Miracle-dz/UniFAR-FTUs huggingface dataset:
Polymer Crystallization of Partially Miscible Polythiophene/Fullerene Mixtures Controls Morphology

Mixtures of polythiophene and fullerene are intensely studied for organic photovoltaic applications. Control of nanoscale morphology of these materials is critical for device performance, but characterization and understanding of this morphology and how it arises is lacking. We use energy-filtered transmission electron microscopy (EFTEM) to obtain high-contrast images of P3HT nanocrystals in organic semiconductor mixtures. Grazing-incidence small-angle X-ray scattering correlates well with the length scales obtained from EFTEM images; we combine the two techniques to follow the morphology evolution under different material processing histories. EFTEM also measures local P3HT concentration in PCBM-rich regions, proving that these components are partially miscible. We determine the P3HT-PCBM χ parameter and Flory–Huggins phase diagram, which predicts miscibility for P3HT volume fractions above 0.42. This miscibility suppresses fullerene crystallization. The nanoscale morphology in these materials, critical .
Intergenerational place-based digital storytelling: a more-than-visual research method

ABSTRACT The use of visual research methods with young people—such as photo diaries and mental mapping—risks reproducing a focus on local-scale micro-politics at the expense of broader contextualization. We contend that combining visual and narrative methods through digital storytelling enables young people to represent their everyday lives within broader social and political processes. Working in post-conflict urban areas in Lebanon, we found that this method allowed young people to disrupt the dominant geopolitical narratives that so often frame their lives and surroundings. This paper provides a detailed explanation of our place-based approach to digital storytelling, including a discussion of ethical and practical concerns. Our approach eschews the individual testimonial technique typical of digital storytelling. Rather than a tool for introspective self-reflection, place-based digital storytelling encourages inter-subjective extrospection, a process of exploring how individual stories unfold within broader geopolitical contexts.
A feasibility randomised controlled trial of targeted oxygen therapy in mechanically ventilated critically ill patients

Background Despite oxygen being the commonest drug administered to critically ill patients we do not know which oxygen saturation (SpO2) target results in optimal survival outcomes in those receiving mechanical ventilation. We therefore conducted a feasibility randomised controlled trial in the United Kingdom (UK) to assess whether it would be possible to host a larger national multi-centre trial to evaluate oxygenation targets in mechanically ventilated patients. Methods We set out to recruit 60 participants across two sites into a trial in which they were randomised to receive conservative oxygenation (SpO2 88–92%) or usual care (control – SpO2 ≥96%). The primary outcome was feasibility; factors related to safety and clinical outcomes were also assessed. Results A total of 34 patients were recruited into the study until it was stopped due to time constraints. A number of key barriers to success were identified during the course of the study. The conservative oxygenation intervention was feasible and appeared to be safe in this small patient cohort and it achieved wide separation of the median time-weighted average (IQR) SpO2 at 91% (90–92%) in conservative oxygenation group versus 97% (96–97%) in control group. Conclusion Whilst conservative oxygenation was a feasible and safe intervention which achieved clear group separation in oxygenation levels, the model used in this trial will require alterations to improve future participant recruitment rates in the UK.
Insight is not always the same: differences between true, false, and induced insights in the matchstick arithmetic task

ABSTRACT There are diverging operationalizations of insight in experimental research, especially when comparing behavioral and neuroimaging research. The question arises how comparable these types of insight are. Here, we set out (1) to evaluate the usefulness of the matchstick arithmetic task for investigating cognitive and neural processes underlying insight-based problem solving, (2) to determine whether the Aha! Experience is diminished over a multitude of trials, and (3) to compare true (correctly solved), false (incorrectly solved), and induced insights. To this end, we analysed solution rates, response times, strength of the Aha! experience, and event-related potentials (ERPs). Our results showed that the Aha! experience was not weakened over 40 trials, but showed the importance of the time for a solution attempt. True, false, and induced insights differed with regard to Aha! ratings and ERP amplitude −2000 to −1500 ms before the response. Our study underscores the importance of the operationalization of insight.
Electrodeposition of HAp coatings on Ti6Al4V alloy and its electrochemical behavior in simulated body fluid solution

Hydroxyapatite (HAp) coatings were prepared on Ti6Al4V substrate by electrodeposition method from electrolyte solution containing Ca(NO3)2, NH4H2PO4 and NaNO3. The results show that the HAp coatings were single phase crystals of HAp. Scanning electron microscope (SEM) images present that HAp/Ti6Al4V have flake shapes which arrange to form like-coral agglomerates. In vitro test of the Ti6Al4V and HAp/Ti6Al4V in simulated body fluid (SBF) solution was investigated with different immersion times. pH of SBF solution decreased and the mass of materials increased. SEM images prove the formation of apatite on the surface of Ti6Al4V and HAp/Ti6Al4V. The corrosion current density during immersion time of substrate is always higher than the one of HAp/Ti6Al4V because the deposited HAp can protect well for the substrate.
An assessment of COVID-19’s impact on Finnish University Leadership

