bluuebunny/crossref_metadata_2025 huggingface dataset:
Image Denoising by Deep Convolution Based on Sparse Representation
Authors: Bian Shengqin , He Xinyu , Xu Zhengguang , Zhang Lixin
Published: May 2023

Noise filtering is a crucial task in digital image processing, performing the function of preprocessing. In this paper, we propose an algorithm that employs deep convolution and soft thresholding iterative algorithms to extract and learn the features of noisy images. The extracted features are acquired through prior and sparse representation theory for image reconstruction. Effective separation of the image and noise is achieved using an end-to-end network of dilated convolution and fully connected layers. Several experiments were performed on public images subject to various levels of Gaussian noise, in order to evaluate the effectiveness of the proposed approach. The results indicated that our algorithm achieved a high peak signal-to-noise ratio (PSNR) and significantly improved the visual effects of the images. Our study supports the effectiveness of our approach and substantiates its potential to be applied to a broad spectrum of image processing tasks.
Assessing the Impacts of COVID-19 and Social Isolation on Mental Health in the United States of America
Authors: Fulk Alexander , Saenz-Escarcega Raul , Kobayashi Hiroko , Maposa Innocent , Agusto Folashade
Published: May 2023

The COVID-19 pandemic has had a devastating impact on the world at large with over 750 million cases and almost 7 million deaths reported thus far. Of those, over 100 million cases and 1 million deaths have occurred in the United States of America (USA). The mental health of the general population has been impacted by several aspects of the pandemic including lockdowns, media sensationalism, social isolation, and spread of the disease. In this paper, we examine the associations that social isolation and COVID-19 infection and related death had with the prevalence of anxiety and depression in the general population of the USA in a state-by-state multiple time-series analysis. Vector Error Correction Models are estimated and we subsequently evaluated the coefficients of the estimated models and calculated their impulse response functions for further interpretation. We found that COVID-19 incidence was positively associated with anxiety across the studied period for a majority of states. Variables related to social isolation had a varied effect depending on the state being considered.
Additive-Assisted Crystallization of 9,10-Diphenylanthracene
Authors: Sonina Alina A. , Cheshkina Darya S. , Kazantsev Maxim S.
Published: May 2023

Crystallization control of organic conjugated small molecules is in high demand for the engineering of functional materials in organic optoelectronics. Here, we report solution additive-assisted crystallization of a model non-planar aromatic hydrocarbon derivative 9,10-diphenylanthracene. Among the studied series of related aromatic hydrocarbons comprising pyrene, perylene, anthracene, tetracene, and rubrene, only tetracene revealed clear reproducible effects allowing one to perform selective crystallization of metastable 9,10-diphenylanthracene polymorphs. Additionally, crystallization of 9,10-diphenylanthracene and pyrene produced a stoichiometric co-crystal (PYR–DPA) having a segregated layered molecular packing with alternating 9,10-diphenylanthracene and pyrene layers. Remarkably, the molecular packing of pyrene within the co-crystal is unique and represented by the herringbone motif, whereas the molecular packing in known pyrene polymorphs is represented by π-stacked molecules. The co-crystal also demonstrated a bright photoluminescence with a photoluminescence quantum yield of 51%. Considering the morphology of 9,10-diphenylanthracene crystals obtained and crystal structures of PYR–DPA co-crystal and tetracene, we have proposed the mechanism of additive-assisted polymorphism based on the inhibition of (111) facet of α-DPA and promoting of the layered structure crystallization corresponding to metastable polymorphs (β- and γ-DPA). We highlight the additive-assisted crystallization approach as a powerful tool for the crystal engineering of functional materials for organic optoelectronics.
Oxidation and Electrical Property Studies on Ferritic Steels as Potential Interconnects in Electrochemical Devices for Energy Conversion
Authors: Dąbek Jarosław , Gaweł Richard , Pyzalski Michał , Brylewski Tomasz
Published: May 2023

This work presents the results of oxidation studies on commercially available Nirosta 4016/1.4016 ferritic steel, which contains 16.3 wt.% chromium, as well as the electrical properties of steel/scale layer systems in order to determine the usefulness of this steel for constructing metallic interconnects in solid oxide fuel cell (SOFC) and solid oxide electrolyzer cell (SOEC) stacks. The E-Brite ferritic steel, consisting of up to 26 wt.% chromium, was chosen as a reference material. High-temperature isothermal oxidation kinetics studies were carried out on both steels at 1073 K for 255, 505, 760 and 1010 h in air atmosphere. These conditions are representative of those present in the cathode compartment of a SOFC and the anode compartment of a SOEC. Area specific resistance (ASR) measurements were performed on steel/scale layer systems, obtained after the previous oxidation of both steels in the above-mentioned conditions, in the air in the temperature range of 573–1073 K using the pseudo-DC four-probe method. On the basis of these studies, complemented by morphology observations, as well as chemical and phase composition analysis of the oxidation products, the usefulness of Nirosta 4016/1.4016 ferritic steel for manufacturing interconnects in energy conversion electrochemical devices operating at 1073 K was confirmed.
Colloidal Synthesis and Optical Properties of Cs2CuCl4 Nanocrystals
Authors: Gu Wanying , Zeng Yicheng , Deng Yuan , Huang Pan , Jin Geyu , Liu Fangze , Wei Jing , Li Hongbo
Published: May 2023

