bluuebunny/crossref_metadata_2025 huggingface dataset:
Performing High-Quality Sublobar Resections: Key Differences Between Wedge Resection and Segmentectomy
Authors: Bottet Benjamin , Hugen Niek , Sarsam Matthieu , Couralet Mathias , Aguir Sonia , Baste Jean-Marc
Published: November 2024

Lung cancer remains the leading cause of cancer-related deaths worldwide. The introduction of targeted treatments and immunotherapy in lung cancer has transformed patient care by offering “precision medicine” focused on the characteristics of the disease. The same concept has emerged in lung cancer surgery. This “precision surgery” aims to determine the best surgical approach based on the characteristics of the cancer, the patient’s cardiorespiratory status, and technological advances in thoracic surgery. While lobectomy continues to be the gold standard for managing early-stage lung cancer, the implementation of screening programs has enabled the earlier detection of smaller tumors. In this context, sublobar resections, particularly segmentectomy, have emerged as valuable options in the treatment of early-stage lung cancer. Recent studies suggest that sublobar resections, including segmentectomy and wedge resection, provide a viable alternative to lobectomy. This review explores the various resection strategies available, tailored to patient and tumor characteristics, and highlights modern preoperative techniques aimed at advancing precision surgery.
Anatomy, Imaging, and Surgical Treatment of Thoracic Lymphadenopathies in Advanced Epithelial Ovarian Cancer
Authors: Rizzo Stefania , Gasparri Maria Luisa , Manganaro Lucia , Del Grande Filippo , Papadia Andrea , Petrella Francesco
Published: November 2024

Ovarian cancer typically presents at advanced stages, with prognosis heavily influenced by the presence of residual disease following cytoreductive surgery. The role of resecting enlarged extra-abdominal lymph nodes during cytoreductive procedures remains contentious. These enlarged lymph nodes are commonly identified through high-resolution imaging techniques such as ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography–computed tomography (PET-CT). A comprehensive understanding of the relevant anatomy, imaging modalities, and surgical techniques for addressing lymphadenopathy in regions such as the supraclavicular fossa, axillae, mediastinum, and pericardiophrenic fat is crucial in determining the feasibility of surgical intervention. An appropriate evaluation of these factors is essential to optimize debulking, which is recognized as the most significant prognostic determinant in patients with ovarian cancer. This review underscores the importance of multidisciplinary approaches in managing advanced ovarian cancer with extra-abdominal lymph node involvement to enhance patient outcomes.
Decoding the Molecular Grammar of TIA1-Dependent Stress Granules in Proteostasis and Welander Distal Myopathy Under Oxidative Stress
Authors: Alcalde-Rey Isabel , Velasco Beatriz Ramos , Alcalde José , Izquierdo José M.
Published: November 2024

T-cell intracellular antigen 1 (TIA1) is an RNA-binding protein (RBP) that plays a multifunctional role in RNA metabolism. TIA1 has three RNA-Recognition Motifs (RRMs) and a prion-like carboxyl C-terminal domain (LCD) with intrinsically disordered regions (IDR) implicated in the dynamics (i.e., formation, assembly, and disassembly) of transient RNA-protein aggregates known as stress granules (SGs). A protein related to TIA1 is its paralog TIA1-related/like protein (TIAR/TIAL1), whose amino acid sequence, structural organisation, and molecular and cellular functions are highly conserved with TIA1. Both proteins are the main components of SGs, which are non-membranous RNA-protein condensates formed under stress to promote cell survival. Welander distal myopathy (WDM) is a late-onset muscular dystrophy that has been linked to a single-nucleotide substitution (c.1362G>A; p.E384K) in the gene encoding the TIA1 protein, which impacts TIA1-dependent SGs dynamics. Herein, we have analysed cellular and molecular aspects by targeting mutagenesis to position 384 to understand its molecular grammar in an amino acid/proteinogenic-dependent or -independent manner under oxidative stress. The observations suggest differential, even opposing, behaviours between TIA1 and TIAR in the presence of specific amino acids with negative and positive charges, and also uncharged acids, at equivalent positions of TIA1 and TIAR, respectively. Collectively, these findings illustrate a characteristic molecular grammar of TIAR- and TIA1-dependent SGs under oxidative conditions, suggesting a gain of versatility between two structurally and functionally highly conserved/related proteins.
Use of Artificial Intelligence in Imaging Dementia
Authors: Aljuhani Manal , Ashraf Azhaar , Edison Paul
Published: November 2024

