Search results for: correlation and prediction

Authors: Huey-Fang Sun, Wei-Kai Weng, Mei-Kuang Chao, Hui-Shan Hsu, Tsai-Yin Shih

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Psycho-social stress is important for mental illness and the presence of emotional and behavioral symptoms to an identifiable event is the central feature of adjustment disorders. However, whether patients with adjustment disorders have been raised in family with poor family functions and social supports and have higher stress perception than their peer group when they both experienced a similar stressful environment remains unknown. The specific aims of the study are to investigate the correlation among the family function, social supports and the level of stress perception and to test the hypothesis that military patients with adjustment disorders would have lower family function, lower social supports and higher stress perception than their healthy colleagues recruited in the same cohort for military services given their common exposure to similar stressful environments. Methods: The study was conducted in four hospitals of northern part of Taiwan from July 1, 2015 to June 30, 2017 and a matched case-control study design was used. The inclusion criteria for potential patient participants were psychiatric inpatients that serviced in military during the study period and met the diagnosis of adjustment disorders. Patients who had been admitted to psychiatric ward before or had illiteracy problem were excluded. A healthy military control sample matched by the same military service unit, gender, and recruited cohort was invited to participate the study as well. Totally 74 participants (37 patients and 37 controls) completed the consent forms and filled out the research questionnaires. Questionnaires used in the study included Perceived Stress Scale (PSS) as a measure of stress perception; Family APGAR as a measure of family function, and Multidimensional Scale of Perceived Social Support (MSPSS) as a measure of social supports. Pearson correlation analysis and t-test were applied for statistical analysis. Results: The analysis results showed that PSS level significantly negatively correlated with three social support subscales (family subscale, r= -.37, P < .05; friend subscale, r= -.38, P < .05; significant other subscale, r= -.39, P < .05). A negative correlation between PSS level and Family APGAR only reached a borderline significant level (P= .06). The t-test results for PSS scores, Family APGAR levels, and three subscale scores of MSPSS between patient and control participants were all significantly different (P < .001, P < .05, P < .05, P < .05, P < .05, respectively) and the patient participants had higher stress perception scores, lower social supports and lower family function scores than the healthy control participants. Conclusions: Our study suggested that family function and social supports were negatively correlated with patients’ subjective stress perception. Military patients with adjustment disorders tended to have higher stress perception and lower family function and social supports than those military peers who remained healthy and still provided services in their military units.

Keywords: adjustment disorders, family function, social support, stress perception

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Authors: Aminu Abdullahi Isyaku

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Semi-arid Bornu basin in northeast Nigeria is characterised with flat topography, thick cover sediments and lack of continuous bedrock outcrops discernible for field geology. This paper presents the methodology for the characterisation of neotectonic surface structures and surface lithology in the north-eastern Bornu basin in northeast Nigeria as an alternative approach to field geological mapping using free multispectral Landsat 7 ETM+, SRTM DEM and ASAR Earth Observation datasets. Spectral lithological mapping herein developed utilised spectral discrimination of the surface features identified on Landsat 7 ETM+ images to infer on the lithology using four steps including; computations of band combination images; band ratio images; supervised image classification and inferences of the lithological compositions. Two complementary approaches to lineament mapping are carried out in this study involving manual digitization and automatic lineament extraction to validate the structural lineaments extracted from the Landsat 7 ETM+ image mosaic covering the study. A comparison between the mapped surface lineaments and lineament zones show good geospatial correlation and identified the predominant NE-SW and NW-SE structural trends in the basin. Topographic profiles across different parts of the Bama Beach Ridge palaeoshorelines in the basin appear to show different elevations across the feature. It is determined that most of the drainage systems in the northeastern Bornu basin are structurally controlled with drainage lines terminating against the paleo-lake border and emptying into the Lake Chad mainly arising from the extensive topographic high-stand Bama Beach Ridge palaeoshoreline.

Keywords: Bornu Basin, lineaments, spectral lithology, tectonics

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Authors: Rafiu Babatunde Ibrahim, Richard Oluseyi Taiwo, Abiodun Toheeb Akintunde

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The relationship between road transport infrastructure and commercial activities in Nigeria has been a topical issue and identified as one of the crucial components for economic development in the country. This study examines road transport infrastructure and commercial activities along selected roads in Ede, Nigeria. The study assesses the characteristics of the selected roads, the condition of road infrastructure, the degree of road network connectivity, maintenance culture for the road infrastructure as well as commercial activities along identified roads in the study area. Stratified Sampling Techniques were used to partition the study area into core, Intermediate and Suburb Township zones. Roads were also classified into Major, Distributor and Access Roads. Field observation and measurement, as well as a questionnaire, were used to obtain primary data from 246 systematically sampled respondents along the roads selected, and they were analyzed using descriptive and inferential statistics. The study revealed that most of the roads were characterized by an incidence of potholes. A total of 448 potholes were observed, where Olowoibida Road accounted for (19.0%), Federal Polytechnic Road (17.4%), and Back to Land Road (16.3%). The majority of the selected roads have no street lights and are of open drainage systems. Also, the condition of road surfaces was observed to be deteriorating. Road network connectivity of the study area was found to be poorly connected with 11% using the alpha index and 40% of Gamma index. It was found that the tailoring business (39) is predominant on major roads and Distributor Roads, while petty trading (35) is dominant on the access road. Results of correlation analysis (r = 0.242) show that there is a low positive correlation between road infrastructure and commercial activities; the significant relationships have indeed explained how important it is in influencing commercial activities across the study area. The study concluded by emphasizing the need for the provision of more roads and proper maintenance of the existing ones. This will no doubt improve the commercial activities along the roads in the study area.

Keywords: road transport, infrastructure, commercial activities, maintenance culture

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Elisa Coraggio, Dawei Han, Weiru Liu, Theo Tryfonas

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Water is one of the most important resources for human society. The world is currently undergoing a wave of urban growth, and pollution problems are of a great impact. Monitoring water quality is a key task for the future of the environment and human species. In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for environmental monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the artificial intelligence algorithm. This study derives the methodology and demonstrates its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for the environment monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a new methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the Artificial Intelligence algorithm. This study derives the methodology and demonstrate its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.

