bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of Real-Time Methods Demonstrating the Effects of Reduced Glutathione on Olfactory Neuroblasts
Authors: Géloën Alain , Berger Emmanuelle
Published: January 2025

The objective of the present study was to compare recent methods for characterizing cell modifications. We studied the effect of extracellular reduced glutathione (GSH) on an olfactory neuroblast cell line (13s24). Three methods were used to monitor, in label-free, noninvasive real-time experiments, cell surface occupancy by measuring impedance (xCELLigence), cell behavior (HoloMonitor cytometry), cell ultrastructure by measuring refractive index (3D Nanolive microscopy). Reduced glutathione dose-dependently increased cell volume and motility and decreased cell adhesion. Cell sorting analyses revealed that after short-term exposure (6 h), GSH reduced F-actin polymerization and extracellular glycoproteins leading to adhesion strength loss. Results support the hypothesis that excreted GSH could modulate disulfide bound-dependent integrin conformations involved in neurogenesis and/or neuronal plasticity. This is the first evidence of a causal link between GSH and changes in cell volume and motility required for cell division, migration, and/or differentiation. Results show the importance of real-time analysis methods, without labelling, in the study of cell responses under culture conditions. The present findings highlight important criteria in the choice of methods, beyond the parameters studied, such as cell preparation time, plate filling time, number of cells studied, friendly use of the devices, and the complexity of data processing.
A Bionic Social Learning Strategy Pigeon-Inspired Optimization for Multi-Unmanned Aerial Vehicle Cooperative Path Planning
Authors: Shen Yankai , Liu Xinan , Ma Xiao , Du Hong , Xin Long
Published: January 2025

This paper proposes a bionic social learning strategy pigeon-inspired optimization (BSLSPIO) algorithm to tackle cooperative path planning for multiple unmanned aerial vehicles (UAVs) with cooperative detection. Firstly, a modified pigeon-inspired optimization (PIO) is proposed, which incorporates a bionic social learning strategy. In this modification, the global best is replaced by the average of the top-ranked solutions in the map and compass operator, while the global center is replaced by the local center in the landmark operator. The paper also proves the algorithm’s convergence and provides complexity analysis. Comparison experiments demonstrate that the proposed method searches for the optimal solution while guaranteeing fast convergence. Subsequently, a path-planning model, detection units’ network model, and cost estimation are constructed. The developed BSLSPIO is utilized to generate feasible paths for UAVs, adhering to time consistency constraints. The simulation results show that the BSLSPIO generates feasible paths at minimum cost and effectively solves the UAVs’ cooperative path-planning problem.
A Review of Chemical Variability and Metal Contamination of Herbaceous Plants in Terms of Health Safety—A Case Study of Tanacetum vulgare L.
Authors: Turek Anna
Published: January 2025

The article presents a review of studies on the chemical diversity of essential oils and extracts from Tanacetum vulgare L. under the influence of environmental factors and the bioaccumulation of heavy metals in the plant. These issues are crucial for consumer safety, especially when they concern plants widely used in alternative medicine. Stress factors to which plants are subjected may result in a decrease in their biological activity or toxic effects (as in the case of Tanacetum vulgare L.). The issue is particularly important in the context of increasing environmental pollution, e.g., with heavy metals, and climate change, which is also a stress factor. The presented review clearly shows that detailed research is required both to identify the components of T. vulgare with specific therapeutic potential and to assess their quantitative changes under the influence of a specific stressor. It was found that there is a need to standardize analytical procedures used in research on the bioaccumulation of metals by medicinal plants. The importance of environmental monitoring in herb-growing areas, as an important element of health policy, is also emphasized.
A New Chaotic Color Image Encryption Algorithm Based on Memristor Model and Random Hybrid Transforms
Authors: Yao Yexia , Xu Xuemei , Jiang Zhaohui
Published: January 2025

This paper skillfully incorporates the memristor model into a chaotic system, creating a two-dimensional (2D) hyperchaotic map. The system’s exceptional chaotic performance is verified through methods such as phase diagrams, bifurcation diagrams, and Lyapunov exponential spectrum. Additionally, a universal framework corresponding to the chaotic system is proposed. To enhance encryption security, the pixel values of the image are preprocessed, and a hash function is used to generate a hash value, which is then incorporated into the secret keys generation process. Existing algorithms typically encrypt the three channels of a color image separately or perform encryption only at the pixel level, resulting in certain limitations in encryption effectiveness. To address this, this paper proposes a novel encryption algorithm based on 2D hyperchaotic maps that extends from single-channel encryption to multi-channel encryption (SEME-TDHM). The SEME-TDHM algorithm combines single-channel and multi-channel random scrambling, followed by local cross-diffusion of pixel values across different planes. By integrating both pixel-level and bit-level diffusion, the randomness of the image information distribution is significantly increased. Finally, the diffusion matrix is decomposed and restored to generate the encrypted color image. Simulation results and comparative analyses demonstrate that the SEME-TDHM algorithm outperforms existing algorithms in terms of encryption effectiveness. The encrypted image maintains a stable information entropy around 7.999, with average NPCR and UACI values close to the ideal benchmarks of 99.6169% and 33.4623%, respectively, further affirming its outstanding encryption effectiveness. Additionally, the histogram of the encrypted image shows a uniform distribution, and the correlation coefficient is nearly zero. These findings indicate that the SEME-TDHM algorithm successfully encrypts color images, providing strong security and practical utility.
A Method for Constructing a Synthesis Health Index for Metro Vehicle Wheelsets
Authors: Luo Min , Zhong Tianyi , Dai Jinzhen
Published: January 2025

