Search results for: general data protection regulation

Authors: Tharaki Hetti Arachchi

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Sri Lanka has undergone numerous changes in the fields of social-economic and cultural processors during the past decades. Consequently, the Sri Lankan construction industry was subjected to rapid growth while contributing a considerable amount to the national economy. The prevailing situation under the Covid-19 pandemic exhibited challenges to almost all of the sectors of the country in attaining success. Although productivity is one of the dimensions that measure the degree of project success, achieving sufficient productivity has become challengeable due to the Covid-19 outbreak. As employee motivation is an influential factor in defining productivity, the present study becomes significant in discovering ways of enhancing construction productivity via employee motivation. The study has adopted a combination of qualitative and quantitative methodologies in attaining the study objectives. While the research population refers to construction professionals in Sri Lanka, the study sample is aimed at Quantity Surveyors in the bottom and middle managements of organizational hierarchies. The data collection was implemented via primary and secondary sources. The primary data collection was accomplished by undertaking semi-structured interviews and online questionnaire surveys while sampling the overall respondents based on the purposive sample method. The responses of the questionnaire survey were gathered in a form of a ‘Likert Scale’ to examine the degree of applicability on each respondent. Overall, 76.36% of primary data were recovered from the expected count while obtaining 60 responses from the questionnaire survey and 24 responses from interviews. Secondary data were obtained by reviewing sources such as research articles, journals, newspapers, books, etc. The findings suggest adopting and enhancing sixteen motivational factors in achieving greater productivity in the Sri Lankan construction sector.

Keywords: Covid 19 pandemic, motivation, quantity surveying, Sri Lanka

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Authors: Noriyuki Suyama

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This paper discusses purchasing behavior in retail stores, with a particular focus on the impact of weather changes on customers' purchasing behavior. Weather conditions are one of the factors that greatly affect the management and operation of retail stores. However, there is very little research on the relationship between weather conditions and marketing from an academic perspective, although there is some importance from a practical standpoint and knowledge based on experience. For example, customers are more hesitant to go out when it rains than when it is sunny, and they may postpone purchases or buy only the minimum necessary items even if they do go out. It is not difficult to imagine that weather has a significant impact on consumer behavior. To the best of the authors' knowledge, there have been only a few studies that have delved into the purchasing behavior of individual customers. According to Hirata (2018), the economic impact of weather in the United States is estimated to be 3.4% of GDP, or "$485 billion ± $240 billion per year. However, weather data is not yet fully utilized. Representative industries include transportation-related industries (e.g., airlines, shipping, roads, railroads), leisure-related industries (e.g., leisure facilities, event organizers), energy and infrastructure-related industries (e.g., construction, factories, electricity and gas), agriculture-related industries (e.g., agricultural organizations, producers), and retail-related industries (e.g., retail, food service, convenience stores, etc.). This paper focuses on the retail industry and advances research on weather. The first reason is that, as far as the author has investigated the retail industry, only grocery retailers use temperature, rainfall, wind, weather, and humidity as parameters for their products, and there are very few examples of academic use in other retail industries. Second, according to NBL's "Toward Data Utilization Starting from Consumer Contact Points in the Retail Industry," labor productivity in the retail industry is very low compared to other industries. According to Hirata (2018) mentioned above, improving labor productivity in the retail industry is recognized as a major challenge. On the other hand, according to the "Survey and Research on Measurement Methods for Information Distribution and Accumulation (2013)" by the Ministry of Internal Affairs and Communications, the amount of data accumulated by each industry is extremely large in the retail industry, so new applications are expected by analyzing these data together with weather data. Third, there is currently a wealth of weather-related information available. There are, for example, companies such as WeatherNews, Inc. that make weather information their business and not only disseminate weather information but also disseminate information that supports businesses in various industries. Despite the wide range of influences that weather has on business, the impact of weather has not been a subject of research in the retail industry, where business models need to be imagined, especially from a micro perspective. In this paper, the author discuss the important aspects of the impact of weather on marketing strategies in the non-food retail industry.

Keywords: consumer behavior, weather marketing, marketing science, big data, retail marketing

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Authors: Shivalaxmi Arumugham

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Child sexual abuse (CSA) has taken several forms, particularly with the advent of internet technologies that provide pedophiles access to their targets anonymously at an affordable rate. To combat CSA which has far-reaching consequences on the physical and psychological health of the victims, a special act, the Protection of Children from Sexual Offences (POCSO) Act, was formulated amongst the existing laws. With its latest amendment criminalizing various online activities about child pornography also known as child sexual abuse materials in 2019, tremendous pressure is speculated on law enforcement to identify offenders online. Effective investigations of CSA cases help in not only to detect perpetrators but also in preventing the re-victimization of children. Understanding the vulnerability of the child population and that the offenders continue to develop stealthier strategies to operate, it is high time that traditional investigation, where the focus is on apprehending and prosecuting the offender, must make a paradigm shift to proactively investigate to prevent victimization at the first place. One of the proactive policing techniques involves understanding the psychology of the offenders and children and operating undercover to catch the criminals before a real child is victimized. With the fundamental descriptive approach to research, the article attempts to identify the multitude of issues associated with the investigation of child sexual abuse cases currently in practice in India. Then, the article contextualizes the various covert operations carried out by numerous law enforcement agencies across the globe. To provide this comprehensive overview, the paper examines various reports, websites, guidelines, protocols, judicial pronouncements, and research articles. Finally, the paper presents the challenges and ethical issues that are to be considered before getting into undercover operations either in the guise of a pedophile or as a child. The research hopes to contribute to the making of standard operating protocols for investigation officers and other relevant policymakers in this regard.

