Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban development.Traditional mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely data.Recent advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations.Nonetheless,challenges persist,particularly concerning accuracy and significant temporal delays.This study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing data.This methodology can facilitate the accurate and timely identification of urban redevelopment activities.The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment.The model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban redevelopment.This research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban development.The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

Network traffic classification:Techniques,datasets,and challenges

In network traffic classification,it is important to understand the correlation between network traffic and its causal application,protocol,or service group,for example,in facilitating lawful interception,ensuring the quality of service,preventing application choke points,and facilitating malicious behavior identification.In this paper,we review existing network classification techniques,such as port-based identification and those based on deep packet inspection,statistical features in conjunction with machine learning,and deep learning algorithms.We also explain the implementations,advantages,and limitations associated with these techniques.Our review also extends to publicly available datasets used in the literature.Finally,we discuss existing and emerging challenges,as well as future research directions.

Macroinvertebrate Community Index (MCI) and Quantitative Macroinvertebrate Community Index (QMCI) Analysis: A Comparative Study between Le Afe and Mulivaifagatoloa Rivers, Upolu Island, Samoa

The diversity of Samoa’s freshwater macroinvertebrates remains largely unexplored, with past studies focusing on specific species without comprehensive cataloguing. This research evaluated the health of Upolu Island’s rural rivers through macroinvertebrate analysis, particularly in the Le Afe and Mulivaifagatoloa Rivers. Collaborating with Samoa’s Water Resources Division in the Ministry of Natural Resources and Environment (MNRE), three sites along each river were sampled, representing a gradient from pristine to anthropogenically impacted areas. A total of 2953 macroinvertebrates were collected and classified into five categories using established identification keys. The Macroinvertebrate Community Index (MCI) and Quantitative Macroinvertebrate Community Index (QMCI) were applied for analysis. The results showed no clear pattern of pollutant-sensitive species prevalence or decline in less disturbed rivers. High MCI scores with low QMCI values indicated numerous low-scoring species, while the opposite suggested a richness of high-scoring taxa. Although MCI and QMCI are tools for monitoring freshwater health, this study lays the groundwork for future research to categorize Samoan macroinvertebrates and assign tolerance scores based on their presence in varying river conditions. .

CNN-based segmentation frameworks for structural component and earthquake damage determinations using UAV images

Buildings undergo various kinds of structural damage during earthquakes,and damage detection and functional assessment of these structures in the aftermath of the events have been challenging issues.Under these circumstances,computer vision techniques offer a promising solution by automating the inspection process.This study presents an effective methodology for automatic structural components and damage detection using unmanned aerial vehicle(UAV)images of damaged buildings.Two types of neural network architectures are considered for appropriate feature extractions in different task detections.The feature pyramid network(FPN)is employed for crack,spall,rebar,and component damage segmentation,while the UNet++network is utilized for the damage state.For network training and validation,a total of 3805 original images of size 1920×1080 pixels are processed by the proposed method and reduced the image pixels.From the FPN,the achieved highest intersection over unions(IoUs)were 0.59,0.93,0.42,and 0.99 for crack,spall,rebar,and components,respectively.These predicted labels were found in close agreement with the labels.Similarly,the UNet++recognized the semantic information and damage state with an IoU of 0.72.This demonstrated the applicability of the proposed method for automated post-earthquake building inspection process accurately without information loss from the original images.

Indicator Selection for Quality Measurement in Maternal Neonatal and Child Health Services: Application of Random Forest Classifier

Quality of Maternal, Neonatal and Child (MNCH) care is an important aspect in ensuring healthy outcomes and survival of mothers and children. To maintain quality in health services provided, organizations and other stakeholders in maternal and child health recommend regular quality measurement. Quality indicators are the key components in the quality measurement process. However, the literature shows neither an indicator selection process nor a set of quality indicators for quality measurement that is universally accepted. The lack of a universally accepted quality indicator selection process and set of quality indicators results in the establishment of a variety of quality indicator selection processes and several sets of quality indicators whenever the need for quality measurement arises. This adds extra processes that render quality measurement process. This study, therefore, aims to establish a set of quality indicators from a broad set of quality indicators recommended by the World Health Organization (WHO). The study deployed a machine learning technique, specifically a random forest classifier to select important indicators for quality measurement. Twenty-nine indicators were identified as important features and among those, eight indicators namely maternal mortality ratio, still-birth rate, delivery at a health facility, deliveries assisted by skilled attendants, proportional breach delivery, normal delivery rate, born before arrival rate and antenatal care visit coverage were identified to be the most important indicators for quality measurement.