This study focuses on the construction of a synthesis health index for metro vehicle wheelsets through theoretical foundation analysis, procedural steps, and practical examples. By analyzing the theoretical foundation, this study explains the advantages of the synthesis health index compared to traditional health indicators. The procedural steps describe the detailed process from data collection, preprocessing, and feature extraction to health index construction. Additionally, practical examples are used to validate the effectiveness and accuracy of this method in real-world applications. The results indicate that this synthesis health index construction method can accurately assess the real-time health status of in-service wheelsets and also reflect and predict the trends of their health status changes.
Deep Reinforcement Learning-Based Task Partitioning Ratio Decision Mechanism in High-Speed Rail Environments with Mobile Edge Computing Server
Authors: Koo Seolwon , Lim Yujin
Published: January 2025

High-speed rail (HSR) environments present unique challenges due to their high mobility and dense passenger traffic, resulting in dynamic and unpredictable task generation patterns. Mobile Edge Computing (MEC) has emerged as a transformative paradigm to address these challenges by deploying computation resources closer to end-users. However, the limited resources of MEC servers necessitate efficient task partitioning, wherein a single task is divided into multiple sub-tasks for parallel processing across MEC servers. In the context of HSR environments, the task partitioning ratio is pivotal in ensuring quality of service (QoS) and optimizing resource utilization, particularly under dynamic and high-demand conditions. This paper proposes a deep reinforcement learning (DRL)-based task partitioning mechanism using Twin Delayed Deep Deterministic Policy Gradient (TD3) for HSR environments with MEC servers (MECSs). The proposed method dynamically adjusts task partitioning ratios by leveraging real-time information about task characteristics and server load conditions. The experimental results show that when the task arrival rate is 20, the delay is improved by about 5% compared to random and about 13% compared to no_partition. When it is 50, there is no significant difference from random and about 2% improvement compared to no_partition. The task throughput is almost the same when it is 20. However, when it is 50, random is much better. We also looked at the performance change according to the number of serving MECSs. In this process, we can also note the research direction of finding an appropriate number of serving MECSs K. The results highlight the efficacy of DRL-based approaches in dynamically adapting to the unique characteristics of HSR environments, achieving optimal resource allocation and maintaining high QoS. This paper contributes to advancing task partitioning strategies for HSR systems and lays the groundwork for future research in MEC-based HSR systems.
Research on Indoor Positioning Systems and Autonomous Mechatronic Systems for Surveillance of Intrabuilding Zones
Authors: Vlăsceanu Alexandru , Avram Mihai , Constantin Victor , Moraru Edgar
Published: January 2025

Given increasingly complex threats, adapting surveillance approaches to meet the necessary security levels is essential. The aim of this paper is to develop a surveillance architecture based on autonomous mechatronic systems (mobile robots and drones) designed to secure areas of strategic interest, covering a predefined space while providing enhanced security at minimal costs. Designing such a system is challenging due to the need for continuous monitoring, raising issues around autonomy, as well as the design of the navigation and command and control systems. This paper presents key surveillance systems, demonstrating their efficiency and further development potential. This paper discusses the steps taken to enable a team of autonomous mobile robots to monitor a known indoor location by using a specialized positioning system. Steps are taken to deploy, implement, and configure the said indoor global positioning system (GPS). Among those steps, a study is performed by attaching one of the mobile beacons to a linear axis and moving said axis to known points. This position is read by both the axis and the positioning system, and the results are compared.
Combining CS Unplugged and L2T2L to Bridge the Computing Illiteracy Gap of the Elderly Population: A Case Study
Authors: Díaz-León José Alfredo , Arbelaitz Olatz , Larrañaga Mikel , Arruarte Ana
Published: January 2025

