Miracle-dz/UniFAR-FTUs huggingface dataset:
of Chemical Engineering ON-LINE MONITORING OF BIOMASS CONCENTRATION BASED ON A CAPACITANCE SENSOR : ASSESSING THE METHODOLOGY FOR DIFFERENT BACTERIA AND YEAST HIGH CELL DENSITY FED-BATCH CULTURES

The performance of an in-situ capacitance sensor for on-line monitoring of biomass concentration was evaluated for some of the most important microorganisms in the biotechnology industry: Escherichia coli, Saccharomyces cerevisiae, Pichia pastoris and Bacillus megaterium. A total of 33 batch and fed-batch cultures were carried out in a bench-scale bioreactor and biomass formation trends were followed by dielectric measurements during the growth phase as well as the induction phase, for 5 recombinant E. coli strains. Permittivity measurements and viable cellular concentrations presented a linear correlation for all the studied conditions. In addition, the permittivity signal was further used for inference of the cellular growth rate. The estimated specific growth rates mirrored the main trends of the metabolic states of the different cells and they can be further used for setting-up control strategies in fed-batch cultures.
Effect of Preoperative Pregabalin on Induced Hypotension and Postoperative Analgesia with Functional Endoscopic Sinus Surgery

Background: Our target was to estimate the effect of pregabalin as premedication on deliberate hypotension and quality of the surgical site during functional endoscopic surgery for nasal sinuses (FESS) and its postoperative analgesic effect.Materials and methods: Eighty patients ASA I-II prepared for FESS were included in this randomized research. The study encompassed 2 groups [control group (C) and pregabalin group (P)]. Patients were given either placebo capsule [group (C)] or pregabalin capsule 150 mg [group (P)] 1 h preoperatively. During surgery mean arterial blood pressure (MAP) was maintained between 55-60 mmHg by intravenous infusion of nitroglycerin (NTG) at a rate of 0.5-10 μg/kg/min. We recorded quality of surgical site [using average category scale (ACS)], the overall dose of NTG used, postoperative pain, the overall dose of morphine used postoperatively, and number of patients requesting analgesia.Results: Pregabalin group (P) showed better ACS, lower overall dose of NTG used, less postoperative pain, lower overall dose of morphine, and less number of patients requesting analgesia than control group (C). Conclusion: Preoperative oral pregabalin improved the quality of the surgical site during FESS, and decreased the required dose of intraoperative hypotensive agent and postoperative morphine with better postoperative analgesia.
Insights into Dynamic Tuning of Magnetic-Resonant Wireless Power Transfer Receivers Based on Switch-Mode Gyrators

Magnetic-resonant wireless power transfer (WPT) has become a reliable contactless source of power for a wide range of applications. WPT spans different power levels ranging from low-power implantable devices up to high-power electric vehicles (EV) battery charging. The transmission range and efficiency of WPT have been reasonably enhanced by resonating the transmitter and receiver coils at a common frequency. Nevertheless, matching between resonance in the transmitter and receiver is quite cumbersome, particularly in single-transmitter multi-receiver systems. The resonance frequency in transmitter and receiver tank circuits has to be perfectly matched, otherwise power transfer capability is greatly degraded. This paper discusses the mistuning effect of parallel-compensated receivers, and thereof a novel dynamic frequency tuning method and related circuit topology and control is proposed and characterized in the system application. The proposed method is based on the concept of switch-mode gyrator emulating variable lossless inductors oriented to enable self-tunability in WPT receivers.
Probabilistic Natural Language Generation with Wasserstein Autoencoders

Probabilistic generation of natural language sentences is an important task in NLP. Existing models such as variational autoencoders (VAE) for sequence generation are extremely difficult to train due to the issues associated with the Kullback-Leibler (KL) loss collapsing to zero. One has to implement various heuristics such as KL weight annealing and word dropout in a carefully engineered manner to successfully train a text VAE. In this paper, we propose the use of Wasserstein autoencoders (WAE) for probabilistic natural language sentence generation. We show that sequence-to-sequence WAEs are more robust towards hyperparameters and can be trained in a straightforward manner without the need for any weight annealing. Empirical evidence shows that the latent space learned by WAEs exhibits properties of continuity and smoothness as in VAEs, while simultaneously achieving much higher BLEU scores for sentence reconstruction.
Developing a Framework for a Program Theory-Based Approach to Evaluating Policy Processes and Outcomes: Health in All Policies in South Australia

