Miracle-dz/UniFAR-FTUs huggingface dataset:
Location Based Channel Resource Allocation for V2V Communications

This paper proposes a channel Resource Allocation (RA) scheme based on location information for Vehicle-to-Vehicle (V2V) communications. The fast time-varying character of the wireless channel makes the channel resource allocation of V2V communications a challenging problem. However, the wireless channel, between the transmitter and the receiver depends on their locations, which are slowly changing. It is better to take the location information into consideration for the channel allocation scheme of V2V. Therefore, we propose a novel location-based RA scheme to maximize the system energy efficiency of both Cellular User Equipments (CUEs) and Vehicular User Equipments (VUEs). Simulation results show that the algorithm effectively improves the energy efficiency of CUEs and VUEs, reducing the algorithm's computational complexity in a large number of user scenarios.
Optic Disc Hemorrhage Detection via A Novel Position-Guided Attention Network with Small Samples on Fundus Images

Optic disc hemorrhage (ODH) is an important lesion factor for eye disease diagnosis. ODH has some specific displaying characteristics on fundus images, including covering fuzzy small domains and displaying similar to vessels, which greatly increase ODH detection difficulty. In this paper, we propose a novel position-guided attention network to detect optic disc hemorrhage on fundus images. Our method greatly takes advantage of the prior knowledge of ODH position information and the multitask learning dependencies related to the ODH segmentation and ODH classification, to build a position information attention module and a correlative feature fusion module. Moreover, we introduce a disc edge strength map on the optic disc to constrain the attention domains. Due to the limitation of ODH labeled data, an online hard case segmentation strategy on small samples are proposed to train our method. Experiments show that our method could greatly reduce the detection of false positives when detecting ODH on fundus images, so as to obtain superior performance on the ODH detection with small samples. Further, a fundus image dataset with professional ODH labels is published to advance the research of ODH detection (https://github.com/JieGenius/disc_hemo_dataset).
On the Rydberg transitions and elemental compositions in the laser produced Al (6063) plasma

3p 2 P1=2,3=2 (n ¼ 3 – 8), ns 2 S1=2 ! 3p 2 P1=2,3=2 (n ¼ 4–6), and the dominant spectral lines of the other constituent elements. We have extracted the relative abundance of the impurities using the relative intensity ratio method. Besides, we have calculated the electron temperature (� 7580 K) from the Boltzmann plot method and the electron number densities (� 1.4 � 10 17 =cm 3 ) from the Stark widths of the aluminum spectral lines. The plasma parameters determined in the present work are in agreement with that reported in the literature. The molecular vibrational transitions of the AlO free radical associated with the B 2 P ! X 2 P band system have also been identified. V C 2011 American Institute of Physics. [doi:10.1063/1.3625552]
High Fidelity Anthropomorphic 3D Printed Models - Accuracy, Precision and Quality Control