In the era where digital technologies are becoming increasingly prevalent, it is anticipated that a majority of the global population will have at least a basic understanding of informatics. However, empirical evidence suggests that a significant portion of the global population remains digitally illiterate. This phenomenon is particularly pronounced in the case of the senior adult population. In light of the aforementioned challenges, this work integrates Computer Science Unplugged exercises, based on games and recreational activities without the use of computers, and L2T2L, a learning-by-teaching methodology whereby university students learn and then, in turn, teach that learning to other populations in a cascading manner. A case study was conducted in Lima, Peru, with the participation of 140 volunteers from centres for the elderly. Thirty-five students and one teacher from the Universidad Científica del Sur were responsible for initiating the transfer of knowledge from the university to the senior citizens, with the assistance of twelve individuals responsible for their care. The results demonstrate that the participants attained a commendable level of comprehension when attempting to complete all of the assigned tasks. Furthermore, the efficacy of L2T2L is evident in its adaptability and suitability for scenarios beyond those for which it was originally designed.
Characterization of Avocado (Persea americana Mill) Seed Extract from the Variety Semil 34 Cultivated in the Dominican Republic
Authors: Sanchez-Rosario Ramon , Castillo Luis , Féliz-Jiménez Alejandra , Vargas Sebastián , Pérez-Romero Ramón , Aquino Mónica , Abutokaikah Maha T.
Published: January 2025

Avocado (Persea americana Mill.) is a widely cultivated fruit known for its nutritional benefits, with the seed representing a significant portion of the fruit that is often discarded as waste. In the Dominican Republic, the cultivar Semil 34 represents 58% of the national production. This study aimed to explore the potential of Semil 34 avocado seed (AS) as a source of bioactive compounds with applications in the food industry. We conducted the chemical characterization of the seed extract, focusing on its total phenolic content, total flavonoids, and antioxidant capacity. High-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS) was employed to identify key phytochemicals, including phenolic acids and flavonoids, that were responsible for the antioxidant properties of the extract. The hydroalcoholic extract of the Semil 34 seeds exhibited an antioxidant capacity of 1743.3 ± 52.3 µM Trolox/g extract, total phenolic content of 25.86 ± 2.17 mg gallic acid equivalents/g extract, and total flavonoid content of 2.09 ± 0.10 mg quercetin equivalents/g extract. However, the extract’s antioxidant capacity was found to be sensitive to pH changes, suggesting the need for stabilization when used in acidic or basic food matrices. The present work identified 53 compounds in the Semil 34 seed extracts; among these, 23 are being reported for the first time in avocado seeds. This study demonstrates the potential of the avocado seed as a source of bioactive compounds and hence a functional ingredient, supporting its value in sustainable production and its possible contribution to environmental goals by reducing waste in the avocado industry.
Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
Authors: Say Tamer , Alkan Mustafa , Kocak Aynur
Published: January 2025

The rapid advancement of synthetic media, while beneficial, has also spawned GAN-generated deepfakes, which pose risks, including misinformation and digital fraud. This paper investigates the detectability of GAN-generated static images, focusing on residual artifacts that are imperceptible to humans but detectable through digital analysis. Our approach introduces three key advancements: (1) a taxonomy for classifying GAN residues in deepfake detection; (2) a unique mixed dataset combining StyleGAN3, ProGAN, and InterfaceGAN to aid cross-model detection research; and (3) a combination of frequency space analysis and RGB color correlation methods to improve artifact detection. Covering three different transform methods, three GAN models, and twelve classification methods, ours is the most comprehensive study of detection of static deepfake face images produced by GANs. Our results demonstrate that artifact-based detection can achieve high accuracy, precision, recall, and F1 scores, challenging prior assumptions about the detectability of synthetic face images.
Laser Applications in Metal Orthodontic Bracket Debonding: A Systematic Review
Authors: Woś Patryk , Kiryk Sylwia , Dyl Tomasz , Kiryk Jan , Horodniczy Tomasz , Szablińska Magdalena , Dubowik Magdalena Aleksandra , Dobrzyński Wojciech , Mikulewicz Marcin , Matys Jacek , Dobrzyński Maciej
Published: January 2025

Objective: The aim of this systematic review was to evaluate the effectiveness and safety of various laser wavelengths for debonding orthodontic metal brackets compared to traditional plier-based methods. The primary outcomes assessed were enamel damage, pulp temperature changes, adhesive remnant index (ARI), and shear bond strength (SBS). Materials and Methods: In September 2024, an electronic search was performed across the PubMed, Web of Science (WoS), and Scopus databases, adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the PICO framework. The initial search yielded 453 records. After eliminating 256 duplicates, 197 unique records were left for screening, which ultimately led to the qualification of 8 articles that met the inclusion criteria for both qualitative and quantitative analyses. The risk of bias in the articles was assessed by two independent reviewers. Results: The included studies demonstrated that laser-assisted debonding generally resulted in less adhesive residue on the enamel surface compared to conventional methods, as evidenced by the reductions in ARI scores reported in two studies. Temperature increases during laser use varied depending on the laser type and power settings. The Nd:YAG (neodymium-yttrium, aluminum, garnet) laser was found to cause significant temperature rises, posing a potential risk to pulp tissue, while the Er:YAG (erbium—yttrium, aluminum, garnet) and Er,Cr:YSGG (erbium, chromium—yttrium, scandium, gallium, garnet) lasers produced only negligible increases in pulp temperature. SBS comparisons revealed no significant differences between the laser-assisted and traditional debonding methods. Additionally, diode lasers demonstrated the potential to minimize enamel damage, particularly when operated at lower power settings. Four publications were assessed as high quality (low risk of bias), and another four as moderate quality (average risk of bias). Conclusions: In conclusion, laser-assisted orthodontic metal bracket debonding, when conducted with appropriately calibrated parameters, is a safe method for preserving tooth tissue. However, its advantages appear to be minimal compared to conventional plier-based methods, highlighting the need for further research to justify its broader clinical application.
Aerodynamic Design and Performance Analysis of a Large-Scale Composite Blade for Wind Turbines
Authors: Lim Semyoung , Ahn Sungjin , Park Hyunbum
Published: January 2025

