bluuebunny/crossref_metadata_2025 huggingface dataset:
Non-Invasive and Minimally Invasive Biomarkers for the Management of Eosinophilic Esophagitis beyond Peak Eosinophil Counts: Filling the Gap in Clinical Practice
Authors: Visaggi Pierfrancesco , Solinas Irene , Baiano Svizzero Federica , Bottari Andrea , Barberio Brigida , Lorenzon Greta , Ghisa Matteo , Maniero Daria , Marabotto Elisa , Bellini Massimo , de Bortoli Nicola , Savarino Edoardo V.
Published: August 2023

Eosinophilic esophagitis (EoE) is a chronic esophageal disease that needs lifelong management and follow-up. The diagnosis requires an upper endoscopy with at least one esophageal biopsy demonstrating >15 eosinophils/high-power field, and often occurs with a diagnostic delay of up to ten years, partly due to the absence of valid non-invasive screening tools. In addition, serial upper endoscopies with esophageal biopsies are mandatory to assess the efficacy of any ongoing treatment in patients with EoE. These procedures are invasive, costly, and, when performed without sedation, are often poorly tolerated by patients. Therefore, there is the clinical need to identify reliable non-invasive or minimally invasive biomarkers that could be used to assess disease activity in clinical practice as a surrogate of peak eosinophil counts on esophageal biopsies. This review summarizes evidence on investigational non-invasive or minimally invasive biomarkers for the diagnosis and follow-up of EoE to report on the state of the art in the field and support future research. We discussed eosinophil-derived mediators including eosinophil cationic protein (ECP), eosinophil-derived neurotoxin (EDN, also known as eosinophil protein X), eosinophil peroxidase (EPO), and major basic protein (MBP) as well as other promising non-eosinophil-derived biomarkers. Although several studies have shown the utility of most biomarkers collected from the serum, esophageal luminal secretions, and feces of EoE patients, numerous limitations currently hamper the integration of such biomarkers in clinical practice. Future studies should aim at validating the utility of non-invasive and minimally invasive biomarkers using rigorous protocols and updated consensus criteria for EoE.
Vitamin D Deficiency (VDD) and Benefits of Supplementation in Veterans with IBS-D
Authors: Kesavan Chandrasekhar , Das Anjali , Goyal Preeya , Jackson Christian S. , Strong Donna D. , Strong Richard M.
Published: August 2023

Many veterans deployed to Gulf War areas suffer from persistent chronic diarrhea that is disabling and affects their quality of life. The causes for this condition have eluded investigators until recently and recent literature has shed light on the effect of vitamin D on the brain–gut axis. This study focused on determining clinical causes contributing to diarrhea and assessed whether reversing the identified causes, specifically vitamin D deficiency (VDD), could reduce the incidence of diarrhea in Gulf War veterans (GWVs). All patients completed a workup that included serologies (IBD, celiac), routine laboratory tests (CBC, chemistry panels, TSH, T4, CRP), cultures for enteric pathogens (C diff, bacteria, viruses, small intestinal bacterial overgrowth (SIBO)), and upper and lower endoscopies with histology and a trial of cholestyramine to exclude choleretic diarrhea and rifaximin for dysbiosis. A total of 4221 veterans were screened for chronic diarrhea, yielding 105 GWVs, of which 69 GWVs had irritable bowel syndrome with diarrhea (IBS-D). Paired t-tests demonstrated that all GWVs had VDD (t-11.62, df68 and sig(2-tailed) 0.0001) (defined as a vitamin D level less than 30 ng/mL with normal ranges of 30–100 ng/mL) but no positive serologies, inflammatory markers, abnormal endoscopies, cultures, or histology to explain their persistent diarrhea. There was no correlation with age, BMI, or inflammation. Some zip codes had a higher frequency of GWVs with VDD, but the number of deployments had no impact. Treatment with vitamin D supplementation (3000–5000 units), given in the morning, based on weight, reduced the number of bowel movements per day (p < 0.0001) without causing hypercalcemia. We suggest that VDD is important in the etiology of IBS-D in GWVs and that vitamin D supplementation significantly reduces diarrhea.
Automatic Refractive Error Estimation Using Deep Learning-Based Analysis of Red Reflex Images
Authors: Linde Glenn , Chalakkal Renoh , Zhou Lydia , Huang Joanna Lou , O’Keeffe Ben , Shah Dhaivat , Davidson Scott , Hong Sheng Chiong
Published: August 2023