ABSTRACT This article maps the management actions that Finnish universities have taken during the COVID-19 pandemic and studies how managers in Finnish higher education coped during the crisis and how they feel about it. The article uses action mapping and a survey that was administered to top- and mid-level managers at Finnish universities, and the findings highlight the importance of collegial coordination and maintenance work abilities among managers.
Mediating the Effects of Climate on the Temperature and Thermal Structure of a Monomictic Reservoir through Use of Hydraulic Facilities

Climate warming can alter the thermal conditions of reservoirs. However, some hydraulic interventions can be explored to mitigate this impact. This study investigates the long-term effects of climate on the temperature and thermal structure of a monomictic reservoir that has had varying operations from 1959 to 2016. Reservoir progressively operated through three distinct periods, namely, (A) deep penstock withdrawal (DPW; 1959–1991), (B) purely selective withdrawal (SW; 1992–2001), and (C) combination of SW and vertical curtain (VC; 2002–2016). Although annual air temperatures are increasing (+0.15 °C decade−1) in the long term, the reservoir’s surface water temperatures have been found to be decreasing (−0.06 °C decade−1). Periods B and C produced colder profiles and exhibited lower heat content and higher potential energy anomaly than Period A. Furthermore, stronger thermoclines, as indicated by Brunt–Vaisala frequency, were observed in the two latter periods. The results of this study show that varying operations bear a stronger influence on the reservoir’s temperature and thermal structure than climate change itself. Mitigating the thermal impacts of climate warming in reservoirs appears promising with the use of SW and VC.
COVID-19: Impacts and perspectives for religious tourism events. The case of Lourdes Pilgrimages

Abstract The breakout of COVID-19 has massively impacted the event industry. Focusing on Lourdes Pilgrimages, the results of the study reveal that the online delivery of the event, has not negatively impacted on the traditional dimension of the pilgrimage; nor on the satisfaction of attendees. Instead, it has potentially offered some opportunities to the Sanctuary, and surrounding areas. As a result, crisis either internal or external to an industry should not be perceived only as negative. A Janusian thinking approach is required to perceive the opportunities related to crisis, and an ambidextrous management approach needed to exploit them.
Local power and the location of subsidized renters in comparative perspective: public support for low- and moderate-income households in the United States, France, and the United Kingdom

Abstract In the context of worsening housing affordability for low- and moderate-income households, we assemble data from metropolitan areas in the United States, France, and the United Kingdom to analyze regional differences in the level and distribution of nationally supported affordable-housing units and renters with tenant-based housing benefits. We examine the location of subsidized renters comparatively, exploring how varying power arrangements between national and local governments over land-use and housing policy shape options for low-income renters. We find that US metropolitan areas are unique in the extent to which many municipalities exclude subsidized renters altogether; subsidized housing is disproportionately situated in areas with historically limited access to resources. The number of municipalities within metropolitan areas does not appear to impact the location of subsidized units, but the ability of localities to exclude is associated with their distribution.
The Automatic Detection of Pedestrians under the High-Density Conditions by Deep Learning Techniques

The automatic detection and tracking of pedestrians under high-density conditions is a challenging task for both computer vision fields and pedestrian flow studies. Collecting pedestrian data is a fundamental task for the modeling and practical implementations of crowd management. Although there are many methods for detecting pedestrians, they may not be easily adopted in the high-density situations. Therefore, we utilized one emerging method based on the deep learning algorithm. Based on the top-view video data of some pedestrian flow experiments recorded by an unmanned aerial vehicle (UAV), we produce our own training datasets. We train the detection model by using Yolo v3, a very popular deep learning model among many available detection models in recent years. We find the detection results are good; e.g., the precisions, recalls, and F1 scores could be larger than 0.95 even when the pedestrian density is as high as 9.0 ped / m 2 . We think this approach could be used for the other pedestrian flow experiments or field data which have similar configurations and can also be useful for automatic crowd density estimation.
The Efficient Resource Scheduling Strategy in Cloud: A Metaheuristic Approach

