Search results for: gnition delay correlation

Authors: Oluyemi O. Awolusi, Abimbola M. Enitan, Sheena Kumari, Faizal Bux

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Nitrification is essential to biological processes designed to remove ammonia and/or total nitrogen. It removes excess nitrogenous compound in wastewater which could be very toxic to the aquatic fauna or cause serious imbalance of such aquatic ecosystem. Efficient nitrification is linked to an in-depth knowledge of the structure and dynamics of the nitrifying community structure within the wastewater treatment systems. In this study, molecular technique was employed for characterizing the microbial structure of activated sludge [ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB)] in a municipal wastewater treatment with intention of linking it to the plant efficiency. PCR based phylogenetic analysis was also carried out. The average operating and environmental parameters as well as specific nitrification rate of plant was investigated during the study. During the investigation the average temperature was 23±1.5oC. Other operational parameters such as mixed liquor suspended solids and chemical oxygen demand inversely correlated with ammonia removal. The dissolved oxygen level in the plant was constantly lower than the optimum (between 0.24 and 1.267 mg/l) during this study. The plant was treating wastewater with influent ammonia concentration of 31.69 and 24.47 mg/L. The influent flow rates (ML/Day) was 96.81 during period. The dominant nitrifiers include: Nitrosomonas spp. Nitrobacter spp. and Nitrospira spp. The AOB had correlation with nitrification efficiency and temperature. This study shows that the specific ammonia oxidizing rate and the specific nitrate formation rates can serve as good indicator of the plant overall nitrification performance.

Keywords: Ammonia monooxygenase α-subunit (amoA) gene, ammonia-oxidizing bacteria (AOB), nitrite-oxidizing bacteria (NOB), specific nitrification rate, PCR.

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Authors: Mayowa Adeyeye

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This study was conducted to investigate the psychological variables motivating sport participation and involvement among student-athletes of tertiary institutions in southwest Nigeria. One thousand three hundred and fifty (N-1350) studentathletes were randomly selected in all sports from nine tertiary institutions in south-west Nigeria. These tertiary institutions include University of Lagos, Lagos State University, Obafemi Awolowo University, Osun State University, University of Ibadan, University of Agriculture Abeokuta, Federal University of Technology Akungba, University of Ilorin, and Kwara State University. The descriptive survey research method was adopted while a self developed validated Likert type questionnaire named Sport Participation Scale (SPS) was used to elicit opinion from respondents. The test-retest reliability value obtained for the instrument, using Pearson Product Moment Correlation Co-efficient was 0.96. Out of the one thousand three hundred and fifty (N-1350) questionnaire administered, only one thousand two hundred and five (N-1286) were correctly filled, coded and analysed using inferential statistics of Chi-Square (X2) while all the tested hypotheses were set at. 05 alpha level. Based on the findings of this study, the result revealed that several psychological factors influence student athletes to continue participation in sport one which includes love for the game, famous athletes as role model and family support. However, the analysis further revealed that the stipends the student-athletes get from their universities have no influence on their participation and involvement in sport.

Keywords: Family support, peer, role model, sport participation, student-athletes.

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Authors: Alireza Ansariyar, Safieh Laaly

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Over the last decades, the vehicle industry has shown increased interest in integrating autonomous, connected, and electrical technologies in vehicle design with the primary hope of improving mobility and road safety while reducing transportation’s environmental impact. Using the State of Maryland (M.D.) in the United States as a pilot study, this research investigates Connected and Autonomous Vehicles (CAVs) fuel consumption and air pollutants including Carbon Monoxide (CO), Particulate Matter (PM), and Nitrogen Oxides (NOx) and utilizes meaningful linear regression models to predict CAV’s environmental effects. Maryland transportation network was simulated in VISUM software, and data on a set of variables were collected through a comprehensive survey. The number of pollutants and fuel consumption were obtained for the time interval 2010 to 2021 from the macro simulation. Eventually, four linear regression models were proposed to predict the amount of C.O., NOx, PM pollutants, and fuel consumption in the future. The results highlighted that CAVs’ pollutants and fuel consumption have a significant correlation with the income, age, and race of the CAV customers. Furthermore, the reliability of four statistical models was compared with the reliability of macro simulation model outputs in the year 2030. The error of three pollutants and fuel consumption was obtained at less than 9% by statistical models in SPSS. This study is expected to assist researchers and policymakers with planning decisions to reduce CAV environmental impacts in M.D.

Keywords: Connected and autonomous vehicles, statistical model, environmental effects, pollutants and fuel consumption, VISUM, linear regression models.

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Authors: M. Tadi, M. Hadi Mohammad Zadeh, Ozge Ogut

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Although how cities’ forms are structured is studied, more efforts are needed on systemic comprehensions and evaluations of the urban morphology through quantitative metrics that are able to describe the performance of a city in relation to its formal properties. More research is required in this direction in order to better describe the urban form characteristics and their impact on the environmental performance of cities and to increase their sustainability stewardship. With the aim of developing a better understanding of the built environment’s systemic structure, the intention of this paper is to present a holistic methodology for studying the behavior of the built environment and investigate the methods for measuring the effect of urban structure to the environmental performance. This goal will be pursued through an inquiry into the morphological components of the urban systems and the complex relationships between them. Particularly, this paper focuses on proximity, referring to the proximity of different land-uses, is a concept with which Integrated Modification Methodology (IMM) explains how land-use allocation might affect the choice of mobility in neighborhoods, and especially, encourage or discourage non-motived mobility. This paper uses proximity to demonstrate that the structure attributes can quantifiably relate to the performing behavior in the city. The target is to devise a mathematical pattern from the structural elements and correlate it directly with urban performance indicators concerned with environmental sustainability. The paper presents some results of this rigorous investigation of urban proximity and its correlation with performance indicators in four different areas in the city of Milan, each of them characterized by different morphological features.

