bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Scenario Simulation of Ecosystem Service Value in Beijing’s Green Belts Based on PLUS Model
Authors: Hu Ziying , Wang Siyuan
Published: February 2025

Urbanization and economic growth have substantially modified the land utilization structure, affecting ecosystem services and their spatial distribution. As a crucial component of Beijing’s urban framework, the city’s green belts, located at the periphery of its core metropolitan area, play a vital role in supplying urban ecosystem services. They also represent a focal point for land use transformation conflicts, making them an important study area. This research utilizes land utilization data from 2000, 2005, 2010, 2015, and 2020 as the primary dataset. It adopts a modified standard equivalent factor and integrates it with the Patch-Generaling Land Use Simulation (PLUS) model to model land utilization in Beijing’s green belts for 2035 under three scenarios: the natural development scenario (NDS), ecological protection scenario (EPS) and cultivated protection scenario (CPS). The study aims to analyze and project the spatial and temporal evolution of ecosystem service values (ESVs) in 2035 under different scenarios in the green belts of Beijing. The results indicate that (1) land use in Beijing’s green belts is dominated by cropland and construction land. Construction land has expanded significantly since 2000, increasing by 500.78 km2, while cropland has decreased by 488.47 km2. Woodland, grassland, and water have also seen a reduction. Overall, there is a trend of woodland and water being converted into cropland, with cropland subsequently transitioning into construction land. (2) In the NDS, construction land increases by 91.76 km2, while cropland, grassland, and water decrease. In EDS, the growth of construction land decelerates to 22.09 km2, the reduction in cropland decelerates, and the conversion of cropland to construction land is limited. Grassland and water remain largely unchanged, and woodland experiences a slight increase. In CPS, the conversion of cropland to construction land is notably reduced, with construction land increasing by 11.97 km2, woodland increasing slightly, and grassland and water decreasing slightly. (3) The ESV ranking across scenarios is as follows: EPS 1830.72 mln yuan > CPS 1816.23 mln yuan > NDS 1723.28 mln yuan. Hydrological regulation and climate regulation are the dominant services in all scenarios. ESV in EPS attains the greatest economic gains. This study contributes to understanding the effects of land utilization changes on ESV, offering valuable empirical evidence for sustainable development decision-making in swiftly urbanizing areas.
Distribution Patterns and Influencing Factors Controlling Soil Carbon in the Heihe River Source Basin, Northeast Qinghai–Tibet Plateau
Authors: Zhao Meiliang , Cao Guangchao , Zhao Qinglin , Ma Yonggui , Zhang Fuling , Li Hongda , He Qixin , Qiu Xunxun
Published: February 2025

Soil organic carbon (SOC) and soil inorganic carbon (SIC) are key components of soil carbon pools in arid ecosystems, playing a crucial role in regional carbon cycling and climate change mitigation. However, the interactions between these two forms of carbon in arid alpine ecosystems remain underexplored. This study was conducted in the Heihe River Basin (HRB) in the northeastern Qinghai–Tibet Plateau, focusing on the distribution and dynamics of SOC and SIC in deep soil layers. Using data from 329 samples collected from 49 soil profiles extending to the bedrock, combined with path analysis, we explored the inter-relationships between SOC and SIC and quantified the influence of environmental factors. The results showed that (1) SOC exhibited a unimodal distribution with elevation, peaking at 3300–3600 m, while SIC continuously decreased with elevation, with reduction rates ranging from −0.39% to −31.18%; (2) SOC and SIC were significantly positively correlated (r = 0.55, p < 0.01), with SOC decreasing with depth and SIC showing an inflection point at 50 cm depth; (3) SOC was primarily driven by nutrient factors, such as total nitrogen (TN), with a path coefficient of 0.988, while SIC was influenced by abiotic factors, including potential evapotranspiration (PET), with a coefficient of −1.987; (4) SOC density accounted for 81.62% of the total soil carbon pool, playing a dominant role in carbon storage, whereas SIC density exhibited dynamic changes, particularly at depths of 110–150 cm. These findings advance our understanding of deep soil carbon dynamics in arid alpine ecosystems and provide critical data for improving carbon management strategies in similar regions.
Study on Subway Station Street Block-Level Land Use Pattern and Plot Ratio Control Based on Machine Learning
Authors: Kuang Mingyi , Fu Fei , Tian Fangzhou , Lin Liwei , Du Can , Zhang Yuesong
Published: February 2025

As urbanization accelerates, megacities are facing challenges such as inefficient land use and traffic congestion, particularly in the context of rail transit-oriented development, where land use optimization remains a significant research gap. Current urban planning still relies heavily on the experience and intuition of government planning departments, without achieving quantitative, intelligent, and scientific decision making. This study takes Panda Avenue Subway Station as a case study to analyze the evolution of land use patterns around subway stations and explore optimization strategies to enhance land development efficiency and spatial utilizationTo fill this research gap, this paper proposes a CNN-AIMatch model based on machine learning algorithm and an enhanced PLUS-Markov prediction model using the increase and decrease of floor area ratio as a control measure, which adopts an increase in plot ratio as a control measure to improve the accuracy of the Kappa coefficient in different plot ratio scenarios and the prediction of 3D urban spatial growth trends. The model effectively overcomes the limitations of the conventional 2D perspective in predicting urban expansion. By simulating urban renewal and ecological preservation scenarios, it provides an innovative solution for land use pattern optimization and plot ratio control at the block level in subway station areas. The goal of this study is to optimize land use and floor area ratio control strategies through the application of this model, intelligently respond to the challenges of high-density development and quality of life assurance, achieve the best use of land, and promote sustainable urban development and the construction of smart cities.
Left Atrial Strain in Patients with Chronic Heart Failure with Preserved Ejection Fraction: A Narrative Review
Authors: Man Dana Emilia , Motofelea Alexandru Catalin , Buda Valentina , Velimirovici Dana Emilia , Bodea Olivia , Duda-Seiman Daniel Marius , Luca Constantin Tudor , Dragan Simona-Ruxanda
Published: February 2025

