bluuebunny/crossref_metadata_2025 huggingface dataset:
Endotoxin-Induced Sepsis on Ceftriaxone-Treated Rats’ Ventilatory Mechanics and Pharmacokinetics
Authors: Simões Juliana Savioli , Rodrigues Rafaela Figueiredo , Zavan Bruno , Emídio Ricardo Murilo Pereira , Soncini Roseli , Boralli Vanessa Bergamin
Published: January 2024

Sepsis can trigger acute respiratory distress syndrome (ARDS), which can lead to a series of physiological changes, modifying the effectiveness of therapy and culminating in death. For all experiments, male Wistar rats (200–250 g) were split into the following groups: control and sepsis-induced by endotoxin lipopolysaccharide (LPS); the control group received only intraperitoneal saline or saline + CEF while the treated groups received ceftriaxone (CEF) (100 mg/kg) IP; previously or not with sepsis induction by LPS (1 mg/kg) IP. We evaluated respiratory mechanics, and alveolar bronchial lavage was collected for nitrite and vascular endothelial growth factor (VEGF) quantification and cell evaluation. For pharmacokinetic evaluation, two groups received ceftriaxone, one already exposed to LPS. Respiratory mechanics shows a decrease in total airway resistance, dissipation of viscous energy, and elastance of lung tissues in all sepsis-induced groups compared to the control group. VEGF and NOx values were higher in sepsis animals compared to the control group, and ceftriaxone was able to reduce both parameters. The pharmacokinetic parameters for ceftriaxone, such as bioavailability, absorption, and terminal half-life, were smaller in the sepsis-induced group than in the control group since clearance was higher in septic animals. Despite the pharmacokinetic changes, ceftriaxone showed a reduction in resistance in the airways. In addition, CEF lowers nitrite levels in the lungs and acts on their adverse effects, reflecting pharmacological therapy of the disease.
Silymarin and Inflammation: Food for Thoughts
Authors: Surai Peter F. , Surai Anton , Earle-Payne Katie
Published: January 2024

Inflammation is a vital defense mechanism, creating hostile conditions for pathogens, preventing the spread of tissue infection and repairing damaged tissues in humans and animals. However, when inflammation resolution is delayed or compromised as a result of its misregulation, the process proceeds from the acute phase to chronic inflammation, leading to the development of various chronic illnesses. It is proven that redox balance disturbances and oxidative stress are among major factors inducing NF-κB and leading to over-inflammation. Therefore, the anti-inflammatory properties of various natural antioxidants have been widely tested in various in vitro and in vivo systems. Accumulating evidence indicates that silymarin (SM) and its main constituent silibinin/silybin (SB) have great potential as an anti-inflammation agent. The main anti-inflammatory mechanism of SM/SB action is attributed to the inhibition of TLR4/NF-κB-mediated signaling pathways and the downregulated expression of pro-inflammatory mediators, including TNF-α, IL-1β, IL-6, IL-12, IL-23, CCL4, CXCL10, etc. Of note, in the same model systems, SM/SB was able to upregulate anti-inflammatory cytokines (IL-4, IL-10, IL-13, TGF-β, etc.) and lipid mediators involved in the resolution of inflammation. The inflammatory properties of SM/SB were clearly demonstrated in model systems based on immune (macrophages and monocytes) and non-immune (epithelial, skin, bone, connective tissue and cancer) cells. At the same time, the anti-inflammatory action of SM/SB was confirmed in a number of in vivo models, including toxicity models, nonalcoholic fatty liver disease, ischemia/reperfusion models, stress-induced injuries, ageing and exercising models, wound healing and many other relevant model systems. It seems likely that the anti-inflammatory activities of SM/SB are key elements on the health-promoting properties of these phytochemicals.
Bioactive Components, Applications, Extractions, and Health Benefits of Winery By-Products from a Circular Bioeconomy Perspective: A Review
Authors: Constantin Oana Emilia , Stoica Florina , Rațu Roxana Nicoleta , Stănciuc Nicoleta , Bahrim Gabriela Elena , Râpeanu Gabriela
Published: January 2024

Significant waste streams produced during winemaking include winery by-products such as pomace, skins, leaves, stems, lees, and seeds. These waste by-products were frequently disposed of in the past, causing resource waste and environmental issues. However, interest has risen in valorizing vineyard by-products to tap into their latent potential and turn them into high-value products. Wine industry by-products serve as a potential economic interest, given that they are typically significant natural bioactive sources that may exhibit significant biological properties related to human wellness and health. This review emphasizes the significance of winery by-product valorization as a sustainable management resource and waste management method. The novelty of this review lies in its comprehensive analysis of the potential of winery by-products as a source of bioactive compounds, extraction techniques, health benefits, and applications in various sectors. Chemical components in winery by-products include bioactive substances, antioxidants, dietary fibers, organic acids, and proteins, all of which have important industrial and therapeutic applications. The bioactives from winery by-products act as antioxidant, antidiabetic, and anticancer agents that have proven potential health-promoting effects. Wineries can switch from a linear waste management pattern to a more sustainable and practical method by adopting a circular bioeconomy strategy. Consequently, the recovery of bioactive compounds that function as antioxidants and health-promoting agents could promote various industries concomitant within the circular economy.
Dietary Marine Oils Selectively Decrease Obesogenic Diet-Derived Carbonylation in Proteins Involved in ATP Homeostasis and Glutamate Metabolism in the Rat Cerebellum
Authors: Moreno Francisco , Méndez Lucía , Raner Ana , Miralles-Pérez Bernat , Romeu Marta , Ramos-Romero Sara , Torres Josep Lluís , Medina Isabel
Published: January 2024