In this study, we determined an aerodynamic configuration to design structures applying composites for large-scale horizontal-axis wind turbine blades. A new aerodynamic and structural design method for large wind turbine blades is presented. The rated power of the wind turbine blade is 25 MW class. The tip speed ratio is 7. The diameter of the designed blade is 260 m. Therefore, thick airfoils were selected to design large-scale wind turbine blades considering structural stiffness and maximum lift coefficients. For the aerodynamic design method, it was designed with the optimal angle of attack having the maximum lift-to-drag ratio. The blade element theory and vortex theory were applied to aerodynamic design. For the aerodynamic design results, its validity was investigated via aerodynamic performance analysis. As a result of analyzing aerodynamic performance, it was confirmed that higher power was generated. At 12.5 m/s of rated wind speed, electrical power was 28.32 MW. The structural design considering the aerodynamic design results was carried out. The composite laminate theory was adopted. Structural safety was evaluated for the designed blades. Finally, the structural design results were analyzed as sufficiently valid.
The Future of Abandoned Shallow Mines as a Function of Precipitation Under Changing Climate
Authors: Conil Nathalie , Al Heib Marwan
Published: January 2025

The paper discusses the future of abandoned underground mines and the main hazards related to their instability, specifically local and large collapses. A database of over 500 large collapses is presented. The analysis of these events reveals that the primary causes are the extraction ratio, ageing and climatic factors (such as heavy rain, and increasing water levels). Furthermore, a back analysis of a large-scale collapse, the Château-Landon collapse of 1910, showed that the collapse resulted from a combination of the mining conditions and slope instability climate factor, particularly the impact of rainfall on mine stability. In the case of Château-Landon, the water had two effects: it reduced the resistance of the chalk and decreased the shear strength of the fault crossing the slope. The back analysis and database contribute to a better understanding of mine collapses caused by mining conditions and the modification of climate conditions.
Probabilistic Analysis of Infinite Slope Stability Considering Variation in Soil Depth
Authors: Kim Taejin , Bong Taeho , Kim Donggeun
Published: January 2025

In probabilistic slope stability analysis, soil depth has been treated as a deterministic variable, although it is a highly variable parameter. This study aims to identify soil depth variability using seismic refraction survey data and to analyze its impact on probabilistic analysis of slope stability. Seismic refraction survey data were collected from 70 slopes in South Korea and employed to identify the variability of soil depth within natural slopes. As a result, the average soil depth across 70 slopes was 2.5 m, with an average coefficient of variation (COV) of 29%, indicating high variability. To investigate the influence of soil depth variability on the probability of slope failure, probabilistic slope stability analysis was conducted by considering the shear strength parameters of soil and soil depth as random variables. Accordingly, the influences of the variability of soil depth on the probabilistic analysis of slope stability were evaluated by comparing the probability of slope failure and distribution of the failure occurrence frequency by depth. Additionally, global sensitivity analysis was conducted to understand the relative contribution of input parameters on the probability of slope failure. Consequently, the probability of slope failure can vary significantly depending on soil depth variability, emphasizing the importance of considering this factor in probabilistic slope stability analysis.
Fast 3D Transmission Tower Detection Based on Virtual Views
Authors: Zhou Liwei , Tan Jiaying , Fu Jing , Shao Guiwei
Published: January 2025

Advanced remote sensing technologies leverage extensive synthetic aperture radar (SAR) satellite data and high-resolution airborne light detection and ranging (LiDAR) data to swiftly capture comprehensive 3D information about electrical grid assets and their surrounding environments. This facilitates in-depth scene analysis for target detection and classification, allowing for the early recognition of potential hazards near transmission towers (TTs). These innovations present a groundbreaking strategy for the automated inspection of electrical grid assets. However, traditional 3D target detection techniques, which involve searching the entire 3D space, are marred by low accuracy and high computational demands. Although deep learning-based 3D target detection methods have significantly improved detection precision, they rely on a large volume of 3D target samples for training and are sensitive to point cloud data density. Moreover, these methods demonstrate low detection efficiency, constraining their application in the automated monitoring of electricity networks. This paper proposes a fast 3D target detection method using virtual views to overcome these challenges related to detection accuracy and efficiency. The method first utilizes cutting-edge 2D splatting technology to project 3D point clouds with diverse densities from a specific viewpoint, generating a 2D virtual image. Then, a novel local–global dual-path feature fusion network based on YOLO is applied to detect TTs on the virtual image, ensuring efficient and accurate identification of their positions and types. Finally, by leveraging the projection transformation between the virtual image and the 3D point cloud, combined with a 3D region growing algorithm, the 3D points belonging to the TTs are extracted from the whole 3D point cloud. The effectiveness of the proposed method in terms of target detection rate and efficiency is validated through experiments on synthetic datasets and outdoor LiDAR point clouds.
An MLLM-Assisted Web Crawler Approach for Web Application Fuzzing
Authors: Yang Wantong , Wang Enze , Gui Zhiwen , Zhou Yuan , Wang Baosheng , Xie Wei
Published: January 2025

