Miracle-dz/UniFAR-FTUs huggingface dataset:
Geodetic evidence of back‐arc spreading in the Mariana Trough

Repeated GPS surveys in the Mariana Islands show that the Mariana block is moving apart from the Philippine Sea plate. The velocities of the islands relative to the Philippine Sea plate range from about 15 mm/yr in the northern islands to about 45 mm/yr near Guam. The data also suggest convergence rates for the Mariana forearc with respect to the Pacific plate of 35–45 mm/yr at 19°N increasing to 55–70 mm/yr at 13.5°N. In addition, the velocity vectors show a slight north‐south expansion of the arc. The estimated location of the Euler pole of the Mariana forearc with respect to the Philippine Sea plate is well south of the geographical point where the back‐arc basin narrows to zero width.
Aesthetic Vs. functional restoration of urban and peri-urban rivers: the Manzanares River in Madrid

The Manzanares River (Madrid, Spain) is living an ongoing process of transformation, essential if we understand the number and intensity of human pressures it has experienced during the last decades. It is also a paradigmatic case study of the present debate about the most suitable restoration approaches of urban and peri-urban rivers: aesthetic-based or functional-oriented alternatives have been proposed in different urban sites worldwide. This work analyses when and how those approaches can be used, which should be selected under the light of the present knowledge on urban rivers, and in what conditions they can coexist. All these questions have been addressed for the specific case of the Manzanares River, allowing some insights which can be transferred to other urban and peri-urban rivers, both in Mediterranean and non-Mediterranean areas. In this work, we propose a multi-functional approach to urban rivers, considering the importance of providing self-sustaining actions, which can rehabilitate essential river processes inside the city, and which offer relevant ecosystems services to the urban citizens. With that aim, we list and present some of the measures which are being taken in the Manzanares River to reach those goals. Among them, rehabilitation of relevant hydromorphologic processes, allowance of natural river forms to develop inside the riverbed, re-connection of the urban reach with upstream and downstream semi-natural and protected river reaches, improvement of riparian stands, and rehabilitation of the river corridor to allow the improvement of the ecological, landscape and recreational connectivity.
Meteorological conditions for the triggering of landslides in Asturias (NW Spain): a preliminary analysis of synoptic patterns

Principality of Asturias is one of the most landslide prone areas in the NW of Spain. Most of landslides take place during intense rainfall events, which point out precipitation as the main triggering factor (DominguezCuesta et al., 1999). Regional climate is characterized by average annual precipitation and temperature of 960 mm and 13.3°C. According to the Köppen-Geiger classification (Peel et al., 2007), the majority of the area presents an Oceanic Temperate climate (Cfb).
Uncovering Fake News by Means of Social Network Analysis

The short access to facts on social media networks in addition to its exponential upward push also made it tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for misstatement of information thru its source, content material, or author and save you the unauthenticated assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for fake information presence. The implementation setup produced most volume 99% category accuracy, even as dataset is tested for binary (real or fake) labelling with multiple epochs.
Investment infrastructure and development assistance impact on headcount poverty reduction in Nigeria: Auto-regressive distributed lag (ARDL) approach

This research analysis includes past and future expectations, which are vital and addresses the following question to examine the relationship between infrastructural investment and the effects of development assistance on poverty headcount reduction? The dynamic relationship between infrastructure investment (percentage of the gross fixed investment) and foreign aid (percentage of net official development assistance) on poverty reduction (headcount) proved inconclusive using four lags. The auto-regressive distributed lag (ARDL) model results revealed an inconclusive, rather impactful relationship and significantly supported the model's efficiency during the study period. Despite the relationship inconclusiveness, it indicated that foreign aid or official assistance was impactful to the optimum in the past and present. Investment in infrastructure was crucial for reducing the poverty headcount in the Nigerian economy. The policy implications suggested that net official assistance inflows and infrastructural development could foster an excellent avenue for foreigners to invest more in the country, bringing capital stock, employment opportunities, and increased per capita income. To boost trade openness, employment, and growth, especially in rural areas, enhances urban development or improve living standards.
and Multimodal Approaches in Teaching and Learning Mathematics, Music and Language Based on the Flipped Classroom Model

Digitalization and multidisciplinary approach are the new trends in Teaching and Learning theory and practice. The article lays emphasis on mathematics teaching and learning knowledge, based on Action Research Theory, New Learning Theory and Variation Theory. Preservice early elementary school teachers created audiovisual video films (AV) and Podcasts (PD) lessons integrating Mathematics, Music and Language during a university course in teacher education. These multidisciplinary lessons were used as intelligent tutors in a flipped classroom model. The lessons are dealing with proportionality, one of the most difficult items in elementary school mathematical curriculum. For explaining proportionality, the preservice teachers have used multiple representations such as manipulatives, fiction stories, videogame Minecraft, “Smash up” in tennis table, musical intervals, self-composed, self-written and self-performed songs about mathematical rules in easy to remember rap-style. Preservice teachers have shown their AV or PD lessons to elementary school students followed by joint discussions
Potencialidades da água de chuva no Semi-Árido brasileiro.

