bluuebunny/crossref_metadata_2025 huggingface dataset:
Surgical Treatment for Endometrial Cancer, Hysterectomy Performed via Minimally Invasive Routes Compared with Open Surgery: A Systematic Review and Network Meta-Analysis
Authors: Natarajan Purushothaman , Delanerolle Gayathri , Dobson Lucy , Xu Cong , Zeng Yutian , Yu Xuan , Marston Kathleen , Phan Thuan , Choi Fiona , Barzilova Vanya , Powell Simon G. , Wyatt James , Taylor Sian , Shi Jian Qing , Hapangama Dharani K.
Published: May 2024

Background: Total hysterectomy with bilateral salpingo-oophorectomy via minimally invasive surgery (MIS) has emerged as the standard of care for early-stage endometrial cancer (EC). Prior systematic reviews and meta-analyses have focused on outcomes reported solely from randomised controlled trials (RCTs), overlooking valuable data from non-randomised studies. This inaugural systematic review and network meta-analysis comprehensively compares clinical and oncological outcomes between MIS and open surgery for early-stage EC, incorporating evidence from randomised and non-randomised studies. Methods: This study was prospectively registered on PROSPERO (CRD42020186959). All original research of any experimental design reporting clinical and oncological outcomes of surgical treatment for endometrial cancer was included. Study selection was restricted to English-language peer-reviewed journal articles published 1 January 1995–31 December 2021. A Bayesian network meta-analysis was conducted. Results: A total of 99 studies were included in the network meta-analysis, comprising 181,716 women and 14 outcomes. Compared with open surgery, laparoscopic and robotic-assisted surgery demonstrated reduced blood loss and length of hospital stay but increased operating time. Compared with laparoscopic surgery, robotic-assisted surgery was associated with a significant reduction in ileus (OR = 0.40, 95% CrI: 0.17–0.87) and total intra-operative complications (OR = 0.38, 95% CrI: 0.17–0.75) as well as a higher disease-free survival (OR = 2.45, 95% CrI: 1.04–6.34). Conclusions: For treating early endometrial cancer, minimal-access surgery via robotic-assisted or laparoscopic techniques appears safer and more efficacious than open surgery. Robotic-assisted surgery is associated with fewer complications and favourable oncological outcomes.
A Comprehensive Review of Fine Chemical Production Using Metal-Modified and Acidic Microporous and Mesoporous Catalytic Materials
Authors: Lantos Joseph , Kumar Narendra , Saha Basudeb
Published: May 2024

Fine chemicals are produced in small annual volume batch processes (often <10,000 tonnes per year), with a high associated price (usually >USD 10/kg). As a result of their usage in the production of speciality chemicals, in areas including agrochemicals, fragrances, and pharmaceuticals, the need for them will remain high for the foreseeable future. This review article assesses current methods used to produce fine chemicals with heterogeneous catalysts, including both well-established and newer experimental methods. A wide range of methods, utilising microporous and mesoporous catalysts, has been explored, including their preparation and modification before use in industry. Their potential drawbacks and benefits have been analysed, with their feasibility compared to newer, recently emerging catalysts. The field of heterogeneous catalysis for fine chemical production is a dynamic and ever-changing area of research. This deeper insight into catalytic behaviour and material properties will produce more efficient, selective, and sustainable processes in the fine chemical industry. The findings from this article will provide an excellent foundation for further exploration and a critical review in the field of fine chemical production using micro- and mesoporous heterogeneous catalysts.
Facile Asymmetric Syntheses of Non-Natural Amino Acid (S)-Cyclopropylglycine by the Developed NADH-Driven Biocatalytic System
Authors: Tang Qian , Li Shanshan , Zhou Liping , Sun Lili , Xin Juan , Li Wei
Published: May 2024

A self-sufficient bifunctional enzyme integrating reductive amination and coenzyme regeneration activities was developed and successfully employed to synthesize (S)-cyclopropylglycine with an improved reaction rate 2.1-fold over the native enzymes and a short bioconversion period of 6 h at a high substrate concentration of 120 g·L−1 and space–time yield of (S)-cyclopropylglycine up to 377.3 g·L−1·d−1, higher than that of any previously reported data. Additionally, (S)-cyclopropylglycine could be continuously synthesized for 90 h with the enzymes packed in a dialysis tube, providing 634.6 g of (S)-cyclopropylglycine with >99.5% ee and over 95% conversion yield up to 12 changes. These results confirmed that the newly developed NADH-driven biocatalytic system could be utilized as a self-sufficient biocatalyst for industrial application in the synthesis of (S)-cyclopropylglycine, which provides a chiral center and cyclopropyl fragment for the frequent synthesis of preclinical/clinical drug molecules.
Quasi-Interpolation on Chebyshev Grids with Boundary Corrections
Authors: Alsharif Faisal
Published: May 2024

