Search results for: situational variables

Authors: Giulio Mangano, Alberto De Marco, Giovanni Zenezini

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The concept of City Logistics (CL) has emerged to improve the impacts of last mile freight distribution in urban areas. In this paper, a System Dynamics (SD) model exploring the dynamics of the diffusion of a ICT platform for CL management across different populations is proposed. For the development of the model two sources have been used. On the one hand, the major diffusion variables and feedback loops are derived from a literature review of existing diffusion models. On the other hand, the parameters are represented by the value propositions delivered by the platform as a response to some of the users’ needs. To extract the most important value propositions the Business Model Canvas approach has been used. Such approach in fact focuses on understanding how a company can create value for her target customers. These variables and parameters are thus translated into a SD diffusion model with three different populations namely municipalities, logistics service providers, and own account carriers. Results show that, the three populations under analysis fully adopt the platform within the simulation time frame, highlighting a strong demand by different stakeholders for CL projects aiming at carrying out more efficient urban logistics operations.

Keywords: city logistics, simulation, system dynamics, business model

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Authors: Chun-Qing Li, Hassan Baji, Wei Yang

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Corrosion of steel in reinforced concrete structures is a major problem worldwide. The volume expansion of corrosion products causes concrete cover cracking, which could lead to delamination of concrete cover. The time to cover cracking plays a key role to the assessment of serviceability of reinforced concrete structures subjected to corrosion. Many analytical, numerical, and empirical models have been developed to predict the time to cracking initiation due to corrosion. In this study, a numerical model based on finite element modeling of corrosion-induced cracking process is used. In order to predict the service life based on time to cover initiation, the numerical approach is coupled with a probabilistic procedure. In this procedure, all the influential factors affecting time to cover cracking are modeled as random variables. The results show that the time to cover cracking is highly variables. It is also shown that rust product expansion ratio and the size of more porous concrete zone around the rebar are the most influential factors in predicting service life of corrosion-affected structures.

Keywords: corrosion, crack width, probabilistic, service life

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Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

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Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

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Authors: Ine Huybrechts, Anja Declercq, Emily Verté, Peter Raeymaeckers, Sibyl Anthierens

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Goal-oriented care is a concept gaining increased interest as an approach to go towards more coordinated and integrated primary care. It places patients’ personal life goals at the core of health care support, hereby shifting the focus from “what’s the matter with this patient” to “what matters to this patient.” In Flanders/Belgium, various primary care providers, health and social care organizations and governmental bodies have picked up this concept and have initiated actions to facilitate this approach. The implementation of goal-oriented care not only happens on the micro-level, but it also requires efforts on the meso- and macro-level. Within implementation research, there is a growing recognition that the context in which an intervention takes place strongly relates to its implementation outcomes. However, when investigating contextual variables, the external context and its impact on implementation processes is often overlooked. This study aims to explore how we can better identify and understand the external context and how it relates to the mechanism of change within the implementation process of goal-oriented care in Flanders/Belgium. Results can be used to support and guide initiatives to introduce innovative approaches such as goal-oriented care inside an organization or in the broader primary care landscape. We have conducted qualitative research, performing in-depth interviews with n=23 respondents who have affinity with the implementation of goal-oriented care within their professional function. This lead to in-depth insights from a wide range of actors, with meso-level and/or macro-level perspectives on the implementation of goal-oriented care. This means that we have interviewed actors that are not only involved with initiatives to implement goal-oriented care, but also actors that actively give form to the external context in which goal-oriented care is implemented. Data were collected using a semi-structured interview guide, audio recorded, and analyzed first inductively and then deductively using various theories and concepts that derive from organizational research. Our preliminary findings suggest t Our findings can contribute to further define actions needed for sustainable implementation of goal-oriented primary care. It gives insights in the dynamics between contextual variables and implementation efforts, hereby indicating towards those contextual variables that can be further shaped to facilitate the implementation of an innovation such as goal-oriented care. hat organizational theories can help understand the mechanism of change of implementation processes with a macro-level perspective. Institutional theories, contingency theories, resources dependency theories and others can expose the mechanism of change for an innovation such as goal-oriented care. Our findings can contribute to further define actions needed for sustainable implementation of goal-oriented primary care. It gives insights in the dynamics between contextual variables and implementation efforts, hereby indicating towards those contextual variables that can be further shaped to facilitate the implementation of an innovation such as goal-oriented care.

Keywords: goal-oriented care, implementation processes, organizational theories, person-centered care, implementation research

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Authors: Omar A. A. Shaebi, Matouk M. Elamari, Salaheddin Allid

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The Information Computing System of Monitoring (ICSM) for the Research Reactor of Tajoura Nuclear Research Centre (TNRC) stopped working since early 1991. According to the regulations, the computer is necessary to operate the reactor up to its maximum power (10 MW). The fund is secured via IAEA to develop a modern computer based data acquisition system to replace the old computer. This paper presents the development of the Labview based data acquisition system to allow automated measurements using National Instruments Hardware and its labview software. The developed system consists of SCXI 1001 chassis, the chassis house four SCXI 1100 modules each can maintain 32 variables. The chassis is interfaced with the PC using NI PCI-6023 DAQ Card. Labview, developed by National Instruments, is used to run and operate the DAQ System. Labview is graphical programming environment suited for high level design. It allows integrating different signal processing components or subsystems within a graphical framework. The results showed system capabilities in monitoring variables, acquiring and saving data. Plus the capability of the labview to control the DAQ.

