Search results for: forest restoration

Authors: Marielcio Lacerda, Angelo Paulino, Elcio Shiguemori, Alvaro Damiao, Lamartine Guimaraes, Camila Anjos

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Accurate information about the terrain is extremely important in disaster management activities or conflict. This paper proposes the use of the Unmanned Aircraft Systems (UAS) at the identification of Airdrop Zones (AZs) and Helicopter Landing Zones (HLZs). In this paper we consider the AZs the zones where troops or supplies are dropped by parachute, and HLZs areas where victims can be rescued. The use of digital image processing enables the automatic generation of an orthorectified mosaic and an actual Digital Surface Model (DSM). This methodology allows obtaining this fundamental information to the terrain’s comprehension post-disaster in a short amount of time and with good accuracy. In order to get the identification and classification of AZs and HLZs images from DJI drone, model Phantom 4 have been used. The images were obtained with the knowledge and authorization of the responsible sectors and were duly registered in the control agencies. The flight was performed on May 24, 2017, and approximately 1,300 images were obtained during approximately 1 hour of flight. Afterward, new attributes were generated by Feature Extraction (FE) from the original images. The use of multispectral images and complementary attributes generated independently from them increases the accuracy of classification. The attributes of this work include the Declivity Map and Principal Component Analysis (PCA). For the classification four distinct classes were considered: HLZ 1 – small size (18m x 18m); HLZ 2 – medium size (23m x 23m); HLZ 3 – large size (28m x 28m); AZ (100m x 100m). The Decision Tree method Random Forest (RF) was used in this work. RF is a classification method that uses a large collection of de-correlated decision trees. Different random sets of samples are used as sampled objects. The results of classification from each tree and for each object is called a class vote. The resulting classification is decided by a majority of class votes. In this case, we used 200 trees for the execution of RF in the software WEKA 3.8. The classification result was visualized on QGIS Desktop 2.12.3. Through the methodology used, it was possible to classify in the study area: 6 areas as HLZ 1, 6 areas as HLZ 2, 4 areas as HLZ 3; and 2 areas as AZ. It should be noted that an area classified as AZ covers the classifications of the other classes, and may be used as AZ, HLZ of large size (HLZ3), medium size (HLZ2) and small size helicopters (HLZ1). Likewise, an area classified as HLZ for large rotary wing aircraft (HLZ3) covers the smaller area classifications, and so on. It was concluded that images obtained through small UAV are of great use in calamity situations since they can provide data with high accuracy, with low cost, low risk and ease and agility in obtaining aerial photographs. This allows the generation, in a short time, of information about the features of the terrain in order to serve as an important decision support tool.

Keywords: disaster management, unmanned aircraft systems, helicopter landing zones, airdrop zones, random forest

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Authors: M. D. D. Chathurangi, M. G. K. Nayanathara, K. M. H. M. M. Gunapala, G. M. R. G. Dayananda, Kavinga Yapa Abeywardena, Deemantha Siriwardana

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Due to the growing popularity of social media platforms at present, there are various concerns, mostly cyberbullying, spam, bot accounts, and the spread of incorrect information. To develop a risk score calculation system as a thorough method for deciphering and exposing unethical social media profiles, this research explores the most suitable algorithms to our best knowledge in detecting the mentioned concerns. Various multiple models, such as Naïve Bayes, CNN, KNN, Stochastic Gradient Descent, Gradient Boosting Classifier, etc., were examined, and the best results were taken into the development of the risk score system. For cyberbullying, the Logistic Regression algorithm achieved an accuracy of 84.9%, while the spam-detecting MLP model gained 98.02% accuracy. The bot accounts identifying the Random Forest algorithm obtained 91.06% accuracy, and 84% accuracy was acquired for fake news detection using SVM.

Keywords: cyberbullying, spam behavior, bot accounts, fake news, machine learning

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Authors: Yuanjin Liu

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Retirement withdrawal strategies are very important to minimize the probability of ruin in retirement. The ruin probability is modeled as a function of initial withdrawal age, gender, asset allocation, inflation rate, and initial withdrawal rate. The ruin probability is obtained based on the 2019 period life table for the Social Security, IRS Required Minimum Distribution (RMD) Worksheets, US historical bond and equity returns, and inflation rates using simulation. Several popular machine learning algorithms of the generalized additive model, random forest, support vector machine, extreme gradient boosting, and artificial neural network are built. The model validation and selection are based on the test errors using hyperparameter tuning and train-test split. The optimal model is recommended for retirees to monitor the ruin probability. The optimal withdrawal strategy can be obtained based on the optimal predictive model.

