Search Results (91)

In recent years, the unsustainable consumption of fossil resources has been causing major ecological concerns, especially for the production of polymeric materials. 2,5-furandicarboxylic acid (FDCA) is one of the most appealing biobased chemical building blocks, because of its potential to replace the industrially widespread petrochemical, terephthalic acid. Camphoric acid (CA) is also an interesting biobased chemical derived from camphor, one of the most widespread fragrances. This work had the objective of combining CA, FDCA and biobased 1,6-hexanediol to synthesize random copolymers for sustainable food packaging applications by means of a solvent-free polycondensation process, obtaining poly(hexamethylene furanoate-co-camphorate)s (PHFC). The optimization of the synthesis made it possible to obtain high molecular weight polyesters with a percentage of camphoric acid up to 17 mol%, which could be compression-molded into films. They were subjected to molecular, structural, thermal and functional characterization via NMR, GPC, WAXS, DSC, and TGA analyses, as well as mechanical and gas permeability tests. Compared to the homopolymer of reference, it was possible to obtain higher flexibility, 430% higher elongation at break, and 223% higher toughness, with comparable, excellent gas permeability properties. Calorimetric evidence suggested that camphoric acid might have enhanced the formation of a partially ordered mesomorph phase in the copolymers under study. Full article

Per- and polyfluoroalkyl substances (PFASs), as a class of “permanent chemicals” with high environmental persistence and bioaccumulation, have attracted much attention. In this study, we focused on the molecular mechanism of the interaction between perfluoroalkyl acids (PFAAs) and peroxisome proliferator-activated receptor δ (PPARδ). Using molecular docking, binding free energy calculation, and structural analysis, we systematically investigated the binding modes, key amino acid residues, and binding energies of 20 structurally diverse PFAAs with PPARδ. The results showed that the binding energies of PFAAs with PPARδ were significantly affected by the molecular weight, the number of hydrogen bond donors, and the melting point of PFAAs. PFAAs with smaller molecular weights and fewer hydrogen bond donors showed stronger binding affinity. The binding sites were concentrated in high-frequency amino acid residues such as TRP-256, ASN-269, and GLY-270, and the interaction forces were dominated by hydrogen and halogen bonds. PFAAs with branched structure of larger molecular weight (e.g., 3m-PFOA, binding energy of −2.92 kcal·mol⁻¹; 3,3m₂-PFOA, binding energy of −2.45 kcal·mol⁻¹) had weaker binding energies than their straight-chain counterparts due to spatial site-blocking effect. In addition, validation group experiments further confirmed the regulation law of binding strength by physicochemical properties. In order to verify the binding stability of the key complexes predicted by molecular docking, and to investigate the dynamic behavior under the conditions of solvation and protein flexibility, molecular dynamics simulations were conducted on PFBA, PFOA, 3,3m₂-PFOA, and PFHxA. The results confirmed the dynamic stability of the binding of the high-affinity ligands selected through docking to PPARδ. Moreover, the influence of molecular weight and branched structure on the binding strength was quantitatively verified from the perspectives of energy and RMSD trajectories. The present study revealed the molecular mechanism of PFAAs interfering with metabolic homeostasis through the PPARδ pathway, providing a theoretical basis for assessing its ecological and health risks. Full article

Traditional hydroxyapatite materials are inherently stiff and brittle, limiting their applications. Flexible ultralong hydroxyapatite nanowires, characterized by nano-scale diameters and micrometer-scale lengths, offer a promising alternative as one-dimensional flexible building blocks for constructing high-performance biomimetic materials. Nature has evolved a variety of high-performance materials with hierarchically ordered structures assembled from nano-scale building blocks, which provide valuable insights into the design and ordered assembly of flexible nanofibers for building high-performance biomimetic materials. Currently, how to distill the structural design principles of natural materials to engineer flexible nanofibers into advanced high-performance biomimetic materials with excellent properties and multifunctions remains a frontier scientific challenge. In 2014, the authors’ research group reported for the first time the calcium oleate precursor solvothermal method for the synthesis of flexible ultralong hydroxyapatite nanowires and their applications. Since then, many soft functional materials and high-performance biomimetic materials have been designed and prepared using flexible ultralong hydroxyapatite nanowires, and their applications in various fields have been explored. These studies demonstrate the successful assembly of flexible ultralong hydroxyapatite nanowires into hierarchical biomimetic structures inspired by natural materials such as enamel, nacre, and bone, which exhibit enhanced mechanical properties, including improved strength, toughness, and flexibility, alongside multifunctional capabilities like thermal insulation and biomedical compatibility. These findings suggest that flexible ultralong hydroxyapatite nanowires provide a versatile platform for designing and constructing advanced biomimetic materials with promising applications in various fields. This review article aims to briefly review recent advances in this exciting and rapidly evolving research field. The synthetic methods, assembly strategies, properties, and applications of flexible ultralong hydroxyapatite nanowires and their derivative biomimetic materials are discussed, enlightening their structural design principles and potential applications. Finally, we propose future research directions and future perspectives in this exciting frontier research field. Full article

