Miracle-dz/UniFAR-FTUs huggingface dataset:
EXPLORING ASYMMETRIC NEXUS BETWEEN TOURISM, ECONOMIC GROWTH AND CO2 EMISSIONS IN THE CONTEXT OF PAKISTAN

This study aims to examine the nexus between tourism, economic growth, and CO 2 emissions in Pakistan. We examined the asymmetric relationship between tourism, economic growth, and CO 2 emissions for the period 1991 to 2019. We applied NARDL technique, and Granger Causality to predict results. Moreover, we also employed ADF, PP unit root test, Zivot and Andrews test for structural breaks. The nonlinear autoregressive distributed lag is the most appropriate econometric estimator in the case if asymmetric association exists among the variables. The NARDL technique is capable to discover the dynamic association between economic growth, tourism, and CO 2 emissions. Our results confirmed the asymmetric nexus between tourism, GDP and CO 2 emissions in the context of Pakistan. The results reported that in the short run as well as long run positive and negative shocks of tourism and GDP affect CO 2 emissions. The outcomes also revealed unidirectional causality between tourism and CO 2 emission, while bi-directional causality between economic growth and CO 2 emissions. Likewise, a bi-directional causality has also been reported between tourism and economic growth. The results are very useful for the macro level policy makers in Pakistan, and hopefully will provide significant insight to the relevant stakeholders.
A Barotropic Solver for High-Resolution Ocean General Circulation Models

High-resolution global ocean general circulation models (OGCMs) play a key role in accurate ocean forecasting. However, the models of the operational forecasting systems are still not in high resolution due to the subsequent high demand for large computation, as well as the low parallel efficiency barrier. Good scalability is an important index of parallel efficiency and is still a challenge for OGCMs. We found that the communication cost in a barotropic solver, namely, the preconditioned conjugate gradient (PCG) method, is the key bottleneck for scalability due to the high frequency of the global reductions. In this work, we developed a new algorithm—a communication-avoiding Krylov subspace method with a PCG (CA-PCG)—to improve scalability and then applied it to the Nucleus for European Modelling of the Ocean (NEMO) as an example. For PCG, inner product operations with global communication were needed in every iteration, while for CA-PCG, inner product operations were only needed every eight iterations. Therefore, the global communication cost decreased from more than 94.5% of the total execution time with PCG to less than 63.4% with CA-PCG. As a result, the execution time of the barotropic modes decreased from more than 17,000 s with PCG to less than 6000 s with CA-PCG, and the total execution time decreased from more than 18,000 s with PCG to less than 6200 s with CA-PCG. Besides, the ratio of the speedup can also be increased from 3.7 to 4.6. In summary, the high process count scalability when using CA-PCG was effectively improved from that using the PCG method, providing a highly effective solution for accurate ocean simulation.
A Review of the Causes of Sustainable Supply Deficiencies in Natural Gas Supply in Ghana

Although Ghana produces natural gas and has several gas reserves, the country still depends on supplies from neighbouring countries for power generation due to challenges like fuel supply inefficiencies and increasing cost of gas production. Gas supply is thus a limiting factor for power generation. This study explores strategies to improve gas supplies for sustainable power generation in Ghana. The study examined the underlying factors inhibiting gas supplies in Ghana, investigated the risks in the gas supply industry, and explored strategies for improving gas supplies in Ghana. The study adopted an inductive approach that utilises both qualitative and quantitative research methods. With the use of questionnaires, data were collected from 151 respondents who were selected by purposive and snowball sampling procedures. The collected data were descriptively and inferentially analysed with analytical tools in the SPSS. The study’s findings conclude that factors that inhibit gas supply in Ghana include the lack of growth and supply investment in the gas supply industry, power sector debt on the gas market, and the lack of an extensive distribution infrastructure/network. Also, the gas industry in Ghana lacks the requisite investment drive which culminates into inefficiency and ultimately undermines efforts to achieve sustainable power generation. Politics influence in issues relating to gas supplies in Ghana inhibits long-term supply relationships and permanent infrastructure in gas supply, both locally and on the world market.
Effects of graphene on thermal properties and thermal stability of polycarbonate/graphene nanocomposite

