Search results for: ABC-VED inventory classification

Authors: Ammar Said Ahmed, M. Reissmann, R. Bortfeldt, G. A. Brockmann

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Milk protein genetic variants are of interest for characterizing domesticated mammalian species and breeds, and for studying associations with economic traits. The aim of this work was to analyze milk protein genetic variation in the Sudanese native cattle breeds, which have been gradually declining in numbers over the last years due to the breed substitution, and indiscriminate crossbreeding. The genetic variation at three milk protein genes αS1-casein (CSN1S1), αS2-casein (CSN1S2) and ƙ-casein (CSN3) was investigated in 250 animals belonging to five Bos indicus cattle breeds of Sudan (Butana, Kenana, White-nile, Erashy and Elgash). Allele specific primers were designed for five SNPs determine the CSN1S1 variants B and C, the CSN1S2 variants A and B, the CSN3 variants A, B and H. Allele, haplotype frequencies and genetic distances (D) were calculated and the phylogenetic tree was constructed. All breeds were found to be polymorphic for the studied genes. The CSN1S1*C variant was found very frequently (>0.63) in all analyzed breeds with highest frequency (0.82) in White-nile cattle. The CSN1S2*A variant (0.77) and CSN3*A variant (0.79) had highest frequency in Kenana cattle. Eleven haplotypes in casein gene cluster were inferred. Six of all haplotypes occurred in all breeds with remarkably deferent frequencies. The estimated D ranged from 0.004 to 0.049. The most distant breeds were White-nile and Kenana (D 0.0479). The results presented contribute to the genetic knowledge of indigenous cattle and can be used for proper definition and classification of the Sudanese cattle breeds as well as breeding, utilization, and potential development of conservation strategies for local breeds.

Keywords: milk protein, genetic variation, casein haplotype, Bos indicus

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Authors: Elaine Lima Da Fonseca, Eliomar Pereira Da Silva Filho

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The modeling of areas susceptible to soil loss by hydro erosive processes consists of a simplified instrument of reality with the purpose of predicting future behaviors from the observation and interaction of a set of geoenvironmental factors. The models of potential areas for soil loss will be obtained through binary logistic regression, artificial neural networks, and support vector machines. The choice of the municipality of Colorado do Oeste in the south of the western Amazon is due to soil degradation due to anthropogenic activities, such as agriculture, road construction, overgrazing, deforestation, and environmental and socioeconomic configurations. Initially, a soil erosion inventory map constructed through various field investigations will be designed, including the use of remotely piloted aircraft, orbital imagery, and the PLANAFLORO/RO database. 100 sampling units with the presence of erosion will be selected based on the assumptions indicated in the literature, and, to complement the dichotomous analysis, 100 units with no erosion will be randomly designated. The next step will be the selection of the predictive parameters that exert, jointly, directly, or indirectly, some influence on the mechanism of occurrence of soil erosion events. The chosen predictors are altitude, declivity, aspect or orientation of the slope, curvature of the slope, composite topographic index, flow power index, lineament density, normalized difference vegetation index, drainage density, lithology, soil type, erosivity, and ground surface temperature. After evaluating the relative contribution of each predictor variable, the erosion susceptibility model will be applied to the municipality of Colorado do Oeste - Rondônia through the SPSS Statistic 26 software. Evaluation of the model will occur through the determination of the values of the R² of Cox & Snell and the R² of Nagelkerke, Hosmer and Lemeshow Test, Log Likelihood Value, and Wald Test, in addition to analysis of the Confounding Matrix, ROC Curve and Accumulated Gain according to the model specification. The validation of the synthesis map resulting from both models of the potential risk of soil erosion will occur by means of Kappa indices, accuracy, and sensitivity, as well as by field verification of the classes of susceptibility to erosion using drone photogrammetry. Thus, it is expected to obtain the mapping of the following classes of susceptibility to erosion very low, low, moderate, very high, and high, which may constitute a screening tool to identify areas where more detailed investigations need to be carried out, applying more efficient social resources.

Keywords: modeling, susceptibility to erosion, artificial intelligence, Amazon

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Authors: Xiaohua Zhu, Lingling Ma, Yongguang Zhao

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Leaf Area Index (LAI) is a key structural characteristic of crops and plays a significant role in precision agricultural management and farmland ecosystem modeling. However, LAI retrieved from different resolution data contain a scaling bias due to the spatial heterogeneity and model non-linearity, that is, there is scale effect during multi-scale LAI estimate. In this article, a typical farmland in semi-arid regions of Chinese Inner Mongolia is taken as the study area, based on the combination of PROSPECT model and SAIL model, a multiple dimensional Look-Up-Table (LUT) is generated for multiple crops LAI estimation from unmanned aerial vehicle (UAV) hyperspectral data. Based on Taylor expansion method and computational geometry model, a scale transfer model considering both difference between inter- and intra-class is constructed for scale effect analysis of LAI inversion over inhomogeneous surface. The results indicate that, (1) the LUT method based on classification and parameter sensitive analysis is useful for LAI retrieval of corn, potato, sunflower and melon on the typical farmland, with correlation coefficient R2 of 0.82 and root mean square error RMSE of 0.43m2/m-2. (2) The scale effect of LAI is becoming obvious with the decrease of image resolution, and maximum scale bias is more than 45%. (3) The scale effect of inter-classes is higher than that of intra-class, which can be corrected efficiently by the scale transfer model established based Taylor expansion and Computational geometry. After corrected, the maximum scale bias can be reduced to 1.2%.

