Dissolution enhancement of gliclazide using pH change approach in presence of twelve stabilizers with various physico-chemical properties

Purpose. The micronization using milling process to enhance dissolution rate is extremely inefficient due to a high energy input, and disruptions in the crystal lattice which can cause physical or chemical instability. Therefore, the aim of the present study is to use in situ micronization process through pH change method to produce micron-size gliclazide particles for fast dissolution hence better bioavailability. Methods. Gliclazide was recrystallized in presence of 12 different stabilizers and the effects of each stabilizer on micromeritic behaviors, morphology of microcrystals, dissolution rate and solid state of recrystallized drug particles were investigated. Results. The results showed that recrystallized samples showed faster dissolution rate than untreated gliclazide particles and the fastest dissolution rate was observed for the samples recrystallized in presence of PEG 1500. Some of the recrystallized drug samples in presence of stabilizers dissolved 100% within the first 5 min showing at least 10 times greater dissolution rate than the dissolution rate of untreated gliclazide powders. Micromeritic studies showed that in situ micronization technique via pH change method is able to produce smaller particle size with a high surface area. The results also showed that the type of stabilizer had significant impact on morphology of recrystallized drug particles. The untreated gliclazide is rod or rectangular shape, whereas the crystals produced in presence of stabilizers, depending on the type of stabilizer, were very fine particles with irregular, cubic, rectangular, granular and spherical/modular shape. The results showed that crystallization of gliclazide in presence of stabilizers reduced the crystallinity of the samples as confirmed by XRPD and DSC results. Conclusion. In situ micronization of gliclazide through pH change method can successfully be used to produce micron-sized drug particles to enhance dissolution rate

Integrated Performance Assessment of Engineering Projects at the Interface of Emergent Properties and Uncertainty

Investigation of the performance of engineering project organizations is critical for understanding and eliminating inefficiencies in today’s dynamic global markets. The existing theoretical frameworks consider project organizations as monolithic systems and attribute the performance of project organizations to the characteristics of the constituents. However, project organizations consist of complex interdependent networks of agents, information, and resources whose interactions give rise to emergent properties that affect the overall performance of project organizations. Yet, our understanding of the emergent properties in project organizations and their impact on project performance is rather limited. This limitation is one of the major barriers towards creation of integrated theories of performance assessment in project organizations. The objective of this paper is to investigate the emergent properties that affect the ability of project organization to cope with uncertainty. Based on the theories of complex systems, we propose and test a novel framework in which the likelihood of performance variations in project organizations could be investigated based on the environment of uncertainty (i.e., static complexity, dynamic complexity, and external source of disruption) as well as the emergent properties (i.e., absorptive capacity, adaptive capacity, and restorative capacity) of project organizations. The existence and significance of different dimensions of the environment of uncertainty and emergent properties in the proposed framework are tested based on the analysis of the information collected from interviews with senior project managers in the construction industry. The outcomes of this study provide a novel theoretical lens for proactive bottom-up investigation of performance in project organizations at the interface of emergent properties and uncertaint

Why the determinacy condition is a weak criterion in rational expectations models

This paper disputes what Blanchard and Kahn have reported as the solution of linear rational expectation(RE) systems many years ago. Their method leads to traditional determinacy condition which is used very much nowadays. In this paper we have a new look to the mathematical procedure of this solution method and the main problem in their solution will be shown. We introduce a new methodology for modeling the systems with expectation, while in future this way of modeling can be used to replace traditional RE models.Rational expectation; Determinacy condition; Stability; Uniqueness; Predictive control

Urban Form and Structure Explain Variability in Spatial Inequality of Property Flood Risk among US Counties

