Miracle-dz/UniFAR-FTUs huggingface dataset:
Process safety analysis using operational data and Bayesian network

Fault detection and diagnosis (FDD) methods have recently experienced significant advances. These methods provide valuable information from an abnormal situation management perspective. However, traditional FDD methods do not consider system failure analysis, which is required from the process safety perspective. This work seeks to overcome this barrier and presents a methodology to assess process system failure probability based on process operational data and system knowledge. The methodology is built using the principal component analysis (PCA) and a Bayesian network (BN). The PCA is used for FDD, while the BN determines the probability of system failure once a fault is detected. This portion of the network is based on the logical relationship of the data, operational thresholds, and system failure conditions. The proposed methodology is tested and verified on a level‐controlled tank system and the real‐life failure scenario of the 2010 Tesoro heat exchanger explosion. The results suggest that the proposed methodology helps to assess process system failure probability based on process operating conditions. The current work is expected to give a stimulus on digital process safety education.
Affine-invariant recognition of gray-scale objects by Fourier descriptors

The affine invariant recognition of two-dimensional objects which have the same shape but a different gray level function is a difficult problem in computer vision. A possible solution to the problem is based on the method of affine invariant moments. However, the moments have a badly conditioned dynamic range and they turn out to be very sensitive to noise. This paper presents a new method which is based on affine invariant Fourier descriptors for planar curves. It suggests a weighted parameterization for the boundary of an object based on its gray level function. The parameter of a boundary point is defined as the sum of gray levels of the area covered by a line from the center of mass to a starting point of the boundary moving along the boundary. The parameterization is an affine mapping with respect to the transformed object and to an arbitrary starting point of the contour finder. This parameterization enables the development of affine invariant Fourier descriptors. The Fourier descriptors consider two features, the contour and the gray level function of the object. The set of Fourier descriptors which characterizes the image pattern is not complete but their capabilities for pattern separation is very good. The method is less vulnerable to noise than the method of affine invariant moments. Application tests under real conditions demonstrate the practicability of the proposed algorithm. The complexity of the algorithm for a pattern with N pixels is 0(N).
A Hybrid Transmitter With Voltage-Mode SST Preemphasis and Current-Mode Transmitter Equalization Capable of Operating at 77 K in DUNE

The Deep Underground Neutrino Experiment (DUNE) requires that the front-end transmitters operate at cryogenic temperature and drive 25–35 m long twin-axial (twinax) cables. To compensate the frequency-dependent channel loss over the long cables and alleviate the de-emphasizing of the low-frequency signal magnitude, a hybrid of a current-mode (CM) transmitter equalization (TXEQ) and a voltage-mode (VM) preemphasis is proposed. The TXEQ employs a finite-impulse response (FIR) filter to boost the high-frequency components while de-emphasizing the low-frequency signal magnitude, thereby flattening the overall channel frequency response and reducing the intersymbol interference (ISI). The VM preemphasis is proposed to further mitigate ISI by boosting the high-frequency portion without degrading the signal magnitude, allowing for high signal swing. The main driver utilizes VM source-series-terminated (SST) output stages, which offers higher signal swing and better power efficiency than the conventional CM logic (CML) drivers. To ensure the lifetime and reliability at cryogenic temperature, the transmitter is implemented in a 65-nm CMOS process operating at 1.1 V of supply voltage and employing transistors with larger than minimum lengths. Silicon measurement results at 77 K have validated the proposed approaches.
Automated Web Testing using Machine Learning and Containerization

Testing practices in software engineering constantly evolves due to the complexity of the systems. This has opened the space for new testing methods to try to integrate artificial intelligence with software testing tools. Automation testing refers to the use of strategies and tools which reduce the need for manual or human involvement in redundant and repetitive tasks that tend to cause human errors and then generating and executing automatically test cases. However, while a test scripts is generated, its reuse may be challenging for several reasons. In our Web systems context, a web page may be modified leading to the adaptation of the testing architecture and the eventual rewriting of the test scripts. It becomes time and effort consuming to create generic test cases that can be applied on any website. Websites keep on changing dynamically and the testers need to adapt to these changes each time and alter the test cases. These changes are often made manually or using external scripts. In this paper, we propose an approach allowing the test scripts to automatically adapt to these eventual changes of the web pages by using containers and a learning technique. We defined and implemented an algorithm on a well designed test framework and successfully evaluated our approach on thousands of websites.
Approximately optimal spatial design approaches for environmental health data

Environmental health research considers the relationship between exposures to environmental contaminants and particular health endpoints. Due to spatial structure associated with either exposures or outcomes, spatial modeling is making rapid inroads in environmental health. Our focus in this paper is on approximately optimal spatial design in the case of one‐time sampling at a large number of spatial locations. If we plan to use spatial processes in building models to analyze the data, it seems equally appropriate to use such models in developing the sampling design.
Multi-domain Phase-field Modeling of Polycrystalline Hafnia-based (Anti-)ferroelectrics Capable of Representing Defects, Wake-up and Fatigue