Quasi-interpolation is a powerful tool for approximating functions using radial basis functions (RBFs) such as Gaussian kernels. This avoids solving large systems of equations as in RBF interpolation. However, quasi-interpolation with Gaussian kernels on compact intervals can have significant errors near the boundaries. This paper proposes a quasi-interpolation method with Gaussian kernels using Chebyshev points and boundary corrections to improve the approximation near the boundaries. The boundary corrections use a linear approximation of the function beyond the interval to estimate the truncation error and add correction terms. Numerical studies on test functions show that the proposed method reduces errors significantly near boundaries compared to quasi-interpolation without corrections, for both equally spaced and Chebyshev points. The convergence and accuracy with the boundary corrections are generally better with Chebyshev points compared to equally spaced points. The proposed method provides an efficient way to perform quasi-interpolation on compact intervals while controlling the boundary errors. This study introduces a novel approach to quasi-interpolation modification, which significantly enhances convergence rates and minimizes errors at boundary points, thereby advancing the methods for boundary approximation.
Unraveling Functional Diversity Patterns in Hyporheic Zones: A Trait-Based Approach Applied to Copepods from the Rio Gamberale Creek
Authors: Tabilio Di Camillo Agostina , Cerasoli Francesco , Di Cicco Mattia , Galassi Diana Maria Paola , Di Lorenzo Tiziana
Published: May 2024

Despite the recognized ecological significance of hyporheic zones, biological investigations into their ecology, especially concerning functional diversity, remain limited. This is particularly true for copepod assemblages, abundant in the transitional zone between groundwater and surface waters, requiring more thorough exploration. To bridge this knowledge gap, our study extensively monitored a hyporheic zone within a mountain creek and examined nine functional traits across twelve copepod species found in this environment. Through the application of RLQ and fourth-corner analysis, as well as functional diversity indices including functional richness, evenness, divergence, and dispersion, we aimed to establish links between functional traits and environmental factors. Our findings revealed relationships between environmental features—specifically electrical conductivity, pH, and ammonium concentration—and the trait composition of copepod assemblages, which were influenced by species abundances. Considering the intimate connection between functional traits and ecosystem services, assessing functional diversity within hyporheic zones offers valuable insights into its functionality in terms of services rendered. This study emphasizes the importance of understanding and managing functional diversity in hyporheic zone dynamics to ensure the health and stability of ecotones and, by extension, riverine and groundwater ecosystems.
Callovian Marine Reptiles of European Russia
Authors: Zverkov Nikolay , Arkhangelsky Maxim , Gulyaev Denis , Ippolitov Alexey , Shmakov Alexey
Published: May 2024

Our knowledge of marine reptiles of the Callovian age (Middle Jurassic) is majorly based on the collections from the Oxford Clay Formation of England, which yielded a diverse marine reptile fauna of plesiosaurians, ichthyosaurians, and thalattosuchians. However, outside of Western Europe, marine reptile remains of this age are poorly known. Here, we survey marine reptiles from the Callovian stage of European Russia. The fossils collected over more than a century from 28 localities are largely represented by isolated bones and teeth, although partial skeletons are also known. In addition to the previously described rhomaleosaurid and metriorhynchids, we identify pliosaurids of the genera Liopleurodon and Simolestes; cryptoclidid plesiosaurians, including Cryptoclidus eurymerus, Muraenosaurus sp., and cf. Tricleidus, and ophthalmosaurid ichthyosaurians, including the iconic Ophthalmosaurus icenicus. These findings expand the ranges of several Callovian marine reptile taxa far to the Eastern Europe, and support the exchange of marine reptile faunas between Western and Eastern European seas in the middle to late Callovian. However, some specimens from the lower Callovian of European Russia show differences from typical representatives of the middle Callovian Oxford Clay fauna, possibly representing the earlier stages of evolution of some of these marine reptiles not yet recorded in Western Europe or elsewhere.
Unraveling the Interplay of KRAS, NRAS, BRAF, and Micro-Satellite Instability in Non-Metastatic Colon Cancer: A Systematic Review
Authors: Orlandi Elena , Giuffrida Mario , Trubini Serena , Luzietti Enrico , Ambroggi Massimo , Anselmi Elisa , Capelli Patrizio , Romboli Andrea
Published: May 2024

Microsatellite Instability (MSI-H) occurs in approximately 15% of non-metastatic colon cancers, influencing patient outcomes positively compared to microsatellite stable (MSS) cancers. This systematic review focuses on the prognostic significance of KRAS, NRAS, and BRAF mutations within MSI-H colon cancer. Through comprehensive searches in databases like MEDLINE, EMBASE, and others until 1 January 2024, we selected 8 pertinent studies from an initial pool of 1918. These studies, encompassing nine trials and five observational studies involving 13,273 patients, provided insights into disease-free survival (DFS), survival after recurrence, and overall survival. The pooled data suggest that while KRAS and BRAF mutations typically predict poorer outcomes in MSS colorectal cancer, their impact is less pronounced in MSI contexts, with implications varying across different stages of cancer and treatment responses. In particular, adverse effects of these mutations manifest significantly upon recurrence rather than affecting immediate DFS. Our findings confirm the complex interplay between genetic mutations and MSI status, emphasizing the nuanced role of MSI in modifying the prognostic implications of KRAS, NRAS, and BRAF mutations in colon cancer. This review underscores the importance of considering MSI alongside mutational status in the clinical decision-making process, aiming to tailor therapeutic strategies more effectively for colon cancer patients.
Prostate-Specific Membrane Antigen Positron Emission Tomography Oncological Applications beyond Prostate Cancer in Comparison to Other Radiopharmaceuticals
Authors: Miceli Alberto , Liberini Virginia , Pepe Giovanna , Dondi Francesco , Vento Antonio , Jonghi Lavarini Lorenzo , Celesti Greta , Gazzilli Maria , Serani Francesca , Guglielmo Priscilla , Buschiazzo Ambra , Filice Rossella , Alongi Pierpaolo , Laudicella Riccardo , Santo Giulia
Published: May 2024

