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The article offers information on several conferences including of 2013 American Association for Applied Linguistics (AAAL) Conference to be held in Dallas, Texas from March 16-19, the 2013 International Conference on English Education to be held in Taiwan from April 20-21, and the 11th Asia Teaching English as a Foreign Language (TEFL) International Conference to be held in the Philippines from October 26-28, 2013.

The University of Texas at Dallas has proposed 24 top journals (UTD24) covering all areas related to management. UTD24 are currently the most authoritative management journals. Institutions or research scholars who have published papers in these journals are considered to have a high academic level. This study uses the paper quality published by an institution in the UTD24 journals to indicate the research quality of this institution and takes the papers published by Chinese Business School in the UTD24 journals from 2000 to 2018 as the research object. By analyzing the staged evolution process and the research hotspot of international research collaboration network (IRC network) of Chinese Business School, this paper summarizes and analyzes the factors that influence the quality of papers published by Chinese Business School in UTD24 journals: degree centrality of an institution, betweenness centrality of an institution, the degree of attention of an institution, the degree of novelty of an institution, and the number of countries cooperating with the institution. This paper divides paper quality into two parts: the total number of papers and citations per paper of an institution. Among them, degree centrality and betweenness centrality of the institution have a significant positive impact on the total number of papers of the institution. The degree of attention and the number of countries cooperating with the institution have a significant positive impact on citations per paper, and the degree of novelty has a significant negative impact on citations per paper. [ABSTRACT FROM AUTHOR]

A continuous monitoring of neurological status can help in reporting the physical and mental health of a person. This can be capitalized for building a healthcare tracking system using a wearable device and a handheld mobile device. In this paper, we have used the non-EEG physiological biosignals dataset which gives practicability among subjects for acquiring data easily from wearable device sensors linearly and comfortably rather than the way of putting the subjects in a cumbersome setup laboratory. This paper proposes a custom fully convolutional-LSTM (FCN-LSTM) network to identify the neurological status of a subject using multivariate time series physiological sensor data. The proposed architecture uses parallel stacks of the convolutional layers and LSTM cells. This combination of different network types is significant for the selected problem as the fully convolutional section of the model extracts the local spatial features in the data, while the LSTM network handles the high-level features and temporal dependencies. The proposed FCN-LSTM model yielded a high accuracy of 98.6% and a precision of 98% on the non-EEG dataset from UT-Dallas. The average accuracy of single-subject results of the dataset using the proposed model was observed to be 99.26%. The results from the proposed model are significantly improved when compared with various state-of-the-art works on this problem. These results strongly suggest that this model, when put on a wearable device, can be effectively used to detect the neurological status or stress that the subject may be going through in real time. [ABSTRACT FROM AUTHOR]

The article announces a call for papers for the Council for Exceptional Children's 57th Annual International Convention slated in April 1979 in Dallas, Texas. Individuals may submit papers, program sessions or other types of convention ideas to program chairperson, Kenneth E. Wyatt. Deadline for submission of papers and convention ideas is June 15, 1978 which could include workshops, demonstrations, and other practical application formats. Topics must have relevance to the exceptional child and other key concerns.

In this paper, we present a method (Action-Fusion) for human action recognition from depth maps and posture data using convolutional neural networks (CNNs). Two input descriptors are used for action representation. The first input is a depth motion image that accumulates consecutive depth maps of a human action, whilst the second input is a proposed moving joints descriptor which represents the motion of body joints over time. In order to maximize feature extraction for accurate action classification, three CNN channels are trained with different inputs. The first channel is trained with depth motion images (DMIs), the second channel is trained with both DMIs and moving joint descriptors together, and the third channel is trained with moving joint descriptors only. The action predictions generated from the three CNN channels are fused together for the final action classification. We propose several fusion score operations to maximize the score of the right action. The experiments show that the results of fusing the output of three channels are better than using one channel or fusing two channels only. Our proposed method was evaluated on three public datasets: 1) Microsoft action 3-D dataset (MSRAction3D); 2) University of Texas at Dallas-multimodal human action dataset; and 3) multimodal action dataset (MAD) dataset. The testing results indicate that the proposed approach outperforms most of existing state-of-the-art methods, such as histogram of oriented 4-D normals and Actionlet on MSRAction3D. Although MAD dataset contains a high number of actions (35 actions) compared to existing action RGB-D datasets, this paper surpasses a state-of-the-art method on the dataset by 6.84%. [ABSTRACT FROM AUTHOR]

