bluuebunny/crossref_metadata_2025 huggingface dataset:
A Cross-Working Condition-Bearing Diagnosis Method Based on Image Fusion and a Residual Network Incorporating the Kolmogorov–Arnold Representation Theorem
Authors: Tang Ziyi , Hou Xinhao , Wang Xin , Zou Jifeng
Published: August 2024

With the optimization and advancement of industrial production and manufacturing, the application scenarios of bearings have become increasingly diverse and highly coupled. This complexity poses significant challenges for the extraction of bearing fault features, consequently affecting the accuracy of cross-condition fault diagnosis methods. To improve the extraction and recognition of fault features and enhance the diagnostic accuracy of models across different conditions, this paper proposes a cross-condition bearing diagnosis method. This method, named MCR-KAResNet-TLDAF, is based on image fusion and a residual network that incorporates the Kolmogorov–Arnold representation theorem. Firstly, the one-dimensional vibration signals of the bearing are processed using Markov transition field (MTF), continuous wavelet transform (CWT), and recurrence plot (RP) methods, converting the resulting images to grayscale. These grayscale images are then multiplied by corresponding coefficients and fed into the R, G, and B channels for image fusion. Subsequently, fault features are extracted using a residual network enhanced by the Kolmogorov–Arnold representation theorem. Additionally, a domain adaptation algorithm combining multiple kernel maximum mean discrepancy (MK-MMD) and conditional domain adversarial network with entropy conditioning (CDAN+E) is employed to align the source and target domains, thereby enhancing the model’s cross-condition diagnostic accuracy. The proposed method was experimentally validated on the Case Western Reserve University (CWRU) dataset and the Jiangnan University (JUN) dataset, which include the 6205-2RS JEM SKF, N205, and NU205 bearing models. The method achieved accuracy rates of 99.36% and 99.889% on the two datasets, respectively. Comparative experiments from various perspectives further confirm the superiority and effectiveness of the proposed model.
Understanding Risk Factors of Recurrent Anxiety Symptomatology in an Older Population with Mild to Severe Depressive Symptoms: A Bayesian Approach
Authors: Maekawa Eduardo , de Sá Martins Mariana Mendes , Nakamura Carina Akemi , Araya Ricardo , Peters Tim J. , Van de Ven Pepijn , Scazufca Marcia
Published: August 2024

Anxiety in older individuals is understudied despite its prevalence. Investigating its occurrence can be challenging, yet understanding the factors influencing its recurrence is important. Gaining insights into these factors through an explainable, probabilistic approach can enhance improved management. A Bayesian network (BN) is well-suited for this purpose. This study aimed to model the recurrence of anxiety symptomatology in an older population within a five-month timeframe. Data included baseline socio-demographic and general health information for older adults aged 60 years or older with at least mild depressive symptoms. A BN model explored the relationship between baseline data and recurrent anxiety symptomatology. Model evaluation employed the Area Under the Receiver Operating Characteristic Curve (AUC). The BN model was also compared to four machine learning models. The model achieved an AUC of 0.821 on the test data, using a threshold of 0.367. The model demonstrated generalisation abilities while being less complex and more explainable than other machine learning models. Key factors associated with recurrence of anxiety symptomatology were: “Not being able to stop or control worrying”; “Becoming easily annoyed or irritable”; “Trouble relaxing”; and “depressive symptomatology severity”. These findings indicate a prioritised sequence of predictors to identify individuals most likely to experience recurrent anxiety symptomatology.
Reduced Element for Adaptive Finite Element Analysis of First-Order IVP with Built-in Error Estimator in Maximum Norm
Authors: Yuan Quan , Yuan Si
Published: August 2024

This paper proposes a novel yet simple approach to the adaptive finite element (FE) analysis of the first-order Initial Value Problems (IVPs) in the maximum norm by introducing the reduced element technique. In the present approach, the FE solution uh of the conventional Galerkin element of degree m + 1 is decomposed into two parts: a reduced solution uRh from the reduced element of degree m obtained by ignoring the highest degree term of uh, and a built-in point-wise error estimator εRh directly given by the ignored term. Since the end node solutions of the reduced element are inherited from the full order element, it gains O(h2m+2) accuracy and achieves a nodal/element accuracy ratio as high as two, which greatly enhances its adaptive capability regarding solving IVPs on long time domains. The related error analysis is addressed and a complete adaptivity algorithm is given. Typical numerical examples of both linear and nonlinear IVPs of both single and systems of equations are presented to show the validity and effectiveness of the proposed approach.
Surveillance Unmanned Ground Vehicle Path Planning with Path Smoothing and Vehicle Breakdown Recovery
Authors: Parsons Tyler , Baghyari Farhad , Seo Jaho , Kim Byeongjin , Kim Mingeuk , Lee Hanmin
Published: August 2024