Purpose/Background: We evaluate how a deep learning model can be applied to extract refractive error metrics from pupillary red reflex images taken by a low-cost handheld fundus camera. This could potentially provide a rapid and economical vision-screening method, allowing for early intervention to prevent myopic progression and reduce the socioeconomic burden associated with vision impairment in the later stages of life. Methods: Infrared and color images of pupillary crescents were extracted from eccentric photorefraction images of participants from Choithram Hospital in India and Dargaville Medical Center in New Zealand. The pre-processed images were then used to train different convolutional neural networks to predict refractive error in terms of spherical power and cylindrical power metrics. Results: The best-performing trained model achieved an overall accuracy of 75% for predicting spherical power using infrared images and a multiclass classifier. Conclusions: Even though the model’s performance is not superior, the proposed method showed good usability of using red reflex images in estimating refractive error. Such an approach has never been experimented with before and can help guide researchers, especially when the future of eye care is moving towards highly portable and smartphone-based devices.
Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case
Authors: Escudero Sanchez Lorena , Buddenkotte Thomas , Al Sa’d Mohammad , McCague Cathal , Darcy James , Rundo Leonardo , Samoshkin Alex , Graves Martin J. , Hollamby Victoria , Browne Paul , Crispin-Ortuzar Mireia , Woitek Ramona , Sala Evis , Schönlieb Carola-Bibiane , Doran Simon J. , Öktem Ozan
Published: August 2023

Artificial intelligence (AI) methods applied to healthcare problems have shown enormous potential to alleviate the burden of health services worldwide and to improve the accuracy and reproducibility of predictions. In particular, developments in computer vision are creating a paradigm shift in the analysis of radiological images, where AI tools are already capable of automatically detecting and precisely delineating tumours. However, such tools are generally developed in technical departments that continue to be siloed from where the real benefit would be achieved with their usage. Significant effort still needs to be made to make these advancements available, first in academic clinical research and ultimately in the clinical setting. In this paper, we demonstrate a prototype pipeline based entirely on open-source software and free of cost to bridge this gap, simplifying the integration of tools and models developed within the AI community into the clinical research setting, ensuring an accessible platform with visualisation applications that allow end-users such as radiologists to view and interact with the outcome of these AI tools.
The Hidden Link between Polycystic Ovary Syndrome and Kidney Stones: Finding from the Tehran Lipid and Glucose Study (TLGS)
Authors: Rostami Dovom Marzieh , Rahmati Maryam , Amanollahi Soudmand Saber , Ziaeefar Pardis , Azizi Fereidoun , Ramezani Tehrani Fahimeh
Published: August 2023

Background: We aimed to investigate the association between kidney stones and polycystic ovarian syndrome (PCOS). Materials and methods: In a cross-sectional study, data from the Tehran Lipid and Glucose Study (TLGS) were used to investigate the risk of kidney stones in women with Polycystic Ovary Syndrome (PCOS). Four distinct phenotypes of PCOS, as defined by the Rotterdam criteria, were examined in a sample of 520 women and compared to a control group of 1638 eumenorrheic non-hirsute healthy women. Univariate and multivariable logistic regression models were employed for analysis. The four PCOS phenotypes were classified as follows: Phenotype A, characterized by the presence of all three PCOS features (anovulation (OA), hyperandrogenism (HA), and polycystic ovarian morphology on ultrasound (PCOM)); Phenotype B, characterized by the presence of anovulation and hyperandrogenism; Phenotype C, characterized by the presence of hyperandrogenism and polycystic ovarian morphology on ultrasound; and Phenotype D, characterized by the presence of anovulation and polycystic ovarian morphology on ultrasound. Results: The prevalence of a history of kidney stones was found to be significantly higher in women with Polycystic Ovary Syndrome (PCOS) compared to healthy controls (12.5% vs. 7.7%, p = 0.001). This increased prevalence was observed across all PCOS phenotypes (p < 0.001). After adjusting for potential risk factors, including age, family history of kidney stones, waist-to-height ratio, total cholesterol, and low-density lipoprotein, the odds ratio for kidney stones in women with PCOS was found to be 1.59 [95% CI: 1.12–2.25, p = 0.01], indicating a 59% increase in risk compared to healthy women. Women with PCOS Phenotype A [OR: 1.97, 95% CI: 1.09–3.55, p = 0.02] and Phenotype D [OR: 3.03, 95% CI: 1.24–7.41, p = 0.01] were found to be at a higher risk for kidney stones. Conclusion: Women with Polycystic Ovary Syndrome (PCOS), particularly those exhibiting menstrual irregularities and polycystic ovarian morphology on ultrasound (PCOM), have been found to be two to three times more likely to develop kidney stones. This increased prevalence should be taken into consideration when providing preventive care and counseling to these individuals.
Applications and Prospects of Artificial Intelligence-Assisted Endoscopic Ultrasound in Digestive System Diseases
Authors: Huang Jia , Fan Xiaofei , Liu Wentian
Published: August 2023

Endoscopic ultrasound (EUS) has emerged as a widely utilized tool in the diagnosis of digestive diseases. In recent years, the potential of artificial intelligence (AI) in healthcare has been gradually recognized, and its superiority in the field of EUS is becoming apparent. Machine learning (ML) and deep learning (DL) are the two main AI algorithms. This paper aims to outline the applications and prospects of artificial intelligence-assisted endoscopic ultrasound (EUS-AI) in digestive diseases over the past decade. The results demonstrated that EUS-AI has shown superiority or at least equivalence to traditional methods in the diagnosis, prognosis, and quality control of subepithelial lesions, early esophageal cancer, early gastric cancer, and pancreatic diseases including pancreatic cystic lesions, autoimmune pancreatitis, and pancreatic cancer. The implementation of EUS-AI has opened up new avenues for individualized precision medicine and has introduced novel diagnostic and treatment approaches for digestive diseases.
A Robust Disturbance-Rejection Controller Using Model Predictive Control for Quadrotor UAV in Tracking Aggressive Trajectory
Authors: Xu Zhixiong , Fan Li , Qiu Wei , Wen Guangwei , He Yunhan
Published: August 2023