Disponibilidade de agua e a gestao de recursos hidricos; Clima e agua de chuva no semi-arido; P1+2: Programa uma terra e duas aguas para um semi-arido sustentavel; Cisternas domiciliares: qualidade de agua para consumo humano; Cisterna rural: agua para consumo animal; Barragem subterrânea: agua para producao de alimentos; Captacao in situ: agua para a producao de alimentos; Irrigacao de salvacao: producao de alimentos.
Language models for extracting Wikidata statements and generating Wikipedia content

In this project, I plan to create language models specifically designed for extracting statements from Wikidata and generating content for Wikipedia in English. These models will operate in a cyclical training process, where the output of one model serves as the input for another, enhancing overall performance. The objective of this project is to enhance the content of Wikidata and English Wikipedia by integrating the outputs of these models with results from ChatGPT. The anticipated outcome is an accelerated development of content for Wikidata and Wikipedia.
GeoRCF-GN: Geography-Aware State Prediction in Dynamic GeoRCF-GN: Geography-Aware State Prediction in Dynamic Networks Networks

In this paper, we formulate a geography-aware model for predictions over dynamic networks using Graph Neural Network (GNN) and Random Convolutional Feature (RCF) extractor. We take traffic and mobile ad-hoc communication networks as our main case studies, and study their special properties to design an architecture that makes use of information about both the graph topology and the geography that the nodes are located in, as geographical factors are crucial in determining future radio signal quality and traffic sensor readings.
Decision Theory

People have been consciously coping with decision problems for at least as long as recorded history exists. Some of the best advice modern theory can offer appears to have been known to the ancients. For example, the biblical Jacob, fearing his brother Esau’s might, divides his camp into two bands, saying, “If Esau come to the one company and smite it, then the other company which is left shall escape” (Genesis, 32:8) – thereby holding a claim to having invented diversification.
Beyond 10,000 Hours: Addressing Misconceptions of the Expert Performance Approach

It has been three decades since K. Anders Ericsson (Ericsson & Smith, 1991) proposed the expert performance approach as a general theoretical and methodological framework for studying the development of expert-level performance. Drawing on Ericsson’s most recent writing, this review corrects four misconceptions about the expert performance approach that have persisted in both the popular and scientific literatures on expertise: (1) anyone can become an expert by putting in 10,000 hours of any kind of practice, (2) the expert performance approach is exclusively concerned with deliberate practice, (3) expert performers can be identified based on reputation or experience, and (4) Ericsson’s claims require that a majority of the variance in performance is explained by deliberate practice. We conclude by making the case for integrating aspects of the expert performance approach into broader learning contexts, including educational and occupational environments. Such in situ experiments will mark the transition of expertise research from the basic science of describing exceptional performance to the applied science of maximizing human potential.
On the predictability of hydrology using land surface models and field soil moisture data

x! Chapter 1! Introduction . 1! 1.1! Introduction . 1! 1.2! Hydrology and Soil Moisture . 1! 1.3! Key Problems for Predicting Soil Moisture . 3! 1.3. 1 Inadequate Soil Moisture Records . 3! 1.3. 2 Scale Inconsistency . 4! 1.3. 3 Vegetation Information . 5! 1.3. 4 Model Deficiency and Model Differences . 6! 1.3. 5 Summary . 6! 1.4! Objectives . 7! 1.5! Thesis Organization . 7! Chapter 2! Literature Review . 9! 2.1! Introduction . 9! 2.2! Importance of Soil Moisture . 9! 2.3! Spatio-temporal Variability of Soil Moisture . 12! 2.3. 1 Temporal Variability . 12! 2.3. 2 Spatial Variability . 13! 2.4! Data Availability of Soil Moisture . 14! 2.4. 1 Ground-based Measurements . 15! 2.4. 2 Remote Sensing Measurements . 16! 2.5! Modelling of Soil Moisture . 17! 2.5. 1 Land Surface Models (LSMs) . 17! 2.5. 1.1 First-generation Models . 18! 2.5. 1.2 Second-generation Models . 18! 2.5. 1.3 Third-generation Models . 20! 2.5. 2 LSM Intercomparison Studies . 22!
: An application of the synthetic control method

