Miracle-dz/UniFAR-FTUs huggingface dataset:
Mining layered technological information in scientific papers: A semi-supervised method

Tech mining is the application of text mining tools to science and technology information resources. The ever-increasing volume of scientific outputs is a boom to technological innovation, but it also complicates efforts to obtain useful and concise information for problem solving. This challenge extends to tech mining, where the development of techniques compatible with big data is an urgent issue. This article introduces a semi-supervised method for extracting layered technological information from scientific papers in order to extend the reach of tech mining. Our method starts with several pre-set seed patterns used to extract candidate phrases by matching the dependency tree of each sentence. Then, after a series of judgements, phrases are divided into two categories: ‘main technique’ and ‘tech-component’. (A technique, for the purposes of this study, is a method or tool used in the article being analysed.) In order to generate new patterns for subsequent iterations, a weighted pattern learning method is also adopted. Finally, multiple iterations of the method are applied to extract technological information from each paper. A dataset from the field of optical switcher is used to verify the method’s effectiveness. Our findings are that (1) by two loops of extraction process in each iteration, our method realises the layered technological information extraction, which contains the ‘part–whole’ relationships between main techniques and tech-components; (2) the recall rate for main techniques is superior to the baseline after iterating 23 rounds; (3) when layering is disregarded, in the aspect of the precision and the volume of techniques, the new method is higher than that for the baseline; and (4) adjusting another two parameters can optimise the efficiency – however, the effect is neither pronounced nor straightforward.
Autonomous thermography: towards the automatic detection and classification of building pathologies

Infrared thermography (IRT) is widely used for the study of the composition of building envelopes and the detection of faults in the building materials with influence on their thermal behaviour such as thermal bridges and moistures. The automation of the interpretation of the thermal images is proposed as a solution to help in the identification of the pathologies, speeding up the inspection process as well as avoiding the loss of detection of existing pathologies, or their misinterpretation. Specifically, thermal bridges and moisture areas are detected and classified, and their contours are extracted through the temperature analysis of the thermal images.
Development of dynamic Bayesian network for the analysis of high-dimensional biomedical data

Inferring gene regulatory networks (GRNs) from time-course expression data is a major challenge in Bioinformatics. Advances in microarray technology have given rise to cheap and easy production of high-dimensional biological datasets, however, accurate analysis and prediction have been hampered by the curse of dimensionality problem whereby the number of features exponentially larger than the number of samples. Therefore, the need for the development of better statistical and predictive methods is continually on the increase. The main aim of this thesis is to develop dynamic Bayesian network (DBN) methods for analysis and prediction temporal biomedical data. A two stage computational bionetwork discovery approach is proposed. In the ovarian cancer case study, 39 out of 592 metabolomic features were selected by the Least Angle Shrinkage and Subset Operator (LASSO) with highest accuracy of 93% and 21 chemical compounds identified. The proposed approach is further improved by the application of swarm optimisation methods for parameter optimization. The improved method was applied to colorectal cancer diagnosis with 1.8% improvement in total accuracy, which was achieved with much less feature subsets of clinical importance than thousands of features when compared to previous studies. In order to address the modelling inefficiencies in inferring GRNs from time-course data, two nonlinear hybrid algorithms were proposed using support vector regression with DBN, and recurrent neural network with DBN. Experiments showed that the proposed method was better at predicting nonlinearities in GRNs than previous methods. Stratified analysis using Ovarian cancer time-course data further showed that the expression levels Prostrate differentiation factor and BTG family member 2 genes, were significantly increased by the cisplatin and oxaliplatin platinum drugs; while expression levels of Polo-like kinase and Cyclin B1 genes, were both decreased by the platinum drugs. The methods and results obtained may be useful in the designing of drugs and vaccines.
Pediatric Heart Rate Variability Normative Values Related to Average Heart Rate and Age in a Developing Country

Cardiac autonomic function can evaluate with Heart Rate Variability (HRV) which is a noninvasive diagnostic method using mathematical algorithms [1]. In the past, many studies have been used to obtain information about the variability of the heart rate [25]. Although there are too many reference values for HRV parameters among adults in each sex and age group [6], there is not enough pediatric study in the literature [7-10]. The recent pediatric studies notice that autonomic nervous system (ANS) changes are associated with behavioral problems and overweight in children. Moreover, De Bock et al. suggested that in normal children, ANS activity changes over time and these changes lead to a decrease in vagal activity and predominance in sympathetic activity [11]. Eyre et al claimed that there may be a genetic difference making different ethnic groups more vulnerable to abnormal cardiovascular risk profiles [12]. These novel observations may have important consequences for our understanding of the high cardiovascular risk profile of which may emerge in childhood and develop in adulthood. Unfortunately, all of pediatric HRV studies were from development coutries, and there is no study to assess normative values of cardiac autonomic function in Turkey. The first aim of the present study was to identify normative values and the main factors of HRV in healthy children in a developing country. The secondary aim of the present study was to analyze the relationship between gender and age in healthy children and their effects on HRV with 24-hour ECG Holter records.
Agility in responding to disruptive digital innovation: Case study of an SME

