Miracle-dz/UniFAR-FTUs huggingface dataset:
Intra- and interannual export of nitrogen and phosphorus in the subtropical Richmond River catchment, Australia

Nitrogen and phosphorus concentrations were measured and exports were calculated in the subtropical Richmond River catchment between July 1994 and June 1996. A stratified sampling approach was adopted owing to the extreme and rapid changes in riverine discharge, which varied by up to 10 000 times over the study period. Nutrient concentrations were lowest during baseflow. During storm discharge, dissolve inorganic and organic and particulate nitrogen and phosphorus concentrations increased two- to five fold, and followed hysteresis patterns that were attributed to the integration and/or depletion of catchment nutrient sources during an event. Dissolved organic nitrogen and particulate phosphorus were the dominant nutrient forms. Land use and antecedent conditions had a large influence on nutrient concentrations and exports. During the 1995–96 year (slightly above the mean annual discharge) 96% of nitrogen and 98% of phosphorus loads were transported in less than 6% of the time. When averaged across the catchment, monthly riverine nutrient loads varied by up to 1061-fold during the study and exports were approximately four-fold greater during the second year relative to the first. The subtropical Richmond River catchment has greater intra- and potential interannual variability in nutrient loads and exports when compared with temperate catchments from other parts of the world. It is suggested that in tropical and subtropical Australian catchments with large intra- and interannual variation in discharge, the need for parameterizing the antecedent conditions, such as the degree of nutrient storage, may make it difficult to model spatial and temporal (short time-scale) nutrient exports. Copyright © 2000 John Wiley & Sons, Ltd.
Radiocarbon decay and oxygen utilization in the Deep Atlantic Ocean

A parameter based on the sum of the concentrations of PO4 and O2 (divided by the Redfield coefficient-ΔO2/δPO4) is used to separate the contributions of the northern and southern components to deep waters in the Atlantic. This separation allows the amount of radiocarbon lost by radiodecay and the amount of oxygen lost to respiration during residence in the deep Atlantic to be calculated. Maps of these quantities reveal strong west to east gradients and weak north to south gradients consistent with ventilation along the western boundary from both ends of the ocean coupled with mixing outward from the boundary. The O2 and 14C deficiences are highly correlated, suggesting an O2 utilization rate of 12 μm/kg per century. The apparent mean isolation time of water in the deep Atlantic is about 200 years.
Were the peseta exchange rate crises forecastable during target zone period?

During the 1990s several fixed or quasi-fixed exchange rate systems collapsed. Currency crises have happened in both developed and emerging countries so it is necessary to forecast and avoid them. However, financial market crises have been extremely difficult to forecast. Economic agents' expectations are nonobservable variables that cannot be ignored in the models. In addition, if it is required to study the European case during the 1990s, the censored disposition of the exchange rate cannot be ignored either. A discrete time target zones model is proposed where these aspects are taken into account. It will be tested in a peseta/deutsche mark exchange rate framework, from June 1989 to December 1998. The results indicate differences between before and after the shift in band widths in August 1993.
Variabilidade Intrassazonal Extrema da Temperatura do ar à Superfície na Península Antártica

A Peninsula Antartica (PA) e uma das regioes da Terra onde o aquecimento foi mais acelerado nos ultimos 50 anos. Ha indicios de que esta tendencia esteja associada, em parte, ao aumento da atividade intrasazonal (IS) tropical. Utilizando dados de estacoes, este estudo mostrou que a variabilidade da temperatura do ar a superficie (Tsup) sobre a PA apresenta um pico estatisticamente significativo na escala IS (78 dias). A influencia da Oscilacao Madden-Julian (MJO) sobre os eventos IS da Tsup na PA foi quantificada: mais de 60% dos eventos foram associados a MJO, via trem de ondas de Rossby com periodicidade em torno de 30-40 dias. Portanto, outros mecanismos precisam ser investigados para explicar a variabilidade IS da Tsup na PA nao associada a MJO (NMJO). Tanto os eventos NMJO quanto o total de eventos (MJO + NMJO) foram mais frequentes na primavera. Alem disso, nos ultimos 12 anos analisados (1998-2009) houve uma maior frequencia de eventos MJO do que NMJO, principalmente na primavera e no inverno.
EEG-Based on Emotion Recognition Using Machine Learning

