Search Results (56)

Multifunctional self-healing supramolecular structural toughened resins, formulated to counteract the insulating properties of epoxy polymers and integrating auto-repair mechanisms, are morphologically and spectroscopically characterized using Tunneling Atomic Force Microscopy (TUNA) and Fourier transform infrared spectroscopy (FT-IR), respectively. Specifically, the multifunctional resin comprises self-healing molecular fillers and electrically conductive carbon nanotubes (CNTs) embedded in the matrix. The selected self-healing molecules can form non-covalent bonds with the hydroxyl (OH) and carbonyl (C=O) groups of the toughened epoxy matrix through their H-bonding donor and acceptor sites. An FT-IR analysis has been conducted to evaluate the interactions that the barbiturate acid derivatives, serving as self-healing fillers, can form with the constituent parts of the toughened epoxy blend. Tunneling Atomic Force Microscopy (TUNA) highlights the morphological characteristics of CNTs, their dispersion within the polymeric matrix, and their affinity for the globular rubber domains. The TUNA technique maps the samples’ electrical conductivity at micro- and nanoscale spatial domains. Detecting electrical currents reveals supramolecular networks, determined by hydrogen bonds, within the samples, showcasing the morphological features of the sample containing an embedded conductive nanofiller in the hosting matrix. Full article

The interactions between dissolved organic matter (DOM) and agrochemicals (e.g., neonicotinoid insecticides, NIs) govern the distribution, migration, and potential environmental risks of agrochemicals. However, the long-term effects of agricultural management on the DOM components and structure, as well as their further influences on the interactions between DOM and agrochemicals, remain unclear. Here, spectroscopic techniques, including Fourier transform infrared spectroscopy, two-dimensional correlation spectroscopy, and three-dimensional excitation–emission matrix fluorescence spectroscopy were employed to delve into the interaction mechanism between the DOM from citrus orchards with distinct cultivation ages (10, 30, and 50 years) and imidacloprid, which is a type of pesticide widely used in agricultural production. The findings revealed that the composition and structure of soil DOM significantly change with increasing cultivation age, characterized by an increase in humic substances and the emergence of new organic components, indicating complex biodegradation and chemical transformation processes of soil organic matter. Imidacloprid primarily interacts with fulvic acid-like fractions of DOM, and its binding affinity decreases with increasing cultivation age. Additionally, the interactions of protein-like fractions with imidacloprid occur after humic-like fractions, suggesting differential binding behaviors among DOM fractions. These results demonstrate that cultivation age significantly influences the composition and structural characteristics of soil DOM in citrus orchards, subsequently affecting its sorption capacity to imidacloprid. This study enhances the understanding of imidacloprid’s environmental behavior and provides theoretical support for the environmental risk management of neonicotinoid pesticides. Full article

Idiopathic pulmonary fibrosis (IPF) is a progressive and incurable chronic interstitial lung disease characterized by excessive fibrosis and impaired lung function. Current treatments, such as pirfenidone and nintedanib, slow disease progression but fail to halt or reverse fibrosis, highlighting the need for novel approaches. Activin A, which belongs to the TGF-β superfamily, is implicated in various fibrosis-related mechanisms, including epithelial–mesenchymal transition (EMT), a process where epithelial cells acquire mesenchymal characteristics, and fibroblast–myofibroblast transformation (FMT), in which fibroblasts differentiate into contractile myofibroblasts. It also promotes inflammatory cytokine release and extracellular matrix buildup. This study aimed to inhibit Activin A activity using synthetic peptides identified through phage display screening. Of the ten peptides isolated, A7, B9, and E10 demonstrated high binding affinity and inhibitory activity. Computational modeling confirmed that these peptides target the receptor-binding domain of Activin A, with peptide E10 exhibiting superior efficacy. Functional assays showed that E10 reduced cell migration, inhibited EMT in A549 cells, and suppressed FMT in fibroblast cultures, even under pro-fibrotic stimulation with TGF-β. These findings underscore the therapeutic potential of targeting Activin A with synthetic peptides, offering a promising avenue for IPF treatment and expanding the arsenal of anti-fibrotic strategies. Full article