As unmanned ground vehicles (UGV) continue to be adapted to new applications, an emerging area lacks proper guidance for global route optimization methodology. This area is surveillance. In autonomous surveillance applications, a UGV is equipped with a sensor that receives data within a specific range from the vehicle while it traverses the environment. In this paper, the ant colony optimization (ACO) algorithm was adapted to the UGV surveillance problem to solve for optimal paths within sub-areas. To do so, the problem was modeled as the covering salesman problem (CSP). This is one of the first applications using ACO to solve the CSP. Then, a genetic algorithm (GA) was used to schedule a fleet of UGVs to scan several sub-areas such that the total distance is minimized. Initially, these paths are infeasible because of the sharp turning angles. Thus, they are improved using two methods of path refinement (namely, the corner-cutting and Reeds–Shepp methods) such that the kinematic constraints of the vehicles are met. Several test case scenarios were developed for Goheung, South Korea, to validate the proposed methodology. The promising results presented in this article highlight the effectiveness of the proposed methodology for UGV surveillance applications.
SDCB-YOLO: A High-Precision Model for Detecting Safety Helmets and Reflective Clothing in Complex Environments
Authors: Yang Xiang , Wang Jizhen , Dong Minggang
Published: August 2024

The correct wearing of safety helmets and reflective vests is of great significance in construction sites, offices, and civil engineering sites. Aiming to address the issues of low detection accuracy and high algorithm complexity caused by complex background environments in the small target detection of safety helmets and reflective clothing using existing algorithms, an improved algorithm based on YOLOv8n is proposed. Firstly, the SE module is utilized to reduce interference in complex environments. Next, the IOU function is modified to speed up calculations. Then, a lightweight universal upsampling operator (CARAFE) is employed to obtain a larger receptive field. Finally, the Bidirectional Feature Pyramid Network is used to replace the Concat module of the original head layer. Based on these four modifications made to the model, this article names the new model SDCB-YOLO, derived from the initial letters of the four respective modules. The experimental results show that the mAP of the SDCB-YOLO model on the test set reached 97.1%, which is 4.6% higher than YOLOv5s and 3.5% higher than YOLOv8n. Additionally, the model boasts a parameter count of 3,094,304, a computational load of 8.4 GFLOPs, and a model size of 6.13 MB. Compared to YOLOv5s, with a parameter count of 7,030,417, a computational cost of 16.0 GFLOPs, and a model size of 13.79 MB, the SDCB-YOLO model is significantly smaller. When compared to YOLOv8n, with a parameter count of 3,011,628, a computational complexity of 8.2 GFLOPs, and a model size of 6.11 MB, the SDCB-YOLO model’s parameters and model size are only slightly increased, while maintaining a comparable computational load. Therefore, the improved detection algorithm presented in this article not only ensures the lightweight nature of the model but also significantly enhances its detection accuracy.
An Extensive Parametric Analysis and Optimization to Design Unidimensional Periodic Acoustic Metamaterials for Noise Attenuation
Authors: Shendy Mohamed , Oluyemi Momoiyioluwa , Maftoon Nima , Salehian Armaghan
Published: August 2024

The presented research delineates an extensive study aimed at obtaining and comparing optimal designs and geometries for one-dimensional periodic acoustic metamaterials to attenuate noise within the audible frequency range of 20 Hz to 20 kHz. Various periodic designs, encompassing diverse geometric parameters and shapes—from Basic-Periodic to Semi-Periodic, Tapered-Diverging, and Tapered-Converging unit cells of repeated patterns—are examined to identify the most effective configurations for this application. A thorough parametric analysis is executed employing FE-Bloch’s theorem across these four configurations to determine their bandgaps and to identify the most effective geometry. A normalization process is utilized to extend the domain of the analysis and the range of the system parameters studied in this work, totaling 202,505 design cases. Finally, the optimal design is identified based on achieving the best bandgaps coverage. The study concludes with the presentation of frequency domain acoustic pressure responses at multiple sensing points along the filters, validating the performance and the obtained bandgaps through these optimal geometries.
Pulsed Electric Field as a Mild Treatment for Extended Shelf-Life and Preservation of Bioactive Compounds in Blood Orange Juice
Authors: Fabroni Simona , Platania Giusy Maria , Amenta Margherita , Ballistreri Gabriele , Galvano Francesco , Nges Ivo Achu , Timpanaro Nicolina
Published: August 2024

The agri-food processing industry predominantly relies on fossil fuels, contributing significantly to greenhouse gas emissions and extensive water use. Climate change requires a conversion of food processing technologies towards sustainability. Our research focuses on testing and validating pulsed electric field (PEF) technology as a mild processing method for stabilizing freshly squeezed, not from concentrate, blood orange juice. Experiments were carried out on a continuous pilot plant endowed with a patented treatment chamber that ensures a constant flow of product without “hot spots” for a homogeneous treatment. Once the operative conditions of the process in terms of energy density were optimized, PEF-treated blood orange juice was tested in order to evaluate the effects on physico-chemical parameters (total soluble solids, total acidity, pH, CIE L*, a*, b*, C*, h color indices, cloud, bioactive compounds (ascorbic acid, total anthocyanins, total and individual flavanones), antioxidant activity (ORAC units, total phenolic content), microbial communities (aerobic mesophilic viable count, yeasts and molds, acid-tolerant microorganisms), residual enzymatic pectinmethylesterase activity, and sensory attributes (flavor, off-flavor, off-odor, color, intensity of odor, acidity, sweetness, bitter, freshness, cooked flavor). Then, in order to simulate the commercial refrigerated distribution of PEF-treated blood orange juice and define its refrigerated shelf-life, the PEF-treated freshly squeezed, not from concentrate, blood orange juice was subjected to physico-chemical, antioxidant, microbiological, and sensory evaluations over twenty days of refrigerated storage at 4 ± 1 °C. The PEF treatment effectively ensured excellent microbial inactivation and enhanced the nutritional and health characteristics of the juice, thereby extending its shelf-life. This study demonstrates the significant potential of the PEF treatment to produce blood orange juice suitable for a new retail segment—freshly squeezed juices with superior health quality, fresh-like characteristics, and extended refrigerated shelf-life.
From the Environment to Molecular Interactions of Nanoplastics: Unraveling the Neurotoxic Impacts and the Implications in Neurodegenerative Processes
Authors: Urani Chiara , Barbieri Raffaella , Alloisio Susanna , Tesauro Marina
Published: August 2024

