Miracle-dz/UniFAR-FTUs huggingface dataset:
Extending Geodemographics Using Data Primitives: A Review and a Methodological Proposal

This paper reviews geodemographic classifications and developments in contemporary classifications. It develops a critique of current approaches and identifiea a number of key limitations. These include the problems associated with the geodemographic cluster label (few cluster members are typical or have the same properties as the cluster centre) and the failure of the static label to describe anything about the underlying neighbourhood processes and dynamics. To address these limitations, this paper proposed a data primitives approach. Data primitives are the fundamental dimensions or measurements that capture the processes of interest. They can be used to describe the current state of an area in a multivariate feature space, and states can be compared over multiple time periods for which data are available, through for example a change vector approach. In this way, emergent social processes, which may be too weak to result in a change in a cluster label, but are nonetheless important signals, can be captured. As states are updated (for example, as new data become available), inferences about different social processes can be made, as well as classification updates if required. State changes can also be used to determine neighbourhood trajectories and to predict or infer future states. A list of data primitives was suggested from a review of the mechanisms driving a number of neighbourhood-level social processes, with the aim of improving the wider understanding of the interaction of complex neighbourhood processes and their effects. A small case study was provided to illustrate the approach. In this way, the methods outlined in this paper suggest a more nuanced approach to geodemographic research, away from a focus on classifications and static data, towards approaches that capture the social dynamics experienced by neighbourhoods.
What Makes Copper-Exchanged SSZ-13 Zeolite Efficient at Cleaning Car Exhaust Gases?

Recently, the outstanding properties of Cu-SSZ-13 (a zeolite in the chabazite structure) for the selective catalytic reduction of nitrous oxides were discovered. However, the true nature of the active site is still not answered satisfactorily. In this work, we identify the active site for the given reaction from first-principles simulations of the total energy of Cu(II) ions in various positions in combination with previously published catalytic activity as a function of the copper exchange level. This attribution is confirmed by the simulation of vibrational properties of CO adsorbed to the reduced Cu(I) species. The relation between energetic considerations, vibrational calculations, and experiment allows a clear statement about the distribution of active sites in the catalyst. We furthermore discuss the structural properties of the active site leading to the high stability under reaction conditions over a large temperature range. The insights from this work allow a more targeted catalyst design and rep.
Imagining the future with citizens: participatory foresight and democratic policy design in Marcoussis, France

Abstract The idea that the future is too serious and strategic to be discussed with people who are not “experts” is so commonly shared that citizens’ voices are lacking in most decision-making processes and future-oriented choices whether we look at political or research and innovation agendas. Yet, proofs to contradict this vision are now multiplying and can be found in a growing number of case studies and scientific publications. Citizens are proving not only to be legitimate actors to contribute to the future agenda-setting of local, national or European levels but also relevant and pertinent contributors. However, this practice of participatory agenda-setting—even though it is getting greater attention—remains relatively experimental, atypical and sporadic. The limits and drawbacks of citizen participation processes have already been highlighted in the literature but rather than calling this practice to an end it actually calls for more experimentation as the difficulties do not diminish the democratic need, legitimacy and value of such governance practices. Especially, since many western democracies are becoming very fragile. We will see in the preliminary part of this paper, the many challenges that undermine not only the functioning of democratic processes but also the citizens’ role and sense of agency in the governance of the society they live in, as well as the role of elected officials and experts regarding agenda-setting, then we will look into the lack of future thinking not only in the political sphere but also the educational and social practices, analyze the values of participatory processes and its potential limits and risks and finally review an experimental case of participatory foresight and policy design from the small town of Marcoussis (France) which successfully conducted a participatory agenda-setting experiment—and voted—after a 2 years-long process, their local agenda for the future together with its inhabitants, civil servants and elected officials. Marcoussis has engaged around 600 people (out of a population of 8000 inhabitants) in 25 moments, using very diverse methods and techniques (forum-theater, philosophy talks, market of ideas for the future, collective distillation) and setting the process in unusual and non-administrative contexts (entering into schools, ‘hacking’ their local popular festivals) to touch a very diverse set of citizens and gather as many contributions as possible. In this case analysis, we will inspect the preliminary conditions of how Marcoussis set up their participatory foresight and agenda-setting process, then review the creative tools and design methods that were experimented and finally, draw the lessons learned in terms of participatory agenda-setting processes and democratic innovations.
Knockout-Tournament Procedures for Large-Scale Ranking and Selection in Parallel Computing Environments