Background: Heart failure with preserved ejection fraction (HFpEF) represents a significant portion of heart failure cases, but diagnosis is challenging due to its diverse presentation and the limitations of traditional echocardiographic parameters. Left atrial (LA) strain provides valuable insights into LA function and is increasingly used to evaluate cardiac function, including left ventricular (LV) diastolic function. LA strain, particularly reservoir strain, is considered a reliable indicator of LV diastolic function and can be used to grade diastolic function and estimate LV filling pressure. Unlike traditional LA measurements, LA strain offers detailed insights into LA function, conduit, and booster-pump phases, making it crucial for evaluating both structural and functional cardiac performance, especially in HFpEF. HFpEF diagnosis currently relies on a combination of echocardiographic parameters, clinical symptoms, and natriuretic peptide levels, encompassing various pathophysiological entities and complicating standardized management. Precise characterization of cardiac pathologies in HFpEF patients is essential. This review assesses global longitudinal strain (GLS) and left atrial strain (LAS) as echocardiographic biomarkers for diagnosing and characterizing HFpEF. Strain imaging, particularly speckle tracking echocardiography, offers a refined assessment of myocardial deformation, providing detailed insights into left heart function beyond traditional measures. Normal ranges for GLS and LAS are discussed, acknowledging demographic and technical influences. Clinical studies confirm the prognostic value of GLS and LAS in HFpEF, especially for predicting cardiovascular outcomes and distinguishing HFpEF from other dyspnea causes. However, variability in strain measurements and false-negative risks necessitate cautious clinical interpretation. The HFA-PEFF scoring system includes these biomarkers but does not fully cover the HFpEF pathology spectrum. Combining GLS and LAS shows promise in defining HFpEF phenogroups, potentially guiding individualized treatments. Global longitudinal strain (GLS) and left atrial strain (LAS) are central to non-invasive HFpEF diagnosis and stratification, with potential for more tailored therapies. Integration of these biomarkers into standard diagnostic practice requires an organized approach, and future guidelines should recommend their combined use for comprehensive HFpEF assessment.
Mechanical Characterization of a Novel Cyclic Olefin-Based Hot-Melt Adhesive
Authors: Rodrigues Vasco C. M. B. , Venâncio Ana T. F. , Marques Eduardo A. S. , Carbas Ricardo J. C. , Klein Armina , Kazuhiro Ejiri , Nelson Björn , Silva Lucas F. M. da
Published: February 2025

A novel hot-melt cyclic olefin-based adhesive was designed as a transparent, non-tacky film of amorphous thermoplastic with a unique polymer micro-structure. The aim of the present paper is to assess the mechanical properties of the 0.1 mm thick COP hot-melt adhesive film through adhesive characterizations tests. The glass transition temperature was determined using dynamic mechanical analysis (DMA). For mechanical characterization, bulk and thick adherend shear specimens were manufactured and tested at a quasi-static rate, where at least three specimens were used to calculate the average and standard deviation values. Tensile tests revealed the effects of molecular chain drawing and reorientation before the onset of strain hardening. Thick adherend shear specimens were used to retrieve shear properties. Fracture behaviour was assessed with the double cantilever beam (DCB) test and end-notched flexure (ENF) test, for characterization under modes I and II, respectively. To study the in-joint behaviour, single lap joints (SLJs) of aluminium and carbon fibre-reinforced polymer (CFRP) were manufactured and tested under different temperatures. Results showed a progressive interfacial failure following adhesive plasticization, allowing deformation prior to failure at 8 MPa. An adhesive failure mode was confirmed through scanning electron microscopy (SEM) analysis of aluminium SLJ. The adhesive exhibits tensile properties comparable to existing adhesives, while demonstrating enhanced lap shear strength and a distinctive failure mechanism. These characteristics suggest potential advantages in applications involving heat and pressure across automotive, electronics and structural bonding sectors.
Autonomous Non-Communicative Navigation Assistance to the Ground Vehicle by an Aerial Vehicle
Authors: Sivarathri Ashok Kumar , Shukla Amit
Published: February 2025

