Search results for: panel regression

Authors: Md Ruhul Amin

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This study was conducted in the experimental field of the Department of Entomology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh during November 2017 to May 2018 with a view to understanding the seasonal abundance of the major sucking pests namely thrips, aphid and red spider mite on rose. The findings showed that the thrips started to build up their population from the middle of January with abundance 1.0 leaf⁻¹, increased continuously, reached to the peak level (2.6 leaf⁻¹) in the middle of February and then declined. Aphid started to build up their population from the second week of November with abundance 6.0 leaf⁻¹, increased continuously, reached to the peak level (8.4 leaf⁻¹) in the last week of December and then declined. Mite started to build up their population from the first week of December with abundance 0.8 leaf⁻¹, increased continuously, reached to the peak level (8.2 leaf⁻¹) in the second week of March and then declined. Thrips and mite prevailed until the last week of April, and aphid showed their abundance till last week of May. The daily mean temperature, relative humidity, and rainfall had an insignificant negative correlation with thrips and significant negative correlation with aphid abundance. The daily mean temperature had significant positive, relative humidity had an insignificant positive, and rainfall had an insignificant negative correlation with mite abundance. The multiple linear regression analysis showed that the weather parameters together contributed 38.1, 41.0 and 8.9% abundance on thrips, aphid and mite on rose, respectively and the equations were insignificant.

Keywords: aphid, mite, thrips, weather factors

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Authors: Yao Song

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Objectives: We aimed to explore the clinicodemographic characteristics and prognosis of grey zone squamous cell cancer (GZSCC) located in the overlapping or ambiguous area of the oral cavity and oropharynx and to identify valuable factors that would improve its differential diagnosis and prognosis. Methods: Information of GZSCC patients in the Surveillance, Epidemiology, and End Results (SEER) database was compared to patients with an oral cavity (OCSCC) and oropharyngeal (OPSCC) squamous cell carcinomas with corresponding HPV status, respectively. Kaplan-Meier method with log-rank test and multivariate Cox regression analysis were applied to assess associations between clinical characteristics and overall survival (OS). A predictive model integrating age, gender, marital status, HPV status, and staging variables was conducted to classify GZSCC patients into three risk groups and verified internally by 10-fold cross validation. Results: A total of 3318 GZSCC, 10792 OPSCC, and 6656 OCSCC patients were identified. HPV-positive GZSCC patients had the best 5-year OS as HPV-positive OPSCC (81% vs. 82%). However, the 5-year OS of HPV-negative/unknown GZSCC (43%/42%) was the worst among all groups, indicating that HPV status and the overlapping nature of tumors were valuable prognostic predictors in GZSCC patients. Compared with the strategy of dividing GZSCC into two groups by HPV status, the predictive model integrating more variables could additionally identify a unique high-risk GZSCC group with the lowest OS rate. Conclusions: GZSCC patients had distinct clinical characteristics and prognoses compared with OPSCC and OCSCC; integrating HPV status and other clinical factors could help distinguish GZSCC and predict their prognosis.

Keywords: GZSCC, OCSCC, OPSCC, HPV

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Authors: Vincent A. Adzika, Franklin N. Glozah, Collins S. K. Ahorlu

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Background: Sickle Cell Disease (SCD) is of major public-health concern globally, with majority of patients living in Africa. Despite its relevance, there is a dearth of research to determine the socio-demographic distribution and psychosocial impact of SCD in Africa. The objective of this study therefore was to examine the socio-demographic distribution and psychosocial consequences of SCD among patients in Ghana and to assess their quality of life and coping mechanisms. Methods: A cross-sectional research design was used, involving the completion of questionnaires on socio-demographic characteristics, quality of life of individuals, anxiety and depression. Participants were 387 male and female patients attending a sickle cell clinic in a public hospital. Results: Results showed no gender and marital status differences in anxiety and depression. However, there were age and level of education variances in depression but not in anxiety. In terms of quality of life, patients were more satisfied by the presence of love, friends, relatives as well as home, community and neighbourhood environment. While pains of varied nature and severity were the major reasons for attending hospital in SCD condition, going to the hospital as well as having Faith in God was the frequently reported mechanisms for coping with an unbearable SCD attacks. Multiple regression analysis showed that some socio-demographic and quality of life indicators had strong associations with anxiety and/or depression. Conclusion: It is recommended that a multi-dimensional intervention strategy incorporating psychosocial dimensions should be considered in the treatment and management of SCD.

Keywords: anxiety, depression, sickle cell disease, socio-demographic quality of life, characteristics, Ghana

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Authors: Ehsan Pegah, Huabei Liu

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Determination of the internal friction angle (φ) in natural soil deposits is an important issue in geotechnical engineering. The main objective of this study was to examine the evaluation of this parameter in overconsolidated granular soil deposits by using the P-wave velocity and the anisotropic components of S-wave velocity (i.e., both the vertical component (SV) and the horizontal component (SH) of S-wave). To this end, seventeen pairs of P-wave and S-wave seismic refraction profiles were carried out at three different granular sites in Iran using non-invasive seismic wave methods. The acquired shot gathers were processed, from which the P-wave, SV-wave and SH-wave velocities were derived. The reference values of φ and overconsolidation ratio (OCR) in the soil deposits were measured through laboratory tests. By assuming cross-anisotropy of the soils, the P-wave and S-wave velocities were utilized to develop an equation for calculating the coefficient of lateral earth pressure at-rest (K₀) based on the theory of elasticity for a cross-anisotropic medium. In addition, to develop an equation for OCR estimation in granular geomaterials in terms of SH/SV velocity ratios, a general regression analysis was performed on the resulting information from this research incorporated with the respective data published in the literature. The calculated K₀ values coupled with the estimated OCR values were finally employed in the Mayne and Kulhawy formula to evaluate φ in granular soil deposits. The results showed that reliable values of φ could be estimated based on the seismic wave velocities. The findings of this study may be used as the appropriate approaches for economic and non-invasive determination of in-situ φ in granular soil deposits using the surface seismic surveys.

