Search Results (584)

Depending on the feedstock type and the pyrolysis conditions, biochars exhibit different physical, chemical, and structural properties, which highly influence their performance in various applications. This study presents a comprehensive characterization of biochar materials derived from the waste wood of pine (Pinus sylvestris L.) and beech (Fagus sylvatica) after low-temperature pyrolysis at 270 °C, followed by chemical activation using zinc chloride. The resulting materials were thoroughly analyzed in terms of their chemical composition (FTIR), thermal behavior (TGA/DTG), structural morphology (SEM and XRD), elemental analysis, and particle size distribution. The successful modification of raw biomass into carbon-rich structures of increased aromaticity and thermal stability was confirmed. Particle size analysis revealed that the activated carbon of Fagus sylvatica (FSAC) exhibited a monomodal distribution, indicating high homogeneity, whereas Pinus sylvestris-activated carbon showed a distinct bimodal distribution. This heterogeneity was supported by elemental analysis, revealing a higher inorganic content in pine-activated carbon, likely contributing to its dimensional instability during activation. These findings suggest that the uniform morphology of beech-activated carbon may be advantageous in filtration and adsorption applications, while pine-activated carbon’s heterogeneous structure could be beneficial for multifunctional systems requiring variable pore architectures. Overall, this study underscored the potential of chemically activated biochar from lignocellulosic residues for customized applications in environmental and material science domains. Full article

Heparan sulfate proteoglycans (HSPGs) within the neuronal niche are expressed during brain development, contributing to multiple aspects of neurogenesis, yet their roles in glial lineage commitment remain elusive. This study utilised three human cell models expanded under basal culture conditions followed by media-induced lineage induction to identify a reproducible and robust model of gliogenesis. SH-SY5Y human neuroblastoma cells (neuronal control), ReNcell CX human neural progenitor cells (astrocyte inductive) and ReNcell VM human neural progenitor (mixed neural induction) models were examined. The cultures were characterised during basal and inductive states via Q-PCR, Western Blotting, immunocytochemistry (ICC) and calcium signalling activity analyses. While the ReNcell lines did not produce fully mature or homogeneous astrocyte cultures, the ReNcell CX cultures most closely resembled an astrocytic phenotype with ReNcell VM cells treated with platelet-derived growth factor (PDGF) biased toward an oligodendrocyte lineage. The glycated variant of surface-bound glypican-2 (GPC2) was found to be associated with lineage commitment, with GPC6 and 6-O HS sulfation upregulated in astrocyte lineage cultures. Syndecan-3 (SDC3) emerged as a lineage-sensitive proteoglycan, with its cytoplasmic domain enriched in progenitor-like states and lost upon differentiation, supporting a role in maintaining neural plasticity. Conversely, the persistence of transmembrane-bound SDC3 in astrocyte cultures suggest continued involvement in extracellular signalling and proteoglycan secretion, demonstrated by increased membrane-bound HS aggregates. This data supports HSPGs and HS GAGs as human neural lineage differentiation and specification markers that may enable better isolation of human neural lineage-specific cell populations and improve our understanding of human neurogenesis. Full article

In order to address the limitations of the number of label samples in practical accurate classification scenarios and the problems of overfitting and an insufficient generalization ability caused by Few-Shot Learning (FSL) in hyperspectral image classification (HSIC), this paper designs and implements a neural architecture search (NAS) for a few-shot HSI classification method that combines meta learning. Firstly, a multi-source domain learning framework was constructed to integrate heterogeneous natural images and homogeneous remote sensing images to improve the information breadth of few-sample learning, enabling the final network to enhance its generalization ability under limited labeled samples by learning the similarity between different data sources. Secondly, by constructing precise and robust search spaces and deploying different units at different locations, the classification accuracy and model transfer robustness of the final network can be improved. This method fully utilizes spatial texture information and rich category information of multi-source data and transfers the learned meta knowledge to the optimal architecture for HSIC execution through precise and robust search space design, achieving HSIC tasks with limited samples. Experimental results have shown that our proposed method achieved an overall accuracy (OA) of 98.57%, 78.39%, and 98.74% for classification on the Pavia Center, Indian Pine, and WHU-Hi-LongKou datasets, respectively. It is fully demonstrated that utilizing spatial texture information and rich category information of multi-source data, and through precise and robust search space design, the learned meta knowledge is fully transmitted to the optimal architecture for HSIC, perfectly achieving classification tasks with few-shot samples. Full article