Alzheimer’s disease is the most common cause of dementia in the elderly population (aged 65 years and over), followed by vascular dementia, Lewy body dementia, and rare types of neurodegenerative diseases, including frontotemporal dementia. There is an unmet need to improve diagnosis and prognosis for patients with dementia, as cycles of misdiagnosis and diagnostic delays are challenging scenarios in neurodegenerative diseases. Neuroimaging is routinely used in clinical practice to support the diagnosis of neurodegenerative diseases. Clinical neuroimaging is amenable to errors owing to varying human judgement as the imaging data are complex and multidimensional. Artificial intelligence algorithms (machine learning and deep learning) enable automation of neuroimaging interpretation and may reduce potential bias and ameliorate clinical decision-making. Graph convolutional network-based frameworks implicitly provide multimodal sparse interpretability to support the detection of Alzheimer’s disease and its prodromal stage, mild cognitive impairment. In patients with amyloid-related imaging abnormalities, radiologists had significantly better detection performances with both ARIA-E (sensitivity higher in the assisted/deep learning method [87%] compared to unassisted [71%]) and for ARIA-H signs (sensitivity was higher in assisted [79%] compared to unassisted [69%]). A convolutional neural network method was developed, and external validation predicted final clinical diagnoses of Alzheimer’s disease, dementia with Lewy bodies, mild cognitive impairment due to Alzheimer’s disease, or cognitively normal with FDG-PET. The translation of artificial intelligence to clinical practice is plagued with technical, disease-related, and institutional challenges. The implementation of artificial intelligence methods in clinical practice has the potential to transform the diagnostic and treatment landscape and improve patient health and outcomes.
Mitrofanoff Appendicovesicostomy in Robotic Paediatric Surgery—A Systematic Review
Authors: Ronconi Di Giuseppe Diana , Claxton Harry , Duhoky Rauand , Piozzi Guglielmo Niccolò , Khan Jim S.
Published: November 2024

Introduction: Proper bladder drainage is crucial. Children with bladder dysfunction may require alternative methods like clean intermittent catheterisation (CIC). However, CIC can be challenging for individuals with impairments. The Mitrofanoff procedure provides a solution by connecting the appendix to the bladder and creating a stoma on the skin, allowing for continent catheterisation. Minimally invasive techniques, including robotics, have been adopted recently. The aim of this study is to review the existing literature on robotic Mitrofanoff procedures. Materials and Methods: A systematic review on paediatric robotic Mitrofanoff procedures on the PubMed, Cochrane, and Scopus databases was conducted according to the PRISMA Statement. Critical appraisals of the included studies were performed with the Newcastle Ottawa Scale. Results: Six studies were included about the robotic Mitrofanoff procedure. Sex was reported in 50% of the studies. Ages were within the twelve-year age limit, as per the inclusion criteria. The mean operative time was 499.3 (±171.1) min. Four of the six studies reported a length of stay with a median of 6 days (±4; range 1.8–23). The incidence of complications was in line with established benchmarks. Only one study compared the Mitrofanoff procedure to open surgery, finding similar outcomes but longer operating times. Port placement and surgical strategy was described. Conclusions: Robotics can offer potential advantages for the Mitrofanoff procedure, despite its application still being in its early stages. This study emphasises the potential safety and efficacy of the robotic approach and promotes the need for further prospective high-quality studies.
The Importance of Hearing Screening and Central Auditory Processing in School-Aged Children
Authors: Skarżyński Piotr Henryk , Czajka Natalia , Bukato Ewelina , Zdanowicz Rita , Kołodziejak Aleksandra , Skarżyński Henryk
Published: November 2024

Background/Objectives: The aim of the study was to evaluate the prevalence of undiagnosed hearing impairment or central auditory processing disorders in children from I and VIII grades of primary schools in Warsaw. Methods: The participants in the study were 15,659 pupils from classes I and VIII attending primary schools in Warsaw. As part of the study, the hearing threshold for air conduction at frequencies of 0.5–8 kHz was determined, and a test assessing central auditory processing was performed: FPT (Frequency Pattern Test), DDT (Dichotic Digit Test), the Auditory Behaviour Scale (SAB), and an interview questionnaire developed for the program. Results: Abnormal test results were found in 1946 children, of which abnormal hearing screening test results were found in 678 children tested, while reduced central auditory processing results were found in 1268 children. Conclusions: The program implemented draws attention to the fact that tests of central auditory processing are included in the testing protocol. As has been shown, peripheral hearing testing alone is not sufficient to exclude abnormalities concerning the sense of hearing.
Estimation and Validation of Snowmelt Runoff Using Degree Day Method in Northwestern Himalayas
Authors: Sunita  , Sood Vishakha , Singh Sartajvir , Gupta Pardeep Kumar , Gusain Hemendra Singh , Tiwari Reet Kamal , Khajuria Varun , Singh Daljit
Published: November 2024

The rivers of the Himalayas heavily rely on the abundance of snow, which serves as a vital source of water to South Asian countries. However, its impact on the hydrological system of the region is mainly felt during the spring season. The melting of snow and consequent base flow significantly contribute to the incoming streamflow. This article examines the evaluation of the proportionate contribution to the total streamflow of Beas River up to Pandoh Dam through the snow melt. To analyze the snow melt, the snowmelt runoff model (SRM) has been utilized via dividing the study area into seven different elevation zones within a range of 853–6582 m and computing the percentage of snow cover, ranging from 15% to 90% across the basin. To validate the accuracy of the model, several metrics, such as coefficient of determination (R2) and volume difference (VD), are utilized. The R2 reveals that over the span of ten years, the daily discharge simulations exhibited efficiency levels ranging from 0.704 to 0.795, with VD falling within the range of 1.47% to 20.68%. This study has revealed that a significant amount of streamflow originates during the summer and monsoon periods, with snowmelt ranging from 10% to 45%. This research provides crucial understanding of the impact of snowmelt on streamflow, supplying essential knowledge on freshwater supply in the area.
Effect of Si Gradient Pattern on the Microstructure and Properties of Laminated Electrical Steel Composites Prepared by Hot-Press Sintering
Authors: Gao Ke , Xu Qiang , Jiao Haitao , Hu Yong
Published: November 2024