Keywords: angle of internal friction, overconsolidation ratio, granular soils, P-wave velocity, SV-wave velocity, SH-wave velocity

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Authors: Chuan Yang, Jing Bie, Zhong Wang, Panagiotis Psimoulis

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Contemporary studies investigating the association between the spatial configuration of the urban network and economic activities at the street level were mostly conducted within space syntax conceptual framework. These findings supported the theory of 'movement economy' and demonstrated the impact of street configuration on the distribution of pedestrian movement and land-use shaping, especially retail activities. However, the effects varied between different urban contexts. In this paper, the relationship between economic activity distribution and the urban configurational characters was examined at the segment level. In the study area, three kinds of neighbourhood types, urban, suburban, and rural neighbourhood, were included. And among all neighbourhoods, three kinds of urban network form, 'tree-like', grid, and organic pattern, were recognised. To investigate the nested effects of urban configuration measured by space syntax approach and urban context, multilevel zero-inflated negative binomial (ZINB) regression models were constructed. Additionally, considering the spatial autocorrelation, spatial lag was also concluded in the model as an independent variable. The random effect ZINB model shows superiority over the ZINB model or multilevel linear (ML) model in the explanation of economic activities pattern shaping over the urban environment. And after adjusting for the neighbourhood type and network form effects, connectivity and syntax centrality significantly affect economic activities clustering. The comparison between accumulative and new established economic activities illustrated the different preferences for economic activity location choice.

Keywords: space syntax, economic activities, multilevel model, Chinese city

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Authors: Habte Tadesse Likassa, Bacha Edosa

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Customer’s satisfaction was very important thing that is required for the existence of banks to be more productive and success in any organization and business area. The main goal of the study is assessing and identifying factors that influence customer’s satisfaction in West Shoa Zone of Commercial Bank of Ethiopia (Holeta, Ginchi, Ambo, Gedo and Bako). Stratified random sampling procedure was used in the study and by using simple random sampling (lottery method) 520 customers were drawn from the target population. By using Probability Proportional Size Techniques sample size for each branch of banks were allocated. Both descriptive and inferential statistics methods were used in the study. A binary logistic regression model was fitted to see the significance of factors affecting customer’s satisfaction in this study. SPSS statistical package was used for data analysis. The result of the study reveals that the overall level of customer’s satisfaction in the study area is low (38.85%) as compared those who were not satisfied (61.15%). The result of study showed that all most all factors included in the study were significantly associated with customer’s satisfaction. Therefore, it can be concluded that based on the comparison of branches on their customers satisfaction by using odd ratio customers who were using Ambo and Bako are less satisfied as compared to customers who were in Holeta branch. Additionally, customers who were in Ginchi and Gedo were more satisfied than that of customers who were in Holeta. Since the level of customers satisfaction was low in the study area, it is more advisable and recommended for concerned body works cooperatively more in maximizing satisfaction of their customers.

Keywords: customers, satisfaction, binary logistic, complain handling process, waiting time

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Authors: Sergio Andres Rojas, Julian Benavides Franco, Juan Tomas Sayago

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An increasing amount of research demonstrates how market mood affects financial markets, but their primary goal is to demonstrate how Trump's tweets impacted US interest rate volatility. Following that lead, this work evaluates the effect that Trump's tweets had during his presidency on local and international stock markets, considering not just volatility but the direction of the movement. Three indexes for Trump's tweets were created relating his activity with movements in the S&P500 using natural language analysis and machine learning algorithms. The indexes consider Trump's tweet activity and the positive or negative market sentiment they might inspire. The first explores the relationship between tweets generating negative movements in the S&P500; the second explores positive movements, while the third explores the difference between up and down movements. A pseudo-investment strategy using the indexes produced statistically significant above-average abnormal returns. The findings also showed that the pseudo strategy generated a higher return in the local market if applied to intraday data. However, only a negative market sentiment caused this effect on daily data. These results suggest that the market reacted primarily to a negative idea reflected in the negative index. In the international market, it is not possible to identify a pervasive effect. A rolling window regression model was also performed. The result shows that the impact on the local and international markets is heterogeneous, time-changing, and differentiated for the market sentiment. However, the negative sentiment was more prone to have a significant correlation most of the time.

Keywords: market sentiment, Twitter market sentiment, machine learning, natural dialect analysis

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Authors: Manuel Jose Oyson

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The Intergovernmental Panel on Climate Change (IPCC) warned in its most recent report for the entire world “to both mitigate and adapt to climate change if it is to effectively avoid harmful climate impacts.” The IPCC observed “with high confidence” a more rapid rise in total anthropogenic greenhouse gas emissions (GHG) emissions from 2000 to 2010 than in the past three decades that “were the highest in human history”, which if left unchecked will entail a continuing process of global warming and can alter the climate system. Current efforts, however, to respond to the threat of global warming, such as the United Nations Framework Convention on Climate Change and the Kyoto Protocol, have focused on states, and fail to involve Transnational Corporations (TNCs) which are responsible for a vast amount of GHG emissions. Involving TNCs in the search for solutions to climate change is consistent with an acknowledgment by contemporary international law that there is an international role for other international persons, including TNCs, and departs from the traditional “state-centric” response to climate change. Putting the focus of GHG emissions away from states recognises that the activities of TNCs “are not bound by national borders” and that the international movement of goods meets the needs of consumers worldwide. Although there is no legally-binding instrument that covers TNC activities or legal responsibilities generally, TNCs have increasingly been made legally responsible under international law for violations of human rights, exploitation of workers and environmental damage, but not for climate change damage. Imposing on TNCs a legally-binding obligation to reduce their GHG emissions or a legal liability for climate change damage is arguably formidable and unlikely in the absence a recognisable source of obligation in international law or municipal law. Instead a recourse to “soft law” and non-legally binding instruments may be a way forward for TNCs to reduce their GHG emissions and help in addressing climate change. Positive effects have been noted by various studies to voluntary approaches. TNCs have also in recent decades voluntarily committed to “soft law” international agreements. This development reflects a growing recognition among corporations in general and TNCs in particular of their corporate social responsibility (CSR). While CSR used to be the domain of “small, offbeat companies”, it has now become part of mainstream organization. The paper argues that TNCs must voluntarily commit to reducing their GHG emissions and helping address climate change as part of their CSR. One, as a serious “global commons problem”, climate change requires international cooperation from multiple actors, including TNCs. Two, TNCs are not innocent bystanders but are responsible for a large part of GHG emissions across their vast global operations. Three, TNCs have the capability to help solve the problem of climate change. Assuming arguendo that TNCs did not strongly contribute to the problem of climate change, society would have valid expectations for them to use their capabilities, knowledge-base and advanced technologies to help address the problem. It would seem unthinkable for TNCs to do nothing while the global environment fractures.

