bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of a Hybrid of the Different Transform Method and Adomian Decomposition Method Algorithms to Solve the Troesch Problem
Authors: Pleszczyński Mariusz , Kaczmarek Konrad , Słota Damian
Published: December 2024

The Troesch problem is a well-known and important problem; the ability to solve it is important due to the practical applications of this problem. In this paper, we propose a method to solve this problem using a combination of two useful algorithms: Different Transform Method (DTM) and Adomian Decomposition Method (ADM). The combination of these two methods resulted in a continuous approximate solution to this problem and eliminated the problems that occur when trying to use each of these methods separately. The great advantages of the DTM method are the continuous form of the solution and the fact that it easy to implement error control. However, in too-complex tasks, this method becomes difficult to use. By using a hybrid of ADM and DTM, we obtain a relatively simple-to-implement method that retains the advantages of the DTM approach.
Bubble Detection in Multiphase Flows Through Computer Vision and Deep Learning for Applied Modeling
Authors: Nizovtseva Irina , Mikushin Pavel , Starodumov Ilya , Makhaeva Ksenia , Kraev Simon , Chernushkin Dmitrii
Published: December 2024

An innovative method for bubble detection and characterization in multiphase flows using advanced computer vision and neural network algorithms is introduced. Building on the research group’s previous findings, this study combines high-speed video capture with advanced deep learning techniques to enhance bubble detection accuracy and dynamic analysis. In order to further develop a robust framework for detecting and analyzing bubble properties in multiphase flows, enabling accurate estimation of essential mass transfer parameters, a YOLOv9-based neural network was implemented for bubble segmentation and trajectory analysis, achieving high accuracy. Key contributions include the development of an averaged mass transfer model integrating experimental data, neural network outputs, and scaling algorithms, as well as validation of the proposed methodology through experimental studies, including high-speed video imaging and comparisons with mass transfer coefficients obtained via the sulfite method. By precisely characterizing critical parameters, the algorithm enables accurate gas transfer rate calculations, ensuring optimal conditions in various industrial applications. The neural network-based algorithm serves as a non-invasive platform for detailed characterization of bubble media, demonstrating high accuracy in experimental validation and significantly outperforming traditional techniques. This approach provides a robust tool for real-time monitoring and modeling of bubble flows, laying the foundation for novel, non-invasive methods to measure multiphase media properties.
Interval Estimation for the Two-Parameter Exponential Distribution Based on Upper Record Value Data Using Bayesian Approaches
Authors: Wu Shu-Fei
Published: December 2024

Using upper record value data, we provide a credible interval estimate for the scale parameter of a two-parameter exponential distribution based on Bayesian methods. Additionally, we propose two Bayesian credible confidence regions for both parameters. In addition to interval estimations for the parameters, we propose a Bayesian prediction interval for a future upper record value. A simulation study is conducted to compare the performance of the proposed Bayesian credible interval, regions and prediction intervals with existing non-Bayesian approaches, focusing on coverage probabilities. The simulation results show that the Bayesian approaches achieve higher coverage probabilities than existing methods. Finally, we use an engineering example to demonstrate all the proposed Bayesian credible estimations for the exponential distribution based on upper record value data.
Multiple and Nonexistence of Positive Solutions for a Class of Fractional Differential Equations with p-Laplacian Operator
Authors: Zhang Haoran , Hao Zhaocai , Bohner Martin
Published: December 2024

Research about multiple positive solutions for fractional differential equations is very important. Based on some outstanding results reported in this field, this paper continue the focus on this topic. By using the properties of the Green function and generalized Avery–Henderson fixed point theorem, we derive three positive solutions of a class of fractional differential equations with a p-Laplacian operator. We also study the nonexistence of positive solutions to the eigenvalue problem of the equation. Three examples are given to illustrate our main result.
Alterations in Biomarkers Associated with Cardiovascular Health and Obesity with Short-Term Lifestyle Changes in Overweight Women: The Role of Exercise and Diet
Authors: Şengün Nezihe , Pala Ragıp , Çınar Vedat , Akbulut Taner , Larion Alin , Padulo Johnny , Russo Luca , Migliaccio Gian Mario
Published: December 2024

Background and Objectives: In this study, the effects of an eight-week exercise and nutrition program on blood lipids, glucose, insulin, insulin resistance (HOMA-IR), leptin, ghrelin, irisin, malondialdehyde (MDA), and Growth Differentiation Factor 15 (GDF15) in overweight women were investigated. Materials and Methods: A total of 48 women volunteers participated in this study. The participants were randomly divided into four groups: control (C), exercise (E), nutrition (N), exercise + nutrition (E + N). While no intervention was applied to group C, the other groups participated in the predetermined programs for 8 weeks. At the beginning and end of this study, body composition was measured and blood samples were taken. Results: It was determined that the body composition components, lipid profile indicators, insulin, glucose, insulin resistance, leptin, ghrelin, irisin, and MDA parameters examined in this study showed positive changes in the intervention groups. Group E had a greater effect on body muscle percentage, MDA, and irisin levels, while group N had a greater effect on blood lipids and ghrelin levels. Conclusions: As a result, it is thought that lifestyle changes are important to improve cardiovascular health and combat obesity, and that maintaining a healthy diet together with exercise may be more effective.
Retrospective Study on Orthodontic Gingival Recession Correction Using Clear Aligners
Authors: Bucur Sorana Maria , Bud Eugen , Decusară Mioara , Olteanu Cristian-Doru
Published: December 2024

