Miracle-dz/UniFAR-FTUs huggingface dataset:
ACTIVITY OF THE IMMOBILIZED Candida rugosa LIPASE A Thesis Submitted In partial fulfillment of the requirement for the degree of MASTER OF SCIENCE IN CHEMISTRY

In the present study Candida rugosa lipase has been immobilized on MCM41 support through sol – gel process. The immobilized enzyme was entrapped by the poly condensation of silica precursors namely tetramethoxyorthosilane /tetraethoxyorthosilane(TEOS) and iso-butyltrimethoxysilane (isoBTMS). The immobilized enzyme was characterized by scanning electron microscopy, Fourier Transformed Infrared (FT–IR) and UV–Visible Spectrophotometer. Kinetic parameters viz., Michaelis-Menten constant (Km), Maximum rate of reaction (Vmax), Turnover number (kcat) and Specificity constant (kcat /Km) for immobilized Candida rugosa lipase were determined by using Michaelis-Menten and LineweaverBurk approach.
Inhibitors of social media as an innovative tool for advertising and marketing communication: evidence from SMES in a developing country

Innovative marketing has given rise to practitioners’ and scholars’ attention in the 21st-century market. Given this, social media marketing has become the order of the day when accessing modern tools for marketing communication. However, the adoption of this innovation comes with its associated challenges, particularly, from a developing country perspective. Against this background, this study sought to examine the inhibitors characterized in the application of social media channels as an innovative tool for advertising and communication among SMEs (small and medium enterprises) in Ghana. For study results to be achieved, a quantitative research approach with a questionnaire of 650 was distributed among managerial staff of the fashion industry in the central part of Ghana of which 512 respondents were duly received and correctly filled for data processing and analysis. Results from the partial least square structural equation method (PLS-SEM), showed that despite the importance of innovation, lack of managerial skills/marketing expertise, perceived cost, regular systems/links upgrade, and financial constraints are significant inhibiting factors affecting the application of social media as advertising and communication tools among SMEs in developing economy. Interestingly, the findings further showed that ’company’s size’ as well as ’availability of social media channel/tool’ significantly control for the outcome variable (internet/social media) as a marketing communication tool. AcknowledgmentThis work is supported by Tomas Bata University in Zlin through; IGA/FaME/2019/002: The role of the institutional environment in fostering entrepreneurship, and further supported by IGA/FaME/2020/002 and IGA/FaME/2019/008. The authors are thankful to Prof. Jaroslav Belás, Prof. Boris Popesko, and Prof. Miloslava Chovancová for their guidance towards developing this manuscript. &nbsp;
Potentially toxic metallic wear nanoparticles and trace metal ions release from metal-on-metal orthopedic implants in the human biological specimens: An Overview of in vivo and ex vivo clinical studies

The use of metallic biomaterials in the medical implant devices has become increasingly prevalent over the past few decades. Patients find themselves being exposed to metals in a variety of ways, ranging from external exposure to instruments such as medical devices to internal exposure via surgical devices being implanted in their bodies. In situ generation of metallic wear nanoparticles, corrosion products and in vivo trace metal ions release from metal and metallic alloys implanted into the body in orthopedic surgery is becoming a major cause for concern regarding the health and safety of patients. The chemical form, particulate vs. ionic, of the metal species in the bodily fluids and tissues is a key to the local nanotoxicity effects arising in the body. Potential health risks are associated with metallic wear debris in the form of nanoparticles in situ generation and the release of in vivo trace metal ions into human biological specimen's circulation. This overview explores how migration of metallic wear nanoparticles and ultratrace metal ions in the area of metal-on-metal orthopedic implants influences the surrounding tissues and bodily fluids, and what the toxicological consequences of this process may be. Specifically, the present article is more informative of indicative multilevel in situ/in vivo/ex vivo analytical/clinical methodologies which will be helpful in a way to plan, understand and lead the analytical innovations in the area of nano-analysis to improve patient outcomes.
Risk Taking and Impulsivity in Boredom: an EEG investigation