Background: Prostate-specific membrane antigen (PSMA) is a type II transmembrane glycoprotein overexpressed on the surface of tumor cells in most of the patients affected by prostate adenocarcinoma (PCa). However, PSMA expression has also been demonstrated in the endothelial cells of newly formed vessels of various solid tumors, suggesting a role for PSMA in neoangiogenesis. In this scenario, gallium-68 (68Ga) or fluoro-18 (18F)-labeled PSMA positron emission tomography (PET) may play a role in tumors other than PCa, generally evaluated employing other radiopharmaceuticals targeting different pathways. This review aims to investigate the detection rate of PSMA-PET compared to other radiopharmaceuticals (especially [18F]FDG) in non-prostate tumors to identify patients who may benefit from the use of such a theragnostic agent. Methods: We performed a bibliographic search on three different databases until February 2024 using the following terms: “positron emission tomography”, “PET”, “PET/CT”, “Prostate-specific membrane antigen”, “PSMA”, “non-prostate”, “not prostate cancer”, “solid tumor”, “FDG”, “Fluorodeoxyglucose”, “FAPi”, “FET”, “MET”, “DOPA”, “choline”, “FCH”, “FES”, “DOTATOC”, “DOTANOC”, and “DOTATATE”. Only original articles edited in English with at least 10 patients were included. Results: Out of a total of 120 articles, only 25 original articles comparing PSMA with other radiotracers were included in this study. The main evidence was demonstrated in renal cell carcinoma, where PSMA showed a higher detection rate compared to [18F]FDG PET/CT, with implications for patient management. PSMA PET may also improve the assessment of other entities, such as gliomas, in defining regions of early neoangiogenesis. Further data are needed to evaluate the potential role of PSMA-PET in triple-negative breast cancer as a novel therapeutic vascular target. Finally, unclear applications of PSMA-PET include thyroid and gastrointestinal tumors. Conclusions: The present review shows the potential use of PSMA-labeled PET/CT in solid tumors beyond PCa, underlining its value over other radiopharmaceuticals (mainly [18F]FDG). Prospective clinical trials with larger sample sizes are crucial to further investigate these possible clinical applications.
The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis
Authors: Uchikov Petar , Khalid Usman , Vankov Nikola , Kraeva Maria , Kraev Krasimir , Hristov Bozhidar , Sandeva Milena , Dragusheva Snezhanka , Chakarov Dzhevdet , Petrov Petko , Dobreva-Yatseva Bistra , Novakov Ivan
Published: May 2024

Background and objectives: This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. Method: A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. Results: Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. Conclusions: When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.
Robust-DSN: A Hybrid Distributed Replication and Encoding Network Grouped with a Distributed Swarm Workflow Scheduler
Authors: Hameed Zeeshan , Barzegar Hamid R. , Ioini Nabil El , Pahl Claus
Published: May 2024

In many distributed applications such as the Internet of Things (IoT), large amounts of data are being generated that require robust storage solutions. Traditional cloud solutions, although efficient, often lack trust and transparency because of centralized management. To address these issues, we present Robust-DSN, a distributed storage network leveraging the hybrid distributed replication and encoding network (HYDREN) and the distributed swarm workflow scheduler (DSWS) as its main components. Our system uses an interplanetary file system (IPFS) as an underlay storage network and segments it into multiple regions to distribute the failure domain and improve the data’s proximity to users. HYDREN incorporates Reed–Solomon encoding and distributed replication to improve file availability, while DSWS optimizes resource allocation across the network. The uploaded file is encoded into chunks and distributed across distinct optimal nodes leveraging lightweight multithreading. Additionally, Robust-DSN verifies the integrity of all chunks by preserving the hashes when uploading and validating each chunk while downloading. The proposed system provides a comprehensive solution for resilient distributed data storage, focusing on the key challenges of data availability, integrity, and performance. The results reveal that compared with a state-of-the-art system, the proposed system improves file recovery by 15%, even with a 50% peer failure rate. Furthermore, with replication factor 4 and the same failure resilience as IPFS, it saves 50% storage and enhances file recovery by 8%. Robust-DSN acts as a distributed storage platform for emerging technologies, expanding storage system capabilities in a wide range of distributed applications.
Object Detection in Hazy Environments, Based on an All-in-One Dehazing Network and the YOLOv5 Algorithm
Authors: Li Aijuan , Xu Guangpeng , Yue Wenpeng , Xu Chuanyan , Gong Chunpeng , Cao Jiaping
Published: May 2024