This paper presents an equilibrium-based modeling framework for emergency response (ER) workload balancing for robust operations in metropolitan areas. The problem is formulated as a non-linear mathematical program (NLP), which determines the optimal maximum workload for each ER station such that the weighted sum of the area-wide expected response time and its variation is minimized. The concept of Marginal Cost of Uncertainty (MCU) is introduced to measure the impact of a station's workload increase on the robustness of the area-wide service performance. The solution of the NLP is proved to be equivalent to a state of equilibrium in which all stations have a minimum MCU. An iterative solution methodology is developed, which adopts a modified version of the Frank-Wolfe decomposition algorithm for convex optimization. The workload is iteratively balanced among adjacent stations until the state of equilibrium is achieved. At equilibrium, no station can reduce its MCU value by unilaterally shifting a part of its workload to any other station(s) in the area. The developed framework is applied to determine the optimal workload balancing strategy for 58 fire stations serving the City of Dallas. The framework is shown to enhance the robustness of the ER service performance especially in situations with imbalanced workloads. [ABSTRACT FROM AUTHOR]

This paper provides an approach for social studies education that includes investigative research into an old school building that has traditionally served predominantly African American children, along with a narrative inquiry into the experiences of one of that school’s former students. We offer a unique approach to experiential global citizenship education, in conjunction with an exemplar of this kind of social studies research. The first half of this paper is a “building biography” of N. W. Harllee School, followed by memories of Dr. Njoki McElroy, who attended Harllee as a young child. In the US, African American life is often misrepresented, devalued, or completely expunged from history books and historical documents. The implication of this novel approach to uncovering the truth about the education of African Americans in the 1930s is that educators around the world can use a similar approach to honor and highlight voices of marginalized people, creating rightful spaces for their stories in our collective history and memory. [ABSTRACT FROM AUTHOR]

Nowadays, in researches on electric vehicle routing problems, in order to improve the delivery efficiency and reduce the routing cost, many important elements are broad discussed such as the customer time window, the routing algorithm, the electric energy consumption, etc. In these, the routing algorithm is the key element to achieve a good solution. Based on this background, the paper investigates the routing algorithm, then adopts the elitist genetic algorithm and proposes an improved neighbor routing initialization method for solving the electric vehicle routing problem. In our method, the electric vehicle energy consumption is used as the main component of the routing system. The neighbor routing initialization enables the routing system to choose the close route from a suitable first customer in the initialization, which makes the routing search faster and find the global optimal route easily. The simulations on the Solomon benchmark data and the Hiland Dairy milk delivery example in Dallas, Texas, USA verifies the good performance of the method. [ABSTRACT FROM AUTHOR]

Drawing on qualitative data from an exploratory study of the experiences of audit pair researchers—pairs of black, white, and Latinx field testers—in an urban nightlife study in Austin, Dallas, and Houston, Texas, this paper examines nightclub owners' use of what I identify as "exclusionary tools"—that is, "dress codes," "steering," and "fake guest lists." Using Critical Race Theory's critique of neutrality, I argue that these exclusionary tactics create a welcoming atmosphere for white patrons while maintaining limits on the participation of African Americans and other peoples of color. This paper offers empirical examples of how these exclusionary practices limit legal challenges to discrimination in the first place and concludes with a discussion of potential methodological approaches for expanding this exploratory study and further identifying systematic exclusionary practices. [ABSTRACT FROM AUTHOR]