Vision-based UAV-AGV (Unmanned Aerial Vehicle–Autonomous Ground Vehicle) systems are prominent for executing tasks in GPS (Global Positioning System)-inaccessible areas. One of the roles of the UAV is guiding the navigation of the AGV. Reactive/mapless navigation assistance to an AGV from a UAV is well known and suitable for computationally less powerful systems. This method requires communication between both agents during navigation as per state of the art. However, communication delays and failures will cause failures in tasks, especially during outdoor missions. In the present work, we propose a mapless technique for the navigation of AGVs assisted by UAVs without communication of obstacles to AGVs. The considered scenario is that the AGV is undergoing sensor and communication module failure and is completely dependent on the UAV for its safe navigation. The goal of the UAV is to take AGV to the destination while guiding it to avoid obstacles. We exploit the autonomous tracking task between the UAV and AGV for obstacle avoidance. In particular, AGV tracking the motion of the UAV is exploited for the navigation of the AGV. YOLO (You Only Look Once) v8 has been implemented to detect the drone by AGV camera. The sliding mode control method is implemented for the tracking motion of the AGV and obstacle avoidance control. The job of the UAV is to localize obstacles in the image plane and guide the AGV without communicating with it. Experimental results are presented to validate the proposed method. This proves to be a significant technique for the safe navigation of the AGV when it is non-communicating and experiencing sudden sensor failure.
Mathematical Modeling and Recursive Algorithms for Constructing Complex Fractal Patterns
Authors: Buriboev Abror Shavkatovich , Sultanov Djamshid , Ibrohimova Zulaykho , Jeon Heung Seok
Published: February 2025

In this paper, we present mathematical geometric models and recursive algorithms to generate and design complex patterns using fractal structures. By applying analytical, iterative methods, iterative function systems (IFS), and L-systems to create geometric models of complicated fractals, we developed fractal construction models, visualization tools, and fractal measurement approaches. We introduced a novel recursive fractal modeling (RFM) method designed to generate intricate fractal patterns with enhanced control over symmetry, scaling, and self-similarity. The RFM method builds upon traditional fractal generation techniques but introduces adaptive recursion and symmetry-preserving transformations to produce fractals with applications in domains such as medical imaging, textile design, and digital art. Our approach differs from existing methods like Barnsley’s IFS and Jacquin’s fractal coding by offering faster convergence, higher precision, and increased flexibility in pattern customization. We used the RFM method to create a mathematical model of fractal objects that allowed for the viewing of polygonal, Koch curves, Cayley trees, Serpin curves, Cantor set, star shapes, circulars, intersecting circles, and tree-shaped fractals. Using the proposed models, the fractal dimensions of these shapes were found, which made it possible to create complex fractal patterns using a wide variety of complicated geometric shapes. Moreover, we created a software tool that automates the visualization of fractal structures. This tool may be used for a variety of applications, including the ornamentation of building items, interior and exterior design, and pattern construction in the textile industry.
Feasibility Exploration and Research Examples of On-Site Metallographic Inspection Methods in the Analysis of Bronze Artifacts—A Case Study of Ming Jiajing Bronze Lions and the Shang Bronze Tripod Vessel with Cicada Designs
Authors: Zhang Kaige , Liu Cheng , Zhang Siyu , Cui Ruihua , Li Yi
Published: February 2025

This study explores a new microdestructive on-site metallographic inspection technique for analyzing metal artifacts. In the current archeometrical work, the metallographic analysis of metal artifacts requires mechanical sampling, which not only damages the integrity of the artifacts but also brings cold working effects to the metallographic structure during the sampling process, making the information inaccurate. This study designed a set of detailed on-site metallographic inspection methods for bronze artifacts, including grinding, sealing, polishing, etching, replicating, cleaning, and other steps. After verifying its safety through simulation experiments, the method was applied to several precious bronze artifacts, including two Ming Dynasty bronze lions from the Xi’an Beilin Museum and a Shang Bronze Tripod Vessel with Cicada Designs from the China Bronze Ware Museum. The metallographic findings show that the in situ metallographic technique can flexibly and accurately reveal the metallographic texture and process information of each localized part of the bronze artifacts, e.g., the heat-affected zone of the coins on the surface of the Ming Dynasty bronze lions proved the casting-inlay process, and the different heat texture of each foot of the Shang Bronze Tripod Vessel with Cicada Designs proved the chronological sequence of its two historical restorations. This study provides a novel approach to the process analysis of bronze artifacts, a method that can provide significant advantages in analyzing the processing techniques of precious and intact artifacts.
The Influence of Contour Form Geometric Features and the Number of Cutting Passes on the Surface Quality Characteristics and Critical Points of Cutting Tools Fabricated by Wire Electrical Discharge Machining (WEDM)
Authors: Alinaghizadeh Amir , Azarhoushang Bahman , Hadad Mohammadjafar
Published: February 2025

Since one of the effective methods for producing the form-cutting tools used in the form-turning process involves utilizing a wire cut machine, the effect of the geometric characteristics of the form contour on reducing the negative effects of the recast layer was investigated in this research. The basic assumption of the components for each type of profile form is based on a combination of four modes, i.e., concave arc, convex arc, flat surface, and oblique surface. Based on this, samples were fabricated as cutting tools with three different radii: a convex arc, a concave arc, and a flat surface. During the wire electrical discharge machining (WEDM) operation, one-pass mode was used to create a rough surface, two passes resulted in a semi-finished surface, and three passes resulted in a finished surface. Furthermore, the difference between the surface quality of the recast layer in the two areas above the workpiece or the wire entry point and the bottom area of the workpiece or the wire exit point was studied. Finally, the effect of the direction, size of the curvature and the number of passes in the electric discharge process of the wire on the recast layer was shown, and it was observed that with the increase in the number of passes in WEDM, the thickness of the recast layer was reduced, along with the uniformity of the cutting contour section in the areas close to the cutting region. The entry of the wire was greater than that in the areas near the exit of the wire.
Uncovering the Host Range–Lifestyle Relationship in the Endophytic and Anthracnose Pathogenic Genus Colletotrichum
Authors: Newfeld Jacy , Ujimatsu Ren , Hiruma Kei
Published: February 2025

