Search results for: integrated short circuit model

Authors: Kinza Qadeer, Muhammad Abdul Qyyum, Moonyong Lee

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Natural gas has become an attractive energy source in comparison with other fossil fuels because of its lower CO₂ and other air pollutant emissions. Therefore, compared to the demand for coal and oil, that for natural gas is increasing rapidly world-wide. The transportation of natural gas over long distances as a liquid (LNG) preferable for several reasons, including economic, technical, political, and safety factors. However, LNG production is an energy-intensive process due to the tremendous amount of power requirements for compression of refrigerants, which provide sufficient cold energy to liquefy natural gas. Therefore, one of the major issues in the LNG industry is to improve the energy efficiency of existing LNG processes through a cost-effective approach that is 'optimization'. In this context, a bio-inspired Krill-herd (KH) step-up approach was examined to enhance the energy efficiency of a single mixed refrigerant (SMR) natural gas liquefaction (LNG) process, which is considered as a most promising candidate for offshore LNG production (FPSO). The optimal design of a natural gas liquefaction processes involves multivariable non-linear thermodynamic interactions, which lead to exergy destruction and contribute to process irreversibility. As key decision variables, the optimal values of mixed refrigerant flow rates and process operating pressures were determined based on the herding behavior of krill individuals corresponding to the minimum energy consumption for LNG production. To perform the rigorous process analysis, the SMR process was simulated in Aspen Hysys® software and the resulting model was connected with the Krill-herd approach coded in MATLAB. The optimal operating conditions found by the proposed approach significantly reduced the overall energy consumption of the SMR process by ≤ 22.5% and also improved the coefficient of performance in comparison with the base case. The proposed approach was also compared with other well-proven optimization algorithms, such as genetic and particle swarm optimization algorithms, and was found to exhibit a superior performance over these existing approaches.

Keywords: energy efficiency, Krill-herd, LNG, optimization, single mixed refrigerant

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Authors: E. Syngelaki, C. C. F. Schinkel, S. Klatt, E. Hörandl

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Environmental influence can alter the conditions for plant development and can trigger changes in epigenetic variation. Thus, the exposure to abiotic environmental stress can lead to different DNA methylation profiles and may have evolutionary consequences for adaptation. Epigenetic control mechanisms may further influence mode of reproduction. The alpine species R. kuepferi has diploid and tetraploid cytotypes, that are mostly sexual and facultative apomicts, respectively. Hence, it is a suitable model system for studying the correlations of mode of reproduction, ploidy, and environmental stress. Diploid and tetraploid individuals were placed in two climate chambers and treated with low (+7°C day/+2°C night, -1°C cold shocks for three nights per week) and warm (control) temperatures (+15°C day/+10°C night). Subsequently, methylation sensitive-Amplified Fragment-Length Polymorphism (AFPL) markers were used to screen genome-wide methylation alterations triggered by stress treatments. The dataset was analyzed for four groups regarding treatment (cold/warm) and ploidy level (diploid/tetraploid), and also separately for full methylated, hemi-methylated and unmethylated sites. Patterns of epigenetic variation suggested that diploids differed significantly in their profiles from tetraploids independent from treatment, while treatments did not differ significantly within cytotypes. Furthermore, diploids are more differentiated than the tetraploids in overall methylation profiles of both treatments. This observation is in accordance with the increased frequency of apomictic seed formation in diploids and maintenance of facultative apomixis in tetraploids during the experiment. Global analysis of molecular variance showed higher epigenetic variation within groups than among them, while locus-by-locus analysis of molecular variance showed a high number (54.7%) of significantly differentiated un-methylated loci. To summarise, epigenetic variation seems to depend on ploidy level, and in diploids may be correlated to changes in mode of reproduction. However, further studies are needed to elucidate the mechanism and possible functional significance of these correlations.

Keywords: apomixis, cold stress, DNA methylation, Ranunculus kuepferi

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Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 167

Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 159

Authors: Frederick M. Dumisani Xaba, Nokuthula G. Khanyile

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There is a growing demand for greater accountability, transparency and ethical conduct based on sound governance principles in the developing world. Funders, donors and sponsors are increasingly demanding more transparency, better value for money and adherence to good governance standards. The drive towards improved governance measures is largely influenced by the need to ‘plug the leaks’, deal with malfeasance, engender greater levels of accountability and good governance and to ultimately attract further funding or investment. This is the case with the Non-Profit Organizations (NPOs) in South Africa in general, and in the province of KwaZulu-Natal in particular. The paper draws from the good governance theory, stakeholder theory and systems thinking to critically examine the requirements for good governance for the NPO sector from a theoretical and legislative point and to systematically looks at the contours of governance currently among the NPOs. The paper did this through the rigorous examination of the vignettes of cases of governance among selected NPOs based in KwaZulu-Natal. The study used qualitative and quantitative research methodologies through document analysis, literature review, semi-structured interviews, focus groups and statistical analysis from the various primary and secondary sources. It found some good cases of good governance but also found frightening levels of poor governance. There was an exponential growth of NPOs registered during the period under review, equally so there was an increase in cases of non-compliance to good governance practices. NPOs operate in an increasingly complex environment. There is contestation for influence and access to resources. Stakeholder management is poorly conceptualized and executed. Recognizing that the NPO sector operates in an environment characterized by complexity, constant changes, unpredictability, contestation, diversity and divergent views of different stakeholders, there is a need to apply legislative and systems thinking approaches to strengthen governance to withstand this turbulence through a capacity development model that recognizes these contextual and environmental challenges.

Keywords: good governance, non-profit organizations, stakeholder theory, systems theory

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Authors: Marine Reymond, Michael Descostes, Marie Muguet, Clemence Besancon, Martine Leermakers, Catherine Beaucaire, Sophie Billon, Patricia Patrier