On one hand, large-scale ranking and selection (R&S) problems require a large amount of computation. On the other hand, parallel computing environments that provide a large capacity for computation are becoming prevalent today, and they are accessible by ordinary users. Therefore, solving large-scale R&S problems in parallel computing environments has emerged as an important research topic in recent years. However, directly implementing traditional stagewise procedures and fully sequential procedures in parallel computing environments may encounter problems because either the procedures require too many simulation observations or the procedures’ selection structures induce too many comparisons and too frequent communications among the processors. In this paper, inspired by the knockout-tournament arrangement of tennis Grand Slam tournaments, we develop new R&S procedures to solve large-scale problems in parallel computing environments. We show that no matter whether the variances of the alternatives are known or not, our procedures can theoretically achieve the lowest growth rate on the expected total sample size with respect to the number of alternatives and thus, are optimal in rate. Moreover, common random numbers can be easily adopted in our procedures to further reduce the total sample size. Meanwhile, the comparison time in our procedures is negligible compared with the simulation time, and our procedures barely request for communications among the processors.
Is There Daily Growth Hysteresis versus Vapor Pressure Deficit in Cherry Fruit?

The growth of cherry fruit is generally described using a double sigmoid model, divided into four growth stages. Abiotic factors are considered to be significant components in modifying fruit growth, and among these, the vapor pressure deficit (VPD) is deemed the most effective. In this study, we investigated sweet cherry fruit growth through the continuous, hourly monitoring of fruit transversal diameter over two consecutive years (2019 and 2020), from the beginning of the third stage to maturation (forth stage). Extensometers were used in the field and VPD was calculated from weather data. The fruit growth pattern up to the end of the third stage demonstrated three critical steps during non-rainy days: shrinkage, stabilization and expansion. In the third stage of fruit growth, a partial clockwise hysteresis curve of circadian growth, as a response to VPD, appeared on random days. The pattern of fruit growth during rainy days was not distinctive, but the amount and duration of rain caused a consequent decrease in the VPD and indirectly boosted fruit growth. At the beginning of the fourth stage, the circadian growth changed and the daily transversal diameter vs VPD formed fully clockwise hysteresis curves for most of this stage. Our findings indicate that hysteresis can be employed to evaluate the initial phenological phase of fruit maturation, as a fully clockwise hysteresis curve was observable only in the fourth stage of fruit growth. There are additional opportunities for its use in the management of fruit production, such as in precision fruit farming.
Diagnosing COVID-19 with Xception + Bi-LSTM: Detecting Anomalies with Grad-CAM

To successfully contain the spread of COVID-19, the importance of swift and accurate testing is unparalleled. Currently, PCR tests are most commonly utilized to detect COVID-19, yet these tests typically consume 24 hours—not a short period of time. Hence, new deep learning algorithms have been under development to accurately and quickly detect COVID-19. With this aim, we have proposed a deep learning model to determine the presence of COVID-19 using X-ray images by combining Xception with Bi-LSTM. Altering the output from the Xception network into a three-dimensional shape rendered the ensuing Bi-LSTM network compatible. Consequently, the novel model yielded a high accuracy rate of 98.5%, one greater than the accuracy rates of VGG16, Densenet, Mobilenet, Mobilenet_v2, Resnet50, and DNN models. Moreover, with the creation of a heatmap, by using a Class Activation Map, our model could specifically locate the anomaly. However, our model could not yield high accuracy when we applied it to the lung ct scan dataset. Even though training and validation accuracy kept rising, the test accuracy was far lower than them. Furthermore, with limitations including a small sample size, inflated accuracy rates for binary classification, and incompatibility with CT images, follow-up research will need to ensue to perfect the model at hand.
The impact of COVID-19 related regulations and restrictions on mobility and potential for sustained climate mitigation across the Netherlands, Sweden and the UK: a data-based commentary

Human behaviour change is necessary to meet targets set by the Paris Agreement to mitigate climate change. Restrictions and regulations put in place globally to mitigate the spread of COVID-19 during 2020 have had a substantial impact on everyday life, including many carbon-intensive behaviours such as transportation. Changes to transportation behaviour may reduce carbon emissions. Behaviour change theory can offer perspective on the drivers and influences of behaviour and shape recommendations for how policy-makers can capitalise on any observed behaviour changes that may mitigate climate change. For this commentary, we aimed to describe changes in data relating to transportation behaviours concerning working from home during the COVID-19 pandemic across the Netherlands, Sweden and the UK. We display these identified changes in a concept map, suggesting links between the changes in behaviour and levels of carbon emissions. We consider these changes in relation to a comprehensive and easy to understand model of behaviour, the Opportunity, Motivation Behaviour (COM-B) model, to understand the capabilities, opportunities and behaviours related to the observed behaviour changes and potential policy to mitigate climate change. There is now an opportunity for policy-makers to increase the likelihood of maintaining pro-environmental behaviour changes by providing opportunities, improving capabilities and maintaining motivation for these behaviours.
Implementation of an Artificial Neural Network for Storm Surge Forecasting

