Miracle-dz/UniFAR-FTUs huggingface dataset:
The development and evaluation of methods for the screening of steroids using molecular imprinted polymers coupled with comprehensive gas chromatography/mass spectrometry and aptamers coupled with liquid chromatography/mass spectrometry

The profiling of hormones is an important requirement in sports dope testing and it is increasingly being used for clinical applications. The screening of samples for the detection of steroids is usually undertaken in urine and these analyses present many challenges. Issues related to the complexity of the matrix, the separation of exogenous from endogenous species and the very low detection limits have placed increasing demands on clinical and International Olympic Commission accredited laboratories. Whilst liquid chromatography (LC) systems provide a highly sensitive detection method for targeted species, gas chromatography, especially when coupled to mass spectrometry, is considered preferable for the screening of a wider range of target and unknown species. Existing chromatography methods are not without their limitations, not least the difficulty in reproducibly derivatising the wide range of steroids that can be exogenously administered, in particular the so called ‘designer steroids’ which deliberately hinder this process. Initially, a pilot study was conducted to investigate whether aptamer-coated magnetic beads could provide a simple, low cost method for the extraction of estradiol from urine, prior to its detection and quantification with mass spectrometry. In reality, the process necessitated the use of additional solvent extraction steps to the standard method applied previously. These additional steps added complexity and were not without issue and the limit of detection was not found to be adequate for the proposed clinical purpose. The project explored the application of comprehensive gas chromatography/mass spectrometry (GC x GC/MS) coupled to bespoke molecular imprinted polymers (MIPS) as an alternative solution to the problem of the screening of multiple steroids in urine. Twelve structurally similar and commercially relevant steroids were used to develop the chromatographic method. Once optimised, these compounds were then extracted from synthetic urine using a MIP templated with testosterone and developed during the project. Significant improvements compared to commercially available C-18 materials were found. All the steroids were identified and detected when spiked at the 10 ng/ml level, as required by World Anti-Doping Agency (WADA) at the time. The method was further validated and then successfully applied to the extraction of designer steroids from synthetic urine, in the presence of the original steroids. Epistane, which has a different structure to testosterone, was not detected indicating the efficacy of the templating process.
Practical Computational Approach for the Stability Analysis of Fuzzy Model-Based Predictive Control of Substrate and Biomass in Activated Sludge Processes

This paper presents a procedure for the closed-loop stability analysis of a certain variant of the strategy called Fuzzy Model-Based Predictive Control (FMBPC), with a model of the Takagi-Sugeno type, applied to the wastewater treatment process known as the Activated Sludge Process (ASP), with the aim of simultaneously controlling the substrate concentration in the effluent (one of the main variables that should be limited according to environmental legislations) and the biomass concentration in the reactor. This case study was chosen both for its environmental relevance and for special process characteristics that are of great interest in the field of nonlinear control, such as strong nonlinearity, multivariable nature, and its complex dynamics, a consequence of its biological nature. The stability analysis, both of fuzzy systems (FS) and the very diverse existing strategies of nonlinear predictive control (NLMPC), is in general a mathematically laborious task and difficult to generalize, especially for processes with complex dynamics. To try to minimize these difficulties, in this article, the focus was placed on the mathematical simplification of the problem, both with regard to the mathematical model of the process and the stability analysis procedures. Regarding the mathematical model, a state-space model of discrete linear time-varying (DLTV), equivalent to the starting fuzzy model (previously identified), was chosen as the base model. Furthermore, in a later step, the DLTV model was approximated to a local model of type discrete linear time-invariant (DLTI). As regards the stability analysis itself, a computational method was developed that greatly simplified this difficult task (in a local environment of an operating point), compared to other existing methods in the literature. The use of the proposed method provides useful conclusions for the closed-loop stability analysis of the considered FMBPC strategy, applied to an ASP process; at the same time, the possibility that the method may be useful in a more general way, for similar fuzzy and predictive strategies, and for other complex processes, was observed.
Growth and doping of silicon carbide with germanium: a review