This study introduces an advanced algorithm for intelligent vehicle target detection in hazy conditions, aiming to bolster the environmental perception capabilities of autonomous vehicles. The proposed approach integrates a hybrid convolutional module (HDC) into an all-in-one dehazing network, AOD-Net, to expand the perceptual domain for image feature extraction and refine the clarity of dehazed images. To accelerate model convergence and enhance generalization, the loss function has been optimized. For practical deployment in intelligent vehicle systems, the ShuffleNetv2 lightweight network module is incorporated into the YOLOv5s network backbone, and the feature pyramid network (FPN) within the neck network has been refined. Additionally, the network employs a global shuffle convolution (GSconv) to balance accuracy with parameter count. To further focus on the target, a convolutional block attention module (CBAM) is introduced, which helps in reducing the network’s parameter count without compromising accuracy. A comparative experiment was conducted, and the results indicated that our algorithm achieved an impressive mean average precision (mAP) of 76.8% at an intersection-over-union (IoU) threshold of 0.5 in hazy conditions, outperforming YOLOv5 by 7.4 percentage points.
Unidirectional Hybrid Three-Phase Rectifier with Boost Converter and Coupled Inductor
Authors: Gonçalves José Teixeira , Valtchev Stanimir , Luis Euclides , Blaabjerg Frede
Published: May 2024

The unidirectional hybrid three-phase rectifier (UHTR) with Boost converter consists of two different rectifiers (Rectifier 1 and 2), where Rectifier 2 consists of modules corresponding to the phases. Each rectifier processes different part of the current waveform at the input, so that the sum of the current waveform parts is sinusoidal, or multilevel. Analyzing existing literature, the UHTR with Boost converter and isolation transformer, is proven as a classic solution with high weight, volume and cost. Therefore, in 2019 the UHTR with Boost converter was proposed, without any isolation transformer. To do this, it was necessary to replace the Boost inductor of each Rectifier 2 module with a coupled inductor. However, the proposed UHTR has not described the coupled inductor in details, neither the control circuit and the decoupling of the power circuit with the control circuit were described. Therefore, this main objective this paper is to provide answers to those aspects, and consequently present the UHTR proposal in a more realistic and practical application way, thus favoring the implementation of the prototype. A simulation of the proposed UHTR with coupled inductor was performed in the PSIM version 12.0 software with a power of 20 kW. The results of the proposed UHTR show that there is no current interaction and it works correctly, having a high PF of 99.92% and low total harmonic distortion of 3.96% at full load. In this way, it is proven that it is possible to implement an UHTR with a Boost converter and using coupled inductor.
Influence Analysis of Voltage Imbalance in Input-Series, Output-Parallel (ISOP) Multichannel IPT System
Authors: Wang Leyu , Sun Pan , Liang Yan , Wu Xusheng , Rong Enguo
Published: May 2024

In order to solve the demand for efficient and stable low-voltage, high-power energy transmission capacity of electric vehicle (EV) fast charging, an ISOP-IPT system based on inductor-capacitor-capacitor series (LCC-S) compensation network is proposed. Firstly, the influence of compensation parameter inconsistency on the system is analyzed. On this basis, considering the resistance of coupler coils, the overall transmission efficiency of the system is analyzed. It is found that the voltage imbalance of the system will affect the working state of inverters, and then affect the stability of the system. The system transmission efficiency increases with the increase in mutual inductance. Moreover, the voltage imbalance caused by the inconsistency of compensation parameters and mutual inductance will reduce the transmission efficiency of the system. Finally, it is concluded that in the parameter design of the ISOP-IPT system, mutual inductance should be improved on the basis of ensuring the input voltage equalization of each channel so as to improve the transmission efficiency and working stability of the system. The experimental platform of a two-channel ISOP-IPT system is built and the maximum efficiency is 94.03%, which verifies the correctness of theoretical analysis.
Power Hardware-in-the-Loop Smart Inverter Testing with Distributed Energy Resource Management Systems
Authors: Chang Hao , Vanfretti Luigi
Published: May 2024

The increasing integration of grid-connected photovoltaic (PV) inverters and inverter-based resource (IBR) systems into the power grid emphasizes the critical need for standardized procedures to ensure their reliability and effective grid support functions. This research is driven by the gap in standardized testing methodologies for smart inverters, which are pivotal for the stability and quality of power in distributed energy systems. We used a Power Hardware-in-the-Loop (PHIL) laboratory setup to conduct a comprehensive analysis of smart inverters within a simulated real-world grid environment. Our approach integrates a Distributed Energy Resource Management System (DERMS) with PHIL testing to evaluate the smart inverter’s performance across various operational modes. The detailed test protocols mimic real-world grid conditions, enabling the examination of the inverter’s dynamic response to grid disturbances, control strategies, and communication protocols. The primary aim of this study is to rigorously test and validate the primary functions of smart inverters, focusing on their impact on overall grid stability and power quality management. This includes advanced features like Volt–VAR, Volt–Watt, dynamic power factor control, and the seamless integration of smart inverters into DERMSs and Advanced Distribution Management Systems (ADMSs). Furthermore, we aim to bridge the current gap in standardized testing procedures, contributing to the establishment of robust standards and operating protocols for smart inverter integration into the grid.
Sparse-View Spectral CT Reconstruction Based on Tensor Decomposition and Total Generalized Variation
Authors: Li Xuru , Wang Kun , Xue Xiaoqin , Li Fuzhong
Published: May 2024

