bluuebunny/crossref_metadata_2025 huggingface dataset:
Carbon-Based Adsorbents for Microplastic Removal from Wastewater
Authors: Anuwa-Amarh Nii Ashitey , Dizbay-Onat Melike , Venkiteshwaran Kaushik , Wu Shenghua
Published: November 2024

Plastics are widely used across various industries due to their flexibility, cost-effectiveness, and durability. This extensive use has resulted in significant plastic pollution, with microplastics (MPs) becoming pervasive contaminants in water bodies worldwide, adversely affecting aquatic ecosystems and human health. This review explores the surface characteristics of carbon-based adsorbents, including biochar, activated carbon, carbon nanotubes (CNTs), and graphene, and their influence on MP removal efficiency. Key surface characteristics such as the carbon content, surface area, pore size, and particle size of adsorbents influenced adsorption efficiency. Additionally, hydrophobic interaction, van der Waals forces, π–π interactions and electrostatic interaction were found to be mechanisms by which microplastics are trapped onto adsorbents. Modified biochar and activated carbon demonstrated high adsorption efficiencies, while CNTs and graphene, with their high carbon contents and well-defined mesopores, showed outstanding performance in MP removal. Although a high surface area was generally associated with better adsorption performance, modifications significantly enhanced efficiency regardless of the initial surface area. This review emphasizes the importance of understanding the relationship between surface characteristics and adsorption efficiency to develop optimized adsorbents for MP removal from wastewater. However, challenges such as the lack of standardized testing methods, variability in biochar performance, and the high cost of regenerating carbon adsorbents remain. Future research should focus on developing cost-effective production methods, optimizing biochar production, and exploring advanced modifications to broaden the application of carbon adsorbents. Integrating advanced adsorbents into existing water treatment systems could further enhance MP removal efficiency. Addressing these challenges can improve the effectiveness and scalability of carbon-based adsorbents, significantly contributing to the mitigation of microplastic pollution in wastewater.
Advancements in Cold Spray Additive Manufacturing: Process, Materials, Optimization, Applications, and Challenges
Authors: Kafle Abishek , Silwal Raman , Koirala Bikram , Zhu Weihang
Published: November 2024

Cold spray additive manufacturing (CSAM) is a cutting-edge high-speed additive manufacturing process enabling the production of high-strength components without relying on traditional high-temperature methods. Unlike other techniques, CSAM produces oxide-free deposits and preserves the feedstock’s original characteristics without adversely affecting the substrate. This makes it ideal for industries requiring materials that maintain structural integrity. This paper explores strategies for improving material quality, focusing on nozzle design, particle size distribution, and fine-tuning of process parameters such as gas pressure, temperature, and spray distance. These factors are key to achieving efficient deposition and optimal bonding, which enhance the mechanical properties of the final products. Challenges in CSAM, including porosity control and achieving uniform coating thickness, are discussed, with solutions offered through the advancements in machine learning (ML). ML algorithms analyze extensive data to predict optimal process parameters, allowing for more precise control, reduced trial-and-error, and improved material usage. Advances in material strength, such as enhanced tensile strength and corrosion resistance, are also highlighted, making CSAM applicable to sectors like aerospace, defense, and automotive. The ability to produce high-performance, durable components positions CSAM as a promising additive-manufacturing technology. By addressing these innovations, this study offers insights into optimizing CSAM processes, guiding future research and industrial applications toward more efficient and high-performing manufacturing systems.
Numerical Investigation of the Fully Damped Wave-Type Magnetohydrodynamic Flow Problem
Authors: Demir Seda , Selvitopi Harun
Published: November 2024

Magnetohydrodynamic (MHD) flow plays a crucial role in various applications, ranging from nuclear fusion devices to MHD pumps. The mathematical modeling of such flows involves convection–diffusion-type equations, with fluid velocity governed by the Navier–Stokes equations and the magnetic field determined by Maxwell’s equations through Ohm’s law. Due to the complexity of these models, most studies on steady and unsteady MHD equations rely on numerical methods, as theoretical solutions are limited to specific cases. In this research, we propose a damped-wave-type mathematical model to describe fluid flow within a channel, taking into account both the velocity and magnetic field components. The model is solved numerically using the finite difference method for time discretization and the finite element method for spatial discretization. Numerical results are displayed graphically for different values of Hartmann numbers, and a detailed analysis and discussion of the solutions are provided.
Adaptive Production Rescheduling System for Managing Unforeseen Disruptions
Authors: Figueroa Andy J. , Poler Raul , Andres Beatriz
Published: November 2024

This work presents a mixed-integer linear programming (MILP) model to solve the production rescheduling problem in a job shop manufacturing system impacted by unexpected events, aiming to minimize production costs and disruptions to the initial schedule. The approach begins by generating an optimal production plan through batch assignments to machines. When unforeseen events, such as machine breakdowns or raw material shortages, occur, a dynamic rescheduling process is triggered, employing an iterative and reactive algorithm to adapt the plan to the real-time conditions on the shop floor. The results demonstrate that this rescheduling method efficiently adjusts to the new conditions while minimizing deviations from the original schedule, achieving solutions within acceptable computational times.
Two–Stage Detection and Localization of Inter–Frame Tampering in Surveillance Videos Using Texture and Optical Flow
Authors: Akhtar Naheed , Hussain Muhammad , Habib Zulfiqar
Published: November 2024

