bluuebunny/crossref_metadata_2025 huggingface dataset:
Interactions of Highly Diluted Arnica montana Extract with Water Across Glass Interfaces
Authors: Jerman Igor , Ogrizek Linda , Pihir Jonatan , Senica Mateja
Published: January 2025

This study explores the physicochemical changes provoked by the physical transmission of highly diluted (HD) solutions of Arnica montana extract on three receiver solutions differing by their pH. Three dilutions (potencies), one modest (D6), one very high (C30), and another ultra-high (C200) extract of A. montana, were used as a source of HD signal transfer. The HD signal transfer was enhanced by an initial knocking at the start of the experiment and then allowed to interact with the receiver solution for 24 h of exposure. The results confirmed the detectability of the HD signal transfer in solutions with different pH, the general effect of this signal on pH increase, the differential effect of the signal depending on the initial dilution level (potency), and the effect on the decrease in both the electrical voltage in water (ORP) and the conductivity. The overall findings of the study offer valuable new insights and suggest innovative approaches for further research, particularly in detecting the HD signal in solutions with varying pH levels, focusing on interactions with hydroxide and hydronium ions.
Bioprostheses and Mechanical Prostheses for Aortic Valve Replacement in Patients Aged 50 to 65 Years Offer Similar Long-Term Survival Rates
Authors: Toporcer Tomáš , Lukačín Štefan , Kraus Andrea , Homola Marián , Bereš Anton , Trebišovský Michal , Radótzy Denis , Rohn Vilém , Kolesár Adrián
Published: January 2025

Background: Aortic valve replacement (AVR) is the definitive therapy for patients with severe aortic valve stenosis (AoS). The aim of this work is to compare the effect of a mechanical prosthesis (MP) and a bioprosthesis (BP) on the survival of patients aged 50–65 years after AVR. Methods: The retrospective analysis included 276 patients aged 50 to 65 years who had undergone isolated AVR for AoS; 161 patients were implanted with an MP and 115 with a BP. Patient survival, adjusted for age, gender and risk parameters affecting survival, was assessed. A subgroup analysis was performed on the 208 patients with a modern valve (prosthesis models that are no longer used in clinical practice were removed from the sample). Results: After adjusting for risk factors for overall survival as well as for age and sex, the implantation of an MP did not have a significant effect on overall survival in comparison to a BP, at a median follow-up of 10.3 years (p = 0.477). The size of the MP had no significant effect on overall survival either (HR: 1.29; 95%CI: 0.16–10.21; p = 0.812). However, the indexed effective orifice area of the BP had a positive effect on overall survival (HR: 0.09; 95%CI: 0.01–0.78; p = 0.029). Conclusions: The estimated survival of patients aged between 50 and 65 years after implantation of a BP with a sufficiently large indexed effective orifice area may exceed that of patients with an MP.
A Rare Case of an Intracardial Ectopic Thyroid in the Right Ventricle
Authors: Wei Chuangwei , Zhao Ying , Song Yanting , Liu Dongting , Zhang Nan , Liu Jiayi , Sun Zhonghua , Wen Zhaoying , Xu Lei
Published: January 2025

The ectopic thyroid gland is an abnormal development of the embryo. Most of the ectopic thyroid occurs in the path around the thyroglossal duct or on the lateral side of the neck. However, ectopic thyroid occurs in the heart, which is rare. We report a case of right ventricular ectopic thyroid. This case highlights the imaging characteristics of computed tomography (CT) and cardiac magnetic resonance (CMR) and discusses the underlying mechanisms for a timely diagnosis.
Composite Forms in the REE2O3–ZrO2–TiO2 System for Minor Actinides (Am, Cm) and REE Immobilization
Authors: Yudintsev Sergey V. , Ojovan Michael I. , Stefanovsky Olga I.
Published: January 2025

The choice of efficient methods for the immobilization of high-level waste (HLW) resulting from the reprocessing of spent nuclear fuel (SNF) is an important scientific and practical task. The current policy of managing HLW within a closed nuclear fuel cycle envisages its vitrification into borosilicate (B-Si) or alumina–phosphate (Al-P) glasses. These wasteforms have rather limited waste loading and can potentially impair their retaining properties on devitrification. The optimal solution for HLW immobilization could be separating radionuclides into groups using dedicated capacious durable matrices. The phases of the Nd2O3–ZrO2–TiO2 system in this respect are promising hosts for the REE (rare earth elements: Nd, Ce, La, Pr, Sm, Gd, Y) –MA (MA: Am, Cm) fraction of HLW. In this manuscript, we present data on the composition of the samples analyzed, their durability in hot water, their behavior under irradiation, and their industrial manufacturing methods.
Deep Reinforcement Learning Based Optimal Operation of Low-Carbon Island Microgrid with High Renewables and Hybrid Hydrogen–Energy Storage System
Authors: Zhu Wangwang , Wen Shuli , Zhao Qiang , Zhang Bing , Huang Yuqing , Zhu Miao
Published: January 2025

