Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation of Hydro Power Plants in the Iberian Market using an Agent-Based Model and Q-Learning

This paper presents the results of an Agent-Based Model developed to simulate the Iberian Electricity Market, with special focus on the modelling of hydro power plants. To simulate the agent’s dynamics in the day-ahead market, it was developed a bidding strategy based on a Q-Learning procedure. In the computation area, the recent years brought the discussion around artificial intelligence to a new upper level to complement traditional models, driven by the increased hardware computer capabilities, as well as new developments in the machine learning area. Reinforcement Learning models, as Q-Learning, are being widely used to represent complex systems such as electricity markets. The developed model is designed to simulate in a detailed way the hydro units that have a large impact in the electricity market common to Portugal and Spain. Apart from describing the developed model, this paper also includes results from its application to the Iberian Market case along 2018.
On the Expressivity of a Parametric Humanoid Emotion Model

Emotion expression is an important part of human-robot interaction. Previous studies typically focused on a small set of emotions and a single channel to express them. We developed an emotion expression model that modulates motion, poses and LED features parametrically, using valence and arousal values. This model does not interrupt the task or gesture being performed and hence can be used in combination with functional behavioural expressions. Even though our model is relatively simple, it is just as capable of expressing emotions as other more complicated models that have been proposed in the literature. We systematically explored the expressivity of our model and found that a parametric model using 5 key motion and pose features can be used to effectively express emotions in the two quadrants where valence and arousal have the same sign. As paradigmatic examples, we tested for happy, excited, sad and tired. By adding a second channel (eye LEDs), the model is also able to express high arousal (anger) and low arousal (relaxed) emotions in the two other quadrants. Our work supports other findings that it remains hard to express moderate arousal emotions in these quadrants for both negative (fear) and positive (content) valence.
A 680 GHz Direct Detection Dual-Channel Polarimetric Receiver

In this work, a 680 GHz direct detection polarimetric receiver for the sensing of cloud ice in the upper atmosphere is presented. The minimum noise figure for the horizontally and vertically polarized channels are 10.5 dB and 10.6 dB, respectively. The receiver utilizes 25 nm indium phosphide (InP) HEMT low noise amplifier MMIC technology, which provide approximately 36 dB of RF gain per channel. Including linear regulators, the polarimeter consumes 1 W of DC power and measures 2.6 cm x 3.1 cm x 5.8 cm. This work represents the first 680 GHz direct detection polarimeter.
Theoretical Analysis of a Super-Mode Waveguide and Design of a Complementary Triangular Hybrid Plasmonic Nano-Antenna

The characteristics of a super-mode waveguide-fed nano-antenna composed of a complementary triangular hybrid plasmonic radiation part have been investigated by two methods of finite element and finite-difference time-domain. Also, a symmetric hybrid plasmonic waveguide (SHPW) has been studied theoretically and numerically to analyze short- and long-range fundamental TM super-modes (TMSR and TMLR) that excite the nano-antenna. The obtained propagation length and figure of merit at 193.5 THz are 150.6 μm (1.27 μm) and 691.77 (16.94) for TMLR (TMSR) super-mode, respectively, which confirm the inevitable loss-confinement trade-off of SHPW. These super-modes cause the nano-antenna to have horizontal and bidirectional radiation patterns due to the existence of the in-phase and out-of phase super-modes. The obtained directivities and efficiencies are 9.34 dBi (7.01 dBi) and 96.82% (9.66%) for TMLR (TMSR) super-mode, respectively, at 193.5 THz. Moreover, the horizontal and bidirectional radiation patterns are appropriate for on-chip wireless links with the quality factor of 69.18 and target tracking systems, respectively. The performance of a single row array of nano-antenna on improving the directivity and efficiency has been studied. The proposed SHPW-fed nano-antenna is quite tolerant to practical fabrication errors and compatible with lift-off and electron beam lithography fabrication processes.
Receive Diversity Options for Single Panel 4G-5G Antennas