Keywords: leaf area index (LAI), scale effect, UAV-based hyperspectral data, look-up-table (LUT), remote sensing

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Authors: M. Yosra, A. Mohamed, T. Sami

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Over the past few years, mobile communication technologies have seen significant evolution. This fact promoted the implementation of many systems in a multi-technological setting. From one system to another, the Quality of Service (QoS) provided to mobile consumers gets better. The growing number of normalized standards extends the available services for each consumer, moreover, most of the available radio frequencies have already been allocated, such as 3G, Wifi, Wimax, and LTE. A study by the Federal Communications Commission (FCC) found that certain frequency bands are partially occupied in particular locations and times. So, the idea of Cognitive Radio (CR) is to share the spectrum between a primary user (PU) and a secondary user (SU). The main objective of this spectrum management is to achieve a maximum rate of exploitation of the radio spectrum. In general, the CR can greatly improve the quality of service (QoS) and improve the reliability of the link. The problem will reside in the possibility of proposing a technique to improve the reliability of the wireless link by using the CR with some routing protocols. However, users declared that the links were unreliable and that it was an incompatibility with QoS. In our case, we choose the QoS parameter "bandwidth" to perform a supervised classification. In this paper, we propose a comparative study between some routing protocols, taking into account the variation of different technologies on the existing spectral bandwidth like 3G, WIFI, WIMAX, and LTE. Due to the simulation results, we observe that LTE has significantly higher availability bandwidth compared with other technologies. The performance of the OLSR protocol is better than other on-demand routing protocols (DSR, AODV and DSDV), in LTE technology because of the proper receiving of packets, less packet drop and the throughput. Numerous simulations of routing protocols have been made using simulators such as NS3.

Keywords: cognitive radio, multi technology, network simulator (NS3), routing protocol

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Authors: Christopher Thornton, Niina Kolehmainen, Kianoush Nazarpour

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Background: Accelerometers are widely used to measure physical activity behavior, including in children. The traditional method for processing acceleration data uses cut points, relying on calibration studies that relate the quantity of acceleration to energy expenditure. As these relationships do not generalise across diverse populations, they must be parametrised for each subpopulation, including different age groups, which is costly and makes studies across diverse populations difficult. A data-driven approach that allows physical activity intensity states to emerge from the data under study without relying on parameters derived from external populations offers a new perspective on this problem and potentially improved results. We evaluated the data-driven approach in a diverse population with a range of rapidly evolving physical and mental capabilities, namely very young children (9-38 months old), where this new approach may be particularly appropriate. Methods: We applied an unsupervised machine learning approach (a hidden semi-Markov model - HSMM) to segment and cluster the accelerometer data recorded from 275 children with a diverse range of physical and cognitive abilities. The HSMM was configured to identify a maximum of six physical activity intensity states and the output of the model was the time spent by each child in each of the states. For comparison, we also processed the accelerometer data using published cut points with available thresholds for the population. This provided us with time estimates for each child’s sedentary (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Data on the children’s physical and cognitive abilities were collected using the Paediatric Evaluation of Disability Inventory (PEDI-CAT). Results: The HSMM identified two inactive states (INS, comparable to SED), two lightly active long duration states (LAS, comparable to LPA), and two short-duration high-intensity states (HIS, comparable to MVPA). Overall, the children spent on average 237/392 minutes per day in INS/SED, 211/129 minutes per day in LAS/LPA, and 178/168 minutes in HIS/MVPA. We found that INS overlapped with 53% of SED, LAS overlapped with 37% of LPA and HIS overlapped with 60% of MVPA. We also looked at the correlation between the time spent by a child in either HIS or MVPA and their physical and cognitive abilities. We found that HIS was more strongly correlated with physical mobility (R²HIS =0.5, R²MVPA= 0.28), cognitive ability (R²HIS =0.31, R²MVPA= 0.15), and age (R²HIS =0.15, R²MVPA= 0.09), indicating increased sensitivity to key attributes associated with a child’s mobility. Conclusion: An unsupervised machine learning technique can segment and cluster accelerometer data according to the intensity of movement at a given time. It provides a potentially more sensitive, appropriate, and cost-effective approach to analysing physical activity behavior in diverse populations, compared to the current cut points approach. This, in turn, supports research that is more inclusive across diverse populations.

Keywords: physical activity, machine learning, under 5s, disability, accelerometer

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Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

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Authors: Leila M. Carvalho, Maria Erica S. Oliveira, Arnoud C. Neto, Elza I. Ida, Massami Shimokomaki, Marta S. Madruga

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The quality of broiler breast meat is changing as a result of the continuing emphasis on genetic selection for a more efficient meat production. Breast meat has been classified as PSE (pale, soft, exudative), DFD (dark, firm, dry) and normal color meat, and recently a third group has emerged: the so-called PFN (pale, firm, non-exudative) meat. This classification was based on pH, color and functional properties. The aim of this work was to confirm the existence of PFN and PSE meat by biochemical characterization and functional properties. Twenty four hours of refrigerated fillet, Pectoralis major, m. samples (n= 838) were taken from Cobb flocks 42-48 days old, obtained in Northeastern Brazil tropical region, the Northeastern, considered to have only dry and wet seasons. Color (L*), pH, water holding capacity (WHC), values were evaluated and compared with PSE group samples. These samples were classified as Normal (465.8), PSE meat (L*≥53; pH<5.8) and PFN (L*≥53; pH>5.8). The occurrence of control meat, PSE and PFN was 69.09%, 11.10% and 19.81%, respectively. Samples from PFN presented 4.0-5.0% higher WHC in relation to PSE meat and similar to control group. These results are explained by the fact that PSE meat syndrome occurs because of higher protein denaturation as the consequence of a simultaneous lower pH values under warm carcass sooner after slaughtering impairing the myofibril proteins functional properties. Conversely, PFN samples follow normal glycolysis rate maintaining the normal proteins activities. In conclusion, the results reported herein confirm the existence of this emerging broiler meat group with similar properties as control group and it should be considered as normal breast meat group.