The regular intake of diets high in saturated fat and sugars increases oxidative stress and has been linked to cognitive decline and premature brain aging. The cerebellum is highly vulnerable to oxidative stress and thus, obesogenic diets might be particularly detrimental to this tissue. However, the precise molecular mechanisms behind obesity-related brain damage are still not clear. Since protein carbonylation, a biomarker of oxidative stress, influences protein functions and is involved in metabolic control, the current investigation addressed the effect of long-term high-fat and high-sucrose diet intake on the cerebellum of Sprague-Dawley rats by deciphering the changes caused in the carbonylated proteome. The antioxidant effects of fish oil supplementation on cerebellar carbonylated proteins were also investigated. Lipid peroxidation products and carbonylated proteins were identified and quantified using immunoassays and 2D-LC-MS/MS in the cerebellum. After 21 weeks of nutritional intervention, the obesogenic diet selectively increased carbonylation of the proteins that participate in ATP homeostasis and glutamate metabolism in the cerebellum. Moreover, the data demonstrated that fish oil supplementation restrained carbonylation of the main protein targets oxidatively damaged by the obesogenic diet, and additionally protected against carbonylation of several other proteins involved in amino acid biosynthesis and neurotransmission. Therefore, dietary interventions with fish oils could help the cerebellum to be more resilient to oxidative damage. The results could shed some light on the effect of high-fat and high-sucrose diets on redox homeostasis in the cerebellum and boost the development of antioxidant-based nutritional interventions to improve cerebellum health.
Anti-Aging Properties of Chitosan-Based Hydrogels Rich in Bilberry Fruit Extract
Authors: Studzińska-Sroka Elżbieta , Paczkowska-Walendowska Magdalena , Erdem Cansu , Paluszczak Jarosław , Kleszcz Robert , Hoszman-Kulisz Marta , Cielecka-Piontek Judyta
Published: January 2024

Photoaging is a process related to an increased level of reactive oxygen species (ROS). Polyphenols can scavenge free radicals in the body, which can delay skin aging. Therefore, our work aimed to prepare a biologically active extract from dry fruits of Vaccinium myrtillus or Vaccinium corymbosum and use it for the preparation of hydrogels for topical application. Therefore, eight different extracts (using V. myrtillus and V. corymbosum and different extraction mixtures: methanol, methanol–water 1:1, water, acetone–water 1:1) were prepared and their phytochemical (total polyphenolic content, total flavonoid content, total anthocyanin content) and biological properties (antioxidant, anti-hyaluronidase, and anti-tyrosinase activity) were assessed. Cytotoxicity towards HaCaT keratinocytes was also determined. Based on the results, the acetone–water extract from V. myrtillus was selected for further study. Using the Design of Experiments approach, chitosan-based hydrogels with bilberry fruit extract were prepared. The content of extract and chitosan were selected as independent factors. The activity of hydrogels depended on the extract content; however, the enzyme-inhibiting (anti-hyaluronidase and anti-tyrosinase) activity resulted from the presence of both the extract and chitosan. Increased concentration of chitosan in the hydrogel base led to increased viscosity of the hydrogel and, consequently, a slower release of active compounds. To get optimal hydrogel characteristics, 1% extract and 2.5% MMW chitosan were utilized. The research suggests the validity of using bilberry fruit extracts in topical preparations with anti-aging properties.
Synthesis and Electrochemical Properties of Fe3O4/C Nanocomposites for Symmetric Supercapacitors
Authors: Bordun Ihor , Szymczykiewicz Ewelina
Published: January 2024

In the work, nanostructured Fe3O4/C composites based on natural raw materials (beet pulp and corn stalks) are synthesised in various ways. Iron chloride FeCl3 is used as an activator. The synthesised composites are investigated using the following methods, scanning electron microscopy, X-ray diffractometry, nitrogen adsorption/desorption porometry, magnetometry, impedance and galvanostatic measurements. The presence of nanosized Fe3O4 magnetite in the synthesised carbon structures is disclosed. Based on the magnetic measurements, the particle size of Fe3O4 is on average 50 nm for the sample of the composite synthesised from beet pulp in one step, 30 nm for the sample of the composite synthesised from beet pulp in two steps, 33 nm for the composite synthesised from corn stalks in one step, and 29 nm for the composite synthesised from corn stalks in two steps. It is shown that the two-step synthesis using pre-carbonised raw materials gives the specific capacitance of the composite made with beet pulp at 96 F/g, and for the composite made with corn stalks at 95 F/g. The high coulombic efficiency (>95%) and the stability of the electrical capacitance during long-term cycling make it possible to use Fe3O4/C composites for the manufacture of supercapacitor electrodes with aqueous electrolytes.
Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS
Authors: Villegas-Ch William , Govea Jaime , Ortiz-Garces Iván
Published: January 2024