Using a single rectangular antenna panel enhances cellular capacity via vertical beamforming (e.g. “Full Dimension MIMO”) but may impact diversity gain. We compare the receive diversity performance of several single-panel signal processing techniques, including: subarrays (with wide or narrow beamwidths), pattern diversity (with various a priori azimuth offset beam steering), and Maximum Ratio Combining. Spatial fading correlation is obtained from the 3GPP TR 25.996 model. We assume a hybrid analog-digital beamformer and derive the SNR for any analog beamformer followed by an MRC digital stage. This allows us to apply various beamforming strategies to the antenna subarrays and determine if there is a benefit. In a simple azimuth-only, single user simulation we find:4R (i.e. 4 outputs) subarrays with narrow steered beams approaches the performance of 8RMRC, while a 4 dB shortfall is found for widebeam subarrays relative to MRC; and pattern diversity is competitive with widebeam subarrays for 4R but not 8R.
Pylon Anti-Vandalism Monitoring System using Machine Learning Approach

A country's economic growth is largely dependent on the stability of its energy sector. The growth of Kenya's power sector is greatly supported by Vision 2030 development blueprint, which is geared at transforming the country into a “middle-income country that is able to provide a high quality of life to all of its citizens by the year 2030”. Though major investments have so far been made in the generation and transmission of power, the distribution sub-sector is yet to reap the full benefits due to a number of challenges ranging from financial to operation and maintenance. Transformers are key components of any power distribution network and despite their specified long operational life, they often fail within a short period of commissioning due to various reasons including vandalism. This has led to great losses to the power distribution sub-sector and consumers alike. This paper proposes an anti-theft system that uses machine learning technique to monitor the pylon's environs against vandals. Results of the study reveals that the system is capable of detecting human images with confidence levels between 68% and 97%.
Conformation and Adsorption Properties in Aqueous Solution for Polycarboxylate Superplasticizers with Different Main Chain Lengths

The conformation in aqueous solution and the adsorption ability to water molecules of polycarboxylate ether (PCE) superplasticizer have important effects on its the properties. In this work, molecular dynamics (MD) simulations were employed to investigate the conformation of PCE molecules and the interaction between PCE and water molecules at microscopic level. Five kinds of PCE molecular models (PCE-2, PCE-6, PCE-10, PCE-14 and PCE-18) with different lengths of main chain were constructed, and the simulation results show that the longer length of main chain makes PCE molecules more stretched. The steric hindrance of PCE enhances from PCE-2 to PCE-14, and then declines from PCE-14 to PCE-18, which result from the greater molecules aggregation. The radial distribution functions (RDFs) indicate that the water absorption capacity of PCE molecule enhanced with the increasing of main chain length, and main chain plays an important role of water molecule capture. Both steric hindrance and water absorption capacity of PCE molecules can effectively affect the diffusion properties of water molecules, and prevent water molecules from contacting with cement particles, which retards the hydration process of cement.
Signal transduction and detection methods of reactive oxygen species in plants

Reactive oxygen species( ROS) play important roles in plant life. Several ROS detecting methods have been applied in in vivo studies of ROS. Among them,fluorescent probes have been widely employed in plant sciences,since researchers can visualize the cellular distribution and changing of ROS with high sensitivity. Currently,advanced protein and genetic engineering created several novel fluorescent approaches,providing new molecular tools for further exploring of the ROS signaling pathways in live plant cells. This review summarizes the ROS signal transduction pathway and detection methods for various ROS species in plants,especially those based on intracellular fluorescence labeled probes.
An Energy Audit Approach Towards Mitigating Power Infrastructural Dearth in Nigeria Universities: ABUAD as a Case Study

