Miracle-dz/UniFAR-FTUs huggingface dataset:
How to Design Accounting Video Lectures to Recover Lost Time

Aim/Purpose The objective of this study is to understand how video lectures of the same length and content as the current face-to-face lectures can be designed and implemented to have a positive effect on student performance, particularly when there is a campus shutdown. Background In a number of South African universities protests by the students are on the increase. Often, they lead to the cancellation of academic activities such as faceto-face classes and examinations. Methodology A quasi-experimental design was used on two video lectures to (1) compare the performance of the students who did not watch the video lectures and those who watched the video lectures, (2) compare the performance of each student who watched the video lectures on the test topics covered in the videos and the test topics not covered in the videos, and (3) determine the factors that influence the effectiveness of the video lectures. Contribution This study contributes to the literature by investigating the effectiveness of video lectures in improving student performance, the factors associated to the effectiveness of such lectures, and the complexity or simplicity of the two video lectures used, and by providing possible solutions to the challenges identified in relation to designing video lectures. Findings In terms of student performance, there is no significant advantage arising from watching the video lectures for the students who watch the video lectures, as compared to those who did not watch the video lectures. It is also found that the student performance on the topics with video lectures is significantly associated to the students’ commitment, prior performance, the quality of the content, and the design of the videos. Recommendations for Practitioners This study recommends how the accounting video lectures can be designed and highlights the environments in which the video lectures of the same length and content as the face-to-face lectures should not be used. Accounting Video Lectures 208 Recommendation for Researchers Researchers should replicate this study by using short length videos of better quality and appropriate length, which incorporate current issues, games, are interactive, and so forth. Impact on Society This study examines the use of educational video lectures in order to minimise the impact of disruptions at university level. Future Research Future studies may use randomly selecting treatment and control groups. They may consider a nationwide research or using qualitative interviews in examining the use of educational video lectures.
Current achievements and the future direction of electrochemical CO2 reduction: A short review

Abstract Electrochemical CO2 reduction (CO2RR) has received much attention for its ability to generate value-added chemicals from a molecule that would otherwise be a waste end-product. Numerous studies have emerged in the past decades, but the renewable and sustainable carbon-neutral CO2 reduction process is yet to be industrialized. Here, we review the progress and bottlenecks of the electrochemical CO2 reduction technologies over the past 15 years (2004–2018) to examine whether CO2RR process is to be applicable in a large-scale. Although the techno-economic analysis and pilot plants based on liquid-phase electrolysis have shown some positive results, current densities of the liquid-phase electrochemical CO2 reduction are well below what techno-economic analyzes have projected due to its intrinsic limitations of solubility. On the other hand, the gas-phase electrolysis of CO2 has shown superior performance parameters compared to the liquid-phase electrolysis, especially in the current densities, showing commercial viability although its techno-economic analysis is yet to be performed. Herein, we offer some perspectives and guidelines where future research in CO2 electrolysis should aim. Based on the performance parameters obtained from the lab-scale gas-phase reactions, we believe that the current negative outlooks towards the industrial feasibility of the CO2 electrolysis system could turn to positive views.
Applying self-attention neural networks for sentiment analysis classification and time-series regression tasks

Many machine learning tasks are structured as sequence modeling problems, predominantly dealing with text and data with a time dimension. It is thus very important to have a model that is good at capturing both short range and long range dependencies across sequence steps. Many approaches have been used over the past few decades, with various neural network architectures becoming the standard in recent years. The main neural network architecture types that have been applied are recurrent neural networks (RNNs) and convolutional neural neworks (CNNs). In this work, we explore a new type of neural network architecture, self-attention networks (SANs), by testing on sequence modeling tasks of sentiment analysis classification and time-series regression. First we perform a detailed comparison between simple SANs, RNNs, and CNNs on six sentiment analysis datasets, where we demonstrate SANs achieving higher classification accuracy while having other better model characteristics over RNNs such as faster training and inference times, lower number of trainable parameters, and consuming less memory during training. Next we propose a more complex self-attention based architecture called ESSAN and use it to achieve state-of-the-art (SOTA) results on the Stanford Sentiment Treebank fine-grained sentiment analysis dataset. Finally, we apply our ESSAN architectures for the regression task of multivariate time-series prediction. Our preliminary results show that ESSAN once again achieves SOTA results, beating previous SOTA RNN with attention architectures.
A Trampoline Group: Feasibility, Implementation, and Outcomes