Cybersecurity is a critical concern in today’s digital age, where organizations face an ever-evolving cyber threat landscape. This study explores the potential of leveraging artificial intelligence and Amazon Web Services to improve cybersecurity practices. Combining the capabilities of OpenAI’s GPT-3 and DALL-E models with Amazon Web Services infrastructure aims to improve threat detection, generate high-quality synthetic training data, and optimize resource utilization. This work begins by demonstrating the ability of artificial intelligence to create synthetic cybersecurity data that simulates real-world threats. These data are essential for training threat detection systems and strengthening an organization’s resilience against cyberattacks. While our research shows the promising potential of artificial intelligence and Amazon Web Services in cybersecurity, it is essential to recognize the limitations. Continued research and refinement of AI models are needed to address increasingly sophisticated threats. Additionally, ethical and privacy considerations must be addressed when employing AI in cybersecurity practices. The results support the notion that this collaboration can revolutionize how organizations address cyber challenges, delivering greater efficiency, speed, and accuracy in threat detection and mitigation.
Review of Traffic Assignment and Future Challenges
Authors: Elimadi Manal , Abbas-Turki Abdeljalil , Koukam Abder , Dridi Mahjoub , Mualla Yazan
Published: January 2024

The problem of traffic assignment consists of determining the routes taken by the users of transportation infrastructure. This problem has been the subject of numerous studies, particularly in analyzing scenarios for developing road infrastructure and pricing strategies. This paper reviews the major progress in the field. Accordingly, it shows that the evolution of intelligent transportation systems and the emergence of connected and autonomous vehicles present new challenges to classical approaches for solving the traffic assignment problem. It addresses two major perspectives: digital twins coupled with artificial intelligence to help decision-makers, and rule-based policy to offer users fair and efficient itineraries while respecting infrastructure capacity.
Cephalometrics in Obstructive Sleep Apnea Patients with Mixed Dentition
Authors: Manrikyan Gayane E. , Khachatryan Samson G. , Markaryan Marina M. , Ter-Poghosyan Hrant Yu. , Hakobyan Anna G. , Manrikyan Mikayel E. , Vardanyan Izabella F.
Published: January 2024

Distal occlusion is one of the most common dentoalveolar anomalies and can be the reason for the obstructive sleep apnea (OSA) syndrome development among children. The aim of the study was to investigate the relationship between cephalometric and OSA parameters in the pediatric population. Methods: The cohort study included 39 children with OSA symptoms. Orthodontic examination consisted of a cephalometric analysis of 39 linear and angular variables. Patients underwent a sleep diagnostic study. Statistical analysis was performed using SPSS 19.0.0. Results: Of the general sample, 53.8% were mouth breathers and 46.2% had a mixed type of breathing. Moreover, 30.8% of patients had bruxism. The mean apnea-hypopnea index and oxygen desaturation index were 4.6/h and 3.9/h, respectively. A 1.06 times increase in the SNA index indicated the anterior position of the upper jaw. The MnPLSN° exceeded the norm by 1.3 times, which indicated the posterior position of the lower jaw and vertical type of the growth. An increase in ANSPNSSPT° by 1.1 times indicated an inclination of the upper jaw in the posterior position and a narrowing of the nasal passages. Patients with pediatric OSA had a significantly smaller lower airway space and MPH parameter. Conclusion: Systematic orthodontic monitoring of children with pediatric OSA is important for diagnosis and timely treatment.
Alleviation Effects of Hoods at the Entrances and Exits of High-Speed Railway Tunnels on the Micro-Pressure Wave
Authors: Ma Weibin , Fang Yufei , Li Tao , Shao Mingyu
Published: January 2024

The MPW that emits from a tunnel’s exit when a high-speed train passes through is a serious environmental problem which increases rapidly with the speed of the train. To alleviate the MPW problem at 400 km/h, the aerodynamic effects caused by the hood located at the entrance or exit of a tunnel are studied by numerical method, and the influences of hood geometry, such as an enlarged cross-section, oblique entrance, and opening holes on the MPW, are also investigated. The research indicates that the enlarged cross-section of the hood at the entrance and exit of the tunnel has opposite effects on the MPW, and the oblique section can alleviate the MPW by extending the rising time of the compression wave and increasing the spatial angle at the hood exit. The pressure gradient can be mitigated through delaying the rising of the compression wave by opening holes on the side wall of the hood, and the relief effects of the holes can reduce the MPW further. The MPW problem when a train passes through a tunnel at 400 km/h can be effectively alleviated by an optimized oblique enlarged hood with opening holes, even up to train speeds of 500 km/h.
The Identification of Polyphenolic Compounds and the Determination of Antioxidant Activity in Extracts and Infusions of Peppermint, Lemon Balm and Lavender
Authors: Sadowska Urszula , Armenta Villavicencio Ruth , Dziadek Kinga , Skoczylas Joanna , Sadowski Szymon Kamil , Kopeć Aneta
Published: January 2024