Spectral computed tomography (CT)-reconstructed images often exhibit severe noise and artifacts, which compromise the practical application of spectral CT imaging technology. Methods that use tensor dictionary learning (TDL) have shown superior performance, but it is difficult to obtain a high-quality pre-trained global tensor dictionary in practice. In order to resolve this problem, this paper develops an algorithm called tensor decomposition with total generalized variation (TGV) for sparse-view spectral CT reconstruction. In the process of constructing tensor volumes, the proposed algorithm utilizes the non-local similarity feature of images to construct fourth-order tensor volumes and uses Canonical Polyadic (CP) tensor decomposition instead of pre-trained tensor dictionaries to further explore the inter-channel correlation of images. Simultaneously, introducing the TGV regularization term to characterize spatial sparsity features, the use of higher-order derivatives can better adapt to different image structures and noise levels. The proposed objective minimization model has been addressed using the split-Bregman algorithm. To assess the performance of the proposed algorithm, several numerical simulations and actual preclinical mice are studied. The final results demonstrate that the proposed algorithm has an enormous improvement in the quality of spectral CT images when compared to several existing competing algorithms.
Enhancement of Two-Dimensional Barcode Restoration Based on Recurrent Feature Reasoning and Structural Fusion Attention Mechanism
Authors: Yi Jinwang , Chen Jianan
Published: May 2024

In practical scenarios, such as in electronics, where barcodes on electronic component carriers often wear out, and in logistics, where package labels frequently get damaged, this type of damage makes the recognition of two-dimensional (2D) barcodes challenging. In this study, a new repair method was introduced for quick response (QR) and PDF417 codes. In addition, a structural fusion attention (SFA) mechanism with a recurrent feature reasoning network was integrated to enhance structural integrity and recognition rates. The proposed method significantly outperforms existing inpainting models in terms of accuracy and robustness, which is demonstrated by the custom dataset provided by the authors. Notably, the approach ensures near-perfect recognition rates despite extensive structural impairments. It achieves an accuracy of 98% for large-area PDF417 occlusions and maintains a recognition rate of 100% for QR codes with 75–90% structural damage. These findings highlight the exceptional ability of the proposed method to restore 2D barcodes impaired by diverse levels of structural occlusion.
Spatial–Temporal Analysis-Based Video Quality Assessment: A Two-Stream Convolutional Network Approach
Authors: He Jianghui , Wang Zhe , Liu Yi , Song Yang
Published: May 2024

In system processing, video inevitably suffers from distortion, which leads to quality degradation and affects the user experience. Therefore, it is of great importance to design an accurate and effective objective video quality assessment (VQA) method. In this paper, by considering the multi-dimensional characteristics for video and visual perceptual mechanism, a two-stream convolutional network for VQA is proposed based on spatial–temporal analysis, named TSCNN-VQA. Specifically, for feature extraction, TSCNN-VQA first extracts spatial and temporal features by two different convolutional neural network branches, respectively. After that, the spatial–temporal joint feature fusion is constructed to obtain the joint spatial–temporal features. Meanwhile, the TSCNN-VQA also integrates an attention module to guarantee that the process conforms to the mechanism that the visual system perceives video information. Finally, the overall quality is obtained by non-linear regression. The experimental results in both the LIVE and CSIQ VQA datasets show that the performance indicators obtained by TSCNN-VQA are higher than those of existing VQA methods, which demonstrates that TSCNN-VQA can accurately evaluate video quality and has better consistency with the human visual system.
A Nonlinear Control Design for Cooperative Adaptive Cruise Control with Time-Varying Communication Delay
Authors: Bonab Parisa Ansari , Sargolzaei Arman
Published: May 2024

Cooperative adaptive cruise control (CACC) is one of the main features of connected and autonomous vehicles (CAVs), which uses connectivity to improve the efficiency of adaptive cruise control (ACC). The addition of reliable communication systems to ACC reduces fuel consumption, maximizes road capacity, and ensures traffic safety. However, the performance, stability, and safety of CACC could be affected by the transmission of outdated data caused by communication delays. This paper proposes a Lyapunov-based nonlinear controller to mitigate the impact of time-varying delays in the communication channel of CACC. This paper uses Lyapunov–Krasovskii functionals in the stability analysis to ensure semi-global uniformly ultimately bounded tracking. The efficaciousness of the proposed CACC algorithm is demonstrated in simulation and through experimental implementation.
Comparing Tactical Analysis Methods in Women’s Soccer Using Positioning Data from Electronic Performance and Tracking Systems
Authors: Oliveira Luis Ángel , Melendi David , García Roberto
Published: May 2024

