The level of labor productivity differs among the European Union countries, especially when you compare a developing country to a more developed country in the EU. This is an issue because the achievement of high labor productivity is a necessary stipulation for a developing economy to realize economic growth and more economic development. On the other hand, the more individuals in an economy with access to the internet (internet connectivity) depicts how developed the economy is in terms of information and communication technology (ICT). Accordingly, the purpose of this paper is to ascertain whether there is a positive relationship between countries having high internet connectivity and labor productivity in the EU. In doing so, Political and entrepreneurial decision-makers can use these findings to decide how much attention or budget to put on the ICT sector to improve labor productivity. To understand the factors that affect labor productivity, Adam Smith and Karl Marx’s theory on labor productivity is used to gain a better understanding. A panel data analysis using a fixed-effect model and pooled OLS regression model is applied in the study to predict the relationship. The result of the study indicates that internet connectivity does not have a significant impact on Labour productivity, or there was not enough evidence showing that they are positively correlated with each other.

BACKGROUND: India—one of the most significant antibiotic users in the world with a high burden of antibiotic resistance—does not have a formal antibiotic surveillance system. No formal studies exist on the sub-national differences in antibiotic use in India except for small hospital or community-based studies. Informed by the WHO Global Action Plan, India developed a national action plan; however only two states have state action plans so far. This suggests that it is important to understand existing antibiotic consumption patterns, sub- national differences and trends over time, and the determinants of antibiotic use so that evidence-informed action plans and programs can be developed in India. AIM: To understand the changing landscape of antibiotic use in India and contribute to relevant policy and programmatic interventions that can improve the appropriate use of antibiotics in the country. Specific objectives included examining the use of systemic antibiotic consumption at the national level, analyzing geographical and temporal variations across states between 2011 and 2019, and understanding the determinants of antibiotic consumption. Additionally, we examined Kerala as a case study to understand the use and availability of data in designing, implementing, and monitoring the state antibiotic action plan. METHODOLOGY: First, we conducted a cross-sectional analysis of antibiotic use in 2019 using the WHO Access-Watch-Reserve (AWaRe) and Defined Daily Doses (DDD) matrices at the national level across product type (Fixed-Dose Combinations [FDCs]; and single formulations [SF]), essentiality (listed in the national list of essential medicines [NLEM]; and not listed), and central regulatory approval status (approved and unapproved). Second, we analyzed trends in consumption rates and patterns at the national, state, and groups of states at different levels of health achievements (‘high focus’ [HF]; and ‘non-high focus’ [nHF]) and compared the appropriateness of use between states and state groups. Third, using a cross-sectional, time series (panel) dataset on antibiotic use, per-capita GDP, per-capita government spending on health, girls' tertiary education enrollment ratio, measles vaccination coverage, and lower respiratory tract infection incidence for the period 2011- 2019, we conducted a quasi- experimental fixed-effects analysis to understand the critical determinants of antibiotic use. Finally, we conducted key-informant interviews and document analysis to understand the use of data in policy formulation, implementation, monitoring, and evaluation of the Kerala state action plan. RESULTS: India's per-capita private-sector antibiotic consumption rate was lower than global rates, but the country has a high consumption rate of broad-spectrum antibiotics, FDCs discouraged by WHO, formulations outside NLEM in FDCs, and unapproved formulations. The overall rate increased from 2011 to 2016 and decreased between 2016 and 2019, registering a net decrease of 3.6%. State consumption rates varied widely— with HF states reporting lower rates. The inappropriate use increased over the years, the share of Access antibiotics decreased (13.1%), and the access-to-watch ratio declined (from 0.59 to 0.49). HF and nHF states showed convergence in the share of the Access and the Access-Watch ratio, while they showed divergence in the use of WHO Discouraged FDCs. The most critical independent determinant of antibiotic use was government spending on health—for every US$12.9 increase in per-capita government spending on health, antibiotic use decreased by 461.4 doses per 1000 population per year after adjusting for other factors. Economic progress (increase in per-capita GDP) and social progress (increase in girls' higher education) were also found to reduce antibiotic use independently. The qualitative case study showed that stakeholders understand and express interest in generating and using data for decision- making, and the action plan document mentions some basic monitoring plans. However, a monitoring and evaluation framework is missing, there is a lack of engagement with the private sector, and there is a lack of understanding among key government policymakers on the importance of using data for surveillance and policy implementation. CONCLUSION AND IMPLICATIONS: There is significant and increasing inappropriate antibiotic use in India's private sector, accounting for 85-90% of total antibiotic use. Increased government spending on health is critical in reducing private-sector antibiotic use. The dearth of data on public sector use is a significant challenge in understanding the total consumption rate. Developing a monitoring and evaluation system through stakeholder engagement is necessary for Indian States to inform, monitor, and evaluate effective antibiotic action plans. We need global efforts to improve the science and methods to measure antibiotic use. / 2023-08-30T00:00:00Z

