Search Results (294)

Introduction: Biodegradable Temporizing Matrix (BTM) is a new synthetic dermal substitute suitable for wound closure and tissue regeneration. The data in paediatric population remain limited. The study purpose is to review the indications for BTM application in paediatric patients; evaluate the short-term and long-term results, including complications and functional outcomes, as well as to share some unique observations regarding the use of BTM in paediatric population. Patients and Methods: Patients undergoing reconstructive surgery and BTM application during the last three years were included. Data collected included patient demographics, primary diagnosis, previous surgical management, post-operative complications and final outcomes. BTM was used in 32 patients. The indications varied including epidermolysis bullosa (n = 6), burns (n = 4), trauma (n = 7), infection (n = 4), ischemia or necrosis (n = 11). Results: The results were satisfying with acceptable aesthetic and functional outcomes. Complications included haematomaunderneath the BTM leading to BTM removal and re-application (n = 1), BTM infection (n = 1) and split-thickness skin graft failure on top of BTM requiring re-grafting (n = 2). Conclusions: BTM can be a good alternative to large skin grafts, locoregional flaps or even free flaps. The big advantages over other dermal substitutes or skin grafts are that BTM is less prone to infection and offers excellent scarring by preserving the normal skin architecture. Specifically in children, BTM might not require grafting, resulting in spontaneous healing with good scarring. In critically ill patients, BTM reduces the operation time and there is no donor site morbidity. BTM should be considered in the reconstructive ladder when discussing defect coverage options in children and young people. Full article

In this paper, MXene nanosheets were used as nano additives for the preparation of MXene-modified polyvinyl chloride (PVC) mixed max membranes (MMMs) for the rejection of lead (Pb²⁺) ions from wastewater. MXene nanosheets were introduced into the PVC matrix to enhance membrane performance, hydrophilicity, contact angle, porosity, and resistance to fouling. Modeling and optimization techniques were used to examine the effects of important operational and fabrication parameters, such as pH, contaminant concentration, nanoadditive (MXene) content, and operating pressure. Predictive models were developed using experimental data to assess the membranes’ performance in terms of flux and Pb²⁺ rejection. The ideal circumstances that struck a balance between long-term operating stability and high removal efficiency were found through multi-variable optimization. The optimized conditions for the best rejection of Pb²⁺ ions and the most stable permeability over time among the membranes that were manufactured were the initial metal ions concentration (2 mg/L), pH (7.89), pressure (2.99 bar), and MXene mass (0.3 g). The possibility of combining MXene nanoparticles with methodical optimization techniques to create efficient membranes for the removal of heavy metals in wastewater treatment applications is highlighted by this work. Full article

Electrochromic (EC) devices are gaining increasing attention for next-generation smart windows and low-power displays due to their reversible color modulation, low operating voltage, and flexible form factors. Recently, electrochromic energy storage devices (EESDs) have emerged as a promising class of multifunctional systems, enabling simultaneous energy storage and real-time visual monitoring. In this study, we report a flexible dual-functional EESD constructed using polyaniline (PANI) films doped with anthraquinone-1-sulfonic acid sodium salt (AQS), coupled with a redox-active PVA-based gel electrolyte also incorporating AQS. The incorporation of AQS into both the polymer matrix and the gel electrolyte introduces synergistic redox activity, facilitating bidirectional Faradaic reactions at the film–electrolyte interface and within the bulk gel phase. The resulting vertically aligned PANI-AQS nanoneedle films provide high surface area and efficient ion pathways, while the AQS-doped gel electrolyte contributes to enhanced ionic conductivity and electrochemical stability. The device exhibits rapid and reversible color switching from light green to deep black (within 2 s), along with a high areal capacitance of 194.2 mF·cm⁻² at 1 mA·cm⁻² and 72.1% capacitance retention over 5000 cycles—representing a 31.5% improvement over undoped systems. These results highlight the critical role of redox-functionalized gel electrolytes in enhancing both the energy storage and optical performance of EESDs, offering a scalable strategy for multifunctional, gel-based electrochemical systems in wearable and smart electronics. Full article

The article presents the quantum signal-induced heap transform (QsiHT) method of the QR-decomposition of multi-qubit operations. This transform can be generated by a given signal, by using different paths, or orders, of processing the data. We propose using the concept of the fast path of calculation of the QsiHT and applying such transforms on each stage of the matrix decomposition. This allows us to build quantum circuits for multi-qubit unitary operation without permutations. Unitary operations with real and complex matrices are considered. The cases of 3- and 4-qubit operations are described in detail with quantum circuits. These circuits use a maximum of 28 and 120 Givens rotation gates for 3- and 4-qubit real operations, respectively. All rotations are performing only on adjacent bit planes. For complex unitary operation, each of the Givens gates is used in pairs with two Z-rotation gates. These two types of rotations and the global phase gate are the universal gate set for multi-qubit operations. The presented approach can be used for implementing quantum circuits for $n$-qubits when $n\ge 2$, with a maximum of ($4^n/2-2^{n-1})$ Givens rotations and no permutations. Full article