Disruptive digital innovation (DDI) often creates hypercompetitive market environment that forces firms to be agile to survive and remain competitive. Whereas most studies have focused on larger firms' effort to be agile, few have looked at how small‐ and medium‐sized enterprises (SMEs) respond to DDI. The study attempts to answer the research question of how SMEs achieve agility to respond to DDI. Drawing on a case study of an innovative SME, our study develops a framework on agility based on the processes of mitigating organizational rigidity, developing innovative capabilities, and balancing the tension of organizational ambidexterity. Specifically, our findings show that for SMEs, mitigating organizational rigidity is enabled by the mechanism of achieving boundary openness while developing innovative capability is enabled by the mechanism of achieving organizational adaptability. At the same time, given the inherent challenges of resource constraints, SMEs also need to balance the tension of organizational ambidexterity.
Data-Aware Task Allocation for Achieving Low Latency in Collaborative Edge Computing

The recent trend in the Internet of Things (IoT) is to distribute and move the computation from centralized cloud devices to edge devices which are closer to data sources. Researchers have proposed collaborative edge computing for IoT where the data and computation tasks are shared among a network of edge devices. One of the important problems in collaborative edge computing is to schedule tasks among edge devices to minimize latency and other performance metrics. Compared to existing works in wireless sensor networks and IoT, there are two additional challenges while scheduling tasks in collaborative edge computing. First, we need to consider the transfer of input data required by different tasks as the data is generated by sensing devices which are located at different geographical places. Second, existing works solve the problem of task scheduling without considering network flow scheduling which can lead to network congestion and long completion times. In this paper, we study the data-aware task allocation problem to jointly schedule task and network flows in collaborative edge computing. We mathematically model the joint problem to minimize the overall completion time of the application. We have proposed a multistage greedy adjustment (MSGA) algorithm where the task scheduling is done by considering both placement of tasks and adjustment of network flows. Performance comparison done using simulation shows that MSGA leads to up to 27% improvement in completion time as compared to benchmark solutions.
Imatinib-Induced Interstitial Lung Disease: A Case Report

For more than a decade, Tyrosine Kinase Inhibitors (TKI) has been standard of care in the treatment of BCR-ABL positive Chronic Myeloid Leukemia (CML). The treatment has revolutionized the approach to CML and a vast majority of patients can now have unaltered lifespan with lifelong treatment. Generally the treatment is well tolerated with high-grade toxicities being rare. In this case report, a 69-year-old male patient with newly diagnosed chronic phase CML had a rapid hematological and compelling molecular response to imatinib mesylate (imatinib), but developed severe progressive Interstitial Lung Disease (ILD) within the first 3 months of treatment. Drug discontinuation and combined immunosuppressive therapy, with corticosteroids and azathioprine, alleviated the pulmonary symptoms, chest radiographs improved significantly, and the patient achieved nearly complete pulmonary recovery. This case report serves to raise awareness of ILD as a rare but very serious complication of TKI treatment in CML, where early diagnosis and intervention is of utmost importance since the condition is potentially life threatening, if unrecognized.
Paws for Thought: The Importance of Dogs in a Seniors Social Intervention

As Australia faces an aging population with an unprecedented life expectancy, it is the community’s obligation to ensure seniors are offered resources to support their wellbeing. Studies investigating loneliness in agedcare facilities attest to the therapeutic properties of dogs for residents’ sense of wellbeing. Consequently, this study aimed to ascertain the effectiveness of a communitybased dog lover’s initiative for the selfmanagement of wellbeing among senior citizens. Our qualitative research investigated whether community gatherings including dogs would produce greater feelings of subjective wellbeing among senior citizens compared to community gatherings without dogs. A conventional content analysis provided support for the supposition that dogs address some of the unmet needs of senior citizens by increasing wellbeing. The multitude of benefits provided by this humananimal friendship undeniably merits inclusion as a community initiative aimed at improving both the wellbeing of our senior citizens and the health of the community at large. It is anticipated that these findings will inspire a new field within social gerontology dedicated to promoting the humananimal bond via community initiatives.
An evaluation of health-related quality of life and its determinants among people living with HIV/AIDS attending tertiary care hospital of Kurnool, India

