Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparation and properties of polyurethane coatings modified by polysilazane

Due to their unique physicochemical properties, polysilazanes exhibit excellent performance when combined with some resin matrixes, which had drawn great research attention. In this article, polyurethane (PU) was firstly prepared by polytetrahydrofuran glycol, isophorone diisocyanate, and 1,4-butanediol as main materials. Then, the prepared PU was blended with polysilazane by mixing the two solutions together, which was cured to films via dip-coating method at room temperature. The structure, thermal stability, and surface properties of the composite coatings were investigated by Fourier-transform infrared spectroscopy, thermogravimetric analysis, scanning electron microscopy, and X-ray photoelectron spectroscopy. The results demonstrated that after modification with polysilazane, the heat resistance, hydrophobicity, and mechanical property of the PU coatings were improved. When the content of polysilazane was 6 wt%, the mechanical property of the composite films was optimized, with a maximum tensile strength of 25.7 MPa and elongation at break of 797%. Meanwhile, the water contact angle of the composite film was 107° and the water absorption reached a minimum of 2.1%, which showed improved hydrophobicity and water resistance.
Advanced techniques for subsurface imaging Bayesian neural networks and Marchenko methods

Estimation of material properties such as density and velocity of the Earth’s subsurface are important in resource exploration, waste and CO2 storage and for monitoring changes underground. These properties can be used to create structural images of the subsurface or for resource characterisation. Seismic data are often the main source of information from which these estimates are derived. However the complex nature of the Earth, limitations in data acquisition and in resolution of images, and various types of noise all mean that estimates of material parameters also come with a level of uncertainty. The physics relating these material parameters to recorded seismic data is usually non-linear, necessitating the use of Monte Carlo inversion methods to solve the estimation problem in a fully probabilistic sense. Such methods are computationally expensive which usually prohibits their use over areas with many data, or for subsurface models that involve many parameters. Furthermore multiple unknown material parameters can be jointly dependent on each datum so trade-offs between parameters deteriorate parameter estimates and increase uncertainty in the results. In this thesis various types of neural networks are trained to provide probabilistic estimates of the subsurface velocity structure. A trained network can rapidly invert data in near realtime, much more rapidly than any traditional non-linear sampling method such as Monte Carlo. The thesis also shows how the density estimation problem can be reformulated to avoid direct trade-offs with velocity, by using a combination of seismic interferometry and Marchenko methods. First this thesis shows how neural networks can provide a full probability density function describing the uncertainty in parameters of interest, by using a
Effects of the type of school attended on students academic performance in Kericho and Kipkelion Districts, Kenya

This study aimed at finding out the effects of the type of school attended on male and female students, academic performance in Kenya. The study sought to explore the influence of the different male and female students’ school and societal socialisation experiences on their academic performance. The study used descriptive research design and two questionnaires for data collection. Descriptive statistics was used in data analysis. The research was guided by the following objectives; To establish whether social classroom interactions affect male and female students academic performance; Establish whether the type of school attended affect male and female students’ academic performance. The research also addresses the following research questions: what social classroom interactions affect male and female students’ academic performance; what effect does the type of school attended have on male and female students’ academic performance. The major findings of the study were that the type of school attended affected students’ academic performance as majority of the girls who qualified to join tertiary institution were from single-sex schools.
CoSafe: Securing Mobile Devices through Mutual Mobility Consistency Verification

As mobile devices play increasingly important roles in our daily lives, it is of great significance to protect personal mobile devices from being lost. Noticing the trend that one person normally carries more than one mobile device, we propose an innovative scheme, called CoSafe, to detect device loss by verifying the motion consistency between a pair of devices. The rationale is that the vibrations perceived on devices carried by the same person should be tightly coupled whereas a lost device would show distinct mobility characteristics from others. Specifically, CoSafe compares the mobility consistency between a pair of devices on three levels, where coarse features (i.e., the mobility state and motion periodicity) are first compared to give fast response and more complex comparison on subtle feature (i.e., the relative phase) is conducted only when needed. In this way, CoSafe can instantly respond and introduce very low computation and communication costs. We implement CoSafe on a Commercial-Off-The-Shelf Android smartphone and a smartwatch, and conduct both trace-driven simulations and real-world experiments to evaluate the performance of CoSafe. The results show that CoSafe achieves a mean false negative ratio and false positive ratio of 1.46 and 3.12 percent, respectively, even under sophisticated stealing attacks.
A new Mismatch cancelation for Quadrature Delta Sigma Modulator