Keywords: broiler breast meat, funcional properties, PFN, PSE

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Authors: Rita Bagheri, Javed Ahmed, Humayra Bashir, M. Irfan Qureshi

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Agriculture lands suffer from a combination of stresses such as salinity and metal contamination including cadmium at the same time. Under such condition of multiple stresses, plant may exhibit unique responses different from the stress occurring individually. Thus, it would be interesting to investigate that how plant respond to combined stress at level of antioxidants and proteome expression, and identifying the proteins which are involved in imparting stress tolerance. With an approach of comparative proteomics and antioxidant analysis, present study investigates the response of Spinacia oleracea to salt (NaCl), cadmium (Cd), and their combination (NaCl+Cd) stress. Two-dimensional gel electrophoresis was used for resolving leaf proteome, and proteins of interest were identified using PDQuest software. A number of proteins expressed differentially, those indicated towards their roles in imparting stress tolerance, were digested by trypsin and analyzed on mass spectrometer for peptide mass fingerprinting (PMF). Data signals were then matched with protein databases using MASCOT. Results show that NaCl, Cd and both together (NaCl+Cd) induce oxidative stress which was highest in combined stress of Cd+NaCl. Correspondingly, the activities of enzymatic antioxidants viz., SOD, APX, GR and CAT, and non-enzymatic antioxidants had highest changes under combined stress compares to single stress over their respective controls. Among the identified proteins, several interesting proteins were identified that may be have role in Spinacia oleracia tolerance in individual and combinatorial stress of salt and cadmium. The functional classification of identified proteins indicates the importance and necessity of keeping higher ratio of defence and disease responsive proteins.

Keywords: Spinacia oleracea, Cd, salinity, proteomics, antioxidants, combinatorial stress

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Authors: Matthew McArthur, Margaret Friend

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The COVID-19 pandemic disrupted the stability of the home environment for families across the world. Potential disruptions include parent work modality (in-person vs. remote), levels of health anxiety, family routines, and caregiving. These disruptions may have interfered with the processes of early vocabulary acquisition, carrying lasting effects over the life course. Our justification for this research is as follows: First, early, stable, caregiver-child reciprocal interactions, which may have been disrupted during the pandemic, contribute to the development of the brain architecture that supports language, cognitive, and social-emotional development. Second, early vocabulary predicts several cognitive outcomes, such as numeracy, literacy, and executive function. Further, disruption in the home is associated with adverse cognitive, academic, socio-emotional, behavioral, and communication outcomes in young children. We are interested in how disruptions related to the COVID-19 pandemic are associated with vocabulary acquisition in children born during the first two waves of the pandemic. We are conducting a moderated online experiment to assess this question. Participants are 16 children (10F) ranging in age from 19 to 39 months (M=25.27) and their caregivers. All child participants were screened for language background, health history, and history of language disorders, and were typically developing. Parents completed a modified version of the COVID-19 Family Stressor Scale (CoFaSS), a published measure of COVID-19-related family stressors. Thirteen items from the original scale were replaced to better capture change in family organization and stability specifically related to disruptions in income, anxiety, family relations, and childcare. Following completion of the modified CoFaSS, children completed a Web-Based version of the Computerized Comprehension Task and the Receptive One Word Picture Vocabulary if 24 months or older or the MacArthur-Bates Communicative Development Inventory if younger than 24 months. We report our preliminary data as a partial correlation analysis controlling for age. Raw vocabulary scores on the CCT, ROWPVT-4, and MCDI were all negatively associated with pandemic-related disruptions related to anxiety (r12=-.321; r1=-.332; r9=-.509), family relations (r12=-.590*; r1=-.155; r9=-.468), and childcare (r12=-.294; r1=-.468; r9=-.177). Although the small sample size for these preliminary data limits our power to detect significance, this trend is in the predicted direction, suggesting that increased pandemic-related disruption across multiple domains is associated with lower vocabulary scores. We anticipate presenting data on a full sample of 50 monolingual English participants. A sample of 50 participants would provide sufficient statistical power to detect a moderate effect size, adhering to a nominal alpha of 0.05 and ensuring a power level of 0.80.

Keywords: COVID-19, early vocabulary, home environment, language acquisition, multiple measures

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Authors: Sanghamitra Roy

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In recent times, India has experienced the phenomenal rise in the number of registered vehicles and vehicular trips, particularly intra-city trips in most of its urban areas. The increase in vehicle ownership and use have increased parking demand immensely and accommodating the same is now a matter of big concern. Most cities do not have adequate off-street parking facilities thus forcing people to park on the streets. This has resulted in decreased carrying capacity, decreased traffic speed, increased congestion, and increased environmental problems. While integrated multi-modal transportation system is the answer to such problems, parking issues will continue to exist. In Kolkata, only 6.4% land is devoted for roads. The consequences of this huge crunch in road spaces coupled with increased parking demand are severe particularly in the CBD and major commercial areas, making the role of off-street parking facilities in Kolkata even more critical. To meaningfully address parking issues, it is important to identify the factors that influence parking demand so that it can be assessed and comprehensive parking policies and plans for the city can be formulated. This paper aims at identifying the factors that contribute towards parking demand for offices in Kolkata and their degree of correlation with parking demand. The study is limited to home-to-work trips located within Kolkata Municipal Corporation (KMC) where parking related issues are most pronounced. The data for the study is collected through personal interviews, questionnaires and direct observations from offices across the wards of KMC. SPSS is used for classification of the data and analyses of the same. The findings of this study will help in re-assessment of the parking requirements specified in The Kolkata Municipal Corporation Building Rules as a step towards alleviating parking related issues in the city.

Keywords: building rules, office spaces, parking demand, urbanization

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Authors: Thewodros K. Geberemariam

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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

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Authors: Wambugu Naftaly, Ruisheng Wang, Zhijun Wang

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Models based on convolutional neural networks (CNNs), in conjunction with Transformer, have excelled in semantic segmentation, a fundamental task for intelligent Earth observation using remote sensing (RS) imagery. Nonetheless, tokenization in the Transformer model undermines object structures and neglects inner-patch local information, whereas CNNs are unable to simulate global semantics due to limitations inherent in their convolutional local properties. The integration of the two methodologies facilitates effective global-local feature aggregation and interactions, potentially enhancing segmentation results. Inspired by the merits of CNNs and Transformers, we introduce a layer-level feature aggregation network (LLFA-Net) to address semantic segmentation of fine-resolution remote sensing (FRRS) images for land cover classification. The simple yet efficient system employs a transposed unit that hierarchically utilizes dense high-level semantics and sufficient spatial information from various encoder layers through a layer-level feature aggregation module (LLFAM) and models global contexts using structured Transformer blocks. Furthermore, the decoder aggregates resultant features to generate rich semantic representation. Extensive experiments on two public land cover datasets demonstrate that our proposed framework exhibits competitive performance relative to the most recent frameworks in semantic segmentation.

