bluuebunny/crossref_metadata_2025 huggingface dataset:
Unveiling a Surgical Revolution: The Use of Conventional Histology versus Ex Vivo Fusion Confocal Microscopy in Breast Cancer Surgery
Authors: Humaran Daniel , Pérez-Anker Javiera , Fernández Pedro L. , Blay Lidia , Pascual Iciar , Castellà Eva , Pérez Laia , Puig Susana , Malvehy Josep , Julián Joan F.
Published: October 2024

Ex vivo fusion confocal microscopy (EVFCM) enables the rapid examination of breast tissue and has the potential to reduce the surgical margins and the necessity for further surgeries. Traditional methods, such as frozen section analysis, are limited by the distortion of tissue and artefacts, leading to false negatives and the need for additional surgeries. This study on observational diagnostic accuracy evaluated the ability of EVFCM to detect breast cancer. A total of 36 breast tissue samples, comprising 20 non-neoplastic and 16 neoplastic cases, were analysed using EVFCM and compared to the results obtained from routine histopathology. A Mohs surgeon experienced in EVFCM (evaluator A) and two breast pathologists unfamiliar with EVFCM (evaluators B and C) performed blinded analyses. EVFCM showed high concordance with the histopathology and the detection of neoplasia, with significant kappa values (p < 0.001). Evaluator A achieved 100% sensitivity and specificity. Evaluators B and C achieved a sensitivity of >87%, a specificity of >94%, positive predictive values of >95%, and negative predictive values of 81% and 94%, respectively. EVFCM therefore offers a promising technique for the assessment of margins in breast-conserving surgery. Its widespread adoption could significantly reduce re-excisions, lower healthcare costs, and improve cosmetic and oncological outcomes.
Genetic and Molecular Heterogeneity of Synovial Sarcoma and Associated Challenges in Therapy
Authors: Lesovaya Ekaterina A. , Fetisov Timur I. , Bokhyan Beniamin Yu. , Maksimova Varvara P. , Kulikov Evgeny P. , Belitsky Gennady A. , Kirsanov Kirill I. , Yakubovskaya Marianna G.
Published: October 2024

Synovial sarcoma (SS) is one of the most common types of pediatric soft tissue sarcoma (STS) being far less frequent in adults. This STS type is characterized by one specific chromosomal translocation SS18-SSX and the associated changes in signaling. However, other genetic and epigenetic abnormalities in SS do not necessarily include SS18-SSX-related events, but abnormalities are more sporadic and do not correlate well with the prognosis and response to therapy. Currently, targeted therapy for synovial sarcoma includes a limited range of drugs, and surgical resection is the mainstay treatment for localized cancer with adjuvant or neoadjuvant chemotherapy and radiotherapy. Understanding the molecular characteristics of synovial sarcoma subtypes is becoming increasingly important for detecting new potential targets and developing innovative therapies. Novel approaches to treating synovial sarcoma include immune-based therapies (such as TCR-T cell therapy to NY-ESO-1, MAGE4, PRAME or using immune checkpoint inhibitors), epigenetic modifiers (HDAC inhibitors, EZH2 inhibitors, BRD disruptors), as well as novel or repurposed receptor tyrosine kinase inhibitors. In the presented review, we aimed to summarize the genetic and epigenetic landscape of SS as well as to find out the potential niches for the development of novel diagnostics and therapies.
Single-Cell Hypertrophy Promotes Contractile Function of Cultured Human Airway Smooth Muscle Cells via Piezo1 and YAP Auto-Regulation
Authors: Ni Kai , Che Bo , Gu Rong , Wang Chunhong , Pan Yan , Li Jingjing , Liu Lei , Luo Mingzhi , Deng Linhong
Published: October 2024

Severe asthma is characterized by increased cell volume (hypertrophy) and enhanced contractile function (hyperresponsiveness) of the airway smooth muscle cells (ASMCs). The causative relationship and underlying regulatory mechanisms between them, however, have remained unclear. Here, we manipulated the single-cell volume of in vitro cultured human ASMCs to increase from 2.7 to 5.2 and 8.2 × 103 μm3 as a simulated ASMC hypertrophy by culturing the cells on micropatterned rectangular substrates with a width of 25 μm and length from 50 to 100 and 200 μm, respectively. We found that as the cell volume increased, ASMCs exhibited a pro-contractile function with increased mRNA expression of contractile proteins, increased cell stiffness and traction force, and enhanced response to contractile stimulation. We also uncovered a concomitant increase in membrane tension and Piezo1 mRNA expression with increasing cell volume. Perhaps more importantly, we found that the enhanced contractile function due to cell volume increase was largely attenuated when membrane tension and Piezo1 mRNA expression were downregulated, and an auto-regulatory loop between Piezo1 and YAP mRNA expression was also involved in perpetuating the contractile function. These findings, thus, provide convincing evidence of a direct link between hypertrophy and enhanced contractile function of ASMCs that was mediated via Piezo1 mRNA expression, which may be specifically targeted as a novel therapeutic strategy to treat pulmonary diseases associated with ASMC hypertrophy such as severe asthma.
Plasma-Activated Water Combined with Chitosan–Oregano Essential Oil Coating to Prolong the Shelf Life of Carp Fillets
Authors: Du Manting , Hu Bin , Zhang Mingli , Liu Xiao
Published: October 2024