Keywords: child sexual abuse, cybercrime against children, covert police operations, investigation of CSA

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Authors: M. Aboalazayem, Essam A. Gouda, Ahmed M. Moussa, Amr E. Flifl

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Field data collecting is considered one of the most difficult work due to the difficulty of accessing large zones such as large lakes. Also, it is well known that the cost of obtaining field data is very expensive. Remotely monitoring of lake water quality (WQ) provides an economically feasible approach comparing to field data collection. Researchers have shown that lake WQ can be properly monitored via Remote sensing (RS) analyses. Using satellite images as a method of WQ detection provides a realistic technique to measure quality parameters across huge areas. Landsat (LS) data provides full free access to often occurring and repeating satellite photos. This enables researchers to undertake large-scale temporal comparisons of parameters related to lake WQ. Satellite measurements have been extensively utilized to develop algorithms for predicting critical water quality parameters (WQPs). The goal of this paper is to use RS to derive WQ indicators in Aswan High Dam Reservoir (AHDR), which is considered Egypt's primary and strategic reservoir of freshwater. This study focuses on using Landsat8 (L-8) band surface reflectance (SR) observations to predict water-quality characteristics which are limited to Turbidity (TUR), total suspended solids (TSS), and chlorophyll-a (Chl-a). ArcGIS pro is used to retrieve L-8 SR data for the study region. Multiple linear regression analysis was used to derive new correlations between observed optical water-quality indicators in April and L-8 SR which were atmospherically corrected by values of various bands, band ratios, and or combinations. Field measurements taken in the month of May were used to validate WQP obtained from SR data of L-8 Operational Land Imager (OLI) satellite. The findings demonstrate a strong correlation between indicators of WQ and L-8 .For TUR, the best validation correlation with OLI SR bands blue, green, and red, were derived with high values of Coefficient of correlation (R2) and Root Mean Square Error (RMSE) equal 0.96 and 3.1 NTU, respectively. For TSS, Two equations were strongly correlated and verified with band ratios and combinations. A logarithm of the ratio of blue and green SR was determined to be the best performing model with values of R2 and RMSE equal to 0.9861 and 1.84 mg/l, respectively. For Chl-a, eight methods were presented for calculating its value within the study area. A mix of blue, red, shortwave infrared 1(SWR1) and panchromatic SR yielded the greatest validation results with values of R2 and RMSE equal 0.98 and 1.4 mg/l, respectively.

Keywords: remote sensing, landsat 8, nasser lake, water quality

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Authors: Anthony Proschka, Deepak Mishra, Merlyn Ramanan, Zurab Baratashvili

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Small and medium-sized businesses receive over 160 billion invoices every year. Since these documents exhibit many subtle differences in layout and text, extracting structured fields such as sender name, amount, and VAT rate from them automatically is an open research question. In this paper, existing work in template-based document extraction is extended, and a system is devised that is able to reliably extract all required fields for up to 70% of all documents in the data set, more than any other previously reported method. The approaches are described for 1) detecting through visual features which template a given document belongs to, 2) automatically generating extraction rules for a given new template by composing regular expressions from multiple components, and 3) computing confidence scores that indicate the accuracy of the automatic extractions. The system can generate templates with as little as one training sample and only requires the ground truth field values instead of detailed annotations such as bounding boxes that are hard to obtain. The system is deployed and used inside a commercial accounting software.

Keywords: data mining, information retrieval, business, feature extraction, layout, business data processing, document handling, end-user trained information extraction, document archiving, scanned business documents, automated document processing, F1-measure, commercial accounting software

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Authors: Mohammed Hmimou, Khalid Amediaz, Imane Sebari, Nabil Bounajma

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Annual crop production is one of the most important macroeconomic indicators for the majority of countries around the world. This information is valuable, especially for exporting countries which need a yield estimation before harvest in order to correctly plan the supply chain. When it comes to estimating agricultural yield, especially for arboriculture, conventional methods are mostly applied. In the case of the citrus industry, the sale before harvest is largely practiced, which requires an estimation of the production when the fruit is on the tree. However, conventional method based on the sampling surveys of some trees within the field is always used to perform yield estimation, and the success of this process mainly depends on the expertise of the ‘estimator agent’. The present study aims to propose a methodology based on the combination of unmanned aerial vehicle (UAV) images and terrestrial laser scanner (TLS) point cloud to estimate citrus production. During data acquisition, a fixed wing and rotatory drones, as well as a terrestrial laser scanner, were tested. After that, a pre-processing step was performed in order to generate point cloud and digital surface model. At the processing stage, a machine vision workflow was implemented to extract points corresponding to fruits from the whole tree point cloud, cluster them into fruits, and model them geometrically in a 3D space. By linking the resulting geometric properties to the fruit weight, the yield can be estimated, and the statistical distribution of fruits size can be generated. This later property, which is information required by importing countries of citrus, cannot be estimated before harvest using the conventional method. Since terrestrial laser scanner is static, data gathering using this technology can be performed over only some trees. So, integration of drone data was thought in order to estimate the yield over a whole orchard. To achieve that, features derived from drone digital surface model were linked to yield estimation by laser scanner of some trees to build a regression model that predicts the yield of a tree given its features. Several missions were carried out to collect drone and laser scanner data within citrus orchards of different varieties by testing several data acquisition parameters (fly height, images overlap, fly mission plan). The accuracy of the obtained results by the proposed methodology in comparison to the yield estimation results by the conventional method varies from 65% to 94% depending mainly on the phenological stage of the studied citrus variety during the data acquisition mission. The proposed approach demonstrates its strong potential for early estimation of citrus production and the possibility of its extension to other fruit trees.

