bluuebunny/crossref_metadata_2025 huggingface dataset:
3D Surgical Planning for Customized Devices in Orthopaedics: Applications in Massive Hip Reconstructions of Oncological Patients
Authors: Haidar Yazan , Belvedere Claudio , Spazzoli Benedetta , Donati Davide Maria , Leardini Alberto
Published: November 2024

This study investigates the morphological impact of using three-dimensional (3D) printed custom implants in surgical hip reconstruction compared to the conventional bone graft and standard size implant methods. An amount of 16 patients at the Rizzoli Orthopaedic Institute who underwent hip reconstruction surgery for tumors involving the P2 pelvis region were selected using stratified sampling. Half of them were randomly selected to receive 3D-printed implants, and the other half were selected to receive standard implants with bone grafts. Six months post-surgery, computed tomography (CT) scans were used to identify the hip joint center of rotation and to measure greater the trochanter offset and acetabular inclination angle. These CT scans were also used to construct a 3D model of the pelvis for 3D measurements. The results show no significant differences in accuracy, using Student’s t-test and Mann–Whitney U-test (p-value > 0.05), between the two methods for reconstructing the hip joint center of rotation or greater trochanter offset. However, 3D-printed implants showed statistically significant greater precision, using Student’s t-test (p-value < 0.05), in reconstructing the acetabular inclination angle compared to the conventional bone graft and standard-sized off-the-shelf implants. This superior precision reduces the risk of impingement of the femur implant neck with the acetabulum implant cup, which directly relates to improved implant survivorship. These findings support the continued exploration of 3D printing technology for personalized orthopedic solutions.
Study on Vibration Damping Characteristics of Mounting Structure Based on the High Damping Alloy Material
Authors: Lin Junzhe , Li Wenbiao , Chen Haiyang , Zhang Hongxu , Zhao Yulai , Ma Hui , Han Qingkai
Published: November 2024

The high damping alloy materials, by virtue of the excellent mechanical properties and damping performance generated by the material’s properties and internal friction mechanism, can be employed directly in the reduction of structural vibrations. On the basis of summarising the current research progress and application status of existing damping alloy materials, the damping characteristics of Fe-12Cr-3Al ferromagnetic high damping alloy material were tested and applied to the vibration reduction of an aero-engine lubricating oil tank. Subsequent to this, vibration characteristics simulation analysis and tests were conducted. The results show that the amplitude reduction rate of mounting structures utilising the Fe-12Cr-3Al ferromagnetic high damping alloy is 30% in comparison to Q235 carbon steel. Additionally, the vibration transmission rate can be diminished by 40%. The utilisation of the Fe-12Cr-3Al ferromagnetic high damping alloy in the aero-engine mounting structure has been observed to result in a discernible reduction in vibration.
The Prevalence of Egg Parasitoids of Two Cobweb Spiders in a Tropical Urban Gradient
Authors: Jiménez-Conejo Natalia , Hanson Paul E. , Chacón-Madrigal Eduardo , Rojas-Malavasi Geovanna
Published: November 2024

Parasitoidism strongly influences the structure of the spiders’ populations, and it can be affected by environmental factors such as those caused by anthropogenic actions. We studied the prevalence of parasitoids in egg sacs and the proportion of eggs parasitized in each egg sac of two synanthropic spider species, one native to the American continent (Parasteatoda tepidariorum) and another recently introduced to the Americas (Latrodectus geometricus). We conducted the study at two scales, along an urban gradient (from highly urbanized to rural sites) and in the vegetation surrounding each sampling site (microscale). We expected to find a larger prevalence of parasitoids in the most urbanized sites and around sampling sites with more vegetation. However, we saw more parasitized egg sacs at the intermediate urbanized site for both species, and the vegetation surrounding the sampling sites did not affect the number of parasitized egg sacs. Therefore, conditions in the site with intermediate urban development favored parasitoids. We also found more parasitized egg sacs in P. tepidariorum than in L. geometricus, which is likely a consequence of native parasites not being adapted to a new host. The proportion of eggs parasitized was similar for both species in all sites, which may be related to the behavior (e.g., searching behavior) and number of spider eggs a female parasitoid can parasitize.
Accelerated Transfer Learning for Cooperative Transportation Formation Change via SDPA-MAPPO (Scaled Dot Product Attention-Multi-Agent Proximal Policy Optimization)
Authors: Budiyanto Almira , Azetsu Keisuke , Matsunaga Nobutomo
Published: November 2024