Previous research on boredom suggest it function as an important self-regulatory signal, indicating that the current state of the environment carries opportunity-costs and therefore driving the need to explore alternative activities. Trait boredom proneness is associated with negative consequences including increased risk-taking and impulsivity. These findings often rely on self-reports and not much is known about the role of state and trait boredom in controlled laboratory tasks, or their neural correlates. Sixty-two participants completed the Balloon Analogue Risk Task and a go/no-go task while electrical brain activity was recorded using EEG. Results showed that state boredom leads to impulsivity and poor performance monitoring, as evident by behavioral, subjective and ERP metrics. Trait boredom was associated with increased risk-taking, and modulated the correlation between errors and state boredom: high boredom proneness increased the sensitivity of trait boredom to errors. Overall, these findings emphasize the involvement of executive functions in the interaction between state and trait boredom.
Foster Parenting Practices that Improve the Placement Stability of Canadian Aboriginal Youth in State Residential Foster Care

Residential instability, or disruptions with children and youth in state-funded foster care, is a common occurrence with large financial and human costs. There is little Canadian or British Columbian (BC) research on what risk and protective factors and what foster parent practices create placement stability with Aboriginal children in [foster] care (CIC). This study was based on qualitative interviews with five veteran foster parents who have 48 years of combined caregiver experience serving BC Aboriginal youth with complex behaviours. The foster parents identified relationship practices between the foster parent, CIC, social worker, and the child welfare agency that were either a risk or protective factors for residential placement stability of their Aboriginal children. Foster parents reported utilizing specific practices of attachment, empathy, and communication that improved relationship building. Understanding what relationship-building practices reduce disruptions with CIC can inform both agency policy and foster parent and social worker training.
IMPACT OF FORMALDEHYDE ON MECHANICAL PROPERTIES OF ATHEROSCLEROTIC CAROTID ARTERIES

: Atherosclerosis is a life threatening cardiovascular disease causing lipoprotein accumulation within intima layer of artery wall and thus its thickening. Later stages are characterized by plaque formation with a lipid core separated from lumen by fibrous cap. In case of carotid arteries (CA), rupture of the plaque often results in a stroke. Detailed understanding of mechanical properties of atherosclerotic arteries and their components is essential for improvement of computer models used for prediction of plaque rupture. Samples of atherosclerotic CA from carotid endarterectomy were kept either in saline solution or in formaldehyde solution and then underwent uniaxial tensile testing in two directions. Statistical analysis of the obtained stress-strain responses shows that formaldehyde treatment of the tissue increases significantly the mean stiffness and reduces the dispersion.
Morrison 1..11

Three-dimensional (3D) printing offers the potential for rapid customization of medical devices. The advent of 3D-printable biomaterials has created the potential for device control in the fourth dimension: 3D-printed objects that exhibit a designed shape change under tissue growth and resorption conditions over time. Tracheobronchomalacia (TBM) is a condition of excessive collapse of the airways during respiration that can lead to life-threatening cardiopulmonary arrests. We demonstrate the successful application of 3D printing technology to produce a personalized medical device for treatment of TBM, designed to accommodate airway growth while preventing external compression over a predetermined time period before bioresorption. We implanted patient-specific 3D-printed external airway splints in three infants with severe TBM. At the time of publication, these infants no longer exhibited life-threatening airway disease and had demonstrated resolution of both pulmonary and extrapulmonary complications of their TBM. Long-term data show continued growth of the primary airways. This process has broad application for medical manufacturing of patient-specific 3D-printed devices that adjust to tissue growth through designed mechanical and degradation behaviors over time.
Sri Lankan women migrant workers and role of family and kinship networks