Keywords: ruin probability, retirement withdrawal strategies, predictive models, optimal model

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Authors: Thomas, Daniel Tajo, Kish

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Sight is primarily associated with the function of gathering and processing near and extended spatial information which is largely used to support self-determined interaction with the environment through self-directed movement and navigation. By contrast, hearing is primarily associated with the function of gathering and processing sequential information which may typically be used to support self-determined communication through the self-directed use of music and language. Blindness or the lack of vision is traditionally characterized by a lack of capacity to access spatial information which, in turn, is presumed to result in a lack of capacity for self-determined interaction with the environment due to limitations in self-directed movement and navigation. However, through a specific protocol of FlashSonar education developed by World Access for the Blind, the function of hearing can be expanded in blind people to carry out some of the functions normally associated with sight, that is to access and process near and extended spatial information to construct three-dimensional acoustic images of the environment. This perceptual education protocol results in a significant restoration in blind people of self-determined environmental interaction, movement, and navigational capacities normally attributed to vision - a new way to see. Thus, by expanding the function of hearing to process spatial information to restore self-determined movement, we are not only changing the meaning of blindness, and what it means to be blind, but we are also recasting the meaning of vision and what it is to see.

Keywords: echolocation, changing, sensory, function

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Authors: Vincent Liu

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Machine learning algorithms (ML) can achieve high accuracy in predicting outcomes compared to classical models. Metaheuristic, nature-inspired algorithms can enhance traditional ML algorithms by optimizing them such as by performing feature selection. We compare ten ML algorithms to predict 30-day hospital readmission rates for diabetes patients in the US using a dataset from UCI Machine Learning Repository with feature selection performed by Greywolf nature-inspired algorithm. The baseline accuracy for the initial random forest model was 65%. After performing feature engineering, SMOTE for class balancing, and Greywolf optimization, the machine learning algorithms showed better metrics, including F1 scores, accuracy, and confusion matrix with improvements ranging in 10%-30%, and a best model of XGBoost with an accuracy of 95%. Applying machine learning this way can improve patient outcomes as unnecessary rehospitalizations can be prevented by focusing on patients that are at a higher risk of readmission.

Keywords: diabetes, machine learning, 30-day readmission, metaheuristic

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Authors: N. Moazami, R. Ranjbar, A. Ashori

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Out of hundreds of microalgal strains reported, only very few of them are capable for production of high content of lipid. Therefore, the key technical challenges include identifying the strains with the highest growth rates and oil contents with adequate composition, which were the main aims of this work. From 147 microalgae screened for high biomass and oil productivity, the Nannochloropsis sp. PTCC 6016, which attained 52% lipid content, was selected for large scale cultivation in Persian Gulf Knowledge Island. Nannochloropsis strain PTCC 6016 belongs to Eustigmatophyceae (Phylum heterokontophyta) isolated from Mangrove forest area of Qheshm Island and Persian Gulf (Iran) in 2008. The strain PTCC 6016 had an average biomass productivity of 2.83 g/L/day and 52% lipid content. The biomass productivity and the oil production potential could be projected to be more than 200 tons biomass and 100000 L oil per hectare per year, in an outdoor algal culture (300 day/year) in the Persian Gulf climate.

Keywords: biofuels, microalgae, Nannochloropsis, raceway open pond, bio-jet

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Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

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Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

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Authors: Hakim Bachiri, Mohammed Znari, Moulay Abdeljalil Ait Baamranne

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Population characteristics of Atlas Barbary sheep (Ammotragus lervia lervia) were investigated 20 years following the 1999 introduction of 10 individuals into the fenced nature reserve of Amassine, High Atlas range, Morocco, for promoting wildlife watching and tourism. Population age-sex structure and density were determined in late winter-early spring during four consecutive years (2016-2019) by direct observation before the dispersal of the herd. In this latter case, the line transect distance sampling was successfully applied. Population size increased from 37 to 62 animals during the four-year study period; the maximal population size being 82 individuals recorded in 2006. An estimated population density ranged from 0.25 to 0.41 Barbary sheep/ha during the study period. The adult sex ratio varied from 91 to 67 per 100 females. The apparent birth rate was 14 to 73/100 females. Juveniles and subadults comprised 27-43% of the population, adult males 26-31% and adult females 29-45%. The survival rate from birth to 1 year of age approximated 35%, for adult males was estimated to average 69%/year. The obtained results would be helpful for developing sustainable population management and habitat restoration plan and assessing the feasibility of potential reintroduction/restocking in other areas of the Atlas range.

