Miracle-dz/UniFAR-FTUs huggingface dataset:
Reactivation of a hyperextended rift system: The Basque–Cantabrian Pyrenees case

This contribution investigates the role of a hyperextended rift system in the formation of the Basque–Cantabrian Pyrenees by discussing their present‐day architecture as well as the inherited rift template. Moreover, this work attempts to decipher the onset of reactivation of a hyperextended system and to discuss the related processes during collision. To carry out this study, two regional, crustal‐scale cross‐sections are presented that provide geological and geophysical information and interpretations across the Central and Western Basque–Cantabrian Pyrenees. Moreover, the two sections are restored back to the Cenomanian and Barremian, corresponding to the end of two independent rift stages respectively. The two sections document different structural styles observed along the orogenic belt. The Central section, involving the Iberian and European plates, shows a thin‐skinned structural style, where the Upper Triassic salt acted as a decoupling level between the sedimentary cover and the underlying basement during both extension and reactivation. The Western section, by contrast, crosses only the Iberian plate (i.e., intra‐plate section) and displays a hybrid situation showing both thin‐ and thick‐skinned structural styles that were conditioned by the irregular distribution of Triassic salt. Extensional deformation was localised in the north (i.e., Bay of Biscay) and less important in the south. Despite compressional reactivation, the northern part of the Western section preserves its rift template, which provides key insights to restore the internal part of the Central section. In contrast to the Western section, the Central section shows stacked depocenters, resulting from overprinted Mesozoic rift events that had a first order control on the subsequent reactivation. This study corroborates the importance of rift inheritance during the onset of convergence by reactivating the most distal and weak part of the rift system (i.e., serpentinised mantle) before starting the collision phase. A key learning is that the understanding of the nature and distribution of decoupling levels at a crustal scale is fundamental to reconstruct the structural evolution during the formation and reactivation of a hyperextended rift system.
Effective Connectivity between Major Nodes of the Limbic System, Salience and Frontoparietal Networks Differentiates Schizophrenia and Mood Disorders from Healthy Controls

This study was conducted to examine whether there are quantitative or qualitative differences in the connectome between psychiatric patients and healthy controls and to delineate the connectome features of major depressive disorder (MDD), schizophrenia (SCZ) and bipolar disorder (BD), as well as the severity of these disorders. Toward this end, we performed an effective connectivity analysis of resting state functional MRI data in these three patient groups and healthy controls. We used spectral Dynamic Causal Modeling (spDCM), and the derived connectome features were further subjected to machine learning. The results outlined a model of five connections, which discriminated patients from controls, comprising major nodes of the limbic system (amygdala (AMY), hippocampus (HPC) and anterior cingulate cortex (ACC)), the salience network (anterior insula (AI), and the frontoparietal and dorsal attention network (middle frontal gyrus (MFG), corresponding to the dorsolateral prefrontal cortex, and frontal eye field (FEF)). Notably, the alterations in the self-inhibitory connection of the anterior insula emerged as a feature of both mood disorders and SCZ. Moreover, four out of the five connectome features that discriminate mental illness from controls are features of mood disorders (both MDD and BD), namely the MFG→FEF, HPC→FEF, AI→AMY, and MFG→AMY connections, whereas one connection is a feature of SCZ, namely the AMY→SPL connectivity. A large part of the variance in the severity of depression (31.6%) and SCZ (40.6%) was explained by connectivity features. In conclusion, dysfunctions in the self-regulation of the salience network may underpin major mental disorders, while other key connectome features shape differences between mood disorders and SCZ, and can be used as potential imaging biomarkers.
The effect of childhood maltreatment on college students’ depression symptoms: The mediating role of subjective well-being and the moderating role of MAOA gene rs6323 polymorphism

ABSTRACT Based on both positive psychology and the gene × environment research paradigm, the current study constructed a moderated mediation model to explore the relationship between childhood maltreatment and college students’ depression symptoms. The sample comprised 301 college students (74 males and 227 females; average age = 19.15 ± 0.63 years of age, age range 17 to 20). The results indicated that: (1) Childhood maltreatment had a significant positive correlation with college students’ depression symptoms, and subjective well-being had a significant negative correlation with childhood maltreatment and college students’ depression symptoms; (2) Subjective well-being appeared to play a mediating role in the relationship between childhood maltreatment and college students’ depression symptoms; (3) The MAOA gene rs6323 polymorphism moderated the indirect path from subjective well-being and college students’ depression symptoms
O MEIO AMBIENTE VIRTUAL E AS CRIPTOMOEDAS: UMA ÁNALISE JURÍDICA SOBRE A ATUAL SITUAÇÃO DOS BITCOINS NA LEGISLAÇÃO BRASILEIRA