Background and Objectives: Our study aims to evaluate the effectiveness of ClearCorrect® clear aligners (The Straumann Group) in improving gingival recession, which developed following prior fixed orthodontic treatment. The specific objectives were to assess changes in recession severity in patients with orthodontic gingival recessions following aligner therapy and to determine whether the initial severity of recession influenced improvement rates across three categories—mild (RT1), moderate (RT2), and severe (RT3)—as defined by the Cairo classification. Materials and Methods: Fifty-six patients with gingival recession from previous orthodontic treatment received aligner therapy for seven to twelve months, focusing on 146 recession sites, primarily in the lower anterior region. Recession severity was classified and tracked as RT1, RT2, and RT3. Statistical analyses, including a Chi-square test, assessed improvement rates across these categories. Results: Among severe recessions (RT3), 52% showed no improvement, while 48% exhibited some improvement, with 16% improving to RT1. In the moderate group (RT2), 63% improved to RT1, and 16% resolved. The mild group (RT1) showed a 58% reduction in recession depth. Chi-square testing confirmed a statistically significant difference in improvement rates across the three groups (p = 1.44 × 10−7). Conclusions: Clear aligners may contribute to gingival recession improvement, particularly in mild and moderate cases (RT1 and RT2), though severe cases (RT3) show a more limited response.
The Comparative Effects of Anakinra and Tocilizumab on Inflammation and Cerebral Vasospasm in an Experimental Subarachnoid Hemorrhage Model
Authors: Kılıç Güven , Engin Berk Enes , Halabi Amir , Tuncer Cengiz , Sungur Mehmet Ali , Alpay Merve , Kurtuluş Adem , Soylu Hakan , Gök Ali
Published: December 2024

Objective: Subarachnoid hemorrhage (SAH) is a life-threatening cerebrovascular condition that triggers a robust inflammatory response and cerebral vasospasm. This study aimed to evaluate the effects of anakinra, an interleukin-1 receptor antagonist, and tocilizumab, an interleukin-6 receptor antagonist, on inflammation and vasospasm in an experimental rat SAH model. Methods: Forty male Sprague Dawley rats (200–250 g) were randomly assigned to five groups: control, SAH, SAH + anakinra (ANA), SAH + tocilizumab (TCZ), and SAH + anakinra + tocilizumab (ANA+TCZ). SAH was induced by injecting non-heparinized arterial blood into the cisterna magna. Treatment groups received anakinra (50 mg/kg twice daily), tocilizumab (8 mg/kg once daily), or their combination for three days. Blood and cerebrospinal fluid (CSF) samples were analyzed for inflammatory markers (IL-1, IL-6, TNF-α, CRP), and histopathological evaluations were conducted to assess vasospasm and apoptosis. Results: SAH significantly increased pro-inflammatory cytokines (IL-1, IL-6, TNF-α, CRP) and fibrinogen levels in serum and CSF while reducing the basilar artery lumen diameter (p < 0.001). Anakinra and tocilizumab treatments significantly reduced inflammatory markers and vasospasm severity compared to the SAH group (p < 0.05). Combination therapy was more effective in reducing inflammation and vasospasm than either treatment alone (p < 0.05). Anakinra showed a stronger effect on IL-1 reduction, while tocilizumab was more effective in lowering IL-6 levels. The ANA+TCZ group exhibited a significant decrease in caspase activity, indicating reduced apoptosis (p < 0.05). Conclusions: Anakinra and tocilizumab effectively mitigated inflammation and vasospasm in an experimental SAH model, with combination therapy showing superior efficacy. These findings suggest that targeting both IL-1 and IL-6 pathways may be a promising therapeutic strategy for managing SAH complications. Further studies are warranted to evaluate long-term outcomes and clinical implications.
CD44 Immunohistochemical Expression in Central and Peripheral Parts of Prostatic Adenocarcinoma: An Institutional Study
Authors: Oguic Romano , Grskovic Antun , Spanjol Josip , Mikolasevic Ivana , Djordjevic Gordana
Published: December 2024