This study aimed to investigate the effects of plasma-activated water (PAW) combined with a chitosan–oregano essential oil (CS-OEO) coating on the physicochemical, microbiological, and sensory attributes of carp fillets during chilled storage. Fresh carp fillets were immersed in sterile deionized water, PAW, CS-OEO, and PAW+CS-OEO, respectively, and stored at 4 °C for 10 d. The results showed that plasma-activated water combined with chitosan–oregano essential oil could effectively inhibit the microbial growth in carp fillets during chilled storage. At 10 d storage, the total viable count of carp fillets treated with PAW+CS-OEO decreased by 2.87 log10 CFU/g when compared to the control. The lowest pH values were found in the carp fillet samples after the PAW+CS-OEO treatment; as well, the carp fillet samples after the PAW+CS-OEO treatment showed the lowest total volatile basic nitrogen, thiobarbituric acid reactive substance values, and K values. In addition, the carp fillets in the PAW+CS-OEO treatment group showed the smallest color changes and the highest scores in sensory properties during chilled storage. This study provides a basis for the potential application of plasma-activated water combined with chitosan–oregano essential oil in the preservation of carp fillets and contributes to developing a new food packaging.
Influence of Si Content on the Microstructure, Wear Resistance, and Corrosion Resistance of FeCoNiCrAl0.7Cu0.3Six High Entropy Alloy
Authors: Xi Rui , Li Yanzhou
Published: October 2024

This study explores microstructure, wear, and corrosion resistance properties of FeCoNiCrAl0.7Cu0.3Six (x = 0, 0.2, 0.3, 0.5) high-entropy alloys. The FeCoNiCrAl0.7Cu0.3Six alloy contains FCC and BCC structures; as the x increases, the FeCoNiCrAl0.7Cu0.3Si0.2, FeCoNiCrAl0.7Cu0.3Si0.4, and FeCoNiCrAl0.7Cu0.3Si0.5 high-entropy alloys transition to BCC structures. The morphological transition in FeCoNiCrAl0.7Cu0.3Six evolves from bamboo leaf-like intergranular features to a discontinuous intergranular structure as Si content increases. The hardness of these alloys gradually increases with higher Si content. The addition of Si promotes a uniform distribution of Cr within and between grains, reducing the intergranular segregation of Cu. Al and Ni show a consistent pattern of elemental distribution throughout the alloy. Wear measurements of FeCoNiCrAl0.7Cu0.3Six alloys demonstrate that adding Si enhances wear resistance, resulting in smoother wear surfaces with reduced deformation. The wear mechanism for all alloys is primarily abrasive, with no brittle fractures observed. Corrosion resistance is optimized when Si content is 0.2, with pitting corrosion being the primary corrosion form.
Improving Hydrophobicity and Water Vapor Barrier Properties in Paper Using Cellulose Nanofiber-Stabilized Cocoa Butter and PLA Emulsions
Authors: Argel-Pérez Shaydier , Velásquez-Cock Jorge , Zuluaga Robin , Gómez-Hoyos Catalina
Published: October 2024

This study explores the use of cellulose nanofiber (CNF)-stabilized Pickering emulsions for paper coatings, focusing on their rheological properties and effects on hydrophilicity and water vapor transmission rate (WVTR). Two types of Pickering emulsions, oil-in-water (O/W), were stabilized with 1 wt% CNF extracted from fique by-products. The oily phases of the emulsions were composed of poly(lactic acid) (PLA) and cocoa butter (CB). The physical stability, viscosity, and viscoelasticity of the emulsions were characterized. The emulsions were applied to the surfaces of Bond and Kraft papers using the rod-coating method. The coating process involved first applying a layer of the PLA emulsion followed by a layer of the CB emulsion. The coated papers were then evaluated by FE-SEM, contact angle, adhesion work, and water vapor transmission rate (WVTR). The results indicated that the coatings effectively produced a slightly hydrophobic surface on the papers, with contact angles approaching 90°. Initially, Kraft paper exhibited a WVTR value of 29.20 ± 1.13 g/m2·h, which significantly decreased to 7.06 ± 2.80 g/m2·h after coating, representing a reduction of 75.82%. Similarly, natural Bond paper showed a WVTR value of 30.56 ± 0.34 g/m2·h, which decreased to 14.37 ± 5.91 g/m2·h after coating, indicating a reduction of 47.02%. These findings demonstrate the potential of CNF-stabilized Pickering emulsions for enhancing the performance of paper coatings in terms of hydrophobicity and moisture barrier properties. The approach of this study aligns with global sustainability goals in packaging materials combining the use of PLA and CB to develop a waterborne coating to enhance the moisture barrier properties, demonstrated by a substantial reduction in water vapor transmission rates, and an improved hydrophobicity of coated papers.
The Effect of Shot Blasting Abrasive Particles on the Microstructure of Thermal Barrier Coatings Containing Ni-Based Superalloy
Authors: Lai Jianping , Shen Xin , Yuan Xiaohu , Li Dingjun , Gong Xiufang , Zhao Fei , Liao Xiaobo , Yu Jiaxin
Published: October 2024

