Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Analytics on Emotional Data Mined from Digital Social Networks with a Focus on Financial Markets

This dissertation is a cumulative dissertation and is comprised of five articles. User-Generated Content (UGC) comprises a substantial part of communication via social media. In this dissertation, UGC that carries and facilitates the exchange of emotions is referred to as “emotional data. ” People “produce” emotional data, that is, they express their emotions via tweets, forum posts, blogs, and so on, or they “consume” it by being influenced by expressed sentiments, feelings, opinions, and the like. Decisions often depend on shared emotions and data – which again lead to new data because decisions may change behaviors or results. “Emotional Data Intelligence” ultimately seeks an answer to the question of how all the different emotions expressed in public online sources influence decision-making processes. The overarching research topic of this dissertation follows the question whether network structures and emotional sentiment data extracted from digital social networks contain predictive information or they are just noise. Underlying data was collected from different social media sources, such as Twitter, blogs, message boards, or online news and social networking sites, such as Xing. By means of methodologies of social network analysis (SNA), sentiment analysis, and predictive analysis the individual contributions of this dissertation study whether sentiment data from social media or online social networking structures can predict real-world behaviors. The focus lies on the analysis of emotional data and network structures and its predictive power for financial markets. With the formal construction of the data analyses methodologies introduced in the individual contributions this dissertation contributes to the theories of social network analysis, sentiment analysis, and predictive analytics.
Temperature dependence of acoustic properties of natural snail mucus by Brillouin spectroscopy

Brillouin light scattering was used to understand how the acoustic and mechanical properties of gastropod mucus are influenced by temperature over the temperature range -11ᴼC ≤ T ≤ 52ᴼC. Two peaks were observed in spectra obtained in this study, one which was present throughout the full temperature range and at a frequency shirt of ~8.0 GHz, while the other peak appeared at a shift of ~18.5 GHZ but only for temperatures below ~ -3. 0ᴼC. Results for the 8.0 GHz liquid mucus peak showed minor changes in both frequency shift and FWHM for temperatures greater than -2. 5ᴼC. As well, the intensity of this peak showed an overall increase as temperature increases. For temperatures below -2. 5ᴼC, the sound velocity for the mucus mode sh owed an overall increase as temperature decreased, with a difference of ~11% between the sound velocity at T = -3. 0ᴼC (1550 m/s) and -11. 0ᴼC (1750 m/s). Moreover, the liquid mucus bulk modulus was also obtained and also shows an overall increase as temperature decreases in this temperature regime. Additionally, this 8.0 GHz peak also revealed an exponential decrease in the FWHM as temperature increased with linewidth ranging from 12 GHz to 0.6 GHz for temperatures T = 11. 0ᴼC and 52. 0ᴼC respectively. Likewise, sound absorption values were also obtained in this study and showed an exponential decrease as temperature increased with values ranging from ~ 120 X 10⁻¹⁵ s²/m to ~15 X 10⁻¹⁵ s²/m over the temperature range -11. 0ᴼC ≤ T ≤ 52ᴼC for all samples. From the sound absorption, the interaction energy between the molecules in the mucus was obtained and showed a large change in its values for temperatures above and below T = 2. 5ᴼC. The second peak occurring at ~18.5 GHz deemed due to ice Ih showed very small changes in frequency and linewidth as temperature decreases, as well it was seen that as the temperature decreased the intensity of this peak generally increased. The sound velocity for this mode increases slightly as temperature decreases with values ranging from 3800 to 3860 m/s for temperatures T = -3. 0ᴼC and T = -11. 0ᴼC respectively. Likewise, the bulk modulus of the ice slightly increases in this temperature regime. Collectively, the results from this work suggest a phase transition between a viscous liquid state to a mixed solid-liquid state. Comparison of the results obtained in this study is made to similar liquid systems (i.e., water, water and polymer mixtures).
Evaluation of the fit of Tolpyralate for Weed Management in Field Corn (Zea mays L.) in Ontario

