25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

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Department of Chemistryclos

Exploring the Contributions by Transportation Features to Urban Economy: An Experiment of a Scalable Tree-Boosting Algorithm with Big Data

Previous studies regarding transportation impacts on economic development in urban areas have three major issues—the limited scope of analysis mostly with the change of property values, the exclusion of smart transportation systems as features despite their potential for urban areas, and stereotyped approaches with limited types of variables. To surmount such limitations, this research adopted the concept of Big Data with machine learning techniques. As such, a total of 67 features from main categories, including the change of business, geographical boundary, socio-economic, land value, transportation, smart transportation, sales, and floating population were analyzed with XGBoost and SHAP algorithms. Given that the rise and fall of business is a major consideration for economic development in urban areas, the change in the total number of sales was selected as a target value. As a result, sales-related features showed the largest contribution to the rise of business, among others. It was also noted that features related to smart transportation systems obviously affected the success of business, even more than traditional ones from transportation. It is thus expected that the findings from this research will provide insights for decision-makers and researchers to make customized policies for boosting economic development in urban areas that are a major part of the urban economy to achieve sustainability

Data-driven stochastic transit assignment modeling using an automatic fare collection system

In modern urban transit networks, buses and subways are not distinguished as different modes of transportation; this makes it challenging to analyze travel behaviors with multiple modes for the purpose of developing policies and plans. With the introduction of Automatic Fare Collection (AFC) systems, these modes are operated along a complex of links and nodes that constitute a multimodal transit network. Methods for analyzing travel behaviors in mass transit have been developed, but previous approaches fail to adequately reflect travel behaviors and network features (e.g., transfers, mode and route preferences). To overcome such limitations, this research proposes a smart card data-based analytical method with which travel behaviors can be efficiently and accurately examined. AFC systems provide a tremendous amount of data that contain detailed trip information, and using these data reinforces the reliability of the proposed data-driven method. The proposed method of analysis involves four core processes: establishing a scheme for how multiple transit modes can be integrated into one multimodal transit network on the basis of information derived from the AFC system, selecting feasible paths, assigning trips using a stochastic approach, and verifying analytical results by comparing them with findings from trip datasets. This method was used to analyze monthly smart card data collected from the AFC system in 2009 in the greater Seoul area. Multimodal transit networks were constructed from 34,852 bus stops and 539 subway stations using smart card data, and in total, 3,614,875 trips were used in the analysis. The final model for stochastic transit assignment was developed using the proposed method, which was verified by comparing actual and assigned trips. The proposed method exhibits high accuracy (83.93%) and a high R-square value (0.981), which supports the strength of the proposed stochastic transit assignment model. The findings reveal new interesting research directions for exploration, such as developing more disaggregated models (e.g., for specific regions, times, and users), considering detailed transfer features (e.g., transferable boundaries, transfer facilities, and transfer times), confirming the method’s applicability by testing it in other cities, and incorporating both multimodal transit and road networks into the proposed model

Assessment of Heavy Metal and Oil-Contaminated Silty Sand Treatment by Electrical Resistance Heating Method

The feasibility of the electrical resistance heating method developed in this study was evaluated for the remediation of multi-contaminated silty sand in terms of environmental and geotechnical aspects. The multi-contaminated silty sand sampled in this study was polluted with 21,081 mg/kg of heavy oils, as well as heavy metals. Silty sand, treated using the electrical resistance _heating method was environmentally, as well as geotechnically, compared with the multi-contaminated silty sand in terms of residual concentration, leaching, shear modulus and modified California bearing ratio (CBR). The remediation test was conducted with a target temperature of 700 °C. The removal efficiency of total petroleum hydrocarbon (TPH) was estimated as 99.99% after remediation in 48 h; most of the heavy metals, as some of the contaminants, were isolated as a crystal in treated silty sand without any harmful leakage, and heavy oil was fully extracted with a form of mist and dust. Moreover, it was also geotechnically found that the decontamination process, including the removal of heavy metals and oils, had an effect on the increase in the internal friction angle, shear modulus and modified CBR of treated silty sand. In conclusion, it is shown that the electrical resistance heating method developed in this study is an environmentally and geotechnically effective technology for the recovery of clean construction fill material from hazardous-waste-contaminated silty sand

Assessment of Heavy Metal and Oil-Contaminated Silty Sand Treatment by Electrical Resistance Heating Method

The feasibility of the electrical resistance heating method developed in this study was evaluated for the remediation of multi-contaminated silty sand in terms of environmental and geotechnical aspects. The multi-contaminated silty sand sampled in this study was polluted with 21,081 mg/kg of heavy oils, as well as heavy metals. Silty sand, treated using the electrical resistance _heating method was environmentally, as well as geotechnically, compared with the multi-contaminated silty sand in terms of residual concentration, leaching, shear modulus and modified California bearing ratio (CBR). The remediation test was conducted with a target temperature of 700 °C. The removal efficiency of total petroleum hydrocarbon (TPH) was estimated as 99.99% after remediation in 48 h; most of the heavy metals, as some of the contaminants, were isolated as a crystal in treated silty sand without any harmful leakage, and heavy oil was fully extracted with a form of mist and dust. Moreover, it was also geotechnically found that the decontamination process, including the removal of heavy metals and oils, had an effect on the increase in the internal friction angle, shear modulus and modified CBR of treated silty sand. In conclusion, it is shown that the electrical resistance heating method developed in this study is an environmentally and geotechnically effective technology for the recovery of clean construction fill material from hazardous-waste-contaminated silty sand