Lead-free copper halide perovskite nanocrystals (NCs) are emerging materials with excellent photoelectric properties. Herein, we present a colloidal synthesis route for orthorhombic Cs2CuCl4 NCs with a well-defined cubic shape and an average diameter of 24 ± 2.1 nm. The Cs2CuCl4 NCs exhibited bright, deep blue photoluminescence, which was attributed to the Cu(II) defects. In addition, passivating the Cs2CuCl4 NCs by Ag+ could effectively improve the photoluminescence quantum yield (PLQY) and environmental stability.
Porous, Tremella-like NiFe2O4 with Ultrathin Nanosheets for ppb-Level Toluene Detection
Authors: Zhang Yanlin , Qu Honglong , Gang Cheng , Guan Hongtao , Dong Chengjun , Yin Zongyou
Published: May 2023

As a typical spinel ferrite, NiFe2O4 is suitable for use in gas sensors. Herein, we report the fabrication of porous, tremella-like NiFe2O4 assembled using porous, ultrathin nanosheets via the coordination of Ni2+ and Fe2+ with 1,4-phenylenediboronic acid. The optical band gap of the NiFe2O4 is estimated to be about 1.7 eV. Furthermore, the NiFe2O4 sensor annealed at 400 °C exhibits a low detection limit of 50 ppb, a fast response/recovery time (11.6 s/41.9 s to 10 ppm toluene), good reproducibility, and long-term stability at 220 °C. The suitable sensing performances can be attributed to the good catalytic activity of NiFe2O4 to toluene oxidation. Moreover, the ultrathin nanosheets with porous structures provide a large number of active sites to significantly favor the diffusion and adsorption/desorption of toluene molecules. This current work provides an insight into fabricating NiFe2O4 using 1,4-phenylenediboronic acid, which is promising for ppb-level toluene detection.
Mesomechanical Aspects of the Strain-Rate Sensitivity of Armco-Iron Pulled in Tension
Authors: Eremin Mikhail , Chirkov Artyom , Danilov Vladimir
Published: May 2023

The phenomenon of the strain-rate sensitivity of metallic materials has been a topic of interest since the first mechanical tests at different strain rates were performed. The problem of its theoretical description appeared simultaneously. Despite the significant number of studies covering this issue, it is necessary to rule out a few drawbacks of previously reported models, which is the goal of this work. Herein, an extension of the elastic–viscoplastic model to a generalized state of stress is proposed while aiming to describe the strain rate sensitivity of Armco-iron samples that were pulled in tension within the framework of the finite-difference method. A mathematical model was formulated using equivalent stress and strain, which alleviated the complexity of the relaxation-type constitutive equations. The critical shear stress (CSS) function describes S-type instability with a single equation. The plastic strain rate was calculated based on the well-known Orowan equation, which is related to dislocation dynamics. In addition, the model took the material’s microstructure into account based on the design of a representative volume element (RVE) using the step-by-step packing (SSP) method. The results of the modeling were compared with the available experimental data and were found to satisfactorily correlate with them. The results suggest that the misfit error between the model and experimental data did not exceed 10% in the range of strain rates under study, which is a reliable outcome.
Genetic Structure and Selection Signature in Flora Scent of Roses by Whole Genome Re-Sequencing
Authors: Sun Meile , Ren Xiangrong , Liu Ya , Yang Jing , Hui Jingtao , Zhang Yukun , Cui Yanhua , Zhang Jun , Lin Guocang , Li Yan
Published: May 2023

Roses are important plants, and they are cultivated worldwide for their beautiful flowers, remarkable scent, and nutritious hips. In this study, we re-sequenced the whole genomes of 57 rose cultivars and one wild species that originated from different regions around the world and had different scents, aiming to evaluate their genetic structure and to detect the potential signature of the selective sweep between different scent groups with single-nucleotide polymorphism (SNP) and indel markers. The roses were sequenced at an average depth of 6× to the reference genome of Rosa ‘Old blush’. A total of 2,375,949 SNPs were obtained. The SNP numbers varied among the 58 samples, with an average of 1,271,906 per sample. The phylogeny and population structure revealed that the roses could be divided into three main clusters; however, the groups were not consistent in terms of geographic origin or scent classification. These indicated that rose cultivars have a complex genetic background due to the extensive hybridization between cultivated roses or wild rosa species worldwide. A selective sweep analysis was conducted to detect the selection signatures in rose scent traits. A total of 2430 candidate genes were identified in the strong scent groups, and were at the top 5% FST compared to the moderate group. These genes were significantly enriched in the KEGG pathways of tyrosine metabolism, cyanoamino acid metabolism, alpha-linolenic acid metabolism (13 genes), and phenylpropanoid biosynthesis (30 genes). When the low-scent group was used as the control, 2604 candidate genes were identified at the top 5% FST that were significantly enriched in the KEGG pathways of alpha-linolenic acid metabolism (15 genes), and glutathione metabolism (23 genes). We also observed genes enriched in pathways including the linoleic acid metabolism (five genes), diterpenoid biosynthesis (six genes), and monoterpenoid biosynthesis (seven genes), although they were not significant. These implied a positive selection of rose cultivars with a strong fragrance in terms of fatty acid derivatives, terpenoids and benzenoids/phenylpropanoids during rose breeding. Our study establishes a foundation for the further large-scale characterization of rose germplasm, improving the genetic knowledge of the background of roses.
Marginal Reef Systems: Resilience in a Rapidly Changing World
Authors: Browne Nicola K. , Bauman Andrew G.
Published: May 2023