Migration from Sri Lanka to other countries has played an important role not only in Gross National Production of the country, but also social and cultural activities in the people. The International Labour Organization (ILO) estimated however, that the total number of legal migrant workers was 25 million and the illegal migrants were in the range of 45 million throughout the world (Sri Lanka Bureau of Foreign Employment -SLBFE 2014). In 2012, migrant worker remittances amounted to $6.1bn, or over 10% of GDP, the highest income from a foreign income source to the government. Many studies have focused on economic, social and cultural impact on the person, family and the society from the women migration to Middle East employment (Colen 1995; Gunatilake 1995; Gamburd, 1995; Wanninayake 2006). The aim of this paper in to explore the impact created by women’s migration on the family network and kinship relation and the find success achieved by their families. Particularly, when a woman leaves her family for foreign employment, instantly gaps and challenges arrive regarding the security of the family and taking care of children. In such situations, how does the family network and kinship networks operate, and with what effect? The study show when the woman absent, family members filling the gaps and share the service as well as how they managed, controlled, consumed, saved or invested remittance which have received as foreign earning by using their family and kinship networks. The study covers the two DS Divisions in administrative district of Kurunegala in North Western Province in Sri Lanka and selected a random sample for collecting data. The study manly depends on qualitative data using in-depth interviews, interview with key informants. © 2016 TAF Publishing. All rights reserved.
Growth of large protein crystals by a large‐scale hanging‐drop method

A method for growing large protein crystals is described. In this method, a cut pipette tip is used to hang large-scale droplets (maximum volume 200 µl) consisting of protein and precipitating agents. A crystal grows at the vapor–liquid interface; thereafter the grown crystal can be retrieved by droplet–droplet contact both for repeated macroseeding and for mounting crystals in a capillary. Crystallization experiments with peroxiredoxin of Aeropyrum pernix K1 (thioredoxin peroxidase, ApTPx) and hen egg white lysozyme demonstrated that this large-scale hanging-drop method could produce a large-volume crystal very effectively. A neutron diffraction experiment confirmed that an ApTPx crystal (6.2 mm3) obtained by this method diffracted to beyond 3.5 A resolution.
A Study of Effects of University ‟ s R & D Capabilities on the Technology Based Start-up

Technology based start-up(hereinafter “startup”) is develop the industry, cultures and are positioned as the universal means to create new value sector in the knowledge-based economy. This is because the traditional industrial capital is changing to the knowledge-based economic environment. Recently the reason that extra attention is focused on start-up, is because they are playing the role of core to pull the growth of the existing industries and create new market and the interest of ordinary people is increasing due to the popularization of technology start-up. However, in order to make a start-up firm and grow to reach the certain level, it is important to have the superiority of item, demand of market and timeliness with the technology based on R&D. In particular, for students who starting the start-up at the university, they are influenced by the R&D capabilities of the university (infra, knowledge, technical level etc) for the development of start-up item. The entrepreneurship and start-up support program are also acting as important factors for the beginning and success of startup. Such factors are playing the role to supplement the uncertain areas difficult for individuals to have a responsibility. This study intends to verify the effects that the R&D capabilities of university give to start-up of university students. In addition, I identified the influence by intermediary role of entrepreneurship and start-up support program and attempted the comprehensive analysis. To accomplish the purpose of the study, I formulated a hypothesis through advanced research and set the questionnaire items. For empirical study, I asked for cooperation to teachers, staffs and related persons of 43 start-up support teams are performing the start-up support projects of Korean government cross the country and carried out the research for university students who participated in start-up related programs and the questionnaires used for this study were total 435 copies. SPSS 22 was used to analyze the collected data. As a result of analysis based on the collected questionnaires, it showed that the intention of start-up, entrepreneurship and start-up support program were influenced directly by the R&D capabilities. In addition, it was identified that the entrepreneurship and start-up support program were effective as partial parameters. These results give message that the R&D capabilities had a major influence for Intention of start-up and in order to encourage high technology start-up of university students, it is needed to expand R&D capabilities of university.
Superiority of RIPASA over modified ALVARADO scoring systems for the diagnosis of acute appendicitis

