Miracle-dz/UniFAR-FTUs huggingface dataset:
SHORT TERM CHARACTERISTICS AND ENVIRONMENTAL AGING OF BIO-RESIN GFRP TESTED IN TENSION AND FOR CONFINEMENT OF CONCRETE CYLINDERS

ii Acknowledgements . iii List of Figures . vi List of Tables . ix Chapter 1 : Introduction . 1 1.1 GENERAL . 1 1.2 OBJECTIVES . 3 1.3 SCOPE . 3 1.4 THESIS FORMAT . 4 Chapter 2 : Mechanical Characteristics of Glass Fibre Reinforced Polymer made of Furfuryl Alcohol Bio-resin . 5 2.
Towards Delay-Minimal Scheduling through Reinforcement Learning in IEEE 802.15.4 DSME

The rise of wireless sensor networks (WSNs) in industrial applications imposes novel demands on existing wireless protocols. The deterministic and synchronous multi-channel extension (DSME) is a recent amendment to the IEEE 802.15. 4 standard, which aims for highly reliable, deterministic traffic in these industrial environments. It offers TDMA-based channel access, where slots are allocated in a distributed manner. In this work, we propose a novel scheduling algorithm for DSME which minimizes the delay in time-critical applications by employing reinforcement learning (RL) on deep neural networks (DNN).
Parthenolide ameliorates colon inflammation through regulating Treg/Th17 balance in a gut microbiota-dependent manner

Inflammatory bowel disease (IBD) is a global health problem in which gut microbiota dysbiosis plays an important pathogenic role. However, the current drugs for IBD treatment are far from optimal. Previous researches indicated that parthenolide (PTL) had not only anti-cancer properties but also strong anti-inflammatory activities. Rationale: To investigate the protective effect of PTL on colon inflammation and demonstrate the underlying gut microbiota-dependent mechanism. Methods: Colon inflammation severity in mouse model was measured by body weight change, mortality, colon length, disease activity index (DAI) score, H&E staining and colonoscopy evaluation. Gut microbiota alteration and short-chain fatty acids (SCFAs) production were analyzed through 16S rRNA sequencing and targeted metabolomics. Luminex cytokine microarray and Enzyme-linked immunosorbent assay (ELISA) were conducted to measure the colon cytokines profile. The frequency of immune cells in lamina propria (LP) and spleen were phenotyped by flow cytometry. Results: The PTL-treated mice showed significantly relieved colon inflammation, as evidenced by a reduction in body weight loss, survival rate, shortening of colon length, DAI score, histology score and colonoscopy score. Notably, when the gut microbiota was depleted using antibiotic cocktails, the protective effect of PTL on colon inflammation disappeared. PTL treatment downregulated the level of proinflammatory cytokines, including IL-1β, TNF-α, IL-6, and IL-17A and upregulated the immunosuppressive cytokine IL-10 in colon tissue. 16S rRNA sequencing indicated that PTL-treated mice exhibited much more abundant gut microbial diversity and flora composition. Targeted metabolomics analysis manifested the increased SCFAs production in PTL-treated mice. Additionally, PTL administration selectively upregulated the frequency of colonic regulatory T (Treg) cells as well as downregulated the ratio of colonic T helper type 17 (Th17) cells, improving the Treg/Th17 balance to maintain intestinal homeostasis. Gut microbiota depletion and fecal microbiota transplantation (FMT) was performed to confirm this gut microbiota-dependent mechanism. Conclusions: PTL ameliorated colon inflammation in a gut microbiota-dependent manner. The underlying protective mechanism was associated with the improved Treg/Th17 balance in intestinal mucosa mediated through the increased microbiota-derived SCFAs production. Collectively, our results demonstrated the role of PTL as a potential gut microbiota modulator to prevent and treat IBD.
Human Infection with Highly Pathogenic Avian Influenza A(H7N9) Virus, China