Keywords: data acquisition, labview, signal conditioning, national instruments

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Authors: Ameur Abdelkader, Abed Bouarfa Hafida

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Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data.

Keywords: predictive analysis, big data, predictive analysis algorithms, CART algorithm

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Authors: Lei Li, Ping Yin, Haneen Khreis

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Stroke is the second leading cause of death and a third leading cause of death and disability worldwide in 2019. Given the significant role of environmental factors in stroke development and progression, it is essential to investigate the effect of air pollution on stroke occurrence while considering the modifying effects of meteorological variables. This study aimed to evaluate the association between short-term exposure to air pollution and the incidence of stroke subtypes in Shenzhen, China, and to explore the potential interactions of meteorological factors with air pollutants. The study analyzed data from January 1, 2006, to December 31, 2014, including 88,214 cases of ischemic stroke and 30,433 cases of hemorrhagic stroke among residents of Shenzhen. Using a time-stratified case–crossover design with conditional quasi-Poisson regression, the study estimated the percentage changes in stroke morbidity associated with short-term exposure to nitrogen dioxide (NO₂), sulfur dioxide (SO₂), particulate matter less than 10 mm in aerodynamic diameter (PM10), carbon monoxide (CO), and ozone (O₃). A five-day moving average of air pollution was applied to capture the cumulative effects of air pollution. The estimates were further stratified by sex, age, education level, and season. The additive and multiplicative interaction between air pollutants and meteorologic variables were assessed by the relative excess risk due to interaction (RERI) and adding the interactive term into the main model, respectively. The study found that NO₂ was positively associated with ischemic stroke occurrence throughout the year and in the cold season (November through April), with a stronger effect observed among men. Each 10 μg/m³ increment in the five-day moving average of NO₂ was associated with a 2.38% (95% confidence interval was 1.36% to 3.41%) increase in the risk of ischemic stroke over the whole year and a 3.36% (2.04% to 4.69%) increase in the cold season. The harmful effect of CO on ischemic stroke was observed only in the cold season, with each 1 mg/m³ increment in the five-day moving average of CO increasing the risk by 12.34% (3.85% to 21.51%). There was no statistically significant additive interaction between individual air pollutants and temperature or relative humidity, as demonstrated by the RERI. The interaction term in the model showed a multiplicative antagonistic effect between NO₂ and temperature (p-value=0.0268). For hemorrhagic stroke, no evidence of the effects of any individual air pollutants was found in the whole population. However, the RERI indicated a statistically additive and multiplicative interaction of temperature on the effects of PM10 and O₃ on hemorrhagic stroke onset. Therefore, the insignificant conclusion should be interpreted with caution. The study suggests that environmental NO₂ and CO might increase the morbidity of ischemic stroke, particularly during the cold season. These findings could help inform policy decisions aimed at reducing air pollution levels to prevent stroke and other health conditions. Additionally, the study provides valuable insights into the interaction between air pollution and meteorological variables, which underscores the need for further research into the complex relationship between environmental factors and health.

Keywords: air pollution, meteorological variables, interactive effect, seasonal pattern, stroke

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Authors: Lule Basha, Eralda Gjika

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The exchange rate is one of the most important economic variables, especially for a small, open economy such as Albania. Its effect is noticeable in one country's competitiveness, trade and current account, inflation, wages, domestic economic activity, and bank stability. This study investigates the fluctuation of Albania’s exchange rates using monthly average foreign currency, Euro (Eur) to Albanian Lek (ALL) exchange rate with a time span from January 2008 to June 2021, and the macroeconomic factors that have a significant effect on the exchange rate. Initially, the Random Forest Regression algorithm is constructed to understand the impact of economic variables on the behavior of monthly average foreign currencies exchange rates. Then the forecast of macro-economic indicators for 12 months was performed using time series models. The predicted values received are placed in the random forest model in order to obtain the average monthly forecast of the Euro to Albanian Lek (ALL) exchange rate for the period July 2021 to June 2022.

Keywords: exchange rate, random forest, time series, machine learning, prediction

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Authors: Wajid Ali Khan

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Extensive experimentation and numerical investigation are performed to predict the machining-induced residual stresses in the end milling of AISI 1045 steel, and an optimization code has been developed using the particle swarm optimization technique. Experiments were conducted using a single factor at a time and design of experiments approach. Regression analysis was done, and a mathematical model of the cutting process was developed, thus predicting the machining-induced residual stress with reasonable accuracy. The mathematical model served as the objective function to be optimized using particle swarm optimization. The relationship between the different cutting parameters and the output variables, force, and residual stresses has been studied. The combined effect of the process parameters, speed, feed, and depth of cut was examined, and it is understood that 85% of the variation of these variables can be attributed to these machining parameters under research. A 3D finite element model is developed to predict the cutting forces and the machining-induced residual stresses in end milling operation. The results were validated experimentally and against the Johnson-cook model available in the literature.