Colletotrichum includes agriculturally and scientifically important pathogens that infect numerous plants. They can also adopt an endophytic lifestyle, refraining from causing disease and/or even promoting plant growth when inoculated on a non-susceptible host. In this manner, the host range of a Colletotrichum fungus can shift, depending on whether it exhibits endophytic or pathogenic lifestyles. Some fungi, such as Colletotrichum tofieldiae, can even shift between pathogenicity and endophytism within the same host depending on the environmental conditions. Here, we aim to disentangle the relationship between lifestyle and host range in Colletotrichum. Specifically, we aim to demonstrate that lifestyle is dependent on the host colonized in many Colletotrichum fungi. We discuss the ways in which pathogenic Colletotrichum species may act endophytically on alternative hosts, how comparative genomics has uncovered candidate molecules (namely effectors, CAZymes, and secondary metabolites) underlying fungal lifestyle, and the merits of using endophytic fungi alongside pathogenic fungi in research, which facilitates the use of reverse genetics to uncover molecular determinants of lifestyle. In particular, we reference the Arabidopsis thaliana–Colletotrichum tofieldiae study system as a model for elucidating the dual roles of plant–fungus interactions, both endophytic and pathogenic, through integrative omics approaches and reverse genetics. This is because C. tofieldiae contains closely related pathogens and endophytes, making it an ideal model for identifying candidate determinants of lifestyle. This approach could identify key molecular targets for effective pathogen management in agriculture. Lastly, we propose a model in which pathogenic lifestyle occupies a different host range than the endophytic lifestyle. This will enhance our understanding of pathogenicity and endophytism in a globally significant fungal genus and lay the groundwork for future research examining molecular determinants of lifestyle in plant-associated fungi.
The Role of Clostridioides difficile Within the One Health Framework: A Review
Authors: Alexiou Sotiris , Diakou Anastasia , Kachrimanidou Melania
Published: February 2025

Clostridioides difficile is the leading cause of antibiotic-associated diarrhea in hospitalized patients. In recent years, the incidence of C. difficile infection (CDI) has increased globally, with a notable rise in community-associated CDI (CA-CDI). The presence of the microorganism in animals, the environment, and food suggests that these sources may contribute to the spread of the infection in the community. This review applies a One Health approach, integrating human, animal, and environmental health, to provide a comprehensive strategy for understanding and managing this pathogen. Findings reveal the widespread dissemination of C. difficile in animals, the environment, and food. The predominant PCR ribotypes identified were RTs 078 and 014/020, followed by RTs 126, 001, 002, 009, 010, and 033. C. difficile strains exhibited resistance to multiple antimicrobial agents, including clindamycin, erythromycin, fluoroquinolones, cephalosporins, and tetracyclines. Discriminative typing methods, such as whole-genome sequencing, revealed clonal relationships between C. difficile strains from humans and animals, indicating either direct transmission or a common environmental source of infection. The high genetic similarity between isolates from the environment and humans indicates potential environmental contamination. Additionally, clusters of C. difficile strains found in food and humans indicate a possible foodborne transmission route. This review summarizes the current knowledge on the role of Clostridioides difficile within the One Health framework.
Inhibitory Potential of Cannabis Biomass Extracts on Livestock-Associated Staphylococcal and Streptococcal Pathogens
Authors: Paulová Tereza , Malíková Lucie , Lanzoni Davide , Taubner Tomáš , Malík Matěj , Houdková Markéta , Pěchoučková Eva
Published: February 2025

Diseases caused by staphylococci and streptococci are a serious burden on livestock production, causing significant losses. In addition, the associated antibiotic resistance of these pathogens often makes treatment impossible or prolonged. Cannabis sativa L. contains many compounds with antibacterial properties and shows great potential as a natural antimicrobial agent for agricultural use against both of these bacterial species. The aim of this study was to compare the in vitro antibacterial activity of ethanol extracts from five cultivars of hemp, namely, Bialobrzeskie, Felina 32, Futura 75, mixed and Santhica 27, against Staphylococcus aureus, Streptococcus agalactiae and Streptococcus dysgalactiae. All five cultivars exhibited a certain degree of inhibitory effect against all the pathogens tested with minimum inhibitory concentrations (MICs) ranging from 128 to 2048 μg/mL. The extract from the Santhica 27 cultivar was the most effective antibacterial agent with the lowest MIC value of 128 μg/mL against Str. agalactiae and two clinical isolates of S. aureus, followed by Bialobrzeskie and mixed cultivars with the same growth-inhibitory potential against Str. agalactiae. The extracts from the Felina 32 and Futura 75 cultivars presented only weak activity with MIC values ranging from 256 to 2048 μg/mL. The extract from the Santhica 27 cultivar appears to be a promising product for future use in the treatment of staphylococcal and streptococcal infections in livestock.
Metagenomic Insights into the Abundance of Iron-Reducing Microorganisms in a Petroleum-Contaminated Iron-Rich Aquifer
Authors: Di He , Zhang Min , Ning Zhuo , Liu Changli , He Ze , Wang Shuaiwei , Kong Siyu , Gan Shuang , Sun Weichao , Xu Zhe , Ti Jinjin
Published: February 2025