Keywords: artificial intelligence, hydroinformatics, numerical modelling, smart cities, water quality

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Authors: George Eduful, Ebenezer A. Jackson, Kingsford A. Atanga

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This paper investigates the impacts of cable length and capacity of transformer on ferroresonant overvoltage in cable-fed transformers. The study was conducted by simulation using the EMTP RV. Results show that ferroresonance can cause dangerous overvoltages ranging from 2 to 5 per unit. These overvoltages impose stress on insulations of transformers and cables and subsequently result in system failures. Undertaking Basic Multiple Regression Analysis (BMR) on the results obtained, a statistical model was obtained in terms of cable length and transformer capacity. The model is useful for ferroresonant prediction and control in cable-fed transformers.

Keywords: ferroresonance, cable-fed transformers, EMTP RV, regression analysis

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Authors: Tesfaye Walle Mekonnen

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Maize is one of the most important staple food crops for most low-income households in the Sub-Saharan (SSA). Combined heat and drought stress is the major production threats that reduce the yield potential of biofortified maize and restrain various macro and micronutrient deficiencies highly prevalent in low-income people who rely solely on maize-based diets, SSA. This problem can be alleviated by crossing the biofortified inbred lines with different nutritional attributes, Fe, Zn, Protein, and Provitamin A, and developing agronomically superior and stable multi-nutrient maize of various genetic backgrounds. This aimed to understand the correlation between biofortified inbred lines per se and hybrid performance under combined heat and drought stress conditions (CSC). The experiment was conducted at CIMMYT, Zimbabwe, using α-lattice design with three replications. The hybrid effect was highly significant for zein fractions (α-, β-, γ- and δ-zein) zinc, (Zn), and iron (Fe) provitamin A, phytic acid, and grain yield. Under CSC, Fe, Zn concentration, provitamin A in grain and grain yield of hybrids were significantly decreased, however, the zein fraction content and phytic acid content increases in grain were increased under CSC. The phenotypic correlation between grain yield with Zn, Fe concentration, and Provitamin A in grain was strongly positive and higher under CSC than in well-watered conditions. The present investigation confirmed that under CSC, Fe, and Zn-enhanced hybrids could be forecasted to a certain scope based on the performance of and scientifically selected for desirable grain yield and related traits with CSC tolerance during hybrid development programs. In conclusion, the development of high-yielding and micronutrient-dense maize variety is possible under CSC, which could reduce the highly prevalent micronutrient in SSA.

Keywords: drought, Fe, heat, maize, protein, zein fractions, Zn

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Authors: Mahesh Kothari, K. D. Gharde

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The ANN and fuzzy logic (FL) models were developed to predict the runoff and sediment yield for catchment of Kal river, India using 21 years (1991 to 2011) rainfall and other hydrological data (evaporation, temperature and streamflow lag by one and two day) and 7 years data for sediment yield modelling. The ANN model performance improved with increasing the input vectors. The fuzzy logic model was performing with R value more than 0.95 during developmental stage and validation stage. The comparatively FL model found to be performing well to ANN in prediction of runoff and sediment yield for Kal river.

Keywords: transferred function, sigmoid, backpropagation, membership function, defuzzification

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Authors: Yoshio Kurosawa, Takao Yamaguchi

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High frequency automotive interior noise above 500 Hz considerably affects automotive passenger comfort. To reduce this noise, sound insulation material is often laminated on body panels or interior trim panels. For a more effective noise reduction, the sound reduction properties of this laminated structure need to be estimated. We have developed a new calculate tool that can roughly calculate the sound absorption and insulation properties of laminate structure and handy for designers. In this report, the outline of this tool and an analysis example applied to floor mat are introduced.

Keywords: automobile, acoustics, porous material, transfer matrix method

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Authors: Bindu I. Somarajan, Viney Gupta, Gagandeep Kaur Walia, Jasbir Kaur, Sunil Kumar, Shikha Gupta, Abadh K. Chaurasia, Dinesh Gupa, Abhinav Kaushik, Aditi Mehta, Vipin Gupta, Arundhati Sharma

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Background: Juvenile onset primary open angle glaucoma (JOAG), affects individuals under the age of 40 years. Studies on a few families of JOAG, that led to the discovery of the Myocilin gene, reported the disease to have an autosomal dominant pattern of inheritance. However, sporadic forms of JOAG been seen to be more common in some populations. Most pathological homozygous mutations in the CYP1B1 gene associated with JOAG have been seen among sporadic cases. Given the higher prevalence of sporadic JOAG cases in our population, we aimed to look for common mutations E229K and R368H, the two most common variants in the CYP1B1 gene associated with glaucoma. Objective: To determine the frequency and evaluate genotype phenotype correlation of CYP1B1 E229K and R368H mutations in a cohort of 120 sporadic Juvenile open angle glaucoma patients.Methods: Unrelated JOAG patients whose first degree relatives had been examined and found to be unaffected were included in the study. The patients and their parents were screened for E229K and R368H mutations. The phenotypic characteristics were compared between probands with and with out these mutations by SPSS v16. Results: Out of 120 JOAG patients included in the study, the E229K mutation was seen in 9 probands (7.5%) and R368H in 7 (5.8%). The average age of onset of the disease (p=0.3) and the highest untreated IOP (p=0.4) among those carrying mutations was not significantly different from those who did not have these mutations. The proportion of probands with angle dysgenesis among those with E229K and R368H mutations was 70% (11 out of 16) in comparison to 65% (67 out of 104) of those who did not harbour these mutations (p=0.56). Similarly the probands with moderate to high myopia among those with E229K and R368H mutations was 20% (3 out of 16) in comparison to 18% (18 out of 104) of those who did not harbour these mutations(p=0.59). Conclusion: The frequency of E229K and R368H mutations of the CYP1B1 gene is low even among sporadic JOAG patients. Moreover there is no clinical correlation between the presence of these mutations and disease severity

