Miracle-dz/UniFAR-FTUs huggingface dataset:
The contribution of elastic geothermobarometry to the debate on HP versus UHP metamorphism

Characterizing the pressure and temperature (P–T) histories of eclogite facies rocks is of key importance for unravelling subduction zone processes at all scales. Accurate P–T estimates provide constraints on tectonic and geochemical processes affecting subduction dynamics and help in interpreting the geophysical images of present‐day converging plates. Conventional equilibrium geothermobarometers are challenged in ultra high pressure (UHP) metamorphic terranes, as minerals may undergo re‐equilibration along their exhumation path. Elastic geobarometry applied to host‐inclusion systems is a complementary method to determine P–T conditions of metamorphism independent from chemical equilibrium. Because only a single measurement, the inclusion strain, is made, only a line in P–T space of possible entrapment conditions, the entrapment isomeke, can be determined. Thus, the entrapment pressure along an isomeke can only be determined if the entrapment temperature is known. An alternative is to calculate entrapment conditions for two types of inclusions that are believed, from petrological evidence such as being in the same garnet growth zone, to have been entrapped at the same time. The intersection between the two sets of isomeke calculated on multiple quartz and zircon inclusions demonstrates that measuring different inclusion phases trapped inside a single host allows unique P–T conditions for the host rock to be determined. Here, we combine Zr‐in‐Rutile thermometry and thermodynamic modelling with micro‐Raman measurements on quartz and zircon inclusions trapped in garnet to obtain pressures and temperatures of equilibration of a quartz–garnet vein from the Proterozoic Ulla gneiss basement and of garnet–kyanite gneiss from the Caledonian Blåhø nappe, both in the Fjørtoft UHP terrane, Norway. We find that the quartz–garnet vein formed at high pressure (1.5–2.5 GPa and 750–800°C) and recrystallized at ~1.2 GPa and 880°C. In contrast, the garnet–kyanite gneiss followed an anticlockwise path with peak P–T at 1.2 GPa and 880°C: these estimates are consistent with previous thermodynamic modelling and suggest that the Ulla gneiss and the Blåhø nappe came into contact at these last conditions. We also discuss a new method to detect hydrostatic versus Non‐hydrostatic stresses near quartz and zircon inclusions in garnet.
Resilience assessment of electrified road networks subject to charging station failures

The number of electric vehicles (EVs) and charging facilities is expected to increase significantly in the near future, further coupling the existing transportation system with the power system. This may bring new stresses and risks to such a system of systems. This paper presents a mathematical framework to analyze the resilience of an electrified road network (ERN) subject to potential failures of its supporting fast‐charging stations (FCSs). Within this framework, a novel linear optimization model is proposed for the first time to solve the system optimal dynamic traffic assignment problem of ERN. The characteristics considered in the modeling framework include the location, capacity, and charging speed of FCSs, as well as the driving range, charging time, and state of charge (SoC) of EVs. The linear model is proposed based on the cell transmission model. It is used as the first‐stage model to assign the traffic under normal FCS operations. A second‐stage model is, then, extended to minimize the total travel time after the stochastic occurrence of FCS failures, that is, in the failure and recovery phases. Two metrics are considered to quantify the ERN performance and the impacts of FCS failures. A numerical example is studied to illustrate the usefulness of the proposed framework for analyzing ERN resilience. The results show that deploying FCSs near the highway entrances and maintaining their operation are relevant factors to enhance the system's resilience. The analysis can provide guidelines to the system operators for effective management of the ERN operation and identify resilience‐critical FCSs for system resilience improvement.
Swelling properties of thermoresponsive/hydrophilic co-networks with functional crosslinked domain structures

For the fine control of the thermoresponsive properties of polymer hydrogels, we focused on the monomer and crosslinker sequence in a network. In this paper, we designed novel thermoresponsive/hydrophilic polymer co-networks with functional crosslinked domain (CD) structures such as a thermoresponsive network with hydrophilic CDs (DCDN gel) and a hydrophilic network with thermoresponsive CDs (NCDD gel). The gel synthesis was based on post-polymerization crosslinking of triblock prepolymers with reactive sites in the outer blocks. The obtained gels showed larger, sharper, and more rapid volume transitions in response to temperature change as compared to the corresponding gel with a random monomer/crosslinker sequence as well as a randomly crosslinked polymer co-network with the same composition. In addition, significant differences were observed in the transition temperature and shrinking kinetics between DCDN and NCDD gels with the same composition and a similar network structure, and the swelling behavior was also controlled by the composition of the triblock prepolymers. These results indicated that the domain structure in the network could effectively function and the monomer/crosslinker sequence exerts a strong effect on the swelling behavior of the gels.
Experimental and numerical simulation studies of liquefied petroleum gas fire in a full–scale compartment