A robust controller for the waypoint tracking of a quadrotor unmanned aerial vehicle (UAV) is proposed in this paper, in which position control and attitude control are effectively decoupled. Model predictive control (MPC) is employed in the position controller. The constraints of motors are imposed on the state and input variables of the optimization equation. This design effectively mitigates the nonlinearity of the attitude loop and enhances the planning efficiency of the position controller. The attitude controller is designed using a nonlinear and robust control law based on SO(3) space, which enables continuous control on the SO(3) manifold. By extending the differential flatness of the quadrotor-UAV to the angular acceleration level, the mapping of the control reference from the position controller to the attitude controller is achieved. Simulations are carried out to demonstrate the capability of the proposed controller. In the simulations, multiple aggressive flight trajectories and severe external disturbances are designed. The results show that the controller is robust, with superior accuracy in tracking aggressive trajectories.
2chADCNN: A Template Matching Network for Season-Changing UAV Aerial Images and Satellite Imagery
Authors: Ren Yan , Liu Yuwei , Huang Zhenjia , Liu Wanquan , Wang Weina
Published: August 2023

Visual navigation based on image matching has become one of the most important research fields for UAVs to achieve autonomous navigation, because of its low cost, strong anti-jamming ability, and high performance. Currently, numerous positioning and navigation methods based on visual information have been proposed for UAV navigation. However, the appearance, shape, color, and texture of objects can change significantly due to different lighting conditions, shadows, and surface coverage during different seasons, such as vegetation cover in summer or ice and snow cover in winter. These changes pose greater challenges for feature-based image matching methods. This encouraged us to overcome the limitations of previous works, which did not consider significant seasonal changes such as snow-covered UAV aerial images, by proposing an image matching method using season-changing UAV aerial images and satellite imagery. Following the pipeline of a two-channel deep convolutional neural network, we first pre-scaled the UAV aerial images, ensuring that the UAV aerial images and satellite imagery had the same ground sampling distance. Then, we introduced attention mechanisms to provide additional supervision for both low-level local features and high-level global features, resulting in a new season-specific feature representation. The similarity between image patches was calculated using a similarity measurement layer composed of two fully connected layers. Subsequently, we conducted template matching to estimate the UAV matching position with the highest similarity. Finally, we validated our proposed method on both synthetic and real UAV aerial image datasets, and conducted direct comparisons with previous popular works. The experimental results demonstrated that our method achieved the highest matching accuracy on multi-temporal and multi-season images.
A Semi-Supervised Stacked Autoencoder Using the Pseudo Label for Classification Tasks
Authors: Lai Jie , Wang Xiaodan , Xiang Qian , Quan Wen , Song Yafei
Published: August 2023

The efficiency and cognitive limitations of manual sample labeling result in a large number of unlabeled training samples in practical applications. Making full use of both labeled and unlabeled samples is the key to solving the semi-supervised problem. However, as a supervised algorithm, the stacked autoencoder (SAE) only considers labeled samples and is difficult to apply to semi-supervised problems. Thus, by introducing the pseudo-labeling method into the SAE, a novel pseudo label-based semi-supervised stacked autoencoder (PL-SSAE) is proposed to address the semi-supervised classification tasks. The PL-SSAE first utilizes the unsupervised pre-training on all samples by the autoencoder (AE) to initialize the network parameters. Then, by the iterative fine-tuning of the network parameters based on the labeled samples, the unlabeled samples are identified, and their pseudo labels are generated. Finally, the pseudo-labeled samples are used to construct the regularization term and fine-tune the network parameters to complete the training of the PL-SSAE. Different from the traditional SAE, the PL-SSAE requires all samples in pre-training and the unlabeled samples with pseudo labels in fine-tuning to fully exploit the feature and category information of the unlabeled samples. Empirical evaluations on various benchmark datasets show that the semi-supervised performance of the PL-SSAE is more competitive than that of the SAE, sparse stacked autoencoder (SSAE), semi-supervised stacked autoencoder (Semi-SAE) and semi-supervised stacked autoencoder (Semi-SSAE).
Multi-UAV Cooperative Trajectory Planning Based on the Modified Cheetah Optimization Algorithm
Authors: Fu Yuwen , Yang Shuai , Liu Bo , Xia E , Huang Duan
Published: August 2023