Keywords: CYP1B1, gene, IOP, JOAG, mutation

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Authors: Alokananda Ghosh, Shraban Sarkar

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The study aims to identify suitable sites for new hospitals with critical obstetric care facilities in Birbhum, one of the vulnerable and underserved districts of Eastern India, considering six main and 14 sub-criteria, using GIS-based Analytic Hierarchy Process (AHP) and Multi-Attribute Utility Theory (MAUT) approach. The criteria were identified through field surveys and previous literature. After collecting expert decisions, a pairwise comparison matrix was prepared using the Saaty scale to calculate the weights through AHP. On the contrary, objective weighting methods, i.e., Entropy and Criteria Importance through Interaction Correlation (CRITIC), were used to perform the MAUT. Finally, suitability maps were prepared by weighted sum analysis. Sensitivity analyses of AHP were performed to explore the effect of dominant criteria. Results from AHP reveal that ‘maternal death in transit’ followed by ‘accessibility and connectivity’, ‘maternal health care service (MHCS) coverage gap’ were three important criteria with comparatively higher weighted values. Whereas ‘accessibility and connectivity’ and ‘maternal death in transit’ were observed to have more imprint in entropy and CRITIC, respectively. While comparing the predictive suitable classes of these three models with the layer of existing hospitals, except Entropy-MAUT, the other two are pointing towards the left-over underserved areas of existing facilities. Only 43%-67% of existing hospitals were in the moderate to lower suitable class. Therefore, the results of the predictive models might bring valuable input in future planning.

Keywords: hospital site suitability, analytic hierarchy process, multi-attribute utility theory, entropy, criteria importance through interaction correlation, multi-criteria decision analysis

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Authors: Yagya Murti Aryal, Bipin Adhikari, Pradeep Gyawali

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The Lesser Himalaya zone of Nepal consists of thrusting and folding belts, which play an important role in the sustainable management of groundwater in the Himalayan regions. The study area is located in the Dolakha and Ramechhap Districts of Bagmati Province, Nepal. Geologically, these districts are situated in the Lesser Himalayas and partly encompass the Higher Himalayan rock sequence, which includes low-grade to high-grade metamorphic rocks. Following the Gorkha Earthquake in 2015, numerous springs dried up, and many others are currently experiencing depletion due to the distortion of the natural groundwater flow. The primary objective of this study is to identify potential groundwater areas and determine suitable sites for artificial groundwater recharge. Two distinct statistical approaches were used to develop models: The Frequency Ratio (FR) and Shannon Entropy (SE) methods. The study utilized both primary and secondary datasets and incorporated significant role and controlling factors derived from field works and literature reviews. Field data collection involved spring inventory, soil analysis, lithology assessment, and hydro-geomorphology study. Additionally, slope, aspect, drainage density, and lineament density were extracted from a Digital Elevation Model (DEM) using GIS and transformed into thematic layers. For training and validation, 114 springs were divided into a 70/30 ratio, with an equal number of non-spring pixels. After assigning weights to each class based on the two proposed models, a groundwater potential map was generated using GIS, classifying the area into five levels: very low, low, moderate, high, and very high. The model's outcome reveals that over 41% of the area falls into the low and very low potential categories, while only 30% of the area demonstrates a high probability of groundwater potential. To evaluate model performance, accuracy was assessed using the Area under the Curve (AUC). The success rate AUC values for the FR and SE methods were determined to be 78.73% and 77.09%, respectively. Additionally, the prediction rate AUC values for the FR and SE methods were calculated as 76.31% and 74.08%. The results indicate that the FR model exhibits greater prediction capability compared to the SE model in this case study.

Keywords: groundwater potential mapping, frequency ratio, Shannon’s Entropy, Lesser Himalaya Zone, sustainable groundwater management

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Authors: Marcel Meinhardt, Manfred Keuser, Thomas Braml

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Cracking of reinforced concrete is a complex phenomenon induced by direct loads or restraints affecting reinforced concrete structures as soon as the tensile strength of the concrete is exceeded. Hence it is important to predict where cracks will be located and how they will propagate. The bond theory and the crack formulas in the actual design codes, for example, DIN EN 1992-1-1, are all based on the assumption that the reinforcement bars are embedded in homogeneous concrete without taking into account the influence of transverse reinforcement and the real stress situation. However, it can often be observed that real structures such as walls, slabs or beams show a crack spacing that is orientated to the transverse reinforcement bars or to the stirrups. In most Finite Element Analysis studies, the smeared crack approach is used for crack prediction. The disadvantage of this model is that the typical strain localization of a crack on element level can’t be seen. The crack propagation in concrete is a discontinuous process characterized by different factors such as the initial random distribution of defects or the scatter of material properties. Such behavior presupposes the elaboration of adequate models and methods of simulation because traditional mechanical approaches deal mainly with average material parameters. This paper concerned with the modelling of the initiation and the propagation of cracks in reinforced concrete structures considering the influence of transverse reinforcement and the real stress distribution in reinforced concrete (R/C) beams/plates in bending action. Therefore, a parameter study was carried out to investigate: (I) the influence of the transversal reinforcement to the stress distribution in concrete in bending mode and (II) the crack initiation in dependence of the diameter and distance of the transversal reinforcement to each other. The numerical investigations on the crack initiation and propagation were carried out with a 2D reinforced concrete structure subjected to quasi static loading and given boundary conditions. To model the uncertainty in the tensile strength of concrete in the Finite Element Analysis correlated normally and lognormally distributed random filed with different correlation lengths were generated. The paper also presents and discuss different methods to generate random fields, e.g. the Covariance Matrix Decomposition Method. For all computations, a plastic constitutive law with softening was used to model the crack initiation and the damage of the concrete in tension. It was found that the distributions of crack spacing and crack widths are highly dependent of the used random field. These distributions are validated to experimental studies on R/C panels which were carried out at the Laboratory for Structural Engineering at the University of the German Armed Forces in Munich. Also, a recommendation for parameters of the random field for realistic modelling the uncertainty of the tensile strength is given. The aim of this research was to show a method in which the localization of strains and cracks as well as the influence of transverse reinforcement on the crack initiation and propagation in Finite Element Analysis can be seen.