Keywords: land cover mapping, semantic segmentation, remote sensing, vision transformer networks, deep learning

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Authors: Chi-Ching Lee, Po-Jung Huang, Kuo-Yang Huang, Petrus Tang

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Background: Recent advances in high-throughput research technologies such as new-generation sequencing and multi-dimensional liquid chromatography makes it possible to dissect the complete transcriptome and proteome in a single run for the first time. However, it is almost impossible for many laboratories to handle and analysis these “BIG” data without the support from a bioinformatics team. We aimed to provide a web-based analysis platform for users with only limited knowledge on bio-computing to study the functional genomics and proteomics. Method: We use NCI-60 as an example dataset to demonstrate the power of the web-based analysis platform and data delivering system: C-eXpress takes a simple text file that contain the standard NCBI gene or protein ID and expression levels (rpkm or fold) as input file to generate a distribution map of gene/protein expression levels in a heatmap diagram organized by color gradients. The diagram is hyper-linked to a dynamic html table that allows the users to filter the datasets based on various gene features. A dynamic summary chart is generated automatically after each filtering process. Results: We implemented an integrated database that contain pre-defined annotations such as gene/protein properties (ID, name, length, MW, pI); pathways based on KEGG and GO biological process; subcellular localization based on GO cellular component; functional classification based on GO molecular function, kinase, peptidase and transporter. Multiple ways of sorting of column and rows is also provided for comparative analysis and visualization of multiple samples.

Keywords: cancer, visualization, database, functional annotation

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Authors: Kristina Colovic, Mari Janikian, Nikolaos Takis, Fotini-Sonia Apergi

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A considerable number of Afghan asylum seekers in Greece are still waiting for answers about their future and status for personal, social and societal advancement. Most have been trapped in a stalemate of continuously postponed or temporarily progressed levels of integration into the EU/Greek process of asylum. Limited quantitative research exists investigating the psychological effects of long-term displacement among Afghans refugees in Greece. The purpose of this study is to investigate factors that are associated with and predict psychological distress symptoms among this population. Data from a sample of native Afghan nationals (N > 70) living in Greece for approximately the last ten years will be collected from May to July 2016. Criteria for participation include the following: being 18 years of age or older, and emigration from Afghanistan to Greece from 2003 onwards (i.e., long-term refugees or part of the 'old system of asylum'). Snowball sampling will be used to recruit participants, as this is considered the most effective option when attempting to study refugee populations. Participants will complete self-report questionnaires, consisting of the Afghan Symptom Checklist (ASCL), a culturally validated measure of psychological distress, the World Health Organization Quality of Life scale (WHOQOL-BREF), an adapted version of the Comprehensive Trauma Inventory-104 (CTI-104), and a modified Psychological Acculturation Scale. All instruments will be translated in Greek, through the use of forward- and back-translations by bilingual speakers of English and Greek, following WHO guidelines. A pilot study with 5 Afghan participants will take place to check for discrepancies in understanding and for further adapting the instruments as needed. Demographic data, including age, gender, year of arrival to Greece and current asylum status will be explored. Three different types of analyses (descriptive statistics, bivariate correlations, and multivariate linear regression) will be used in this study. Descriptive findings for respondent demographics, psychological distress symptoms, traumatic life events and quality of life will be reported. Zero-order correlations will assess the interrelationships among demographic, traumatic life events, psychological distress, and quality of life variables. Lastly, a multivariate linear regression model will be estimated. The findings from the study will contribute to understanding the determinants of acculturation, distress and trauma on daily functioning for Afghans in Greece. The main implications of the current study will be to advocate for capacity building and empower communities through effective program evaluation and design for mental health services for all refugee populations in Greece.

Keywords: Afghan refugees, evaluation, Greece, mental health, quality of life

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Authors: C. M. Williams, R. English, P. King, I. M. Brown

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Introduction: Pseudoangiomatous Stromal Hyperplasia (PASH) is a benign fibrous proliferation of breast stroma affecting predominantly premenopausal women with no significant increased risk of breast cancer. Informal recommendations for management have continued to evolve over recent years from surgical excision to observation, although there are no specific national guidelines. This study assesses the safety of a non-surgical approach to PASH management by review of cases at a single centre. Methods: Retrospective case series review (January 2011 – August 2016) was conducted on consecutive PASH cases. Diagnostic classification (clinical, radiological and histological), management outcomes, and breast cancer incidence were recorded. Results: 43 patients were followed up for median of 25 months (3-64) with 75% symptomatic at presentation. 12% of cases (n=5) had a radiological score (BIRADS MMG or US) ≥ 4 of which 3 were confirmed malignant. One further malignancy was detected and proven radiologically occult and contralateral. No patients were diagnosed with a malignancy during follow-up. Treatment evolved from 67% surgical in 2011 to 33% in 2016. Conclusions: The management of PASH has transitioned in line with other published experience. The preliminary findings suggest this appears safe with no evidence of missed malignancies; however, longer follow up is required to confirm long-term safety. Recommendations: PASH with suspicious radiological findings ( ≥ U4/R4) warrants multidisciplinary discussion for excision. In the absence of histological or radiological suspicion of malignancy, PASH can be safely managed without surgery.