O artigo tem o objetivo de analisar a situação atual das criptomoedas no que tange a sua classificação jurídica e a possibilidade de reparação dos danos causados quando ocorre a transmissão desses direitos mediante as hipóteses de existência de vícios do consentimento. O trabalho constatou que as criptomoedas podem ser equiparadas a bens jurídicos moveis e que existe a possibilidade de aplicação das normas civis para a reparação civil dos danos sofridos por transações com criptomoedas efetuadas com alguma espécie de defeito do negócio jurídico. Para tanto, empregou-se uma metodologia jurídico-teórica com uma técnica de pesquisa bibliográfica e documental.
Performance Evaluation of Sustainable Concrete Comprising Waste Polypropylene Food Tray Fibers and Palm Oil Fuel Ash Exposed to Sulfate and Acid Attacks

Sulfate and acid attacks cause material degradation, which is a severe durability concern for cementitious materials. The performance of concrete composites comprising waste plastic food trays (WPFTs) as low-cost fibers and palm oil fuel ash (POFA) exposed to acid and sulfate solutions has been evaluated in an immersion period of 12 months. In this study, visual assessment, mass variation, compressive strength, and microstructural analyses are investigated. For ordinary Portland cement (OPC), six concrete mixtures, including 0–1% WPFT fibers with a length of 20 mm, were prepared. In addition, another six mixtures with similar fiber dosages were cast, with 30% POFA replacing OPC. It was discovered that adding WPFT fibers and POFA to concrete reduced its workability. POFA concrete mixes were found to have higher long-term compressive strength than OPC concrete mixes cured in water. As a result of the positive interaction between POFA and WPFT fibers, both the crack formation and spalling of concrete samples exposed to acid and sulfate solutions were reduced, as was the strength loss. The study’s findings show that using WPFT fibers combined with POFA to develop a novel fiber-reinforced concrete subjected to chemical solutions is technically and environmentally feasible. WPFT fibers have a significant protective effect on concrete against chemical attacks.
Parallel Tiled Code for Computing General Linear Recurrence Equations

In this article, we present a technique that allows us to generate parallel tiled code to calculate general linear recursion equations (GLRE). That code deals with multidimensional data and it is computing-intensive. We demonstrate that data dependencies available in an original code computing GLREs do not allow us to generate any parallel code because there is only one solution to the time partition constraints built for that program. We show how to transform the original code to another one that exposes dependencies such that there are two linear distinct solutions to the time partition restrictions derived from these dependencies. This allows us to generate parallel 2D tiled code computing GLREs. The wavefront technique is used to achieve parallelism, and the generated code conforms to the OpenMP C/C++ standard. The experiments that we conducted with the resulting parallel 2D tiled code show that this code is much more efficient than the original serial code computing GLREs. Code performance improvement is achieved by allowing parallelism and better locality of the target code.
X3SEG: Model-Agnostic Explanations For The Semantic Segmentation Of 3D Point Clouds With Prototypes And Criticism

The proposed $\text{X}^{3}\text{S}\text{e}\text{g}$ approach generates model-agnostic, example-based explanations for the semantic segmentation of 3D point clouds. It retrieves the most similar 3D point sets (prototypes) as well as the most dissimilar point sets (criticism) to the spatially connected 3D point set which is to be explained. $\text{X}^{3}\text{S}\text{e}\text{g}$ comprises three methods for a holistic understanding of point-by-point class predictions: encompassing, selective, and predictive $\text{X}^{3}\text{S}\text{e}\text{g}$. Prototypes and criticism are identified from a particularly generated prototype database by combining different similarity measures. To the best of our knowledge, $\text{X}^{3}\text{S}\text{e}\text{g}$ is the first model-agnostic explainable artificial intelligence (XAI) approach providing example-based explanations for the semantic segmentation of 3D data with prototypes and criticism. It is demonstrated on RangeNet53++[1] predictions for 3D point cloud data from the SemanticKITTI dataset [2].
Determination of Caffeic Acid in Ethanolic Extract of Spent Coffee Grounds by High-Performance Liquid Chromatography with UV Detection