Keywords: crack initiation, crack modelling, crack propagation, cracks, numerical simulation, random fields, reinforced concrete, stochastic

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Authors: Hussain M. Al-foudari, Weizhong Chen, James M. Bishop

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Correlations between oceanographic variables and monthly catch rates of total shrimp and those of each of the major species (Penaeus semisulcatus, Metapenaeus affinis and Parapenaeopsis stylifera) showed significant differences for particular conditions. Catches of P. semisulcatus were basically positively correlated with temperature, i.e., the higher the temperature, the higher the catch rate, while those of M. affinis and P. stylifera were negatively correlated with temperature, i.e., high catch rates occurred in the low temperature waters. Thus, during the months January and April, P. semisulcatus preferred waters with high temperature, usually the offshore and southern areas, while M. affinis and P. stylifera preferred waters with low temperature, usually inshore and northern areas. The relationships between the catch rate of P. semisulcatus and salinity were not so clear. Results indicated that although salinity was one of the factors affecting the distribution of P. semisulcatus, it was not the principal factor, and impacts from other variables, such as temperature, might overshadow the correlation between the catch rates of P. semisulcatus and salinity. The relationship between shrimp catch rates and dissolved oxygen (DO) also showed mixed results. The catch rates of M. affinis increased with a decrease of surface DO in November 2013, but decreased with lower bottom DO in December. These results indicated that DO might be a factor affecting distributions of the shrimp; however; the true correlation between catch rate and DO might be easily overshadowed by other environmental variables. Catch rates of P. semisulcatus did not show any relationship with depth. P. semisulcatus is a migratory species and widely distributed in Kuwait's waters.During the shrimp season from July through December, P. semisulcatus occurs in almost all areas in Kuwait's waters irrespective of water depth. The catch rates of M. affinis and P. stylifera, however, showed clear relationships with depth. Both species had significantly higher catch rates in shallower waters, indicative of their restricted distribution.

Keywords: Kuwait, Penaeus semisulcatus, Metapenaeus affinis, Parapenaeopsis stylifera, Arabian gulf

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Authors: Kiyanoush Ghalavand

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Aquaculture production is inherently a risky business, and farmers face a variety of weather, pest, disease, inptut supply, and market related risks. There are many factors out farmers control and unpredictable. Insurance has an important role in aquaculture production and is a tool to support farmers against threats. Investigation of factors affecting aquaculture farmers' adoption of aquaculture insurance strategy was the objective of this study. The purpose of this study was determining the related factors to adoption of insurance by aquaculture farmers in Khuzestan province, Iran. The research design was a descriptive and correlation surveying method. Aquaculture farmers in Khuzestan province were the target population for this study. A random sample of aquaculture selected (N=1830, n =139). The main result of the study reveled that exist correlation between the level of education, knowledge about purpose of insurance, participation in extension course, visit with insurance organization, and contact with extension agents to the adoption of insurance by aquaculture farmers were significantly positive. By using Bartlett's test and KMO test, determine whether research variables are appropriate for factor analysis (Sig = 0.000, Bartlett test = 0.9724, KMO = 0.74). The number of factors was determined using a split plot, eigenvalue, and percent of variance. An examination of the items and their factors loadings was used to understand the nature of the nine factors. To reduce subjectivity, items with factor loading equal to or greater than 0.5 were considered most important when factors were labeled. The nine factors were labeled (1) Extension and education activities, (2) Economical characteristics, (3) Governmental support, (4) communicational channel, (5) local leaders, (6) Facilitate in given damage (7) Motivation establishing, (8) Given damage in appropriate methods and (9) Appropriate activities by insurance organization. The results obtained from the factors analysis reveal that the nine factors explain percentage75 of the variation of the adoption of insurance of the adoption of insurance by aquaculture farmers in Khuzestan province.

Keywords: aquaculture farmers, insurance, factorial analysis, Khuzestan province, risks

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Authors: Zan Azma Nasruddin, Irwan Mazlin, Nor Aziah Daud, Fauziah Redzuan, Fariza Hanis Abdul Razak

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This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process.

Keywords: convolutional neural network, Makhraj recognition, speech recognition, signal processing, tensorflow

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Authors: Autcha Araveeporn

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This paper presents a study about a nonparametric regression model consisting of a smoothing spline method and a penalized spline regression method. We also compare the techniques used for estimation and prediction of nonparametric regression model. We tried both methods with crude oil prices in dollars per barrel and the Stock Exchange of Thailand (SET) index. According to the results, it is concluded that smoothing spline method performs better than that of penalized spline regression method.

Keywords: nonparametric regression model, penalized spline regression method, smoothing spline method, Stock Exchange of Thailand (SET)

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Authors: Yavuz Erdoğan, Hidayet Çiftçi