Although, in recent years, it has been common to monitor players in team sports using EPTSs (Electronic Performance and Tracking Systems) devices, most of the studies have focused on the optimization of individual performance rather than collective work or tactical analysis. Moreover, almost all these studies focus on men’s teams with little focus on women’s teams. In this work, data from women’s soccer teams at different levels (competition and grassroots) have been collected using both a low-cost personally developed EPTS and a commercial EPTS. With these systems, we have built a dataset consisting of more than 16 million records, paying special attention to spatio-temporal variables collected in the form of geographical coordinates. Different methods have been applied to the collected dataset to solve the problem of determining the position (individual role) of each player on the field based solely on spatio-temporal variables. The methods include algorithms based on clustering, centroid calculation, and computer vision. We have verified the effectiveness of these methods and propose an alternative method based on image recognition algorithms applied to heat maps generated from the position of the players monitored during the matches. As shown in this paper, the validity of the proposed method has been verified, exceeding the performance of existing methods and extending the range of application of these techniques.
Power Flow Calculation for AC/DC Power Systems with Line-Commutated Converter–Modular Multilevel Converter (LCC-MMC) Hybrid High-Voltage Direct Current (HVDC) Based on the Holomorphic Embedding Method
Authors: Luo Yongjian , Liu Chengxi , He Zaiyu , Jiang Youjin , Chen Qi
Published: May 2024

To harness the strengths and mitigate the limitations of line-commutated converter (LCC) and modular multilevel converter (MMC) HVDC systems, the LCC-MMC hybrid HVDC system has been developed. This paper proposes a holomorphic embedding (HE)-based power flow calculation method for AC/DC power systems with the LCC-MMC hybrid HVDC system. Firstly, the methodology involves establishing the mathematical model of the LCC-MMC hybrid HVDC system and its control modes. Subsequently, the HE formulation for the constructed LCC-MMC model is derived using the HE method. The proposed method simplifies the HE formulation using substitute variables, making it adaptable to various control modes. The designed HE formulation facilitates the derivation of the germ solution and the calculation of high-order power series coefficients. A sequential solution flow chart compatible with AC power flow algorithms is constructed to enhance the practicality of the HE method for real-world large-scale systems. The accuracy and effectiveness of the proposed method are verified through numerical tests on the modified IEEE 14-bus, IEEE 57-bus, and large 2383-bus system. The proposed method provides a reliable and efficient solution for power flow analysis in hybrid HVDC systems.
Sampling-Based Machine Learning Models for Intrusion Detection in Imbalanced Dataset
Authors: Fan Zongwen , Sohail Shaleeza , Sabrina Fariza , Gu Xin
Published: May 2024

Cybersecurity is one of the important considerations when adopting IoT devices in smart applications. Even though a huge volume of data is available, data related to attacks are generally in a significantly smaller proportion. Although machine learning models have been successfully applied for detecting security attacks on smart applications, their performance is affected by the problem of such data imbalance. In this case, the prediction model is preferable to the majority class, while the performance for predicting the minority class is poor. To address such problems, we apply two oversampling techniques and two undersampling techniques to balance the data in different categories. To verify their performance, five machine learning models, namely the decision tree, multi-layer perception, random forest, XGBoost, and CatBoost, are used in the experiments based on the grid search with 10-fold cross-validation for parameter tuning. The results show that both the oversampling and undersampling techniques can improve the performance of the prediction models used. Based on the results, the XGBoost model based on the SMOTE has the best performance in terms of accuracy at 75%, weighted average precision at 82%, weighted average recall at 75%, weighted average F1 score at 78%, and Matthews correlation coefficient at 72%. This indicates that this oversampling technique is effective for multi-attack prediction under a data imbalance scenario.
Dual-Frequency, Dual-Mode Reconfigurable Digital Atmospheric Radar Receiver Design
Authors: Zhang Zengmao , Xu Qingchen , Hu Xiong , Cai Bing , Wu Yongkun , Yang Junfeng , Zhao Mingliang
Published: May 2024

A new dual-frequency, dual-mode reconfigurable digital receiver based on Field-Programmable Gate Array (FPGA) dynamic reconfiguration is proposed, which is based on a common hardware platform of high-bandwidth RF front-end, high-speed data acquisition, and real-time signal processing. The receiver adopts the design of dynamically reconfigurable down-conversion, filter extraction, and matched filtering in the digital domain. In this study, we completed the design and development of the digital receiver, experimental platform construction, and field detection test with hardware and software cooperation. The experimental results show that the receiver achieves full digital reception and signal processing for 53.8 MHz stratosphere–troposphere (ST) detection and 35.0 MHz meteor detection and successfully acquired the number of meteors versus time, the meteor trail, and low-altitude atmospheric radial winds. This dual-frequency, dual-mode reconfigurable digital receiver can be applied to new-generation multifunction integrated radar systems such as dual-frequency ST/meteor radars.
Diagnostics of Exercise-Induced Laryngeal Obstruction Using Machine Learning: A Narrative Review
Authors: Mæstad Rune , Kvidaland Haakon Kristian , Clemm Hege , Røksund Ola Drange , Arghandeh Reza
Published: May 2024