Hybrid hydrogen–energy storage systems play a significant role in the operation of islands microgrid with high renewable energy penetration: maintaining balance between the power supply and load demand. However, improper operation leads to undesirable costs and increases risks to voltage stability. Here, multi-time-scale scheduling is developed to reduce power costs and improve the operation performance of an island microgrid by integrating deep reinforcement learning with discrete wavelet transform to decompose and mitigate power fluctuations. Specifically, in the day-ahead stage, hydrogen production and the hydrogen blending ratio in gas turbines are optimized to minimize operational costs while satisfying the load demands of the island. In the first intraday stage, rolling adjustments are implemented to smooth renewable energy fluctuations and increase system stability by adjusting lithium battery and hydrogen production equipment operations. In the second intraday stage, real-time adjustments are applied to refine the first-stage plan and to compensate for real-time power imbalances. To verify the proposed multi-stage scheduling framework, real-world island data from Shanghai, China, are utilized in the case studies. The numerical simulation results demonstrate that the proposed innovative optimal operation strategy can simultaneously reduce both the costs and emissions of island microgrids.
PJ-YOLO: Prior-Knowledge and Joint-Feature-Extraction Based YOLO for Infrared Ship Detection
Authors: Liu Yongjie , Li Chaofeng , Fu Guanghua
Published: January 2025

Infrared ship images have low resolution and limited recognizable features, especially for small targets, leading to low accuracy and poor generalization of traditional detection methods. To address this, we design a prior knowledge auxiliary loss for leveraging the unique brightness distribution of infrared ship images, we construct a joint feature extraction module that sufficiently captures context awareness, channel differentiation, and global information, and then we propose a prior-knowledge- and joint-feature-extraction-based YOLO (PJ-YOLO) for use in detecting infrared ships. Additionally, a residual deformable attention module is designed to integrate multi-scale information, enhancing detail capture. Experimental results on the SFISD and InfiRray Ships datasets demonstrate that the proposed PJ-YOLO achieves state-of-the-art detection performance for infrared ship targets. In particular, PJ-YOLO achieves improvements of 1.6%, 5.0%, and 2.8% in mAP50, mAP75, and mAP50:95 on the SFISD dataset, respectively.
Beachrock as a Natural Coastal Resource in Antiquity: Evidence from Southern Israel
Authors: Bar Amir , Haddad Elie , Asscher Yotam , Galili Ehud , Bookman Revital , Zviely Dov
Published: January 2025

Beachrock is a sedimentary rock that forms rapidly through the cementation of beach-associated clasts by calcium carbonate within the intertidal zone. On the southern coast of Israel, Holocene beachrock exposures typically appear as linear, shore-parallel platforms that dip seawards. In the archeological record, beachrock was exploited as a natural resource for various purposes, including the production of querns, millstones, basins, building stones, and other architectural elements. At Tel Yavne, a site continuously settled from the Chalcolithic period to the present day, excavations in the eastern and southern fringes revealed an extensive industrial compound dating to the Byzantine and early Islamic periods. This compound underscores the settlement’s economic and industrial prominence during those eras. Numerous beachrock artifacts were uncovered, and this study examines their practical applications while characterizing their composition and microstructure. This research highlights the role of beachrock as a key resource, providing insights into inland–coastal economic interactions in antiquity and broadening our understanding of its use within the socio-economic landscape of the region.
Numerical Analysis and Modeling of a Semi-Submersible Floating Wind Turbine Platform with Large Amplitude Motions Subjected to Extreme Wind and Wave Loads
Authors: Lyu Weishan , Falzarano Jeffrey
Published: January 2025

The objective of this study is to predict the large amplitude motions of floating wind turbine platforms and to emphasize the significance of nonlinear forces when these platforms are subjected to combined wind and wave loads. The analysis utilizes the 5 MW OC4 semi-submersible model. First, we couple the OpenFAST v3.1.0 with SIMDYN, validate the effectiveness of the coupled program, and highlight the considerable impact of nonlinearity on the results, particularly in relation to the heave and pitch motions of offshore wind platforms under extreme environmental conditions. We then discuss the primary reasons for this phenomenon. Ultimately, this study proposes an optimized model aimed at mitigating the nonlinear effects associated with such conditions.
Band Weight-Optimized BiGRU Model for Large-Area Bathymetry Inversion Using Satellite Images
Authors: Xi Xiaotao , Guo Gongju , Gu Jianxiang
Published: January 2025