Grit particles remaining on the substrate surface after grit blasting are generally considered to impair the thermal performance of thermal barrier coatings (TBCs). However, the specific mechanisms by which these particles degrade the multilayer structure of TBCs during thermal cycling have not yet been fully elucidated. In this study, the superalloy substrate was grit-blasted using various processing parameters, followed by the deposition of thermal barrier coatings (TBCs) consisting of a metallic bond coat (BC) and a ceramic top coat (TC). After thermal shock tests, local thinning or discontinuities in the thermally grown oxide (TGO) layer were observed in TBCs where large grit particles were embedded at the BC/substrate interface. Moreover, cracks originated at the concave positions of the TGO layer and propagated vertically towards BC; these cracks may be associated with additional stress imposed by the foreign grit particles during thermal cycling. At the BC/substrate interface, crack origins were observed in the vicinity of large grit particles (~50 μm).
Bio-Pesticidal Potential of Nanostructured Lipid Carriers Loaded with Thyme and Rosemary Essential Oils against Common Ornamental Flower Pests
Authors: Múnera-Echeverri Alejandro , Múnera-Echeverri José Luis , Segura-Sánchez Freimar
Published: October 2024

The encapsulation of essential oils (EOs) in nanostructured lipid carriers (NLCs) represents a modern and sustainable approach within the agrochemical industry. This research evaluated the colloidal properties and insecticidal activity of NLCs loaded with thyme essential oil (TEO-NLC) and rosemary essential oil (REO-NLC) against three common arthropod pests of ornamental flowers: Frankliniella occidentalis, Myzus persicae, and Tetranychus urticae. Gas chromatography–mass spectrometry (GC-MS) analysis identified the major chemical constituents of the EOs, with TEO exhibiting a thymol chemotype and REO exhibiting an α-pinene chemotype. NLCs were prepared using various homogenization techniques, with high shear homogenization (HSH) providing the optimal particle size, size distribution, and surface electrical charge. A factorial design was employed to evaluate the effects of EO concentration, surfactant concentration, and liquid lipid/solid lipid ratio on the physicochemical properties of the nanosuspensions. The final TEO-NLC formulation had a particle size of 347.8 nm, a polydispersity index of 0.182, a zeta potential of −33.8 mV, an encapsulation efficiency of 71.9%, and a loading capacity of 1.18%. The REO-NLC formulation had a particle size of 288.1 nm, a polydispersity index of 0.188, a zeta potential of −34 mV, an encapsulation efficiency of 80.6%, and a loading capacity of 1.40%. Evaluation of contact toxicity on leaf disks showed that TEO-NLC exhibited moderate insecticidal activity against the western flower thrips and mild acaricidal activity against the two-spotted spider mite, while REO-NLC demonstrated limited effects. These findings indicate that TEO-NLCs show potential as biopesticides for controlling specific pests of ornamental flowers, and further optimization of the administration dosage could significantly enhance their effectiveness.
Area–Time-Efficient High-Radix Modular Inversion Algorithm and Hardware Implementation for ECC over Prime Fields
Authors: Li Yamin
Published: October 2024

Elliptic curve cryptography (ECC) is widely used for secure communications, because it can provide the same level of security as RSA with a much smaller key size. In constrained environments, it is important to consider efficiency, in terms of execution time and hardware costs. Modular inversion is a key time-consuming calculation used in ECC. Its hardware implementation requires extensive hardware resources, such as lookup tables and registers. We investigate the state-of-the-art modular inversion algorithms, and evaluate the performance and cost of the algorithms and their hardware implementations. We then propose a high-radix modular inversion algorithm aimed at reducing the execution time and hardware costs. We present a detailed radix-8 hardware implementation based on 256-bit primes in Verilog HDL and compare its cost performance to other implementations. Our implementation on the Altera Cyclone V FPGA chip used 1227 ALMs (adaptive logic modules) and 1037 registers. The modular inversion calculation took 3.67 ms. The AT (area–time) factor was 8.30, outperforming the other implementations. We also present an implementation of ECC using the proposed radix-8 modular inversion algorithm. The implementation results also showed that our modular inversion algorithm was more efficient in area–time than the other algorithms.
Employing Different Algorithms of Lightweight Convolutional Neural Network Models in Image Distortion Classification
Authors: Ahmed Ismail Taha , Abdulazeez Falah Amer , Hammad Baraa Tareq
Published: October 2024

