Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning Approach to Forex Trading using Price Changes Trend

An automated program that generates profits from the foreign exchange market is attractive to all forex investors. Reinforcement learning has been widely used in financial market trading to maximize profits. In this study, we interested to generate profit from high-frequency currency trading with Deep Q-Network. It has known that the state signals are extracted from forex price data, e.g. mean, maximum, minimum, standard deviation in the interval time. We introduced novel price change trend features that aim to capture directions and speed of price movement over different time and apply it to be the state signal for reinforcement learning. The state signals are consist of the following two parts: the market features that including novel slope of price changes trends features and the agent's current position features. There are three actions -open position, close position, and hold or do nothing position. The reward signal was formulated by tanh function that is used to maximize the profit. We tested our proposed features with Deep Q-Network in the EUR/USD market from 01-Aug-2021 to 30-Dec-2021. The experimental results shown that in comparisons of reinforcement learning with base line features, our proposed features outperforms better than for all performance metrics in both 3000 and 6000 ticks feature vector. These results confirm that the slope of price changes trends features are useful for forecasting under the reinforcement learning approach.
Simultaneous Modulation of Cortical Activity and Phantom Pain in a Patient with Brachial Plexus Injury

Brachial plexus injury may induce complete loss of sensation and motor control in parts of the affected arm, leading to phantom limb pain (PLP). PLP is associated with functional reorganization at the somatosensory cortex and alteration in the functional connectivity of the related pain brain areas. Several therapies have previously been suggested for PLP relief. Transcutaneous electrical nerve stimulation (TENS) is a non-invasive intervention used for pain relief and stroke rehabilitation. However, the underlying mechanism of TENS on cortical activity associated with PLP relief is not well-known. The effect of TENS (20Hz) intervention on somatosensory activity, cortical functional connectivity, and possible PLP reduction was investigated in this study in the case of a 55 year old subject with brachial plexus injury inducing complete paralysis below the elbow in the left arm. We recorded EEG data and the patient's pain level before and after the intervention. Somatosensory evoked potentials (SEPs) and functional connectivity between pain-related areas were assessed from EEG data. The results showed that the application of TENS suppressed somatosensory evoked potentials. In addition, the intervention enhanced the functional connectivity (a) between the primary somatosensory cortex and the anterior cingulate cortex (SI-ACC), also (b) between the primary somatosensory cortex and the medial prefrontal cortex (SI-MPC). Moreover, these results were associated with PLP reduction following TENS. We compared the results with our previous research on a large healthy population to assess the cortical changes.
Mixed Model On the Bayesian Zero Inflated Spatio-Temporal for Relative Risk Diagnostic of DHF Incidences

Bayesian modeling of count data with local characteristics is commonly found in many cases, such as dengue hemorrhagic fever (DHF). The DHF data are is generally containing excess zeros, especially when it is measured as spatial and temporal data. Regression model of Bayesian zero-inflated Poisson spatio-temporal (BZIP S-T) is often used to analyze such data. The BZIP S-T, called the convolution extended (CE) model, has been introduced, but it does not adjust the excess zeros yet. The BZIP S-T structure is the mixing of three main components; spatial heterogeneity, two random effects (locally and globally) and temporal trend. The BZIP S-T is expressed as a generalized linear model because the DHF data is not continuous and skewed. The performance of both models was verified by using DHF data in 10 districts of Kendari-Indonesia for 84 months, the period of 2008-2014, as response variables. Rainfall and population density are as predictors. Markov Chain Monte Carlo (MCMC) method is to estimate the parameters of both models via their full conditional distributions, respectively. Estimation parameters of both models achieve the convergence at 10,000 iterations with 20,000 burn-in. They were confirmed that trace plots are in the same zone (no extreme value), evolutions of ergodic mean are stable in confidence interval, and autocorrelations of plot are sufficient the Markov Chain properties. Both models indicating the rainfall and the population density statistically have a significant impact on dengue cases in Kendari city. The BZIP S-T deviance is smaller than the CE deviance, then the BZIP S-T is the better model. The BZIP S-T investigation shows that Puwatu and Kadia district are consistent as DHF highest locations in Kendari. Both districts are endemic DHF locations.
21st century change in ocean response to climate forcing

Modeling globally averaged information on climate forcing from the land surface temperature data, the sea surface temperatures (SST) and the empirically determined relationship between the changes in SST and the turbulent diffusion of heat into the upper ocean demonstrates a consistent link. The modeling is accurate throughout the 20th century despite the different phases of the Interdecadal Pacific Oscillation (IPO) or the strong divergence between land and ocean surface warming. It only fails during the last 15 years when SST drops well below the trend. The finding reinforces the view that slower global warming over the previous 15 years is not a caused by a negative phase of the IPO or by the variations in the upper ocean (top 700 m) warming but results from a change in the ocean behavior leading to increased heat transfer into the deeper ocean.
An Improved Integer Wavelet Image Coding Based on Zerotree Framework

