Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Electrorheology to Improve Crude Oil Flowing Properties Through Pipeline

This chapter describes a new technology based on electrorheology (ER) that simulta‐ neously reduces oil viscosity and suppresses flow turbulence. The application of a strong electric field in a small section of the oil pipeline causes the polarization of suspended particles contained in the oil phase aggregating them into short chains along the flow direction. This particle aggregation breaks the rotational symmetry and makes the oil viscosity anisotropic, which in turn reduces the oil viscosity along the flow direction. Simultaneously, oil viscosity increases in the direction perpendicular to the flow, which supresses flow turbulence. This green technology does not involve the addition of chemicals to the oil phase and is environmentally friendly. This approach is also energy efficient because it only targets the particles suspended in the oil phase causing the formation of aggregates. Furthermore, heat is not required to reduce the viscosity of the oil phase. Neutron scattering experiments and field tests demonstrate the effectiveness of this technology. Electrorheology will play an important role during enhanced heavy oil recovery and/or production operations and transportation via pipeline.
Higgs Boson Pair Production via Gluon Fusion: Full NLO QCD Corrections

The measured properties of the particle detected six years ago at the LHC at CERN indicate that it is compatible with the Higgs boson predicted by the Standard Model. However, the theoretical and experimental uncertainties allow associations with extended models. Therefore it is of essential importance to investigate the properties of this particle in more detail. The determination of the Higgs potential is crucial to test whether this particle causes electroweak symmetry breaking. The self-coupling strength has to be determined to measure the Higgs potential. This can be achieved in a first step by measuring the trilinear self-coupling in Higgs pair production. At the LHC, the dominant process of Higgs pair production is the loop induced gluon fusion. Therefore the main goal of this thesis is the calculation of the next-to-leading order (NLO) QCD corrections considering the complete top-quark mass dependence in the framework of the Standard Model. The relevant two-loop integrals cannot be calculated analytically with the currently known methods. Instead a numerical integration is required. The main challenge is the extraction of the ultraviolet, the infrared and the collinear divergences from the amplitudes. For this purpose, a modified end-point subtraction has been developed for the extraction of the IR-singularities. Publicly available programs have been used for the real corrections. The differential cross section has been obtained as a distribution in the invariant Higgs pair mass. It shows that the main contributions to the cross section emerge from the invariant Higgs pair masses between 300 and 800 GeV and that the heavy-top limit is a reasonable approximation for invariant Higgs pair masses only up to about 600 GeV. Further, it can be observed that for an invariant Higgs pair mass up to 400-600 GeV the NLO QCD corrections can be reasonably approximated by the K-factor of the triangular contributions alone. The obtained hadronic cross section implies a negative contribution of about −15% from NLO mass effects compared to the previous known heavy-top limit results involving the full leading-order mass dependence. Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-169490 Dissertation Published Version Originally published at: Glaus, Seraina. Higgs Boson Pair Production via Gluon Fusion: Full NLO QCD Corrections. 2018, University of Zurich, Faculty of Science. Higgs Boson Pair Production via Gluon Fusion: Full NLO QCD Corrections Dissertation zur Erlangung der naturwissenschaftlichen Doktorwürde (Dr. sc. nat.) vorgelegt der Mathematisch-naturwissenschaftlichen Fakultät der Universität Zürich von Seraina Glaus
ECG-Quality ssessment of ry-Electrode ooperative ensors

Classical approaches for measuring high-quality ECG require the use of gel electrodes and individually shielded cables, which limit patient comfort, especially in long-term use. We recently introduced a novel sensing architecture— so-called cooperative sensors—that allow the use of active dry electrodes connected by two unshielded wires. The aim of this work is to qualitatively evaluate an ECG recorded with a dry-electrode cooperative-sensor system. To that end, preliminary observations were made on three healthy subjects. The ECGs were concurrently recorded with cooperative sensors and a gold-standard 12-lead ECG device during a stress test on a stationary bicycle. First experimental measurements demonstrated the reliability of the approach for a wearable 12-lead ECG monitoring system tested in real settings.
Distinct Volume Subsets | SIAM Journal on Discrete Mathematics | Vol. 29, No. 1 | Society for Industrial and Applied Mathematics

