Miracle-dz/UniFAR-FTUs huggingface dataset:
IoT based Hybrid Green Energy driven Street Lighting System

In this paper, a hybrid energy solution is implemented for trivial scale green energy generation as an option for limited conventional energy source, standalone and manual system for the street illumination system and emergency e-vehicle charging. This hybrid energy system consists of two renewable energy sources as Solar PV panel and VAWT along with IoT based control method with the coordination of microcontroller provides effective controlling, monitoring, fault detection and preventive maintenance alert which makes the system intelligent and energy-efficient, resulting in less manpower requirement automation and saving in energy. The Solar PV Panel utilizes the photon energy from sunlight and VAWT utilizes the aerodynamic losses produced by moving vehicles for the generation of power. It provides the real-time monitoring of all connected street lights. Also, it can be an efficient, automated and attractive option for the development of smart cities.
Helicons in Solid-State Plasma of Cylindrical Configuration

Helicon electromagnetic eigenwaves of a cylindrical solid-state plasma waveguide that located in a coaxial constant magnetic field have been studied. It is shown that helicons are bulk-surface waves. In the case of ideal medium conductivity of waveguide, their propagation is accompanied by a surface current flowing along the generating lines of cylinder. The nonreciprocity effect of helicons that have the identical field distributions, but differing in the direction of propagation along the azimuthal coordinate, as well as when the external magnetic field is reversed the direction, has been noted.
Domination of Technological Tools in Cartographic Advancement: Perspective of Spatial Analysis

Recent advancement in technology is incomparable to any other discipline. Besides the credit of development diverse information and communication technologies, these advancements had also led to the development of various tools, applications and gadgets. Information and Communication Technology (ICT) as an academic discipline, it is one of forefront disciplines that had experienced one of the most enormous progresses lately. Similarly, communication paradigm also paves ways for the advancement of diverse cartographic techniques beyond mere making maps to a more sophisticated tool of communication. This study reviewed how the traditional cartographic techniques of map making completely transformed from conventional map making to a sophisticated digitized tool of spatial referencing in addition integration of modern mutual Spatial, Temporal, Geostatistics and Statistical data for spatial referencing and identifications. During the 1990s, cartographic techniques have been undergoing series of developments and advancements that facilitates the exact identification of more precise locations with precision on surface of the earth. Though, these forms of spatial identifications are only possible with aid of computer devices and other lightweight devices such as mobile devices, phones, gadgets and terminals. However, the emergence of geographic information system and its application in spatial analysis in 1960s have simplified the identification of geographical locations, spatial referencing and projections which led to the coordination of system that consequently gave birth to the fasted-growing GIS-Computer Aided Design for spatial analyst. Moreover, GIS and other related tools of spatial analyses have capability of enabling both individuals and cooperate decision making that are beyond the scopes of simple visual analyses.
3D Mirrored Object Selection for Occluded Objects in Virtual Environments

There is an increasing demand for the manipulation of virtual objects in 3D virtual reality (VR) space, which begins with the user selecting a desired object. Existing selection methods aim to provide an intuitive and natural experience that minimizes user fatigue. Although typical ray-casting methods are effective when objects in the virtual space are sparsely or evenly placed, the selection becomes challenging when these objects are smaller or farther away, particularly when they overlap. In this study, we propose a method to address these challenges via an effective selection process. Based on 3D mirroring, the proposed method provides a new interaction metaphor that allows for a more efficient selection of occluded virtual objects. We conduct systematic experiments with 21 subjects and use various levels of target visibility determined by object size, spatial density, and degrees of occlusion. The results indicate that, in occluded 3D environments, the proposed 3D mirrored selection process outperforms traditional gaze-supported selection processes and offers better user satisfaction.
Design and Analysis of Terahertz Transmitarray Using 1-bit Liquid Crystal Phase Shifter

This paper presents a 1-bit terahertz liquid crystal (LC) beam-steering antenna. It consists of a feed horn antenna and a flat rectangular transmitarray with 20×20 LC phase shifter unit cells. The unit cell could achieve phase difference within 180°±20° from 329 to 366.4GHz using double-layer of LC when the permittivity of the LC varies between 2.37 (state 0) and 3.29 (state 1). The full wave simulation results indicate that it can realize beam scanning over ±40° at 340GHz. The gain of the transmitarray is more than 21.4dBi.
Comparison of the Pedestrian Crossing Intention Parameters and Research on Intention Recognition Model Under Different Road Conditions