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

This paper presents a new data fusion multiscale observation product (MOP) for flood emergencies. The MOP was created by integrating multiple sources of contributed open-source data with traditional spaceborne remote sensing imagery in order to provide a sequence of high spatial and temporal resolution flood inundation maps. The study focuses on the 2015 Memorial Day floods that caused up to USD 61 million dollars of damage. The Hydraulic Engineering Center River Analysis System (HEC-RAS) model was used to simulate water surfaces for the northern part of the Trinity River in Dallas, using reservoir surcharge releases and topographic data provided by the U.S. Army Corps of Engineers. A measure of fit assessment is performed on the MOP flood maps with the HEC-RAS simulated flood inundation output to quantify spatial differences. Estimating possible flood inundation using individual datasets that vary spatially and temporally allow to gain an understanding of how much each observational dataset contributes to the overall water estimation. Results show that water surfaces estimated by MOP are comparable with the simulated output for the duration of the flood event. Additionally, contributed data, such as Civil Air Patrol, although they may be geographically sparse, become an important data source when fused with other observation data. [ABSTRACT FROM AUTHOR]

This article presents the hardware and software architectures used to implement the Modbus Extension (ModbusE) IIoT gateway, the performance of the acquisition cycle at the PRU real-time programmable core level, the acquisition cycle communication flow—dispatcher—OPC UA server (Linux)—OPC UA client (Windows) as well as the performance analysis of data communications between the IIoT ModbusE gateway and the OPC UA client (Windows). In order to be able to implement both the ModbusE acquisition cycle and the OPC UA server, the BeagleBone Black Board was chosen as the hardware platform. This board uses the Sitara AM335x processor (Texas Instruments (TI), Dallas, TX, USA) from Texas Instruments. Thus, the acquisition cycle was implemented on the PRU0 real-time core, and the OPC UA server, running under the Linux operating system, was implemented on the ARM Cortex A8 processor. From the analysis of the communication speed of the messages between the OPC UA client and the ModbusE servers, it was found that the ModbusE acquisition cycle speed was higher than the acquisition speed of the OPC UA client. [ABSTRACT FROM AUTHOR]

Power generation using small wind turbines connected to AC grids has been gaining attention and contributions in recent years. As small wind turbines are connected to remote areas as support energy systems, there are not extensive contributions connecting those small turbines to AC grids. This paper presents the integration of a small wind generation system which is AC-grid-connected. The system is composed of a 160 W commercial small wind turbine with a permanent magnet synchronous generator and a 140 W Texas Instruments (Dallas, TX, USA) development kit devoted to connecting photovoltaic panels to AC grids. Several experimental tests were developed to characterize the devices, e.g., to obtain the power–current curves of the synchronous generator. Moreover, a mathematical model of the flyback converter is developed in detail in order to design a new converter controller. All the control capacity of the development kit is used to extract the maximum power of the synchronous generator, to reject the oscillation produced by the inverter and to connect the system to the AC grid. Experimental results show that is possible to integrate these devices to provide energy to power systems with some achievable adaptations. [ABSTRACT FROM AUTHOR]

This paper considers the problem of estimating road user costs (RUCs), such as travel time and delays, for roadway construction projects under time and data limitations. RUCs measure the mobility impacts of construction work zones on public roads. RUC metrics are typically used in construction phases to determine the project's incentives and disincentives. However, determining RUC can be challenging due to scarce traffic data availability, incomplete construction plans, and limited time to conduct analysis. In this paper, we use traffic microsimulation to estimate the RUC for each hour of a typical 24-h day using 11 construction cases in Dallas, Texas. Our primary contribution includes developing relationships between peak-hour RUC measures with respect to the total daily values. Such relationships would allow an estimation of the full day RUC based only on the analysis of the peak hour. The main results suggest that the peak-hour travel time as a fraction of the daily value is, on average, 8.2%, comparable to typical percentages of the peak-hour traffic volumes. Furthermore, the models for predictive relationships using construction scenario demand and capacity show high correlation coefficients, presenting a potential for the development of such relationships for roadway construction projects. [ABSTRACT FROM AUTHOR]

Reports that the directors of Dallas, Texas-based Kimberly-Clark Corp. have approved the previously announced plan to spin-off its paper and Canadian pulp operations. Repurchase of shares; Companies to go through spin-off; Filings with the Securities and Exchange Commission.