Accurate and timely storm surge forecasts are essential during tropical cyclone events in order to assess the magnitude and location of the impacts. Coupled ocean‐atmosphere dynamical models provide accurate measures of storm surge but remain too computationally expensive to run for real‐time forecasting purposes. Therefore, it is common to utilize a parametric vortex model, implemented within a hydrodynamic model, which decreases computational time at the expense of forecast accuracy. Recently, data‐driven neural networks are being implemented as an alternative due to their combined efficiency and high accuracy. This work seeks to examine how an artificial neural network (ANN) can be used to make accurate storm surge predictions, and explores the added value of using a recurrent neural network (RNN). In particular, it is concerned with determining the parameters needed to successfully implement a neural network model for the Mid‐Atlantic Bight region. The neural network models were trained with modeled data resulting from coupling of the Hybrid Weather Research and Forecasting cyclone model (HWCM) and the Advanced Circulation Model. An ensemble of synthetic, but physically plausible, cyclones were simulated using the HWCM and used as input for the hydrodynamic model. Tests of the ANN were conducted to investigate the optimal lead‐time configuration of the input data and the neural network architecture needed to minimize storm surge forecast errors. Results highlight the accuracy of the ANN in forecasting moderate storm surge levels, while indicating a deficiency in capturing the magnitude of the peak values, which is improved in the implementation of the RNN.
Learning Homogeneous and Heterogeneous Co-Occurrences for Unsupervised Cross-Modal Retrieval

Image-text retrieval, which focuses on unifying both visual and textual representations, is one of the major tasks of cross-modal information processing. With attention mechanism, previous methods performing well take advantage of not only the correspondence in image-text level but also the semantic alignment between the regions in images and corresponding words. However, few of them comprehend the importance of combing the semantic relationship between multimodalities and semantic correspondences in one modality at the same time. Inspired by the heterogeneous information learning of heterogeneous graph network, we propose a novel method called Homogeneous and Heterogeneous Co-Occurrences (H2CO) which mainly consists of two modules to achieve modal co-occurrence in a query of corporations. Specifically, Homogeneous Co-Occurrence Module captures correspondences with neighbors from single modal of regions and words respectively, while Heterogeneous Co-Occurrence Module aims to learn the relations about neighbors across modalities. Finally, the proposed method can aggregate the neighborhood features from both intra modality and inter modality at the same time, thus performs better on image-text matching for considering much more semantic information. Extensive experimental results on MS COCO and Flickr30K show the superior performance of our proposed modal over the state-of-the-arts.
Subthreshold transport in mono- and multilayered MoS2 FETs

The subthreshold transport properties in exfoliated MoS2 FETs are reported. The temperature dependence of subthreshold characteristics in multilayered MoS2 FETs behaves in the same way as that of conventional semiconductors, while conductance fluctuations and random telegraphic signals in the subthreshold region of Ids–Vgs characteristics were observed much more frequently in monolayered FETs than in multilayered ones. This fact is understandable from the viewpoint of a three-dimensional to two-dimensional percolation transport process in MoS2 with defects, which should be located in the MoS2 layer or at the MoS2/SiO2 interface. Thus, it is suggested that few-layered MoS2 FETs are more viable for practical applications from the viewpoint of suppressing defect-induced current fluctuations.
Evaluación de una técnica de protección física en el éxito de establecimiento de dos especies leñosas nativas en zonas con hormigas cortadoras

Introducción y objetivos: La restauración de bosques en sitios degradados por actividades antrópicas es una actividad importante en el manejo de sistemas montanos. Aún hace falta investigación tendiente a reducir el efecto de la herbivoría y de las condiciones ambientales hostiles sobre el establecimiento de plantines de árboles. Nos propusimos evaluar la efectividad de una protección física usando tubos de policloruro de vinilo (PVC) en la plantación de dos especies arbóreas nativas de las Sierras de Córdoba (Argentina) en un área con presencia de hormigas cortadoras. M&M: Las especies de estudio fueron escallonia (Escallonia cordobensis) y maitén (Maytenus boaria). El área de plantación estuvo a 1840 m snm. Usamos 230 plantines por especie, 100 de ellos con y 130 sin protección de PVC. Fueron plantadas durante la estación húmeda en sitios con vegetación baja. Medimos semestralmente y por dos años la altura, la supervivencia y los daños por herbivoría. Resultados: Para escallonia y maitén, respectivamente, la protección aumentó la supervivencia a los dos años en 24 y 26%, aumentó la altura en 7,3 y 12,6 cm, y redujo la herbivoría por hormigas cortadoras en 30 y 51% en comparación con los plantines sin protección. Conclusiones: El efecto favorable de las protecciones en la supervivencia y crecimiento podría deberse conjuntamente a la disminución de la herbivoría por hormigas cortadoras y al resguardo contra las condiciones abióticas desfavorables. Recomendamos estudios de más años, y tendientes a entender los mecanismos que subyacen a la mejora en el establecimiento de los plantines.
Landauer’s Principle of Minimum Energy Might Place Limits on the Detectability of Gravitons of Certain Mass