This paper proposes a new mismatch cancelation technique for quadrature delta-sigma modulators (QDSM). In this approach, a high speed and simple structure dynamic element matching (DEM) based on homogenization and time-division (HTD) is designed. In addition, I and Q digital-to-analog converters (DACs) are merged into one complex DAC (C_DAC) for quadrature mismatch cancelation which leads to near-perfect I/Q balance. A third-order multi-bit continuous-time (CT) QDSM for a WCDMA LOW-IF receiver is designed and implemented in 180 nm CMOS technology to investigate the effects of the proposed DEM. The proposed DEM method and DWA algorithm are applied to the QDSM with 2% mismatch errors in DAC cells and compared two outputs PSD effects. Simulation results show that the modulator achieves a signal-to-noise ratio (SNR) of 74 dB and 74.2 dB for the proposed method and DWA, respectively, while the proposed method is simpler and faster than the data weighted averaging (DWA) algorithm.
Searching for biomarkers of disease-free survival in head and neck cancers using PET/CT radiomics

The goals of this thesis were to (1) study methodologies for radiomics data analysis, and (2) apply such methods to identify biomarkers of disease-free survival in head and neck cancers. Procedures for radiomics feature extraction and feature exploration in biomarker discovery were implemented with the PythonTM programming language. The code is available at https://github.com/gsel9/biorad. In a retrospective study of disease-free survival as response to radiotherapy, radiomics features were extracted from PET/CT images of 198 head and neck cancers patients. A total of 513 features were obtained by combining the radiomics features with clinical factors and PET parameters. Combinations of seven feature selection and 10 classification algorithms were evaluated in terms of their ability to predict patient treatment response. By using a combination of MultiSURF feature selection and Extreme Gradient Boosting classification, subgroup analyses ofHPV negative oropharyngeal (HPV unrelated) cancers gave 76.4 ± 13.2 % area under the Receiver Operating Characteristic curve (AUC). This performance was superior to the baseline of 54 % for disease-free survival outcomes in the patient subgroup. Four features were identified as prognostic of disease-free survival in the HPV unrelated cohort. Among these were two CT features capturing intratumour heterogeneity. Another feature described tumour shape and was, contrary to the CT features, significantly correlated with the tumour volume. The fourth feature was themedian CT intensity. Determining the prognostic value of these features in an independent cohort will elucidate the relevance of tumour volume and intratumour heterogeneity in treatment of HPV unrelated head and neck cancer.
Efficient Certificateless Aggregate Signature With Conditional Privacy Preservation in IoV

As an extension of traditional vehicular ad hoc networks, the Internet of Vehicles (IoV) enables information collection and dissemination, which brings a lot of convenience and benefits to the intelligent transportation systems. However, the booming IoV confronts a few challenges in the aspects of vehicle location privacy preservation and the authenticity of the transmitted information. In order to meet these challenges, we propose an efficient certificateless aggregate signature scheme with conditional privacy preservation in this article. Our scheme utilizes the technique of full aggregation to reduce the bandwidth resources and computing overhead. Besides, the conditional privacy preservation in IoV system is realized by using pseudonym mechanism. We demonstrate that the proposed scheme is secure against the Type-I and Type-II adversaries in the random oracle under the computational Diffie–Hellman assumption. In addition, the proposed scheme is compared with related works from aspects of computation cost, communication efficiency, and security requirements. The comparison results show that the proposed scheme is efficient, and it is suitable for resource-constrained environments.
Ultrafast VCSEL-based plasmonic polymerase chain reaction with real-time label-free amplicon detection for point-of-care diagnostics