Emotion is of great significance in human's daily interaction. Contemporarily, emotion recognition systems are beneficial in various areas, ranging from human-computer interaction (HCI) system to healthcare safety. So far, the Electroencephalogram (EEG) has received much attention because of its sensitivity to mood changes. However, the relationship hasn't been fully researched. To investigate the relationship among emotions, bands, channels and find a better classifier, this experiment organize the framework based on the DEAP dataset. After the data preprocessing, EEG data is reduced by principal component analysis (PCA). Then to create the emotion feature vectors, the discrete wavelet transform (DWT) is chosen as the parameter. Lastly, to categorize the EEG signal, support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), XGboost model is created. According to the experiment, this paper obtained the following relevant conclusions: 1) RF, SVM, KNN and XGboost classifier, performed with the accuracy of 77.01%, 80.44%, 66.75% and 77.75%, respectively; 2) In temperal and whole brain regions, gamma has the highest recognition ability, reaching 76.770/0 and 77.02%. The beta performs best in occipital, parietal, and central regions with 78.00%, 77.26%, and 78.00%, respectively; 3) The higher the frequency, the greater the oscillatory dynamics of brain activity on positive / negative emotions; 4) The AF3, AF4 area has a greater effect in reflecting emotions because of less irrelevant influences.
Self‐lubrication for Long Runout Landslides: Examination by computer simulation

This paper describes the use of a discrete particle computer simulation to test whether the apparent low friction exhibited by long runout landslides could be explained in terms of simple granular mechanics. The flow structure consists of an active basal shear zone supporting the majority of the landslide, which travels as a relatively solid plug. The observed runout appears to depend on the total energy dissipation which is a trade-off between (1) the inelasticity of the particles, which governs the energy dissipated in each event, and, (2), the size of the dissipating shear zone, which governs the number of particles that are actively dissipating energy. Very inelastic particles quickly dissipate the work performed at boundaries and result in thin dissipative zones. As a result, these two factors balance and the runout is nearly independent of particle inelasticity. Along the same lines, increasing the boundary roughness was found to increase the size of the dissipative shear zone and markedly decrease the runout. Also, a distribution of particle sizes destroyed ordered structures within the body which, once again, increases the dissipation and decreases the runout. Unfortunately, the simulations also indicate that the runout should be independent of the depth of the flow and thus cannot account for the observed volumetric effect on runout.
I-V Lissajous Pattern: a Promising Condition Monitoring Technique for High Voltage Assets

The current-voltage (I-V) characteristics of critical high voltage assets such as power transformers, overhead transmission lines and underground cables are affected by various factors including the asset’s health state along with other operating and environmental conditions. Understanding the current-voltage characteristics of such assets is essential for reliable and efficient operation of power transmission and distribution networks. The measurement of such parameters does not call for additional hardware as it utilises easily accessible voltage and current data at both terminals of the asset. The I-V characteristic can be considered as a unique fingerprint for the asset during its normal operation. variations in the fingerprint can be easily detected using a proper software code. The variation can be observed in real time and analysed using digital image processing and artificial intelligence-based techniques to identify and quantify any abnormal event or a fault at early stage. This paper discusses the potential of using the I-V Lissajous pattern to detect incipient faults and abnormal events of high voltage assets. Some case studies on power transformer and overhead transmission line are presented. Results show that I-V Lissajous pattern is a promising technique for real time asset management of high voltage critical equipment.
Amplitude-frequency dependencies of wave attenuation in single-crystal quartz: Experimental study

[1] The experiments have been conducted to investigate the effect of strain amplitude and frequency on the compressional and shear wave attenuation in quartz samples of three types: the intact quartz, fractured quartz, and smoky quartz. The measurements were performed using the reflection method on a pulse frequency of 1 MHz with changing strain in the range 0.3 ≤ ɛ ≤ 2.0 μstrain under a confining pressure of 10 MPa and at ambient temperature. The essential difference in amplitude-frequency characteristics of wave attenuation in three quartz types has been detected. The intact quartz shows the more “simple” behavior in comparison with the fractured and smoky quartz. The attenuation (the inverse quality factor Q) depends on strain amplitude as Q−1(ɛ) ∼ ɛ−n, where n ≅ 0.005–0.085, with the greatest decrease in the smoky and fractured quartz reaching of about 15%. Relaxation spectra of attenuation are presented in the frequency range from 0.4 to 1.4 MHz. The dependence Qp−1(f) ∼ f−1.2 characterizes the intact and fractured quartz, whereas the smoky quartz has the relaxation peak. The dependence Qs−1(f) ∼ f−0.84 presents S wave relaxation spectrum in the intact quartz; in the fractured and smoky quartz, the attenuation peaks take place. The strain amplitude variation exerts influence on the relaxation strength, the peak frequency, and the width of the relaxation peak. Such behavior of attenuation can be explained by a joint action of viscoelastic and microplastic mechanisms. These results can be considered as a contribution for providing the experimental background to the theory of attenuation in rocks. They can also be used in solving applied problems in material science, seismic prospecting, etc.
Statistical analysis of the sources of the cross‐polar potential for southward IMF, based on particle precipitation characteristics