Bioactive compounds are molecules that may have therapeutic potential with influence on oxidative stress, pro-inflammatory state and metabolic disorders. Herbs are recognized as a significant source of natural antioxidants. The aim of this study was to determine the antioxidant properties of peppermint (Mentha piperita L.), lemon balm (Melissa officinalis L.) and lavender (Lavandula angustifolia Mill.). Fresh plant materials were harvested and obtained from the Experimental Station of Agriculture University in Kraków, Poland. Proximate analysis (protein, fat, ash and dry matter) was performed on herbs. Total polyphenol content and polyphenol profile antioxidant activity (ABTS and FRAP methods) were measured in an extract prepared with deionized distilled hot water (infusion), 70% methanol or 70% methanol acidified with formic acid. It was found that the water extract of peppermint had the highest polyphenol content and antioxidant activity measured by the FRAP method. The highest antioxidant activity measured with the ABTS method was in methanolic lemon balm extract. The highest amount of total phenolic compound was determined in the water extract of peppermint. The water and acidified methanol extract of peppermint contained a higher amount of naringin, rutin, hesperidin and rosmarinic acid. Evaluated plants can be used as natural antioxidants instead of synthetic antioxidants in the food and pharmaceutical industries.
Efficacy of Different Irrigation Activation Techniques on Dentinal Tubule Penetration of the Novel AH-Plus Bioceramic Sealer
Authors: Almasri Alhasan , Abduljalil Mohamad , Aksoy Umut
Published: January 2024

This study aimed to assess the penetration of AH-Plus (AHP) and AH-Plus Bioceramic (AHPB) sealers into dentinal tubules subsequent to different irrigation activation protocols. One hundred fifty teeth were divided into five groups according to the final irrigation protocol: conventional syringe irrigation (CSI), EndoActivator (EA), passive ultrasonic irrigation (PUI), Er,Cr:YSGG laser, and SWEEPS. Then, the teeth were divided into two subgroups (n = 15) according to the canal sealer type. The maximum penetration depth (MPD) point and percentage of the sealer were evaluated using confocal laser scanning microscopy. Then, the data were statistically analyzed. The highest MPD values were observed in the SWEEPS groups, while the lowest value was in the CSI groups. Considering the effect of the sealer type and irrigation protocol interaction, there was a significant difference between the Er,Cr:YSGG laser, and EA groups for the AHPB sealer (p < 0.05), while it was insignificant for the AHP sealer (p > 0.05). The AHP and AHPB sealers statistically presented similar penetration properties. Laser-activated irrigation presented a higher sealer penetration compared to the other techniques.
Transformation of the Shape and Spectrum of an Ultrawideband Electromagnetic Pulse in a “Gigantic” Coaxial Line Filled with Magnetized Plasma
Authors: Zudin Ilya , Gushchin Mikhail , Korobkov Sergey , Strikovskiy Askold , Katkov Alexandr , Kochedykov Vasiliy , Petrova Irina
Published: January 2024

A propagation of ultrawideband electromagnetic pulses (UWB EMPs) through magnetized plasma has been experimentally studied using a “gigantic” coaxial line, which has been developed at IAP RAS for laboratory modeling of ionospheric effects. This coaxial line is 1.4 m in diameter and 10 m in length and is installed inside the chamber of the large-scale Krot plasma device. The line can be filled with rf inductively coupled plasma, magnetized or not. It allows one to explore the propagation of UWB EMPs in plasma along a long path without refraction and divergence and obtain a physical picture of EMP transformation. Under conditions where the duration of the UWB EMP is comparable to the period of electron plasma oscillations (fp−1), the period of cyclotron rotation of electrons (fc−1), or even significantly shorter, a complex of effects of transformation of the waveform and frequency spectrum of the pulse occurs. Without ambient magnetic field, a UWB EMP is distorted due to the effects of the cutoff and frequency dispersion. In dense magnetized plasma, i.e., when fp >> fc, the UWB EMP breaks into two wave packets, the high-frequency one (f > fp) and low-frequency one (f < fc). In rare magnetized plasma (fp << fc), the cyclotron absorption produces a long train of damped oscillations at a frequency close to the cyclotron frequency fc following the UWB EMP.
Image Processing and Analysis for Biotechnology and Bioprocess Engineering
Authors: Jung Sang-Kyu
Published: January 2024

The development of high-performance computing hardware and image processing algorithms has led to the widespread application of image analysis in various fields [...]
Effects of Fingerroot (Boesenbergia pandurata) Oil on Microflora as an Antimicrobial Agent and on the Formation of Heterocyclic Amines in Fried Meatballs
Authors: Soikam Panida , Rachtanapun Chitsiri , Suriyarak Sarisa , Weiss Jochen , Gibis Monika
Published: January 2024