Keywords: citrus, digital surface model, point cloud, terrestrial laser scanner, UAV, yield estimation, 3D modeling

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Authors: R. Simutis, V. Galvanauskas, D. Levisauskas, J. Repsyte, V. Grincas

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This paper deals with advanced state estimation algorithms for estimation of biomass concentration and specific growth rate in a typical fed-batch biotechnological process. This biotechnological process was represented by a nonlinear mass-balance based process model. Extended Kalman Filter (EKF) and Particle Filter (PF) was used to estimate the unmeasured state variables from oxygen uptake rate (OUR) and base consumption (BC) measurements. To obtain more general results, a simplified process model was involved in EKF and PF estimation algorithms. This model doesn’t require any special growth kinetic equations and could be applied for state estimation in various bioprocesses. The focus of this investigation was concentrated on the comparison of the estimation quality of the EKF and PF estimators by applying different measurement noises. The simulation results show that Particle Filter algorithm requires significantly more computation time for state estimation but gives lower estimation errors both for biomass concentration and specific growth rate. Also the tuning procedure for Particle Filter is simpler than for EKF. Consequently, Particle Filter should be preferred in real applications, especially for monitoring of industrial bioprocesses where the simplified implementation procedures are always desirable.

Keywords: biomass concentration, extended Kalman filter, particle filter, state estimation, specific growth rate

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Authors: Yongjiang Zhang, Kallol Barai, Umesh R. Hodeghatta, Trang Tran, Vikas Dhiman

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Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using a handheld spectroradiometer and leaf water potential data using a pressure chamber were collected from wild blueberry plants. Machine learning techniques, including multiple regression analysis and random forest models, were employed to predict leaf water potential (MPa). We explored the optimal wavelength bands for simple differences (RY1-R Y2), simple ratios (RY1/RY2), and normalized differences (|RY1-R Y2|/ (RY1-R Y2)). NDWI ((R857 - R1241)/(R857 + R1241)), SD (R2188 – R2245), and SR (R1752 / R1756) emerged as top predictors for predicting leaf water potential, significantly contributing to the highest model performance. The base learner models achieved an R-squared value of approximately 0.81, indicating their capacity to explain 81% of the variance. Research is underway to develop a neural vegetation index (NVI) that automates the process of index development by searching for specific wavelengths in the space ratio of linear functions of reflectance. The NVI framework could work across species and predict different physiological parameters.

Keywords: hyperspectral reflectance, water potential, spectral indices, machine learning, wild blueberries, optimal bands

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Telesphore Tiendrebeogo, Oumarou Sié

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Cloud computing technologies have attracted considerable interest in recent years. Thus, these latters have become more important for many existing database applications. It provides a new mode of use and of offer of IT resources in general. Such resources can be used “on demand” by anybody who has access to the internet. Particularly, the Cloud platform provides an ease to use interface between providers and users, allow providers to develop and provide software and databases for users over locations. Currently, there are many Cloud platform providers support large scale database services. However, most of these only support simple keyword-based queries and can’t response complex query efficiently due to lack of efficient in multi-attribute index techniques. Existing Cloud platform providers seek to improve performance of indexing techniques for complex queries. In this paper, we define a new cloud computing architecture based on a Distributed Hash Table (DHT) and design a prototype system. Next, we perform and evaluate our cloud computing indexing structure based on a hyperbolic tree using virtual coordinates taken in the hyperbolic plane. We show through our experimental results that we compare with others clouds systems to show our solution ensures consistence and scalability for Cloud platform.

Keywords: virtual coordinates, cloud, hyperbolic plane, storage, scalability, consistency

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Authors: Hassina Gheribi, Salim Boukebbab

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The measurement of the parts manufactured on CMM (coordinate measuring machine) is based on the association of a surface of perfect geometry to the group of dots palpated via a mathematical calculation of the distances between the palpated points and itself surfaces. Surfaces not being never perfect, they are measured by a number of points higher than the minimal number necessary to define them mathematically. However, the central problems of three-dimensional metrology are the estimate of, the orientation parameters, location and intrinsic of this surface. Including the numerical uncertainties attached to these parameters help the metrologist to make decisions to be able to declare the conformity of the part to specifications fixed on the design drawing. During this paper, we will present a data-processing model in Visual Basic-6 which makes it possible automatically to determine the whole of these parameters, and their uncertainties.

Keywords: coordinate measuring machines (CMM), associated surface, uncertainties of measurement, acquisition and modeling

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Authors: Shahin A. Abdel-Naby, Asad T. Hassan, Stuart Loch, Michael Fogle, Negil R. Badnell, Michael S. Pindzola

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Accurate and reliable laboratory astrophysical data for electron-ion recombination are needed for plasma modeling. Dielectronic recombination (DR) rate coefficients are calculated for boron-like nitrogen and oxygen ions using state-of-the-art multi-configuration Breit-Pauli atomic structure AUTOSTRUCTURE collisional package within the generalized collisional-radiative framework. The calculations are performed in intermediate coupling scheme associated with n = 0 (2  2) and n = 1 (2  3) core-excitations. Good agreements are found between the theoretically convoluted rate coefficients and the experimental measurements performed at CRYRING heavy-ion storage ring for both ions. Fitting coefficients for the rate coefficients are produced for these ions in the temperature range q2(102-107) K, where q is the ion charge before recombination.