A method for cooperative transportation, which required formation change in a traveling environment, is gaining interest. Deep reinforcement learning is used in formation changes for multi-robot cases. The MADDPG (Multi-Agent Deep Deterministic Policy Gradient) method is popularly used for recognized environments. On the other hand, re-learning may be required in unrecognized circumstances by using the MADDPG method. Although the development of MADDPG using model-based learning and imitation learning has been applied to reduce learning time, it is unclear how the learning results are transferred when the number of robots changes. For example, in the GASIL-MADDPG (Generative adversarial self-imitation learning and Multi-agent Deep Deterministic Policy Gradient) method, how the results of three robot training can be transferred to the four robots’ neural networks is uncertain. Nowadays, Scaled Dot Product Attention (SDPA) has attracted attention and is highly impactful for its speed and accuracy in natural language processing. When transfer learning is combined with fast computation, the efficiency of edge-level re-learning is improved. This paper proposes a formation change algorithm that allows easy and fast multi-robot knowledge transfer using SDPA combined with MAPPO (Multi-Agent Proximal Policy Optimization), compared to other methods. This algorithm applies SDPA to multi-robot formation learning and performs fast learning by transferring the acquired knowledge of formation changes to a certain number of robots. The proposed algorithm is verified by simulating the robot formation change and was able to achieve dramatic high-speed learning capabilities. The proposed SDPA-MAPPO (Scaled Dot Product Attention-Multi-Agent Proximal Policy Optimization) learned 20.83 times faster than the Deep Dyna-Q method. Furthermore, using transfer learning from a three-robot to five-robot case, the method shows that the learning time can be reduced by about 56.57 percent. A scenario of three-robot to five-robot is chosen based on the number of robots often used in cooperative robots.
Kekulé Structure of Angularly Connected Even Ring Systems
Authors: Brezovnik Simon
Published: November 2024

An even ring system G is a simple 2-connected plane graph with all interior vertices of degree 3, all exterior vertices of either degree 2 or 3, and all finite faces of an even length. G is angularly connected if all of the peripheral segments of G have odd lengths. In this paper, we show that every angularly connected even ring system G, which does not contain any triple of altogether-adjacent peripheral faces, has a perfect matching. This was achieved by finding an appropriate edge coloring of G, derived from the proof of the existence of a proper face 3-coloring of the graph. Additionally, an infinite family of graphs that are face 3-colorable has been identified. When interpreted in the context of the inner dual of G, this leads to the introduction of 3-colorable graphs containing cycles of lengths 4 and 6, which is a supplementation of some already known results. Finally, we have investigated the concept of the Clar structure and Clar set within the aforementioned family of graphs. We found that a Clar set of an angularly connected even ring system cannot in general be obtained by minimizing the cardinality of the set A. This result is in contrast to the previously known case for the subfamily of benzenoid systems, which admit a face 3-coloring. Our results open up avenues for further research into the properties of Clar and Fries sets of angularly connected even ring systems.
Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme
Authors: Hisyam Ng Hibatul Azizi , Mahmoodi Toktam
Published: November 2024

Machine learning is taking on a significant role in materializing a new vision of 6G. 6G aspires to provide more use cases, handle high-complexity tasks, and improvise the current 5G and beyond 5G infrastructure. Artificial Intelligence (AI) and machine learning (ML) are the optimal candidates to support and deliver these aspirations. Traffic steering functions encompass many opportunities to help enable new use cases and improve overall performance. The emergence and advancement of the non-terrestrial network is another driving factor for creating an intelligence selection scheme to have a dynamic traffic steering function. With service-based architecture, 5G and 6G are data-driven architectures that use massive transactional data to emerge a new approach to handling highly complex processes. A highly complex process, a massive volume of data, and a short timeframe require a scheme using machine learning techniques to resolve the challenges. In this paper, the study creates a scheme to use the massive historical data and provide a decision scheme that enables dynamic traffic steering functions addressing the future emergence of the heterogeneous transport network and aligns with the Open Radio Access Network (O-RAN). The proposed scheme in this paper gives an inference to be programmed in the telecommunication nodes. It provides a novel scheme to enable dynamic traffic steering functions for the 6G transport network. The study shows an appropriate data size to create a high-performance multi-output classification model that produces more than 90% accuracy for traffic steering functions.
Navigating Fish Immunity: Focus on Mucosal Immunity and the Evolving Landscape of Mucosal Vaccines
Authors: Hopo Mai G. , Mabrok Mahmoud , Abu-Elala Nermeen , Yu Yongyao
Published: November 2024

The growing role of aquaculture in global food security has underscored the need for advanced immunological insights to protect fish health and boost productivity. As aquaculture’s importance rises, understanding fish immunity is crucial for developing effective vaccination strategies. Fish possess a specialized immune system with unique mucosal structures that enable resilience in aquatic environments. This review examines critical advances in fish mucosal immunity, particularly focusing on mucosal vaccines that target infection at primary entry points, such as the gills, skin, and gastrointestinal tract. Mucosal vaccination has demonstrated a compelling capacity to stimulate localized and systemic immune responses, offering enhanced protection against waterborne pathogens. Additionally, this review addresses knowledge gaps from previous research on the global aquaculture vaccines market by offering a regional perspective on industry developments, recent trends, and innovative vaccine formulations. In doing so, it highlights the role of mucosal vaccines in overcoming the specific challenges of fish farming and supporting sustainable aquaculture. This synthesis of current methodologies, industry practices, and future directions contributes to a deeper understanding of fish immunology, ultimately informing strategies to achieve optimal disease management and bolster global aquaculture resilience.
Natural Compounds Targeting Thymic Stromal Lymphopoietin (TSLP): A Promising Therapeutic Strategy for Atopic Dermatitis
Authors: Suleman Muhammad , Moltrasio Chiara , Tricarico Paola Maura , Marzano Angelo Valerio , Crovella Sergio
Published: November 2024