Marginal coral reefs live under sub-optimal environmental conditions (e [...]
Safety Concern and Regulatory Status of Chemicals Used in Cosmetics and Personal Care Products
Authors: Kaushik Manthan , Farooq Uzma , Ali Mohd Shoab , Ansari Mohammad Javed , Iqbal Zeenat , Mirza Mohd Aamir
Published: May 2023

Cosmetics and personal care products (PCPs) are a few of the most commonly used products across the globe with a whopping market share of approximately USD 500 billion. These products are used for cleansing purposes and for improving the quality and beauty of the face, hair, and skin. There are many chemical substances involved in the manufacturing of cosmetics and PCPs. These chemical substances incorporated in cosmetics or PCPs are crucial to develop high-quality products with superior appearance, applicability, and stability; however, excessive use of such chemicals in cosmetics and PCPs has become a safety concern as many of these are reported to cause severe health complications. Overuse of cosmetics and PCPs with hazardous material should be minimized, especially by pregnant women and children. Gynecologists advise pregnant women not to use cosmetics and PCPs with hazardous chemicals. The implementation of a lawful framework is crucial to establish the safety of cosmetics and PCPs. Cosmetic companies/industries must be strictly regulated and made compliant to the guidelines in order to protect human health and minimize safety concerns. In this review, hazardous chemicals incorporated in the personal care products/cosmetics and their related risk and health complications have been discussed in detail. Additionally, regulatory status and clinical trials of chemical substances that involve toxicity and causing severe complications have also been discussed.
Lot-to-Lot Variance in Immunoassays—Causes, Consequences, and Solutions
Authors: Luo Yunyun , Pehrsson Martin , Langholm Lasse , Karsdal Morten , Bay-Jensen Anne-Christine , Sun Shu
Published: May 2023

Immunoassays, which have gained popularity in clinical practice and modern biomedical research, play an increasingly important role in quantifying various analytes in biological samples. Despite their high sensitivity and specificity, as well as their ability to analyze multiple samples in a single run, immunoassays are plagued by the problem of lot-to-lot variance (LTLV). LTLV negatively affects assay accuracy, precision, and specificity, leading to considerable uncertainty in reported results. Therefore, maintaining consistency in technical performance over time presents a challenge in reproducing immunoassays. In this article, we share our two-decade-long experience and delve into the reasons for and locations of LTLV, as well as explore methods to mitigate its effects. Our investigation identifies potential contributing factors, including quality fluctuation in critical raw materials and deviations in manufacturing processes. These findings offer valuable insights to developers and researchers working with immunoassays, emphasizing the importance of considering lot-to-lot variance in assay development and application.
Automated Skeletal Bone Age Assessment with Two-Stage Convolutional Transformer Network Based on X-ray Images
Authors: Mao Xiongwei , Hui Qinglei , Zhu Siyu , Du Wending , Qiu Chenhui , Ouyang Xiaoping , Kong Dexing
Published: May 2023

Human skeletal development is continuous and staged, and different stages have various morphological characteristics. Therefore, bone age assessment (BAA) can accurately reflect the individual’s growth and development level and maturity. Clinical BAA is time consuming, highly subjective, and lacks consistency. Deep learning has made considerable progress in BAA in recent years by effectively extracting deep features. Most studies use neural networks to extract global information from input images. However, clinical radiologists are highly concerned about the ossification degree in some specific regions of the hand bones. This paper proposes a two-stage convolutional transformer network to improve the accuracy of BAA. Combined with object detection and transformer, the first stage mimics the bone age reading process of the pediatrician, extracts the hand bone region of interest (ROI) in real time using YOLOv5, and proposes hand bone posture alignment. In addition, the previous information encoding of biological sex is integrated into the feature map to replace the position token in the transformer. The second stage extracts features within the ROI by window attention, interacts between different ROIs by shifting the window attention to extract hidden feature information, and penalizes the evaluation results using a hybrid loss function to ensure its stability and accuracy. The proposed method is evaluated on the data from the Pediatric Bone Age Challenge organized by the Radiological Society of North America (RSNA). The experimental results show that the proposed method achieves a mean absolute error (MAE) of 6.22 and 4.585 months on the validation and testing sets, respectively, and the cumulative accuracy within 6 and 12 months reach 71% and 96%, respectively, which is comparable to the state of the art, markedly reducing the clinical workload and realizing rapid, automatic, and high-precision assessment.
Detection of M2-Type Anti-Mitochondrial Autoantibodies against Specific Subunits in the Diagnosis of Primary Biliary Cholangitis in Patients with Discordant Results
Authors: Arnaldos-Pérez Cristina , Pérez-Isidro Albert , Bolos Uma , Domènech Carmen , Ballús Judit , Rodríguez-Tajes Sergio , Londoño María Carlota , Viñas Odette , Ruiz-Ortiz Estíbaliz
Published: May 2023