The capacity for autonomous functionality serves as the fundamental ability and driving force for the cross-generational upgrading of unmanned aerial vehicles (UAVs). With the disruptive transformation of artificial intelligence technology, autonomous trajectory planning based on intelligent algorithms has emerged as a key technique for enhancing UAVs’ capacity for autonomous behavior, thus holding significant research value. To address the challenges of UAV trajectory planning in complex 3D environments, this paper proposes a multi-UAV cooperative trajectory-planning method based on a Modified Cheetah Optimization (MCO) algorithm. Firstly, a spatiotemporal cooperative trajectory planning model is established, incorporating UAV-cooperative constraints and performance constraints. Evaluation criteria, including fuel consumption, altitude, and threat distribution field cost functions, are introduced. Then, based on its parent Cheetah Optimization (CO) algorithm, the MCO algorithm incorporates a logistic chaotic mapping strategy and an adaptive search agent strategy, thereby improving the home-returning mechanism. Finally, extensive simulation experiments are conducted using a considerably large test dataset containing functions with the following four characteristics: unimodal, multimodal, separable, and inseparable. Meanwhile, a strategy for dimensionality reduction searching is employed to solve the problem of autonomous trajectory planning in real-world scenarios. The results of a conducted simulation demonstrate that the MCO algorithm outperforms several other related algorithms, showcasing smaller trajectory costs, a faster convergence speed, and stabler performance. The proposed algorithm exhibits a certain degree of correctness, effectiveness, and advancement in solving the problem of multi-UAV cooperative trajectory planning.
Detecting Pump-and-Dumps with Crypto-Assets: Dealing with Imbalanced Datasets and Insiders’ Anticipated Purchases
Authors: Fantazzini Dean , Xiao Yufeng
Published: August 2023

Detecting pump-and-dump schemes involving cryptoassets with high-frequency data is challenging due to imbalanced datasets and the early occurrence of unusual trading volumes. To address these issues, we propose constructing synthetic balanced datasets using resampling methods and flagging a pump-and-dump from the moment of public announcement up to 60 min beforehand. We validated our proposals using data from Pumpolymp and the CryptoCurrency eXchange Trading Library to identify 351 pump signals relative to the Binance crypto exchange in 2021 and 2022. We found that the most effective approach was using the original imbalanced dataset with pump-and-dumps flagged 60 min in advance, together with a random forest model with data segmented into 30-s chunks and regressors computed with a moving window of 1 h. Our analysis revealed that a better balance between sensitivity and specificity could be achieved by simply selecting an appropriate probability threshold, such as setting the threshold close to the observed prevalence in the original dataset. Resampling methods were useful in some cases, but threshold-independent measures were not affected. Moreover, detecting pump-and-dumps in real-time involves high-dimensional data, and the use of resampling methods to build synthetic datasets can be time-consuming, making them less practical.
7T Magnetic Compatible Multimodality Electrophysiological Signal Recording System
Authors: Pan Jiadong , Xia Jie , Zhang Fan , Zhang Luxi , Zhang Shaomin , Pan Gang , Dong Shurong
Published: August 2023

This paper developed a comprehensive magnetic resonance imaging (MRI)-compatible electrophysiological (EP) acquisition system, which can acquire various physiological electrical signals, including electrocardiography (ECG), electromyography (EMG), electroencephalography (EEG) and electrocorticogram (ECoG), and EP recording combined with multimodal stimulation. The system is designed to be compatible with the 7-Tesla (7T) ultra-high field MRI environment, providing convenience for neuroscience and physiological research. To achieve MRI compatibility, the device uses magnetically compatible materials and shielding measures on the hardware and algorithm processing on the software side. Different filtering algorithms are adopted for different signals to suppress all kinds of interference in the MRI environment. The system can allow input signals up to ±0.225 V and channels up to 256. The equipment has been tested and proven to be able to collect a variety of physiological electrical signals effectively. When scanned under the condition of a 7T high-intensity magnetic field, the system does not generate obvious heating and can meet the safety requirements of MRI and EEG acquisition requirements. Moreover, an algorithm is designed and improved to efficiently and automatically remove the gradient artifact (GA) noise generated by MRI, which is a thousand-fold gradient artifact. Overall, this work proposes a complete, portable, MRI-compatible system that can collect a variety of physiological electrical signals and integrate more efficient GA removal algorithms.
High-Performance FPGA Streaming Data Concentrator for GEM Electronic Measurement System for WEST Tokamak
Authors: Kolasiński Piotr , Poźniak Krzysztof T. , Wojeński Andrzej , Linczuk Paweł , Kasprowicz Grzegorz , Chernyshova Maryna , Mazon Didier , Czarski Tomasz , Colnel Julian , Malinowski Karol , Guibert Denis
Published: August 2023