Nanoplastics (NPs) represent an escalating hazard to both humans and the ecosystem due to their pervasive presence. This review delves into (i) the widespread occurrence of NPs across the different environmental matrices, including food; (ii) routes and estimates for human exposure; (iii) the mechanisms of blood–brain barrier (BBB) crossing; and (iv) implications for human health, with a specific focus on molecular features associated with neurotoxicity and neurodegenerative processes. The impact of NPs on the central nervous system, their ability to cross the BBB and the underpinning mechanisms, the potential to initiate neurotoxicity by fostering β-amyloid aggregation, and their interactions with metallo-enzymes (such as superoxide dismutase) are elucidated. The analysis of transcriptomics and epigenomic results, including microRNA dysregulation, unveil how NPs could contribute to neurological disorders. The need for considering overlaps among diverse pathogenetic mechanisms when probing the effects of NPs is discussed. Additional urgent needs are the development of reliable in vitro models for neurotoxicity studies able to mimic the complexity of the nervous system and the exposure of such models to more environmentally relevant NPs. Finally, the development of extremely sensitive detection and analysis methodologies to quantify NPs in environmental and biological matrices is a pressing priority.
Accurate MRI-Based Brain Tumor Diagnosis: Integrating Segmentation and Deep Learning Approaches
Authors: Ashimgaliyev Medet , Matkarimov Bakhyt , Barlybayev Alibek , Li Rita Yi Man , Zhumadillayeva Ainur
Published: August 2024

Magnetic Resonance Imaging (MRI) is vital in diagnosing brain tumours, offering crucial insights into tumour morphology and precise localisation. Despite its pivotal role, accurately classifying brain tumours from MRI scans is inherently complex due to their heterogeneous characteristics. This study presents a novel integration of advanced segmentation methods with deep learning ensemble algorithms to enhance the classification accuracy of MRI-based brain tumour diagnosis. We conduct a thorough review of both traditional segmentation approaches and contemporary advancements in region-based and machine learning-driven segmentation techniques. This paper explores the utility of deep learning ensemble algorithms, capitalising on the diversity of model architectures to augment tumour classification accuracy and robustness. Through the synergistic amalgamation of sophisticated segmentation techniques and ensemble learning strategies, this research addresses the shortcomings of traditional methodologies, thereby facilitating more precise and efficient brain tumour classification.
Adsorption Separation of Various Polar Dyes in Water by Oil Sludge-Based Porous Carbon
Authors: Cheng Huanquan , Peng Longgui , Liu Jia , Ma Cuiying , Hao Fangtao , Zheng Bin , Yang Jianye
Published: August 2024

The pollution caused by printing and dyeing wastewater is increasingly severe, posing significant harm to aquatic plants and animals. In this study, porous carbon was synthesized via the high-temperature pyrolysis of light and heavy organic matter present in oily sludge, utilizing low oil content sludge as the raw material and zinc chloride as a chemical activator. The results exhibited a significant increase in the specific surface area of the oily sludge-based porous carbon, from 4.95 m²/g to 10.95 m²/g. The effects of various parameters such as pH, amount of sorbent, dye concentration, temperature, and contact time on dye removal have been studied. The results showed that the oil sludge-based porous carbon exhibited high efficiency in removing Malachite Green from aqueous solutions, which has low polarity and remains consistently above 97%. The removal rate of Crystal Violet, which is more polar, was as low as 24.14%. The corresponding adsorption capacities were 33.41 mg/g for Malachite Green, 16.41 mg/g for Crystal Violet, and 13.56 mg/g for Methylene Blue. The adsorption capacity of OSC700 for three types of dyes was characterized by monolayer adsorption, primarily driven by chemical adsorption, with significant contributions from electrostatic and hydrophobic effects. The adsorption process was spontaneous, exothermic, and accompanied by an increase in entropy. For less polar substances, the adsorption on oily sludge-based porous carbon is primarily driven by aromatic functional groups on the carbon surface, hydrophobicity, π-π electron-donor-acceptor (π-π EDA) interactions, and surface hydrogen bond formation. In contrast, for more polar dyes, electrostatic and hydrophobic interactions dominate, with electrostatic adsorption being the predominant mechanism and minimal hydrogen bond formation during adsorption.
Trajectory-Tracking Control of Unmanned Vehicles Based on Adaptive Variable Parameter MPC
Authors: Chen Wenjue , Liu Fuchao , Zhao Hailin
Published: August 2024