The recent increase in zoonotic avian influenza A(H7N9) disease in China is a cause of public health concern. Most of the A(H7N9) viruses previously reported have been of low pathogenicity. We report the fatal case of a patient in China who was infected with an A(H7N9) virus having a polybasic amino acid sequence at its hemagglutinin cleavage site (PEVPKRKRTAR/GL), a sequence suggestive of high pathogenicity in birds. Its neuraminidase also had R292K, an amino acid change known to be associated with neuraminidase inhibitor resistance. Both of these molecular features might have contributed to the patient’s adverse clinical outcome. The patient had a history of exposure to sick and dying poultry, and his close contacts had no evidence of A(H7N9) disease, suggesting human-to-human transmission did not occur. Enhanced surveillance is needed to determine whether this highly pathogenic avian influenza A(H7N9) virus will continue to spread.
Assessment of volume status with cardiovascular risk factors in hemodialysis patients

INTRODUCTION: Chronic fluid overload is a common issue in patients undergoing hemodialysis. In present study, we aimed to investigate fluid volume parameters by bioimpedance analysis (BIA) and their relationship to hemodynamic parameters as systolic and diastolic blood pressure, pulse wave velocity (PWv), PP, cardiac index (CI) and cardiac output (CO) in patients ongoing hemodialysis. METHODS: One hundred end stage renal disease patients who were medically stable and undergoing dialysis treatment for at least 3 months were enrolled to the study. Body compositions were analyzed with the BIA technique (BCM, Fresenius) that estimates systolic and diastolic blood pressure, body mass index (BMI) lean tissue index (LTI), extracellular volume (ECW), intracellular volume (ICW), lean tissue mass (LTM) and phase angle levels. PWv, pulse pressure, CI, CO, systolic pressure augmentation (AIx) and total vascular resistance (TVR) were evaluated with PWv analysis monitor (Mobil-O-Graph). RESULTS: The mean age was 47.5 ± 16.2. Ultrafiltration volume and overhydration frequency were positively correlated with systolic blood pressure, ECW, ICW, BMI (p<0.05) and hemodynamic parameters as PWv, PP, CO, TVR, CI and AIx (p<0.001), and negatively correlated with LTI (p <0.05). PP, CO, TVR, CI, AIx, Kt/V, E/I and phi50 were the predictors of pulse wave velocity (p<0.05). Systolic blood pressure, PP, CO, TVR, CI, AIx, PWv, ECW, ICW and E/I were the predictors of overhydration (p<0.001). DISCUSSION and CONCLUSION: Present study concluded that overhydration is associated with increased PWv, systolic blood pressure, PP and vascular resistance
CHALLENGES OF REGULAR TEACHERS IN IMPLEMENTING INCLUSIVE EDUCATION IN SCHOOLS OF MALDIVES

Inclusive education is in the early stages of implementation in the Republic of Maldives. Exploring the challenges faced by regular teachers will contribute greatly to the policymakers. This study explored the challenges faced by regular teachers (RTs) in implementing inclusive education in government primary schools in the Republic of Maldives. The instruments used in this mixed method research was a survey questionnaire and interviews. A survey questionnaire of three sections with an open-ended question on teachers' challenges was administered to randomly selected regular teachers (N=125) from 11 schools and six geographical locations. A semi-structured interview guide was used for regular teachers (N= 12). The data from the open-ended question was analysed for categories and the interview transcripts were thematically analysed. The challenges of RTs in implementing inclusive education in mainstream schools of Maldives were found to be lack of resources, large class size, lack of time, lack of trained teachers, and lack of knowledge. It is recommended for policymakers to provide necessary resources, bring changes to current policy of teacher allocation, provide regular teachers with skills and knowledge through professional development programs and conduct awareness programs to foster positive attitudes. It is proposed for policymakers to build partnerships with higher education institutions to develop innovative teacher education programs in preparing skilled teachers for implementing inclusive education in the Republic of Maldives.
The Effects of Orientation, Window Size, and Lighting Control to Climate-Based Daylight Performance and Lighting Energy Demand on Buildings in Tropical Area