Web application fuzzing faces significant challenges in achieving comprehensive test interface (attack surface) coverage, primarily due to the complexity of user interactions and dynamic website architectures. While web crawlers can automatically access and extract critical website information—including form fields and request parameters—which are essential for generating effective fuzzing test cases, current crawler technologies exhibit three primary limitations: (i) insufficient capabilities in analyzing page relationships and determining page states; (ii) lack of functionality-aware exploration capabilities, resulting in generated inputs with poor contextual relevance; (iii) generation of unstructured operation sequences that fail to execute effectively due to their incompatibility with state-based testing logic. To address these challenges, we propose CrawlMLLM, a framework using multi-modal large language models to simulate human web browsing. It includes three core components: page state mining, functionality analysis, and automatic operation generation. Evaluations show 163% code coverage improvements over SOTA work. When integrated with vulnerability audit tools, CrawlMLLM found 44 vulnerabilities in three vulnerable web applications versus 34 by the baseline. In six real-world applications, CrawlMLLM detected 20 vulnerabilities while the next best method found six.
Biomechanical Insights into Ski Mountaineering: Kinematics and Muscular Activation in Uphill Movements
Authors: Donno Lucia , Francia Carlalberto , Motta Filippo , LoMauro Antonella , Gorla Carlo , Scaccabarozzi Diego , Tarabini Marco , Galli Manuela
Published: January 2025

Ski mountaineering (Skimo) combines mountain climbing and skiing, with ascents on skis or by carrying them, followed by ski descents. Despite its popularity, the literature lacks integrated biomechanical analyses during indoor training, limiting evidence-based recommendations for training, injury prevention, and performance improvement in this sport. This study analyzed four Skimo athletes during uphill walking and running on a 9° inclined treadmill at 7 km/h and 8 km/h. Kinematics was assessed by an optoelectronic system, and surface electromyography recorded the muscular activity of twelve bilateral muscles (Erector Spinae, Rectus Abdominis, Rectus Femoris, Biceps Femoris, Tibialis Anterior, Gastrocnemius Lateralis). Rectus Femoris and Biceps Femoris co-activation supported knee stability during the load support phase, especially while running. Running at 8 km/h was the most demanding condition, with increased knee flexion throughout the task cycle. Switching from 7 to 8 km/h reduced the maximum extension and increased the maximum flexion of both hip and knee and required the Gastrocnemius’ recruitment in the swing phase. Regardless of task and speed, Rectus Abdominis and Erector Spinae played a key role in stabilizing the trunk. This study provided a biomechanical characterization of two motor gestures typical in Skimo, highlighting how task typology and velocity could influence kinematics and muscle activation.
Effects of Long-Term Land Use Patterns on Labile Organic Carbon Fractions and Carbon Pool Management Index of Mollisols Humus Layers
Authors: Duo Xinqu , Wu Jinggui , Cheng Wei
Published: January 2025

Land use patterns significantly influence the quantity and composition of litter in the soil humus layers, thereby affecting the dynamics of soil organic carbon. However, the differences in labile organic carbon fractions and the carbon sequestration index under different land use patterns, as well as their impact on soil carbon storage in the humus layers of mollisols—without migration loss and soil erosion—remain unclear. Labile organic carbon is classified into fractions such as dissolved organic carbon, easily oxidized carbon, particulate organic carbon, and microbial biomass carbon, which are identified through different chemical extraction methods. This study investigates the impact of long-term land use patterns on organic carbon dynamics, organic carbon pools, KOS, and CPMI in mollisols across five treatments: SC (continuous soybean cultivation), MC (continuous maize cultivation), MSR (maize–soybean rotation), GB (grass belt), and FB (forest belt). It also selects three soil depths (0–20 cm, 20–40 cm, and 40–60 cm) over an 11-year period for analysis. The results indicate that soil organic carbon, labile organic carbon fractions (EOC, POC, DOC, and MBC), and CPMI decrease with soil depth, while KOS increases. Non-tillage treatments enhance SOC accumulation in the humus layers, with FB exhibiting the highest organic carbon content, surpassing GB, MC, SC, and MSR by 22.88%, 52.35%, 60.64%, and 80.12%, respectively. Non-tillage treatments can enhance the accumulation of labile organic carbon fractions, aligning with the observed trends in soil organic carbon, with the FB treatment identified as optimal. Additionally, these treatments can increase labile organic carbon fractions and CPMI, thereby improving soil stability. To minimize SOC loss, land use patterns should encourage the conversion of farmland to grassland and forest, with the FB treatment recommended as the optimal strategy for the protection of mollisols and the sustainable development of these soils over the long term. This approach is significant for understanding the soil carbon cycle, rationally planning land use strategies, and providing a reference for enhancing soil quality and ecosystem carbon sinks.
Valorization of Agri-Food Waste into PHA and Bioplastics: From Waste Selection to Transformation
Authors: Arriaga Marta , Pinar Francisco Javier , Izarra Irene , Amo Jesús del , Vicente Javier , Fernández-Morales Francisco Jesús , Mena Javier
Published: January 2025