This study focuses on developing a control methodology for exoskeleton robots designed for lower limb rehabilitation, specifically addressing the needs of elderly individuals and pediatric therapy. The approach centers on implementing an affine state-feedback controller to effectively regulate and stabilize the knee-joint exoskeleton robot at a desired position. The robot’s dynamics are nonlinear, accounting for unknown parameters, solid and viscous frictions, and external disturbances. To ensure robust stabilization, the Lyapunov approach is utilized to derive a set of Linear Matrix Inequality (LMI) conditions, guaranteeing the stability of the position error. The derivation of these LMI conditions is grounded in a comprehensive theoretical framework that employs advanced mathematical tools, including the matrix inversion lemma, Young’s inequality, the Schur complement, the S-procedure, and specific congruence transformations. Simulation results are presented to validate the proposed LMI conditions, demonstrating the effectiveness of the control strategy in achieving robust and accurate positioning of the lower limb rehabilitation exoskeleton robotic system. Full article

Traditionally, traffic characteristics such as speed, volume, and travel time are obtained from a range of sensors and systems such as inductive loop detectors (ILDs), automatic number plate recognition cameras (ANPR), and GPS-equipped floating cars. However, many issues associated with these data have been identified in the existing literature. Although roadside surveillance cameras cover most road segments, especially on freeways, existing techniques to extract traffic data (e.g., speed measurements of individual vehicles) from video are not accurate enough to be employed in a proactive traffic management system. Therefore, this paper aims to develop a technique for estimating traffic data from video captured by surveillance cameras. This paper then develops a deep learning-based video processing algorithm for detecting, tracking, and predicting highly disaggregated vehicle-based data, such as trajectories and speed, and transforms such data into aggregated traffic characteristics such as speed variance, average speed, and flow. By taking traffic observations from a high-quality LiDAR sensor as ‘ground truth’, the results indicate that the developed technique estimates lane-based traffic volume with an accuracy of 97%. With the application of the deep learning model, the computer vision technique can estimate individual vehicle-based speed calculations with an accuracy of 90–95% for different angles when the objects are within 50 m of the camera. The developed algorithm was then utilised to obtain dynamic traffic characteristics from a freeway in southern China and employed in a statistical model to predict monthly crashes. Full article

In order to systematize and expand knowledge about copper-containing composite materials as hybrid ion exchangers, in this study, fine metallic copper particles were dispersed within the matrix of a carboxyl cation exchanger (CCE) with a macroporous and gel-type structure thanks to the reduction of Cu₂O particles precipitated within the matrix earlier. It was possible to introduce as much as 22.0 wt% Cu⁰ into a gel-type polymeric carrier (G/H#Cu) when an ascorbic acid solution was used to act as a reducer of Cu₂O and a reagent transforming the functional groups from Na⁺ into the H⁺ form. The extremely high shrinkage of the porous skeleton containing –COOH groups (in a wet and also dry state) and its limited affinity for water protected the copper from oxidation without the use of special conditions. When macroporous CCE was used as a host material, the composite material (M/H#Cu) contained 18.5 wt% Cu, and copper particles were identified inside the resin beads, but not on their surface where Cu²⁺ ions appeared during drying. Thermal analysis in an air atmosphere and under N₂ showed that dispersing metallic copper within the resin matrix accelerated its decomposition in both media, whereby M/H#Cu decomposed faster than G/H#Cu. It was found that G/H#Cu contained 6.0% bounded water, less than M/H#Cu (7.5%), and that the solid residue after combustion of G/H#Cu and M/H#Cu was CuO (26.28% and 22.80%), while after pyrolysis the solid residue (39.35% and 26.23%) was a mixture of carbon (50%) and metallic copper (50%). The presented composite materials thanks to the antimicrobial, catalytic, reducing, deoxygenating and hydrophobic properties of metallic copper can be used for point-of-use and column water/wastewater treatment systems. Full article