Abstract This paper review the research works made so far in associating Ge isoelectronic element to SiC crystals, either by incorporating it inside SiC matrix or for assisting SiC epitaxial growth. The incorporation mechanism and level of incorporation of Ge in SiC during crystal growth with different techniques (sublimation, chemical vapor deposition, vapor-liquid-solid) are compared and discussed. Ge doping level as high as 2-3x1020 at.cm−3 can be reached without affecting SiC crystalline quality but generating some strain. Higher Ge incorporation levels up to few at% can be reached using farer-to-equilibrium conditions such as ion implantation or molecular beam epitaxy. The former allows retaining 4H-SiC polytype while the latter leads exclusively to defective 3C-SiC polytype. Adding Ge to SiC crystal growth was also used for promoting 3C-SiC heteroepitaxy on Si and on α-SiC substrates, the latter case being more successful. The reported modifications and improvements of SiC crystalline and electronic properties by the incorporation of Ge element are discussed in order to draw or clearer picture of SiC:Ge material. Based on such discussion, some short- and long-term perspectives are proposed
Radial Growth Response of European Larch Provenances to Interannual Climate Variation in Poland

The present study identified the similarities and differences in the radial growth responses of 20 provenances of 51-year-old European larch (Larix decidua Mill.) trees from Poland to the climatic conditions at three provenance trials situated in the Polish lowlands (Siemianice), uplands (Bliżyn) and mountains (Krynica). A chronology of radial growth indices was developed for each of 60 European larch populations, which highlighted the interannual variations in the climate-mediated radial growth of their trees. With the aid of principal component, correlation and multiple regression analysis, supra-regional climatic elements were identified to which all the larch provenances reacted similarly at all three provenance trials. They increased the radial growth in years with a short, warm and precipitation-rich winter; a cool and humid summer and when high precipitation in late autumn of the previous year was noted. Moreover, other climatic elements were identified to which two groups of the larch provenances reacted differently at each provenance trial. In the lowland climate, the provenances reacted differently to temperature in November to December of the previous year and July and to precipitation in September. In the upland climate, the provenances differed in growth sensitivity to precipitation in October of the previous year and June–September. In the mountain climate, the provenances responded differently to temperature and precipitation in September of the previous year and to precipitation in February, June and September of the year of tree ring formation. The results imply that both climatic factors and origin (genotype), i.e., the genetic factor, mediate the climate–growth relationships of larch provenances.
What Corporates Can Do to Help an Innovation Ecosystem Thrive -- and Why They Should Do It

Given the pace of change in nearly every aspect of society, transformative innovation is imperative. At the same time transformation is very difficult for large established companies. Open innovation – collaboration with outside entities such as startups -- is a powerful tool for exploring both business model and technological innovation. A thriving ecosystem provides a healthy environment in which dramatically different types of entities can find each other and the resources they need to explore and ultimately engage in transformative innovation. Given these benefits, corporates can and should play a role in the creation and growth of thriving ecosystems. We describe the work done to create the life sciences ecosystem in Boston/Cambridge through the eyes of Author 2 who was a central leader in that effort. And we describe in detail both the benefits corporates can enjoy, and the role corporates can play in developing a thriving ecosystem.
Human-induced loading and dynamic response of footbridges in the vertical direction due to restricted pedestrian traffic

Abstract Despite extensive research in the field of human-induced vibration in the last twenty years, there is still lack of reliable load and response models to assess vibration serviceability of footbridges under human-induced excitation, especially with reference to crowded conditions. This paper aims to provide a reliable characterization of pedestrian-induced loading on footbridges in restricted traffic condition, and to compare the reliability of current guidelines and advanced spectral models in the serviceability assessment of footbridges with respect to vertical vibrations. Numerical simulations of restricted pedestrian traffic are carried out through an agent-based model, considering variable pedestrian densities and deck widths. The probability distribution of the step frequency and the power spectral density function of the modal force are obtained numerically. Based on the numerical results, a Modified Generalized Equivalent Spectral Model is proposed. Finally, two numerical examples are analyzed to assess the reliability of current guidelines and of the Modified Generalized Equivalent Spectral Model in the estimate of the maximum footbridge acceleration.
First-principles calculations of the water molecules and hydroxylated iron surface

