can help planners predict the impact of neighborhood-level changes, that are otherwise overlooked. However, limited research has compared the impacts of the built environment for different networks and for individual transit modes. This paper addresses this gap by developing built environment and transit use models for three multimodal networks, Amsterdam, Boston and Melbourne, using a consistent methodology. A sample of train, tram and bus sites with similar station-area built environments are selected and tested to establish if impacts differ by mode. It is the first study that develops neighborhood-level indicators for multiple locations using a consistent approach. This study compares results for ordinary least squares regression and two-stage least squares (2SLS) regression to examine the impact of transit supply endogeneity on results. Instrumented values are derived for bus and tram frequency in Melbourne and bus frequency in Boston. For other mode and city combinations, the 2SLS approach is less effective at removing endogeneity. Results confirm that different associations exist between the built environment and transit modes, after accounting for mode location bias, and that this is true in multiple networks. Local access and pedestrian connectivity are more important for bus use than other modes. Tram is related to commercial density. This finding is consistent for all samples. Land use mix and bicycle connectivity also tend to be associated with higher tram use. Train use is highest where opportunities exist to transfer with bus. Population density is commonly linked to ridership, but its significance varies by mode and network. More research is needed to understand the behavioral factors driving modal differences to help planners target interventions that result in optimal integration of land use with transit modes. [ABSTRACT FROM AUTHOR]

Immediately after a disruption, in order to minimize the negative impact inflicted on the society, its imperative to re-establish the interdicted critical services enabled by the infrastructure networks. In this paper, we study the stochastic network recovery problem that tackles the planning of restoration activities (considering limited resources) on interdicted infrastructure network links so that the pre-disruption critical service flows can be re-established as quickly as possible. As an illustrative case study, we consider a disaster scenario on a road infrastructure network that obstructs the flow of relief-aid commodities and search-and-rescue teams between critical service providing facilities and locations in need of these critical services. As in the case of many realistic applications, we consider the amount of demand for critical services as stochastic. First, we present a Markov decision process (MDP) formulation for the stochastic road network recovery problem (SRNRP), then we propose an approximate dynamic programming (ADP) approach to heuristically solve SRNRP. We develop basis functions to capture the important complex network interactions that can be used to approximate cost-to-go values for the MDP states. We conduct computational experiments on a set of small-scale randomly generated instances and demonstrate that the ADP approach provides near-optimal results regardless of the demand distribution and network topology. In order to develop a practical approach suitable for solving real world sized instances, we propose a framework where we first develop an ADP model and derive a policy on a spatially aggregated network of large scale instance. Next, we show the performance of this policy through computational testing on the large scale disaggregated network. Moreover, we provide managerial insights by assessing the importance of each basis function in the ADP model contributing to the recovery policies. We test this approach on a case study based on the Boston road infrastructure network. We observe that, as the urgency of re-establishing services increases or the resources become more scarce, the information gained from the network characteristics and short-term decisions should be the main driving factors to derive recovery policies. The results of all experiments strongly evidence the significance of utilizing the inherent network interactions and attributes to generate basis function sets for ADP models that yield high-quality recovery policies. [ABSTRACT FROM AUTHOR]

• Measuring crowding through archived and real-time data using object detection tools. • Object detection tools with surveillance video to quantify transit performance. • Logistic regression models with automatic passenger counts and train operations data. • Estimation of left-behind passengers at stations with farecard use only at entrance. • Estimation and evaluation of waiting time as a transit service reliability measure. Crowding is one of the most common problems for public transportation systems worldwide, and extreme crowding can lead to passengers being left behind when they are unable to board the first arriving bus or train. This paper combines existing data sources with an emerging technology for object detection to estimate the number of passengers that are left behind on subway platforms. The methodology proposed in this study has been developed and applied to the subway in Boston, Massachusetts. Trains are not currently equipped with automated passenger counters, and farecard data is only collected on entry to the system. An analysis of crowding from inferred origin–destination data was used to identify stations with high likelihood of passengers being left behind during peak hours. Results from North Station during afternoon peak hours are presented here. Image processing and object detection software was used to count the number of passengers that were left behind on station platforms from surveillance video feeds. Automatically counted passengers and train operations data were used to develop logistic regression models that were calibrated to manual counts of left behind passengers on a typical weekday with normal operating conditions. The models were validated against manual counts of left behind passengers on a separate day with normal operations. The results show that by fusing passenger counts from video with train operations data, the number of passengers left behind during a day's rush period can be estimated within 10 % of their actual number. [ABSTRACT FROM AUTHOR]