This study aimed to determine the antibacterial activity of the essential oil of fingerroot (Boesenbergia pandurata) (EOF) as a natural preservative in ground meat and its effect on the formation of heterocyclic amines (HAs) in pan-fried meatballs. EOF was applied either by adding it to ground pork or marinating pork in it before grinding. In addition, the antibacterial activity of EOF was tested. Aerobic mesophilic total viable count (TVC), lactic acid bacteria (LAB), and Enterobacteriaceae bacteria were monitored. The results show that EOF exhibited strong antibacterial activity when added at concentrations of 1.0 and 2.5 wt%. Antimicrobial activity against TVC, LAB, and especially Enterobacteriaceae bacteria was observed at all EOF concentrations (0.25, 0.5, 1.0, and 2.5 wt%). A 2.5% concentration of EOF applied by marinating trimmings can extend the shelf-life of ground pork to 18 days, while 2.5% EOF applied via addition can extend the shelf-life to 15 days, compared with 3 days for the control sample. After frying the meatballs, the inhibitory effect on the formation of heterocyclic amines was only significant for MeIQx with the highest addition of EOF (2.5 wt%). Significant increases in the concentrations of all other HAs were determined by adding EOF (2.5 wt%).
Hybrid Clustering Algorithm Based on Improved Density Peak Clustering
Authors: Guo Limin , Qin Weijia , Cai Zhi , Su Xing
Published: January 2024

In the era of big data, unsupervised learning algorithms such as clustering are particularly prominent. In recent years, there have been significant advancements in clustering algorithm research. The Clustering by Density Peaks algorithm is known as Clustering by Fast Search and Find of Density Peaks (density peak clustering). This clustering algorithm, proposed in Science in 2014, automatically finds cluster centers. It is simple, efficient, does not require iterative computation, and is suitable for large-scale and high-dimensional data. However, DPC and most of its refinements have several drawbacks. The method primarily considers the overall structure of the data, often resulting in the oversight of many clusters. The choice of truncation distance affects the calculation of local density values, and varying dataset sizes may necessitate different computational methods, impacting the quality of clustering results. In addition, the initial assignment of labels can cause a ‘chain reaction’, i.e., if one data point is incorrectly labeled, it may lead to more subsequent data points being incorrectly labeled. In this paper, we propose an improved density peak clustering method, DPC-MS, which uses the mean-shift algorithm to find local density extremes, making the accuracy of the algorithm independent of the parameter dc. After finding the local density extreme points, the allocation strategy of the DPC algorithm is employed to assign the remaining points to appropriate local density extreme points, forming the final clusters. The robustness of this method in handling uncertain dataset sizes adds some application value, and several experiments were conducted on synthetic and real datasets to evaluate the performance of the proposed method. The results show that the proposed method outperforms some of the more recent methods in most cases.
Preliminary Experiments of a Real-World Authentication Mechanism Based on Facial Recognition and Fully Homomorphic Encryption
Authors: Crihan Georgiana , Dumitriu Luminița , Crăciun Marian Viorel
Published: January 2024

In the current context in which user authentication is the first line of defense against emerging attacks and can be considered a defining element of any security infrastructure, the need to adopt alternative, non-invasive, contactless, and scalable authentication mechanisms is mandatory. This paper presents initial research on the design, implementation, and evaluation of a multi-factor authentication mechanism that combines facial recognition with a fully homomorphic encryption algorithm. The goal is to minimize the risk of unauthorized access and uphold user confidentiality and integrity. The proposed device is implemented on the latest version of the Raspberry Pi and Arduino ESP 32 modules, which are wirelessly connected to the computer system. Additionally, a comprehensive evaluation, utilizing various statistical parameters, demonstrates the performance, the limitations of the encryption algorithms proposed to secure the biometric database, and also the security implications over the system resources. The research results illustrate that the Brakerski–Gentry–Vaikuntanathan algorithm can achieve higher performance and efficiency when compared to the Brakerski–Fan–Vercauteren algorithm, and proved to be the best alternative for the designed mechanism because it effectively enhances the level of security in computer systems, showing promise for deployment and seamless integration into real-world scenarios of network architectures.
Improved Selective Deep-Learning-Based Clustering Ensemble
Authors: Qian Yue , Yao Shixin , Wu Tianjun , Huang You , Zeng Lingbin
Published: January 2024

Clustering ensemble integrates multiple base clustering results to improve the stability and robustness of the single clustering method. It consists of two principal steps: a generation step, which is about the creation of base clusterings, and a consensus function, which is the integration of all clusterings obtained in the generation step. However, most of the existing base clustering algorithms used in the generation step are shallow clustering algorithms such as k-means. These shallow clustering algorithms do not work well or even fail when dealing with large-scale, high-dimensional unstructured data. The emergence of deep clustering algorithms provides a solution to address this challenge. Deep clustering combines the unsupervised commonality of deep representation learning to address complex high-dimensional data clustering, which has achieved excellent performance in many fields. In light of this, we introduce deep clustering into clustering ensemble and propose an improved selective deep-learning-based clustering ensemble algorithm (ISDCE). ISDCE exploits the deep clustering algorithm with different initialization parameters to generate multiple diverse base clusterings. Next, ISDCE constructs ensemble quality and diversity evaluation metrics of base clusterings to select higher-quality and rich-diversity candidate base clusterings. Finally, a weighted graph partition consensus function is utilized to aggregate the candidate base clusterings to obtain a consensus clustering result. Extensive experimental results on various types of datasets demonstrate that ISDCE performs significantly better than existing clustering ensemble approaches.
Numerical Simulation of Liquid Film Characteristics during Atomization of Aluminum Alloy Powder
Authors: Liu Yingjie , Hu Qiang , Zhao Xinming , Zhang Shaoming
Published: January 2024