The use of daylight offers a positive contribution to building energy savings yet also provoke a shortfall anticipation such as the large amount of light and visual discomfort. Moreover, building energy demand will also depend on the lighting control system combined with occupants’ behaviour of which is still rarely studied. This paper addressed the evaluation of climate-based daylight performance in a simulated reference office with a single unglazed opening measuring various window-to-wall ratio on one of the short façades facing different orientations. Modelling and simulation were performed in Radiance and Daysim using weather data of Yogyakarta and Bandung, Indonesia. Six different lighting control conditions were included. The following metrics observed are: average daylight factor, spatial daylight autonomy, spatial useful daylight illuminance, simplified daylight glare probability and lighting energy demand due to the lighting control system. Sensitivity analysis using multiple linear regression was also included. Excluding the average daylight factor, the results yielded similar trends for each metrics in two cities. However, the trend of sDA and sUDI-e in Yogyakarta are getting higher than Bandung in contrast with lighting energy demand. Moreover, combination switch-off occupancy & dimming system shows the most efficient lighting control system in all conditions.
From urban climate to building and material scale: a multiscale modeling approach

Modeling the local urban climate is a natural extension of the field of building simulation. To achieve appropriate modeling of the urban climate, a multiscale approach is proposed. Several models at different scales, such as city, neighborhood and street canyon, building and material scale, allow predicting, analyzing and mitigating negative effects of local heat islands on urban thermal comfort and building cooling demand.
An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows

Simplifying the task of resource management and scheduling for customers, while still delivering complex Quality-of-Service (QoS), is key to cloud computing. Many autoscaling policies have been proposed in the past decade to decide on behalf of cloud customers when and how to provision resources to a cloud application utilizing cloud elasticity features. However, in prior work, when a new policy is proposed, it is seldom compared to the state-of-the-art, and is often compared only to static provisioning using a predefined QoS target. This reduces the ability of cloud customers and of cloud operators to choose and deploy an autoscaling policy. In our work, we conduct an experimental performance evaluation of autoscaling policies, using as application model workflows, a commonly used formalism for automating resource management for applications with well-defined yet complex structure. We present a detailed comparative study of general state-of-the-art autoscaling policies, along with two new workflow-specific policies. To understand the performance differences between the 7 policies, we conduct various forms of pairwise and group comparisons. We report both individual and aggregated metrics. Our results highlight the trade-offs between the suggested policies, and thus enable a better understanding of the current state-of-the-art.
Energy Consumption of Institutional Buildings Considering the Urban Climate in Rome

Institutional buildings are very important considering the impact on energy consumption of a city. Due to the conditions of operation, the size and the density of occupants, institutional buildings represent a challenge to save energy in both cold and hot seasons. Urban climate influences the building performance, typically reducing thermal demand in winter and increasing in summer. The context is relevant in determining urban heat island effect, shadows, ventilation and in general the thermal environment that surrounds buildings. This paper develops a strategy to estimate the energy penalty obtained by including in building performance simulation the urban effects. Urban Weather Generator v. 4 beta and TRNSYS v. 17 tools are used to obtain base and urban energy demand through the whole year in three cases of study in Rome.
DrCoding: Using Transformers to Predict ICD-9 Codes from Discharge Summaries

Manual ICD code assignment underpins the way the health care system tracks diagnoses and procedures but continues to be error-prone and inconsistent between trained human coders. With the increasing adoption of electronic medical records, the interest in automating this process continues to grow. In this project, we train recent state-of-the-art Transformer-based models on clinical discharge summaries with the aim of improving the performance on the ICD-9 assignment task over existing LSTM-based methods. Fine-tuning BioBERT pre-trained context embed-dings which resulted in the best overall performance with an F1 score of 51.0%, outperforming the LSTM baseline by 12.1%. Augmenting Transformer-based models with ICD code descriptions was also shown to slightly increase the F1 score by 1.2%.
Nationwide Evaluation of Potential Energy Savings and Payback of Integrated Building and Battery Energy Storage System through Model Predictive Controls