Atopic dermatitis (AD) is a chronic inflammatory skin disease with rising prevalence, marked by eczematous lesions, itching, and a weakened skin barrier often tied to filaggrin gene mutations. This breakdown allows allergen and microbe entry, with thymic stromal lymphopoietin (TSLP) playing a crucial role by activating immune pathways that amplify the allergic response. TSLP’s central role in AD pathogenesis makes it a promising therapeutic target. Consequently, in this study, we used the virtual drug screening, molecular dynamics simulation, and binding free energies calculation approaches to explore the African Natural Product Database against the TSLP protein. The molecular screening identified four compounds with high docking scores, namely SA_0090 (−7.37), EA_0131 (−7.10), NA_0018 (−7.03), and WA_0006 (−6.99 kcal/mol). Furthermore, the KD analysis showed a strong binding affinity of these compounds with TSLP, with values of −5.36, −5.36, −5.34, and −5.32 kcal/mol, respectively. Moreover, the strong binding affinity of these compounds was further validated by molecular dynamic simulation analysis, which revealed that the WA_0006-TSLP is the most stable complex with the lowest average RMSD. However, the total binding free energies were −40.5602, −41.0967, −27.3293, and −51.3496 kcal/mol, respectively, showing the strong interaction between the selected compounds and TSLP. Likewise, these compounds showed excellent pharmacokinetics characteristics. In conclusion, this integrative approach provides a foundation for the development of safe and effective treatments for AD, potentially offering relief to millions of patients worldwide.
The Interpersonal Theory of Suicide and Relationship Satisfaction: A Daily Diary Study
Authors: Love Heather A. , Morgan Preston
Published: November 2024

Romantic relationships serve as one of the most important relationships in adults’ lives, yet the influence of relational dynamics on suicide ideation (SI) is limited and longitudinal associations are unclear. The Interpersonal Theory of Suicide has been applied to romantic relationships broadly and supports motivations of suicide (thwarted belonging and perceived burdensomeness) and relationship satisfaction to be predictors of SI. An online daily diary study (n = 94 adults) was conducted to examine romantic relationship dynamics and mental health indicators in adults over 10 days. Multilevel growth modeling results revealed that higher perceived burdensomeness, but not relational satisfaction or thwarted belonging, was associated with higher initial levels of SI. However, perceived burdensomeness, thwarted belonging, and relationship satisfaction were not associated with rates of change in SI over time. Further, no interaction effects between either relationship satisfaction and thwarted belonging or perceived burdensomeness were found in association with trajectories of SI over the 10-day period. The results of this study indicate that relational satisfaction, through a commonly used global assessment of relational wellbeing, may not have a substantial influence on SI, particularly in individuals with mild SI levels. This may be due to relational satisfaction being relatively consistent, while SI is prone to short-term fluctuations. However, additional research is recommended to address other relational dynamics’ influences on SI.
Effects of Parent Involvement in Homework on Students’ Negative Emotions in Chinese Students: Moderating Role of Parent–Child Communication and Mediating Role of Family Responsibility
Authors: Li Jiayin , Liu Xiaomeng , Zhu Deqi , Jiang Haozhe
Published: November 2024

Chinese parents’ involvement in children’s homework has become a hot topic, which not only affects students’ learning but also leads to mental health problems. This study aimed to examine how parent involvement in homework affects students’ negative emotions, focusing on the mediating role of family responsibility and the moderating role of parent–child communication in it. The study uses data from the CFPS 2020 database by Peking University, with a sample size of 6906, resulting in 494 valid observations after data cleaning. Data were analyzed using SPSS 26.0 and SPSS Macro PROCESS, which examined the correlation coefficients, mediation effects, and moderated mediation among the variables. The results found that parent involvement in homework had a significant effect on students’ negative emotions. Family responsibility played a partial mediating role between parent involvement in homework and students’ negative emotions. Parent–child communication played a significant moderating role in the relationship between parent involvement in homework and family responsibility on students’ negative emotions. The results are consistent with the Family Systems Theory, and help to reduce the negative emotions of students and promote the physical and mental health of children.
Flexural Strength of Structural Beams Cast Using Combined Normal-Weight and Lightweight Concrete Mixtures
Authors: Jahami Ali , Dayaa Lynn , Assaad Joseph J. , Baalbaki Oussama , Khatib Jamal
Published: November 2024

Limited investigations have evaluated the potential of using layered sections of normal-weight and lightweight concrete (NWC and LWC) mixtures in structural beams and slabs. The main objective of this paper is to assess the flexural strength properties of layered reinforced concrete (RC) beams, which help conserve natural resources and reduce construction weight. Six RC beams cast with different NWC/LWC combinations are tested to determine the damage patterns, concrete strains, ultimate load, displacements at failure, and ductility. The test results showed that the LWC cast in the tension zone (and up to the neutral axis) has a negligible effect on the beam’s stiffness and ultimate load since the overall behavior remains governed by the yielding of tensile steel reinforcement. Nevertheless, the deflection at failure and ductility seem to gradually curtail when the NWC is partially replaced by LWC at different elevations across the beam’s cross-section. A finite element analysis using ABAQUS software 6.14 is performed, and the results are compared with experimental data for model validation. Such data can be of interest to structural engineers and consultants aiming for optimized design of slabs and beams using layered concrete casting, which helps reduce the overall construction weight while maintaining the structural integrity of members.
Effects of Soil–Structure Interaction on the Seismic Response of RC Frame–Shear Wall Building Structures Under Far-Field Long-Period Ground Motions
Authors: Yang Ke , Cai Pengrong , Zhang Zixuan , Hou Qinglin , Zheng Ruige , Hao Bin , Wang Bo
Published: November 2024