Power and ICT infrastructures are vital drivers for modern 21st century education. The Sustainable Development Goal (SDG) 7 and African Union (AU) agenda 2063 highlight sustainable energy and access to quality education. The power infrastructure deficit in Nigeria adversely impact the university education in the country. This research work examined this problem with Afe Babalola University, Ado Ekiti (ABUAD), Nigeria as a case study, by auditing the various power infrastructures, determining the investment cost and developing a synchronized distributed power blend for optimal use. The power houses were modeled using the existing infrastructure parameters. The investment cost was evaluated using the life-cycle cost approach and the resulting audit data was used to estimate the fuel consumption and the cost saving when the generators were synchronized. The optimal performance of its generating sets resulted in a cost saving equivalent of $197898.50 per annum. The case study met its power infrastructural needs relatively to others.
Short-term Forecasting of User Power Load in China Based on XGBoost

The forecasting of the user’s short-term power load has always been a matter of concern for the power supply department. The traditional ensemble learning method for load prediction is generally random forest (RF), and XGBoost as a newly proposed ensemble learning method has improved algorithm generalization ability and prediction accuracy compared with RF. This paper considers the influence of climatic factors such as wind speed, humidity on short-term load, and establishes a short-term load forecasting model based on XGBoost in combination with historical load data. Experiments were conducted using users’ load data from an industrial park in China, and compared with the RF. The experiment shows the effectiveness and superiority of XGBoost algorithm in short-term load forecasting.
Contributions of Emotional Overload, Emotion Dysregulation, and Impulsivity to Eating Patterns in Obese Patients with Binge Eating Disorder and Seeking Bariatric Surgery

Background: Binge eating disorder (BED) is very frequently observed in patients considered for weight loss surgery and seems to influence their outcome critically. Literature highlights a global emotional overload in individuals with BED, but little is known on the mechanisms involved. The present study aimed to focus on emotion regulation, impulsivity, depression, and anxiety in people with and without BED and fulfilling inclusion criteria for bariatric surgery. Doing so, we sought to individualize factors related to BED. Then, we examined the contribution of depression, anxiety, emotion regulation difficulties, and impulsivity to inappropriate eating behaviors observed in patients with BED. Methods: A sample of 121 individuals (79.3% female, mean age: 40.82 ± 9.26, mean current body mass index (BMI): 44.92 kg/m2 ± 7.55) seeking bariatric surgery were recruited at the Champagne Ardenne Specialized Center in Obesity in Reims, France from November 2017 to October 2018. They were stratified as with or without BED according to the binge eating scale. Characteristics identified in univariate analyses as differentiating the two groups were then included in multivariable analyses. Results: Multivariable analyses showed that limited access to emotional regulation strategies was significantly associated with BED. Furthermore, inappropriate eating behaviors were independently associated with age, depression severity, anxiety, emotional dysregulation, and impulsivity in BED group. Conclusions: The present findings are indicative of an association between emotion deficit and BED in obese patients seeking bariatric surgery. Patients with BED could benefit from the addition of an emotion regulation intervention.
Refractory density model of cortical direction selectivity: Lagged-nonlagged, transient-sustained, and On-Off thalamic neuron-based mechanisms and intracortical amplification

A biophysically detailed description of the mechanisms of the primary vision is still being developed. We have incorporated a simplified, filter-based description of retino-thalamic visual signal processing into the detailed, conductance-based refractory density description of the neuronal population activity of the primary visual cortex. We compared four mechanisms of the direction selectivity (DS), three of them being based on asymmetrical projections of different types of thalamic neurons to the cortex, distinguishing between (i) lagged and nonlagged, (ii) transient and sustained, and (iii) On and Off neurons. The fourth mechanism implies a lack of subcortical bias and is an epiphenomenon of intracortical interactions between orientation columns. The simulations of the cortical response to moving gratings have verified that first three mechanisms provide DS to an extent compared with experimental data and that the biophysical model realistically reproduces characteristics of the visual cortex activity, such as membrane potential, firing rate, and synaptic conductances. The proposed model reveals the difference between the mechanisms of both the intact and the silenced cortex, favoring the second mechanism. In the fourth case, DS is weaker but significant; it completely vanishes in the silenced cortex.DS in the On-Off mechanism derives from the nonlinear interactions within the orientation map. Results of simulations can help to identify a prevailing mechanism of DS in V1. This is a step towards a comprehensive biophysical modeling of the primary visual system in the frameworks of the population rate coding concept.
Rail Surface Defects Detection Based on Faster R-CNN

