Search

• We analyzed older driver at-fault crashes at unsignalized intersections in Alabama. • We estimated random parameter models. • We introduced a deep learning approach based on Artificial Neural Networks. • We identified variables that increase the likelihood of injuries and fatalities. • We discussed possible countermeasures to address older driver at-fault crashes. Introduction: The research described in this paper explored the factors contributing to the injury severity resulting from the male and female older driver (65 years and older) at-fault crashes at unsignalized intersections in Alabama. Method: Random parameter logit models of injury severity were estimated. The estimated models identified a variety of statistically significant factors influencing the injury severities resulting from older driver at-fault crashes. Results : According to these models, some variables were found to be significant only in one model (male or female) but not in the other one. For example, variables such as driver under the influence of alcohol/drugs, horizontal curve, and stop sign were found significant only in the male model. On the other hand, variables such as intersection approaches on tangents with flat grade, and driver older than 75 years were found significant only in the female model. In addition, variables such as making turning maneuver, freeway-ramp junction, high speed approach, and so forth were found significant in both models. Estimation findings showed that two parameters in the male model and another two parameters in the female model could be modeled as random parameters, indicating their varying influences on the injury severity due to unobserved effects. In addition to the random parameter logit approach, a deep learning approach based on Artificial Neural Networks was introduced to predict the outcome of the crashes based on 164 variables that are listed in the crash database. The artificial intelligence (AI)-based method achieved an accuracy of 76% indicating the role of the variables in deciding the final outcome. Practical Applications: Future plans are set to study the use of AI on large sized datasets to achieve a relatively high-performance, and hence to be able to identify which variables contribute the most to the final outcome. [ABSTRACT FROM AUTHOR]

The growing utilization of wind energy resources has been evidenced to have had substantial positive impacts on mitigating climate change consequences. Unlike stationary wind turbines, mobile wind turbines (MWTs) can travel along the local transportation system (TS) via a truck, supplying power to microgrids (MGs). This spatiotemporal flexibility can provide significant benefits, including enhancing system resilience in the aftermath of high-impact low-probability (HILP) events and contributing to energy system decarbonization. In this paper, we propose a two-stage stochastic optimization model for the service restoration problem by coordinating the routing and scheduling of MWTs with hydrogen storage systems (HSSs) in MGs. The first-stage problem focuses on optimizing the deployment of MWTs based on the shortest-path information obtained through Dijkstra's algorithm. The second-stage problem aims to minimize the expected power outage costs while accounting for wind energy uncertainties estimated through a Monte Carlo simulation approach. Case studies on an integrated transportation and energy network – a central Alabama interstate TS and four IEEE 33-node test power systems – demonstrate the effectiveness of the proposed restoration scheme in boosting MGs resilience and reducing carbon emissions. • Design a two-stage model to assign mobile wind turbines in hydrogen-based microgrids. • Use the shortest-path algorithm to enhance efficiency of MWTs' dispatch decision. • Integrate microgrids and a transportation system, enabling use of wind energy. • Discuss insight on striking balance between power outage costs and carbon emissions. [ABSTRACT FROM AUTHOR]

Natural and induced fractures in porous media pose significant challenges in subsurface applications such as carbon storage, hydrocarbon production, and aquifer contamination mitigation. These highly permeable fractures disrupt the natural flow in porous formations, leading to issues including reduced pressure buildup and early fluid breakthrough. To address these challenges, foam injection serves as an effective method for fluid diversion, offering enhanced mobility control in fractured media. However, the foam's performance must be carefully optimized considering factors such as reservoir conditions, in-situ fluid compatibility, rock properties like pore structure, permeability, and mineralogy, as well as fracture roughness, homogeneity, and orientation. This study investigates the effects of inclination angles (0°, 5°, 10°, and 15°) of smooth, saw-cut fractures on the performance of supercritical carbon dioxide foam in three distinct carbonate rocks: Indiana limestone, Fond Du Lac dolomite, and Alabama Silver limestone. These rocks exhibit a wide range of permeabilities, from 0.4 mD to 5.4 D. The findings are compared with previous research on Berea sandstone. It is demonstrated that optimal foam performance is more readily achieved in fractured porous media with higher permeability, larger pore throat sizes, and mineralogically compatible formations. Unlike previous work where studies focused on the individual impact of specific parameters, including a previous study conducted by our group to explore the effect of fracture orientation, the novelty of this paper is that it addresses multiple parameters at the same time and the interconnected roles that they play. This interplay of fracture orientation, rock type, and flow properties is what determines the ideal conditions that must be optimized to achieve the required foam performance. [Display omitted] [ABSTRACT FROM AUTHOR]