Suppose that a and d are positive integers with a ≥ 2. Let h a,d ( n ) be the largest integer t such that any set of n points in R d contains a subset of t points for which all the nonzero volumes of the (cid:2) ta (cid:3) subsets of order a are distinct. Beginning with Erd˝os in 1957, the function h 2 ,d ( n ) has been closely studied and is known to be at least a power of n . We improve the best known bound for h 2 ,d ( n ) and show that h a,d ( n ) is at least a power of n for all a and d .
"Weird" Fuzzy Notations: An Algebraic Interpretation

Traditionally, fuzzy logic used non-standard notations like m1/x1 + . + mn/xn for a function that attains the value m1 at x1, . , and the value mn at xn. In this paper, we provide an algebraic explanation for these notations. Mathematics Subject Classification: 03B52
The classifying space of the 1 + 1 dimensional cobordism category.

One of the more basic mathematical structures is the l -h l cobordism category of 1-manifolds and surfaces. It has played an important role in string theory and is at the root of l -f l dimensional topological field theory (see for example [A]). The structure of this category is however not well-understood. Here we analyse its classifying space and by way of it the underlying combinatorics. We are able to compute its fundamental group and give a complete description of the classifying spaces of two important subcategories. Some applications towards the classification of l +1 dimensional topological field theories are derived.
Wall distance calculation using the Eikonal/Hamilton‐Jacobi equations on unstructured meshes: A finite element approach

Purpose – The purpose of this paper is to numerically solve Eikonal and Hamilton‐Jacobi equations using the finite element method; to use both explicit Taylor Galerkin (TG) and implicit methods to obtain shortest wall distances; to demonstrate the implemented methods on some realistic problems; and to use iterative generalized minimal residual method (GMRES) method in the solution of the equations.Design/methodology/approach – The finite element method along with both the explicit and implicit time discretisations is employed. Two different forms of governing equations are also employed in the solution. The Eikonal equation in its original form is used in the explicit Taylor Galerkin discretisation to save computational time. For implicit method, however, the convection‐diffusion form in its conservation form is used to maintain spatial stability.Findings – The finite element solution obtained is both accurate and smooth. As expected the implicit method is much faster than the explicit method. Though the .
Machine Learning for Android Ransomware Detection

—Effective detection of ransomware is becoming increasingly important due to the influx of smartphones and the increasing amount of personal data being stored in smartphones. Ransomware, which often demands payment for the safe return of data, encrypts a user’s personal data and renders it useless without proper decryption. In this paper, we present a model for an Android ransomware intrusion detection system that is an improvement over the previous works on the detection of Android malware families. This was accomplished through sufficient data preprocessing using information gain and the effective use of machine learning classifiers, Decision Tree, Naïve Bayes, and OneR. Network traffic data was used for this classification. Of the three classifiers, the decision tree classifier produced the best classification results.
Angular accelerating white light

Significant interest has been devoted to tailoring optical fields that transversely accelerate during propagation in the form of Airy, Weber and Mathieu beams. In this work, we introduce a new type of optical field that exhibits controlled angular acceleration during propagation which is achieved by superpositions of Bessel beams with non-canonical phase functions. We demonstrate these angular accelerating fields by modulating the phase and amplitude of a supercontinuum source with the use of a phase-only spatial light modulator (SLM). We illustrate that by considering only the first diffraction order when the SLM is encoded with a blazed grating, the SLM is capable of tailoring the spatial profile of broadband sources without any wavelength dependence. By digitally simulating free-space propagation on the SLM, we compare the effects of real and digital propagation on the angular rotation rates of the resulting optical fields for various wavelengths. The development of controlled angular accelerating optical fields will be useful in areas such as particle manipulation, plasma control, material processing and non-linear optics.
Guaranteed cost control for discrete-time networked control systems with random Markov delays