Integer wavelet transforms have many advantages. However, their energy concentration performance is much worse than that of common wavelet image transform algorithms. This is not beneficial to embedded zerotree wavelet encoding. For this reason, a novel algorithm is proposed to deal with this problem from two aspects. First, because subband coefficients by integer wavelet transform have smaller dynamic change value and worse energy concentration, a new method called ? integer square quantization threshold algorithm ? is adopted, in which quantization thresholds are chosen from the square of integers beginning with 1. At the same time, the quantization threshold is adjustable according to the importance of the interested region in the image. Secondly, a new approach based on index table and RLE coding is proposed to simplify the coding and decoding processes. Experiments demonstrate that the new algorithm improves not only compression quality, but also compression efficiency.
An Adaptive Fuzzy-Based Modeling for Contrast Stretching

Contrast is the difference in brightness between two adjacent pixels. Contrast is based on human perception abilities. Fuzzy statistics of digital images are analyzed and represented for producing gray distribution of histogram specification in the spatial domain of the input image. The histogram mapping produces an enhanced image with smooth nature and has less noise. The probability of gray value is generated and the fuzzy value is determined from the position of the input image pixel. The results are indicative of the good performance of the proposed fuzzy-based method. The inverse transform of the real values is mapped with the input image to generate the fuzzy value. The transformation function is a single valued and monotonically increasing brightness of the input image.
Inventory systems of perishable commodities

This paper deals with the blood-bank model; namely, an inventory system in which both arrival of items and demand are stochastic and items stored have finite lifetimes. We assume that the arrival and demand processes are independent Poisson processes. We use an analogy with queueing models with impatient customers to obtain some of the important characteristics of the system.
Vector theory analysis and numerical calculation for beam sampling grating used in ICF

Beam sampling grating are used to send a known fraction of transmitted light into a calorimeter for energy diagnostics in final focusing optical system of ICF. This paper discusses diffractive efficiency as a function of period, slot depth, duty cycle and incident angle. The algorithm that we applied is rigorous coupled wave analysis. By calculation, we can get some useful information for practical fabrication of the beam sampling grating.
Driveline Oscillation Damping for Hybrid Electric Vehicles Using Extended-State-Observer-Based Compensator

Driveline oscillation is a significant concern in the context of hybrid electric vehicles (HEVs), because it can adversely affect the vehicles’ sustainability. The reason for this is that the oscillation not only diminishes the longevity of components due to high mechanical contact stress but also results in poor driving comfort, which in turn reduces customer satisfaction. To address the issue of driveline oscillation effectively, two critical challenges, namely the time-varying torque load and driveline backlash, need to be tackled. To this end, this study constructs a control-oriented model of a second-order system plus a dead zone for the driveline backlash. An extended state observer is designed in order to estimate the unmeasurable load torque. As such, an extended-state-observer-based compensator is proposed to suppress driveline oscillations for HEVs. To evaluate the control and observation performance of the proposed extended-state-observer-based compensator, simulation and engine-in-loop experiments are conducted. Results obtained in the time and frequency domains reveal that the proposed control scheme substantially reduces driveline oscillation.
Uncertainties in Recorded Photon Radiation Doses at Sellafield

Individual monitoring for external radiation has been carried out on the Sellafield Site using film badges since 1950 with the results recorded in external dose records for about 20,000 workers. This paper considers the likely uncertainties in the recorded photon doses taking into account the main characteristics of the types of film badge used on the site and their performance in the radiation fields found in the workplace. Overall, it is concluded that the recorded cumulative annual photon doses should be within a factor of 2 of the true dose in comparison to the response of an ideal dosemeter, while the recorded annual dose is more likely to be an overestimate rather than an underestimate noting the possible influences of limit of detection of the badge, storage dose and the entry of notional doses onto the record.
Flow-Induced Forces for a Group of One Large and Several Small Structures in the Sheared Turbulent Flow