This study uses ordinary Portland–sulfate–silicate composite cement as the matrix and investigates the effects of water–cement ratio, HPMC dosage, and PCS dosage on the performance of specialized grouting materials for subway structure repair during operation through single-factor experiments and orthogonal experiments. Multifactorial variance analysis was employed to quantitatively evaluate the sensitivity of each factor and their interactions to slurry flowability, setting time, anti-dispersibility, and compressive strength. The results show that the water–cement ratio is the most critical factor affecting the performance of the grouting material, with extremely significant impacts on all performance indicators; HPMC dosage significantly affects flowability, setting time, and anti-dispersibility; PCS dosage primarily influences 2 h compressive strength; the interaction between water–cement ratio and HPMC dosage has a significant impact on anti-dispersibility. Principal component analysis revealed the trade-off relationship between flowability, setting time, and strength. The study established a sensitivity ranking for the performance of specialized grouting materials: water–cement ratio > HPMC dosage > PCS dosage > interaction, providing a theoretical basis and methodological reference for the formulation optimization of specialized grouting materials for subway structure repair during operation. Full article

Background/Objective: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer death and tumors with an extremely poor prognosis. In the present study, novel biomarker candidates useful for the early diagnosis and prognosis of human invasive PDAC were investigated. Methods: Biomarker candidates were first selected based on the proteomic/bioinformatic and clinico-pathological analyses of 10 and 100 patients with PDAC, respectively, operated at Osaka Metropolitan University Hospital (Exp. 1). Next, the expression and secretion of the target protein and its EV mRNA were investigated in pancreatic cancer cells in vitro and in a Balb/c nude mouse model. In addition, the protein and EV mRNA levels of candidate molecules were measured in the blood serum of 36 PDAC and 10 IPMN patients, and diagnostic significance was assessed (Exp. 2). Results: A significant elevation of peroxiredoxin 3 (PRX3), a mitochondrial matrix protein, was found in PDAC via LC-Ms/Ms analysis. In Exp. 1, PRX3 overexpression was found in PDAC and PanIN lesions and was associated with a tumor infiltrative growth pattern (INFc) and poor overall 1-year patient survival. The prognostic value was significantly improved when PRX3 was combined with serum SPan-1 and DUPAN-2 markers in survival analyses. Furthermore, the PRX3 protein and its extracellular vesicle (EV: exosome and oncosome)-incorporated mRNA were secreted at detectable levels from PANC-1, MIAPaCa-2, and SW1990 cells into the blood of Balb/c nude mice bearing tumors. The overexpression of PRX3 was positively correlated with that of cancer stem cell marker CD44 variant 9 (CD44v9), P-Nrf2, and FOXO3a, as well as the generation of reactive oxygen species. In Exp. 2, a significant increase in PRX3 protein and EV mRNA was detected in the blood serum of PDAC subjects compared to IPMN patients and healthy controls. Significantly higher PRX3 protein levels were found in the IPMN group. The elevation of PRX3 EV mRNA was significantly associated with poor patient survival. Conclusions: These results indicate that PRX3 may become a novel early biomarker for PDAC diagnosis and prognosis. Full article

Accurate prediction of natural gas production is of great significance for optimizing development strategies, simplifying production management, and promoting decision-making. This paper utilizes partial differentiation to effectively capture the spatiotemporal characteristics of natural gas data and the advantages of grey prediction models. By introducing the fractional damping accumulation operator, a new fractional order partial grey prediction model is established. The new model utilizes partial capture of details and features in the data, improves model accuracy through fractional order accumulation, and extends the metadata of the classic grey prediction model from time series to matrix series, effectively compensating for the phenomenon of inaccurate results caused by data fluctuations in the model. Meanwhile, the principle of data accumulation is effectively expressed in matrix form, and the least squares method is used to estimate the parameters of the model. The time response equation of the model is obtained through multiplication transformation, and the modelling steps are elaborated in detail. Finally, the new model is applied to the prediction of natural gas production in Qinghai Province, China, selecting energy production related to natural gas production, including raw coal production, oil production, and electricity generation, as relevant variables. To verify the effectiveness of the new model, we started by selecting the number of relevant variables, divided them into three categories for analysis based on the number of relevant variables, and compared them with five other grey prediction models. The results showed that in the seven simulation experiments of the three types of experiments, the average relative error of the new model was less than 2%, indicating that the new model has strong stability. When selecting the other three types of energy production as related variables, the best effect was achieved with an average relative error of 0.3821%, and the natural gas production for the next nine months was successfully predicted. Full article