In this study, electrical steel laminated composites with positive Si gradient (PO-G), counter Si gradient (CO-G), and cross Si gradient (CR-G) were fabricated by hot-press sintering, cold rolling and annealing. The microstructure evolution during processing, as well as the magnetic and mechanical properties were investigated. The results indicate that the microstructure of the high-silicon layer and medium-silicon layer in the hot-pressed composites featured columnar grains throughout the thickness. The microstructure of the low-silicon layer in the hot-pressed CO-G sample consisted of equiaxed grains. However, a mixed structure dominated by columnar grains with some equiaxed grains was observed in the inner low-silicon layer of the PO-G and CR-G samples. Following cold rolling, the thickness ratio of each layer remained largely unchanged. After annealing, the microstructure of each layer transformed into columnar grains. The average grain size of the high-silicon layer, medium-silicon layer, and low-silicon layers in the three composites were approximately 20–23 μm, 33–38 μm, and 42–49 μm, respectively. Compared with the CO-G and CR-G samples, the annealed PO-G composite exhibited lower core loss at 400–1000 Hz and superior tensile strength. Furthermore, the core loss of the three composites was greater than that of the initial medium-silicon and high-silicon materials. This can be attributed to the increased hysteresis loss due to the existence of multi-layer interface.
Hierarchical Nanostructured Copper by Dealloying MnCu Alloy Ribbon for High-Performance Glucose Sensing
Authors: Wang Jinyi , Fan Bowen , Song Jiana , Chen Chen , Ji Yuan , Yu Jincheng
Published: November 2024

Electrochemical glucose sensing is vital for biomedical applications, particularly in diabetes management and continuous health monitoring. Among electrochemical sensors, non-enzyme-based sensors offer advantages such as cost-effectiveness, robust anti-interference capabilities, and environmental stability compared to enzyme-based ones. This study focuses on the development of non-enzyme-based glucose sensors utilizing hierarchical nanostructured copper (Cu) electrodes. The electrodes are fabricated by selectively dissolving components from an alloy precursor. Specifically, MnCu alloy ribbons prepared by melt rolling were used due to their favorable properties, and electrochemical dealloying was employed to create nanostructured Cu with high electrocatalytic activity for glucose oxidation. Three-dimensional bicontinuous nanoporous copper with an average pore size of 34 nm~86 nm and an average ligament size range of 45 nm~125 nm can be obtained. The optimized hierarchical nanostructured Cu electrodes exhibited excellent performance, including high sensitivity (0.652 mA·mM−1·cm−2), a wide linear detection range (0.001 mM to 1.5 mM), a low detection limit (0.73 μM), and a rapid response time. This work demonstrates the potential of nanostructured Cu in the advancement of non-enzyme-based glucose sensors.
The Effect of Mn3+ Substitution on the Electric Field Gradient in a HoFe1−xMnxO3 (x = 0–0.7) System
Authors: Knyazev Yuriy V. , Pavlovskii Maksim S. , Balaev Timofei D. , Semenov Sergey V. , Skorobogatov Stanislav A. , Sokolov Aleksey E. , Gokhfeld Denis M. , Shaykhutdinov Kirill A.
Published: November 2024

The effect of the Mn3+ ion on the local distortions of FeO6 octahedra in orthoferrite samples was investigated. Mössbauer spectroscopy measurements for a series of HoFe1−xMnxO3 (x = 0–0.7) orthoferrite samples with the space group Pnma were carried out at temperatures above the Néel point (700 K). The electric field gradient (EFG) tensor on Fe ions for these compounds was found using first-principle calculations. The concentration dependence of quadrupole splitting was obtained using experimental and theoretical data. Mn3+ cations were found to affect the Mössbauer spectra mainly due to distortions of the crystal lattice. Theoretical calculations show that the values of all electric field gradient components increase significantly with the manganese concentration in the system, and the eigenvectors exx and eyy of the electric field gradient tensor sharply change their direction at concentrations of x > 0.1.
Structures and Luminescent Sensing Properties of Terbium Metal–Organic Frameworks with Methyl-Decorated Phenanthroline Ligand
Authors: Ovchinnikova Anna A. , Demakov Pavel A. , Ryadun Alexey A. , Fedin Vladimir P. , Dybtsev Danil N.
Published: November 2024