The guaranteed cost control for a class of uncertain discrete-time networked control systems with random delays is ad- dressed. The sensor-to-controller (S-C) and contraller-to-actuator (C-A) random network-induced delays are modeled as two Markov chains. The focus is on the design of a two-mode-dependent guar- anteed cost controller, which depends on both the current S-C delay and the most recently available C-A delay. The resulting closed-loop systems are special jump linear systems. Sufficient conditions for existence of guaranteed cost controller and an up- per bound of cost function are established based on stochastic Lyapunov-Krasovakii functions and linear matrix inequality (LMI) approach. A simulation example illustrates the effectiveness of the proposed method.
Integration of Solar PV With Low-Voltage Weak Grid System: Using Normalized Laplacian Kernel Adaptive Kalman Filter and Learning Based InC Algorithm

This paper proposes a novel normalized Laplacian kernel adaptive Kalman filter (NLKAKF) based control technique and learning based incremental conductance (LIC) maximum power point tracking (MPPT) algorithm, for low-voltage weak grid-integrated solar photovoltaic (PV) system. Here, a two-stage topology of three-phase grid integrated solar PV system is implemented, where the loads are connected at the point of common coupling. Proposed LIC is the improved form of incremental conductance (InC) algorithm, where inherent problems of traditional InC technique, such as steady-state oscillation, slow dynamic responses, and fixed step size issues, are successfully mitigated. The prime objective of proposed NLKAKF control is to meet the active power requirement of the loads from generated solar PV power, and after feeding load, excess power is fed to the grid. However, when generated PV power is less than the required load power, then NLKAKF control meets the load by taking extra required power from the grid. During this process, power quality is improved at the grid. The controller action provides reactive power compensation, power factor correction, and harmonics filtering and mitigation of other power quality issues. Moreover, when the solar irradiation is zero than voltage source converter acts as a distribution static compensator (DSTATCOM), which enhances the utilization factor of the system. The proposed techniques are modeled and their performances are verified experimentally on a developed prototype, in solar insolation variation conditions, unbalanced loading, as well as in different grid disturbances such as overvoltage, undervoltage, phase imbalance, harmonics distortion in the grid voltage, etc.
Oxygen vacancy kinetics in ferroelectric PbZr0.4Ti0.6O3

Oxygen vacancy kinetics in ferroelectric PbZr0.4Ti0. 6O3 were studied by oxygen18 (O18) tracer self-diffusion in epitaxial thin films as well as bulk polycrystalline samples. O18 exchange annealing was carried out at an oxygen partial pressure of 250mbar and temperatures between 250 and 400°C. Isotope depth profiling was performed by secondary ion mass spectrometry as well as neutral secondary mass spectrometry. The observed concentration depth profiles of the polycrystalline samples show two distinct diffusion paths, namely, bulk diffusion and grain boundary (GB) diffusion. It appears to be of type B-kinetics in the investigated temperature range, with DGB∕Dbulk⪢100. Donor doped samples with different levels of Nb5+ (1–4mol.%) were also investigated. The effect on the diffusion depth profiles, however, were negligible and can solely be attributed due to the change in the samples microstructure as induced by the dopants. A diffusion coefficient for the bulk diffusion of the O18 isotope, Dbulk=10±5×10−8cm2∕.
When Should We Not Interpret? The Analyst’s Transformative Act as a Vital Contribution to the Patient’s Sense of Being Real and Alive

Should the analyst do anything else besides interpret? What constitutes an act within the analytic setting? And what signifies an act as analytic? The author reviews previous contributions to this literature, putting forward the idea that the analyst’s act is never isolated from the context of the analysis and the whole of the transference-countertransference relationship. Yet, under certain circumstances, it is not interpretation or the understanding of something that facilitates the transformation, but the experiencing of something that the analyst does. Through the careful 10 examination of previous conceptualizations (enactment, interpretive act), the author proposes that the analyst’s transformative act is a conscious-spontaneous act, not a reenactment of past. This idea will be discussed in light of the idea of playing and some recent thinking concerning ontological psychoanalysis.
An Extended Ultimatum Game for Multi-Party Access Control in Social Networks

