Spatial mapping of land susceptibility to dust emissions using optimization of attentive Interpretable Tabular Learning (TabNet) model.

Dust pollution poses significant risks to human health, air quality, and food safety, necessitating the identification of dust occurrence and the development of dust susceptibility maps (DSMs) to mitigate its effects. This research aims to detect dust occurrence using satellite images and prepare a DSM for Bushehr province, Iran, by enhancing the attentive interpretable tabular learning (TabNet) model through three swarm-based metaheuristic algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), and hunger games search (HGS). A spatial database incorporating dust occurrence areas was created using Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2022, including 15 influential criteria related to climate, soil, topography, and land cover. Four models were employed for modeling and DSM generation: TabNet, TabNet-PSO, TabNet-GWO, and TabNet-HGS. Evaluation of the modeling results using performance metrics indicated that the TabNet-HGS model outperformed the other models in both training (mean absolute error (MAE) = 0.055, root-mean-square error (RMSE) = 0.1, coefficient of determination (R2) = 0.959), and testing (MAE = 0.063, RMSE = 0.114, R2 = 0.947) data. Following TabNet-HGS, the TabNet-PSO, TabNet-GWO, and TabNet models demonstrated progressively lower accuracy. The validation of the DSM was performed by assessing receiver operating characteristic (ROC) curves, revealing that the TabNet-HGS, TabNet-PSO, TabNet-GWO, and TabNet models exhibited the highest modeling accuracy, with corresponding area under the curve (AUC) values of 0.994, 0.986, 0.98, and 0.832, respectively. These results highlight the enhanced accuracy of dust susceptibility modeling achieved by integrating swarm-based metaheuristic algorithms with the TabNet model. The dust susceptibility map provides valuable insights into the sources, pathways, and impacts of dust particles on the environment and human health in the study area.

Temporal dynamics of urban gas pipeline risks.

Urban gas pipelines pose significant risks to public safety and infrastructure integrity, necessitating thorough risk assessment methodologies to mitigate potential hazards. This study investigates the dynamics of population distribution, demographic characteristics, and building structures to assess the risk associated with gas pipelines. Using geospatial analysis techniques, we analyze population distribution patterns during both day and night periods. Additionally, we conduct an in-depth vulnerability assessment considering multiple criteria maps, highlighting areas of heightened vulnerability in proximity to gas pipelines and older buildings. This study incorporated the concept of individual risk and the intrinsic parameters of gas pipelines to develop a hazard map. Hazard analysis identifies areas with elevated risks, particularly around main pipeline intersections and high-pressure zones. Integrating hazard and vulnerability assessments, we generate risk maps for both day and night periods, providing valuable insights into spatial risk distribution dynamics. The findings underscore the importance of considering temporal variations in risk assessment and integrating demographic and structural factors into hazard analysis for informed decision-making in pipeline management and safety measures.

Integrated Augmented and Virtual Reality Technologies for Realistic Fire Drill Training.

In this article, we propose a novel fire drill training system designed specifically to integrate augmented reality (AR) and virtual reality (VR) technologies into a single head-mounted display device to provide realistic as well as safe and diverse experiences. Applying hybrid AR/VR technologies in fire drill training may be beneficial because they can overcome limitations such as space-time constraints, risk factors, training costs, and difficulties in real environments. The proposed system can improve training effectiveness by transforming arbitrary real spaces into real-time, realistic virtual fire situations, and by interacting with tangible training props. Moreover, the system can create intelligent and realistic fire effects in AR by estimating not only the object type but also its physical properties. Our user studies demonstrated the potential of integrated AR/VR for improving training and education.

A new approach based on biology-inspired metaheuristic algorithms in combination with random forest to enhance the flood susceptibility mapping.