In order to meet government contestability policy ambitions, the United Kingdom Civil Aviation Authority (CAA) has undertaken a range of initiatives to create a competitive market for terminal air navigation services (TANS). This paper examines the critical dynamics underlying recent TANS service delivery changes at the nine United Kingdom airports that fall within the Single European Sky performance scheme (SES) using industry data and Porter's five forces model. Interviews with CAA, NATS and airport operators, along with publically available material, are used to explore the various elements impacting competition for TANS at these airports. Competition is intense among a very small number of companies. In addition to optimizing service cost, airport operators require greater value for money including alignment to strategic-operational goals, closer integration with other airside functions and payment structures that are performance based. Gatwick and Birmingham airports changed service provisions due to concerns about the value for money proposition offered by NATS Services Ltd (NSL). This paper also illustrates how competition has changed the customer orientation of NSL. It has been forced to evolve quickly from an expensive, perceived as somewhat arrogant, organisation to one that must be capable of aligning to the cost and service requirements of its customers. Importantly for the ATM industry this paper provides evidence that competition drives lower service costs and provides greater value for money for airlines and airport operators. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has significantly affected people's behavioral patterns and schedules because of stay-at-home orders and a reduction of social interactions. Therefore, the shape of electrical loads associated with residential buildings has also changed. In this paper, we quantify the changes and perform a detailed analysis on how the load shapes have changed, and we make potential recommendations for utilities to handle peak load and demand response. Our analysis incorporates data from before and after the onset of the COVID-19 pandemic, from an Alabama Power Smart Neighborhood with energy-efficient/smart devices, using around 40 advanced metering infrastructure data points. This paper highlights the energy usage pattern changes between weekdays and weekends pre– and post–COVID-19 pandemic times. The weekend usage patterns look similar pre– and post–COVID-19 pandemic, but weekday patterns show significant changes. We also compare energy use of the Smart Neighborhood with a traditional neighborhood to better understand how energy-efficient/smart devices can provide energy savings, especially because of increased work-from-home situations. HVAC and water heating remain the largest consumers of electricity in residential homes, and our findings indicate an even further increase in energy use by these systems. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines, with reservoir simulation, the impact of applying “best practices” and capitalizing on reservoir architecture to restrict CO2 plume movement and maximize reservoir storage capacity. Specifically, the paper explores the value gained from using horizontal CO2 injection wells and taking advantage of any existing shale “baffles” in conjunction with other reservoir characteristics that influence flow such as capillary pressure, pore volume trapping, and CO2 dissolution. Recent geologic and reservoir data collected from the Tuscaloosa Formation at the Mississippi Test Site (a Southeastern Regional Carbon Sequestration Partnership CO2 sequestration pilot test) will be used as the case study for evaluating alternative CO2 injection and storage engineering concepts. The Tuscaloosa Formation is a thick, porous, permeable, regionally extensive saline reservoir occurring throughout the Gulf Coast and is considered a high priority setting for large-scale CO2 storage. [Copyright &y& Elsevier]

Abstract: Ethanol is produced from organic material and is important as a strategic and cleaner alternative to fossil fuels. The cost of producing ethanol, which ultimately affects its price and demand, depends to a large extent on the delivered cost of raw materials. For a planned ethanol production facility, the cost of raw material is a function of (1) the facility location, (2) the facility size, and (3) the method of procurement. In a previous paper, two of the authors provided a methodology for computing a marginal price “surface” to better understand the location decision under several facility size scenarios. The methodology was implemented in a GIS-based decision support system to investigate the economic feasibility of locating a switchgrass-to-ethanol conversion facility in Alabama. In this paper, we use the methodology to construct delivered cost surfaces in order to evaluate two methods of procurement pricing; fixed and discriminatory. In the fixed pricing method of procurement, the facility pays one fixed price per delivered tonne. In the discriminatory pricing method of procurement, the facility pays a source-specific price per tonne before delivery and then pays the cost of delivery transportation. The results from the current study confirm the cost advantage of discriminatory over fixed pricing. However, it was observed that the relative cost advantage showed considerable variation as a function of location and facility size. [Copyright &y& Elsevier]

This paper reports the results of scratch tests on three rocks (Bonne Terre Dolomite, Alabama Marble and Dunnville Sandstone) conducted at depths of cut ranging from 0.2 mm to 2.6 mm. The experiments were performed on slabs of thickness slightly smaller than the cutter width, with a camera recording the entire cutting process. The test results reveal the existence of three cutting regimes: ductile, fragmentation, and brittle with increasing depth of cut. The regimes differ by the failure mode, the dependence of the specific energy ϵ on the depth of cut d , and the fragment shapes. In the ductile regime, ϵ ∼ d 0 and the fragments are constitutive grains of the rock or powder as a result of intense shearing. In the fragmentation regime, ϵ ∼ d − 1 / 2 and the fragments is a mix of ductile-type particles and flake-like chips also the product of shear failure. Finally, the brittle regime is characterized by ϵ ∼ d − 1 and is essentially associated with chip-like fragments that come in two varieties depending on whether they are created by shear or by tension. The d − 1 -dependence of the specific energy can only be observed if the rock is sufficiently homogeneous at the scale of the experiment. The distinction between different modes of failure is based on evidence from video recording and fractography with a Scanning Electron Microscope. It is shown that the dominance of tensile over shear fracture is intimately related to the existence of a crushed zone, which acts as a wedge to initiate a tensile macrocrack in a compressive stress field. Finally, an analysis of the energy partitioning in the brittle regime indicates that the energy dissipated in creating new surfaces with a tensile crack is actually negligible compared to the energy expended in the formation of the wedge. This result indicates that the mode I toughness cannot be determined from the specific energy in the brittle regime, on the basis of a model of a crack parallel to the free surface, in contradiction with published claims to that effect. Finally, this experimental investigation clarifies some confusion in the literature, rooted on assuming the existence of only two modes of failure, ductile and brittle. • Scratch tests on rock slabs reveal ductile, fragmentation, and brittle regimes. • The scaling of the specific energy with depth of cut depends on the cutting regime. • Flake-like chips produced in the fragmentation regime are created by shear. • The crushed zone acts as a wedge to initiate tensile cracks in brittle regime. • The energy dissipated in the propagation of a tensile crack is negligible. [ABSTRACT FROM AUTHOR]