Keywords: benign breast disease, conservative management, malignancy, pseudoangiomatous stromal hyperplasia, surgical excision

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Authors: Wei Wen Yang, Emily Chia Yu Su

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Dengue fever is a mosquito-borne tropical infectious disease caused by the dengue virus. After infection, symptoms usually start from three to fourteen days. Dengue virus may cause a high fever and at least two of the following symptoms, severe headache, severe eye pain, joint pains, muscle or bone pain, vomiting, feature skin rash, and mild bleeding manifestation. In addition, recovery will take at least two to seven days. Dengue fever has rapidly spread in tropical and subtropical areas in recent years. Several phenomena around the world such as global warming, urbanization, and international travel are the main reasons in boosting the spread of dengue. In Taiwan, epidemics occur annually, especially during summer and fall seasons. On the other side, Singapore government also has announced the amounts number of dengue cases spreading in Singapore. As the serious epidemic of dengue fever outbreaks in Taiwan and Singapore, countries around the Asia-Pacific region are becoming high risks of susceptible to the outbreaks and local hub of spreading the virus. To improve public safety and public health issues, firstly, we are going to use Microsoft Excel and SAS EG to do data preprocessing. Secondly, using support vector machines and decision trees builds predict model, and analyzes the infectious cases between Taiwan and Singapore. By comparing different factors causing vector mosquito from model classification and regression, we can find similar spreading patterns where the disease occurred most frequently. The result can provide sufficient information to predict the future dengue infection outbreaks and control the diffusion of dengue fever among countries.

Keywords: dengue fever, Taiwan, Singapore, Aedes aegypti

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Authors: Shamsulhaq Amin

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Drought can arise from several hydrometeorological phenomena that result in insufficient precipitation, soil moisture, and surface and groundwater flow, leading to conditions that are considerably drier than the usual water content or availability. Drought is often assessed using indices that are associated with meteorological, agricultural, and hydrological phenomena. In order to effectively handle drought disasters, it is essential to accurately determine the kind, intensity, and extent of the drought using drought characterization. This information is critical for managing the drought before, during, and after the rehabilitation process. Over a hundred drought assessments have been created in literature to evaluate drought disasters, encompassing a range of factors and variables. Some models utilise solely hydrometeorological drivers, while others employ remote sensing technology, and some incorporate a combination of both. Comprehending the entire notion of drought and taking into account drought indices along with their calculation processes are crucial for researchers in this discipline. Examining several drought metrics in different studies requires additional time and concentration. Hence, it is crucial to conduct a thorough examination of approaches used in drought indices in order to identify the most straightforward approach to avoid any discrepancies in numerous scientific studies. In case of practical application in real-world, categorizing indices relative to their usage in meteorological, agricultural, and hydrological phenomena might help researchers maximize their efficiency. Users have the ability to explore different indexes at the same time, allowing them to compare the convenience of use and evaluate the benefits and drawbacks of each. Moreover, certain indices exhibit interdependence, which enhances comprehension of their connections and assists in making informed decisions about their suitability in various scenarios. This study provides a comprehensive assessment of various drought indices, analysing their types and computation methodologies in a detailed and systematic manner.

Keywords: drought classification, drought severity, drought indices, agriculture, hydrological

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Authors: Brindha Senthil Kumar, Payel Chakraborty, Senthil Kumar Nachimuthu, Arindam Maitra, Prem Nath

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Gastric cancer is predominantly caused by demographic and diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (ECG) from diet and lifestyle factors using supervised machine learning algorithms. For this study, 160 healthy individual and 80 cases were selected who had been followed for 3 years (2016-2019), at Civil Hospital, Aizawl, Mizoram. A dataset containing 11 features that are core risk factors for the gastric cancer were extracted. Supervised machine algorithms: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Multilayer perceptron, and Random Forest were used to analyze the dataset using Python Jupyter Notebook Version 3. The obtained classified results had been evaluated using metrics parameters: minimum_false_positives, brier_score, accuracy, precision, recall, F1_score, and Receiver Operating Characteristics (ROC) curve. Data analysis results showed Naive Bayes - 88, 0.11; Random Forest - 83, 0.16; SVM - 77, 0.22; Logistic Regression - 75, 0.25 and Multilayer perceptron - 72, 0.27 with respect to accuracy and brier_score in percent. Naive Bayes algorithm out performs with very low false positive rates as well as brier_score and good accuracy. Naive Bayes algorithm classification results in predicting ECG showed very satisfactory results using only diet cum lifestyle factors which will be very helpful for the physicians to educate the patients and public, thereby mortality of gastric cancer can be reduced/avoided with this knowledge mining work.

Keywords: Early Gastric cancer, Machine Learning, Diet, Lifestyle Characteristics

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Authors: Mohammed Ahmed Ahmed Odah

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DNA barcoding is a crucial method for assessing and monitoring species diversity amidst escalating threats to global biodiversity. The author explores DNA barcoding's potential as a robust and reliable tool for biodiversity assessment. It begins with a comprehensive review of existing literature, delving into the theoretical foundations, methodologies and applications of DNA barcoding. The suitability of various DNA regions, like the COI gene, as universal barcodes is extensively investigated. Additionally, the advantages and limitations of different DNA sequencing technologies and bioinformatics tools are evaluated within the context of DNA barcoding. To evaluate the efficacy of DNA barcoding, diverse ecosystems, including terrestrial, freshwater and marine habitats, are sampled. Extracted DNA from collected specimens undergoes amplification and sequencing of the target barcode region. Comparison of the obtained DNA sequences with reference databases allows for the identification and classification of the sampled organisms. Findings demonstrate that DNA barcoding accurately identifies species, even in cases where morphological identification proves challenging. Moreover, it sheds light on cryptic and endangered species, aiding conservation efforts. The author also investigates patterns of genetic diversity and evolutionary relationships among different taxa through the analysis of genetic data. This research contributes to the growing knowledge of DNA barcoding and its applicability for biodiversity assessment. The advantages of this approach, such as speed, accuracy and cost-effectiveness, are highlighted, along with areas for improvement. By unlocking the genetic code, DNA barcoding enhances our understanding of biodiversity, supports conservation initiatives and informs evidence-based decision-making for the sustainable management of ecosystems.