Keywords: residual stresses, end milling, 1045 steel, optimization

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Authors: Manojkumar Sabanayagam

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The Riemann hypothesis is an unsolved millennium problem and the search for a solution to the Riemann hypothesis is to study the pattern of prime number distribution. The scope of this paper is to identify the solution for the critical strip and the critical line axis, which has the non-trivial zero solutions using complex plane functions. The Riemann graphical plot is constructed using a linear complex variable function (X+iY) and is applicable only when X>1. But the investigation shows that complex variable behavior has two zones. The first zone is the transformation zone, where the definition of the complex plane should be a non-linear variable which is the critical strip zone in the graph (X=0 to 1). The second zone is the transformed zone (X>1) defined using linear variables conventionally. This paper deals with the Non-linear function in the transformation zone derived using cosine and sinusoidal time lag w.r.t imaginary number ‘i’. The alternate complex variable (Cosθ+i Sinθ) is used to understand the variables in the critical strip zone. It is concluded that the non-trivial zeros present in the Real part 0.5 are because the linear function is not the correct approach in the critical strip. This paper provides the solution to Reimann's hypothesis.

Keywords: Reimann hypothesis, critical strip, complex plane, transformation zone

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Authors: Partha Saha, Uttam Kumar Banerjee

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Maternal and Child Healthcare (MCH) has always been regarded as one of the important issues globally. Reduction of maternal and child mortality rates and increase of healthcare service coverage were declared as one of the targets in Millennium Development Goals till 2015 and thereafter as an important component of the Sustainable Development Goals. Over the last decade, worldwide MCH indicators have improved but could not match the expected levels. Progress of both maternal and child mortality rates have been monitored by several researchers. Each of the studies has stated that only less than 26% of low-income and middle income countries (LMICs) were on track to achieve targets as prescribed by MDG4. Average worldwide annual rate of reduction of under-five mortality rate and maternal mortality rate were 2.2% and 1.9% as on 2011 respectively whereas rates should be minimum 4.4% and 5.5% annually to achieve targets. In spite of having proven healthcare interventions for both mothers and children, those could not be scaled up to the required volume due to fragmented health systems, especially in the developing and under-developed countries. In this research, a knowledge discovery based interactive Decision Support System (DSS) has been developed on web platform which would assist healthcare policy makers to develop evidence-based policies. To achieve desirable results in MCH, efficient resource planning is very much required. In maximum LMICs, resources are big constraint. Knowledge, generated through this system, would help healthcare managers to develop strategic resource planning for combatting with issues like huge inequity and less coverage in MCH. This system would help healthcare managers to accomplish following four tasks. Those are a) comprehending region wise conditions of variables related with MCH, b) identifying relationships within variables, c) segmenting regions based on variables status, and d) finding out segment wise key influential variables which have major impact on healthcare indicators. Whole system development process has been divided into three phases. Those were i) identifying contemporary issues related with MCH services and policy making; ii) development of the system; and iii) verification and validation of the system. More than 90 variables under three categories, such as a) educational, social, and economic parameters; b) MCH interventions; and c) health system building blocks have been included into this web-based DSS and five separate modules have been developed under the system. First module has been designed for analysing current healthcare scenario. Second module would help healthcare managers to understand correlations among variables. Third module would reveal frequently-occurring incidents along with different MCH interventions. Fourth module would segment regions based on previously mentioned three categories and in fifth module, segment-wise key influential interventions will be identified. India has been considered as case study area in this research. Data of 601 districts of India has been used for inspecting effectiveness of those developed modules. This system has been developed by importing different statistical and data mining techniques on Web platform. Policy makers would be able to generate different scenarios from the system before drawing any inference, aided by its interactive capability.

Keywords: maternal and child heathcare, decision support systems, data mining techniques, low and middle income countries

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Authors: Apipol Penkitti

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The new normal or a new way of life stemmed from the COVID-19 outbreak, gave rise to a new form of social media: audio-based social platforms (ABSPs), known as Clubhouse, Twitter space, and Facebook live audio room. These platforms, on which audio-based communication is featured, became popular in a short span of time. The objective of the research study is to understand ABSPs users’ behaviors in Thailand. The study, in which functional attitude theory, uses and gratifications theory, and social influence theory are referred to, is conducted through consumer perceived utilitarian, hedonic, and social value that affect engagement. This research study is mixed method paradigm, utilizing Model of Triangulation as its framework. The data acquisition is proceeded through questionnaires from a sample of 384 male, female and LGBTQA+ individuals aged 25 - 34 who, from various occupations, have used audio-based social platform applications. This research study employs the structural equation modeling to analyze the relationships between variables, and it uses the semi - structured interviewing to comprehend the rationality of the variables in the study. The study found that hedonic value directly affects engagement.