Keywords: atlas mountains, barbary sheep, demography, management

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Authors: Rana Parween, Rob Marchant

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With the continued loss of global biodiversity despite the application of modern conservation techniques, it has become crucial to investigate non-conventional methods. Accelerated destruction of ecosystems due to altered land use, climate change, cultural and social change, necessitates the exploration of society-biodiversity attitudes and links. While the loss of species and their extinction is a well-known and well-documented process that attracts much-needed attention from researchers, academics, government and non-governmental organizations, the loss of traditional ecological knowledge and practices is more insidious and goes unnoticed. The growing availability of 'indirect experiences' such as the internet and media are leading to a disaffection towards nature and the 'Extinction of Experience'. Exacerbated by the lack of documentation of traditional practices and skills, there is the possibility for the 'extinction' of traditional practices and skills before they are fully recognized and captured. India, as a mega-biodiverse country, is also known for its historical conservation strategies entwined in traditional beliefs. Indigenous communities hold skillsets, knowledge, and traditions that have accumulated over multiple generations and may play an important role in conserving biodiversity today. This study explores the differences in knowledge and attitudes towards conserving biodiversity, of three different stakeholder groups living around Jim Corbett National Park, based on their age, traditions, and association with the protected area. A triangulation designed multi-strategy investigation collected qualitative and quantitative data through a questionnaire survey of village elders, the general public, and forest officers. Following an inductive approach to analyzing qualitative data, the thematic content analysis was followed. All coding and analysis were completed using NVivo 11. Although the village elders and some general public had vast amounts of traditional knowledge, most of it was related to animal husbandry and the medicinal value of plants. Village elders were unfamiliar with the concept of the term ‘biodiversity’ albeit their way of life and attitudes ensured that they care for the ecosystem without having the scientific basis underpinning biodiversity conservation. Inherently, village elders were keen to conserve nature; the superimposition of governmental policies without any tangible benefit or consultation was seen as detrimental. Alienating villagers and consequently the village elders who are the reservoirs of traditional knowledge would not only be damaging to the social network of the area but would also disdain years of tried and tested techniques held by the elders. Forest officers advocated for biodiversity and conservation education for women and children. Women, across all groups, when questioned about nature conservation, showed more interest in learning and participation. Biodiversity not only has an ethical and cultural value, but also plays a role in ecosystem function and, thus, provides ecosystem services and supports livelihoods. Therefore, underpinning and using traditional knowledge and incorporating them into programs of biodiversity conservation should be explored with a sense of urgency.

Keywords: biological diversity, mega-biodiverse countries, traditional ecological knowledge, society-biodiversity links

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Authors: Raghad A. Altowairgi

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With the wide spread of social media platforms, young people tend to use them extensively as the first means of communication due to their ease and modernity. But these platforms often create a fertile ground for bullies to practice their aggressive behavior against their victims. Platform usage cannot be reduced, but intelligent mechanisms can be implemented to reduce the abuse. This is where machine learning comes in. Understanding and classifying text can be helpful in order to minimize the act of cyberbullying. Artificial intelligence techniques have expanded to formulate an applied tool to address the phenomenon of cyberbullying. In this research, machine learning models are built to classify text into two classes; cyberbullying and non-cyberbullying. After preprocessing the data in 4 stages; removing characters that do not provide meaningful information to the models, tokenization, removing stop words, and lowering text. BoW and TF-IDF are used as the main features for the five classifiers, which are; logistic regression, Naïve Bayes, Random Forest, XGboost, and Catboost classifiers. Each of them scores 92%, 90%, 92%, 91%, 86% respectively.

Keywords: cyberbullying, machine learning, Bag-of-Words, term frequency-inverse document frequency, natural language processing, Catboost

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Authors: Hemant Kumar, R. N. K. Sharma, A. P. Krishna

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The study has been carried out on the Saranda in Jharkhand and a part of Odisha state. Geospatial data of Hyperion, a remote sensing satellite, have been used. This study has used a wide variety of patterns related to image processing to enhance and extract the mining class of Fe and Mn ores.Landsat-8, OLI sensor data have also been used to correctly explore related minerals. In this way, various processes have been applied to increase the mineralogy class and comparative evaluation with related frequency done. The Hyperion dataset for hyperspectral remote sensing has been specifically verified as an effective tool for mineral or rock information extraction within the band range of shortwave infrared used. The abundant spatial and spectral information contained in hyperspectral images enables the differentiation of different objects of any object into targeted applications for exploration such as exploration detection, mining.

Keywords: Hyperion, hyperspectral, sensor, Landsat-8

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Authors: Norah Mohammed Alshahrani, Abdulaziz Almaleh

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Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD and they are Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then Feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by the Support Vector Machine (SVM), achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: autism spectrum disorder, machine learning, feature selection, support vector machine

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Authors: Samrat Paul, Satyajit Pahari, Krishnendu Basak, Amitava Roy