Keywords: climate change law, corporate social responsibility, greenhouse gas emissions, transnational corporations

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Authors: Lavanya Annadi, Fahimeh Jafari

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Transportation brings immense benefits to society, but it also has its costs. Costs include such as the cost of infrastructure, personnel and equipment, but also the loss of life and property in traffic accidents on the road, delays in travel due to traffic congestion and various indirect costs in terms of air transport. More research has been done to identify the various factors that affect road accidents, such as road infrastructure, traffic, sociodemographic characteristics, land use, and the environment. The aim of this research is to predict the probabilistic crash prediction of vehicles using machine learning due to natural and structural reasons by excluding spontaneous reasons like overspeeding etc., in the United States. These factors range from weather factors, like weather conditions, precipitation, visibility, wind speed, wind direction, temperature, pressure, and humidity to human made structures like road structure factors like bump, roundabout, no exit, turning loop, give away, etc. Probabilities are dissected into ten different classes. All the predictions are based on multiclass classification techniques, which are supervised learning. This study considers all crashes that happened in all states collected by the US government. To calculate the probability, multinomial expected value was used and assigned a classification label as the crash probability. We applied three different classification models, including multiclass Logistic Regression, Random Forest and XGBoost. The numerical results show that XGBoost achieved a 75.2% accuracy rate which indicates the part that is being played by natural and structural reasons for the crash. The paper has provided in-deep insights through exploratory data analysis.

Keywords: road safety, crash prediction, exploratory analysis, machine learning

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Authors: Usha Loganathan, Haresh Pandya

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Analysis and correlation of soil properties represent an important outset for precision agriculture and is currently promoted and implemented in the developed world. Establishing relationships among indices of soil salinity has always been a challenging task in salt affected soils necessitating unique approaches for their reclamation and management to sustain long term productivity of Soil. Soil salinity indices like Electrical Conductivity (EC) and Sodium Adsorption Ratio (SAR) are normally used to characterize soils as either sodic or saline sodic. Currently, Determination of Soil sodium adsorption ratio is a more accepted and reliable measure of soil salinity. However, it involves arduous and protracted laboratory investigations which demand evolving new and economical methods to determine SAR based on simple soil salinity index. A linear regression model to predict soil SAR from soil electrical conductivity has been developed and presented in this paper as per which, soil SAR could very well be worked out as a pedotransfer function of soil EC. The present study was carried out in Orathupalayam (11.09-11.11 N latitude and 74.54-77.59 E longitude) in the vicinity of Orathupalayam Reservoir of Noyyal River Basin, India, over a period of 3 consecutive years from September 2013 through February 2016 in different locations chosen randomly through different seasons. The research findings are discussed in the light of micro level farming practices in India and recommend determination of SAR as a low cost technology aiding in the effective management of salt affected agricultural land.

Keywords: electrical conductivity, orathupalayam, pedotranfer function, sodium adsorption ratio

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Authors: Zelalem Markos Borko

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Work-related stress is a reaction that occurs when the work weight progress toward becoming excessive. Therefore, unless properly managed, stress leads to high employee turnover, decreased performance, illness and absenteeism. Yet, little has been addressed regarding work-related stress and its predictors in the study area. Therefore, the objective of this study was to assess stress prevalence and its predictors in the study area. To that effect, a cross-sectional study design was conducted on 281 employees from the Ethiopian Federal Bureau of Investigation by using stratified random sampling techniques. Survey questionnaire scales were employed to collect data. Data were analyzed by percentage, Pearson correlation coefficients, simple linear regression, multiple linear regressions, independent t-test and one-way ANOVA statistical techniques. In the present study13.9% of participants faced high stress, whereas 13.5% of participants faced low stress and the rest 72.6% of officers experienced moderate stress. There is no significant group difference among workers due to age, gender, marital status, educational level, years of service and police rank. This study concludes that factors such as role conflict, performance over-utilization, role ambiguity, and qualitative and quantitative role overload together predict 39.6% of work-related stress. This indicates that 60.4% of the variation in stress is explained by other factors, so other additional research should be done to identify additional factors predicting stress. To prevent occupational stress among police, the Ethiopian Federal Bureau of Investigation should develop strategies based on factors that will help to develop stress reduction management.

Keywords: work-related stress, Ethiopian federal bureau of investigation, predictors, Addis Ababa

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Authors: Alicia Carranza, Hildemar Dos Santos, W. Lawrence Beeson, R. Patti Herring, Kimberly R. Freeman, Adam Arechiga

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Early exposure to trauma can impact health-related behaviors later in life, which poses a considerable challenge for young adults transitioning into independence when they are lacking the necessary skills and support to live a healthy life. The study will be a non-experimental, mixed methods pre- and post-test (where subjects will serve as their own controls) to determine the impact of an eight-week trauma-informed healthy lifestyle program on self-efficacy for adopting health-promoting behaviors and health outcomes among young adults. Forty-two adults, ages 18-24 who are living in Orange County, CA will be recruited to participate in the eight-week trauma-informed healthy living program. Baseline and post-intervention assessments will be conducted to assess changes in self-efficacy for nutrition and physical exercise, sleep quality and quantity, body mass index (kg/m2), and coping skills used by comparing pre- to post-intervention. Some of the planned activities include cooking demonstrations, mindful eating activities and media literacy using Instagram. Frequencies analyses, paired t-test, and multiple regression will be used to determine if there was a change in coping skills. The results of this study can serve to assess the potential for mitigating the effects of Adverse Childhood Experiences (ACEs), or other toxic stress, experienced during adolescence across the lifespan. Young adults who learn how to cope with stress in a healthy way and engage in a healthy lifestyle can be better prepared to role model that behavior to their children.

