Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Consumption Forecasting using a Deep Learning Energy-Level Based Prediction

Smart grids benefit the consumers via enabling informed participation. As a result, customers have more choices, which drives a range of buying trends and actions. One such important application is accurate forecasts of energy demands (loads) at individual sites, but load forecasting is still a challenge. Most load forecasting research has used traditional numerical value estimation, which is a regression style prediction; however, energy consumers will not know if the forecasted load is low or high. Level prediction can be performed by performing regression first and then classifying the values into energy levels, or by using a classification model to predict the levels directly. The Random Forest (RF) classification algorithm is shown to have outperformed other machine learning algorithms for level predictions. This paper provides a deep learning-based classification approach. The results of this deep learning-based classification were compared to those of the RF classification algorithm, it was observed that the deep learning method outperformed the RF method, particularly for high numbers of energy levels.
Peripheral ablation of type III adenylyl cyclase induces hyperalgesia and eliminates KOR-mediated analgesia in mice

Ca2+/calmodulin-stimulated group I adenylyl cyclase (AC) isoforms AC1 and AC8 have been involved in nociceptive processing and morphine responses. However, whether AC3, another member of group I ACs, is involved in nociceptive transmission and regulates opioid receptor signaling remains elusive. Here, we report that conditional KO of AC3 (AC3 CKO) in L3 and L4 DRGs robustly facilitated the mouse nociceptive responses, decreased voltage-gated potassium (Kv) channel currents, and increased neuronal excitability. Furthermore, we report AC3 CKO eliminated the analgesic effect of κ-opioid receptor (KOR) agonist and its inhibition on Kv channel by classical Gαi/o signaling or nonclassical direct interaction of KOR and AC3 proteins. Interestingly, significantly upregulated AC1 level and cAMP concentration were detected in AC3-deficient DRGs. Inhibition of AC1 completely reversed cAMP upregulation, neuronal excitability enhancement, and nociceptive behavioral hypersensitivity in AC3-CKO mice. Our findings suggest a crucial role of peripheral AC3 in nociceptive modulation and KOR opioid analgesia.
NAD+ capping of RNA in Archaea and Mycobacteria

Chemical modifications of RNA affect essential properties of transcripts, such as their translation, localization and stability. 5’-end RNA capping with the ubiquitous redox cofactor nicotinamide adenine dinucleotide (NAD+) has been discovered in organisms ranging from bacteria to mammals. However, the hypothesis that NAD+ capping might be universal in all domains of life has not been proven yet, as information on this RNA modification is missing for Archaea. Likewise, this RNA modification has not been studied in the clinically important Mycobacterium genus. Here, we demonstrate that NAD+ capping occurs in the archaeal and mycobacterial model organisms Methanosarcina barkeri and Mycobacterium smegmatis. Moreover, we identify the NAD+-capped transcripts in M. smegmatis, showing that this modification is more prevalent in stationary phase, and revealing that mycobacterial NAD+-capped transcripts include non-coding small RNAs, such as Ms1. Furthermore, we show that mycobacterial RNA polymerase incorporates NAD+ into RNA, and that the genes of NAD+-capped transcripts are preceded by promoter elements compatible with σA/σF dependent expression. Taken together, our findings demonstrate that NAD+ capping exists in the archaeal domain of life, suggesting that it is universal to all living organisms, and define the NAD+-capped RNA landscape in mycobacteria, providing a basis for its future exploration.
Validation of the Arabic Version of the Refugee Health Screener-13 Among Perinatal Syrian Refugees in Lebanon

Background: Refugee mothers are at a significantly increased risk of suffering from perinatal depression. However, available tools to screen for it often use a western paradigm of mental health, may not be culturally appropriate, and may not account for the background stress experienced by refugees. The objective of this study is to test and validate RHS-13, which was developed by consultation with refugees from different backgrounds, among a group of Syrian refugee mothers living in Beka’s valley in Lebanon. Methodology: The tool was tested on a sample of 103 women. Internal consistency of the items for each subscale was assessed using Cronbach’s alpha. An interitem correlation was performed to examine the most correlated items on the scale. Pearson correlation coefficient was calculated between each subscale and its diagnostic proxy. To find the best fit cut-point between sensitivity and specificity, ROC curves were used. Results: RHS-13 is a reliable and valid tool to detect depression and anxiety among perinatal Syrian mothers (Internal consistency 0.803) (correlation with PHQ: 0.63), (correlation with GAD-7: 0.73). The best fit cut-points were 12, 15, and 25 indicating the severity of symptoms from mild to severe, respectively. Conclusion: RHS-13 is a valid tool among perinatal refugees in Lebanon and can be used by primary care physicians, mental health providers, social workers working with refugees from the Middle East to screen mothers for depression and anxiety.
Lenses matching of compound eye for target positioning

