Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid long short-term memory deep learning model and Dijkstra’s Algorithm for fastest travel route recommendation considering eco-routing factors

ABSTRACT The exponential growth of vehicles has led to increased traffic congestion and pollutants emission in cities. Intelligent transportation systems (ITS) shall be built by integrating Artificial Intelligence(AI). Planning optimal route with minimum travel time is a great challenge for the traffic management system. A hybrid model integrating long-short-term memory(LSTM) deep-learning model and Dijkstra’s algorithm for recommending the fastest route is proposed in this work. The proposed hybrid approach reduces the travel time to a greater extent. Vehicle speed greatly influences eco-driving factors such as fuel consumption and exhaust emission. Thus, for each road links, the fuel consumption and emission of pollutants such as carbon monoxide(CO), nitrogen oxides(NOx), and hydrocarbon(HC) are estimated for different type of vehicles. The results showed the proposed approach decreases fuel consumption, CO emission, NOx emission, and HC emission up to 54.5%, 61.5%, 64.2%, and 81.9%, respectively, compared to the traditional shortest path.
An Octagon Antenna Array For Satellite Direct Broadcasting Reception During Moving

In this paper, a novel octagon conformal antenna array has been presented. The proposed array characterize by lightweight, low cost, and a wide return loss bandwidth (RLBW), in addition to that it has a wide axial ratio bandwidth (ARBW). The proposed antenna consists of 8-faces in octagon form, each face consists of two slotted spiral substrate integrated waveguide (SIW) antenna. In order to cover 360° around the vehicle during moving, an omnidirectional pattern will be needed, consequently, two feeding techniques can be performed, conventional feed or sequential feed. The superior characteristic of the proposed antenna makes it a good solution to receive the satellite signals in the vehicle during moving. Using the spiral shape overcomes the slotted SIW antenna bandwidth limitation to get more than 35 % RLBW, and 10 % ARBW, in addition to 7 dBi gain within a single face. For the omnidirectional pattern about 5dBi directivity has been achieved.
Overdispersion Models in SAS

Overdispersion Models in SAS provides a friendly methodology-based introduction to the ubiquitous phenomenon of overdispersion. A basic yet rigorous introduction to the several different overdispersion models, an effective omnibus test for model adequacy, and fully functioning commented SAS codes are given for numerous examples. The examples, many of which use the GLIMMIX, GENMOD, and NLMIXED procedures, cover a variety of fields of application, including pharmaceutical, health care, and consumer products. The book is ideal as a textbook for an MS-level introductory course on estimation methods for overdispersion and generalized linear models as well as a first reading for students interested in pursuing this fertile area of research for further study. Topics covered include quasi-likelihood models; likelihood overdispersion binomial, Poisson and multinomial models; generalized overdispersion linear models (GLOM); goodness-of-fit for overdispersion binomial models; Kappa statistics; marginal and conditional models; generalized estimating equations (GEE); ratio estimation; small sample bias correction of GEE; generalized linear mixed models (GLMM); and generalized linear overdispersion mixed models (GLOMM).
ADAR2 Protein Is Associated with Overall Survival in GBM Patients and Its Decrease Triggers the Anchorage-Independent Cell Growth Signature

Background: Epitranscriptomic mechanisms, such as A-to-I RNA editing mediated by ADAR deaminases, contribute to cancer heterogeneity and patients’ stratification. ADAR enzymes can change the sequence, structure, and expression of several RNAs, affecting cancer cell behavior. In glioblastoma, an overall decrease in ADAR2 RNA level/activity has been reported. However, no data on ADAR2 protein levels in GBM patient tissues are available; and most data are based on ADARs overexpression experiments. Methods: We performed IHC analysis on GBM tissues and correlated ADAR2 levels and patients’ overall survival. We silenced ADAR2 in GBM cells, studied cell behavior, and performed a gene expression/editing analysis. Results: GBM tissues do not all show a low/no ADAR2 level, as expected by previous studies. Although, different amounts of ADAR2 protein were observed in different patients, with a low level correlating with a poor patient outcome. Indeed, reducing the endogenous ADAR2 protein in GBM cells promotes cell proliferation and migration and changes the cell’s program to an anchorage-independent growth mode. In addition, deep-seq data and bioinformatics analysis indicated multiple RNAs are differently expressed/edited upon siADAR2. Conclusion: ADAR2 protein is an important deaminase in GBM and its amount correlates with patient prognosis.
Piano Multi-Pitch Estimator Using CNN-Stacked LSTM