The textile industry, renowned for its comfort-providing role, is undergoing a significant transformation to address its environmental impact. The escalating environmental impact of the textile industry, characterised by substantial contributions to global carbon emissions, wastewater, and the burgeoning issue of textile waste, demands urgent attention. This study aims at identifying the feasibility of the future use of textile scraps in the construction and architecture industry by analysing the effect of different binders. In this study, synthetic knitted post-consumer-waste fabrics were taken from a waste market for use as a reinforcement, and different binders were used as the matrix. In the experiment phase, the waste fabrics were mixed with synthetic binders and hydraulic binders to form brick samples. The mechanical and thermal properties of these samples were tested and compared with those of clay bricks. In terms of mechanical properties, unsaturated polyester resin (UPR) samples showed the highest mechanical strength, while acrylic glue (GL) samples had the lowest mechanical strength. White cement (WC) samples showed moderate mechanical properties. Through several tests, it was observed that UPR samples showed the highest values of tensile, bending, and compressive strengths, i.e., 0.111 MPa, 0.134 MPa, and 3.114 MPa, respectively. For WC, the tensile, bending, and compressive strengths were 0.064 MPa, 0.106 MPa, and 2.670 MPa, respectively. For GL, the least favourable mechanical behaviour was observed, i.e., 0.0162 MPa, 0.0492 MPa, and 1.542 MPa, respectively. In terms of thermal conductivity, WC samples showed exceptional resistance to heat transfer. They showed a minimum temperature rise of 54.3 °C after 15 min, as compared to 57.3 °C for GL-based samples and 58.1 °C for UPR. When it comes to polymeric binders, UPR showed better thermal insulation properties, whereas GL allowed for faster heat transfer for up to 10 min of heating. This study explores a circular textile system by assessing the potential of using textile waste as a building material, contributing to greener interior design. This study demonstrated the usefulness of adding short, recycled PET fibres as a reinforcement in UPR composites. The use of the PET fibre avoids the need to use a surface treatment to improve interfacial adhesion to the UPR matrix because of the chemical affinity between the two polyesters, i.e., the PET fibre and the unsaturated polyester resin. This can find application in the construction field, such as in the reinforcement of wooden structural elements, infill walls, and partition walls, or in furniture or for decorative purposes. Full article

Vector control of an induction machine (IM) is typically performed by using cascade control structures with conventional linear proportional–integral (PI) controllers, the inner loop being designed for current control and the outer loop for rotor flux and speed control. In this paper, starting with the dq model of the IM, advanced control algorithms are proposed for the two control loops of the cascade structure. For the current inner loop, after the decoupling of the two dq currents, predictive control algorithms are employed to independently control the currents, considering the constraints imposed by the electrical signal physics limitations. Since the outer loop has a nonlinear affine multivariable plant model, a homotopy-based variant of feedback linearization is used to obtain a nonsingular decoupling matrix of the feedback transformation even when the rotor flux is zero at the start-up of the motor. During the continuous variation in the homotopy parameter, the plant model is variable and, for this reason, model-free algorithms are used to control the flux and speed of the IM due to their capabilities to manage complex dynamics from data without requiring knowledge of the plant model. The performances of the proposed cascade control strategy with advanced algorithms in the two loops were tested by simulation and compared with those obtained with conventional PI controllers, resulting in better dynamic behavior for predictive and model-free control. Full article