Compound eye, as a new imaging method with multi-lens for a large field of view, could complete target positioning and detection fastly, especially at close range. Therefore it could be applicated in the fields of military and medical treatment and aviation with vast market potential and development prospect. Yet the compound eye imaging method designed use three layer construction of multiple lens array arranged in a curved surface and refractive lens and imaging sensor of CMOS. In order to simplify process structure and increase the imaging area of every sub-eye, the imaging area of every eye is coved with the whole CMOS. Therefore, for several imaging point of one target, the corresponding lens of every imaging point is unkonown, and thus to identify. So an algorithm was put forward. Firstly, according to the Regular Geometry relationship of several adjacent lenses, data organization of seven lenses with a main lens was built. Subsequently, by the data organization, when one target was caught by several unknown lenses, we search every combined type of the received lenses. And for every combined type, two lenses were selected to combine and were used to calculate one three-dimensional (3D) coordinate of the target. If the 3D coordinates are same to the some combine type of the lenses numbers, in theory, the lenses and the imaging points are matched. So according to error of the 3D coordinates is calculated by the different seven lenses numbers combines, the unknown lenses could be distinguished. The experimental results show that the presented algorithm is feasible and can complete matching task for imaging points and corresponding lenses.
Selected Financial Applications

This article shows how to use some of Mathematica’s built-in fi nancial functions and de fi ne new functions useful for the practical analysis of real-world fi nancial data. The main topics covered are linear programming and its application in bond portfolio management, conditional value-at-risk minimization, introductory time-series analysis, simulation, bootstrapping, robust equity portfolio optimization and arti fi cial intelligence.
Analysis of the performance of four-cascaded single-stage distributed amplifiers

In this paper, the analysis, design, and implementation of a hybrid broad-band distributed amplifier based on four-cascaded single-stage distributed amplifiers (4-CSSDAs) is demonstrated. The available gain produced by this amplifier is significantly higher than conventional distributed amplifiers (CDAs) using the same number of active devices. Simulation results for two versions of the 4-CSSDA are demonstrated, one with low interstage characteristic impedance (Zo/sub int/) and the other with high Zo/sub int/. Simulation results show a number of advantages of the proposed amplifier over the CDA, in particular, that the available gain can be increased by increasing the Zo/sub int/ of the amplifier stages. Simulation predicts that gains approaching 50 dB could be achieved by appropriate design modification. Measured results for implementation of the two versions of the 4-CSSDA are reported. These show excellent performance with high gain, good gain flatness, input and output matching, flat group delays, and a low noise figure across a 1-10-GHz bandwidth. The amplifier configuration achieved a measured wide-band performance (0.8-10.8 GHz) with up to 39/spl plusmn/2-dB flat gain using discrete packaged active devices.
Consistent SLAM using Local Optimization with Virtual Prior Topologies

In the present work we address the problem of achieving a consistent estimator for SLAM. We propose a novel method capable of computing approximately consistent global uncertainties without scaling in complexity with the total size of the explored area. The method allows arbitrary selection of local areas for optimization, introducing a methodology for building a virtual prior in bounded time. The constructed prior topologies serve as a way of contextualizing the local optimizations, resulting in consistent uncertainty estimations. This overcomes several shortcomings of previous approaches that rely on conditioning (fixing variables) and/or sliding window marginalization. Evaluations are presented in different simulated scenarios comparing results against a global batch optimization and other canonical approaches.
Data preprocessing for non-image data: using convolution neural networks for network intrusion detection systems

: Application of machine learning models in network intrusion detection systems has been the subject of extensive investigation and testing. Modern day networks produce data in quantities that put more emphasis on the accuracy and precision of the intrusion detection systems. This produces the drive for more accurate and time-efﬁcient intrusion detection s ystems, and machine learning was investigated as a viable solution. Research into machine learning models in other ﬁelds has yielded several different algorithms and approaches, highly specialised to those particular data types. Testing for intrusion detection has found that the models that process the network data best tend to yield higher accuracy and lower false-positive rates, whereas those models that perform best on their original data have struggled. One such model that under performed in intrusion detection when compared to the original ﬁeld is convolution neural n etwork. This paper aims to investigate preprocessing methods for network data to increase the effectiveness of using a convolution neural network model as part of a network intrusion detection system. Speciﬁcally, the paper will analyse the use of the DeepInsight architecture,using a modiﬁed t-distributed stochastic neighbour embedding technique, the positioning of features in isolation and a control class of simple reshaping data from vector to matrix form.
Turning an Articulated 3-PPSR Manipulator into a Parallel Continuum Robot