Assessment of Ground Water Dynamics and Potential Zones in Urban Areas: A Case Study of Voi Town, Kenya

Water plays a role in sustaining all the biotic elements. Unfortunately, in the recent times with persistent climate change impacts, parts of the world are facing cases of inadequate water causing stress and increased vulnerability among the people. This is the case with urban areas across the globe as their populations keep increasing with little to no attention paid to urban planning that allows sustainable management of resources amidst rapid development. Urban areas are surrounded by high yielding aquifers that have better water services from groundwater. However, the urban sprawl phenomena have limited attempts in assessing ground water potential in urban areas contributing to urban water scarcity. Therefore, the study aims to look at the problem of urban water scarcity, by analyzing the levels and distribution of groundwater in Voi town using remote sensing and GIS techniques, in order to suggest suitable sites for underground water exploration in regard to the overall urban water supply. From the analysis, the results showed that the area majorly has low to potential zones of groundwater. High potential areas were very few and were mostly on the western side of the area. Very low potential zones were seen on the east and north side of the area.

Personality Trait Detection via Transfer Learning

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications.

Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers

Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008-2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota （SD） U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit （PSU） level based on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service （NASS） ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency （FSA） Common Land Unit （CLU） and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.

Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer

Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change, which requires accurate assessment to quantify the damages. Various remote sensing products and indices have been used in the past for this purpose. This paper utilizes the moderate resolution imaging spectroradiometer （MODIS） weekly normalized difference vegetation index （NDVI） product to detect and further quantify flood damages on corn within the major corn producing states in the Midwest region of the US. County-level analyses were performed by taking weighted average of all pure corn pixels （〉90%） masked by the United States Department of Agriculture （USDA） Cropland Data Layer （CDL）. The NDVI-based time-series difference between flood years and normal year （median of years 2000-2014） was used to detect flood occur- rences. To further measure the impact of the flood on corn yield, regression analysis between change in NDVI and change in corn yield as independent and dependent variables respectively was performed for 30 different flooding events within growing seasons of the corn. With the R2 value of 0.85, the model indicates statistically significant linear relation between the NDVI and corn yield. Testing the predictability of the model with 10 new cases, the average relative error of the model was only 4.47%. Furthermore, small error （4.8%） of leave-one-out cross validation （LOOCV） along with smaller statistical error indicators （root mean square error （RMSE）, mean absolute error （MAE）, and mean bias error （MBE））, further validated the accuracy of the model. Utilizing the linear regression approach, change in NDVI during the growing season of corn appeared to be a good indicator to quantify the yield loss due to flood. Additionally, with the 250 m MODIS-based NDVI, these yield losses can be estimated up to field level.

Inventory of Atmospheric Pollutant Emissions from Burning of Crop Residues in China Based on Satellite-retrieved Farmland Data

The burning of crop residues emits large quantities of atmospheric aerosols.Published studies have developed inventories of emissions from crop residue burning based on statistical data.In contrast,this study used satellite-retrieved land-cover data(1 km×1 km)as activity data to compile an inventory of atmospheric pollutants emitted from the burning of crop residues in China in 2015.The emissions of PM10,PM2.5,VOCs,NOx,SO2,CO,and NH3 from burning crop straw on nonirrigated farmland in China in 2015 were 610.5,598.4,584.4,230.6,35.4,3329.3,and 36.1 Gg(1 Gg=109 g),respectively;the corresponding emissions from burning paddy rice residues were 234.1,229.7,342.3,57.5,57.5,1122.1,and 21.5 Gg,respectively.The emissions from crop residue burning showed large spatial and temporal variations.The emissions of particulate matter and gaseous pollutants from crop residue burning in nonirrigated farmland were highest in east China,particularly in Shandong,Henan,Anhui,and Sichuan provinces.Emissions from burning paddy rice residue were highest in east and central China,with particularly high levels in Shandong,Jiangsu,Zhejiang,and Hunan provinces.The monthly variations in atmospheric pollutant emissions were similar among different regions,with the highest levels observed in October in north,northeast,northwest,east,and southwest China and in June and July in central and south China.The developed inventory of emissions from crop residue burning is expected to help improve air quality models by providing high-resolution spatial and temporal data.

Smart Cities as Cyber-Physical Social Systems

The emerging prototype for a Smart City is one of an urban environment with a new generation of inno- vative services for transportation, energy distribution, healthcare, environmental monitoring, business, commerce, emergency response, and social activities. Enabling the technology for such a setting re- quires a viewpoint of Smart Cities as cyber-physical systems （CPSs） that include new software platforms and strict requirements for mobility, security, safety, privacy, and the processing of massive amounts of information. This paper identifies some key defining characteristics of a Smart City, discusses some lessons learned from viewing them as CPSs, and outlines some fundamental research issues that remain largely open.