The majority of applications use automatic image recognition technologies to carry out a range of tasks. Therefore, it is crucial to identify and classify image distortions to improve image quality. Despite efforts in this area, there are still many challenges in accurately and reliably classifying distorted images. In this paper, we offer a comprehensive analysis of models of both non-lightweight and lightweight deep convolutional neural networks (CNNs) for the classification of distorted images. Subsequently, an effective method is proposed to enhance the overall performance of distortion image classification. This method involves selecting features from the pretrained models’ capabilities and using a strong classifier. The experiments utilized the kadid10k dataset to assess the effectiveness of the results. The K-nearest neighbor (KNN) classifier showed better performance than the naïve classifier in terms of accuracy, precision, error rate, recall and F1 score. Additionally, SqueezeNet outperformed other deep CNN models, both lightweight and non-lightweight, across every evaluation metric. The experimental results demonstrate that combining SqueezeNet with KNN can effectively and accurately classify distorted images into the correct categories. The proposed SqueezeNet-KNN method achieved an accuracy rate of 89%. As detailed in the results section, the proposed method outperforms state-of-the-art methods in accuracy, precision, error, recall, and F1 score measures.
An Efficient Detection Mechanism of Network Intrusions in IoT Environments Using Autoencoder and Data Partitioning
Authors: Xiao Yiran , Feng Yaokai , Sakurai Kouichi
Published: October 2024

In recent years, with the development of the Internet of Things and distributed computing, the “server-edge device” architecture has been widely deployed. This study focuses on leveraging autoencoder technology to address the binary classification problem in network intrusion detection, aiming to develop a lightweight model suitable for edge devices. Traditional intrusion detection models face two main challenges when directly ported to edge devices: inadequate computational resources to support large-scale models and the need to improve the accuracy of simpler models. To tackle these issues, this research utilizes the Extreme Learning Machine for its efficient training speed and compact model size to implement autoencoders. Two improvements over the latest related work are proposed: First, to improve data purity and ultimately enhance detection performance, the data are partitioned into multiple regions based on the prediction results of these autoencoders. Second, autoencoder characteristics are leveraged to further investigate the data within each region. We used the public dataset NSL-KDD to test the behavior of the proposed mechanism. The experimental results show that when dealing with multi-class attacks, the model’s performance was significantly improved, and the accuracy and F1-Score were improved by 3.5% and 2.9%, respectively, maintaining its lightweight nature.
Modeling and Comparison of Design Features of Pendulum and Radial Micro-Hydropower Plants Considering the Influence of Variable Design Parameters
Authors: Zhilkashinova Almira , Ocheredko Igor , Azamatov Bagdat , Nurbaev Mergen , Dogadkin Dmitry , Abilev Madi
Published: October 2024

This article provides a comparative analysis of pendulum and radial micro-hydropower plants. The novelty of this study lies in the comparative analysis of units that are fundamentally different in design to achieve the most rational option for low-speed rivers. It has been established that a pendulum micro-hydropower plant has a high torque with relatively small dimensions but operates cyclically. At a diameter of 1 m and a blade area of 0.3 m2, the peak torque was 140 N·m. At the same time, the design is sensitive to the blade area and at 0.6 m2 and a lever length of 1.5 m, the torque reached 430 N·m. A radial micro-hydropower plant has lower torque but operates constantly. At an area of 1.23 m2 and a diameter of 1 m, the torque was 40.4 N·m. Accordingly, in terms of specific area with a diameter of 1 m, a pendulum micro-hydropower plant has up to 12 times more torque. It has been established that the pendulum hydropower plant best satisfies the requirements for converting a low river speed into high revolutions of a current generator.
Dynamic Ultrasound Assessment and Guided Medial Plantar Nerve Hydrodissection for Master Knot of Henry Syndrome
Authors: Wu Wei-Ting , Özçakar Levent , Chang Ke-Vin
Published: October 2024

A 27-year-old female presented with persistent right medial plantar pain that developed over six months following an ankle sprain. The pain, described as sharp and radiating to the toes, progressively worsened, affecting her ability to walk. An initial ultrasound examination suggested medial plantar nerve compression by a lipoma, prompting her referral for ultrasound-guided hydrodissection. During the pre-procedure assessment, sono-palpation (palpation using the ultrasound transducer) localized the pain to the Master Knot of Henry—where the medial plantar nerve, artery, and flexor tendons intersect. No lipoma but a normal fat pad was observed. Ultrasound-guided hydrodissection with 5% dextrose mixed with lidocaine and saline was performed. After two sessions, her pain significantly decreased, with her visual analogue scale score dropping from 8 to 5 after the first session and to 2 after the second, allowing her to resume normal activities. This case highlights the value of ultrasound in accurately diagnosing and treating conditions involving the Master Knot of Henry.
Artificial Intelligence in Uropathology
Authors: Leite Katia Ramos Moreira , Melo Petronio Augusto de Souza
Published: October 2024

The global population is currently at unprecedented levels, with an estimated 7.8 billion people inhabiting the planet. We are witnessing a rise in cancer cases, attributed to improved control of cardiovascular diseases and a growing elderly population. While this has resulted in an increased workload for pathologists, it also presents an opportunity for advancement. The accurate classification of tumors and identification of prognostic and predictive factors demand specialized expertise and attention. Fortunately, the rapid progression of artificial intelligence (AI) offers new prospects in medicine, particularly in diagnostics such as image and surgical pathology. This article explores the transformative impact of AI in the field of uropathology, with a particular focus on its application in diagnosing, grading, and prognosticating various urological cancers. AI, especially deep learning algorithms, has shown significant potential in improving the accuracy and efficiency of pathology workflows. This comprehensive review is dedicated to providing an insightful overview of the primary data concerning the utilization of AI in diagnosing, predicting prognosis, and determining drug responses for tumors of the urinary tract. By embracing these advancements, we can look forward to improved outcomes and better patient care.
Relationship between Choroidal Thickness and Anterior Scleral Thickness in Patients with Keratoconus
Authors: Burguera-Giménez Neus , Díez-Ajenjo M.ª Amparo , Briceno-Lopez Celeste , Burguera Noemí , Luque-Cobija M.ª José , Peris-Martínez Cristina
Published: October 2024