Background and Objectives: Prostate cancer is one of the most commonly diagnosed cancers in the male population and the fifth leading cause of cancer death worldwide in men as of 2022. One of the potential biomarkers that can predict the progression of the disease is the transmembrane adhesion molecule CD44s. The aims of this study were to determine the expression of CD44s in prostate cancer in the central tumor mass and in the tumor periphery of the disease and to compare it with the clinicopathological parameters (PSA, Gleason score, surgical margins, and biochemical recurrence of the disease) in patients treated with radical prostatectomy. Materials and Methods: The research was randomized retrospectively during the period from 2001 to 2006. Tissue microarrays of 121 archival acinar prostate carcinoma samples were immunohistochemically evaluated for CD44s expression. The immunoexpression was determined semiquantitatively, taking into account the percentage (0 (0–5%), 1 (6–24%), 2 (26–75%), and 3 (76–100%) and intensity of the membranous staining of the tumor cells (0 absent; 1 weak at 400×; 2 intermediates at 100×; 3 strong at 40×) and calculated to obtain a final score (0–3 were regarded as negative; 4–6 were regarded as positive). Results: For statistical purposes, we divided the tumors into two categories: Gleason grade group 1 makes up 80.7% and grade group 2, which includes all the remaining Gleason grade groups (out of 2–5), accounts for 19.3% of the tumors. Grade group 1 had the highest incidence of score 4 (positive expression). There were statistically significantly more positive expressions in those tumors with negative prostatectomy margins (chi square: p = 0.001; Cramer V: 0.319). There was no correlation between CD44s expression and biochemical recurrence (p = 0.218), nor with the preoperative PSA values (p = 0.165). In the grade group 1 tumors, the CD44s immunoexpression and status of prostatectomy margin were statistically significantly related with negative margins (p = 0.028). An analysis of the expression of CD44s according to the localization in the central part of the tumor mass and on the periphery of the cancer in the group of tumors with a positive margin did not show a significant correlation because the sample was too small. Descriptively, it can be noted that the expression on the periphery was higher, and the central/peripheral expression ratio was higher in favor of the periphery. Conclusions: Our results provide insight into the possible value of CD44s expression for predicting the behavior of prostate tumors and the justification of therapy after a prostatectomy. Also hypothetically, they indicate a protective role of CD44s in a group of well-differentiated tumors at the periphery of the tumor mass. Therefore, it is useful to study the CD44s molecule further in this sense.
Evaluation of In Vitro Inhibition of β-Hematin Formation: A Step Towards a Comprehensive Understanding of the Mechanism of Action of New Arylamino Alcohols
Authors: Damiani Céline , Soler Floriane , Le Govic Yohann , Totet Anne , Bentzinger Guillaume , Bouchut Anne , Mustière Romain , Agnamey Patrice , Dassonville-Klimpt Alexandra , Sonnet Pascal
Published: December 2024

Currently, artemisinin-based combination therapy is recommended as first-line treatment of uncomplicated falciparum malaria. Arylamino alcohols (AAAs) such as mefloquine (MQ) are the preferred partner drugs due to their longer half-life, reliable absorption and strong antimalarial activity. However, the mode of action of MQ remains poorly understood and its neurotoxicity limits its use. Furthermore, the emergence of drug-resistant parasites requires development of new antimalarial drugs. The aim of this study was to evaluate the β-hematin inhibition capacity of three pairs of enantiopure AAAs 1–3 (a/S and b/R) derived from MQ or enpiroline (ENP), a pyridine-based MQ analog with strong antimalarial activity. Inhibition of β-hematin—the synthetic counterpart of hemozoin formation—was determined for each compound. Antimalarial activity against W2 and 3D7 Plasmodium falciparum strains as well as percentages of inhibition of β-hematin formation were compared to those of reference molecules, i.e., chloroquine (CQ), MQ and ENP. Furthermore, a cytotoxicity study on the human-derived hepatocarcinoma cell line HepG2 was performed. With high antimalarial activity, stronger ability to inhibit β-hematin formation and low cytotoxicity, AAAs 1a-b and 2a are the most promising. These findings provide a better understanding of their potential mechanisms of action and may pave the way toward developing new lead compounds.
The Role of ClpV in the Physiology and Pathogenicity of Xanthomonas citri subsp. citri Strain zlm1908
Authors: Li Ya , Wu Zilin , Liu Dengyan , Cong Kexin , Dai Jiajun , Xu Wenjie , Ke Yingtong , He Xinyi
Published: December 2024

Xanthomonas citri subsp. citri (Xcc) is a Gram-negative bacterium responsible for citrus canker, a significant threat to citrus crops. ClpV is a critical protein in the type VI secretion system (T6SS) as an ATPase involved in bacterial motility, adhesion, and pathogenesis to the host for some pathogenic bacteria. In order to investigate the function of clpV gene in Xcc, the clpV-deletion strain ΔclpV was constructed, its biological properties were evaluated, and the differences in gene expression levels between the wild-type strain and ΔclpV were analyzed by transcriptomics. The results exhibited significantly reduced biofilm formation, extracellular polysaccharide synthesis, and swarming motility in ΔclpV compared to the wild-type strain. Although the clpV-deletion did not significantly affect bacterial growth or pathogenicity in terms of disease symptoms on citrus leaves, the mutant showed increased sensitivity to environmental stresses (NaCl, SDS, and H2O2) and antibiotics (β-lactams and aminoglycosides). Transcriptome analysis revealed that clpV-deletion altered the expression of motility-related genes and the efflux pump gene mexH. Our findings underscore the importance of ClpV in maintaining biofilm integrity and suggest a multifaceted role in adaptive strategies of Xcc, positioning ClpV as a potential target for mitigating citrus canker disease.
Reorientation Transition Between Square and Hexagonal Skyrmion Lattices near the Saturation into the Homogeneous State in Quasi-Two-Dimensional Chiral Magnets
Authors: Leonov Andrey O.
Published: December 2024