EVALUATION OF THE FIT OF TOLPYRALATE FOR WEED MANAGEMENT IN FIELD CORN (Zea mays L.) IN ONTARIO Nicole M. Langdon University of Guelph, 2020 Advisor: Dr. P. H. Sikkema Thirty-six experiments were conducted over two years (2018, 2019) to evaluate tolpyralate, a benzoylpyrazole 4-hydroxyphenyl-pyruvate dioxygenase inhibitor, for weed management in corn in Ontario. The importance of adjuvants (MSO and UAN) with tolpyralate + atrazine and the influence of Roundup Weathermax® was determined for control of nine annual weed species, glyphosate-resistant (GR) Canada fleabane and GR waterhemp. Optimal control was achieved with tolpyralate + atrazine + MSO and when tolpyralate + atrazine was coapplied with Roundup Weathermax®. Tolpyralate and tolpyralate + atrazine has residual activity on GR Canada fleabane, comparable to current industry standards. Three weed species exhibited a time-of-day effect to tolpyralate + atrazine, although, consistent control was achieved regardless of application time. Corn injury was greatest from tolpyralate + atrazine when applied at the 2X rate alone and/or co-applied with glyphosate at 1 WAA; there was no further increase in corn injury when co-applied with glyphosate.
Mechanistic, ecological, and evolutionary consequences of artificial light at night for insects: review and prospective

The alternation of light and dark periods on a daily or seasonal time scale is of utmost importance for the synchronization of physiological and behavioral processes in the environment. For the last 2 decades, artificial light at night (ALAN) has strongly increased worldwide, disrupting the photoperiod and its related physiological processes, and impacting the survival and reproduction of wild animals. ALAN is now considered as a major concern for biodiversity and human health. Here, we present why insects are relevant biological models to investigate the impact of ALAN. First the phenotypic responses to ALAN and their underpinning mechanisms are reviewed. The consequences for population dynamics, and the community composition and functioning are described in the second part. Because ALAN provides new and widespread selective pressure, we inventory evolutionary changes in response to this anthropogenic change. Finally, we identify promising future avenues, focusing on the necessity of understanding evolutionary processes that could help stakeholders consider darkness as a resource to preserve biodiversity as well as numerous ecosystem services in which insects are involved.
DSMC Simulation of thermal transpiration and accomodation pumps

The Direct Simulation Monte Carlo (DSMC) technique is employed to evaluate several configurations of thermal transpiration and accommodation pumps. There is renewed interest in these rarefied flow pumping concepts for Micro-Electro-Mechanical Systems (MEMS) due to advances in micro-fabrication. The simulation results are compared with existing data to understand gas-surface interaction uncertainties in the experiments. Parametric studies are performed to determine the effects of Knudsen number and surface temperature and roughness on the maximum pump pressure ratio.
Telemedicina durante la epidemia de COVID-19 en Chile: Guía de Buenas Prácticas y Recomendaciones

Documento desarrollado por el Centro Nacional en Sistemas de Información en Salud (CENS), que provee recomendaciones para la prestación de servicios a través de telemedicina, que incluyen informar al paciente sobre la teleconsulta;recomendaciones a los pacientes sobre con qué contar antes de la consulta;trabajo colaborativo entre profesionales, es prioritario mantener la privacidad y confidencialidad del paciente, y;utilizar cada oportunidad disponible para educar en la prevención, e identificar casos sospechosos de COVID-19
On the predictability of U.S. stock market using machine learning and deep learning techniques