In petroleum-contaminated aquifers, iron (III) serves as an electron acceptor, enabling microbial degradation of organic matter. While previous studies have focused on iron reduction and organic matter degradation under laboratory conditions, research on iron-associated microorganisms in petroleum-contaminated aquifers is limited. To explore the diversity and distribution of such microorganisms in natural settings, this study used metagenomic analysis of an iron-rich, petroleum-contaminated aquifer. Sixteen groundwater samples from both pollution source and background areas were collected for species annotation and functional gene identification. Results show more than 7000 species were identified as iron-reducing microorganisms (IRMs), including several previously well-characterized iron-reducing species (e.g., Geobacter luticola and Geobacter sulfurreducens). However, the majority of IRMs were not found in existing iron-reducing microbial databases. Some of them, such as Sulfurospirillum sp. and Extensimonas perlucida, could be taxonomically classified at the species level, while most were only annotated as unclassified bacteria. In the contamination source zone, these microorganisms proliferated extensively, which led to hydrocarbon degradation predominantly driven by iron reduction in the aquifer. This study enhances our understanding of hydrocarbon-degrading microorganisms and supports the management of petroleum-contaminated sites.
Exploring the Grapevine Microbiome: Insights into the Microbial Ecosystem of Grape Berries
Authors: Minerdi Daniela , Sabbatini Paolo
Published: February 2025

Plant growth, health, and resilience to stress are intricately linked to their associated microbiomes. Grapevine, functioning as a holobiont, forms essential relationships with fungi and bacteria across both its belowground (roots) and aboveground (leaves and berries) compartments. The root microbiome exhibits a stable, site-specific structure, whereas the microbiomes of ephemeral tissues such as leaves and berries, which regenerate annually, display more stochastic assembly patterns across growing seasons. Among these, grape berries represent a critical component in viticulture due to their direct influence on wine quality and flavor complexity. Berries provide a unique ecological niche, hosting diverse microbial communities composed of yeasts, bacteria, and fungi that interact with the grapevine and its surrounding environment. These microorganisms are not only pivotal to berry development but also contribute significantly to the synthesis of secondary metabolites and fermentation processes, ultimately shaping the sensory and organoleptic properties of wine. This review consolidates current knowledge on the grapevine microbiome, with a particular emphasis on the microbial dynamics of grape berries.
Small Molecules Identified by an In Silico Docking Screen Targeting Anaphase-Promoting Complex/Cyclosome Subunit 1 (APC1) Potentiate Paclitaxel-Induced Breast Cancer Cell Death
Authors: Schuyler Scott C. , Gupta Rythm , Nguyen Tran Thi Bao , Weng Cheng-Ye , Chen Hsin-Yu
Published: February 2025

Delaying mitotic cell cycle progression has been proposed as a strategy to potentiate the effects of anti-mitotic anti-cancer drugs that induce multipolar mitotic spindles. Toward this end, we have performed an in silico docking screen targeting anaphase-promoting complex/cyclosome subunit 1 (APC1) at a conserved 10-amino acid surface site that was modeled to interact via a single hydrogen bond with the essential mitotic anaphase-promoting complex/cyclosome (APC/C) co-factor cell division cycle 20 (CDC20). Five molecules were identified after screening 15,000 small molecules. As a secondary in cellulo bioactivity screening, MDA-MB-231 genomically unstable aneuploid breast cancer cells were exposed to each compound in the absence and presence of 10 nM paclitaxel or 1 nM eribulin, the likely clinically relevant doses of these drugs in these cells. Two of the five compounds, which share a common 2-(trifluoromethyl)quinazolin-4-amine chemical structure, induced elevated levels of cell death in combination with paclitaxel, as observed by fluorescence-activated cell sorting (FACS). These two compounds will now serve as a starting point for further optimization and target validation experiments and for additional in silico screens in search of other chemically related small molecules that display more potent but specific anti-cancer cell effects.
A Semi-Micro Extraction Spectrophotometric Determination of Iron Using 4-Nitrocatechol and Xylometazoline Hydrochloride
Authors: Racheva Petya V. , Saravanska Antoaneta D. , Toncheva Galya K. , Kiradzhiyska Denitsa D. , Milcheva Nikolina P. , Divarova Vidka V. , Pencheva Ina P. , Stojnova Kirila T. , Delchev Vassil B. , Gavazov Kiril B.
Published: February 2025