The 3D modelling and printing of anthropomorphic 3D models based on CT are essential to modern anthropology and bioengineering. The accuracy and precision of those models are vital for their usability, which presents more challenges for their manufacturing and QA. In this study, three human humeri marked with X-ray-positive labels were CT scanned. The labels provided reference points for the subsequent measurements. The exported DICOM datasets were segmented in Slicer3D and 3D-printed with an FDM 3D printer with and without thermal deformation compensation. Three raters measured the bones and the 3D-printed models using a manual caliper. The DICOM measurements were conducted using the volume rendering module of Radiant DICOM viewer. The average distances between the reference points on the actual bones were used as ground truth to assess the accuracy of the DICOM dataset and the 3D-printed models. The average bias (AB) of the volume rendering measurements was $-\mathbf{3}. \mathbf{24} \pm \mathbf{1}. \mathbf{13}\mathbf{mm}$. The mean weighted bias (WB) was $-\mathbf{24}. \mathbf{35}\pm \mathbf{9}. \mathbf{1}\boldsymbol{\mu} \mathbf{m}/$ mm. The mean weighted absolute difference (WAD) was 25.93 $\pm \mathbf{8}. \mathbf{85}\boldsymbol{\mu} \mathbf{m}/\mathbf{mm}$. In the 3D-printed model without thermal compensation the AB was $\mathbf{0}. \mathbf{11}\pm \mathbf{0}. \mathbf{27}\mathbf{mm}$, the WB was $-\mathbf{1}. \mathbf{89}\pm \mathbf{4}. \mathbf{68}\boldsymbol{\mu}\mathbf{m}/\mathbf{mm}$, and the WAD was 7.85 $\pm \mathbf{4}. \mathbf{35}\boldsymbol{\mu}\mathbf{m}/\mathbf{mm}$. The anthropomorphic 3D-printed models are an accurate instrument for the representation of the spatial properties of the skeleton. They can provide fast and reproducible results in morphometric studies on bone morphology. Thus 3D-printed models are a more reliable tool than the measurements of Volume renderings, which clinicians frequently use. *
The Possibility of Post-industrial Landscapes Reclamation Based on the Study of Soil Quality Indicators and Variance Analysis

Elucidation of the ecological situation in the territories affected by mining and chemical works is a primary task for the formation of a strategy for the restoration of the natural environment. Total organic carbon is one of the most important components of soils. It is evaluated the level of total organic carbon in the soils of post-industrial landscapes. A software tool was created that examines the influence of factors on measurement results on the basis of laboratory studies. As a result of one-factor and two-factor analysis of variance, it was established that depth and distance do not affect the results of measurements, and the presence of systematic errors in the results of measurements is insignificant.
Identification of lncRNA-related protein-coding genes using multi-omics data based on deep learning and matrix completion

Long noncoding RNAs (lncRNAs) can regulate the expression of protein-coding genes (PCGs) to cause disease. Identifying lncRNA-PCG associations (LGAs) is beneficial in revealing the pathogenic mechanism of lncRNA. Nevertheless, it remains challenging due to the heterogeneity of lncRNA expression and the complexity of its regulatory patterns. Biological experiments have been designed to identify LGAs, but they cannot be used on a large scale due to time and financial constraints. Therefore, the design of computational methods becomes crucial for LGA research. Here, we propose a new computational model, DNNMC, to reveal potential LGAs based on deep neural networks and inductive matrix completion using association and multi-omics data. We first integrated LGA and multi-omics similarity to construct lncRNA and PCG similarity networks. Subsequently, deep graph convolutional networks were used for feature learning of lncRNAs and PCGs. These learned features and the known LGA matrix were finally used as input to the inductive matrix completion module for predicting potential LGAs. Experimental results on three datasets demonstrated that DNNMC outperformed other machine learning methods in predicting LGA relationships. Furthermore, multi-omics features were shown to improve the performance of LGA identification. In conclusion, we propose a new LGA prediction method, DNNMC, which can effectively complete the LGA prediction task and help to reveal the regulatory mechanism of lncRNAs in diseases.
Predicting drug transcriptional response similarity using Signed Graph Convolutional Network

Exploring the transcriptional response after employing chemical compounds assists in treating gene-related diseases and understanding biological activity of compounds. Calculating the similarity of drug transcriptional response can help to discover novel compounds that have the similar biological activity to known drugs for treating the same disease. Considering the transcriptional profiles of compounds are limited and harder to get than the structure of compounds, it is worth modeling the structure-transcriptional response similarity relationship. In this paper, we propose a signed graph convolutional network (SGCN)-based method, namely SGCN-DTRS, to predict drug transcriptional response similarity, which is quantitatively measured by Connectivity Map (CMap) scores, from their structures. SGCNDTRS constructs a CMap signed network from compounds and their CMap scores in the training data, which takes compounds as nodes, molecular structural representations of compounds as the attributes of nodes, and similarity relations between compounds as edges. Then SGCN-DTRS learns the CMap compound embeddings to predict CMap scores of pairwise compounds. Extensive experiments verify the superiority of the proposed method against the compared state-of-the-art methods and reveal that the relational information of the CMap data, which is learned from CMap signed network, is important for the CMap score prediction. SGCN-DTRS can not only work for the compounds in the training set but also is applicable to unseen compounds.
Predictability and finite‐time instability of the northern winter circulation