Conventional market theories are considered to be inconsistent approach in modern financial analysis. This thesis focuses mainly on the application of sophisticated machine learning and deep learning techniques in stock market statistical predictability and economic significance over the benchmark conventional efficient market hypothesis and econometric models. Five chapters and three publishable papers were proposed altogether, and each chapter is developed to solve specific identifiable problem(s). Chapter one gives the general introduction of the thesis. It presents the statement of the research problems identified in the relevant literature, the objective of the study and the significance of the study. Chapter two applies a plethora of machine learning techniques to forecast the direction of the U.S. stock market. The notable sophisticated techniques such as regularization, discriminant analysis, classification trees, Bayesian and neural networks were employed. The empirical findings revealed that the discriminant analysis classifiers, classification trees, Bayesian classifiers and penalized binary probit models demonstrate significant outperformance over the binary probit models both statistically and economically, proving significant alternatives to portfolio managers. Chapter three focuses mainly on the application of regression training (RT) techniques to forecast the U.S. equity premium. The RT models demonstrate significant evidence of equity premium predictability both statistically and economically relative to the benchmark historical average, delivering significant utility gains. Chapter four investigates the statistical predictive power and economic significance of financial stock market data by deep learning techniques. Chapter five give the summary, conclusion and present area(s) of further research. The techniques are proven to be robust both statistically and economically when forecasting the equity premium out-of-sample using recursive window method. Overall, the deep learning techniques produced the best result in this thesis. They seek to provide meaningful economic information on mean-variance portfolio investment for investors who are timing the market to earn future gains at minimal risk.
Vertical cloud structure of warm conveyor belts – a comparison and evaluation of ERA5 reanalysis, CloudSat and CALIPSO data

Abstract. Warm conveyor belts (WCBs) are important cyclone-related airstreams that are responsible for most of the cloud and precipitation formation in the extratropics. They can also substantially influence the dynamics of cyclones and the upper-level flow. So far, most of the knowledge about WCBs is based on model data from analyses, reanalyses and forecast data with only a few observational studies available. The aim of this work is to gain a detailed observational perspective on the vertical cloud and precipitation structure of WCBs during their inflow, ascent and outflow and to evaluate their representation in the new ERA5 reanalysis dataset. To this end, satellite observations from the CloudSat radar and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar are combined with an ERA5-based WCB climatology for nine Northern Hemisphere winters. Based on a case study and a composite analysis, the main findings can be summarized as follows. (i) WCB air masses are part of deep, strongly precipitating clouds, with cloud-top heights at 9–10 km during their ascent and an about 2–3 km deep layer with supercooled liquid water co-existing with ice above the melting layer. The maximum surface precipitation occurs when the WCB is at about 2–4 km height. (ii) Convective clouds can be observed above the inflow and during the ascent. (iii) At upper levels, the WCB outflow is typically located near the top of a 3 km deep cirrus layer. (iv) There is a large variability between WCBs in terms of cloud structure, peak reflectivity and associated surface precipitation. (v) The WCB trajectories with the highest radar reflectivities are mainly located over the North Atlantic and North Pacific, and – apart from the inflow – they occur at relatively low latitudes. They are associated with particularly deep and strongly precipitating clouds that occur not only during the ascent but also in the inflow and outflow regions. (vi) ERA5 represents the WCB clouds remarkably well in terms of position, thermodynamic phase and frozen hydrometeor distribution, although it underestimates the high ice and snow values in the mixed-phase clouds near the melting layer. (vii) In the lower troposphere, high potential vorticity is diabatically produced along the WCB in areas with high reflectivities and hydrometeor contents, and at upper levels, low potential vorticity prevails in the cirrus layer in the WCB outflow. The study provides important observational insight into the internal cloud structure of WCBs and emphasizes the ability of ERA5 to essentially capture the observed pattern but also reveals many small- and mesoscale structures observed by the remote sensing instruments but not captured by ERA5.
Climate risk and finance

Climate risks, whether physical risks or transition risks, represent an increasingly important issue for companies, bankers and institutional investors. This article provides a review of the recent literature on the relationship between climate risks and finance. It examines institutional investors’ perceptions of climate risks and reports findings on the impact of climate risks on the value of real estate, debt and equity.
Comparison of level of Stress among Bedside Nurses Working in Different Specialties at Private Sector Tertiary Care Hospital in Karachi, Pakistan