This paper reflects two studies designed to explore feasibility and outcomes of a movement-based trampoline group for children with autism and/or sensory processing challenges. A pre experimental A- B single subject pilot study was used to examine feasibility and sensitivity of outcomes, followed by a single group pretest posttest design to examine outcomes. Each group received eight to ten, 50-minute sessions of trampoline activity. Motor outcomes were administered once a week as well as pre- post- measures of motor function. Measures from Study 1 informed Study 2. Parent report pre- post- measures of social functioning were added to Study 2. All procedures were feasible and sensitive outcome measures were identified. In Study 1, outcomes (e.g. broad jump, heel toe walking, beads in box, beads on rod and hand strength) did not consistently show change. Broad jump and one foot standing balance were suggested for future use. In Study 2 significant improvements were noted in broad jump. Social competency and participation as measured by the Social Skills Improvement System, and the Sensory Processing Three Dimensions Occupational Performance Scale showed significant change. A visual analog scale completed by parents was also sensitive to changes in both motor and social abilities. This study demonstrated the feasibility of conducting a trampoline group program for children with autism and/or sensory processing challenges. Motor gains were reported as well as gains in psychosocial functioning.
Effects of additional colostrum feeding on the levels of protein fractions in calves’ serum

The aim of this study was to investigate the impact of additional colostrum feeding to new born calves on levels of selected serum protein fractions. The study was conducted on a commercial dairy farm. Twenty-four cows of Polish Holstein-Friesian breed and their calves were included in the trial. Chemical composition, the somatic cell count (SCC), the total bacteria count (TBC) and immunoglobulin G (IgG) concentration were determined in colostrum samples. Blood was collected via jugular vein puncture from a total of 24 calves at the 36th hour of life. Total serum protein (TSP), as well as its fractions (albumins, globulins) and IgG concentration were determined. Greater IgG, total protein, albumins and globulins concentrations were found in serum of calves fed colostrum four times compared with three times on the first day of life. It was concluded that increasing the number of colostrum feedings to four times can be beneficial to forming of passive immunity of newborn calves.
Adsorptive removal of alizarin dye from wastewater using maghemite nanoadsorbents

ABSTRACT This is an investigation of the adsorptive removal of anthraquinone dyes, resembled by Alizarin, by utilizing maghemite iron oxide (γ-Fe2O3) nanoparticles in aqueous media. The adsorption process was affected by several parameters such as solution pH, adsorbent amount, contact time, and temperature. After optimizing the parameters affecting the adsorption, the process was successful in removing Alizarin dye with an efficiency exceeding 95%. Best adsorption results were achieved at a pH of 11 and contact time of 60 min. The adsorption was shown to follow the Langmuir model suggesting a monolayer and homogeneous coverage. The maximum adsorption capacity (qm ) was found to be 23.2 mg/g at pH = 11. A thermodynamic study showed that the adsorption process is exothermic and spontaneous at room temperature. The Gibbs free energy of adsorption (-6.79 kJ/mol) obtained in this study suggests a physisorption process. This finding has facilitated the regeneration of the Fe2O3 nanocatalyst. Both NaOH and HNO3 at dilute levels were tested for the regeneration of the nanocatalyst. Regeneration with HNO3 was successful up to four successive removal cycles with an efficiency >80%. Photodegradation experiments utilizing a UV light were also successful in maximizing the adsorption removal efficiency. A sorption mechanism based on the results obtained in this work is also proposed.
Exploring Cognitive Load of Single and Mixed Mental Models Gesture Sets for UAV Navigation

We compare four gesture sets for controlling a UAV in terms of cognitive load, intuitiveness, easiness, learnability, and memorability, by means of users' subjective feedback. Additionally, we evaluate the level of cognitive load associated with each gesture set under study using dual-task performance measures (errors and response time) and time perception. Our participants used all four gesture sets under study in a Wizard of Oz based simulated environment. Results confirm our hypothesis that mixed mental model gesture sets perform worse than single mental model gesture sets in terms of all the considered attributes. However, we did not find a significant difference in cognitive load between the three classes of mental models identified in our previous work.
Robust and adaptive control strategies for closed-loop climate engineering