Keywords: audio based social platform, engagement, hedonic, perceived value, social, utilitarian

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Authors: Ghada Abo-Zaid

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The main objective of this study was to examine the association between the elevated level of CRP and incidence of hypertension before and after adjusting by age, BMI, gender, SES, smoking, diabetes, cholesterol LDL and cholesterol HDL and to determine whether the association were differ by race. Method: Cross sectional data for participations from age 17 to age 74 years who included in The National Health and Nutrition Examination Survey (NHANES) from 1999 to 2010 were analysed. CRP level was classified into three categories ( > 3mg/L, between 1mg/LL and 3mg/L, and < 3 mg/L). Blood pressure categorization was done using JNC 7 algorithm Hypertension defined as either systolic blood pressure (SBP) of 140 mmHg or more and disystolic blood pressure (DBP) of 90mmHg or greater, otherwise a self-reported prior diagnosis by a physician. Pre-hypertension was defined as (139 > SBP > 120 or 89 > DPB > 80). Multinominal regression model was undertaken to measure the association between CRP level and hypertension. Results: In univariable models, CRP concentrations > 3 mg/L were associated with a 73% greater risk of incident hypertension compared with CRP concentrations < 1 mg/L (Hypertension: odds ratio [OR] = 1.73; 95% confidence interval [CI], 1.50-1.99). Ethnic comparisons showed that American Mexican had the highest risk of incident hypertension (odds ratio [OR] = 2.39; 95% confidence interval [CI], 2.21-2.58).This risk was statistically insignificant, however, either after controlling by other variables (Hypertension: OR = 0.75; 95% CI, 0.52-1.08,), or categorized by race [American Mexican: odds ratio [OR] = 1.58; 95% confidence interval [CI], 0,58-4.26, Other Hispanic: odds ratio [OR] = 0.87; 95% confidence interval [CI], 0.19-4.42, Non-Hispanic white: odds ratio [OR] = 0.90; 95% confidence interval [CI], 0.50-1.59, Non-Hispanic Black: odds ratio [OR] = 0.44; 95% confidence interval [CI], 0.22-0,87]. The same results were found for pre-hypertension, and the Non-Hispanic black showed the highest significant risk for Pre-Hypertension (odds ratio [OR] = 1.60; 95% confidence interval [CI], 1.26-2.03). When CRP concentrations were between 1.0-3.0 mg/L, in an unadjusted models prehypertension was associated with higher likelihood of elevated CRP (OR = 1.37; 95% CI, 1.15-1.62). The same relationship was maintained in Non-Hispanic white, Non-Hispanic black, and other race (Non-Hispanic white: OR = 1.24; 95% CI, 1.03-1.48, Non-Hispanic black: OR = 1.60; 95% CI, 1.27-2.03, other race: OR = 2.50; 95% CI, 1.32-4.74) while the association was insignificant with American Mexican and other Hispanic. In the adjusted model, the relationship between CRP and prehypertension were no longer available. In contrary, Hypertension was not independently associated with elevated CRP, and the results were the same after grouped by race or adjusted by the confounder variables. The same results were obtained when SBP or DBP were on a continuous measure. Conclusions: This study confirmed the existence of an association between hypertension, prehypertension and elevated level of CRP, however this association was no longer available after adjusting by other variables. Ethic group differences were statistically significant at the univariable models, while it disappeared after controlling by other variables.

Keywords: CRP, hypertension, ethnicity, NHANES, blood pressure

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Authors: Olakunle Felix Adekunle

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In the recent years several non-tariff provisions have been regarded as means holding back transboundary environmental damages. Affected countries have then increasingly come up with trade policies to compensate for or to In recent years, several non‐tariff trade provisions have been regarded as means of holding back transboundary environmental damages. Affected countries have then increasingly come up with trade policies to compensate for or to enforce the adoption of environmental policies elsewhere. These non‐tariff trade constraints are claimed to threaten the freedom of trading across nations, as well as the harmonization sought towards the distribution of income and policy measures. Therefore the ‘greening’ of world trade issues essentially ranges over whether there ought or ought not to be a trade‐off between trade and environmental policies. The impacts of free trade and environmental policies on major economic variables (such as trade flows, balances of trade, resource allocation, output, consumption and welfare) are thus studied here, and so is the EKC hypothesis, when such variables are played against the resulting emission levels. The policy response is seen as a political game, played here by two representative parties named North and South. Whether their policy choices, simulated by four scenarios, are right or wrong depends on their policy goals, split into economic and environmental ones.

Keywords: environmental, policies, strategies, constraint

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Authors: Jonardan Koner

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The study attempts to find out the impact of infrastructural investment on state economic growth in India. It further tries to determine the magnitude of the impact of infrastructural investment on economic indicator, i.e., per-capita income (PCI) in Indian States. The study uses panel regression technique to measure the impact of infrastructural investment on per-capita income (PCI) in Indian States. Panel regression technique helps incorporate both the cross-section and time-series aspects of the dataset. In order to analyze the difference in impact of the explanatory variables on the explained variables across states, the study uses Fixed Effect Panel Regression Model. The conclusions of the study are that infrastructural investment has a desirable impact on economic development and that the impact is different for different states in India. We analyze time series data (annual frequency) ranging from 1991 to 2010. The study reveals that the infrastructural investment significantly explains the variation of economic indicators.