A chromogenic solvent extraction system for Fe(III) based on 4-nitrocatechol (4NC) and xylometazoline hydrochloride (XMH) was investigated. The optimum conditions for extraction spectrophotometric determination of iron were found. Under these conditions, the formula of the extracted complex was (XMH+)3[Fe(4NC)3] and the apparent molar absorption coefficient at λmax = 464 nm was 1.9 × 105 dm3 mol−1 cm−1 (4-fold enrichment). To validate the aforementioned formula and gather information about the spin state of the central Fe(III) ion, a combined experimental-theoretical approach was employed. This approach entailed the experimental determination of the 4NC:Fe and XMH:Fe molar ratios and the optimization of potential color-bearing structures utilizing the B3LYP/6-311G computational chemistry method. The effect of foreign ions was thoroughly examined, and a sensitive, simple, and inexpensive analytical procedure was proposed, which was successfully applied for determining iron in pharmaceutical and industrial samples.
Liquid Metal–Polymer Hydrogel Composites for Sustainable Electronics: A Review
Authors: Hajalilou Abdollah
Published: February 2025

Hydrogels, renowned for their hydrophilic and viscoelastic properties, have emerged as key materials for flexible electronics, including electronic skins, wearable devices, and soft sensors. However, the application of pure double network hydrogel-based composites is limited by their poor chemical stability, low mechanical stretchability, and low sensitivity. Recent research has focused on overcoming these limitations by incorporating conductive fillers, such as liquid metals (LMs), into hydrogel matrices or creating continuous conductive paths through LMs within the polymer matrix. LMs, including eutectic gallium and indium (EGaIn) alloys, offer exceptional electromechanical, electrochemical, thermal conductivity, and self-repairing properties, making them ideal candidates for diverse soft electronic applications. The integration of LMs into hydrogels improves conductivity and mechanical performance while addressing the challenges posed by rigid fillers, such as mismatched compliance with the hydrogel matrix. This review explores the incorporation of LMs into hydrogel composites, the challenges faced in achieving optimal dispersion, and the unique functionalities introduced by these composites. We also discuss recent advances in the use of LM droplets for polymerization processes and their applications in various fields, including tissue engineering, wearable devices, biomedical applications, electromagnetic shielding, energy harvesting, and storage. Additionally, 3D-printable hydrogels are highlighted. Despite the promise of LM-based hydrogels, challenges such as macrophase separation, weak interfacial interactions between LMs and polymer networks, and the difficulty of printing LM inks onto hydrogel substrates limit their broader application. However, this review proposes solutions to these challenges.
Flaxseed Improves Glucose and Lipid Metabolism in Mexican Subjects with Type 2 Diabetes: A Parallel Randomized Clinical Trial
Authors: López-Toledo Sabina , Pineda De la Cruz María Cruz , Gutiérrez-Hurtado Itzae Adonai , Gijón-Soriano Ana L. , Martínez-Martínez Enrique , Valencia-Santiago Carlos , Orellana-Centeno José E. , Ramírez-García Sergio A. , Pacheco-Cruz Royer
Published: February 2025

Introduction: Type 2 diabetes is a significant health concern in the 21st century, and its prevalence continues to rise despite efforts to promote preventive lifestyle changes. This increase has led to higher economic burdens, prompting the search for non-pharmacological methods to manage glucose levels. Objective: To assess the effects of flaxseed consumption on biochemical markers (glucose, glycated hemoglobin, total cholesterol, and triglycerides) in adult patients with Type 2 diabetes in Oaxaca, Mexico. Materials and Methods: Participants were recruited and randomized into clinical trials between April and June 2023, and the study protocol was approved by a Human Research Ethics Committee. Results: Consuming 16 g of flaxseed daily for three months led to a significant decrease in glucose, total cholesterol, and triglyceride levels (p < 0.001) in Type 2 diabetes patients in the intervention group (n = 82). Belonging to the control group (n = 84) was correlated with presenting higher levels of glycated hemoglobin (Spearman’s Rho 0.640; p < 0.001), higher levels of glucose (Spearman’s Rho 0.352; p < 0.001), total cholesterol (Spearman’s Rho 0.796; p < 0.001), and triglycerides (Spearman’s Rho 0.700; p < 0.001). Conclusions: A daily intake of 16 g of flaxseed is an effective supplementary treatment for adult Mexican patients with Type 2 diabetes, as evidenced by reduced levels of glycated hemoglobin, glucose, cholesterol, and triglycerides in the intervention group. Potential implications for clinical practice: Healthcare providers may consider recommending flaxseed in the diets of patients with obesity, prediabetes, or Type 2 diabetes to improve glucose and lipid metabolism and overall metabolic health.
Innovative Perspectives on Phytochemicals in Human Nutrition
Authors: Melini Valentina , Ruzzi Maurizio
Published: February 2025

Phytochemicals are chemical compounds naturally occurring in plants [...]
Editorial on Special Issue “Pharmacokinetics, Pharmacodynamics, and Drug Interactions”
Authors: Zhou Zhu
Published: February 2025

Pharmacokinetics describes the absorption, distribution, metabolism, and excretion of a drug, while pharmacodynamics focuses on the effects of the drug on the body [...]
Multifunctional Flexible Hard Coatings with Weathering Resistance and Heat-Shielding Properties
Authors: Chen Yuxi , Tian Shenglan , Ruan Jincheng , Chen Ruyu , Qu Lijie , Li Luming
Published: February 2025