Rail surface defects detection is very important for improving railway safety. Therefore, we use Faster R-CNN to conduct research on the rail surface defects detection. Firstly, a data set of rail surface defect images is established, and then the training set of random segmentation is used for effective training to a certain extent, and the corresponding verification set is verified. After undergoing corresponding effective training, the model is tested accordingly. From the detection results we can see that AP is 97.8%. Compared with other algorithms, the recognition effect of Faster R-CNN is higher, therefore this method can efficiently identify the rail surface defects.
An Efficient Low-Latency Algorithm and Implementation for Rate-Matching and Bit-Interleaving in 5G NR

5G New Radio (NR) is primarily characterized by multi-Gbps throughput and up to 10X lower latency than LTE. 5G adopts Low-Density Parity Check (LDPC) code as the channel coding candidate for data channels. Rate-Matching and BitInterleaving functions are performed after LDPC encoding in the transmit signal chain. The purpose of Rate Matching is to select a specific set of encoded bits for transmission by the process of puncturing and/or repetition to support HARQ operation. The output of the Rate-Matching butter goes through row-column permutation function in Bit-Interleaving process. The rate-matched bits from the circular butter are written in row-first order into another butter and read in column-first order. While copying the bits from the rate-matching circular butter, the filler bits are skipped and does not enter the row-column butter. The ratematching butter to row-column butter copy operation accounts for the overall latency in the transmit chain. This paper addresses the latency aspects while processing the large Transport Blocks corresponding to the maximum downlink (DL) throughput. An efficient M-parallel look-ahead pointers generation algorithm is proposed to read M-interleaved bits directly from rate-matching butter, avoiding row-column permutation operation, and thus the need of a separate butter, where M is programmed for a target latency.
Pentafluorobenzylation and Detection of Sodium Monofluoroacetate (Compound 1080) in Veterinary Samples by Gas Chromatography Tandem Quadrupole Mass Sectrometry with Multiple Reaction Monitoring.

RATIONALE The analytical detection of chemical residues from sodium monofluoroacetate (MFA) ingestion in targeted predatory wildlife and in pesticide misuse incidents perpetrated against nuisance companion animals remains a concern in veterinary forensic toxicology. There is a current need for chemically stable sample extracts with reliable and specific diagnostic methods for trace quantities in diverse diagnostic matrices. METHODS Biphasic pentafluorobenzylation provided a simple combined extraction and derivatization procedure for removing MFA in a chemically stable form from a complex matrix such as stomach contents. Analysis of the derivatized extract by gas chromatography tandem quadrupole mass spectrometry (GC/MS/MS) with Multiple Reaction Monitoring (MRM) approaches specific to MFA provided greater specificity than simple scan or Selected Ion Monitoring approaches. RESULTS Collision-induced dissociation in GC/MS/MS showed that pentafluorobenzyl derivatized MFA (M+ 258) generated m/z 258➔130, 149, 161, 177, 178, 180.1, and 181.1 transitions. Of these, the transition m/z 258➔181 provided a peak for quantitation while m/z 258➔161 and 258➔178 provided specificity for qualifying MFA. Similarly, pentafluorobenzyl derivatized 2-chloropropionic acid (M+ 288) was used as an internal standard which generated m/z 288➔181 and 161. Of these, the transition m/z 288➔181 provided a peak for quantitation while m/z 288➔161 and 181➔161 served to qualify the internal standard. CONCLUSIONS The method was validated with a calculated limit of detection of 0.35 ppm and limit of quantitation of 1.09 ppm MFA. The method should have adequate sensitivity and reliability for veterinary toxicology labs analyzing specimens from animals poisoned by this predacide.
The Role of Social Cues for Goal Disambiguation in Human-Robot Cooperation