Background: Acute appendicitis is the most common abdominal emergency. The diagnosis is primarily clinical. Various scores have been established based on clinical, laboratory and radiological investigations for the accurate diagnosis and to reduce the negative appendicectomy rates.Methods: A prospective observational study in the Department of General Surgery was conducted from February 2018 to September 2019. A total of 50 cases were studied. Alvarado and Ripasa scores were calculated and accordingly decision was taken to operate or conserved. Diagnosis was confirmed on histopathological evaluation and the scores were compared on the basis of their sensitivity, specificity, positive predictive value, negative predictive value.Results: RIPASA score is a more valuable tool for diagnosing acute appendicitis with 78.72% sensitivity and specificity 33.33%, in spite of sophisticated investigations like CT, thus reducing the cost of treatment especially in developing countries. In our study. Negative appendicectomy rate was 6% (3 cases out of 50) which is appreciably less than traditional literature.Conclusions: Our study concludes that in the diagnosis of acute appendicitis Ripasa score is more specific than Modified Alvarado score. It helps in categorization for the proper management. It not only reduces the number of "missed appendicitis" but also reduces the cost of treatment by avoiding imaging modalities.
Magnetic white dwarfs in post-common-envelope binaries

Magnitude-limited samples have shown that 20–25 per cent of cataclysmic variables contain white dwarfs with magnetic fields of Mega Gauss strength, in stark contrast to the approximately 5 per cent of single white dwarfs with similar magnetic field strengths. Moreover, the lack of identifiable progenitor systems for magnetic cataclysmic variables leads to considerable challenges when trying to understand how these systems form and evolve. Here, we present a sample of six magnetic white dwarfs in detached binaries with low-mass stellar companions where we have constrained the stellar and binary parameters including, for the first time, reliable mass estimates for these magnetic white dwarfs. We find that they are systematically more massive than non-magnetic white dwarfs in detached binaries. These magnetic white dwarfs generally have cooling ages of more than 1 Gyr and reside in systems that are very close to Roche lobe filling. Our findings are more consistent with these systems being temporarily detached cataclysmic variables, rather than pre-cataclysmic binaries, but we cannot rule out the latter possibility. We find that these systems can display unusual asymmetric light curves that may offer a way to identify them in larger numbers in future. Seven new candidate magnetic white dwarf systems are also presented, three of which have asymmetric light curves. Finally, we note that several newly identified magnetic systems have archival spectra where there is no clear evidence of magnetism, meaning that these binaries have been previously missed. Nevertheless, there remains a clear lack of younger detached magnetic white dwarf systems.
Machine Learning Percolation Model

Recent advances in machine learning have become increasingly popular in the applications of phase transitions and critical phenomena. By machine learning approaches, we try to identify the physical characteristics in the two-dimensional percolation model. To achieve this, we adopt Monte Carlo simulation to generate dataset at first, and then we employ several approaches to analyze the dataset. Four kinds of convolutional neural networks (CNNs), one variational autoencoder (VAE), one convolutional VAE (cVAE), one principal component analysis (PCA), and one $k$-means are used for identifying order parameter, the permeability, and the critical transition point. The former three kinds of CNNs can simulate the two order parameters and the permeability with high accuracy, and good extrapolating performance. The former two kinds of CNNs have high anti-noise ability. To validate the robustness of the former three kinds of CNNs, we also use the VAE and the cVAE to generate new percolating configurations to add perturbations into the raw configurations. We find that there is no difference by using the raw or the perturbed configurations to identify the physical characteristics, under the prerequisite of corresponding labels. In the case of lacking labels, we use unsupervised learning to detect the physical characteristics. The PCA, a classical unsupervised learning, performs well when identifying the permeability but fails to deduce order parameter. Hence, we apply the fourth kinds of CNNs with different preset thresholds, and identify a new order parameter and the critical transition point. Our findings indicate that the effectiveness of machine learning still needs to be evaluated in the applications of phase transitions and critical phenomena.
Active Hybrid Classification