In this article, we aim to answer an important set of questions about the potential longitudinal effects of repeated sharing and privacy settings decisions over jointly managed content among users in a social network. We model user interactions through a repeated game in a network graph. We present a variation of the one-shot Ultimatum Game, wherein individuals interact with peers to make a decision on a piece of shared content. The outcome of this game is either success or failure, wherein success implies that a satisfactory decision for all parties is made and failure instead implies that the parties could not reach an agreement. Our proposed game is grounded in empirical data about individual decisions in repeated pairwise negotiations about jointly managed content in a social network. We consider both a “continuous” privacy model as well the “discrete” case of a model wherein privacy values are to be chosen among a fixed set of options. We formally demonstrate that over time, the system converges toward a “fair” state, wherein each individual’s preferences are accounted for. Our discrete model is validated by way of a user study, where participants are asked to propose privacy settings for own shared content from a small, discrete set of options.
Traffic sign recognition based on YOLOv5

With the development of intelligent transportation, traffic sign detection has become a very important task. Traffic sign detection requires the positioning and classification of traffic signs in the road environment. Due to the complexity and diversity of road environments, traffic signs can only take up a small proportion of videos or pictures, and common algorithms still show high false detection rate and high time and calculation overhead in practical application. So far, traffic sign detection is still a difficult task. At the same time, most traffic sign data sets used by algorithms at the present stage have unreasonable data structures or incomplete database types. Blind application of unstructured data sets easily lead to the difficulty of obtaining good results in model training. Therefore, this paper decided to carry out the traffic sign detection task based on the lightweight YOLOv5 (You Only Look Once) neural network model. In order to achieve excellent target detection effect, this paper adopts optimized Tsinghua-Tencent-100K (TT100K) data set to train the model. The experimental results show that the trained models mAP@0.5 and mAP@0.5:0.95 reach 59% and 40% respectively, which basically meet the requirements of application.
Deep-learning-based OFDM channel compensation hardware implementation algorithm design

In order to solve the problem that some high-performance deep learning neural networks are not ideal for application in embedded devices due to the defects of high complexity and large computation. From the perspective of easy hardware implementation, the hardware implementation algorithm of OFDM channel compensation based on deep learning is researched and designed, and the channel compensation scheme of conventional pre-equalization + DNN fine compensation is designed based on the idea of joint efficient implementation of FPGA + GPU.
Penalized logistic regressions predict stock price trends

In recent years, machine learning and data mining methods have been applied to stock price forecasting problems. However, due to the time series characteristics, data noise and multicollinearity of stock price data, many traditional machine learning methods cannot accurately predict the trend of stock price movement. This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) penalized logistic regression model and the ridge penalized regression model to the stock price prediction problem and evaluates their performance. First, we select the stock data of CMA, EQR and IRM from 2016-07-21 to 2020-07-13. Then we apply the TTR package to calculate 17 technical indicators. After that, we divide the data set into the training set and test set, train the model using the training set, and evaluate the performance of models on the test set. Finally, we introduce Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) as comparative models and evaluate the predictive performance of models by evaluating indicators such as sensitivity, specificity, and ROC curve. After the above work, we find that LASSO penalized logistic regression has the best prediction performance, reaching an accuracy of 0.81 on the EQR data set, and the ridge has a prediction performance of 0.79, which is much higher than RF, SVM and ANN.
Simulation and testing platform of oil and gas station industrial control system based on digital twin technology

Aiming at the outstanding problems of long on-site debugging time and high risk of operation and parameter adjustment of industrial control systems in important production and processing bases of oil and gas stations, such as joint stations, transfer stations, and water injection stations, this paper adopts digital twin technology, rapid physical access technology for equipment, PLC and Configuration technology develop the simulation and test platform of the oil and gas station industrial control system, build the mapping relationship between the virtual space simulation model and the measured system entity, and realize the rapid access and reliability verification of the oil and gas station and warehouse PLC industrial control system. The platform significantly improves the operation quality of the industrial control system and reduces the occurrence of production safety accidents.
A Novel SEPIC Converter with Magnetic Coupled Inductor for High Static Gain