Background: The importance of evaluating policy processes to achieve health equity is well recognised but such evaluation encounters methodological, theoretical and political challenges. This paper describes how a program theorybased evaluation framework can be developed and tested, using the example of an evaluation of the South Australian Health in All Policies (HiAP) initiative. Methods: A framework of the theorised components and relationships of the HiAP initiative was produced to guide evaluation. The framework was the product of a collaborative, iterative process underpinned by a policy-research partnership and drew on social and political science theory and relevant policy literature. Results: The process engaged key stakeholders to capture both HiAP specific and broader bureaucratic knowledge and was informed by a number of social and political science theories. The framework provides a basis for exploring the interactions between framework components and how they shape policy-making and public policy. It also enables an assessment of HiAP’s success in integrating health and equity considerations in policies, thereby laying a foundation for predicting the impacts of resulting policies. Conclusion: The use of a program theory-based evaluation framework developed through a consultative process and informed by social and political science theory has accommodated the complexity of public policy-making. The framework allows for examination of HiAP processes and impacts, and for the tracking of contribution towards distal outcomes through the explicit articulation of the underpinning program theory.
Decomposing American immobility: Compositional and rate components of interstate, intrastate, and intracounty migration and mobility decline

Background: American migration rates have declined by nearly half since the mid-20th century, but it is not clear why. While the emerging literature on the topic stresses the salience of shifts in the composition of the American population, estimates of the contribution of population aging, increasing diversity, rising homeownership, and other shifts vary widely. Furthermore, it is unclear whether and how compositional shifts differ in their effects on migration over different geographic scales. Objective: To gauge the contribution of compositional shifts to concomitant declines in migration over various distances, while allowing for group variations in the rates at which declines occur. Methods: Drawing on individual-level IPUMS Current Population Survey data from 1982 to 2015, I use the Oaxaca–Blinder method to decompose declines in interstate migration, intrastate migration, and intracounty mobility. Results: Between a quarter and a third of declines since 1982 are attributable to aging and increasing diversity. Changing ethnoracial composition exerts a stronger influence on interstate migration, while aging has a stronger effect on local mobility. Results also reveal more dramatic declines among non-Latino Whites and those under age 35, as well as a marked delay and decline in peak mobility rates with each successive birth cohort. Conclusions: Results point to social and economic shifts as the key drivers of American immobility, and the need for reorientation within the emerging literature. Future research should investigate the causes of group-specific rates of decline and focus on local mobility, where declines are most concentrated and where rising immobility is most problematic.
Observations of reactive nitrogen oxide fluxes by eddy covariance above two midlatitude North American mixed hardwood forests

Abstract. Significant knowledge gaps persist in the understanding of forest–atmosphere exchange of reactive nitrogen oxides, partly due to a lack of direct observations. Chemical transport models require representations of dry deposition over a variety of land surface types, and the role of canopy exchange of NOx (= NO + NO2) is highly uncertain. Biosphere–atmosphere exchange of NOx and NOy (= NOx + HNO3 + PANs + RONO2 + pNO3− + .) was measured by eddy covariance above a mixed hardwood forest in central Ontario (Haliburton Forest and Wildlife Reserve, or HFWR), and a mixed hardwood forest in northern lower Michigan (Program for Research on Oxidants: Photochemistry, Emissions and Transport, or PROPHET) during the summers of 2011 and 2012 respectively. NOx and NOy mixing ratios were measured by a custom-built two-channel analyser based on chemiluminescence, with selective NO2 conversion via LED photolysis and NOy conversion via a hot molybdenum converter. Consideration of interferences from water vapour and O3, and random uncertainty of the calculated fluxes are discussed. NOy flux observations were predominantly of deposition at both locations. In general, the magnitude of deposition scaled with NOy mixing ratios. Average midday (12:00–16:00) deposition velocities at HFWR and PROPHET were 0.20 ± 0.25 and 0.67 ± 1.24 cm s−1 respectively. Average nighttime (00:00–04:00) deposition velocities were 0.09 ± 0.25 cm s−1 and 0.08 ± 0.16 cm s−1 respectively. At HFWR, a period of highly polluted conditions (NOy concentrations up to 18 ppb) showed distinctly different flux characteristics than the rest of the campaign. Integrated daily average NOy flux was −0.14 mg (N) m−2 day−1 and −0.34 mg (N) m−2 day−1 (net deposition) at HFWR and PROPHET respectively. Concurrent wet deposition measurements were used to estimate the contributions of dry deposition to total reactive nitrogen oxide inputs, found to be 22 and 40% at HFWR and PROPHET respectively.
Ground Water in Certain Sites in Egypt and Its Treatments Using a New Modified Ion Exchange Resin—Characterization of Water and Modified Ion Exchange