Keywords: nutrition, healthy lifestyle, trauma-informed, stress management

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Authors: Linnan Zhang

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Linguists, as well as psychologists, have shown great interests in implicit causality in reference processing. However, most frequently-used approaches to this issue are psychological experiments (such as eye tracking or self-paced reading, etc.). This research is a corpus-based one and is assisted with statistical tool – software R. The main focus of the present study is about the competition between verb-based implicit causality and theme structure’s influence on anaphora bias in Mandarin Chinese sentences. In Accessibility Theory, it is believed that salience, which is also known as accessibility, and relevance are two important factors in reference processing. Theme structure, which is a special syntactic structure in Chinese, determines the salience of an antecedent on the syntactic level while verb-based implicit causality is a key factor to the relevance between antecedent and anaphora. Therefore, it is a study about anaphora, combining psychology with linguistics. With analysis of the sentences from corpus as well as the statistical analysis of Multinomial Logistic Regression, major findings of the present study are as follows: 1. When the sentence is stated in a ‘cause-effect’ structure, the theme structure will always be the antecedent no matter forward biased verbs or backward biased verbs co-occur; in non-theme structure, the anaphora bias will tend to be the opposite of the verb bias; 2. When the sentence is stated in a ‘effect-cause’ structure, theme structure will not always be the antecedent and the influence of verb-based implicit causality will outweigh that of theme structure; moreover, the anaphora bias will be the same with the bias of verbs. All the results indicate that implicit causality functions conditionally and the noun in theme structure will not be the high-salience antecedent under any circumstances.

Keywords: accessibility theory, anaphora, theme strcture, verb-based implicit causality

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Authors: Tagel Gebrehiwot, Carolina Castilla

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Food insecurity and child malnutrition are among the most critical issues in Ethiopia. Accordingly, different reform programs have been carried to improve household food security. The Food Security Program (FSP) (among others) was introduced to combat the persistent food insecurity problem in the country. The FSP combines a safety net component called the Productive Safety Net Program (PSNP) started in 2005. The goal of PSNP is to offer multi-annual transfers, such as food, cash or a combination of both to chronically food insecure households to break the cycle of food aid. Food or cash transfers are the main elements of PSNP. The case for cash transfers builds on the Sen’s analysis of ‘entitlement to food’, where he argues that restoring access to food by improving demand is a more effective and sustainable response to food insecurity than food aid. Cash-based schemes offer a greater choice of use of the transfer and can allow a greater diversity of food choice. It has been proven that dietary diversity is positively associated with the key pillars of food security. Thus, dietary diversity is considered as a measure of household’s capacity to access a variety of food groups. Studies of dietary diversity among Ethiopian rural households are somewhat rare and there is still a dearth of evidence on the impact of PSNP on household dietary diversity. In this paper, we examine the impact of the Ethiopia’s PSNP on household dietary diversity and child nutrition using panel household surveys. We employed different methodologies for identification. We exploit the exogenous increase in kebeles’ PSNP budget to identify the effect of the change in the amount of money households received in transfers between 2012 and 2014 on the change in dietary diversity. We use three different approaches to identify this effect: two-stage least squares, reduced form IV, and generalized propensity score matching using a continuous treatment. The results indicate the increase in PSNP transfers between 2012 and 2014 had no effect on household dietary diversity. Estimates for different household dietary indicators reveal that the effect of the change in the cash transfer received by the household is statistically and economically insignificant. This finding is robust to different identification strategies and the inclusion of control variables that determine eligibility to become a PSNP beneficiary. To identify the effect of PSNP participation on children height-for-age and stunting we use a difference-in-difference approach. We use children between 2 and 5 in 2012 as a baseline because by then they have achieved long-term failure to grow. The treatment group comprises children ages 2 to 5 in 2014 in PSNP participant households. While changes in height-for-age take time, two years of additional transfers among children who were not born or under the age of 2-3 in 2012 have the potential to make a considerable impact on reducing the prevalence of stunting. The results indicate that participation in PSNP had no effect on child nutrition measured as height-for-age or probability of beings stunted, suggesting that PSNP should be designed in a more nutrition-sensitive way.

Keywords: continuous treatment, dietary diversity, impact, nutrition security

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Authors: Olabisi Badmos, Daniel Callo-Concha, Babatunde Agbola, Andreas Rienow, Klaus Greve, Carsten Jurgens

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Slums are important components of city development planning, especially in Africa where slum growth is on par with urban growth. Purposefully, our knowledge on the residential choice of slum dwellers, which contributes to population growth in slums, is limited. This is the case in Lagos, a megacity reportedly dominated by slum dwellers. Thus, this study aims to disclose the factors influencing the residential choices and causes of people to remain in Lagos slums. Data was collected through questionnaire administration and focus group discussions. Descriptive statistics were used to analyze and describe the factors influencing residential location choice; logistic regression was utilized to determine the extent to which the neighborhood and household attributes, influence slum dwellers decisions to remain in the slums. Results showed that movement to Lagos was the main cause of population growth in slums; most of the migrants were from closer geopolitical zones (in Nigeria). Further, the movement patterns observed support two theories of human mobility in slums: slum as a sink, and as a final destination. Also, the factors that brought most of the slum dwellers to the slums (cheap housing, proximity to work etc.) differs from the ones that made them stay (Gender, employment status, housing status etc.). This study concludes that residential choice and intention to stay are the major contributors to population growth in a slum. It is therefore important for Lagos state Government to incorporate these elements of residential choices of slum dwellers in their slum management policies if the city aims to be free of slums by 2030