The cloud computing is evolving as a high-performance computing platform due to broad-scale, flexible computational architecture and heterogeneous collection of autonomous systems. Cloud technology uses concept of virtualization for managing resources, which develops resource scheduling as a key issue. The scheduling of cloud tasks is an NP-complete problem and therefore irreconcilable with particular solution. Also, with the huge collection of a database system, the management of resources and tasks becomes complex with specific to the completion time requirements and cost constraints. To resolve this problem, a number of meta-heuristic algorithms have been developed. Due to redundant wastage of resources and time, the under and over-provisioning is one kind of issues leads to either degradation in performance or wastage of cloud resources. To overcome these kinds of problems, we introduce a task scheduling approach by incorporating reinforcement learning along with the nature-inspired meta-heuristic optimization to maximizing cloud throughput, minimizing completion time & production cost in IaaS cloud. By reinforcement learning, the agent will choose appropriate action among a set of available actions and the scheduler succeeds towards task allocation resulted to decrease makespan and increasing system utilization rate.
Controlling growth cone behavior through substrate patterning

Throughout the process of development, billions of neuronal axons are responsible for navigating through the nervous system and synapsing onto their appropriate targets. To establish their individual paths, neurons are guided by a complex and dynamic map of biochemical, topographic, and mechanical signals. At the tip of each neurite lies the growth cone – a dynamic structure responsible for interpreting extracellular cues and steering neuronal growth in the appropriate direction. Growth cones in vivo exhibit various morphologies and behavioral changes, though the underlying mechanisms of these changes remain widely unknown. Through the use of substrate patterning techniques, we examined the relationship between growth cone morphology and behavior with the purpose of optimizing guidance in engineered regenerative systems. We investigated multiple substrate patterning techniques in order to achieve easily observable manipulations to growth cone morphology. We found that common micro-contact printing and microfluidic techniques are poor at creating isolated regions of overlapping biochemical cues. We also found that established protein patterning methods for soft substrates
A Tightly-Coupled Positioning System of Online Calibrated RGB-D Camera and Wheel Odometry Based on SE(2) Plane Constraints

The emergence of Automated Guided Vehicle (AGV) has greatly increased the efficiency of the transportation industry, which put forward the urgent requirement for the accuracy and ease of use of 2D planar motion robot positioning. Multi-sensor fusion positioning has gradually become an important technical route to improve overall efficiency when dealing with AGV positioning. As a sensor directly acquiring depth, the RGB-D camera has received extensive attention in indoor positioning in recent years, while wheel odometry is the sensor that comes with most two-dimensional planar motion robots, and its parameters will not change over time. Both the RGB-D camera and the wheel odometry are commonly used sensors for indoor robot positioning, but the existing research on the fusion of RGB-D and wheel odometry is limited based on classic filtering algorithms; few fusion solutions based on optimization algorithm of them are available at present. To ensure the practicability and greatly improve the accuracy of RGB-D and odometry fusion positioning scheme, this paper proposed a tightly-coupled positioning scheme of online calibrated RGB-D camera and wheel odometry based on SE(2) plane constraints. Experiments have proved that the angle accuracy of the extrinsic parameter in the calibration part is less than 0.5 degrees, and the displacement of the extrinsic parameter reaches the millimeter level. The field-test positioning accuracy of the positioning system we proposed having reached centimeter-level on the dataset without pre-calibration, which is better than ORB-SLAM2 relying solely on RGB-D cameras. The experimental results verify the excellent performance of the frame in positioning accuracy and ease of use and prove that it can be a potential promising technical solution in the field of two-dimensional AGV positioning.
Leak-Off Pressure Using Weakly Correlated Geospatial Information and Machine Learning Algorithms

Leak-off pressure (LOP) is a key parameter to determine the allowable weight of drilling mud in a well and the in situ horizontal stress. The LOP test is run in situ and is frequently used by the petroleum industry. If the well pressure exceeds the LOP, wellbore instability may occur, with hydraulic fracturing and large mud losses in the formation. A reliable prediction of LOP is required to ensure safe and economical drilling operations. The prediction of LOP is challenging because it is affected by the usually complex earlier geological loading history, and the values of LOP and their measurements can vary significantly geospatially. This paper investigates the ability of machine learning algorithms to predict leak-off pressure on the basis of geospatial information of LOP measurements. About 3000 LOP test data were collected from 1800 exploration wells offshore Norway. Three machine learning algorithms (the deep neural network (DNN), random forest (RF), and support vector machine (SVM) algorithms) optimized by three hyperparameter search methods (the grid search, randomized search and Bayesian search) were compared with multivariate regression analysis. The Bayesian search algorithm needed fewer iterations than the grid search algorithms to find an optimal combination of hyperparameters. The three machine learning algorithms showed better performance than the multivariate linear regression when the features of the geospatial inputs were properly scaled. The RF algorithm gave the most promising results regardless of data scaling. If the data were not scaled, the DNN and SVM algorithms, even with optimized parameters, did not provide significantly improved test scores compared to the multivariate regression analysis. The analyses also showed that when the number of data points in a geographical setting is much smaller than that of other geographical areas, the prediction accuracy reduces significantly.
Fabrication of Poly (Acrylonitrile-Co-Methyl Methacrylate) Nanofibers Containing Boron via Electrospinning Method: A Study on Size Distribution, Thermal, Crystalline, and Mechanical Strength Properties