RF-CLASS： A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSDIS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.

Seismicity,structure and tectonics in the Arctic region

The "Arctic" region,where the North Pole occupies the center of the Arctic Ocean,has been affecting the environmental variation of the Earth from geological time to the present.However,the seismic activities in the area are not adequately monitored.Therefore,by conducting long term monitoring of seismic phenomenon as sustainable parameters,our understanding of both the tectonic evolution of the Earth and the dynamic interaction between the cryosphere and geosphere in surface layers of the Earth will increase.In this paper,the association of the seismicity and structure of the Arctic region,particularly focused on Eurasian continent and surrounding oceans,and its relationship with regional evolution during the Earth’s history is studied.The target areas cover representative tectonic provinces in the Eurasian Arctic,such as the wide area of Siberia,Baikal Rift Zone.Far East Russia,Arctic Ocean together with Greenland and Northern Canada.Based on discussion including characteristics of seismicity,heterogeneous structure of the crust and upper mantle,tectonic history and recent dynamic features of the Earth’s surface in the Arctic are summarized.

Infrasound Signals and Their Source Location Inferred from Array Deployment in the Lützow-Holm Bay Region, East Antarctica: January-June 2015

Characteristic features of infrasound waves observed in the Antarctic represent a physical interaction relating surface environment in the continental margin and surrounding Southern Ocean. Source location of several infrasound events is demonstrated by using combination of two array deployments along a coast of the Lützow-Holm Bay (LHB), East Antarctica, for data retrieving period in January-June 2015. These infrasound arrays being established in January 2013 clearly detected temporal variations in frequency content and propagation direction of the identified seven large events. Many of these sources are assumed to have cryoseismic origins;the ice-quakes associated with calving of glaciers, discharge of sea-ice, collision between sea-ice and icebergs around the LHB. Detail and continuous measurements of infrasound waves in the Antarctic are a proxy for monitoring regional environment as well as climate change in high southern latitude.

Data Security Issues and Challenges in Cloud Computing: A Conceptual Analysis and Review

Cloud computing is a set of Information Technology services offered to users over the web on a rented base. Such services enable the organizations to scale-up or scale-down their in-house foundations. Generally, cloud services are provided by a third-party supplier who possesses the arrangement. Cloud computing has many advantages such as flexibility, efficiency, scalability, integration, and capital reduction. Moreover, it provides an advanced virtual space for organizations to deploy their applications or run their operations. With disregard to the possible benefits of cloud computing services, the organizations are reluctant to invest in cloud computing mainly due to security concerns. Security is one of the main challenges that hinder the growth of cloud computing. At the same time, service providers strive to reduce the risks over the clouds and increase their reliability in order to build mutual trust between them and the cloud customers. Various security issues and challenges are discussed in this research, and possible opportunities are stated.

Seismic Tremors and Their Relation to Cryosphere Dynamics in April 2015 around the Lützow-Holm Bay, East Antarctica

Characteristics and statistics of seismic tremors occurring during April 2015 were investigated by using short-period and broadband seismographs deployed at Syowa Station (SYO), in the Lützow-Holm Bay (LHB), East Antarctica. In order to examine a relationship between surface environments in particular cryosphere variation, the MODIS satellite images were utilized for comparison with the detected tremor events. Since a large volume of sea-ice was discharged during the April, together with several large icebergs passed through from the west to the east at northern edge of the fast sea-ice of LHB, it was expected to detect seismic tremors involving cryospehre dynamics. During the month, a total number of 49 tremor events including short duration ice shocks were identified. Majority of the events (N = 39) had their duration times more than 15 minutes, which were divided into both tremors and ice shocks on the basis of experienced definition at SYO. Cryospheric sources recorded by seismic tremors were classified into several origins (collision, calving, crevassing, crashing, etc.): “crevassing events” along the large cracks inside the fast sea-ice in LHB (04 April), “discharge events” of fast sea-ice from the Bay (07 April), “collision events” between iceberg and the edge of fast sea-ice (14 April), “crashing movement” between fragmentation of fast sea-ice and packed sea-ice (18 April), and other origins. In particular, strong amplitude tremors with harmonic overtones were assumed to be occurred independently from whether condition, because these overtone tremors were identified at less stormy days by comparison with infrasound data at SYO.

Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multicriteria

Rainfall induced landslides are a common threat to the communities living on dangerous hillslopes in Chittagong Metropolitan Area, Bangladesh. Extreme population pressure, indiscriminate hill cutting, increased precipitation events due to global warming and associated unplanned urbanization in the hills are exaggerating landslide events. The aim of this article is to prepare a scientifically accurate landslide susceptibility map by combining landslide initiation and runout maps. Land cover, slope, soil permeability, surface geology, precipitation, aspect, and distance to hill cut, road cut, drainage and stream network factor maps were selected by conditional independence test. The locations of 56 landslides were collected by field surveying. A weight of evidence(Wo E) method was applied to calculate the positive(presence of landslides) and negative(absence of landslides) factor weights. A combination of analytical hierarchical process(AHP) and fuzzymembership standardization(weighs from 0 to 1) was applied for performing a spatial multi-criteria evaluation. Expert opinion guided the decision rule for AHP. The Flow-R tool that allows modeling landslide runout from the initiation sources was applied. The flow direction was calculated using the modified Holmgren's algorithm. The AHP landslide initiation and runout susceptibility maps were used to prepare a combined landslide susceptibility map. The relative operating characteristic curve was used for model validation purpose. The accuracy of Wo E, AHP, and combined susceptibility map was calculated 96%, 97%, and 98%, respectively.

Nutritional Epidemiological Study to Estimate Usual Intake and to Define Optimum Nutrient Profiling Choice in the Diet of Egyptian Youths

Objectives: To define optimum food and nutrient profiling in gender-specific and age group-specific variant regression models. Setting: 481 subjects of both sexes (18.4 years old) from Giza urban were set. Design: Dietary assessment used the 24-h dietary recall data to calculate the estimated energy and (24) nutrients eaten by each individual. Four indices—food variety diversity score, healthy eating index (HEI), mean probability of nutrients adequacy (MPA) and nutrient rich food (NRF9.3) index score were used for assessing the profiling of the diet. Results: A total of 163 individual food items were consumed by the participants within the 24-h dietary recall with an average daily intake of (6.6) different food varieties. Grains were the top contributors of energy and 10 macro and micro nutrients followed by the meat group. Based on the MPA data, the mean acceptable intake (AI) of dietary calcium (32.9%) and vitamin C (30%) were limiting in the diet. The diet profiling consumed by the teenagers aged 14.8 years was inferior compared to that consumed by subjects aging 23.9 years. Linear regression analyses were conducted between the 4 indices as the dependent variable and all possible combinations of 16 nutrients of interest as independent variables. NRF9.3 was the optimum nutrient index and correlated negatively with markers of abdominal obesity. Conclusion: Implementation of nutrition intervention program was directed to youths to include age appropriate good healthy foods to decrease the risk of nutrient deficiencies.

Modelling and Optimal Design of Hybrid Power System Photovoltaic/Solid Oxide Fuel Cell for a Mediterranean City

This work presents a hybrid power system consisting of photovoltaic and solid oxide fuel cell(PV-SOFC)for electricity production and hydrogen production.The simulation of this hybrid system is adjusted for Bou-Zedjar city in north Algeria.Homer software was used for this simulation to calculate the power output and the total net present cost.The method used depends on the annual average monthly values of clearness index and radiation for which the energy contributions are determined for each component of PV/SOFC hybrid system.The economic study is more important criterion in the proposed hybrid system,and the results show that the cost is very suitable for the use of this hybrid system,which ensures that the area is fed continuously with the sufficient energy for the load which assumed to be 500 kW in the peak season.The optimized results of the present study show that the photovoltaic is capable of generating 8733 kW electricity while the SOFC produces 500 kW electricity.The electrolyzer is capable of producing 238750 kg of hydrogen which is used as fuel in the SOFC to compensate the energy lack in nights and during peak season.

Some Features of Neural Networks as Nonlinearly Parameterized Models of Unknown Systems Using an Online Learning Algorithm

This paper deals with deriving the properties of updated neural network model that is exploited to identify an unknown nonlinear system via the standard gradient learning algorithm. The convergence of this algorithm for online training the three-layer neural networks in stochastic environment is studied. A special case where an unknown nonlinearity can exactly be approximated by some neural network with a nonlinear activation function for its output layer is considered. To analyze the asymptotic behavior of the learning processes, the so-called Lyapunov-like approach is utilized. As the Lyapunov function, the expected value of the square of approximation error depending on network parameters is chosen. Within this approach, sufficient conditions guaranteeing the convergence of learning algorithm with probability 1 are derived. Simulation results are presented to support the theoretical analysis.