Search results for: pressure gradient

Authors: Junjie Wu, Hongjuan Hou, Eric Hu, Yongping Yang

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Solar aided power generation (SAPG) technology has been proven as an efficient way to make use of solar energy for power generation purpose. In an SAPG plant, a solar field consisting of parabolic solar collectors is normally used to supply the solar heat in order to displace the high pressure/temperature extraction steam. To understand the performance of such a SAPG plant, a new simulation model was developed by the authors recently, in which the boiler was treated, as a series of heat exchangers unlike other previous models. Through the simulations using the new model, it was found the outlet properties of reheated steam, e.g. temperature, would decrease due to the introduction of the solar heat. The changes make the (lower stage) turbines work under off-design condition. As a result, the whole plant’s performance may not be optimal. In this paper, the second solar filed was proposed to increase the inlet temperature of steam to be reheated, in order to bring the outlet temperature of reheated steam back to the designed condition. A 600MW SAPG plant was simulated as a case study using the new model to understand the impact of the second solar field on the plant performance. It was found in the study, the 2nd solar field would improve the plant’s performance in terms of cycle efficiency and solar-to-electricity efficiency by 1.91% and 6.01%. The solar-generated electricity produced by per aperture area under the design condition was 187.96W/m2, which was 26.14% higher than the previous design.

Keywords: solar-aided power generation system, off-design performance, coal-saving performance, boiler modelling, integration schemes

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Authors: Joselito Medina-Marin, Maria G. Serna-Diaz, Juan C. Seck-Tuoh-Mora, Norberto Hernandez-Romero, Irving Barragán-Vite

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Wind speed forecasting is an important issue for planning wind power generation facilities. The accuracy in the wind speed prediction allows a good performance of wind turbines for electricity generation. A model based on artificial neural networks is presented in this work. A dataset with atmospheric information about air temperature, atmospheric pressure, wind direction, and wind speed in Pachuca, Hidalgo, México, was used to train the artificial neural network. The data was downloaded from the web page of the National Meteorological Service of the Mexican government. The records were gathered for three months, with time intervals of ten minutes. This dataset was used to develop an iterative algorithm to create 1,110 ANNs, with different configurations, starting from one to three hidden layers and every hidden layer with a number of neurons from 1 to 10. Each ANN was trained with the Levenberg-Marquardt backpropagation algorithm, which is used to learn the relationship between input and output values. The model with the best performance contains three hidden layers and 9, 6, and 5 neurons, respectively; and the coefficient of determination obtained was r²=0.9414, and the Root Mean Squared Error is 1.0559. In summary, the ANN approach is suitable to predict the wind speed in Pachuca City because the r² value denotes a good fitting of gathered records, and the obtained ANN model can be used in the planning of wind power generation grids.

Keywords: wind power generation, artificial neural networks, wind speed, coefficient of determination

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Authors: Babu Shersad, Partha Banerjee

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Reduction in haemoglobin levels has been implicated to be a cause for reduced exercise tolerance and cardiovascular complications of chronic renal diseases. Trends of hemoglobin levels in patients on haemodialysis could be an indicator of efficacy of hemodialysis and an indicator of quality of life in haemodialysis patients. In the UAE, the rate of growth (of patients on dialysis) is 10 to 15 per cent per year. The primary mode of haemodialysis in the region is based on in-patient hospital-based hemodialysis units. The increase in risk of cardiovascular and cerebrovascular morbidity as well as mortality in pre-dialysis Chronic Renal Disease has been reported. However, data on the health burden on haemodialysis in standalone dialysis facilities is very scarce. This is mainly due to the paucity of ambulatory centres for haemodialysis in the region. AMSA is the first center to offer standalone dialysis in the UAE and a study over a one year period was performed. Patient data was analyzed using a questionnaire for 45 patients with an average of 2.5 dialysis sessions per week. All patients were on chronic haemodialysis as outpatients. The trends of haemoglobin levels as an independent variable were evaluated. These trends were interpreted in comparison with other parameters of renal function (creatinine, uric acid, blood pressure and ferritin). Trends indicate an increase in hemoglobin levels with increased supplementation of iron and erythropoietin over time. The adequacy of hemodialysis shows improvement concomitantly. This, in turn, correlates with better patient outcomes and has a direct impact on morbidity and mortality. This study is a pilot study and further studies are indicated so that objective parameters can be studied and validated for hemodialysis in the region.

Keywords: haemodialysis, haemoglobin in haemodialysis, haemodialysis parameters, erythropoietic agents in haemodialysis

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Authors: Donludee Jaisut, Norihisa Kato, Thanutchaporn Kumrungsee, Kiyoshi Kawai, Somkiat Prachayawarakorn, Patchalee Tungtrakul

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Jasmine Rice is a principle food of Thai people. However, glycemic index of jasmine rice is in high level, risk of type II diabetes after consuming. Burdock root is a good source of non-starch polysaccharides such as inulin. Inulin acts as prebiotic and helps reduce blood-sugar level. The purpose of this research was to reduce digestion rate of jasmine rice by coating burdock root extract on rice surface, using top-spay fluidized bed coating technique. Coating experiments were performed by spraying burdock root solution onto Jasmine rice kernels (Khao Dawk Mali-105; KDML), which had an initial moisture content of 11.6% wet basis, suspended in the fluidized bed. The experimental conditions were: solution spray rates of 31.7 mL/min, atomization pressure of 1.5 bar, spray time of 10 min, time of drying after spraying of 30 s, superficial air velocity of 3.2 m/s and drying temperatures of 60°C. The coated rice quality was evaluated in terms of the moisture content, texture, whiteness and digestion rate. The results showed that initial and final moisture contents of samples were the same in concentration 8% (v/v) and 10% (v/v). The texture was insignificantly changed from that of uncoated sample. The whiteness values were varied on concentration of burdock root extract. Coated samples were slower digested.