The paper presents developments and significant improvements of the soft X-ray measurement system installed at the WEST tokamak. In the introduction, a brief discussion is carried out in the scope of energy shortage, fusion energy as a remedy, and the necessity of impurities monitoring in the scope of stable and long plasma discharge. This requires high-speed and accurate measurement systems due to the intense data streams that need to be processed online. For that reason, the SXR GEM FPGA-based system was designed by the Institute of Electronic Systems of the Warsaw University of Technology, the Institute of Plasma Physics and Laser Microfusion from Warsaw, and installed at the WEST tokamak in collaboration with the Institute for Magnetic Fusion Research, Commissariat à l’Énergie Atomique. It is the second-generation system, whereas the first was installed at JET tokamak. The article describes the architecture of the designed electronic part of the system. The paper presents an entirely new approach for the data concentration module implemented in FPGA. The main premise is to select only active data and send them chronologically to the embedded computer with high throughput. It is the essential component for long-term plasma operations, about 1 min, now carried out at WEST tokamak during the C3 campaign. The paper describes the laboratory tests under the exploitation of various radiation sources and the implementation of the solution and measurements during tokamak plasmas. Previous and current data acquisition methods are compared. The results show that implementing a new local trigger has significantly improved the system performance compared to the global trigger-based acquisition. The results are approximately 17 times better in the scope of performance and more than 20 times better in terms of data compression. The described design was successfully applied during the most recent 2023 experimental campaign at the WEST tokamak.
An Internet of Things-Based Home Telehealth System for Smart Healthcare by Monitoring Sleep and Water Usage: A Preliminary Study
Authors: Tang Zunyi , Jiang Linlin , Zhu Xin , Huang Ming
Published: August 2023

Recently, the Internet of Things (IoT) has attracted wide attention from many fields, especially healthcare, because of its large capacities for information perception and collection. In this paper, we present an IoT-based home telehealth system for providing smart healthcare management for individuals, especially older people. Each client node of the system is mainly composed of an electronic water meter that records the user’s daily water usage, in order to analyze their living patterns and lifestyle as well as ascertain their well-being, and an unobtrusive sleep sensor that monitors the user’s physiological parameters during sleep, such as heart rate (HR), respiratory rate (RR), body movement (BM), and their states on the bed or outside the bed. The collected data can be transmitted to a remote centralized cloud service by a wireless home gateway for analyzing the living pattern and rhythm of users. Furthermore, the periodic feedback of results can be provided to users themselves, as well as their family and health advisers. In the present study, data was collected from a total of 18 older subjects for one year to evaluate the effectiveness of the proposed system. By analyzing living patterns and rhythm, preliminary results indicate the effectiveness of the telehealth system and suggest the potential of the system regarding improvement in the quality of life (QoL) of older people and promotion of their health.
Automated Assessment of Initial Answers to Questions in Conversational Intelligent Tutoring Systems: Are Contextual Embedding Models Really Better?
Authors: Carmon Colin M. , Morgan Brent , Hu Xiangen , Graesser Arthur C.
Published: August 2023

This paper assesses the ability of semantic text models to assess student responses to electronics questions compared with that of expert human judges. Recent interest in text similarity has led to a proliferation of models that can potentially be used for assessing student responses. However, it is unclear whether these models perform as well as early models of distributional semantics. We assessed 5166 response pairings of 219 participants across 118 electronics questions and scored each with 13 different computational text models, including models that use Regular Expressions, distributional semantics, embeddings, contextual embeddings, and combinations of these features. Regular Expressions performed the best out of the stand-alone models. We show other semantic text models performing comparably to the Latent Semantic Analysis model that was originally used for the current task, and in a small number of cases outperforming the model. Models trained on a domain-specific electronics corpus for the task performed better than models trained on general language or Newtonian physics. Furthermore, semantic text models combined with RegEx outperformed stand-alone models in agreement with human judges. Tuning the performance of these recent models in Automatic Short Answer Grading tasks for conversational intelligent tutoring systems requires empirical analysis, especially in domain-specific areas such as electronics. Therefore, the question arises as to how well recent contextual embedding models compare with earlier distributional semantic language models on this task of answering questions about electronics. These results shed light on the selection of appropriate computational techniques for text modeling to improve the accuracy, recall, weighted agreement, and ultimately the effectiveness of automatic scoring in conversational ITSs.
A Novel Elastic Sensor Sheet for Pressure Injury Monitoring: Design, Integration, and Performance Analysis
Authors: Mohammad Amini Mohammad , Farajzadeh Devin Mohammad Ghassem , Alves Paulo , Fanaei Sheikholeslami Davood , Hariri Fatemeh , Dionísio Rogério , Faghihi Mahdi , Reinaldo Fernando , Metrôlho José Carlos , Fonseca Luis
Published: August 2023

This study presents the SENSOMATT sensor sheet, a novel, non-invasive pressure monitoring technology intended for placement beneath a mattress. The development and design process of the sheet, which includes a novel sensor arrangement, material selection, and incorporation of an elastic rubber sheet, is investigated in depth. Highlighted features include the ability to adjust to varied mattress sizes and the incorporation of AI technology for pressure mapping. A comparison with conventional piezoelectric contact sensor sheets demonstrates the better accuracy of the SENSOMATT sensor for monitoring pressures beneath a mattress. The report highlights the sensor network’s cost-effectiveness, durability, and enhanced data measurement, alongside the problems experienced in its design. Evaluations of performance under diverse settings contribute to a full understanding of its potential pressure injury prediction and patient care applications. Proposed future paths for the SENSOMATT sensor sheet include clinical validation, more cost and performance improvement, wireless connection possibilities, and improved long-term monitoring data analysis. The study concludes that the SENSOMATT sensor sheet has the potential to transform pressure injury prevention techniques in healthcare.
ConKgPrompt: Contrastive Sample Method Based on Knowledge-Guided Prompt Learning for Text Classification
Authors: Wang Qian , Zeng Cheng , Li Bing , He Peng
Published: August 2023