Background: HIV/AIDS is known to affect an individual not only physically but also mentally, socially, and financially. It is a syndrome that builds a vacuum in a person affecting his/her life as a whole. Combined with ART, Quality of life (QoL) is an important component in the evaluation of the wellbeing of people living with HIV/AIDS (PLHIV). The objective of this study is to assess health related quality of life of HIV/AIDS patients attending ART clinic Tertiary care hospital, GGH, Kurnool and to determine the association of socio-demographic and disease related variables with health related quality of life.Methods: A cross-sectional study was conducted from April 2019 to June 2019 involving 400 purposively selected HIV-positive patients of age >18 years, who were taking highly active anti-retroviral therapy for the past 6months from the ART center, GGH, Kurnool. After obtaining IEC clearance and informed consent, WHOQOL-BREF instrument was used for data collection. Data analysis was performed using IBM SPSS version 26.0. Results: Out of the 400 participants, 60% were males. The mean age of the participants was 38.5±10.54 years. Overall quality of life had a mean score of 69.71. Physical (82.57) and level of independence (78.78) domains showed higher mean score when compared to psychological (63.82), environmental (61.49) and Social (60.26) domains.Conclusions: Among study subjects 15.5% had excellent QoL (≥80), nearly 69.75% had good QoL (60-79) and 14.75% had poor QoL (<60). Low QoL scores were seen in the social domain, suggesting that more social interventions are required in this population.
Outcome prediction for patients with anterior circulation acute ischemic stroke following endovascular treatment: A single-center study

Previous studies have identified various factors associated with the outcomes of acute ischemic stroke (AIS) but considered only 1 or 2 predictive factors. The present study aimed to use outcome-related factors derived from biochemical, imaging and clinical data to establish a logistic regression model that can predict the outcome of patients with AIS following endovascular treatment (EVT). The data of 118 patients with anterior circulation AIS (ACAIS) who underwent EVT between October 2014 and August 2018 were retrospectively analyzed. The patients were divided into 2 groups based on the modified Rankin Scale score at three months after surgery, where 0–2 points were considered to indicate a favorable outcome and 3–6 points were considered a poor outcome. Non-conditional logistic stepwise regression was used to identify independent variables that were significantly associated with patient outcome, which were subsequently used to establish a predictive statistical model, receiver operating characteristic (ROC) curve was used to show the performance of statistical model and analyze the specific association between each factor and outcome. Among the 118 patients, 47 (39.83%) exhibited a good and 71 (60.17%) exhibited a poor outcome. Multivariate analysis revealed that the predictive model was statistically significant (χ2=78.92; P<0.001), and that the predictive accuracy of the model was 83.1%, which was higher compared with that obtained using only a single factor. ROC curve analysis shows the area under curve of the statistical model was 0.823, the analysis of diagnostic threshold for prognostic factors indicated that age, diffusion-weighted imaging lesion volume, glucose on admission, National Institutes of Health Stroke Scale score on admission and hypersensitive C-reactive protein were valuable predictive factors for the outcome of EVT (P<0.05). In conclusion, a predictive model based on non-conditional logistic stepwise regression analysis was able to predict the outcome of EVT for patients with ACAIS.
VHDL auto-generation tool for optimized hardware acceleration of convolutional neural networks on FPGA (VGT)

Convolutional Neural Network (CNN), a popular machine learning algorithm, has been proven as a highly accurate and effective algorithm that has been used in a variety of applications such as handwriting digit recognition, visual recognition, and image classification. State-of-the-art CNNs are computationally intensive, yet their parallel and modular nature make platforms like Field Programmable Gate Arrays (FPGAs) well suited for the acceleration process. Typically, Convolutional Neural Networks take a very long development round to be implemented or accelerated using FPGAs, hence in this thesis, we propose a VHDL generation tool (VGT), which through VHDL code (CNN architecture) can be on the fly generated for different CNN models (benchmarked and hand-tuned). The generated code or architecture is highly optimized, where it is modular, highly parallel, reconfigurable, scalable, fully pipelined, and adaptive to different CNN models. We demonstrate the automatic VHDL generation tool and its adaptability by implementing a small-scale CNN model “LeNet-5” and a large-scale one “AlexNet”. The generated code for the small-scale model does not incorporate any external memory management for the CNN parameters, whereas parameters are automatically hard-coded as constants unlike how it is typically done for large-scale models. On a Xilinx Virtex-7 running at 200 MHZ, the system is capable of processing up to 125k 28×28 Images per second for LeNet-5 and achieved a peak performance of 611.52 GOP/s for AlexNet.
A low cost modular actuator for dynamic robots

This thesis details the hardware and control development for a low-cost modular actuator, intended for use in highly dynamic robots. A small 12 degree of freedom quadruped robot has built using these actuators, on which several control experiments have been performed. Despite the relatively low cost of the actuators, the quadruped has demonstrated unprecedented dynamic behaviors for a robot of this scale and number of degrees of freedom, such as a full 360∘ backflip from standing on flat ground. Several other implementations of these actuators are also discussed, a including bilateral teleoperation and haptic feedback system and a 6 degree of freedom lower-body biped robot. Thesis Supervisor: Sangbae Kim Title: Associate Professor
Mineralocorticoid Receptor Antagonists in the Treatment of Coronary Artery Disease, Myocardial Infarction and Heart Failure