Background: M2-type anti-mitochondrial autoantibodies are considered the hallmark of primary biliary cholangitis and are directed mainly against the E2 subunits of the 2-oxo acid dehydrogenase complex enzymes (PDC, BCOADC and OGDC). The aim of this study was to determine whether a Dot-blot that includes these E2 subunits separately could confirm the results of methods with non-separated subunits in patients with low positive or discordant results between techniques. Methods: Sera of 24 patients with low positive or discordant results and of 10 patients with clear positive results by non-separated subunits methods were analyzed by Dot-blot with separated subunits. Results: Autoantibodies against E2 subunits of PDC, BCOADC or OGDC were detected in all patients, except in one case from the low positive or discordant results group, by Dot-blot with separated subunits. Conclusions: It would be advisable to use methods that include the three E2 subunits, and a Dot-blot with separated subunits could confirm doubtful cases by non-separated assays.
Linear EUS Accuracy in Preoperative Staging of Gastric Cancer: A Retrospective Multicenter Study
Authors: de Nucci Germana , Gabbani Tommaso , Impellizzeri Giovanna , Deiana Simona , Biancheri Paolo , Ottaviani Laura , Frazzoni Leonardo , Mandelli Enzo Domenico , Soriani Paola , Vecchi Maurizio , Manes Gianpiero , Manno Mauro
Published: May 2023

Introduction: Preoperative gastric cancer (GC) staging is the most reliable prognostic factor that affects therapeutic strategies. Contrast-enhanced computed tomography (CECT) and radial endoscopic ultrasound (R-EUS) scans are the most commonly used staging tools for GC. The accuracy of linear EUS (L-EUS) in this setting is still controversial. The aim of this retrospective multicenter study was to evaluate the accuracy of L-EUS and CECT in preoperative GC staging, with regards to depth of tumor invasion (T staging) and nodal involvement (N staging). Materials and methods: 191 consecutive patients who underwent surgical resection for GC were retrospectively enrolled. Preoperative staging had been performed using both L-EUS and CECT, and the results were compared to postoperative staging by histopathologic analysis of surgical specimens. Results: L-EUS diagnostic accuracy for depth of invasion of the GC was 100%, 60%, 74%, and 80% for T1, T2, T3, and T4, respectively. CECT accuracy for T staging was 78%, 55%, 45%, and 10% for T1, T2, T3, and T4, respectively. L-EUS diagnostic accuracy for N staging of GC was 85%, significantly higher than CECT accuracy (61%). Conclusions: Our data suggest that L-EUS has a higher accuracy than CECT in preoperative T and N staging of GC.
Remote Data Acquisition Using UAVs and Custom Sensor Node Technology
Authors: Bernier-Vega Agustin , Barton Kyle , Olson Isaac , Rodriguez Juan , Cantu Genesis , Ozcelik Selahattin
Published: May 2023

Precision agriculture technology has advanced rapidly in the 21st century. Despite this, the vast majority of US farmers do not employ any form of precision agriculture. Reasons for this include the high initial cost, lack of internet connectivity in rural areas, and complex setup and operation. The basis of this project was to create a low-cost, energy-efficient data collection system using an unmanned aerial vehicle (UAV) as a mobile sink node in a local wireless system. This was accomplished through the design and manufacture of custom sensor nodes and a custom drone-mounted wireless receiver node. The sensor node and drone node enclosures were 3D printed and assembled using low-cost materials and internal components. The system was successfully tested in a field where it collected soil data, including soil moisture, soil temperature, and electrical conductivity. The cost and scalability of the system are discussed, as well as potential improvements and comparisons with existing technologies. The system was concluded to have many potential applications in its current state but with room to expand and improve its operation and features.
Refund of Consumption Tax to Low-Income People: Impact Assessment Using Difference-in-Differences
Authors: Tonetto Jorge Luis , Fochezatto Adelar , da Silva Giovanni Padilha
Published: May 2023

One way to reduce inequality and poverty is to promote tax justice. In 2021, the government of the state of Rio Grande do Sul, Brazil, implemented a program (the Devolve-ICMS Program) that refunds consumption tax to low-income citizens (cashback). This study aims to evaluate the impacts of this Program using a differences-in-differences model and having, as response variables, the monthly sum of electronic invoices issued to the Program’s beneficiaries, as well as their value. The database used includes all invoices issued to the target population during the 12 months before the Program’s implementation and the 14 months after its implementation, resulting in 7.7 million records. To receive the tax refund, the eligible population must pick up a Citizen Card, made available by the state government, which was done by a significant part of this population. The treatment group is composed of eligible citizens who have the Card, whereas the control group comprises eligible citizens who do not have it. The results show that the Program is effective, as it has reduced tax pressure on poor people and increased both the number of invoices issued and their value.
The Impact of the Stock Market on Liquidity and Economic Growth: Evidence of Volatile Market
Authors: Chikwira Collin , Mohammed Jahed Iqbal
Published: May 2023