The barrier performance of containment liners against heavy metals and other contaminants is a critical element in ensuring environmental safety. However, the high concentration of multivalent cations in landfill leachate raises concerns about the effectiveness of conventional barriers (e.g., sodium bentonite). To address concerns regarding the high permeability and elevated heavy metal concentrations in effluents from sodium bentonite (Na-B) barriers, this study proposes the use of new complex-modified sorbent bentonite—specifically treated with disodium ethylenediaminetetraacetate (EDTA-2Na) and sodium tripolyphosphate (STPP). Batch adsorption and flexible-wall permeability tests in extreme synthetic leachate demonstrate that the complex-modified sodium bentonite not only maintains low permeability but also enhances contaminant adsorption capacity of barriers. When modified with 2% EDTA-2Na and 4% STPP (by mass), the maximum Zn(II) adsorption capacity of bentonite was measured at 43.22 and 48.22 μg/g, respectively. These values correspond to enhancements by a factor of 1.99 and 2.32 compared to the unmodified Na-B. Simultaneously, the hydraulic conductivity met the permeability requirements for engineering barrier systems (k < 1 × 10⁻⁷ cm/s) throughout the tested range of confining pressures. Microscopic analyses confirmed the successful incorporation of functional groups into bentonite by both EDTA-2Na and STPP. STPP-induced electrostatic repulsion, promoting ordered particle stacking and dense structure formation. EDTA-2Na physically filled pores to block ion migration pathways while electrochemically counteracting double-layer compression under high ionic strength. This effective strategy resolves the long-standing trade-off between permeability and adsorption capacity in conventional bentonite, providing a theoretical basis for designing barrier materials in complex contaminated sites. Full article

Pixel value ordering (PVO) is a widely used framework for reversible data hiding (RDH). As the demand for higher embedding capacity continues to grow, achieving a proper balance between capacity and image quality has become increasingly important. In this paper, we propose a novel PVO-based multi-pixel embedding RDH scheme for grayscale images, which improves capacity by embedding multiple bits of data within multiple pixels in each block. A PVO recovery strategy is designed to guarantee reversibility while minimizing image distortion when multiple bits are embedded per block. Moreover, an improved flexible spatial location strategy is introduced, which defines pixel positions within a block using twelve modes. By selecting the optimal mode for each block, the number of expandable prediction errors is increased, further enhancing embedding capacity. In addition, the artificial lemming algorithm (ALA) is employed to optimize embedding parameters, enabling a better balance between capacity and visual quality for a given payload. Experimental results demonstrate that the proposed method achieves significantly improved embedding capacity while maintaining high image quality, offering a well-balanced performance compared to similar PVO-based schemes. Full article

The deparser stage in the Protocol-Independent Switch Architecture (PISA) is often overshadowed by parser and match-action optimizations. Yet, it remains a critical performance bottleneck in P4-programmable FPGA data planes. Challenges associated with the deparser stem from dynamic header layouts, variable emission orders, and alignment constraints, which often necessitate resource-intensive designs, such as wide, dynamic crossbar routing. While compile-time specialization techniques can reduce logic usage, they sacrifice runtime adaptability: any change to the protocol graph, including adding, removing, or reordering headers, requires full hardware resynthesis and re-implementation, limiting their practicality for evolving or multi-tenant workloads. This work presents a unified FPGA-targeted deparser architecture that merges templated and overlay concepts within a hardware–software co-design framework. At design time, template parameters define upper bounds on protocol complexity, enabling resource-efficient synthesis tailored to specific workloads. Within these bounds, runtime reconfiguration is supported through overlay control tables derived from static deparser DAG analysis, which capture the per-path emission order, header alignments, and offsets. These tables drive protocol-agnostic, chunk-based emission blocks that eliminate the overhead of crossbar interconnects, thereby significantly reducing complexity and resource usage. The proposed design sustains high throughput while preserving the flexibility needed for in-field updates and long-term protocol evolution. Full article

