A Low-Cost and Environmentally Friendly Electrochemical Biosensor for the Determination of Estradiol.

Estradiol is a natural estrogen belonging to the group of natural steroid hormones. This paper presents new electrochemical biosensors-simple and low-cost tools for the determination of β-estradiol. The receptor layer of the sensor is the enzyme laccase, which was immobilized on the substrate surface using the soft plasma polymerization technique.

Treatment of Ochronotic Osteoarthropathy and the Evaluation of Selected Lower Limb Muscle Properties, Including the Patellar Tendon: A Case Report and Mini Literature Review.

Alkaptonuria (AKU) is a rare genetic disorder characterized by elevated levels of circulating homogentisic acid (HGA), which accumulates in connective tissues. The musculoskeletal system is particularly susceptible to HGA deposition, often resulting in severe ochronotic osteoarthropathy, especially in the hips, shoulders, knees, and spine. However, little is known about the effects of AKU on skeletal muscle tissue.

Identification of the Parameters of the Szpica-Warakomski Method's Rectilinear Trend Complementary to the Gaussian Characteristic Area Method in the Functional Evaluation of Gas Injectors.

The Fit for 55 and Euro 7 regulations significantly reduce CO emissions from combustion sources. This will be reflected in the regulations governing the approval of in-service vehicles, including those using alternative fuels. The present study focused on the rapid diagnostics of the technical condition of gas injectors.

Machine learning-assisted early detection of keratoconus: a comparative analysis of corneal topography and biomechanical data.

Keratoconus is a progressive eye disease characterized by the thinning and bulging of the cornea, leading to visual impairment. Early and accurate diagnosis is crucial for effective management and treatment. This study investigates the application of machine learning models to identify keratoconus based on corneal topography and biomechanical data.

Magnetic Fe,Co-Nanocarbon Frameworks Derived from Fe-Doped Zeolitic Imidazolate Framework-67 as Highly Active Catalysts for 5-Hydroxymethylfurfural Oxidation.

Zeolitic imidazolate frameworks (ZIFs) have recently emerged as promising precursors for the synthesis of heteroatom-doped nanocarbon materials. The chemical and structural features of these frameworks are influenced by the synthesis methodology, which directly affects their catalytic efficiency and stability. This study aims to investigate such frameworks by exploring a Co-ZIF structure doped with iron.

Combined method of identification of free oscillations of perforated riffled plates.

Presented experimental-numerical method of prediction of modal characteristics of rectangular perforated plates with riffles. The technique allows to analyze the oscillation frequency of plates with holes and stamped riffles. The methodology is based on numerical modeling by FE-methods and experiments to obtain the regularities of changes in the natural oscillation frequency of plate.

Improvement in classification capabilities of surface water samples based on analysis of multidimensional data from gas sensor array.

Introduction And Objective: It has been proven that e-noses can successfully differentiate between drainage and river water samples. However, it was supposed that the classification accuracy in the previous article from the series could have been refined. The aim of the article was to improve the classification accuracy of surface water samples analyzed with a gas sensor array.

Unsupervised Clustering and Ensemble Learning for Classifying Lip Articulation in Fingerspelling.

This paper presents a new methodology for analyzing lip articulation during fingerspelling aimed at extracting robust visual patterns that can overcome the inherent ambiguity and variability of lip shape. The proposed approach is based on unsupervised clustering of lip movement trajectories to identify consistent articulatory patterns across different time profiles. The methodology is not limited to using a single model.

Epoxy Adhesive Materials as Protective Coatings: Strength Property Analysis Using Machine Learning Algorithms.

This study analyzed the mechanical properties of epoxy adhesive materials used as functional coatings, focusing on how physical modifications impact their microstructure and strength. Compositions based on Epidian 5, 53 and 57 resins were cured using TFF, Z-1, or PAC curing agents and modified with various fillers: mineral (CaCO calcium carbonate), active (activated carbon filler, CWZ-22), and nanostructured (montmorillonite, ZR-2) fillers. The best results were achieved with calcium carbonate (10-20 wt%) in Epidian 5 or 53 resins cured with TFF or Z-1, yielding tensile strength up to 64 MPa, compressive strength up to 145 MPa, and bending strength up to 123 MPa.