To overcome the limitations in the perception performance of individual robots and homogeneous robot teams, this paper presents a distributed multi-robot collaborative SLAM method based on air–ground cross-domain cooperation. By integrating environmental perception data from UAV and UGV teams across air and ground domains, this method enables more efficient, robust, and globally consistent autonomous positioning and mapping. First, to address the challenge of significant differences in the field of view between UAVs and UGVs, which complicates achieving a unified environmental understanding, this paper proposes an iterative registration method based on semantic and geometric features assistance. This method calculates the correspondence probability of the air–ground loop closure keyframes using these features and iteratively computes the rotation angle and translation vector to determine the coordinate transformation matrix. The resulting matrix provides strong initialization for back-end optimization, which helps to significantly reduce global pose estimation errors. Next, to overcome the convergence difficulties and high computational complexity of large-scale distributed back-end nonlinear pose graph optimization, this paper introduces a multi-level partitioning majorization–minimization DPGO method incorporating loss kernel optimization. This method constructs a multi-level, balanced pose subgraph based on the coupling degree of robot nodes. Then, it uses the minimization substitution function of non-trivial loss kernel optimization to gradually converge the distributed pose graph optimization problem to a first-order critical point, thereby significantly improving global pose estimation accuracy. Finally, experimental results on benchmark SLAM datasets and the GRACO dataset demonstrate that the proposed method effectively integrates environmental feature information from air–ground cross-domain UAV and UGV teams, achieving high-precision global pose estimation and map construction. Full article

In the era of homogenized competition, brand knowledge has become a critical factor that influences consumer purchasing decisions. However, traditional single-layer network models fail to capture the multi-dimensional semantic relationships embedded in brand-related textual data. To address this gap, this study proposes a BKMN framework integrating TF-IDF and TextRank algorithms for comprehensive brand knowledge discovery. By analyzing 19,875 consumer reviews of a mobile phone brand from JD website, we constructed a tri-layer network comprising TF-IDF-derived keywords, TextRank-derived keywords, and their overlapping nodes. The model incorporates co-occurrence matrices and centrality metrics (degree, closeness, betweenness, eigenvector) to identify semantic hubs and interlayer associations. The results reveal that consumers prioritize attributes such as “camera performance”, “operational speed”, “screen quality”, and “battery life”. Notably, the overlap layer exhibits the highest node centrality, indicating convergent consumer focus across algorithms. The network demonstrates small-world characteristics (average path length = 1.627) with strong clustering (average clustering coefficient = 0.848), reflecting cohesive consumer discourse around key features. Meanwhile, this study proposes the Mul-LSTM model for sentiment analysis of reviews, achieving a 93% sentiment classification accuracy, revealing that consumers have a higher proportion of positive attitudes towards the brand’s cell phones, which provides a quantitative basis for enterprises to understand users’ emotional tendencies and optimize brand word-of-mouth management. This research advances brand knowledge modeling by synergizing heterogeneous algorithms and multilayer network analysis. Its practical implications include enabling enterprises to pinpoint competitive differentiators and optimize marketing strategies. Future work could extend the framework to incorporate sentiment dynamics and cross-domain applications in smart home or cosmetic industries. Full article