Text classification aims to classify text according to pre-defined categories. Despite the success of existing methods based on the fine-tuning paradigm, there is a significant gap between fine-tuning and pre-training. Currently, prompt learning methods can bring state of the art (SOTA) performance to pre-trained language models (PLMs) in text classification and transform a classification problem into a masked language modeling problem. The crucial step of prompt learning is to construct a map between original labels and the label extension words. However, most mapping construction methods consider only labels themselves; relying solely on a label is not sufficient to achieve accurate prediction of mask tokens, especially in classification tasks where semantic features and label words are highly interrelated. Therefore, the accurate prediction of mask tokens requires one to consider additional factors beyond just label words. To this end, we propose a contrastive sample method based on knowledge-guided prompt learning framework (ConKgPrompt) for text classification. Specifically, this framework utilizes external knowledge bases (KBs) to expand the label vocabulary of verbalizers at multiple granularities. In the contrastive sample module, we incorporate supervised contrastive learning to make representations more expressive. Our approach was validated on four benchmark datasets, and extensive experimental results and analysis demonstrated the effectiveness of each module of the ConKgPrompt method.
Decentralized QFT Controller Design Based on the Equivalent Subsystems Method
Authors: Kozáková Alena , Čápková Romana , Kozák Štefan
Published: August 2023

Since the 1970s various decentralized control methodologies have been developed to deal with the challenge of controlling complex and/or spatially distributed systems with multiple inputs and multiple outputs (MIMO), e.g., chemical plants, power systems, water systems, etc. In general, the use of distributed information and control structures requires the synthesis of control laws in a constrained (decentralized) information structure. The article presents a novel frequency domain robust decentralized controller design method that is appropriate for uncertain dynamic MIMO systems with equal numbers of input and output variables, which consist of interconnected physical subsystems and are given as a set of square transfer function matrices. The main framework of the proposed method provides the Equivalent Subsystems Method (ESM), whereby the overall closed-loop system under a decentralized controller is stable if, and only if, all the individual closed-loop equivalent subsystems are stable. By generating equivalent subsystems for all transfer matrices, which describe the uncertain MIMO system, the individual uncertain equivalent subsystems are obtained as sets of respective frequency responses. Such representation allows the application of the QFT (quantitative feedback theory) approach to independently design local single-input single-output (SISO) robust controllers which constitute the resulting decentralized controller implemented in real subsystems. The designed controller ensures robust stability of the overall closed-loop system and the required performance as specified by the standard QFT performance specification types in both the equivalent subsystems and the overall closed-loop system. Compared to the existing method and references therein, the proposed method reduces the conservatism of the robust stability conditions and enables the exploitation of the benefits by the SISO QFT approach in the independent design of the robust decentralized controller. The developed design procedure is verified and illustrated in a case study on the robust decentralized level controller design of the quadruple tank process.
An Efficient and Lightweight Model for Automatic Modulation Classification: A Hybrid Feature Extraction Network Combined with Attention Mechanism
Authors: Ma Zhao , Fang Shengliang , Fan Youchen , Li Gaoxing , Hu Haojie
Published: August 2023

This paper proposes a hybrid feature extraction convolutional neural network combined with a channel attention mechanism (HFECNET-CA) for automatic modulation recognition (AMR). Firstly, we designed a hybrid feature extraction backbone network. Three different forms of convolution kernels were used to extract features from the original I/Q sequence on three branches, respectively, learn the spatiotemporal features of the original signal from different “perspectives” through the convolution kernels with different shapes, and perform channel fusion on the output feature maps of the three branches to obtain a multi-domain mixed feature map. Then, the deep features of the signal are extracted by connecting multiple convolution layers in the time domain. Secondly, a plug-and-play channel attention module is constructed, which can be embedded into any feature extraction layer to give higher weight to the more valuable channels in the output feature map to achieve the purpose of feature correction for the output feature map. The experimental results on the RadiomL2016.10A dataset show that the designed HFECNET-CA has higher recognition accuracy and fewer trainable parameters compared to other networks. Under 20 SNRs, the average recognition accuracy reached 63.92%, and the highest recognition accuracy reached 93.64%.
Development of an Extended Reality-Based Collaborative Platform for Engineering Education: Operator 5.0
Authors: Mourtzis Dimitris , Angelopoulos John
Published: August 2023

With the shift towards the human centric, sustainable, and resilient Industry 5.0, the need for training operators in complex industrial systems has become increasingly crucial. This paper explores the significance of collaborative extended reality (XR)-based engineering education in the preparation of the next generation of operators, denoted as Operator 5.0. By leveraging immersive technologies, operators can gain hands-on training experience in virtual or augmented environments. By incorporating these elements, operators can undergo comprehensive and personalized training, resulting in improved performance, reduced downtime, enhanced safety, and increased operational efficiency. Additionally, the framework is tested within a laboratory environment in three different case studies, focusing on maintenance and repair operations in the context of modern manufacturing in order to test its functionalities. Therefore, in this research, the current developments have been debugged and examined in order to test all of the functionalities of the digital platform so that the revised and improved version of the digital platform can be tested with a wider industrial and educational audience.
YOLO-Drone: An Optimized YOLOv8 Network for Tiny UAV Object Detection
Authors: Zhai Xianxu , Huang Zhihua , Li Tao , Liu Hanzheng , Wang Siyuan
Published: August 2023