I revisit the well-known phase transition between the hexagonal skyrmion lattice and the homogeneous state within the phenomenological Dzyaloshinskii theory for chiral magnets, which includes only the exchange, Dzyaloshinskii–Moriya, and Zeeman energy contributions. I show that, in a narrow field range near the saturation field, the hexagonal skyrmion order gradually transforms into a square arrangement of skyrmions. Then, by the second-order phase transition during which the lattice period diverges, the square skyrmion lattice releases a set of repulsive isolated skyrmions. On decreasing the magnetic field, isolated skyrmions re-condense into the square lattice at the same critical field as soon as their eigen-energy becomes negative with respect to the field-aligned state. The underlying reason for the reorientation transition between two skyrmion orders can be deduced from the energy density distribution within isolated skyrmions surrounded by the homogeneous state. When the negative energy within the ring-shaped area at the skyrmion outskirt outweighs the positive energy amount around the skyrmion axis, skyrmions tend to form the square lattice, in which the overlap of skyrmion profiles results in smaller energy losses as compared with the hexagonal crystal. With the further decreasing field, the hexagonal lattice achieves smaller energy density in comparison with the square one due to the denser packing of individual skyrmions.
Geometrical and Optical Properties of Optical Components Manufactured by Means of Stereolithography
Authors: Bartels Dominic , Heise Miriam , Schmidt Michael
Published: December 2024

Additive manufacturing (AM) is a fast-growing technology that supports the rapid fabrication of prototypes. Already, employing AM technologies in early product development stages can help to accelerate the time from conceptualisation phase to the market entry. Furthermore, advances in the field of materials, such as highly transparent resins in the visible wavelength spectrum, also open application potentials for the design and fabrication of optical components. Three highly transparent resins from different suppliers are studied in this work regarding their influence on the geometrical and optical properties of additively manufactured components. The different materials were processed using a masked stereolithography 3D printer. Geometrical and optical properties of the generated test samples were analysed by means of laser scanning microscopy and spectrophotometry, respectively. A clear correlation between optical properties of the resin and the solidified samples was identified. It was further found that while spin coating does not significantly affect the geometrical properties, a strong influence on the resulting optical properties could be observed. The highest transmission properties, in the range of up to 90%, were determined for samples that were spin-coated on both surfaces. Thus, applying a spin coating operation to additively manufactured optical components is recommended for the highest transmission properties.
Chronic Kidney Disease of Unknown Etiology: A Global Health Threat in Rural Agricultural Communities—Prevalence, Suspected Causes, Mechanisms, and Prevention Strategies
Authors: Ben Khadda Zineb , Lahmamsi Haitam , El Karmoudi Yahya , Ezrari Said , El Hanafi Laila , Sqalli Houssaini Tarik
Published: December 2024

Chronic Kidney Disease of Unknown Etiology (CKDu) is a worldwide hidden health threat that is associated with progressive loss of kidney functions without showing any initial symptoms until reaching end-stage renal failure, eventually leading to death. It is a growing health problem in Asia, Central America, Africa, and the Middle East, with identified hotspots. CKDu disease mainly affects young men in rural farming communities, while its etiology is not related to hypertension, kidney stones, diabetes, or other known causes. The main suspected causal factors are heat-stress, dehydration, exposure to agrochemicals, heavy metals and use of hard water, infections, mycotoxins, nephrotoxic agents, altitude, and genetic factors. This review gives an overview of CKDu and sheds light on its medical history, geographic distribution, and worldwide prevalence. It also summarizes the suspected causal factors, their proposed mechanisms of action, as well as the main methods used in the CKDu prior detection and surveillance. In addition, mitigation measures to reduce the burden of CKDu are also discussed. Further investigation utilizing more robust study designs would provide a better understanding of the risk factors linked to CKDu and their comparison between affected regions.
Preclinical Study of a Dual-Target Molecular Probe Labeled with 68Ga Targeting SSTR2 and FAP
Authors: Liu Huanhuan , Zhang Xiaojun , Pan Yue , Zhang Jingfeng , Wen Hui , Zhang Cong , Xu Xiaodan , Ma Guangyu , Wang Ruimin , Zhang Jinming
Published: December 2024