Unmanned aerial vehicles (UAVs) is a rapidly expanding area of research due to their versatile usage, such as inspection of places inaccessible to humans and surveillance missions. This creates a demand for a reliable model that can accurately describe the dynamics of the system in order to improve the performance of the vehicle. System identification is a common tool used for the modelling of a system and is essential for developing an accurate and reliable model. The aim of this master's thesis is to develop an accurate non-linear grey-box model, with six degrees of freedom, of a fixed-wing UAV as well as a linearized version of the model. After a literature study a suitable model structure with sixstates and 28 parameters was chosen. The moment of inertia matrix is estimated separately using physical experiments,and the other parameters, related to the aerodynamic coefficients of the UAV, are estimated using flight experiments. Flight experiments are designed in order to capture all of the system dynamics and data was collected accordingly. The parameters are estimated using a prediction error method, which requires the solution of an optimal control problem. The derived models of the UAV are compared to each other and evaluated using model validation. In conclusion, the non-linear grey-box model shows great potential in becoming an accurate model, but further investigation and refining of the model is necessary.

The influx of unmanned aerial systems over the last decade has increased need for airspace awareness. Monitoring solutions such as drone detection, tracking, and classification become increasingly important to maintain compliance for regulatory and security purposes, as well as for recognizing aircraft that may not be so. Vision systems offer significant size, weight, power, and cost (SWaP-C) advantages, which motivates exploration of algorithms to further aid with monitoring performance. A method to classify aircraft using vision systems to measure their motion characteristics is explored. It builds on the assumption that at least continuous visual detection or at most visual tracking of an object of interest is already accomplished. Monocular vision is in part limited by range/scale ambiguity, where range and scale information of an object projected onto the image plane of a camera using a pin- hole model is generally lost. In an indirect effort to attempt to recover scale information via identity, classification of aircraft can aid in improvement of. These measured motion characteristics can then be used to classify the perceived object based on its unique motion profile over time, using signal classification techniques. The study is not limited to just unmanned aircraft, but includes full scale aircraft in the simulated dataset used to provide a representative set of aircraft scale and motion. / Doctor of Philosophy / The influx of small drones over the last decade has increased need for airspace awareness to ensure they do not become a nuisance when operated by unqualified or ill-intentioned personnel. Monitoring airspace around locations where drone usage would be unwanted or a security issue is increasingly necessary, especially for more range and endurance capable fixed wing (airplane) drones. This work presents a solution utilizing a single camera to address the classification part of fixed wing drone monitoring, as cameras are extremely common, generally cheap, information rich sensors. Once an aircraft of interest is detected, classifying it can provide additional information regarding its intentions. It can also help improve visual detection and tracking performance since classification can help change expectations of where and how the aircraft may continue to travel. Most existing visual classification works rely on features visible on the aircraft itself or its silhouette shape. This work discusses an approach to classification by characterizing visually perceived motion of an aircraft as it flies through the air. The study is not limited to just drones, but includes full scale aircraft in the simulated dataset used. Video of an airplane is used to extract motion from each frame. This motion is condensed to and expressed as a single time signal, that is then classified using a neural network trained to recognize audio samples using a time-frequency representation called a spectrogram. This transfer learning approach with Resnet based spectrogram classification is able to achieve 90.9% precision on the simulated test set used.