This paper focuses on the effect of soil–structure interaction (SSI) on the seismic response of high-rise RC frame–shear wall structures under far-field long-period ground motions. Elastic–plastic time–history analyses were performed using ABAQUS. The effects of the ground motion type, soil type, and structural frequency on the seismic response are analyzed and quantitatively evaluated. On this basis, the influence mechanism of SSI on the seismic response under far-field long-period ground motions is discussed and revealed through a ground motion spectrum analysis. The results show that the consideration of the SSI effect leads to an increase in the displacement response and a decrease in the shear response. The SSI coefficient of the base shear is all less than 1, ranging from 0.5 to 1. The SSI effect under far-field long-period ground motions is more pronounced than that under ordinary ground motions. The shear force reduction in the current code may not be applicable to the structural design considering the SSI effect under far-field long-period ground motions. The displacement response amplification of the SSI effect on loess soil (Site 2) is more remarkable than that on sand soil (Site 1). The SSI effect can reduce the structural frequency, especially for the structures with fewer floors on the softer soil site. The “bimodal characteristic” of the acceleration response spectrum for far-field long-period ground motions may lead to shear force amplification when SSI is considered.
A Change Management View on Technology Adoption in Hotel Organizations: A Review and a Conceptual Framework
Authors: Somera Francis Albert R. , Petrova Krassie
Published: November 2024

In response to the rapid advancement in smart technology and the 2019–2020 pandemic, the hospitality industry has accelerated its adoption of innovative technologies. However, new technologies are often disruptive and may not be aligned with the organization’s strategy, technological expertise, and employee and customer expectations. This negatively affects the perceived value of the new technology and its adoption and continuous use. The review of the relevant research presented in this paper indicates that despite the many potential points of intersection between technology acceptance and adoption theories and studies of entrepreneurship and change management, these connections have not been explored in sufficient depth in the hospitality context. Drawing on extant theories, this study proposes a novel conceptual framework for the management of technology adoption in hotel organizations. The four stages of the framework represent the process of technology adoption from initiation to institutionalization to new challenges, connecting the theoretical constructs with the tangible needs of the hotel organizations. The framework considers technology adoption as a process of change that involves capability and leadership building. It shows how managing technology adoption through an integrated change management and digital capability-building perspective can lead to sustainable digital innovation. Practitioners can use the framework to systematically evaluate the potential of new and emerging technologies and develop the required digital competencies while managing organizational culture shifts and user resistance to change.
Benchmarking Pretrained Models for Speech Emotion Recognition: A Focus on Xception
Authors: Hassan Ahmed , Masood Tehreem , Ahmed Hassan A. , Shahzad H. M. , Tayyab Khushi Hafiz Muhammad
Published: November 2024

Speech emotion recognition (SER) is an emerging technology that utilizes speech sounds to identify a speaker’s emotional state. Computational intelligence is receiving increasing attention from academics, health, and social media applications. This research was conducted to identify emotional states in verbal communication. We applied a publicly available dataset called RAVDEES. The data augmentation process involved adding noise, applying time stretching, shifting, and pitch, and extracting the features zero cross rate (ZCR), chroma shift, Mel-Frequency Cepstral Coefficients (MFCC), and a spectrogram. In addition, we used many pretrained deep learning models, such as VGG16, ResNet50, Xception, InceptionV3, and DenseNet121. Out of all of the deep learning models, Xception yielded superior outcomes. Furthermore, we improved performance by changing the Xception model to include hyperparameters and additional layers. We used a variety of performance evaluation parameters to test the proposed model. These included F1-score, accuracy, misclassification rate (MCR), precision, sensitivity, specificity, negative predictive value, false negative rate, false positive rate, false discovery rate, false omission rate, and false discovery rate. The model that we suggested demonstrated an overall accuracy of 98%, with an MCR of 2%. Additionally, it attained precision, sensitivity, and specificity values of 91.99%, 91.78%, and 98.68%, respectively. Additional models attained an F1-score of 91.83%. Our suggested model demonstrated superiority compared to other cutting-edge techniques
Exploring the Role of News Outlets in the Rise of a Conspiracy Theory: Hydroxychloroquine in the Early Days of COVID-19
Authors: Dickinson Robert , Makowski Dominique , van Marwijk Harm , Ford Elizabeth
Published: November 2024