ABSTRACT To provide atomic scale understanding of the initial corrosion process of Fe in aqueous environment, the electronic interaction of between water molecules and hydroxyl groups with the (110) and (100) surface of Fe is investigated in a density functional theory approach. Using periodic slab models, stable structures and electronic states are calculated, and the dynamical behavior of water molecules and hydroxylated iron surface under finite temperature is resolved by first-principles molecular dynamics. The position of hydroxyl groups on surface does not change from the initial bridge position, while water molecules move from the initial on-top to bridge site, and the molecular surface tilts from a parallel position, the hydrogen bonds are also formed between water molecules and hydroxyl groups. This result suggests that the interaction due to the hydrogen bonding is dominant.
Supervised Strategies of Multiblock Data Analysis: A Unified Approach and Extension

Within the framework of multiblock data analysis, a unified approach of supervised methods is discussed. It encompasses multiblock redundancy analysis (MB-RA) and multiblock partial least squares (MB-PLS) regression. Moreover, we develop new supervised strategies of multiblock data analysis, which can be seen as variants of one or the other of these two methods. They are respectively refered to as multiblock weighted redundancy analysis (MB-WRA) and multiblock weighted covariate analysis (MB-WCov). The four methods are based on the determination of latent variables associated with the various blocks of variables. They are derived from clear optimization criteria whose aim is to maximize either the sum of the covariances or the sum of squared covariances between the latent variable associated with the response block of variables and the block latent variables associated with the various explanatory blocks of variables. We also propose indices to help better interpreting the outcomes of the analyses. The methods are illustrated and compared based on simulated and real datasets.
Self-learning growth simulator for modelling forest stand dynamics in changing conditions

Finnish forest structures vary from even-aged planted forests to two- and multi-storied mixed stands. Also, the range of silvicultural systems in use has increased because thinning from above and continuous cover management are gaining popularity. The data currently available for modelling stand dynamics are insufficient to allow the development of unbiased and reliable models for the simulation of all possible transitions between various current and future stand conditions. Therefore, the models should allow temporal and regional calibration along the accumulation of new information on forest development. If the calibration process is automated, the simulators that use these models constitute a self-learning system that adapts to the properties of new data on stand dynamics. The current study first developed such a model set for stand dynamics that is technically suitable for simulating the stand development in all stand structures, silvicultural systems and their transitions. The model set consists of individual-tree models for diameter increment and survival and a stand-level model for ingrowth. The models were based on the permanent sample plots of the 10th and 11th national forest inventories of Finland. Second, a system for calibrating the models based on additional data was presented. This optimization-based system allows different types and degrees of calibration, depending on the intended use of the models and the amount of data available for calibration. The calibration method was demonstrated with two external datasets where a set of sample plots had been measured two times at varying measurement intervals.
S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.
A novel model to eliminate the doubly near-far problem in wireless powered communication network

In this paper, the newly emerging wireless powered communication network is studied. In doing so, the performance of the global controller (GC) is evaluated, which coordinates the wireless energy transmissions between two sensor nodes. Both the sensors have the same harvested energy for uplink (UL) transmission of information through time-division-multiple-access. Afterwards, the information transmission time is optimised to maximise the common throughput of both the sensors with a total time constraint based on the user’s UL channels along with the same harvested energy value. Further, due to the “doubly near-far” phenomenon, a remote sensor from the GC, which has poor channel conditions than a nearer user, has to transmit more time in the UL for maximum common throughput. To overcome this problem, the energy exchange (EEx) model is proposed where both sensors ﬁrst harvest the same amount of wireless energy and then exchange energy to nullify the different channel conditions between sensors and GC to send their independent information in the UL. Simulation results demonstrate the EEx Model’s effectiveness
They’re Called “Specialty Courts” for a Reason: A Criminologically-informed and Evidence-based Reply to Lucas (2020)

ABSTRACT We recently published an article in Victims & Offenders in which we argued that: (1) the generality of deviance is real (i.e., offenders rarely specialize in any form of criminal or deviant behavior), (2) specialty courts typically assume – either implicitly or explicitly – that offenders do, in fact, specialize primarily in a particular form of criminal behavior, and (3) that there is thus a mismatch between a well-documented criminological reality and the administrative reality of specialty courts. Lucas recently took issue with our arguments and claimed that specialty courts are well-run, evidence based, and sufficiently flexible to be consistently effective at reducing recidivism, and that our relative pessimism is rooted in our “misinterpretation” of the evidence we cite. We find no merit in either of his arguments and we present here a criminologically-informed and evidence-based discussion of the generality of deviance and specialty courts.
An Econometric Approach Regarding the Impact of Fiscal Pressure on Equilibrium: Evidence from Electricity, Gas and Oil Companies Listed on the New York Stock Exchange