Liquefied petroleum gas fuel fire hazard characteristics such as thermal, toxic, and radiation hazards were investigated in a full–scale compartment. A 0.3 m × 0.3 m sand–filled burner was used as a fire source with quasi–steady–state heat release rate of 40 kW and 160 kW for 300 s and 600 s respectively. The experimental study was carried out in the compartment of size 2.4 m × 3.6 m × 2.4 m with a doorway opening of size 0.8 m × 1 m for ventilation condition. Temperatures, gas concentrations, and irradiance measurements were carried out at different locations in the compartment with respect to time. Numerical simulation studies were performed for validation and predictions for different door aspect ratios using the computational fluid dynamics tool, fire dynamics simulator. Experimental measurements temperatures, velocities, heat flux, and species concentrations at different locations were reported in transient analysis. Numerical predictions were carried out for different door aspect opening ratios and discussed the impact of the thermal, toxic, and radiation hazards. Comparative evaluation of numerical predictions was in good agreement with measurements of experimental data.
Shearlet-based compressed sensing with non-local similarity for MRI breast image reconstruction

Magnetic resonance imaging (MRI) requires long detection time and makes patients uncomfortable. The proposed compressed sensing MRI compressed sensing with shearlet dictionary and non ‐ local similarity model is established with shearlet dictionary and non ‐ local similarity. The shearlet dictionary is adopted in MRI compressed sensing to represent breast tissues with sparser data in different scales and directions. The non ‐ local similarity of an image is integrated to the model to preserve the lesion details of the reconstructed MRI images. The proposed model is solved by the split Bregman algorithm to obtain the optimized image iteratively. Experiments are performed on practical MRI breast images with sampling data of 13% and 10%. With the decrease of sampling data, the proposed method can reconstruct the image with better visual effect and higher peak signal ‐ to ‐ noise ratio (PSNR) and structural similarity index (SSIM) than traditional methods. There is an improvement of 13 dB of PSNR and 0.2 of SSIM under 10% data. The proposed method can reconstruct MRI images with less data and higher reconstruction quality compared with the traditional methods.
Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing

Real-time electricity pricing mechanisms are emerging as a key component of the smart grid. However, prior work has not fully addressed the challenges of multi-step prediction (Predicting multiple time steps into the future) that is accurate, robust and real-time. This paper proposes a novel Artificial Intelligence-based approach, Robust Intelligent Price Prediction in Real-time (RIPPR), that overcomes these challenges. RIPPR utilizes Variational Mode Decomposition (VMD) to transform the spot price data stream into sub-series that are optimized for robustness using the Particle Swarm Optimization (PSO) algorithm. These sub-series are inputted to a Random Vector Functional Link neural network algorithm for real-time multi-step prediction. A mirror extension removal of VMD, including continuous and discrete spaces in the PSO, is a further novel contribution that improves the effectiveness of RIPPR. The superiority of the proposed RIPPR is demonstrated using three empirical studies of multi-step price prediction of the Australian electricity market.
Collagen matrices: biomaterials for selective cell response

This thesis covers two types of biomaterials: collagen matrices for cell culture and segmental-long-spacing (SLS) collagen. They are both based on type I collagen, yet have distinct configurations. Cells are supported by extracellular matrix (ECM) proteins which can actively influence differentiation, growth, and function of cells. We used type-I collagen, which is the most abundant fibrous protein in the ECM, to establish a model physical context for cell-cell interaction suitable to study the impacts of fat cells (adipocytes) on breast cancer cells. This model system incorporates interactions with the collagen matrix to selectively manipulate adipocyte development or function as defined by triglyceride accumulation as well as leptin and adiponectin secretion. The morphology and elasticity of collagen matrices in the presence and absence of adipocytes, as measured by atomic force microscopy (AFM), indicates that adipocytes altered collagen elasticity in a manner dependent both on adipocyte developmental stage and the collagen preparation. Overall, it is feasible to selectively manipulate the physiology and functionality of adipocytes by timing the addition of specific collagen overlays in combination with specific tissue-culture plates. These findings have implications for analysis of adipocyte development in vitro as well as potential therapeutic implications to specifically alter adipocyte functionality, which is reported to have impacts on regulating the proliferation of breast cancer cells. As to the SLS collagen, we prepared the samples in different pH environments to seek the influence of pH on the formation of SLS collagen. The result reveals that the less acidic environment (pH=4) favours the formation of long fibers, which is more similar to the native-type configuration of type I collagen aggregation. On the contrary, in the more acidic environment (pH=2), the individual block-like SLS collagen is the major product. Yet, the specific mechanism of the synthesis of SLS collagen still requires more investigation. This work provides the basis for further study to synthesize SLS collagen with tunable length, thickness, and/or surface banding distance, with various applications such as matrices for the cell culture.
A computationally intelligent neural network‐based nonlinear autoregressive exogenous balancing approach for real‐time processing in industrial applications using big data