This research explores several variations of the automatic music transcription method, specifically in the pitch estimation task. Pitch estimation in this research mainly converts an acoustic piano song recording into a digitally transcribed song format. First, several techniques, including short-time Fast-Fourier transform and constant-Q transform, provide a spectrogram representation of a wav piano recording. Then it is fed into a combination of Convolutional Neural Network (ConvNet) and Long Short-Term Memory (LSTM) neural network. This transcription is a digitally transcribed song in the format of a MIDI file. For training purposes, the MAESTRO dataset was used for conducting training, which every training varies the learning rate value and spectrogram representation. This research obtains a model that can perform pitch estimation with a 90.14% F1 score and an average user evaluation of 8.4 out of 10.
Only Words Count; the Rest Is Mere Chattering: A Cross-Disciplinary Approach to the Verbal Expression of Emotional Experience

The analysis of sequences of words and prosody, meter, and rhythm provided in an interview addressing the capacity to identify and describe emotions represents a powerful tool to reveal emotional processing. The ability to express and identify emotions was analyzed by means of the Toronto Structured Interview for Alexithymia (TSIA), and TSIA transcripts were analyzed by Natural Language Processing to shed light on verbal features. The brain correlates of the capacity to translate emotional experience into words were determined through cortical thickness measures. A machine learning methodology proved that individuals with deficits in identifying and describing emotions (n = 7) produced language distortions, frequently used the present tense of auxiliary verbs, and few possessive determiners, as well as scarcely connected the speech, in comparison to individuals without deficits (n = 7). Interestingly, they showed high cortical thickness at left temporal pole and low at isthmus of the right cingulate cortex. Overall, we identified the neuro-linguistic pattern of the expression of emotional experience.
Blockchain-Based Autonomous Notarization System Using National eID Card

In recent years, the spread of information and communication technology has led to the emergence of e-government, which is the electronic replacement of government services. E-government is said to be compatible with blockchain technology, which has led to various studies on the possibility. Notarization, one of the functions of the government, has particularly been examined for the potential adoption of blockchain technology. However, because the notary public must authenticate the document’s contents during the notarization process, they have been difficult to replace with smart contracts. In this study, we focus only on fixed date notarizations and propose a fully automated notarization system by combining a national eID card with Public Key Infrastructure and smart contracts. A fixed date is a notarization that allows a notary public to guarantee that a document existed, regardless of the authenticity of the document’s content. Therefore, it can be replaced by a smart contract. Specifically, our proposed system automatically authenticates the creator and the document for electronic documents signed with a national eID card and uses the transaction receipt generated when the information is stored on the blockchain as a certificate of notarization. Verification of the signed data is done inside the blockchain by smart contracts, which eliminates the need for a verification authority. We further demonstrate the effectiveness of the proposed method in a Japanese use case as proof of concept.
Analysis of Multi-Hop Relaying in VLC-Based Vehicular Networks

Vehicular communications has gained a lot of attention over the past few years due to the rise in autonomous vehicles and intelligent transportation systems. However, due to the large numbers of vehicles that need to communicate with each others, traditional communication using Radio frequency might not be adequate. Visible light communications (VLC)was shown to be a suitable replacement as vehicles already have head and taillights which can support VLC with no need to install additional hardware. This paper analyzes the performance of a VLC based vehicular network employing decode and forward multi-hop relaying. Additionally, the optimum location and power allocation of the relays in order to optimizes the bit error rate is studied. It is found that the best achievable BER and data rate is when the relay at half the distance between the source and destination.
Optimized design procedure of rectenna impedance matching networks for IoT devices