The finite-time instability and associated predictability of atmospheric of atmospheric circulations are defined in terms of the largest singular values, and associated singular vectors, of the linear evolution operator determined form given equations of motion. These quantities are calculated in both a barotropic and a three-level quasi-geostrophic model, using as basic states realistic large-scale northern wintertime flows that represent the climatological state, regime composites, and specific realizations of these regimes. for time-invariant basic states, the singular vectors are compared with the corresponding normal-mode solutions; it is shown that the perturbations defined (at the initial time) by the singular vectors have much larger growth rates than the normal modes, and possess a more localized spatial structure. The regimes studied have opposite values of the Pacific/North American (PNA) index, and growth rates for the barotropic basis states appear to confirm earlier studies that the barotropic instability of the negative PNA states may be larger than the corresponding positive PNA states. The evolution of the singular-vector perturbations, with emphasis on the vertical structure, is compared for time-evolving and time-invariant baroclinic basis states; the effects of nonlinearity are also discusses. It is shown that, in the baroclinic model, interactions between synoptic-scale eddies in the time-evolving basic state and in the perturbation field are fundamental for studying the predictability of transitions in the large-scale circulation. Consequently results obtained from linear calculations using very smooth basic states cannot properly account for such predictability. These results form the basis of a technique used to initialize ensembles of forecasts made with a primitive-equation model, and are described in the companion paper (Mureau et al. 1993).
Engineering Students' Engagement and Their Perspective on Compulsory Classroom Attendance

The link between class attendance, student engagement, and student success is controversial in Higher Education. Some universities monitor student attendance in the classroom. The difference between monitoring or recording attendance and enforcing a compulsory attendance policy should be clarified. This paper aims to explore engineering students' experience of applying attendance monitoring and enforcing a mandatory attendance policy. The relation between increasing the attendance rate and student engagement is also investigated. The primary research questions are: what are student perspectives on compulsory attendance monitoring? and why do senior engineering students attend classes? A pilot case study investigates senior undergraduate engineering student experience before and after the attendance monitoring system was performed. A survey was used to determine the view of students on how the compulsory attendance system influences their motivation and engagement in the classroom and what motivated them to attend before the compulsory policy became in effect. The findings can enrich higher education teaching and learning authorities with thoughts on future strategic policies and strategic research in the digital transformation era.
Fine-Tuned Framework With Transfer Learning for Pulmonary Nodule Detection

Cancer is one of the leading cause of death globally with lung cancer having the highest elevated mortality rate. Chances of survival in lung cancer patients are comparatively higher if it is detected in its early stages. Hence, Computer-Aided Detection (CAD) systems that can provide a faster diagnosis of lung cancer could potentially be life-saving. For this, a pulmonary nodule classification model using Computed Tomography (CT) scans based on Convolutional Neural Network (CNN) is proposed. The framework uses augmented images of the pulmonary nodules that are extracted from CT scans which are available in the LIDC-IDRI dataset. The framework further relies on using transfer learning techniques like VGG16 and ResNet50 on the resultant images to build a well-trained model. The proposed model also explores the effectiveness of fine-tuning to enhance classification accuracy and decrease data losses. The highest accuracy achieved by the proposed model is 84.16% with a loss of 39.17%.
Mediation Analysis and Mixed-Effects Models for the Identification of Stage-specific Imaging Genetics Patterns in Alzheimer’s Disease