Recent progress in microfluidics and optical systems has made enormous impact in the advancement of nucleic acid amplification and detection. However, commercial and currently reported microfluidic PCR devices have not yet found their utilization in point-of-care (POC) applications. This is due to long amplification time, high power requirement, and bulky size of commercial PCR machines or cost-inefficiency, complex fabrication and operation of microfluidic chips. In this work, we present a compact PCR device in which fast amplification is accomplished by photothermal heating of gold nanorods evenly dispersed in PCR reaction by a vertical-cavity surface-emitting laser (VCSEL). This thermocycler offers sub-ten-minute amplification time for 30 thermal cycles with high temperature stability and PCR products comparable to conventional bench-top machines. The proposed device is approximately 100mm×50mm×50mm in size, and its small footprint is obtained by hardware miniaturization. Retaining conventional sample volumes (20μL) makes our device more user-friendly in terms of sample loading and capable of more sensitive amplicon detection for on-site assays. Also, its cost-effectiveness due to disposable AuNRs and inexpensive light source outweigh surface plasmon heating methods utilizing embedded Au films with limited lifetimes and other previously presented plasmonic thermocyclers.
Perinatal Psychosocial Assessment-What are the Views of Health Professionals Working in the Private Obstetric Sector?

Background: Women are not universally or routinely screened ante-or postnatally for psychosocial risk factors, depression and anxiety in the private sector in Australia. There are limited studies that explore health professionals’ views on screening or perceived barriers to the screening process. Aim: The aim of this study was to discuss the health professionals’ views of psychosocial screening and assessment who work in the private obstetric sector. Methods: Semi-structured face-to-face interviews were completed with 11 midwives, 1 social worker and 2 obstetricians. Three hospital sites were chosen, of which only one currently screens women for psychosocial risk factors. Thematic analysis was applied to interview transcripts. Three researchers then discussed reoccurring themes and a consensus in themes and subthemes was reached. Results: Only one hospital was screening women and had midwives trained in psychosocial assessment including depression screening. There were mixed views on the process and barriers to screening were identified, e.g. lack of support systems, cultural barriers, inaccurate answers, power barriers with obstetricians, husband interference, fear and powerlessness. Benefits were recognised: early identification of difficulties, standardisation and patient-focused care. Concerns were, however, also evident: suicide ideation, intrusiveness of questions, whether women responded honestly, not wanting to screen all women. Conclusion: There was an identified concern by midwives that obstetricians did not take seriously any concerns highlighted by the midwife about women’s psychosocial problems. There was a sense of a lack of ‘ownership’ of the women, therefore a feeling of helplessness in addressing their needs. Suggestions were made: appropriate education and training of midwives, flagging high risk women, more in-house resources and external resources/community links and employing a central midwife with interest and expertise in psychosocial screening.
A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.
Business intelligence using the fuzzy-Kano model

Today, understanding customer satisfaction is becoming a difficult and complex task for companies due to the explosive growth of the voice of the customer in online reviews. This has pushed companies to rethink their business strategies and resort to business intelligence techniques in order to help them in analyzing customer requirements and market trends. This paper proposes a decision support framework for dynamically transforming the voice of the customer data into actionable insight. The framework measures the customer satisfaction by extracting key products’ aspects along with customers’ sentiments from online reviews using a text mining technique: the latent Dirichlet allocation approach. We apply the Fuzzy-Kano model to classify the real customer requirements, then, map them dynamically to the SWOT matrix. The proposed approach is extensively tested on an empirical dataset based on several performance metrics including accuracy, precision, recall, and F-score. The reported results showed that latent Dirichlet allocation approach has correctly extracted aspects with 97.4% accuracy and 92.4 % precision.
A periodic DFT study on adsorption of small molecules (CH4, CO, H2O, H2S, NH3) on the WO3 (001) surface-supported Au

Gas molecules (such as CH4, CO, H2O, H2S, NH3) adsorption on the pure and Au-doped WO3(001) surface have been studied by Density functional theory calculations with generalized gradient approximation. Based on the the calculation of adsorption energy, we found the most stable adsorption site for gas molecules by comparing the adsorption energies of different gas molecules on the WO3(001) surface. We have also compared the adsorption energy of five different gas molecules on the WO3(001) surface, our calculation results show that when the five kinds of gases are adsorbed on the pure WO3(001) surface, the order of the surface adsorption energy is CO > H2S > CH4 > H2O > NH3. And the results show that NH3 is the most easily adsorbed gas among the other four gases adsorbed on the surface of pure WO3(001) surface. We also calculated the five different gases on the Au-doped WO3(001) surface. The order of adsorption energy was found to be different from the previous calculation: CO > CH4 > H2S > H2O > NH3. These results provide a new route for the potential applications of Au-doped WO3 in gas molecules adsorption.
Chronic stress in sheep: assessment tools and their use in different management conditions