Evaluating the hydrodynamic force fluctuations acting on each structure in a group of subsea objects of different cross-section shapes, sizes and relative positions represents a challenge due to the sensitivity of the vortex shedding process, especially for a variety of sheared flows. The present study uses the numerical 2D computational fluid dynamics model to estimate the flow-induced forces on a group of small circular and D-shaped cylinders in the linear and parabolic sheared flow, which are placed in proximity to a larger structure of the squared cross-section. This allows us to evaluate loads, which are affected by the presence of subsea equipment located on the seabed. The average Reynolds number of the considered linear flow profile is 3900, while the parabolic flow profile has the maximum Reynolds number of 3900. The k-ω SST turbulence model is used for simulations. The work demonstrates the effect of the cross-sectional shape of smaller cylinders on hydrodynamic coefficients, explores the effect from the spacing in between the structures and highlights differences between loads in the linearly sheared and parabolic flow. The results obtained show that the presence of the squared cylinder notably influences the mean drag coefficient on the first cylinder, for both circular and D-shaped cylinders. The parabolic sheared flow profile in this series leads to the highest mean drag and the highest amplitudes of the fluctuating drag and lift coefficients.
Associations between Pharmacological Treatment Patterns during the Initial Treatment Period and the Relapse or Recurrence of Anxiety Disorders: A Nationwide Retrospective Cohort Study

Although the importance of proper pharmacological treatment for preventing the relapse/recurrence of anxiety disorders is well known, a real-world data-based study has not been conducted. We aimed to investigate the effect of the initial pharmacological patterns related to continuous treatment and the choice of medication on the relapse/recurrence of anxiety disorders. We used claim data from the Health Insurance Review and Assessment Service, South Korea, of 34,378 adults who received psychiatric medications, including antidepressants, after being newly diagnosed with anxiety disorders. We compared the relapse/recurrence rate in the patients receiving continuous pharmacological treatment with those who discontinued treatment early using Cox’s proportional-hazards model. Patients receiving continuous pharmacological treatment experienced a higher risk of relapse/recurrence than those who discontinued treatment. Using three or more antidepressants during the initial treatment period decreased the risk of relapse/recurrence (adjusted hazard ratio (aHR) = 0.229 (0.204–0.256)); however, the combined use of antidepressants from the beginning of treatment increased the risk (aHR = 1.215 (1.131–1.305)). Factors other than continuous pharmacological treatment should be considered to effectively prevent the relapse/recurrence of anxiety disorders. The active use of antidepressants, including switching or adding medications based on progress and frequent follow-up visits during the acute phase, were significantly associated with a reduction in the relapse/recurrence of anxiety disorders.
China’s central bank digital currency (CBDC): an assessment of money and power relations

ABSTRACT This article reflects on the meaning of China’s Central Bank Digital Currency (CDBC) with respect to money and power relations to argue that it will not represent a rupture with the dollar centric international monetary and financial system (IMFS). It combines three theoretical approaches – on the nature of money, on currency internationalisation and on currency hierarchy – to build an analytical framework that adds to the literature that strives to understand the dynamics between money and state power. This framework stresses the fundamental place of currency dominance and of an underlying currency hierarchy that explains the broader configuration and evolution of the IMFS. While there is an enormous asymmetry between the economic weight of China and the international use of its currency (physical or digital), it does not necessarily follow that this tension will be resolved by assembling a fairer distribution of the economic and political advantages that come along with issuing a currency that is used by the rest of the world (the so-called ‘exorbitant privilege’).
Maximum confidence measure-based dual-microphone beamforming direction and beamwidth steering algorithm for robust speech recognition

Abstract This paper proposes a maximum confidence measure-based closed-loop dual-microphone beamforming direction and beamwidth steering algorithm to facilitate robust speech recognition. This technique involves feeding back the confidence measure reported through a back-end speech recognizer, automatically steering a front-end microphone array to optimally identify the correct speaker direction and array beamwidth. The technique enables users to move around freely and directly improves overall system performance. The experimental results from a voice command task show that the proposed approach demonstrated superior performance.
High-Fidelity Model Identification for Synchronous Reluctance Motor Drives

This article presents an accurate model identification method for the synchronous reluctance machine (SynRel), considering the nonlinear magnetic behavior and spatial harmonics. The availability of the high-fidelity magnetic model is essential for high-performance control and modeling the vibro-acoustic behavior of the electric drive, allowing to predict not only the average torque but also the torque ripple behavior versus rotor position. The magnetic coenergy variation is calculated based on the flux linkages in order to yield the torque ripple. During the identification, the current disturbances introduced by the spatial effects and the inverter nonlinearities are properly suppressed by the merged proportional-integral (PI) controller with a repetitive technique. Besides, the inverter nonlinearity and digital control effects are carefully addressed to improve identification accuracy. The effectiveness of the proposed identification method is demonstrated by the comparison of the finite element (FE) simulation results and experiments for two SynRel prototypes.
PDD-Net: Plant Disease Diagnoses Using Multilevel and Multiscale Convolutional Neural Network Features