Purpose: To evaluate the relationship between choroidal thickness (CT) and anterior scleral thickness (AST) in patients with subclinical keratoconus (SKC) and established keratoconus (KC). Methods: This single-center prospective case-control study included 97 eyes of 97 patients: 44 KC eyes, 14 SKC eyes, and 39 age- and axial length (AL)-matched healthy eyes. Using swept-source optical coherence tomography, the AST was manually measured in four directions and the CT was obtained automatically from the Early Treatment Diabetic Retinopathy Study (ETDRS) grid. Principal component analysis (PCA) was used to linearly reduce the dimensionality of nine CT inputs to one significant component, CT1. A multivariate model was created to evaluate the association between CT1, AST, and several ocular parameters in SKC and KC patients. Partial correlation was then performed to adjust the confounding factors and to examine the effect of AST on CT1. Results: The PCA showed that CT1 accounts for 86.54% of the total variance in the nine original CTs of the ETDRS grid. The CT1 model was associated with age, AL, and AST in the superior meridian in SKC eyes, whereas in KC eyes, it was correlated with gender, age, AL, and AST in the inferior meridian (p < 0.001). The partial correlation between CT1 and AST in the superior zone was found to be significant, positive, and strong in SKC eyes (r = 0.79, p = 0.019), whereas a significant, positive, and moderate correlation between CT1 and AST at the inferior zone (r = 0.41, p = 0.017) was observed in KC eyes. Conclusions: Choroidal tissue was significantly correlated with the anterior sclera across the vertical meridian. This relationship was observed over the superior sclera in SKC eyes, whereas in established KC, it was over the inferior sclera. These results reveal new insights regarding the interactions between the anterior and posterior structures of the KC eyes and confirm the enigma of the pathophysiology of KC.
Molecular Typing of Pseudomonas aeruginosa Isolates Collected in Abidjan Hospitals (Côte d’Ivoire) Using the Multiple-Locus Variable Number of Tandem Repeats Method
Authors: Essoh Christiane , Hauck Yolande , Ouassa Timothée , Touré Daouda , Djatchi Richmond , Loukou Guillaume Yao , N’Guetta Simon-Pierre Assanvo , Vergnaud Gilles , Pourcel Christine
Published: October 2024

Background/objectives: Pseudomonas aeruginosa can cause community-acquired infections affecting various body sites. The present retrospective study investigated the genetic diversity of 173 isolates (166 clinical, 7 environmental) of P. aeruginosa collected from clinical pathology laboratories in Abidjan, Côte d’Ivoire (2001–2011). Methods: Multiple-Locus Variable Number of Tandem Repeats (VNTR) Analysis (MLVA) using 13 loci was applied to all isolates and compared to published MLVA data. The antibiotics status of the isolates was compiled when available and compared to published profiles. Results: Among 95 isolates analyzed for their antibiotics status, 14 displayed concerning resistance profiles: five multidrug-resistant (MDR) and nine extensively drug-resistant (XDR). MLVA typing revealed a high genetic diversity (>130 genotypes), with many genotypes represented by a single strain. Notably, thirteen clusters (≥4 related isolates) were observed. Some clusters displayed close genetic relatedness to isolates from France, Korea, and well-studied strains (ST560, LES and PA14). Comparative analysis suggested the presence of international high-risk MDR clones (CC233, CC111) in Côte d’Ivoire. Importantly, MLVA clustering revealed a close relationship of CC235-MDR strains with a locally identified cluster (group 9). Conclusions: These findings support MLVA as a reliable and cost-effective tool for low-resource settings, allowing the selection of relevant strains for future whole genome sequence analyses. This approach can improve outbreak investigations and public health interventions aimed at curbing MDR P. aeruginosa transmission within hospitals and at the national level.
Flower-Shaped Plasma Cells in Multiple Myeloma with Morphological Heterogeneity
Authors: Hosoi Hiroki , Tane Misato , Sogabe Makiko , Iwamoto Ryuta , Minoura Naoto , Murata Shogo , Mushino Toshiki , Nishikawa Akinori , Murata Shin-Ichi , Sonoki Takashi
Published: October 2024

Background: Flower-shaped nuclei in plasma cells are rare in multiple myeloma. Case presentation: We report on an 88-year-old male who presented with a mass lesion in the clavicular region. A biopsy of the mass revealed an increase in mature plasma cells with round nuclei. In contrast, a bone marrow examination showed increased plasma cells with flower-shaped nuclei. The patient tested negative for human T-lymphotropic virus type-1 and was diagnosed with multiple myeloma. Conclusions: While multiple myeloma is known for intra-tumor heterogeneity, reports of morphological heterogeneity based on the site of tumor sampling are limited. In this case, the presence of plasma cells with flower-shaped nuclei enabled the identification of site-dependent morphological tumor heterogeneity.
Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals
Authors: Palanisamy Sivamani , Rajaguru Harikumar
Published: October 2024