Currently, using satellite images combined with deep learning models has become an efficient approach for bathymetry inversion. However, only limited bands are usually used for bathymetry inversion in most methods, and they rarely applied for large-area bathymetry inversion (it is important for methods to be used in operational environments). Aiming to utilize all band information of satellite optical image data, this paper first proposes the Band Weight-Optimized Bidirectional Gated Recurrent Unit (BWO_BiGRU) model for bathymetry inversion. To further improve the accuracy, the Stumpf model is incorporated into the BWO_BiGRU model to form another new model—Band Weight-Optimized and Stumpf’s Bidirectional Gated Recurrent Unit (BWOS_BiGRU). In addition, using RANSAC to accurately extract in situ water depth points from the ICESat-2 dataset can accelerate computation speed and improve convergence efficiency compared to DBSCAN. This study was conducted in the eastern bay of Shark Bay, Australia, covering an extensive shallow-water area of 1725 km2. A series of experiments were performed using Stumpf, Band-Optimized Bidirectional LSTM (BoBiLSTM), BWO_BiGRU, and BWOS_BiGRU models to infer bathymetry from EnMAP, Sentinel-2, and Landsat 9 satellite images. The results show that when using EnMAP hyperspectral images, the bathymetry inversion of BWO_BiGRU and BWOS_BiGRU models outperform Stumpf and BoBiLSTM models, with RMSEs of 0.64 m and 0.63 m, respectively. Additionally, the BWOS_BiGRU model is particularly effective in nearshore water areas (depth between 0 and 5 m) of multispectral images. In general, comparing to multispectral satellite images, using the proposed BWO_BiGRU model to infer hyperspectral satellite images can achieve better bathymetry inversion results for large-area bathymetry maps.
Psychopathological Influences on Surgical and Clinical Outcomes in Lumbar Disk Herniation: Prediction Models and Literature Analysis
Authors: Galieri Gianluca , Orlando Vittorio , Mazzucchi Edoardo , Pignotti Fabrizio , Cusumano Davide , Bazzu Paola , Mantini Sofia , Altieri Roberto , Barbarisi Manlio , Olivi Alessandro , Sabatino Giovanni , La Rocca Giuseppe
Published: January 2025

Background/Objectives: Lumbar disk herniation (LDH) significantly affects quality of life due to lower back and radiating leg pain. Surgical intervention, such as discectomy, is effective for symptom relief when conservative measures fail; however, psychological factors like anxiety, depression, and maladaptive coping strategies may negatively impact surgical outcomes. This study aims to assess the role of preoperative psychological evaluations in predicting postoperative recovery and to identify key psychological and functional predictors of surgical success. Methods: A prospective study was conducted on 888 patients undergoing microdiscectomy for LDH at Mater Olbia Hospital between December 2020 and December 2023. Preoperative evaluations included the Visual Analog Scale, Symptom Checklist 90-R, Oswestry Disability Index, and Short Form 36. Logistic regression models and ROC curve analysis were used to identify significant predictors of outcomes and evaluate model accuracy. Results: Preoperative pain levels and emotional well-being emerged as the strongest determinants of postoperative improvement in the Oswestry Disability Index. The predictive model demonstrated high specificity (90.2%) in identifying patients likely to benefit from surgery. Clinically significant improvements were achieved by 69% of patients, highlighting the importance of psychological and functional assessments. Conclusions: Preoperative psychological assessment is critical in predicting outcomes of lumbar disk herniation surgery. Addressing psychological factors preoperatively enhances recovery, supports personalized treatment planning, and improves patient education. These findings advocate for an integrated care model that considers both physical and psychological health, optimizing surgical outcomes and patient satisfaction.
Precision Medicine in Myeloid Neoplasia: Challenges and Opportunities
Authors: Hochman Michael J. , Muniz Joshua P. , Papadantonakis Nikolaos
Published: January 2025

High-risk myeloid neoplasms encompass a group of hematologic malignancies known to cause significant cytopenias, which are accompanied by the risk of end-organ damage. They tend to have an aggressive clinical course and limit life expectancy in the absence of effective treatments. The adoption of precision medicine approaches has been limited by substantive diversity in somatic mutations, limited fraction of patients with targetable genetic lesions, and the prolonged turnaround times of pertinent genetic tests. Efforts to incorporate targeted agents into first-line treatment, rapidly determine pre-treatment molecular or cytogenetic aberrations, and evaluate functional vulnerabilities ex vivo hold promise for advancing the use of precision medicine in these malignancies. Given the relative accessibility of malignant cells from blood and bone marrow, precision medicine strategies hold great potential to shape future standard-of-care approaches to patients with high-risk myeloid malignancies. This review aims to summarize the development of the targeted therapies currently available to treat these blood cancers, most notably acute myeloid leukemia, and also evaluate future opportunities and challenges related to the integration of personalized approaches.
Intention to Use Cryptocurrencies for Business Transactions: The Case of North Carolina
Authors: Ullah Shakir
Published: January 2025

Financial technologies and payment applications have revolutionized money flow recently, with cryptocurrencies offering decentralization, though still limited in transactional use. This study investigates the factors influencing the use of cryptocurrencies for business transactions in North Carolina (NC). This exploratory research utilizes an extended technology acceptance model (TAM) using survey data collected from 228 North Carolina residents and applying Partial Least Squares Structural Equation Modeling (PLS-SEM) to find the relationship between the independent and dependent variables. Our results indicate that perceived usefulness, social influence, and personal innovativeness significantly impact users’ intentions to adopt cryptocurrencies as a medium of exchange. A surprising finding is that ownership has a negative effect on the intention to use cryptos for business transactions. The findings imply that regulators and cryptocurrency issuers should make the system more useful, take full advantage of social media to promote cryptos, and encourage crypto holders to use cryptos for their intended utility rather than just as speculative instruments.
Monitoring of Soil Salinity in the Weiku Oasis Based on Feature Space Models with Typical Parameters Derived from Sentinel-2 MSI Images
Authors: Tashpolat Nigara , Reheman Abuduwaili
Published: January 2025