Keywords: DNA barcoding, biodiversity assessment, genetic code, species identification, taxonomic resolution, next-generation sequencing

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Authors: Atour Taghipour

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In the real world each manufacturing company is an independent business unit. These business units are linked to each other through upstream and downstream linkages. The management of these linkages is called coordination which, could be considered as a difficult engineering task. The degree of difficulty of coordination depends on the type and the nature of information exchanged between partners as well as the structure of relationship from mutual to the network structure. The literature of manufacturing systems comprises a wide range of varieties of methods and approaches of coordination. In fact, two main streams of research can be distinguished: central coordination versus decentralized coordination. In the centralized systems a high degree of information exchanges is required. The high degree of information exchanges sometimes leads to difficulties when independent members do not want to share information. In order to address these difficulties, decentralized approaches of coordination of operations planning decisions based on some minimal information sharing have been proposed in many academic disciplines. This paper first proposes a framework of analysis in order to analyze the proposed approaches in the literature, based on this framework which includes the similarities between approaches we categorize the existing approaches. This classification can be used as a research map for future researches. The result of our paper highlights several opportunities for future research. First, it is proposed to develop more dynamic and stochastic mechanisms of planning coordination of manufacturing units. Second, in order to exploit the complementarities of approaches proposed by diverse science discipline, we propose to integrate the techniques of coordination. Finally, based on our approach we proposed to develop coordination standards to guaranty both the complementarity of these approaches as well as the freedom of companies to adopt any planning tools.

Keywords: network coordination, manufacturing, operations planning, supply chain

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Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

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Authors: Mohammad Saber Eslamlou

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Morphology of Islamic cities has been extensively studied by researchers of Islamic cities and different theories could be found about it. In this regard, there exist much difference in method of analysis, classification, recognition, confrontation and comparative method of urban morphology. The present paper aims to examine the previous methods, approaches and insights and that how Dr. Giulia Annalinda Neglia dealt with the analysis of morphology of Islamic cities. Neglia is assistant professor in University of Bari, Italy (UNIBA) who has published numerous papers and books on Islamic cities. I introduce her works in the field of morphology of Islamic cities. And then, her thoughts, insights and research methodologies are presented and analyzed in critical perspective. This is a qualitative research on her written works, which have been classified in three major categories. The first category consists mainly of her works on morphology and physical shape of Islamic cities. The results of her works’ review suggest that she has used Moratoria typology in investigating morphology of Islamic cities. Moreover, overall structure of the cities under investigation is often described linear; however, she’s against to define a single framework for the recognition of morphology in Islamic cities. She states that ‘to understand the physical complexity and irregularities in Islamic cities, it is necessary to study the urban fabric by typology method, focusing on transformation processes of the buildings’ form and their surrounding open spaces’ and she believes that fabric of each region in the city follows from the principles of an specific period or urban pattern, in particular, Hellenistic and Roman structures. Furthermore, she believes that it is impossible to understand the morphology of a city without taking into account the obvious and hidden developments associated with it, because form of building and their surrounding open spaces are written history of the city.

Keywords: city, Islamic city, Giulia Annalinda Neglia, morphology

Procedia PDF Downloads 98

Authors: Rayanne Branco Dos Santos Lima, Lorena Pinheiro Barbosa, Kamila Ferreira Lima, Victor Manuel Tegoma Ruiz, Monyka Brito Lima Dos Santos, Maria Wendiane Gueiros Gaspar, Luzia Camila Coelho Ferreira, Leandro Cardozo Dos Santos Brito, Deyse Maria Alves Rocha

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Introduction: The lack of regulation of the baby's sleep-wake pattern in the first years of life affects the health of thousands of women. Maternal sleep deprivation can trigger or aggravate psychosomatic problems such as depression, anxiety and stress that can directly influence maternal safety, with consequences for the baby's and mother's sleep. Such conditions can affect the family's quality of life and child development. Objective: To correlate maternal security with maternal state anxiety scores and the mother's and baby's total sleep time. Method: Cross-sectional study carried out with 96 mothers of babies aged 10 to 24 months, accompanied by nursing professionals linked to a Federal University in Northeast Brazil. Study variables were maternal security, maternal state anxiety scores, infant latency and sleep time, and total nocturnal sleep time of mother and infant. Maternal safety was calculated using a four-point Likert scale (1=not at all safe, 2=somewhat safe, 3=very safe, 4=completely safe). Maternal anxiety was measured by State-Trait Anxiety Inventory, state-anxiety subscale whose scores vary from 20 to 80 points, and the higher the score, the higher the anxiety levels. Scores below 33 are considered mild; from 33 to 49, moderate and above 49, high. As for the total nocturnal sleep time, values between 7-9 hours of sleep were considered adequate for mothers, and values between 9-12 hours for the baby, according to the guidelines of the National Sleep Foundation. For the sleep latency time, a time equal to or less than 20 min was considered adequate. It is noteworthy that the latency time and the time of night sleep of the mother and the baby were obtained by the mother's subjective report. To correlate the data, Spearman's correlation was used in the statistical package R version 3.6.3. Results: 96 women and babies participated, aged 22 to 38 years (mean 30.8) and 10 to 24 months (mean 14.7), respectively. The average of maternal security was 2.89 (unsafe); Mean maternal state anxiety scores were 43.75 (moderate anxiety). The babies' average sleep latency time was 39.6 min (>20 min). The mean sleep times of the mother and baby were, respectively, 6h and 42min and 8h and 19min, both less than the recommended nocturnal sleep time. Maternal security was positively correlated with maternal state anxiety scores (rh=266, p=0.009) and negatively correlated with infant sleep latency (rh= -0.30. P=0.003). Baby sleep time was positively correlated with maternal sleep time. (rh 0.46, p<0.001). Conclusion: The more secure the mothers considered themselves, the higher the anxiety scores and the shorter the baby's sleep latency. Also, the longer the baby sleeps, the longer the mother sleeps. Thus, interventions are needed to promote the quality and efficiency of sleep for both mother and baby.