According to Landauer’s principle, the energy of a particle may be used to record or erase N number of information bits within the thermal bath. The maximum number of information N recorded by the particle in the heat bath is found to be inversely proportional to its temperature T. If at least one bit of information is transferred from the particle to the medium, then the particle might exchange information with the medium. Therefore for at least one bit of information, the limiting mass that can carry or transform information assuming a temperature T= 2.73 K is equal to m = 4.71810-40 kg which is many orders of magnitude smaller that the masse of most of today’s elementary particles. Next, using the corresponding temperature of a graviton relic and assuming at least one bit of information the corresponding graviton mass is calculated and from that, a relation for the number of information N carried by a graviton as a function of the graviton mass mgr is derived. Furthermore, the range of information number contained in a graviton is also calculated for the given range of graviton mass as given by Nieto and Goldhaber, from which we find that the range of the graviton is inversely proportional to the information number N. Finally, treating the gravitons as harmonic oscillators in an enclosure of size R we derive the range of a graviton as a function of the cosmological parameters in the present era
Evidence for high-elevation salar recharge and interbasin groundwater flow in the Western Cordillera of the Peruvian Andes

Abstract. Improving our understanding of hydrogeological processes on the western flank of the central Andes is critical to communities living in this arid region. Groundwater emerging as springs at low elevations provides water for drinking, agriculture, and baseflow. Some springs also have recreation or religious significance. However, the high elevation sources of recharge and specific groundwater flowpaths that support these springs and convey groundwater to lower elevations in southern Peru remain poorly quantified in this geologically complex environment. The objectives of this study were to identify recharge zones and groundwater flowpaths supporting natural springs east of the city of Arequipa in the volcanic mountain terrain, particularly, the potential for recharge within the high-elevation closed-basin Lagunas Salinas salar. We used geochemical and isotopic tracers in springs, surface waters (rivers and lakes), and precipitation (rain and snow) sampled from March 2019 through February 2020. We obtained monthly samples from six springs, bimonthly samples from four rivers, and various samples from high-elevation springs during the dry season. We analyzed stable water isotopes (δ18O and δ2H) and general chemistry of springs, rivers, local rainfall, and snow from Pichu Pichu volcano. The monthly isotopic composition of spring water was invariable over time, suggesting that the springs receive a stable source of groundwater recharge and are not supported by relatively short groundwater flowpaths. The chemistry of springs in the low- and mid-elevations (2500 to 2900 masl) point towards a mix of recharge from the salar (4300 masl) and mountain-block recharge (MBR) in or above a queñuales forest ecosystem at ~4000 masl on the adjacent Pichu Pichu volcano. Springs at higher elevation closer to the salar and in a region with a high degree of faulting had higher chloride concentrations indicating higher proportions of interbasin groundwater flow from the salar. We conclude that while the salar is a closed basin, surface water from the salar recharges through the lacustrine sediments, mixes with mountain-block groundwater, and is incorporated into the regional groundwater flow system. Groundwater flow in the mountain block and the subsequent interbasin groundwater flow is accommodated through extensive faulting and fracturing. Our findings provide valuable information on the flowpaths and zones of recharge that support low-elevation springs in this arid region. In this study, high-elevation forests and a closed-basin salar are important sources of recharge. These features should be carefully managed to prevent impacts to the down-valley springs and streams.
Did the COVID-19 Pandemic Make Consumers Shop Alone? The Role of Emotions and Interdependent Self-Construal

The paper aims to assess the impact of the COVID-19 pandemic and possible future global epidemic events on shopping behavioral patterns. Specifically, the paper investigates consumer pandemic-related isolation behavior (which manifests itself via preference for shopping without leaving home, and avoiding contact with other people while shopping offline) as a consequence of consumer interdependent self-construal, with the mediating role of consumer pandemic-related emotions of disgust, fear for oneself, fear for others, and sadness. The results of two surveys conducted in different stages of the COVID-19 pandemic in Poland (October 2020, and January 2021, respectively) suggest two opposing indirect effects of interdependent self-construal on isolation behavior: a positive effect through disgust, and a negative effect through sadness. Additionally, a positive indirect effect through fear was visible in the second study. Moreover, two dimensions of interdependent self-construal (i.e., vertical and horizontal) are demonstrated to have opposing effects (a positive effect and a negative one, respectively) on pandemic-related disgust, and in turn on isolation behavior. The above results indicate that, in the context of the pandemic, consumer self-construal influences pandemic-related emotions, and in turn consumers’ tendency to isolate themselves. Implications for marketers and society were discussed from the perspective of economic and sustainability goals.