Aiming at the problems of the poor trajectory-tracking performance and low control accuracy of unmanned vehicles under complex working conditions, we first estimate the lateral force of tires using the square root cubature Kalman filter (SRCKF) in order to correct the lateral stiffness of the tires online, which reduces the model bias caused by constant lateral stiffness, and then adopt a Gaussian function-based adaptive time-domain model predictive control method to improve the trajectory-tracking control accuracy of unmanned vehicles under complex working conditions. Finally, the proposed control algorithm is validated via Carsim and MATLAB/Simulink joint simulation. The results show that compared with the classical model predictive control (MPC) algorithm, the proposed control algorithm reduces the average lateral tracking error by 73.07% and the peak beta and the peak yaw rate by 50.89% and 47.51%, respectively, so that the unmanned vehicle is able to maintain good tracking performance and control accuracy.
Containment Control-Guided Boundary Information for Semantic Segmentation
Authors: Liu Wenbo , Zhang Junfeng , Zhao Chunyu , Huang Yi , Deng Tao , Yan Fei
Published: August 2024

Real-time semantic segmentation is a challenging task in computer vision, especially in complex scenes. In this study, a novel three-branch semantic segmentation model is designed, aiming to effectively use boundary information to improve the accuracy of semantic segmentation. The proposed model introduces the concept of containment control in a pioneering way, which treats image interior elements as well as image boundary elements as followers and leaders in containment control, respectively. Based on this, we utilize two learnable feature fusion matrices in the high-level semantic information stage of the model to quantify the fusion process of internal and boundary features. Further, we design a dedicated loss function to update the parameters of the feature fusion matrices based on the criterion of containment control, which enables fine-grained communication between target features. In addition, our model incorporates a Feature Enhancement Unit (FEU) to tackle the challenge of maximizing the utility of multi-scale features essential for semantic segmentation tasks through the meticulous reconstruction of these features. The proposed model proves effective on the publicly available Cityscapes and CamVid datasets, achieving a trade-off between effectiveness and speed.
Methods for Reducing Ring Artifacts in Tomographic Images Using Wavelet Decomposition and Averaging Techniques
Authors: Lipowicz Paweł , Borowska Marta , Dardzińska-Głębocka Agnieszka
Published: August 2024

Computed tomography (CT) is one of the fundamental imaging modalities used in medicine, allowing for the acquisition of accurate cross-sectional images of internal body tissues. However, during the acquisition and reconstruction process, various artifacts can arise, and one of them is ring artifacts. These artifacts result from the inherent limitations of CT scanner components and the properties of the scanned material, such as detector defects, non-uniform distribution of radiation from the source, or the presence of metallic elements within the scanning region. The purpose of this study was to identify and reduce ring artifacts in tomographic images using image decomposition and average filtering methods. In this study, tests were conducted on the effectiveness of identifying ring artifacts using wavelet decomposition methods for images. The test was performed on a Shepp–Logan phantom with implemented artifacts of different intensity levels. The analysis was performed using different wavelet families, and linear approximation methods were used to filter the image in the identified areas. Additional filtering was performed using moving average methods and empirical mode decomposition (EMD) techniques. Image comparison methods, i.e., RMSE, SSIM and MS-SSIM, were used to evaluate performance. The results of this study showed a significant improvement in the quality of tomographic phantom images. The authors obtained more than 50% improvement in image quality with reference to the image without any filtration. The different wavelet families had different efficiencies with relation to the identification of the induction regions of ring artifacts. The Haar wavelet and Coiflet 1 showed the best performance in identifying artifact induction regions, with comparative RMSE values for these wavelets of 0.1477 for Haar and 0.1469 for Coiflet 1. The applied additional moving average filtering and EMD permitted us to improve image quality, which is confirmed by the results of the image comparison. The obtained results allow us to assess how the used methods affect the reduction in ring artifacts in phantom images with induced artifacts.
MISAO: Ultra-Short-Term Photovoltaic Power Forecasting with Multi-Strategy Improved Snow Ablation Optimizer
Authors: Zhang Xu , Ye Jun , Ma Shenbing , Gao Lintao , Huang Hui , Xie Qiman
Published: August 2024