Keywords: Atomic data, atomic processes, electron-ion collision, plasma

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Authors: M. S. Mrudula, M. R. Gopinathan Nair

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In order to utilize the natural rubber for developing new green polymeric materials for specialty applications, we have prepared natural rubber and polyethylene oxide based polymeric networks by two shot method. The polymeric networks thus formed have been used as chelating exchanger for metal ion binding. Chelating exchangers are, in general, coordinating copolymers containing one or more electron donor atoms such as N, S, O, and P that can form coordinate bonds with metals. Hydrogels are water- swollen network of hydrophilic homopolymer or copolymers. They acquire a great interest due to the facility of the incorporation of different chelating groups into the polymeric networks. Such polymeric hydrogels are promising materials in the field of hydrometallurgical applications and water purification due to their chemical stability. The current study discusses the swelling response of the polymeric networks as a function of time, temperature, pH and [NaCl] and sorption studies. Equilibrium swelling has been observed to depend on both structural aspects of the polymers and environmental factors. Metal ion sorption shows that these polymeric networks can be used for removal, separation, and enrichment of metal ions from aqueous solutions and can play an important role for environmental remediation of municipal and industrial wastewater.

Keywords: block copolymer, adsorption, chelating exchanger, swelling study, polymer, metal complexes

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Authors: Alexa L. Walsh, Christine Hartl, Juliette Ferdschneider, Lezode Kipoliongo, Eleonora Feketeova

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The investigation of COVID-19 and its effects has been at the forefront of clinical research since its emergence in the United States in 2020. Although the possibility of severe infection in immunocompromised individuals has been documented, within the general population of pregnant individuals, there remains to be vaccine hesitancy and uncertainty regarding how the virus may affect the individual and fetus. To combat this hesitancy, this study aims to evaluate the effects of COVID-19 infection on maternal and neonatal complication rates. This retrospective study was conducted by manual chart review of women who were diagnosed with COVID-19 during pregnancy (n = 78) and women who were not diagnosed with COVID-19 during pregnancy (n = 1,124) that gave birth at Garnet Health Medical Centers between 1/1/2019-1/1/2021. Both the COVID+ and COVID- groups exhibited similar median ages, BMI, and parity. The rates of complications were compared between the groups and statistical significance was determined using Chi-squared analysis. Results demonstrated a statistically higher rate of PROM, polyhydramnios, oligohydramnios, GDM, DVT/PE, preterm birth, and the overall incidence of any birth complication in the population that was infected with COVID-19 during their pregnancy. With this information, obstetrical providers can be better prepared for the management of COVID-19+ pregnancies and continue to educate their patients on the benefits of vaccination.

Keywords: complications, COVID-19, Gynecology, Obstetrics

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Authors: Matthias Leitner, Gernot Pottlacher

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Experimental determination of critical point data like critical temperature, critical pressure, critical volume and critical compressibility of high-melting metals such as niobium is very rare due to the outstanding experimental difficulties in reaching the necessary extreme temperature and pressure regimes. Experimental techniques to achieve such extreme conditions could be diamond anvil devices, two stage gas guns or metal samples hit by explosively accelerated flyers. Electrical pulse-heating under increased pressures would be another choice. This technique heats thin wire samples of 0.5 mm diameter and 40 mm length from room temperature to melting and then further to the end of the stable phase, the spinodal line, within several microseconds. When crossing the spinodal line, the sample explodes and reaches the gaseous phase. In our laboratory, pulse-heating experiments can be performed under variation of the ambient pressure from 1 to 5000 bar and allow a direct determination of critical point data for low-melting, but not for high-melting metals. However, the critical point also can be estimated by extrapolating the liquid phase density according to theoretical models. A reasonable prerequisite for the extrapolation is the existence of data that cover as much as possible of the liquid phase and at the same time exhibit small uncertainties. Ohmic pulse-heating was therefore applied to determine thermal volume expansion, and from that density of niobium over the entire liquid phase. As a first step, experiments under ambient pressure were performed. The second step will be to perform experiments under high-pressure conditions. During the heating process, shadow images of the expanding sample wire were captured at a frame rate of 4 × 105 fps to monitor the radial expansion as a function of time. Simultaneously, the sample radiance was measured with a pyrometer operating at a mean effective wavelength of 652 nm. To increase the accuracy of temperature deduction, spectral emittance in the liquid phase is also taken into account. Due to the high heating rates of about 2 × 108 K/s, longitudinal expansion of the wire is inhibited which implies an increased radial expansion. As a consequence, measuring the temperature dependent radial expansion is sufficient to deduce density as a function of temperature. This is accomplished by evaluating the full widths at half maximum of the cup-shaped intensity profiles that are calculated from each shadow image of the expanding wire. Relating these diameters to the diameter obtained before the pulse-heating start, the temperature dependent volume expansion is calculated. With the help of the known room-temperature density, volume expansion is then converted into density data. The so-obtained liquid density behavior is compared to existing literature data and provides another independent source of experimental data. In this work, the newly determined off-critical liquid phase density was in a second step utilized as input data for the estimation of niobium’s critical point. The approach used, heuristically takes into account the crossover from mean field to Ising behavior, as well as the non-linearity of the phase diagram’s diameter.

Keywords: critical point data, density, liquid metals, niobium, ohmic pulse-heating, volume expansion

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Authors: Yayun Hsu, Henry Horng-Shing Lu

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In this paper, a new method is proposed to extending the method of connected component labeling from processing binary images to multi-scale modeling of images. By using the adaptive threshold of multi-scale attributes, this approach minimizes the possibility of missing those important components with weak intensities. In addition, the computational cost of this approach remains similar to that of the typical approach of component labeling. Then, this methodology is applied to grain boundary detection and Drosophila Brain-bow neuron segmentation. These demonstrate the feasibility of the proposed approach in the analysis of challenging microscopy images for scientific discovery.