Hard, flexible, transparent, and hydrophobic multifunctional coatings have a wide range of applications, but they do not adequately protect against harsh conditions, especially photoaging. In this study, SiO2 and Al2O3 nanoparticles were first modified by silazane and epoxy-functionalized silanes and then reacted with a polyetheramine curing agent to prepare highly crosslinked multifunctional hybrid coatings at room temperature. Due to the integration of siloxane nanoparticles and a polymer network, the multifunctional coatings presented outstanding hardness (4H), flexibility (bending diameter of 10 mm), and transmittance (>97%). The introduction of low-surface-energy PDMS and methyl-rich HMDS endowed the coatings with good hydrophobicity (water contact angle = 141.37°). The high reflectivity of SiO2 and Al2O3 in the solar spectral region can help prevent photoaging of the coatings, improve their heat-shielding effect, and broaden their application scenarios. Compared with the traditional manufacturing methods, this study did not need ultraviolet irradiation, and the multifunctional transparent coatings could be prepared through a simple and efficient step-by-step strategy.
Finite Element Modeling-Assisted Deep Subdomain Adaptation Method for Tool Condition Monitoring
Authors: Jing Cong , He Xin , Xu Guichang , Li Luyang , Yao Yunfeng
Published: February 2025

To reduce the experimental costs associated with tool condition monitoring (TCM) under new cutting conditions, a finite element modeling (FEM)-assisted deep subdomain adaptive network (DSAN) approach is proposed. Initially, an FEM technique is employed to construct a cutting tool model for the new cutting condition (target domain), and the similarity between simulated and experimental data is assessed to obtain valid simulated samples for the target domain. Subsequently, the time–frequency Markov representation method is utilized to extract imaging features from the simulated samples, which serve as input features for the monitoring model. Then, a DSAN model is established to facilitate the transfer from simulation to reality, with the source domain comprising a simulated sample set under new cutting conditions that includes various types of tool conditions obtained through FEM, and the target domain containing only a limited number of normal tool condition samples under new cutting conditions. The application analysis has demonstrated the effectiveness of the proposed method, achieving a classification accuracy of 99%. The proposed approach can significantly reduce experimental costs and obtain high-precision diagnostics of tool conditions with a small sample size.
Surface Topography Analysis and Surface Roughness Prediction Model of Diamond Wire-Sawed NdFeB Magnet Based on Optimized Back Propagation Neural Network
Authors: Li Guanzheng , Zhang Xingchun , Gao Yufei , Cui Fan , Shi Zhenyu
Published: February 2025

Wire sawing is an important process in the cutting of NdFeB magnets and the sawed surface topography and surface roughness (SR) are important indicators for assessing surface quality. This paper analyzed the effects of process parameters on the sawed NdFeB surface topography and SR based on orthogonal experiments and then presented an SR prediction model called ISSA-BP, which was based on a BP neural network using an improved sparrow search algorithm (ISSA). For the problem of insufficient optimization capability of the traditional sparrow search algorithm (SSA), Cubic chaotic mapping, Latin hypercube sampling, the sine–cosine algorithm, Levy flight, and Cauchy mutation were introduced to improve the traditional sparrow search algorithm (SSA) to obtain ISSA, improving algorithm convergence speed and global optimization. The ISSA was then used to optimize the initial weights and thresholds of the BP neural network for predicting Ra. Research shows that the sawed surface topography reflects a combination of brittle and ductile material removal. As the workpiece feed speed and size decrease and the wire speed increases, there is a reduction in SR. Compared with the SSA-BP and traditional BP models, the ISSA-BP prediction model has reduced various error indicators such as mean absolute error (MAE) and mean square error (MSE). The mean absolute error (MAE) of the prediction model optimized by the ISSA is 0.064475, the mean square error (MSE) is 0.0072297, the root mean square error (RMSE) is 0.085028, and the mean absolute percentage error (MAPE) is 3.7171%. The research results provide an experimental basis and technical support for predicting the SR and optimizing the process parameters in diamond wire-sawing NdFeB.
Improving Breakthrough Curve Calculations in Dynamic PSA Processes Through a Rigorous Cubic State Equation
Authors: Gutiérrez-González Daniel , Luna-Solano Guadalupe , Cantú-Lozano Denis , Aguilar-Uscanga María Guadalupe , Gómez-Rodríguez Javier , Urrea-García Galo Rafael
Published: February 2025

This study evaluates the improvement of numerical accuracy in the PSA model for ethanol dehydration using molecular sieves. By incorporating cubic state equations for vapor density determination, significant errors were identified when applying the ideal gas assumption to the ethanol–water mixture, particularly under moderate pressure conditions. The integration of the Peng–Robinson equation demonstrated a 5–10% improvement in calculation accuracy compared to the ideal gas law. This enhancement is crucial for achieving reliable predictions under non-ideal conditions, enabling more accurate estimations of real process dynamics across varying scenarios. Notably, improved accuracy in the PSA model is essential for designing more efficient and reliable industrial applications, especially at moderate pressures. The results indicate that the Peng–Robinson equation provides a more accurate representation of the density of the ethanol–water vapor mixture, contributing significantly to more accurate simulation results of the PSA process.
Comparative Study on Preparation of Aluminum-Rare Earth Master Alloy Fine Powders by Mechanical Pulverization and Gas Atomization Methods
Authors: Bai Huiyi , Ji Yunping , Li Yiming , Wang Haoqi , Kang Xueliang , Ren Huiping , Lv Wei
Published: February 2025