Parallel Continuum Robots (PCR) have received a lot of attention in recent years. This paper presents a new 6-degrees-of-freedom PCR derived from the conventional 3-PPSR parallel manipulator. This robot is driven by three limbs consisting of two flexible rods each and replacing the spherical and revolute joints of the original version. Each limb is mounted onto two linear axes arranged in series. To allow a direct comparison between the articulated and the continuum version, the parallel mechanism of an industrial manipulator has been replaced by an elastic structure of the same size. The simulations and the experiments show that the flexible counterpart of the manipulator is able to achieve a larger workspace, increasing the range of motion by 150% for rotations and by 157% in elevation. Moreover, the position repeatability is improved by 47% (reaching 3.4 µm) and the orientation repeatability by 57% (reaching 14.3 µrad). This can be explained by the removal of the spherical and revolute joints but also by the constant stress in the structure that acts as an anti backlash system on leadscrew actuators.
Regional and global lightning activity effect on the composition and properties of the upper troposphere/lower stratosphere

The prediction of lightning-generated nitrogen oxides in the troposphere and low stratosphere is evaluated using the Weather and Research Forecasting model coupled with Chemistry (WRF-Chem) with nested grid spacing of 15 and 5 km where convection is parameterized. The responses of nitrogen oxides, ozone, temperature and peroxyacetyl nitrate (PAN) are investigated using matching of WRF-Chem sensitivity tests. A high resolution simulation was performed for the Black sea region to examine feedbacks between atmospheric electricity and local weather. The result obtained shows that storms, occurring near the seaside, cause stronger effect on the land. Temperature fields show that continental difference is bigger than the coast one. The maximum temperature field difference is registered above 10 km altitude.
Numerical Analysis on Droplet Deformation and Breakup in High Gravity Field

Research slope stability under rainfall condition in geotechnical centrifuge is an ideal test method, however, the influence of high centrifugal force field produced by running geotechnical centrifuge cannot be neglected. Droplet deformation and breakup under different gravity and of different diameters were studied with VOF method, the results shows that the process of droplet deformation and breakup is similar under condition of different g-value and diameters, droplet breakup in a very short time in high gravity field, and with the increase of g-value, the breakup time of droplet became shorter, with the increase of droplet diameter, the breakup time of droplet became longer under same gravity acceleration. Studies in this paper have important significance in developing geotechnical centrifuge artificial rainfall equipment.
High Frequency Receiver Front-End Module for Active Antenna Applications

This research is based on the analysis and development of an integrated receiver frontend module for high gain active antenna systems at the K-band (20GHz). In the design of conventional satellite receivers (such as reflector antennas), the system is usually specified by the gain/directivity, gain-to-temperature ratio (G/T) and radiation pattern requirements. The challenge in high gain active antenna systems development, in addition to beam-forming/beam-steering requirements, is to develop transmit/receive modules which will meet the power, noise and radiation pattern requirements of the conventional antenna. In order to guarantee an optimal design, it is important to be able to translate the specifications from the system level to the transistor level. The focus is on the development of a single-channel CMOS-based integrated receiver module. The G/T requirement is analysed to derive the noise figure and gain specifications for the low noise amplifier(LNA). An LNA design in 65nm CMOS is demonstrated to achieve a 2.6 dB noise figure and uses only 7mW of DC power. The digital phased shifter specifications are studied. The generation of ”quantization lobes” is analysed and used to estimate the number of bits based on side-lobe level requirements. The design of a 5-bit digital phase shifter based on quadrature signal modulation and a unique digital control logic is presented and tested at 20GHz. The phase shifter is shown to achieve < 5.5◦ rms phase error and > 10 dB input and output return loss between 16 − 21GHz. The effect of pattern tapering on the side-lobe level is investigated and used to specify the minimum dynamic range for a variable gain amplifier (VGA). A VGA design is demonstrated to meet this dynamic range with low phase-frequency variation. A schematic level design of the proposed single-channel array is studied featuring a hybrid coupler and switch for polarisation requirements, as well as a low-voltage bandgap reference circuit. Simulations results verify that the receiver can be used to generate two hands of polarisation (right and left) with < 1.1 dB axial ratio.
An Anthropomorphic Prosthetic Hand with an Active, Selectively Lockable Differential Mechanism: Towards Affordable Dexterity

Over the last decade, adaptive tendon driven devices have gained an increased interest from the research community for their lightweight, compact, and affordable design features attributed to the utilisation of underactuation, differential mechanisms, and structural compliance. Although adaptive tendon driven devices are capable of efficiently executing stable grasps under significant object pose uncertainties with simplistic control algorithms, they lack the controllability over individual fingers in comparison to traditional fully actuated designs. In this paper, we focus on the development of a selectively lockable differential mechanism that is powered through a small and low torque servo to provide increased autonomy to highly underactuated and adaptive prosthetic hands, without compromising the weight, cost, and compactness of the device. The proposed prosthetic hand is experimentally validated through four tests: i) grasping posture and gesture execution experiments, ii) grasping experiments with everyday life objects, iii) force exertion experiments, and iv) Electromyography (EMG) based control of the prosthetic hand.
Batoid Fishes: Inspiration for the Next Generation of Underwater Robots