In this paper, for the first time, a multi-domain phase-field device-level framework for polycrystalline hafnia-based ferroelectric (FE) and anti-ferroelectric (AFE) thin films is developed to describe (i) pristine states with different dominant phases, (ii) wake-up driven by either depolarization or tetragonal (t-) to orthorhombic (o-) phase transition, (iii) fatigue induced by o- to monoclinic (m-) phase transition, and (iv) imprint resulting from defect accumulation at phase boundaries during electric field cycling. The model shows excellent agreements with a wide range of electrical polarization-voltage (P-V) measurements in both pristine and after field cycling while being consistent to density functional theory (DFT) calculations, the phase analysis from x-ray diffraction (XRD), and the observations reported in literature; therefore, this work not only provides a fundamental theoretical platform for device-level simulations with polycrystalline hafnia thin films, but also opens new opportunities for device designs using the mixture of phases in hafnium oxide.
Study on Mechanical Properties of Two-Component Polyurethane Based on Multi-Scale Molecular Simulation

Mechanical properties determine the use of two-component polyurethane materials. The compatibility of two components in the polyether polyol-MDI molecular system greatly influences the formation of mechanical properties in polyurethane materials. In this paper, we studied and evaluated the compatibility and mechanical properties of two-component polyurethane at multiple scales by combining molecular dynamics simulation with macroscopic experiments, which is an important guideline for synthesizing and preparing two-component polyurethanes. We evaluated the stability of the two-component polyurethane system by calculating the solubility parameter, binding energy, and diffusion coefficient at four temperatures with three isocyanate contents. The Perl scripting language obtained the mechanical properties of the MDI-polyether polyol system. The MD calculation results show that the solubility parameter of two-component polyurethane negatively correlated with temperature, and the intermolecular binding energy and MDI diffusion coefficient positively correlated with temperature. When the mass ratio of polyether polyol to isocyanate was 1:0.6, the solubility parameter difference between the two was 1.43 (J/cm3)1/2, the intermolecular binding energy was 531.68 kcal/mol, and the two-component system was more stable. A macroscopic direct tensile test was employed to assess the polyurethane elastomers’ tensile properties. Our results show that the tensile strength of polyurethane elastomers increased with the increase in isocyanate content and decrease in temperature. Furthermore, the elongation at the break decreased, and the modulus increased, which is consistent with the law of molecular simulation.
Atomic parity nonconservation in Ra

We report on a theoretical analysis of the suitability of the 7s (2)S(1/2) 6d (2)D(3/2) transition in singly ionized radium to measure parity nonconservation, in the light of an experiment planned at the KVI of the University of Groningen. Relativistic coupled-cluster theory has been employed to perform an ab initio calculation of the parity-nonconserving electric dipole amplitude of this transition, including single, double, and leading triple excitations. We discuss the prospects for a sub-1% precision test of the electroweak theory of particle physics.
Evaluation of the Pragmatic Implementation of a Digital Health Intervention Promoting Healthy Nutrition, Physical Activity, and Gestational Weight Gain for Women Entering Pregnancy at a High Body Mass Index

txt4two is a multi-modality intervention promoting healthy pregnancy nutrition, physical activity (PA), and gestational weight gain (GWG), which had been previously evaluated in a pilot randomized controlled trial (RCT). This study aimed to evaluate a pragmatic implementation of an adapted version of txt4two in a public tertiary hospital. Using a consecutive cohort design, txt4two was delivered to women with a pre-pregnancy BMI > 25 kg/m2, between 10 + 0 to 17 + 6 weeks. Control and intervention cohorts (n = 150) were planned, with surveys and weight measures at baseline and 36 weeks. The txt4two cohort received a dietetic goal-setting appointment and program (SMS, website, and videos). The navigation of disparate hospital systems and the COVID-19 pandemic saw adaptation and adoption take two years. The intervention cohort (n = 35; 43% full data) demonstrated significant differences (mean (SD)), compared to the control cohort (n = 97; 45% full data) in vegetable intake (+0.9 (1.2) versus +0.1 (0.7), p = 0.03), fiber-diet quality index (+0.6 (0.8) versus 0.1 (0.5), p = 0.012), and total diet quality index (+0.7 (1.1) versus +0.2 (±0.6), p = 0.008), but not for PA or GWG. Most (85.7%) intervention participants found txt4two extremely or moderately useful, and 92.9% would recommend it. Embedding the program in a non-RCT context raised implementation challenges. Understanding the facilitators and barriers to adaptation and adoption will strengthen the evidence for the refinement of implementation plans.
Analytical and Experimental Investigation of Electromagnetic Tracking System for Location and Orientation Estimation