Two new Tb(III) metal–organic frameworks based on 4,7-dimethylphenanthroline (dmphen) and flexible ligand trans-1,4-cyclohexanedicarboxylate (chdc2−) were synthesized and characterized. Their crystallographic formulae are [Tb2(dmphen)2(H2O)2(chdc)3]·2DMF (1; DMF = N,N-dimethylformamide) and [Tb2(dmphen)2(NO3)2(chdc)2]·2DMF (2). Among some differences in their synthetic conditions, the most important one is apparently the using of terbium(III) nitrate instead of terbium(III) chloride as a metal precursor in the synthesis of 2, providing a nitrate coordination to Tb3+, and its subsequent notable structural differences to 1. Compound 1 was found to have a layered hcb structure with intralayer windows ca. 10 × 8 Å2 in size. Its layer-to-layer packing leaves narrow channels running across these windows, with 18% as a total solvent-accessible volume in the coordination structure. Compound 2 was found to have a layered sql structure with smaller intralayer windows of ca. 8 × 6 Å2 in size. Methyl substituents on the phen ligands do not affect the topology of the framework but seem to have a substantial effect on the packing density, as well as the pore volume of the resulting MOF. A high 18.4% luminescence quantum yield was found for 2. Its emission lifetime of 0.695(12) ms belongs to a typical range for phosphorescent Tb(III)-carboxylate complexes. A quenching of its emission by different nitroaromatic molecules was found. A linear concentration dependence on 3-nitrotoluene and 4-nitro-m-xylene at micromolar concentrations was found during luminescent titration experiments (LOD values ca. 350 nM), suggesting this MOF to be a viable and highly sensitive luminescent sensor for such substrates.
The Influence of Milling Conditions on the Structure and Properties of Fe3O4 Nanoparticles for Biomedical Applications
Authors: Mikheev Vladislav A. , Nizamov Timur R. , Nikolenko Polina I. , Ivanova Anna V. , Novikov Alexander I. , Dorofievich Irina V. , Lileev Alexey S. , Abakumov Maxim A. , Shchetinin Igor V.
Published: November 2024

In this work, a new two-stage scalable method for the synthesis of magnetite nanoparticles for biomedical applications is proposed. The influence of the milling time, medium, and surfactants on the formation of the structure, magnetic, and functional properties of magnetite nanoparticles has been studied. Comprehensive investigation of the formation of the structure and properties of magnetite nanoparticles has been carried out using X-ray diffraction analysis, scanning and transmission electron microscopy, Mössbauer spectroscopy, measurements of magnetic properties, specific loss power (SLP), and cytotoxicity. It was shown that the milling medium of water with the addition of trisodium citrate is a harsher milling condition compared to octadecene-1 with the addition of oleic acid. Continuous milling for 50 h allowed to obtain a fraction of colloidally stable nanoparticles at the level of 80–90%. Harsher milling conditions led to the formation of a larger fraction of superparamagnetic particles, which reduced the coercivity and SLP. The maximum SLP value of 1140 W/g was reached by large particles, while nanoparticles had decreased SLP values of 100–190 W/g, which was completely determined by the coercivity dependence. Different synthesis conditions allowed obtaining particles with different cytotoxicity against PC-3 cells.
Prime Basking Sites and Communal Basking in the Lizard, Lacerta bilineata; High Risk for Juveniles?
Authors: Meek Roger , Luiselli Luca
Published: November 2024

Sunlight and the heat it provides are important ecological resources for reptiles especially for those species living in temperate zones that bask extensively to maximize heat uptake. Sun basking has both benefits and costs for reptiles, giving heat that provides the energy to drive physiology but basking in open patches increases risk of predation due to higher visibility. Prime basking sites are believed to increase benefits for reptiles that include, in addition to open sunlit areas, facilitate detection of predators and prey and escape to nearby refuges. However, if such sites are limited, both inter and intra-specific interference may occur and this kind of competition may impact on a reptile’s ability to access prime basking sites, and as a consequence, its capacity to thermoregulate to optimum body temperatures. This may be especially important for juveniles, for whom rapid growth is a key factor in survivorship. We studied communal basking and interaction events at prime basking sites in the European green lizard, Lacerta bilineata, in a hedgerow in western France. We compared basking behaviour of adults and juveniles with sympatric adult wall lizards Podarcis muralis using non-invasive photographic-mark-recapture. Adult L. bilineata were more evenly distributed across basking sights compared to juveniles but significant differences were only detected between males and juveniles. Juvenile L. bilineata abandoned basking sites at the approach of both adult males and females and were aggressively removed by adult male L. bilineata. We found inter-specific communal basking between both adult and juvenile L. bilineata with adult wall lizards P. muralis. Communal basking was observed between male and female L. bilineata but not between adult males or between adult female L. bilineata. Communal basking was in proportionally greater frequency in juveniles compared to adult L. bilineata.
Visual Indicator for Intradialytic Hypotension Prediction Using Variation and Compensation of Heart Rate
Authors: Bae Tae-Wuk , Park Ji-Hyun , Park Jong-Won , Kwon Kee-Koo , Kim Kwang-Yong
Published: November 2024

Background: To date, most intradialytic hypotension (IDH) studies have proposed technologies to comprehensively predict the occurrence of IDH using the patient’s baseline information and ultrafiltration (UF) information, but it is difficult to apply the technologies while identifying the patient’s condition in real time. Methods: In this study, we propose an IDH indicator that uses heart rate (HR) change information to identify the patient’s condition in real time and visually shows whether IDH has occurred. The data used were collected from 40 dialysis patients in a clinical trial conducted in the Artificial Kidney Unit at Yeungnam University Medical Center, Korea, from 18 July to 29 November 2023. Results: The IDH indicator infers changes in the patient’s blood pressure during dialysis by analyzing the upper and lower maximum HRs based on the real-time average HR. Medical staff can respond to IDH in real time by looking at the IDH indicator, which visually expresses changes in the patient’s HR. In addition, we propose a multilayer perceptron structure that inputs upper and lower maximum HR information based on the average HR for the time interval accumulated in real time. In learning using 40 min of data up to 5 min before IDH occurred, models using two and five layers showed excellent performance, with accuracy of 88.6% and 85.2%, respectively. Conclusions: By combining IDH visual indicators and the multi-layer perceptron method, medical staff can effectively respond to IDH in real time.
ChMinMaxPat: Investigations on Violence and Stress Detection Using EEG Signals
Authors: Bektas Omer , Kirik Serkan , Tasci Irem , Hajiyeva Rena , Aydemir Emrah , Dogan Sengul , Tuncer Turker
Published: November 2024