Improper use of hydroxychloroquine to treat COVID-19 has been linked to 17,000 preventable deaths. This content analysis study investigates the emergence of this conspiracy theory, the role of the news media in perpetuating and disseminating it, and whether coverage differed by outlet political alignment. We searched Nexis for relevant media from 17–31 March 2020. A total of 128 media pieces were coded qualitatively and thematically analysed. The news media amplified the voices of right-wing political elites and used a variety of manipulative tactics in reporting on hydroxychloroquine. Powerful ingroup/outgroup mechanisms polarised the American public and created a schism between Trump supporters and the public health apparatus that reflected the political asymmetry in reporting on hydroxychloroquine. The widespread use of optimistic framings and anecdotal evidence contributed to public misunderstandings of the evidence. Therefore, strategic and interventionist public health efforts are required to combat misinformation. This study informs discussions of how politicised media coverage catalyses conspiracism.
Accuracy and Outcomes of Sentinel Lymph Node Biopsy in Male with Breast Cancer: A Narrative Review and Expert Opinion
Authors: Cipolla Calogero , Gebbia Vittorio , D’Agati Eleonora , Greco Martina , Mesi Chiara , Scandurra Giuseppa , Sambataro Daniela , Valerio Maria Rosaria
Published: November 2024

Male breast cancer (MBC) is a rare disease, accounting for less than 1% of all breast cancer cases. Sentinel lymph node biopsy (SLNB) has emerged as a less invasive alternative to axillary lymph node dissection (ALND) for axillary staging in breast cancer, offering reduced morbidity and comparable accuracy. However, the application of SLNB in MBC remains underexplored, with limited male-specific data and treatment protocols often extrapolated from female breast cancer studies. Available evidence suggests that SLNB in men demonstrates high diagnostic accuracy, with low false-negative rates and a high sentinel lymph node identification rate. Despite this, there is ongoing debate about its long-term impact on clinical outcomes, particularly for patients with sentinel node metastasis, where ALND may still provide superior survival outcomes in some cases. Predictive tools are being developed to identify better patients who may benefit from SLNB alone, potentially reducing the need for more invasive procedures. As the role of SLNB continues to evolve in MBC management, further prospective research is needed to refine its application and assess its long-term oncologic outcomes.
Automated Quantitative Susceptibility and Morphometry MR Study: Feasibility and Interrelation Between Clinical Score, Lesion Load, Deep Grey Matter and Normal-Appearing White Matter in Multiple Sclerosis
Authors: Manasseh Gibran , Hilbert Tom , Fartaria Mário João , Deverdun Jeremy , Cuadra Meritxell Bach , Maréchal Bénédicte , Kober Tobias , Dunet Vincent
Published: November 2024

Introduction: Lesion load (LL), deep gray matter (DGM) and normal-appearing white matter (NAWM) susceptibility and morphometry may help in monitoring brain changes in multiple sclerosis (MS) patients. We aimed at evaluating the feasibility of a fully automated segmentation and the potential interrelation between these biomarkers and clinical disability. Methods: Sixty-six patients with brain MRIs and clinical evaluations (Expanded Disability Status Scale [EDSS]) were retrospectively included. Automated prototypes were used for the segmentation and morphometry of brain regions (MorphoBox) and MS lesions (LeManPV). Susceptibility maps were estimated using standard post-processing (RESHARP and TVSB). Spearman’s rho was computed to evaluate the interrelation between biomarkers and EDSS. Results: We found (i) anticorrelations between the LL and right thalamus susceptibility (rho = −0.46, p < 0.001) and between the LL and NAWM susceptibility (rho = [−0.68 to −0.25], p ≤ 0.05); (ii) an anticorrelation between LL and DGM (rho = [−0.71 to −0.36], p < 0.04) and WM morphometry (rho = [−0.64 to −0.28], p ≤ 0.01); and (iii) a positive correlation between EDSS and LL (rho = [0.28 to 0.5], p ≤ 0.03) and anticorrelation between EDSS and NAWM susceptibility (rho = [−0.29 to −0.38], p < 0.014). Conclusions: Fully automated brain morphometry and susceptibility monitoring is feasible in MS patients. The lesion load, thalamus and NAWM susceptibility values and trophicity are interrelated and correlate with disability.
High Serum Adrenomedullin and Mid-Regional Pro-Atrial Natriuretic Peptide Concentrations in Early Pregnancy Predict the Development of Gestational Hypertension
Authors: Jagodzinska Aleksandra , Wsol Agnieszka , Gondek Agata , Cudnoch-Jedrzejewska Agnieszka
Published: November 2024