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²²⁶Ra is one of the radionuclides resulting from the disintegration of ²³⁸U. Due to its half-life (1600 y) and its high specific activity (3.7 x 1010 Bq/g), ²²⁶Ra is found at the ultra-trace level in the natural environment (usually below 1 Bq/L, i.e. 10-13 mol/L). Because of its decay in ²²²Rn, a radioactive gas with a shorter half-life (3.8 days) which is difficult to control and dangerous for humans when inhaled, ²²⁶Ra is subject to a dedicated monitoring in surface waters especially in the context of uranium mining. In natural waters, radionuclides occur in dissolved, colloidal or particular forms. Due to the size of colloids, generally ranging between 1 nm and 1 µm and their high specific surface areas, the colloidal fraction could be involved in the transport of trace elements, including radionuclides in the environment. The colloidal fraction is not always easy to determine and few existing studies focus on ²²⁶Ra. In the present study, a complete multidisciplinary approach is proposed to assess the colloidal transport of ²²⁶Ra. It includes water sampling by conventional filtration (0.2µm) and the innovative Diffusive Gradient in Thin Films technique to measure the dissolved fraction (<10nm), from which the colloidal fraction could be estimated. Suspended matter in these waters were also sampled and characterized mineralogically by X-Ray Diffraction, infrared spectroscopy and scanning electron microscopy. All of these data, which were acquired on a rehabilitated former uranium mine, allowed to build a geochemical model using the geochemical calculation code PhreeqC to describe, as accurately as possible, the colloidal transport of ²²⁶Ra. Colloidal transport of ²²⁶Ra was found, for some of the sampling points, to account for up to 95% of the total ²²⁶Ra measured in water. Mineralogical characterization and associated geochemical modelling highlight the role of barite, a barium sulfate mineral well known to trap ²²⁶Ra into its structure. Barite was shown to be responsible for the colloidal ²²⁶Ra fraction despite the presence of kaolinite and ferrihydrite, which are also known to retain ²²⁶Ra by sorption.

Keywords: colloids, mining context, radium, transport

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Authors: Benbiao Song, Yan Gao, Zhuo Liu

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Geostatistical modeling is the key technic for reservoir characterization, the quality of geological models will influence the prediction of reservoir performance greatly, but few studies have been done to quantify the factors impacting geostatistical reservoir modeling accuracy. In this study, 16 fluvial prototype models have been established to represent different geological complexity, 6 cases range from 16 to 361 wells were defined to reproduce all those 16 prototype models by different methodologies including SIS, object-based and MPFS algorithms accompany with different constraint parameters. Modeling accuracy ratio was defined to quantify the influence of each factor, and ten realizations were averaged to represent each accuracy ratio under the same modeling condition and parameters association. Totally 5760 simulations were done to quantify the relative contribution of each factor to the simulation accuracy, and the results can be used as strategy guide for facies modeling in the similar condition. It is founded that data density, geological trend and geological complexity have great impact on modeling accuracy. Modeling accuracy may up to 90% when channel sand width reaches up to 1.5 times of well space under whatever condition by SIS and MPFS methods. When well density is low, the contribution of geological trend may increase the modeling accuracy from 40% to 70%, while the use of proper variogram may have very limited contribution for SIS method. It can be implied that when well data are dense enough to cover simple geobodies, few efforts were needed to construct an acceptable model, when geobodies are complex with insufficient data group, it is better to construct a set of robust geological trend than rely on a reliable variogram function. For object-based method, the modeling accuracy does not increase obviously as SIS method by the increase of data density, but kept rational appearance when data density is low. MPFS methods have the similar trend with SIS method, but the use of proper geological trend accompany with rational variogram may have better modeling accuracy than MPFS method. It implies that the geological modeling strategy for a real reservoir case needs to be optimized by evaluation of dataset, geological complexity, geological constraint information and the modeling objective.

Keywords: fluvial facies, geostatistics, geological trend, modeling strategy, modeling accuracy, variogram

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Authors: Aziza S. Al Adhoobi, Amna Al Ruheili, Saud M. Al Jufaili

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This study investigates the potential effects of climate change on the habitat suitability of two Aphaniops species (Teleostei: Aphaniidae) found in the Oman Mountains and the Southwestern Arabian Coast. Aphaniops kruppi, an endemic species, is found in various water bodies such as wadis, springs, aflaj, spring-fed streams, and some coastal backwaters. Aphaniops stoliczkanus, on the other hand, inhabits brackish and freshwater habitats, particularly in the lower parts of wadies and aflaj, and exhibits euryhaline characteristics. Using Maximum Entropy Modeling (MaxEnt) in conjunction with ArcGIS (10.8.2) and CHELSA bioclimatic variables, topographic indices, and other pertinent environmental factors, the study modeled the potential impacts of climate change based on three Representative Concentration Pathways (RCPs 2.6, 7.0, 8.5) for the periods 2011-2040, 2041-2070, and 2071-2100. The model demonstrated exceptional predictive accuracy, achieving AUC values of 0.992 for A. kruppi and 0.983 for A. stoliczkanus. For A. kruppi, the most influential variables were the mean monthly climate moisture index (Cmi_m), the mean diurnal range (Bio2), and the sediment transport index (STI), accounting for 39.9%, 18.3%, and 8.4%, respectively. As for A. stoliczkanus, the key variables were the sediment transport index (STI), stream power index (SPI), and precipitation of the coldest quarter (Bio19), contributing 31%, 20.2%, and 13.3%, respectively. A. kruppi showed an increase in habitat suitability, especially in low and medium suitability areas. By 2071-2100, high suitability areas increased slightly by 0.05% under RCP 2.6, but declined by -0.02% and -0.04% under RCP 7.0 and 8.5, respectively. A. stoliczkanus exhibited a broader range of responses. Under RCP 2.6, all suitability categories increased by 2071-2100, with high suitability areas increasing by 0.01%. However, low and medium suitability areas showed mixed trends under RCP 7.0 and 8.5, with declines of -0.17% and -0.16%, respectively. The study highlights that climatic and topographical factors significantly influence the habitat suitability of Aphaniops species in Oman. Therefore, species-specific conservation strategies are crucial to address the impacts of climate change.

Keywords: Aphaniops kruppi, Aphaniops stoliczkanus, Climate change, Habitat suitability, MaxEnt

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Authors: D. P. N. De Silva, N. P. P. Liyanage

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Zebrafish (Danio rerio) in the family Cyprinidae, is a tropical freshwater fish widely used as a model organism in scientific research. Use of effective and economical anaesthetic is very important when handling fish. Clove oil (active ingredient: eugenol) was identified as a natural product which is safer and economical compared to synthetic chemicals like methanesulfonate (MS-222). Therefore, the aim of this study was to identify the most effective dosage of clove oil solution as an anaesthetic on mature Zebrafish. Clove oil solution was prepared by mixing pure clove oil with 94% ethanol at a ratio of 1:9 respectively. From that solution, different volumes were selected as (0.4 ml, 0.6 ml and 0.8 ml) and dissolved in one liter of conditioned water (dosages : 0.4 ml/L, 0.6 ml/L and 0.8 ml/L). Water quality parameters (pH, temperature and conductivity) were measured before and after adding clove oil solution. Mature Zebrafish with similar standard length (2.76 ± 0.1 cm) and weight (0.524 ± 0.1 g) were selected for this experiment. Time taken for loss of equilibrium (initiation phase) and complete loss of movements including opercular movement (anaesthetic phase) were measured. To detect the efficacy on anaesthetic recovery, time taken to begin opercular movements (initiation of recovery phase) until swimming (post anaesthetic phase) were observed. The results obtained were analyzed according to the analysis of variance (ANOVA) and Tukeys’ method using SPSS version 17.0 at 95% confidence interval (p<0.5). According to the results, there was no significant difference at the initiation phase of anaesthesia in all three doses though the time taken was varied from 0.14 to 0.41 minutes. Mean value of the time taken to complete the anaesthetic phase at 0.4 ml/L dosage was significantly different with 0.6 ml/L and 0.8 ml/L dosages independently (p=0.01). There was no significant difference among recovery times at all dosages but 0.8 ml/L dosage took longer time compared to 0.6 ml/L dosage. The water quality parameters (pH and temperature) were stable throughout the experiment except conductivity, which increased with the higher dosage. In conclusion, the best dosage need to anaesthetize Zebrafish using clove oil solution was 0.6 ml/L due to its fast initiation of anaesthesia and quick recovery compared to the other two dosages. Therefore clove oil can be used as a good substitute for synthetic anaesthetics because of its efficacy at a lower dosage with higher safety at a low cost.