Climate engineering aims to offset human-driven climate change through engineering interventions. This thesis focuses on the deployment of Solar Radiation Management (SRM) methods which aim to counteract radiative forcing generated by the concentration of atmospheric CO2. The climate system is investigated as a closed-loop control problem with uncertainties in its dynamics mitigated by robust and adaptive control strategies. Indeed, an adaptive controller for climate engineering is presented for the first time in a multi-variable control scheme. A low order three-box energy model is developed for the climate system to investigate such adaptive control strategies. Climate engineering measures are then deployed in 3 boxes, thus representing northern, southern and central (equatorial) bands. It is demonstrated that, through the on-line estimation of the controller parameters, adaptive control can overcome key-issues related to uncertainties of the climate model, external radiative forcing and actuator dynamics. The use of adaptive control provides a robust means of dealing with unforeseeable abrupt perturbations and the parametrisation of the model considered, while still providing bounds on stability and control performance. Importantly, the convergence of the controller is guaranteed through the Lyapunov stability criterion. Moreover, an analytical model describing the main latitudinal dynamics of the Earth’s climate with closed-loop control has been developed. This model has analytical solution and allows for quick evaluations of non-uniform climate engineering strategies. Multi-objective analyses are considered and analytical expressions for control laws with latitudinal resolution are obtained in several scenarios. Results are broadly comparable with the literature, demonstrating model’s utility in rapidly assessing climate engineering controls laws. Using the PDE model, ice line dynamics are investigated and a Lyapunov stability analysis is employed to estimate the maximum insolation reduction before the current climate falls into an ice-covered state. This provides an extreme operational boundary for future climate engineering ventures. Finally, the PDE model is employed to investigate strategies involving the deployment of space shields. The grade of obscuration provided at each latitude is estimated and an optimization process performed in order to minimize the shield size and to find the ideal orbit to counteract 2xCO2 concentration.
Knowledge-sharing and collaborative behaviour: An empirical study on a Portuguese higher education institution

Collaboration has been considered a way to address the challenges of the 21st century, fostering the necessary innovation, growth and productivity for all parties involved. Several studies reveal that collaboration can be strongly influenced by knowledge sharing. The literature suggests that this topic is quite relevant and that there is an evident lack of empirical studies that properly investigate the relationship between knowledge-sharing and collaborative behaviour in Higher Education Institutions (HEIs). In this context, the purpose of this work is to examine whether knowledge-sharing intention has a positive relationship with collaborative behaviour among professors and researchers in a public Portuguese HEI, taking into account other constructs that can have effect on the knowledge-sharing intention. In order to reach this objective, a conceptual research model was developed based on the theory of reasoned action. The empirical study was conducted based on a questionnaire, and the data analysis was performed using partial least squares. The results indicate that intrinsic motivation and networking are the factors that positively affect the attitude towards knowledge sharing. Nevertheless, it is concluded that trust is the variable that more strongly affects the knowledge-sharing intention. Finally, the study identified that knowledge-sharing intention has a positive influence in collaborative behaviour. It is considered that this study can contribute to support institutions’ management in defining strategies and developing actions in order to promote an organisational culture based on knowledge management that significantly leads to knowledge-sharing and collaboration relationships.
Land Utility Planning Layout Model Based on Constrained Conditions Cellular Automata

The deficiency of the current technique of general land use planning layout in our country is discussed,and the standard cellular automata's cellular meaning,rule definition etc are expanded.The land use planning layout model is presented under neighbor match promoting,constrained with suitable,constrained with inheritance,constrained with target of land use planning.And by taking Dongxing city of Guangxi autonomous region as an example,the simulatation of the model and the result analysis are made.
Studying Perturbations on the Input of Two-Layer Neural Networks with ReLU Activation

Studying Perturbations on the Input of Two-Layer Neural Networks with ReLU Activation Salman Alsubaihi Neural networks was shown to be very susceptible to small and imperceptible perturbations on its input. In this thesis, we study perturbations on two-layer piecewise linear networks. Such studies are essential in training neural networks that are robust to noisy input. One type of perturbations we consider is `∞ norm bounded perturbations. Training Deep Neural Networks (DNNs) that are robust to norm bounded perturbations, or adversarial attacks, remains an elusive problem. While verification based methods are generally too expensive to robustly train large networks, it was demonstrated in [1] that bounded input intervals can be inexpensively propagated per layer through large networks. This interval bound propagation (IBP) approach lead to high robustness and was the first to be employed on large networks. However, due to the very loose nature of the IBP bounds, particularly for large networks, the required training procedure is complex and involved. In this work, we closely examine the bounds of a block of layers composed of an affine layer followed by a ReLU nonlinearity followed by another affine layer. In doing so, we propose probabilistic bounds, true bounds with overwhelming probability, that are provably tighter than IBP bounds in expectation. We then extend this result to deeper networks through blockwise propagation and show that we can achieve orders of magnitudes tighter bounds compared to IBP. With such tight bounds, we demonstrate that a simple standard training procedure can achieve the best robustness-accuracy tradeoff across several architectures on both MNIST and CIFAR10. We, also, consider 5 Gaussian perturbations, where we build on a previous work that derives the first and second output moments of a two-layer piecewise linear network [2]. In this work, we derive an exact expression for the second moment, by dropping the zero mean assumption in [2].
INHIBITORY EFFECT OF CRUDE EXTRACTS DERIVED FROM AROMATIC PLANTS AGAINST WHITE MOLD OF BRASSICA JUNCEA VAR. TUMIDA