Deep learning based neural networks and their variants have gained popularity due to their inherent flexibility to handle unforeseen especially when a chaotic time series big data are required to be dealt with. There are innumerable applications that are beneficiary of vast interest in computational intelligent approaches that include but not limited to robotics, healthcare, transport, industrial, decision making, and gaming. This paper attempts to investigate the effectiveness of using a neural nonlinear autoregressive with exogenous inputs (NARX) controller in an emerging application field of balancing systems like inverted pendulum (IP) using big data. This paper's aim has been to control an IP cart system by designing a neural NARX controller, and the focus is primarily on real‐time processing in industrial applications grounded on big data ecosystems. In the proposed work, an IP system is mathematically modeled and first controlled utilizing a combination of classical proportional‐integral‐derivative (PID) controllers for cart and pendulum. Second, a chaotic time series input–output data are obtained and are used to train two NARX controllers for cart and pendulum, respectively. Both the controllers are designed as single‐input single‐output systems with one layer each at input and output with suitable number of hidden layers and neurons. Performance comparison of NARX system behavior with PID controller indicates that the NARX controllers successfully adapt to two different kinds of unknown inputs and effectively stabilize the plant. Simulation results confirm that NARX controllers follow the training parameters and exhibit superior performance and overall system stability than PID control. Experimental results demonstrate the effectiveness of the approach.
Technical involvements and pressure applied influence movement demands in elite Australian Football Match-play

ABSTRACT Match activity analyses in Australian Football (AF) have provided information on the loads of match-play and identified contextual factors influencing physical activity. Match physical activity has been shown to be influenced by technical and tactical factors. This study examined the association of in-game contextual factors and technical involvements on the activity demands of elite AF players. Global positioning system data were recorded from 35 players in 13 matches throughout the 2019 Australian Football League (AFL) season. Technical involvements – player possession, and defensive pressure applied – were attained from Champion Data (AFL statistics provider), while possession phases and the commencing possession chain event were manually coded. Mixed models examined the influence possession chain contextual factors had on the total distance (TD) and high-speed running (HSR). During attacking phases, physical activity increased when a player had a possession, when a greater number of opposition players applied pressure, and when play was initiated from a turnover (p ≤ 0.001). During defensive phases, physical activity was greatest when an individual player applied pressure, however, also increased when the total number of players applying pressure increased and when play was initiated from a turnover or kick-in (p ≤ 0.001). Overall, this study combines the three constructs of AF performance (physical, technical and tactical) and demonstrates that TD and HSR demands are greatest in a chain when a player was directly involved in the play or applying pressure on the opposition.
Attribute-guided attention and dependency learning for improving person re-identification based on data analysis technology

ABSTRACT Person re-identification (Re-ID) can determine whether a pedestrian target can be matched across diverse regions or cameras, thereby alleviating the problem between massive surveillance data and inefficient manual retrieval. Inspired by attribute-person recognition (APR) network, this paper proposes an improved Re-ID method based on attribute learning, which uses an attribute-guided attention mechanism module and an attribute dependency learning module to learn fine-grained attribute features and rich dependencies among them. After that, a joint model with the integration of attribute recognition and person identity recognition is built for end-to-end training. Experimental results show that the proposed method can effectively improve Re-ID accuracy and achieve a competitive recognition performance.
Quantisation compensated data-driven iterative learning control for nonlinear systems

This work presents a quantisation compensation-based data-driven iterative learning control (QC-DDILC) scheme by incorporating a uniform quantiser and an encoding–decoding mechanism (E-DM) to deal with the problem of limited communication resources in a networked control system. Since it is directly aimed at a nonlinear nonaffine system, an iterative dynamic linearisation method is employed to transfer it to a linear data model. Then, the QC-DDILC method is developed by the use of optimisation technique for the learning control law and the parameter updating law, respectively, where the quantised output from the E-DM is used. Since the direct output measurement of the system is unavailable, the linear data model is also acted as an iterative predictive model to estimate the system outputs utilised as the compensator in the consequent QC-DDILC. The proposed QC-DDILC is a data-driven method without relying on any explicit mechanism model information. The convergence analysis is conducted by using the mathematical tools of contraction mapping and induction. Simulations verify the theoretical results.
Consumers’ contribution to a climate neutral EU: What influences the adoption of food-related mitigation options?