Keywords: anaesthetics, clove oil, zebrafish, Cyprinidae

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Authors: Omil Nady Mahrous Maximous

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The article deals with the use of modern information technologies to achieve pro-ecological marketing goals in company-customer relationships. The purpose of the article is to show the possibilities of implementing modern information technologies. In B2C relationships, marketing departments face challenges stemming from the need to quickly segment customers and the current fragmentation of data across many systems, which significantly hinders the achievement of marketing goals. Thus, Article proposes the use of modern IT solutions in the field of marketing activities of companies, taking into account their environmental goals. As a result, its importance for the economic and social development of the emerging countries has increased. While traditional companies emphasize profit maximization as a core business principle, social enterprises must solve social problems at the expense of profit. This rationale gives social enterprises an edge over traditional businesses by meeting the needs of those at the bottom of the pyramid. This also represents a major challenge for social business, since social business acts on the one hand for the benefit of the public and on the other strives for financial stability. Otherwise, the company is unlikely to be fired from the company. Cultures play a role in business communication and research. Using the example of language in international relations, the article presents the problem of the articulation of research cultures in management and linguistics and of cultures as such. After an overview of current research on language in international relations, this article presents the approach to communication in international economy from a linguistic point of view and tries to explain the problems of communication in business starting from linguistic research. A step towards interdisciplinary research that brings together research in the fields of management and linguistics.

Keywords: international marketing, marketing mix, marketing research, small and medium-sized enterprises, strategic marketing, B2B digital marketing strategy, digital marketing, digital marketing maturity model, SWOT analysis consumer behavior, experience, experience marketing, marketing employee organizational performance, internal marketing, internal customer, direct marketing, mobile phones mobile marketing, Sms advertising

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Authors: Kottaridi Klimentia, Demopoulos Vasilis, Sidiropoulos Anastasios, Ihara Diego, Nikolaidis Vasileios, Antonopoulos Dimitrios

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Aflatoxins are highly poisonous and carcinogenic compounds produced by species of the genus Aspergillus spp. that can infect a variety of agricultural foods, including dried figs. Biological and environmental factors, such as population, pathogenicity, and aflatoxinogenic capacity of the strains, topography, soil, and climate parameters of the fig orchards, are believed to have a strong effect on aflatoxin levels. Existing methods for aflatoxin detection and measurement, such as high performance liquid chromatography (HPLC), and enzyme-linked immunosorbent assay (ELISA), can provide accurate results, but the procedures are usually time-consuming, sample-destructive, and expensive. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the health and financial impact of a contaminated crop. Consequently, there is interest in developing a tool that predicts aflatoxin levels based on topography and soil analysis data of fig orchards. This paper describes the development of a risk assessment tool for the contamination of aflatoxin on dried figs, based on the location and altitude of the fig orchards, the population of the fungus Aspergillus spp. in the soil, and soil parameters such as pH, saturation percentage (SP), electrical conductivity (EC), organic matter, particle size analysis (sand, silt, clay), the concentration of the exchangeable cations (Ca, Mg, K, Na), extractable P, and trace of elements (B, Fe, Mn, Zn and Cu), by employing machine learning methods. In particular, our proposed method integrates three machine learning techniques, i.e., dimensionality reduction on the original dataset (principal component analysis), metric learning (Mahalanobis metric for clustering), and k-nearest neighbors learning algorithm (KNN), into an enhanced model, with mean performance equal to 85% by terms of the Pearson correlation coefficient (PCC) between observed and predicted values.

Keywords: aflatoxins, Aspergillus spp., dried figs, k-nearest neighbors, machine learning, prediction

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Authors: Inna R. Edara, Haw-Lin Wu

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Positive psychology investigates human strengths and virtues and promotes well-being. Relying on this assumption, positive interventions have been continuously designed to build pleasure and happiness, joy and contentment, engagement and meaning, hope and optimism, satisfaction and gratitude, spirituality, and various other positive measures of well-being. In line with this model of positive psychology and interventions, this study investigated certain measures of well-being in a group of 45 students enrolled in an 18-week positive psychology course and simultaneously engaged in service-oriented interventions that they chose for themselves based on the course content and individual interests. Students’ well-being was measured at the beginning and end of the course. The well-being indicators included positive automatic thoughts, optimism and hope, satisfaction with life, and spirituality. A paired-samples t-test conducted to evaluate the impact of class content and service-oriented interventions on students’ scores of well-being indicators indicated statistically significant increase from pre-class to post-class scores. There were also significant gender differences in post-course well-being scores, with females having higher levels of well-being than males. A two-way between groups analysis of variance indicated a significant interaction effect of age by gender on the post-course well-being scores, with females in the age group of 56-65 having the highest scores of well-being in comparison to the males in the same age group. Regression analyses indicated that positive automatic thought significantly predicted hope and satisfaction with life in the pre-course analysis. In the post-course regression analysis, spiritual transcendence made a significant contribution to optimism, and positive automatic thought made a significant contribution to both hope and satisfaction with life. Finally, a significant test between pre-course and post-course regression coefficients indicated that the regression coefficients at pre-course were significantly different from post-course coefficients, suggesting that the positive psychology course and the interventions were helpful in raising the levels of well-being. The overall results suggest a substantial increase in the participants’ well-being scores after engaging in the positive-oriented interventions, implying a need for designing more positive interventions in education to promote well-being.  