Background and Objectives: Electroencephalography (EEG) signals, often termed the letters of the brain, are one of the most cost-effective methods for gathering valuable information about brain activity. This study presents a new explainable feature engineering (XFE) model designed to classify EEG data for violence detection. The primary objective is to assess the classification capability of the proposed XFE model, which uses a next-generation feature extractor, and to obtain interpretable findings for EEG-based violence and stress detection. Materials and Methods: In this research, two distinct EEG signal datasets were used to obtain classification and explainable results. The recommended XFE model utilizes a channel-based minimum and maximum pattern (ChMinMaxPat) feature extraction function, which generates 15 distinct feature vectors from EEG data. Cumulative weight-based neighborhood component analysis (CWNCA) is employed to select the most informative features from these vectors. Classification is performed by applying an iterative and ensemble t-algorithm-based k-nearest neighbors (tkNN) classifier to each feature vector. Information fusion is achieved through iterative majority voting (IMV), which consolidates the 15 tkNN classification results. Finally, the Directed Lobish (DLob) symbolic language generates interpretable outputs by leveraging the identities of the selected features. Together, the tkNN classifier, IMV-based information fusion, and DLob-based explainable feature extraction transform the model into a self-organizing explainable feature engineering (SOXFE) framework. Results: The ChMinMaxPat-based model achieved over 70% accuracy on both datasets with leave-one-record-out (LORO) cross-validation (CV) and over 90% accuracy with 10-fold CV. For each dataset, 15 DLob strings were generated, providing explainable outputs based on these symbolic representations. Conclusions: The ChMinMaxPat-based SOXFE model demonstrates high classification accuracy and interpretability in detecting violence and stress from EEG signals. This model contributes to both feature engineering and neuroscience by enabling explainable EEG classification, underscoring the potential importance of EEG analysis in clinical and forensic applications.
Absorbable Haemostatic Artefacts as a Diagnostic Challenge in Postoperative Follow-Up After Oncological Resection in Head and Neck Tumours: Systematic Review of Current Literature and Two Case Reports
Authors: Barbera Giorgio , Lobbia Guido , Ghiozzi Federica , Rovescala Alessandra , Franzina Carlotta , Sina Sokol , Nocini Riccardo
Published: November 2024

Objectives: This article aims to define the clinical, radiological, and pathological characteristics of non-resorbed oxidised cellulose-induced pseudotumours to raise awareness among surgeons and radiologists, to prevent misdiagnosis, and avoid unnecessary invasive procedures and delays in adjuvant oncological treatments. Methods: A systematic review of oxidised resorbable cellulose (ORC)-induced pseudotumours of the head and neck was conducted following PRISMA 2020 guidelines. Articles were retrieved from PubMed, Scopus, Cochrane, and Web of Science. Two ORC-induced pseudotumour cases from the Maxillofacial Surgery Department of Verona are also presented. Results: In most cases, pseudotumours were monitored using ultrasound. Further investigations included CT, MRI, PET-CT, and scintigraphy. Ultrasound images showed stable, elongated, and non-homogeneous masses. In CT scans, pseudotumours showed a liquefied core, and none or only peripheral enhancement. In MRI, pseudotumours presented none or only peripheral enhancement, and a heterogeneous pattern in T2-weighted images. 18-FDG PET scans demonstrated an FDG-avid mass (SUV 7.5). Scintigraphy was inconclusive. Cytology indicated a granulomatous reaction without neoplastic cells. Where surgical excision was performed, a granulomatous reaction with the presence of oxidised cellulose fibres was confirmed. Conclusions: Surgeons should consider artifacts from retained oxidised absorbable haemostatic material when suspecting tumour recurrence or metastasis on postoperative imaging, especially if certain features are present. Fine-needle aspiration cytology (FNAC) is a useful diagnostic tool, but surgical excision may be needed if FNAC is inconclusive or impractical. Collaboration between surgeons and radiologists is essential to avoid misdiagnosis and delays in treatment. Documenting the use and location of haemostatic material in operative reports would aid future understanding of these phenomena.
Dermoscopy, Line-Field Confocal Optical Coherence Tomography, Reflectance Confocal Microscopy, and Ultra-High-Frequency Ultrasound: Clues for the Diagnosis of Hidrocystomas
Authors: Erasti Maria , D’Onghia Martina , Batsikosta Anastasia , Suppa Mariano , Tognetti Linda , Cappilli Simone , Peris Ketty , La Marca Francesca , Perrot Jean Luc , Rubegni Giovanni , Rubegni Pietro , Cinotti Elisa
Published: November 2024