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder marked by motor and non-motor symptoms, including cognitive decline, mood disturbances, and sensory deficits. While dopaminergic treatments remain the gold standard, they present long-term side effects and limited impact on non-motor symptoms. Transcranial Pulse Stimulation (TPS) has emerged as a promising adjunct therapy in neurological and psychiatric conditions, but its effects in PD remain underexplored. This open-label, single-arm trial protocol involves 14 PD participants and outlines a personalized 12-session treatment approach combined with a homogeneously distributed TPS intervention among patients with PD. The approach addresses the subject’s most prominent symptoms, as identified through validated clinical assessments, encompassing domains related to both motor and non-motor symptoms. Over 2.5 months, besides the intervention sessions, the 14 participants will undergo an MRI brain scan, a baseline assessment, a post-treatment assessment, and a 1-month follow-up assessment. The study aims to determine whether personalized TPS is a feasible and safe intervention and whether it improves PD symptoms across multiple functional domains. This study represents the first structured attempt to evaluate a multimodal, personalized TPS intervention in patients with PD. It addresses gaps in current treatment approaches and may support the development of future strategies for integrated, symptom-targeted neuromodulation. Full article

Linear passive electrical devices/components are usually characterized in the frequency domain by their impedance, i.e., the ratio of the voltage and current phasors. The use of the impedance concept does not raise particular concerns in low-frequency regimes; however, things become more complicated when it comes to rapid time-varying phenomena, mainly because the voltage depends not only on the position of the points between which it is defined but also on the choice of the integration path that connects them. In this article, based on first principles (Maxwell equations and Poynting vector flow considerations), we discuss the concept of impedance and define it unequivocally for a class of electrical devices/components with rotational symmetry. Two application examples are presented and discussed. One simple example concerns the per-unit-length impedance of a homogeneous cylindrical wire subject to the skin effect. The other, which is more elaborate, concerns a heterogeneous structure that consists of a dielectric disk sandwiched between two metal plates. For the lossless situation, the high-frequency impedance of this device (circular parallel plate capacitor) reaches zero when the frequency reaches a certain critical frequency f_c; then, it becomes inductive and increases enormously when the frequency reaches another critical frequency at 1.6 f_c. The influence of losses on the impedance of the device is thoroughly investigated and evaluated. Impedance corrections due to dielectric losses are analyzed using a frequency-dependent Debye permittivity model. The impedance corrections due to plate losses are analyzed by considering radial current distributions on the outer and inner surfaces of the plates, the latter exhibiting significant variations near the critical frequencies of the device. Full article

Time series data are fundamental for analyzing temporal dynamics and patterns, enabling researchers and practitioners to model, forecast, and support decision-making across a wide range of domains, such as finance, climate science, environmental studies, and signal processing. In the context of high-dimensional time series, the Vector Autoregressive model (VAR) is widely used, wherein each variable is modeled as a linear combination of lagged values of all variables in the system. However, the traditional VAR framework relies on the assumption of stationarity, which states that the autoregressive coefficients remain constant over time. Unfortunately, this assumption often fails in practice, especially in systems subject to structural breaks or evolving temporal dynamics. The Time-Varying Vector Autoregressive (TV-VAR) model has been developed to address this limitation, allowing model parameters to vary over time and thereby offering greater flexibility in capturing non-stationary behavior. In this study, we propose an enhanced modeling approach for the TV-VAR framework by incorporating minimization solvers in generalized additive models and one-sided kernel smoothing techniques. The effectiveness of the proposed methodology is assessed using simulations based on non-homogeneous Markov chains, accompanied by a detailed discussion of its advantages and limitations. Finally, we illustrate the practical utility of our approach using an application to real-world financial data. Full article

In the last decade, magnetic nanoparticles (MNPs) have attracted much attention for the implementation of non-invasive approaches suitable for the diagnosis and treatment of vascular diseases. In this work, the optimization of novel vascular grafts loaded with Nickel-based nanoparticles via electrospinning is proposed. Two different polycarbonate urethanes—i.e., Corethane A80 (COT) and Chronoflex AL80 (CHF)—were used to fabricate 3D electrospun nanocomposite grafts. SEM analysis showed a homogeneous distribution of fibers, with slight differences in terms of average diameters as a function of the polymer used—(1.14 ± 0.18) µm for COT, and (1.33 ± 0.23) µm for CHF—that tend to disappear in the presence of MNPs—(1.26 ± 0.19) µm and (1.26 ± 0.213) µm for COT/NPs and CHF/NPs, respectively. TGA analyses confirmed the higher ability of CHF to entrap MNPs in the fibers—18.25% with respect to 14.63% for COT—while DSC analyses suggested an effect of MNPs on short-range rearrangements of hard/soft micro-domains of CHF. Accordingly, mechanical tests confirmed a decay of mechanical strength in the presence of MNPs with some differences depending on the matrix—from (6.16 ± 0.33) MPa to (4.55 ± 0.2) MPa (COT), and from (3.67 ± 0.18) MPa to (2.97 ± 0.22) MPa (CNF). The in vitro response revealed that the presence of MNPs did not negatively affect cell viability after 7 days in in vitro culture, suggesting a promising use of these materials as smart vascular grafts able to support the actuation function of vessel wall muscles. Full article