The goal of this research is to expand the field of conformal predictions using Random Survival Forests. The standard Conformal Random Survival Forest can predict with a fixed certainty whether something will survive up until a certain time point. This research is the first to show that there is little practical use in the standard Conformal Random Survival Forest algorithm. It turns out that the confidence guarantees of the conformal prediction framework are violated if the Standard algorithm makes predictions for a user-controlled fixed time point. To solve this challenge, this thesis proposes two algorithms that specialize in conformal predictions for a fixed point in time: a Fixed Time algorithm and a Hybrid algorithm. Both algorithms transform the survival data that is used by the split evaluation metric in the Random Survival Forest algorithm. The algorithms are evaluated and compared along six different set prediction evaluation criteria. The prediction performance of the Hybrid algorithm outperforms the prediction performance of the Fixed Time algorithm in most cases. Furthermore, the Hybrid algorithm is more stable than the Fixed Time algorithm when the predicting job extends to various time points. The hybrid Conformal Random Survival Forest should thus be considered by anyone who wants to make conformal survival predictions at usercontrolled time points. / Målet med denna avhandling är att utöka området för konformitetsprediktion med hjälp av Random Survival Forests. Standardutförandet av Conformal Random Survival Forest kan förutsäga med en viss säkerhet om någonting kommer att överleva fram till en viss tidpunkt. Denna avhandling är den första som visar att det finns liten praktisk användning i standardutförandet av Conformal Random Survival Forest-algoritmen. Det visar sig att konfidensgarantierna för konformitetsprediktionsramverket bryts om standardalgoritmen gör förutsägelser för en användarstyrd fast tidpunkt. För att lösa denna utmaning, föreslår denna avhandling två algoritmer som specialiserar sig i konformitetsprediktion för en bestämd tidpunkt: en fast-tids algoritm och en hybridalgoritm. Båda algoritmerna omvandlar den överlevnadsdata som används av den delade utvärderingsmetoden i Random Survival Forest-algoritmen. Uppskattningsförmågan för hybridalgoritmen överträffar den för fast-tids algoritmen i de flesta fall. Dessutom är hybrid algoritmen stabilare än fast-tids algoritmen när det förutsägelsejobbet sträcker sig till olika tidpunkter. Hybridalgoritmen för Conformal Random Survival Forest bör därför föredras av den som vill göra konformitetsprediktion av överlevnad vid användarstyrda tidpunkter.

Greenhouse Gas emission increase is largely attributed to carbon dioxide emissions as the major gas causing climate change and atmospheric warming. According to Environmental Kuznets Curve Theory (EKC), the increase in economic growth is expected to reduce the environmental pollution from carbon dioxide emission caused at the beginning stages of economic growth. In this thesis, I examined the impact of economic growth on carbon dioxide emission. The key hypothesis tested in this study is the Environmental Kuznets Curve hypothesis. Data from 1967 to 2016 from over 15 countries in North and South America, published by the World Bank were used. Since EKC posits a non-linear relationship between economic growth (GDP/capita) and Carbon dioxide emission, I used a quadratic component in the regression model. I analyzed the data using the OLS regression as my baseline model. Each country is unique in many respects that are hard to capture by a set of variables in econometrics model. This poses a challenge to estimating an unbiased estimate. Using panel data model allowed controlling for time invariant unobserved country-specific factors that could bias the estimates. I estimated a fixed effect panel regression to examine the relationship between carbon dioxide emissions and economic growth is primarily measured with Gross Domestic Product (GDP) per capita. The results of the fixed effect panel regression showed that all variables are significant, except export and inflation which were not significant. OLS could not solve the issue of heterogeneity among the variables. Estimating country-specific fixed effects model eliminates unobserved heterogeneity across countries and, therefore provides relatively unbiased estimates compared to OLS estimates. The positive correlation between Total CO2 emissions, CO2 emissions from Solid, and CO2 emissions from gas and GDP per capita suggests that carbon dioxide emissions increase as GDP/ capita increases before the turning point. The negative correlation between Total CO2 emissions, CO2 emissions from Solid, and CO2 emissions from gas and GDP per capita squared suggests that there is a polynomial (quadratic) form which is like that of inverted U-shape of the EKC curve. The coefficient, although it is very small, suggests the impact of the negative relationship after the turning point at the vertex of EKC curve is fractional. As expected, the result indicates a higher population causes an increase in total CO2 emissions. The result from CO2 emissions from liquid shows a negative relationship between the dependent variable CO2 emissions from liquid and the independent variable GDP per capita at the highest level of significance. This result is different from that of total carbon dioxide emissions, CO2 emissions from Solid, and CO2 emissions from gas. Carbon emission from liquid looks different from carbon emissions from solid and gas. There are high and constant emission throughout all the years and in all countries used in the analysis. EKC hypothesis is proven to be true for total carbon dioxide emissions, carbon dioxide emission from solid and gas. The hypothesized correlation between GDPs per capita square and CO2 emissions is statistically supported for Total CO2 emission, CO2 emission from solid and CO2 emission from gas. CO2 emissions from Solid, and CO2 emissions from gas and GDP per capita squared suggest that there is a polynomial (quadratic) form which is like that of inverted U-shape of the EKC curve. This proves that EKC model is proven to be true for my data. Policies like population policies can help in increasing growth in GDP per capita and reducing growth in the amount of carbon dioxide emissions. Population policies could play a significant role aimed at mitigating and reducing climate change.