Keywords: Built environment, ecology, sustainable indicators, sustainability, urban morphology.

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Authors: Mahmood Ahmad, Asadullah Madni, Muhammad Usman, Abubakar Munir, Naveed Akhtar, Haji M. Shoaib Khan

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This article demonstrated development of controlled release system of an NSAID drug, Diclofenac sodium employing different ratios of Ethyl cellulose. Diclofenac sodium and ethyl cellulose in different proportions were processed by microencapsulation based on phase separation technique to formulate microcapsules. The prepared microcapsules were then compressed into tablets to obtain controlled release oral formulations. In-vitro evaluation was performed by dissolution test of each preparation was conducted in 900 ml of phosphate buffer solution of pH 7.2 maintained at 37 ± 0.5 °C and stirred at 50 rpm. At predetermined time intervals (0, 0.5, 1.0, 1.5, 2, 3, 4, 6, 8, 10, 12, 16, 20 and 24 hrs). The drug concentration in the collected samples was determined by UV spectrophotometer at 276 nm. The physical characteristics of diclofenac sodium microcapsules were according to accepted range. These were off-white, free flowing and spherical in shape. The release profile of diclofenac sodium from microcapsules was found to be directly proportional to the proportion of ethylcellulose and coat thickness. The in-vitro release pattern showed that with ratio of 1:1 and 1:2 (drug: polymer), the percentage release of drug at first hour was 16.91 and 11.52 %, respectively as compared to 1:3 which is only 6.87 % with in this time. The release mechanism followed higuchi model for its release pattern. Tablet Formulation (F2) of present study was found comparable in release profile the marketed brand Phlogin-SR, microcapsules showed an extended release beyond 24 h. Further, a good correlation was found between drug release and proportion of ethylcellulose in the microcapsules. Microencapsulation based on coacervation found as good technique to control release of diclofenac sodium for making the controlled release formulations.

Keywords: Diclofenac sodium, Microencapsulationtechnology, Ethylcellulose, In-Vitro Release Profile

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Authors: R. S. Chandan, M. Vasudevan, Deecaraman, B. M. Gurupadayya

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In Pharmaceutical industries, it is very important to remove drug residues from the equipment and areas used. The cleaning procedure must be validated, so special attention must be devoted to the methods used for analysis of trace amounts of drugs. A rapid, sensitive and specific reverse phase ultra performance liquid chromatographic (UPLC) method was developed for the quantitative determination of Albendazole in cleaning validation swab samples. The method was validated using an ACQUITY HSS C₁₈, 50 x 2.1mm, 1.8μ column with a isocratic mobile phase containing a mixture of 1.36g of Potassium dihydrogenphosphate in 1000mL MilliQ water, 2mL of triethylamine and pH adjusted to 2.3 ± 0.05 with ortho-phosphoric acid, Acetonitrile and Methanol (50:40:10 v/v). The flow rate of the mobile phase was 0.5 mL min^-1 with a column temperature of 35⁰C and detection wavelength at 254nm using PDA detector. The injection volume was 2µl. Cotton swabs, moisten with acetonitrile were used to remove any residue of drug from stainless steel, teflon, rubber and silicon plates which mimic the production equipment surface and the mean extraction-recovery was found to be 91.8. The selected chromatographic condition was found to effectively elute Albendazole with retention time of 0.67min. The proposed method was found to be linear over the range of 0.2 to 150µg/mL and correlation coefficient obtained is 0.9992. The proposed method was found to be accurate, precise, reproducible and specific and it can also be used for routine quality control analysis of these drugs in biological samples either alone or in combined pharmaceutical dosage forms.

Keywords: Cleaning validation, Albendazole, residues, swab analysis, UPLC.

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Authors: Nuralam Sam, Irawan Yusuf, Irfan Idris, Endi Adnan

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Frozen shoulder (FS) is an insidious, painful condition caused by an inflammatory condition that causes fibrosis of the glenohumeral joint capsule, which causes progressive stiffness and restriction of the active and passive range of motion (ROM) of the shoulder. The studies of FS are still limited. This single-blinded randomized controlled trial involved participants with FS. The study participants were divided into two groups. The Prolotherapy group was the study group, and the Normal Saline (NS) group was the control group. Both groups were given injections at weeks 0, 2, 4, and 6. Matrix Metalloproteinase-1 (MMP-1) and Tissue Inhibitor Metalloproteinase-1 (TIMP-1) were measured at week six and week 12 after the last injection. The Disabilities of The Arm, Shoulder, and Hand (DASH) Score and ROM were measured at weeks 0, 2, 4, and 6 before and after injection and week 12. Comparative analysis was performed using repeated measures Paired T-Test, and data processing to assess correlation was using ANOVA. The result showed a significant decrease in The Disability of the Arm, Shoulder, and Hand (DASH) score in prolotherapy injection patients in each measurement week (p < 0.05). While the measurement of ROM, each direction of shoulder motion showed a significant difference in average each week, from week 0 to week 6 (p < 0.05). Dextrose prolotherapy injection results significantly improved the functional outcome of the shoulder joint and ROM. They did not show significant results in assessing the specific biomarker, MMP-1, and TIMP-1, in tissue repair. This study suggests an alternative to injection prolotherapy in FS patients; it has minimal adverse effects and is efficient in time and cost.

Keywords: Frozen Shoulder, ROM, DASH Score, prolotherapy, MMP-1, TIMP-1.