There have a large number of pedestrian-vehicle accidents on the pedestrian crossing area in China every year, causing huge loss of life and property. In view of different road conditions, it’s crucial to establish a more accurate crossing intention recognition model to improve the safety of pedestrians. In this work, a pedestrian crossing area was chosen. Due to construction reasons, two road conditions appeared in the same crossing area at different periods, namely a condition with a zebra crossing and that without a zebra crossing. We compared pedestrian crossing intention parameters under two road conditions in the same crossing area. The results found that there was a great difference in the characterization parameters of pedestrian crossing intention when the site with and without a zebra crossing. Additionally, a more comprehensive crossing intention characteristic parameters set was established. The characteristic parameters were pedestrian speed, the distance between vehicle and crossing area, time to collision (TTC), and safe vehicle deceleration (SVD), pedestrian age, pedestrian gender, group, respectively. The pedestrian intention recognition model for the site with a and without a zebra crossing were established by long short-term memory network integrated with the attention mechanism (AT-LSTM). When the model recognized pedestrian crossing intention 0.6 seconds in advance, the recognition accuracies were 93.05% and 93.89% respectively. The research results are of great significance for improving the safety of autonomous vehicles in the future, and there are also important to improve pedestrian safety.
Research on Finger Vein Recognition Based on Sub-Convolutional Neural Network

Aiming at the problem that the traditional deep learning finger vein recognition algorithm reduces the recognition rate when there are fewer training samples and training times, a finger vein recognition method based on multi-convolution and multi-scale features is proposed. Two relatively independent sub-convolutional networks with different granularities are used for feature extraction. The convolutional layer uses the LeakyReLU activation function, and the pooling layer uses the maximum value downsampling method. For the object classification of the model, feature extraction from a single layer of different feature extraction as a result, the samples are finally trained through the softmax classifier. Experimental results show that the use of two relatively independent sub-convolutional networks with different granularities can more effectively extract features, and the accuracy rate can reach 95.1% when the sample set is small. At the same time, in the case of picture rotation, the error rate of this method is only 0.0373.
Microcontroller based Automatic Sun Tracking Solar Panel

To learn a hierarchal representation of data, deep learning techniques can be used that use multiple processing layers, and produce state of art results. Many models and methods are designed in deep learning for classification in natural language processing (NLP). Various classification algorithms have been used for Arabic documents classification, but they have two problems High dimensional feature representation and the low accuracy of the classification. In this work, an important experiment is made by using deep related models and methods for classifying Arabic text also compare our model with various models. Also to forward a full understand, present and future of deep learning in Arabic text classification and have obtained encouraging results.
Tryptophan Metabolism: A Versatile Area Providing Multiple Targets for Pharmacological Intervention

The essential amino acid L-tryptophan (Trp) undergoes extensive metabolism along several pathways, resulting in production of many biologically active metabolites which exert profound effects on physiological processes. The disturbance in Trp metabolism and disposition in many disease states provides a basis for exploring multiple targets for pharmaco-therapeutic interventions. In particular, the kynurenine pathway of Trp degradation is currently at the forefront of immunological research and immunotherapy. In this review, I shall consider mammalian Trp metabolism in health and disease and outline the intervention targets. It is hoped that this account will provide a stimulus for pharmacologists and others to conduct further studies in this rich area of biomedical research and therapeutics.
Leveraging Machine Learning Techniques for Architecting Self-Adaptive IoT Systems

The use of IoT systems is increasing day by day. However, these systems due to their heterogeneity and inherently dynamic nature, face different uncertainties from the context, environment, etc. Such uncertainties can have a big impact on the overall system QoS, especially on energy efficiency and data traffic. This calls for better ways of architecting IoT systems that may self-adapt to keep the desired QoS. This paper presents an approach that leverages the use of machine learning (ML) techniques to perform a proactive adaptation of IoT architectures using self-adaptation patterns. It i) continuously monitors the QoS parameters; ii) forecasts possible deviations from the acceptable QoS parameters; iii) selects the best adaptation pattern based on forecasts using reinforcement learning (RL) techniques; iv) checks the quality of the selected decision using feedback mechanisms; and v) continuously performs the loop of the forecast, adaptation, and feedback. The results of our evaluations show that our approach can provide accurate QoS forecasts and further improve the energy efficiency of the system while maintaining the required data traffic.
Understanding Merge Conflicts and Resolutions in Git Rebases

Software merging is an important activity during software development. Merge conflicts may arise and degrade the software quality. Empirical studies on software merging are helpful to understand developers’ needs and the challenges of detecting and resolving conflicts. Existing studies collect merges by identifying commits that have more than one parent commit. Different from these explicit merges, rebasing branches is used to merge other changes but rewrites the evolutionary history. Hence, existing studies fail to identify implicit merges performed by rebasing branches. Consequently, the results of these studies may fail to provide comprehensive insights on software merging. In our study, we leverage the recently updated APIs of GitHub to study rebase activities in the pull requests. Our study shows that rebasing is widely used in pull requests. And our results indicate that, to resolve textual conflicts, developers adopt similar strategies shown in existing studies on explicit merges. However, in 34.2% of non-conflict rebase scenarios, developers add new changes during the rebase process. And this indicates that there are some new challenges of validating rebases. Our results provide useful insights for improving the state-of-the-art techniques on resolving conflicts and validating rebases.
The neural bases of spatial attention and perceptual rhythms

