cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6468754
title: Resource Guide for State DOT's Maintenance Equipment Fleet Management Decisions

description: This research created a guide for state Departments of Transportation fleet management on utilizing equipment fleet management system data to make informed fleet management decisions. The research team took the historical equipment fleet data from the Oklahoma Department of Transportation and developed workflows, algorithms, and examples to demonstrate the use of historical equipment data for equipment decisions. Specifically, the research team demonstrated 1) the use of life cycle analysis and dynamic programming models for equipment replacement decisions; 2) developed algorithms to calculate equipment rental rates that can be used by the Department to forecast and allocate equipment operational budget among field districts and central office, 3) developed a procedure to make own-rent/lease decisions based on historical equipment management data. Using the two class codes of equipment (Class Code 5355 – 2 Yd. front-end loaders and Class Code 5385 – 1/2-ton fleetside pickup trucks) as examples, the research team presented the result of the equipment replacement models using both life cycle analysis and dynamic programming approaches and discussed the difference of those two methods. In addition, the impact of model input parameters (specifically depreciation cost estimation using both straight line and double declining balance depreciation calculation methods) on equipment replacement outcomes is discussed. The framework for deciding between on renting or owning for the two class codes of equipment was developed. Also, the equipment rental rates for the most frequently used equipment by ODOT were updated per class code.
description: Tran-SET Project: 20OPOSU037
DOI: 10.5281/zenodo.6468753
title: Resource Guide for State DOT's Maintenance Equipment Fleet Management Decisions

description: This research created a guide for state Departments of Transportation fleet management on utilizing equipment fleet management system data to make informed fleet management decisions. The research team took the historical equipment fleet data from the Oklahoma Department of Transportation and developed workflows, algorithms, and examples to demonstrate the use of historical equipment data for equipment decisions. Specifically, the research team demonstrated 1) the use of life cycle analysis and dynamic programming models for equipment replacement decisions; 2) developed algorithms to calculate equipment rental rates that can be used by the Department to forecast and allocate equipment operational budget among field districts and central office, 3) developed a procedure to make own-rent/lease decisions based on historical equipment management data. Using the two class codes of equipment (Class Code 5355 – 2 Yd. front-end loaders and Class Code 5385 – 1/2-ton fleetside pickup trucks) as examples, the research team presented the result of the equipment replacement models using both life cycle analysis and dynamic programming approaches and discussed the difference of those two methods. In addition, the impact of model input parameters (specifically depreciation cost estimation using both straight line and double declining balance depreciation calculation methods) on equipment replacement outcomes is discussed. The framework for deciding between on renting or owning for the two class codes of equipment was developed. Also, the equipment rental rates for the most frequently used equipment by ODOT were updated per class code.
description: Tran-SET Project: 20OPOSU037
DOI: 10.6084/m9.figshare.19611567
title: Data_smail_charif_semi-directive_ interviews_ ESD_France

description: Thirty-four semi-directive interviews conducted with local ESD actors in two municipalities in the south of France
DOI: 10.6084/m9.figshare.19611567.v1
title: Data_smail_charif_semi-directive_ interviews_ ESD_France

description: Thirty-four semi-directive interviews conducted with local ESD actors in two municipalities in the south of France
DOI: 10.5281/zenodo.6468766
title: Deep Reinforcement Learning-based Project Prioritization for Rapid Post-Disaster Recovery of Transportation Infrastructure Systems

description: Among various natural hazards that threaten transportation infrastructure, flooding represents a major hazard in Region 6's states to roadways as it challenges their design, operation, efficiency, and safety. The catastrophic flooding disaster event generally leads to massive obstruction of traffic, direct damage to highway/bridge structures/pavement, and indirect damages to economic activities and regional communities that may cause loss of many lives. After disasters strike, reconstruction and maintenance of an enormous number of damaged transportation infrastructure systems require each DOT to take extremely expensive and long-term processes. In addition, planning and organizing post-disaster reconstruction and maintenance projects of transportation infrastructures are extremely challenging for each DOT because they entail a massive number and the broad areas of the projects with various considerable factors and multi-objective issues including social, economic, political, and technical factors. Yet, amazingly, a comprehensive, integrated, data-driven approach for organizing and prioritizing post-disaster transportation reconstruction projects remains elusive. In addition, DOTs in Region 6 still need to improve the current practice and systems to robustly identify and accurately predict the detailed factors and their impacts affecting post-disaster transportation recovery. The main objective of this proposed research is to develop a deep reinforcement learning-based project prioritization system for rapid post-disaster reconstruction and recovery of damaged transportation infrastructure systems. This project also aims to provide a means to facilitate the systematic optimization and prioritization of the post-disaster reconstruction and maintenance plan of transportation infrastructure by focusing on social, economic, and technical aspects. The outcomes from this project would help engineers and decision-makers in Region 6's State DOTs optimize and sequence transportation recovery processes at a regional network level with necessary recovery factors and evaluating its long-term impacts after disasters.
description: Tran-SET Project: 20PPLSU14
DOI: 10.5281/zenodo.6468765
title: Deep Reinforcement Learning-based Project Prioritization for Rapid Post-Disaster Recovery of Transportation Infrastructure Systems