Keywords: microscopic image processing, scientific data mining, multi-scale modeling, data mining

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Authors: Lincoln Panjaitan, Antonius Sumarlin

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The development of property businesses in the metropolitan area is growing rapidly forcing big real estate developers to come up with various strategies in winning the heart of consumers. This empirical research is focusing on how the two schools of marketing thoughts; namely, Modern and postmodern marketing employed by the preceding developers to retain consumers’ commitment toward their prospective brands. The data was collected from three different properties of PT. Intiland Tbk using accidental sampling technique. The data of 600 respondents was then put into Structural Equation Model (SEM). The result of the study suggests that both schools of thought can equally produce commitment and loyalty of consumers; however, the difference lays where the loyalty belongs to. The first is more toward developer’s brand and the latter is more toward the co-creation value of the housing community.

Keywords: consumer loyalty, consumer commitment, knowledge sharing platform, marketing mix

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Authors: Peifang Hsieh

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The prevention of child abuse has aroused serious concerns in Taiwan because of the disparity between the increasing amount of reported child abuse cases that doubled over the past decade and the scarcity of social workers. New Taipei city, with the most population in Taiwan and over 70% of its 4 million citizens are migrant families in which the needs of children can be easily neglected due to insufficient support from relatives and communities, sees urgency for a social support system, by preemptively identifying and outreaching high-risk families of child abuse, so as to offer timely assistance and preventive measure to safeguard the welfare of the children. Big data analysis is the inspiration. As it was clear that high-risk families of child abuse have certain characteristics in common, New Taipei city decides to consolidate detailed background information data from departments of social affairs, education, labor, and health (for example considering status of parents’ employment, health, and if they are imprisoned, fugitives or under substance abuse), to cross-reference for accurate and prompt identification of the high-risk families in need. 'The Service Center for High-Risk Families' (SCHF) was established to integrate data cross-departmentally. By utilizing the machine learning 'random forest method' to build a risk prediction model which can early detect families that may very likely to have child abuse occurrence, the SCHF marks high-risk families red, yellow, or green to indicate the urgency for intervention, so as to those families concerned can be provided timely services. The accuracy and recall rates of the above model were 80% and 65%. This prediction model can not only improve the child abuse prevention process by helping social workers differentiate the risk level of newly reported cases, which may further reduce their major workload significantly but also can be referenced for future policy-making.

Keywords: child abuse, high-risk families, big data analysis, risk prediction model

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Authors: E. K. Mendez, N. A. Salazar, C. E. Orrego

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There is undoubted proof that increasing the intake of fruit lessens the risk of hypertension, coronary heart disease, stroke, and probable evidence that lowers the risk of cancer. Proper fruit drying is an excellent alternative to make their shelf-life longer, commercialization easier, and ready-to-eat healthy products or ingredients. The conventional way of drying is by hot air forced convection. However, this process step often requires a very long residence time; furthermore, it is highly energy consuming and detrimental to the product quality. Nowadays, power ultrasound (US) technic has been considered as an emerging and promising technology for industrial food processing. Most of published works dealing with drying food assisted by US have studied the effect of ultrasonic pre-treatment prior to air-drying on food and the airborne US conditions during dehydration. In this work a new approach was tested taking in to account drying time and two quality parameters of mango slices dehydrated by convection assisted by 20 KHz power US applied directly using a holed plate as product support and sound transmitting surface. During the drying of mango (Mangifera indica L.) slices (ca. 6.5 g, 0.006 m height and 0.040 m diameter), their weight was recorded every hour until final moisture content (10.0±1.0 % wet basis) was reached. After previous tests, optimization of three drying parameters - frequencies (2, 5 and 8 minutes each half-hour), air temperature (50-55-60⁰C) and power (45-70-95W)- was attempted by using a Box–Behnken design under the response surface methodology for the optimal drying time, color parameters and rehydration rate of dried samples. Assays involved 17 experiments, including a quintuplicate of the central point. Dried samples with and without US application were packed in individual high barrier plastic bags under vacuum, and then stored in the dark at 8⁰C until their analysis. All drying assays and sample analysis were performed in triplicate. US drying experimental data were fitted with nine models, among which the Verna model resulted in the best fit with R2 > 0.9999 and reduced χ2 ≤ 0.000001. Significant reductions in drying time were observed for the assays that used lower frequency and high US power. At 55⁰C, 95 watts and 2 min/30 min of sonication, 10% moisture content was reached in 211 min, as compared with 320 min for the same test without the use of US (blank). Rehydration rates (RR), defined as the ratio of rehydrated sample weight to that of dry sample and measured, was also larger than those of blanks and, in general, the higher the US power, the greater the RR. The direct contact and intermittent US treatment of mango slices used in this work improve drying rates and dried fruit rehydration ability. This technique can thus be used to reduce energy processing costs and the greenhouse gas emissions of fruit dehydration.