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Cyberbullying, a phenomenon among adolescents, is defined as actions that use information and communication technologies such as social media to support deliberate, repeated, and hostile behaviour by an individual or group. With the advancement in communication and information technology, cyberbullying has expanded its boundaries among students in schools. Thus, parents, psychologists, educators, and lawmakers must become aware of the potential risks of this phenomenon. In the light of these perspectives, this study aims to investigate the relationships between cyberbullying perception and Facebook attitudes of Turkish students. A survey method was used for the study and the data were collected by “Cyberbullying Perception Scale”, “Facebook Attitude Scale” and “Personal Information Form”. For this purpose, study has been conducted during 2014-2015 academic year, with a total of 748 students with 493 male (%65.9) and 255 female (%34.1) from randomly selected high schools. In the analysis of data Pearson correlation and multiple regression analysis, multivariate analysis of variance (MANOVA) and Scheffe post hoc test has been used. At the end of the study, the results displayed a negative correlation between Turkish students’ Facebook attitudes and cyberbullying perception (r=-.210; p<0.05). In order to identify the predictors of students’ cyberbullying perception, multiple regression analysis was used. As a result, significant relations were detected between cyberbullying perception and independent variables (F=5.102; p<0.05). Independent variables together explain 11.0% of the total variance in cyberbullying scores. The variables that significantly predict the students’ cyberbullying perception are Facebook attitudes (t=-5.875; p<0.05), and gender (t=3.035; p<0.05). In order to calculate the effects of independent variables on students’ Facebook attitudes and cyberbullying perception MANOVA was conducted. The results of the MANOVA indicate that the Facebook attitudes and cyberbullying perception were significantly differed according to students’ gender, age, educational attainment of the mother, educational attainment of the father, income of the family and daily usage of internet.

Keywords: facebook, cyberbullying, attitude, internet usage

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Authors: H. Hosseini, B. V. Berdyshev, I. Iskopintsev

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This paper presents a rheological model for producing shape-memory thermoplastic polymers. Shape-memory occurs as a result of internal rearrangement of the structural elements of a polymer. A non-linear viscoelastic model was developed that allows qualitative and quantitative prediction of the stress-strain behavior of shape-memory polymers during heating. This research was done to develop a technique to determine the maximum possible change in size of heat-shrinkable products during heating. The rheological model used in this work was particularly suitable for defining process parameters and constructive parameters of the processing equipment.

Keywords: elastic deformation, heating, shape-memory polymers, stress-strain behavior, viscoelastic model

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Authors: U. Chattaraj, K. Dhusiya, M. Raviteja

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Perception reaction time of drivers is an outcome of human thought process, which is vague and approximate in nature and also varies from driver to driver. So, in this study a fuzzy logic based model for prediction of the same has been presented, which seems suitable. The control factors, like, age, experience, intensity of driving of the driver, speed of the vehicle and distance of stimulus have been considered as premise variables in the model, in which the perception reaction time is the consequence variable. Results show that the model is able to explain the impacts of the control factors on perception reaction time properly.

Keywords: driver, fuzzy logic, perception reaction time, premise variable

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Authors: Chau So Ling

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I.INTRODUCTION: Educators have always been interested in exploring factors that contribute to students’ academic success. It is beyond question that language, as a medium of instruction, will affect student learning. This paper tries to investigate whether language is a crucial factor in determining students’ achievement in their studies. II. BACKGROUND AND SIGNIFICANCE OF STUDY: The issue of using English as a medium of instruction in Hong Kong is a special topic because Hong Kong is a post-colonial and international city which a British colony. In such a specific language environment, researchers in the education field have always been interested in investigating students’ language proficiency and its relation to academic achievement and other related educational indicators such as motivation to learn, self-esteem, learning effectiveness, self-efficacy, etc. Along this line of thought, this study specifically focused on business education. III. METHODOLOGY: The methodology in this study involved two sequential stages, namely, a focus group interview and a data analysis. The whole study was directed towards both qualitative and quantitative aspects. The subjects of the study were divided into two groups. For the first group participating in the interview, a total of ten high school students were invited. They studied Business Studies, and their English standard was varied. The theme of the discussion was “Does English affect your learning and examination results of Business Studies?” The students were facilitated to discuss the extent to which English standard affected their learning of Business subjects and requested to rate the correlation between English and performance of Business Studies on a five-point scale. The second stage of the study involved another group of students. They were high school graduates who had taken the public examination for entering universities. A database containing their public examination results for different subjects has been obtained for the purpose of statistical analysis. Hypotheses were tested and evidence was obtained from the focus group interview to triangulate the findings. V. MAJOR FINDINGS AND CONCLUSION: By sharing of personal experience, the discussion of focus group interviews indicated that higher English standards could help the students achieve better learning and examination performance. In order to end the interview, the students were asked to indicate the correlation between English proficiency and performance of Business Studies on a five-point scale. With point one meant least correlated, ninety percent of the students gave point four for the correlation. The preliminary results illustrated that English plays an important role in students’ learning of Business Studies, or at least this was what the students perceived, which set the hypotheses for the study. After conducting the focus group interview, further evidence had to be gathered to support the hypotheses. The data analysis part tried to find out the relationship by correlating the students’ public examination results of Business Studies and levels of English standard. The results indicated a positive correlation between their English standard and Business Studies examination performance. In order to highlight the importance of the English language to the study of Business Studies, the correlation between the public examination results of other non-business subjects was also tested. Statistical results showed that language does play a role in affecting students’ performance in studying Business subjects than the other subjects. The explanation includes the dynamic subject nature, examination format and study requirements, the specialist language used, etc. Unlike Science and Geography, students in their learning process might find it more difficult to relate business concepts or terminologies to their own experience, and there are not many obvious physical or practical activities or visual aids to serve as evidence or experiments. It is well-researched in Hong Kong that English proficiency is a determinant of academic success. Other research studies verified such a notion. For example, research revealed that the more enriched the language experience, the better the cognitive performance in conceptual tasks. The ability to perform this kind of task is particularly important to students taking Business subjects. Another research was carried out in the UK, which was geared towards identifying and analyzing the reasons for underachievement across a cohort of GCSE students taking Business Studies. Results showed that weak language ability was the main barrier to raising students’ performance levels. It seemed that the interview result was successfully triangulated with data findings. Although education failure cannot be restricted to linguistic failure and language is just one of the variables to play in determining academic achievement, it is generally accepted that language does affect students’ academic performance. It is just a matter of extent. This paper provides recommendations for business educators on students’ language training and sheds light on more research possibilities in this area.