Surveillance cameras provide security and protection through real-time monitoring or through the investigation of recorded videos. The authenticity of surveillance videos cannot be taken for granted, but tampering detection is challenging. Existing techniques face significant limitations, including restricted applicability, poor generalizability, and high computational complexity. This paper presents a robust detection system to meet the challenges of frame duplication (FD) and frame insertion (FI) detection in surveillance videos. The system leverages the alterations in texture patterns and optical flow between consecutive frames and works in two stages; first, suspicious tampered videos are detected using motion residual–based local binary patterns (MR–LBPs) and SVM; second, by eliminating false positives, the precise tampering location is determined using the consistency in the aggregation of optical flow and the variance in MR–LBPs. The system is extensively evaluated on a large COMSATS Structured Video Tampering Evaluation Dataset (CSVTED) comprising challenging videos with varying quality of tampering and complexity levels and cross–validated on benchmark public domain datasets. The system exhibits outstanding performance, achieving 99.5% accuracy in detecting and pinpointing tampered regions. It ensures the generalization and wide applicability of the system while maintaining computational efficiency.
Enhanced Subspace Iteration Technique for Probabilistic Modal Analysis of Statically Indeterminate Structures
Authors: Cao Hongfei , Peng Xi , Xu Bin , Qin Fengjiang , Yang Qiuwei
Published: November 2024

In structural stochastic dynamic analysis, the consideration of the randomness in the physical parameters of the structure necessitates the establishment of numerous stochastic finite element models and the subsequent computation of their corresponding vibration modes. When the complete analysis is applied to calculate the vibration modes for each sample of the stochastic finite element model, a substantial computational expense is incurred. To enhance computational efficiency, this work presents an extended subspace iteration method aimed at rapidly determining the vibration modal parameters of statically indeterminate structures. The essence of this proposed method revolves around efficiently constructing reduced basis vectors during the subspace iteration process, utilizing flexibility disassembly perturbation and the Krylov subspace. This extended subspace iteration method proves particularly advantageous for the modal analysis of finite element models that incorporate a multitude of random variables. The proposed modal random analysis method has been validated using both a truss structure and a beam structure. The results demonstrate that the proposed method achieves substantial savings in computational time. Specifically, for the truss structure, the calculation time of the proposed method is approximately 1.2% and 65% of that required by the comprehensive analysis method and the combined approximation method, respectively. For the beam structure, on average, the computational time of the proposed method is roughly 2.1% of a full analysis and approximately 48.2% of the Ritz vector method’s time requirement. Compared to existing stochastic modal analysis algorithms, the proposed method offers improved computational accuracy and efficiency, particularly in scenarios involving high-discreteness random analyses.
Salivary Metabolomics in Patients with Long COVID-19 Infection
Authors: Machado Luiz , Prudente Robson , Franco Estefânia , Gatto Mariana , Mota Gustavo , Pagan Luana , Brizola Luís , dos Santos Maércio , Cunha Thulio , Sabino-Silva Robinson , Goulart Luiz , Martins Mario , Santos Paula , Maia Larissa , Albuquerque André , Ferreira Eloara , Baldi Bruno , Okoshi Marina , Tanni Suzana
Published: November 2024

Background: Long COVID-19 has been characterized by the presence of symptoms lasting longer than 4 weeks after the acute infection. The pathophysiology of clinical manifestations still lacks knowledge. Objective: The objective of this paper was to evaluate metabolite abundance in the saliva of long COVID patients 60 days after hospital discharge. Methods: A convenience sample was composed of 30 post-discharge patients with long COVID and seven non-COVID-19 controls. All COVID-19 patients were evaluated by demographic characteristics, spirometry, 6 min walk test (6mWT), Saint George Respiratory Questionnaire (SGRQ), and body composition. Metabolomics was performed on saliva. Results: The long COVID-19 patients were 60.4 ± 14.3 years-old, and 66% male. Their lean body mass was 30.7 ± 7.3 kg and fat mass, 34.4 ± 13.7 kg. Spirometry evaluation showed forced vital capacity (FVC) of 3.84 ± 0.97 L with 96.0 ± 14.0% of the predicted value, and forced expiratory volume in the first second (FEV1) of 3.11 ± 0.83 L with 98.0 ± 16.0 of the predicted value. The long COVID-19 patients had reduced maximal inspiratory (90.1 ± 31.6 cmH2O) and maximal expiratory (97.3 ± 31.0 cmH2O) pressures. SGRQ showed domain symptoms of 32.3 ± 15.2, domain activities of 41.9 ± 25.6, and domain impact 13.7 ± 11.4, with a mean of 24.3 ± 14.9%. Physical capacity measured by distance covered in the 6mWT was 418.2 ± 130 m with a 73.3% (22.3–98.1) predictive value. The control group consisted of 44.1 ± 10.7-year-old men with a body mass index of 26.5 ± 1.66 Kg/m2. Metabolomics revealed 19 differentially expressed metabolites; expression was lower in 16 metabolites, and 2 metabolites were absent in the COVID-19 patients compared to controls. Calenduloside G methyl ester (p = 0.03), Gly Pro Lys (p = 0.0001), and creatine (p = 0.0001) expressions were lower in patients than controls. Conclusions: Long COVID-19 patients present less abundance of calenduloside G methyl ester, Gly Pro Lys, and creatine in saliva than healthy controls. Lower creatine abundance may be related to reduced physical capacity and fatigue
Methane Generation Potential of the Easily Degradable Group of Landfilled Municipal Solid Waste
Authors: Takeda Cíntia Minori , Maciel-Silva Francisco Weshley , Forster-Carneiro Tânia , Miguel Miriam Gonçalves
Published: November 2024