With the widespread use of UAVs in commercial and industrial applications, UAV detection is receiving increasing attention in areas such as public safety. As a result, object detection techniques for UAVs are also developing rapidly. However, the small size of drones, complex airspace backgrounds, and changing light conditions still pose significant challenges for research in this area. Based on the above problems, this paper proposes a tiny UAV detection method based on the optimized YOLOv8. First, in the detection head component, a high-resolution detection head is added to improve the device’s detection capability for small targets, while the large target detection head and redundant network layers are cut off to effectively reduce the number of network parameters and improve the detection speed of UAV; second, in the feature extraction stage, SPD-Conv is used to extract multi-scale features instead of Conv to reduce the loss of fine-grained information and enhance the model’s feature extraction capability for small targets. Finally, the GAM attention mechanism is introduced in the neck to enhance the model’s fusion of target features and improve the model’s overall performance in detecting UAVs. Relative to the baseline model, our method improves performance by 11.9%, 15.2%, and 9% in terms of P (precision), R (recall), and mAP (mean average precision), respectively. Meanwhile, it reduces the number of parameters and model size by 59.9% and 57.9%, respectively. In addition, our method demonstrates clear advantages in comparison experiments and self-built dataset experiments and is more suitable for engineering deployment and the practical applications of UAV object detection systems.
CANARY: An Adversarial Robustness Evaluation Platform for Deep Learning Models on Image Classification
Authors: Sun Jiazheng , Chen Li , Xia Chenxiao , Zhang Da , Huang Rong , Qiu Zhi , Xiong Wenqi , Zheng Jun , Tan Yu-An
Published: August 2023

The vulnerability of deep-learning-based image classification models to erroneous conclusions in the presence of small perturbations crafted by attackers has prompted attention to the question of the models’ robustness level. However, the question of how to comprehensively and fairly measure the adversarial robustness of models with different structures and defenses as well as the performance of different attack methods has never been accurately answered. In this work, we present the design, implementation, and evaluation of Canary, a platform that aims to answer this question. Canary uses a common scoring framework that includes 4 dimensions with 26 (sub)metrics for evaluation. First, Canary generates and selects valid adversarial examples and collects metrics data through a series of tests. Then it uses a two-way evaluation strategy to guide the data organization and finally integrates all the data to give the scores for model robustness and attack effectiveness. In this process, we use Item Response Theory (IRT) for the first time to ensure that all the metrics can be fairly calculated into a score that can visually measure the capability. In order to fully demonstrate the effectiveness of Canary, we conducted large-scale testing of 15 representative models trained on the ImageNet dataset using 12 white-box attacks and 12 black-box attacks and came up with a series of in-depth and interesting findings. This further illustrates the capabilities and strengths of Canary as a benchmarking platform. Our paper provides an open-source framework for model robustness evaluation, allowing researchers to perform comprehensive and rapid evaluations of models or attack/defense algorithms, thus inspiring further improvements and greatly benefiting future work.
DTV Essential Hidden Area Decoder for DTV Broadcasting
Authors: Kim Tae-hun , Han Chan-ho , Park Kil-houm
Published: August 2023

Terrestrial, satellite, and internet High Definition (HD) and Ultra High Definition (UHD) broadcasting have experienced notable advancements in recent years, yet the potential within the DTV Essential Hidden Area (DEHA) in each frame remains largely untapped. This study focuses on exploring and harnessing the capabilities of the DEHA via the introduction of a DEHA decoder. Through experimental analysis, the proposed decoder effectively reveals previously unnoticed DEHA in diverse HDTV broadcasting systems. By incorporating overlaid three-digit values, DEHA text, a 16 × 15 checked board pattern, and a QR code, the decoder enables the easy identification and extraction of the embedded DEHA service. Furthermore, the research investigates the potential of utilizing the embedded DEHA for transmitting program-related metadata, encompassing technical information, specific camera details, and post-production technologies. Leveraging a checked pattern block image, a seamless and efficient transfer mechanism within the embedded DEHA is established. The utilization of the embedded DEHA holds promising opportunities for enhancing DTV service, leveraging its inherent synchronization advantages within video content. Moreover, compliance with established broadcasting standards, such as ATSC, ISDB, and DVB, ensures compatibility and interoperability. This study emphasizes the significance of the DEHA in terrestrial, satellite, and internet broadcasting, unveiling new possibilities for innovation and improvement in the industry.
Comprehensive Review and Analysis of Glazing Systems towards Nearly Zero-Energy Buildings: Energy Performance, Thermal Comfort, Cost-Effectiveness, and Environmental Impact Perspectives
Authors: Moghaddam Saman Abolghasemi , Serra Catarina , Gameiro da Silva Manuel , Simões Nuno
Published: August 2023