Keywords: academic performance, language, learning, medium of instruction

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Authors: Hind Hassan Arruwaite

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Conceptual metaphor is a cognitive semantic tool that provides access to people's conceptual systems. The correlation in the human conceptual system surpasses limited time and specific cultures. The universal associations provide universal schemas that organize people's conceptualization of the world. The study aims to explore how the cultural associations used in conceptual metaphors create commonalities and harmony between people of the world. In the research methodology, the researcher implemented Critical Metaphor Analysis, Metaphor Candidate Identification and Metaphor Identification Procedure models to deliver qualitative and descriptive findings. The semantic tension was the key criterion in identifying metaphorically used words in the headlines. The research materials are the oil trade conceptual metaphors used in the headlines of Arab News and Saudi Gazette Newspapers. The data will be uploaded to the self-constructed corpus to examine electronic lists for identifying conceptual metaphors. The study investigates the types of conceptual metaphors used in the headlines of the newspapers, the cultural associations identified in the conceptual metaphors, and whether the identified cultural associations in conceptual metaphors create universal conceptual schemas. The study aligned with previous seminal works on conceptual metaphor theory in emphasizing the distinctive power of conceptual metaphors in exposing the cultural associations that unify people's perceptions. The correlation of people conceptualization provides universal schemas that involve elements of human sensorimotor experiences. The study contributes to exposing the shared cultural associations that ensure the commonality of all humankind's thinking mechanism.

Keywords: critical discourse analysis, critical metaphor analysis, conceptual metaphor theory, primary and specific metaphors, corpus-driven approach, universal associations, image schema, sensorimotor experience, oil trade

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Authors: Mustafa M. Donma, Orkide Donma

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Metabolisms of macrominerals, those of calcium, phosphorus and magnesium, are closely associated with the metabolism of vitamin D. Particularly magnesium, the second most abundant intracellular cation, is related to biochemical and metabolic processes in the body, such as those of carbohydrates, proteins and lipids. The status of each mineral was investigated in obesity to some extent. Their products and ratios may possibly give much more detailed information about the matter. The aim of this study is to investigate possible relations between each macromineral and some obesity-related parameters. This study was performed on 235 children, whose ages were between 06-18 years. Aside from anthropometric measurements, hematological analyses were performed. TANITA body composition monitor using bioelectrical impedance analysis technology was used to establish some obesity-related parameters including basal metabolic rate (BMR), total fat, mineral and muscle masses. World Health Organization body mass index (BMI) percentiles for age and sex were used to constitute the groups. The values above 99^th percentile were defined as morbid obesity. Those between 95^th and 99^th percentiles were included into the obese group. The overweight group comprised of children whose percentiles were between 95 and 85. Children between the 85^th and 15^th percentiles were defined as normal. Metabolic syndrome (MetS) components (waist circumference, fasting blood glucose, triacylglycerol, high density lipoprotein cholesterol, systolic pressure, diastolic pressure) were determined. High performance liquid chromatography was used to determine Vitamin D status by measuring 25-hydroxy cholecalciferol (25-hydroxy vitamin D₃, 25(OH)D). Vitamin D values above 30.0 ng/ml were accepted as sufficient. SPSS statistical package program was used for the evaluation of data. The statistical significance degree was accepted as p < 0.05. The important points were the correlations found between vitamin D and magnesium as well as phosphorus (p < 0.05) that existed in the group with normal BMI values. These correlations were lost in the other groups. The ratio of phosphorus to magnesium was even much more highly correlated with vitamin D (p < 0.001). The negative correlation between magnesium and total fat mass (p < 0.01) was confined to the MetS group showing the inverse relationship between magnesium levels and obesity degree. In this group, calcium*magnesium product exhibited the highest correlation with total fat mass (p < 0.001) among all groups. Only in the MetS group was a negative correlation found between BMR and calcium*magnesium product (p < 0.05). In conclusion, magnesium is located at the center of attraction concerning its relationships with vitamin D, fat mass and MetS. The ratios and products derived from macrominerals including magnesium have pointed out stronger associations other than each element alone. Final considerations have shown that unique correlations of magnesium as well as calcium*magnesium product with total fat mass have drawn attention particularly in the MetS group, possibly due to the derangements in some basic elements of carbohydrate as well as lipid metabolism.

Keywords: macrominerals, metabolic syndrome, pediatric obesity, vitamin D

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Authors: Andrea L. Arnett, Ann T. Packard, Yolanda I. Garces, Kenneth W. Merrell

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Objective: Pathologic response following neoadjuvant chemoradiation (CRT) has been associated with improved overall survival (OS). Conflicting results have been reported regarding the pathologic predictive value of positron emission tomography (PET) response in patients with stage III lung cancer. The aim of this study was to evaluate the correlation between post-treatment PET response and pathologic response utilizing novel FDG-PET parameters. Methods: This retrospective study included patients with non-metastatic, stage IIIA (N2) NSCLC cancer treated with CRT followed by resection. All patients underwent PET prior to and after neoadjuvant CRT. Univariate analysis was utilized to assess correlations between PET response, nodal clearance, pCR, and near-complete pathologic response (defined as the microscopic residual disease or less). Maximal standard uptake value (SUV), standard uptake ratio (SUR) [normalized independently to the liver (SUR-L) and blood pool (SUR-BP)], metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured pre- and post-chemoradiation. Results: A total of 44 patients were included for review. Median age was 61.9 years, and median follow-up was 2.6 years. Histologic subtypes included adenocarcinoma (72.2%) and squamous cell carcinoma (22.7%), and the majority of patients had the T2 disease (59.1%). The rate of pCR and near-complete pathologic response within the primary lesion was 28.9% and 44.4%, respectively. The average reduction in SUVmₐₓ was 9.2 units (range -1.9-32.8), and the majority of patients demonstrated some degree of favorable treatment response. SUR-BP and SUR-L showed a mean reduction of 4.7 units (range -0.1-17.3) and 3.5 units (range –1.7-12.6), respectively. Variation in PET response was not significantly associated with histologic subtype, concurrent chemotherapy type, stage, or radiation dose. No significant correlation was found between pathologic response and absolute change in MTV or TLG. Reduction in SUVmₐₓ and SUR were associated with increased rate of pathologic response (p ≤ 0.02). This correlation was not impacted by normalization of SUR to liver versus mediastinal blood pool. A threshold of > 75% decrease in SUR-L correlated with near-complete response, with a sensitivity of 57.9% and specificity of 85.7%, as well as positive and negative predictive values of 78.6% and 69.2%, respectively (diagnostic odds ratio [DOR]: 5.6, p=0.02). A threshold of >50% decrease in SUR was also significantly associated pathologic response (DOR 12.9, p=0.2), but specificity was substantially lower when utilizing this threshold value. No significant association was found between nodal PET parameters and pathologic nodal clearance. Conclusions: Our results suggest that treatment response to neoadjuvant therapy as assessed on PET imaging can be a predictor of pathologic response when evaluated via SUV and SUR. SUR parameters were associated with higher diagnostic odds ratios, suggesting improved predictive utility compared to SUVmₐₓ. MTV and TLG did not prove to be significant predictors of pathologic response but may warrant further investigation in a larger cohort of patients.