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Authors: V. H. Mankar, T. S. Das, S. K. Sarkar

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In this paper, we have proposed a novel blind watermarking architecture towards its hardware implementation in VLSI. In order to facilitate this hardware realization, cellular automata (CA) concept is introduced. The CA has been already accepted as an attractive structure for VLSI implementation because of its modularity, parallelism, high performance and reliability. The hardware realizable multiresolution spread spectrum watermarking techniques are very few in numbers in spite of their best ever resiliency against signal impairments. This is because of the computational cost and complexity associated with their different filter banks and lifting techniques. The concept of cellular automata theory in order to form a new transform domain technique i.e. Cellular Automata Transform (CAT) have been incorporated. Since CA provides spreading sequences having very low cross-correlation properties, the CA based pseudorandom sequence generator is considered in the present work. Considering the watermarking technique as a digital communication process, an error control coding (ECC) must be incorporated in the data hiding schemes. Besides the hardware implementation of entire CA based data hiding technique, the individual blocks of the algorithm using CA provide the best result than that of some other methods irrespective of the hardware and software technique. The Cellular Automata Transform, CA based PN sequence generator, and CA ECC are the requisite blocks that are developed not only to meet the reliable hardware requirements but also for the basic spread spectrum watermarking features. The proposed algorithm shows statistical invisibility and resiliency against various common signal-processing operations. This algorithmic design utilizes the existing allocated bandwidth in the data transmission channel in a more efficient manner.

Keywords: Cellular automata, watermarking, error control coding, PN sequence, VLSI.

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Authors: Muhammad Aqil, Ichiro Kita, Moses Macalinao

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Reliable water level forecasts are particularly important for warning against dangerous flood and inundation. The current study aims at investigating the suitability of the adaptive network based fuzzy inference system for continuous water level modeling. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of the network. For this study, water levels data are available for a hydrological year of 2002 with a sampling interval of 1-hour. The number of antecedent water level that should be included in the input variables is determined by two statistical methods, i.e. autocorrelation function and partial autocorrelation function between the variables. Forecasting was done for 1-hour until 12-hour ahead in order to compare the models generalization at higher horizons. The results demonstrate that the adaptive networkbased fuzzy inference system model can be applied successfully and provide high accuracy and reliability for river water level estimation. In general, the adaptive network-based fuzzy inference system provides accurate and reliable water level prediction for 1-hour ahead where the MAPE=1.15% and correlation=0.98 was achieved. Up to 12-hour ahead prediction, the model still shows relatively good performance where the error of prediction resulted was less than 9.65%. The information gathered from the preliminary results provide a useful guidance or reference for flood early warning system design in which the magnitude and the timing of a potential extreme flood are indicated.

Keywords: Neural Network, Fuzzy, River, Forecasting

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Authors: Akram Mahdaviparsa, Tamera McCuen, Vahideh Karimimansoob

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Fire service is responsible for protecting life, assets, and natural resources from fire and other hazardous incidents. Search and rescue in unfamiliar buildings is a vital part of firefighters’ responsibilities. Providing firefighters with precise building information in an easy-to-understand format is a potential solution for mitigating the negative consequences of fire hazards. The negative effect of insufficient knowledge about a building’s indoor environment impedes firefighters’ capabilities and leads to lost property. A data rich building information modeling (BIM) is a potentially useful source in three-dimensional (3D) visualization and data/information storage for fire emergency response. Therefore, this research’s purpose is prioritizing the required information for firefighters from the most important information to the least important. A survey was carried out with firefighters working in the Norman Fire Department to obtain the importance of each building information item. The results show that “the location of exit doors, windows, corridors, elevators, and stairs”, “material of building elements”, and “building data” are the three most important information specified by firefighters. The results also implied that the 2D model of architectural, structural and way finding is more understandable in comparison with the 3D model, while the 3D model of MEP system could convey more information than the 2D model. Furthermore, color in visualization can help firefighters to understand the building information easier and quicker. Sufficient internal consistency of all responses was proven through developing the Pearson Correlation Matrix and obtaining Cronbach’s alpha of 0.916. Therefore, the results of this study are reliable and could be applied to the population.

Keywords: BIM, building fire response, ranking, visualization.

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Authors: P. C. Boisteanu, M. G. Usturoi, Roxana Lazar, B. V. Avarvarei

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The paper is included within the framework of a complex research program, which was initiated from the hypothesis arguing on the existence of a correlation between pineal indolic and peptide hormones and the somatic development rhythm, including thus the epithalamium-epiphysis complex involvement. At birds, pineal gland contains a circadian oscillator, playing a main role in the temporal organization of the cerebral functions. The secretion of pineal indolic hormones is characterized by a high endogenous rhythmic alternation, modulated by the light/darkness (L/D) succession and by temperature as well. The research has been carried out using 100 chicken broilers - “Ross" commercial hybrid, randomly allocated in two experimental batches: Lc batch, reared under a 12L/12D lighting schedule and Lexp batch, which was photic pinealectomised through continuous exposition to light (150 lux, 24 hours, 56 days). Chemical and physical features of the meat issued from breast fillet and thighs muscles have been studied, determining the dry matter, proteins, fat, collagen, salt content and pH value, as well. Besides the variations of meat chemical composition in relation with lighting schedule, other parameters have been studied: live weight dynamics, feed intake and somatic development degree. The achieved results became significant since chickens have 7 days of age, some variations of the studied parameters being registered, revealing that the pineal gland physiologic activity, in relation with the lighting schedule, could be interpreted through the monitoring of the somatic development technological parameters, usually studied within the chicken broilers rearing aviculture practice.

Keywords: lighting schedule, physic-chemical characteristics ofmeat, pineal gland at birds.