This paper presents the development of an electromagnetic (EM) tracking system that consists of a pyramid field generator (PFG) of five excitation coils and a sensing coil for location and orientation estimation. The system measures the induced electromotive forces (EMF) of the sensing coil with the change of the magnetic field contributed by the PFG. The proposed modeling distributed magnetic flux (DMF) method that divides the multi-turns sensing coil into multiple layers and concentric circles for the distributed elements is utilized to determine EMF induced in the sensing coils for investigating the effects of translational and angular displacement. The finite element analysis numerically verifies the DMF method and validates the Artificial Neural Network (ANN)-based estimation models. The calibration and ANN-based estimation process are applied to inversely estimate the location and orientation of the sensing coil based on the DMF method and the experimental EM tracking system. The proposed design and a prototype of the EM tracking system have experimentally verified the DMF method and showed the validity of the location and orientation estimation.
A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network

In greenhouses, sensors are needed to measure the variables of interest. They help farmers and allow automatic controllers to determine control actions to regulate the environmental conditions that favor crop growth. This paper focuses on the problem of the lack of monitoring and control systems in traditional Mediterranean greenhouses. In such greenhouses, most farmers manually operate the opening of the vents to regulate the temperature during the daytime. Therefore, the state of vent opening is not recorded because control systems are not usually installed due to economic reasons. The solution presented in this paper consists of developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) as a soft sensor to estimate vent opening using the measurements of different inside and outside greenhouse climate variables as input data. A dataset from a traditional greenhouse located in Almería (Spain) was used. The data were processed and analyzed to study the relationships between the measured climate variables and the state of vent opening, both statistically (using correlation coefficients) and graphically (with regression analysis). The dataset (with 81 recorded days) was then used to train, validate, and test a set of candidate LSTM-based networks for the soft sensor. The results show that the developed soft sensor can estimate the actual opening of the vents with a mean absolute error of 4.45%, which encourages integrating the soft sensor as part of decision support systems for farmers and using it to calculate other essential variables, such as greenhouse ventilation rate.
Characterization of an Electronic Nicotine Delivery System (ENDS) Aerosol Generation Platform to Determine Exposure Risks

Evaluating vaping parameters that influence electronic nicotine delivery system (ENDS) emission profiles and potentially hazardous exposure levels is essential to protecting human health. We developed an automated multi-channel ENDS aerosol generation system (EAGS) for characterizing size-resolved particle emissions across pod- and mod-type devices using real-time monitoring instruments, an exposure chamber, and vaping parameters including different ventilation rates, device type and age, e-liquid formulation, and atomizer setup. Results show the ENDS device type, e-liquid flavoring, and nicotine content can affect particle emissions. In general, pod-type devices have unimodal particle size distributions and higher number emissions, while mod-type devices have bimodal size distributions and higher mass emissions. For pod-type devices, later puff fractions emit lower aerosols, which is potentially associated with the change of coil resistance and power during ageing. For a mod-type device, an atomizer with a lower resistance coil and higher power generates larger particle emissions than an atomizer with a greater resistance coil and lower power. The unventilated scenario produces higher particle emission factors, except for particle mass emission from pod-type devices. The data provided herein indicate the EAGS can produce realistic and reproducible puff profiles of pod- and mod-type ENDS devices and therefore is a suitable platform for characterizing ENDS-associated exposure risks.
Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence

The knowledge of product particle size distribution (PSD) in crystallization processes is of high interest for the pharmaceutical and fine chemical industries, as well as in research and development. Not only can the efficiency of crystallization/production processes and product quality be increased but also new equipment can be qualitatively characterized. A large variety of analytical methods for PSDs is available, most of which have underlying assumptions and corresponding errors affecting the measurement of the volume of individual particles. In this work we present a method for the determination of particle volumes in a bulk sample via micro-computed tomography and the application of artificial intelligence. The particle size of bulk samples of sucrose were measured with this method and compared to classical indirect measurement methods. Advantages of the workflow are presented.
Hybrid CNN-SVR Blood Pressure Estimation Model Using ECG and PPG Signals