Hybrid crowd-machine classifiers can achieve superior performance by combining the cost-effectiveness of automatic classification with the accuracy of human judgment. This paper shows how crowd and machines can support each other in tackling classification problems. Specifically, we propose an architecture that orchestrates active learning and crowd classification and combines them in a virtuous cycle. We show that when the pool of items to classify is finite we face learning vs. exploitation trade-off in hybrid classification, as we need to balance crowd tasks optimized for creating a training dataset with tasks optimized for classifying items in the pool. We define the problem, propose a set of heuristics and evaluate the approach on three real-world datasets with different characteristics in terms of machine and crowd classification performance, showing that our active hybrid approach significantly outperforms baselines.
Unequal Error Protection Achieves Threshold Gains on BEC and BSC via Higher Fidelity Messages

Because of their capacity-approaching performance, graph-based codes have a wide range of applications, including communications and storage. In these codes, unequal error protection (UEP) can offer performance gains with limited rate loss. Recent empirical results in magnetic recording (MR) systems show that extra protection for the parity bits of a low-density parity-check (LDPC) code via constrained coding results in significant density gains. In particular, when UEP is applied via more reliable parity bits, higher fidelity messages of parity bits are spread to all bits by message passing algorithm, enabling performance gains. Threshold analysis is a tool to measure the effectiveness of a graph-based code or coding scheme. In this paper, we provide a theoretical analysis of this UEP idea using extrinsic information transfer (EXIT) charts in the binary erasure channel (BEC) and the binary symmetric channel (BSC). We use EXIT functions to investigate the effect of change in mutual information of parity bits on the overall coding scheme. We propose a setup in which parity bits of a repeat-accumulate (RA) LDPC code have lower erasure or crossover probabilities than input information bits. We derive the a-priori and extrinsic mutual information functions for check nodes and variable nodes of the code. After applying our UEP setup to the information functions, we formulate a linear programming problem to find the optimal degree distribution that maximizes the code rate under the decoding convergence constraint. Results show that UEP via higher fidelity parity bits achieves up to about $17\%$ and $28\%$ threshold gains on BEC and BSC, respectively.
Artificial intelligence prediction of stock prices using social media

The primary objective of this work is to develop a Neural Network based on LSTM to predict stock market movements using tweets. Word embeddings, used in the LSTM network, are initialised using Stanford's GloVe embeddings, pretrained specifically on 2 billion tweets. To overcome the limited size of the dataset, an augmentation strategy is proposed to split each input sequence into 150 subsets. To achieve further improvements in the original configuration, hyperparameter optimisation is performed. The effects of variation in hyperparameters such as dropout rate, batch size, and LSTM hidden state output size are assessed individually. Furthermore, an exhaustive set of parameter combinations is examined to determine the optimal model configuration. The best performance on the validation dataset is achieved by hyperparameter combination 0.4,8,100 for the dropout, batch size, and hidden units respectively. The final testing accuracy of the model is 76.14%.
Iterative Optimisation with an Innovation CNN for Pose Refinement

Object pose estimation from a single RGB image is a challenging problem due to variable lighting conditions and viewpoint changes. The most accurate pose estimation networks implement pose refinement via reprojection of a known, textured 3D model, however, such methods cannot be applied without high quality 3D models of the observed objects. In this work we propose an approach, namely an Innovation CNN, to object pose estimation refinement that overcomes the requirement for reprojecting a textured 3D model. Our approach improves initial pose estimation progressively by applying the Innovation CNN iteratively in a stochastic gradient descent (SGD) framework. We evaluate our method on the popular LINEMOD and Occlusion LINEMOD datasets and obtain state-of-the-art performance on both datasets.