The process of atomizing aluminum alloy powder using a rotating disk was studied by numerical simulation and experimental verification. The motion characteristics of the molten metal thin liquid film and the evolution law of atomization into droplets were systematically studied with different disk shapes and speeds. The results showed that the slippage of the liquid film on the surface of the spherical disk was smaller, the liquid film spread more evenly, and the velocity distribution was more uniform. Under the same working condition, the boundary diameter of the continuous liquid film on the spherical disk was 21–29% larger, and the maximum liquid film velocity increased by approximately 19%. In other words, the liquid film obtained more energy at the same rotational speed, the energy utilization rate was higher, and the liquid filaments produced by the splitting region of the disk surface were finer and greater in number. The data showed that the average thickness of the liquid film on the surfaces of different disk shapes was more affected by the speed of the flat disk, and the thickness on the spherical disk was relatively stable and uniform, but the difference in thickness between the two disk shapes decreased from 4.2 μm to 0.3 μm when the speed increased from 10,000 rpm to 60,000 rpm. In particular, the influence of the disk shape on the liquid film thickness became smaller when the speed increased to a certain range. At the same time, the characteristics of the liquid film during the spreading movement of molten metal on the disk and the mechanisms of the primary and secondary breakage of the liquid film were obtained through this simulation study.
The Chaperone System in Tumors of the Vocal Cords: Quantity and Distribution Changes of Hsp10, Hsp27, Hsp60, and Hsp90 during Carcinogenesis
Authors: Pitruzzella Alessandro , Fucarino Alberto , Modica Michele Domenico , Lentini Vincenzo Luca , Vella Claudio , Burgio Stefano , Calabrò Federica , Intili Giorgia , Rappa Francesca
Published: January 2024

Laryngeal squamous cell carcinoma (LSCC) constitutes a noteworthy subset of head and neck cancers, contributing to about 4.5% of all malignancies. Its clinical behavior and characteristics exhibit variations contingent upon the specific anatomical site affected, with the glottis, supraglottis, and subglottis emerging as the most prevalent locations. Notably, squamous cell carcinoma represents a predominant histological type, accounting for 85% to 95% of all laryngeal cancers. The gender disparity is evident, with a higher incidence among males, exhibiting a ratio of 3.9:1. Moreover, disparities among racial groups are observed, as African American patients tend to manifest the condition at a younger age, coupled with lower overall survival rates compared to their Caucasian, Hispanic, and Asian counterparts. The primary etiological factors implicated in the onset of laryngeal cancer are tobacco and alcohol consumption, with a direct correlation to the intensity and duration of usage. Importantly, the risk diminishes gradually following cessation, necessitating a substantial period of at least 15 years for a return to baseline rates. Given the diverse nature of laryngeal SCC, treatment modalities are tailored based on the specific site and stage of the disease. Therapeutic interventions, such as radiotherapy, transoral laser microsurgery, open horizontal partial laryngectomy, or total laryngectomy, are employed with the overarching goal of preserving organ function. This study delves into the intricate realm of laryngeal SCC, specifically exploring the involvement of heat shock proteins (HSPs) in disease progression. This research meticulously examines the expression levels of Hsp10, Hsp27, Hsp60, and Hsp90 in dysplastic and benign tissue samples extracted from the right vocal cord, utilizing immunohistochemistry analysis. The focal point of the investigation revolves around unraveling the intricate role of these molecular chaperones in tissue differentiation mechanisms and cellular homeostasis, particularly within the inflammatory milieu characteristic of the tumor phenotype. The findings from this study serve as a robust histopathological foundation, paving the way for more in-depth analyses of the underlying mechanisms governing the contribution of the four chaperones to the development of squamous cell carcinoma in the larynx. Additionally, the data gleaned from this research hint at the potential of these four chaperones as valuable biomarkers, not only for diagnostic purposes but also for prognostication and ongoing patient monitoring. As our understanding of the molecular intricacies deepens, the prospect of targeted therapeutic interventions and personalized treatment strategies for laryngeal SCC becomes increasingly promising.
Temperature and Reaction Time’s Effects on N80 Steel Corrosion Behavior in Supercritical CO2 and Formation Water Environments
Authors: Wang Hanwen , Zhang Liwei , Gan Manguang , Su Xuebin , Wang Yan , Xue Quan , Mei Kaiyuan , Fu Xiaojuan
Published: January 2024