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Authors: Lanka Dinushke Weerasiri, Subrat Das, Daniel Fabijanic, William Yang

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Fluidization at vacuum pressure has been a topic that is of growing research interest. Several industrial applications (such as drying, extractive metallurgy, and chemical vapor deposition (CVD)) can potentially take advantage of vacuum pressure fluidization. Particularly, the fine chemical industry requires processing under safe conditions for thermolabile substances, and reduced pressure fluidized beds offer an alternative. Fluidized beds under vacuum conditions provide optimal conditions for treatment of granular materials where the reduced gas pressure maintains an operational environment outside of flammability conditions. The fluidization at low-pressure is markedly different from the usual gas flow patterns of atmospheric fluidization. The different flow regimes can be characterized by the dimensionless Knudsen number. Nevertheless, hydrodynamics of bubbling vacuum fluidized beds has not been investigated to author’s best knowledge. In this work, the two-fluid numerical method was used to determine the impact of reduced pressure on the fundamental properties of a fluidized bed. The slip flow model implemented by Ansys Fluent User Defined Functions (UDF) was used to determine the interphase momentum exchange coefficient. A wide range of operating pressures was investigated (1.01, 0.5, 0.25, 0.1 and 0.03 Bar). The gas was supplied by a uniform inlet at 1.5U_mf and 2U_mf. The predicted minimum fluidization velocity (U_mf) shows excellent agreement with the experimental data. The results show that the operating pressure has a notable impact on the bed properties and its hydrodynamics. Furthermore, it also shows that the existing Gorosko correlation that predicts bed expansion is not applicable under reduced pressure conditions.

Keywords: Computational fluid dynamics, fluidized bed, gas-solid flow, vacuum pressure, slip flow, minimum fluidization velocity.

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Authors: G. Karunakaran, M. Jagathambal, N. Van Minh, E. Kolesnikov, A. Gusev, O. V. Zakharova, E. V. Scripnikova, E. D. Vishnyakova, D. Kuznetsov

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Iron oxide nanoparticles (Fe₂O₃NPs) are widely used in different applications due to its ecofriendly nature and biocompatibility. Hence, in this investigation, biosynthesized Fe₂O₃NPs influence on flax (Linum usitatissimum L.) plant was examined. The biosynthesized nanoparticles were found to be cubic phase which is confirmed by XRD analysis. FTIR analysis confirmed the presence of functional groups corresponding to the iron oxide nanoparticle. The elemental analysis also confirmed that the obtained nanoparticle is iron oxide nanoparticle. The scanning electron microscopy and the transmission electron microscopy confirm that the average particle size was around 56 nm. The effect of Fe₂O₃NPs on seed germination followed by biochemical analysis was carried out using standard methods. The results obtained after four days and 11 days of seed vigor studies showed that the seedling length (cm), average number of seedling with leaves, increase in root length (cm) was found to be enhanced on treatment with iron oxide nanoparticles when compared to control. A positive correlation was noticed with the dose of the nanoparticle and plant growth, which may be due to changes in metabolic activity. Hence, to evaluate the change in metabolic activity, peroxidase and catalase activities were estimated. It was clear from the observation that higher concentration of iron oxide nanoparticles (Fe₂O₃NPs 1000 mg/L) has enhanced peroxidase and catalase activities and in turn plant growth. Thus, this study clearly showed that biosynthesized iron oxide nanoparticles will be an effective nano-nutrient for agriculture applications.

Keywords: Catalase, fertilizer, iron oxide nanoparticles, Linum usitatissimum L., nano-nutrient, peroxidase.

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Authors: Dr. H. B. Kekre, Sudeep D. Thepade

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Panoramic view generation has always offered novel and distinct challenges in the field of image processing. Panoramic view generation is nothing but construction of bigger view mosaic image from set of partial images of the desired view. The paper presents a solution to one of the problems of image seascape formation where some of the partial images are color and others are grayscale. The simplest solution could be to convert all image parts into grayscale images and fusing them to get grayscale image panorama. But in the multihued world, obtaining the colored seascape will always be preferred. This could be achieved by picking colors from the color parts and squirting them in grayscale parts of the seascape. So firstly the grayscale image parts should be colored with help of color image parts and then these parts should be fused to construct the seascape image. The problem of coloring grayscale images has no exact solution. In the proposed technique of panoramic view generation, the job of transferring color traits from reference color image to grayscale image is done by palette based method. In this technique, the color palette is prepared using pixel windows of some degrees taken from color image parts. Then the grayscale image part is divided into pixel windows with same degrees. For every window of grayscale image part the palette is searched and equivalent color values are found, which could be used to color grayscale window. For palette preparation we have used RGB color space and Kekre-s LUV color space. Kekre-s LUV color space gives better quality of coloring. The searching time through color palette is improved over the exhaustive search using Kekre-s fast search technique. After coloring the grayscale image pieces the next job is fusion of all these pieces to obtain panoramic view. For similarity estimation between partial images correlation coefficient is used.

Keywords: Panoramic View, Similarity Estimate, Color Transfer, Color Palette, Kekre's Fast Search, Kekre's LUV

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Authors: S. Bouhouche, M. Lahreche, S. Ziani, J. Bast

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Modern manufacturing facilities are large scale, highly complex, and operate with large number of variables under closed loop control. Early and accurate fault detection and diagnosis for these plants can minimise down time, increase the safety of plant operations, and reduce manufacturing costs. Fault detection and isolation is more complex particularly in the case of the faulty analog control systems. Analog control systems are not equipped with monitoring function where the process parameters are continually visualised. In this situation, It is very difficult to find the relationship between the fault importance and its consequences on the product failure. We consider in this paper an approach to fault detection and analysis of its effect on the production quality using an adaptive centring and scaling in the pickling process in cold rolling. The fault appeared on one of the power unit driving a rotary machine, this machine can not track a reference speed given by another machine. The length of metal loop is then in continuous oscillation, this affects the product quality. Using a computerised data acquisition system, the main machine parameters have been monitored. The fault has been detected and isolated on basis of analysis of monitored data. Normal and faulty situation have been obtained by an artificial neural network (ANN) model which is implemented to simulate the normal and faulty status of rotary machine. Correlation between the product quality defined by an index and the residual is used to quality classification.