Keywords: sleep, anxiety, infant, mother-child relations

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Authors: Qingsong Xiong, Cheng Yuan, Qingzhao Kong, Haibei Xiong

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Deep-learning-driven approaches based on vibration responses have attracted larger attention in rapid structural condition assessment while obtaining sufficient measured training data with corresponding labels is relevantly costly and even inaccessible in practical engineering. This study proposes an integrated label propagation network for structural condition assessment, which is able to diffuse the labels from continuously-generating measurements by intact structure to those of missing labels of damage scenarios. The integrated network is embedded with damage-sensitive features extraction by deep autoencoder and pseudo-labels propagation by optimized fuzzy clustering, the architecture and mechanism which are elaborated. With a sophisticated network design and specified strategies for improving performance, the present network achieves to extends the superiority of self-supervised representation learning, unsupervised fuzzy clustering and supervised classification algorithms into an integration aiming at assessing damage conditions. Both numerical simulations and full-scale laboratory shaking table tests of a two-story building structure were conducted to validate its capability of detecting post-earthquake damage. The identifying accuracy of a present network was 0.95 in numerical validations and an average 0.86 in laboratory case studies, respectively. It should be noted that the whole training procedure of all involved models in the network stringently doesn’t rely upon any labeled data of damage scenarios but only several samples of intact structure, which indicates a significant superiority in model adaptability and feasible applicability in practice.

Keywords: autoencoder, condition assessment, fuzzy clustering, label propagation

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Authors: Padmini S. Navalgund, Manasi Naik, Ujwala Patil

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Road hump is a semi-cylindrical elevation on the road made across specific locations of the road. The vehicle needs to maneuver the hump by reducing the speed to avoid car damage and pass over the road hump safely. Road Humps on road surfaces, if identified in advance, help to maintain the security and stability of vehicles, especially in adverse visibility conditions, viz. night scenarios. We have proposed a deep learning architecture adaptation by implementing the MISH activation function and developing a new classification loss function called "Effective Focal Loss" for Indian road humps detection in adverse light scenarios. We captured images comprising of marked and unmarked road humps from two different types of cameras across South India to build a heterogeneous dataset. A heterogeneous dataset enabled the algorithm to train and improve the accuracy of detection. The images were pre-processed, annotated for two classes viz, marked hump and unmarked hump. The dataset from these images was used to train the single-stage object detection algorithm. We utilised an algorithm to synthetically generate reduced visible road humps scenarios. We observed that our proposed framework effectively detected the marked and unmarked hump in the images in clear and ad-verse light environments. This architectural adaptation sets up an option for early detection of Indian road humps in reduced visibility conditions, thereby enhancing the autonomous driving technology to handle a wider range of real-world scenarios.

Keywords: Indian road hump, reduced visibility condition, low light condition, adverse light condition, marked hump, unmarked hump, YOLOv9

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Authors: Ayu Dwi Hardiyanti, Bernardus Anggit Winahyu, I. Gusti Agung Ayu Sugita Sari, Lestari Sutra Simamora, I. Wayan Warmada

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The Penosogan Formation sandstone, that has Middle Miosen age, has been deemed as a reservoir potential based on sample data from sandstone outcrop in Kebakalan and Kedawung villages, Karangsambung sub-district, Kebumen Regency, Central Java. This research employs the following analytical methods; petrography, X-ray diffraction (XRD), and porosity test. Based on the presence of micritic sandstone, muddy micrite, and muddy sandstone, the Penosogan Formation sandstone has a fine-coarse granular size and middle-to-fine sorting. The composition of the sandstone is mostly made up of plagioclase, skeletal grain, and traces of micrite. The percentage of clay minerals based on petrographic analysis is 10% and appears to envelop grain, resulting enveloping grain which reduces the porosity of rocks. The porosity types as follows: interparticle, vuggy, channel, and shelter, with an equant form of cement. Moreover, the diagenesis process involves compaction, cementation, authigenic mineral growth, and dissolving due to feldspar alteration. The maturity of the reservoir can be seen through the X-ray diffraction analysis results, using ethylene glycol solution for clay minerals fraction transformed from smectite–illite. Porosity test analysis showed that the Penosogan Formation sandstones has a porosity value of 22% based on the Koeseomadinata classification, 1980. That shows high maturity is very influential for the quality of reservoirs sandstone of the Penosogan Formation.

Keywords: sandstone reservoir, Penosogan Formation, smectite, XRD

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Authors: S. Abdi Goudazri, R. Ziaie Moayed, A. Nazeri

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Loose soils normally are of weak bearing capacity due to their structural nature. Being exposed to heavy traffic loads, they would fail in most cases. To tackle the aforementioned issue, geotechnical engineers have come up with different approaches; one of which is making use of geosynthetic-reinforced soil-aggregate systems. As these polymeric reinforcements have highlighted economic and environmentally-friendly features, they have become widespread in practice during the last decades. The present research investigates the efficiency of four different types of these reinforcements in increasing the bearing capacity of two-layered soil sections using a series California Bearing Ratio (CBR) test. The studied sections are comprised of a 10 cm-thick layer of no. 161 Firouzkooh sand (weak subgrade) and a 10 cm-thick layer of compacted aggregate materials (base course) classified as SP and GW according to the United Soil Classification System (USCS), respectively. The aggregate layer was compacted to the relative density (Dr) of 95% at the optimum water content (Wopt) of 6.5%. The applied reinforcements were including two kinds of geocomposites (type A and B), a geotextile, and a geogrid that were embedded at the interface of the lower and the upper layers of the soil-aggregate system. As the standard CBR mold was not appropriate in height for this study, the mold used for soaked CBR tests were utilized. To make a comparison between the results of stress-settlement behavior in the studied specimens, CBR values pertinent to the penetrations of 2.5 mm and 5 mm were considered. The obtained results demonstrated 21% and 24.5% increments in the amount of CBR value in the presence of geocomposite type A and geogrid, respectively. On the other hand, the effect of both geotextile and geocomposite type B on CBR values was generally insignificant in this research.