For millions of years, aquatic species have utilized the principles of unsteady hydrodynamics for propulsion and maneuvering. They have evolved high-endurance swimming that can outperform current underwater vehicle technology in the areas of stealth, maneuverability and control authority. Batoid fishes, including the manta ray, Manta birostris, the cownose ray, Rhinoptera bonasus, and the Atlantic stingray, Dasyatis sabina, have been identified as a high-performing species due to their ability to migrate long distances, maneuver in spaces the size of their tip-to-tip wing span, produce enough thrust to leap out of the water, populate many underwater regions, and attain sustained swimming speeds of 2.8 m/s with low flapping/ undulating frequencies. These characteristics make batoid fishes an ideal platform to emulate in the design of a bio-inspired autonomous underwater vehicle. The enlargedpectoral finsofeachrayundergoescomplexmotionsthatcouplespanwise curvature with a chordwise traveling wave to produce thrust and to maneuver. Researchersareinvestigatingtheseamazingspeciestounderstandthebiologicalprinciples for locomotion. The continuum of swimming motions—from undulatory to oscillatory—demonstrates the range of capabilities, environments, and behaviors exhibited by these fishes. Direct comparisons between observed swimming motions and the underlying cartilage structure of the pectoral fin have been made. A simple yet powerful analytical model to describe the swimming motions of batoid fishes has been developed and is being used to quantify their hydrodynamic performance. This model is also being used as the design target for artificial pectoral fin design. Various strategies have been employed to replicate pectoral fin motion. Active tensegrity structures, electro-active polymers, and fluid muscles are three structure/actuator approaches that have successfully demonstrated pectoral-finlike motions. This paper explores these recent studies to understand the relationship between form and swimming function of batoid fishes and describes attempts to emulate their abilities in the next generation of bio-inspired underwater vehicles.
Predicting Lumen Degradation of Light Emitting Diodes Using Hybrid Particle Filter Trained Neural Networks

High power white light emitting diodes (LEDs) are the widely opted eco-friendly alternate light source over incandescent lamps due to their lower power consumption and longer lifetime. The emerging market demand for LEDs brings about the critical need for adequate reliability testing and lifetime prognosis as they are predominantly used in uncontrolled environments. The common wear-out failure modes of LEDs include light output (luminosity) degradation and color shift. Commercially available LEDs have prolonged lifetime of about 70,000h which makes it impractical to obtain real-time degradation data, which subsequently complicates the ability to accurately predict the lumen maintenance life. Several attempts have been made by researchers to develop physics of failure models and / or data driven models for predicting the remaining useful life (RUL) of LEDs. However, these methods lack generalizability and do not address the prediction uncertainties caused by unit-to-unit variation. This calls for the need to develop robust prediction algorithms capable of handling variations in degradation trends due to manufacturing process, system design and environmental / operating conditions. This study proposes a hybrid prognostic approach which combines particle filters (PF) and neural networks (NN). The run-to-failure trend of one LED in a lot is used to model a feedforward neural network and the model parameters are optimized using the particle filter algorithm. The PF trained NN model is further used for RUL prediction for other LED devices in the lot enabling variations to be accounted for and naturally embedded into the prognostic framework. The accuracy of proposed hybrid approach was evaluated using RMSE as the performance metric.
Inspection ROV for Nuclear Pressure Water Reactors

This thesis was carried out at KTH in cooperation with the company WesDyne. The aim was to evaluate the possibility of using quadcopter technology in conjunction with visual inspection of nuclear reactor vessels. WesDyne already develops equipment for this purpose. Today the inspections are regulated by the ASME standard that sets the requirements of the inspections. The project began with a literature study to gain knowledge in the eld, followed by the development of a mathematical model. For greater advantage of the model it was chosen to build a prototype for verication. A number of eld tests were made to compare the model and prototype properties. The veried model was then used to carry out inspections simulated both by allowing a human operator but also a computer-based operator to control the model. Several tests were made in the model to evaluate dierent subsystems inuence. To evaluate real environment situations, which was not modeled, also eld tests were performed to investigate this. The conclusion drawn was that the quadcopter technology is possible to use to perform visual inspections in accordance with the ASME standard. However, there remains some work to done before reaching anished product. Development and verication of the mathematical model provides a good tool for future work.