Keywords: ultrasonic assisted drying, fruit drying, mango slices, contact ultrasonic drying

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Authors: Santos Cabrera, Halbert Yesid, Moncada Nino, Alvaro Fernando, Rincon Cuta, Yeisson Alexis

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The present work evaluates the feasibility of implementing a health and use monitoring system (HUMS), based on vibration analysis as a condition-based maintenance program for the UH60L 'Blackhawk' helicopters. The mixed approach used consists of contributions from national and international experts, the analysis of data extracted from the software (Meridium), the correlation of variables derived from the diagnosis of availability, the development, and application of the HUMS system, the evaluation of the latter through of the use of instruments designed for the collection of information using the DELPHI method and data capture with the device installed in the helicopter studied. The results obtained in the investigation reflect the context of maintenance in aerial operations, a reduction of operation and maintenance costs of over 2%, better use of human resources, improvement in availability (5%), and fulfillment of the aircraft’s security standards, enabling the implementation of the monitoring system (HUMS) in the condition-based maintenance program. New elements are added to the study of maintenance based on condition -specifically, in the determination of viability based on qualitative and quantitative data according to the methodology. The use of condition-based maintenance will allow organizations to adjust and reconfigure their strategic, logistical, and maintenance capabilities, aligning them with their strategic objectives of responding quickly and adequately to changes in the environment and operational requirements.

Keywords: air transportation sustainability, HUMS, maintenance based condition, maintenance blackhawk capability

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Authors: Mohammad Shahjahan, Md. Monoarul Haque

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In the research work information and data were collected from both types of sources, direct and indirect. Numerological, qualitative and participatory analysis methods have been followed. There were two principal sources of collecting information and data. Firstly, the data provided by the service recipients (300 nos. of women and children victims) in the Victim Support Centre and service providing policemen, executives and staffs (60 nos.). Secondly, data collected from Specialists, Criminologists and Sociologists involved in victim support services through Consultative Interview, KII, Case Study and FGD etc. The initial data collection has been completed with the help of questionnaires as per strategic variations and with the help of guidelines. It is to be noted that the main objective of this research was to determine whether services provided to the victims for their facilities, treatment/medication and rehabilitation by different government/non-government organizations was veritable at all. At the same time socio-economic background and demographic characteristics of the victims have also been revealed through this research. The results of the study show that although the number of victims has increased gradually due to socio-economic, political and cultural realities in Bangladesh, the number of victim support centers has not increased as expected. Awareness among the victims about the effectiveness of the 8 centers working in this regard is also not up to the mark. Two thirds of the victims coming to get service were not cognizant regarding the victim support services at all before getting the service. Most of those who have finally been able to come under the services of the Victim Support Center through various means, have received sheltering (15.5%), medical services (13.32%), counseling services (13.10%) and legal aid (12.66%). The opportunity to stay in security custody and psycho-physical services were also notable. Usually, women and children from relatively poor and marginalized families of the society come to victim support center for getting services. Among the women, young unmarried women are the biggest victims of crime. Again, women and children employed as domestic workers are more affected. A number of serious negative impacts fall on the lives of the victims. Being deprived of employment opportunities (26.62%), suffering from psycho-somatic disorder (20.27%), carrying sexually transmitted diseases (13.92%) are among them. It seems apparent to urgently enact distinct legislation, increase the number of Victim Support Centers, expand the area and purview of services and take initiative to increase public awareness and to create mass movement.

Keywords: crime, victim, support, Bangladesh

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Authors: Zichun Guo

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Guangzhou's house price has long been lower than the other three major cities; with the gradual increase in Guangzhou's house price, the influencing factors of house price have gradually been paid attention to; this paper tries to use house price data and POI (Point of Interest) data, and explores the distribution of house price and influencing factors by applying the Kriging spatial interpolation method and geographically weighted regression model in ArcGIS. The results show that the interpolation result of house price has a significant relationship with the economic development and development potential of the region and that different POI types have different impacts on the growth of house prices in different regions.

Keywords: POI, house price, spatial heterogeneity, Guangzhou

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Authors: Colleen McMillan, Alice Schmidt-Hanbidge, Beth Archer-Kuhn, Heather Boynton, Judith Hughes

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It is well known that social work students enter the profession with higher scores of adverse childhood experiences (ACE). Add to that the fact that COVID-19 has forced higher education institutions to shift to online teaching and learning, where students, faculty and field educators in social work education have reported increased stressors as well as posing challenges in developing relationships with students and being able to identify mental health challenges including those related to trauma. This multi-institutional project included three Canadian post-secondary institutions at five sites (the University of Waterloo, the University of Calgary and the University of Manitoba) and partners; Desire To Learn (D2L), The Centre for Teaching Excellence at the University of Waterloo and the Taylor Institute for Teaching and Learning. A sequential mixed method research design was used. Survey data was collected from students, faculty and field education staff from the 3 universities using the Qualtrics Insight Platform, followed by virtual focus group data with students to provide greater clarity to the quantitative data. Survey data was analyzed using SPSS software, while focus group data was transcribed verbatim and organized with N-Vivo 12. Thematic analysis used line-by-line coding and constant comparative methods within and across focus groups. The following three objectives of the study were achieved: 1) Establish a Canadian baseline on trauma informed pedagogy and student experiences of trauma informed teaching in the online higher education environment during a pandemic; 2) Identify and document educator and student experiences of online learning regarding the ability to process trauma experiences; and, 3) Transfer the findings into a trauma informed pedagogical model for Social Work as a first step toward developing a universal trauma informed teaching model. The trauma informed pedagogy model would be presented in relation to the study findings.