Three-dimensional (3D) hydrogels provide tissue-like complexities and allow for the spatial orientation of cells, leading to more realistic cellular responses in pathophysiological environments. There is a growing interest in developing multifunctional hydrogels using ternary mixtures for biomedical applications. This study examined the biocompatibility and suitability of human auricular chondrocytes from microtia cultured onto steam-sterilized 3D Chitosan/Gelatin/Poly(Vinyl Alcohol) (CS/Gel/PVA) hydrogels as scaffolds for tissue engineering applications. Hydrogels were prepared in a polymer ratio (1:1:1) through freezing/thawing and freeze-drying and were sterilized by autoclaving. The macrostructure of the resulting hydrogels was investigated by scanning electron microscopy (SEM), showing a heterogeneous macroporous structure with a pore size between 50 and 500 μm. Fourier-transform infrared (FTIR) spectra showed that the three polymers interacted through hydrogen bonding between the amino and hydroxyl moieties. The profile of amino acids present in the gelatin and the hydrogel was determined by ultra-performance liquid chromatography (UPLC), suggesting that the majority of amino acids interacted during the formation of the hydrogel. The cytocompatibility, viability, cell growth and formation of extracellular matrix (ECM) proteins were evaluated to demonstrate the suitability and functionality of the 3D hydrogels for the culture of auricular chondrocytes. The cytocompatibility of the 3D hydrogels was confirmed using a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, reaching 100% viability after 72 h. Chondrocyte viability showed a high affinity of chondrocytes for the hydrogel after 14 days, using the Live/Dead assay. The chondrocyte attachment onto the 3D hydrogels and the formation of an ECM were observed using SEM. Immunofluorescence confirmed the expression of elastin, aggrecan and type II collagen, three of the main components found in an elastic cartilage extracellular matrix. These results demonstrate the suitability and functionality of a CS/Gel/PVA hydrogel as a 3D support for the auricular chondrocytes culture, suggesting that these hydrogels are a potential biomaterial for cartilage tissue engineering applications, aimed at the regeneration of elastic cartilage. Full article

The liver is the central metabolic organ and produces 85–90% of the proteins found in plasma. Accordingly, the plasma proteome is an attractive source of liver disease biomarkers that reflects the different cell types present in this organ, as well as the processes such as responses to acute and chronic injury or the formation of an extracellular matrix. In the first part, we summarize the biomarkers routinely used in clinical evaluations and their biological relevance in the different stages of non-malignant liver disease. Later, we describe the current proteomic approaches, including mass spectrometry and affinity-based techniques, that allow a more comprehensive assessment of the liver function but also require complex data processing. The many approaches of analysis and interpretation and their potential caveats are delineated. While these advances hold the promise to transform our understanding of liver diseases and support the development and validation of new liver-related drugs, an interdisciplinary collaboration is needed. Full article

In this paper, we present a novel nonlinear model predictive control (NMPC) algorithm based on the Laguerre function for dynamic positioning ships to solve the problems of input saturation, unknown time-varying disturbances, and heavy computation. The nonlinear model of a dynamic positioning ship is presented as a linear model, transformed from a standard affine nonlinear state-space model by precise feedback linearization. The environmental disturbance is overcome using an integrator. The time cost of the proposed nonlinear control algorithm is decreased by inducing the Laguerre function to describe the feedback-linearization system input increments. The Laguerre function reduces the matrix dimensions of the nonlinear optimization problem. The simulation results for a DP supply vessel showed that the novel algorithm maintained the effective control performance of the original nonlinear model predictive control algorithm and had a reduced computation load to satisfy the requirements of real-time operation. Full article

In order to realize the stable clamping of large cabin parts, this paper studied the clamping force optimization of the clamping mechanism for large-mass and large-size cabin parts. Firstly, three kinds of contact models are introduced. Then, a clamping matrix is constructed for a particular clamping configuration. The nonlinear friction cone constraint at the contact point is transformed into a linear affine constraint in a smooth Riemannian manifold using the special structure of the positive definite symmetric matrix. Finally, a large cabin part used in the aerospace field is used as an example for calculating and simulating the clamping force optimization. Different optimization algorithms are used to calculate the initial value which is put into the gradient flow optimization method of the clamping force for optimization calculation. Meanwhile, the normal clamping force value of a 2 t object is measured using relevant experimental equipment. The simulation results and experimental results show that the gradient flow optimization method of the clamping force can quickly complete the clamping force optimization of large-mass and large-size cabin parts. The actual measured value of the normal clamping force is close to the simulated convergence value. The distribution of the normal force of the clamping mechanisms and the convergence value of the clamping force for each clamping mechanism can provide some references for determining the output clamping force of the clamping mechanism and confirming a reasonable distribution of the clamping mechanism. It also confirms the feasibility and effectiveness of this method applied to the clamping force optimization for a large axial part. Full article