Objective: Currently, 68Ga-labeled somatostatin analogs (SSAs) are the most commonly used imaging agents for patients with neuroendocrine tumors (NETs) in clinical practice, demonstrating good results in tumor diagnosis. For applications in peptide receptor radionuclide therapy (PRRT), targeted drugs should have high tumor uptake and prolonged tumor retention time. To enhance the uptake and retention of tracers in NETs, our goal is to design a 68Ga-labeled heterodimer for optimizing pharmacokinetics and assess whether this form is more efficacious than its monomeric equivalents. Methods: Using the somatostatin analog TATE and quinoline-based compound FAPI-46 as raw materials, we designed and synthesized 68Ga-labeled TATE-46. The labeling efficiency and stability were verified by Radio-HPLC. The receptor binding properties and tumor targeting were examined both in vitro and in vivo by using NCI-H727 (SSTR2/FAP, positive) and Mc38 (SSTR2/FAP, negative) cell lines and tumor-bearing mouse models. Preclinical evaluation was performed through cell uptake, pharmacokinetics, Micro PET, and biodistribution studies, and the results were compared with [68Ga]Ga-DOTA-TATE and [68Ga]Ga -FAPI-46. Immunohistochemistry and HE staining were performed on tumor tissues from tumor-bearing mice for further validation. Results: [68Ga]Ga-TATE-46 showed comparable SSTR2 and FAP targeting ability to monomeric TATE and FAPI-46 in cell uptake and PET imaging studies. [68Ga]Ga-TATE-46 exhibited significantly higher uptake in NCI-H727 (SSTR2/FAP, positive) tumors compared to [68Ga]Ga-DOTA-TATE (p < 0.001) and [68Ga]Ga-FAPI-46 (p < 0.001). No increased uptake of [68Ga]Ga-TATE-46 was observed in MC38 tumors (SSTR2/FAP, negative). Additionally, excess DOTA-TATE and/or unlabeled FAPI-46 significantly blocked the uptake of [68Ga]Ga-TATE-46 in NCI-H727 tumors (p < 0.001), confirming its dual-receptor targeting characteristics. The ex vivo biodistribution, immunofluorescence and immunohistochemistry results were in line with the in vivo imaging findings. Conclusion: Compared with 68Ga-labeled FAPI-46 and DOTA-TATE mono-specific tracers, the dual-target tracer [68Ga]Ga-TATE-46 improves tumor uptake, extends tumor retention, and enhances pharmacokinetics. It is an effective probe for non-invasive detection of tumors expressing FAP and SSTR2, and it is worth further studying its application in the expression of sstr2 and FAP-related tumors.
Computation of the Multi-Spheres Scattering Coefficient Using the Prime Index Method
Authors: Huang Fangcheng , Santini Carlo , Mangini Fabio , Frezza Fabrizio
Published: December 2024

The analytical-numerical evaluation of the scattering of electromagnetic waves by multiple spheres requires the computation of numerous coefficients. For this purpose, many contributions, available in the literature, have traditionally employed the recursion method. In the present paper, we introduce a novel approach, based on primes and indices, which can be conveniently applied to the computation of the Wigner 3-j symbols, the Wigner D-function, and the Gaunt coefficients. By considering a series-expansion form, our method proves to be easily applicable to a variety of similar problems. We provide examples of coefficient calculations and compare the results with those retrieved from previous publications, demonstrating the advantages of our approach.
The Use of Digital Twins to Address Smart Tourist Destinations’ Future Challenges
Authors: Florido-Benítez Lázaro
Published: December 2024

This research aims to conceptualise the use of the digital twins (DT) tool in tourism to address smart tourist destinations’ future challenges to enhance tourists’ experiences and residents’ quality of life through better services developed by DTs. This paper investigated the use of DT technology to tackle the new challenges facing smart tourist destinations (STDs) in terms of urban planning, sustainability, security, marketing, and tourism activities by using data on this topic from expert researchers and public and private organisations. Also, this research adopted a systematic review approach to analyse and illustrate the existing literature on the topic of DTs and their use for STDs, which helped us develop the DT concept in a STD context. The findings of this research reveal that DT technology is emerging as a disruptive technology tool that is being used to improve the management of STDs in terms of their efficiency, safety, sustainability, environmental protection, productivity, and energy consumption. Moreover, DT technology is a nascent tool in the tourism and marketing industries, but its versatility in relation to its capacity to analyse data and predict the behaviour of tourism and business ecosystems to address relevant issues at STDs gives it a key role in the tourism industry. Finally, a new definition of DT technology has been included in this study within a tourism and STDs context, filling the gap in this topic in the tourism industry according to some researchers’ suggestions. There are limited scientific publications about DT technology that jointly tackle the DT technology and STDs sphere. Accordingly, this manuscript defines DT technology and provides a new viewpoint of this technology that will help to initiate academic discussions on DT tourism within the context of STDs.
Molecular Dynamics Simulation on the Process of Ultrasonic Viscosity Reduction
Authors: Shi Jianchao , Tong Li , Sun Lin , Jiang Tao , Yu Xiaofeng , Yu Kaijie , Lu Shaobo , Xu Weiwei
Published: December 2024

In this work, through experiments and molecular dynamics simulations, it was found that the viscosity of heavy oil decreased significantly after ultrasonic treatment, and the viscosity reduction rate can be up to 60%. The simulation results show that under the action of ultrasound, the macromolecules in the heavy oil were broken into small molecular hydrocarbons accounting for 89.2% with fewer carbon numbers (<6) and simple structures, as well as small molecules containing heteroatoms. The fracture rate of different bonds in the macromolecule under the action of ultrasound was in the range of 25% to 43%. The simulation results provide a theoretical basis for the industrial application of ultrasonic viscosity reduction.
Affinity-Enriched Plasma Proteomics for Biomarker Discovery in Abdominal Aortic Aneurysms
Authors: Palstrøm Nicolai Bjødstrup , Nielsen Kristian Boje , Campbell Amanda Jessica , Soerensen Mette , Rasmussen Lars Melholt , Lindholt Jes Sanddal , Beck Hans Christian
Published: December 2024

Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by the weakening and dilation of the abdominal aorta. Few diagnostic biomarkers have been proposed for this condition. We performed mass spectrometry-based proteomics analysis of affinity-enriched plasma from 45 patients with AAA and 45 matched controls to identify changes to the plasma proteome and potential diagnostic biomarkers. Gene ontology analysis revealed a significant upregulation of the proteins involved in inflammation, coagulation, and extracellular matrix in AAA patients, while proteins related to angiogenesis were among those downregulated. Using recursive feature elimination, we identified a subset of 10 significantly regulated proteins that were highly predictive of AAA. A random forest classifier trained on these proteins achieved an area under the curve (AUC) of 0.93 [95% CI: 0.91–0.95] using cross-validation. Further validation in a larger cohort is necessary to confirm these results.
Automated Visual Inspection for Precise Defect Detection and Classification in CBN Inserts
Authors: Zeng Li , Wan Feng , Zhang Baiyun , Zhu Xu
Published: December 2024

In the high-stakes domain of precision manufacturing, Cubic Boron Nitride (CBN) inserts are pivotal for their hardness and durability. However, post-production surface defects on these inserts can compromise product integrity and performance. This paper proposes an automated detection and classification system using machine vision to scrutinize these surface defects. By integrating an optical bracket, a high-resolution industrial camera, precise lighting, and an advanced development board, the system employs digital image processing to ascertain and categorize imperfections on CBN inserts. The methodology initiates with a high-definition image capture by the imaging platform, tailored for CBN insert inspection. A suite of defect detection algorithms undergoes comparative analysis to discern their efficacy, emphasizing the impact of algorithm parameters and dataset diversity on detection precision. The most effective algorithm is then encapsulated into a versatile application, ensuring compatibility with various operating systems. Empirical verification of the system shows that the detection accuracy of multiple defect types exceeds 90%, and the tooth surface recognition efficiency significantly reaches three frames per second, with the front and side cutting surfaces of the tool in each frame. This breakthrough indicates a scalable, reliable solution for automatically detecting and classifying surface defects on CBN inserts, paving the way for enhanced quality control in automated, high-speed production lines.
A XGBoost-Based Prediction Method for Meat Sheep Transport Stress Using Wearable Photoelectric Sensors and Infrared Thermometry
Authors: Ma Ruiqin , Chen Runqing , Liang Buwen , Li Xinxing
Published: December 2024

Transportation pressure poses a serious threat to the health of live sheep and the quality of their meat. So, the edible Hu sheep was chosen as the research object for meat sheep. We constructed a systematic biosignal detecting, processing, and modeling method. The biosignal sensing was performed with wearable sensors (photoelectric sensor and infrared temperature measurement) for physiological dynamic sensing and continuous monitoring of the transport environment of meat sheep. Core waveform extraction and modern spectral estimation methods are used to determine and strip out the target signal waveform from it for the purpose of accurate sensing and the acquisition of key transport parameters. Subsequently, we built a qualitative stress assessment method based on external manifestations with reference to the Karolinska drowsiness scale to establish stage classification rules for monitoring data in the transportation environment of meat sheep. Finally, machine learning algorithms such as Gaussian Naive Bayes (GaussianNB), Passive-Aggressive Aggregative Classifier (PAC), Nearest Centroid (NC), K-Nearest Neighbor Classification (KNN), Random Forest (RF), Support Vector Classification (SVC), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGB) were established to predict the classification models of transportation stress in meat sheep. Their classification results were compared. The results show that SVC and GBDT algorithms are more effective and the overall model classification accuracy reached 86.44% and 91.53%. XGB has the best results. The accuracy of the assessment of the transport stress state of meat sheep after the optimization of three parameters was 100%, 90.91%, and 93.33%, and the classification accuracy of the overall model reached 94.92%. The final results achieved improve transport reliability, reduce transport risk, and solve the problems of inefficient meat sheep transport supervision and quality control.
Sensor-Based Frailty Assessment Using Fitbit
Authors: Hosseinalizadeh Mohammad , Asghari Mehran , Toosizadeh Nima
Published: December 2024

This study evaluated the reliability of Fitbit in assessing frailty based on motor and heart rate (HR) parameters through a validated upper extremity function (UEF) test, which involves 20 s of rapid elbow flexion. For motor performance, participants completed six trials of full elbow flexion using their right arm, with and without weight. Fitbit and a commercial motion sensor were worn on the right arm. For HR measurements, an ECG system was placed on the left chest alongside the Fitbit on the left wrist. Motor parameters assessing speed, flexibility, weakness, exhaustion, and HR before, during, and after UEF were measured. A total of 42 participants (age = 22 ± 3) were recruited. For motor parameters, excellent agreement was observed between the wearable sensor and Fitbit, except for flexibility (ICC = 0.87 ± 0.09). For HR parameters, ICC values showed weak agreement between ECG and Fitbit for HR increase and recovery (ICC = 0.24 ± 0.11), while moderate to stronger agreement was seen for mean HR during baseline, task, and post-task (ICC = 0.81 ± 0.13). Fitbit is a reliable tool for assessing frailty through motor parameters and provides reasonably accurate HR estimates during baseline, task, and recovery periods. However, Fitbit’s ability to track rapid HR changes during activity is limited.
Extreme R-CNN: Few-Shot Object Detection via Sample Synthesis and Knowledge Distillation
Authors: Zhang Shenyong , Wang Wenmin , Wang Zhibing , Li Honglei , Li Ruochen , Zhang Shixiong
Published: December 2024