Overlooked diseases in agriculture severely impact crop growth, which results in significant losses for farmers. Unfortunately, manual field visits for plant disease diagnosis (PDD) are costly and time consuming. Although various methods of PDD have been proposed, many challenges have yet to be investigated, such as early stage leaf disease diagnosis, class variations in diseases, cluttered backgrounds, and computational complexity of the diagnosis system. In this paper, we propose a Convolutional Neural Network (CNN)-based PDD framework (i.e., PDD-Net), which employs data augmentation techniques and incorporates multilevel and multiscale features to create a class and scale-invariant architecture. The Flatten-T Swish (FTS) activation function is utilized to prevent gradient vanishing and exploding problems, while the focal loss function is used to mitigate the impact of class imbalance during PDD-Net training. The PDD-Net method outperforms baseline models, achieving an average precision of 92.06%, average recall of 92.71%, average F1 score of 92.36%, and accuracy of 93.79% on the PlantVillage dataset. It also achieves an average precision of 86.41%, average recall of 85.77%, average F1 score of 86.02%, and accuracy of 86.98% on the cassava leaf disease dataset. These results demonstrate the efficiency and robustness of PDD-Net in plant disease diagnosis.
Coccidioidomycosis and Host Microbiome Interactions: What We Know and What We Can Infer from Other Respiratory Infections

Between 70 and 80% of Valley fever patients receive one or more rounds of antibiotic treatment prior to accurate diagnosis with coccidioidomycosis. Antibiotic treatment and infection (bacterial, viral, fungal, parasitic) often have negative implications on host microbial dysbiosis, immunological responses, and disease outcome. These perturbations have focused on the impact of gut dysbiosis on pulmonary disease instead of the implications of direct lung dysbiosis. However, recent work highlights a need to establish the direct effects of the lung microbiota on infection outcome. Cystic fibrosis, chronic obstructive pulmonary disease, COVID-19, and M. tuberculosis studies suggest that surveying the lung microbiota composition can serve as a predictive factor of disease severity and could inform treatment options. In addition to traditional treatment options, probiotics can reverse perturbation-induced repercussions on disease outcomes. The purpose of this review is to speculate on the effects perturbations of the host microbiome can have on coccidioidomycosis progression. To do this, parallels are drawn to aa compilation of other host microbiome infection studies.
Joint Computation Offloading and Resource Allocation for MIMO-NOMA Assisted Multi-User MEC Systems

This paper investigates the resource allocation and computation offloading problem for multi-access edge computing (MEC) systems, where multiple mobile users (MUs) equipped with multiple antennas access the base station in a non-orthogonal multiple access manner. We jointly optimize the offloading ratio, computational frequency and transmit precoding matrix of each MU to minimize the total energy consumption of all MUs while satisfying the latency constraints. The problem is formulated as a non-convex optimization problem and a two-layer iterative method is proposed to solve the problem efficiently with low complexity. Specifically, we first decompose the original problem into several subproblems, and then sequentially solve these subproblems in an alternative fashion. Furthermore, we also discuss the optimal decoding order of MUs under two different scenarios. Firstly, when the MUs’ channel conditions are similar, by deriving closed-form expressions for energy consumptions of all MUs, we prove that the optimal decoding order is only determined by the latency requirements. On the other hand, when the MUs’ channel conditions are different, we show that the optimal decoding order is determined by both the channel conditions and the latency requirements. As such, we propose a metric aiming to balance the effects of channel conditions and latency requirements on the MUs’ decoding order. Simulation results validate the convergence of the proposed method and demonstrate its superiority over benchmark algorithms.
Lipid Nanocarrier-Based Drug Delivery Systems: Therapeutic Advances in the Treatment of Lung Cancer

Abstract Although various treatments are currently being developed, lung cancer still has a very high mortality rate. Moreover, while various strategies for the diagnosis and treatment of lung cancer are being used in clinical settings, in many cases, lung cancer does not respond to treatment and presents reducing survival rates. Cancer nanotechnology, also known as nanotechnology in cancer, is a relatively new topic of study that brings together scientists from a variety of fields, including chemistry, biology, engineering, and medicine. The use of lipid-based nanocarriers to aid drug distribution has already had a significant impact in several scientific fields. Lipid-based nanocarriers have been demonstrated to help stabilize therapeutic compounds, overcome barriers to cellular and tissue absorption, and improve in vivo drug delivery to specific target areas. For this reason, lipid-based nanocarriers are being actively researched and used for lung cancer treatment and vaccine development. This review discusses the improvements in drug delivery achieved with lipid-based nanocarriers, the obstacles that still exist with in vivo applications, and the current clinical and experimental applications of lipid-based nanocarriers in lung cancer treatment and management.