Herein, we report on the synthesis of Ni and Zn clusters on the surface of TiO₂ as well as their bimetallic NiZn analogs. The materials were prepared by incipient wet impregnation of colloidal TiO₂ followed by microwave (MW) irradiation to graft the clusters to TiO₂ surface. The materials were further immobilized onto glass slides and exhibited high surface area, high mechanical stability, and porosity with accessible pores. The main species responsible for visible light degradation of microcystin LR via the interface charge transfer (IFCT) of excited e⁻ to surface metal clusters were found to be O₂^•− and h⁺. The optimal nominal grafting concentration was 0.5 wt.% for Ni and 1.0 wt.% for Zn, while for the bimetal modification (NiZn), the optimal nominal concentration was 0.5 wt.%. Compared to monometallic, bimetallic grafting showed a lower kinetic constant, albeit still improved compared to bare TiO₂. Bimetal-modified titania showed a lower photocurrent compared to single metal-grafted TiO₂ and poorer interfacial charge transport, namely, more recombination sites—possibly at the interface between the Ni and Zn domains. This work highlights the efficiency of using MW irradiation for grafting sub-nano-sized metallic species to TiO₂ in a homogeneous way. However, further strategies using MW irradiation for the structural design of bimetallic cocatalysts can be implemented in the future. Full article

Ventricular chamber development involves the coordinated maturation of diverse cardiomyocyte cell populations. In the human fetal heart, single-cell and single-nucleus RNA sequencing technologies and spatial transcriptomics reveal marked regional gene expression differences. In contrast, the mouse ventricular wall appears more homogeneous, except for a transient hybrid cardiomyocyte population co-expressing compact (Hey2) and trabecular (Irx3, Nppa, Bmp10) markers, indicating a transitional lineage state. To further investigate this, we used in situ hybridization (ISH) to examine the expression of a selected set of cardiomyocyte markers in normal and left ventricular non-compaction cardiomyopathy (LVNC) mouse models. In developing mouse ventricles, the expression of key marker genes was largely restricted to two wide myocardial domains, compact and trabecular myocardium, suggesting a less complex regional organization than the human fetal heart. Other markers labeled endocardial and coronary endothelial cells rather than cardiomyocytes, differing from patterns observed in the human heart. In the LVNC model, various markers exhibited altered spatial expression, indicating that the precise regional organization of gene expression is critical for normal ventricular wall maturation. These findings underscore the critical role of spatially regulated gene programs in ventricular chamber development and point to their potential involvement in cardiomyopathy pathogenesis. Full article

Point-of-care testing (POCT) has garnered widespread attention due to its rapid, convenient, and efficient detection capabilities, particularly playing an increasingly pivotal role in medical diagnostics and significantly improving the efficiency and quality of healthcare services. Nanozymes, as novel enzyme-mimicking materials, have emerged as a research hotspot owing to their superior catalytic performance, low cost, and robust stability. This review provides a systematic overview of the fundamental characteristics and classifications of nanozymes, along with various sensing strategies employed in POCT applications, colorimetric, electrochemical, fluorescent, chemiluminescent, and surface-enhanced Raman scattering (SERS)-based approaches. Furthermore, this review highlights innovative designs that enhance the sensitivity and accuracy of POCT across multiple domains, such as biomarker detection, environmental monitoring, and food safety analysis, thereby offering novel perspectives for the practical implementation of nanozymes in point-of-care diagnostics. Finally, this review analyzes current challenges in nanozyme-based POCT systems, including limitations in optimizing catalytic activity, ensuring nanozyme homogeneity, and achieving large-scale production, while proposing future development trajectories. Full article