Keywords: hope, optimism, positive automatic thoughts, satisfaction with life, spirituality, well-being

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Authors: Diandra Cristache

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The transition of traditional manuscript studies into the digital framework closely affects the methodological premises upon which manuscript descriptions are modeled, created, and questioned for the purpose of research. This paper intends to explore the issue by presenting a methodological investigation into the process of modeling, creating, and questioning manuscript metadata. The investigation is founded on a close observation of the Polonsky Greek Manuscripts Project, a collaboration between the Universities of Cambridge and Heidelberg. More than just providing a realistic ground for methodological exploration, along with a complete metadata set for computational demonstration, the case study also contributes to a broader purpose: outlining general methodological principles for making the most out of manuscript metadata by means of research-oriented digital outputs. The analysis mainly focuses on the scholarly approach to manuscript descriptions, in the specific instance where the act of metadata recording does not have a programmatic research purpose. Close attention is paid to the encounter of 'traditional' practices in manuscript studies with the formal constraints of the digital framework: does the shift in practices (especially from the straight narrative of free writing towards the hierarchical constraints of the TEI encoding model) impact the structure of metadata and its capability to respond specific research questions? It is argued that flexible structure of TEI and traditional approaches to manuscript description lead to a proliferation of markup: does an 'encyclopedic' descriptive approach ensure the epistemological relevance of the digital outputs to metadata? To provide further insight on the computational approach to manuscript metadata, the metadata of the Polonsky project are processed with techniques of distant reading and data networking, thus resulting in a new group of digital outputs (relational graphs, geographic maps). The computational process and the digital outputs are thoroughly illustrated and discussed. Eventually, a retrospective analysis evaluates how the digital outputs respond to the scientific expectations of research, and the other way round, how the requirements of research questions feed back into the creation and enrichment of metadata in an iterative loop.

Keywords: digital manuscript studies, digital outputs to manuscripts metadata, metadata interoperability, methodological issues

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Authors: Siti Rahmawati

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Indonesia has had long period of monetary economic crisis and it is followed by an upward trend in the price of fuel oil. This situation impacts all aspects of tuna fishermen community. For instance, the basic needs of fishing communities increase and the lower purchasing power then lead to economic and social instability as well as the health of fishermen household. To understand this AHP method is applied to acknowledge the model of tuna fisheries management priorities and cold chain marketing channel and the utilization levels that impact on human health. The study is designed as a development research with the number of 180 respondents. The data were analyzed by Analytical Hierarchy Process (AHP) method. The development of tuna fishery business can improve productivity of production with economic empowerment activities for coastal communities, improving the competitiveness of products, developing fish processing centers and provide internal capital for the development of optimal fishery business. From economic aspects, fishery business is more attracting because the benefit cost ratio of 2.86. This means that for 10 years, the economic life of this project can work well as B/C> 1 and therefore the rate of investment is economically viable. From the health aspects, tuna can reduce the risk of dying from heart disease by 50%, because tuna contain selenium in the human body. The consumption of 100 g of tuna meet 52.9% of the selenium in the body and activating the antioxidant enzyme glutathione peroxidaxe which can protect the body from free radicals and stimulate various cancers. The results of the analytic hierarchy process that the quality of tuna products is the top priority for export quality as well as quality control in order to compete in the global market. The implementation of the policy can increase the income of fishermen and reduce the poverty of fishermen households and have impact on the human health whose has high risk of disease.

Keywords: management of tuna, social, economic, health

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Authors: Shreya Poddar

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Legal drafting and revising are recognized as highly demanding tasks for legal professionals. This paper introduces an approach to automate and refine these processes through the use of advanced Artificial Intelligence (AI). The method employs Large Language Models (LLMs), with a specific focus on 'Chain of Thoughts' (CoT) and knowledge injection via prompt engineering. This approach differs from conventional methods that depend on comprehensive training or fine-tuning of models with extensive legal knowledge bases, which are often expensive and time-consuming. The proposed method incorporates knowledge injection directly into prompts, thereby enabling the AI to generate more accurate and contextually appropriate legal texts. This approach substantially decreases the necessity for thorough model training while preserving high accuracy and relevance in drafting. Additionally, the concept of guardrails is introduced. These are predefined parameters or rules established within the AI system to ensure that the generated content adheres to legal standards and ethical guidelines. The practical implications of this method for legal work are considerable. It has the potential to markedly lessen the time lawyers allocate to document drafting and revision, freeing them to concentrate on more intricate and strategic facets of legal work. Furthermore, this method makes high-quality legal drafting more accessible, possibly reducing costs and expanding the availability of legal services. This paper will elucidate the methodology, providing specific examples and case studies to demonstrate the effectiveness of 'Chain of Thoughts' and knowledge injection in legal drafting. The potential challenges and limitations of this approach will also be discussed, along with future prospects and enhancements that could further advance legal work. The impact of this research on the legal industry is substantial. The adoption of AI-driven methods by legal professionals can lead to enhanced efficiency, precision, and consistency in legal drafting, thereby altering the landscape of legal work. This research adds to the expanding field of AI in law, introducing a method that could significantly alter the nature of legal drafting and practice.

Keywords: AI-driven legal drafting, legal automation, futureoflegalwork, largelanguagemodels

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Authors: Lizel Bertie, Kim Johnston

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Parental emotion regulation plays a central role in the socialisation of emotion, especially when teaching young children to cope with negative emotions. Despite evidence which shows non-supportive parental responses to children’s expression of negative emotions has implications for the social and emotional development of the child, few studies have investigated risk factors which impact parental emotion socialisation processes. The current study aimed to explore the extent to which parental stress contributes to both difficulties in parental emotion regulation and non-supportive parental responses to children’s expression of negative emotions. In addition, the study examined whether parental use of expressive suppression as an emotion regulation strategy facilitates the influence of parental stress on non-supportive responses by testing the relations in a mediation model. A sample of 140 Australian adults, who identified as parents with children aged 5 to 10 years, completed an online questionnaire. The measures explored recent symptoms of depression, anxiety, and stress, the use of expressive suppression as an emotion regulation strategy, and hypothetical parental responses to scenarios related to children’s expression of negative emotions. A mediated regression indicated that parents who reported higher levels of stress also reported higher levels of expressive suppression as an emotion regulation strategy and increased use of non-supportive responses in relation to young children’s expression of negative emotions. These findings suggest that parents who experience heightened symptoms of stress are more likely to both suppress their emotions in parent-child interaction and engage in non-supportive responses. Furthermore, higher use of expressive suppression strongly predicted the use of non-supportive responses, despite the presence of parental stress. Contrary to expectation, no indirect effect of stress on non-supportive responses was observed via expressive suppression. The findings from the study suggest that parental stress may become a more salient manifestation of psychological distress in a sub-clinical population of parents while contributing to impaired parental responses. As such, the study offers support for targeting overarching factors such as difficulties in parental emotion regulation and stress management, not only as an intervention for parental psychological distress, but also the detection and prevention of maladaptive parenting practices.