Keywords: infrastructural investment, multiple regression, panel regression techniques, economic development, fixed effect dummy variable model

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Authors: Eva Šmelová, Alena Berčíková

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The issue of school maturity and readiness of a child for a successful start of compulsory education is one of the long-term monitored areas, especially in the context of education and psychology. In the context of the curricular reform in the Czech Republic, the issue has recently gained importance. Analyses of research in this area suggest a lack of a broader overview of indicators informing about the current level of children’s school maturity and school readiness. Instead, various studies address partial issues. Between 2009 and 2013 a research study was performed at the Faculty of Education, Palacký University Olomouc (Czech Republic) focusing on children’s maturity and readiness for compulsory education. In this study, social skills were of marginal interest; the main focus was on the mental area. This previous research is smoothly linked with the present study, the objective of which is to identify the level of school maturity and school readiness in selected characteristics of social skills as part of the adaptation process after enrolment in compulsory education. In this context, the following research question has been formulated: During the process of adaptation to the school environment, which social skills are weakened? The method applied was observation, for the purposes of which the authors developed a research tool – record sheet with 11 items – social skills that a child should have by the end of preschool education. The items were assessed by first-grade teachers at the beginning of the school year. The degree of achievement and intensity of the skills were assessed for each child using an assessment scale. In the research, the authors monitored a total of three independent variables (gender, postponement of school attendance, participation in inclusive education). The effect of these independent variables was monitored using 11 dependent variables. These variables are represented by the results achieved in selected social skills. Statistical data processing was assisted by the Computer Centre of Palacký University Olomouc. Statistical calculations were performed using SPSS v. 12.0 for Windows and STATISTICA: StatSoft STATISTICA CR, Cz (software system for data analysis). The research sample comprised 115 children. In their paper, the authors present the results of the research and at the same time point to possible areas of further investigation. They also highlight possible risks associated with weakened social skills.

Keywords: compulsory education, curricular reform, educational diagnostics, pupil, school curriculum, school maturity, school readiness, social skills

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Authors: Rizwan Fazal

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This study investigates the behavior of external sector of Pakistan economy and its impact on economic growth, using quarterly data for the period 1990:01-2010:04. External sector indices used in this study are financial integration, net foreign assets and trade integration. Augmented Ducky fuller confirms that all variables of external sector are non-stationary at level, but at first difference it becomes stationary. The co-integration test suggests one co-integrating variables in the study. The analysis is based on Vector Auto Regression model followed by Vector Error Correction Model. The empirical findings show that financial integration play important role in increasing economic growth in Pakistan economy while trade integration has negative effect on economic growth of Pakistan in the long run. However, the short run confirms that output lag accounts for error correction. The estimated CUSUM and CUSUMQ stability test provide information that the period of the study equation remains stable.

Keywords: financial integration, trade integration, net foreign assets, gross domestic product

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Authors: Maneesha Pahlani, Najmin Sultana

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A hospital is one of the most suitable places for acquiring an infection because it harbors a high population of virulent strains of microorganisms that may be resistant to antibiotics, especially the prevalence of Methicillin-Resistant Staphylococcus Aureus (MRSA) infections. The hospital-acquired infection has become a global challenge. In developed countries, healthcare-associated infections occur in 5-15% of hospitalized clients, affecting 9-37% of those admitted to intensive care units (ICU). A non-experimental descriptive study was conducted among 50 nursing officers working in a selected hospital in Bangalore to assess the nursing officers’ level of knowledge regarding the prevention and management of MRSA infections and to associate the pre-test knowledge mean scores of nursing officers with selected socio-demographic variables. Data was collected using a structured questionnaire consisting of socio-demographic data and a structured questionnaire on knowledge regarding the prevention and management of MRSA infections. The data was analyzed in terms of frequencies and percentages for the analysis of demographic variables and computing chi-square to determine the association between knowledge means scores and selected demographic variables. The study findings revealed that the nursing officer had an overall good level of knowledge (63.05%) regarding the prevention and management of MRSA infections, and there is no significant association found between the level of knowledge mean scores for prevention and management of MRSA infection with the selected socio-demographic variables. However, the categorization of knowledge items showed that the nursing officer must thoroughly receive education on correct guidance and information regarding MRSA infection control policy, including measures and practices on hygiene precautions and information regarding antibiotic resistance for effective nursing care to patients with MRSA infections. The conclusions drawn from the study findings showed that it is necessary that the nursing officer thoroughly receive education on correct guidance and information regarding MRSA infection control policy, including measures and practices on hygiene precautions and information regarding antibiotic resistance to provide effective nursing care to patients with MRSA infection as they constantly care for the patient who can be at risk for multi-drug resistance organisms to reduce the risk of MRSA infection in hospital care settings as well community settings.