Effective vertical seismic profiling (VSP) of upgoing and downgoing wave separation is essential for high-quality imaging. However, VSP wavefield separation is particularly challenging under complex geological conditions. Existing solutions encompass one derived from the mathematical characteristics of upgoing and downgoing waves, employing signal decomposition methodologies, and another that utilizes data-driven machine learning techniques, achieving wavefield separation by training sample data to identify the distinct characteristics of upgoing and downgoing waves. This study introduces a VSP wave-separation method using signal knowledge representation, primarily by constructing knowledge representations of upgoing and downgoing waves. Physics-informed recurrent neural network FWI and Poynting vector physical knowledge representation yielded accurate velocity models. Axial gradient information was utilized to construct morphological knowledge representations of upgoing and downgoing waves. Directional differentiation knowledge representations were established based on kinematic characteristic disparities between upgoing and downgoing waves in the time-depth domain. These wave knowledge representations (KRs) built a dual convolutional autoencoder. Its distinct branches extracted up/down wave information, while the KRs, transformed into loss functions, enabled knowledge-driven unsupervised VSP wave separation. The proposed methodology was validated using a homogeneous layer and Marmousi models, demonstrating the effective separation of upgoing and downgoing waves from the VSP seismic records. Full article

The Bundelkhand granite (BG) constitutes the bulk of the granitoid complex in the Bundelkhand Craton and preserves imprints of its evolution from the magmatic to a protracted hydrothermal stage as deduced from the petrography. In order to reconstruct such a path of evolution in this study, thermobarometric calculations were attempted on the mineral chemistry of the major (hornblende, plagioclase, biotite) and minor (epidote, apatite) magmatic phases. They yielded magmatic temperatures and pressures (in excess of 700 °C and ~5 kbar), although not consistently, and indicate mid-crustal conditions at the onset of crystallization. Temperatures in the hydrothermal regime within the BG are better constrained by the chemistry of the chlorite and epidote minerals (340 to 160 °C) that conform with the ranges of homogenization temperatures of aqueous–biphase inclusions in matrix quartz in the BG and subordinate quartz veins. These reconstructions indicate that fluid within the BG evolved down to lower temperatures and towards the deposition of quartz and, more importantly, bears a striking similarity to the temperature–salinity characteristics of fluid in the giant quartz reef system. Scanty mixed aqueous–carbonic inclusions in the BG are indicative of the CO₂-poor nature of the BG magma and the exsolution of CO₂ at lower pressure (~2.6 kbar). The dominant mechanism of fluid evolution in the BG appears to be the incursion of meteoric fluid, which caused fluid dilution. Laser Raman microspectrometry reveals many types of solid phases in aqueous–carbonic inclusions in the BG domain. The occurrence of unusual, effervescent-type inclusions, though infrequent, bears a striking similarity to that reported in the giant quartz reef domain. Thus, the highlight of the present work is the convincing fluid inclusion evidence that genetically links the BG with the giant quartz reef system, although many cited discrepancies arise from the radiometric dates. We visualize the episodic release of silica-transporting fluid to the major fracture system (now occupied by the giant reef) from the BG, thus making the fluid in the two domains virtually indistinguishable. Full article

An improved interaction integral method has been proposed for heterogeneous materials with complex interfaces based on the postprocessing routine of commercial finite element software. This approach introduces a suitably designed auxiliary field to eliminate the derivative terms of material parameters in the interaction integral, which enables the direct extraction of the stress intensity factor at the crack tip without considering the material interfaces. This paper utilizes the postprocessing routine of commercial finite element software to extract the simulation results from the specified analysis step to obtain the stress intensity factor. Validation via homogeneous material cases shows excellent agreement with the theoretical solutions. For two-dimensional/three-dimensional heterogeneous materials, the domain-independence of the present method still stands, even when the integral domain intersects interfaces. This new method improves the efficiency of parameter extraction and extends the scope of application of commercial finite element software for calculating the fracture parameters of heterogeneous materials. Full article