Flash floods are one of the worst natural disasters, causing massive economic losses and many deaths. Creating a flood susceptibility map (FSM) that pinpoints the areas most at risk of flooding is a crucial non-structural solution for managing floods. This study aimed to assess the efficacy of combinations of the random forest (RF) model with three biology-inspired metaheuristic algorithms, namely invasive weed optimization (IWO), slime mould algorithm (SMA), and satin bowerbird optimization (SBO), for flood susceptibility mapping in Estahban town, Iran. Initially, synthetic-aperture radar (SAR) (Sentinel-1) and optical (Landsat-8) satellite images were integrated to monitor the flooded areas during the July 2022 monsoon in the study area. A dataset of 509 flood occurrence points was created to identify flood-prone areas using remote sensing techniques, considering the monitored flood areas. The dataset also included twelve flood-related criteria: topography, land cover, and climate. The holdout method was employed for modeling, with a ratio of 70:30 used for the train/test split. Data pre-processing techniques were conducted to improve model performance, including determining criteria importance and addressing multicollinearity issues using certainty factor (CF), multicollinearity, and information gain ratio (IGR) methods. Then FSM was prepared using RF, RF-IWO, RF-SBO, and RF-SMA models. The findings of this research revealed that the RF-IWO model was the best predictive model of flood susceptibility modeling, with root-mean-square-error (RMSE) (0.211 and 0.0.27), mean-absolute-error (MAE) (0.103 and 0.15), and coefficient-of-determination (R2) (0.821 and 0.707) in the training and testing phases, respectively. Receiver operating characteristic (ROC) curve analysis of FSM revealed that the most accurate models were the RF-IWO (area under the curve (AUC) = 0.983), RF-SBO (AUC = 0.979), RF-SMA (AUC = 0.963), and RF (AUC = 0.959), respectively. Integrating biology-inspired computing algorithms with machine learning algorithms presents a novel approach to enhancing the accuracy of FSMs.

Dust detection and susceptibility mapping by aiding satellite imagery time series and integration of ensemble machine learning with evolutionary algorithms.

To mitigate the impact of dust on human health and the environment, it is crucial to create a model and map that identifies the areas susceptible to dust. The present study focused on identifying dust occurrences in the Bushehr province of Iran between 2002 and 2022 using moderate-resolution imaging spectroradiometer (MODIS) imagery. Subsequently, an ensemble machine learning model was improved to prepare a dust susceptibility map (DSM). The study employed differential evolution (DE), genetic algorithm (GA), and flower pollination algorithm (FPA) - three evolutionary algorithms - to enhance the random forest (RF) ensemble model. A spatial database was created for modeling, including 519 dust occurrence points (extracted from MODIS imagery) and 15 factors affecting dust (Slope, bulk density, aspect, clay, altitude, sand, rainfall, lithology, soil order, distance to river, soil texture, normalized difference vegetation index (NDVI), soil water content, land cover, and wind speed). By utilizing the differential evolution (DE) algorithm, we determined the significance of these factors in impacting dust occurrences. The results indicated that altitude, wind speed, and land cover were the most influential factors, while the distance to the river, bulk density, and soil texture had less impact on dust occurrence. Data were preprocessed using multicollinearity analysis and the frequency ratio (FR) approach. For this research, three RF-based meta-heuristic optimization algorithms, namely RF-FPA, RF-GA, and RF-DE, were created for DSM. The effectiveness prediction of the constructed models by indexes of root-mean-square-error (RMSE), the area under the receiver operating characteristic (AUC-ROC), and coefficient of determination (R2) from best to worst were RF-DE (RMSE = 0.131, AUC-ROC = 0.988, and R2 = 0.93), RF-GA (RMSE = 0.141, AUC-ROC = 0.986, and R2 = 0.919), RF-FPA (RMSE = 0.157, AUC-ROC = 0.981, and R2 = 0.9), and RF (RMSE = 0.173, AUC-ROC = 0.964, and R2 = 0.878). The results showed that combining evolutionary algorithms with an RF model improves the accuracy of dust susceptibility modeling.

Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood susceptibility mapping using radar satellite imagery.

Floods are the natural disaster that occurs most frequently due to the weather and causes the most widespread destruction. The purpose of the proposed research is to analyze flood susceptibility mapping (FSM) in the Sulaymaniyah province of Iraq. This study employed a genetic algorithm (GA) to fine-tune parallel ensemble-based machine learning algorithms (random forest (RF) and bootstrap aggregation (Bagging)). Four machine learning algorithms (RF, Bagging, RF-GA, and Bagging-GA) were used to build FSM in the study area. To provide inputs into parallel ensemble-based machine learning algorithms, we gathered and processed data from meteorological (Rainfall), satellite image (flood inventory, normalized difference vegetation index (NDVI), aspect, land cover, altitude, stream power index (SPI), plan curvature, topographic wetness index (TWI), slope) and geographic sources (geology). For this research, Sentinel-1 synthetic aperture radar (SAR) satellite images were utilized to locate flooded areas and create an inventory map of floods. To train and validate the model, we employed 70 % and 30 % of 160 selected flood locations, respectively. Multicollinearity, frequency ratio (FR), and Geodetector methods were used for data preprocessing. Four metrics were utilized to assess the FSM performance: the root mean square error (RMSE), the area under the receiver-operator characteristic curve (AUC-ROC), the Taylor diagram, and the seed cell area index (SCAI). The results exhibited that all the suggested models have high accuracy of prediction, but the performance of Bagging-GA (RMSE (Train = 0.1793, Test = 0.4543)) was slightly better than RF-GA (RMSE (Train = 0.1803, Test = 0.4563)), Bagging (RMSE (Train = 0.2191, Test = 0.4566)), and RF (RMSE (Train = 0.2529, Test = 0.4724)). According to the ROC index, the Bagging-GA model (AUC = 0.935) was the most accurate in flood susceptibility modeling, followed by the RF-GA (AUC = 0.904), the Bagging (AUC = 0.872), and the RF (AUC = 0.847) models. The study's identification of high-risk flood zones and the most significant factors contributing to flooding make it a helpful resource for flood management.