Municipal solid waste (MSW) remains in sanitary landfills for many years. To maintain a circular economy, assessing the feasibility of reinserting MSW excavated from sanitary landfills into the production chain is important. This reduces environmental impacts, helping to minimize soil, water, and air pollution resulting from the decomposition of waste in landfills. In addition, it promotes economic benefits from the energy recovery of waste, such as biomass, which can generate electricity and heat, contributing to a sustainable energy matrix. The present study aimed to evaluate the easily degradable MSW group with 24 years of landfilling (ED-24) regarding its potential for methane generation. The ED group consisted of putrescible organic matter, wood, paper, cardboard, and pruning landfilled at a sanitary landfill in Southeastern Brazil. The feasibility of valuing ED-24 as a substrate for anaerobic digestion was assessed by analyzing its physical, chemical, and biochemical characterization and calculating its theoretical methane yield (TMY). The total volatile solids (TVS) and holo-cellulose contents of ED-24 were 73.45% and 61.39%, respectively, on a dry-weight basis. These values were in the range of those determined for non-landfilled lignocellulosic materials. Thus, 24 years of landfilling partially degraded the anaerobically lignocellulosic materials. The TMY of ED-24 was 233.41 mL CH4/g TVS, indicating a potential to generate methane. Despite the high lignin value, ED-24 can be valued as a substrate for anaerobic digestion.
Multimodal Approach of Improving Spatial Abilities
Authors: Balla Tamás , Tóth Róbert , Zichar Marianna , Hoffmann Miklós
Published: November 2024

Spatial abilities, which are sources of our capacity to understand visual and spatial relations among objects, as well as the abilities to generate, retain, retrieve, and transform well-structured visual information are important in several scientific fields and workplaces. Various tests have already been prepared to measure these abilities, including the Mental Cutting Test, which is considered the golden standard of measurement. However, much less attention is paid to how to prepare students for this test, as well as how to develop these skills most effectively. The purpose of this research is to study the multimodal approach of improving these skills and its effectiveness, i.e., the mixed use of 2D tools similar to the paper-based test, and 3D tools, including augmented reality and web-based interfaces in training students for those kinds of tasks. We want to demonstrate and prove with tests that multimodal modes of training can significantly affect the effectiveness of developing these skills. Moreover, through appropriately combining these methods, they can reinforce each other to form a multimodal approach, which is the most effective way for developing spatial skills and improving students’ performance related to the Mental Cutting Test.
Effects of Soluble and Insoluble Fibre on Glycolipid Metabolism and Gut Microbiota in High-Fat-Diet-Induced Obese Mice
Authors: Ren Han , Dong Sihao , Li Li , Zhao Wei
Published: November 2024

Background: Dietary fibre can alleviate or reduce the risk of obesity and obesity-induced abnormalities in glycolipid metabolism. However, the effects of different types of dietary fibre or their combinations on obesity remain unclear. Here, we explored the effects of different ratios of inulin soluble dietary fibre (ISDF) and barley leaf insoluble dietary fibre (BLIDF) on the body weight, glycolipid metabolism and gut microbiota of obese mice. Methods: Seven experimental groups were treated with different combinations of soluble and insoluble fibre, comprising HFD (high-fat diet without dietary fibre), BLIDF, ISDF, I3S1DF (insoluble/soluble = 3:1), I2S2DF (insoluble/soluble = 1:1), I1S3DF (insoluble/soluble = 1:3) and MIX (inulin, BLIDF and matcha powder fibre; insoluble/soluble = 3.6:1) groups. Results: Our results showed that the BLIDF, ISDF and MIX treatments decreased the body weight gain of the HFD mice significantly after eight-week interventions. All the fibre intervention groups except the MIX group displayed lower fasting blood glucose and glycosylated serum protein levels than the HFD group. BLIDF, ISDF, I3S1DF and I2S2DF improved the glucose tolerance of the mice. Moreover, none of the dietary fibre interventions affected the liver lipid metabolism, while I3S1DF and I1S3DF improved the abnormal serum lipid metabolism. BLIDF, ISDF, I3S1DF and I2S2DF reduced the serum IL-6 levels, and BLIDF and I1S3DF increased SOD activity significantly. Additionally, all the dietary fibre interventions decreased the Firmicutes to Bacteroidota (F/B) ratio and increased the abundance of beneficial gut microbes differently. Conclusions: In short, our results suggest that different ratios of soluble and insoluble dietary fibre have unique impacts on mice body weight, glycolipid metabolism, inflammation and gut microbiota. The ratio of soluble to insoluble dietary fibre intake should be considered for specific health goals in the future.
The Different Effects of Noradrenaline on Rhabdomyosarcoma and Ewing’s Sarcoma Cancer Hallmarks—Implications for Exercise Oncology
Authors: Weeber Peter , Bremer Stephanie , Haferanke Jonas , Regina Carla , Schönfelder Martin , Wackerhage Henning , von Luettichau Irene
Published: November 2024