Keywords: emotion regulation, emotion socialisation, expressive suppression, non-supportive responses, parental stress

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Authors: Fatemeh Rouhi, Hadi Nassiri

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Earning forecast is a key element in economic decisions but there are some situations, such as conflicts of interest in financial reporting, complexity and lack of direct access to information has led to the phenomenon of information asymmetry among individuals within the organization and external investors and creditors that appear. The adverse selection and moral hazard in the investor's decision and allows direct assessment of the difficulties associated with data by users makes. In this regard, the role of trustees in corporate governance disclosure is crystallized that includes controls and procedures to ensure the lack of movement in the interests of the company's management and move in the direction of maximizing shareholder and company value. Therefore, the earning forecast of companies in the capital market and the need to identify factors influencing this study was an attempt to make relationship between moral hazard and corporate governance with earning forecast quality companies operating in the capital market and its impact on Earnings Forecasts quality by the company to be established. Getting inspiring from the theoretical basis of research, two main hypotheses and sub-hypotheses are presented in this study, which have been examined on the basis of available models, and with the use of Panel-Data method, and at the end, the conclusion has been made at the assurance level of 95% according to the meaningfulness of the model and each independent variable. In examining the models, firstly, Chow Test was used to specify either Panel Data method should be used or Pooled method. Following that Housman Test was applied to make use of Random Effects or Fixed Effects. Findings of the study show because most of the variables are positively associated with moral hazard with earnings forecasts quality, with increasing moral hazard, earning forecast quality companies listed on the Tehran Stock Exchange is increasing. Among the variables related to corporate governance, board independence variables have a significant relationship with earnings forecast accuracy and earnings forecast bias but the relationship between board size and earnings forecast quality is not statistically significant.

Keywords: corporate governance, earning forecast quality, moral hazard, financial sciences

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Authors: Thomas Ruppert, Shana Jones, J. Scott Pippin

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State and federal government agencies within the United States have recently invested substantial resources into studies of future flood risk conditions associated with climate change and sea-level rise. A review of numerous case studies has uncovered several key themes that speak to an overall incoherence within current flood risk assessment procedures in the U.S. context. First, there are substantial local differences in the quality of available information about basic infrastructure, particularly with regard to local stormwater features and essential facilities that are fundamental components of effective flood hazard planning and mitigation. Second, there can be substantial mismatch between regulatory Flood Insurance Rate Maps (FIRMs) as produced by the National Flood Insurance Program (NFIP) and other 'current condition' flood assessment approaches. This is of particular concern in areas where FIRMs already seem to underestimate extant flood risk, which can only be expected to become a greater concern if future FIRMs do not appropriately account for changing climate conditions. Moreover, while there are incentives within the NFIP’s Community Rating System (CRS) to develop enhanced assessments that include future flood risk projections from climate change, the incentive structures seem to have counterintuitive implications that would tend to promote moral hazard. In particular, a technical finding of higher future risk seems to make it easier for a community to qualify for flood insurance savings, with much of these prospective savings applied to individual properties that have the most physical risk of flooding. However, there is at least some case study evidence to indicate that recognition of these issues is prompting broader discussion about the need to move beyond FIRMs as a standalone local flood planning standard. The paper concludes with approaches for developing climate adaptation and flood resilience strategies in the U.S. that move away from the social welfare model being applied through NFIP and toward more of an informed risk approach that transfers much of the investment responsibility over to individual private property owners.

Keywords: climate change adaptation, flood risk, moral hazard, sea-level rise

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Authors: Muhammad Ali Siddiquee, Md. Alauddin, Md. Omar Faruque, Zakir Hossain Howlader, Mohammad Asaduzzaman

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Rice bran (RB) and rice bran oil (RBO) are explored as prominent food components worldwide. In this study, fermented rice bran (FRB) was produced by employing edible gram-positive bacteria (Lactobacillus acidophilus, Lactobacillus bulgaricus, and Bifidobacterium bifidum) at 125 x 10⁵ spore g⁻¹ of rice bran, and investigated to evaluate nutritional quality. The crude rice bran oil (CRBO) was extracted from RB, and its quality was also investigated compared to market-available rice bran oil (MRBO) in Bangladesh. We found that fermentation of rice bran with lactic acid bacteria increased total proteins (29.52%), fat (5.38%), ash (48.47%), crude fiber (38.96%), and moisture (61.04%) and reduced the carbohydrate content (36.61%). We also found that essential amino acids (methionine, tryptophan, threonine, valine, leucine, lysine, histidine, and phenylalanine) and non-essential amino acids (alanine, aspartate, glycine, glutamine, proline, serine, and tyrosine) were increased in FRB except methionine and proline. Moreover, total phenolic content, tannin content, flavonoid content, and antioxidant activity were increased in FRB. The RBO analysis showed that γ-oryzanol content (10.00mg/g) was found in CRBO compared to MRBO (ranging from 7.40 to 12.70 mg/g) and Vitamin-E content 0.20% was found higher in CRBO compared to MRBO (ranging 0.097 to 0.12%). The total saturated (25.16%) and total unsaturated fatty acids (74.44%) were found in CRBO, whereas MRBO contained total saturated (22.08 to 24.13%) and total unsaturated fatty acids (71.91 to 83.29%), respectively. The physiochemical parameters were found satisfactory in all samples except acid value and peroxide value higher in CRBO. Finally, animal experiments showed that FRB and CRBO reduce the body weight, glucose, and lipid profile in high-fat diet-induced animal models. Thus, FRB and RBO could be value-added food supplements for human health.

Keywords: fermented rice bran, crude rice bran oil, amino acids, proximate composition, gamma-oryzanol, fatty acids, heavy metals, physiochemical parameters

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Authors: Wisam Ismail, Sarah Hosgood, Michael Nicholson