Keywords: geosynthetics, geogrid, geotextile, CBR test, increasing bearing capacity

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Authors: Mehdi Boukri, Mohammed Naboussi Farsi, Mounir Naili, Omar Amellal, Mohamed Belazougui, Ahmed Mebarki, Nabila Guessoum, Brahim Mezazigh, Mounir Ait-Belkacem, Nacim Yousfi, Mohamed Bouaoud, Ikram Boukal, Aboubakr Fettar, Asma Souki

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The seismic risk to which the urban centres are more and more exposed became a world concern. A co-operation on an international scale is necessary for an exchange of information and experiments for the prevention and the installation of action plans in the countries prone to this phenomenon. For that, the 1990s was designated as 'International Decade for Natural Disaster Reduction (IDNDR)' by the United Nations, whose interest was to promote the capacity to resist the various natural, industrial and environmental disasters. Within this framework, it was launched in 1996, the RADIUS project (Risk Assessment Tools for Diagnosis of Urban Areas Against Seismic Disaster), whose the main objective is to mitigate seismic risk in developing countries, through the development of a simple and fast methodological and operational approach, allowing to evaluate the vulnerability as well as the socio-economic losses, by probable earthquake scenarios in the exposed urban areas. In this paper, we will present the adaptation and application of this methodology to the Algerian context for the seismic risk evaluation in urban areas potentially exposed to earthquakes. This application consists to perform an earthquake scenario in the urban centre of Constantine city, located at the North-East of Algeria, which will allow the building seismic damage estimation of this city. For that, an inventory of 30706 building units was carried out by the National Earthquake Engineering Research Centre (CGS). These buildings were digitized in a data base which comprises their technical information by using a Geographical Information system (GIS), and then they were classified according to the RADIUS methodology. The study area was subdivided into 228 meshes of 500m on side and Ten (10) sectors of which each one contains a group of meshes. The results of this earthquake scenario highlights that the ratio of likely damage is about 23%. This severe damage results from the high concentration of old buildings and unfavourable soil conditions. This simulation of the probable seismic damage of the building and the GIS damage maps generated provide a predictive evaluation of the damage which can occur by a potential earthquake near to Constantine city. These theoretical forecasts are important for decision makers in order to take the adequate preventive measures and to develop suitable strategies, prevention and emergency management plans to reduce these losses. They can also help to take the adequate emergency measures in the most impacted areas in the early hours and days after an earthquake occurrence.

Keywords: seismic risk, mitigation, RADIUS, urban areas, Algeria, earthquake scenario, Constantine

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Authors: Parul Johri, Mala Trivedi

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Major Intrinsic proteins (MIPs), actively involved in the passive transport of small polar molecules across the membranes of almost all living organisms. MIPs that specifically transport water molecules are named aquaporins (AQPs). The permeability of membranes is actively controlled by the regulation of the amount of different MIPs present but also in some cases by phosphorylation and dephosphorylation of the channel. Based on sequence similarity, MIPs have been classified into many categories. All of the proteins are made up of the 20 amino acids, the only difference is there in their orientations. Again all the 20 amino acids are made up of the basic five elements namely: carbon, hydrogen, oxygen, sulphur and nitrogen. These elements are responsible for giving the amino acids the properties of hydrophilicity/hydrophobicity which play an important role in protein interactions. The hydrophobic amino acids characteristically have greater number of carbon atoms as carbon is the main element which contributes to hydrophobic interactions in proteins. It is observed that the carbon level of proteins in different species is different. In the present work, we have taken a sample set of 150 aquaporins proteins from Uniprot database and a dynamic programming code was written to calculate the carbon percentage for each sequence. This carbon percentage was further used to barcode the aqauporins of animals and plants. The protein taken from Oryza sativa, Zea mays and Arabidopsis thaliana preferred to have carbon percentage of 31.8 to 35, whereas on the other hand sequences taken from Mus musculus, Saccharomyces cerevisiae, Homo sapiens, Bos Taurus, and Rattus norvegicus preferred to have carbon percentage of 31 to 33.7. This clearly demarks the carbon range in the aquaporin proteins from plant and animal origin. Hence the atom level analysis of protein sequences can provide us with better results as compared to the residue level comparison.

Keywords: aquaporins, carbon, dynamic prgramming, MIPs

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Authors: Silvija Šiljeg, Ivan Marić, Ante Šiljeg

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The accessibility of urban green spaces (UGS) is an integral element in the quality of life. Due to rapid urbanization, UGS studies have become a key element in urban planning. The potential benefits of space for its inhabitants are frequently analysed. A functional transport network system and the optimal spatial distribution of urban green surfaces are the prerequisites for maintaining the environmental equilibrium of the urban landscape. An accessibility analysis was conducted as part of the Urban Green Belts Project (UGB). The development of a GIS database for Zadar was the first step in generating the UGS accessibility indicator. Data were collected using the supervised classification method of multispectral LANDSAT images and manual vectorization of digital orthophoto images (DOF). An analysis of UGS accessibility according to the ANGst standard was conducted in the first phase of research. The accessibility indicator was generated on the basis of seven objective measurements, which included average UGS surface per capita and accessibility according to six functional levels of green surfaces. The generated indicator was compared with subjective measurements obtained by conducting a survey (718 respondents) within statistical units. The collected data reflected individual assessments and subjective evaluations of UGS accessibility. This study highlighted the importance of using objective and subjective measures in the process of understanding the accessibility of urban green surfaces. It may be concluded that when evaluating UGS accessibility, residents emphasize the immediate residential environment, ignoring higher UGS functional levels. It was also concluded that large areas of UGS within a city do not necessarily generate similar satisfaction with accessibility. The heterogeneity of output results may serve as guidelines for the further development of a functional UGS city network.

Keywords: urban green spaces (UGS), accessibility indicator, subjective and objective measurements, Zadar

Procedia PDF Downloads 262