The valorization of agri-food waste is a pivotal component in developing the circular economy, wherein waste is given a second life through various conversion technologies. This review aims to provide an overview of the current state of knowledge on the valorization of agri-food waste, with a particular focus on volatile fatty acids (VFAs) and subsequent bioplastics production. To this end, a comprehensive literature search was conducted using specialist bibliographic databases. The study primarily focuses on reviewing the biological production of PHBV (poly(3-hydroxybutyrate-co-3-hydroxyvalerate)), a type of PHA, due to its relevance as a substitute for fossil-based plastics. Significant attention has been directed toward exploring the potential of agri-food wastes, such as whey, potato peelings, and brewery bagasse, as raw materials for their transformation into a tailor-made VFA solution, rich in valeric and propionic acids. Additionally, existing operational strategies and waste co-treatments have been reviewed. The produced VFAs present multiple applications, including single-cell oils, Omega-3-rich oils, and different types of PHA. Factors influencing the bio-polymerization of VFAs to PHBV, such as the type of substrate, operating conditions (pH and retention time), and the presence of specific microorganisms, have also been reviewed. Due to its physicochemical properties, PHBV has applications in sectors such as agriculture and packaging. This review concludes that transforming agri-food waste into PHBV has the potential to integrate environmental and economic benefits within a circular bioeconomy model, fostering technological innovation and the sustainable use of waste resources.
Design and Evaluation of Noise Simulation Algorithm Using MATLAB Ray Tracing Engine for Noise Assessment and Prediction
Authors: Kalisalvan Precin , Ab Karim Mohd Sayuti , Musa Siti Nurmaya
Published: January 2025

The Malaysian Department of Occupational Safety and Health (DOSH) reported that noise-induced hearing loss (NIHL) accounted for 92% of occupational diseases in 2019. To address this, accurate risk assessment is crucial. The current noise evaluation methods are complex and time-consuming, relying on manual calculations and field measurements. An easy-to-use, open-source noise simulator that directly compares the output with national standards would help mitigate this issue. This research aims to develop an advanced noise evaluation tool to assess and predict unregulated workplace noise, providing tailored safety recommendations. Using a representative plant layout, the Sound Pressure Level (SPL) is calculated using MATLAB’s ray tracing propagation model. The model simulates all possible transmission paths from the source to the receiver to derive the resultant SPL. A noise simulation application featuring a graphical user interface (GUI) built with MATLAB’s App Designer (version: R2024a) automates these computations. The simulation results are validated against the DOSH’s safety standards in Malaysia. Additional safety metrics, such as the recommended maximum exposure time and the required Noise Reduction Rating (NRR) for hearing protection, are calculated based on the SPLs for hazardous locations. The simulation algorithm’s functionality is validated against manual calculations, with an average deviation of just 3.06 dB, demonstrating the model’s precision. This tool can assess and predict indoor noise levels, provide information on optimal exposure limits, and recommend necessary protective measures, ultimately reducing the risk of NIHL in factory environments. It can potentially optimise plant floor operations for existing and new facilities, ensuring safer shift operations and reducing worker noise hazard exposure.
Analyzing the Corporate Business Intelligence Impact: A Case Study in the Financial Sector
Authors: Yetgin Serap Akcan , Altas Hilal
Published: January 2025

Business intelligence is the process and methods that enable businesses to effectively analyze large amounts of data and transform it into meaningful information, helping to increase efficiency and productivity in businesses, thus enabling businesses to gain competitive advantage. In this context, business intelligence improves data management and decision-making processes and plays a critical role in strategic management. The main purpose of this study is to analyze the transition process of business intelligence solutions in financial institutions in detail, to increase efficiency in reporting processes, and to optimize decision-making processes. The study examines the ‘Cheque Report’, which reports the status of cheques in XY Financial Institution. Within the scope of the study, the transition process to business intelligence in the financial institution examined the ‘Cheque Report’ in three stages: in the first stage, reports were prepared manually; in the second stage, they were prepared with PL/SQL, and in the last stage, they were prepared with a business intelligence solution, and their outputs were compared. As a result, it was observed that with the use of business intelligence, fast and direct access to reports, data security, freedom from person dependency, and efficiency in internal information sharing are provided.
Experimental and Numerical Research on End-Wall Flow Mechanism of High-Loading Tandem Stators
Authors: Zhang Chuanhai , Liu Baojie , Qiu Ming , Jiang Zhuyu , Hao Yan
Published: January 2025