The subway is crucial for urban operations, especially during holidays. Unlike traditional studies using smart card data, this research analyzes National Day holiday subway travel patterns with Hangzhou’s 2021 mobile payment data, covering 42 days from 6 September to 17 October for comprehensive comparison. Considering spatial passenger flow correlations, a Composite Weight (CW) matrix integrating network distance and time is defined and integrated into a Spatial Error Model (SEM), Spatial autoregressive model (SAR), and Spatial Durbin Model (SDM) to create CW-SEM, CW-SAR, and CW-SDM. The CW matrix innovatively considers network distance and time, overcoming traditional spatial weight matrix limitations to accurately and dynamically capture passenger flow spatial correlations. The results show the following: (1) Hangzhou saw 37% and 49% increases in average daily passenger flow during the extended holiday versus workdays and weekends, with holiday peak hour flow declining 16% compared to workdays but increasing 18% versus weekends, likely due to shifted travel purposes from commuting to tourism; (2) strong spatial passenger flow correlations existed in both workdays and weekends, attributed to urban functional zoning and transport network connectivity; (3) key factors such as population, social media activity, commercial facilities and transportation hubs show significant positive correlations with holiday passenger flow. Medical facility reveals significant negative correlations with holiday passenger flow. These findings highlight the need to incorporate spatial variations into major holiday subway travel studies for urban planning and traffic management insights. Full article

Leveraging the human brain as a paradigm of energy-efficient computation, considerable attention has been paid to photonic neurons and neural networks to achieve higher computing efficiency and lower energy consumption. This study experimentally demonstrates on-chip silicon photonic neurons and neural networks based on thermally tunable microring resonators (MRRs) implement weighting and nonlinear operations. The weight component consists of eight cascaded MRRs thermally tuned within wavelength division multiplexing (WDM) architecture. The nonlinear response depends on the MRR’s nonlinear transmission spectrum, which is analogous to the rectified linear unit (ReLU) function. The matrix multiplication and recognition task of digits 2, 3, and 5 represented by seven-segment digital tube are successfully completed by using the photonic neural networks constructed by the photonic neurons based on the on-chip thermally tunable MRR as the nonlinear units. The power consumption of the nonlinear unit was about 5.65 mW, with an extinction ratio of about 25 dB between different digits. The proposed photonic neural network is CMOS-compatible, which makes it easy to construct scalable and large-scale multilayer neural networks. These findings reveal that there is great potential for highly integrated and scalable neuromorphic photonic chips. Full article

The technology of road extraction serves as a crucial foundation for urban intelligent renewal and green sustainable development. Its outcomes can optimize transportation network planning, reduce resource waste, and enhance urban resilience. Deep learning-based approaches have demonstrated outstanding performance in road extraction, particularly excelling in complex scenarios. However, extracting roads from remote sensing data remains challenging due to several factors that limit accuracy: (1) Roads often share similar visual features with the background, such as rooftops and parking lots, leading to ambiguous inter-class distinctions; (2) Roads in complex environments, such as those occluded by shadows or trees, are difficult to detect. To address these issues, this paper proposes an improved model based on Graph Convolutional Networks (GCNs), named FR-SGCN (Hierarchical Depth-wise Separable Graph Convolutional Network Incorporating Graph Reasoning and Attention Mechanisms). The model is designed to enhance the precision and robustness of road extraction through intelligent techniques, thereby supporting precise planning of green infrastructure. First, high-dimensional features are extracted using ResNeXt, whose grouped convolution structure balances parameter efficiency and feature representation capability, significantly enhancing the expressiveness of the data. These high-dimensional features are then segmented, and enhanced channel and spatial features are obtained via attention mechanisms, effectively mitigating background interference and intra-class ambiguity. Subsequently, a hybrid adjacency matrix construction method is proposed, based on gradient operators and graph reasoning. This method integrates similarity and gradient information and employs graph convolution to capture the global contextual relationships among features. To validate the effectiveness of FR-SGCN, we conducted comparative experiments using 12 different methods on both a self-built dataset and a public dataset. The proposed model achieved the highest F1 score on both datasets. Visualization results from the experiments demonstrate that the model effectively extracts occluded roads and reduces the risk of redundant construction caused by data errors during urban renewal. This provides reliable technical support for smart cities and sustainable development. Full article

A composite-coated inclusion is embedded in a matrix, where the conductivity (permittivity) of the phases is assumed to be complex-valued. The purpose of this paper is to demonstrate that a non-zero flux can arise under specific conditions related to the conductivities of the components in the absence of external sources. These conditions are unattainable with conventional positive conductivities but can be satisfied when the conductivities are negative or complex—a scenario achievable in the context of metamaterials. The problem is formulated as a spectral boundary value problem for the Laplace equation, featuring a linear conjugation condition defined on a smooth curve L. This curve divides the plane ${\mathbb{R}}^2$ into two regions, $D^{+}$ and $D^{-}\ni \infty$. The spectral parameter appears in the boundary condition, drawing parallels with the Steklov eigenvalue problem. The case of a circular annulus is analyzed using the method of functional equations. The complete set of eigenvalues is derived by applying the classical theory of self-adjoint operators in Hilbert space. Full article