Polycarbonate (PC) /graphene nanocomposite was prepared using multilayer graphene (MLG) with loadings of 0.5, 1, and 3 wt% via melt mixing process. Morphological, structural, and thermal properties of the PC/MLG nanocomposites are investigated to look into the influence of MLG on the nanocomposite. A significant increase (∼6.4°C) in glass transition temperature is observed upon the addition of 3 wt% of MLG into the polycarbonate matrix. This increase in glass transition temperature may be due to the interaction between the MLG and polycarbonate polymer matrix. The specific heat capacity of pure PC and PC/MLG nanocomposites varies linearly with temperature below their glass transition. Upon the addition of MLGs, the overall thermal stability of PC/MLG nanocomposites increases with MLG loadings. A maximum increase about 29.23°C in Tonset of thermal decomposition is observed in PC/MLG nanocomposite having 3 wt% of MLG loading. The activation energy (Ea) of thermal decomposition is also calculated by kinetic analysis of thermal decomposition of the PC/MLG nanocomposites using Horowitz–Metzger and Broido’s methods.
Insecticide resistance outpaces behavioural adaptation, as a response to Long-Lasting Insecticidal Net distribution, in malaria vectors in Burkina Faso

The decline in malaria across Africa has been largely attributed to vector control using Long-Lasting Insecticidal Nets (LLINs). However, this intervention has prompted widespread insecticide resistance (IR) and been associated with changes in mosquito behaviour that reduce their contact with LLINs. The relative importance and rate at which IR and behavioural adaptations emerge are poorly understood. We conducted surveillance of mosquito behaviour and IR at 12 sites in Burkina Faso to assess the magnitude and temporal dynamics of insecticide and behavioural resistance in vectors in the 2-year following mass LLIN distribution. Insecticide resistance was present in all vector populations and increased rapidly over the study. In contrast, no longitudinal shifts in LLIN-avoidance behaviours (earlier or outdoor biting and resting) were detected. There was a moderate shift in vector species composition from Anopheles coluzzii to Anopheles gambiae which coincided with a reduction in the proportion of bites preventable by LLINs; possibly driven by between-species variation in behaviour. These findings indicate that adaptations based on insecticide resistance arise and intensify more rapidly than behavioural shifts within mosquito vectors. However, longitudinal shifts in mosquito vector species composition were evident within 2 years following a mass LLIN distribution. This ecological shift was characterized by a relative increase in the moderately more exophagic species (An. gambiae) and coincided with a predicted decline in the degree of protection expected from LLINs. Although human exposure fell through the study period due to reducing vector densities and infection rates, such ecological shifts in vector species along with insecticide resistance were likely to have eroded the efficacy of LLINs. While both adaptations impact malaria control, the rapid increase of the former indicates it is the most rapid strategy but interventions targeting both will be needed.
Critical Success Factors of Knowledge on Sustainability in Malaysian Higher Education.