Keywords: burdock root, digestion, drying, rice

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Authors: Seokyoon Moon, Yun-Ho Ahn, Heejoong Kim, Sujin Hong, Yunseok Lee, Youngjune Park

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Gas hydrate is a non-stoichiometric crystalline compound consisting of host water-framework and low molecular weight guest molecules. Small gaseous molecules such as CH₄, CO₂, and N₂ can be captured in the host water framework lattices of the gas hydrate with specific temperature and pressure conditions. The three well-known crystal structures of structure I (sI), structure II (sII), and structure H (sH) are determined by the size and shape of guest molecules. In this study, we measured the phase equilibria of binary (2,2-dimethyl-1-propanol + CO₂, CH₄, N₂) hydrates to explore their fundamental thermodynamic characteristics. We identified the structure of the binary gas hydrate by employing synchrotron high-resolution powder diffraction (HRPD), and the guest distributions in the lattice of gas hydrate were investigated via dispersive Raman and ¹³C solid-state nuclear magnetic resonance (NMR) spectroscopies. The end-to-end distance of 2,2-dimethyl-1-propanol was calculated to be 7.76 Å, which seems difficult to be enclathrated in large cages of sI or sII. However, due to the flexibility of the host water framework, binary hydrates of sI or sII types can be formed with the help of small gas molecule. Also, the synchrotron HRPD patterns revealed that the binary hydrate structure highly depends on the type of help gases; a cubic Fd3m sII hydrate was formed with CH₄ or N₂, and a cubic Pm3n sI hydrate was formed with CO₂. Interestingly, dispersive Raman and ¹³C NMR spectra showed that the unique tuning phenomenon occurred in binary (2,2-dimethyl-1-propanol + CO₂) hydrate. By optimizing the composition of NPA, we can achieve both thermodynamic stability and high CO₂ storage capacity for the practical application to CO₂ capture.

Keywords: clathrate, gas hydrate, neopentyl alcohol, CO₂, tuning phenomenon

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Authors: Huan Peng, Chenye Zeng, Zhao Wang

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Alzheimer’s disease (AD) is an age-related neurodegenerative disease that is a leading cause of dementia syndromes and has become a huge burden on society and families. The main pathological features of AD involve excessive deposition of β-amyloid (Aβ) and Tau proteins in the brain, resulting in loss of neurons, expansion of neuroinflammation, and cognitive dysfunction in patients. Researchers have found effective drugs to clear the brain of error-accumulating proteins or to slow the loss of neurons, but their direct administration has key bottlenecks such as single-drug limitation, rapid blood clearance rate, impenetrable blood-brain barrier (BBB), and poor ability to target tissues and cells. Therefore, we are committed to seeking a suitable and efficient delivery system. Inspired by the possibility that exosomes may be involved in the secretion and transport mechanism of many signaling molecules or proteins in the brain, exosomes have attracted extensive attention as natural nanoscale drug carriers. We selected exosomes derived from bone marrow mesenchymal stem cells (MSC-EXO) with low immunogenicity and exosomes derived from hippocampal neurons (HT22-EXO) that may have excellent homing ability to overcome the deficiencies of oral or injectable pathways and bypass the BBB through nasal administration and evaluated their delivery ability and effect on AD. First, MSC-EXO and HT22 cells were isolated and cultured, and MSCs were identified by microimaging and flow cytometry. Then MSC-EXO and HT22-EXO were obtained by gradient centrifugation and qEV SEC separation column, and a series of physicochemical characterization were performed by transmission electron microscope, western blot, nanoparticle tracking analysis and dynamic light scattering. Next, exosomes labeled with lipophilic fluorescent dye were administered to WT mice and APP/PS1 mice to obtain fluorescence images of various organs at different times. Finally, APP/PS1 mice were administered intranasally with two exosomes 20 times over 40 days and 20 μL each time. Behavioral analysis and pathological section analysis of the hippocampus were performed after the experiment. The results showed that MSC-EXO and HT22-EXO were successfully isolated and characterized, and they had good biocompatibility. MSC-EXO showed excellent brain enrichment in APP/PS1 mice after intranasal administration, could improve the neuronal damage and reduce inflammation levels in the hippocampus of APP/PS1 mice, and the improvement effect was significantly better than HT22-EXO. However, intranasal administration of the two exosomes did not cause depression and anxious-like phenotypes in APP/PS1 mice, nor significantly improved the short-term or spatial learning and memory ability of APP/PS1 mice, and had no significant effect on the content of Aβ plaques in the hippocampus, which also meant that MSC-EXO could use their own advantages in combination with other drugs to clear Aβ plaques. The possibility of realizing highly effective non-invasive synergistic treatment for AD provides new strategies and ideas for clinical research.