Changes in Long-Term Functional Independence in Patients with Moderate and Severe Ischemic Stroke: Comparison of the Responsiveness of the Modified Barthel Index and the Functional Independence Measure.

This study investigated the long-term functional changes in patients with moderate-to-severe ischemic stroke. In addition, we investigated whether there was a difference between the modified Barthel Index (MBI) and Functional Independence Measure (FIM) according to severity. To evaluate the changes in the long-term functional independence of the subjects, six evaluations were conducted over 2 years, and the evaluation was performed using MBI and FIM. A total of 798 participants participated in this study, of which 673 were classified as moderate and 125 as severe. During the first 3 months, the moderate group showed greater recovery than the severe group. The period of significant change in the National Institutes of Health Stroke Scale (NIHSS) score was up to 6 months after onset in the moderate group, and up to 3 months after onset in the severe group. In the severe group, MBI evaluation showed significant changes up to 6 months after onset, whereas FIM showed significant changes up to 18-24 months. Our results showed that functional recovery of patients with ischemic stroke in the 3 months after onset was greater in the moderate group than in the severe group. FIM is more appropriate than MBI for evaluating the functional status of patients with severe stroke.

Understanding of the Lower Extremity Motor Recovery After First-Ever Ischemic Stroke.

BACKGROUND: We aimed to verify the validity of the proportional recovery model for the lower extremity. METHODS: We reviewed clinical data of patients enrolled in the Korean Stroke Cohort for Functioning and Rehabilitation between August 2012 and May 2015. Recovery proportion was calculated as the amount of motor recovery over initial motor impairment, measured as the Fugl-Meyer Assessment of Lower Extremity score. We used the logistic regression method to model the probability of achieving the full Fugl-Meyer Assessment of Lower Extremity score, whereby we considered the ceiling effect of the score. To show the difference in the prevalence of achieving the full Fugl-Meyer Assessment of Lower Extremity score between 3 and 6 months poststroke, we constructed a marginal model through the generalized estimating equation method. We also performed the propensity score matching analysis to show the dependency of recovery proportion on the initial motor deficit at 3 and 6 months poststroke. RESULTS: We evaluated 1085 patients. The recovery proportions at 3 and 6 months poststroke were 0.67±0.42 and 0.75±0.39, respectively. A 1-unit decrease in the initial neurological impairment and the age at stroke onset increased the probability of achieving the full Fugl-Meyer Assessment of Lower Extremity score, which occurred at both 3 and 6 months poststroke. The prevalence of those who reach full lower limb motor recovery differs significantly between 3 and 6 months poststroke. We also found out that the recovery proportion at both 3 and 6 months poststroke is determined by the initial motor deficits of the lower limb. These results are not consistent with the proportional recovery model. CONCLUSIONS: Our results demonstrated that the proportional recovery model for the lower limb is invalid.

Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020.

In recent months, the world has been affected by the infectious coronavirus disease and Iran is one of the most affected countries. The Iranian government's health facilities for an urgent investigation of all provinces do not exist simultaneously. There is no management tool to identify the vulnerabilities of Iranian provinces in prioritizing health services. The aim of this study was to prepare a coronavirus vulnerability map of Iranian provinces using geographic information system (GIS) to monitor the disease. For this purpose, four criteria affecting coronavirus, including population density, percentage of older people, temperature, and humidity, were prepared in the GIS. A multiscale geographically weighted regression (MGWR) model was used to determine the vulnerability of coronavirus in Iran. An adaptive neuro-fuzzy inference system (ANFIS) model was used to predict vulnerability in the next two months. Results indicated that, population density and older people have a more significant impact on coronavirus in Iran. Based on MGWR models, Tehran, Mazandaran, Gilan, and Alborz provinces were more vulnerable to coronavirus in February and March. The ANFIS model findings showed that West Azerbaijan, Zanjan, Fars, Yazd, Semnan, Sistan and Baluchistan, and Tehran provinces were more vulnerable in April and May.