Stock markets serve as a conduit for money and liquidity, which are necessary for economic growth and stability. This study aimed to determine whether stock market impacts are communicated in an economically unstable environment, characterised by volatility, high inflation rates, and political instability. The research used a time series Vector Autoregressive model (VAR) with quarterly data from between 2013 and 2022. The study revealed that there is a positive statistically significant association between the stock market and economic growth at the 10% level. On the other hand, the stock market liquidity has no major influence on Zimbabwe’s economic development. As a result, the study advises policymakers to evaluate the rules regulating the stock market carefully and to relax some of the requirements for firms to be listed on the stock exchange. The stock market will become more liquid as a result of this because it will draw more internal and external businesses to being listed. The ZSE should also develop a framework for the gradual implementation of the commodity derivatives exchange as Zimbabwe’s substantial mineral reserves and robust agriculture may bring significant income to the country’s economy.
Using Pyramidal Training to Address Challenging Behavior in an Early Childhood Education Classroom
Authors: Thompson Courtney , MacNaul Hannah
Published: May 2023

Challenging behavior exhibited by students in a school setting is one of the most significant obstructions to student learning. These behaviors often warrant specialized interventions delivered by educators in the presence of typically developing peers; however, the availability of personnel to prepare educators to implement said interventions is limited. One viable solution may be to leverage a pyramidal training model in which training is provided in tiers, allowing for more individuals to be trained within a shorter period. In the current study, one researcher utilized pyramidal training to prepare four educators to implement functional communication training without extinction to decrease aggression toward peers for one student in an inclusionary early childhood education setting. With written instruction only (similar to what a teacher might receive as part of a behavior intervention plan), all educators implemented the intervention with low fidelity (M = 15% steps completed correctly). Post-intervention, all educators were able to implement the intervention with the trainer at or above 80% fidelity, and skills improved to 100% fidelity during in situ training with the student. For the student, aggression was completely decreased to zero levels, and functional communication responses increased. Moreover, all results were maintained after the holiday break without additional training. Implications for research and practice will be discussed.
Dual-Band Antenna on 3D-Printed Substrate for 2.4/5.8 GHz ISM-Band Applications
Authors: Olan-Nuñez Karen N. , Murphy-Arteaga Roberto S.
Published: May 2023

In this paper, we present a dual-band antenna working at 2.45 GHz and 5.8 GHz. The design is based on two radiating rectangular slots with one upper Split Ring Resonator (SRR) to enhance the radiation pattern at 5.8 GHz, one Complementary Split Ring Resonator (CSRR), and three Split Rings (SR) to improve the input reflection coefficient. The dual-band antenna covers the 2.45 GHz and 5.8 GHz Industrial, Scientific and Medical (ISM) bands. The total size of the proposed antenna is 31 × 70 × 70 mm3. The proposed antenna was manufactured and tested. The attained gain is better than 7.5 dBi, and 6.5 dBi at 2.4 GHz and 5.8 GHz, respectively. The measured gain and input reflection coefficient have good correlation with the simulated results, covering the ISM bands around 2.45 GHz and 5.8 GHz. The performance of the proposed antenna is remarkable and suitable for applications where the size is not a severe limitation, such as sensors, LPWAN/WLAN modules, health care devices, base stations, IoT, and control applications.
GFAM: A Gender-Preserving Face Aging Model for Age Imbalance Data
Authors: Li Suli , Lee Hyo Jong
Published: May 2023

The objective of face aging is to generate facial images that present the effects of aging. The existing one-hot encoding method for aging and/or rejuvenation patterns overlooks the personalized patterns for different genders and races, causing errors such as a male beard appearing on an aged female face. A gender-preserving face aging model is proposed to address these issues, termed GFAM. GFAM employs a generative adversarial network and includes several subnetworks that simulate the aging process between two adjacent age groups to learn specific aging effects. Specifically, the proposed model introduces a gender classifier and gender loss function that uses gender information as a self-guiding mechanism for maintaining gender attributes. To maintain the identity information of synthetic faces, the proposed model also introduces an identity-preserving module. Additionally, age balance loss is used to mitigate the impact of imbalanced age distribution and enhance the accuracy of aging predictions. Moreover, we construct a dataset with balanced age distribution for the task of face age progression, referred to as Age_FR. This dataset is expected to facilitate current research efforts. Ablation studies have been conducted to extensively evaluate the performance improvements achieved by our method. We obtained relative improvements of 3.75% higher than the model without the gender preserving module. The experimental results provide evidence of the effectiveness of the proposed method, both through qualitative and quantitative analyses. Notably, the mean face verification accuracy for the age-progressed groups (0–20, 31–40, 41–50, and 51–60) was found to be 100%, 99.83%, 99.79%, and 99.11%, respectively, highlighting the robustness of our approach across various age ranges.
MISO System with Intelligent Reflecting Surface-Assisted Cellular Networks
Authors: Kassem Mona , Al Haj Hassan Hussein , Nasser Abbass , Mansour Ali , Yao Koffi-Clément
Published: May 2023