Objectives: Adrenomedullin (AM) and natriuretic peptide levels are elevated in pre-eclampsia. The aim of the present study was to determine AM and natriuretic peptide concentrations before 20 weeks of pregnancy in women who later developed gestational hypertension and in normal pregnancies. Methods: 95 pregnant Caucasian women were included in the study. Gestational hypertension (GH) was diagnosed in 18 patients. The control group consisted of 41 patients with normal pregnancies (non-GH). Blood samples were taken during the first trimester of pregnancy. Results: Analysis of NT-proBNP showed no significant differences between the group of patients who later developed GH and those with normal pregnancies. Patients who developed GH later in pregnancy had higher levels of both MR-proANP (p < 0.001) and adrenomedullin (p < 0.001). Higher levels of MR-proANP were found in the GH with pre-eclampsia group compared with the GH without pre-eclampsia group. Higher levels of AM (p < 0.05) and MR-proANP (p < 0.005) correlated with the risk of preterm birth. Conclusions: (1) Plasma adrenomedullin and MR-proANP concentrations were higher before the 20th week of pregnancy in women who later developed GH; (2) NT-proBNP concentrations did not differ between women with pregnancy-induced hypertension and normal pregnancies; (3) MR-proANP concentrations were highest in patients who developed pre-eclampsia in advanced pregnancy; and (4) there was a correlation between higher plasma adrenomedullin, MR-proANP concentrations before the 20th week of pregnancy, and the risk of preterm birth.
CAD-EYE: An Automated System for Multi-Eye Disease Classification Using Feature Fusion with Deep Learning Models and Fluorescence Imaging for Enhanced Interpretability
Authors: Khalid Maimoona , Sajid Muhammad Zaheer , Youssef Ayman , Khan Nauman Ali , Hamid Muhammad Fareed , Abbas Fakhar
Published: November 2024

Background: Diabetic retinopathy, hypertensive retinopathy, glaucoma, and contrast-related eye diseases are well-recognized conditions resulting from high blood pressure, rising blood glucose, and elevated eye pressure. Later-stage symptoms usually include patches of cotton wool, restricted veins in the optic nerve, and buildup of blood in the optic nerve. Severe consequences include damage of the visual nerve, and retinal artery obstruction, and possible blindness may result from these conditions. An early illness diagnosis is made easier by the use of deep learning models and artificial intelligence (AI). Objectives: This study introduces a novel methodology called CAD-EYE for classifying diabetic retinopathy, hypertensive retinopathy, glaucoma, and contrast-related eye issues. Methods: The proposed system combines the features extracted using two deep learning (DL) models (MobileNet and EfficientNet) using feature fusion to increase the diagnostic system efficiency. The system uses fluorescence imaging for increasing accuracy as an image processing algorithm. The algorithm is added to increase the interpretability and explainability of the CAD-EYE system. This algorithm was not used in such an application in the previous literature to the best of the authors’ knowledge. The study utilizes datasets sourced from reputable internet platforms to train the proposed system. Results: The system was trained on 65,871 fundus images from the collected datasets, achieving a 98% classification accuracy. A comparative analysis demonstrates that CAD-EYE surpasses cutting-edge models such as ResNet, GoogLeNet, VGGNet, InceptionV3, and Xception in terms of classification accuracy. A state-of-the-art comparison shows the superior performance of the model against previous work in the literature. Conclusions: These findings support the usefulness of CAD-EYE as a diagnosis tool that can help medical professionals diagnose an eye disease. However, this tool will not be replacing optometrists.
The Efficacy of Er:YAG Laser in the Extraction of Impacted Third Molars: A Randomized Clinical Trial
Authors: Sologova Diana , Diachkova Ekaterina , Sologova Susanna , Smolyarchuk Elena , Margaryan Arus , Grigorevskikh Ekaterina , Petruk Pavel , Tumanova Elizaveta , Svitich Oxana , Tarasenko Svetlana
Published: November 2024

(1) Background: Impacted third molar extraction with a scalpel and rotary instruments is one of the most traumatic surgeries in dentistry. Therefore, it is necessary to discover less traumatic methods and instruments to reduce the risk of postoperative complications. (2) Methods: This study is reported in accordance with the CONSORT guidelines. The study aim is to assess the effectiveness of an Er:YAG laser with a wavelength of 2.94 μm, cutting and rotating instruments in the extraction of lower third molars in comparison with the traditional instruments using clinical and radiology parameters. In the control group, the impacted third molars were extracted with the traditional instruments, like scalpel and rotary instruments; in the test group, the impacted third molars were extracted with an Er:YAG laser. As per the inclusion and exclusion criteria, we enrolled 60 patients who were randomly assigned into two groups (Er:YAG laser group and control group). The efficacy of the Er:YAG laser was assessed by postoperative pain, collateral swelling, mouth opening, and radiology parameters such as radiographic infrabony defects and radiographic bone height after tooth extraction. (3) Results: The results showed that the clinical postoperative parameters like pain, collateral swelling, and mouth opening were less pronounced in the Er:YAG laser group than those in the control group (p < 0.001). According to the data of the radiology parameters (RBH and RID), the regeneration of the socket after extraction was better in the laser group than in the control group (p < 0.001). (4) Conclusions: Based on the obtained results of clinical and radiology parameters assessment, it was shown that third molar extraction using an Er:YAG laser is a less traumatic method than extraction using a scalpel and rotary instruments.
Quantum Circuit Architecture Search on a Superconducting Processor
Authors: Linghu Kehuan , Qian Yang , Wang Ruixia , Hu Meng-Jun , Li Zhiyuan , Li Xuegang , Xu Huikai , Zhang Jingning , Ma Teng , Zhao Peng , Liu Dong E. , Hsieh Min-Hsiu , Wu Xingyao , Du Yuxuan , Tao Dacheng , Jin Yirong , Yu Haifeng
Published: November 2024