In this paper, SEPIC converter, voltage multiplier, and magnetic coupling technique are applied to obtain high static gain. The proposed converter model has various advantages such as low duty ratio, low switching voltage, and high efficiency for applications with low input and high output voltages. Magnetic coupling configuration is presented in this paper and applied here to achieve a large static gain and low switching voltage. This converter's control system is simple, and conduction can be operated in a continuous manner because the gating pulses for both switches are the same. The theoretical and simulation model is analyzed here, As this proposed model shows, it can be applied to high static gain applications, including renewable energy, fuel cell, and micro-grid. In this model for analysis Vg= 20 V and Vo= 340 V, at nominal power has a gain of 17 times and 91.4 percent efficiency.
An 8.2-10.2 GHz Digitally Controlled Oscillator in 28-nm CMOS Using Constantly-Conducting NMOS Biased Switchable Capacitor

This paper presents an 8.2-10.2 GHz digitally controlled oscillator (DCO) in a 28 nm technology. The proposed DCO utilizes a switchable capacitor (SC) structure with a constantly conducting NMOS pair, featuring an SC bank with unitary cells arranged in a matrix. With the unitary weighted capacitor bank, the DCO demonstrates an inherently monotonic tuning with a range of 24.3 %. The finest tuning resolution is 17 kHz thanks to the customized fringe capacitor. The DCO shows a phase noise of −115.1 dBc/Hz at 1 MHz offset from 9 GHz carrier frequency with 13 m W power consumption, achieving a −183 dBc/Hz FoM and −190.6 dBc/Hz $\mathbf{FoM}_{\mathrm{T}}$.
A 17 Gb/s 10.7 pJ/b 4FSK Transceiver System for Point to Point Communication in 65 nm CMOS

This paper presents a novel 145–185 GHz transceiver (TRX) with 4 frequency-shift keying (4FSK) modulation. The proposed non-coherent 4FSK design removes the need for separate modulator and demodulator blocks reducing the power consumption and complexity. The proposed TX generates four different RF frequencies based on the two parallel streams of binary input data, and the RX employs a slot power divider to divide the 4FSK RF signal into two paths, where the 4FSK RF signal is demodulated and data is recovered by enveloped detectors and digital buffers. Both the transmitter and receiver are fabricated in a 65 nm CMOS technology with a total core area of $0.6\ mm^{2}$. The TRX architecture achieves 17 Gb/s over 18 cm link distance while consuming only 182 mW power.
280.2/309.2 GHz, 18.2/9.3 dB Gain, 1.48/1.4 dB Gain-per-mW, 3-Stage Amplifiers in 65nm CMOS Adopting $\text{Double-embedded-}G_{max}\text{-core}$

This paper reports a sub-THz high-gain amplifier design technique which is more flexible and suitable for performance optimization based on a $\text{double}\text{-}\text{embedded}\text{-}G_{max}\text{-}\text{core}$. The $\text{double}\text{-} \text{embedded} \text{-}G_{max}\text{-}\text{cort}$ is implemented by adopting an additional linear, lossless, and reciprocal (LLR) network that satisfies the $G_{max}\text{-}\text{condition} (Y_{21}/Y_{12}=\text{-}G_{max})$ on to an $N\text{-}\text{stage}\ \ \text{pseudo}\text{-}G_{max}\text{-}\text{cores}$ where each stage satisfies the stability factor $k_{i}\! \! =\! \! 1$ and phase delay of $2\mathrm{m}\pi/N$. Implemented in a 65nm CMOS, the three-stage 280.2 and 309.2 GHz amplifiers achieve power gains of 18.2 and 9.3 dB and gain-per-mW of 1.48 and 1.4 dB/mW, respectively.