Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach.

This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the fruit fly optimization algorithm (CatBoost-FOA), to spatially assess and map noise pollution prone areas in Tehran city, Iran. To spatially model areas susceptible to noise pollution, we established a comprehensive spatial database encompassing data for the annual average Leq (equivalent continuous sound level) from 2019 to 2022. This database was enriched with critical spatial criteria influencing noise pollution, including urban land use, traffic volume, population density, and normalized difference vegetation index (NDVI).

Alzheimer's disease diagnosis in the metaverse.

Background And Objective: The importance of early diagnosis of Alzheimer's Disease (AD) is by no means negligible because no cure has been recognized for it rather than some therapies only lowering the pace of progression. The research gap reveals information on the lack of an automatic non-invasive approach toward the diagnosis of AD, in particular with the help of Virtual Reality (VR) and Artificial Intelligence. Another perspective highlights that current VR studies fail to incorporate a comprehensive range of cognitive tests and consider design notes for elderlies, leading to unreliable results.

Epidemic exposure risk assessment in digital contact tracing: A fuzzy logic approach.

Background: Bluetooth low energy (BLE)-based contact-tracing applications were widely used during the COVID-19 pandemic. However, the use of only the received signal strength feature for proximity calculations may not be adaptable to different virus variants or scalable for other potential epidemic diseases.

Objective: This study presents a novel framework in regard to evaluating and classifying personal exposure risk that considers both contact features, which include distance and length of contact, and environment features, which include crowd size and the number of recently infected cases in the environment.

Solving water scarcity challenges in arid regions: A novel approach employing human-based meta-heuristics and machine learning algorithm for groundwater potential mapping.

Addressing water scarcity challenges in arid regions is a pressing concern and demands innovative solutions for accurate groundwater potential mapping (GPM). This study presents a comprehensive evaluation of advanced modeling techniques to enhance the precision of GPM. This study, conducted in the Zayandeh Rood watershed, Iran, employed a spatial database comprising 16 influential factors on groundwater potential and data from 175 wells.

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.

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.

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.

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.

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).

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)).

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.

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.

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.

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.

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.

Revisiting the Proportional Recovery Model in View of the Ceiling Effect of Fugl-Meyer Assessment.

Background And Purpose: The aim of this study was to verify the validity of the proportional recovery model in view of the ceiling effect of the Fugl-Meyer Assessment.

Methods: We reviewed the medical records of patients enrolled in the Korean Stroke Cohort for Functioning and Rehabilitation between August 2012 and May 2015. Recovery proportion was defined as the actual change in Fugl-Meyer Assessment score of the upper extremity between 7 days and 6 months poststroke, relative to the initial neurological impairment.

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 (PM and PM), nitrogen dioxide (NO), sulfur dioxide (SO), and ozone (O) in four-seasons (spring, summer, autumn, and winter).

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.

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.

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).

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.

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.

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.

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.