Chronic stress occurs when animals are unable to deal with a persistent stressor with species-typical responses, or when several stressors are present concurrently. Chronic stress is most frequently considered in intensive systems, but it may also be a welfare concern for extensively managed species, such as the sheep. Here we review behavioural and physiological responses of sheep to experimentally induced chronic stressors to determine relevant indicators of chronic stress. Neuroendocrine responses to chronic stress are difficult to interpret because initial responses are followed by an apparent normalisation. Thus, cortisol or catecholamines may be at or below pre-stress levels during chronic stress, but this varies with different stressors. Chronic stress can also affect reproductive function, impair body and wool growth and meat quality, reduce immune function, and is associated with greater parasite burdens in sheep. Chronic stress induces alterations in behaviour patterns, particularly activity and feeding, and circadian rhythms of behaviour. Stereotypic behaviours, however, are infrequent in sheep and may occur only in experimental conditions of social isolation. Behavioural and physiological data suggest that rough handling and sheepdogs may be sources of chronic stress for sheep. Social subordination and weaning also act as chronic stressors, leading to higher parasitism in these animals and a greater response to additional stressors. Lameness and parasitism are associated with physiological and behavioural responses indicating that these are severe forms of chronic stress in sheep. It is unclear whether environmental stressors, such as weather and food availability, induce chronic stress in sheep. Under-nutrition may, however, be a welfare concern through its impact on lamb survival. The existence of many sources of chronic stress in the management of sheep suggests that the welfare of this species requires more attention than it has currently received.
Selection for rapid uptake of scarce or fluctuating resource explains vulnerability of glycolysis to imbalance

Glycolysis is a conserved central pathway in energy metabolism that converts glucose to pyruvate with net production of two ATP molecules. Because ATP is produced only in the lower part of glycolysis (LG), preceded by an initial investment of ATP in the upper glycolysis (UG), achieving robust start-up of the pathway upon activation presents a challenge: a sudden increase in glucose concentration can throw a cell into a self-sustaining imbalanced state in which UG outpaces LG, glycolytic intermediates accumulate and the cell is unable to maintain high ATP concentration needed to support cellular functions. Such metabolic imbalance can result in “substrate-accelerated death”, a phenomenon observed in prokaryotes and eukaryotes when cells are exposed to an excess of substrate that previously limited growth. Here, we address why evolution has apparently not eliminated such a costly vulnerability and propose that it is a manifestation of an evolutionary trade-off, whereby the glycolysis pathway is adapted to quickly secure scarce or fluctuating resource at the expense of vulnerability in an environment with ample resource. To corroborate this idea, we perform evolutionary simulations of a simplified yeast glycolysis pathway consisting of UG, LG, phosphate transport between a vacuole and a cytosol, and a general ATP demand reaction. The pathway is evolved in constant or fluctuating resource environments by allowing mutations that affect the (maximum) reaction rate constants, reflecting changing expression levels of different glycolytic enzymes. We demonstrate that under limited constant resource, the population evolves to a genotype that is balanced but exhibits strongly imbalanced dynamics under ample resource conditions. Furthermore, when resource availability is fluctuating, the imbalanced phenotype enjoys a fitness advantage over balanced dynamics: when glucose is abundant, imbalanced pathways can quickly accumulate glycolytic intermediate FBP as intracellular storage that is used during periods of starvation to maintain high ATP concentration needed for growth. Our model further predicts that in environments with fluctuating resource, competition for glucose can result in stable coexistence of balanced and imbalanced cells, as well as repeated cycles of population crashes and recoveries that depend on such polymorphism. Overall, we demonstrate the importance of ecological and evolutionary arguments for understanding seemingly maladaptive aspects of cellular metabolism.
A Comparative simulation on the grounding grid system of a wind turbine with FEA software

The mathematical models proposed in the literature are used to determine the grounding resistance value for electrical installations. These models are mathematical expressions that change according to grounding type and the mechanical properties of the grounding system elements. Besides, the inclusion of nonlinear terms is another important issue in the modeling studies. For the modeling of nonlinear systems and the analysis of the faults that may occur, the finite element analysis (FEA) approach provides realistic results. In the present study, the simulation of a grid fault that may occur in the grounding system of a sample wind turbine (WT) is modeled by 2D electrostatic solver in FEA software. The simulation results prove that the findings are similar to the realistic faults in the grounding system.