Detection of Defects in Solid Carbide Cutting Tools During Creep-Feed Flute Grinding (CFG) Using Recurrence Analysis.

This study presents a comprehensive analysis of defect detection in the manufacturing process of solid carbide milling tools. The creep-feed flute grinding technique was used to fabricate a milling tool, with cutting force signals recorded and examined using recurrence analysis and conventional statistical methods. The analysis identified four distinct dynamic fluctuations (cutting force amplitude jumps), which showed a direct correlation with the formation of microcracks on the flute surface.

Unveiling the Fundamental Principles of Reconfigurable Resistance States in Silver/Poly(Ethylene Glycol) Nanofluids.

Developing novel memristive systems aims to implement key principles of biological neuron assemblies - plasticity, adaptivity, and self-organization - into artificial devices for parallel, energy-efficient computing. Solid-state memristive devices, such as crossbar arrays and percolated nanoparticle (NP) networks, already demonstrate these properties. However, closer similarity to neural networks is expected from liquid-state systems, including polymer melts, which remain largely unexplored.

Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study.

: Recent advancements in machine learning (ML) have significantly influenced the analysis of brain signals, particularly electroencephalography (EEG), enhancing the detection of complex neural patterns. ML enables large-scale data processing, offering novel opportunities for diagnosing and treating mental disorders. However, challenges such as data variability, noise, and model interpretability remain significant.

Post-mortem evidence of microplastic bioaccumulation in human organs: insights from advanced imaging and spectroscopic analysis.

Humans are chronically exposed to airborne particulate matter and environmental microplastics through food, water, and consumer products. These anthropogenic pollutants may accumulate in human tissues, but their distribution and chemical identity remain poorly understood. In this study, we analyzed samples of human brain, liver, thyroid, kidney, heart, skeletal muscle, and lung tissue collected post-mortem to assess the presence and composition of micro- and nanoplastics (MNPs).

Novel Research on Selected Mechanical and Environmental Properties of the Polyurethane-Based P3HB Nanobiocomposites.

This study focused on hybrid nanobiocomposite polymers produced with the use of poly(3-hydroxybutyrate), P3HB and aliphatic polyurethane (PU) as a matrix, including variable quantities of organomodified montmorillonite (Cloisite30B). Mechanical, thermal, and biodegradability tests were conducted to evaluate their properties. The nanobiocomposites were tested using monotonic tensile tests, which revealed that the addition of PU and organomodified montmorillonite reduced the stiffness and strain at break compared to native P3HB.

Research on Energy Management in Forward Extrusion Processes Based on Experiment and Finite Element Method Application.

This paper advances the forward extrusion process by integrating sustainable methodologies and optimizing energy efficiency. This research investigates the impact of die geometry and elongation coefficients on energy usage and process efficiency, employing finite element method (FEM) simulations alongside empirical analysis. Artificial neural networks and experimental data were utilized to predict process energy.

The Influence of Fibre and Fly Ash Additions on the Properties of Self-Compacting Concrete.

Self-compacting concrete (SCC) is an innovative building material that is distinguished by its ability to flow and fill forms without the need for mechanical vibration. The aim of this research was to determine the effect of different types of fibres-steel, glass, and polypropylene-on the properties of both the fresh mix (consistency, density, air content, and viscosity) and the hardened concrete (compressive strength, tensile strength in bending, density, water absorption, and frost resistance). Attention was also paid to CO emissions associated with cement production and the potential of their reduction by using alternative materials.

Lightweight Artificial Aggregates Produced from Water Reservoir Sediment and Industrial Waste-Ecological and Technological Aspect.

The use of mineral waste for the production of lightweight artificial aggregate is an important element of activities for sustainable development in construction and the implementation of the objectives of the circular economy. The article presents the physical, mechanical, and ecological properties of an innovative artificial aggregate produced from bottom sediments, concrete dust, and municipal solid waste incineration fly ash. The obtained research results confirm that the developed material achieves technological properties comparable to artificial aggregates available on the market, both commercial and those derived from recycling.