Background: Exercise has beneficial effects on cancer and its treatment, but the underlying mechanisms are poorly understood. Some studies have linked the positive impact of exercise to catecholamine signaling. In contrast, cancer stress studies have typically reported that catecholamines worsen cancer hallmarks and outcomes. Here, we aimed to investigate whether adrenergic receptor isoform expression can explain the contradictory effects of catecholamines in cancer. Methods: We cultured two pediatric sarcoma cancer cell lines that either express (A673 cell line) or do not express (RD cell line) adrenergic receptors. The cells were treated with a 5× dilution series of noradrenaline to assess the effects of noradrenaline on cell numbers. After these dose-finding experiments, we treated both cancer cell lines with 60 μM noradrenaline to examine its effect on cell proliferation and migration and cAMP signaling. Results: Treatment with 60 μM noradrenaline significantly decreased the cell numbers by 61.89% ± 10.36 (p ≤ 0.001), decreased cell proliferation by 15.88% ± 6.76 (p ≤ 0.05), decreased cell migration after 24 h (p ≤ 0.001), and increased cAMP concentrations 38-fold (p ≤ 0.001) in the A673 cells, which express adrenergic receptors, but not in the RD cells, which do not express adrenergic receptors. Conclusions: Our results indicate, as a proof of principle, that the effects of catecholamines on cancer progression and metastasis might depend on the expressions of the nine adrenergic receptor isoforms. As cancers express adrenergic and other receptors differentially, this has implications for the response of cancers to exercise, stress, and medication and may help to further personalize cancer treatments.
Therapies for Chronic Spontaneous Urticaria: Present and Future Developments
Authors: Asero Riccardo , Calzari Paolo , Vaienti Silvia , Cugno Massimo
Published: November 2024

Chronic spontaneous urticaria (CSU) is a complex dermatological condition characterized by recurrent wheals and/or angioedema lasting for more than six weeks, significantly impairing patients’ quality of life. According to European guidelines, the first step in treatment involves second-generation H1-antihistamines (sgAHs), which block peripheral H1 receptors to alleviate symptoms. In cases with inadequate responses, the dose of antihistamines can be increased by up to fourfold. If symptoms persist despite this adjustment, the next step involves the use of omalizumab, a monoclonal anti-IgE antibody, which has shown efficacy in the majority of cases. However, a subset of patients remains refractory, necessitating alternative treatments such as immunosuppressive agents like cyclosporine or azathioprine. To address these unmet needs, several new therapeutic targets are being explored. Among them, significant attention is being given to drugs that block Bruton’s tyrosine kinase (BTK), such as remibrutinib, which reduces mast cell activation. Therapies like dupilumab, which target the interleukin-4 (IL-4) and IL-13 pathways, are also under investigation. Additionally, molecules targeting the Mas-related G protein-coupled receptor X2 (MRGPRX2), and those inhibiting the tyrosine kinase receptor Kit, such as barzolvolimab, show promise in clinical studies. These emerging treatments offer new options for patients with difficult-to-treat CSU and have the potential to modify the natural course of the disease by targeting key immune pathways, helping to achieve longer-term remission. Further research is essential to better elucidate the pathophysiology of CSU and optimize treatment protocols to achieve long-term benefits in managing this condition. Altogether, the future of CSU treatments that target pathogenetic mechanisms seems promising.
Characterization of Holmium-Doped Fiber Using AOM and Considering Pair-Induced Quenching and Fiber Length
Authors: Barmenkov Yuri , Muniz-Cánovas Pablo , Cruz José-Luis , Andrés Miguel V.
Published: November 2024

In this paper, we present the results of an experimental study on the characterization of holmium-doped silica fiber. A standard acousto-optic modulator controls the output power of the ytterbium-doped fiber laser operating at 1134.5 nm and serving as a pump source of the holmium-doped fiber under test. This technique allows us to measure the lifetimes of 5I7 and 5I8 energy levels of Ho3+ ions. The effects of the fiber length and the concentration-dependent pair-induced quenching on the accuracy of the fluorescence lifetime measurement are considered. The results of this study are compared with those obtained using the exponential and Förster decay functions used for such types of measurements. It is demonstrated that the knowledge of two fiber parameters, the pump saturation power and the fluorescence saturation power, together with the fiber absorption spectrum, permits one to obtain the absorption cross-sections at the pump and other key wavelengths, the effective concentration of the active ions, and the quantum efficiency of the fluorescence from the laser level. The results of this study are applicable to the reliable characterization of any type of heavily doped gain fibers and to the further numerical modeling and optimization of fiber lasers.
A Sensitive Frequency Band Study for Distributed Acoustical Sensing Monitoring Based on the Coupled Simulation of Gas–Liquid Two-Phase Flow and Acoustic Processes
Authors: Li Zhong , Wu Yi , Yang Yanming , Li Mengbo , Sheng Leixiang , Guo Huan , Jiao Jingang , Li Zhenbo , Sui Weibo
Published: November 2024