Keywords: Modeling, fault detection and diagnosis, parameters estimation, neural networks, Fault Detection and Diagnosis (FDD), pickling process.

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Authors: Navneet Singh Deora, Aastha Deswal, H. N. Mishra

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Pasta is one of the most widely consumed food products around the world. Rapid determination of the moisture content in pasta will assist food processors to provide online quality control of pasta during large scale production. Rapid Fourier transform near-infrared method (FT-NIR) was developed for determining moisture content in pasta. A calibration set of 150 samples, a validation set of 30 samples and a prediction set of 25 samples of pasta were used. The diffuse reflection spectra of different types of pastas were measured by FT-NIR analyzer in the 4,000-12,000cm^-1 spectral range. Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 10 to 15 percent (w.b) of the pasta. The prediction models based on partial least squares (PLS) regression, were developed in the near-infrared. Conventional criteria such as the R², the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre-processing (vector normalization, minimum-maximum normalization and multiplicative scatter correction) methods. Spectra of pasta sample were treated with different mathematic pre-treatments before being used to build models between the spectral information and moisture content. The moisture content in pasta predicted by FT-NIR methods had very good correlation with their values determined via traditional methods (R² = 0.983), which clearly indicated that FT-NIR methods could be used as an effective tool for rapid determination of moisture content in pasta. The best calibration model was developed with min-max normalization (MMN) spectral pre-processing (R² = 0.9775). The MMN pre-processing method was found most suitable and the maximum coefficient of determination (R²) value of 0.9875 was obtained for the calibration model developed.

Keywords: FT-NIR, Pasta, moisture determination.

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Authors: L. Basha, E. Gjika

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This study is focused on analyzing climate change in Albania and its potential effects on cereal yields. Initially, monthly temperature and rainfalls in Albania were studied for the period 1960-2021. Climacteric variables are important variables when trying to model cereal yield behavior, especially when significant changes in weather conditions are observed. For this purpose, in the second part of the study, linear and nonlinear models explaining cereal yield are constructed for the same period, 1960-2021. The multiple linear regression analysis and lasso regression method are applied to the data between cereal yield and each independent variable: average temperature, average rainfall, fertilizer consumption, arable land, land under cereal production, and nitrous oxide emissions. In our regression model, heteroscedasticity is not observed, data follow a normal distribution, and there is a low correlation between factors, so we do not have the problem of multicollinearity. Machine learning methods, such as Random Forest (RF), are used to predict cereal yield responses to climacteric and other variables. RF showed high accuracy compared to the other statistical models in the prediction of cereal yield. We found that changes in average temperature negatively affect cereal yield. The coefficients of fertilizer consumption, arable land, and land under cereal production are positively affecting production. Our results show that the RF method is an effective and versatile machine-learning method for cereal yield prediction compared to the other two methods: multiple linear regression and lasso regression method.

Keywords: Cereal yield, climate change, machine learning, multiple regression model, random forest.

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Authors: B. Ghanbarian-Alavijeh, A.M. Liaghat, S. Sohrabi

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Saturated hydraulic conductivity is one of the soil hydraulic properties which is widely used in environmental studies especially subsurface ground water. Since, its direct measurement is time consuming and therefore costly, indirect methods such as pedotransfer functions have been developed based on multiple linear regression equations and neural networks model in order to estimate saturated hydraulic conductivity from readily available soil properties e.g. sand, silt, and clay contents, bulk density, and organic matter. The objective of this study was to develop neural networks (NNs) model to estimate saturated hydraulic conductivity from available parameters such as sand and clay contents, bulk density, van Genuchten retention model parameters (i.e. r θ , α , and n) as well as effective porosity. We used two methods to calculate effective porosity: : (1) eff s FC φ =θ -θ , and (2) inf φ =θ -θ eff s , in which s θ is saturated water content, FC θ is water content retained at -33 kPa matric potential, and inf θ is water content at the inflection point. Total of 311 soil samples from the UNSODA database was divided into three groups as 187 for the training, 62 for the validation (to avoid over training), and 62 for the test of NNs model. A commercial neural network toolbox of MATLAB software with a multi-layer perceptron model and back propagation algorithm were used for the training procedure. The statistical parameters such as correlation coefficient (R2), and mean square error (MSE) were also used to evaluate the developed NNs model. The best number of neurons in the middle layer of NNs model for methods (1) and (2) were calculated 44 and 6, respectively. The R2 and MSE values of the test phase were determined for method (1), 0.94 and 0.0016, and for method (2), 0.98 and 0.00065, respectively, which shows that method (2) estimates saturated hydraulic conductivity better than method (1).

Keywords: Neural network, Saturated hydraulic conductivity, Soil physical properties.

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Authors: N. A. Samat, S. H. Mohd Imam Ma’arof

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Dengue disease is an infectious vector-borne viral disease that is commonly found in tropical and sub-tropical regions, especially in urban and semi-urban areas, around the world and including Malaysia. There is no currently available vaccine or chemotherapy for the prevention or treatment of dengue disease. Therefore prevention and treatment of the disease depend on vector surveillance and control measures. Disease risk mapping has been recognized as an important tool in the prevention and control strategies for diseases. The choice of statistical model used for relative risk estimation is important as a good model will subsequently produce a good disease risk map. Therefore, the aim of this study is to estimate the relative risk for dengue disease based initially on the most common statistic used in disease mapping called Standardized Morbidity Ratio (SMR) and one of the earliest applications of Bayesian methodology called Poisson-gamma model. This paper begins by providing a review of the SMR method, which we then apply to dengue data of Perak, Malaysia. We then fit an extension of the SMR method, which is the Poisson-gamma model. Both results are displayed and compared using graph, tables and maps. Results of the analysis shows that the latter method gives a better relative risk estimates compared with using the SMR. The Poisson-gamma model has been demonstrated can overcome the problem of SMR when there is no observed dengue cases in certain regions. However, covariate adjustment in this model is difficult and there is no possibility for allowing spatial correlation between risks in adjacent areas. The drawbacks of this model have motivated many researchers to propose other alternative methods for estimating the risk.