Keywords: Alzheimer’s disease, exosomes derived from mesenchymal stem cell, intranasal administration, therapy-carrier dual roles

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Authors: Lutfi Abosrra, Alseddeq Alabaoub, Nuri Elhaloudi

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Corrosion processes in the Oil & Gas industry can lead to failures that are usually costly to repair, costly in terms of loss of contaminated product, in terms of environmental damage and possibly costly in terms of human safety. This article describes the results of the corrosion review and criticality assessment done at Mellitah Gas (SRU unit) for pressure equipment and piping system. The information gathered through the review was intended for developing a qualitative RBI study. The corrosion criticality assessment has been carried out by applying company procedures and industrial recommended practices such as API 571, API 580/581, ASME PCC 3, which provides a guideline for establishing corrosion integrity assessment. The corrosion review is intimately related to the probability of failure (POF). During the corrosion study, the process units are reviewed by following the applicable process flow diagrams (PFDs) in the presence of Mellitah’s personnel from process engineering, inspection, and corrosion/materials and reliability engineers. The expected corrosion damage mechanism (internal and external) was identified, and the corrosion rate was estimated for every piece of equipment and corrosion loop in the process units. A combination of both Consequence and Likelihood of failure was used for determining the corrosion risk. A qualitative consequence of failure (COF) for each individual item was assigned based on the characteristics of the fluid as per its flammability, toxicity, and pollution into three levels (High, Medium, and Low). A qualitative probability of failure (POF)was applied to evaluate the internal and external degradation mechanism, a high-level point-based (0 to 10) for the purpose of risk prioritizing in the range of Low, Medium, and High.

Keywords: corrosion, criticality assessment, RBI, POF, COF

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Authors: Saeideh Lotfi Nikoo, Azadeh Ghaheri, Reza Omani Samani

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Context: In most cultures around the globe, infertility is recognized as a crisis and exposed infertile couples are under psychosocial pressure. Indeed, the quality of life (QoL) for infertile women is lower in comparison with fertile control. Objective, The purpose of this study, was to investigate the impact of social support and hope on QoL in women undergoing infertility treatment. Methods: A cross-sectional study. Patient(s): In this cross-sectional study, 350 infertile women were recruited who were referred to an infertility clinic for the first time and had no history of Assisted Reproductive Techniques (ART) failure. Intervention(s): Questionnaires on the Fertility Quality of Life (FertiQoL), Multi-dimensional Scale of Perceived Social Support (family and friends), and Snyder Hope Scale (pathway and agency) were used to collect data. Data analysis was done by univariate and multivariate analysis. P value <0.05 was considered statistically significant. Result(s): Multivariate analysis indicated that infertile women with a higher score of social support (by family & friends) (b= 0.59 (CI 95%: 0.03, 1.15) (P = 0.040), b= 0.61 (CI 95%: 0.17, 1.04) (P = 0.006)) and hope (pathway & agency) (b= 0.94 (CI 95%: 0.29, 1.59) (P = 0.005), b= 1.13 (CI 95%: 0.45, 1.82) (P = 0.001) respectively) have significantly better Core FertiQoL. The result revealed that social support and hope are significantly and positively associated with other subscales of FertiQoL as well. Conclusions: According to the results, lifestyle interventions such as receiving social support, building a sound family with effective communication, and providing appropriate health education are of crucial importance to address psychological distress and improve the fertility QoL of women experiencing fertility problems.

Keywords: inertility, social support, infertile women, hope

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Authors: Jeni A. Popescu, Sorin G. Tomescu, Valeriu A. Vilag

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The paper focuses on the potential methods of increasing the performance of a microturbine by combining additional elements available for utilization in a cogeneration plant. The activity is carried out within the framework of a project aiming to develop, manufacture and test a microturbine functional model with high potential in energetic industry utilization. The main goal of the analysis is to determine the parameters of the fluid flow passing through each section of the turbine, based on limited data available in literature for the focus output power range or provided by experimental studies, starting from a reference cycle, and considering different cycle options, including simple, intercooled and recuperated options, in order to optimize a small cogeneration plant operation. The studied configurations operate under the same initial thermodynamic conditions and are based on a series of assumptions, in terms of individual performance of the components, pressure/velocity losses, compression ratios, and efficiencies. The thermodynamic analysis evaluates the expected performance of the microturbine cycle, while providing a series of input data and limitations to be included in the development of the experimental plan. To simplify the calculations and to allow a clear estimation of the effect of heat transfer between fluids, the working fluid for all the thermodynamic evolutions is, initially, air, the combustion being modelled by simple heat addition to the system. The theoretical results, along with preliminary experimental results are presented, aiming for a correlation in terms of microturbine performance.

Keywords: cogeneration, microturbine, performance, thermodynamic analysis

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Authors: A. Matiushkina, A. Bazhenova, I. Litvinov, E. Kornilova, A. Dubavik, A. Orlova