The objective of this study was to evaluate antifungal activity and resistance inducing potential of crude extracts derived from neem (Azadirachta indica) and ginger (Zingiber officinale) against three isolates of Sclerotinia sclerotiorum, the causal agent of mustard white mold under in vitro and in vivo conditions. In addition, enzymatic tests were carried out to assess the effect of crude extracts on activities of resistance-inducing enzymes in mustard leaves. The results showed that ethanol extracts of neem and ginger at concentration 2 g/l were able to reduce mycelial growth of the pathogen (isolate 3) by 61.5 and 44.3%, respectively. The ethanol extracts of neem and ginger at concentration 2 g/l reduced infection radius on plant leaves from 9.7 in control to 3.1 and 3.4, respectively, due to antifungal efficacy. In addition, ethanol extracts of neem and ginger at concentration 2 g/l decreased infection radius (isolate 1) on plant leaves from 9.5 in control to 2.1 and 2.3, respectively, seven days after application. Enzymatic analyses showed significant increase in level of chitinases, β-1,3-glucanase, Phenylalanine ammonia lyase and Peroxidase due to application of ethanol extracts of neem and ginger.
Pigmental Composition at of Freshwater Algae Different Levels of Nitrogen and Physicochemical Parameters of Palhi River, Baybay, Leyte

Freshwater algae are diverse group of organisms which impart important a functions in the ecosystem pertaining to water quality. Cultured freshwater algae were used to determine the effects of nitrogen in different levels on the pigment composition, , and total carotenoids. A three-month study chlorophyll a chlorophyll b of some physico-chemical properties of Palhi River as well as pigment content of freshwater algae conducted to find correlations on the different parameters was with that of the pigment concentration. Results of the study showed a strong positive correlation between chlorophyll a and nitrogen level, yet a weak correlation with and total carotenoids on chlorophyll b cultured freshwater algae. oth pH (6.06-6.92) and total dissolved solids (249-397 B ppm) are within the standard limits set by DENR, WHO and of Palhi river USEPA. However, the average total suspended solids (63.0 ppm) and total nitrogen (10.3 ppm) exceeded that of the tolerable limits set by national and international standards. he pigment composition of the freshwater algae showed T a strong negative correlation with the nitrogen level in Palhi River which could be attributed to the dynamics, biodiversity and complexities of the river system.
Learning Management Through Matching: A Field Experiment Using Mechanism Design

We place young professionals into established firms to shadow middle managers. Using random assignment into program participation, we find positive average effects on wage employment, but no average effect on the likelihood of self-employment. We match individuals to firms using a deferred-acceptance algorithm, and show how this allows us to identify heterogeneous treatment effects by firm and intern characteristics. We find striking heterogeneity in self-employment effects, and show that some assignment mechanisms can substantially outperform random matching in generating employment and income effects. These results demonstrate the potential for matching algorithms to improve the design of field experiments.
Multi-Objective Optimisation of Stereo Vision Algorithms

The process of stereo vision matches one or more images to recover the depth information of the pictured scene. Great progress is currently being made within this field, with algorithmic research, computational power developments, and cheaper cameras all contributing to give stereo vision great future potential as the depth measuring system of choice. One of the challenges of the stereo vision approach is the multitude of control parameters, which all affect algorithm behaviour. These parameters have traditionally been tuned by hand, with some limited use of computerized optimisation techniques. However, the process of evolutionary computation provides a promising method of optimisation of such complex problems. This thesis explored the possibility of applying a multi-objective evolutionary optimisation approach to the stereo algorithm parameter problem. In this regard, an automatic parameter optimisation framework based on the multi-objective optimisation algorithm NSGA-II was developed and tested. In order to judge the performance of the framework and the validity of the multi-objective approach, three different stereo algorithms were tested and a series of near pareto-optimal parameter sets were produced. One parameter set per algorithm was submitted to the official KITTI stereo vision benchmark ranking, and was able to improve upon the current official results.
On the Deployment of Wireless Sensor Networks for Air Quality Mapping: Optimization Models and Algorithms

Wireless sensor networks (WSNs) are widely used in environmental applications where the aim is to sense physical phenomena, such as temperature and air pollution. A careful deployment of sensors is necessary in order to get a better knowledge of these physical phenomena while ensuring the minimum deployment cost. In this paper, we focus on using WSN for air pollution mapping and tackle the optimization problem of sensor deployment. Unlike most of the existing deployment approaches that are either generic or assume that sensors have a given detection range, we define an appropriate coverage formulation based on an interpolation formula that is adapted to the characteristics of air pollution sensing. We derive, from this formulation, two deployment models for air pollution mapping using the integer linear programming while ensuring the connectivity of the network and taking into account the sensing error of nodes. We analyze the theoretical complexity of our models and propose the heuristic algorithms based on the linear programming relaxation and binary search. We perform extensive simulations on a dataset of the Lyon city, France, in order to assess the computational complexity of our proposal and evaluate the impact of the deployment requirements on the obtained results.