Keywords: Dengue disease, Disease mapping, Standardized Morbidity Ratio, Poisson-gamma model, Relative risk.

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Authors: Mai. M. Toughan, Ahmed A. A. Sallam, Ashraf O. Abd El-Latif

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Termites are eusocial insects that are found on all continents except Antarctica. Termites have serious destructive impact, damaging local huts and crops of poor subsistence. The annual cost of termite damage and its control is determined in the billions globally. In Egypt, most of these damages are due to the subterranean termite species especially the sand termite, P. hypostoma. Pyrethroids became the primary weapon for subterranean termite control, after the use of chlorpyrifos as a soil termiticide was banned. Despite the important role of pyrethroids in termite control, its extensive use in pest control led to the eventual rise of insecticide resistance which may make many of the pyrethroids ineffective. The ability to diagnose the precise mechanism of pyrethroid resistance in any insect species would be the key component of its management at specified location for a specific population. In the present study, detailed toxicological and biochemical studies was conducted on the mechanism of pyrethroid resistance in P. hypostoma. The susceptibility of field populations of P. hypostoma against deltamethrin, α-cypermethrin and ƛ-cyhalothrin was evaluated. The obtained results revealed that the workers of P. hypostoma have developed high resistance level against the tested pyrethroids. Studies carried out through estimation of detoxification enzyme activity indicated that enhanced esterase and cytochrome P450 activities were probably important mechanisms for pyrethroid resistance in field populations. Elevated esterase activity and also additional esterase isozyme were observed in the pyrethroid-resistant populations compared to the susceptible populations. Strong positive correlation between cytochrome P450 activity and pyrethroid resistance was also reported. |Deltamethrin could be recommended as a resistance-breaking pyrethroid that is active against resistant populations of P. hypostoma.

Keywords: Psammotermes hypostoma, pyrethroid resistance, esterase, cytochrome P450.

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Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin

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Copper is an essential raw material used in the construction industry. During 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two hybrid price prediction models using artificial neural network and long short-term memory (ANN-LSTM), by Python, that can forecast the average monthly copper prices, traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022 and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices, and economic indicators of the three major exporting countries of copper depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation, and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-month prediction model is better than the 1-month prediction model; but still, both models can act as predicting tools for diverse economic situations.

Keywords: Copper prices, prediction model, neural network, time series forecasting.

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Authors: Sarina Md Yusof, Suhana Aiman, Mohd Khairi Zawi, Hosni Hasan, Azila Azreen Md Radzi

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Failure in mastery of motor skills proficiency during childhood has been seen as a detrimental factor for children to be physically active. Lack of motor skills proficiency tends to reduce children’s competency and confidence level to participate in physical activity. As a consequence of less participation in physical activity, children will turn to be overweight and obese. It has been suggested that children who master motor skill proficiency will be more involved in physical activity thus preventing them from being overweight. Obesity has become a serious childhood health issues worldwide. Previous studies have found that children who were overweight and obese were generally less active however these studies focused on one gender. This study aims to compare motor skill proficiency of underweight, normal-weight, overweight and obese young boys as well as to determine the relationship between motor skills proficiency and body composition. 112 boys aged between 8 to 10 years old participated in this study. Participants were assigned to four groups; underweight, normal-weight, overweight and obese using BMI-age percentile chart for children. Bruininks- Oseretsky Test Second Edition-Short Form was administered to assess their motor skill proficiency. Meanwhile, body composition was determined by the skinfold thickness measurement. Result indicated that underweight and normal children were superior in motor skills proficiency compared to overweight and obese children (p < 0.05). A significant strong inverse correlation between motor skills proficiency and body composition (r = -0.849) is noted. The findings of this study could be explained by non-contributory mass that carried by overweight and obese children leads to biomechanical movement inefficiency which will become detrimental to motor skills proficiency. It can be concluded that motor skills proficiency is inversely correlated with body composition.

Keywords: Motor skills proficiency, body composition, obesity.

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Authors: Manat Srivanit, Hokao Kazunori

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Facing the concern of the population to its environment and to climatic change, city planners are now considering the urban climate in their choices of planning. The urban climate, representing different urban morphologies across central Bangkok metropolitan area (BMA), are used to investigates the effects of both the composition and configuration of variables of urban morphology indicators on the summer diurnal range of urban climate, using correlation analyses and multiple linear regressions. Results show first indicate that approximately 92.6% of the variation in the average maximum daytime near-surface air temperature (Ta) was explained jointly by the two composition variables of urban morphology indicators including open space ratio (OSR) and floor area ratio (FAR). It has been possible to determine the membership of sample areas to the local climate zones (LCZs) using these urban morphology descriptors automatically computed with GIS and remote sensed data. Finally result found the temperature differences among zones of large separation, such as the city center could be respectively from 35.48±1.04ºC (Mean±S.D.) warmer than the outskirt of Bangkok on average for maximum daytime near surface temperature to 28.27±0.21ºC for extreme event and, can exceed as 8ºC. A spatially disaggregation of urban thermal responsiveness map would be helpful for several reasons. First, it would localize urban areas concerned by different climate behavior over summer daytime and be a good indicator of urban climate variability. Second, when overlaid with a land cover map, this map may contribute to identify possible urban management strategies to reduce heat wave effects in BMA.