Objective: This paper explores machine learning methods for exercise-induced laryngeal obstruction (EILO) diagnostics. Traditional diagnostic approaches like CLE scoring face subjectivity, limiting precise objective assessments. Machine learning is introduced as a theoretical solution to potentially overcome these limitations and improve diagnostic precision. Methods: A narrative review was conducted to explore the integration of machine learning techniques in the diagnostics of EILO. Result: Three machine learning methods for the segmentation of laryngeal images were discovered: fully convolutional network, Mask R-CNN, and 3D VOSNet. Our findings reveal that the integration of machine learning with EILO diagnostics remains a largely untapped research domain, providing significant room for further exploration. Conclusions: The integration of ML techniques for EILO diagnostics has the potential to be a helpful tool for clinicians. The application of computer vision ML methods, such as image segmentation, to delineate laryngeal structures paves the way for a more objective assessment. While challenges persist, especially in differences in patients’ laryngeal anatomy, the synergy of ML and medical expertise is an important field to explore in the years to come.
FireXplainNet: Optimizing Convolution Block Architecture for Enhanced Wildfire Detection and Interpretability
Authors: Khan Muneeb A. , Park Heemin
Published: May 2024

The early detection of wildfires is a crucial challenge in environmental monitoring, pivotal for effective disaster management and ecological conservation. Traditional detection methods often fail to detect fires accurately and in a timely manner, resulting in significant adverse consequences. This paper presents FireXplainNet, a Convolutional Neural Network (CNN) base model, designed specifically to address these limitations through enhanced efficiency and precision in wildfire detection. We optimized data input via specialized preprocessing techniques, significantly improving detection accuracy on both the Wildfire Image and FLAME datasets. A distinctive feature of our approach is the integration of Local Interpretable Model-agnostic Explanations (LIME), which facilitates a deeper understanding of and trust in the model’s predictive capabilities. Additionally, we have delved into optimizing pretrained models through transfer learning, enriching our analysis and offering insights into the comparative effectiveness of FireXplainNet. The model achieved an accuracy of 87.32% on the FLAME dataset and 98.70% on the Wildfire Image dataset, with inference times of 0.221 and 0.168 milliseconds, respectively. These performance metrics are critical for the application of real-time fire detection systems, underscoring the potential of FireXplainNet in environmental monitoring and disaster management strategies.
A Broadband Three-Way Series Doherty Power Amplifier with Deep Power Back-Off Efficiency Enhancement for 5G Application
Authors: Que Xianfeng , Li Jun , Wang Yanjie
Published: May 2024

This article presents a new broadband three-way series Doherty power amplifier (DPA) topology, which enables a broadband output power back-off (OBO) efficiency enhancement of up to 10 dB or higher. The proposed DPA topology achieves Doherty load modulation and three-way power combining through a transformer, which requires only a low coupling factor, thus facilitating its implementation in double-sided PCBs or monolithic microwave integrated circuit (MMIC) processes. The design equations for the proposed DPA topology are proposed and analyzed in detail. A proof-of-concept PA at the 2.1–2.8 GHz band using commercial GaN transistors was designed and fabricated to validate the proposed concept. Within the operating frequency band, it achieves a saturated output power (Psat) of 44.5–46.5 dBm with a peak drain efficiency (DE) of 60–72%, and 43–52% DE at 10 dB OBO. Moreover, under a 20 MHz long-term evolution (LTE)-modulated signal, the PA demonstrates a 36.8–37.5 dBm average output power (Pavg) and 47–53% average drain efficiency (DEavg). Notably, the adjacent channel leakage ratio (ACLR) is as low as −35–−28.2 dBc without any digital predistortion (DPD).
A Novel Direct Current Circuit Breaker with a Gradually Increasing Counter-Current
Authors: Chen Jinchao , Liu Siyuan , Jin Jingyong , Chen Yifan , Liu Zhiyuan , Geng Yingsan
Published: May 2024

A reliable and cost-effective mechanical direct current circuit breaker (DCCB) is a promising solution for DC interruption. However, the typical mechanical DCCB has difficulty in interrupting a rated current, because the high oscillating current superimposed on the rated current generates a steep current slope at current zero-crossing (CZC) points, which makes it difficult for the vacuum interrupter to extinguish the arc. The objective of this paper is to present a novel DCCB topology with a gradually increasing counter-current. It utilizes a full-controlled converter, a semi-controlled full bridge, and an LC oscillation branch to generate a gradually increasing counter-current, which is superimposed on any fault current and generates a smooth current slope at CZC points. The proposed DCCB topology is modeled with PSCAD, and the current slope and the initial transient interruption voltage (ITIV) at CZC are analyzed and compared with the typical mechanical DCCB. The results indicate that the current slope at CZC decreases by 57–84% in full-range current interruptions, and the ITIV can be reduced by the same extent. Additionally, the performance of the proposed DCCB is evaluated in a four-terminal HVDC system. A cost and performance comparison is conducted among the main topologies. The obtained results show that the proposed DCCB is a reliable solution for the multi-terminal HVDC system.
Single and Mixed Sensory Anomaly Detection in Connected and Automated Vehicle Sensor Networks
Authors: Kim Tae Hoon , Ojo Stephen , Krichen Moez , Alamro Meznah A.
Published: May 2024