The increase in installed PV capacity worldwide and the intermittent nature of solar resources highlight the importance of power prediction for grid integration of this technology. Therefore, there is an urgent need for an effective prediction model, but the choice of model hyperparameters greatly affects the prediction performance. In this paper, a multi-strategy improved snowmelt algorithm (MISAO) is proposed for optimizing intrinsic computing-expressive empirical mode decomposition with adaptive noise (ICEEMDAN) and weighted least squares support vector machine for PV power forecasting. Firstly, a cyclic chaotic mapping initialization strategy is used to generate a uniformly distributed high-quality population, which facilitates the algorithm to enter the appropriate search domain quickly. Secondly, the Gaussian diffusion strategy enhances the local exploration ability of the intelligences and extends their search in the solution space, effectively preventing them from falling into local optima. Finally, a stochastic follower search strategy is employed to reserve better candidate solutions for the next iteration, thus achieving a robust exploration–exploitation balance. With these strategies, the optimization performance of MISAO is comprehensively improved. In order to comprehensively evaluate the optimization performance of MISAO, a series of numerical optimization experiments were conducted using IEEE CEC2017 and test sets, and the effectiveness of each improvement strategy was verified. In terms of solution accuracy, convergence speed, robustness, and scalability, MISAO was compared with the basic SAO, various state-of-the-art optimizers, and some recently developed improved algorithms. The results showed that the overall optimization performance of MISAO is excellent, with Friedman average rankings of 1.80 and 1.82 in the two comparison experiments. In most of the test cases, MISAO delivered more accurate and reliable solutions than its competitors. In addition, the altered algorithm was applied to the selection of hyperparameters for the ICEEMDAN-WLSSVM PV prediction model, and seven neural network models, including WLSSVM, ICEEMDAN-WLSSVM, and MISAO-ICEEMDAN-WLSSVM, were used to predict the PV power under three different weather types. The results showed that the models have high prediction accuracy and stability. The MAPE, MAE and RMSE of the proposed model were reduced by at least 25.3%, 17.8% and 13.3%, respectively. This method is useful for predicting the output power, which is conducive to the economic dispatch of the grid and the stable operation of the power system.
Enhancing 3D Printing with Procedural Generation and STL Formatting Using Python
Authors: Kopowski Jakub , Mreła Aleksandra , Mikołajewski Dariusz , Rojek Izabela
Published: August 2024

Three-dimensional printing has become a fast-growing industry. The first phase of this technology is the design of a 3D object to personalize it and optimize its production. This paper explores the procedural generation of the 3D model. The article aims to present the method of procedurally generating 3D objects in Python. Procedural content generation is the automated creation of content using algorithms. Most often, as part of procedural generation, a small number of input parameters and pseudo-random processes are used to generate content that will meet the requirements. The programming techniques for object customization in Python optimize the manufacturing process. Moreover, procedural generation speeds up the model design, and if developers use 3D scanning methods and artificial intelligence, production can be personalized, which is in line with the concept of Industry 4.0.
Advancing BIM and Sustainability with Coopetition: Evidence from the Portuguese Stone Industry
Authors: Silva Agostinho da , Cardoso Antonio J. Marques
Published: August 2024

The construction industry plays a crucial role in the global economy but faces persistent challenges such as inefficiency, high costs, and significant environmental impact. Building Information Modelling (BIM) has been proposed as a solution to enhance efficiency and sustainability through digital representations of construction projects. However, the full potential of BIM has yet to be realized. A contributing factor to this gap is that construction manufacturing companies, which produce upstream parts and products used downstream in construction, are often overlooked in discussions of BIM’s benefits. This study explores the potential of coopetition networks to help manufacturing companies better align with BIM dimensions. Coopetition networks, which integrate competitive and cooperative strategies, present a promising method to enhance the effectiveness of manufacturing companies. Focusing on the Portuguese Ornamental Stone industry, the study employs an experimental pilot network facilitated by the Industrial Internet of Things (IIoT) to assess the effects of competition on labour productivity, on-time delivery, and environmental performance among stone companies. The findings indicate that coopetition networks significantly improve alignment with BIM requirements, enhancing operational efficiency and sustainability. Despite being limited by a small sample size, this research offers valuable insights into the role of manufacturing companies in BIM-enhanced construction projects and the broader applicability of coopetition networks in advancing BIM objectives. These results highlight the potential of coopetition networks as a strategic approach to improving performance in the construction industry.
Enriched Z-Contractions and Fixed-Point Results with Applications to IFS
Authors: Alraddadi Ibrahim , Din Muhammad , Ishtiaq Umar , Akram Mohammad , Argyros Ioannis K.
Published: August 2024

In this manuscript, we initiate a large class of enriched (d,Z)-Z-contractions defined on Banach spaces and prove the existence and uniqueness of the fixed point of these contractions. We also provide an example to support our results and give an existence condition for the uniqueness of the solution to the integral equation. The results provided in the manuscript extend, generalize, and modify the existence results. Our research introduces novel fixed-point results under various contractive conditions. Furthermore, we discuss the iterated function system associated with enriched (d,Z)-Z-contractions in Banach spaces and define the enriched Z-Hutchinson operator. A result regarding the convergence of Krasnoselskii’s iteration method and the uniqueness of the attractor via enriched (d,Z)-Z-contractions is also established. Our discoveries not only confirm but also significantly build upon and broaden several established findings in the current body of literature.
Plaque Characteristics Derived from Intravascular Optical Coherence Tomography That Predict Cardiovascular Death
Authors: Lee Juhwan , Gharaibeh Yazan , Zimin Vladislav N. , Kim Justin N. , Hassani Neda S. , Dallan Luis A. P. , Pereira Gabriel T. R. , Makhlouf Mohamed H. E. , Hoori Ammar , Wilson David L.
Published: August 2024