Attentional processes allow the brain to overcome its processing capacities limitations by enhancing relevant visual information and suppressing irrelevant information. Thus attention plays a critical role, shaping our perception of the world. Several models have been proposed to describe the neuronal bases of attention and its mechanistic underlyings. Recent electrophysiological evidence show that attentional processes rely on oscillatory brain activities that correlate with rhythmic changes in cognitive performance. In the present review, we first take a historical perspective on how attention is viewed, from the initial spotlight theory of attention to the recent dynamic view of attention selection and we review their supporting psychophysical evidence. Based on recent prefrontal electrophysiological evidence, we refine the most recent models of attention sampling by proposing a rhythmic and continuous model of attentional sampling. In particular, we show that attention involves a continuous exploration of space, shifting within and across visual hemifield at specific alpha and theta rhythms, independently of the current attentional load. In addition, we show that this prefrontal attentional spotlight implements conjointly selection and suppression mechanisms, and is captured by salient incoming items. Last, we argue that this attention spotlight implements a highly flexible alternation of attentional exploration and exploitation epochs, depending on ongoing task contingencies. In a last part, we review the local and network oscillatory mechanisms that correlate with rhythmic attentional sampling, describing multiple rhythmic generators and complex network interactions.
A Low Profile Wideband Linearly Polarized Slot Antenna Using a Circular AMC Surface

In this paper, a novel low-profile wideband linearly polarized slot antenna based on an artificial magnetic conductor (AMC) reflector is designed. It consists of an elliptical slot antenna and an AMC reflector consisting of 4 × 5 slotted circular patches. The [-90°, +90o] in-phase reflection bandwidth of the AMC is 5.68 - 8.52 GHz (40%). Utilizing the in-phase reflection of the AMC surface, the spatial phase delay caused by the air layer between the upper antenna and the lower AMC reflector is reduced, so that the profile of the antenna is reduced compared with the antenna using a metal reflector. The measured and simulated results show that the antenna has an impedance bandwidth of 5.06 - 7.92 GHz (44.07%), a peak gain of 8.05 dBi, a 3-dB gain bandwidth of 5.3 - 7.3 GHz (31.7%), and a height of 0.101 λL (λL is the wavelength at the lowest operating frequency).
A Reduced Switch Count Switched Capacitor Based High Voltage Gain Bidirectional DC-DC Converter for Grid Integration of BTS

A high voltage gain bidirectional DC-DC converter with reduced switch count is presented in this work. Renewable energy sources such as solar PV array, that are available at low DC voltage, is being used for supporting the DC load of a base transceiver station along with battery energy storage. Hence, the low voltage DC grid system requires a bidirectional DC-DC converter for the bidirectional power flow with the AC grid. This converter has a high gain compared to other existing topologies with reduced active switch count. The performance of it is validated with detailed simulations and a hardware prototype. Moreover, the performance of the system is analyzed and studied for step-up and step-down operations with a solar PV array, BES and a load connected to DC grid.
Adhesion Receptors as Therapeutic Targets

Cell adhesion receptors and their ligands as therapeutic targets - an overview, M.A. Horton laminin isoforms and their integrin receptors, G.O. Delwel and A. Sonnenberg the intracellular adhesion molecules (ICAMs), C. Gahmberg et al selectins and their ligands, S. Watson functional domains of cell adhesion molecules, A.P. Mould and M.J. Humphries signal transduction by integrins and other adhesion receptors, R.J. Juliano regulation of integrin mediated adhesion, R.C. Bates and G. Burns Adhesive platelet glycoproteins and platelet function, N. Keiffer leukocyte-endothelium interactions, C. Godin adhesion receptors in epithelial tumours, M. Pignatelli and J. Wilding anti-adhesive extracellular matrix proteins, J. Lawler integrins as therapeutic targets in bone disease, M.A. Horton and G. rodan identification and development of integrin/ligand antagonists for the treatment of human disease, T. Gadek and B.K. Blackburn adhesion molecules and cytokines in experimental inflammatory disease, P.A. Ward preclinical development of c7E3 Fab - a mouse/human chimeric monoclonal antibody fragment that inhibits platelet function by blockade of GPIIb/IIIa receptors with observations on the immunogenicity of c7E3 Fab in humans, R.E. Jordan et al clinical trials of adhesion receptor antagonists in renal transplantation, B. le Mauff and M. Hourmant.
DOA Estimation for Arbitrarily Distributed Subarrays in UAV Swarm