Keywords: Urban climate, Urban morphology, Local climate zone, Urban planning, GIS and remote sensing

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Authors: M. Seguini, D. Nedjar

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An accuracy nonlinear analysis of a deep beam resting on elastic perfectly plastic soil is carried out in this study. In fact, a nonlinear finite element modeling for large deflection and moderate rotation of Euler-Bernoulli beam resting on linear and nonlinear random soil is investigated. The geometric nonlinear analysis of the beam is based on the theory of von Kàrmàn, where the Newton-Raphson incremental iteration method is implemented in a Matlab code to solve the nonlinear equation of the soil-beam interaction system. However, two analyses (deterministic and probabilistic) are proposed to verify the accuracy and the efficiency of the proposed model where the theory of the local average based on the Monte Carlo approach is used to analyze the effect of the spatial variability of the soil properties on the nonlinear beam response. The effect of six main parameters are investigated: the external load, the length of a beam, the coefficient of subgrade reaction of the soil, the Young’s modulus of the beam, the coefficient of variation and the correlation length of the soil’s coefficient of subgrade reaction. A comparison between the beam resting on linear and nonlinear soil models is presented for different beam’s length and external load. Numerical results have been obtained for the combination of the geometric nonlinearity of beam and material nonlinearity of random soil. This comparison highlighted the need of including the material nonlinearity and spatial variability of the soil in the geometric nonlinear analysis, when the beam undergoes large deflections.

Keywords: Finite element method, geometric nonlinearity, material nonlinearity, soil-structure interaction, spatial variability.

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Authors: Ammar Ayad Issa Al-Rifaie, Zuhreya Muazu, Maysam Ali Abdulwahid, Dermot Gleeson

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The aim of this audit was to examine the efficiency of alcohol history documentation and screening for hazardous drinkers at the Medical Admission Unit (MAU) of Northern General Hospital (NGH), Sheffield, to identify any potential for enhancing clinical practice. Data were collected from medical clerking sheets, ICE system and directly from 82 patients by three junior medical doctors using both CAGE questionnaire and AUDIT-C tool for newly admitted patients to MAU in NGH, in the period between January and March 2015. Alcohol consumption was documented in around two-third of the patient sample and this was documented fairly accurately by health care professionals. Some used subjective words such as 'social drinking' in the alcohol units’ section of the history. CAGE questionnaire was applied to only four patients and none of the patients had documented advice, education or referral to an alcohol liaison team. AUDIT-C tool had identified 30.4%, while CAGE 10.9%, of patients admitted to the NGH MAU as hazardous drinkers. The amount of alcohol the patient consumes positively correlated with the score of AUDIT-C (Pearson correlation 0.83). Re-audit is planned to be carried out after integrating AUDIT-C tool as labels in the notes and presenting a brief teaching session to junior doctors. Alcohol misuse screening is not adequately undertaken and no appropriate action is being offered to hazardous drinkers. CAGE questionnaire is poorly applied to patients and when satisfactory and adequately used has low sensitivity to detect hazardous drinkers in comparison with AUDIT-C tool. Re-audit of alcohol screening practice after introducing AUDIT-C tool in clerking sheets (as labels) is required to compare the findings and conclude the audit cycle.

Keywords: Alcohol screening, AUDIT-C, CAGE, Hazardous drinking.

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Authors: Ime R. Udotong, Samuel I. Eduok, Joseph P. Essien, Basil N. Ita

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Sediment and mangrove root samples from Iko River Estuary, Nigeria were analyzed for microbial and polycyclic aromatic hydrocarbon (PAH) content. The total heterotrophic bacterial (THB) count ranged from 1.1x107 to 5.1 x107 cfu/g, total fungal (TF) count ranged from 1.0x106 to 2.7x106 cfu/g, total coliform (TC) count ranged from 2.0x104 to 8.0x104cfu/g while hydrocarbon utilizing bacterial (HUB) count ranged from 1.0x 105 to 5.0 x 105cfu/g. There was a range of positive correlation (r = 0.72 to 0.93) between THB count and total HUB count, respectively. The organisms were Staphylococcus aureus, Bacillus cereus, Flavobacterium breve, Pseudomonas aeruginosa, Erwinia amylovora, Escherichia coli, Enterobacter sp, Desulfovibrio sp, Acinetobacter iwoffii, Chromobacterium violaceum, Micrococcus sedentarius, Corynebacterium sp, and Pseudomonas putrefaciens. The PAH were Naphthalene, 2-Methylnaphthalene, Acenapthylene, Acenaphthene, Fluorene, Phenanthene, Anthracene, Fluoranthene, Pyrene, Benzo(a)anthracene, Chrysene, Benzo(b)fluoranthene, Benzo(k)fluoranthene, Benzo(a)pyrene, Dibenzo(a,h)anthracene, Benzo(g,h,l)perylene ,Indeno(1,2,3-d)pyrene with individual PAH concentrations that ranged from 0.20mg/kg to 1.02mg/kg, 0.20mg/kg to 1.07mg/kg and 0.2mg/kg to 4.43mg/kg in the benthic sediment, epipellic sediment and mangrove roots, respectively. Total PAH ranged from 6.30 to 9.93mg/kg, 6.30 to 9.13mg/kg and 9.66 to 16.68mg/kg in the benthic sediment, epipellic sediment and mangrove roots, respectively. The high concentrations in the mangrove roots are indicative of bioaccumulation of the pollutant in the plant tissue. The microorganisms are of ecological significance and the detectable quantities of polycyclic aromatic hydrocarbon could be partitioned and accumulated in tissues of infaunal and epifaunal organisms in the study area.

Keywords: Hydrocarbonoclastic bacteria, Iko River estuary, Mangrove, Polycyclic aromatic hydrocarbon.