Tandem blades have been recognized for their potential to enhance the loading capacity of compressors. However, tandem stators currently do not exhibit advantages due to insufficient understanding of the complex end-wall flow mechanisms. To address this, an extensive study was conducted on tandem stators using experimental and numerical methods. The analysis focused on loss development, three-dimensional flow structures, and interaction mechanisms between front and rear blades. The results indicated the following: (1) The rear blade’s influence on the front blade has contrasting effects in mid-span and end-wall regions. The stagnation action of the rear blade increases front blade load, leading to greater corner separation losses. (2) Mid-span flow losses account for over 60% of total losses near stalls, primarily due to increased mixing losses from the migration of front blade corner separations towards the mid-span region in the rear blade channel. (3) At higher mass flow rates, corner separations occur in the rear blade, driven by significant circumferential pressure differences at the front section of the rear blade, causing end-wall fluid migration towards suction surfaces. (4) Corner stalls predominantly occur in the front blade, with associated losses exceeding those in conventional blades.
Inversion of Aerosol Chemical Composition in the Beijing–Tianjin–Hebei Region Using a Machine Learning Algorithm
Authors: Li Baojiang , Cheng Gang , Shang Chunlin , Si Ruirui , Shao Zhenping , Zhang Pu , Zhang Wenyu , Kong Lingbin
Published: January 2025

Aerosols and their chemical composition exert an influence on the atmospheric environment, global climate, and human health. However, obtaining the chemical composition of aerosols with high spatial and temporal resolution remains a challenging issue. In this study, using the NR-PM1 collected in the Beijing area from 2012 to 2013, we found that the annual average concentration was 41.32 μg·m−3, with the largest percentage of organics accounting for 49.3% of NR-PM1, followed by nitrates, sulfates, and ammonium. We then established models of aerosol chemical composition based on a machine learning algorithm. By comparing the inversion accuracies of single models—namely MLR (Multivariable Linear Regression) model, SVR (Support Vector Regression) model, RF (Random Forest) model, KNN (K-Nearest Neighbor) model, and LightGBM (Light Gradient Boosting Machine)—with that of the combined model (CM) after selecting the optimal model, we found that although the accuracy of the KNN model was the highest among the other single models, the accuracy of the CM model was higher. By employing the CM model to the spatially and temporally matched AOD (aerosol optical depth) data and meteorological data of the Beijing–Tianjin–Hebei region, the spatial distribution of the annual average concentrations of the four components was obtained. The areas with higher concentrations are mainly situated in the southwest of Beijing, and the annual average concentrations of the four components in Beijing’s southwest are 28 μg·m−3, 7 μg·m−3, 8 μg·m−3, and 15 μg·m−3 for organics, sulfates, ammonium, and nitrates, respectively. This study not only provides new methodological ideas for obtaining aerosol chemical composition concentrations based on satellite remote sensing data but also provides a data foundation and theoretical support for the formulation of atmospheric pollution prevention and control policies.
On the Derivation of a Fast Solver for Nonlinear Systems of Equations Utilizing Frozen Substeps with Applications
Authors: Liu Mingming , Shateyi Stanford
Published: January 2025

In this manuscript, we propose a multi-step framework for solving nonlinear systems of algebraic equations. To improve the solver’s efficiency, the Jacobian matrix is held constant during the second sub-step, while a specialized strategy is applied in the third sub-step to maximize convergence speed without necessitating additional Jacobian evaluations. The proposed method achieves fifth-order convergence for simple roots, with its theoretical convergence established. Finally, computational experiments are conducted to illustrate the performance of the proposed solver in addressing nonlinear equation systems.
Comprehensive Assessment of Novel Technologies for Electrolyte Filling in Lithium-Ion Battery Production
Authors: Hinkers Michael , Borzutzki Kristina , Krätzig Oliver , Degen Florian
Published: January 2025

Electric vehicles play a pivotal role in the decarbonization of the mobility sector. However, their success depends on low-cost, high-performance batteries, requiring continuous optimization of their production processes. Electrolyte filling is a critical and costly bottleneck in the cell assembly and influences the quality and safety of the cells, offering great potential for identifying process optimizations. The aim of this study is to complement existing studies by analyzing and evaluating novel technologies for electrolyte filling and thus to provide guidance for industry and science. A systematic literature and patent search led to the identification of sixteen relevant technologies. These were evaluated by a group of experts from the scientific community to identify the most promising technologies. As a result of this evaluation, five technologies emerged that were assessed as positive compared to the state of the art. Overall, the results of this study indicate that the dominating trend in electrolyte filling will be direct pressurization of the battery cells with increasing pressures. Apart from this trend, no other fundamentally new process technologies for industrial use are currently foreseeable. Our findings indicate that both academics and practitioners should focus future research and industrial efforts on optimizing and understanding the current process.
Release Profile and Antibacterial Activity of Thymus sibthorpii Essential Oil-Incorporated, Optimally Stabilized Type I Collagen Hydrogels
Authors: Ersanli Caglar , Skoufos Ioannis , Fotou Konstantina , Tzora Athina , Bayon Yves , Mari Despoina , Sarafi Eleftheria , Nikolaou Konstantina , Zeugolis Dimitrios I.
Published: January 2025