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The research hypothesis is that early post-transplant allograft fibrosis will be linked to donor factors and that acute rejection and/or delayed graft function in the recipient will be independent risk factors for the development of fibrosis. This research hypothesis is to explore whether acute rejection/delay graft function has an effect on the renal transplant fibrosis within the first year post live donor kidney transplant between 1998 and 2009. Methods: The study has been designed to identify five time points of the renal transplant biopsies [0 (pre-transplant), 1 month, 3 months, 6 months and 12 months] for 300 live donor renal transplant patients over 12 years period between March 1997 – August 2009. Paraffin fixed slides were collected from Leicester General Hospital and Leicester Royal Infirmary. These were routinely sectioned at a thickness of 4 Micro millimetres for standardization. Conclusions: Fibrosis at 1 month after the transplant was found significantly associated with baseline fibrosis (p<0.001) and HTN in the transplant recipient (p<0.001). Dialysis after the transplant showed a weak association with fibrosis at 1 month (p=0.07). The negative coefficient for HTN (-0.05) suggests a reduction in fibrosis in the absence of HTN. Fibrosis at 1 month was significantly associated with fibrosis at baseline (p 0.01 and 95%CI 0.11 to 0.67). Fibrosis at 3, 6 or 12 months was not found to be associated with fibrosis at baseline (p=0.70. 0.65 and 0.50 respectively). The amount of fibrosis at 1 month is significantly associated with graft survival (p=0.01 and 95%CI 0.02 to 0.14). Rejection and severity of rejection were not found to be associated with fibrosis at 1 month. The amount of fibrosis at 1 month was significantly associated with graft survival (p=0.02) after adjusting for baseline fibrosis (p=0.01). Both baseline fibrosis and graft survival were significant predictive factors. The amount of fibrosis at 1 month was not found to be significantly associated with rejection (p=0.64) after adjusting for baseline fibrosis (p=0.01). The amount of fibrosis at 1 month was not found to be significantly associated with rejection severity (p=0.29) after adjusting for baseline fibrosis (p=0.04). Fibrosis at baseline and HTN in the recipient were found to be predictive factors of fibrosis at 1 month. (p 0.02, p <0.001 respectively). Age of the donor, their relation to the patient, the pre-op Creatinine, artery, kidney weight and warm time were not found to be significantly associated with fibrosis at 1 month. In this complex model baseline fibrosis, HTN in the recipient and cold time were found to be predictive factors of fibrosis at 1 month (p=0.01,<0.001 and 0.03 respectively). Donor age was found to be a predictive factor of fibrosis at 6 months. The above analysis was repeated for 3, 6 and 12 months. No associations were detected between fibrosis and any of the explanatory variables with the exception of the donor age which was found to be a predictive factor of fibrosis at 6 months.

Keywords: fibrosis, transplant, renal, rejection

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Authors: Zhaojun Wang, Zongdi Sun, Qinqin Cui, Xingwan Ren

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Based on multivariate statistical analysis theory, this paper uses the principal component analysis method, Mahalanobis distance analysis method and fitting method to establish the photovoltaic health model to evaluate the health of photovoltaic panels. First of all, according to weather conditions, the photovoltaic panel variable data are classified into five categories: sunny, cloudy, rainy, foggy, overcast. The health of photovoltaic panels in these five types of weather is studied. Secondly, a scatterplot of the relationship between the amount of electricity produced by each kind of weather and other variables was plotted. It was found that the amount of electricity generated by photovoltaic panels has a significant nonlinear relationship with time. The fitting method was used to fit the relationship between the amount of weather generated and the time, and the nonlinear equation was obtained. Then, using the principal component analysis method to analyze the independent variables under five kinds of weather conditions, according to the Kaiser-Meyer-Olkin test, it was found that three types of weather such as overcast, foggy, and sunny meet the conditions for factor analysis, while cloudy and rainy weather do not satisfy the conditions for factor analysis. Therefore, through the principal component analysis method, the main components of overcast weather are temperature, AQI, and pm2.5. The main component of foggy weather is temperature, and the main components of sunny weather are temperature, AQI, and pm2.5. Cloudy and rainy weather require analysis of all of their variables, namely temperature, AQI, pm2.5, solar radiation intensity and time. Finally, taking the variable values in sunny weather as observed values, taking the main components of cloudy, foggy, overcast and rainy weather as sample data, the Mahalanobis distances between observed value and these sample values are obtained. A comparative analysis was carried out to compare the degree of deviation of the Mahalanobis distance to determine the health of the photovoltaic panels under different weather conditions. It was found that the weather conditions in which the Mahalanobis distance fluctuations ranged from small to large were: foggy, cloudy, overcast and rainy.

Keywords: fitting, principal component analysis, Mahalanobis distance, SPSS, MATLAB

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Authors: Mohammad Anwarul Kabir, Fayezul Islam

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Language invariably is learned / taught to be used primarily as means of communications. Teaching a language for its native audience differs from teaching it to non-native audience. Moreover, teaching a language for communication only is different from teaching it for specific purposes. Arabic language is primarily regarded as the language of the Quran and the Sunnah (Prophetic tradition). Arabic is, therefore, learnt and spread all over the globe. However, Arabic is also a cultural heritage shared by all Islamic nations which has used Arabic for a long period to record the contributions of Muslim thinkers made in the field of wide spectrum of knowledge and scholarship. That is why the phenomenon of teaching Arabic by different educational institutes became quite rife, and the idea of teaching Arabic for specific purposes is heavily discussed in the academic sphere. Although the number of learners of Arabic is increasing consistently, yet their purposes vary. These include religious purpose, international trade, diplomatic purpose, better livelihood in the Arab world extra. By virtue of this high demand for learning Arabic, numerous institutes have been established all over the world including Bangladesh. This paper aims at focusing on the current status of the language institutes which has been established for learning Arabic for specific purposes in Bangladesh including teaching methodology, curriculum, and teachers’ quality. Such curricula and using its materials resulted in a lot of problems. The least, it confused teachers and students as well. Islamic educationalists have been working hard to professionally meet the need. They are following a systematic approach of stating clear and achievable goals, building suitable content, and applying new technology to present these learning experiences and evaluate them. It also suggests a model for designing instructional systems that responds to the need of non-Arabic speaking Islamic communities and provide the knowledge needed in both linguistic and cultural aspects. It also puts forward a number of suggestions for the improvement of the teaching / learning Arabic for specific purposes in Bangladesh after a detailed investigation in the following areas: curriculum, teachers’ skills, method of teaching and assessment policy.

Keywords: communication, Quran, sunnah, educational institutes, specific purposes, curriculum, method of teaching

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Authors: Kholoud Abdulaziz, Kholoud Al-Najdi, Abdullah Kadri, Konstantinos E. Kakosimos

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The Middle East area is highly affected by air pollution induced by anthropogenic and natural phenomena. There is evidence that air pollution, especially particulates, greatly affects the population health. Many studies have raised a warning of the high concentration of particulates and their affect not just around industrial and construction areas but also in the immediate working and living environment. One of the methods to study air quality is continuous and periodic monitoring using active or passive samplers. Active monitoring and sampling are the default procedures per the European and US standards. However, in many cases they have been inefficient to accurately capture the spatial variability of air pollution due to the small number of installations; which eventually is attributed to the high cost of the equipment and the limited availability of users with expertise and scientific background. Another alternative has been found to account for the limitations of the active methods that is the passive sampling. It is inexpensive, requires no continuous power supply, and easy to assemble which makes it a more flexible option, though less accurate. This study aims to investigate and evaluate the use of passive sampling for particulate matter pollution monitoring in dry tropical climates, like in the Middle East. More specifically, a number of field measurements have be conducted, both indoors and outdoors, at Qatar and the results have been compared with active sampling equipment and the reference methods. The samples have been analyzed, that is to obtain particle size distribution, by applying existing laboratory techniques (optical microscopy) and by exploring new approaches like the white light interferometry to. Then the new parameters of the well-established model have been calculated in order to estimate the atmospheric concentration of particulates. Additionally, an extended literature review will investigate for new and better models. The outcome of this project is expected to have an impact on the public, as well, as it will raise awareness among people about the quality of life and about the importance of implementing research culture in the community.