Soil salinization, as one of the types of land degradation, is a global threat. It not only poses serious ecological problems, but also poses great challenges for the sustainable utilization of land resources, especially in arid and semi-arid areas. The Weiku Oasis is undoubtedly one of the typical areas under severe salinization. The wide spread of saline soil brings numerous negative impacts to the local region. To prevent the escalation of soil salinization, timely monitoring of soil salinization is urgently needed for informed decision-making. Remote sensing technology can obtain large-scale datasets in a short period, allowing researchers to carry out the rapid and accurate investigation of soil salinization. Sentinel-2 images have a relatively high spatial resolution and provide red-edge bands data, referring to bands 5, 6, and 7, and the use of red-edge bands is a new approach to estimate soil salinization in the Weiku Oasis. In this study, we selected five typical indices (NDre1, RNDSI, MSAVI, NDWI, SI3, with the first two being red-edge indices) from twenty potential indices to construct multiple two-dimensional feature space models. Consequently, an optimal and novel monitoring index for soil salinization in the Weiku Oasis was developed. The result showed that: (1) The monitoring index MSAVI-RNDSI, which includes red-edge indices, had the highest inversion accuracy of R2 = 0.7998 and MAE = 3.3444; (2) The red-edge salinity indices effectively captured the conditions of salinization, with the feature space model composed of red-edge indices achieving an average inversion accuracy of R2 = 0.7902; (3) Land-use type was identified as the primary factor affecting the degree of soil salinization in the study area. The proposed approach provides a highly accurate and high-resolution soil salinity mapping strategy.
Understanding the Rise and Fall of Rural Specialty Agriculture from Social–Ecological Land System Perspective: A Longitudinal Case Study in China
Authors: Wang Xue , Wang Mingyu , Gong Lianbing , Yu Chuangchuang
Published: January 2025

Specialty agriculture, promoted in China to enhance rural revitalization and farmers’ wellbeing, depends heavily on local social–ecological conditions and land transformations. However, existing studies often emphasize socio-institutional successes while oversimplifying ecological complexity and neglecting failures. This study examines the rise and fall of rural specialty agriculture through a social–ecological land system perspective, using the cherry tomato industry in C Town, China, as a case study. Drawing on qualitative data from field investigations and interviews, we adapt an extended Social–Ecological Systems Framework (SESF) to analyze interactions among governance subsystems, land resources, and external shocks, and their role in social–ecological transitions. Our findings show that the shift from farmer-led self-organization to government-driven and enterprise-dominated governance significantly undermined system resilience and adaptability. Although external interventions initially spurred industrialization and scaling, they marginalized farmers, leading to the system’s collapse under stressors like market fluctuations and environmental challenges. Our findings highlight the importance of inclusive governance for sustaining land-based agricultural systems, and point to the need of prioritizing farmer autonomy, sustainable land use planning, and adaptive governance to ensure the resilience and long-term sustainability of specialty agriculture systems.
Understanding Scapulohumeral Periarthritis: A Comprehensive Systematic Review
Authors: Iordan Daniel-Andrei , Leonard Stoica , Matei Daniela Viorelia , Sardaru Dragos-Petrica , Onu Ilie , Onu Ana
Published: January 2025

Background: This systematic review examines the clinical presentations and prevalence of scapulohumeral periarthritis (SP) by synthesizing the relevant literature from open-access articles from international databases (Medline, Pedro, and EBSCO). Methods: Keywords guiding the review included ‘scapulohumeral periarthritis’, ‘clinical forms’, ‘incidence’, ‘impingement syndrome, ‘calcifying tendinitis’, ‘bicipital tendonitis’, ‘shoulder bursitis’, ‘adhesive capsulitis or frozen shoulder’, ‘rotator cuff tears’, ‘functional assessment’, and ‘clinical trials’. Eligible studies included randomized controlled trials, nonrandomized controlled trials, cross-sectional studies, and review articles published between 1972 and 2024. Results: Our screening identified 2481 initial articles, of which 621 were further reviewed for eligibility resulting in 107 articles that met the relevance criteria. The findings highlight six distinct clinical forms of SP, such as partial rotator cuff tears and calcific tendinitis, each characterized by specific pathological features and prevalence patterns. Key factors contributing to SP include injuries, scapular instability, acromion deformities, and degenerative rotator cuff changes. Functional assessments, including the Neer, Hawkins, Pain Arc, and Yocum tests, demonstrated diagnostic value in distinguishing SP from other shoulder conditions. Conclusions: By comprehensively analyzing the clinical forms, functional assessment methods, and prevalent lesions of SP, functional testing can improve early diagnosis and guide personalized physiotherapy protocols for optimal rehabilitation in the physiotherapist’s practice.
Electrochemical Corrosion Properties and Protective Performance of Coatings Electrodeposited from Deep Eutectic Solvent-Based Electrolytes: A Review
Authors: Protsenko Vyacheslav S.
Published: January 2025