The present work is a comprehensive of drinking water quality. Eleven groundwater samples were taken from various rural regions of Egypt, the groundwater samples were investigated for chemical, radiometric and heavy metals analyses, the major cations including; sodium ( ), potassium ( ), calcium ( ) and magnesium ( ) ions species, the major anions of chloride ( ), sulphate ( ), nitrite ( ), phosphate ( ). Radiometric analyses in water expressed as the gross alpha and beta activity concentrations, heavy metals analyses including arsenic ( ), lead ( ), cobalt ( ), manganese ( ), iron ( ) and cadmium ( ) ions. The groundwater samples were found to contain high concentrations of heavy metals than the limited values of the world health organization (WHO). Heavy metals speciation were performed using MinteqA2 geochemical code. A modified exchange resin was prepared by polymerization of the condensed dioxalayl p-sulphanilamide with phenol, this ion exchange resin was examined by the different techniques such as; x-ray diffraction, infra red spectra (IR), and electronic microscopic, it was found a good adsorbent material that used for the reduction of heavy metals from contaminated groundwater samples.
Bayesian Optimization for Dynamic Problems

We propose practical extensions to Bayesian optimization for solving dynamic problems. We model dynamic objective functions using spatiotemporal Gaussian process priors which capture all the instances of the functions over time. Our extensions to Bayesian optimization use the information learnt from this model to guide the tracking of a temporally evolving minimum. By exploiting temporal correlations, the proposed method also determines when to make evaluations, how fast to make those evaluations, and it induces an appropriate budget of steps based on the available information. Lastly, we evaluate our technique on synthetic and real-world problems.
Evaluating Language Models of Tonal Harmony

This study borrows and extends probabilistic language models from natural language processing to discover the syntactic properties of tonal harmony. Language models come in many shapes and sizes, but their central purpose is always the same: to predict the next event in a sequence of letters, words, notes, or chords. However, few studies employing such models have evaluated the most state-of-the-art architectures using a large-scale corpus of Western tonal music, instead preferring to use relatively small datasets containing chord annotations from contemporary genres like jazz, pop, and rock. Using symbolic representations of prominent instrumental genres from the common-practice period, this study applies a flexible, data-driven encoding scheme to (1) evaluate Finite Context (or n-gram) models and Recurrent Neural Networks (RNNs) in a chord prediction task; (2) compare predictive accuracy from the best-performing models for chord onsets from each of the selected datasets; and (3) explain differences between the two model architectures in a regression analysis. We find that Finite Context models using the Prediction by Partial Match (PPM) algorithm outperform RNNs, particularly for the piano datasets, with the regression model suggesting that RNNs struggle with particularly rare chord types.
Lipid metabolism during endurance exercise 1–3

Endogenous triacylglycerols represent an important source of fuel for endurance exercise. Triacylglycerol oxidation increases progressively during exercise; the specific rate is determined by energy requirements of working muscles, fatty acid delivery to muscle mitochondria, and the oxidation of other substrates. The catecholamine response to exercise increases lipolysis of adipose tissue triacylglycerols and, presumably, intramuscular triacylglycerols. In addition, increases in adipose tissue and muscle blood flow decrease fatty acid reesterification and facilitate the delivery of released fatty acids to skeletal muscle. Alterations in fatty acid mobilization and the relative use of adipose and intramuscular triacylglycerols during exercise depend, in large part, on degree of fitness and exercise intensity. Compared with untrained persons exercising at the same absolute intensity, persons who have undergone endurance training have greater fat oxidation during exercise without increased lipolysis. Available evidence suggests that the training-induced increase in fat oxidation is due primarily to increased oxidation of non-plasma-derived fatty acids, perhaps from intramuscular triacylglycerol stores. Fat oxidation is lower in high-intensity exercise than in moderate-intensity exercise, in part because of decreased fatty acid delivery to exercising muscles. Parenteral lipid supplementation during high-intensity exercise increases fat oxidation, but the effect of ingesting long-chain or medium-chain triacylglycerols on substrate metabolism during exercise is less clear. This review discusses the relation between fatty acid mobilization and oxidation during exercise and the effect of endurance training, exercise intensity, and lipid supplementation on these responses. Am J Clin Nutr 2000;72(suppl):558S–63S.
Estimation of Graphical Models through Structured Norm Minimization

Estimation of Markov Random Field and covariance models from high-dimensional data represents a canonical problem that has received a lot of attention in the literature. A key assumption, widely employed, is that of {\em sparsity} of the underlying model. In this paper, we study the problem of estimating such models exhibiting a more intricate structure comprising simultaneously of {\em sparse, structured sparse} and {\em dense} components. Such structures naturally arise in several scientific fields, including molecular biology, finance, and political science. We introduce a general framework based on a novel structured norm that enables us to estimate such complex structures from high-dimensional data. The resulting optimization problem is convex and we introduce a linearized multi-block alternating direction method of multipliers (ADMM) algorithm to solve it efficiently. We illustrate the superior performance of the proposed framework on a number of synthetic data sets generated from both random and structured networks. Further, we apply the method to a number of real data sets and discuss the results.
Measuring Technology Acceptance Level of Teachers by Using Unified Theory of Acceptance and Use of Technology