Keywords: air pollution, passive samplers, interferometry, indoor, outdoor

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Authors: Jae Ni Jang, Young Uk Kim

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Background: Chat Generative Pre-training Transformer-3 (ChatGPT; San Francisco, California, Open Artificial Intelligence) is an artificial intelligence chatbot based on a large language model designed to generate human-like text. As the usage of ChatGPT is increasing among less knowledgeable patients, medical students, and anesthesia and pain medicine residents or trainees, we aimed to evaluate the accuracy of ChatGPT-3 responses to questions about the American Society of Anesthesiologists (ASA) classification based on patients’ underlying diseases and assess the quality of the generated responses. Methods: A total of 47 questions were submitted to ChatGPT using textual prompts. The questions were designed for ChatGPT-3 to provide answers regarding ASA classification in response to common underlying diseases frequently observed in adult patients. In addition, we created 18 questions regarding the ASA classification for pediatric patients and pregnant women. The accuracy of ChatGPT’s responses was evaluated by cross-referencing with Miller’s Anesthesia, Morgan & Mikhail’s Clinical Anesthesiology, and the American Society of Anesthesiologists’ ASA Physical Status Classification System (2020). Results: Out of the 47 questions pertaining to adults, ChatGPT -3 provided correct answers for only 23, resulting in an accuracy rate of 48.9%. Furthermore, the responses provided by ChatGPT-3 regarding children and pregnant women were mostly inaccurate, as indicated by a 28% accuracy rate (5 out of 18). Conclusions: ChatGPT provided correct responses to questions relevant to the daily clinical routine of anesthesiologists in approximately half of the cases, while the remaining responses contained errors. Therefore, caution is advised when using ChatGPT to retrieve anesthesia-related information. Although ChatGPT may not yet be suitable for clinical settings, we anticipate significant improvements in ChatGPT and other large language models in the near future. Regular assessments of ChatGPT's ASA classification accuracy are essential due to the evolving nature of ChatGPT as an artificial intelligence entity. This is especially important because ChatGPT has a clinically unacceptable rate of error and hallucination, particularly in pediatric patients and pregnant women. The methodology established in this study may be used to continue evaluating ChatGPT.

Keywords: American Society of Anesthesiologists, artificial intelligence, Chat Generative Pre-training Transformer-3, ChatGPT

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Authors: Wondmnew Derebe Yohannis

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Farming irrigation plays a crucial role in rural development strategies, impacting both annual household income and livelihood. This research aims to evaluate the factors influencing irrigation participation and assess the impact of small-scale irrigation on rural households' annual income. The study collected data from 287 farmers in the Dahana district of northern Ethiopia. The research investigates the driving forces behind farmers' decisions to adopt small-scale irrigation and its effect on annual income gain. The findings reveal that several factors positively influence the probability of adoption, including access to credit, cultivated land size, livestock holding, extension contact, and the education level of the household head. Conversely, the distance to local markets and water schemes negatively affects the likelihood of adoption. To understand the differences in annual income between farm households that adopted irrigation and those that did not, a simultaneous equations model with endogenous switching regression is estimated. This accounts for the heterogeneity in the adoption decision and unobservable characteristics of farmers and their farms. The analysis compares the expected income gain under actual and counterfactual scenarios, considering whether the farm household adopted irrigation or not. The study reveals that the group of farm households that adopted irrigation has distinct characteristics compared to those that did not adopt it. Furthermore, the research demonstrates that the adoption of irrigation practices leads to an increase in annual income. Interestingly, the impact of small-scale irrigation on annual income is greater for the farm households that actually adopted irrigation compared to those in the counterfactual scenario where they did not adopt. Based on the findings, the researcher concludes that small-scale irrigation is a practical solution for meeting household financial needs in the study area. It is recommended that investments in small-scale irrigation continue to further improve the livelihoods of rural farming communities by enhancing annual income gains.

Keywords: small-scale irrigation, income, rural farm households, endogenous switching regression, user, non-user

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Authors: Sitti Rahmawati

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Payment system of cataract surgery implemented by professional attendant of doctor and nurse has been increasing, through health insurance program and this has become one of the factors that affects a lot of government in the budget establishment. This system has been implemented in purpose of quality and expenditure control, i.e., controlling health overpayment to obtain benefit (moral hazard) by the user of insurance or health service provider. The increasing health cost becomes the main issue that hampers the society to receive required health service in cash payment-system. One of the efforts that should be taken by the government in health payment is by securing health insurance through society's health insurance. The objective of the study is to learn the capability of a patient to pay cataract eye operation for the elders. Method of study sample population in this study was patients who obtain health insurance board card for the society that was started in the first of tri-semester (January-March) 2015 and claimed in Indonesian software-Case Based Group as a purposive sampling of 40 patients. Results of the study show that total unit cost analysis of surgery service unit was obtained $75 for unit cost without AFC and salary of nurse and doctor. The operation tariff that has been implemented today at Anutapura hospitals in eye department is tariff without AFC and the salary of the employee is $80. The operation tariff of the unit cost calculation with double distribution model at $65. Conclusion, the calculation result of actual unit cost that is much greater causes incentive distribution system provided to an ophthalmologist at $37 and nurse at $20 for one operation. The surgery service tariff is still low; consequently, the hospital receives low revenue and the quality of health insurance in eye operation department is relatively low. In purpose of increasing the service quality, it requires adequately high cost to equip medical equipment and increase the number of professional health attendant in serving patients in cataract eye operation at hospital.