Building appropriate models is crucial for imaging tasks in many fields but often challenging due to the richness of the systems. In radio astronomy, for example, wide-field observations can contain various and superposed structures that require different descriptions, such as filaments, point sources or compact objects. This work presents an automatic pipeline that iteratively adapts probabilistic models for such complex systems in order to improve the reconstructed images. It uses the Bayesian imaging library NIFTy, which is formulated in the language of information field theory. Starting with a preliminary reconstruction using a simple and flexible model, the pipeline employs deep learning and clustering methods to identify and separate different objects. In a further step, these objects are described by adding new building blocks to the model, allowing for a component separation in the next reconstruction step. This procedure can be repeated several times for refinement to iteratively improve the overall reconstruction. In addition, the individual components can be modeled at different resolutions allowing us to focus on important parts of the emission field without getting computationally too expensive. Full article

Neuromorphic circuits emulate the brain’s massively parallel, energy-efficient, and robust information processing by reproducing the behavior of neurons and synapses in dense networks. Memristive technologies have emerged as key enablers of such systems, offering compact and low-power implementations. In particular, locally active memristors (LAMs), with their ability to amplify small perturbations within a locally active domain to generate action potential-like responses, provide powerful building blocks for neuromorphic circuits and offer new perspectives on the mechanisms underlying neuronal firing dynamics. This paper introduces a novel second-order locally active memristor (LAM) governed by two coupled state variables, enabling richer nonlinear dynamics compared to conventional first-order devices. Even when the capacitances controlling the states are equal, the device retains two independent memory states, which broaden the design space for hysteresis tuning and allow flexible modulation of the current–voltage response. The second-order LAM is then integrated into a FitzHugh–Nagumo neuron circuit. The proposed circuit exhibits oscillatory firing behavior under specific parameter regimes and is further investigated under both DC and AC external stimulation. A comprehensive analysis of its equilibrium points is provided, followed by bifurcation diagrams and Lyapunov exponent spectra for key system parameters, revealing distinct regions of periodic, chaotic, and quasi-periodic dynamics. Representative time-domain patterns corresponding to these regimes are also presented, highlighting the circuit’s ability to reproduce a rich variety of neuronal firing behaviors. Finally, two unknown system parameters are estimated using the Aquila Optimization algorithm, with a cost function based on the system’s return map. Simulation results confirm the algorithm’s efficiency in parameter estimation. Full article

In this work, a novel solid polymer electrolyte with a disordered structure has been designed, combining polyethylene glycol (PEG) as the flexible segments and hexamethylene diisocyanate (HDI) as the rigid segments. The synthesis was realized by alternating flexible PEG with rigid HDI through a peptide bond (–CO–NH–), which disrupts the ordered structures of PEG, generating electron-deficient Lewis acid groups. The pathbreaking introduction of HDI blocks not only bridges links between the PEG molecules but also generates electron-deficient Lewis acid groups. Therefore, the original ordered structures of PEG are disrupted by both the alternating chains between PEG and HDI and the Lewis acid groups. As a result, the PEG_H/L4000 electrolytes (PEG molecular weight of 4000) exhibit a strong anion-capture ability that decreases the crystallinity of polymers, which further achieves a high ionic conductivity close to 10⁻³ S·cm⁻¹ with the lithium-ion transference numbers up to 0.88. The symmetric Li|PEG_H/L4000|Li cells maintain a low and stable voltage polarization for more than 800 h at 0.1 mA·cm⁻². Furthermore, the LiFePO₄|PEG_H/L4000|Li all-solid-state cells perform well both in cycling and rate performances. The design of polymer disordered structures for polymer electrolytes provides a new thought for manufacturing all-solid-state lithium-ion batteries with high safety as well as long life. Full article

Liquid crystalline block copolymers (LCBCPs) have emerged as an adaptable hybrid class at the intersection of self-assembling block copolymers and liquid crystalline ordering, producing multi-tiered architectures that can be finely programmed for multifunctional performance. This review surveys recent advances in their structure–property relationships and highlights applications spanning nanotechnology, biomedical systems, flexible photonics, stimuli-responsive, energy storage, and soft robotics. Particular emphasis is placed on how molecular design enables precise tuning of structural, optical, mechanical, and stimuli-responsive functions, positioning LCBCPs as strong candidates for next-generation functional materials. We also discuss current challenges, including scalability, phase control, and advanced characterization, and outline promising research directions to accelerate their translation from laboratory concepts to real-world technologies. Full article