Keywords: lung cancer, positron emission tomography (PET), standard uptake ratio (SUR), standard uptake value (SUV)

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Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

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This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

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Authors: Jasbir Arora, Indu Talwar, Daisy Sahni, Vidya Rattan

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Introduction: Age estimation is a crucial step to build the identity of a person, both in case of deceased and alive. In adults, age can be estimated on the basis of six regressive (Attrition, Secondary dentine, Dentine transparency, Root resorption, Cementum apposition and Periodontal Disease) changes in teeth qualitatively using scoring system and quantitatively by micrometric method. The present research was designed to establish the reliability of qualitative (method 1) and quantitative (method 2) of age estimation among North Indians and to compare the efficacy of these two methods. Method: 250 single-rooted extracted teeth (18-75 yrs.) were collected from Department of Oral Health Sciences, PGIMER, Chandigarh. Before extraction, periodontal score of each tooth was noted. Labiolingual sections were prepared and examined under light microscope for regressive changes. Each parameter was scored using Gustafson’s 0-3 point score system (qualitative), and total score was calculated. For quantitative method, each regressive change was measured quantitatively in form of 18 micrometric parameters under microscope with the help of measuring eyepiece. Age was estimated using linear and multiple regression analysis in Gustafson’s method and Kedici’s method respectively. Estimated age was compared with actual age on the basis of absolute mean error. Results: In pooled data, by Gustafson’s method, significant correlation (r= 0.8) was observed between total score and actual age. Total score generated an absolute mean error of ±7.8 years. Whereas, for Kedici’s method, a value of correlation coefficient of r=0.5 (p<0.01) was observed between all the eighteen micrometric parameters and known age. Using multiple regression equation, age was estimated, and an absolute mean error of age was found to be ±12.18 years. Conclusion: Gustafson’s (qualitative) method was found to be a better predictor for age estimation among North Indians.

Keywords: forensic odontology, age estimation, North India, teeth

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Authors: Jie Lin, Yiwen Pan, Li Zhang, Zhangyong Xia

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Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of lipids, immune cells, and extracellular matrix in the arterial walls. This pathological process can lead to the formation of plaques that can obstruct blood flow and trigger various cardiovascular diseases such as heart attack and stroke. The underlying molecular mechanisms still remain unclear, although many studies revealed the dysfunction of endothelial cells, recruitment and activation of monocytes and macrophages, and the production of pro-inflammatory cytokines and chemokines in atherosclerosis. This study aimed to identify hub genes involved in the progression of atherosclerosis and to analyze their biological function in silico, thereby enhancing our understanding of the disease’s molecular mechanisms. Through the analysis of microarray data, we examined the gene expression in media and neo-intima from plaques, as well as distant macroscopically intact tissue, across a cohort of 32 hypertensive patients. Initially, 112 differentially expressed genes (DEGs) were identified. Subsequent immune infiltration analysis indicated a predominant presence of 27 immune cell types in the atherosclerosis group, particularly noting an increase in monocytes and macrophages. In the Weighted gene co-expression network analysis (WGCNA), 10 modules with a minimum of 30 genes were defined as key modules, with blue, dark, Oliver green and sky-blue modules being the most significant. These modules corresponded respectively to monocyte, activated B cell, and activated CD4 T cell gene patterns, revealing a strong morphological-genetic correlation. From these three gene patterns (modules morphology), a total of 2509 key genes (Gene Significance >0.2, module membership>0.8) were extracted. Six hub genes (CD36, DPP4, HMOX1, PLA2G7, PLN2, and ACADL) were then identified by intersecting 2509 key genes, 102 DEGs with lipid-related genes from the Genecard database. The bio-functional analysis of six hub genes was estimated by a robust classifier with an area under the curve (AUC) of 0.873 in the ROC plot, indicating excellent efficacy in differentiating between the disease and control group. Moreover, PCA visualization demonstrated clear separation between the groups based on these six hub genes, suggesting their potential utility as classification features in predictive models. Protein-protein interaction (PPI) analysis highlighted DPP4 as the most interconnected gene. Within the constructed key gene-drug network, 462 drugs were predicted, with ursodeoxycholic acid (UDCA) being identified as a potential therapeutic agent for modulating DPP4 expression. In summary, our study identified critical hub genes implicated in the progression of atherosclerosis through comprehensive bioinformatic analyses. These findings not only advance our understanding of the disease but also pave the way for applying similar analytical frameworks and predictive models to other diseases, thereby broadening the potential for clinical applications and therapeutic discoveries.