Traditional object detectors require extensive instance-level annotations for training. Conversely, few-shot object detectors, which are generally fine-tuned using limited data from unknown classes, tend to show biases toward base categories and are susceptible to variations within these unknown samples. To mitigate these challenges, we introduce a Two-Stage Fine-Tuning Approach (TFA) named Extreme R-CNN, designed to operate effectively with extremely limited original samples through the integration of sample synthesis and knowledge distillation. Our approach involves synthesizing new training examples via instance clipping and employing various data-augmentation techniques. We enhance the Faster R-CNN architecture by decoupling the regression and classification components of the Region of Interest (RoI), allowing synthetic samples to train the classification head independently of the object-localization process. Comprehensive evaluations on the Microsoft COCO and PASCAL VOC datasets demonstrate significant improvements over baseline methods. Specifically, on the PASCAL VOC dataset, the average precision for novel categories is enhanced by up to 15 percent, while on the more complex Microsoft COCO benchmark it is enhanced by up to 6.1 percent. Remarkably, in the 1-shot scenario, the AP50 of our model exceeds that of the baseline model in the 10-shot setting within the PASCAL VOC dataset, confirming the efficacy of our proposed method.
Brain and Subjective Responses to Indoor Environments Related to Concentration and Creativity
Authors: Wang Ze-Yu , Cho Ji Young , Hong Yi-Kyung
Published: December 2024

Electroencephalograms (EEGs) can be used to study the influence of environmental elements on human emotions, cognition, and behavior. EEGs can reveal unconscious responses and fill in the gaps left by subjective responses provided in survey questionnaires or interviews. EEG research on the impact of classroom design elements on concentration and creativity is scarce; the design elements studied have not been diverse enough. In addition, no researchers have examined the brain and subjective responses to multiple indoor environmental elements regarding concentration and creativity. Thus, the purpose of this study was to explore how the human brain responds to different indoor environmental elements as shown by objective EEG signals related to concentration and creativity, and their similarities and differences to subjective self-reported responses. The experimental stimuli included 16 images combining four indoor environmental elements—classroom space shape, furniture arrangement, ceiling height, and color—along with images of white walls, a full-window wall with a view of nature, and a windowless scenario, totaling 19 images. The brainwaves of 20 people collected from eight channels were analyzed to determine the concentration index (CI) for concentration and relative theta (RT) for creativity. As a subjective response, participants were asked to choose the stimuli in which they felt they could best concentrate and be most creative in a self-report format. The results showed the following tendencies: (a) More brainwaves in the parietal and occipital lobes than in the prefrontal or frontal lobes; (b) a higher CI with rectilinear shapes, traditional frontal furniture arrangements, and red walls; (c) a higher RT with curvilinear shapes, collaborative furniture arrangements, white walls, and a full view of nature; and (d) participants selected white walls and a front-facing furniture layout as supportive of concentration and a full view of nature, curvilinear shape, and collaborative furniture layout for creative thinking. The results showed that similarities in brain and subjective responses were related to furniture layout and shape, whereas differences existed in color. This study contributes to the understanding of the characteristics of indoor environments that appear to enhance the manifestation of concentration and creativity.
Pseudo Multi-Modal Approach to LiDAR Semantic Segmentation
Authors: Kim Kyungmin
Published: December 2024

To improve the accuracy and reliability of LiDAR semantic segmentation, previous studies have introduced multi-modal approaches that utilize additional modalities, such as 2D RGB images, to provide complementary information. However, these methods increase the cost of data collection, sensor hardware requirements, power consumption, and computational complexity. We observed that multi-modal approaches improve the semantic alignment of 3D representations. Motivated by this observation, we propose a pseudo multi-modal approach. To this end, we introduce a novel class-label-driven artificial 2D image construction method. By leveraging the close semantic alignment between image and text features of vision–language models, artificial 2D images are synthesized by arranging LiDAR class label text features. During training, the semantic information encoded in the artificial 2D images enriches the 3D features through knowledge distillation. The proposed method significantly reduces the burden of training data collection and facilitates more effective learning of semantic relationships in the 3D backbone network. Extensive experiments on two benchmark datasets demonstrate that the proposed method improves performance by 2.2–3.5 mIoU over the baseline using only LiDAR data, achieving performance comparable to that of real multi-modal approaches.
A Hierarchical-Based Learning Approach for Multi-Action Intent Recognition
Authors: Hollinger David , Pollard Ryan S. , Schall Mark C. , Chen Howard , Zabala Michael
Published: December 2024