We developed a method for determining the caffeic acid in spent coffee grounds. The spent coffee ground solution was prepared by blending 3 g spent coffee grounds with 60 mL ethanol/water (40/60 v/v) for 2 h on a hot plate magnetic stirrer (60 °C, 350 rpm). The mixture was filtered and the filtrate was concentrated under vacuum (60 °C) to 5 mL. The method employed a reversed-phase high-performance liquid chromatography with a UV detector. We used a Phenomenex Luna column (250 × 4.6 mm; i.d., 5 µm) under isocratic elution, and the mobile phase was acetonitrile-methanol-aqueous formic acid (10:10:80 v/v), with a flow rate of 0.9 mL/min. Analysis was performed at 324 nm. The column temperature was set at 27 °C temperature. The results showed that this method was selective for quantifying the caffeic acid in spent coffee grounds with good linearity in the range of 1.31–17.07 μg/mL. The detection and quantitation limits were 0.28 and 0.84 μg/mL, respectively. The mean intraday and interday recoveries were 83.80–95.17% and 82.16–97.40%, respectively. Intraday and interday precision expressed as the relative standard deviation (RSD) were below 7.3%. There was 0.17% ± 0.006 w/w caffeic acid in the spent coffee grounds (RSD = 3.63%, n = 3).
Antarctic Slope Current Modulates Ocean Heat Intrusions Towards Totten Glacier

The Totten ice shelf (TIS) in East Antarctica has received increasing attention in recent years due to high basal melt rates, which have been linked to a presence of warm modified Circumpolar Deep Water (mCDW) observed at the ice front. We show that mCDW on‐shelf intrusions towards the TIS strengthen when the Antarctic Slope Current (ASC) weakens. This demonstrates that the ASC has a blocking effect and ASC weakening leads to on‐shelf intrusions, as proposed by previous observational studies. The interannual variability of the ASC is controlled primarily by atmospheric and oceanic conditions beyond our regional model domain. We further show that heat intrusions onto the continental shelf off the TIS are not influenced by off‐shelf warming but are enhanced with coastal freshening, suggesting positive feedback whereby ice melt and freshening upstream could start a chain reaction, leading to increased melt, and further coastal freshening.
Impulsive adaptive pinning synchronization of Lur'e networks with cluster‐tree topology via parameters variation protocols

This article is devoted to studying the problem of globally and exponentially adaptive cluster synchronization for a class of Lur'e dynamical networks with multiple time‐varying delays and hybrid couplings. A novel impulsive adaptive pinning feedback control protocol is proposed with fully considering the cluster‐tree topology structures of the networks and only imposed on the Lur'e systems in current cluster which exist directed paths with those Lur'e systems in the other clusters. In view of the concept of average impulsive interval, the comparison principle, the extended parameter variation methods, and the reductio ad absurdum, sufficient conditions are acquired for achieving the cluster synchronization of the derivative coupled Lur'e networks with considering different functions of the impulsive effects, respectively. Furthermore, according to the designed adaptive updating law, suitable feedback control strengths are obtained, which largely save the control costs. Finally, the effectiveness of the control schemes and the theoretical results have been proved by executing two numerical simulation examples.
Him-Net: A New Neural Network Approach for SAR and Optical Image Template Matching1

SAR and optical images provide highly complementary information about observed scenes. The integrated use of these two data is desired in many data fusion tasks. However, traditional similarity methods cannot correctly match SAR and optical images due to the significant non-linear radio-metric difference between them. This paper proposed a template matching neural network based on stereo matching for SAR and optical image matching. Unlike the classical template matching methods doing feature extraction and similarity calculating separately, our network is a complete end-to-end approach, which allows optimizing the matching between the SAR and optical images through training. Moreover, a heatmap loss function is designed for image template matching, and better result is obtained. Our experiments confirmed our proposed network advantage over the state-of-the-art similarity approaches (such as NCC, CARMI, DeepMatch, and QATM) and superior matching performance.
CEO turnover in public and private organizations: analysis of the relevance of different performance horizons

PurposeThis paper investigates how past performance changes, prior CEO replacements and changes in the chairperson impact CEO turnover in public and large private businesses.Design/methodology/approachWe analyze 1,679 CEO replacements documented in a sample of 1,493 Spanish public and private firms during 1998–2004 by computing dynamic binary choice models that control for endogeneity in CEO turnovers.FindingsThe results reveal that different performance horizons (short- and long-term) explain the dissimilar rate of CEO turnover between public and private firms. Private firms exercise monitoring patience and path dependency characterizes the evaluation of CEOs, while public companies' short-termism leads to higher CEO turnover rates as a reaction to poor short-term economic results, and alternative controls—ownership and changes in the chairperson—improve the monitoring of management.Originality/valueOur results show the importance of controlling for path dependency to examine more accurately top executives' performance. The findings confirm that exposure to market controls affects the functioning of internal controls in evaluating CEOs and shows a short-term performance horizon that could be behind the recent moves of public firms going private or restraining shareholders' power.
Cam-Guided U-Net With Adversarial Regularization For Defect Segmentation