Keywords: economic evaluation, cataract operation, health attendant, health insurance system

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Authors: Hanna Gulema, Meaza Demissie, Alemayehu Worku, Tesfaye Assebe Yadeta, Yemane Berhane

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Background: In patriarchal societies, female caregivers decide on food allocation within a family based on prevailing gender and age norms, which may lead to inequality that does not favor young adolescent girls. This study evaluated the effect of a community-based social norm intervention involving female caregivers in West Hararghe, Ethiopia. The intervention was engaging female caregivers along with other adult influential community members to deliberate and act on food allocation social norms in a process referred to as Social Analysis and Action (SAA). Method: We used data from a large quasi-experimental study to compare family eating practices between those who participated in the Social Analyses and Action intervention and those who did not. The respondents were female caregivers in households with young adolescent girls (ages 13 and 14 years). The study’s outcome was the practice of family eating together from the same dish. The difference in difference (DID) analysis with the Mixed effect logistic regression model was used to examine the effect of the intervention. Result: The results showed improved family eating practices in both groups, but the improvement was greater in the intervention group. The DID analysis showed an 11.99 percentage points greater improvement in the intervention arm than in the control arm. The mixed-effect regression produced an adjusted odds ratio of 2.08 (95% CI [1.06–4.09]) after controlling selected covariates, p-value 0.033. Conclusions: The involvement of influential adult community members significantly improves the family practice of eating together in households where adolescent girls are present in our study. The intervention has great potential to minimize household food allocation inequalities and thus improve the nutritional status of young adolescents. Further studies are necessary to evaluate the effectiveness of the intervention in different social norm contexts to formulate policy and guidelines for scale-up.

Keywords: family eating practice, social norm intervention, adolescence girls, caregiver

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Authors: A. Durgadevi, S. Pushpavanam

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For the past several decades, CO2 levels have been dramatically increasing in the atmosphere due to the man-made emissions such as fossil fuel-fired power plants. With the increase in CO2 emissions, CO2 concentration in the atmosphere has increased resulting in global warming. This shows the need to study different ways to capture the emitted CO2 directly from the exhausts of power plants or atmosphere. There are several ways to remove CO2, such as absorption into a liquid solvent, adsorption into a solid, cryogenic separation, permeation through membranes and photochemical conversion. In most industries, the absorption of CO2 in chemical solvents (in absorption towers) is used for CO2 capture. In these towers, the mass transfer along with chemical reactions take place between the gas and liquid phase. This helps in the separation of CO2 from other gases. It is important to understand these processes in detail. These flow patterns are difficult to maintain in large scale industrial absorbers. So to get accurate information controlled gas-liquid absorption experiments are carried out in milli-channels in this work under controlled atmosphere. The absorption experiments of CO2 in varying concentrations of sodium hydroxide solution are carried out in T-junction glass milli-channels with a circular cross section (inner diameter of 2mm). The gas and liquid flow rates are controlled by a mass flow controller (MFC) and a Harvard syringe pump respectively. The slug flow in the channel is recorded using a camera and the videos are analysed. The gas slug of pure CO2 is found to decrease in size along the length of the channel due to absorption of gas in the liquid. This is also captured with the model developed and the mass transfer characteristics are studied. The pressure drop across the channel is determined by sum of the pressure drops from the gas slugs and the liquid plugs. A dimensionless correlation for the mass transfer coefficient is developed in terms of Sherwood number and compared with the existing correlations in the literature. They are found to be in close agreement with each other. In this case, due to the presence of chemical reaction, the enhancement of mass transfer is obtained. This is quantified with the help of an enhancement factor.

Keywords: absorption, enhancement factor, mass transfer coefficient, Sherwood number

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Authors: Elżbieta Turska

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Introduction: One of the most difficult times in a person’s life is adolescence. The experiences in this period may shape the future life of this person to a large extent. This is the reason why many young people experience sadness, dejection, hopelessness, sense of worthlessness, as well as losing interest in various activities and social relationships, all of which are often classified as mood disorders. As many as 15-40% adolescents experience depressed moods and for most of them they resolve and are not carried into adulthood. However, (5-6%) of those affected by mood disorders develop the depressive syndrome and as many as (1-3%) develop full-blown clinical depression. Materials: A large questionnaire was given to 2508 students, aged 13–16 years old, and one of its parts was the Burns checklist, i.e. the standard test for identifying depressed mood. The questionnaire asked about many aspects of the student’s life, it included a total of 53 questions, most of which had subquestions. It is important to note that the data suffered from many problems, the most important of which were missing data and collinearity. Aim: In order to identify the correlates of mood disorders we built predictive models which were then trained and validated. Our aim was not to be able to predict which students suffer from mood disorders but rather to explore the factors influencing mood disorders. Methods: The problems with data described above practically excluded using all classical statistical methods. For this reason, we attempted to use the following Artificial Intelligence (AI) methods: classification trees with surrogate variables, random forests and xgboost. All analyses were carried out with the use of the mlr package for the R programming language. Resuts: The predictive model built by classification trees algorithm outperformed the other algorithms by a large margin. As a result, we were able to rank the variables (questions and subquestions from the questionnaire) from the most to least influential as far as protection against mood disorder is concerned. Thirteen out of twenty most important variables reflect the relationships with parents. This seems to be a really significant result both from the cognitive point of view and also from the practical point of view, i.e. as far as interventions to correct mood disorders are concerned.

Keywords: mood disorders, adolescents, family, artificial intelligence

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Authors: Iyd Eqqab Maree, Moouyad Ibrahim Abbood

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The purpose behind this study is to predict damping effect for steel cantilever beam by using two methods of passive viscoelastic constrained layer damping. First method is Matlab Program, this method depend on the Ross, Kerwin and Unger (RKU) model for passive viscoelastic damping. Second method is experimental lab (frequency domain method), in this method used the half-power bandwidth method and can be used to determine the system loss factors for damped steel cantilever beam. The RKU method has been applied to a cantilever beam because beam is a major part of a structure and this prediction may further leads to utilize for different kinds of structural application according to design requirements in many industries. In this method of damping a simple cantilever beam is treated by making sandwich structure to make the beam damp, and this is usually done by using viscoelastic material as a core to ensure the damping effect. The use of viscoelastic layers constrained between elastic layers is known to be effective for damping of flexural vibrations of structures over a wide range of frequencies. The energy dissipated in these arrangements is due to shear deformation in the viscoelastic layers, which occurs due to flexural vibration of the structures. The theory of dynamic stability of elastic systems deals with the study of vibrations induced by pulsating loads that are parametric with respect to certain forms of deformation. There is a very good agreement of the experimental results with the theoretical findings. The main ideas of this thesis are to find the transition region for damped steel cantilever beam (4mm and 8mm thickness) from experimental lab and theoretical prediction (Matlab R2011a). Experimentally and theoretically proved that the transition region for two specimens occurs at modal frequency between mode 1 and mode 2, which give the best damping, maximum loss factor and maximum damping ratio, thus this type of viscoelastic material core (3M468) is very appropriate to use in automotive industry and in any mechanical application has modal frequency eventuate between mode 1 and mode 2.

Keywords: 3M-468 material core, loss factor and frequency, domain method, bioinformatics, biomedicine, MATLAB

Procedia PDF Downloads 271