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Magnetic-luminescent complexes based on superparamagnetic iron oxide nanoparticles (SPIONs) and semiconductor quantum dots (QDs) have been recognized as a new class of materials that have high potential in modern medicine. These materials can serve for theranostics of oncological diseases, and also as a target agent for drug delivery. They combine the qualities characteristic of magnetic nanoparticles, that is, magneto-controllability and the ability to local heating under the influence of an external magnetic field, as well as phosphors, due to luminescence of which, for example, early tumor imaging is possible. The complexity of creating complexes is the energy transfer between particles, which quenches the luminescence of QDs in complexes with SPIONs. In this regard, a relatively new type of alloyed (CdₓZn₁₋ₓSeᵧS₁₋ᵧ)-ZnS QDs is used in our work. The presence of a sufficiently thick gradient semiconductor shell in alloyed QDs makes it possible to reduce the probability of energy transfer from QDs to SPIONs in complexes. At the same time, Forster Resonance Energy Transfer (FRET) is a perfect instrument to confirm the formation of complexes based on QDs and different-type energy acceptors. The formation of complexes in the aprotic bipolar solvent dimethyl sulfoxide is ensured by the coordination of the carboxyl group of the stabilizing QD molecule (L-cysteine) on the surface iron atoms of the SPIONs. An analysis of the photoluminescence (PL) spectra has shown that a sequential increase in the SPIONs concentration in the samples is accompanied by effective quenching of the luminescence of QDs. However, it has not confirmed the formation of complexes yet, because of a decrease in the PL intensity of QDs due to reabsorption of light by SPIONs. Therefore, a study of the PL kinetics of QDs at different SPIONs concentrations was made, which demonstrates that an increase in the SPIONs concentration is accompanied by a symbatic reduction in all characteristic PL decay times. It confirms the FRET from QDs to SPIONs, which indicates the QDs/SPIONs complex formation, rather than a spontaneous aggregation of QDs, which is usually accompanied by a sharp increase in the percentage of the QD fraction with the shortest characteristic PL decay time. The complexes have been studied by the magnetic circular dichroism (MCD) spectroscopy that allows one to estimate the response of magnetic material to the applied magnetic field and also can be useful to check SPIONs aggregation. An analysis of the MCD spectra has shown that the complexes have zero residual magnetization, which is an important factor for using in biomedical applications, and don't contain SPIONs aggregates. Cell penetration, biocompatibility, and stability of QDs/SPIONs complexes in cancer cells have been studied using HeLa cell line. We have found that the complexes penetrate in HeLa cell and don't demonstrate cytotoxic effect up to 25 nM concentration. Our results clearly demonstrate that alloyed (CdₓZn₁₋ₓSeᵧS₁₋ᵧ)-ZnS QDs can be successfully used in complexes with SPIONs reached new hybrid nanostructures, which combine bright luminescence for tumor imaging and magnetic properties for targeted drug delivery and magnetic hyperthermia of tumors. Acknowledgements: This work was supported by the Ministry of Science and Higher Education of Russian Federation, goszadanie no. 2019-1080 and was financially supported by Government of Russian Federation, Grant 08-08.

Keywords: alloyed quantum dots, magnetic circular dichroism, magneto-luminescent complexes, superparamagnetic iron oxide nanoparticles

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Jhabakhar Aryal, Kesh Bahadur Rana, Durga Prasad Neupane

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This research analyzes the nature and impacts of the 2015 unofficial blockade in Nepal, a significant event that triggered an economic and humanitarian crisis. While official channels denied claims of involvement, Nepal perceived the blockade as orchestrated by India due to concerns about the newly adopted constitution and Madheshi infringements. The study adopts a qualitative approach, utilizing semi-structured interviews, document analysis, and content analysis to gather data from various perspectives. Employing a "colonial hangover lens," it investigates if colonial legacies continue to influence postcolonial nation dynamics, focusing on India's potential attempt to exert influence over Nepal. The findings suggest that the 2015 blockade had profound consequences for Nepal, potentially reflecting lingering colonial power dynamics in the region. Despite India's denials, a significant portion of Nepalis perceived the blockade as an act of external pressure. Examining these perceptions offers valuable insights into postcolonial relations and their impact on regional stability. The 2015 unofficial blockade serves as a critical case study in understanding the complex interplay of internal dynamics, external influences, and historical legacies in shaping the geopolitics of the region. This research contributes to a deeper understanding of these factors and their ongoing implications for Nepal and its relationship with India.

Keywords: blockade, unofficial, constitution, Madhesis, India, Nepal, postcolonial, regional stability, geopolitics

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Authors: A. S. Al-Fatesh, A. A. Ibrahim, A. M. AlSharekh, F. S. Alqahtani, S. O. Kasim, A. H. Fakeeha

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Hydrogen is the expected future fuel since it produces energy without any pollution. It can be used as a fuel directly or through the fuel cell. It is also used in chemical and petrochemical industry as reducing agent or in hydrogenation processes. It is produced by different methods such as reforming of hydrocarbon, electrolytic method and methane decomposition. The objective of the present paper is to study the decomposition of methane reaction at 700°C and 800°C. The catalysts were prepared via impregnation method using 20%Fe and different proportions of combined alumina and silica support using the following ratios [100%, 90%, 80%, and 0% Al₂O₃/SiO₂]. The prepared catalysts were calcined and activated at 600 OC and 500 OC respectively. The reaction was carried out in fixed bed reactor at atmospheric pressure using 0.3g of catalyst and feed gas ratio of 1.5/1 CH₄/N₂ with a total flow rate 25 mL/min. Catalyst characterizations (TPR, TGA, BET, XRD, etc.) have been employed to study the behavior of catalysts before and after the reaction. Moreover, a brief description of the weight loss and the CH₄ conversions versus time on stream relating the different support ratios over 20%Fe/Al₂O₃/SiO₂ catalysts has been added as well. The results of TGA analysis provided higher weights losses for catalysts operated at 700°C than 800°C. For the 90% Al₂O₃/SiO₂, the activity decreases with the time on stream using 800°C reaction temperature from 73.9% initial CH₄ conversion to 46.3% for a period of 300min, whereas the activity for the same catalyst increases from 47.1% to 64.8% when 700°C reaction temperature is employed. Likewise, for 80% Al₂O₃/SiO₂ the trend of activity is similar to that of 90% Al₂O₃/SiO₂ but with a different rate of activity variation. It can be inferred from the activity results that the ratio of Al₂O₃ to SiO₂ is crucial and it is directly proportional with the activity. Whenever the Al/Si ratio decreases the activity declines. Indeed, the CH₄ conversion of 100% SiO₂ support was less than 5%.