description: Among various natural hazards that threaten transportation infrastructure, flooding represents a major hazard in Region 6's states to roadways as it challenges their design, operation, efficiency, and safety. The catastrophic flooding disaster event generally leads to massive obstruction of traffic, direct damage to highway/bridge structures/pavement, and indirect damages to economic activities and regional communities that may cause loss of many lives. After disasters strike, reconstruction and maintenance of an enormous number of damaged transportation infrastructure systems require each DOT to take extremely expensive and long-term processes. In addition, planning and organizing post-disaster reconstruction and maintenance projects of transportation infrastructures are extremely challenging for each DOT because they entail a massive number and the broad areas of the projects with various considerable factors and multi-objective issues including social, economic, political, and technical factors. Yet, amazingly, a comprehensive, integrated, data-driven approach for organizing and prioritizing post-disaster transportation reconstruction projects remains elusive. In addition, DOTs in Region 6 still need to improve the current practice and systems to robustly identify and accurately predict the detailed factors and their impacts affecting post-disaster transportation recovery. The main objective of this proposed research is to develop a deep reinforcement learning-based project prioritization system for rapid post-disaster reconstruction and recovery of damaged transportation infrastructure systems. This project also aims to provide a means to facilitate the systematic optimization and prioritization of the post-disaster reconstruction and maintenance plan of transportation infrastructure by focusing on social, economic, and technical aspects. The outcomes from this project would help engineers and decision-makers in Region 6's State DOTs optimize and sequence transportation recovery processes at a regional network level with necessary recovery factors and evaluating its long-term impacts after disasters.
description: Tran-SET Project: 20PPLSU14
DOI: 10.5281/zenodo.6468687
title: Design of experiment (DOE) used in the study: Lightweight design of variable-stiffness imperfection-insensitive cylinders enabled by continuous tow shearing and machine learning

description: There are five input variables that are changed for this design of experiment (DOE) within the following range: \(\begin{eqnarray} 0.05 \leq r_{CTS} \leq 0.20 \nonumber \\ 1 \leq n \leq 12 \nonumber \\ 0 \leq {c_2}_{ratio} \leq 1 \\ 0 \leq \theta_1 \leq 75 \nonumber \\ 0 \leq \theta_2 \leq 75 \nonumber \end{eqnarray}\) For the sake of simplicity, these variables are respectively called v1, v2, v3, v4, v5. The DOE consist of 2000 points created with Latin Hyper-cube Sampling, as available in the LHS toolbox (Carnell, R. lhs: Latin Hypercube Samples, 2021. R package version 1.1.3.). The outputs evaluated with this design of experiment (DOE) are the critical buckling load $P_{critical}$, $b_{factor}$ obtained with Koiter's asymptotic approach, and the mass.
DOI: 10.5281/zenodo.6468686
title: Design of experiment (DOE) used in the study: Lightweight design of variable-stiffness imperfection-insensitive cylinders enabled by continuous tow shearing and machine learning

description: There are five input variables that are changed for this design of experiment (DOE) within the following range: \(\begin{eqnarray} 0.05 \leq r_{CTS} \leq 0.20 \nonumber \\ 1 \leq n \leq 12 \nonumber \\ 0 \leq {c_2}_{ratio} \leq 1 \\ 0 \leq \theta_1 \leq 75 \nonumber \\ 0 \leq \theta_2 \leq 75 \nonumber \end{eqnarray}\) For the sake of simplicity, these variables are respectively called v1, v2, v3, v4, v5. The DOE consist of 2000 points created with Latin Hyper-cube Sampling, as available in the LHS toolbox (Carnell, R. lhs: Latin Hypercube Samples, 2021. R package version 1.1.3.). The outputs evaluated with this design of experiment (DOE) are the critical buckling load $P_{critical}$, $b_{factor}$ obtained with Koiter's asymptotic approach, and the mass.
DOI: 10.5281/zenodo.6468788
title: Expanding the Concept of Comprehensive Area Ratio Parameter to the South-Central States: Towards Simplifying the Structural Evaluation of Flexible Pavements at the Network Level

