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In the tumor microenvironment, various immune and stromal cells, such as fibroblasts and vascular endothelial cells, contribute to tumor growth and progression by interacting with cancer cells. Tumor-associated macrophages (TAMs) have attracted attention as major players in the tumor microenvironment. The origin of TAMs is believed to be the infiltration of monocytes derived from bone marrow progenitor cells into tumor tissues and their differentiation into macrophages, whereas tissue-resident macrophages derived from yolk sacs have recently been reported. TAMs infiltrating tumor tissues act in a tumor-promoting manner through immunosuppression, angiogenesis, and the promotion of cancer cell invasion. Reflecting the nature of TAMs, increased TAM invasion and TAM-specific gene expression in tumor tissues may be the new biomarkers for cancer. Moreover, new therapeutic strategies targeting TAMs, such as transformation into immunostimulatory macrophages, suppression of TAM infiltration, and promotion of phagocytosis, are being investigated, and many clinical trials are underway. As the origin and function of TAMs are further elucidated, TAM-targeted therapy is expected to become a new option for the immunotherapy of various cancers, including oral cancers. Full article

System representation via computational graphs has become a cornerstone of modern machine learning, underpinning the gradient-based training of complex models. We have previously introduced the Partial Lagrangian Method—a divide-and-conquer approach that decomposes the Lagrangian into link-wise components—to derive and evaluate the equations of motion for robot systems with dynamically changing structures. That method leverages the symbolic expressiveness of computational graphs with automatic differentiation to streamline dynamic analysis. In this paper, we advance this framework by establishing a principled way to encode time-dependent differential equations as computational graphs. Our approach, which augments the state vector and applies the chain rule, constructs fully time-independent graphs directly from the Lagrangian, eliminating the erroneous time-derivative embeddings that previously required manual correction. Because our transformation is derived from first principles, it guarantees graph correctness and generalizes to any system governed by variational dynamics. We validate the method on a simple serial-link robotic arm, showing that it faithfully reproduces the standard equations of motion without graph failure. Furthermore, by compactly representing state variables, the resulting computational graph achieves a seven-fold reduction in evaluation time compared to our prior implementation. The proposed framework thus offers a more intuitive, scalable, and efficient design and analysis of complex dynamic systems. Full article

Methane concentration anomalies during coal mining operations are identified as important factors triggering major safety accidents. This study aimed to address the key issues of insufficient adaptability of existing detection methods in dynamic and complex underground environments and limited characterization capabilities for non-uniform sampling data. Specifically, an intelligent diagnostic model was proposed by integrating the improved Dung Beetle Optimization Algorithm (SGDBO) with Transformer-SVM. A dual-path feature fusion architecture was innovatively constructed. First, the original sequence length of samples was unified by interpolation algorithms to adapt to deep learning model inputs. Meanwhile, statistical features of samples (such as kurtosis and differential standard deviation) were extracted to deeply characterize local mutation characteristics. Then, the Transformer network was utilized to automatically capture the temporal dependencies of concentration time series. Additionally, the output features were concatenated with manual statistical features and input into the LSSVM classifier to form a complementary enhancement diagnostic mechanism. Sine chaotic mapping initialization and a golden sine search mechanism were integrated into DBO. Subsequently, the SGDBO algorithm was employed to optimize the hyperparameters of the Transformer-LSSVM hybrid model, breaking through the bottleneck of traditional parameter optimization falling into local optima. Experiments reveal that this model can significantly improve the classification accuracy and robustness of anomaly curve discrimination. Furthermore, core technical support can be provided to construct coal mine safety monitoring systems, demonstrating critical practical value for ensuring national energy security production. Full article