This paper presents some of the most significant findings in the design of a hydraulic servomechanism for flight controls, which were primarily achieved by the first author during his activity in an aviation institute. These results are grouped into four main topics. The first one outlines a classical theory, from the 1950s–1970s, of the analysis of nonlinear automatic systems and namely the issue of absolute stability. The uninformed public may be misled by the adjective “absolute”. This is not a “maximalist” solution of stability but rather highlights in the system of equations a nonlinear function that describes, for the case of hydraulic servomechanisms, the flow-control dependence in the distributor spool. This function is odd, and it is therefore located in quadrants 1 and 3. The decision regarding stability is made within the so-called Lurie problem and is materialized by a matrix inequality, called the Lefschetz condition, which must be satisfied by the parameters of the electrohydraulic servomechanism and also by the components of the control feedback vector. Another approach starts from a classical theorem of V. M. Popov, extended in a stochastic framework by T. Morozan and I. Ursu, which ends with the description of the local and global spool valve flow-control characteristics that ensure stability in the large with respect to bounded perturbations for the mechano-hydraulic servomechanism. We add that a conjecture regarding the more pronounced flexibility of mathematical models in relation to mathematical instruments (theories) was used. Furthermore, the second topic concerns, the importance of the impedance characteristic of the mechano-hydraulic servomechanism in preventing flutter of the flight controls is emphasized. Impedance, also called dynamic stiffness, is defined as the ratio, in a dynamic regime, between the output exerted force (at the actuator rod of the servomechanism) and the displacement induced by this force under the assumption of a blocked input. It is demonstrated in the paper that there are two forms of the impedance function: one that favors the appearance of flutter and another that allows for flutter damping. It is interesting to note that these theoretical considerations were established in the institute’s reports some time before their introduction in the Aviation Regulation AvP.970. However, it was precisely the absence of the impedance criterion in the regulation at the appropriate time that ultimately led, by chance or not, to a disaster: the crash of a prototype due to tailplane flutter. A third topic shows how an important problem in the theory of automatic systems of the 1970s–1980s, namely the robust synthesis of the servomechanism, is formulated, applied and solved in the case of an electrohydraulic servomechanism. In general, the solution of a robust servomechanism problem consists of two distinct components: a servo-compensator, in fact an internal model of the exogenous dynamics, and a stabilizing compensator. These components are adapted in the case of an electrohydraulic servomechanism. In addition to the classical case mentioned above, a synthesis problem of an anti-windup (anti-saturation) compensator is formulated and solved. The fourth topic, and the last one presented in detail, is the synthesis of a fuzzy supervised neurocontrol (FSNC) for the position tracking of an electrohydraulic servomechanism, with experimental validation, in the laboratory, of this control law. The neurocontrol module is designed using a single-layered perceptron architecture. Neurocontrol is in principle optimal, but it is not free from saturation. To this end, in order to counteract saturation, a Mamdani-type fuzzy logic was developed, which takes control when neurocontrol has saturated. It returns to neurocontrol when it returns to normal, respectively, when saturation is eliminated. What distinguishes this FSNC law is its simplicity and efficiency and especially the fact that against quite a few opponents in the field, it still works very well on quite complicated physical systems. Finally, a brief section reviews some recent works by the authors, in which current approaches to hydraulic servomechanisms are presented: the backstepping control synthesis technique, input delay treated with Lyapunov–Krasovskii functionals, and critical stability treated with Lyapunov–Malkin theory. Full article

The steel strip is an important and ideal material for the automotive and aerospace industries due to its superior machinability, cost efficiency, and flexibility. However, surface defects such as inclusions, spots, and scratches can significantly impact product performance and durability. Accurately identifying these defects remains challenging due to the complex texture structures and subtle variations in the material. In order to tackle this challenge, we propose a Differentiable Edge-guided Pyramid Aggregation Network (DEPANet) to utilize edge information for improving segmentation performance. DEPANet adopts an end-to-end encoder-decoder framework, where the encoder consisting of three key components: a backbone network, a Differentiable Edge Feature Pyramid network (DEFP), and Edge-aware Feature Aggregation Modules (EFAMs). The backbone network is designed to extract overall features from the strip steel surface, while the proposed DEFP utilizes learnable Laplacian operators to extract multiscale edge information of defects across scales. In addition, the proposed EFAMs aggregate the overall features generating from the backbone and the edge information obtained from DEFP using the Convolutional Block Attention Module (CBAM), which combines channel attention and spatial attention mechanisms, to enhance feature expression. Finally, through the decoder, implemented as a Feature Pyramid Network (FPN), the multiscale edge-enhanced features are progressively upsampled and fused to reconstruct high-resolution segmentation maps, enabling precise defect localization and robust handling of defects across various sizes and shapes. DEPANet demonstrates superior segmentation accuracy, edge preservation, and feature representation on the SD-saliency-900 dataset, outperforming other state-of-the-art methods and delivering more precise and reliable defect segmentation. Full article