This paper proposes an architecture based on Intelligent Reflecting Surfaces (IRSs) to improve the performance of future cellular networks. Specifically, we investigate the use of IRSs in combination with statistical Channel State Information (CSI) to enhance the coverage of Base Stations (BSs) in Multiple-Input Single-Output (MISO) systems. Furthermore, we exploit IRSs to reduce the complexity of the proposed architecture, and therefore the total cost, by reducing the number of required antennas at the transmitters (i.e., BSs). At first, we consider a Rayleigh fading channel between the transmitter and the receiver, and we assume the existence of a Line of Sight (LoS) between the BS and the IRS, as well as between the IRS and the destination. In the second part, we investigate the case of a Single-User Multiple-Input Single-Output (SU-MISO) system, where we study the benefits of IRSs in terms of coverage of the BS; then, we formulate a problem for a Multi-User Multiple-Input Single-Output (MU-MISO) system where the IRS is considered as a block of resources that can assist a certain number of users. The problem of managing the IRS resources is formulated as a nonlinear integer problem. We solve the optimization problem using an exhaustive search and propose two low-complexity heuristic algorithms. The performance of the system is evaluated with respect to a variable number of users, the position of the IRS, the required SNR, and the size of the cell. Simulation results corroborate the proposed approach and show that the introduction of the IRS in the network architecture enhances the overall performance of the network, extends the coverage area, enhances users’ satisfaction, and improves the SNR value, while optimizing the required number of antennas at the BS.
A Comparative Analysis of Cross-Validation Techniques for a Smart and Lean Pick-and-Place Solution with Deep Learning
Authors: Kee Elven , Chong Jun Jie , Choong Zi Jie , Lau Michael
Published: May 2023

As one of the core applications of computer vision, object detection has become more important in scenarios requiring high accuracy but with limited computational resources such as robotics and autonomous vehicles. Object detection using machine learning running on embedded device such as Raspberry Pi provides the high possibility to detect any custom objects without the recalibration of camera. In this work, we developed a smart and lean object detection model for shipping containers by using the state-of-the-art deep learning TensorFlow model and deployed it to a Raspberry Pi. Using EfficientDet-Lite2, we explored the different cross-validation strategies (Hold-out and K-Fold). The experimental results show that compared with the baseline EfficientDet-Lite2 algorithm, our model improved the mean average precision (mAP) by 44.73% for the Hold-out dataset and 6.26% for K-Fold cross-validation. We achieved Average Precision (AP) of more than 80% and best detection scores of more than 93% for the Hold-out dataset. For the 5-Fold lean dataset, the results show the Average Precision across the three lightweight models are generally high as the models achieved more than 50% average precision, with YOLOv4 Tiny performing better than EfficientDet-Lite2 and Single Shot Detector (SSD) MobileNet V2 Feature Pyramid Network (FPN) 320 as a lightweight model.
Adaptive SLAM Methodology Based on Simulated Annealing Particle Swarm Optimization for AUV Navigation
Authors: Zhou Liqian , Wang Meng , Zhang Xin , Qin Ping , He Bo
Published: May 2023

Simultaneous localization and mapping (SLAM) is crucial and challenging for autonomous underwater vehicle (AUV) autonomous navigation in complex and uncertain ocean environments. However, inaccurate time-varying observation noise parameters may lead to filtering divergence and poor mapping accuracy. In addition, particles are easily trapped in local extrema during the resampling, which may lead to inaccurate state estimation. In this paper, we propose an innovative simulated annealing particle swarm optimization-adaptive unscented FastSLAM (SAPSO-AUFastSLAM) algorithm. To cope with the unknown observation noise, the maximum a posteriori probability estimation algorithm is introduced into SLAM to recursively correct the measurement noise. Firstly, the Sage–Husa (SH) based unscented particle filter (UPF) algorithm is proposed to estimate time-varying measurement noise adaptively in AUV path estimation for improving filtering accuracy. Secondly, the SH-based unscented Kalman filter (UKF) algorithm is proposed to enhance mapping accuracy in feature estimation. Thirdly, SAPSO-based resampling is proposed to optimize posterior particles. The random judgment mechanism is used to update feasible solutions iteratively, which makes particles disengage local extreme values and achieve optimal global effects. The effectiveness and accuracy of the proposed algorithm are evaluated through simulation and sea trial data. The average AUV navigation accuracy of the presented SAPSO-AUFastSLAM method is improved by 18.0% compared to FastSLAM, 6.5% compared to UFastSLAM, and 5.9% compared to PSO-UFastSLAM.
Partial Discharge Detection and Recognition in Insulated Overhead Conductor Based on Bi-LSTM with Attention Mechanism
Authors: Xi Yanhui , Zhou Feng , Zhang Weijie
Published: May 2023