Defect segmentation is critical in real-wold industrial product quality assessment. There are usually a huge number of normal (defect-free) images but a very limited number of annotated anomalous images. This poses huge challenges to exploiting Fully-Convolutional Networks (FCN), e.g., UNet, as they require sufficient anomalous images with defect annotations during training. To further leverage the information from normal data, a novel CAM-guided U-Net with adversarial regularization (CAM-UNet-AR) is proposed. We first modify the existing CAM-UNet to incorporate the CAMs for both normal and anomalous classes and fine-tune the segmentation network using a combined loss which jointly considers pixel-wise classification, foreground segmentation and boundary segmentation. Secondly, an auxiliary adversarial regularization module (ARM) is proposed to facilitate the segmentation network to encode the “normal components” from training images into consistent representations. Extensive experiments on MVTec AD dataset show the superiority of our proposed network over multiple state-of-the-art U-Net variants.
An analysis of critical success factors towards sustainable supply chain management – in the context of an engine manufacturing industry

ABSTRACT Appropriate consideration and selection of practices, success factors are of utmost importance of the best use of resources, time and money to facilitate Sustainable Supply Chain Management (SSCM) development. This study presents an integrated framework based on Analytic Hierarchy Process (AHP) and Interpretive Structural Modelling (ISM) to evaluate potential alternatives for the SSCM. The criteria used for the selection of various enhancers of sustainability, which are developed along with the categories are environmental, economic, social, technical, organisational and business. An Indian-based engine manufacturing industry is taken to build up the model. AHP is employed to select the best criteria from the various Critical Success Factors (CSFs) listed under the categories based on the calculated weights. ISM and Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis are employed to model the relationship among the various CSFs and to select the best from the developed matrix. The result indicates ‘support from senior management for environmental activities’, ‘environmental teamwork’ and ‘government support’ are the main CSFs for the achievement of sustainability of the supply chain.
Soft Sensor Transferability: A Survey

: Soft Sensors (SSs) are inferential dynamical models employed in industries to perform prediction of process hard-to-measure variables based on their relation with easily accessible ones. They allow implementation of real-time control and monitoring of the plants and present other advantages in terms of costs and efforts. Given the complexity of industrial processes, these models are generally designed with data-driven black-box machine learning (ML) techniques. ML methods work well only if the data on which the prediction is performed share the same distribution with the one on which the model was trained. This is not always possible, since plants can often show new working conditions. Even similar plants show different data distributions, making SSs not scalable between them. Models should then be created from scratch with highly time-consuming procedures. Transfer Learning (TL) is a ﬁeld of ML that re-uses the knowledge from one task to learn a new different, but related, one. TL techniques are mainly used for classiﬁcation tasks. Only recently TL techniques have been adopted in the SS ﬁeld. The proposed survey reports the state of the art of TL techniques for nonlinear dynamical SSs design. Methods and applications are discussed and the new directions of this research ﬁeld are depicted.
Protean and Boundaryless Career Attitude as Determinants of Well-being Among Indian IT Temporary Agency Workers

The growth of temporary and non-standard employment arrangements post liberalization drives temporary agency workers (TAWs), especially of the information technology (IT) sector, to be agents of their own career identities. As a result, TAWs embrace self-managed/adaptable career management approaches namely protean career (PCO) and boundaryless career (BCO) orientations for their long-term sustainability and well-being. This study investigated the direct effects of PCO and BCO on TAW’s well-being, also, their indirect effects thorough perceived employability (PE). Study participants were 246 TAWs employed by leading Indian IT recruitment agencies and deployed to work for various IT clients. Data were collected through validated questionnaire and analysed through structural equation modelling. Findings confirm direct effects of PCO and BCO on TAW well-being and partial mediation effects of PE. Thus, to boost TAWs’ well-being, organizations may consider developing their protean attitudes via employee engagement programmes, staff agencies through counselling to map workers values/interest with their assigned projects, TAWs workers themselves proactively participating in the challenging assignment at the client place.
Transportation Network Companies: Drivers’ Perceptions of Ride-Sharing Regarding Climate Change and Extreme Weather

The transportation sector is a major factor contributing to climate change. Transportation Network Companies (TNC) may become part of solutions to reduce emissions and their drivers play an important role in doing so. This study aims to understand TNC driver’s perceptions of climate change, to understand how climate change and extreme weather affects their business and how they see their role in contributing to or mitigating climate change. We conducted an in-person survey of TNC drivers in Nevada, USA, and analyzed the derived information with descriptive statistics and content analysis. Among the 75 TNC drivers, almost half believe climate change is happening and is caused by human activities. We found TNC drivers and their business are affected by extreme weather events. Currently the drivers do not see their role in mitigating climate change and lack the awareness of green initiatives already in place by TNCs’. We conclude that TNCs could increase their climate change responsibility by providing driver incentives for cars with reduced emissions or by geographically expanding customer incentives for using sustainable TNC options such as car-pooling. By doing so, TNC may play a role in reducing global greenhouse gas emissions and traffic congestion; thus, contributing to improved sustainable transportation practices.