6501 research outputs found
Sort by
EFFECTIVE INTEGRATION OF WASTE PLASTICS IN HOT MIX ASPHALT MIXTURES: TOWARDS SUSTAINABLE INFRASTRUCTURE
No full textThe study investigated the potential of waste plastics such as low density polyethylene(LDPE), high density polyethylene(HDPE), polyethylene terephthalate(PET) and polystyrene (PS) for designing superior performing asphalt mixtures. Both wet mixing and dry mixing methods were adopted for designing plastic modified asphalt mixtures. Initially LDPE and HDPE were selected for developing plastic modified binders, based on melting characterization, determined from differential scanning calorimeter(DSC). In wet mixing process, the influence of dosage, size of plastic, blending duration and interaction between plastic and asphalt were assessed. Plastics were found to exhibit phase separation with asphalt, while finer particles take only longer to segregate than coarser particles. Pretreating PE with waste cooking oil and compatibilizing with Styrene-Butadiene-Styrene(SBS) reduced phase separation and improved the storage stability of modified binder. Through dry mixing method, it was found that degree of crystallinity (Xc) can be a potential parameter to identify a PE behavior rather than source of plastic. PE with Xc value less than 60% was observed to behave as a binding agent, while the tendency to behave as a filler increased with higher Xc values. PS exhibited soft amorphous behavior in hot mix asphalt mixture due to glass transition and PET exhibited filler behavior owing to its crystalline phase. Overall, plastic modification improved resistance to rutting and moisture damage. While intermediate cracking performance varied significantly with the type and behavior of plastic, thermal cracking behavior was found to be least influence
INTEGRATING DATA-DRIVEN AND MECHANISM-DRIVEN MODELING IN DRUG DISCOVERY AND HEPATOTOXICITY EVALUATION
No full textThe pharmacological activities and hepatotoxicity significantly influence the success or failure of new drugs during drug discovery and development. Traditional experimental methods, such as animal models, are costly and time-consuming for chemical testing. There is a great need to develop in vitro and computational modeling to help identify the pharmacological activities and hepatotoxicity potential in the early stage of drug discovery and safety evaluation. In this dissertation, new computational models and associated modeling frameworks were described for predicting the biological profile and hepatotoxicity of chemicals. Firstly, a novel data mining and Quantitative Structure-Activity Relationship (QSAR) workflow was developed to construct a virtual bioprofile for opioids, facilitating the screening of new analgesic opioids. Next, a mechanistic model consisting of structural alerts and in vitro ARE activation was developed to predict chemical hepatotoxicity potentially mediated through the oxidative stress pathway. Lastly, an interpretable deep neural network approach, incorporating in vitro assay results, transcriptome data, and pathway ontology knowledge, was employed to construct a virtual adverse outcome pathway network encompassing various toxicity pathways. This model\u27s interpretation can unveil potential toxicity mechanisms, aiding in identifying chemicals and drugs of concern to human health
A Comprehensive ML and AI Framework for Intersection Safety: Assessing Contributing Factors, Surrogate Safety Measures, Non-Compliance Behaviors, and Cost-Inclusive Methodology
No full textIntersection safety, particularly in traffic management, has emerged as a pressing concern due to its substantial contribution to road crashes and fatalities. In recent years, understanding road users\u27 behavior and conflicts at intersections has become essential for evaluating traffic safety. According to the Federal Highway Administration (FHWA), in 2020, over 50% of fatal and injury crashes occurred at or near intersections, necessitating further investigation. This study addresses this issue through an innovative blend of comprehensive literature review, advanced machine learning algorithms, artificial intelligence (AI) technologies, surrogate safety measures (SSMs), non-compliance behavior, and emphasizing the economic aspects of traffic incidents. First, the study conducts a detailed analysis of intersection-related crashes in New Jersey over five years, utilizing cutting-edge machine learning techniques such as RandomForest, XGBoost, LightGBM, CatBoost, and Ensemble models, along with Shapely Additive Explanations (SHAP) impact value techniques. These methods are employed to pinpoint critical factors contributing to crash severity. Second, it explores SSMs like Time-to-Collision (TTC) and Post-Encroachment Time (PET), employing various data collection methods, including videography, LiDAR, and GPS tracking. The effectiveness of these measures is assessed using Extreme Value Theory (EVT)-based models to predict crash probabilities, aiming to improve safety while understanding the financial implications of crashes. Third, a significant innovation is the development of an AI-based video analytic tool integrating advanced detection models like YOLO-v5 and DeepSORT algorithms. This tool enables real-time safety evaluation, i.e., SSMs (e.g., TTC, PET) for vehicle-to-vehicle and pedestrian-to-vehicle conflicts, and non-compliance behaviors – such as pedestrians walking outside the crosswalk and vehicles running red lights, aligning safety metrics with cost estimates. Fourth, the study analyzes comprehensive data from Chicago to establish a correlation between red-light violations and crashes, quantifying their economic burden and guiding the prioritization of safety interventions based on their cost-effectiveness. Fifth, this study evaluates the precision and applicability of trajectory data from video analytics, GPS tracking devices, and On-Board Units (OBUs) to assess the efficacy of Connected Vehicle (CV)-related safety applications. In conclusion, this study proposes actionable, cost-inclusive recommendations for enhancing intersection safety. It advocates for a comprehensive approach that blends traditional traffic safety measures with AI and machine learning techniques, maintaining a cost-conscious perspective. This methodology not only deepens the understanding of intersection dynamics but also fosters the creation of effective, economically viable, and data-driven safety interventions. This represents a significant step forward in reducing road fatalities and injuries and managing the economic impact of traffic crashes. As the first of its kind, this study can provide transportation agencies with valuable information about intersection safety from different standpoints, including data collection, analysis, economic impact, and safety countermeasures
Evaluation of Bridge Deterioration Factors: From Design Parameters to Community Impact.
Get PDFThe structural health and economic variables influencing the state of bridges in New Jersey are thoroughly examined in this thesis. The predictive modeling of bridge conditions using stepwise selection and linear logistic regression approaches is the primary focus of the opening chapter. With its precise predictions about bridge condition, either being fair/good, our model offers insightful information about resource allocation and maintenance by identifying bridge features which affect the deterioration of bridges the most. This gives effective infrastructure management, which not only relies on the identification of the important predictors and their effects on bridge conditions but also ranks them from the feature with the most effect to those with little to no effect. The second section of the analysis has two parts. We first started by examining the time-to-failure of bridges under various load scenarios (ADT/Live Loads, Environmental Loads/Conditions) and for different bridge materials in the first section. We also looked into the probability of failure using complex statistical techniques while recommending ways to improve bridge reliability. For the second part, we investigate the impacts of the median household income of the people living in a county on bridge conditions. The analysis shows that areas with lower household income have a higher proportion of bridges in fair condition, and this is likely due to lower road funds generated by authorities to supplement the federal road fund in areas with low household income. This insight, combined with statistical analysis to find the time-to-failure of the bridges, suggests prioritizing specific bridge types in low-income areas to ensure longevity despite limited funds. In the third chapter, we address the skew angle of the bridges and its influence on structural integrity because, from the previous analysis, we found that the skew angle has a 0.7% effect on the hazard/probability of deterioration of the bridge. After analyzing different skew angles and how they affect the distribution of stress, we offer recommendations for designing bridges for maximum structural stability. According to our data, bridges with skew angles of more than 45 degrees had a higher likelihood of deterioration, whereas bridges with skew angles between 15 and 30 degrees offer the optimal balance between structural integrity and design flexibility, thereby limiting stress concentrations
Protein and Polysaccharide-Based Optical Materials for Biomedical Applications.