Insulated overhead conductor (IOC) faults cannot be detected by the ordinary protection devices due to the existence of the insulation layer. The failure of insulated overhead conductors is regularly accompanied by partial discharge (PD); thus, IOC faults are often judged by the PDs of insulated overhead conductors. In this paper, an intelligent PD detection model based on bidirectional long short-term memory with attention mechanism (AM-Bi-LSTM) is proposed for judging IOC faults. First, the original signals are processed using discrete wavelet transform (DWT) for de-noising, and then the signal statistical-feature and entropy-feature vectors are fused to characterize the PD signals. Finally, an AM-Bi-LSTM network is proposed for PD detection, in which the AM is able to assign the inputs different weights and highlight their effective characteristics; thus, the identification accuracy and computational complexity have been greatly improved. The validity and accuracy of the proposed model were evaluated with an ENET common dataset. The experiment results demonstrate that the AM-Bi-LSTM model exhibits a higher performance than the existing models, such as LSTM, Bi-LSTM, and AM-LSTM.
SiamPRA: An Effective Network for UAV Visual Tracking
Authors: Li Jiafeng , Zhang Kang , Gao Zheng , Yang Liheng , Zhuo Li
Published: May 2023

The visual navigation system is an important module in intelligent unmanned aerial vehicle (UAV) systems as it helps to guide them autonomously by tracking visual targets. In recent years, tracking algorithms based on Siamese networks have demonstrated outstanding performance. However, their application to UAV systems has been challenging due to the limited resources available in such systems.This paper proposes a simple and efficient tracking network called the Siamese Pruned ResNet Attention (SiamPRA) network and applied to embedded platforms that can be deployed on UAVs. SiamPRA is base on the SiamFC network and incorporates ResNet-24 as its backbone. It also utilizes the spatial-channel attention mechanism, thereby achieving higher accuracy while reducing the number of computations. Further, sparse training and pruning are used to reduce the size of the model while maintaining high precision. Experimental results on the challenging benchmarks VOT2018, UAV123 and OTB100 show that SiamPRA has a higher accuracy and lower complexity than other tracking networks.
Finding the Top-K Heavy Hitters in Data Streams: A Reconfigurable Accelerator Based on an FPGA-Optimized Algorithm
Authors: Ebrahim Ali
Published: May 2023

This paper presents a novel approach for accelerating the top-k heavy hitters query in data streams using Field Programmable Gate Arrays (FPGAs). Current hardware acceleration approaches rely on the direct and strict mapping of software algorithms into hardware, limiting their performance and practicality due to the lack of hardware optimizations at an algorithmic level. The presented approach optimizes a well-known software algorithm by carefully relaxing some of its requirements to allow for the design of a practical and scalable hardware accelerator that outperforms current state-of-the-art accelerators while maintaining near-perfect accuracy. This paper details the design and implementation of an optimized FPGA accelerator specifically tailored for computing the top-k heavy hitters query in data streams. The presented accelerator is entirely specified at the C language level and is easily reproducible with High-Level Synthesis (HLS) tools. Implementation on Intel Arria 10 and Stratix 10 FPGAs using Intel HLS compiler showed promising results—outperforming prior state-of-the-art accelerators in terms of throughput and features.
Quantum Machine Learning—An Overview
Authors: Tychola Kyriaki A. , Kalampokas Theofanis , Papakostas George A.
Published: May 2023

Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed. This paper aims to address these challenges by exploring the current state of quantum machine learning and benchmarking the performance of quantum and classical algorithms in terms of accuracy. Specifically, we conducted experiments with three datasets for binary classification, implementing Support Vector Machine (SVM) and Quantum SVM (QSVM) algorithms. Our findings suggest that the QSVM algorithm outperforms classical SVM on complex datasets, and the performance gap between quantum and classical models increases with dataset complexity, as simple models tend to overfit with complex datasets. While there is still a long way to go in terms of developing quantum hardware with sufficient resources, quantum machine learning holds great potential in areas such as unsupervised learning and generative models. Moving forward, more efforts are needed to explore new quantum learning models that can leverage the power of quantum mechanics to overcome the limitations of classical machine learning.
The Effects of VR and TP Visual Cues on Motor Imagery Subjects and Performance
Authors: Yang Jingcheng , Zhu Shixuan , Ding Peng , Wang Fan , Gong Anmin , Fu Yunfa
Published: May 2023

This study objectively evaluated the effects of Virtual Reality Visual Cues (VRVCs) and Traditional Plane Visual Cues (TPVCs) on motor imagery (MI) subjects and Brain-Computer Interface (BCI) performance when building a classification model for MI-BCIs. Four metrics, namely, imagery stability, brain activation and connectivity, classification accuracy, and fatigue level, were used to evaluate the effects of TPVCs and VRVCs on subjects and MI-BCI performance. Nine male subjects performed four types of MI (left/right-hand grip strength) under VRVCs and TPVCs while EEG and fNIRS signals were acquired. FBCSP and HFD were used to extract features, and KNN was used to evaluate MI-BCI accuracy. Rt-DTW was used to evaluate MI stability. PSD topography and the brain functional network were used to assess brain activation and connectivity. Cognitive load and fNIRS mean features were used to evaluate fatigue. The mean classification accuracies of the four types of MI under TPVCs and VRVCs were 50.83% and 51.32%, respectively. However, MI was more stable under TPVCs. VRVCs enhanced the connectivity of the brain functional network during MI and increased the subjects’ fatigue level. This study’s head-mounted VRVCs increased the subjects’ cognitive load and fatigue level. By comparing the performance of an MI-BCI under VRVCs and TPVCs using multiple metrics, this study provides insights for the future integration of MI-BCIs with VR.
GUI Component Detection-Based Automated Software Crash Diagnosis
Authors: Nam Seong-Guk , Seo Yeong-Seok
Published: May 2023