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Fishing is one of the pivotal livelihood activities, especially in developing countries. Today it is considered an important occupation for human society from the era of human settlement began. In simple terms, non-target catches of any species during fishing can be considered as ‘bycatch,’ and fishing bycatch is neither a new fishery management issue nor a new problem. Sundarban is one of the world’s largest mangrove land expanding up to 10,200 sq. km in India and Bangladesh. This largest mangrove biome resource is used by the local inhabitants commercially to run their livelihood, especially by forest fringe villagers (FFVs). In Sundarban, over-fishing, especially post larvae collection of wild Penaeus monodon, is one of the major concerns, as during the collection of P. monodon, different aquatic species are destroyed as a result of bycatch mortality which changes in productivity and may negatively impact entire biodiversity, of the ecosystem. Wild prawn seed collection gear like a small mesh sized net poses a serious threat to aquatic stocks, where the collection isn’t only limited to prawn seed larvae. As prawn seed collection processes are inexpensive, require less monetary investment, and are lucrative; people are easily engaged here as their source of income. Wildlife Trust of India’s (WTI) intervention in selected forest fringe villages of Sundarban Tiger Reserve (STR) was to estimate and reduce the mortality of aquatic bycatches by involving local communities in newly developed release method and their time engagement in prawn seed collection (PSC) by involving them in Alternate Income Generation (AIG). The study was conducted for their taxonomic identification during the period of March to October 2019. Collected samples were preserved in 70% ethyl alcohol for identification, and all the preserved bycatch samples were identified morphologically by the expertise of the Zoological Survey of India (ZSI), Kolkata. Around 74 different aquatic species, where 11 different species are molluscs, 41 fish species, out of which 31 species were identified, and 22 species of crustacean collected, out of which 18 species were identified. Around 13 different species belong to a different order, and families were unable to identify them morphologically as they were collected in the juvenile stage. The study reveals that for collecting one single prawn seed, eight individual life of associated faunas are being lost. Zero bycatch mortality is not practical; rather, collectors should focus on bycatch reduction by avoiding capturing, allowing escaping, and mortality reduction, and must make changes in their fishing method by increasing net mesh size, which will avoid non-target captures. But as the prawns are small in size (generally 1-1.5 inches in length), thus increase net size making economically less or no profit for collectors if they do so. In this case, returning bycatches is considered one of the best ways to a reduction in bycatch mortality which is a more sustainable practice.

Keywords: bycatch mortality, biodiversity, mangrove biome resource, sustainable practice, Alternate Income Generation (AIG)

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Authors: Zegrar Ahmed, Ghabi Mohamed

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The degradation of arid and semi-arid ecosystems in Algeria has become a palpable fact that only hinders progress and rural development. In these exceptionally fragile environments, the decline of vegetation is done according to an alarming increase and wind erosion dominates. The ecosystem is subjected to a long hot dry season and low annual average rainfall. The urgency of the fight against desertification is imposed by the very nature of the process that tends to self-accelerate, resulting when human intervention is not forthcoming the irreversibility situations, preventing any possibility of restoration state of these zones. These phenomena have led to different degradation processes, such as the destruction of vegetation, soil erosion, and deterioration of the physical environment. In this study, the work is mainly based on the criteria for classification and identification of physical parameters for spatial analysis and multi-sources to determine the vulnerability of major steppe formations and their impact on desertification. we used Landsat data with two different dates March 2010 and November 2014 in order to determine the changes in land cover, sand moving and land degradation for the diagnosis of the desertification Phenomenon. The application, through specific processes, including the supervised classification was used to characterize the main steppe formations. An analysis of the vulnerability of plant communities was conducted to assign weights and identify areas most susceptible to desertification. Vegetation indices are used to characterize the steppe formations to determine changes in land use.

Keywords: remote sensing, SIG, ecosystem, degradation, desertification

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Authors: Thuy Hang Le

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Traditional agroforestry systems are land-use practices still widespread in tropical and subtropical countries, while in Europe have significantly decreased due to land-use intensification, land abandonment, and urbanization. Nevertheless, scientific evidence reveals that traditional agroforestry systems significantly support biodiversity and ecosystem services and may positively contribute to socioeconomic rural regional development. We worked out a review that follows the PRISMA approach and compiled comprehensive information on traditional agroforestry systems in Europe. Based on the differentiation of different land-use systems, also considering the agricultural as well as forestry components, we compiled information regarding current distribution, management (agrodiversity), biodiversity and agrobiodiversity, ecosystem and landscape services, threats, and restoration initiatives. From a total of 3,304 studies that dealt with agroforestry systems in Europe, both “modern” (e.g., buffer strip) and “traditional” (e.g., meadow orchards), we filtered out 158 studies from 35 European countries which represent the basis for in-depth investigation. We found, for example, that the traditional pastoral agroforestry system in the Mediterranean region, the so-called Dehesa, can harbor up to 300 plant species as well as 238 bird species, of which 134 are breeding birds. With regard to carbon storage, the traditional orchard agroforestry system in Germany stocks ranged between 6.5 and 9.8 Mg C ha−1, showing significantly higher values compared to an intensively used grassland with around 3.4 to 6.7 Mg C ha−1. With the remarkably high benefit for biodiversity and ecosystem services provided, the important role and multifunctionality of traditional agroforestry systems in Europe should be acknowledged and promoted.