Keywords: MRSA, nursing officers, knowledge, preventive and management

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Authors: Wu Hu

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There is an increasing amount of research bridging the gap between transportation and subjective well-being (SWB). However, travel SWB research in this area is still sporadic. Therefore, we are in need of a more systematic body of work that examines travel SWB considering various work occupations, working conditions, commuting variabilities, and other related variables, and develops updated qualitative and quantitative methods to inform the transportation design. In this Super Mario Guide, the author reflects on the related elements involved with travel SWB under four categories (having Super Mario as the protagonist): 1. the starting point including variables like living conditions; 2. the commuter including the commuter’s age, gender, occupation, and others; 3. the commuting including commuting environment, vehicles, commuting time, commuting vehicles flexibility and variability and others; 4. destination including the workplace conditions, the corporate culture on working flexibility, the employer supportiveness and others. In addition, with the rise of new vehicles such as auto-driving, this research can play a significant role to better understand travel SWB and to guide the design of more efficient travelling systems so as to improve worker performance and general SWB. The author also shares thoughts on promising areas for future research.

Keywords: transportation, subjective well-being (SWB), commuting, happiness

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Authors: Najmin Sultana, Maneesha Pahlani

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A hospital is one of the most suitable places for acquiring an infection because it harbors a high population of virulent strains of microorganisms that may be resistant to antibiotics, especially the prevalence of Methicillin-Resistant Staphylococcus Aureus (MRSA) infections. The hospital-acquired infection has become a global challenge. In developed countries, healthcare-associated infections occur in 5-15% of hospitalized clients, affecting 9-37% of those admitted to intensive care units (ICU). A non-experimental descriptive study was conducted among 50 nursing officers working in a selected hospital in bengaluru to assess the nursing officers’ level of knowledge regarding the prevention and management of MRSA infections and to associate the pre-test knowledge mean scores of nursing officers with selected socio-demographic variables. Data was collected using a structured questionnaire consisting of socio-demographic data and a structured questionnaire on knowledge regarding the prevention and management of MRSA infections. The data was analyzed in terms of frequencies and percentages for the analysis of demographic variables and computing chi-square to determine the association between knowledge means scores and selected demographic variables. The study findings revealed that the nursing officer had an overall good level of knowledge (63.05%) regarding the prevention and management of MRSA infections, and there is no significant association found between the level of knowledge mean scores for prevention and management of MRSA infection with the selected socio-demographic variables. However, the categorization of knowledge items showed that the nursing officer must thoroughly receive education on correct guidance and information regarding MRSA infection control policy, including measures and practices on hygiene precautions and information regarding antibiotic resistance for effective nursing care to patients with MRSA infections. The conclusions drawn from the study findings showed that it is necessary that the nursing officer thoroughly receive education on correct guidance and information regarding MRSA infection control policy, including measures and practices on hygiene precautions and information regarding antibiotic resistance to provide effective nursing care to patients with MRSA infection as they constantly care for the patient who can be at risk for multi-drug resistance organisms to reduce the risk of MRSA infection in hospital care settings as well community settings.

Keywords: MRSA, knowledge, nursing officers', prevention and management

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Authors: Cüneyit Akar, Mustafa Başaran, Ufuk Uluçınar

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The purpose of this research is to examine the critical thinking dispositions of pre-service teachers in terms of several variables. In the line of this aim, we have investigated what their levels of critical thinking dispositions and whether there is any significant different in their critical thinking dispositions. Also, we have examined the relations between their critical thinking dispositions and their parents’ education statues, the number of their siblings, family income levels, and their religiosity level. 202 pre-service teachers who are studying at different departments at faculty of education at Uşak University participated in this research. In study, critical thinking dispositions scale by one of researchers was utilized and its validity and reliability was performed. The findings indicate that the level of their critical thinking dispositions was found to be .376 (arithmetic mean). On the other hand, we found that there is no significant difference in terms of their gender and the department at which they are studying. Furthermore, although there aren’t significant relationships between critical thinking dispositions and their mother education statues, their income levels, their religiosity levels and the number of their siblings; there is any significant positively at low level the relation between thinking dispositions and father educational statues. The findings obtained will be discussed together with literature and other research’ results.

Keywords: preservice teachers, critical thinking dispositions, pedagogy, education

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Authors: Mahshid Hatamzad, Geanette Polanco

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Improving the performance of Winter Road Maintenance (WRM) can increase the traffic safety and reduce the cost as well as environmental impacts. This study evaluates the efficiency of WRM technique, named salting, in the Arctic area by using Data Envelopment Analysis (DEA), which is a non-parametric linear method to measure the efficiencies of decision-making units (DMUs) based on handling multiple inputs and multiple outputs at the same time that their associated weights are not known. Here, roads are considered as DMUs for which the efficiency must be determined. The three input variables considered are traffic flow, road area and WRM cost. In addition, the two output variables included are level of safety in the roads and environment impacts resulted from WRM, which is also considered as an uncontrollable factor in the second scenario. The results show the performance of DMUs from the most efficient WRM to the inefficient/least efficient one and this information provides decision makers with technical support and the required suggested improvements for inefficient WRM, in order to achieve a cost-effective WRM and a safe road transportation during wintertime in the Arctic areas.