Keywords: Al₂O₃, SiO₂, CH₄ decomposition, hydrogen, iron

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Authors: O. B. Oriolowo, O. J. John

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Empirical findings have shown insects to be nutritious and good source of food for man. However, human food preferences are not only determined by nutritional values of food consumed but, more importantly, by sociology and economic pressure. This study examined the interrelation between science and sociology in sustaining the acceptance of entomophagy among college students to combat food insecurity. A twenty items five Likert scale, College Students Entomophagy Questionnaire (CSEQ), was used to elucidate information from the respondents. The reliability coefficient was obtained to be 0.91 using Spearman-Brown Prophecy formula. Three research questions and three hypotheses were raised. Also, quantitative nutritional analysis of few insects and some established conventional protein sources were undertaking in order to compare their nutritional status. The data collected were analyzed using descriptive statistics of percentages and inferential statistics of correlation and Analysis of Variance (ANOVA). The results obtained showed that entomophagy has cultural heritage among different tribes in Nigeria and is an acceptable practice; it cuts across every social stratum and is practiced among both major religions. Moreover, insects compared favourably in term of nutrient contents when compared with the conventional animal protein sources analyzed. However, there is a gradual decline in the practice of entomophagy among students, which may be attributed to the influence of western civilization. This study, therefore, recommended an intensification of research and enlightenment of people on the usefulness of entomophagy so as to preserve its cultural heritage as well as boost human food security.

Keywords: entomophagy, food security, malnutrition, poverty alleviation, sociology

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Authors: Narin Charoenphun, Praopen Rattanadee, Chaiporn Phaephiromrat

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One of the most important intricacies of cooking is seasoning which is the process of adding salt, herbs, or spices to food to enhance the flavor. Sodium chloride (NaCl) was added in seasoning powder for taste-improving and shelf life of products. However, the raised blood pressure caused by eating too much NaCl may damage the arteries leading to the heart. Interestingly, NaCl replacement with other substance is essential for consumer. The objective of this study was to investigate the effects of NaCl replacement with potassium chloride (KCl) on the sensory characteristics and physiochemical properties of longan seasoning powder. Five longan seasoning Powder were replaced sodium chloride with KCl at 0, 25, 50 75 and 100%. Mixture design with 2 replications was performed. Sensory characteristics on overall flavor, saltiness, sweetness, bitterness and overall liking were investigated using 12 descriptive trained panelists. Results revealed that NaCl and KCl had effects on saltiness, bitterness and overall liking. As the level of KCl substituted increased, the overall flavor and sweetness of powdered seasoning from longan were not significantly (p < 0.05). This resulted in the decrease of overall liking of the products. In addition, increasing the level of KCl substituted resulted in the drop of saltiness but out of bitterness of the products. Saltiness of powdered seasoning from longan with replacement levels of 50, 75 and 100% KCl different when compared to that of 0% KCl. Bitterness of powdered seasoning from longan with replacement levels of 50, 75 and 100% KCl different when compared to that of 0% KCl. Moreover, consumer acceptance test was conducted (n=100). In conclusion, the optimum formulation contained of 32.0% longan powder, 28.0% sugar, 15.0% NaCl, 5% KCl, 16.0% pork powder, 3.0% pepper powder, and 3.0% garlic powder that would meet acceptability scores of at least 7 or like moderately.

Keywords: longan, seasoning, NaCl, KCl

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Authors: Florian Wagner, Christoph Schmidt, Vincent Bagnoud

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Understanding the interaction of ultra-intense laser pulses with overcritical targets is of major interest for many applications such as laser-driven ion acceleration, fast ignition in the frame of inertial confinement fusion or high harmonic generation and the creation of attosecond pulses. One particular aspect of this interaction is the shift of the critical surface, where the laser pulse is stopped and the absorption is at maximum, due to the radiation pressure induced by the laser pulse, also referred to as laser hole boring. We investigate laser-hole boring experimentally by measuring the backscattered spectrum which is doppler-broadened because of the movement of the reflecting surface. Using the high-power, high-energy laser system PHELIX in Darmstadt, we gathered an extensive set of data for different laser intensities ranging from 10^18 W/cm2 to 10^21 W/cm2, two different levels of the nanosecond temporal contrast (10^6 vs. 10^11), elliptical and linear polarization and varying target configurations. In this contribution we discuss how the maximum velocity of the critical surface depends on these parameters. In particular we show that by increasing the temporal contrast the maximum hole boring velocity is decreased by more than a factor of three. Our experimental findings are backed by a basic analytical model based on momentum and mass conservation as well as particle in cell simulations. These results are of particular importance for fast ignition since they contribute to a better understanding of the transport of the ignitor pulse into the overdense region.

Keywords: laser-hole boring, interaction of ultra-intense lasers with overcritical targets, fast ignition, relativistic laser motter interaction

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Authors: Gaurav Kumar Sinha

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The oil refining industry, significant in its energy consumption and carbon emissions, faces increasing pressure to reduce its environmental footprint. This article explores the application of energy efficiency and sustainability analytics as crucial tools for reducing carbon emissions in oil refineries. Through a comprehensive review of current practices and technologies, this study highlights innovative analytical approaches that can significantly enhance energy efficiency. We focus on the integration of advanced data analytics, including machine learning and predictive modeling, to optimize process controls and energy use. These technologies are examined for their potential to not only lower energy consumption but also reduce greenhouse gas emissions. Additionally, the article discusses the implementation of sustainability analytics to monitor and improve environmental performance across various operational facets of oil refineries. We explore case studies where predictive analytics have successfully identified opportunities for reducing energy use and emissions, providing a template for industry-wide application. The challenges associated with deploying these analytics, such as data integration and the need for skilled personnel, are also addressed. The paper concludes with strategic recommendations for oil refineries aiming to enhance their sustainability practices through the adoption of targeted analytics. By implementing these measures, refineries can achieve significant reductions in carbon emissions, aligning with global environmental goals and regulatory requirements.