Variational quantum algorithms (VQAs) have shown strong evidence to gain provable computational advantages in diverse fields such as finance, machine learning, and chemistry. However, the heuristic ansatz exploited in modern VQAs is incapable of balancing the trade-off between expressivity and trainability, which may lead to degraded performance when executed on noisy intermediate-scale quantum (NISQ) machines. To address this issue, here, we demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique, i.e., quantum architecture search (QAS), to enhance VQAs on an 8-qubit superconducting quantum processor. In particular, we apply QAS to tailor the hardware-efficient ansatz toward classification tasks. Compared with heuristic ansätze, the ansatz designed by QAS improves the test accuracy from 31% to 98%. We further explain this superior performance by visualizing the loss landscape and analyzing effective parameters of all ansätze. Our work provides concrete guidance for developing variable ansätze to tackle various large-scale quantum learning problems with advantages.
Quantum Computing in Community Detection for Anti-Fraud Applications
Authors: Wang Yanbo (Justin) , Yang Xuan , Ju Chao , Zhang Yue , Zhang Jun , Xu Qi , Wang Yiduo , Gao Xinkai , Cao Xiaofeng , Ma Yin , Wu Jie
Published: November 2024

Fraud detection within transaction data is crucial for maintaining financial security, especially in the era of big data. This paper introduces a novel fraud detection method that utilizes quantum computing to implement community detection in transaction networks. We model transaction data as an undirected graph, where nodes represent accounts and edges indicate transactions between them. A modularity function is defined to measure the community structure of the graph. By optimizing this function through the Quadratic Unconstrained Binary Optimization (QUBO) model, we identify the optimal community structure, which is then used to assess the fraud risk within each community. Using a Coherent Ising Machine (CIM) to solve the QUBO model, we successfully divide 308 nodes into four communities. We find that the CIM computes faster than the classical Louvain and simulated annealing (SA) algorithms. Moreover, the CIM achieves better community structure than Louvain and SA as quantified by the modularity function. The structure also unambiguously identifies a high-risk community, which contains almost 70% of all the fraudulent accounts, demonstrating the practical utility of the method for banks’ anti-fraud business.
Effectiveness of the VFSMOD Model in Simulating Sediment Trapping by a Vegetative Filter Strip: Case of the Lobo Reservoir in Daloa (Central–West Côte d’Ivoire)
Authors: Koua Tanoh Jean-Jacques , Kouassi Kouakou Hervé , Kouassi Kouakou Lazare , Allou Koffi Alfred , Jeong Jaehak
Published: November 2024

The Lobo reservoir, designed to supply water to the Daloa city population (central west of Côte d’Ivoire), is facing the phenomenon of eutrophication due to the agricultural plots located upstream of the reservoir inputs. Studies have highlighted the reservoir pollution and sedimentation problems. This study was initiated to test the effectiveness of a transfer model in the fight against the eutrophication problem. The objective of this study was to determine the optimal width of a vegetative filter strip on the banks of the Lobo reservoir, where the water intake installations of the drunk water company SODECI are located, using the VFSMOD model. The methodological approach is structured into four (4) main stages: 1—Evaluation of runoff from the source of pollution (contributing surface) during a rainy episode; 2—Calculation of the incoming runoff volume and the sediment load; 3—Simulation of runoff reduction and incoming sediments within the grassy filter strip; and 4—Determination of the optimal width of the grassy strip. The results obtained showed a contributing source area of 13.5 ha and 2% as the average slope. A runoff volume of 4680 m3 coming from the source of pollution is reduced by 1119 m3 (24%) through a grassy strip that is 3 m wide. The sediment load goes from 132 kg to 33 kg, a reduction of 75% in the quantity of sediment entering. The scenarios tested showed that a grassy strip width equal to 3 m should allow for a reduction of 75% of upstream sediments. This grassy strip width of 3 m should be considered for implementing a vegetative filter strip around the Lobo reservoir.
Mathematical Modeling Approach and Exploration of Geometric Properties as Part of an Outdoor Activity for Primary-School Pupils in Out-of-School Learning
Authors: Bočková Veronika , Rumanová Lucia
Published: November 2024

School-age children and being outdoors are intrinsically linked. Education and the outdoors offer unique opportunities to extend the learning potential of children at this age in a more engaging way. From the point of view of mathematics, this way of learning is very suitable and certainly motivating for the pupils since mathematics, especially geometry, is not very popular among pupils. Therefore, in this article, we describe how we used an out-of-school learning experience for second-level primary school pupils (sixth to ninth grade) to link mathematics to outdoor learning. Activities that we solved with pupils in the outdoor environment were solved in the teaching process using only modeling. Practical applications were illustrated through interesting topographic fieldwork, which we analyzed for their appropriate integration into the teaching process by means of a priori analysis. The inclusion of these practical problems was preceded by research on 781 pupils of primary schools who solved application problems related to the circle and the square. It was clear from the research that pupils have a significant problem with the geometric interpretation of simple geometric concepts, which can be improved with the use of mathematical modeling and the linking of similar problems that can be carried out in a non-school environment.
A Receiver Position Estimation Method Based on LSTM for Multi-Transmitter Single-Receiver Wireless Power Transfer Systems
Authors: Dai Zhuoyue , Yang Yongmin , Luo Yanting , Chen Suiyu , Lin Zhilong
Published: November 2024