The Spanish antitrust authorities charged car makers with information sharing and collusion in the Spanish market in the period 2006 - 2013. Using data on car prices for models sold in a set of European countries, in the period 1993 - 2011, we measure how explicit collusion affected car prices in Spain in that period using the synthetic control method. By following a demand-driven approach, that is, based on the fact that the elasticity of demand is heterogeneous across segments, we focus the analysis on the most representative car segments within the Spanish and European market, selecting a representative car model for each of them. Our results indicate that sharing information caused higher prices in A and B segments, highlighting the great power of explicit collusion. In particular, we show how in oligopolies with a large number of initial members and in cartels that increase its size over time, such an agreement can be maintained, even in periods of economic crisis. Focusing on the segment with the largest market share, the C-segment, there is no evidence that sharing information had an effect of increasing final prices, suggesting that higher competition within the segment may succeed in nullifying the harmful effects of explicit collusion on social welfare.
Advecting Superspecies: Efficiently Modeling Transport of Organic Aerosol With a Mass‐Conserving Dimensionality Reduction Method

The chemical transport model LOTOS‐EUROS uses a volatility basis set (VBS) approach to represent the formation of secondary organic aerosol (SOA) in the atmosphere. Inclusion of the VBS approximately doubles the dimensionality of LOTOS‐EUROS and slows computation of the advection operator by a factor of two. This complexity limits SOA representation in operational forecasts. We develop a mass‐conserving dimensionality reduction method based on matrix factorization to find latent patterns in the VBS tracers that correspond to a smaller set of superspecies. Tracers are reversibly compressed to superspecies before transport, and the superspecies are subsequently decompressed to tracers for process‐based SOA modeling. This physically interpretable data‐driven method conserves the total concentration and phase of the tracers throughout the process. The superspecies approach is implemented in LOTOS‐EUROS and found to accelerate the advection operator by a factor of 1.5–1.8. Concentrations remain numerically stable over model simulation times of 2 weeks, including simulations at higher spatial resolutions than the data‐driven models were trained on. The reversible compression of VBS tracers enables detailed, process‐based SOA representation in LOTOS‐EUROS operational forecasts in a computationally efficient manner. Beyond this case study, the physically consistent data‐driven approach developed in this work enforces conservation laws that are essential to other Earth system modeling applications, and generalizes to other processes where computational benefit can be gained from a two‐way mapping between detailed process variables and their representation in a reduced‐dimensional space.
Flow-sensitive control-flow analysis in linear-log time

The flexibility of dynamically typed languages such as JavaScript, Python, Ruby, and Scheme comes at the cost of run-time type checks. Some of these checks can be eliminated via control-flow analysis. However, traditional control-flow analysis (CFA) is not ideal for this task as it ignores flow-sensitive information that can be gained from dynamic type predicates, such as JavaScript's instanceof and Scheme's pair? , and from type-restricted operators, such as Scheme's car. Yet, adding flow-sensitivity to a traditional CFA worsens the already significant compile-time cost of traditional CFA. This makes it unsuitable for use in just-in-time compilers. In response, this dissertation presents a fast, flow-sensitive type-recovery algorithm based on the linear-time, flow-insensitive sub-0CFA. The algorithm has been implemented as an experimental optimization into Chez Scheme compiler, where it has proven to be effective, justifying the elimination of about 60% of run-time type checks in a large set of bench-marks. The algorithm processes on average over 100,000 lines of code per second and scales well asymptotically, running in only O(n log n) time. This compile-time performance and scalability is achieved through a novel combination of data structures and algorithms.
Business Process Management Journal An integrated framework for understanding the driving forces behind non-sequential process of internationalisation among firms

Purpose – Is non-sequential internationalization process solely technology enabled or a product of mutually interdependent forces? This paper aims to show that even though the sequential approach in the process model is intuitively appealing, not all firms follow such a path. Hence, integrated framework to explain how the international market entry process has changed with respect to the sequential approach is presented. Design/methodology/approach – The data banks of the Swedish Trade Council and the Chambers of Commerce are used to identify small and medium-sized enterprises (SMEs) with between 50 and 250 employees (in compliance with European Union’s definition of SMEs). Data are collected through the use of questionnaires and in-depth interviews from 60 Swedish SMEs operating in other countries and foreign SMEs operating in Sweden that tends not to develop in incremental stages with respect to their international. Findings – Some of the findings are: that the sequential model is by no means reflective of, or appropriate for all firms’ approaches to international business; the usefulness gained by using an international network approach to study the international activity of a firm; and the interplay between the identified driving forces behind a non-sequential internationalization process. Research limitations/implications – Even though market entry patterns of firms investigated demonstrate a non-sequential internationalization process, they are still within the general theoretical framework where the basic assumption of the sequential internationalization model can maintain its validity. Firms are indeed exposed to irrecoverable transaction costs that hamper their behaviours and complicate export supply responses in international markets. However, the magnitude of the costs and speed of internationalization is dependent on the ability of the firms to take advantage of the enablers of non-sequential internationalization pattern. This is particularly important for firms to and from developing countries and emerging markets and their propensities to succeed in their internationalization endeavours. Originality/value – Knowledge acquired from the empirical study of firms that tend not to develop in incremental stages with respect to their international activities, and therefore start international activities by entering very distant markets and multiple countries right from birth without prior experience, is used to develop an integrated framework which aptly depicts that non-sequential internationalization process is not solely enabled by technology, as commonly envisaged in literature, but a product of mutually interdependent forces. Consequently, this study provides a holistic view on the driving forces behind the rapid internationalization process encountered by many SMEs today.
OfﬁceHours: A System for Student Supervisor Matching through Reinforcement Learning