This study aimed to investigate whether plaque characteristics derived from intravascular optical coherence tomography (IVOCT) could predict a long-term cardiovascular (CV) death. This study was a single-center, retrospective study on 104 patients who had undergone IVOCT-guided percutaneous coronary intervention. Plaque characterization was performed using Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) software developed by our group. A total of 31 plaque features, including lesion length, lumen, calcium, fibrous cap (FC), and vulnerable plaque features (e.g., microchannel), were computed from the baseline IVOCT images. The discriminatory power for predicting CV death was determined using univariate/multivariate logistic regressions. Of 104 patients, CV death was identified in 24 patients (23.1%). Univariate logistic regression revealed that lesion length, calcium angle, calcium thickness, FC angle, FC area, and FC surface area were significantly associated with CV death (p < 0.05). In the multivariate logistic analysis, only the FC surface area (OR 2.38, CI 0.98–5.83, p < 0.05) was identified as a significant determinant for CV death, highlighting the importance of the 3D lesion analysis. The AUC of FC surface area for predicting CV death was 0.851 (95% CI 0.800–0.927, p < 0.05). Patients with CV death had distinct plaque characteristics (i.e., large FC surface area) in IVOCT. Studies such as this one might someday lead to recommendations for pharmaceutical and interventional approaches.
Dendrochronological Analysis of Pinus pinea in Central Chile and South Spain for Sustainable Forest Management
Authors: Loewe-Muñoz Verónica , Cachinero-Vivar Antonio M. , Camarero Jesús Julio , Río Rodrigo Del , Delard Claudia , Navarro-Cerrillo Rafael M.
Published: August 2024

Pinus pinea is an important Mediterranean species due to its adaptability and tolerance to aridity and its high-quality pine nuts. Different forest types located in Mediterranean native and non-native environments provide the opportunity to perform comparative studies on the species’ response to climate change. The aims of this study were to elucidate growth patterns of the species growing in native and exotic habitats and to analyze its response to climatic fluctuations, particularly drought, in both geographical contexts. Understanding stone pine (Pinus pinea) growth responses to climate variability in native and exotic habitats by comparing natural stands and plantations may provide useful information to plan adequate management under climate change. By doing so, we enhance the understanding of P. pinea’s adaptability and provide practical approaches to its sustainable management. In this study, we reconstructed and compared the stem radial growth of seven stone pine stands, two in southern Spain and five in central–southern Chile, growing under different climatic conditions. We quantified the relationships between growth variability and climate variables (total rainfall, mean temperature, and SPEI drought index). Growth was positively correlated with autumn rainfall in plantations and with autumn–winter rainfall in natural stands. Growth was also enhanced by high autumn-to-spring rainfall in the driest Chilean plantation, whereas in the wettest and coolest plantation, such correlation was found in winter and summer. A negative impact of summer temperature was found only in one of the five Chilean plantations and in a Spanish site. The correlation between SPEI and tree-ring width indices showed different patterns between and within countries. Overall, exotic plantations showed lower sensitivity to climate variability than native stands. Therefore, stone pine plantations may be useful to assist in mitigating climate change.
The Multifaceted Impact of Environmental Pollutants on Health and Ecosystems
Authors: Piscopo Marina , Marinaro Carmela , Lettieri Gennaro
Published: August 2024

Environmental pollutants have pervasive and far-reaching effects on both ecosystems and human health [...]
Neurosteroid Levels in GBA Mutated and Non-Mutated Parkinson’s Disease: A Possible Factor Influencing Clinical Phenotype?
Authors: Cavallieri Francesco , Lucchi Chiara , Grisanti Sara , Monfrini Edoardo , Fioravanti Valentina , Toschi Giulia , Di Rauso Giulia , Rossi Jessica , Di Fonzo Alessio , Biagini Giuseppe , Valzania Franco
Published: August 2024

Neurosteroids are pleiotropic molecules involved in various neurodegenerative diseases with neuroinflammation. We assessed neurosteroids’ serum levels in a cohort of Parkinson’s Disease (PD) patients with heterozygous glucocerebrosidase (GBA) mutations (GBA-PD) compared with matched cohorts of consecutive non-mutated PD (NM-PD) patients and healthy subjects with (GBA-HC) and without (NM-HC) GBA mutations. A consecutive cohort of GBA-PD was paired for age, sex, disease duration, Hoehn and Yahr stage, and comorbidities with a cohort of consecutive NM-PD. Two cohorts of GBA-HC and HC were also considered. Clinical assessment included the Movement Disorder Society revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) and the Montreal Cognitive Assessment (MoCA). Serum samples were processed and analyzed by liquid chromatography coupled with the triple quadrupole mass spectrometry. Twenty-two GBA-PD (males: 11, age: 63.68), 22 NM-PD (males: 11, age: 63.05), 14 GBA-HC (males: 8; age: 49.36), and 15 HC (males: 4; age: 60.60) were studied. Compared to NM-PD, GBA-PD showed more hallucinations and psychosis (p < 0.05, Fisher’s exact test) and higher MDS-UPDRS part-II (p < 0.05). Most of the serum neurosteroids were reduced in both GBA-PD and NM-PD compared to the respective control cohorts, except for 5α-dihydroprogesterone. Allopregnanolone was the only neurosteroid significantly lower (p < 0.01, Dunn’s test) in NM-PD compared to GBA-PD patients. Only in GBA-PD, allopregnanolone, and pregnanolone levels correlated (Spearman) with a more severe MDS-UPDRS part-III. Allopregnanolone levels also negatively correlated with MoCA scores, and pregnanolone levels correlated with more pronounced bradykinesia. This pilot study provides the first observation of changes in neurosteroid peripheral levels in GBA-PD. The involvement of the observed changes in the development of neuropsychological and motor symptoms of GBA-PD deserves further attention.
RNA Surveillance Factor SMG5 Is Essential for Mouse Embryonic Stem Cell Differentiation
Authors: Chen Chengyan , Wei Yanling , Jiang Xiaoning , Li Tangliang
Published: August 2024