The application of deep eutectic solvents (DESs) as an innovative class of environmentally friendly liquid media represents a significant advancement in materials science, especially for the development and enhancement of structural materials. Among the promising applications, DESs are particularly attractive for the electrodeposition of corrosion-resistant coatings. It is established that corrosion-resistant and protective coatings, including those based on metals, alloys, and composite materials, can be synthesized using both traditional aqueous electrolytes and non-aqueous systems, such as organic solvents and ionic liquids. The integration of DESs in electroplating introduces a unique capacity for precise control over microstructure, chemical composition, and morphology, thereby improving the electrochemical corrosion resistance and protective performance of coatings. This review focuses on the electrodeposition of corrosion-resistant and protective coatings from DES-based electrolytes, emphasizing their environmental, technological, and economic benefits relative to traditional aqueous and organic solvent systems. Detailed descriptions are provided for the electrodeposition processes of coatings based on zinc, nickel, and chromium from DES-based baths. The corrosion–electrochemical behavior and protective characteristics of the resulting coatings are thoroughly analyzed, highlighting the potential and future directions for developing anti-corrosion and protective coatings using DES-assisted electroplating techniques.
Experimental–Analytical Method for Determining the Dynamic Coefficients of Turning Tools
Authors: Nowakowski Lukasz , Blasiak Slawomir , Skrzyniarz Michal , Rolek Jaroslaw
Published: January 2025

The article presents an analytical and experimental method for determining the dynamic coefficients of cutting tools, with particular emphasis on turning tools. The method involves aligning the acceleration profile obtained from empirical investigations with a mathematical model describing the oscillations of the cutting tool tip. The stiffness (k) and damping (c) coefficients determined using this approach enable the design of tools with desired dynamic characteristics, tailored to specific machining processes, such as machining with long overhangs. From the perspective of mechanical dynamics, selecting appropriate stiffness and damping values allows for the design of tools with optimal dynamic properties. High stiffness reduces the occurrence of deformations under external forces, while adequate damping facilitates the rapid attenuation of vibrations, thereby minimising their adverse effects on the machining process. The developed method could serve as a practical tool for identifying the dynamic parameters applicable to a wide variety of cutting tools. The analysis includes three types of turning tools: one with a steel shank, another with a carbide-core steel shank, and a third with a carbon fibre-core steel shank. The results of the tests indicate that the E-A20Q SDUCL 11 tool is best suited for operations requiring high stability and minimal vibration, owing to its favourable damping and stiffness properties.
Possibilities of Using Specific Jominy Distance in ANN Models for Predicting Low-Alloy Steels’ Microstructure
Authors: Marohnić Tea , Smokvina Hanza Sunčana , Iljkić Dario , Basan Robert
Published: January 2025

Understanding the volume fractions of microstructure constituents such as ferrite, pearlite, bainite, and martensite in low-alloy steels is critical for tailoring mechanical properties to specific engineering applications. To address the complexity of these relationships, this study explores the use of artificial neural networks (ANNs) as a robust tool for predicting these microstructure constituents based on alloy composition, specific Jominy distance, and heat treatment parameters. Unlike previous ANN-based predictions that rely on the hardness after quenching as an input parameter, this study excludes hardness. The developed model relies on readily available input parameters, enabling accurate estimation of microstructure composition prior to heat treatment, which significantly improves its practicality for process planning, optimization, and reducing trial-and-error on industrial applications. Three different input configurations were tested to evaluate the predictive capabilities of ANNs, with results showing that the use of specific Jominy distance as an input variable enhances model performance. Furthermore, the findings suggest that specific Jominy distance could serve as a practical alternative to detailed chemical composition data in industrial applications. The predictions for ferrite, pearlite, and martensite were more accurate than those for bainite, which can be attributed to the complex nature of bainite formation.
Mechanical Properties, Corrosion Damage Evolution Laws, and Durability Deterioration Indicators of High-Performance Concrete Exposed to Saline Soil Environment for 8 Years
Authors: Wang Xiaoming , Yu Hongfa , Tan Yongshan , Wu Chengyou , Wu Peng , Ma Haiyan , Ding Zhigang , Liu Lianxin
Published: January 2025

Most of the current studies rely on simulated brine corrosion environments and lack long-term investigations into concrete corrosion damage evolution under actual corrosive conditions. In this paper, high-performance concrete (HPC) with various mix ratios is designed in the context of the Qinghai Salt Lake region in China, and the evolution of corrosion damage of HPC with different water–binder ratios (W/B) and different fly ash (FA) admixtures under long-term field exposure conditions is obtained by testing the ultrasonic velocity and strengths of the HPC in the field exposure of the HPC in the Qinghai Salt Lake region. The results show that the corrosion resistance of HPC is related to its water–binder ratio and mineral admixture type and dosage under the exposure of 8 years in Qinghai Salt Lake area. HPC with a fly ash dosage of 15–35% and silica fume dosage of 10% exhibits better corrosion resistance when the water–binder ratio (W/B) is between 0.24 and 0.38. The dependence relationship between the corrosion resistance coefficient of HPC and the relative dynamic elastic modulus (Erd) and 28 d standard maintenance strength was also established. The Erd of HPC with a corrosion resistance coefficient of 0.80 or above was 0.73–0.93, not 0.60, which provides an important experimental basis for determining the corrosion damage index of HPC in the high-saline brine environment of the salt lake.
Effect of Porosity on Tribological Properties of Medical-Grade 316L Stainless Steel Manufactured by Laser-Based Powder Bed Fusion
Authors: Barrionuevo Germán Omar , Walczak Magdalena , Mendez Patricio , La Fé-Perdomo Iván , Chiluisa-Palomo Erika , Navas-Pinto Wilson , Cree Duncan E.
Published: January 2025