We describe OfﬁceHours, a recommender system that assists students in ﬁnding potential supervisors for their dissertation projects. OfﬁceHours is an interactive recommender system that combines reinforcement learning techniques with a novel interface that assists the student in formulating their query and allows active engagement in directing their search. Students can directly manipulate document features (keywords) extracted from scientiﬁc articles written by faculty members to indicate their interests and reinforcement learning is used to model the student’s interests by allowing the system to trade off between exploration and exploitation. The goal of system is to give the student the opportunity to more effectively search for possible project supervisors in a situation where the student may have difﬁculties formulating their query or when very little information may be available on faculty members’ websites about their research interests.
Renewable and Sustainable Energy Reviews

Blockchains or distributed ledgers are an emerging technology that has drawn considerable interest from energy supply ﬁrms, startups, technology developers, ﬁnancial institutions, national governments and the academic community. Numerous sources coming from these backgrounds identify blockchains as having the potential to bring signiﬁcant beneﬁts and innovation. Blockchains promise transparent, tamper-proof and secure systems that can enable novel business solutions, especially when combined with smart contracts. This work provides a comprehensive overview of fundamental principles that underpin blockchain technologies, such as system architectures and distributed consensus algorithms. Next, we focus on blockchain solutions for the energy industry and inform the state-of-the-art by thoroughly reviewing the literature and current business cases. To our knowledge, this is one of the ﬁrst academic, peer-reviewed works to provide a systematic review of blockchain activities and initiatives in the energy sector. Our study reviews 140 blockchain research projects and startups from which we construct a map of the potential and relevance of blockchains for energy applications. These initiatives were systematically classiﬁed into diﬀerent groups according to the ﬁeld of activity, implementation platform and consensus strategy used. 1 Opportunities, potential challenges and limitations for a number of use cases are discussed, ranging from emerging peer-to-peer (P2P) energy trading and Internet of Things (IoT) applications, to decentralised marketplaces, electric vehicle charging and e-mobility. For each of these use cases, our contribution is twofold: ﬁrst, in identifying the technical challenges that blockchain technology can solve for that application as well as its potential drawbacks, and second in brieﬂy presenting the research and industrial projects and startups that are currently applying blockchain technology to that area. The paper ends with a discussion of challenges and market barriers the technology needs to overcome to get past the hype phase, prove its commercial viability and ﬁnally be adopted in the mainstream.
Adaptive Total Variation Based on Feature Scale

The widely used Total Variation de-noising algorithm can preserve sharp edge, while removing noise. However, since fixed regularization parameter over entire image, small details and textures are often lost in the process. In this paper, we propose a modified Total Variation algorithm to better preserve smaller-scaled features. This is done by allowing an adaptive regularization parameter to control the amount of de-noising in any region of image, according to relative information of local feature scale. Experimental results demonstrate the efficient of the proposed algorithm. Compared with standard Total Variation, our algorithm can better preserve smaller-scaled features and show better performance. Keywords— Adaptive, de-noising, feature scale, regularization parameter, Total Variation.
Journal of International Women's Studies Journal of International Women

This paper examines the nature of the feminization of agriculture, and factors influencing the phenomenon in citrus producing pocket areas of Sindhuli district in Central Nepal. Presenting the intra-household division of work in agriculture among better-off family members in rural farms, emerging themes from narratives of women farmers’ lived-experience as farmers are discussed. Based on the narratives, this paper explores how household members’ everyday lifestyles regarding agriculture and non-agriculture shape their lives differently. Outlining the problematic of gendered agricultural engagement, three in-depth case analyses of farm families have been presented through data collected from narratives and participant observation as part of ethnographic study. These case studies highlight how women attribute economic and non-economic rationales for their life choices, using agency as an analytical lens. Finally, given the choice dilemma individuals face to reshape their lives, this paper shows how and why women farmers’ socially ascribed responsibility for livestock rearing spurs the feminization debate. It also demonstrates the need to further explore the extent of livestock feminization to the pattern of gendered mobility and non-mobility.