Alzheimer’s disease (AD) is one of the most common and severe forms of Senile Dementia. Genome-wide association studies (GWAS) have identified dozens of AD susceptible loci. To better understand potential mechanism-of-action for AD, quantitative brain imaging features have been studied as mediators linking genetic variants to AD outcomes. In this study, Mediation analysis, Chow test and Mixed-effects Models are used to investigate the biological pathways by which genetic variants affect both brain structures/functions and disease diagnosis. We analyzed the imaging and genetics data collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project, including a Polygenic Hazard Score (PHS) and 13 imaging quantitative traits (QTs) extracted from the AV45 PET scans quantifying the amyloid deposition in different brain regions of subjects from four separate diagnostic groups. Mediation analysis assessed the mediating effects of image QTs between PHS and diagnosis, whereas Chow test and Linear Mixed-Effects models were used to characterize intra-group differences in the associations between genetic scores and imaging QTs for different disease stages. Results show that promising stage-specific imaging QTs that mediate the genetic effect of the studied PHS on disease status have been identified, providing novel insights into the predictive power of the PHS and the mediating power of amyloid imaging QTs with respect to multiple stages over the AD progression.
Soft Exoskeleton for Hand Rehabilitation: An Overview

Robotics based rehabilitation have attracted much attention during the past two decades. Recent intensive research publications in the area indicates that soft exoskeleton is one of the promising technologies for robotic-based rehabilitation. This paper aims to review the up-to-date research work in soft exoskeletons material, manufacturing, sensing and control for hand rehabilitation. Applied materials, its preferred properties, and manufacturing technologies of soft exoskeletons are reviewed. Different position and force sensing technologies as well as recent control techniques applying bio-signals as control signals are reviewed. The major challenges, which are also recommendations for future research work, are highlighted.
Optimization of Experimental Parameters for Co(II) Ions Biosorption onto Algae Biochars

In this study, the removal Co(II) ions from aqueous media using biochars obtained from the marine algae biomass is proposed. Due to the high content of carbon, marine algae biomass can be a suitable raw material for biochar production. Therefore, the biochars obtained by pyrolysis of algae biomass (Ulva lactuca sp.) at two different temperatures: 320°C (BC-320) and 550°C (BC-550) under limited oxygen conditions, were used as biosorbents for the removal of Co(II) ions in aqueous media. The biosorption process was examined as a function of initial pH, biosorbent dose, initial Co(II) ions concentration, contact time and temperature, and the optimal conditions were selected step by step. The highest biosorption efficiency for Co(II) ions was obtained at a pH of 5.0, biosorbent dose of 4.0 g/L, a contact time of 60 min and temperature of 22°C. Under these conditions, the experimental removal percent is higher than 99 %, when the initial concentration of Co(II) ions is in the range of 12- 71 mg/L.
BioMDSE: A Multimodal Deep Learning-Based Search Engine Framework for Biofilm Documents Classifications

As biofilms research grows rapidly, a corpus of bibliographic literature (i.e., documents) is increasing at an incredible rate. Many researchers often need to inspect these large document collections, including (1) text, (2) images, and (3) captions, to understand underlying biological mechanisms and make a critical decision. However, researchers have great difficulty in exploring such ever-growing large datasets in labor-intensive processes. Thus, automation of such tasks is urgently required for the automatic identification or classification of a large volume of document collections. To address this problem, we present a multimodal deep learning-based approach to automatically classify documents for a specialized information retrieval technique based on biofilm images, captions, and texts, which is a major source of information for the classification of documents. Images, captions, and texts from biofilm documents are represented in a large vector space. Then, they are fed into convolutional neural networks (CNNs), to improve similarity matching and relevance. Our extensive experiments and analysis will take captions, texts, or images as unimodal models as inputs and concatenate them all into multimodal models. The trained models for this classification approach in turn help a search engine to precisely identify relevant and domain-specific documents from a large volume of document collections for further research direction in biofilm development.
Design of a Software-Defined Multi-Channel Function Generator