Technology has undergone a lot of radical changes in the last years which have caused the implemention of new paradigms in different sectors. It is almost impossible for education not to be affected from this change in technology. It has shifted from the traditional applications to the technology use in the classrooms. In this case, teachers’ role in the application of technology into education has become an essential part of the research in the field. Also, in higher education, teachers become the key factors to the effective use of technology in the teaching and learning processes. Thus, teachers’ technology acceptance level remains an important issue. The term, technology acceptance refers to the adoption and use of technologies in the way they were designed for. The purpose of this study is to examine the technology acceptance level of teachers at Anadolu University School of Foreign Languages to test the Unified Theory of Acceptance and Use of Technology (UTAUT) that determines the variables influencing individuals’ technology acceptance. Firstly, a Likert scale type of scale was administered to measure UTAUT and the attitudes of teachers towards technology. The participants are the 44 lecturers who have started using technology recently at Anadolu University School of Foreign Languages. The findings reveal that the overall technology acceptance level of participants is average.
A Biosecure Composting System for Tilletia controversa Kühn-Infected Wheat Waste

Tilletia controversa Kuhn (TCK) has strong infectivity and viability and may cause great cut hazard to wheat and other crops. Composting treatment of TCK-infected wheat waste may be an effective method to eliminate the further contamination. This study applied microbial fermentation composting technique using mixed crop straws and manure to establish a bio-friendly composting method. The change of physicochemical properties of the compost was monitored regularly to detect the time course of TCK degradation and confirm the inactivation of TCK germination activity. The results of regular sampling indicate that the germination rate of TCK declines with the composting progress, and composting ten days under the condition of 50℃ to 60℃ completely inactivates the germination ability of TCK. Thus the present study provides a bio-friendly composting method for degrading TCK-infected wheat crops under TCK outbreak.
Aviva Stadium: A Case Study in Integrated Parametric Design

The nature of large complex buildings requires specialized skills across a multi-disciplinary team and high levels of collaboration and communication. By taking a parametric approach to design and construction, high quality results can be delivered on budget on time. This type of approach facilitates the opportunity for design teams to work in an iterative manner. A parametric model reduces the time associated with complex design changes while providing a centralized method for coordinating communication. In this paper the recently completed Aviva Stadium is used to illustrate the ways in which these benefits manifest themselves on built work. The authors identify the moments in the design and construction process that truly justify the effort in implementing a parametric approach. By approaching design in this way a “design conversation” can take place between parties involved, resulting in a better building.
3 D Spectral Nonrigid Registration of Facial Expression Scans

In this paper, we introduce a new template-based spectral nonrigid registration method in which the target is represented using multilevel partition of unity (MPU) implicit surfaces and the template is embedded in a discrete Laplace-Beltrami based spectral representation using the manifold harmonics transform (MHT). The implicit surface parametrization of the target allows us to avoid computing correspondences during the registration as in classical nonrigid iterative closest point (ICP) techniques. It also allows us to denoise the 3D scans and fill the holes by interpolating the noisy 3D data and to incorporate different types of 3D surfaces into our model, independently of their original parametrization. We take advantage of spectral geometry processing methods to compute a spectral embedding of the template and use it as a parametric surface deformation model. We optimize the nonrigid deformation directly in the spectral domain, thus effectively reducing the size of the parameter space as compared with the classical per vertex affine transformation deformation model. In addition, we introduce a new 3D facial expressions database, EPFL3DFace, on which we apply the proposed method to nonrigidly register 3D face scans that contain different expressions. This database consists of 3D scans of 120 subjects posing 35 different facial expressions. These include various standard prototypical facial expressions as well as individual action units, visemes, and the facial movement of biting one’s own upper lip, which are suitable for a large variety of
Tensor Monte Carlo: particle methods for the GPU era

Multi-sample, importance-weighted variational autoencoders (IWAE) give tighter bounds and more accurate uncertainty estimates than variational autoencoders (VAE) trained with a standard single-sample objective. However, IWAEs scale poorly: as the latent dimensionality grows, they require exponentially many samples to retain the benefits of importance weighting. While sequential Monte-Carlo (SMC) can address this problem, it is prohibitively slow because the resampling step imposes sequential structure which cannot be parallelised, and moreover, resampling is non-differentiable which is problematic when learning approximate posteriors. To address these issues, we developed tensor Monte-Carlo (TMC) which gives exponentially many importance samples by separately drawing $K$ samples for each of the $n$ latent variables, then averaging over all $K^n$ possible combinations. While the sum over exponentially many terms might seem to be intractable, in many cases it can be computed efficiently as a series of tensor inner-products. We show that TMC is superior to IWAE on a generative model with multiple stochastic layers trained on the MNIST handwritten digit database, and we show that TMC can be combined with standard variance reduction techniques.