Keywords: energy efficiency, sustainability analytics, carbon emissions, oil refineries, data analytics, machine learning, predictive modeling, process optimization, greenhouse gas reduction, environmental performance

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Authors: Ömer Faruk Ada, Türker Baş, M. Yaman Öztek

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In this study, we aim to investigate the skills to be owned by procurement professionals in order to fulfill their developing and changing role completely. Market conditions, competitive pressure, and high financial costs make it more important than ever for organizations to be able to use resources more efficiently. Research shows that procurement expenses consist more than 50 % of the operating expenses. With increasing profit impact of procurement, reviewing the position of the procurement function within the organization has become inevitable. This study is significant as it indicates the necessary skills that procurement professionals must have to keep in step with the transformation of procurement units from transaction oriented to value chain oriented. In this study, the transformation of procurement is investigated from the perspective of procurement professionals and we aim to answer following research questions: • How do procurement professionals perceive their role within the organization? • How has their role changed and what challenges have they had to face? • What portfolio of skills do they believe will enable them to fulfill their role effectively? Literature review consists of the first part of the study by investigating the changing role of procurement from different perspectives. In the second part, we present the results of the in-depth interviews with 15 procurement professionals and we used descriptive analysis as a methodology. In the light of these results, we classified procurement skills under operational, tactical and strategic levels and Procurement Skills Framework has been developed. This study shows the differences in the perception of purchasing by professionals and the organizations. The differences in the perception are considered as an important barrier beyond the procurement transformation. Although having the necessary skills has a significant effect for procurement professionals to fulfill their role completely and keep in step with the transformation of the procurement function, It is not the only factor and the degree of high-level management and organizational support has also a direct impact during this transformation.

Keywords: procuement skills, procurement transformation, strategic procurement, value chain

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Authors: Lusine Tadevosyan, Natella Mirzoyan, Anna Yeritsyan, Narek Avetisyan

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Due to high quality, the artesian groundwater is the main source of water supply for the fisheries in Ararat Valley, Armenia. From 1.6 billion m3 abstracted groundwater in 2016, half was used by fish farms. Yet, the inefficient water use, typical for low-intensity aquaculture systems in Ararat Valley, has become a key environmental issue in Armenia. In addition to excessive pure groundwater exploitation, which along with other sectors of groundwater use in this area resulted in the reduction of artesian zone by approximately 67% during last 20 years, the negative environmental impact of these productions is magnified by the discharge of large volumes of wastewater into receiving water bodies. In turn, unsustainable use of artesian groundwater in Ararat Valley along with increasingly strict policy measures on water use had a devastating impact on small and/or medium scale aquaculture: over the last two years approximately 100 fish farms have permanently seized their operations. The current project aims at the introduction of efficient and environmentally friendly fish farming practices (e.g., Recirculating Aquaculture Systems) in Ararat Valley fisheries in order to support current levels of fish production and simultaneously reduce the negative environmental pressure of aquaculture facilities in Armenia. Economic and environmental analysis of current small and medium scale operational systems and subsequently developed environmentally–friendly and economically sustainable system configurations will be presented.

Keywords: aquaculture, groundwater, recirculation, sustainability

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Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

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Authors: Won Yeol Choi, Sangmo Kang

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The propulsion of a bacterial flagellum in a viscous fluid has attracted many interests in the field of biological hydrodynamics, but remains yet fully understood and thus still a challenging problem. In this study, therefore, we have numerically investigated the flow around a steadily rotating micro-sized spring to further understand such bacterial flagellum propulsion. Note that a bacterium gains thrust (propulsive force) by rotating the flagellum connected to the body through a bio motor to move forward. For the investigation, we convert the spring model from the micro scale to the macro scale using a similitude law (scale law) and perform simulations on the converted macro-scale model using a commercial software package, CFX v13 (ANSYS). To scrutinize the propulsion characteristics of the flagellum through the simulations, we make parameter studies by changing some flow parameters, such as the pitch, helical radius and rotational speed of the spring and the Reynolds number (or fluid viscosity), expected to affect the thrust force experienced by the rotating spring. Results show that the propulsion characteristics depend strongly on the parameters mentioned above. It is observed that the forward thrust increases in a linear fashion with either of the rotational speed or the fluid viscosity. In addition, the thrust is directly proportional to square of the helical radius and but the thrust force is increased and then decreased based on the peak value to the pitch. Finally, we also present the appropriate flow and pressure fields visualized to support the observations.

Keywords: fluid viscosity, hydrodynamics, similitude, propulsive force

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Authors: A. B. Benshak, M. G. Yilkangnha, V. Y. Nanle

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Social crises and violent conflict can inflict direct (short-term) impact on the environment like poisoning water bodies, climate change, deforestation, destroying the chemical component of the soil due to the chemical and biological weapons used. It can also impact the environment indirectly (long-term), e.g., the destruction of political and economic infrastructure to manage the environmental resources and breaking down traditional conservation practices, population displacement and refugee flows which puts pressure on the already inadequate resources, infrastructure, facilities, amenities, services etc. This study therefore examines the impact of social crises on the environment in Jos Plateau State with emphasis on the long-term impact, analyze the relationship between crises and the environment and assess the perception of people on social crises because much work have concentrated on other repercussions such as the economy, health etc that are more politically expedient. The data for this research were collected mostly through interviews, questionnaire, dailies and reports on the subject matter. The data and findings were presented in tables and results showed that the environment is directly and indirectly impacted by crises and that these impacts can in turn result to a continuous cycle of violent activities if not addressed because of the inadequacies in the supply of infrastructural facilities, resources and so on caused by the inflow of displaced population. Recommendations were made on providing security to minimize conflict occurrences in Jos and its environs, minimizing the impact of social crises on the environment, provision of adequate infrastructural facilities to carter for population rise, renewal and regeneration schemes, etc. which will go a long way in mitigating the impact of crises on the environment.