Background: Stressful situation are among the challenging experiences especially in clinical setting. The aim of this study was to compare the level of work-related stress among nurses working in Intensive Care Units (ICUs), General ward and Emergency setting. Methods: This cross-sectional study was conducted at different private tertiary hospitals of Karachi in 2017-2018. Two stage sampling technique including quota and convenient sampling technique was used to collect the data. A total of 225 participants were recruited. Pre-validated questionnaire was used which contained different stress related categories. Midwives, nursing assistants, part time nurses and nurses employed at management positions were excluded from the study. Data were presented as frequency and percentage and mean and standard deviation. The chi-square test was used for examine association of categorical variables. The SPSS used for data analysis. Results: The mean age of participants was 26.5±2.4 years. Stress among nurses working in emergency department was found to be highest in all categories. Management related stress was 77.3% and 69.3% due to criticism by supervisor and inadequate support by supervisor, respectively. Patient related stress was 72% and 64% due to health risk posed by patient contact and communication about death to patient. About 81.3% of the nurses of emergency department reported high level of stress due to covering work for another employee followed by 66.7% related to working overtime. Moreover, 86.7% and 62.7% of participants had high level of stress due to inadequate salary and lack of recognition. Conclusion: The study concludes that the level of stress varies within different departments in the hospitals. There are several work-related stressors that found more in Emergency department than ICU or General ward.
What Jobs are Being Done at Home During the Covid-19 Crisis? Evidence from Firm-Level Surveys

The threat of COVID-19 has increased the health risks of going to an office or factory, leading more workers to do their jobs remotely. In this paper, we provide results from firm surveys on both small and large businesses on the prevalence and productivity of remote work, and expectations about the persistence of remote work once the COVID-19 crisis ends. We present four main findings. First, while overall levels of remote work are high, there is considerable variation across industries. The Dingel and Neiman (2020) measure of suitability for remote work does a remarkably good job of predicting the industry level patterns of remote work - highlighting the challenge of moving many industries to remote work. Second, remote work is much more common in industries with better educated and better paid workers. Third, in our larger survey, employers think that there has been less productivity loss from remote working in better educated and higher paid industries. Fourth, more than one-third of firms that had employees switch to remote work believe that remote work will remain more common at their company even after the COVID-19 crisis ends.
Multimodality Practices in Hearing Impairment EFL Classroom

Abstract This article provides a report of teacher’s experience in using ICT-integrated multimodal texts to help heard-of-hearing learners develop or improve their literacy skills especially in mastering reading comprehension. Additionally, this study tried to investigate the impacts of the use of the materials on students’ learning. The study was carried out in a special need school. The research is a case study of an English teacher and three hard-of-hearing learners. The material consists of video observation, photographs, field notes, documents, and interviews with the teacher and the students. The findings showed that multimodal texts which were built with diverse modes or semiotic resources such as color, sound, motion, written text, and gesture could ease the teacher to help the students perform better in the area of reading comprehension although she also encountered some challenges. Moreover, the students perceived the use of multimodal texts positively as it allowed them to participate more actively in the classroom activities. I believe that the result of the study can serve as a consideration for English teachers in teaching readings to hard hearing learners. Keyword: Hard hearing learners, multimodal texts, reading for hard hearing
HORMONAL REGULATION OF ROOT MORPHOGENESIS IN CALLUS CULTURE OF COWPEA (Vigna unguiculata L. WALP)