Background/Objectives: Hidrocystomas, eccrine and apocrine, are rare cystic lesions that form benign tumors of the sweat glands. This study aimed to describe the clinical features of hidrocystomas and evaluate the role of non-invasive imaging techniques, including dermoscopy, Line-field Confocal Optical Coherence Tomography (LC-OCT), Reflectance Confocal Microscopy (RCM), and Ultra-High-Frequency Ultrasound (UHFUS), in their diagnosis. Methods: In total, seven cases of hidrocystomas were collected from the Dermatologic Clinic of the University of Siena, Italy. Predefined dermoscopic, LC-OCT, RCM, and UHFUS features were retrospectively described. Results: Overall, hidrocystomas were located on the face, mainly presenting as blue/purple-bluish translucent papules (71%). Dermoscopic examination revealed a homogeneous purple-bluish color (71%), white pale halo (71%), and shiny globules at the periphery (57%). LC-OCT identified a hyporeflective cupoliform round structure in the dermis with a bright and thick contour, while UHFUS showed a round structure in the dermis filled with hypoechoic content. Conclusions: Non-invasive imaging techniques could significantly enhance the diagnostic accuracy of hidrocystomas, aid in differentiation from other lesions, and minimize unnecessary biopsies.
Construction of a Deep Learning Model for Unmanned Aerial Vehicle-Assisted Safe Lightweight Industrial Quality Inspection in Complex Environments
Authors: Jing Zhongyuan , Wang Ruyan
Published: November 2024

With the development of mobile communication technology and the proliferation of the number of Internet of Things (IoT) terminal devices, a large amount of data and intelligent applications are emerging at the edge of the Internet, giving rise to the demand for edge intelligence. In this context, federated learning, as a new distributed machine learning method, becomes one of the key technologies to realize edge intelligence. Traditional edge intelligence networks usually rely on terrestrial communication base stations as parameter servers to manage communication and computation tasks among devices. However, this fixed infrastructure is difficult to adapt to the complex and ever-changing heterogeneous network environment. With its high degree of flexibility and mobility, the introduction of unmanned aerial vehicles (UAVs) into the federated learning framework can provide enhanced communication, computation, and caching services in edge intelligence networks, but the limited communication bandwidth and unreliable communication environment increase system uncertainty and may lead to a decrease in overall energy efficiency. To address the above problems, this paper designs a UAV-assisted federated learning with a privacy-preserving and efficient data sharing method, Communication-efficient and Privacy-protection for FL (CP-FL). A network-sparsifying pruning training method based on a channel importance mechanism is proposed to transform the pruning training process into a constrained optimization problem. A quantization-aware training method is proposed to automate the learning of quantization bitwidths to improve the adaptability between features and data representation accuracy. In addition, differential privacy is applied to the uplink data on this basis to further protect data privacy. After the model parameters are aggregated on the pilot UAV, the model is subjected to knowledge distillation to reduce the amount of downlink data without affecting the utility. Experiments on real-world datasets validate the effectiveness of the scheme. The experimental results show that compared with other federated learning frameworks, the CP-FL approach can effectively mitigate the communication overhead, as well as the computation overhead, and has the same outstanding advantage in terms of the balance between privacy and usability in differential privacy preservation.
How and Why Teachers Use Technology: Distinct Integration Practices in K-12 Education
Authors: Raave Doris Kristina , Saks Katrin , Pedaste Margus , Roldan Roa Eric
Published: November 2024

This study investigates the integration of digital technology in K-12 education by examining the practices of Estonian primary and lower secondary school teachers. Through latent profile analysis, we identified six distinct profiles of teachers’ digital technology integration practices, each characterised by unique patterns of how and why technology is integrated. These profiles—Motivating Facilitators, Depth Facilitators, Efficiency Engineers, Engaging Innovators, Depth Divers, and Motivating Traditionalists—highlight the necessity for context-specific professional development that addresses teachers’ diverse needs and motivations. The study contributes to the growing literature on technology-enhanced learning by providing valuable insights into designing effective professional development programs and promoting the strategic use of digital technologies to enhance student learning.
Efficient Consistency Check Based on Perceived Initial Deviation
Authors: Zhang Liwen , Wang Fanglue , Song Zhihuan , Huang Kaifeng , Hu Yanli , Zhuo Guiying
Published: November 2024

The behavior recorded by an information system is often different from the behavior in the initial model since the business process is constantly changing in actual operation. In order to enable event logs to be replayed well to process the model, the set of optimal alignments first needs to be determined. All the corresponding activities in each alignment are compared to the existing method, which will cause a lot of unnecessary work. Thus, we propose that the non-optimal alignment is perceived beforehand according to the relationship between the location of the initial deviation and the number of deviations. The perceptible regions in the process model are divided based on the behavioral characteristics of various substructures. The comparison of an alignment is terminated if the location of the initial deviation is less than the previous value in the perceptible region. This alignment is judged to be non-optimal. Otherwise, the alignment with optimal probability is completely compared. The OPS plug-in was executed in the data sets from various networks and BPIC2020, and the results showed that the search efficiency could be improved under the premise of guaranteeing optimality.
Advances in Robotic Surgery: A Review of New Surgical Platforms
Authors: Picozzi Paola , Nocco Umberto , Labate Chiara , Gambini Isabella , Puleo Greta , Silvi Federica , Pezzillo Andrea , Mantione Rocco , Cimolin Veronica
Published: November 2024