Achieving carbon reduction is essential in advancing China’s dual carbon goals and promoting a green transformation in the transportation sector. Changes in energy structure and intensity constitute key drivers for sustainable and low-carbon development in this field. To explore the spatial spillover effects of the energy structure and intensity on the green transition of transportation, this study constructs a panel dataset of 30 Chinese provinces from 2007 to 2020. It employs a super-efficiency SBM model, non-parametric kernel density estimation, and a spatial Markov chain to verify and quantify the spatial spillover effects of green total factor productivity (GTFP) in the transportation sector. A dynamic spatial Durbin model is then used for empirical estimation. The main findings are as follows: (1) GTFP in China’s transportation sector exhibits a distinct spatial pattern of “high in the east, low in the west”, with an evident path dependence and structural divergence in its evolution; (2) GTFP displays spatial clustering characteristics, with “high–high” and “low–low” agglomeration patterns, and the spatial Markov chain confirms that the GTFP levels of neighboring regions significantly influence local transitions; (3) the optimization of the energy structure significantly promotes both local and neighboring GTFP in the short term, although the effect weakens over the long term; (4) a reduction in energy intensity also exerts a significant positive effect on GTFP, but with clear regional heterogeneity: the effects are more pronounced in the eastern and central regions, whereas the western and northeastern regions face risks of negative spillovers. Drawing on the empirical findings, several policy recommendations are proposed, including implementing regionally differentiated strategies for energy structure adjustment, enhancing transportation’s energy efficiency, strengthening cross-regional policy coordination, and establishing green development incentive mechanisms, with the aim of supporting the green and low-carbon transformation of the transportation sector both theoretically and practically. Full article

Electrocardiogram (ECG) and phonocardiogram (PCG) signals are widely used in the early prevention and diagnosis of cardiovascular diseases (CVDs) due to their ability to accurately reflect cardiac conditions from different physiological perspectives and their ease of acquisition. Currently, some studies have explored the joint use of ECG and PCG signals for disease screening, but few studies have considered the trade-off between classification performance and energy consumption in model design. In this study, we propose a multimodal CVDs detection framework based on Spiking Neural Networks (SNNs), which integrates ECG and PCG signals. A differential fusion strategy at the signal level is employed to generate a fused EPCG signal, from which time–frequency features are extracted using the Adaptive Superlets Transform (ASLT). Two separate Spiking Convolutional Neural Network (SCNN) models are then trained on the ECG and EPCG signals, respectively. A confidence-based dynamic decision-level (CDD) fusion strategy is subsequently employed to perform the final classification. The proposed method is validated on the PhysioNet/CinC Challenge 2016 dataset, achieving an accuracy of 89.74%, an AUC of 89.08%, and an energy consumption of 209.6 $\mathsf{\mu }$J. This method not only achieves better balancing performance compared to unimodal signals but also realizes an effective balance between model energy consumption and classification effect, which provides an effective idea for the development of low-power, multimodal medical diagnostic systems. Full article

In the early faults of transformer windings, there are obvious variation characteristics of the spatial leakage magnetic field. Taking the leakage magnetic field as the fault characteristic quantity can establish an active defense system for transformer defects and faults, thereby increasing the service life of the equipment. However, the installation method of the optical fiber leakage magnetic field sensor, the principle of leakage magnetic field protection, the research and development of the protection device, and the dynamic model testing of the protection device are all key links in realizing the leakage magnetic field monitoring and active defense system. This paper first analyzes the symmetry of the winding leakage magnetic field, proposes invasive and non-invasive installation methods for optical fiber sensors based on different application scenarios, presents the principle of leakage magnetic field differential protection, and develops a protection device. The feasibility of the protection scheme proposed in this paper was verified through dynamic model experiments, and the early fault active defense system was put into actual on-site operation. Full article

The present study investigates the manufacturing and characterization of poly(lactic acid) (PLA)-based composites with raw and treated Poaceae, with loadings of 5, 10, and 20% wt. Before composite fabrication, the lignocellulosic fillers were analyzed using Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and microscopy to assess their chemical composition, thermal stability, and morphological features. Composites were prepared by melting PLA in a molten state with fillers, followed by injection molding. Comprehensive characterization of the obtained composites included microscopic analysis, melt flow index (MFI) testing, and differential scanning calorimetry (DSC), as well as mechanical tests (tensile and bending tests, impact test). The addition of Poaceae fibers to the PLA matrix significantly affected the mechanical and rheological properties of the composites. Incorporating 5% of cooked or alkalized fibers increased the flexural strength by 57% and 54%, respectively, compared to neat PLA. The modulus of elasticity for the composite with 20% alkalized fibers increased by as much as 35%. The fibers acted as nucleating agents, reducing the cold crystallization temperature (T_cc) by up to 15.6 °C, while alkaline residues contributed to an increased melt flow index (MFI). The conducted research provides a valuable basis and insights into the design of sustainable bio-based composites. Full article