Affecting sodium reabsorption and potassium excretion in the kidney, mineralocorticoid receptor antagonists (MRA) were originally developed as antihypertensive drugs. After several large clinical trials, the concept of MR blockade has nowadays become a main treatment paradigm in heart failure with reduced ejection fraction (HFrEF) and for patients after myocardial infarction (MI) with left ventricular (LV) dysfunction. Recent analyses also point to a beneficial effect of early MRA treatment in patients with acute MI without LV dysfunction, however, there is no clear evidence yet. Although promising data from preclinical settings suggest that MRAs mediate favorable anti-atherogenic effects, clinical studies in patients with stable coronary artery disease (CAD) have not been able to detect differences of hard clinical outcomes. The concept might still be pursued using the most recent MRA, like the non-steroidal MR antagonist finerenone, and larger clinical trials need to be performed. Here, we review the current impact of MRA in patients with CAD and focus on the conflicting evidence of preclinical and clinical data in patients with stable CAD and preserved ejection fraction and summarize the current indications for MRA in these patients according to the guidelines.
Comparison of Ozone and Extracorporeal Shockwave Therapy in the Treatment of Chronic Lateral Epicondylitis

Objective: The aim of the current retrospective study was to compare the effects of ozone injections and Extracorporeal Shockwave Therapy in the alleviation of pain associated with chronic lateral epicondylitis. Method: A Retrospective Cohort Study was performed, and data was collected from the documented medical records of 89 patients with unilateral chronic lateral epicondylitis. Patients who received local injections of ozone (n=49) and Extracorporeal Shockwave Therapy (n=40) were evaluated. Pain assessment was made by means of Verhaar scores before and after the first injection and the at 1st, 3rd, 6th, and 9th months after treatment. The two groups were compared with respect to baseline demographics including age, gender, dominant and affected sides and Verhaar scores. Results: Evaluation of pain scores after the treatment showed significant differences between the two groups. Assessment of pain at the 3rd and 9th months after treatment demonstrated that the ozone group had statistically significantly better scores at rest (p<0.001), on compression (p<0.001) and during activity (p<0.001). Conclusion: Our results demonstrated that ozone injection may be superior over Extracorporeal Shockwave Therapy which is an effective therapeutic option for longterm pain relief in patients with chronic lateral epicondylitis who are refractory to conservative treatment.
The correlation of clinical and subclinical presentations with dengue serotypes and plasma viral load: the case of children with dengue hemorrhagic fever in Vietnam

Numerous studies have been carried out on patients afflicted with dengue hemorrhagic fever (DHF), but various issues related to the disease, including the characteristics of the dengue virus (DENV), remain unclear. To address this deficiency, the current research was conducted to determine the correlation of clinical and subclinical presentations with dengue serotypes and plasma viral load. This prospective cohort study, which was performed at Tien Giang General Hospital from 2009 to 2014, involved 481 children who were under 15 years of age and had DHF for less than 72 hours. Results showed that among the patients, the highest proportion were composed of those suffering from DENV-1 infection (44.7%). The progression of the disease to dengue shock syndrome (DSS) owing to infection with DENV-2 and DENV-1 was significantly higher than that caused by infection with DENV-3 and DENV-4. No statistically significant differences in DENV viremia were found between the non-shock DHF and DSS groups. Finally, no correlation was found between dengue plasma viral load and clinical and subclinical presentations. The findings led to the conclusion that dengue serotypes can be used as a basis in ascertaining the prognosis of DSS and DHF.
Adaptive robotic tutors for scaffolding self-regulated learning

This thesis explores how to utilise social robotic tutors to tackle the problem of providing children with enough personalised scaffolding to develop Self-Regulated Learning (SRL) skills. SRL is an important 21st century skill and correlates with measures of academic performance. The dynamics of social interactions when human tutors are scaffolding SRL are modelled, a computational model for how these strategies can be personalised to the learner is developed, and a framework for long-term SRL guidance from an autonomous social robotic tutor is created. To support the scaffolding of SRL skills the robot uses an Open Learner Model (OLM) visualisation to highlight the developing skills or gaps in learners' knowledge. An OLM shows the learner's competency or skill level on a screen to help the learner reflect on their performance. The robot also supports the development of meta-cognitive planning or forethought by summarising the OLM content and giving feedback on learners' SRL skills. Both short and longer-term studies are presented, which show the benefits of fully autonomous adaptive robotic tutors for scaffolding SRL skills. These benefits include the learners reflecting more on their developing competencies and skills, greater adoption SRL processes, and increased learning gain.