Post-Stroke Depression and Cognitive Aging: A Multicenter, Prospective Cohort Study.

Background: This study investigated the impact of post-stroke depression (PSD) on cognitive aging in elderly stroke patients. Methods: This study was an interim analysis of the Korean Stroke Cohort for Functioning and Rehabilitation. Among 10,636 patients with first-ever stroke, a total of 3215 patients with normal cognitive function three months post-stroke were included in the analysis. PSD was defined using the Korean Geriatric Depression Scale Short Form (K-GDS-SF) at three months. Cognitive aging was defined as a decline in the Korean version of the Mini-Mental Status Examination (K-MMSE) score to less than the second percentile. Results: The hazard ratio (HR) of PSD for cognitive decline was 2.16 (95% CI, 1.34−3.50, p < 0.01) in the older group (age ≥65 years), and 1.02 (95% CI, 0.50−2.07, n.s.) in the younger group (age <65 years). When the older group was divided by sex, the HR was 2.50 (95% CI, 1.26−4.96, p < 0.01) in male patients and 1.80 (95% CI, 0.93−3.51, n.s.) in female patients. However, women showed a higher incidence of cognitive decline in both the PSD and no PSD groups. Among K-GDS-SF factors, "Negative judgment about the past, present, and future" increased the HR of PSD in older male patients. Conclusions: Early PSD increased the HR for cognitive decline in older stroke patients, mainly in males. Specifically, older male patients with negative thinking were at increased risk of cognitive decline. The findings also suggest that older women may be at risk for cognitive decline. Therefore, preventive interventions for cognitive decline should be tailored differently for men and women.

COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms.

The reduction of population concentration in some urban land uses is one way to prevent and reduce the spread of COVID-19 disease. Therefore, the objective of this study is to prepare the risk mapping of COVID-19 in Tehran, Iran, using machine learning algorithms according to socio-economic criteria of land use. Initially, a spatial database was created using 2282 locations of patients with COVID-19 from 2 February 2020 to 21 March 2020 and eight socio-economic land uses affecting the disease-public transport stations, supermarkets, banks, automated teller machines (ATMs), bakeries, pharmacies, fuel stations, and hospitals. The modeling was performed using three machine learning algorithms that included random forest (RF), adaptive neuro-fuzzy inference system (ANFIS), and logistic regression (LR). Feature selection was performed using the OneR method, and the correlation between land uses was obtained using the Pearson coefficient. We deployed 70% and 30% of COVID-19 patient locations for modeling and validation, respectively. The results of the receiver operating characteristic (ROC) curve and the area under the curve (AUC) showed that the RF algorithm, which had a value of 0.803, had the highest modeling accuracy, which was followed by the ANFIS algorithm with a value of 0.758 and the LR algorithm with a value of 0.747. The results showed that the central and the eastern regions of Tehran are more at risk. Public transportation stations and pharmacies were the most correlated with the location of COVID-19 patients in Tehran, according to the results of the OneR technique, RF, and LR algorithms. The results of the Pearson correlation showed that pharmacies and banks are the most incompatible in distribution, and the density of these land uses in Tehran has caused the prevalence of COVID-19.

Status Of Dysphagia After Ischemic Stroke: A Korean Nationwide Study.