Keywords: biodiversity, ecosystem services, landscape services, traditional agroforestry systems

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Authors: Carla Maria Furuno Rimkus

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The technologies related to virtual reality and augmented reality in combination with mobile technologies, are being more consolidated and used each day. The increasing technological availability along with the decrease of their acquisition and maintenance costs, have favored the expansion of its use in the field of historic heritage. In this context it is focused, in this article, on the potential of mobile applications in the dissemination of the architectural heritage, using the technology of Augmented Reality. From this perspective approach, it is discussed about the process of producing an application for mobile devices on the Android platform, which combines the technologies of geometric modeling with augmented reality (AR) and access to interactive multimedia contents with cultural, social and historic information of the historic building that we take as the object of study: a block with a set of buildings built in the XVIII century, known as "Quarteirão dos Trapiches", which was modeled in 3D, coated with the original texture of its facades and displayed on AR. From this perspective approach, this paper discusses about methodological aspects of the development of this application regarding to the process and the project development tools, and presents our considerations on methodological aspects of developing an application for the Android system, focused on the dissemination of the architectural heritage, in order to encourage the tourist potential of the city in a sustainable way and to contribute to develop the digital documentation of the heritage of the city, meeting a demand of tourists visiting the city and the professionals who work in the preservation and restoration of it, consisting of architects, historians, archaeologists, museum specialists, among others.

Keywords: augmented reality, architectural heritage, geometric modeling, mobile applications

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Authors: Abdelfatah Elsenosy, Mahmoud Ebrahim

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Background: Distal radius fractures are commonly encountered in orthopedic practice, especially in elderly patients. A number of clinical papers have supported the idea that anatomic restoration of the distal end of the radius is essential to gain superior results. Aim and objectives: The aim of the study is to systematically review the literature for the management of distal end radius in elderly persons (conservative treatment versus percutaneous wire fixation) as regards radiological and functional outcomes. Subjects and methods: Studies were identified from the Medline, Cochrane, EMBASE, and Google Scholar databases were searched until 2019 using combinations of the following search terms: distal radius fracture, conservative treatment, non-operative treatment, and nonsurgical treatment, surgical treatment, operative, elderly, and older. Reference lists of relevant studies were manually searched. Results: There was no statistical significance difference between CI and PKF groups’ frequency of complication in all of the selected studies. Based on the results, we recommend more analysis regarding every parameter of the radiographic and functional results and specific complications related to each fixation need to be accomplished, which requires more Randomized controlled trials (RCTs) with high quality. Conclusion: Surgical treatment seems to be more effective distal radius fracture compared with conservative treatment when the radiographic outcomes were analyzed, and no significant differences were detected in the functional outcomes and complication rate.

Keywords: radius, fracture, surgical, RCTs, conservative, radiographic, outcomes, orthopedic

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Authors: Pietro D'Ambrosio, Roberta D'Ambrosio

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The upgrading of the architectural and urban heritage of the urban historic centers almost always involves the planning for the reuse and refunctionalization of the structures. Such interventions have complexities linked to the need to take into account the urban and social context in which the structure and its intrinsic characteristics such as historical and artistic value are inserted. To these, of course, we have to add the need to make a preliminary estimate of recovery costs and more generally to assess the economic and financial sustainability of the whole project of re-socialization. Particular difficulties are encountered during the pre-assessment of costs since it is often impossible to perform analytical surveys and structural tests for both structural conditions and obvious cost and time constraints. The methodology proposed in this work, based on a multidisciplinary and data-driven approach, is aimed at obtaining, at very low cost, reasonably priced economic evaluations of the interventions to be carried out. In addition, the specific features of the approach used, derived from the predictive analysis techniques typically applied in complex IT domains (big data analytics), allow to obtain as a result indirectly the evaluation process of a shared database that can be used on a generalized basis to estimate such other projects. This makes the methodology particularly indicated in those cases where it is expected to intervene massively across entire areas of historical city centers. The methodology has been partially tested during a study aimed at assessing the feasibility of a project for the reuse of the monumental complex of San Massimo, located in the historic center of Salerno, and is being further investigated.

Keywords: evaluation, methodology, restoration, reuse

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Authors: Yue Sun, Yuan Wang

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Most of the earliest national industries in Wuhan are located along the Hanjiang River, and Qiaokou is considered to be a gathering place for Dahankou old industrial base. Zongguan Waterworks, Pacific Soap Factory, Fuxin Flour Factory, Nanyang Tobacco Factory and other hundred-year-old factories are located along Hanjiang River in Qiaokou District, especially the Gutian Industrial Zone, which was listed as one of 156 national restoration projects at the beginning of the founding of the People’s Republic of China. After decades of development, Qiaokou has become the gathering place of the chemical industry and secondary industry, causing damage to the city and serious pollution, becoming a marginalized area forgotten by the central city. In recent years, with the accelerated pace of urban renewal, Qiaokou has been constantly reforming and innovating, and has begun drastic changes in the transformation of old cities and the development of new districts. These factories have been listed as key reconstruction projects, and a large number of industrial heritage with historical value and full urban memory have been relocated, demolished and reformed, with only a few factory buildings preserved. Through the methods of industrial archaeology, image analysis, typology and field investigation, this paper analyzes and summarizes the spatial characteristics of industrial heritage in Qiaokou District, explores urban renewal from the perspective of industrial heritage protection, and provides design strategies for the regeneration of urban industrial sites and industrial heritage.