As a crucial vehicle for advancing the transition to a green low-carbon economy, the green supply chain plays a pivotal role in alleviating pollution pressures and facilitating the green transformation of products. Existing studies mainly focus on static optimization and cost coordination in green supply chains, with limited attention to the dynamic impact of consumer behavior on green production and channel coordination. Based on consumer green preferences and the evolution of reference prices, we developed a differential game model for a two-tier green supply chain composed of a manufacturer and a retailer. The model incorporates green goodwill and consumer memory variables to capture the dynamic interaction among product greenness, sales effort, and consumer perception. By comparing the dynamic optimal response paths under integrated and non-integrated strategies, the study analyzes how reference price effects and goodwill accumulation influence decision-making and system performance. The results show that the stable reference price of green products is significantly higher than the actual selling price. When consumer environmental awareness is strong, cooperative strategies can markedly improve both green performance and supply chain profits, offering potential for Pareto improvement. This research enhances behavior-oriented modeling in green supply chains and provides theoretical and empirical support for designing collaboration mechanisms in green product promotion. Full article

This work presents an efficient analytical technique for obtaining approximate solutions to time-fractional system partial differential equations. The proposed method combines the Natural generalized Laplace transform with the decomposition method to construct a systematic solution framework. A general formulation of the method is developed for a broad class of time-fractional system equations. In particular, we check the validity and effectiveness of the approach by providing three illustrative examples, confirming its accuracy and applicability in solving both linear and nonlinear fractional problems. Full article

As a crucial modeling tool for stochastic financial markets, the Lévy risk model effectively characterizes the evolution of risks during enterprise operations. Through dynamic evaluation and quantitative analysis of risk indicators under specific dividend- distribution strategies, this model can provide theoretical foundations for optimizing corporate capital allocation. Addressing the inadequate adaptability of traditional single-period threshold strategies in time-varying market environments, this paper proposes a dividend strategy based on multiperiod dynamic threshold adjustments. By implementing periodic modifications of threshold parameters, this strategy enhances the risk model’s dynamic responsiveness to market fluctuations and temporal variations. Within the framework of the spectrally negative Lévy risk model, this paper constructs a stochastic control model for multiperiod threshold dividend strategies. We derive the integro-differential equations for the expected present value of aggregate dividend payments before ruin and the Gerber–Shiu function, respectively. Combining the methodologies of the discounted increment density, the operator introduced by Dickson and Hipp, and the inverse Laplace transforms, we derive the explicit solutions to these integro-differential equations. Finally, numerical simulations of the related results are conducted using given examples, thereby demonstrating the feasibility of the analytical method proposed in this paper. Full article

With the ongoing transformation of the energy structure and the advancement of smart grid development, green and sustainable development of substations has become an inevitable trend. As the core driving force of substation transformation, novel technologies remain at the pilot application stage, and their performance evaluations are yet to be clarified. In view of this, this paper proposes a comprehensive evaluation framework for the application effectiveness of novel technologies in green construction for substations. Firstly, based on the feature for the whole life cycle of the technologies, an evaluation index system is established covering multiple dimensions and stages, including resource conservation, technical performance enhancement, and economic benefits. Secondly, on the basis of AHP group decision and EWM combination weights, a variable-weight model is constructed by combining projection gray target evaluation to enable significant differentiation in cross-technology comparative analysis. Finally, a case study is conducted on pilot applications of multiple novel technologies in substations within a specific region, and the results indicate that novel technologies which demonstrate better sustainable development effects throughout the entire life cycle have a broader prospect for promotion. Full article

The rapid growth of global urbanisation has resulted in significant environmental pollution, with urban water pollution emerging as a critical factor in comprehensive urban development. The present study employs panel data from 268 Chinese cities between 2013 and 2022, utilising entropy weighting and a two-effect fixed-effects model to empirically analyse how urban water pollution control promotes comprehensive urban development. The research findings reveal that water pollution control significantly promotes comprehensive urban development, but there are differences across urban regions and scales, with greater effectiveness observed in central and western regions and medium-sized and small cities. This paper also highlights that water pollution control can promote urban development by optimising industrial structure and proposes that governments should formulate regionally differentiated water pollution control policies, establish a ‘Regional Water Environment Governance and Industrial Transformation Coordination Centre,’ and implement the ‘River and Lake Chief System+’ policy. Full article