This paper proposes a parallel direct solution of flexible multibody systems based on block Gaussian elimination. The Craig–Bampton method is utilized to model flexible bodies within the multibody system, resulting in a reduction in the size of the system equations. To address the time integration problem, an implicit stiff scheme is adopted to obtain large time step sizes. When forming the linearized systemic equations, global sparsity in the Jacobian matrix and similar local sparsity in submatrices can be observed. Subsequently, block Gaussian elimination is introduced for the direct solution of these linearized equations. The algorithm is designed to be parallelizable at the algorithm level, with a specific processing order for the submatrices of the constraints. The stability of the method is guaranteed by the positive definite and symmetric properties in the diagonal matrices in the Craig–Bampton method. The parallel efficiency and numerical stability of the method are confirmed through numerical examples in homemade codes parallelized by OpenMP. Full article

Enhancing the detection capabilities of rotated objects in aerial imagery is a vital aspect of the burgeoning field of remote sensing technology. The objective is to identify and localize objects oriented in arbitrary directions within the image. In recent years, the capacity for rotated object detection has seen continuous improvement. However, existing methods largely employ traditional backbone networks, where static convolutions excel at extracting features from objects oriented at a specific angle. In contrast, most objects in aerial imagery are oriented in various directions. This poses a challenge for backbone networks to extract high-quality features from objects of different orientations. In response to the challenge above, we propose the Dynamic Rotational Convolution (DRC) module. By integrating it into the ResNet backbone network, we form the backbone network presented in this paper, DRC-ResNet. Within the proposed DRC module, rotation parameters are predicted by the Adaptive Routing Unit (ARU), employing a data-driven approach to adaptively rotate convolutional kernels to extract features from objects oriented in various directions within different images. Building upon this foundation, we introduce a conditional computation mechanism that enables convolutional kernels to more flexibly and efficiently adapt to the dramatic angular changes of objects within images. To better integrate key information within images after obtaining features rich in angular details, we propose the Multi-Order Spatial-Channel Aggregation Block (MOSCAB) module, which is aimed at enhancing the integration capacity of key information in images through selective focusing and global information aggregation. Meanwhile, considering the significant semantic gap between features at different levels during the feature pyramid fusion process, we propose a new multi-scale fusion network named AugFPN+. This network reduces the semantic gap between different levels before feature fusion, achieves more effective feature integration, and minimizes the spatial information loss of small objects to the greatest extent possible. Experiments conducted on popular benchmark datasets DOTA-V1.0 and HRSC2016 demonstrate that our proposed model has achieved mAP scores of $77.56\%$ and $90.4\%$, respectively, significantly outperforming current rotated detection models. Full article

In this paper, we examine an ultra-compact on-chip balanced filter based on novel hybrid resonators (NHRs) comprising short transmission line sections (STLSs) and series LC blocks using low-temperature co-fired ceramic (LTCC) technology. Based on a rigorous theoretical analysis, the proposed NHR demonstrates the potential for intrinsic ultra-wideband differential-mode (DM) and common-mode (CM) suppression without any additional suppressing structures. Furthermore, the resonance of NHRs was determined by four degrees of freedom, providing flexibility for miniaturization. Theoretical extensions of the Nth-order topology can be easily achieved by the simple coupling schemes that occur exclusively between STLSs. For verification, a balanced filter covering the 5G band n78 with an area of 0.065λ_g × 0.072λ_g was designed using the proposed optimization-based design procedure. An ultra-low insertion loss of 0.8 dB was obtained. The quasi-full CM stopband with a 20 dB rejection level ranged from 0 to 12.9 GHz. And the ultra-wide upper DM stopband with a 20 dB rejection level ranged from 4.4 to 11.5 GHz. Good agreement between the theoretical, simulated, and measured results indicate the validity of the proposed design principle. Full article