OBJECTIVE: To identify the incidence of dysphagia after ischemic stroke and determine factors affecting the presence of dysphagia. DESIGN: Retrospective case-control study. This was an interim analysis of a prospective multicenter Korean stroke cohort. SETTING: Acute care university hospitals. PARTICIPANTS: Patients (N=6000) with first-ever acute ischemic stroke. Patients were divided into 2 groups according to the presence or absence of dysphagia confirmed at 7 days after onset using the American Speech-Language-Hearing Association National Outcomes Measurement System (ASHA-NOMS) scale, which was determined after conducting screening or standardized tests. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Age at stroke onset, body mass index (BMI), premorbid modified Rankin Scale (mRS), brainstem lesions, National Institutes of Health Stroke Scale (NIHSS), poststroke mRS, and ASHA-NOMS swallowing level at poststroke day 7 were evaluated. RESULTS: Among patients with ischemic stroke, 32.3% (n=1940) had dysphagia at 7 days after stroke onset. At discharge, 80.5% (n=1561) still had dysphagia. The prediction model for the presence of dysphagia identified age at onset, underweight (BMI <18.5 kg/m2), premorbid mRS, brainstem lesions, and NIHSS as independent predictors. The odds ratio (OR) for the presence of dysphagia significantly increased with underweight (OR, 1.6684; 95% confidence interval [CI], 1.27-2.20), increased age at onset (OR, 1.0318; 95% CI, 1.03-1.04), premorbid mRS (OR, 1.1832; 95% CI, 1.13-1.24), brainstem lesions (OR, 1.6494; 95% CI, 1.39-1.96), and NIHSS (OR, 1.2073; 95% CI, 1.19-1.23). CONCLUSIONS: The incidence of dysphagia after ischemic stroke was 32.3%. The prediction model for the presence of dysphagia identified age, low BMI, premorbid disabilities, brainstem lesions, and NIHSS as predictive factors.

Effects of air pollution in Spatio-temporal modeling of asthma-prone areas using a machine learning model.

Industrialization and increasing urbanization have led to increased air pollution, which has a devastating effect on public health and asthma. This study aimed to model the spatial-temporal of asthma in Tehran, Iran using a machine learning model. Initially, a spatial database was created consisting of 872 locations of asthma children and six air pollution parameters, including carbon monoxide (CO), particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) in four-seasons (spring, summer, autumn, and winter). Spatial-temporal modeling and mapping of asthma-prone areas were performed using a random forest (RF) model. For Spatio-temporal modeling and assessment, 70% and 30% of the dataset were used, respectively. The Spearman correlation and RF model findings showed that during different seasons, the PM2.5 parameter had the most important effect on asthma occurrence in Tehran. The assessment of the Spatio-temporal modeling of asthma using the receiver operating characteristic (ROC)-area under the curve (AUC) showed an accuracy of 0.823, 0.821, 0.83, and 0.827, respectively for spring, summer, autumn, and winter. According to the results, asthma occurs more often in autumn than in other seasons.

Asthma-prone areas modeling using a machine learning model.

Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran considering environmental, spatial factors. Initially, we built a spatial database using 872 locations of children with asthma and 13 environmental factors affecting the disease-distance to parks and streets, rainfall, temperature, humidity, pressure, wind speed, particulate matter (PM 10 and PM 2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), and nitrogen dioxide (NO2). Subsequently, utilizing this spatial database, a random forest (RF) machine learning model, and a geographic information system, we prepared a map of asthma-prone areas. For modeling and validation, we deployed 70% and 30%, respectively, of the locations of children with asthma. The results of spatial autocorrelation and RF model showed that the criteria of distance to parks and streets as well as PM 2.5 and PM 10 had the greatest impact on asthma occurrence in the study area. Spatial autocorrelation analyses indicated that the distribution of asthma cases was not random. According to receiver operating characteristic results, the RF model had good accuracy (the area under the curve was 0.987 and 0.921, respectively, for training and testing data).

Performance Analysis of IoT-Based Health and Environment WSN Deployment.

With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.

Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods.

This paper investigates the capabilities of the evolutionary fuzzy genetic (FG) approach and compares it with three neuro-fuzzy methods-neuro-fuzzy with grid partitioning (ANFIS-GP), neuro-fuzzy with subtractive clustering (ANFIS-SC), and neuro-fuzzy with fuzzy c-means clustering (ANFIS-FCM)-in terms of modeling long-term air temperatures for sustainability based on geographical information. In this regard, to estimate long-term air temperatures for a 40-year (1970-2011) period, the models were developed using data for the month of the year, latitude, longitude, and altitude obtained from 71 stations in Turkey. The models were evaluated with respect to mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and the determination coefficient (R2). All data were divided into three parts and every model was tested on each. The FG approach outperformed the other models, enhancing the MAE, RMSE, NSE, and R2 of the ANFIS-GP model, which yielded the highest accuracy among the neuro-fuzzy models by 20%, 30%, and 4%, respectively. A geographical information system was used to obtain temperature maps using estimates of the optimal models, and the results of the model were assessed using it.

A Survey of Marker-Less Tracking and Registration Techniques for Health & Environmental Applications to Augmented Reality and Ubiquitous Geospatial Information Systems.