Nonsense-mediated mRNA decay (NMD) is a highly conserved post-transcriptional gene expression regulatory mechanism in eukaryotic cells. NMD eliminates aberrant mRNAs with premature termination codons to surveil transcriptome integrity. Furthermore, NMD fine-tunes gene expression by destabilizing RNAs with specific NMD features. Thus, by controlling the quality and quantity of the transcriptome, NMD plays a vital role in mammalian development, stress response, and tumorigenesis. Deficiencies of NMD factors result in early embryonic lethality, while the underlying mechanisms are poorly understood. SMG5 is a key NMD factor. In this study, we generated an Smg5 conditional knockout mouse model and found that Smg5-null results in early embryonic lethality before E13.5. Furthermore, we produced multiple lines of Smg5 knockout mouse embryonic stem cells (mESCs) and found that the deletion of Smg5 in mESCs does not compromise cell viability. Smg5-null delays differentiation of mESCs. Mechanistically, our study reveals that the c-MYC protein, but not c-Myc mRNA, is upregulated in SMG5-deficient mESCs. The overproduction of c-MYC protein could be caused by enhanced protein synthesis upon SMG5 loss. Furthermore, SMG5-null results in dysregulation of alternative splicing on multiple stem cell differentiation regulators. Overall, our findings underscore the importance of SMG5-NMD in regulating mESC cell-state transition.
Extracellular Vesicles Induce Nuclear Factor-κB Activation and Interleukin-8 Synthesis through miRNA-191-5p Contributing to Inflammatory Processes: Potential Implications in the Pathogenesis of Chronic Obstructive Pulmonary Disease
Authors: Carpi Sara , Polini Beatrice , Nieri Dario , Doccini Stefano , Conti Maria , Bazzan Erika , Pagnini Marta , Santorelli Filippo Maria , Cecchini Marco , Nieri Paola , Celi Alessandro , Neri Tommaso
Published: August 2024

Extracellular vesicles (EVs) play a pivotal role in a variety of physiologically relevant processes, including lung inflammation. Recent attention has been directed toward EV-derived microRNAs (miRNAs), such as miR-191-5p, particularly in the context of inflammation. Here, we investigated the impact of miR-191-5p-enriched EVs on the activation of NF-κB and the expression of molecules associated with inflammation such as interleukin-8 (IL-8). To this aim, cells of bronchial epithelial origin, 16HBE, were transfected with miR-191-5p mimic and inhibitor and subsequently subjected to stimulations to generate EVs. Then, bronchial epithelial cells were exposed to the obtained EVs to evaluate the activation of NF-κB and IL-8 levels. Additionally, we conducted a preliminary investigation to analyze the expression profiles of miR-191-5p in EVs isolated from the plasma of patients diagnosed with chronic obstructive pulmonary disease (COPD). Our initial findings revealed two significant observations. First, the exposure of bronchial epithelial cells to miR-191-5p-enriched EVs activated the NF-kB signaling and increased the synthesis of IL-8. Second, we discovered the presence of miR-191-5p in peripheral blood-derived EVs from COPD patients and noted a correlation between miR-191-5p levels and inflammatory and functional parameters. Collectively, these data corroborate and further expand the proinflammatory role of EVs, with a specific emphasis on miR-191-5p as a key cargo involved in this process. Consequently, we propose a model in which miR-191-5p, carried by EVs, plays a role in airway inflammation and may contribute to the pathogenesis of COPD.
Nano-Formulations of Natural Antioxidants for the Treatment of Liver Cancer
Authors: Cristani Mariateresa , Citarella Andrea , Carnamucio Federica , Micale Nicola
Published: August 2024

Oxidative stress is a key factor in the pathological processes that trigger various chronic liver diseases, and significantly contributes to the development of hepatocarcinogenesis. Natural antioxidants reduce oxidative stress by neutralizing free radicals and play a crucial role in the treatment of free-radical-induced liver diseases. However, their efficacy is often limited by poor bioavailability and metabolic stability. To address these limitations, recent advances have focused on developing nano-drug delivery systems that protect them from degradation and enhance their therapeutic potential. Among the several critical benefits, they showed to be able to improve bioavailability and targeted delivery, thereby reducing off-target effects by specifically directing the antioxidant to the liver tumor site. Moreover, these nanosystems led to sustained release, prolonging the therapeutic effect over time. Some of them also exhibited synergistic effects when combined with other therapeutic agents, allowing for improved overall efficacy. This review aims to discuss recent scientific advances in nano-formulations containing natural antioxidant molecules, highlighting their potential as promising therapeutic approaches for the treatment of liver cancer. The novelty of this review lies in its comprehensive focus on the latest developments in nano-formulations of natural antioxidants for the treatment of liver cancer.
An Improved Binary Walrus Optimizer with Golden Sine Disturbance and Population Regeneration Mechanism to Solve Feature Selection Problems
Authors: Geng Yanyu , Li Ying , Deng Chunyan
Published: August 2024