Morphogenesis in high plants is controlled by manipulating the balance of auxins and cytokinins. In cowpea however, morphogenesis is not fully understood and very difficult to regulate. This study established a routine system for callus induction and root regeneration from primary leaves of cowpea (Vigna unguiculata L. Walp). Embryonic axes were used to establish seedling under in vitro condition and primary leaves from the in vitro seedlings were used are explants for the establishment of callus culture using different concentrations of 2, 4-dichlorophenoxyacetic acid (2, 4-D). To regenerate root, calli were subcultured on hormone free media or media fortified with 2. 22µM N6-Benzylaminopurine (BAP). Callus induction was efficient (85.03%) on Murashige and Skoog (MS) basal salts with B5 vitamins (MSB5) fortified with 6. 78μM 2, 4-Dichlorophenoxyacetic acid (2, 4-D) and induction frequency gradually decreases with reduction in the concentration of 2, 4-D. When calli were subcultured on MSB5 in the presence of N6-Benzylaminopurine (BAP), roots developed in 85% of the calli, with mean number of roots per callus ranging from 1 – 7. The presence of BAP was essential for roots development, as calli subcultured on hormone free media failed to develop roots, indicating the significance of hormonal balance in plant morphogenesis. The use of callus culture as experimental materials could help in understanding the processes of shoots and roots morphogenesis in cowpea. Root cultures could also be used in the production of secondary metabolites of pharmaceutical and other industrial values from genetically engineered callus.
An Early Assessment of Curfew and Second COVID-19 Lock-down on Virus Propagation in France

This note provides an early assessment of the reinforced measures to curb the COVID-19 pandemic in France, which include a curfew of selected areas and culminate in a second COVID-19-related lock-down that started on October 30, 2020 and is still ongoing. We analyse the change in virus propagation across age groups and across departements using an acceleration index introduced in Baunez et al. (2020). We find that while the pandemic is still in the acceleration regime, acceleration decreased notably with curfew measures and this more rapidly so for the more vulnerable population group, that is, for people older than 60. Acceleration continued to decline under lock-down, but more so for the active population under 60 than for those above 60. For the youngest population aged 0 to 19, curfew measures did not reduce acceleration but lock-down does. This suggests that if health policies aim at protecting the elderly population generally more at risk to suffer severe consequences from COVID-19, curfew measures may be effective enough. However, looking at the departmental map of France, we find that curfews have not necessarily been imposed in departements where acceleration was the largest.
Investment Sensitivity to Lender Default Shocks

We investigate how idiosyncratic lender shocks impact corporate investment. Lenders with recent default experience write stricter loan contracts, leading to a reduction in real investment for borrowing firms. The decline in investment is not attributable to loan riskiness, borrowers agency costs, the lender-borrower relationship nexus, or to lender capitalization, but is less pronounced when non-bank lenders increase. The findings remain robust when controlling for lender fragility, macroeconomic conditions, aggregate defaults in the economy, and excluding defaulters from the same industry or geographic region. The evidence suggests that defaults inform lenders about investment opportunities and their screening ability, and adjustments to this information have real economic consequences.
The Antecendence of E-Commerce Adoption by Micro, Small, and Medium sized Enterprise (Msme) with E-Commerce Training as Moderation

At present, MSMEs in Indonesia are inhibited by the lack of market information and fierce competition of product marketing, so MSMEs need a new marketing media to market their products, one of which is using e-commerce media. There are many frameworks that can be utilized to measure the adoption of e-commerce by MSMEs. One of them is the TOE (technology, organization, environment) framework. In the TOE framework, e-commerce adoption is measured using technology, organization, and environment variables. Currently, there are different and contradictory findings between one study and another in the TOE framework, thus requiring a new study regarding other variables affecting the TOE framework for e-commerce adoption. This study aims to determine which factors can influence the TOE framework for e-commerce adoption by MSMEs. Accordingly, e-commerce training is used as a moderation of technology, organization, and environment towards the adoption of e-commerce by MSMEs. Sample collection is done by saturation sampling technique to all members of the population who met the population criteria, namely as many as 158 MSMEs. Based on the questionnaires distributed, 120 respondents were obtained, or 75.96% of all questionnaires were distributed. The analysis conducted in this study uses Partial Least Square (PLS) analysis. After the analysis, it is known that technology and the environment have a significant influence on e-commerce adoption by MSMEs. The organization has no significant effect on the adoption of e-commerce by MSMEs. While e-commerce training turned out to be insignificant in moderating technology, organization, and environment towards the adoption of e-commerce by MSMEs.