Connected and automated vehicles (CAVs), integrated with sensors, cameras, and communication networks, are transforming the transportation industry and providing new opportunities for consumers to enjoy personalized and seamless experiences. The fast proliferation of connected vehicles on the road and the growing trend of autonomous driving create vast amounts of data that need to be analyzed in real time. Anomaly detection in CAVs refers to identifying any unusual or unforeseen behavior in the data generated by vehicles’ various sensors and components. Anomaly detection aims to identify any unusual behavior that might indicate a problem or a malfunction in the vehicle. To identify and detect anomalies efficiently, a method must deal with noisy data, missing data, dynamic frequency data, and low- and high-magnitude data, and it must be accurate enough to detect anomalies in a dynamic sensor streaming environment. Therefore, this paper proposes a fast and efficient hard-voting-based technique named FT-HV, comprising three fine-tuned machine learning algorithms to detect and classify anomaly behavior in CAVs for single and mixed sensory datasets. In experiments, we evaluate our approach on the benchmark Sensor Anomaly dataset that contains data from various vehicle sensors at low and high magnitudes. Further, it contains single and mixed anomaly types that are challenging to detect and identify. The results reveal that the proposed approach outperforms existing solutions for detecting single anomaly types at low magnitudes and detecting mixed anomaly types in all settings. Furthermore, this research is envisioned to help detect and identify anomalies early and efficiently promote safer and more resilient CAVs.
Investigation of IEEE 802.16e LDPC Code Application in PM-DQPSK System
Authors: Xue Jiaxin , Li Yupeng , Zhang Yichao , Wu Xiao , Zhang Yanyue
Published: May 2024

With the development of the Internet and information technology, optical fiber communication systems need to meet people’s information demand for large capacity and high speed. High-order phase modulation and channel multiplexing can improve the capacity and data rate of optical fiber communication systems, but they also bring the problem of bit error. To improve the transmission quality and reliability of optical fiber communication systems, forward error correction (FEC) coding techniques are commonly used, which serve as the fundamental approach to enhance the quality and reliability of fiber optic communication systems, ensuring that the received data remain accurate and reliable. The FEC in optical fiber communication systems is divided into three generations. The first generation FEC is mainly hard decision codewords, represented as RS code. The second generation FEC is mainly cascaded code, which stands for interleaved cascaded code. The third generation of FEC mainly refers to soft decision codes, which are represented as low-density parity-check (LDPC) codes. As a kind of FEC, LDPC codes stand out as pivotal contributors in the field of optical communication and have gained remarkable attention due to exceptional error correction performance and low decoding complexity. Based on IEEE802.16e standard, LDPC code with specific code length and rate is compiled and simulated in MATLAB and VPItransmissionMaker 10.1 and successfully incorporated into polarization multiplexed differential quadrature phase shift keying (PM-DQPSK) coherent optical transmission system. The simulation results indicate that the bit error rate (BER) can be reduced to 10−3 when the optical signal-to-noise ratio (OSNR) reaches 14.2 dB, and the BER experiences a reduction by nearly three orders of magnitude when the OSNR is 17.2 dB. These findings underscore the efficacy of LDPC codes in significantly improving the performance of optical communication systems, particularly in scenarios demanding robust error correction capabilities. This study provides valuable, significant results regarding the potential of LDPC codes for enhancing the reliability of optical transmission in real-world applications.
Flexible Deployment of Machine Learning Inference Pipelines in the Cloud–Edge–IoT Continuum
Authors: Bogacka Karolina , Sowiński Piotr , Danilenka Anastasiya , Biot Francisco Mahedero , Wasielewska-Michniewska Katarzyna , Ganzha Maria , Paprzycki Marcin , Palau Carlos E.
Published: May 2024

Currently, deploying machine learning workloads in the Cloud–Edge–IoT continuum is challenging due to the wide variety of available hardware platforms, stringent performance requirements, and the heterogeneity of the workloads themselves. To alleviate this, a novel, flexible approach for machine learning inference is introduced, which is suitable for deployment in diverse environments—including edge devices. The proposed solution has a modular design and is compatible with a wide range of user-defined machine learning pipelines. To improve energy efficiency and scalability, a high-performance communication protocol for inference is propounded, along with a scale-out mechanism based on a load balancer. The inference service plugs into the ASSIST-IoT reference architecture, thus taking advantage of its other components. The solution was evaluated in two scenarios closely emulating real-life use cases, with demanding workloads and requirements constituting several different deployment scenarios. The results from the evaluation show that the proposed software meets the high throughput and low latency of inference requirements of the use cases while effectively adapting to the available hardware. The code and documentation, in addition to the data used in the evaluation, were open-sourced to foster adoption of the solution.