In this paper, we consider the problem of DOA estimation for the arbitrarily distributed subarrays with application in unmanned aerial vehicle (UAV) swarm, where multiple small UAVs equipped with uniform linear array (ULA) are separated by unknown intervals due to the dynamic moving. Three parameters are used to formulate the steering vector, i.e., the direction of arrivals (DOAs) of the target users, the intervals of UAVs, and the orientation angles of UAVs. We first estimate the orientation angles using an auxiliary user and then obtain the DOAs via a search free rooting method, regardless of the intervals of the UAVs. The deterministic Cramér-Rao bound (CRB) of the DOA and orientation angle are derived in closed-form. Finally, numerical examples are provided to corroborate the proposed studies.
State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method

This study aims to design and assess the simulation of the state of charge (SoC) estimation on lead-acid batteries using the Coulomb counting (CC) and feed-forward neural network (FFNN) method. Also, this study compared the effectiveness of each technique. CC and FFNN methods were designed and simulated in Simulink, and the results were analyzed. The two estimation results are compared to see the level of efficiency of each technique. The results of this study show that the feed-forward neural network method is better than the Coulomb counting method in load variation with a ratio of 5.56% on the first data input and 0.46% on the second data input, and 5.78% in temperature variation.
Effects of milrinone on cerebral perfusion and postoperative cognitive function in spine surgery: Secondary analysis of a CONSORT-compliant randomized controlled trial

Abstract Objective: To compare the effects of milrinone, sodium nitroprusside (SNP), and nitroglycerin (NTG) on induced hypotension, cerebral perfusion, and postoperative cognitive function in elderly patients undergoing spine surgery. Methods: Sixty patients >60 years scheduled for lumbar fusion surgery were assigned to receive milrinone (group M), SNP (group S), or NTG (group N). The administration of the study drug was initiated immediately after perivertebral muscle retraction and was stopped after completion of interbody fusion. Target blood pressure was a decrease of 30% in systolic blood pressure from baseline or mean blood pressure of 60 to 65 mm Hg. The regional cerebral venous oxygen saturation (rSVO2), as a measure of cerebral perfusion, and the change in perioperative Mini-Mental State Examination (MMSE) score, as a measure of postoperative cognitive function, were assessed. Results: During the administration of the study drug, the overall and lowest intraoperative rSVO2 values were significantly higher (P = .01 and P = .01, respectively), and the duration of rSVO2 <60% was shorter in group M than in the other groups (P = .03). In group M, intraoperative rSVO2 was not different from the basal value, whereas in groups S and N, rSVO2 was significantly lower than the basal value during the administration of the study drug, but then returned to the basal value after terminating the study drug. Basal MMSE scores were comparable among the 3 groups. The MMSE score on postoperative day 5 was higher in group M than the other groups. Conclusions: Milrinone used to induce hypotension resulted in better intraoperative cerebral perfusion and postoperative cognitive function compared to SNP and nitroglycerin.
Multi-modal Adversarial Training for Crisis-related Data Classification on Social Media

Social media platforms such as Twitter are increasingly used to collect data of all kinds. During natural disasters, users may post text and image data on social media platforms to report information about infrastructure damage, injured people, cautions and warnings. Effective processing and analysing tweets in real time can help city organisations gain situational awareness of the affected citizens and take timely operations. With the advances in deep learning techniques, recent studies have significantly improved the performance in classifying crisis-related tweets. However, deep learning models are vulnerable to adversarial examples, which may be imperceptible to the human, but can lead to model's misclassification. To process multi-modal data as well as improve the robustness of deep learning models, we propose a multi-modal adversarial training method for crisis-related tweets classification in this paper. The evaluation results clearly demonstrate the advantages of the proposed model in improving the robustness of tweet classification.
Diffusion of Saccharides and Amino Acids in Cross-linked Polymers

Diffusion coefficients of saccharides and amino acids were measured in cross-linked polymers such as dextran gels, polyacrylamide gels and photo-crosslinkable resins of different gel concentrations or various degrees of crosslinkage. Comparison of diffusion coefficients in gels with those in polymer solutions indicated that the diffusional velocity in gels was restricted not only by the interaction between diffusing substances and gel components but also by the steric hindrance of gel matrix. The diffusion coefficients in dextran gels and polyacrylamide gels were appreciably lower than those in polymer solutions, and the degree of lowering depended on both the gel concentration and the size of diffusing substances. The photo-crosslinkable resins basically composed of polyethylene glycol showed a higher permeability than dextran and polyacrylamide gels on account of a weak interaction between diffusing substances and resin component. Furthermore, an attempt was made to correlate the decrease of diffusion c.