In the present study, an immersion experiment was carried out to examine how N80 steel corrodes when exposed to formation water containing dissolved CO2 and supercritical CO2 (Sc-CO2) along with water vapor. We employed electrochemical and surface analysis methods to examine the influence of various factors, including the temperature and duration of immersion, on the extent of corrosion. The results show that the corrosion patterns of N80 steel in a supercritical CO2 environment and CO2-saturated formation water differed significantly. The presence of similar corrosion features was suggested by the constant structure of the corrosion products identified in the formation water. However, the morphology of the corrosion product was complex in the supercritical CO2 environment, exhibiting features of pitting and localized corrosion. Furthermore, a non-linear trend in the corrosion rate was observed between 40 °C and 120 °C. Specifically, the rate of corrosion declined from 40 °C to 80 °C, but it then resumed its growth from 80 °C to 120 °C. These findings suggest that very high temperatures could lead to the destruction of corrosion products and subsequently enhance the corrosion process.
DCSPose: A Dual-Channel Siamese Framework for Unseen Textureless Object Pose Estimation
Authors: Yue Zhen , Han Zhenqi , Yang Xiulong , Liu Lizhuang
Published: January 2024

The demand for object pose estimation is steadily increasing, and deep learning has propelled the advancement of this field. However, the majority of research endeavors face challenges in their applicability to industrial production. This is primarily due to the high cost of annotating 3D data, which places higher demands on the generalization capabilities of neural network models. Additionally, existing methods struggle to handle the abundance of textureless objects commonly found in industrial settings. Finally, there is a strong demand for real-time processing capabilities in industrial production processes. Therefore, in this study, we introduced a dual-channel Siamese framework to address these challenges in industrial applications. The architecture employs a Siamese structure for template matching, enabling it to learn the matching capability between the templates constructed from high-fidelity simulated data and real-world scenes. This capacity satisfies the requirements for generalization to unseen objects. Building upon this, we utilized two feature extraction channels to separately process RGB and depth information, addressing the limited feature issue associated with textureless objects. Through our experiments, we demonstrated that this architecture effectively estimates the three-dimensional pose of objects, achieving a 6.0% to 10.9% improvement compared to the state-of-the-art methods, while exhibiting robust generalization and real-time processing capabilities.
Composite Foundation Settlement Prediction Based on LSTM–Transformer Model for CFG
Authors: Li Zichao , Peng Yipu , Li Jian , Tang Zhiyuan
Published: January 2024

Roadbed construction typically employs layered and staged filling, characterized by a periodic feature of ‘layered filling—filling interval’. The load and settlement histories established during staged construction offer crucial insights into long-term deformation under filling loads. However, models often rely solely on post-construction settlement data, neglecting the rich filling data. To accurately predict composite foundation ground (CFG) settlement, an LSTM–Transformer deep learning model is used. Five factors from the ‘fill height–time–foundation settlement’ curve are extracted as input variables. The first-layer LSTM model’s gate units capture long-term dependencies, while the second-layer Transformer model’s self-attention mechanism focuses on key features, efficiently and accurately predicting ground settlement. The model is trained and analyzed based on the newly constructed Changsha–Zhuzhou–Xiangtan intercity railway section CSLLXZQ-1, which has a CFG pile composite foundation. The research shows that the proposed LSTM–Transformer model for the settlement prediction of composite foundations has an average absolute error, mean absolute percentage error, and root mean square error of 0.224, 0.563%, and 0.274, respectively. Compared to SVM, LSTM, and Transformer neural network models, it demonstrates higher prediction accuracy, indicating better reliability and practicality. This can provide a new approach and method for the settlement prediction of newly constructed CFG composite foundations.
Geochemical Constraints on the Evolution of Late- to Post-Orogenic Granites in the Arabian Shield, with a Specific Focus on Jabal Al Bayda Area in the Central Hijaz Region, Saudi Arabia
Authors: Abd El-Naby Hamdy H. , Dawood Yehia H.
Published: January 2024

The Jabal Al Bayda, located in the Hijaz terrain of northwest Saudi Arabia, comprises magmatic rocks that represent the ending phase in the Precambrian development of the Arabian Shield. Two granitic suites have been studied petrologically and geochemically, the monzogranite and alkali granite suites, to gain knowledge about their origin and geotectonic implications. The geochemical characteristics of the monzogranites align with their formation in a subduction-related environment. These rocks have a composition that is rich in strontium and barium, and low in rubidium, and displays a high-K calc-alkaline to shoshonitic nature. In contrast, alkali granites typically have lower concentrations of Sr and Ba, and higher rubidium contents. The differences in geochemical composition between monzogranites and alkali granites found in Jabal Al Bayda indicate differences in their origin and geotectonic environment. The evolution of granitoid magmatism in the Jabal Al Bayda area is linked to the Hijaz orogenic cycle, during which northwest-dipping subduction led to the formation of the Midyan, Hijaz, and Jeddah arc assemblage, followed by the collision and accretion of these arcs along the Yanbu and Bir Umq sutures. Due to crustal thickening during the subduction-related stage, the deeper parts of the overlying metagraywackes and metatonalites contribute melt to the early crustal magma, which eventually solidifies to form monzogranites. Later on, during the post-orogenic stage, anatexis of metapelites can occur, leading to the generation of magmas that give rise to alkali granites.
Recent Advances in Bio-Based Wood Protective Systems: A Comprehensive Review
Authors: Calovi Massimo , Zanardi Alessia , Rossi Stefano
Published: January 2024