The sensitivity of gas and water phases to DAS acoustic frequency bands can be used to interpret the production profile of horizontal wells. DAS typically collects acoustic signals in the kilohertz range, presenting a key challenge in identifying the sensitive frequency bands of the gas and water phases in the production well for accurate interpretation. In this study, a gas–water two-phase flow–acoustic coupling model for a horizontal well is developed by integrating a gas–water separation flow model with a pipeline acoustic model. The model simulates the sound pressure level (SPL) and amplitude variations of acoustic waves under different flow patterns, spatial locations, and gas–water ratio schemes. The results demonstrate that within the same flow pattern, an increase in the gas–water ratio significantly elevates acoustic amplitude and SPL peaks within the 5–50 Hz frequency band. Analysis of oil field DAS data reveals that the amplitude response range for stages with a lower gas–water ratio falls within 5–10 Hz, whereas stages with a higher gas–water ratio exhibit an amplitude response range of 10–50 Hz.
Investigation into the Geological Origin and Influencing Factors of Selenium-Enriched Soil in Licheng, Jinan, Shandong Province
Authors: Li Jie , Bi Naishuang , Cao Yanling , Yin Haishun , Qin Pinrui , Liu Lian , Wu Bo , Jin Lijie , Tian Mingyang , Niu Zhixiang , Zhao Baoju
Published: November 2024

The geochemical classification of soils is crucial for the scientific development and utilization of land, particularly in the investigation of selenium-rich soils, which plays a guiding role in the cultivation layout of local selenium-rich agricultural products. This study involves the collection and analysis of over 6000 samples of soil, water quality, and crops from the entire area of Licheng District in Jinan City, Shandong Province. By analyzing the data in conjunction with the geochemical classification standards for soil nutrient levels, soil environmental geochemical levels, and comprehensive soil quality geochemical levels, we delineated a distribution area of selenium-rich soils covering 192.26 km2 in Licheng District, providing a bibliographic basis for the scientific planning of agriculture and forestry in the region.
Fazang’s Perspective on the Arousal of the Thought of Enlightenment 發心: An Examination Through the Awakening of Faith in Mahāyāna 大乘起信論 as Cited in the Huayanjing Tanxuanji 華嚴經探玄記
Authors: Kim Jiyun
Published: November 2024

This study examines the parts of the Awakening of Faith in Mahāyāna (AFM) quoted in the Huayanjing tanxuanji (Tanxuanji) and sheds light on how Fazang, as a Huayan master, understood and utilized the AFM in the Tanxuanji. In the Tanxuanji, the AFM is quoted 36 times, and the arousal of the thought of enlightenment is most frequently mentioned. Therefore, this study focuses on the arousal of the thought of enlightenment and reveals Fazang’s unique understanding of this concept within Huayan thought. First, Fazang understood the stage of the aspiration to awakening through the perfection of faith as corresponding to the first abode. Second, Fazang explains several chapters of the Avataṃsaka-sūtra beyond the first stage of abode in the Tanxuanji by quoting the aspiration to awakening through the perfection of faith, which stems from his theory of becoming buddha by the completion of faith. Third, while Fazang categorized the AFM within the final teaching in terms of its concept of arousal of the thought of enlightenment, he may have recognized the AFM as aligning with either the same teaching of the one vehicle or the distinct teaching of the one vehicle. Fourth, Fazang’s view on the arousal of the thought of enlightenment in the Tanxuanji reflects his distinctive perspective evident in the Fazangshu. This understanding of Fazang explains why the arousal of the thought of enlightenment was quoted most frequently in the Tanxuanji.
Mural Unicystic Ameloblastoma of the Mandible: A Case Report
Authors: Al Azawi Mina , Shinas Nikolaos , Zisis Vasileios , Shosho Dhurata , Poulopoulos Athanasios , Kashtwari Deeba
Published: November 2024