Keywords: industrial heritage, urban renewal, protection, urban memory

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Authors: Jia-Yu Wu, Chih-Han Chang, Shu-Fen Chuang, Rong-Yang Lai

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Objective: This study was to examine the stress distribution of tooth with different class V restorations under different loading situations and geometry by 3D finite element (FE) analysis. `Methods: A series of FE models of mandibular premolars containing class V cavities were constructed using micro-CT. The class V cavities were assigned as the combinations of different cavity depths x occlusal -gingival heights: 1x2, 1x4, 2x2, and 2x4 mm. Three alveolar bone loss conditions were examined: 0, 1, and 2 mm. 200 N force was exerted on the buccal cusp tip under various directions (vertical, V; obliquely 30° angled, O; oblique and parallel the individual occlusal cavity wall, P). A 3-D FE analysis was performed and the von-Mises stress was used to summarize the data of stress distribution and maximum stress. Results: The maximal stress did not vary in different alveolar bone heights. For each geometry, the maximal stress was found at bilateral corners of the cavity. The peak stress of restorations was significantly higher under load P compared to those under loads V and O while the latter two were similar. 2x2mm cavity exhibited significantly increased (2.88 fold) stress under load P compared to that under load V, followed by 1x2mm (2.11 fold), 2x4mm (1.98 fold) and 1x4mm (1.1fold). Conclusion: Load direction causes the greatest impact on the results of stress, while the effect of alveolar bone loss is minor. Load direction parallel to the cavity wall may enhance the stress concentration especially in deep and narrow class cavities.

Keywords: class v restoration, finite element analysis, loading situation, stress

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Authors: Meshal Al-Samhan, Abdullah Al-Marshed

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Recently, there has been a tremendous increase in interest in nanotechnology applications and nanomaterials in the oilfield. In the last decade, the global increase in oil production resulted in large amounts of produced water, causing a significant problem for all producing countries and companies. This produced water deserves special attention and a study of its characteristics to understand and determine how it can be treated and later used for suitable applications such as water injection for Enhance Oil Recovery (EOR) without harming the environment. This work aims to investigate the prepared compatible mixed water (seawater and effluent water) response to nanoparticles for EOR water injection. The evaluation of different mix seawater/effluent water ratios (60/40,70/30) for their characteristics prior to nanofluid preparation using Inductive Couple Plasma (ICP) analysis, potential zeta test, and OLI software (the OLI Systems is a recognised leader in aqueous chemistry). This step of the work revealed the suitability of the water mix with a lower effluent-water ratio. Also, OLI predicted that the 60:40 mix needs to be balanced around temperatures of 70 ºC to avoid the mass accumulation of calcium sulfate and strontium sulfate. Later the prepared nanofluid was tested for interfacial tension (IFT) and wettability restoration in the sandstone rock; the Al2O3 nanofluid at 0.06 wt% concentration reduced the IFT by more than 16% with moderate water wet contact angle. The study concluded that the selected nanoparticle Al2O3 had demonstrated excellent performance in decreasing the interfacial tension with respect to the selected water mix type (60/40) at low nanoparticles wt%.

Keywords: nano AL2O3, sanstone, nanofluid, IFT, wettability

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Authors: Kamyar Tolouei, Ehsan Moosavi

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In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework.

Keywords: grade uncertainty, metaheuristic algorithms, open-pit mine, production scheduling optimization

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Authors: E. Marei, A. Hammad, S. Ismail, A. El-Gindy

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This study aims to investigate the ability of different formula of mixed bacteria as a biological treatments of wastewater after primary treatment as a bio-treatment and bio-removal and bio-adsorbent of different heavy metals in natural circumstances. The wastewater was collected from Sarpium forest site-Ismailia Governorate, Egypt. These treatments were mixture of free cells and mixture of immobilized cells of different bacteria. These different formulas of mixed bacteria were prepared under Lab. condition. The obtained data indicated that, as a result of wastewater bio-treatment, the removal rate was found to be 76.92 and 76.70% for biological oxygen demand, 79.78 and 71.07% for chemical oxygen demand, 32.45 and 36.84 % for ammonia nitrogen as well as 91.67 and 50.0% for phosphate after 24 and 28 hrs with mixed free cells and mixed immobilized cells, respectively. Moreover, the bio-removals of different heavy metals were found to reach 90.0 and 50. 0% for Cu ion, 98.0 and 98.5% for Fe ion, 97.0 and 99.3% for Mn ion, 90.0 and 90.0% Pb, 80.0% and 75.0% for Zn ion after 24 and 28 hrs with mixed free cells and mixed immobilized cells, respectively. The results indicated that 13.86 and 17.43% of removal efficiency and reduction of total dissolved solids were achieved after 24 and 28 hrs with mixed free cells and mixed immobilized cells, respectively.