This study aims to investigate Malaysia’s Institutes of Teacher Education (ITE) students’ knowledge on sustainability toward environmentallysustainable behaviour (ESB). The model adapted is based on Hines, Hungerford and Tomera’s Environmental Responsible Behaviour Model which was established since 1987.The nature of this study was descriptive, and the data were collected using cross-sectional survey. A total of 425 participants responded to a questionnaire developed for this study. Several procedure was carried out in order to assess the validity and reliability of the questionnaire using PLS-SEM.The study revealed that the Malaysian Higher Education students showed a high level of knowledge of sustainability action skills, knowledge of sustainability strategies and knowledge of sustainability issues toward ESB and the environment. Findings associated with students’ gender, age, ethnicity, field of study and academic program were also reported and discussed.First, the participating students came from Institutions of Teacher Education. Although this institution is a major teacher training centre in the nation, further studies should include samples from other public and private institutions. Second, this study did not examine the correlation among the three main components: knowledge of sustainability action skills, knowledge of sustainability strategies and knowledge of sustainability issues.Limited studies have been conducted in Malaysia on ITE although the institutionsare supporting ESD through the Sustainable ITE program. Therefore, this study is exploring the critical success factors of knowledge on sustainability action skills, knowledge on sustainability strategies and knowledge on sustainability issues on environmentally sustainable behaviour ofteacher candidate students in Malaysia. In addition, past studies have recommended to investigate and look into the environmentally sustainablebehaviour among the younger generations, especially those with higher education levels, especially because inevitably, they are agents of change, and also they will be affected by the environmental issues that are caused by the current human activities. This paper also providesvaluable insights that foster a much better understanding in regards to the role of Institutions of Teacher Education (ITE) in assisting sustainable development, addressingthe important key issues to be considered in education system.
THE RELATIONSHIP BETWEEN RENEWABLE ENERGY PRODUCTION AND EMPLOYMENT IN EUROPEAN UNION COUNTRIES: PANEL DATA ANALYSIS

Renewable energy, which is the type of energy that plays a major role in the development and growth of countries, plays an important role in today’s world, considering that the life of depleted energy resources is finite. Compared to fossil energy sources, except that its source is infinite, a noticeable difference in carbon dioxide emission is seen as the reason for preferring renewable energy sources. Many studies in the literature investigate the relationship between the consumption of renewable energy resources and economic growth. This study was conducted on renewable energy production in 27 European Union member countries explores its impact on employment. In the study, panel ARDL test was conducted with the data for the years 2006-2019. According to the results of the study, renewable energy generation has a positive effect on employment in European Union countries in the long term. In the long run, a 1% increase in renewable energy primary production increases employment by 0.08%. Increasing the use of renewable energy resources should go beyond being a policy recommendation in international conventions. The fact that these resources provide employment in large areas can be a good alternative today with high unemployment rates.
Mathematics as a gendered subject: a deeper insight into students’ attitudes in Irish post-primary schools

This study examines gender differences in students’ attitudes to mathematics in Irish post-primary schools. Large-scale international assessments have shown the achievement gap between boys and girls to be narrowing in recent years, particularly in lower post-primary education. However, women and girls remain significantly under-represented in advanced level mathematics courses and STEM careers. The current research aims to contextualise this state of inequity, providing a deeper understanding of gendered attitudes to mathematics in Ireland today. To this end, Irish data from the 2015 Trends in International Mathematics and Science Study is further analysed, focusing specifically on the student background questionnaire. Various aspects of students’ attitudes to mathematics are investigated, including their interest in mathematics, views on mathematics class, confidence and mathematical self-concept, and perceived importance of mathematics as a subject. The findings show consistent and significant attitudinal gender differences. These differences were generally strongest regarding confidence and self-concept, and attitudes to mathematical problem-solving. Differences regarding attitudes to mathematics lessons were small to negligible.
Improved Adherence to Positive Airway Pressure Treatment after Covering National Health Insurance in Patient with Obstructive Sleep Apnea: A Tertiary Sleep Center Review

Hyo-Eun Yoon, Chang-Jin Jeon, Jaechun Hwang, Ho-Won Lee, Ji-Ye Jeon Graduate School of Life Science, Kyungpook National University, Daegu, Department of Biology, School of Life Sciences, College of Natural Sciences, and Brain Science and Engineering Institute, Kyungpook National University, Daegu, Department of Neurology, Kyungpook National University School of Medicine, Kyungpook National University Chilgok Hospital, Daegu, Brain Science and Engineering Institute, Kyungpook National University, Daegu, Korea
The robot consciousness based on empirical knowledge