Background and Clinical Significance: Among the odontogenic tumors, ameloblastoma is one of the most notorious, although it remains relatively rare, accounting for approximately one percent of all oral tumors. This neoplasm, derived from odontogenic epithelium, may arise from the developing enamel organ, epithelial cell rests of dental lamina, epithelial lining of odontogenic cysts, and basal cells of oral epithelium. This is a case presentation of a mural unicystic ameloblastoma, the most aggressive subtype and the one with the highest chance of recurrence. Case Presentation: A patient was referred by his dentist for root canal treatment at the Emergency Dental Clinic of Boston University. The patient complained of mandibular numbness. A panoramic radiograph was acquired, revealing a radiolucent lesion in the right mandible. Clinical examination detected a soft swelling perforating the buccal cortex in the area of #27–#30. A Cone-Beam CT (CBCT) was acquired in the Oral and Maxillofacial Radiology Clinic revealing a well-defined, partially corticated entity in the periapical area of teeth #27 through #30, with evidence of scalloping borders. The internal structure was unilocular and uniformly low-density. The entity caused interruption of the lamina dura of the associated teeth and inferior displacement of the inferior alveolar canal. Differential diagnoses included unicystic ameloblastoma (UA) and central giant cell granuloma as a second less likely diagnosis. An incisional biopsy was performed for further evaluation. Biopsy confirmed UA with mural involvement. Conclusions: UAs typically exhibit less aggressive behavior. However, cases like this one, where mural involvement is noted and no associated impaction is detected, underline the possibility of variable radiographic presentation and the significance of a multidisciplinary approach in correct diagnosis and treatment. Histological subtyping is crucial for guiding treatment.
Impacts of Spatial and Temporal Resolution on Remotely Sensed Corn and Soybean Emergence Detection
Authors: Gao Feng , Anderson Martha , Houborg Rasmus
Published: November 2024

Crop emergence is critical for crop growth modeling, crop condition monitoring, and crop yield estimation. Ground collections of crop emergence dates are time-consuming and can only include limited fields. Remote sensing time series have been used to detect crop emergence. However, the impacts of the temporal and spatial resolutions of these time series on crop emergence detection have not been thoroughly evaluated. This paper assesses corn and soybean emergence detection using various remote sensing datasets (i.e., VENµS, Planet Fusion, Sentinel-2, Landsat, and Harmonized Landsat and Sentinel-2 (HLS)) with diverse spatial and temporal resolutions. The green-up dates from the remote sensing time series are detected using the within-season emergence (WISE) algorithm and assessed using ground emergence observations and planting records of corn, soybeans, and alfalfa from the Beltsville Agricultural Research Center (BARC) fields in Maryland, USA, from 2019 to 2023. Our results showed that most emergence events (~95%) could be detected when the frequency of usable observations reached ten days or less. Planet Fusion captured all crop emergences and outperformed other datasets, with a mean difference (MD) of <1 day, a mean absolute difference (MAD) of <5 days, and a root mean square error (RMSE) of <6 days compared to the ground-observed emergence dates. The HLS and Sentinel-2 time series captured most emergences of corn and soybeans with MD < 3 days, MAD < 7 days, and RMSE < 9 days. Landsat detected less than half of the crop emergences in recent years when both Landsat-8 and -9 were available. In our study area, temporal revisit plays a more crucial role in emergence detection than spatial resolution. Spatial resolutions from 5 to 30 m are suitable for field-level summaries in the study area. However, the 30 m HLS lacked sub-field details in fields with mixed cropping systems. The findings from this study could benefit remotely sensed crop emergence detection from local to regional scales.
Axis Estimation of Spaceborne Targets via Inverse Synthetic Aperture Radar Image Sequence Based on Regression Network
Authors: Guo Wenjing , Yang Qi , Wang Hongqiang , Luo Chenggao
Published: November 2024

Axial estimation is an important task for detecting non-cooperative space targets in orbit, with inverse synthetic aperture radar (ISAR) imaging serving as a fundamental approach to facilitate this process. However, most of the existing axial estimation methods usually rely on manually extracting and matching features of key corner points or linear structures in the images, which may result in a degradation in estimation accuracy. To address these issues, this paper proposes an axial estimation method for spaceborne targets via ISAR image sequences based on a regression network. Firstly, taking the ALOS satellite as an example, its Computer-Aided Design (CAD) model is constructed through a prior analysis of its structural features. Subsequently, target echoes are generated using electromagnetic simulation software, followed by imaging processing, analysis of imaging characteristics, and the determination of axial labels. Finally, in contrast to traditional classification approaches, this study introduces a straightforward yet effective regression network specifically designed for ISAR image sequences. This network transforms the classification loss into a loss function constrained by the minimum mean square error, which can be utilized to adaptively perform the feature extraction and estimation of axial parameters. The effectiveness of the proposed method is validated through both electromagnetic simulations and experimental data.
Tensor-Based Few-Shot Learning for Cross-Domain Hyperspectral Image Classification
Authors: Tang Haojin , Yang Xiaofei , Tang Dong , Dong Yiru , Zhang Li , Xie Weixin
Published: November 2024

Few-shot learning (FSL) is an effective solution for cross-domain hyperspectral image (HSI) classification, which could address the limited labeled samples of the target domain. Current FSL methods mostly utilize the 3D-CNN to transform the spatial and spectral information into a single feature to model an HSI, which means that spatial and spectral information are treated equally in the feature-modeling process. However, spectral information is considered to be more domain-invariant than spatial information. Treating the spatial and spectral information equally may result in parameter redundancy and undesirable cross-domain classification performance. In this paper, we attempt to use tensor mathematics for modeling the HSI and propose a novel few-shot learning method, called tensor-based few-shot Learning (TFSL) for cross-domain HSI classification, which aims to guide the model to focus on the extraction of domain-invariant spectral dependencies. Specifically, we first propose a spatial–spectral tensor decomposition (SSTD) model to provide a mathematical explanation of the input HSI, representing the local spatial–spectral information as 1D and 2D local tensors to reduce the data redundancy. Additionally, a tensor-based hybrid two-stream (THT) model is proposed for extracting the domain-invariant spatial–spectral tensor feature by using 1D-CNN and 2D-CNN. Furthermore, we adopt a 1D-CNN tensor feature enhancement block to enhance the spectral feature of hybrid two-stream tensors and guide the THT model to concentrate on the modeling of spectral dependencies. Finally, the proposed TFSL is evaluated on four public HSI datasets, and the extensive experimental results demonstrate that the proposed TFSL significantly outperforms other advanced FSL methods.
Satellite Image Restoration via an Adaptive QWNNM Model
Authors: Xu Xudong , Zhang Zhihua , Crabbe M. James C.
Published: November 2024