The matter of fiscal pressure is more current than ever in most countries around the world for various reasons. In the first place, disruptive phenomena such as financial crises put tremendous pressure on worldwide economies. Secondly, high taxes trigger an overall reduction in the level of investments aiming at creating stable and well-paid jobs. Thirdly, the income generated by the majority of taxpayers is subject to excessive taxation, which may fuel tax evasion acts. On these grounds, the article is the first empirical research investigating the impact of fiscal pressure on the financial equilibrium of energy companies listed on the New York Stock Exchange. The sample included 88 electricity, gas, and oil companies from around the world, which were analyzed over a time span of 16 years, including the periods before, during, and after the 2008 global financial crisis. The methodology entailed estimating econometric models via Panel Least Squares (cross-section weights) with and without time fixed effects. Empirical results showed that fiscal pressure had a stronger impact on the short-term and long-term equilibrium of electricity and oil companies than on the equilibrium of gas companies. The study can serve as a compass for the managers of energy companies interested in estimating the evolution of company equilibrium state when considering other potential financial downturns.
Simulated annealing for SVM parameters optimization in student’s performance prediction

High education is an important and critical part of education all over the world. In recent months, the outbreak of the Covid-19 pandemic has turned the world increasingly to online education; therefore, the need for improving this education system became an urgent matter. Online learning systems are a primal environment for acquiring educational data which can be from different sources, especially academic institutions. These data can be mainly used to analyze and extract utilizable information to help in understanding university students’ performance and identifying factors that affect it. To extract some meaningful information from these large volumes of data, academic organizations must mine the data with high accuracy. Optimization is the process of achieving the best solution for a problem, in this work, three different real datasets were selected, pre-processed, cleaned, and filtered for applying Support Vector Machine (SVM) with Multilayer perceptron kernel (MLP kernel) and optimize its parameters using Simulated Annealing (SA) algorithm to improve the objective function value. While exploring solution space, SA offers the possibility of accepting worse neighbor solutions in a controlled manner to escape from local minima. The results show that the designed system can determine the best SVM parameters using SA and therefore presents better model evaluation.
Quantum phases of mixtures of atoms and molecules on optical lattices

We investigate the phase diagram of a two-species Bose-Hubbard model including a conversion term, by which two particles from the first species can be converted into one particle of the second species, and vice versa. The model can be related to ultracold atom experiments in which a Feshbach resonance produces long-lived bound states viewed as diatomic molecules. The model is solved exactly by means of quantum Monte Carlo simulations. We show that an ``inversion of population'' occurs, depending on the parameters, where the second species becomes more numerous than the first species. The model also exhibits an exotic incompressible ``super-Mott'' phase where the particles from both species can flow with signs of superfluidity, but without global supercurrent. We present two phase diagrams, one in the chemical potential, conversion plane, the other in the chemical potential, detuning plane.
Winter Precipitation Changes in California Under Global Warming: Contributions of CO2, Uniform SST Warming, and SST Change Patterns

Climate models project a wetting winter over California, distinct from the drying in all other Mediterranean climate regions under warming. Dynamic effect via an eastward extension of the westerly jet stream over the North Pacific is the root cause for the wetting California. Utilizing four sets of prescribed‐SST (sea surface temperature) simulations, the relative effects of direct CO2 radiative forcing, uniform SST warming, and SST change patterns are investigated. The wetting winter is dominated by uniform SST warming mainly through its effect on the jet extension. As a fundamental response to the enhanced upper‐tropospheric warming in the tropics, the jet extension in coupled simulations is well reproduced under uniform SST warming and robust among models. An El Niño‐like SST warming pattern has a secondary contribution to the jet extension via atmospheric teleconnection, but with large inter‐model spread. Relative to the SST forcing, the direct CO2 radiative effect is negligible.