Keywords: atherosclerosis, hub genes, drug prediction, bioinformatics

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Authors: Kantha Siddhanth Gujjari, Shaani Singhal, Robert Andrew Danks, Adrian Praeger

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Background: Trigeminal neuralgia (TN) is a heterogenous pain syndrome characterised by short paroxysms of lancinating facial pain in the distribution of the trigeminal nerve, often triggered by usually innocuous stimuli. TN has a low prevalence of less than 0.1%, of which 80% to 90% is caused by compression of the trigeminal nerve from an adjacent artery or vein. The root entry zone of the trigeminal nerve is most sensitive to neurovascular conflict (NVC), causing dysmyelination. Whilst microvascular decompression (MVD) is an effective treatment for TN with NVC, all patients do not achieve long-term pain relief. Pre-operative scoring systems by Panczykowski and Hardaway have been proposed but have not been externally validated. These pre-operative scoring systems are composite scores calculated according to a subtype of TN, presence and degree of neurovascular conflict, and response to medical treatments. There is discordance in the assessment of NVC identified on pre-operative magnetic resonance imaging (MRI) between neurosurgeons and radiologists. To our best knowledge, the prognostic impact for MVD of this difference of interpretation has not previously been investigated in the form of a composite scoring system such as those suggested by Panczykowski and Hardaway. Aims: This study aims to identify prognostic factors and externally validate the proposed scoring systems by Panczykowski and Hardaway for TN. A secondary aim is to investigate the prognostic difference between a neurosurgeon's interpretation of NVC on MRI compared with a radiologist’s. Methods: This retrospective cohort study included 95 patients who underwent de novo MVD in a single neurosurgical unit in Melbourne. Data was recorded from patients’ hospital records and neurosurgeon’s correspondence from perioperative clinic reviews. Patient demographics, type of TN, distribution of TN, response to carbamazepine, neurosurgeon, and radiologist interpretation of NVC on MRI, were clearly described prospectively and preoperatively in the correspondence. Scoring systems published by Panczykowski et al. and Hardaway et al. were used to determine composite scores, which were compared with the recurrence of TN recorded during follow-up over 1-year. Categorical data analysed using Pearson chi-square testing. Independent numerical and nominal data analysed with logistical regression. Results: Logistical regression showed that a Panczykowski composite score of greater than 3 points was associated with a higher likelihood of pain-free outcome 1-year post-MVD with an OR 1.81 (95%CI 1.41-2.61, p=0.032). The composite score using neurosurgeon’s impression of NVC had an OR 2.96 (95%CI 2.28-3.31, p=0.048). A Hardaway composite score of greater than 2 points was associated with a higher likelihood of pain-free outcome 1 year post-MVD with an OR 3.41 (95%CI 2.58-4.37, p=0.028). The composite score using neurosurgeon’s impression of NVC had an OR 3.96 (95%CI 3.01-4.65, p=0.042). Conclusion: Composite scores developed by Panczykowski and Hardaway were validated for the prediction of response to MVD in TN. A composite score based on the neurosurgeon’s interpretation of NVC on MRI, when compared with the radiologist’s had a greater correlation with pain-free outcomes 1 year post-MVD.

Keywords: de novo microvascular decompression, neurovascular conflict, prognosis, trigeminal neuralgia

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Authors: C. Vázquez, L. M. Gigirey, C. P. del Oro, S. Seoane

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Poor vision is common among older people, and several studies show connections between visual impairment and cognitive function. 15 older adult live in Galician Government nursing homes, and cognitive decline is one of the main reasons of admission. Objectives: (1) To evaluate functional far and near vision of older people with cognitive impairment. (2) To determine connections between visual and cognitive state of “our” residents. Methodology: A total of 364 older adults (aged 65 years or more) underwent a visual and cognitive screening. We tested presenting visual acuity (binocular visual acuity with habitual correction if warn) for distance and near vision (E-Snellen, usual working distance for near vision). Binocular presenting visual acuity less than 0.3 was used as cut point for diagnosis of visual impairment. Exclusion criteria included immobilized residents unable to reach the USC Dual Sensory Loss Unit for visual screening. To screen cognition we employed the mini-mental examination test (Spanish version). Analysis of categorical variables was performed using chi-square tests. We utilized Pearson and Spearman correlation tests and the variance analysis to determine differences between groups of interest (SPSS 19.0 version). Results: the percentage of residents with cognitive decline reaches 32.2% Prevalence of visual impairment for distance and near vision increases among those subjects with cognitive impairment respect those with normal cognition. Shift correlation exists between distance visual acuity and mini-mental test (age and sex controlled), and moderate association was found in case of near vision (p<0.01). Conclusion: First results shows that people with cognitive impairment have poor functional distance and near vision than those with normal cognition. Next step will be to analyse the individual contribution of distance and near vision loss on cognition.

Keywords: visual impairment, cognition, aging, nursing homes

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Authors: Mounir Azzam, Valerie Graw, Andreas Rienow

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The Syrian war, which commenced in 2011, has resulted in significant changes in the real estate market in the Damascus metropolitan area, with rising levels of insecurity and disputes over tenure rights. The quest for spatial justice is, therefore, imperative, and this study performs a spatiotemporal analysis to investigate the impact of the war on real estate differentiation. Using the hedonic price models including 2,411 housing transactions over the period 2010-2022, this study aims to understand the spatial dynamics of the real estate market in wartime. Our findings indicate that war variables have had a significant impact on the differentiation and depreciation of property prices. Notably, property attributes have a more substantial impact on real estate values than district location, with severely damaged buildings in Damascus city resulting in an 89% decline in prices, while prices in Rural Damascus districts have decreased by 50%. Additionally, this study examines the urban texture of Damascus using correlation and homogeneity statistics derived from the gray-level co-occurrence matrix obtained from Google Earth Engine. We monitored 250 samples from hedonic datasets within three different years of the Syrian war (2015, 2019, and 2022). Our findings show that correlation values were highly differentiated, particularly among Rural Damascus districts, with a total decline of 87.2%. While homogeneity values decreased overall between 2015 and 2019, they improved slightly after 2019. The findings have valuable implications, not only for investment prospects in setting up a successful reconstruction strategy but also for spatial justice of property rights in strongly encouraging sustainable real estate development.

Keywords: hedonic price, real estate differentiation, reconstruction strategy, spatial justice, urban texture analysis

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