Understanding the relationship between urban form and structure and spatial variation of property flood risk has been a longstanding challenge in urban planning and city flood risk management. Yet limited data-driven insights exist regarding the extent to which variation in spatial inequality of property flood risk in cities can be explained by heterogenous features of urban form and structure. In this study, we explore eight key features (i.e., population density, point of interest density, road density, minority segregation, income segregation, urban centrality index, gross domestic product, and human mobility index) related to urban form and structure to explain variability in spatial inequality of property flood risk among 2567 US counties. Using rich datasets related to property flood risk, we quantify spatial inequality in property flood risk and delineate features of urban form and structure using high-resolution human mobility and facility distribution data. We identify significant variation in spatial inequality of property flood risk among US counties with coastline and metropolitan counties having the greatest spatial inequality of property flood risk. The results also reveal variations in spatial inequality of property flood risk can be effectively explained based on principal components of development density, economic activity, and centrality and segregation. Using a classification and regression tree model, we demonstrate how these principal components interact and form pathways that explain levels of spatial inequality in property flood risk in US counties. The findings offer important insights for the understanding of the complex interplay between urban form and structure and spatial inequality of property flood risk and have important implications for integrated urban design strategies to address property flood risk as cities continue to expand and develop

A System-of-Systems Framework for Performance Assessment in Complex Construction Projects

Performance inefficiency is a critical challenge facing the construction industry. Despite the efforts made in the existing body of literature, an integrated theory of performance assessment facilitating a bottom-up understanding of the dynamic behaviors, uncertainties, and interdependencies between the constituents in construction projects is still missing. The traditional paradigm for performance assessment z is mainly based on a reductionism perspective, in which construction projects are identified as monolithic systems. However, complex construction projects are systems-of-systems. Systems-of-systems have unique traits that are different from those of monolithic systems. Failure to investigate construction projects as systems-of-systems has led to theoretical and methodological limitations in the creation of integrated tools and techniques for better assessment of performance in complex construction projects. To address these theoretical and methodological limitations, a system-of-systems framework is proposed as a theoretical lens and methodological structure toward creation of tools and techniques for integrated performance assessment of complex construction projects. Two principles (i.e., base-level abstraction and multi-level aggregation) are used to develop the proposed framework. The proposed framework facilitates a bottom-up evaluation of the dynamic behaviors, uncertainties, and interdependencies between the constituents in construction projects. The capabilizties of the proposed framework show its potential in addressing the limitations pertaining to the traditional frameworks for performance assessment. Hence, it can be adopted and tested by researchers to advance the body of knowledge and create integrated theories of performance assessment in complex construction projects

Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features

This study investigates the interplay among social demographics, built environment characteristics, and environmental hazard exposure features in determining community level cancer prevalence. Utilizing data from five Metropolitan Statistical Areas in the United States: Chicago, Dallas, Houston, Los Angeles, and New York, the study implemented an XGBoost machine learning model to predict the extent of cancer prevalence and evaluate the importance of different features. Our model demonstrates reliable performance, with results indicating that age, minority status, and population density are among the most influential factors in cancer prevalence. We further explore urban development and design strategies that could mitigate cancer prevalence, focusing on green space, developed areas, and total emissions. Through a series of experimental evaluations based on causal inference, the results show that increasing green space and reducing developed areas and total emissions could alleviate cancer prevalence. The study and findings contribute to a better understanding of the interplay among urban features and community health and also show the value of interpretable machine learning models for integrated urban design to promote public health. The findings also provide actionable insights for urban planning and design, emphasizing the need for a multifaceted approach to addressing urban health disparities through integrated urban design strategies

Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas

Urban flood risk emerges from complex and nonlinear interactions among multiple features related to flood hazard, flood exposure, and social and physical vulnerabilities, along with the complex spatial flood dependence relationships. Existing approaches for characterizing urban flood risk, however, are primarily based on flood plain maps, focusing on a limited number of features, primarily hazard and exposure features, without consideration of feature interactions or the dependence relationships among spatial areas. To address this gap, this study presents an integrated urban flood-risk rating model based on a novel unsupervised graph deep learning model (called FloodRisk-Net). FloodRisk-Net is capable of capturing spatial dependence among areas and complex and nonlinear interactions among flood hazards and urban features for specifying emergent flood risk. Using data from multiple metropolitan statistical areas (MSAs) in the United States, the model characterizes their flood risk into six distinct city-specific levels. The model is interpretable and enables feature analysis of areas within each flood-risk level, allowing for the identification of the three archetypes shaping the highest flood risk within each MSA. Flood risk is found to be spatially distributed in a hierarchical structure within each MSA, where the core city disproportionately bears the highest flood risk. Multiple cities are found to have high overall flood-risk levels and low spatial inequality, indicating limited options for balancing urban development and flood-risk reduction. Relevant flood-risk reduction strategies are discussed considering ways that the highest flood risk and uneven spatial distribution of flood risk are formed.Comment: 24 page