Diagnosing tuberculosis (TB) in children presents significant challenges, necessitating the identification of reliable biomarkers for accurate diagnosis. In this study, we investigated plasma matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) as potential diagnostic markers. A prospective case–control study involved 167 children classified into confirmed TB, unconfirmed TB, and unlikely TB control groups. Plasma levels of MMPs (MMP 1, 2, 3, 7, 8, 9, 12, and 13) and TIMPs (TIMP 1, 2, 3, and 4) were measured using multiplex assays. Elevated baseline levels of MMP-1, MMP-2, MMP-7, MMP-9, TIMP-1, TIMP-2, TIMP-3, and TIMP-4 were observed in active TB cases compared to unlikely TB controls. Receiver operating characteristics (ROC) analysis identified MMP-1, MMP-2, MMP-9, and TIMP-1 as potential biomarkers with over 80% sensitivity and specificity. A three-MMP signature (MMP-1, MMP-2, and MMP-9) demonstrated 100% sensitivity and specificity. The findings suggest that a baseline MMP signature could serve as an accurate biomarker for diagnosing pediatric TB, enabling early intervention and effective management. Full article

This paper addresses the problem of optimal $H_2$-filtering for a class of continuous-time linear McKean–Vlasov stochastic differential equations under sampled measurements. The main tool used to solve the filtering problem is a forward jump matrix linear differential equation with a Riccati-type jumping operator. More specifically, the stabilizing solution of such a jump Riccati-type equation plays a key role. Full article

Multi-sensor fault diagnosis, especially when using heterogeneous sensors, substantially enhances the accuracy of fault detection in asynchronous motors operating under high-interference conditions. A critical challenge in multi-sensor fault diagnosis lies in effectively fusing data from different sensors. Deep learning offers a promising solution by transforming multi-sensor data into a unified representation, thereby facilitating robust data fusion. However, existing approaches often fail to fully exploit inter-sensor correlations and inherent prior physical knowledge. To address this limitation, we propose a novel graph neural network-based model that emphasizes graph structure construction for heterogeneous multi-sensor information fusion. Our framework includes (1) a multi-task enhanced autoencoder for node feature extraction, enabling discriminative representation learning, particularly with heterogeneous sensor data; (2) an adjacency matrix builder integrated with physical prior constraints to improve the generalization and robustness of the model; and (3) a graph isomorphism network to derive graph-level representations for fault classification. Our experimental results demonstrate the model’s effectiveness in diagnosing faults, as it achieves superior performance compared to conventional methods on two heterogeneous asynchronous motor datasets. Full article

The landscape visual sensitivity (LVS) assessment is recognized as a critical tool for identifying areas most sensitive to landscape changes and for informing multi-resource optimization and allocation strategies. However, conventional large-scale LVS assessment criteria and methodologies developed for natural landscapes do not satisfy the precision-oriented assessment requirements of streetscape visual sensitivity (SVS) in historic districts, nor do they facilitate the operational linkage between assessment outcomes and planning applications. This study proposes an innovative SVS–PAP assessment methodology, which is a systematic integration of the SVS assessment and public esthetic perception (PAP) evaluation. The SVS assessment criteria framework was first improved through the integration of enriched multi-modal datasets. Subjective weights were obtained via the analytic hierarchy process (AHP), incorporating expert and public judgments, while objective weights were derived through the entropy weight method (EWM) based on data information entropy. The integration of both approaches enhances the methodological rigor and scientific validity of SVS weight determination. An SVS–PAP analytical matrix was subsequently constructed through integration of SVS assessments and PAP-based scenic beauty estimation (SBE), enabling the derivation of planning strategies. An empirical validation conducted in Anshandao Historic District yielded four key findings: (1) The SVS–PAP methodology, which integrates subjective–objective evaluation factors and incorporates broad public participation, demonstrates strong scientific validity and reliability, establishing a novel paradigm for SVS assessment and strategic planning; (2) The technical framework—leveraging multi-modal data and GIS spatial analysis techniques—improves assessment precision, operability, and replicability; (3) The planning and management strategies formulated by the SVS–PAP analytical matrix were verified as reasonable, demonstrating effective planning-transition capability; (4) Notably, historical and cultural influences showed significantly higher weighting coefficients across assessment criteria compared to non-historic streetscape assessments. Overall, these research results address the persistent undervaluation of the esthetic and spiritual values of historic landscapes in multi-resource value trade-off and decision-making processes, demonstrating both theoretical and practical significance through a systematic methodological advancement. Full article