The use of template molecules as chemical scaffolds that significantly influence the course of the reaction has recently been intensively studied. Peptide nucleic acids (PNA) are molecules that mimic natural nucleic acids. They are a promising matrix in such reactions because they possess high affinity and specificity in their interactions. The manner of PNA interaction is predictable based on sequence complementarity. Recently, we report the visible light-induced metathesis reaction in peptides containing a diselenide bond. Herein, we present an efficient and straightforward method of the visible light-driven diselenide-based metathesis of peptide–nucleic acid conjugates. Compared to a similar photochemical transformation in peptides, a significant increase in the metathesis efficiency was obtained due to the template effect. Full article

Traditional non-coplanar calibration methods, represented by Tsai’s method, are difficult to apply in multi-camera-based stereo vision measurements because of insufficient calibration accuracy, inconvenient operation, etc. Based on projective theory and matrix transformation theory, a novel mathematical model is established to characterize the transformation from targets’ 3D affine coordinates to cameras’ image coordinates. Then, novel non-coplanar calibration methods for both monocular and binocular camera systems are proposed in this paper. To further improve the stability and accuracy of calibration methods, a novel circular feature points extraction method based on region Otsu algorithm and radial section scanning method is proposed to precisely extract the circular feature points. Experiments verify that our novel calibration methods are easy to operate, and have better accuracy than several classical methods, including Tsai’s and Zhang’s methods. Intrinsic and extrinsic parameters of multi-camera-systems can be calibrated simultaneously by our methods. Our novel circular feature points extraction algorithm is stable, and with high precision can effectively improve calibration accuracy for coplanar and non-coplanar methods. Real stereo measurement experiments demonstrate that the proposed calibration method and feature extraction method have high accuracy and stability, and can further serve for complicated shape and deformation measurements, for instance, stereo-DIC measurements, etc. Full article

Metal additive manufacturing (AM) is a disruptive production technology, widely adopted in innovative industries that revolutionizes design and manufacturing. The interest in quality control of AM systems has grown substantially over the last decade, driven by AM’s appeal for intricate, high-value, and low-volume production components. Geometry-dependent process conditions in AM yield unique challenges, especially regarding quality assurance. This study contributes to the development of machine learning models to enhance in-process monitoring and control technology, which is a critical step in cost reduction in metal AM. As the part is built layer upon layer, the features of each layer have an influence on the quality of the final part. Layer-wise in-process sensing can be used to retrieve condition-related features and help detect defects caused by improper process conditions. In this work, layer-wise monitoring using optical tomography (OT) imaging was employed as a data source, and a machine-learning (ML) technique was utilized to detect anomalies that can lead to defects. The major defects analyzed in this experiment were gas pores and lack of fusion defects. The Random Forest Classifier ML algorithm is employed to segment anomalies from optical images, which are then validated by correlating them with defects from computerized tomography (CT) data. Further, 3D mapping of defects from CT data onto the OT dataset is carried out using the affine transformation technique. The developed anomaly detection model’s performance is evaluated using several metrics such as confusion matrix, dice coefficient, accuracy, precision, recall, and intersection-over-union (IOU). The k-fold cross-validation technique was utilized to ensure robustness and generalization of the model’s performance. The best detection accuracy of the developed anomaly detection model is 99.98%. Around 79.40% of defects from CT data correlated with the anomalies detected from the OT data. Full article