The main aim of this contribution is to identify factors that influence consumers’ willingness to adopt food-related options to mitigate climate change and reveal how these factors are interlinked. In-depth interviews were carried out with 32 consumers in Germany, France and the UK; the results were analysed and described in the context of Alphabet Theory. The allocation of findings from the interviews to the theoretical framework helps to identify interrelations, to provide explanations and thereby achieve to take a holistic view on the topic, without neglecting contextual information. The findings of this study indicate that a higher level of knowledge will result in more personal responsibility assumed by the consumers and will increase their willingness to adopt mitigation options. Hence, public information campaigns and education programs initiated to enhance consumers’ knowledge on climate-friendly food choices. At the same time, climate-friendly food products need to be easily available and identifiable to help consumers to act according to their attitudes.
The sorption of metal ions on nanoscale zero-valent iron

The injection of the colloidal suspensions of nano-iron (nZVI) into an aquifer is a novel method of removing metal ions from acidic water. In the batch tests, the equilibrium study of the sorption of metal ions, Cu(II) and Zn(II), on Green Tea nanoscale Zero-Valent Ion (GT-nZVI) was carried out. The sorption of metal ions on this reactive material was described using the Langmuir, Freundlich and Sips models. This last model described in a better way the sorption equilibrium in the tested range of concentrations and temperature. The value of determination coefficient (R 2 ) for the Sips model, for copper and zinc, was 0.9735 to 0.9995, respectively. GT-nZVI has very good properties in removing Cu(II) and Zn(II) from acidic water. The high values of qmaxS, the maximum adsorption capacity in the Sips model, amounting to 348.0 and 267.3 mg/g for Cu(II) and Zn(II), indicate the high adsorption capacity of GT-nZVI. The analyzed metals have good or very good affinity with GT-nZVI.
A Multi-Objective Bayesian Approach with Dynamic Optimization (MOBADO). A Hybrid of Decision Theory and Machine Learning Applied to Customs Fraud Control in Spain

This paper studies the economically significant problem of the optimization of customs fraud control, which is a critical issue for many countries. The European Union (EU) alone handles 4693 tons of goods every minute (2018 figures). Even though 70% of goods are imported at zero tariff, the EU raised EUR 25.4 billions in 2018, and customs-related income transferred by member states to the EU accounts for nearly 13% of its overall budget. In this field, (a) the conflicting objectives are qualitative and cannot be reduced to a common measure (security and terrorism, health, drug market access control, taxes, etc.); (b) each submitted item has dozens of characteristics; (c) there are constraints; and (d) risk analysis systems have to make decisions in real time. Although the World Customs Organization has promoted the use of artificial intelligence to increase the precision of controls, the problem is very complex due to the data characteristics and interpretability, which is a requirement established by customs officers. In this paper, we propose a new Bayesian-based hybrid approach combining machine learning and multi-objective linear programming (MOLP), called multi-objective Bayesian with dynamic optimization (MOBADO). We demonstrate that it is possible to more than double (with a 237% increase) the precision of current inspection systems, freeing up almost 50% of human resources, and outperform past results with respect to each of the above objectives. MOBADO is an optimization technique that could be combined with any artificial intelligence approach capable of optimizing the quality of multi-objective risk analysis in real time.
Identification of overlapped wool/cashmere fibers based on multi-focus image fusion and convolutional neural network

ABSTRACT Identification of cashmere and wool fibers has always been one of the essential topics in the field of textile. To solve this problem, this paper proposes anew overlapped fiber recognition method based on multi-focus image fusion and convolutional neural network. Firstly, the images of wool and cashmere fibers were captured by an optical microscope and adigital camera. Secondly, the fiber images with clear features were acquired by the multi-focus image fusion method. Then, image preprocessing operations were used to obtain single fiber images. Finally, the features of overlapped fibers are extracted and classified by the classical convolutional neural network. The results show that when the ratio of the training set to the test set is 8:2, the recognition accuracy of the method proposed in this paper reaches the highest of 98.7%, which can be used to realize the fast and accurate identification of overlapped fibers.
Engaging Politically Disenfranchised Students in Governance

New Jersey City University is an urban, Minority- and Hispanic-Serving Institution with a First-Generation-to-College, commuter, and immigrant student population. How can we engage our students who feel powerless, distrustful, or even threatened by government actors in governance? Will perceptions of governance change with increased exposure to political elites in their communities? Using Community Engaged Learning methods, we asked students to attend civic meetings and courtrooms to observe the inner workings of governance and engage in dialogue with political elites. Journals and surveys reveal that students deconstructed pre-conceived notions of powerlessness, humanized government actors, and became hopeful about change in their communities.
Comparing in-class scenario-based learning to scenario-based eLearning through an interactive, self-paced case study

Abstract Scenario-Based Learning (SBL) is a learning method that promotes authentic learning through real-world circumstances. eLearning software allows for the creation of robust Scenario-Based eLearning (SBeL). These modules harness student-content interaction to provide an effective learning experience. This study compares the perceived learning, flow, and enjoyment of two business classes who completed an in-class or an eLearning case study. Results indicate no significant differences in how students perceive these experiences, suggesting equivalency of the two methods.