Keywords: environment, impact, long-term, social crises

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Authors: Yosi Maria Wijaya, Florisma Arista Riti Tegu

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Background: Hypertension in pregnancy can be prevented by controlling the lifestyle. However, the majority of research on this topic has been conducted on lifestyle in women with normal pregnancy. Few studies of lifestyle have focused on Indonesian pregnant women with hypertension. Aim: The purpose of this study is to determine the association of demographic characteristics and the lifestyle of pregnant women who have hypertension. Methods: In this cross-sectional study, 76 women with hypertension during pregnancy were recruited from primary health care, West Java, Indonesia. Inclusion criteria were gestational age ≥ 28 weeks with the blood pressure systole ≥ 140 mmHg and diastole ≥ 90 mmHg. Data were collected using two instruments: demographic data and Health Promoting Life Style Profile (HPLP II). Data were analyzed with descriptive statistic and linear regression analysis. Results: The majority of participants were married, mean age was 27.96 years old (SD=6.77) with the mean of gestational age 33.21 (SD=3.49), most of them unemployed (94.7%) and more than a half participants have an education less than twelve years (59.2%). The total score of lifestyle was 2.44 (SD=0.34), more than a half participants experience unhealthy lifestyle (59.2%). Lifestyle was predicted by income, education years, occupation, and access to health care services, accounting for 20.8% of the total variance. Conclusion: Pregnant women with hypertension with low income, low level of education, non-occupational and hard to access health care services were related to unhealthy lifestyle. Understanding the lifestyle and associated factors contributes to health care providers ability to design effective interventions intended to improve healthy lifestyle among pregnant women with hypertension.

Keywords: demographic characteristics, hypertension, lifestyle, pregnancy

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Authors: M. McHugh, D. Lillis

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Mobile applications are being used to perform a wide variety of tasks in day-to-day life, ranging from checking email to controlling your home heating. Application developers have recognized the potential to transform a smart device into a medical device, by using a mobile medical application i.e. a mobile phone or a tablet. When initially conceived these mobile medical applications performed basic functions e.g. BMI calculator, accessing reference material etc.; however, increasing complexity offers clinicians and patients a range of functionality. As this complexity and functionality increases, so too does the potential risk associated with using such an application. Examples include any applications that provide the ability to inflate and deflate blood pressure cuffs, as well as applications that use patient-specific parameters and calculate dosage or create a dosage plan for radiation therapy. If an unapproved mobile medical application is marketed by a medical device organization, then they face significant penalties such as receiving an FDA warning letter to cease the prohibited activity, fines and possibility of facing a criminal conviction. Regulatory bodies have finalized guidance intended for mobile application developers to establish if their applications are subject to regulatory scrutiny. However, regulatory controls appear contradictory with the approaches taken by mobile application developers who generally work with short development cycles and very little documentation and as such, there is the potential to stifle further improvements due to these regulations. The research presented as part of this paper details how by adopting development techniques, such as agile software development, mobile medical application developers can meet regulatory requirements whilst still fostering innovation.

Keywords: agile, applications, FDA, medical, mobile, regulations, software engineering, standards

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Authors: Rkia Lbouhmadi, Mir Youssef

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Fatty acids such as DocosaHexaenoic Acid (DHA) and EicosaPentaenoic Acid (EPA) are of growing interest for their positive impact on human health. Oils rich in omega-3 are in high demand, particularly for incorporation into infant milk. Generally omega-3 fatty acids are extracted from oily fish, putting additional pressure on global fish stocks that is experiencing an over exploitation. Therefore, this present work aimed to study the capacity of tree different strains of microalgae for producing lipids rich on Omega-3 fatty acids such as EPA and DHA that can be used to enrich infantile milk. Three different strains were selected for this study; Parachlorella kessleri (GEPEA UMR-CNRS6144, University of Nantes) and Cyclotella spp and Scenedesmus spp (collected from different water bodies that are located in the region of Agadir, Morocco). it examined the impact of various culture conditions on EPA and DHA accumulation in three strains. Lipid composition was analyzed using GC-MS and FTIR. Following a comparative analysis between regular and microalgal oil-supplemented formula milk was carried out by incorporating large droplets of fat containing microalgal fatty acids coated with added phospholipids into the formula milk. Results indicated that culture conditions such as light intensity affected fatty acides production. With 40% increase in Polyunsaturated Fatty Acids (PUFA) compared to Saturated Fatty Acids (SFA). In conclusion, it exploratory study indicates that incorporating large milk phospholipid-coated lipid droplets enriched with microalgae lipids into infant formula may offer improved nutritional benefits for newborns, resembling human milk.