This study presents an automated software crash-diagnosis technique using a state transition graph (STG) based on GUI-component detection. An STG is a graph representation of the state changes in an application that are caused by actions that are executed in the GUI, which avoids redundant test cases and generates bug-reproduction scenarios. The proposed technique configures the software application STG using computer vision and artificial intelligence technologies and performs automated GUI testing without human intervention. Four experiments were conducted to evaluate the performance of the proposed technique: a detection-performance analysis of the GUI-component detection model, code-coverage measurement, crash-detection-performance analysis, and crash-detection-performance analysis in a self-configured multi-crash environment. The GUI-component detection model obtained a macro F1-score of 0.843, even with a small training dataset for the deep-learning model in the detection-performance analysis. Furthermore, the proposed technique achieved better performance results than the baseline Monkey in terms of code coverage, crash detection, and multi-crash detection.
Performance Analysis of a Reconfigurable-Intelligent-Surfaces-Assisted V2V Communication System
Authors: Li Kainan , Zhou Siyuan , Tan Guoping
Published: May 2023

Novel reconfigurable Intelligent Surfaces (RISs) area technology can improve the communication performance by changing the wireless transmission environment. Introducing RIS technology into Vehicle-to-Vehicle (V2V) communication environments can enhance the communication reliability by creating Line-of-Sight (LoS) communication links, thereby effectively improving the communication performance. However, in RIS-assisted V2V large-scale communication networks, the stochasticity of network nodes and random interference can impact performance. In this article, we examine the outage communication transmission performance of an RIS-assisted V2V communication network. We select the signal transmission mode based on the obstacle presence between vehicles and use the stochastic geometry theory to calculate the probabilities of the two modes: direct mode and RIS-assisted mode. By deriving the communication distance distribution and the aggregate interference distribution, we evaluate V2V communication in the direct mode to assess its transmission performance in two scenarios and obtain the overall outage probability. The numerical results demonstrate a better performance in RIS-assisted V2V networks, with an improved optimal phase shift scheme over that of the original V2V network. Monte Carlo simulation validated our analytical findings.
Handover Decision-Making Algorithm for 5G Heterogeneous Networks
Authors: Goh Mark Irwin , Mbulwa Abbas Ibrahim , Yew Hoe Tung , Kiring Aroland , Chung Seng Kheau , Farzamnia Ali , Chekima Ali , Haldar Manas Kumar
Published: May 2023

The evolution of 5G small cell networks has led to the advancement of vertical handover decision-making algorithms. A mobile terminal (MT) tends to move from one place to another and, as the 5G network coverage is small, user network access will change frequently and lead to a high probability of unnecessary handover, which is a waste of network resources and causes degradation of service quality. This paper aims to reduce the number of unnecessary handovers in 5G heterogeneous networks by proposing a handover decision-making algorithm that integrates the dwelling time prediction technique and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed algorithm reduces the number of unnecessary handovers by estimating the connection time to a small cell network using the dwell time prediction technique. The TOPSIS evaluates the network quality and chooses the best network based on user preference. The result shows that the proposed handover algorithm reduces the number of unnecessary handovers to small cell networks in high-speed scenarios. It also saves the network connection cost by up to 27.51% compared with the TOPSIS-based handover algorithm. As for throughput achievement, the proposed algorithm yields an improvement of 5.12%. The proposed algorithm significantly reduces the number of unnecessary handovers in the high-speed scenario while fulfilling user preferences.
Design and Implementation of an Orbitrap Mass Spectrometer Data Acquisition System for Atmospheric Molecule Identification
Authors: Wang Wei , Li Yongping
Published: May 2023

Orbitrap mass spectrometers have gained widespread popularity in ground-based environmental component analysis. However, their application in atmospheric exploration for space missions remains limited. Existing data acquisition solutions for Orbitrap instruments primarily rely on commercial systems and computer-based spectrum analysis. In this study, we developed a self-designed data acquisition solution specifically tailored for atmospheric molecule detection. The implementation involved directly integrating a spectrum analysis algorithm onto a field programmable gate array (FPGA), enabling miniaturization, real-time performance, and meeting the desired requirements. The system comprises signal conditioning circuits, analog-to-digital conversion (ADC) circuits, programmable logic circuits, and related software. These components facilitate real-time spectrum analysis and signal processing on hardware, enabling high-speed acquisition and analysis of signals generated by the Orbitrap. Experimental results demonstrate that the system can sample front-end analog signals at a rate of 25 MHz and differentiate signal spectra with an error margin of less than 7 kHz. This establishes the viability of the designed data acquisition system for atmospheric mass spectrometry analysis.