Social interaction is the new frontier in contemporary robotics: we want to build robots that blend with ease into our daily social environments, following their norms and rules. The cognitive skill that bootstraps social awareness in humans is known as "intention reading" and it allows us to interpret other agents’ actions and assign them meaning. Given its centrality for humans, it is likely that intention reading will foster the development of robotic social understanding. In this paper, we present an artificial cognitive architecture for intention reading in human-robot interaction (HRI) that makes use of social cues to disambiguate goals. This is accomplished by performing a low-level action encoding paired with a high-level probabilistic goal inference. We introduce a new clustering algorithm that has been developed to differentiate multi-sensory human social cues by performing several levels of clustering on different feature-spaces, paired with a Bayesian network that infers the underlying intention. The model has been validated through an interactive HRI experiment involving a joint manipulation game performed by a human and a robotic arm in a toy block scenario. The results show that the artificial agent was capable of reading the intention of its partner and cooperate in mutual interaction, thus validating the novel methodology and the use of social cues to disambiguate goals, other than demonstrating the advantages of intention reading in social HRI.
Optimal Battery Location for Minimizing the Total Cost of Generation in a Power System

Wind is one of the major and fast growing source of green energy in the world. However, it's naturally variable and this reduces its percentage penetration in a power system because of costs associated with balancing reserves. One of the solution being proposed in the recent times is the use of battery energy storage systems (BESS). This is coupled by the fact that there has been continuous reduction in the cost of BESS which has been experienced recently. The objective of this research study is to find optimal BESS location for minimum total cost of power generation in a power system with wind power generation and branch flow power limits. The study used the IEEE 30 bus system. Simulations were done using MATPOWER. Particle swam optimization technique was used to determine optimal BESS location. Results show that bus number 24 was BESS optimal location for several wind location buses.
Alcohol and hepatocarcinogenesis

An excessive alcohol intake may result in fatty liver, acute/chronic hepatitis, cirrhosis, and lead to hepatocellular carcinoma (HCC). The aim of this review is to clarify the present condition and the mechanisms of alcohol-related hepatocarcinogenesis and clinical risk factors for alcohol-related HCC. There are several possible mechanisms through which alcohol may induce hepatocarcinogenesis, including the mutagenic effects of acetaldehyde toxicity through the formation of protein and DNA adducts and the production of reactive oxygen species due to the excessive hepatic deposition of iron, changes to lipid peroxidation and metabolism, inflammation and an impaired immune response and modifications to DNA methylation. Furthermore, it has been reported that alcohol accelerates liver carcinogenesis through several signaling pathways including gut-liver axis. From a clinical perspective, it is well known that alcohol interacts with other factors, such as age, gender, viral hepatitis, obesity, and diabetes leading to an increased risk of HCC.
Joint Routing and Sketch Configuration in Software-Defined Networking

Traffic measurement is very important for various applications, such as traffic engineering and attack detection, in software-defined networks. Due to limited resources (e.g., computing, memory) on SDN switches, sketches have been widely used for efficient traffic measurement. Meanwhile, multiple independent sketches are required for different application requirements, such as heavy hitter detection and flow size distribution estimation. To avoid the measurement redundancy, we configure which sketch(es) will measure flow on each switch. If traffic measurement is performed on each switch without considering network routing, due to traffic dynamics, it will cause massive measurement overhead on a switch, which may exceed the switch’s computing capacity. In this paper, we study the joint optimization of flow routing and sketch configuration in SDNs. We formulate this problem as an integer linear programming and prove its NP-hardness. To solve this problem, we propose a rounding-based offline algorithm and a primal-dual-based online algorithm for different application scenarios. To deal with bursty traffic, we also design an adaptive sampling mechanism for high-fidelity traffic measurement. We formally analyze the approximation performance or competitive ratio of the proposed algorithms. The extensive simulation results show the high efficiency of our proposed algorithms. For example, the online algorithm can improve the network throughput 30% compared with the state-of-the-art.