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Authors: Naci Büyükkaracığan

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Hydroclimatic observation values ​​are used in the planning of the project of water resources. Climate variables are the first of the values ​​used in planning projects. At the same time, the climate system is a complex and interactive system involving the atmosphere, land surfaces, snow and bubbles, the oceans and other water structures. The amount and distribution of precipitation, which is an important climate parameter, is a limiting environmental factor for dispersed living things. Trend analysis is applied to the detection of the presence of a pattern or trend in the data set. Many trends work in different parts of the world are usually made for the determination of climate change. The detection and attribution of past trends and variability in climatic variables is essential for explaining potential future alteration resulting from anthropogenic activities. Parametric and non-parametric tests are used for determining the trends in climatic variables. In this study, trend tests were applied to annual total precipitation data obtained in period of 1972 and 2012, in the Konya Basin. Non-parametric trend tests, (Sen’s T, Spearman’s Rho, Mann-Kendal, Sen’s T trend, Wald-Wolfowitz) and parametric test (mean square) were applied to annual total precipitations of 15 stations for trend analysis. The linear slopes (change per unit time) of trends are calculated by using a non-parametric estimator developed by Sen. The beginning of trends is determined by using the Mann-Kendall rank correlation test. In addition, homogeneities in precipitation trends are tested by using a method developed by Van Belle and Hughes. As a result of tests, negative linear slopes were found in annual total precipitations in Konya.

Keywords: Trend analysis, precipitation, hydroclimatology, Konya, Turkey.

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Authors: Xiaoshu Lu, Esa-Pekka Takala, Risto Toivonen

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This paper systematically investigates the timedependent health outcomes for office staff during computer work using the developed mathematical model. The model describes timedependent health outcomes in multiple body regions associated with computer usage. The association is explicitly presented with a doseresponse relationship which is parametrized by body region parameters. Using the developed model we perform extensive investigations of the health outcomes statically and dynamically. We compare the risk body regions and provide various severity rankings of the discomfort rate changes with respect to computer-related workload dynamically for the study population. Application of the developed model reveals a wide range of findings. Such broad spectrum of investigations in a single report literature is lacking. Based upon the model analysis, it is discovered that the highest average severity level of the discomfort exists in neck, shoulder, eyes, shoulder joint/upper arm, upper back, low back and head etc. The biggest weekly changes of discomfort rates are in eyes, neck, head, shoulder, shoulder joint/upper arm and upper back etc. The fastest discomfort rate is found in neck, followed by shoulder, eyes, head, shoulder joint/upper arm and upper back etc. Most of our findings are consistent with the literature, which demonstrates that the developed model and results are applicable and valuable and can be utilized to assess correlation between the amount of computer-related workload and health risk.

Keywords: Computer-related workload, health outcomes, dynamic association, dose-response relationship, systematic analysis.

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Authors: Matthias Feiner, Francisco Javier Fernández García, Michael Arneman, Martin Kipfmüller

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To ensure reliability in miniaturized devices or processes with increased heat fluxes, very efficient cooling methods have to be employed in order to cope with small available cooling surfaces. To address this problem, a certain type of evaporator/heat exchanger was developed: It is called a swirl evaporator due to its flow characteristic. The swirl evaporator consists of a concentrically eroded screw geometry in which a capillary tube is guided, which is inserted into a pocket hole in components with high heat load. The liquid refrigerant R32 is sprayed through the capillary tube to the end face of the blind hole and is sucked off against the injection direction in the screw geometry. Its inner diameter is between one and three millimeters. The refrigerant is sprayed into the pocket hole via a small tube aligned in the center of the bore hole and is sucked off on the front side of the hole against the direction of injection. The refrigerant is sucked off in a helical geometry (twisted flow) so that it is accelerated against the hot wall (centrifugal acceleration). This results in an increase in the critical heat flux of up to 40%. In this way, more heat can be dissipated on the same surface/available installation space. This enables a wide range of technical applications. To optimize the design for the needs in various fields of industry, like the internal tool cooling when machining nickel base alloys like Inconel 718, a correlation-based model of the swirl-evaporator was developed. The model is separated into 3 subgroups with overall 5 regimes. The pressure drop and heat transfer are calculated separately. An approach to determine the locality of phase change in the capillary and the swirl was implemented. A test stand has been developed to verify the simulation.

Keywords: Helically-shaped, oil-free, R32, swirl-evaporator, twist flow.

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Authors: A. Yavar, S. Sarmani, K. S. Khoo

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The analysis of trace elements in human hair provides crucial insights into an individual's nutritional status and environmental exposure. This research aimed to examine the levels of toxic and essential elements in the scalp hair of school children aged 12-17 from three villages (Anglong Romiot (AR), Svay Romiot (SR), and Kampong Kong (KK)) in Cambodia's Kandal province, a region where residents are especially vulnerable to toxic elements, notably arsenic (As), due to their dietary habits, lifestyle, and environmental conditions. The scalp hair samples were analyzed using the k0-Instrumental Neutron Activation method (k0-INAA), with a six-hour irradiation period in the Malaysian Nuclear Agency (MNA) research reactor followed by High Purity Germanium (HPGe) detector use to identify the gamma peaks of radionuclides. The analysis identified 31 elements in the human hair from the study area, including As, Au, Br, Ca, Ce, Co, Dy, Eu-152m, Hg-197, Hg-203, Ho, Ir, K, La, Lu, Mn, Na, Pa, Pt-195m, Pt-197, Sb, Sc-46, Sc-47, Sm, Sn-117m, W-181, W-187, Yb-169, Yb-175, Zn, and Zn-69m. The accuracy of the method was verified through the analysis of ERM-DB001-human hair as a Certified Reference Material (CRM), with the results demonstrating consistency with the certified values. Given the prevalent arsenic pollution in the research area, the study also examined the relationship between the concentration of As and other elements using Pearson's correlation test. The outcomes offer a comprehensive resource for future investigations into toxic and essential element presence in the region. In the main body of the paper, a more extensive discussion on the implications of arsenic pollution and the correlations observed is provided to enhance understanding and inform future research directions.

Keywords: Human scalp hair, toxic and essential elements, Kandal Province, Cambodia, k0-Instrumental Neutron Activation Method.

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