This review emphasizes the recent ongoing shift in the wood coating industry towards bio-based resources and circular economy principles, promoting eco-friendly alternatives. In addressing wood’s vulnerabilities, this study investigates the use of natural compounds and biopolymers to enhance wood coatings. These materials contribute to protective matrices that safeguard wood surfaces against diverse challenges. Essential oils, vegetable oils, and bio-based polymers are explored for their potential in crafting eco-friendly and durable coating matrices. Furthermore, this review covers efforts to counter weathering and biological decay through the application of various natural compounds and extracts. It evaluates the effectiveness of different bio-based alternatives to traditional chemical preservatives and highlights promising candidates. This review also delves into the incorporation of sustainable pigments and dyes into wood coatings to enhance both protective and aesthetic qualities. Innovative pigments are able to provide visually appealing solutions in line with sustainability principles. As the wood coating industry embraces bio-based resources and the circular economy, researchers are actively developing protective solutions that encompass the coating matrix, preservatives, bio-based fillers, and natural-pigment dyes. This review showcases the continuous efforts of academia and industry to enhance wood coatings’ effectiveness, durability, and sustainability, while maintaining their aesthetic appeal.
Analyzing the Influence of Diverse Background Noises on Voice Transmission: A Deep Learning Approach to Noise Suppression
Authors: Nogales Alberto , Caracuel-Cayuela Javier , García-Tejedor Álvaro J.
Published: January 2024

This paper presents an approach to enhancing the clarity and intelligibility of speech in digital communications compromised by various background noises. Utilizing deep learning techniques, specifically a Variational Autoencoder (VAE) with 2D convolutional filters, we aim to suppress background noise in audio signals. Our method focuses on four simulated environmental noise scenarios: storms, wind, traffic, and aircraft. The training dataset has been obtained from public sources (TED-LIUM 3 dataset, which includes audio recordings from the popular TED-TALK series) combined with these background noises. The audio signals were transformed into 2D power spectrograms, upon which our VAE model was trained to filter out the noise and reconstruct clean audio. Our results demonstrate that the model outperforms existing state-of-the-art solutions in noise suppression. Although differences in noise types were observed, it was challenging to definitively conclude which background noise most adversely affects speech quality. The results have been assessed with objective (mathematical metrics) and subjective (listening to a set of audios by humans) methods. Notably, wind noise showed the smallest deviation between the noisy and cleaned audio, perceived subjectively as the most improved scenario. Future work should involve refining the phase calculation of the cleaned audio and creating a more balanced dataset to minimize differences in audio quality across scenarios. Additionally, practical applications of the model in real-time streaming audio are envisaged. This research contributes significantly to the field of audio signal processing by offering a deep learning solution tailored to various noise conditions, enhancing digital communication quality.
Can Neural Networks Do Arithmetic? A Survey on the Elementary Numerical Skills of State-of-the-Art Deep Learning Models
Authors: Testolin Alberto
Published: January 2024

Creating learning models that can exhibit sophisticated reasoning abilities is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In the past few years there has been an explosion of neural network architectures, datasets, and benchmarks specifically designed to tackle mathematical problems, reporting impressive achievements in disparate fields such as automated theorem proving, numerical integration, and the discovery of new conjectures or matrix multiplication algorithms. However, despite this notable success it is still unclear whether deep learning models possess an elementary understanding of quantities and numbers. This survey critically examines the recent literature, concluding that even state-of-the-art architectures and large language models often fall short when probed with relatively simple tasks designed to test basic numerical and arithmetic knowledge.
Simulating the Wind Energy Distribution in the Coastal Hilly Area of the Jiaodong Peninsula Using the Weather Research and Forecasting Model
Authors: Song Yunhai , He Sen , Zhou Zhenzhen , Wang Liwei , Yang Yufeng , Li Zheng , Gao Zhiqiu
Published: January 2024

This study simulated the wind energy density distribution in the Jiaodong Peninsula region using the Weather Research and Forecasting (WRF) Model. The impacts of different boundary-layer and near-surface parameterization schemes on the simulated wind speed and direction were investigated. The results indicate that the Yonsei University (YSU) scheme and the Quasi-Normal Scale Elimination (QNSE) scheme performed optimally for wind speed and wind direction. We also conducted a sensitivity test of the simulation results for atmospheric pressure, air temperature, and relative humidity. The statistical analysis showed that the YSU scheme performed optimally, while the MRF and BL schemes performed poorly. Following this, the wind energy distribution in the coastal hilly areas of the Jiaodong Peninsula was simulated using the YSU boundary-layer parameterization scheme. The modeled wind energy density in the mountainous and hilly areas of the Jiaodong Peninsula were higher than that in other regions. The wind energy density exhibits a seasonal variation, with the highest values in spring and early summer and the lowest in summer. In spring, the wind energy density over the Bohai Sea is higher than over the Yellow Sea, while the opposite trend is modeled in summer.