Keywords: trauma informed pedagogy, higher education, social work, mental health

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Authors: Gonul Kurt, Semra Aciksoz

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All types of interventions that increase the knowledge and awareness of youths about Sexually Transmitted Diseases (STD) are considered to be important for safe sex life and sexual health. The aim of this study was to determine the knowledge levels of nursing students about STD and evaluate the effectiveness of peer education and brochure methods to increase the knowledge and awareness about STD. This interventional study was carried out by participation of nursing students attending the first and second grade in a school of nursing on February–May 2015. The study participants were 200 undergraduate nursing student volunteers. The students were given education by peer trainers and brochure methods. First-grade students were divided into five groups with block randomization method and each group were given education by five peer trainers. Second-grade students were given education with brochure by the researchers. The knowledge level of study groups was evaluated before and after educational intervention. The data were collected using the “Data Collection Form” and “Sexually Transmitted Diseases Information Form”. The questionnaire forms developed by the researchers after the literature review. The SPSS 15.0 package software was used for the evaluation of the data obtained from the study. Data were analyzed by Mann-Whitney-U-Test, Wilcoxon Signed Ranks Test and Mc Nemar Test. A p value of <0.05 was regarded as statistically significant. All of participants in the study were female nursing students. The mean age of students was 18.99±0.32 years old in the peer education group and 20.04±0.37 in the brochure education group. There was no statistically significant difference between knowledge levels of the students in both groups before the education (p>0.05). It was determined that an increase in knowledge levels of the students in both groups after the education. This increase was statistically significant (p<0.05). It was determined that knowledge level of the students about STD in brochure group was higher than the peer education group (p<0.001). The results of this study indicate that brochure education method was more effective than the peer education method in both increasing knowledge and awareness about STD.

Keywords: education method, knowledge, nursing students, sexually transmitted diseases

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Authors: Shahin A. Abdel-Naby, Asad T. Hassan

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Electron-ion recombination data are needed for plasma modeling. The recombination processes include radiative recombination (RR), dielectronic recombination (DR), and trielectronic recombination (TR). When a free electron is captured by an ion with simultaneous excitation of its core, a doubly-exited intermediate state may be formed. The doubly excited state relaxes either by electron emission (autoionization) or by radiative decay (photon emission). DR process takes place when the relaxation occurs to a bound state by photon emission. Reliable laboratory astrophysics data (theory and experiment) for DR rate coefficients are needed to determine the charge state distribution in photoionized sources such as X-ray binaries and active galactic nuclei. DR rate coefficients for NIII and OIV ions are calculated using state-of-the-art multi-configuration Breit-Pauli atomic structure AUTOSTRUCTURE collisional package within the generalized collisional-radiative framework. Level-resolved calculations for RR and DR rate coefficients from the ground and metastable initial states are produced in an intermediate coupling scheme associated with Δn = 0 (2→2) and Δn = 1 (2 →3) core-excitations. DR cross sections for these ions are convoluted with the experimental electron-cooler temperatures to produce DR rate coefficients. Good agreements are found between these rate coefficients and the experimental measurements performed at the CRYRING heavy-ion storage ring for both ions.

Keywords: atomic data, atomic process, electron-ion collision, plasmas

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Authors: Aneta Nitsch-Osuch, Sylwia Dyk, Izabela Gołebiak

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Background. Since 2014 the pertussis vaccine is recommended to Polish health care workers who have close contacts with infants. Although this recommendation is implemented into the National Immunization Programme, its realization has remained unknown. The Purpose: The aim of the study, conducted at the department of Social Medicine and Public Health (Medical University of Warsaw, Poland), was to describe a perception, knowledge and coverage rates regarding pertussis vaccination among nursing staff. According to the authors' knowledge, it was the first study related to this topic in our country. Material and Methods: A total number of 543 nurses who work at pediatric or neonatal wards was included into the study (501 women and 42 men), average age was 47 years. All nurses were asked to fulfill the anonymous survey, previously validated. Results: 1. Coverage rates: The analysis of results revealed that only 4% of responders reported they were vaccinated with Tdpa within past 10 years, while 8% declared they would plan the vaccine in the future. 35% of responders would consider the Tdpa vaccine whether there is some kind of the reimbursement. 2. Perception and knowledge of the disease and vaccination: The majority (82%) of nurses did not recognize pertussis as a re-emerging infectious disease. 54% of them believed that obligatory vaccinations in the childhood protect against the disease and the protection is a life-long one. Only 15% of nurses considered pertussis as a possible nosocomial infection. The current epidemiology of the disease was known to 6% of responders, while 24% of them were familiar with pertussis vaccination schedules for infants, children and adolescents, but only 9% of responders knew that adults older than 19 years are recommended to be vaccinated with Tdpa every 10 years. Many nurses (82%) would expect more educational activities related to pertussis and methods of its prophylaxis. Conclusions: The pertussis vaccine coverage rate among Polish nurses is extremely low. This is a result of not enough knowledge about the disease and its prevention. Educational activities addressed to health care workers and reimbursement of the pertussis vaccine are required to improve awareness and increase of vaccine coverage rates in the future.

Keywords: coverage, nurse, pertussis, vaccine

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Authors: Gorbunova Tatiana L.

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The research is dedicated to the study of the polluted river inflow impact on the Black Sea coastal marine environment within the watercourse’s plumes in the Greater Sochi area applying bioassay methods using freshwater and marine microalgae. River waters were analyzed using microalgae Chlorella vulgaris Beijer and sea waters were tested with marine diatoms Phaeodactylum tricornutum Bohlin. Experiments included algae cell abundancy growth assessments in acute (24 hours), sub-acute (72 hours) and chronic (168 hours / 7 days) tests. The increase in algal cell growth rates compared to the control in the summer period was detected as a consequence of the recreational activities intensification during the tourism seasonal peak. Most of the analyzed samples demonstrated a significant effect of algae cell growth stimulation compared to the control. It is established that under the impact of contaminants carried by river drain to the sea, the capacity of the coastal marine ecosystem is partially capable of compensating for its effect on the coastal biocenosis, but the general trends of the impact processes remain constant.