The following paper chronicles my experiences as a high school theatre teacher during the school closures and state shutdowns caused by the COVID-19 Pandemic in the spring of 2020. The critical reflection identifies struggles and success in the areas of communication, participation and overall emotional and mental health of students and teachers, due to the environmental shift from in person to online learning. My school community (Dallas, TX) and personal pedagogical lens are described to help further contextualize the inquiries and conclusions of this reflection. It is important to note that this paper acknowledges the ongoing nature of the COVID-19 Pandemic and does not seek to provide permanent answers or solutions to an ever-evolving crisis. The aim of this paper is to reflect on my unique struggles and success as an online educator and to explore the impact of theatre on child development, specifically during times of traumatic crisis. [ABSTRACT FROM AUTHOR]

Transportation has been identified as one of the major barriers to successful reentry for prisoners released to community in the United States. We foregrounded transportation and mobility in our design consistent with the new mobilities paradigm and investigated the mobility needs of returning citizens from the perspective of service providers and employers in Dallas, Texas. We interviewed 17 participants who directly served returning citizens in their professional roles as part of a conventional content analytic design that focused specifically on transportation and mobility among their clients. The findings include five primary themes: 1) Returning citizens rely primarily on public transit; 2) Access to cars is rare, complicated, but advantageous; 3) Support lays a road to successful reentry; 4) Transportation is critical for successful reentry, and; 5) Returning citizens face a complex network of obligations. We utilized mobilities literature and assemblage thinking to interpret our findings as an expansion of the carceral mobilities literature both conceptually and geo-spatially as a reentry mobility assemblage. The paper concludes with a consideration of the possibilities for social service practice, research, and pedagogy through a mobilities lens. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic forced many educators and students around the world to shift abruptly from a face-to-face environment to an online learning management system (LMS). This change of environment proved especially difficult for English language learners (ELLs) in the United States. Some of the challenges for ELLs include technology issues, lack of facility with the LMS, limited comprehension of written instructions, time management difficulties, social isolation, and low motivation. This paper discusses those challenges and examines the benefits and shortcomings of using an LMS for lessons, assessments, and communication among ELLs. This qualitative study investigates the experiences of ELL students enrolled in a Dallas, Texas area high school during the COVID-19 pandemic and their attitudes towards completing coursework through an LMS platform. [ABSTRACT FROM AUTHOR]

In this paper, the authors present their efforts in exploring a new type of traffic data, referred to as internet-connected vehicle (ICV) data, for traffic congestion management and operational planning. Most currently manufactured vehicles contain onboard GPS and cellular modules, and they constantly connect to automobile manufacturers' clouds via cellular networks and upload their status. Some automobile manufacturers have recently redistributed the nonpersonal part of such data, such as geolocation, to third-party organizations for innovative applications. Compared with the traditional vehicle GPS data, the ICV data contain high-resolution GPS waypoints accompanied with the vehicles' abnormal moving events (e.g., hard braking). The ICV data also have huge potential in congestion management and operational planning. They explore to identify and analyze traffic congestion on both freeways and arterials using the ICV data. The ICV data adopted for this research are redistributed by Wejo Data Service, representing 10%–15% of all moving vehicles in the Dallas–Fort Worth (DFW) area in Texas. Through one case study for a freeway segment and one for an arterial segment, new traffic performance metrics based on the characteristics of ICV data have been presented. The highlights of these efforts are as follows: (I) queue length and propagation at freeway bottlenecks can be directly measured based on where and when most internet-connected vehicles slow down and join the queue; (II) an internet-connected vehicle's actual delay time on arterials can be directly measured according to its slow movement percentage, without assuming the nondelay travel speed; and (III) the ICV data set are also combined with the high-resolution traffic signal events to generate a ground-truth time-space diagram (TSD) on arterials—a common visualization of arterial signal performance for transportation planning and operations. [ABSTRACT FROM AUTHOR]