Feature selection (FS) is a significant dimensionality reduction technique in machine learning and data mining that is adept at managing high-dimensional data efficiently and enhancing model performance. Metaheuristic algorithms have become one of the most promising solutions in FS owing to their powerful search capabilities as well as their performance. In this paper, the novel improved binary walrus optimizer (WO) algorithm utilizing the golden sine strategy, elite opposition-based learning (EOBL), and population regeneration mechanism (BGEPWO) is proposed for FS. First, the population is initialized using an iterative chaotic map with infinite collapses (ICMIC) chaotic map to improve the diversity. Second, a safe signal is obtained by introducing an adaptive operator to enhance the stability of the WO and optimize the trade-off between exploration and exploitation of the algorithm. Third, BGEPWO innovatively designs a population regeneration mechanism to continuously eliminate hopeless individuals and generate new promising ones, which keeps the population moving toward the optimal solution and accelerates the convergence process. Fourth, EOBL is used to guide the escape behavior of the walrus to expand the search range. Finally, the golden sine strategy is utilized for perturbing the population in the late iteration to improve the algorithm’s capacity to evade local optima. The BGEPWO algorithm underwent evaluation on 21 datasets of different sizes and was compared with the BWO algorithm and 10 other representative optimization algorithms. The experimental results demonstrate that BGEPWO outperforms these competing algorithms in terms of fitness value, number of selected features, and F1-score in most datasets. The proposed algorithm achieves higher accuracy, better feature reduction ability, and stronger convergence by increasing population diversity, continuously balancing exploration and exploitation processes and effectively escaping local optimal traps.
Experimental Study on the Mechanical Properties of Metallurgical Slag Aggregate Concrete and Artificial Aggregate Concrete
Authors: Zhang Xueyuan , Gao Meiling , Zhang Daoming , Zhang Biao , Wang Mengyao
Published: August 2024

Three types of aggregate, including metallurgical slag aggregate (steel slag, copper slag, and iron sand), rare earth porcelain sand (REPS) aggregate as artificial aggregate, and recycled aggregate, were selected to produce concrete with the same basic mixture proportions in order to investigate the influence of aggregate types and aggregate replacement rates on their mechanical properties. Three levels of aggregate replacement rate—20%, 35%, and 50% for coarse aggregate (CA) and 20%, 30%, and 40% for fine aggregate (FA)—were employed in this study. The results indicate that replacing natural sand with metallurgical slag aggregate as FA enhances the mechanical properties of concrete. Among these, iron sand (IS) shows superior enhancement effects compared with copper slag (CS), and CS outperforms steel slag (SS). Specifically, at a 30% IS replacement rate, the compressive strength and splitting tensile strength of IS aggregate concrete are 32.8% and 35.6% higher than those of natural aggregate concrete, respectively. REPS used as CA demonstrates significant improvements in compressive strength, while REPS used as FA notably enhances splitting tensile strength. For recycled aggregate concrete with recycled coarse aggregate replacement rates of 35% and 50%, mechanical properties are effectively strengthened by incorporating CS as FA at a 30% replacement rate and REPS as CA at a 20% substitution ratio, respectively. Additionally, XRF and XRD techniques were employed to confirm aggregate composition and were combined with SEM and EDS techniques to analyze the concrete microstructure, clarifying the strengthening mechanisms of metallurgical and artificial aggregates on concrete.
Sludge Recycling from Non-Lime Purification of Electrolysis Wastewater: Bridge from Contaminant Removal to Waste-Derived NOX SCR Catalyst
Authors: Gao Ju , Sun Fucheng , Liu Pei , Zhou Jizhi , Zhang Yufeng
Published: August 2024

Catalysts for the selective catalytic reduction (NOX SCR) of nitrogen oxides can be obtained from sludge in industrial waste treatment, and, due to the complex composition of sludge, NOX SCR shows various SCR efficiencies. In the current work, an SCR catalyst developed from the sludge produced with Fe/C micro-electrolysis Fenton technology (MEF) in wastewater treatment was investigated, taking into account various sludge compositions, Fe/C ratios, and contaminant contents. It was found that, at about 300 °C, the NOX removal rate could reach 100% and there was a wide decomposition temperature zone. The effect of individual components of electroplating sludge, i.e., P, Fe and Ni, on NOX degradation performance of the obtained solids was investigated. It was found that the best effect was achieved when the Fe/P was 8/3 wt%, and variations in the Ni content had a limited effect on the NOX degradation performance. When the Fe/C was 1:2 and the Fe/C/P was 1:2:0.4, the electroplating sludge formed after treatment with Fe/C MEF provided the best NOX removal rate at 100%. Moreover, the characterization results show that the activated carbon was also involved in the catalytic reduction degradation of NOX. An excessive Fe content may cause agglomeration on the catalyst surface and thus affect the catalytic efficiency. The addition of P effectively reduces the catalytic reaction temperature, and the formation of phosphate promotes the generation of adsorbed oxygen, which in turn contributes to improvements in catalytic efficiency. Therefore, our work suggests that controlling the composition in the sludge is an efficient way to modulate SCR catalysis, providing a bridge from contaminant-bearing waste to efficient catalyst.