Amid growing demands for heritage conservation and precision urban governance, this study proposes a multimodal framework to analyze tourist perception and behavior in Macau’s Historic Centre. We integrate geotagged social media images and text, ultra-wideband (UWB) pedestrian trajectories, and a LiDAR-derived 3D digital twin to examine the interplay among spatial configuration, movement, and affect. Visual content in tourist photos is classified with You Only Look Once (YOLOv8), and sentiment polarity in Weibo posts is estimated with a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. UWB data provide fine-grained trajectories, and all modalities are georeferenced within the digital twin. Results indicate that iconic landmarks concentrate visual attention, pedestrian density, and positive sentiment, whereas peripheral sites show lower footfall yet strong emotional resonance. We further identify three coupling typologies that differentiate tourist experiences across spatial contexts. The study advances multimodal research on historic urban centers by delivering a reproducible framework that aligns image, text, and trajectory data to extract microscale patterns. Theoretically, it elucidates how spatial configuration, movement intensity, and affective expression co-produce experiential quality. Using Macau’s Historic Centre as an empirical testbed, the findings inform heritage revitalization, wayfinding, and crowd-management strategies. Full article

To address the inherent challenge of poor interfacial compatibility in lignin/poly(butylene adipate-co-terephthalate) (PBAT) composites, lignin was extracted from Camellia oleifera shells and subjected to sequential solvent fractionation using ethanol, acetone, and tetrahydrofuran (THF). Two representative fractions—acetone-soluble (ACL) and THF-soluble (THFL)—were selected for composite preparation with PBAT via solvent casting. The influence of lignin fractionation on the structural and performance characteristics of the resulting composites was systematically evaluated through Fourier-transform infrared (FTIR) spectroscopy, the water contact angle (WCA), differential scanning calorimetry (DSC), tensile testing, and scanning electron microscopy (SEM). The results revealed that the abundant hydroxyl groups and benzene rings present in both ACL and THFL facilitated hydrogen bonding and conjugation interactions with the PBAT matrix, significantly improving interfacial adhesion. Notably, the ACL fraction effectively suppressed phase separation and increased the glass transition temperature (T_g) by 1.9 °C, leading to a 13.9% enhancement in tensile strength compared to neat PBAT. More strikingly, the incorporation of only 7 wt% THFL resulted in a remarkable 31% improvement in tensile strength. This substantial enhancement was primarily attributed to the favorable polarity match between THFL and PBAT, as well as the nucleating effect of THFL, which increased the crystallinity of PBAT by 25.3%. This study highlights the effectiveness of sequential lignin fractionation in tailoring the interfacial properties of biodegradable polymer composites. It also provides a promising strategy for the high-value utilization of lignin toward the development of high-performance, environmentally friendly materials. Full article

Background/Objectives: Herein, we investigated the effect of platelet-rich fibrin (PRF) treatment combined with no-ozone cold plasma (NCP) on growth factor levels, rat bone-marrow stem cell (rBMSC) proliferation, and alkaline phosphatase (ALP) activity in the early stage of differentiation into osteoblasts. Methods: The PRF used in the experiment was prepared by collecting blood from the jugular vein of rats, followed by centrifugation. The obtained PRF was treated with NCP, and the cell culture media were conditioned with the PRF extracts alone or with NCP-treated PRF extracts. Three different experimental groups were defined: no treatment (NT); cell culture media extracted from PRF (PRF); and cell culture media extracted from PRF treated with NCP (PRF + NCP). Enzyme-linked immunosorbent assays were performed to determine the levels of transforming growth factor (TGF)-β and platelet-derived growth factor (PDGF) AB. Water-soluble Tetrazolium-1 assay was performed to measure cell proliferation in rBMSCs. To analyze cell differentiation into osteoblasts, ALP staining and real-time PCR were performed. Results: Growth factor levels increased in response to treatment (TGF-β: p < 0.001, PDGF AB: p < 0.05), and the cell proliferation rate increased with treatment (145.29% and 150.05% for PRF and the PRF + NCP groups, respectively, relative to the NT group, p < 0.001). Evaluation of the ALP staining intensity and mRNA expression levels showed that the ALP activity was highest in the PRF + NCP group (p < 0.001). Conclusions: Our results confirmed that NCP treatment enhanced the release of several different growth factors contained in PRF to the culture media and that treatment with PRF and NCP increased the proliferation of rBMSCs and their differentiation into osteoblasts. Full article