The multi-transmitter single-receiver wireless power transfer (MTSR-WPT) system has good tolerance for coil misalignment because the magnetic fields generated by multiple transmitters can be shaped to adapt to position changes in the receiver coil. In order to achieve magnetic field shaping of the MTSR-WPT system and increase power transfer efficiency, accurately estimating the position of the receiver coil is a key issue that needs to be addressed. In this article, a receiver position estimation method based on long short-term memory (LSTM) is proposed, which utilizes a data-driven approach to establish a neural network model. By learning the relationship between the measured time-series voltage data of the transmitter coils and the position of the receiver coil, the proposed model can achieve accurate position estimation of the receiver. Compared with previous works, the proposed method does not require communication between the transmitter and receiver, which is conducive to simplifying the system structure and reducing costs. In addition, the proposed LSTM-based method requires less derivation of complex formulas and the internal mechanism analysis of the system. Finally, a MTSR-WPT prototype is built to verify the proposed method. The experimental results show that the proposed LSTM-based method can achieve high-accuracy position estimation of the receiver. When the receiver moves within a range of 160 mm × 160 mm, the average error between the estimated receiver coil position using the proposed method and the actual receiver coil position is less than 2.40 mm.
Vehicle Target Detection Using the Improved YOLOv5s Algorithm
Authors: Dong Zhaopeng
Published: November 2024

This paper explores the application of the YOLOv5s algorithm integrated with the DeepSORT tracking detection algorithm in vehicle target detection, leveraging its advantages in data processing, loss function, network structure, and training strategy. Regarding detection frame regression, adopting Focal-EIOU can improve vehicle detection accuracy by precisely measuring overlap and better handle complex scenarios, enhancing the overall performance. The CoordConv convolution layer with more spatial position information is employed to enhance the original network structure’s convolution layer and improve vehicle positioning accuracy. The principle and effectiveness of the Shuffle Attention mechanism are analyzed and added to the YOLOv5s network structure to enhance training, improve detection accuracy and running speed. And the DeepSORT tracking detection algorithm is designed to achieve high-speed operation and high-accuracy matching in target tracking, enabling efficient and reliable tracking of objects. Simultaneously, the network structure is optimized to enhance algorithmic speed and performance. To meet the requirements of vehicle detection in practical transportation systems, real-world vehicle images are collected as a dataset for model training to achieve accurate vehicle detection. The results show that the accuracy rate P of the improved YOLOv5s algorithm is increased by 0.484%, and mAP_0.5:0.95 reaches 92.221%, with an increase of 1.747%.
Data-Driven Bus Trajectory Tracking Based on Feedforward–Feedback Model-Free Adaptive Iterative Learning Control
Authors: Xiu Weijie , Xie Yongqiang , Ren Ye , Wang Li
Published: November 2024

This paper presents a scheme for the feedforward–feedback longitudinal trajectory tracking control of buses. The scheme is specifically designed to address the periodic and repetitive nature of bus operations. First, the vehicle’s longitudinal dynamics are linearized along the iterative axis via full-form dynamic linearization (FFDL), and parameters such as the pseudo-gradient are estimated with data and a projection algorithm to grasp the dynamic characteristics of the system. To better handle complex real-world traffic conditions, we then propose the forward and backward structure. At the same time, the iterative axis design performance index is verified, and the forward partial control law, namely, model-free adaptive iterative learning control (MFAILC), is derived. In order to further enhance the robustness to disturbance and other factors, the control law of the feedback part is designed with active disturbance rejection control (ADRC). A key advantage of this control approach is its sole reliance on the data generated during vehicle operation, without the need for specific information about the controlled vehicle. This feature enables the method to be adaptable to different vehicle types and resilient to various disturbances. Finally, MATLAB simulations are used to verify the practicality of the proposed method.
Flexible DC Optimization Control Technology Based on Zonal Interconnection of Urban Power Grids
Authors: Zhu Zimin , Nan Dongliang , Duan Qingxi , Liu Zhen , Wu Xiaofang
Published: November 2024

Aiming at the problems of insufficient power supply capacity, unbalanced load, and poor safety and reliability of the urban power grid, a flexible DC optimization control strategy is proposed to support the interconnection of power grid zones. Firstly, the steady-state and transient optimal control objectives are established to meet the safety and economic operation requirements of the grid; secondly, comprehensive evaluation indexes are constructed, covering load balancing, network loss, and voltage deviation, and a step-by-step approximation method is used to quickly solve the optimal power of the flexible DC, so as to realize the optimal control of the urban grid in the steady state. Meanwhile, a multi-terminal flexible DC adaptive sag control strategy based on the power margin of the converter station is designed to reasonably allocate the emergency power support in each partition after a fault and reduce the frequency deviation of the heavily loaded nodes. Taking the urban grid as an example, a simulation model of the grid structure with a 750 KV outer ring and a 220 KV inner ring is constructed, and the simulation results verify the effectiveness of the proposed method and its engineering practicability.