The global interest in nearly zero-energy buildings (NZEBs) has led to their establishment as mandatory building objectives in Europe for all new constructions starting in 2021. The principles outlined in the Energy Performance of Building Directive (EPBD) emphasize the significance of reducing energy demand through various energy efficiency measures to achieve NZEB status. Among these measures, the utilization of high-performance glazing systems plays a crucial role in ensuring natural light, ventilation, favorable solar gain, aesthetics, and positive psychological effects in buildings, while maintaining high energy performance and thermal comfort without burdening the budget or harming the environment. The use of increasingly larger glazing areas makes this topic of great relevance. Nevertheless, numerous studies frequently overlook certain crucial aspects of glazing systems in their assessments. This review study aims to assess different glazing solutions based on four critical perspectives called “EThCE”: Energy performance, thermal comfort, cost-effectiveness, and environmental impact, considering their interrelationships. Furthermore, the importance of adopting a comprehensive approach for selecting the optimal glazing solution for NZEBs is discussed. Additionally, the relationship between glazing systems and climate change is taken into account. Ultimately, the authors propose a comprehensive approach, including all the influential factors, to assist designers and homeowners in making informed decisions regarding glazing system selection for new NZEBs or NZEB retrofits in different situations.
A SOC Correction Method Based on Unsynchronized Full Charge and Discharge Control Strategy in Multi-Branch Battery System
Authors: Huang Siyi , Kang Jianqiang , Zhao Bowen , Wu Oukai , Wang Jing V.
Published: August 2023

LiFePO4 batteries exhibit voltage plateau and voltage hysteresis characteristics during charging and discharging processes; however, the estimation of state-of-charge relies on voltage detection. Thus, the estimation accuracy of SOC is low in a traditional method. In this paper, a full charge and discharge SOC correction method is proposed; i.e., the SOC is corrected to 100% when the battery is fully charged and to 0% when fully discharged, and the actual usable capacity is corrected using the fully discharged capacity after being fully charged. Thereby, the cumulative error of the ampere-hour integration method is dynamically corrected. In engineering applications, however, the battery systems do not always undergo full charge and discharge cycling due to the operating conditions. By making full use of the distributed control characteristics of the multi-branch topology battery system, the present work proposes an optimized system control strategy to realize the unsynchronized full charge and discharge cluster by cluster, which extends the application of the full charge and discharge SOC correction method. The experimental results verify the accuracy of the proposed SOC correction method and the feasibility of the control strategy. A more reliable and efficient battery management scheme is provided for LFP battery system, which has high practical value in engineering.
Medium-Term Hydrothermal Scheduling of the Infiernillo Reservoir Using Stochastic Dual Dynamic Programming (SDDP): A Case Study in Mexico
Authors: Marín Cruz Ignacio , Badaoui Mohamed , Mota Palomino Ricardo
Published: August 2023

This article aims to obtain and evaluate medium-term operating policies for the hydrothermal scheduling problem by using the stochastic dual dynamic programming (SDDP) approach. To this end, to feed the mathematical model and build the probability distribution functions that best fit each month of the actual inflow volume, monthly inflow data recorded from 1938 to 2018 for the Infiernillo reservoir located in Mexico were employed. Moreover, we simulated inflow volume scenarios using the Monte Carlo method for each month of a one-year planning period. The SDDP approach to solving the optimization problem consisted of the simulation of one forward scenario per iteration and the stabilization of the total operating cost as a convergence criterion, which results in an operating policy. We then assessed its quality by estimating the one-sided optimality gap. It is worth mentioning that the best operation policy required scenario trees of up to 17,000 inflow realizations per stage. Additionally, to study the evolution of the expected value along the planning horizon of the main variables involved in the medium-term hydrothermal scheduling problem, we simulated the best operation policy over 10,000 inflow scenarios. Finally, to show the practical value of the proposed approach, we report its computational complexity.
Studies on Flow Characteristics of Gas–Liquid Multiphase Pumps Applied in Petroleum Transportation Engineering—A Review
Authors: Li Huichuang , Zhang Wenwu , Hu Liwei , Zhu Baoshan , Wang Fujun
Published: August 2023

Flow and phase separation in gas–liquid multiphase pumps is easy to occur, which deteriorates their performance and mixed transportability. Many research achievements have been made in the experiment, CFD simulation and performance improvement of multiphase pumps. However, there are many challenges for the test technology, accurate numerical model development and gas–liquid flow control. This paper is mainly aimed at critically reviewing various technologies for experimental observation, flow calculation and analysis, and the optimization design of gas–liquid multiphase pumps. In this regard, the experimental results including the energy performance, flow pattern and bubble movement in the multiphase pump are presented in detail. Discussions on the turbulence model, multiphase flow model and bubble balance model are carried out for the flow prediction in such pumps. Various numerical results are presented, including energy performance, bubble distribution, vorticity, phase interaction and pressure fluctuation. Moreover, the flow control and optimization strategy are briefly introduced. Having carried out an extensive literature review of flow characteristics in multiphase pumps, the deficiencies of relevant fields and suggestions for future research direction are given.