There are several proposed physical processes which may contribute to the cross‐polar potential and thus drive ionospheric convection around the polar caps. It is generally believed that magnetic reconnection is the dominant process, however dynamos such as viscous interaction and impulsive penetration are other possible contributors. A comprehensive statistical study has been conducted using data from the DMSP F13 satellite for passages along the northern hemisphere dawn‐dusk meridian, with focus on typical two‐cell convection patterns during times of steady southward IMF conditions. The results show that the low‐latitude dynamo (viscous interaction or reconnection in the LLBL) on average accounts for only 1–2 kV of the total potential drop, values lower than those previously predicted. At rare occasions this dynamo can be a significant source of energy, however, contributing to more than 20 kV of the cross‐polar potential.
Tropical Pacific Interannual Variability and CO2 Climate Change

Abstract In this paper, an attempt is made to estimate possible sensitivities of El Nino-Southern Oscillation (ENSO)-related effects in a climate with increased carbon dioxide (CO2). To illustrate this sensitivity, results are shown from two different interactive ocean-atmosphere model configurations and an atmospheric model with prescribed heating anomalies. In the first, an atmospheric general circulation model (GCM) is coupled to a global coarse-grid dynamical ocean GCM (coupled model). In the second, the same atmospheric model is coupled to a simple nondynamic slab-ocean mixed-layer model (mixed-layer model). In the third, an atmospheric model is run in perpetual January mode with observed sea surface temperatures (SSTs) and prescribed tropical tropospheric heating anomalies (prescribed-heating model). Results from the coupled model show that interannual SST variability (with warm and cold events relative to the mean SST) continues to occur in the tropics with a doubling of CO2. This variability is su.
Design of H-Bridge Circuit for CMOS Stimulator and its Application Analysis

The relationship between nerve signals and electrical signals promotes the develop of neural stimulators. The Complementary Metal Oxide Semiconductor (CMOS) technology brings benefits to the development of implantable nerve stimulators. This paper reviews the theoretical basis of neural stimulator as well as its common structure. In part 3, an experiment based on software simulation is conducted to study the behavior of ‘H’ bridge. Some optimization of digital to analog converter is discussed in part 4. Experiment shows that the ‘H’ bridge has stale control to produce biphasic current. Studies show that current mirrors widely used in the design of neural stimulator which is included in DAC and also used in the output driver to increase output resistance. Blocking capacitors and optimized DAC are equipped in the stimulators to address the safety issues. CMOS technology, with its excellent characteristics such as low power consumption, provides the possibility of optimal design scheme for neurostimulators and neurostimulators have great potential for development.
Detection and Tracking of Dynamic Ocean Carbon Uptake Regimes Built Upon Spatial Target-Driver Relationships via Adaptive Hierarchical Clustering

Our research focuses on detecting and tracking ocean carbon regimes, which are crucial indicators for understanding the impacts of climate change on ocean carbon uptake. Geoscientific datasets in Earth System Sciences often contain local and distinct statistical distributions at a regional scale. This poses a significant challenge in applying conventional clustering algorithms for data analysis. Based on the observed limitations of prominent methods, in our study, we propose a framework that enhances well-established unsupervised machine-learning methods tailored to applications on geoscientific datasets. We define a carbon uptake regime as a region characterized by common relationships between the carbon uptake and its drivers, as simulated by a multi-annual hydrodynamic model simulation. As a first step, we compute multivariate linear regressions capturing local spatial relations between carbon dioxide uptake and its drivers to discover such regimes. This is followed by an agglomerative hierarchical clustering constructed upon the collection of regional multivariate linear regression models. To overcome the emerging limitations of a global cut for partitioning, which is inadequate to capture the local statistical distributions, we present a novel, straightforward and adaptive approach to detect and visualize ocean carbon uptake regimes in this work. This method relies on the distance-variance selection technique and detects multiple local cuts on the dendrogram by considering both the compactness and similarity of the clusters. Detecting meaningful and well-defined carbon uptake regimes is vital for their tracking over time. The tracking is performed through a simple yet effective approach where summary structures derived from the clusters are traced over time. Applied over longer time scales, this novel method will enable marine scientists to effectively monitor the impacts of climate change on the ocean carbon cycle more.
Drifter Launch Strategies Based on Lagrangian Templates

Abstract A basin-scale, reduced-gravity model is used to study how drifter launch strategies affect the accuracy of Eulerian velocity fields reconstructed from limited Lagrangian data. Optimal dispersion launch sites are found by tracking strongly hyperbolic singular points in the flow field. Lagrangian data from drifters launched from such locations are found to provide significant improvement in the reconstruction accuracy over similar but randomly located initial deployments. The eigenvalues of the hyperbolic singular points in the flow field determine the intensity of the local particle dispersion and thereby provide a natural timescale for initializing subsequent launches. Aligning the initial drifter launch in each site along an outflowing manifold ensures both high initial particle dispersion and the eventual sampling of regions of high kinetic energy, two factors that substantially affect the accuracy of the Eulerian reconstruction. Reconstruction error is reduced by a factor of ∼2.5 by using a co.
The Cambridge Handbook of Computational Psychology