Most existing augmented reality (AR) applications are suitable for cases in which only a small number of real world entities are involved, such as superimposing a character on a single surface. In this case, we only need to calculate pose of the camera relative to that surface. However, when an AR health or environmental application involves a one-to-one relationship between an entity in the real-world and the corresponding object in the computer model (geo-referenced object), we need to estimate the pose of the camera in reference to a common coordinate system for better geo-referenced object registration in the real-world. New innovations in developing cheap sensors, computer vision techniques, machine learning, and computing power have helped to develop applications with more precise matching between a real world and a virtual content. AR Tracking techniques can be divided into two subcategories: marker-based and marker-less approaches. This paper provides a comprehensive overview of marker-less registration and tracking techniques and reviews their most important categories in the context of ubiquitous Geospatial Information Systems (GIS) and AR focusing to health and environmental applications. Basic ideas, advantages, and disadvantages, as well as challenges, are discussed for each subcategory of tracking and registration techniques. We need precise enough virtual models of the environment for both calibrations of tracking and visualization. Ubiquitous GISs can play an important role in developing AR in terms of providing seamless and precise spatial data for outdoor (e.g., environmental applications) and indoor (e.g., health applications) environments.

Enhancing response coordination through the assessment of response network structural dynamics.

Preparing for intensifying threats of emergencies in unexpected, dangerous, and serious natural or man-made events, and consequent management of the situation, is highly demanding in terms of coordinating the personnel and resources to support human lives and the environment. This necessitates prompt action to manage the uncertainties and risks imposed by such extreme events, which requires collaborative operation among different stakeholders (i.e., the personnel from both the state and local communities). This research aims to find a way to enhance the coordination of multi-organizational response operations. To do so, this manuscript investigates the role of participants in the formed coordination response network and also the emergence and temporal dynamics of the network. By analyzing an inter-personal response coordination operation to an extreme bushfire event, the networks' and participants' structural change is evaluated during the evolution of the operation network over four time durations. The results reveal that the coordination response network becomes more decentralized over time due to the high volume of communication required to exchange information. New emerging communication structures often do not fit the developed plans, which stress the need for coordination by feedback in addition to by plan. In addition, we find that the participant's brokering role in the response operation network identifies a formal and informal coordination role. This is useful for comparison of network structures to examine whether what really happens during response operations complies with the initial policy.

Volumetric Analysis of 3-D-Cultured Colonies in Wet Alginate Spots Using 384-Pillar Plate.

The volumetric analysis of three-dimensional (3-D)-cultured colonies in alginate spots has been proposed to increase drug efficacy. In a previously developed pillar/well chip platform, colonies within spots are usually stained and dried for analysis of cell viability using two-dimensional (2-D) fluorescent images. Since the number of viable cells in colonies is directly related to colony volume, we proposed the 3-D analysis of colonies for high-accuracy cell viability calculation. The spots were immersed in buffer, and the 3-D volume of each colony was calculated from the 2-D stacking fluorescent images of the spot with different focal positions. In the experiments with human gastric carcinoma cells and anticancer drugs, we compared cell viability values calculated using the 2-D area and 3-D volume of colonies in the wet and dried alginate spots, respectively. The IC50 value calculated using the 3-D volume of the colonies (9.5 µM) was less than that calculated in the 2-D area analysis (121.5 µM). We observed that the colony showed a more sensitive drug response regarding volume calculated from the 3-D image reconstructed using several confocal images than regarding colony area calculated in the 2-D analysis.

Strategies for sustainable development of industrial park in Ulsan, South Korea--from spontaneous evolution to systematic expansion of industrial symbiosis.

The Korea National Cleaner Production Center (KNCPC) affiliated to the Korea Institute of Industrial Technology (KITECH) has started a 15 year, 3-phase EIP master plan with the support of Ministry of Commerce, Industry, and Energy (MOCIE). A total of 6 industrial parks, including industrial parks in Ulsan city, known as the industrial capital of South Korea, are planning projects to find the feasibility of shifting existing industrial parks to eco-industrial parks. The basic survey shows that Ulsan industrial complex has been continuously evolving from conventional industrial complexes to eco-industrial parks by spontaneous industrial symbiosis. This paper describes the Korean national policies and the developmental activities of this vision to drive the global trend of innovation for converting the existing industrial parks to eco-industrial parks through inter-industry waste, energy, and material exchange in Ulsan Industrial complexes. In addition, the primary and supportive components of the Ulsan EIP pilot project, which will be implemented for 5 years is elaborated with its schedules and economic benefits.