In this paper a synthesis method of the rectifier and matching stages of a rectenna is presented, which allows to reach an optimal solution in a relatively short time, overcoming some criticalities that may be encountered in the design phase. Starting from a set of initial hypotheses of a general nature (e.g. a certain number of possible impedance matching or rectifier topologies), a simulation approach to the problem will be presented through the ADS software, divided into 3 process phases with subsequent refinements, 1) circuit simulation with ideal components, 2) circuit simulation with micro strip components, 3) layout design and simulation. Through the aid of the ADS optimization tools, the goal is to reduce the number of circuits that the designer must evaluate in the last phase, that is the layout design and simulation and obtain the highest performances.
Inpainting for videos with dynamic objects using texture and structure reconstruction

This paper describes a novel inpainting approach for removing marked dynamic objects from videos captured with a camera, so long as the objects occlude parts of the scene with a static background. Proposed approach allow to remove objects or restore missing or tainted regions present in a video sequence by utilizing spatial and temporal information from neighboring scenes. The algorithm iteratively performs following operations: achieve frame; update the scene model; update positions of moving objects; replace parts of the frame occupied by the objects marked for remove with use of a background model. In this paper, we extend an image inpainting algorithm based texture and structure reconstruction by incorporating an improved strategy for video. An image inpainting approach based on the construction of a composite curve for the restoration of the edges of objects in a frame using the concepts of parametric and geometric continuity is presented. It is shown that this approach allows to restore the curved edges and provide more flexibility for curve design in damaged frame by interpolating the boundaries of objects by cubic splines. After edge restoration stage, a texture reconstruction using patch-based method is carried out. We demonstrate the performance of a new approach via several examples, showing the effectiveness of our algorithm and compared with state-of-the-art video inpainting methods.
Rapid Detection of Aspergillus flavus and Quantitative Determination of Aflatoxin B1 in Grain Crops Using a Portable Raman Spectrometer Combined with Colloidal Au Nanoparticles

Aspergillus flavus and Aflatoxins in grain crops give rise to a serious threat to food security and cause huge economic losses. In particular, aflatoxin B1 has been identified as a Class I carcinogen to humans by the International Agency for Research on Cancer (IARC). Compared with conventional methods, Surface-Enhanced Raman Scattering (SERS) has paved the way for the detection of Aspergillus flavus and Aflatoxins in grain crops as it is a rapid, nondestructive, and sensitive analytical method. In this work, the rapid detection of Aspergillus flavus and quantification of Aflatoxin B1 in grain crops were performed by using a portable Raman spectrometer combined with colloidal Au nanoparticles (AuNPs). With the increase of the concentration of Aspergillus flavus spore suspension in the range of 102–108 CFU/mL, the better the combination of Aspergillus flavus spores and AuNPs, the better the enhancement effect of AuNPs solution on the Aspergillus flavus. A series of different concentrations of aflatoxin B1 methanol solution combined with AuNPs were determined based on SERS and their spectra were similar to that of solid powder. Moreover, the characteristic peak increased gradually with the increase of concentration in the range of 0.0005–0.01 mg/L and the determination limit was 0.0005 mg/L, which was verified by HPLC in ppM concentration. This rapid detection method can greatly shorten the detection time from several hours or even tens of hours to a few minutes, which can help to take effective measures to avoid causing large economic losses.
Improved Space Vector Based PWM Technique for Three Phase Z-Source Inverter

This article proposes a new space vector pulse width modulation (SVPWM) switching technique for three phase three level Z source inverter (3L-ZSI). The main feature of the proposed maximum boost control technique is that it eliminates the sixth frequency inductor current ripple, which is normally present in the conventional maximum boost control technique. Additionally, the proposed technique facilitates to reduce the switching frequency of the impedance network and common mode voltage, resulting in reduced size of the passive components. The vectors, “- - -” and “+ + +” of three level switching vectors have been implemented after proper modification for the concept of 3L- ZSI. The proposed technique is analyzed with respect to neutral point clamped (NPC), T-type NPC, and controlled diode bridge clamped 3L-ZSI. The efficacy of the proposed technique has been verified by both simulation and experimental results.
Industrial DC Microgrid Analysis Through Linear Power Flow