An Empirical Analysis for Mode Choice in a Short-Distance Trip with Personal Rapid Transit

Recently, there have been emerging demands for new transportation modes, such as personal rapid transit (PRT), to improve the connectivity of first and last mile travel. Advancement of ICT and growing concerns over environmental issues reinforce such demands through which specific transportation modes can satisfy the need of each individual for short-distance trips. Although PRT has received particular attention for short-distance trips, it is true that recent approaches have been developed to analyze the behavior of travelers for mid- to long-distance trips that are not relevant for short-distance trips. This study proposed a suitable approach using logistic regression models that could assist the understanding of features which determine mode choice in a short-distance trip. The mode choice for PRT in short-distance trips in this study was based on the data from the survey. After considering various factors, it was apparent that the purpose of the trip together with weather conditions impacted significantly on travelers’ mode choices to PRT in short-distance trips. Additionally, it is expected that this study will play an important initial role in analyzing emerging transportation modes that can more easily respond to new demands for short-distance trips

Evaluation of countermeasures for red light running by traffic simulator–based surrogate safety measures

<p><b>Objective</b>: The conflicts among motorists entering a signalized intersection with the red light indication have become a national safety issue. Because of its sensitivity, efforts have been made to investigate the possible causes and effectiveness of countermeasures using comparison sites and/or before-and-after studies. Nevertheless, these approaches are ineffective when comparison sites cannot be found, or crash data sets are not readily available or not reliable for statistical analysis. Considering the random nature of red light running (RLR) crashes, an inventive approach regardless of data availability is necessary to evaluate the effectiveness of each countermeasure face to face.</p> <p><b>Method</b>: The aims of this research are to (1) review erstwhile literature related to red light running and traffic safety models; (2) propose a practical methodology for evaluation of RLR countermeasures with a microscopic traffic simulation model and surrogate safety assessment model (SSAM); (3) apply the proposed methodology to actual signalized intersection in Virginia, with the most prevalent scenarios—increasing the yellow signal interval duration, installing an advance warning sign, and an RLR camera; and (4) analyze the relative effectiveness by RLR frequency and the number of conflicts (rear-end and crossing).</p> <p><b>Results</b>: All scenarios show a reduction in RLR frequency (−7.8, −45.5, and −52.4%, respectively), but only increasing the yellow signal interval duration results in a reduced total number of conflicts (−11.3%; a surrogate safety measure of possible RLR-related crashes). An RLR camera makes the greatest reduction (−60.9%) in crossing conflicts (a surrogate safety measure of possible angle crashes), whereas increasing the yellow signal interval duration results in only a 12.8% reduction of rear-end conflicts (a surrogate safety measure of possible rear-end crash).</p> <p><b>Conclusions</b>: Although increasing the yellow signal interval duration is advantageous because this reduces the total conflicts (a possibility of total RLR-related crashes), each countermeasure shows different effects by RLR-related conflict types that can be referred to when making a decision. Given that each intersection has different RLR crash issues, evaluated countermeasures are directly applicable to enhance the cost and time effectiveness, according to the situation of the target intersection. In addition, the proposed methodology is replicable at any site that has a dearth of crash data and/or comparison sites in order to test any other countermeasures (both engineering and enforcement countermeasures) for RLR crashes.</p

Electrochemical C(sp(3))-H Functionalization of gamma-Lactams Based on Hydrogen Atom Transfer

We describe the electrochemical alpha-amidoalkylation of gamma-lactams based on transition-metal-free cross-coupling via hydrogen atom transfer. The highly selective hydrogen atom transfer process allows for a broad substrate scope including both inter- and intramolecular reactions. Also, the construction of quaternary centers was realized by a double hydrogen atom transfer protocol to afford spirocycles. Detailed mechanistic studies including experimental and computational studies are provided to support the reaction pathway

Modeling public acceptance of demand-responsive transportation: An integrated UTAUT and ITM framework

Demand-responsive transportation (DRT) is a flexible form of shared mobility in which service provision is shaped by the user demand. DRT has been considered a sustainable mobility solution, as it reduces CO2 emissions from fixed-route services and encourages a mode shift from private cars to shared mobility. Given that public acceptance is a key for the wider diffusion of DRT, this study explored the factors affecting usage intention for DRT in the Republic of Korea. Drawing on the unified theory of acceptance and use of technology (UTAUT) and the initial trust model (ITM), a conceptual framework was developed that linked attitudinal and psychological factors to behavioral intention for DRT usage. 1168 valid observations were collected from adults aged 19–64 years in the Republic of Korea using a structured online survey, and analyzed using structural equation modeling. The results showed that the four UTAUT constructs (performance expectancy, social influence, facilitating conditions, and environmental concerns) were directly related to intention for DRT usage. Indirect impacts of perceived safety, structural assurance, familiarity, performance expectancy, and effort expectancy on initial trust were also found. Consequently, the constructs with the greatest total effect on usage intention were (in order of relevance) initial trust, performance expectancy, social influence, and structural assurance. As one of the few attempts to examine public acceptance of DRT, it is expected that findings from this study could contribute to the literature by providing insights into potential users’ attitudes toward DRT. This study further offers guidance on designing interventions intended to promote a transition toward increased operational efficiency through policy developments for DRT, thereby achieving sustainable development