Introduction: The objective of this retrospective longitudinal study was to assess and compare the surgical changes and stability of the maxilla, mandible, and mandibular condyle, in patients who have undergone surgery first approach (SFA) followed by conventional braces or Invisalign (Inv) treatment. Methods: Thirty patients had a cone beam computed tomography (CBCT) exam taken at three timepoints: T1 (presurgery), T2 (immediately after surgery), and T3 at the completion of orthodontics treatment. After the cranial base registration, twenty-seven landmarks were located on each time point using axial, sagittal, and coronal cross-sectional views in the ITK-SNAP software. In addition, seventeen skeletal angular and linear variables were measured using the 3D Slicer software. Result: In general, both Groups had similar sugical changes (T2-T1) and stability (T3-T2). There was a statistically significant longer postsurgical orthodontic time in the Fixed Group (x̅ = five months). Skeletally, comparing T3-T2 the B point showed a statistically significant inferior position in the Fixed Group compared to the Inv Group (1.3 mm), resulting in a larger increase in mandibular plane angle in the Fixed Group (x̅ = 2.7 degrees). Conclusion: The Fixed appliances and clear aligner therapy in the surgery first approach resulted in similar skeletal changes and stability, except for the mandibular plane angle, which showed a greater increase in the Fixed Group. This result may suggest that patients with hyperdivergent skeletal pattern could benefit from aligner therapy for the postsurgical orthodontic phase.

No description available.

<p> An Experimental investigation has been conducted to determine the effects on degree of reduction and efficiency of utilization of gases of changes in the levels of several variables when reducing fixed beds of commercial hematite pellets. The effects on reduction behaviour of changes in the levels of reducing gas composition and flow rate, and reaction temperature, were determined by graphical and numerical techniques.</p> / Thesis / Master of Engineering (MEngr)

Rumberger (2020) observed that "dropping out of school has economic and social consequences both for dropouts themselves and for the country as a whole" (p. 151). Every year, many Brazilians drop out of school due to work, early pregnancies, marriage, drug consumption, crime, etc. Dropping out of school can occur due to learning challenges, poor attendance, discipline problems, or a lack of access to high school institutions. Dropouts can experience depression and anxiety and are more likely to attempt suicide. The present dissertation includes two different papers about dropouts. The first paper uses fixed effect regression to show the main characteristics of dropouts who both left high school before completing it and registered for the Brazilian National Exam (ENEM). The results demonstrate that dropouts who take the ENEM are males, hail from low-income families, are younger (less than 17 years old), and are less likely to possess computers. When analyzing the 2015 and 2016 data set, which included dropouts who took the ENEM to receive high school certification, the results show that thew are more likely to have dropped out of school during their basic education (1st to 9th grade). In the second paper, I evaluated differences in achievement "between dropout registrants and current students, and dropout registrants and graduates" each comparison using the same data set (ENEM), but restricted to 2015 and 2016, due to the availability of a larger number of predictive variables of dropouts. The results indicate that dropout registrants did worse than all groups in essay writing but performed similarly to current students in math and language in 2016. When comparing the achievement of dropout registrants and graduates, the results show more pronounced differences, but in essay writing, the effect size varied from 0.22SD to 0.35SD.