Building-integrated microgrids (BIMGs) are rising in popularity due to their flexibility in incorporating multiple distributed energy resources including renewable energy sources and battery energy storage systems (BESS) and their natural suitability for demand response. To date, there have been almost no studies about the effect of various climate, building type, and electricity prices on BESS’s potential for peak load reduction and energy cost savings. In this study, all sixteen U.S. Department of Energy (DOE) commercial reference building types are simulated with weather data from eighty U.S. cities across all eight ASHRAE climate zones using model predictive control (MPC) algorithm and incorporating BESS and varying electricity price schemes for Intelligent Building to Battery (B2B) control. Results show that cities in colder climate zones can expect up to 3% greater cost and demand savings than in hotter climate zones; additionally, cities with time-of-use price scheme can expect up to 60% shorter payback period than those with tiered prices.
Detecting crisis-related tweets by example

Messages obtained from social media sources can be extremely helpful in crisis situations, but discovering relevant ones in a ﬂood of other data is not trivial. Methods have so far focused on universal detection models for crises or speciﬁc types of them. Event-speciﬁc models would fare better than those, but data collection and training is costly and may take too much time for realtime analysis and response. As a compromise, collecting a small amount of example messages manually is feasible and can be performed through a search for certain keywords or hashtags. Few-shot models can generalize to new examples with such a small handful of examples, and do not need be trained anew for each event. We show how such models can be used to detect crisis-relevant tweets with just 10 to 100 examples and counterexamples. We also apply a new type of few-shot model that does not require counterexamples (one-way prototypical networks).
Improvement of deep learning techniques for ICD-9 code classiﬁcation using MIMIC-III medical notes

ICD-9 code (International Classiﬁcation of Diseases, version 9) classiﬁcation has long been a burden on our healthcare system due to the time-intensive process of manual curation of disease codes. We apply current state of the art NLP models to the task of automatic ICD-9 code classiﬁcation on free-text clinical notes of MIMIC-III [1]. Our goal was to exceed the baselines established by Huang et al. [2], which achieved an F1 score of 0.696 and an accuracy of 0.896 from their best model, a GRU RNN, trained and tested on notes of type “Discharge summary. ” With our best model, based on ﬁne-tuning BlueBERT, we are able to achieve an accuracy of 0.8521 and F1 of 0.7074. We also evaluate the performance of these models when trained on notes of all types, which has not been done before to the best of our knowledge.
Targeting of Malawi's Farm Input Subsidy Programme based on Efficiency: Big Pain, Small Gain?

The Farm Input Subsidy Programme (FISP) in Malawi was introduced in the 2005/2006 season against a background of bad weather affecting production and prolonged food shortages. Vouchers are distributed empowering eligible farmers to exchange them for fixed quantities of inputs at subsidized prices. Since its inception, there has been a debate at national level about whether the FISP’s potential has been fully exploited, with policy makers exploring options to improve the programme. Proposals include targeting efficient and productive farmers to maximize returns. In this paper, we evaluate the effectiveness of these proposed changes to the existing FISP design by utilizing two waves of the LSMS-ISA survey merged with climatic data. We estimate how the national demand for agricultural inputs varies according to a variation in the targeting criteria by means of a two-stage demand system. Then, we identify more efficient farmers by means of a stochastic frontier approach. We observe a mismatch between voucher recipients and efficiency, with approximately 60% of vouchers being allocated to the three bottom quintiles of efficiency. This mismatch is observed also at the spatial level with more vouchers going to districts characterized by less efficient production. While concerns on the distributional impacts of the new criteria are discussed together with some suggestions for spatially diversifying the structuring of the policy and incentivizing crop diversification, our results highlight a high substitutability of commercial with subsidized inputs by new eligible farmers. Consequently, simulating the targeting policy variation we obtain an outcome that would lead only to a limited increase in predicted food expenditure ranging from 0.27% to 0.8% and maize production from 0.2% to 1.3. Scope for analysing different adjustments in the functioning of FISP are, thus, proposed to policy makers.
ALBERT Premium for SQuAD 2.0