The potential of laser-based powder bed fusion (L-PBF) technology for producing functional components relies on its capability of maintaining or even improving the mechanical properties of the processed material. This improvement is associated with the microstructure resulting from the high thermal gradient and fast cooling rate. However, this microstructural advantage may be counterbalanced by the lack of full density, which could be tolerated to a certain degree for applications such as biomedical implants and medical equipment. In this study, medical-grade 316L stainless steel specimens with porosities ranging from 1.7 to 9.1% were additively manufactured by L-PBF using different combinations of laser power and scanning speeds. Tribological properties were evaluated by pin-on-disc testing in dry conditions against a silicon nitride test body and analyzed in the context of microstructural characterization by optical and electron microscopy. The results reveal that higher porosity allows for a diminishing wear rate, which is explained by the capacity of the pores to retain wear debris related with the three-body abrasion. This research provides practical insights into the design of medical wear-resistant components, thereby enhancing our understanding of the potential of L-PBF in the fields of materials science and biomedical engineering.
Optimizing Injection Molding Tool Design with Additive Manufacturing: A Focus on Thermal Performance and Process Efficiency
Authors: Chalicheemalapalli Jayasankar Deviprasad , Tröster Thomas , Marten Thorsten
Published: January 2025

Injection molding plays a pivotal role in modern manufacturing, enabling the mass production of complex components with high precision. However, traditional tooling methods often face challenges related to thermal management, design constraints, and material efficiency. This study examines the use of additive manufacturing (AM) in the development and optimization of injection molding tools to overcome these limitations. A novel prototype was fabricated using AM techniques, incorporating integrated cooling channels and optimized lattice structures to enhance thermal performance and simplify the manufacturing process. Experimental validation demonstrated the prototype’s effective integration into a vacuum-assisted resin transfer molding (VA-LRTM) system without requiring modifications to existing tooling setups. The results showed significant improvements in temperature regulation, reduced cycle times, and consistent mechanical properties of the molded components compared to conventional approaches. By reducing the number of tool components and eliminating the need for support structures during manufacturing, AM also minimized material waste and post-processing requirements. This research highlights the transformative potential of additive manufacturing in injection molding tool design, offering increased flexibility, cost efficiency, and enhanced functionality to meet the evolving demands of modern industrial applications.
Microstructures and Mechanical Properties of SUS 630 Stainless Steel: Effects of Age Hardening in a Tin Bath and Atmospheric Environments
Authors: Chen Kuan-Jen , Chuang Fu-Sung
Published: January 2025

This study investigates the solution-aging treatment of precipitation-hardening SUS 630 stainless steel, alongside an analysis of the carbon emissions generated by the energy consumed during aging treatments. By employing atmospheric and liquid tin as aging media, the research comprehensively explores the effects of aging treatments on the characteristics of 630 stainless steel. The maximum hardness value for the 630 stainless steel was observed after atmospheric aging at 500 °C for 1 h. The given 630 stainless steel obtained its maximum hardness value after atmospheric aging at 500 °C for 1 h, indicating that the formation of secondary precipitates strengthens the steel’s performance. By leveraging the intrinsic characteristics of liquid tin, using it as an aging medium (Sn bath aging) significantly improves the efficiency of the aging process, achieving mechanical properties comparable to those of atmosphere-aged steel. The 630 stainless steel aged in a Sn bath exhibited a refined martensitic matrix with substantial precipitate formation, contributing to superior impact toughness and dynamic fatigue resistance. With an equivalent mass and performance, Sn bath aging notably reduced the duration of the treatment compared to atmospheric aging, leading to substantial energy savings and reduced carbon emissions. The Sn bath treatment, recognized in metallurgical science and heat treatment for its excellent thermal conductivity and recyclability, shows potential to enhance process efficiency and enable low carbon emissions in the heat treatment industry. By highlighting the differences between aging methods, this study provides solutions for optimizing heat treatment processes and thereby achieving industrial advancement and sustainability goals.
Multi-Objective Optimization of Injection Molding Process Parameters for Junction Boxes Based on BP Neural Network and NSGA-II Algorithm
Authors: Hong Tengjiao , Huang Dong , Ding Fengjuan , Zhang Liyong , Dong Fulong , Chen Lei
Published: January 2025