Background/Objectives: Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive cardiovascular disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD. Methods: This research involves a total of 41 subjects sourced from the CapnoBase database, consisting of 21 normal subjects and 20 CVD cases. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression (LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, and SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate (GDR), Error rate, and Jaccard Index (JI). Results: The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving the highest accuracy of 95.12% along with the minimum error rate of 4.88%. In addition to that, it provides an MCC and kappa value of 0.90, a GDR and F1 score of 95%, and a Jaccard Index of 90.48%. Conclusions: This study demonstrated that heuristic-based optimization and machine learning classification of PPG signals are highly effective for the non-invasive detection of cardiovascular disease.
Pulp Vitality Diagnosis by Means of an Optical Pulp Scanning Device
Authors: Briseño Marroquín Benjamín , Borgschulte Markus , Savic Alexander , Ertl Thomas Peter , Wolf Thomas Gerhard
Published: October 2024

This study aimed to evaluate the diagnostic accuracy of an Optical Pulp Scanning (OPS) device. A total of 421 teeth from 107 emergency patients were investigated. Pulp vitality was assessed using conventional diagnostic methods (ConvDia), cold pulp testing (CPT), and a final diagnosis (FinDia) based on the pulp chamber contents (PCCs) of teeth requiring endodontic treatment (EmeTe). Following ConvDia, OPS screening was performed, endodontic access was prepared, and the FinDia was established. The specificity, sensitivity, as well as positive and negative predictive values of ConvDia, CPT, and FinDia were calculated against the OPS results. The null hypothesis stated that specificity and sensitivity would be equal to or lower than 90% and 80%, respectively. OPS sensitivity was 66.2% and 72.2% (EmeTe) for ConvDia, 66.2% and 69.8% (EmeTe) for CPT, and 76.9% for FinDia. Specificity was 26.4% and 25.5% (EmeTe) for ConvDia, 26.9% and 21.7% (EmeTe) for CPT, and 31.5% for FinDia. These results suggest that, at its current stage of development, the OPS device does not provide an acceptable level of diagnostic accuracy, particularly in vital teeth.
A Butterfly Algorithm That Combines Chaos Mapping and Fused Particle Swarm Optimization for UAV Path Planning
Authors: Wang Linlin , Zhang Xin , Zheng Huilong , Wang Chuanyun , Gao Qian , Zhang Tong , Li Zhongyi , Shao Jing
Published: October 2024

Effective path planning is essential for autonomous drone flight to enhance task efficiency. Many researchers have applied swarm intelligence algorithms to drone path planning. For instance, the traditional Butterfly Optimization Algorithm (BOA) has been used for this purpose. However, traditional BOA faces challenges such as slow convergence and susceptibility to being trapped in local optima. An Improved Butterfly Optimization Algorithm (IBOA) has been developed to identify optimal routes to address these limitations. Initially, ICMIC mapping is utilized to establish the butterfly community, enhancing the initial population’s diversity and preventing premature algorithm convergence. Following this, a population reset strategy is introduced, replacing weaker individuals over a specified number of iterations while maintaining a constant population size. This strategy enhances the algorithm’s ability to avoid local optima and increases its robustness. Additionally, characteristics of the Particle Swarm Optimization (PSO) algorithm are integrated to enhance the butterfly’s location update mechanism, accelerating the algorithm’s convergence rate. To evaluate the performance of the IBOA algorithm, this study designed a CEC2020 function test experiment and compared it with several swarm intelligence algorithms. The results showed that IBOA achieved the best performance in 70% of the function tests, outperforming 75% of the other algorithms. In the path planning experiments within a simulated environment, IBOA quickly converged to the optimal path, and the paths it planned were the shortest and safest compared to those generated by other algorithms.
U-Space Social and Environmental Performance Indicators
Authors: Krstić Simić Tatjana , Ganić Emir , Mirković Bojana , Baena Miguel , LeGriffon Ingrid , Barrado Cristina
Published: October 2024

The social potential of Urban Air Mobility (UAM) as a greener and faster transportation system in and around urban environments is indisputable. Nevertheless, the success of UAM introduction and its wide use will strongly depend on acceptance by the citizens and future UAM users. The impact on overall quality of life, as a multidimensional concept that encompasses physical health, mental and emotional well-being, economic status, education, and the environment, is becoming a significant issue. This paper aims to describe the performance framework for the assessment of the social and environmental impact of UAM. The specific objectives are to identify the full range of UAM’s impacts on citizens’ quality of life and to propose a set of indicators that enables the quantification and assessment of the identified impacts. Firstly, the main issues (focus areas) were identified, namely, noise, visual pollution, and privacy concerns, followed by access and equity, economic aspect, emissions, public safety, and impact on wildlife. In the next step, for each identified focus area, performance indicators were defined along with the several cross-cutting areas for a geographical, temporal, demographic, socioeconomic, and behavioral resolution. The proposed performance framework could enable more efficient mitigation measures and possibly contribute to wider adoption of the UAM operations.
Digital Stories and Inclusive Cultures at School: A Research Study in an Italian Primary Multicultural Classroom
Authors: Manganello Flavio , Baldacci Mara
Published: October 2024