Antimicrobial resistance is one of the drastically increasing major global health threats due to the misuse and overuse of antibiotics as traditional antimicrobial agents, which render urgent the need for alternative and safer antimicrobial agents, such as essential oils (EOs). Although the strong antimicrobial activity of various EOs has already been studied and revealed, their characteristic high sensitivity and volatility drives the need towards a more efficient drug administration method via a biomaterial system. Herein, the potential of Thymus sibthorpii EO incorporated in functionalized antibacterial collagen hydrogels was investigated. At first, the optimally stabilized type I collagen hydrogels via six different multi-arm poly (ethylene glycol) succinimidyl glutarate (starPEG) crosslinkers were determined by assessing the free amine content and the resistance to enzymatic degradation. Subsequently, 0.5, 1, and 2% v/v of EO were incorporated into optimized collagen hydrogels, and the release profile, as well as release kinetics, were studied. Finally, biomaterial cytocompatibility tests were performed. Thymus sibthorpii EO was released from the hydrogel matrix via Fickian diffusion and showed sustained release and 0.5% v/v EO-loaded hydrogels showed adequate antibacterial activity against Staphylococcus aureus and did not show any statistically significant difference compared to penicillin (p < 0.05). Moreover, none of the fabricated composite antibacterial scaffolds displayed any cytotoxicity on NIH-3T3 fibroblasts. In conclusion, this work presents an innovative antibacterial biomaterial system for tissue engineering applications, which could serve as a promising alternative to antibiotics, contributing to coping with the issue of antimicrobial resistance.
Perturbations in Microbial Communities at Hydrothermal Vents of Panarea Island (Aeolian Islands, Italy)
Authors: Gallo Annamaria , Sposito Fabio , Longo Manfredi , Lazzaro Gianluca , Caruso Cinzia Giuseppina , Morici Sabina , Scappuzzo Sergio Scirè , Radovic Slobodanka , Villanova Valeria , Vecchioni Luca , Arculeo Marco , Alduina Rosa
Published: January 2025

Marine hydrothermal ecosystems represent extreme environments connected to submarine volcanic areas characterized by vents, having high temperatures and particular chemical compositions. The hydrothermal marine system of Panarea, located in one of the seven small islands belonging to the Aeolian Archipelago (southern Tyrrhenian Sea), is characterized by a range of vents exhibiting diverse physical and chemical conditions. We aimed to analyze the microbial community of a peculiar hot spring belonging to the Panarea hydrothermal field, known as “Black Point” (BP), in two separate sampling expeditions (May and August). Our results demonstrated that the chemical–physical variations within this hydrothermal vent, such as temperature fluctuations, mineral content, and hydrothermal fluid dynamics, play a role in shaping the structure and diversity of microbial communities. The differences between the two sampling expeditions suggest that seasonal changes, i.e., in temperature, pH, and redox potential (Eh), could drive microbial community shifts over time.
DICER1: The Argonaute Endonuclease Family Member and Its Role in Pediatric and Youth Pathology
Authors: Sergi Consolato M. , Minervini Fabrizio
Published: January 2025

In 2001, two enzyme-encoding genes were recognized in the fruit fly Drosophila melanogaster. The genetic material, labeled Dicer-1 and Dicer-2, encodes ribonuclease-type enzymes with slightly diverse target substrates. The human orthologue is DICER1. It is a gene, which has been positioned on chromosome 14q32.13. It contains 27 exons, which are linking the two enzyme domains. DICER1 is found in all organ systems. It has been proved that it is paramount in human development. The protein determined by DICER1 is a ribonuclease (RNase). This RNase belongs to the RNase III superfamily, formally known as ’endoribonuclease’. It has been determined that the function of RNase III proteins is set to identify and degrade double-stranded molecules of RNA. DICER1 is a vital “housekeeping” gene. The multi-domain enzyme is key for small RNA processing. This enzyme functions in numerous pathways, including RNA interference paths, DNA damage renovation, and response to viruses. At the protein level, DICER is also involved in several human diseases, of which the pleuro-pulmonary blastoma is probably the most egregious entity. Numerous studies have determined the full range of DICER1 functions and the corresponding relationship to tumorigenic and non-neoplastic diseases. In fact, genetic mutations (somatic and germline) have been detected in DICER1 and are genetically associated with at least two clinical syndromes: DICER1 syndrome and GLOW syndrome. The ubiquity of this enzyme in the human body makes it an exquisite target for nanotechnology-supported therapies and repurposing drug approaches.