description: The surface deflection bowl data collected through falling weight deflectometer (FWD) test is utilized by highway agencies in assessing the performance of the flexible pavement. However, a robust method to evaluate pavement sections utilizing FWD data from all the sensors is seldom developed. There is always a need for DOTs and highway agencies to have a simplified procedure, which can be directly implemented in agencies' databases. This study focuses on expanding and validating the concept of previously developed area ratio parameters towards the pavement section of South-Central States (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas) in effectively analyzing the pavement performances. Simulation-based deflections are utilized to develop enhanced deflection-based parameters and to reduce the need for extensive FWD testing in the field. Ninety-seven pavement sections in these states are considered to implement and validate simplified procedures that will be readily available to various transportation agencies to evaluate their pavement conditions at the network level. Due to this purpose, a pavement ranking chart is proposed for the five South-Central states, which categorizes the pavement section into very good, good, fair, and poor pavement sections. Eventually, load-induced effects concerning developed parameters are effectively analyzed to predict the remaining service life of the flexible pavement structures. The developed methodologies will be helpful for DOTs and highway agencies to carry out the rehabilitation and maintenance work in time and estimate the budget required in these procedures.
description: Tran-SET Project: 20PUTSA34
DOI: 10.5281/zenodo.6468787
title: Expanding the Concept of Comprehensive Area Ratio Parameter to the South-Central States: Towards Simplifying the Structural Evaluation of Flexible Pavements at the Network Level

description: The surface deflection bowl data collected through falling weight deflectometer (FWD) test is utilized by highway agencies in assessing the performance of the flexible pavement. However, a robust method to evaluate pavement sections utilizing FWD data from all the sensors is seldom developed. There is always a need for DOTs and highway agencies to have a simplified procedure, which can be directly implemented in agencies' databases. This study focuses on expanding and validating the concept of previously developed area ratio parameters towards the pavement section of South-Central States (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas) in effectively analyzing the pavement performances. Simulation-based deflections are utilized to develop enhanced deflection-based parameters and to reduce the need for extensive FWD testing in the field. Ninety-seven pavement sections in these states are considered to implement and validate simplified procedures that will be readily available to various transportation agencies to evaluate their pavement conditions at the network level. Due to this purpose, a pavement ranking chart is proposed for the five South-Central states, which categorizes the pavement section into very good, good, fair, and poor pavement sections. Eventually, load-induced effects concerning developed parameters are effectively analyzed to predict the remaining service life of the flexible pavement structures. The developed methodologies will be helpful for DOTs and highway agencies to carry out the rehabilitation and maintenance work in time and estimate the budget required in these procedures.
description: Tran-SET Project: 20PUTSA34
DOI: 10.5281/zenodo.6383950
title: Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions.

description: This dataset contains the raw experimental data and the analysis script for the paper Merl, R., Stöckl, T., Palan, S., 2022. "Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions", Journal of Banking and Finance 106490, https://doi.org/10.1016/j.jbankfin.2022.106490. Instructions: 1. Unpack all files into one folder.<br> 2. Open R version 4.1.2 and set the working directory to the folder with all the files.<br> 3. Run Script.R. In case the SPTools package is not available from GitHub anymore, you can also find it included in this dataset so you can install it from here.
description: {"references": ["Merl, R., St\u00f6ckl, T., Palan, S., 2022. \"Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions\", Journal of Banking and Finance 106490, https://doi.org/10.1016/j.jbankfin.2022.106490."]}
DOI: 10.5281/zenodo.6383949
title: Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions.

description: This dataset contains the raw experimental data and the analysis script for the paper Merl, R., Stöckl, T., Palan, S., 2022. "Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions", Journal of Banking and Finance 106490, https://doi.org/10.1016/j.jbankfin.2022.106490. Instructions: 1. Unpack all files into one folder.<br> 2. Open R version 4.1.2 and set the working directory to the folder with all the files.<br> 3. Run Script.R. In case the SPTools package is not available from GitHub anymore, you can also find it included in this dataset so you can install it from here.
description: {"references": ["Merl, R., St\u00f6ckl, T., Palan, S., 2022. \"Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions\", Journal of Banking and Finance 106490, https://doi.org/10.1016/j.jbankfin.2022.106490."]}
DOI: 10.6084/m9.figshare.13190708.v3
title: Significant variants / genes

description: The variants, and linked genes, that are significantly associated with body weight at multiple ages, in both diet-independent and diet-dependent manners, are enumerated in these files.
DOI: 10.6084/m9.figshare.19611651.v1
title: Dayekh et al Patient Partnership 2022.xlsx