Keywords: wastewater bio-treatment , bio-sorption heavy metals, biological desalination, immobilized bacteria, free cell bacteria

Procedia PDF Downloads 178

Authors: Anudeep Appe, Bhanu Poluparthi, Lakshmi Kasivajjula, Udai Mv, Sobha Bagadi, Punya Modi, Aditya Singh, Hemanth Gunupudi, Spenser Troiano, Jeff Paul, Justin Stovall, Justin Yamamoto

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The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data is considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since its data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for ex. quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP (SHapley Additive exPlanations), a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: competition, DAGs, facility, healthcare, machine learning, market share, random forest, SHAP

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Authors: Istijabatul Aliyah, Bambang Setioko, Rara Sugiarti

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Surakarta is one of the cities which are formed with the concept of Javanese cosmology. As a traditional town of Java, Surakarta is known as ‘the paradise’ of traditional markets. Since its establishment, Surakarta is formed with Catur Gatra Tunggal or Four Single-Slot concept (palace, square, mosques, and markets). Current development in Surakarta downtown today indicates that traditional markets have improved themselves in both physical and non-physical aspects. The efforts start from the market façade revitalization, restoration and the overall development of market; up to social activities, competition between traders or large celebrations in the neighbourhood market. This research was conducted in Surakarta, which is aimed at: identifying the role of traditional market revitalization efforts in the development of a city. This study employs several methods of analysis, namely: 1) Spatial analysis for mapping the distribution of traditional markets in the city constellation, 2) Category-Based Analysis (CBA) to classify the revitalization of traditional markets that has an influence in the development of the city, and 3) Interactive Method of Analysis. The results of this research indicate that the presence of a constellation of traditional markets in Surakarta is dominated by the presence of Gede Market, not only as the oldest traditional market, but also as a center of economic and socio-cultural activities of the community. The role of traditional market revitalization in the development of a town is as an Urban Catalyst towards a MICE city in the sense that the revitalization effort, even done in a relatively short time and not yet covering the overall objects, is able to establish brand image of Surakarta as a city of culture which is friendly and ready to be MICE tourism city.

Keywords: traditional market revitalization, urban catalyst, MICE tourism, Surakarta

Procedia PDF Downloads 367

Authors: Jing Jiang, Wenhuan XU, Lei Zhang, Shiyi Zhang, Tongli Wang

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Climate data quality significantly affects the reliability of ecological modeling. In the Asia Pacific (AP) region, low-quality climate data hinders ecological modeling. ClimateAP, a software developed in 2017, generates high-quality climate data for the AP region, benefiting researchers in forestry and agriculture. However, its adoption remains limited. This study aims to confirm the validity of biologically relevant variable data generated by ClimateAP during the normal climate period through comparison with the currently available gridded data. Climate data from 2,366 weather stations were used to evaluate the prediction accuracy of ClimateAP in comparison with the commonly used gridded data from WorldClim1.4. Univariate regressions were applied to 48 monthly biologically relevant variables, and the relationship between the observational data and the predictions made by ClimateAP and WorldClim was evaluated using Adjusted R-Squared and Root Mean Squared Error (RMSE). Locations were categorized into mountainous and flat landforms, considering elevation, slope, ruggedness, and Topographic Position Index. Univariate regressions were then applied to all biologically relevant variables for each landform category. Random Forest (RF) models were implemented for the climatic niche modeling of Cunninghamia lanceolata. A comparative analysis of the prediction accuracies of RF models constructed with distinct climate data sources was conducted to evaluate their relative effectiveness. Biologically relevant variables were obtained from three unpublished Chinese meteorological datasets. ClimateAPv3.0 and WorldClim predictions were obtained from weather station coordinates and WorldClim1.4 rasters, respectively, for the normal climate period of 1961-1990. Occurrence data for Cunninghamia lanceolata came from integrated biodiversity databases with 3,745 unique points. ClimateAP explains a minimum of 94.74%, 97.77%, 96.89%, and 94.40% of monthly maximum, minimum, average temperature, and precipitation variances, respectively. It outperforms WorldClim in 37 biologically relevant variables with lower RMSE values. ClimateAP achieves higher R-squared values for the 12 monthly minimum temperature variables and consistently higher Adjusted R-squared values across all landforms for precipitation. ClimateAP's temperature data yields lower Adjusted R-squared values than gridded data in high-elevation, rugged, and mountainous areas but achieves higher values in mid-slope drainages, plains, open slopes, and upper slopes. Using ClimateAP improves the prediction accuracy of tree occurrence from 77.90% to 82.77%. The biologically relevant climate data produced by ClimateAP is validated based on evaluations using observations from weather stations. The use of ClimateAP leads to an improvement in data quality, especially in non-mountainous regions. The results also suggest that using biologically relevant variables generated by ClimateAP can slightly enhance climatic niche modeling for tree species, offering a better understanding of tree species adaptation and resilience compared to using gridded data.