Advanced Non-Destructive Testing Simulation and Modeling Approaches for Fiber-Reinforced Polymer Pipes: A Review.

Fiber-reinforced polymer (FRP) pipes have emerged as a preferred alternative to conventional metallic piping systems in various industries, including chemical processing, marine, and oil and gas industries, owing to their superior corrosion resistance, high strength-to-weight ratio, and extended service life. However, ensuring the long-term reliability and structural integrity of FRP pipes presents significant challenges, primarily because of their anisotropic and heterogeneous nature, which complicates defect detection and characterization. Traditional non-destructive testing (NDT) methods, which are widely applied, often fail to address these complexities, necessitating the adoption of advanced digital techniques.

Advances in 3D Printing Applications for Personalized Orthopedic Surgery: From Anatomical Modeling to Patient-Specific Implants.

Three-dimensional (3D) printing has gained substantial interest among scientists and surgeons over the past decade due to its broad potential in medical applications. Its clinical utility has been increasingly recognized, demonstrating promising outcomes for patient care. Currently, 3D printing technology enables surgeons to enhance operative precision by facilitating the creation of patient-specific anatomical models, customized implants, biological tissues, and even surgical instruments.

Corn-derived biochar mitigates oxidative stress and increases the content of essential elements in lettuce leaves grown in phthalate-polluted soil.

Phthalic acid esters (PAEs) are recognized markers of microplastic pollution of the environment. The study assessed the effects of different biochars (BC) derived from sewage sludge (SS), corn residues (CR), sunflower (SF), and residues from biogas production (BG) on lettuce grown in PAEs-polluted soil. The BC varied in composition, porosity, and carbon structure, with CR-BC exhibiting the highest surface area and optimal aliphatic carbon content, making it the most effective for soil application.

Corrosion Behavior of Shot Peened Ti6Al4V Alloy Fabricated by Conventional and Additive Manufacturing.

Ti6Al4V titanium alloy is one of the most studied for its properties after additive manufacturing. Due to its widely use in medical applications, its properties are investigated in various aspects of surface layer property improvement and later compared to conventionally manufactured Ti-6Al-4V. In this study, the corrosion behavior in a 0.

Comments on "Part I: determination of a structure/property transformation mechanism responsible for changes in the point of zero change of anatase titania with decreasing particle size" by M. Leffler, A. Mirich, J. Fee, S. March and S. L. Suib, , 2024, 14, 30543.

The work mentioned in the title of this comment appears to establish a relationship between the point of zero charge of oxide minerals (anatase, goethite, and others) and particle size. While the methods to establish the point of zero charge used in the experimental work of the paper are non-standard and need to be critically assessed, the apparent increase of the point of zero charge with particle size is clearly not a general feature and some literature data observed by standard methods for measuring points of zero charge actually suggest the absence of a universal trend. Since part II of article series (, 2024, 14, 30317) also builds on this apparent relationship, we believe readers should be cautioned about it.

Adsorption Mechanisms of Cellulose Derivatives with Different Chemical Nature on Bentonite - pH Perspective.

The goal of this study is to answer the question of how pH influences the adsorption behavior of cellulose derivatives with different chemical natures (cationic cellulose-AHCE; nonionic hydroxyethyl cellulose, and anionic carboxymethyl cellulose) on the surface of the natural adsorbent bentonite-nanoclay N-HB. The diversity of polysaccharide adsorption is investigated, the mechanisms of this process are determined, and the adsorption layer structure as well as the stability of the systems are characterized. The studies are carried out by UV-vis spectrophotometry, Fourier transform infrared, X-ray diffraction, scanning electron microscopy-electron dispersive spectroscopy, X-ray photoelectron spectroscopy, zeta potential, and thermogravimetric analysis.

Residual self-attention vision transformer for detecting acquired vitelliform lesions and age-related macular drusen.

Retinal diseases recognition is still a challenging task. Many deep learning classification methods and their modifications have been developed for medical imaging. Recently, Vision Transformers (ViT) have been applied for classification of retinal diseases with great success.