Keywords: Lagos, population growth, residential decision choices, slum

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Authors: Pule P. Mpopetsi, Warren M. Potts, Nicola James, Amber Childs

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Increased CO₂ production and the consequent ocean acidification (OA) have been identified as one of the greatest threats to both calcifying and non-calcifying marine organisms. Traditionally, marine fishes, as non-calcifying organisms, were considered to have a higher tolerance to near-future OA conditions owing to their well-developed ion regulatory mechanisms. However, recent studies provide evidence to suggest that they may not be as resilient to near-future OA conditions as previously thought. In addition, earlier life stages of marine fishes are thought to be less tolerant than juveniles and adults of the same species as they lack well-developed ion regulatory mechanisms for maintaining homeostasis. This study focused on the effects of near-future OA on larval Argyrosomus japonicus, an estuarine-dependent marine fish species, in order to identify the tipping point of tolerance for the larvae of this species. Larval A. japonicus in the present study were reared from the egg up to 22 days after hatching (DAH) under three treatments. The three treatments, (pCO₂ 353 µatm; pH 8.03), (pCO₂ 451 µatm; pH 7.93) and (pCO₂ 602 µatm; pH 7.83) corresponded to levels predicted to occur in year 2050, 2068 and 2090 respectively under the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (IPCC RCP) 8.5 model. Size-at-hatch, growth, development, and metabolic responses (standard and active metabolic rates and metabolic scope) were assessed and compared between the three treatments throughout the rearing period. Five earlier larval life stages (hatchling – flexion/post-flexion) were identified by the end of the experiment. There were no significant differences in size-at-hatch (p > 0.05), development or the active metabolic (p > 0.05) or metabolic scope (p > 0.05) of fish in the three treatments throughout the study. However, the standard metabolic rate was significantly higher in the year 2068 treatment but only at the flexion/post-flexion stage which could be attributed to differences in developmental rates (including the development of the gills) between the 2068 and the other two treatments. Overall, the metabolic scope was narrowest in the 2090 treatment but varied according to life stage. Although not significantly different, metabolic scope in the 2090 treatment was noticeably lower at the flexion stage compared to the other two treatments, and the development appeared slower, suggesting that this could be the stage most prone to OA. The study concluded that, in isolation, OA levels predicted to occur between 2050 and 2090 will not negatively affect size-at-hatch, growth, development, and metabolic responses of larval A. japonicus up to 22 DAH (flexion/post-flexion stage). The present study also identified the tipping point of tolerance (where negative impacts will begin) in larvae of the species to be between the years 2090 and 2100.

Keywords: climate change, ecology, marine, ocean acidification

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Authors: Faseluka Olamide Tope

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Giving quality education to students with special needs requires that all necessary resources should be harnessed and digital devices has become important part of resources used as instructional materials in educating students with special needs. Teachers who will make use of these technologies are considered as a part of the most important elements in any educational programme and the effective usage of these technologies largely depends on them. Out of numerous determinants of the effective use of these digital devices, this study examines teachers’ attitude and knowledge as predictors of effective use of digital technology for education of special needs student in Oyo state, Nigeria. The descriptive survey research design of the expo-facto type was adopted for the study, using simple random sampling technique. The study was carried out among sixty (60) participants. Two research questions and two research hypotheses were formulated and used. The data collected through the research instruments for the study were analysedusing frequency, percentage, mean and standard deviation, Pearson, Product, Moment Correlation (PPMC) and Multiple Regression Analysis. The study revealed a significant relationship between teachers attitude (50, < 0.05) and effective use of digital technologies for special needs students. Furthermore, there was a significant contribution F (F=4.289; R=0.876 and R2 =0.758) in the joint contribution of the independent variable  (teacher’s attitude and teacher’s knowledge) and dependent variable (effective use of digital technologies) while teachers knowledge have the highest contribution(b=7.926, t=4.376), the study therefore revealed that teachers attitude and knowledge are potent factors that predicts the effective usage of digital technologies for the education of special needs student. The study recommended that due to the ever-changing nature of technology which comes with new features, teachers should be equipped with appropriate knowledge in order to effectively make use of them and teachers should also develop right attitude toward the use of digital technologies

Keywords: teachers’ knowledge, teachers’ attitude, digital devices, special needs students

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Authors: Zixuan Zhao, Lingbin Du, Le Wang, Youqing Wang, Yi Yang, Jingjun Chen, Hengjin Dong

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Objectives: Few results from public attitudes for lung cancer screening are available both in China and abroad. This study aimed to identify preferred lung cancer screening modalities in a Chinese population and predict uptake rates of different modalities. Materials and Methods: A discrete choice experiment questionnaire was administered to 392 Chinese individuals aged 50–74 years who were at high risk for lung cancer. Each choice set had two lung screening options and an option to opt-out, and respondents were asked to choose the most preferred one. Both mixed logit analysis and stepwise logistic analysis were conducted to explore whether preferences were related to respondent characteristics and identify which kinds of respondents were more likely to opt out of any screening. Results: On mixed logit analysis, attributes that were predictive of choice at 1% level of statistical significance included the screening interval, screening venue, and out-of-pocket costs. The preferred screening modality seemed to be screening by low-dose computed tomography (LDCT) + blood test once a year in a general hospital at a cost of RMB 50; this could increase the uptake rate by 0.40 compared to the baseline setting. On stepwise logistic regression, those with no endowment insurance were more likely to opt out; those who were older and housewives/househusbands, and those with a health check habit and with commercial endowment insurance were less likely to opt out from a screening programme. Conclusions: There was considerable variance between real risk and self-perceived risk of lung cancer among respondents, and further research is required in this area. Lung cancer screening uptake can be increased by offering various screening modalities, so as to help policymakers further design the screening modality.