Collision of Environmental Injustice and Sea Level Rise: Assessment of Risk Inequality in Flood-induced Pollutant Dispersion from Toxic Sites in Texas

Global sea-level rise causes increasing threats of coastal flood and subsequent pollutant dispersion. However, there are still few studies on the disparity arising from such threats and the extent to which different communities could be exposed to flood-induced pollution dispersion from toxic sites under future sea level rise. To address this gap, this study selects Texas (a U.S. state with a large number of toxic sites and significant flood hazards) as the study area and investigates impacts of flood-induced pollutant dispersion on different communities under current (2018) and future (2050) flood hazard scenarios.The results show, currently, north coastline in Texas bears higher threats and vulnerable communities (i.e., low income, minorities and unemployed) are disproportionally exposed to these threats. In addition, the future sea-level rise and the exacerbated flood hazards will put additional threats on more (about 10%) Texas residents, among which vulnerable communities will still be disproportionately exposed to the increased threats. Our study reveals the facts that potential coastal pollutant dispersion will further aggravate the environmental injustice issues at the intersection of toxic sites and flood hazards for vulnerable populations and exacerbate risk inequalities. Given the dire impacts of flood-induced pollution dispersion on public health, the findings have important implications for specific actions from the policy makers to mitigate the inequitable risks

Unraveling Fundamental Properties of Power System Resilience Curves using Unsupervised Machine Learning

The standard model of infrastructure resilience, the resilience triangle, has been the primary way of characterizing and quantifying infrastructure resilience. However, the theoretical model merely provides a one-size-fits-all framework for all infrastructure systems. Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance. Limited empirical studies hindered our ability to fully understand and predict resilience characteristics in infrastructure systems. To address this gap, this study examined over 200 resilience curves related to power outages in three major extreme weather events. Using unsupervised machine learning, we examined different curve archetypes, as well as the fundamental properties of each resilience curve archetype. The results show two primary archetypes for power system resilience curves, triangular, and trapezoidal curves. Triangular curves characterize resilience behavior based on 1. critical functionality threshold, 2. critical functionality recovery rate, and 3. recovery pivot point. Trapezoidal archetypes explain resilience curves based on 1. duration of sustained function loss and 2. constant recovery rate. The longer the duration of sustained function loss, the slower the constant rate of recovery. The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructures

Frameworks for Systemic and Structural Analysis of Financial Innovations in Infrastructure

Financial innovations have emerged globally to close the gap between the rising global demand for infrastructures and the availability of financing sources offered by traditional financing mechanisms such as fuel taxation, tax-exempt bonds, and federal and state funds. The key to sustainable innovative financing mechanisms is effective policymaking. This paper discusses the theoretical framework of a research study whose objective is to structurally and systemically assess financial innovations in global infrastructures. The research aims to create analysis frameworks, taxonomies and constructs, and simulation models pertaining to the dynamics of the innovation process to be used in policy analysis. Structural assessment of innovative financing focuses on the typologies and loci of innovations and evaluates the performance of different types of innovative financing mechanisms. Systemic analysis of innovative financing explores the determinants of the innovation process using the System of Innovation approach. The final deliverables of the research include propositions pertaining to the constituents of System of Innovation for infrastructure finance which include the players, institutions, activities, and networks. These static constructs are used to develop a hybrid Agent-Based/System Dynamics simulation model to derive propositions regarding the emergent dynamics of the system. The initial outcomes of the research study are presented in this paper and include: (a) an archetype for mapping innovative financing mechanisms, (b) a System of Systems-based analysis framework to identify the dimensions of Systems of Innovation analyses, and (c) initial observations regarding the players, institutions, activities, and networks of the System of Innovation in the context of the U.S. transportation infrastructure financing