Due to channel noise and random atmospheric turbulence, retrieved satellite images are always distorted and degraded and so require further restoration before use in various applications. The latest quaternion-based weighted nuclear norm minimization (QWNNM) model, which utilizes the idea of low-rank matrix approximation and the quaternion representation of multi-channel satellite images, can achieve image restoration and enhancement. However, the QWNNM model ignores the impact of noise on similarity measurement, lacks the utilization of residual image information, and fixes the number of iterations. In order to address these drawbacks, we propose three adaptive strategies: adaptive noise-resilient block matching, adaptive feedback of residual image, and adaptive iteration stopping criterion in a new adaptive QWNNM model. Both simulation experiments with known noise/blurring and real environment experiments with unknown noise/blurring demonstrated that the effectiveness of adaptive QWNNM models outperformed the original QWNNM model and other state-of-the-art satellite image restoration models in very different technique approaches.
Spatiotemporal Dynamics and Prediction of Habitat Quality Based on Land Use and Cover Change in Jiangsu, China
Authors: Shi Ge , Chen Chuang , Cao Qingci , Zhang Jingran , Xu Jinghai , Chen Yu , Wang Yutong , Liu Jiahang
Published: November 2024

Analyzing the spatiotemporal evolution characteristics of urban land use and habitat quality is crucial for the sustainable development of urban ecological environments. This study utilizes the land use data of Jiangsu Province for the years 2000, 2010, and 2020, applying the FLUS model to investigate the driving force behind land expansion and to simulate a prediction for the land use of 2030. By integrating the InVEST model and landscape pattern indices, this study analyzes the spatiotemporal evolution characteristics of habitat quality in Jiangsu Province and uses geographical detector analysis to examine the synergistic effects of influencing factors. The results indicate that, from 2000 to 2020, habitat degradation in Jiangsu Province progressively increased, with the spatial distribution of degradation levels showing a gradual change. Under the ecological protection scenario in 2030, habitat fragmentation was alleviated. Conversely, under the economic development scenario, habitat quality further deteriorated, resulting in the largest area of low-quality regions. Minimal changes occurred under the natural development scenario. (2) The landscape indices in Jiangsu Province experienced significant changes from 2000 to 2020. The continuous expansion of urban land into other land use types led to a trend of fragmentation, with a clear increasing trend in dispersion, sprawl, and Shannon’s diversity index, accompanied by a decrease in cohesion. (3) The dominant interacting factors affecting habitat quality were combinations of socioeconomic factors with other factors, indicating that the economy largely determines the spatial distribution pattern of habitat quality. The findings of this study provide optimization strategies for future spatial planning of land use types in Jiangsu Province and offer references for habitat quality restoration efforts in the region.
Development of a High-Speed Precision Ultrasonic-Assisted Spindle for Ultra-Precision Optical Mold Machining
Authors: Wang Dingwen , Hong Qiu , Yin Shaohui
Published: November 2024

Ultrasonic vibration-assisted grinding is a critical method for machining ultra-hard optical molds. However, current ultrasonic-assisted grinding spindles, as essential foundational equipment, face limitations in maintaining ultra-high rotational speed, high precision, and a compact structure during ultrasonic operation. This study presents a novel ultra-precision ultrasonic-assisted high-speed aerostatic spindle for grinding ultra-hard optical molds, developed through theoretical calculations, FEM, and CFD simulations. The spindle features a simple and compact design (φ60 mm outer diameter × 194 mm length), operates at an ultrasonic frequency of 41.23 kHz, and is driven by an impulse turbine providing torque up to 50.4 N•mm, achieving speeds exceeding 40,000 r/min. Aerostatic bearings provide axial and radial load capacities of 89 N and 220 N, respectively. The results demonstrate that the proposed high-speed precision ultrasonic spindle exhibits both feasibility and potential for practical application.
A Multi-Scale CNN for Transfer Learning in sEMG-Based Hand Gesture Recognition for Prosthetic Devices
Authors: Fratti Riccardo , Marini Niccolò , Atzori Manfredo , Müller Henning , Tiengo Cesare , Bassetto Franco
Published: November 2024