Keywords: environmental impacts, DEA, risk and safety, WRM

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Authors: Jia Guo, Qinyuan Huang, Qinyi Zhong, Yanjing Zeng, Yimeng Li, James Wiley, Kin Cheung, Jyu-Lin Chen

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Context: Mothers with abdominal obesity, particularly in China, face challenges in managing their health due to family responsibilities. Existing diabetes prevention programs do not cater specifically to this demographic. Research Aim: To assess the feasibility, acceptability, and efficacy of an mHealth-based diabetes prevention program tailored for Chinese mothers with abdominal obesity in reducing weight-related variables and diabetes risk. Methodology: A randomized controlled trial was conducted in Changsha, China, where the mHealth group received personalized modules and health messages, while the control group received general health education. Data were collected at baseline, 3 months, and 6 months. Findings: The mHealth intervention significantly improved waist circumference, modifiable diabetes risk scores, daily steps, self-efficacy for physical activity, social support for physical activity, and physical health satisfaction compared to the control group. However, no differences were found in BMI and certain other variables. Theoretical Importance: The study demonstrates the feasibility and efficacy of a tailored mHealth intervention for Chinese mothers with abdominal obesity, emphasizing the potential for such programs to improve health outcomes in this population. Data Collection: Data on various variables including weight-related measures, diabetes risk scores, behavioral and psychological factors were collected at baseline, 3 months, and 6 months from participants in the mHealth and control groups. Analysis Procedures: Generalized estimating equations were used to analyze the data collected from the mHealth and control groups at different time points during the study period. Question Addressed: The study addressed the effectiveness of an mHealth-based diabetes prevention program tailored for Chinese mothers with abdominal obesity in improving various health outcomes compared to traditional general health education approaches. Conclusion: The tailored mHealth intervention proved to be feasible and effective in improving weight-related variables, physical activity, and physical health satisfaction among Chinese mothers with abdominal obesity, highlighting its potential for delivering diabetes prevention programs to this population.

Keywords: type 2 diabetes, mHealth, obesity, prevention, mothers

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Authors: Chia-Chen Chen, Mu-Yen Chen

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In recent years, with the vigorous growth of hardware and software technologies of smart mobile devices coupling with the rapid increase of social network influence, mobile commerce also presents the commercial operation mode of the future mainstream. For the time being, SoLoMo has become one of the very popular commercial models, its full name and meaning mainly refer to that users can obtain three key service types through smart mobile devices (Mobile) and omnipresent network services, and then link to the social (Social) web site platform to obtain the information exchange, again collocating with position and situational awareness technology to get the service suitable for the location (Local), through anytime, anywhere and any personal use of different mobile devices to provide the service concept of seamless integration style, and more deriving infinite opportunities of the future. The study tries to explore the use intention of users with SoLoMo mobile application formula, proposing research model to integrate TAM, ISSM, IDT and network externality, and with questionnaires to collect data and analyze results to verify the hypothesis, results show that perceived ease-of-use (PEOU), perceived usefulness (PU), and network externality have significant impact on the use intention with SoLoMo mobile application formula, and the information quality, relative advantages and observability have impacts on the perceived usefulness, and further affecting the use intention.

Keywords: SoLoMo (social, local, and mobile), technology acceptance model, innovation diffusion theory, network externality

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Authors: Nadine Sammy, Mark Wilson, Samuel Vine

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The Theory of Challenge and Threat States in Athletes (TCTSA) states that self-efficacy is an antecedent of challenge and threat. These states result from conscious and unconscious evaluations of situational demands and personal resources and are represented by both cognitive and physiological markers. Challenge is considered a more adaptive stress response as it is associated with a more efficient cardiovascular profile, as well as better performance and attention effects compared with threat. Self-efficacy is proposed to influence challenge/threat because an individual’s belief that they have the skills necessary to execute the courses of action required to succeed contributes to a perception that they can cope with the demands of the situation. This study experimentally examined the effects of self-efficacy on cardiovascular responses (challenge and threat), demand and resource evaluations, performance and attention under pressurised conditions. Forty-five university students were randomly assigned to either a control (n=15), low self-efficacy (n=15) or high self-efficacy (n=15) group and completed baseline and pressurised golf putting tasks. Self-efficacy was manipulated using false feedback adapted from previous studies. Measures of self-efficacy, cardiovascular reactivity, demand and resource evaluations, task performance and attention were recorded. The high self-efficacy group displayed more favourable cardiovascular reactivity, indicative of a challenge state, compared with the low self-efficacy group. The former group also reported high resource evaluations, but no task performance or attention effects were detected. These findings demonstrate that levels of self-efficacy influence cardiovascular reactivity and perceptions of resources under pressurised conditions.