Aiming at the high-value application of rare earth elements lanthanum (La), an Al-50% La alloy was selected and prepared in a vacuum medium-frequency induction furnace. The geometric characteristics of the Al-50% La alloy powders were compared and studied, with the powders prepared by two different methods: mechanical pulverization and gas atomization. The results showed that an Al-49.09% La master alloy was obtained, and the only intermediate phase containing La in the experimental alloy was Al11La3. From the perspectives of chemical and phase composition, La has a high yield. Additionally, an Al-La alloy with controllable rare earth intermediate phases can be obtained. The Al-La alloy powders prepared by the mechanical pulverization method are irregular in shape, but the particle size is relatively small, ranging from 0.25 to 66.9 μm. Submicron powders were obtained, with 4.38% of the powders having an equivalent particle size of less than 1 μm. Considering the characteristic of the selective laser melting (SLM) process forming micro-melt pools, a small amount of submicron Al-La alloy powders prepared by the mechanical pulverization method can be used as a trace additive for SLM preparation of CP-Ti. The powders prepared by gas atomization have good sphericity, with a particle size range of 1.65 to 76.0 μm. Among them, the powders with a size of 2–10 μm account for 75.52%, and this part of the powders can be used for the powder metallurgy preparation of composite materials.
An SVM-Based Anomaly Detection Method for Power System Security Analysis Using Particle Swarm Optimization and t-SNE for High-Dimensional Data Classification
Authors: Tao Ye , Yan Jiongcheng , Niu Enquan , Zhai Pengming , Zhang Shuolin
Published: February 2025

Research on the detection and identification of anomalies in electric power systems is crucial for ensuring their secure and stable operation. Anomaly detection models based on Support Vector Machines (SVMs) effectively process high-dimensional data while maintaining strong generalization capabilities. However, the performance of SVMs significantly depends on the choice of parameters, where improper parameter settings can lead to overfitting or underfitting, consequently decreasing the accuracy of anomaly detection. Furthermore, the dimensions of anomaly data in electric power systems are often unknown, making it difficult for existing methods to maintain a high precision in multidimensional data detection, and the segmentation of such data lacks intuitive display. In response, this article proposes an improved SVM model for electric power system anomaly detection, enhanced by parameter optimization algorithms, alongside a method for nonlinear dimension reduction and visualization using t-Distributed Stochastic Neighbor Embedding (t-SNE). Initially, traditional SVM parameters are optimized using the following four algorithms: Grid Search (GS), Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Artificial Bee Colony (ABC) algorithms, in order to establish the optimized SVM model for electric power system anomaly detection. Finally, the effectiveness of the proposed method is verified through simulations. The simulation results indicate that, in the IEEE-14 node system case study, the accuracy for normal data reaches 97.58%, the accuracy for load step change detection reaches 99.52%, the accuracy for bad data detection reaches 99.92%, and the accuracy under fault conditions reaches 100%.
A Comprehensive Optimization for Path Planning: Combining Improved ACO and Smoothing Techniques
Authors: Li Yuanao , Cui Chang , Zhao Qiang
Published: February 2025

The ant colony algorithm is an approach for path planning that is used in multiple industries. This paper proposes an improved robot path planning method, referred to as Improved-ACO. First, the heuristic information calculation is optimized to increase algorithm efficiency and shorten convergence time. Secondly, an enhanced Tanh function is included into the heuristic information, allowing dynamic modifications during the search period and preventing the algorithm’s convergence to local optima. Then, a novel pheromone update strategy is employed to accelerate convergence. Next, a novel pheromone diffusion mechanism is proposed to strengthen the ants’ search capability. Additionally, a collision avoidance system and improved B-spline curves are included for path smoothing, guaranteeing that the optimized pathways conform to the robot’s kinematic limitations. Simulation results indicate that the improved ant colony algorithm decreases the average number of turns by 37.5% and accelerates convergence time by 39.45% relative to existing methods across diverse map dimensions. The experiments confirm that Improved-ACO achieves rapid convergence and constructs smooth curves that adhere to the robot’s kinematic constraints. Consequently, Improved-ACO is confirmed as an efficient and adaptable route planning method for robotic navigation under complicated situations.
Structure-Based Deep Learning Framework for Modeling Human–Gut Bacterial Protein Interactions
Authors: Kiouri Despoina P. , Batsis Georgios C. , Chasapis Christos T.
Published: February 2025

Background: The interaction network between the human host proteins and the proteins of the gut bacteria is essential for the establishment of human health, and its dysregulation directly contributes to disease development. Despite its great importance, experimental data on protein–protein interactions (PPIs) between these species are sparse due to experimental limitations. Methods: This study presents a deep learning-based framework for predicting PPIs between human and gut bacterial proteins using structural data. The framework leverages graph-based protein representations and variational autoencoders (VAEs) to extract structural embeddings from protein graphs, which are then fused through a Bi-directional Cross-Attention module to predict interactions. The model addresses common challenges in PPI datasets, such as class imbalance, using focal loss to emphasize harder-to-classify samples. Results: The results demonstrated that this framework exhibits robust performance, with high precision and recall across validation and test datasets, underscoring its generalizability. By incorporating proteoforms in the analysis, the model accounts for the structural complexity within proteomes, making predictions biologically relevant. Conclusions: These findings offer a scalable tool for investigating the interactions between the host and the gut microbiota, potentially yielding new treatment targets and diagnostics for disorders linked to the microbiome.