Infrasound data contain contributions from incoherent noise, coherent noise and signals of interest. The design of an infrasound array to target sources of interest requires a quantification of array response, individual sensor response, propagation effects (topography and meteorological conditions), signal spectrum and the noise environment. The Comprehensive Nuclear-Test-Ban Treaty community has spent significant effort in quantifying the acoustic field in rural environments for frequencies up to 7 Hz. Given that the nuclear monitoring and tactical infrasound community have growing interests in monitoring sources in or near populated regions, there is an emergent need to measure and understand acoustic fields in these environments as well. This paper focuses on quantification of the acoustic field in three different urban environments: (1) arrays installed within Dallas, TX, a metropolitan area, (2) an array installed at the rural–suburban interface near San Diego, CA and (3) an array installed in Vicksburg, MS, a small city with multiple major transportation corridors. A minimum of five months of data was recorded and used for each site characterization. The analysis focuses on frequencies from 0.1 to 45 Hz. The quantification of the data from these three sites is accomplished with statistical noise models that capture the total ambient acoustic field, separation of the coherent portion of the field and trend analysis to link temporal and seasonal variations to wind speed and anthropogenic activities. The resulting physical interpretation of the data demonstrates that the total acoustic field in urban regions is overall higher and inversely related to the number of coherent detections observed by an array. Furthermore, this study presents an analysis framework for characterizing additional urban arrays and provides a basis for future array site selection and installation. [ABSTRACT FROM AUTHOR]

Dallas-Fort Worth, the fastest growing region in Texas, has been criticized for decades for auto-centric and corporate development, which has restricted the sustainable design of cultural venues in need of permeable access to the community. Meanwhile, the city’s arts districts – Bishop Arts District and Deep Ellum, officially designated as Cultural Districts in the Dallas Cultural Plan – are known for their clusters of murals, live music venues, and independent businesses, which exemplify the transformation of the forgotten historic streetcar corridors into bohemian cultural destinations. This paper examines the revitalization of Deep Ellum and the Bishop Arts District that have both become successful local destinations with walkable commercial streets. First, we review the literature on cultural placemaking in a post-industrial urban context. Next, using US Census data, we explore the socio-demographic environment of the two neighbourhoods. Then, synthesizing a Geographic Information System (GIS) data set and Google Street imagery, we analyse storefronts that have been modified for cultural use. Urban development of cultural districts shows that reusing storefronts can and should be contextually adapted to the existing built landscape and socio-cultural environment to satisfy both communities’ needs and to enhance local economic growth. In view of cultural placemaking, the city should develop a policy that increases pedestrian and transit access to cultural districts, affordable housing, and spaces for artists and residents, and spurs innovative, often post-retail, cultural storefront repurposing through community engagement in order to build incremental, long-term resilience. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is being used regularly in many different fields. This paper compares traditional econometric methods that have better explanations of data analysis to ML methods, focusing on predicting, understanding and unpacking ML methods which have higher prediction accuracies of four key transport‐planning variables: household vehicle‐miles traveled (continuous variable), household vehicle ownership (count variable), mode choice (categorical variable), and land use change (categorical variable with strong spatial interactions). Here, the results of ten ML methods are compared to methods of ordinary least squares (OLS), multinomial logit (MNL), negative binomial and spatial auto‐regressive (SAR). The U.S.'s 2017 National Household Travel Survey and land use data sets from the Dallas‐Ft. Worth region of Texas are used. Results suggest traditional econometric methods work pretty well on the more continuous responses (VMT and vehicle ownership), but the random forest (RF), gradient boosting decision trees (GBDT), and extreme gradient boosting (XGBoost) methods delivered the best results, though the RF model required 30 to almost 60 times more computing time than XGBoost and GBDT methods. The RF, GBDT, XGBoost, light gradient boosting method (lightGBM), and catboost offer better results than other methods for the two "classification" cases, with lightGBM being the most time‐efficient. Importantly, ML methods captured the plateauing effect modelers may expect when extrapolating covariate effects. [ABSTRACT FROM AUTHOR]