Keywords: cardiovascular, challenge, performance, threat

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Authors: Hamid Goharnejad, Amir Hossein Eghbali

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Recent investigations have demonstrated the global sea level rise due to climate change impacts. In this study climate changes study the effects of increasing water level in the strait of Hormuz. The probable changes of sea level rise should be investigated to employ the adaption strategies. The climatic output data of a GCM (General Circulation Model) named CGCM3 under climate change scenario of A1b and A2 were used. Among different variables simulated by this model, those of maximum correlation with sea level changes in the study region and least redundancy among themselves were selected for sea level rise prediction by using stepwise regression. One models of Discrete Wavelet artificial Neural Network (DWNN) was developed to explore the relationship between climatic variables and sea level changes. In these models, wavelet was used to disaggregate the time series of input and output data into different components and then ANN was used to relate the disaggregated components of predictors and predictands to each other. The results showed in the Shahid Rajae Station for scenario A1B sea level rise is among 64 to 75 cm and for the A2 Scenario sea level rise is among 90 to 105 cm. Furthermore the result showed a significant increase of sea level at the study region under climate change impacts, which should be incorporated in coastal areas management.

Keywords: climate change scenarios, sea-level rise, strait of Hormuz, forecasting

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Authors: Noah Erskine, Jaime Barratt, John Cairney

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The current study aims to provide an in-depth analysis and extension of the Environmental Stress Hypothesis (ESH) framework, focusing on the complex interplay between poor motor skills and internalising problems like anxiety and depression. Using an integrative research review methodology, this study synthesizes findings from 38 articles, both empirical and theoretical, building upon the foundational work of the model. The hypothesis posits that poor motor skills serve as a primary stressor, leading to internalising problems through various secondary stressors. A rigorous comparison of data was conducted, considering study design, findings, and methodologies - while giving special attention to variables such as age, sex, and comorbidities. The study also enhances the ESH framework by introducing resource buffers, including optimism and familial support, as additional influencing factors. This multi-level approach yields a more nuanced and comprehensive ESH framework, highlighting the need for future studies to consider intersectional variables and how they may vary across various life stages.

Keywords: motor coordination, mental health, developmental coordination disorders, paediatric comorbidities, obesity, peer problems

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Authors: Alireza Vafaei Sadr, Vida Abedi, Jiang Li, Ramin Zand

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Missing data is a common challenge in medical research and can lead to biased or incomplete results. When the data bias leaks into models, it further exacerbates health disparities; biased algorithms can lead to misclassification and reduced resource allocation and monitoring as part of prevention strategies for certain minorities and vulnerable segments of patient populations, which in turn further reduce data footprint from the same population – thus, a vicious cycle. This study compares the performance of six imputation techniques grouped into Linear and Non-Linear models on two different realworld electronic health records (EHRs) datasets, representing 17864 patient records. The mean absolute percentage error (MAPE) and root mean squared error (RMSE) are used as performance metrics, and the results show that the Linear models outperformed the Non-Linear models in terms of both metrics. These results suggest that sometimes Linear models might be an optimal choice for imputation in laboratory variables in terms of imputation efficiency and uncertainty of predicted values.

Keywords: EHR, machine learning, imputation, laboratory variables, algorithmic bias

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Authors: Ali Almadan, Anoop Krishnan, Ajita Rattani

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Facial recognition is a widely adopted technology in surveillance, border control, healthcare, banking services, and lately, in mobile user authentication with Apple introducing “Face ID” moniker with iPhone X. A lot of research has been conducted in the area of face recognition on datasets captured by surveillance cameras, DSLR, and mobile devices. Recently, face recognition technology has also been deployed on body-worn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic so far, without the availability of any publicly available datasets with a sufficient sample size. This paper aims to advance research in the area of face recognition using body-worn cameras. To this aim, the contribution of this work is two-fold: (1) collection of a dataset consisting of a total of 136,939 facial images of 102 subjects captured using body-worn cameras in in-door and daylight conditions and (2) evaluation of various deep-learning architectures for face identification on the collected dataset. Experimental results suggest a maximum True Positive Rate(TPR) of 99.86% at False Positive Rate(FPR) of 0.000 obtained by SphereFace based deep learning architecture in daylight condition. The collected dataset and the baseline algorithms will promote further research and development. A downloadable link of the dataset and the algorithms is available by contacting the authors.

Keywords: face recognition, body-worn cameras, deep learning, person identification

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Authors: Kathrin Endres, Daniela Greven

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Although chatbots are a widely known and established communication instrument in B2C customer services, B2B industries still hesitate to implement chatbots due to the incertitude of customer acceptance. While many studies examine the chatbot acceptance of B2C consumers, few studies are focusing on the B2B sector, where the customer is represented by a buying center consisting of several stakeholders. This study investigates the challenges of chatbot acceptance in B2B industries compared to challenges of chatbot acceptance from current B2C literature by interviewing experts from German chatbot vendors. The results show many similarities between the customer requirements of B2B customers and B2C consumers. Still, due to several stakeholders involved in the buying center, the features of the chatbot users are more diverse but obfuscated at the same time. Using a factorial survey, this study further examines the customer acceptance of varying situations of B2B chatbot designs based on the chatbot variables transparency, fault tolerance, complexity of products, value of products, as well as transfer to live chat service employees. The findings show that all variables influence the propensity to use the chatbot. The results contribute to a better understanding of how firms in B2B industries can design chatbots to advance their customer service and enhance customer satisfaction.

Keywords: chatbots, technology acceptance, B2B customer service, customer satisfaction

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