Get PDFRecent advances in biomedical research, particularly in optical applications, have sparked a transformative movement towards replacing synthetic polymers with more biocompatible and sustainable alternatives. Most often made from plastics or glass, these materials ignite immune responses from the body, and their production is based on environmentally harsh oil-based processes. Biopolymers, including both polysaccharides and proteins, have emerged as a potential candidate for optical biomaterials due to their inherent biocompatibility, biodegradability, and sustainability, derived from their existence in nature and being recognized by the immune system. Current extraction and fabrication methods for these biomaterials, including thermal drawing, extrusion and printing, mold casting, dry-jet wet spinning, hydrogel formations, and nanoparticles, aim to create optical materials in cost-effective and environmentally friendly manners for a wide range of applications. Present and future applications include optical waveguides and sensors, imaging and diagnostics, optical fibers, and waveguides, as well as ocular implants using biopolymers, which will revolutionize these fields, specifically their uses in the healthcare industry
Novel Inhibitors to MmpL3 Transporter of Mycobacterium tuberculosis by Structure-Based High-Throughput Virtual Screening and Molecular Dynamics Simulations
Get PDFTuberculosis (TB)-causing bacterium Mycobacterium tuberculosis (Mtb) utilizes mycolic acids for building the mycobacterial cell wall, which is critical in providing defense against external factors and resisting antibiotic action. MmpL3 is a secondary resistance nodulation division transporter that facilitates the coupled transport of mycolic acid precursor into the periplasm using the proton motive force, thus making it an attractive drug target for TB infection. In 2019, X-ray crystal structures of MmpL3 from M. smegmatis were solved with a promising inhibitor SQ109, which showed promise against drug-resistant TB in Phase II clinical trials. Still, there is a pressing need to discover more effective MmpL3 inhibitors to counteract rising antibiotic resistance. In this study, structure-based high-throughput virtual screening combined with molecular dynamics (MD) simulations identified potential novel MmpL3 inhibitors. Approximately 17 million compounds from the ZINC15 database were screened against the SQ109 binding site on the MmpL3 protein using drug property filters and glide XP docking scores. From this, the top nine compounds and the MmpL3-SQ109 crystal complex structure each underwent 2 × 200 ns MD simulations to probe the inhibitor binding energetics to MmpL3. Four of the nine compounds exhibited stable binding properties and favorable drug properties, suggesting these four compounds could be potential novel inhibitors of MmpL3 for M. tuberculosis
Editorial: Introduction to Special Issue on modern day experiential exercises
Get PDF[No abstract available
The “Henry Rifle” on the German Stage: Karl May’s Depiction of the American West as “Dark and Bloody Grounds”
Get PDFTHE EVALUATION OF NON-SPECIFIC RISK INDICATORS IN IMPROVING DETECTION OF PSYCHOSIS-SPECTRUM LIABILITY
Get PDFPsychosis-spectrum disorders remain a leading cause of disability for both individuals and society, with early identification and prevention efforts representing a promising avenue of research for addressing these concerns. One potential impediment to improving early risk identification is the historical focus on indicators thought to be exclusive to the psychosis-spectrum. This focus often comes at the expense of non-specific risk factors (e.g., disrupted sleep, adverse childhood experiences) which contribute to the risk of developing psychosis as well as other mental illnesses. Research suggests the inclusion of these non-specific factors may improve our ability to identify those at risk. The present research collected data on a wide array of both specific and non-specific risk factors to develop a new, more holistic measure of psychosis-spectrum risk. A novel brief measure was developed, the Inclusive Psychosis Risk Inventory (IPRI), which compared favorably to existing psychosis-spectrum risk measures when looking at multiple fit indices as well as when predicting quality of life. The results of this study suggest the IPRI may provide a more holistic, comprehensive snapshot of psychosis-spectrum risk by including both non-specific and specific risk indicators within a single measure. Future research should seek to replicate these findings in more diverse samples and investigate the IPRI’s ability to predict clinical outcomes
Estimating changing marshland habitat and conservation potential for diamondback terrapins in New Jersey under climate change and development pressures
Get PDFThe diamondback terrapin, a brackish water turtle native to the eastern US, is listed as a species of ‘special concern’ in the state of New Jersey, due to decreasing habitat from development and changing climatic conditions. Diamondback terrapins reside in saline marshes and wetlands and nest in sandy substrate, primarily beaches and dunes, in June and July. The state of New Jersey is vulnerable to both sea level rise, leaving diamondback terrapin habitats and nesting areas at risk of inundation under future climate scenarios, and, as the most densely populated state, subject to continual development pressures on potentially conservable land. Changing sea level and climatic conditions will cause accretion and migration of marshes into open grassy land, yielding new potential terrapin habitat, though changing temperatures could affect the availability of male-producing nesting sites sex ratios and impact potential nesting patterns. This study spatially modeled lost, gained, and changed habitat and nesting areas under sea level rise scenarios for 2050 and 2100 in New Jersey and quantifies these by municipality to offer insights into potential conservable land that may mitigate these changes for the vulnerable species. Results indicate an overall decrease in potential habitat coupled with a decrease in both overall and male-producing nesting ranges