In recent decades, the development of surgical systems which minimize patient impact has been a major focus for surgeons and researchers, leading to the advent of robotic systems for minimally invasive surgery. These technologies offer significant patient benefits, including enhanced outcome quality and accuracy, reduced invasiveness, lower blood loss, decreased postoperative pain, diminished infection risk, and shorter hospitalization and recovery times. Surgeons benefit from the elimination of human tremor, ergonomic advantages, improved vision systems, better access to challenging anatomical areas, and magnified 3DHD visualization of the operating field. Since 2000, Intuitive Surgical has developed multiple generations of master-slave multi-arm robots, securing over 7000 patents, which created significant barriers for competitors. This monopoly resulted in the widespread adoption of their technology, now used in over 11 million surgeries globally. With the expiration of key patents, new robotic platforms featuring innovative designs, such as modular systems, are emerging. This review examines advancements in robotic surgery within the fields of general, urological, and gynecological surgery. The objective is to analyze the current robotic surgical platforms, their technological progress, and their impact on surgical practices. By examining these platforms, this review provides insights into their development, potential benefits, and future directions in robotic-assisted surgery.
Scalable Transformer Accelerator with Variable Systolic Array for Multiple Models in Voice Assistant Applications
Authors: Chang Seok-Woo , Kim Dong-Sun
Published: November 2024

Transformer model is a type of deep learning model that has quickly become fundamental in natural language processing (NLP) and other machine learning tasks. Transformer hardware accelerators are usually designed for specific models, such as Bidirectional Encoder Representations from Transformers (BERT), and vision Transformer models, like the ViT. In this study, we propose a Scalable Transformer Accelerator Unit (STAU) for multiple models, enabling efficient handling of various Transformer models used in voice assistant applications. Variable Systolic Array (VSA) centralized design, along with control and data preprocessing in embedded processors, enables matrix operations of varying sizes. In addition, we propose an efficient variable structure and a row-wise data input method for natural language processing where the word count changes. The proposed scalable Transformer accelerator accelerates text summarization, audio processing, image search, and generative AI used in voice assistance.
A Cascade Fractional-N Synthesizer Topology of DLL and Frequency Multiplier for 5G+ Communication Systems
Authors: Nam Kyu-Hyun , Hong Nam-Pyo , Park Jun-Seok
Published: November 2024

This study presents a synthesizer topology based on a delay-locked loop (DLL) and programmable frequency multiplier for 5G+ communication systems. The proposed synthesizer comprises a 512-phase DLL, an intermediate frequency generator (IFG), and an RF frequency multiplier (RFFM). The 512-phase DLL provides 512 delayed pulses through a chain of 256 delay units and single-to-differential complementary converters (S2DCs). The IFG comprises I/Q-multiplexers, I/Q-accumulators, an XOR, and an S2DC. The I/Q-multiplexer outputs switch to the phase lag or lead waveforms at every rising or falling edge of the outputs, which makes the I/Q-multiplexer output frequency, fMX, programmable. The IF, fIF, is two times fMX, and fIF is up-converted to RF, fRF, through the RFFM. When the reference clock frequency, fref, is 156.25 MHz, the fIF range is 156.863–312.5 MHz and the fRF dynamic range is approximately 1.89–9.96 GHz. The channel resolution range is 3.698–38.609 MHz. Consequently, the proposed synthesizer provides a wide 134% output frequency bandwidth and a finer channel resolution smaller than fref. The presented synthesizer is fabricated in a 65 nm CMOS process. The total power consumption is 15 mW, and the rms jitter integrated from 1 kHz to 100 MHz measured as 107.6 fs.
Compact DINO-ViT: Feature Reduction for Visual Transformer
Authors: Chandranegara Didih Rizki , Niedziela Przemysław , Cyganek Bogusław
Published: November 2024

Research has been ongoing for years to discover image features that enable their best classification. One of the latest developments in this area is the Self-Distillation with No Labels Vision Transformer—DINO-ViT features. However, even for a single image, their volume is significant. Therefore, for this article we proposed to substantially reduce their size, using two methods: Principal Component Analysis and Neighborhood Component Analysis. Our developed methods, PCA-DINO and NCA-DINO, showed a significant reduction in the volume of the features, often exceeding an order of magnitude while maintaining or slightly reducing the classification accuracy, which was confirmed by numerous experiments. Additionally, we evaluated the Uniform Manifold Approximation and Projection (UMAP) method, showing the superiority of the PCA and NCA approaches. Our experiments involving modifications to patch size, attention heads, and noise insertion in DINO-ViT demonstrated that both PCA-DINO and NCA-DINO exhibited reliable accuracy. While NCA-DINO is optimal for high-performance applications despite its higher computational cost, PCA-DINO offers a faster, more resource-efficient solution, depending on the application-specific requirements. The code for our method is available on GitHub.
Applications of Machine Learning in Cancer Imaging: A Review of Diagnostic Methods for Six Major Cancer Types
Authors: Dumachi Andreea Ionela , Buiu Cătălin
Published: November 2024