Keywords: microalgae oil, INFANT MILK, EPA, DHA

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Authors: Saba Gachpaz, Hamid Reza Heidari

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The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. To achieve this, more resources should be consumed and, besides other environmental concerns, highlight sustainable agricultural development. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for ten different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: land suitability, machine learning, random forest, sustainable agriculture

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Authors: Hung-Jen Lin, Chien-Hao Wang, Chien-Peng Huang, Yu-Sheng Tseng, En-Cheng Yang, Joe-Air Jiang

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Bees’ incoming and outgoing behavior is a decisive index which can indicate the health condition of a colony. Traditional methods for monitoring the behavior of honey bees (Apis mellifera) take too much time and are highly labor-intensive, and the lack of automation and synchronization disables researchers and beekeepers from obtaining real-time information of beehives. To solve these problems, this study proposes to use an Internet of Things (IoT)-based system for counting honey bees’ incoming and outgoing activities using an infrared interruption technique, while environmental factors are recorded simultaneously. The accuracy of the established system is verified by comparing the counting results with the outcomes of manual counting. Moreover, this highly -accurate device is appropriate for providing quantitative information regarding honey bees’ incoming and outgoing behavior. Different statistical analysis methods, including one-way ANOVA and two-way ANOVA, are used to investigate the influence of environmental factors, such as temperature, humidity, illumination and ambient pressure, on bees’ incoming and outgoing behavior. With the real-time data, a standard model is established using the outcomes from analyzing the relationship between environmental factors and bees’ incoming and outgoing behavior. In the future, smart control systems, such as a temperature control system, can also be combined with the proposed system to create an appropriate colony environment. It is expected that the proposed system will make a considerable contribution to the apiculture and researchers.

Keywords: ANOVA, environmental factors, honey bee, incoming and outgoing behavior

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Authors: Raman Kumar Singh

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Urbanization and economic growth are considered to be the most striking features of the past century. There is currently a radical demographic shift in progress worldwide, wherein people are moving from rural to urban areas at an increasing rate. The UN-Habitat report 2005 indicates that in 2025, 61 per cent of the 5 billion world population will reside in the urban areas with about 85 per cent of the development process taking place in the urban hinterlands widely referred to as ‘peri-urban’, ‘suburbs’, ‘urban fringe’, ‘city edge’, ‘metropolitan shadow’, or ‘urban sprawl’. In this context the study is broadly concerned with understanding the development of the industrial hub in the Gurgaon and its impact on the immediate neighbourhood. However studies have revealed that with the increase of industrial development the growth pattern changes rapidly, not only the growth of the urban area but the overall economy shifts from more agrarian to non-agrarian, with the change in the occupational pattern of the people. The process is mainly known as tertiarization, where a number of tertiary activities increase in comparison to primary or secondary. The change in the occupational pattern creates a pull factor on its immediate neighbourhood, which triggers the in- migrations from the rural areas as people come in the core urban area in search of the better job opportunities and increased standards of living. But this gives way to the unplanned growth of the urban fringe and the villages which tend to accommodate the migrants and in turn the pressure on the socio-economic infrastructure increases. Therefore, it becomes increasing necessary for the government institution and policy level intervention to provide an overall socio-economic growth along with rapid industrial growth.

Keywords: policy intervention, urban morphology, urban industrial hub, livelihood transformation

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Authors: Monika Szpringer, Mariola Wojciechowska, Robert Dutkiewicz, Grażyna Nowak-Starz, Marzena Olędzka

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The main aim of this article is to determine the phenomenon of stress among academic teachers as well as to identify the extent to which the teachers experience work-related psychological risks. It is also important to support academic teachers trade unions in scope of stress-oriented activities, including psychological dangers in the assessment of risk in the workplace (college). The authors used a method of a diagnostic survey with a polling as a technique and authors’ questionnaire as a tool. The survey was conducted between September and December of 2013 and it comprised 1890 academic teachers from five voivodeships. The study reveals that 84.0% of the respondents found the work of an academic teacher to be borne with a considerable stress. The percentage values of the most frequent causes of stress are as follows: frequent changes of both organisational and didactic matters as well as overwhelming bureaucracy (77.8 %), time pressure regarding professional development and related risk of losing job (68.2 %), difficult working conditions (45.4%), conflicts and rivalry between teachers (44.1%), excessive amount of duties as well as increasing requirements and demanding attitude of students (33.7%). Work-related stress affects or significantly affects the private life of 69 % and 66.4 % of the respondents respectively. The majority of the people surveyed deals with stress by undertaking various activities, with 40% pointing at using various substances, mostly cigarettes and alcohol (p > 0,05) Physical ailments were experienced by 81% of the respondents, in 9% they were rare and 8 % of the respondents had never experienced such disorders. The entire group of the surveyed people (100 %) claimed that they have no possibility of contacting a psychologist at their workplace (p > 0.05), and they stated that the need of contacting specialists does exist.

Keywords: stress, academic teachers, psychological risks, work-related

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Authors: Mariusz Hofman

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The paper ‘Theoretical framework for value creation in Project-Oriented Companies’ is designed to determine, how organisations create value and whether this allows them to achieve market success. An assumption has been made that there are two routes to achieving this value. The first one is to create intangible assets (i.e. the resources of human, structural and relational capital), while the other one is to create added value (understood as the surplus of revenue over costs). It has also been assumed that the combination of the achieved added value and unique intangible assets translates to the success of a project-oriented company. The purpose of the paper is to present hypothetical and deductive model which describing the modus operandi of such companies and approach to model operationalisation. All the latent variables included in the model are theoretical constructs with observational indicators (measures). The existence of a latent variable (construct) and also submodels will be confirmed based on a covariance matrix which in turn is based on empirical data, being a set of observational indicators (measures). This will be achieved with a confirmatory factor analysis (CFA). Due to this statistical procedure, it will be verified whether the matrix arising from the adopted theoretical model differs statistically from the empirical matrix of covariance arising from the system of equations. The fit of the model with the empirical data will be evaluated using χ2, RMSEA and CFI (Comparative Fit Index). How well the theoretical model fits the empirical data is assessed through a number of indicators. If the theoretical conjectures are confirmed, an interesting development path can be defined for project-oriented companies. This will let such organisations perform efficiently in the face of the growing competition and pressure on innovation.

Keywords: value creation, project-oriented company, structural equation modelling

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