Keywords: algae abundance growth, bioassay, microalgae, modeling

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Authors: Syahrir Andi Mangga, Ipranta

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The geological, paleomagnetic, geochemical and geophysical Investigation in The Sumatra Region has yielded some new data, has stimulated a reassessment of stratigraphy, structure, tectonic evolution and which can show a Sumatra geodynamic model. Sumatra island has in the margin of southwest part of the Eurasia plate in the Sundaland cratonic block and occurred as the amalgamation of allochtonous microplates, continental fragments, Island arc and accrctionary by foreland complex which assembled prior to Tertiary. The allochtonous rocks (terranes), can be divided into 4 (four) Terranes with Paleozoic to Mesosoic in age, had different origin, lithology and are separated by a Suture as main fault with trending NW-SE. The terranes are: the Tigapuluh-Bohorok (East Sumatra block / Sibumasu block), Permo-Carboniferous in age and is characterized by the rock types formed in glacio-marine and was intruded by Late Triassic to Early Jurrasic granitics, occupied in the Eastern part of Sumatra, the paleomagnetic data shown 41° South. Tanjung Karang - Gunung Kasih Terrane, is composed of higher metamorphic rocks and supposed to be pre-Carboniferous in age, covered by Mesozoic sedimentary rocks and were intruded by granitic-dioritic rocks, occupied in the Southern part of Sumatra, the paleomagnetic data shown 19° North. The Kuantan-Duabelas Mountain (West Sumatra block) is occupied by metamorphic, sedimentary and volcanic rocks of Paleozoic - Mesozoic (Carboniferous - Triassic) in age, contains a Cathaysion fauna and flora and are intruded by the Mesozoic granitoid rocks. The terrane occurred in the western part of Sumatra. Meanwhile, the Gumai-Garba (Waloya Terrane) which is occupied by the tectonite/melange, metasediment, carbonate and volcanic rocks of Mesozoic (Jurassic - Cretaceous) in age, are intruted by the Late Cretaceous granitoid rocks, the paleomagnetic data shown 30° - 31° South.

Keywords: tectonostratigraphy, amalgamation, allochtonous, terranes, sumatra

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Authors: Ivette Gonzalez-Rivera, Diana B. Paz-Trejo, Oscar Galicia-Castillo, David N. Velazquez-Martinez, Hugo Sanchez-Castillo

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Prolonged exposure to stress can cause disorders related with dysfunction in the prefrontal cortex such as generalized anxiety and depression. These disorders involve alterations in neurotransmitter systems; the serotonergic system—a target of the drugs that are commonly used as a treatment to these disorders—is one of them. Recent studies suggest that 5-HT1A receptors play a pivotal role in the serotonergic system regulation and in stress responses. In the same way, there is increasing evidence that astrocytes are involved in the pathophysiology of stress. The aim of this study was to examine the effects of 8-OH-DPAT, a selective agonist of 5-HT1A receptors, in the behavioral signs of anxiety and anhedonia as well as in the number of astrocytes in the medial prefrontal cortex (mPFC) after exposure to chronic stress. They used 50 male Wistar rats of 250-350 grams housed in standard laboratory conditions and treated in accordance with the ethical standards of use and care of laboratory animals. A protocol of chronic unpredictable stress was used for 10 consecutive days during which the presentation of stressors such as motion restriction, water deprivation, wet bed, among others, were used. 40 rats were subjected to the stress protocol and then were divided into 4 groups of 10 rats each, which were administered 8-OH-DPAT (Tocris, USA) intraperitoneally with saline as vehicle in doses 0.0, 0.3, 1.0 and 2.0 mg/kg respectively. Another 10 rats were not subjected to the stress protocol or the drug. Subsequently, all the rats were measured in an open field test, a forced swimming test, sucrose consume, and a cero maze test. At the end of this procedure, the animals were sacrificed, the brain was removed and the tissue of the mPFC (Bregma: 4.20, 3.70, 2.70, 2.20) was processed in immunofluorescence staining for astrocytes (Anti-GFAP antibody - astrocyte maker, ABCAM). Statistically significant differences were found in the behavioral tests of all groups, showing that the stress group with saline administration had more indicators of anxiety and anhedonia than the control group and the groups with administration of 8-OH-DPAT. Also, a dose dependent effect of 8-OH-DPAT was found on the number of astrocytes in the mPFC. The results show that 8-OH-DPAT can modulate the effect of stress in both behavioral and anatomical level. Also they indicate that 5-HT1A receptors and astrocytes play an important role in the stress response and may modulate the therapeutic effect of serotonergic drugs, so they should be explored as a fundamental part in the treatment of symptoms of stress and in the understanding of the mechanisms of stress responses.

Keywords: anxiety, prefrontal cortex, serotonergic system, stress

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Authors: Teera Intararuang

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The purpose of this research is to study on the challenge and opportunity for waterway route management for promoting cultural tourism in Angsila District, Chonburi Province. To accomplish the goals and objectives, qualitative research will be applied. The research instruments used are observation, basic interviews, in-depth interviews, and interview key local performance. The study also uses both primary data and secondary data. From research result, it is revealed that all respondents had appreciated and strongly agree to promote their waterway route tourism as an intend for further increase for their income. However, it has some challenges to success this project due to natural obstacles such as water level, seasons and high temperature. Moreover, they lack financial support from government sectors also.

Keywords: Angsila community, waterway tourism route, cultural tourism, way of life

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