This paper demonstrates the design of a multi-channel function generator. The pattern generators are fully software defined allowing maximum flexibility. Four different patterns are chosen as examples to demonstrate the ability to generate any desired waveform using simple software implementation. The design is built on an embedded FPGA/SoC platform (AMD Zynq Ultrascale+) which proves feasibility to deploy this design in a real-world product.
Challenges and Opportunities of Integrating, and Securing IoT technology in public places

Due to the recent advancements in Internet of Things (IoT), smart environments have gained popularity, and grown from a theoretical concept to one with applications all over the world. In this survey, the novel uses of IoT in public places, such as smart airports, smart railway stations, and smart ports, are reviewed. Since these places are often crowded, and frequently used, it is crucial for IoT technologies to be secure, robust, efficient, and integrated effectively. For cybersecurity attacks, AI-enabled cyber-defense, mutual authentication, are covered. While for non-cybersecurity, object detection, and passenger risk assessment, are covered. The biggest obstacle in the integration of IoT is the lack of user awareness and skills of stakeholders. Making IoT a form of entertainment, or an experience, as well as using it for forecasting, and maintenance, are some of the ways IoT can be more effectively integrated in public places. The main themes, proposed solutions, and findings, are reviewed.
Trajectory and Power Optimization for Buffer-Assisted Amplify-and-Forward UAV Relay

This paper proposes a buffer-assisted amplify-and-forward (AF) unmanned aerial vehicle (UAV) relay for communication between two ground nodes without a direct link. These communications are essential in disaster rescue areas where fairness plays a significant role. To achieve fairness, we aim to maximize the minimum information rate. The traditional way of prefixed scheduling the time slots to transfer and receive at the UAV does not guarantee that the communication system uses high signal-to-noise ratio (SNR) communication links instead of low SNR links. Therefore, we choose to employ a buffer at the UAV to store the information and transfer it to the destination nodes in the high SNR links. The pairing of time slots is necessary here since we are using a buffer, and we do not know in which time slot data is transmitted to the destination node after it is received at the UAV. Consequently, we formulate a fairness maximization problem by jointly optimizing the trajectory and power control. Unfortunately, this formulation results in a non-convex problem. We propose a solution based on the principles of the minorize-maximize (MM) algorithm and linear programming relaxation techniques to solve the fairness problem and pairing of time slots. Numerical results demonstrate that the trajectory, power control, and paired slots favor the UAV and ground nodes to communicate in the high SNR channel links, thus maintaining fairness.
Design of Millimetre-Wave Passive Mixer in 45-nm SOI CMOS Technology

Design of a millimetre-wave passive mixer in 45nm SOI CMOS technology is presented in this work. To balance the conversion loss of the mixer with linearity, bias conditions are carefully selected. Moreover, transformer baluns are used to convert single-ended signal to differential ones for both LO and RF ports. Based on the electromagnetic simulations, the designed mixer has a return loss better than -10 dB for the RF port, and the conversion loss of the designed mixer is 10 dB, while a 14-dBm LO power is used. Meanwhile, under the same LO power, the P1dB of the designed mixer is 10 dBm. The overall size of the designed mixer with all RF and DC pads is only $0.95 \times 0.6$ mm$^{\mathbf{2}}$.
A Low Profile Magneto-electric Dipole Antenna based on Spoof Surface Plasmon Polaritons

A novel low profile magneto-electric (ME) dipole antenna based on spoof surface plasmon polaritons (SSPPs) is proposed. The magneto and electric dipole of the ME antenna both are composed of slow-wave SSPPs structure, which effectively decrease the size of the antenna. A coplanar waveguide (CPW) is used to transform the guided wave into slow-wave to further expand the impedance bandwidth. An antenna prototype is designed and simulated. Simulated results show that the designed antenna achieves an impedance bandwidth of 22.8% for VSWR < 2 from 2.44 to 3.07 GHz and 7.8% 1-dB gain bandwidth from 2.44 to 2.65 GHz with a low profile height (0.10$\lambda_{0}$, $\lambda_{0}$ is the wavelength of 2.55GHz) and high front-to-back ratios.