An active stage of relativistic astrophysics started in 1963 since in this year, quasars were discovered, Kerr solution had been found and the first Texas Symposium on Relativistic Astrophysics was organized in Dallas. Five years later, in 1967–1968 pulsars were discovered and their model as rotating neutron stars (NSs) had been proposed, meanwhile Wheeler claimed that Kerr and Schwarzschild vacuum solutions of Einstein equations provide an efficient approach for astronomical objects with different masses. Wheeler suggested to call these objects black holes. NSs were observed in different spectral band of electromagnetic radiation. In addition, a neutrino signal had been found for SN1987A. Therefore, multi-messenger astronomy demonstrated its efficiency for decades even before observations of the first gravitational radiation sources. However, usually, one has only manifestations of black holes in a weak gravitational field limit and sometimes a model with a black hole could be substituted with an alternative approach which very often looks much less natural, however, it is necessary to find observational evidences to reject such an alternative model. At the moment, only few astronomical signatures for strong gravitational field are found, including a shape of relativistic iron K α line, size and shape of shadows near black holes at the Galactic Center (GC) and M87, trajectories of bright stars near the GC. After two observational runs, the LIGO–Virgo collaboration provided a confirmation for a presence of mergers for 10 binary black holes and one binary NS system where gravitational wave signals were found. In addition, in the last years, a remarkable progress has been reached in a development of observational facilities to investigate a gravitational potential, for instance, the number of telescopes operating in the Event Horizon Telescope network is increasing and accuracy of a shadow reconstruction near the GC is improving, meanwhile largest VLT, Keck telescopes with adaptive optics and especially GRAVITY facilities observe bright IR stars at the GC with perfect accuracy. More options for precision observations of bright stars will be available with creating extremely large telescopes Thirty Meter Telescope (TMT) and E-ELT. It is clear that the GC (Sgr A ∗ ) is a specific object for observations. Our solar system is located at a distance around 8 kpc from the GC. Earlier, theorists proposed a number of different models including exotic ones for GC such as boson star, fermion ball, neutrino ball, a cluster of NSs. Later, some of these models were ruled out or essentially constrained with consequent observations and theoretical considerations. Currently, a supermassive black hole with mass around 4 × 1 0 6 M ⊙ is the most natural model for GC. Using results of observations for trajectories of bright stars in paper [A. F. Zakharov, P. Jovanović, D. Borka and V. B. Jovanović, J. Cosmol. Astropart. Phys.05 (2016) 045] the authors got a graviton mass constraint which is comparable and consistent with constraints obtained recently by the LIGO–Virgo collaboration. Later, we consider opportunities to improve current graviton mass constraints with future observations of bright stars [A. F. Zakharov, P. Jovanović, D. Borka and V. B. Jovanović, J. Cosmol. Astropart. Phys.04 (2018) 050]. Similarly, from an analysis of bright star trajectories, one could constrain a tidal charge which was predicted by a gravity theory with an additional dimension [A. F. Zakharov, Eur. Phys. J. C78 (2018) 689]. [ABSTRACT FROM AUTHOR]

Dallas/Fort Worth International Airport (DFW) was one of the first airports in the USA to receive Federal Aviation Administration (FAA) approval to fly small unmanned aerial system (sUAS) aircraft in highly restricted Class B airspace. To date, almost 100 public and commercial flights have been flown at DFW in support of law enforcement activities, fire safety, airport construction and private industry videography. The path to FAA issuance of sUAS certificates of authorization (COA) was paved by a concerted effort among a broad cross-section of aviation professionals with an interest in integrating UAS operations into the DFW environment. The DFW UAS Work Group worked diligently on many aspects of sUAS operations, ranging from COA application to UAS education to flight procedures development. Collaboration between airport representatives and various entities within the FAA resulted in an executed letter of agreement that supports public and commercial sUAS flights over airport property. This paper describes the methodology applied by the work group that led to successful sUAS flight operations. The paper also identifies barriers encountered and the processes applied to mitigate those issues. The author and the DFW UAS Work Group would like to share information that would hopefully assist other airports in developing a sUAS flight programme. [ABSTRACT FROM AUTHOR]