This project aims at achieving a higher performance on the SQuAD 2.0 challenge. Our methods include data preprocessing, improving the ALBERT model, output postprocessing, and ensembling. First, we use data augmentation to increase the size of the training set. Then, we implement several improvements on ALBERT, including incorporating a question answerability classiﬁcation component, adding extra layers, and tuning the hyperparameters. Finally, after ensembling these models, we use named entity recognition to award the predicted answers with types of words corresponding to the question with a higher probability. Our proposed method reaches the EM/F1 scores of 79 . 949 / 82 . 584 on the test PCE-division leaderboard.
Activity-Travel: Enhancing FEATHERS Framework

8 Travel demand modeling has evolved from the traditional four-step models to tour-based models which 9 eventually became the basis of the advanced Activity-Based Models (ABM). The added value of the ABM 10 over others is its ability to test various policy scenarios by considering the complete activity-travel pattern 11 of individuals living in the region. However, the majority of the ABM restricts residents’ activities within 12 the study area which results in distorted travel patterns. The external travel is modeled separately via 13 external models which are insensitive to policy tests that an ABM is capable of analyzing. Consequently, 14 to minimize external travel, transport planners tend to define a larger study area. This approach, however, 15 requires huge resources which significantly deterred the worldwide penetration of ABM. To overcome 16 these limitations, this study presents a framework to model residents’ travel and activities outside the 17 study area as part of the complete activity-travel schedule. This is realized by including the Catchment 18 Area (CA), a region outside the study area, in the destination choice models. Within the destination choice 19 models, a top-level model is introduced that specifies for each activity its destination inside or outside the 20 study area. For activities to be performed inside the study area, the detailed land use information is utilized 21 to determine the exact location. However, for activities in the CA, another series of models are presented 22 that use land use information obtained from open-source platforms in order to minimize the data collection 23 efforts. These modifications are implemented in FEATHERS, an ABM operational for Flanders, Belgium 24 and the methodology is tested on three medium-sized regions within Flanders. The results indicate 25 improvements in the model outputs by defining medium-sized regions as study areas as compared to 26 defining a large study area. Furthermore, the Points of Interests (POI) density is also found to be 27 significant in many cases. Lastly, a comprehensive validation framework is presented to compare the 28 results of the ABM for the medium-sized regions against the ABM for Flanders. The validation includes 29 the (dis)aggregate distribution of activities, trips, and tours in time, space and structure (e.g. transport 30 modes used and types of activities performed) through eleven measures. The results demonstrate similar 31 distributions between the two ABM (i.e. ABM for medium-sized regions and for Flanders) and thus 32 confirms the validity of the proposed methodology. This study, therefore, shall lead to the development of 33 ABM for medium-sized regions. 34
Inhabitants’ Adaptive Strategies: A Case of Interactive Control in Residential Buildings

Building inhabitants act more complexly than architects, engineers and designers assume. Inhabitant actions vary depending on comfort preferences, body type, clothing preference, cultural habits, knowledge of building science, controls, awareness of sustainable practices and contingencies of life. These parameters are not definitive: there might be more of which we are not aware. The more people involved, the more complexity increases. This paper summarizes inhabitants’ adaptive strategies in residential buildings through a qualitative study on 14 households in Vancouver. It elucidates inhabitants’ comfort preferences, barriers to sustainable adaptation and the adaptive impact of a proposed system Light Weight Look Ahead Options (LWLA).
Demand Controlled Ventilation for Electric Demand Side Flexibility

The intermittency of renewable electricity generation and the increasing peak demand due to electric mobility pose challenges to the electric grid operation. Demand side flexibility (DSF) is becoming an important technique to counter these challenges, where the air-conditioning demand of commercial buildings has a particularly high potential to serve as a flexible load. This paper proposes demand controlled ventilation (DCV) as a method to increase the DSF of air-conditioned commercial buildings. Two DCV strategies are proposed based on (1) occupancy-based ventilation according to ASHRAE Standard 62.1 and (2) an indoor air quality (IAQ) model. The increase/changes in DSF are quantified for both the strategies. For a test case comprising a zone of an office building in Singapore, both DCV strategies are shown to increase the DSF when compared to a conservative ventilation strategy. For instance, the IAQbased DCV strategy allows for scheduling strategic reserves six-fold higher than the conservative ventilation strategy.