This study explores the effectiveness of digital storytelling in enhancing communicative competence—encompassing both linguistic and narrative skills—and fostering inclusivity in a multicultural primary classroom in Genoa, Italy. The quasi-experimental intervention spanned four months and involved eight lessons designed to engage students from diverse cultural backgrounds, particularly those with migratory backgrounds. Pre- and post-intervention assessments were conducted to evaluate linguistic proficiency, narrative competence, and educational inclusion. The results from the Italian language proficiency test demonstrated that second-generation students typically performed at the B1 level, while students with more recent migratory backgrounds scored lower. However, narrative competence showed notable improvements among A2+ level students, who outperformed their peers in certain dimensions despite linguistic challenges. Observational rubrics used throughout the intervention revealed increased classroom inclusivity, active participation, and cultural exchange. Despite limitations related to the sample size and intervention duration, the findings suggest that digital storytelling can significantly enhance communicative competence and promote a more inclusive classroom environment. Future research should investigate the long-term effects of digital storytelling on multilingual classrooms and its broader applicability.
Comparing the Impact of Non-Cognitive Skills in STEM and Non-STEM Contexts in Kazakh Secondary Education
Authors: Sultanova Gulbakhyt , Shora Nurym
Published: October 2024

The role of non-cognitive skills in academic achievement has garnered increasing attention in educational research. This study explores the impact of non-cognitive skills on academic achievement in STEM and non-STEM subjects in secondary education. Survey data from 795 teachers and 12,965 students across 20 STEM schools in Kazakhstan were analyzed to examine the impact of 26 non-cognitive skills on performance in math, physics, first language, and history. Regression and mediation analyses were conducted to investigate how students’ self-assessment of non-cognitive skills directly affects academic achievement and how these effects are mediated by teachers’ assessments. The findings indicate that non-cognitive skills exhibit varying direct and total effects, with mediated effects showing greater consistency across different subjects. In math and physics, information processing skill and grit show the strongest direct and total effects on academic achievement. In first language and history, responsibility management and teamwork skill are most influential. Except for capacity for optimism and growth mindset, all skills demonstrated mediated effects across the four subjects. This research informs curriculum development and equitable policies by showing how non-cognitive skills impact academic performance across subjects. It studies the case of Kazakhstan, adding to global education discourse and offering valuable insights for enhancing STEM education.
Comprehensive Preview Decision Method for Direction and Speed of Intelligent Vehicle Based on Rules and Learning
Authors: Guan Hsin , Xue Pengcheng , Zhan Jun , Chen Haoyuan , Gao Shenzhen , Zhao Yunda , Jin Hao
Published: October 2024

Decision-making is a key part of autonomous driving systems. Human-like decision-making in complex scenarios is important for enhancing drivers’ trust in and acceptance of autonomous driving systems. In order to improve their human-like features and their adaptability to complex scenarios, this paper proposes a comprehensive preview decision method for direction and speed based on rules and learning. First, a decision-making structure including maneuvering ability judgment, motion trajectory prediction, safety judgment, legitimacy judgment, comprehensive performance evaluation, and parameters learning is presented. Second, a learning method for the longitudinal safety distance threshold based on a BP neural network is introduced. Third, inverse reinforcement learning (IRL) is used to learn the comprehensive performance evaluation weight coefficient for different driving styles. Finally, the DJI AD4CHE dataset is processed and used to train the parameters. Car-following and lane-changing scenarios are simulated to verify the effectiveness of decision-making. The simulation results show that the proposed method can reflect human-like decision-making in multiple scenarios.
Vehicle and Pedestrian Detection Based on Improved YOLOv7-Tiny
Authors: Liang Zhen , Wang Wei , Meng Ruifeng , Yang Hongyu , Wang Jinlei , Gao He , Li Biao , Fan Jungeng
Published: October 2024

To improve the detection accuracy of vehicles and pedestrians in traffic scenes using object detection algorithms, this paper presents modifications, compression, and deployment of the single-stage typical algorithm YOLOv7-tiny. In the model improvement section: firstly, to address the problem of small object missed detection, shallower feature layer information is incorporated into the original feature fusion branch, forming a four-scale detection head; secondly, a Multi-Stage Feature Fusion (MSFF) module is proposed to fully integrate shallow, middle, and deep feature information to extract more comprehensive small object information. In the model compression section: the Layer-Adaptive Magnitude-based Pruning (LAMP) algorithm and the Torch-Pruning library are combined, setting different pruning rates for the improved model. In the model deployment section: the V7-tiny-P2-MSFF model, pruned by 45% using LAMP, is deployed on the embedded platform NVIDIA Jetson AGX Xavier. Experimental results show that the improved and pruned model achieves a 12.3% increase in mAP@0.5 compared to the original model, with parameter volume, computation volume, and model size reduced by 76.74%, 7.57%, and 70.94%, respectively. Moreover, the inference speed of a single image for the pruned and quantized model deployed on Xavier is 9.5 ms.
Texture-Image-Oriented Coverless Data Hiding Based on Two-Dimensional Fractional Brownian Motion
Authors: Chang Yen-Ching , Liu Jui-Chuan , Chang Ching-Chun , Chang Chin-Chen
Published: October 2024