Continuous blood pressure (BP) measurement is vital in monitoring patients’ health with a high risk of cardiovascular disease. The complex and dynamic nature of the cardiovascular system can influence BP through many factors, such as cardiac output, blood vessel wall elasticity, circulated blood volume, peripheral resistance, respiration, and emotional behavior. Yet, traditional BP measurement methods in continuously estimating the BP are cumbersome and inefficient. This paper presents a novel hybrid model by integrating a convolutional neural network (CNN) as a trainable feature extractor and support vector regression (SVR) as a regression model. This model can automatically extract features from the electrocardiogram (ECG) and photoplethysmography (PPG) signals and continuously estimates the systolic blood pressure (SBP) and diastolic blood pressure (DBP). The CNN takes the correct topology of input data and establishes the relationship between ECG and PPG features and BP. A total of 120 patients with available ECG, PPG, SBP, and DBP data are selected from the MIMIC III database to evaluate the performance of the proposed model. This novel model achieves an overall Mean Absolute Error (MAE) of 1.23 ± 2.45 mmHg (MAE ± STD) for SBP and 3.08 ± 5.67 for DBP, all of which comply with the accuracy requirements of the AAMI SP10 standard.
Efficacy of In Vitro Lithium Chloride Treatments on Dermacentor reticulatus

Simple Summary Dermacentor reticulatus is a significant parasite and vector of various pathogens which are dangerous to humans and animals. Lithium chloride is currently one of the most promising potential alternatives against Varroa destructor, a detrimental mite parasite of honeybees. Furthermore, it shows efficacy against Dermanyssus gallinae, a severe pest of poultry, in vitro. In the present study, we report for the first time that the biocidal efficacy of lithium chloride extends to D. reticulatus as well, with 100% mortality at a minimum concentration of 1.38 M and an LC50 of 0.654 M. Our results may contribute to a comprehensive understanding of the lithium ion. Abstract Dermacentor reticulatus (Fabr., 1794) (Acari: Ixodidae) is parasite that spreads many diseases which are dangerous to humans and animals. Microelement lithium was found to have promising potential against the detrimental bee pest Varroa destructor. Furthermore, its effectiveness was confirmed against Dermanyssus gallinae, a major parasite of poultry, in vitro. In the present study, we investigated whether the efficacy of lithium chloride extends to other parasitic species, such as D. reticulatus. Our results revealed, for the first time, that the effectiveness of lithium chloride extends to D. reticulatus, confirmed to have 100% mortality at a relatively high minimum concentration of 1.38 M in vitro. The 24 h and 48 h median lethal concentration (LC50) values proved to be 0.654 M and 0.481 M, respectively, for this species. Our pilot study may contribute to a better understanding of the properties of lithium ion. Furthermore, it may elicit further studies aiming to reveal whether the different environmental mineral conditions may influence the D. reticulatus population. Further studies might reveal whether lithium has any possible veterinary relevance.
Acoustic radiation and active control from a smart functionally graded submerged hollow cylinder

The 3D piezoelasticity theory and the spatial state-space approach are used to study the steady-state non-axisymmetric sound radiation and scattering characteristics of an infinitely long, arbitrarily thick, orthotropic functionally graded hollow circular cylinder, coupled with an inner (actuator) layer of functionally graded piezoceramic material. The method of stationary phase is employed for evaluation of the radiated far-field pressure integral. Numerical simulations include water-submerged air-filled steel-PZT4 composite cylinders driven by harmonic electromechanical loads or insonified by an obliquely incident plane sound wave. Also, when the actuator layer is operating in the active vibration mode, the effects of the number of control modes on the input voltage required for partial or complete cancellation of the far-field and internal radiated pressures as well as on the backscattering form function and noise reduction amplitudes are examined. Limiting cases are considered and validity of formulation is established by comparison with available data as well as with the aid of a commercial finite element package.
Effect of Pore‐Wall Roughness and Péclet Number on Conservative Solute Transport in Saturated Porous Media

While modeling solute transport has been an active subject of research in the past few decades, the influence of pore‐wall roughness on contaminant migration has not yet been addressed. We therefore conduct particle tracking simulations in three porous domains that have different pore‐wall roughness characteristics. Specifically, we consider five surface fractal dimensions ds = 1.0, 1.1, 1.2, 1.4, and 1.6, and four different Péclet numbers Pe = 10, 102, 103, and 105. Overall, arrival time distributions are simulated for 60 scenarios (3 domains × $\times $ 5 surface fractal dimensions × $\times $ 4 Péclet numbers) some of which show heavy‐tailed patterns indicating non‐Fickian transport. To interpret the simulations and quantify the transport behavior, we analyze the resulting arrival time distributions by the continuous time random walk (CTRW) approach. Results show that, on average, as the surface fractal dimension increases from 1.0 to 1.6, the CTRW model parameters β $\beta $ , an exponent showing the degree of anomalous transport, v, the average solute velocity, and t2, the cut‐off time to Fickian transport, remain nearly constant. However, the dispersion coefficient, D, increases and the characteristic transition time, t1, decreases. We found t1 and D are more sensitive to pore‐wall roughness compared to the other CTRW parameters. We also found that as the Péclet number increases from 10 to 105, on average, v and D increase, t1 and β $\beta $ decrease, and t2 remains nearly constant. The simulations demonstrate that the exponent β $\beta $ and the dispersion coefficient are correlated to the average solute velocity.