Keywords: lung cancer, screening, China., discrete choice experiment

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Authors: Harini Chakkera

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Background: Inpatient hyperglycemia is an established independent risk factor among several patient cohorts with hospital readmission. This has not been studied after kidney transplantation. Nearly one-third of patients who have undergone a kidney transplant reportedly experience 30-day readmission. Methods: Data on first-time solitary kidney transplantations were retrieved between September 2015 to December 2018. Information was linked to the electronic health record to determine a diagnosis of diabetes mellitus and extract glucometeric and insulin therapy data. Univariate logistic regression analysis and the XGBoost algorithm were used to predict 30-day readmission. We report the average performance of the models on the testing set on five bootstrapped partitions of the data to ensure statistical significance. Results: The cohort included 1036 patients who received kidney transplantation, and 224 (22%) experienced 30-day readmission. The machine learning algorithm was able to predict 30-day readmission with an average AUC of 77.3% (95% CI 75.30-79.3%). We observed statistically significant differences in the presence of pretransplant diabetes, inpatient-hyperglycemia, inpatient-hypoglycemia, and minimum and maximum glucose values among those with higher 30-day readmission rates. The XGBoost model identified the index admission length of stay, presence of hyper- and hypoglycemia and recipient and donor BMI values as the most predictive risk factors of 30-day readmission. Additionally, significant variations in the therapeutic management of blood glucose by providers were observed. Conclusions: Suboptimal glucose metrics during hospitalization after kidney transplantation is associated with an increased risk for 30-day hospital readmission. Optimizing the hospital blood glucose management, a modifiable factor, after kidney transplantation may reduce the risk of 30-day readmission.

Keywords: kidney, transplant, diabetes, insulin

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Authors: Said Belaaouad

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This study aimed to explore the quantitative structure-activity relationship (QSAR) of 1,2,4-Triazine-3(2H)-one derivative as a potential anticancer agent against breast cancer. The electronic descriptors were obtained using the Density Functional Theory (DFT) method, and a multiple linear regression techniques was employed to construct the QSAR model. The model exhibited favorable statistical parameters, including R2=0.849, R2adj=0.656, MSE=0.056, R2test=0.710, and Q2cv=0.542, indicating its reliability. Among the descriptors analyzed, absolute electronegativity (χ), total energy (TE), number of hydrogen bond donors (NHD), water solubility (LogS), and shape coefficient (I) were identified as influential factors. Furthermore, leveraging the validated QSAR model, new derivatives of 1,2,4-Triazine-3(2H)-one were designed, and their activity and pharmacokinetic properties were estimated. Subsequently, molecular docking (MD) and molecular dynamics (MD) simulations were employed to assess the binding affinity of the designed molecules. The Tubulin colchicine binding site, which plays a crucial role in cancer treatment, was chosen as the target protein. Through the simulation trajectory spanning 100 ns, the binding affinity was calculated using the MMPBSA script. As a result, fourteen novel Tubulin-colchicine inhibitors with promising pharmacokinetic characteristics were identified. Overall, this study provides valuable insights into the QSAR of 1,2,4-Triazine-3(2H)-one derivative as potential anticancer agent, along with the design of new compounds and their assessment through molecular docking and dynamics simulations targeting the Tubulin-colchicine binding site.

Keywords: QSAR, molecular docking, ADMET, 1, 2, 4-triazin-3(2H)-ones, breast cancer, anticancer, molecular dynamic simulations, MMPBSA calculation

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

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

Keywords: FT-NIR, pasta, moisture determination, food engineering

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Authors: Getnet Gedefaw, Fentanesh Endalew, Bitewush Azmeraw, Bethelhem Walelign, Eyob Shitie

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Introduction: The issue of menstrual hygiene is often overlooked and has not been sufficiently addressed in the fields of reproductive health in low and middle-income countries. Inadequate menstrual hygiene practices can increase the risk of various infectious and chronic obstetric and gynaecological complications for girls and adolescents. Hence, this study seeks to investigate the knowledge, attitudes, and practices related to menstrual hygiene, along with the factors influencing them, among high school students. Methods: A facility based cross-sectional study was conducted involving a total of 423 study subjects. A systematic random sampling technique was utilized. Data was entered and analyzed through Epi data 3.1 and SPSS 22, respectively. Both univariable and multivariable logistic regression models were employed. A p-value of less than 0.05 was considered statistically significant. Results: This study revealed that 365(89.2%), 200(48.9%) and 196(47.9%) of the study participants have good knowledge, good practice, and good attitudes about menstrual hygiene, respectively. Being higher grade students (grade 10) [AOR=3.96, 95% CI =2.0-7.8] and having good practice of menstrual hygiene (AOR=2.52, 95% CI= 1.26-5) had a positive association with menstrual hygiene knowledge. Whereas maternal education level (AOR=1.86, 95% CI=1.18-2.9) and being a grade 10 student (AOR=2.3, 95% CI=1.48-3.56) were associated factors for practising menstrual hygiene. Additionally, being higher grade students (AOR=1.9, 95% CI=1.2-2.8), age ≥18 years (AOR=1.67, 95% CI=1.09-2.55) were statistically and positively associated with the attitude of menstrual hygiene. Conclusion: The study findings indicated that the knowledge of the study participants regarding menstrual hygiene was high, while their attitudes and practices towards menstrual hygiene were low. It is suggested that raising awareness among reproductive health groups and educating their families and parents could potentially lead to a positive change in their poor practices and attitudes towards menstrual hygiene.