Local-scale government ordinances that attempt to delay or displace oil and gas drilling in their territories are common in regions with hydrocarbon extraction activities. Drawing on literature from policy mobilities and resource and energy governance, this paper analyzes policymaking processes that resulted in a December 2013 ordinance in Dallas, Texas, which established a 1500 foot (457.2 meter) setback between gas wells and residences, making drilling (with hydraulic fracturing) nearly impossible. Dallas was not the first city in the region to adopt an oil and gas drilling ordinance; indeed, many regulatory provisions were copied from other regional cities. This paper explains policy mobility in the Dallas policymaking process in terms of anti-political practices and hydrocarbon institutions that, overall, determine neoliberal hydrocarbon governance. City governments cede some of the political process to gas drilling task forces that work to render setbacks technical. Legal classification of subsurface hydrocarbons as the mineral estate creates a legal gray area that confounds municipal regulatory authority and gives discursive power to mineral owners to threaten municipal officials with lawsuits. Both of these anti-political strategies encouraged selective copying and morphing of other policy provisions by the Dallas city government. Adopting longer municipal setback distance regulations represents a type of contestation of neoliberalism situated between complete deregulation and overt opposition. [ABSTRACT FROM AUTHOR]

We propose a macrofinance model that rationalizes robust features in equity index option markets. When rare disasters are followed by economic recoveries, the slope of the implied volatility term structure is positive in good times but turns negative in bad times. Additionally, implied volatility decreases with moneyness in bad times (volatility skew), whereas the shape becomes a smile in good times in the presence of rare economic booms. Our theory contributes to understanding the dynamics of the implied volatility surface yet keeping standard asset-pricing moments realistic. This paper was accepted by Gustavo Manso, finance. Funding: The authors are grateful to HEC Montreal, the University of Texas at Dallas, and particularly to the Canadian Derivatives Institute for generous financial support. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4587. [ABSTRACT FROM AUTHOR]

Dallas and Houston are merely 90 minutes apart and a significant number of people commute between the two cities in heavy traffic. With a goal to reducing the travel time and allowing commuters to begin their day with a review of what will come when they arrive at work, Texas Central Partners, LLC proposed the development of a high-speed rail between the cities. However, it soon turned out that the proposition conflicted with property rights an imminent domain issues. The purpose of this paper is to analyze the proposition of Texas Central Partners in relation to imminent domain and property rights. [ABSTRACT FROM AUTHOR]

Summary: With the grid's evolution, the end‐users demand becomes more vital for demand side management (DSM). Accurate load forecasting (LF) is critical for power system planning and using advanced demand response (DR) strategies. To design efficient and precise LF, information about various factors that influence end‐users demand is required. In this paper, the impact of different factors on electrical demand and capacity of climatic factors existence and their variation is discussed and analysed. The Pearson correlation coefficient (PCC) is utilized to express the degree of electric demand correlation with metrological and calendar factors. Then, the optimal‐Bayesian regularization algorithm (BRA) based on ANN for LF is presented. The effect of the number of neurons in hidden layers on output is observed to select the most appropriate option. Additionally, heating degree days (HDDs) and cooling degree days (CDDs) indices are investigated to consider the impact of air conditioners' (ACs) loads in different seasons. Case studies on data from Dallas, Texas, USA, are used to demonstrate the influence of various factors on electrical demand. The proposed algorithm's effectiveness for LF and error formulations shows that optimal‐BRA‐enabled LF presents better accuracy than state‐of‐the‐art approaches. Thus, the proposed electric demand prediction strategy could help the system operator know DR potential at different times better, leading to optimal system resources dispatching through DR actions. [ABSTRACT FROM AUTHOR]