The energy shift towards renewable energies has brought an increased interest into DC microgrids as a decentralized option with increased efficiency and easier control that can facilitate this transition. Many efforts have been made into the optimization and planning of DC microgrids. In this paper, the authors propose an approach in which a planned DC microgrid can be simulated through the use of linear power flow. This approach allows to study the evolution of the steady state of the microgrid over time. The proposed method allows to simulate the contributions and power limits of batteries, and to obtain the voltages, the currents, and the power injections at each time step. With the generated results, the expected performance and benefits of a DC industrial microgrid can be assessed. The simulation results obtained using Python showcase the information and insights obtainable with the proposed approach.
Label-Aware Recurrent Reading for Multi-Label Classification

Multi-label classification (MLC) is an essential branch of natural language processing where a given instance may be associated with multiple labels. Recently, neural network approaches invested considerable dependency between labels and the instance, achieving state-of-the-art performance. However, the existing methods ignore the hidden correlations between each document's semantic information and labels. In this paper, inspired by the cognitive process of human reading, we propose a Label-Aware Recurrent Reading (LARD) network based on neuroscience. LARD modeled the MLC problem as a decision-making process of recurrent reading and constructs label-aware document representation according to the top-down mechanism of neuroscience. The model outputs the prediction of all labels after each reading, and in the process of recurrent reading, the prediction accuracy is improved. Besides, the attention mechanism is applied to make the weight of words dynamically adjust according to the topdown classification prediction information, taking into account the different contributions of words to labels. Experiments show that our model has better performance than the existing models.
Numerical Evaluation of the Channel Estimation in 5G NR Based on Machine Learning

This paper deal with the numerical evaluation of the wireless channel estimation based on machine learning approach in the multiuser multiantenna downlink system. As sounding sequence in frequency domain, the CSI-RS sequence are used. The convolution neural network is applied for the task of channel estimation in frequency domain using LS channel estimation on CSI-RS pilots signals. Training data set is generated using the open source 5G channel model QuaDRiGa for static scenario. CNN-based channel estimation algorithm is trained with the data from LS channel estimation and the true channels to obtain the parameters of CNN. CSIRS resource is configured according to 5G NR standard. Numerical results for selected user demonstrate the better performance of the learning-based channel estimation compared the least squares channel estimation method in the frequency response domain and MSE of channel estimation. The accuracy of channel estimate is improved with increase of training epoch and with reducing the learning rate. The obtained results show that machine learning-based channel estimation methods can be used for channel estimation in modern 5G networks or other networks as an alternative to existing methods.
MYRF: A New Regulator of Cardiac and Early Gonadal Development—Insights from Single Cell RNA Sequencing Analysis

De novo variants in the myelin regulatory factor (MYRF), a transcription factor involved in the differentiation of oligodendrocytes, have been linked recently to the cardiac and urogenital syndrome, while familiar variants are associated with nanophthalmos. Here, we report for the first time on a patient with a de novo stop-gain variant in MYRF (p.Q838*) associated with Scimitar syndrome, 46,XY partial gonadal dysgenesis (GD) and severe hyperopia. Since variants in MYRF have been described in both 46,XX and 46,XY GD, we assumed a role of MYRF in the early development of the bipotential gonad. We used publicly available single cell sequencing data of human testis and ovary from different developmental stages and analysed them for MYRF expression. We identified MYRF expression in the subset of coelomic epithelial cells at stages of gonadal ridge development in 46,XX and 46,XY individuals. Differential gene expression analysis revealed significantly upregulated genes. Within these, we identified CITED2 as a gene containing a MYRF binding site. It has been shown that Cited2−/− mice have gonadal defects in both testis and ovary differentiation, as well as defects in heart development and establishment of the left–right axis. This makes MYRF a potential candidate as an early regulator of gonadal and heart development via upregulation of the transcriptional cofactor CITED2.
Network Intrusion Detection via Flow-to-Image Conversion and Vision Transformer Classification