Keywords: menstrual hygiene, menstruation, students, reproductive health

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Authors: Colin Schmidt, Adrien Lecossier, Pascal Crubleau, Philippe Blanchard, Simon Richir

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Introduction:The widespread availability of artificial intelligence, exemplified by Generative Pre-trained Transformers (GPT) relying on large language models (LLM), has caused a seismic shift in the realm of knowledge. Everyone now has the capacity to swiftly learn how these models can either serve them well or not. Today, conversational AI like ChatGPT is grounded in neural transformer models, a significant advance in natural language processing facilitated by the emergence of renowned LLMs constructed using neural transformer architecture. Inventiveness of an LLM : OpenAI's GPT-3 stands as a premier LLM, capable of handling a broad spectrum of natural language processing tasks without requiring fine-tuning, reliably producing text that reads as if authored by humans. However, even with an understanding of how LLMs respond to questions asked, there may be lurking behind OpenAI’s seemingly endless responses an inventive model yet to be uncovered. There may be some unforeseen reasoning emerging from the interconnection of neural networks here. Just as a Soviet researcher in the 1940s questioned the existence of Common factors in inventions, enabling an Under standing of how and according to what principles humans create them, it is equally legitimate today to explore whether solutions provided by LLMs to complex problems also share common denominators. Theory of Inventive Problem Solving (TRIZ) : We will revisit some fundamentals of TRIZ and how Genrich ALTSHULLER was inspired by the idea that inventions and innovations are essential means to solve societal problems. It's crucial to note that traditional problem-solving methods often fall short in discovering innovative solutions. The design team is frequently hampered by psychological barriers stemming from confinement within a highly specialized knowledge domain that is difficult to question. We presume ChatGPT Utilizes TRIZ 40. Hence, the objective of this research is to decipher the inventive model of LLMs, particularly that of ChatGPT, through a comparative study. This will enhance the efficiency of sustainable innovation processes and shed light on how the construction of a solution to a complex problem was devised. Description of the Experimental Protocol : To confirm or reject our main hypothesis that is to determine whether ChatGPT uses TRIZ, we will follow a stringent protocol that we will detail, drawing on insights from a panel of two TRIZ experts. Conclusion and Future Directions : In this endeavor, we sought to comprehend how an LLM like GPT addresses complex challenges. Our goal was to analyze the inventive model of responses provided by an LLM, specifically ChatGPT, by comparing it to an existing standard model: TRIZ 40. Of course, problem solving is our main focus in our endeavours.

Keywords: artificial intelligence, Triz, ChatGPT, inventiveness, problem-solving

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Authors: Muftah H. El-Naas, Ameera F. Mohammad, Mabruk I. Suleiman, Mohamed Al Musharfy, Ali H. Al-Marzouqi

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CO₂ capture and storage technologies play a significant role in contributing to the control of climate change through the reduction of carbon dioxide emissions into the atmosphere. The present study evaluates and optimizes CO₂ capture through a process, where carbon dioxide is passed into pH adjusted high salinity water and reacted with sodium chloride to form a precipitate of sodium bicarbonate. This process is based on a modified Solvay process with higher CO₂ capture efficiency, higher sodium removal, and higher pH level without the use of ammonia. The process was tested in a bubble column semi-batch reactor and was optimized using response surface methodology (RSM). CO₂ capture efficiency and sodium removal were optimized in terms of major operating parameters based on four levels and variables in Central Composite Design (CCD). The operating parameters were gas flow rate (0.5–1.5 L/min), reactor temperature (10 to 50 ^oC), buffer concentration (0.2-2.6%) and water salinity (25-197 g NaCl/L). The experimental data were fitted to a second-order polynomial using multiple regression and analyzed using analysis of variance (ANOVA). The optimum values of the selected variables were obtained using response optimizer. The optimum conditions were tested experimentally using desalination reject brine with salinity ranging from 65,000 to 75,000 mg/L. The CO₂ capture efficiency in 180 min was 99% and the maximum sodium removal was 35%. The experimental and predicted values were within 95% confidence interval, which demonstrates that the developed model can successfully predict the capture efficiency and sodium removal using the modified Solvay method.

Keywords: CO2 capture, water desalination, Response Surface Methodology, bubble column reactor

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Authors: Arun K. Yadav, Adam J. Carroll, Gonzalo M. Estavillo, Greg J. Rebetzke, Barry J. Pogson

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Water limits crop productivity, so selecting for minimal yield-gap in drier environments is critical to mitigate against climate change and land-use pressures. To date, no markers measured in glasshouses have been reported to predict field-based drought tolerance. In the field, the best measure of drought tolerance is yield-gap; but this requires multisite trials that are an order of magnitude more resource intensive and can be impacted by weather variation. We investigated the responses of relative water content (RWC), stomatal conductance (gs), chlorophyll content and metabolites in flag leaves of commercial wheat (Triticum aestivum L.) cultivars to three drought treatments in the glasshouse and field environments. We observed strong genetic associations between glasshouse-based RWC, metabolites and Yield gap-based Drought Tolerance (YDT): the ratio of yield in water-limited versus well-watered conditions across 24 field environments spanning sites and seasons. Critically, RWC response to glasshouse drought was strongly associated with both YDT (r2 = 0.85, p < 8E-6) and RWC under field drought (r2 = 0.77, p < 0.05). Multiple regression analyses revealed that 98% of genetic YDT variance was explained by drought responses of four metabolites: serine, asparagine, methionine and lysine (R2 = 0.98; p < 0.01). Fitted coefficients suggested that, for given levels of serine and asparagine, stronger methionine and lysine accumulation was associated with higher YDT. Collectively, our results demonstrate that high-throughput, targeted metabolic phenotyping of glasshouse-grown plants may be an effective tool for the selection of wheat cultivars with high YDT in the field.