Machine learning (ML) methods have revolutionized cancer analysis by enhancing the accuracy of diagnosis, prognosis, and treatment strategies. This paper presents an extensive study on the applications of machine learning in cancer analysis, with a focus on three primary areas: a comparative analysis of medical imaging techniques (including X-rays, mammography, ultrasound, CT, MRI, and PET), various AI and ML techniques (such as deep learning, transfer learning, and ensemble learning), and the challenges and limitations associated with utilizing ML in cancer analysis. The study highlights the potential of ML to improve early detection and patient outcomes while also addressing the technical and practical challenges that must be overcome for its effective clinical integration. Finally, the paper discusses future directions and opportunities for advancing ML applications in cancer research.
A Diagnostic Approach to Improving the Energy Efficiency of Production Processes—2E-DAmIcS Methodology
Authors: Hamrol Adam , Kujawińska Agnieszka , Brzozowski Krzysztof , Jasiulewicz-Kaczmarek Małgorzata
Published: November 2024

This article presents the issue of energy waste in manufacturing processes, focusing on reducing unnecessary energy consumption and CO2 emissions. A significant challenge in modern production is identifying and minimizing energy waste, which not only increases operational costs but also contributes to environmental degradation. An improvement methodology referred to as 2E-DAmIcS is proposed. A distinguishing feature of the methodology is a risk map of energy waste in the production process. Application of the methodology is demonstrated using the example of a lead–acid battery production process. It is shown that even small but well-diagnosed changes to the process make it possible to significantly reduce energy consumption. The proposed methodology offers practical tools for managers and decision-makers in various industries to systematically identify and minimize energy waste. It highlights the importance of cross-disciplinary collaboration among specialists in technology, energy consumption, and statistical analysis to optimize energy use. By applying this approach, companies can achieve both financial savings and environmental benefits, contributing to more sustainable production practices.
Efficient Simulator for P2P Energy Trading: Customizable Bid Preferences for Trading Agents
Authors: Takeda Yasuhiro , Suzuki Yosuke , Fukamachi Kota , Yamada Yuji , Tanaka Kenji
Published: November 2024

Given the accelerating global movement towards decarbonization, the importance of promoting renewable energy (RE) adoption and ensuring efficient transactions in energy markets is increasing worldwide. However, renewable energy sources, including photovoltaic (PV) systems, are subject to output fluctuations due to weather conditions, requiring large-scale backup power to balance supply and demand. This makes trading electricity from large-scale PV systems connected to the existing grid challenging. To address this, peer-to-peer (P2P) energy markets where individual prosumers can trade excess power within their local communities have been garnering attention. This study introduces a simulator for P2P energy trading, designed to account for the diverse behaviors and objectives of participants within a market mechanism. The simulator incorporates two risk aversion parameters: one related to transaction timing, expressed through order prices, and another related to forecast errors, managed by adjusting trade volumes. This allows participants to customize their trading strategies, resulting in more realistic analyses of trading outcomes. To explore the effects of these risk aversion settings, we conduct a case study with 120 participants, including both consumers and prosumers, using real data from household smart meters collected on sunny and cloudy days. Our analysis shows that participants with higher aversion to transaction timing tend to settle trades earlier, often resulting in unnecessary transactions due to forecast inaccuracies. Furthermore, trading outcomes are significantly influenced by weather conditions: sunny days typically benefit buyers through lower settlement prices, while cloudy days favor sellers who execute trades closer to their actual needs. These findings demonstrate the trade-off between early execution and forecast error losses, emphasizing the simulator’s ability to analyze trading outcomes while accounting for participant risk aversion preferences.
Producing and Testing the Properties of Biodiesel Sourced from Hemp Oil
Authors: Kurczyński Dariusz , Wcisło Grzegorz
Published: November 2024

Organic matter is converted into a variety of fuels, including potential replacements for transport fuels. New sources of raw materials are being sought for their acquisition. One such raw material that is currently attracting a growing degree of attention is hemp. The objective of this study was to produce biodiesel from hemp oil to ascertain its selected properties and to compare them with the properties of biodiesel obtained from rapeseed oil and the properties of diesel fuel. A reactor designed for the non-industrial, local conversion of available raw materials into fatty acid esters was used for the manufacture of biodiesel. The properties of hemp oil biodiesel were evaluated in comparison with those of rapeseed oil biodiesel, with properties of diesel fuel, and with the requirements set forth in the EN 14214 standard, pertaining to the specification of fatty acid methyl esters for utilization in compression-ignition internal combustion engines. The kinematic viscosity value of the hemp oil biodiesel yielded just below the upper limit defined in the standard. Furthermore, research has demonstrated that such biodiesel contains a considerable proportion of esters of linoleic and linolenic acids, which are susceptible to oxidation. The content of linolenic acid ester in esters produced from hemp oil is clearly higher than the content of this ester in esters obtained from rapeseed oil. This higher content contributes to the high value of the iodine number, significantly exceeding the standard requirements. The remaining designated properties of hemp oil biodiesel are in accordance with the requirements laid down in the standard and exhibit similarities to those of rapeseed oil biodiesel. Further research is recommended to enhance the characteristics of hemp oil biodiesel and its utilization in compression-ignition engines.