Advancements in neural network approaches have enhanced the effectiveness of surface Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity. However, current deep learning architectures struggle to achieve good generalization and robustness, often demanding significant computational resources. The goal of this paper was to develop a robust model that can quickly adapt to new users using Transfer Learning. We propose a Multi-Scale Convolutional Neural Network (MSCNN), pre-trained with various strategies to improve inter-subject generalization. These strategies include domain adaptation with a gradient-reversal layer and self-supervision using triplet margin loss. We evaluated these approaches on several benchmark datasets, specifically the NinaPro databases. This study also compared two different Transfer Learning frameworks designed for user-dependent fine-tuning. The second Transfer Learning framework achieved a 97% F1 Score across 14 classes with an average of 1.40 epochs, suggesting potential for on-site model retraining in cases of performance degradation over time. The findings highlight the effectiveness of Transfer Learning in creating adaptive, user-specific models for sEMG-based prosthetic hands. Moreover, the study examined the impacts of rectification and window length, with a focus on real-time accessible normalizing techniques, suggesting significant improvements in usability and performance.
Inclusion Detection in Injection-Molded Parts with the Use of Edge Masking
Authors: Rotter Pawel , Klemiato Maciej , Knapik Dawid , Rosół Maciej , Putynkowski Grzegorz
Published: November 2024

The algorithm and prototype presented in the article are part of a quality control system for plastic objects coming from injection-molding machines. Some objects contain a flaw called inclusion, which is usually observed as a local discoloration and disqualifies the object. The objects have complex, irregular geometry with many edges. This makes inclusion detection difficult, because local changes in the image at inclusions are much less significant than grayscale changes at the edges. In order to exclude edges from calculations, the presented method first classifies the object and then matches it with the corresponding mask of edges, which is prepared off-line and stored in the database. Inclusions are detected based on the analysis of local variations in the surface grayscale in the unmasked part of the image under inspection. Experiments were performed on real objects rejected from production by human quality controllers. The proposed approach allows tuning the algorithm to achieve very high sensitivity without false detections at edges. Based on input from the controllers, the algorithm was tuned to detect all the inclusions. At 100% recall, 87% precision was achieved, which is acceptable for industrial applications.
Challenges of the Biopharmaceutical Industry in the Application of Prescriptive Maintenance in the Industry 4.0 Context: A Comprehensive Literature Review
Authors: Carvalho Johnderson Nogueira de , Silva Felipe Rodrigues da , Nascimento Erick Giovani Sperandio
Published: November 2024

The biopharmaceutical industry has specificities related to the optimization of its processes, the effectiveness of the maintenance of the productive park in the face of regulatory requirements. and current concepts of modern industry. Current research on the subject points to investments in the health area using the current tools and concepts of Industry 4.0 (I4.0) with the objective of a more assertive production, reduction of maintenance costs, reduction of operating risks, and minimization of equipment idle time. In this context, this study aims to characterize the current knowledge about the challenges of the biopharmaceutical industry in the application of prescriptive maintenance, which derives from predictive maintenance, in the context of I4.0. To achieve this, a systematic review of the literature was carried out in the scientific knowledge bases IEEE Xplore, Scopus, Web of Science, Science Direct, and Google Scholar, considering works such as Reviews, Article Research, and Conference Abstracts published between 2018 and 2023. The results obtained revealed that prescriptive maintenance offers opportunities for improvement in the production process, such as cost reduction and greater proximity to all actors in the areas of production, maintenance, quality, and management. The limitations presented in the literature include a reduced number of models, the lack of a clearer understanding of its construction, lack of applications directly linked to the biopharmaceutical industry, and lack of measurement of costs and implementation time of these models. There are significant advances in this area including the implementation of more elaborate algorithms used in artificial intelligence neural networks, the advancement of the use of decision support systems as well as the collection of data in a more structured and intelligent way. It is concluded that for the adoption of prescriptive maintenance in the pharmaceutical industry, issues such as the definition of data entry and analysis methods, interoperability between “shop floor” and corporate systems, as well as the integration of technologies existing in the world, must be considered for I4.0.
Hypervector Approximation of Complex Manifolds for Artificial Intelligence Digital Twins in Smart Cities
Authors: Kahawala Sachin , Madhusanka Nuwan , De Silva Daswin , Osipov Evgeny , Mills Nishan , Manic Milos , Jennings Andrew
Published: November 2024

The United Nations Sustainable Development Goal 11 aims to make cities and human settlements inclusive, safe, resilient and sustainable. Smart cities have been studied extensively as an overarching framework to address the needs of increasing urbanisation and the targets of SDG 11. Digital twins and artificial intelligence are foundational technologies that enable the rapid prototyping, development and deployment of systems and solutions within this overarching framework of smart cities. In this paper, we present a novel AI approach for hypervector approximation of complex manifolds in high-dimensional datasets and data streams such as those encountered in smart city settings. This approach is based on hypervectors, few-shot learning and a learning rule based on single-vector operation that collectively maintain low computational complexity. Starting with high-level clusters generated by the K-means algorithm, the approach interrogates these clusters with the Hyperseed algorithm that approximates the complex manifold into fine-grained local variations that can be tracked for anomalies and temporal changes. The approach is empirically evaluated in the smart city setting of a multi-campus tertiary education institution where diverse sensors, buildings and people movement data streams are collected, analysed and processed for insights and decisions.