• OPTICS, HDBSCAN* used to define tolling zones for distance-based pricing schemes. • Clustering features (link speeds, marginal cost tolls) for tolling zone definition. • Experiments on Singapore Expressway, Boston CBD networks for various pricing schemes. • Results show that distance-based schemes outperform cordon-based schemes. • OPTICS, HDBSCAN*, using marginal costs tolls, suitable for tolling zone definitions. Real-time network control strategies such as congestion pricing have been used in a number of metropolitan areas around the world for traffic congestion mitigation. Recent advances in Global Navigation Satellite System (GNSS) technology have led to increasing interest in distance- or usage-based road pricing as an effective alternative to traditional facility-, cordon- and area-based pricing that typically rely on fixed infrastructure. In this paper, we propose the use of feature-variant clustering methods, OPTICS and HDBSCAN*, as a systematic approach for tolling zone definition to operationalize distance-based tolling schemes. Subsequently, we develop a framework for predictive distance-based toll optimization to evaluate network performance for the various tolling zone definitions derived from the aforementioned feature-variant clustering methods. In this framework, for a specific tolling zone definition, tolling function parameters are optimized using a simulation-based Dynamic Traffic Assignment (DTA) model operating within a rolling horizon scheme. Predictive optimization is integrated with the guidance information generation. Behavioral models capture drivers' responses to the tolls in terms of trip cancellation and choices of mode, route and departure time. Experiments on the real-world Expressway and Major Arterials network of Singapore demonstrate improved effectiveness of distance-based toll optimization given tolling zone definitions derived from feature-variant clustering, compared to fixed cordon-based pricing, adaptive cordon-based pricing, as well as distance-based pricing with ad hoc tolling zone definitions. Further, the results indicate that the use of the marginal link cost tolls as a clustering feature produces the most robust tolling zone definitions and yields significant improvements in social welfare over ad hoc zone definitions and cordon-pricing. Finally, experiments on the Boston CBD network also demonstrate the effectiveness of distance-based toll optimization schemes on urban traffic networks. [ABSTRACT FROM AUTHOR]

Marine debris pollution is becoming an increasingly serious problem, affecting not only navigational safety but also the health of marine ecosystems. Carbon emission causes ocean acidification, which greatly harms marine lives. In order to respond to these environmental issues, this paper studies marine debris collection with consideration of carbon cap using logistics network. To the most of our knowledge, this is first work to study marine debris removal from logistics way. We proposed a three-phase marine debris collection framework: (1) identifying initial locations of the debris; (2) utilizing GNOME software to predict the drifting trajectory of the debris; and (3) formulating vessel routing as a mixed integer nonlinear programming with consideration of carbon emission. The model seeks to minimize the total costs including fuel cost, berth cost, unloading cost in the harbor, vessel rent, vessel insurance, and labor cost, with respect to the constraints of carbon cap, vessel capacities, and time windows. After the linearization process, a Branch-and-Cut (B&C) algorithm with multiple variable fixing techniques and valid inequalities is used to solve the proposed model. The debris dispersion located in Boston vicinity is used as a case study to validate the proposed model and algorithm. The results reveal that the farther away the debris location is from the shore, the higher the collection cost and the more the carbon emission would be. The daily collection cost in the closest location and in the farthest location is $140 lower and $210 higher than that in the initial location. The carbon emission in the farthest location is 21% and 32% higher than that in the initial location and in the closest location. Sensitivity analysis is also carried out for the fuel price and carbon cap to test their impact on total costs These results show that proposed model and algorithm can significantly improve the efficiency of marine debris collection process, reduce carbon emission and total costs. • A three-phase structured framework for marine debris cleanup is firstly proposed. • Vessel velocity has been used as a variable to trade off carbon emission and cost. • A mixed integer nonlinear programming vessel routing problem is firstly formulated. • Α Branch-and-Cut algorithm combined with linearization is used to solve the model. • The vicinity of Boston case study is used to validate the model and algorithm. [ABSTRACT FROM AUTHOR]