Keywords: drought stress, grain yield, metabolomics, stomatal conductance, wheat

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Authors: Fazle Rabbi, Mashuda Khanom Tithi, Tasnim Mirza, Sanjida Rowshan Anannya, Ahmed Hossain

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Background: Low Back Pain (LBP) is one of the common health problems among the garment workers that causes workers absenteeism from the work. The purpose of the study is to identify the association between occupational factors and LBP among the female garment workers in Bangladesh. Materials and Methods: A cross-sectional study was conducted with 487 female garment workers from three compliant garment factories of Bangladesh. Face-to-face interview on four different LBP measures along with questions on socio-demographic, occupational, and physical factors were used to collect the data. Result: The prevalence rates for LBP lasts for at least one day during the last six months, chronic pain, intense pain, and seeking medical care for LBP were found 63.04%, 38.60%, 13.76%, and 18.89%, respectively among the female garments workers. The multivariate logistic regression analysis indicates that duration of employment (>5 years), regular weight bearing and extended weekly working hours (>48 hours) are positively associated with LBP. Besides, age, BMI, family income, marital status and number of children are also found positively associated with the LBP measures. Conclusion: The prevalence of LBP among female garment workers in Bangladesh is found high. The duration of employment (>5 years), regular weight bearing and extended weekly working hours (>48 hours) play a significant role in developing LBP among the female workers. Factories need to consider training programs on the appropriate technique of weight bearing. It is also important to conduct regular screening programs to identify LBP, especially with married, overweight/obese and older age group to reduce the occurrence of LBP.

Keywords: Bangladesh, garment workers, low back pain, occupational health

Procedia PDF Downloads 198

Authors: Ni Wayan Sinthia Widiastuti, Komang Rahayu Indrawati

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The increasing number of tourists coming to Bali, causing accommodation facilities, such as hotels have increased. The existence of hotel needs will be the source of labor and cost efficiency, so that hotel management employs employees with different working status. The hospitality industry is one of the sectors that require organizational citizenship behavior because, the main goal of every hotel, in general, was to provide the best service and quality to tourists. The purpose of this study was to determine the differences in organizational citizenship behavior based on work status of employees at the Hotel in Bali in terms of quality of work life. Research sample was chosen randomly through two-stage cluster sampling which succeeds to obtain 126 samples from 11 hotels in Denpasar, Bali. The subjects consisted of 64 employees with Employment Agreement of Uncertain Time or who is often called a permanent employee and 62 employees with Employment Agreement of Certain Time or better known as contract employees, outsourcing, and daily workers. Instruments in this study were the scale of organizational citizenship behavior and the scale of quality of work life. The results of ANCOVA analysis showed there were differences in organizational citizenship behavior based on employee work status in terms of quality of work life. Differences in organizational citizenship behavior and quality of work life based on work status of employees using comparative test was analysis by independent sample t-test shows there were differences in organizational citizenship behavior and quality of work life between employees with different working status in hotels in Bali. The result of the regression analysis showed the functional relationship between quality of work life and organizational citizenship behavior.

Keywords: hotel in Bali, organizational citizenship behavior, quality of work life, work status of employees

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Authors: Hayriye Anıl, Görkem Kar

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In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

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Authors: Bataineh Abdallah, Alabdallah Ghaith, Alkharabshe Abdalhameed

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Social media is an umbrella term for different types of online communication channels. The most prominent forms can be divided into four categories: Collaborative projects (e.g. Wikipedia, comparison-shopping sites), blogs (e.g. Twitter), content communities (e.g. Youtube), social networking sites (e.g. Facebook) social media allow consumers to share their opinions, criticisms and suggestions in public. Facebook launched in 2004, initially targeted college students and later started including everyone has become the most popular sites amongst the young generation for connecting with friends and relatives and for the communication of ideas. In 2013 Facebook penetration rate reached 41.4% of the population making it the most popular social networking site in Jordan. Accordingly, the purpose of this research is to examine the impact of perceived usefulness, perceived ease of use, perceived trust, perceived enjoyment and subjective norms on users' satisfaction and continuance intention to use Facebook in Jordan. Using a structured questionnaire, the primary data was collected from 584 users who have an active Facebook accounts. Multiple regression analysis was employed to test the research model and hypotheses. The research findings indicate that perceived usefulness, perceived ease of use, perceived trust, perceived enjoyment, and subjective norms have a positive and significant effect on users' satisfaction and continuance intention to use Facebook. The findings also indicated that the strongest predictors, based on beta values, on both users' satisfaction and continuance intention to use Facebook is subjective norms and respectively, perceived enjoyment, perceived usefulness, perceived ease of us, and perceived trust. Research results, recommendations, and future research opportunities are also discussed.

Keywords: perceived usefulness, perceived ease of use, perceived trust, perceived enjoyment, perceived subjective norms, users' satisfaction, continuance intention, Facebook

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Authors: Wondmnew Derebe Yohannis

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The enhanced utilization of modern beehives holds significant potential to enhance the livelihoods of smallholder farmers who heavily rely on mixed crop-livestock farming for their income. Recognizing this, the distribution of improved beehives has been implemented across various regions in Ethiopia, including the Bugina district. However, the precise impact of these improved beehives on farmers' income has received limited attention. To address this gap, this study aims to assess the influence of adopting upgraded beehives on rural households' income and asset accumulation. To conduct this research, survey data was gathered from a sample of 350 households selected through random sampling. The collected data was then analyzed using an econometric stochastic frontier model (ESRM) approach. The findings reveal that the adoption of improved beehives has resulted in higher annual income and asset growth for beekeepers. On average, those who adopted the improved beehives earned approximately 6,077 Ethiopian Birr (ETB) more than their counterparts who did not adopt these beehives. However, it is worth noting that the impact of adoption would have been even greater for non-adopters, as evidenced by the negative transitional heterogeneity effect of 1792 ETB. Furthermore, the analysis indicates that the decision to adopt or not adopt improved beehives was driven by individual self-selection. The adoption of improved beehives also led to an increase in fixed assets for households, establishing it as a viable strategy for poverty reduction. Overall, this study underscores the positive effect of adopting improved beehives on rural households' income and asset holdings, showcasing its potential to uplift smallholder farmers and serve as an alternative mechanism for reducing poverty.

Keywords: impact, adoption, endogenous switching regression, income, improved beehives

Procedia PDF Downloads 55