This book is a definitive reference source for the growing, increasingly more important, and interdisciplinary field of computational cognitive modeling, that is, computational psychology. It combines breadth of coverage with definitive statements by leading scientists in this field. Research in computational cognitive modeling explores the essence of cognition through developing detailed, process-based understanding by specifying computational mechanisms, structures, and processes. Computational models provide both conceptual clarity and precision at the same time. This book substantiates this approach through overviews and many examples.
Dynamic Human-Aware Task Planner for Human-Robot Collaboration in Industrial Scenario

The collaboration between humans and robots in industrial scenarios is one of the key challenges for Industry 4.0. In particular, industrial robots offer accuracy and efficiency, while humans have experience and the capability to manage complex situations. Combining these features can enhance the industrial process by avoiding the user manipulates heavy weights and allowing him to dedicate his efforts to tasks where flexibility, quality and experience make the difference in the final product. However, the collaboration between humans and robots raises several new problems to be addressed like safety, tasks scheduling and operator ergonomics. For example, human presence in the robot workspace introduces various elements of complexity into robot planning due to its dynamism and unpredictability. Planning must take into account how to coordinate the tasks between the robot and the human and be quick in re-planning to respond reactively to the operator's trigger. For this purpose, this work proposes a hierarchical Human-Aware Task Planner framework capable of generate a suitable plan to complete the process and manage user interrupts in order to have a constantly updated plan. The method is evaluated in a real industrial scenario and in a specific complex assembly task like the draping of carbon fiber plies.
Science Perspectives on Psychological the Smartphone Psychology Manifesto on Behalf Of: Association for Psychological Science the Smartphone Psychology Manifesto Previous Research Using Mobile Electronic Devices What Smartphones Can Do Now and Will Be Able to Do in the near Future

Smartphones such as the iPhone, Blackberry, and Android are not just new communication technologies. They are an occasion to rethink what psychology could be. If psychology had no history—if it was invented today and had no methodologi-cal inertia—what research methods would we use for gathering behavioral data? I think that we would use smartphones, because they are ubiquitous, unobtrusive, intimate, sensor-rich, computationally powerful, and remotely accessible. Smartphones offer huge potential to gather precise, objective, sustained, and ecologically valid data on the real-world behaviors and experiences of millions of people where they already are, without requiring them to come into labs (Dufau et al. Smartphones can also run controlled but perceptually and behaviorally rich surveys, tests, and experiments. Downloadable software applications for smartphones (which I will call " psych apps, " though they can be used in any behavioral science) could become our central way of recruiting, obtaining consent from, observing, experimenting on, and debriefing participants—anyone, anytime, anywhere. This manifesto argues that if we start taking smartphones seriously now as research tools, psychology could become much more powerful, sophisticated, international, applicable, and grounded in real-world behavior. Smartphones could revolutionize all fields of psychology and other behavioral sciences, if we grasp their potential and develop the right research skills, psych apps, data analysis tools, and human subjects protections. How could such humble little devices have such power to advance our science? A $700 iPhone doesn't look like much compared with a $2 million MRI brain scanner. Yet smart-phones are becoming very common, powerful, and multifunc-tional—an all-in-one lifestyle technology, a sort of electronic Swiss Army knife (Barkhuus & Polichar, 2011). Worldwide, mobile broadband users (who typically use smartphones) when most of today's psychology majors are in their mid-30s (with some who entered academia just going up for tenure), most of the projected eight billion people in the world will carry smartphones. Each smartphone will have more computing power and memory, faster connectivity, better sensors, more input–output options, and more software apps than any Abstract By 2025, when most of today's psychology undergraduates will be in their mid-30s, more than 5 billion people on our planet will be using ultra-broadband, sensor-rich smartphones far beyond the abilities of today's iPhones, Androids, and Blackberries. Although smartphones were not designed for psychological research, they can collect vast amounts of ecologically valid data, easily and quickly, from large global samples. If participants download the right " psych apps, …
Effects of External Archives on the Performance of Multi-Objective Evolutionary Algorithms on Real-World Problems

External archives have attracted more and more attention in the evolutionary multi-objective optimization (EMO) community. This is because a solution set selected from an external archive is usually better than the final population of an EMO algorithm. Whereas the effects of subset selection from external archives have already been investigated on artificial test problems, its effects on real-world problems have not been examined. In this paper, we examine the effects of subset selection from external archives for ten EMO algorithms on two real-world problem suites. Experimental results show that the performance improvement by subset selection is large for most algorithms and many problems but small for a few algorithms and a few problems (i.e., algorithm dependent and problem dependent).