Many factors affect the quality of the injection molding of plastic products, including the process parameters, mold materials, type and geometry of plastic parts, cooling system, pouring system, etc. A multi-objective optimization method for injection molding process parameters based on the BP neural network and NSGA-II algorithm is proposed to address the problem of product quality defects caused by unreasonable process parameter settings. Taking the junction box shell as the object, numerical simulation was carried out using Moldflow2019 software and a six-factor five-level orthogonal experiment was designed to explore the influence of injection molding process parameters, such as the mold temperature, melt temperature, injection pressure, holding pressure, holding time, and cooling time, on the volume shrinkage rate and warpage deformation of the junction box. Based on a numerical simulation, the BP neural network and NSGA-II algorithm were used to optimize the optimal combination of injection molding process parameters, volume shrinkage rate, and warpage deformation. The research results indicate that the melt temperature has the most significant impact on the quality of the injection molding of junction boxes, followed by the holding time, holding pressure, cooling time, injection pressure, and mold temperature. After optimization using the BP neural network and the NSGA-II algorithm, the optimal process parameter combination was obtained with a melt temperature of 230.03 °C, a mold temperature of 51.27 °C, an injection pressure of 49.13 MPa, a holding pressure of 69.01 MPa, a holding time of 15.48 s, and a cooling time of 34.91 s. At this time, the volume shrinkage rate and warpage deformation of the junction box were 6.905% and 0.991 mm, respectively, which decreased by 33.2% and 3.8% compared to the average volume shrinkage rate (10.34884%) and warpage deformation (1.030764 mm) before optimization. The optimization effect was significant. In addition, the errors between the volume shrinkage rate and warpage deformation predicted by BP-NSGA-II and the simulated values using Moldflow software were 1.9% and 3.4%, respectively, indicating that the optimization method based on the BP neural network model and NSGA-II algorithm is reliable.
Iron Oxide Powders Containing Arsenic from Water Treatment Processes Mixed with Cement as Environmental and Structural Solution
Authors: Colorado Henry A. , Rendón Giraldo Jeiser , Montoya Manuela , Correa Mauricio , Gómez Marroquín Mery Cecilia , Monteiro Sergio Neves
Published: January 2025

This study explores the stabilization and utilization of hazardous waste (HW) derived from iron oxide powders containing arsenic, a byproduct of a water purification process. Cement paste samples were prepared with varying waste content (0.0%, 2.5%, 10% and 20% by weight) through mechanical mixing of all the components. Utilizing this waste offers two key environmental benefits: first, it addresses the issue of large-scale waste production globally by providing a method for its stabilization; second, it reduces cement consumption in concrete by serving as an admixture and filler, thereby lowering the cement industry’s significant CO2 emissions. After 28 days, compressive strength and density tests were conducted, and the microstructure was examined using scanning electron microscopy and X-ray diffraction. The results demonstrated compressive strengths exceeding 20 MPa, with the presence of calcite, portlandite, and ettringite phases in the samples. Additionally, Weibull statistics were conducted over a wide number of samples per composition in order to account for the variability of the compression properties, which can be important for deciding the applications. The results showed that the prepared formulations can be used in structural applications such as walls, infrastructure, sidewalks, and soil stabilization.
Deterministic and Stochastic Machine Learning Classification Models: A Comparative Study Applied to Companies’ Capital Structures
Authors: Hair Joseph F. , Fávero Luiz Paulo , Junior Wilson Tarantin , Duarte Alexandre
Published: January 2025

Corporate financing decisions, particularly the choice between equity and debt, significantly impact a company’s financial health and value. This study predicts binary corporate debt levels (high or low) using supervised machine learning (ML) models and firms’ characteristics as predictive variables. Key features include companies’ size, tangibility, profitability, liquidity, growth opportunities, risk, and industry. Deterministic models, represented by logistic regression and multilevel logistic regression, and stochastic approaches that incorporate a certain degree of randomness or probability, including decision trees, random forests, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks, were evaluated using usual metrics. The results indicate that decision trees, random forest, and XGBoost excelled in the training phase but showed higher overfitting when evaluated in the test sample. Deterministic models, in contrast, were less prone to overfitting. Notably, all models delivered statistically similar results in the test sample, emphasizing the need to balance performance, simplicity, and interpretability. These findings provide actionable insights for managers to benchmark their company’s debt level and improve financing strategies. Furthermore, this study contributes to ML applications in corporate finance by comparing deterministic and stochastic models in predicting capital structure, offering a robust tool to enhance managerial decision-making and optimize financial strategies.
Golden Angle Modulation in Complex Dimension Two
Authors: Hu Kejia , Li Hongyi , Zhao Di , Jiang Yuan
Published: January 2025

In this paper, we propose a new geometric-shaping design for golden angle modulation (GAM) based on the complex geometric properties of open symmetrized bidisc, termed Bd-GAM, for future generation wireless communication systems. Inspired from the circular symmetric structure of the GAM, we construct the modulation schemes, Bd-GAM1 and Bd-GAM2. Specifically, we consider MI-optimized probabilistic modulation scheme with the geometrics properties of symmetric bidisc. With minimum SNR and entropy constraint, Bd-GAM1 and Bd-GAM2 can overcome the shaping-loss. Compared with the existed golden angle modulation introduced, the new design improves the mutual information, and the distance between adjacent constellation points.
Harnessing Unsupervised Ensemble Learning for Biomedical Applications: A Review of Methods and Advances
Authors: Ahsen Mehmet Eren
Published: January 2025