Electrospun polymeric nanofibers have attracted great attention in filtration systems and protective clothes. One of them is polyacrylonitrile (PAN) nanofibers, which are a suitable choice for the fabrication of protective clothes in the defense industry, due to their good fiber formation and easy optimization with chemical reagents. They do not possess adequate properties for protection against chemical, biological, and radiological agents. In this research, poly (acrylonitrile-co-methyl methacrylate) (PANMM) nanofibers and PANMM nanofibers containing 10B were fabricated via the electrospinning method. The study of the morphology of nanofibers, using scanning electron microscopy (SEM), revealed that smooth and knotted fibers with an average diameter of 259 ± 64 nm were obtained, using 12% (w/v) of PANMM in the solution as the optimal concentration for the electrospinning process. This sample was doped with boron (10%, 30%, and 50% (w/w)) to fabricate the samples of PANMM + boric acid (BA) nanofibers. The results demonstrated an increasing trend in the diameter of the nanofibers with an increase in BA up to 50%. At this concentration, smooth fibers were formed with lower knots. Furthermore, the presence of B-O and O-H groups was observed using Fourier transform infrared (FTIR) spectroscopy. To study the tensile properties, the nanofibrous web was tested, and the results showed that introducing 10B to PANMM nanofiber structures reduced the strength of the nanofibers. Thermal gravimetric analysis (TGA) showed that BA-modified PANMM nanofibers had lower thermal degradability, as compared with pure PANMM.
Application of Data Mining Technology in the Analysis of E-commerce Emotional Law

The rapid development of the Internet has brought opportunities for enterprises. Data mining is becoming more and more important in e-commerce websites. In e-commerce, data mining is helpful to discover the development trend of the industry and help enterprises make correct decisions. This paper summarizes the current data mining methods of e-commerce, describes the application of e-commerce website in some data mining applications, classifies its objects, and analyzes the functions of network data mining. It is an important tool to support the experimental research and design of e-commerce system oriented to emotion. The technology of Intelligent Emotion Recognition Based on modern mathematical model and the simulation model of emotion oriented intelligent e-commerce system based on computational intelligence are proposed. For the future of e-commerce practice to provide a reference, especially the development and application of software. The progress in this field is outstanding.
Diversifying Focused Testing for Unit Testing

Software changes constantly, because developers add new features or modifications. This directly affects the effectiveness of the test suite associated with that software, especially when these new modifications are in a specific area that no test case covers. This article tackles the problem of generating a high-quality test suite to cover repeatedly a given point in a program, with the ultimate goal of exposing faults possibly affecting the given program point. Both search-based software testing and constraint solving offer ready, but low-quality, solutions to this: Ideally, a maximally diverse covering test set is required, whereas search and constraint solving tend to generate test sets with biased distributions. Our approach, Diversified Focused Testing (DFT), uses a search strategy inspired by GödelTest. We artificially inject parameters into the code branching conditions and use a bi-objective search algorithm to find diverse inputs by perturbing the injected parameters, while keeping the path conditions still satisfiable. Our results demonstrate that our technique, DFT, is able to cover a desired point in the code at least 90% of the time. Moreover, adding diversity improves the bug detection and the mutation killing abilities of the test suites. We show that DFT achieves better results than focused testing, symbolic execution, and random testing by achieving from 3% to 70% improvement in mutation score and up to 100% improvement in fault detection across 105 software subjects.
Rheological behaviour of a La-based bulk metallic glass (BMG) used in 3D printing

ABSTRACT To be used as a new kind of three-dimensional printing material, rheological behaviour of a La-based bulk metallic glass (BMG) was studied over a wide range of high temperatures and strain rates by uniaxial compression experiments. The variations in peak stress and steady stress with temperature and strain rate were analysed. Three rheological regions were revealed for the BMG: a non-Newtonian, a Newtonian and an abnormal region which are determined by the temperature and strain rate. The variation on viscosity further indicates that the rheological mode transforms from Newtonian fluid to non-Newtonian fluid with the increase of strain rates at the same temperature. The results from scanning electron microscope test also confirm the microstructural changes that occurred at three different rheological modes. The crystallisation kinetics of La55Al25Ni5Cu10Co5 was studied by thermal analysis and the result show that the crystallisation process of La55Al25Ni5Cu10Co5 glassy alloy is the diffusion-controlled growth. The relationship among steady-state stress, strain rates and temperatures are determined by using hyperbolic function under high-temperature compression test. The curve shows that the logarithmic relationship between the steady-state stress and strain rates satisfies the linear relationship. Besides, the activation energy Q of this amorphous alloy in Newtonian rheological region is 131.31 kJ/mol.