In an AI-immersing age, scholars look for new possibilities of employing AI technology to their fields, and how to strengthen security and protect privacy is no exception. In a coverless data hiding domain, the embedding capacity of an image generally depends on the size of a chosen database. Therefore, choosing a suitable database is a critical issue in coverless data hiding. A novel coverless data hiding approach is proposed by applying deep learning models to generate texture-like cover images or code images. These code images are then used to construct steganographic images to transmit covert messages. Effective mapping tables between code images in the database and hash sequences are established during the process. The cover images generated by a two-dimensional fractional Brownian motion (2D FBM) are simply called fractional Brownian images (FBIs). The only parameter, the Hurst exponent, of the 2D FBM determines the patterns of these cover images, and the seeds of a random number generator determine the various appearances of a pattern. Through the 2D FBM, we can easily generate as many FBIs of multifarious sizes, patterns, and appearances as possible whenever and wherever. In the paper, a deep learning model is treated as a secret key selecting qualified FBIs as code images to encode corresponding hash sequences. Both different seeds and different deep learning models can pick out diverse qualified FBIs. The proposed coverless data hiding scheme is effective when the amount of secret data is limited. The experimental results show that our proposed approach is more reliable, efficient, and of higher embedding capacity, compared to other coverless data hiding methods.
Multi-Task Diffusion Learning for Time Series Classification
Authors: Zheng Shaoqiu , Liu Zhen , Tian Long , Ye Ling , Zheng Shixin , Peng Peng , Chu Wei
Published: October 2024

Current deep learning models for time series often face challenges with generalizability in scenarios characterized by limited samples or inadequately labeled data. By tapping into the robust generative capabilities of diffusion models, which have shown success in computer vision and natural language processing, we see potential for improving the adaptability of deep learning models. However, the specific application of diffusion models in generating samples for time series classification tasks remains underexplored. To bridge this gap, we introduce the MDGPS model, which incorporates multi-task diffusion learning and gradient-free patch search (MDGPS). Our methodology aims to bolster the generalizability of time series classification models confronted with restricted labeled samples. The multi-task diffusion learning module integrates frequency-domain classification with random masked patches diffusion learning, leveraging frequency-domain feature representations and patch observation distributions to improve the discriminative properties of generated samples. Furthermore, a gradient-free patch search module, utilizing the particle swarm optimization algorithm, refines time series for specific samples through a pre-trained multi-task diffusion model. This process aims to reduce classification errors caused by random patch masking. The experimental results on four time series datasets show that the proposed MDGPS model consistently surpasses other methods, achieving the highest classification accuracy and F1-score across all datasets: 95.81%, 87.64%, 82.31%, and 100% in accuracy; and 95.21%, 82.32%, 78.57%, and 100% in F1-Score for Epilepsy, FD-B, Gesture, and EMG, respectively. In addition, evaluations in a reinforcement learning scenario confirm MDGPS’s superior performance. Ablation and visualization experiments further validate the effectiveness of its individual components.
A State Estimation Method for Rectangular Singular Systems Based on State Decomposition
Authors: He Shuying , Wen Chenglin , Wang Di , Zheng Jinhui
Published: October 2024

This paper proposes a state estimation method of linear discrete rectangular singular systems. The system is observable and regular, and the system matrix is rectangular without full column rank. To give the estimation of the state, the state is decomposed into two parts based on QR factorization, and the weighted least squares method is used to obtain the prediction of one part of the state. Then, the partial measurement equation is used to obtain the prediction of the other part of the state, and the projection theorem is used to obtain the state estimation value. Combined with the data-driven idea, a Kalman filtering algorithm based on historical data modeling is established. Finally, the feasibility and effectiveness of our approach is discussed and verified through performance analysis and numerical simulation perspectives.
High-Data-Rate Modulators Based on Graphene Transistors: Device Circuit Co-Design Proposals
Authors: Pacheco-Sanchez Anibal , Ramos-Silva J. Noé , Mavredakis Nikolaos , Ramírez-García Eloy , Jiménez David
Published: October 2024

The multifunctionality feature of graphene field-effect transistors (GFETs) is exploited here to design circuit building blocks of high-data-rate modulators by using a physics-based compact model. Educated device performance projections are obtained with the experimentally calibrated model and used to choose an appropriate improved feasible GFET for these applications. Phase-shift and frequency-shift keying (PSK and FSK) modulation schemes are obtained with 0.6 GHz GFET-based multifunctional circuits used alternatively in different operation modes: inverting and in-phase amplification and frequency multiplication. An adequate baseband signal applied to the transistors’ input also serves to enhance the device and circuit performance reproducibility since the impact of traps is diminished. Quadrature PSK is also achieved by combining two GFET-based multifunctional circuits. This device circuit co-design proposal intends to boost the heterogeneous implementation of graphene devices with incumbent technologies into a single chip: the baseband pulses can be generated with CMOS technology as a front end of line and the multifunctional GFET-based circuits as a back end of line.