Advancements in data availability and computational techniques, including machine learning, have transformed the field of bioinformatics, enabling the robust analysis of complex, high-dimensional, and heterogeneous biomedical data. This paper explores how diverse bioinformatics tasks, including differential expression analysis, network inference, and somatic mutation calling, can be reframed as binary classification tasks, thereby providing a unifying framework for their analysis. Traditional single-method approaches often fail to generalize across datasets due to differences in data distributions, noise levels, and underlying biological contexts. Ensemble learning, particularly unsupervised ensemble approaches, emerges as a compelling solution by integrating predictions from multiple algorithms to leverage their strengths and mitigate weaknesses. This review focuses on the principles and recent advancements in ensemble learning, with a particular emphasis on unsupervised ensemble methods. These approaches demonstrate their ability to address critical challenges in bioinformatics, such as the lack of labeled data and the integration of predictions from algorithms operating on different scales. Overall, this paper highlights the transformative potential of ensemble learning in advancing predictive accuracy, robustness, and interpretability across diverse bioinformatics applications.
3F4 Hypergeometric Functions as a Sum of a Product of 1F2 Functions
Authors: Straton Jack C.
Published: January 2025

This paper shows that certain F43 hypergeometric functions can be expanded in sums of pair products of F21 functions. In special cases, the F43 hypergeometric functions reduce to F32 functions. Further special cases allow one to reduce the F32 functions to F21 functions, and the sums to products of F10 (Bessel) and F21 functions. The class of hypergeometric functions with summation theorems are thereby expanded beyond those expressible as pair-products of F12 functions, F23 functions, and generalized Whittaker functions, into the realm of Fqp functions where p<q for both the summand and terms in the series.
Validation of Psychometric Tools for Assessing Fatigue, Mood, and Sleep Quality: Application in the PREVES-STOP Study
Authors: Di Lorenzo Giuseppe , Buonerba Carlo , Baio Raffaele , Strianese Oriana , Cappuccio Francesca , Verde Antonio , Calabrese Alessia Nunzia , Montanaro Vittorino , Fortino Federica , Tufano Antonio , Zarrella Roberta , Pucci Luigi , Ferro Matteo , Ingenito Concetta , Riccio Vittorio , Costa Emma , Riccio Giovanni , Errico Carla , Buonocore Anna , Gallo Ilaria , Amoruso Gianluca , Verze Paolo , Costabile Ferdinando , Scafuri Luca
Published: January 2025

Background and Objectives: Environmental pollution in regions like the Sarno River Basin in southern Italy significantly affects physical and psychological health. This study aimed to validate three novel psychometric tools—REST, HEAL-BDLC, and PEACE—for assessing fatigue, mood disturbances, and sleep quality in environmentally exposed populations. While correlations with heavy metal exposure will be addressed in a separate manuscript, this study focuses solely on psychometric validation. Materials and Methods: The PREVES-STOP Initiative recruited 88 participants aged 30–65 years from the Sarno River Basin. Participants completed psychometric questionnaires tailored to measure fatigue (REST), symptoms of depression and anxiety (HEAL-BDLC), and sleep quality (PEACE). Internal consistency, construct validity, and reliability were analyzed using Cronbach’s alpha, correlation analyses, and principal component analysis (PCA). A subgroup received a nutraceutical intervention for us to explore their responsiveness to change over a two-week period. Results: REST (α = 0.969), HEAL-BDLC (α = 0.962), and PEACE (α = 0.736) demonstrated strong reliability. PCA confirmed the unidimensional structure of REST and the two-component structure of HEAL-BDLC (depression and anxiety dimensions) and PEACE (insomnia and sleep quality). Correlations with established measures, such as the WHO Well-Being Index, supported construct validity. Among the intervention participants, significant improvements were observed in fatigue (−12.5 REST median score), mood (−13.0 HEAL-BDLC median score), sleep (+1.5 PEACE median score), and overall well-being (+4.0 WHO-5 median score). Conclusions: REST, HEAL-BDLC, and PEACE are reliable and valid instruments for assessing nuanced health outcomes in environmentally exposed populations. They hold potential for guiding public health interventions and evaluating environmental remediation impacts. These findings lay the groundwork for future studies linking psychometric outcomes with heavy metal exposure.
Novel Insight of N6-Methyladenosine in Cardiovascular System
Authors: Zhang Huan , Lu Wei , Tang Haoyue , Chen Aiqun , Gao Xiaofei , Zhu Congfei , Zhang Junjie
Published: January 2025

N6-methyladenosine (m6A) is the most common and abundant internal co-transcriptional modification in eukaryotic RNAs. This modification is catalyzed by m6A methyltransferases, known as “writers”, including METTL3/14 and WTAP, and removed by demethylases, or “erasers”, such as FTO and ALKBH5. It is recognized by m6A-binding proteins, or “readers”, such as YTHDF1/2/3, YTHDC1/2, IGF2BP1/2/3, and HNRNPA2B1. Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality worldwide. Recent studies indicate that m6A RNA modification plays a critical role in both the physiological and pathological processes involved in the initiation and progression of CVDs. In this review, we will explore how m6A RNA methylation impacts both the normal and disease states of the cardiovascular system. Our focus will be on recent advancements in understanding the biological functions, molecular mechanisms, and regulatory factors of m6A RNA methylation, along with its downstream target genes in various CVDs, such as atherosclerosis, ischemic diseases, metabolic disorders, and heart failure. We propose that the m6A RNA methylation pathway holds promise as a potential therapeutic target in cardiovascular disease.