How to make robot conscious is an important goal of artificial intelligence. Despite the emergence of some very creative ideas, no convincing way to realize consciousness on a machine has been proposed so far. For example, the integrated information theory of consciousness proposes that consciousness can exist in any place that can reasonably process information, whether brain or machine. It points out that a physical system must satisfy two basic conditions to cause the emergence of consciousness: it must have rich information, and it must be highly integrated. However, the theory does not give an idea of how to realize consciousness on a machine. In this paper, we propose the robot consciousness based on empirical knowledge. We believe that the empirical knowledge of robots is an important basis for robot consciousness, any cognitive experience of robot can lead to the generation of consciousness. We firstly propose a formal framework for describing robot empirical knowledge; then we discuss the robot consciousness based on its own empirical knowledge; finally, we propose a cost-oriented evolutionary method of robot consciousness.
The Mediating Role of Coaching Behavior in The Effects of Intrinsic Motivation on Work Addiction Among Nurses

Backround: Nursing holds a critical place in the health care system. The work performance of nurses has an impact on patient safety and the quality of clinical care. To improve work performance, nurses should be assisted by their senior managers and leaders who use coaching behavior as a strategy tool in the management to guide employees to realize their full potential and maximize it. Aim: This study aims to investigate whether coaching behavior has a mediating role in the effects of intrinsic motivation on work addiction among nurses. Design: This research study utilized a cross sectionl survey design. Methods: A total of 269 nurses who were working at Ankara Education and Research Hospital were included in the study. Statistical analyses were performed using SPSS version 20 for Windows. Funding:The mean work addiction total score was 3,26±0,72; the mean coaching behavior scale score was 2,89±1,02 and the mean intrinsic motivation scale score was 3,86±0,78 for the participants. Work addiction showed a positive linear correlation with coaching behavior and intrinsic motivation (r=0,243, r=0,148, respectively, p<0,05). Besides, there was a positive linear correlation between coaching behavior and intrinsic motivation (r=0,140 p<0,05). Conclusion: We performed Sobel Test to investigate the mediating effect among the variables and found that coaching behavior had a fully mediating role in the effects of intrinsic motivation on work addiction.
Lighting and color design in the live music show between new technologies and practice

The choice of light and color to complete the performance of a singer or performer in a live music show is an important and critical issue that can contribute to the success of the event. As part of the show, whether it’s a concert, a musical, or a fashion show, the lighting designer's design choices can make an event unforgettable or sink its quality invalidating the efforts of all the production. In this paper we introduce the evolution of this sector from a technological, methodological and research point of view, comparing it with today’s reality of the professional practice of the stage lighting designer.
Low-literate versus literate customer experience: Dimensions, consequences and moderators

This study explores the lived phenomenon of customer experience (CX) across the customer decision journey of two divergent segments of customers in a naturalistic environment. Specifically, the study focusses on understanding the similarities and differences with respect to—(a) dimensions of CX, (b) consequences of CX, (c) moderators of CX, and (d) situating CX in customer decision journey. The findings are in congruence with the various theories from cognitive psychology, environmental psychology, and economic geography. The findings contribute to existing research that is primarily focussed on conceptualizing and measuring CX by providing an in-depth analysis of its formation for these two customer segments. Managers can utilize the findings to identify these touchpoints that lead to relevant customer responses. In all, the propositions drawn from the study findings are assimilated in the theoretical framework that adds to the nascent research on CX and customer decision journey.
The Mental Health and Job Burnout of Nurses During the COVID-19 Outbreak: Resilience as a Mediator