In recent years, computer networks have become an indispensable part of our life, and these networks are vulnerable to various type of network attacks, compromising the security of our data and the freedom of our communications. In this paper, we propose a new intrusion detection method that uses image conversion from network data flow to produce an RGB image that can be classified using advanced deep learning models. In this method, we proposed to use the decision tree algorithm to identify the important features, and a windowing and overlapping mechanism to convert the varying input size to a standard size image for the classifier. We then use a Vision Transfomer (ViT) classifier to classify the resulting image. Our experimental results show that we can achieve 98.5% accuracy in binary classification on the CIC IDS2017 dataset, and 96.3% on the UNSW-NB15 dataset, which is 8.09% higher than the next best algorithm, the Deep Belief Network with Improved Kernel-Based Extreme Learning (DBN-KELM) method. For multi-class classification, our proposed method can achieve a testing accuracy of 96.4%, which is 5.6% higher than the next best method, the DBN-KELM.
A Novel Design Nomogram for Optimization of Micro Search Coil Magnetometer for Energy Monitoring in Smart Buildings

In this paper, a new analytical method to achieve the maximum signal-to-noise ratio (SNR) of a micro search coil magnetometer (µSCM) is presented. A planar spiral inductor was utilized to miniaturize conventional bulky search coil magnetometers. First, dimensional analysis was applied to identify three dimensionless parameters for the µSCM’s key performance indices (sensitivity (Se), noise, and SNR). The effect of the parameters on the µSCM’s performance was carefully investigated, and a novel 4D nomogram was developed. Furthermore, an SNR analysis considering noise sources of a low-noise amplifier was performed. By combining the results from the nomogram and the effect of the noise sources from the amplifier circuit, optimum values for the dimensionless parameters were calculated. According to the calculation results, the dominant noise source varied with an increase in the track width ratio to the outer diameter. Seven different samples were fabricated by a single-mask lithography process. The sensitivity of 1612 mV/mT was demonstrated at a 50 Hz input magnetic field, which was better than the previous µSCM (Se = 6.5 mV/mT) by more than 2 orders of magnitude. Finally, one of the fabricated µSCMs was employed to measure the online power consumption of a personal computer while different types of software were running.
Optimised germination protocol for jackfruit seeds and evaluation of methods for extraction of DNA suitable for genetic analysis

Jackfruit (Artocarpus heterophyllus) is an underutilised plant that is promising in curbing food and nutritional security in sub-Saharan Africa. However, high level of secondary metabolites in its tissues significantly hampers its genetic characterisation for breeding purposes. Primarily, the compounds react with DNA during the extraction process, thus reducing its yield and quality. The utilisation of leaves from jackfruit seedlings is a potentially effective approach of addressing the challenge, however, limited information is available on efficient jackfruit seed germination procedures. Elucidating effective methods of jackfruit seed germination, and optimising protocols for DNA extraction is crucial in promoting its genetic characterisation studies for identification of superior varieties for propagation. The objective of this study was to evaluate methods of jackfruit seed germination, and DNA extraction procedures using jackfruit leaves. Pre-treatment of seeds with 3% hydrogen peroxide was effective in enhancing seed germination within a short time, compared to distilled water and 3% hydrochloric acid. We optimised a DNA extraction technique by combining CTAB-SDS based approach with an enhanced solvent extraction method. The technique yielded high quantity and quality of DNA from jackfruit leaves, which was appropriate for downstream polymerase chain reaction analysis. The sequence-related amplified polymorphism (SRAP) and simple sequence repeat (SSR) amplifications confirmed the effectiveness of the optimised CTAB-SDS based protocol for extraction of high quality DNA.