Keywords: climate data validation, data quality, Asia pacific climate, climatic niche modeling, random forest models, tree species

Procedia PDF Downloads 50

Authors: M. Elanchezhian, C. Gunasekaran, A. Agnes Deepa, M. Salahudeen

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In recent years climate change is one of the most emerging threats facing by biodiversity both the animals and plants species. Elevated carbon dioxide and ozone concentrations, extreme temperature, changes in rainfall patterns, insects-plant interaction are the main criteria that affect biodiversity. In the present study, which emphasis the climate change and its effects on terrestrial insect diversity in Mukuruthi National Park a protected areas of Western Ghats in India. Sampling was done seasonally at the three areas using pitfall traps, over the period of January to December 2013. The statistical findings were done by Shannon wiener diversity index (H). A significant seasonal variation pattern was detected for total insect’s diversity at the different study areas. Totally nine orders of insects were recorded. Diversity and abundance of terrestrial insects shows much difference between the Natural, Shoal forest and the Grasslands.

Keywords: biodiversity, climate change, mukuruthi national park, terrestrial invertebrates

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Authors: Medjadj Tarek, Ghribi Hayet

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This study aims to develop a flood sensitivity assessment tool using machine learning (ML) techniques and geographic information system (GIS). The importance of this study is integrating the geographic information systems (GIS) and machine learning (ML) techniques for mapping flood risks, which help decision-makers to identify the most vulnerable areas and take the necessary precautions to face this type of natural disaster. To reach this goal, we will study the case of the city of M'sila, which is among the areas most vulnerable to floods. This study drew a map of flood-prone areas based on the methodology where we have made a comparison between 3 machine learning algorithms: the xGboost model, the Random Forest algorithm and the K Nearest Neighbour algorithm. Each of them gave an accuracy respectively of 97.92 - 95 - 93.75. In the process of mapping flood-prone areas, the first model was relied upon, which gave the greatest accuracy (xGboost).

Keywords: Geographic information systems (GIS), machine learning (ML), emergency mapping, flood disaster management

Procedia PDF Downloads 70

Authors: Vaishali Anagal, Vasudha Gokhale, Sharvey Dhongde

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Old buildings have significance in many aspects. The manifold significance may include historic, architectural and cultural aspects. In a cultural city like Pune, India, numerous residential buildings exist in the core city whose age may range between 60-100 years. These represent the city’s history and culture. Most of them are still in use as residential buildings with adaptations in various degrees. Some of these buildings are enlisted as ‘Heritage Buildings’ by local municipal authority. However, there are number of buildings that have heritage value although they are not enlisted as heritage sites. A lot of these buildings have already been pulled down for several reasons such as end of technical life, inadequacy for users, increasing floor area ratios, inflating land prices and changing lifestyles etc. Literature suggest that place attachment and residential satisfaction are positively related. It also indicates that length of residency is positively correlated with the place attachment. Residential satisfaction is associated with number of factors including socio demographic characteristics of users, housing characteristics, neighborhood characteristics and behavioral characteristics. This research paper poses an inquiry about the dynamics of co-relation between place attachment and residential satisfaction in case of old residential buildings. The motive of this enquiry is to examine if place attachment can serve as a strong ground for restoration of these old buildings and evade the devastation of emblems of cultural heritage of the city. The methodology includes questionnaire survey of users as well as a qualitative assessment regarding place attachment and residential satisfaction. About 20 residential buildings in the core city of Pune are selected for this purpose. The results of survey are analyzed and conclusions are drawn.

Keywords: place attachment, residential satisfaction, old residential buildings, housing characteristics, cultural heritage

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Authors: K. Sangeeth, P. Rekha, P. Preeja, P. Divya, R. Arya, R. Maneesha

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Wireless sensor networks (WSN) are small or tiny devices that consists of different sensors to sense physical parameters like air pressure, temperature, vibrations, movement etc., process these data and sends it to the central data center to take decisions. The WSN domain, has wide range of applications such as monitoring and detecting natural hazards like landslides, forest fire, avalanche, flood monitoring and also in healthcare applications. With such different applications, it is being taught in undergraduate/post graduate level in many universities under department of computer science. But the cost and infrastructure required to purchase WSN nodes for having the students getting hands on expertise on these devices is expensive. This paper gives overview about the remote triggered lab that consists of more than 100 WSN nodes that helps the students to remotely login from anywhere in the world using the World Wide Web, configure the nodes and learn the WSN concepts in intuitive way. It proposes new way called over the air programming (OTAP) and its internals that program the 100 nodes simultaneously and view the results without the nodes being physical connected to the computer system, thereby allowing for sparse deployment.

Keywords: WSN, over the air programming, virtual lab, AT45DB

Procedia PDF Downloads 353