Recent applications of wearable inertial measurement units (IMUs) for predicting human movement have often entailed estimating action-level (e.g., walking, running, jumping) and joint-level (e.g., ankle plantarflexion angle) motion. Although action-level or joint-level information is frequently the focus of movement intent prediction, contextual information is necessary for a more thorough approach to intent recognition. Therefore, a combination of action-level and joint-level information may offer a more comprehensive approach to predicting movement intent. In this study, we devised a novel hierarchical-based method combining action-level classification and subsequent joint-level regression to predict joint angles 100 ms into the future. K-nearest neighbors (KNN), bidirectional long short-term memory (BiLSTM), and temporal convolutional network (TCN) models were employed for action-level classification, and a random forest model trained on action-specific IMU data was used for joint-level prediction. A joint-level action-generic model trained on multiple actions (e.g., backward walking, kneeling down, kneeling up, running, and walking) was also used for predicting the joint angle. Compared with a hierarchical-based approach, the action-generic model had lower prediction error for backward walking, kneeling down, and kneeling up. Although the TCN and BiLSTM classifiers achieved classification accuracies of 89.87% and 89.30%, respectively, they did not surpass the performance of the action-generic random forest model when used in combination with an action-specific random forest model. This may have been because the action-generic approach was trained on more data from multiple actions. This study demonstrates the advantage of leveraging large, disparate data sources over a hierarchical-based approach for joint-level prediction. Moreover, it demonstrates the efficacy of an IMU-driven, task-agnostic model in predicting future joint angles across multiple actions.
A New Sensorized Approach Based on a DeepLabCut Model and IR Thermography for Characterizing the Thermal Profile in Knees During Exercise
Authors: Crisafulli Davide , Spataro Marta , De Marchis Cristiano , Risitano Giacomo , Milone Dario
Published: December 2024

The knee is one of the joints most vulnerable to disease and injury, particularly in athletes and older adults. Surface temperature monitoring provides insights into the health of the analysed area, supporting early diagnosis and monitoring of conditions such as osteoarthritis and tendon injuries. This study presents an innovative approach that combines infrared thermography techniques with a Resnet 152 (DeepLabCut based) to detect and monitor temperature variations across specific knee regions during repeated sit-to-stand exercises. Thermal profiles are then analysed in relation to weight distribution data collected using a Wii Balance Board during the exercise. DeepLabCut was used to automate the selection of the region of interest (ROI) for temperature assessments, improving data accuracy compared to traditional time-consuming semi-automatic methods. This integrative approach enables precise and marker-free measurements, offering clinically relevant data that can aid in the diagnosis of knee pathologies, evaluation of the rehabilitation progress, and assessment of treatment effectiveness. The results emphasize the potential of combining thermography with DeepLabCut-driven data analysis to develop accessible, non-invasive tools for joint health monitoring or preventive diagnostics of pathologies.
Exploring Components, Sensors, and Techniques for Cancer Detection via eNose Technology: A Systematic Review
Authors: Ramírez Washington , Pillajo Verónica , Ramírez Eileen , Manzano Ibeth , Meza Doris
Published: December 2024

This paper offers a systematic review of advancements in electronic nose technologies for early cancer detection with a particular focus on the detection and analysis of volatile organic compounds present in biomarkers such as breath, urine, saliva, and blood. Our objective is to comprehensively explore how these biomarkers can serve as early indicators of various cancers, enhancing diagnostic precision and reducing invasiveness. A total of 120 studies published between 2018 and 2023 were examined through systematic mapping and literature review methodologies, employing the PICOS (Population, Intervention, Comparison, Outcome, and Study design) methodology to guide the analysis. Of these studies, 65.83% were ranked in Q1 journals, illustrating the scientific rigor of the included research. Our review synthesizes both technical and clinical perspectives, evaluating sensor-based devices such as gas chromatography–mass spectrometry and selected ion flow tube–mass spectrometry with reported incidences of 30 and 8 studies, respectively. Key analytical techniques including Support Vector Machine, Principal Component Analysis, and Artificial Neural Networks were identified as the most prevalent, appearing in 22, 24, and 13 studies, respectively. While substantial improvements in detection accuracy and sensitivity are noted, significant challenges persist in sensor optimization, data integration, and adaptation into clinical settings. This comprehensive analysis bridges existing research gaps and lays a foundation for the development of non-invasive diagnostic devices. By refining detection technologies and advancing clinical applications, this work has the potential to transform cancer diagnostics, offering higher precision and reduced reliance on invasive procedures. Our aim is to provide a robust knowledge base for researchers at all experience levels, presenting insights on sensor capabilities, metrics, analytical methodologies, and the transformative impact of emerging electronic nose technologies in clinical practice.
A Dual-Path Computational Ghost Imaging Method Based on Convolutional Neural Networks
Authors: Wang Hexiao , Wu Jianan , Wang Mingcong , Xia Yu
Published: December 2024

Ghost imaging is a technique for indirectly reconstructing images by utilizing the second-order or higher-order correlation properties of the light field, which exhibits a robust ability to resist interference. On the premise of ensuring the quality of the image, effectively broadening the imaging range can improve the practicality of the technology. In this paper, a dual-path computational ghost imaging method based on convolutional neural networks is proposed. By using the dual-path detection structure, a wider range of target image information can be obtained, and the imaging range can be expanded. In this paper, for the first time, we try to use the two-channel probe as the input of the convolutional neural network and successfully reconstruct the target image. In addition, the network model incorporates a self-attention mechanism, which can dynamically adjust the network focus and further improve the reconstruction efficiency. Simulation results show that the method is effective. The method in this paper can effectively broaden the imaging range and provide a new idea for the practical application of ghost imaging technology.