description: This data set is linked to a study on the implementation of patient partnership at a Lebanese hospital. It includes the responses of each participant to each question in the interview guides. It also includes demographic and clinical data on patient partners (PP) and healthcare professionals (HP).
DOI: 10.6084/m9.figshare.19611651
title: Dayekh et al Patient Partnership 2022.xlsx

description: This data set is linked to a study on the implementation of patient partnership at a Lebanese hospital. It includes the responses of each participant to each question in the interview guides. It also includes demographic and clinical data on patient partners (PP) and healthcare professionals (HP).
DOI: 10.6084/m9.figshare.19611651.v3
title: Dayekh et al Patient Partnership 2022.xlsx

description: This data set is linked to a study on the implementation of patient partnership at a Lebanese hospital. It includes the responses of each participant to each question in the interview guides. It also includes demographic and clinical data on patient partners (PP) and healthcare professionals (HP).
DOI: 10.6084/m9.figshare.19611651.v2
title: Dayekh et al Patient Partnership 2022.xlsx

description: This data set is linked to a study on the implementation of patient partnership at a Lebanese hospital. It includes the responses of each participant to each question in the interview guides. It also includes demographic and clinical data on patient partners (PP) and healthcare professionals (HP).
DOI: 10.5281/zenodo.6468830
title: An Innovative Thermo-Energy Harvesting Module for Asphalt Roadway Pavement

description: The importance of green technologies for generating renewable energy and sustainable development is widely accepted. Road surfaces are exposed to solar radiation that generates thermal gradients and heat flow in the pavement layers. The heat stored can be harvested providing an untapped source of renewable energy. This report presents the design, construction, and assessment of an improved thermoelectric energy prototype for harvesting heat energy from roadway pavements. To accomplish this, various prototype designs were simulated using Finite Element (FE) analysis, followed by design construction and laboratory testing of the most promising prototypes to evaluate their power harvesting capabilities. The main design components of these prototypes are a heat collector/transfer plate, thermoelectric generators (TEG), and a cooling module consisting of a heat sink, phase change material, and an insulation box. The results suggest a direct relationship between thermal gradients and power generation and point out the importance of the cooling module in maintaining the efficiency of the harvester. An optimum harvester design would generate an average power output of 29 mWatt or 835 J over 8 hours per day in South Texas. Extrapolating this output for an installation that covers a length of 1 kilometer of a roadway could produce an average of 23.2 kWh/day, which appears to be a promising independent source of power for roadside signage and sensors.
description: Tran-SET Project: 20PUTSA42
DOI: 10.5281/zenodo.6468829
title: An Innovative Thermo-Energy Harvesting Module for Asphalt Roadway Pavement

description: The importance of green technologies for generating renewable energy and sustainable development is widely accepted. Road surfaces are exposed to solar radiation that generates thermal gradients and heat flow in the pavement layers. The heat stored can be harvested providing an untapped source of renewable energy. This report presents the design, construction, and assessment of an improved thermoelectric energy prototype for harvesting heat energy from roadway pavements. To accomplish this, various prototype designs were simulated using Finite Element (FE) analysis, followed by design construction and laboratory testing of the most promising prototypes to evaluate their power harvesting capabilities. The main design components of these prototypes are a heat collector/transfer plate, thermoelectric generators (TEG), and a cooling module consisting of a heat sink, phase change material, and an insulation box. The results suggest a direct relationship between thermal gradients and power generation and point out the importance of the cooling module in maintaining the efficiency of the harvester. An optimum harvester design would generate an average power output of 29 mWatt or 835 J over 8 hours per day in South Texas. Extrapolating this output for an installation that covers a length of 1 kilometer of a roadway could produce an average of 23.2 kWh/day, which appears to be a promising independent source of power for roadside signage and sensors.
description: Tran-SET Project: 20PUTSA42
DOI: 10.7303/syn28450881.4
title: Transcriptional landscape of human microglia implicates age, sex and APOE-related immunometabolic pathway perturbations
DOI: 10.6084/m9.figshare.19611672
title: Intermediary artifact.xlsx

description: File with the record of the texts considered relevant for the research, such as sustainability aspects, concepts, terms related to sustainable SE and promising elements on the subject as taxonomies and propositions extrated from GT method.
DOI: 10.6084/m9.figshare.19611672.v1
title: Intermediary artifact.xlsx

description: File with the record of the texts considered relevant for the research, such as sustainability aspects, concepts, terms related to sustainable SE and promising elements on the subject as taxonomies and propositions extrated from GT method.