In this paper I recall some of the experiences in having the great opportunity to work with Dr. Wernher von Braun, Krafft Ehricke, and other members of the Peenemunde Group, beginning in September 1951 who were developing the Redstone liquid propellant rocket at Redstone Arsenal, Huntsville, Alabama. [ABSTRACT FROM AUTHOR]

Abstract: The 42nd Annual Binghamton Geomorphology Symposium convened on October 21–23, 2011, in Mobile, Alabama, USA. The topic of the meeting was zoogeomorphology and ecosystem engineering. Speakers represented a variety of perspectives from the disciplines of geomorphology and ecology, and 21 posters were also presented covering a wide range of topics in biogeomorphology. This special issue presents the 15 invited papers presented at the symposium. [Copyright &y& Elsevier]

Abstract: This paper examines how national and local newspapers in the United States frame Chinese product recalls in 2007. First, it reviews literature on international communication and framing. Second, it content analyzes the presentations of the Chinese product recalls in two leading national newspapers, including the New York Times and the USA today, and six major newspapers from five Southern states, including Georgia, South Carolina, Louisiana, Mississippi, and Alabama. Third, it compares media representations of Chinese products in the two national newspapers with those in Southern newspapers. We argue that American media are still dominated by stories from the U.S. perspectives and Chinese sources are less likely to be cited. Even though Southern newspapers play an important role in constructing local knowledge, their limited scope of coverage and perspectives constrain their role in reporting international news. [Copyright &y& Elsevier]

Organizations carry out a variety of business processes in order to serve their clients. Usually supported by information technology and systems, process execution data is logged in an event log. Process mining uses this event log to discover the process' control-flow, its performance, information about the resources, etc. A common assumption is that the cases are executed independently of each other. However, batch work – the collective execution of cases for specific activities – is a common phenomenon in operational processes to save costs or time. Existing research has mainly focused on discovering individual batch tasks. However, beyond this narrow setting, batch processing may consist of the execution of several linked tasks. In this work, we present a novel algorithm which can also detect parallel, sequential and concurrent batching over several connected tasks, i.e., subprocesses. The proposed algorithm is evaluated on synthetic logs generated by a business process simulator, as well as on a real-world log obtained from a hospital's digital whiteboard system. The evaluation shows that batch processing at the subprocess level can be reliably detected. • This paper presents the novel Batch Activity Mining Algorithm (BAMA). • BAMA automatically detects batching behavior in process execution data (event log). • BAMA is the first to detect batching at both the task and the subprocess level. • BAMA is empirically evaluated using both synthetic and real-world data. [ABSTRACT FROM AUTHOR]

• Developed a methodology for sequential Bayesian design of experiments. • Minimized the maximum model prediction uncertainty for key output variables. • Methodology applied to an aqueous monoethanolamine pilot plant. • Two iteration resulted in 50% reduction in uncertainty of CO 2 capture prediction. • Methodology is generic and can be readily applied to other process systems. In this paper, a methodology is developed for sequential design of experiments (SDoE) for process systems and applied to a solvent-based CO 2 capture system. In this approach, the prior knowledge of the system is used to prioritize process data collection at specific operating conditions. These data are then incorporated into a Bayesian inference methodology for updating a stochastic model by refining estimations of its underlying parameters, and the updated model is then used to generate the next set of test runs. Thus, the new knowledge obtained from the data is used to guide subsequent iterations of the experimental runs, ensuring that the overall data collection is maximally informative given that most experimental campaigns, especially at pilot or higher-scale plants, are costly, time-consuming, and resource-limited. The test run objective for this work was to minimize the maximum model prediction uncertainty for key output variables, but the methodology is generic and can be readily applied to other test run objectives. This methodology is applied to an aqueous monoethanolamine (MEA) pilot plant campaign at the National Carbon Capture Center (NCCC) in Wilsonville, Alabama, USA. The SDoE framework was utilized for two iterations, while collecting 18 sets of data representing different process conditions, and this resulted in an overall average reduction in uncertainty of approximately 50% in the prediction of CO 2 capture percentage. Moreover, 11 additional data sets were obtained with variation of absorber packing height for further model validation. This work shows the capability of the SDoE framework to maximize learning given limited resources, allowing for the reduction of model uncertainty, which is of great importance for many applications including reduction of technical risk associated with scale-up and economic analysis. [ABSTRACT FROM AUTHOR]