Background: The COVID-19 pandemic has had adverse psychological, social, and economic consequences around the world. Nurses and physicians have been more negatively affected by this pandemic as compared to other occupational groups. Objectives: The present survey aimed to investigate the job burnout and mental health of nurses during the COVID-19 pandemic and to explore the mediating role of resilience in the relationship between mental health and job burnout. Methods: This cross-sectional study was carried out in two public hospitals of Kashan, Iran from March 2020 until the end of June 2020. A total of 108 nurses participated in this study. The assessment tools included the General Health Questionnaire-28 (GHQ-28), nurses’ job Burnout Questionnaire, and Resilience Scale. Hierarchical linear regression models were used to evaluate the mediating role of resilience in the relationship between job burnout and mental health. Results: The results showed that 61.1% of the participants had mild to severe mental health problems, while 13% reported no job burnout. In terms of resilience, most participants (88.9%) reported moderate resilience. Based on the results, resilience partially mediated the effect of mental health problems on job burnout. The present findings highlighted the mediating role of resilience in the relationship between job burnout and mental health problems among nurses. Conclusions: The present findings suggested that evaluation of resilience resources and traits might be helpful in predicting individuals at risk of psychological problems and job burnout.
Deep Learning for Ensemble Forecasting

3. Science Challenge While both climate and weather forecast systems have continued to improve due to substantial efforts to improve computational capabilities, observations, and numerical models, the atmosphere is a chaotic system, and this puts a fundamental limit on our ability to make predictions. Forecasts made by high-resolution models initialized with only slightly different atmospheric states can quickly diverge. Quantifying uncertainty in forecasts is essential to adequately understand them and to make the best-informed policy decisions particularly when it comes to hydrology, extreme weather (including extreme precipitation events), and climate. Currently, forecast uncertainty at the time of prediction is assessed using ensemble systems. These systems initialize multiple forecast models with slightly different initial states or different representations of atmospheric physics and use the spread of the resulting predictions to estimate uncertainty. Additionally, the ensemble mean is typically more predictive than a single deterministic forecast. Ensemble forecasts come at extreme computational cost that limits their potential applications: typical ensembles involve 5-100 members that each use the computational resources of running a single forecast model. Nonetheless, quantifying forecast uncertainty is valuable enough that many of the world’s premier weather forecasting agencies (NOAA [1] and ECMWF [2] for instance) provide operational ensemble weather forecasts. Ensembles have recently been applied to climate modeling [3] where the need for assessing confidence in climate predictions is critical, and thus the need for informing ensemble details for climate is also critical. Ensembles also have potential for use in sensitivity studies as a way to isolate changes associated with the chaotic nature of the system from changes associated with a variable of interest. More efficient methods for accurately generating ensemble spread and improving the overall ensemble forecast would be a boon for weather forecasting, climate modeling, and our understanding of atmospheric dynamics and processes. Recently, deep convolutional neural networks have been demonstrated as very effective for many tasks in the field of atmospheric science including forecasting [4], satellite retrievals [5], downscaling/super resolution [6], atmospheric state classification, and many others. They have also been successfully applied as post-processing for ensemble forecasts [7,8]. These machine learning tools are particularly well suited for problems that involve very large, gridded datasets where complex non-linear relationships must be learned, and more conventional methods may not be up to the task. They could be a powerful tool for predicting forecast uncertainty or estimating likely ensemble spread using fewer ensemble members and for understanding parameters and conditions that lead to growth of uncertainty in predictions. A Deep Learning (DL) based approach to ensemble modeling and uncertainty estimation has the potential for massive computational savings for existing ensemble systems and would allow for
Ultrawideband Dual-Polarized Antenna for LTE600/LTE700/GSM850/GSM900 Application

An ultrawideband antenna is proposed for base station using loop resonator below the dual-polarized antenna. The designed loop resonator below is able to introduce new resonance to the antenna for ultrawideband operation. The energy is coupled by the loop dipole. This design can effectively enhance the bandwidth without using extra antenna space, which contributes to the multiarray arrangement in the future. Consequently, a full parameter wideband dual-polarized antenna is obtained. Simulation and measurement exhibit a wide bandwidth of 54.3% (|S11| < −15 dB) from 550 to 960 MHz with polarization isolation of more than 31 dB. Desirable stable radiation patterns are also obtained over the whole band. It is a good candidate for the future base station communication system, etc.